From 2a48780d16b098498248820dc45b6693fe09d0f5 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Thu, 7 May 2026 17:18:53 +0200
Subject: [PATCH 01/10] Apply latest templates and dev environment updates
 (#169)

* Move essdiffraction to dev deps and simplify test

* Add dev and user features to pixi.toml

* Apply latest templates

* Isolate tagged docs deployments from dev runs

* Remove resolved dev documentation

* Remove resolved ADP implementation plan
---
 .copier-answers.yml                           |    2 +-
 .github/workflows/docs.yml                    |   14 +-
 .github/workflows/issues-labels.yml           |  152 +-
 .github/workflows/pr-labels.yml               |    7 +
 .github/workflows/release-pr.yml              |    2 +-
 docs/dev/adp_implementation.md                |  321 ----
 docs/dev/architecture.md                      |   84 +-
 docs/dev/cryspy-dwf-bug.md                    |  126 --
 docs/dev/plan_powder-chart-y-range.md         |  138 --
 docs/docs/installation-and-setup/index.md     |   10 +-
 pixi.lock                                     | 1450 +++++++++++++----
 pixi.toml                                     |   94 +-
 pyproject.toml                                |    2 +-
 .../dream/test_package_import.py              |   21 +-
 14 files changed, 1400 insertions(+), 1023 deletions(-)
 delete mode 100644 docs/dev/adp_implementation.md
 delete mode 100644 docs/dev/cryspy-dwf-bug.md
 delete mode 100644 docs/dev/plan_powder-chart-y-range.md

diff --git a/.copier-answers.yml b/.copier-answers.yml
index 929409549..a807c84da 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.11.0-7-gb5113cf
+_commit: v0.11.1-12-gbb9bb30
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/diffraction-app
 app_doi: 10.5281/zenodo.18163581
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 117416a3f..def30827c 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -27,12 +27,16 @@ on:
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
-# Allow only one concurrent deployment to gh-pages at a time.
-# All docs workflows share the same concurrency group to prevent race conditions
-# when multiple branches/tags trigger simultaneous deployments.
+# - Non-tagged pushes and pull requests all use `docs-dev` group, so
+#   they cancel each other.
+# - Tagged pushes use their own group like docs-v1.2.3, so they do not
+#   cancel non-tagged runs, and non-tagged runs do not cancel them.
 concurrency:
-  group: docs-gh-pages-deploy
-  cancel-in-progress: false
+  group: >-
+    ${{ startsWith(github.ref, 'refs/tags/v')
+      && format('docs-{0}', github.ref_name)
+      || 'docs-dev' }}
+  cancel-in-progress: true
 
 # Set the environment variables to be used in all jobs defined in this workflow
 env:
diff --git a/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml
index 31a69ad20..56ab5b19a 100644
--- a/.github/workflows/issues-labels.yml
+++ b/.github/workflows/issues-labels.yml
@@ -1,6 +1,6 @@
-# Verifies if an issue has at least one of the `[scope]` and one of the
-# `[priority]` labels. If not, the bot adds labels with a warning emoji
-# to indicate that those labels need to be added.
+# Verifies if the current issue has at least one real `[scope]` label and one
+# real `[priority]` label. If either is missing, the workflow adds a reminder
+# label with a warning emoji.
 
 name: Issue labels check
 
@@ -13,8 +13,6 @@ permissions:
 
 jobs:
   check-labels:
-    if: github.actor != 'easyscience[bot]'
-
     runs-on: ubuntu-latest
 
     concurrency:
@@ -25,27 +23,127 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v6
 
-      - name: Setup easyscience[bot]
-        id: bot
-        uses: ./.github/actions/setup-easyscience-bot
+      - name: Sync missing-label reminders
+        uses: ./.github/actions/github-script
         with:
-          app-id: ${{ vars.EASYSCIENCE_APP_ID }}
-          private-key: ${{ secrets.EASYSCIENCE_APP_KEY }}
+          script: |
+            const fs = require('fs');
 
-      - name: Check for required [scope] label
-        uses: trstringer/require-label-prefix@v1
-        with:
-          secret: ${{ steps.bot.outputs.token }}
-          prefix: '[scope]'
-          labelSeparator: ' '
-          addLabel: true
-          defaultLabel: '[scope] ⚠️ label needed'
-
-      - name: Check for required [priority] label
-        uses: trstringer/require-label-prefix@v1
-        with:
-          secret: ${{ steps.bot.outputs.token }}
-          prefix: '[priority]'
-          labelSeparator: ' '
-          addLabel: true
-          defaultLabel: '[priority] ⚠️ label needed'
+            const issueNumber = context.issue.number;
+            const action = context.payload.action;
+            const changedLabel = context.payload.label?.name ?? null;
+            const labels = context.payload.issue.labels.map(({ name }) => name);
+            const requirements = [
+              {
+                prefix: '[scope] ',
+                reminder: '[scope] ⚠️ label needed',
+              },
+              {
+                prefix: '[priority] ',
+                reminder: '[priority] ⚠️ label needed',
+              },
+            ];
+
+            const labelsToAdd = [];
+            const labelsToRemove = [];
+            const evaluations = [];
+
+            console.log(`::group::Issue label check for #${issueNumber}`);
+            console.log(`Event action: ${action}`);
+            if (changedLabel) {
+              console.log(`Event label: ${changedLabel}`);
+            }
+            console.log(
+              `Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
+            );
+
+            for (const { prefix, reminder } of requirements) {
+              const matchingRealLabels = labels.filter(
+                (name) => name.startsWith(prefix) && name !== reminder,
+              );
+              const hasRealLabel = matchingRealLabels.length > 0;
+              const hasReminderLabel = labels.includes(reminder);
+
+              evaluations.push({
+                prefix,
+                reminder,
+                matchingRealLabels,
+                hasReminderLabel,
+              });
+
+              if (hasRealLabel && hasReminderLabel) {
+                labelsToRemove.push(reminder);
+              } else if (!hasRealLabel && !hasReminderLabel) {
+                labelsToAdd.push(reminder);
+              }
+            }
+
+            for (const evaluation of evaluations) {
+              if (evaluation.matchingRealLabels.length > 0) {
+                console.log(
+                  `Found required ${evaluation.prefix}label(s): ${evaluation.matchingRealLabels.join(', ')}`,
+                );
+              } else {
+                console.log(`Missing required ${evaluation.prefix}label.`);
+              }
+
+              if (evaluation.hasReminderLabel) {
+                console.log(`Reminder label already present: ${evaluation.reminder}`);
+              }
+            }
+
+            if (labelsToAdd.length > 0) {
+              console.log(`Adding reminder labels: ${labelsToAdd.join(', ')}`);
+              await github.rest.issues.addLabels({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                labels: labelsToAdd,
+              });
+            }
+
+            for (const name of labelsToRemove) {
+              console.log(`Removing reminder label: ${name}`);
+              await github.rest.issues.removeLabel({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                name,
+              });
+            }
+
+            if (labelsToAdd.length === 0 && labelsToRemove.length === 0) {
+              console.log('No label changes required.');
+            }
+
+            console.log('::endgroup::');
+
+            if (process.env.GITHUB_STEP_SUMMARY) {
+              const summaryLines = [
+                '### Issue Label Check',
+                `- Issue: #${issueNumber}`,
+                `- Event: ${action}`,
+                `- Trigger label: ${changedLabel ?? '(none)'}`,
+                `- Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
+                '',
+                '#### Requirement status',
+                ...evaluations.map((evaluation) => {
+                  const status =
+                    evaluation.matchingRealLabels.length > 0
+                      ? `found ${evaluation.matchingRealLabels.join(', ')}`
+                      : 'missing';
+                  const reminder = evaluation.hasReminderLabel
+                    ? `reminder present: ${evaluation.reminder}`
+                    : `reminder absent: ${evaluation.reminder}`;
+                  return `- ${evaluation.prefix}: ${status}; ${reminder}`;
+                }),
+                '',
+                `- Labels to add: ${labelsToAdd.length > 0 ? labelsToAdd.join(', ') : '(none)'}`,
+                `- Labels to remove: ${labelsToRemove.length > 0 ? labelsToRemove.join(', ') : '(none)'}`,
+              ];
+
+              fs.appendFileSync(
+                process.env.GITHUB_STEP_SUMMARY,
+                `${summaryLines.join('\n')}\n`,
+              );
+            }
diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml
index 642cd3184..25710633b 100644
--- a/.github/workflows/pr-labels.yml
+++ b/.github/workflows/pr-labels.yml
@@ -9,6 +9,9 @@
 # (e.g. by manual labeling, another workflow, or GitHub App).
 #
 # These are separate GitHub events, so two workflow runs can be started.
+# The `concurrency` configuration below ensures that only the latest run
+# for the same PR remains active, canceling any previous in-progress
+# run.
 
 name: PR labels check
 
@@ -16,6 +19,10 @@ on:
   pull_request_target:
     types: [opened, labeled, unlabeled, synchronize]
 
+concurrency:
+  group: pr-labels-${{ github.event.pull_request.number }}
+  cancel-in-progress: true
+
 permissions:
   pull-requests: read
 
diff --git a/.github/workflows/release-pr.yml b/.github/workflows/release-pr.yml
index 0e716c27e..3fa073b90 100644
--- a/.github/workflows/release-pr.yml
+++ b/.github/workflows/release-pr.yml
@@ -48,7 +48,7 @@ jobs:
           gh pr create \
             --base ${{ env.DEFAULT_BRANCH }} \
             --head ${{ env.SOURCE_BRANCH }} \
-            --title "Release: merge ${{ env.SOURCE_BRANCH }} into ${{ env.DEFAULT_BRANCH }}" \
+            --title "🎉 Release: merge ${{ env.SOURCE_BRANCH }} into ${{ env.DEFAULT_BRANCH }}" \
             --label "[bot] release" \
             --body "This PR is created automatically to trigger the release pipeline. It merges the accumulated changes from \`${{ env.SOURCE_BRANCH }}\` into \`${{ env.DEFAULT_BRANCH }}\`.
 
diff --git a/docs/dev/adp_implementation.md b/docs/dev/adp_implementation.md
deleted file mode 100644
index 4d66e0b74..000000000
--- a/docs/dev/adp_implementation.md
+++ /dev/null
@@ -1,321 +0,0 @@
-# ADP Implementation Plan
-
-**Date:** 2026-04-12 **Status:** Design approved — awaiting
-implementation
-
----
-
-## 1. Goal
-
-Extend ADP (Atomic Displacement Parameter) support from the current
-Biso-only implementation to all four CIF-standard types: **Biso**,
-**Uiso**, **Bani**, **Uani**. The design uses type-neutral parameter
-names (`adp_iso`, `adp_11`…`adp_23`) so that switching ADP type is a
-one-line operation on `adp_type` without creating or destroying
-parameters.
-
----
-
-## 2. Design Summary
-
-### 2.1 Two Sibling Collections on Structure
-
-Following CIF conventions (`_atom_site` + `_atom_site_aniso` are
-separate loops), the structure owns two sibling collections:
-
-```
-Structure
-├── cell              (CategoryItem)
-├── space_group       (CategoryItem)
-├── atom_sites        (CategoryCollection of AtomSite)
-└── atom_site_aniso   (CategoryCollection of AtomSiteAniso)
-```
-
-Every atom always has an entry in both collections, kept in sync by
-`Structure._update_categories()`. This eliminates conditional existence
-checks throughout the codebase.
-
-### 2.2 Type-Neutral Parameter Names
-
-Parameters on `AtomSite` and `AtomSiteAniso` use type-neutral names
-whose physical meaning is determined by `atom_site.adp_type`:
-
-| Parameter  | Location        | CIF names (order depends on `adp_type`)                  |
-| ---------- | --------------- | -------------------------------------------------------- |
-| `adp_type` | `AtomSite`      | `_atom_site.adp_type`                                    |
-| `adp_iso`  | `AtomSite`      | `_atom_site.B_iso_or_equiv`, `_atom_site.U_iso_or_equiv` |
-| `adp_11`   | `AtomSiteAniso` | `_atom_site_aniso.B_11`, `_atom_site_aniso.U_11`         |
-| `adp_22`   | `AtomSiteAniso` | `_atom_site_aniso.B_22`, `_atom_site_aniso.U_22`         |
-| `adp_33`   | `AtomSiteAniso` | `_atom_site_aniso.B_33`, `_atom_site_aniso.U_33`         |
-| `adp_12`   | `AtomSiteAniso` | `_atom_site_aniso.B_12`, `_atom_site_aniso.U_12`         |
-| `adp_13`   | `AtomSiteAniso` | `_atom_site_aniso.B_13`, `_atom_site_aniso.U_13`         |
-| `adp_23`   | `AtomSiteAniso` | `_atom_site_aniso.B_23`, `_atom_site_aniso.U_23`         |
-
-### 2.3 Dual CIF Names — Static Read, Reordered Write
-
-Each parameter's `CifHandler` carries both CIF name variants. The
-existing infrastructure handles this:
-
-- **Reading (deserialization):** `param_from_cif()` and
-  `category_collection_from_cif()` iterate `_cif_handler.names` and stop
-  at the first match. A CIF file with `_atom_site.U_iso_or_equiv` is
-  read correctly regardless of name order.
-- **Writing (serialization):** `param_to_cif()` and
-  `category_collection_to_cif()` always use `names[0]`. The `adp_type`
-  setter reorders the `names` list so that the correct CIF tag is
-  emitted first.
-
-Example — when `adp_type` changes from `'Biso'` to `'Uiso'`:
-
-```python
-# adp_type setter reorders CIF names on adp_iso
-self._adp_iso._cif_handler._names = [
-    '_atom_site.U_iso_or_equiv',
-    '_atom_site.B_iso_or_equiv',
-]
-```
-
-No new core CIF infrastructure is needed.
-
-### 2.4 ADP Type Enum
-
-```python
-class AdpTypeEnum(str, Enum):
-    BISO = 'Biso'
-    UISO = 'Uiso'
-    BANI = 'Bani'
-    UANI = 'Uani'
-```
-
-`adp_type` on `AtomSite` is a `StringDescriptor` validated by
-`MembershipValidator(allowed=...)` using all four enum values.
-
-### 2.5 Auto-Conversion on Type Switch
-
-Setting `adp_type` triggers value conversion. The physics:
-
-- **B ↔ U (isotropic):** `B = 8π²U`
-- **Iso → Ani:** diagonal `adp_11 = adp_22 = adp_33 = adp_iso`,
-  off-diagonal `adp_12 = adp_13 = adp_23 = 0`
-- **Ani → Iso:** `adp_iso = (adp_11 + adp_22 + adp_33) / 3`
-
-The `adp_type` setter on `AtomSite` performs the conversion and updates
-both the isotropic parameter and the aniso parameters on the sibling
-collection.
-
-### 2.6 Collection Sync via `_update_categories()`
-
-`Structure._update_categories()` reconciles the two collections:
-
-| Event                              | Sync action                                               |
-| ---------------------------------- | --------------------------------------------------------- |
-| Atom added to `atom_sites`         | Create matching `AtomSiteAniso` entry with defaults (0.0) |
-| Atom removed from `atom_sites`     | Remove matching `AtomSiteAniso` entry                     |
-| Atom label renamed in `atom_sites` | Rekey the matching `AtomSiteAniso` entry                  |
-
-The sync is driven by the dirty flag — any parameter or collection
-change sets `_need_categories_update = True`, and the next
-serialization, plot, or fit call triggers `_update_categories()`.
-
-### 2.7 Inactive Aniso Values
-
-When `adp_type` is `'Biso'` or `'Uiso'`, the aniso parameters exist with
-value `0.0` but are not read by calculators. This avoids introducing
-`None` into the `float`-based `Parameter` system.
-
----
-
-## 3. User-Facing API
-
-### 3.1 Parameter Access Pattern
-
-Parameters are accessed via the standard two-level pattern:
-
-```python
-# CategoryItem.Parameter
-structure.cell.length_a = 3.88
-
-# CategoryCollection[item_id].Parameter
-structure.atom_sites['Si'].adp_type = 'Biso'
-structure.atom_sites['Si'].adp_iso = 0.47
-structure.atom_site_aniso['Si'].adp_11 = 0.05
-```
-
-### 3.2 Switching ADP Type
-
-```python
-# Switch from Biso to Uiso — auto-converts value
-structure.atom_sites['Si'].adp_type = 'Uiso'
-# adp_iso now holds U_iso value (B / 8π²)
-
-# Switch to anisotropic — seeds diagonal from iso
-structure.atom_sites['Si'].adp_type = 'Uani'
-# atom_site_aniso['Si'].adp_11/22/33 seeded from adp_iso
-# adp_iso recalculated as mean of diagonal
-
-# Switch back to isotropic — collapses tensor to scalar
-structure.atom_sites['Si'].adp_type = 'Biso'
-# adp_iso = mean(adp_11, adp_22, adp_33), converted B→U if needed
-```
-
-### 3.3 Creating Atoms
-
-```python
-# adp_iso replaces the old b_iso keyword
-structure.atom_sites.create(
-    label='Si',
-    type_symbol='Si',
-    fract_x=0.0,
-    fract_y=0.0,
-    fract_z=0.0,
-    adp_type='Biso',
-    adp_iso=0.47,
-)
-# atom_site_aniso['Si'] is auto-created by _update_categories()
-```
-
-### 3.4 CIF Output
-
-```cif
-loop_
-_atom_site.label
-_atom_site.type_symbol
-_atom_site.fract_x
-_atom_site.fract_y
-_atom_site.fract_z
-_atom_site.adp_type
-_atom_site.B_iso_or_equiv
-_atom_site.occupancy
-Si  Si  0.00000000  0.00000000  0.00000000  Biso  0.47000000  1.00000000
-
-loop_
-_atom_site_aniso.label
-_atom_site_aniso.B_11
-_atom_site_aniso.B_22
-_atom_site_aniso.B_33
-_atom_site_aniso.B_12
-_atom_site_aniso.B_13
-_atom_site_aniso.B_23
-Si  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
-```
-
-When `adp_type = 'Uiso'`, the CIF tag becomes
-`_atom_site.U_iso_or_equiv` and the aniso loop uses
-`_atom_site_aniso.U_*` tags.
-
----
-
-## 4. Implementation Phases
-
-### Phase 1: Rename `b_iso` → `adp_iso` and Expand `adp_type`
-
-**Files to modify:**
-
-1. **`atom_sites/default.py`** — `AtomSite`:
-   - Rename `_b_iso` Parameter to `_adp_iso` with dual CIF names
-     `['_atom_site.B_iso_or_equiv', '_atom_site.U_iso_or_equiv']`.
-   - Expand `_adp_type` validator to accept all four values from
-     `AdpTypeEnum`.
-   - Add `adp_type` setter logic: reorder CIF names on `_adp_iso`,
-     perform B↔U conversion.
-   - Rename property `b_iso` → `adp_iso`.
-
-2. **`atom_sites/enums.py`** (new) — `AdpTypeEnum` with BISO, UISO,
-   BANI, UANI members plus `default()` and `description()` methods.
-
-3. **Calculator bridges** — update `cryspy.py` and `crysfml.py` to read
-   `atom.adp_iso.value` instead of `atom.b_iso.value`, and pass the
-   correct type based on `atom.adp_type.value`.
-
-4. **CIF data files** — update all `.cif` files in `data/` that
-   reference `b_iso`.
-
-5. **Tutorials** — update all `*.py` scripts that use `b_iso`.
-
-6. **Tests** — update all unit/functional/integration tests.
-
-### Phase 2: Add `AtomSiteAniso` Sibling Collection
-
-**Files to create:**
-
-1. **`atom_site_aniso/`** package under
-   `datablocks/structure/categories/`:
-   - `__init__.py` — imports `AtomSiteAniso` and
-     `AtomSiteAnisoCollection`.
-   - `default.py` — `AtomSiteAniso` (CategoryItem) with `label`
-     (StringDescriptor) and six Parameters (`adp_11`…`adp_23`) each with
-     dual CIF names. `AtomSiteAnisoCollection` (CategoryCollection).
-   - `factory.py` — `AtomSiteAnisoFactory`.
-   - `enums.py` — if needed (likely shared with Phase 1 enum).
-
-2. **`structure/item/base.py`** — add `_atom_site_aniso` attribute and
-   `atom_site_aniso` read-only property on Structure.
-
-3. **`structure/item/base.py`** — override `_update_categories()` to
-   reconcile `atom_sites` and `atom_site_aniso` collections (add
-   missing, remove stale, rekey on label change).
-
-**Files to modify:**
-
-4. **`atom_sites/default.py`** — `adp_type` setter also reorders CIF
-   names on all six aniso parameters (accessed via parent structure's
-   `atom_site_aniso` collection).
-
-5. **Calculator bridges** — when `adp_type` is `'Bani'` or `'Uani'`,
-   read from `atom_site_aniso[label]` instead of `adp_iso`.
-
-6. **CIF serialization** — works automatically (Structure's `as_cif`
-   emits all CategoryCollections found in `vars(self)`).
-
-### Phase 3: ADP Symmetry Constraints
-
-1. **`crystallography.py`** — add symmetry constraint functions for
-   anisotropic ADPs based on space group and Wyckoff position.
-
-2. **`AtomSites._update()`** — call aniso symmetry constraints in
-   addition to coordinate constraints.
-
----
-
-## 5. Breaking Changes
-
-| Change                                    | Scope                             | Migration                          |
-| ----------------------------------------- | --------------------------------- | ---------------------------------- |
-| `b_iso` → `adp_iso`                       | All code referencing `atom.b_iso` | Mechanical rename (greppable)      |
-| `b_iso=0.5` → `adp_iso=0.5` in `create()` | Tutorials, tests, user scripts    | Mechanical rename                  |
-| New `atom_site_aniso` on Structure        | Structure API surface grows       | Additive — no existing code breaks |
-
-The project is in beta, so no deprecation path is needed.
-
----
-
-## 6. Design Decisions
-
-### Why type-neutral names (`adp_iso`) instead of type-specific (`b_iso`, `u_iso`)?
-
-With type-specific names, switching ADP type would require
-creating/destroying parameters, migrating constraints and free flags,
-and updating every reference. Type-neutral names make switching a
-one-liner on `adp_type` — the parameter object stays the same, only its
-value and CIF tag change.
-
-### Why always-present aniso collection instead of on-demand?
-
-Conditional existence of `atom_site_aniso` would require guards
-everywhere: serialization, calculators, parameter tables, constraint
-wiring, UI. Always-present with 0.0 defaults eliminates all those
-branches.
-
-### Why sync via `_update_categories()` instead of coupled add/remove?
-
-Loose coupling: `AtomSites` and `AtomSiteAnisoCollection` don't know
-about each other. `Structure` coordinates them at update time. This
-follows the existing dirty-flag pattern and keeps categories
-independent.
-
-### Why reorder `_cif_handler.names` instead of dynamic CIF handler?
-
-The existing serialization/deserialization pipeline uses `names[0]` for
-writing and iterates all names for reading. Reordering the list is a
-2-line operation in the `adp_type` setter with zero core infrastructure
-changes.
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 6cb8c67e2..390aa77bd 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -338,7 +338,8 @@ per-key flags.
 
 ADP support covers four CIF-standard types: **Biso**, **Uiso**,
 **Bani**, **Uani**. The design uses **type-neutral parameter names** so
-that switching type is a one-line operation on `adp_type`.
+that switching type is a one-line operation on `adp_type`: parameter
+objects stay stable while their values and CIF output names change.
 
 **Two sibling collections.** Following CIF conventions (`_atom_site` and
 `_atom_site_aniso` are separate loops), isotropic and anisotropic data
@@ -346,27 +347,96 @@ live in separate collections on `Structure`. Every atom always has an
 entry in both collections; when `adp_type` is isotropic, the aniso
 parameters hold `0.0` and are ignored by calculators.
 
-**Type-neutral names.** `atom_site.adp_iso` is the type-neutral
-isotropic ADP parameter. Its physical meaning (B or U) is determined by
-`atom_site.adp_type`. Similarly, `atom_site_aniso.adp_11`…`adp_23` are
+```shell
+Structure
+├── cell              (CategoryItem)
+├── space_group       (CategoryItem)
+├── atom_sites        (CategoryCollection of AtomSite)
+└── atom_site_aniso   (CategoryCollection of AtomSiteAniso)
+```
+
+**Type-neutral names.** `atom_site.adp_iso` is the isotropic ADP
+parameter. Its physical meaning (B or U) is determined by
+`atom_site.adp_type`. Similarly, `atom_site_aniso.adp_11`...`adp_23` are
 type-neutral tensor components.
 
+| Parameter  | Location        | CIF names                                                |
+| ---------- | --------------- | -------------------------------------------------------- |
+| `adp_type` | `AtomSite`      | `_atom_site.adp_type`                                    |
+| `adp_iso`  | `AtomSite`      | `_atom_site.B_iso_or_equiv`, `_atom_site.U_iso_or_equiv` |
+| `adp_11`   | `AtomSiteAniso` | `_atom_site_aniso.B_11`, `_atom_site_aniso.U_11`         |
+| `adp_22`   | `AtomSiteAniso` | `_atom_site_aniso.B_22`, `_atom_site_aniso.U_22`         |
+| `adp_33`   | `AtomSiteAniso` | `_atom_site_aniso.B_33`, `_atom_site_aniso.U_33`         |
+| `adp_12`   | `AtomSiteAniso` | `_atom_site_aniso.B_12`, `_atom_site_aniso.U_12`         |
+| `adp_13`   | `AtomSiteAniso` | `_atom_site_aniso.B_13`, `_atom_site_aniso.U_13`         |
+| `adp_23`   | `AtomSiteAniso` | `_atom_site_aniso.B_23`, `_atom_site_aniso.U_23`         |
+
+`adp_type` is a `StringDescriptor` validated against `AdpTypeEnum`
+(`Biso`, `Uiso`, `Bani`, `Uani`).
+
 **Dual CIF names.** Each parameter's `CifHandler` carries both CIF name
 variants (e.g.
 `['_atom_site.B_iso_or_equiv', '_atom_site.U_iso_or_equiv']`). Reading
 tries each name until a match is found; writing uses `names[0]`. The
 `adp_type` setter reorders the list so the correct tag is emitted.
 
+For example, switching from `Biso` to `Uiso` makes
+`_atom_site.U_iso_or_equiv` the first CIF name on `adp_iso`; switching
+from `Bani` to `Uani` does the same for all six `_atom_site_aniso.U_*`
+tensor tags. No dynamic CIF handler is needed.
+
 **Auto-conversion.** Setting `adp_type` triggers value conversion: B ↔ U
 via `B = 8π²U`; iso → ani seeds the diagonal; ani → iso averages the
 diagonal.
 
+| Transition | Conversion rule                                                       |
+| ---------- | --------------------------------------------------------------------- |
+| B ↔ U      | `B = 8π²U`                                                            |
+| Iso → Ani  | `adp_11 = adp_22 = adp_33 = adp_iso`; off-diagonal terms become `0.0` |
+| Ani → Iso  | `adp_iso = (adp_11 + adp_22 + adp_33) / 3`                            |
+
 **Collection sync.** `Structure._update_categories()` reconciles the two
 collections: adds missing aniso entries, removes stale ones, and rekeys
-on label rename.
+on label rename. Sync follows the datablock dirty-flag pattern: category
+or parameter changes mark the structure as needing an update, and the
+next serialisation, plot, or fit call performs the reconciliation.
+
+| Event                              | Sync action                                               |
+| ---------------------------------- | --------------------------------------------------------- |
+| Atom added to `atom_sites`         | Create matching `AtomSiteAniso` entry with `0.0` defaults |
+| Atom removed from `atom_sites`     | Remove matching `AtomSiteAniso` entry                     |
+| Atom label renamed in `atom_sites` | Rekey the matching `AtomSiteAniso` entry                  |
+
+**User-facing access.** ADP parameters follow the same two-level access
+pattern as other category parameters:
+
+```python
+structure.atom_sites['Si'].adp_type = 'Biso'
+structure.atom_sites['Si'].adp_iso = 0.47
+structure.atom_site_aniso['Si'].adp_11 = 0.05
+```
+
+Creating an atom uses `adp_iso`; the matching `atom_site_aniso` entry is
+created by `_update_categories()`:
+
+```python
+structure.atom_sites.create(
+    label='Si',
+    type_symbol='Si',
+    fract_x=0.0,
+    fract_y=0.0,
+    fract_z=0.0,
+    adp_type='Biso',
+    adp_iso=0.47,
+)
+```
 
-See [`adp_implementation.md`](adp_implementation.md) for the full
-implementation plan.
+**Design rule.** ADP parameter names are type-neutral because
+type-specific names (`b_iso`, `u_iso`, etc.) would require replacing
+parameter objects when the ADP type changes, which would break
+constraints, free flags, and existing references. The always-present
+`atom_site_aniso` collection avoids conditional branches in
+serialisation, calculators, parameter tables, constraint wiring, and UI.
 
 ---
 
diff --git a/docs/dev/cryspy-dwf-bug.md b/docs/dev/cryspy-dwf-bug.md
deleted file mode 100644
index 5f475ecc6..000000000
--- a/docs/dev/cryspy-dwf-bug.md
+++ /dev/null
@@ -1,126 +0,0 @@
-# CrysPy Bug: Wrong Rotation Matrix Indices in `calc_power_dwf_aniso`
-
-## Summary
-
-`calc_power_dwf_aniso` in CrysPy 0.7.8 reads the rotation matrix from
-the wrong slice of `reduced_symm_elems`, producing incorrect
-Debye–Waller factors for every anisotropic atom.
-
-## Affected Version
-
-CrysPy **0.7.8** (and likely all versions sharing this code path).
-
-## Location
-
-`cryspy/A_functions_base/debye_waller_factor.py`, function
-`calc_power_dwf_aniso`, around line 177.
-
-## Root Cause
-
-`reduced_symm_elems` has shape `(13, N_symmetry_operations)` with the
-following layout per column:
-
-| Indices | Content                             |
-| ------- | ----------------------------------- |
-| 0–3     | Translation: $b_1, b_2, b_3, b_d$   |
-| 4–12    | Rotation matrix $R$ (row-major 3×3) |
-
-The buggy code extracts the rotation matrix from indices **0–8** (i.e.
-`symm_elems_r[0:9]`), which mixes the translation vector into the
-rotation matrix:
-
-```python
-# BUGGY (current code)
-r_11, r_12, r_13 = symm_elems_r[0], symm_elems_r[1], symm_elems_r[2]
-r_21, r_22, r_23 = symm_elems_r[3], symm_elems_r[4], symm_elems_r[5]
-r_31, r_32, r_33 = symm_elems_r[6], symm_elems_r[7], symm_elems_r[8]
-```
-
-The correct indices are **4–12**:
-
-```python
-# FIXED
-r_11, r_12, r_13 = symm_elems_r[4], symm_elems_r[5], symm_elems_r[6]
-r_21, r_22, r_23 = symm_elems_r[7], symm_elems_r[8], symm_elems_r[9]
-r_31, r_32, r_33 = symm_elems_r[10], symm_elems_r[11], symm_elems_r[12]
-```
-
-For comparison, `calc_pr1` in the same codebase (structure factor phase
-calculation) correctly uses indices 4–12 for the rotation.
-
-## Impact
-
-Every structure-factor calculation that uses anisotropic ADPs produces
-wrong results. The isotropic DWF (`calc_power_dwf_iso`) is unaffected
-and works correctly, so the bug only manifests when anisotropic
-displacement parameters ($B_\text{ani}$ or $U_\text{ani}$) are used.
-
-## Verification
-
-We verified the fix by comparing isotropic and anisotropic calculations
-for the LBCO structure (space group $I\,4/m\,m\,m$) where $B_\text{iso}$
-and the equivalent diagonal anisotropic tensor must give identical
-$|F|^2$ values:
-
-| Metric                      | Buggy code                  | Patched code |
-| --------------------------- | --------------------------- | ------------ | ------------------------------ | ---------------------- |
-| $\max                       | y*\text{iso} - y*\text{ani} | $            | ~11 (first peak: 72.1 vs 61.0) | $1.78 \times 10^{-13}$ |
-| $\chi^2$ (Biso)             | 14.98                       | 14.98        |
-| $\chi^2$ (Bani, equivalent) | 184.10                      | 14.98        |
-
-## Workaround
-
-Monkey-patch at import time (used in EasyDiffraction):
-
-```python
-from cryspy.A_functions_base import debye_waller_factor as _dwf_mod
-
-def _patched_calc_power_dwf_aniso(index_hkl, beta, symm_elems_r, flag_beta=False):
-    b_11, b_22, b_33 = beta[0], beta[1], beta[2]
-    b_12, b_13, b_23 = beta[3], beta[4], beta[5]
-    h, k, l = index_hkl[0], index_hkl[1], index_hkl[2]
-    # Corrected indices: rotation starts at 4, not 0
-    r_11, r_12, r_13 = symm_elems_r[4], symm_elems_r[5], symm_elems_r[6]
-    r_21, r_22, r_23 = symm_elems_r[7], symm_elems_r[8], symm_elems_r[9]
-    r_31, r_32, r_33 = symm_elems_r[10], symm_elems_r[11], symm_elems_r[12]
-    h_s = h * r_11 + k * r_21 + l * r_31
-    k_s = h * r_12 + k * r_22 + l * r_32
-    l_s = h * r_13 + k * r_23 + l * r_33
-    power = (
-        b_11 * np.square(h_s) + b_22 * np.square(k_s) + b_33 * np.square(l_s)
-        + 2.0 * b_12 * h_s * k_s
-        + 2.0 * b_13 * h_s * l_s
-        + 2.0 * b_23 * k_s * l_s
-    )
-    dder = {}
-    if flag_beta:
-        ones_b = np.ones_like(b_11)
-        dder['beta'] = np.stack([
-            ones_b * np.square(h_s),
-            ones_b * np.square(k_s),
-            ones_b * np.square(l_s),
-            ones_b * 2.0 * h_s * k_s,
-            ones_b * 2.0 * h_s * l_s,
-            ones_b * 2.0 * k_s * l_s,
-        ], axis=0)
-    return power, dder
-
-_dwf_mod.calc_power_dwf_aniso = _patched_calc_power_dwf_aniso
-```
-
-## Suggested Fix
-
-In `cryspy/A_functions_base/debye_waller_factor.py`, function
-`calc_power_dwf_aniso`, change:
-
-```diff
--    r_11, r_12, r_13 = symm_elems_r[0], symm_elems_r[1], symm_elems_r[2]
--    r_21, r_22, r_23 = symm_elems_r[3], symm_elems_r[4], symm_elems_r[5]
--    r_31, r_32, r_33 = symm_elems_r[6], symm_elems_r[7], symm_elems_r[8]
-+    r_11, r_12, r_13 = symm_elems_r[4], symm_elems_r[5], symm_elems_r[6]
-+    r_21, r_22, r_23 = symm_elems_r[7], symm_elems_r[8], symm_elems_r[9]
-+    r_31, r_32, r_33 = symm_elems_r[10], symm_elems_r[11], symm_elems_r[12]
-```
-
-The same correction must be applied to the analytical-derivative branch
-of the same function if it shares the same index convention.
diff --git a/docs/dev/plan_powder-chart-y-range.md b/docs/dev/plan_powder-chart-y-range.md
deleted file mode 100644
index c987fe47a..000000000
--- a/docs/dev/plan_powder-chart-y-range.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Powder Chart Y-Range Fix Plan
-
-**Date:** 2026-05-06 **Status:** Phase 2 verified — complete
-
----
-
-## 1. Goal
-
-Fix the Plotly composite powder measured-vs-calculated chart so the main
-intensity row is not anchored to zero. The y-axis range should be
-derived from all displayed main-row intensity series: measured
-(`Imeas`), calculated (`Icalc`), and background (`Ibkg`) when present.
-
-The intended display range is:
-
-```text
-lower = min(Imeas, Icalc, Ibkg) - margin
-upper = max(Imeas, Icalc, Ibkg) + margin
-```
-
-where `margin` is controlled by a dedicated constant of about 5% of the
-main intensity span. The lower bound must use `min - margin`, not
-`min + margin`, so the lowest displayed point remains visible with
-padding below it.
-
----
-
-## 2. Current Findings
-
-- The affected code is `PlotlyPlotter._get_main_intensity_range()` in
-  `src/easydiffraction/display/plotters/plotly.py`.
-- It currently uses only `y_meas` and `y_calc`, then forces
-  `lower_limit = min(0.0, main_y_min)`. That explains positive powder
-  charts being truncated to a `0..max` range.
-- `PowderMeasVsCalcSpec` already carries optional `y_bkg`, and
-  `Plotter._plot_meas_vs_calc_data()` already filters
-  `pattern.intensity_bkg` into the spec for powder Bragg plots.
-- Existing Plotly unit tests assert the current `0.0..max` y-range in
-  the residual scale-match tests, so those expectations must change.
-- Repository memory notes confirm the background line is part of the
-  composite powder plot and should be considered display data.
-
----
-
-## 3. Scope
-
-### In Scope
-
-- Add a module-level constant in
-  `src/easydiffraction/display/plotters/plotly.py`, likely
-  `MAIN_INTENSITY_RANGE_MARGIN_FRACTION = 0.05`.
-- Update `_get_main_intensity_range()` to compute min/max over `y_meas`,
-  `y_calc`, and non-empty `y_bkg` when present.
-- Apply symmetric visual padding outside the data range using the new
-  constant.
-- Preserve the existing empty-filtered-range behavior: empty required
-  series should still return a harmless fallback range.
-- Preserve residual scale matching by letting `_get_residual_limit()`
-  use the newly padded main range and the existing
-  `residual_height_fraction`, so the residual row remains adjusted to
-  the main row size as it is now.
-- Add/update focused unit tests for range calculation and affected
-  residual-scale expectations.
-
-### Out of Scope
-
-- No public plotting API changes.
-- No user-configurable y-axis margin in this step.
-- No changes to ASCII plotting unless a later review shows the same main
-  view problem exists there.
-- No refactor of plot layout, Bragg tick sizing, hover templates, or
-  facade routing.
-
----
-
-## 4. Decisions
-
-- Use `min(Imeas, Icalc, Ibkg) - margin` for the lower y-axis bound and
-  `max(Imeas, Icalc, Ibkg) + margin` for the upper y-axis bound.
-- Keep the residual plot scaled to the main intensity row, preserving
-  the current matched-scale behavior after the main range gains padding.
-
----
-
-## 5. Implementation Checklist
-
-- [ ] Create branch `feature/powder-chart-y-range` if requested.
-- [x] In `src/easydiffraction/display/plotters/plotly.py`, add the
-      dedicated 5% y-range margin constant near the other Plotly layout
-      constants.
-- [x] Update `_get_main_intensity_range()` so it includes background
-      intensity when available and uses the padded min/max range instead
-      of anchoring positive data to zero.
-- [x] Keep zero-span data explicit and stable, using a small fallback
-      range around the datum because a percentage margin is undefined.
-- [x] Confirm `_get_residual_limit()` continues to scale the residual
-      row from the updated main y-range and existing residual height
-      fraction.
-- [x] Stop after Phase 1 and request review before adding or running
-      tests, following the repo workflow.
-
----
-
-## 6. Phase 2 Verification Checklist
-
-- [x] Add or update tests in
-      `tests/unit/easydiffraction/display/plotters/test_plotly.py` for:
-  - positive-only `Imeas`/`Icalc` data no longer starting at zero;
-  - `Ibkg` lowering or raising the main y-range when present;
-  - 5% padding on both ends of the main row;
-  - residual scale-match expectations after padding changes the main row
-    span;
-  - empty filtered arrays retaining the existing fallback behavior.
-- [x] Keep the existing facade propagation test in
-      `tests/unit/easydiffraction/display/test_plotting.py` unless the
-      implementation reveals a missing background handoff case.
-- [x] Run `pixi run fix`.
-- [x] Run `pixi run check` until clean.
-- [x] Run `pixi run unit-tests`.
-- [x] Run `pixi run integration-tests`.
-- [x] Run `pixi run script-tests`.
-
----
-
-## 7. Likely Files
-
-- `src/easydiffraction/display/plotters/plotly.py`
-- `tests/unit/easydiffraction/display/plotters/test_plotly.py`
-- `tests/unit/easydiffraction/display/test_plotting.py` only if a
-  facade-level test gap is discovered during verification.
-
----
-
-## 8. Suggested Commit Message
-
-```text
-Fix powder chart y-axis range
-```
diff --git a/docs/docs/installation-and-setup/index.md b/docs/docs/installation-and-setup/index.md
index fdea87bfc..41036f1d9 100644
--- a/docs/docs/installation-and-setup/index.md
+++ b/docs/docs/installation-and-setup/index.md
@@ -244,9 +244,10 @@ once using the command line, as shown below.
 
 - Navigate to your existing Pixi project, created as described in the
   [Installing with Pixi](#installing-with-pixi) section.
-- Add JupyterLab and the Pixi kernel for Jupyter:
+- Add JupyterLab, Interactive Python shell and the Pixi kernel for
+  Jupyter:
   ```txt
-  pixi add --pypi jupyterlab pixi-kernel
+  pixi add --pypi jupyterlab ipython pixi-kernel
   ```
 - Download all the EasyDiffraction tutorials to the `tutorials/`
   directory:
@@ -263,9 +264,10 @@ once using the command line, as shown below.
 
 ### Classical Run Tutorials Locally
 
-- Install Jupyter Notebook and IPython kernel:
+- Install Jupyter Notebook, Interactive Python shell and the IPython
+  kernel:
   ```txt
-  pip install notebook ipykernel
+  pip install notebook ipython ipykernel
   ```
 - Add the virtual environment as a Jupyter kernel:
   ```txt
diff --git a/pixi.lock b/pixi.lock
index 36a2ecb00..4ff7fdb61 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -2,6 +2,7 @@ version: 6
 environments:
   default:
     channels:
+    - url: https://conda.anaconda.org/nodefaults/
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
@@ -19,7 +20,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.4.0-py314h680f03e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
@@ -65,7 +66,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.17.0-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.18.2-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda
@@ -82,7 +83,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-6_h0358290_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.5-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.0-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_18.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_18.conda
@@ -94,14 +95,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.32-pthreads_h94d23a6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.21-h280c20c_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.0-hf4e2dac_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_18.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42-h5347b49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py314h67df5f8_1.conda
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+  timestamp: 1777744489106
 - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda
   sha256: 21f6c8a20fe050d09bfda3fb0a9c3493936ce7d6e1b3b5f8b01319ee46d6c6f6
   md5: 6639b6b0d8b5a284f027a2003669aa65
diff --git a/pixi.toml b/pixi.toml
index 546620d4a..958851cf2 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,27 +1,3 @@
-#######################
-# ENVIRONMENT VARIABLES
-#######################
-
-# Platform-independent
-
-[activation.env]
-PYTHONIOENCODING = 'utf-8'
-
-# Platform-specific
-
-# Ensures the main package is used from the source code during
-# development, even if main package is installed in editable mode
-# via uv. This is important because `pixi update` might replace
-# the installed version with the latest released version.
-
-# Unix/macOS
-[target.unix.activation.env]
-PYTHONPATH = "${PIXI_PROJECT_ROOT}/src${PYTHONPATH:+:${PYTHONPATH}}"
-
-# Windows
-[target.win.activation.env]
-PYTHONPATH = "${PIXI_PROJECT_ROOT}/src;%PYTHONPATH%"
-
 ###########
 # WORKSPACE
 ###########
@@ -32,49 +8,65 @@ PYTHONPATH = "${PIXI_PROJECT_ROOT}/src;%PYTHONPATH%"
 platforms = ['win-64', 'linux-64', 'osx-arm64']
 
 # Channels for fetching packages
-channels = ['conda-forge']
+channels = ['nodefaults', 'conda-forge']
 
-#####################
-# SYSTEM REQUIREMENTS
-#####################
+##########
+# FEATURES
+##########
 
-[system-requirements]
+# Default features:
 
+[activation.env]
+PYTHONIOENCODING = 'utf-8'
+
+[system-requirements]
 # Set minimum supported version for macOS to be 14.0 to ensure packages
-# like `skipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64)
+# like `scipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64)
 # are used instead of building from source. This is a workaround for
 # Pixi, see https://github.com/prefix-dev/pixi/issues/5667
 macos = '14.0'
-
 # Set minimum supported version for glibc to be 2.35 to ensure packages
 # like `crysfml` that only have wheels for glibc 2.35+
 # (manylinux_2_35_x86_64) are used.
 #libc = { family = 'glibc', version = '2.35' }
 libc = '2.35'
 
-##########
-# FEATURES
-##########
+# Non-default features:
+
+# Set specific Python versions to be used in CI testing.
+
+[feature.py-min.dependencies]
+python = '3.12.*'
+
+[feature.py-max.dependencies]
+python = '3.14.*'
 
-# Default feature configuration
+# Development dependencies for local development and testing with
+# editable installations.
 
-[dependencies]
+[feature.dev.dependencies]
 nodejs = '*'      # Required for Prettier (non-Python formatting)
 jupyterlab = '*'  # Jupyter notebooks
+ipython = '*'     # Interactive Python shell
 pixi-kernel = '*' # Pixi Jupyter kernel
 gsl = '*'         # GNU Scientific Library; required for diffpy.pdffit2
 
-[pypi-dependencies] # == [feature.default.pypi-dependencies]
-pip = '*' # Native package installer
+[feature.dev.pypi-dependencies]
+pip = '*'
 #pycrysfml = { version = ">=0.4.0", index = "https://easyscience.github.io/pypi/" }
 easydiffraction = { path = '.', editable = true, extras = ['dev'] }
 
-# Specific features: Set specific Python versions
+# User-like behavior for testing with pip-installed dependencies instead
+# of editable installations.
 
-[feature.py-min.dependencies]
-python = '3.12.*'
-[feature.py-max.dependencies]
-python = '3.14.*'
+[feature.user.dependencies]
+jupyterlab = '*'  # Jupyter notebooks
+ipython = '*'     # Interactive Python shell
+pixi-kernel = '*' # Pixi Jupyter kernel
+
+[feature.user.pypi-dependencies]
+pip = '*'
+easydiffraction = '*'
 
 ##############
 # ENVIRONMENTS
@@ -84,12 +76,18 @@ python = '3.14.*'
 
 # The `default` feature is always included in all environments.
 # Additional features can be specified per environment.
-py-312-env = { features = ['py-min'] }
-py-314-env = { features = ['py-max'] }
 
-# The `default` environment is always created and includes the `default` feature.
-# It does not need to be specified explicitly unless non-default features are included.
-default = { features = ['py-max'] }
+# Specific environments for CI testing with different Python versions.
+py-312-env = { features = ['py-min', 'dev'] }
+py-314-env = { features = ['py-max', 'dev'] }
+
+# The `default` environment is developer-oriented for local development
+# and testing with editable installation of the current package.
+default = { features = ['py-max', 'dev'] }
+
+# The `user` environment allows testing with pip-installed dependencies
+# instead of editable installation of the current package.
+user = { features = ['py-max', 'user'] }
 
 #######
 # TASKS
diff --git a/pyproject.toml b/pyproject.toml
index c0943384c..5faffe6b6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,6 @@ classifiers = [
 ]
 requires-python = '>=3.12'
 dependencies = [
-  'essdiffraction', # ESS-specific diffraction library
   'numpy',          # Numerical computing library
   'colorama',       # Color terminal output
   'tabulate',       # Pretty-print tabular data for terminal output
@@ -53,6 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 dev = [
+  'essdiffraction',                  # ESS-specific diffraction library
   'GitPython',                       # Interact with Git repositories
   'build',                           # Building the package
   'pre-commit',                      # Pre-commit hooks
diff --git a/tests/integration/scipp-analysis/dream/test_package_import.py b/tests/integration/scipp-analysis/dream/test_package_import.py
index 05f80a068..45b5bc95b 100644
--- a/tests/integration/scipp-analysis/dream/test_package_import.py
+++ b/tests/integration/scipp-analysis/dream/test_package_import.py
@@ -51,28 +51,9 @@ def get_base_version(
 def test_package_import(
     package_name: str,
 ) -> None:
-    """Verify installed package is not older than PyPI latest version.
-
-    Uses >= comparison to support both:
-    - Real releases where installed == latest
-    - Dev builds where installed (e.g., 999.0.0) > latest
-    """
+    """Verify  that the package is installed and can be fetched from PyPI."""
     installed_version = get_installed_version(package_name)
     latest_version = get_latest_version(package_name)
 
     assert installed_version is not None, f'Package {package_name} is not installed.'
     assert latest_version is not None, f'Could not fetch latest version for {package_name}.'
-
-    # Compare only MAJOR.MINOR.PATCH, ignoring local version identifiers
-    installed_base = Version(get_base_version(installed_version))
-    latest_base = Version(get_base_version(latest_version))
-
-    if installed_base < latest_base:
-        pytest.skip(
-            f'Installed {package_name} is older than latest PyPI release: '
-            f'{installed_base} < {latest_base}',
-        )
-
-    assert installed_base >= latest_base, (
-        f'Package {package_name} is outdated: Installed={installed_base}, Latest={latest_base}'
-    )

From 3467c721df60fe2fb94889ee75c644bf6a5fde8f Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Wed, 13 May 2026 23:26:45 +0200
Subject: [PATCH 02/10] Add initial Bayesian analysis implementation with BUMPS
 DREAM (#170)

* Add Bayesian analysis design note

* Add arviz for Bayesian analysis

* Remove old package structure documentation

* Update package structure docs with refln reorganization

* Plan Bayesian DREAM implementation

* Register BUMPS DREAM minimizer

* Add Bayesian fit result models

* Thread random seed through fitting

* Implement DREAM sampling

* Add Bayesian fit result display

* Plot Bayesian parameter correlations

* Add posterior ArviZ plots

* Add posterior predictive plots

* Refine Bayesian fit UX

* Fix DREAM sampler progress reporting

* Fix posterior pair plot axis sharing

* Improve Bayesian demo parameter choices

* Expose DREAM sampler settings

* Render posterior marginals as KDE curves

* Add posterior pair density contours

* Switch Bayesian demo to correlated pair

* Add Bayesian tracking and posterior plot updates

* Add posterior draws and log-posterior tracking

* Use population mean log-posterior for progress tracking

* Polish Bayesian progress and posterior plots

* Fix DREAM diagnostics and posterior KDEs

* Refine posterior plot styling

* Improve posterior pair plot UX

* Add uncertainty-based fit bounds method

* Tighten posterior pair plot layout

* Make fit bounds helper a setter

* Refine posterior contour layering

* Fix posterior contour layering

* Preserve pair plot sample hover

* Enforce pair plot trace z-order

* Restore pair plot axis frames

* Split posterior diagnostic warnings

* Strengthen pair plot axis borders

* Free broad_gauss and set bounds from uncertainty

* Draw explicit pair plot borders

* Align pair plot y-axis titles

* Unify plot axis border colors

* Lock posterior pair axis ranges

* Align pair plot title with modebar

* Update diagonal subplot axis styling

* Expose DREAM init and align pair labels

* Tighten fit bounds multiplier from 5 to 4

* Match pair plot axis title font sizes

* Disable Rich highlighting in Bayesian summaries

* Tune DREAM and model parameters in fit

* Expand Bayesian tutorial walkthrough

* Apply linting rules and formatting

* Add LBCO Bayesian tutorial

* Make posterior pair plots responsive

* Add DREAM bound validation and flexible parameter selection

* Adopt fixed-aspect CSS for posterior pair plots

* Add posterior pair plot rendering modes

* Add multiline axis titles to posterior pair plots

* Redesign correlation heatmap display

* Simplify tutorial parameter loops and bounds

* Improve hover template readability

* Improve posterior plot binning and styling

* Refactor posterior x-axis range to static method

* Change posterior predictive default style to band

* Add separate caching for band summaries

* Reduce sampling steps and adjust plot ranges

* Update posterior pair plot marker and background

* Update notebooks

* Fix docstring errors in Plotter class

* Standardize axis title font size

* Improve Bayesian fit summary display

* Improve Bayesian fit result display

* Adjust posterior and band fill transparency

* Update fit bounds and plot range in ed-21

* Improve posterior table diagnostic notes

* Add parallel DREAM sampling

* Add parallel=0 description to DREAM tutorial

* Fix test expectations for r-hat and parallel=0

* Add Bayesian DREAM to architecture

* Clean up

* Fix Bayesian diagnostics and DREAM cleanup edge cases

* Fix square matrix plot spacing and aspect ratio

* Apply the latest templates

* Bump setup-pixi to v0.9.5

* Bump dependencies

* Bump pixi.lock from v6 to v7

* Add correlation-based limits to posterior pair plots

* Improve posterior summary plot styling

* Add single-crystal posterior predictive scatter plot

* Add Tb2TiO7 Bayesian tutorial

* Set default correlation and pair-plot limit to six

* Bump copier template to v0.11.3

* Update tutorials

* Adjust posterior pair plot margins and title shift

* Generalize pair-plot layout to square matrix

* Switch DREAM defaults and refine Bayesian fit displays

* Show uncertainty multiplier in pair plot titles

* Refine Bayesian tutorial examples

* Shorten fit-bounds multiplier constant name

* Bump dependencies

* Add ADR documents

* Improve CI and codecov settings

* Fix structure factor support for X-ray

* Increase integration coverage for Bayesian fit helpers

* Remove unused benchmark dependency and track serial benchmarks

* Normalize styled console output in Windows integration tests
---
 .codecov.yml                                  |    26 +
 .copier-answers.yml                           |     2 +-
 .github/actions/setup-pixi/action.yml         |     2 +-
 .github/copilot-instructions.md               |    13 +-
 .github/workflows/coverage.yml                |     2 -
 .github/workflows/dashboard.yml               |     6 +-
 .github/workflows/issues-labels.yml           |   149 +-
 .github/workflows/pr-labels.yml               |    74 +-
 .github/workflows/test.yml                    |     6 +-
 CONTRIBUTING.md                               |     6 +
 codecov.yml                                   |    13 -
 docs/architecture/package-structure-full.md   |   425 -
 docs/architecture/package-structure-short.md  |   224 -
 docs/dev/adr_analysis-cif-fit-state.md        |   220 +
 .../adr_parameter-correlation-persistence.md  |   114 +
 docs/dev/adr_parameter-posterior-summary.md   |   681 +
 docs/dev/adr_undo-fit.md                      |   162 +
 docs/dev/architecture.md                      |   103 +-
 docs/dev/issues_open.md                       |    24 +
 docs/dev/package-structure-full.md            |    38 +-
 docs/dev/package-structure-short.md           |     8 +-
 docs/docs/installation-and-setup/index.md     |    13 +-
 docs/docs/tutorials/ed-13.ipynb               |     2 +-
 docs/docs/tutorials/ed-21.ipynb               |   730 +
 docs/docs/tutorials/ed-21.py                  |   354 +
 docs/docs/tutorials/ed-22.ipynb               |   427 +
 docs/docs/tutorials/ed-22.py                  |   269 +
 docs/docs/tutorials/index.json                |    14 +
 docs/docs/tutorials/index.md                  |    16 +
 docs/mkdocs.yml                               |    11 +-
 pixi.lock                                     | 21172 ++++++++--------
 pyproject.toml                                |     2 +-
 src/easydiffraction/analysis/analysis.py      |    40 +-
 .../analysis/calculators/cryspy.py            |    10 +-
 .../analysis/categories/fit/default.py        |    16 +-
 .../analysis/fit_helpers/__init__.py          |     6 +
 .../analysis/fit_helpers/bayesian.py          |   777 +
 .../analysis/fit_helpers/reporting.py         |    38 +-
 .../analysis/fit_helpers/tracking.py          |   391 +-
 src/easydiffraction/analysis/fitting.py       |     4 +
 .../analysis/minimizers/__init__.py           |     2 +
 .../analysis/minimizers/base.py               |    85 +-
 .../analysis/minimizers/bumps_dream.py        |   948 +
 .../analysis/minimizers/enums.py              |    11 +
 src/easydiffraction/core/variable.py          |    78 +
 src/easydiffraction/display/plotters/ascii.py |     2 +
 src/easydiffraction/display/plotters/base.py  |     5 +
 .../display/plotters/plotly.py                |   549 +-
 src/easydiffraction/display/plotting.py       |  3711 ++-
 src/easydiffraction/project/project.py        |     5 +
 .../test_analysis_and_fit_category_support.py |   360 +
 .../fitting/test_bayesian_dream.py            |   178 +
 .../fitting/test_bayesian_helper_support.py   |   670 +
 .../fitting/test_bayesian_tracker_and_base.py |   451 +
 .../fitting/test_bumps_dream_support.py       |   559 +
 .../fitting/test_cli_entrypoints.py           |   173 +
 .../analysis/calculators/test_cryspy.py       |    29 +
 .../analysis/fit_helpers/test_bayesian.py     |   385 +
 .../analysis/fit_helpers/test_reporting.py    |    66 +
 .../analysis/minimizers/test_base.py          |    39 +
 .../analysis/minimizers/test_bumps.py         |    12 +
 .../analysis/minimizers/test_bumps_dream.py   |   362 +
 .../analysis/minimizers/test_enums.py         |     7 +
 .../analysis/minimizers/test_factory.py       |    12 +-
 .../easydiffraction/core/test_parameters.py   |    84 +
 .../display/plotters/test_plotly.py           |   152 +-
 .../easydiffraction/display/test_plotting.py  |  1131 +-
 .../easydiffraction/project/test_project.py   |    14 +
 tools/generate_package_docs.py                |     4 +-
 69 files changed, 25035 insertions(+), 11639 deletions(-)
 create mode 100644 .codecov.yml
 delete mode 100644 codecov.yml
 delete mode 100644 docs/architecture/package-structure-full.md
 delete mode 100644 docs/architecture/package-structure-short.md
 create mode 100644 docs/dev/adr_analysis-cif-fit-state.md
 create mode 100644 docs/dev/adr_parameter-correlation-persistence.md
 create mode 100644 docs/dev/adr_parameter-posterior-summary.md
 create mode 100644 docs/dev/adr_undo-fit.md
 create mode 100644 docs/docs/tutorials/ed-21.ipynb
 create mode 100644 docs/docs/tutorials/ed-21.py
 create mode 100644 docs/docs/tutorials/ed-22.ipynb
 create mode 100644 docs/docs/tutorials/ed-22.py
 create mode 100644 src/easydiffraction/analysis/fit_helpers/bayesian.py
 create mode 100644 src/easydiffraction/analysis/minimizers/bumps_dream.py
 create mode 100644 tests/integration/fitting/test_analysis_and_fit_category_support.py
 create mode 100644 tests/integration/fitting/test_bayesian_dream.py
 create mode 100644 tests/integration/fitting/test_bayesian_helper_support.py
 create mode 100644 tests/integration/fitting/test_bayesian_tracker_and_base.py
 create mode 100644 tests/integration/fitting/test_bumps_dream_support.py
 create mode 100644 tests/integration/fitting/test_cli_entrypoints.py
 create mode 100644 tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
 create mode 100644 tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py

diff --git a/.codecov.yml b/.codecov.yml
new file mode 100644
index 000000000..d001c41da
--- /dev/null
+++ b/.codecov.yml
@@ -0,0 +1,26 @@
+# Codecov configuration
+# https://docs.codecov.com/docs/codecov-yaml
+
+github_checks:
+  annotations: true
+
+comment:
+  layout: 'reach, diff, flags, files'
+  behavior: default
+  require_changes: false
+  require_base: false
+  require_head: true
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        # Make project coverage informational (won't block PR)
+        informational: true
+    patch:
+      default:
+        target: auto
+        # Require patch coverage but with threshold
+        threshold: 1%
diff --git a/.copier-answers.yml b/.copier-answers.yml
index a807c84da..009e6acc4 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.11.1-12-gbb9bb30
+_commit: v0.11.4
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/diffraction-app
 app_doi: 10.5281/zenodo.18163581
diff --git a/.github/actions/setup-pixi/action.yml b/.github/actions/setup-pixi/action.yml
index ec7d7ba7b..dc3232c61 100644
--- a/.github/actions/setup-pixi/action.yml
+++ b/.github/actions/setup-pixi/action.yml
@@ -33,7 +33,7 @@ inputs:
 runs:
   using: 'composite'
   steps:
-    - uses: prefix-dev/setup-pixi@v0.9.4
+    - uses: prefix-dev/setup-pixi@v0.9.5
       with:
         environments: ${{ inputs.environments }}
         activate-environment: ${{ inputs.activate-environment }}
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 18b01c478..0a54cc941 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -77,7 +77,7 @@
 ## Testing
 
 - Every new module, class, or bug fix ships with tests. See
-  `docs/architecture/architecture.md` §10 for the full strategy.
+  `docs/dev/architecture.md` §10 for the full strategy.
 - Unit tests mirror the source tree:
   `src/easydiffraction/<pkg>/<mod>.py` →
   `tests/unit/easydiffraction/<pkg>/test_<mod>.py`. Verify with
@@ -102,8 +102,8 @@
 
 - Before any structural/design change (new categories, factories,
   switchable-category wiring, datablocks, CIF serialisation), read
-  `docs/architecture/architecture.md` and follow documented patterns.
-  Localised bug fixes or test updates need only this file.
+  `docs/dev/architecture.md` and follow documented patterns. Localised
+  bug fixes or test updates need only this file.
 - Project is in beta: no legacy shims, no deprecation warnings — update
   tests and tutorials to the current API.
 - Minimal diffs; don't reformat working code. Fix only what's asked;
@@ -146,13 +146,12 @@ Non-trivial changes use a two-phase workflow:
 
 Notes:
 
-- `pixi run fix` regenerates `docs/architecture/package-structure-*.md`
+- `pixi run fix` regenerates `docs/dev/package-structure-*.md`
   automatically — never edit those by hand. Don't review auto-fixes;
   accept and move on. Then `pixi run check` until clean.
 - Open issues / design questions / planned improvements live in
-  `docs/architecture/issues_open.md` (priority-ordered). On resolution,
-  move to `docs/architecture/issues_closed.md` and update
-  `architecture.md` if affected.
+  `docs/dev/issues_open.md` (priority-ordered). On resolution, move to
+  `docs/dev/issues_closed.md` and update `architecture.md` if affected.
 
 ### Planning
 
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
index 95c190c63..d96e5b87d 100644
--- a/.github/workflows/coverage.yml
+++ b/.github/workflows/coverage.yml
@@ -5,8 +5,6 @@ on:
   push:
     branches:
       - develop
-    # Do not run on version tags (those are handled by other workflows)
-    tags-ignore: ['v*']
   # Trigger the workflow on pull request
   pull_request:
   # Allows you to run this workflow manually from the Actions tab
diff --git a/.github/workflows/dashboard.yml b/.github/workflows/dashboard.yml
index 9d1f2b0bd..573e30868 100644
--- a/.github/workflows/dashboard.yml
+++ b/.github/workflows/dashboard.yml
@@ -32,9 +32,13 @@ jobs:
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
 
+      # Install badgery into the active Pixi environment without modifying
+      # pixi.toml/pixi.lock. Using `pixi add` here re-solves the whole project
+      # and rebuilds the local editable package, which can cause intermittent
+      # Linux CI failures (`Text file busy`, os error 26).
       - name: Install badgery
         shell: bash
-        run: pixi add --pypi --git https://github.com/enhantica/badgery badgery
+        run: pixi run python -m pip install git+https://github.com/enhantica/badgery
 
       - name: Run docstring coverage and code complexity/maintainability checks
         run: |
diff --git a/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml
index 56ab5b19a..4fbf4fab1 100644
--- a/.github/workflows/issues-labels.yml
+++ b/.github/workflows/issues-labels.yml
@@ -1,6 +1,5 @@
-# Verifies if the current issue has at least one real `[scope]` label and one
-# real `[priority]` label. If either is missing, the workflow adds a reminder
-# label with a warning emoji.
+# Verifies if the current issue has at least one `[scope]` label and one
+# `[priority]` label. If either is missing, the workflow adds a reminder label.
 
 name: Issue labels check
 
@@ -20,130 +19,22 @@ jobs:
       cancel-in-progress: true
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-
-      - name: Sync missing-label reminders
-        uses: ./.github/actions/github-script
+      - name: Ensure [scope] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
         with:
-          script: |
-            const fs = require('fs');
-
-            const issueNumber = context.issue.number;
-            const action = context.payload.action;
-            const changedLabel = context.payload.label?.name ?? null;
-            const labels = context.payload.issue.labels.map(({ name }) => name);
-            const requirements = [
-              {
-                prefix: '[scope] ',
-                reminder: '[scope] ⚠️ label needed',
-              },
-              {
-                prefix: '[priority] ',
-                reminder: '[priority] ⚠️ label needed',
-              },
-            ];
-
-            const labelsToAdd = [];
-            const labelsToRemove = [];
-            const evaluations = [];
-
-            console.log(`::group::Issue label check for #${issueNumber}`);
-            console.log(`Event action: ${action}`);
-            if (changedLabel) {
-              console.log(`Event label: ${changedLabel}`);
-            }
-            console.log(
-              `Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
-            );
-
-            for (const { prefix, reminder } of requirements) {
-              const matchingRealLabels = labels.filter(
-                (name) => name.startsWith(prefix) && name !== reminder,
-              );
-              const hasRealLabel = matchingRealLabels.length > 0;
-              const hasReminderLabel = labels.includes(reminder);
-
-              evaluations.push({
-                prefix,
-                reminder,
-                matchingRealLabels,
-                hasReminderLabel,
-              });
-
-              if (hasRealLabel && hasReminderLabel) {
-                labelsToRemove.push(reminder);
-              } else if (!hasRealLabel && !hasReminderLabel) {
-                labelsToAdd.push(reminder);
-              }
-            }
-
-            for (const evaluation of evaluations) {
-              if (evaluation.matchingRealLabels.length > 0) {
-                console.log(
-                  `Found required ${evaluation.prefix}label(s): ${evaluation.matchingRealLabels.join(', ')}`,
-                );
-              } else {
-                console.log(`Missing required ${evaluation.prefix}label.`);
-              }
-
-              if (evaluation.hasReminderLabel) {
-                console.log(`Reminder label already present: ${evaluation.reminder}`);
-              }
-            }
-
-            if (labelsToAdd.length > 0) {
-              console.log(`Adding reminder labels: ${labelsToAdd.join(', ')}`);
-              await github.rest.issues.addLabels({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                labels: labelsToAdd,
-              });
-            }
-
-            for (const name of labelsToRemove) {
-              console.log(`Removing reminder label: ${name}`);
-              await github.rest.issues.removeLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                name,
-              });
-            }
-
-            if (labelsToAdd.length === 0 && labelsToRemove.length === 0) {
-              console.log('No label changes required.');
-            }
-
-            console.log('::endgroup::');
-
-            if (process.env.GITHUB_STEP_SUMMARY) {
-              const summaryLines = [
-                '### Issue Label Check',
-                `- Issue: #${issueNumber}`,
-                `- Event: ${action}`,
-                `- Trigger label: ${changedLabel ?? '(none)'}`,
-                `- Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
-                '',
-                '#### Requirement status',
-                ...evaluations.map((evaluation) => {
-                  const status =
-                    evaluation.matchingRealLabels.length > 0
-                      ? `found ${evaluation.matchingRealLabels.join(', ')}`
-                      : 'missing';
-                  const reminder = evaluation.hasReminderLabel
-                    ? `reminder present: ${evaluation.reminder}`
-                    : `reminder absent: ${evaluation.reminder}`;
-                  return `- ${evaluation.prefix}: ${status}; ${reminder}`;
-                }),
-                '',
-                `- Labels to add: ${labelsToAdd.length > 0 ? labelsToAdd.join(', ') : '(none)'}`,
-                `- Labels to remove: ${labelsToRemove.length > 0 ? labelsToRemove.join(', ') : '(none)'}`,
-              ];
-
-              fs.appendFileSync(
-                process.env.GITHUB_STEP_SUMMARY,
-                `${summaryLines.join('\n')}\n`,
-              );
-            }
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[scope]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[scope] ⚠️ label needed'
+
+      - name: Ensure [priority] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
+        with:
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[priority]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[priority] ⚠️ label needed'
diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml
index 25710633b..fcb8a78cb 100644
--- a/.github/workflows/pr-labels.yml
+++ b/.github/workflows/pr-labels.yml
@@ -1,17 +1,9 @@
 # Verifies if a pull request has at least one label from a set of valid
 # labels before it can be merged.
 #
-# NOTE:
-# This workflow may be triggered twice in quick succession when a PR is
-# created:
-#   1) `opened`  — when the pull request is initially created
-#   2) `labeled` — if labels are added immediately after creation
-# (e.g. by manual labeling, another workflow, or GitHub App).
-#
-# These are separate GitHub events, so two workflow runs can be started.
-# The `concurrency` configuration below ensures that only the latest run
-# for the same PR remains active, canceling any previous in-progress
-# run.
+# The label validation is delegated to `mheap/github-action-required-labels`,
+# which checks the current PR labels via the GitHub API and can add or update a
+# PR comment when the required label set is missing.
 
 name: PR labels check
 
@@ -19,45 +11,35 @@ on:
   pull_request_target:
     types: [opened, labeled, unlabeled, synchronize]
 
-concurrency:
-  group: pr-labels-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
 permissions:
-  pull-requests: read
+  issues: write
+  pull-requests: write
 
 jobs:
   check-labels:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Check for valid labels
-        run: |
-          PR_LABELS=$(echo '${{ toJson(github.event.pull_request.labels.*.name) }}' | jq -r '.[]')
-
-          echo "Current PR labels: $PR_LABELS"
-          VALID_LABELS=(
-            "[bot] release"
-            "[scope] bug"
-            "[scope] documentation"
-            "[scope] enhancement"
-            "[scope] maintenance"
-            "[scope] significant"
-          )
-
-          found=false
-          for label in "${VALID_LABELS[@]}"; do
-            if echo "$PR_LABELS" | grep -Fxq "$label"; then
-              echo "✅ Found valid label: $label"
-              found=true
-              break
-            fi
-          done
-
-          if [ "$found" = false ]; then
-            echo "ERROR: PR must have at least one of the following labels:"
-            for label in "${VALID_LABELS[@]}"; do
-              echo "       - $label"
-            done
-            exit 1
-          fi
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Setup easyscience[bot]
+        id: bot
+        uses: ./.github/actions/setup-easyscience-bot
+        with:
+          app-id: ${{ vars.EASYSCIENCE_APP_ID }}
+          private-key: ${{ secrets.EASYSCIENCE_APP_KEY }}
+
+      - uses: mheap/github-action-required-labels@v5
+        with:
+          token: ${{ steps.bot.outputs.token }}
+          add_comment: true
+          mode: minimum
+          count: 1
+          labels: |
+            [bot] release
+            [scope] bug
+            [scope] documentation
+            [scope] enhancement
+            [scope] maintenance
+            [scope] significant
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index d0298f95b..86bfa6063 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -248,10 +248,10 @@ jobs:
               exit 1
             fi
 
-            whl_url="file://$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
+            whl_abs_path="$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
 
-            echo "Adding easydiffraction from: $whl_url"
-            pixi add --pypi "easydiffraction[dev] @ ${whl_url}"
+            echo "Adding easydiffraction from: $whl_abs_path"
+            pixi add --pypi "easydiffraction[dev] @ ${whl_abs_path}"
 
             echo "Exiting pixi project directory"
             cd ..
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e8dc420f2..9e5b93cff 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -249,6 +249,12 @@ or to run only Python linting checks:
 pixi run py-lint-check
 ```
 
+To add missing license headers:
+
+```bash
+pixi run spdx-add
+```
+
 Some formatting issues can be fixed automatically:
 
 ```bash
diff --git a/codecov.yml b/codecov.yml
deleted file mode 100644
index f62b13ab4..000000000
--- a/codecov.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# Codecov configuration
-# https://docs.codecov.com/docs/codecovyml-reference
-
-coverage:
-  status:
-    project:
-      default:
-        # Make project coverage informational (won't block PR)
-        informational: true
-    patch:
-      default:
-        # Require patch coverage but with threshold
-        threshold: 1%
diff --git a/docs/architecture/package-structure-full.md b/docs/architecture/package-structure-full.md
deleted file mode 100644
index be35bb076..000000000
--- a/docs/architecture/package-structure-full.md
+++ /dev/null
@@ -1,425 +0,0 @@
-# Package Structure (full)
-
-```
-📦 easydiffraction
-├── 📁 analysis
-│   ├── 📁 calculators
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   ├── 🏷️ class PowderReflnRecord
-│   │   │   └── 🏷️ class CalculatorBase
-│   │   ├── 📄 crysfml.py
-│   │   │   └── 🏷️ class CrysfmlCalculator
-│   │   ├── 📄 cryspy.py
-│   │   │   └── 🏷️ class CryspyCalculator
-│   │   ├── 📄 factory.py
-│   │   │   └── 🏷️ class CalculatorFactory
-│   │   └── 📄 pdffit.py
-│   │       └── 🏷️ class PdffitCalculator
-│   ├── 📁 categories
-│   │   ├── 📁 aliases
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class Alias
-│   │   │   │   └── 🏷️ class Aliases
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class AliasesFactory
-│   │   ├── 📁 constraints
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class Constraint
-│   │   │   │   └── 🏷️ class Constraints
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class ConstraintsFactory
-│   │   ├── 📁 fit
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Fit
-│   │   │   ├── 📄 enums.py
-│   │   │   │   └── 🏷️ class FitModeEnum
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class FitFactory
-│   │   ├── 📁 joint_fit_experiments
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class JointFitExperiment
-│   │   │   │   └── 🏷️ class JointFitExperiments
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class JointFitExperimentsFactory
-│   │   └── 📄 __init__.py
-│   ├── 📁 fit_helpers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 metrics.py
-│   │   ├── 📄 reporting.py
-│   │   │   └── 🏷️ class FitResults
-│   │   └── 📄 tracking.py
-│   │       ├── 🏷️ class _TerminalLiveHandle
-│   │       └── 🏷️ class FitProgressTracker
-│   ├── 📁 minimizers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   └── 🏷️ class MinimizerBase
-│   │   ├── 📄 bumps.py
-│   │   │   ├── 🏷️ class _EasyDiffractionFitness
-│   │   │   └── 🏷️ class BumpsMinimizer
-│   │   ├── 📄 bumps_amoeba.py
-│   │   │   └── 🏷️ class BumpsAmoebaMinimizer
-│   │   ├── 📄 bumps_de.py
-│   │   │   └── 🏷️ class BumpsDEMinimizer
-│   │   ├── 📄 bumps_lm.py
-│   │   │   └── 🏷️ class BumpsLmMinimizer
-│   │   ├── 📄 dfols.py
-│   │   │   └── 🏷️ class DfolsMinimizer
-│   │   ├── 📄 enums.py
-│   │   │   └── 🏷️ class MinimizerTypeEnum
-│   │   ├── 📄 factory.py
-│   │   │   └── 🏷️ class MinimizerFactory
-│   │   ├── 📄 lmfit.py
-│   │   │   └── 🏷️ class LmfitMinimizer
-│   │   ├── 📄 lmfit_least_squares.py
-│   │   │   └── 🏷️ class LmfitLeastSquaresMinimizer
-│   │   └── 📄 lmfit_leastsq.py
-│   │       └── 🏷️ class LmfitLeastsqMinimizer
-│   ├── 📄 __init__.py
-│   ├── 📄 analysis.py
-│   │   ├── 🏷️ class AnalysisDisplay
-│   │   └── 🏷️ class Analysis
-│   ├── 📄 fitting.py
-│   │   └── 🏷️ class Fitter
-│   └── 📄 sequential.py
-│       └── 🏷️ class SequentialFitTemplate
-├── 📁 core
-│   ├── 📄 __init__.py
-│   ├── 📄 category.py
-│   │   ├── 🏷️ class CategoryItem
-│   │   └── 🏷️ class CategoryCollection
-│   ├── 📄 collection.py
-│   │   └── 🏷️ class CollectionBase
-│   ├── 📄 datablock.py
-│   │   ├── 🏷️ class DatablockItem
-│   │   └── 🏷️ class DatablockCollection
-│   ├── 📄 diagnostic.py
-│   │   └── 🏷️ class Diagnostics
-│   ├── 📄 factory.py
-│   │   └── 🏷️ class FactoryBase
-│   ├── 📄 guard.py
-│   │   └── 🏷️ class GuardedBase
-│   ├── 📄 identity.py
-│   │   └── 🏷️ class Identity
-│   ├── 📄 metadata.py
-│   │   ├── 🏷️ class TypeInfo
-│   │   ├── 🏷️ class Compatibility
-│   │   └── 🏷️ class CalculatorSupport
-│   ├── 📄 singleton.py
-│   │   ├── 🏷️ class SingletonBase
-│   │   └── 🏷️ class ConstraintsHandler
-│   ├── 📄 validation.py
-│   │   ├── 🏷️ class DataTypeHints
-│   │   ├── 🏷️ class DataTypes
-│   │   ├── 🏷️ class ValidationStage
-│   │   ├── 🏷️ class ValidatorBase
-│   │   ├── 🏷️ class TypeValidator
-│   │   ├── 🏷️ class RangeValidator
-│   │   ├── 🏷️ class MembershipValidator
-│   │   ├── 🏷️ class RegexValidator
-│   │   └── 🏷️ class AttributeSpec
-│   └── 📄 variable.py
-│       ├── 🏷️ class GenericDescriptorBase
-│       ├── 🏷️ class GenericStringDescriptor
-│       ├── 🏷️ class GenericNumericDescriptor
-│       ├── 🏷️ class GenericParameter
-│       ├── 🏷️ class StringDescriptor
-│       ├── 🏷️ class NumericDescriptor
-│       └── 🏷️ class Parameter
-├── 📁 crystallography
-│   ├── 📄 __init__.py
-│   ├── 📄 crystallography.py
-│   └── 📄 space_groups.py
-│       └── 🏷️ class _RestrictedUnpickler
-├── 📁 datablocks
-│   ├── 📁 experiment
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 background
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class BackgroundBase
-│   │   │   │   ├── 📄 chebyshev.py
-│   │   │   │   │   ├── 🏷️ class PolynomialTerm
-│   │   │   │   │   └── 🏷️ class ChebyshevPolynomialBackground
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   │   └── 🏷️ class BackgroundTypeEnum
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class BackgroundFactory
-│   │   │   │   └── 📄 line_segment.py
-│   │   │   │       ├── 🏷️ class LineSegment
-│   │   │   │       └── 🏷️ class LineSegmentBackground
-│   │   │   ├── 📁 calculation
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class Calculation
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class CalculationFactory
-│   │   │   ├── 📁 data
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   │   ├── 🏷️ class PdDataPointBaseMixin
-│   │   │   │   │   ├── 🏷️ class PdCwlDataPointMixin
-│   │   │   │   │   ├── 🏷️ class PdTofDataPointMixin
-│   │   │   │   │   ├── 🏷️ class PdCwlDataPoint
-│   │   │   │   │   ├── 🏷️ class PdTofDataPoint
-│   │   │   │   │   ├── 🏷️ class PdDataBase
-│   │   │   │   │   ├── 🏷️ class PdCwlData
-│   │   │   │   │   └── 🏷️ class PdTofData
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class DataFactory
-│   │   │   │   └── 📄 total_pd.py
-│   │   │   │       ├── 🏷️ class TotalDataPoint
-│   │   │   │       ├── 🏷️ class TotalDataBase
-│   │   │   │       └── 🏷️ class TotalData
-│   │   │   ├── 📁 diffrn
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class DefaultDiffrn
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class DiffrnFactory
-│   │   │   ├── 📁 excluded_regions
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class ExcludedRegion
-│   │   │   │   │   └── 🏷️ class ExcludedRegions
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExcludedRegionsFactory
-│   │   │   ├── 📁 experiment_type
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class ExperimentType
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExperimentTypeFactory
-│   │   │   ├── 📁 extinction
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 becker_coppens.py
-│   │   │   │   │   └── 🏷️ class BeckerCoppensExtinction
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExtinctionFactory
-│   │   │   ├── 📁 instrument
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class InstrumentBase
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   │   ├── 🏷️ class CwlInstrumentBase
-│   │   │   │   │   ├── 🏷️ class CwlScInstrument
-│   │   │   │   │   └── 🏷️ class CwlPdInstrument
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class InstrumentFactory
-│   │   │   │   └── 📄 tof.py
-│   │   │   │       ├── 🏷️ class TofScInstrument
-│   │   │   │       └── 🏷️ class TofPdInstrument
-│   │   │   ├── 📁 linked_crystal
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class LinkedCrystal
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class LinkedCrystalFactory
-│   │   │   ├── 📁 linked_phases
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class LinkedPhase
-│   │   │   │   │   └── 🏷️ class LinkedPhases
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class LinkedPhasesFactory
-│   │   │   ├── 📁 peak
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class PeakBase
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   │   ├── 🏷️ class CwlPseudoVoigt
-│   │   │   │   │   ├── 🏷️ class CwlPseudoVoigtEmpiricalAsymmetry
-│   │   │   │   │   └── 🏷️ class CwlThompsonCoxHastings
-│   │   │   │   ├── 📄 cwl_mixins.py
-│   │   │   │   │   ├── 🏷️ class CwlBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class EmpiricalAsymmetryMixin
-│   │   │   │   │   └── 🏷️ class FcjAsymmetryMixin
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class PeakFactory
-│   │   │   │   ├── 📄 tof.py
-│   │   │   │   │   ├── 🏷️ class TofPseudoVoigt
-│   │   │   │   │   ├── 🏷️ class TofJorgensen
-│   │   │   │   │   ├── 🏷️ class TofJorgensenVonDreele
-│   │   │   │   │   └── 🏷️ class TofDoubleJorgensenVonDreele
-│   │   │   │   ├── 📄 tof_mixins.py
-│   │   │   │   │   ├── 🏷️ class TofGaussianBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class TofLorentzianBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class TofBackToBackExponentialMixin
-│   │   │   │   │   └── 🏷️ class TofDoubleExponentialMixin
-│   │   │   │   ├── 📄 total.py
-│   │   │   │   │   └── 🏷️ class TotalGaussianDampedSinc
-│   │   │   │   └── 📄 total_mixins.py
-│   │   │   │       └── 🏷️ class TotalBroadeningMixin
-│   │   │   ├── 📁 refln
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   │   ├── 🏷️ class PowderReflnBase
-│   │   │   │   │   ├── 🏷️ class PowderCwlRefln
-│   │   │   │   │   ├── 🏷️ class PowderTofRefln
-│   │   │   │   │   ├── 🏷️ class PowderReflnDataBase
-│   │   │   │   │   ├── 🏷️ class PowderCwlReflnData
-│   │   │   │   │   └── 🏷️ class PowderTofReflnData
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   │   ├── 🏷️ class Refln
-│   │   │   │   │   └── 🏷️ class ReflnData
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ReflnFactory
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   │   ├── 🏷️ class ExperimentBase
-│   │   │   │   ├── 🏷️ class ScExperimentBase
-│   │   │   │   └── 🏷️ class PdExperimentBase
-│   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   └── 🏷️ class BraggPdExperiment
-│   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   ├── 🏷️ class CwlScExperiment
-│   │   │   │   └── 🏷️ class TofScExperiment
-│   │   │   ├── 📄 enums.py
-│   │   │   │   ├── 🏷️ class SampleFormEnum
-│   │   │   │   ├── 🏷️ class ScatteringTypeEnum
-│   │   │   │   ├── 🏷️ class RadiationProbeEnum
-│   │   │   │   ├── 🏷️ class BeamModeEnum
-│   │   │   │   ├── 🏷️ class CalculatorEnum
-│   │   │   │   ├── 🏷️ class PeakProfileTypeEnum
-│   │   │   │   └── 🏷️ class ExtinctionModelEnum
-│   │   │   ├── 📄 factory.py
-│   │   │   │   └── 🏷️ class ExperimentFactory
-│   │   │   └── 📄 total_pd.py
-│   │   │       └── 🏷️ class TotalPdExperiment
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   │       └── 🏷️ class Experiments
-│   ├── 📁 structure
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 atom_site_aniso
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class AtomSiteAniso
-│   │   │   │   │   └── 🏷️ class AtomSiteAnisoCollection
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class AtomSiteAnisoFactory
-│   │   │   ├── 📁 atom_sites
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class AtomSite
-│   │   │   │   │   └── 🏷️ class AtomSites
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   │   └── 🏷️ class AdpTypeEnum
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class AtomSitesFactory
-│   │   │   ├── 📁 cell
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class Cell
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class CellFactory
-│   │   │   ├── 📁 space_group
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class SpaceGroup
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class SpaceGroupFactory
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   │   └── 🏷️ class Structure
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class StructureFactory
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   │       └── 🏷️ class Structures
-│   └── 📄 __init__.py
-├── 📁 display
-│   ├── 📁 plotters
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 ascii.py
-│   │   │   └── 🏷️ class AsciiPlotter
-│   │   ├── 📄 base.py
-│   │   │   ├── 🏷️ class BraggTickSet
-│   │   │   ├── 🏷️ class PowderMeasVsCalcSpec
-│   │   │   ├── 🏷️ class XAxisType
-│   │   │   └── 🏷️ class PlotterBase
-│   │   └── 📄 plotly.py
-│   │       ├── 🏷️ class PowderCompositeRows
-│   │       └── 🏷️ class PlotlyPlotter
-│   ├── 📁 tablers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   └── 🏷️ class TableBackendBase
-│   │   ├── 📄 pandas.py
-│   │   │   └── 🏷️ class PandasTableBackend
-│   │   └── 📄 rich.py
-│   │       └── 🏷️ class RichTableBackend
-│   ├── 📄 __init__.py
-│   ├── 📄 base.py
-│   │   ├── 🏷️ class RendererBase
-│   │   └── 🏷️ class RendererFactoryBase
-│   ├── 📄 plotting.py
-│   │   ├── 🏷️ class PlotterEngineEnum
-│   │   ├── 🏷️ class _MeasVsCalcPlotOptions
-│   │   ├── 🏷️ class _PowderMeasVsCalcSeries
-│   │   ├── 🏷️ class Plotter
-│   │   └── 🏷️ class PlotterFactory
-│   ├── 📄 tables.py
-│   │   ├── 🏷️ class TableEngineEnum
-│   │   ├── 🏷️ class TableRenderer
-│   │   └── 🏷️ class TableRendererFactory
-│   └── 📄 utils.py
-│       └── 🏷️ class JupyterScrollManager
-├── 📁 io
-│   ├── 📁 cif
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 handler.py
-│   │   │   └── 🏷️ class CifHandler
-│   │   ├── 📄 parse.py
-│   │   └── 📄 serialize.py
-│   ├── 📄 __init__.py
-│   └── 📄 ascii.py
-├── 📁 project
-│   ├── 📁 categories
-│   │   ├── 📁 display
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Display
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class DisplayFactory
-│   │   └── 📄 __init__.py
-│   ├── 📄 __init__.py
-│   ├── 📄 project.py
-│   │   └── 🏷️ class Project
-│   └── 📄 project_info.py
-│       └── 🏷️ class ProjectInfo
-├── 📁 summary
-│   ├── 📄 __init__.py
-│   └── 📄 summary.py
-│       └── 🏷️ class Summary
-├── 📁 utils
-│   ├── 📁 _vendored
-│   │   ├── 📁 jupyter_dark_detect
-│   │   │   ├── 📄 __init__.py
-│   │   │   └── 📄 detector.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 theme_detect.py
-│   ├── 📄 __init__.py
-│   ├── 📄 enums.py
-│   │   └── 🏷️ class VerbosityEnum
-│   ├── 📄 environment.py
-│   ├── 📄 logging.py
-│   │   ├── 🏷️ class IconifiedRichHandler
-│   │   ├── 🏷️ class ConsoleManager
-│   │   ├── 🏷️ class LoggerConfig
-│   │   ├── 🏷️ class ExceptionHookManager
-│   │   ├── 🏷️ class Logger
-│   │   └── 🏷️ class ConsolePrinter
-│   └── 📄 utils.py
-├── 📄 __init__.py
-└── 📄 __main__.py
-```
diff --git a/docs/architecture/package-structure-short.md b/docs/architecture/package-structure-short.md
deleted file mode 100644
index ba2a0b33b..000000000
--- a/docs/architecture/package-structure-short.md
+++ /dev/null
@@ -1,224 +0,0 @@
-# Package Structure (short)
-
-```
-📦 easydiffraction
-├── 📁 analysis
-│   ├── 📁 calculators
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 crysfml.py
-│   │   ├── 📄 cryspy.py
-│   │   ├── 📄 factory.py
-│   │   └── 📄 pdffit.py
-│   ├── 📁 categories
-│   │   ├── 📁 aliases
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 constraints
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 fit
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   ├── 📄 enums.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 joint_fit_experiments
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   └── 📄 __init__.py
-│   ├── 📁 fit_helpers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 metrics.py
-│   │   ├── 📄 reporting.py
-│   │   └── 📄 tracking.py
-│   ├── 📁 minimizers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 bumps.py
-│   │   ├── 📄 bumps_amoeba.py
-│   │   ├── 📄 bumps_de.py
-│   │   ├── 📄 bumps_lm.py
-│   │   ├── 📄 dfols.py
-│   │   ├── 📄 enums.py
-│   │   ├── 📄 factory.py
-│   │   ├── 📄 lmfit.py
-│   │   ├── 📄 lmfit_least_squares.py
-│   │   └── 📄 lmfit_leastsq.py
-│   ├── 📄 __init__.py
-│   ├── 📄 analysis.py
-│   ├── 📄 fitting.py
-│   └── 📄 sequential.py
-├── 📁 core
-│   ├── 📄 __init__.py
-│   ├── 📄 category.py
-│   ├── 📄 collection.py
-│   ├── 📄 datablock.py
-│   ├── 📄 diagnostic.py
-│   ├── 📄 factory.py
-│   ├── 📄 guard.py
-│   ├── 📄 identity.py
-│   ├── 📄 metadata.py
-│   ├── 📄 singleton.py
-│   ├── 📄 validation.py
-│   └── 📄 variable.py
-├── 📁 crystallography
-│   ├── 📄 __init__.py
-│   ├── 📄 crystallography.py
-│   └── 📄 space_groups.py
-├── 📁 datablocks
-│   ├── 📁 experiment
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 background
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 chebyshev.py
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 line_segment.py
-│   │   │   ├── 📁 calculation
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 data
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 total_pd.py
-│   │   │   ├── 📁 diffrn
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 excluded_regions
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 experiment_type
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 extinction
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 becker_coppens.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 instrument
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 tof.py
-│   │   │   ├── 📁 linked_crystal
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 linked_phases
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 peak
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   ├── 📄 cwl_mixins.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   ├── 📄 tof.py
-│   │   │   │   ├── 📄 tof_mixins.py
-│   │   │   │   ├── 📄 total.py
-│   │   │   │   └── 📄 total_mixins.py
-│   │   │   ├── 📁 refln
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   ├── 📄 bragg_pd.py
-│   │   │   ├── 📄 bragg_sc.py
-│   │   │   ├── 📄 enums.py
-│   │   │   ├── 📄 factory.py
-│   │   │   └── 📄 total_pd.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   ├── 📁 structure
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 atom_site_aniso
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 atom_sites
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 cell
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 space_group
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   └── 📄 __init__.py
-├── 📁 display
-│   ├── 📁 plotters
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 ascii.py
-│   │   ├── 📄 base.py
-│   │   └── 📄 plotly.py
-│   ├── 📁 tablers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 pandas.py
-│   │   └── 📄 rich.py
-│   ├── 📄 __init__.py
-│   ├── 📄 base.py
-│   ├── 📄 plotting.py
-│   ├── 📄 tables.py
-│   └── 📄 utils.py
-├── 📁 io
-│   ├── 📁 cif
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 handler.py
-│   │   ├── 📄 parse.py
-│   │   └── 📄 serialize.py
-│   ├── 📄 __init__.py
-│   └── 📄 ascii.py
-├── 📁 project
-│   ├── 📁 categories
-│   │   ├── 📁 display
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   └── 📄 __init__.py
-│   ├── 📄 __init__.py
-│   ├── 📄 project.py
-│   └── 📄 project_info.py
-├── 📁 summary
-│   ├── 📄 __init__.py
-│   └── 📄 summary.py
-├── 📁 utils
-│   ├── 📁 _vendored
-│   │   ├── 📁 jupyter_dark_detect
-│   │   │   ├── 📄 __init__.py
-│   │   │   └── 📄 detector.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 theme_detect.py
-│   ├── 📄 __init__.py
-│   ├── 📄 enums.py
-│   ├── 📄 environment.py
-│   ├── 📄 logging.py
-│   └── 📄 utils.py
-├── 📄 __init__.py
-└── 📄 __main__.py
-```
diff --git a/docs/dev/adr_analysis-cif-fit-state.md b/docs/dev/adr_analysis-cif-fit-state.md
new file mode 100644
index 000000000..c39fc192b
--- /dev/null
+++ b/docs/dev/adr_analysis-cif-fit-state.md
@@ -0,0 +1,220 @@
+# ADR: Analysis CIF Fit State
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`analysis/analysis.cif` currently persists analysis configuration such
+as `_fit.minimizer_type`, `_fit.mode`, aliases, constraints, and
+joint-fit weights. It does not persist analysis-owned fit state such as
+fit bounds, bound provenance, pre-fit scalar snapshots, or latest
+fit-status metadata.
+
+At the same time, parameter CIF serialization already carries the
+committed parameter `value`, the current `free` state, and the current
+`uncertainty` via CIF bracket notation. That data belongs to the model
+and should remain in structure or experiment CIF files.
+
+The missing piece is analysis-owned fit state:
+
+- fit controls that apply to parameters during fitting but are not part
+  of the model itself
+- latest fit-status metadata shown by `display.fit_results()`
+- deterministic and Bayesian fit metadata that should survive project
+  reloads and command-line workflows
+
+This separation matters because:
+
+- Bayesian plotting after reload needs `fit_min`, `fit_max`, and bound
+  provenance even when raw posterior arrays are absent
+- command-line users need a saved pre-fit starting state to recover from
+  a poor minimization run
+- `analysis.fit_results` already changes by fit type, but its persisted
+  projection should have a stable analysis-owned home
+
+The current architecture document still describes fit results as
+runtime-only. This ADR proposes a narrower persisted projection of the
+latest fit state, not a direct dump of backend runtime objects.
+
+## Decision
+
+### 1. `analysis/analysis.cif` becomes the home of analysis-owned fit state
+
+The analysis CIF file will persist:
+
+- fit configuration
+- aliases and constraints
+- joint-fit weights
+- per-parameter fit controls owned by analysis
+- latest fit-status metadata common to deterministic and Bayesian fits
+- fit-type-specific extensions defined in separate ADRs
+
+Committed parameter values remain in structure or experiment CIF files.
+They are not duplicated into `analysis/analysis.cif`.
+
+### 2. Add a real `_fit_parameter` loop
+
+Introduce a new analysis-owned loop category:
+
+```cif
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+```
+
+Fields:
+
+- `param_unique_name`
+- `fit_min`
+- `fit_max`
+- `fit_bounds_uncertainty_multiplier`
+- `start_value`
+- `start_uncertainty`
+
+Rationale:
+
+- `fit_min` and `fit_max` are required to restore deterministic and
+  Bayesian fit controls faithfully.
+- `fit_bounds_uncertainty_multiplier` preserves the provenance of
+  uncertainty-derived bounds for restored Bayesian plot annotations.
+- `start_value` and `start_uncertainty` capture the last committed
+  pre-fit scalar state and enable fit recovery workflows, especially in
+  command-line usage.
+- `start_uncertainty` preserves a user-visible pre-fit uncertainty
+  instead of treating it as disposable fit residue.
+
+### 3. Add a generic `_fit_result` single-item category
+
+Persist the latest fit-status metadata shared across fit types in a
+single analysis-owned category:
+
+- `result_kind`
+- `success`
+- `message`
+- `iterations`
+- `fitting_time`
+- `reduced_chi_square`
+
+Suggested CIF fragment:
+
+```cif
+_fit_result.result_kind deterministic
+_fit_result.success yes
+_fit_result.message "Fit converged"
+_fit_result.iterations 37
+_fit_result.fitting_time 1.82
+_fit_result.reduced_chi_square 1.031
+```
+
+`result_kind` distinguishes the latest persisted fit projection, for
+example `deterministic` or `bayesian`.
+
+### 4. Persist only stable fit-status fields here
+
+The `_fit_result` category is for generic status fields that are stable
+across engines and already belong to the result model.
+
+It should not persist backend runtime objects or arbitrary engine
+payloads.
+
+Metrics such as R-factors shown by `display.fit_results()` are derived
+from observed and calculated data and can be recomputed after load when
+needed. They do not need to be part of the first persisted fit-state
+schema.
+
+### 5. Fit-type-specific extensions build on this ADR
+
+This ADR defines the common `analysis.cif` contract for deterministic
+and Bayesian fitting.
+
+Fit-type-specific extensions are layered on top:
+
+- Bayesian persistence extends this with `_bayesian_*` categories and an
+  HDF5 sidecar, as described in `adr_parameter-posterior-summary.md`.
+- Future fit-specific summaries should follow the same pattern: generic
+  shared fields in `_fit_result`, specialized fields in separate
+  categories.
+
+### 6. Restore order is analysis-first, fit-type-second
+
+Load order should be:
+
+1. standard analysis configuration
+2. aliases and constraints
+3. joint-fit weights
+4. `_fit_parameter`
+5. `_fit_result`
+6. fit-type-specific extensions such as `_bayesian_*`
+
+This ensures that generic fit controls are available before restoring
+specialized fit summaries.
+
+### 7. Suggested full `analysis.cif` example
+
+```cif
+_fit.minimizer_type "bumps (dream)"
+_fit.mode single
+
+loop_
+_alias.label
+_alias.param_unique_name
+biso_Co1 cosio.atom_site.Co1.adp_iso
+biso_Co2 cosio.atom_site.Co2.adp_iso
+
+loop_
+_constraint.expression
+"biso_Co2 = biso_Co1"
+
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+
+_fit_result.result_kind bayesian
+_fit_result.success yes
+_fit_result.message "Sampler converged"
+_fit_result.iterations 3000
+_fit_result.fitting_time 82.4
+_fit_result.reduced_chi_square 1.031
+
+# Bayesian-specific extension categories follow here.
+```
+
+## Consequences
+
+### Positive
+
+- `analysis.cif` becomes the single analysis-owned source of fit state.
+- Deterministic and Bayesian persistence share one common base schema.
+- Fit bounds, bound provenance, and start values survive project
+  reloads.
+- Command-line workflows gain a persisted pre-fit starting state.
+
+### Trade-offs
+
+- The architecture document must be updated because fit state is no
+  longer entirely runtime-only.
+- Analysis persistence becomes more stateful and must be kept in sync
+  with live parameter objects.
+- Some existing serializer assumptions will need refactoring so that
+  `analysis.cif` owns fit metadata rather than individual parameters.
+
+## Deferred Work
+
+- Bayesian-specific categories and HDF5 sidecar details remain in
+  `adr_parameter-posterior-summary.md`.
+- Undo semantics for `start_value` and `start_uncertainty` are defined
+  in a separate ADR.
+- Correlation-matrix persistence is defined in a separate ADR.
diff --git a/docs/dev/adr_parameter-correlation-persistence.md b/docs/dev/adr_parameter-correlation-persistence.md
new file mode 100644
index 000000000..9c494908b
--- /dev/null
+++ b/docs/dev/adr_parameter-correlation-persistence.md
@@ -0,0 +1,114 @@
+# ADR: Parameter Correlation Persistence
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`plot_param_correlations()` can currently visualize either:
+
+- deterministic parameter correlations derived from engine covariance
+- Bayesian correlations derived from posterior samples
+
+After project reload, this correlation information is not available
+unless the underlying runtime objects are rebuilt. For Bayesian fits,
+full posterior samples may not always be restored. For deterministic
+fits, engine covariance is typically not persisted at all.
+
+The correlation matrix is an analysis-owned summary, not model state. It
+therefore belongs in `analysis/analysis.cif`, not in structure or
+experiment CIF files.
+
+## Decision
+
+### 1. Add a `_fit_parameter_correlation` loop category
+
+Persist pairwise parameter correlations in a new analysis-owned loop:
+
+- `source_kind`
+- `param_unique_name_i`
+- `param_unique_name_j`
+- `correlation`
+
+Suggested example:
+
+```cif
+loop_
+_fit_parameter_correlation.source_kind
+_fit_parameter_correlation.param_unique_name_i
+_fit_parameter_correlation.param_unique_name_j
+_fit_parameter_correlation.correlation
+posterior cosio.atom_site.Co1.adp_iso cosio.atom_site.Co2.adp_iso 0.87
+```
+
+`source_kind` records how the correlation was obtained, for example:
+
+- `deterministic`
+- `posterior`
+
+### 2. Store only the upper triangle excluding the diagonal
+
+Each row stores one unordered parameter pair with
+`param_unique_name_i < param_unique_name_j` in a stable ordering.
+
+The diagonal is omitted because it is always 1.0 and can be rebuilt on
+load.
+
+This keeps the CIF loop compact while remaining lossless for the
+correlation matrix.
+
+### 3. Treat the loop as a summary, not a replacement for raw samples
+
+For Bayesian fits, `_fit_parameter_correlation` is a persisted summary.
+It does not replace posterior samples or posterior pair data.
+
+This means:
+
+- correlation heatmaps can be restored from the loop alone
+- posterior pair plots still require posterior samples
+
+### 4. Deterministic and Bayesian fits share the same loop schema
+
+The same loop category is used for both deterministic and Bayesian fit
+results. The distinction is carried by `source_kind`, not by separate
+loop names.
+
+### 5. Suggested restore behavior
+
+On load:
+
+- if `_fit_parameter_correlation` is present, correlation summaries are
+  restored into a lightweight analysis-owned correlation structure
+- if it is absent, correlation plots fall back to whatever live runtime
+  information is available
+
+### 6. Suggested user-facing behavior
+
+```python
+# Restored from analysis.cif when available
+project.display.plotter.plot_param_correlations()
+```
+
+If only the correlation loop is restored, the user still gets the
+correlation heatmap without needing raw posterior samples.
+
+## Consequences
+
+### Positive
+
+- Deterministic and Bayesian correlation summaries survive reload.
+- Correlation heatmaps no longer depend entirely on runtime-only data.
+- The schema is compact and fit-type-agnostic.
+
+### Trade-offs
+
+- The correlation loop is a derived summary, so it must be kept in sync
+  with the latest fit result.
+- Restored correlation data is not enough for posterior pair plots or
+  predictive summaries.
+
+## Deferred Work
+
+- optional storage of covariance matrices in addition to correlations
+- multiple named correlation sources for the same saved project
+- full correlation restoration for pair-plot density surfaces
diff --git a/docs/dev/adr_parameter-posterior-summary.md b/docs/dev/adr_parameter-posterior-summary.md
new file mode 100644
index 000000000..dd7999ba1
--- /dev/null
+++ b/docs/dev/adr_parameter-posterior-summary.md
@@ -0,0 +1,681 @@
+# ADR: Parameter-Level Posterior Projection and Bayesian Persistence
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`GenericParameter` already stores fit-adjacent helper data such as
+`uncertainty`, `fit_min`, and `fit_max`, while `value` remains the
+single scalar used by calculations.
+
+Bayesian DREAM currently keeps posterior state only on
+`analysis.fit_results` via `BayesianFitResults`, including
+`posterior_samples`, `posterior_parameter_summaries`,
+`posterior_predictive`, diagnostics, and sampler settings. The current
+architecture document describes this state as runtime-only and not
+serialized.
+
+`analysis.fit_results` already changes by analysis type: deterministic
+fits use `FitResults`, while posterior-capable fits such as DREAM use
+`BayesianFitResults`. This ADR preserves that result-model split.
+
+The user-facing need is more local: when a posterior-capable fit has
+completed, each parameter should expose a compact Bayesian summary for
+inspection without forcing users to traverse `analysis.fit_results`. At
+the same time, `parameter.value` must remain the only scalar used by the
+live model, minimizer setup, constraints, and category updates.
+
+There is also a staleness problem. After a manual parameter edit or a
+new fit, fit-derived helper data must be cleared or replaced as a group.
+Keeping old posterior summaries after the active parameter state changes
+would mislead users.
+
+One more implementation constraint matters: minimizers currently apply
+final fitted values through `_set_value_from_minimizer(...)` and then
+write `param.uncertainty = ...` directly. If `uncertainty` and posterior
+metadata become read-only fit outputs and the public `value` setter
+starts clearing stale fit metadata on manual edits, fit-result
+application must move to a dedicated internal update path so that valid
+fit outputs are installed atomically instead of being cleared
+accidentally.
+
+## Decision
+
+### 1. Add one optional posterior object to each parameter
+
+Each `GenericParameter` will gain a read-only `posterior` property whose
+default value is `None`.
+
+This property is convenience metadata only. It does not participate in
+calculations. `parameter.value` remains the only scalar used by the live
+model.
+
+### 2. Reuse the existing Bayesian summary container
+
+Do not add separate flat parameter attributes such as `median`,
+`map_estimate`, `interval_95`, `r_hat`, or `ess_bulk`.
+
+Instead, the parameter-level projection reuses the existing
+`PosteriorParameterSummary` object already produced for
+`BayesianFitResults`. This keeps one grouped summary shape for display,
+inspection, and later persistence.
+
+The summary object currently provides the right level of detail:
+
+- `map_estimate`
+- `median`
+- `uncertainty`
+- `interval_68`
+- `interval_95`
+- `r_hat`
+- `ess_bulk`
+
+`unique_name` and `display_name` remain redundant but acceptable when
+the object is attached to a parameter.
+
+Different communities use different names for this scalar spread term:
+`standard deviation`, `estimated standard deviation` (`e.s.d.`), and
+`standard uncertainty` (`s.u.` / `su`). The public EasyDiffraction API
+selects `uncertainty` as the canonical name because it already matches
+the existing parameter API, aligns better with CIF terminology, and can
+be used consistently for both deterministic and Bayesian results. For
+Bayesian summaries, this `uncertainty` value is computed from the
+posterior standard deviation.
+
+The internal field names stay compact and code-oriented. User-facing
+tables, summaries, and plot annotations should use these friendly
+labels:
+
+- `map_estimate` -> `MAP estimate`
+- `median` -> `Median`
+- `uncertainty` -> `Standard uncertainty`
+- `interval_68` -> `68% credible interval`
+- `interval_95` -> `95% credible interval`
+- `r_hat` -> `R-hat`
+- `ess_bulk` -> `Bulk ESS`
+
+Asymmetric interval information remains explicit through the stored
+lower and upper bounds. For example, `parameter.posterior.interval_95`
+returns a `(lower, upper)` tuple rather than one symmetric uncertainty
+value.
+
+Example user access:
+
+```python
+param = project.phases['lbco'].cell.length_a
+
+current_value = param.value
+current_uncertainty = param.uncertainty
+
+if param.posterior is not None:
+  map_estimate = param.posterior.map_estimate
+  median = param.posterior.median
+  uncertainty = param.posterior.uncertainty
+  low95, high95 = param.posterior.interval_95
+  r_hat = param.posterior.r_hat
+  ess_bulk = param.posterior.ess_bulk
+```
+
+### 3. Make fit outputs read-only for the user
+
+`uncertainty` becomes a read-only fit output on the public parameter
+API. `posterior` is also read-only.
+
+Users may inspect these properties, but only internal fit-application
+paths may set or clear them.
+
+This keeps the writable public parameter state focused on user intent:
+`value`, `free`, `fit_min`, `fit_max`, and related configuration.
+
+### 4. Populate it only for posterior-capable fit methods
+
+Only fit methods that actually produce posterior summaries may populate
+`parameter.posterior`.
+
+At present this means only `bumps (dream)`.
+
+Do not add a generic minimizer-capability abstraction yet. That would
+introduce a new abstraction before there is a second concrete posterior
+fit method. When another posterior-capable minimizer exists, the shared
+contract can be extracted then.
+
+### 5. Treat parameter-level posterior data as a projection, not the canonical result
+
+The canonical Bayesian result remains `analysis.fit_results`.
+
+`parameter.posterior` is a synchronized projection of the current fit
+result for user convenience. It must never be the only place where
+Bayesian information lives, because it cannot represent joint posterior
+arrays, predictive summaries, or cross-parameter correlations.
+
+### 6. Clear or replace fit-derived metadata as a group
+
+Fit-derived helper data on a parameter must be coherent.
+
+The following policy applies:
+
+- Manual edit of `parameter.value` clears `parameter.uncertainty` and
+  `parameter.posterior`.
+- A deterministic fit replaces `value` and `uncertainty`, then clears
+  `posterior`.
+- A posterior fit replaces `value`, `uncertainty`, and `posterior`
+  together.
+
+Manual user edits of `uncertainty` and `posterior` are not supported,
+because both are read-only fit outputs.
+
+Configuration attributes such as `free`, `fit_min`, `fit_max`, units,
+and `fit_bounds_uncertainty_multiplier` are not cleared by this policy.
+They are parameter configuration or user intent, not posterior output.
+
+### 7. Add a dedicated internal fit-application path
+
+To support the clearing policy above, fit application must not rely on
+public setters alone.
+
+Implementation should add private helpers on `GenericParameter` to set
+and clear fit-derived state explicitly, for example:
+
+- `_set_uncertainty(...)`
+- `_set_posterior(...)`
+- `_clear_fit_metadata()`
+- `_apply_deterministic_fit_update(...)`
+- `_apply_posterior_fit_update(...)`
+
+The exact helper names can be refined during implementation, but the
+design requirement is fixed: manual user edits clear stale metadata,
+while internal fit application installs fresh metadata atomically.
+
+### 8. Commit MAP to `parameter.value` after Bayesian fits
+
+After a posterior-capable fit, `parameter.value` is committed from the
+maximum-a-posteriori estimate.
+
+MAP is chosen because it is a coherent joint point estimate across all
+free parameters. Marginal medians remain available on
+`parameter.posterior`, but they are summary data rather than the active
+live model state.
+
+### 9. Keep `uncertainty` as a convenience scalar after Bayesian fits
+
+`parameter.uncertainty` remains a single convenience scalar even after
+posterior fits.
+
+For posterior-capable fits, populate it from posterior standard
+deviation and expose it as `uncertainty`. This preserves one scalar API
+term across deterministic and Bayesian results while keeping the
+calculation itself statistically conventional.
+
+Asymmetric interval information is not squeezed into
+`parameter.uncertainty`; it remains available only via
+`parameter.posterior`.
+
+### 10. Persist canonical Bayesian state at analysis level
+
+Canonical Bayesian state is owned by `analysis.fit_results`, not by
+individual parameters.
+
+When the active result is a `BayesianFitResults` instance, persistence
+must save enough data to restore two distinct capability levels:
+
+- summary-only restore for parameter inspection and tables
+- full restore for posterior plots and predictive plots
+
+`parameter.posterior` is never serialized as a per-parameter property.
+It is always rebuilt from the canonical analysis-level persisted data.
+
+### 11. Persist fit-control and Bayesian metadata in `analysis/analysis.cif`
+
+The existing `analysis/analysis.cif` file remains the text metadata
+entry point for analysis persistence.
+
+The persisted fit-control and Bayesian metadata is split into explicit
+CIF categories.
+
+#### 11.1 `_fit_parameter` loop
+
+Stores analysis-owned per-parameter fit metadata that is not currently
+covered by parameter CIF serialization.
+
+This loop exists because the committed parameter CIF representation
+already carries the active `value`, current `free` state, and current
+`uncertainty`, but it does not carry fit bounds, bound provenance, or
+the pre-fit uncertainty snapshot needed by undo. Those fields are
+required for Bayesian plot ranges, pair-plot bound annotations, and
+clean fit rollback after project reload.
+
+Fields:
+
+- `param_unique_name`
+- `fit_min`
+- `fit_max`
+- `fit_bounds_uncertainty_multiplier`
+- `start_value`
+- `start_uncertainty`
+
+`fit_min` and `fit_max` are required for restored Bayesian plotting.
+`fit_bounds_uncertainty_multiplier` is required if restored plots should
+preserve the uncertainty-derived bound annotation exactly. `start_value`
+and `start_uncertainty` are required for clean cross-session undo. If
+omitted, restored fit reports may show `N/A` for `start` and `change`,
+and undo may need to clear uncertainty as a compatibility fallback.
+
+#### 11.2 `_bayesian_result` single item
+
+Stores one saved Bayesian result header with these fields:
+
+- `schema_version`
+- `sampler_name`
+- `point_estimate_name`
+- `success`
+- `sampler_completed`
+- `reduced_chi_square`
+- `fitting_time`
+- `best_log_posterior`
+- `credible_interval_inner`
+- `credible_interval_outer`
+- `has_posterior_samples`
+- `has_posterior_predictive`
+- `sidecar_file`
+
+For the current design, `point_estimate_name` is always `map`.
+
+#### 11.3 `_bayesian_sampler` single item
+
+Stores resolved sampler settings actually used for the run:
+
+- `steps`
+- `burn`
+- `thin`
+- `pop`
+- `parallel`
+- `init`
+- `random_seed`
+
+This persists the existing runtime `sampler_settings` in an explicit,
+schema-driven form rather than as an open-ended key/value mapping.
+
+#### 11.4 `_bayesian_convergence` single item
+
+Stores top-level convergence and shape metadata:
+
+- `converged`
+- `max_r_hat`
+- `min_ess_bulk`
+- `n_draws`
+- `n_chains`
+- `n_parameters`
+
+Per-parameter `r_hat` and `ess_bulk` remain in the parameter summary
+loop described below.
+
+#### 11.5 `_bayesian_parameter_posterior` loop
+
+Stores one canonical posterior summary row per sampled parameter. These
+rows are the source used to rebuild `parameter.posterior` on load.
+
+Fields:
+
+- `order_index`
+- `unique_name`
+- `display_name`
+- `map_estimate`
+- `median`
+- `uncertainty`
+- `interval_68_lower`
+- `interval_68_upper`
+- `interval_95_lower`
+- `interval_95_upper`
+- `ess_bulk`
+- `r_hat`
+
+`order_index` defines the parameter order used by posterior sample
+columns in the sidecar arrays.
+
+#### 11.6 `_bayesian_predictive_dataset` loop
+
+Stores one manifest row per persisted posterior predictive summary.
+
+Fields:
+
+- `experiment_name`
+- `x_axis_name`
+- `x_path`
+- `map_prediction_path`
+- `lower_95_path`
+- `upper_95_path`
+- `lower_68_path`
+- `upper_68_path`
+- `draws_path`
+- `n_x`
+- `n_draws_cached`
+
+This loop tells the loader which arrays to read from the sidecar file
+for each experiment-level predictive summary.
+
+#### 11.7 Suggested CIF fragments
+
+The active parameter value remains in the structure or experiment CIF as
+it does today, for example:
+
+```cif
+_atom_site_U_iso_or_equiv 0.0319(21)
+```
+
+Analysis-owned fit-control and Bayesian metadata then lives in
+`analysis/analysis.cif`, for example:
+
+```cif
+_fit.minimizer_type "bumps (dream)"
+_fit.mode single
+
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+
+_bayesian_result.schema_version 1
+_bayesian_result.sampler_name dream
+_bayesian_result.point_estimate_name map
+_bayesian_result.success yes
+_bayesian_result.sampler_completed yes
+_bayesian_result.reduced_chi_square 1.031
+_bayesian_result.fitting_time 82.4
+_bayesian_result.best_log_posterior -1542.77
+_bayesian_result.credible_interval_inner 0.68
+_bayesian_result.credible_interval_outer 0.95
+_bayesian_result.has_posterior_samples yes
+_bayesian_result.has_posterior_predictive yes
+_bayesian_result.sidecar_file "bayesian_data.h5"
+
+_bayesian_sampler.steps 3000
+_bayesian_sampler.burn 600
+_bayesian_sampler.thin 1
+_bayesian_sampler.pop 20
+_bayesian_sampler.parallel 0
+_bayesian_sampler.init lhs
+_bayesian_sampler.random_seed 12345
+
+_bayesian_convergence.converged yes
+_bayesian_convergence.max_r_hat 1.01
+_bayesian_convergence.min_ess_bulk 812.4
+_bayesian_convergence.n_draws 2400
+_bayesian_convergence.n_chains 20
+_bayesian_convergence.n_parameters 2
+
+loop_
+_bayesian_parameter_posterior.order_index
+_bayesian_parameter_posterior.unique_name
+_bayesian_parameter_posterior.display_name
+_bayesian_parameter_posterior.map_estimate
+_bayesian_parameter_posterior.median
+_bayesian_parameter_posterior.uncertainty
+_bayesian_parameter_posterior.interval_68_lower
+_bayesian_parameter_posterior.interval_68_upper
+_bayesian_parameter_posterior.interval_95_lower
+_bayesian_parameter_posterior.interval_95_upper
+_bayesian_parameter_posterior.ess_bulk
+_bayesian_parameter_posterior.r_hat
+0 cosio.atom_site.Co1.adp_iso "Co1 ADP" 0.0319 0.0317 0.0021 0.0298 0.0339 0.0278 0.0361 812.4 1.01
+1 cosio.atom_site.Co2.adp_iso "Co2 ADP" 0.0320 0.0318 0.0020 0.0300 0.0338 0.0281 0.0359 830.7 1.00
+
+loop_
+_bayesian_predictive_dataset.experiment_name
+_bayesian_predictive_dataset.x_axis_name
+_bayesian_predictive_dataset.x_path
+_bayesian_predictive_dataset.map_prediction_path
+_bayesian_predictive_dataset.lower_95_path
+_bayesian_predictive_dataset.upper_95_path
+_bayesian_predictive_dataset.lower_68_path
+_bayesian_predictive_dataset.upper_68_path
+_bayesian_predictive_dataset.draws_path
+_bayesian_predictive_dataset.n_x
+_bayesian_predictive_dataset.n_draws_cached
+hrpt ttheta /predictive/hrpt/x /predictive/hrpt/map_prediction /predictive/hrpt/lower_95 /predictive/hrpt/upper_95 /predictive/hrpt/lower_68 /predictive/hrpt/upper_68 /predictive/hrpt/draws 2500 200
+```
+
+### 12. Persist bulk arrays in `analysis/bayesian_data.h5`
+
+Large numerical arrays are stored outside CIF text in a single sidecar
+file:
+
+- `analysis/bayesian_data.h5`
+
+The sidecar is optional. Summary-only restore remains valid without it.
+
+HDF5 is selected instead of NPZ because the persisted Bayesian payload
+is a structured collection of named datasets with heterogeneous shapes,
+optional groups, and potentially large predictive arrays. HDF5 provides
+explicit hierarchical storage, dataset metadata, selective reads, and a
+better long-term path for compression or chunking. NPZ is simpler, but
+it is flatter and less suitable once the saved Bayesian state grows
+beyond a small set of arrays.
+
+#### 12.1 Required core HDF5 dataset paths when posterior samples are saved
+
+- `posterior_parameter_samples`
+- `posterior_log_posterior`
+- `posterior_draw_index`
+
+Expected array shapes:
+
+- `posterior_parameter_samples`: `(n_draws, n_chains, n_parameters)`
+- `posterior_log_posterior`: `(n_draws, n_chains)` when available
+- `posterior_draw_index`: `(n_draws,)` when available
+
+If `posterior_log_posterior` or `posterior_draw_index` are unavailable,
+their corresponding header flags remain false and the arrays may be
+omitted.
+
+#### 12.2 Predictive dataset keys
+
+Posterior predictive arrays are addressed through the
+`_bayesian_predictive_dataset` manifest rather than inferred from file
+ordering.
+
+Recommended HDF5 dataset naming is:
+
+- `predictive__<experiment>__x`
+- `predictive__<experiment>__map_prediction`
+- `predictive__<experiment>__lower_95`
+- `predictive__<experiment>__upper_95`
+- `predictive__<experiment>__lower_68`
+- `predictive__<experiment>__upper_68`
+- `predictive__<experiment>__draws`
+
+The manifest, not the naming convention, is the source of truth.
+
+Recommended HDF5 group layout is:
+
+- `/posterior/parameter_samples`
+- `/posterior/log_posterior`
+- `/posterior/draw_index`
+- `/predictive/<experiment>/x`
+- `/predictive/<experiment>/map_prediction`
+- `/predictive/<experiment>/lower_95`
+- `/predictive/<experiment>/upper_95`
+- `/predictive/<experiment>/lower_68`
+- `/predictive/<experiment>/upper_68`
+- `/predictive/<experiment>/draws`
+
+The manifest remains the canonical mapping used by the loader, so this
+layout is recommended rather than mandatory.
+
+#### 12.3 What is not persisted in the sidecar
+
+Do not persist backend-specific runtime objects such as `engine_result`,
+the DREAM driver, or ArviZ `InferenceData`.
+
+Those objects can be rebuilt from canonical saved arrays when needed, or
+left unavailable after load.
+
+### 13. Restore flow and partial-availability policy
+
+Persistence must support both full and partial restore.
+
+#### 13.1 Save flow
+
+When `Project.save()` sees `analysis.fit_results` as a
+`BayesianFitResults` instance:
+
+1. It writes standard analysis configuration to `analysis/analysis.cif`.
+2. It appends `_fit_parameter`, `_bayesian_result`, `_bayesian_sampler`,
+   `_bayesian_convergence`, and `_bayesian_parameter_posterior`.
+3. If posterior predictive summaries are available, it also writes the
+   `_bayesian_predictive_dataset` manifest.
+4. If posterior sample arrays or predictive arrays are available, it
+   writes `analysis/bayesian_data.h5`.
+
+#### 13.2 Load flow
+
+When `Project.load()` restores `analysis/analysis.cif`:
+
+1. Standard analysis configuration is restored first.
+2. If `_fit_parameter` is present, fit bounds, bound provenance, and
+   optional pre-fit scalar snapshots are restored by matching
+   `param_unique_name` to live parameters.
+3. If Bayesian categories are present, a lightweight
+   `BayesianFitResults` instance is rebuilt from persisted metadata and
+   parameter summary rows.
+4. `parameter.posterior` is rebuilt by matching summary rows to live
+   parameters via `unique_name`.
+5. `parameter.value` and `parameter.uncertainty` continue to come from
+   the normal project serialization path; Bayesian restore does not
+   overwrite them.
+6. If `analysis/bayesian_data.h5` exists and matches the manifest,
+   `posterior_samples` and `posterior_predictive` are restored.
+7. If the sidecar is missing or incomplete, the restore degrades to
+   summary-only mode without failing the whole project load.
+
+#### 13.3 Partial restore behavior
+
+The chosen partial-restore policy is:
+
+- `parameter.posterior` and `display.fit_results()` remain available
+  from saved metadata and summaries.
+- Bayesian-only plots requiring canonical posterior arrays remain
+  unavailable unless those arrays were restored successfully.
+- Missing sidecar data should produce a clear warning, not a hard load
+  failure.
+
+Example user access after load:
+
+```python
+project = Project.load('/path/to/project')
+
+param = project.phases['lbco'].cell.length_a
+posterior = param.posterior
+
+if posterior is not None:
+    print(posterior.map_estimate)
+    print(posterior.uncertainty)
+    print(posterior.interval_68)
+
+project.analysis.display.fit_results()
+```
+
+Example plotting behavior after load:
+
+```python
+# Works with summary-only restore
+project.analysis.display.fit_results()
+
+# Requires canonical posterior arrays from analysis/bayesian_data.h5
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+```
+
+### 14. Keep parameter posterior data rebuilt, not duplicated
+
+`parameter.posterior` is always rebuilt from the canonical
+`_bayesian_parameter_posterior` loop in `analysis/analysis.cif`.
+
+Do not serialize posterior summaries again inside each parameter's own
+CIF representation. Duplicating the same posterior summary data across
+structure and experiment files would create multiple sources of truth.
+
+## Consequences
+
+### Positive
+
+- Users get a compact Bayesian overview directly from each parameter.
+- The parameter API stays tidy because posterior helpers are grouped
+  into one optional object rather than many flat attributes.
+- `uncertainty` and posterior metadata become clearly fit-owned rather
+  than mixed user-editable and fit-editable state.
+- `analysis.fit_results` remains the canonical source for full Bayesian
+  state.
+- `analysis/analysis.cif` becomes the home for fit-control metadata that
+  does not belong in structure or experiment CIF files.
+- Bayesian save/load gains a clear split between text metadata in
+  `analysis/analysis.cif` and bulk numerical arrays in
+  `analysis/bayesian_data.h5`.
+- Partial restore works for summaries even when full posterior arrays
+  are absent.
+- The design matches the current rule that `value` is the only active
+  scalar used for calculations.
+
+### Trade-offs
+
+- `GenericParameter` now holds an optional reference to analysis-derived
+  metadata.
+- Any manual user edit invalidates fit-derived metadata more eagerly
+  than today.
+- `uncertainty` becomes a read-only public property, so fit-result
+  application code must be updated to use dedicated internal helpers
+  rather than a mix of `_set_value_from_minimizer(...)` and public
+  uncertainty assignment.
+- Bayesian persistence now spans both CIF metadata and a binary sidecar,
+  so save/load code must validate consistency between the two.
+
+## Layering and Ownership
+
+To keep `core/` free of Bayesian computations, the parameter object must
+not compute posterior summaries itself.
+
+The summary object is created by posterior-capable fit code and then
+attached to parameters as already-computed metadata. `core/variable.py`
+should avoid eager runtime imports of Bayesian helper modules; a
+type-only import is acceptable.
+
+## Deferred Work
+
+This ADR now defines persistence for one canonical saved Bayesian run.
+
+It still defers:
+
+- support for multiple saved Bayesian runs per project
+- plot-ready cache layers beyond canonical posterior and predictive data
+- persistence-time compression or chunking strategies beyond the first
+  HDF5 sidecar implementation
+- persistence for future posterior-capable minimizers beyond DREAM
+- enabling currently unsupported single-crystal predictive draw plots
+
+## Implementation Notes
+
+- The first implementation should only populate `parameter.posterior`
+  for DREAM.
+- Existing `PosteriorParameterSummary` instances should be reused rather
+  than copied into a second summary type unless implementation reveals a
+  concrete layering problem that cannot be resolved cleanly.
+- `parameter.posterior` should always be rebuilt from restored canonical
+  Bayesian data rather than serialized redundantly at parameter level.
+- The sidecar reader and writer should be isolated behind explicit
+  serializer helpers instead of being implemented inline in
+  `Project.save()` and `Project.load()`.
+
+## Chosen Defaults
+
+- `parameter.value` remains committed to MAP after posterior fits.
+- If a project is loaded without full posterior arrays, restoring only
+  `parameter.posterior` is acceptable for table display and parameter
+  inspection.
+- Posterior plotting remains unavailable unless the canonical Bayesian
+  containers needed by those plots are also restored.
diff --git a/docs/dev/adr_undo-fit.md b/docs/dev/adr_undo-fit.md
new file mode 100644
index 000000000..299741308
--- /dev/null
+++ b/docs/dev/adr_undo-fit.md
@@ -0,0 +1,162 @@
+# ADR: Undo Fit
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+The new `_fit_parameter.start_value` and
+`_fit_parameter.start_uncertainty` fields in `analysis/analysis.cif`
+capture the last committed pre-fit scalar state for each fitted
+parameter. This is useful when a minimization run produces a poor result
+and the user wants to return to the state from immediately before the
+fit.
+
+This need is especially important in command-line workflows, where the
+user may save a project after a bad fit and reopen it later expecting a
+simple way to roll back to the pre-fit state.
+
+However, these snapshots alone do not define undo semantics. The API
+owner, rollback scope, and interaction with fit-derived metadata must be
+explicit.
+
+## Decision
+
+### 1. Add an analysis-owned `undo_fit()` operation
+
+The rollback operation belongs on `Analysis`, for example:
+
+```python
+project.analysis.undo_fit()
+```
+
+`Analysis` owns the fit lifecycle, fit metadata, and persisted
+`analysis.cif` state, so it is the correct owner for this operation.
+
+### 2. Initial undo scope is scalar rollback plus posterior clear
+
+The first undo implementation restores each fitted parameter's saved
+pre-fit scalar state and clears fit state that belongs only to the
+discarded fit.
+
+It does not attempt to restore every possible runtime detail of the
+previous fit result.
+
+After `undo_fit()`:
+
+- `parameter.value` is restored from `_fit_parameter.start_value`
+- `parameter.uncertainty` is restored from
+  `_fit_parameter.start_uncertainty`
+- `parameter.posterior` is cleared
+- `analysis.fit_results` is cleared
+
+This gives the user a safe, predictable return to the pre-fit visible
+parameter state without pretending to restore a full historical result.
+
+If an older saved project lacks `start_uncertainty`, clearing
+`parameter.uncertainty` remains an acceptable compatibility fallback.
+
+### 3. Undo does not roll back user configuration
+
+The initial undo operation does not revert:
+
+- aliases
+- constraints
+- fit bounds
+- minimizer type
+- fit mode
+- joint-fit weights
+
+These are analysis configuration, not fit output.
+
+### 4. Undo is single-level for now
+
+Only the latest saved pre-fit state is addressable.
+
+The initial API does not create a stack of historical fits. Supporting
+multiple undo levels would require a dedicated snapshot history design
+and is deferred.
+
+### 5. Persisted scalar snapshots are the rollback anchors
+
+The minimum persisted state required for clean cross-session undo is the
+pair of `_fit_parameter.start_value` and
+`_fit_parameter.start_uncertainty` defined in
+`adr_analysis-cif-fit-state.md`.
+
+If a parameter has no saved `start_value`, `undo_fit()` leaves that
+parameter unchanged.
+
+If a parameter has no saved `start_uncertainty`, `undo_fit()` may clear
+that parameter's uncertainty as a compatibility fallback for older saved
+projects.
+
+### 6. Suggested user flow
+
+```python
+project.analysis.fit()
+
+# Decide that the latest fit should be discarded.
+project.analysis.undo_fit()
+
+# Save the recovered state if desired.
+project.save()
+```
+
+### 7. Add a top-level `undo-fit` CLI command
+
+Because command-line recovery is one of the main motivations for this
+feature, undo must also be exposed through the existing top-level CLI.
+
+Suggested command:
+
+```bash
+python -m easydiffraction undo-fit PROJECT_DIR
+```
+
+This command should:
+
+- load the saved project from `PROJECT_DIR`
+- execute `project.analysis.undo_fit()`
+- save the recovered state back to the same project directory by default
+- support `--dry` to perform the rollback in memory without overwriting
+  project files
+- emit a clear message describing whether the latest fit snapshot was
+  successfully discarded
+
+Suggested dry-run form:
+
+```bash
+python -m easydiffraction undo-fit PROJECT_DIR --dry
+```
+
+If the project does not contain a usable undo snapshot, the command
+should fail with a clear non-zero exit status instead of silently doing
+nothing.
+
+## Consequences
+
+### Positive
+
+- Users gain a simple recovery path after a poor fit.
+- The feature works naturally with saved projects and both Python and
+  command-line workflows.
+- The initial scope stays small and does not require full historical fit
+  snapshots.
+
+### Trade-offs
+
+- Undo restores pre-fit scalar parameter state, not a full historical
+  `fit_results` object.
+- Older saved projects that do not carry `start_uncertainty` may still
+  fall back to clearing uncertainty.
+- Multi-level undo remains unsupported.
+
+## Deferred Work
+
+- exact restoration of previous posterior-derived projections beyond the
+  scalar parameter snapshot
+- multi-level undo / redo
+- user-facing confirmation or preview APIs
+- rollback of fit-type-specific persisted summaries beyond parameter
+  values
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 390aa77bd..48a411619 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -527,28 +527,28 @@ from .line_segment import LineSegmentBackground
 
 ### 5.5 All Factories
 
-| Factory                      | Domain                 | Tags resolve to                                                                                                                                                             |
-| ---------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `BackgroundFactory`          | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                    |
-| `PeakFactory`                | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                |
-| `InstrumentFactory`          | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                     |
-| `DataFactory`                | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                       |
-| `ReflnFactory`               | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                     |
-| `ExtinctionFactory`          | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                   |
-| `LinkedCrystalFactory`       | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                             |
-| `ExcludedRegionsFactory`     | Excluded regions       | `ExcludedRegions`                                                                                                                                                           |
-| `LinkedPhasesFactory`        | Linked phases          | `LinkedPhases`                                                                                                                                                              |
-| `ExperimentTypeFactory`      | Experiment descriptors | `ExperimentType`                                                                                                                                                            |
-| `CellFactory`                | Unit cells             | `Cell`                                                                                                                                                                      |
-| `SpaceGroupFactory`          | Space groups           | `SpaceGroup`                                                                                                                                                                |
-| `AtomSitesFactory`           | Atom sites             | `AtomSites`                                                                                                                                                                 |
-| `AtomSiteAnisoFactory`       | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                   |
-| `AliasesFactory`             | Parameter aliases      | `Aliases`                                                                                                                                                                   |
-| `ConstraintsFactory`         | Parameter constraints  | `Constraints`                                                                                                                                                               |
-| `FitModeFactory`             | Fit-mode category      | `FitMode`                                                                                                                                                                   |
-| `JointFitExperimentsFactory` | Joint-fit weights      | `JointFitExperiments`                                                                                                                                                       |
-| `CalculatorFactory`          | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                 |
-| `MinimizerFactory`           | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
+| Factory                      | Domain                 | Tags resolve to                                                                                                                                                                                    |
+| ---------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `BackgroundFactory`          | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                                           |
+| `PeakFactory`                | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                                       |
+| `InstrumentFactory`          | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                                            |
+| `DataFactory`                | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                                              |
+| `ReflnFactory`               | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                                            |
+| `ExtinctionFactory`          | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                                          |
+| `LinkedCrystalFactory`       | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                                                    |
+| `ExcludedRegionsFactory`     | Excluded regions       | `ExcludedRegions`                                                                                                                                                                                  |
+| `LinkedPhasesFactory`        | Linked phases          | `LinkedPhases`                                                                                                                                                                                     |
+| `ExperimentTypeFactory`      | Experiment descriptors | `ExperimentType`                                                                                                                                                                                   |
+| `CellFactory`                | Unit cells             | `Cell`                                                                                                                                                                                             |
+| `SpaceGroupFactory`          | Space groups           | `SpaceGroup`                                                                                                                                                                                       |
+| `AtomSitesFactory`           | Atom sites             | `AtomSites`                                                                                                                                                                                        |
+| `AtomSiteAnisoFactory`       | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                                          |
+| `AliasesFactory`             | Parameter aliases      | `Aliases`                                                                                                                                                                                          |
+| `ConstraintsFactory`         | Parameter constraints  | `Constraints`                                                                                                                                                                                      |
+| `FitModeFactory`             | Fit-mode category      | `FitMode`                                                                                                                                                                                          |
+| `JointFitExperimentsFactory` | Joint-fit weights      | `JointFitExperiments`                                                                                                                                                                              |
+| `CalculatorFactory`          | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                                        |
+| `MinimizerFactory`           | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
 
 > **Note:** `ExperimentFactory` and `StructureFactory` are _builder_
 > factories with `from_cif_path`, `from_cif_str`, `from_data_path`, and
@@ -675,6 +675,7 @@ the choice.
 | `dfols`                 | `DfolsMinimizer`             |
 | `bumps`                 | `BumpsMinimizer`             |
 | `bumps (lm)`            | `BumpsLmMinimizer`           |
+| `bumps (dream)`         | `BumpsDreamMinimizer`        |
 | `bumps (amoeba)`        | `BumpsAmoebaMinimizer`       |
 | `bumps (de)`            | `BumpsDEMinimizer`           |
 
@@ -825,14 +826,45 @@ workflow:
   only `single`; >1 → all three).
 - Joint-fit weights: `joint_fit_experiments` (`CategoryCollection` of
   per-experiment weight entries); sibling of `fit`, not a child.
+- Fit results: `analysis.fit_results` stores the latest runtime result
+  object. This is `FitResults` for deterministic fits and
+  `BayesianFitResults` for Bayesian DREAM runs.
 - Parameter tables: `show_all_params()`, `show_fittable_params()`,
   `show_free_params()`, `how_to_access_parameters()`
 - Fitting: `fit()` dispatches single/joint through the callable `fit`
   category; `fit_sequential()` handles sequential mode (sets `fit.mode`
-  to `'sequential'` internally). `display.fit_results()` shows results.
+  to `'sequential'` internally). `fit()` accepts optional `random_seed`
+  for stochastic minimizers; deterministic minimizers reject non-`None`
+  seeds. `display.fit_results()` dispatches through the active runtime
+  result object.
 - Aliases and constraints (single-type categories; no public `_type`
   getter or setter)
 
+#### 6.4.1 Bayesian DREAM Runtime Results
+
+Bayesian sampling is integrated as a normal minimizer selection with tag
+`'bumps (dream)'`. It does not create a parallel `Analysis` stack or a
+new persisted results category.
+
+- `BayesianFitResults` extends `FitResults` with runtime-only posterior
+  state such as `posterior_samples`, `posterior_parameter_summaries`,
+  `posterior_predictive`, `diagnostics`, and `sampler_settings`.
+- Posterior arrays and predictive caches remain runtime-only; they are
+  not serialized into CIF or project directories.
+- `sampler_settings` records the resolved stochastic settings actually
+  used for the run, including `random_seed`, `steps`, `burn`, `thin`,
+  `pop`, and `parallel`.
+- The current user-facing DREAM controls live on the active minimizer
+  object, for example `project.analysis.fit.minimizer.steps`, `burn`,
+  `thin`, `pop`, `parallel`, and `init`.
+- `plot_param_correlations()` uses posterior samples when available and
+  otherwise falls back to deterministic covariance or engine-derived
+  correlations.
+- Bayesian-only plotting methods are exposed explicitly rather than by
+  overloading deterministic plot calls: `plot_posterior_pairs()`,
+  `plot_param_distribution(param)`, and
+  `plot_posterior_predictive(expt_name, ...)`.
+
 ---
 
 ## 7. Project — The Top-Level Façade
@@ -894,7 +926,9 @@ project_dir/
 saved project re-opens with the same display backends. Per-experiment
 calculator selection (`_calculation.calculator_type`) lives in each
 experiment file, and fit configuration (`_fit.minimizer_type`,
-`_fit.mode`) lives in `analysis/analysis.cif`.
+`_fit.mode`) lives in `analysis/analysis.cif`. Runtime fit outputs,
+including `analysis.fit_results`, posterior chains, posterior predictive
+summaries, and convergence diagnostics, are not serialized.
 
 ### 7.3 Verbosity
 
@@ -1052,6 +1086,27 @@ project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)
 project.save()
 ```
 
+### 8.4.1 Bayesian Refinement
+
+```python
+# Deterministic pre-fit remains explicit
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fit()
+
+# Switch to Bayesian sampling using the same entry point
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+project.analysis.fit.minimizer.steps = 1000
+project.analysis.fit.minimizer.parallel = 0
+project.analysis.fit(random_seed=11)
+
+# Runtime-only Bayesian summaries and plots
+project.analysis.display.fit_results()
+project.display.plotter.plot_param_correlations()
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+```
+
 ### 8.5 TOF Experiment (tutorial ed-7)
 
 ```python
diff --git a/docs/dev/issues_open.md b/docs/dev/issues_open.md
index 014aa4675..21976afde 100644
--- a/docs/dev/issues_open.md
+++ b/docs/dev/issues_open.md
@@ -180,6 +180,30 @@ minimiser.
 
 ---
 
+## 16. 🟢 Add Serial Pattern-Generation Benchmarks
+
+**Type:** Performance
+
+The dev environment previously installed `pytest-benchmark`, but the
+repository does not currently define any benchmark tests. At the same
+time, the integration, script, and notebook pytest tasks all run with
+`pytest-xdist`, so benchmark plugins only add warning noise and do not
+provide reliable performance regression coverage.
+
+Performance regressions are still worth tracking, especially for single
+diffraction-pattern calculation where backend or profile changes can
+quietly slow interactive workflows.
+
+**Fix:** add a dedicated serial benchmark task outside the normal
+parallel pytest suite. Benchmark representative single-pattern
+calculations on fixed datasets and calculators, run without `-n auto`,
+and define regression thresholds only after measurements are stable on a
+controlled runner.
+
+**Depends on:** nothing.
+
+---
+
 ## 17. 🟢 Use PDF-Specific CIF Names for Total Scattering
 
 **Type:** Naming
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index ae233b21d..8083f39b0 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -6,6 +6,7 @@
 │   ├── 📁 calculators
 │   │   ├── 📄 __init__.py
 │   │   ├── 📄 base.py
+│   │   │   ├── 🏷️ class PowderReflnRecord
 │   │   │   └── 🏷️ class CalculatorBase
 │   │   ├── 📄 crysfml.py
 │   │   │   └── 🏷️ class CrysfmlCalculator
@@ -48,10 +49,16 @@
 │   │   └── 📄 __init__.py
 │   ├── 📁 fit_helpers
 │   │   ├── 📄 __init__.py
+│   │   ├── 📄 bayesian.py
+│   │   │   ├── 🏷️ class PosteriorParameterSummary
+│   │   │   ├── 🏷️ class PosteriorPredictiveSummary
+│   │   │   ├── 🏷️ class PosteriorSamples
+│   │   │   └── 🏷️ class BayesianFitResults
 │   │   ├── 📄 metrics.py
 │   │   ├── 📄 reporting.py
 │   │   │   └── 🏷️ class FitResults
 │   │   └── 📄 tracking.py
+│   │       ├── 🏷️ class SamplerProgressUpdate
 │   │       ├── 🏷️ class _TerminalLiveHandle
 │   │       └── 🏷️ class FitProgressTracker
 │   ├── 📁 minimizers
@@ -65,12 +72,18 @@
 │   │   │   └── 🏷️ class BumpsAmoebaMinimizer
 │   │   ├── 📄 bumps_de.py
 │   │   │   └── 🏷️ class BumpsDEMinimizer
+│   │   ├── 📄 bumps_dream.py
+│   │   │   ├── 🏷️ class _DreamRunContext
+│   │   │   ├── 🏷️ class _DreamDriverResult
+│   │   │   ├── 🏷️ class _DreamProgressMonitor
+│   │   │   └── 🏷️ class BumpsDreamMinimizer
 │   │   ├── 📄 bumps_lm.py
 │   │   │   └── 🏷️ class BumpsLmMinimizer
 │   │   ├── 📄 dfols.py
 │   │   │   └── 🏷️ class DfolsMinimizer
 │   │   ├── 📄 enums.py
-│   │   │   └── 🏷️ class MinimizerTypeEnum
+│   │   │   ├── 🏷️ class MinimizerTypeEnum
+│   │   │   └── 🏷️ class DreamPopulationInitializationEnum
 │   │   ├── 📄 factory.py
 │   │   │   └── 🏷️ class MinimizerFactory
 │   │   ├── 📄 lmfit.py
@@ -169,9 +182,6 @@
 │   │   │   │   │   ├── 🏷️ class PdDataBase
 │   │   │   │   │   ├── 🏷️ class PdCwlData
 │   │   │   │   │   └── 🏷️ class PdTofData
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   │   ├── 🏷️ class Refln
-│   │   │   │   │   └── 🏷️ class ReflnData
 │   │   │   │   ├── 📄 factory.py
 │   │   │   │   │   └── 🏷️ class DataFactory
 │   │   │   │   └── 📄 total_pd.py
@@ -257,6 +267,20 @@
 │   │   │   │   │   └── 🏷️ class TotalGaussianDampedSinc
 │   │   │   │   └── 📄 total_mixins.py
 │   │   │   │       └── 🏷️ class TotalBroadeningMixin
+│   │   │   ├── 📁 refln
+│   │   │   │   ├── 📄 __init__.py
+│   │   │   │   ├── 📄 bragg_pd.py
+│   │   │   │   │   ├── 🏷️ class PowderReflnBase
+│   │   │   │   │   ├── 🏷️ class PowderCwlRefln
+│   │   │   │   │   ├── 🏷️ class PowderTofRefln
+│   │   │   │   │   ├── 🏷️ class PowderReflnDataBase
+│   │   │   │   │   ├── 🏷️ class PowderCwlReflnData
+│   │   │   │   │   └── 🏷️ class PowderTofReflnData
+│   │   │   │   ├── 📄 bragg_sc.py
+│   │   │   │   │   ├── 🏷️ class Refln
+│   │   │   │   │   └── 🏷️ class ReflnData
+│   │   │   │   └── 📄 factory.py
+│   │   │   │       └── 🏷️ class ReflnFactory
 │   │   │   └── 📄 __init__.py
 │   │   ├── 📁 item
 │   │   │   ├── 📄 __init__.py
@@ -352,7 +376,13 @@
 │   │   └── 🏷️ class RendererFactoryBase
 │   ├── 📄 plotting.py
 │   │   ├── 🏷️ class PlotterEngineEnum
+│   │   ├── 🏷️ class PosteriorPairPlotStyleEnum
 │   │   ├── 🏷️ class _MeasVsCalcPlotOptions
+│   │   ├── 🏷️ class _PowderMeasVsCalcSeries
+│   │   ├── 🏷️ class _PosteriorDistributionContext
+│   │   ├── 🏷️ class _PosteriorPairsContext
+│   │   ├── 🏷️ class _CorrelationHeatmapContext
+│   │   ├── 🏷️ class _PosteriorPairsLegendState
 │   │   ├── 🏷️ class Plotter
 │   │   └── 🏷️ class PlotterFactory
 │   ├── 📄 tables.py
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index 30b4daf72..9ce283eb5 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -31,6 +31,7 @@
 │   │   └── 📄 __init__.py
 │   ├── 📁 fit_helpers
 │   │   ├── 📄 __init__.py
+│   │   ├── 📄 bayesian.py
 │   │   ├── 📄 metrics.py
 │   │   ├── 📄 reporting.py
 │   │   └── 📄 tracking.py
@@ -40,6 +41,7 @@
 │   │   ├── 📄 bumps.py
 │   │   ├── 📄 bumps_amoeba.py
 │   │   ├── 📄 bumps_de.py
+│   │   ├── 📄 bumps_dream.py
 │   │   ├── 📄 bumps_lm.py
 │   │   ├── 📄 dfols.py
 │   │   ├── 📄 enums.py
@@ -85,7 +87,6 @@
 │   │   │   ├── 📁 data
 │   │   │   │   ├── 📄 __init__.py
 │   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 bragg_sc.py
 │   │   │   │   ├── 📄 factory.py
 │   │   │   │   └── 📄 total_pd.py
 │   │   │   ├── 📁 diffrn
@@ -128,6 +129,11 @@
 │   │   │   │   ├── 📄 tof_mixins.py
 │   │   │   │   ├── 📄 total.py
 │   │   │   │   └── 📄 total_mixins.py
+│   │   │   ├── 📁 refln
+│   │   │   │   ├── 📄 __init__.py
+│   │   │   │   ├── 📄 bragg_pd.py
+│   │   │   │   ├── 📄 bragg_sc.py
+│   │   │   │   └── 📄 factory.py
 │   │   │   └── 📄 __init__.py
 │   │   ├── 📁 item
 │   │   │   ├── 📄 __init__.py
diff --git a/docs/docs/installation-and-setup/index.md b/docs/docs/installation-and-setup/index.md
index 41036f1d9..ee86679ba 100644
--- a/docs/docs/installation-and-setup/index.md
+++ b/docs/docs/installation-and-setup/index.md
@@ -254,8 +254,8 @@ once using the command line, as shown below.
   ```txt
   pixi run easydiffraction download-all-tutorials
   ```
-- Start JupyterLab in the `tutorials/` directory to access the
-  notebooks:
+- Start the JupyterLab server in the `tutorials/` directory to access
+  the notebooks:
   ```txt
   pixi run jupyter lab tutorials/
   ```
@@ -278,13 +278,14 @@ once using the command line, as shown below.
   ```txt
   python -m easydiffraction download-all-tutorials
   ```
-- Launch the Jupyter Notebook server (opens browser automatically at
-  `http://localhost:8888/`):
+- Start the Jupyter Notebook server in the `tutorials/` directory to
+  access the notebooks:
   ```txt
   jupyter notebook tutorials/
   ```
-- Open one of the `*.ipynb` files and select the
-  `EasyDiffraction Python kernel` to get started.
+- Your web browser should open automatically. Click on one of the
+  `*.ipynb` files and select the `EasyDiffraction Python kernel` to get
+  started.
 
 ### Run Tutorials via Google Colab
 
diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 4d605962c..69eae9aa9 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -2665,7 +2665,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "tags,title,-all",
+   "cell_metadata_filter": "title,tags,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
new file mode 100644
index 000000000..405bc8589
--- /dev/null
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -0,0 +1,730 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Bayesian Analysis: LBCO, HRPT\n",
+    "\n",
+    "This tutorial demonstrates a practical two-stage workflow for powder\n",
+    "diffraction analysis with EasyDiffraction.\n",
+    "\n",
+    "In the first stage, we run a fast local refinement to obtain a sensible\n",
+    "point estimate and parameter uncertainties. In the second stage, we use\n",
+    "these refined values to define fit bounds and then sample the posterior\n",
+    "distribution with DREAM.\n",
+    "\n",
+    "The example uses constant-wavelength neutron powder diffraction data\n",
+    "for La0.5Ba0.5CoO3 measured on HRPT at PSI.\n",
+    "\n",
+    "The goal is not only to obtain a good fit, but also to answer Bayesian\n",
+    "questions such as:\n",
+    "\n",
+    "- Which parameter values are most probable?\n",
+    "- How broad are the credible intervals?\n",
+    "- Which parameters are strongly correlated?\n",
+    "- How much uncertainty propagates into the calculated diffraction\n",
+    "  pattern?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Container\n",
+    "\n",
+    "The project object keeps structures, experiments, fit settings, and\n",
+    "plotting utilities together in a single place. We will build the full\n",
+    "workflow inside this object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Step 2: Build the Structural Model\n",
+    "\n",
+    "We define a simple cubic perovskite model for LBCO. La and Ba share the\n",
+    "same crystallographic site with equal occupancy, while Co and O occupy\n",
+    "the remaining ideal perovskite positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.structures.create(name='lbco')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure = project.structures['lbco']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.space_group.name_h_m = 'P m -3 m'\n",
+    "structure.space_group.it_coordinate_system_code = '1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.cell.length_a = 3.88"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "The atom-site definitions below form the starting structural model. The\n",
+    "parameters are intentionally reasonable rather than fully optimized,\n",
+    "because the refinement step will improve them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.atom_sites.create(\n",
+    "    label='La',\n",
+    "    type_symbol='La',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0,\n",
+    "    fract_z=0,\n",
+    "    wyckoff_letter='a',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.5151,\n",
+    "    occupancy=0.5,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='Ba',\n",
+    "    type_symbol='Ba',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0,\n",
+    "    fract_z=0,\n",
+    "    wyckoff_letter='a',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.5151,\n",
+    "    occupancy=0.5,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='Co',\n",
+    "    type_symbol='Co',\n",
+    "    fract_x=0.5,\n",
+    "    fract_y=0.5,\n",
+    "    fract_z=0.5,\n",
+    "    wyckoff_letter='b',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.2190,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='O',\n",
+    "    type_symbol='O',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0.5,\n",
+    "    fract_z=0.5,\n",
+    "    wyckoff_letter='c',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=1.3916,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define the Diffraction Experiment\n",
+    "\n",
+    "Next we download the measured powder pattern, create a neutron powder\n",
+    "experiment, and configure the instrument, profile, background, and\n",
+    "excluded regions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "#### Download the Measured Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = ed.download_data(id=3, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "#### Create the Experiment Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.experiments.add_from_data_path(\n",
+    "    name='hrpt',\n",
+    "    data_path=data_path,\n",
+    "    sample_form='powder',\n",
+    "    beam_mode='constant wavelength',\n",
+    "    radiation_probe='neutron',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment = project.experiments['hrpt']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "#### Set Instrument and Peak-Profile Parameters\n",
+    "\n",
+    "These values provide the initial instrument description for the local\n",
+    "refinement. Later, a subset of them will be refined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.instrument.setup_wavelength = 1.494\n",
+    "experiment.instrument.calib_twotheta_offset = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.peak.broad_gauss_u = 0.1\n",
+    "experiment.peak.broad_gauss_v = -0.1\n",
+    "experiment.peak.broad_gauss_w = 0.1204\n",
+    "experiment.peak.broad_lorentz_y = 0.0844"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "#### Add Background Points and Excluded Regions\n",
+    "\n",
+    "The line-segment background is defined by a few anchor points. We also\n",
+    "exclude regions that are not intended to contribute to the fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.background.create(id='1', x=10, y=168.5585)\n",
+    "experiment.background.create(id='2', x=30, y=164.3357)\n",
+    "experiment.background.create(id='3', x=50, y=166.8881)\n",
+    "experiment.background.create(id='4', x=110, y=175.4006)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.excluded_regions.create(id='1', start=0, end=10)\n",
+    "experiment.excluded_regions.create(id='2', start=100, end=180)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "#### Link the Structural Phase to the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_phases.create(id='lbco', scale=9.1351)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "## Step 4: Run an Initial Local Refinement\n",
+    "\n",
+    "Before Bayesian sampling, it is useful to run a deterministic fit. This\n",
+    "gives us:\n",
+    "\n",
+    "- a good point estimate near the best-fit region,\n",
+    "- uncertainties from the local optimizer,\n",
+    "- a quick check that the model and experiment are configured\n",
+    "  sensibly.\n",
+    "\n",
+    "In this tutorial we refine only a small set of parameters that are easy\n",
+    "to interpret in the later Bayesian stage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.cell.length_a.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_phases['lbco'].scale.free = True\n",
+    "experiment.peak.broad_gauss_u.free = True\n",
+    "experiment.peak.broad_gauss_v.free = True\n",
+    "experiment.instrument.calib_twotheta_offset.free = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30",
+   "metadata": {},
+   "source": [
+    "We choose the BUMPS Levenberg-Marquardt minimizer as a fast local\n",
+    "optimizer. Its main purpose here is to provide a stable starting point\n",
+    "and uncertainty estimates for the Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer_type = 'bumps (lm)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35",
+   "metadata": {},
+   "source": [
+    "The correlation plot shows how strongly the fitted parameters move\n",
+    "together in the local refinement. The measured-vs-calculated plots show\n",
+    "how well the refined model reproduces the data globally and in a zoomed\n",
+    "region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38",
+   "metadata": {},
+   "source": [
+    "## Step 5: Prepare for Bayesian Sampling\n",
+    "\n",
+    "DREAM requires finite bounds for the free parameters. Instead of\n",
+    "setting them manually, we derive them from the uncertainties estimated\n",
+    "in the local refinement.\n",
+    "\n",
+    "The helper method `set_fit_bounds_from_uncertainty` centers the bounds\n",
+    "on the current parameter value and expands them by a chosen multiple of\n",
+    "the reported uncertainty.\n",
+    "\n",
+    "Default `multiplier` is 8 to give a wide range for the sampler to\n",
+    "explore, but here we use 3 to speed up the tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    param.set_fit_bounds_from_uncertainty(multiplier=3.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41",
+   "metadata": {},
+   "source": [
+    "Displaying the free parameters again is a convenient way to confirm\n",
+    "that the fit bounds have been assigned as expected before launching the\n",
+    "sampler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43",
+   "metadata": {},
+   "source": [
+    "## Step 6: Configure and Run DREAM\n",
+    "\n",
+    "We now switch from the local minimizer to the Bayesian DREAM sampler.\n",
+    "\n",
+    "The settings below are intentionally small so the tutorial runs\n",
+    "quickly. For production analysis you would usually increase the number\n",
+    "of steps (`steps`) and often the burn-in (`burn`) as well. When\n",
+    "needed, the DREAM API also lets you tune how chains are initialized\n",
+    "through the `init` setting. Other sampler settings such as `thin` and\n",
+    "`pop` can be adjusted as well. The current EasyDiffraction default\n",
+    "also uses `parallel=0`, which tells BUMPS DREAM to use all available\n",
+    "CPUs for population evaluations.\n",
+    "\n",
+    "The default `steps` value is 1000, and real analyses often need more\n",
+    "to achieve good convergence and posterior sampling. Here we use a much\n",
+    "smaller value to keep the tutorial fast, but this is not recommended\n",
+    "for production analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer.steps = 100  # lower than the default 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48",
+   "metadata": {},
+   "source": [
+    "## Step 7: Inspect Bayesian Results\n",
+    "\n",
+    "The fit-results display now includes sampler settings, convergence\n",
+    "diagnostics, committed parameter values, and posterior summary\n",
+    "statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50",
+   "metadata": {},
+   "source": [
+    "The correlation and posterior-pair plots are complementary:\n",
+    "\n",
+    "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
+    "  matrix.\n",
+    "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
+    "  posterior contours off-diagonal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_pairs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53",
+   "metadata": {},
+   "source": [
+    "The one-dimensional posterior distributions below make it easier to\n",
+    "inspect individual parameters in isolation, including asymmetry or\n",
+    "multimodality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    project.display.plotter.plot_param_distribution(param)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55",
+   "metadata": {},
+   "source": [
+    "Finally, the posterior predictive plot propagates the sampled parameter\n",
+    "uncertainty into the calculated diffraction pattern. Comparing this to\n",
+    "the zoomed measured-vs-calculated view helps assess whether the sampled\n",
+    "model family explains the data in the region of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57",
+   "metadata": {},
+   "source": [
+    "A final zoomed measured-vs-calculated plot is useful for checking how\n",
+    "the posterior-supported model behaves in a narrow region of the pattern\n",
+    "after the Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
new file mode 100644
index 000000000..d91173c37
--- /dev/null
+++ b/docs/docs/tutorials/ed-21.py
@@ -0,0 +1,354 @@
+# %% [markdown]
+# # Bayesian Analysis: LBCO, HRPT
+#
+# This tutorial demonstrates a practical two-stage workflow for powder
+# diffraction analysis with EasyDiffraction.
+#
+# In the first stage, we run a fast local refinement to obtain a sensible
+# point estimate and parameter uncertainties. In the second stage, we use
+# these refined values to define fit bounds and then sample the posterior
+# distribution with DREAM.
+#
+# The example uses constant-wavelength neutron powder diffraction data
+# for La0.5Ba0.5CoO3 measured on HRPT at PSI.
+#
+# The goal is not only to obtain a good fit, but also to answer Bayesian
+# questions such as:
+#
+# - Which parameter values are most probable?
+# - How broad are the credible intervals?
+# - Which parameters are strongly correlated?
+# - How much uncertainty propagates into the calculated diffraction
+#   pattern?
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Step 1: Create a Project Container
+#
+# The project object keeps structures, experiments, fit settings, and
+# plotting utilities together in a single place. We will build the full
+# workflow inside this object.
+
+# %%
+project = ed.Project()
+
+# %% [markdown]
+# ## Step 2: Build the Structural Model
+#
+# We define a simple cubic perovskite model for LBCO. La and Ba share the
+# same crystallographic site with equal occupancy, while Co and O occupy
+# the remaining ideal perovskite positions.
+
+# %%
+project.structures.create(name='lbco')
+
+# %%
+structure = project.structures['lbco']
+
+# %%
+structure.space_group.name_h_m = 'P m -3 m'
+structure.space_group.it_coordinate_system_code = '1'
+
+# %%
+structure.cell.length_a = 3.88
+
+# %% [markdown]
+# The atom-site definitions below form the starting structural model. The
+# parameters are intentionally reasonable rather than fully optimized,
+# because the refinement step will improve them.
+
+# %%
+structure.atom_sites.create(
+    label='La',
+    type_symbol='La',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5151,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Ba',
+    type_symbol='Ba',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5151,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Co',
+    type_symbol='Co',
+    fract_x=0.5,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='b',
+    adp_type='Biso',
+    adp_iso=0.2190,
+)
+structure.atom_sites.create(
+    label='O',
+    type_symbol='O',
+    fract_x=0,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='c',
+    adp_type='Biso',
+    adp_iso=1.3916,
+)
+
+# %% [markdown]
+# ## Step 3: Define the Diffraction Experiment
+#
+# Next we download the measured powder pattern, create a neutron powder
+# experiment, and configure the instrument, profile, background, and
+# excluded regions.
+
+# %% [markdown]
+# Download the measured data from the repository. Alternatively, you
+# could use your own data file by providing the path to it instead of
+# downloading from the repository.
+
+# %%
+data_path = ed.download_data(id=3, destination='data')
+
+# %% [markdown]
+# Create the experiment object and specify the sample form, beam mode,
+# and radiation probe.
+
+# %%
+project.experiments.add_from_data_path(
+    name='hrpt',
+    data_path=data_path,
+    sample_form='powder',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+# %%
+experiment = project.experiments['hrpt']
+
+# %% [markdown]
+# Link the structural phase to the experiment.
+
+# %%
+experiment.linked_phases.create(id='lbco', scale=9.1351)
+
+# %% [markdown]
+# Set instrument and peak profile parameters.
+#
+# These values provide the initial instrument description for the local
+# refinement. Later, a subset of them will be refined.
+
+# %%
+experiment.instrument.setup_wavelength = 1.494
+experiment.instrument.calib_twotheta_offset = 0.0
+
+# %%
+experiment.peak.broad_gauss_u = 0.1
+experiment.peak.broad_gauss_v = -0.1
+experiment.peak.broad_gauss_w = 0.1204
+experiment.peak.broad_lorentz_y = 0.0844
+
+# %% [markdown]
+# Add background points and excluded regions.
+#
+# The line-segment background is defined by a few anchor points. We also
+# exclude regions that are not intended to contribute to the fit.
+
+# %%
+experiment.background.create(id='1', x=10, y=168.5585)
+experiment.background.create(id='2', x=30, y=164.3357)
+experiment.background.create(id='3', x=50, y=166.8881)
+experiment.background.create(id='4', x=110, y=175.4006)
+
+# %%
+experiment.excluded_regions.create(id='1', start=0, end=10)
+experiment.excluded_regions.create(id='2', start=100, end=180)
+
+# %% [markdown]
+# ## Step 4: Run an Initial Local Refinement
+#
+# Before Bayesian sampling, it is useful to run a deterministic fit. This
+# gives us:
+#
+# - a good point estimate near the best-fit region,
+# - uncertainties from the local optimizer,
+# - a quick check that the model and experiment are configured
+#   sensibly.
+#
+# In this tutorial we refine only a small set of parameters that are easy
+# to interpret in the later Bayesian stage.
+
+# %%
+structure.cell.length_a.free = True
+
+# %%
+experiment.linked_phases['lbco'].scale.free = True
+experiment.peak.broad_gauss_u.free = True
+experiment.peak.broad_gauss_v.free = True
+experiment.instrument.calib_twotheta_offset.free = True
+
+# %% [markdown]
+# We choose the BUMPS Levenberg-Marquardt minimizer as a fast local
+# optimizer. Its main purpose here is to provide a stable starting point
+# and uncertainty estimates for the Bayesian run.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation plot shows how strongly the fitted parameters move
+# together in the local refinement. The measured-vs-calculated plots show
+# how well the refined model reproduces the data globally and in a zoomed
+# region.
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+
+# %% [markdown]
+# ## Step 5: Prepare for Bayesian Sampling
+#
+# DREAM requires finite bounds for the free parameters. Instead of
+# setting them manually, we derive them from the uncertainties estimated
+# in the local refinement.
+#
+# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
+# on the current parameter value and expands them by a chosen multiple of
+# the reported uncertainty.
+#
+# The default `multiplier` is 4. If the local refinement is very tight,
+# or if you expect a broader posterior, increase it explicitly.
+#
+# Show unset fit bounds before setting them from the local refinement uncertainties.
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# Set fit bounds for all free parameters using the default multiplier of
+# 4. In this tutorial that means the posterior pair plot will later
+# refer to a `±4 × uncertainty` region in its title. To use a different
+# region, pass another value, for example `multiplier=6`.
+
+# %%
+for param in project.free_parameters:
+    param.set_fit_bounds_from_uncertainty()
+
+# %% [markdown]
+# Displaying the free parameters again is a convenient way to confirm
+# that the fit bounds have been assigned as expected before launching the
+# sampler.
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# ## Step 6: Configure and Run DREAM
+#
+# We now switch from the local minimizer to the Bayesian DREAM sampler.
+#
+# The settings below are intentionally small so the tutorial runs
+# quickly. For production analysis you would usually increase the number
+# of steps (`steps`) and often the burn-in (`burn`) as well. When
+# needed, the DREAM API also lets you tune how chains are initialized
+# through the `init` setting. Other sampler settings such as `thin` and
+# `pop` can be adjusted as well. The current EasyDiffraction defaults
+# use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells
+# BUMPS-DREAM to use all available CPUs for population evaluations.
+#
+# The `burn` setting is auto-resolved when left unset. With the default
+# `steps=3000` this gives `burn=600`, but if you override `steps` and
+# keep `burn=None`, the effective burn-in is recomputed automatically.
+# Here we use a much smaller step count to keep the tutorial fast, but
+# this is not recommended for production analysis.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+
+# %%
+project.analysis.fit.minimizer.steps = 300  # lower than the default 3000
+
+# %%
+project.analysis.fit()
+
+# %% [markdown]
+# ## Step 7: Inspect Bayesian Results
+#
+# The fit-results display now includes sampler settings, convergence
+# diagnostics, committed parameter values, and posterior summary
+# statistics.
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation and posterior-pair plots are complementary:
+#
+# - `plot_param_correlations` summarizes pairwise structure in a compact
+#   matrix.
+# - `plot_posterior_pairs` shows marginal densities on the diagonal and
+#   posterior contours off-diagonal. In this tutorial its title also
+#   reminds you that the display region follows the `±4 × uncertainty`
+#   bounds defined above, while numeric subplot ranges are omitted to
+#   keep the grid readable.
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_posterior_pairs()
+
+# %% [markdown]
+# The one-dimensional posterior distributions below make it easier to
+# inspect individual parameters in isolation, including asymmetry or
+# multimodality.
+
+# %%
+for param in project.free_parameters:
+    project.display.plotter.plot_param_distribution(param)
+
+# %% [markdown]
+# Finally, the posterior predictive plot propagates the sampled parameter
+# uncertainty into the calculated diffraction pattern. Comparing this to
+# the zoomed measured-vs-calculated view helps assess whether the sampled
+# model family explains the data in the region of interest.
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+
+# %% [markdown]
+# A final zoomed measured-vs-calculated plot is useful for checking how
+# the posterior-supported model behaves in a narrow region of the pattern
+# after the Bayesian run.
+
+# %%
+project.display.plotter.plot_posterior_predictive(
+    expt_name='hrpt',
+    x_min=92,
+    x_max=93,
+)
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
new file mode 100644
index 000000000..cc880314e
--- /dev/null
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Bayesian Analysis: Tb2TiO7, HEiDi\n",
+    "\n",
+    "This tutorial demonstrates a practical two-stage workflow for single-crystal\n",
+    "diffraction analysis with EasyDiffraction.\n",
+    "\n",
+    "In the first stage, we run a fast local refinement to obtain a sensible\n",
+    "point estimate and parameter uncertainties. In the second stage, we use\n",
+    "these refined values to define fit bounds and then sample the posterior\n",
+    "distribution with BUMPS-DREAM.\n",
+    "\n",
+    "The example uses constant-wavelength neutron single-crystal diffraction data\n",
+    "for Tb2TiO7 measured on HEiDi at FRM II."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Container"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Step 2: Build the Structural Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure_path = ed.download_data(id=20, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.structures.add_from_cif_path(structure_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure = project.structures['tbti']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define the Diffraction Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = ed.download_data(id=19, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.experiments.add_from_data_path(\n",
+    "    name='heidi',\n",
+    "    data_path=data_path,\n",
+    "    sample_form='single crystal',\n",
+    "    beam_mode='constant wavelength',\n",
+    "    radiation_probe='neutron',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment = project.experiments['heidi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_crystal.id = 'tbti'\n",
+    "experiment.linked_crystal.scale = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.instrument.setup_wavelength = 0.793"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.extinction.mosaicity = 35000\n",
+    "experiment.extinction.radius = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Step 4: Run an Initial Local Refinement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.atom_sites['O1'].fract_x.free = True\n",
+    "\n",
+    "structure.atom_sites['Ti'].occupancy.free = False\n",
+    "structure.atom_sites['O1'].occupancy.free = False\n",
+    "structure.atom_sites['O2'].occupancy.free = False\n",
+    "\n",
+    "structure.atom_sites['Tb'].adp_iso.free = True\n",
+    "structure.atom_sites['Ti'].adp_iso.free = True\n",
+    "structure.atom_sites['O1'].adp_iso.free = True\n",
+    "structure.atom_sites['O2'].adp_iso.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_crystal.scale.free = True\n",
+    "experiment.extinction.radius.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "## Step 5: Prepare for Bayesian Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    param.set_fit_bounds_from_uncertainty(multiplier=1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29",
+   "metadata": {},
+   "source": [
+    "## Step 6: Configure and Run BUMPS-DREAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer.steps = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_pairs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    project.display.plotter.plot_param_distribution(param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='heidi')"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
new file mode 100644
index 000000000..aaa727180
--- /dev/null
+++ b/docs/docs/tutorials/ed-22.py
@@ -0,0 +1,269 @@
+# %% [markdown]
+# # Bayesian Analysis: Tb2TiO7, HEiDi
+#
+# This tutorial demonstrates a practical two-stage workflow for single-crystal
+# diffraction analysis with EasyDiffraction.
+#
+# In the first stage, we run a fast local refinement to obtain a sensible
+# point estimate and parameter uncertainties. In the second stage, we use
+# these refined values to define fit bounds and then sample the posterior
+# distribution with BUMPS-DREAM.
+#
+# The example uses constant-wavelength neutron single-crystal diffraction data
+# for Tb2TiO7 measured on HEiDi at FRM II.
+#
+# The goal is not only to obtain a good fit, but also to answer Bayesian
+# questions such as:
+#
+# - Which parameter values are most probable?
+# - How broad are the credible intervals?
+# - Which parameters are strongly correlated?
+# - How much uncertainty propagates into the calculated reflection
+#   intensities?
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Step 1: Create a Project Container
+#
+# The project object keeps structures, experiments, fit settings, and
+# plotting utilities together in a single place. We will build the full
+# workflow inside this object.
+
+# %%
+project = ed.Project()
+
+# %% [markdown]
+# ## Step 2: Build the Structural Model
+#
+# For this example we start from a CIF file describing the Tb2TiO7
+# pyrochlore structure. Loading the structure from CIF is convenient
+# because it preserves a realistic starting
+# model without rebuilding the full structure by hand.
+
+# %%
+structure_path = ed.download_data(id=20, destination='data')
+
+# %%
+project.structures.add_from_cif_path(structure_path)
+
+# %%
+structure = project.structures['tbti']
+
+# %% [markdown]
+# ## Step 3: Define the Diffraction Experiment
+#
+# Next we download the measured reflection data, create a neutron
+# single-crystal experiment, and configure the crystal link,
+# wavelength, and extinction model.
+
+# %%
+data_path = ed.download_data(id=19, destination='data')
+
+# %%
+project.experiments.add_from_data_path(
+    name='heidi',
+    data_path=data_path,
+    sample_form='single crystal',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+# %%
+experiment = project.experiments['heidi']
+
+# %% [markdown]
+# Link the crystal structure to the experiment and set its scale factor.
+
+# %%
+experiment.linked_crystal.id = 'tbti'
+experiment.linked_crystal.scale = 1.0
+
+# %% [markdown]
+# Set the instrument wavelength and starting extinction parameters.
+# These values provide the initial experiment description for the local
+# refinement.
+
+# %%
+experiment.instrument.setup_wavelength = 0.793
+
+# %%
+experiment.extinction.mosaicity = 35000
+experiment.extinction.radius = 10
+
+# %% [markdown]
+# ## Step 4: Run an Initial Local Refinement
+#
+# Before Bayesian sampling, it is useful to run a deterministic fit. This
+# gives us:
+#
+# - a good point estimate near the best-fit region,
+# - uncertainties from the local optimizer,
+# - a quick check that the model and experiment are configured
+#   sensibly.
+#
+# In this tutorial we refine a small set of structural and extinction
+# parameters while keeping occupancies fixed.
+
+# %%
+structure.atom_sites['O1'].fract_x.free = True
+
+structure.atom_sites['Ti'].occupancy.free = False
+structure.atom_sites['O1'].occupancy.free = False
+structure.atom_sites['O2'].occupancy.free = False
+
+structure.atom_sites['Tb'].adp_iso.free = True
+structure.atom_sites['Ti'].adp_iso.free = True
+structure.atom_sites['O1'].adp_iso.free = True
+structure.atom_sites['O2'].adp_iso.free = True
+
+# %%
+experiment.linked_crystal.scale.free = True
+experiment.extinction.radius.free = True
+
+# %% [markdown]
+# We keep using the default LMFIT Levenberg-Marquardt minimizer as a fast local
+# optimizer. Its main purpose here is to provide a stable starting point
+# and uncertainty estimates for the Bayesian run.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit()
+
+# %% [markdown]
+# The fit-results display summarizes the locally refined values and their
+# estimated uncertainties.
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation plot shows how strongly the refined parameters move
+# together in the local refinement. The measured-vs-calculated plot shows
+# how well the refined crystal model reproduces the measured reflection
+# intensities.
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+
+# %% [markdown]
+# ## Step 5: Prepare for Bayesian Sampling
+#
+# DREAM requires finite bounds for the free parameters. Instead of
+# setting them manually, we derive them from the uncertainties estimated
+# in the local refinement.
+#
+# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
+# on the current parameter value and expands them by a chosen multiple of
+# the reported uncertainty.
+#
+# The default `multiplier` is 4. In this single-crystal tutorial we use
+# a tighter value of `1.5` to keep the sampling window closer to the
+# locally refined solution.
+#
+# Show unset fit bounds before setting them from the local refinement
+# uncertainties.
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# Set fit bounds for all free parameters using `multiplier=1.5`. In this
+# tutorial that means the posterior pair plot will later refer to a
+# `±1.5 × uncertainty` region in its title. To widen the sampling window,
+# increase the multiplier explicitly.
+
+# %%
+for param in project.free_parameters:
+    param.set_fit_bounds_from_uncertainty(multiplier=1.5)
+
+# %% [markdown]
+# Displaying the free parameters again is a convenient way to confirm
+# that the fit bounds have been assigned as expected before launching the
+# sampler.
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# ## Step 6: Configure and Run DREAM
+#
+# We now switch from the local minimizer to the Bayesian DREAM sampler.
+#
+# The settings below are intentionally small so the tutorial runs
+# quickly. For production analysis you would usually increase the number
+# of steps (`steps`) and often the burn-in (`burn`) as well. When
+# needed, the DREAM API also lets you tune how chains are initialized
+# through the `init` setting. Other sampler settings such as `thin` and
+# `pop` can be adjusted as well. The current EasyDiffraction defaults
+# use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells
+# BUMPS-DREAM to use all available CPUs for population evaluations.
+#
+# The `burn` setting is auto-resolved when left unset. Here we override
+# `steps` with a smaller value to keep the tutorial fast, and the
+# effective burn-in is recomputed automatically.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+
+# %%
+project.analysis.fit.minimizer.steps = 500  # lower than the default 3000
+
+# %%
+project.analysis.fit()
+
+# %% [markdown]
+# ## Step 7: Inspect Bayesian Results
+#
+# The fit-results display now includes sampler settings, convergence
+# diagnostics, committed parameter values, and posterior summary
+# statistics.
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation and posterior-pair plots are complementary:
+#
+# - `plot_param_correlations` summarizes pairwise structure in a compact
+#   matrix.
+# - `plot_posterior_pairs` shows marginal densities on the diagonal and
+#   posterior contours off-diagonal. In this tutorial its title also
+#   reminds you that the display region follows the `±1.5 × uncertainty`
+#   bounds defined above, while numeric subplot ranges are omitted to
+#   keep the grid readable.
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_posterior_pairs()
+
+# %% [markdown]
+# The one-dimensional posterior distributions below make it easier to
+# inspect individual parameters in isolation, including asymmetry or
+# multimodality.
+
+# %%
+for param in project.free_parameters:
+    project.display.plotter.plot_param_distribution(param)
+
+# %% [markdown]
+# Finally, the posterior predictive plot propagates the sampled
+# parameter uncertainty into the calculated single-crystal reflection
+# intensities.
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='heidi')
diff --git a/docs/docs/tutorials/index.json b/docs/docs/tutorials/index.json
index dcd0cb870..40145a15e 100644
--- a/docs/docs/tutorials/index.json
+++ b/docs/docs/tutorials/index.json
@@ -131,5 +131,19 @@
     "title": "Instrument calibration: BEER at ESS",
     "description": "Instrument calibration of BEER at ESS using neutron powder diffraction data simulated with McStas",
     "level": "intermediate"
+  },
+  "21": {
+    "url": "https://easyscience.github.io/diffraction-lib/{version}/tutorials/ed-21/ed-21.ipynb",
+    "original_name": "",
+    "title": "Bayesian Analysis: LBCO, HRPT",
+    "description": "Bayesian analysis of the La0.5Ba0.5CoO3 crystal structure - Markov Chain Monte Carlo (MCMC) sampling.",
+    "level": "advanced"
+  },
+  "22": {
+    "url": "https://easyscience.github.io/diffraction-lib/{version}/tutorials/ed-22/ed-22.ipynb",
+    "original_name": "",
+    "title": "Bayesian Analysis: Tb2TiO7, HEiDi",
+    "description": "Bayesian analysis of the Tb2TiO7 crystal structure - Markov Chain Monte Carlo (MCMC) sampling.",
+    "level": "advanced"
   }
 }
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index 69b9d0ca9..a11c73e60 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -98,6 +98,22 @@ The tutorials are organized into the following categories:
 - [BEER McStas](ed-20.ipynb) – Rietveld refinement based on the data
   simulated with McStas for the BEER instrument at ESS.
 
+## Bayesian Analysis
+
+- [LBCO Bayesian](ed-21.ipynb) – Demonstrates how to perform a Bayesian
+  analysis of the La0.5Ba0.5CoO3 crystal structure using constant
+  wavelength neutron powder diffraction data from HRPT at PSI. This
+  tutorial covers the use of Markov Chain Monte Carlo (MCMC) sampling to
+  explore the posterior distribution of the refined parameters,
+  providing insights into parameter uncertainties and correlations.
+- [Tb2TiO7 Bayesian](ed-22.ipynb) – Another example of a Bayesian
+  analysis. This tutorial focuses on the Tb2TiO7 crystal structure using
+  constant wavelength neutron single crystal diffraction data from HEiDi
+  at FRM II. Similar to the LBCO Bayesian tutorial, it covers the use of
+  MCMC sampling to explore the posterior distribution of the refined
+  parameters, providing insights into parameter uncertainties and
+  correlations in the context of single crystal diffraction data.
+
 ## Workshops & Schools
 
 - [DMSC Summer School](ed-13.ipynb) – A workshop tutorial that
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 45c2ce5fb..16091a5d6 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -148,8 +148,10 @@ plugins:
         python:
           paths: ['src'] # Change 'src' to your actual sources directory
           options:
+            annotations_path: source
             docstring_style: numpy
-            group_by_category: false
+            group_by_category: true
+            members_order: source
             heading_level: 1
             show_root_heading: true
             show_root_full_path: false
@@ -214,6 +216,9 @@ nav:
       - Simulated Data:
           - LBCO+Si McStas: tutorials/ed-9.ipynb
           - BEER McStas: tutorials/ed-20.ipynb
+      - Bayesian Analysis:
+          - LBCO Bayesian: tutorials/ed-21.ipynb
+          - Tb2TiO7 Bayesian: tutorials/ed-22.ipynb
       - Workshops & Schools:
           - DMSC Summer School: tutorials/ed-13.ipynb
   - API Reference:
@@ -229,5 +234,5 @@ nav:
       - project: api-reference/project.md
       - summary: api-reference/summary.md
       - utils: api-reference/utils.md
-  - Command-Line Interface:
-      - Command-Line Interface: cli/index.md
+  - Command-Line:
+      - Command-Line: cli/index.md
diff --git a/pixi.lock b/pixi.lock
index 4ff7fdb61..f1532e2b2 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,8 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-arm64
+- name: win-64
 environments:
   default:
     channels:
@@ -6,49 +10,91 @@ environments:
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@@ -71,47 +117,14 @@ environments:
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+  - pytest>=3.6.0 ; extra == 'test'
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+  sha256: 2727d939c5b4257cb01ea404372803b477f5176e5a347c43beaf89cd5072e853
+  requires_dist:
+  - importlib-metadata>=1.6.0 ; python_full_version < '3.8'
+  - packaging>=20.9
+  requires_python: '>=3.5'
+- pypi: https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl
+  name: toolz
+  version: 1.1.0
+  sha256: 15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl
+  name: aiosignal
+  version: 1.4.0
+  sha256: 053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e
+  requires_dist:
+  - frozenlist>=1.1.0
+  - typing-extensions>=4.2 ; python_full_version < '3.13'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fc/b6/6b8de4c0a7d7ab3004c439c80c5c1e0a3e8d78bbae19379b01960383d9e5/pydantic_core-2.46.4-cp314-cp314-win_amd64.whl
+  name: pydantic-core
+  version: 2.46.4
+  sha256: 811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
+  name: pydantic
+  version: 2.13.4
+  sha256: 45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba
+  requires_dist:
+  - annotated-types>=0.6.0
+  - pydantic-core==2.46.4
+  - typing-extensions>=4.14.1
+  - typing-inspection>=0.4.2
+  - email-validator>=2.0.0 ; extra == 'email'
+  - tzdata ; python_full_version >= '3.9' and sys_platform == 'win32' and extra == 'timezone'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl
+  name: contourpy
+  version: 1.3.3
+  sha256: cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77
+  requires_dist:
+  - numpy>=1.25
+  - furo ; extra == 'docs'
+  - sphinx>=7.2 ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - bokeh ; extra == 'bokeh'
+  - selenium ; extra == 'bokeh'
+  - contourpy[bokeh,docs] ; extra == 'mypy'
+  - bokeh ; extra == 'mypy'
+  - docutils-stubs ; extra == 'mypy'
+  - mypy==1.17.0 ; extra == 'mypy'
+  - types-pillow ; extra == 'mypy'
+  - contourpy[test-no-images] ; extra == 'test'
+  - matplotlib ; extra == 'test'
+  - pillow ; extra == 'test'
+  - pytest ; extra == 'test-no-images'
+  - pytest-cov ; extra == 'test-no-images'
+  - pytest-rerunfailures ; extra == 'test-no-images'
+  - pytest-xdist ; extra == 'test-no-images'
+  - wurlitzer ; extra == 'test-no-images'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
+  name: multidict
+  version: 6.7.1
+  sha256: 7eee46ccb30ff48a1e35bb818cc90846c6be2b68240e42a78599166722cea709
+  requires_dist:
+  - typing-extensions>=4.1.0 ; python_full_version < '3.11'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/ff/1c/a28b27effb13a381fe077ea3e3e78f6debd6315f2b3edff67bbb93d0ef51/gemmi-0.7.5-cp314-cp314-win_amd64.whl
+  name: gemmi
+  version: 0.7.5
+  sha256: 419c36d9ea0f28dda0ff0d6db17035170d0888ca78aff82a0f9f604613aec58f
+  requires_python: '>=3.9'
diff --git a/pyproject.toml b/pyproject.toml
index 5faffe6b6..f81f23031 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,6 +47,7 @@ dependencies = [
   'darkdetect',     # Detecting dark mode (system-level)
   'pandas',         # Displaying tables in Jupyter notebooks
   'plotly',         # Interactive plots
+  'arviz',          # Bayesian analysis summaries and posterior plotting
   'py3Dmol',        # Visualisation of crystal structures
 ]
 
@@ -63,7 +64,6 @@ dev = [
   'pytest',                          # Testing
   'pytest-cov',                      # Test coverage
   'pytest-xdist',                    # Enable parallel testing
-  'pytest-benchmark',                # Benchmarking tests
   'ruff',                            # Linting and formatting code
   'radon',                           # Code complexity and maintainability
   'validate-pyproject[all]',         # Validate pyproject.toml
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index c132e409c..23b9fcf3f 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -184,9 +184,11 @@ def free_params(self) -> None:
         """Print only currently free (varying) parameters."""
         project = self._analysis.project
         self._flush_structure_categories()
-        structures_params = project.structures.free_parameters
-        experiments_params = project.experiments.free_parameters
-        free_params = structures_params + experiments_params
+        free_params = getattr(project, 'free_parameters', None)
+        if free_params is None:
+            structures_params = project.structures.free_parameters
+            experiments_params = project.experiments.free_parameters
+            free_params = structures_params + experiments_params
 
         if not free_params:
             log.warning('No free parameters found.')
@@ -513,7 +515,13 @@ def joint_fit_experiments(self) -> object:
         """Per-experiment weight collection for joint fitting."""
         return self._joint_fit_experiments
 
-    def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool = False) -> None:
+    def _run_fit(
+        self,
+        verbosity: str | None = None,
+        *,
+        use_physical_limits: bool = False,
+        random_seed: int | None = None,
+    ) -> None:
         """
         Execute fitting for all experiments.
 
@@ -542,6 +550,8 @@ def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool =
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
         """
         verb = VerbosityEnum(verbosity if verbosity is not None else self.project.verbosity)
 
@@ -563,10 +573,20 @@ def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool =
         # Run the fitting process
         mode = FitModeEnum(self._fit.mode.value)
         if mode is FitModeEnum.JOINT:
-            self._fit_joint(verb, structures, experiments, use_physical_limits=use_physical_limits)
+            self._fit_joint(
+                verb,
+                structures,
+                experiments,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
+            )
         elif mode is FitModeEnum.SINGLE:
             self._fit_single(
-                verb, structures, experiments, use_physical_limits=use_physical_limits
+                verb,
+                structures,
+                experiments,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
             )
         elif mode is FitModeEnum.SEQUENTIAL:
             log.error(
@@ -585,6 +605,7 @@ def _fit_joint(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run joint fitting across all experiments with weights.
@@ -599,6 +620,8 @@ def _fit_joint(
             Project experiments collection.
         use_physical_limits : bool
             Whether to use physical limits as fit bounds.
+        random_seed : int | None
+            Optional random seed passed to stochastic minimizers.
         """
         mode = FitModeEnum.JOINT
         # Auto-populate joint_fit_experiments if empty
@@ -622,6 +645,7 @@ def _fit_joint(
             analysis=self,
             verbosity=verb,
             use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
         )
 
         # After fitting, get the results
@@ -634,6 +658,7 @@ def _fit_single(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run single-mode fitting for each experiment independently.
@@ -648,6 +673,8 @@ def _fit_single(
             Project experiments collection.
         use_physical_limits : bool
             Whether to use physical limits as fit bounds.
+        random_seed : int | None
+            Optional random seed passed to stochastic minimizers.
         """
         mode = FitModeEnum.SINGLE
         expt_names = experiments.names
@@ -666,6 +693,7 @@ def _fit_single(
                 analysis=self,
                 verbosity=verb,
                 use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
             )
 
             # After fitting, snapshot parameter values before
diff --git a/src/easydiffraction/analysis/calculators/cryspy.py b/src/easydiffraction/analysis/calculators/cryspy.py
index 3c2da252c..7bac27385 100644
--- a/src/easydiffraction/analysis/calculators/cryspy.py
+++ b/src/easydiffraction/analysis/calculators/cryspy.py
@@ -315,13 +315,19 @@ def _powder_refln_core_arrays(
         try:
             indices = np.asarray(phase_block['index_hkl'], dtype=int)
             sin_theta_over_lambda = np.asarray(phase_block['sthovl'], dtype=float)
-            f_nucl = np.asarray(phase_block['f_nucl'])
         except KeyError:
             return None
 
+        structure_factor = phase_block.get('f_nucl')
+        if structure_factor is None:
+            structure_factor = phase_block.get('f_charge')
+        if structure_factor is None:
+            return None
+        structure_factor = np.asarray(structure_factor)
+
         if indices.shape[0] != EXPECTED_HKL_INDEX_ROWS:
             return None
-        return indices, sin_theta_over_lambda, f_nucl
+        return indices, sin_theta_over_lambda, structure_factor
 
     @staticmethod
     def _powder_refln_d_spacing(
diff --git a/src/easydiffraction/analysis/categories/fit/default.py b/src/easydiffraction/analysis/categories/fit/default.py
index c002fe399..7a9ebcb12 100644
--- a/src/easydiffraction/analysis/categories/fit/default.py
+++ b/src/easydiffraction/analysis/categories/fit/default.py
@@ -148,6 +148,7 @@ def run(
         verbosity: str | None = None,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """
         Execute fitting for the owning analysis.
@@ -158,6 +159,8 @@ def run(
             Console output verbosity override.
         use_physical_limits : bool, default=False
             Whether to fall back to physical limits as fit bounds.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
 
         Raises
         ------
@@ -168,16 +171,25 @@ def run(
         if parent is None:
             msg = 'Fit category is not attached to an Analysis object.'
             raise RuntimeError(msg)
-        parent._run_fit(verbosity=verbosity, use_physical_limits=use_physical_limits)
+        parent._run_fit(
+            verbosity=verbosity,
+            use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
+        )
 
     def __call__(
         self,
         verbosity: str | None = None,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """Execute :meth:`run` for convenience."""
-        self.run(verbosity=verbosity, use_physical_limits=use_physical_limits)
+        self.run(
+            verbosity=verbosity,
+            use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
+        )
 
     def from_cif(self, block: object, idx: int = 0) -> None:
         """
diff --git a/src/easydiffraction/analysis/fit_helpers/__init__.py b/src/easydiffraction/analysis/fit_helpers/__init__.py
index 4e798e209..c7c53862a 100644
--- a/src/easydiffraction/analysis/fit_helpers/__init__.py
+++ b/src/easydiffraction/analysis/fit_helpers/__init__.py
@@ -1,2 +1,8 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
new file mode 100644
index 000000000..e10d9c698
--- /dev/null
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -0,0 +1,777 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian fit result models and posterior data containers."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import arviz as az
+import numpy as np
+from rich.text import Text
+
+from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor
+from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor_squared
+from easydiffraction.analysis.fit_helpers.metrics import calculate_rb_factor
+from easydiffraction.analysis.fit_helpers.metrics import calculate_weighted_r_factor
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
+from easydiffraction.analysis.fit_helpers.reporting import _build_parameter_row
+from easydiffraction.analysis.fit_helpers.reporting import _format_optional_float
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.logging import log
+from easydiffraction.utils.utils import render_table
+
+R_HAT_CONVERGENCE_THRESHOLD = 1.01
+ESS_BULK_CONVERGENCE_THRESHOLD = 400.0
+POSTERIOR_SAMPLE_NDIM = 3
+DEFAULT_CI_LEVELS = (0.68, 0.95)
+DEFAULT_CREDIBLE_INTERVAL_LEVELS = DEFAULT_CI_LEVELS
+IntervalLevels = tuple[float, ...]
+SettingsMap = dict[str, object] | None
+DiagnosticsMap = dict[str, object] | None
+
+
+@dataclass(slots=True)
+class PosteriorParameterSummary:
+    r"""
+    Posterior summary statistics for one fitted parameter.
+
+    Attributes
+    ----------
+    unique_name : str
+        Unique parameter name used across EasyDiffraction.
+    display_name : str
+        Human-readable label used in plots and tables.
+    map_value : float
+        Maximum-a-posteriori or best-sampled parameter value.
+    median : float
+        Posterior median value.
+    standard_deviation : float
+        Posterior standard deviation.
+    interval_68 : tuple[float, float]
+        Central 68% interval.
+    interval_95 : tuple[float, float]
+        Central 95% interval.
+    ess_bulk : float | None, default=None
+        Bulk effective sample size when available.
+    r_hat : float | None, default=None
+        Rank-normalized split-$\hat{R}$ when available.
+    """
+
+    unique_name: str
+    display_name: str
+    map_value: float
+    median: float
+    standard_deviation: float
+    interval_68: tuple[float, float]
+    interval_95: tuple[float, float]
+    ess_bulk: float | None = None
+    r_hat: float | None = None
+
+
+@dataclass(slots=True)
+class PosteriorPredictiveSummary:
+    """
+    Posterior predictive summaries for one experiment.
+
+    Attributes
+    ----------
+    experiment_name : str
+        Experiment identifier.
+    x_axis_name : str
+        Name of the x-axis used for the predictive arrays.
+    x : np.ndarray
+        X-axis values for the predictive curves.
+    map_prediction : np.ndarray
+        Prediction corresponding to the committed point estimate.
+    lower_95 : np.ndarray | None, default=None
+        Lower bound of the 95% credible interval.
+    upper_95 : np.ndarray | None, default=None
+        Upper bound of the 95% credible interval.
+    lower_68 : np.ndarray | None, default=None
+        Lower bound of the 68% credible interval.
+    upper_68 : np.ndarray | None, default=None
+        Upper bound of the 68% credible interval.
+    draws : np.ndarray | None, default=None
+        Optional capped predictive draws with shape ``(n_draws, n_x)``.
+    """
+
+    experiment_name: str
+    x_axis_name: str
+    x: np.ndarray
+    map_prediction: np.ndarray
+    lower_95: np.ndarray | None = None
+    upper_95: np.ndarray | None = None
+    lower_68: np.ndarray | None = None
+    upper_68: np.ndarray | None = None
+    draws: np.ndarray | None = None
+
+
+@dataclass(slots=True)
+class PosteriorSamples:
+    """
+    Posterior samples and sample statistics from a Bayesian fit.
+
+    Attributes
+    ----------
+    parameter_names : list[str]
+        Parameter names in the preserved EasyDiffraction order.
+    parameter_samples : np.ndarray
+        Sample array with shape ``(n_draws, n_chains, n_parameters)``.
+    log_posterior : np.ndarray | None, default=None
+        Log-posterior values with shape ``(n_draws, n_chains)`` when
+        available.
+    draw_index : np.ndarray | None, default=None
+        Optional draw or generation indices associated with the first
+        axis of ``parameter_samples``.
+    """
+
+    parameter_names: list[str]
+    parameter_samples: np.ndarray
+    log_posterior: np.ndarray | None = None
+    draw_index: np.ndarray | None = None
+
+    def flattened(self) -> np.ndarray:
+        """
+        Return flattened posterior samples by parameter.
+
+        Returns
+        -------
+        np.ndarray
+            Array with shape ``(n_draws * n_chains, n_parameters)``.
+        """
+        return np.asarray(self.parameter_samples).reshape(-1, len(self.parameter_names))
+
+    def to_arviz(self) -> object:
+        """
+        Convert posterior samples to an ArviZ ``InferenceData`` object.
+
+        Returns
+        -------
+        object
+            ArviZ ``InferenceData`` instance built from the stored
+            posterior samples.
+
+        Raises
+        ------
+        ValueError
+            If the stored arrays do not have the expected shapes.
+        """
+        posterior_array = np.asarray(self.parameter_samples, dtype=float)
+        if posterior_array.ndim != POSTERIOR_SAMPLE_NDIM:
+            msg = 'Posterior sample array must have shape (n_draws, n_chains, n_parameters).'
+            raise ValueError(msg)
+
+        n_draws, n_chains, n_parameters = posterior_array.shape
+        if n_parameters != len(self.parameter_names):
+            msg = 'Posterior sample array does not match the parameter name list length.'
+            raise ValueError(msg)
+
+        posterior_dict = {
+            name: np.transpose(posterior_array[:, :, index], (1, 0))
+            for index, name in enumerate(self.parameter_names)
+        }
+
+        sample_stats: dict[str, np.ndarray] | None = None
+        if self.log_posterior is not None:
+            log_posterior = np.asarray(self.log_posterior, dtype=float)
+            if log_posterior.shape != (n_draws, n_chains):
+                msg = 'Log-posterior array must match the first two posterior sample axes.'
+                raise ValueError(msg)
+            sample_stats = {'lp': np.transpose(log_posterior, (1, 0))}
+
+        data = {'posterior': posterior_dict}
+        if sample_stats is not None:
+            data['sample_stats'] = sample_stats
+
+        return az.from_dict(data)
+
+
+SummaryList = list[PosteriorParameterSummary] | None
+PredictiveMap = dict[str, PosteriorPredictiveSummary] | None
+
+
+@dataclass(kw_only=True)
+class BayesianFitResults(FitResults):
+    """
+    Container for Bayesian fit results and posterior summaries.
+
+    Attributes
+    ----------
+    success : bool, default=False
+        Whether the Bayesian fit produced usable posterior results.
+    parameters : list[object] | None, default=None
+        Final committed parameter objects.
+    reduced_chi_square : float | None, default=None
+        Reduced chi-square evaluated at the committed point estimate.
+    engine_result : object | None, default=None
+        Opaque backend result object.
+    starting_parameters : list[object] | None, default=None
+        Starting parameter objects or snapshots.
+    fitting_time : float | None, default=None
+        Total fitting time in seconds.
+    sampler_name : str, default='dream'
+        Sampler identifier.
+    point_estimate_name : str, default='map'
+        Name of the point estimate committed back to the project.
+    posterior_samples : PosteriorSamples | None, default=None
+        Stored posterior samples.
+    posterior_parameter_summaries : SummaryList, default=None
+        Posterior summaries for each sampled parameter.
+    posterior_predictive : PredictiveMap, default=None
+        Posterior predictive summaries keyed by experiment name.
+    credible_interval_levels : IntervalLevels, default=DEFAULT_CI_LEVELS
+        Interval levels available in the summaries.
+    sampler_settings : SettingsMap, default=None
+        Sampler settings recorded for reproducibility.
+    convergence_diagnostics : DiagnosticsMap, default=None
+        Convergence diagnostics and status metadata.
+    sampler_completed : bool, default=False
+        Whether the sampler completed a run and returned posterior data.
+    best_log_posterior : float | None, default=None
+        Best log-posterior value reported by the sampler.
+    """
+
+    success: bool = False
+    parameters: list[object] | None = None
+    reduced_chi_square: float | None = None
+    engine_result: object | None = None
+    starting_parameters: list[object] | None = None
+    fitting_time: float | None = None
+    sampler_name: str = 'dream'
+    point_estimate_name: str = 'map'
+    posterior_samples: PosteriorSamples | None = None
+    posterior_parameter_summaries: SummaryList = None
+    posterior_predictive: PredictiveMap = None
+    credible_interval_levels: IntervalLevels = DEFAULT_CI_LEVELS
+    sampler_settings: SettingsMap = None
+    convergence_diagnostics: DiagnosticsMap = None
+    sampler_completed: bool = False
+    best_log_posterior: float | None = None
+
+    def __post_init__(self) -> None:
+        """
+        Initialize inherited FitResults state and normalize containers.
+        """
+        super().__init__(
+            success=self.success,
+            parameters=self.parameters,
+            reduced_chi_square=self.reduced_chi_square,
+            engine_result=self.engine_result,
+            starting_parameters=self.starting_parameters,
+            fitting_time=self.fitting_time,
+        )
+        self.posterior_parameter_summaries = (
+            list(self.posterior_parameter_summaries)
+            if self.posterior_parameter_summaries is not None
+            else []
+        )
+        self.posterior_predictive = (
+            dict(self.posterior_predictive) if self.posterior_predictive is not None else {}
+        )
+        self.sampler_settings = dict(self.sampler_settings) if self.sampler_settings else {}
+        self.convergence_diagnostics = (
+            dict(self.convergence_diagnostics) if self.convergence_diagnostics is not None else {}
+        )
+
+    def display_results(
+        self,
+        y_obs: list[float] | None = None,
+        y_calc: list[float] | None = None,
+        y_err: list[float] | None = None,
+        f_obs: list[float] | None = None,
+        f_calc: list[float] | None = None,
+    ) -> None:
+        """
+        Render a Bayesian fit summary with posterior diagnostics.
+
+        Parameters
+        ----------
+        y_obs : list[float] | None, default=None
+            Observed intensities for pattern R-factor metrics.
+        y_calc : list[float] | None, default=None
+            Calculated intensities for pattern R-factor metrics.
+        y_err : list[float] | None, default=None
+            Standard deviations of observed intensities for wR.
+        f_obs : list[float] | None, default=None
+            Observed structure-factor magnitudes for Bragg R.
+        f_calc : list[float] | None, default=None
+            Calculated structure-factor magnitudes for Bragg R.
+        """
+        metrics = _calculate_fit_quality_metrics(
+            y_obs=y_obs,
+            y_calc=y_calc,
+            y_err=y_err,
+            f_obs=f_obs,
+            f_calc=f_calc,
+        )
+
+        self._display_summary_header()
+        _print_fit_quality_metrics(metrics)
+
+        console.print('📈 Committed parameters:')
+        _render_committed_parameter_table(self.parameters)
+
+        console.print('📊 Posterior parameter summaries:')
+        _render_posterior_summary_table(
+            parameters=self.parameters,
+            posterior_parameter_summaries=self.posterior_parameter_summaries,
+        )
+
+        self._print_table_notes()
+
+    def _print_table_notes(self) -> None:
+        """
+        Print parameter and posterior-diagnostic notes below tables.
+        """
+        super()._print_table_notes()
+        for note in _posterior_table_notes(self.posterior_parameter_summaries):
+            log.warning(note)
+
+    def _display_summary_header(self) -> None:
+        """Render the high-level Bayesian fit summary."""
+        status_icon, overall_status = _format_bayesian_overall_status(
+            success=self.success,
+            sampler_completed=self.sampler_completed,
+            convergence_diagnostics=self.convergence_diagnostics,
+        )
+        fitting_time = _format_optional_float(self.fitting_time, suffix=' seconds')
+        goodness_of_fit = _format_optional_float(self.reduced_chi_square)
+
+        console.paragraph('Bayesian fit results')
+        console.print(f'{status_icon} Overall status: {overall_status}')
+        if self.message:
+            console.print(f'💬 Sampler status: {self.message}')
+        console.print(f'🧪 Sampler: {self.sampler_name}')
+        console.print(
+            f'🎯 Committed point estimate: {_format_point_estimate_name(self.point_estimate_name)}'
+        )
+        sampler_completed = 'yes' if self.sampler_completed else 'no'
+        console.print(f'🔁 Sampler completed: {sampler_completed}')
+        console.print(f'⏱️ Fitting time: {fitting_time}')
+        console.print(f'📏 Goodness-of-fit (reduced χ²): {goodness_of_fit}')
+        if self.best_log_posterior is not None:
+            console.print(f'📉 Best log-posterior: {self.best_log_posterior:.2f}')
+
+        sampler_settings = _format_sampler_settings(self.sampler_settings)
+        if sampler_settings is not None:
+            console.print(Text(f'⚙️ Sampler settings: {sampler_settings}'))
+
+        convergence_summary = _format_convergence_summary(self.convergence_diagnostics)
+        if convergence_summary is not None:
+            console.print(Text.from_markup(f'📊 Convergence: {convergence_summary}'))
+
+
+def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict[str, object]:
+    """
+    Compute convergence diagnostics from posterior samples.
+
+    Parameters
+    ----------
+    posterior_samples : PosteriorSamples
+        Posterior samples container.
+
+    Returns
+    -------
+    dict[str, object]
+        Convergence metrics keyed by diagnostic name.
+    """
+    inference_data = posterior_samples.to_arviz()
+    rhat_dataset = az.rhat(inference_data)
+    ess_dataset = az.ess(inference_data, method='bulk')
+
+    r_hat_by_parameter = _dataset_to_scalar_dict(rhat_dataset)
+    ess_bulk_by_parameter = _dataset_to_scalar_dict(ess_dataset)
+
+    finite_r_hat = [value for value in r_hat_by_parameter.values() if value is not None]
+    finite_ess_bulk = [value for value in ess_bulk_by_parameter.values() if value is not None]
+
+    max_r_hat = max(finite_r_hat, default=None)
+    min_ess_bulk = min(finite_ess_bulk, default=None)
+
+    converged = len(finite_r_hat) == len(r_hat_by_parameter) and len(finite_ess_bulk) == len(
+        ess_bulk_by_parameter
+    )
+    if max_r_hat is not None and max_r_hat > R_HAT_CONVERGENCE_THRESHOLD:
+        converged = False
+    if min_ess_bulk is not None and min_ess_bulk < ESS_BULK_CONVERGENCE_THRESHOLD:
+        converged = False
+
+    return {
+        'converged': converged,
+        'r_hat_by_parameter': r_hat_by_parameter,
+        'ess_bulk_by_parameter': ess_bulk_by_parameter,
+        'max_r_hat': max_r_hat,
+        'min_ess_bulk': min_ess_bulk,
+        'n_draws': int(posterior_samples.parameter_samples.shape[0]),
+        'n_chains': int(posterior_samples.parameter_samples.shape[1]),
+        'n_parameters': len(posterior_samples.parameter_names),
+    }
+
+
+def summarize_posterior_parameters(
+    parameter_names: list[str],
+    posterior_samples: PosteriorSamples,
+    map_values: np.ndarray,
+    parameter_display_names: list[str] | None = None,
+    convergence_diagnostics: dict[str, object] | None = None,
+) -> list[PosteriorParameterSummary]:
+    """
+    Build posterior parameter summaries in EasyDiffraction order.
+
+    Parameters
+    ----------
+    parameter_names : list[str]
+        Sampled parameter names in EasyDiffraction order.
+    posterior_samples : PosteriorSamples
+        Posterior sample container.
+    map_values : np.ndarray
+        MAP or best-sampled parameter values in the same order.
+    parameter_display_names : list[str] | None, default=None
+        Human-readable parameter names in the same order.
+    convergence_diagnostics : dict[str, object] | None, default=None
+        Optional convergence diagnostics keyed by parameter name.
+
+    Returns
+    -------
+    list[PosteriorParameterSummary]
+        Summary rows matching the input parameter order.
+
+    Raises
+    ------
+    ValueError
+        If the posterior sample array is incompatible with the parameter
+        name list.
+    """
+    flattened = posterior_samples.flattened()
+    if flattened.shape[1] != len(parameter_names):
+        msg = 'Posterior samples do not match the sampled parameter name list length.'
+        raise ValueError(msg)
+    if parameter_display_names is not None and len(parameter_display_names) != len(
+        parameter_names
+    ):
+        msg = 'Posterior display-name list must match the sampled parameter name list length.'
+        raise ValueError(msg)
+
+    r_hat_by_parameter = {}
+    ess_bulk_by_parameter = {}
+    if convergence_diagnostics is not None:
+        r_hat_by_parameter = convergence_diagnostics.get('r_hat_by_parameter', {})
+        ess_bulk_by_parameter = convergence_diagnostics.get('ess_bulk_by_parameter', {})
+
+    summaries: list[PosteriorParameterSummary] = []
+    for index, parameter_name in enumerate(parameter_names):
+        values = flattened[:, index]
+        interval_68 = tuple(np.quantile(values, [0.16, 0.84]).tolist())
+        interval_95 = tuple(np.quantile(values, [0.025, 0.975]).tolist())
+        display_name = (
+            parameter_display_names[index]
+            if parameter_display_names is not None
+            else parameter_name
+        )
+        summaries.append(
+            PosteriorParameterSummary(
+                unique_name=parameter_name,
+                display_name=display_name,
+                map_value=float(map_values[index]),
+                median=float(np.median(values)),
+                standard_deviation=float(np.std(values, ddof=1)),
+                interval_68=(float(interval_68[0]), float(interval_68[1])),
+                interval_95=(float(interval_95[0]), float(interval_95[1])),
+                ess_bulk=_maybe_scalar(ess_bulk_by_parameter.get(parameter_name)),
+                r_hat=_maybe_scalar(r_hat_by_parameter.get(parameter_name)),
+            )
+        )
+
+    return summaries
+
+
+def standard_deviations_from_summaries(
+    summaries: list[PosteriorParameterSummary],
+) -> np.ndarray:
+    """
+    Return posterior standard deviations in summary order.
+
+    Parameters
+    ----------
+    summaries : list[PosteriorParameterSummary]
+        Posterior summaries in parameter order.
+
+    Returns
+    -------
+    np.ndarray
+        Standard deviations in the same order.
+    """
+    return np.array([summary.standard_deviation for summary in summaries], dtype=float)
+
+
+def _dataset_to_scalar_dict(dataset: object) -> dict[str, float | None]:
+    values: dict[str, float | None] = {}
+    for name, data_array in dataset.data_vars.items():
+        values[name] = _maybe_scalar(np.asarray(data_array).reshape(-1)[0])
+    return values
+
+
+def _maybe_scalar(value: object) -> float | None:
+    if value is None:
+        return None
+    scalar = float(value)
+    if not np.isfinite(scalar):
+        return None
+    return scalar
+
+
+def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
+    if not sampler_settings:
+        return None
+
+    parts = [
+        f'{key}={sampler_settings[key]}'
+        for key in ('steps', 'burn', 'thin', 'pop', 'init', 'samples')
+        if key in sampler_settings
+    ]
+    return ', '.join(parts) if parts else None
+
+
+def _calculate_fit_quality_metrics(
+    *,
+    y_obs: list[float] | None,
+    y_calc: list[float] | None,
+    y_err: list[float] | None,
+    f_obs: list[float] | None,
+    f_calc: list[float] | None,
+) -> dict[str, float | None]:
+    """Compute optional fit-quality metrics for summary rendering."""
+    metrics: dict[str, float | None] = {
+        'rf': None,
+        'rf2': None,
+        'wr': None,
+        'br': None,
+    }
+    if y_obs is not None and y_calc is not None:
+        metrics['rf'] = calculate_r_factor(y_obs, y_calc) * 100
+        metrics['rf2'] = calculate_r_factor_squared(y_obs, y_calc) * 100
+    if y_obs is not None and y_calc is not None and y_err is not None:
+        metrics['wr'] = calculate_weighted_r_factor(y_obs, y_calc, y_err) * 100
+    if f_obs is not None and f_calc is not None:
+        metrics['br'] = calculate_rb_factor(f_obs, f_calc) * 100
+    return metrics
+
+
+def _print_fit_quality_metrics(metrics: dict[str, float | None]) -> None:
+    """Render any available fit-quality metrics."""
+    metric_labels = (
+        ('📏 R-factor (Rf)', metrics['rf']),
+        ('📏 R-factor squared (Rf²)', metrics['rf2']),
+        ('📏 Weighted R-factor (wR)', metrics['wr']),
+        ('📏 Bragg R-factor (BR)', metrics['br']),
+    )
+    for label, value in metric_labels:
+        if value is not None:
+            console.print(f'{label}: {value:.2f}%')
+
+
+def _format_point_estimate_name(point_estimate_name: str) -> str:
+    """Return a user-facing label for the committed point estimate."""
+    normalized_name = point_estimate_name.strip().lower().replace('_', ' ')
+    if normalized_name == 'map':
+        return 'Max posterior'
+    return point_estimate_name.replace('_', ' ').title()
+
+
+def _format_bayesian_overall_status(
+    *,
+    success: bool,
+    sampler_completed: bool,
+    convergence_diagnostics: dict[str, object],
+) -> tuple[str, str]:
+    """Return icon and text for Bayesian run status."""
+    if not success:
+        return '❌', 'failed'
+
+    converged = convergence_diagnostics.get('converged')
+    if converged is False:
+        return '⚠️', 'completed with warnings'
+    if sampler_completed:
+        return '✅', 'completed'
+    return '✅', 'posterior available'
+
+
+def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> str | None:
+    if not convergence_diagnostics:
+        return None
+
+    parts: list[str] = []
+    converged = convergence_diagnostics.get('converged')
+    if converged is not None:
+        status = 'passed' if converged else '[red]failed[/red]'
+        parts.append(f'status={status}')
+
+    max_r_hat = _maybe_scalar(convergence_diagnostics.get('max_r_hat'))
+    if max_r_hat is not None:
+        parts.append(f'max_r_hat={_format_r_hat(max_r_hat)}')
+
+    min_ess_bulk = _maybe_scalar(convergence_diagnostics.get('min_ess_bulk'))
+    if min_ess_bulk is not None:
+        parts.append(f'min_ess_bulk={_format_ess_bulk(min_ess_bulk)}')
+
+    n_draws = convergence_diagnostics.get('n_draws')
+    n_chains = convergence_diagnostics.get('n_chains')
+    if n_draws is not None and n_chains is not None:
+        parts.append(f'draws={n_draws}, chains={n_chains}')
+
+    return ', '.join(parts) if parts else None
+
+
+def _render_committed_parameter_table(parameters: list[object]) -> None:
+    headers = [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'max posterior',
+        'uncertainty',
+        'change',
+    ]
+    alignments = [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    rows = [_build_parameter_row(parameter) for parameter in parameters]
+    render_table(
+        columns_headers=headers,
+        columns_alignment=alignments,
+        columns_data=rows,
+    )
+
+
+def _render_posterior_summary_table(
+    *,
+    parameters: list[object],
+    posterior_parameter_summaries: list[PosteriorParameterSummary],
+) -> None:
+    if not posterior_parameter_summaries:
+        console.print('No posterior parameter summaries available.')
+        return
+
+    parameters_by_name = {parameter.unique_name: parameter for parameter in parameters}
+    headers = [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'median',
+        '95% interval',
+        'r-hat',
+        'ess bulk',
+    ]
+    alignments = [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    rows = [
+        _build_posterior_summary_row(summary, parameters_by_name)
+        for summary in posterior_parameter_summaries
+    ]
+    render_table(
+        columns_headers=headers,
+        columns_alignment=alignments,
+        columns_data=rows,
+    )
+
+
+def _build_posterior_summary_row(
+    summary: PosteriorParameterSummary,
+    parameters_by_name: dict[str, object],
+) -> list[str]:
+    parameter = parameters_by_name.get(summary.unique_name)
+    identity = getattr(parameter, '_identity', None)
+    datablock = getattr(identity, 'datablock_entry_name', 'N/A')
+    category = getattr(identity, 'category_code', 'N/A')
+    entry = getattr(identity, 'category_entry_name', '') or ''
+    parameter_name = getattr(parameter, 'name', summary.display_name)
+    units = getattr(parameter, 'units', 'N/A')
+
+    return [
+        datablock,
+        category,
+        entry,
+        parameter_name,
+        units,
+        f'{summary.median:.4f}',
+        _format_interval(summary.interval_95),
+        _format_r_hat(summary.r_hat),
+        _format_ess_bulk(summary.ess_bulk),
+    ]
+
+
+def _format_interval(interval: tuple[float, float]) -> str:
+    return f'[{interval[0]:.4f}, {interval[1]:.4f}]'
+
+
+def _format_r_hat(value: float | None) -> str:
+    if value is None or not np.isfinite(value):
+        return 'N/A'
+    formatted = f'{value:.3f}'
+    if value > R_HAT_CONVERGENCE_THRESHOLD:
+        return f'[red]{formatted}[/red]'
+    return formatted
+
+
+def _format_ess_bulk(value: float | None) -> str:
+    if value is None or not np.isfinite(value):
+        return 'N/A'
+    formatted = f'{value:.1f}'
+    if value < ESS_BULK_CONVERGENCE_THRESHOLD:
+        return f'[red]{formatted}[/red]'
+    return formatted
+
+
+def _posterior_table_notes(
+    posterior_parameter_summaries: list[PosteriorParameterSummary],
+) -> list[str]:
+    """Return warning notes for posterior summary diagnostics."""
+    if not posterior_parameter_summaries:
+        return []
+
+    has_failed_r_hat = any(
+        summary.r_hat is not None and summary.r_hat > R_HAT_CONVERGENCE_THRESHOLD
+        for summary in posterior_parameter_summaries
+    )
+    has_failed_ess_bulk = any(
+        summary.ess_bulk is not None and summary.ess_bulk < ESS_BULK_CONVERGENCE_THRESHOLD
+        for summary in posterior_parameter_summaries
+    )
+
+    if not has_failed_r_hat and not has_failed_ess_bulk:
+        return []
+
+    notes: list[str] = []
+    if has_failed_r_hat:
+        notes.append(
+            f'[red]r-hat > {R_HAT_CONVERGENCE_THRESHOLD:.2f}[/red]: '
+            'Consider longer sampling, better initialization, or reparameterization.'
+        )
+    if has_failed_ess_bulk:
+        notes.append(
+            f'[red]ess bulk < {ESS_BULK_CONVERGENCE_THRESHOLD:.0f}[/red]: '
+            'Consider longer sampling or reparameterization.'
+        )
+    return notes
diff --git a/src/easydiffraction/analysis/fit_helpers/reporting.py b/src/easydiffraction/analysis/fit_helpers/reporting.py
index a83ad7bf5..aefad27b6 100644
--- a/src/easydiffraction/analysis/fit_helpers/reporting.py
+++ b/src/easydiffraction/analysis/fit_helpers/reporting.py
@@ -108,8 +108,10 @@ def display_results(
 
         console.paragraph('Fit results')
         console.print(f'{status_icon} Success: {self.success}')
-        console.print(f'⏱️ Fitting time: {self.fitting_time:.2f} seconds')
-        console.print(f'📏 Goodness-of-fit (reduced χ²): {self.reduced_chi_square:.2f}')
+        fitting_time = _format_optional_float(self.fitting_time, suffix=' seconds')
+        goodness_of_fit = _format_optional_float(self.reduced_chi_square)
+        console.print(f'⏱️ Fitting time: {fitting_time}')
+        console.print(f'📏 Goodness-of-fit (reduced χ²): {goodness_of_fit}')
         if rf is not None:
             console.print(f'📏 R-factor (Rf): {rf:.2f}%')
         if rf2 is not None:
@@ -125,10 +127,10 @@ def display_results(
             'category',
             'entry',
             'parameter',
+            'units',
             'start',
             'fitted',
             'uncertainty',
-            'units',
             'change',
         ]
         alignments = [
@@ -136,10 +138,10 @@ def display_results(
             'left',
             'left',
             'left',
+            'left',
             'right',
             'right',
             'right',
-            'left',
             'right',
         ]
 
@@ -221,10 +223,10 @@ def _build_parameter_row(param: object) -> list[str]:
         param._identity.category_code,
         param._identity.category_entry_name or '',
         name,
+        units,
         start,
         fitted,
         uncertainty,
-        units,
         relative_change,
     ]
 
@@ -248,3 +250,29 @@ def _compute_relative_change(param: object) -> str:
     change = ((param.value - param._fit_start_value) / param._fit_start_value) * 100
     arrow = '↑' if change > 0 else '↓'
     return f'{abs(change):.2f} % {arrow}'
+
+
+def _format_optional_float(
+    value: float | None,
+    *,
+    suffix: str = '',
+) -> str:
+    """
+    Format an optional float for console output.
+
+    Parameters
+    ----------
+    value : float | None
+        Value to format.
+    suffix : str, default=''
+        Optional suffix appended to formatted numeric values.
+
+    Returns
+    -------
+    str
+        ``'N/A'`` when the value is ``None``; otherwise a formatted
+        string with two decimal places.
+    """
+    if value is None:
+        return 'N/A'
+    return f'{value:.2f}{suffix}'
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 99f9c8b68..b5cfb11a2 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -3,6 +3,7 @@
 
 import time
 from contextlib import suppress
+from dataclasses import dataclass
 
 import numpy as np
 
@@ -29,8 +30,29 @@
 from easydiffraction.utils.logging import ConsoleManager
 
 SIGNIFICANT_CHANGE_THRESHOLD = 0.01  # 1% threshold
-DEFAULT_HEADERS = ['iteration', 'χ²', 'improvement [%]']
-DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
+SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
+TRACKING_MODE_FIT = 'fit'
+TRACKING_MODE_SAMPLER = 'sampling'
+DEFAULT_HEADERS = ['iteration', 'time (s)', 'χ²', 'change / status']
+DEFAULT_ALIGNMENTS = ['center', 'center', 'center', 'center']
+SAMPLER_HEADERS = ['iteration', 'progress', 'time (s)', 'log posterior', 'phase']
+SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center', 'center']
+
+
+@dataclass(frozen=True, slots=True)
+class SamplerProgressUpdate:
+    """
+    Normalized sampler progress payload forwarded by monitor hooks.
+    """
+
+    iteration: int
+    total_iterations: int
+    phase: str
+    progress_percent: float
+    log_posterior: float
+    reduced_chi2: float
+    elapsed_time: float
+    force_report: bool = False
 
 
 class _TerminalLiveHandle:
@@ -97,10 +119,20 @@ def __init__(self) -> None:
         self._previous_chi2: float | None = None
         self._last_chi2: float | None = None
         self._last_iteration: int | None = None
+        self._last_reported_iteration: int | None = None
         self._best_chi2: float | None = None
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
+        self._start_time: float | None = None
+        self._end_time: float | None = None
         self._verbosity: VerbosityEnum = VerbosityEnum.FULL
+        self._last_progress_time: float | None = None
+        self._tracking_mode: str = TRACKING_MODE_FIT
+        self._sampler_total_iterations: int | None = None
+        self._last_sampler_phase: str | None = None
+        self._last_sampler_progress_percent: float | None = None
+        self._last_sampler_log_posterior: float | None = None
+        self._last_sampler_elapsed_time: float | None = None
 
         self._df_rows: list[list[str]] = []
         self._display_handle: object | None = None
@@ -112,9 +144,19 @@ def reset(self) -> None:
         self._previous_chi2 = None
         self._last_chi2 = None
         self._last_iteration = None
+        self._last_reported_iteration = None
         self._best_chi2 = None
         self._best_iteration = None
         self._fitting_time = None
+        self._start_time = None
+        self._end_time = None
+        self._last_progress_time = None
+        self._tracking_mode = TRACKING_MODE_FIT
+        self._sampler_total_iterations = None
+        self._last_sampler_phase = None
+        self._last_sampler_progress_percent = None
+        self._last_sampler_log_posterior = None
+        self._last_sampler_elapsed_time = None
 
     def track(
         self,
@@ -140,6 +182,16 @@ def track(
 
         reduced_chi2 = calculate_reduced_chi_square(residuals, len(parameters))
 
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            if self._previous_chi2 is None:
+                self._previous_chi2 = reduced_chi2
+                self._best_chi2 = reduced_chi2
+            elif self._best_chi2 is None or reduced_chi2 < self._best_chi2:
+                self._best_chi2 = reduced_chi2
+
+            self._last_chi2 = reduced_chi2
+            return residuals
+
         row: list[str] = []
 
         # First iteration, initialize tracking
@@ -150,6 +202,7 @@ def track(
 
             row = [
                 str(self._iteration),
+                self._format_elapsed_time(),
                 f'{reduced_chi2:.2f}',
                 '',
             ]
@@ -164,6 +217,7 @@ def track(
 
                 row = [
                     str(self._iteration),
+                    self._format_elapsed_time(),
                     f'{reduced_chi2:.2f}',
                     f'{change_in_percent:.1f}% ↓',
                 ]
@@ -175,7 +229,7 @@ def track(
             self.add_tracking_info(row)
 
         # Update best chi-square if better
-        if reduced_chi2 < self._best_chi2:
+        if self._best_chi2 is None or reduced_chi2 < self._best_chi2:
             self._best_chi2 = reduced_chi2
             self._best_iteration = self._iteration
 
@@ -185,6 +239,46 @@ def track(
 
         return residuals
 
+    def track_sampler_progress(self, update: SamplerProgressUpdate) -> None:
+        """
+        Update progress from a sampler monitor.
+
+        Parameters
+        ----------
+        update : SamplerProgressUpdate
+            Sampler iteration, phase, timing, and fit-quality payload.
+        """
+        self._iteration = update.iteration
+        self._tracking_mode = TRACKING_MODE_SAMPLER
+        self._sampler_total_iterations = max(1, update.total_iterations)
+
+        clamped_iteration = min(max(1, update.iteration), self._sampler_total_iterations)
+        clamped_progress = min(max(update.progress_percent, 0.0), 100.0)
+        previous_phase = self._last_sampler_phase
+        self._last_sampler_phase = update.phase
+        self._last_sampler_progress_percent = clamped_progress
+        self._last_sampler_log_posterior = update.log_posterior
+        self._last_sampler_elapsed_time = update.elapsed_time
+
+        row = self._initial_sampler_progress_row(
+            update=update,
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+        )
+        if not row:
+            row = self._continued_sampler_progress_row(
+                update=update,
+                previous_phase=previous_phase,
+                clamped_iteration=clamped_iteration,
+                clamped_progress=clamped_progress,
+            )
+
+        if row:
+            self.add_tracking_info(row)
+
+        self._last_chi2 = update.reduced_chi2
+        self._last_iteration = update.iteration
+
     @property
     def best_chi2(self) -> float | None:
         """Best recorded reduced chi-square value or None."""
@@ -208,13 +302,17 @@ def fitting_time(self) -> float | None:
     def start_timer(self) -> None:
         """Begin timing of a fit run."""
         self._start_time = time.perf_counter()
+        self._end_time = None
 
     def stop_timer(self) -> None:
         """Stop timing and store elapsed time for the run."""
+        if self._start_time is None:
+            self._fitting_time = None
+            return
         self._end_time = time.perf_counter()
         self._fitting_time = self._end_time - self._start_time
 
-    def start_tracking(self, minimizer_name: str) -> None:
+    def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT) -> None:
         """
         Initialize display and headers and announce the minimizer.
 
@@ -222,14 +320,23 @@ def start_tracking(self, minimizer_name: str) -> None:
         ----------
         minimizer_name : str
             Name of the minimizer used for the run.
+        mode : str, default=TRACKING_MODE_FIT
+            Tracking mode for the run.
         """
+        self._tracking_mode = (
+            TRACKING_MODE_SAMPLER if mode == TRACKING_MODE_SAMPLER else TRACKING_MODE_FIT
+        )
+
         if self._verbosity is VerbosityEnum.SILENT:
             return
         if self._verbosity is VerbosityEnum.SHORT:
             return
 
         console.print(f"🚀 Starting fit process with '{minimizer_name}'...")
-        console.print('📈 Goodness-of-fit (reduced χ²) change:')
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            console.print('📈 Bayesian sampling progress:')
+        else:
+            console.print('📈 Goodness-of-fit progress:')
 
         # Reset rows and create an environment-appropriate handle
         self._df_rows = []
@@ -237,8 +344,8 @@ def start_tracking(self, minimizer_name: str) -> None:
 
         # Initial empty table; subsequent updates will reuse the handle
         render_table(
-            columns_headers=DEFAULT_HEADERS,
-            columns_alignment=DEFAULT_ALIGNMENTS,
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
             columns_data=self._df_rows,
             display_handle=self._display_handle,
         )
@@ -250,41 +357,291 @@ def add_tracking_info(self, row: list[str]) -> None:
         Parameters
         ----------
         row : list[str]
-            Columns corresponding to DEFAULT_HEADERS.
+            Columns corresponding to the active tracking headers.
         """
+        if row:
+            iteration_cell = row[0].split('/', maxsplit=1)[0]
+            if iteration_cell.isdigit():
+                self._last_reported_iteration = int(iteration_cell)
         self._df_rows.append(row)
         if self._verbosity is not VerbosityEnum.FULL:
             return
         # Append and update via the active handle (Jupyter or
         # terminal live)
         render_table(
-            columns_headers=DEFAULT_HEADERS,
-            columns_alignment=DEFAULT_ALIGNMENTS,
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
             columns_data=self._df_rows,
             display_handle=self._display_handle,
         )
 
     def finish_tracking(self) -> None:
         """Finalize progress display and print best result summary."""
-        # Add last iteration as last row
-        row: list[str] = [
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            self._finalize_sampler_tracking_row()
+        else:
+            self._finalize_fit_tracking_row()
+
+        if self._verbosity is not VerbosityEnum.FULL:
+            return
+
+        self._close_display_handle()
+        self._print_completion_summary()
+
+    def _initial_sampler_progress_row(
+        self,
+        *,
+        update: SamplerProgressUpdate,
+        clamped_iteration: int,
+        clamped_progress: float,
+    ) -> list[str]:
+        if self._previous_chi2 is not None and self._best_chi2 is not None:
+            return []
+
+        self._previous_chi2 = update.reduced_chi2
+        self._best_chi2 = update.reduced_chi2
+        self._best_iteration = update.iteration
+        self._last_progress_time = update.elapsed_time
+        return self._sampler_progress_row(
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+            log_posterior=update.log_posterior,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+        )
+
+    def _continued_sampler_progress_row(
+        self,
+        *,
+        update: SamplerProgressUpdate,
+        previous_phase: str | None,
+        clamped_iteration: int,
+        clamped_progress: float,
+    ) -> list[str]:
+        if self._best_chi2 is not None and update.reduced_chi2 < self._best_chi2:
+            self._best_chi2 = update.reduced_chi2
+            self._best_iteration = update.iteration
+
+        if not self._should_render_sampler_row(
+            iteration=update.iteration,
+            previous_phase=previous_phase,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+            force_report=update.force_report,
+            clamped_iteration=clamped_iteration,
+        ):
+            return []
+
+        self._last_progress_time = update.elapsed_time
+        return self._sampler_progress_row(
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+            log_posterior=update.log_posterior,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+        )
+
+    def _should_render_sampler_row(
+        self,
+        *,
+        iteration: int,
+        previous_phase: str | None,
+        phase: str,
+        elapsed_time: float,
+        force_report: bool,
+        clamped_iteration: int,
+    ) -> bool:
+        if iteration == self._last_reported_iteration:
+            return False
+
+        return (
+            force_report
+            or previous_phase != phase
+            or self._last_progress_time is None
+            or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+            or clamped_iteration >= self._sampler_total_iterations
+        )
+
+    def _sampler_progress_row(
+        self,
+        *,
+        clamped_iteration: int,
+        clamped_progress: float,
+        log_posterior: float,
+        phase: str,
+        elapsed_time: float,
+    ) -> list[str]:
+        return [
+            self._sampler_iteration_label(clamped_iteration),
+            f'{clamped_progress:.1f}%',
+            self._format_elapsed_time(elapsed_time),
+            f'{log_posterior:.2f}',
+            phase,
+        ]
+
+    def _finalize_sampler_tracking_row(self) -> None:
+        row = self._final_sampler_tracking_row()
+        if row is None:
+            return
+
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        if self._rows_match_on_columns(self._df_rows[-1], row, (0, 1, 3, 4)):
+            self._replace_last_tracking_row(row)
+            return
+
+        if self._df_rows[-1] != row:
+            self.add_tracking_info(row)
+
+    def _final_sampler_tracking_row(self) -> list[str] | None:
+        if self._last_iteration is None or self._sampler_total_iterations is None:
+            return None
+
+        final_progress = self._resolved_final_sampler_progress()
+        elapsed_time = self._resolved_final_sampler_elapsed_time()
+        log_posterior = (
+            f'{self._last_sampler_log_posterior:.2f}'
+            if self._last_sampler_log_posterior is not None
+            else ''
+        )
+        return [
+            self._sampler_iteration_label(self._last_iteration),
+            f'{final_progress:.1f}%',
+            self._format_elapsed_time(elapsed_time),
+            log_posterior,
+            self._last_sampler_phase or 'sampling',
+        ]
+
+    def _finalize_fit_tracking_row(self) -> None:
+        row = self._final_fit_tracking_row()
+        if row is None:
+            return
+
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        if self._rows_match_on_columns(self._df_rows[-1], row, (0, 2)):
+            self._replace_last_tracking_row(row)
+            return
+
+        if self._df_rows[-1][:3] != row[:3]:
+            self.add_tracking_info(row)
+
+    def _final_fit_tracking_row(self) -> list[str] | None:
+        if self._last_iteration is None:
+            return None
+
+        return [
             str(self._last_iteration),
+            self._format_elapsed_time(self._fitting_time),
             f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
             '',
         ]
-        self.add_tracking_info(row)
 
-        if self._verbosity is not VerbosityEnum.FULL:
-            return
+    def _resolved_final_sampler_progress(self) -> float:
+        if self._last_sampler_progress_percent is not None:
+            return self._last_sampler_progress_percent
+
+        if self._last_iteration is None or self._sampler_total_iterations is None:
+            msg = 'Sampler progress is unavailable without final iteration counts.'
+            raise RuntimeError(msg)
+        return (
+            100.0
+            * min(self._last_iteration, self._sampler_total_iterations)
+            / self._sampler_total_iterations
+        )
+
+    def _resolved_final_sampler_elapsed_time(self) -> float | None:
+        if self._fitting_time is not None:
+            return self._fitting_time
+        return self._last_sampler_elapsed_time
+
+    def _sampler_iteration_label(self, iteration: int) -> str:
+        if self._sampler_total_iterations is None:
+            msg = 'Sampler iteration labels require a configured total iteration count.'
+            raise RuntimeError(msg)
+        clamped_iteration = min(iteration, self._sampler_total_iterations)
+        return f'{clamped_iteration}/{self._sampler_total_iterations}'
 
-        # Close terminal live if used
+    def _close_display_handle(self) -> None:
         if self._display_handle is not None and hasattr(self._display_handle, 'close'):
             with suppress(Exception):
                 self._display_handle.close()
 
-        # Print best result
+    def _print_completion_summary(self) -> None:
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            console.print('✅ Bayesian sampling complete.')
+            return
+
         console.print(
             f'🏆 Best goodness-of-fit (reduced χ²) is {self._best_chi2:.2f} '
             f'at iteration {self._best_iteration}'
         )
         console.print('✅ Fitting complete.')
+
+    def _headers(self) -> list[str]:
+        """Return column headers for the active tracking mode."""
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return SAMPLER_HEADERS
+        return DEFAULT_HEADERS
+
+    def _alignments(self) -> list[str]:
+        """Return column alignments for the active tracking mode."""
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return SAMPLER_ALIGNMENTS
+        return DEFAULT_ALIGNMENTS
+
+    def _current_elapsed_time(self) -> float | None:
+        """Return elapsed run time in seconds when timing is active."""
+        if self._start_time is None:
+            return None
+
+        end_time = self._end_time if self._end_time is not None else time.perf_counter()
+        return max(end_time - self._start_time, 0.0)
+
+    def _format_elapsed_time(self, elapsed_time: float | None = None) -> str:
+        """Format elapsed time in seconds with two decimal places."""
+        resolved_time = elapsed_time
+        if resolved_time is None:
+            resolved_time = self._current_elapsed_time()
+        if resolved_time is None:
+            return ''
+        return f'{resolved_time:.2f}'
+
+    @staticmethod
+    def _rows_match_on_columns(
+        current_row: list[str],
+        new_row: list[str],
+        column_indices: tuple[int, ...],
+    ) -> bool:
+        """
+        Return whether two tracking rows match on selected columns.
+        """
+        return all(
+            len(current_row) > index
+            and len(new_row) > index
+            and current_row[index] == new_row[index]
+            for index in column_indices
+        )
+
+    def _replace_last_tracking_row(self, row: list[str]) -> None:
+        """
+        Replace the last rendered tracking row and refresh the view.
+        """
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        self._df_rows[-1] = row
+        if self._verbosity is not VerbosityEnum.FULL:
+            return
+
+        render_table(
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
+            columns_data=self._df_rows,
+            display_handle=self._display_handle,
+        )
diff --git a/src/easydiffraction/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py
index 9b0360143..db5590e1b 100644
--- a/src/easydiffraction/analysis/fitting.py
+++ b/src/easydiffraction/analysis/fitting.py
@@ -39,6 +39,7 @@ def fit(
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """
         Run the fitting process.
@@ -65,6 +66,8 @@ def fit(
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
         """
         # Enforce symmetry constraints (e.g. ADP) before collecting
         # free parameters so that components fixed by site symmetry are
@@ -118,6 +121,7 @@ def objective_function(engine_params: dict[str, Any]) -> np.ndarray:
             objective_function,
             verbosity=verbosity,
             use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
         )
 
     def _process_fit_results(
diff --git a/src/easydiffraction/analysis/minimizers/__init__.py b/src/easydiffraction/analysis/minimizers/__init__.py
index 7dddb75ee..1006eefb7 100644
--- a/src/easydiffraction/analysis/minimizers/__init__.py
+++ b/src/easydiffraction/analysis/minimizers/__init__.py
@@ -4,8 +4,10 @@
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
 from easydiffraction.analysis.minimizers.bumps_amoeba import BumpsAmoebaMinimizer
 from easydiffraction.analysis.minimizers.bumps_de import BumpsDEMinimizer
+from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
 from easydiffraction.analysis.minimizers.bumps_lm import BumpsLmMinimizer
 from easydiffraction.analysis.minimizers.dfols import DfolsMinimizer
+from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
 from easydiffraction.analysis.minimizers.lmfit import LmfitMinimizer
 from easydiffraction.analysis.minimizers.lmfit_least_squares import LmfitLeastSquaresMinimizer
 from easydiffraction.analysis.minimizers.lmfit_leastsq import LmfitLeastsqMinimizer
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index c0b39eb33..bbc639f10 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -41,6 +41,7 @@ def __init__(
         self._best_chi2: float | None = None
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
+        self._resolved_random_seed: int | None = None
         self.tracker: FitProgressTracker = FitProgressTracker()
 
     def _start_tracking(
@@ -60,7 +61,7 @@ def _start_tracking(
         """
         self.tracker.reset()
         self.tracker._verbosity = verbosity
-        self.tracker.start_tracking(minimizer_name)
+        self.tracker.start_tracking(minimizer_name, mode=self._tracking_mode())
         self.tracker.start_timer()
 
     def _stop_tracking(self) -> None:
@@ -68,6 +69,11 @@ def _stop_tracking(self) -> None:
         self.tracker.stop_timer()
         self.tracker.finish_tracking()
 
+    @staticmethod
+    def _tracking_mode() -> str:
+        """Return the tracker mode for the current minimizer."""
+        return 'fit'
+
     @abstractmethod
     def _prepare_solver_args(self, parameters: list[Any]) -> dict[str, Any]:
         """
@@ -124,7 +130,38 @@ def _finalize_fit(
         self._warn_boundary_parameters(parameters)
         self._warn_physical_limit_violations(parameters)
         success = self._check_success(raw_result)
-        self.result = FitResults(
+        self.result = self._build_fit_results(
+            parameters=parameters,
+            raw_result=raw_result,
+            success=success,
+        )
+        return self.result
+
+    def _build_fit_results(
+        self,
+        *,
+        parameters: list[object],
+        raw_result: object,
+        success: bool,
+    ) -> FitResults:
+        """
+        Build the final fit-result object for this minimizer.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters after the solver finished.
+        raw_result : object
+            Backend-specific solver output object.
+        success : bool
+            Whether the minimizer considers the run successful.
+
+        Returns
+        -------
+        FitResults
+            Aggregated outcome of the fit.
+        """
+        return FitResults(
             success=success,
             parameters=parameters,
             reduced_chi_square=self.tracker.best_chi2,
@@ -132,7 +169,6 @@ def _finalize_fit(
             starting_parameters=parameters,
             fitting_time=self.tracker.fitting_time,
         )
-        return self.result
 
     @staticmethod
     def _warn_boundary_parameters(parameters: list[object]) -> None:
@@ -231,6 +267,33 @@ def _warn_physical_limit_violations(parameters: list[object]) -> None:
     def _check_success(self, raw_result: object) -> bool:
         """Determine whether the fit was successful."""
 
+    def _resolve_random_seed(self, random_seed: int | None) -> int | None:
+        """
+        Validate or normalize the random seed for this minimizer.
+
+        Parameters
+        ----------
+        random_seed : int | None
+            User-provided random seed.
+
+        Returns
+        -------
+        int | None
+            Seed accepted by the minimizer, or ``None`` when not used.
+
+        Raises
+        ------
+        ValueError
+            If this minimizer does not support ``random_seed``.
+        """
+        if random_seed is None:
+            self._resolved_random_seed = None
+            return None
+
+        minimizer_name = self.name or self.__class__.__name__
+        msg = f"Minimizer '{minimizer_name}' does not support random_seed."
+        raise ValueError(msg)
+
     def fit(
         self,
         parameters: list[object],
@@ -238,6 +301,7 @@ def fit(
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> FitResults:
         """
         Run the full minimization workflow.
@@ -255,6 +319,8 @@ def fit(
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
 
         Returns
         -------
@@ -264,16 +330,21 @@ def fit(
         if use_physical_limits:
             self._apply_physical_limits(parameters)
 
+        resolved_random_seed = self._resolve_random_seed(random_seed)
+
         minimizer_name = self.name or 'Unnamed Minimizer'
         if self.method is not None and f'({self.method})' not in minimizer_name:
             minimizer_name += f' ({self.method})'
 
         self._start_tracking(minimizer_name, verbosity=verbosity)
 
-        solver_args = self._prepare_solver_args(parameters)
-        raw_result = self._run_solver(objective_function, **solver_args)
-
-        self._stop_tracking()
+        try:
+            solver_args = self._prepare_solver_args(parameters)
+            if resolved_random_seed is not None:
+                solver_args['random_seed'] = resolved_random_seed
+            raw_result = self._run_solver(objective_function, **solver_args)
+        finally:
+            self._stop_tracking()
 
         return self._finalize_fit(parameters, raw_result)
 
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
new file mode 100644
index 000000000..c9af1012b
--- /dev/null
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -0,0 +1,948 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bumps minimizer variant using the DREAM sampler."""
+
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+import numpy as np
+from bumps.fitproblem import FitProblem
+from bumps.fitters import FITTERS
+from bumps.fitters import FitDriver
+from bumps.fitters import monitor as bumps_monitor
+from bumps.mapper import MPMapper
+from bumps.mapper import can_pickle
+from scipy.optimize import OptimizeResult
+
+from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+from easydiffraction.analysis.fit_helpers.bayesian import standard_deviations_from_summaries
+from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
+from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+from easydiffraction.analysis.minimizers.factory import MinimizerFactory
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.utils.logging import log
+
+DEFAULT_METHOD = 'dream'
+DEFAULT_MAX_ITERATIONS = 3000
+DEFAULT_BURN_FRACTION = 0.2
+DEFAULT_MIN_BURN = 50
+DEFAULT_THIN = 1
+DEFAULT_POP = 4
+DEFAULT_PARALLEL = 0
+DEFAULT_INIT = DreamPopulationInitializationEnum.LHS
+DEFAULT_ALPHA = 0.0
+DEFAULT_OUTLIER_TEST = 'none'
+DEFAULT_TRIM = False
+MAX_RANDOM_SEED = int(np.iinfo(np.uint32).max)
+TOTAL_PROGRESS_POINTS = 25
+DREAM_SAMPLE_ARRAY_NDIM = 3
+DREAM_DRIVER_FAILURES = (ArithmeticError, RuntimeError, TypeError, ValueError)
+
+
+@dataclass(slots=True)
+class _DreamRunContext:
+    """Prepared driver state and metadata for one DREAM run."""
+
+    driver: FitDriver
+    parameter_names: list[str]
+    parameter_display_names: list[str]
+    parameter_uids: list[str]
+    sampler_settings: dict[str, object]
+    starting_values: np.ndarray
+    starting_uncertainties: list[float | None]
+
+
+@dataclass(slots=True)
+class _DreamDriverResult:
+    """
+    Raw driver outcome captured before EasyDiffraction normalization.
+    """
+
+    best_values: object | None
+    best_nllf: float | None
+    raw_state: object | None
+    error: Exception | None = None
+
+
+class _DreamProgressMonitor(bumps_monitor.Monitor):
+    """
+    Progress monitor translating DREAM updates into chi-square rows.
+    """
+
+    def __init__(
+        self,
+        *,
+        tracker: object,
+        n_points: int,
+        n_parameters: int,
+        total_generations: int,
+        burn_steps: int,
+    ) -> None:
+        self._tracker = tracker
+        self._n_points = n_points
+        self._n_parameters = n_parameters
+        self._total_generations = max(1, total_generations)
+        self._burn_steps = max(0, burn_steps)
+        burn_target_count, sampling_target_count = self._phase_progress_point_counts(
+            total_generations=self._total_generations,
+            burn_steps=self._burn_steps,
+        )
+        self._burn_targets = self._progress_targets(
+            start=1,
+            stop=self._burn_steps,
+            target_count=burn_target_count,
+        )
+        self._sampling_targets = self._progress_targets(
+            start=self._burn_steps + 1,
+            stop=self._total_generations,
+            target_count=sampling_target_count,
+        )
+        self._next_burn_target_index = 0
+        self._next_sampling_target_index = 0
+
+    @staticmethod
+    def config_history(history: object) -> None:
+        """Declare the history fields needed for progress updates."""
+        history.requires(time=1, step=1, value=1, population_values=1)
+
+    def __call__(self, history: object) -> None:
+        """Forward sampler progress to the shared fit tracker."""
+        step = int(history.step[0]) if history.step else 0
+        generation = max(1, step)
+        if not self._should_report(generation):
+            return
+        nllf = float(history.value[0])
+        reduced_chi2 = self._reduced_chi_square_from_nllf(nllf)
+        log_posterior = self._population_mean_log_posterior(history)
+        self._tracker.track_sampler_progress(
+            SamplerProgressUpdate(
+                iteration=generation,
+                total_iterations=self._total_generations,
+                phase=self._phase_name(generation),
+                progress_percent=self._progress_percent(generation),
+                log_posterior=log_posterior,
+                reduced_chi2=reduced_chi2,
+                elapsed_time=float(history.time[0]),
+                force_report=True,
+            )
+        )
+
+    def final(self, history: object, best: dict[str, object]) -> None:
+        """Record the final DREAM state in the shared fit tracker."""
+        if not history.time or best.get('value') is None:
+            return
+        step = int(history.step[0]) if history.step else 0
+        generation = max(1, step)
+        best_nllf = float(best['value'])
+        reduced_chi2 = self._reduced_chi_square_from_nllf(best_nllf)
+        self._tracker.track_sampler_progress(
+            SamplerProgressUpdate(
+                iteration=generation,
+                total_iterations=self._total_generations,
+                phase=self._phase_name(generation),
+                progress_percent=self._progress_percent(generation),
+                log_posterior=self._population_mean_log_posterior(history),
+                reduced_chi2=reduced_chi2,
+                elapsed_time=float(history.time[0]),
+                force_report=True,
+            )
+        )
+
+    @staticmethod
+    def _progress_targets(
+        *,
+        start: int,
+        stop: int,
+        target_count: int,
+    ) -> list[int]:
+        """
+        Return monotonically increasing reporting targets for one phase.
+        """
+        if target_count < 1 or stop < start:
+            return []
+
+        targets = np.linspace(start, stop, num=target_count)
+        rounded = np.rint(targets).astype(int)
+        unique_targets = sorted({int(value) for value in rounded if start <= value <= stop})
+        if start not in unique_targets:
+            unique_targets.insert(0, start)
+        if stop not in unique_targets:
+            unique_targets.append(stop)
+        return unique_targets
+
+    @staticmethod
+    def _phase_progress_point_counts(
+        *,
+        total_generations: int,
+        burn_steps: int,
+    ) -> tuple[int, int]:
+        """Return proportional burn and sampling progress counts."""
+        total_points = min(TOTAL_PROGRESS_POINTS, max(1, total_generations))
+        burn_generations = min(max(0, burn_steps), total_generations)
+        sampling_generations = max(total_generations - burn_generations, 0)
+
+        if burn_generations == 0:
+            return 0, total_points
+        if sampling_generations == 0:
+            return total_points, 0
+
+        burn_target_count = round(total_points * burn_generations / total_generations)
+        burn_target_count = min(
+            max(burn_target_count, 1),
+            burn_generations,
+            total_points - 1,
+        )
+        sampling_target_count = min(
+            max(total_points - burn_target_count, 1),
+            sampling_generations,
+        )
+        return burn_target_count, sampling_target_count
+
+    def _should_report(self, generation: int) -> bool:
+        """Return whether the current generation should be rendered."""
+        clamped_generation = min(max(1, generation), self._total_generations)
+        if self._phase_name(clamped_generation) == 'burn-in':
+            return self._consume_progress_target(
+                clamped_generation,
+                phase_targets=self._burn_targets,
+                target_index_name='_next_burn_target_index',
+            )
+
+        return self._consume_progress_target(
+            clamped_generation,
+            phase_targets=self._sampling_targets,
+            target_index_name='_next_sampling_target_index',
+        )
+
+    def _consume_progress_target(
+        self,
+        generation: int,
+        *,
+        phase_targets: list[int],
+        target_index_name: str,
+    ) -> bool:
+        """
+        Advance a phase target pointer when the generation reaches it.
+        """
+        target_index = getattr(self, target_index_name)
+        should_report = False
+        while target_index < len(phase_targets) and generation >= phase_targets[target_index]:
+            target_index += 1
+            should_report = True
+        setattr(self, target_index_name, target_index)
+        return should_report
+
+    def _phase_name(self, generation: int) -> str:
+        """Return the current sampler phase name."""
+        clamped_generation = min(generation, self._total_generations)
+        if clamped_generation <= self._burn_steps:
+            return 'burn-in'
+        return 'sampling'
+
+    def _progress_percent(self, generation: int) -> float:
+        """Return DREAM progress as a percentage."""
+        clamped_generation = min(generation, self._total_generations)
+        return 100.0 * clamped_generation / self._total_generations
+
+    @staticmethod
+    def _population_mean_log_posterior(history: object) -> float:
+        """Return the mean log-posterior across the population."""
+        population_values = history.population_values[0] if history.population_values else None
+        if population_values is None:
+            return -float(history.value[0])
+
+        nllf_values = np.asarray(population_values, dtype=float)
+        finite_mask = np.isfinite(nllf_values)
+        if not np.any(finite_mask):
+            return -float(history.value[0])
+        return float(np.mean(-nllf_values[finite_mask]))
+
+    def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
+        """
+        Convert DREAM's negative log-likelihood to reduced chi-square.
+        """
+        dof = self._n_points - self._n_parameters
+        chi_square = 2.0 * nllf
+        if dof <= 0:
+            return chi_square
+        return chi_square / dof
+
+
+@MinimizerFactory.register
+class BumpsDreamMinimizer(BumpsMinimizer):
+    """Bumps minimizer using the DREAM Bayesian sampler."""
+
+    type_info = TypeInfo(
+        tag=MinimizerTypeEnum.BUMPS_DREAM,
+        description='Bumps library with DREAM Bayesian sampling',
+    )
+
+    def __init__(
+        self,
+        name: str = MinimizerTypeEnum.BUMPS_DREAM,
+        method: str = DEFAULT_METHOD,
+        max_iterations: int = DEFAULT_MAX_ITERATIONS,
+    ) -> None:
+        super().__init__(
+            name=name,
+            method=method,
+            max_iterations=max_iterations,
+        )
+        self._burn: int | None = None
+        self._thin: int = DEFAULT_THIN
+        self._pop: int = DEFAULT_POP
+        self._parallel: int = DEFAULT_PARALLEL
+        self._init: DreamPopulationInitializationEnum = DEFAULT_INIT
+
+    @property
+    def steps(self) -> int:
+        """Number of DREAM generations retained after burn-in."""
+        return self._validated_positive_integer('steps', self.max_iterations)
+
+    @steps.setter
+    def steps(self, value: int) -> None:
+        self.max_iterations = self._validated_positive_integer('steps', value)
+
+    @property
+    def burn(self) -> int | None:
+        """Explicit DREAM burn-in generations or ``None`` for auto."""
+        return self._burn
+
+    @burn.setter
+    def burn(self, value: int | None) -> None:
+        if value is None:
+            self._burn = None
+            return
+        self._burn = self._validated_non_negative_integer('burn', value)
+
+    @property
+    def thin(self) -> int:
+        """DREAM thinning interval."""
+        return self._thin
+
+    @thin.setter
+    def thin(self, value: int) -> None:
+        self._thin = self._validated_positive_integer('thin', value)
+
+    @property
+    def pop(self) -> int:
+        """DREAM population multiplier."""
+        return self._pop
+
+    @pop.setter
+    def pop(self, value: int) -> None:
+        self._pop = self._validated_positive_integer('pop', value)
+
+    @property
+    def parallel(self) -> int:
+        """DREAM parallel worker count; ``0`` uses all CPUs."""
+        return self._parallel
+
+    @parallel.setter
+    def parallel(self, value: int) -> None:
+        self._parallel = self._validated_non_negative_integer('parallel', value)
+
+    @property
+    def init(self) -> DreamPopulationInitializationEnum:
+        """DREAM population initializer."""
+        return self._init
+
+    @init.setter
+    def init(self, value: DreamPopulationInitializationEnum | str) -> None:
+        self._init = self._validated_init(value)
+
+    def _resolve_random_seed(self, random_seed: int | None) -> int:
+        """
+        Return a user-provided or generated random seed.
+
+        Parameters
+        ----------
+        random_seed : int | None
+            User-provided random seed.
+
+        Returns
+        -------
+        int
+            Seed to use for the DREAM run.
+        """
+        if random_seed is None:
+            generator = np.random.default_rng()
+            random_seed = int(generator.integers(0, np.iinfo(np.int32).max))
+
+        integer_seed = self._validated_random_seed_value(random_seed)
+
+        self._resolved_random_seed = integer_seed
+        return self._resolved_random_seed
+
+    @staticmethod
+    def _validated_random_seed_value(random_seed: object) -> int:
+        """Validate and normalize a DREAM random seed."""
+        if isinstance(random_seed, bool):
+            msg = f'DREAM random_seed must be an integer between 0 and {MAX_RANDOM_SEED}.'
+            raise TypeError(msg)
+
+        integer_seed = int(random_seed)
+        if integer_seed != random_seed or integer_seed < 0 or integer_seed > MAX_RANDOM_SEED:
+            msg = f'DREAM random_seed must be an integer between 0 and {MAX_RANDOM_SEED}.'
+            raise ValueError(msg)
+        return integer_seed
+
+    @staticmethod
+    def _tracking_mode() -> str:
+        """Use sampler-style progress reporting for DREAM runs."""
+        return 'sampling'
+
+    def _prepare_solver_args(
+        self,
+        parameters: list[object],
+    ) -> dict[str, object]:
+        """
+        Prepare DREAM solver arguments in EasyDiffraction order.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            List of parameters to be sampled.
+
+        Returns
+        -------
+        dict[str, object]
+            BUMPS parameters plus EasyDiffraction parameter metadata.
+        """
+        self._validate_sampled_parameter_bounds(parameters)
+        solver_args = super()._prepare_solver_args(parameters)
+        solver_args['parameter_names'] = [parameter.unique_name for parameter in parameters]
+        solver_args['parameter_display_names'] = [
+            getattr(parameter, 'name', parameter.unique_name) for parameter in parameters
+        ]
+        solver_args['parameter_uids'] = [parameter._minimizer_uid for parameter in parameters]
+        solver_args['starting_uncertainties'] = [parameter.uncertainty for parameter in parameters]
+        return solver_args
+
+    @classmethod
+    def _validate_sampled_parameter_bounds(
+        cls,
+        parameters: list[object],
+    ) -> None:
+        """
+        Validate finite ordered bounds for sampled DREAM parameters.
+        """
+        issues: list[str] = []
+        for parameter in parameters:
+            parameter_name = cls._parameter_name_for_bound_validation(parameter)
+            parameter_issues = cls._parameter_bound_issues(parameter)
+            if parameter_issues:
+                issues.append(f'- {parameter_name}: {"; ".join(parameter_issues)}')
+
+        if not issues:
+            return
+
+        message = 'DREAM requires finite valid bounds for every sampled parameter:\n' + '\n'.join(
+            issues
+        )
+        raise ValueError(message)
+
+    @staticmethod
+    def _parameter_name_for_bound_validation(parameter: object) -> str:
+        """Return the user-facing name for DREAM bound validation."""
+        unique_name = getattr(parameter, 'unique_name', None)
+        if unique_name:
+            return str(unique_name)
+
+        parameter_name = getattr(parameter, 'name', None)
+        if parameter_name:
+            return str(parameter_name)
+        return '<unknown parameter>'
+
+    @classmethod
+    def _parameter_bound_issues(
+        cls,
+        parameter: object,
+    ) -> list[str]:
+        """Return bound-validation issues for one sampled parameter."""
+        lower_bound = getattr(parameter, 'fit_min', None)
+        upper_bound = getattr(parameter, 'fit_max', None)
+        value = getattr(parameter, 'value', None)
+        issues: list[str] = []
+
+        lower_is_finite = cls._is_finite_bound_value(lower_bound)
+        upper_is_finite = cls._is_finite_bound_value(upper_bound)
+        value_is_finite = cls._is_finite_bound_value(value)
+
+        if not lower_is_finite:
+            issues.append(f'fit_min must be finite (got {lower_bound!r})')
+        if not upper_is_finite:
+            issues.append(f'fit_max must be finite (got {upper_bound!r})')
+
+        bounds_are_ordered = lower_is_finite and upper_is_finite and lower_bound < upper_bound
+        if lower_is_finite and upper_is_finite and not bounds_are_ordered:
+            issues.append(f'fit_min ({lower_bound}) must be smaller than fit_max ({upper_bound})')
+
+        if not value_is_finite:
+            issues.append(f'starting value must be finite (got {value!r})')
+        elif bounds_are_ordered and not lower_bound <= value <= upper_bound:
+            issues.append(f'starting value {value} is outside [{lower_bound}, {upper_bound}]')
+
+        return issues
+
+    @staticmethod
+    def _is_finite_bound_value(value: object) -> bool:
+        """Return whether a bound-validation value is finite."""
+        try:
+            return bool(np.isfinite(value))
+        except TypeError:
+            return False
+
+    @staticmethod
+    def _validated_positive_integer(name: str, value: float) -> int:
+        """Validate a DREAM setting that must be a positive integer."""
+        if isinstance(value, bool):
+            msg = f"DREAM setting '{name}' must be a positive integer."
+            raise TypeError(msg)
+
+        integer_value = int(value)
+        if integer_value != value or integer_value < 1:
+            msg = f"DREAM setting '{name}' must be a positive integer."
+            raise ValueError(msg)
+        return integer_value
+
+    @staticmethod
+    def _validated_non_negative_integer(name: str, value: float) -> int:
+        """
+        Validate a DREAM setting that must be a non-negative integer.
+        """
+        if isinstance(value, bool):
+            msg = f"DREAM setting '{name}' must be a non-negative integer."
+            raise TypeError(msg)
+
+        integer_value = int(value)
+        if integer_value != value or integer_value < 0:
+            msg = f"DREAM setting '{name}' must be a non-negative integer."
+            raise ValueError(msg)
+        return integer_value
+
+    @staticmethod
+    def _validated_init(
+        value: DreamPopulationInitializationEnum | str,
+    ) -> DreamPopulationInitializationEnum:
+        """Validate a DREAM population initializer."""
+        try:
+            return DreamPopulationInitializationEnum(value)
+        except ValueError:
+            valid_values = ', '.join(
+                initialization.value for initialization in DreamPopulationInitializationEnum
+            )
+            msg = f"DREAM setting 'init' must be one of: {valid_values}."
+            raise ValueError(msg) from None
+
+    def _resolved_burn(self, steps: int) -> int:
+        """Return the configured or automatic DREAM burn-in length."""
+        if self.burn is None:
+            proposed_burn = max(DEFAULT_MIN_BURN, int(steps * DEFAULT_BURN_FRACTION))
+            return min(proposed_burn, max(steps - 1, 0))
+
+        burn = self.burn
+        if burn >= steps:
+            msg = "DREAM setting 'burn' must be smaller than 'steps'."
+            raise ValueError(msg)
+        return burn
+
+    def _sampler_settings(
+        self,
+        *,
+        random_seed: int,
+        steps: int,
+        burn: int,
+        n_parameters: int,
+    ) -> dict[str, object]:
+        """Build the sampler settings dictionary recorded in results."""
+        samples = steps * self.pop * n_parameters
+        return {
+            'random_seed': int(random_seed),
+            'steps': int(steps),
+            'burn': int(burn),
+            'thin': int(self.thin),
+            'pop': int(self.pop),
+            'parallel': int(self.parallel),
+            'init': self.init.value,
+            'samples': int(samples),
+            'alpha': float(DEFAULT_ALPHA),
+            'outliers': DEFAULT_OUTLIER_TEST,
+            'trim': DEFAULT_TRIM,
+        }
+
+    def _run_solver(
+        self,
+        objective_function: object,
+        **kwargs: object,
+    ) -> object:
+        """
+        Run the DREAM sampler and normalize its posterior outputs.
+
+        Parameters
+        ----------
+        objective_function : object
+            Objective function returning residuals.
+        **kwargs : object
+            Solver arguments including BUMPS parameters and random seed.
+
+        Returns
+        -------
+        object
+            Normalized DREAM result stored in an ``OptimizeResult``.
+        """
+        context = self._prepare_run_context(objective_function=objective_function, kwargs=kwargs)
+        driver_result = self._execute_driver(
+            driver=context.driver,
+            random_seed=int(context.sampler_settings['random_seed']),
+        )
+        if driver_result.error is not None:
+            return self._failure_result(
+                context=context,
+                message=f'DREAM sampling failed: {driver_result.error}',
+                raw_state=driver_result.raw_state,
+                sampler_completed=False,
+            )
+        if driver_result.best_values is None or driver_result.raw_state is None:
+            return self._failure_result(
+                context=context,
+                message='DREAM sampling did not produce usable posterior samples.',
+                raw_state=driver_result.raw_state,
+                sampler_completed=False,
+            )
+
+        return self._build_success_result(
+            context=context,
+            raw_state=driver_result.raw_state,
+            best_nllf=driver_result.best_nllf,
+        )
+
+    def _prepare_run_context(
+        self,
+        *,
+        objective_function: object,
+        kwargs: dict[str, object],
+    ) -> _DreamRunContext:
+        """Prepare a driver and metadata for one DREAM solver run."""
+        bumps_params = kwargs.get('bumps_params')
+        parameter_names = kwargs.get('parameter_names')
+        parameter_display_names = kwargs.get('parameter_display_names')
+        parameter_uids = kwargs.get('parameter_uids')
+        random_seed = int(kwargs.get('random_seed'))
+        starting_uncertainties = kwargs.get('starting_uncertainties')
+
+        fitness = _EasyDiffractionFitness(bumps_params, objective_function)
+        fitness.nllf()
+        fitclass = next(cls for cls in FITTERS if cls.id == self.method)
+        steps = self.steps
+        burn = self._resolved_burn(steps)
+        init = self.init
+        sampler_settings = self._sampler_settings(
+            random_seed=random_seed,
+            steps=steps,
+            burn=burn,
+            n_parameters=len(bumps_params),
+        )
+        driver = self._build_driver(
+            fitclass=fitclass,
+            fitness=fitness,
+            steps=steps,
+            burn=burn,
+            init=init,
+            sampler_settings=sampler_settings,
+            n_parameters=len(bumps_params),
+        )
+        starting_values = np.array([parameter.value for parameter in bumps_params], dtype=float)
+        resolved_uncertainties = (
+            list(starting_uncertainties)
+            if starting_uncertainties is not None
+            else [None] * len(bumps_params)
+        )
+        return _DreamRunContext(
+            driver=driver,
+            parameter_names=parameter_names,
+            parameter_display_names=parameter_display_names,
+            parameter_uids=parameter_uids,
+            sampler_settings=sampler_settings,
+            starting_values=starting_values,
+            starting_uncertainties=resolved_uncertainties,
+        )
+
+    def _build_driver(
+        self,
+        *,
+        fitclass: object,
+        fitness: object,
+        steps: int,
+        burn: int,
+        init: DreamPopulationInitializationEnum,
+        sampler_settings: dict[str, object],
+        n_parameters: int,
+    ) -> FitDriver:
+        """Build and clip the BUMPS DREAM driver."""
+        total_generations = int(steps + burn + 1)
+        problem = FitProblem(fitness)
+        progress_monitor = _DreamProgressMonitor(
+            tracker=self.tracker,
+            n_points=fitness.numpoints(),
+            n_parameters=n_parameters,
+            total_generations=total_generations,
+            burn_steps=int(burn),
+        )
+        mapper = self._build_mapper(problem)
+        try:
+            driver = FitDriver(
+                fitclass=fitclass,
+                problem=problem,
+                monitors=[progress_monitor],
+                mapper=mapper,
+                steps=steps,
+                burn=burn,
+                thin=self.thin,
+                pop=self.pop,
+                init=init.value,
+                samples=sampler_settings['samples'],
+                alpha=DEFAULT_ALPHA,
+                outliers=DEFAULT_OUTLIER_TEST,
+                trim=DEFAULT_TRIM,
+            )
+            driver.clip()
+        except Exception:
+            MPMapper.stop_mapper()
+            raise
+        else:
+            return driver
+
+    def _build_mapper(self, problem: FitProblem) -> object | None:
+        """Return a DREAM mapper for the configured parallel setting."""
+        if self.parallel == 1:
+            return None
+
+        if not can_pickle(problem):
+            log.warning(
+                'DREAM parallel evaluation requires a picklable '
+                'problem; falling back to serial execution.'
+            )
+            return None
+
+        return MPMapper.start_mapper(problem, [], cpus=self.parallel)
+
+    @staticmethod
+    def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResult:
+        """
+        Run the DREAM driver under a deterministic RNG-state guard.
+        """
+        numpy_rng = np.random.mtrand._rand
+        numpy_state = numpy_rng.get_state()
+        python_state = random.getstate()
+        try:
+            validated_seed = BumpsDreamMinimizer._validated_random_seed_value(random_seed)
+            numpy_rng.seed(validated_seed)
+            random.seed(validated_seed)
+            best_values, best_nllf = driver.fit()
+        except DREAM_DRIVER_FAILURES as error:  # pragma: no cover - backend-specific
+            return _DreamDriverResult(
+                best_values=None,
+                best_nllf=None,
+                raw_state=getattr(driver.fitter, 'state', None),
+                error=error,
+            )
+        finally:
+            MPMapper.stop_mapper()
+            numpy_rng.set_state(numpy_state)
+            random.setstate(python_state)
+
+        return _DreamDriverResult(
+            best_values=best_values,
+            best_nllf=float(best_nllf),
+            raw_state=getattr(driver.fitter, 'state', None),
+        )
+
+    @staticmethod
+    def _failure_result(
+        *,
+        context: _DreamRunContext,
+        message: str,
+        raw_state: object,
+        sampler_completed: bool,
+    ) -> OptimizeResult:
+        """
+        Build a normalized failure result for an incomplete DREAM run.
+        """
+        return OptimizeResult(
+            x=context.starting_values,
+            dx=None,
+            fun=None,
+            success=False,
+            status=-1,
+            message=message,
+            var_names=context.parameter_names,
+            posterior_samples=None,
+            posterior_parameter_summaries=[],
+            convergence_diagnostics={},
+            sampler_settings=context.sampler_settings,
+            sampler_completed=sampler_completed,
+            raw_state=raw_state,
+            best_log_posterior=None,
+            starting_values=context.starting_values,
+            starting_uncertainties=context.starting_uncertainties,
+        )
+
+    def _build_success_result(
+        self,
+        *,
+        context: _DreamRunContext,
+        raw_state: object,
+        best_nllf: float | None,
+    ) -> OptimizeResult:
+        """Normalize a completed DREAM run into an OptimizeResult."""
+        draw_index, parameter_samples_array, log_posterior = raw_state.chains()
+        if (
+            parameter_samples_array.ndim != DREAM_SAMPLE_ARRAY_NDIM
+            or parameter_samples_array.size == 0
+        ):
+            return self._failure_result(
+                context=context,
+                message='DREAM sampling did not return a usable posterior sample array.',
+                raw_state=raw_state,
+                sampler_completed=True,
+            )
+
+        state_best_values, best_log_posterior = raw_state.best()
+        best_by_name = dict(zip(raw_state.labels, state_best_values, strict=True))
+        label_to_index = {label: index for index, label in enumerate(raw_state.labels)}
+        ordered_indices = [label_to_index[uid] for uid in context.parameter_uids]
+        ordered_samples = np.asarray(parameter_samples_array, dtype=float)[:, :, ordered_indices]
+        map_values = np.array([best_by_name[uid] for uid in context.parameter_uids], dtype=float)
+        posterior_samples = PosteriorSamples(
+            parameter_names=context.parameter_names,
+            parameter_samples=ordered_samples,
+            log_posterior=np.asarray(log_posterior, dtype=float),
+            draw_index=np.asarray(draw_index, dtype=float),
+        )
+        convergence_diagnostics = compute_convergence_diagnostics(posterior_samples)
+        posterior_parameter_summaries = summarize_posterior_parameters(
+            parameter_names=context.parameter_names,
+            posterior_samples=posterior_samples,
+            map_values=map_values,
+            parameter_display_names=context.parameter_display_names,
+            convergence_diagnostics=convergence_diagnostics,
+        )
+        posterior_standard_deviations = standard_deviations_from_summaries(
+            posterior_parameter_summaries
+        )
+
+        if not convergence_diagnostics.get('converged', True):
+            log.warning('Convergence diagnostics indicate the posterior may be poorly mixed.')
+
+        return OptimizeResult(
+            x=map_values,
+            dx=posterior_standard_deviations,
+            fun=float(best_nllf),
+            success=True,
+            status=0,
+            message='DREAM sampling completed',
+            var_names=context.parameter_names,
+            posterior_samples=posterior_samples,
+            posterior_parameter_summaries=posterior_parameter_summaries,
+            convergence_diagnostics=convergence_diagnostics,
+            sampler_settings=context.sampler_settings,
+            sampler_completed=True,
+            raw_state=raw_state,
+            best_log_posterior=float(best_log_posterior),
+            starting_values=context.starting_values,
+            starting_uncertainties=context.starting_uncertainties,
+        )
+
+    @staticmethod
+    def _sync_result_to_parameters(
+        parameters: list[object],
+        raw_result: object,
+    ) -> None:
+        """
+        Commit MAP values on success and restore starts on failure.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters being optimized.
+        raw_result : object
+            DREAM result object.
+        """
+        if hasattr(raw_result, 'x'):
+            if getattr(raw_result, 'success', False):
+                values = raw_result.x
+                uncertainties = getattr(raw_result, 'dx', None)
+            else:
+                values = getattr(raw_result, 'starting_values', raw_result.x)
+                uncertainties = getattr(raw_result, 'starting_uncertainties', None)
+        else:
+            values = raw_result
+            uncertainties = None
+
+        if values is None:
+            return
+
+        for index, parameter in enumerate(parameters):
+            parameter._set_value_from_minimizer(float(values[index]))
+            if uncertainties is None:
+                parameter.uncertainty = None
+                continue
+
+            uncertainty = uncertainties[index]
+            parameter.uncertainty = None if uncertainty is None else float(uncertainty)
+
+    def _build_fit_results(
+        self,
+        *,
+        parameters: list[object],
+        raw_result: object,
+        success: bool,
+    ) -> BayesianFitResults:
+        """
+        Build the Bayesian fit result container.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters after the solver finished.
+        raw_result : object
+            Normalized DREAM solver output.
+        success : bool
+            Whether DREAM produced usable posterior samples.
+
+        Returns
+        -------
+        BayesianFitResults
+            Bayesian result object for the finished run.
+        """
+        fit_results = BayesianFitResults(
+            success=success,
+            parameters=parameters,
+            reduced_chi_square=self.tracker.best_chi2,
+            engine_result=getattr(raw_result, 'raw_state', raw_result),
+            starting_parameters=parameters,
+            fitting_time=self.tracker.fitting_time,
+            sampler_name='dream',
+            point_estimate_name='map',
+            posterior_samples=getattr(raw_result, 'posterior_samples', None),
+            posterior_parameter_summaries=getattr(raw_result, 'posterior_parameter_summaries', []),
+            posterior_predictive={},
+            credible_interval_levels=(0.68, 0.95),
+            sampler_settings=getattr(raw_result, 'sampler_settings', {}),
+            convergence_diagnostics=getattr(raw_result, 'convergence_diagnostics', {}),
+            sampler_completed=getattr(raw_result, 'sampler_completed', False),
+            best_log_posterior=getattr(raw_result, 'best_log_posterior', None),
+        )
+        fit_results.message = getattr(raw_result, 'message', '')
+        fit_results.iterations = int(fit_results.sampler_settings.get('steps', self.steps))
+        fit_results.result = raw_result
+        return fit_results
diff --git a/src/easydiffraction/analysis/minimizers/enums.py b/src/easydiffraction/analysis/minimizers/enums.py
index cdfe4ecb8..0d5bb086d 100644
--- a/src/easydiffraction/analysis/minimizers/enums.py
+++ b/src/easydiffraction/analysis/minimizers/enums.py
@@ -16,6 +16,7 @@ class MinimizerTypeEnum(StrEnum):
     DFOLS = 'dfols'
     BUMPS = 'bumps'
     BUMPS_LM = 'bumps (lm)'
+    BUMPS_DREAM = 'bumps (dream)'
     BUMPS_AMOEBA = 'bumps (amoeba)'
     BUMPS_DE = 'bumps (de)'
 
@@ -45,7 +46,17 @@ def description(self) -> str:
                 'BUMPS library using the default Levenberg-Marquardt method'
             ),
             MinimizerTypeEnum.BUMPS_LM: ('BUMPS library with Levenberg-Marquardt method'),
+            MinimizerTypeEnum.BUMPS_DREAM: ('BUMPS library with DREAM Bayesian sampling'),
             MinimizerTypeEnum.BUMPS_AMOEBA: ('BUMPS library with Nelder-Mead simplex method'),
             MinimizerTypeEnum.BUMPS_DE: ('BUMPS library with differential evolution method'),
         }
         return descriptions.get(self, '')
+
+
+class DreamPopulationInitializationEnum(StrEnum):
+    """Supported DREAM population initializers."""
+
+    EPS = 'eps'
+    COV = 'cov'
+    LHS = 'lhs'
+    RANDOM = 'random'
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 84bbd2f42..303c5bbe5 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -22,6 +22,8 @@
 
 # ======================================================================
 
+DEFAULT_FIT_BOUNDS_MULTIPLIER = 4.0
+
 
 class GenericDescriptorBase(GuardedBase):
     """
@@ -280,6 +282,7 @@ def __init__(
         self._fit_min = self._fit_min_spec.default
         self._fit_max_spec = AttributeSpec(data_type=DataTypes.NUMERIC, default=np.inf)
         self._fit_max = self._fit_max_spec.default
+        self._fit_bounds_uncertainty_multiplier: float | None = None
         self._start_value_spec = AttributeSpec(data_type=DataTypes.NUMERIC, default=0.0)
         self._start_value = self._start_value_spec.default
         self._constrained_spec = self._BOOL_SPEC_TEMPLATE
@@ -414,6 +417,7 @@ def fit_min(self, v: float) -> None:
         self._fit_min = self._fit_min_spec.validated(
             v, name=f'{self.unique_name}.fit_min', current=self._fit_min
         )
+        self._fit_bounds_uncertainty_multiplier = None
 
     @property
     def fit_max(self) -> float:
@@ -426,6 +430,80 @@ def fit_max(self, v: float) -> None:
         self._fit_max = self._fit_max_spec.validated(
             v, name=f'{self.unique_name}.fit_max', current=self._fit_max
         )
+        self._fit_bounds_uncertainty_multiplier = None
+
+    @property
+    def fit_bounds_uncertainty_multiplier(self) -> float | None:
+        """
+        Multiplier used for uncertainty-derived fit bounds, if known.
+        """
+        return self._fit_bounds_uncertainty_multiplier
+
+    def set_fit_bounds_from_uncertainty(
+        self,
+        multiplier: float = DEFAULT_FIT_BOUNDS_MULTIPLIER,
+        *,
+        clip_to_limits: bool = True,
+    ) -> None:
+        """
+        Set fit bounds from the current standard uncertainty.
+
+        Parameters
+        ----------
+        multiplier : float, default=DEFAULT_FIT_BOUNDS_MULTIPLIER
+            Positive finite factor applied symmetrically to the current
+            parameter uncertainty.
+        clip_to_limits : bool, default=True
+            Whether to clip the resolved fit bounds to the parameter's
+            physical lower and upper limits when those are finite.
+
+        Raises
+        ------
+        ValueError
+            If the current value, uncertainty, or multiplier is missing,
+            invalid, or produces non-increasing bounds.
+        """
+        name = self.unique_name
+        value = self.value
+        uncertainty = self.uncertainty
+
+        if value is None or not np.isfinite(float(value)):
+            msg = f'Cannot set fit bounds for {name}: current value is missing or invalid.'
+            raise ValueError(msg)
+
+        resolved_multiplier = float(multiplier)
+        if isinstance(multiplier, bool) or not np.isfinite(resolved_multiplier):
+            msg = 'multiplier must be a positive finite number.'
+            raise ValueError(msg)
+        if resolved_multiplier <= 0:
+            msg = 'multiplier must be a positive finite number.'
+            raise ValueError(msg)
+
+        if uncertainty is None or uncertainty <= 0 or not np.isfinite(float(uncertainty)):
+            msg = f'Cannot set fit bounds for {name}: uncertainty is missing or invalid.'
+            raise ValueError(msg)
+
+        lower = float(value) - resolved_multiplier * float(uncertainty)
+        upper = float(value) + resolved_multiplier * float(uncertainty)
+
+        if clip_to_limits:
+            physical_lower = float(self._physical_lower_bound())
+            physical_upper = float(self._physical_upper_bound())
+            if np.isfinite(physical_lower):
+                lower = max(lower, physical_lower)
+            if np.isfinite(physical_upper):
+                upper = min(upper, physical_upper)
+
+        if lower >= upper:
+            msg = (
+                f'Cannot set fit bounds for {name}: resolved lower bound {lower} '
+                f'is not below upper bound {upper}.'
+            )
+            raise ValueError(msg)
+
+        self.fit_min = lower
+        self.fit_max = upper
+        self._fit_bounds_uncertainty_multiplier = resolved_multiplier
 
 
 # ======================================================================
diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index a0c95fe11..4aeb5577a 100644
--- a/src/easydiffraction/display/plotters/ascii.py
+++ b/src/easydiffraction/display/plotters/ascii.py
@@ -131,6 +131,8 @@ def plot_powder_meas_vs_calc(
             title=plot_spec.title,
             height=plot_spec.height,
         )
+        if plot_spec.predictive_lower_95 is not None and plot_spec.predictive_upper_95 is not None:
+            console.print('Posterior predictive bands are available with the Plotly engine only.')
         if plot_spec.bragg_tick_sets:
             console.print('Bragg peak subplot rows are available with the Plotly engine only.')
 
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index b1a19a274..d4be0d67a 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -60,6 +60,11 @@ class PowderMeasVsCalcSpec:
     bragg_peaks_height_fraction: float
     height: int | None = None
     y_bkg: np.ndarray | None = None
+    predictive_lower_95: np.ndarray | None = None
+    predictive_upper_95: np.ndarray | None = None
+    predictive_draws: np.ndarray | None = None
+    y_calc_name: str | None = None
+    y_calc_line_dash: str | None = None
 
 
 class XAxisType(StrEnum):
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 887879dd3..cb4914c3f 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -65,6 +65,15 @@
 COMPOSITE_MARGIN_RIGHT = 30
 COMPOSITE_MARGIN_TOP = 40
 COMPOSITE_MARGIN_BOTTOM = 45
+TITLE_FONT_SIZE = 14
+AXIS_TITLE_FONT_SIZE = 12
+PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.14)'
+PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
+PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
+PREDICTIVE_DRAW_PLOT_CAP = 50
+PREDICTIVE_DRAW_ARRAY_NDIM = 2
+FIXED_ASPECT_WRAPPER_META_KEY = 'fixed_aspect_wrapper'
+FIXED_ASPECT_WRAPPER_CLASS_NAME = 'ed-fixed-aspect-plotly-wrapper'
 
 
 @dataclass(frozen=True)
@@ -157,6 +166,11 @@ def _correlation_grid_color(cls) -> str:
             return 'rgba(110, 145, 190, 0.35)'
         return 'rgba(120, 140, 160, 0.28)'
 
+    @classmethod
+    def _axis_frame_color(cls) -> str:
+        """Return the shared axis-frame color for Plotly figures."""
+        return cls._correlation_grid_color()
+
     @classmethod
     def _legend_background_color(cls) -> str:
         """Return a half-transparent legend background color."""
@@ -208,7 +222,7 @@ def plot_correlation_heatmap(
                 'yanchor': 'middle',
             },
             hoverongaps=False,
-            hovertemplate=f'x: %{{x}}<br>y: %{{y}}<br>corr: %{{z:.{precision}f}}<extra></extra>',
+            hovertemplate=f'%{{x}}<br>%{{y}}<br>correlation: %{{z:.{precision}f}}<extra></extra>',
         )
         label_trace = self._get_correlation_label_trace(
             corr_df,
@@ -461,12 +475,13 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str:
         """
         Return a shared hover template for composite powder traces.
         """
+        calc_label = plot_spec.y_calc_name or 'Icalc'
         if plot_spec.y_bkg is None:
             return (
                 'x: %{x:,.2f}<br>'
                 'Imeas: %{customdata[0]:,.2f}<br>'
-                'Icalc: %{customdata[1]:,.2f}<br>'
-                'Imeas - Icalc: %{customdata[2]:,.2f}'
+                f'{calc_label}: %{{customdata[1]:,.2f}}<br>'
+                f'Imeas - {calc_label}: %{{customdata[2]:,.2f}}'
                 '<extra></extra>'
             )
 
@@ -474,8 +489,8 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str:
             'x: %{x:,.2f}<br>'
             'Imeas: %{customdata[0]:,.2f}<br>'
             'Ibkg: %{customdata[1]:,.2f}<br>'
-            'Icalc: %{customdata[2]:,.2f}<br>'
-            'Imeas - Icalc: %{customdata[3]:,.2f}'
+            f'{calc_label}: %{{customdata[2]:,.2f}}<br>'
+            f'Imeas - {calc_label}: %{{customdata[3]:,.2f}}'
             '<extra></extra>'
         )
 
@@ -778,6 +793,90 @@ def _modebar_legend_toggle_post_script() -> str:
 window.requestAnimationFrame(installLegendToggleButton);
 """
 
+    @classmethod
+    def _html_post_script(cls, fig: object) -> str | None:
+        """Return concatenated HTML post scripts for a Plotly figure."""
+        scripts: list[str] = []
+        if cls._has_visible_legend(fig):
+            scripts.append(cls._modebar_legend_toggle_post_script())
+        if not scripts:
+            return None
+        return '\n'.join(cls._scoped_html_post_script(script) for script in scripts)
+
+    @staticmethod
+    def _scoped_html_post_script(script: str) -> str:
+        """
+        Return one HTML post script wrapped in its own block scope.
+        """
+        return '{\n' + script.strip() + '\n}'
+
+    @staticmethod
+    def _figure_meta(fig: object) -> dict[str, object] | None:
+        """Return figure layout metadata when available."""
+        layout = getattr(fig, 'layout', None)
+        if layout is None:
+            return None
+
+        meta = getattr(layout, 'meta', None)
+        if isinstance(meta, dict):
+            return meta
+
+        layout_kwargs = getattr(layout, 'kwargs', None)
+        if isinstance(layout_kwargs, dict):
+            meta = layout_kwargs.get('meta')
+            if isinstance(meta, dict):
+                return meta
+        return None
+
+    @classmethod
+    def _fixed_aspect_wrapper_aspect_ratio(cls, fig: object) -> str | None:
+        """Return the fixed aspect ratio requested for inline HTML."""
+        meta = cls._figure_meta(fig)
+        if not isinstance(meta, dict):
+            return None
+
+        wrapper = meta.get(FIXED_ASPECT_WRAPPER_META_KEY)
+        if not isinstance(wrapper, dict):
+            return None
+
+        aspect_ratio = wrapper.get('aspect_ratio')
+        if not isinstance(aspect_ratio, str):
+            return None
+
+        aspect_ratio = aspect_ratio.strip()
+        if not aspect_ratio:
+            return None
+        return aspect_ratio
+
+    @classmethod
+    def _wrap_html_figure(cls, fig: object, html_fig: str) -> str:
+        """Wrap inline Plotly HTML in a fixed-aspect container."""
+        aspect_ratio = cls._fixed_aspect_wrapper_aspect_ratio(fig)
+        if aspect_ratio is None:
+            return html_fig
+
+        return (
+            '<style>\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} {{\n'
+            '    width: 100%;\n'
+            f'    aspect-ratio: {aspect_ratio};\n'
+            '}\n\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} > div,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plotly-graph-div,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .js-plotly-plot,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plotly,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plot-container,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .svg-container {{\n'
+            '    display: block;\n'
+            '    width: 100% !important;\n'
+            '    height: 100% !important;\n'
+            '}\n'
+            '</style>\n\n'
+            f'<div class="{FIXED_ASPECT_WRAPPER_CLASS_NAME}">\n'
+            f'{html_fig}\n'
+            '</div>'
+        )
+
     @staticmethod
     def _get_figure(
         data: object,
@@ -855,9 +954,7 @@ def _show_figure(
         if in_pycharm() or display is None or HTML is None:
             fig.show(config=config)
         else:
-            post_script = None
-            if self._has_visible_legend(fig):
-                post_script = self._modebar_legend_toggle_post_script()
+            post_script = self._html_post_script(fig)
             html_fig = pio.to_html(
                 fig,
                 include_plotlyjs='cdn',
@@ -865,6 +962,7 @@ def _show_figure(
                 config=config,
                 post_script=post_script,
             )
+            html_fig = self._wrap_html_figure(fig, html_fig)
             display(HTML(html_fig))
 
     @classmethod
@@ -900,6 +998,7 @@ def _get_layout(
             },
             title={
                 'text': title,
+                'font': {'size': TITLE_FONT_SIZE},
             },
             legend={
                 'bgcolor': cls._legend_background_color(),
@@ -909,14 +1008,22 @@ def _get_layout(
                 'y': 1.0,
             },
             xaxis={
-                'title_text': axes_labels[0],
+                'title': {
+                    'text': axes_labels[0],
+                    'font': {'size': AXIS_TITLE_FONT_SIZE},
+                },
                 'showline': True,
+                'linecolor': cls._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
             },
             yaxis={
-                'title_text': axes_labels[1],
+                'title': {
+                    'text': axes_labels[1],
+                    'font': {'size': AXIS_TITLE_FONT_SIZE},
+                },
                 'showline': True,
+                'linecolor': cls._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
             },
@@ -1165,11 +1272,7 @@ def _get_main_intensity_range(cls, plot_spec: PowderMeasVsCalcSpec) -> tuple[flo
         if min(y_meas.size, y_calc.size) == 0:
             return 0.0, 1.0
 
-        main_series = [y_meas, y_calc]
-        if plot_spec.y_bkg is not None:
-            y_bkg = np.asarray(plot_spec.y_bkg)
-            if y_bkg.size > 0:
-                main_series.append(y_bkg)
+        main_series = cls._main_intensity_series(plot_spec, y_meas=y_meas, y_calc=y_calc)
 
         main_y_min = float(min(np.min(series) for series in main_series))
         main_y_max = float(max(np.max(series) for series in main_series))
@@ -1180,6 +1283,49 @@ def _get_main_intensity_range(cls, plot_spec: PowderMeasVsCalcSpec) -> tuple[flo
 
         return main_y_min - 1.0, main_y_max + 1.0
 
+    @classmethod
+    def _main_intensity_series(
+        cls,
+        plot_spec: PowderMeasVsCalcSpec,
+        *,
+        y_meas: np.ndarray,
+        y_calc: np.ndarray,
+    ) -> list[np.ndarray]:
+        main_series = [y_meas, y_calc]
+        for values in (
+            plot_spec.y_bkg,
+            plot_spec.predictive_lower_95,
+            plot_spec.predictive_upper_95,
+        ):
+            cls._append_non_empty_series(main_series, values)
+
+        predictive_draws = cls._predictive_draw_array(plot_spec.predictive_draws)
+        if predictive_draws is not None:
+            main_series.extend(predictive_draws)
+        return main_series
+
+    @staticmethod
+    def _append_non_empty_series(
+        main_series: list[np.ndarray],
+        values: np.ndarray | None,
+    ) -> None:
+        if values is None:
+            return
+
+        array = np.asarray(values)
+        if array.size > 0:
+            main_series.append(array)
+
+    @staticmethod
+    def _predictive_draw_array(values: object | None) -> np.ndarray | None:
+        if values is None:
+            return None
+
+        predictive_draws = np.asarray(values)
+        if predictive_draws.ndim != PREDICTIVE_DRAW_ARRAY_NDIM or predictive_draws.size == 0:
+            return None
+        return predictive_draws
+
     @classmethod
     def _get_residual_limit(cls, plot_spec: PowderMeasVsCalcSpec) -> float:
         """Return a symmetric residual limit matched to the main row."""
@@ -1221,11 +1367,40 @@ def plot_powder_meas_vs_calc(
         layout = self._get_powder_composite_rows(plot_spec)
         x_min, x_max = self._composite_x_range(np.asarray(plot_spec.x))
         main_y_min, main_y_max = self._get_main_intensity_range(plot_spec)
-        residual_limit = None
         hover_data = self._powder_meas_vs_calc_hover_data(plot_spec)
         hover_template = self._powder_meas_vs_calc_hover_template(plot_spec)
+        fig = self._create_powder_composite_figure(layout)
+        self._add_predictive_band_traces(fig=fig, plot_spec=plot_spec)
+        self._add_main_intensity_traces(
+            fig=fig,
+            plot_spec=plot_spec,
+            hover_data=hover_data,
+            hover_template=hover_template,
+        )
+        self._add_predictive_draw_traces(fig=fig, plot_spec=plot_spec)
+        self._add_bragg_tick_traces(fig=fig, plot_spec=plot_spec, layout=layout)
+        residual_limit = self._add_residual_trace(
+            fig=fig,
+            plot_spec=plot_spec,
+            layout=layout,
+            hover_data=hover_data,
+            hover_template=hover_template,
+        )
+        self._configure_powder_composite_layout(fig=fig, plot_spec=plot_spec, layout=layout)
+        self._configure_powder_composite_axes(
+            fig=fig,
+            plot_spec=plot_spec,
+            layout=layout,
+            x_range=(x_min, x_max),
+            main_y_range=(main_y_min, main_y_max),
+            residual_limit=residual_limit,
+        )
+
+        self._show_figure(fig)
 
-        fig = make_subplots(
+    @staticmethod
+    def _create_powder_composite_figure(layout: PowderCompositeRows) -> object:
+        return make_subplots(
             rows=layout.row_count,
             cols=1,
             shared_xaxes=True,
@@ -1233,58 +1408,146 @@ def plot_powder_meas_vs_calc(
             row_heights=layout.row_heights,
         )
 
-        main_traces = (
-            (
-                ('meas', plot_spec.y_meas),
-                ('bkg', plot_spec.y_bkg),
-                ('calc', plot_spec.y_calc),
-            )
-            if plot_spec.y_bkg is not None
-            else (
-                ('meas', plot_spec.y_meas),
-                ('calc', plot_spec.y_calc),
+    def _add_predictive_band_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+    ) -> None:
+        if plot_spec.predictive_lower_95 is None or plot_spec.predictive_upper_95 is None:
+            return
+
+        lower_trace, upper_trace = self._get_predictive_band_traces(
+            x=plot_spec.x,
+            lower=plot_spec.predictive_lower_95,
+            upper=plot_spec.predictive_upper_95,
+        )
+        fig.add_trace(lower_trace, row=1, col=1)
+        fig.add_trace(upper_trace, row=1, col=1)
+
+    def _add_main_intensity_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        hover_data: object,
+        hover_template: str,
+    ) -> None:
+        meas_trace = self._get_powder_trace(
+            plot_spec.x,
+            plot_spec.y_meas,
+            'meas',
+            customdata=hover_data,
+            hovertemplate=hover_template,
+        )
+        fig.add_trace(meas_trace, row=1, col=1)
+
+        if plot_spec.y_bkg is not None:
+            bkg_trace = self._get_powder_trace(
+                plot_spec.x,
+                plot_spec.y_bkg,
+                'bkg',
+                customdata=hover_data,
+                hovertemplate=hover_template,
             )
+            fig.add_trace(bkg_trace, row=1, col=1)
+
+        calc_trace = self._get_powder_trace(
+            plot_spec.x,
+            plot_spec.y_calc,
+            'calc',
+            customdata=hover_data,
+            hovertemplate=hover_template,
         )
-        for label, y_values in main_traces:
+        if plot_spec.y_calc_name is not None:
+            calc_trace.name = plot_spec.y_calc_name
+        if plot_spec.y_calc_line_dash is not None:
+            calc_trace.line.dash = plot_spec.y_calc_line_dash
+        fig.add_trace(calc_trace, row=1, col=1)
+
+    def _add_predictive_draw_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+    ) -> None:
+        predictive_draws = self._predictive_draw_array(plot_spec.predictive_draws)
+        if predictive_draws is None:
+            return
+
+        draw_cap = min(predictive_draws.shape[0], PREDICTIVE_DRAW_PLOT_CAP)
+        for index in range(draw_cap):
             fig.add_trace(
-                self._get_powder_trace(
-                    plot_spec.x,
-                    y_values,
-                    label,
-                    customdata=hover_data,
-                    hovertemplate=hover_template,
+                go.Scatter(
+                    x=plot_spec.x,
+                    y=predictive_draws[index],
+                    mode='lines',
+                    line={'color': PREDICTIVE_DRAW_COLOR, 'width': 1},
+                    name='Posterior draw' if index == 0 else None,
+                    showlegend=index == 0,
+                    hovertemplate=(
+                        'Posterior draw<br>x: %{x:,.2f}<br>y: %{y:,.2f}<extra></extra>'
+                    ),
                 ),
                 row=1,
                 col=1,
             )
 
-        if layout.bragg_row is not None:
-            for idx, tick_set in enumerate(plot_spec.bragg_tick_sets):
-                color = BRAGG_TICK_COLORS[idx % len(BRAGG_TICK_COLORS)]
-                fig.add_trace(
-                    self._get_bragg_tick_trace(
-                        tick_set=tick_set,
-                        row_y=float(idx + 1),
-                        color=color,
-                    ),
-                    row=layout.bragg_row,
-                    col=1,
-                )
+    def _add_bragg_tick_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
+        if layout.bragg_row is None:
+            return
 
-        if layout.residual_row is not None and plot_spec.y_resid is not None:
-            residual_limit = self._get_residual_limit(plot_spec)
+        for idx, tick_set in enumerate(plot_spec.bragg_tick_sets):
+            color = BRAGG_TICK_COLORS[idx % len(BRAGG_TICK_COLORS)]
             fig.add_trace(
-                self._get_powder_trace(
-                    plot_spec.x,
-                    plot_spec.y_resid,
-                    'resid',
-                    customdata=hover_data,
-                    hovertemplate=hover_template,
+                self._get_bragg_tick_trace(
+                    tick_set=tick_set,
+                    row_y=float(idx + 1),
+                    color=color,
                 ),
-                row=layout.residual_row,
+                row=layout.bragg_row,
                 col=1,
             )
 
+    def _add_residual_trace(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        hover_data: object,
+        hover_template: str,
+    ) -> float | None:
+        if layout.residual_row is None or plot_spec.y_resid is None:
+            return None
+
+        residual_limit = self._get_residual_limit(plot_spec)
+        fig.add_trace(
+            self._get_powder_trace(
+                plot_spec.x,
+                plot_spec.y_resid,
+                'resid',
+                customdata=hover_data,
+                hovertemplate=hover_template,
+            ),
+            row=layout.residual_row,
+            col=1,
+        )
+        return residual_limit
+
+    def _configure_powder_composite_layout(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
         fig.update_layout(
             height=self._composite_figure_height(plot_spec, layout),
             margin={
@@ -1293,7 +1556,10 @@ def plot_powder_meas_vs_calc(
                 't': COMPOSITE_MARGIN_TOP,
                 'b': COMPOSITE_MARGIN_BOTTOM,
             },
-            title={'text': plot_spec.title},
+            title={
+                'text': plot_spec.title,
+                'font': {'size': TITLE_FONT_SIZE},
+            },
             legend={
                 'bgcolor': self._legend_background_color(),
                 'xanchor': 'right',
@@ -1303,10 +1569,64 @@ def plot_powder_meas_vs_calc(
             },
         )
 
-        for row_idx in range(1, layout.row_count + 1):
+    def _configure_powder_composite_axes(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        x_range: tuple[float | None, float | None],
+        main_y_range: tuple[float, float],
+        residual_limit: float | None,
+    ) -> None:
+        self._configure_shared_composite_axes(
+            fig=fig,
+            row_count=layout.row_count,
+            x_min=x_range[0],
+            x_max=x_range[1],
+        )
+        fig.update_xaxes(showticklabels=(layout.row_count == 1), row=1, col=1)
+        fig.update_yaxes(
+            title_text=plot_spec.axes_labels[1],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
+            range=list(main_y_range),
+            row=1,
+            col=1,
+        )
+
+        if layout.bragg_row is not None:
+            self._configure_bragg_axes(fig=fig, plot_spec=plot_spec, layout=layout)
+        if layout.residual_row is not None and residual_limit is not None:
+            self._configure_residual_axes(
+                fig=fig,
+                plot_spec=plot_spec,
+                layout=layout,
+                residual_limit=residual_limit,
+            )
+            return
+
+        terminal_row = layout.bragg_row if layout.bragg_row is not None else 1
+        fig.update_xaxes(
+            title_text=plot_spec.axes_labels[0],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
+            row=terminal_row,
+            col=1,
+        )
+
+    def _configure_shared_composite_axes(
+        self,
+        *,
+        fig: object,
+        row_count: int,
+        x_min: float | None,
+        x_max: float | None,
+    ) -> None:
+        axis_frame_color = self._axis_frame_color()
+        for row_idx in range(1, row_count + 1):
             x_axis_kwargs = {
                 'matches': 'x',
                 'showline': True,
+                'linecolor': axis_frame_color,
                 'mirror': True,
                 'zeroline': False,
                 'tickformat': ',.6~g',
@@ -1317,6 +1637,7 @@ def plot_powder_meas_vs_calc(
             fig.update_xaxes(row=row_idx, col=1, **x_axis_kwargs)
             fig.update_yaxes(
                 showline=True,
+                linecolor=axis_frame_color,
                 mirror=True,
                 zeroline=False,
                 tickformat=',.6~g',
@@ -1325,50 +1646,86 @@ def plot_powder_meas_vs_calc(
                 col=1,
             )
 
-        fig.update_xaxes(showticklabels=(layout.row_count == 1), row=1, col=1)
+    @staticmethod
+    def _configure_bragg_axes(
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
         fig.update_yaxes(
-            title_text=plot_spec.axes_labels[1],
-            range=[main_y_min, main_y_max],
-            row=1,
+            tickmode='array',
+            tickvals=[float(idx + 1) for idx in range(len(plot_spec.bragg_tick_sets))],
+            ticktext=[tick_set.phase_id for tick_set in plot_spec.bragg_tick_sets],
+            range=[float(len(plot_spec.bragg_tick_sets)) + 0.5, 0.5],
+            showgrid=False,
+            row=layout.bragg_row,
+            col=1,
+        )
+        fig.update_xaxes(
+            showticklabels=layout.residual_row is None,
+            row=layout.bragg_row,
             col=1,
         )
 
-        if layout.bragg_row is not None:
-            fig.update_yaxes(
-                # title_text='Bragg peaks',
-                tickmode='array',
-                tickvals=[float(idx + 1) for idx in range(len(plot_spec.bragg_tick_sets))],
-                ticktext=[tick_set.phase_id for tick_set in plot_spec.bragg_tick_sets],
-                range=[float(len(plot_spec.bragg_tick_sets)) + 0.5, 0.5],
-                showgrid=False,
-                row=layout.bragg_row,
-                col=1,
-            )
-            fig.update_xaxes(
-                showticklabels=layout.residual_row is None,
-                row=layout.bragg_row,
-                col=1,
-            )
-
-        if layout.residual_row is not None and plot_spec.y_resid is not None:
-            residual_tick_limit = self._get_display_tick_limit(residual_limit)
-            fig.update_yaxes(
-                # title_text='Residual',
-                range=[-residual_limit, residual_limit],
-                tickmode='array',
-                tickvals=[-residual_tick_limit, 0.0, residual_tick_limit],
-                scaleanchor='y',
-                scaleratio=1,
-                zeroline=False,
-                row=layout.residual_row,
-                col=1,
-            )
-            fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=layout.residual_row, col=1)
-        else:
-            terminal_row = layout.bragg_row if layout.bragg_row is not None else 1
-            fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=terminal_row, col=1)
+    def _configure_residual_axes(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        residual_limit: float,
+    ) -> None:
+        residual_tick_limit = self._get_display_tick_limit(residual_limit)
+        fig.update_yaxes(
+            range=[-residual_limit, residual_limit],
+            tickmode='array',
+            tickvals=[-residual_tick_limit, 0.0, residual_tick_limit],
+            scaleanchor='y',
+            scaleratio=1,
+            zeroline=False,
+            row=layout.residual_row,
+            col=1,
+        )
+        fig.update_xaxes(
+            title_text=plot_spec.axes_labels[0],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
+            row=layout.residual_row,
+            col=1,
+        )
 
-        self._show_figure(fig)
+    @staticmethod
+    def _get_predictive_band_traces(
+        *,
+        x: np.ndarray,
+        lower: np.ndarray,
+        upper: np.ndarray,
+    ) -> tuple[go.Scatter, go.Scatter]:
+        """
+        Return Plotly traces for a filled predictive interval band.
+        """
+        lower_trace = go.Scatter(
+            x=x,
+            y=lower,
+            mode='lines',
+            line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
+            hoverinfo='skip',
+            showlegend=False,
+            legendgroup='predictive_band',
+        )
+        upper_trace = go.Scatter(
+            x=x,
+            y=upper,
+            mode='lines',
+            line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
+            fill='tonexty',
+            fillcolor=PREDICTIVE_BAND_COLOR,
+            name='95% interval',
+            hoverinfo='skip',
+            legendgroup='predictive_band',
+            legendrank=35,
+        )
+        return lower_trace, upper_trace
 
     def plot_single_crystal(
         self,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 61e93f683..34b0da107 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -16,6 +16,7 @@
 import numpy as np
 import pandas as pd
 
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
 from easydiffraction.datablocks.experiment.item.base import intensity_category_for
 from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
 from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
@@ -30,6 +31,10 @@
 from easydiffraction.display.plotters.base import BraggTickSet
 from easydiffraction.display.plotters.base import PowderMeasVsCalcSpec
 from easydiffraction.display.plotters.base import XAxisType
+from easydiffraction.display.plotters.plotly import (
+    AXIS_TITLE_FONT_SIZE as PLOTLY_AXIS_TITLE_FONT_SIZE,
+)
+from easydiffraction.display.plotters.plotly import TITLE_FONT_SIZE as PLOTLY_TITLE_FONT_SIZE
 from easydiffraction.display.plotters.plotly import PlotlyPlotter
 from easydiffraction.display.tables import TableRenderer
 from easydiffraction.utils.environment import in_jupyter
@@ -63,12 +68,108 @@ def description(self) -> str:
         return ''
 
 
-DEFAULT_CORRELATION_THRESHOLD = 0.7
+class PosteriorPairPlotStyleEnum(StrEnum):
+    """Available posterior pair-plot rendering modes."""
+
+    AUTO = 'auto'
+    FAST = 'fast'
+    FULL = 'full'
+
+
+DEFAULT_CORRELATION_THRESHOLD: float | None = None
+DEFAULT_CORRELATION_MAX_PARAMETERS = 6
 EXPECTED_COVAR_NDIM = 2
 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25
 DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION = 0.10
 DEFAULT_RESID_HEIGHT = DEFAULT_RESIDUAL_HEIGHT_FRACTION
 DEFAULT_BRAGG_ROW = DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION
+DEFAULT_POSTERIOR_PREDICTIVE_DRAWS = 200
+DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
+FULL_POSTERIOR_PAIR_COVARIANCE_RANK = 2
+POSTERIOR_FLATTENED_SAMPLE_NDIM = 2
+MIN_POSTERIOR_PARAMETER_COUNT = 2
+MIN_POSTERIOR_SAMPLE_COUNT = 2
+POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
+POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR = 'rgb(44, 160, 44)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR = 'rgba(44, 160, 44, 0.22)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH = 1
+POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
+POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
+POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.14)'
+POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(214, 39, 40, 0.26)'
+POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
+POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
+POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Max posterior'
+POSTERIOR_POINT_ESTIMATE_LINE_DASH = 'dot'
+POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME = '95% interval'
+POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
+POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
+POSTERIOR_CONTOUR_FILL_COLORSCALE = [
+    [0.0, 'rgba(224, 233, 255, 0.62)'],
+    [0.35, 'rgba(183, 203, 255, 0.70)'],
+    [0.60, 'rgba(138, 169, 252, 0.78)'],
+    [0.82, 'rgba(96, 131, 242, 0.84)'],
+    [1.0, 'rgba(58, 86, 224, 0.90)'],
+]
+POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE = [
+    [0.0, 'rgba(255, 224, 224, 0.62)'],
+    [0.35, 'rgba(250, 188, 188, 0.70)'],
+    [0.60, 'rgba(245, 148, 148, 0.78)'],
+    [0.82, 'rgba(237, 104, 104, 0.84)'],
+    [1.0, 'rgba(215, 48, 39, 0.90)'],
+]
+POSTERIOR_CONTOUR_LINE_COLORSCALE = [
+    [0.0, 'rgba(183, 203, 255, 0.94)'],
+    [0.35, 'rgba(183, 203, 255, 0.94)'],
+    [0.35, 'rgba(138, 169, 252, 0.95)'],
+    [0.60, 'rgba(138, 169, 252, 0.95)'],
+    [0.60, 'rgba(96, 131, 242, 0.96)'],
+    [0.82, 'rgba(96, 131, 242, 0.96)'],
+    [0.82, 'rgba(58, 86, 224, 0.98)'],
+    [1.0, 'rgba(58, 86, 224, 0.98)'],
+]
+POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE = [
+    [0.0, 'rgba(250, 188, 188, 0.94)'],
+    [0.35, 'rgba(250, 188, 188, 0.94)'],
+    [0.35, 'rgba(245, 148, 148, 0.95)'],
+    [0.60, 'rgba(245, 148, 148, 0.95)'],
+    [0.60, 'rgba(237, 104, 104, 0.96)'],
+    [0.82, 'rgba(237, 104, 104, 0.96)'],
+    [0.82, 'rgba(215, 48, 39, 0.98)'],
+    [1.0, 'rgba(215, 48, 39, 0.98)'],
+]
+POSTERIOR_PAIR_SCATTER_MAX_POINTS = 1500
+POSTERIOR_PAIR_MAX_DENSITY_SAMPLES = 4000
+POSTERIOR_PAIR_MIN_DENSITY_SAMPLES = 800
+POSTERIOR_PAIR_TARGET_DENSITY_SAMPLE_BUDGET = 24000
+POSTERIOR_PAIR_MAX_CONTOUR_GRID_SIZE = 96
+POSTERIOR_PAIR_MIN_CONTOUR_GRID_SIZE = 56
+POSTERIOR_PAIR_TARGET_CONTOUR_GRID_POINT_BUDGET = 73728
+POSTERIOR_PAIR_AUTO_MAX_CONTOUR_PARAMETERS = 6
+PAIR_PLOT_CELL_SIZE_PIXELS = 190
+PAIR_PLOT_MIN_CELL_SIZE_PIXELS = 90
+PAIR_PLOT_MIN_SIZE_PIXELS = 680
+PAIR_PLOT_MARGIN_PIXELS = 120
+PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS = 980
+PAIR_PLOT_SUBPLOT_SPACING = 0.01
+POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
+POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = PLOTLY_AXIS_TITLE_FONT_SIZE
+POSTERIOR_PAIR_TITLE_FONT_SIZE = PLOTLY_TITLE_FONT_SIZE
+POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
+POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
+SQUARE_MATRIX_TITLE_YSHIFT_PIXELS = 12
+POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)'
+SQUARE_MATRIX_FIXED_ASPECT_RATIO = '1 / 1'
+SQUARE_MATRIX_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
+SQUARE_MATRIX_LEFT_MARGIN_PIXELS = 40
+SQUARE_MATRIX_RIGHT_MARGIN_PIXELS = 24
+SQUARE_MATRIX_TOP_MARGIN_PIXELS = 40
+SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS = 40
+SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
+SQUARE_MATRIX_TITLE_LEFT_PADDING_PIXELS = 14
+POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
+POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE = 6
 
 
 @dataclass(frozen=True)
@@ -90,6 +191,70 @@ class _PowderMeasVsCalcSeries:
     y_bkg: np.ndarray | None = None
 
 
+@dataclass(frozen=True)
+class _PosteriorDistributionContext:
+    """Inputs needed to build a posterior distribution plot."""
+
+    fit_results: object
+    parameter_name: str
+    values: np.ndarray
+    label: str
+    title: str
+    summary: object | None
+
+
+@dataclass(frozen=True)
+class _PosteriorPairsContext:
+    """Inputs needed to build a posterior pair plot."""
+
+    fit_results: object
+    parameter_names: list[str]
+    labels: list[str]
+    annotation_labels: list[str]
+    title: str
+    density_samples: np.ndarray
+    scatter_samples: np.ndarray
+    show_contours: bool
+    contour_grid_size: int
+    axis_frame_color: str
+    axis_ranges: list[tuple[float, float]]
+
+    @property
+    def n_parameters(self) -> int:
+        """Return the number of plotted parameters."""
+        return len(self.parameter_names)
+
+
+@dataclass(frozen=True)
+class _CorrelationHeatmapContext:
+    """Inputs needed to build a correlation matrix plot."""
+
+    corr_df: pd.DataFrame
+    row_labels: list[str]
+    col_labels: list[str]
+    threshold: float | None
+    precision: int
+
+    @property
+    def n_rows(self) -> int:
+        """Return the number of displayed rows."""
+        return self.corr_df.shape[0]
+
+    @property
+    def n_cols(self) -> int:
+        """Return the number of displayed columns."""
+        return self.corr_df.shape[1]
+
+
+@dataclass(slots=True)
+class _PosteriorPairsLegendState:
+    """Legend-visibility state for posterior pair plots."""
+
+    show_density: bool = True
+    show_scatter: bool = True
+    show_contour: bool = True
+
+
 class Plotter(RendererBase):
     """User-facing plotting facade backed by concrete plotters."""
 
@@ -562,6 +727,9 @@ def plot_param_correlations(
         self,
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
         precision: int = 2,
+        *,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        show_diagonal: bool = True,
     ) -> None:
         """
         Plot the parameter correlation matrix from the latest fit.
@@ -570,44 +738,57 @@ def plot_param_correlations(
         the active engine is Plotly, an interactive heatmap is shown.
         Otherwise, a rounded correlation table is rendered.
 
-        Only the lower triangle is shown (without the diagonal), since
-        the matrix is symmetric and diagonal values are always ``1``.
+        By default the lower triangle is shown with blank diagonal cells
+        so the grid stays square, like posterior pair plots. Set
+        ``show_diagonal=False`` to trim the empty outer row and column.
 
         Parameters
         ----------
         threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
             Minimum absolute off-diagonal correlation required for a
-            parameter to be shown. Parameters are kept only if they
-            participate in at least one pair with ``abs(correlation) >=
-            threshold``. Set to ``None`` or ``0`` to show the full
-            matrix.
+            parameter to be shown. When omitted, an automatic cutoff is
+            chosen so the displayed matrix stays at or below
+            ``max_parameters x max_parameters`` when possible. Set to
+            ``0`` to show the full matrix.
         precision : int, default=2
             Number of decimal places to show in the table fallback.
+        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
+            Maximum number of parameters to display when ``threshold``
+            is omitted. Ignored when ``threshold`` is provided.
+        show_diagonal : bool, default=True
+            Whether to retain blank diagonal cells in the displayed
+            lower-triangle matrix.
         """
         corr_df = self._get_param_correlation_dataframe()
         if corr_df is None:
             return
 
-        corr_df = self._filter_correlation_dataframe(corr_df, threshold=threshold)
+        corr_df, resolved_threshold = self._resolve_correlation_filter(
+            corr_df,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
         if corr_df is None:
             return
 
         corr_df = self._mask_correlation_lower_triangle(corr_df)
-        title = 'Refined parameter correlation matrix'
-        if threshold is not None and threshold > 0:
-            title += f' with |correlation| >= {threshold:.2f}'
+        title = self._correlation_filtered_title(
+            'Refined parameter correlation matrix',
+            resolved_threshold,
+        )
 
         is_graphical = self._backend._supports_graphical_heatmap
         display_corr_df, row_numbers, col_numbers = self._trim_correlation_display_dataframe(
             corr_df,
             preserve_all_rows=not is_graphical,
+            show_diagonal=show_diagonal,
         )
 
         if is_graphical:
             self._plot_correlation_heatmap(
                 display_corr_df,
                 title,
-                threshold=threshold,
+                threshold=resolved_threshold,
                 precision=precision,
             )
             return
@@ -618,11 +799,439 @@ def plot_param_correlations(
                 display_corr_df,
                 row_numbers=row_numbers,
                 col_numbers=col_numbers,
-                threshold=threshold,
+                threshold=resolved_threshold,
                 precision=precision,
             )
         )
 
+    @classmethod
+    def _resolve_correlation_filter(
+        cls,
+        corr_df: pd.DataFrame,
+        *,
+        threshold: float | None,
+        max_parameters: int | None = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        min_parameters: int = 1,
+    ) -> tuple[pd.DataFrame | None, float]:
+        """Return a filtered matrix and effective threshold."""
+        if threshold is not None:
+            filtered_corr_df = cls._filter_correlation_dataframe(corr_df, threshold=threshold)
+            return filtered_corr_df, float(threshold)
+        if max_parameters is None:
+            return corr_df, 0.0
+        validated_max_parameters = cls._validated_max_parameter_count(
+            max_parameters,
+            minimum=min_parameters,
+        )
+        return cls._auto_filtered_correlation_dataframe(
+            corr_df,
+            max_parameters=validated_max_parameters,
+            min_parameters=min_parameters,
+        )
+
+    @staticmethod
+    def _validated_max_parameter_count(
+        max_parameters: int,
+        *,
+        minimum: int,
+    ) -> int:
+        """Return a validated parameter-count limit."""
+        if not isinstance(max_parameters, int) or isinstance(max_parameters, bool):
+            msg = 'max_parameters must be an integer.'
+            raise TypeError(msg)
+        if max_parameters < minimum:
+            msg = f'max_parameters must be at least {minimum}.'
+            raise ValueError(msg)
+        return max_parameters
+
+    @staticmethod
+    def _auto_filtered_correlation_dataframe(
+        corr_df: pd.DataFrame,
+        *,
+        max_parameters: int,
+        min_parameters: int = 1,
+    ) -> tuple[pd.DataFrame, float]:
+        """Return an auto-limited matrix for default display."""
+        if corr_df.shape[0] <= max_parameters:
+            return corr_df, 0.0
+
+        abs_corr = np.abs(corr_df.to_numpy(copy=True))
+        np.fill_diagonal(abs_corr, 0.0)
+        positive_values = np.unique(abs_corr[abs_corr > 0.0])
+        for candidate in np.sort(positive_values):
+            keep_mask = (abs_corr >= candidate).any(axis=0)
+            if min_parameters <= int(keep_mask.sum()) <= max_parameters:
+                labels = corr_df.index[keep_mask]
+                return corr_df.loc[labels, labels], float(candidate)
+
+        if positive_values.size == 0:
+            return corr_df.iloc[:max_parameters, :max_parameters], 0.0
+
+        parameter_strength = np.max(abs_corr, axis=0)
+        top_indices = np.argsort(-parameter_strength, kind='stable')[:max_parameters]
+        top_indices.sort()
+        labels = corr_df.index[top_indices]
+        return corr_df.loc[labels, labels], 0.0
+
+    @staticmethod
+    def _correlation_filtered_title(base_title: str, threshold: float) -> str:
+        """Return a plot title with a correlation cutoff."""
+        if threshold <= 0:
+            return base_title
+        return f'{base_title} with |correlation| ≥ {threshold:.2f}'
+
+    @staticmethod
+    def _posterior_pair_title(multiplier: float | None) -> str:
+        """
+        Return the posterior pair title with its displayed bound scale.
+        """
+        if multiplier is None:
+            return 'Posterior pair plot'
+        return f'Posterior pair plot in ±{multiplier:g} × uncertainty region'  # noqa: RUF001
+
+    @staticmethod
+    def _posterior_pair_uncertainty_multiplier(
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> float | None:
+        """
+        Return a shared uncertainty-bound multiplier for a pair plot.
+        """
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        multiplier: float | None = None
+
+        for parameter_name in parameter_names:
+            parameter = parameters_by_name.get(parameter_name)
+            if parameter is None:
+                return None
+
+            current = getattr(parameter, 'fit_bounds_uncertainty_multiplier', None)
+            if current is None or not np.isfinite(float(current)):
+                return None
+
+            current_value = float(current)
+            if multiplier is None:
+                multiplier = current_value
+                continue
+            if not np.isclose(multiplier, current_value):
+                return None
+
+        return multiplier
+
+    def plot_posterior_pairs(
+        self,
+        parameters: list[object] | None = None,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
+        *,
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+    ) -> None:
+        """
+        Plot posterior pair relationships for sampled parameters.
+
+        Parameters
+        ----------
+        parameters : list[object] | None, default=None
+            Optional subset of sampled parameters to include. When
+            provided, ``threshold`` and ``max_parameters`` are ignored.
+        style : PosteriorPairPlotStyleEnum | str, default='auto'
+            Pair-plot rendering mode. Defaults to ``'auto'``. ``'auto'``
+            keeps contours for compact plots and disables them for wide
+            grids. ``'fast'`` always skips contours. ``'full'`` always
+            renders contours.
+        threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
+            Minimum absolute off-diagonal correlation required for a
+            parameter to be auto-selected. When omitted, an automatic
+            cutoff keeps the plot at or below ``max_parameters``
+            parameters when possible. Set to ``0`` to show all sampled
+            parameters.
+        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
+            Maximum number of parameters to auto-select when
+            ``parameters`` is omitted and ``threshold`` is ``None``.
+            Must be at least ``2``.
+        """
+        plot = self._build_posterior_pairs_plot(
+            parameters=parameters,
+            style=style,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
+        if plot is None:
+            return
+        self._show_plot_figure(plot)
+
+    def plot_param_distribution(
+        self,
+        param: object,
+    ) -> None:
+        """
+        Plot the posterior distribution for one sampled parameter.
+
+        Parameters
+        ----------
+        param : object
+            Parameter descriptor or string identifier selecting the
+            posterior to plot. Strings may be unique names or
+            user-facing labels.
+        """
+        plot = self._build_param_distribution_plot(param)
+        if plot is None:
+            return
+        self._show_plot_figure(plot)
+
+    def plot_posterior_predictive(
+        self,
+        expt_name: str,
+        style: str = 'band',
+        x_min: float | None = None,
+        x_max: float | None = None,
+        *,
+        show_residual: bool | None = None,
+        x: object | None = None,
+    ) -> None:
+        """
+        Plot posterior predictive checks for supported experiments.
+
+        Parameters
+        ----------
+        expt_name : str
+            Experiment name to plot.
+        style : str, default='band'
+            ``'band'`` shows the 95% credible interval, ``'draws'``
+            shows sampled predictive curves, and ``'band+draws'`` shows
+            both together. Single-crystal plots currently render only
+            the interval-based reflection check.
+        x_min : float | None, default=None
+            Lower bound for the x-axis range.
+        x_max : float | None, default=None
+            Upper bound for the x-axis range.
+        show_residual : bool | None, default=None
+            Whether to include the residual row in supported powder
+            composite plots.
+        x : object | None, default=None
+            Optional explicit x-axis data to override stored values.
+
+        Raises
+        ------
+        ValueError
+            If ``style`` is not one of ``'band'``, ``'draws'``, or
+            ``'band+draws'``.
+        """
+        if style not in {'band', 'draws', 'band+draws'}:
+            msg = "style must be 'band', 'draws', or 'band+draws'."
+            raise ValueError(msg)
+
+        if self._project is None:
+            log.warning('Plotter is not attached to a project.')
+            return
+
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior predictive plots currently require the Plotly backend.')
+            return
+
+        self._update_project_categories(expt_name)
+        experiment = self._project.experiments[expt_name]
+        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, x)
+
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_residual=show_residual,
+            x=x,
+        )
+
+        if sample_form == SampleFormEnum.SINGLE_CRYSTAL:
+            self._plot_single_crystal_posterior_predictive(
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=x_axis,
+                scattering_type=scattering_type,
+                plot_options=plot_options,
+                style=style,
+            )
+            return
+
+        if sample_form != SampleFormEnum.POWDER:
+            log.warning('Posterior predictive plots currently support powder experiments only.')
+            return
+
+        if scattering_type == ScatteringTypeEnum.BRAGG:
+            self._plot_posterior_predictive_data(
+                experiment=experiment,
+                expt_name=expt_name,
+                plot_options=plot_options,
+                x_axis=x_axis,
+                style=style,
+            )
+            return
+
+        self._plot_non_bragg_posterior_predictive(
+            experiment=experiment,
+            expt_name=expt_name,
+            plot_options=plot_options,
+            x_axis=x_axis,
+            sample_form=sample_form,
+            scattering_type=scattering_type,
+            style=style,
+        )
+
+    def _plot_single_crystal_posterior_predictive(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+        scattering_type: object,
+        plot_options: _MeasVsCalcPlotOptions,
+        style: str,
+    ) -> None:
+        """Render a single-crystal posterior predictive scatter plot."""
+        if scattering_type != ScatteringTypeEnum.BRAGG:
+            log.warning(
+                'Single-crystal posterior predictive plots currently support Bragg data only.'
+            )
+            return
+        if x_axis not in {XAxisType.INTENSITY_CALC, 'intensity_calc'}:
+            log.warning(
+                'Single-crystal posterior predictive plots currently support '
+                "x='intensity_calc' only."
+            )
+            return
+        if plot_options.show_residual:
+            log.warning(
+                'Posterior predictive residuals are unavailable for '
+                'single-crystal plots; ignoring show_residual=True.'
+            )
+        if style != 'band':
+            log.warning(
+                'Single-crystal posterior predictive plots currently support '
+                'style="band" only; rendering the 95% interval.'
+            )
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=False,
+        )
+        if summary is None:
+            return
+
+        pattern = intensity_category_for(experiment)
+        y_meas_raw = getattr(pattern, 'intensity_meas', None)
+        if y_meas_raw is None:
+            log.warning(f'No measured data available for experiment {expt_name}.')
+            return
+        y_meas = np.asarray(y_meas_raw, dtype=float)
+        if y_meas.shape != np.asarray(summary.map_prediction).shape:
+            log.warning(
+                'Single-crystal posterior predictive values do not match the '
+                'measured reflection array shape.'
+            )
+            return
+
+        y_meas_su_raw = getattr(pattern, 'intensity_meas_su', None)
+        if y_meas_su_raw is None:
+            log.warning(f'No measurement uncertainties for experiment {expt_name}')
+            y_meas_su = np.zeros_like(y_meas)
+        else:
+            y_meas_su = np.asarray(y_meas_su_raw, dtype=float)
+            if y_meas_su.shape != y_meas.shape:
+                log.warning(
+                    'Single-crystal posterior predictive uncertainties do not '
+                    'match the measured reflection array shape.'
+                )
+                return
+
+        self._plot_single_crystal_posterior_predictive_summary(
+            expt_name=expt_name,
+            summary=summary,
+            y_meas=y_meas,
+            y_meas_su=y_meas_su,
+            axes_labels=self._get_axes_labels(
+                SampleFormEnum.SINGLE_CRYSTAL,
+                ScatteringTypeEnum.BRAGG,
+                XAxisType.INTENSITY_CALC,
+            ),
+        )
+
+    def _plot_non_bragg_posterior_predictive(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        plot_options: _MeasVsCalcPlotOptions,
+        x_axis: object,
+        sample_form: object,
+        scattering_type: object,
+        style: str,
+    ) -> None:
+        """Render non-Bragg posterior predictive summaries."""
+        pattern = intensity_category_for(experiment)
+        y_meas = getattr(pattern, 'intensity_meas', None)
+        if y_meas is None:
+            log.warning(f'No measured data available for experiment {expt_name}.')
+            return
+
+        ctx = self._prepare_powder_context(
+            pattern,
+            expt_name,
+            experiment.type,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
+        )
+        if ctx is None:
+            return
+
+        if plot_options.show_residual:
+            log.warning(
+                'Posterior predictive residuals are unavailable for non-Bragg '
+                'summary plots; ignoring show_residual=True.'
+            )
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=style in {'draws', 'band+draws'},
+        )
+        if summary is None:
+            return
+
+        filtered_summary = self._filtered_posterior_predictive_summary(
+            summary=summary,
+            x_min=ctx['x_min'],
+            x_max=ctx['x_max'],
+            include_draws=style in {'draws', 'band+draws'},
+        )
+        if filtered_summary is None:
+            log.warning(
+                f'No posterior predictive data available within the requested x-range '
+                f'for experiment {expt_name}.'
+            )
+            return
+
+        filtered_y_meas = self._filtered_y_array(
+            y_meas,
+            ctx['x_array'],
+            ctx['x_min'],
+            ctx['x_max'],
+        )
+
+        axes_labels = self._get_axes_labels(sample_form, scattering_type, x_axis)
+        self._plot_posterior_predictive_summary(
+            expt_name=expt_name,
+            summary=filtered_summary,
+            y_meas=filtered_y_meas,
+            axes_labels=axes_labels,
+            show_band=style in {'band', 'band+draws'},
+            show_draws=style in {'draws', 'band+draws'},
+        )
+
     @staticmethod
     def _filter_correlation_dataframe(
         corr_df: pd.DataFrame,
@@ -697,6 +1306,7 @@ def _trim_correlation_display_dataframe(
         corr_df: pd.DataFrame,
         *,
         preserve_all_rows: bool,
+        show_diagonal: bool,
     ) -> tuple[pd.DataFrame, list[int], list[int]]:
         """
         Trim empty outer rows/columns from the lower-triangle view.
@@ -711,6 +1321,8 @@ def _trim_correlation_display_dataframe(
         preserve_all_rows : bool
             Whether to keep the full row list so row labels continue to
             identify all numeric column headers in tabular output.
+        show_diagonal : bool
+            Whether blank diagonal cells should remain visible.
 
         Returns
         -------
@@ -722,7 +1334,7 @@ def _trim_correlation_display_dataframe(
         row_numbers = list(range(1, num_rows + 1))
         col_numbers = list(range(1, num_cols + 1))
 
-        if min(num_rows, num_cols) <= 1:
+        if show_diagonal or min(num_rows, num_cols) <= 1:
             return corr_df, row_numbers, col_numbers
 
         if preserve_all_rows:
@@ -739,16 +1351,19 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
             Square correlation matrix labeled by parameter unique names,
             or ``None`` if unavailable.
         """
-        result = self._get_fit_result_for_correlation()
-        if result is None:
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
             return None
-        raw_result, var_names, fit_results = result
 
-        covar = getattr(raw_result, 'covar', None)
-        if covar is not None:
-            return self._correlation_from_covariance(covar, var_names, fit_results.parameters)
+        corr_df = self._posterior_correlation_dataframe(fit_results)
+        if corr_df is not None:
+            return corr_df
 
-        corr_df = self._get_param_correlation_dataframe_from_engine_params(
+        raw_result = self._raw_fit_result_for_correlation(fit_results)
+        if raw_result is None:
+            return None
+
+        corr_df = self._correlation_dataframe_from_engine_result(
             raw_result=raw_result,
             parameters=fit_results.parameters,
         )
@@ -757,21 +1372,2587 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
 
         log.warning(
             'Correlation matrix is unavailable for this fit. '
-            'Use the lmfit minimizer and ensure covariance estimation succeeds.'
+            'Use a minimizer that returns covariance information or posterior samples.'
         )
         return None
 
-    def _get_fit_result_for_correlation(
+    def _posterior_correlation_dataframe(
         self,
-    ) -> tuple[object, list[str], object] | None:
-        """
-        Validate and return the raw fit result for correlation.
+        fit_results: object,
+    ) -> pd.DataFrame | None:
+        """Return posterior-sample correlations when available."""
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            return None
+        return self._correlation_from_posterior_samples(posterior_samples)
 
-        Returns
+    @staticmethod
+    def _raw_fit_result_for_correlation(fit_results: object) -> object | None:
+        """Return raw fit results for correlation fallback."""
+        raw_result = getattr(fit_results, 'result', None)
+        if raw_result is None:
+            raw_result = getattr(fit_results, 'engine_result', None)
+        if raw_result is None:
+            log.warning('No raw fit result available. Correlation matrix cannot be plotted.')
+            return None
+
+        var_names = getattr(raw_result, 'var_names', None)
+        if not var_names:
+            log.warning('Fit result does not expose variable names for a correlation matrix.')
+            return None
+        return raw_result
+
+    def _correlation_dataframe_from_engine_result(
+        self,
+        *,
+        raw_result: object,
+        parameters: list[object],
+    ) -> pd.DataFrame | None:
+        """Return correlations derived from engine result fields."""
+        covar = getattr(raw_result, 'covar', None)
+        if covar is not None:
+            return self._correlation_from_covariance(
+                covar,
+                getattr(raw_result, 'var_names', None),
+                parameters,
+            )
+        return self._get_param_correlation_dataframe_from_engine_params(
+            raw_result=raw_result,
+            parameters=parameters,
+        )
+
+    def _build_posterior_pairs_plot(
+        self,
+        *,
+        parameters: list[object] | None,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int | None = None,
+    ) -> object | None:
+        """
+        Build a Plotly posterior pair plot.
+
+        Parameters
+        ----------
+        parameters : list[object] | None
+            Optional subset of sampled parameters to include.
+        style : PosteriorPairPlotStyleEnum | str, default='auto'
+            Posterior pair-plot rendering mode. Defaults to ``'auto'``.
+        threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
+            Absolute-correlation cutoff for auto-selected parameters.
+        max_parameters : int | None, default=None
+            Maximum number of auto-selected parameters. ``None`` keeps
+            the full posterior parameter set.
+
+        Returns
+        -------
+        object | None
+            Plotly figure, or ``None`` when posterior plotting is
+            unavailable.
+        """
+        context = self._posterior_pairs_context(
+            parameters,
+            style=style,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
+        if context is None:
+            return None
+
+        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
+        subplot_title_annotations: list[dict[str, object]] = []
+        subplot_border_shapes: list[dict[str, object]] = []
+        legend_state = _PosteriorPairsLegendState()
+        fig = make_subplots(
+            rows=context.n_parameters,
+            cols=context.n_parameters,
+            shared_xaxes='columns',
+            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+        )
+
+        for row_index in range(context.n_parameters):
+            for col_index in range(context.n_parameters):
+                self._populate_posterior_pair_panel(
+                    fig=fig,
+                    context=context,
+                    row_index=row_index,
+                    col_index=col_index,
+                    legend_state=legend_state,
+                    subplot_title_annotations=subplot_title_annotations,
+                    subplot_border_shapes=subplot_border_shapes,
+                )
+
+        self._finalize_posterior_pairs_figure(
+            fig=fig,
+            context=context,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
+        )
+        return fig
+
+    def _posterior_pairs_context(
+        self,
+        parameters: list[object] | None,
+        *,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int | None = None,
+    ) -> _PosteriorPairsContext | None:
+        """Return the resolved inputs for a posterior pair plot."""
+        posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
+        if posterior_samples is None or fit_results is None:
+            return None
+
+        plot_style = self._validated_posterior_pair_plot_style(style)
+
+        parameter_names, resolved_threshold = self._resolved_posterior_pair_parameter_names(
+            fit_results=fit_results,
+            parameters=parameters,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
+        if parameter_names is None:
+            return None
+        if len(parameter_names) < MIN_POSTERIOR_PARAMETER_COUNT:
+            log.warning('Posterior pair plots require at least two sampled parameters.')
+            return None
+
+        selected_samples = self._selected_posterior_samples(posterior_samples, parameter_names)
+        if selected_samples is None:
+            return None
+
+        n_parameters = len(parameter_names)
+        show_contours = self._posterior_pair_show_contours(
+            n_parameters=n_parameters,
+            style=plot_style,
+        )
+        if plot_style is PosteriorPairPlotStyleEnum.AUTO and not show_contours:
+            log.warning(
+                'Posterior pair plot auto mode disabled contours for '
+                f'{n_parameters} parameters. Use style="full" to force '
+                'contours.'
+            )
+
+        density_samples = self._thin_posterior_samples(
+            selected_samples,
+            max_points=self._posterior_pair_density_max_points(n_parameters),
+        )
+        scatter_samples = self._thin_posterior_samples(
+            selected_samples,
+            max_points=POSTERIOR_PAIR_SCATTER_MAX_POINTS,
+        )
+        uncertainty_multiplier = self._posterior_pair_uncertainty_multiplier(
+            fit_results,
+            parameter_names,
+        )
+
+        return _PosteriorPairsContext(
+            fit_results=fit_results,
+            parameter_names=parameter_names,
+            labels=self._posterior_plot_labels(fit_results, parameter_names),
+            annotation_labels=self._square_matrix_axis_title_labels(parameter_names),
+            title=self._correlation_filtered_title(
+                self._posterior_pair_title(uncertainty_multiplier),
+                resolved_threshold,
+            ),
+            density_samples=density_samples,
+            scatter_samples=scatter_samples,
+            show_contours=show_contours,
+            contour_grid_size=self._posterior_pair_contour_grid_size(n_parameters),
+            axis_frame_color=self._plot_axis_frame_color(),
+            axis_ranges=self._posterior_pair_axis_ranges(
+                fit_results=fit_results,
+                parameter_names=parameter_names,
+                density_samples=selected_samples,
+            ),
+        )
+
+    def _resolved_posterior_pair_parameter_names(
+        self,
+        *,
+        fit_results: object,
+        parameters: list[object] | None,
+        threshold: float | None,
+        max_parameters: int | None,
+    ) -> tuple[list[str] | None, float]:
+        """Return pair-plot names and the effective cutoff."""
+        parameter_names = self._resolve_posterior_parameter_names(
+            fit_results=fit_results,
+            parameters=parameters,
+        )
+        if parameter_names is None:
+            return None, 0.0
+        if parameters is not None:
+            return parameter_names, 0.0
+
+        corr_df = self._posterior_correlation_dataframe(fit_results)
+        if corr_df is None:
+            return parameter_names, 0.0
+
+        filtered_corr_df, resolved_threshold = self._resolve_correlation_filter(
+            corr_df.loc[parameter_names, parameter_names],
+            threshold=threshold,
+            max_parameters=max_parameters,
+            min_parameters=MIN_POSTERIOR_PARAMETER_COUNT,
+        )
+        if filtered_corr_df is None:
+            return None, 0.0
+        return list(filtered_corr_df.index), resolved_threshold
+
+    def _posterior_pair_axis_ranges(
+        self,
+        *,
+        fit_results: object,
+        parameter_names: list[str],
+        density_samples: np.ndarray,
+    ) -> list[tuple[float, float]]:
+        """Return per-parameter axis ranges for a pair plot."""
+        axis_ranges: list[tuple[float, float]] = []
+        for index, parameter_name in enumerate(parameter_names):
+            lower_bound, upper_bound = self._posterior_parameter_bounds(
+                fit_results=fit_results,
+                parameter_name=parameter_name,
+            )
+            axis_ranges.append(
+                self._posterior_axis_bounds(
+                    density_samples[:, index],
+                    lower_bound=lower_bound,
+                    upper_bound=upper_bound,
+                )
+            )
+        return axis_ranges
+
+    def _populate_posterior_pair_panel(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row_index: int,
+        col_index: int,
+        legend_state: _PosteriorPairsLegendState,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Populate one panel in the posterior pair plot grid."""
+        row = row_index + 1
+        col = col_index + 1
+        if col_index > row_index:
+            self._hide_posterior_pair_panel(fig=fig, row=row, col=col)
+            return
+
+        if row_index == col_index:
+            self._add_posterior_pair_diagonal(
+                fig=fig,
+                context=context,
+                row=row,
+                col=col,
+                parameter_index=col_index,
+                legend_state=legend_state,
+            )
+        else:
+            self._add_posterior_pair_off_diagonal(
+                fig=fig,
+                context=context,
+                row=row,
+                col=col,
+                row_index=row_index,
+                col_index=col_index,
+                legend_state=legend_state,
+            )
+
+        self._configure_posterior_pair_panel_axes(
+            fig=fig,
+            context=context,
+            row=row,
+            col=col,
+            row_index=row_index,
+            col_index=col_index,
+        )
+        self._collect_posterior_pair_panel_decorations(
+            fig=fig,
+            context=context,
+            row_index=row_index,
+            col_index=col_index,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
+        )
+
+    @staticmethod
+    def _hide_posterior_pair_panel(
+        *,
+        fig: object,
+        row: int,
+        col: int,
+    ) -> None:
+        """Hide an upper-triangle panel in the pair plot grid."""
+        fig.update_xaxes(visible=False, row=row, col=col)
+        fig.update_yaxes(visible=False, row=row, col=col)
+
+    def _add_posterior_pair_diagonal(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        parameter_index: int,
+        legend_state: _PosteriorPairsLegendState,
+    ) -> None:
+        """Add the diagonal marginal-density panel."""
+        go = __import__('plotly.graph_objects', fromlist=['Histogram'])
+        density_values = context.density_samples[:, parameter_index]
+        density_trace = self._posterior_density_trace(
+            fit_results=context.fit_results,
+            parameter_name=context.parameter_names[parameter_index],
+            values=density_values,
+            trace_name=context.labels[parameter_index],
+        )
+        if density_trace is None:
+            fig.add_trace(
+                go.Histogram(
+                    x=density_values,
+                    nbinsx=40,
+                    histnorm='probability density',
+                    marker={'color': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR},
+                    showlegend=False,
+                    hovertemplate=self._posterior_pair_density_hovertemplate(
+                        context.parameter_names[parameter_index]
+                    ),
+                ),
+                row=row,
+                col=col,
+            )
+            return
+
+        self._style_posterior_pair_marginal_density_trace(density_trace)
+        density_trace.hovertemplate = self._posterior_pair_density_hovertemplate(
+            context.parameter_names[parameter_index]
+        )
+        density_trace.name = 'Marginal density'
+        density_trace.legendgroup = 'posterior-marginal-density'
+        density_trace.showlegend = legend_state.show_density
+        fig.add_trace(density_trace, row=row, col=col)
+        legend_state.show_density = False
+        y_axis_range = self._posterior_density_axis_range(np.asarray(density_trace.y))
+        if y_axis_range is not None:
+            fig.update_yaxes(range=list(y_axis_range), row=row, col=col)
+
+    def _add_posterior_pair_off_diagonal(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        row_index: int,
+        col_index: int,
+        legend_state: _PosteriorPairsLegendState,
+    ) -> None:
+        """Add one off-diagonal pair-relationship panel."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+        x_density_values = context.density_samples[:, col_index]
+        y_density_values = context.density_samples[:, row_index]
+        x_scatter_values = context.scatter_samples[:, col_index]
+        y_scatter_values = context.scatter_samples[:, row_index]
+        contour_traces = None
+        if context.show_contours:
+            contour_traces = self._posterior_contour_traces(
+                fit_results=context.fit_results,
+                x_parameter_name=context.parameter_names[col_index],
+                y_parameter_name=context.parameter_names[row_index],
+                x_values=x_density_values,
+                y_values=y_density_values,
+                grid_size=context.contour_grid_size,
+            )
+        sample_hovertemplate = self._posterior_pair_scatter_hovertemplate(
+            x_parameter_name=context.parameter_names[col_index],
+            y_parameter_name=context.parameter_names[row_index],
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=x_scatter_values,
+                y=y_scatter_values,
+                mode='markers',
+                marker={
+                    'color': POSTERIOR_SCATTER_MARKER_COLOR,
+                    'size': POSTERIOR_PAIR_SAMPLE_MARKER_SIZE,
+                },
+                name='Posterior samples',
+                legendgroup='posterior-samples',
+                showlegend=legend_state.show_scatter,
+                hoverinfo='skip',
+                zorder=0,
+            ),
+            row=row,
+            col=col,
+        )
+        legend_state.show_scatter = False
+        if contour_traces is not None:
+            contour_traces[0].name = 'Posterior contours'
+            contour_traces[0].legendgroup = 'posterior-contours'
+            contour_traces[0].showlegend = legend_state.show_contour
+            contour_traces[1].legendgroup = 'posterior-contours'
+            contour_traces[1].showlegend = False
+            fig.add_trace(contour_traces[0], row=row, col=col)
+            fig.add_trace(contour_traces[1], row=row, col=col)
+            legend_state.show_contour = False
+        fig.add_trace(
+            go.Scatter(
+                x=x_scatter_values,
+                y=y_scatter_values,
+                mode='markers',
+                marker={
+                    'color': 'rgba(0, 0, 0, 0)',
+                    'size': POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE,
+                },
+                showlegend=False,
+                hovertemplate=sample_hovertemplate,
+                zorder=3,
+            ),
+            row=row,
+            col=col,
+        )
+
+    @staticmethod
+    def _configure_posterior_pair_panel_axes(
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        row_index: int,
+        col_index: int,
+    ) -> None:
+        """Apply axis styling and labels to one pair-plot panel."""
+        is_diagonal = row_index == col_index
+        fig.update_xaxes(
+            showline=True,
+            mirror=True,
+            range=list(context.axis_ranges[col_index]),
+            zeroline=False,
+            showgrid=False,
+            layer='above traces',
+            linecolor=context.axis_frame_color,
+            linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            row=row,
+            col=col,
+        )
+        fig.update_yaxes(
+            showline=True,
+            mirror=True,
+            zeroline=False,
+            showgrid=False,
+            layer='above traces',
+            linecolor=context.axis_frame_color,
+            linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            row=row,
+            col=col,
+        )
+        if not is_diagonal:
+            fig.update_yaxes(range=list(context.axis_ranges[row_index]), row=row, col=col)
+        if is_diagonal:
+            fig.update_yaxes(
+                title_text=None,
+                row=row,
+                col=col,
+            )
+        fig.update_xaxes(title_text=None, row=row, col=col)
+
+    @staticmethod
+    def _collect_posterior_pair_panel_decorations(
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Collect annotations and frame shapes for one pair panel."""
+        row = row_index + 1
+        col = col_index + 1
+        subplot = fig.get_subplot(row, col)
+        x_mid = 0.5 * (subplot.xaxis.domain[0] + subplot.xaxis.domain[1])
+        y_mid = 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1])
+        if col_index == 0:
+            subplot_title_annotations.append({
+                'x': subplot.xaxis.domain[0],
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]),
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': context.annotation_labels[row_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        if row_index == context.n_parameters - 1:
+            subplot_title_annotations.append({
+                'x': x_mid,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': subplot.yaxis.domain[0],
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': context.annotation_labels[col_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+        subplot_border_shapes.extend([
+            {
+                'type': 'line',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': x_mid,
+                'x1': x_mid,
+                'y0': subplot.yaxis.domain[0],
+                'y1': subplot.yaxis.domain[1],
+                'line': {
+                    'color': POSTERIOR_PAIR_GUIDE_LINE_COLOR,
+                    'width': 1,
+                },
+                'layer': 'above',
+            },
+            {
+                'type': 'line',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': subplot.xaxis.domain[0],
+                'x1': subplot.xaxis.domain[1],
+                'y0': y_mid,
+                'y1': y_mid,
+                'line': {
+                    'color': POSTERIOR_PAIR_GUIDE_LINE_COLOR,
+                    'width': 1,
+                },
+                'layer': 'above',
+            },
+            {
+                'type': 'rect',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': subplot.xaxis.domain[0],
+                'x1': subplot.xaxis.domain[1],
+                'y0': subplot.yaxis.domain[0],
+                'y1': subplot.yaxis.domain[1],
+                'line': {
+                    'color': context.axis_frame_color,
+                    'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+                },
+                'fillcolor': 'rgba(0, 0, 0, 0)',
+                'layer': 'above',
+            },
+        ])
+
+    @staticmethod
+    def _square_matrix_title_annotation(
+        title: str,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
+        """Return a top-left title annotation for matrix plots."""
+        return {
+            'x': 0.0,
+            'xref': 'paper',
+            'xanchor': 'left',
+            'xshift': -Plotter._square_matrix_title_left_shift(annotation_labels),
+            'y': 1.0,
+            'yref': 'paper',
+            'yanchor': 'bottom',
+            'yshift': SQUARE_MATRIX_TITLE_YSHIFT_PIXELS,
+            'text': title,
+            'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+            'showarrow': False,
+        }
+
+    @staticmethod
+    def _posterior_pair_title_annotation(
+        title: str,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
+        """Return the outer title annotation for the pair plot."""
+        return Plotter._square_matrix_title_annotation(
+            title,
+            annotation_labels,
+        )
+
+    @staticmethod
+    def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int:
+        """Return the title shift relative to the shared left margin."""
+        extra_margin = Plotter._square_matrix_extra_axis_title_margin(annotation_labels)
+        return max(
+            0,
+            SQUARE_MATRIX_LEFT_MARGIN_PIXELS
+            + extra_margin
+            - SQUARE_MATRIX_TITLE_LEFT_PADDING_PIXELS,
+        )
+
+    @staticmethod
+    def _square_matrix_gap_data_width(n_parameters: int) -> float:
+        """Return the gap width matching pair-plot spacing."""
+        if n_parameters <= 1:
+            return 0.0
+
+        denominator = 1.0 - PAIR_PLOT_SUBPLOT_SPACING * (n_parameters - 1)
+        if denominator <= 0:
+            return 0.0
+        return PAIR_PLOT_SUBPLOT_SPACING * n_parameters / denominator
+
+    @classmethod
+    def _square_matrix_plot_extent(cls, n_parameters: int) -> float:
+        """Return the inner plot extent for one square matrix plot."""
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        return float(n_parameters + max(0, n_parameters - 1) * gap_width)
+
+    @classmethod
+    def _square_matrix_target_plot_size_pixels(cls, n_parameters: int) -> float:
+        """Return the target inner size for one square matrix plot."""
+        cell_size = cls._posterior_pair_cell_size_pixels(
+            n_parameters,
+            available_width_pixels=PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS,
+        )
+        return cell_size * cls._square_matrix_plot_extent(n_parameters)
+
+    @classmethod
+    def _square_matrix_layout_meta(
+        cls,
+        *,
+        n_parameters: int,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
+        """Return wrapper metadata for square matrix plots."""
+        margins = cls._square_matrix_layout_margin(annotation_labels)
+        plot_size = cls._square_matrix_target_plot_size_pixels(n_parameters)
+        aspect_width = round(plot_size + int(margins['l']) + int(margins['r']))
+        aspect_height = round(plot_size + int(margins['t']) + int(margins['b']))
+        return {
+            SQUARE_MATRIX_FIXED_ASPECT_META_KEY: {
+                'aspect_ratio': f'{aspect_width} / {aspect_height}',
+            }
+        }
+
+    def _finalize_posterior_pairs_figure(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Apply final layout settings to the posterior pair plot."""
+        fig.update_layout(
+            autosize=True,
+            margin=self._square_matrix_layout_margin(context.annotation_labels),
+            bargap=0.05,
+            annotations=[
+                self._posterior_pair_title_annotation(
+                    context.title,
+                    context.annotation_labels,
+                ),
+                *subplot_title_annotations,
+            ],
+            shapes=subplot_border_shapes,
+            meta=self._square_matrix_layout_meta(
+                n_parameters=context.n_parameters,
+                annotation_labels=context.annotation_labels,
+            ),
+            legend={
+                'bgcolor': 'rgba(0, 0, 0, 0)',
+                'xanchor': 'right',
+                'x': 0.995,
+                'yanchor': 'top',
+                'y': 0.995,
+                'groupclick': 'togglegroup',
+            },
+        )
+
+    @staticmethod
+    def _square_matrix_layout_margin(annotation_labels: list[str]) -> dict[str, int | bool]:
+        """Return outer margins sized for multiline matrix labels."""
+        extra_margin = Plotter._square_matrix_extra_axis_title_margin(annotation_labels)
+        return {
+            'autoexpand': False,
+            'l': SQUARE_MATRIX_LEFT_MARGIN_PIXELS + extra_margin,
+            'r': SQUARE_MATRIX_RIGHT_MARGIN_PIXELS,
+            't': SQUARE_MATRIX_TOP_MARGIN_PIXELS,
+            'b': SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS + extra_margin,
+        }
+
+    @staticmethod
+    def _square_matrix_extra_axis_title_margin(annotation_labels: list[str]) -> int:
+        """Return extra margin needed for multiline axis labels."""
+        if not annotation_labels:
+            return 0
+
+        max_line_count = max(
+            Plotter._square_matrix_axis_title_line_count(label) for label in annotation_labels
+        )
+        return max(0, max_line_count - 1) * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
+
+    @staticmethod
+    def _square_matrix_axis_title_line_count(label: str) -> int:
+        """Return the number of display lines in one axis title."""
+        if not label:
+            return 1
+        return label.count('<br>') + 1
+
+    @staticmethod
+    def _posterior_pair_cell_size_pixels(
+        n_parameters: int,
+        *,
+        available_width_pixels: float,
+    ) -> int:
+        """Return an estimated square cell size for a pair plot."""
+        if n_parameters < 1:
+            return PAIR_PLOT_CELL_SIZE_PIXELS
+
+        plot_width = max(
+            PAIR_PLOT_MIN_CELL_SIZE_PIXELS,
+            available_width_pixels - PAIR_PLOT_MARGIN_PIXELS,
+        )
+        cell_size = plot_width / n_parameters
+        return round(
+            min(
+                PAIR_PLOT_CELL_SIZE_PIXELS,
+                max(PAIR_PLOT_MIN_CELL_SIZE_PIXELS, cell_size),
+            )
+        )
+
+    @classmethod
+    def _posterior_pair_figure_height_pixels(cls, n_parameters: int) -> int:
+        """
+        Return the initial figure height for a responsive pair plot.
+        """
+        cell_size = cls._posterior_pair_cell_size_pixels(
+            n_parameters,
+            available_width_pixels=PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS,
+        )
+        return max(
+            PAIR_PLOT_MIN_SIZE_PIXELS,
+            cell_size * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
+        )
+
+    @staticmethod
+    def _posterior_pair_contour_panel_count(n_parameters: int) -> int:
+        """Return the number of lower-triangle contour panels."""
+        if n_parameters < MIN_POSTERIOR_PARAMETER_COUNT:
+            return 1
+        return n_parameters * (n_parameters - 1) // 2
+
+    @classmethod
+    def _posterior_pair_density_max_points(cls, n_parameters: int) -> int:
+        """Return a KDE sample cap for interactive pair plots."""
+        panel_count = cls._posterior_pair_contour_panel_count(n_parameters)
+        estimated_limit = round(
+            POSTERIOR_PAIR_TARGET_DENSITY_SAMPLE_BUDGET / panel_count,
+        )
+        return max(
+            POSTERIOR_PAIR_MIN_DENSITY_SAMPLES,
+            min(POSTERIOR_PAIR_MAX_DENSITY_SAMPLES, estimated_limit),
+        )
+
+    @classmethod
+    def _posterior_pair_contour_grid_size(cls, n_parameters: int) -> int:
+        """Return contour grid size for current pair-plot width."""
+        panel_count = cls._posterior_pair_contour_panel_count(n_parameters)
+        estimated_grid_size = round(
+            np.sqrt(POSTERIOR_PAIR_TARGET_CONTOUR_GRID_POINT_BUDGET / panel_count),
+        )
+        return max(
+            POSTERIOR_PAIR_MIN_CONTOUR_GRID_SIZE,
+            min(POSTERIOR_PAIR_MAX_CONTOUR_GRID_SIZE, estimated_grid_size),
+        )
+
+    @staticmethod
+    def _validated_posterior_pair_plot_style(
+        style: PosteriorPairPlotStyleEnum | str,
+    ) -> PosteriorPairPlotStyleEnum:
+        """Return a validated posterior pair-plot rendering mode."""
+        try:
+            return PosteriorPairPlotStyleEnum(style)
+        except ValueError as exc:
+            supported_styles = ', '.join(item.value for item in PosteriorPairPlotStyleEnum)
+            msg = f'style must be one of {supported_styles} for posterior pair plots.'
+            raise ValueError(msg) from exc
+
+    @staticmethod
+    def _posterior_pair_show_contours(
+        *,
+        n_parameters: int,
+        style: PosteriorPairPlotStyleEnum,
+    ) -> bool:
+        """Return whether contours should be rendered."""
+        if style is PosteriorPairPlotStyleEnum.FULL:
+            return True
+        if style is PosteriorPairPlotStyleEnum.FAST:
+            return False
+        return n_parameters <= POSTERIOR_PAIR_AUTO_MAX_CONTOUR_PARAMETERS
+
+    def _plot_axis_frame_color(self) -> str:
+        """
+        Return the shared axis-frame color for Plotly-backed plots.
+        """
+        axis_frame_color = getattr(self._backend, '_axis_frame_color', None)
+        if callable(axis_frame_color):
+            return axis_frame_color()
+        return PlotlyPlotter._axis_frame_color()
+
+    def _plot_legend_background_color(self) -> str:
+        """Return the shared legend background for Plotly plots."""
+        legend_background_color = getattr(self._backend, '_legend_background_color', None)
+        if callable(legend_background_color):
+            return legend_background_color()
+        return PlotlyPlotter._legend_background_color()
+
+    def _posterior_contour_traces(
+        self,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+        grid_size: int,
+    ) -> tuple[object, object] | None:
+        """
+        Return filled and line contour traces for posterior pair plots.
+        """
+        go = __import__('plotly.graph_objects', fromlist=['Contour'])
+
+        bounds = self._posterior_pair_bounds(
+            fit_results=fit_results,
+            x_parameter_name=x_parameter_name,
+            y_parameter_name=y_parameter_name,
+            x_values=x_values,
+            y_values=y_values,
+        )
+        density_surface = self._posterior_pair_density_surface(
+            x_values=x_values,
+            y_values=y_values,
+            x_bounds=bounds[0],
+            y_bounds=bounds[1],
+            grid_size=grid_size,
+        )
+        if density_surface is None:
+            return None
+
+        x_grid, y_grid, density = density_surface
+        fill_colorscale, line_colorscale = self._posterior_pair_contour_colorscales(
+            x_values,
+            y_values,
+        )
+        contour_start = float(np.max(density) * 0.20)
+        contour_end = float(np.max(density) * 0.95)
+        contour_size = float(np.max(density) * 0.15)
+        fill_density = np.array(density, copy=True)
+        fill_density[fill_density < contour_start] = np.nan
+        fill_trace = go.Contour(
+            x=x_grid,
+            y=y_grid,
+            z=fill_density,
+            contours={
+                'coloring': 'fill',
+                'showlabels': False,
+                'showlines': False,
+                'start': contour_start,
+                'end': contour_end,
+                'size': contour_size,
+            },
+            colorscale=fill_colorscale,
+            zmin=contour_start,
+            zmax=contour_end,
+            connectgaps=False,
+            hoverinfo='skip',
+            showscale=False,
+            showlegend=False,
+            zorder=1,
+        )
+        line_trace = go.Contour(
+            x=x_grid,
+            y=y_grid,
+            z=density,
+            contours={
+                'coloring': 'lines',
+                'showlabels': False,
+                'start': contour_start,
+                'end': contour_end,
+                'size': contour_size,
+            },
+            colorscale=line_colorscale,
+            zmin=contour_start,
+            zmax=contour_end,
+            line={'width': 0.9},
+            hoverinfo='skip',
+            showscale=False,
+            showlegend=False,
+            zorder=2,
+        )
+        return fill_trace, line_trace
+
+    def _build_param_distribution_plot(
+        self,
+        param: object,
+    ) -> object | None:
+        """
+        Build a Plotly posterior distribution plot for one parameter.
+
+        Parameters
+        ----------
+        param : object
+            Parameter descriptor to plot.
+
+        Returns
+        -------
+        object | None
+            Plotly figure, or ``None`` when posterior plotting is
+            unavailable.
+        """
+        context = self._posterior_distribution_context(param)
+        if context is None:
+            return None
+
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram'])
+        fig, layout_factory = self._posterior_distribution_figure(
+            go=go,
+            title=context.title,
+            label=context.label,
+        )
+        histogram_bin_edges = self._posterior_distribution_histogram_bin_edges(context.values)
+        density_trace = self._posterior_density_trace(
+            fit_results=context.fit_results,
+            parameter_name=context.parameter_name,
+            values=context.values,
+            trace_name='Marginal density',
+        )
+        if density_trace is not None:
+            self._style_posterior_pair_marginal_density_trace(density_trace)
+            density_trace.hovertemplate = self._posterior_pair_density_hovertemplate(
+                context.parameter_name
+            )
+        x_axis_range = self._posterior_distribution_x_axis_range(
+            values=context.values,
+            density_trace=density_trace,
+            histogram_bin_edges=histogram_bin_edges,
+        )
+        y_axis_range = self._posterior_distribution_y_axis_range(
+            values=context.values,
+            density_trace=density_trace,
+            histogram_bin_edges=histogram_bin_edges,
+        )
+
+        self._add_posterior_distribution_interval_traces(
+            fig=fig,
+            summary=context.summary,
+            y_axis_range=y_axis_range,
+        )
+        self._add_posterior_distribution_histogram(
+            fig=fig,
+            go=go,
+            values=context.values,
+            histogram_bin_edges=histogram_bin_edges,
+        )
+        self._add_posterior_distribution_density_trace(fig=fig, density_trace=density_trace)
+        self._add_posterior_distribution_reference_traces(
+            fig=fig,
+            summary=context.summary,
+            values=context.values,
+            y_axis_range=y_axis_range,
+        )
+        self._apply_posterior_distribution_layout(
+            fig=fig,
+            layout_factory=layout_factory,
+            title=context.title,
+            label=context.label,
+            x_axis_range=x_axis_range,
+            y_axis_range=y_axis_range,
+        )
+        return fig
+
+    def _posterior_distribution_context(
+        self,
+        param: object,
+    ) -> _PosteriorDistributionContext | None:
+        """Return the context for a posterior distribution plot."""
+        posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
+        if posterior_samples is None or fit_results is None:
+            return None
+
+        parameter_names = self._resolve_posterior_parameter_names(
+            fit_results=fit_results,
+            parameters=[param],
+        )
+        if parameter_names is None:
+            return None
+
+        parameter_name = parameter_names[0]
+        samples = self._selected_posterior_samples(posterior_samples, [parameter_name])
+        if samples is None:
+            return None
+
+        label = self._posterior_plot_labels(fit_results, [parameter_name])[0]
+        return _PosteriorDistributionContext(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+            values=samples[:, 0],
+            label=label,
+            title=f'Posterior distribution: {parameter_name}',
+            summary=self._posterior_summary_by_name(fit_results).get(parameter_name),
+        )
+
+    def _posterior_distribution_figure(
+        self,
+        *,
+        go: object,
+        title: str,
+        label: str,
+    ) -> tuple[object, object | None]:
+        """Return the figure and optional backend layout factory."""
+        layout_factory = getattr(self._backend, '_get_layout', None)
+        if callable(layout_factory):
+            figure = go.Figure(layout=layout_factory(title, [label, 'Probability density']))
+            return figure, layout_factory
+        return go.Figure(), layout_factory
+
+    def _posterior_distribution_y_axis_range(
+        self,
+        *,
+        values: np.ndarray,
+        density_trace: object | None,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> tuple[float, float] | None:
+        """Return the y-axis range for a posterior distribution plot."""
+        density_sources = []
+        histogram_density = self._posterior_distribution_histogram_density(
+            values,
+            histogram_bin_edges,
+        )
+        if histogram_density is not None:
+            density_sources.append(histogram_density)
+        if density_trace is not None:
+            density_sources.append(np.asarray(density_trace.y, dtype=float))
+        if not density_sources:
+            return None
+        return self._posterior_density_axis_range(np.concatenate(density_sources))
+
+    @staticmethod
+    def _posterior_distribution_x_axis_range(
+        *,
+        values: np.ndarray,
+        density_trace: object | None,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> tuple[float, float] | None:
+        """Return the x-axis range for a posterior distribution plot."""
+        if density_trace is not None:
+            density_x = np.asarray(density_trace.x, dtype=float)
+            return float(density_x[0]), float(density_x[-1])
+
+        if histogram_bin_edges is not None:
+            return (
+                float(histogram_bin_edges[0]),
+                float(histogram_bin_edges[-1]),
+            )
+
+        finite_values = np.asarray(values, dtype=float)
+        finite_values = finite_values[np.isfinite(finite_values)]
+        if finite_values.size == 0:
+            return None
+        return float(np.min(finite_values)), float(np.max(finite_values))
+
+    @staticmethod
+    def _posterior_distribution_histogram_bin_edges(values: np.ndarray) -> np.ndarray | None:
+        """Return histogram bin edges used by the distribution plot."""
+        finite_values = np.asarray(values, dtype=float)
+        finite_values = finite_values[np.isfinite(finite_values)]
+        if finite_values.size == 0:
+            return None
+
+        bin_edges = np.histogram_bin_edges(finite_values, bins='auto')
+        if bin_edges.size >= MIN_POSTERIOR_SAMPLE_COUNT:
+            return np.asarray(bin_edges, dtype=float)
+        return None
+
+    @staticmethod
+    def _posterior_distribution_histogram_density(
+        values: np.ndarray,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> np.ndarray | None:
+        """Return densities matching the rendered histogram bins."""
+        if histogram_bin_edges is None:
+            return None
+
+        histogram_density, _ = np.histogram(
+            np.asarray(values, dtype=float),
+            bins=histogram_bin_edges,
+            density=True,
+        )
+        return np.asarray(histogram_density, dtype=float)
+
+    def _add_posterior_distribution_interval_traces(
+        self,
+        *,
+        fig: object,
+        summary: object | None,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Add credible-interval bands to the distribution plot."""
+        if summary is None or y_axis_range is None:
+            return
+
+        fig.add_trace(
+            self._posterior_interval_band_trace(
+                x0=summary.interval_95[0],
+                x1=summary.interval_95[1],
+                y_axis_range=y_axis_range,
+                trace_name='95% credible interval',
+                color=POSTERIOR_INTERVAL_95_FILL_COLOR,
+            )
+        )
+        fig.add_trace(
+            self._posterior_interval_band_trace(
+                x0=summary.interval_68[0],
+                x1=summary.interval_68[1],
+                y_axis_range=y_axis_range,
+                trace_name='68% credible interval',
+                color=POSTERIOR_INTERVAL_68_FILL_COLOR,
+            )
+        )
+
+    @staticmethod
+    def _add_posterior_distribution_histogram(
+        *,
+        fig: object,
+        go: object,
+        values: np.ndarray,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> None:
+        """Add the histogram trace for a posterior distribution plot."""
+        histogram_kwargs: dict[str, object] = {}
+        if (
+            histogram_bin_edges is not None
+            and histogram_bin_edges.size >= MIN_POSTERIOR_SAMPLE_COUNT
+        ):
+            histogram_kwargs['xbins'] = {
+                'start': float(histogram_bin_edges[0]),
+                'end': float(histogram_bin_edges[-1]),
+                'size': float(histogram_bin_edges[1] - histogram_bin_edges[0]),
+            }
+
+        fig.add_trace(
+            go.Histogram(
+                x=values,
+                histnorm='probability density',
+                marker={
+                    'color': POSTERIOR_HISTOGRAM_FILL_COLOR,
+                    'line': {'color': POSTERIOR_HISTOGRAM_LINE_COLOR, 'width': 1},
+                },
+                opacity=0.82,
+                name='Posterior histogram',
+                hovertemplate='sample=%{x:.4f}<br>density: %{y:.2f}<extra></extra>',
+                **histogram_kwargs,
+            )
+        )
+
+    @staticmethod
+    def _add_posterior_distribution_density_trace(
+        *,
+        fig: object,
+        density_trace: object | None,
+    ) -> None:
+        """Add the KDE trace for a posterior distribution plot."""
+        if density_trace is None:
+            return
+
+        density_trace.name = 'Marginal density'
+        density_trace.showlegend = True
+        fig.add_trace(density_trace)
+
+    def _add_posterior_distribution_reference_traces(
+        self,
+        *,
+        fig: object,
+        summary: object | None,
+        values: np.ndarray,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Add posterior median and MAP reference lines."""
+        if y_axis_range is None:
+            return
+
+        fig.add_trace(
+            self._posterior_reference_line_trace(
+                x_value=float(np.median(values)),
+                y_axis_range=y_axis_range,
+                trace_name='Median',
+                color=POSTERIOR_MEDIAN_LINE_COLOR,
+                dash='dash',
+            )
+        )
+        if summary is None:
+            return
+
+        fig.add_trace(
+            self._posterior_reference_line_trace(
+                x_value=summary.map_value,
+                y_axis_range=y_axis_range,
+                trace_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+                color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
+                dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+            )
+        )
+
+    @staticmethod
+    def _apply_posterior_distribution_layout(
+        *,
+        fig: object,
+        layout_factory: object | None,
+        title: str,
+        label: str,
+        x_axis_range: tuple[float, float] | None,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Apply layout settings to the distribution plot."""
+        if callable(layout_factory):
+            fig.update_layout(
+                title={
+                    'text': title,
+                    'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+                }
+            )
+        else:
+            fig.update_layout(
+                title={
+                    'text': title,
+                    'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+                },
+                xaxis_title=label,
+                yaxis_title='Probability density',
+                legend={
+                    'bgcolor': 'rgba(0, 0, 0, 0)',
+                    'xanchor': 'right',
+                    'x': 1.0,
+                    'yanchor': 'top',
+                    'y': 1.0,
+                },
+            )
+            fig.update_xaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
+            fig.update_yaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
+        if x_axis_range is not None:
+            fig.update_xaxes(range=list(x_axis_range))
+        if y_axis_range is not None:
+            fig.update_yaxes(range=list(y_axis_range))
+
+    def _show_plot_figure(self, figure: object) -> None:
+        """Display a figure through the active backend when possible."""
+        show_figure = getattr(self._backend, '_show_figure', None)
+        if callable(show_figure):
+            show_figure(figure)
+            return
+        figure.show()
+
+    @staticmethod
+    def _style_posterior_pair_marginal_density_trace(density_trace: object) -> None:
+        """Apply pair-plot-specific styling to a marginal KDE trace."""
+        density_trace.line = {
+            'color': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR,
+            'width': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH,
+        }
+        density_trace.fillcolor = POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+
+    @staticmethod
+    def _posterior_pair_density_hovertemplate(parameter_name: str) -> str:
+        """Return the hover template for a marginal density trace."""
+        return f'{parameter_name}: %{{x:.4f}}<br>density: %{{y:.4f}}<extra></extra>'
+
+    @staticmethod
+    def _posterior_pair_scatter_hovertemplate(
+        *,
+        x_parameter_name: str,
+        y_parameter_name: str,
+    ) -> str:
+        """Return the hover template for pair-plot sample points."""
+        return f'{x_parameter_name}: %{{x:.4f}}<br>{y_parameter_name}: %{{y:.4f}}<extra></extra>'
+
+    @staticmethod
+    def _posterior_pair_correlation_value(
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> float | None:
+        """Return the sample correlation for one contour panel."""
+        finite_mask = np.isfinite(x_values) & np.isfinite(y_values)
+        if np.count_nonzero(finite_mask) < MIN_POSTERIOR_SAMPLE_COUNT:
+            return None
+
+        correlation_matrix = np.corrcoef(x_values[finite_mask], y_values[finite_mask])
+        correlation_value = float(correlation_matrix[0, 1])
+        if not np.isfinite(correlation_value):
+            return None
+        return correlation_value
+
+    @staticmethod
+    def _posterior_pair_contour_colorscales(
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> tuple[list[list[float | str]], list[list[float | str]]]:
+        """Return sign-aware contour palettes for one panel."""
+        correlation_value = Plotter._posterior_pair_correlation_value(x_values, y_values)
+        if correlation_value is not None and correlation_value < 0:
+            return (
+                POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE,
+                POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE,
+            )
+        return POSTERIOR_CONTOUR_FILL_COLORSCALE, POSTERIOR_CONTOUR_LINE_COLORSCALE
+
+    def _posterior_density_trace(
+        self,
+        *,
+        fit_results: object,
+        parameter_name: str,
+        values: np.ndarray,
+        trace_name: str,
+    ) -> object | None:
+        """Return a filled KDE trace for one posterior marginal."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        lower_bound, upper_bound = self._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+        )
+        density_curve = self._posterior_density_curve(
+            values,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+        )
+        if density_curve is None:
+            return None
+
+        grid, density = density_curve
+        return go.Scatter(
+            x=grid,
+            y=density,
+            mode='lines',
+            line={'color': POSTERIOR_DENSITY_LINE_COLOR, 'width': 2},
+            fill='tozeroy',
+            fillcolor=POSTERIOR_DENSITY_FILL_COLOR,
+            name=trace_name,
+            showlegend=False,
+            hovertemplate='%{x:.4f}<br>density: %{y:.2f}<extra></extra>',
+        )
+
+    @staticmethod
+    def _posterior_density_axis_range(
+        density_values: np.ndarray,
+    ) -> tuple[float, float] | None:
+        """Return a padded y-axis range for posterior density plots."""
+        data = np.asarray(density_values, dtype=float)
+        data = data[np.isfinite(data)]
+        if data.size == 0:
+            return None
+
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        upper_padding = 0.08 * data_range if data_range > 0 else max(abs(data_max), 1.0) * 0.05
+        if upper_padding == 0:
+            upper_padding = 1e-6
+        lower = min(0.0, data_min)
+        return lower, data_max + upper_padding
+
+    @staticmethod
+    def _posterior_interval_band_trace(
+        *,
+        x0: float,
+        x1: float,
+        y_axis_range: tuple[float, float],
+        trace_name: str,
+        color: str,
+    ) -> object:
+        """Return a hideable credible-interval band trace."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        return go.Scatter(
+            x=[x0, x1, x1, x0, x0],
+            y=[
+                y_axis_range[0],
+                y_axis_range[0],
+                y_axis_range[1],
+                y_axis_range[1],
+                y_axis_range[0],
+            ],
+            mode='lines',
+            fill='toself',
+            fillcolor=color,
+            line={'color': color, 'width': 0},
+            name=trace_name,
+            showlegend=True,
+            hoverinfo='skip',
+        )
+
+    @staticmethod
+    def _posterior_reference_line_trace(
+        *,
+        x_value: float,
+        y_axis_range: tuple[float, float],
+        trace_name: str,
+        color: str,
+        dash: str,
+    ) -> object:
+        """
+        Return a named vertical reference line for posterior plots.
+        """
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        return go.Scatter(
+            x=[x_value, x_value],
+            y=[y_axis_range[0], y_axis_range[1]],
+            mode='lines',
+            line={'color': color, 'width': 2, 'dash': dash},
+            name=trace_name,
+            showlegend=True,
+            hovertemplate=f'{trace_name}: %{{x:.4f}}<extra></extra>',
+        )
+
+    @staticmethod
+    def _posterior_parameter_bounds(
+        *,
+        fit_results: object,
+        parameter_name: str,
+    ) -> tuple[float | None, float | None]:
+        """Return finite fit bounds for a posterior parameter."""
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        parameter = parameters_by_name.get(parameter_name)
+        if parameter is None:
+            return None, None
+
+        lower_bound = getattr(parameter, 'fit_min', None)
+        upper_bound = getattr(parameter, 'fit_max', None)
+        lower = (
+            float(lower_bound)
+            if lower_bound is not None and np.isfinite(float(lower_bound))
+            else None
+        )
+        upper = (
+            float(upper_bound)
+            if upper_bound is not None and np.isfinite(float(upper_bound))
+            else None
+        )
+        return lower, upper
+
+    @classmethod
+    def _posterior_pair_bounds(
+        cls,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> tuple[tuple[float, float], tuple[float, float]]:
+        """Return plotting bounds for a posterior pair panel."""
+        x_lower, x_upper = cls._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=x_parameter_name,
+        )
+        y_lower, y_upper = cls._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=y_parameter_name,
+        )
+        return (
+            cls._posterior_axis_bounds(x_values, lower_bound=x_lower, upper_bound=x_upper),
+            cls._posterior_axis_bounds(y_values, lower_bound=y_lower, upper_bound=y_upper),
+        )
+
+    @staticmethod
+    def _posterior_axis_bounds(
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> tuple[float, float]:
+        """Return finite plotting bounds for one posterior axis."""
+        data = np.asarray(values, dtype=float)
+        data = data[np.isfinite(data)]
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        padding = 0.05 * data_range if data_range > 0 else max(abs(data_min), 1.0) * 0.05
+        if padding == 0:
+            padding = 1e-6
+
+        resolved_lower = lower_bound if lower_bound is not None else data_min - padding
+        resolved_upper = upper_bound if upper_bound is not None else data_max + padding
+        return float(resolved_lower), float(resolved_upper)
+
+    @classmethod
+    def _posterior_density_curve(
+        cls,
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+        grid_size: int = 256,
+    ) -> tuple[np.ndarray, np.ndarray] | None:
+        """Estimate a boundary-aware posterior density curve."""
+        gaussian_kde = __import__('scipy.stats', fromlist=['gaussian_kde']).gaussian_kde
+
+        data = np.asarray(values, dtype=float)
+        data = data[np.isfinite(data)]
+        if data.size < MIN_POSTERIOR_SAMPLE_COUNT:
+            return None
+
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        padding = 0.05 * data_range if data_range > 0 else max(abs(data_min), 1.0) * 0.05
+        if padding == 0:
+            padding = 1e-6
+
+        grid_lower = lower_bound if lower_bound is not None else data_min - padding
+        grid_upper = upper_bound if upper_bound is not None else data_max + padding
+        if grid_upper <= grid_lower:
+            return None
+
+        grid = np.linspace(grid_lower, grid_upper, num=grid_size)
+        if np.allclose(data, data[0]):
+            bandwidth = max(abs(data[0]) * 0.01, 1e-6)
+            density = np.exp(-0.5 * ((grid - data[0]) / bandwidth) ** 2)
+            density /= bandwidth * np.sqrt(2.0 * np.pi)
+        else:
+            kde = gaussian_kde(data)
+            density = cls._evaluate_reflected_1d_kde(
+                kde,
+                grid,
+                lower_bound=lower_bound,
+                upper_bound=upper_bound,
+            )
+
+        area = np.trapezoid(density, grid)
+        if area <= 0:
+            return None
+        return grid, density / area
+
+    @classmethod
+    def _posterior_pair_density_surface(
+        cls,
+        *,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+        x_bounds: tuple[float, float],
+        y_bounds: tuple[float, float],
+        grid_size: int = 96,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
+        """Estimate a 2D KDE surface for a posterior pair panel."""
+        gaussian_kde = __import__('scipy.stats', fromlist=['gaussian_kde']).gaussian_kde
+
+        x_data = np.asarray(x_values, dtype=float)
+        y_data = np.asarray(y_values, dtype=float)
+        mask = np.isfinite(x_data) & np.isfinite(y_data)
+        x_data = x_data[mask]
+        y_data = y_data[mask]
+        if x_data.size < MIN_POSTERIOR_SAMPLE_COUNT or y_data.size < MIN_POSTERIOR_SAMPLE_COUNT:
+            return None
+
+        if np.allclose(x_data, x_data[0]) and np.allclose(y_data, y_data[0]):
+            return None
+
+        pair_data = np.vstack([x_data, y_data])
+        covariance = np.cov(pair_data)
+        if np.linalg.matrix_rank(covariance) < FULL_POSTERIOR_PAIR_COVARIANCE_RANK:
+            return None
+
+        x_grid = np.linspace(x_bounds[0], x_bounds[1], num=grid_size)
+        y_grid = np.linspace(y_bounds[0], y_bounds[1], num=grid_size)
+        mesh_x, mesh_y = np.meshgrid(x_grid, y_grid)
+        try:
+            density = cls._evaluate_reflected_2d_kde(
+                gaussian_kde(pair_data),
+                mesh_x=mesh_x,
+                mesh_y=mesh_y,
+                x_bounds=x_bounds,
+                y_bounds=y_bounds,
+            )
+        except (np.linalg.LinAlgError, ValueError):
+            return None
+        if not np.any(np.isfinite(density)):
+            return None
+        return x_grid, y_grid, density
+
+    @staticmethod
+    def _reflection_positions_1d(
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> list[np.ndarray]:
+        """Return mirrored positions for boundary-corrected KDEs."""
+        reflected = [values]
+        if lower_bound is not None:
+            reflected.append(2.0 * lower_bound - values)
+        if upper_bound is not None:
+            reflected.append(2.0 * upper_bound - values)
+        return reflected
+
+    @classmethod
+    def _evaluate_reflected_1d_kde(
+        cls,
+        kde: object,
+        grid: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> np.ndarray:
+        """Evaluate a 1D KDE using mirrored-boundary correction."""
+        density = np.zeros_like(grid, dtype=float)
+        for reflected_grid in cls._reflection_positions_1d(
+            grid,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+        ):
+            density += np.asarray(kde(reflected_grid), dtype=float)
+        return density
+
+    @classmethod
+    def _evaluate_reflected_2d_kde(
+        cls,
+        kde: object,
+        *,
+        mesh_x: np.ndarray,
+        mesh_y: np.ndarray,
+        x_bounds: tuple[float, float],
+        y_bounds: tuple[float, float],
+    ) -> np.ndarray:
+        """Evaluate a 2D KDE using mirrored-boundary correction."""
+        x_positions = cls._reflection_positions_1d(
+            mesh_x.ravel(),
+            lower_bound=x_bounds[0],
+            upper_bound=x_bounds[1],
+        )
+        y_positions = cls._reflection_positions_1d(
+            mesh_y.ravel(),
+            lower_bound=y_bounds[0],
+            upper_bound=y_bounds[1],
+        )
+        density = np.zeros(mesh_x.size, dtype=float)
+        for reflected_x in x_positions:
+            for reflected_y in y_positions:
+                positions = np.vstack([reflected_x, reflected_y])
+                density += np.asarray(kde(positions), dtype=float)
+        return density.reshape(mesh_x.shape)
+
+    def _get_or_build_posterior_predictive_summary(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+        include_draws: bool = True,
+    ) -> object | None:
+        """Return a cached or built predictive summary."""
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None
+
+        posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_predictive is None or posterior_samples is None:
+            return None
+
+        x_axis_name = getattr(x_axis, 'value', x_axis)
+        draw_cache_key = self._posterior_predictive_key(
+            expt_name,
+            str(x_axis_name),
+            include_draws=True,
+        )
+        band_cache_key = self._posterior_predictive_key(
+            expt_name,
+            str(x_axis_name),
+            include_draws=False,
+        )
+        cache_key = draw_cache_key if include_draws else band_cache_key
+        summary = posterior_predictive.get(cache_key)
+        if summary is None and not include_draws:
+            summary = posterior_predictive.get(draw_cache_key)
+        if summary is not None:
+            return summary
+
+        summary = self._build_posterior_predictive_summary(
+            fit_results=fit_results,
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=include_draws,
+        )
+        if summary is None:
+            return None
+
+        posterior_predictive[cache_key] = summary
+        return summary
+
+    def _build_posterior_predictive_summary(
+        self,
+        *,
+        fit_results: object,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+        include_draws: bool = True,
+    ) -> object | None:
+        """Build posterior predictive summaries from posterior draws."""
+        sampling_inputs = self._posterior_predictive_sampling_inputs(fit_results)
+        if sampling_inputs is None:
+            return None
+
+        flattened_samples, parameter_names = sampling_inputs
+        sampled_parameters = self._posterior_predictive_parameters(
+            fit_results=fit_results,
+            parameter_names=parameter_names,
+        )
+        if sampled_parameters is None:
+            return None
+
+        predictive_data = self._evaluate_posterior_predictive_draws(
+            flattened_samples=flattened_samples,
+            sampled_parameters=sampled_parameters,
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if predictive_data is None:
+            return None
+
+        map_prediction, x_values, predictive_draw_array = predictive_data
+        lower_68, upper_68 = np.quantile(predictive_draw_array, [0.16, 0.84], axis=0)
+        lower_95, upper_95 = np.quantile(predictive_draw_array, [0.025, 0.975], axis=0)
+        x_axis_name = getattr(x_axis, 'value', x_axis)
+
+        return PosteriorPredictiveSummary(
+            experiment_name=expt_name,
+            x_axis_name=str(x_axis_name),
+            x=np.asarray(x_values, dtype=float),
+            map_prediction=np.asarray(map_prediction, dtype=float),
+            lower_95=np.asarray(lower_95, dtype=float),
+            upper_95=np.asarray(upper_95, dtype=float),
+            lower_68=np.asarray(lower_68, dtype=float),
+            upper_68=np.asarray(upper_68, dtype=float),
+            draws=predictive_draw_array if include_draws else None,
+        )
+
+    @staticmethod
+    def _posterior_predictive_sampling_inputs(
+        fit_results: object,
+    ) -> tuple[np.ndarray, list[str]] | None:
+        """Return predictive-sampling arrays and parameter names."""
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            return None
+
+        flattened_samples = np.asarray(posterior_samples.flattened(), dtype=float)
+        parameter_names = getattr(posterior_samples, 'parameter_names', None)
+        if flattened_samples.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM or not parameter_names:
+            log.warning('Posterior samples are unavailable for predictive summaries.')
+            return None
+        return flattened_samples, list(parameter_names)
+
+    @staticmethod
+    def _posterior_predictive_parameters(
+        *,
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> list[object] | None:
+        """Return fitted parameters in posterior sample order."""
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        sampled_parameters: list[object] = []
+        for name in parameter_names:
+            parameter = parameters_by_name.get(name)
+            if parameter is None:
+                log.warning(
+                    'Posterior predictive summaries require matching fitted parameters for '
+                    f"'{name}'."
+                )
+                return None
+            sampled_parameters.append(parameter)
+        return sampled_parameters
+
+    def _evaluate_posterior_predictive_draws(
+        self,
+        *,
+        flattened_samples: np.ndarray,
+        sampled_parameters: list[object],
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
+        """Return MAP and sampled predictive curves."""
+        original_values = np.array(
+            [parameter.value for parameter in sampled_parameters],
+            dtype=float,
+        )
+        original_uncertainties = [parameter.uncertainty for parameter in sampled_parameters]
+        predictive_draws: list[np.ndarray] = []
+        draw_indices = self._posterior_predictive_draw_indices(flattened_samples.shape[0])
+
+        try:
+            map_prediction, x_values = self._evaluate_posterior_predictive_state(
+                sampled_parameters=sampled_parameters,
+                values=original_values,
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=x_axis,
+            )
+            if map_prediction is None or x_values is None:
+                return None
+
+            for index in draw_indices:
+                prediction, current_x = self._evaluate_posterior_predictive_state(
+                    sampled_parameters=sampled_parameters,
+                    values=flattened_samples[index],
+                    experiment=experiment,
+                    expt_name=expt_name,
+                    x_axis=x_axis,
+                )
+                if prediction is None or current_x is None:
+                    return None
+                if prediction.shape != map_prediction.shape or current_x.shape != x_values.shape:
+                    log.warning('Posterior predictive draws returned inconsistent array shapes.')
+                    return None
+                predictive_draws.append(prediction)
+        finally:
+            self._restore_posterior_predictive_parameters(
+                sampled_parameters=sampled_parameters,
+                original_values=original_values,
+                original_uncertainties=original_uncertainties,
+                expt_name=expt_name,
+            )
+
+        return (
+            np.asarray(map_prediction, dtype=float),
+            np.asarray(x_values, dtype=float),
+            np.asarray(predictive_draws, dtype=float),
+        )
+
+    def _restore_posterior_predictive_parameters(
+        self,
+        *,
+        sampled_parameters: list[object],
+        original_values: np.ndarray,
+        original_uncertainties: list[float | None],
+        expt_name: str,
+    ) -> None:
+        """Restore parameter state after predictive sampling."""
+        for parameter, value, uncertainty in zip(
+            sampled_parameters,
+            original_values,
+            original_uncertainties,
+            strict=True,
+        ):
+            parameter._set_value_from_minimizer(float(value))
+            parameter.uncertainty = uncertainty
+        self._update_project_categories(expt_name)
+
+    def _evaluate_posterior_predictive_state(
+        self,
+        *,
+        sampled_parameters: list[object],
+        values: np.ndarray,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> tuple[np.ndarray | None, np.ndarray | None]:
+        """Evaluate one posterior predictive state for an experiment."""
+        for parameter, value in zip(sampled_parameters, values, strict=True):
+            parameter._set_value_from_minimizer(float(value))
+
+        self._update_project_categories(expt_name)
+        pattern = intensity_category_for(experiment)
+        x_name = getattr(x_axis, 'value', x_axis)
+        x_values = getattr(pattern, x_name, None)
+        y_calc = getattr(pattern, 'intensity_calc', None)
+        if x_values is None or y_calc is None:
+            log.warning(
+                f'Posterior predictive data is unavailable for experiment {expt_name}. '
+                'Ensure calculated intensities and the selected x axis are available.'
+            )
+            return None, None
+
+        return np.asarray(y_calc, dtype=float), np.asarray(x_values, dtype=float)
+
+    @staticmethod
+    def _posterior_predictive_draw_indices(n_draws: int) -> np.ndarray:
+        """
+        Select evenly spaced posterior draws for predictive summaries.
+        """
+        if n_draws <= DEFAULT_POSTERIOR_PREDICTIVE_DRAWS:
+            return np.arange(n_draws, dtype=int)
+
+        return np.unique(
+            np.linspace(
+                0,
+                n_draws - 1,
+                num=DEFAULT_POSTERIOR_PREDICTIVE_DRAWS,
+                dtype=int,
+            )
+        )
+
+    @staticmethod
+    def _posterior_predictive_key(
+        expt_name: str,
+        x_axis_name: str,
+        *,
+        include_draws: bool = True,
+    ) -> str:
+        """Return the cache key for a posterior predictive summary."""
+        key_suffix = 'draws' if include_draws else 'band'
+        return f'{expt_name}:{x_axis_name}:{key_suffix}'
+
+    def _get_posterior_inference_data(
+        self,
+    ) -> tuple[object | None, object | None]:
+        """
+        Return posterior inference data for the current Bayesian fit.
+
+        Returns
+        -------
+        tuple[object | None, object | None]
+            ``(inference_data, fit_results)`` when posterior samples are
+            available, otherwise ``(None, None)``.
+        """
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior plots currently require the Plotly plotting backend.')
+            return None, None
+
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None, None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            log.warning('Posterior samples are unavailable. Run a Bayesian fit first.')
+            return None, None
+
+        return posterior_samples.to_arviz(), fit_results
+
+    def _get_posterior_samples_and_fit_results(
+        self,
+    ) -> tuple[object | None, object | None]:
+        """Return posterior samples and fit results for plotting."""
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior plots currently require the Plotly plotting backend.')
+            return None, None
+
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None, None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            log.warning('Posterior samples are unavailable. Run a Bayesian fit first.')
+            return None, None
+
+        return posterior_samples, fit_results
+
+    def _plot_posterior_predictive_summary(
+        self,
+        *,
+        expt_name: str,
+        summary: object,
+        y_meas: np.ndarray,
+        axes_labels: list[str],
+        show_band: bool,
+        show_draws: bool,
+    ) -> None:
+        """Render posterior predictive summaries using Plotly."""
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
+        axis_frame_color = self._plot_axis_frame_color()
+
+        fig = go.Figure()
+        if show_band:
+            fig.add_trace(
+                go.Scatter(
+                    x=summary.x,
+                    y=summary.lower_95,
+                    mode='lines',
+                    line={'color': 'rgba(0, 0, 0, 0)'},
+                    hoverinfo='skip',
+                    showlegend=False,
+                )
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=summary.x,
+                    y=summary.upper_95,
+                    mode='lines',
+                    line={'color': 'rgba(0, 0, 0, 0)'},
+                    fill='tonexty',
+                    fillcolor=POSTERIOR_INTERVAL_95_FILL_COLOR,
+                    name=POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME,
+                    hoverinfo='skip',
+                    legendrank=30,
+                )
+            )
+
+        if show_draws:
+            draws = getattr(summary, 'draws', None)
+            if draws is None:
+                log.warning('Posterior predictive draws are unavailable for plotting.')
+                return
+
+            draw_cap = min(len(draws), DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP)
+            for index in range(draw_cap):
+                fig.add_trace(
+                    go.Scatter(
+                        x=summary.x,
+                        y=draws[index],
+                        mode='lines',
+                        line={'color': POSTERIOR_DRAW_LINE_COLOR, 'width': 1},
+                        name='Posterior draw' if index == 0 else None,
+                        showlegend=index == 0,
+                        legendrank=40,
+                    )
+                )
+
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=y_meas,
+                mode='lines+markers',
+                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
+                name='Measured',
+                legendrank=10,
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=summary.map_prediction,
+                mode='lines',
+                line={
+                    'color': POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
+                    'width': 2,
+                    'dash': POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+                },
+                name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+                legendrank=20,
+            )
+        )
+        fig.update_layout(
+            title={
+                'text': f"Posterior predictive for experiment 🔬 '{expt_name}'",
+                'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+            },
+            margin={
+                'autoexpand': True,
+                'r': 30,
+                't': 40,
+                'b': 45,
+            },
+            legend={
+                'bgcolor': self._plot_legend_background_color(),
+                'xanchor': 'right',
+                'x': 1.0,
+                'yanchor': 'top',
+                'y': 1.0,
+            },
+            xaxis_title=axes_labels[0],
+            yaxis_title=axes_labels[1],
+        )
+        fig.update_xaxes(
+            title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+            showline=True,
+            linecolor=axis_frame_color,
+            mirror=True,
+            zeroline=False,
+        )
+        fig.update_yaxes(
+            title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+            showline=True,
+            linecolor=axis_frame_color,
+            mirror=True,
+            zeroline=False,
+        )
+        self._show_plot_figure(fig)
+
+    def _plot_single_crystal_posterior_predictive_summary(
+        self,
+        *,
+        expt_name: str,
+        summary: PosteriorPredictiveSummary,
+        y_meas: np.ndarray,
+        y_meas_su: np.ndarray,
+        axes_labels: list[str],
+    ) -> None:
+        """Render single-crystal posterior predictive checks."""
+        if summary.lower_95 is None or summary.upper_95 is None:
+            log.warning(
+                'Single-crystal posterior predictive plots require 95% predictive intervals.'
+            )
+            return
+
+        map_prediction = np.asarray(summary.map_prediction, dtype=float)
+        lower_95 = np.asarray(summary.lower_95, dtype=float)
+        upper_95 = np.asarray(summary.upper_95, dtype=float)
+        if lower_95.shape != map_prediction.shape or upper_95.shape != map_prediction.shape:
+            log.warning('Single-crystal posterior predictive interval arrays have invalid shapes.')
+            return
+
+        go = __import__('plotly.graph_objects', fromlist=['Figure'])
+        trace = PlotlyPlotter._get_single_crystal_trace(
+            x_calc=map_prediction,
+            y_meas=y_meas,
+            y_meas_su=y_meas_su,
+        )
+        trace.error_x = {
+            'type': 'data',
+            'array': np.maximum(0.0, upper_95 - map_prediction),
+            'arrayminus': np.maximum(0.0, map_prediction - lower_95),
+            'visible': True,
+        }
+        trace.customdata = np.column_stack((lower_95, upper_95, y_meas_su))
+        trace.hovertemplate = (
+            'Predicted I²: %{x:,.2f}<br>'
+            '95% interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]<br>'
+            'Measured I²: %{y:,.2f}<br>'
+            'su(I²meas): %{customdata[2]:,.2f}<extra></extra>'
+        )
+
+        fig = go.Figure(
+            data=[trace],
+            layout=PlotlyPlotter._get_layout(
+                f"Posterior predictive reflection check for experiment 🔬 '{expt_name}'",
+                axes_labels,
+                shapes=[PlotlyPlotter._get_diagonal_shape()],
+            ),
+        )
+        self._show_plot_figure(fig)
+
+    def _filtered_posterior_predictive_summary(
+        self,
+        *,
+        summary: PosteriorPredictiveSummary,
+        x_min: float,
+        x_max: float,
+        include_draws: bool,
+    ) -> PosteriorPredictiveSummary | None:
+        """Return a predictive summary filtered to an x-range."""
+        x_filtered = self._filtered_y_array(summary.x, summary.x, x_min, x_max)
+        if np.asarray(x_filtered).size == 0:
+            return None
+
+        draws = None
+        if include_draws and summary.draws is not None:
+            draws = np.asarray(
+                [self._filtered_y_array(draw, summary.x, x_min, x_max) for draw in summary.draws],
+                dtype=float,
+            )
+
+        return PosteriorPredictiveSummary(
+            experiment_name=summary.experiment_name,
+            x_axis_name=summary.x_axis_name,
+            x=x_filtered,
+            map_prediction=self._filtered_y_array(summary.map_prediction, summary.x, x_min, x_max),
+            lower_95=(
+                None
+                if summary.lower_95 is None
+                else self._filtered_y_array(summary.lower_95, summary.x, x_min, x_max)
+            ),
+            upper_95=(
+                None
+                if summary.upper_95 is None
+                else self._filtered_y_array(summary.upper_95, summary.x, x_min, x_max)
+            ),
+            lower_68=(
+                None
+                if summary.lower_68 is None
+                else self._filtered_y_array(summary.lower_68, summary.x, x_min, x_max)
+            ),
+            upper_68=(
+                None
+                if summary.upper_68 is None
+                else self._filtered_y_array(summary.upper_68, summary.x, x_min, x_max)
+            ),
+            draws=draws,
+        )
+
+    def _plot_posterior_predictive_data(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        plot_options: _MeasVsCalcPlotOptions,
+        x_axis: object,
+        style: str,
+    ) -> None:
+        """Render posterior predictive curves on the powder layout."""
+        pattern = intensity_category_for(experiment)
+        ctx = self._prepare_powder_context(
+            pattern,
+            expt_name,
+            experiment.type,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
+        )
+        if ctx is None:
+            return
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=style in {'draws', 'band+draws'},
+        )
+        if summary is None:
+            return
+
+        y_meas = self._filtered_y_array(
+            pattern.intensity_meas,
+            ctx['x_array'],
+            ctx['x_min'],
+            ctx['x_max'],
+        )
+        y_bkg_raw = getattr(pattern, 'intensity_bkg', None)
+        y_bkg = (
+            self._filtered_y_array(y_bkg_raw, ctx['x_array'], ctx['x_min'], ctx['x_max'])
+            if y_bkg_raw is not None
+            else None
+        )
+        y_calc = self._filtered_y_array(
+            summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max']
+        )
+        show_residual = True if plot_options.show_residual is None else plot_options.show_residual
+        y_resid = y_meas - y_calc if show_residual else None
+
+        predictive_lower_95 = None
+        predictive_upper_95 = None
+        if style in {'band', 'band+draws'}:
+            predictive_lower_95 = self._filtered_y_array(
+                summary.lower_95,
+                summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
+            )
+            predictive_upper_95 = self._filtered_y_array(
+                summary.upper_95,
+                summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
+            )
+
+        predictive_draws = None
+        if style in {'draws', 'band+draws'}:
+            draws = getattr(summary, 'draws', None)
+            if draws is None:
+                log.warning('Posterior predictive draws are unavailable for plotting.')
+                return
+            predictive_draws = np.asarray(
+                [
+                    self._filtered_y_array(draw, summary.x, ctx['x_min'], ctx['x_max'])
+                    for draw in draws
+                ],
+                dtype=float,
+            )
+
+        if np.asarray(ctx['x_filtered']).size == 0:
+            bragg_tick_sets = ()
+        else:
+            bragg_tick_sets = self._extract_bragg_tick_sets(
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=ctx['x_axis'],
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+
+        plot_spec = PowderMeasVsCalcSpec(
+            x=ctx['x_filtered'],
+            y_meas=y_meas,
+            y_calc=y_calc,
+            y_resid=y_resid,
+            bragg_tick_sets=bragg_tick_sets,
+            axes_labels=ctx['axes_labels'],
+            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            residual_height_fraction=DEFAULT_RESID_HEIGHT,
+            bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
+            height=self._composite_plot_height(),
+            y_bkg=y_bkg,
+            predictive_lower_95=predictive_lower_95,
+            predictive_upper_95=predictive_upper_95,
+            predictive_draws=predictive_draws,
+            y_calc_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+            y_calc_line_dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+        )
+        self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
+
+    @staticmethod
+    def _resolve_posterior_parameter_names(
+        *,
+        fit_results: object,
+        parameters: list[object] | None,
+    ) -> list[str] | None:
+        """
+        Resolve posterior parameter names from descriptors.
+
+        Parameters
+        ----------
+        fit_results : object
+            Bayesian fit result exposing posterior samples.
+        parameters : list[object] | None
+            Optional parameter subset.
+
+        Returns
+        -------
+        list[str] | None
+            Posterior parameter names in plotting order, or ``None`` if
+            the selection cannot be resolved.
+        """
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        available_names = getattr(posterior_samples, 'parameter_names', None)
+        if not available_names:
+            log.warning('Posterior samples do not expose parameter names.')
+            return None
+
+        if parameters is None:
+            return list(available_names)
+
+        resolved_names: list[str] = []
+        for parameter in parameters:
+            resolved_name = Plotter._resolve_posterior_parameter_name(
+                fit_results=fit_results,
+                available_names=list(available_names),
+                parameter=parameter,
+            )
+            if resolved_name is None:
+                return None
+            resolved_names.append(resolved_name)
+        return resolved_names
+
+    @staticmethod
+    def _resolve_posterior_parameter_name(
+        *,
+        fit_results: object,
+        available_names: list[str],
+        parameter: object,
+    ) -> str | None:
+        """
+        Resolve one posterior parameter selection into a unique name.
+        """
+        if isinstance(parameter, str):
+            return Plotter._resolve_posterior_parameter_name_from_string(
+                fit_results=fit_results,
+                available_names=available_names,
+                selection=parameter,
+            )
+
+        unique_name = getattr(parameter, 'unique_name', None)
+        if unique_name is None:
+            log.warning(
+                'Posterior parameter selection expects parameter objects '
+                'or strings matching a unique name or label.'
+            )
+            return None
+        if unique_name not in available_names:
+            log.warning(f'Posterior samples do not contain the selected parameter: {unique_name}')
+            return None
+        return str(unique_name)
+
+    @staticmethod
+    def _resolve_posterior_parameter_name_from_string(
+        *,
+        fit_results: object,
+        available_names: list[str],
+        selection: str,
+    ) -> str | None:
+        """Resolve a string posterior parameter selection."""
+        stripped_selection = selection.strip()
+        if not stripped_selection:
+            log.warning('Posterior parameter selection cannot use an empty string.')
+            return None
+        if stripped_selection in available_names:
+            return stripped_selection
+
+        selection_candidates = Plotter._posterior_parameter_selection_candidates(
+            fit_results=fit_results,
+            available_names=available_names,
+        )
+        matching_names = [
+            unique_name
+            for unique_name, candidates in selection_candidates.items()
+            if stripped_selection in candidates
+        ]
+        if len(matching_names) == 1:
+            return matching_names[0]
+        if len(matching_names) > 1:
+            matches = ', '.join(matching_names)
+            log.warning(
+                f"Posterior parameter selection '{stripped_selection}' is ambiguous. "
+                f'Matches: {matches}'
+            )
+            return None
+
+        log.warning(
+            f'Posterior samples do not contain the selected parameter or label: '
+            f'{stripped_selection}'
+        )
+        return None
+
+    @staticmethod
+    def _posterior_parameter_selection_candidates(
+        *,
+        fit_results: object,
+        available_names: list[str],
+    ) -> dict[str, set[str]]:
+        """
+        Return string identifiers accepted for posterior selection.
+        """
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        summaries_by_name = Plotter._posterior_summary_by_name(fit_results)
+        plot_labels = dict(
+            zip(
+                available_names,
+                Plotter._posterior_plot_labels(fit_results, available_names),
+                strict=True,
+            )
+        )
+        candidates_by_name: dict[str, set[str]] = {}
+        for unique_name in available_names:
+            candidates = {unique_name}
+            parameter = parameters_by_name.get(unique_name)
+            if parameter is not None:
+                parameter_name = getattr(parameter, 'name', None)
+                if isinstance(parameter_name, str) and parameter_name.strip():
+                    candidates.add(parameter_name.strip())
+
+            summary = summaries_by_name.get(unique_name)
+            summary_label = getattr(summary, 'display_name', None)
+            if isinstance(summary_label, str) and summary_label.strip():
+                candidates.add(summary_label.strip())
+
+            plot_label = plot_labels.get(unique_name)
+            if isinstance(plot_label, str) and plot_label.strip():
+                candidates.add(plot_label.strip())
+
+            candidates_by_name[unique_name] = candidates
+        return candidates_by_name
+
+    @staticmethod
+    def _selected_posterior_samples(
+        posterior_samples: object,
+        parameter_names: list[str],
+    ) -> np.ndarray | None:
+        """Return flattened posterior samples in the selected order."""
+        available_names = getattr(posterior_samples, 'parameter_names', None)
+        if not available_names:
+            return None
+
+        name_to_index = {name: index for index, name in enumerate(available_names)}
+        try:
+            indices = [name_to_index[name] for name in parameter_names]
+        except KeyError:
+            return None
+
+        flattened = np.asarray(posterior_samples.flattened(), dtype=float)
+        if flattened.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM:
+            return None
+        return flattened[:, indices]
+
+    @staticmethod
+    def _thin_posterior_samples(
+        samples: np.ndarray,
+        *,
+        max_points: int = 4000,
+    ) -> np.ndarray:
+        """Downsample posterior samples for interactive plotting."""
+        if samples.shape[0] <= max_points:
+            return samples
+
+        indices = np.linspace(0, samples.shape[0] - 1, num=max_points, dtype=int)
+        return samples[indices]
+
+    @staticmethod
+    def _posterior_plot_labels(
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> list[str]:
+        """
+        Return readable posterior plot labels for selected parameters.
+        """
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        labels: list[str] = []
+        for parameter_name in parameter_names:
+            parameter = parameters_by_name.get(parameter_name)
+            if parameter is None:
+                labels.append(parameter_name)
+                continue
+
+            entry_name = getattr(getattr(parameter, '_identity', None), 'category_entry_name', '')
+            short_name = getattr(parameter, 'name', parameter_name)
+            if entry_name:
+                labels.append(f'{entry_name} {short_name}')
+            else:
+                labels.append(short_name)
+        return labels
+
+    @staticmethod
+    def _square_matrix_axis_title_labels(
+        parameter_names: list[str],
+    ) -> list[str]:
+        """Return compact multiline labels for square-matrix axes."""
+        return [Plotter._square_matrix_axis_title_label(name) for name in parameter_names]
+
+    @staticmethod
+    def _square_matrix_axis_title_label(unique_name: str) -> str:
+        """Return one compact multiline axis title."""
+        normalized_name = unique_name.strip()
+        if not normalized_name or '.' not in normalized_name:
+            return normalized_name
+
+        name_parts = [part.strip() for part in normalized_name.split('.') if part.strip()]
+        if not name_parts:
+            return normalized_name
+
+        return '<br>'.join([*(f'{part}.' for part in name_parts[:-1]), name_parts[-1]])
+
+    @staticmethod
+    def _posterior_summary_by_name(
+        fit_results: object,
+    ) -> dict[str, object]:
+        """Return posterior summaries keyed by unique parameter name."""
+        summaries = getattr(fit_results, 'posterior_parameter_summaries', [])
+        return {summary.unique_name: summary for summary in summaries}
+
+    def _get_fit_result_for_correlation(
+        self,
+    ) -> object | None:
+        """
+        Validate and return the fit result for correlation.
+
+        Returns
         -------
-        tuple[object, list[str], object] | None
-            A tuple of ``(raw_result, var_names, fit_results)`` when all
-            required data is present, or ``None`` otherwise.
+        object | None
+            Fit result object when available, or ``None`` otherwise.
         """
         if self._project is None:
             log.warning('Plotter is not attached to a project.')
@@ -781,18 +3962,46 @@ def _get_fit_result_for_correlation(
         if fit_results is None:
             log.warning('No fit results available. Run fit() first.')
             return None
+        return fit_results
 
-        raw_result = getattr(fit_results, 'engine_result', None)
-        if raw_result is None:
-            log.warning('No raw fit result available. Correlation matrix cannot be plotted.')
+    @staticmethod
+    def _correlation_from_posterior_samples(
+        posterior_samples: object,
+    ) -> pd.DataFrame | None:
+        """
+        Convert posterior samples into a correlation DataFrame.
+
+        Parameters
+        ----------
+        posterior_samples : object
+            Posterior sample container exposing ``flattened()`` and
+            ``parameter_names``.
+
+        Returns
+        -------
+        pd.DataFrame | None
+            Correlation matrix labeled by posterior parameter names, or
+            ``None`` if the sample array is invalid.
+        """
+        parameter_names = getattr(posterior_samples, 'parameter_names', None)
+        if not parameter_names:
+            log.warning('Posterior samples do not expose parameter names.')
             return None
 
-        var_names = getattr(raw_result, 'var_names', None)
-        if not var_names:
-            log.warning('Fit result does not expose variable names for a correlation matrix.')
+        flattened = np.asarray(posterior_samples.flattened(), dtype=float)
+        if flattened.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM or flattened.shape[1] != len(
+            parameter_names
+        ):
+            log.warning('Posterior sample array has an invalid shape for correlations.')
+            return None
+        if flattened.shape[0] < MIN_POSTERIOR_SAMPLE_COUNT:
+            log.warning('At least two posterior draws are required for correlations.')
             return None
 
-        return raw_result, var_names, fit_results
+        corr = np.corrcoef(flattened, rowvar=False)
+        corr = np.nan_to_num(corr, nan=0.0, posinf=0.0, neginf=0.0)
+        np.fill_diagonal(corr, 1.0)
+        return pd.DataFrame(corr, index=parameter_names, columns=parameter_names)
 
     @staticmethod
     def _correlation_from_covariance(
@@ -938,12 +4147,437 @@ def _plot_correlation_heatmap(
         precision : int
             Number of decimals to show in plot labels and hover text.
         """
-        self._backend.plot_correlation_heatmap(
+        figure = self._build_correlation_heatmap_plot(
             corr_df,
             title,
             threshold=threshold,
             precision=precision,
         )
+        self._show_plot_figure(figure)
+
+    def _build_correlation_heatmap_plot(
+        self,
+        corr_df: pd.DataFrame,
+        title: str,
+        *,
+        threshold: float | None,
+        precision: int,
+    ) -> object:
+        """Build a compact pair-plot-style correlation heatmap."""
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Heatmap'])
+        context = _CorrelationHeatmapContext(
+            corr_df=corr_df,
+            row_labels=self._square_matrix_axis_title_labels(corr_df.index.tolist()),
+            col_labels=self._square_matrix_axis_title_labels(corr_df.columns.tolist()),
+            threshold=threshold,
+            precision=precision,
+        )
+        plot_extent = self._square_matrix_plot_extent(context.n_cols)
+        x_edges = self._correlation_heatmap_edges(context.n_cols)
+        y_edges = self._correlation_heatmap_edges(context.n_rows)
+        x_centers = self._correlation_heatmap_centers(context.n_cols)
+        y_centers = self._correlation_heatmap_centers(context.n_rows)
+
+        heatmap = go.Heatmap(
+            z=self._correlation_heatmap_values(context.corr_df),
+            x=x_edges,
+            y=y_edges,
+            customdata=self._correlation_heatmap_customdata(context.corr_df),
+            zmin=-1.0,
+            zmax=1.0,
+            zmid=0.0,
+            colorscale=self._plot_correlation_colorscale(),
+            showscale=False,
+            hoverongaps=False,
+            hovertemplate=(
+                '%{customdata[0]}<br>'
+                '%{customdata[1]}<br>'
+                f'correlation: %{{z:.{context.precision}f}}<extra></extra>'
+            ),
+        )
+        label_trace = PlotlyPlotter._get_correlation_label_trace(
+            context.corr_df,
+            x_centers=x_centers,
+            y_centers=y_centers,
+            threshold=context.threshold,
+            precision=context.precision,
+        )
+        traces = [heatmap]
+        if label_trace is not None:
+            traces.append(label_trace)
+        fig = go.Figure(data=traces)
+
+        fig.update_layout(
+            autosize=True,
+            margin=self._square_matrix_layout_margin([
+                *context.row_labels,
+                *context.col_labels,
+            ]),
+            annotations=self._correlation_heatmap_annotations(
+                title=title,
+                context=context,
+                plot_extent=plot_extent,
+                x_centers=x_centers,
+                y_centers=y_centers,
+            ),
+            shapes=self._correlation_heatmap_grid_shapes(context),
+            meta=self._square_matrix_layout_meta(
+                n_parameters=context.n_cols,
+                annotation_labels=[*context.row_labels, *context.col_labels],
+            ),
+            showlegend=False,
+        )
+        fig.update_xaxes(
+            range=[0.0, plot_extent],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            constrain='domain',
+        )
+        fig.update_yaxes(
+            range=[plot_extent, 0.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            constrain='domain',
+            scaleanchor='x',
+            scaleratio=1,
+        )
+        return fig
+
+    @classmethod
+    def _correlation_heatmap_edges(cls, n_parameters: int) -> np.ndarray:
+        """Return expanded heatmap edges with pair-plot-like gaps."""
+        if n_parameters <= 0:
+            return np.asarray([0.0], dtype=float)
+
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        if np.isclose(gap_width, 0.0):
+            return np.arange(n_parameters + 1, dtype=float)
+
+        widths = [1.0 if index % 2 == 0 else gap_width for index in range(2 * n_parameters - 1)]
+        return np.concatenate(([0.0], np.cumsum(np.asarray(widths, dtype=float))))
+
+    @classmethod
+    def _correlation_heatmap_centers(cls, n_parameters: int) -> np.ndarray:
+        """Return visible-cell centers for a gapped heatmap."""
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        return np.arange(n_parameters, dtype=float) * (1.0 + gap_width) + 0.5
+
+    @staticmethod
+    def _correlation_heatmap_values(corr_df: pd.DataFrame) -> np.ndarray:
+        """Return a gapped heatmap array for correlation cells."""
+        n_rows, n_cols = corr_df.shape
+        expanded = np.full((2 * n_rows - 1, 2 * n_cols - 1), np.nan, dtype=float)
+        expanded[0::2, 0::2] = corr_df.to_numpy(dtype=float)
+        return expanded
+
+    @staticmethod
+    def _correlation_heatmap_customdata(
+        corr_df: pd.DataFrame,
+    ) -> np.ndarray:
+        """Return hover labels for a correlation heatmap."""
+        n_rows, n_cols = corr_df.shape
+        expanded = np.empty((2 * n_rows - 1, 2 * n_cols - 1, 2), dtype=object)
+        expanded[..., 0] = ''
+        expanded[..., 1] = ''
+        for row_index, row_label in enumerate(corr_df.index):
+            for col_index, col_label in enumerate(corr_df.columns):
+                expanded[2 * row_index, 2 * col_index, 0] = str(col_label)
+                expanded[2 * row_index, 2 * col_index, 1] = str(row_label)
+        return expanded
+
+    def _correlation_heatmap_annotations(
+        self,
+        *,
+        title: str,
+        context: _CorrelationHeatmapContext,
+        plot_extent: float,
+        x_centers: np.ndarray,
+        y_centers: np.ndarray,
+    ) -> list[dict[str, object]]:
+        """Return pair-plot-like title and axis annotations."""
+        annotations = [
+            self._square_matrix_title_annotation(
+                title,
+                [*context.row_labels, *context.col_labels],
+            )
+        ]
+        for row_index, row_label in enumerate(context.row_labels):
+            annotations.append({
+                'x': 0.0,
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': 1.0 - (float(y_centers[row_index]) / plot_extent),
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': row_label,
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        for col_index, col_label in enumerate(context.col_labels):
+            annotations.append({
+                'x': float(x_centers[col_index]) / plot_extent,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': 0.0,
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': col_label,
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+        return annotations
+
+    def _correlation_heatmap_grid_shapes(
+        self,
+        context: _CorrelationHeatmapContext,
+    ) -> list[dict[str, object]]:
+        """Return per-cell borders for the visible lower triangle."""
+        axis_frame_color = self._plot_axis_frame_color()
+        gap_width = self._square_matrix_gap_data_width(context.n_cols)
+        return [
+            {
+                'type': 'rect',
+                'xref': 'x',
+                'yref': 'y',
+                'x0': float(col_index * (1.0 + gap_width)),
+                'x1': float(col_index * (1.0 + gap_width) + 1.0),
+                'y0': float(row_index * (1.0 + gap_width)),
+                'y1': float(row_index * (1.0 + gap_width) + 1.0),
+                'layer': 'above',
+                'line': {
+                    'color': axis_frame_color,
+                    'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+                },
+                'fillcolor': 'rgba(0, 0, 0, 0)',
+            }
+            for row_index in range(context.n_rows)
+            for col_index in range(context.n_cols)
+            if col_index <= row_index
+        ]
+
+    def _populate_correlation_heatmap_panel(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Populate one panel in the correlation-matrix grid."""
+        row = row_index + 1
+        col = col_index + 1
+        if col_index > row_index:
+            self._hide_posterior_pair_panel(fig=fig, row=row, col=col)
+            return
+
+        value = context.corr_df.iat[row_index, col_index]
+        if not pd.isna(value):
+            self._add_correlation_heatmap_value_panel(
+                fig=fig,
+                context=context,
+                row_index=row_index,
+                col_index=col_index,
+                value=float(value),
+            )
+
+        self._configure_correlation_heatmap_panel_axes(fig=fig, row=row, col=col)
+        self._collect_correlation_heatmap_panel_decorations(
+            fig=fig,
+            context=context,
+            row_index=row_index,
+            col_index=col_index,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
+        )
+
+    def _add_correlation_heatmap_value_panel(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        value: float,
+    ) -> None:
+        """Add one colored cell and optional text label."""
+        go = __import__('plotly.graph_objects', fromlist=['Heatmap'])
+        row = row_index + 1
+        col = col_index + 1
+        hovertemplate = (
+            f'{context.corr_df.columns[col_index]}<br>'
+            f'{context.corr_df.index[row_index]}<br>'
+            f'correlation: %{{z:.{context.precision}f}}<extra></extra>'
+        )
+        fig.add_trace(
+            go.Heatmap(
+                z=[[value]],
+                x=[0.0, 1.0],
+                y=[0.0, 1.0],
+                zmin=-1.0,
+                zmax=1.0,
+                zmid=0.0,
+                colorscale=self._plot_correlation_colorscale(),
+                showscale=False,
+                hoverongaps=False,
+                hovertemplate=hovertemplate,
+            ),
+            row=row,
+            col=col,
+        )
+
+        if (
+            context.threshold is not None
+            and context.threshold > 0
+            and abs(value) < context.threshold
+        ):
+            return
+
+        fig.add_trace(
+            go.Scatter(
+                x=[0.5],
+                y=[0.5],
+                mode='text',
+                text=[f'{value:.{context.precision}f}'],
+                textposition='middle center',
+                textfont={'color': PlotlyPlotter._correlation_label_color()},
+                hoverinfo='skip',
+                showlegend=False,
+            ),
+            row=row,
+            col=col,
+        )
+
+    @staticmethod
+    def _configure_correlation_heatmap_panel_axes(
+        *,
+        fig: object,
+        row: int,
+        col: int,
+    ) -> None:
+        """Hide ticks and titles for one correlation-matrix panel."""
+        fig.update_xaxes(
+            range=[0.0, 1.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            row=row,
+            col=col,
+        )
+        fig.update_yaxes(
+            range=[1.0, 0.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            row=row,
+            col=col,
+        )
+
+    def _collect_correlation_heatmap_panel_decorations(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Collect labels and frames for one correlation cell."""
+        row = row_index + 1
+        col = col_index + 1
+        subplot = fig.get_subplot(row, col)
+        x_mid = 0.5 * (subplot.xaxis.domain[0] + subplot.xaxis.domain[1])
+        y_mid = 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1])
+
+        if col_index == 0:
+            subplot_title_annotations.append({
+                'x': subplot.xaxis.domain[0],
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': y_mid,
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': context.row_labels[row_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        if row_index == context.n_rows - 1:
+            subplot_title_annotations.append({
+                'x': x_mid,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': subplot.yaxis.domain[0],
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': context.col_labels[col_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+
+        subplot_border_shapes.append({
+            'type': 'rect',
+            'xref': 'paper',
+            'yref': 'paper',
+            'x0': subplot.xaxis.domain[0],
+            'x1': subplot.xaxis.domain[1],
+            'y0': subplot.yaxis.domain[0],
+            'y1': subplot.yaxis.domain[1],
+            'line': {
+                'color': self._plot_axis_frame_color(),
+                'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            },
+            'fillcolor': 'rgba(0, 0, 0, 0)',
+            'layer': 'above',
+        })
+
+    def _plot_correlation_colorscale(self) -> list[tuple[float, str]]:
+        """Return the active correlation colorscale."""
+        correlation_colorscale = getattr(self._backend, '_correlation_colorscale', None)
+        if callable(correlation_colorscale):
+            return correlation_colorscale()
+        return PlotlyPlotter._correlation_colorscale()
 
     @staticmethod
     def _format_correlation_table_dataframe(
@@ -1283,6 +4917,7 @@ def _plot_powder_bragg_meas_vs_calc(
                 x_min=ctx['x_min'],
                 x_max=ctx['x_max'],
             )
+
         plot_spec = PowderMeasVsCalcSpec(
             x=ctx['x_filtered'],
             y_meas=series.y_meas,
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index 7dc42cf6d..58d7e97fb 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -171,6 +171,11 @@ def parameters(self) -> list:
         """Return parameters from all structures and experiments."""
         return self.structures.parameters + self.experiments.parameters
 
+    @property
+    def free_parameters(self) -> list:
+        """Return free parameters from structures and experiments."""
+        return self.structures.free_parameters + self.experiments.free_parameters
+
     @property
     def as_cif(self) -> str:
         """Export whole project as CIF text."""
diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
new file mode 100644
index 000000000..7a52c63d8
--- /dev/null
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -0,0 +1,360 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import re
+
+import pytest
+
+ANSI_ESCAPE_RE = re.compile(r'\x1b\[[0-?]*[ -/]*[@-~]')
+
+
+def _unstyled_output(text: str) -> str:
+    return ANSI_ESCAPE_RE.sub('', text)
+
+
+def _make_project():
+    class ExpCol:
+        def __init__(self) -> None:
+            self._names: list[str] = []
+
+        @property
+        def names(self):
+            return self._names
+
+        @property
+        def parameters(self):
+            return []
+
+        @property
+        def fittable_parameters(self):
+            return []
+
+        @property
+        def free_parameters(self):
+            return []
+
+    class Project:
+        experiments = ExpCol()
+        structures = ExpCol()
+        _varname = 'proj'
+        verbosity = 'full'
+
+    return Project()
+
+
+def _make_project_with_names(names: list[str]):
+    class ExpCol:
+        def __init__(self, names):
+            self._names = names
+
+        def __len__(self):
+            return len(self._names)
+
+        @property
+        def names(self):
+            return self._names
+
+        @property
+        def parameters(self):
+            return []
+
+        @property
+        def fittable_parameters(self):
+            return []
+
+        @property
+        def free_parameters(self):
+            return []
+
+    class Project:
+        experiments = ExpCol(names)
+        structures = ExpCol([])
+        _varname = 'proj'
+        verbosity = 'full'
+
+    return Project()
+
+
+def test_fit_mode_enum_members_default_and_descriptions():
+    from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+
+    assert FitModeEnum.SINGLE == 'single'
+    assert FitModeEnum.JOINT == 'joint'
+    assert FitModeEnum.SEQUENTIAL == 'sequential'
+    assert FitModeEnum.default() is FitModeEnum.SINGLE
+    assert all(member.description() for member in FitModeEnum)
+
+
+def test_fit_instantiation_defaults_and_run_paths():
+    from easydiffraction.analysis.categories.fit.default import Fit
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+
+    fit = Fit()
+
+    assert fit._identity.category_code == 'fit'
+    assert fit.mode.value == 'single'
+    assert fit.minimizer_type.value == 'lmfit (leastsq)'
+    assert fit.minimizer is None
+
+    with pytest.raises(
+        RuntimeError,
+        match=r'Fit category is not attached to an Analysis object\.',
+    ):
+        fit.run()
+
+    calls: list[tuple[str | None, bool, int | None]] = []
+
+    class Parent:
+        fitter = object()
+
+        @staticmethod
+        def _run_fit(
+            verbosity: str | None = None,
+            *,
+            use_physical_limits: bool = False,
+            random_seed: int | None = None,
+        ) -> None:
+            calls.append((verbosity, use_physical_limits, random_seed))
+
+    fit._parent = Parent()
+    fit(verbosity='silent', use_physical_limits=True, random_seed=7)
+
+    assert calls == [('silent', True, 7)]
+
+    class ParentWithMinimizer:
+        fitter = type('FitterHolder', (), {'minimizer': 'MIN'})()
+
+    fit._parent = ParentWithMinimizer()
+    assert fit.minimizer == 'MIN'
+
+    shown: list[str] = []
+    monkeypatch = pytest.MonkeyPatch()
+    monkeypatch.setattr(fit_mod.MinimizerFactory, 'show_supported', lambda: shown.append('shown'))
+    Fit.show_available_minimizers()
+    monkeypatch.undo()
+
+    assert shown == ['shown']
+
+
+def test_fit_from_cif_warns_on_invalid_minimizer(monkeypatch):
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+    from easydiffraction.analysis.categories.fit.default import Fit
+
+    fit = Fit()
+    fit._minimizer_type._value = 'bad-minimizer'
+
+    class Parent:
+        fitter = None
+
+    warnings: list[str] = []
+    fit._parent = Parent()
+    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    monkeypatch.setattr(
+        fit_mod,
+        'Fitter',
+        lambda value: (_ for _ in ()).throw(ValueError('bad minimizer')),
+    )
+    monkeypatch.setattr(fit_mod.log, 'warning', lambda message: warnings.append(message))
+
+    fit.from_cif(object())
+
+    assert warnings == ['bad minimizer']
+
+
+def test_fit_fallback_paths_without_parent_or_project(capsys, monkeypatch):
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+    from easydiffraction.analysis.categories.fit.default import Fit
+
+    fit = Fit()
+
+    assert fit.minimizer is None
+
+    fit.show_modes()
+    out = capsys.readouterr().out
+    assert 'single' in out
+    assert 'joint' in out
+    assert 'sequential' in out
+
+    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    fit.from_cif(object())
+
+
+def test_fit_show_modes_for_single_and_multiple_experiments(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    single = Analysis(project=_make_project_with_names(['e1']))
+    single.fit.show_modes()
+    out_single = capsys.readouterr().out
+    assert 'Fit modes' in out_single
+    assert 'single' in out_single
+    assert 'joint' not in out_single
+
+    multi = Analysis(project=_make_project_with_names(['e1', 'e2']))
+    multi.fit.show_modes()
+    out_multi = capsys.readouterr().out
+    assert 'joint' in out_multi
+    assert 'sequential' in out_multi
+
+
+def test_show_minimizer_types_prints(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names([]))
+    analysis.fit.show_minimizer_types()
+    out = capsys.readouterr().out
+    assert 'Minimizer types' in out
+    assert 'lmfit (leastsq)' in out
+
+
+def test_analysis_help_and_mode_switching(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names(['e1', 'e2']))
+    assert analysis.fit.mode.value == 'single'
+    analysis.fit.mode = 'joint'
+    assert analysis.fit.mode.value == 'joint'
+    assert len(analysis.joint_fit_experiments) == 0
+
+    analysis.help()
+    out = _unstyled_output(capsys.readouterr().out)
+    assert "Help for 'Analysis'" in out
+    assert 'fit' in out
+    assert 'display' in out
+    assert 'Properties' in out
+    assert 'Methods' in out
+    assert 'fit_sequential()' in out
+
+
+def test_display_fit_results_warns_when_no_results(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names([]))
+    analysis.display.fit_results()
+    out = capsys.readouterr().out
+    assert 'No fit results available' in out
+
+
+def test_display_fit_results_calls_process_fit_results(monkeypatch):
+    from easydiffraction.analysis.analysis import Analysis
+
+    process_called = {'called': False, 'args': None}
+
+    def fake_process_fit_results(structures, experiments):
+        process_called['called'] = True
+        process_called['args'] = (structures, experiments)
+
+    class Project:
+        structures = object()
+        _varname = 'proj'
+        verbosity = 'full'
+
+        class Experiments:
+            names: list[str] = []
+
+            @staticmethod
+            def values():
+                return []
+
+            @property
+            def parameters(self):
+                return []
+
+            @property
+            def fittable_parameters(self):
+                return []
+
+            @property
+            def free_parameters(self):
+                return []
+
+        experiments = Experiments()
+
+    analysis = Analysis(project=Project())
+    analysis.fit_results = object()
+    monkeypatch.setattr(analysis.fitter, '_process_fit_results', fake_process_fit_results)
+
+    analysis.display.fit_results()
+
+    assert process_called['called'] is True
+
+
+def test_analysis_display_as_cif_and_constraints(monkeypatch, capsys):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project())
+    rendered: dict[str, str] = {}
+
+    monkeypatch.setattr(analysis_mod, 'render_cif', lambda text: rendered.setdefault('text', text))
+    analysis.display.as_cif()
+    assert 'text' in rendered
+
+    analysis.display.constraints()
+    assert 'No constraints' in capsys.readouterr().out
+
+    class FakeExpr:
+        value = 'x = y + 1'
+
+    class FakeConstraint:
+        expression = FakeExpr()
+
+    analysis.constraints._items = [FakeConstraint()]
+    captured: dict[str, object] = {}
+    monkeypatch.setattr(analysis_mod, 'render_table', lambda **kwargs: captured.update(kwargs))
+    analysis.display.constraints()
+    out = capsys.readouterr().out
+    assert 'User defined constraints' in out
+    assert captured['columns_data'][0][0] == 'x = y + 1'
+
+
+def test_discover_helpers_and_snapshot_params():
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.analysis.analysis import _discover_method_rows
+    from easydiffraction.analysis.analysis import _discover_property_rows
+
+    class Demo:
+        @property
+        def alpha(self):
+            """Alpha property."""
+            return 1
+
+        @property
+        def beta(self):
+            """Beta property."""
+            return 2
+
+        @beta.setter
+        def beta(self, value):
+            return None
+
+        def do_thing(self):
+            """Do a thing."""
+
+        def _private(self):
+            return None
+
+    property_rows = _discover_property_rows(Demo)
+    method_rows = _discover_method_rows(Demo)
+
+    assert len(property_rows) == 2
+    assert 'alpha' in [row[1] for row in property_rows]
+    assert next(row for row in property_rows if row[1] == 'beta')[2] == '✓'
+    assert 'do_thing()' in [row[1] for row in method_rows]
+    assert '_private()' not in [row[1] for row in method_rows]
+
+    analysis = Analysis(project=_make_project())
+
+    class FakeParam:
+        unique_name = 'p1'
+        value = 1.23
+        uncertainty = 0.01
+        units = 'A'
+
+    class FakeResults:
+        parameters = [FakeParam()]
+
+    analysis._snapshot_params('expt1', FakeResults())
+    assert analysis._parameter_snapshots['expt1']['p1']['value'] == 1.23
+    assert analysis._parameter_snapshots['expt1']['p1']['uncertainty'] == 0.01
diff --git a/tests/integration/fitting/test_bayesian_dream.py b/tests/integration/fitting/test_bayesian_dream.py
new file mode 100644
index 000000000..23e0de589
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_dream.py
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import tempfile
+
+import numpy as np
+
+from easydiffraction import ExperimentFactory
+from easydiffraction import Project
+from easydiffraction import StructureFactory
+from easydiffraction import download_data
+
+TEMP_DIR = tempfile.gettempdir()
+
+
+def _create_lbco_project() -> Project:
+    model = StructureFactory.from_scratch(name='lbco')
+    model.space_group.name_h_m = 'P m -3 m'
+    model.cell.length_a = 3.88
+    model.atom_sites.create(
+        label='La',
+        type_symbol='La',
+        fract_x=0,
+        fract_y=0,
+        fract_z=0,
+        wyckoff_letter='a',
+        occupancy=0.5,
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='Ba',
+        type_symbol='Ba',
+        fract_x=0,
+        fract_y=0,
+        fract_z=0,
+        wyckoff_letter='a',
+        occupancy=0.5,
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='Co',
+        type_symbol='Co',
+        fract_x=0.5,
+        fract_y=0.5,
+        fract_z=0.5,
+        wyckoff_letter='b',
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='O',
+        type_symbol='O',
+        fract_x=0,
+        fract_y=0.5,
+        fract_z=0.5,
+        wyckoff_letter='c',
+        adp_iso=0.1,
+    )
+
+    data_path = download_data(id=3, destination=TEMP_DIR)
+    experiment = ExperimentFactory.from_data_path(name='hrpt', data_path=data_path)
+    experiment.instrument.setup_wavelength = 1.494
+    experiment.instrument.calib_twotheta_offset = 0.0
+    experiment.peak.broad_gauss_u = 0.1
+    experiment.peak.broad_gauss_v = -0.1
+    experiment.peak.broad_gauss_w = 0.2
+    experiment.peak.broad_lorentz_x = 0.0
+    experiment.peak.broad_lorentz_y = 0.0
+    experiment.linked_phases.create(id='lbco', scale=5.0)
+    experiment.background.create(id='1', x=10, y=170)
+    experiment.background.create(id='2', x=165, y=170)
+
+    project = Project(name='lbco_bayesian')
+    project.structures.add(model)
+    project.experiments.add(experiment)
+    return project
+
+
+def _dream_parameters(project: Project) -> tuple[object, object, object]:
+    structure = project.structures['lbco']
+    experiment = project.experiments['hrpt']
+    return (
+        structure.cell.length_a,
+        experiment.linked_phases['lbco'].scale,
+        experiment.instrument.calib_twotheta_offset,
+    )
+
+
+def _configure_small_dream(project: Project) -> None:
+    project.analysis.fit.minimizer_type = 'bumps (dream)'
+    minimizer = project.analysis.fit.minimizer
+    minimizer.steps = 20
+    minimizer.burn = 5
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = 'lhs'
+
+
+def test_small_bounded_dream_refinement_produces_posterior_results():
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    length_a.fit_min = 3.84
+    length_a.fit_max = 3.92
+    scale.fit_min = 1.0
+    scale.fit_max = 12.0
+    offset.fit_min = -1.0
+    offset.fit_max = 1.0
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=11)
+
+    results = project.analysis.fit_results
+    assert results.success is True
+    assert results.sampler_completed is True
+    assert results.sampler_name == 'dream'
+    assert results.posterior_samples is not None
+    assert results.posterior_samples.parameter_samples.ndim == 3
+    assert results.sampler_settings['random_seed'] == 11
+    assert results.sampler_settings['init'] == 'lhs'
+    assert len(results.posterior_parameter_summaries) == 3
+
+
+def test_lm_prefit_followed_by_dream_uses_uncertainty_based_bounds():
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    project.analysis.fit.minimizer_type = 'bumps (lm)'
+    project.analysis.fit(verbosity='silent')
+
+    for parameter in (length_a, scale, offset):
+        assert parameter.uncertainty is not None
+        parameter.set_fit_bounds_from_uncertainty(multiplier=4)
+        assert np.isfinite(parameter.fit_min)
+        assert np.isfinite(parameter.fit_max)
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=13)
+
+    results = project.analysis.fit_results
+    assert results.success is True
+    assert results.posterior_samples is not None
+    assert results.sampler_settings['random_seed'] == 13
+    assert len(results.posterior_parameter_summaries) == 3
+
+
+def test_bayesian_fit_results_are_runtime_only_after_save_load(tmp_path):
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    length_a.fit_min = 3.84
+    length_a.fit_max = 3.92
+    scale.fit_min = 1.0
+    scale.fit_max = 12.0
+    offset.fit_min = -1.0
+    offset.fit_max = 1.0
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=17)
+
+    assert project.analysis.fit_results.posterior_samples is not None
+
+    proj_dir = tmp_path / 'dream_project'
+    project.save_as(str(proj_dir))
+
+    analysis_cif = proj_dir / 'analysis' / 'analysis.cif'
+    assert analysis_cif.is_file()
+    assert 'posterior' not in analysis_cif.read_text().lower()
+
+    loaded = Project.load(str(proj_dir))
+    assert loaded.analysis.fit_results is None
diff --git a/tests/integration/fitting/test_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py
new file mode 100644
index 000000000..a4eef9007
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_helper_support.py
@@ -0,0 +1,670 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import re
+
+import numpy as np
+import pytest
+
+ANSI_ESCAPE_RE = re.compile(r'\x1b\[[0-?]*[ -/]*[@-~]')
+
+
+def _unstyled_output(text: str) -> str:
+    return ANSI_ESCAPE_RE.sub('', text)
+
+
+class Identity:
+    def __init__(self) -> None:
+        self.datablock_entry_name = 'db'
+        self.category_code = 'cat'
+        self.category_entry_name = 'entry'
+
+
+class Param:
+    def __init__(self, unique_name: str, start: float, value: float, uncertainty: float) -> None:
+        self._identity = Identity()
+        self._fit_start_value = start
+        self.unique_name = unique_name
+        self.name = unique_name
+        self.value = value
+        self.uncertainty = uncertainty
+        self.units = 'arb'
+
+
+def test_posterior_samples_flatten_and_to_arviz():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a', 'b'],
+        parameter_samples=np.array(
+            [
+                [[1.0, 10.0], [2.0, 20.0]],
+                [[3.0, 30.0], [4.0, 40.0]],
+            ],
+            dtype=float,
+        ),
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+
+    flattened = posterior_samples.flattened()
+    inference_data = posterior_samples.to_arviz()
+
+    assert flattened.shape == (4, 2)
+    np.testing.assert_allclose(flattened[:, 0], np.array([1.0, 2.0, 3.0, 4.0]))
+    np.testing.assert_allclose(flattened[:, 1], np.array([10.0, 20.0, 30.0, 40.0]))
+    assert set(inference_data.posterior.data_vars) == {'a', 'b'}
+    assert inference_data.posterior['a'].shape == (2, 2)
+    assert inference_data.sample_stats['lp'].shape == (2, 2)
+
+
+def test_posterior_samples_to_arviz_validates_shapes():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.array([1.0, 2.0]),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array must have shape \(n_draws, n_chains, n_parameters\)\.',
+    ):
+        posterior_samples.to_arviz()
+
+
+def test_posterior_samples_to_arviz_validates_name_and_log_posterior_lengths():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    wrong_names = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((2, 2, 2), dtype=float),
+    )
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array does not match the parameter name list length\.',
+    ):
+        wrong_names.to_arviz()
+
+    wrong_log_posterior = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((2, 2, 1), dtype=float),
+        log_posterior=np.ones((3, 2), dtype=float),
+    )
+    with pytest.raises(
+        ValueError,
+        match=r'Log-posterior array must match the first two posterior sample axes\.',
+    ):
+        wrong_log_posterior.to_arviz()
+
+
+def test_compute_convergence_diagnostics_treats_non_finite_values_as_not_converged(
+    monkeypatch,
+):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((4, 2, 1), dtype=float),
+    )
+
+    fake_dataset = type('FakeDataset', (), {'data_vars': {'a': np.array([np.nan], dtype=float)}})
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.rhat',
+        lambda inference_data: fake_dataset,
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.ess',
+        lambda inference_data, method='bulk': type(
+            'FakeDataset', (), {'data_vars': {'a': np.array([4000.0], dtype=float)}}
+        ),
+    )
+
+    diagnostics = compute_convergence_diagnostics(posterior_samples)
+
+    assert diagnostics['converged'] is False
+    assert diagnostics['r_hat_by_parameter'] == {'a': None}
+    assert diagnostics['ess_bulk_by_parameter'] == {'a': 4000.0}
+    assert diagnostics['max_r_hat'] is None
+    assert diagnostics['min_ess_bulk'] == pytest.approx(4000.0)
+
+
+def test_summarize_posterior_parameters_preserves_order_and_display_names():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['beta', 'alpha'],
+        parameter_samples=np.array(
+            [
+                [[2.0, 1.0], [2.2, 1.2]],
+                [[1.8, 0.8], [2.1, 1.1]],
+            ],
+            dtype=float,
+        ),
+    )
+
+    summaries = summarize_posterior_parameters(
+        parameter_names=['beta', 'alpha'],
+        posterior_samples=posterior_samples,
+        map_values=np.array([2.05, 1.05]),
+        parameter_display_names=['Beta width', 'Alpha shift'],
+        convergence_diagnostics={
+            'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
+            'ess_bulk_by_parameter': {'beta': 120.0, 'alpha': 800.0},
+        },
+    )
+
+    assert [summary.unique_name for summary in summaries] == ['beta', 'alpha']
+    assert [summary.display_name for summary in summaries] == ['Beta width', 'Alpha shift']
+    assert summaries[0].r_hat == pytest.approx(1.02)
+    assert summaries[0].ess_bulk == pytest.approx(120.0)
+    assert summaries[1].r_hat == pytest.approx(1.0)
+    assert summaries[1].ess_bulk == pytest.approx(800.0)
+
+
+def test_summarize_posterior_parameters_validates_display_name_length():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['alpha'],
+        parameter_samples=np.ones((2, 2, 1), dtype=float),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior display-name list must match the sampled parameter name list length\.',
+    ):
+        summarize_posterior_parameters(
+            parameter_names=['alpha'],
+            posterior_samples=posterior_samples,
+            map_values=np.array([1.0]),
+            parameter_display_names=['Alpha', 'Extra'],
+        )
+
+
+def test_standard_deviations_from_summaries_returns_float_array():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import standard_deviations_from_summaries
+
+    values = standard_deviations_from_summaries([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='A',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.2,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+        ),
+        PosteriorParameterSummary(
+            unique_name='b',
+            display_name='B',
+            map_value=2.0,
+            median=2.0,
+            standard_deviation=0.3,
+            interval_68=(1.9, 2.1),
+            interval_95=(1.8, 2.2),
+        ),
+    ])
+
+    np.testing.assert_allclose(values, np.array([0.2, 0.3]))
+    assert values.dtype == float
+
+
+def test_bayesian_format_helpers_cover_edge_cases():
+    from easydiffraction.analysis.fit_helpers.bayesian import _calculate_fit_quality_metrics
+    from easydiffraction.analysis.fit_helpers.bayesian import _dataset_to_scalar_dict
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_bayesian_overall_status
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_convergence_summary
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_point_estimate_name
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_sampler_settings
+    from easydiffraction.analysis.fit_helpers.bayesian import _maybe_scalar
+
+    dataset = type(
+        'FakeDataset',
+        (),
+        {'data_vars': {'a': np.array([np.nan], dtype=float), 'b': np.array([3.0], dtype=float)}},
+    )
+
+    assert _maybe_scalar(None) is None
+    assert _maybe_scalar(float('inf')) is None
+    assert _maybe_scalar(3.0) == pytest.approx(3.0)
+    assert _dataset_to_scalar_dict(dataset) == {'a': None, 'b': 3.0}
+    assert _format_sampler_settings({}) is None
+    assert (
+        _format_sampler_settings({'steps': 10, 'burn': 2, 'samples': 40})
+        == 'steps=10, burn=2, samples=40'
+    )
+    assert _format_point_estimate_name('map') == 'Max posterior'
+    assert _format_point_estimate_name('best_sample') == 'Best Sample'
+    assert _format_bayesian_overall_status(
+        success=False,
+        sampler_completed=False,
+        convergence_diagnostics={},
+    ) == ('❌', 'failed')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=False,
+        convergence_diagnostics={'converged': False},
+    ) == ('⚠️', 'completed with warnings')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=True,
+        convergence_diagnostics={'converged': True},
+    ) == ('✅', 'completed')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=False,
+        convergence_diagnostics={},
+    ) == ('✅', 'posterior available')
+    assert _format_convergence_summary({}) is None
+    assert _format_convergence_summary({
+        'converged': False,
+        'max_r_hat': 1.02,
+        'min_ess_bulk': 200.0,
+        'n_draws': 30,
+        'n_chains': 8,
+    }) == (
+        'status=[red]failed[/red], max_r_hat=[red]1.020[/red], '
+        'min_ess_bulk=[red]200.0[/red], draws=30, chains=8'
+    )
+
+    metrics = _calculate_fit_quality_metrics(
+        y_obs=[10.0, 20.0],
+        y_calc=[9.5, 19.5],
+        y_err=[1.0, 1.0],
+        f_obs=[5.0, 6.0],
+        f_calc=[5.1, 5.9],
+    )
+
+    assert metrics['rf'] is not None
+    assert metrics['rf2'] is not None
+    assert metrics['wr'] is not None
+    assert metrics['br'] is not None
+
+
+def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(capsys, monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.utils.logging import Logger
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
+
+    results = BayesianFitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+        sampler_name='dream',
+        sampler_completed=True,
+        sampler_settings={
+            'random_seed': 1313900679,
+            'steps': 200,
+            'burn': 50,
+            'thin': 1,
+            'pop': 4,
+            'init': 'lhs',
+            'samples': 3200,
+        },
+        convergence_diagnostics={
+            'converged': False,
+            'max_r_hat': 1.107,
+            'min_ess_bulk': 125.9,
+            'n_draws': 200,
+            'n_chains': 16,
+        },
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+        best_log_posterior=-12.34,
+    )
+    results.message = 'DREAM sampling completed'
+
+    results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
+
+    out = _unstyled_output(capsys.readouterr().out)
+    assert 'Bayesian fit results' in out
+    assert 'Overall status: completed with warnings' in out
+    assert 'Sampler status: DREAM sampling completed' in out
+    assert 'Sampler: dream' in out
+    assert 'Sampler completed: yes' in out
+    assert 'steps=200' in out
+    assert 'init=lhs' in out
+    assert 'random_seed=1313900679' not in out
+    assert 'status=failed' in out
+    assert 'max_r_hat=1.107' in out
+    assert 'min_ess_bulk=125.9' in out
+    assert 'Posterior parameter summaries:' in out
+    assert 'Success: True' not in out
+    assert 'datablock' in out
+    assert 'category' in out
+    assert 'entry' in out
+    assert '95% interval' in out
+    assert '68% interval' not in out
+    assert 'std' not in out
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
+
+
+def test_render_posterior_summary_table_without_summaries_prints_notice(capsys):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    bayesian._render_posterior_summary_table(parameters=[], posterior_parameter_summaries=[])
+
+    assert 'No posterior parameter summaries available.' in capsys.readouterr().out
+
+
+def test_build_posterior_summary_row_restores_identifier_columns():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+
+    parameter = Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    summary = PosteriorParameterSummary(
+        unique_name='a',
+        display_name='a',
+        map_value=1.2,
+        median=1.15,
+        standard_deviation=0.05,
+        interval_68=(1.1, 1.2),
+        interval_95=(1.0, 1.3),
+        r_hat=1.107,
+        ess_bulk=125.9,
+    )
+
+    row = _build_posterior_summary_row(summary, {'a': parameter})
+
+    assert row == [
+        'db',
+        'cat',
+        'entry',
+        'a',
+        'arb',
+        '1.1500',
+        '[1.0000, 1.3000]',
+        '[red]1.107[/red]',
+        '[red]125.9[/red]',
+    ]
+
+
+def test_render_committed_parameter_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_committed_parameter_table([
+        Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    ])
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'max posterior',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
+
+
+def test_render_posterior_summary_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_posterior_summary_table(
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+    )
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'median',
+        '95% interval',
+        'r-hat',
+        'ess bulk',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.1500',
+            '[1.0000, 1.3000]',
+            '[red]1.107[/red]',
+            '[red]125.9[/red]',
+        ]
+    ]
+
+
+def test_posterior_table_notes_split_failed_diagnostics():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    notes = _posterior_table_notes([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='a',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.1,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+            r_hat=1.02,
+            ess_bulk=100.0,
+        )
+    ])
+
+    assert len(notes) == 2
+    assert 'r-hat' in notes[0]
+    assert 'ess bulk' in notes[1]
+
+
+def test_bayesian_helpers_cover_non_warning_and_default_display_paths():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_ess_bulk
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_r_hat
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    summary = PosteriorParameterSummary(
+        unique_name='missing',
+        display_name='Missing',
+        map_value=1.0,
+        median=1.0,
+        standard_deviation=0.1,
+        interval_68=(0.9, 1.1),
+        interval_95=(0.8, 1.2),
+        r_hat=1.0,
+        ess_bulk=500.0,
+    )
+
+    row = _build_posterior_summary_row(summary, {})
+
+    assert row == [
+        'N/A',
+        'N/A',
+        '',
+        'Missing',
+        'N/A',
+        '1.0000',
+        '[0.8000, 1.2000]',
+        '1.000',
+        '500.0',
+    ]
+    assert _format_r_hat(None) == 'N/A'
+    assert _format_r_hat(1.0) == '1.000'
+    assert _format_ess_bulk(None) == 'N/A'
+    assert _format_ess_bulk(500.0) == '500.0'
+    assert _posterior_table_notes([]) == []
+    assert _posterior_table_notes([summary]) == []
+
+
+def test_fitresults_display_results_prints_and_table(capsys):
+    from easydiffraction.analysis.fit_helpers.reporting import FitResults
+
+    params = [Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)]
+
+    results = FitResults(
+        success=True,
+        parameters=params,
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+    )
+
+    results.display_results(
+        y_obs=[10.0, 20.0],
+        y_calc=[9.5, 19.5],
+        y_err=[1.0, 1.0],
+        f_obs=[5.0, 6.0],
+        f_calc=[5.1, 5.9],
+    )
+
+    out = _unstyled_output(capsys.readouterr().out)
+    assert 'Fit results' in out
+    assert 'Success: True' in out
+    assert 'reduced χ²' in out
+    assert 'R-factor (Rf)' in out
+    assert 'R-factor squared (Rf²)' in out
+    assert 'Weighted R-factor (wR)' in out
+    assert 'Bragg R-factor (BR)' in out
+    assert 'Fitted parameters:' in out
+    assert any(char in out for char in ('╒', '┌', '+', '─'))
+
+
+def test_fitresults_display_results_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import reporting
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(reporting, 'render_table', fake_render_table)
+
+    reporting.FitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+    ).display_results()
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'fitted',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
diff --git a/tests/integration/fitting/test_bayesian_tracker_and_base.py b/tests/integration/fitting/test_bayesian_tracker_and_base.py
new file mode 100644
index 000000000..f63cb111f
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py
@@ -0,0 +1,451 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pytest
+
+
+class DummyParam:
+    def __init__(self, value: float) -> None:
+        self.value = value
+        self.fit_min = -np.inf
+        self.fit_max = np.inf
+        self.unique_name = f'param_{value}'
+
+    def _physical_lower_bound(self) -> float:
+        return -np.inf
+
+    def _physical_upper_bound(self) -> float:
+        return np.inf
+
+
+def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker.start_tracking('dummy')
+    tracker.start_timer()
+
+    tracker.track(np.array([2.0, 1.0]), parameters=[1])
+    out = capsys.readouterr().out
+    assert 'Goodness-of-fit' in out
+
+    tracker.track(np.array([1.9, 1.0]), parameters=[1])
+    tracker.track(np.array([0.1, 0.1]), parameters=[1])
+
+    tracker.stop_timer()
+    tracker.finish_tracking()
+
+    out = capsys.readouterr().out
+    assert 'Best goodness-of-fit' in out
+    assert tracker.best_iteration is not None
+
+
+def test_tracker_sampler_progress_renders_and_completes(monkeypatch, capsys):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker.start_tracking('dream', mode='sampling')
+    tracker.start_timer()
+    tracker.track_sampler_progress(
+        SamplerProgressUpdate(
+            iteration=1,
+            total_iterations=10,
+            phase='burn-in',
+            progress_percent=10.0,
+            log_posterior=-12.0,
+            reduced_chi2=5.0,
+            elapsed_time=0.0,
+            force_report=True,
+        )
+    )
+    tracker.track_sampler_progress(
+        SamplerProgressUpdate(
+            iteration=10,
+            total_iterations=10,
+            phase='sampling',
+            progress_percent=100.0,
+            log_posterior=-3.0,
+            reduced_chi2=1.0,
+            elapsed_time=6.0,
+            force_report=False,
+        )
+    )
+    tracker.stop_timer()
+    tracker.finish_tracking()
+
+    out = capsys.readouterr().out
+    assert 'Bayesian sampling progress' in out
+    assert 'Bayesian sampling complete.' in out
+    assert tracker.best_chi2 == pytest.approx(1.0)
+    assert tracker.best_iteration == 10
+
+
+def test_tracker_helper_error_paths_and_short_mode(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.utils.enums import VerbosityEnum
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker._verbosity = VerbosityEnum.SHORT
+    tracker.stop_timer()
+    tracker.start_tracking('dream', mode='sampling')
+    tracker.finish_tracking()
+
+    with pytest.raises(RuntimeError, match='Sampler progress is unavailable'):
+        FitProgressTracker()._resolved_final_sampler_progress()
+    with pytest.raises(RuntimeError, match='Sampler iteration labels require'):
+        FitProgressTracker()._sampler_iteration_label(1)
+
+    assert FitProgressTracker._rows_match_on_columns(['1', 'a'], ['1', 'b'], (0,)) is True
+
+
+def test_tracker_final_sampler_row_replaces_last_row(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: None)
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = 'sampling'
+    tracker._sampler_total_iterations = 10
+    tracker._last_iteration = 10
+    tracker._last_sampler_progress_percent = 100.0
+    tracker._last_sampler_log_posterior = -3.0
+    tracker._last_sampler_phase = 'sampling'
+    tracker._last_sampler_elapsed_time = 5.0
+    tracker._df_rows = [['10/10', '90.0%', '4.00', '-3.00', 'sampling']]
+
+    tracker.finish_tracking()
+
+    assert tracker._df_rows[-1] == ['10/10', '100.0%', '5.00', '-3.00', 'sampling']
+
+
+def test_make_display_handle_uses_terminal_live_when_available(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+
+    class FakeLive:
+        def __init__(self, *, console, auto_refresh):
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.started = False
+            self.stopped = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.stopped = True
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(tracking_mod, 'Live', FakeLive)
+    monkeypatch.setattr(tracking_mod.ConsoleManager, 'get', lambda: 'console')
+
+    handle = tracking_mod._make_display_handle()
+
+    assert isinstance(handle, tracking_mod._TerminalLiveHandle)
+    assert handle._live.console == 'console'
+    assert handle._live.auto_refresh is True
+    assert handle._live.started is True
+
+    handle.close()
+
+    assert handle._live.stopped is True
+
+
+def test_tracker_misc_helper_paths(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    render_calls: list[dict[str, object]] = []
+    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: render_calls.append(kwargs))
+    monkeypatch.setattr(
+        tracking_mod, 'calculate_reduced_chi_square', lambda residuals, n_params: 3.0
+    )
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    tracker._previous_chi2 = 5.0
+    tracker._best_chi2 = 5.0
+
+    residuals = np.array([1.0, -1.0], dtype=float)
+
+    assert np.array_equal(tracker.track(residuals, [1.0]), residuals)
+    assert tracker.best_chi2 == pytest.approx(3.0)
+
+    tracker.start_timer()
+    tracker.stop_timer()
+    assert tracker.fitting_time is not None
+
+    tracker.reset()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    assert tracker._headers() == tracking_mod.SAMPLER_HEADERS
+    assert tracker._alignments() == tracking_mod.SAMPLER_ALIGNMENTS
+    assert tracker._current_elapsed_time() is None
+    assert tracker._format_elapsed_time() == ''
+
+    tracker._replace_last_tracking_row(['1'])
+
+    assert tracker._df_rows == [['1']]
+    assert len(render_calls) == 1
+
+
+def test_tracker_final_rows_cover_fallbacks_and_close_suppression():
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    tracker._sampler_total_iterations = 10
+    tracker._last_iteration = 8
+    tracker._last_sampler_elapsed_time = 2.5
+
+    assert tracker._final_sampler_tracking_row() == ['8/10', '80.0%', '2.50', '', 'sampling']
+
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_FIT
+    tracker._fitting_time = 1.5
+    assert tracker._final_fit_tracking_row() == ['8', '1.50', '', '']
+
+    class BadHandle:
+        @staticmethod
+        def close() -> None:
+            message = 'boom'
+            raise RuntimeError(message)
+
+    tracker._display_handle = BadHandle()
+    tracker._close_display_handle()
+
+
+def test_minimizer_base_fit_flow_and_finalize():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    @dataclass
+    class DummyResult:
+        success: bool = True
+
+    class DummyMinimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.synced = False
+
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {'ok': True}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            residuals = objective_function(kwargs.get('engine_parameters'))
+            self.tracker.track(residuals=np.array(residuals), parameters=[1])
+            return DummyResult(success=True)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            self.synced = True
+            if parameters:
+                parameters[0].value = 42
+
+        def _check_success(self, raw_result):
+            return getattr(raw_result, 'success', False)
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            assert engine_params == {'ok': True}
+            return np.array([0.0, 0.0])
+
+    minimizer = DummyMinimizer()
+    params = [DummyParam(1.0), DummyParam(2.0)]
+    objective = minimizer._create_objective_function(
+        parameters=params,
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    result = minimizer.fit(parameters=params, objective_function=objective)
+
+    assert result.success is True
+    assert minimizer.synced is True
+    assert isinstance(result.parameters, list)
+    assert result.parameters[0].value == 42
+    assert minimizer.tracker.fitting_time is not None
+    assert minimizer.tracker.fitting_time >= 0.0
+
+
+def test_minimizer_base_create_objective_function_uses_compute_residuals():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def _prepare_solver_args(self, parameters):
+            return {}
+
+        def _run_solver(self, objective_function, **kwargs):
+            return None
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([1.0, 2.0, 3.0])
+
+    minimizer = Minimizer()
+    objective = minimizer._create_objective_function(
+        parameters=[],
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    np.testing.assert_allclose(objective({}), np.array([1.0, 2.0, 3.0]))
+
+
+def test_minimizer_base_fit_stops_tracking_when_solver_prep_fails():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.started = False
+            self.stopped = False
+
+        def _start_tracking(self, minimizer_name, verbosity=None):
+            self.started = True
+
+        def _stop_tracking(self):
+            self.stopped = True
+
+        def _prepare_solver_args(self, parameters):
+            message = 'prep failed'
+            raise ValueError(message)
+
+        def _run_solver(self, objective_function, **kwargs):
+            message = 'should not run solver'
+            raise AssertionError(message)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+    minimizer = Minimizer()
+
+    with pytest.raises(ValueError, match='prep failed'):
+        minimizer.fit(parameters=[DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
+
+    assert minimizer.started is True
+    assert minimizer.stopped is True
+
+
+def test_minimizer_base_applies_physical_limits_and_warns(monkeypatch):
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    warnings: list[str] = []
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.base.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    class BoundaryParam(DummyParam):
+        def __init__(self) -> None:
+            super().__init__(5.0)
+            self.unique_name = 'boundary'
+
+        def _physical_lower_bound(self) -> float:
+            return 0.0
+
+        def _physical_upper_bound(self) -> float:
+            return 10.0
+
+    class DummyResult:
+        success = True
+
+    class Minimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            residuals = objective_function(kwargs.get('engine_parameters'))
+            self.tracker.track(residuals=np.array(residuals), parameters=[1])
+            return DummyResult()
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            parameters[0].value = 11.0
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([0.0, 0.0])
+
+    minimizer = Minimizer()
+    parameter = BoundaryParam()
+    objective = minimizer._create_objective_function(
+        parameters=[parameter],
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    result = minimizer.fit(
+        parameters=[parameter],
+        objective_function=objective,
+        use_physical_limits=True,
+    )
+
+    assert result.success is True
+    assert parameter.fit_min == 0.0
+    assert parameter.fit_max == 10.0
+    assert parameter._outside_physical_limits is True
+    assert any('upper bound' in message for message in warnings)
+    assert any('physical upper limit' in message for message in warnings)
+
+
+def test_minimizer_base_rejects_random_seed_when_not_supported():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            return object()
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([0.0])
+
+    minimizer = Minimizer(name='dummy')
+
+    with pytest.raises(
+        ValueError,
+        match=r"Minimizer 'dummy' does not support random_seed\.",
+    ):
+        minimizer.fit(parameters=[], objective_function=lambda _: np.array([0.0]), random_seed=7)
diff --git a/tests/integration/fitting/test_bumps_dream_support.py b/tests/integration/fitting/test_bumps_dream_support.py
new file mode 100644
index 000000000..bf87549a4
--- /dev/null
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -0,0 +1,559 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+
+class FakeParam:
+    def __init__(
+        self,
+        uid: str,
+        value: float,
+        uncertainty: float | None = None,
+        *,
+        fit_min: float | None = 0.0,
+        fit_max: float | None = 1.0,
+    ) -> None:
+        self._minimizer_uid = uid
+        self.unique_name = uid
+        self.name = uid.upper()
+        self.value = value
+        self.uncertainty = uncertainty
+        self.fit_min = fit_min
+        self.fit_max = fit_max
+
+    def _set_value_from_minimizer(self, value: float) -> None:
+        self.value = value
+
+
+def test_type_info_and_default_init():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+    from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+    assert minimizer.steps == 3000
+
+
+def test_dream_progress_monitor_allocates_rows_by_phase_ratio():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=100,
+        n_parameters=3,
+        total_generations=100,
+        burn_steps=40,
+    )
+
+    assert len(monitor._burn_targets) == 10
+    assert len(monitor._sampling_targets) == 15
+
+
+def test_dream_progress_monitor_helper_edge_cases():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=2,
+        n_parameters=3,
+        total_generations=10,
+        burn_steps=2,
+    )
+
+    assert _DreamProgressMonitor._progress_targets(start=3, stop=2, target_count=2) == []
+    assert _DreamProgressMonitor._progress_targets(start=1, stop=5, target_count=0) == []
+    assert _DreamProgressMonitor._phase_progress_point_counts(
+        total_generations=10, burn_steps=0
+    ) == (0, 10)
+    assert _DreamProgressMonitor._phase_progress_point_counts(
+        total_generations=10, burn_steps=10
+    ) == (10, 0)
+    assert _DreamProgressMonitor._population_mean_log_posterior(
+        SimpleNamespace(population_values=[], value=[2.5])
+    ) == pytest.approx(-2.5)
+    assert _DreamProgressMonitor._population_mean_log_posterior(
+        SimpleNamespace(population_values=[np.array([np.nan, np.inf])], value=[1.5])
+    ) == pytest.approx(-1.5)
+    assert monitor._reduced_chi_square_from_nllf(4.0) == pytest.approx(8.0)
+
+
+def test_init_accepts_enum_or_string_and_rejects_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+
+    minimizer.init = 'random'
+    assert minimizer.init is DreamPopulationInitializationEnum.RANDOM
+
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'init' must be one of: eps, cov, lhs, random\.",
+    ):
+        minimizer.init = 'bad-init'
+
+
+def test_resolve_random_seed_returns_provided_or_generated(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolve_random_seed(17) == 17
+    assert minimizer._resolved_random_seed == 17
+
+    generator = SimpleNamespace(integers=lambda *args, **kwargs: 123456)
+    monkeypatch.setattr(np.random, 'default_rng', lambda: generator)
+
+    assert minimizer._resolve_random_seed(None) == 123456
+    assert minimizer._resolved_random_seed == 123456
+
+
+def test_dream_numeric_validators_reject_boolean_inputs():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        TypeError,
+        match=r"DREAM setting 'steps' must be a positive integer\.",
+    ):
+        minimizer.steps = True
+    with pytest.raises(
+        TypeError,
+        match=r"DREAM setting 'parallel' must be a non-negative integer\.",
+    ):
+        minimizer.parallel = True
+    with pytest.raises(TypeError, match='DREAM random_seed must be an integer'):
+        minimizer._validated_random_seed_value(random_seed=True)
+
+
+@pytest.mark.parametrize('seed', [-1, np.iinfo(np.uint32).max + 1])
+def test_resolve_random_seed_rejects_out_of_range_values(seed):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r'DREAM random_seed must be an integer between 0 and 4294967295\.',
+    ):
+        minimizer._resolve_random_seed(seed)
+
+
+def test_resolved_burn_uses_auto_or_explicit_and_validates():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolved_burn(steps=200) == 50
+
+    minimizer.burn = 20
+    assert minimizer._resolved_burn(steps=200) == 20
+
+    minimizer.burn = 200
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'burn' must be smaller than 'steps'\.",
+    ):
+        minimizer._resolved_burn(steps=200)
+
+
+def test_sampler_settings_include_init_and_sample_count():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+
+    settings = minimizer._sampler_settings(
+        random_seed=7,
+        steps=10,
+        burn=2,
+        n_parameters=3,
+    )
+
+    assert settings['random_seed'] == 7
+    assert settings['parallel'] == 0
+    assert settings['init'] == 'lhs'
+    assert settings['samples'] == 120
+
+
+@pytest.mark.parametrize(
+    ('fit_min', 'fit_max', 'value', 'message'),
+    [
+        (None, 1.0, 0.5, r'fit_min must be finite'),
+        (0.0, np.inf, 0.5, r'fit_max must be finite'),
+        (2.0, 1.0, 1.5, r'fit_min \(2\.0\) must be smaller than fit_max \(1\.0\)'),
+        (0.0, 1.0, 2.0, r'starting value 2\.0 is outside \[0\.0, 1\.0\]'),
+    ],
+)
+def test_prepare_solver_args_rejects_invalid_dream_bounds(fit_min, fit_max, value, message):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameter = FakeParam('alpha', value, fit_min=fit_min, fit_max=fit_max)
+
+    with pytest.raises(ValueError, match=message):
+        minimizer._prepare_solver_args([parameter])
+
+
+def test_prepare_solver_args_lists_all_offending_dream_parameters():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [
+        FakeParam('alpha', 2.0, fit_min=0.0, fit_max=1.0),
+        FakeParam('beta', 0.5, fit_min=None, fit_max=1.0),
+    ]
+
+    with pytest.raises(
+        ValueError,
+        match=r'alpha: .*outside \[0\.0, 1\.0\][\s\S]*beta: .*fit_min',
+    ):
+        minimizer._prepare_solver_args(parameters)
+
+
+def test_sync_result_to_parameters_restores_starting_values_on_failure():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [FakeParam('a', 10.0, uncertainty=0.7), FakeParam('b', 20.0, uncertainty=0.8)]
+    raw_result = SimpleNamespace(
+        x=np.array([99.0, 88.0]),
+        success=False,
+        starting_values=np.array([1.5, 2.5]),
+        starting_uncertainties=[0.1, None],
+    )
+
+    minimizer._sync_result_to_parameters(parameters, raw_result)
+
+    assert parameters[0].value == 1.5
+    assert parameters[0].uncertainty == 0.1
+    assert parameters[1].value == 2.5
+    assert parameters[1].uncertainty is None
+
+
+def test_build_mapper_falls_back_for_serial_and_unpicklable(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 1
+    assert minimizer._build_mapper('problem') is None
+
+    warnings: list[str] = []
+    minimizer.parallel = 0
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: False
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    assert minimizer._build_mapper('problem') is None
+    assert any('falling back to serial execution' in message for message in warnings)
+
+
+def test_run_solver_preserves_parameter_order_and_forwards_init():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.steps = 4
+    minimizer.burn = 1
+    minimizer.thin = 1
+    minimizer.pop = 2
+    minimizer.init = 'lhs'
+
+    draw_index = np.array([0.0, 1.0])
+    parameter_samples = np.array(
+        [
+            [[1.0, 10.0], [2.0, 20.0]],
+            [[3.0, 30.0], [4.0, 40.0]],
+        ],
+        dtype=float,
+    )
+    log_posterior = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float)
+
+    class FakeState:
+        labels = ['uid_a', 'uid_b']
+
+        def chains(self):
+            return draw_index, parameter_samples, log_posterior
+
+        def best(self):
+            return np.array([11.0, 22.0]), 3.5
+
+    fake_fitter = SimpleNamespace(id='dream')
+
+    with (
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FITTERS', [fake_fitter]),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+            return_value={'converged': True},
+        ),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+            return_value=[
+                PosteriorParameterSummary(
+                    unique_name='beta',
+                    display_name='Beta',
+                    map_value=22.0,
+                    median=21.0,
+                    standard_deviation=0.4,
+                    interval_68=(20.5, 21.5),
+                    interval_95=(20.0, 22.0),
+                ),
+                PosteriorParameterSummary(
+                    unique_name='alpha',
+                    display_name='Alpha',
+                    map_value=11.0,
+                    median=10.5,
+                    standard_deviation=0.3,
+                    interval_68=(10.0, 11.0),
+                    interval_95=(9.5, 11.5),
+                ),
+            ],
+        ) as summarize_mock,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+            return_value=np.array([0.4, 0.3]),
+        ),
+    ):
+        driver_instance = mock_driver_cls.return_value
+        driver_instance.clip = MagicMock()
+        driver_instance.fit.return_value = (np.array([22.0, 11.0]), 0.25)
+        driver_instance.fitter = SimpleNamespace(state=FakeState())
+
+        result = minimizer._run_solver(
+            lambda values: np.array([0.0, 0.0]),
+            bumps_params=[
+                SimpleNamespace(name='uid_a', value=1.0),
+                SimpleNamespace(name='uid_b', value=2.0),
+            ],
+            parameter_names=['beta', 'alpha'],
+            parameter_display_names=['Beta', 'Alpha'],
+            parameter_uids=['uid_b', 'uid_a'],
+            random_seed=17,
+            starting_uncertainties=[0.01, 0.02],
+        )
+
+    assert mock_driver_cls.call_args.kwargs['init'] == 'lhs'
+    np.testing.assert_allclose(result.x, np.array([22.0, 11.0]))
+    np.testing.assert_allclose(result.dx, np.array([0.4, 0.3]))
+    assert result.posterior_samples.parameter_names == ['beta', 'alpha']
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 0], parameter_samples[:, :, 1]
+    )
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 1], parameter_samples[:, :, 0]
+    )
+    assert result.sampler_settings['init'] == 'lhs'
+    assert result.sampler_settings['random_seed'] == 17
+    assert summarize_mock.call_args.kwargs['parameter_names'] == ['beta', 'alpha']
+
+
+def test_build_driver_stops_mapper_when_driver_clip_fails():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with (
+        patch.object(minimizer, '_build_mapper', return_value='mapper'),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'
+        ),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+        ) as stop_mapper,
+    ):
+        mock_driver_cls.return_value.clip.side_effect = RuntimeError('clip failed')
+
+        with pytest.raises(RuntimeError, match='clip failed'):
+            minimizer._build_driver(
+                fitclass=object(),
+                fitness=SimpleNamespace(numpoints=lambda: 10),
+                steps=10,
+                burn=2,
+                init=minimizer.init,
+                sampler_settings={'samples': 40},
+                n_parameters=1,
+            )
+
+    stop_mapper.assert_called_once()
+
+
+def test_execute_driver_stops_mapper_when_seed_is_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    driver = SimpleNamespace(fit=MagicMock(), fitter=SimpleNamespace(state=None))
+
+    with patch(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+    ) as stop_mapper:
+        result = BumpsDreamMinimizer._execute_driver(driver=driver, random_seed=-1)
+
+    assert isinstance(result.error, ValueError)
+    driver.fit.assert_not_called()
+    stop_mapper.assert_called_once()
+
+
+def test_run_solver_failure_paths_return_failure_results(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamDriverResult
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamRunContext
+
+    minimizer = BumpsDreamMinimizer()
+    context = _DreamRunContext(
+        driver=object(),
+        parameter_names=['alpha'],
+        parameter_display_names=['Alpha'],
+        parameter_uids=['uid_alpha'],
+        sampler_settings={'random_seed': 7},
+        starting_values=np.array([1.0]),
+        starting_uncertainties=[0.1],
+    )
+
+    monkeypatch.setattr(
+        minimizer,
+        '_prepare_run_context',
+        lambda *, objective_function, kwargs: context,
+    )
+    monkeypatch.setattr(
+        minimizer,
+        '_execute_driver',
+        lambda *, driver, random_seed: _DreamDriverResult(
+            best_values=None,
+            best_nllf=None,
+            raw_state='state',
+            error=RuntimeError('boom'),
+        ),
+    )
+
+    failed = minimizer._run_solver(lambda _: np.array([0.0]))
+
+    assert failed.success is False
+    assert failed.message == 'DREAM sampling failed: boom'
+    assert failed.sampler_completed is False
+
+    monkeypatch.setattr(
+        minimizer,
+        '_execute_driver',
+        lambda *, driver, random_seed: _DreamDriverResult(
+            best_values=np.array([1.0]),
+            best_nllf=0.5,
+            raw_state=None,
+            error=None,
+        ),
+    )
+
+    unusable = minimizer._run_solver(lambda _: np.array([0.0]))
+
+    assert unusable.success is False
+    assert unusable.message == 'DREAM sampling did not produce usable posterior samples.'
+    assert unusable.sampler_completed is False
+
+
+def test_build_success_result_handles_invalid_samples_and_warns_when_not_converged(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamRunContext
+
+    minimizer = BumpsDreamMinimizer()
+    context = _DreamRunContext(
+        driver=object(),
+        parameter_names=['alpha'],
+        parameter_display_names=['Alpha'],
+        parameter_uids=['uid_alpha'],
+        sampler_settings={'random_seed': 7},
+        starting_values=np.array([1.0]),
+        starting_uncertainties=[0.1],
+    )
+
+    class BadState:
+        labels = ['uid_alpha']
+
+        @staticmethod
+        def chains():
+            return np.array([0.0]), np.array([1.0]), np.array([0.0])
+
+        @staticmethod
+        def best():
+            return np.array([1.0]), 0.5
+
+    failed = minimizer._build_success_result(context=context, raw_state=BadState(), best_nllf=0.5)
+
+    assert failed.success is False
+    assert failed.sampler_completed is True
+
+    class GoodState:
+        labels = ['uid_alpha']
+
+        @staticmethod
+        def chains():
+            return (
+                np.array([0.0, 1.0]),
+                np.ones((2, 2, 1), dtype=float),
+                np.zeros((2, 2), dtype=float),
+            )
+
+        @staticmethod
+        def best():
+            return np.array([1.0]), 0.5
+
+    warnings: list[str] = []
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+        lambda posterior_samples: {'converged': False},
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+        lambda **kwargs: [
+            PosteriorParameterSummary(
+                unique_name='alpha',
+                display_name='Alpha',
+                map_value=1.0,
+                median=1.0,
+                standard_deviation=0.2,
+                interval_68=(0.9, 1.1),
+                interval_95=(0.8, 1.2),
+            )
+        ],
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+        lambda summaries: np.array([0.2]),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    successful = minimizer._build_success_result(
+        context=context,
+        raw_state=GoodState(),
+        best_nllf=0.5,
+    )
+
+    assert successful.success is True
+    assert successful.sampler_completed is True
+    assert any('poorly mixed' in message for message in warnings)
diff --git a/tests/integration/fitting/test_cli_entrypoints.py b/tests/integration/fitting/test_cli_entrypoints.py
new file mode 100644
index 000000000..636c0ca4c
--- /dev/null
+++ b/tests/integration/fitting/test_cli_entrypoints.py
@@ -0,0 +1,173 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from typer.testing import CliRunner
+
+runner = CliRunner()
+
+
+def test_cli_version_invokes_show_version(monkeypatch):
+    import easydiffraction as ed
+    import easydiffraction.__main__ as main_mod
+
+    called = {'ok': False}
+
+    def fake_show_version() -> None:
+        print('VERSION_OK')
+        called['ok'] = True
+
+    monkeypatch.setattr(ed, 'show_version', fake_show_version)
+
+    result = runner.invoke(main_mod.app, ['--version'])
+
+    assert result.exit_code == 0
+    assert called['ok'] is True
+    assert 'VERSION_OK' in result.stdout
+
+
+def test_cli_help_shows_and_exits_zero():
+    import easydiffraction.__main__ as main_mod
+
+    result = runner.invoke(main_mod.app, ['--help'])
+
+    assert result.exit_code == 0
+    assert 'EasyDiffraction command-line interface' in result.stdout
+
+
+def test_cli_subcommands_call_utils(monkeypatch):
+    import easydiffraction as ed
+    import easydiffraction.__main__ as main_mod
+
+    calls: list[str] = []
+    monkeypatch.setattr(ed, 'list_tutorials', lambda: calls.append('LIST'))
+    monkeypatch.setattr(
+        ed,
+        'download_all_tutorials',
+        lambda destination='tutorials', overwrite=False: calls.append('DOWNLOAD_ALL'),
+    )
+    monkeypatch.setattr(
+        ed,
+        'download_tutorial',
+        lambda id, destination='tutorials', overwrite=False: calls.append(f'DOWNLOAD_{id}'),
+    )
+
+    list_result = runner.invoke(main_mod.app, ['list-tutorials'])
+    download_all_result = runner.invoke(main_mod.app, ['download-all-tutorials'])
+    download_one_result = runner.invoke(main_mod.app, ['download-tutorial', '1'])
+
+    assert list_result.exit_code == 0
+    assert download_all_result.exit_code == 0
+    assert download_one_result.exit_code == 0
+    assert calls == ['LIST', 'DOWNLOAD_ALL', 'DOWNLOAD_1']
+
+
+def test_cli_fit_loads_and_fits(monkeypatch, tmp_path):
+    import easydiffraction.__main__ as main_mod
+    from easydiffraction.project.project import Project
+
+    calls: list[str] = []
+
+    class FakeInfo:
+        _path = '/some/path'
+
+    class FakeExperiment:
+        name = 'exp1'
+
+    class FakeProject:
+        info = FakeInfo()
+        experiments = [FakeExperiment()]
+
+        class _analysis:
+            @staticmethod
+            def fit() -> None:
+                calls.append('FIT')
+
+            class display:
+                @staticmethod
+                def fit_results() -> None:
+                    calls.append('DISPLAY')
+
+        analysis = _analysis()
+
+        class _display:
+            class _plotter:
+                @staticmethod
+                def plot_param_correlations() -> None:
+                    calls.append('PLOT_CORR')
+
+                @staticmethod
+                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
+                    calls.append(f'PLOT_{expt_name}_{show_residual}')
+
+            plotter = _plotter()
+
+        display = _display()
+
+    fake_project = FakeProject()
+
+    project_dir = tmp_path / 'proj'
+    project_dir.mkdir()
+    (project_dir / 'project.cif').write_text('_project.id test\n')
+
+    monkeypatch.setattr(Project, 'load', staticmethod(lambda path: fake_project))
+
+    result = runner.invoke(main_mod.app, ['fit', str(project_dir)])
+
+    assert result.exit_code == 0
+    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_True']
+
+
+def test_cli_fit_dry_clears_path(monkeypatch, tmp_path):
+    import easydiffraction.__main__ as main_mod
+    from easydiffraction.project.project import Project
+
+    class FakeInfo:
+        _path = '/some/path'
+
+    class FakeExperiment:
+        name = 'exp1'
+
+    class FakeProject:
+        info = FakeInfo()
+        experiments = [FakeExperiment()]
+
+        class _analysis:
+            @staticmethod
+            def fit() -> None:
+                return None
+
+            class display:
+                @staticmethod
+                def fit_results() -> None:
+                    return None
+
+        analysis = _analysis()
+
+        class _display:
+            class _plotter:
+                @staticmethod
+                def plot_param_correlations() -> None:
+                    return None
+
+                @staticmethod
+                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
+                    return None
+
+            plotter = _plotter()
+
+        display = _display()
+
+    fake_project = FakeProject()
+
+    project_dir = tmp_path / 'proj'
+    project_dir.mkdir()
+    (project_dir / 'project.cif').write_text('_project.id test\n')
+
+    monkeypatch.setattr(Project, 'load', staticmethod(lambda path: fake_project))
+
+    result = runner.invoke(main_mod.app, ['fit', '--dry', str(project_dir)])
+
+    assert result.exit_code == 0
+    assert fake_project.info._path is None
diff --git a/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py b/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
index 5fdda3ee4..6cebe4c33 100644
--- a/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
+++ b/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
@@ -173,3 +173,32 @@ def test_last_powder_refln_records_reads_tof_time_and_d_spacing():
     assert records[0].d_spacing == pytest.approx(3.21)
     assert records[0].f_calc == pytest.approx(6.0)
     assert records[0].f_squared_calc == pytest.approx(36.0)
+
+
+def test_last_powder_refln_records_reads_xray_charge_structure_factor():
+    from easydiffraction.analysis.calculators.cryspy import CryspyCalculator
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+
+    calculator = CryspyCalculator()
+    calculator._last_powder_phase_blocks = {
+        'phase_exp': {
+            'index_hkl': np.array([[1], [1], [1]]),
+            'sthovl': np.array([0.2]),
+            'ttheta_hkl': np.array([np.pi / 3]),
+            'f_charge': np.array([8.0 - 6.0j]),
+        }
+    }
+    structure = SimpleNamespace(name='phase')
+    experiment = SimpleNamespace(
+        name='exp',
+        type=SimpleNamespace(beam_mode=SimpleNamespace(value=BeamModeEnum.CONSTANT_WAVELENGTH)),
+    )
+
+    records = calculator.last_powder_refln_records(structure, experiment, phase_id='phase-x')
+
+    assert len(records) == 1
+    assert records[0].phase_id == 'phase-x'
+    assert records[0].two_theta == pytest.approx(60.0)
+    assert records[0].d_spacing == pytest.approx(2.5)
+    assert records[0].f_calc == pytest.approx(10.0)
+    assert records[0].f_squared_calc == pytest.approx(100.0)
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
new file mode 100644
index 000000000..3b641e6d6
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -0,0 +1,385 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+
+class Identity:
+    def __init__(self) -> None:
+        self.datablock_entry_name = 'db'
+        self.category_code = 'cat'
+        self.category_entry_name = 'entry'
+
+
+class Param:
+    def __init__(self, unique_name: str, start: float, value: float, uncertainty: float) -> None:
+        self._identity = Identity()
+        self._fit_start_value = start
+        self.unique_name = unique_name
+        self.name = unique_name
+        self.value = value
+        self.uncertainty = uncertainty
+        self.units = 'arb'
+
+
+def test_module_import():
+    import easydiffraction.analysis.fit_helpers.bayesian as MUT
+
+    assert MUT.__name__ == 'easydiffraction.analysis.fit_helpers.bayesian'
+
+
+def test_posterior_samples_flatten_and_to_arviz():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a', 'b'],
+        parameter_samples=np.array(
+            [
+                [[1.0, 10.0], [2.0, 20.0]],
+                [[3.0, 30.0], [4.0, 40.0]],
+            ],
+            dtype=float,
+        ),
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+
+    flattened = posterior_samples.flattened()
+    inference_data = posterior_samples.to_arviz()
+
+    assert flattened.shape == (4, 2)
+    np.testing.assert_allclose(flattened[:, 0], np.array([1.0, 2.0, 3.0, 4.0]))
+    np.testing.assert_allclose(flattened[:, 1], np.array([10.0, 20.0, 30.0, 40.0]))
+    assert set(inference_data.posterior.data_vars) == {'a', 'b'}
+    assert inference_data.posterior['a'].shape == (2, 2)
+    assert inference_data.sample_stats['lp'].shape == (2, 2)
+
+
+def test_posterior_samples_to_arviz_validates_shapes():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.array([1.0, 2.0]),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array must have shape \(n_draws, n_chains, n_parameters\)\.',
+    ):
+        posterior_samples.to_arviz()
+
+
+def test_compute_convergence_diagnostics_treats_non_finite_values_as_not_converged(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((4, 2, 1), dtype=float),
+    )
+
+    fake_dataset = type('FakeDataset', (), {'data_vars': {'a': np.array([np.nan], dtype=float)}})
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.rhat',
+        lambda inference_data: fake_dataset,
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.ess',
+        lambda inference_data, method='bulk': type(
+            'FakeDataset', (), {'data_vars': {'a': np.array([4000.0], dtype=float)}}
+        ),
+    )
+
+    diagnostics = compute_convergence_diagnostics(posterior_samples)
+
+    assert diagnostics['converged'] is False
+    assert diagnostics['r_hat_by_parameter'] == {'a': None}
+    assert diagnostics['ess_bulk_by_parameter'] == {'a': 4000.0}
+    assert diagnostics['max_r_hat'] is None
+    assert diagnostics['min_ess_bulk'] == pytest.approx(4000.0)
+
+
+def test_summarize_posterior_parameters_preserves_order_and_display_names():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['beta', 'alpha'],
+        parameter_samples=np.array(
+            [
+                [[2.0, 1.0], [2.2, 1.2]],
+                [[1.8, 0.8], [2.1, 1.1]],
+            ],
+            dtype=float,
+        ),
+    )
+
+    summaries = summarize_posterior_parameters(
+        parameter_names=['beta', 'alpha'],
+        posterior_samples=posterior_samples,
+        map_values=np.array([2.05, 1.05]),
+        parameter_display_names=['Beta width', 'Alpha shift'],
+        convergence_diagnostics={
+            'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
+            'ess_bulk_by_parameter': {'beta': 120.0, 'alpha': 800.0},
+        },
+    )
+
+    assert [summary.unique_name for summary in summaries] == ['beta', 'alpha']
+    assert [summary.display_name for summary in summaries] == ['Beta width', 'Alpha shift']
+    assert summaries[0].r_hat == pytest.approx(1.02)
+    assert summaries[0].ess_bulk == pytest.approx(120.0)
+    assert summaries[1].r_hat == pytest.approx(1.0)
+    assert summaries[1].ess_bulk == pytest.approx(800.0)
+
+
+def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(capsys, monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.utils.logging import Logger
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
+
+    results = BayesianFitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+        sampler_name='dream',
+        sampler_completed=True,
+        sampler_settings={
+            'random_seed': 1313900679,
+            'steps': 200,
+            'burn': 50,
+            'thin': 1,
+            'pop': 4,
+            'init': 'lhs',
+            'samples': 3200,
+        },
+        convergence_diagnostics={
+            'converged': False,
+            'max_r_hat': 1.107,
+            'min_ess_bulk': 125.9,
+            'n_draws': 200,
+            'n_chains': 16,
+        },
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+        best_log_posterior=-12.34,
+    )
+    results.message = 'DREAM sampling completed'
+
+    results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
+
+    out = capsys.readouterr().out
+    assert 'Bayesian fit results' in out
+    assert 'Overall status: completed with warnings' in out
+    assert 'Sampler status: DREAM sampling completed' in out
+    assert 'Sampler: dream' in out
+    assert 'Sampler completed: yes' in out
+    assert 'steps=200' in out
+    assert 'init=lhs' in out
+    assert 'random_seed=1313900679' not in out
+    assert 'status=failed' in out
+    assert 'max_r_hat=1.107' in out
+    assert 'min_ess_bulk=125.9' in out
+    assert 'Posterior parameter summaries:' in out
+    assert 'Success: True' not in out
+    assert 'datablock' in out
+    assert 'category' in out
+    assert 'entry' in out
+    assert '95% interval' in out
+    assert '68% interval' not in out
+    assert 'std' not in out
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
+
+
+def test_build_posterior_summary_row_restores_identifier_columns():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+
+    parameter = Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    summary = PosteriorParameterSummary(
+        unique_name='a',
+        display_name='a',
+        map_value=1.2,
+        median=1.15,
+        standard_deviation=0.05,
+        interval_68=(1.1, 1.2),
+        interval_95=(1.0, 1.3),
+        r_hat=1.107,
+        ess_bulk=125.9,
+    )
+
+    row = _build_posterior_summary_row(summary, {'a': parameter})
+
+    assert row == [
+        'db',
+        'cat',
+        'entry',
+        'a',
+        'arb',
+        '1.1500',
+        '[1.0000, 1.3000]',
+        '[red]1.107[/red]',
+        '[red]125.9[/red]',
+    ]
+
+
+def test_render_committed_parameter_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_committed_parameter_table([
+        Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    ])
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'max posterior',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
+
+
+def test_render_posterior_summary_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_posterior_summary_table(
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+    )
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'median',
+        '95% interval',
+        'r-hat',
+        'ess bulk',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.1500',
+            '[1.0000, 1.3000]',
+            '[red]1.107[/red]',
+            '[red]125.9[/red]',
+        ]
+    ]
+
+
+def test_posterior_table_notes_split_failed_diagnostics():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    notes = _posterior_table_notes([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='a',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.1,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+            r_hat=1.02,
+            ess_bulk=100.0,
+        )
+    ])
+
+    assert len(notes) == 2
+    assert 'r-hat' in notes[0]
+    assert 'ess bulk' in notes[1]
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
index ee8014f23..bb824f829 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
@@ -59,3 +59,69 @@ def __init__(self, start, value, uncertainty, name='p', units='u'):
     assert 'Fitted parameters:' in out
     # Table border: accept common border glyphs from Rich/tabulate
     assert any(ch in out for ch in ('╒', '┌', '+', '─'))
+
+
+def test_fitresults_display_results_places_units_after_parameter(monkeypatch):
+    class Identity:
+        def __init__(self):
+            self.datablock_entry_name = 'db'
+            self.category_code = 'cat'
+            self.category_entry_name = 'entry'
+
+    class Param:
+        def __init__(self):
+            self._identity = Identity()
+            self._fit_start_value = 1.0
+            self.value = 1.2
+            self.uncertainty = 0.05
+            self.name = 'a'
+            self.units = 'arb'
+
+    from easydiffraction.analysis.fit_helpers import reporting
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(reporting, 'render_table', fake_render_table)
+
+    reporting.FitResults(success=True, parameters=[Param()]).display_results()
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'fitted',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_base.py b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
index 23d385e7c..0ad0ca127 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_base.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import numpy as np
+import pytest
 
 
 class _DummyParam:
@@ -119,3 +120,41 @@ def _compute_residuals(
     )
     out = f({})
     assert np.allclose(out, np.array([1.0, 2.0, 3.0]))
+
+
+def test_minimizer_base_fit_stops_tracking_when_solver_prep_fails():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class M(MinimizerBase):
+        def __init__(self):
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.started = False
+            self.stopped = False
+
+        def _start_tracking(self, minimizer_name, verbosity=None):
+            self.started = True
+
+        def _stop_tracking(self):
+            self.stopped = True
+
+        def _prepare_solver_args(self, parameters):
+            msg = 'prep failed'
+            raise ValueError(msg)
+
+        def _run_solver(self, objective_function, **kwargs):
+            msg = 'should not run solver'
+            raise AssertionError(msg)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            pass
+
+        def _check_success(self, raw_result):
+            return True
+
+    minimizer = M()
+
+    with pytest.raises(ValueError, match='prep failed'):
+        minimizer.fit(parameters=[_DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
+
+    assert minimizer.started is True
+    assert minimizer.stopped is True
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
index 668524eaf..1a73fdbcd 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
@@ -38,6 +38,18 @@ def test_default_max_iterations():
     assert m.max_iterations == 1000
 
 
+def test_bumps_minimizer_rejects_random_seed():
+    from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+
+    m = BumpsMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r"Minimizer 'bumps' does not support random_seed\.",
+    ):
+        m._resolve_random_seed(17)
+
+
 def test_is_subclass_of_base():
     from easydiffraction.analysis.minimizers.base import MinimizerBase
     from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
new file mode 100644
index 000000000..eea9f0898
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -0,0 +1,362 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+
+class FakeParam:
+    """Minimal stand-in for an EasyDiffraction parameter."""
+
+    def __init__(
+        self,
+        uid: str,
+        value: float,
+        uncertainty: float | None = None,
+        *,
+        fit_min: float | None = 0.0,
+        fit_max: float | None = 1.0,
+    ) -> None:
+        self._minimizer_uid = uid
+        self.unique_name = uid
+        self.name = uid.upper()
+        self.value = value
+        self.uncertainty = uncertainty
+        self.fit_min = fit_min
+        self.fit_max = fit_max
+
+    def _set_value_from_minimizer(self, value: float) -> None:
+        self.value = value
+
+
+def test_module_import():
+    import easydiffraction.analysis.minimizers.bumps_dream as MUT
+
+    assert MUT.__name__ == 'easydiffraction.analysis.minimizers.bumps_dream'
+
+
+def test_type_info_and_default_init():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+    from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+    assert minimizer.steps == 3000
+
+
+def test_dream_progress_monitor_allocates_rows_by_phase_ratio():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=100,
+        n_parameters=3,
+        total_generations=100,
+        burn_steps=40,
+    )
+
+    assert len(monitor._burn_targets) == 10
+    assert len(monitor._sampling_targets) == 15
+
+
+def test_init_accepts_enum_or_string_and_rejects_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+
+    minimizer.init = 'random'
+    assert minimizer.init is DreamPopulationInitializationEnum.RANDOM
+
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'init' must be one of: eps, cov, lhs, random\.",
+    ):
+        minimizer.init = 'bad-init'
+
+
+def test_resolve_random_seed_returns_provided_or_generated(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolve_random_seed(17) == 17
+    assert minimizer._resolved_random_seed == 17
+
+    generator = SimpleNamespace(integers=lambda *args, **kwargs: 123456)
+    monkeypatch.setattr(np.random, 'default_rng', lambda: generator)
+
+    assert minimizer._resolve_random_seed(None) == 123456
+    assert minimizer._resolved_random_seed == 123456
+
+
+@pytest.mark.parametrize('seed', [-1, np.iinfo(np.uint32).max + 1])
+def test_resolve_random_seed_rejects_out_of_range_values(seed):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r'DREAM random_seed must be an integer between 0 and 4294967295\.',
+    ):
+        minimizer._resolve_random_seed(seed)
+
+
+def test_resolved_burn_uses_auto_or_explicit_and_validates():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolved_burn(steps=200) == 50
+
+    minimizer.burn = 20
+    assert minimizer._resolved_burn(steps=200) == 20
+
+    minimizer.burn = 200
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'burn' must be smaller than 'steps'\.",
+    ):
+        minimizer._resolved_burn(steps=200)
+
+
+def test_sampler_settings_include_init_and_sample_count():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+
+    settings = minimizer._sampler_settings(
+        random_seed=7,
+        steps=10,
+        burn=2,
+        n_parameters=3,
+    )
+
+    assert settings['random_seed'] == 7
+    assert settings['parallel'] == 0
+    assert settings['init'] == 'lhs'
+    assert settings['samples'] == 120
+
+
+@pytest.mark.parametrize(
+    ('fit_min', 'fit_max', 'value', 'message'),
+    [
+        (None, 1.0, 0.5, r'fit_min must be finite'),
+        (0.0, np.inf, 0.5, r'fit_max must be finite'),
+        (2.0, 1.0, 1.5, r'fit_min \(2\.0\) must be smaller than fit_max \(1\.0\)'),
+        (0.0, 1.0, 2.0, r'starting value 2\.0 is outside \[0\.0, 1\.0\]'),
+    ],
+)
+def test_prepare_solver_args_rejects_invalid_dream_bounds(
+    fit_min,
+    fit_max,
+    value,
+    message,
+):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameter = FakeParam('alpha', value, fit_min=fit_min, fit_max=fit_max)
+
+    with pytest.raises(ValueError, match=message):
+        minimizer._prepare_solver_args([parameter])
+
+
+def test_prepare_solver_args_lists_all_offending_dream_parameters():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [
+        FakeParam('alpha', 2.0, fit_min=0.0, fit_max=1.0),
+        FakeParam('beta', 0.5, fit_min=None, fit_max=1.0),
+    ]
+
+    with pytest.raises(
+        ValueError,
+        match=r'alpha: .*outside \[0\.0, 1\.0\][\s\S]*beta: .*fit_min',
+    ):
+        minimizer._prepare_solver_args(parameters)
+
+
+def test_sync_result_to_parameters_restores_starting_values_on_failure():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [FakeParam('a', 10.0, uncertainty=0.7), FakeParam('b', 20.0, uncertainty=0.8)]
+    raw_result = SimpleNamespace(
+        x=np.array([99.0, 88.0]),
+        success=False,
+        starting_values=np.array([1.5, 2.5]),
+        starting_uncertainties=[0.1, None],
+    )
+
+    minimizer._sync_result_to_parameters(parameters, raw_result)
+
+    assert parameters[0].value == 1.5
+    assert parameters[0].uncertainty == 0.1
+    assert parameters[1].value == 2.5
+    assert parameters[1].uncertainty is None
+
+
+def test_run_solver_preserves_parameter_order_and_forwards_init():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.steps = 4
+    minimizer.burn = 1
+    minimizer.thin = 1
+    minimizer.pop = 2
+    minimizer.init = 'lhs'
+
+    draw_index = np.array([0.0, 1.0])
+    parameter_samples = np.array(
+        [
+            [[1.0, 10.0], [2.0, 20.0]],
+            [[3.0, 30.0], [4.0, 40.0]],
+        ],
+        dtype=float,
+    )
+    log_posterior = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float)
+
+    class FakeState:
+        labels = ['uid_a', 'uid_b']
+
+        def chains(self):
+            return draw_index, parameter_samples, log_posterior
+
+        def best(self):
+            return np.array([11.0, 22.0]), 3.5
+
+    fake_fitter = SimpleNamespace(id='dream')
+
+    with (
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FITTERS', [fake_fitter]),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+            return_value={'converged': True},
+        ),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+            return_value=[
+                PosteriorParameterSummary(
+                    unique_name='beta',
+                    display_name='Beta',
+                    map_value=22.0,
+                    median=21.0,
+                    standard_deviation=0.4,
+                    interval_68=(20.5, 21.5),
+                    interval_95=(20.0, 22.0),
+                ),
+                PosteriorParameterSummary(
+                    unique_name='alpha',
+                    display_name='Alpha',
+                    map_value=11.0,
+                    median=10.5,
+                    standard_deviation=0.3,
+                    interval_68=(10.0, 11.0),
+                    interval_95=(9.5, 11.5),
+                ),
+            ],
+        ) as summarize_mock,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+            return_value=np.array([0.4, 0.3]),
+        ),
+    ):
+        driver_instance = mock_driver_cls.return_value
+        driver_instance.clip = MagicMock()
+        driver_instance.fit.return_value = (np.array([22.0, 11.0]), 0.25)
+        driver_instance.fitter = SimpleNamespace(state=FakeState())
+
+        result = minimizer._run_solver(
+            lambda values: np.array([0.0, 0.0]),
+            bumps_params=[
+                SimpleNamespace(name='uid_a', value=1.0),
+                SimpleNamespace(name='uid_b', value=2.0),
+            ],
+            parameter_names=['beta', 'alpha'],
+            parameter_display_names=['Beta', 'Alpha'],
+            parameter_uids=['uid_b', 'uid_a'],
+            random_seed=17,
+            starting_uncertainties=[0.01, 0.02],
+        )
+
+    assert mock_driver_cls.call_args.kwargs['init'] == 'lhs'
+    np.testing.assert_allclose(result.x, np.array([22.0, 11.0]))
+    np.testing.assert_allclose(result.dx, np.array([0.4, 0.3]))
+    assert result.posterior_samples.parameter_names == ['beta', 'alpha']
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 0], parameter_samples[:, :, 1]
+    )
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 1], parameter_samples[:, :, 0]
+    )
+    assert result.sampler_settings['init'] == 'lhs'
+    assert result.sampler_settings['random_seed'] == 17
+    assert summarize_mock.call_args.kwargs['parameter_names'] == ['beta', 'alpha']
+
+
+def test_build_driver_stops_mapper_when_driver_clip_fails():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with (
+        patch.object(minimizer, '_build_mapper', return_value='mapper'),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'
+        ),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+        ) as stop_mapper,
+    ):
+        mock_driver_cls.return_value.clip.side_effect = RuntimeError('clip failed')
+
+        with pytest.raises(RuntimeError, match='clip failed'):
+            minimizer._build_driver(
+                fitclass=object(),
+                fitness=SimpleNamespace(numpoints=lambda: 10),
+                steps=10,
+                burn=2,
+                init=minimizer.init,
+                sampler_settings={'samples': 40},
+                n_parameters=1,
+            )
+
+    stop_mapper.assert_called_once()
+
+
+def test_execute_driver_stops_mapper_when_seed_is_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    driver = SimpleNamespace(fit=MagicMock(), fitter=SimpleNamespace(state=None))
+
+    with patch(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+    ) as stop_mapper:
+        result = BumpsDreamMinimizer._execute_driver(driver=driver, random_seed=-1)
+
+    assert isinstance(result.error, ValueError)
+    driver.fit.assert_not_called()
+    stop_mapper.assert_called_once()
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_enums.py b/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
index 23e44e5bd..5551b9b7a 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
@@ -9,12 +9,19 @@ def test_module_import():
 
 
 def test_enum_members():
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
     from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 
     assert MinimizerTypeEnum.LMFIT == 'lmfit'
     assert MinimizerTypeEnum.LMFIT_LEASTSQ == 'lmfit (leastsq)'
     assert MinimizerTypeEnum.LMFIT_LEAST_SQUARES == 'lmfit (least_squares)'
     assert MinimizerTypeEnum.DFOLS == 'dfols'
+    assert MinimizerTypeEnum.BUMPS_DREAM == 'bumps (dream)'
+
+    assert DreamPopulationInitializationEnum.EPS == 'eps'
+    assert DreamPopulationInitializationEnum.COV == 'cov'
+    assert DreamPopulationInitializationEnum.LHS == 'lhs'
+    assert DreamPopulationInitializationEnum.RANDOM == 'random'
 
 
 def test_enum_default():
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_factory.py b/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
index 7351d84b2..406a5e093 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
@@ -9,11 +9,12 @@ def test_minimizer_factory_list_and_show(capsys):
 
     lst = MinimizerFactory.supported_tags()
     assert isinstance(lst, list)
-    assert len(lst) >= 4
+    assert len(lst) >= 5
     assert 'lmfit' in lst
     assert 'lmfit (leastsq)' in lst
     assert 'lmfit (least_squares)' in lst
     assert 'dfols' in lst
+    assert 'bumps (dream)' in lst
     MinimizerFactory.show_supported()
     out = capsys.readouterr().out
     assert 'Supported types' in out
@@ -75,3 +76,12 @@ def test_minimizer_factory_create_lmfit_least_squares():
     m = MinimizerFactory.create('lmfit (least_squares)')
     assert isinstance(m, LmfitLeastSquaresMinimizer)
     assert m.method == 'least_squares'
+
+
+def test_minimizer_factory_create_bumps_dream():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.factory import MinimizerFactory
+
+    m = MinimizerFactory.create('bumps (dream)')
+    assert isinstance(m, BumpsDreamMinimizer)
+    assert m.method == 'dream'
diff --git a/tests/unit/easydiffraction/core/test_parameters.py b/tests/unit/easydiffraction/core/test_parameters.py
index e7d102d9d..a6c5b7adf 100644
--- a/tests/unit/easydiffraction/core/test_parameters.py
+++ b/tests/unit/easydiffraction/core/test_parameters.py
@@ -103,6 +103,90 @@ def test_parameter_fit_bounds_assign_and_read():
     assert np.isclose(p.fit_max, 10.0)
 
 
+def test_parameter_set_fit_bounds_from_uncertainty_sets_bounds_and_returns_none():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='d',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.d']),
+    )
+    p.value = 2.0
+    p.uncertainty = 0.25
+
+    result = p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+    assert result is None
+    assert np.isclose(p.fit_min, 1.0)
+    assert np.isclose(p.fit_max, 3.0)
+
+
+def test_parameter_set_fit_bounds_from_uncertainty_uses_default_multiplier():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='default_multiplier',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.default_multiplier']),
+    )
+    p.value = 2.0
+    p.uncertainty = 0.25
+
+    p.set_fit_bounds_from_uncertainty()
+
+    assert np.isclose(p.fit_min, 1.0)
+    assert np.isclose(p.fit_max, 3.0)
+
+
+def test_parameter_set_fit_bounds_from_uncertainty_clips_to_physical_limits():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.validation import DataTypes
+    from easydiffraction.core.validation import RangeValidator
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='bounded',
+        value_spec=AttributeSpec(
+            data_type=DataTypes.NUMERIC,
+            default=1.0,
+            validator=RangeValidator(ge=0.5, le=1.5),
+        ),
+        cif_handler=CifHandler(names=['_param.bounded']),
+    )
+    p.value = 1.0
+    p.uncertainty = 0.3
+
+    p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+    assert np.isclose(p.fit_min, 0.5)
+    assert np.isclose(p.fit_max, 1.5)
+
+
+def test_parameter_set_fit_bounds_from_uncertainty_requires_valid_uncertainty():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='invalid',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.invalid']),
+    )
+    p.value = 2.0
+    p.uncertainty = None
+
+    with pytest.raises(
+        ValueError,
+        match=r'Cannot set fit bounds for invalid: uncertainty is missing or invalid\.',
+    ):
+        p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+
 def _make_param() -> object:
     from easydiffraction.core.validation import AttributeSpec
     from easydiffraction.core.variable import Parameter
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index 3905ee278..5c3c8dd50 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -13,62 +13,19 @@ def test_module_import():
     assert expected_module_name == actual_module_name
 
 
-def test_default_template_name_prefers_jupyter_theme(monkeypatch):
+def test_get_layout_sets_title_and_axis_title_font_sizes():
     import easydiffraction.display.plotters.plotly as pp
 
-    monkeypatch.setattr(pp, 'in_jupyter', lambda: True)
-    monkeypatch.setattr(pp, 'is_dark', lambda: True)
-    monkeypatch.setattr(pp.darkdetect, 'isDark', lambda: False)
+    layout = pp.PlotlyPlotter._get_layout('Title', ['x axis', 'y axis'])
 
-    assert pp.PlotlyPlotter._default_template_name() == 'plotly_dark'
-
-
-def test_correlation_colorscale_uses_black_center_in_dark_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: True))
-
-    assert pp.PlotlyPlotter._correlation_colorscale()[1] == (0.5, '#000000')
-
-
-def test_default_template_name_uses_system_theme_outside_jupyter(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp, 'in_jupyter', lambda: False)
-    monkeypatch.setattr(pp, 'is_dark', lambda: False)
-    monkeypatch.setattr(pp.darkdetect, 'isDark', lambda: False)
-
-    assert pp.PlotlyPlotter._default_template_name() == 'plotly_white'
-
-
-def test_correlation_colorscale_uses_white_center_in_light_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: False))
-
-    assert pp.PlotlyPlotter._correlation_colorscale()[1] == (0.5, '#f7f7f7')
-
-
-def test_legend_background_color_uses_light_overlay_in_light_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: False))
-
-    assert pp.PlotlyPlotter._legend_background_color() == 'rgba(255, 255, 255, 0.5)'
-
-
-def test_legend_background_color_uses_dark_overlay_in_dark_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: True))
-
-    assert pp.PlotlyPlotter._legend_background_color() == 'rgba(0, 0, 0, 0.5)'
+    assert layout.title.font.size == pp.TITLE_FONT_SIZE
+    assert layout.xaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
+    assert layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
 
 
 def test_get_trace_and_plot(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
-    # Arrange: force non-PyCharm branch and stub fig.show/HTML/display so nothing opens
     monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
 
     shown = {'count': 0}
@@ -83,7 +40,6 @@ def update_yaxes(self, **kwargs):
         def show(self, **kwargs):
             shown['count'] += 1
 
-    # Patch go.Scatter and go.Figure to minimal dummies
     class DummyScatter:
         def __init__(self, **kwargs):
             self.kwargs = kwargs
@@ -122,7 +78,6 @@ def __init__(self, html):
 
     plotter = pp.PlotlyPlotter()
 
-    # Exercise _get_powder_trace
     x = [0, 1, 2]
     y = [1, 2, 3]
     trace = plotter._get_powder_trace(x, y, label='calc')
@@ -278,6 +233,57 @@ def __init__(self, html):
     assert captured['displayed_html'] == '<div>plot</div>'
 
 
+def test_show_figure_wraps_fixed_aspect_html(monkeypatch):
+    import easydiffraction.display.plotters.plotly as pp
+
+    monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
+
+    captured = {}
+
+    class DummyLayout:
+        def __init__(self):
+            self.meta = {
+                'fixed_aspect_wrapper': {
+                    'aspect_ratio': '1 / 1',
+                }
+            }
+            self.showlegend = False
+
+    class DummyFig:
+        def __init__(self):
+            self.data = []
+            self.layout = DummyLayout()
+
+        def show(self, **kwargs):
+            captured['show_called'] = True
+
+    class DummyPIO:
+        @staticmethod
+        def to_html(fig, include_plotlyjs=None, full_html=None, config=None, post_script=None):
+            captured['post_script'] = post_script
+            return '<div>plot</div>'
+
+    def dummy_display(obj):
+        captured['displayed_html'] = obj.html
+
+    class DummyHTML:
+        def __init__(self, html):
+            self.html = html
+
+    monkeypatch.setattr(pp, 'pio', DummyPIO)
+    monkeypatch.setattr(pp, 'display', dummy_display)
+    monkeypatch.setattr(pp, 'HTML', DummyHTML)
+
+    plotter = pp.PlotlyPlotter()
+    plotter._show_figure(DummyFig())
+
+    assert captured.get('show_called') is not True
+    assert captured['post_script'] is None
+    assert 'aspect-ratio: 1 / 1;' in captured['displayed_html']
+    assert 'ed-fixed-aspect-plotly-wrapper' in captured['displayed_html']
+    assert '<div>plot</div>' in captured['displayed_html']
+
+
 def test_plotly_single_crystal_trace_and_plot(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
@@ -779,6 +785,9 @@ def fake_show_figure(self, fig):
     assert fig.layout.xaxis2.matches == 'x'
     assert fig.layout.yaxis2.title.text is None
     assert fig.layout.xaxis2.title.text == '2θ (degree)'
+    assert fig.layout.title.font.size == pp.TITLE_FONT_SIZE
+    assert fig.layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
+    assert fig.layout.xaxis2.title.font.size == pp.AXIS_TITLE_FONT_SIZE
     assert [trace.name for trace in fig.data] == [
         'Measured (Imeas)',
         'Total calculated (Icalc)',
@@ -786,6 +795,49 @@ def fake_show_figure(self, fig):
     ]
 
 
+def test_plot_powder_meas_vs_calc_styles_predictive_max_posterior_and_band(monkeypatch):
+    import easydiffraction.display.plotters.plotly as pp
+
+    from easydiffraction.display.plotters.base import PowderMeasVsCalcSpec
+
+    captured = {}
+
+    def fake_show_figure(self, fig):
+        captured['fig'] = fig
+
+    monkeypatch.setattr(pp.PlotlyPlotter, '_show_figure', fake_show_figure)
+
+    plotter = pp.PlotlyPlotter()
+    plotter.plot_powder_meas_vs_calc(
+        plot_spec=PowderMeasVsCalcSpec(
+            x=np.array([1.0, 2.0, 3.0]),
+            y_meas=np.array([10.0, 12.0, 11.0]),
+            y_calc=np.array([9.0, 11.0, 10.5]),
+            y_resid=np.array([1.0, 1.0, 0.5]),
+            bragg_tick_sets=(),
+            axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
+            title='Powder',
+            residual_height_fraction=0.25,
+            bragg_peaks_height_fraction=0.15,
+            height=None,
+            predictive_lower_95=np.array([8.0, 9.0, 10.0]),
+            predictive_upper_95=np.array([10.0, 11.0, 12.0]),
+            y_calc_name='Max posterior',
+            y_calc_line_dash='dot',
+        ),
+    )
+
+    fig = captured['fig']
+    predictive_band_trace = next(trace for trace in fig.data if trace.name == '95% interval')
+    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+    residual_trace = next(trace for trace in fig.data if trace.name == 'Residual (Imeas - Icalc)')
+
+    assert predictive_band_trace.fillcolor == pp.PREDICTIVE_BAND_COLOR
+    assert predictive_band_trace.legendrank == 35
+    assert max_posterior_trace.line.dash == 'dot'
+    assert predictive_band_trace.legendrank < residual_trace.legendrank
+
+
 def test_plot_powder_meas_vs_calc_keeps_exact_residual_scale_match(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 5e1cc4daf..c0ef16158 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -2,6 +2,10 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import re
+from types import MethodType
+from types import SimpleNamespace
+
+import numpy as np
 
 import pytest
 
@@ -233,6 +237,897 @@ class Experiment:
     assert tick_sets == ()
 
 
+def _make_bayesian_plotter_fixture():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import Plotter
+
+    samples = np.array(
+        [
+            [[3.8900, 0.0760, -0.1170, 0.6290], [3.8908, 0.0775, -0.1185, 0.6300]],
+            [[3.8912, 0.0785, -0.1190, 0.6310], [3.8916, 0.0790, -0.1200, 0.6320]],
+        ],
+        dtype=float,
+    )
+    parameter_names = ['length_a', 'broad_gauss_u', 'broad_gauss_v', 'twotheta_offset']
+    posterior_samples = PosteriorSamples(
+        parameter_names=parameter_names,
+        parameter_samples=samples,
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+    parameters = [
+        SimpleNamespace(unique_name='length_a', name='length_a', fit_min=3.8895, fit_max=3.8920),
+        SimpleNamespace(
+            unique_name='broad_gauss_u', name='broad_gauss_u', fit_min=0.05, fit_max=0.11
+        ),
+        SimpleNamespace(
+            unique_name='broad_gauss_v', name='broad_gauss_v', fit_min=-0.14, fit_max=-0.10
+        ),
+        SimpleNamespace(
+            unique_name='twotheta_offset', name='twotheta_offset', fit_min=0.625, fit_max=0.64
+        ),
+    ]
+    summaries = [
+        PosteriorParameterSummary(
+            unique_name=name,
+            display_name=name,
+            map_value=float(samples[-1, -1, index]),
+            median=float(np.median(samples[:, :, index])),
+            standard_deviation=float(np.std(samples[:, :, index], ddof=1)),
+            interval_68=tuple(np.quantile(samples[:, :, index], [0.16, 0.84]).tolist()),
+            interval_95=tuple(np.quantile(samples[:, :, index], [0.025, 0.975]).tolist()),
+        )
+        for index, name in enumerate(parameter_names)
+    ]
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=summaries,
+        posterior_predictive={},
+        parameters=parameters,
+    )
+    plotter = Plotter()
+    plotter._get_posterior_samples_and_fit_results = MethodType(
+        lambda self: (posterior_samples, fit_results),
+        plotter,
+    )
+    plotter._get_fit_result_for_correlation = MethodType(lambda self: fit_results, plotter)
+    return plotter, fit_results, posterior_samples
+
+
+def test_correlation_from_posterior_samples_returns_labeled_dataframe():
+    from easydiffraction.display.plotting import Plotter
+
+    _, _, posterior_samples = _make_bayesian_plotter_fixture()
+
+    corr_df = Plotter()._correlation_from_posterior_samples(posterior_samples)
+
+    assert list(corr_df.index) == posterior_samples.parameter_names
+    assert list(corr_df.columns) == posterior_samples.parameter_names
+    np.testing.assert_allclose(np.diag(corr_df.to_numpy()), np.ones(len(corr_df)))
+
+
+def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TOP_MARGIN_PIXELS
+
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    assert figure.layout.title.text is None
+    assert figure.layout.autosize is True
+    assert figure.layout.width is None
+    assert figure.layout.height is None
+    assert (
+        figure.layout.meta['fixed_aspect_wrapper']['aspect_ratio']
+        == plotter._square_matrix_layout_meta(
+            n_parameters=4,
+            annotation_labels=[
+                'length_a',
+                'broad_gauss_u',
+                'broad_gauss_v',
+                'twotheta_offset',
+            ],
+        )['fixed_aspect_wrapper']['aspect_ratio']
+    )
+    assert [annotation.text for annotation in figure.layout.annotations] == [
+        'Posterior pair plot',
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
+    ]
+    assert figure.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
+    assert figure.layout.annotations[0].yshift == SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift([
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
+    ])
+    assert figure.layout.margin.t == SQUARE_MATRIX_TOP_MARGIN_PIXELS
+    assert figure.layout.margin.b == SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    subplot = figure.get_subplot(1, 1)
+    bottom_subplot = figure.get_subplot(4, 1)
+    assert subplot.yaxis.showticklabels is False
+    assert subplot.yaxis.ticks == ''
+    assert subplot.yaxis.ticklen == 0
+    assert subplot.yaxis.title.text is None
+    assert bottom_subplot.xaxis.showticklabels is False
+    assert bottom_subplot.xaxis.title.text is None
+    assert figure.layout.paper_bgcolor is None
+    assert figure.layout.plot_bgcolor is None
+    assert len(figure.layout.shapes) == 30
+    assert any(trace.name == 'Posterior contours' for trace in figure.data)
+    sample_trace = next(trace for trace in figure.data if trace.name == 'Posterior samples')
+    hover_trace = next(
+        trace
+        for trace in figure.data
+        if getattr(trace, 'mode', None) == 'markers'
+        and getattr(trace.marker, 'color', None) == 'rgba(0, 0, 0, 0)'
+    )
+    assert sample_trace.marker.size == POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
+    assert hover_trace.marker.size == POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
+
+
+def test_build_posterior_pairs_plot_fast_mode_skips_contours():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None, style='fast')
+
+    assert all(trace.name != 'Posterior contours' for trace in figure.data)
+
+
+def test_build_posterior_pairs_plot_sign_colors_contours_and_marginals():
+    from easydiffraction.display.plotting import POSTERIOR_CONTOUR_FILL_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_CONTOUR_LINE_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    marginal_traces = [trace for trace in figure.data if trace.name == 'Marginal density']
+    assert marginal_traces
+    assert all(
+        trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR for trace in marginal_traces
+    )
+    assert all(
+        trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH for trace in marginal_traces
+    )
+    assert all(
+        trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR for trace in marginal_traces
+    )
+
+    fill_contours = [
+        trace
+        for trace in figure.data
+        if trace.type == 'contour' and trace.contours.coloring == 'fill'
+    ]
+    line_contours = [
+        trace
+        for trace in figure.data
+        if trace.type == 'contour' and trace.contours.coloring == 'lines'
+    ]
+    fill_end_colors = {trace.colorscale[-1][1] for trace in fill_contours}
+    line_end_colors = {trace.colorscale[-1][1] for trace in line_contours}
+
+    assert POSTERIOR_CONTOUR_FILL_COLORSCALE[-1][1] in fill_end_colors
+    assert POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE[-1][1] in fill_end_colors
+    assert POSTERIOR_CONTOUR_LINE_COLORSCALE[-1][1] in line_end_colors
+    assert POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE[-1][1] in line_end_colors
+
+
+def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_LEFT_MARGIN_PIXELS
+
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    dotted_parameter_names = [
+        'lbco.cell.length_a',
+        'hrpt.peak.broad_gauss_u',
+        'hrpt.peak.broad_gauss_v',
+        'hrpt.instrument.twotheta_offset',
+    ]
+    fit_results.posterior_samples.parameter_names = dotted_parameter_names
+    for index, unique_name in enumerate(dotted_parameter_names):
+        fit_results.parameters[index].unique_name = unique_name
+        fit_results.posterior_parameter_summaries[index].unique_name = unique_name
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    annotation_texts = [annotation.text for annotation in figure.layout.annotations]
+    assert 'hrpt.<br>peak.<br>broad_gauss_u' in annotation_texts
+    assert 'hrpt.<br>instrument.<br>twotheta_offset' in annotation_texts
+    assert all('None broad_gauss_u' not in text for text in annotation_texts)
+    assert figure.layout.margin.l > SQUARE_MATRIX_LEFT_MARGIN_PIXELS
+    assert figure.layout.margin.b > SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+
+
+def test_build_posterior_pairs_plot_uses_full_names_in_hovertemplates():
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    dotted_parameter_names = [
+        'lbco.cell.length_a',
+        'hrpt.peak.broad_gauss_u',
+        'hrpt.peak.broad_gauss_v',
+        'hrpt.instrument.twotheta_offset',
+    ]
+    fit_results.posterior_samples.parameter_names = dotted_parameter_names
+    for index, unique_name in enumerate(dotted_parameter_names):
+        fit_results.parameters[index].unique_name = unique_name
+        fit_results.posterior_parameter_summaries[index].unique_name = unique_name
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    hovertemplates = {
+        trace.hovertemplate
+        for trace in figure.data
+        if getattr(trace, 'hovertemplate', None) is not None
+    }
+
+    assert (
+        'lbco.cell.length_a: %{x:.4f}<br>hrpt.peak.broad_gauss_u: %{y:.4f}<extra></extra>'
+    ) in hovertemplates
+    assert (
+        'hrpt.instrument.twotheta_offset: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
+    ) in hovertemplates
+
+
+def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
+    from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
+    from easydiffraction.display.plotting import Plotter
+
+    cell_size = Plotter._posterior_pair_cell_size_pixels(8, available_width_pixels=980)
+
+    assert cell_size < PAIR_PLOT_CELL_SIZE_PIXELS
+
+
+def test_posterior_pair_density_budget_scales_with_parameter_count():
+    from easydiffraction.display.plotting import Plotter
+
+    assert Plotter._posterior_pair_density_max_points(
+        8
+    ) < Plotter._posterior_pair_density_max_points(4)
+    assert Plotter._posterior_pair_contour_grid_size(
+        8
+    ) < Plotter._posterior_pair_contour_grid_size(4)
+
+
+def test_posterior_pairs_context_thins_kde_samples_and_preserves_axis_ranges():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SCATTER_MAX_POINTS
+    from easydiffraction.display.plotting import Plotter
+
+    parameter_count = 8
+    sample_count = 5000
+    parameter_names = [f'param_{index}' for index in range(parameter_count)]
+    samples = np.zeros((1, sample_count, parameter_count), dtype=float)
+    samples[0, 1, 0] = 100.0
+    samples[0, 2, 1] = -50.0
+    for index in range(2, parameter_count):
+        samples[0, :, index] = np.linspace(index, index + 1, sample_count, dtype=float)
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=parameter_names,
+        parameter_samples=samples,
+        log_posterior=np.zeros((1, sample_count), dtype=float),
+    )
+    parameters = [
+        SimpleNamespace(unique_name=name, name=name, fit_min=None, fit_max=None)
+        for name in parameter_names
+    ]
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=[],
+        posterior_predictive={},
+        parameters=parameters,
+    )
+    plotter = Plotter()
+    plotter._get_posterior_samples_and_fit_results = MethodType(
+        lambda self: (posterior_samples, fit_results),
+        plotter,
+    )
+
+    context = plotter._posterior_pairs_context(parameters=None)
+
+    assert context is not None
+    assert context.density_samples.shape == (
+        Plotter._posterior_pair_density_max_points(parameter_count),
+        parameter_count,
+    )
+    assert context.scatter_samples.shape == (POSTERIOR_PAIR_SCATTER_MAX_POINTS, parameter_count)
+    assert context.show_contours is False
+    assert context.contour_grid_size == Plotter._posterior_pair_contour_grid_size(parameter_count)
+    assert context.axis_ranges[0][1] > 100.0
+    assert context.axis_ranges[1][0] < -50.0
+
+
+def test_build_posterior_pairs_plot_rejects_unknown_style():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    with pytest.raises(
+        ValueError,
+        match=r'style must be one of auto, fast, full for posterior pair plots\.',
+    ):
+        plotter._build_posterior_pairs_plot(parameters=None, style='slow')
+
+
+def test_build_param_distribution_plot_returns_plotly_figure():
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_68_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_95_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_POINT_ESTIMATE_LINE_DASH
+
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    parameter = fit_results.parameters[0]
+
+    figure = plotter._build_param_distribution_plot(parameter)
+
+    assert figure.layout.title.text == 'Posterior distribution: length_a'
+    assert {trace.name for trace in figure.data} >= {
+        'Posterior histogram',
+        'Marginal density',
+        '68% credible interval',
+        '95% credible interval',
+        'Median',
+        'Max posterior',
+    }
+    marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
+    histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
+    interval_68_trace = next(
+        trace for trace in figure.data if trace.name == '68% credible interval'
+    )
+    interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
+    max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
+    assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    assert marginal_trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+    assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
+    assert histogram_trace.xbins.size is not None
+    assert interval_68_trace.fillcolor == POSTERIOR_INTERVAL_68_FILL_COLOR
+    assert interval_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
+    assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
+    assert figure.layout.xaxis.range is not None
+    assert tuple(figure.layout.xaxis.range) == (
+        float(marginal_trace.x[0]),
+        float(marginal_trace.x[-1]),
+    )
+    assert figure.layout.yaxis.range is not None
+
+
+def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_95_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_POINT_ESTIMATE_LINE_DASH
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotters.plotly import PlotlyPlotter
+
+    captured: dict[str, object] = {}
+
+    plotter = Plotter()
+    plotter._backend = SimpleNamespace(
+        _show_figure=lambda figure: captured.setdefault('fig', figure)
+    )
+
+    plotter._plot_posterior_predictive_summary(
+        expt_name='hrpt',
+        summary=SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            map_prediction=np.array([9.0, 10.0, 11.0]),
+        ),
+        y_meas=np.array([9.5, 10.5, 11.5]),
+        axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
+        show_band=True,
+        show_draws=False,
+    )
+
+    fig = captured['fig']
+    upper_band_trace = fig.data[1]
+    measured_trace = next(trace for trace in fig.data if trace.name == 'Measured')
+    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+
+    assert upper_band_trace.name == '95% interval'
+    assert upper_band_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
+    assert upper_band_trace.legendrank == 30
+    assert measured_trace.legendrank == 10
+    assert max_posterior_trace.legendrank == 20
+    assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
+    assert fig.layout.legend.x == 1.0
+    assert fig.layout.legend.y == 1.0
+    assert fig.layout.legend.bgcolor == PlotlyPlotter._legend_background_color()
+    assert fig.layout.margin.r == 30
+    assert fig.layout.margin.t == 40
+    assert fig.layout.margin.b == 45
+    assert fig.layout.xaxis.showline is True
+    assert fig.layout.xaxis.mirror is True
+    assert fig.layout.xaxis.zeroline is False
+    assert fig.layout.xaxis.linecolor == PlotlyPlotter._axis_frame_color()
+    assert fig.layout.yaxis.showline is True
+    assert fig.layout.yaxis.mirror is True
+    assert fig.layout.yaxis.zeroline is False
+    assert fig.layout.yaxis.linecolor == PlotlyPlotter._axis_frame_color()
+
+
+@pytest.mark.parametrize(
+    ('x_values', 'y_values', 'x_bounds', 'y_bounds'),
+    [
+        (np.ones(8), np.arange(8, dtype=float), (0.5, 1.5), (0.0, 7.0)),
+        (
+            np.arange(8, dtype=float),
+            2.0 * np.arange(8, dtype=float) + 1.0,
+            (0.0, 7.0),
+            (1.0, 15.0),
+        ),
+    ],
+)
+def test_posterior_pair_density_surface_returns_none_for_rank_deficient_samples(
+    x_values,
+    y_values,
+    x_bounds,
+    y_bounds,
+):
+    from easydiffraction.display.plotting import Plotter
+
+    surface = Plotter._posterior_pair_density_surface(
+        x_values=np.asarray(x_values, dtype=float),
+        y_values=np.asarray(y_values, dtype=float),
+        x_bounds=x_bounds,
+        y_bounds=y_bounds,
+    )
+
+    assert surface is None
+
+
+def test_plot_posterior_predictive_data_uses_max_posterior_label_and_dash(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import XAxisType
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Pattern:
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 12.0, 11.0])
+        intensity_bkg = np.array([1.0, 1.0, 1.0])
+
+    class Experiment:
+        type = ExptType()
+        data = Pattern()
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._backend = SimpleNamespace(
+        plot_powder_meas_vs_calc=lambda *, plot_spec: captured.setdefault('plot_spec', plot_spec)
+    )
+
+    monkeypatch.setattr(
+        Plotter,
+        '_get_or_build_posterior_predictive_summary',
+        lambda self, **kwargs: SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            map_prediction=np.array([9.0, 11.0, 10.5]),
+            draws=None,
+        ),
+    )
+    monkeypatch.setattr(Plotter, '_extract_bragg_tick_sets', lambda self, **kwargs: ())
+
+    plotter._plot_posterior_predictive_data(
+        experiment=Experiment(),
+        expt_name='hrpt',
+        plot_options=SimpleNamespace(x_min=None, x_max=None, show_residual=None, x=None),
+        x_axis=XAxisType.TWO_THETA,
+        style='band',
+    )
+
+    plot_spec = captured['plot_spec']
+    assert plot_spec.y_calc_name == 'Max posterior'
+    assert plot_spec.y_calc_line_dash == 'dot'
+
+
+def test_build_param_distribution_plot_accepts_unique_name_string():
+    plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
+    unique_name = 'phase.cell.length_a'
+    posterior_samples.parameter_names[0] = unique_name
+    fit_results.parameters[0].unique_name = unique_name
+    fit_results.posterior_parameter_summaries[0].unique_name = unique_name
+
+    figure = plotter._build_param_distribution_plot(unique_name)
+
+    assert figure.layout.title.text == f'Posterior distribution: {unique_name}'
+
+
+def test_build_param_distribution_plot_accepts_user_facing_label_string():
+    plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
+    unique_name = 'phase.cell.length_a'
+    posterior_samples.parameter_names[0] = unique_name
+    fit_results.parameters[0].unique_name = unique_name
+    fit_results.posterior_parameter_summaries[0].unique_name = unique_name
+    fit_results.posterior_parameter_summaries[0].display_name = 'Cell a'
+
+    figure = plotter._build_param_distribution_plot('Cell a')
+
+    assert figure.layout.title.text == f'Posterior distribution: {unique_name}'
+
+
+def test_resolve_posterior_parameter_names_warns_on_ambiguous_label(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import Plotter
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['phase_a.length_a', 'phase_b.length_a'],
+        parameter_samples=np.ones((2, 2, 2), dtype=float),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='phase_a.length_a',
+                display_name='length_a',
+                map_value=1.0,
+                median=1.0,
+                standard_deviation=0.1,
+                interval_68=(0.9, 1.1),
+                interval_95=(0.8, 1.2),
+            ),
+            PosteriorParameterSummary(
+                unique_name='phase_b.length_a',
+                display_name='length_a',
+                map_value=2.0,
+                median=2.0,
+                standard_deviation=0.1,
+                interval_68=(1.9, 2.1),
+                interval_95=(1.8, 2.2),
+            ),
+        ],
+        parameters=[
+            SimpleNamespace(unique_name='phase_a.length_a', name='length_a'),
+            SimpleNamespace(unique_name='phase_b.length_a', name='length_a'),
+        ],
+    )
+    warning_messages: list[str] = []
+
+    monkeypatch.setattr(
+        'easydiffraction.display.plotting.log.warning',
+        lambda message: warning_messages.append(message),
+    )
+
+    result = Plotter._resolve_posterior_parameter_names(
+        fit_results=fit_results,
+        parameters=['length_a'],
+    )
+
+    assert result is None
+    assert warning_messages
+    assert 'ambiguous' in warning_messages[0]
+    assert 'phase_a.length_a' in warning_messages[0]
+    assert 'phase_b.length_a' in warning_messages[0]
+
+
+def test_build_posterior_predictive_summary_restores_parameter_state(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+    from easydiffraction.display.plotting import Plotter
+
+    class FakePredictiveParameter:
+        def __init__(self, unique_name, value, uncertainty):
+            self.unique_name = unique_name
+            self.value = value
+            self.uncertainty = uncertainty
+
+        def _set_value_from_minimizer(self, value):
+            self.value = value
+
+    sampled_parameters = [
+        FakePredictiveParameter('a', 1.0, 0.1),
+        FakePredictiveParameter('b', 2.0, 0.2),
+    ]
+    posterior_samples = SimpleNamespace(
+        parameter_names=['a', 'b'],
+        flattened=lambda: np.array(
+            [
+                [1.0, 2.0],
+                [1.1, 2.1],
+                [0.9, 1.9],
+                [1.2, 2.2],
+            ],
+            dtype=float,
+        ),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        parameters=sampled_parameters,
+    )
+    plotter = Plotter()
+
+    def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_axis):
+        x = np.array([0.0, 1.0], dtype=float)
+        y = np.array([values[0] + values[1], values[0] - values[1]], dtype=float)
+        return y, x
+
+    monkeypatch.setattr(Plotter, '_evaluate_posterior_predictive_state', fake_evaluate)
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    summary = plotter._build_posterior_predictive_summary(
+        fit_results=fit_results,
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+    )
+
+    assert isinstance(summary, PosteriorPredictiveSummary)
+    assert summary.experiment_name == 'hrpt'
+    assert summary.x_axis_name == 'two_theta'
+    assert summary.draws.shape == (4, 2)
+    np.testing.assert_allclose(summary.map_prediction, np.array([3.0, -1.0]))
+    np.testing.assert_allclose([parameter.value for parameter in sampled_parameters], [1.0, 2.0])
+    assert [parameter.uncertainty for parameter in sampled_parameters] == [0.1, 0.2]
+
+
+def test_build_posterior_predictive_summary_omits_draws_when_not_requested(monkeypatch):
+    from easydiffraction.display.plotting import Plotter
+
+    class FakePredictiveParameter:
+        def __init__(self, unique_name, value, uncertainty):
+            self.unique_name = unique_name
+            self.value = value
+            self.uncertainty = uncertainty
+
+        def _set_value_from_minimizer(self, value):
+            self.value = value
+
+    sampled_parameters = [
+        FakePredictiveParameter('a', 1.0, 0.1),
+        FakePredictiveParameter('b', 2.0, 0.2),
+    ]
+    posterior_samples = SimpleNamespace(
+        parameter_names=['a', 'b'],
+        flattened=lambda: np.array(
+            [
+                [1.0, 2.0],
+                [1.1, 2.1],
+                [0.9, 1.9],
+                [1.2, 2.2],
+            ],
+            dtype=float,
+        ),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        parameters=sampled_parameters,
+    )
+    plotter = Plotter()
+
+    def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_axis):
+        x = np.array([0.0, 1.0], dtype=float)
+        y = np.array([values[0] + values[1], values[0] - values[1]], dtype=float)
+        return y, x
+
+    monkeypatch.setattr(Plotter, '_evaluate_posterior_predictive_state', fake_evaluate)
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    summary = plotter._build_posterior_predictive_summary(
+        fit_results=fit_results,
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=False,
+    )
+
+    assert summary is not None
+    assert summary.draws is None
+    np.testing.assert_allclose(summary.lower_95.shape, (2,))
+    np.testing.assert_allclose(summary.upper_95.shape, (2,))
+
+
+def test_get_or_build_posterior_predictive_summary_rebuilds_draws_after_band_cache(
+    monkeypatch,
+):
+    from easydiffraction.display.plotting import Plotter
+
+    fit_results = SimpleNamespace(
+        posterior_predictive={},
+        posterior_samples=object(),
+    )
+    band_summary = SimpleNamespace(draws=None)
+    draw_summary = SimpleNamespace(draws=np.ones((2, 2), dtype=float))
+    build_calls: list[bool] = []
+    plotter = Plotter()
+
+    monkeypatch.setattr(Plotter, '_get_fit_result_for_correlation', lambda self: fit_results)
+
+    def fake_build(
+        self,
+        *,
+        fit_results,
+        experiment,
+        expt_name,
+        x_axis,
+        include_draws=True,
+    ):
+        del fit_results, experiment, expt_name, x_axis
+        build_calls.append(include_draws)
+        return draw_summary if include_draws else band_summary
+
+    monkeypatch.setattr(Plotter, '_build_posterior_predictive_summary', fake_build)
+
+    summary_band = plotter._get_or_build_posterior_predictive_summary(
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=False,
+    )
+    summary_draws = plotter._get_or_build_posterior_predictive_summary(
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=True,
+    )
+
+    assert summary_band is band_summary
+    assert summary_draws is draw_summary
+    assert build_calls == [False, True]
+
+
+def test_plot_posterior_predictive_defaults_to_band_for_bragg(monkeypatch):
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Experiment:
+        type = ExptType()
+
+    class Project:
+        experiments = {'hrpt': Experiment()}
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._set_project(Project())
+
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    def fake_plot_posterior_predictive_data(
+        self,
+        *,
+        experiment,
+        expt_name,
+        plot_options,
+        x_axis,
+        style,
+    ):
+        captured['experiment'] = experiment
+        captured['expt_name'] = expt_name
+        captured['style'] = style
+        captured['x_axis'] = x_axis
+        captured['show_residual'] = plot_options.show_residual
+
+    monkeypatch.setattr(
+        Plotter, '_plot_posterior_predictive_data', fake_plot_posterior_predictive_data
+    )
+
+    plotter.plot_posterior_predictive('hrpt')
+
+    assert captured['experiment'] is Project.experiments['hrpt']
+    assert captured['expt_name'] == 'hrpt'
+    assert captured['style'] == 'band'
+    assert captured['show_residual'] is None
+
+
+def test_plot_posterior_predictive_non_bragg_filters_x_range_and_warns_for_residual(
+    monkeypatch,
+):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+    warnings: list[str] = []
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.TOTAL})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Pattern:
+        x = np.array([1.0, 2.0, 3.0])
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 20.0, 30.0])
+
+    class Experiment:
+        type = ExptType()
+        data = Pattern()
+
+    class Project:
+        experiments = {'pdf': Experiment()}
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._set_project(Project())
+
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+    monkeypatch.setattr(
+        Plotter,
+        '_get_or_build_posterior_predictive_summary',
+        lambda self, **kwargs: PosteriorPredictiveSummary(
+            experiment_name='pdf',
+            x_axis_name='two_theta',
+            x=np.array([1.0, 2.0, 3.0]),
+            map_prediction=np.array([9.0, 19.0, 29.0]),
+            lower_95=np.array([8.0, 18.0, 28.0]),
+            upper_95=np.array([10.0, 20.0, 30.0]),
+        ),
+    )
+    monkeypatch.setattr('easydiffraction.display.plotting.log.warning', warnings.append)
+
+    def fake_plot_summary(
+        self,
+        *,
+        expt_name,
+        summary,
+        y_meas,
+        axes_labels,
+        show_band,
+        show_draws,
+    ):
+        captured['expt_name'] = expt_name
+        captured['summary'] = summary
+        captured['y_meas'] = y_meas
+        captured['axes_labels'] = axes_labels
+        captured['show_band'] = show_band
+        captured['show_draws'] = show_draws
+
+    monkeypatch.setattr(Plotter, '_plot_posterior_predictive_summary', fake_plot_summary)
+
+    plotter.plot_posterior_predictive('pdf', x_min=1.5, x_max=2.5, show_residual=True)
+
+    assert captured['expt_name'] == 'pdf'
+    np.testing.assert_allclose(captured['summary'].x, np.array([2.0]))
+    np.testing.assert_allclose(captured['summary'].map_prediction, np.array([19.0]))
+    np.testing.assert_allclose(captured['summary'].lower_95, np.array([18.0]))
+    np.testing.assert_allclose(captured['summary'].upper_95, np.array([20.0]))
+    np.testing.assert_allclose(captured['y_meas'], np.array([20.0]))
+    assert captured['show_band'] is True
+    assert captured['show_draws'] is False
+    assert any('ignoring show_residual=True' in warning for warning in warnings)
+
+
 def test_extract_bragg_tick_sets_uses_derived_d_spacing_for_cwl_ticks():
     import numpy as np
 
@@ -641,7 +1536,7 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations(threshold=0.1, precision=3)
+    p.plot_param_correlations(threshold=0.1, precision=3, show_diagonal=False)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [
@@ -660,7 +1555,12 @@ def test_plot_param_correlations_renders_plotly_heatmap(monkeypatch):
     import numpy as np
 
     import easydiffraction.display.plotters.plotly as plotly_mod
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
     from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TOP_MARGIN_PIXELS
 
     captured = {}
 
@@ -694,44 +1594,70 @@ class Project:
     p = Plotter()
     p.engine = 'plotly'
     p._set_project(Project())
-    p.plot_param_correlations(threshold=0.1)
+    p.plot_param_correlations()
 
     fig = captured['fig']
+    heatmap = fig.data[0]
+    text_trace = fig.data[1]
+    gap_width = Plotter._square_matrix_gap_data_width(2)
     assert len(fig.data) == 2
-    assert fig.data[0].type == 'heatmap'
-    assert list(fig.data[0].x) == [0.0, 1.0]
-    assert list(fig.data[0].y) == [0.0, 1.0]
-    assert fig.data[0].xgap in (None, 0)
-    assert fig.data[0].ygap in (None, 0)
-    assert fig.data[0].colorbar.lenmode == 'fraction'
-    assert fig.data[0].colorbar.len == 1.0
-    assert fig.data[0].colorbar.title.text == ''
-    assert fig.data[0].hovertemplate == 'x: %{x}<br>y: %{y}<br>corr: %{z:.2f}<extra></extra>'
-    assert pytest.approx(fig.data[0].z[0][0], rel=1e-9) == -0.5
-    assert fig.data[1].type == 'scatter'
-    assert fig.data[1].mode == 'text'
-    assert list(fig.data[1].x) == [0.5]
-    assert list(fig.data[1].y) == [0.5]
-    assert list(fig.data[1].text) == ['-0.50']
-    assert fig.data[1].textposition == 'middle center'
-    assert fig.data[1].hoverinfo == 'skip'
-    assert fig.layout.xaxis.side == 'bottom'
-    assert fig.layout.xaxis.tickangle < 0
-    assert list(fig.layout.xaxis.tickvals) == [0.5]
-    assert list(fig.layout.xaxis.ticktext) == ['phase.scale']
+    assert heatmap.type == 'heatmap'
+    assert list(heatmap.x) == pytest.approx([0.0, 1.0, 1.0 + gap_width, 2.0 + gap_width])
+    assert list(heatmap.y) == pytest.approx([0.0, 1.0, 1.0 + gap_width, 2.0 + gap_width])
+    assert heatmap.showscale is False
+    assert heatmap.hovertemplate == (
+        '%{customdata[0]}<br>%{customdata[1]}<br>correlation: %{z:.2f}<extra></extra>'
+    )
+    assert list(heatmap.customdata[2][0]) == ['phase.scale', 'phase.cell.length_c']
+    assert pytest.approx(np.nanmin(np.asarray(heatmap.z, dtype=float)), rel=1e-9) == -0.5
+    assert text_trace.type == 'scatter'
+    assert text_trace.mode == 'text'
+    assert list(text_trace.x) == [0.5]
+    assert list(text_trace.y) == pytest.approx([1.5 + gap_width])
+    assert list(text_trace.text) == ['-0.50']
+    assert text_trace.textposition == 'middle center'
+    assert text_trace.hoverinfo == 'skip'
+    assert [annotation.text for annotation in fig.layout.annotations] == [
+        'Refined parameter correlation matrix',
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+    ]
+    assert fig.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
+    assert fig.layout.annotations[0].yshift == SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+    ])
+    assert fig.layout.margin.t == SQUARE_MATRIX_TOP_MARGIN_PIXELS
+    assert fig.layout.margin.b == (
+        SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS + 2 * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
+    )
+    assert (
+        fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio']
+        == Plotter._square_matrix_layout_meta(
+            n_parameters=2,
+            annotation_labels=[
+                'phase.<br>scale',
+                'phase.<br>cell.<br>length_c',
+                'phase.<br>scale',
+                'phase.<br>cell.<br>length_c',
+            ],
+        )['fixed_aspect_wrapper']['aspect_ratio']
+    )
     assert fig.layout.xaxis.showline is False
     assert fig.layout.xaxis.mirror is False
-    assert fig.layout.xaxis.layer == 'above traces'
-    assert list(fig.layout.yaxis.tickvals) == [0.5]
-    assert list(fig.layout.yaxis.ticktext) == ['phase.cell.length_c']
     assert fig.layout.yaxis.showline is False
     assert fig.layout.yaxis.mirror is False
-    assert fig.layout.yaxis.layer == 'above traces'
-    assert fig.layout.yaxis.ticklabelstandoff == 8
-    assert len(fig.layout.shapes) == 1
-    assert fig.layout.shapes[-1].type == 'rect'
-    assert fig.layout.shapes[-1].xref == 'paper'
-    assert fig.layout.shapes[-1].yref == 'paper'
+    assert fig.layout.xaxis.showticklabels is False
+    assert fig.layout.yaxis.showticklabels is False
+    assert fig.layout.xaxis.title.text is None
+    assert fig.layout.yaxis.title.text is None
+    assert fig.layout.paper_bgcolor is None
+    assert fig.layout.plot_bgcolor is None
+    assert len(fig.layout.shapes) == 3
+    assert all(shape.type == 'rect' for shape in fig.layout.shapes)
 
 
 def test_plot_param_correlations_plotly_labels_respect_threshold(monkeypatch):
@@ -785,17 +1711,128 @@ class Project:
     p = Plotter()
     p.engine = 'plotly'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0.7)
 
     fig = captured['fig']
-    assert len(fig.data) == 2
-    assert fig.data[0].type == 'heatmap'
-    assert fig.data[1].type == 'scatter'
-    assert fig.data[1].mode == 'text'
-    assert list(fig.data[1].text) == ['-0.91', '0.83', '-0.89', '0.82']
+    heatmap_traces = [trace for trace in fig.data if trace.type == 'heatmap']
+    text_traces = [trace for trace in fig.data if trace.type == 'scatter' and trace.mode == 'text']
+    assert len(heatmap_traces) == 1
+    assert len(text_traces) == 1
+    assert list(text_traces[0].text) == ['-0.91', '0.83', '-0.89', '0.82']
+    assert len(fig.layout.shapes) == 15
 
 
-def test_plot_param_correlations_filters_by_default_threshold(monkeypatch):
+def test_plot_param_correlations_limits_default_table_to_six_parameters(monkeypatch):
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.tables import TableRenderer
+
+    captured = {}
+
+    class FakeTabler:
+        def render(self, df):
+            captured['df'] = df
+
+    monkeypatch.setattr(TableRenderer, 'get', staticmethod(lambda: FakeTabler()))
+
+    class Param:
+        def __init__(self, uid, unique_name):
+            self._minimizer_uid = uid
+            self.unique_name = unique_name
+
+    class RawResult:
+        covar = None
+        var_names = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6']
+
+        class ParamResult:
+            def __init__(self, correl):
+                self.correl = correl
+
+        params = {
+            'p1': ParamResult({'p2': 0.95}),
+            'p2': ParamResult({'p1': 0.95, 'p3': 0.94}),
+            'p3': ParamResult({'p2': 0.94, 'p4': 0.93}),
+            'p4': ParamResult({'p3': 0.93, 'p5': 0.92}),
+            'p5': ParamResult({'p4': 0.92, 'p6': 0.91}),
+            'p6': ParamResult({'p5': 0.91}),
+        }
+
+    class FitResults:
+        engine_result = RawResult()
+        parameters = [
+            Param('p1', 'phase.scale'),
+            Param('p2', 'phase.cell.length_a'),
+            Param('p3', 'phase.background'),
+            Param('p4', 'phase.profile.u'),
+            Param('p5', 'phase.profile.v'),
+            Param('p6', 'phase.profile.w'),
+        ]
+
+    class Analysis:
+        fit_results = FitResults()
+
+    class Project:
+        analysis = Analysis()
+
+    p = Plotter()
+    p.engine = 'asciichartpy'
+    p._set_project(Project())
+    p.plot_param_correlations()
+
+    df = captured['df']
+    assert [column.strip() for column in df.columns.get_level_values(0)] == [
+        'parameter',
+        '1',
+        '2',
+        '3',
+        '4',
+        '5',
+        '6',
+    ]
+    assert list(df.index) == [0, 1, 2, 3, 4, 5]
+    assert list(df.iloc[:, 0]) == [
+        'phase.scale',
+        'phase.cell.length_a',
+        'phase.background',
+        'phase.profile.u',
+        'phase.profile.v',
+        'phase.profile.w',
+    ]
+    assert df.iloc[0, 1] == ''
+    assert _strip_markup(df.iloc[1, 1]).strip() == '0.95'
+    assert _strip_markup(df.iloc[2, 2]).strip() == '0.94'
+    assert _strip_markup(df.iloc[3, 3]).strip() == '0.93'
+    assert _strip_markup(df.iloc[4, 4]).strip() == '0.92'
+    assert _strip_markup(df.iloc[5, 5]).strip() == '0.91'
+    assert df.iloc[5, 6] == ''
+
+
+def test_plot_posterior_pairs_uses_default_max_parameter_limit(monkeypatch):
+    from easydiffraction.display.plotting import DEFAULT_CORRELATION_MAX_PARAMETERS
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    plotter = Plotter()
+
+    def fake_build(self, *, parameters, style, threshold, max_parameters):
+        captured['parameters'] = parameters
+        captured['style'] = style
+        captured['threshold'] = threshold
+        captured['max_parameters'] = max_parameters
+        return object()
+
+    monkeypatch.setattr(Plotter, '_build_posterior_pairs_plot', fake_build)
+    monkeypatch.setattr(Plotter, '_show_plot_figure', lambda self, figure: None)
+
+    plotter.plot_posterior_pairs()
+
+    assert captured['parameters'] is None
+    assert captured['style'] == 'auto'
+    assert captured['threshold'] is None
+    assert captured['max_parameters'] == DEFAULT_CORRELATION_MAX_PARAMETERS
+
+
+def test_plot_param_correlations_shows_full_table_when_threshold_is_zero(monkeypatch):
     from easydiffraction.display.plotting import Plotter
     from easydiffraction.display.tables import TableRenderer
 
@@ -843,18 +1880,28 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [
         'parameter',
         '1',
+        '2',
+        '3',
     ]
-    assert list(df.index) == [0, 1]
+    assert list(df.index) == [0, 1, 2]
     assert df.iloc[0, 0] == 'phase.scale'
     assert df.iloc[0, 1] == ''
+    assert df.iloc[0, 2] == ''
+    assert df.iloc[0, 3] == ''
     assert df.iloc[1, 0] == 'phase.cell.length_a'
     assert _strip_markup(df.iloc[1, 1]).strip() == '0.82'
+    assert df.iloc[1, 2] == ''
+    assert df.iloc[1, 3] == ''
+    assert df.iloc[2, 0] == 'phase.background'
+    assert _strip_markup(df.iloc[2, 1]).strip() == '0.25'
+    assert _strip_markup(df.iloc[2, 2]).strip() == '0.00'
+    assert df.iloc[2, 3] == ''
 
 
 def test_plot_param_correlations_hides_subthreshold_table_values(monkeypatch):
@@ -909,7 +1956,7 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0.7, show_diagonal=False)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [
diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py
index 92907f66c..090ce5404 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from types import SimpleNamespace
+
 
 def test_module_import():
     import easydiffraction.project.project as MUT
@@ -49,3 +51,15 @@ def test_project_verbosity_invalid():
     p = Project()
     with pytest.raises(ValueError, match="'verbose' is not a valid VerbosityEnum"):
         p.verbosity = 'verbose'
+
+
+def test_project_free_params_aggregate_structures_and_experiments():
+    from easydiffraction.project.project import Project
+
+    project = Project()
+    structure_param = object()
+    experiment_param = object()
+    project._structures = SimpleNamespace(free_parameters=[structure_param])
+    project._experiments = SimpleNamespace(free_parameters=[experiment_param])
+
+    assert project.free_parameters == [structure_param, experiment_param]
diff --git a/tools/generate_package_docs.py b/tools/generate_package_docs.py
index 671a4f0ec..1d860557e 100644
--- a/tools/generate_package_docs.py
+++ b/tools/generate_package_docs.py
@@ -3,7 +3,7 @@
 
 """Generate project package structure markdown files.
 
-Outputs two docs under docs/architecture/:
+Outputs two docs under docs/dev/:
  - package-structure-short.md  (folders/files only)
  - package-structure-full.md   (folders/files and classes)
 
@@ -21,7 +21,7 @@
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
 SRC_ROOT = REPO_ROOT / 'src' / 'easydiffraction'
-DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'architecture'
+DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'dev'
 
 
 IGNORE_DIRS = {

From efadbcdc94c56bcffff9987c70ed61a32b1a162a Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Thu, 14 May 2026 17:35:55 +0200
Subject: [PATCH 03/10] Improve chart and table display API (#171)

* Add display UX ADR and plan

* Rename display settings category to rendering

* Add grouped display facade

* Move constraint display and standardize CIF helpers

* Refine display pattern routing

* Implement state-aware pattern display

* Update display UX documentation

* Complete display UX verification and test updates

* Address display UX review fixes

* Fix single-crystal display availability checks
---
 docs/dev/adr_display-ux.md                    | 227 ++++++
 docs/dev/architecture.md                      |  59 +-
 docs/dev/package-structure-full.md            |  12 +-
 docs/dev/package-structure-short.md           |   3 +-
 docs/dev/plan_display-ux.md                   | 238 ++++++
 docs/docs/tutorials/ed-1.ipynb                |  10 +-
 docs/docs/tutorials/ed-1.py                   |  10 +-
 docs/docs/tutorials/ed-10.ipynb               |   6 +-
 docs/docs/tutorials/ed-10.py                  |   6 +-
 docs/docs/tutorials/ed-11.ipynb               |  12 +-
 docs/docs/tutorials/ed-11.py                  |  12 +-
 docs/docs/tutorials/ed-12.ipynb               |  12 +-
 docs/docs/tutorials/ed-12.py                  |  12 +-
 docs/docs/tutorials/ed-13.ipynb               | 100 ++-
 docs/docs/tutorials/ed-13.py                  |  98 ++-
 docs/docs/tutorials/ed-14.ipynb               |  12 +-
 docs/docs/tutorials/ed-14.py                  |  12 +-
 docs/docs/tutorials/ed-15.ipynb               |  14 +-
 docs/docs/tutorials/ed-15.py                  |  14 +-
 docs/docs/tutorials/ed-16.ipynb               |  14 +-
 docs/docs/tutorials/ed-16.py                  |  14 +-
 docs/docs/tutorials/ed-17.ipynb               |  34 +-
 docs/docs/tutorials/ed-17.py                  |  34 +-
 docs/docs/tutorials/ed-18.ipynb               |   6 +-
 docs/docs/tutorials/ed-18.py                  |   6 +-
 docs/docs/tutorials/ed-2.ipynb                |  18 +-
 docs/docs/tutorials/ed-2.py                   |  18 +-
 docs/docs/tutorials/ed-20.ipynb               |  18 +-
 docs/docs/tutorials/ed-20.py                  |  18 +-
 docs/docs/tutorials/ed-21.ipynb               | 162 ++--
 docs/docs/tutorials/ed-21.py                  |  22 +-
 docs/docs/tutorials/ed-22.ipynb               | 266 +++++--
 docs/docs/tutorials/ed-22.py                  |  20 +-
 docs/docs/tutorials/ed-3.ipynb                |  66 +-
 docs/docs/tutorials/ed-3.py                   |  66 +-
 docs/docs/tutorials/ed-4.ipynb                |   4 +-
 docs/docs/tutorials/ed-4.py                   |   4 +-
 docs/docs/tutorials/ed-5.ipynb                |  14 +-
 docs/docs/tutorials/ed-5.py                   |  14 +-
 docs/docs/tutorials/ed-6.ipynb                |  38 +-
 docs/docs/tutorials/ed-6.py                   |  38 +-
 docs/docs/tutorials/ed-7.ipynb                |  48 +-
 docs/docs/tutorials/ed-7.py                   |  48 +-
 docs/docs/tutorials/ed-8.ipynb                |  12 +-
 docs/docs/tutorials/ed-8.py                   |  12 +-
 docs/docs/tutorials/ed-9.ipynb                |  10 +-
 docs/docs/tutorials/ed-9.py                   |  10 +-
 .../user-guide/analysis-workflow/analysis.md  |   8 +-
 .../user-guide/analysis-workflow/project.md   |   4 +-
 docs/docs/user-guide/first-steps.md           |  27 +-
 src/easydiffraction/__main__.py               |   6 +-
 src/easydiffraction/analysis/analysis.py      |  30 +-
 .../categories/constraints/default.py         |  19 +
 src/easydiffraction/display/plotters/ascii.py |   9 +
 src/easydiffraction/display/plotters/base.py  |   4 +
 .../display/plotters/plotly.py                |  35 +
 src/easydiffraction/display/plotting.py       | 269 +++++--
 src/easydiffraction/io/cif/serialize.py       |  14 +-
 .../project/categories/display/__init__.py    |   8 -
 .../project/categories/display/default.py     | 117 ---
 .../project/categories/rendering/__init__.py  |   8 +
 .../project/categories/rendering/default.py   | 131 ++++
 .../{display => rendering}/factory.py         |   6 +-
 src/easydiffraction/project/display.py        | 689 ++++++++++++++++++
 src/easydiffraction/project/project.py        |  21 +-
 src/easydiffraction/project/project_info.py   |   5 +-
 .../test_analysis_and_fit_category_support.py |   3 +-
 .../fitting/test_analysis_display.py          |  20 +-
 .../fitting/test_cli_entrypoints.py           |  31 +-
 tests/integration/fitting/test_plotting.py    |  22 +-
 .../analysis/test_analysis_coverage.py        |   4 +-
 .../easydiffraction/display/test_plotting.py  | 134 +++-
 .../categories/display/test_default.py        |  55 --
 .../categories/display/test_factory.py        |  23 -
 .../categories/rendering/test_default.py      |  55 ++
 .../categories/rendering/test_factory.py      |  23 +
 .../easydiffraction/project/test_display.py   | 479 ++++++++++++
 .../easydiffraction/project/test_project.py   |  11 +
 .../project/test_project_load.py              |  10 +-
 .../project/test_project_save.py              |   4 +-
 tests/unit/easydiffraction/test___main__.py   |  31 +-
 81 files changed, 3228 insertions(+), 990 deletions(-)
 create mode 100644 docs/dev/adr_display-ux.md
 create mode 100644 docs/dev/plan_display-ux.md
 delete mode 100644 src/easydiffraction/project/categories/display/__init__.py
 delete mode 100644 src/easydiffraction/project/categories/display/default.py
 create mode 100644 src/easydiffraction/project/categories/rendering/__init__.py
 create mode 100644 src/easydiffraction/project/categories/rendering/default.py
 rename src/easydiffraction/project/categories/{display => rendering}/factory.py (70%)
 create mode 100644 src/easydiffraction/project/display.py
 delete mode 100644 tests/unit/easydiffraction/project/categories/display/test_default.py
 delete mode 100644 tests/unit/easydiffraction/project/categories/display/test_factory.py
 create mode 100644 tests/unit/easydiffraction/project/categories/rendering/test_default.py
 create mode 100644 tests/unit/easydiffraction/project/categories/rendering/test_factory.py
 create mode 100644 tests/unit/easydiffraction/project/test_display.py

diff --git a/docs/dev/adr_display-ux.md b/docs/dev/adr_display-ux.md
new file mode 100644
index 000000000..264092e22
--- /dev/null
+++ b/docs/dev/adr_display-ux.md
@@ -0,0 +1,227 @@
+# ADR: Display UX Facade
+
+## Status
+
+Accepted.
+
+## Context
+
+The current user-facing display API mixes presentation actions, analysis
+reports, and renderer configuration:
+
+```python
+project.display.plotter.plot_meas(expt_name='hrpt')
+project.display.plotter.plot_calc(expt_name='hrpt')
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.plotter.plot_param_correlations()
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+
+project.analysis.display.free_params()
+project.analysis.display.fit_results()
+```
+
+This has several UX problems:
+
+- `plot_` is redundant below any display or chart object.
+- `plotter` and `tabler` expose backend implementation language to
+  scientists using notebooks.
+- `project.display` and `project.analysis.display` overlap without a
+  clear user-facing rule.
+- `plot_meas`, `plot_calc`, and `plot_meas_vs_calc` force users to
+  choose a plot state that the project can often infer.
+- Bayesian and deterministic chart names are not systematic.
+- The existing `project.display` category is serialized to CIF, so it
+  should not also become a broad transient display facade.
+
+EasyDiffraction is aimed at scientists, often non-programmers, so the
+display API should prioritize discoverability, clear names, and safe
+defaults.
+
+## Decision
+
+Use `project.display` as the user-facing facade for display actions.
+Move serialized renderer settings out of that facade and into a separate
+project category named `project.rendering`.
+
+Renderer settings:
+
+```python
+project.rendering.chart_engine = 'plotly'
+project.rendering.table_engine = 'pandas'
+project.rendering.show_chart_engines()
+project.rendering.show_table_engines()
+project.rendering.show_config()
+```
+
+Suggested CIF names:
+
+- `_rendering.chart_engine`
+- `_rendering.table_engine`
+
+No legacy loader is required for `_display.plotter_type` or
+`_display.tabler_type`. The project is in beta, so this cleanup may
+break old project files rather than carrying compatibility code.
+
+The selected display API is grouped:
+
+```python
+project.display.pattern(expt_name='hrpt')
+
+project.display.parameters.free()
+project.display.parameters.fittable()
+project.display.parameters.all()
+project.display.parameters.access()
+project.display.parameters.cif_uids()
+
+project.display.fit.results()
+project.display.fit.correlations()
+project.display.fit.series(param, versus=temperature)
+
+project.display.posterior.pairs()
+project.display.posterior.distribution(param)
+project.display.posterior.predictive(expt_name='hrpt')
+
+project.display.show_pattern_options(expt_name='hrpt')
+```
+
+`project.analysis.display` is removed from the primary public API. Its
+current responsibilities move to clearer homes:
+
+| Current method               | New home                                                       |
+| ---------------------------- | -------------------------------------------------------------- |
+| `all_params()`               | `project.display.parameters.all()`                             |
+| `fittable_params()`          | `project.display.parameters.fittable()`                        |
+| `free_params()`              | `project.display.parameters.free()`                            |
+| `how_to_access_parameters()` | `project.display.parameters.access()`                          |
+| `parameter_cif_uids()`       | `project.display.parameters.cif_uids()`                        |
+| `fit_results()`              | `project.display.fit.results()`                                |
+| `constraints()`              | `project.analysis.constraints.show()`                          |
+| `as_cif()`                   | `project.analysis.as_cif` and `project.analysis.show_as_cif()` |
+
+`project.analysis` and `project.info` should follow the same CIF display
+pattern as structures and experiments:
+
+- `as_cif` is a read-only property returning CIF text as a string.
+- `show_as_cif()` pretty-prints the CIF text with a header.
+
+## Pattern Display
+
+Use `pattern()` as the main experiment chart:
+
+```python
+project.display.pattern(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt', x_min=40, x_max=55)
+```
+
+By default, `pattern()` uses `include='auto'` and displays as much
+useful information as the project state supports:
+
+- measured data if present
+- calculated data if a model/calculation is available
+- background if defined and relevant
+- Bragg ticks if phases/reflections are available
+- residual if both measured and calculated data are available and the
+  experiment type supports a residual panel
+- excluded regions if available
+- uncertainty bands where posterior predictive data exists
+
+Specific subsets are selected with `include`:
+
+```python
+project.display.pattern(expt_name='hrpt', include='auto')
+project.display.pattern(expt_name='hrpt', include='measured')
+project.display.pattern(expt_name='hrpt', include='calculated')
+project.display.pattern(
+    expt_name='hrpt',
+    include=('measured', 'calculated', 'background', 'residual', 'bragg'),
+)
+```
+
+`include` was chosen over alternatives:
+
+| Name          | Reason not selected                             |
+| ------------- | ----------------------------------------------- |
+| `layers`      | Sounds graphical rather than user intent.       |
+| `components`  | Precise, but longer.                            |
+| `content`     | Too broad.                                      |
+| `view`        | Better for presets than arbitrary combinations. |
+| `series`      | Does not fit residual rows or Bragg ticks well. |
+| boolean flags | Explicit, but scales poorly.                    |
+
+Add discovery for supported pattern content:
+
+```python
+project.display.show_pattern_options(expt_name='hrpt')
+```
+
+The table should show option name, description, availability for the
+experiment, whether `include='auto'` includes it, and the reason an
+option is unavailable.
+
+Initial option names:
+
+- `auto`
+- `measured`
+- `calculated`
+- `background`
+- `residual`
+- `bragg`
+- `excluded`
+- `uncertainty`
+
+`uncertainty` should be implemented immediately where posterior
+predictive data exists. It should be unavailable, with a clear reason,
+when no posterior predictive data is present.
+
+## Deterministic And Bayesian Consistency
+
+Use these naming rules:
+
+- `pattern()` shows the current point-estimate experiment view.
+- `fit.results()` reports the latest fit result.
+- `fit.correlations()` shows parameter relationships from the latest
+  fit.
+- `fit.series(param, versus=...)` shows fitted parameter values across a
+  sequence of fit results or experiments.
+- `posterior.*` names are used only when posterior samples are required.
+
+## Rejected Alternatives
+
+Flat display facade:
+
+```python
+project.display.pattern(expt_name='hrpt')
+project.display.parameters(scope='free')
+project.display.fit_results()
+project.display.correlations()
+project.display.parameter_series(param, versus=temperature)
+project.display.posterior_pairs()
+project.display.posterior_distribution(param)
+project.display.posterior_predictive(expt_name='hrpt')
+```
+
+This is shorter but would make `project.display` grow into a long flat
+list.
+
+Separate `charts` and `tables` namespaces were also rejected because
+users should not need to decide the output type before asking for
+information. Some outputs may render as a chart or a table depending on
+backend and state.
+
+Separate `measured()` and `calculated()` methods were rejected because
+they duplicate `pattern(..., include=...)`.
+
+## Consequences
+
+- The main display workflow becomes more discoverable through grouped
+  namespaces and tab completion.
+- Renderer configuration becomes clearly separate from display actions.
+- Existing tutorials and public API docs must be updated to the selected
+  API.
+- Constraints remain owned by the analysis constraints category.
+- There is no legacy CIF compatibility path for `_display.plotter_type`
+  or `_display.tabler_type`.
+- `project.analysis` and `project.info` need CIF access cleanup for
+  consistency with structure and experiment objects.
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 48a411619..85fc0ad4e 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -879,15 +879,16 @@ project = ed.Project(name='my_project')
 
 It owns and coordinates all components:
 
-| Property              | Type          | Description                              |
-| --------------------- | ------------- | ---------------------------------------- |
-| `project.info`        | `ProjectInfo` | Metadata: name, title, description, path |
-| `project.structures`  | `Structures`  | Collection of structure datablocks       |
-| `project.experiments` | `Experiments` | Collection of experiment datablocks      |
-| `project.display`     | `Display`     | Plot/table engine selection and facades  |
-| `project.analysis`    | `Analysis`    | Minimiser, fitting, aliases, constraints |
-| `project.summary`     | `Summary`     | Report generation                        |
-| `project.verbosity`   | `str`         | Console output level (full/short/silent) |
+| Property              | Type             | Description                              |
+| --------------------- | ---------------- | ---------------------------------------- |
+| `project.info`        | `ProjectInfo`    | Metadata: name, title, description, path |
+| `project.structures`  | `Structures`     | Collection of structure datablocks       |
+| `project.experiments` | `Experiments`    | Collection of experiment datablocks      |
+| `project.rendering`   | `Rendering`      | Plot/table engine selection              |
+| `project.display`     | `ProjectDisplay` | Pattern/report facade                    |
+| `project.analysis`    | `Analysis`       | Minimiser, fitting, aliases, constraints |
+| `project.summary`     | `Summary`        | Report generation                        |
+| `project.verbosity`   | `str`            | Console output level (full/short/silent) |
 
 ### 7.1 Data Flow
 
@@ -922,7 +923,7 @@ project_dir/
 ```
 
 `project.cif` carries both the `_project.*` metadata and the
-`_display.*` engine preferences (`plotter_type`, `tabler_type`), so a
+`_rendering.*` engine preferences (`chart_engine`, `table_engine`), so a
 saved project re-opens with the same display backends. Per-experiment
 calculator selection (`_calculation.calculator_type`) lives in each
 experiment file, and fit configuration (`_fit.minimizer_type`,
@@ -1064,7 +1065,7 @@ project.experiments['hrpt'].calculation.calculator_type = 'cryspy'
 project.analysis.fit.minimizer_type = 'lmfit'
 
 # Plot before fitting
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)
+project.display.pattern(expt_name='hrpt')
 
 # Select free parameters
 project.structures['lbco'].cell.length_a.free = True
@@ -1073,14 +1074,14 @@ project.experiments['hrpt'].instrument.calib_twotheta_offset.free = True
 project.experiments['hrpt'].background['10'].y.free = True
 
 # Inspect free parameters
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # Fit and show results
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # Plot after fitting
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)
+project.display.pattern(expt_name='hrpt')
 
 # Save
 project.save()
@@ -1100,11 +1101,11 @@ project.analysis.fit.minimizer.parallel = 0
 project.analysis.fit(random_seed=11)
 
 # Runtime-only Bayesian summaries and plots
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
-project.display.plotter.plot_posterior_pairs()
-project.display.plotter.plot_param_distribution(param)
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+project.display.fit.results()
+project.display.fit.correlations()
+project.display.posterior.pairs()
+project.display.posterior.distribution(param)
+project.display.posterior.predictive(expt_name='hrpt')
 ```
 
 ### 8.5 TOF Experiment (tutorial ed-7)
@@ -1225,8 +1226,8 @@ that owns calculator selection —
 `experiment.calculation.calculator_type` and
 `experiment.calculation.show_calculator_types()` — instead of the
 selector being exposed at the experiment owner level. The same pattern
-applies to `display` on `Project`, which owns `plotter_type` and
-`tabler_type` (see §9.4.1).
+applies to `display` on `Project`, which owns `chart_engine` and
+`table_engine` (see §9.4.1).
 
 **Design decisions:**
 
@@ -1248,14 +1249,14 @@ recognises three distinct selector families. They share a similar
 `<name>_type` shape so the user can inspect and set them uniformly, but
 their intent and ownership differ:
 
-| Family                             | User intent                     | Examples                                                                    | CIF                                                                            |
-| ---------------------------------- | ------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
-| Backend selector                   | Pick an execution backend       | `fit.minimizer_type`, `calculation.calculator_type`, `display.plotter_type` | `_fit.minimizer_type`, `_calculation.calculator_type`, `_display.plotter_type` |
-| Switchable-category impl. selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                | category-owned type tag such as `_peak.profile_type`                           |
-| Semantic value selector            | Pick a scientific/analysis mode | `fit.mode`                                                                  | `_fit.mode`                                                                    |
+| Family                             | User intent                     | Examples                                                                      | CIF                                                                              |
+| ---------------------------------- | ------------------------------- | ----------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
+| Backend selector                   | Pick an execution backend       | `fit.minimizer_type`, `calculation.calculator_type`, `rendering.chart_engine` | `_fit.minimizer_type`, `_calculation.calculator_type`, `_rendering.chart_engine` |
+| Switchable-category impl. selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                  | category-owned type tag such as `_peak.profile_type`                             |
+| Semantic value selector            | Pick a scientific/analysis mode | `fit.mode`                                                                    | `_fit.mode`                                                                      |
 
 Backend selectors and semantic value selectors live on a dedicated
-configuration category (`fit`, `calculation`, `display`). Switchable-
+configuration category (`fit`, `calculation`, `rendering`). Switchable-
 category implementation selectors are owned by the host (typically the
 experiment) because switching them replaces the category instance, as
 described in §9.3.
@@ -1272,8 +1273,8 @@ expt.calculation.show_calculator_types()
 expt.show_extinction_types()
 project.analysis.fit.show_minimizer_types()
 project.analysis.fit.show_modes()
-project.display.show_plotter_types()
-project.display.show_tabler_types()
+project.rendering.show_chart_engines()
+project.rendering.show_table_engines()
 ```
 
 Available calculators are filtered by `engine_imported` (whether the
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index 8083f39b0..e71f099de 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -402,14 +402,20 @@
 │   └── 📄 ascii.py
 ├── 📁 project
 │   ├── 📁 categories
-│   │   ├── 📁 display
+│   │   ├── 📁 rendering
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Display
+│   │   │   │   └── 🏷️ class Rendering
 │   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class DisplayFactory
+│   │   │       └── 🏷️ class RenderingFactory
 │   │   └── 📄 __init__.py
 │   ├── 📄 __init__.py
+│   ├── 📄 display.py
+│   │   ├── 🏷️ class PatternOptionStatus
+│   │   ├── 🏷️ class ParameterDisplay
+│   │   ├── 🏷️ class FitDisplay
+│   │   ├── 🏷️ class PosteriorDisplay
+│   │   └── 🏷️ class ProjectDisplay
 │   ├── 📄 project.py
 │   │   └── 🏷️ class Project
 │   └── 📄 project_info.py
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index 9ce283eb5..573142dae 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -198,12 +198,13 @@
 │   └── 📄 ascii.py
 ├── 📁 project
 │   ├── 📁 categories
-│   │   ├── 📁 display
+│   │   ├── 📁 rendering
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
 │   │   └── 📄 __init__.py
 │   ├── 📄 __init__.py
+│   ├── 📄 display.py
 │   ├── 📄 project.py
 │   └── 📄 project_info.py
 ├── 📁 summary
diff --git a/docs/dev/plan_display-ux.md b/docs/dev/plan_display-ux.md
new file mode 100644
index 000000000..4088ac657
--- /dev/null
+++ b/docs/dev/plan_display-ux.md
@@ -0,0 +1,238 @@
+# Plan: Display UX Facade
+
+## Goal
+
+Implement the display UX approach described in
+`docs/dev/adr_display-ux.md`.
+
+The end-user API should become:
+
+```python
+project.display.pattern(expt_name='hrpt')
+
+project.display.parameters.free()
+project.display.parameters.fittable()
+project.display.parameters.all()
+project.display.parameters.access()
+project.display.parameters.cif_uids()
+
+project.display.fit.results()
+project.display.fit.correlations()
+project.display.fit.series(param, versus=temperature)
+
+project.display.posterior.pairs()
+project.display.posterior.distribution(param)
+project.display.posterior.predictive(expt_name='hrpt')
+
+project.display.show_pattern_options(expt_name='hrpt')
+```
+
+Renderer configuration should move to:
+
+```python
+project.rendering.chart_engine = 'plotly'
+project.rendering.table_engine = 'pandas'
+project.rendering.show_chart_engines()
+project.rendering.show_table_engines()
+project.rendering.show_config()
+```
+
+## Branch
+
+Use implementation branch:
+
+```text
+feature/display-ux
+```
+
+## Decisions
+
+- Use grouped display namespaces under `project.display`.
+- Use `project.display.fit.series(param, versus=...)` for sequential fit
+  parameter plots.
+- Use `pattern(..., include='auto')` as the default experiment chart.
+- Use `include` rather than `layers`, `components`, `content`, `view`,
+  `series`, or boolean flags.
+- Do not add `project.display.constraints()`.
+- Move constraint reporting to `project.analysis.constraints.show()`.
+- Rename the serialized project display category to `rendering`.
+- Do not add legacy CIF loading for `_display.plotter_type` or
+  `_display.tabler_type`.
+- Standardize CIF display helpers on `project.analysis` and
+  `project.info`: `as_cif` should be a read-only property returning CIF
+  text, and `show_as_cif()` should pretty-print CIF text with a header.
+- Implement `include='uncertainty'` immediately where posterior
+  predictive data exists.
+- No compatibility aliases or deprecation warnings are required unless
+  release policy separately requires them.
+
+## Likely Files To Change
+
+- `src/easydiffraction/project/project.py`
+- `src/easydiffraction/project/project_info.py`
+- `src/easydiffraction/project/categories/display/default.py`
+- `src/easydiffraction/project/categories/display/factory.py`
+- `src/easydiffraction/project/categories/display/__init__.py`
+- new `src/easydiffraction/project/categories/rendering/...`
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/display/tables.py`
+- `src/easydiffraction/analysis/analysis.py`
+- `src/easydiffraction/analysis/categories/constraints/default.py`
+- `src/easydiffraction/__main__.py`
+- `docs/dev/architecture.md`
+- `docs/docs/user-guide/**/*.md`
+- `docs/docs/tutorials/*.py`
+- tests under `tests/unit/easydiffraction/`
+- tests under `tests/integration/fitting/`
+
+Do not edit generated tutorial notebooks directly. Update tutorial `.py`
+files and regenerate notebooks in Phase 2 if required.
+
+## Resolved Questions
+
+- Legacy CIF `_display.plotter_type` and `_display.tabler_type` do not
+  need to load into `project.rendering`. No legacy code is required.
+- `project.analysis` and `project.info` need consistency with structure
+  and experiment objects: `as_cif` should be a read-only property that
+  returns CIF text, and `show_as_cif()` should pretty-print CIF text
+  with a header.
+- `include='uncertainty'` should be implemented immediately where
+  posterior predictive data exists.
+
+## Phase 1 - Implementation
+
+Do not create or run tests in Phase 1 unless explicitly requested. Every
+completed Phase 1 implementation step must be staged with explicit paths
+and committed locally before moving to the next implementation step or
+the Phase 1 review gate. Use atomic commits, inspect the worktree before
+each commit, and stage only the files changed for that step.
+
+- [x] Rename the serialized project display category to rendering.
+  - Move or recreate the category package as
+    `src/easydiffraction/project/categories/rendering/`.
+  - Rename user-facing settings from `plotter_type` and `tabler_type` to
+    `chart_engine` and `table_engine`.
+  - Add `show_chart_engines()`, `show_table_engines()`, and
+    `show_config()`.
+  - Update CIF names to `_rendering.chart_engine` and
+    `_rendering.table_engine`.
+  - Do not add legacy loading for `_display.plotter_type` or
+    `_display.tabler_type`.
+
+- [x] Add the new `project.display` facade.
+  - Add a facade object that is not the serialized rendering category.
+  - Add `pattern(...)` and `show_pattern_options(...)`.
+  - Add `parameters`, `fit`, and `posterior` namespace objects.
+
+- [x] Implement `pattern(..., include='auto')`.
+  - Replace common user-facing calls to `plot_meas`, `plot_calc`, and
+    `plot_meas_vs_calc` with one state-aware method.
+  - Support explicit includes for `measured`, `calculated`,
+    `background`, `residual`, `bragg`, `excluded`, and `uncertainty`
+    where data is available.
+  - Implement `uncertainty` immediately for experiments with posterior
+    predictive data.
+  - Render a clear warning or error when a requested include is not
+    available.
+
+- [x] Move parameter table displays under `project.display.parameters`.
+  - Implement `all()`, `fittable()`, `free()`, `access()`, and
+    `cif_uids()`.
+  - Remove the primary public need for `project.analysis.display`.
+
+- [x] Move fit displays under `project.display.fit`.
+  - Implement `results()`.
+  - Implement `correlations()`.
+  - Implement `series(param, versus=...)`.
+
+- [x] Move Bayesian displays under `project.display.posterior`.
+  - Implement `pairs()`.
+  - Implement `distribution(param)`.
+  - Implement `predictive(expt_name=...)`.
+
+- [x] Move constraint reporting to
+      `project.analysis.constraints.show()`.
+  - Do not add `project.display.constraints()`.
+
+- [x] Standardize CIF display helpers.
+  - Convert `project.analysis.as_cif()` to a read-only
+    `project.analysis.as_cif` property.
+  - Ensure `project.analysis.show_as_cif()` pretty-prints CIF text with
+    a header.
+  - Convert `project.info.as_cif()` to a read-only `project.info.as_cif`
+    property.
+  - Ensure `project.info.show_as_cif()` pretty-prints CIF text with a
+    header.
+
+- [x] Update docs, tutorials, and architecture text.
+  - Replace old public display examples with the selected API.
+  - Update `docs/dev/architecture.md`.
+  - Update tutorial `.py` files only; regenerate notebooks in Phase 2 if
+    required.
+
+- [x] Stop at the Phase 1 review gate.
+  - Summarize changed files and open questions.
+  - Suggest next verification commands.
+  - Wait for user approval before Phase 2.
+
+Suggested Phase 1 commit messages:
+
+```text
+Rename display settings category to rendering
+Add grouped display facade
+Implement state-aware pattern display
+Move analysis display reports to display facade
+Standardize CIF display helpers
+Update display UX documentation
+```
+
+## Phase 2 - Verification
+
+After Phase 1 is reviewed and approved:
+
+- [x] Add or update unit tests for the rendering category.
+- [x] Add or update unit tests for the display facade namespaces.
+- [x] Add or update plotting integration tests for `pattern(...)`.
+- [x] Add or update analysis display integration tests for parameter and
+      fit report methods.
+- [x] Regenerate tutorial notebooks if tutorial `.py` files changed.
+- [x] Run formatting and checks.
+- [x] Run unit tests.
+- [x] Run integration tests.
+- [x] Run script tests.
+
+Verification commands:
+
+```sh
+pixi run notebook-prepare
+pixi run fix
+pixi run check
+pixi run unit-tests
+pixi run integration-tests
+pixi run script-tests
+```
+
+Run `pixi run notebook-prepare` only if tutorial `.py` files changed.
+Run `pixi run integration-tests` only after the relevant unit and
+focused integration tests are passing.
+
+## Suggested Commit Message
+
+```text
+Plan display UX facade implementation
+```
+
+## Suggested Pull Request
+
+Title:
+
+```text
+Improve chart and table display API
+```
+
+Description:
+
+This change makes display commands easier to discover and use in
+notebooks. Experiment patterns, parameter tables, fit reports, and
+Bayesian plots are grouped under `project.display`, while renderer
+settings move to `project.rendering`.
diff --git a/docs/docs/tutorials/ed-1.ipynb b/docs/docs/tutorials/ed-1.ipynb
index 53635a4c0..f108a808b 100644
--- a/docs/docs/tutorials/ed-1.ipynb
+++ b/docs/docs/tutorials/ed-1.ipynb
@@ -167,7 +167,7 @@
    "outputs": [],
    "source": [
     "# Show fit results summary\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -178,7 +178,7 @@
    "outputs": [],
    "source": [
     "# Show parameter correlations\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -234,7 +234,7 @@
    "outputs": [],
    "source": [
     "# Show fit results summary\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -245,7 +245,7 @@
    "outputs": [],
    "source": [
     "# Show parameter correlations\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -267,7 +267,7 @@
    "outputs": [],
    "source": [
     "# Plot measured vs. calculated diffraction patterns\n",
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-1.py b/docs/docs/tutorials/ed-1.py
index 0e2a5e553..e6312d4ac 100644
--- a/docs/docs/tutorials/ed-1.py
+++ b/docs/docs/tutorials/ed-1.py
@@ -61,11 +61,11 @@
 
 # %%
 # Show fit results summary
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
 # Show parameter correlations
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # ## Step 5: Perform Analysis (with constraints)
@@ -95,11 +95,11 @@
 
 # %%
 # Show fit results summary
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
 # Show parameter correlations
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
 # Show defined experiment names
@@ -107,4 +107,4 @@
 
 # %%
 # Plot measured vs. calculated diffraction patterns
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
diff --git a/docs/docs/tutorials/ed-10.ipynb b/docs/docs/tutorials/ed-10.ipynb
index 1e14322b2..2b23af884 100644
--- a/docs/docs/tutorials/ed-10.ipynb
+++ b/docs/docs/tutorials/ed-10.ipynb
@@ -207,8 +207,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations(threshold=0.75)"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations(threshold=0.75)"
    ]
   },
   {
@@ -226,7 +226,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='pdf', show_residual=True)"
+    "project.display.pattern(expt_name='pdf')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-10.py b/docs/docs/tutorials/ed-10.py
index 08f33f0ce..9fb0b9c61 100644
--- a/docs/docs/tutorials/ed-10.py
+++ b/docs/docs/tutorials/ed-10.py
@@ -82,11 +82,11 @@
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations(threshold=0.75)
+project.display.fit.results()
+project.display.fit.correlations(threshold=0.75)
 
 # %% [markdown]
 # ## Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='pdf', show_residual=True)
+project.display.pattern(expt_name='pdf')
diff --git a/docs/docs/tutorials/ed-11.ipynb b/docs/docs/tutorials/ed-11.ipynb
index ff9669a26..963894d86 100644
--- a/docs/docs/tutorials/ed-11.ipynb
+++ b/docs/docs/tutorials/ed-11.ipynb
@@ -82,8 +82,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.show_supported_engines()\n",
-    "project.display.plotter.show_current_engine()"
+    "project.rendering.show_chart_engines()\n",
+    "project.rendering.show_config()"
    ]
   },
   {
@@ -94,7 +94,7 @@
    "outputs": [],
    "source": [
     "# Set global plot range for plots\n",
-    "project.display.plotter.x_max = 40"
+    "project.rendering.plotter.x_max = 40"
    ]
   },
   {
@@ -238,8 +238,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -257,7 +257,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='nomad', show_residual=False)"
+    "project.display.pattern(expt_name='nomad', include=('measured', 'calculated'))"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-11.py b/docs/docs/tutorials/ed-11.py
index a48d2fd68..e4a1ec991 100644
--- a/docs/docs/tutorials/ed-11.py
+++ b/docs/docs/tutorials/ed-11.py
@@ -21,12 +21,12 @@
 # ## Set Plotting Engine
 
 # %%
-project.display.plotter.show_supported_engines()
-project.display.plotter.show_current_engine()
+project.rendering.show_chart_engines()
+project.rendering.show_config()
 
 # %%
 # Set global plot range for plots
-project.display.plotter.x_max = 40
+project.rendering.plotter.x_max = 40
 
 # %% [markdown]
 # ## Add Structure
@@ -94,11 +94,11 @@
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # ## Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='nomad', show_residual=False)
+project.display.pattern(expt_name='nomad', include=('measured', 'calculated'))
diff --git a/docs/docs/tutorials/ed-12.ipynb b/docs/docs/tutorials/ed-12.ipynb
index 77e648e0b..176ab1475 100644
--- a/docs/docs/tutorials/ed-12.ipynb
+++ b/docs/docs/tutorials/ed-12.ipynb
@@ -87,7 +87,7 @@
    "source": [
     "# Keep the auto-selected engine. Alternatively, you can uncomment the\n",
     "# line below to explicitly set the engine to the required one.\n",
-    "# project.display.plotter.engine = 'plotly'"
+    "# project.rendering.chart_engine = 'plotly'"
    ]
   },
   {
@@ -98,8 +98,8 @@
    "outputs": [],
    "source": [
     "# Set global plot range for plots\n",
-    "project.display.plotter.x_min = 2.0\n",
-    "project.display.plotter.x_max = 30.0"
+    "project.rendering.plotter.x_min = 2.0\n",
+    "project.rendering.plotter.x_max = 30.0"
    ]
   },
   {
@@ -278,8 +278,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -297,7 +297,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='xray_pdf')"
+    "project.display.pattern(expt_name='xray_pdf')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-12.py b/docs/docs/tutorials/ed-12.py
index 78c722ced..090f948b6 100644
--- a/docs/docs/tutorials/ed-12.py
+++ b/docs/docs/tutorials/ed-12.py
@@ -26,12 +26,12 @@
 # %%
 # Keep the auto-selected engine. Alternatively, you can uncomment the
 # line below to explicitly set the engine to the required one.
-# project.display.plotter.engine = 'plotly'
+# project.rendering.chart_engine = 'plotly'
 
 # %%
 # Set global plot range for plots
-project.display.plotter.x_min = 2.0
-project.display.plotter.x_max = 30.0
+project.rendering.plotter.x_min = 2.0
+project.rendering.plotter.x_max = 30.0
 
 # %% [markdown]
 # ## Add Structure
@@ -113,11 +113,11 @@
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # ## Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='xray_pdf')
+project.display.pattern(expt_name='xray_pdf')
diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 69eae9aa9..616c536c5 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -266,8 +266,8 @@
     "[documentation](https://docs.easydiffraction.org/lib/user-guide/analysis-workflow/experiment/#measured-data-category)\n",
     "for more details about the measured data and its format.\n",
     "\n",
-    "To visualize the measured data, we can use the `plot_meas` method of\n",
-    "the project."
+    "To visualize the measured data, we can use the `pattern` method of\n",
+    "the project's `display` facade with `include='measured'`."
    ]
   },
   {
@@ -277,7 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas(expt_name='sim_si')"
+    "project_1.display.pattern(expt_name='sim_si', include='measured')"
    ]
   },
   {
@@ -330,8 +330,8 @@
    "metadata": {},
    "source": [
     "To visualize the effect of excluding the high TOF region, we can plot\n",
-    "the measured data again. The excluded region will be omitted from the\n",
-    "plot and is not used in the fitting process."
+    "the measured data again. The excluded region will be highlighted on\n",
+    "the plot and is not used in the fitting process."
    ]
   },
   {
@@ -341,7 +341,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas(expt_name='sim_si')"
+    "project_1.display.pattern(expt_name='sim_si', include=('measured', 'excluded'))"
    ]
   },
   {
@@ -979,7 +979,7 @@
     "#### Show Free Parameters\n",
     "\n",
     "We can check which parameters are free to be refined by calling the\n",
-    "`free_params` method of the `analysis.display` object of the project."
+    "`free` method of the `display.parameters` object of the project."
    ]
   },
   {
@@ -1002,7 +1002,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.analysis.display.free_params()"
+    "project_1.display.parameters.free()"
    ]
   },
   {
@@ -1016,8 +1016,8 @@
     "diffraction pattern with the calculated diffraction pattern based on\n",
     "the initial parameters of the structure and the instrument. This\n",
     "provides an indication of how well the initial parameters match the\n",
-    "measured data. The `plot_meas_vs_calc` method of the project allows\n",
-    "this comparison."
+    "measured data. The `pattern` method of the project's `display`\n",
+    "facade allows this comparison."
    ]
   },
   {
@@ -1027,7 +1027,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si')"
+    "project_1.display.pattern(expt_name='sim_si')"
    ]
   },
   {
@@ -1059,7 +1059,7 @@
    "outputs": [],
    "source": [
     "project_1.analysis.fit()\n",
-    "project_1.analysis.display.fit_results()"
+    "project_1.display.fit.results()"
    ]
   },
   {
@@ -1101,7 +1101,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si')"
+    "project_1.display.pattern(expt_name='sim_si')"
    ]
   },
   {
@@ -1132,9 +1132,9 @@
     "`quad` terms were not part of the data reduction and are therefore set\n",
     "to 0 by default.\n",
     "\n",
-    "The `plot_meas_vs_calc` method of the project allows us to plot the\n",
-    "measured and calculated diffraction patterns in the d-spacing axis by\n",
-    "setting the `d_spacing` parameter to `True`."
+    "The `pattern` method of the project's `display` facade allows us to\n",
+    "plot the measured and calculated diffraction patterns in the\n",
+    "d-spacing axis by setting `x='d_spacing'`."
    ]
   },
   {
@@ -1144,7 +1144,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si', x='d_spacing')"
+    "project_1.display.pattern(expt_name='sim_si', x='d_spacing')"
    ]
   },
   {
@@ -1348,12 +1348,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas(expt_name='sim_lbco')\n",
+    "project_2.display.pattern(expt_name='sim_lbco', include='measured')\n",
     "\n",
     "project_2.experiments['sim_lbco'].excluded_regions.create(id='1', start=0, end=55000)\n",
     "project_2.experiments['sim_lbco'].excluded_regions.create(id='2', start=105500, end=200000)\n",
     "\n",
-    "project_2.display.plotter.plot_meas(expt_name='sim_lbco')"
+    "project_2.display.pattern(expt_name='sim_lbco', include=('measured', 'excluded'))"
    ]
   },
   {
@@ -1938,10 +1938,10 @@
    "id": "146",
    "metadata": {},
    "source": [
-    "Use the `plot_meas_vs_calc` method of the project to visualize the\n",
-    "measured and calculated diffraction patterns before fitting. Then, use\n",
-    "the `fit` method of the `analysis` object of the project to perform\n",
-    "the fitting process."
+    "Use the `pattern` method of the project's `display` facade to\n",
+    "visualize the measured and calculated diffraction patterns before\n",
+    "fitting. Then, use the `fit` method of the `analysis` object of the\n",
+    "project to perform the fitting process."
    ]
   },
   {
@@ -1959,10 +1959,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')\n",
+    "project_2.display.pattern(expt_name='sim_lbco')\n",
     "\n",
     "project_2.analysis.fit()\n",
-    "project_2.analysis.display.fit_results()"
+    "project_2.display.fit.results()"
    ]
   },
   {
@@ -2036,7 +2036,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')"
+    "project_2.display.pattern(expt_name='sim_lbco')"
    ]
   },
   {
@@ -2090,9 +2090,9 @@
     "project_2.structures['lbco'].cell.length_a.free = True\n",
     "\n",
     "project_2.analysis.fit()\n",
-    "project_2.analysis.display.fit_results()\n",
+    "project_2.display.fit.results()\n",
     "\n",
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')"
+    "project_2.display.pattern(expt_name='sim_lbco')"
    ]
   },
   {
@@ -2132,8 +2132,8 @@
    "id": "163",
    "metadata": {},
    "source": [
-    "Use the `plot_meas_vs_calc` method of the project and set the\n",
-    "`d_spacing` parameter to `True`."
+    "Use the `pattern` method of the project's `display` facade and set\n",
+    "`x='d_spacing'`."
    ]
   },
   {
@@ -2151,7 +2151,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco', x='d_spacing')"
+    "project_2.display.pattern(expt_name='sim_lbco', x='d_spacing')"
    ]
   },
   {
@@ -2180,9 +2180,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas_vs_calc(\n",
-    "    expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40\n",
-    ")"
+    "project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40)"
    ]
   },
   {
@@ -2242,11 +2240,9 @@
     "project_2.experiments['sim_lbco'].peak.exp_rise_alpha_1.free = True\n",
     "\n",
     "project_2.analysis.fit()\n",
-    "project_2.analysis.display.fit_results()\n",
+    "project_2.display.fit.results()\n",
     "\n",
-    "project_2.display.plotter.plot_meas_vs_calc(\n",
-    "    expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40\n",
-    ")"
+    "project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40)"
    ]
   },
   {
@@ -2296,9 +2292,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_2.display.plotter.plot_meas_vs_calc(\n",
-    "    expt_name='sim_lbco', x='d_spacing', x_min=1.53, x_max=1.7\n",
-    ")"
+    "project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.53, x_max=1.7)"
    ]
   },
   {
@@ -2420,10 +2414,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si', x='d_spacing', x_min=1, x_max=1.7)\n",
-    "project_2.display.plotter.plot_meas_vs_calc(\n",
-    "    expt_name='sim_lbco', x='d_spacing', x_min=1, x_max=1.7\n",
-    ")"
+    "project_1.display.pattern(expt_name='sim_si', x='d_spacing', x_min=1, x_max=1.7)\n",
+    "project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1, x_max=1.7)"
    ]
   },
   {
@@ -2520,10 +2512,10 @@
    "id": "196",
    "metadata": {},
    "source": [
-    "You can use the `plot_meas_vs_calc` method of the project to visualize\n",
-    "the patterns. Then, set the `free` attribute of the `scale` parameter\n",
-    "of the Si phase to `True` to allow the fitting process to adjust the\n",
-    "scale factor."
+    "You can use the `pattern` method of the project's `display` facade to\n",
+    "visualize the patterns. Then, set the `free` attribute of the `scale`\n",
+    "parameter of the Si phase to `True` to allow the fitting process to\n",
+    "adjust the scale factor."
    ]
   },
   {
@@ -2544,7 +2536,7 @@
     "# Before optimizing the parameters, we can visualize the measured\n",
     "# diffraction pattern and the calculated diffraction pattern based on\n",
     "# the two phases: LBCO and Si.\n",
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')\n",
+    "project_2.display.pattern(expt_name='sim_lbco')\n",
     "\n",
     "# As you can see, the calculated pattern is now the sum of both phases,\n",
     "# and Si peaks are visible in the calculated pattern. However, their\n",
@@ -2554,14 +2546,14 @@
     "\n",
     "# Now we can perform the fit with both phases included.\n",
     "project_2.analysis.fit()\n",
-    "project_2.analysis.display.fit_results()\n",
+    "project_2.display.fit.results()\n",
     "\n",
     "# Let's plot the measured diffraction pattern and the calculated\n",
     "# diffraction pattern both for the full range and for a zoomed-in region\n",
     "# around the previously unexplained peak near 95,000 μs. The calculated\n",
     "# pattern will be the sum of the two phases.\n",
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')\n",
-    "project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco', x_min=88000, x_max=101000)"
+    "project_2.display.pattern(expt_name='sim_lbco')\n",
+    "project_2.display.pattern(expt_name='sim_lbco', x_min=88000, x_max=101000)"
    ]
   },
   {
@@ -2665,7 +2657,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "title,tags,-all",
+   "cell_metadata_filter": "tags,title,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-13.py b/docs/docs/tutorials/ed-13.py
index e89f42887..436c13b93 100644
--- a/docs/docs/tutorials/ed-13.py
+++ b/docs/docs/tutorials/ed-13.py
@@ -144,11 +144,11 @@
 # [documentation](https://docs.easydiffraction.org/lib/user-guide/analysis-workflow/experiment/#measured-data-category)
 # for more details about the measured data and its format.
 #
-# To visualize the measured data, we can use the `plot_meas` method of
-# the project.
+# To visualize the measured data, we can use the `pattern` method of
+# the project's `display` facade with `include='measured'`.
 
 # %%
-project_1.display.plotter.plot_meas(expt_name='sim_si')
+project_1.display.pattern(expt_name='sim_si', include='measured')
 
 # %% [markdown]
 # If you zoom in on the highest TOF peak (around 120,000 μs), you will
@@ -179,11 +179,11 @@
 
 # %% [markdown]
 # To visualize the effect of excluding the high TOF region, we can plot
-# the measured data again. The excluded region will be omitted from the
-# plot and is not used in the fitting process.
+# the measured data again. The excluded region will be highlighted on
+# the plot and is not used in the fitting process.
 
 # %%
-project_1.display.plotter.plot_meas(expt_name='sim_si')
+project_1.display.pattern(expt_name='sim_si', include=('measured', 'excluded'))
 
 # %% [markdown]
 # #### Set Instrument Parameters
@@ -583,7 +583,7 @@
 # #### Show Free Parameters
 #
 # We can check which parameters are free to be refined by calling the
-# `free_params` method of the `analysis.display` object of the project.
+# `free` method of the `display.parameters` object of the project.
 
 # %% [markdown] tags=["doc-link"]
 # 📖 See
@@ -594,7 +594,7 @@
 # - show only free parameters of the project.
 
 # %%
-project_1.analysis.display.free_params()
+project_1.display.parameters.free()
 
 # %% [markdown]
 # #### Visualize Diffraction Patterns
@@ -603,11 +603,11 @@
 # diffraction pattern with the calculated diffraction pattern based on
 # the initial parameters of the structure and the instrument. This
 # provides an indication of how well the initial parameters match the
-# measured data. The `plot_meas_vs_calc` method of the project allows
-# this comparison.
+# measured data. The `pattern` method of the project's `display`
+# facade allows this comparison.
 
 # %%
-project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si')
+project_1.display.pattern(expt_name='sim_si')
 
 # %% [markdown]
 # #### Run Fitting
@@ -622,7 +622,7 @@
 
 # %%
 project_1.analysis.fit()
-project_1.analysis.display.fit_results()
+project_1.display.fit.results()
 
 # %% [markdown]
 # #### Check Fit Results
@@ -647,7 +647,7 @@
 # pattern is now based on the refined parameters.
 
 # %%
-project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si')
+project_1.display.pattern(expt_name='sim_si')
 
 # %% [markdown]
 # #### TOF vs d-spacing
@@ -673,12 +673,12 @@
 # `quad` terms were not part of the data reduction and are therefore set
 # to 0 by default.
 #
-# The `plot_meas_vs_calc` method of the project allows us to plot the
-# measured and calculated diffraction patterns in the d-spacing axis by
-# setting the `d_spacing` parameter to `True`.
+# The `pattern` method of the project's `display` facade allows us to
+# plot the measured and calculated diffraction patterns in the
+# d-spacing axis by setting `x='d_spacing'`.
 
 # %%
-project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si', x='d_spacing')
+project_1.display.pattern(expt_name='sim_si', x='d_spacing')
 
 # %% [markdown]
 # As you can see, the calculated diffraction pattern now matches the
@@ -789,12 +789,12 @@
 # **Solution:**
 
 # %% tags=["solution", "hide-input"]
-project_2.display.plotter.plot_meas(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco', include='measured')
 
 project_2.experiments['sim_lbco'].excluded_regions.create(id='1', start=0, end=55000)
 project_2.experiments['sim_lbco'].excluded_regions.create(id='2', start=105500, end=200000)
 
-project_2.display.plotter.plot_meas(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco', include=('measured', 'excluded'))
 
 # %% [markdown]
 # #### Exercise 2.2: Set Instrument Parameters
@@ -1106,19 +1106,19 @@
 # **Hint:**
 
 # %% [markdown] tags=["dmsc-school-hint"]
-# Use the `plot_meas_vs_calc` method of the project to visualize the
-# measured and calculated diffraction patterns before fitting. Then, use
-# the `fit` method of the `analysis` object of the project to perform
-# the fitting process.
+# Use the `pattern` method of the project's `display` facade to
+# visualize the measured and calculated diffraction patterns before
+# fitting. Then, use the `fit` method of the `analysis` object of the
+# project to perform the fitting process.
 
 # %% [markdown]
 # **Solution:**
 
 # %% tags=["solution", "hide-input"]
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco')
 
 project_2.analysis.fit()
-project_2.analysis.display.fit_results()
+project_2.display.fit.results()
 
 # %% [markdown]
 # #### Exercise 5.3: Find the Misfit in the Fit
@@ -1160,7 +1160,7 @@
 # peak positions.
 
 # %% tags=["solution", "hide-input"]
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco')
 
 # %% [markdown]
 # #### Exercise 5.4: Refine the LBCO Lattice Parameter
@@ -1187,9 +1187,9 @@
 project_2.structures['lbco'].cell.length_a.free = True
 
 project_2.analysis.fit()
-project_2.analysis.display.fit_results()
+project_2.display.fit.results()
 
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco')
 
 # %% [markdown]
 # One of the main goals of this study was to refine the lattice
@@ -1209,14 +1209,14 @@
 # **Hint:**
 
 # %% [markdown] tags=["dmsc-school-hint"]
-# Use the `plot_meas_vs_calc` method of the project and set the
-# `d_spacing` parameter to `True`.
+# Use the `pattern` method of the project's `display` facade and set
+# `x='d_spacing'`.
 
 # %% [markdown]
 # **Solution:**
 
 # %% tags=["solution", "hide-input"]
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco', x='d_spacing')
+project_2.display.pattern(expt_name='sim_lbco', x='d_spacing')
 
 # %% [markdown]
 # #### Exercise 5.6: Refine the Peak Profile Parameters
@@ -1233,9 +1233,7 @@
 # perfectly describe the peak at about 1.38 Å, as can be seen below:
 
 # %%
-project_2.display.plotter.plot_meas_vs_calc(
-    expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40
-)
+project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40)
 
 # %% [markdown]
 # The peak profile parameters are determined based on both the
@@ -1268,11 +1266,9 @@
 project_2.experiments['sim_lbco'].peak.exp_rise_alpha_1.free = True
 
 project_2.analysis.fit()
-project_2.analysis.display.fit_results()
+project_2.display.fit.results()
 
-project_2.display.plotter.plot_meas_vs_calc(
-    expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40
-)
+project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.35, x_max=1.40)
 
 # %% [markdown]
 # #### Exercise 5.7: Find Undefined Features
@@ -1295,9 +1291,7 @@
 # **Solution:**
 
 # %% tags=["solution", "hide-input"]
-project_2.display.plotter.plot_meas_vs_calc(
-    expt_name='sim_lbco', x='d_spacing', x_min=1.53, x_max=1.7
-)
+project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1.53, x_max=1.7)
 
 # %% [markdown]
 # #### Exercise 5.8: Identify the Cause of the Unexplained Peaks
@@ -1362,10 +1356,8 @@
 # confirm this hypothesis.
 
 # %% tags=["solution", "hide-input"]
-project_1.display.plotter.plot_meas_vs_calc(expt_name='sim_si', x='d_spacing', x_min=1, x_max=1.7)
-project_2.display.plotter.plot_meas_vs_calc(
-    expt_name='sim_lbco', x='d_spacing', x_min=1, x_max=1.7
-)
+project_1.display.pattern(expt_name='sim_si', x='d_spacing', x_min=1, x_max=1.7)
+project_2.display.pattern(expt_name='sim_lbco', x='d_spacing', x_min=1, x_max=1.7)
 
 # %% [markdown]
 # #### Exercise 5.10: Create a Second Structure – Si as Impurity
@@ -1420,10 +1412,10 @@
 # **Hint:**
 
 # %% [markdown] tags=["dmsc-school-hint"]
-# You can use the `plot_meas_vs_calc` method of the project to visualize
-# the patterns. Then, set the `free` attribute of the `scale` parameter
-# of the Si phase to `True` to allow the fitting process to adjust the
-# scale factor.
+# You can use the `pattern` method of the project's `display` facade to
+# visualize the patterns. Then, set the `free` attribute of the `scale`
+# parameter of the Si phase to `True` to allow the fitting process to
+# adjust the scale factor.
 
 # %% [markdown]
 # **Solution:**
@@ -1432,7 +1424,7 @@
 # Before optimizing the parameters, we can visualize the measured
 # diffraction pattern and the calculated diffraction pattern based on
 # the two phases: LBCO and Si.
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco')
 
 # As you can see, the calculated pattern is now the sum of both phases,
 # and Si peaks are visible in the calculated pattern. However, their
@@ -1442,14 +1434,14 @@
 
 # Now we can perform the fit with both phases included.
 project_2.analysis.fit()
-project_2.analysis.display.fit_results()
+project_2.display.fit.results()
 
 # Let's plot the measured diffraction pattern and the calculated
 # diffraction pattern both for the full range and for a zoomed-in region
 # around the previously unexplained peak near 95,000 μs. The calculated
 # pattern will be the sum of the two phases.
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco')
-project_2.display.plotter.plot_meas_vs_calc(expt_name='sim_lbco', x_min=88000, x_max=101000)
+project_2.display.pattern(expt_name='sim_lbco')
+project_2.display.pattern(expt_name='sim_lbco', x_min=88000, x_max=101000)
 
 # %% [markdown]
 # All previously unexplained peaks are now accounted for in the pattern,
diff --git a/docs/docs/tutorials/ed-14.ipynb b/docs/docs/tutorials/ed-14.ipynb
index 75e6805b0..48665fa53 100644
--- a/docs/docs/tutorials/ed-14.ipynb
+++ b/docs/docs/tutorials/ed-14.ipynb
@@ -254,7 +254,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+    "project.display.pattern(expt_name='heidi')"
    ]
   },
   {
@@ -307,7 +307,7 @@
    "outputs": [],
    "source": [
     "# Show fit results summary\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -337,7 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+    "project.display.pattern(expt_name='heidi')"
    ]
   },
   {
@@ -405,7 +405,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -415,7 +415,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -425,7 +425,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+    "project.display.pattern(expt_name='heidi')"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-14.py b/docs/docs/tutorials/ed-14.py
index c29d3eb88..3ff5f25f0 100644
--- a/docs/docs/tutorials/ed-14.py
+++ b/docs/docs/tutorials/ed-14.py
@@ -85,7 +85,7 @@
 # ## Step 4: Perform Analysis I (ADP iso)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+project.display.pattern(expt_name='heidi')
 
 # %%
 structure.atom_sites['O1'].fract_x.free = True
@@ -110,7 +110,7 @@
 
 # %%
 # Show fit results summary
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
 structure.show_as_cif()
@@ -119,7 +119,7 @@
 project.experiments.show_names()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+project.display.pattern(expt_name='heidi')
 
 # %% [markdown]
 # ## Step 5: Perform Analysis (ADP aniso)
@@ -147,13 +147,13 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+project.display.pattern(expt_name='heidi')
 
 # %%
 structure.show_as_cif()
diff --git a/docs/docs/tutorials/ed-15.ipynb b/docs/docs/tutorials/ed-15.ipynb
index d1ece4eb6..ab93cd536 100644
--- a/docs/docs/tutorials/ed-15.ipynb
+++ b/docs/docs/tutorials/ed-15.ipynb
@@ -208,7 +208,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='senju')"
+    "project.display.pattern(expt_name='senju')"
    ]
   },
   {
@@ -262,7 +262,7 @@
    "outputs": [],
    "source": [
     "# Show fit results summary\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -292,7 +292,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='senju')"
+    "project.display.pattern(expt_name='senju')"
    ]
   },
   {
@@ -344,7 +344,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -364,7 +364,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -374,7 +374,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -384,7 +384,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='senju')"
+    "project.display.pattern(expt_name='senju')"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-15.py b/docs/docs/tutorials/ed-15.py
index c198fc234..659af7214 100644
--- a/docs/docs/tutorials/ed-15.py
+++ b/docs/docs/tutorials/ed-15.py
@@ -67,7 +67,7 @@
 # ## Step 4: Perform Analysis I (ADP iso)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='senju')
+project.display.pattern(expt_name='senju')
 
 # %%
 experiment.linked_crystal.scale.free = True
@@ -86,7 +86,7 @@
 
 # %%
 # Show fit results summary
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
 structure.show_as_cif()
@@ -95,7 +95,7 @@
 project.experiments.show_names()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='senju')
+project.display.pattern(expt_name='senju')
 
 # %% [markdown]
 # ## Step 5: Perform Analysis (ADP aniso)
@@ -114,19 +114,19 @@
 structure.show_as_cif()
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %%
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='senju')
+project.display.pattern(expt_name='senju')
 
 # %%
 structure.show_as_cif()
diff --git a/docs/docs/tutorials/ed-16.ipynb b/docs/docs/tutorials/ed-16.ipynb
index f7a839dae..5da587e9d 100644
--- a/docs/docs/tutorials/ed-16.ipynb
+++ b/docs/docs/tutorials/ed-16.ipynb
@@ -455,7 +455,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -465,7 +465,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='nomad')"
+    "project.display.pattern(expt_name='nomad')"
    ]
   },
   {
@@ -551,7 +551,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -570,8 +570,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -589,7 +589,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -599,7 +599,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='nomad')"
+    "project.display.pattern(expt_name='nomad')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-16.py b/docs/docs/tutorials/ed-16.py
index 91d48b1e6..bde9af470 100644
--- a/docs/docs/tutorials/ed-16.py
+++ b/docs/docs/tutorials/ed-16.py
@@ -190,10 +190,10 @@
 # #### Plot Measured vs Calculated (Before Fit)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='nomad')
+project.display.pattern(expt_name='nomad')
 
 # %% [markdown]
 # #### Set Fitting Parameters
@@ -231,21 +231,21 @@
 # #### Show Free Parameters
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated (After Fit)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='nomad')
+project.display.pattern(expt_name='nomad')
diff --git a/docs/docs/tutorials/ed-17.ipynb b/docs/docs/tutorials/ed-17.ipynb
index d2ee917d1..d370ca274 100644
--- a/docs/docs/tutorials/ed-17.ipynb
+++ b/docs/docs/tutorials/ed-17.ipynb
@@ -569,7 +569,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -587,7 +587,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20')"
+    "project.display.pattern(expt_name='d20')"
    ]
   },
   {
@@ -678,7 +678,7 @@
    "outputs": [],
    "source": [
     "project.apply_params_from_csv(row_index=0)\n",
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20')"
+    "project.display.pattern(expt_name='d20')"
    ]
   },
   {
@@ -698,7 +698,7 @@
    "outputs": [],
    "source": [
     "project.apply_params_from_csv(row_index=-1)\n",
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20')"
+    "project.display.pattern(expt_name='d20')"
    ]
   },
   {
@@ -736,9 +736,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_series(structure.cell.length_a, versus=temperature)\n",
-    "project.display.plotter.plot_param_series(structure.cell.length_b, versus=temperature)\n",
-    "project.display.plotter.plot_param_series(structure.cell.length_c, versus=temperature)"
+    "project.display.fit.series(structure.cell.length_a, versus=temperature)\n",
+    "project.display.fit.series(structure.cell.length_b, versus=temperature)\n",
+    "project.display.fit.series(structure.cell.length_c, versus=temperature)"
    ]
   },
   {
@@ -756,23 +756,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['Co1'].adp_iso,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['Si'].adp_iso,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O1'].adp_iso,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O2'].adp_iso,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O3'].adp_iso,\n",
     "    versus=temperature,\n",
     ")"
@@ -793,23 +793,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['Co2'].fract_x,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['Co2'].fract_z,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O1'].fract_z,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O2'].fract_z,\n",
     "    versus=temperature,\n",
     ")\n",
-    "project.display.plotter.plot_param_series(\n",
+    "project.display.fit.series(\n",
     "    structure.atom_sites['O3'].fract_z,\n",
     "    versus=temperature,\n",
     ")"
diff --git a/docs/docs/tutorials/ed-17.py b/docs/docs/tutorials/ed-17.py
index f929cd47c..c1dc3a79a 100644
--- a/docs/docs/tutorials/ed-17.py
+++ b/docs/docs/tutorials/ed-17.py
@@ -280,13 +280,13 @@
 # #### Show parameter correlations
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Compare measured and calculated patterns for the first fit.
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='d20')
+project.display.pattern(expt_name='d20')
 
 # %% [markdown]
 # #### Run Sequential Fitting
@@ -329,7 +329,7 @@ def extract_diffrn(file_path):
 
 # %%
 project.apply_params_from_csv(row_index=0)
-project.display.plotter.plot_meas_vs_calc(expt_name='d20')
+project.display.pattern(expt_name='d20')
 
 # %% [markdown]
 #
@@ -337,7 +337,7 @@ def extract_diffrn(file_path):
 
 # %%
 project.apply_params_from_csv(row_index=-1)
-project.display.plotter.plot_meas_vs_calc(expt_name='d20')
+project.display.pattern(expt_name='d20')
 
 # %% [markdown]
 # #### Plot Parameter Evolution
@@ -351,31 +351,31 @@ def extract_diffrn(file_path):
 # Plot unit cell parameters vs. temperature.
 
 # %%
-project.display.plotter.plot_param_series(structure.cell.length_a, versus=temperature)
-project.display.plotter.plot_param_series(structure.cell.length_b, versus=temperature)
-project.display.plotter.plot_param_series(structure.cell.length_c, versus=temperature)
+project.display.fit.series(structure.cell.length_a, versus=temperature)
+project.display.fit.series(structure.cell.length_b, versus=temperature)
+project.display.fit.series(structure.cell.length_c, versus=temperature)
 
 # %% [markdown]
 # Plot isotropic displacement parameters vs. temperature.
 
 # %%
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['Co1'].adp_iso,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['Si'].adp_iso,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O1'].adp_iso,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O2'].adp_iso,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O3'].adp_iso,
     versus=temperature,
 )
@@ -384,23 +384,23 @@ def extract_diffrn(file_path):
 # Plot selected fractional coordinates vs. temperature.
 
 # %%
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['Co2'].fract_x,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['Co2'].fract_z,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O1'].fract_z,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O2'].fract_z,
     versus=temperature,
 )
-project.display.plotter.plot_param_series(
+project.display.fit.series(
     structure.atom_sites['O3'].fract_z,
     versus=temperature,
 )
diff --git a/docs/docs/tutorials/ed-18.ipynb b/docs/docs/tutorials/ed-18.ipynb
index 47a25154a..c1200010a 100644
--- a/docs/docs/tutorials/ed-18.ipynb
+++ b/docs/docs/tutorials/ed-18.ipynb
@@ -144,7 +144,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -154,7 +154,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -164,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-18.py b/docs/docs/tutorials/ed-18.py
index fb44972fa..7c4fff9fc 100644
--- a/docs/docs/tutorials/ed-18.py
+++ b/docs/docs/tutorials/ed-18.py
@@ -47,10 +47,10 @@
 # ## Show Results
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
diff --git a/docs/docs/tutorials/ed-2.ipynb b/docs/docs/tutorials/ed-2.ipynb
index 4a52b569c..927087e9d 100644
--- a/docs/docs/tutorials/ed-2.ipynb
+++ b/docs/docs/tutorials/ed-2.ipynb
@@ -345,7 +345,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -355,7 +355,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -365,7 +365,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -429,7 +429,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -439,7 +439,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -449,7 +449,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -497,7 +497,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -507,7 +507,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -517,7 +517,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-2.py b/docs/docs/tutorials/ed-2.py
index fa4918c05..c2772ba6b 100644
--- a/docs/docs/tutorials/ed-2.py
+++ b/docs/docs/tutorials/ed-2.py
@@ -162,13 +162,13 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %% [markdown]
 # ## Step 5: Perform Analysis (with constraints)
@@ -199,13 +199,13 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %% [markdown]
 # ## Step 6: Switch calculator engine
@@ -220,10 +220,10 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
diff --git a/docs/docs/tutorials/ed-20.ipynb b/docs/docs/tutorials/ed-20.ipynb
index b9a47a281..4ad0e666c 100644
--- a/docs/docs/tutorials/ed-20.ipynb
+++ b/docs/docs/tutorials/ed-20.ipynb
@@ -515,7 +515,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='expt_s2')"
+    "project.display.pattern(expt_name='expt_s2')"
    ]
   },
   {
@@ -525,7 +525,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='expt_n2')"
+    "project.display.pattern(expt_name='expt_n2')"
    ]
   },
   {
@@ -576,7 +576,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fittable_params()"
+    "project.display.parameters.fittable()"
    ]
   },
   {
@@ -731,8 +731,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -752,7 +752,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='expt_s2')"
+    "project.display.pattern(expt_name='expt_s2')"
    ]
   },
   {
@@ -762,7 +762,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='expt_n2')"
+    "project.display.pattern(expt_name='expt_n2')"
    ]
   },
   {
@@ -780,7 +780,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(\n",
+    "project.display.pattern(\n",
     "    expt_name='expt_s2',\n",
     "    x='d_spacing',\n",
     "    x_min=2.08,\n",
@@ -795,7 +795,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(\n",
+    "project.display.pattern(\n",
     "    expt_name='expt_n2',\n",
     "    x='d_spacing',\n",
     "    x_min=2.08,\n",
diff --git a/docs/docs/tutorials/ed-20.py b/docs/docs/tutorials/ed-20.py
index 0ac55fdbf..e48e860ba 100644
--- a/docs/docs/tutorials/ed-20.py
+++ b/docs/docs/tutorials/ed-20.py
@@ -245,10 +245,10 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='expt_s2')
+project.display.pattern(expt_name='expt_s2')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='expt_n2')
+project.display.pattern(expt_name='expt_n2')
 
 # %% [markdown]
 # ## Perform Analysis
@@ -268,7 +268,7 @@
 # #### Set Free Parameters
 
 # %%
-project.analysis.display.fittable_params()
+project.display.parameters.fittable()
 
 # %%
 ferrite.atom_sites['Fe'].adp_iso.free = True
@@ -347,8 +347,8 @@
 # Show fit results and parameter correlations.
 
 # %%
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
@@ -356,16 +356,16 @@
 # Show full range in TOF.
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='expt_s2')
+project.display.pattern(expt_name='expt_s2')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='expt_n2')
+project.display.pattern(expt_name='expt_n2')
 
 # %% [markdown]
 # Show selected peaks in d-spacing.
 
 # %%
-project.display.plotter.plot_meas_vs_calc(
+project.display.pattern(
     expt_name='expt_s2',
     x='d_spacing',
     x_min=2.08,
@@ -373,7 +373,7 @@
 )
 
 # %%
-project.display.plotter.plot_meas_vs_calc(
+project.display.pattern(
     expt_name='expt_n2',
     x='d_spacing',
     x_min=2.08,
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 405bc8589..33f77196f 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -218,7 +218,9 @@
    "id": "14",
    "metadata": {},
    "source": [
-    "#### Download the Measured Data"
+    "Download the measured data from the repository. Alternatively, you\n",
+    "could use your own data file by providing the path to it instead of\n",
+    "downloading from the repository."
    ]
   },
   {
@@ -236,7 +238,8 @@
    "id": "16",
    "metadata": {},
    "source": [
-    "#### Create the Experiment Object"
+    "Create the experiment object and specify the sample form, beam mode,\n",
+    "and radiation probe."
    ]
   },
   {
@@ -270,7 +273,25 @@
    "id": "19",
    "metadata": {},
    "source": [
-    "#### Set Instrument and Peak-Profile Parameters\n",
+    "Link the structural phase to the experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_phases.create(id='lbco', scale=9.1351)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "Set instrument and peak profile parameters.\n",
     "\n",
     "These values provide the initial instrument description for the local\n",
     "refinement. Later, a subset of them will be refined."
@@ -279,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,10 +323,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22",
+   "id": "24",
    "metadata": {},
    "source": [
-    "#### Add Background Points and Excluded Regions\n",
+    "Add background points and excluded regions.\n",
     "\n",
     "The line-segment background is defined by a few anchor points. We also\n",
     "exclude regions that are not intended to contribute to the fit."
@@ -314,7 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "23",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,24 +356,6 @@
     "experiment.excluded_regions.create(id='2', start=100, end=180)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "25",
-   "metadata": {},
-   "source": [
-    "#### Link the Structural Phase to the Experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment.linked_phases.create(id='lbco', scale=9.1351)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "27",
@@ -442,7 +445,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -463,7 +466,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -473,7 +476,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -491,8 +494,10 @@
     "on the current parameter value and expands them by a chosen multiple of\n",
     "the reported uncertainty.\n",
     "\n",
-    "Default `multiplier` is 8 to give a wide range for the sampler to\n",
-    "explore, but here we use 3 to speed up the tutorial."
+    "The default `multiplier` is 4. If the local refinement is very tight,\n",
+    "or if you expect a broader posterior, increase it explicitly.\n",
+    "\n",
+    "Show unset fit bounds before setting them from the local refinement uncertainties."
    ]
   },
   {
@@ -502,23 +507,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40",
+   "metadata": {},
+   "source": [
+    "Set fit bounds for all free parameters using the default multiplier of\n",
+    "4. In this tutorial that means the posterior pair plot will later\n",
+    "refer to a `±4 × uncertainty` region in its title. To use a different\n",
+    "region, pass another value, for example `multiplier=6`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40",
+   "id": "41",
    "metadata": {},
    "outputs": [],
    "source": [
     "for param in project.free_parameters:\n",
-    "    param.set_fit_bounds_from_uncertainty(multiplier=3.5)"
+    "    param.set_fit_bounds_from_uncertainty()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "41",
+   "id": "42",
    "metadata": {},
    "source": [
     "Displaying the free parameters again is a convenient way to confirm\n",
@@ -529,16 +545,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "42",
+   "id": "43",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "43",
+   "id": "44",
    "metadata": {},
    "source": [
     "## Step 6: Configure and Run DREAM\n",
@@ -550,20 +566,21 @@
     "of steps (`steps`) and often the burn-in (`burn`) as well. When\n",
     "needed, the DREAM API also lets you tune how chains are initialized\n",
     "through the `init` setting. Other sampler settings such as `thin` and\n",
-    "`pop` can be adjusted as well. The current EasyDiffraction default\n",
-    "also uses `parallel=0`, which tells BUMPS DREAM to use all available\n",
-    "CPUs for population evaluations.\n",
+    "`pop` can be adjusted as well. The current EasyDiffraction defaults\n",
+    "use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells\n",
+    "BUMPS-DREAM to use all available CPUs for population evaluations.\n",
     "\n",
-    "The default `steps` value is 1000, and real analyses often need more\n",
-    "to achieve good convergence and posterior sampling. Here we use a much\n",
-    "smaller value to keep the tutorial fast, but this is not recommended\n",
-    "for production analysis."
+    "The `burn` setting is auto-resolved when left unset. With the default\n",
+    "`steps=3000` this gives `burn=600`, but if you override `steps` and\n",
+    "keep `burn=None`, the effective burn-in is recomputed automatically.\n",
+    "Here we use a much smaller step count to keep the tutorial fast, but\n",
+    "this is not recommended for production analysis."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44",
+   "id": "45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +590,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45",
+   "id": "46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,17 +600,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "46",
+   "id": "47",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 100  # lower than the default 1000"
+    "project.analysis.fit.minimizer.steps = 300  # lower than the default 3000"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47",
+   "id": "48",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +619,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48",
+   "id": "49",
    "metadata": {},
    "source": [
     "## Step 7: Inspect Bayesian Results\n",
@@ -615,16 +632,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "49",
+   "id": "50",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "50",
+   "id": "51",
    "metadata": {},
    "source": [
     "The correlation and posterior-pair plots are complementary:\n",
@@ -632,32 +649,35 @@
     "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
     "  matrix.\n",
     "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
-    "  posterior contours off-diagonal."
+    "  posterior contours off-diagonal. In this tutorial its title also\n",
+    "  reminds you that the display region follows the `±4 × uncertainty`\n",
+    "  bounds defined above, while numeric subplot ranges are omitted to\n",
+    "  keep the grid readable."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51",
+   "id": "52",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "52",
+   "id": "53",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_pairs()"
+    "project.display.posterior.pairs()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "53",
+   "id": "54",
    "metadata": {},
    "source": [
     "The one-dimensional posterior distributions below make it easier to\n",
@@ -668,17 +688,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54",
+   "id": "55",
    "metadata": {},
    "outputs": [],
    "source": [
     "for param in project.free_parameters:\n",
-    "    project.display.plotter.plot_param_distribution(param)"
+    "    project.display.posterior.distribution(param)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55",
+   "id": "56",
    "metadata": {},
    "source": [
     "Finally, the posterior predictive plot propagates the sampled parameter\n",
@@ -690,16 +710,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "56",
+   "id": "57",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
+    "project.display.posterior.predictive(expt_name='hrpt')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "57",
+   "id": "58",
    "metadata": {},
    "source": [
     "A final zoomed measured-vs-calculated plot is useful for checking how\n",
@@ -710,11 +730,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "58",
+   "id": "59",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)"
+    "project.display.posterior.predictive(\n",
+    "    expt_name='hrpt',\n",
+    "    x_min=92,\n",
+    "    x_max=93,\n",
+    ")"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index d91173c37..e890f55bc 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -213,7 +213,7 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # The correlation plot shows how strongly the fitted parameters move
@@ -222,10 +222,10 @@
 # region.
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %% [markdown]
 # ## Step 5: Prepare for Bayesian Sampling
@@ -244,7 +244,7 @@
 # Show unset fit bounds before setting them from the local refinement uncertainties.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # Set fit bounds for all free parameters using the default multiplier of
@@ -262,7 +262,7 @@
 # sampler.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # ## Step 6: Configure and Run DREAM
@@ -304,7 +304,7 @@
 # statistics.
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # The correlation and posterior-pair plots are complementary:
@@ -318,10 +318,10 @@
 #   keep the grid readable.
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_posterior_pairs()
+project.display.posterior.pairs()
 
 # %% [markdown]
 # The one-dimensional posterior distributions below make it easier to
@@ -330,7 +330,7 @@
 
 # %%
 for param in project.free_parameters:
-    project.display.plotter.plot_param_distribution(param)
+    project.display.posterior.distribution(param)
 
 # %% [markdown]
 # Finally, the posterior predictive plot propagates the sampled parameter
@@ -339,7 +339,7 @@
 # model family explains the data in the region of interest.
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+project.display.posterior.predictive(expt_name='hrpt')
 
 # %% [markdown]
 # A final zoomed measured-vs-calculated plot is useful for checking how
@@ -347,7 +347,7 @@
 # after the Bayesian run.
 
 # %%
-project.display.plotter.plot_posterior_predictive(
+project.display.posterior.predictive(
     expt_name='hrpt',
     x_min=92,
     x_max=93,
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
index cc880314e..965e4ed82 100644
--- a/docs/docs/tutorials/ed-22.ipynb
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -35,7 +35,16 @@
     "distribution with BUMPS-DREAM.\n",
     "\n",
     "The example uses constant-wavelength neutron single-crystal diffraction data\n",
-    "for Tb2TiO7 measured on HEiDi at FRM II."
+    "for Tb2TiO7 measured on HEiDi at FRM II.\n",
+    "\n",
+    "The goal is not only to obtain a good fit, but also to answer Bayesian\n",
+    "questions such as:\n",
+    "\n",
+    "- Which parameter values are most probable?\n",
+    "- How broad are the credible intervals?\n",
+    "- Which parameters are strongly correlated?\n",
+    "- How much uncertainty propagates into the calculated reflection\n",
+    "  intensities?"
    ]
   },
   {
@@ -61,7 +70,11 @@
    "id": "4",
    "metadata": {},
    "source": [
-    "## Step 1: Create a Project Container"
+    "## Step 1: Create a Project Container\n",
+    "\n",
+    "The project object keeps structures, experiments, fit settings, and\n",
+    "plotting utilities together in a single place. We will build the full\n",
+    "workflow inside this object."
    ]
   },
   {
@@ -79,7 +92,12 @@
    "id": "6",
    "metadata": {},
    "source": [
-    "## Step 2: Build the Structural Model"
+    "## Step 2: Build the Structural Model\n",
+    "\n",
+    "For this example we start from a CIF file describing the Tb2TiO7\n",
+    "pyrochlore structure. Loading the structure from CIF is convenient\n",
+    "because it preserves a realistic starting\n",
+    "model without rebuilding the full structure by hand."
    ]
   },
   {
@@ -117,7 +135,11 @@
    "id": "10",
    "metadata": {},
    "source": [
-    "## Step 3: Define the Diffraction Experiment"
+    "## Step 3: Define the Diffraction Experiment\n",
+    "\n",
+    "Next we download the measured reflection data, create a neutron\n",
+    "single-crystal experiment, and configure the crystal link,\n",
+    "wavelength, and extinction model."
    ]
   },
   {
@@ -156,10 +178,18 @@
     "experiment = project.experiments['heidi']"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "Link the crystal structure to the experiment and set its scale factor."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,10 +197,20 @@
     "experiment.linked_crystal.scale = 1.0"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "Set the instrument wavelength and starting extinction parameters.\n",
+    "These values provide the initial experiment description for the local\n",
+    "refinement."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +220,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "16",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,16 +230,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17",
+   "id": "19",
    "metadata": {},
    "source": [
-    "## Step 4: Run an Initial Local Refinement"
+    "## Step 4: Run an Initial Local Refinement\n",
+    "\n",
+    "Before Bayesian sampling, it is useful to run a deterministic fit. This\n",
+    "gives us:\n",
+    "\n",
+    "- a good point estimate near the best-fit region,\n",
+    "- uncertainties from the local optimizer,\n",
+    "- a quick check that the model and experiment are configured\n",
+    "  sensibly.\n",
+    "\n",
+    "In this tutorial we refine a small set of structural and extinction\n",
+    "parameters while keeping occupancies fixed."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +269,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,10 +277,20 @@
     "experiment.extinction.radius.free = True"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "We keep using the default LMFIT Levenberg-Marquardt minimizer as a fast local\n",
+    "optimizer. Its main purpose here is to provide a stable starting point\n",
+    "and uncertainty estimates for the Bayesian run."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,65 +300,111 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
     "project.analysis.fit()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "The fit-results display summarizes the locally refined values and their\n",
+    "estimated uncertainties."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "The correlation plot shows how strongly the refined parameters move\n",
+    "together in the local refinement. The measured-vs-calculated plot shows\n",
+    "how well the refined crystal model reproduces the measured reflection\n",
+    "intensities."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "23",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+    "project.display.pattern(expt_name='heidi')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "25",
+   "id": "30",
    "metadata": {},
    "source": [
-    "## Step 5: Prepare for Bayesian Sampling"
+    "## Step 5: Prepare for Bayesian Sampling\n",
+    "\n",
+    "DREAM requires finite bounds for the free parameters. Instead of\n",
+    "setting them manually, we derive them from the uncertainties estimated\n",
+    "in the local refinement.\n",
+    "\n",
+    "The helper method `set_fit_bounds_from_uncertainty` centers the bounds\n",
+    "on the current parameter value and expands them by a chosen multiple of\n",
+    "the reported uncertainty.\n",
+    "\n",
+    "The default `multiplier` is 4. In this single-crystal tutorial we use\n",
+    "a tighter value of `1.5` to keep the sampling window closer to the\n",
+    "locally refined solution.\n",
+    "\n",
+    "Show unset fit bounds before setting them from the local refinement\n",
+    "uncertainties."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32",
+   "metadata": {},
+   "source": [
+    "Set fit bounds for all free parameters using `multiplier=1.5`. In this\n",
+    "tutorial that means the posterior pair plot will later refer to a\n",
+    "`±1.5 × uncertainty` region in its title. To widen the sampling window,\n",
+    "increase the multiplier explicitly."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,28 +412,53 @@
     "    param.set_fit_bounds_from_uncertainty(multiplier=1.5)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "34",
+   "metadata": {},
+   "source": [
+    "Displaying the free parameters again is a convenient way to confirm\n",
+    "that the fit bounds have been assigned as expected before launching the\n",
+    "sampler."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "29",
+   "id": "36",
    "metadata": {},
    "source": [
-    "## Step 6: Configure and Run BUMPS-DREAM"
+    "## Step 6: Configure and Run DREAM\n",
+    "\n",
+    "We now switch from the local minimizer to the Bayesian DREAM sampler.\n",
+    "\n",
+    "The settings below are intentionally small so the tutorial runs\n",
+    "quickly. For production analysis you would usually increase the number\n",
+    "of steps (`steps`) and often the burn-in (`burn`) as well. When\n",
+    "needed, the DREAM API also lets you tune how chains are initialized\n",
+    "through the `init` setting. Other sampler settings such as `thin` and\n",
+    "`pop` can be adjusted as well. The current EasyDiffraction defaults\n",
+    "use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells\n",
+    "BUMPS-DREAM to use all available CPUs for population evaluations.\n",
+    "\n",
+    "The `burn` setting is auto-resolved when left unset. Here we override\n",
+    "`steps` with a smaller value to keep the tutorial fast, and the\n",
+    "effective burn-in is recomputed automatically."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30",
+   "id": "37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +468,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,72 +478,120 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32",
+   "id": "39",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 500"
+    "project.analysis.fit.minimizer.steps = 500  # lower than the default 3000"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33",
+   "id": "40",
    "metadata": {},
    "outputs": [],
    "source": [
     "project.analysis.fit()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "41",
+   "metadata": {},
+   "source": [
+    "## Step 7: Inspect Bayesian Results\n",
+    "\n",
+    "The fit-results display now includes sampler settings, convergence\n",
+    "diagnostics, committed parameter values, and posterior summary\n",
+    "statistics."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34",
+   "id": "42",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43",
+   "metadata": {},
+   "source": [
+    "The correlation and posterior-pair plots are complementary:\n",
+    "\n",
+    "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
+    "  matrix.\n",
+    "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
+    "  posterior contours off-diagonal. In this tutorial its title also\n",
+    "  reminds you that the display region follows the `±1.5 × uncertainty`\n",
+    "  bounds defined above, while numeric subplot ranges are omitted to\n",
+    "  keep the grid readable."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35",
+   "id": "44",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36",
+   "id": "45",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_pairs()"
+    "project.display.posterior.pairs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46",
+   "metadata": {},
+   "source": [
+    "The one-dimensional posterior distributions below make it easier to\n",
+    "inspect individual parameters in isolation, including asymmetry or\n",
+    "multimodality."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37",
+   "id": "47",
    "metadata": {},
    "outputs": [],
    "source": [
     "for param in project.free_parameters:\n",
-    "    project.display.plotter.plot_param_distribution(param)"
+    "    project.display.posterior.distribution(param)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48",
+   "metadata": {},
+   "source": [
+    "Finally, the posterior predictive plot propagates the sampled\n",
+    "parameter uncertainty into the calculated single-crystal reflection\n",
+    "intensities."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "38",
+   "id": "49",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_predictive(expt_name='heidi')"
+    "project.display.posterior.predictive(expt_name='heidi')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index aaa727180..bf0f2e19e 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -141,7 +141,7 @@
 # estimated uncertainties.
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # The correlation plot shows how strongly the refined parameters move
@@ -150,10 +150,10 @@
 # intensities.
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+project.display.pattern(expt_name='heidi')
 
 # %% [markdown]
 # ## Step 5: Prepare for Bayesian Sampling
@@ -174,7 +174,7 @@
 # uncertainties.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # Set fit bounds for all free parameters using `multiplier=1.5`. In this
@@ -192,7 +192,7 @@
 # sampler.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # ## Step 6: Configure and Run DREAM
@@ -232,7 +232,7 @@
 # statistics.
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # The correlation and posterior-pair plots are complementary:
@@ -246,10 +246,10 @@
 #   keep the grid readable.
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %%
-project.display.plotter.plot_posterior_pairs()
+project.display.posterior.pairs()
 
 # %% [markdown]
 # The one-dimensional posterior distributions below make it easier to
@@ -258,7 +258,7 @@
 
 # %%
 for param in project.free_parameters:
-    project.display.plotter.plot_param_distribution(param)
+    project.display.posterior.distribution(param)
 
 # %% [markdown]
 # Finally, the posterior predictive plot propagates the sampled
@@ -266,4 +266,4 @@
 # intensities.
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='heidi')
+project.display.posterior.predictive(expt_name='heidi')
diff --git a/docs/docs/tutorials/ed-3.ipynb b/docs/docs/tutorials/ed-3.ipynb
index f99298a2f..03fba5148 100644
--- a/docs/docs/tutorials/ed-3.ipynb
+++ b/docs/docs/tutorials/ed-3.ipynb
@@ -175,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.show_supported_engines()"
+    "project.rendering.show_chart_engines()"
    ]
   },
   {
@@ -193,7 +193,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.show_config()"
+    "project.rendering.show_config()"
    ]
   },
   {
@@ -492,7 +492,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt', include='measured')"
    ]
   },
   {
@@ -774,7 +774,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt', include='calculated')"
    ]
   },
   {
@@ -792,7 +792,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -802,7 +802,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
@@ -822,7 +822,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# project.analysis.display.all_params()"
+    "# project.display.parameters.all()"
    ]
   },
   {
@@ -840,7 +840,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fittable_params()"
+    "project.display.parameters.fittable()"
    ]
   },
   {
@@ -858,7 +858,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -876,7 +876,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# project.analysis.display.how_to_access_parameters()"
+    "# project.display.parameters.access()"
    ]
   },
   {
@@ -1014,7 +1014,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -1033,7 +1033,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -1051,7 +1051,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -1061,7 +1061,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
@@ -1120,7 +1120,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -1139,7 +1139,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -1157,7 +1157,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -1167,7 +1167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
@@ -1226,7 +1226,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -1245,7 +1245,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -1263,7 +1263,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -1273,7 +1273,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
@@ -1356,7 +1356,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.constraints()"
+    "project.analysis.constraints.show()"
    ]
   },
   {
@@ -1374,7 +1374,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -1393,7 +1393,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -1411,7 +1411,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -1421,7 +1421,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
@@ -1508,7 +1508,7 @@
    },
    "outputs": [],
    "source": [
-    "project.analysis.display.constraints()"
+    "project.analysis.constraints.show()"
    ]
   },
   {
@@ -1544,7 +1544,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -1563,8 +1563,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -1582,7 +1582,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -1592,7 +1592,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)"
+    "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-3.py b/docs/docs/tutorials/ed-3.py
index de1f74c63..271b8e8f0 100644
--- a/docs/docs/tutorials/ed-3.py
+++ b/docs/docs/tutorials/ed-3.py
@@ -68,13 +68,13 @@
 # Show supported plotting engines.
 
 # %%
-project.display.plotter.show_supported_engines()
+project.rendering.show_chart_engines()
 
 # %% [markdown]
 # Show current plotting configuration.
 
 # %%
-project.display.plotter.show_config()
+project.rendering.show_config()
 
 # %% [markdown]
 # ## Step 2: Define Structure
@@ -219,7 +219,7 @@
 # #### Show Measured Data
 
 # %%
-project.display.plotter.plot_meas(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt', include='measured')
 
 # %% [markdown]
 # #### Set Instrument
@@ -328,16 +328,16 @@
 # #### Show Calculated Data
 
 # %%
-project.display.plotter.plot_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt', include='calculated')
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Show Parameters
@@ -345,25 +345,25 @@
 # Show all parameters of the project.
 
 # %%
-# project.analysis.display.all_params()
+# project.display.parameters.all()
 
 # %% [markdown]
 # Show all fittable parameters.
 
 # %%
-project.analysis.display.fittable_params()
+project.display.parameters.fittable()
 
 # %% [markdown]
 # Show only free parameters.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # Show how to access parameters in the code.
 
 # %%
-# project.analysis.display.how_to_access_parameters()
+# project.display.parameters.access()
 
 # %% [markdown]
 # #### Set Fit Mode
@@ -417,23 +417,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Save Project State
@@ -456,23 +456,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Save Project State
@@ -495,23 +495,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Save Project State
@@ -546,29 +546,29 @@
 # Show defined constraints.
 
 # %%
-project.analysis.display.constraints()
+project.analysis.constraints.show()
 
 # %% [markdown]
 # Show free parameters.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Save Project State
@@ -605,7 +605,7 @@
 # Show defined constraints.
 
 # %%
-project.analysis.display.constraints()
+project.analysis.constraints.show()
 
 
 # %% [markdown]
@@ -618,24 +618,24 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)
+project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
 # %% [markdown]
 # #### Save Project State
diff --git a/docs/docs/tutorials/ed-4.ipynb b/docs/docs/tutorials/ed-4.ipynb
index 3a77ab8bb..bdf0343c4 100644
--- a/docs/docs/tutorials/ed-4.ipynb
+++ b/docs/docs/tutorials/ed-4.ipynb
@@ -687,7 +687,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='npd', x_min=35.5, x_max=38.3)"
+    "project.display.pattern(expt_name='npd', x_min=35.5, x_max=38.3)"
    ]
   },
   {
@@ -697,7 +697,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='xrd', x_min=29.0, x_max=30.4)"
+    "project.display.pattern(expt_name='xrd', x_min=29.0, x_max=30.4)"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-4.py b/docs/docs/tutorials/ed-4.py
index 8a868277f..a0ea3f623 100644
--- a/docs/docs/tutorials/ed-4.py
+++ b/docs/docs/tutorials/ed-4.py
@@ -315,7 +315,7 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='npd', x_min=35.5, x_max=38.3)
+project.display.pattern(expt_name='npd', x_min=35.5, x_max=38.3)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='xrd', x_min=29.0, x_max=30.4)
+project.display.pattern(expt_name='xrd', x_min=29.0, x_max=30.4)
diff --git a/docs/docs/tutorials/ed-5.ipynb b/docs/docs/tutorials/ed-5.ipynb
index 02ef6045c..3fa6294b9 100644
--- a/docs/docs/tutorials/ed-5.ipynb
+++ b/docs/docs/tutorials/ed-5.ipynb
@@ -426,7 +426,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20')"
+    "project.display.pattern(expt_name='d20')"
    ]
   },
   {
@@ -436,7 +436,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20', x_min=41, x_max=54)"
+    "project.display.pattern(expt_name='d20', x_min=41, x_max=54)"
    ]
   },
   {
@@ -507,7 +507,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -582,7 +582,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -592,7 +592,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -610,7 +610,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20')"
+    "project.display.pattern(expt_name='d20')"
    ]
   },
   {
@@ -620,7 +620,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='d20', x_min=42, x_max=52)"
+    "project.display.pattern(expt_name='d20', x_min=42, x_max=52)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-5.py b/docs/docs/tutorials/ed-5.py
index 72e54feb9..a82990a32 100644
--- a/docs/docs/tutorials/ed-5.py
+++ b/docs/docs/tutorials/ed-5.py
@@ -200,10 +200,10 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='d20')
+project.display.pattern(expt_name='d20')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='d20', x_min=41, x_max=54)
+project.display.pattern(expt_name='d20', x_min=41, x_max=54)
 
 # %% [markdown]
 # #### Set Free Parameters
@@ -243,7 +243,7 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Set Constraints
@@ -274,19 +274,19 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='d20')
+project.display.pattern(expt_name='d20')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='d20', x_min=42, x_max=52)
+project.display.pattern(expt_name='d20', x_min=42, x_max=52)
 
 # %% [markdown]
 # ## Summary
diff --git a/docs/docs/tutorials/ed-6.ipynb b/docs/docs/tutorials/ed-6.ipynb
index ae8f5a84d..3c89362d1 100644
--- a/docs/docs/tutorials/ed-6.ipynb
+++ b/docs/docs/tutorials/ed-6.ipynb
@@ -385,7 +385,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -395,7 +395,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)"
+    "project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)"
    ]
   },
   {
@@ -437,7 +437,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -465,7 +465,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -483,7 +483,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -493,7 +493,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)"
+    "project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)"
    ]
   },
   {
@@ -537,7 +537,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -565,7 +565,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -583,7 +583,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -593,7 +593,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)"
+    "project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)"
    ]
   },
   {
@@ -635,7 +635,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -663,7 +663,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -681,7 +681,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -691,7 +691,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)"
+    "project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)"
    ]
   },
   {
@@ -738,7 +738,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -766,7 +766,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -776,7 +776,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -794,7 +794,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+    "project.display.pattern(expt_name='hrpt')"
    ]
   },
   {
@@ -804,7 +804,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)"
+    "project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-6.py b/docs/docs/tutorials/ed-6.py
index 341178e61..14d93515e 100644
--- a/docs/docs/tutorials/ed-6.py
+++ b/docs/docs/tutorials/ed-6.py
@@ -179,10 +179,10 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)
+project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)
 
 # %% [markdown]
 # ### Perform Fit 1/4
@@ -200,7 +200,7 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
@@ -209,16 +209,16 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)
+project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)
 
 # %% [markdown]
 # ### Perform Fit 2/4
@@ -238,7 +238,7 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
@@ -247,16 +247,16 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)
+project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)
 
 # %% [markdown]
 # ### Perform Fit 3/4
@@ -274,7 +274,7 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
@@ -283,16 +283,16 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)
+project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)
 
 # %% [markdown]
 # ### Perform Fit 4/4
@@ -315,7 +315,7 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
@@ -324,19 +324,19 @@
 project.analysis.fit()
 
 # %%
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.pattern(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=48, x_max=51)
+project.display.pattern(expt_name='hrpt', x_min=48, x_max=51)
 
 # %% [markdown]
 # ## Summary
diff --git a/docs/docs/tutorials/ed-7.ipynb b/docs/docs/tutorials/ed-7.ipynb
index 7a66c6f66..31294b586 100644
--- a/docs/docs/tutorials/ed-7.ipynb
+++ b/docs/docs/tutorials/ed-7.ipynb
@@ -345,8 +345,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')\n",
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd')\n",
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -387,7 +387,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -406,7 +406,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -424,7 +424,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -434,7 +434,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -473,7 +473,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -492,7 +492,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -510,7 +510,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -520,7 +520,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -579,7 +579,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -598,7 +598,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -616,7 +616,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -626,7 +626,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -668,7 +668,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.display.free_params()"
+    "project.display.parameters.free()"
    ]
   },
   {
@@ -687,7 +687,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -705,7 +705,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -723,7 +723,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd')"
+    "project.display.pattern(expt_name='sepd')"
    ]
   },
   {
@@ -733,7 +733,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -745,7 +745,7 @@
    },
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x='d_spacing')"
+    "project.display.pattern(expt_name='sepd', x='d_spacing')"
    ]
   },
   {
@@ -860,7 +860,7 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()"
+    "project.display.fit.results()"
    ]
   },
   {
@@ -878,7 +878,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -896,7 +896,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)"
+    "project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)"
    ]
   },
   {
@@ -906,7 +906,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x='d_spacing')"
+    "project.display.pattern(expt_name='sepd', x='d_spacing')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-7.py b/docs/docs/tutorials/ed-7.py
index 5305549a7..ed3dcdaec 100644
--- a/docs/docs/tutorials/ed-7.py
+++ b/docs/docs/tutorials/ed-7.py
@@ -140,8 +140,8 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd')
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %% [markdown]
 # ### Perform Fit 1/5
@@ -158,23 +158,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %% [markdown]
 # ### Perform Fit 2/5
@@ -189,23 +189,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %% [markdown]
 # ### Perform Fit 3/5
@@ -228,23 +228,23 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %% [markdown]
 # ### Perform Fit 4/5
@@ -262,32 +262,32 @@
 # Show free parameters after selection.
 
 # %%
-project.analysis.display.free_params()
+project.display.parameters.free()
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Show parameter correlations
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd')
+project.display.pattern(expt_name='sepd')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x='d_spacing')
+project.display.pattern(expt_name='sepd', x='d_spacing')
 
 
 # %% [markdown]
@@ -334,19 +334,19 @@
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
+project.display.fit.results()
 
 # %% [markdown]
 # #### Show parameter correlations
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700)
+project.display.pattern(expt_name='sepd', x_min=23200, x_max=23700)
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='sepd', x='d_spacing')
+project.display.pattern(expt_name='sepd', x='d_spacing')
diff --git a/docs/docs/tutorials/ed-8.ipynb b/docs/docs/tutorials/ed-8.ipynb
index 5df563205..248d4f59e 100644
--- a/docs/docs/tutorials/ed-8.ipynb
+++ b/docs/docs/tutorials/ed-8.ipynb
@@ -630,7 +630,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='wish_5_6')"
+    "project.display.pattern(expt_name='wish_5_6')"
    ]
   },
   {
@@ -640,7 +640,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='wish_4_7')"
+    "project.display.pattern(expt_name='wish_4_7')"
    ]
   },
   {
@@ -659,8 +659,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -678,7 +678,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='wish_5_6')"
+    "project.display.pattern(expt_name='wish_5_6')"
    ]
   },
   {
@@ -688,7 +688,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='wish_4_7')"
+    "project.display.pattern(expt_name='wish_4_7')"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-8.py b/docs/docs/tutorials/ed-8.py
index a9adb7c8f..82beedd7b 100644
--- a/docs/docs/tutorials/ed-8.py
+++ b/docs/docs/tutorials/ed-8.py
@@ -333,27 +333,27 @@
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='wish_5_6')
+project.display.pattern(expt_name='wish_5_6')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='wish_4_7')
+project.display.pattern(expt_name='wish_4_7')
 
 # %% [markdown]
 # #### Run Fitting
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='wish_5_6')
+project.display.pattern(expt_name='wish_5_6')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='wish_4_7')
+project.display.pattern(expt_name='wish_4_7')
 
 # %% [markdown]
 # ## Summary
diff --git a/docs/docs/tutorials/ed-9.ipynb b/docs/docs/tutorials/ed-9.ipynb
index 4c4d7eb5b..0bfd09490 100644
--- a/docs/docs/tutorials/ed-9.ipynb
+++ b/docs/docs/tutorials/ed-9.ipynb
@@ -509,7 +509,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas(expt_name='mcstas')"
+    "project.display.pattern(expt_name='mcstas', include='measured')"
    ]
   },
   {
@@ -564,7 +564,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas(expt_name='mcstas')"
+    "project.display.pattern(expt_name='mcstas', include=('measured', 'excluded'))"
    ]
   },
   {
@@ -660,8 +660,8 @@
    "outputs": [],
    "source": [
     "project.analysis.fit()\n",
-    "project.analysis.display.fit_results()\n",
-    "project.display.plotter.plot_param_correlations()"
+    "project.display.fit.results()\n",
+    "project.display.fit.correlations()"
    ]
   },
   {
@@ -679,7 +679,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='mcstas')"
+    "project.display.pattern(expt_name='mcstas')"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-9.py b/docs/docs/tutorials/ed-9.py
index c9108b5f0..ffae09108 100644
--- a/docs/docs/tutorials/ed-9.py
+++ b/docs/docs/tutorials/ed-9.py
@@ -227,7 +227,7 @@
 # Show measured data as loaded from the file.
 
 # %%
-project.display.plotter.plot_meas(expt_name='mcstas')
+project.display.pattern(expt_name='mcstas', include='measured')
 
 # %% [markdown]
 # Add excluded regions.
@@ -246,7 +246,7 @@
 # Show measured data after adding excluded regions.
 
 # %%
-project.display.plotter.plot_meas(expt_name='mcstas')
+project.display.pattern(expt_name='mcstas', include=('measured', 'excluded'))
 
 # %% [markdown]
 # Show experiment as CIF.
@@ -294,11 +294,11 @@
 
 # %%
 project.analysis.fit()
-project.analysis.display.fit_results()
-project.display.plotter.plot_param_correlations()
+project.display.fit.results()
+project.display.fit.correlations()
 
 # %% [markdown]
 # #### Plot Measured vs Calculated
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='mcstas')
+project.display.pattern(expt_name='mcstas')
diff --git a/docs/docs/user-guide/analysis-workflow/analysis.md b/docs/docs/user-guide/analysis-workflow/analysis.md
index bf1d4ee87..b3c9e3d53 100644
--- a/docs/docs/user-guide/analysis-workflow/analysis.md
+++ b/docs/docs/user-guide/analysis-workflow/analysis.md
@@ -247,11 +247,11 @@ Now, you can inspect the fitted parameters to see how they have changed
 during the refinement process, select more parameters to be refined, and
 perform additional fits as needed.
 
-To plot the measured vs calculated data after the fit, you can use the
-`plot_meas_vs_calc` method of the `analysis` object:
+To plot the measured and calculated data after the fit, you can use the
+`pattern` method of the `display` object:
 
 ```python
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)
+project.display.pattern(expt_name='hrpt')
 ```
 
 ## Constraints
@@ -319,7 +319,7 @@ To view the defined constraints, you can use the `show_constraints`
 method:
 
 ```python
-project.analysis.display.constraints()
+project.analysis.constraints.show()
 ```
 
 The example of the output is:
diff --git a/docs/docs/user-guide/analysis-workflow/project.md b/docs/docs/user-guide/analysis-workflow/project.md
index 7959f9f85..5e8e8b417 100644
--- a/docs/docs/user-guide/analysis-workflow/project.md
+++ b/docs/docs/user-guide/analysis-workflow/project.md
@@ -115,8 +115,8 @@ data_<span class="red"><b>La0.5Ba0.5CoO3</b></span>
 <span class="blue"><b>_project</b>.title</span>       "La0.5Ba0.5CoO3 from neutron diffraction at HRPT@PSI"
 <span class="blue"><b>_project</b>.description</span> "neutrons, powder, constant wavelength, HRPT@PSI"
 
-<span class="blue"><b>_display</b>.plotter_type</span>  asciichartpy
-<span class="blue"><b>_display</b>.tabler_type</span>   rich
+<span class="blue"><b>_display</b>.chart_engine</span>  asciichartpy
+<span class="blue"><b>_display</b>.table_engine</span>   rich
 </pre>
 </div>
 
diff --git a/docs/docs/user-guide/first-steps.md b/docs/docs/user-guide/first-steps.md
index b8f69070d..66f96e908 100644
--- a/docs/docs/user-guide/first-steps.md
+++ b/docs/docs/user-guide/first-steps.md
@@ -125,23 +125,22 @@ project.analysis.fit.show_minimizer_types()
 EasyDiffraction provides several methods for showing the available
 parameters grouped in different categories. For example, you can use:
 
-- `project.analysis.display.all_params()` – to display all available
+- `project.display.parameters.all()` – to display all available
   parameters for the analysis step.
-- `project.analysis.display.fittable_params()` – to display only the
+- `project.display.parameters.fittable()` – to display only the
   parameters that can be fitted during the analysis.
-- `project.analysis.display.free_params()` – to display the parameters
-  that are currently free to be adjusted during the fitting process.
+- `project.display.parameters.free()` – to display the parameters that
+  are currently free to be adjusted during the fitting process.
 
-Finally, you can use the
-`project.analysis.display.how_to_access_parameters()` method to get a
-brief overview of how to access and modify parameters in the analysis
-step, along with their unique identifiers in the CIF format. This can be
-particularly useful for users who are new to the EasyDiffraction API or
-those who want to quickly understand how to work with parameters in
-their projects.
+Finally, you can use the `project.display.parameters.access()` method to
+get a brief overview of how to access and modify parameters in the
+analysis step, along with their unique identifiers in the CIF format.
+This can be particularly useful for users who are new to the
+EasyDiffraction API or those who want to quickly understand how to work
+with parameters in their projects.
 
-An example of the output for the
-`project.analysis.display.how_to_access_parameters()` method is:
+An example of the output for the `project.display.parameters.access()`
+method is:
 
 |     | Code variable                                       | Unique ID for CIF          |
 | --- | --------------------------------------------------- | -------------------------- |
@@ -160,7 +159,7 @@ To see the available plotters, you can use the `display` category on the
 `Project` instance:
 
 ```python
-project.display.show_plotter_types()
+project.rendering.show_chart_engines()
 ```
 
 An example of the output is:
diff --git a/src/easydiffraction/__main__.py b/src/easydiffraction/__main__.py
index bd5ae9be5..18220904f 100644
--- a/src/easydiffraction/__main__.py
+++ b/src/easydiffraction/__main__.py
@@ -99,10 +99,10 @@ def fit(
     if dry:
         project.info._path = None
     project.analysis.fit()
-    project.analysis.display.fit_results()
-    project.display.plotter.plot_param_correlations()
+    project.display.fit.results()
+    project.display.fit.correlations()
     for expt in project.experiments:
-        project.display.plotter.plot_meas_vs_calc(expt_name=expt.name, show_residual=True)
+        project.display.pattern(expt_name=expt.name)
     # project.summary.show_report()
 
 
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index 23b9fcf3f..e3c01607f 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from __future__ import annotations
+
 from contextlib import suppress
 
 import numpy as np
@@ -102,7 +104,7 @@ class AnalysisDisplay:
     Accessed via ``analysis.display``.
     """
 
-    def __init__(self, analysis: 'Analysis') -> None:
+    def __init__(self, analysis: Analysis) -> None:
         self._analysis = analysis
 
     def _flush_structure_categories(self) -> None:
@@ -342,20 +344,7 @@ def parameter_cif_uids(self) -> None:
 
     def constraints(self) -> None:
         """Print a table of all user-defined symbolic constraints."""
-        analysis = self._analysis
-        if not analysis.constraints._items:
-            log.warning('No constraints defined.')
-            return
-
-        rows = [[constraint.expression.value] for constraint in analysis.constraints]
-
-        console.paragraph('User defined constraints')
-        render_table(
-            columns_headers=['expression'],
-            columns_alignment=['left'],
-            columns_data=rows,
-        )
-        console.print(f'Constraints enabled: {analysis.constraints.enabled}')
+        self._analysis.constraints.show()
 
     def fit_results(self) -> None:
         """
@@ -381,10 +370,7 @@ def fit_results(self) -> None:
 
     def as_cif(self) -> None:
         """Render the analysis section as CIF in console."""
-        cif_text: str = self._analysis.as_cif()
-        paragraph_title: str = 'Analysis 🧮 info as cif'
-        console.paragraph(paragraph_title)
-        render_cif(cif_text)
+        self._analysis.show_as_cif()
 
 
 class Analysis:
@@ -900,6 +886,7 @@ def _update_categories(
             self.constraints_handler.set_constraints(self.constraints)
             self.constraints_handler.apply()
 
+    @property
     def as_cif(self) -> str:
         """
         Serialize the analysis section to a CIF string.
@@ -911,3 +898,8 @@ def as_cif(self) -> str:
         """
         self._update_categories()
         return analysis_to_cif(self)
+
+    def show_as_cif(self) -> None:
+        """Pretty-print the analysis section as CIF text."""
+        console.paragraph('Analysis info as CIF')
+        render_cif(self.as_cif)
diff --git a/src/easydiffraction/analysis/categories/constraints/default.py b/src/easydiffraction/analysis/categories/constraints/default.py
index 63a4264d4..fc1d74fde 100644
--- a/src/easydiffraction/analysis/categories/constraints/default.py
+++ b/src/easydiffraction/analysis/categories/constraints/default.py
@@ -18,6 +18,9 @@
 from easydiffraction.core.validation import RegexValidator
 from easydiffraction.core.variable import StringDescriptor
 from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.logging import log
+from easydiffraction.utils.utils import render_table
 
 
 class Constraint(CategoryItem):
@@ -132,3 +135,19 @@ def create(self, *, expression: str) -> None:
         item.expression = expression
         self.add(item)
         self._enabled = True
+
+    def show(self) -> None:
+        """Print a table of all user-defined symbolic constraints."""
+        if not self._items:
+            log.warning('No constraints defined.')
+            return
+
+        rows = [[constraint.expression.value] for constraint in self]
+
+        console.paragraph('User defined constraints')
+        render_table(
+            columns_headers=['expression'],
+            columns_alignment=['left'],
+            columns_data=rows,
+        )
+        console.print(f'Constraints enabled: {self.enabled}')
diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index 4aeb5577a..c81af0b0e 100644
--- a/src/easydiffraction/display/plotters/ascii.py
+++ b/src/easydiffraction/display/plotters/ascii.py
@@ -61,6 +61,7 @@ def plot_powder(
         axes_labels: object,
         title: str,
         height: int | None = None,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
         """
         Render a line plot for powder diffraction data.
@@ -83,6 +84,8 @@ def plot_powder(
             Figure title printed above the chart.
         height : int | None, default=None
             Number of text rows to allocate for the chart.
+        excluded_ranges : tuple[tuple[float, float], ...], default=()
+            Excluded x-ranges to print below the selected x-range.
         """
         # Intentionally unused; kept for a consistent display API
         del axes_labels
@@ -100,6 +103,11 @@ def plot_powder(
         console.print(
             f'Displaying data for selected x-range from {x[0]} to {x[-1]} ({len(x)} points)'
         )
+        if excluded_ranges:
+            formatted_ranges = ', '.join(
+                f'[{start:,.2f}, {end:,.2f}]' for start, end in excluded_ranges
+            )
+            console.print(f'Excluded regions: {formatted_ranges}')
         console.print(f'Legend:\n{legend}')
 
         padded = '\n'.join(' ' + line for line in chart.splitlines())
@@ -130,6 +138,7 @@ def plot_powder_meas_vs_calc(
             axes_labels=plot_spec.axes_labels,
             title=plot_spec.title,
             height=plot_spec.height,
+            excluded_ranges=plot_spec.excluded_ranges,
         )
         if plot_spec.predictive_lower_95 is not None and plot_spec.predictive_upper_95 is not None:
             console.print('Posterior predictive bands are available with the Plotly engine only.')
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index d4be0d67a..4cf43d669 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -65,6 +65,7 @@ class PowderMeasVsCalcSpec:
     predictive_draws: np.ndarray | None = None
     y_calc_name: str | None = None
     y_calc_line_dash: str | None = None
+    excluded_ranges: tuple[tuple[float, float], ...] = ()
 
 
 class XAxisType(StrEnum):
@@ -229,6 +230,7 @@ def plot_powder(
         axes_labels: object,
         title: str,
         height: int | None,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
         """
         Render a line plot for powder diffraction data.
@@ -250,6 +252,8 @@ def plot_powder(
             Figure title.
         height : int | None
             Backend-specific height (text rows or pixels).
+        excluded_ranges : tuple[tuple[float, float], ...], default=()
+            Closed x-intervals to highlight as excluded regions.
         """
 
     @abstractmethod
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index cb4914c3f..d41c1f9eb 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -70,6 +70,7 @@
 PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.14)'
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
+EXCLUDED_REGION_FILL_COLOR = 'rgba(120, 120, 120, 0.16)'
 PREDICTIVE_DRAW_PLOT_CAP = 50
 PREDICTIVE_DRAW_ARRAY_NDIM = 2
 FIXED_ASPECT_WRAPPER_META_KEY = 'fixed_aspect_wrapper'
@@ -1038,6 +1039,7 @@ def plot_powder(
         axes_labels: object,
         title: str,
         height: int | None = None,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
         """
         Render a line plot for powder diffraction data.
@@ -1059,6 +1061,8 @@ def plot_powder(
             Figure title.
         height : int | None, default=None
             Ignored; Plotly auto-sizes based on renderer.
+        excluded_ranges : tuple[tuple[float, float], ...], default=()
+            Excluded x-ranges to shade on the figure.
         """
         # Intentionally unused; accepted for API compatibility
         del height
@@ -1075,8 +1079,33 @@ def plot_powder(
         )
 
         fig = self._get_figure(data, layout)
+        self._add_excluded_region_vrects(fig=fig, excluded_ranges=excluded_ranges)
         self._show_figure(fig)
 
+    @staticmethod
+    def _add_excluded_region_vrects(
+        *,
+        fig: object,
+        excluded_ranges: tuple[tuple[float, float], ...],
+        row: object | None = None,
+        col: int | None = None,
+    ) -> None:
+        """Shade excluded x-ranges on a Plotly figure."""
+        for start, end in excluded_ranges:
+            add_kwargs = {
+                'x0': start,
+                'x1': end,
+                'fillcolor': EXCLUDED_REGION_FILL_COLOR,
+                'opacity': 1.0,
+                'line_width': 0,
+                'layer': 'below',
+            }
+            if row is not None:
+                add_kwargs['row'] = row
+            if col is not None:
+                add_kwargs['col'] = col
+            fig.add_vrect(**add_kwargs)
+
     @staticmethod
     def _get_bragg_tick_trace(
         tick_set: BraggTickSet,
@@ -1386,6 +1415,12 @@ def plot_powder_meas_vs_calc(
             hover_data=hover_data,
             hover_template=hover_template,
         )
+        self._add_excluded_region_vrects(
+            fig=fig,
+            excluded_ranges=plot_spec.excluded_ranges,
+            row='all',
+            col=1,
+        )
         self._configure_powder_composite_layout(fig=fig, plot_spec=plot_spec, layout=layout)
         self._configure_powder_composite_axes(
             fig=fig,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 34b0da107..a258579e8 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -179,6 +179,9 @@ class _MeasVsCalcPlotOptions:
     x_min: float | None = None
     x_max: float | None = None
     show_residual: bool | None = None
+    show_background: bool | None = None
+    show_bragg: bool | None = None
+    show_excluded: bool = False
     x: object | None = None
 
 
@@ -373,6 +376,18 @@ def _filtered_y_array(
         mask = (x_array >= lower_bound) & (x_array <= upper_bound)
         return y_array[mask]
 
+    def _filtered_optional_y_array(
+        self,
+        y_array: object | None,
+        x_array: object,
+        x_min: object,
+        x_max: object,
+    ) -> object | None:
+        """Filter an optional y-array by inclusive x-range limits."""
+        if y_array is None:
+            return None
+        return self._filtered_y_array(y_array, x_array, x_min, x_max)
+
     @staticmethod
     def _get_axes_labels(
         sample_form: object,
@@ -574,6 +589,8 @@ def plot_meas(
         x_min: float | None = None,
         x_max: float | None = None,
         x: object | None = None,
+        *,
+        show_excluded: bool = False,
     ) -> None:
         """
         Plot measured diffraction data for an experiment.
@@ -588,16 +605,23 @@ def plot_meas(
             Upper bound for the x-axis range.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
+        show_excluded : bool, default=False
+            Whether to show excluded fitting regions on supported plots.
         """
         self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_excluded=show_excluded,
+            x=x,
+        )
         self._plot_meas_data(
+            experiment,
             intensity_category_for(experiment),
             expt_name,
             experiment.type,
-            x_min=x_min,
-            x_max=x_max,
-            x=x,
+            plot_options,
         )
 
     def plot_calc(
@@ -606,6 +630,8 @@ def plot_calc(
         x_min: float | None = None,
         x_max: float | None = None,
         x: object | None = None,
+        *,
+        show_excluded: bool = False,
     ) -> None:
         """
         Plot calculated diffraction pattern for an experiment.
@@ -620,16 +646,23 @@ def plot_calc(
             Upper bound for the x-axis range.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
+        show_excluded : bool, default=False
+            Whether to show excluded fitting regions on supported plots.
         """
         self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_excluded=show_excluded,
+            x=x,
+        )
         self._plot_calc_data(
+            experiment,
             intensity_category_for(experiment),
             expt_name,
             experiment.type,
-            x_min=x_min,
-            x_max=x_max,
-            x=x,
+            plot_options,
         )
 
     def plot_meas_vs_calc(
@@ -639,6 +672,7 @@ def plot_meas_vs_calc(
         x_max: float | None = None,
         *,
         show_residual: bool | None = None,
+        show_excluded: bool = False,
         x: object | None = None,
     ) -> None:
         """
@@ -656,17 +690,29 @@ def plot_meas_vs_calc(
             When ``None``, powder Bragg plots include the residual by
             default while other measured-vs-calculated plots keep the
             historical no-residual default.
+        show_excluded : bool, default=False
+            Whether to show excluded fitting regions on supported plots.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
         """
-        self._update_project_categories(expt_name)
-        experiment = self._project.experiments[expt_name]
         plot_options = _MeasVsCalcPlotOptions(
             x_min=x_min,
             x_max=x_max,
             show_residual=show_residual,
+            show_excluded=show_excluded,
             x=x,
         )
+        self._plot_meas_vs_calc_request(expt_name=expt_name, plot_options=plot_options)
+
+    def _plot_meas_vs_calc_request(
+        self,
+        *,
+        expt_name: str,
+        plot_options: _MeasVsCalcPlotOptions,
+    ) -> None:
+        """Render a measured-vs-calculated request from plot options."""
+        self._update_project_categories(expt_name)
+        experiment = self._project.experiments[expt_name]
         self._plot_meas_vs_calc_data(
             experiment=experiment,
             expt_name=expt_name,
@@ -990,6 +1036,7 @@ def plot_posterior_predictive(
         x_max: float | None = None,
         *,
         show_residual: bool | None = None,
+        show_excluded: bool = False,
         x: object | None = None,
     ) -> None:
         """
@@ -1011,6 +1058,8 @@ def plot_posterior_predictive(
         show_residual : bool | None, default=None
             Whether to include the residual row in supported powder
             composite plots.
+        show_excluded : bool, default=False
+            Whether to show excluded fitting regions on supported plots.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
 
@@ -1024,6 +1073,28 @@ def plot_posterior_predictive(
             msg = "style must be 'band', 'draws', or 'band+draws'."
             raise ValueError(msg)
 
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_residual=show_residual,
+            show_excluded=show_excluded,
+            x=x,
+        )
+
+        self._plot_posterior_predictive_request(
+            expt_name=expt_name,
+            style=style,
+            plot_options=plot_options,
+        )
+
+    def _plot_posterior_predictive_request(
+        self,
+        *,
+        expt_name: str,
+        style: str,
+        plot_options: _MeasVsCalcPlotOptions,
+    ) -> None:
+        """Render a posterior predictive request from plot options."""
         if self._project is None:
             log.warning('Plotter is not attached to a project.')
             return
@@ -1034,13 +1105,9 @@ def plot_posterior_predictive(
 
         self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
-        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, x)
-
-        plot_options = _MeasVsCalcPlotOptions(
-            x_min=x_min,
-            x_max=x_max,
-            show_residual=show_residual,
-            x=x,
+        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(
+            experiment.type,
+            plot_options.x,
         )
 
         if sample_form == SampleFormEnum.SINGLE_CRYSTAL:
@@ -1221,6 +1288,15 @@ def _plot_non_bragg_posterior_predictive(
             ctx['x_min'],
             ctx['x_max'],
         )
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
 
         axes_labels = self._get_axes_labels(sample_form, scattering_type, x_axis)
         self._plot_posterior_predictive_summary(
@@ -1230,6 +1306,7 @@ def _plot_non_bragg_posterior_predictive(
             axes_labels=axes_labels,
             show_band=style in {'band', 'band+draws'},
             show_draws=style in {'draws', 'band+draws'},
+            excluded_ranges=excluded_ranges,
         )
 
     @staticmethod
@@ -3377,6 +3454,7 @@ def _plot_posterior_predictive_summary(
         axes_labels: list[str],
         show_band: bool,
         show_draws: bool,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
         """Render posterior predictive summaries using Plotly."""
         go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
@@ -3452,6 +3530,15 @@ def _plot_posterior_predictive_summary(
                 legendrank=20,
             )
         )
+        for start, end in excluded_ranges:
+            fig.add_vrect(
+                x0=start,
+                x1=end,
+                fillcolor='rgba(120, 120, 120, 0.16)',
+                opacity=1.0,
+                line_width=0,
+                layer='below',
+            )
         fig.update_layout(
             title={
                 'text': f"Posterior predictive for experiment 🔬 '{expt_name}'",
@@ -3627,12 +3714,14 @@ def _plot_posterior_predictive_data(
             ctx['x_min'],
             ctx['x_max'],
         )
-        y_bkg_raw = getattr(pattern, 'intensity_bkg', None)
-        y_bkg = (
-            self._filtered_y_array(y_bkg_raw, ctx['x_array'], ctx['x_min'], ctx['x_max'])
-            if y_bkg_raw is not None
-            else None
+        y_bkg = self._filtered_optional_y_array(
+            getattr(pattern, 'intensity_bkg', None),
+            ctx['x_array'],
+            ctx['x_min'],
+            ctx['x_max'],
         )
+        if not self._show_background_enabled(plot_options, background_available=y_bkg is not None):
+            y_bkg = None
         y_calc = self._filtered_y_array(
             summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max']
         )
@@ -3669,7 +3758,7 @@ def _plot_posterior_predictive_data(
                 dtype=float,
             )
 
-        if np.asarray(ctx['x_filtered']).size == 0:
+        if np.asarray(ctx['x_filtered']).size == 0 or not self._show_bragg_enabled(plot_options):
             bragg_tick_sets = ()
         else:
             bragg_tick_sets = self._extract_bragg_tick_sets(
@@ -3679,6 +3768,15 @@ def _plot_posterior_predictive_data(
                 x_min=ctx['x_min'],
                 x_max=ctx['x_max'],
             )
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
 
         plot_spec = PowderMeasVsCalcSpec(
             x=ctx['x_filtered'],
@@ -3697,6 +3795,7 @@ def _plot_posterior_predictive_data(
             predictive_draws=predictive_draws,
             y_calc_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
             y_calc_line_dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+            excluded_ranges=excluded_ranges,
         )
         self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 
@@ -4646,18 +4745,19 @@ def _format_correlation_table_dataframe(
 
     def _plot_meas_data(
         self,
+        experiment: object,
         pattern: object,
         expt_name: str,
         expt_type: object,
-        x_min: object = None,
-        x_max: object = None,
-        x: object = None,
+        plot_options: _MeasVsCalcPlotOptions,
     ) -> None:
         """
         Plot measured pattern using the current engine.
 
         Parameters
         ----------
+        experiment : object
+            Experiment object used for excluded-range extraction.
         pattern : object
             Object with x-axis arrays (``two_theta``,
             ``time_of_flight``, ``d_spacing``) and ``meas`` array.
@@ -4665,20 +4765,16 @@ def _plot_meas_data(
             Experiment name for the title.
         expt_type : object
             Experiment type with scattering/beam enums.
-        x_min : object, default=None
-            Optional minimum x-axis limit.
-        x_max : object, default=None
-            Optional maximum x-axis limit.
-        x : object, default=None
-            X-axis type. If ``None``, auto-detected from beam mode.
+        plot_options : _MeasVsCalcPlotOptions
+            X-range, excluded-region, and x-axis selection options.
         """
         ctx = self._prepare_powder_context(
             pattern,
             expt_name,
             expt_type,
-            x_min,
-            x_max,
-            x,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
         )
         if ctx is None:
             return
@@ -4689,6 +4785,15 @@ def _plot_meas_data(
         y_meas = self._filtered_y_array(
             pattern.intensity_meas, ctx['x_array'], ctx['x_min'], ctx['x_max']
         )
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
 
         self._backend.plot_powder(
             x=ctx['x_filtered'],
@@ -4697,22 +4802,24 @@ def _plot_meas_data(
             axes_labels=ctx['axes_labels'],
             title=f"Measured data for experiment 🔬 '{expt_name}'",
             height=self.height,
+            excluded_ranges=excluded_ranges,
         )
 
     def _plot_calc_data(
         self,
+        experiment: object,
         pattern: object,
         expt_name: str,
         expt_type: object,
-        x_min: object = None,
-        x_max: object = None,
-        x: object = None,
+        plot_options: _MeasVsCalcPlotOptions,
     ) -> None:
         """
         Plot calculated pattern using the current engine.
 
         Parameters
         ----------
+        experiment : object
+            Experiment object used for excluded-range extraction.
         pattern : object
             Object with x-axis arrays (``two_theta``,
             ``time_of_flight``, ``d_spacing``) and ``calc`` array.
@@ -4720,20 +4827,16 @@ def _plot_calc_data(
             Experiment name for the title.
         expt_type : object
             Experiment type with scattering/beam enums.
-        x_min : object, default=None
-            Optional minimum x-axis limit.
-        x_max : object, default=None
-            Optional maximum x-axis limit.
-        x : object, default=None
-            X-axis type. If ``None``, auto-detected from beam mode.
+        plot_options : _MeasVsCalcPlotOptions
+            X-range, excluded-region, and x-axis selection options.
         """
         ctx = self._prepare_powder_context(
             pattern,
             expt_name,
             expt_type,
-            x_min,
-            x_max,
-            x,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
         )
         if ctx is None:
             return
@@ -4744,6 +4847,15 @@ def _plot_calc_data(
         y_calc = self._filtered_y_array(
             pattern.intensity_calc, ctx['x_array'], ctx['x_min'], ctx['x_max']
         )
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
 
         self._backend.plot_powder(
             x=ctx['x_filtered'],
@@ -4752,6 +4864,7 @@ def _plot_calc_data(
             axes_labels=ctx['axes_labels'],
             title=f"Calculated data for experiment 🔬 '{expt_name}'",
             height=self.height,
+            excluded_ranges=excluded_ranges,
         )
 
     def _plot_meas_vs_calc_data(
@@ -4837,12 +4950,23 @@ def _plot_meas_vs_calc_data(
             if y_bkg_raw is not None
             else None
         )
+        if not self._show_background_enabled(plot_options, background_available=y_bkg is not None):
+            y_bkg = None
 
         powder_series = _PowderMeasVsCalcSeries(
             y_meas=y_meas,
             y_calc=y_calc,
             y_bkg=y_bkg,
         )
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
 
         if sample_form == SampleFormEnum.POWDER and scattering_type == ScatteringTypeEnum.BRAGG:
             self._plot_powder_bragg_meas_vs_calc(
@@ -4852,6 +4976,7 @@ def _plot_meas_vs_calc_data(
                 series=powder_series,
                 plot_options=plot_options,
                 title=title,
+                excluded_ranges=excluded_ranges,
             )
             return
 
@@ -4863,6 +4988,7 @@ def _plot_meas_vs_calc_data(
             if plot_options.show_residual is None
             else plot_options.show_residual,
             title=title,
+            excluded_ranges=excluded_ranges,
         )
 
     def _plot_single_crystal_meas_vs_calc(
@@ -4901,13 +5027,14 @@ def _plot_powder_bragg_meas_vs_calc(
         series: _PowderMeasVsCalcSeries,
         plot_options: _MeasVsCalcPlotOptions,
         title: str,
+        excluded_ranges: tuple[tuple[float, float], ...],
     ) -> None:
         """
         Render the composite powder Bragg measured-vs-calculated plot.
         """
         show_residual = True if plot_options.show_residual is None else plot_options.show_residual
         y_resid = series.y_meas - series.y_calc if show_residual else None
-        if np.asarray(ctx['x_filtered']).size == 0:
+        if np.asarray(ctx['x_filtered']).size == 0 or not self._show_bragg_enabled(plot_options):
             bragg_tick_sets = ()
         else:
             bragg_tick_sets = self._extract_bragg_tick_sets(
@@ -4930,9 +5057,30 @@ def _plot_powder_bragg_meas_vs_calc(
             bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
             height=self._composite_plot_height(),
             y_bkg=series.y_bkg,
+            excluded_ranges=excluded_ranges,
         )
         self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 
+    @staticmethod
+    def _show_background_enabled(
+        plot_options: object,
+        *,
+        background_available: bool,
+    ) -> bool:
+        """Return whether the background curve should be shown."""
+        show_background = getattr(plot_options, 'show_background', None)
+        if show_background is None:
+            return background_available
+        return show_background and background_available
+
+    @staticmethod
+    def _show_bragg_enabled(plot_options: object) -> bool:
+        """Return whether Bragg reflection rows should be shown."""
+        show_bragg = getattr(plot_options, 'show_bragg', None)
+        if show_bragg is None:
+            return True
+        return show_bragg
+
     def _plot_line_meas_vs_calc(
         self,
         ctx: dict[str, object],
@@ -4941,6 +5089,7 @@ def _plot_line_meas_vs_calc(
         *,
         show_residual: bool,
         title: str,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
         """
         Render the non-composite line version of measured-vs-calculated.
@@ -4958,8 +5107,36 @@ def _plot_line_meas_vs_calc(
             axes_labels=ctx['axes_labels'],
             title=title,
             height=self.height,
+            excluded_ranges=excluded_ranges,
         )
 
+    @staticmethod
+    def _excluded_ranges(
+        *,
+        experiment: object,
+        x_min: float | None,
+        x_max: float | None,
+    ) -> tuple[tuple[float, float], ...]:
+        """Return excluded x-ranges clipped to the current view."""
+        excluded_regions = getattr(experiment, 'excluded_regions', None)
+        if excluded_regions is None:
+            return ()
+
+        clipped_ranges: list[tuple[float, float]] = []
+        lower_bound = -np.inf if x_min is None else float(x_min)
+        upper_bound = np.inf if x_max is None else float(x_max)
+
+        for region in excluded_regions:
+            start = float(region.start.value)
+            end = float(region.end.value)
+            clipped_start = max(start, lower_bound)
+            clipped_end = min(end, upper_bound)
+            if clipped_start > clipped_end:
+                continue
+            clipped_ranges.append((clipped_start, clipped_end))
+
+        return tuple(clipped_ranges)
+
     @staticmethod
     def _extract_bragg_tick_sets(
         experiment: object,
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index 0b868b6c1..b71e69e22 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -334,9 +334,9 @@ def _as_cif_text(section: object) -> str:
 def project_config_to_cif(project: object) -> str:
     """Render project-level configuration to ``project.cif`` text."""
     lines: list[str] = [_as_cif_text(project.info)]
-    display = getattr(project, 'display', None)
-    if display is not None:
-        lines.extend(('', _as_cif_text(display)))
+    rendering = getattr(project, 'rendering', None)
+    if rendering is not None:
+        lines.extend(('', _as_cif_text(rendering)))
     return '\n'.join(lines)
 
 
@@ -350,7 +350,7 @@ def project_to_cif(project: object) -> str:
     if getattr(project, 'experiments', None):
         parts.append(_as_cif_text(project.experiments))
     if getattr(project, 'analysis', None):
-        parts.append(project.analysis.as_cif())
+        parts.append(_as_cif_text(project.analysis))
     if getattr(project, 'summary', None):
         parts.append(project.summary.as_cif())
     return '\n\n'.join([p for p in parts if p])
@@ -455,9 +455,9 @@ def project_config_from_cif(project: object, cif_text: str) -> None:
 
     _populate_project_info_from_block(project.info, block)
 
-    display = getattr(project, 'display', None)
-    if display is not None:
-        display.from_cif(block)
+    rendering = getattr(project, 'rendering', None)
+    if rendering is not None:
+        rendering.from_cif(block)
 
 
 def analysis_from_cif(analysis: object, cif_text: str) -> None:
diff --git a/src/easydiffraction/project/categories/display/__init__.py b/src/easydiffraction/project/categories/display/__init__.py
deleted file mode 100644
index d0862c2e0..000000000
--- a/src/easydiffraction/project/categories/display/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-"""Project display category exports."""
-
-from __future__ import annotations
-
-from easydiffraction.project.categories.display.default import Display
-from easydiffraction.project.categories.display.factory import DisplayFactory
diff --git a/src/easydiffraction/project/categories/display/default.py b/src/easydiffraction/project/categories/display/default.py
deleted file mode 100644
index d8287b09b..000000000
--- a/src/easydiffraction/project/categories/display/default.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-"""Project display category."""
-
-from __future__ import annotations
-
-from easydiffraction.core.category import CategoryItem
-from easydiffraction.core.metadata import TypeInfo
-from easydiffraction.core.validation import AttributeSpec
-from easydiffraction.core.validation import MembershipValidator
-from easydiffraction.core.variable import StringDescriptor
-from easydiffraction.display.plotting import Plotter
-from easydiffraction.display.plotting import PlotterEngineEnum
-from easydiffraction.display.tables import TableEngineEnum
-from easydiffraction.display.tables import TableRenderer
-from easydiffraction.io.cif.handler import CifHandler
-from easydiffraction.io.cif.parse import read_cif_str
-from easydiffraction.project.categories.display.factory import DisplayFactory
-
-
-@DisplayFactory.register
-class Display(CategoryItem):
-    """Display engine selection and access for a project."""
-
-    type_info = TypeInfo(
-        tag='default',
-        description='Project display category',
-    )
-
-    def __init__(self) -> None:
-        super().__init__()
-
-        self._plotter = Plotter()
-        self._tabler = TableRenderer.get()
-
-        self._plotter_type = StringDescriptor(
-            name='plotter_type',
-            description='Plot renderer backend type',
-            value_spec=AttributeSpec(
-                default=self._plotter.engine,
-                validator=MembershipValidator(
-                    allowed=[member.value for member in PlotterEngineEnum],
-                ),
-            ),
-            cif_handler=CifHandler(names=['_display.plotter_type']),
-        )
-        self._tabler_type = StringDescriptor(
-            name='tabler_type',
-            description='Table renderer backend type',
-            value_spec=AttributeSpec(
-                default=self._tabler.engine,
-                validator=MembershipValidator(
-                    allowed=[member.value for member in TableEngineEnum],
-                ),
-            ),
-            cif_handler=CifHandler(names=['_display.tabler_type']),
-        )
-
-        self._identity.category_code = 'display'
-
-    @property
-    def plotter_type(self) -> StringDescriptor:
-        """Plot renderer backend type."""
-        return self._plotter_type
-
-    @plotter_type.setter
-    def plotter_type(self, value: str) -> None:
-        self._plotter.engine = value
-        self._plotter_type.value = self._plotter.engine
-
-    @property
-    def tabler_type(self) -> StringDescriptor:
-        """Table renderer backend type."""
-        return self._tabler_type
-
-    @tabler_type.setter
-    def tabler_type(self, value: str) -> None:
-        self._tabler.engine = value
-        self._tabler_type.value = self._tabler.engine
-
-    @property
-    def plotter(self) -> Plotter:
-        """Live plotting facade bound to the owning project."""
-        parent = getattr(self, '_parent', None)
-        if parent is not None:
-            self._plotter._set_project(parent)
-        return self._plotter
-
-    @property
-    def tabler(self) -> TableRenderer:
-        """Live table-rendering facade."""
-        return self._tabler
-
-    def show_plotter_types(self) -> None:
-        """Print supported plot renderer backends."""
-        self.plotter.show_supported_engines()
-
-    def show_tabler_types(self) -> None:
-        """Print supported table renderer backends."""
-        self.tabler.show_supported_engines()
-
-    def from_cif(self, block: object, idx: int = 0) -> None:
-        """Populate this display category from a CIF block."""
-        del idx
-        plotter_type = read_cif_str(block, '_display.plotter_type')
-        if plotter_type is not None:
-            if plotter_type == self._plotter.engine:
-                self._plotter_type.value = plotter_type
-            else:
-                self.plotter_type = plotter_type
-
-        tabler_type = read_cif_str(block, '_display.tabler_type')
-        if tabler_type is not None:
-            if tabler_type == self._tabler.engine:
-                self._tabler_type.value = tabler_type
-            else:
-                self.tabler_type = tabler_type
diff --git a/src/easydiffraction/project/categories/rendering/__init__.py b/src/easydiffraction/project/categories/rendering/__init__.py
new file mode 100644
index 000000000..3e9889159
--- /dev/null
+++ b/src/easydiffraction/project/categories/rendering/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project rendering category exports."""
+
+from __future__ import annotations
+
+from easydiffraction.project.categories.rendering.default import Rendering
+from easydiffraction.project.categories.rendering.factory import RenderingFactory
diff --git a/src/easydiffraction/project/categories/rendering/default.py b/src/easydiffraction/project/categories/rendering/default.py
new file mode 100644
index 000000000..f8970cb55
--- /dev/null
+++ b/src/easydiffraction/project/categories/rendering/default.py
@@ -0,0 +1,131 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project rendering category."""
+
+from __future__ import annotations
+
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.display.plotting import Plotter
+from easydiffraction.display.plotting import PlotterEngineEnum
+from easydiffraction.display.tables import TableEngineEnum
+from easydiffraction.display.tables import TableRenderer
+from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.io.cif.parse import read_cif_str
+from easydiffraction.project.categories.rendering.factory import RenderingFactory
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.utils import render_table
+
+
+@RenderingFactory.register
+class Rendering(CategoryItem):
+    """Chart and table engine selection for a project."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Project rendering category',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._plotter = Plotter()
+        self._tabler = TableRenderer.get()
+
+        self._chart_engine = StringDescriptor(
+            name='chart_engine',
+            description='Chart renderer backend type',
+            value_spec=AttributeSpec(
+                default=self._plotter.engine,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in PlotterEngineEnum],
+                ),
+            ),
+            cif_handler=CifHandler(names=['_rendering.chart_engine']),
+        )
+        self._table_engine = StringDescriptor(
+            name='table_engine',
+            description='Table renderer backend type',
+            value_spec=AttributeSpec(
+                default=self._tabler.engine,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in TableEngineEnum],
+                ),
+            ),
+            cif_handler=CifHandler(names=['_rendering.table_engine']),
+        )
+
+        self._identity.category_code = 'rendering'
+
+    @property
+    def chart_engine(self) -> StringDescriptor:
+        """Chart renderer backend type."""
+        return self._chart_engine
+
+    @chart_engine.setter
+    def chart_engine(self, value: str) -> None:
+        self._plotter.engine = value
+        self._chart_engine.value = self._plotter.engine
+
+    @property
+    def table_engine(self) -> StringDescriptor:
+        """Table renderer backend type."""
+        return self._table_engine
+
+    @table_engine.setter
+    def table_engine(self, value: str) -> None:
+        self._tabler.engine = value
+        self._table_engine.value = self._tabler.engine
+
+    @property
+    def plotter(self) -> Plotter:
+        """Live plotting facade bound to the owning project."""
+        parent = getattr(self, '_parent', None)
+        if parent is not None:
+            self._plotter._set_project(parent)
+        return self._plotter
+
+    @property
+    def tabler(self) -> TableRenderer:
+        """Live table-rendering facade."""
+        return self._tabler
+
+    def show_chart_engines(self) -> None:
+        """Print supported chart renderer backends."""
+        self.plotter.show_supported_engines()
+
+    def show_table_engines(self) -> None:
+        """Print supported table renderer backends."""
+        self.tabler.show_supported_engines()
+
+    def show_config(self) -> None:
+        """Print the current rendering configuration."""
+        console.paragraph('Current rendering configuration')
+        render_table(
+            columns_headers=['Setting', 'Value'],
+            columns_alignment=['left', 'left'],
+            columns_data=[
+                ['Chart engine', self.chart_engine.value],
+                ['Table engine', self.table_engine.value],
+            ],
+        )
+
+    def from_cif(self, block: object, idx: int = 0) -> None:
+        """Populate this rendering category from a CIF block."""
+        del idx
+        chart_engine = read_cif_str(block, '_rendering.chart_engine')
+        if chart_engine is not None:
+            if chart_engine == self._plotter.engine:
+                self._chart_engine.value = chart_engine
+            else:
+                self.chart_engine = chart_engine
+
+        table_engine = read_cif_str(block, '_rendering.table_engine')
+        if table_engine is not None:
+            if table_engine == self._tabler.engine:
+                self._table_engine.value = table_engine
+            else:
+                self.table_engine = table_engine
diff --git a/src/easydiffraction/project/categories/display/factory.py b/src/easydiffraction/project/categories/rendering/factory.py
similarity index 70%
rename from src/easydiffraction/project/categories/display/factory.py
rename to src/easydiffraction/project/categories/rendering/factory.py
index 0d1be80a9..c2bdf7c5f 100644
--- a/src/easydiffraction/project/categories/display/factory.py
+++ b/src/easydiffraction/project/categories/rendering/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Factory for project display categories."""
+"""Factory for project rendering categories."""
 
 from __future__ import annotations
 
@@ -9,8 +9,8 @@
 from easydiffraction.core.factory import FactoryBase
 
 
-class DisplayFactory(FactoryBase):
-    """Create project display category instances."""
+class RenderingFactory(FactoryBase):
+    """Create project rendering category instances."""
 
     _default_rules: ClassVar[dict] = {
         frozenset(): 'default',
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
new file mode 100644
index 000000000..74c510fac
--- /dev/null
+++ b/src/easydiffraction/project/display.py
@@ -0,0 +1,689 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project display facade grouping charts and reports."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from easydiffraction.datablocks.experiment.item.base import intensity_category_for
+from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+from easydiffraction.display.plotting import PlotterEngineEnum
+from easydiffraction.display.plotting import PosteriorPairPlotStyleEnum
+from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
+from easydiffraction.utils.utils import render_table
+
+if TYPE_CHECKING:
+    from easydiffraction.project.project import Project
+
+
+_PATTERN_OPTION_DESCRIPTIONS: dict[str, str] = {
+    'auto': 'Show the most informative available pattern view.',
+    'measured': 'Measured diffraction intensities.',
+    'calculated': 'Calculated diffraction intensities.',
+    'background': 'Calculated background intensities when present.',
+    'residual': 'Measured minus calculated residuals when supported.',
+    'bragg': 'Bragg reflection tick marks when reflection data exists.',
+    'excluded': 'Excluded fitting regions when defined on the experiment.',
+    'uncertainty': 'Posterior predictive uncertainty bands when available.',
+}
+
+
+@dataclass(frozen=True, slots=True)
+class PatternOptionStatus:
+    """Availability metadata for one ``display.pattern`` option."""
+
+    name: str
+    description: str
+    available: bool
+    auto_included: bool
+    reason: str
+
+
+class ParameterDisplay:
+    """Parameter-table namespace under ``project.display``."""
+
+    def __init__(self, project: Project) -> None:
+        self._project = project
+
+    def all(self) -> None:
+        """Show all structure and experiment parameters."""
+        self._project.analysis.display.all_params()
+
+    def fittable(self) -> None:
+        """Show all currently fittable parameters."""
+        self._project.analysis.display.fittable_params()
+
+    def free(self) -> None:
+        """Show all currently free parameters."""
+        self._project.analysis.display.free_params()
+
+    def access(self) -> None:
+        """Show Python access paths for all parameters."""
+        self._project.analysis.display.how_to_access_parameters()
+
+    def cif_uids(self) -> None:
+        """Show CIF unique identifiers for all parameters."""
+        self._project.analysis.display.parameter_cif_uids()
+
+
+class FitDisplay:
+    """Fit-report namespace under ``project.display``."""
+
+    def __init__(self, project: Project) -> None:
+        self._project = project
+
+    def results(self) -> None:
+        """Show the latest fit summary and fitted parameter table."""
+        self._project.analysis.display.fit_results()
+
+    def correlations(
+        self,
+        threshold: float | None = None,
+        precision: int = 2,
+        *,
+        max_parameters: int = 6,
+        show_diagonal: bool = True,
+    ) -> None:
+        """Show parameter correlations from the latest fit."""
+        self._project.rendering.plotter.plot_param_correlations(
+            threshold=threshold,
+            precision=precision,
+            max_parameters=max_parameters,
+            show_diagonal=show_diagonal,
+        )
+
+    def series(
+        self,
+        param: object,
+        versus: object | None = None,
+    ) -> None:
+        """Plot one fitted parameter across sequential results."""
+        self._project.rendering.plotter.plot_param_series(param=param, versus=versus)
+
+
+class PosteriorDisplay:
+    """Posterior-plot namespace under ``project.display``."""
+
+    def __init__(self, project: Project) -> None:
+        self._project = project
+
+    def pairs(
+        self,
+        parameters: list[object] | None = None,
+        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
+        *,
+        threshold: float | None = None,
+        max_parameters: int = 6,
+    ) -> None:
+        """Plot posterior pair relationships for sampled parameters."""
+        self._project.rendering.plotter.plot_posterior_pairs(
+            parameters=parameters,
+            style=style,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
+
+    def distribution(self, param: object) -> None:
+        """Plot one sampled parameter's posterior distribution."""
+        self._project.rendering.plotter.plot_param_distribution(param)
+
+    def predictive(
+        self,
+        expt_name: str,
+        style: str = 'band',
+        x_min: float | None = None,
+        x_max: float | None = None,
+        *,
+        show_residual: bool | None = None,
+        x: object | None = None,
+    ) -> None:
+        """Plot posterior predictive summaries for one experiment."""
+        self._project.rendering.plotter.plot_posterior_predictive(
+            expt_name=expt_name,
+            style=style,
+            x_min=x_min,
+            x_max=x_max,
+            show_residual=show_residual,
+            x=x,
+        )
+
+
+class ProjectDisplay:
+    """Grouped display facade exposed as ``project.display``."""
+
+    def __init__(self, project: Project) -> None:
+        self._project = project
+        self._parameters = ParameterDisplay(project)
+        self._fit = FitDisplay(project)
+        self._posterior = PosteriorDisplay(project)
+
+    @property
+    def parameters(self) -> ParameterDisplay:
+        """Parameter-table namespace."""
+        return self._parameters
+
+    @property
+    def fit(self) -> FitDisplay:
+        """Fit-report namespace."""
+        return self._fit
+
+    @property
+    def posterior(self) -> PosteriorDisplay:
+        """Posterior-plot namespace."""
+        return self._posterior
+
+    def pattern(
+        self,
+        expt_name: str,
+        x_min: float | None = None,
+        x_max: float | None = None,
+        include: str | tuple[str, ...] = 'auto',
+        *,
+        x: object | None = None,
+    ) -> None:
+        """Show a pattern view for one experiment."""
+        normalized_include = self._normalize_include(include)
+        statuses = self._pattern_option_statuses(expt_name)
+
+        if normalized_include == ('auto',):
+            auto_include = self._auto_include(statuses)
+            if x is not None:
+                auto_include = tuple(option for option in auto_include if option != 'excluded')
+            if not auto_include:
+                msg = self._status_by_name(statuses, 'auto').reason
+                raise ValueError(msg)
+            if 'uncertainty' in auto_include:
+                self._project.rendering.plotter._plot_posterior_predictive_request(
+                    expt_name=expt_name,
+                    style='band',
+                    plot_options=_MeasVsCalcPlotOptions(
+                        x_min=x_min,
+                        x_max=x_max,
+                        show_residual=True if 'residual' in auto_include else None,
+                        show_background='background' in auto_include,
+                        show_bragg='bragg' in auto_include,
+                        show_excluded='excluded' in auto_include,
+                        x=x,
+                    ),
+                )
+                return
+            self._show_point_estimate_pattern(
+                expt_name=expt_name,
+                x_min=x_min,
+                x_max=x_max,
+                include=auto_include,
+                statuses=statuses,
+                x=x,
+            )
+            return
+
+        self._validate_requested_include(statuses, normalized_include)
+        if x is not None and 'excluded' in normalized_include:
+            msg = "Excluded-region overlays currently require the experiment's default x-axis."
+            raise ValueError(msg)
+
+        if 'uncertainty' in normalized_include:
+            self._project.rendering.plotter._plot_posterior_predictive_request(
+                expt_name=expt_name,
+                style='band',
+                plot_options=_MeasVsCalcPlotOptions(
+                    x_min=x_min,
+                    x_max=x_max,
+                    show_residual=True if 'residual' in normalized_include else None,
+                    show_background='background' in normalized_include,
+                    show_bragg='bragg' in normalized_include,
+                    show_excluded='excluded' in normalized_include,
+                    x=x,
+                ),
+            )
+            return
+
+        self._show_point_estimate_pattern(
+            expt_name=expt_name,
+            x_min=x_min,
+            x_max=x_max,
+            include=normalized_include,
+            statuses=statuses,
+            x=x,
+        )
+
+    def show_pattern_options(self, expt_name: str) -> None:
+        """Show available ``pattern(include=...)`` options."""
+        statuses = self._pattern_option_statuses(expt_name)
+        render_table(
+            columns_headers=['Option', 'Description', 'Available', 'Auto', 'Reason'],
+            columns_alignment=['left', 'left', 'center', 'center', 'left'],
+            columns_data=[
+                [
+                    status.name,
+                    status.description,
+                    'yes' if status.available else 'no',
+                    'yes' if status.auto_included else 'no',
+                    status.reason or '-',
+                ]
+                for status in statuses
+            ],
+        )
+
+    @staticmethod
+    def _normalize_include(include: str | tuple[str, ...]) -> tuple[str, ...]:
+        """Validate and normalize a ``pattern(include=...)`` value."""
+        values = (include,) if isinstance(include, str) else include
+        if not values:
+            msg = 'include must contain at least one option.'
+            raise ValueError(msg)
+
+        normalized = tuple(dict.fromkeys(values))
+        unknown = [value for value in normalized if value not in _PATTERN_OPTION_DESCRIPTIONS]
+        if unknown:
+            msg = f'Unknown pattern include option(s): {unknown}.'
+            raise ValueError(msg)
+        if 'auto' in normalized and len(normalized) > 1:
+            msg = "include='auto' cannot be combined with other options."
+            raise ValueError(msg)
+        return normalized
+
+    @staticmethod
+    def _status_by_name(
+        statuses: list[PatternOptionStatus],
+        option_name: str,
+    ) -> PatternOptionStatus:
+        """Return one pattern option status by name."""
+        for status in statuses:
+            if status.name == option_name:
+                return status
+        msg = f'Unknown pattern option: {option_name}.'
+        raise ValueError(msg)
+
+    @staticmethod
+    def _with_available_options(
+        status_by_name: dict[str, PatternOptionStatus],
+        required: tuple[str, ...],
+        optional: tuple[str, ...],
+    ) -> tuple[str, ...]:
+        """Return required options plus available optional ones."""
+        include = list(required)
+        include.extend(
+            option_name for option_name in optional if status_by_name[option_name].available
+        )
+        return tuple(include)
+
+    @classmethod
+    def _auto_include(
+        cls,
+        statuses: list[PatternOptionStatus],
+    ) -> tuple[str, ...]:
+        """Return the effective include tuple for ``include='auto'``."""
+        status_by_name = {status.name: status for status in statuses}
+        optional_point_estimate = ('background', 'residual', 'bragg', 'excluded')
+
+        if status_by_name['uncertainty'].available:
+            return cls._with_available_options(
+                status_by_name,
+                ('measured', 'calculated', 'uncertainty'),
+                optional_point_estimate,
+            )
+        if status_by_name['measured'].available and status_by_name['calculated'].available:
+            return cls._with_available_options(
+                status_by_name,
+                ('measured', 'calculated'),
+                optional_point_estimate,
+            )
+        if status_by_name['measured'].available:
+            return cls._with_available_options(
+                status_by_name,
+                ('measured',),
+                ('excluded',),
+            )
+        if status_by_name['calculated'].available:
+            return cls._with_available_options(
+                status_by_name,
+                ('calculated',),
+                ('excluded',),
+            )
+        return ()
+
+    @classmethod
+    def _validate_requested_include(
+        cls,
+        statuses: list[PatternOptionStatus],
+        include: tuple[str, ...],
+    ) -> None:
+        """
+        Raise a clear error when a requested include is unavailable.
+        """
+        status_by_name = {status.name: status for status in statuses}
+        unavailable = [
+            option_name
+            for option_name in include
+            if option_name != 'auto' and not status_by_name[option_name].available
+        ]
+        if unavailable:
+            option_name = unavailable[0]
+            msg = status_by_name[option_name].reason
+            raise ValueError(msg)
+
+        include_set = set(include)
+        if 'background' in include_set and not {'measured', 'calculated'}.issubset(include_set):
+            msg = 'background requires both measured and calculated data in the same view.'
+            raise ValueError(msg)
+        if 'bragg' in include_set and not {'measured', 'calculated'}.issubset(include_set):
+            msg = 'bragg requires both measured and calculated data in the same view.'
+            raise ValueError(msg)
+        if 'residual' in include_set and not {'measured', 'calculated'}.issubset(include_set):
+            msg = 'residual requires both measured and calculated data in the same view.'
+            raise ValueError(msg)
+        if 'excluded' in include_set and not include_set.intersection({
+            'measured',
+            'calculated',
+            'uncertainty',
+        }):
+            msg = 'excluded requires measured, calculated, or uncertainty data in the same view.'
+            raise ValueError(msg)
+
+    def _show_point_estimate_pattern(
+        self,
+        *,
+        expt_name: str,
+        x_min: float | None,
+        x_max: float | None,
+        include: tuple[str, ...],
+        statuses: list[PatternOptionStatus],
+        x: object | None,
+    ) -> None:
+        """
+        Dispatch a point-estimate pattern view to the live plotter.
+        """
+        self._validate_requested_include(statuses, include)
+        include_set = set(include)
+        if include_set == {'measured'}:
+            self._project.rendering.plotter.plot_meas(
+                expt_name=expt_name,
+                x_min=x_min,
+                x_max=x_max,
+                x=x,
+                show_excluded=False,
+            )
+            return
+        if include_set == {'measured', 'excluded'}:
+            self._project.rendering.plotter.plot_meas(
+                expt_name=expt_name,
+                x_min=x_min,
+                x_max=x_max,
+                x=x,
+                show_excluded=True,
+            )
+            return
+        if include_set == {'calculated'}:
+            self._project.rendering.plotter.plot_calc(
+                expt_name=expt_name,
+                x_min=x_min,
+                x_max=x_max,
+                x=x,
+                show_excluded=False,
+            )
+            return
+        if include_set == {'calculated', 'excluded'}:
+            self._project.rendering.plotter.plot_calc(
+                expt_name=expt_name,
+                x_min=x_min,
+                x_max=x_max,
+                x=x,
+                show_excluded=True,
+            )
+            return
+        if {'measured', 'calculated'}.issubset(include_set):
+            self._project.rendering.plotter._plot_meas_vs_calc_request(
+                expt_name=expt_name,
+                plot_options=_MeasVsCalcPlotOptions(
+                    x_min=x_min,
+                    x_max=x_max,
+                    show_residual='residual' in include_set,
+                    show_background='background' in include_set,
+                    show_bragg='bragg' in include_set,
+                    show_excluded='excluded' in include_set,
+                    x=x,
+                ),
+            )
+            return
+
+        msg = (
+            'Point-estimate pattern views currently support include values '
+            "'measured', 'calculated', or combinations containing both."
+        )
+        raise ValueError(msg)
+
+    def _pattern_option_statuses(self, expt_name: str) -> list[PatternOptionStatus]:
+        """Return availability details for the requested experiment."""
+        self._project.rendering.plotter._update_project_categories(expt_name)
+        experiment = self._project.experiments[expt_name]
+        pattern = intensity_category_for(experiment)
+        sample_form = experiment.type.sample_form.value
+        scattering_type = experiment.type.scattering_type.value
+        has_linked_structure = self._has_linked_structure_for_calculation(experiment)
+
+        measured_available = self._has_nonempty_value(getattr(pattern, 'intensity_meas', None))
+        calculated_available = has_linked_structure and self._has_nonempty_value(
+            getattr(pattern, 'intensity_calc', None)
+        )
+        background_available = (
+            sample_form == SampleFormEnum.POWDER.value
+            and scattering_type == ScatteringTypeEnum.BRAGG.value
+            and measured_available
+            and calculated_available
+            and self._has_nonempty_value(getattr(experiment, 'background', None))
+            and self._has_nonempty_value(getattr(pattern, 'intensity_bkg', None))
+        )
+        bragg_available = (
+            measured_available
+            and calculated_available
+            and sample_form == SampleFormEnum.POWDER.value
+            and scattering_type == ScatteringTypeEnum.BRAGG.value
+            and self._has_nonempty_value(getattr(experiment, 'refln', None))
+        )
+        residual_available = (
+            sample_form == SampleFormEnum.POWDER.value
+            and measured_available
+            and calculated_available
+        )
+        has_excluded_regions = self._has_nonempty_value(
+            getattr(experiment, 'excluded_regions', None)
+        )
+        uncertainty_available, uncertainty_reason = self._uncertainty_status(
+            measured_available=measured_available,
+            sample_form=sample_form,
+            scattering_type=scattering_type,
+        )
+
+        auto_include = self._auto_include([
+            PatternOptionStatus(
+                name='measured',
+                description=_PATTERN_OPTION_DESCRIPTIONS['measured'],
+                available=measured_available,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='calculated',
+                description=_PATTERN_OPTION_DESCRIPTIONS['calculated'],
+                available=calculated_available,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='background',
+                description=_PATTERN_OPTION_DESCRIPTIONS['background'],
+                available=background_available,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='residual',
+                description=_PATTERN_OPTION_DESCRIPTIONS['residual'],
+                available=residual_available,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='bragg',
+                description=_PATTERN_OPTION_DESCRIPTIONS['bragg'],
+                available=bragg_available,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='excluded',
+                description=_PATTERN_OPTION_DESCRIPTIONS['excluded'],
+                available=has_excluded_regions,
+                auto_included=False,
+                reason='',
+            ),
+            PatternOptionStatus(
+                name='uncertainty',
+                description=_PATTERN_OPTION_DESCRIPTIONS['uncertainty'],
+                available=uncertainty_available,
+                auto_included=False,
+                reason='',
+            ),
+        ])
+
+        return [
+            PatternOptionStatus(
+                name='auto',
+                description=_PATTERN_OPTION_DESCRIPTIONS['auto'],
+                available=bool(auto_include),
+                auto_included=True,
+                reason='' if auto_include else 'No supported pattern content is available.',
+            ),
+            PatternOptionStatus(
+                name='measured',
+                description=_PATTERN_OPTION_DESCRIPTIONS['measured'],
+                available=measured_available,
+                auto_included='measured' in auto_include,
+                reason='' if measured_available else 'Measured intensities are unavailable.',
+            ),
+            PatternOptionStatus(
+                name='calculated',
+                description=_PATTERN_OPTION_DESCRIPTIONS['calculated'],
+                available=calculated_available,
+                auto_included='calculated' in auto_include,
+                reason='' if calculated_available else 'Calculated intensities are unavailable.',
+            ),
+            PatternOptionStatus(
+                name='background',
+                description=_PATTERN_OPTION_DESCRIPTIONS['background'],
+                available=background_available,
+                auto_included='background' in auto_include,
+                reason=''
+                if background_available
+                else (
+                    'Background display currently requires powder Bragg '
+                    'measured and calculated data plus defined '
+                    'background points.'
+                ),
+            ),
+            PatternOptionStatus(
+                name='residual',
+                description=_PATTERN_OPTION_DESCRIPTIONS['residual'],
+                available=residual_available,
+                auto_included='residual' in auto_include,
+                reason=''
+                if residual_available
+                else ('Residuals currently require powder measured and calculated data.'),
+            ),
+            PatternOptionStatus(
+                name='bragg',
+                description=_PATTERN_OPTION_DESCRIPTIONS['bragg'],
+                available=bragg_available,
+                auto_included='bragg' in auto_include,
+                reason=''
+                if bragg_available
+                else (
+                    'Bragg tick marks require powder Bragg measured and '
+                    'calculated data with reflection rows.'
+                ),
+            ),
+            PatternOptionStatus(
+                name='excluded',
+                description=_PATTERN_OPTION_DESCRIPTIONS['excluded'],
+                available=has_excluded_regions,
+                auto_included='excluded' in auto_include,
+                reason=''
+                if has_excluded_regions
+                else ('No excluded regions are defined for this experiment.'),
+            ),
+            PatternOptionStatus(
+                name='uncertainty',
+                description=_PATTERN_OPTION_DESCRIPTIONS['uncertainty'],
+                available=uncertainty_available,
+                auto_included='uncertainty' in auto_include,
+                reason=uncertainty_reason,
+            ),
+        ]
+
+    @staticmethod
+    def _has_nonempty_value(value: object | None) -> bool:
+        """Return whether a plotting input has content."""
+        if value is None:
+            return False
+
+        try:
+            return len(value) > 0
+        except TypeError:
+            return True
+
+    def _has_linked_structure_for_calculation(self, experiment: object) -> bool:
+        """Return whether the experiment links to a known structure."""
+        structure_names = set(getattr(self._project.structures, 'names', ()))
+
+        linked_phases = getattr(experiment, 'linked_phases', None)
+        if self._has_nonempty_value(linked_phases):
+            for linked_phase in linked_phases:
+                identity = getattr(linked_phase, '_identity', None)
+                category_entry_name = getattr(identity, 'category_entry_name', None)
+                if category_entry_name in structure_names:
+                    return True
+
+        linked_crystal = getattr(experiment, 'linked_crystal', None)
+        linked_crystal_id = getattr(getattr(linked_crystal, 'id', None), 'value', None)
+        return linked_crystal_id in structure_names
+
+    def _uncertainty_status(
+        self,
+        *,
+        measured_available: bool,
+        sample_form: str,
+        scattering_type: str,
+    ) -> tuple[bool, str]:
+        """
+        Return whether posterior predictive uncertainty is available.
+        """
+        if not measured_available:
+            return False, 'Uncertainty bands require measured data.'
+
+        supported_sample_form = sample_form == SampleFormEnum.POWDER.value or (
+            sample_form == SampleFormEnum.SINGLE_CRYSTAL.value
+            and scattering_type == ScatteringTypeEnum.BRAGG.value
+        )
+        if not supported_sample_form:
+            return (
+                False,
+                ('Posterior predictive pattern views are unavailable for this experiment type.'),
+            )
+
+        fit_results = getattr(self._project.analysis, 'fit_results', None)
+        if fit_results is None:
+            return False, 'No fit results are available.'
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
+        if posterior_samples is None or posterior_predictive is None:
+            return False, 'Posterior predictive data is unavailable.'
+
+        if self._project.rendering.chart_engine.value != PlotterEngineEnum.PLOTLY.value:
+            return False, 'Uncertainty bands currently require the Plotly chart engine.'
+
+        return True, ''
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index 58d7e97fb..3f3ac4fb4 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -16,8 +16,9 @@
 from easydiffraction.datablocks.structure.collection import Structures
 from easydiffraction.io.cif.serialize import project_config_to_cif
 from easydiffraction.io.cif.serialize import project_to_cif
-from easydiffraction.project.categories.display import Display
-from easydiffraction.project.categories.display import DisplayFactory
+from easydiffraction.project.categories.rendering import Rendering
+from easydiffraction.project.categories.rendering import RenderingFactory
+from easydiffraction.project.display import ProjectDisplay
 from easydiffraction.project.project_info import ProjectInfo
 from easydiffraction.summary.summary import Summary
 from easydiffraction.utils.enums import VerbosityEnum
@@ -82,8 +83,9 @@ def __init__(
         self._info: ProjectInfo = ProjectInfo(name, title, description)
         self._structures = Structures()
         self._experiments = Experiments()
-        self._display = DisplayFactory.create('default')
-        self._display._parent = self
+        self._rendering = RenderingFactory.create('default')
+        self._rendering._parent = self
+        self._display = ProjectDisplay(self)
         self._analysis = Analysis(self)
         self._summary = Summary(self)
         self._saved = False
@@ -152,8 +154,13 @@ def experiments(self, experiments: Experiments) -> None:
         self._experiments = experiments
 
     @property
-    def display(self) -> Display:
-        """Display configuration and facades bound to the project."""
+    def rendering(self) -> Rendering:
+        """Rendering configuration bound to the project."""
+        return self._rendering
+
+    @property
+    def display(self) -> ProjectDisplay:
+        """Current display entry-point bound to the project."""
         return self._display
 
     @property
@@ -373,7 +380,7 @@ def save(self) -> None:
         analysis_dir = self._info.path / 'analysis'
         analysis_dir.mkdir(parents=True, exist_ok=True)
         with (analysis_dir / 'analysis.cif').open('w') as f:
-            f.write(self.analysis.as_cif())
+            f.write(self.analysis.as_cif)
             console.print('├── 📁 analysis/')
             console.print('│   └── 📄 analysis.cif')
 
diff --git a/src/easydiffraction/project/project_info.py b/src/easydiffraction/project/project_info.py
index 94247f331..9bdfac7b0 100644
--- a/src/easydiffraction/project/project_info.py
+++ b/src/easydiffraction/project/project_info.py
@@ -2,6 +2,8 @@
 # SPDX-License-Identifier: BSD-3-Clause
 """Project metadata container used by Project."""
 
+from __future__ import annotations
+
 import datetime
 import pathlib
 
@@ -121,6 +123,7 @@ def parameters(self) -> None:
         """List parameters (not implemented)."""
 
     # TODO: Consider moving to io.cif.serialize
+    @property
     def as_cif(self) -> str:
         """Export project metadata to CIF."""
         return project_info_to_cif(self)
@@ -129,6 +132,6 @@ def as_cif(self) -> str:
     def show_as_cif(self) -> None:
         """Pretty-print CIF via shared utilities."""
         paragraph_title: str = f"Project 📦 '{self.name}' info as CIF"
-        cif_text: str = self.as_cif()
+        cif_text: str = self.as_cif
         console.paragraph(paragraph_title)
         render_cif(cif_text)
diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
index 7a52c63d8..0ed65e4fc 100644
--- a/tests/integration/fitting/test_analysis_and_fit_category_support.py
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -282,6 +282,7 @@ def free_parameters(self):
 
 def test_analysis_display_as_cif_and_constraints(monkeypatch, capsys):
     import easydiffraction.analysis.analysis as analysis_mod
+    import easydiffraction.analysis.categories.constraints.default as constraints_mod
     from easydiffraction.analysis.analysis import Analysis
 
     analysis = Analysis(project=_make_project())
@@ -302,7 +303,7 @@ class FakeConstraint:
 
     analysis.constraints._items = [FakeConstraint()]
     captured: dict[str, object] = {}
-    monkeypatch.setattr(analysis_mod, 'render_table', lambda **kwargs: captured.update(kwargs))
+    monkeypatch.setattr(constraints_mod, 'render_table', lambda **kwargs: captured.update(kwargs))
     analysis.display.constraints()
     out = capsys.readouterr().out
     assert 'User defined constraints' in out
diff --git a/tests/integration/fitting/test_analysis_display.py b/tests/integration/fitting/test_analysis_display.py
index 2f5b0e450..bc549ee42 100644
--- a/tests/integration/fitting/test_analysis_display.py
+++ b/tests/integration/fitting/test_analysis_display.py
@@ -1,53 +1,53 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-"""Integration tests for Analysis display methods and CIF serialization."""
+"""Integration tests for project display reports and analysis CIF helpers."""
 
 
 def test_display_all_params(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.all_params()
+    project.display.parameters.all()
 
 
 def test_display_fittable_params(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.fittable_params()
+    project.display.parameters.fittable()
 
 
 def test_display_free_params(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.free_params()
+    project.display.parameters.free()
 
 
 def test_display_how_to_access_parameters(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.how_to_access_parameters()
+    project.display.parameters.access()
 
 
 def test_display_parameter_cif_uids(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.parameter_cif_uids()
+    project.display.parameters.cif_uids()
 
 
 def test_display_constraints_empty(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.constraints()
+    project.analysis.constraints.show()
 
 
 def test_display_fit_results(lbco_fitted_project):
     project = lbco_fitted_project
     assert project.analysis.fit_results is not None
-    project.analysis.display.fit_results()
+    project.display.fit.results()
 
 
 def test_display_as_cif(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.display.as_cif()
+    project.analysis.show_as_cif()
 
 
 def test_analysis_as_cif(lbco_fitted_project):
     project = lbco_fitted_project
-    cif_text = project.analysis.as_cif()
+    cif_text = project.analysis.as_cif
     assert isinstance(cif_text, str)
     assert len(cif_text) > 0
 
diff --git a/tests/integration/fitting/test_cli_entrypoints.py b/tests/integration/fitting/test_cli_entrypoints.py
index 636c0ca4c..34cc85ad8 100644
--- a/tests/integration/fitting/test_cli_entrypoints.py
+++ b/tests/integration/fitting/test_cli_entrypoints.py
@@ -92,16 +92,21 @@ def fit_results() -> None:
         analysis = _analysis()
 
         class _display:
-            class _plotter:
+            class _fit:
                 @staticmethod
-                def plot_param_correlations() -> None:
-                    calls.append('PLOT_CORR')
+                def results() -> None:
+                    calls.append('DISPLAY')
 
                 @staticmethod
-                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
-                    calls.append(f'PLOT_{expt_name}_{show_residual}')
+                def correlations() -> None:
+                    calls.append('PLOT_CORR')
 
-            plotter = _plotter()
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name: str, **kwargs) -> None:
+                del kwargs
+                calls.append(f'PLOT_{expt_name}_False')
 
         display = _display()
 
@@ -116,7 +121,7 @@ def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
     result = runner.invoke(main_mod.app, ['fit', str(project_dir)])
 
     assert result.exit_code == 0
-    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_True']
+    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_False']
 
 
 def test_cli_fit_dry_clears_path(monkeypatch, tmp_path):
@@ -146,16 +151,20 @@ def fit_results() -> None:
         analysis = _analysis()
 
         class _display:
-            class _plotter:
+            class _fit:
                 @staticmethod
-                def plot_param_correlations() -> None:
+                def results() -> None:
                     return None
 
                 @staticmethod
-                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
+                def correlations() -> None:
                     return None
 
-            plotter = _plotter()
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name: str, **kwargs) -> None:
+                del expt_name, kwargs
 
         display = _display()
 
diff --git a/tests/integration/fitting/test_plotting.py b/tests/integration/fitting/test_plotting.py
index 1cb06c41b..393e78c3d 100644
--- a/tests/integration/fitting/test_plotting.py
+++ b/tests/integration/fitting/test_plotting.py
@@ -1,29 +1,29 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-"""Integration tests for the Plotter facade on a fitted project."""
+"""Integration tests for ``project.display.pattern``."""
 
 
-def test_plot_meas(lbco_fitted_project):
+def test_pattern_auto(lbco_fitted_project):
     project = lbco_fitted_project
-    project.display.plotter.plot_meas(expt_name='hrpt')
+    project.display.pattern(expt_name='hrpt')
 
 
-def test_plot_calc(lbco_fitted_project):
+def test_pattern_measured(lbco_fitted_project):
     project = lbco_fitted_project
-    project.display.plotter.plot_calc(expt_name='hrpt')
+    project.display.pattern(expt_name='hrpt', include='measured')
 
 
-def test_plot_meas_vs_calc(lbco_fitted_project):
+def test_pattern_measured_vs_calculated(lbco_fitted_project):
     project = lbco_fitted_project
-    project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+    project.display.pattern(expt_name='hrpt', include=('measured', 'calculated'))
 
 
-def test_plot_meas_with_range(lbco_fitted_project):
+def test_pattern_with_range(lbco_fitted_project):
     project = lbco_fitted_project
-    project.display.plotter.plot_meas(expt_name='hrpt', x_min=20, x_max=80)
+    project.display.pattern(expt_name='hrpt', x_min=20, x_max=80)
 
 
-def test_plot_meas_vs_calc_with_range(lbco_fitted_project):
+def test_show_pattern_options(lbco_fitted_project):
     project = lbco_fitted_project
-    project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=20, x_max=80)
+    project.display.show_pattern_options(expt_name='hrpt')
diff --git a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
index 089e1f6b8..9ff2926ea 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
@@ -72,7 +72,7 @@ def test_empty_constraints_warns(self, capsys):
         assert 'No constraints' in out
 
     def test_constraints_with_items(self, capsys, monkeypatch):
-        import easydiffraction.analysis.analysis as mod
+        import easydiffraction.analysis.categories.constraints.default as constraints_mod
         from easydiffraction.analysis.analysis import Analysis
 
         a = Analysis(project=_make_project())
@@ -91,7 +91,7 @@ class FakeConstraint:
         def fake_render_table(**kwargs):
             captured.update(kwargs)
 
-        monkeypatch.setattr(mod, 'render_table', fake_render_table)
+        monkeypatch.setattr(constraints_mod, 'render_table', fake_render_table)
         a.display.constraints()
         out = capsys.readouterr().out
         assert 'User defined constraints' in out
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index c0ef16158..08540bf07 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -94,19 +94,43 @@ def __init__(self):
     p = Plotter()
 
     # Error paths (now log errors via console; messages are printed)
-    p._plot_meas_data(Ptn(two_theta=None, intensity_meas=None), 'E', ExptType())
+    p._plot_meas_data(
+        object(),
+        Ptn(two_theta=None, intensity_meas=None),
+        'E',
+        ExptType(),
+        _MeasVsCalcPlotOptions(),
+    )
     out = capsys.readouterr().out
     assert 'No two_theta data available for experiment E' in out
 
-    p._plot_meas_data(Ptn(two_theta=[1], intensity_meas=None), 'E', ExptType())
+    p._plot_meas_data(
+        object(),
+        Ptn(two_theta=[1], intensity_meas=None),
+        'E',
+        ExptType(),
+        _MeasVsCalcPlotOptions(),
+    )
     out = capsys.readouterr().out
     assert 'No measured data available for experiment E' in out
 
-    p._plot_calc_data(Ptn(two_theta=None, intensity_calc=None), 'E', ExptType())
+    p._plot_calc_data(
+        object(),
+        Ptn(two_theta=None, intensity_calc=None),
+        'E',
+        ExptType(),
+        _MeasVsCalcPlotOptions(),
+    )
     out = capsys.readouterr().out
     assert 'No two_theta data available for experiment E' in out
 
-    p._plot_calc_data(Ptn(two_theta=[1], intensity_calc=None), 'E', ExptType())
+    p._plot_calc_data(
+        object(),
+        Ptn(two_theta=[1], intensity_calc=None),
+        'E',
+        ExptType(),
+        _MeasVsCalcPlotOptions(),
+    )
     out = capsys.readouterr().out
     assert 'No calculated data available for experiment E' in out
 
@@ -154,13 +178,24 @@ def test_plotter_routes_to_ascii_plotter(monkeypatch):
     from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
     from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
     from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
 
     called = {}
 
-    def fake_plot_powder(self, x, y_series, labels, axes_labels, title, height=None):
+    def fake_plot_powder(
+        self,
+        x,
+        y_series,
+        labels,
+        axes_labels,
+        title,
+        height=None,
+        excluded_ranges=(),
+    ):
         called['labels'] = tuple(labels)
         called['axes'] = tuple(axes_labels)
         called['title'] = title
+        called['excluded_ranges'] = excluded_ranges
 
     monkeypatch.setattr(ascii_mod.AsciiPlotter, 'plot_powder', fake_plot_powder)
 
@@ -178,9 +213,16 @@ def __init__(self):
 
     p = Plotter()
     p.engine = 'asciichartpy'  # ensure AsciiPlotter
-    p._plot_meas_data(Ptn(), 'E', ExptType())
+    p._plot_meas_data(
+        object(),
+        Ptn(),
+        'E',
+        ExptType(),
+        _MeasVsCalcPlotOptions(),
+    )
     assert called['labels'] == ('meas',)
     assert 'Measured data' in called['title']
+    assert called['excluded_ranges'] == ()
 
 
 def test_extract_bragg_tick_sets_groups_and_filters():
@@ -742,7 +784,15 @@ class Experiment:
     plotter._plot_posterior_predictive_data(
         experiment=Experiment(),
         expt_name='hrpt',
-        plot_options=SimpleNamespace(x_min=None, x_max=None, show_residual=None, x=None),
+        plot_options=SimpleNamespace(
+            x_min=None,
+            x_max=None,
+            show_residual=None,
+            show_background=None,
+            show_bragg=None,
+            show_excluded=False,
+            x=None,
+        ),
         x_axis=XAxisType.TWO_THETA,
         style='band',
     )
@@ -752,6 +802,73 @@ class Experiment:
     assert plot_spec.y_calc_line_dash == 'dot'
 
 
+def test_plot_meas_vs_calc_request_respects_background_and_bragg_flags():
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
+
+    captured: dict[str, object] = {}
+
+    class FakeBackend:
+        def plot_powder_meas_vs_calc(self, *, plot_spec):
+            captured['plot_spec'] = plot_spec
+
+    class Pattern:
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 12.0, 11.0])
+        intensity_calc = np.array([9.0, 11.0, 10.5])
+        intensity_bkg = np.array([1.0, 1.0, 1.0])
+
+    class Refln:
+        phase_id = np.array(['phase-a'])
+        two_theta = np.array([2.0])
+        index_h = np.array([1])
+        index_k = np.array([0])
+        index_l = np.array([1])
+        f_squared_calc = np.array([50.0])
+        f_calc = np.array([7.0])
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Experiment:
+        data = Pattern()
+        type = ExptType()
+        refln = Refln()
+
+    plotter = Plotter()
+    plotter._backend = FakeBackend()
+    plotter._plot_meas_vs_calc_data(
+        experiment=Experiment(),
+        expt_name='E1',
+        plot_options=_MeasVsCalcPlotOptions(
+            show_background=False,
+            show_bragg=False,
+        ),
+    )
+
+    plot_spec = captured['plot_spec']
+    assert plot_spec.y_bkg is None
+    assert plot_spec.bragg_tick_sets == ()
+
+    plotter._plot_meas_vs_calc_data(
+        experiment=Experiment(),
+        expt_name='E1',
+        plot_options=_MeasVsCalcPlotOptions(
+            show_background=True,
+            show_bragg=True,
+        ),
+    )
+
+    plot_spec = captured['plot_spec']
+    assert np.allclose(plot_spec.y_bkg, np.array([1.0, 1.0, 1.0]))
+    assert len(plot_spec.bragg_tick_sets) == 1
+
+
 def test_build_param_distribution_plot_accepts_unique_name_string():
     plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
     unique_name = 'phase.cell.length_a'
@@ -1105,6 +1222,7 @@ def fake_plot_summary(
         axes_labels,
         show_band,
         show_draws,
+        excluded_ranges,
     ):
         captured['expt_name'] = expt_name
         captured['summary'] = summary
@@ -1112,6 +1230,7 @@ def fake_plot_summary(
         captured['axes_labels'] = axes_labels
         captured['show_band'] = show_band
         captured['show_draws'] = show_draws
+        captured['excluded_ranges'] = excluded_ranges
 
     monkeypatch.setattr(Plotter, '_plot_posterior_predictive_summary', fake_plot_summary)
 
@@ -1125,6 +1244,7 @@ def fake_plot_summary(
     np.testing.assert_allclose(captured['y_meas'], np.array([20.0]))
     assert captured['show_band'] is True
     assert captured['show_draws'] is False
+    assert captured['excluded_ranges'] == ()
     assert any('ignoring show_residual=True' in warning for warning in warnings)
 
 
diff --git a/tests/unit/easydiffraction/project/categories/display/test_default.py b/tests/unit/easydiffraction/project/categories/display/test_default.py
deleted file mode 100644
index c43171e31..000000000
--- a/tests/unit/easydiffraction/project/categories/display/test_default.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-import gemmi
-
-
-def test_display_defaults():
-    from easydiffraction.project.categories.display.default import Display
-
-    display = Display()
-
-    assert display.type_info.tag == 'default'
-    assert display._identity.category_code == 'display'
-    assert display.plotter_type.value == display.plotter.engine
-    assert display.tabler_type.value == display.tabler.engine
-
-
-def test_display_plotter_binds_parent():
-    from easydiffraction.project.categories.display.default import Display
-
-    display = Display()
-    parent = object()
-    display._parent = parent
-
-    plotter = display.plotter
-
-    assert plotter._project is parent
-
-
-def test_display_setters_update_engines():
-    from easydiffraction.project.categories.display.default import Display
-
-    display = Display()
-
-    display.plotter_type = 'plotly'
-    display.tabler_type = 'rich'
-
-    assert display.plotter_type.value == 'plotly'
-    assert display.plotter.engine == 'plotly'
-    assert display.tabler_type.value == 'rich'
-    assert display.tabler.engine == 'rich'
-
-
-def test_display_from_cif_restores_types():
-    from easydiffraction.project.categories.display.default import Display
-
-    display = Display()
-    block = gemmi.cif.read_string(
-        'data_test\n_display.plotter_type plotly\n_display.tabler_type rich\n',
-    ).sole_block()
-
-    display.from_cif(block)
-
-    assert display.plotter_type.value == 'plotly'
-    assert display.tabler_type.value == 'rich'
diff --git a/tests/unit/easydiffraction/project/categories/display/test_factory.py b/tests/unit/easydiffraction/project/categories/display/test_factory.py
deleted file mode 100644
index b335a0530..000000000
--- a/tests/unit/easydiffraction/project/categories/display/test_factory.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-import pytest
-
-
-def test_display_factory_default_and_create():
-    from easydiffraction.project.categories.display.default import Display
-    from easydiffraction.project.categories.display.factory import DisplayFactory
-
-    assert DisplayFactory.default_tag() == 'default'
-    assert 'default' in DisplayFactory.supported_tags()
-
-    display = DisplayFactory.create('default')
-
-    assert isinstance(display, Display)
-
-
-def test_display_factory_rejects_unknown_tag():
-    from easydiffraction.project.categories.display.factory import DisplayFactory
-
-    with pytest.raises(ValueError, match=r"Unsupported type: 'missing'"):
-        DisplayFactory.create('missing')
diff --git a/tests/unit/easydiffraction/project/categories/rendering/test_default.py b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
new file mode 100644
index 000000000..edb884272
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+import gemmi
+
+
+def test_rendering_defaults():
+    from easydiffraction.project.categories.rendering.default import Rendering
+
+    rendering = Rendering()
+
+    assert rendering.type_info.tag == 'default'
+    assert rendering._identity.category_code == 'rendering'
+    assert rendering.chart_engine.value == rendering.plotter.engine
+    assert rendering.table_engine.value == rendering.tabler.engine
+
+
+def test_rendering_plotter_binds_parent():
+    from easydiffraction.project.categories.rendering.default import Rendering
+
+    rendering = Rendering()
+    parent = object()
+    rendering._parent = parent
+
+    plotter = rendering.plotter
+
+    assert plotter._project is parent
+
+
+def test_rendering_setters_update_engines():
+    from easydiffraction.project.categories.rendering.default import Rendering
+
+    rendering = Rendering()
+
+    rendering.chart_engine = 'plotly'
+    rendering.table_engine = 'rich'
+
+    assert rendering.chart_engine.value == 'plotly'
+    assert rendering.plotter.engine == 'plotly'
+    assert rendering.table_engine.value == 'rich'
+    assert rendering.tabler.engine == 'rich'
+
+
+def test_rendering_from_cif_restores_types():
+    from easydiffraction.project.categories.rendering.default import Rendering
+
+    rendering = Rendering()
+    block = gemmi.cif.read_string(
+        'data_test\n_rendering.chart_engine plotly\n_rendering.table_engine rich\n',
+    ).sole_block()
+
+    rendering.from_cif(block)
+
+    assert rendering.chart_engine.value == 'plotly'
+    assert rendering.table_engine.value == 'rich'
diff --git a/tests/unit/easydiffraction/project/categories/rendering/test_factory.py b/tests/unit/easydiffraction/project/categories/rendering/test_factory.py
new file mode 100644
index 000000000..0f2812e5c
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/rendering/test_factory.py
@@ -0,0 +1,23 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+import pytest
+
+
+def test_rendering_factory_default_and_create():
+    from easydiffraction.project.categories.rendering.default import Rendering
+    from easydiffraction.project.categories.rendering.factory import RenderingFactory
+
+    assert RenderingFactory.default_tag() == 'default'
+    assert 'default' in RenderingFactory.supported_tags()
+
+    rendering = RenderingFactory.create('default')
+
+    assert isinstance(rendering, Rendering)
+
+
+def test_rendering_factory_rejects_unknown_tag():
+    from easydiffraction.project.categories.rendering.factory import RenderingFactory
+
+    with pytest.raises(ValueError, match=r"Unsupported type: 'missing'"):
+        RenderingFactory.create('missing')
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
new file mode 100644
index 000000000..46f22296a
--- /dev/null
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -0,0 +1,479 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Unit tests for project/display.py."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+
+from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
+from easydiffraction.project.display import PatternOptionStatus
+from easydiffraction.project.display import ProjectDisplay
+
+
+def _make_project_stub() -> tuple[SimpleNamespace, list[tuple[str, tuple, dict]]]:
+    calls: list[tuple[str, tuple, dict]] = []
+
+    def record(name: str):
+        def _recorder(*args, **kwargs):
+            calls.append((name, args, kwargs))
+
+        return _recorder
+
+    analysis_display = SimpleNamespace(
+        all_params=record('all_params'),
+        fittable_params=record('fittable_params'),
+        free_params=record('free_params'),
+        how_to_access_parameters=record('how_to_access_parameters'),
+        parameter_cif_uids=record('parameter_cif_uids'),
+        fit_results=record('fit_results'),
+    )
+    plotter = SimpleNamespace(
+        plot_param_correlations=record('plot_param_correlations'),
+        plot_param_series=record('plot_param_series'),
+        plot_posterior_pairs=record('plot_posterior_pairs'),
+        plot_param_distribution=record('plot_param_distribution'),
+        plot_posterior_predictive=record('plot_posterior_predictive'),
+        _plot_posterior_predictive_request=record('_plot_posterior_predictive_request'),
+        plot_meas=record('plot_meas'),
+        plot_calc=record('plot_calc'),
+        plot_meas_vs_calc=record('plot_meas_vs_calc'),
+        _plot_meas_vs_calc_request=record('_plot_meas_vs_calc_request'),
+    )
+    project = SimpleNamespace(
+        analysis=SimpleNamespace(display=analysis_display),
+        rendering=SimpleNamespace(plotter=plotter),
+    )
+    return project, calls
+
+
+def _make_statuses(
+    *,
+    measured: bool = False,
+    calculated: bool = False,
+    background: bool = False,
+    residual: bool = False,
+    bragg: bool = False,
+    excluded: bool = False,
+    uncertainty: bool = False,
+    uncertainty_reason: str = 'Posterior predictive data is unavailable.',
+) -> list[PatternOptionStatus]:
+    return [
+        PatternOptionStatus(
+            name='auto',
+            description='auto',
+            available=True,
+            auto_included=True,
+            reason='',
+        ),
+        PatternOptionStatus(
+            name='measured',
+            description='measured',
+            available=measured,
+            auto_included=False,
+            reason='' if measured else 'Measured unavailable',
+        ),
+        PatternOptionStatus(
+            name='calculated',
+            description='calculated',
+            available=calculated,
+            auto_included=False,
+            reason='' if calculated else 'Calculated unavailable',
+        ),
+        PatternOptionStatus(
+            name='background',
+            description='background',
+            available=background,
+            auto_included=False,
+            reason='' if background else 'Background unavailable',
+        ),
+        PatternOptionStatus(
+            name='residual',
+            description='residual',
+            available=residual,
+            auto_included=False,
+            reason='' if residual else 'Residual unavailable',
+        ),
+        PatternOptionStatus(
+            name='bragg',
+            description='bragg',
+            available=bragg,
+            auto_included=False,
+            reason='' if bragg else 'Bragg unavailable',
+        ),
+        PatternOptionStatus(
+            name='excluded',
+            description='excluded',
+            available=excluded,
+            auto_included=False,
+            reason='' if excluded else 'Excluded unavailable',
+        ),
+        PatternOptionStatus(
+            name='uncertainty',
+            description='uncertainty',
+            available=uncertainty,
+            auto_included=False,
+            reason='' if uncertainty else uncertainty_reason,
+        ),
+    ]
+
+
+def test_parameter_display_delegates_to_analysis_display():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+
+    display.parameters.all()
+    display.parameters.fittable()
+    display.parameters.free()
+    display.parameters.access()
+    display.parameters.cif_uids()
+
+    assert [name for name, _args, _kwargs in calls] == [
+        'all_params',
+        'fittable_params',
+        'free_params',
+        'how_to_access_parameters',
+        'parameter_cif_uids',
+    ]
+
+
+def test_fit_display_delegates_to_analysis_and_rendering():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+
+    display.fit.results()
+    display.fit.correlations(
+        threshold=0.75,
+        precision=3,
+        max_parameters=4,
+        show_diagonal=False,
+    )
+    display.fit.series(param='scale', versus='temperature')
+
+    assert calls[0] == ('fit_results', (), {})
+    assert calls[1] == (
+        'plot_param_correlations',
+        (),
+        {
+            'threshold': 0.75,
+            'precision': 3,
+            'max_parameters': 4,
+            'show_diagonal': False,
+        },
+    )
+    assert calls[2] == (
+        'plot_param_series',
+        (),
+        {'param': 'scale', 'versus': 'temperature'},
+    )
+
+
+def test_posterior_display_delegates_to_rendering_plotter():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+
+    display.posterior.pairs(parameters=['a'], threshold=0.5, max_parameters=3)
+    display.posterior.distribution('a')
+    display.posterior.predictive(
+        'hrpt',
+        style='draws',
+        x_min=1.0,
+        x_max=2.0,
+        show_residual=True,
+        x='d_spacing',
+    )
+
+    assert calls[0][0] == 'plot_posterior_pairs'
+    assert calls[0][2]['parameters'] == ['a']
+    assert calls[0][2]['threshold'] == 0.5
+    assert calls[0][2]['max_parameters'] == 3
+    assert calls[1] == ('plot_param_distribution', ('a',), {})
+    assert calls[2] == (
+        'plot_posterior_predictive',
+        (),
+        {
+            'expt_name': 'hrpt',
+            'style': 'draws',
+            'x_min': 1.0,
+            'x_max': 2.0,
+            'show_residual': True,
+            'x': 'd_spacing',
+        },
+    )
+
+
+def test_pattern_auto_routes_measured_and_excluded_to_plot_meas():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        excluded=True,
+    )
+
+    display.pattern('hrpt')
+
+    assert calls == [
+        (
+            'plot_meas',
+            (),
+            {
+                'expt_name': 'hrpt',
+                'x_min': None,
+                'x_max': None,
+                'x': None,
+                'show_excluded': True,
+            },
+        )
+    ]
+
+
+def test_pattern_uncertainty_routes_to_posterior_predictive():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        calculated=True,
+        residual=True,
+        excluded=True,
+        uncertainty=True,
+    )
+
+    display.pattern(
+        'hrpt',
+        x_min=1.0,
+        x_max=2.0,
+        include=('measured', 'calculated', 'uncertainty', 'residual', 'excluded'),
+    )
+
+    assert calls == [
+        (
+            '_plot_posterior_predictive_request',
+            (),
+            {
+                'expt_name': 'hrpt',
+                'style': 'band',
+                'plot_options': _MeasVsCalcPlotOptions(
+                    x_min=1.0,
+                    x_max=2.0,
+                    show_residual=True,
+                    show_background=False,
+                    show_bragg=False,
+                    show_excluded=True,
+                    x=None,
+                ),
+            },
+        )
+    ]
+
+
+def test_pattern_measured_and_calculated_suppresses_background_and_bragg():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        calculated=True,
+        background=True,
+        bragg=True,
+    )
+
+    display.pattern('hrpt', include=('measured', 'calculated'))
+
+    assert calls == [
+        (
+            '_plot_meas_vs_calc_request',
+            (),
+            {
+                'expt_name': 'hrpt',
+                'plot_options': _MeasVsCalcPlotOptions(
+                    x_min=None,
+                    x_max=None,
+                    show_residual=False,
+                    show_background=False,
+                    show_bragg=False,
+                    show_excluded=False,
+                    x=None,
+                ),
+            },
+        )
+    ]
+
+
+def test_pattern_measured_and_calculated_can_enable_background_and_bragg():
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        calculated=True,
+        background=True,
+        residual=True,
+        bragg=True,
+        excluded=True,
+    )
+
+    display.pattern(
+        'hrpt',
+        include=('measured', 'calculated', 'background', 'residual', 'bragg', 'excluded'),
+    )
+
+    assert calls == [
+        (
+            '_plot_meas_vs_calc_request',
+            (),
+            {
+                'expt_name': 'hrpt',
+                'plot_options': _MeasVsCalcPlotOptions(
+                    x_min=None,
+                    x_max=None,
+                    show_residual=True,
+                    show_background=True,
+                    show_bragg=True,
+                    show_excluded=True,
+                    x=None,
+                ),
+            },
+        )
+    ]
+
+
+def test_pattern_option_statuses_ignore_placeholder_arrays_without_usable_state(monkeypatch):
+    pattern = SimpleNamespace(
+        intensity_meas=[1.0, 2.0],
+        intensity_calc=[0.0, 0.0],
+        intensity_bkg=[0.0, 0.0],
+    )
+
+    experiment = SimpleNamespace(
+        type=SimpleNamespace(
+            sample_form=SimpleNamespace(value=SampleFormEnum.POWDER.value),
+            scattering_type=SimpleNamespace(value=ScatteringTypeEnum.BRAGG.value),
+        ),
+        linked_phases=[],
+        background=[],
+        refln=[],
+        excluded_regions=[],
+    )
+    project = SimpleNamespace(
+        experiments={'hrpt': experiment},
+        structures=SimpleNamespace(names=['phase-a']),
+        analysis=SimpleNamespace(fit_results=None),
+        rendering=SimpleNamespace(
+            plotter=SimpleNamespace(_update_project_categories=lambda expt_name: None),
+            chart_engine=SimpleNamespace(value='plotly'),
+        ),
+    )
+    display = ProjectDisplay(project)
+
+    monkeypatch.setattr(
+        'easydiffraction.project.display.intensity_category_for', lambda expt: pattern
+    )
+
+    statuses = {status.name: status for status in display._pattern_option_statuses('hrpt')}
+
+    assert statuses['measured'].available is True
+    assert statuses['calculated'].available is False
+    assert statuses['background'].available is False
+    assert statuses['bragg'].available is False
+    assert statuses['measured'].auto_included is True
+    assert statuses['calculated'].auto_included is False
+
+
+def test_pattern_auto_routes_single_crystal_with_calculated_data(monkeypatch):
+    calls: list[tuple[str, tuple, dict]] = []
+
+    def record(name: str):
+        def _recorder(*args, **kwargs):
+            calls.append((name, args, kwargs))
+
+        return _recorder
+
+    pattern = SimpleNamespace(
+        intensity_meas=[10.0, 12.0],
+        intensity_calc=[9.5, 11.5],
+    )
+    experiment = SimpleNamespace(
+        type=SimpleNamespace(
+            sample_form=SimpleNamespace(value=SampleFormEnum.SINGLE_CRYSTAL.value),
+            scattering_type=SimpleNamespace(value=ScatteringTypeEnum.BRAGG.value),
+        ),
+        linked_crystal=SimpleNamespace(id=SimpleNamespace(value='si')),
+        excluded_regions=[],
+    )
+    project = SimpleNamespace(
+        experiments={'heidi': experiment},
+        structures=SimpleNamespace(names=['si']),
+        analysis=SimpleNamespace(fit_results=None),
+        rendering=SimpleNamespace(
+            plotter=SimpleNamespace(
+                _update_project_categories=lambda expt_name: None,
+                _plot_meas_vs_calc_request=record('_plot_meas_vs_calc_request'),
+            ),
+            chart_engine=SimpleNamespace(value='plotly'),
+        ),
+    )
+    display = ProjectDisplay(project)
+
+    monkeypatch.setattr(
+        'easydiffraction.project.display.intensity_category_for',
+        lambda expt: pattern,
+    )
+
+    display.pattern('heidi')
+
+    assert calls == [
+        (
+            '_plot_meas_vs_calc_request',
+            (),
+            {
+                'expt_name': 'heidi',
+                'plot_options': _MeasVsCalcPlotOptions(
+                    x_min=None,
+                    x_max=None,
+                    show_residual=False,
+                    show_background=False,
+                    show_bragg=False,
+                    show_excluded=False,
+                    x=None,
+                ),
+            },
+        )
+    ]
+
+
+def test_pattern_rejects_excluded_with_custom_x():
+    project, _calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        excluded=True,
+    )
+
+    with pytest.raises(ValueError, match='default x-axis'):
+        display.pattern('hrpt', include=('measured', 'excluded'), x='d_spacing')
+
+
+def test_show_pattern_options_renders_table(monkeypatch):
+    project, _calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    display._pattern_option_statuses = lambda expt_name: _make_statuses(
+        measured=True,
+        calculated=True,
+    )
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr('easydiffraction.project.display.render_table', fake_render_table)
+
+    display.show_pattern_options('hrpt')
+
+    assert captured['columns_headers'] == ['Option', 'Description', 'Available', 'Auto', 'Reason']
+    assert captured['columns_alignment'] == ['left', 'left', 'center', 'center', 'left']
+    assert captured['columns_data'][0][0] == 'auto'
+    assert captured['columns_data'][1][0] == 'measured'
diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py
index 090ce5404..e0ff677dd 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -63,3 +63,14 @@ def test_project_free_params_aggregate_structures_and_experiments():
     project._experiments = SimpleNamespace(free_parameters=[experiment_param])
 
     assert project.free_parameters == [structure_param, experiment_param]
+
+
+def test_project_exposes_rendering_and_display_facades():
+    from easydiffraction.project.categories.rendering import Rendering
+    from easydiffraction.project.display import ProjectDisplay
+    from easydiffraction.project.project import Project
+
+    project = Project()
+
+    assert isinstance(project.rendering, Rendering)
+    assert isinstance(project.display, ProjectDisplay)
diff --git a/tests/unit/easydiffraction/project/test_project_load.py b/tests/unit/easydiffraction/project/test_project_load.py
index c788f0a74..448345777 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -81,16 +81,16 @@ def test_round_trips_fit_mode(self, tmp_path):
 
         assert loaded.analysis.fit.mode.value == 'joint'
 
-    def test_round_trips_display_configuration(self, tmp_path):
+    def test_round_trips_rendering_configuration(self, tmp_path):
         original = Project(name='d1')
-        original.display.plotter_type = 'asciichartpy'
-        original.display.tabler_type = 'rich'
+        original.rendering.chart_engine = 'asciichartpy'
+        original.rendering.table_engine = 'rich'
         original.save_as(str(tmp_path / 'proj'))
 
         loaded = Project.load(str(tmp_path / 'proj'))
 
-        assert loaded.display.plotter_type.value == 'asciichartpy'
-        assert loaded.display.tabler_type.value == 'rich'
+        assert loaded.rendering.chart_engine.value == 'asciichartpy'
+        assert loaded.rendering.table_engine.value == 'rich'
 
     def test_round_trips_constraints(self, tmp_path):
         original = Project(name='c1')
diff --git a/tests/unit/easydiffraction/project/test_project_save.py b/tests/unit/easydiffraction/project/test_project_save.py
index bf632e11c..08292525d 100644
--- a/tests/unit/easydiffraction/project/test_project_save.py
+++ b/tests/unit/easydiffraction/project/test_project_save.py
@@ -24,8 +24,8 @@ def test_project_save_as_writes_core_files(tmp_path, monkeypatch):
     from easydiffraction.summary.summary import Summary
 
     # Monkeypatch as_cif producers to avoid heavy internals
-    monkeypatch.setattr(ProjectInfo, 'as_cif', lambda self: 'info')
-    monkeypatch.setattr(Analysis, 'as_cif', lambda self: 'analysis')
+    monkeypatch.setattr(ProjectInfo, 'as_cif', property(lambda self: 'info'))
+    monkeypatch.setattr(Analysis, 'as_cif', property(lambda self: 'analysis'))
     monkeypatch.setattr(Summary, 'as_cif', lambda self: 'summary')
 
     p = Project(name='p1')
diff --git a/tests/unit/easydiffraction/test___main__.py b/tests/unit/easydiffraction/test___main__.py
index 76d150c6d..9edd2bc27 100644
--- a/tests/unit/easydiffraction/test___main__.py
+++ b/tests/unit/easydiffraction/test___main__.py
@@ -95,16 +95,21 @@ def fit_results():
         analysis = _analysis()
 
         class _display:
-            class _plotter:
+            class _fit:
                 @staticmethod
-                def plot_param_correlations():
-                    calls.append('PLOT_CORR')
+                def results():
+                    calls.append('DISPLAY')
 
                 @staticmethod
-                def plot_meas_vs_calc(expt_name, *, show_residual=False):
-                    calls.append(f'PLOT_{expt_name}_{show_residual}')
+                def correlations():
+                    calls.append('PLOT_CORR')
 
-            plotter = _plotter()
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name, **kwargs):
+                del kwargs
+                calls.append(f'PLOT_{expt_name}_False')
 
         display = _display()
 
@@ -119,7 +124,7 @@ def plot_meas_vs_calc(expt_name, *, show_residual=False):
 
     result = runner.invoke(main_mod.app, ['fit', str(proj_dir)])
     assert result.exit_code == 0
-    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_True']
+    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_False']
 
 
 def test_cli_fit_dry_clears_path(monkeypatch, tmp_path):
@@ -149,16 +154,20 @@ def fit_results():
         analysis = _analysis()
 
         class _display:
-            class _plotter:
+            class _fit:
                 @staticmethod
-                def plot_param_correlations():
+                def results():
                     pass
 
                 @staticmethod
-                def plot_meas_vs_calc(expt_name, *, show_residual=False):
+                def correlations():
                     pass
 
-            plotter = _plotter()
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name, **kwargs):
+                del expt_name, kwargs
 
         display = _display()
 

From a6e80995b5805a19c1a9876d3aa9b3589f692ca1 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Thu, 14 May 2026 21:49:59 +0200
Subject: [PATCH 04/10] Improve parameter discoverability, constraint
 semantics, and CIF output (#172)

* Reorganize dev documentation files

* Hide refln in parameter summary displays

* Exclude symmetry-fixed parameters from fittable displays

* Rename parameter constraint state flags

* Adopt bulk-created loop items into collections

* Add help methods to display facades

* Add help discovery helpers to analysis display

* Add activity indicator implementation plan

* Omit empty CIF fragments from serialization

* Add Quick Reference page to docs

* Uncomment display calls, simplify save in tutorial
---
 .../adr_analysis-cif-fit-state.md             |   0
 .../adr_parameter-correlation-persistence.md  |   0
 .../adr_parameter-posterior-summary.md        |   0
 .../dev/{ => ADR-suggestions}/adr_undo-fit.md |   0
 docs/dev/{ => ADRs}/adr_display-ux.md         |  52 ++-
 docs/dev/ADRs/adr_help-discoverability.md     |  65 +++
 docs/dev/{ => Issues}/issues_closed.md        |  12 +
 docs/dev/{ => Issues}/issues_open.md          |  31 +-
 docs/dev/architecture.md                      |  44 +-
 .../progress-activity-indicator.md            | 407 ++++++++++++++++++
 docs/dev/plan_display-ux.md                   | 238 ----------
 docs/docs/quick-reference/index.md            | 371 ++++++++++++++++
 docs/docs/tutorials/ed-3.ipynb                | 166 +++----
 docs/docs/tutorials/ed-3.py                   |  26 +-
 docs/mkdocs.yml                               |   6 +-
 src/easydiffraction/analysis/analysis.py      | 165 +++----
 src/easydiffraction/analysis/fitting.py       |   2 +-
 src/easydiffraction/analysis/sequential.py    |   6 +-
 src/easydiffraction/core/collection.py        |  13 +
 src/easydiffraction/core/datablock.py         |   8 +-
 src/easydiffraction/core/singleton.py         |   8 +-
 src/easydiffraction/core/variable.py          |  44 +-
 .../crystallography/crystallography.py        |  46 +-
 .../experiment/categories/data/bragg_pd.py    |   4 +-
 .../experiment/categories/data/total_pd.py    |   2 +-
 .../experiment/categories/refln/bragg_sc.py   |   2 +-
 .../categories/atom_sites/default.py          |  30 +-
 .../structure/categories/cell/default.py      |   6 +-
 src/easydiffraction/io/cif/serialize.py       |  30 +-
 src/easydiffraction/project/display.py        |  17 +
 src/easydiffraction/project/project.py        |   2 +-
 src/easydiffraction/summary/summary.py        |   5 +
 src/easydiffraction/utils/utils.py            |  85 ++++
 tests/functional/test_structure_workflow.py   |  24 +-
 .../fitting/test_exploration_help.py          |  21 +
 .../easydiffraction/analysis/test_analysis.py |  12 +
 .../analysis/test_analysis_access_params.py   | 257 ++++++++++-
 .../easydiffraction/core/test_datablock.py    |  65 ++-
 .../easydiffraction/core/test_parameters.py   |  28 +-
 .../test_crystallography_coverage.py          |  26 +-
 .../test_crystallography_wyckoff.py           |  14 +-
 .../categories/data/test_bragg_pd.py          |  25 ++
 .../categories/data/test_total_pd.py          |  25 ++
 .../categories/refln/test_bragg_sc.py         |  29 ++
 .../io/cif/test_serialize_more.py             |  44 ++
 .../easydiffraction/project/test_display.py   |  35 ++
 .../easydiffraction/summary/test_summary.py   |  15 +
 .../unit/easydiffraction/utils/test_utils.py  |  27 ++
 48 files changed, 1836 insertions(+), 704 deletions(-)
 rename docs/dev/{ => ADR-suggestions}/adr_analysis-cif-fit-state.md (100%)
 rename docs/dev/{ => ADR-suggestions}/adr_parameter-correlation-persistence.md (100%)
 rename docs/dev/{ => ADR-suggestions}/adr_parameter-posterior-summary.md (100%)
 rename docs/dev/{ => ADR-suggestions}/adr_undo-fit.md (100%)
 rename docs/dev/{ => ADRs}/adr_display-ux.md (80%)
 create mode 100644 docs/dev/ADRs/adr_help-discoverability.md
 rename docs/dev/{ => Issues}/issues_closed.md (90%)
 rename docs/dev/{ => Issues}/issues_open.md (97%)
 create mode 100644 docs/dev/implementation-plans/progress-activity-indicator.md
 delete mode 100644 docs/dev/plan_display-ux.md
 create mode 100644 docs/docs/quick-reference/index.md

diff --git a/docs/dev/adr_analysis-cif-fit-state.md b/docs/dev/ADR-suggestions/adr_analysis-cif-fit-state.md
similarity index 100%
rename from docs/dev/adr_analysis-cif-fit-state.md
rename to docs/dev/ADR-suggestions/adr_analysis-cif-fit-state.md
diff --git a/docs/dev/adr_parameter-correlation-persistence.md b/docs/dev/ADR-suggestions/adr_parameter-correlation-persistence.md
similarity index 100%
rename from docs/dev/adr_parameter-correlation-persistence.md
rename to docs/dev/ADR-suggestions/adr_parameter-correlation-persistence.md
diff --git a/docs/dev/adr_parameter-posterior-summary.md b/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
similarity index 100%
rename from docs/dev/adr_parameter-posterior-summary.md
rename to docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
diff --git a/docs/dev/adr_undo-fit.md b/docs/dev/ADR-suggestions/adr_undo-fit.md
similarity index 100%
rename from docs/dev/adr_undo-fit.md
rename to docs/dev/ADR-suggestions/adr_undo-fit.md
diff --git a/docs/dev/adr_display-ux.md b/docs/dev/ADRs/adr_display-ux.md
similarity index 80%
rename from docs/dev/adr_display-ux.md
rename to docs/dev/ADRs/adr_display-ux.md
index 264092e22..47e9b5c28 100644
--- a/docs/dev/adr_display-ux.md
+++ b/docs/dev/ADRs/adr_display-ux.md
@@ -2,12 +2,12 @@
 
 ## Status
 
-Accepted.
+Accepted and implemented.
 
 ## Context
 
-The current user-facing display API mixes presentation actions, analysis
-reports, and renderer configuration:
+The previous user-facing display API mixed presentation actions,
+analysis reports, and renderer configuration:
 
 ```python
 project.display.plotter.plot_meas(expt_name='hrpt')
@@ -32,7 +32,7 @@ This has several UX problems:
 - `plot_meas`, `plot_calc`, and `plot_meas_vs_calc` force users to
   choose a plot state that the project can often infer.
 - Bayesian and deterministic chart names are not systematic.
-- The existing `project.display` category is serialized to CIF, so it
+- The previous `project.display` category was serialized to CIF, so it
   should not also become a broad transient display facade.
 
 EasyDiffraction is aimed at scientists, often non-programmers, so the
@@ -55,7 +55,7 @@ project.rendering.show_table_engines()
 project.rendering.show_config()
 ```
 
-Suggested CIF names:
+CIF names:
 
 - `_rendering.chart_engine`
 - `_rendering.table_engine`
@@ -87,7 +87,8 @@ project.display.show_pattern_options(expt_name='hrpt')
 ```
 
 `project.analysis.display` is removed from the primary public API. Its
-current responsibilities move to clearer homes:
+current responsibilities move to clearer homes, while the implementation
+may keep the existing helpers as internal delegation targets:
 
 | Current method               | New home                                                       |
 | ---------------------------- | -------------------------------------------------------------- |
@@ -100,7 +101,7 @@ current responsibilities move to clearer homes:
 | `constraints()`              | `project.analysis.constraints.show()`                          |
 | `as_cif()`                   | `project.analysis.as_cif` and `project.analysis.show_as_cif()` |
 
-`project.analysis` and `project.info` should follow the same CIF display
+`project.analysis` and `project.info` follow the same CIF display
 pattern as structures and experiments:
 
 - `as_cif` is a read-only property returning CIF text as a string.
@@ -119,13 +120,17 @@ By default, `pattern()` uses `include='auto'` and displays as much
 useful information as the project state supports:
 
 - measured data if present
-- calculated data if a model/calculation is available
-- background if defined and relevant
-- Bragg ticks if phases/reflections are available
+- calculated data if linked structure state and calculated intensities
+  are available
+- background if powder Bragg measured and calculated data plus defined
+  background points are available
+- Bragg ticks if powder Bragg measured and calculated data plus
+  reflection rows are available
 - residual if both measured and calculated data are available and the
   experiment type supports a residual panel
-- excluded regions if available
-- uncertainty bands where posterior predictive data exists
+- excluded regions if available on the experiment
+- uncertainty bands where posterior predictive data exists and the chart
+  engine supports them
 
 Specific subsets are selected with `include`:
 
@@ -156,11 +161,11 @@ Add discovery for supported pattern content:
 project.display.show_pattern_options(expt_name='hrpt')
 ```
 
-The table should show option name, description, availability for the
+The table shows option name, description, availability for the
 experiment, whether `include='auto'` includes it, and the reason an
 option is unavailable.
 
-Initial option names:
+Pattern option names:
 
 - `auto`
 - `measured`
@@ -171,9 +176,17 @@ Initial option names:
 - `excluded`
 - `uncertainty`
 
-`uncertainty` should be implemented immediately where posterior
-predictive data exists. It should be unavailable, with a clear reason,
-when no posterior predictive data is present.
+`uncertainty` is available where posterior predictive data exists for a
+supported experiment and the active chart engine can render bands. It is
+unavailable, with a clear reason, when no posterior predictive data is
+present.
+
+Explicit combinations are validated against the same project state used
+by `include='auto'`. `background`, `bragg`, and `residual` require both
+measured and calculated data in the same view. `excluded` requires
+measured, calculated, or uncertainty content in the same view, and
+excluded-region overlays currently require the experiment's default
+x-axis.
 
 ## Deterministic And Bayesian Consistency
 
@@ -223,5 +236,8 @@ they duplicate `pattern(..., include=...)`.
 - Constraints remain owned by the analysis constraints category.
 - There is no legacy CIF compatibility path for `_display.plotter_type`
   or `_display.tabler_type`.
-- `project.analysis` and `project.info` need CIF access cleanup for
+- `project.analysis` and `project.info` CIF access is standardized for
   consistency with structure and experiment objects.
+- Pattern option availability is computed from live project state,
+  linked structures, calculated intensities, and experiment-specific
+  content instead of placeholder arrays alone.
diff --git a/docs/dev/ADRs/adr_help-discoverability.md b/docs/dev/ADRs/adr_help-discoverability.md
new file mode 100644
index 000000000..8fd019f39
--- /dev/null
+++ b/docs/dev/ADRs/adr_help-discoverability.md
@@ -0,0 +1,65 @@
+# ADR: Help Method Discoverability
+
+## Status
+
+Accepted and implemented.
+
+## Context
+
+EasyDiffraction is used by scientists who often explore the API in
+notebooks. The main object graph already exposes many focused objects:
+projects, project metadata, structures, experiments, categories,
+parameters, analysis helpers, summaries, and display facades. Users need
+a consistent way to discover the next useful operation from any of these
+objects without reading source code.
+
+Most model objects inherit `GuardedBase`, `CategoryItem`,
+`CategoryCollection`, `DatablockItem`, or `DatablockCollection`, which
+already provide `help()` output. Plain facade classes such as display
+namespaces and summaries do not inherit those base classes, so they need
+the same discovery behavior explicitly.
+
+## Decision
+
+Every primary public object should provide a `help()` method. This
+includes:
+
+- parameters and descriptors
+- category items and category collections
+- datablock items and datablock collections
+- project-level objects such as `Project`, `ProjectInfo`, `Analysis`,
+  `Summary`, and `Rendering`
+- display facades such as `project.display`,
+  `project.display.parameters`, `project.display.fit`,
+  `project.display.posterior`, and `analysis.display`
+
+`help()` output uses the existing console/table presentation style. It
+lists public properties and methods discovered from the class MRO, uses
+the first docstring paragraph as the description, and skips private
+names. Specialized containers can append domain-specific tables, such as
+collection items or datablock categories, after the generic section.
+
+Plain helper and facade classes use `render_object_help()` so their
+output stays consistent with `GuardedBase.help()` without forcing those
+classes into the guarded object hierarchy.
+
+## Consequences
+
+Users can call `help()` while navigating through the object graph:
+
+```python
+project.help()
+project.display.help()
+project.display.parameters.help()
+project.analysis.display.help()
+project.summary.help()
+project.experiments.help()
+project.experiments['hrpt'].help()
+project.experiments['hrpt'].background.help()
+project.experiments['hrpt'].background['1'].help()
+```
+
+New user-facing objects should either inherit an existing help-capable
+base class or define `help()` by delegating to `render_object_help()`.
+When a class represents a collection or owner, its help output should
+guide users to the next object level where practical.
diff --git a/docs/dev/issues_closed.md b/docs/dev/Issues/issues_closed.md
similarity index 90%
rename from docs/dev/issues_closed.md
rename to docs/dev/Issues/issues_closed.md
index a5ae2621a..fee4f327b 100644
--- a/docs/dev/issues_closed.md
+++ b/docs/dev/Issues/issues_closed.md
@@ -4,6 +4,18 @@ Issues that have been fully resolved. Kept for historical reference.
 
 ---
 
+## 77. Add Help Methods to Public Discovery Facades
+
+Added consistent `help()` methods for plain user-facing facade classes
+that do not inherit the guarded object hierarchy: `project.display`,
+`project.display.parameters`, `project.display.fit`,
+`project.display.posterior`, `analysis.display`, and `project.summary`.
+Introduced `render_object_help()` so these helpers share the same
+property and method table style as `GuardedBase.help()`. Documented the
+convention in `docs/dev/ADRs/adr_help-discoverability.md`.
+
+---
+
 ## Restore Minimiser Variant Support
 
 Used thin subclasses (approach A) to restore lmfit algorithm variants.
diff --git a/docs/dev/issues_open.md b/docs/dev/Issues/issues_open.md
similarity index 97%
rename from docs/dev/issues_open.md
rename to docs/dev/Issues/issues_open.md
index 21976afde..bb41887f7 100644
--- a/docs/dev/issues_open.md
+++ b/docs/dev/Issues/issues_open.md
@@ -689,12 +689,13 @@ the archived planning notes left two follow-up questions open:
 
 ---
 
-## 40. 🟢 Implement Resetting `.constrained` to `False`
+## 40. 🟢 Implement Resetting `.user_constrained` to `False`
 
 **Type:** Feature
 
-`ConstraintsHandler` has a TODO to implement changing the `.constrained`
-attribute back to `False` when constraints are removed.
+`ConstraintsHandler` has a TODO to implement changing the
+`.user_constrained` attribute back to `False` when constraints are
+removed.
 
 **TODOs:**
 
@@ -1263,27 +1264,6 @@ deviate: e.g. `show_minimizer_types()` instead of
 
 ---
 
-## 77. 🟡 Add `help()` to `Project` and Enrich Existing `help()` Methods
-
-**Type:** API discoverability
-
-`help()` exists on `CategoryItem`, `CollectionBase`, `DatablockItem`,
-and `Analysis`, but **not on `Project`**. The user's primary entry point
-lacks discoverability. Additionally, each `help()` level should guide
-the user to the next level:
-
-1. `project.help()` → attributes: info, experiments, structures,
-   analysis, summary.
-2. `project.experiments.help()` → list experiments and how to select.
-3. `project.experiments['name'].help()` → list categories.
-4. `experiment.peak.help()` → list public attributes.
-5. `experiment.background.help()` → list items + array accessors.
-6. `experiment.background['id'].help()` → list attributes.
-
-**Depends on:** nothing.
-
----
-
 ## 79. 🟢 Verify Completeness of Analysis CIF Serialisation
 
 **Type:** Correctness
@@ -1518,7 +1498,7 @@ operation is possible (e.g. in automated pipelines or tests).
 | 37  | Rename experiment `.type` property               | 🟢 Low   | Naming           |
 | 38  | Fix `@typechecked`/gemmi in factories            | 🟡 Med   | Bug              |
 | 39  | Improve `_update_priority` handling              | 🟢 Low   | Design           |
-| 40  | Implement resetting `.constrained` to `False`    | 🟢 Low   | Feature          |
+| 40  | Reset `.user_constrained` to `False`             | 🟢 Low   | Feature          |
 | 41  | Check `_mark_dirty` in `_set_value`              | 🟢 Low   | Cleanup          |
 | 42  | MkDocs type unpacking in validation              | 🟢 Low   | Docs             |
 | 43  | Fix summary display inconsistencies              | 🟢 Low   | UX               |
@@ -1555,7 +1535,6 @@ operation is possible (e.g. in automated pipelines or tests).
 | 74  | Sync property type hints + custom lint rules     | 🟡 Med   | Tooling          |
 | 75  | `show_supported_calculators()` on Analysis       | 🟢 Low   | API completeness |
 | 76  | Consistent `_type` suffix in switchable APIs     | 🟡 Med   | Naming           |
-| 77  | Add `help()` to Project + enrich existing        | 🟡 Med   | Discoverability  |
 | 79  | Verify analysis CIF serialisation completeness   | 🟢 Low   | Correctness      |
 | 80  | Resolve `Any` vs `object` annotation policy      | 🟢 Low   | Code style       |
 | 81  | Enforce docstrings on all public methods         | 🟡 Med   | Code quality     |
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 85fc0ad4e..7cc3abe92 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -187,16 +187,16 @@ execution order within a datablock (e.g. background before data).
 
 ### 2.4 DatablockItem and DatablockCollection
 
-| Aspect             | `DatablockItem`                             | `DatablockCollection`          |
-| ------------------ | ------------------------------------------- | ------------------------------ |
-| CIF analogy        | A single `data_` block                      | Collection of data blocks      |
-| Examples           | Structure, BraggPdExperiment                | Structures, Experiments        |
-| Category discovery | Scans `vars(self)` for categories           | N/A                            |
-| Update cascade     | `_update_categories()` — sorted by priority | N/A                            |
-| Parameters         | Aggregated from all categories              | Aggregated from all datablocks |
-| Fittable params    | N/A                                         | Non-constrained `Parameter`s   |
-| Free params        | N/A                                         | Fittable + `free == True`      |
-| Dirty flag         | `_need_categories_update`                   | N/A                            |
+| Aspect             | `DatablockItem`                             | `DatablockCollection`                                    |
+| ------------------ | ------------------------------------------- | -------------------------------------------------------- |
+| CIF analogy        | A single `data_` block                      | Collection of data blocks                                |
+| Examples           | Structure, BraggPdExperiment                | Structures, Experiments                                  |
+| Category discovery | Scans `vars(self)` for categories           | N/A                                                      |
+| Update cascade     | `_update_categories()` — sorted by priority | N/A                                                      |
+| Parameters         | Aggregated from all categories              | Aggregated from all datablocks                           |
+| Fittable params    | N/A                                         | `Parameter`s not blocked by user or symmetry constraints |
+| Free params        | N/A                                         | Fittable + `free == True`                                |
+| Dirty flag         | `_need_categories_update`                   | N/A                                                      |
 
 When any `Parameter.value` is set, it propagates
 `_need_categories_update = True` up to the owning `DatablockItem`.
@@ -210,7 +210,7 @@ GuardedBase
 └── GenericDescriptorBase               # name, value (validated via AttributeSpec), description
     ├── GenericStringDescriptor         # _value_type = DataTypes.STRING
     └── GenericNumericDescriptor        # _value_type = DataTypes.NUMERIC, + units
-        └── GenericParameter            # + free, uncertainty, fit_min, fit_max, constrained, symmetry_fixed
+        └── GenericParameter            # + free, uncertainty, fit_min, fit_max, user_constrained, symmetry_constrained
 ```
 
 CIF-bound concrete classes add a `CifHandler` for serialisation:
@@ -325,14 +325,15 @@ via the `crystallography` module during `_update_categories()`.
 
 Parameters that are fully determined by symmetry (e.g. `lattice_b` in
 cubic, `fract_y` of an atom on a 4-fold axis, off-diagonal ADPs forced
-to zero by site symmetry) are flagged as `symmetry_fixed = True` on the
-`Parameter`. This forces `free = False`; any subsequent attempt to set
-`free = True` on such a parameter is ignored with a warning. Flags are
-recomputed on every `_update_categories()` so that changing the space
-group, Wyckoff letter, or ADP type re-evaluates which parameters are
-fixed. Surface helpers `cell_symmetry_fixed_flags(...)` and
-`atom_site_symmetry_fixed_flags(...)` in `crystallography` expose the
-per-key flags.
+to zero by site symmetry) are flagged as `symmetry_constrained = True`
+on the `Parameter`. This forces `free = False`; any subsequent attempt
+to set `free = True` on such a parameter is ignored with a warning.
+Flags are recomputed on every `_update_categories()` so that changing
+the space group, Wyckoff letter, or ADP type re-evaluates which
+parameters are symmetry constrained. Surface helpers
+`cell_symmetry_constrained_flags(...)` and
+`atom_site_symmetry_constrained_flags(...)` in `crystallography` expose
+the per-key flags.
 
 ### 4.2 Atomic Displacement Parameters (ADP)
 
@@ -830,7 +831,10 @@ workflow:
   object. This is `FitResults` for deterministic fits and
   `BayesianFitResults` for Bayesian DREAM runs.
 - Parameter tables: `show_all_params()`, `show_fittable_params()`,
-  `show_free_params()`, `how_to_access_parameters()`
+  `show_free_params()`, `how_to_access_parameters()` Compact
+  summary-style parameter displays intentionally hide the large
+  loop-backed experiment categories `pd_data`, `total_data`, and `refln`
+  in `all()`, `access()`, and `cif_uids()` so the output stays readable.
 - Fitting: `fit()` dispatches single/joint through the callable `fit`
   category; `fit_sequential()` handles sequential mode (sets `fit.mode`
   to `'sequential'` internally). `fit()` accepts optional `random_seed`
diff --git a/docs/dev/implementation-plans/progress-activity-indicator.md b/docs/dev/implementation-plans/progress-activity-indicator.md
new file mode 100644
index 000000000..e776e0127
--- /dev/null
+++ b/docs/dev/implementation-plans/progress-activity-indicator.md
@@ -0,0 +1,407 @@
+# Progress Activity Indicator Implementation Plan
+
+**Status:** Proposed  
+**Date:** 2026-05-14
+
+## Goal
+
+Add a small activity indicator for fitting and other long-running
+calculations. The indicator should read as the same feature in terminal
+and Jupyter, while using environment-appropriate rendering underneath.
+
+This is an activity indicator, not a numeric progress bar. Most
+deterministic minimizers do not expose a reliable total work estimate,
+and the existing fit progress table updates only when meaningful fit
+state changes. A spinner-style indicator communicates that work is
+continuing without implying a percentage that may be unavailable.
+
+## User-Facing Behavior
+
+### Visibility
+
+The indicator is controlled by existing verbosity:
+
+| Verbosity | Behavior                                                                                             |
+| --------- | ---------------------------------------------------------------------------------------------------- |
+| `silent`  | Show nothing. No table, no activity indicator, no status line.                                       |
+| `short`   | Show the activity indicator, but not the detailed fit progress table. Keep existing short summaries. |
+| `full`    | Show the detailed progress table and the activity indicator below it.                                |
+
+This changes the current fit tracker behavior where `short` returns
+before creating any live progress output.
+
+### Labels
+
+Use the following labels:
+
+| Work type                         | Label        |
+| --------------------------------- | ------------ |
+| Deterministic single fit          | `fitting`    |
+| Sequential fit                    | `fitting`    |
+| Bayesian DREAM burn phase         | `burn-in`    |
+| Bayesian DREAM sampling phase     | `sampling`   |
+| Posterior predictive plots/checks | `processing` |
+| Posterior pair plots              | `processing` |
+| Other long calculations           | `processing` |
+
+The label must be updateable while work is running. DREAM should switch
+from `burn-in` to `sampling` when sampler progress reports the phase
+change.
+
+### Visual Style
+
+Use compact Unicode spinner frames:
+
+```text
+⠋ ⠙ ⠹ ⠸ ⠼ ⠴ ⠦ ⠧ ⠇ ⠏
+```
+
+Examples:
+
+```text
+⠋ fitting
+⠴ burn-in
+⠇ sampling
+⠙ processing
+```
+
+The indicator should be a single line. In `full` mode it appears below
+the progress table. In `short` mode it appears as the only live progress
+element.
+
+## Current Code Paths
+
+### Fit Progress
+
+The single-fit and sampler progress lifecycle is owned by
+`src/easydiffraction/analysis/fit_helpers/tracking.py`.
+
+Important methods:
+
+- `FitProgressTracker.start_tracking(...)`
+- `FitProgressTracker.add_tracking_info(...)`
+- `FitProgressTracker.track_sampler_progress(...)`
+- `FitProgressTracker.finish_tracking(...)`
+- `_make_display_handle()`
+- `_TerminalLiveHandle`
+
+The existing table update path uses `render_table(...)`, which delegates
+to `TableRenderer` and then to either:
+
+- `PandasTableBackend` in Jupyter
+- `RichTableBackend` in terminal
+
+### Sequential Fit
+
+Sequential fitting currently has separate progress output in
+`src/easydiffraction/analysis/sequential.py`.
+
+Important functions:
+
+- `fit_sequential(...)`
+- `_run_fit_loop(...)`
+- `_report_chunk_progress(...)`
+
+Sequential fit should reuse the same activity indicator abstraction
+rather than adding a separate spinner implementation.
+
+### Posterior And Other Long Calculations
+
+Posterior display work is routed through:
+
+- `src/easydiffraction/project/display.py`
+- `src/easydiffraction/display/plotting.py`
+
+Important entry points include:
+
+- `PosteriorDisplay.pairs(...)`
+- `PosteriorDisplay.predictive(...)`
+- `Plotter.plot_posterior_pairs(...)`
+- `Plotter.plot_posterior_predictive(...)`
+
+These should use the generic `processing` label if an activity indicator
+is added to those paths.
+
+## Architecture
+
+### Add A Shared Activity Indicator
+
+Add a small shared display helper, preferably:
+
+```text
+src/easydiffraction/display/progress.py
+```
+
+Recommended public/internal shape:
+
+```python
+class ActivityIndicator:
+    def __init__(self, label: str = "processing", *, verbosity: VerbosityEnum) -> None: ...
+    def start(self) -> None: ...
+    def update(self, *, label: str | None = None, content: object | None = None) -> None: ...
+    def stop(self, *, final_label: str | None = None) -> None: ...
+```
+
+The exact class name can change during implementation, but it should
+provide these capabilities:
+
+- no output in `silent`
+- live output in `short` and `full`
+- label updates while running
+- optional table/content rendering above the indicator in `full`
+- terminal and Jupyter implementations behind one API
+- safe cleanup on exceptions
+
+### Terminal Rendering
+
+Use Rich for terminal rendering.
+
+Preferred implementation:
+
+- keep using `rich.live.Live`
+- render a `rich.console.Group`
+- group content should be:
+  - the progress table renderable, when present
+  - the activity indicator line
+
+The indicator line can be either:
+
+- Rich's built-in `Spinner`, if it works cleanly inside the existing
+  `Live` setup, or
+- a local unicode-frame renderable driven by the same frame list.
+
+Do not create a second independent `Live` instance for the same output
+area. The table and spinner should be refreshed together through one
+live handle.
+
+### Jupyter Rendering
+
+Use an IPython `DisplayHandle` and HTML.
+
+The Jupyter spinner should be browser-driven CSS animation, not a
+Python-loop animation. This matters because Python may not regain
+control during expensive calculations, but CSS keeps animating once the
+HTML has been displayed.
+
+Recommended HTML structure:
+
+```html
+<div class="ed-activity">
+  <span class="ed-activity-spinner"></span>
+  <span class="ed-activity-label">fitting</span>
+</div>
+```
+
+The table HTML and spinner HTML can be updated together in the same
+display handle, or the spinner can have its own display handle below the
+table. Prefer a single display handle if it keeps table-and-spinner
+replacement simpler and avoids duplicated output cells.
+
+### Table Rendering Refactor
+
+The existing table backends mostly print/update directly. For a clean
+combined table-plus-spinner render, add a way to build table renderables
+without immediately displaying them.
+
+Possible approach:
+
+1. Keep `render_table(...)` working for existing callers.
+2. Add a backend method that returns a renderable representation:
+   - Rich: return `rich.table.Table`
+   - Pandas: return HTML from `Styler.to_html()`
+3. Let the activity indicator compose that renderable with the spinner.
+
+This avoids hard-coding table internals in the tracker and keeps normal
+table rendering backwards compatible.
+
+## Fit Tracker Integration
+
+### State
+
+Add fields to `FitProgressTracker`:
+
+- `_activity_indicator`
+- `_activity_label`
+
+The label is derived from tracking mode:
+
+- fit mode -> `fitting`
+- sampler mode -> initial label from sampler phase if known, otherwise
+  `sampling`
+
+### `start_tracking(...)`
+
+Update behavior:
+
+1. Set tracking mode.
+2. Return immediately only for `silent`.
+3. Print the existing start/header messages only where appropriate:
+   - keep current full messages
+   - keep short mode concise
+4. Create the activity indicator for `short` and `full`.
+5. In `full`, render the initial empty progress table plus the
+   indicator.
+6. In `short`, render only the indicator.
+
+### `add_tracking_info(...)`
+
+Update behavior:
+
+1. Always store row state for `full` mode.
+2. In `full`, refresh the table plus the indicator.
+3. In `short`, do not render the table; keep the indicator running.
+
+### `track_sampler_progress(...)`
+
+Update the activity label from `SamplerProgressUpdate.phase`:
+
+- phase `burn-in` -> label `burn-in`
+- phase `sampling` -> label `sampling`
+- any other phase -> normalized phase string if user-facing, otherwise
+  `processing`
+
+The existing sampler table's `phase` column remains unchanged.
+
+### `finish_tracking(...)`
+
+Update behavior:
+
+1. Finalize the last table row as today.
+2. Stop the activity indicator for `short` and `full`.
+3. In `full`, print the current completion summary.
+4. In `short`, keep or add only a concise completion line if the current
+   short behavior expects one.
+5. In `silent`, print nothing.
+
+Use `try/finally` in minimizer execution paths so the indicator is
+stopped when a solver raises.
+
+## Sequential Fit Integration
+
+Sequential fit should use the same `ActivityIndicator`.
+
+Recommended behavior:
+
+- `silent`: no output.
+- `short`: show one activity indicator labelled `fitting`; keep concise
+  chunk summaries when chunks finish.
+- `full`: show one activity indicator labelled `fitting`; keep detailed
+  chunk summaries.
+
+Implementation points:
+
+1. Create the indicator in `fit_sequential(...)` after preflight checks
+   and before `_run_fit_loop(...)`.
+2. Pass it into `_run_fit_loop(...)`, or wrap `_run_fit_loop(...)` in a
+   context manager.
+3. Update the indicator content after each chunk if the implementation
+   supports content text, for example:
+
+   ```text
+   ⠼ fitting  chunk 3/20
+   ```
+
+4. Stop the indicator in a `finally` block before printing final
+   completion output.
+
+Do not duplicate spinner frame logic in `sequential.py`.
+
+## Posterior And Generic Processing Integration
+
+The first implementation can focus on fitting and sequential fitting.
+After that, add a small context helper for generic long calculations:
+
+```python
+with activity_indicator("processing", verbosity=VerbosityEnum(project.verbosity)):
+    ...
+```
+
+Use this for:
+
+- posterior predictive summary generation
+- posterior pair plot construction when sample thinning, density grids,
+  or figure construction take noticeable time
+- any future calculation where total progress is unknown
+
+The generic helper should default to `processing`.
+
+## Testing Plan
+
+### Unit Tests
+
+Add tests for the shared progress helper:
+
+- `silent` does not create display handles or print output.
+- `short` starts the indicator.
+- `full` can compose content plus indicator.
+- label updates replace the visible label.
+- `stop()` suppresses cleanup errors.
+
+Extend tracker tests:
+
+- `FitProgressTracker.start_tracking(...)` starts the indicator in
+  `short`.
+- `silent` still shows nothing.
+- full fit mode uses label `fitting`.
+- sampler updates switch labels from `burn-in` to `sampling`.
+- finalization stops the indicator on success.
+- finalization stops the indicator when solver preparation or solver
+  execution raises.
+
+Extend sequential tests:
+
+- `fit_sequential(..., verbosity="short")` starts and stops the shared
+  indicator.
+- `fit_sequential(..., verbosity="silent")` does not start it.
+- chunk progress does not create a separate spinner.
+
+### Rendering Tests
+
+Terminal:
+
+- fake or monkeypatch `Live` and assert one live handle receives grouped
+  table-plus-indicator content.
+
+Jupyter:
+
+- fake `DisplayHandle` and assert generated HTML contains the activity
+  container and the selected label.
+- assert CSS animation is included once, not duplicated on every table
+  update if avoidable.
+
+### Regression Tests
+
+Keep existing tests passing:
+
+- `tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py`
+- `tests/integration/fitting/test_bayesian_tracker_and_base.py`
+- sequential tests under `tests/integration/fitting/test_sequential.py`
+
+## Implementation Sequence
+
+1. Add `display/progress.py` with a minimal `ActivityIndicator`.
+2. Add tests for verbosity behavior and label updates.
+3. Refactor table rendering just enough to allow Rich renderable and
+   Jupyter HTML composition.
+4. Wire `FitProgressTracker` to the activity indicator.
+5. Update tracker tests for `short`, `full`, `silent`, and sampler phase
+   labels.
+6. Wire sequential fitting to the shared activity indicator.
+7. Update sequential tests.
+8. Add generic `processing` context helper.
+9. Add the helper to posterior predictive and posterior pairs if
+   profiling or user feedback shows those operations need visible
+   activity feedback.
+10. Run focused unit tests, then the relevant integration tests.
+
+## Open Design Checks
+
+- Whether `short` mode should print the existing start line before the
+  spinner or show only the spinner until completion.
+- Whether terminal output should use Rich's built-in `Spinner` or the
+  explicit EasyDiffraction frame list. Prefer the explicit list if
+  consistency with Jupyter matters more than Rich defaults.
+- Whether the table and spinner should share one Jupyter display handle.
+  Prefer one handle unless it complicates the existing pandas backend.
+- Whether generic display/plot operations need a public verbosity
+  argument, or should only read `project.verbosity`.
diff --git a/docs/dev/plan_display-ux.md b/docs/dev/plan_display-ux.md
deleted file mode 100644
index 4088ac657..000000000
--- a/docs/dev/plan_display-ux.md
+++ /dev/null
@@ -1,238 +0,0 @@
-# Plan: Display UX Facade
-
-## Goal
-
-Implement the display UX approach described in
-`docs/dev/adr_display-ux.md`.
-
-The end-user API should become:
-
-```python
-project.display.pattern(expt_name='hrpt')
-
-project.display.parameters.free()
-project.display.parameters.fittable()
-project.display.parameters.all()
-project.display.parameters.access()
-project.display.parameters.cif_uids()
-
-project.display.fit.results()
-project.display.fit.correlations()
-project.display.fit.series(param, versus=temperature)
-
-project.display.posterior.pairs()
-project.display.posterior.distribution(param)
-project.display.posterior.predictive(expt_name='hrpt')
-
-project.display.show_pattern_options(expt_name='hrpt')
-```
-
-Renderer configuration should move to:
-
-```python
-project.rendering.chart_engine = 'plotly'
-project.rendering.table_engine = 'pandas'
-project.rendering.show_chart_engines()
-project.rendering.show_table_engines()
-project.rendering.show_config()
-```
-
-## Branch
-
-Use implementation branch:
-
-```text
-feature/display-ux
-```
-
-## Decisions
-
-- Use grouped display namespaces under `project.display`.
-- Use `project.display.fit.series(param, versus=...)` for sequential fit
-  parameter plots.
-- Use `pattern(..., include='auto')` as the default experiment chart.
-- Use `include` rather than `layers`, `components`, `content`, `view`,
-  `series`, or boolean flags.
-- Do not add `project.display.constraints()`.
-- Move constraint reporting to `project.analysis.constraints.show()`.
-- Rename the serialized project display category to `rendering`.
-- Do not add legacy CIF loading for `_display.plotter_type` or
-  `_display.tabler_type`.
-- Standardize CIF display helpers on `project.analysis` and
-  `project.info`: `as_cif` should be a read-only property returning CIF
-  text, and `show_as_cif()` should pretty-print CIF text with a header.
-- Implement `include='uncertainty'` immediately where posterior
-  predictive data exists.
-- No compatibility aliases or deprecation warnings are required unless
-  release policy separately requires them.
-
-## Likely Files To Change
-
-- `src/easydiffraction/project/project.py`
-- `src/easydiffraction/project/project_info.py`
-- `src/easydiffraction/project/categories/display/default.py`
-- `src/easydiffraction/project/categories/display/factory.py`
-- `src/easydiffraction/project/categories/display/__init__.py`
-- new `src/easydiffraction/project/categories/rendering/...`
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/display/tables.py`
-- `src/easydiffraction/analysis/analysis.py`
-- `src/easydiffraction/analysis/categories/constraints/default.py`
-- `src/easydiffraction/__main__.py`
-- `docs/dev/architecture.md`
-- `docs/docs/user-guide/**/*.md`
-- `docs/docs/tutorials/*.py`
-- tests under `tests/unit/easydiffraction/`
-- tests under `tests/integration/fitting/`
-
-Do not edit generated tutorial notebooks directly. Update tutorial `.py`
-files and regenerate notebooks in Phase 2 if required.
-
-## Resolved Questions
-
-- Legacy CIF `_display.plotter_type` and `_display.tabler_type` do not
-  need to load into `project.rendering`. No legacy code is required.
-- `project.analysis` and `project.info` need consistency with structure
-  and experiment objects: `as_cif` should be a read-only property that
-  returns CIF text, and `show_as_cif()` should pretty-print CIF text
-  with a header.
-- `include='uncertainty'` should be implemented immediately where
-  posterior predictive data exists.
-
-## Phase 1 - Implementation
-
-Do not create or run tests in Phase 1 unless explicitly requested. Every
-completed Phase 1 implementation step must be staged with explicit paths
-and committed locally before moving to the next implementation step or
-the Phase 1 review gate. Use atomic commits, inspect the worktree before
-each commit, and stage only the files changed for that step.
-
-- [x] Rename the serialized project display category to rendering.
-  - Move or recreate the category package as
-    `src/easydiffraction/project/categories/rendering/`.
-  - Rename user-facing settings from `plotter_type` and `tabler_type` to
-    `chart_engine` and `table_engine`.
-  - Add `show_chart_engines()`, `show_table_engines()`, and
-    `show_config()`.
-  - Update CIF names to `_rendering.chart_engine` and
-    `_rendering.table_engine`.
-  - Do not add legacy loading for `_display.plotter_type` or
-    `_display.tabler_type`.
-
-- [x] Add the new `project.display` facade.
-  - Add a facade object that is not the serialized rendering category.
-  - Add `pattern(...)` and `show_pattern_options(...)`.
-  - Add `parameters`, `fit`, and `posterior` namespace objects.
-
-- [x] Implement `pattern(..., include='auto')`.
-  - Replace common user-facing calls to `plot_meas`, `plot_calc`, and
-    `plot_meas_vs_calc` with one state-aware method.
-  - Support explicit includes for `measured`, `calculated`,
-    `background`, `residual`, `bragg`, `excluded`, and `uncertainty`
-    where data is available.
-  - Implement `uncertainty` immediately for experiments with posterior
-    predictive data.
-  - Render a clear warning or error when a requested include is not
-    available.
-
-- [x] Move parameter table displays under `project.display.parameters`.
-  - Implement `all()`, `fittable()`, `free()`, `access()`, and
-    `cif_uids()`.
-  - Remove the primary public need for `project.analysis.display`.
-
-- [x] Move fit displays under `project.display.fit`.
-  - Implement `results()`.
-  - Implement `correlations()`.
-  - Implement `series(param, versus=...)`.
-
-- [x] Move Bayesian displays under `project.display.posterior`.
-  - Implement `pairs()`.
-  - Implement `distribution(param)`.
-  - Implement `predictive(expt_name=...)`.
-
-- [x] Move constraint reporting to
-      `project.analysis.constraints.show()`.
-  - Do not add `project.display.constraints()`.
-
-- [x] Standardize CIF display helpers.
-  - Convert `project.analysis.as_cif()` to a read-only
-    `project.analysis.as_cif` property.
-  - Ensure `project.analysis.show_as_cif()` pretty-prints CIF text with
-    a header.
-  - Convert `project.info.as_cif()` to a read-only `project.info.as_cif`
-    property.
-  - Ensure `project.info.show_as_cif()` pretty-prints CIF text with a
-    header.
-
-- [x] Update docs, tutorials, and architecture text.
-  - Replace old public display examples with the selected API.
-  - Update `docs/dev/architecture.md`.
-  - Update tutorial `.py` files only; regenerate notebooks in Phase 2 if
-    required.
-
-- [x] Stop at the Phase 1 review gate.
-  - Summarize changed files and open questions.
-  - Suggest next verification commands.
-  - Wait for user approval before Phase 2.
-
-Suggested Phase 1 commit messages:
-
-```text
-Rename display settings category to rendering
-Add grouped display facade
-Implement state-aware pattern display
-Move analysis display reports to display facade
-Standardize CIF display helpers
-Update display UX documentation
-```
-
-## Phase 2 - Verification
-
-After Phase 1 is reviewed and approved:
-
-- [x] Add or update unit tests for the rendering category.
-- [x] Add or update unit tests for the display facade namespaces.
-- [x] Add or update plotting integration tests for `pattern(...)`.
-- [x] Add or update analysis display integration tests for parameter and
-      fit report methods.
-- [x] Regenerate tutorial notebooks if tutorial `.py` files changed.
-- [x] Run formatting and checks.
-- [x] Run unit tests.
-- [x] Run integration tests.
-- [x] Run script tests.
-
-Verification commands:
-
-```sh
-pixi run notebook-prepare
-pixi run fix
-pixi run check
-pixi run unit-tests
-pixi run integration-tests
-pixi run script-tests
-```
-
-Run `pixi run notebook-prepare` only if tutorial `.py` files changed.
-Run `pixi run integration-tests` only after the relevant unit and
-focused integration tests are passing.
-
-## Suggested Commit Message
-
-```text
-Plan display UX facade implementation
-```
-
-## Suggested Pull Request
-
-Title:
-
-```text
-Improve chart and table display API
-```
-
-Description:
-
-This change makes display commands easier to discover and use in
-notebooks. Experiment patterns, parameter tables, fit reports, and
-Bayesian plots are grouped under `project.display`, while renderer
-settings move to `project.rendering`.
diff --git a/docs/docs/quick-reference/index.md b/docs/docs/quick-reference/index.md
new file mode 100644
index 000000000..e0d3fd202
--- /dev/null
+++ b/docs/docs/quick-reference/index.md
@@ -0,0 +1,371 @@
+---
+icon: material/clipboard-text-outline
+---
+
+# :material-clipboard-text-outline: Quick Reference
+
+This page is a short refresher for day-to-day EasyDiffraction work. It
+collects the commands you are most likely to need when returning to a
+project, preparing a quick refinement, or checking how to inspect
+parameters and results.
+
+For complete explanations, use the [User Guide](../user-guide/index.md)
+and [Tutorials](../tutorials/index.md).
+
+## Start a Session
+
+Import the package and create or load a project:
+
+```python
+import easydiffraction as ed
+
+project = ed.Project(name='lbco_hrpt')
+```
+
+```python
+from easydiffraction import Project
+
+project = Project.load('lbco_hrpt')
+```
+
+Check the installed version:
+
+```python
+ed.show_version()
+```
+
+## Get Example Data
+
+Download a dataset by ID into a local directory:
+
+```python
+structure_path = ed.download_data(id=1, destination='data')
+data_path = ed.download_data(id=3, destination='data')
+```
+
+For tutorial notebooks:
+
+```python
+ed.list_tutorials()
+ed.download_tutorial(id=1, destination='tutorials')
+ed.download_all_tutorials(destination='tutorials')
+```
+
+## Build a Project
+
+Load a structure from CIF:
+
+```python
+project.structures.add_from_cif_path(structure_path)
+project.structures.show_names()
+
+structure = project.structures['lbco']
+```
+
+Create a structure directly:
+
+```python
+project.structures.create(name='lbco')
+structure = project.structures['lbco']
+
+structure.space_group.name_h_m = 'P m -3 m'
+structure.space_group.it_coordinate_system_code = '1'
+structure.cell.length_a = 3.88
+```
+
+Add an atom site:
+
+```python
+structure.atom_sites.create(
+    label='O',
+    type_symbol='O',
+    fract_x=0,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='c',
+    adp_iso=0.5,
+)
+```
+
+Load an experiment from measured data:
+
+```python
+project.experiments.add_from_data_path(
+    name='hrpt',
+    data_path=data_path,
+    sample_form='powder',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+experiment = project.experiments['hrpt']
+```
+
+Set common experiment parameters:
+
+```python
+experiment.instrument.setup_wavelength = 1.494
+experiment.instrument.calib_twotheta_offset = 0.6
+
+experiment.peak.broad_gauss_u = 0.1
+experiment.peak.broad_gauss_v = -0.1
+experiment.peak.broad_gauss_w = 0.1
+experiment.peak.broad_lorentz_y = 0.1
+```
+
+Add background points and excluded regions:
+
+```python
+experiment.background.create(id='1', x=10, y=170)
+experiment.background.create(id='2', x=30, y=170)
+
+experiment.excluded_regions.create(id='1', start=0, end=5)
+experiment.excluded_regions.create(id='2', start=165, end=180)
+```
+
+Link a structure to an experiment:
+
+```python
+experiment.linked_phases.create(id='lbco', scale=10.0)
+```
+
+## Inspect the Project
+
+Show names, CIF text, and plotting options:
+
+```python
+project.structures.show_names()
+project.experiments.show_names()
+
+structure.show_as_cif()
+experiment.show_as_cif()
+
+project.display.show_pattern_options(expt_name='hrpt')
+```
+
+Open the main display views:
+
+```python
+project.display.pattern(expt_name='hrpt')
+project.display.parameters.all()
+project.display.parameters.fittable()
+project.display.parameters.free()
+project.display.parameters.access()
+project.display.parameters.cif_uids()
+```
+
+## Show Tables and Select Types
+
+EasyDiffraction uses two related display patterns:
+
+- `show_*()` usually lists supported choices or displays a current
+  configuration.
+- `.show()` on a loop-style object usually prints rows you have already
+  created.
+
+Show created loop contents:
+
+```python
+experiment.background.show()
+experiment.excluded_regions.show()
+project.analysis.constraints.show()
+```
+
+List supported type choices. The current selection is marked in the
+output:
+
+```python
+experiment.show_peak_profile_types()
+experiment.show_background_types()
+experiment.calculation.show_calculator_types()
+
+project.analysis.fit.show_modes()
+project.analysis.fit.show_minimizer_types()
+
+project.rendering.show_chart_engines()
+project.rendering.show_table_engines()
+project.rendering.show_config()
+```
+
+Change the active type by assigning the corresponding `*_type` property:
+
+```python
+experiment.peak_profile_type = 'pseudo-voigt'
+experiment.background_type = 'line-segment'
+experiment.calculation.calculator_type = 'cryspy'
+
+project.analysis.fit.mode = 'single'
+project.analysis.fit.minimizer_type = 'lmfit'
+
+project.rendering.chart_engine = 'plotly'
+project.rendering.table_engine = 'rich'
+```
+
+For single-crystal experiments, extinction uses the same pattern:
+
+```python
+experiment.show_extinction_types()
+experiment.extinction_type = 'becker-coppens'
+```
+
+## Find Commands with Help
+
+Use help methods when you do not remember the exact command. The most
+useful starting points are the project-level display and analysis
+facades:
+
+```python
+project.display.help()
+project.display.parameters.help()
+project.display.fit.help()
+project.analysis.help()
+```
+
+Drill into project collections to see what they contain:
+
+```python
+project.structures.help()
+project.experiments.help()
+```
+
+Then inspect one structure or experiment:
+
+```python
+structure = project.structures['lbco']
+experiment = project.experiments['hrpt']
+
+structure.help()
+experiment.help()
+```
+
+Category-level help shows available parameters and methods. This is
+often the fastest way to remember exact names:
+
+```python
+structure.cell.help()
+structure.atom_sites.help()
+structure.atom_sites['O'].help()
+
+experiment.instrument.help()
+experiment.peak.help()
+experiment.background.help()
+experiment.background['1'].help()
+```
+
+Individual parameters also expose help. Use this when you need to check
+whether a parameter is writable, free, constrained, or has fit bounds:
+
+```python
+structure.cell.length_a.help()
+structure.atom_sites['O'].adp_iso.help()
+
+experiment.instrument.calib_twotheta_offset.help()
+experiment.linked_phases['lbco'].scale.help()
+```
+
+The usual navigation pattern is:
+
+```text
+project → structures/experiments → structure/experiment → category → item → parameter
+```
+
+## Refine Parameters
+
+Mark parameters as free:
+
+```python
+structure.cell.length_a.free = True
+structure.atom_sites['O'].adp_iso.free = True
+
+experiment.instrument.calib_twotheta_offset.free = True
+experiment.peak.broad_gauss_u.free = True
+experiment.background['1'].y.free = True
+experiment.linked_phases['lbco'].scale.free = True
+```
+
+Choose calculators and minimizers:
+
+```python
+experiment.calculation.show_calculator_types()
+experiment.calculation.calculator_type = 'cryspy'
+
+project.analysis.fit.show_modes()
+project.analysis.fit.mode = 'single'
+
+project.analysis.fit.show_minimizer_types()
+project.analysis.fit.minimizer_type = 'lmfit'
+```
+
+Run a fit and inspect the result:
+
+```python
+project.analysis.fit()
+
+project.display.fit.results()
+project.display.fit.correlations()
+project.display.pattern(expt_name='hrpt')
+```
+
+## Add Simple Constraints
+
+Create aliases from parameter objects, then define a constraint
+expression using those aliases:
+
+```python
+project.analysis.aliases.create(
+    label='biso_la',
+    param=project.structures['lbco'].atom_sites['La'].adp_iso,
+)
+project.analysis.aliases.create(
+    label='biso_ba',
+    param=project.structures['lbco'].atom_sites['Ba'].adp_iso,
+)
+
+project.analysis.constraints.create(expression='biso_ba = biso_la')
+```
+
+Show the created constraints:
+
+```python
+project.analysis.constraints.show()
+```
+
+Then fit again:
+
+```python
+project.analysis.fit()
+project.display.fit.results()
+```
+
+## Save and Reuse Work
+
+Save a project directory for later:
+
+```python
+project.save_as(dir_path='lbco_hrpt')
+project.save()
+```
+
+Load it again:
+
+```python
+project = ed.Project.load('lbco_hrpt')
+```
+
+Run a saved project from the command line:
+
+```bash
+python -m easydiffraction fit lbco_hrpt
+python -m easydiffraction fit lbco_hrpt --dry
+```
+
+## Command-Line Reminders
+
+```bash
+python -m easydiffraction --help
+python -m easydiffraction --version
+python -m easydiffraction list-tutorials
+python -m easydiffraction download-tutorial 1 --destination tutorials
+python -m easydiffraction download-all-tutorials --destination tutorials
+python -m easydiffraction fit PROJECT_DIR
+```
diff --git a/docs/docs/tutorials/ed-3.ipynb b/docs/docs/tutorials/ed-3.ipynb
index 03fba5148..0f103f8aa 100644
--- a/docs/docs/tutorials/ed-3.ipynb
+++ b/docs/docs/tutorials/ed-3.ipynb
@@ -822,7 +822,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# project.display.parameters.all()"
+    "project.display.parameters.all()"
    ]
   },
   {
@@ -876,7 +876,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# project.display.parameters.access()"
+    "project.display.parameters.access()"
    ]
   },
   {
@@ -1068,24 +1068,6 @@
    "cell_type": "markdown",
    "id": "103",
    "metadata": {},
-   "source": [
-    "#### Save Project State"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "104",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.save_as(dir_path='lbco_hrpt', temporary=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "105",
-   "metadata": {},
    "source": [
     "### Perform Fit 2/5\n",
     "\n",
@@ -1095,7 +1077,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "106",
+   "id": "104",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1107,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "107",
+   "id": "105",
    "metadata": {},
    "source": [
     "Show free parameters after selection."
@@ -1116,7 +1098,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "108",
+   "id": "106",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1125,7 +1107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "109",
+   "id": "107",
    "metadata": {},
    "source": [
     "#### Run Fitting"
@@ -1134,7 +1116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "110",
+   "id": "108",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1144,7 +1126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "111",
+   "id": "109",
    "metadata": {},
    "source": [
     "#### Plot Measured vs Calculated"
@@ -1153,7 +1135,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "112",
+   "id": "110",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,7 +1145,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "113",
+   "id": "111",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1172,7 +1154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "114",
+   "id": "112",
    "metadata": {},
    "source": [
     "#### Save Project State"
@@ -1181,16 +1163,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "115",
+   "id": "113",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.save_as(dir_path='lbco_hrpt', temporary=True)"
+    "project.save()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "116",
+   "id": "114",
    "metadata": {},
    "source": [
     "### Perform Fit 3/5\n",
@@ -1201,7 +1183,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "117",
+   "id": "115",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1213,7 +1195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "118",
+   "id": "116",
    "metadata": {},
    "source": [
     "Show free parameters after selection."
@@ -1222,7 +1204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "119",
+   "id": "117",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1231,7 +1213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "120",
+   "id": "118",
    "metadata": {},
    "source": [
     "#### Run Fitting"
@@ -1240,7 +1222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "121",
+   "id": "119",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1250,7 +1232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "122",
+   "id": "120",
    "metadata": {},
    "source": [
     "#### Plot Measured vs Calculated"
@@ -1259,7 +1241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "123",
+   "id": "121",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1269,7 +1251,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "124",
+   "id": "122",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1278,25 +1260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "125",
-   "metadata": {},
-   "source": [
-    "#### Save Project State"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "126",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.save_as(dir_path='lbco_hrpt', temporary=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "127",
+   "id": "123",
    "metadata": {},
    "source": [
     "### Perform Fit 4/5\n",
@@ -1309,7 +1273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "128",
+   "id": "124",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,7 +1289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "129",
+   "id": "125",
    "metadata": {},
    "source": [
     "Set constraints."
@@ -1334,7 +1298,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "130",
+   "id": "126",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1343,7 +1307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "131",
+   "id": "127",
    "metadata": {},
    "source": [
     "Show defined constraints."
@@ -1352,7 +1316,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "132",
+   "id": "128",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1361,7 +1325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "133",
+   "id": "129",
    "metadata": {},
    "source": [
     "Show free parameters."
@@ -1370,7 +1334,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "134",
+   "id": "130",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1379,7 +1343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "135",
+   "id": "131",
    "metadata": {},
    "source": [
     "#### Run Fitting"
@@ -1388,7 +1352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "136",
+   "id": "132",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1398,7 +1362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "137",
+   "id": "133",
    "metadata": {},
    "source": [
     "#### Plot Measured vs Calculated"
@@ -1407,7 +1371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "138",
+   "id": "134",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1417,7 +1381,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "139",
+   "id": "135",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,25 +1390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "140",
-   "metadata": {},
-   "source": [
-    "#### Save Project State"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "141",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.save_as(dir_path='lbco_hrpt', temporary=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "142",
+   "id": "136",
    "metadata": {},
    "source": [
     "### Perform Fit 5/5\n",
@@ -1457,7 +1403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "143",
+   "id": "137",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1419,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "144",
+   "id": "138",
    "metadata": {},
    "source": [
     "Set more constraints."
@@ -1482,7 +1428,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "145",
+   "id": "139",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1493,7 +1439,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "146",
+   "id": "140",
    "metadata": {},
    "source": [
     "Show defined constraints."
@@ -1502,7 +1448,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "147",
+   "id": "141",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1513,7 +1459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "148",
+   "id": "142",
    "metadata": {},
    "source": [
     "Set structure parameters to be refined."
@@ -1522,7 +1468,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "149",
+   "id": "143",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1531,7 +1477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "150",
+   "id": "144",
    "metadata": {},
    "source": [
     "Show free parameters after selection."
@@ -1540,7 +1486,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "151",
+   "id": "145",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1549,7 +1495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "152",
+   "id": "146",
    "metadata": {},
    "source": [
     "#### Run Fitting"
@@ -1558,7 +1504,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "153",
+   "id": "147",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1569,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "154",
+   "id": "148",
    "metadata": {},
    "source": [
     "#### Plot Measured vs Calculated"
@@ -1578,7 +1524,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "155",
+   "id": "149",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1588,7 +1534,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "156",
+   "id": "150",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1597,7 +1543,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "157",
+   "id": "151",
    "metadata": {},
    "source": [
     "#### Save Project State"
@@ -1606,16 +1552,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "158",
+   "id": "152",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.save_as(dir_path='lbco_hrpt', temporary=True)"
+    "project.save()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "159",
+   "id": "153",
    "metadata": {},
    "source": [
     "## Step 5: Summary\n",
@@ -1625,7 +1571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "160",
+   "id": "154",
    "metadata": {},
    "source": [
     "#### Show Project Summary"
@@ -1634,7 +1580,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "161",
+   "id": "155",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/docs/tutorials/ed-3.py b/docs/docs/tutorials/ed-3.py
index 271b8e8f0..fcccb5968 100644
--- a/docs/docs/tutorials/ed-3.py
+++ b/docs/docs/tutorials/ed-3.py
@@ -345,7 +345,7 @@
 # Show all parameters of the project.
 
 # %%
-# project.display.parameters.all()
+project.display.parameters.all()
 
 # %% [markdown]
 # Show all fittable parameters.
@@ -363,7 +363,7 @@
 # Show how to access parameters in the code.
 
 # %%
-# project.display.parameters.access()
+project.display.parameters.access()
 
 # %% [markdown]
 # #### Set Fit Mode
@@ -435,12 +435,6 @@
 # %%
 project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
-# %% [markdown]
-# #### Save Project State
-
-# %%
-project.save_as(dir_path='lbco_hrpt', temporary=True)
-
 # %% [markdown]
 # ### Perform Fit 2/5
 #
@@ -478,7 +472,7 @@
 # #### Save Project State
 
 # %%
-project.save_as(dir_path='lbco_hrpt', temporary=True)
+project.save()
 
 # %% [markdown]
 # ### Perform Fit 3/5
@@ -513,12 +507,6 @@
 # %%
 project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
-# %% [markdown]
-# #### Save Project State
-
-# %%
-project.save_as(dir_path='lbco_hrpt', temporary=True)
-
 # %% [markdown]
 # ### Perform Fit 4/5
 #
@@ -570,12 +558,6 @@
 # %%
 project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)
 
-# %% [markdown]
-# #### Save Project State
-
-# %%
-project.save_as(dir_path='lbco_hrpt', temporary=True)
-
 # %% [markdown]
 # ### Perform Fit 5/5
 #
@@ -641,7 +623,7 @@
 # #### Save Project State
 
 # %%
-project.save_as(dir_path='lbco_hrpt', temporary=True)
+project.save()
 
 # %% [markdown]
 # ## Step 5: Summary
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 16091a5d6..fdd6a30b0 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -176,6 +176,8 @@ nav:
       - Introduction: introduction/index.md
   - Installation & Setup:
       - Installation & Setup: installation-and-setup/index.md
+  - Quick Reference:
+      - Quick Reference: quick-reference/index.md
   - User Guide:
       - User Guide: user-guide/index.md
       - Glossary: user-guide/glossary.md
@@ -221,6 +223,8 @@ nav:
           - Tb2TiO7 Bayesian: tutorials/ed-22.ipynb
       - Workshops & Schools:
           - DMSC Summer School: tutorials/ed-13.ipynb
+  - Command-Line:
+      - Command-Line: cli/index.md
   - API Reference:
       - API Reference: api-reference/index.md
       - analysis: api-reference/analysis.md
@@ -234,5 +238,3 @@ nav:
       - project: api-reference/project.md
       - summary: api-reference/summary.md
       - utils: api-reference/utils.md
-  - Command-Line:
-      - Command-Line: cli/index.md
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index e3c01607f..484c3e03d 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -16,7 +16,6 @@
 from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
 from easydiffraction.analysis.fit_helpers.tracking import _make_display_handle
 from easydiffraction.analysis.fitting import Fitter
-from easydiffraction.core.guard import GuardedBase
 from easydiffraction.core.singleton import ConstraintsHandler
 from easydiffraction.core.variable import NumericDescriptor
 from easydiffraction.core.variable import Parameter
@@ -26,75 +25,23 @@
 from easydiffraction.utils.enums import VerbosityEnum
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
+from easydiffraction.utils.utils import _help_method_rows
+from easydiffraction.utils.utils import _help_property_rows
 from easydiffraction.utils.utils import render_cif
+from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
 
+_SUMMARY_HIDDEN_PARAMETER_CATEGORIES = frozenset({'pd_data', 'total_data', 'refln'})
 
-def _discover_property_rows(cls: type) -> list[list[str]]:
-    """
-    Discover public properties from the class MRO.
 
-    Parameters
-    ----------
-    cls : type
-        The class to inspect.
-
-    Returns
-    -------
-    list[list[str]]
-        Table rows with ``[index, name, writable, description]``.
-    """
-    seen: dict = {}
-    for base in cls.mro():
-        for key, attr in base.__dict__.items():
-            if key.startswith('_') or not isinstance(attr, property):
-                continue
-            if key not in seen:
-                seen[key] = attr
-
-    rows = []
-    for i, key in enumerate(sorted(seen), 1):
-        prop = seen[key]
-        writable = '✓' if prop.fset else '✗'
-        doc = GuardedBase._first_sentence(prop.fget.__doc__ if prop.fget else None)
-        rows.append([str(i), key, writable, doc])
-    return rows
+def _discover_property_rows(cls: type) -> list[list[str]]:
+    """Return public property rows for analysis help tables."""
+    return _help_property_rows(cls)
 
 
 def _discover_method_rows(cls: type) -> list[list[str]]:
-    """
-    Discover public methods from the class MRO.
-
-    Parameters
-    ----------
-    cls : type
-        The class to inspect.
-
-    Returns
-    -------
-    list[list[str]]
-        Table rows with ``[index, name(), description]``.
-    """
-    seen_methods: set = set()
-    methods_list: list = []
-    for base in cls.mro():
-        for key, attr in base.__dict__.items():
-            if key.startswith('_') or key in seen_methods:
-                continue
-            if isinstance(attr, property):
-                continue
-            raw = attr
-            if isinstance(raw, (staticmethod, classmethod)):
-                raw = raw.__func__
-            if callable(raw):
-                seen_methods.add(key)
-                methods_list.append((key, raw))
-
-    rows = []
-    for i, (key, method) in enumerate(sorted(methods_list), 1):
-        doc = GuardedBase._first_sentence(getattr(method, '__doc__', None))
-        rows.append([str(i), f'{key}()', doc])
-    return rows
+    """Return public method rows for analysis help tables."""
+    return _help_method_rows(cls)
 
 
 class AnalysisDisplay:
@@ -107,6 +54,10 @@ class AnalysisDisplay:
     def __init__(self, analysis: Analysis) -> None:
         self._analysis = analysis
 
+    def help(self) -> None:
+        """Print available analysis-display methods."""
+        render_object_help(self)
+
     def _flush_structure_categories(self) -> None:
         """
         Flush pending category updates so symmetry flags are fresh.
@@ -116,12 +67,23 @@ def _flush_structure_categories(self) -> None:
             structure._need_categories_update = True
             structure._update_categories()
 
+    @staticmethod
+    def _summary_parameters(
+        params: list[StringDescriptor | NumericDescriptor | Parameter],
+    ) -> list[StringDescriptor | NumericDescriptor | Parameter]:
+        """Return parameters suitable for compact summary displays."""
+        return [
+            param
+            for param in params
+            if param._identity.category_code not in _SUMMARY_HIDDEN_PARAMETER_CATEGORIES
+        ]
+
     def all_params(self) -> None:
         """Print all parameters for structures and experiments."""
         project = self._analysis.project
         self._flush_structure_categories()
-        structures_params = project.structures.parameters
-        experiments_params = project.experiments.parameters
+        structures_params = self._summary_parameters(project.structures.parameters)
+        experiments_params = self._summary_parameters(project.experiments.parameters)
 
         if not structures_params and not experiments_params:
             log.warning('No parameters found.')
@@ -138,15 +100,17 @@ def all_params(self) -> None:
             'fittable',
         ]
 
-        console.paragraph('All parameters for all structures (🧩 data blocks)')
-        df = Analysis._get_params_as_dataframe(structures_params)
-        filtered_df = df[filtered_headers]
-        tabler.render(filtered_df)
+        if structures_params:
+            console.paragraph('All parameters for all structures (🧩 data blocks)')
+            df = Analysis._get_params_as_dataframe(structures_params)
+            filtered_df = df[filtered_headers]
+            tabler.render(filtered_df)
 
-        console.paragraph('All parameters for all experiments (🔬 data blocks)')
-        df = Analysis._get_params_as_dataframe(experiments_params)
-        filtered_df = df[filtered_headers]
-        tabler.render(filtered_df)
+        if experiments_params:
+            console.paragraph('All parameters for all experiments (🔬 data blocks)')
+            df = Analysis._get_params_as_dataframe(experiments_params)
+            filtered_df = df[filtered_headers]
+            tabler.render(filtered_df)
 
     def fittable_params(self) -> None:
         """Print all fittable parameters."""
@@ -172,15 +136,17 @@ def fittable_params(self) -> None:
             'free',
         ]
 
-        console.paragraph('Fittable parameters for all structures (🧩 data blocks)')
-        df = Analysis._get_params_as_dataframe(structures_params)
-        filtered_df = df[filtered_headers]
-        tabler.render(filtered_df)
+        if structures_params:
+            console.paragraph('Fittable parameters for all structures (🧩 data blocks)')
+            df = Analysis._get_params_as_dataframe(structures_params)
+            filtered_df = df[filtered_headers]
+            tabler.render(filtered_df)
 
-        console.paragraph('Fittable parameters for all experiments (🔬 data blocks)')
-        df = Analysis._get_params_as_dataframe(experiments_params)
-        filtered_df = df[filtered_headers]
-        tabler.render(filtered_df)
+        if experiments_params:
+            console.paragraph('Fittable parameters for all experiments (🔬 data blocks)')
+            df = Analysis._get_params_as_dataframe(experiments_params)
+            filtered_df = df[filtered_headers]
+            tabler.render(filtered_df)
 
     def free_params(self) -> None:
         """Print only currently free (varying) parameters."""
@@ -225,14 +191,14 @@ def how_to_access_parameters(self) -> None:
         code.
         """
         project = self._analysis.project
-        structures_params = project.structures.parameters
-        experiments_params = project.experiments.parameters
+        structures_params = self._summary_parameters(project.structures.parameters)
+        experiments_params = self._summary_parameters(project.experiments.parameters)
         all_params = {
             'structures': structures_params,
             'experiments': experiments_params,
         }
 
-        if not all_params:
+        if not structures_params and not experiments_params:
             log.warning('No parameters found.')
             return
 
@@ -291,14 +257,14 @@ def parameter_cif_uids(self) -> None:
         creating CIF-based constraints.
         """
         project = self._analysis.project
-        structures_params = project.structures.parameters
-        experiments_params = project.experiments.parameters
+        structures_params = self._summary_parameters(project.structures.parameters)
+        experiments_params = self._summary_parameters(project.experiments.parameters)
         all_params = {
             'structures': structures_params,
             'experiments': experiments_params,
         }
 
-        if not all_params:
+        if not structures_params and not experiments_params:
             log.warning('No parameters found.')
             return
 
@@ -412,27 +378,7 @@ def display(self) -> AnalysisDisplay:
 
     def help(self) -> None:
         """Print a summary of analysis properties and methods."""
-        console.paragraph("Help for 'Analysis'")
-
-        cls = type(self)
-
-        prop_rows = _discover_property_rows(cls)
-        if prop_rows:
-            console.paragraph('Properties')
-            render_table(
-                columns_headers=['#', 'Name', 'Writable', 'Description'],
-                columns_alignment=['right', 'left', 'center', 'left'],
-                columns_data=prop_rows,
-            )
-
-        method_rows = _discover_method_rows(cls)
-        if method_rows:
-            console.paragraph('Methods')
-            render_table(
-                columns_headers=['#', 'Name', 'Description'],
-                columns_alignment=['right', 'left', 'left'],
-                columns_data=method_rows,
-            )
+        render_object_help(self)
 
     # ------------------------------------------------------------------
     #  Parameter helpers
@@ -475,7 +421,8 @@ def _get_params_as_dataframe(
                 }
             if isinstance(param, Parameter):
                 record |= {
-                    ('fittable', 'left'): True,
+                    ('fittable', 'left'): not param.user_constrained
+                    and not param.symmetry_constrained,
                     ('free', 'left'): param.free,
                     ('min', 'right'): param.fit_min,
                     ('max', 'right'): param.fit_max,
@@ -551,7 +498,7 @@ def _run_fit(
             log.warning('No experiments found in the project. Cannot run fit.')
             return
 
-        # Apply constraints before fitting so that constrained
+        # Apply constraints before fitting so that user-constrained
         # parameters are marked and excluded from the free parameter
         # list built by the fitter.
         self._update_categories()
diff --git a/src/easydiffraction/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py
index db5590e1b..343c15742 100644
--- a/src/easydiffraction/analysis/fitting.py
+++ b/src/easydiffraction/analysis/fitting.py
@@ -81,7 +81,7 @@ def fit(
             expt_free_params.extend(
                 p
                 for p in expt.parameters
-                if isinstance(p, Parameter) and not p.constrained and p.free
+                if isinstance(p, Parameter) and not p.user_constrained and p.free
             )
         params = structures.free_parameters + expt_free_params
 
diff --git a/src/easydiffraction/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index 220179f89..25feb184e 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -452,7 +452,7 @@ def _build_template(project: object) -> SequentialFitTemplate:
     free_names: list[str] = []
     initial_params: dict[str, float] = {}
     for p in all_params:
-        if isinstance(p, Parameter) and not p.constrained and p.free:
+        if isinstance(p, Parameter) and not p.user_constrained and p.free:
             free_names.append(p.unique_name)
             initial_params[p.unique_name] = p.value
 
@@ -612,7 +612,9 @@ def _check_seq_preconditions(project: object) -> list[str]:
     from easydiffraction.core.variable import Parameter  # noqa: PLC0415
 
     free_params = [
-        p for p in project.parameters if isinstance(p, Parameter) and not p.constrained and p.free
+        p
+        for p in project.parameters
+        if isinstance(p, Parameter) and not p.user_constrained and p.free
     ]
     if not free_params:
         msg = 'No free parameters found. Mark at least one parameter as free.'
diff --git a/src/easydiffraction/core/collection.py b/src/easydiffraction/core/collection.py
index 520bf94f9..356469354 100644
--- a/src/easydiffraction/core/collection.py
+++ b/src/easydiffraction/core/collection.py
@@ -79,6 +79,19 @@ def __setitem__(self, name: str, item: GuardedBase) -> None:
         self._items.append(item)
         self._rebuild_index()
 
+    def _adopt_items(self, items: list[GuardedBase]) -> None:
+        """
+        Replace items and link each child to this collection.
+        """
+        for item in self._items:
+            item._parent = None
+
+        for item in items:
+            item._parent = self
+
+        self._items = items
+        self._rebuild_index()
+
     def __delitem__(self, name: str) -> None:
         """Delete an item by key or raise ``KeyError`` if missing."""
         for i, item in enumerate(self._items):
diff --git a/src/easydiffraction/core/datablock.py b/src/easydiffraction/core/datablock.py
index f6f7ae056..0ac201760 100644
--- a/src/easydiffraction/core/datablock.py
+++ b/src/easydiffraction/core/datablock.py
@@ -187,8 +187,12 @@ def parameters(self) -> list:
 
     @property
     def fittable_parameters(self) -> list:
-        """All non-constrained Parameters in this collection."""
-        return [p for p in self.parameters if isinstance(p, Parameter) and not p.constrained]
+        """All Parameters not blocked by constraints or symmetry."""
+        return [
+            p
+            for p in self.parameters
+            if isinstance(p, Parameter) and not p.user_constrained and not p.symmetry_constrained
+        ]
 
     @property
     def free_parameters(self) -> list:
diff --git a/src/easydiffraction/core/singleton.py b/src/easydiffraction/core/singleton.py
index 59ade7c38..9f997e535 100644
--- a/src/easydiffraction/core/singleton.py
+++ b/src/easydiffraction/core/singleton.py
@@ -34,7 +34,7 @@ def get(cls) -> Self:
 # ======================================================================
 
 
-# TODO: Implement changing atrr '.constrained' back to False
+# TODO: Implement changing attr '.user_constrained' back to False
 #  when removing constraints
 class ConstraintsHandler(SingletonBase):
     """
@@ -95,7 +95,7 @@ def apply(self) -> None:
         For each constraint:
         - Evaluate RHS using current values of aliased parameters
         - Locate the dependent parameter via direct alias reference
-        - Update its value and mark it as constrained
+        - Update its value and mark it as user constrained
         """
         if not self._parsed_constraints:
             return  # Nothing to apply
@@ -132,8 +132,8 @@ def apply(self) -> None:
                 # Get the actual parameter object we want to update
                 param = self._alias_to_param[lhs_alias].param
 
-                # Update its value and mark it as constrained
-                param._set_value_constrained(rhs_value)
+                # Update its value and mark it as user constrained
+                param._set_value_user_constrained(rhs_value)
 
             except (ValueError, TypeError, ArithmeticError, KeyError, AttributeError) as error:
                 print(f"Failed to apply constraint '{lhs_alias} = {rhs_expr}': {error}")
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 303c5bbe5..55af6cba9 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -285,10 +285,10 @@ def __init__(
         self._fit_bounds_uncertainty_multiplier: float | None = None
         self._start_value_spec = AttributeSpec(data_type=DataTypes.NUMERIC, default=0.0)
         self._start_value = self._start_value_spec.default
-        self._constrained_spec = self._BOOL_SPEC_TEMPLATE
-        self._constrained = self._constrained_spec.default
-        self._symmetry_fixed_spec = self._BOOL_SPEC_TEMPLATE
-        self._symmetry_fixed = self._symmetry_fixed_spec.default
+        self._user_constrained_spec = self._BOOL_SPEC_TEMPLATE
+        self._user_constrained = self._user_constrained_spec.default
+        self._symmetry_constrained_spec = self._BOOL_SPEC_TEMPLATE
+        self._symmetry_constrained = self._symmetry_constrained_spec.default
 
     def _physical_lower_bound(self) -> float:
         """
@@ -325,22 +325,22 @@ def _minimizer_uid(self) -> str:
         return self.unique_name.replace('.', '__')
 
     @property
-    def constrained(self) -> bool:
+    def user_constrained(self) -> bool:
         """Whether this parameter is part of a constraint expression."""
-        return self._constrained
+        return self._user_constrained
 
-    def _set_value_constrained(self, v: object) -> None:
+    def _set_value_user_constrained(self, v: object) -> None:
         """
         Set the value from a constraint expression.
 
         Bypasses validation and marks the parent datablock dirty, like
         ``_set_value_from_minimizer``, because constraints are applied
         inside the minimizer loop where trial values may exceed
-        physical-range validators. Flags the parameter as constrained.
-        Used exclusively by ``ConstraintsHandler.apply()``.
+        physical-range validators. Flags the parameter as user
+        constrained. Used exclusively by ``ConstraintsHandler.apply()``.
         """
         self._value = v
-        self._constrained = True
+        self._user_constrained = True
         parent_datablock = self._datablock_item()
         if parent_datablock is not None:
             parent_datablock._need_categories_update = True
@@ -356,24 +356,24 @@ def free(self, v: bool) -> None:
         validated = self._free_spec.validated(
             v, name=f'{self.unique_name}.free', current=self._free
         )
-        if validated and self._symmetry_fixed:
+        if validated and self._symmetry_constrained:
             log.warning(
-                f"Parameter '{self.unique_name}' is fixed by symmetry. Ignoring free=True."
+                f"Parameter '{self.unique_name}' is constrained by symmetry. Ignoring free=True."
             )
             self._free = False
             return
         self._free = validated
 
     @property
-    def symmetry_fixed(self) -> bool:
+    def symmetry_constrained(self) -> bool:
         """
-        Whether this parameter is fixed by crystallographic symmetry.
+        Return whether symmetry constrains this parameter.
         """
-        return self._symmetry_fixed
+        return self._symmetry_constrained
 
-    def _set_symmetry_fixed(self, *, value: bool) -> None:
+    def _set_symmetry_constrained(self, *, value: bool) -> None:
         """
-        Mark or unmark this parameter as fixed by symmetry.
+        Mark or unmark this parameter as constrained by symmetry.
 
         When set to True, ``free`` is forced to False and any subsequent
         attempt to set ``free = True`` is ignored with a warning. When
@@ -383,14 +383,14 @@ def _set_symmetry_fixed(self, *, value: bool) -> None:
         Parameters
         ----------
         value : bool
-            New symmetry-fixed state.
+            New symmetry-constrained state.
         """
-        validated = self._symmetry_fixed_spec.validated(
+        validated = self._symmetry_constrained_spec.validated(
             value,
-            name=f'{self.unique_name}.symmetry_fixed',
-            current=self._symmetry_fixed,
+            name=f'{self.unique_name}.symmetry_constrained',
+            current=self._symmetry_constrained,
         )
-        self._symmetry_fixed = validated
+        self._symmetry_constrained = validated
         if validated:
             self._free = False
 
diff --git a/src/easydiffraction/crystallography/crystallography.py b/src/easydiffraction/crystallography/crystallography.py
index 7e97be444..4bc0dba5c 100644
--- a/src/easydiffraction/crystallography/crystallography.py
+++ b/src/easydiffraction/crystallography/crystallography.py
@@ -118,7 +118,7 @@ def _crystal_system_from_name_hm(name_hm: str) -> str | None:
     return crystal_system
 
 
-_CELL_FIXED_AXES_BY_SYSTEM: dict[str, set[str]] = {
+_CELL_CONSTRAINED_AXES_BY_SYSTEM: dict[str, set[str]] = {
     'cubic': {'lattice_b', 'lattice_c', 'angle_alpha', 'angle_beta', 'angle_gamma'},
     'tetragonal': {'lattice_b', 'angle_alpha', 'angle_beta', 'angle_gamma'},
     'orthorhombic': {'angle_alpha', 'angle_beta', 'angle_gamma'},
@@ -129,7 +129,7 @@ def _crystal_system_from_name_hm(name_hm: str) -> str | None:
 }
 
 
-def _cell_fixed_axes(crystal_system: str) -> set[str]:
+def _cell_constrained_axes(crystal_system: str) -> set[str]:
     """
     Return cell keys that are dependent on others for a crystal system.
 
@@ -145,14 +145,14 @@ def _cell_fixed_axes(crystal_system: str) -> set[str]:
     Returns
     -------
     set[str]
-        Subset of cell keys that are fixed by symmetry.
+        Subset of cell keys that are constrained by symmetry.
     """
-    return _CELL_FIXED_AXES_BY_SYSTEM.get(crystal_system, set())
+    return _CELL_CONSTRAINED_AXES_BY_SYSTEM.get(crystal_system, set())
 
 
-def cell_symmetry_fixed_flags(name_hm: str) -> dict[str, bool]:
+def cell_symmetry_constrained_flags(name_hm: str) -> dict[str, bool]:
     """
-    Return per-key flags indicating which cell parameters are fixed.
+    Return cell-parameter symmetry-constraint flags.
 
     Parameters
     ----------
@@ -162,16 +162,16 @@ def cell_symmetry_fixed_flags(name_hm: str) -> dict[str, bool]:
     Returns
     -------
     dict[str, bool]
-        Mapping of cell key to ``True`` when the parameter is fixed by
-        symmetry (dependent on another parameter or set to a fixed
-        angle), ``False`` when it is independent. Returns all keys
-        ``False`` when the space group cannot be resolved.
+        Mapping of cell key to ``True`` when the parameter is
+        constrained by symmetry (dependent on another parameter or set
+        to a fixed angle), ``False`` when it is independent. Returns all
+        keys ``False`` when the space group cannot be resolved.
     """
     crystal_system = _crystal_system_from_name_hm(name_hm)
     if crystal_system is None:
         return dict.fromkeys(_CELL_KEYS, False)
-    fixed = _cell_fixed_axes(crystal_system)
-    return {key: key in fixed for key in _CELL_KEYS}
+    constrained = _cell_constrained_axes(crystal_system)
+    return {key: key in constrained for key in _CELL_KEYS}
 
 
 def _get_wyckoff_exprs(
@@ -218,13 +218,13 @@ def _get_wyckoff_exprs(
     return [sympify(comp.strip()) for comp in components]
 
 
-def _fract_fixed_flags(parsed_exprs: list[Expr]) -> dict[str, bool]:
+def _fract_constrained_flags(parsed_exprs: list[Expr]) -> dict[str, bool]:
     """
-    Return per-axis flags marking coordinates fixed by site symmetry.
+    Return fractional-coordinate symmetry-constraint flags.
 
-    For each axis (x, y, z), the coordinate is considered fixed when the
-    corresponding symbol does not appear as a free symbol in any of the
-    Wyckoff position expressions.
+    For each axis (x, y, z), the coordinate is considered constrained
+    when the corresponding symbol does not appear as a free symbol in
+    any of the Wyckoff position expressions.
 
     Parameters
     ----------
@@ -235,7 +235,7 @@ def _fract_fixed_flags(parsed_exprs: list[Expr]) -> dict[str, bool]:
     -------
     dict[str, bool]
         Mapping ``'fract_x' / 'fract_y' / 'fract_z'`` to ``True`` if
-        that axis is fixed by symmetry.
+        that axis is constrained by symmetry.
     """
     x, y, z = symbols('x y z')
     symbols_xyz = (x, y, z)
@@ -271,10 +271,10 @@ def _apply_fract_constraints(
         'y': sympify(atom_site['fract_y']),
         'z': sympify(atom_site['fract_z']),
     }
-    fixed_flags = _fract_fixed_flags(parsed_exprs)
+    constrained_flags = _fract_constrained_flags(parsed_exprs)
 
     for i, axis in enumerate(axes):
-        if fixed_flags[f'fract_{axis}']:
+        if constrained_flags[f'fract_{axis}']:
             evaluated = simplify(parsed_exprs[i].subs(substitutions))
             atom_site[f'fract_{axis}'] = float(evaluated)
 
@@ -312,13 +312,13 @@ def apply_atom_site_symmetry_constraints(
     return atom_site
 
 
-def atom_site_symmetry_fixed_flags(
+def atom_site_symmetry_constrained_flags(
     name_hm: str,
     coord_code: int,
     wyckoff_letter: str,
 ) -> dict[str, bool]:
     """
-    Return per-axis flags marking coordinates fixed by site symmetry.
+    Return atom-site symmetry-constraint flags.
 
     Parameters
     ----------
@@ -339,7 +339,7 @@ def atom_site_symmetry_fixed_flags(
     parsed_exprs = _get_wyckoff_exprs(name_hm, coord_code, wyckoff_letter)
     if parsed_exprs is None:
         return {'fract_x': False, 'fract_y': False, 'fract_z': False}
-    return _fract_fixed_flags(parsed_exprs)
+    return _fract_constrained_flags(parsed_exprs)
 
 
 # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py b/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
index 0f8633010..b1e30aa0a 100644
--- a/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
+++ b/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
@@ -572,7 +572,7 @@ def _create_items_set_xcoord_and_id(self, values: object) -> None:
         # TODO: split into multiple methods
 
         # Create items
-        self._items = [self._item_type() for _ in range(values.size)]
+        self._adopt_items([self._item_type() for _ in range(values.size)])
 
         # Set two-theta values
         for p, v in zip(self._items, values, strict=True):
@@ -651,7 +651,7 @@ def _create_items_set_xcoord_and_id(self, values: object) -> None:
         # TODO: split into multiple methods
 
         # Create items
-        self._items = [self._item_type() for _ in range(values.size)]
+        self._adopt_items([self._item_type() for _ in range(values.size)])
 
         # Set time-of-flight values
         for p, v in zip(self._items, values, strict=True):
diff --git a/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py b/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
index 42b3ca8ab..31ac92e76 100644
--- a/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
+++ b/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
@@ -349,7 +349,7 @@ def _create_items_set_xcoord_and_id(self, values: object) -> None:
         # TODO: split into multiple methods
 
         # Create items
-        self._items = [self._item_type() for _ in range(values.size)]
+        self._adopt_items([self._item_type() for _ in range(values.size)])
 
         # Set r values
         for p, v in zip(self._items, values, strict=True):
diff --git a/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py b/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
index 52329abb0..ada32c70f 100644
--- a/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
+++ b/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
@@ -271,7 +271,7 @@ def _create_items_set_hkl_and_id(
         # TODO: split into multiple methods
 
         # Create items
-        self._items = [self._item_type() for _ in range(indices_h.size)]
+        self._adopt_items([self._item_type() for _ in range(indices_h.size)])
 
         # Set indices
         for item, index_h, index_k, index_l in zip(
diff --git a/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py b/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
index 562ad18d6..6c91a8561 100644
--- a/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
@@ -558,8 +558,8 @@ def _apply_atomic_coordinates_symmetry_constraints(self) -> None:
         Uses the parent structure's space-group symbol, IT coordinate
         system code and each atom's Wyckoff letter.  Atoms without a
         Wyckoff letter are silently skipped. Coordinates fully
-        determined by site symmetry are flagged as ``symmetry_fixed`` so
-        they cannot be marked refinable.
+        determined by site symmetry are flagged as
+        ``symmetry_constrained`` so they cannot be marked refinable.
         """
         structure = self._parent
         space_group_name = structure.space_group.name_h_m.value
@@ -568,7 +568,7 @@ def _apply_atomic_coordinates_symmetry_constraints(self) -> None:
             wl = atom.wyckoff_letter.value
             if not wl:
                 # TODO: Decide how to handle this case
-                self._clear_fract_symmetry_fixed(atom)
+                self._clear_fract_symmetry_constrained(atom)
                 continue
             dummy_atom = {
                 'fract_x': atom.fract_x.value,
@@ -581,7 +581,7 @@ def _apply_atomic_coordinates_symmetry_constraints(self) -> None:
                 coord_code=space_group_coord_code,
                 wyckoff_letter=wl,
             )
-            fixed_flags = ecr.atom_site_symmetry_fixed_flags(
+            constrained_flags = ecr.atom_site_symmetry_constrained_flags(
                 name_hm=space_group_name,
                 coord_code=space_group_coord_code,
                 wyckoff_letter=wl,
@@ -589,17 +589,17 @@ def _apply_atomic_coordinates_symmetry_constraints(self) -> None:
             atom.fract_x.value = dummy_atom['fract_x']
             atom.fract_y.value = dummy_atom['fract_y']
             atom.fract_z.value = dummy_atom['fract_z']
-            atom._fract_x._set_symmetry_fixed(value=fixed_flags['fract_x'])
-            atom._fract_y._set_symmetry_fixed(value=fixed_flags['fract_y'])
-            atom._fract_z._set_symmetry_fixed(value=fixed_flags['fract_z'])
+            atom._fract_x._set_symmetry_constrained(value=constrained_flags['fract_x'])
+            atom._fract_y._set_symmetry_constrained(value=constrained_flags['fract_y'])
+            atom._fract_z._set_symmetry_constrained(value=constrained_flags['fract_z'])
 
     @staticmethod
-    def _clear_fract_symmetry_fixed(atom: AtomSite) -> None:
+    def _clear_fract_symmetry_constrained(atom: AtomSite) -> None:
         """
-        Reset the ``symmetry_fixed`` flag on all fract coordinates.
+        Clear fractional-coordinate symmetry constraints.
         """
         for axis_param in (atom._fract_x, atom._fract_y, atom._fract_z):
-            axis_param._set_symmetry_fixed(value=False)
+            axis_param._set_symmetry_constrained(value=False)
 
     def _apply_adp_symmetry_constraints(self) -> None:
         """
@@ -607,9 +607,9 @@ def _apply_adp_symmetry_constraints(self) -> None:
 
         For each atom with an anisotropic ADP type and a Wyckoff letter,
         enforces the tensor constraints dictated by the site symmetry.
-        Tensor components fixed by symmetry are flagged as
-        ``symmetry_fixed`` (which also forces ``free = False``), and
-        ``adp_iso`` is flagged as fixed for all anisotropic atoms.
+        Tensor components constrained by symmetry are flagged as
+        ``symmetry_constrained`` (which also forces ``free = False``),
+        and ``adp_iso`` is flagged as fixed for all anisotropic atoms.
         """
         structure = self._parent
         aniso_types = {AdpTypeEnum.BANI.value, AdpTypeEnum.UANI.value}
@@ -620,7 +620,7 @@ def _apply_adp_symmetry_constraints(self) -> None:
         for atom in self._items:
             is_aniso = atom.adp_type.value in aniso_types
             # Isotropic ADP is not refinable for aniso atoms
-            atom._adp_iso._set_symmetry_fixed(value=is_aniso)
+            atom._adp_iso._set_symmetry_constrained(value=is_aniso)
             if not is_aniso:
                 continue
             wl = atom.wyckoff_letter.value
@@ -654,7 +654,7 @@ def _apply_adp_symmetry_constraints(self) -> None:
             for key, is_free in zip(adp_keys, ref_i, strict=False):
                 param = getattr(aniso_entry, key)
                 param.value = dummy[key]
-                param._set_symmetry_fixed(value=not is_free)
+                param._set_symmetry_constrained(value=not is_free)
 
     def _sync_iso_from_aniso(self) -> None:
         """
diff --git a/src/easydiffraction/datablocks/structure/categories/cell/default.py b/src/easydiffraction/datablocks/structure/categories/cell/default.py
index 4c02ab713..712629501 100644
--- a/src/easydiffraction/datablocks/structure/categories/cell/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/cell/default.py
@@ -106,7 +106,7 @@ def _apply_cell_symmetry_constraints(self) -> None:
         Uses the parent structure's space-group symbol to determine
         which lattice parameters are dependent and sets them
         accordingly. Dependent parameters are also flagged as
-        ``symmetry_fixed`` so they cannot be marked refinable.
+        ``symmetry_constrained`` so they cannot be marked refinable.
         """
         dummy_cell = {
             'lattice_a': self.length_a.value,
@@ -122,7 +122,7 @@ def _apply_cell_symmetry_constraints(self) -> None:
             cell=dummy_cell,
             name_hm=space_group_name,
         )
-        fixed_flags = ecr.cell_symmetry_fixed_flags(name_hm=space_group_name)
+        constrained_flags = ecr.cell_symmetry_constrained_flags(name_hm=space_group_name)
 
         param_by_key = {
             'lattice_a': self._length_a,
@@ -134,7 +134,7 @@ def _apply_cell_symmetry_constraints(self) -> None:
         }
         for key, param in param_by_key.items():
             param.value = dummy_cell[key]
-            param._set_symmetry_fixed(value=fixed_flags[key])
+            param._set_symmetry_constrained(value=constrained_flags[key])
 
     def _update(
         self,
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index b71e69e22..0527ac690 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -87,7 +87,7 @@ def format_param_value(param: object) -> str:
     - Free parameter with uncertainty: value with esd in brackets,
       e.g. ``3.89(20)``
 
-    Constrained (dependent) parameters are always written without
+    User-constrained (dependent) parameters are always written without
     brackets, even if their ``free`` flag is ``True``, because they are
     not independently varied by the minimizer.
 
@@ -98,7 +98,7 @@ def format_param_value(param: object) -> str:
     ----------
     param : object
         A descriptor or parameter exposing ``.value`` and optionally
-        ``.free``, ``.constrained``, and ``.uncertainty``.
+        ``.free``, ``.user_constrained``, and ``.uncertainty``.
 
     Returns
     -------
@@ -108,10 +108,10 @@ def format_param_value(param: object) -> str:
     from easydiffraction.core.variable import Parameter  # noqa: PLC0415
 
     is_free = param.free if isinstance(param, Parameter) else False
-    is_constrained = param.constrained if isinstance(param, Parameter) else False
+    is_user_constrained = param.user_constrained if isinstance(param, Parameter) else False
     value = param.value  # type: ignore[attr-defined]
 
-    if not is_free or is_constrained or not isinstance(value, (int, float)):
+    if not is_free or is_user_constrained or not isinstance(value, (int, float)):
         return format_value(value)
 
     precision = 8
@@ -279,13 +279,21 @@ def datablock_item_to_cif(
     parts: list[str] = [header]
 
     # First categories
-    parts.extend(v.as_cif for v in vars(datablock).values() if isinstance(v, CategoryItem))
+    parts.extend(
+        cif_text
+        for cif_text in (v.as_cif for v in vars(datablock).values() if isinstance(v, CategoryItem))
+        if cif_text
+    )
 
     # Then collections
     parts.extend(
-        category_collection_to_cif(v, max_display=max_loop_display)
-        for v in vars(datablock).values()
-        if isinstance(v, CategoryCollection)
+        cif_text
+        for cif_text in (
+            category_collection_to_cif(v, max_display=max_loop_display)
+            for v in vars(datablock).values()
+            if isinstance(v, CategoryCollection)
+        )
+        if cif_text
     )
 
     return '\n\n'.join(parts)
@@ -715,11 +723,7 @@ def category_collection_from_cif(
     array = np.array(loop.values, dtype=str).reshape(num_rows, num_cols)
 
     # Pre-create default items in the collection
-    self._items = [self._item_type() for _ in range(num_rows)]
-
-    # Set parent for each item to enable identity resolution
-    for item in self._items:
-        object.__setattr__(item, '_parent', self)  # noqa: PLC2801
+    self._adopt_items([self._item_type() for _ in range(num_rows)])
 
     # Set those items' parameters, which are present in the loop
     for row_idx in range(num_rows):
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index 74c510fac..2071463d5 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -13,6 +13,7 @@
 from easydiffraction.display.plotting import PlotterEngineEnum
 from easydiffraction.display.plotting import PosteriorPairPlotStyleEnum
 from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
+from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
 
 if TYPE_CHECKING:
@@ -68,6 +69,10 @@ def cif_uids(self) -> None:
         """Show CIF unique identifiers for all parameters."""
         self._project.analysis.display.parameter_cif_uids()
 
+    def help(self) -> None:
+        """Print available parameter-display methods."""
+        render_object_help(self)
+
 
 class FitDisplay:
     """Fit-report namespace under ``project.display``."""
@@ -103,6 +108,10 @@ def series(
         """Plot one fitted parameter across sequential results."""
         self._project.rendering.plotter.plot_param_series(param=param, versus=versus)
 
+    def help(self) -> None:
+        """Print available fit-display methods."""
+        render_object_help(self)
+
 
 class PosteriorDisplay:
     """Posterior-plot namespace under ``project.display``."""
@@ -150,6 +159,10 @@ def predictive(
             x=x,
         )
 
+    def help(self) -> None:
+        """Print available posterior-display methods."""
+        render_object_help(self)
+
 
 class ProjectDisplay:
     """Grouped display facade exposed as ``project.display``."""
@@ -175,6 +188,10 @@ def posterior(self) -> PosteriorDisplay:
         """Posterior-plot namespace."""
         return self._posterior
 
+    def help(self) -> None:
+        """Print display namespaces and methods."""
+        render_object_help(self)
+
     def pattern(
         self,
         expt_name: str,
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index 3f3ac4fb4..e7508201f 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -342,7 +342,7 @@ def save(self) -> None:
         console.print(self.info.path.resolve())
 
         # Apply constraints so dependent parameters are flagged
-        # before serialization (constrained params are written
+        # before serialization (user-constrained params are written
         # without brackets).
         self._analysis._update_categories()
 
diff --git a/src/easydiffraction/summary/summary.py b/src/easydiffraction/summary/summary.py
index 768abb4b2..27826dab7 100644
--- a/src/easydiffraction/summary/summary.py
+++ b/src/easydiffraction/summary/summary.py
@@ -6,6 +6,7 @@
 from easydiffraction.core.variable import Parameter
 from easydiffraction.io.cif.serialize import summary_to_cif
 from easydiffraction.utils.logging import console
+from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
 
 
@@ -28,6 +29,10 @@ def __init__(self, project: object) -> None:
         """
         self.project = project
 
+    def help(self) -> None:
+        """Print available summary-report methods."""
+        render_object_help(self)
+
     @staticmethod
     def _fmt_row(
         pretty_name: str,
diff --git a/src/easydiffraction/utils/utils.py b/src/easydiffraction/utils/utils.py
index 0e301e30c..a81f1cb0b 100644
--- a/src/easydiffraction/utils/utils.py
+++ b/src/easydiffraction/utils/utils.py
@@ -563,6 +563,91 @@ def render_table(
     tabler.render(df, display_handle=display_handle)
 
 
+def _help_first_sentence(docstring: str | None) -> str:
+    """Return the first paragraph of a docstring on one line."""
+    if not docstring:
+        return ''
+    first_para = docstring.strip().split('\n\n')[0]
+    return ' '.join(line.strip() for line in first_para.splitlines())
+
+
+def _help_property_rows(cls: type) -> list[list[str]]:
+    """Return public property rows for object help tables."""
+    seen: dict[str, property] = {}
+    for base in cls.mro():
+        for key, attr in base.__dict__.items():
+            if key.startswith('_') or not isinstance(attr, property):
+                continue
+            if key not in seen:
+                seen[key] = attr
+
+    rows = []
+    for i, key in enumerate(sorted(seen), 1):
+        prop = seen[key]
+        writable = '✓' if prop.fset else '✗'
+        doc = _help_first_sentence(prop.fget.__doc__ if prop.fget else None)
+        rows.append([str(i), key, writable, doc])
+    return rows
+
+
+def _help_method_rows(cls: type) -> list[list[str]]:
+    """Return public method rows for object help tables."""
+    seen: set[str] = set()
+    methods = []
+    for base in cls.mro():
+        for key, attr in base.__dict__.items():
+            if key.startswith('_') or key in seen:
+                continue
+            if isinstance(attr, property):
+                continue
+            raw = attr
+            if isinstance(raw, (staticmethod, classmethod)):
+                raw = raw.__func__
+            if callable(raw):
+                seen.add(key)
+                methods.append((key, raw))
+
+    rows = []
+    for i, (key, method) in enumerate(sorted(methods), 1):
+        doc = _help_first_sentence(getattr(method, '__doc__', None))
+        rows.append([str(i), f'{key}()', doc])
+    return rows
+
+
+def render_object_help(obj: object, title: str | None = None) -> None:
+    """
+    Print public properties and methods for a plain helper object.
+
+    Parameters
+    ----------
+    obj : object
+        Object whose public API should be summarized.
+    title : str | None, default=None
+        Optional display name. Uses the class name when omitted.
+    """
+    cls = type(obj)
+    display_title = title or cls.__name__
+    console.paragraph(f"Help for '{display_title}'")
+
+    prop_rows = _help_property_rows(cls)
+    if prop_rows:
+        console.paragraph('Properties')
+        render_table(
+            columns_headers=['#', 'Name', 'Writable', 'Description'],
+            columns_alignment=['right', 'left', 'center', 'left'],
+            columns_data=prop_rows,
+        )
+
+    method_rows = _help_method_rows(cls)
+    if method_rows:
+        console.paragraph('Methods')
+        render_table(
+            columns_headers=['#', 'Name', 'Description'],
+            columns_alignment=['right', 'left', 'left'],
+            columns_data=method_rows,
+        )
+
+
 def render_cif(cif_text: str) -> None:
     """
     Display CIF text as a formatted table in Jupyter or terminal.
diff --git a/tests/functional/test_structure_workflow.py b/tests/functional/test_structure_workflow.py
index a128e73d9..f2e4a9193 100644
--- a/tests/functional/test_structure_workflow.py
+++ b/tests/functional/test_structure_workflow.py
@@ -162,9 +162,9 @@ def test_special_position_fract_cannot_be_freed(self, monkeypatch):
         s._update_categories()
 
         atom = s.atom_sites['La']
-        assert atom.fract_x.symmetry_fixed is True
-        assert atom.fract_y.symmetry_fixed is True
-        assert atom.fract_z.symmetry_fixed is True
+        assert atom.fract_x.symmetry_constrained is True
+        assert atom.fract_y.symmetry_constrained is True
+        assert atom.fract_z.symmetry_constrained is True
 
         monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
         for parameter in ('fract_x', 'fract_y', 'fract_z'):
@@ -183,12 +183,12 @@ def test_cubic_cell_has_only_a_free(self):
         s._need_categories_update = True
         s._update_categories()
 
-        assert s.cell.length_a.symmetry_fixed is False
-        assert s.cell.length_b.symmetry_fixed is True
-        assert s.cell.length_c.symmetry_fixed is True
-        assert s.cell.angle_alpha.symmetry_fixed is True
-        assert s.cell.angle_beta.symmetry_fixed is True
-        assert s.cell.angle_gamma.symmetry_fixed is True
+        assert s.cell.length_a.symmetry_constrained is False
+        assert s.cell.length_b.symmetry_constrained is True
+        assert s.cell.length_c.symmetry_constrained is True
+        assert s.cell.angle_alpha.symmetry_constrained is True
+        assert s.cell.angle_beta.symmetry_constrained is True
+        assert s.cell.angle_gamma.symmetry_constrained is True
 
     def test_general_position_remains_refinable(self):
         project = _make_project()
@@ -208,7 +208,7 @@ def test_general_position_remains_refinable(self):
         s._update_categories()
 
         atom = s.atom_sites['La']
-        assert atom.fract_x.symmetry_fixed is False
+        assert atom.fract_x.symmetry_constrained is False
         atom.fract_x.free = True
         assert atom.fract_x.free is True
 
@@ -222,14 +222,14 @@ def test_changing_space_group_updates_flags(self, monkeypatch):
         # Start in P 1: cell free
         s._need_categories_update = True
         s._update_categories()
-        assert s.cell.length_b.symmetry_fixed is False
+        assert s.cell.length_b.symmetry_constrained is False
         s.cell.length_b.free = True
         assert s.cell.length_b.free is True
 
         # Switch to cubic: length_b becomes fixed
         s.space_group.name_h_m = 'P m -3 m'
         s._update_categories()
-        assert s.cell.length_b.symmetry_fixed is True
+        assert s.cell.length_b.symmetry_constrained is True
         assert s.cell.length_b.free is False
 
         # Setting free=True is now ignored with a warning
diff --git a/tests/integration/fitting/test_exploration_help.py b/tests/integration/fitting/test_exploration_help.py
index d86004691..08e2bcf54 100644
--- a/tests/integration/fitting/test_exploration_help.py
+++ b/tests/integration/fitting/test_exploration_help.py
@@ -65,6 +65,27 @@ def test_experiment_show_as_cif(lbco_fitted_project):
     expt.show_as_cif()
 
 
+def test_experiment_show_as_cif_omits_empty_category_gaps(lbco_fitted_project, monkeypatch):
+    import re
+
+    import easydiffraction.datablocks.experiment.item.base as experiment_base
+
+    captured = {}
+
+    def fake_render_cif(cif_text):
+        captured['cif_text'] = cif_text
+
+    monkeypatch.setattr(experiment_base, 'render_cif', fake_render_cif)
+
+    project = lbco_fitted_project
+    expt = project.experiments['hrpt']
+    expt.show_as_cif()
+
+    cif_text = captured['cif_text']
+    assert re.search(r'_pd_phase_block\.scale\n[^\n]+\n\nloop_', cif_text) is not None
+    assert '\n\n\n' not in cif_text
+
+
 def test_experiment_as_cif(lbco_fitted_project):
     project = lbco_fitted_project
     expt = project.experiments['hrpt']
diff --git a/tests/unit/easydiffraction/analysis/test_analysis.py b/tests/unit/easydiffraction/analysis/test_analysis.py
index a4d2c7faa..c5b2ef248 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis.py
@@ -67,6 +67,18 @@ def test_analysis_help(capsys):
     assert 'fit_sequential()' in out
 
 
+def test_analysis_display_help(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    a = Analysis(project=_make_project_with_names([]))
+    a.display.help()
+    out = capsys.readouterr().out
+    assert "Help for 'AnalysisDisplay'" in out
+    assert 'all_params()' in out
+    assert 'fit_results()' in out
+    assert 'how_to_access_parameters()' in out
+
+
 def test_display_fit_results_warns_when_no_results(capsys):
     """Test that display.fit_results logs a warning when fit() has not been run."""
     from easydiffraction.analysis.analysis import Analysis
diff --git a/tests/unit/easydiffraction/analysis/test_analysis_access_params.py b/tests/unit/easydiffraction/analysis/test_analysis_access_params.py
index b7d9f8956..96667689b 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis_access_params.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis_access_params.py
@@ -2,32 +2,45 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 
-def test_how_to_access_parameters_prints_paths_and_uids(capsys, monkeypatch):
-    import easydiffraction.analysis.analysis as analysis_mod
-    from easydiffraction.analysis.analysis import Analysis
+def _make_param(
+    db,
+    cat,
+    entry,
+    name,
+    val,
+    *,
+    user_constrained=False,
+    symmetry_constrained=False,
+):
     from easydiffraction.core.validation import AttributeSpec
     from easydiffraction.core.variable import Parameter
     from easydiffraction.io.cif.handler import CifHandler
 
-    # Build two parameters with identity metadata set directly
-    def make_param(db, cat, entry, name, val):
-        p = Parameter(
-            name=name,
-            value_spec=AttributeSpec(default=0.0),
-            cif_handler=CifHandler(names=[f'_{cat}.{name}']),
-        )
-        p.value = val
-        # Inject identity metadata (avoid parent chain)
-        p._identity.datablock_entry_name = lambda: db
-        p._identity.category_code = cat
-        if entry:
-            p._identity.category_entry_name = lambda: entry
-        else:
-            p._identity.category_entry_name = lambda: ''
-        return p
-
-    p1 = make_param('db1', 'catA', '', 'alpha', 1.0)
-    p2 = make_param('db2', 'catB', 'row1', 'beta', 2.0)
+    param = Parameter(
+        name=name,
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=[f'_{cat}.{name}']),
+    )
+    param.value = val
+    param._identity.datablock_entry_name = lambda: db
+    param._identity.category_code = cat
+    if entry:
+        param._identity.category_entry_name = lambda: entry
+    else:
+        param._identity.category_entry_name = lambda: ''
+    if user_constrained:
+        param._user_constrained = True
+    if symmetry_constrained:
+        param._set_symmetry_constrained(value=True)
+    return param
+
+
+def test_how_to_access_parameters_prints_paths_and_uids(capsys, monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    p1 = _make_param('db1', 'catA', '', 'alpha', 1.0)
+    p2 = _make_param('db2', 'catB', 'row1', 'beta', 2.0)
 
     class Coll:
         def __init__(self, params):
@@ -82,3 +95,203 @@ def fake_render_table2(**kwargs):
     # Unique names are datablock.category[.entry].parameter
     assert any('db1 catA  alpha' in r.replace('.', ' ') for r in flat_rows2)
     assert any('db2 catB row1 beta' in r.replace('.', ' ') for r in flat_rows2)
+
+
+def test_how_to_access_parameters_skips_large_loop_categories(capsys, monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    visible = _make_param('db1', 'catA', '', 'alpha', 1.0)
+    data_param = _make_param('db2', 'pd_data', '1', 'intensity_meas', 2.0)
+    refln_param = _make_param('db2', 'refln', '1', 'f_calc', 3.0)
+
+    class Coll:
+        def __init__(self, params):
+            self.parameters = params
+
+    class Project:
+        _varname = 'proj'
+
+        def __init__(self):
+            self.structures = Coll([visible])
+            self.experiments = Coll([data_param, refln_param])
+
+    captured = {}
+
+    def fake_render_table(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(analysis_mod, 'render_table', fake_render_table)
+    Analysis(Project()).display.how_to_access_parameters()
+
+    out = capsys.readouterr().out
+    assert 'How to access parameters' in out
+
+    flat_rows = [' '.join(map(str, row)) for row in captured.get('columns_data') or []]
+    assert any("proj.structures['db1'].catA.alpha" in row for row in flat_rows)
+    assert not any('pd_data' in row for row in flat_rows)
+    assert not any('refln' in row for row in flat_rows)
+
+
+def test_parameter_cif_uids_skips_large_loop_categories(monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    visible = _make_param('db1', 'catA', '', 'alpha', 1.0)
+    data_param = _make_param('db2', 'pd_data', '1', 'intensity_meas', 2.0)
+    refln_param = _make_param('db2', 'refln', '1', 'f_calc', 3.0)
+
+    class Coll:
+        def __init__(self, params):
+            self.parameters = params
+
+    class Project:
+        _varname = 'proj'
+
+        def __init__(self):
+            self.structures = Coll([visible])
+            self.experiments = Coll([data_param, refln_param])
+
+    captured = {}
+
+    def fake_render_table(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(analysis_mod, 'render_table', fake_render_table)
+    Analysis(Project()).display.parameter_cif_uids()
+
+    flat_rows = [' '.join(map(str, row)) for row in captured.get('columns_data') or []]
+    assert any('db1 catA  alpha' in row.replace('.', ' ') for row in flat_rows)
+    assert not any('pd_data' in row for row in flat_rows)
+    assert not any('refln' in row for row in flat_rows)
+
+
+def test_all_params_skips_large_loop_categories(monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    structure_param = _make_param('s1', 'cell', '', 'length_a', 4.0)
+    visible_experiment_param = _make_param('e1', 'instrument', '', 'wavelength', 1.5)
+    data_param = _make_param('e1', 'pd_data', '1', 'intensity_meas', 10.0)
+    refln_param = _make_param('e1', 'refln', '1', 'f_calc', 12.0)
+
+    class Coll:
+        def __init__(self, params):
+            self.parameters = params
+
+        def __iter__(self):
+            return iter(())
+
+    class Project:
+        def __init__(self):
+            self.structures = Coll([structure_param])
+            self.experiments = Coll([visible_experiment_param, data_param, refln_param])
+
+    rendered = []
+
+    class FakeTableRenderer:
+        def render(self, df):
+            rendered.append(df)
+
+    monkeypatch.setattr(
+        analysis_mod.TableRenderer, 'get', staticmethod(lambda: FakeTableRenderer())
+    )
+    Analysis(Project()).display.all_params()
+
+    assert len(rendered) == 2
+    experiment_categories = rendered[1]['category', 'left'].tolist()
+    experiment_parameters = rendered[1]['parameter', 'left'].tolist()
+    assert experiment_categories == ['instrument']
+    assert experiment_parameters == ['wavelength']
+
+
+def test_all_params_marks_constrained_parameters_not_fittable(monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    refinable_param = _make_param('s1', 'cell', '', 'length_a', 4.0)
+    user_constrained_param = _make_param('s1', 'cell', '', 'length_b', 4.0, user_constrained=True)
+    symmetry_constrained_param = _make_param(
+        's1',
+        'cell',
+        '',
+        'length_c',
+        4.0,
+        symmetry_constrained=True,
+    )
+
+    class Coll:
+        def __init__(self, params):
+            self.parameters = params
+
+        def __iter__(self):
+            return iter(())
+
+    class Project:
+        def __init__(self):
+            self.structures = Coll([
+                refinable_param,
+                user_constrained_param,
+                symmetry_constrained_param,
+            ])
+            self.experiments = Coll([])
+
+    rendered = []
+
+    class FakeTableRenderer:
+        def render(self, df):
+            rendered.append(df)
+
+    monkeypatch.setattr(
+        analysis_mod.TableRenderer, 'get', staticmethod(lambda: FakeTableRenderer())
+    )
+    Analysis(Project()).display.all_params()
+
+    structure_df = rendered[0]
+    assert structure_df['parameter', 'left'].tolist() == ['length_a', 'length_b', 'length_c']
+    assert structure_df['fittable', 'left'].tolist() == [True, False, False]
+
+
+def test_fittable_params_excludes_symmetry_constrained_parameters(monkeypatch):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    visible_param = _make_param('s1', 'cell', '', 'length_a', 4.0)
+    symmetry_constrained_param = _make_param(
+        's1',
+        'cell',
+        '',
+        'length_c',
+        4.0,
+        symmetry_constrained=True,
+    )
+
+    class Coll:
+        def __init__(self, params, fittable_params):
+            self.parameters = params
+            self.fittable_parameters = fittable_params
+
+        def __iter__(self):
+            return iter(())
+
+    class Project:
+        def __init__(self):
+            self.structures = Coll(
+                [visible_param, symmetry_constrained_param],
+                [visible_param],
+            )
+            self.experiments = Coll([], [])
+
+    rendered = []
+
+    class FakeTableRenderer:
+        def render(self, df):
+            rendered.append(df)
+
+    monkeypatch.setattr(
+        analysis_mod.TableRenderer, 'get', staticmethod(lambda: FakeTableRenderer())
+    )
+    Analysis(Project()).display.fittable_params()
+
+    structure_df = rendered[0]
+    assert structure_df['parameter', 'left'].tolist() == ['length_a']
diff --git a/tests/unit/easydiffraction/core/test_datablock.py b/tests/unit/easydiffraction/core/test_datablock.py
index b41f68b74..98d0dc252 100644
--- a/tests/unit/easydiffraction/core/test_datablock.py
+++ b/tests/unit/easydiffraction/core/test_datablock.py
@@ -33,8 +33,8 @@ def __init__(self):
             # Set actual values via setter
             self._p1.value = 1.0
             self._p2.value = 2.0
-            # Make p2 constrained and not free
-            self._p2._constrained = True
+            # Make p2 user constrained and not free
+            self._p2._user_constrained = True
             self._p2._free = False
             # Mark p1 free to be included in free_parameters
             self._p1.free = True
@@ -67,7 +67,7 @@ def cat(self):
     # parameters collection aggregates from both blocks (p1 & p2 each)
     params = coll.parameters
     assert len(params) == 4
-    # fittable excludes constrained parameters
+    # fittable excludes user-constrained parameters
     fittable = coll.fittable_parameters
     assert all(isinstance(p, Parameter) for p in fittable)
     assert len(fittable) == 2  # only p1 from each block
@@ -76,6 +76,65 @@ def cat(self):
     assert free_params == fittable
 
 
+def test_datablock_collection_fittable_excludes_symmetry_constrained_parameters():
+    from easydiffraction.core.category import CategoryItem
+    from easydiffraction.core.datablock import DatablockCollection
+    from easydiffraction.core.datablock import DatablockItem
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    class Cat(CategoryItem):
+        def __init__(self):
+            super().__init__()
+            self._identity.category_code = 'cat'
+            self._identity.category_entry_name = 'e1'
+            self._free_param = Parameter(
+                name='free_param',
+                description='',
+                value_spec=AttributeSpec(default=0.0),
+                units='',
+                cif_handler=CifHandler(names=['_cat.free_param']),
+            )
+            self._fixed_param = Parameter(
+                name='fixed_param',
+                description='',
+                value_spec=AttributeSpec(default=0.0),
+                units='',
+                cif_handler=CifHandler(names=['_cat.fixed_param']),
+            )
+            self._free_param.value = 1.0
+            self._fixed_param.value = 2.0
+            self._free_param.free = True
+            self._fixed_param._set_symmetry_constrained(value=True)
+
+        @property
+        def free_param(self):
+            return self._free_param
+
+        @property
+        def fixed_param(self):
+            return self._fixed_param
+
+    class Block(DatablockItem):
+        def __init__(self, name):
+            super().__init__()
+            self._identity.datablock_entry_name = lambda: name
+            self._cat = Cat()
+
+        @property
+        def cat(self):
+            return self._cat
+
+    coll = DatablockCollection(item_type=Block)
+    coll.add(Block('A'))
+
+    fittable = coll.fittable_parameters
+
+    assert all(isinstance(p, Parameter) for p in fittable)
+    assert [p.name for p in fittable] == ['free_param']
+
+
 def test_datablock_item_help(capsys):
     from easydiffraction.core.category import CategoryItem
     from easydiffraction.core.datablock import DatablockItem
diff --git a/tests/unit/easydiffraction/core/test_parameters.py b/tests/unit/easydiffraction/core/test_parameters.py
index a6c5b7adf..f81e35e87 100644
--- a/tests/unit/easydiffraction/core/test_parameters.py
+++ b/tests/unit/easydiffraction/core/test_parameters.py
@@ -199,43 +199,43 @@ def _make_param() -> object:
     )
 
 
-def test_parameter_symmetry_fixed_default_is_false():
+def test_parameter_symmetry_constrained_default_is_false():
     p = _make_param()
-    assert p.symmetry_fixed is False
+    assert p.symmetry_constrained is False
 
 
-def test_parameter_set_symmetry_fixed_forces_free_false():
+def test_parameter_set_symmetry_constrained_forces_free_false():
     p = _make_param()
     p.free = True
     assert p.free is True
-    p._set_symmetry_fixed(value=True)
-    assert p.symmetry_fixed is True
+    p._set_symmetry_constrained(value=True)
+    assert p.symmetry_constrained is True
     assert p.free is False
 
 
-def test_parameter_free_true_ignored_when_symmetry_fixed(monkeypatch):
+def test_parameter_free_true_ignored_when_symmetry_constrained(monkeypatch):
     from easydiffraction.utils.logging import Logger
 
     p = _make_param()
-    p._set_symmetry_fixed(value=True)
+    p._set_symmetry_constrained(value=True)
     monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
     p.free = True
     assert p.free is False
-    assert p.symmetry_fixed is True
+    assert p.symmetry_constrained is True
     monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
 
 
-def test_parameter_free_false_allowed_when_symmetry_fixed():
+def test_parameter_free_false_allowed_when_symmetry_constrained():
     p = _make_param()
-    p._set_symmetry_fixed(value=True)
+    p._set_symmetry_constrained(value=True)
     p.free = False  # should not warn or raise
     assert p.free is False
 
 
-def test_parameter_clearing_symmetry_fixed_allows_free_true():
+def test_parameter_clearing_symmetry_constrained_allows_free_true():
     p = _make_param()
-    p._set_symmetry_fixed(value=True)
-    p._set_symmetry_fixed(value=False)
+    p._set_symmetry_constrained(value=True)
+    p._set_symmetry_constrained(value=False)
     p.free = True
     assert p.free is True
-    assert p.symmetry_fixed is False
+    assert p.symmetry_constrained is False
diff --git a/tests/unit/easydiffraction/crystallography/test_crystallography_coverage.py b/tests/unit/easydiffraction/crystallography/test_crystallography_coverage.py
index 13590dbea..7b548aca0 100644
--- a/tests/unit/easydiffraction/crystallography/test_crystallography_coverage.py
+++ b/tests/unit/easydiffraction/crystallography/test_crystallography_coverage.py
@@ -91,25 +91,29 @@ def test_triclinic(self):
         assert result['angle_beta'] == 85.0
         assert result['angle_gamma'] == 75.0
 
-    def test_invalid_name_hm_returns_cell_unchanged(self):
+    def test_invalid_name_hm_returns_cell_unchanged(self, monkeypatch):
+        from easydiffraction.utils.logging import Logger
+
         cell = _make_cell()
         original = dict(cell)
+        monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
         result = apply_cell_symmetry_constraints(cell, 'NOT A REAL SG')
         assert result == original
+        monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
 
 
 # ------------------------------------------------------------------
-# cell_symmetry_fixed_flags
+# cell_symmetry_constrained_flags
 # ------------------------------------------------------------------
 
 
-class TestCellSymmetryFixedFlags:
+class TestCellSymmetryConstrainedFlags:
     def test_cubic_only_a_is_free(self):
         from easydiffraction.crystallography.crystallography import (
-            cell_symmetry_fixed_flags,
+            cell_symmetry_constrained_flags,
         )
 
-        flags = cell_symmetry_fixed_flags('F m -3 m')
+        flags = cell_symmetry_constrained_flags('F m -3 m')
         assert flags == {
             'lattice_a': False,
             'lattice_b': True,
@@ -121,10 +125,10 @@ def test_cubic_only_a_is_free(self):
 
     def test_monoclinic_b_and_beta_free(self):
         from easydiffraction.crystallography.crystallography import (
-            cell_symmetry_fixed_flags,
+            cell_symmetry_constrained_flags,
         )
 
-        flags = cell_symmetry_fixed_flags('P 21/c')
+        flags = cell_symmetry_constrained_flags('P 21/c')
         assert flags['lattice_a'] is False
         assert flags['lattice_b'] is False
         assert flags['lattice_c'] is False
@@ -134,19 +138,19 @@ def test_monoclinic_b_and_beta_free(self):
 
     def test_triclinic_all_free(self):
         from easydiffraction.crystallography.crystallography import (
-            cell_symmetry_fixed_flags,
+            cell_symmetry_constrained_flags,
         )
 
-        flags = cell_symmetry_fixed_flags('P 1')
+        flags = cell_symmetry_constrained_flags('P 1')
         assert all(v is False for v in flags.values())
 
     def test_invalid_returns_all_false(self, monkeypatch):
         from easydiffraction.crystallography.crystallography import (
-            cell_symmetry_fixed_flags,
+            cell_symmetry_constrained_flags,
         )
         from easydiffraction.utils.logging import Logger
 
         monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
-        flags = cell_symmetry_fixed_flags('NOT A REAL SG')
+        flags = cell_symmetry_constrained_flags('NOT A REAL SG')
         assert all(v is False for v in flags.values())
         monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
diff --git a/tests/unit/easydiffraction/crystallography/test_crystallography_wyckoff.py b/tests/unit/easydiffraction/crystallography/test_crystallography_wyckoff.py
index 952f3004c..377ed7f2f 100644
--- a/tests/unit/easydiffraction/crystallography/test_crystallography_wyckoff.py
+++ b/tests/unit/easydiffraction/crystallography/test_crystallography_wyckoff.py
@@ -55,32 +55,32 @@ def test_valid_applies_constraints(self):
         assert result is not None
 
 
-class TestAtomSiteSymmetryFixedFlags:
+class TestAtomSiteSymmetryConstrainedFlags:
     def test_special_position_all_fixed(self):
         from easydiffraction.crystallography.crystallography import (
-            atom_site_symmetry_fixed_flags,
+            atom_site_symmetry_constrained_flags,
         )
 
         # P m -3 m (IT 221), Wyckoff 'a' = (0,0,0): all three axes fixed
-        flags = atom_site_symmetry_fixed_flags('P m -3 m', '1', 'a')
+        flags = atom_site_symmetry_constrained_flags('P m -3 m', '1', 'a')
         assert flags == {'fract_x': True, 'fract_y': True, 'fract_z': True}
 
     def test_general_position_all_free(self):
         from easydiffraction.crystallography.crystallography import (
-            atom_site_symmetry_fixed_flags,
+            atom_site_symmetry_constrained_flags,
         )
 
         # P 1 (IT 1), Wyckoff 'a' is the general position
-        flags = atom_site_symmetry_fixed_flags('P 1', '1', 'a')
+        flags = atom_site_symmetry_constrained_flags('P 1', '1', 'a')
         assert flags == {'fract_x': False, 'fract_y': False, 'fract_z': False}
 
     def test_invalid_returns_all_false(self, monkeypatch):
         from easydiffraction.crystallography.crystallography import (
-            atom_site_symmetry_fixed_flags,
+            atom_site_symmetry_constrained_flags,
         )
         from easydiffraction.utils.logging import Logger
 
         monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
-        flags = atom_site_symmetry_fixed_flags('NOT REAL', None, 'a')
+        flags = atom_site_symmetry_constrained_flags('NOT REAL', None, 'a')
         assert flags == {'fract_x': False, 'fract_y': False, 'fract_z': False}
         monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
diff --git a/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_bragg_pd.py b/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_bragg_pd.py
index 1a3f233cb..c50a095e5 100644
--- a/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_bragg_pd.py
+++ b/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_bragg_pd.py
@@ -4,6 +4,18 @@
 import numpy as np
 
 
+def _experiment_stub(name='exp1'):
+    from easydiffraction.core.identity import Identity
+
+    class ExperimentStub:
+        def __init__(self):
+            self._parent = None
+            self._identity = Identity(owner=self)
+            self._identity.datablock_entry_name = lambda: name
+
+    return ExperimentStub()
+
+
 def test_pd_cwl_data_point_defaults():
     from easydiffraction.datablocks.experiment.categories.data.bragg_pd import PdCwlDataPoint
 
@@ -95,6 +107,19 @@ def test_pd_tof_data_collection_create_and_properties():
     assert coll._items[2].point_id.value == '3'
 
 
+def test_pd_data_items_resolve_experiment_datablock_name():
+    from easydiffraction.datablocks.experiment.categories.data.bragg_pd import PdCwlData
+
+    coll = PdCwlData()
+    coll._parent = _experiment_stub('hrpt')
+
+    coll._create_items_set_xcoord_and_id(np.array([10.0, 20.0]))
+
+    param = coll._items[0].intensity_meas
+    assert param._identity.datablock_entry_name == 'hrpt'
+    assert param.unique_name == 'hrpt.pd_data.1.intensity_meas'
+
+
 def test_pd_data_calc_status_exclusion():
     from easydiffraction.datablocks.experiment.categories.data.bragg_pd import PdCwlData
 
diff --git a/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_total_pd.py b/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_total_pd.py
index 41e638e58..3198158e2 100644
--- a/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_total_pd.py
+++ b/tests/unit/easydiffraction/datablocks/experiment/categories/data/test_total_pd.py
@@ -4,6 +4,18 @@
 import numpy as np
 
 
+def _experiment_stub(name='exp1'):
+    from easydiffraction.core.identity import Identity
+
+    class ExperimentStub:
+        def __init__(self):
+            self._parent = None
+            self._identity = Identity(owner=self)
+            self._identity.datablock_entry_name = lambda: name
+
+    return ExperimentStub()
+
+
 def test_total_data_point_defaults():
     from easydiffraction.datablocks.experiment.categories.data.total_pd import TotalDataPoint
 
@@ -89,3 +101,16 @@ def test_total_data_type_info():
 
     assert TotalData.type_info.tag == 'total-pd'
     assert TotalData.type_info.description == 'Total scattering (PDF) data'
+
+
+def test_total_data_items_resolve_experiment_datablock_name():
+    from easydiffraction.datablocks.experiment.categories.data.total_pd import TotalData
+
+    coll = TotalData()
+    coll._parent = _experiment_stub('pdf-exp')
+
+    coll._create_items_set_xcoord_and_id(np.array([1.0, 2.0]))
+
+    param = coll._items[0].g_r_meas
+    assert param._identity.datablock_entry_name == 'pdf-exp'
+    assert param.unique_name == 'pdf-exp.total_data.1.g_r_meas'
diff --git a/tests/unit/easydiffraction/datablocks/experiment/categories/refln/test_bragg_sc.py b/tests/unit/easydiffraction/datablocks/experiment/categories/refln/test_bragg_sc.py
index fc7175d4a..ffae191b7 100644
--- a/tests/unit/easydiffraction/datablocks/experiment/categories/refln/test_bragg_sc.py
+++ b/tests/unit/easydiffraction/datablocks/experiment/categories/refln/test_bragg_sc.py
@@ -4,6 +4,18 @@
 import numpy as np
 
 
+def _experiment_stub(name='exp1'):
+    from easydiffraction.core.identity import Identity
+
+    class ExperimentStub:
+        def __init__(self):
+            self._parent = None
+            self._identity = Identity(owner=self)
+            self._identity.datablock_entry_name = lambda: name
+
+    return ExperimentStub()
+
+
 def test_refln_data_point_defaults():
     from easydiffraction.datablocks.experiment.categories.refln.bragg_sc import Refln
 
@@ -74,6 +86,23 @@ def test_refln_data_d_spacing_and_stol():
     np.testing.assert_array_almost_equal(coll.sin_theta_over_lambda, stol)
 
 
+def test_refln_items_resolve_experiment_datablock_name():
+    from easydiffraction.datablocks.experiment.categories.refln.bragg_sc import ReflnData
+
+    coll = ReflnData()
+    coll._parent = _experiment_stub('sc-exp')
+
+    coll._create_items_set_hkl_and_id(
+        np.array([1.0, 2.0]),
+        np.array([0.0, 0.0]),
+        np.array([0.0, 1.0]),
+    )
+
+    param = coll._items[0].intensity_meas
+    assert param._identity.datablock_entry_name == 'sc-exp'
+    assert param.unique_name == 'sc-exp.refln.1.intensity_meas'
+
+
 def test_refln_data_type_info():
     from easydiffraction.datablocks.experiment.categories.refln.bragg_sc import ReflnData
 
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize_more.py b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
index 0f98fe1a7..1add7692d 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize_more.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
@@ -40,6 +40,50 @@ def __init__(self):
     assert '7' in out
 
 
+def test_datablock_item_to_cif_skips_empty_category_fragments():
+    import easydiffraction.io.cif.serialize as MUT
+    from easydiffraction.core.category import CategoryCollection
+    from easydiffraction.core.category import CategoryItem
+    from easydiffraction.io.cif.handler import CifHandler
+
+    class Item(CategoryItem):
+        def __init__(self, val):
+            super().__init__()
+            self._p = type('P', (), {})()
+            self._p._cif_handler = CifHandler(names=['_aa'])
+            self._p.value = val
+
+        @property
+        def parameters(self):
+            return [self._p]
+
+        @property
+        def as_cif(self) -> str:
+            return MUT.category_item_to_cif(self)
+
+    class EmptyItem(CategoryItem):
+        @property
+        def parameters(self):
+            return []
+
+        @property
+        def as_cif(self) -> str:
+            return ''
+
+    class DB:
+        def __init__(self):
+            self._identity = type('I', (), {'datablock_entry_name': 'block1'})()
+            self.item = Item(42)
+            self.empty_item = EmptyItem()
+            self.coll = CategoryCollection(item_type=Item)
+            self.coll['row1'] = Item(7)
+            self.empty_coll = CategoryCollection(item_type=Item)
+
+    out = MUT.datablock_item_to_cif(DB())
+    assert out == 'data_block1\n\n_aa 42.\n\nloop_\n_aa\n7.'
+    assert '\n\n\n' not in out
+
+
 def test_datablock_collection_to_cif_concatenates_blocks():
     import easydiffraction.io.cif.serialize as MUT
 
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index 46f22296a..8e8f7695d 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -141,6 +141,41 @@ def test_parameter_display_delegates_to_analysis_display():
     ]
 
 
+def test_project_display_help_lists_namespaces_and_methods(capsys):
+    project, _calls = _make_project_stub()
+    display = ProjectDisplay(project)
+
+    display.help()
+    out = capsys.readouterr().out
+
+    assert "Help for 'ProjectDisplay'" in out
+    assert 'parameters' in out
+    assert 'fit' in out
+    assert 'posterior' in out
+    assert 'pattern()' in out
+    assert 'show_pattern_options()' in out
+
+
+def test_nested_project_display_help_lists_methods(capsys):
+    project, _calls = _make_project_stub()
+    display = ProjectDisplay(project)
+
+    display.parameters.help()
+    display.fit.help()
+    display.posterior.help()
+    out = capsys.readouterr().out
+
+    assert "Help for 'ParameterDisplay'" in out
+    assert 'all()' in out
+    assert 'access()' in out
+    assert "Help for 'FitDisplay'" in out
+    assert 'results()' in out
+    assert 'correlations()' in out
+    assert "Help for 'PosteriorDisplay'" in out
+    assert 'pairs()' in out
+    assert 'predictive()' in out
+
+
 def test_fit_display_delegates_to_analysis_and_rendering():
     project, calls = _make_project_stub()
     display = ProjectDisplay(project)
diff --git a/tests/unit/easydiffraction/summary/test_summary.py b/tests/unit/easydiffraction/summary/test_summary.py
index b8af02667..6c34baf11 100644
--- a/tests/unit/easydiffraction/summary/test_summary.py
+++ b/tests/unit/easydiffraction/summary/test_summary.py
@@ -50,6 +50,21 @@ class R:
     assert 'FITTING' in out
 
 
+def test_summary_help(capsys):
+    from easydiffraction.summary.summary import Summary
+
+    class P:
+        pass
+
+    s = Summary(P())
+    s.help()
+    out = capsys.readouterr().out
+    assert "Help for 'Summary'" in out
+    assert 'show_report()' in out
+    assert 'show_project_info()' in out
+    assert 'show_fitting_details()' in out
+
+
 def test_module_import():
     import easydiffraction.summary.summary as MUT
 
diff --git a/tests/unit/easydiffraction/utils/test_utils.py b/tests/unit/easydiffraction/utils/test_utils.py
index 13564ca44..8c3d2e760 100644
--- a/tests/unit/easydiffraction/utils/test_utils.py
+++ b/tests/unit/easydiffraction/utils/test_utils.py
@@ -142,6 +142,33 @@ def test_render_table_terminal_branch(capsys, monkeypatch):
     assert ('╒' in out and '╕' in out) or ('┌' in out and '┐' in out)
 
 
+def test_render_object_help_prints_public_api(capsys):
+    import easydiffraction.utils.utils as MUT
+
+    class Example:
+        @property
+        def value(self):
+            """Visible value."""
+            return 1
+
+        def run(self):
+            """Run visible action."""
+
+        def _hidden(self):
+            """Hidden action."""
+
+    MUT.render_object_help(Example())
+    out = capsys.readouterr().out
+    assert "Help for 'Example'" in out
+    assert 'Properties' in out
+    assert 'value' in out
+    assert 'Visible value.' in out
+    assert 'Methods' in out
+    assert 'run()' in out
+    assert 'Run visible action.' in out
+    assert '_hidden' not in out
+
+
 def test_is_dev_version_with_dev_suffix(monkeypatch):
     import easydiffraction.utils.utils as MUT
 

From 2d759a95ed1b1fe0c0a7f04a84f20cfa967adeda Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Fri, 15 May 2026 07:34:42 +0200
Subject: [PATCH 05/10] Improve live fitting feedback and posterior uncertainty
 displays (#173)

* Add shared activity indicator helper

* Wire fit tracker activity indicator

* Reuse activity indicator for sequential fitting

* Restore shared display handle compatibility

* Show processing indicator for posterior displays

* Prevent notebook progress table stretching

* Add activity label constants and styling

* Refine progress indicator styling

* Refactor progress indicator context manager

* Format posterior predictive call

* Rename 95% interval, remove 68% interval

* Refactor progress indicator to use dynamic renderable

* Share display handle across multi-experiment fits

* Add timed progress updates for fit tracking

* Prevent progress indicator errors on exception

* Remove completed progress indicator plan
---
 .../progress-activity-indicator.md            | 407 ----------------
 docs/dev/package-structure-full.md            |   5 +-
 docs/dev/package-structure-short.md           |   1 +
 docs/docs/tutorials/ed-21.ipynb               |   6 +-
 docs/docs/tutorials/ed-21.py                  |   6 +-
 src/easydiffraction/analysis/analysis.py      |  62 +--
 .../analysis/fit_helpers/tracking.py          | 253 +++++-----
 src/easydiffraction/analysis/sequential.py    |  49 +-
 .../display/plotters/plotly.py                |   2 +-
 src/easydiffraction/display/plotting.py       |  16 +-
 src/easydiffraction/display/progress.py       | 439 ++++++++++++++++++
 src/easydiffraction/display/tablers/base.py   |  23 +
 src/easydiffraction/display/tablers/pandas.py |  36 +-
 src/easydiffraction/display/tablers/rich.py   |  18 +-
 src/easydiffraction/display/tables.py         |  56 ++-
 src/easydiffraction/project/display.py        |  99 ++--
 src/easydiffraction/utils/utils.py            |  32 ++
 .../fitting/test_bayesian_tracker_and_base.py | 145 ++++--
 .../analysis/fit_helpers/test_tracking.py     |  40 +-
 .../analysis/minimizers/test_base.py          |  43 ++
 .../easydiffraction/analysis/test_analysis.py |  95 ++++
 .../analysis/test_sequential.py               | 131 ++++++
 .../display/plotters/test_plotly.py           |   4 +-
 .../display/tablers/test_pandas.py            |  12 +
 .../display/tablers/test_rich.py              |   6 +-
 .../easydiffraction/display/test_plotting.py  |   9 +-
 .../easydiffraction/display/test_progress.py  | 210 +++++++++
 .../easydiffraction/display/test_tables.py    |  29 ++
 .../easydiffraction/project/test_display.py   |  33 +-
 29 files changed, 1550 insertions(+), 717 deletions(-)
 delete mode 100644 docs/dev/implementation-plans/progress-activity-indicator.md
 create mode 100644 src/easydiffraction/display/progress.py
 create mode 100644 tests/unit/easydiffraction/display/test_progress.py

diff --git a/docs/dev/implementation-plans/progress-activity-indicator.md b/docs/dev/implementation-plans/progress-activity-indicator.md
deleted file mode 100644
index e776e0127..000000000
--- a/docs/dev/implementation-plans/progress-activity-indicator.md
+++ /dev/null
@@ -1,407 +0,0 @@
-# Progress Activity Indicator Implementation Plan
-
-**Status:** Proposed  
-**Date:** 2026-05-14
-
-## Goal
-
-Add a small activity indicator for fitting and other long-running
-calculations. The indicator should read as the same feature in terminal
-and Jupyter, while using environment-appropriate rendering underneath.
-
-This is an activity indicator, not a numeric progress bar. Most
-deterministic minimizers do not expose a reliable total work estimate,
-and the existing fit progress table updates only when meaningful fit
-state changes. A spinner-style indicator communicates that work is
-continuing without implying a percentage that may be unavailable.
-
-## User-Facing Behavior
-
-### Visibility
-
-The indicator is controlled by existing verbosity:
-
-| Verbosity | Behavior                                                                                             |
-| --------- | ---------------------------------------------------------------------------------------------------- |
-| `silent`  | Show nothing. No table, no activity indicator, no status line.                                       |
-| `short`   | Show the activity indicator, but not the detailed fit progress table. Keep existing short summaries. |
-| `full`    | Show the detailed progress table and the activity indicator below it.                                |
-
-This changes the current fit tracker behavior where `short` returns
-before creating any live progress output.
-
-### Labels
-
-Use the following labels:
-
-| Work type                         | Label        |
-| --------------------------------- | ------------ |
-| Deterministic single fit          | `fitting`    |
-| Sequential fit                    | `fitting`    |
-| Bayesian DREAM burn phase         | `burn-in`    |
-| Bayesian DREAM sampling phase     | `sampling`   |
-| Posterior predictive plots/checks | `processing` |
-| Posterior pair plots              | `processing` |
-| Other long calculations           | `processing` |
-
-The label must be updateable while work is running. DREAM should switch
-from `burn-in` to `sampling` when sampler progress reports the phase
-change.
-
-### Visual Style
-
-Use compact Unicode spinner frames:
-
-```text
-⠋ ⠙ ⠹ ⠸ ⠼ ⠴ ⠦ ⠧ ⠇ ⠏
-```
-
-Examples:
-
-```text
-⠋ fitting
-⠴ burn-in
-⠇ sampling
-⠙ processing
-```
-
-The indicator should be a single line. In `full` mode it appears below
-the progress table. In `short` mode it appears as the only live progress
-element.
-
-## Current Code Paths
-
-### Fit Progress
-
-The single-fit and sampler progress lifecycle is owned by
-`src/easydiffraction/analysis/fit_helpers/tracking.py`.
-
-Important methods:
-
-- `FitProgressTracker.start_tracking(...)`
-- `FitProgressTracker.add_tracking_info(...)`
-- `FitProgressTracker.track_sampler_progress(...)`
-- `FitProgressTracker.finish_tracking(...)`
-- `_make_display_handle()`
-- `_TerminalLiveHandle`
-
-The existing table update path uses `render_table(...)`, which delegates
-to `TableRenderer` and then to either:
-
-- `PandasTableBackend` in Jupyter
-- `RichTableBackend` in terminal
-
-### Sequential Fit
-
-Sequential fitting currently has separate progress output in
-`src/easydiffraction/analysis/sequential.py`.
-
-Important functions:
-
-- `fit_sequential(...)`
-- `_run_fit_loop(...)`
-- `_report_chunk_progress(...)`
-
-Sequential fit should reuse the same activity indicator abstraction
-rather than adding a separate spinner implementation.
-
-### Posterior And Other Long Calculations
-
-Posterior display work is routed through:
-
-- `src/easydiffraction/project/display.py`
-- `src/easydiffraction/display/plotting.py`
-
-Important entry points include:
-
-- `PosteriorDisplay.pairs(...)`
-- `PosteriorDisplay.predictive(...)`
-- `Plotter.plot_posterior_pairs(...)`
-- `Plotter.plot_posterior_predictive(...)`
-
-These should use the generic `processing` label if an activity indicator
-is added to those paths.
-
-## Architecture
-
-### Add A Shared Activity Indicator
-
-Add a small shared display helper, preferably:
-
-```text
-src/easydiffraction/display/progress.py
-```
-
-Recommended public/internal shape:
-
-```python
-class ActivityIndicator:
-    def __init__(self, label: str = "processing", *, verbosity: VerbosityEnum) -> None: ...
-    def start(self) -> None: ...
-    def update(self, *, label: str | None = None, content: object | None = None) -> None: ...
-    def stop(self, *, final_label: str | None = None) -> None: ...
-```
-
-The exact class name can change during implementation, but it should
-provide these capabilities:
-
-- no output in `silent`
-- live output in `short` and `full`
-- label updates while running
-- optional table/content rendering above the indicator in `full`
-- terminal and Jupyter implementations behind one API
-- safe cleanup on exceptions
-
-### Terminal Rendering
-
-Use Rich for terminal rendering.
-
-Preferred implementation:
-
-- keep using `rich.live.Live`
-- render a `rich.console.Group`
-- group content should be:
-  - the progress table renderable, when present
-  - the activity indicator line
-
-The indicator line can be either:
-
-- Rich's built-in `Spinner`, if it works cleanly inside the existing
-  `Live` setup, or
-- a local unicode-frame renderable driven by the same frame list.
-
-Do not create a second independent `Live` instance for the same output
-area. The table and spinner should be refreshed together through one
-live handle.
-
-### Jupyter Rendering
-
-Use an IPython `DisplayHandle` and HTML.
-
-The Jupyter spinner should be browser-driven CSS animation, not a
-Python-loop animation. This matters because Python may not regain
-control during expensive calculations, but CSS keeps animating once the
-HTML has been displayed.
-
-Recommended HTML structure:
-
-```html
-<div class="ed-activity">
-  <span class="ed-activity-spinner"></span>
-  <span class="ed-activity-label">fitting</span>
-</div>
-```
-
-The table HTML and spinner HTML can be updated together in the same
-display handle, or the spinner can have its own display handle below the
-table. Prefer a single display handle if it keeps table-and-spinner
-replacement simpler and avoids duplicated output cells.
-
-### Table Rendering Refactor
-
-The existing table backends mostly print/update directly. For a clean
-combined table-plus-spinner render, add a way to build table renderables
-without immediately displaying them.
-
-Possible approach:
-
-1. Keep `render_table(...)` working for existing callers.
-2. Add a backend method that returns a renderable representation:
-   - Rich: return `rich.table.Table`
-   - Pandas: return HTML from `Styler.to_html()`
-3. Let the activity indicator compose that renderable with the spinner.
-
-This avoids hard-coding table internals in the tracker and keeps normal
-table rendering backwards compatible.
-
-## Fit Tracker Integration
-
-### State
-
-Add fields to `FitProgressTracker`:
-
-- `_activity_indicator`
-- `_activity_label`
-
-The label is derived from tracking mode:
-
-- fit mode -> `fitting`
-- sampler mode -> initial label from sampler phase if known, otherwise
-  `sampling`
-
-### `start_tracking(...)`
-
-Update behavior:
-
-1. Set tracking mode.
-2. Return immediately only for `silent`.
-3. Print the existing start/header messages only where appropriate:
-   - keep current full messages
-   - keep short mode concise
-4. Create the activity indicator for `short` and `full`.
-5. In `full`, render the initial empty progress table plus the
-   indicator.
-6. In `short`, render only the indicator.
-
-### `add_tracking_info(...)`
-
-Update behavior:
-
-1. Always store row state for `full` mode.
-2. In `full`, refresh the table plus the indicator.
-3. In `short`, do not render the table; keep the indicator running.
-
-### `track_sampler_progress(...)`
-
-Update the activity label from `SamplerProgressUpdate.phase`:
-
-- phase `burn-in` -> label `burn-in`
-- phase `sampling` -> label `sampling`
-- any other phase -> normalized phase string if user-facing, otherwise
-  `processing`
-
-The existing sampler table's `phase` column remains unchanged.
-
-### `finish_tracking(...)`
-
-Update behavior:
-
-1. Finalize the last table row as today.
-2. Stop the activity indicator for `short` and `full`.
-3. In `full`, print the current completion summary.
-4. In `short`, keep or add only a concise completion line if the current
-   short behavior expects one.
-5. In `silent`, print nothing.
-
-Use `try/finally` in minimizer execution paths so the indicator is
-stopped when a solver raises.
-
-## Sequential Fit Integration
-
-Sequential fit should use the same `ActivityIndicator`.
-
-Recommended behavior:
-
-- `silent`: no output.
-- `short`: show one activity indicator labelled `fitting`; keep concise
-  chunk summaries when chunks finish.
-- `full`: show one activity indicator labelled `fitting`; keep detailed
-  chunk summaries.
-
-Implementation points:
-
-1. Create the indicator in `fit_sequential(...)` after preflight checks
-   and before `_run_fit_loop(...)`.
-2. Pass it into `_run_fit_loop(...)`, or wrap `_run_fit_loop(...)` in a
-   context manager.
-3. Update the indicator content after each chunk if the implementation
-   supports content text, for example:
-
-   ```text
-   ⠼ fitting  chunk 3/20
-   ```
-
-4. Stop the indicator in a `finally` block before printing final
-   completion output.
-
-Do not duplicate spinner frame logic in `sequential.py`.
-
-## Posterior And Generic Processing Integration
-
-The first implementation can focus on fitting and sequential fitting.
-After that, add a small context helper for generic long calculations:
-
-```python
-with activity_indicator("processing", verbosity=VerbosityEnum(project.verbosity)):
-    ...
-```
-
-Use this for:
-
-- posterior predictive summary generation
-- posterior pair plot construction when sample thinning, density grids,
-  or figure construction take noticeable time
-- any future calculation where total progress is unknown
-
-The generic helper should default to `processing`.
-
-## Testing Plan
-
-### Unit Tests
-
-Add tests for the shared progress helper:
-
-- `silent` does not create display handles or print output.
-- `short` starts the indicator.
-- `full` can compose content plus indicator.
-- label updates replace the visible label.
-- `stop()` suppresses cleanup errors.
-
-Extend tracker tests:
-
-- `FitProgressTracker.start_tracking(...)` starts the indicator in
-  `short`.
-- `silent` still shows nothing.
-- full fit mode uses label `fitting`.
-- sampler updates switch labels from `burn-in` to `sampling`.
-- finalization stops the indicator on success.
-- finalization stops the indicator when solver preparation or solver
-  execution raises.
-
-Extend sequential tests:
-
-- `fit_sequential(..., verbosity="short")` starts and stops the shared
-  indicator.
-- `fit_sequential(..., verbosity="silent")` does not start it.
-- chunk progress does not create a separate spinner.
-
-### Rendering Tests
-
-Terminal:
-
-- fake or monkeypatch `Live` and assert one live handle receives grouped
-  table-plus-indicator content.
-
-Jupyter:
-
-- fake `DisplayHandle` and assert generated HTML contains the activity
-  container and the selected label.
-- assert CSS animation is included once, not duplicated on every table
-  update if avoidable.
-
-### Regression Tests
-
-Keep existing tests passing:
-
-- `tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py`
-- `tests/integration/fitting/test_bayesian_tracker_and_base.py`
-- sequential tests under `tests/integration/fitting/test_sequential.py`
-
-## Implementation Sequence
-
-1. Add `display/progress.py` with a minimal `ActivityIndicator`.
-2. Add tests for verbosity behavior and label updates.
-3. Refactor table rendering just enough to allow Rich renderable and
-   Jupyter HTML composition.
-4. Wire `FitProgressTracker` to the activity indicator.
-5. Update tracker tests for `short`, `full`, `silent`, and sampler phase
-   labels.
-6. Wire sequential fitting to the shared activity indicator.
-7. Update sequential tests.
-8. Add generic `processing` context helper.
-9. Add the helper to posterior predictive and posterior pairs if
-   profiling or user feedback shows those operations need visible
-   activity feedback.
-10. Run focused unit tests, then the relevant integration tests.
-
-## Open Design Checks
-
-- Whether `short` mode should print the existing start line before the
-  spinner or show only the spinner until completion.
-- Whether terminal output should use Rich's built-in `Spinner` or the
-  explicit EasyDiffraction frame list. Prefer the explicit list if
-  consistency with Jupyter matters more than Rich defaults.
-- Whether the table and spinner should share one Jupyter display handle.
-  Prefer one handle unless it complicates the existing pandas backend.
-- Whether generic display/plot operations need a public verbosity
-  argument, or should only read `project.verbosity`.
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index e71f099de..dccf421a4 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -59,7 +59,6 @@
 │   │   │   └── 🏷️ class FitResults
 │   │   └── 📄 tracking.py
 │   │       ├── 🏷️ class SamplerProgressUpdate
-│   │       ├── 🏷️ class _TerminalLiveHandle
 │   │       └── 🏷️ class FitProgressTracker
 │   ├── 📁 minimizers
 │   │   ├── 📄 __init__.py
@@ -385,6 +384,10 @@
 │   │   ├── 🏷️ class _PosteriorPairsLegendState
 │   │   ├── 🏷️ class Plotter
 │   │   └── 🏷️ class PlotterFactory
+│   ├── 📄 progress.py
+│   │   ├── 🏷️ class _TerminalLiveHandle
+│   │   ├── 🏷️ class ActivityIndicator
+│   │   └── 🏷️ class _ActivityIndicatorContext
 │   ├── 📄 tables.py
 │   │   ├── 🏷️ class TableEngineEnum
 │   │   ├── 🏷️ class TableRenderer
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index 573142dae..b4e46ec73 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -186,6 +186,7 @@
 │   ├── 📄 __init__.py
 │   ├── 📄 base.py
 │   ├── 📄 plotting.py
+│   ├── 📄 progress.py
 │   ├── 📄 tables.py
 │   └── 📄 utils.py
 ├── 📁 io
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 33f77196f..f0d5123d9 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -734,11 +734,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.posterior.predictive(\n",
-    "    expt_name='hrpt',\n",
-    "    x_min=92,\n",
-    "    x_max=93,\n",
-    ")"
+    "project.display.posterior.predictive(expt_name='hrpt', x_min=92, x_max=93)"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index e890f55bc..7c6f9bdb5 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -347,8 +347,4 @@
 # after the Bayesian run.
 
 # %%
-project.display.posterior.predictive(
-    expt_name='hrpt',
-    x_min=92,
-    x_max=93,
-)
+project.display.posterior.predictive(expt_name='hrpt', x_min=92, x_max=93)
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index 484c3e03d..a2a4f7dee 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -14,12 +14,12 @@
 from easydiffraction.analysis.categories.fit import FitFactory
 from easydiffraction.analysis.categories.fit import FitModeEnum
 from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
-from easydiffraction.analysis.fit_helpers.tracking import _make_display_handle
 from easydiffraction.analysis.fitting import Fitter
 from easydiffraction.core.singleton import ConstraintsHandler
 from easydiffraction.core.variable import NumericDescriptor
 from easydiffraction.core.variable import Parameter
 from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.display.progress import make_display_handle
 from easydiffraction.display.tables import TableRenderer
 from easydiffraction.io.cif.serialize import analysis_to_cif
 from easydiffraction.utils.enums import VerbosityEnum
@@ -614,37 +614,43 @@ def _fit_single(
 
         short_display_handle = self._fit_single_print_header(verb, expt_names, mode)
         short_rows: list[list[str]] = []
+        self.fitter.minimizer.tracker._set_shared_display_handle(short_display_handle)
 
-        for expt_name in expt_names:
-            if verb is VerbosityEnum.FULL:
-                console.print(f"📋 Using experiment 🔬 '{expt_name}' for '{mode.value}' fitting")
+        try:
+            for expt_name in expt_names:
+                if verb is VerbosityEnum.FULL:
+                    console.print(
+                        f"📋 Using experiment 🔬 '{expt_name}' for '{mode.value}' fitting"
+                    )
 
-            experiment = experiments[expt_name]
-            self.fitter.fit(
-                structures,
-                [experiment],
-                analysis=self,
-                verbosity=verb,
-                use_physical_limits=use_physical_limits,
-                random_seed=random_seed,
-            )
+                experiment = experiments[expt_name]
+                self.fitter.fit(
+                    structures,
+                    [experiment],
+                    analysis=self,
+                    verbosity=verb,
+                    use_physical_limits=use_physical_limits,
+                    random_seed=random_seed,
+                )
 
-            # After fitting, snapshot parameter values before
-            # they get overwritten by the next experiment's fit
-            results = self.fitter.results
-            self._snapshot_params(expt_name, results)
-            self.fit_results = results
+                # After fitting, snapshot parameter values before
+                # they get overwritten by the next experiment's fit
+                results = self.fitter.results
+                self._snapshot_params(expt_name, results)
+                self.fit_results = results
 
-            # Short mode: append one summary row and update in-place
-            if verb is VerbosityEnum.SHORT:
-                self._fit_single_update_short_table(
-                    short_rows, expt_name, results, short_display_handle
-                )
+                # Short mode: append one summary row and update in-place
+                if verb is VerbosityEnum.SHORT:
+                    self._fit_single_update_short_table(
+                        short_rows, expt_name, results, short_display_handle
+                    )
+        finally:
+            self.fitter.minimizer.tracker._set_shared_display_handle(None)
 
-        # Short mode: close the display handle
-        if short_display_handle is not None and hasattr(short_display_handle, 'close'):
-            with suppress(Exception):
-                short_display_handle.close()
+            # Short mode: close the display handle
+            if short_display_handle is not None and hasattr(short_display_handle, 'close'):
+                with suppress(Exception):
+                    short_display_handle.close()
 
     @staticmethod
     def _fit_single_print_header(
@@ -680,7 +686,7 @@ def _fit_single_print_header(
         )
         console.print("🚀 Starting fit process with 'lmfit'...")
         console.print('📈 Goodness-of-fit (reduced χ²) per experiment:')
-        return _make_display_handle()
+        return make_display_handle()
 
     def _snapshot_params(self, expt_name: str, results: object) -> None:
         """
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index b5cfb11a2..0915ae929 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -1,35 +1,29 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from __future__ import annotations
+
+import sys
 import time
-from contextlib import suppress
 from dataclasses import dataclass
-
-import numpy as np
-
-from easydiffraction.utils.logging import console
-
-try:
-    from IPython.display import HTML
-    from IPython.display import DisplayHandle
-    from IPython.display import display
-except ImportError:
-    display = None
-    clear_output = None
+from typing import TYPE_CHECKING
 
 from easydiffraction.analysis.fit_helpers.metrics import calculate_reduced_chi_square
+from easydiffraction.display.progress import ACTIVITY_LABEL_BURN_IN
+from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
+from easydiffraction.display.progress import ACTIVITY_LABEL_SAMPLING
+from easydiffraction.display.progress import ActivityIndicator
+from easydiffraction.display.progress import _TerminalLiveHandle as _SharedTerminalLiveHandle
+from easydiffraction.display.progress import make_display_handle
 from easydiffraction.utils.enums import VerbosityEnum
-from easydiffraction.utils.environment import in_jupyter
-from easydiffraction.utils.utils import render_table
-
-try:
-    from rich.live import Live
-except ImportError:  # pragma: no cover - rich always available in app env
-    Live = None  # type: ignore[assignment]
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.utils import build_table_renderable
 
-from easydiffraction.utils.logging import ConsoleManager
+if TYPE_CHECKING:
+    import numpy as np
 
 SIGNIFICANT_CHANGE_THRESHOLD = 0.01  # 1% threshold
+FIT_PROGRESS_UPDATE_SECONDS = 5.0
 SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
 TRACKING_MODE_FIT = 'fit'
 TRACKING_MODE_SAMPLER = 'sampling'
@@ -38,6 +32,13 @@
 SAMPLER_HEADERS = ['iteration', 'progress', 'time (s)', 'log posterior', 'phase']
 SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center', 'center']
 
+_TerminalLiveHandle = _SharedTerminalLiveHandle
+
+
+def _make_display_handle() -> object | None:
+    """Return a backward-compatible generic live display handle."""
+    return make_display_handle()
+
 
 @dataclass(frozen=True, slots=True)
 class SamplerProgressUpdate:
@@ -55,56 +56,6 @@ class SamplerProgressUpdate:
     force_report: bool = False
 
 
-class _TerminalLiveHandle:
-    """
-    Adapter that exposes update()/close() for terminal live updates.
-
-    Wraps a rich.live.Live instance but keeps the tracker decoupled from
-    the underlying UI mechanism.
-    """
-
-    def __init__(self, live: object) -> None:
-        self._live = live
-
-    def update(self, renderable: object) -> None:
-        """
-        Refresh the live display with a new renderable.
-
-        Parameters
-        ----------
-        renderable : object
-            A Rich-compatible renderable to display.
-        """
-        self._live.update(renderable, refresh=True)
-
-    def close(self) -> None:
-        """Stop the live display, suppressing any errors."""
-        with suppress(Exception):
-            self._live.stop()
-
-
-def _make_display_handle() -> object | None:
-    """
-    Create and initialize a display/update handle for the environment.
-
-    - In Jupyter, returns an IPython DisplayHandle and creates a
-    placeholder. - In terminal, returns a _TerminalLiveHandle backed by
-    rich Live. - If neither applies, returns None.
-    """
-    if in_jupyter() and display is not None and HTML is not None:
-        h = DisplayHandle()
-        # Create an empty placeholder area to update in place
-        h.display(HTML(''))
-        return h
-    if Live is not None:
-        # Reuse the shared Console to coordinate with logging output
-        # and keep consistent width
-        live = Live(console=ConsoleManager.get(), auto_refresh=True)
-        live.start()
-        return _TerminalLiveHandle(live)
-    return None
-
-
 class FitProgressTracker:
     """
     Track and report reduced chi-square during optimization.
@@ -135,11 +86,13 @@ def __init__(self) -> None:
         self._last_sampler_elapsed_time: float | None = None
 
         self._df_rows: list[list[str]] = []
-        self._display_handle: object | None = None
-        self._live: object | None = None
+        self._activity_indicator: ActivityIndicator | None = None
+        self._activity_label: str = ACTIVITY_LABEL_FITTING
+        self._shared_display_handle: object | None = None
 
     def reset(self) -> None:
         """Reset internal state before a new optimization run."""
+        self._stop_activity_indicator()
         self._iteration = 0
         self._previous_chi2 = None
         self._last_chi2 = None
@@ -157,6 +110,8 @@ def reset(self) -> None:
         self._last_sampler_progress_percent = None
         self._last_sampler_log_posterior = None
         self._last_sampler_elapsed_time = None
+        self._df_rows = []
+        self._activity_label = ACTIVITY_LABEL_FITTING
 
     def track(
         self,
@@ -193,47 +148,51 @@ def track(
             return residuals
 
         row: list[str] = []
+        elapsed_time = self._current_elapsed_time()
 
-        # First iteration, initialize tracking
         if self._previous_chi2 is None:
             self._previous_chi2 = reduced_chi2
             self._best_chi2 = reduced_chi2
             self._best_iteration = self._iteration
+            self._last_progress_time = elapsed_time
 
             row = [
                 str(self._iteration),
-                self._format_elapsed_time(),
+                self._format_elapsed_time(elapsed_time),
                 f'{reduced_chi2:.2f}',
                 '',
             ]
-
-        # Subsequent iterations, check for significant changes
         else:
             change = (self._previous_chi2 - reduced_chi2) / self._previous_chi2
 
-            # Improvement check
             if change > SIGNIFICANT_CHANGE_THRESHOLD:
                 change_in_percent = change * 100
 
                 row = [
                     str(self._iteration),
-                    self._format_elapsed_time(),
+                    self._format_elapsed_time(elapsed_time),
                     f'{reduced_chi2:.2f}',
                     f'{change_in_percent:.1f}% ↓',
                 ]
 
                 self._previous_chi2 = reduced_chi2
+                self._last_progress_time = elapsed_time
+            elif self._should_render_fit_row(elapsed_time):
+                row = [
+                    str(self._iteration),
+                    self._format_elapsed_time(elapsed_time),
+                    f'{reduced_chi2:.2f}',
+                    '',
+                ]
+                self._last_progress_time = elapsed_time
 
-        # Output if there is something new to display
         if row:
             self.add_tracking_info(row)
 
-        # Update best chi-square if better
         if self._best_chi2 is None or reduced_chi2 < self._best_chi2:
             self._best_chi2 = reduced_chi2
             self._best_iteration = self._iteration
 
-        # Store last chi-square and iteration
         self._last_chi2 = reduced_chi2
         self._last_iteration = self._iteration
 
@@ -259,6 +218,7 @@ def track_sampler_progress(self, update: SamplerProgressUpdate) -> None:
         self._last_sampler_progress_percent = clamped_progress
         self._last_sampler_log_posterior = update.log_posterior
         self._last_sampler_elapsed_time = update.elapsed_time
+        self._set_activity_label(self._activity_label_for_sampler_phase(update.phase))
 
         row = self._initial_sampler_progress_row(
             update=update,
@@ -326,29 +286,20 @@ def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT)
         self._tracking_mode = (
             TRACKING_MODE_SAMPLER if mode == TRACKING_MODE_SAMPLER else TRACKING_MODE_FIT
         )
+        self._df_rows = []
+        self._activity_label = self._default_activity_label()
 
         if self._verbosity is VerbosityEnum.SILENT:
             return
-        if self._verbosity is VerbosityEnum.SHORT:
-            return
 
-        console.print(f"🚀 Starting fit process with '{minimizer_name}'...")
-        if self._tracking_mode == TRACKING_MODE_SAMPLER:
-            console.print('📈 Bayesian sampling progress:')
-        else:
-            console.print('📈 Goodness-of-fit progress:')
-
-        # Reset rows and create an environment-appropriate handle
-        self._df_rows = []
-        self._display_handle = _make_display_handle()
+        if self._verbosity is VerbosityEnum.FULL:
+            console.print(f"🚀 Starting fit process with '{minimizer_name}'...")
+            if self._tracking_mode == TRACKING_MODE_SAMPLER:
+                console.print('📈 Bayesian sampling progress:')
+            else:
+                console.print('📈 Goodness-of-fit progress:')
 
-        # Initial empty table; subsequent updates will reuse the handle
-        render_table(
-            columns_headers=self._headers(),
-            columns_alignment=self._alignments(),
-            columns_data=self._df_rows,
-            display_handle=self._display_handle,
-        )
+        self._start_activity_indicator()
 
     def add_tracking_info(self, row: list[str]) -> None:
         """
@@ -364,16 +315,8 @@ def add_tracking_info(self, row: list[str]) -> None:
             if iteration_cell.isdigit():
                 self._last_reported_iteration = int(iteration_cell)
         self._df_rows.append(row)
-        if self._verbosity is not VerbosityEnum.FULL:
-            return
-        # Append and update via the active handle (Jupyter or
-        # terminal live)
-        render_table(
-            columns_headers=self._headers(),
-            columns_alignment=self._alignments(),
-            columns_data=self._df_rows,
-            display_handle=self._display_handle,
-        )
+        if self._verbosity is VerbosityEnum.FULL:
+            self._refresh_activity_indicator()
 
     def finish_tracking(self) -> None:
         """Finalize progress display and print best result summary."""
@@ -382,11 +325,19 @@ def finish_tracking(self) -> None:
         else:
             self._finalize_fit_tracking_row()
 
-        if self._verbosity is not VerbosityEnum.FULL:
+        if self._verbosity is VerbosityEnum.SILENT:
             return
 
-        self._close_display_handle()
-        self._print_completion_summary()
+        self._stop_activity_indicator()
+        if self._verbosity is VerbosityEnum.FULL and not self._cleanup_during_exception():
+            self._print_completion_summary()
+
+    @staticmethod
+    def _cleanup_during_exception() -> bool:
+        """
+        Return whether cleanup runs during exception handling.
+        """
+        return sys.exc_info()[0] is not None
 
     def _initial_sampler_progress_row(
         self,
@@ -511,7 +462,7 @@ def _final_sampler_tracking_row(self) -> list[str] | None:
             f'{final_progress:.1f}%',
             self._format_elapsed_time(elapsed_time),
             log_posterior,
-            self._last_sampler_phase or 'sampling',
+            self._last_sampler_phase or TRACKING_MODE_SAMPLER,
         ]
 
     def _finalize_fit_tracking_row(self) -> None:
@@ -566,16 +517,14 @@ def _sampler_iteration_label(self, iteration: int) -> str:
         clamped_iteration = min(iteration, self._sampler_total_iterations)
         return f'{clamped_iteration}/{self._sampler_total_iterations}'
 
-    def _close_display_handle(self) -> None:
-        if self._display_handle is not None and hasattr(self._display_handle, 'close'):
-            with suppress(Exception):
-                self._display_handle.close()
-
     def _print_completion_summary(self) -> None:
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
             console.print('✅ Bayesian sampling complete.')
             return
 
+        if self._best_chi2 is None or self._best_iteration is None:
+            return
+
         console.print(
             f'🏆 Best goodness-of-fit (reduced χ²) is {self._best_chi2:.2f} '
             f'at iteration {self._best_iteration}'
@@ -611,6 +560,11 @@ def _format_elapsed_time(self, elapsed_time: float | None = None) -> str:
             return ''
         return f'{resolved_time:.2f}'
 
+    def _should_render_fit_row(self, elapsed_time: float | None) -> bool:
+        if elapsed_time is None or self._last_progress_time is None:
+            return False
+        return elapsed_time - self._last_progress_time >= FIT_PROGRESS_UPDATE_SECONDS
+
     @staticmethod
     def _rows_match_on_columns(
         current_row: list[str],
@@ -636,12 +590,67 @@ def _replace_last_tracking_row(self, row: list[str]) -> None:
             return
 
         self._df_rows[-1] = row
-        if self._verbosity is not VerbosityEnum.FULL:
+        if self._verbosity is VerbosityEnum.FULL:
+            self._refresh_activity_indicator()
+
+    def _default_activity_label(self) -> str:
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return ACTIVITY_LABEL_SAMPLING
+        return ACTIVITY_LABEL_FITTING
+
+    @staticmethod
+    def _activity_label_for_sampler_phase(phase: str) -> str:
+        normalized_phase = phase.strip().lower()
+        if normalized_phase == 'burn-in':
+            return ACTIVITY_LABEL_BURN_IN
+        if normalized_phase == 'sampling':
+            return ACTIVITY_LABEL_SAMPLING
+        if normalized_phase:
+            return normalized_phase
+        return ACTIVITY_LABEL_SAMPLING
+
+    def _set_shared_display_handle(self, display_handle: object | None) -> None:
+        self._shared_display_handle = display_handle
+
+    def _start_activity_indicator(self) -> None:
+        self._activity_indicator = ActivityIndicator(
+            self._activity_label,
+            verbosity=self._verbosity,
+            display_handle=self._shared_display_handle,
+        )
+        self._activity_indicator.start()
+        self._refresh_activity_indicator()
+
+    def _stop_activity_indicator(self) -> None:
+        if self._activity_indicator is None:
             return
 
-        render_table(
+        self._activity_indicator.stop()
+        self._activity_indicator = None
+
+    def _set_activity_label(self, label: str) -> None:
+        if label == self._activity_label:
+            return
+
+        self._activity_label = label
+        self._refresh_activity_indicator()
+
+    def _refresh_activity_indicator(self) -> None:
+        if self._activity_indicator is None:
+            return
+
+        if self._verbosity is VerbosityEnum.FULL:
+            self._activity_indicator.update(
+                label=self._activity_label,
+                content=self._table_renderable(),
+            )
+            return
+
+        self._activity_indicator.update(label=self._activity_label)
+
+    def _table_renderable(self) -> object:
+        return build_table_renderable(
             columns_headers=self._headers(),
             columns_alignment=self._alignments(),
             columns_data=self._df_rows,
-            display_handle=self._display_handle,
         )
diff --git a/src/easydiffraction/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index 25feb184e..a52d38b1a 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -17,6 +17,8 @@
 from typing import TYPE_CHECKING
 from typing import Any
 
+from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
+from easydiffraction.display.progress import ActivityIndicator
 from easydiffraction.io.ascii import extract_data_paths_from_dir
 from easydiffraction.utils.enums import VerbosityEnum
 from easydiffraction.utils.logging import console
@@ -529,9 +531,11 @@ def _report_chunk_progress(
 
     if verbosity is VerbosityEnum.SHORT:
         status = '✅' if successful else '❌'
-        print(f'{status} Chunk {chunk_idx}/{total_chunks}: {num_files} files, avg χ² = {chi2_str}')
+        console.print(
+            f'{status} Chunk {chunk_idx}/{total_chunks}: {num_files} files, avg χ² = {chi2_str}'
+        )
     elif verbosity is VerbosityEnum.FULL:
-        print(
+        console.print(
             f'Chunk {chunk_idx}/{total_chunks}: '
             f'{num_files} files, {len(successful)} succeeded, '
             f'avg reduced χ² = {chi2_str}'
@@ -540,7 +544,7 @@ def _report_chunk_progress(
             status = '✅' if r.get('fit_success') else '❌'
             rchi2 = r.get('reduced_chi_squared')
             rchi2_str = f'{rchi2:.2f}' if rchi2 is not None else '—'
-            print(f'  {status} {Path(r["file_path"]).name}: χ² = {rchi2_str}')
+            console.print(f'  {status} {Path(r["file_path"]).name}: χ² = {rchi2_str}')
 
 
 def _apply_diffrn_metadata(
@@ -653,7 +657,7 @@ def _setup_csv_and_recovery(
         num_skipped = len(already_fitted)
         log.info(f'Resuming: {num_skipped} files already fitted, skipping.')
         if verb is not VerbosityEnum.SILENT:
-            print(f'📂 Resuming from CSV: {num_skipped} files already fitted.')
+            console.print(f'📂 Resuming from CSV: {num_skipped} files already fitted.')
         if recovered_params is not None:
             template = replace(template, initial_params=recovered_params)
     else:
@@ -755,6 +759,7 @@ def _run_fit_loop(
     csv_info: tuple[Path, list[str]],
     extract_diffrn: Callable | None,
     verb: VerbosityEnum,
+    indicator: ActivityIndicator | None,
 ) -> None:
     """
     Execute the chunk-based fitting loop.
@@ -773,6 +778,8 @@ def _run_fit_loop(
         User callback for diffrn metadata.
     verb : VerbosityEnum
         Output verbosity.
+    indicator : ActivityIndicator | None
+        Shared sequential-fit activity indicator.
     """
     csv_path, header = csv_info
     total_chunks = len(chunks)
@@ -789,6 +796,8 @@ def _run_fit_loop(
 
             _append_to_csv(csv_path, header, results)
             _report_chunk_progress(chunk_idx, total_chunks, results, verb)
+            if indicator is not None:
+                indicator.update()
 
             # Propagate last successful params
             last_ok = _find_last_successful(results)
@@ -841,16 +850,15 @@ def fit_sequential(
     if mp.parent_process() is not None:
         return
 
-    project = analysis.project
-    verb = VerbosityEnum(project.verbosity)
+    verb = VerbosityEnum(analysis.project.verbosity)
 
-    _check_seq_preconditions(project)
+    _check_seq_preconditions(analysis.project)
 
     data_paths = extract_data_paths_from_dir(data_dir, file_pattern=file_pattern)
-    template = _build_template(project)
+    template = _build_template(analysis.project)
 
     csv_path, header, already_fitted, template = _setup_csv_and_recovery(
-        project,
+        analysis.project,
         template,
         verb,
     )
@@ -860,7 +868,7 @@ def fit_sequential(
         remaining.reverse()
     if not remaining:
         if verb is not VerbosityEnum.SILENT:
-            print('✅ All files already fitted. Nothing to do.')
+            console.print('✅ All files already fitted. Nothing to do.')
         return
 
     max_workers, chunk_size = _resolve_workers(max_workers, chunk_size)
@@ -874,15 +882,30 @@ def fit_sequential(
         )
         console.print('📈 Goodness-of-fit (reduced χ²):')
 
+    indicator = None
+    if verb is not VerbosityEnum.SILENT:
+        indicator = ActivityIndicator(ACTIVITY_LABEL_FITTING, verbosity=verb)
+        indicator.start()
+
     pool_cm, main_mod, main_file_bak, main_spec_bak = _create_pool_context(max_workers)
     try:
-        _run_fit_loop(pool_cm, chunks, template, (csv_path, header), extract_diffrn, verb)
+        _run_fit_loop(
+            pool_cm,
+            chunks,
+            template,
+            (csv_path, header),
+            extract_diffrn,
+            verb,
+            indicator,
+        )
     finally:
+        if indicator is not None:
+            indicator.stop()
         _restore_main_state(main_mod, main_file_bak, main_spec_bak)
 
     if verb is not VerbosityEnum.SILENT:
-        print(
+        console.print(
             f'✅ Sequential fitting complete: '
             f'{len(already_fitted) + len(remaining)} files processed.'
         )
-        print(f'📄 Results saved to: {csv_path}')
+        console.print(f'📄 Results saved to: {csv_path}')
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index d41c1f9eb..fbbda288f 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -1755,7 +1755,7 @@ def _get_predictive_band_traces(
             line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
             fill='tonexty',
             fillcolor=PREDICTIVE_BAND_COLOR,
-            name='95% interval',
+            name='95% credible interval',
             hoverinfo='skip',
             legendgroup='predictive_band',
             legendrank=35,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index a258579e8..2ca6dff0f 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -97,12 +97,11 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
 POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
 POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.14)'
-POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(214, 39, 40, 0.26)'
 POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Max posterior'
 POSTERIOR_POINT_ESTIMATE_LINE_DASH = 'dot'
-POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME = '95% interval'
+POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME = '95% credible interval'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
 POSTERIOR_CONTOUR_FILL_COLORSCALE = [
@@ -1175,7 +1174,7 @@ def _plot_single_crystal_posterior_predictive(
         if style != 'band':
             log.warning(
                 'Single-crystal posterior predictive plots currently support '
-                'style="band" only; rendering the 95% interval.'
+                'style="band" only; rendering the 95% credible interval.'
             )
 
         summary = self._get_or_build_posterior_predictive_summary(
@@ -2597,15 +2596,6 @@ def _add_posterior_distribution_interval_traces(
                 color=POSTERIOR_INTERVAL_95_FILL_COLOR,
             )
         )
-        fig.add_trace(
-            self._posterior_interval_band_trace(
-                x0=summary.interval_68[0],
-                x1=summary.interval_68[1],
-                y_axis_range=y_axis_range,
-                trace_name='68% credible interval',
-                color=POSTERIOR_INTERVAL_68_FILL_COLOR,
-            )
-        )
 
     @staticmethod
     def _add_posterior_distribution_histogram(
@@ -3614,7 +3604,7 @@ def _plot_single_crystal_posterior_predictive_summary(
         trace.customdata = np.column_stack((lower_95, upper_95, y_meas_su))
         trace.hovertemplate = (
             'Predicted I²: %{x:,.2f}<br>'
-            '95% interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]<br>'
+            '95% credible interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]<br>'
             'Measured I²: %{y:,.2f}<br>'
             'su(I²meas): %{customdata[2]:,.2f}<extra></extra>'
         )
diff --git a/src/easydiffraction/display/progress.py b/src/easydiffraction/display/progress.py
new file mode 100644
index 000000000..1cde4d1c2
--- /dev/null
+++ b/src/easydiffraction/display/progress.py
@@ -0,0 +1,439 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Environment-aware activity indicator for long-running tasks."""
+
+from __future__ import annotations
+
+import html
+from contextlib import AbstractContextManager
+from contextlib import suppress
+from time import monotonic
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+try:
+    from IPython.display import HTML
+    from IPython.display import DisplayHandle
+except ImportError:  # pragma: no cover - optional dependency
+    HTML = None
+    DisplayHandle = None
+
+from rich.console import Group
+from rich.live import Live
+from rich.protocol import is_renderable
+from rich.text import Text
+
+from easydiffraction.utils.enums import VerbosityEnum
+from easydiffraction.utils.environment import in_jupyter
+from easydiffraction.utils.logging import ConsoleManager
+
+ACTIVITY_LABEL_BURN_IN = 'Burn-in...'
+ACTIVITY_LABEL_FITTING = 'Fitting...'
+ACTIVITY_LABEL_PROCESSING = 'Processing...'
+ACTIVITY_LABEL_SAMPLING = 'Sampling...'
+ACTIVITY_ACCENT_COLOR = '#d97706'
+ACTIVITY_TERMINAL_STYLE = ACTIVITY_ACCENT_COLOR
+
+SPINNER_FRAMES: tuple[str, ...] = (
+    '⠋',
+    '⠙',
+    '⠹',
+    '⠸',
+    '⠼',
+    '⠴',
+    '⠦',
+    '⠧',
+    '⠇',
+    '⠏',
+)
+_SPINNER_FRAME_SECONDS = 0.1
+_JUPYTER_SPINNER_SECONDS = 1.0
+
+
+class _TerminalLiveHandle:
+    """
+    Adapter exposing update()/close() for terminal live updates.
+
+    Wraps a ``rich.live.Live`` instance so callers can treat terminal
+    and notebook handles through a single update-oriented interface.
+    """
+
+    def __init__(self, *, console: object) -> None:
+        self._renderable: object = Text('')
+        self._live = Live(
+            console=console,
+            auto_refresh=True,
+            refresh_per_second=1 / _SPINNER_FRAME_SECONDS,
+            get_renderable=self._get_renderable,
+        )
+        self._live.start()
+
+    def _get_renderable(self) -> object:
+        renderable = self._renderable
+        if callable(renderable):
+            return renderable()
+        return renderable
+
+    def update(self, renderable: object) -> None:
+        """
+        Refresh the live display with a new renderable.
+
+        Parameters
+        ----------
+        renderable : object
+            A Rich-compatible renderable to display.
+        """
+        self._renderable = renderable
+        self._live.refresh()
+
+    def close(self) -> None:
+        """Stop the live display, suppressing any errors."""
+        with suppress(Exception):
+            self._live.stop()
+
+
+def make_display_handle() -> object | None:
+    """
+    Create a generic in-place display handle for the active environment.
+
+    Returns
+    -------
+    object | None
+        An IPython ``DisplayHandle`` in notebooks, a terminal live
+        handle in the console, or ``None`` if neither is available.
+    """
+    if in_jupyter() and DisplayHandle is not None and HTML is not None:
+        handle = DisplayHandle()
+        with suppress(Exception):
+            handle.display(HTML(''))
+        return handle
+
+    return _TerminalLiveHandle(console=ConsoleManager.get())
+
+
+class ActivityIndicator:
+    """
+    Render a live activity indicator for long-running work.
+
+    Parameters
+    ----------
+    label : str, default=ACTIVITY_LABEL_PROCESSING
+        User-facing activity label.
+    verbosity : VerbosityEnum
+        Output verbosity controlling whether live display is shown.
+    display_handle : object | None, default=None
+        Optional existing live display handle to reuse.
+    """
+
+    def __init__(
+        self,
+        label: str = ACTIVITY_LABEL_PROCESSING,
+        *,
+        verbosity: VerbosityEnum,
+        display_handle: object | None = None,
+    ) -> None:
+        self._label = label
+        self._verbosity = verbosity
+        self._content: object | None = None
+        self._provided_display_handle = display_handle
+        self._display_handle: object | None = None
+        self._live: object | None = None
+        self._running = False
+        self._keep_stopped_label = False
+        self._started_at = monotonic()
+
+    def start(self) -> None:
+        """
+        Start the live activity indicator.
+
+        Returns
+        -------
+        None
+            Starts live rendering unless verbosity is silent.
+        """
+        if self._verbosity is VerbosityEnum.SILENT or self._running:
+            return
+
+        self._running = True
+        self._keep_stopped_label = False
+        self._started_at = monotonic()
+
+        if self._provided_display_handle is not None:
+            self._display_handle = self._provided_display_handle
+            self._refresh()
+            return
+
+        if in_jupyter() and DisplayHandle is not None and HTML is not None:
+            handle = DisplayHandle()
+            self._display_handle = handle
+            with suppress(Exception):
+                handle.display(HTML(self._render_html()))
+            return
+
+        live = Live(
+            console=ConsoleManager.get(),
+            auto_refresh=True,
+            refresh_per_second=1 / _SPINNER_FRAME_SECONDS,
+            get_renderable=self._terminal_renderable,
+        )
+        live.start()
+        self._live = live
+
+    def update(
+        self,
+        *,
+        label: str | None = None,
+        content: object | None = None,
+    ) -> None:
+        """
+        Refresh the current label and optional rendered content.
+
+        Parameters
+        ----------
+        label : str | None, default=None
+            Replacement activity label. When ``None``, keep the current
+            label.
+        content : object | None, default=None
+            Optional content rendered above the indicator. When
+            ``None``, keep the current content.
+        """
+        if label is not None:
+            self._label = label
+        if content is not None:
+            self._content = content
+        self._refresh()
+
+    def stop(self, *, final_label: str | None = None) -> None:
+        """
+        Stop live rendering and keep optional final content visible.
+
+        Parameters
+        ----------
+        final_label : str | None, default=None
+            Optional final label to leave in place after stopping. When
+            omitted, only the current content remains visible.
+        """
+        self._running = False
+        self._keep_stopped_label = final_label is not None
+        if final_label is not None:
+            self._label = final_label
+
+        self._refresh()
+
+        if self._live is not None:
+            with suppress(Exception):
+                self._live.stop()
+        self._live = None
+        self._display_handle = None
+
+    def _terminal_renderable(self) -> object:
+        """Return the terminal renderable for the current state."""
+        renderables: list[object] = []
+        content = self._terminal_content()
+        if content is not None:
+            renderables.append(content)
+
+        indicator_line = self._terminal_indicator_line()
+        if indicator_line is not None:
+            renderables.append(indicator_line)
+
+        if not renderables:
+            return Text('')
+
+        if len(renderables) == 1:
+            return renderables[0]
+
+        return Group(*renderables)
+
+    def _refresh(self) -> None:
+        if self._verbosity is VerbosityEnum.SILENT:
+            return
+
+        if self._display_handle is not None:
+            self._refresh_display_handle()
+            return
+
+        if self._live is not None:
+            with suppress(Exception):
+                self._live.refresh()
+
+    def _refresh_display_handle(self) -> None:
+        if self._display_handle is None:
+            return
+
+        if (
+            HTML is not None
+            and DisplayHandle is not None
+            and isinstance(self._display_handle, DisplayHandle)
+        ):
+            with suppress(Exception):
+                self._display_handle.update(HTML(self._render_html()))
+            return
+
+        renderable: object = self._terminal_renderable()
+        if isinstance(self._display_handle, _TerminalLiveHandle):
+            renderable = self._terminal_renderable
+        with suppress(Exception):
+            self._display_handle.update(renderable)
+
+    def _terminal_content(self) -> object | None:
+        if self._content is None:
+            return None
+        if is_renderable(self._content):
+            return self._content
+        return Text(str(self._content))
+
+    def _terminal_indicator_line(self) -> Text | None:
+        if self._running:
+            frame = self._current_frame()
+            return Text(f'{frame} {self._label}', style=ACTIVITY_TERMINAL_STYLE)
+        if self._keep_stopped_label:
+            return Text(self._label, style=ACTIVITY_TERMINAL_STYLE)
+        return None
+
+    def _current_frame(self) -> str:
+        elapsed = monotonic() - self._started_at
+        frame_index = int(elapsed / _SPINNER_FRAME_SECONDS) % len(SPINNER_FRAMES)
+        return SPINNER_FRAMES[frame_index]
+
+    def _render_html(self) -> str:
+        content_html = self._html_content()
+        indicator_html = self._html_indicator()
+
+        sections = [section for section in (content_html, indicator_html) if section]
+        if not sections:
+            return ''
+
+        body = ''.join(sections)
+        return f'{self._html_style()}<div class="ed-activity-stack">{body}</div>'
+
+    def _html_content(self) -> str:
+        if self._content is None:
+            return ''
+        if isinstance(self._content, str):
+            return self._content
+
+        data = getattr(self._content, 'data', None)
+        if isinstance(data, str):
+            return data
+
+        text = html.escape(str(self._content))
+        return f'<pre class="ed-activity-pre">{text}</pre>'
+
+    def _html_indicator(self) -> str:
+        safe_label = html.escape(self._label)
+
+        if self._running:
+            return (
+                '<div class="ed-activity">'
+                '<span class="ed-activity-spinner" aria-hidden="true"></span>'
+                f'<span class="ed-activity-label">{safe_label}</span>'
+                '</div>'
+            )
+
+        if self._keep_stopped_label:
+            return (
+                '<div class="ed-activity">'
+                f'<span class="ed-activity-label">{safe_label}</span>'
+                '</div>'
+            )
+
+        return ''
+
+    @staticmethod
+    def _html_style() -> str:
+        keyframes = []
+        total_frames = len(SPINNER_FRAMES)
+        for index, frame in enumerate(SPINNER_FRAMES):
+            percent = int(index * 100 / total_frames)
+            keyframes.append(f'{percent}% {{ content: "{frame}"; }}')
+        keyframes.append(f'100% {{ content: "{SPINNER_FRAMES[0]}"; }}')
+        keyframe_css = ' '.join(keyframes)
+
+        return (
+            '<style>'
+            '.ed-activity-stack {'
+            'display: flex;'
+            'flex-direction: column;'
+            'align-items: flex-start;'
+            'gap: 0.35rem;'
+            'margin-top: 0.5rem;'
+            '}'
+            '.ed-activity {'
+            'display: inline-flex;'
+            'align-items: center;'
+            'gap: 0.45rem;'
+            f'color: {ACTIVITY_ACCENT_COLOR};'
+            'font-size: 0.95rem;'
+            'font-weight: 400;'
+            'line-height: 1.1;'
+            '}'
+            '.ed-activity-label {'
+            'font-family: var(--jp-ui-font-family, -apple-system, BlinkMacSystemFont, '
+            '"Segoe UI", sans-serif);'
+            '}'
+            '.ed-activity-pre {'
+            'margin: 0;'
+            'font-family: ui-monospace, SFMono-Regular, Menlo, monospace;'
+            'white-space: pre-wrap;'
+            '}'
+            '.ed-activity-spinner {'
+            'font-family: ui-monospace, SFMono-Regular, Menlo, monospace;'
+            '}'
+            '.ed-activity-spinner::before {'
+            f'animation: ed-activity-frames {_JUPYTER_SPINNER_SECONDS}s steps(1) infinite;'
+            'content: "⠋";'
+            'display: inline-block;'
+            'width: 1ch;'
+            '}'
+            f'@keyframes ed-activity-frames {{ {keyframe_css} }}'
+            '</style>'
+        )
+
+
+class _ActivityIndicatorContext(AbstractContextManager[ActivityIndicator]):
+    """Context manager wrapper for ``ActivityIndicator``."""
+
+    def __init__(self, *, label: str, verbosity: VerbosityEnum) -> None:
+        self._indicator = ActivityIndicator(label, verbosity=verbosity)
+
+    def __enter__(self) -> ActivityIndicator:
+        self._indicator.start()
+        return self._indicator
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        del exc_type
+        del exc_value
+        del traceback
+        self._indicator.stop()
+
+
+def activity_indicator(
+    label: str = ACTIVITY_LABEL_PROCESSING,
+    *,
+    verbosity: VerbosityEnum,
+) -> AbstractContextManager[ActivityIndicator]:
+    """
+    Manage an activity indicator around a block of work.
+
+    Parameters
+    ----------
+    label : str, default=ACTIVITY_LABEL_PROCESSING
+        User-facing activity label.
+    verbosity : VerbosityEnum
+        Output verbosity controlling whether live display is shown.
+
+    Returns
+    -------
+    AbstractContextManager[ActivityIndicator]
+        Context manager that starts the indicator on entry and stops it
+        on exit.
+    """
+    return _ActivityIndicatorContext(label=label, verbosity=verbosity)
diff --git a/src/easydiffraction/display/tablers/base.py b/src/easydiffraction/display/tablers/base.py
index 75922fce3..0bc2bddfb 100644
--- a/src/easydiffraction/display/tablers/base.py
+++ b/src/easydiffraction/display/tablers/base.py
@@ -98,6 +98,29 @@ def _rich_border_color(self) -> str:
     def _pandas_border_color(self) -> str:
         return self._rich_to_hex(self._rich_border_color)
 
+    @abstractmethod
+    def build_renderable(
+        self,
+        alignments: object,
+        df: object,
+    ) -> object:
+        """
+        Build a backend-native table representation.
+
+        Parameters
+        ----------
+        alignments : object
+            Iterable of column justifications (e.g., ``'left'`` or
+            ``'center'``) corresponding to the data columns.
+        df : object
+            Index-aware DataFrame with data to render.
+
+        Returns
+        -------
+        object
+            Backend-native renderable, such as a Rich table or HTML.
+        """
+
     @abstractmethod
     def render(
         self,
diff --git a/src/easydiffraction/display/tablers/pandas.py b/src/easydiffraction/display/tablers/pandas.py
index affa3ff8f..823c2cb09 100644
--- a/src/easydiffraction/display/tablers/pandas.py
+++ b/src/easydiffraction/display/tablers/pandas.py
@@ -241,6 +241,38 @@ def render(
         object
             Backend-defined return value (commonly ``None``).
         """
-        color = self._pandas_border_color
-        styler = self._apply_styling(df, alignments, color)
+        styler = self._build_styler(alignments, df)
         self._update_display(styler, display_handle)
+
+    def build_renderable(
+        self,
+        alignments: object,
+        df: object,
+    ) -> object:
+        """
+        Build notebook HTML for the provided table.
+
+        Parameters
+        ----------
+        alignments : object
+            Iterable of column justifications (e.g. 'left').
+        df : object
+            Index-aware DataFrame whose index is shown as the first
+            column.
+
+        Returns
+        -------
+        object
+            HTML string representation of the styled table.
+        """
+        styler = self._build_styler(alignments, df)
+        return styler.to_html()
+
+    def _build_styler(
+        self,
+        alignments: object,
+        df: object,
+    ) -> object:
+        """Return a configured pandas Styler for the provided table."""
+        color = self._pandas_border_color
+        return self._apply_styling(df, alignments, color)
diff --git a/src/easydiffraction/display/tablers/rich.py b/src/easydiffraction/display/tablers/rich.py
index 8daafb6ba..e23334be3 100644
--- a/src/easydiffraction/display/tablers/rich.py
+++ b/src/easydiffraction/display/tablers/rich.py
@@ -66,24 +66,27 @@ def _to_html(table: Table) -> str:
             "<pre style='margin:0; font-size: 0.9em !important; ' ",
         )
 
-    def _build_table(self, df: object, alignments: object, color: str) -> Table:
+    def build_renderable(
+        self,
+        alignments: object,
+        df: object,
+    ) -> object:
         """
         Construct a Rich Table with formatted data and alignment.
 
         Parameters
         ----------
-        df : object
-            DataFrame-like object providing rows to render.
         alignments : object
             Iterable of text alignment values for columns.
-        color : str
-            Rich color name used for borders/index style.
+        df : object
+            DataFrame-like object providing rows to render.
 
         Returns
         -------
-        Table
+        object
             A :class:`~rich.table.Table` configured for display.
         """
+        color = self._rich_border_color
         table = Table(
             title=None,
             box=RICH_TABLE_BOX,
@@ -172,6 +175,5 @@ def render(
         object
             Backend-defined return value (commonly ``None``).
         """
-        color = self._rich_border_color
-        table = self._build_table(df, alignments, color)
+        table = self.build_renderable(alignments, df)
         self._update_display(table, display_handle)
diff --git a/src/easydiffraction/display/tables.py b/src/easydiffraction/display/tables.py
index cda6f0d19..574d245a0 100644
--- a/src/easydiffraction/display/tables.py
+++ b/src/easydiffraction/display/tables.py
@@ -75,6 +75,47 @@ def show_config(self) -> None:
         console.paragraph('Current tabler configuration')
         TableRenderer.get().render(df)
 
+    @staticmethod
+    def _prepare_dataframe(df: object) -> tuple[object, object]:
+        """
+        Normalize input table data for backend consumption.
+
+        Parameters
+        ----------
+        df : object
+            DataFrame with a two-level column index where the second
+            level provides per-column alignment.
+
+        Returns
+        -------
+        tuple[object, object]
+            Normalized ``(alignments, dataframe)`` pair.
+        """
+        prepared_df = df.copy()
+        prepared_df.index += 1
+
+        alignments = prepared_df.columns.get_level_values(1)
+        prepared_df.columns = prepared_df.columns.get_level_values(0)
+        return alignments, prepared_df
+
+    def build_renderable(self, df: object) -> object:
+        """
+        Build a backend-native renderable without displaying it.
+
+        Parameters
+        ----------
+        df : object
+            DataFrame with a two-level column index where the second
+            level provides per-column alignment.
+
+        Returns
+        -------
+        object
+            Backend-native renderable, such as a Rich table or HTML.
+        """
+        alignments, prepared_df = self._prepare_dataframe(df)
+        return self._backend.build_renderable(alignments, prepared_df)
+
     def render(self, df: object, display_handle: object | None = None) -> object:
         """
         Render a DataFrame as a table using the active backend.
@@ -94,19 +135,8 @@ def render(self, df: object, display_handle: object | None = None) -> object:
         object
             Backend-specific return value (usually ``None``).
         """
-        # Work on a copy to avoid mutating the original DataFrame
-        df = df.copy()
-
-        # Force starting index from 1
-        df.index += 1
-
-        # Extract column alignments
-        alignments = df.columns.get_level_values(1)
-
-        # Remove alignments from df (Keep only the first index level)
-        df.columns = df.columns.get_level_values(0)
-
-        return self._backend.render(alignments, df, display_handle)
+        alignments, prepared_df = self._prepare_dataframe(df)
+        return self._backend.render(alignments, prepared_df, display_handle)
 
 
 class TableRendererFactory(RendererFactoryBase):
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index 2071463d5..6bab7df3d 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -13,6 +13,9 @@
 from easydiffraction.display.plotting import PlotterEngineEnum
 from easydiffraction.display.plotting import PosteriorPairPlotStyleEnum
 from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
+from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
+from easydiffraction.display.progress import activity_indicator
+from easydiffraction.utils.enums import VerbosityEnum
 from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
 
@@ -128,12 +131,16 @@ def pairs(
         max_parameters: int = 6,
     ) -> None:
         """Plot posterior pair relationships for sampled parameters."""
-        self._project.rendering.plotter.plot_posterior_pairs(
-            parameters=parameters,
-            style=style,
-            threshold=threshold,
-            max_parameters=max_parameters,
-        )
+        with activity_indicator(
+            ACTIVITY_LABEL_PROCESSING,
+            verbosity=VerbosityEnum(self._project.verbosity),
+        ):
+            self._project.rendering.plotter.plot_posterior_pairs(
+                parameters=parameters,
+                style=style,
+                threshold=threshold,
+                max_parameters=max_parameters,
+            )
 
     def distribution(self, param: object) -> None:
         """Plot one sampled parameter's posterior distribution."""
@@ -150,14 +157,18 @@ def predictive(
         x: object | None = None,
     ) -> None:
         """Plot posterior predictive summaries for one experiment."""
-        self._project.rendering.plotter.plot_posterior_predictive(
-            expt_name=expt_name,
-            style=style,
-            x_min=x_min,
-            x_max=x_max,
-            show_residual=show_residual,
-            x=x,
-        )
+        with activity_indicator(
+            ACTIVITY_LABEL_PROCESSING,
+            verbosity=VerbosityEnum(self._project.verbosity),
+        ):
+            self._project.rendering.plotter.plot_posterior_predictive(
+                expt_name=expt_name,
+                style=style,
+                x_min=x_min,
+                x_max=x_max,
+                show_residual=show_residual,
+                x=x,
+            )
 
     def help(self) -> None:
         """Print available posterior-display methods."""
@@ -213,19 +224,23 @@ def pattern(
                 msg = self._status_by_name(statuses, 'auto').reason
                 raise ValueError(msg)
             if 'uncertainty' in auto_include:
-                self._project.rendering.plotter._plot_posterior_predictive_request(
-                    expt_name=expt_name,
-                    style='band',
-                    plot_options=_MeasVsCalcPlotOptions(
-                        x_min=x_min,
-                        x_max=x_max,
-                        show_residual=True if 'residual' in auto_include else None,
-                        show_background='background' in auto_include,
-                        show_bragg='bragg' in auto_include,
-                        show_excluded='excluded' in auto_include,
-                        x=x,
-                    ),
-                )
+                with activity_indicator(
+                    ACTIVITY_LABEL_PROCESSING,
+                    verbosity=VerbosityEnum(self._project.verbosity),
+                ):
+                    self._project.rendering.plotter._plot_posterior_predictive_request(
+                        expt_name=expt_name,
+                        style='band',
+                        plot_options=_MeasVsCalcPlotOptions(
+                            x_min=x_min,
+                            x_max=x_max,
+                            show_residual=True if 'residual' in auto_include else None,
+                            show_background='background' in auto_include,
+                            show_bragg='bragg' in auto_include,
+                            show_excluded='excluded' in auto_include,
+                            x=x,
+                        ),
+                    )
                 return
             self._show_point_estimate_pattern(
                 expt_name=expt_name,
@@ -243,19 +258,23 @@ def pattern(
             raise ValueError(msg)
 
         if 'uncertainty' in normalized_include:
-            self._project.rendering.plotter._plot_posterior_predictive_request(
-                expt_name=expt_name,
-                style='band',
-                plot_options=_MeasVsCalcPlotOptions(
-                    x_min=x_min,
-                    x_max=x_max,
-                    show_residual=True if 'residual' in normalized_include else None,
-                    show_background='background' in normalized_include,
-                    show_bragg='bragg' in normalized_include,
-                    show_excluded='excluded' in normalized_include,
-                    x=x,
-                ),
-            )
+            with activity_indicator(
+                ACTIVITY_LABEL_PROCESSING,
+                verbosity=VerbosityEnum(self._project.verbosity),
+            ):
+                self._project.rendering.plotter._plot_posterior_predictive_request(
+                    expt_name=expt_name,
+                    style='band',
+                    plot_options=_MeasVsCalcPlotOptions(
+                        x_min=x_min,
+                        x_max=x_max,
+                        show_residual=True if 'residual' in normalized_include else None,
+                        show_background='background' in normalized_include,
+                        show_bragg='bragg' in normalized_include,
+                        show_excluded='excluded' in normalized_include,
+                        x=x,
+                    ),
+                )
             return
 
         self._show_point_estimate_pattern(
diff --git a/src/easydiffraction/utils/utils.py b/src/easydiffraction/utils/utils.py
index a81f1cb0b..2fe66e739 100644
--- a/src/easydiffraction/utils/utils.py
+++ b/src/easydiffraction/utils/utils.py
@@ -563,6 +563,38 @@ def render_table(
     tabler.render(df, display_handle=display_handle)
 
 
+def build_table_renderable(
+    columns_data: object,
+    columns_alignment: object,
+    columns_headers: object = None,
+) -> object:
+    """
+    Build a table renderable for the active display backend.
+
+    Parameters
+    ----------
+    columns_data : object
+        A list of rows, where each row is a list of cell values.
+    columns_alignment : object
+        A list of alignment strings (e.g. ``'left'``, ``'right'``,
+        ``'center'``) matching the number of columns.
+    columns_headers : object, default=None
+        Optional list of column header strings.
+
+    Returns
+    -------
+    object
+        Backend-native renderable, such as a Rich table or HTML.
+    """
+    headers = [
+        (col, align) for col, align in zip(columns_headers, columns_alignment, strict=False)
+    ]
+    df = pd.DataFrame(columns_data, columns=pd.MultiIndex.from_tuples(headers))
+
+    tabler = TableRenderer.get()
+    return tabler.build_renderable(df)
+
+
 def _help_first_sentence(docstring: str | None) -> str:
     """Return the first paragraph of a docstring on one line."""
     if not docstring:
diff --git a/tests/integration/fitting/test_bayesian_tracker_and_base.py b/tests/integration/fitting/test_bayesian_tracker_and_base.py
index f63cb111f..e6988a0f3 100644
--- a/tests/integration/fitting/test_bayesian_tracker_and_base.py
+++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py
@@ -27,7 +27,25 @@ def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
     import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
 
-    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    events: list[tuple[str, object]] = []
+
+    class FakeIndicator:
+        def __init__(self, label, *, verbosity, display_handle=None):
+            del display_handle
+            events.append(('init', label, verbosity))
+
+        def start(self):
+            events.append(('start', None))
+
+        def update(self, *, label=None, content=None):
+            events.append(('update', label))
+            del content
+
+        def stop(self):
+            events.append(('stop', None))
+
+    monkeypatch.setattr(tracking_mod, 'ActivityIndicator', FakeIndicator)
+    monkeypatch.setattr(tracking_mod, 'build_table_renderable', lambda **kwargs: 'table')
 
     tracker = FitProgressTracker()
     tracker.start_tracking('dummy')
@@ -46,6 +64,7 @@ def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
     out = capsys.readouterr().out
     assert 'Best goodness-of-fit' in out
     assert tracker.best_iteration is not None
+    assert ('init', tracking_mod.ACTIVITY_LABEL_FITTING, tracker._verbosity) in events
 
 
 def test_tracker_sampler_progress_renders_and_completes(monkeypatch, capsys):
@@ -53,7 +72,25 @@ def test_tracker_sampler_progress_renders_and_completes(monkeypatch, capsys):
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
     from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
 
-    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    events: list[tuple[str, object]] = []
+
+    class FakeIndicator:
+        def __init__(self, label, *, verbosity, display_handle=None):
+            del display_handle
+            events.append(('init', label, verbosity))
+
+        def start(self):
+            events.append(('start', None))
+
+        def update(self, *, label=None, content=None):
+            events.append(('update', label))
+            del content
+
+        def stop(self):
+            events.append(('stop', None))
+
+    monkeypatch.setattr(tracking_mod, 'ActivityIndicator', FakeIndicator)
+    monkeypatch.setattr(tracking_mod, 'build_table_renderable', lambda **kwargs: 'table')
 
     tracker = FitProgressTracker()
     tracker.start_tracking('dream', mode='sampling')
@@ -90,6 +127,8 @@ def test_tracker_sampler_progress_renders_and_completes(monkeypatch, capsys):
     assert 'Bayesian sampling complete.' in out
     assert tracker.best_chi2 == pytest.approx(1.0)
     assert tracker.best_iteration == 10
+    assert ('update', tracking_mod.ACTIVITY_LABEL_BURN_IN) in events
+    assert ('update', tracking_mod.ACTIVITY_LABEL_SAMPLING) in events
 
 
 def test_tracker_helper_error_paths_and_short_mode(monkeypatch):
@@ -97,7 +136,24 @@ def test_tracker_helper_error_paths_and_short_mode(monkeypatch):
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
     from easydiffraction.utils.enums import VerbosityEnum
 
-    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    events: list[tuple[str, object]] = []
+
+    class FakeIndicator:
+        def __init__(self, label, *, verbosity, display_handle=None):
+            del display_handle
+            events.append(('init', label, verbosity))
+
+        def start(self):
+            events.append(('start', None))
+
+        def update(self, *, label=None, content=None):
+            events.append(('update', label))
+            del content
+
+        def stop(self):
+            events.append(('stop', None))
+
+    monkeypatch.setattr(tracking_mod, 'ActivityIndicator', FakeIndicator)
 
     tracker = FitProgressTracker()
     tracker._verbosity = VerbosityEnum.SHORT
@@ -111,14 +167,17 @@ def test_tracker_helper_error_paths_and_short_mode(monkeypatch):
         FitProgressTracker()._sampler_iteration_label(1)
 
     assert FitProgressTracker._rows_match_on_columns(['1', 'a'], ['1', 'b'], (0,)) is True
+    assert events == [
+        ('init', tracking_mod.ACTIVITY_LABEL_SAMPLING, VerbosityEnum.SHORT),
+        ('start', None),
+        ('update', tracking_mod.ACTIVITY_LABEL_SAMPLING),
+        ('stop', None),
+    ]
 
 
-def test_tracker_final_sampler_row_replaces_last_row(monkeypatch):
-    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+def test_tracker_final_sampler_row_replaces_last_row():
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
 
-    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: None)
-
     tracker = FitProgressTracker()
     tracker._tracking_mode = 'sampling'
     tracker._sampler_total_iterations = 10
@@ -137,44 +196,32 @@ def test_tracker_final_sampler_row_replaces_last_row(monkeypatch):
 def test_make_display_handle_uses_terminal_live_when_available(monkeypatch):
     import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
 
-    class FakeLive:
-        def __init__(self, *, console, auto_refresh):
-            self.console = console
-            self.auto_refresh = auto_refresh
-            self.started = False
-            self.stopped = False
-
-        def start(self):
-            self.started = True
-
-        def stop(self):
-            self.stopped = True
-
-    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
-    monkeypatch.setattr(tracking_mod, 'Live', FakeLive)
-    monkeypatch.setattr(tracking_mod.ConsoleManager, 'get', lambda: 'console')
-
-    handle = tracking_mod._make_display_handle()
+    sentinel = object()
 
-    assert isinstance(handle, tracking_mod._TerminalLiveHandle)
-    assert handle._live.console == 'console'
-    assert handle._live.auto_refresh is True
-    assert handle._live.started is True
+    monkeypatch.setattr(tracking_mod, 'make_display_handle', lambda: sentinel)
 
-    handle.close()
-
-    assert handle._live.stopped is True
+    assert tracking_mod._make_display_handle() is sentinel
 
 
 def test_tracker_misc_helper_paths(monkeypatch):
     import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.utils.enums import VerbosityEnum
 
     render_calls: list[dict[str, object]] = []
-    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: render_calls.append(kwargs))
+    update_calls: list[dict[str, object]] = []
     monkeypatch.setattr(
         tracking_mod, 'calculate_reduced_chi_square', lambda residuals, n_params: 3.0
     )
+    monkeypatch.setattr(
+        tracking_mod,
+        'build_table_renderable',
+        lambda **kwargs: render_calls.append(kwargs) or 'renderable',
+    )
+
+    class FakeIndicator:
+        def update(self, *, label=None, content=None):
+            update_calls.append({'label': label, 'content': content})
 
     tracker = FitProgressTracker()
     tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
@@ -197,13 +244,21 @@ def test_tracker_misc_helper_paths(monkeypatch):
     assert tracker._current_elapsed_time() is None
     assert tracker._format_elapsed_time() == ''
 
+    tracker._verbosity = VerbosityEnum.FULL
+    tracker._activity_indicator = FakeIndicator()
     tracker._replace_last_tracking_row(['1'])
 
     assert tracker._df_rows == [['1']]
     assert len(render_calls) == 1
+    assert update_calls == [
+        {
+            'label': tracking_mod.ACTIVITY_LABEL_FITTING,
+            'content': 'renderable',
+        }
+    ]
 
 
-def test_tracker_final_rows_cover_fallbacks_and_close_suppression():
+def test_tracker_final_rows_cover_fallbacks_and_activity_labels():
     import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
 
@@ -218,15 +273,19 @@ def test_tracker_final_rows_cover_fallbacks_and_close_suppression():
     tracker._tracking_mode = tracking_mod.TRACKING_MODE_FIT
     tracker._fitting_time = 1.5
     assert tracker._final_fit_tracking_row() == ['8', '1.50', '', '']
-
-    class BadHandle:
-        @staticmethod
-        def close() -> None:
-            message = 'boom'
-            raise RuntimeError(message)
-
-    tracker._display_handle = BadHandle()
-    tracker._close_display_handle()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    assert tracker._default_activity_label() == tracking_mod.ACTIVITY_LABEL_SAMPLING
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_FIT
+    assert tracker._default_activity_label() == tracking_mod.ACTIVITY_LABEL_FITTING
+    assert (
+        tracker._activity_label_for_sampler_phase('burn-in') == tracking_mod.ACTIVITY_LABEL_BURN_IN
+    )
+    assert (
+        tracker._activity_label_for_sampler_phase('sampling')
+        == tracking_mod.ACTIVITY_LABEL_SAMPLING
+    )
+    assert tracker._activity_label_for_sampler_phase('annealing') == 'annealing'
+    assert tracker._activity_label_for_sampler_phase('') == tracking_mod.ACTIVITY_LABEL_SAMPLING
 
 
 def test_minimizer_base_fit_flow_and_finalize():
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
index 561eb54c3..877a46b1a 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
@@ -13,11 +13,11 @@ def test_module_import():
 
 
 def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
-    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    import easydiffraction.display.progress as progress_mod
     from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
 
-    # Force terminal branch (not notebook): tracking imports in_jupyter directly
-    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    # Force terminal branch (not notebook) in the shared progress layer.
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
 
     tracker = FitProgressTracker()
     tracker.start_tracking('dummy')
@@ -43,3 +43,37 @@ def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
     out2 = capsys.readouterr().out
     assert 'Best goodness-of-fit' in out2
     assert tracker.best_iteration is not None
+
+
+def test_tracker_fit_adds_timed_rows_and_resets_counter(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    chi2_values = iter([5.0, 4.0, 3.97, 3.97, 3.97, 3.97])
+    perf_counter_values = iter([0.0, 0.0, 2.0, 6.9, 7.1, 11.9, 12.2])
+
+    monkeypatch.setattr(
+        tracking_mod,
+        'calculate_reduced_chi_square',
+        lambda residuals, n_parameters: next(chi2_values),
+    )
+    monkeypatch.setattr(
+        tracking_mod.time,
+        'perf_counter',
+        lambda: next(perf_counter_values),
+    )
+
+    tracker = FitProgressTracker()
+    tracker.start_timer()
+
+    for _ in range(6):
+        tracker.track(np.array([1.0]), parameters=[1.0])
+
+    assert tracker._df_rows == [
+        ['1', '0.00', '5.00', ''],
+        ['2', '2.00', '4.00', '20.0% ↓'],
+        ['4', '7.10', '3.97', ''],
+        ['6', '12.20', '3.97', ''],
+    ]
+    assert tracker._last_progress_time == 12.2
+    assert tracker._previous_chi2 == 4.0
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_base.py b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
index 0ad0ca127..3b18f5354 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_base.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
@@ -158,3 +158,46 @@ def _check_success(self, raw_result):
 
     assert minimizer.started is True
     assert minimizer.stopped is True
+
+
+def test_minimizer_base_fit_preserves_solver_prep_error_during_cleanup(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    monkeypatch.setattr(
+        tracking_mod.FitProgressTracker,
+        '_start_activity_indicator',
+        lambda self: None,
+    )
+    monkeypatch.setattr(
+        tracking_mod.FitProgressTracker,
+        '_stop_activity_indicator',
+        lambda self: None,
+    )
+    monkeypatch.setattr(tracking_mod.console, 'print', lambda *args, **kwargs: None)
+
+    class M(MinimizerBase):
+        def __init__(self):
+            super().__init__(name='dummy', method='m', max_iterations=5)
+
+        def _prepare_solver_args(self, parameters):
+            del parameters
+            msg = 'prep failed'
+            raise ValueError(msg)
+
+        def _run_solver(self, objective_function, **kwargs):
+            del objective_function, kwargs
+            msg = 'should not run solver'
+            raise AssertionError(msg)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            del parameters, raw_result
+
+        def _check_success(self, raw_result):
+            del raw_result
+            return True
+
+    minimizer = M()
+
+    with pytest.raises(ValueError, match='prep failed'):
+        minimizer.fit(parameters=[_DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
diff --git a/tests/unit/easydiffraction/analysis/test_analysis.py b/tests/unit/easydiffraction/analysis/test_analysis.py
index c5b2ef248..637facc02 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from types import SimpleNamespace
+
 
 def test_module_import():
     import easydiffraction.analysis.analysis as MUT
@@ -132,3 +134,96 @@ def values(self):
     a.display.fit_results()
 
     assert process_called['called'], '_process_fit_results should be called'
+
+
+def test_fit_single_short_reuses_tracker_display_handle(monkeypatch):
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.utils.enums import VerbosityEnum
+
+    class Handle:
+        def __init__(self) -> None:
+            self.closed = False
+
+        def close(self) -> None:
+            self.closed = True
+
+    class Experiments:
+        def __init__(self) -> None:
+            self.names = ['e1']
+
+        def __getitem__(self, name: str) -> object:
+            del name
+            return object()
+
+    class Tracker:
+        def __init__(self) -> None:
+            self.best_iteration = 7
+            self.display_handles: list[object | None] = []
+
+        def _set_shared_display_handle(self, display_handle: object | None) -> None:
+            self.display_handles.append(display_handle)
+
+    project = SimpleNamespace(
+        experiments=Experiments(),
+        structures=object(),
+        _varname='proj',
+    )
+    analysis = Analysis(project=project)
+    tracker = Tracker()
+    analysis.fitter.minimizer = SimpleNamespace(tracker=tracker)
+    analysis.fitter.results = None
+
+    handle = Handle()
+    short_display_handles: list[object | None] = []
+
+    def fake_make_display_handle() -> Handle:
+        return handle
+
+    def fake_fit(
+        structures: object,
+        experiments: list[object],
+        *,
+        analysis: object,
+        verbosity: object,
+        use_physical_limits: bool,
+        random_seed: int | None,
+    ) -> None:
+        del structures, experiments, analysis, verbosity, use_physical_limits, random_seed
+        analysis_obj = fake_fit.analysis_obj
+        analysis_obj.fitter.results = SimpleNamespace(
+            reduced_chi_square=1.23,
+            success=True,
+            parameters=[],
+        )
+
+    fake_fit.analysis_obj = analysis
+
+    def fake_update_short_table(
+        self,
+        short_rows: list[list[str]],
+        expt_name: str,
+        results: object,
+        display_handle: object | None,
+    ) -> None:
+        del self, expt_name, results
+        short_rows.append(['e1', '1.23', '7', '✅'])
+        short_display_handles.append(display_handle)
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.analysis.make_display_handle', fake_make_display_handle
+    )
+    monkeypatch.setattr(analysis, '_snapshot_params', lambda expt_name, results: None)
+    monkeypatch.setattr(analysis.fitter, 'fit', fake_fit)
+    monkeypatch.setattr(Analysis, '_fit_single_update_short_table', fake_update_short_table)
+
+    analysis._fit_single(
+        VerbosityEnum.SHORT,
+        project.structures,
+        project.experiments,
+        use_physical_limits=False,
+        random_seed=None,
+    )
+
+    assert tracker.display_handles == [handle, None]
+    assert short_display_handles == [handle]
+    assert handle.closed is True
diff --git a/tests/unit/easydiffraction/analysis/test_sequential.py b/tests/unit/easydiffraction/analysis/test_sequential.py
index 3179a0b13..5e01b586d 100644
--- a/tests/unit/easydiffraction/analysis/test_sequential.py
+++ b/tests/unit/easydiffraction/analysis/test_sequential.py
@@ -4,7 +4,9 @@
 
 from __future__ import annotations
 
+import contextlib
 import csv
+from types import SimpleNamespace
 
 import pytest
 
@@ -14,6 +16,8 @@
 from easydiffraction.analysis.sequential import _build_csv_header
 from easydiffraction.analysis.sequential import _read_csv_for_recovery
 from easydiffraction.analysis.sequential import _write_csv_header
+from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
+from easydiffraction.utils.enums import VerbosityEnum
 
 
 # ------------------------------------------------------------------
@@ -300,3 +304,130 @@ def test_fields_accessible(self):
         assert template.diffrn_field_names == ['temp']
         assert template.minimizer_tag == 'lmfit'
         assert template.calculator_tag == 'cryspy'
+
+
+def test_fit_sequential_short_starts_and_stops_shared_indicator(monkeypatch, tmp_path):
+    import easydiffraction.analysis.sequential as sequential_mod
+
+    events: list[tuple[object, ...]] = []
+    template = _minimal_template()
+
+    class FakeIndicator:
+        def __init__(self, label, *, verbosity):
+            events.append(('init', label, verbosity))
+
+        def start(self):
+            events.append(('start',))
+
+        def update(self):
+            events.append(('update',))
+
+        def stop(self):
+            events.append(('stop',))
+
+    def fake_run_fit_loop(
+        pool_cm, chunks, template_arg, csv_info, extract_diffrn, verb, indicator
+    ):
+        del pool_cm, csv_info, extract_diffrn
+        assert chunks == [['scan_001.xye']]
+        assert template_arg == template
+        assert verb is VerbosityEnum.SHORT
+        assert indicator is not None
+        indicator.update()
+
+    monkeypatch.setattr(sequential_mod, 'ActivityIndicator', FakeIndicator)
+    monkeypatch.setattr(sequential_mod.mp, 'parent_process', lambda: None)
+    monkeypatch.setattr(sequential_mod, '_check_seq_preconditions', lambda project: None)
+    monkeypatch.setattr(
+        sequential_mod,
+        'extract_data_paths_from_dir',
+        lambda data_dir, file_pattern='*': ['scan_001.xye'],
+    )
+    monkeypatch.setattr(sequential_mod, '_build_template', lambda project: template)
+    monkeypatch.setattr(
+        sequential_mod,
+        '_setup_csv_and_recovery',
+        lambda project, template_arg, verb: (
+            tmp_path / 'results.csv',
+            ['file_path'],
+            set(),
+            template_arg,
+        ),
+    )
+    monkeypatch.setattr(sequential_mod, '_resolve_workers', lambda max_workers, chunk_size: (1, 1))
+    monkeypatch.setattr(
+        sequential_mod,
+        '_create_pool_context',
+        lambda max_workers: (contextlib.nullcontext(None), None, None, None),
+    )
+    monkeypatch.setattr(sequential_mod, '_run_fit_loop', fake_run_fit_loop)
+    monkeypatch.setattr(sequential_mod, '_restore_main_state', lambda *args: None)
+
+    analysis = SimpleNamespace(
+        project=SimpleNamespace(verbosity='short'),
+        fitter=SimpleNamespace(selection='lmfit'),
+    )
+
+    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
+
+    assert events == [
+        ('init', ACTIVITY_LABEL_FITTING, VerbosityEnum.SHORT),
+        ('start',),
+        ('update',),
+        ('stop',),
+    ]
+
+
+def test_fit_sequential_silent_does_not_start_indicator(monkeypatch, tmp_path):
+    import easydiffraction.analysis.sequential as sequential_mod
+
+    template = _minimal_template()
+
+    class FailingIndicator:
+        def __init__(self, *args, **kwargs):
+            message = 'silent mode should not create an activity indicator'
+            raise AssertionError(message)
+
+    def fake_run_fit_loop(
+        pool_cm, chunks, template_arg, csv_info, extract_diffrn, verb, indicator
+    ):
+        del pool_cm, csv_info, extract_diffrn
+        assert chunks == [['scan_001.xye']]
+        assert template_arg == template
+        assert verb is VerbosityEnum.SILENT
+        assert indicator is None
+
+    monkeypatch.setattr(sequential_mod, 'ActivityIndicator', FailingIndicator)
+    monkeypatch.setattr(sequential_mod.mp, 'parent_process', lambda: None)
+    monkeypatch.setattr(sequential_mod, '_check_seq_preconditions', lambda project: None)
+    monkeypatch.setattr(
+        sequential_mod,
+        'extract_data_paths_from_dir',
+        lambda data_dir, file_pattern='*': ['scan_001.xye'],
+    )
+    monkeypatch.setattr(sequential_mod, '_build_template', lambda project: template)
+    monkeypatch.setattr(
+        sequential_mod,
+        '_setup_csv_and_recovery',
+        lambda project, template_arg, verb: (
+            tmp_path / 'results.csv',
+            ['file_path'],
+            set(),
+            template_arg,
+        ),
+    )
+    monkeypatch.setattr(sequential_mod, '_resolve_workers', lambda max_workers, chunk_size: (1, 1))
+    monkeypatch.setattr(
+        sequential_mod,
+        '_create_pool_context',
+        lambda max_workers: (contextlib.nullcontext(None), None, None, None),
+    )
+    monkeypatch.setattr(sequential_mod, '_run_fit_loop', fake_run_fit_loop)
+    monkeypatch.setattr(sequential_mod, '_restore_main_state', lambda *args: None)
+
+    analysis = SimpleNamespace(
+        project=SimpleNamespace(verbosity='silent'),
+        fitter=SimpleNamespace(selection='lmfit'),
+    )
+
+    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index 5c3c8dd50..5b5836229 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -828,7 +828,9 @@ def fake_show_figure(self, fig):
     )
 
     fig = captured['fig']
-    predictive_band_trace = next(trace for trace in fig.data if trace.name == '95% interval')
+    predictive_band_trace = next(
+        trace for trace in fig.data if trace.name == '95% credible interval'
+    )
     max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
     residual_trace = next(trace for trace in fig.data if trace.name == 'Residual (Imeas - Icalc)')
 
diff --git a/tests/unit/easydiffraction/display/tablers/test_pandas.py b/tests/unit/easydiffraction/display/tablers/test_pandas.py
index 2a2828a9c..e4a77c5b6 100644
--- a/tests/unit/easydiffraction/display/tablers/test_pandas.py
+++ b/tests/unit/easydiffraction/display/tablers/test_pandas.py
@@ -33,3 +33,15 @@ def test_apply_styling_returns_styler(self):
         df = pd.DataFrame({'A': [1.0], 'B': [2.0]})
         styler = backend._apply_styling(df, ['left', 'right'], '#aabbcc')
         assert hasattr(styler, 'to_html')
+
+    def test_build_renderable_returns_html(self):
+        from easydiffraction.display.tablers.pandas import PandasTableBackend
+
+        pytest.importorskip('jinja2')
+        backend = PandasTableBackend()
+        df = pd.DataFrame({'A': [1.0], 'B': [2.0]})
+
+        html = backend.build_renderable(['left', 'right'], df)
+
+        assert isinstance(html, str)
+        assert '<table' in html
diff --git a/tests/unit/easydiffraction/display/tablers/test_rich.py b/tests/unit/easydiffraction/display/tablers/test_rich.py
index e23241e2c..c7599865a 100644
--- a/tests/unit/easydiffraction/display/tablers/test_rich.py
+++ b/tests/unit/easydiffraction/display/tablers/test_rich.py
@@ -13,13 +13,13 @@ def test_rich_table_box_constant(self):
 
         assert isinstance(RICH_TABLE_BOX, Box)
 
-    def test_build_table_returns_table(self):
+    def test_build_renderable_returns_table(self):
         from easydiffraction.display.tablers.rich import RichTableBackend
 
         backend = RichTableBackend()
         df = pd.DataFrame({'Col': [1.0, 2.0]})
         df.index += 1
-        table = backend._build_table(df, ['left'], 'grey35')
+        table = backend.build_renderable(['left'], df)
         assert isinstance(table, Table)
 
     def test_to_html_returns_string(self):
@@ -28,7 +28,7 @@ def test_to_html_returns_string(self):
         backend = RichTableBackend()
         df = pd.DataFrame({'Col': [1.0]})
         df.index += 1
-        table = backend._build_table(df, ['left'], 'grey35')
+        table = backend.build_renderable(['left'], df)
         html = backend._to_html(table)
         assert isinstance(html, str)
         assert '<pre' in html
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 08540bf07..18f0afe81 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -607,7 +607,6 @@ def test_build_posterior_pairs_plot_rejects_unknown_style():
 
 
 def test_build_param_distribution_plot_returns_plotly_figure():
-    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_68_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
@@ -623,16 +622,12 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert {trace.name for trace in figure.data} >= {
         'Posterior histogram',
         'Marginal density',
-        '68% credible interval',
         '95% credible interval',
         'Median',
         'Max posterior',
     }
     marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
     histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
-    interval_68_trace = next(
-        trace for trace in figure.data if trace.name == '68% credible interval'
-    )
     interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
     max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
     assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
@@ -640,7 +635,7 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
     assert histogram_trace.xbins.size is not None
-    assert interval_68_trace.fillcolor == POSTERIOR_INTERVAL_68_FILL_COLOR
+    assert '68% credible interval' not in {trace.name for trace in figure.data}
     assert interval_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
     assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
     assert figure.layout.xaxis.range is not None
@@ -685,7 +680,7 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     measured_trace = next(trace for trace in fig.data if trace.name == 'Measured')
     max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
 
-    assert upper_band_trace.name == '95% interval'
+    assert upper_band_trace.name == '95% credible interval'
     assert upper_band_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
     assert upper_band_trace.legendrank == 30
     assert measured_trace.legendrank == 10
diff --git a/tests/unit/easydiffraction/display/test_progress.py b/tests/unit/easydiffraction/display/test_progress.py
new file mode 100644
index 000000000..f690159ed
--- /dev/null
+++ b/tests/unit/easydiffraction/display/test_progress.py
@@ -0,0 +1,210 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for display/progress.py."""
+
+from __future__ import annotations
+
+from easydiffraction.utils.enums import VerbosityEnum
+
+
+def test_make_display_handle_uses_terminal_live_when_available(monkeypatch):
+    import easydiffraction.display.progress as progress_mod
+
+    class FakeLive:
+        def __init__(
+            self,
+            renderable=None,
+            *,
+            console,
+            auto_refresh,
+            refresh_per_second,
+            get_renderable=None,
+        ):
+            self.renderable = renderable
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.refresh_per_second = refresh_per_second
+            self.get_renderable = get_renderable
+            self.started = False
+            self.stopped = False
+            self.refresh_calls = 0
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.stopped = True
+
+        def refresh(self):
+            self.refresh_calls += 1
+
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(progress_mod, 'Live', FakeLive)
+    monkeypatch.setattr(progress_mod.ConsoleManager, 'get', lambda: 'console')
+
+    handle = progress_mod.make_display_handle()
+
+    assert isinstance(handle, progress_mod._TerminalLiveHandle)
+    assert handle._live.console == 'console'
+    assert handle._live.auto_refresh is True
+    assert handle._live.get_renderable is not None
+    assert handle._live.started is True
+
+    handle.update('content')
+
+    assert handle._live.refresh_calls == 1
+    assert handle._live.get_renderable() == 'content'
+
+    handle.close()
+
+    assert handle._live.stopped is True
+
+
+def test_activity_indicator_silent_does_not_create_handles():
+    from easydiffraction.display.progress import ActivityIndicator
+
+    indicator = ActivityIndicator(verbosity=VerbosityEnum.SILENT)
+    indicator.start()
+
+    assert indicator._live is None
+    assert indicator._display_handle is None
+
+
+def test_activity_indicator_html_style_prevents_stretching():
+    import easydiffraction.display.progress as progress_mod
+
+    indicator = progress_mod.ActivityIndicator(verbosity=VerbosityEnum.FULL)
+    style = indicator._html_style()
+
+    assert 'align-items: flex-start;' in style
+    assert progress_mod.ACTIVITY_ACCENT_COLOR in style
+    assert 'font-weight: 400;' in style
+    assert 'var(--jp-ui-font-family' in style
+
+
+def test_activity_indicator_terminal_line_uses_accent_style():
+    import easydiffraction.display.progress as progress_mod
+
+    indicator = progress_mod.ActivityIndicator(label='Fitting...', verbosity=VerbosityEnum.FULL)
+    indicator._running = True
+
+    line = indicator._terminal_indicator_line()
+
+    assert line is not None
+    assert line.style == progress_mod.ACTIVITY_TERMINAL_STYLE
+    assert 'bold' not in str(line.style)
+
+
+def test_activity_indicator_terminal_live_uses_dynamic_renderable(monkeypatch):
+    import easydiffraction.display.progress as progress_mod
+
+    class FakeLive:
+        def __init__(
+            self,
+            renderable=None,
+            *,
+            console,
+            auto_refresh,
+            refresh_per_second,
+            get_renderable=None,
+        ):
+            self.renderable = renderable
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.refresh_per_second = refresh_per_second
+            self.get_renderable = get_renderable
+            self.refresh_calls = 0
+            self.started = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.started = False
+
+        def refresh(self):
+            self.refresh_calls += 1
+
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(progress_mod, 'Live', FakeLive)
+    monkeypatch.setattr(progress_mod.ConsoleManager, 'get', lambda: 'console')
+
+    indicator = progress_mod.ActivityIndicator(label='Fitting...', verbosity=VerbosityEnum.FULL)
+    indicator.start()
+    indicator._current_frame = lambda: 'X'
+
+    assert indicator._live is not None
+    assert indicator._live.get_renderable is not None
+    assert indicator._live.renderable is None
+    assert indicator._live.get_renderable().plain == 'X Fitting...'
+
+    indicator.update(label='Sampling...')
+
+    assert indicator._live.refresh_calls == 1
+    assert indicator._live.get_renderable().plain == 'X Sampling...'
+
+
+def test_activity_indicator_render_html_uses_current_label():
+    from easydiffraction.display.progress import ActivityIndicator
+
+    indicator = ActivityIndicator(label='Fitting...', verbosity=VerbosityEnum.FULL)
+    indicator._running = True
+
+    html = indicator._render_html()
+
+    assert 'Fitting...' in html
+
+
+def test_activity_indicator_updates_shared_terminal_handle_without_ipython(monkeypatch):
+    import easydiffraction.display.progress as progress_mod
+
+    class FakeLive:
+        def __init__(
+            self,
+            renderable=None,
+            *,
+            console,
+            auto_refresh,
+            refresh_per_second,
+            get_renderable=None,
+        ):
+            self.renderable = renderable
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.refresh_per_second = refresh_per_second
+            self.get_renderable = get_renderable
+            self.refresh_calls = 0
+            self.started = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.started = False
+
+        def refresh(self):
+            self.refresh_calls += 1
+
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(progress_mod, 'HTML', None)
+    monkeypatch.setattr(progress_mod, 'DisplayHandle', None)
+    monkeypatch.setattr(progress_mod, 'Live', FakeLive)
+    monkeypatch.setattr(progress_mod.ConsoleManager, 'get', lambda: 'console')
+
+    handle = progress_mod.make_display_handle()
+    indicator = progress_mod.ActivityIndicator(
+        label='Fitting...',
+        verbosity=VerbosityEnum.FULL,
+        display_handle=handle,
+    )
+    indicator._current_frame = lambda: 'X'
+
+    indicator.start()
+
+    assert handle._live.refresh_calls == 1
+    assert handle._live.get_renderable().plain == 'X Fitting...'
+
+    indicator.update(label='Sampling...')
+
+    assert handle._live.refresh_calls == 2
+    assert handle._live.get_renderable().plain == 'X Sampling...'
diff --git a/tests/unit/easydiffraction/display/test_tables.py b/tests/unit/easydiffraction/display/test_tables.py
index 2fb16fe13..9920e7e12 100644
--- a/tests/unit/easydiffraction/display/test_tables.py
+++ b/tests/unit/easydiffraction/display/test_tables.py
@@ -2,6 +2,8 @@
 # SPDX-License-Identifier: BSD-3-Clause
 """Tests for display/tables.py (TableEngineEnum, TableRenderer, TableRendererFactory)."""
 
+from types import SimpleNamespace
+
 import pandas as pd
 
 
@@ -59,3 +61,30 @@ def test_render(self, monkeypatch, capsys):
 
         # Reset singleton to not leak state
         monkeypatch.setattr(TableRenderer, '_instance', None)
+
+    def test_build_renderable_normalizes_dataframe(self, monkeypatch):
+        from easydiffraction.display.tables import TableRenderer
+
+        monkeypatch.setattr(TableRenderer, '_instance', None)
+
+        headers = [('Col', 'left')]
+        df = pd.DataFrame([['val']], columns=pd.MultiIndex.from_tuples(headers))
+        calls: dict[str, object] = {}
+
+        def fake_build_renderable(alignments, prepared_df):
+            calls['alignments'] = list(alignments)
+            calls['columns'] = list(prepared_df.columns)
+            calls['index'] = list(prepared_df.index)
+            return 'renderable'
+
+        renderer = TableRenderer.get()
+        renderer._backend = SimpleNamespace(build_renderable=fake_build_renderable)
+
+        assert renderer.build_renderable(df) == 'renderable'
+        assert calls == {
+            'alignments': ['left'],
+            'columns': ['Col'],
+            'index': [1],
+        }
+
+        monkeypatch.setattr(TableRenderer, '_instance', None)
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index 8e8f7695d..b67d7c022 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -4,15 +4,18 @@
 
 from __future__ import annotations
 
+from contextlib import contextmanager
 from types import SimpleNamespace
 
 import pytest
 
 from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
 from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
 from easydiffraction.display.plotting import _MeasVsCalcPlotOptions
 from easydiffraction.project.display import PatternOptionStatus
 from easydiffraction.project.display import ProjectDisplay
+from easydiffraction.utils.enums import VerbosityEnum
 
 
 def _make_project_stub() -> tuple[SimpleNamespace, list[tuple[str, tuple, dict]]]:
@@ -47,6 +50,7 @@ def _recorder(*args, **kwargs):
     project = SimpleNamespace(
         analysis=SimpleNamespace(display=analysis_display),
         rendering=SimpleNamespace(plotter=plotter),
+        verbosity='full',
     )
     return project, calls
 
@@ -207,9 +211,19 @@ def test_fit_display_delegates_to_analysis_and_rendering():
     )
 
 
-def test_posterior_display_delegates_to_rendering_plotter():
+def test_posterior_display_delegates_to_rendering_plotter(monkeypatch):
+    import easydiffraction.project.display as display_mod
+
     project, calls = _make_project_stub()
     display = ProjectDisplay(project)
+    indicator_calls: list[tuple[str, VerbosityEnum]] = []
+
+    @contextmanager
+    def fake_activity_indicator(label, *, verbosity):
+        indicator_calls.append((label, verbosity))
+        yield object()
+
+    monkeypatch.setattr(display_mod, 'activity_indicator', fake_activity_indicator)
 
     display.posterior.pairs(parameters=['a'], threshold=0.5, max_parameters=3)
     display.posterior.distribution('a')
@@ -239,6 +253,10 @@ def test_posterior_display_delegates_to_rendering_plotter():
             'x': 'd_spacing',
         },
     )
+    assert indicator_calls == [
+        (ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL),
+        (ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL),
+    ]
 
 
 def test_pattern_auto_routes_measured_and_excluded_to_plot_meas():
@@ -266,7 +284,9 @@ def test_pattern_auto_routes_measured_and_excluded_to_plot_meas():
     ]
 
 
-def test_pattern_uncertainty_routes_to_posterior_predictive():
+def test_pattern_uncertainty_routes_to_posterior_predictive(monkeypatch):
+    import easydiffraction.project.display as display_mod
+
     project, calls = _make_project_stub()
     display = ProjectDisplay(project)
     display._pattern_option_statuses = lambda expt_name: _make_statuses(
@@ -276,6 +296,14 @@ def test_pattern_uncertainty_routes_to_posterior_predictive():
         excluded=True,
         uncertainty=True,
     )
+    indicator_calls: list[tuple[str, VerbosityEnum]] = []
+
+    @contextmanager
+    def fake_activity_indicator(label, *, verbosity):
+        indicator_calls.append((label, verbosity))
+        yield object()
+
+    monkeypatch.setattr(display_mod, 'activity_indicator', fake_activity_indicator)
 
     display.pattern(
         'hrpt',
@@ -303,6 +331,7 @@ def test_pattern_uncertainty_routes_to_posterior_predictive():
             },
         )
     ]
+    assert indicator_calls == [(ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL)]
 
 
 def test_pattern_measured_and_calculated_suppresses_background_and_bragg():

From 1a94b5930f795448c6f76b1fb0baf62e24c5d213 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Fri, 15 May 2026 20:40:56 +0200
Subject: [PATCH 06/10] Improve Bayesian posterior displays and DREAM CLI
 robustness (#174)

* Use full samples for pair-plot marginal KDEs

* Support posterior distribution defaults

* Support ASCII posterior predictive plots

* Add ASCII posterior density plots

* Update console heading color to deep_sky_blue3

* Guard DREAM multiprocessing for direct script entry points

* Remove unused colorama and tabulate dependencies

* Normalize docstring formatting

* Add DREAM multiprocessing CLI workflow issue

* Resample ASCII plots to terminal width

* Simplify posterior distribution display in tutorials

* Fit ASCII plot range to terminal width

* Use 'processing' label for sampler tracking

* Clean up code formatting

* Add posterior display documentation

* Refactor plotting and ASCII chart constants

* Simplify posterior display calls in tutorials
---
 docs/dev/Issues/issues_open.md                | 196 ++++++------
 docs/docs/quick-reference/index.md            |  12 +
 docs/docs/tutorials/ed-21.ipynb               |   3 +-
 docs/docs/tutorials/ed-21.py                  |   3 +-
 docs/docs/tutorials/ed-22.ipynb               |   3 +-
 docs/docs/tutorials/ed-22.py                  |   3 +-
 pixi.lock                                     |   2 -
 pyproject.toml                                |   2 -
 .../analysis/fit_helpers/tracking.py          |   3 +-
 .../analysis/minimizers/bumps_dream.py        |  61 +++-
 src/easydiffraction/display/plotters/ascii.py |  52 +++-
 src/easydiffraction/display/plotters/base.py  |   8 +
 .../display/plotters/plotly.py                |   1 +
 src/easydiffraction/display/plotting.py       | 217 +++++++++----
 src/easydiffraction/project/display.py        |  20 +-
 src/easydiffraction/utils/logging.py          |   4 +-
 .../fitting/test_bayesian_tracker_and_base.py |   7 +-
 .../fitting/test_bumps_dream_support.py       | 157 +++++++++-
 .../analysis/fit_helpers/test_reporting.py    |   2 +-
 .../display/plotters/test_ascii.py            |  94 ++++++
 .../easydiffraction/display/test_plotting.py  | 288 ++++++++++++++++++
 .../display/test_plotting_coverage.py         |   8 +-
 .../easydiffraction/project/test_display.py   |  44 +++
 23 files changed, 1019 insertions(+), 171 deletions(-)

diff --git a/docs/dev/Issues/issues_open.md b/docs/dev/Issues/issues_open.md
index bb41887f7..8805fc8fb 100644
--- a/docs/dev/Issues/issues_open.md
+++ b/docs/dev/Issues/issues_open.md
@@ -1424,6 +1424,31 @@ threaded because each step's output is the next step's input.
 
 ---
 
+## 95. 🟡 Re-Enable DREAM Multiprocessing in CLI Workflows
+
+**Type:** Performance / CLI robustness
+
+On macOS and other spawn-based platforms, running Bayesian scripts via
+direct CLI entry points such as `python script.py` can fail during BUMPS
+`MPMapper` startup because worker processes re-import `__main__` and
+re-execute top-level tutorial code. The current defensive workaround is
+to fall back to serial execution for these direct-script entry points,
+which avoids the crash but disables DREAM multiprocessing in terminal
+workflows.
+
+**Possible solution:** keep the existing tracker-state cleanup before
+pickling and mapper startup, but replace the blanket serial fallback
+with an EasyDiffraction-controlled multiprocessing context policy. For
+direct CLI script entry points, prefer a `fork` context when available
+so workers do not re-import the tutorial top level. Keep the existing
+behavior for import-safe module entry points and for platforms where
+`fork` is unavailable. Document the tradeoff clearly because `fork` on
+macOS is less conservative than `spawn`.
+
+**Depends on:** related to issue 89, but independent.
+
+---
+
 ## 90. 🟢 Show Experiment Number/Total During Sequential Fitting
 
 **Type:** UX
@@ -1464,88 +1489,89 @@ operation is possible (e.g. in automated pipelines or tests).
 
 ## Summary
 
-| #   | Issue                                            | Severity | Type             |
-| --- | ------------------------------------------------ | -------- | ---------------- |
-| 3   | Rebuild joint-fit weights                        | 🟡 Med   | Fragility        |
-| 5   | `Analysis` as `DatablockItem`                    | 🟡 Med   | Consistency      |
-| 8   | Explicit `create()` signatures                   | 🟡 Med   | API safety       |
-| 9   | Future enum extensions                           | 🟢 Low   | Design           |
-| 10  | Unify update orchestration                       | 🟢 Low   | Maintainability  |
-| 11  | Document `_update` contract                      | 🟢 Low   | Maintainability  |
-| 13  | Suppress redundant dirty-flag sets               | 🟢 Low   | Performance      |
-| 14  | Finer-grained change tracking                    | 🟢 Low   | Performance      |
-| 15  | Validate joint-fit weights                       | 🟡 Med   | Correctness      |
-| 17  | Use PDF-specific CIF names                       | 🟢 Low   | Naming           |
-| 18  | Move CIF v2→v1 conversion out of calculator      | 🟢 Low   | Maintainability  |
-| 19  | Debug-mode logging for calculator imports        | 🟢 Low   | Diagnostics      |
-| 20  | Redirect/suppress CrysPy stderr                  | 🟢 Low   | UX               |
-| 21  | Clarify CrysPy TOF background CIF tags           | 🟡 Med   | Correctness      |
-| 22  | Check SC instrument mapping in CrysPy            | 🟢 Low   | Correctness      |
-| 23  | Investigate PyCrysFML pattern length discrepancy | 🟢 Low   | Correctness      |
-| 24  | Process defaults on experiment creation          | 🟢 Low   | Design           |
-| 25  | Refactor data `_update` methods                  | 🟡 Med   | Maintainability  |
-| 26  | Clarify `dtype` usage in data arrays             | 🟢 Low   | Cleanup          |
-| 27  | Handle zero uncertainty in Bragg PD              | 🟢 Low   | Correctness      |
-| 28  | Clarify Bragg PD data collection description     | 🟢 Low   | Cleanup          |
-| 29  | Standardise CIF ID validator pattern             | 🟡 Med   | Consistency      |
-| 30  | Make `refinement_status` default an Enum         | 🟢 Low   | Design           |
-| 31  | Rename PD data point mixins                      | 🟢 Low   | Naming           |
-| 32  | Move common methods to `DatablockCollection`     | 🟡 Med   | Maintainability  |
-| 33  | Make `_update_categories` abstract               | 🟡 Med   | Design           |
-| 34  | Auto-extract `PeakProfileTypeEnum`               | 🟢 Low   | Design           |
-| 35  | Rename `BeamModeEnum` members to CWL/TOF         | 🟢 Low   | Naming           |
-| 36  | Common `EnumBase` class                          | 🟢 Low   | Design           |
-| 37  | Rename experiment `.type` property               | 🟢 Low   | Naming           |
-| 38  | Fix `@typechecked`/gemmi in factories            | 🟡 Med   | Bug              |
-| 39  | Improve `_update_priority` handling              | 🟢 Low   | Design           |
-| 40  | Reset `.user_constrained` to `False`             | 🟢 Low   | Feature          |
-| 41  | Check `_mark_dirty` in `_set_value`              | 🟢 Low   | Cleanup          |
-| 42  | MkDocs type unpacking in validation              | 🟢 Low   | Docs             |
-| 43  | Fix summary display inconsistencies              | 🟢 Low   | UX               |
-| 44  | Merge parameter record construction              | 🟢 Low   | Cleanup          |
-| 45  | Decide alias/constraint descriptor default       | 🟢 Low   | Design           |
-| 46  | Rename `JointFitExperiments` id + descriptions   | 🟢 Low   | Naming           |
-| 47  | Improve error handling in crystallography        | 🟢 Low   | Diagnostics      |
-| 48  | Fix CrysPy TOF instrument default                | 🟢 Low   | Bug workaround   |
-| 49  | Automate space group CIF name variants           | 🟢 Low   | Maintainability  |
-| 50  | Clarify `Cell._update` minimizer param           | 🟢 Low   | Cleanup          |
-| 51  | Access space group for Wyckoff letters           | 🟢 Low   | Design           |
-| 52  | Rename line-segment `y` to `intensity`           | 🟢 Low   | Naming           |
-| 53  | Move `show()` to `CategoryCollection`            | 🟢 Low   | Maintainability  |
-| 54  | Add `point_id` to excluded regions               | 🟢 Low   | Completeness     |
-| 55  | Fix Jupyter scroll disabling for MkDocs          | 🟢 Low   | Docs / UX        |
-| 56  | Make ASCII plot width configurable               | 🟢 Low   | UX               |
-| 57  | Clean up CIF deserialisation helpers             | 🟢 Low   | Maintainability  |
-| 58  | Move `ProjectInfo` CIF methods to `serialize`    | 🟢 Low   | Maintainability  |
-| 59  | Add CIF name validation in parse                 | 🟢 Low   | Robustness       |
-| 60  | Unify `mkdir` usage                              | 🟢 Low   | Cleanup          |
-| 61  | Clarify logger default reaction mode             | 🟢 Low   | Design           |
-| 62  | Complete `render_table` → `TableRenderer`        | 🟢 Low   | Cleanup          |
-| 63  | Fix calculator `calculate_pattern` signature     | 🟢 Low   | Design           |
-| 64  | Check unused-if-loading-from-CIF code            | 🟢 Low   | Cleanup          |
-| 65  | Replace all bare `print()` with logging          | 🟡 Med   | Code quality     |
-| 66  | Error-handling strategy: `log.error` vs `raise`  | 🟡 Med   | Design           |
-| 67  | Custom validation for params and category types  | 🟡 Med   | Design           |
-| 68  | `@typechecked` on all public methods?            | 🟢 Low   | Design           |
-| 69  | Shorter public API names via `__init__`          | 🟢 Low   | API ergonomics   |
-| 70  | Standardise class member ordering + headers      | 🟡 Med   | Code style       |
-| 71  | `_update_priority` reference table               | 🟢 Low   | Documentation    |
-| 72  | Warn on all switchable-category type changes     | 🟡 Med   | UX               |
-| 73  | Unify setter parameter naming                    | 🟢 Low   | Code style       |
-| 74  | Sync property type hints + custom lint rules     | 🟡 Med   | Tooling          |
-| 75  | `show_supported_calculators()` on Analysis       | 🟢 Low   | API completeness |
-| 76  | Consistent `_type` suffix in switchable APIs     | 🟡 Med   | Naming           |
-| 79  | Verify analysis CIF serialisation completeness   | 🟢 Low   | Correctness      |
-| 80  | Resolve `Any` vs `object` annotation policy      | 🟢 Low   | Code style       |
-| 81  | Enforce docstrings on all public methods         | 🟡 Med   | Code quality     |
-| 82  | Document `param-docstring-fix` workflow          | 🟢 Low   | Documentation    |
-| 83  | Remove redundant parameter listing               | 🟢 Low   | Cleanup          |
-| 84  | Serialise `None` as `.` in CIF output            | 🟡 Med   | Correctness      |
-| 85  | Retain per-experiment fitted params for plotting | 🟡 Med   | Correctness      |
-| 86  | Auto-resolve `plot_param` x-axis + add units     | 🟢 Low   | UX               |
-| 87  | Redesign tutorial grouping/categorisation        | 🟢 Low   | Documentation    |
-| 88  | Fix Dataset 26 description (47 not 57)           | 🟢 Low   | Data             |
-| 89  | Parallel independent fits for single mode        | 🟡 Med   | Performance      |
-| 90  | Show experiment number during sequential fitting | 🟢 Low   | UX               |
-| 91  | Disable TODO checks in CodeFactor PRs            | 🟢 Low   | CI / Tooling     |
-| 92  | Make `save()` respect verbosity                  | 🟢 Low   | UX               |
+| #   | Issue                                            | Severity | Type                         |
+| --- | ------------------------------------------------ | -------- | ---------------------------- |
+| 3   | Rebuild joint-fit weights                        | 🟡 Med   | Fragility                    |
+| 5   | `Analysis` as `DatablockItem`                    | 🟡 Med   | Consistency                  |
+| 8   | Explicit `create()` signatures                   | 🟡 Med   | API safety                   |
+| 9   | Future enum extensions                           | 🟢 Low   | Design                       |
+| 10  | Unify update orchestration                       | 🟢 Low   | Maintainability              |
+| 11  | Document `_update` contract                      | 🟢 Low   | Maintainability              |
+| 13  | Suppress redundant dirty-flag sets               | 🟢 Low   | Performance                  |
+| 14  | Finer-grained change tracking                    | 🟢 Low   | Performance                  |
+| 15  | Validate joint-fit weights                       | 🟡 Med   | Correctness                  |
+| 17  | Use PDF-specific CIF names                       | 🟢 Low   | Naming                       |
+| 18  | Move CIF v2→v1 conversion out of calculator      | 🟢 Low   | Maintainability              |
+| 19  | Debug-mode logging for calculator imports        | 🟢 Low   | Diagnostics                  |
+| 20  | Redirect/suppress CrysPy stderr                  | 🟢 Low   | UX                           |
+| 21  | Clarify CrysPy TOF background CIF tags           | 🟡 Med   | Correctness                  |
+| 22  | Check SC instrument mapping in CrysPy            | 🟢 Low   | Correctness                  |
+| 23  | Investigate PyCrysFML pattern length discrepancy | 🟢 Low   | Correctness                  |
+| 24  | Process defaults on experiment creation          | 🟢 Low   | Design                       |
+| 25  | Refactor data `_update` methods                  | 🟡 Med   | Maintainability              |
+| 26  | Clarify `dtype` usage in data arrays             | 🟢 Low   | Cleanup                      |
+| 27  | Handle zero uncertainty in Bragg PD              | 🟢 Low   | Correctness                  |
+| 28  | Clarify Bragg PD data collection description     | 🟢 Low   | Cleanup                      |
+| 29  | Standardise CIF ID validator pattern             | 🟡 Med   | Consistency                  |
+| 30  | Make `refinement_status` default an Enum         | 🟢 Low   | Design                       |
+| 31  | Rename PD data point mixins                      | 🟢 Low   | Naming                       |
+| 32  | Move common methods to `DatablockCollection`     | 🟡 Med   | Maintainability              |
+| 33  | Make `_update_categories` abstract               | 🟡 Med   | Design                       |
+| 34  | Auto-extract `PeakProfileTypeEnum`               | 🟢 Low   | Design                       |
+| 35  | Rename `BeamModeEnum` members to CWL/TOF         | 🟢 Low   | Naming                       |
+| 36  | Common `EnumBase` class                          | 🟢 Low   | Design                       |
+| 37  | Rename experiment `.type` property               | 🟢 Low   | Naming                       |
+| 38  | Fix `@typechecked`/gemmi in factories            | 🟡 Med   | Bug                          |
+| 39  | Improve `_update_priority` handling              | 🟢 Low   | Design                       |
+| 40  | Reset `.user_constrained` to `False`             | 🟢 Low   | Feature                      |
+| 41  | Check `_mark_dirty` in `_set_value`              | 🟢 Low   | Cleanup                      |
+| 42  | MkDocs type unpacking in validation              | 🟢 Low   | Docs                         |
+| 43  | Fix summary display inconsistencies              | 🟢 Low   | UX                           |
+| 44  | Merge parameter record construction              | 🟢 Low   | Cleanup                      |
+| 45  | Decide alias/constraint descriptor default       | 🟢 Low   | Design                       |
+| 46  | Rename `JointFitExperiments` id + descriptions   | 🟢 Low   | Naming                       |
+| 47  | Improve error handling in crystallography        | 🟢 Low   | Diagnostics                  |
+| 48  | Fix CrysPy TOF instrument default                | 🟢 Low   | Bug workaround               |
+| 49  | Automate space group CIF name variants           | 🟢 Low   | Maintainability              |
+| 50  | Clarify `Cell._update` minimizer param           | 🟢 Low   | Cleanup                      |
+| 51  | Access space group for Wyckoff letters           | 🟢 Low   | Design                       |
+| 52  | Rename line-segment `y` to `intensity`           | 🟢 Low   | Naming                       |
+| 53  | Move `show()` to `CategoryCollection`            | 🟢 Low   | Maintainability              |
+| 54  | Add `point_id` to excluded regions               | 🟢 Low   | Completeness                 |
+| 55  | Fix Jupyter scroll disabling for MkDocs          | 🟢 Low   | Docs / UX                    |
+| 56  | Make ASCII plot width configurable               | 🟢 Low   | UX                           |
+| 57  | Clean up CIF deserialisation helpers             | 🟢 Low   | Maintainability              |
+| 58  | Move `ProjectInfo` CIF methods to `serialize`    | 🟢 Low   | Maintainability              |
+| 59  | Add CIF name validation in parse                 | 🟢 Low   | Robustness                   |
+| 60  | Unify `mkdir` usage                              | 🟢 Low   | Cleanup                      |
+| 61  | Clarify logger default reaction mode             | 🟢 Low   | Design                       |
+| 62  | Complete `render_table` → `TableRenderer`        | 🟢 Low   | Cleanup                      |
+| 63  | Fix calculator `calculate_pattern` signature     | 🟢 Low   | Design                       |
+| 64  | Check unused-if-loading-from-CIF code            | 🟢 Low   | Cleanup                      |
+| 65  | Replace all bare `print()` with logging          | 🟡 Med   | Code quality                 |
+| 66  | Error-handling strategy: `log.error` vs `raise`  | 🟡 Med   | Design                       |
+| 67  | Custom validation for params and category types  | 🟡 Med   | Design                       |
+| 68  | `@typechecked` on all public methods?            | 🟢 Low   | Design                       |
+| 69  | Shorter public API names via `__init__`          | 🟢 Low   | API ergonomics               |
+| 70  | Standardise class member ordering + headers      | 🟡 Med   | Code style                   |
+| 71  | `_update_priority` reference table               | 🟢 Low   | Documentation                |
+| 72  | Warn on all switchable-category type changes     | 🟡 Med   | UX                           |
+| 73  | Unify setter parameter naming                    | 🟢 Low   | Code style                   |
+| 74  | Sync property type hints + custom lint rules     | 🟡 Med   | Tooling                      |
+| 75  | `show_supported_calculators()` on Analysis       | 🟢 Low   | API completeness             |
+| 76  | Consistent `_type` suffix in switchable APIs     | 🟡 Med   | Naming                       |
+| 79  | Verify analysis CIF serialisation completeness   | 🟢 Low   | Correctness                  |
+| 80  | Resolve `Any` vs `object` annotation policy      | 🟢 Low   | Code style                   |
+| 81  | Enforce docstrings on all public methods         | 🟡 Med   | Code quality                 |
+| 82  | Document `param-docstring-fix` workflow          | 🟢 Low   | Documentation                |
+| 83  | Remove redundant parameter listing               | 🟢 Low   | Cleanup                      |
+| 84  | Serialise `None` as `.` in CIF output            | 🟡 Med   | Correctness                  |
+| 85  | Retain per-experiment fitted params for plotting | 🟡 Med   | Correctness                  |
+| 86  | Auto-resolve `plot_param` x-axis + add units     | 🟢 Low   | UX                           |
+| 87  | Redesign tutorial grouping/categorisation        | 🟢 Low   | Documentation                |
+| 88  | Fix Dataset 26 description (47 not 57)           | 🟢 Low   | Data                         |
+| 89  | Parallel independent fits for single mode        | 🟡 Med   | Performance                  |
+| 95  | Re-enable DREAM multiprocessing in CLI workflows | 🟡 Med   | Performance / CLI robustness |
+| 90  | Show experiment number during sequential fitting | 🟢 Low   | UX                           |
+| 91  | Disable TODO checks in CodeFactor PRs            | 🟢 Low   | CI / Tooling                 |
+| 92  | Make `save()` respect verbosity                  | 🟢 Low   | UX                           |
diff --git a/docs/docs/quick-reference/index.md b/docs/docs/quick-reference/index.md
index e0d3fd202..fb6835d3e 100644
--- a/docs/docs/quick-reference/index.md
+++ b/docs/docs/quick-reference/index.md
@@ -306,6 +306,18 @@ project.display.fit.correlations()
 project.display.pattern(expt_name='hrpt')
 ```
 
+After a Bayesian fit, inspect posterior displays:
+
+```python
+project.display.posterior.distribution(param)
+project.display.posterior.distribution()
+project.display.posterior.pairs()
+project.display.posterior.predictive(expt_name='hrpt')
+```
+
+Call `project.display.posterior.distribution()` without `param` to plot
+the marginal distribution for each free parameter one by one.
+
 ## Add Simple Constraints
 
 Create aliases from parameter objects, then define a constraint
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index f0d5123d9..d5fe45749 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -692,8 +692,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for param in project.free_parameters:\n",
-    "    project.display.posterior.distribution(param)"
+    "project.display.posterior.distribution()"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 7c6f9bdb5..8129f6e3e 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -329,8 +329,7 @@
 # multimodality.
 
 # %%
-for param in project.free_parameters:
-    project.display.posterior.distribution(param)
+project.display.posterior.distribution()
 
 # %% [markdown]
 # Finally, the posterior predictive plot propagates the sampled parameter
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
index 965e4ed82..c6de427d8 100644
--- a/docs/docs/tutorials/ed-22.ipynb
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -570,8 +570,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for param in project.free_parameters:\n",
-    "    project.display.posterior.distribution(param)"
+    "project.display.posterior.distribution()"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index bf0f2e19e..da04c4b5d 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -257,8 +257,7 @@
 # multimodality.
 
 # %%
-for param in project.free_parameters:
-    project.display.posterior.distribution(param)
+project.display.posterior.distribution()
 
 # %% [markdown]
 # Finally, the posterior predictive plot propagates the sampled
diff --git a/pixi.lock b/pixi.lock
index f1532e2b2..4d6614f72 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -8145,7 +8145,6 @@ packages:
   - asciichartpy
   - asteval
   - bumps
-  - colorama
   - crysfml
   - cryspy
   - darkdetect
@@ -8162,7 +8161,6 @@ packages:
   - rich
   - scipy
   - sympy
-  - tabulate
   - typeguard
   - typer
   - uncertainties
diff --git a/pyproject.toml b/pyproject.toml
index f81f23031..71bf2f71b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,8 +24,6 @@ classifiers = [
 requires-python = '>=3.12'
 dependencies = [
   'numpy',          # Numerical computing library
-  'colorama',       # Color terminal output
-  'tabulate',       # Pretty-print tabular data for terminal output
   'asciichartpy',   # ASCII charts for terminal output
   'pooch',          # Data downloader
   'typer',          # Command-line interface creation
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 0915ae929..0f8e4c970 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -11,6 +11,7 @@
 from easydiffraction.analysis.fit_helpers.metrics import calculate_reduced_chi_square
 from easydiffraction.display.progress import ACTIVITY_LABEL_BURN_IN
 from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
+from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
 from easydiffraction.display.progress import ACTIVITY_LABEL_SAMPLING
 from easydiffraction.display.progress import ActivityIndicator
 from easydiffraction.display.progress import _TerminalLiveHandle as _SharedTerminalLiveHandle
@@ -595,7 +596,7 @@ def _replace_last_tracking_row(self, row: list[str]) -> None:
 
     def _default_activity_label(self) -> str:
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
-            return ACTIVITY_LABEL_SAMPLING
+            return ACTIVITY_LABEL_PROCESSING
         return ACTIVITY_LABEL_FITTING
 
     @staticmethod
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index c9af1012b..256ff9a55 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -4,7 +4,9 @@
 
 from __future__ import annotations
 
+import multiprocessing
 import random
+import sys
 from dataclasses import dataclass
 
 import numpy as np
@@ -726,14 +728,65 @@ def _build_mapper(self, problem: FitProblem) -> object | None:
         if self.parallel == 1:
             return None
 
-        if not can_pickle(problem):
+        if self._requires_serial_mapper_for_spawn_main_module():
             log.warning(
-                'DREAM parallel evaluation requires a picklable '
-                'problem; falling back to serial execution.'
+                'DREAM parallel evaluation requires an import-safe main '
+                'module on spawn-based multiprocessing; falling back to '
+                'serial execution.'
             )
             return None
 
-        return MPMapper.start_mapper(problem, [], cpus=self.parallel)
+        shared_display_handle = getattr(self.tracker, '_shared_display_handle', None)
+        activity_indicator = getattr(self.tracker, '_activity_indicator', None)
+        if shared_display_handle is not None:
+            self.tracker._set_shared_display_handle(None)
+        if activity_indicator is not None:
+            self.tracker._activity_indicator = None
+
+        try:
+            if not can_pickle(problem):
+                log.warning(
+                    'DREAM parallel evaluation requires a picklable '
+                    'problem; falling back to serial execution.'
+                )
+                return None
+
+            return MPMapper.start_mapper(problem, [], cpus=self.parallel)
+        except RuntimeError as error:
+            message = str(error)
+            if 'bootstrapping phase' not in message:
+                raise
+            log.warning(
+                'DREAM parallel evaluation requires an import-safe main '
+                'module on spawn-based multiprocessing; falling back to '
+                'serial execution.'
+            )
+            return None
+        finally:
+            if activity_indicator is not None:
+                self.tracker._activity_indicator = activity_indicator
+            if shared_display_handle is not None:
+                self.tracker._set_shared_display_handle(shared_display_handle)
+
+    @staticmethod
+    def _requires_serial_mapper_for_spawn_main_module() -> bool:
+        """
+        Return whether direct-script spawn startup should stay serial.
+        """
+        start_method = multiprocessing.get_start_method(allow_none=True)
+        if start_method is None:
+            start_method = multiprocessing.get_start_method()
+        if start_method != 'spawn':
+            return False
+
+        main_module = sys.modules.get('__main__')
+        if main_module is None:
+            return False
+
+        return (
+            getattr(main_module, '__file__', None) is not None
+            and getattr(main_module, '__spec__', None) is None
+        )
 
     @staticmethod
     def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResult:
diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index c81af0b0e..467467001 100644
--- a/src/easydiffraction/display/plotters/ascii.py
+++ b/src/easydiffraction/display/plotters/ascii.py
@@ -10,6 +10,8 @@
 
 from __future__ import annotations
 
+import shutil
+
 import asciichartpy
 import numpy as np
 
@@ -22,13 +24,55 @@
 DEFAULT_COLORS = {
     'meas': asciichartpy.blue,
     'calc': asciichartpy.red,
+    'posterior': asciichartpy.red,
+    'density': asciichartpy.green,
     'resid': asciichartpy.green,
 }
+ASCII_CHART_OFFSET = 3
+ASCII_CHART_LEFT_PADDING = 15
+ASCII_CHART_FALLBACK_POINT_COUNT = 80
+ASCII_CHART_MIN_POINT_COUNT = 2
 
 
 class AsciiPlotter(PlotterBase):
     """Terminal-based plotter using ASCII art."""
 
+    @staticmethod
+    def _chart_point_count() -> int:
+        """Return the number of points that fit the current terminal."""
+        fallback_columns = (
+            ASCII_CHART_FALLBACK_POINT_COUNT + ASCII_CHART_OFFSET + ASCII_CHART_LEFT_PADDING
+        )
+        columns = shutil.get_terminal_size(fallback=(fallback_columns, DEFAULT_HEIGHT)).columns
+        return max(
+            ASCII_CHART_MIN_POINT_COUNT,
+            columns - ASCII_CHART_OFFSET - ASCII_CHART_LEFT_PADDING,
+        )
+
+    @classmethod
+    def _resample_series_for_chart(
+        cls,
+        y_series: object,
+    ) -> list[list[float]]:
+        """Return y-series resampled to the available chart width."""
+        target_point_count = cls._chart_point_count()
+        resampled_series: list[list[float]] = []
+        for series in y_series:
+            series_array = np.ravel(np.asarray(series, dtype=float))
+            if (
+                series_array.size <= target_point_count
+                or series_array.size < ASCII_CHART_MIN_POINT_COUNT
+            ):
+                resampled_series.append(series_array.tolist())
+                continue
+
+            source_positions = np.linspace(0.0, 1.0, series_array.size)
+            target_positions = np.linspace(0.0, 1.0, target_point_count)
+            resampled_series.append(
+                np.interp(target_positions, source_positions, series_array).tolist()
+            )
+        return resampled_series
+
     @staticmethod
     def _get_legend_item(label: str) -> str:
         """
@@ -94,8 +138,12 @@ def plot_powder(
         if height is None:
             height = DEFAULT_HEIGHT
         colors = [DEFAULT_COLORS[label] for label in labels]
-        config = {'height': height, 'colors': colors}
-        y_series = [y.tolist() for y in y_series]
+        config = {
+            'height': height,
+            'colors': colors,
+            'offset': ASCII_CHART_OFFSET,
+        }
+        y_series = self._resample_series_for_chart(y_series)
 
         chart = asciichartpy.plot(y_series, config)
 
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index 4cf43d669..6910321be 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -191,6 +191,14 @@ class XAxisType(StrEnum):
         'mode': 'lines',
         'name': 'Total calculated (Icalc)',
     },
+    'posterior': {
+        'mode': 'lines',
+        'name': 'Max posterior',
+    },
+    'density': {
+        'mode': 'lines',
+        'name': 'Marginal density',
+    },
     'bkg': {
         'mode': 'lines',
         'name': 'Background (Ibkg)',
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index fbbda288f..afa99da90 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -38,6 +38,7 @@
     'meas': 'rgb(31, 119, 180)',
     'bkg': 'rgb(140, 140, 140)',
     'calc': 'rgb(214, 39, 40)',
+    'posterior': 'rgb(214, 39, 40)',
     'resid': 'rgb(44, 160, 44)',
 }
 
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 2ca6dff0f..3380141d3 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -214,6 +214,7 @@ class _PosteriorPairsContext:
     labels: list[str]
     annotation_labels: list[str]
     title: str
+    marginal_density_samples: np.ndarray
     density_samples: np.ndarray
     scatter_samples: np.ndarray
     show_contours: bool
@@ -330,10 +331,11 @@ def _auto_x_range_for_ascii(
             Tuple of ``(x_min, x_max)``, possibly narrowed.
         """
         if self._engine == 'asciichartpy' and (x_min is None or x_max is None):
-            max_intensity_pos = np.argmax(pattern.intensity_meas)
-            half_range = 50
-            start = max(0, max_intensity_pos - half_range)
-            end = min(len(x_array) - 1, max_intensity_pos + half_range)
+            max_intensity_pos = int(np.argmax(pattern.intensity_meas))
+            target_point_count = min(len(x_array), AsciiPlotter._chart_point_count())
+            start = max(0, max_intensity_pos - target_point_count // 2)
+            end = min(len(x_array) - 1, start + target_point_count - 1)
+            start = max(0, end - target_point_count + 1)
             x_min = x_array[start]
             x_max = x_array[end]
         return x_min, x_max
@@ -998,6 +1000,9 @@ def plot_posterior_pairs(
             ``parameters`` is omitted and ``threshold`` is ``None``.
             Must be at least ``2``.
         """
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            console.paragraph(self._posterior_pair_title(None))
+
         plot = self._build_posterior_pairs_plot(
             parameters=parameters,
             style=style,
@@ -1022,6 +1027,10 @@ def plot_param_distribution(
             posterior to plot. Strings may be unique names or
             user-facing labels.
         """
+        if self.engine == PlotterEngineEnum.ASCII.value:
+            self._plot_ascii_param_distribution(param)
+            return
+
         plot = self._build_param_distribution_plot(param)
         if plot is None:
             return
@@ -1048,8 +1057,10 @@ def plot_posterior_predictive(
         style : str, default='band'
             ``'band'`` shows the 95% credible interval, ``'draws'``
             shows sampled predictive curves, and ``'band+draws'`` shows
-            both together. Single-crystal plots currently render only
-            the interval-based reflection check.
+            both together. ASCII powder plots fall back to measured and
+            max-posterior lines without uncertainty bands or draws.
+            Single-crystal plots currently render only the interval-
+            based reflection check.
         x_min : float | None, default=None
             Lower bound for the x-axis range.
         x_max : float | None, default=None
@@ -1098,10 +1109,6 @@ def _plot_posterior_predictive_request(
             log.warning('Plotter is not attached to a project.')
             return
 
-        if self.engine != PlotterEngineEnum.PLOTLY.value:
-            log.warning('Posterior predictive plots currently require the Plotly backend.')
-            return
-
         self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
         x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(
@@ -1110,6 +1117,12 @@ def _plot_posterior_predictive_request(
         )
 
         if sample_form == SampleFormEnum.SINGLE_CRYSTAL:
+            if self.engine != PlotterEngineEnum.PLOTLY.value:
+                log.warning(
+                    'Single-crystal posterior predictive plots currently '
+                    'require the Plotly backend.'
+                )
+                return
             self._plot_single_crystal_posterior_predictive(
                 experiment=experiment,
                 expt_name=expt_name,
@@ -1236,6 +1249,10 @@ def _plot_non_bragg_posterior_predictive(
         style: str,
     ) -> None:
         """Render non-Bragg posterior predictive summaries."""
+        show_draws = self.engine == PlotterEngineEnum.PLOTLY.value and style in {
+            'draws',
+            'band+draws',
+        }
         pattern = intensity_category_for(experiment)
         y_meas = getattr(pattern, 'intensity_meas', None)
         if y_meas is None:
@@ -1263,7 +1280,7 @@ def _plot_non_bragg_posterior_predictive(
             experiment=experiment,
             expt_name=expt_name,
             x_axis=x_axis,
-            include_draws=style in {'draws', 'band+draws'},
+            include_draws=show_draws,
         )
         if summary is None:
             return
@@ -1272,7 +1289,7 @@ def _plot_non_bragg_posterior_predictive(
             summary=summary,
             x_min=ctx['x_min'],
             x_max=ctx['x_max'],
-            include_draws=style in {'draws', 'band+draws'},
+            include_draws=show_draws,
         )
         if filtered_summary is None:
             log.warning(
@@ -1632,6 +1649,7 @@ def _posterior_pairs_context(
                 self._posterior_pair_title(uncertainty_multiplier),
                 resolved_threshold,
             ),
+            marginal_density_samples=selected_samples,
             density_samples=density_samples,
             scatter_samples=scatter_samples,
             show_contours=show_contours,
@@ -1640,7 +1658,7 @@ def _posterior_pairs_context(
             axis_ranges=self._posterior_pair_axis_ranges(
                 fit_results=fit_results,
                 parameter_names=parameter_names,
-                density_samples=selected_samples,
+                samples=selected_samples,
             ),
         )
 
@@ -1681,7 +1699,7 @@ def _posterior_pair_axis_ranges(
         *,
         fit_results: object,
         parameter_names: list[str],
-        density_samples: np.ndarray,
+        samples: np.ndarray,
     ) -> list[tuple[float, float]]:
         """Return per-parameter axis ranges for a pair plot."""
         axis_ranges: list[tuple[float, float]] = []
@@ -1692,7 +1710,7 @@ def _posterior_pair_axis_ranges(
             )
             axis_ranges.append(
                 self._posterior_axis_bounds(
-                    density_samples[:, index],
+                    samples[:, index],
                     lower_bound=lower_bound,
                     upper_bound=upper_bound,
                 )
@@ -1777,7 +1795,7 @@ def _add_posterior_pair_diagonal(
     ) -> None:
         """Add the diagonal marginal-density panel."""
         go = __import__('plotly.graph_objects', fromlist=['Histogram'])
-        density_values = context.density_samples[:, parameter_index]
+        density_values = context.marginal_density_samples[:, parameter_index]
         density_trace = self._posterior_density_trace(
             fit_results=context.fit_results,
             parameter_name=context.parameter_names[parameter_index],
@@ -2457,13 +2475,52 @@ def _build_param_distribution_plot(
         )
         return fig
 
+    def _plot_ascii_param_distribution(
+        self,
+        param: object,
+    ) -> None:
+        """Render one posterior marginal on the ASCII backend."""
+        context = self._posterior_distribution_context(param)
+        if context is None:
+            return
+
+        lower_bound, upper_bound = self._posterior_parameter_bounds(
+            fit_results=context.fit_results,
+            parameter_name=context.parameter_name,
+        )
+        density_curve = self._posterior_density_curve(
+            context.values,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+        )
+        if density_curve is None:
+            log.warning(
+                f'Posterior distribution is unavailable for parameter {context.parameter_name}.'
+            )
+            return
+
+        grid, density = density_curve
+        self._backend.plot_powder(
+            x=grid,
+            y_series=[density],
+            labels=['density'],
+            axes_labels=[context.label, 'Probability density'],
+            title=context.title,
+            height=self.height,
+        )
+
     def _posterior_distribution_context(
         self,
         param: object,
     ) -> _PosteriorDistributionContext | None:
         """Return the context for a posterior distribution plot."""
-        posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
-        if posterior_samples is None or fit_results is None:
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            log.warning('Posterior samples are unavailable. Run a Bayesian fit first.')
             return None
 
         parameter_names = self._resolve_posterior_parameter_names(
@@ -3446,7 +3503,20 @@ def _plot_posterior_predictive_summary(
         show_draws: bool,
         excluded_ranges: tuple[tuple[float, float], ...] = (),
     ) -> None:
-        """Render posterior predictive summaries using Plotly."""
+        """
+        Render posterior predictive summaries using the active backend.
+        """
+        if self.engine == PlotterEngineEnum.ASCII.value:
+            self._plot_ascii_posterior_predictive_lines(
+                expt_name=expt_name,
+                x=np.asarray(summary.x, dtype=float),
+                y_meas=np.asarray(y_meas, dtype=float),
+                y_calc=np.asarray(summary.map_prediction, dtype=float),
+                axes_labels=axes_labels,
+                excluded_ranges=excluded_ranges,
+            )
+            return
+
         go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
         axis_frame_color = self._plot_axis_frame_color()
 
@@ -3566,6 +3636,27 @@ def _plot_posterior_predictive_summary(
         )
         self._show_plot_figure(fig)
 
+    def _plot_ascii_posterior_predictive_lines(
+        self,
+        *,
+        expt_name: str,
+        x: np.ndarray,
+        y_meas: np.ndarray,
+        y_calc: np.ndarray,
+        axes_labels: list[str],
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
+    ) -> None:
+        """Render posterior predictive lines on the ASCII backend."""
+        self._backend.plot_powder(
+            x=x,
+            y_series=[y_meas, y_calc],
+            labels=['meas', 'posterior'],
+            axes_labels=axes_labels,
+            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            height=self.height,
+            excluded_ranges=excluded_ranges,
+        )
+
     def _plot_single_crystal_posterior_predictive_summary(
         self,
         *,
@@ -3677,6 +3768,10 @@ def _plot_posterior_predictive_data(
         style: str,
     ) -> None:
         """Render posterior predictive curves on the powder layout."""
+        show_draws = self.engine == PlotterEngineEnum.PLOTLY.value and style in {
+            'draws',
+            'band+draws',
+        }
         pattern = intensity_category_for(experiment)
         ctx = self._prepare_powder_context(
             pattern,
@@ -3693,7 +3788,7 @@ def _plot_posterior_predictive_data(
             experiment=experiment,
             expt_name=expt_name,
             x_axis=x_axis,
-            include_draws=style in {'draws', 'band+draws'},
+            include_draws=show_draws,
         )
         if summary is None:
             return
@@ -3715,8 +3810,32 @@ def _plot_posterior_predictive_data(
         y_calc = self._filtered_y_array(
             summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max']
         )
-        show_residual = True if plot_options.show_residual is None else plot_options.show_residual
-        y_resid = y_meas - y_calc if show_residual else None
+        excluded_ranges = (
+            self._excluded_ranges(
+                experiment=experiment,
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+            if plot_options.show_excluded
+            else ()
+        )
+        if self.engine == PlotterEngineEnum.ASCII.value:
+            if plot_options.show_residual:
+                log.warning(
+                    'Posterior predictive residuals are unavailable for '
+                    'ASCII summary plots; ignoring show_residual=True.'
+                )
+            self._plot_ascii_posterior_predictive_lines(
+                expt_name=expt_name,
+                x=np.asarray(ctx['x_filtered'], dtype=float),
+                y_meas=np.asarray(y_meas, dtype=float),
+                y_calc=np.asarray(y_calc, dtype=float),
+                axes_labels=list(ctx['axes_labels']),
+                excluded_ranges=excluded_ranges,
+            )
+            return
+
+        y_resid = y_meas - y_calc if plot_options.show_residual is not False else None
 
         predictive_lower_95 = None
         predictive_upper_95 = None
@@ -3735,15 +3854,14 @@ def _plot_posterior_predictive_data(
             )
 
         predictive_draws = None
-        if style in {'draws', 'band+draws'}:
-            draws = getattr(summary, 'draws', None)
-            if draws is None:
+        if show_draws:
+            if summary.draws is None:
                 log.warning('Posterior predictive draws are unavailable for plotting.')
                 return
             predictive_draws = np.asarray(
                 [
                     self._filtered_y_array(draw, summary.x, ctx['x_min'], ctx['x_max'])
-                    for draw in draws
+                    for draw in summary.draws
                 ],
                 dtype=float,
             )
@@ -3758,37 +3876,28 @@ def _plot_posterior_predictive_data(
                 x_min=ctx['x_min'],
                 x_max=ctx['x_max'],
             )
-        excluded_ranges = (
-            self._excluded_ranges(
-                experiment=experiment,
-                x_min=ctx['x_min'],
-                x_max=ctx['x_max'],
+        self._backend.plot_powder_meas_vs_calc(
+            plot_spec=PowderMeasVsCalcSpec(
+                x=ctx['x_filtered'],
+                y_meas=y_meas,
+                y_calc=y_calc,
+                y_resid=y_resid,
+                bragg_tick_sets=bragg_tick_sets,
+                axes_labels=ctx['axes_labels'],
+                title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+                residual_height_fraction=DEFAULT_RESID_HEIGHT,
+                bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
+                height=self._composite_plot_height(),
+                y_bkg=y_bkg,
+                predictive_lower_95=predictive_lower_95,
+                predictive_upper_95=predictive_upper_95,
+                predictive_draws=predictive_draws,
+                y_calc_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+                y_calc_line_dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+                excluded_ranges=excluded_ranges,
             )
-            if plot_options.show_excluded
-            else ()
         )
 
-        plot_spec = PowderMeasVsCalcSpec(
-            x=ctx['x_filtered'],
-            y_meas=y_meas,
-            y_calc=y_calc,
-            y_resid=y_resid,
-            bragg_tick_sets=bragg_tick_sets,
-            axes_labels=ctx['axes_labels'],
-            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
-            residual_height_fraction=DEFAULT_RESID_HEIGHT,
-            bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
-            height=self._composite_plot_height(),
-            y_bkg=y_bkg,
-            predictive_lower_95=predictive_lower_95,
-            predictive_upper_95=predictive_upper_95,
-            predictive_draws=predictive_draws,
-            y_calc_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
-            y_calc_line_dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
-            excluded_ranges=excluded_ranges,
-        )
-        self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
-
     @staticmethod
     def _resolve_posterior_parameter_names(
         *,
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index 6bab7df3d..c248c9ad3 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -16,6 +16,7 @@
 from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
 from easydiffraction.display.progress import activity_indicator
 from easydiffraction.utils.enums import VerbosityEnum
+from easydiffraction.utils.logging import log
 from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
 
@@ -142,9 +143,22 @@ def pairs(
                 max_parameters=max_parameters,
             )
 
-    def distribution(self, param: object) -> None:
-        """Plot one sampled parameter's posterior distribution."""
-        self._project.rendering.plotter.plot_param_distribution(param)
+    def distribution(self, param: object | None = None) -> None:
+        """
+        Plot posterior distributions for one or all free parameters.
+        """
+        plotter = self._project.rendering.plotter
+        if param is not None:
+            plotter.plot_param_distribution(param)
+            return
+
+        free_parameters = getattr(self._project, 'free_parameters', None)
+        if not free_parameters:
+            log.warning('No free parameters found.')
+            return
+
+        for free_parameter in free_parameters:
+            plotter.plot_param_distribution(free_parameter)
 
     def predictive(
         self,
diff --git a/src/easydiffraction/utils/logging.py b/src/easydiffraction/utils/logging.py
index a94aaddef..3c4da8ee5 100644
--- a/src/easydiffraction/utils/logging.py
+++ b/src/easydiffraction/utils/logging.py
@@ -682,7 +682,7 @@ def paragraph(cls, title: str) -> None:
         ----------
         title : str
             Heading text; substrings enclosed in single quotes are
-            rendered without the bold-blue style.
+            rendered without the bold deep_sky_blue3 style.
         """
         parts = re.split(r"('.*?')", title)
         text = Text()
@@ -690,7 +690,7 @@ def paragraph(cls, title: str) -> None:
             if part.startswith("'") and part.endswith("'"):
                 text.append(part)
             else:
-                text.append(part, style='bold blue')
+                text.append(part, style='bold deep_sky_blue3')
         formatted = f'{text.markup}'
         if not in_jupyter():
             formatted = f'\n{formatted}'
diff --git a/tests/integration/fitting/test_bayesian_tracker_and_base.py b/tests/integration/fitting/test_bayesian_tracker_and_base.py
index e6988a0f3..91ec0e572 100644
--- a/tests/integration/fitting/test_bayesian_tracker_and_base.py
+++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py
@@ -127,6 +127,7 @@ def stop(self):
     assert 'Bayesian sampling complete.' in out
     assert tracker.best_chi2 == pytest.approx(1.0)
     assert tracker.best_iteration == 10
+    assert ('init', tracking_mod.ACTIVITY_LABEL_PROCESSING, tracker._verbosity) in events
     assert ('update', tracking_mod.ACTIVITY_LABEL_BURN_IN) in events
     assert ('update', tracking_mod.ACTIVITY_LABEL_SAMPLING) in events
 
@@ -168,9 +169,9 @@ def stop(self):
 
     assert FitProgressTracker._rows_match_on_columns(['1', 'a'], ['1', 'b'], (0,)) is True
     assert events == [
-        ('init', tracking_mod.ACTIVITY_LABEL_SAMPLING, VerbosityEnum.SHORT),
+        ('init', tracking_mod.ACTIVITY_LABEL_PROCESSING, VerbosityEnum.SHORT),
         ('start', None),
-        ('update', tracking_mod.ACTIVITY_LABEL_SAMPLING),
+        ('update', tracking_mod.ACTIVITY_LABEL_PROCESSING),
         ('stop', None),
     ]
 
@@ -274,7 +275,7 @@ def test_tracker_final_rows_cover_fallbacks_and_activity_labels():
     tracker._fitting_time = 1.5
     assert tracker._final_fit_tracking_row() == ['8', '1.50', '', '']
     tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
-    assert tracker._default_activity_label() == tracking_mod.ACTIVITY_LABEL_SAMPLING
+    assert tracker._default_activity_label() == tracking_mod.ACTIVITY_LABEL_PROCESSING
     tracker._tracking_mode = tracking_mod.TRACKING_MODE_FIT
     assert tracker._default_activity_label() == tracking_mod.ACTIVITY_LABEL_FITTING
     assert (
diff --git a/tests/integration/fitting/test_bumps_dream_support.py b/tests/integration/fitting/test_bumps_dream_support.py
index bf87549a4..4bcfed845 100644
--- a/tests/integration/fitting/test_bumps_dream_support.py
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import threading
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 from unittest.mock import patch
@@ -33,6 +34,20 @@ def _set_value_from_minimizer(self, value: float) -> None:
         self.value = value
 
 
+def _simulate_import_safe_spawn_main_module(monkeypatch) -> None:
+    import sys
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.multiprocessing.get_start_method',
+        lambda allow_none=True: 'spawn',
+    )
+    monkeypatch.setitem(
+        sys.modules,
+        '__main__',
+        SimpleNamespace(__file__='pytest_runner.py', __spec__=object()),
+    )
+
+
 def test_type_info_and_default_init():
     from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
     from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
@@ -258,6 +273,7 @@ def test_build_mapper_falls_back_for_serial_and_unpicklable(monkeypatch):
 
     warnings: list[str] = []
     minimizer.parallel = 0
+    _simulate_import_safe_spawn_main_module(monkeypatch)
     monkeypatch.setattr(
         'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: False
     )
@@ -267,7 +283,146 @@ def test_build_mapper_falls_back_for_serial_and_unpicklable(monkeypatch):
     )
 
     assert minimizer._build_mapper('problem') is None
-    assert any('falling back to serial execution' in message for message in warnings)
+    assert warnings == [
+        'DREAM parallel evaluation requires a picklable problem; falling back to serial execution.'
+    ]
+
+
+def test_build_mapper_temporarily_clears_shared_display_handle(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 0
+    _simulate_import_safe_spawn_main_module(monkeypatch)
+    handle = object()
+    activity_indicator = object()
+    observed_tracker_state: list[tuple[object | None, object | None]] = []
+
+    minimizer.tracker._set_shared_display_handle(handle)
+    minimizer.tracker._activity_indicator = activity_indicator
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle',
+        lambda problem: (
+            observed_tracker_state.append((
+                minimizer.tracker._shared_display_handle,
+                minimizer.tracker._activity_indicator,
+            ))
+            or True
+        ),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.start_mapper',
+        lambda problem, args, cpus: ('mapper', cpus),
+    )
+
+    mapper = minimizer._build_mapper('problem')
+
+    assert mapper == ('mapper', 0)
+    assert observed_tracker_state == [(None, None)]
+    assert minimizer.tracker._shared_display_handle is handle
+    assert minimizer.tracker._activity_indicator is activity_indicator
+
+
+def test_build_mapper_allows_real_can_pickle_with_live_tracker_state(monkeypatch):
+    from bumps.fitproblem import FitProblem
+    from bumps.parameter import Parameter as BumpsParameter
+
+    from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 0
+    _simulate_import_safe_spawn_main_module(monkeypatch)
+    bumps_params = [BumpsParameter(value=1.0, name='alpha')]
+
+    def objective(values):
+        _ = minimizer.tracker._activity_indicator
+        return np.array([float(values[0])], dtype=float)
+
+    problem = FitProblem(_EasyDiffractionFitness(bumps_params, objective))
+    minimizer.tracker._set_shared_display_handle(threading.RLock())
+    minimizer.tracker._activity_indicator = SimpleNamespace(lock=threading.RLock())
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.start_mapper',
+        lambda problem, args, cpus: ('mapper', cpus),
+    )
+
+    assert minimizer._build_mapper(problem) == ('mapper', 0)
+
+
+def test_build_mapper_falls_back_for_spawn_bootstrap_runtime_error(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 0
+    warnings: list[str] = []
+    _simulate_import_safe_spawn_main_module(monkeypatch)
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: True
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.start_mapper',
+        lambda problem, args, cpus: (_ for _ in ()).throw(
+            RuntimeError('current process has finished its bootstrapping phase')
+        ),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    assert minimizer._build_mapper('problem') is None
+    assert warnings == [
+        (
+            'DREAM parallel evaluation requires an import-safe main '
+            'module on spawn-based multiprocessing; falling back to '
+            'serial execution.'
+        )
+    ]
+
+
+def test_build_mapper_falls_back_before_starting_spawn_for_direct_script(monkeypatch):
+    import sys
+
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 0
+    warnings: list[str] = []
+    pickle_checks: list[object] = []
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.multiprocessing.get_start_method',
+        lambda allow_none=True: 'spawn',
+    )
+    monkeypatch.setitem(
+        sys.modules,
+        '__main__',
+        SimpleNamespace(__file__='docs/docs/tutorials/ed-21.py', __spec__=None),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle',
+        lambda problem: pickle_checks.append(problem) or True,
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.start_mapper',
+        lambda problem, args, cpus: (_ for _ in ()).throw(AssertionError('unexpected mapper')),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    assert minimizer._build_mapper('problem') is None
+    assert pickle_checks == []
+    assert warnings == [
+        (
+            'DREAM parallel evaluation requires an import-safe main '
+            'module on spawn-based multiprocessing; falling back to '
+            'serial execution.'
+        )
+    ]
 
 
 def test_run_solver_preserves_parameter_order_and_forwards_init():
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
index bb824f829..9758f70f8 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
@@ -57,7 +57,7 @@ def __init__(self, start, value, uncertainty, name='p', units='u'):
     assert 'Weighted R-factor (wR)' in out
     assert 'Bragg R-factor (BR)' in out
     assert 'Fitted parameters:' in out
-    # Table border: accept common border glyphs from Rich/tabulate
+    # Table border: accept common border glyphs from Rich
     assert any(ch in out for ch in ('╒', '┌', '+', '─'))
 
 
diff --git a/tests/unit/easydiffraction/display/plotters/test_ascii.py b/tests/unit/easydiffraction/display/plotters/test_ascii.py
index 49dd76537..a0e84dc42 100644
--- a/tests/unit/easydiffraction/display/plotters/test_ascii.py
+++ b/tests/unit/easydiffraction/display/plotters/test_ascii.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+import os
+
 import numpy as np
 
 
@@ -23,6 +25,28 @@ def test_ascii_plotter_plot_minimal(capsys):
     assert 'Displaying data for selected x-range' in out
 
 
+def test_ascii_plotter_plot_supports_max_posterior_legend(capsys):
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    x = np.array([0.0, 1.0, 2.0])
+    y_meas = np.array([1.0, 2.0, 3.0])
+    y_map = np.array([0.5, 1.5, 2.5])
+    plotter = AsciiPlotter()
+
+    plotter.plot_powder(
+        x=x,
+        y_series=[y_meas, y_map],
+        labels=['meas', 'posterior'],
+        axes_labels=['x', 'y'],
+        title='Posterior predictive',
+        height=5,
+    )
+
+    out = capsys.readouterr().out
+    assert 'Measured (Imeas)' in out
+    assert 'Max posterior' in out
+
+
 def test_ascii_plotter_plot_single_crystal(capsys):
     from easydiffraction.display.plotters.ascii import AsciiPlotter
 
@@ -85,3 +109,73 @@ def test_ascii_plotter_plot_powder_meas_vs_calc_announces_plotly_only_bragg_row(
     assert 'Legend:' in out
     assert 'Residual (Imeas - Icalc)' in out
     assert 'Bragg peak subplot rows are available with the Plotly engine only.' in out
+
+
+def test_ascii_plotter_plot_resamples_to_detected_terminal_width(monkeypatch):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_LEFT_PADDING
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_MIN_POINT_COUNT
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_OFFSET
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    captured: dict[str, object] = {}
+
+    def fake_plot(series, config):
+        captured['call'] = (series, config)
+        return 'chart'
+
+    monkeypatch.setattr(
+        ascii_mod.shutil,
+        'get_terminal_size',
+        lambda fallback: os.terminal_size((44, 24)),
+    )
+    monkeypatch.setattr(ascii_mod.asciichartpy, 'plot', fake_plot)
+
+    AsciiPlotter().plot_powder(
+        x=np.arange(256, dtype=float),
+        y_series=[np.linspace(0.0, 1.0, 256)],
+        labels=['density'],
+        axes_labels=['x', 'y'],
+        title='Width test',
+        height=5,
+    )
+
+    series, config = captured['call']
+    assert len(series[0]) == max(
+        ASCII_CHART_MIN_POINT_COUNT,
+        44 - ASCII_CHART_OFFSET - ASCII_CHART_LEFT_PADDING,
+    )
+    assert config['offset'] == ASCII_CHART_OFFSET
+
+
+def test_ascii_plotter_plot_uses_fallback_width_when_terminal_size_unavailable(monkeypatch):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_FALLBACK_POINT_COUNT
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_OFFSET
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    captured: dict[str, object] = {}
+
+    def fake_plot(series, config):
+        captured['call'] = (series, config)
+        return 'chart'
+
+    monkeypatch.setattr(
+        ascii_mod.shutil,
+        'get_terminal_size',
+        lambda fallback: os.terminal_size(fallback),
+    )
+    monkeypatch.setattr(ascii_mod.asciichartpy, 'plot', fake_plot)
+
+    AsciiPlotter().plot_powder(
+        x=np.arange(256, dtype=float),
+        y_series=[np.linspace(0.0, 1.0, 256)],
+        labels=['density'],
+        axes_labels=['x', 'y'],
+        title='Fallback width test',
+        height=5,
+    )
+
+    series, config = captured['call']
+    assert len(series[0]) == ASCII_CHART_FALLBACK_POINT_COUNT
+    assert config['offset'] == ASCII_CHART_OFFSET
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 18f0afe81..c32424044 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -585,6 +585,7 @@ def test_posterior_pairs_context_thins_kde_samples_and_preserves_axis_ranges():
     context = plotter._posterior_pairs_context(parameters=None)
 
     assert context is not None
+    assert context.marginal_density_samples.shape == (sample_count, parameter_count)
     assert context.density_samples.shape == (
         Plotter._posterior_pair_density_max_points(parameter_count),
         parameter_count,
@@ -596,6 +597,74 @@ def test_posterior_pairs_context_thins_kde_samples_and_preserves_axis_ranges():
     assert context.axis_ranges[1][0] < -50.0
 
 
+def test_posterior_pair_diagonal_matches_standalone_distribution_when_thinned():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import Plotter
+
+    sample_count = 5001
+    angle = np.linspace(0.0, 12.0 * np.pi, sample_count, dtype=float)
+    sample_axis = np.linspace(-1.0, 1.0, sample_count, dtype=float)
+    samples = np.empty((1, sample_count, 2), dtype=float)
+    samples[0, :, 0] = 3.8913 + 0.00016 * np.sin(angle) + 0.00003 * np.cos(2.0 * angle)
+    samples[0, :, 1] = 0.0780 + 0.0024 * np.cos(0.5 * angle) + 0.0005 * sample_axis**2
+    parameter_names = ['length_a', 'broad_gauss_u']
+    posterior_samples = PosteriorSamples(
+        parameter_names=parameter_names,
+        parameter_samples=samples,
+        log_posterior=np.zeros((1, sample_count), dtype=float),
+    )
+    parameters = [
+        SimpleNamespace(unique_name='length_a', name='length_a', fit_min=3.8909, fit_max=3.8917),
+        SimpleNamespace(
+            unique_name='broad_gauss_u',
+            name='broad_gauss_u',
+            fit_min=0.074,
+            fit_max=0.082,
+        ),
+    ]
+    summaries = [
+        PosteriorParameterSummary(
+            unique_name=name,
+            display_name=name,
+            map_value=float(samples[0, -1, index]),
+            median=float(np.median(samples[:, :, index])),
+            standard_deviation=float(np.std(samples[:, :, index], ddof=1)),
+            interval_68=tuple(np.quantile(samples[:, :, index], [0.16, 0.84]).tolist()),
+            interval_95=tuple(np.quantile(samples[:, :, index], [0.025, 0.975]).tolist()),
+        )
+        for index, name in enumerate(parameter_names)
+    ]
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=summaries,
+        posterior_predictive={},
+        parameters=parameters,
+    )
+    plotter = Plotter()
+    plotter._get_posterior_samples_and_fit_results = MethodType(
+        lambda self: (posterior_samples, fit_results),
+        plotter,
+    )
+    plotter._get_fit_result_for_correlation = MethodType(lambda self: fit_results, plotter)
+
+    pair_figure = plotter._build_posterior_pairs_plot(parameters=parameters)
+    distribution_figure = plotter._build_param_distribution_plot(parameters[0])
+
+    pair_trace = next(
+        trace
+        for trace in pair_figure.data
+        if trace.name == 'Marginal density'
+        and trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
+    )
+    distribution_trace = next(
+        trace for trace in distribution_figure.data if trace.name == 'Marginal density'
+    )
+
+    np.testing.assert_allclose(pair_trace.x, distribution_trace.x)
+    np.testing.assert_allclose(pair_trace.y, distribution_trace.y)
+
+
 def test_build_posterior_pairs_plot_rejects_unknown_style():
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
@@ -646,6 +715,34 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert figure.layout.yaxis.range is not None
 
 
+def test_plot_param_distribution_routes_ascii_to_marginal_density(monkeypatch):
+    from types import SimpleNamespace
+
+    plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
+    captured: dict[str, object] = {}
+    plotter.engine = 'asciichartpy'
+    plotter._backend = SimpleNamespace(
+        plot_powder=lambda **kwargs: captured.setdefault('powder', kwargs)
+    )
+
+    plotter.plot_param_distribution(fit_results.parameters[0])
+
+    values = posterior_samples.flattened()[:, 0]
+    density_curve = plotter._posterior_density_curve(
+        values,
+        lower_bound=fit_results.parameters[0].fit_min,
+        upper_bound=fit_results.parameters[0].fit_max,
+    )
+
+    assert density_curve is not None
+    assert captured['powder']['labels'] == ['density']
+    assert captured['powder']['axes_labels'] == ['length_a', 'Probability density']
+    assert captured['powder']['title'] == 'Posterior distribution: length_a'
+    assert captured['powder']['height'] == plotter.height
+    np.testing.assert_allclose(captured['powder']['x'], density_curve[0])
+    np.testing.assert_allclose(captured['powder']['y_series'][0], density_curve[1])
+
+
 def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(monkeypatch):
     from types import SimpleNamespace
 
@@ -657,6 +754,7 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     captured: dict[str, object] = {}
 
     plotter = Plotter()
+    plotter.engine = 'plotly'
     plotter._backend = SimpleNamespace(
         _show_figure=lambda figure: captured.setdefault('fig', figure)
     )
@@ -797,6 +895,172 @@ class Experiment:
     assert plot_spec.y_calc_line_dash == 'dot'
 
 
+def test_plot_posterior_predictive_request_allows_ascii_for_powder_bragg(monkeypatch):
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Experiment:
+        type = ExptType()
+
+    class Project:
+        experiments = {'hrpt': Experiment()}
+
+    plotter = Plotter()
+    plotter.engine = 'asciichartpy'
+    plotter._set_project(Project())
+
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    def fake_plot_posterior_predictive_data(
+        self,
+        *,
+        experiment,
+        expt_name,
+        plot_options,
+        x_axis,
+        style,
+    ):
+        captured['experiment'] = experiment
+        captured['expt_name'] = expt_name
+        captured['style'] = style
+        captured['x_axis'] = x_axis
+        captured['show_residual'] = plot_options.show_residual
+
+    monkeypatch.setattr(
+        Plotter, '_plot_posterior_predictive_data', fake_plot_posterior_predictive_data
+    )
+
+    plotter.plot_posterior_predictive('hrpt')
+
+    assert captured['experiment'] is Project.experiments['hrpt']
+    assert captured['expt_name'] == 'hrpt'
+    assert captured['style'] == 'band'
+    assert captured['show_residual'] is None
+
+
+def test_plot_posterior_predictive_summary_routes_ascii_to_measured_and_map(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+    plotter = Plotter()
+    plotter.engine = 'asciichartpy'
+    plotter._backend = SimpleNamespace(
+        plot_powder=lambda **kwargs: captured.setdefault('powder', kwargs)
+    )
+
+    plotter._plot_posterior_predictive_summary(
+        expt_name='pdf',
+        summary=SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            map_prediction=np.array([9.0, 10.0, 11.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            draws=np.array([[8.5, 9.5, 10.5]]),
+        ),
+        y_meas=np.array([9.5, 10.5, 11.5]),
+        axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
+        show_band=True,
+        show_draws=True,
+        excluded_ranges=((1.2, 1.4),),
+    )
+
+    assert captured['powder']['labels'] == ['meas', 'posterior']
+    np.testing.assert_allclose(captured['powder']['x'], np.array([1.0, 2.0, 3.0]))
+    np.testing.assert_allclose(
+        captured['powder']['y_series'][0],
+        np.array([9.5, 10.5, 11.5]),
+    )
+    np.testing.assert_allclose(
+        captured['powder']['y_series'][1],
+        np.array([9.0, 10.0, 11.0]),
+    )
+    assert captured['powder']['excluded_ranges'] == ((1.2, 1.4),)
+
+
+def test_plot_posterior_predictive_data_routes_ascii_to_line_plot_without_intervals(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import XAxisType
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Pattern:
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 12.0, 11.0])
+        intensity_bkg = np.array([1.0, 1.0, 1.0])
+
+    class Experiment:
+        type = ExptType()
+        data = Pattern()
+
+    plotter = Plotter()
+    plotter.engine = 'asciichartpy'
+    plotter._backend = SimpleNamespace(
+        plot_powder=lambda **kwargs: captured.setdefault('powder', kwargs),
+        plot_powder_meas_vs_calc=lambda **kwargs: captured.setdefault('composite', kwargs),
+    )
+
+    monkeypatch.setattr(
+        Plotter,
+        '_get_or_build_posterior_predictive_summary',
+        lambda self, **kwargs: SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            map_prediction=np.array([9.0, 11.0, 10.5]),
+            draws=None,
+        ),
+    )
+
+    plotter._plot_posterior_predictive_data(
+        experiment=Experiment(),
+        expt_name='hrpt',
+        plot_options=SimpleNamespace(
+            x_min=None,
+            x_max=None,
+            show_residual=None,
+            show_background=None,
+            show_bragg=None,
+            show_excluded=False,
+            x=None,
+        ),
+        x_axis=XAxisType.TWO_THETA,
+        style='band+draws',
+    )
+
+    assert 'composite' not in captured
+    assert captured['powder']['labels'] == ['meas', 'posterior']
+    np.testing.assert_allclose(captured['powder']['x'], np.array([1.0, 2.0, 3.0]))
+    np.testing.assert_allclose(
+        captured['powder']['y_series'][0],
+        np.array([10.0, 12.0, 11.0]),
+    )
+    np.testing.assert_allclose(
+        captured['powder']['y_series'][1],
+        np.array([9.0, 11.0, 10.5]),
+    )
+
+
 def test_plot_meas_vs_calc_request_respects_background_and_bragg_flags():
     from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
     from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
@@ -1947,6 +2211,30 @@ def fake_build(self, *, parameters, style, threshold, max_parameters):
     assert captured['max_parameters'] == DEFAULT_CORRELATION_MAX_PARAMETERS
 
 
+def test_plot_posterior_pairs_prints_title_before_ascii_backend_warning(monkeypatch):
+    import easydiffraction.display.plotting as plotting_mod
+
+    from easydiffraction.display.plotting import Plotter
+
+    events: list[tuple[str, str]] = []
+    plotter = Plotter()
+    plotter.engine = 'asciichartpy'
+
+    monkeypatch.setattr(
+        plotting_mod.console, 'paragraph', lambda text: events.append(('title', text))
+    )
+    monkeypatch.setattr(
+        plotting_mod.log, 'warning', lambda message: events.append(('warning', message))
+    )
+
+    plotter.plot_posterior_pairs()
+
+    assert events == [
+        ('title', 'Posterior pair plot'),
+        ('warning', 'Posterior plots currently require the Plotly plotting backend.'),
+    ]
+
+
 def test_plot_param_correlations_shows_full_table_when_threshold_is_zero(monkeypatch):
     from easydiffraction.display.plotting import Plotter
     from easydiffraction.display.tables import TableRenderer
diff --git a/tests/unit/easydiffraction/display/test_plotting_coverage.py b/tests/unit/easydiffraction/display/test_plotting_coverage.py
index 806e68da2..542dfa4f5 100644
--- a/tests/unit/easydiffraction/display/test_plotting_coverage.py
+++ b/tests/unit/easydiffraction/display/test_plotting_coverage.py
@@ -254,11 +254,13 @@ def test_unknown_name_returns_none(self):
 
 
 class TestAutoXRangeForAscii:
-    def test_narrows_range_for_ascii(self):
+    def test_narrows_range_for_ascii(self, monkeypatch):
+        from easydiffraction.display.plotters.ascii import AsciiPlotter
         from easydiffraction.display.plotting import Plotter
 
         p = Plotter()
         p.engine = 'asciichartpy'
+        monkeypatch.setattr(AsciiPlotter, '_chart_point_count', lambda: 80)
 
         class Ptn:
             intensity_meas = np.zeros(200)
@@ -266,8 +268,8 @@ class Ptn:
         Ptn.intensity_meas[100] = 10.0  # max at index 100
         x_array = np.arange(200, dtype=float)
         x_min, x_max = p._auto_x_range_for_ascii(Ptn(), x_array, None, None)
-        assert x_min == 50.0
-        assert x_max == 150.0
+        assert x_min == 60.0
+        assert x_max == 139.0
 
     def test_no_narrowing_when_limits_provided(self):
         from easydiffraction.display.plotting import Plotter
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index b67d7c022..b56f8fce0 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -50,6 +50,7 @@ def _recorder(*args, **kwargs):
     project = SimpleNamespace(
         analysis=SimpleNamespace(display=analysis_display),
         rendering=SimpleNamespace(plotter=plotter),
+        free_parameters=[],
         verbosity='full',
     )
     return project, calls
@@ -259,6 +260,49 @@ def fake_activity_indicator(label, *, verbosity):
     ]
 
 
+def test_posterior_distribution_without_param_plots_all_free_parameters():
+    project, calls = _make_project_stub()
+    project.free_parameters = ['a', 'b']
+    project.rendering.plotter.engine = 'plotly'
+    display = ProjectDisplay(project)
+
+    display.posterior.distribution()
+
+    assert calls == [
+        ('plot_param_distribution', ('a',), {}),
+        ('plot_param_distribution', ('b',), {}),
+    ]
+
+
+def test_posterior_distribution_without_param_plots_all_free_parameters_for_ascii():
+    project, calls = _make_project_stub()
+    project.free_parameters = ['a', 'b']
+    project.rendering.plotter.engine = 'asciichartpy'
+    display = ProjectDisplay(project)
+
+    display.posterior.distribution()
+
+    assert calls == [
+        ('plot_param_distribution', ('a',), {}),
+        ('plot_param_distribution', ('b',), {}),
+    ]
+
+
+def test_posterior_distribution_without_param_warns_when_no_free_parameters(monkeypatch):
+    import easydiffraction.project.display as display_mod
+
+    project, calls = _make_project_stub()
+    display = ProjectDisplay(project)
+    warnings: list[str] = []
+
+    monkeypatch.setattr(display_mod.log, 'warning', warnings.append)
+
+    display.posterior.distribution()
+
+    assert calls == []
+    assert warnings == ['No free parameters found.']
+
+
 def test_pattern_auto_routes_measured_and_excluded_to_plot_meas():
     project, calls = _make_project_stub()
     display = ProjectDisplay(project)

From 6a4887e5025b16ab9ee4b23068a32dadfe4e182b Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Sun, 17 May 2026 09:45:00 +0200
Subject: [PATCH 07/10] Refactor fit modes and sequential analysis workflow
 (#175)

* Rename Bayesian MAP labels to best posterior sample

* Add ADR for fit mode categories

* Refine fit-mode categories ADR with active-sibling pattern

* Add open questions and drop fitting.mode mirror in ADR

* Add implementation plan for fit-mode-categories ADR

* Rename CIF field _fitting.mode to _fitting.mode_type

* Tighten high-risk steps in fit-mode-categories plan

* Add BoolDescriptor for CIF-bound boolean values

* Add fitting category replacing fit configuration surface

* Add fitting_mode_type selector and fitting accessor on Analysis

* Rename joint_fit_experiments category to joint_fit

* Add sequential_fit category with persisted scan settings

* Add sequential_fit_extract category for scan metadata rules

* Replace fit category with Analysis.fit() method

* Drive sequential fitting from sequential_fit settings

* Auto-populate joint_fit rows and validate before fitting

* Add instance-aware help filter and hide inactive mode categories

* Serialize only active mode-specific analysis categories

* Restore mode before mode-specific analysis sections

* Update tutorials, docs, and exports for new fitting API

* Resolve ZIP extraction relative to saved project

* Add live progress tables for sequential fitting

* Set sequential fitting mode and save project

* Complete fit-mode categories and refactor progress

* Fix sequential replay, styling, and docs

* Simplify sequential progress table headers

* Add progress and time columns to sequential tables

* Unify sequential and single fit spinners on ActivityIndicator

* Fix spacing in chunk file range display

* Render sequential progress as bordered table with index column

* Render sequential progress with shared single-fit table renderable

* Add fit.series_all to plot every fitted parameter

* Unify fit.series to plot single or all fitted parameters

* Encapsulate chunk progress in dataclass

* Use string paths for versus parameter

* Unify ASCII plot width handling across terminal charts

* Sort ASCII parameter series by x before plotting

* Skip fit reports for sequential mode

* List all analysis files on save

* Bump dependencies

* Add resumed sequential-fit tutorial for Co2SiO4

* Update data index reference and hash

* Clear sequential fit state and make ZIP extraction explicit

* Update data index reference and hash

* Normalize sequential CSV paths for resumed fits

* Update data index reference and hash

* Add resume fit tutorial and fix paths

* Normalize CSV relative paths to POSIX style

* Remove project save step from tutorial

* Accept fit-mode categories ADR

* Move Quick Reference to end of nav

* Lower coverage threshold to 65%
---
 .../adr_parameter-posterior-summary.md        |   43 +-
 docs/dev/ADRs/adr_display-ux.md               |    7 +-
 docs/dev/ADRs/adr_fit-mode-categories.md      |  781 +++++++++++++
 docs/dev/Issues/issues_open.md                |  247 +++-
 docs/dev/architecture.md                      |  273 +++--
 docs/dev/package-structure-full.md            |   40 +-
 docs/dev/package-structure-short.md           |   14 +-
 docs/docs/quick-reference/index.md            |   47 +-
 docs/docs/tutorials/ed-15.ipynb               |    4 +-
 docs/docs/tutorials/ed-15.py                  |    4 +-
 docs/docs/tutorials/ed-16.ipynb               |    6 +-
 docs/docs/tutorials/ed-16.py                  |    6 +-
 docs/docs/tutorials/ed-17.ipynb               |  104 +-
 docs/docs/tutorials/ed-17.py                  |   51 +-
 docs/docs/tutorials/ed-2.ipynb                |    4 +-
 docs/docs/tutorials/ed-2.py                   |    4 +-
 docs/docs/tutorials/ed-20.ipynb               |    4 +-
 docs/docs/tutorials/ed-20.py                  |    4 +-
 docs/docs/tutorials/ed-21.ipynb               |   10 +-
 docs/docs/tutorials/ed-21.py                  |   10 +-
 docs/docs/tutorials/ed-22.ipynb               |    8 +-
 docs/docs/tutorials/ed-22.py                  |    8 +-
 docs/docs/tutorials/ed-23.ipynb               |  241 ++++
 docs/docs/tutorials/ed-23.py                  |   79 ++
 docs/docs/tutorials/ed-3.ipynb                |    8 +-
 docs/docs/tutorials/ed-3.py                   |    8 +-
 docs/docs/tutorials/ed-4.ipynb                |    4 +-
 docs/docs/tutorials/ed-4.py                   |    4 +-
 docs/docs/tutorials/ed-8.ipynb                |    4 +-
 docs/docs/tutorials/ed-8.py                   |    4 +-
 docs/docs/tutorials/index.md                  |    3 +
 .../user-guide/analysis-workflow/analysis.md  |   18 +-
 docs/docs/user-guide/first-steps.md           |    2 +-
 docs/mkdocs.yml                               |    5 +-
 pixi.lock                                     | 1004 ++++++++---------
 pyproject.toml                                |    2 +-
 src/easydiffraction/__main__.py               |    5 +-
 src/easydiffraction/analysis/__init__.py      |   14 +
 src/easydiffraction/analysis/analysis.py      |  449 +++++---
 .../analysis/categories/__init__.py           |   13 +
 .../analysis/categories/fit/__init__.py       |    6 -
 .../analysis/categories/fit/default.py        |  212 ----
 .../analysis/categories/fitting/__init__.py   |    5 +
 .../analysis/categories/fitting/default.py    |  125 ++
 .../factory.py                                |    6 +-
 .../analysis/categories/joint_fit/__init__.py |    8 +
 .../default.py                                |   44 +-
 .../categories/{fit => joint_fit}/factory.py  |    6 +-
 .../joint_fit_experiments/__init__.py         |    5 -
 .../categories/sequential_fit/__init__.py     |    7 +
 .../categories/sequential_fit/default.py      |  124 ++
 .../categories/sequential_fit/factory.py      |   17 +
 .../sequential_fit_extract/__init__.py        |   14 +
 .../sequential_fit_extract/default.py         |  162 +++
 .../sequential_fit_extract/factory.py         |   17 +
 .../analysis/{categories/fit => }/enums.py    |    8 +-
 .../analysis/fit_helpers/bayesian.py          |   28 +-
 .../analysis/minimizers/bumps_dream.py        |   13 +-
 src/easydiffraction/analysis/sequential.py    |  705 +++++++++---
 src/easydiffraction/core/__init__.py          |    2 +
 src/easydiffraction/core/guard.py             |   38 +-
 src/easydiffraction/core/variable.py          |   49 +
 src/easydiffraction/display/plotters/ascii.py |   66 +-
 src/easydiffraction/display/plotters/base.py  |    2 +-
 src/easydiffraction/display/plotting.py       |  233 +++-
 src/easydiffraction/display/progress.py       |   68 +-
 src/easydiffraction/io/ascii.py               |   26 +-
 src/easydiffraction/io/cif/serialize.py       |  201 +++-
 src/easydiffraction/project/display.py        |   19 +-
 src/easydiffraction/project/project.py        |   77 +-
 src/easydiffraction/summary/summary.py        |    2 +-
 src/easydiffraction/utils/utils.py            |    4 +-
 tests/functional/test_fitting_workflow.py     |   12 +-
 .../functional/test_switchable_categories.py  |    2 +-
 tests/integration/fitting/conftest.py         |    5 +-
 .../test_analysis_and_fit_category_support.py |  116 +-
 .../fitting/test_analysis_display.py          |    4 +-
 .../fitting/test_aniso_adp_fitting.py         |    5 +-
 .../fitting/test_bayesian_dream.py            |   28 +-
 .../fitting/test_bayesian_helper_support.py   |   24 +-
 .../fitting/test_bumps_dream_support.py       |    6 +-
 tests/integration/fitting/test_multi.py       |    6 +-
 ..._powder-diffraction_constant-wavelength.py |    6 +-
 .../test_powder-diffraction_joint-fit.py      |   16 +-
 .../test_powder-diffraction_time-of-flight.py |    4 +-
 .../integration/fitting/test_project_load.py  |   13 +-
 tests/integration/fitting/test_sequential.py  |  127 ++-
 .../categories/fitting/test_default.py        |   37 +
 .../categories/fitting/test_factory.py        |   24 +
 .../categories/sequential_fit/test_default.py |   35 +
 .../categories/sequential_fit/test_factory.py |   24 +
 .../sequential_fit_extract/test_default.py    |   66 ++
 .../sequential_fit_extract/test_factory.py    |   32 +
 .../analysis/categories/test_fit.py           |   73 +-
 .../categories/test_joint_fit_experiments.py  |   16 +-
 .../analysis/fit_helpers/test_bayesian.py     |   12 +-
 .../analysis/minimizers/test_bumps_dream.py   |    4 +-
 .../easydiffraction/analysis/test_analysis.py |   88 +-
 .../analysis/test_analysis_coverage.py        |    4 +-
 .../easydiffraction/analysis/test_enums.py    |   22 +
 .../analysis/test_sequential.py               |  418 ++++++-
 .../display/plotters/test_ascii.py            |  131 ++-
 .../display/plotters/test_plotly.py           |    6 +-
 .../easydiffraction/display/test_plotting.py  |   37 +-
 .../display/test_plotting_coverage.py         |   50 +-
 .../easydiffraction/display/test_progress.py  |  105 ++
 .../io/cif/test_serialize_more.py             |   15 +-
 tests/unit/easydiffraction/io/test_ascii.py   |   45 +
 .../easydiffraction/project/test_display.py   |   11 +-
 .../easydiffraction/project/test_project.py   |   47 +
 .../project/test_project_load.py              |   10 +-
 .../project/test_project_save.py              |   24 +
 .../easydiffraction/summary/test_summary.py   |    4 +-
 .../summary/test_summary_details.py           |    4 +-
 tests/unit/easydiffraction/test___main__.py   |   57 +
 115 files changed, 5770 insertions(+), 1850 deletions(-)
 create mode 100644 docs/dev/ADRs/adr_fit-mode-categories.md
 create mode 100644 docs/docs/tutorials/ed-23.ipynb
 create mode 100644 docs/docs/tutorials/ed-23.py
 delete mode 100644 src/easydiffraction/analysis/categories/fit/__init__.py
 delete mode 100644 src/easydiffraction/analysis/categories/fit/default.py
 create mode 100644 src/easydiffraction/analysis/categories/fitting/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/fitting/default.py
 rename src/easydiffraction/analysis/categories/{joint_fit_experiments => fitting}/factory.py (65%)
 create mode 100644 src/easydiffraction/analysis/categories/joint_fit/__init__.py
 rename src/easydiffraction/analysis/categories/{joint_fit_experiments => joint_fit}/default.py (64%)
 rename src/easydiffraction/analysis/categories/{fit => joint_fit}/factory.py (70%)
 delete mode 100644 src/easydiffraction/analysis/categories/joint_fit_experiments/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit/default.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit_extract/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
 create mode 100644 src/easydiffraction/analysis/categories/sequential_fit_extract/factory.py
 rename src/easydiffraction/analysis/{categories/fit => }/enums.py (73%)
 create mode 100644 tests/unit/easydiffraction/analysis/categories/fitting/test_default.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/fitting/test_factory.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/sequential_fit/test_default.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/sequential_fit/test_factory.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_default.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_factory.py
 create mode 100644 tests/unit/easydiffraction/analysis/test_enums.py

diff --git a/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md b/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
index dd7999ba1..f81e64472 100644
--- a/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
+++ b/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
@@ -54,7 +54,7 @@ model.
 ### 2. Reuse the existing Bayesian summary container
 
 Do not add separate flat parameter attributes such as `median`,
-`map_estimate`, `interval_95`, `r_hat`, or `ess_bulk`.
+`best_sample_value`, `interval_95`, `r_hat`, or `ess_bulk`.
 
 Instead, the parameter-level projection reuses the existing
 `PosteriorParameterSummary` object already produced for
@@ -63,7 +63,7 @@ inspection, and later persistence.
 
 The summary object currently provides the right level of detail:
 
-- `map_estimate`
+- `best_sample_value`
 - `median`
 - `uncertainty`
 - `interval_68`
@@ -87,7 +87,7 @@ The internal field names stay compact and code-oriented. User-facing
 tables, summaries, and plot annotations should use these friendly
 labels:
 
-- `map_estimate` -> `MAP estimate`
+- `best_sample_value` -> `Best posterior sample`
 - `median` -> `Median`
 - `uncertainty` -> `Standard uncertainty`
 - `interval_68` -> `68% credible interval`
@@ -109,7 +109,7 @@ current_value = param.value
 current_uncertainty = param.uncertainty
 
 if param.posterior is not None:
-  map_estimate = param.posterior.map_estimate
+  best_sample_value = param.posterior.best_sample_value
   median = param.posterior.median
   uncertainty = param.posterior.uncertainty
   low95, high95 = param.posterior.interval_95
@@ -187,15 +187,15 @@ The exact helper names can be refined during implementation, but the
 design requirement is fixed: manual user edits clear stale metadata,
 while internal fit application installs fresh metadata atomically.
 
-### 8. Commit MAP to `parameter.value` after Bayesian fits
+### 8. Commit best posterior sample to `parameter.value` after Bayesian fits
 
 After a posterior-capable fit, `parameter.value` is committed from the
-maximum-a-posteriori estimate.
+best posterior sample.
 
-MAP is chosen because it is a coherent joint point estimate across all
-free parameters. Marginal medians remain available on
-`parameter.posterior`, but they are summary data rather than the active
-live model state.
+The best posterior sample is chosen because it is a coherent joint point
+estimate across all free parameters. Marginal medians remain available
+on `parameter.posterior`, but they are summary data rather than the
+active live model state.
 
 ### 9. Keep `uncertainty` as a convenience scalar after Bayesian fits
 
@@ -279,7 +279,7 @@ Stores one saved Bayesian result header with these fields:
 - `has_posterior_predictive`
 - `sidecar_file`
 
-For the current design, `point_estimate_name` is always `map`.
+For the current design, `point_estimate_name` is always `best_sample`.
 
 #### 11.3 `_bayesian_sampler` single item
 
@@ -320,7 +320,7 @@ Fields:
 - `order_index`
 - `unique_name`
 - `display_name`
-- `map_estimate`
+- `best_sample_value`
 - `median`
 - `uncertainty`
 - `interval_68_lower`
@@ -342,7 +342,7 @@ Fields:
 - `experiment_name`
 - `x_axis_name`
 - `x_path`
-- `map_prediction_path`
+- `best_sample_prediction_path`
 - `lower_95_path`
 - `upper_95_path`
 - `lower_68_path`
@@ -382,7 +382,7 @@ cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
 
 _bayesian_result.schema_version 1
 _bayesian_result.sampler_name dream
-_bayesian_result.point_estimate_name map
+_bayesian_result.point_estimate_name best_sample
 _bayesian_result.success yes
 _bayesian_result.sampler_completed yes
 _bayesian_result.reduced_chi_square 1.031
@@ -413,7 +413,7 @@ loop_
 _bayesian_parameter_posterior.order_index
 _bayesian_parameter_posterior.unique_name
 _bayesian_parameter_posterior.display_name
-_bayesian_parameter_posterior.map_estimate
+_bayesian_parameter_posterior.best_sample_value
 _bayesian_parameter_posterior.median
 _bayesian_parameter_posterior.uncertainty
 _bayesian_parameter_posterior.interval_68_lower
@@ -429,7 +429,7 @@ loop_
 _bayesian_predictive_dataset.experiment_name
 _bayesian_predictive_dataset.x_axis_name
 _bayesian_predictive_dataset.x_path
-_bayesian_predictive_dataset.map_prediction_path
+_bayesian_predictive_dataset.best_sample_prediction_path
 _bayesian_predictive_dataset.lower_95_path
 _bayesian_predictive_dataset.upper_95_path
 _bayesian_predictive_dataset.lower_68_path
@@ -437,7 +437,7 @@ _bayesian_predictive_dataset.upper_68_path
 _bayesian_predictive_dataset.draws_path
 _bayesian_predictive_dataset.n_x
 _bayesian_predictive_dataset.n_draws_cached
-hrpt ttheta /predictive/hrpt/x /predictive/hrpt/map_prediction /predictive/hrpt/lower_95 /predictive/hrpt/upper_95 /predictive/hrpt/lower_68 /predictive/hrpt/upper_68 /predictive/hrpt/draws 2500 200
+hrpt ttheta /predictive/hrpt/x /predictive/hrpt/best_sample_prediction /predictive/hrpt/lower_95 /predictive/hrpt/upper_95 /predictive/hrpt/lower_68 /predictive/hrpt/upper_68 /predictive/hrpt/draws 2500 200
 ```
 
 ### 12. Persist bulk arrays in `analysis/bayesian_data.h5`
@@ -482,7 +482,7 @@ ordering.
 Recommended HDF5 dataset naming is:
 
 - `predictive__<experiment>__x`
-- `predictive__<experiment>__map_prediction`
+- `predictive__<experiment>__best_sample_prediction`
 - `predictive__<experiment>__lower_95`
 - `predictive__<experiment>__upper_95`
 - `predictive__<experiment>__lower_68`
@@ -497,7 +497,7 @@ Recommended HDF5 group layout is:
 - `/posterior/log_posterior`
 - `/posterior/draw_index`
 - `/predictive/<experiment>/x`
-- `/predictive/<experiment>/map_prediction`
+- `/predictive/<experiment>/best_sample_prediction`
 - `/predictive/<experiment>/lower_95`
 - `/predictive/<experiment>/upper_95`
 - `/predictive/<experiment>/lower_68`
@@ -573,7 +573,7 @@ param = project.phases['lbco'].cell.length_a
 posterior = param.posterior
 
 if posterior is not None:
-    print(posterior.map_estimate)
+  print(posterior.best_sample_value)
     print(posterior.uncertainty)
     print(posterior.interval_68)
 
@@ -673,7 +673,8 @@ It still defers:
 
 ## Chosen Defaults
 
-- `parameter.value` remains committed to MAP after posterior fits.
+- `parameter.value` remains committed to the best posterior sample after
+  posterior fits.
 - If a project is loaded without full posterior arrays, restoring only
   `parameter.posterior` is acceptable for table display and parameter
   inspection.
diff --git a/docs/dev/ADRs/adr_display-ux.md b/docs/dev/ADRs/adr_display-ux.md
index 47e9b5c28..23a780812 100644
--- a/docs/dev/ADRs/adr_display-ux.md
+++ b/docs/dev/ADRs/adr_display-ux.md
@@ -77,7 +77,7 @@ project.display.parameters.cif_uids()
 
 project.display.fit.results()
 project.display.fit.correlations()
-project.display.fit.series(param, versus=temperature)
+project.display.fit.series(param, versus='diffrn.ambient_temperature')
 
 project.display.posterior.pairs()
 project.display.posterior.distribution(param)
@@ -197,7 +197,8 @@ Use these naming rules:
 - `fit.correlations()` shows parameter relationships from the latest
   fit.
 - `fit.series(param, versus=...)` shows fitted parameter values across a
-  sequence of fit results or experiments.
+  sequence of fit results or experiments, using a persisted `diffrn.*`
+  path for `versus`.
 - `posterior.*` names are used only when posterior samples are required.
 
 ## Rejected Alternatives
@@ -209,7 +210,7 @@ project.display.pattern(expt_name='hrpt')
 project.display.parameters(scope='free')
 project.display.fit_results()
 project.display.correlations()
-project.display.parameter_series(param, versus=temperature)
+project.display.parameter_series(param, versus='diffrn.ambient_temperature')
 project.display.posterior_pairs()
 project.display.posterior_distribution(param)
 project.display.posterior_predictive(expt_name='hrpt')
diff --git a/docs/dev/ADRs/adr_fit-mode-categories.md b/docs/dev/ADRs/adr_fit-mode-categories.md
new file mode 100644
index 000000000..278d3752a
--- /dev/null
+++ b/docs/dev/ADRs/adr_fit-mode-categories.md
@@ -0,0 +1,781 @@
+# ADR: Fit Mode Categories and Fit Execution API
+
+## Status
+
+Accepted and implemented.
+
+## Date
+
+2026-05-16
+
+## Context
+
+The current analysis API uses `project.analysis.fit` as both a category
+and a callable execution entry point:
+
+```python
+project.analysis.fit.minimizer_type = 'lmfit (leastsq)'
+project.analysis.fit.mode = 'joint'
+project.analysis.fit()
+```
+
+This creates three problems:
+
+- the same name, `fit`, represents both configuration and execution
+- mode-specific configuration is not part of the normal switchable
+  category pattern used elsewhere in the API
+- `project.analysis.help()` always shows `joint_fit_experiments`, even
+  when the active fit mode is not joint
+
+The separate `fit_sequential(...)` method creates another inconsistency.
+Sequential fitting is selected by `fit.mode = 'sequential'`, but the
+actual run must be started through `fit_sequential(...)` because
+sequential-specific settings such as `data_dir`, `max_workers`,
+`chunk_size`, `file_pattern`, and `reverse` are currently method
+arguments instead of persisted analysis configuration.
+
+The rest of the API already has a clearer convention for switchable
+categories. For example, peak profile selection is owned by the
+experiment:
+
+```python
+project.experiments['hrpt'].show_peak_profile_types()
+project.experiments['hrpt'].peak_profile_type = 'pseudo-voigt'
+project.experiments['hrpt'].peak.broad_gauss_u = 0.1
+```
+
+The owner-level selector switches or configures the active category
+shape, and the active category exposes only the parameters that make
+sense for that selected type.
+
+Fit modes should follow the same conceptual pattern:
+
+- the `Analysis` owner selects the fit mode
+- common fitting configuration lives in a stable category
+- mode-specific settings live in mode-specific sibling categories
+- help output and CIF serialization reflect the active mode
+
+### Precedent: how `atom_site_aniso` handles a conditional category
+
+The repository already has one closely related precedent: the
+`atom_site_aniso` collection on a structure is only meaningful when at
+least one `atom_site` has `adp_type` set to `Bani` or `Uani`. Its
+implementation is instructive because it deliberately does **not** hide
+the category from public discovery:
+
+- `Structure.atom_site_aniso` is always present as a property and always
+  appears in help output.
+- When inactive, the collection is simply empty.
+- A private `_sync_atom_site_aniso()` reconciles its contents from
+  `atom_sites` whenever categories update: rows for anisotropic atoms
+  are added, rows for isotropic or stale atoms are removed.
+- CIF serialization naturally drops the empty loop, so there is no
+  separate "serialize only when active" rule.
+
+This is a viable alternative pattern for fit modes, and it is
+intentionally rejected by this ADR (see _Alternatives Considered_). The
+key differences that motivate a new pattern for fit modes are:
+
+- `atom_site_aniso` rows are **derived** from a per-atom selector
+  (`atom_site.adp_type`). For fit modes, the selector is owner-level
+  (`Analysis.fitting_mode_type`) and the mode-specific categories
+  (`joint_fit`, `sequential_fit`, `sequential_fit_extract`) carry
+  independent, user-edited settings that cannot be derived from anything
+  else.
+- `atom_site_aniso` has one conditional category. Fit modes introduce a
+  family of mutually exclusive categories; showing all of them as
+  always-present empty surfaces would clutter `help()` output and invite
+  users to configure a mode that is not active.
+- For sequential fitting, configuration must be authoritative for CLI
+  workflows. "Empty when inactive" is ambiguous on reload — was the mode
+  never used, or was it cleared on a previous run?
+
+Fit modes therefore call for an explicit **active-sibling** pattern (see
+§2 and §7) rather than the auto-synced always-present pattern used by
+`atom_site_aniso`.
+
+This ADR intentionally does not preserve the existing public API as a
+compatibility surface. The follow-up migration plan may describe file,
+test, and documentation changes, but the target design is not required
+to keep legacy runtime aliases.
+
+## Decision
+
+### 1. Split fitting configuration from fit execution
+
+`Analysis.fit()` becomes the public operation that executes the current
+fit mode.
+
+Common fitting configuration moves to a dedicated category:
+
+```python
+project.analysis.fitting.minimizer_type = 'lmfit (leastsq)'
+project.analysis.fit()
+```
+
+`project.analysis.fit` is no longer a category. It is an action method.
+
+The common `fitting` category owns configuration shared by all fit
+modes. Initially this includes:
+
+- `minimizer_type`
+
+Additional settings that apply to all fit modes can be added here later.
+Verbosity remains a call-level or project-level concern and does not
+need to be persisted in this category.
+
+**Single source of truth.** `Analysis.fitting_mode_type` is the only
+writable surface for the active mode, and the only place the mode is
+stored at runtime. The CIF field `_fitting.mode_type` (§8) is
+synthesized directly from `analysis.fitting_mode_type` at serialization
+time and applied back to the selector on load. There is no mirror
+descriptor on the `fitting` category. This keeps the runtime model free
+of duplicated state.
+
+### 2. Add an owner-level fitting-mode selector
+
+`Analysis` owns the fitting-mode selector, following the existing
+switchable-category style used by experiment categories.
+
+The selector name must start with the public category name. This mirrors
+`peak_profile_type` and `show_peak_profile_types()`: the category is
+`peak`, and the selected aspect is the peak profile. For fitting, the
+category is `fitting`, and the selected aspect is the fitting mode.
+
+```python
+project.analysis.show_fitting_mode_types()
+project.analysis.fitting_mode_type = 'sequential'
+```
+
+The selector is backed by `FitModeEnum` and accepts:
+
+- `single`
+- `joint`
+- `sequential`
+
+`show_fitting_mode_types()` should show all fitting modes, mark the
+current mode, and describe the execution requirements for each mode. It
+should not hide `sequential` simply because the project currently has
+only one experiment. Sequential fitting uses one template experiment
+plus files from `sequential_fit.data_dir`, so filtering it out based on
+experiment count is misleading.
+
+The selector changes the active fit mode and controls which
+mode-specific public categories are visible and serialized.
+
+Note that this is **not** the same mechanism as `peak_profile_type`.
+`peak_profile_type` swaps the concrete class behind a single category
+(`peak`); `fitting_mode_type` swaps which _sibling_ category
+(`joint_fit` / `sequential_fit`) is active and visible. The `fitting`
+category itself does not change shape. This is a new pattern — call it
+the **active-sibling selector** — and it is documented here as a
+first-class convention for owners that gate sibling categories on a
+run-time choice. Future categories with the same shape should follow the
+same naming and lifecycle rules.
+
+### 3. Keep mode-specific categories as flat Analysis siblings
+
+Mode-specific configuration lives in direct children of `Analysis`.
+These categories are not nested under `fitting`.
+
+Public API:
+
+```python
+project.analysis.fitting_mode_type = 'joint'
+project.analysis.joint_fit.create(experiment_id='sepd', weight=0.7)
+project.analysis.joint_fit.create(experiment_id='nomad', weight=0.3)
+project.analysis.fit()
+```
+
+```python
+project.analysis.fitting_mode_type = 'sequential'
+project.analysis.sequential_fit.data_dir = 'data/d20_scan'
+project.analysis.sequential_fit.file_pattern = '*.xye'
+project.analysis.sequential_fit.max_workers = 'auto'
+project.analysis.sequential_fit.reverse = True
+project.analysis.fit()
+```
+
+There is no `single_fit` category initially because single fitting has
+no mode-specific persisted settings. If single-mode settings are added
+later, they should be placed in a `single_fit` category using the same
+pattern.
+
+### 4. Replace `joint_fit_experiments` with `joint_fit`
+
+The user-facing joint-mode category is named `joint_fit`.
+
+It is a collection keyed by experiment id. Each item contains:
+
+- `experiment_id`
+- `weight`
+
+Suggested API:
+
+```python
+project.analysis.joint_fit.create(experiment_id='sepd', weight=0.7)
+project.analysis.joint_fit.create(experiment_id='nomad', weight=0.3)
+project.analysis.joint_fit['sepd'].weight = 0.8
+```
+
+When joint mode is active, `joint_fit` represents the experiments
+participating in the joint fit. Auto-population and validation are
+specified deterministically:
+
+- On `fit()` in joint mode, any project experiment without a
+  corresponding `joint_fit` row is added automatically with
+  `weight = 1.0`.
+- A `joint_fit` row whose `experiment_id` does not match any project
+  experiment raises an error before fitting starts. It is not silently
+  pruned, because that would mask user typos.
+- Switching `fitting_mode_type` to `joint` does **not** auto-populate.
+  Auto-population happens only at execution time so that intermediate
+  configuration states are never silently mutated.
+
+Execution requirements for joint fitting:
+
+- at least two project experiments
+- every joint-fit row references an existing experiment
+- every participating experiment has one weight (after auto-population)
+
+Weights remain relative weights. The fitting implementation may
+normalize them internally.
+
+### 5. Add a `sequential_fit` category
+
+The user-facing sequential-mode category is named `sequential_fit`.
+
+It is a single-item category with these persisted fields:
+
+- `data_dir`
+- `file_pattern`
+- `max_workers`
+- `chunk_size`
+- `reverse`
+
+Suggested defaults:
+
+- `data_dir`: unset, required before execution
+- `file_pattern`: `*`
+- `max_workers`: `1`
+- `chunk_size`: unset, resolved from `max_workers` at runtime
+- `reverse`: `false`
+
+`max_workers` accepts either a positive integer or the token `auto`. It
+is stored as a single descriptor and normalized to a positive integer by
+a runtime resolver before being passed to the worker pool; consumers
+never see the raw `auto` token. Whether the descriptor type is a
+dedicated union descriptor or a string descriptor with validation is an
+implementation detail.
+
+`chunk_size` allows an unset value and serializes that unset value as
+CIF null (`.`).
+
+Relative `data_dir` values are resolved relative to the project
+directory when the project has a saved path. For an unsaved project,
+`fit()` raises a clear error if `data_dir` is relative — sequential
+fitting requires either a saved project or an absolute `data_dir`. This
+keeps saved projects portable across Python and CLI workflows and
+rejects the ambiguous CWD-dependent case explicitly.
+
+`reverse` is represented by a boolean descriptor. If the current
+descriptor layer has no dedicated boolean descriptor, one is introduced
+rather than storing boolean state as an arbitrary string. (Introducing a
+boolean descriptor is a small prerequisite for this ADR; the
+implementation plan should call it out separately.)
+
+Execution requirements for sequential fitting:
+
+- exactly one project structure
+- exactly one template project experiment
+- project has a saved path
+- at least one free parameter
+- `sequential_fit.data_dir` is set and resolves to input data files
+
+### 6. Add a `sequential_fit_extract` category
+
+Sequential fits often need per-file metadata such as temperature,
+pressure, field strength, or other scan coordinates. This information is
+scientifically important and is also used by parameter-series plots.
+
+The current `extract_diffrn` callback solves this in Python notebooks,
+but a Python callable cannot be serialized in a portable way or invoked
+from the generic CLI. Replace that callback with a persisted extraction
+rule collection named `sequential_fit_extract`.
+
+Suggested API:
+
+```python
+project.analysis.sequential_fit_extract.create(
+    id='temperature',
+    target='diffrn.ambient_temperature',
+    pattern=r'^TEMP\s+([0-9.]+)',
+)
+project.analysis.sequential_fit_extract.create(
+    id='pressure',
+    target='diffrn.ambient_pressure',
+    pattern=r'^PRESSURE\s+([0-9.]+)',
+)
+```
+
+Each extraction rule contains:
+
+- `id`
+- `target`
+- `pattern`
+- `required`
+
+`target` is a descriptor path relative to the template experiment, for
+example `diffrn.ambient_temperature`. It replaces the weaker name
+`param_suffix`, because the rule is assigning to a known descriptor and
+to a known output CSV column. Initial supported targets are numeric
+descriptors under `experiment.diffrn`. `target` is validated at
+`create()` time against the current template experiment and rejected if
+it does not resolve to a writable numeric descriptor; this fails fast
+rather than waiting until the first sequential run.
+
+`pattern` is a regular expression applied **line by line** to the input
+data file (`re.search` on each line until the first match). The regex
+must contain exactly one capture group, and the captured text must be
+convertible to `float`. To bound worst-case behaviour on untrusted CIF
+input, patterns are validated at `create()` time and rejected if they
+contain backreferences or nested quantifiers; a future revision may
+adopt a timeout-based engine.
+
+`required` controls failure behavior. If `required` is false and the
+pattern is not found, the target value is left empty for that file. If
+`required` is true, the file result is marked failed with a clear error.
+
+Extracted values are written to `analysis/results.csv` under the column
+name `diffrn.<field>` (dots are preserved). Downstream consumers such as
+`display.fit.series(...)` must use that exact column name.
+
+The corresponding CIF fragment is:
+
+```cif
+loop_
+_sequential_fit_extract.id
+_sequential_fit_extract.target
+_sequential_fit_extract.pattern
+_sequential_fit_extract.required
+temperature diffrn.ambient_temperature "^TEMP\s+([0-9.]+)" false
+pressure    diffrn.ambient_pressure    "^PRESSURE\s+([0-9.]+)" false
+```
+
+During sequential fitting, each worker should:
+
+1. load the data file
+2. apply all `sequential_fit_extract` rules
+3. assign extracted values to the target descriptors on the worker
+   experiment
+4. include those values in the result row as `diffrn.<field>` columns
+5. run the fit
+
+Dataset replay should also apply `diffrn.*` values from
+`analysis/results.csv` back onto the template experiment. This keeps:
+
+```python
+temperature = 'diffrn.ambient_temperature'
+project.display.fit.series(param, versus=temperature)
+```
+
+working after a sequential fit and after reloading a saved project.
+
+A runtime-only Python hook may still be useful for advanced notebook
+workflows, but it is not part of the persisted CLI-ready contract
+defined by this ADR.
+
+### 7. Make help output instance-aware
+
+Help rendering should support instance-aware filtering through a common
+hook rather than special-casing `Analysis` alone. Class-level MRO
+discovery remains the default, but an object may filter or extend the
+properties and methods shown by `help()` based on its current state.
+
+The implementation should add an optional help-filter hook to the common
+help path used by `render_object_help()`, `GuardedBase.help()`, and
+`CategoryItem.help()`. The exact function names can be chosen during
+implementation, but the contract is:
+
+- The hook can only **hide** members that class-MRO discovery already
+  produced; it cannot inject members that do not exist on the class.
+  This keeps `help()` consistent with `dir()` and IDE completion.
+- Hidden members remain programmatically accessible. Direct attribute
+  access to an inactive mode-specific category (e.g. reading
+  `analysis.sequential_fit` while in `joint` mode) returns the
+  underlying object unchanged. Mutating it does not raise, but its
+  values are not serialized while the mode is inactive (§8). This avoids
+  surprising errors in notebooks where a user is iterating on
+  configuration before switching modes.
+
+This hook is useful beyond fitting. Any object with conditional workflow
+surfaces, backend-dependent options, or selected-type-specific
+categories can use the same mechanism later.
+
+For this ADR, `Analysis.help()` must use instance-aware filtering. It
+should not only inspect class-level properties because mode-specific
+categories are conditional workflow surfaces.
+
+The help output should show common analysis properties and only the
+category relevant to the active fit mode.
+
+For `single` mode, help should show fitting configuration and the
+`fit()` operation, but no joint or sequential category:
+
+```text
+Properties
+fitting
+display
+
+Methods
+fit()
+show_fitting_mode_types()
+```
+
+For `joint` mode, help should additionally show:
+
+```text
+joint_fit
+```
+
+For `sequential` mode, help should additionally show:
+
+```text
+sequential_fit
+sequential_fit_extract
+```
+
+Inactive mode categories should not be advertised in help output. Direct
+access to an inactive mode category may either raise a clear mode error
+or remain an internal implementation detail, but the public discovery
+surface should only show categories relevant to the selected mode.
+
+### 8. Serialize common and active mode-specific categories
+
+Persist common fitting configuration in `analysis/analysis.cif` using a
+category name that matches the new Python category:
+
+```cif
+_fitting.minimizer_type "lmfit (leastsq)"
+_fitting.mode_type sequential
+```
+
+Persist only the active mode-specific category.
+
+Sequential example:
+
+```cif
+_fitting.minimizer_type "lmfit (leastsq)"
+_fitting.mode_type sequential
+
+_sequential_fit.data_dir "data/d20_scan"
+_sequential_fit.file_pattern "*.xye"
+_sequential_fit.max_workers auto
+_sequential_fit.chunk_size .
+_sequential_fit.reverse true
+
+loop_
+_sequential_fit_extract.id
+_sequential_fit_extract.target
+_sequential_fit_extract.pattern
+_sequential_fit_extract.required
+temperature diffrn.ambient_temperature "^TEMP\s+([0-9.]+)" false
+```
+
+Joint example:
+
+```cif
+_fitting.minimizer_type "lmfit (leastsq)"
+_fitting.mode_type joint
+
+loop_
+_joint_fit.experiment_id
+_joint_fit.weight
+sepd  0.7
+nomad 0.3
+```
+
+Single example:
+
+```cif
+_fitting.minimizer_type "lmfit (leastsq)"
+_fitting.mode_type single
+```
+
+Inactive mode-specific categories should not be serialized. This avoids
+stale settings from a previously selected mode affecting CLI behavior
+after reload. Because `sequential_fit_extract` is part of the sequential
+workflow, it is serialized only when the active fitting mode is
+`sequential`.
+
+### 9. Restore mode before mode-specific settings
+
+Deserialization order must be:
+
+1. restore the common `fitting` category
+2. read `_fitting.mode_type`
+3. set `analysis.fitting_mode_type`
+4. restore the active mode-specific category, if present
+5. restore active child collections such as `sequential_fit_extract`
+6. restore other analysis categories such as aliases and constraints
+
+This mirrors the switchable-category restoration pattern used by
+experiment categories: the active mode is known before mode-specific
+fields are loaded.
+
+### 10. The CLI runs the configured fit mode
+
+The top-level CLI `fit` command should load the project and execute:
+
+```python
+project.analysis.fit()
+```
+
+It should not need a separate `fit-sequential` command for the normal
+case. Sequential mode can be run from CLI because its required settings
+are persisted in `analysis/analysis.cif`.
+
+CLI options may override saved configuration for one invocation, for
+example `--fitting-mode`, `--data-dir`, or `--max-workers`, but the core
+model is that the saved project contains the selected mode and its
+mode-specific settings. CLI overrides are **per-invocation only** and
+are never written back to the project on disk. Persisting a new mode or
+new settings requires an explicit save step.
+
+## Consequences
+
+### Positive
+
+- `fit()` has one meaning: execute fitting.
+- `fitting` has one meaning: common fitting configuration.
+- Fit modes follow the same owner-level selection style as existing
+  switchable categories.
+- `joint_fit` and `sequential_fit` are visible only when relevant.
+- Sequential fitting becomes runnable from CLI without a special Python
+  method call.
+- Sequential scan metadata becomes serializable and CLI-friendly through
+  `sequential_fit_extract`.
+- Instance-aware help becomes a reusable capability for conditional
+  public surfaces.
+- CIF structure is flat, explicit, and aligned with public API names.
+- Mode-specific configuration can grow independently without polluting
+  the common fitting category.
+
+### Trade-offs
+
+- Help rendering needs an instance-aware extension hook instead of pure
+  class-MRO discovery.
+- Switching fit mode changes the visible public surface of `Analysis`,
+  which requires clear help and error messages.
+- The new API intentionally breaks the current `analysis.fit` category
+  and `fit_sequential(...)` method shape.
+- Regex extraction rules cover common file-header metadata but are less
+  flexible than an arbitrary Python callback.
+
+### Compatibility
+
+This ADR does not require runtime compatibility aliases. Consistent with
+the project's beta-stage policy of no legacy shims, loading an old
+project that still contains `_fit.minimizer_type`, `_fit.mode`, or
+`_joint_fit_experiments.*` raises a clear error pointing at the new CIF
+names. There is no silent auto-migration on load.
+
+The following public API shapes are replaced by the new design:
+
+- `project.analysis.fit.minimizer_type`
+- `project.analysis.fit.mode`
+- `project.analysis.fit_sequential(...)`
+- `project.analysis.joint_fit_experiments`
+
+The replacement API is:
+
+- `project.analysis.fitting.minimizer_type`
+- `project.analysis.fitting_mode_type`
+- `project.analysis.joint_fit`
+- `project.analysis.sequential_fit`
+- `project.analysis.sequential_fit_extract`
+
+The `project.analysis.fit()` spelling remains, but it changes from a
+callable category invocation to a real `Analysis` method.
+
+## Alternatives Considered
+
+### Name the selector `fit_mode_type`
+
+Rejected.
+
+`fit_mode_type` and `show_fit_mode_types()` would imply a public
+`fit_mode` category, which does not exist. The project convention is
+that switchable-category selector names begin with the public category
+name (`peak_profile_type` for `peak`, `background_type` for
+`background`). For a selector hosted on `Analysis` that gates the
+`fitting`-related siblings, `fitting_mode_type` is the closest fit.
+
+### Mirror `atom_site_aniso`: always present, auto-synced, empty when inactive
+
+Rejected for fit modes (see _Context \u2014 Precedent_ for the
+comparison).
+
+`atom_site_aniso` keeps the category always visible and derives its
+contents from a per-atom selector. Applying the same shape to fit modes
+would mean `joint_fit`, `sequential_fit`, and `sequential_fit_extract`
+always appear in `help()` and CIF, with auto-sync rules that try to
+reconcile them with `fitting_mode_type`.
+
+This is rejected because:
+
+- The mode-specific categories carry independent user-edited state that
+  cannot be derived from any other object.
+- Three always-visible mutually exclusive categories make `help()`
+  output and CIF files harder to read than a single active sibling.
+- For CLI workflows, \"empty category\" and \"unused mode\" must be
+  distinguishable on reload; explicit serialization of only the active
+  category preserves that distinction.
+
+The precedent is still informative: `atom_site_aniso` shows that the
+codebase accepts non-uniform category visibility patterns when they fit
+the underlying data model. The active-sibling pattern introduced here is
+the right tool for an owner-level mode selector.
+
+### Keep `analysis.fit` as a callable category
+
+Rejected.
+
+It keeps the current naming ambiguity where `fit` is both a category and
+an operation. It also leaves no clean place for mode-specific
+configuration without either nesting categories under `fit` or adding
+always-visible sibling categories.
+
+### Put all mode-specific settings into `fitting`
+
+Rejected.
+
+This would make `fitting` contain `data_dir`, `max_workers`, and
+joint-fit weights even when those fields do not apply to the active
+mode. It weakens help output and makes CIF harder to read.
+
+### Use `analysis.fitting.mode` as the public selector
+
+Rejected for the public API.
+
+Although `_fitting.mode_type` is the CIF spelling, the public selector
+should follow the existing switchable-category owner style:
+
+```python
+project.analysis.fitting_mode_type = 'sequential'
+```
+
+A separate `fitting.mode` descriptor on the runtime `fitting` category
+is also rejected: it would duplicate state already held by
+`fitting_mode_type`. `_fitting.mode_type` is synthesized at
+serialization time instead of being mirrored on a runtime object.
+
+### Replace the `fitting` category object per fit mode
+
+Rejected.
+
+A concrete `SequentialFitting` object could expose sequential fields
+directly, but switching by assigning a property on the object being
+replaced creates stale-reference hazards:
+
+```python
+fitting = project.analysis.fitting
+project.analysis.fitting_mode_type = 'sequential'
+# fitting may now point to the old object
+```
+
+Keeping `fitting` stable and adding active sibling mode categories gives
+better long-term API stability.
+
+### Persist inactive mode-specific categories
+
+Rejected.
+
+Persisting inactive categories would make saved projects ambiguous for
+CLI workflows. The selected mode should determine which mode-specific
+category is authoritative.
+
+## Follow-up Questions
+
+The core design in this ADR is implemented. The questions below are
+follow-up design topics that may need future ADRs if behaviour changes.
+
+### Architectural / API
+
+- **Direct access to inactive mode categories.** \u00a77 specifies the
+  lenient behaviour: reading `analysis.sequential_fit` in `joint` mode
+  returns the underlying object, mutation does not raise, but values are
+  not serialized. Open: is this the right trade-off, or should access
+  raise a `ModeError` to prevent silent data loss on save?
+
+### Data model
+
+- **`joint_fit` and experiment lifecycle.** Stale rows raise at `fit()`
+  time. Open: should `joint_fit` also listen for experiment-collection
+  changes and prune (or warn) on experiment deletion, or remain passive
+  until execution?
+- **`joint_fit` weight bounds.** Default weight is `1.0`. Open: is
+  `weight = 0` allowed (effective exclusion), and what is the upper
+  bound, if any? Should weights share the validator used by free
+  parameters?
+- **`sequential_fit_extract` target scope.** Targets are initially
+  numeric descriptors under `experiment.diffrn`. Open:
+  - are nested descriptors (`diffrn.foo.bar`) allowed, or only one
+    level?
+  - is the same target reachable from two rules (last wins, error, or
+    merge)?
+  - how is the supported-prefix list extended when new
+    sample-environment categories appear?
+- **`sequential_fit_extract` failure aggregation.** A failed `required`
+  rule marks the file failed. Open: does one failure abort the whole
+  sequential run, just exclude that file from results, or apply a
+  max-failure threshold?
+- **Extraction caching.** Files may be re-read on resume or partial
+  replay. Open: is extraction re-run each time, or cached alongside
+  results in `analysis/results.csv`?
+
+### Persistence & CLI
+
+- **CIF round-trip for `auto` `max_workers`.** The on-disk value is the
+  token `auto`. Open: when CLI overrides resolve `auto` to a concrete
+  integer for one run, is that integer ever written back, or is the
+  token always preserved on disk regardless of runtime resolution?
+- **Serialization order for `_fitting.*`.** \u00a79 specifies
+  deserialization order. Open: pin serialization order too (mode first,
+  then `minimizer_type`, then mode-specific siblings) so generated files
+  are stable for diffing?
+- **Failure mid-sequential-run.** Open: if `fit()` fails partway through
+  a sequential scan, what is the state of `analysis/results.csv` and the
+  persisted `sequential_fit` \u2014 resumable, discarded, or left as-is
+  for manual recovery?
+- **CLI override of `sequential_fit_extract`.** Overrides are listed for
+  `--fitting-mode`, `--data-dir`, `--max-workers`. Open: are extraction
+  rules overridable from the CLI (for example
+  `--extract id=temperature:target=...:pattern=...`), or only via the
+  project file?
+
+### Help & discovery
+
+- **`dir()` consistency.** The hook hides members from `help()` only.
+  Open: should `dir(analysis)` likewise hide inactive categories, or
+  always reflect the full class surface (affects tab completion)?
+
+### Scope
+
+- **`single` mode \"future-proofing.\"** \u00a73 leaves `single_fit` as
+  optional future work. Open: is there any current setting that would
+  qualify \u2014 for example, per-experiment selection when a project
+  contains multiple experiments and the user wants to run `single`
+  against one of them?
+- **Migration error timing.** Compatibility says loading old CIF raises.
+  Open: does \"raises\" mean at load of `project.cif`, or at first
+  access of `analysis`? This affects how users discover the break and
+  whether a project can be partially loaded for inspection.
+
+## Deferred Work
+
+- Optional `single_fit` category if single-mode-specific settings are
+  introduced.
+- A separate ADR for changing switchable category selectors globally
+  from owner-level names such as `peak_profile_type` toward
+  category-owned selectors such as `peak.profile_type`.
diff --git a/docs/dev/Issues/issues_open.md b/docs/dev/Issues/issues_open.md
index 8805fc8fb..3e961f1cc 100644
--- a/docs/dev/Issues/issues_open.md
+++ b/docs/dev/Issues/issues_open.md
@@ -14,14 +14,14 @@ needed.
 
 **Type:** Fragility
 
-`joint_fit_experiments` is created once when `fit.mode` becomes
-`'joint'`. If experiments are added, removed, or renamed afterwards, the
-weight collection is stale. Joint fitting can fail with missing keys or
-run with incorrect weights.
+`joint_fit` is created once when `fit.mode` becomes `'joint'`. If
+experiments are added, removed, or renamed afterwards, the weight
+collection is stale. Joint fitting can fail with missing keys or run
+with incorrect weights.
 
-**Fix:** rebuild or validate `joint_fit_experiments` at the start of
-every joint fit. At minimum, `fit()` should assert that the weight keys
-exactly match `project.experiments.names`.
+**Fix:** rebuild or validate `joint_fit` at the start of every joint
+fit. At minimum, `fit()` should assert that the weight keys exactly
+match `project.experiments.names`.
 
 **Depends on:** nothing.
 
@@ -32,7 +32,7 @@ exactly match `project.experiments.names`.
 **Type:** Consistency
 
 `Analysis` owns categories (`Aliases`, `Constraints`,
-`JointFitExperiments`) but does not extend `DatablockItem`. Its ad-hoc
+`JointFitCollection`) but does not extend `DatablockItem`. Its ad-hoc
 `_update_categories()` iterates over a hard-coded list and does not
 participate in standard category discovery, parameter enumeration, or
 CIF serialisation.
@@ -161,6 +161,156 @@ is needed.
 
 ---
 
+## 15. 🟡 Decide Whether Inactive Fit-Mode Categories Stay Lenient
+
+**Type:** API design
+
+`Analysis` currently allows direct access to inactive mode-specific
+categories such as `joint_fit` or `sequential_fit`. The values remain
+editable, but inactive sections are hidden from help and dropped during
+serialization.
+
+**Fix:** confirm whether this lenient access is the long-term contract,
+or replace it with a dedicated mode error to prevent silent state loss
+on save.
+
+**Depends on:** nothing.
+
+---
+
+## 16. 🟡 Clarify `joint_fit` Lifecycle Outside Execution
+
+**Type:** Fragility
+
+`joint_fit` is validated and auto-populated at `fit()` time, but it does
+not react when experiments are later renamed or removed.
+
+**Fix:** decide whether `joint_fit` should stay passive until execution,
+or listen for experiment lifecycle changes and prune or warn earlier.
+
+**Depends on:** nothing.
+
+---
+
+## 17. 🟡 Define `joint_fit.weight` Bounds
+
+**Type:** Data model
+
+Joint-fit rows currently allow any non-negative weight, but the public
+contract is still unclear about whether `0` means exclusion and whether
+an upper bound should exist.
+
+**Fix:** define the supported range and validator semantics for
+`joint_fit.weight`.
+
+**Depends on:** nothing.
+
+---
+
+## 18. 🟡 Define `sequential_fit_extract` Target Scope
+
+**Type:** Data model
+
+Sequential extract rules currently target one numeric descriptor under
+`experiment.diffrn`. Open questions remain around nested targets,
+duplicate rules writing the same target, and how additional supported
+prefixes should be introduced when new environment categories appear.
+
+**Fix:** pin the allowed target grammar and duplicate-target behaviour
+in architecture and validation rules.
+
+**Depends on:** nothing.
+
+---
+
+## 19. 🟡 Decide Sequential Extraction Failure Policy
+
+**Type:** Runtime behaviour
+
+Today a failed required extract rule marks that file as failed and the
+run continues. The overall aggregation policy is still undefined.
+
+**Fix:** decide whether one failed file should abort the whole run,
+remain an isolated row-level failure, or count toward a configurable
+failure threshold.
+
+**Depends on:** nothing.
+
+---
+
+## 20. 🟢 Decide Whether Sequential Extraction Should Be Cached
+
+**Type:** Performance
+
+Sequential metadata extraction currently re-reads input files when the
+run is repeated or resumed.
+
+**Fix:** decide whether extracted `diffrn.*` values should be cached in
+`analysis/results.csv` only, or also in a dedicated reusable cache.
+
+**Depends on:** nothing.
+
+---
+
+## 21. 🟢 Decide How Mid-Run Sequential Failures Persist
+
+**Type:** Recovery design
+
+If a sequential fit fails partway through, the recovery and persistence
+contract for `analysis/results.csv` is not fully specified.
+
+**Fix:** define whether partial CSV output is authoritative for resume,
+left untouched for manual recovery, or replaced on the next run.
+
+**Depends on:** nothing.
+
+---
+
+## 22. 🟢 Decide Whether CLI Should Override Extract Rules
+
+**Type:** CLI design
+
+The CLI can override mode and worker settings, but persisted
+`sequential_fit_extract` rules are not yet overridable from the command
+line.
+
+**Fix:** decide whether extraction rules stay project-file-only or gain
+an explicit CLI override syntax.
+
+**Depends on:** nothing.
+
+---
+
+## 23. 🟢 Align `dir()` With Help Filtering
+
+**Type:** Discoverability
+
+`help()` now hides inactive analysis categories by fitting mode, while
+`dir()` and tab completion still expose the full class surface.
+
+**Fix:** decide whether `dir()` should mirror the help filter or remain
+an always-complete developer surface.
+
+**Depends on:** nothing.
+
+---
+
+## 24. 🟢 Decide Whether `single_fit` Needs a Future Category
+
+**Type:** Scope planning
+
+Single mode currently has no dedicated persisted category. Future
+single-mode settings could require one, but the threshold is not yet
+defined.
+
+**Fix:** decide what concrete single-mode behaviour would justify a
+`single_fit` category instead of keeping the mode configuration on the
+owner only.
+
+**Depends on:** nothing.
+
+---
+
 ## 15. 🟡 Validate Joint-Fit Weights Before Residual Normalisation
 
 **Type:** Correctness
@@ -784,18 +934,18 @@ formatting for `StringDescriptor` values.
 
 ---
 
-## 46. 🟢 Rename `JointFitExperiments` ID and Improve Descriptions
+## 46. 🟢 Improve `JointFitItem` Descriptions
 
 **Type:** Naming
 
-`JointFitExperiments` uses `name='id'` with a TODO suggesting a better
-name, and two description fields are incomplete.
+`JointFitItem` uses `name='experiment_id'`, but two description fields
+are still incomplete.
 
 **TODOs:**
 
-- [default.py](src/easydiffraction/analysis/categories/joint_fit_experiments/default.py#L33)
-- [default.py](src/easydiffraction/analysis/categories/joint_fit_experiments/default.py#L34)
-- [default.py](src/easydiffraction/analysis/categories/joint_fit_experiments/default.py#L43)
+- [default.py](src/easydiffraction/analysis/categories/joint_fit/default.py#L31)
+- [default.py](src/easydiffraction/analysis/categories/joint_fit/default.py#L32)
+- [default.py](src/easydiffraction/analysis/categories/joint_fit/default.py#L41)
 
 **Depends on:** nothing.
 
@@ -1487,6 +1637,72 @@ operation is possible (e.g. in automated pipelines or tests).
 
 ---
 
+## 93. 🟡 Eliminate Flicker in Live Progress Tables
+
+**Type:** UX
+
+The shared `ActivityIndicator` / Rich `Live` region used by single fit,
+sequential fit, and DREAM sampling visibly flickers in terminals
+whenever the live renderable grows (new rows appended) or is updated at
+a moderate rate. The effect is most pronounced in sequential fit because
+rows are added more frequently than in single fit.
+
+**Findings from current investigation:**
+
+- Both single fit (`FitProgressTracker._refresh_activity_indicator`) and
+  sequential fit (`_report_chunk_progress`) push a fresh
+  `build_table_renderable(...)` into
+  `ActivityIndicator.update(content=...)` on each progress event. The
+  Rich `Table` instance is rebuilt from scratch every time.
+- `_TerminalLiveHandle` / `ActivityIndicator` start `rich.live.Live`
+  with `auto_refresh=True`,
+  `refresh_per_second=1/_SPINNER_FRAME_SECONDS` (≈10 Hz), and
+  `vertical_overflow='visible'`. At every refresh tick, Rich re-renders
+  the full multi-line region (table + spinner line), which on many
+  terminals causes a visible flicker that scales with row count.
+- Earlier attempts to mitigate this in sequential fit by switching to a
+  single-line spinner-only `Live` and printing rows above it (so Rich's
+  print-above-live mechanism handled them) removed flicker entirely, but
+  produced a different visual style from single fit and could not show
+  the closing border during the run. That approach was reverted for
+  consistency with single fit; flicker came back with it.
+- `vertical_overflow='visible'` is required so the growing table is not
+  clipped, but it also forces Rich to repaint the whole region rather
+  than scroll/append.
+- The spinner animation itself drives the refresh rate; lowering
+  `refresh_per_second` reduces flicker frequency but makes the spinner
+  feel sluggish.
+- Single fit appears smoother in practice mainly because content changes
+  are throttled (`FIT_PROGRESS_UPDATE_SECONDS = 5.0`) and rows grow
+  slowly; the underlying mechanism is the same and it still flickers
+  when many iterations are appended quickly.
+
+**Possible directions (not yet evaluated):**
+
+- Decouple spinner refresh from content refresh: drive `Live` at a low
+  `refresh_per_second` (e.g. 2–4 Hz) and update content explicitly only
+  when a new row arrives, while animating the spinner via the label
+  string rather than Rich's renderable diff.
+- Render the table once as static `console.print(...)` above a
+  single-line spinner-only `Live`, and re-print only the _new_ row(s) on
+  each update — restore the streaming approach but emit the bottom
+  border at the end (accept the trade-off that the closing border is not
+  visible during the run, or print it as part of every update with ANSI
+  cursor movement).
+- Use `rich.live.Live(transient=False, auto_refresh=False)` and call
+  `live.refresh()` manually only when content changes; let the spinner
+  animate via a separate background timer or label updates.
+- Investigate `rich.progress.Progress` with custom columns and a table
+  panel — Rich has optimised diff rendering there.
+- Evaluate the actual cause on macOS Terminal / iTerm2 / VS Code
+  terminal separately — flicker behaviour differs across emulators.
+
+**Depends on:** nothing. Affects single fit, sequential fit, and DREAM
+sampler progress displays — any fix should keep their visuals consistent
+(issue #93 should be solved for all three at once).
+
+---
+
 ## Summary
 
 | #   | Issue                                            | Severity | Type                         |
@@ -1529,7 +1745,7 @@ operation is possible (e.g. in automated pipelines or tests).
 | 43  | Fix summary display inconsistencies              | 🟢 Low   | UX                           |
 | 44  | Merge parameter record construction              | 🟢 Low   | Cleanup                      |
 | 45  | Decide alias/constraint descriptor default       | 🟢 Low   | Design                       |
-| 46  | Rename `JointFitExperiments` id + descriptions   | 🟢 Low   | Naming                       |
+| 46  | Improve `JointFitItem` descriptions              | 🟢 Low   | Naming                       |
 | 47  | Improve error handling in crystallography        | 🟢 Low   | Diagnostics                  |
 | 48  | Fix CrysPy TOF instrument default                | 🟢 Low   | Bug workaround               |
 | 49  | Automate space group CIF name variants           | 🟢 Low   | Maintainability              |
@@ -1575,3 +1791,4 @@ operation is possible (e.g. in automated pipelines or tests).
 | 90  | Show experiment number during sequential fitting | 🟢 Low   | UX                           |
 | 91  | Disable TODO checks in CodeFactor PRs            | 🟢 Low   | CI / Tooling                 |
 | 92  | Make `save()` respect verbosity                  | 🟢 Low   | UX                           |
+| 93  | Eliminate flicker in live progress tables        | 🟡 Med   | UX                           |
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 7cc3abe92..d6a63b6d5 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -242,6 +242,34 @@ arguments so they can be used as mixins safely (e.g.
 | `MembershipValidator` | Value must be in an allowed set        |
 | `RegexValidator`      | Value must match a pattern             |
 
+### 2.7 String Paths vs Live Descriptors in Public APIs
+
+Public APIs reference parameters in one of two ways. The choice is not
+stylistic — it follows the call site's role:
+
+- **Setup-time / schema-level APIs use string paths** (CIF-style
+  `'category.attribute'`). The targeted descriptor may not yet exist on
+  any concrete object (e.g. an extraction rule applies uniformly to
+  files about to be loaded), and the value must round-trip through CIF.
+  Examples:
+  `sequential_fit_extract.create(target='diffrn.ambient_temperature', ...)`,
+  alias/constraint definitions persisted in project CIF.
+- **Cross-experiment selectors use the same string paths at runtime.**
+  `project.display.fit.series(..., versus=...)` selects a persisted
+  `diffrn.*` column in `analysis/results.csv` and the matching field
+  across experiments; it does not use one experiment's current live
+  descriptor value. Example:
+  `project.display.fit.series(param=structure.cell.length_a, versus='diffrn.ambient_temperature')`.
+- **Concrete model parameters still use live descriptors.** The call
+  needs the parameter's `unique_name`, `description`, and `units`, and
+  it refers to one exact fitted quantity in the model. Example:
+  `param=structure.cell.length_a` in `project.display.fit.series(...)`.
+
+When adding a new public API, place it on one side of this rule rather
+than accepting both. Use string paths when the value names a persisted
+field or cross-experiment selector, and use live descriptors when the
+value names one concrete model parameter.
+
 ---
 
 ## 3. Experiment System
@@ -528,28 +556,28 @@ from .line_segment import LineSegmentBackground
 
 ### 5.5 All Factories
 
-| Factory                      | Domain                 | Tags resolve to                                                                                                                                                                                    |
-| ---------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `BackgroundFactory`          | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                                           |
-| `PeakFactory`                | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                                       |
-| `InstrumentFactory`          | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                                            |
-| `DataFactory`                | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                                              |
-| `ReflnFactory`               | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                                            |
-| `ExtinctionFactory`          | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                                          |
-| `LinkedCrystalFactory`       | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                                                    |
-| `ExcludedRegionsFactory`     | Excluded regions       | `ExcludedRegions`                                                                                                                                                                                  |
-| `LinkedPhasesFactory`        | Linked phases          | `LinkedPhases`                                                                                                                                                                                     |
-| `ExperimentTypeFactory`      | Experiment descriptors | `ExperimentType`                                                                                                                                                                                   |
-| `CellFactory`                | Unit cells             | `Cell`                                                                                                                                                                                             |
-| `SpaceGroupFactory`          | Space groups           | `SpaceGroup`                                                                                                                                                                                       |
-| `AtomSitesFactory`           | Atom sites             | `AtomSites`                                                                                                                                                                                        |
-| `AtomSiteAnisoFactory`       | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                                          |
-| `AliasesFactory`             | Parameter aliases      | `Aliases`                                                                                                                                                                                          |
-| `ConstraintsFactory`         | Parameter constraints  | `Constraints`                                                                                                                                                                                      |
-| `FitModeFactory`             | Fit-mode category      | `FitMode`                                                                                                                                                                                          |
-| `JointFitExperimentsFactory` | Joint-fit weights      | `JointFitExperiments`                                                                                                                                                                              |
-| `CalculatorFactory`          | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                                        |
-| `MinimizerFactory`           | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
+| Factory                  | Domain                 | Tags resolve to                                                                                                                                                                                    |
+| ------------------------ | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `BackgroundFactory`      | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                                           |
+| `PeakFactory`            | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                                       |
+| `InstrumentFactory`      | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                                            |
+| `DataFactory`            | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                                              |
+| `ReflnFactory`           | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                                            |
+| `ExtinctionFactory`      | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                                          |
+| `LinkedCrystalFactory`   | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                                                    |
+| `ExcludedRegionsFactory` | Excluded regions       | `ExcludedRegions`                                                                                                                                                                                  |
+| `LinkedPhasesFactory`    | Linked phases          | `LinkedPhases`                                                                                                                                                                                     |
+| `ExperimentTypeFactory`  | Experiment descriptors | `ExperimentType`                                                                                                                                                                                   |
+| `CellFactory`            | Unit cells             | `Cell`                                                                                                                                                                                             |
+| `SpaceGroupFactory`      | Space groups           | `SpaceGroup`                                                                                                                                                                                       |
+| `AtomSitesFactory`       | Atom sites             | `AtomSites`                                                                                                                                                                                        |
+| `AtomSiteAnisoFactory`   | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                                          |
+| `AliasesFactory`         | Parameter aliases      | `Aliases`                                                                                                                                                                                          |
+| `ConstraintsFactory`     | Parameter constraints  | `Constraints`                                                                                                                                                                                      |
+| `FitModeFactory`         | Fit-mode category      | `FitMode`                                                                                                                                                                                          |
+| `JointFitFactory`        | Joint-fit weights      | `JointFitCollection`                                                                                                                                                                               |
+| `CalculatorFactory`      | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                                        |
+| `MinimizerFactory`       | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
 
 > **Note:** `ExperimentFactory` and `StructureFactory` are _builder_
 > factories with `from_cif_path`, `from_cif_str`, `from_data_path`, and
@@ -724,41 +752,41 @@ line-segment points.
 
 #### CategoryCollections — factory-created (get all three)
 
-| Class                           | Factory                      |
-| ------------------------------- | ---------------------------- |
-| `LineSegmentBackground`         | `BackgroundFactory`          |
-| `ChebyshevPolynomialBackground` | `BackgroundFactory`          |
-| `PdCwlData`                     | `DataFactory`                |
-| `PdTofData`                     | `DataFactory`                |
-| `TotalData`                     | `DataFactory`                |
-| `ReflnData`                     | `ReflnFactory`               |
-| `PowderCwlReflnData`            | `ReflnFactory`               |
-| `PowderTofReflnData`            | `ReflnFactory`               |
-| `ExcludedRegions`               | `ExcludedRegionsFactory`     |
-| `LinkedPhases`                  | `LinkedPhasesFactory`        |
-| `AtomSites`                     | `AtomSitesFactory`           |
-| `AtomSiteAnisoCollection`       | `AtomSiteAnisoFactory`       |
-| `Aliases`                       | `AliasesFactory`             |
-| `Constraints`                   | `ConstraintsFactory`         |
-| `JointFitExperiments`           | `JointFitExperimentsFactory` |
+| Class                           | Factory                  |
+| ------------------------------- | ------------------------ |
+| `LineSegmentBackground`         | `BackgroundFactory`      |
+| `ChebyshevPolynomialBackground` | `BackgroundFactory`      |
+| `PdCwlData`                     | `DataFactory`            |
+| `PdTofData`                     | `DataFactory`            |
+| `TotalData`                     | `DataFactory`            |
+| `ReflnData`                     | `ReflnFactory`           |
+| `PowderCwlReflnData`            | `ReflnFactory`           |
+| `PowderTofReflnData`            | `ReflnFactory`           |
+| `ExcludedRegions`               | `ExcludedRegionsFactory` |
+| `LinkedPhases`                  | `LinkedPhasesFactory`    |
+| `AtomSites`                     | `AtomSitesFactory`       |
+| `AtomSiteAnisoCollection`       | `AtomSiteAnisoFactory`   |
+| `Aliases`                       | `AliasesFactory`         |
+| `Constraints`                   | `ConstraintsFactory`     |
+| `JointFitCollection`            | `JointFitFactory`        |
 
 #### CategoryItems that are ONLY children of collections (NO metadata)
 
-| Class                | Parent collection               |
-| -------------------- | ------------------------------- |
-| `LineSegment`        | `LineSegmentBackground`         |
-| `PolynomialTerm`     | `ChebyshevPolynomialBackground` |
-| `AtomSite`           | `AtomSites`                     |
-| `AtomSiteAniso`      | `AtomSiteAnisoCollection`       |
-| `PdCwlDataPoint`     | `PdCwlData`                     |
-| `PdTofDataPoint`     | `PdTofData`                     |
-| `TotalDataPoint`     | `TotalData`                     |
-| `Refln`              | `ReflnData`                     |
-| `LinkedPhase`        | `LinkedPhases`                  |
-| `ExcludedRegion`     | `ExcludedRegions`               |
-| `Alias`              | `Aliases`                       |
-| `Constraint`         | `Constraints`                   |
-| `JointFitExperiment` | `JointFitExperiments`           |
+| Class            | Parent collection               |
+| ---------------- | ------------------------------- |
+| `LineSegment`    | `LineSegmentBackground`         |
+| `PolynomialTerm` | `ChebyshevPolynomialBackground` |
+| `AtomSite`       | `AtomSites`                     |
+| `AtomSiteAniso`  | `AtomSiteAnisoCollection`       |
+| `PdCwlDataPoint` | `PdCwlData`                     |
+| `PdTofDataPoint` | `PdTofData`                     |
+| `TotalDataPoint` | `TotalData`                     |
+| `Refln`          | `ReflnData`                     |
+| `LinkedPhase`    | `LinkedPhases`                  |
+| `ExcludedRegion` | `ExcludedRegions`               |
+| `Alias`          | `Aliases`                       |
+| `Constraint`     | `Constraints`                   |
+| `JointFitItem`   | `JointFitCollection`            |
 
 #### Non-category classes — factory-created (get `type_info` only)
 
@@ -825,8 +853,8 @@ workflow:
   or `'sequential'`. `fit.show_minimizer_types()` lists supported
   minimizers; `fit.show_modes()` filters modes by experiment count (≤1 →
   only `single`; >1 → all three).
-- Joint-fit weights: `joint_fit_experiments` (`CategoryCollection` of
-  per-experiment weight entries); sibling of `fit`, not a child.
+- Joint-fit weights: `joint_fit` (`CategoryCollection` of per-experiment
+  weight entries); sibling of `fit`, not a child.
 - Fit results: `analysis.fit_results` stores the latest runtime result
   object. This is `FitResults` for deterministic fits and
   `BayesianFitResults` for Bayesian DREAM runs.
@@ -835,12 +863,12 @@ workflow:
   summary-style parameter displays intentionally hide the large
   loop-backed experiment categories `pd_data`, `total_data`, and `refln`
   in `all()`, `access()`, and `cif_uids()` so the output stays readable.
-- Fitting: `fit()` dispatches single/joint through the callable `fit`
-  category; `fit_sequential()` handles sequential mode (sets `fit.mode`
-  to `'sequential'` internally). `fit()` accepts optional `random_seed`
-  for stochastic minimizers; deterministic minimizers reject non-`None`
-  seeds. `display.fit_results()` dispatches through the active runtime
-  result object.
+- Fitting: `fitting.minimizer_type` stores the shared minimizer
+  selection; `fitting_mode_type` stores the active mode on `Analysis`
+  itself; `fit()` dispatches to the current mode using the persisted
+  sibling categories `joint_fit`, `sequential_fit`, and
+  `sequential_fit_extract`. `display.fit_results()` dispatches through
+  the active runtime result object.
 - Aliases and constraints (single-type categories; no public `_type`
   getter or setter)
 
@@ -859,8 +887,8 @@ new persisted results category.
   used for the run, including `random_seed`, `steps`, `burn`, `thin`,
   `pop`, and `parallel`.
 - The current user-facing DREAM controls live on the active minimizer
-  object, for example `project.analysis.fit.minimizer.steps`, `burn`,
-  `thin`, `pop`, `parallel`, and `init`.
+  object, for example `project.analysis.fitting.minimizer.steps`,
+  `burn`, `thin`, `pop`, `parallel`, and `init`.
 - `plot_param_correlations()` uses posterior samples when available and
   otherwise falls back to deterministic covariance or engine-derived
   correlations.
@@ -930,10 +958,10 @@ project_dir/
 `_rendering.*` engine preferences (`chart_engine`, `table_engine`), so a
 saved project re-opens with the same display backends. Per-experiment
 calculator selection (`_calculation.calculator_type`) lives in each
-experiment file, and fit configuration (`_fit.minimizer_type`,
-`_fit.mode`) lives in `analysis/analysis.cif`. Runtime fit outputs,
-including `analysis.fit_results`, posterior chains, posterior predictive
-summaries, and convergence diagnostics, are not serialized.
+experiment file, and fit configuration (`_fitting.minimizer_type`,
+`_fitting.mode_type`) lives in `analysis/analysis.cif`. Runtime fit
+outputs, including `analysis.fit_results`, posterior chains, posterior
+predictive summaries, and convergence diagnostics, are not serialized.
 
 ### 7.3 Verbosity
 
@@ -954,18 +982,19 @@ project.verbosity = 'short'
 ```
 
 **Resolution order:** methods that produce console output (e.g.
-`analysis.fit()`, `experiments.add_from_data_path()`) accept an optional
-`verbosity` keyword argument. When the argument is `None` (the default),
-the method reads `project.verbosity`. When a string is passed, it
-overrides the project-level setting for that single call.
+`analysis.fit()`, `experiments.add_from_data_path()`) read
+`project.verbosity`.
 
 ```python
 # Use project-level default for all operations
 project.verbosity = 'short'
 project.analysis.fit()                         # → short mode
 
-# Override for a single call
-project.analysis.fit(verbosity='silent')       # → silent, project stays short
+# Override for one fit, then restore the project default
+original_verbosity = project.verbosity
+project.verbosity = 'silent'
+project.analysis.fit()                         # → silent
+project.verbosity = original_verbosity
 ```
 
 **Output styles per level:**
@@ -1066,7 +1095,7 @@ project.experiments['hrpt'].linked_phases.create(id='lbco', scale=10.0)
 # Calculator is auto-resolved per experiment; override if needed
 project.experiments['hrpt'].calculation.show_calculator_types()
 project.experiments['hrpt'].calculation.calculator_type = 'cryspy'
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.minimizer_type = 'lmfit'
 
 # Plot before fitting
 project.display.pattern(expt_name='hrpt')
@@ -1095,14 +1124,14 @@ project.save()
 
 ```python
 # Deterministic pre-fit remains explicit
-project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fitting.minimizer_type = 'bumps (lm)'
 project.analysis.fit()
 
 # Switch to Bayesian sampling using the same entry point
-project.analysis.fit.minimizer_type = 'bumps (dream)'
-project.analysis.fit.minimizer.steps = 1000
-project.analysis.fit.minimizer.parallel = 0
-project.analysis.fit(random_seed=11)
+project.analysis.fitting.minimizer_type = 'bumps (dream)'
+project.analysis.fitting.minimizer.steps = 1000
+project.analysis.fitting.minimizer.parallel = 0
+project.analysis.fit()
 
 # Runtime-only Bayesian summaries and plots
 project.display.fit.results()
@@ -1221,13 +1250,18 @@ Single-type categories (no public `_type` property):
 - **Experiment:** `diffrn`, `linked_crystal`, `excluded_regions`,
   `linked_phases`.
 - **Structure:** `cell`, `space_group`, `atom_sites`, `atom_site_aniso`.
-- **Analysis:** `aliases`, `constraints`.
-
-`fit` is a dedicated analysis category. Its public selector surface is
-`fit.minimizer_type` and `fit.mode`; there is no separate owner-level
-proxy API. Likewise, `calculation` is a dedicated experiment category
-that owns calculator selection —
-`experiment.calculation.calculator_type` and
+- **Analysis:** `aliases`, `constraints`, `fitting`, `sequential_fit`.
+
+`fitting` is a dedicated analysis configuration category, but the fit
+mode selector lives on the owner as `analysis.fitting_mode_type`. This
+is the project's active-sibling selector pattern: the owner stores the
+authoritative mode and decides which sibling categories are active,
+shown in help, and serialized. `joint_fit`, `sequential_fit`, and
+`sequential_fit_extract` remain direct `Analysis` siblings even when
+inactive. See the fit-mode ADR for the full contract:
+[`adr_fit-mode-categories.md`](ADRs/adr_fit-mode-categories.md).
+Likewise, `calculation` is a dedicated experiment category that owns
+calculator selection — `experiment.calculation.calculator_type` and
 `experiment.calculation.show_calculator_types()` — instead of the
 selector being exposed at the experiment owner level. The same pattern
 applies to `display` on `Project`, which owns `chart_engine` and
@@ -1253,17 +1287,20 @@ recognises three distinct selector families. They share a similar
 `<name>_type` shape so the user can inspect and set them uniformly, but
 their intent and ownership differ:
 
-| Family                             | User intent                     | Examples                                                                      | CIF                                                                              |
-| ---------------------------------- | ------------------------------- | ----------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
-| Backend selector                   | Pick an execution backend       | `fit.minimizer_type`, `calculation.calculator_type`, `rendering.chart_engine` | `_fit.minimizer_type`, `_calculation.calculator_type`, `_rendering.chart_engine` |
-| Switchable-category impl. selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                  | category-owned type tag such as `_peak.profile_type`                             |
-| Semantic value selector            | Pick a scientific/analysis mode | `fit.mode`                                                                    | `_fit.mode`                                                                      |
-
-Backend selectors and semantic value selectors live on a dedicated
-configuration category (`fit`, `calculation`, `rendering`). Switchable-
-category implementation selectors are owned by the host (typically the
-experiment) because switching them replaces the category instance, as
-described in §9.3.
+| Family                             | User intent                     | Examples                                                                          | CIF                                                                                  |
+| ---------------------------------- | ------------------------------- | --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
+| Backend selector                   | Pick an execution backend       | `fitting.minimizer_type`, `calculation.calculator_type`, `rendering.chart_engine` | `_fitting.minimizer_type`, `_calculation.calculator_type`, `_rendering.chart_engine` |
+| Switchable-category impl. selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                      | category-owned type tag such as `_peak.profile_type`                                 |
+| Active-sibling selector            | Pick the active sibling surface | `analysis.fitting_mode_type`                                                      | owner-owned tag such as `_fitting.mode_type`                                         |
+
+Backend selectors live on a dedicated configuration category (`fitting`,
+`calculation`, `rendering`). Switchable-category implementation
+selectors are owned by the host (typically the experiment) because
+switching them replaces the category instance, as described in §9.3.
+Active-sibling selectors are also owner-level, but they do not swap one
+category implementation for another. Instead, they select which sibling
+category family is authoritative while the shared configuration category
+keeps a stable shape.
 
 ### 9.5 Discoverable Supported Options
 
@@ -1275,8 +1312,8 @@ expt.show_peak_profile_types()
 expt.show_background_types()
 expt.calculation.show_calculator_types()
 expt.show_extinction_types()
-project.analysis.fit.show_minimizer_types()
-project.analysis.fit.show_modes()
+project.analysis.fitting.show_minimizer_types()
+project.analysis.show_fitting_mode_types()
 project.rendering.show_chart_engines()
 project.rendering.show_table_engines()
 ```
@@ -1316,10 +1353,10 @@ but internal dispatch always uses the enum:
 
 ```python
 # ✅ Correct — compare with enum
-if self._fit.mode.value == FitModeEnum.JOINT:
+if self._fitting_mode_type is FitModeEnum.JOINT:
 
 # ❌ Wrong — compare with raw string
-if self._fit.mode.value == 'joint':
+if self._fitting_mode_type == 'joint':
 ```
 
 ### 9.7 Flat Category Structure — No Nested Categories
@@ -1344,30 +1381,29 @@ Owner
     └── CategoryB   ← WRONG: CategoryB is a child of CategoryA
 ```
 
-**Example — `fit` and `joint_fit_experiments`:** `fit` is a
-`CategoryItem` holding the active minimizer and fitting mode.
-`joint_fit_experiments` is a separate `CategoryCollection` holding
-per-experiment weights. Both are direct children of `Analysis`, not
-nested:
+**Example — `fit` and `joint_fit`:** `fit` is a `CategoryItem` holding
+the active minimizer and fitting mode. `joint_fit` is a separate
+`CategoryCollection` holding per-experiment weights. Both are direct
+children of `Analysis`, not nested:
 
 ```python
 # ✅ Correct — sibling categories on Analysis
-project.analysis.fit.mode = 'joint'
-project.analysis.joint_fit_experiments['npd'].weight = 0.7
+project.analysis.fitting_mode_type = 'joint'
+project.analysis.joint_fit['npd'].weight = 0.7
 
-# ❌ Wrong — joint_fit_experiments as a child of fit
-project.analysis.fit.joint_fit_experiments['npd'].weight = 0.7
+# ❌ Wrong — joint_fit as a child of fit
+project.analysis.fitting.joint_fit['npd'].weight = 0.7
 ```
 
 In CIF output, sibling categories appear as independent blocks:
 
 ```
-_fit.minimizer_type  lmfit
-_fit.mode            joint
+_fitting.mode_type       joint
+_fitting.minimizer_type  lmfit
 
 loop_
-_joint_fit_experiment.id
-_joint_fit_experiment.weight
+_joint_fit.experiment_id
+_joint_fit.weight
 npd  0.7
 xrd  0.3
 ```
@@ -1564,9 +1600,10 @@ Run `pixi run unit-tests-coverage` for a per-module report.
 
 ## 11. Issues
 
-- **Open:** [`issues_open.md`](issues_open.md) — prioritised backlog.
-- **Closed:** [`issues_closed.md`](issues_closed.md) — resolved items
-  for reference.
+- **Open:** [`issues_open.md`](Issues/issues_open.md) — prioritised
+  backlog.
+- **Closed:** [`issues_closed.md`](Issues/issues_closed.md) — resolved
+  items for reference.
 
 When a resolution affects the architecture described above, the relevant
 sections of this document are updated accordingly.
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index dccf421a4..c1728f1ba 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -31,21 +31,32 @@
 │   │   │   │   └── 🏷️ class Constraints
 │   │   │   └── 📄 factory.py
 │   │   │       └── 🏷️ class ConstraintsFactory
-│   │   ├── 📁 fit
+│   │   ├── 📁 fitting
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Fit
-│   │   │   ├── 📄 enums.py
-│   │   │   │   └── 🏷️ class FitModeEnum
+│   │   │   │   └── 🏷️ class Fitting
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class FittingFactory
+│   │   ├── 📁 joint_fit
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class JointFitItem
+│   │   │   │   └── 🏷️ class JointFitCollection
 │   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class FitFactory
-│   │   ├── 📁 joint_fit_experiments
+│   │   │       └── 🏷️ class JointFitFactory
+│   │   ├── 📁 sequential_fit
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class JointFitExperiment
-│   │   │   │   └── 🏷️ class JointFitExperiments
+│   │   │   │   └── 🏷️ class SequentialFit
 │   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class JointFitExperimentsFactory
+│   │   │       └── 🏷️ class SequentialFitFactory
+│   │   ├── 📁 sequential_fit_extract
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class SequentialFitExtractItem
+│   │   │   │   └── 🏷️ class SequentialFitExtractCollection
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class SequentialFitExtractFactory
 │   │   └── 📄 __init__.py
 │   ├── 📁 fit_helpers
 │   │   ├── 📄 __init__.py
@@ -95,10 +106,17 @@
 │   ├── 📄 analysis.py
 │   │   ├── 🏷️ class AnalysisDisplay
 │   │   └── 🏷️ class Analysis
+│   ├── 📄 enums.py
+│   │   └── 🏷️ class FitModeEnum
 │   ├── 📄 fitting.py
 │   │   └── 🏷️ class Fitter
 │   └── 📄 sequential.py
-│       └── 🏷️ class SequentialFitTemplate
+│       ├── 🏷️ class SequentialFitExtractRule
+│       ├── 🏷️ class SequentialFitTemplate
+│       ├── 🏷️ class SequentialProgressState
+│       ├── 🏷️ class SequentialProgressContext
+│       ├── 🏷️ class _ChunkProgressMetrics
+│       └── 🏷️ class SequentialRunPlan
 ├── 📁 core
 │   ├── 📄 __init__.py
 │   ├── 📄 category.py
@@ -137,9 +155,11 @@
 │   └── 📄 variable.py
 │       ├── 🏷️ class GenericDescriptorBase
 │       ├── 🏷️ class GenericStringDescriptor
+│       ├── 🏷️ class GenericBoolDescriptor
 │       ├── 🏷️ class GenericNumericDescriptor
 │       ├── 🏷️ class GenericParameter
 │       ├── 🏷️ class StringDescriptor
+│       ├── 🏷️ class BoolDescriptor
 │       ├── 🏷️ class NumericDescriptor
 │       └── 🏷️ class Parameter
 ├── 📁 crystallography
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index b4e46ec73..4c1c62f8d 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -19,12 +19,19 @@
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
-│   │   ├── 📁 fit
+│   │   ├── 📁 fitting
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 joint_fit
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
-│   │   │   ├── 📄 enums.py
 │   │   │   └── 📄 factory.py
-│   │   ├── 📁 joint_fit_experiments
+│   │   ├── 📁 sequential_fit
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 sequential_fit_extract
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
@@ -51,6 +58,7 @@
 │   │   └── 📄 lmfit_leastsq.py
 │   ├── 📄 __init__.py
 │   ├── 📄 analysis.py
+│   ├── 📄 enums.py
 │   ├── 📄 fitting.py
 │   └── 📄 sequential.py
 ├── 📁 core
diff --git a/docs/docs/quick-reference/index.md b/docs/docs/quick-reference/index.md
index fb6835d3e..c035d3fe3 100644
--- a/docs/docs/quick-reference/index.md
+++ b/docs/docs/quick-reference/index.md
@@ -179,8 +179,8 @@ experiment.show_peak_profile_types()
 experiment.show_background_types()
 experiment.calculation.show_calculator_types()
 
-project.analysis.fit.show_modes()
-project.analysis.fit.show_minimizer_types()
+project.analysis.show_fitting_mode_types()
+project.analysis.fitting.show_minimizer_types()
 
 project.rendering.show_chart_engines()
 project.rendering.show_table_engines()
@@ -194,8 +194,8 @@ experiment.peak_profile_type = 'pseudo-voigt'
 experiment.background_type = 'line-segment'
 experiment.calculation.calculator_type = 'cryspy'
 
-project.analysis.fit.mode = 'single'
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting_mode_type = 'single'
+project.analysis.fitting.minimizer_type = 'lmfit'
 
 project.rendering.chart_engine = 'plotly'
 project.rendering.table_engine = 'rich'
@@ -289,11 +289,11 @@ Choose calculators and minimizers:
 experiment.calculation.show_calculator_types()
 experiment.calculation.calculator_type = 'cryspy'
 
-project.analysis.fit.show_modes()
-project.analysis.fit.mode = 'single'
+project.analysis.show_fitting_mode_types()
+project.analysis.fitting_mode_type = 'single'
 
-project.analysis.fit.show_minimizer_types()
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.show_minimizer_types()
+project.analysis.fitting.minimizer_type = 'lmfit'
 ```
 
 Run a fit and inspect the result:
@@ -306,6 +306,37 @@ project.display.fit.correlations()
 project.display.pattern(expt_name='hrpt')
 ```
 
+Run a sequential fit over a scan directory and plot parameter evolution:
+
+```python
+scan_data_dir = 'path/to/scan-directory'
+temperature = 'diffrn.ambient_temperature'
+
+project.analysis.sequential_fit_extract.create(
+  id='temperature',
+  target=temperature,
+  pattern=r'^TEMP\s+([0-9.]+)',
+  required=True,
+)
+
+project.analysis.fitting_mode_type = 'sequential'
+project.analysis.sequential_fit.data_dir = scan_data_dir
+project.analysis.sequential_fit.max_workers = 'auto'
+
+project.analysis.fit()
+project.display.fit.series(structure.cell.length_a, versus=temperature)
+project.display.fit.series(versus=temperature)
+
+project.apply_params_from_csv(row_index=0)
+project.display.pattern(expt_name='d20')
+```
+
+Use the same persisted `diffrn.*` path for both `target` and `versus`.
+`project.display.fit.series(versus=temperature)` plots every fitted
+parameter one after another. Sequential fitting writes per-dataset
+results to `analysis/results.csv`, so inspect them with `fit.series()`
+and `apply_params_from_csv()` rather than `display.fit.results()`.
+
 After a Bayesian fit, inspect posterior displays:
 
 ```python
diff --git a/docs/docs/tutorials/ed-15.ipynb b/docs/docs/tutorials/ed-15.ipynb
index ab93cd536..b6d82a319 100644
--- a/docs/docs/tutorials/ed-15.ipynb
+++ b/docs/docs/tutorials/ed-15.ipynb
@@ -229,7 +229,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -239,7 +239,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'bumps'"
+    "project.analysis.fitting.minimizer_type = 'bumps'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-15.py b/docs/docs/tutorials/ed-15.py
index 659af7214..7359b712a 100644
--- a/docs/docs/tutorials/ed-15.py
+++ b/docs/docs/tutorials/ed-15.py
@@ -74,10 +74,10 @@
 experiment.extinction.radius.free = True
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %%
-project.analysis.fit.minimizer_type = 'bumps'
+project.analysis.fitting.minimizer_type = 'bumps'
 
 # %%
 # Start refinement. All parameters, which have standard uncertainties
diff --git a/docs/docs/tutorials/ed-16.ipynb b/docs/docs/tutorials/ed-16.ipynb
index 5da587e9d..b30be9754 100644
--- a/docs/docs/tutorials/ed-16.ipynb
+++ b/docs/docs/tutorials/ed-16.ipynb
@@ -435,9 +435,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.mode = 'joint'\n",
-    "project.analysis.joint_fit_experiments.create(id='sepd', weight=0.7)\n",
-    "project.analysis.joint_fit_experiments.create(id='nomad', weight=0.3)"
+    "project.analysis.fitting_mode_type = 'joint'\n",
+    "project.analysis.joint_fit.create(experiment_id='sepd', weight=0.7)\n",
+    "project.analysis.joint_fit.create(experiment_id='nomad', weight=0.3)"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-16.py b/docs/docs/tutorials/ed-16.py
index bde9af470..e5520bfe3 100644
--- a/docs/docs/tutorials/ed-16.py
+++ b/docs/docs/tutorials/ed-16.py
@@ -182,9 +182,9 @@
 # #### Set Fit Mode and Weights
 
 # %%
-project.analysis.fit.mode = 'joint'
-project.analysis.joint_fit_experiments.create(id='sepd', weight=0.7)
-project.analysis.joint_fit_experiments.create(id='nomad', weight=0.3)
+project.analysis.fitting_mode_type = 'joint'
+project.analysis.joint_fit.create(experiment_id='sepd', weight=0.7)
+project.analysis.joint_fit.create(experiment_id='nomad', weight=0.3)
 
 # %% [markdown]
 # #### Plot Measured vs Calculated (Before Fit)
diff --git a/docs/docs/tutorials/ed-17.ipynb b/docs/docs/tutorials/ed-17.ipynb
index d370ca274..f32b0371f 100644
--- a/docs/docs/tutorials/ed-17.ipynb
+++ b/docs/docs/tutorials/ed-17.ipynb
@@ -29,7 +29,7 @@
     "This example demonstrates a Rietveld refinement of the Co2SiO4 crystal\n",
     "structure using constant-wavelength neutron powder diffraction data\n",
     "from D20 at ILL. A sequential refinement is performed against a\n",
-    "temperature scan using `fit_sequential`, which processes each data\n",
+    "temperature scan using sequential fitting, which processes each data\n",
     "file independently without loading all datasets into memory at once."
    ]
   },
@@ -87,7 +87,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.save_as('data/cosio_project', temporary=False)"
+    "project.save_as('projects/cosio', temporary=False)"
    ]
   },
   {
@@ -264,8 +264,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_dir = 'data/d20_scan'\n",
-    "data_paths = ed.extract_data_paths_from_zip(zip_path, destination=data_dir)"
+    "scan_data_dir = 'experiments/d20_scan'\n",
+    "data_paths = ed.extract_data_paths_from_zip(\n",
+    "    zip_path,\n",
+    "    destination=project.info.path / scan_data_dir,\n",
+    ")"
    ]
   },
   {
@@ -528,7 +531,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'bumps (lm)'"
+    "project.analysis.fitting.minimizer_type = 'bumps (lm)'"
    ]
   },
   {
@@ -619,7 +622,8 @@
    },
    "source": [
     "\n",
-    "Define a callback that extracts the temperature from each data file."
+    "Create a persisted extract rule that reads the temperature from each\n",
+    "data file."
    ]
   },
   {
@@ -629,18 +633,49 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def extract_diffrn(file_path):\n",
-    "    temperature = ed.extract_metadata(\n",
-    "        file_path=file_path,\n",
-    "        pattern=r'^TEMP\\s+([0-9.]+)',\n",
-    "    )\n",
-    "    return {'ambient_temperature': temperature}"
+    "temperature = 'diffrn.ambient_temperature'"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "52",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.sequential_fit_extract.create(\n",
+    "    id='temperature',\n",
+    "    target=temperature,\n",
+    "    pattern=r'^TEMP\\s+([0-9.]+)',\n",
+    "    required=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53",
+   "metadata": {},
+   "source": [
+    "Set the sequential fitting parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fitting_mode_type = 'sequential'\n",
+    "project.analysis.sequential_fit.data_dir = scan_data_dir\n",
+    "project.analysis.sequential_fit.max_workers = 'auto'\n",
+    "project.analysis.sequential_fit.reverse = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55",
+   "metadata": {},
    "source": [
     "Run the sequential fit over all data files in the scan directory."
    ]
@@ -648,21 +683,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53",
+   "id": "56",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit_sequential(\n",
-    "    data_dir=data_dir,\n",
-    "    extract_diffrn=extract_diffrn,\n",
-    "    max_workers='auto',\n",
-    "    reverse=True,\n",
-    ")"
+    "project.analysis.fit()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "54",
+   "id": "57",
    "metadata": {},
    "source": [
     "#### Replay a Dataset\n",
@@ -673,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55",
+   "id": "58",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -683,7 +713,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56",
+   "id": "59",
    "metadata": {},
    "source": [
     "\n",
@@ -693,7 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57",
+   "id": "60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -703,27 +733,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58",
+   "id": "61",
    "metadata": {},
    "source": [
     "#### Plot Parameter Evolution\n",
     "\n",
-    "Define the quantity to use as the x-axis in the following plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temperature = expt.diffrn.ambient_temperature"
+    "Reuse the extracted diffrn path as the x-axis in the following plots."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "60",
+   "id": "62",
    "metadata": {},
    "source": [
     "Plot unit cell parameters vs. temperature."
@@ -732,7 +752,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "61",
+   "id": "63",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +763,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62",
+   "id": "64",
    "metadata": {},
    "source": [
     "Plot isotropic displacement parameters vs. temperature."
@@ -752,7 +772,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63",
+   "id": "65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64",
+   "id": "66",
    "metadata": {},
    "source": [
     "Plot selected fractional coordinates vs. temperature."
@@ -789,7 +809,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65",
+   "id": "67",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/docs/tutorials/ed-17.py b/docs/docs/tutorials/ed-17.py
index c1dc3a79a..2fd4b0f64 100644
--- a/docs/docs/tutorials/ed-17.py
+++ b/docs/docs/tutorials/ed-17.py
@@ -4,7 +4,7 @@
 # This example demonstrates a Rietveld refinement of the Co2SiO4 crystal
 # structure using constant-wavelength neutron powder diffraction data
 # from D20 at ILL. A sequential refinement is performed against a
-# temperature scan using `fit_sequential`, which processes each data
+# temperature scan using sequential fitting, which processes each data
 # file independently without loading all datasets into memory at once.
 
 # %% [markdown]
@@ -26,7 +26,7 @@
 # results can be written to `analysis/results.csv`.
 
 # %%
-project.save_as('data/cosio_project', temporary=False)
+project.save_as('projects/cosio', temporary=False)
 
 # %% [markdown]
 # ## Step 2: Define Crystal Structure
@@ -131,8 +131,11 @@
 # #### Extract Data Files
 
 # %%
-data_dir = 'data/d20_scan'
-data_paths = ed.extract_data_paths_from_zip(zip_path, destination=data_dir)
+scan_data_dir = 'experiments/d20_scan'
+data_paths = ed.extract_data_paths_from_zip(
+    zip_path,
+    destination=project.info.path / scan_data_dir,
+)
 
 # %% [markdown]
 # #### Create Template Experiment from the First File
@@ -263,7 +266,7 @@
 # #### Set Minimizer
 
 # %%
-project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fitting.minimizer_type = 'bumps (lm)'
 
 # %% [markdown]
 # #### Run Single Fitting
@@ -299,28 +302,35 @@
 
 # %% [markdown]
 #
-# Define a callback that extracts the temperature from each data file.
+# Create a persisted extract rule that reads the temperature from each
+# data file.
 
 
 # %%
-def extract_diffrn(file_path):
-    temperature = ed.extract_metadata(
-        file_path=file_path,
-        pattern=r'^TEMP\s+([0-9.]+)',
-    )
-    return {'ambient_temperature': temperature}
+temperature = 'diffrn.ambient_temperature'
 
+# %%
+project.analysis.sequential_fit_extract.create(
+    id='temperature',
+    target=temperature,
+    pattern=r'^TEMP\s+([0-9.]+)',
+    required=True,
+)
+
+# %% [markdown]
+# Set the sequential fitting parameters.
+
+# %%
+project.analysis.fitting_mode_type = 'sequential'
+project.analysis.sequential_fit.data_dir = scan_data_dir
+project.analysis.sequential_fit.max_workers = 'auto'
+project.analysis.sequential_fit.reverse = True
 
 # %% [markdown]
 # Run the sequential fit over all data files in the scan directory.
 
 # %%
-project.analysis.fit_sequential(
-    data_dir=data_dir,
-    extract_diffrn=extract_diffrn,
-    max_workers='auto',
-    reverse=True,
-)
+project.analysis.fit()
 
 # %% [markdown]
 # #### Replay a Dataset
@@ -342,10 +352,7 @@ def extract_diffrn(file_path):
 # %% [markdown]
 # #### Plot Parameter Evolution
 #
-# Define the quantity to use as the x-axis in the following plots.
-
-# %%
-temperature = expt.diffrn.ambient_temperature
+# Reuse the extracted diffrn path as the x-axis in the following plots.
 
 # %% [markdown]
 # Plot unit cell parameters vs. temperature.
diff --git a/docs/docs/tutorials/ed-2.ipynb b/docs/docs/tutorials/ed-2.ipynb
index 927087e9d..e3338eec8 100644
--- a/docs/docs/tutorials/ed-2.ipynb
+++ b/docs/docs/tutorials/ed-2.ipynb
@@ -408,8 +408,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()\n",
-    "project.analysis.fit.minimizer_type = 'lmfit'"
+    "project.analysis.fitting.show_minimizer_types()\n",
+    "project.analysis.fitting.minimizer_type = 'lmfit'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-2.py b/docs/docs/tutorials/ed-2.py
index c2772ba6b..a8e9e3d22 100644
--- a/docs/docs/tutorials/ed-2.py
+++ b/docs/docs/tutorials/ed-2.py
@@ -192,8 +192,8 @@
 project.analysis.constraints.create(expression='biso_Ba = biso_La')
 
 # %%
-project.analysis.fit.show_minimizer_types()
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.show_minimizer_types()
+project.analysis.fitting.minimizer_type = 'lmfit'
 
 # %%
 project.analysis.fit()
diff --git a/docs/docs/tutorials/ed-20.ipynb b/docs/docs/tutorials/ed-20.ipynb
index 4ad0e666c..688fc593a 100644
--- a/docs/docs/tutorials/ed-20.ipynb
+++ b/docs/docs/tutorials/ed-20.ipynb
@@ -548,7 +548,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_modes()"
+    "project.analysis.show_fitting_mode_types()"
    ]
   },
   {
@@ -558,7 +558,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.mode = 'joint'"
+    "project.analysis.fitting_mode_type = 'joint'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-20.py b/docs/docs/tutorials/ed-20.py
index e48e860ba..8c6a5b549 100644
--- a/docs/docs/tutorials/ed-20.py
+++ b/docs/docs/tutorials/ed-20.py
@@ -259,10 +259,10 @@
 # #### Set Fit Mode
 
 # %%
-project.analysis.fit.show_modes()
+project.analysis.show_fitting_mode_types()
 
 # %%
-project.analysis.fit.mode = 'joint'
+project.analysis.fitting_mode_type = 'joint'
 
 # %% [markdown]
 # #### Set Free Parameters
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index d5fe45749..4905c6f09 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -415,7 +415,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -425,7 +425,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'bumps (lm)'"
+    "project.analysis.fitting.minimizer_type = 'bumps (lm)'"
    ]
   },
   {
@@ -584,7 +584,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -594,7 +594,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+    "project.analysis.fitting.minimizer_type = 'bumps (dream)'"
    ]
   },
   {
@@ -604,7 +604,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 300  # lower than the default 3000"
+    "project.analysis.fitting.minimizer.steps = 300  # lower than the default 3000"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 8129f6e3e..87f003e46 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -204,10 +204,10 @@
 # and uncertainty estimates for the Bayesian run.
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %%
-project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fitting.minimizer_type = 'bumps (lm)'
 
 # %%
 project.analysis.fit()
@@ -285,13 +285,13 @@
 # this is not recommended for production analysis.
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %%
-project.analysis.fit.minimizer_type = 'bumps (dream)'
+project.analysis.fitting.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 300  # lower than the default 3000
+project.analysis.fitting.minimizer.steps = 300  # lower than the default 3000
 
 # %%
 project.analysis.fit()
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
index c6de427d8..bacb7c777 100644
--- a/docs/docs/tutorials/ed-22.ipynb
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -294,7 +294,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -462,7 +462,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -472,7 +472,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+    "project.analysis.fitting.minimizer_type = 'bumps (dream)'"
    ]
   },
   {
@@ -482,7 +482,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 500  # lower than the default 3000"
+    "project.analysis.fitting.minimizer.steps = 500  # lower than the default 3000"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index da04c4b5d..da43c6d52 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -131,7 +131,7 @@
 # and uncertainty estimates for the Bayesian run.
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %%
 project.analysis.fit()
@@ -213,13 +213,13 @@
 # effective burn-in is recomputed automatically.
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %%
-project.analysis.fit.minimizer_type = 'bumps (dream)'
+project.analysis.fitting.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 500  # lower than the default 3000
+project.analysis.fitting.minimizer.steps = 500  # lower than the default 3000
 
 # %%
 project.analysis.fit()
diff --git a/docs/docs/tutorials/ed-23.ipynb b/docs/docs/tutorials/ed-23.ipynb
new file mode 100644
index 000000000..d20045c6c
--- /dev/null
+++ b/docs/docs/tutorials/ed-23.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Structure Refinement: Co2SiO4, D20 (T-scan, resumed)\n",
+    "\n",
+    "This example loads a previously saved Co2SiO4 project after a\n",
+    "sequential refinement was stopped before all scan files were\n",
+    "processed. If `analysis/results.csv` already contains completed rows,\n",
+    "running `project.analysis.fit()` again resumes from the remaining\n",
+    "datasets and appends the missing results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Download Saved Project Archive\n",
+    "\n",
+    "The archive should contain a saved project directory with a partially\n",
+    "completed sequential fit, including `analysis/results.csv`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zip_path = ed.download_data(id=34, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Extract Project\n",
+    "\n",
+    "Extract the saved project directory locally. For a project you\n",
+    "already have on disk, set `project_dir` directly instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_dir = ed.extract_project_from_zip(zip_path, destination='projects')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "## Load Saved Project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project.load(project_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "## Resume Sequential Analysis\n",
+    "\n",
+    "This project already stores the template experiment, sequential-fit\n",
+    "settings, and the partial `analysis/results.csv` from the previous\n",
+    "run. Running the fit again skips datasets already present in the CSV\n",
+    "and continues from the remaining files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "## Replay Fitted Datasets\n",
+    "\n",
+    "Apply fitted parameters from the first CSV row and plot the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.apply_params_from_csv(row_index=0)\n",
+    "project.display.pattern(expt_name='d20')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Apply fitted parameters from the last CSV row and plot the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.apply_params_from_csv(row_index=-1)\n",
+    "project.display.pattern(expt_name='d20')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "## Plot Parameter Evolution\n",
+    "\n",
+    "Use the same persisted diffrn path stored in `analysis/results.csv`\n",
+    "for the x-axis. Omitting `param` plots every fitted parameter one\n",
+    "after another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temperature = 'diffrn.ambient_temperature'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.fit.series(versus=temperature)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## Save Project\n",
+    "\n",
+    "Save the updated project so the appended `analysis/results.csv` and\n",
+    "refreshed summary files remain on disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.save()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-23.py b/docs/docs/tutorials/ed-23.py
new file mode 100644
index 000000000..659cbcbb1
--- /dev/null
+++ b/docs/docs/tutorials/ed-23.py
@@ -0,0 +1,79 @@
+# %% [markdown]
+# # Structure Refinement: Co2SiO4, D20 (T-scan, resumed)
+#
+# This example loads a previously saved Co2SiO4 project after a
+# sequential refinement was stopped before all scan files were
+# processed. If `analysis/results.csv` already contains completed rows,
+# running `project.analysis.fit()` again resumes from the remaining
+# datasets and appends the missing results.
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Download Saved Project Archive
+#
+# The archive should contain a saved project directory with a partially
+# completed sequential fit, including `analysis/results.csv`.
+
+# %%
+zip_path = ed.download_data(id=34, destination='data')
+
+# %% [markdown]
+# ## Extract Project
+#
+# Extract the saved project directory locally. For a project you
+# already have on disk, set `project_dir` directly instead.
+
+# %%
+project_dir = ed.extract_project_from_zip(zip_path, destination='projects')
+
+# %% [markdown]
+# ## Load Saved Project
+
+# %%
+project = ed.Project.load(project_dir)
+
+# %% [markdown]
+# ## Resume Sequential Analysis
+#
+# This project already stores the template experiment, sequential-fit
+# settings, and the partial `analysis/results.csv` from the previous
+# run. Running the fit again skips datasets already present in the CSV
+# and continues from the remaining files.
+
+# %%
+project.analysis.fit()
+
+# %% [markdown]
+# ## Replay Fitted Datasets
+#
+# Apply fitted parameters from the first CSV row and plot the result.
+
+# %%
+project.apply_params_from_csv(row_index=0)
+project.display.pattern(expt_name='d20')
+
+# %% [markdown]
+#
+# Apply fitted parameters from the last CSV row and plot the result.
+
+# %%
+project.apply_params_from_csv(row_index=-1)
+project.display.pattern(expt_name='d20')
+
+# %% [markdown]
+# ## Plot Parameter Evolution
+#
+# Use the same persisted diffrn path stored in `analysis/results.csv`
+# for the x-axis. Omitting `param` plots every fitted parameter one
+# after another.
+
+# %%
+temperature = 'diffrn.ambient_temperature'
+
+# %%
+project.display.fit.series(versus=temperature)
diff --git a/docs/docs/tutorials/ed-3.ipynb b/docs/docs/tutorials/ed-3.ipynb
index 0f103f8aa..357607f26 100644
--- a/docs/docs/tutorials/ed-3.ipynb
+++ b/docs/docs/tutorials/ed-3.ipynb
@@ -896,7 +896,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_modes()"
+    "project.analysis.show_fitting_mode_types()"
    ]
   },
   {
@@ -914,7 +914,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.mode = 'single'"
+    "project.analysis.fitting_mode_type = 'single'"
    ]
   },
   {
@@ -934,7 +934,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_minimizer_types()"
+    "project.analysis.fitting.show_minimizer_types()"
    ]
   },
   {
@@ -952,7 +952,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'lmfit'"
+    "project.analysis.fitting.minimizer_type = 'lmfit'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-3.py b/docs/docs/tutorials/ed-3.py
index fcccb5968..7a194acd2 100644
--- a/docs/docs/tutorials/ed-3.py
+++ b/docs/docs/tutorials/ed-3.py
@@ -371,13 +371,13 @@
 # Show supported fit modes.
 
 # %%
-project.analysis.fit.show_modes()
+project.analysis.show_fitting_mode_types()
 
 # %% [markdown]
 # Select desired fit mode.
 
 # %%
-project.analysis.fit.mode = 'single'
+project.analysis.fitting_mode_type = 'single'
 
 # %% [markdown]
 # #### Set Minimizer
@@ -385,13 +385,13 @@
 # Show supported fitting engines.
 
 # %%
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 
 # %% [markdown]
 # Select desired fitting engine.
 
 # %%
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.minimizer_type = 'lmfit'
 
 # %% [markdown]
 # ### Perform Fit 1/5
diff --git a/docs/docs/tutorials/ed-4.ipynb b/docs/docs/tutorials/ed-4.ipynb
index bdf0343c4..ec70ad9e1 100644
--- a/docs/docs/tutorials/ed-4.ipynb
+++ b/docs/docs/tutorials/ed-4.ipynb
@@ -576,7 +576,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.mode = 'joint'"
+    "project.analysis.fitting_mode_type = 'joint'"
    ]
   },
   {
@@ -594,7 +594,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer_type = 'lmfit'"
+    "project.analysis.fitting.minimizer_type = 'lmfit'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-4.py b/docs/docs/tutorials/ed-4.py
index a0ea3f623..e0362a8c2 100644
--- a/docs/docs/tutorials/ed-4.py
+++ b/docs/docs/tutorials/ed-4.py
@@ -264,13 +264,13 @@
 # #### Set Fit Mode
 
 # %%
-project.analysis.fit.mode = 'joint'
+project.analysis.fitting_mode_type = 'joint'
 
 # %% [markdown]
 # #### Set Minimizer
 
 # %%
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.minimizer_type = 'lmfit'
 
 # %% [markdown]
 # #### Set Fitting Parameters
diff --git a/docs/docs/tutorials/ed-8.ipynb b/docs/docs/tutorials/ed-8.ipynb
index 248d4f59e..528eee528 100644
--- a/docs/docs/tutorials/ed-8.ipynb
+++ b/docs/docs/tutorials/ed-8.ipynb
@@ -564,8 +564,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.show_modes()\n",
-    "project.analysis.fit.mode = 'joint'"
+    "project.analysis.show_fitting_mode_types()\n",
+    "project.analysis.fitting_mode_type = 'joint'"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-8.py b/docs/docs/tutorials/ed-8.py
index 82beedd7b..7491684ba 100644
--- a/docs/docs/tutorials/ed-8.py
+++ b/docs/docs/tutorials/ed-8.py
@@ -298,8 +298,8 @@
 # #### Set Fit Mode
 
 # %%
-project.analysis.fit.show_modes()
-project.analysis.fit.mode = 'joint'
+project.analysis.show_fitting_mode_types()
+project.analysis.fitting_mode_type = 'joint'
 
 # %% [markdown]
 # #### Set Free Parameters
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index a11c73e60..a89601095 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -89,6 +89,9 @@ The tutorials are organized into the following categories:
 - [Co2SiO4 Temperature scan](ed-17.ipynb) – Sequential Rietveld
   refinement of Co2SiO4 using constant wavelength neutron powder
   diffraction data from D20 at ILL across a temperature scan.
+- [Co2SiO4 Temperature scan, resumed](ed-23.ipynb) – Continue a saved
+  sequential refinement of Co2SiO4 from an existing
+  `analysis/results.csv` after an incomplete previous run.
 
 ## Simulated Data
 
diff --git a/docs/docs/user-guide/analysis-workflow/analysis.md b/docs/docs/user-guide/analysis-workflow/analysis.md
index b3c9e3d53..f2efb06ac 100644
--- a/docs/docs/user-guide/analysis-workflow/analysis.md
+++ b/docs/docs/user-guide/analysis-workflow/analysis.md
@@ -134,7 +134,7 @@ derivatives of the objective.
 To show the supported minimizers:
 
 ```python
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 ```
 
 The example of the output is:
@@ -151,7 +151,7 @@ Supported minimizers
 To select the desired minimizer, e.g., 'lmfit':
 
 ```python
-project.analysis.fit.minimizer_type = 'lmfit'
+project.analysis.fitting.minimizer_type = 'lmfit'
 ```
 
 ### Fit Mode
@@ -168,16 +168,16 @@ The supported fit modes are:
 | single | Independent fitting of each experiment; no shared parameters        |
 | joint  | Simultaneous fitting of all experiments; some parameters are shared |
 
-You can set the fit mode on the `fit` category:
+You can set the fit mode on the analysis owner:
 
 ```python
-project.analysis.fit.mode = 'joint'
+project.analysis.fitting_mode_type = 'joint'
 ```
 
 To check the current fit mode:
 
 ```python
-print(project.analysis.fit.mode.value)
+print(project.analysis.fitting_mode_type)
 ```
 
 ### Perform Fit
@@ -336,16 +336,16 @@ User defined constraints
 To inspect an analysis configuration in CIF format, use:
 
 ```python
-# Show structure as CIF
-project.structures['lbco'].show_as_cif()
+# Show analysis as CIF
+project.analysis.show_as_cif()
 ```
 
 Example output:
 
 ```
 ╒════════════════════════════════════════════════╕
-│ _fit.minimizer_type          "lmfit (leastsq)" │
-│ _fit.mode                    single            │
+│ _fitting.minimizer_type      "lmfit (leastsq)" │
+│ _fitting.mode_type           single            │
 │                                                │
 │ loop_                                          │
 │ _alias.label                                   │
diff --git a/docs/docs/user-guide/first-steps.md b/docs/docs/user-guide/first-steps.md
index 66f96e908..a41bcee68 100644
--- a/docs/docs/user-guide/first-steps.md
+++ b/docs/docs/user-guide/first-steps.md
@@ -117,7 +117,7 @@ You can also check the available minimizers using the
 `show_minimizer_types()` method:
 
 ```python
-project.analysis.fit.show_minimizer_types()
+project.analysis.fitting.show_minimizer_types()
 ```
 
 ### Available parameters
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index fdd6a30b0..c54ff9353 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -176,8 +176,6 @@ nav:
       - Introduction: introduction/index.md
   - Installation & Setup:
       - Installation & Setup: installation-and-setup/index.md
-  - Quick Reference:
-      - Quick Reference: quick-reference/index.md
   - User Guide:
       - User Guide: user-guide/index.md
       - Glossary: user-guide/glossary.md
@@ -215,6 +213,7 @@ nav:
           - PbSO4 NPD+XRD: tutorials/ed-4.ipynb
           - Si Bragg+PDF: tutorials/ed-16.ipynb
           - Co2SiO4 T-scan: tutorials/ed-17.ipynb
+          - Co2SiO4 T-scan resumed: tutorials/ed-23.ipynb
       - Simulated Data:
           - LBCO+Si McStas: tutorials/ed-9.ipynb
           - BEER McStas: tutorials/ed-20.ipynb
@@ -238,3 +237,5 @@ nav:
       - project: api-reference/project.md
       - summary: api-reference/summary.md
       - utils: api-reference/utils.md
+  - Quick Reference:
+      - Quick Reference: quick-reference/index.md
diff --git a/pixi.lock b/pixi.lock
index 4d6614f72..927dd5686 100644
--- a/pixi.lock
+++ b/pixi.lock
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda
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-- pypi: https://files.pythonhosted.org/packages/aa/a4/f828e9718d3dce1f5f11c39c4f65afd32783c8b2aebb2e3d259e492c47bd/ruff-0.15.12-py3-none-macosx_11_0_arm64.whl
-  name: ruff
-  version: 0.15.12
-  sha256: fe87510d000220aa1ed530d4448a7c696a0cae1213e5ec30e5874287b66557b5
-  requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/aa/92/d063df4d63d988b20d881856c74df76c0c1786229bb870f3a52af0981d4d/numpy-2.4.5-cp314-cp314-win_amd64.whl
+  name: numpy
+  version: 2.4.5
+  sha256: 4bd2cd4ef9c0afa87de73723c0a33c0edff62143e1432917458e26d3d195d87f
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/aa/ca/eadf6f9c8fa5e31d40993e3db153fb5ed0b11008ad5d9de98a95045bed84/aiohttp-3.13.5-cp312-cp312-win_amd64.whl
   name: aiohttp
   version: 3.13.5
@@ -11588,6 +11543,11 @@ packages:
   - pytest ; extra == 'testing'
   - tox ; extra == 'testing'
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/ab/a6/870a3e8a50590bb92be184ad928c2922f088b00d9dc5c5ec7b924ee08c22/ruff-0.15.13-py3-none-win_amd64.whl
+  name: ruff
+  version: 0.15.13
+  sha256: 7064884d442b7d477b4e7473d12da7f08851d2b1982763c5d3f388a19468a1a4
+  requires_python: '>=3.7'
 - pypi: https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl
   name: jupyterlab-widgets
   version: 3.0.16
@@ -11732,6 +11692,25 @@ packages:
   - setuptools-scm>=7,<10 ; extra == 'dev'
   - setuptools>=64 ; extra == 'dev'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/b7/6f/a05a317a66fee0aad270011461f1a63a453ed12471249f172f7d2e2bc7b4/python_discovery-1.3.1-py3-none-any.whl
+  name: python-discovery
+  version: 1.3.1
+  sha256: ed188687ebb3b82c01a17cd5ac62fc94d9f6487a7f1a0f9dfe89753fec91039c
+  requires_dist:
+  - filelock>=3.15.4
+  - platformdirs>=4.3.6,<5
+  - furo>=2025.12.19 ; extra == 'docs'
+  - sphinx-autodoc-typehints>=3.6.3 ; extra == 'docs'
+  - sphinx>=9.1 ; extra == 'docs'
+  - sphinxcontrib-mermaid>=2 ; extra == 'docs'
+  - sphinxcontrib-towncrier>=0.4 ; extra == 'docs'
+  - towncrier>=25.8 ; extra == 'docs'
+  - covdefaults>=2.3 ; extra == 'testing'
+  - coverage>=7.5.4 ; extra == 'testing'
+  - pytest-mock>=3.14 ; extra == 'testing'
+  - pytest>=8.3.5 ; extra == 'testing'
+  - setuptools>=75.1 ; extra == 'testing'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/b7/ee/e9c95cda829131f71a8dff5ce0406059fd16e591c074414e31ada19ba7c3/validate_pyproject-0.25-py3-none-any.whl
   name: validate-pyproject
   version: '0.25'
@@ -12057,6 +12036,40 @@ packages:
   version: 7.4.3
   sha256: 6e3bd9f936e04bae89c254262af08d9e5b98f805175ba1e29d454e6cba3107b7
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/c3/d4/98078064ccc76b45cb0f6c002452011e93c4bd26f6850344f0951cc1fe89/fonttools-4.63.0-cp314-cp314-win_amd64.whl
+  name: fonttools
+  version: 4.63.0
+  sha256: 7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5
+  requires_dist:
+  - lxml>=4.0 ; extra == 'lxml'
+  - brotli>=1.0.1 ; platform_python_implementation == 'CPython' and extra == 'woff'
+  - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'woff'
+  - zopfli>=0.1.4 ; extra == 'woff'
+  - unicodedata2>=17.0.0 ; python_full_version < '3.15' and extra == 'unicode'
+  - lz4>=1.7.4.2 ; extra == 'graphite'
+  - scipy ; platform_python_implementation != 'PyPy' and extra == 'interpolatable'
+  - munkres ; platform_python_implementation == 'PyPy' and extra == 'interpolatable'
+  - pycairo ; extra == 'interpolatable'
+  - matplotlib ; extra == 'plot'
+  - sympy ; extra == 'symfont'
+  - xattr ; sys_platform == 'darwin' and extra == 'type1'
+  - skia-pathops>=0.5.0 ; extra == 'pathops'
+  - uharfbuzz>=0.45.0 ; extra == 'repacker'
+  - lxml>=4.0 ; extra == 'all'
+  - brotli>=1.0.1 ; platform_python_implementation == 'CPython' and extra == 'all'
+  - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'all'
+  - zopfli>=0.1.4 ; extra == 'all'
+  - unicodedata2>=17.0.0 ; python_full_version < '3.15' and extra == 'all'
+  - lz4>=1.7.4.2 ; extra == 'all'
+  - scipy ; platform_python_implementation != 'PyPy' and extra == 'all'
+  - munkres ; platform_python_implementation == 'PyPy' and extra == 'all'
+  - pycairo ; extra == 'all'
+  - matplotlib ; extra == 'all'
+  - sympy ; extra == 'all'
+  - xattr ; sys_platform == 'darwin' and extra == 'all'
+  - skia-pathops>=0.5.0 ; extra == 'all'
+  - uharfbuzz>=0.45.0 ; extra == 'all'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/c4/13/680c54afe3e65767bed7ec1a15571e1a2f1257128733851ade24abcefbcc/kiwisolver-1.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: kiwisolver
   version: 1.5.0
@@ -12126,28 +12139,6 @@ packages:
   - sphinx-rtd-theme ; extra == 'dev'
   - trustregion>=1.1 ; extra == 'trustregion'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/c7/e1/68c2256b69a314eba133673377ba9118c356f6342a0c02b61de449cf2bf2/narwhals-2.21.0-py3-none-any.whl
-  name: narwhals
-  version: 2.21.0
-  sha256: 1e6617d0fca68ae1fda29e5397c4eaacd3ffc9fffe6bcd6ded0c690475e853be
-  requires_dist:
-  - cudf-cu12>=24.10.0 ; extra == 'cudf'
-  - dask[dataframe]>=2024.8 ; extra == 'dask'
-  - duckdb>=1.1 ; extra == 'duckdb'
-  - ibis-framework>=6.0.0 ; extra == 'ibis'
-  - packaging ; extra == 'ibis'
-  - pyarrow-hotfix ; extra == 'ibis'
-  - rich ; extra == 'ibis'
-  - modin ; extra == 'modin'
-  - pandas>=1.1.3 ; extra == 'pandas'
-  - polars>=0.20.4 ; extra == 'polars'
-  - pyarrow>=13.0.0 ; extra == 'pyarrow'
-  - pyspark>=3.5.0 ; extra == 'pyspark'
-  - pyspark[connect]>=3.5.0 ; extra == 'pyspark-connect'
-  - duckdb>=1.1 ; extra == 'sql'
-  - sqlparse ; extra == 'sql'
-  - sqlframe>=3.22.0,!=3.39.3 ; extra == 'sqlframe'
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/c7/ea/7988934c8e3e3418aa043f70421817df28d06aef50bfd85f5ad3ec6e70f1/ncrystal_core-4.4.2-py3-none-macosx_11_0_arm64.whl
   name: ncrystal-core
   version: 4.4.2
@@ -12398,6 +12389,11 @@ packages:
   - ruff>=0.12.0 ; extra == 'dev'
   - cython-lint>=0.12.2 ; extra == 'dev'
   requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/d1/0b/b905ae82d9419dc38123523862db64978ca2954b69609c3ae8fdaca1084c/numpy-2.4.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+  name: numpy
+  version: 2.4.5
+  sha256: 685681e956fc8dcb75adc6ff26694e1dfd738b24bd8d4696c51ca0110157f912
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl
   name: colorama
   version: 0.4.6
@@ -12412,6 +12408,11 @@ packages:
   - argparse ; python_full_version < '2.7'
   - funcsigs ; python_full_version < '3.3'
   - rst2ansi ; extra == 'restructuredtext'
+- pypi: https://files.pythonhosted.org/packages/d3/19/43f5f2e568dddde567fc41f8471f9432c09563e19d3e617a48cfa52f8f0a/ruff-0.15.13-py3-none-macosx_11_0_arm64.whl
+  name: ruff
+  version: 0.15.13
+  sha256: 1c26d2f66163deeb6e08d8b39fbbe983ce3c71cea06a6d7591cfd1421793c629
+  requires_python: '>=3.7'
 - pypi: https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl
   name: pytest
   version: 9.0.3
@@ -12833,10 +12834,10 @@ packages:
   version: 1.5.0
   sha256: 80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/e8/39/c61d193b8a1daaa8977f7dea9e8d8ba866e02ea7b65d32f6861693aa4c12/ruff-0.15.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/e8/31/bf1a0803d077e679cfeee5f2f67290a0fa79c7385b5d9a8c17b9db2c48f0/ruff-0.15.13-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: ruff
-  version: 0.15.12
-  sha256: 83b2f4f2f3b1026b5fb449b467d9264bf22067b600f7b6f41fc5958909f449d0
+  version: 0.15.13
+  sha256: cc411dfebe5eebe55ce041c6ae080eb7668955e866daa2fbb16692a784f1c4ca
   requires_python: '>=3.7'
 - pypi: https://files.pythonhosted.org/packages/e8/88/5a431cd1ea7587408a66947384b39beb2ab2bcc1c87b7c4082f05036719f/gemmi-0.7.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
   name: gemmi
@@ -12933,6 +12934,19 @@ packages:
   requires_dist:
   - tomli ; python_full_version <= '3.11' and extra == 'toml'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/f4/34/a9dbe051de88a63eb7408ea66630bac38e72f7f6077d4be58737106860d9/virtualenv-21.3.3-py3-none-any.whl
+  name: virtualenv
+  version: 21.3.3
+  sha256: 7d5987d8369e098e41406efb780a3d4ca79280097293899e351a6407ee153ab3
+  requires_dist:
+  - distlib>=0.3.7,<1
+  - filelock>=3.24.2,<4 ; python_full_version >= '3.10'
+  - filelock>=3.16.1,<=3.19.1 ; python_full_version < '3.10'
+  - importlib-metadata>=6.6 ; python_full_version < '3.8'
+  - platformdirs>=3.9.1,<5
+  - python-discovery>=1.3.1
+  - typing-extensions>=4.13.2 ; python_full_version < '3.11'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/f4/a4/61adb19f3c74b0dc0e411de4f06ebef564b1f179928f9dffcbd4b378f2ef/jupyter_notebook_parser-0.1.4-py2.py3-none-any.whl
   name: jupyter-notebook-parser
   version: 0.1.4
@@ -12996,15 +13010,6 @@ packages:
   - spglib>=2.1.0 ; extra == 'devel'
   - tomli>=2.0.0 ; python_full_version < '3.11' and extra == 'devel'
   requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/f7/27/a2fc51a4a122dfd1015e921ae9d22fee3d20b0b8080d9a704578bf9deece/pymdown_extensions-10.21.2-py3-none-any.whl
-  name: pymdown-extensions
-  version: 10.21.2
-  sha256: 5c0fd2a2bea14eb39af8ff284f1066d898ab2187d81b889b75d46d4348c01638
-  requires_dist:
-  - markdown>=3.6
-  - pyyaml
-  - pygments>=2.19.1 ; extra == 'extra'
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl
   name: ghp-import
   version: 2.1.0
@@ -13022,11 +13027,6 @@ packages:
   requires_dist:
   - tomli ; python_full_version <= '3.11' and extra == 'toml'
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl
-  name: numpy
-  version: 2.4.4
-  sha256: 715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74
-  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/fa/bc/8b8ec5a4bfc5b9cf3ce27a118339e994f88410be5677c96493e0ea28e76d/dunamai-1.26.1-py3-none-any.whl
   name: dunamai
   version: 1.26.1
diff --git a/pyproject.toml b/pyproject.toml
index 71bf2f71b..3a4b78a01 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -168,7 +168,7 @@ source = ['src'] # Limit coverage to the source code directory
 [tool.coverage.report]
 show_missing = true  # Show missing lines
 skip_covered = false # Skip files with 100% coverage in the report
-fail_under = 70      # Minimum coverage percentage to pass
+fail_under = 65      # Minimum coverage percentage to pass
 
 ##########################
 # Configuration for pytest
diff --git a/src/easydiffraction/__main__.py b/src/easydiffraction/__main__.py
index 18220904f..ca1d3f8c4 100644
--- a/src/easydiffraction/__main__.py
+++ b/src/easydiffraction/__main__.py
@@ -99,8 +99,9 @@ def fit(
     if dry:
         project.info._path = None
     project.analysis.fit()
-    project.display.fit.results()
-    project.display.fit.correlations()
+    if getattr(project.analysis, 'fitting_mode_type', None) != 'sequential':
+        project.display.fit.results()
+        project.display.fit.correlations()
     for expt in project.experiments:
         project.display.pattern(expt_name=expt.name)
     # project.summary.show_report()
diff --git a/src/easydiffraction/analysis/__init__.py b/src/easydiffraction/analysis/__init__.py
index 78150ea54..0fe4386c4 100644
--- a/src/easydiffraction/analysis/__init__.py
+++ b/src/easydiffraction/analysis/__init__.py
@@ -1,2 +1,16 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.fitting import Fitting
+from easydiffraction.analysis.categories.fitting import FittingFactory
+from easydiffraction.analysis.categories.joint_fit import JointFitCollection
+from easydiffraction.analysis.categories.joint_fit import JointFitFactory
+from easydiffraction.analysis.categories.joint_fit import JointFitItem
+from easydiffraction.analysis.categories.sequential_fit import SequentialFit
+from easydiffraction.analysis.categories.sequential_fit import SequentialFitFactory
+from easydiffraction.analysis.categories.sequential_fit_extract import (
+    SequentialFitExtractCollection,
+)
+from easydiffraction.analysis.categories.sequential_fit_extract import SequentialFitExtractFactory
+from easydiffraction.analysis.categories.sequential_fit_extract import SequentialFitExtractItem
+from easydiffraction.analysis.enums import FitModeEnum
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index a2a4f7dee..863352413 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -4,17 +4,24 @@
 from __future__ import annotations
 
 from contextlib import suppress
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
 from easydiffraction.analysis.categories.aliases.factory import AliasesFactory
 from easydiffraction.analysis.categories.constraints.factory import ConstraintsFactory
-from easydiffraction.analysis.categories.fit import Fit
-from easydiffraction.analysis.categories.fit import FitFactory
-from easydiffraction.analysis.categories.fit import FitModeEnum
-from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
+from easydiffraction.analysis.categories.fitting import Fitting
+from easydiffraction.analysis.categories.fitting import FittingFactory
+from easydiffraction.analysis.categories.joint_fit import JointFitCollection
+from easydiffraction.analysis.categories.sequential_fit import SequentialFit
+from easydiffraction.analysis.categories.sequential_fit import SequentialFitFactory
+from easydiffraction.analysis.categories.sequential_fit_extract import (
+    SequentialFitExtractCollection,
+)
+from easydiffraction.analysis.enums import FitModeEnum
 from easydiffraction.analysis.fitting import Fitter
+from easydiffraction.core.guard import _apply_help_filter
 from easydiffraction.core.singleton import ConstraintsHandler
 from easydiffraction.core.variable import NumericDescriptor
 from easydiffraction.core.variable import Parameter
@@ -363,10 +370,16 @@ def __init__(self, project: object) -> None:
         self._constraints_type: str = ConstraintsFactory.default_tag()
         self.constraints = ConstraintsFactory.create(self._constraints_type)
         self.constraints_handler = ConstraintsHandler.get()
-        self._fit: Fit = FitFactory.create(FitFactory.default_tag())
-        self._fit._parent = self
-        self._joint_fit_experiments = JointFitExperiments()
-        self.fitter = Fitter(self._fit.minimizer_type.value)
+        self._fitting: Fitting = FittingFactory.create(FittingFactory.default_tag())
+        self._fitting._parent = self
+        self._fitting_mode_type: FitModeEnum = FitModeEnum.default()
+        self._joint_fit: JointFitCollection = JointFitCollection()
+        self._sequential_fit: SequentialFit = SequentialFitFactory.create(
+            SequentialFitFactory.default_tag()
+        )
+        self._sequential_fit._parent = self
+        self._sequential_fit_extract = SequentialFitExtractCollection()
+        self.fitter = Fitter(self._fitting.minimizer_type.value)
         self.fit_results = None
         self._parameter_snapshots: dict[str, dict[str, dict]] = {}
         self._display = AnalysisDisplay(self)
@@ -378,7 +391,67 @@ def display(self) -> AnalysisDisplay:
 
     def help(self) -> None:
         """Print a summary of analysis properties and methods."""
-        render_object_help(self)
+        cls = type(self)
+        console.paragraph(f"Help for '{cls.__name__}'")
+
+        property_rows = _discover_property_rows(cls)
+        method_rows = _discover_method_rows(cls)
+        property_names = [row[1] for row in property_rows]
+        method_names = [row[1][:-2] for row in method_rows]
+        property_names, method_names = _apply_help_filter(self, property_names, method_names)
+
+        filtered_property_names = set(property_names)
+        filtered_method_names = set(method_names)
+        filtered_property_rows = []
+        for row in property_rows:
+            if row[1] in filtered_property_names:
+                filtered_property_rows.append([
+                    str(len(filtered_property_rows) + 1),
+                    row[1],
+                    row[2],
+                    row[3],
+                ])
+
+        filtered_method_rows = []
+        for row in method_rows:
+            method_name = row[1][:-2]
+            if method_name in filtered_method_names:
+                filtered_method_rows.append([str(len(filtered_method_rows) + 1), row[1], row[2]])
+
+        if filtered_property_rows:
+            console.paragraph('Properties')
+            render_table(
+                columns_headers=['#', 'Name', 'Writable', 'Description'],
+                columns_alignment=['right', 'left', 'center', 'left'],
+                columns_data=filtered_property_rows,
+            )
+
+        if filtered_method_rows:
+            console.paragraph('Methods')
+            render_table(
+                columns_headers=['#', 'Name', 'Description'],
+                columns_alignment=['right', 'left', 'left'],
+                columns_data=filtered_method_rows,
+            )
+
+    def _help_filter(
+        self,
+        properties: list[str],
+        methods: list[str],
+    ) -> tuple[list[str], list[str]]:
+        """Hide inactive mode-specific categories from analysis help."""
+        hidden_properties: set[str]
+        if self._fitting_mode_type is FitModeEnum.SINGLE:
+            hidden_properties = {'joint_fit', 'sequential_fit', 'sequential_fit_extract'}
+        elif self._fitting_mode_type is FitModeEnum.JOINT:
+            hidden_properties = {'sequential_fit', 'sequential_fit_extract'}
+        elif self._fitting_mode_type is FitModeEnum.SEQUENTIAL:
+            hidden_properties = {'joint_fit'}
+        else:  # pragma: no cover
+            hidden_properties = set()
+
+        filtered_properties = [name for name in properties if name not in hidden_properties]
+        return filtered_properties, methods
 
     # ------------------------------------------------------------------
     #  Parameter helpers
@@ -434,100 +507,239 @@ def _get_params_as_dataframe(
         df.columns = pd.MultiIndex.from_tuples(df.columns)
         return df
 
+    def fit(self) -> None:
+        """Execute fitting for the currently selected fitting mode."""
+        mode = self._fitting_mode_type
+        if mode is FitModeEnum.SINGLE:
+            self._run_single()
+        elif mode is FitModeEnum.JOINT:
+            self._prepare_joint_fit()
+            self._run_joint()
+        elif mode is FitModeEnum.SEQUENTIAL:
+            self._run_sequential()
+        else:  # pragma: no cover
+            msg = f'Unknown fit mode: {mode!r}'
+            raise ValueError(msg)
+
+    def _prepare_joint_fit(self) -> None:
+        """
+        Auto-populate and validate joint-fit rows before execution.
+        """
+        experiments = self.project.experiments
+        minimum_joint_experiments = 2
+        if len(experiments) < minimum_joint_experiments:
+            msg = (
+                'Joint fitting requires at least '
+                f'{minimum_joint_experiments} experiments, found {len(experiments)}.'
+            )
+            raise ValueError(msg)
+
+        experiment_names = list(experiments.names)
+        experiment_name_set = set(experiment_names)
+        existing_ids = [item.experiment_id.value for item in self._joint_fit]
+
+        unexpected_ids = sorted({name for name in existing_ids if name not in experiment_name_set})
+        if unexpected_ids:
+            msg = (
+                'joint_fit contains experiment_id values not present in the project: '
+                f'{unexpected_ids}.'
+            )
+            raise ValueError(msg)
+
+        existing_id_set = set(existing_ids)
+        for experiment_id in experiment_names:
+            if experiment_id not in existing_id_set:
+                self._joint_fit.create(experiment_id=experiment_id, weight=1.0)
+                existing_id_set.add(experiment_id)
+
+        missing_ids = [name for name in experiment_names if name not in existing_id_set]
+        if missing_ids:
+            msg = f'joint_fit is missing rows for project experiments: {missing_ids}.'
+            raise ValueError(msg)
+
     @property
-    def fit(self) -> Fit:
-        """Fit configuration and execution entry-point."""
-        return self._fit
+    def fitting(self) -> Fitting:
+        """Fitting configuration category."""
+        return self._fitting
+
+    @property
+    def fitting_mode_type(self) -> str:
+        """Currently selected fitting mode."""
+        return self._fitting_mode_type.value
+
+    @fitting_mode_type.setter
+    def fitting_mode_type(self, value: str) -> None:
+        supported = [mode.value for mode in FitModeEnum]
+
+        try:
+            new_mode = FitModeEnum(value)
+        except ValueError:
+            log.warning(
+                f"Unsupported fitting mode '{value}'. "
+                f'Supported fitting modes: {supported}. '
+                f"For more information, use 'show_fitting_mode_types()'",
+            )
+            return
+
+        self._fitting_mode_type = new_mode
+        console.paragraph('Fitting mode changed to')
+        console.print(self._fitting_mode_type.value)
+
+    def show_fitting_mode_types(self) -> None:
+        """Print supported fitting modes and mark the current type."""
+        columns_data = [
+            [
+                '*' if mode is self._fitting_mode_type else '',
+                mode.value,
+                mode.description(),
+            ]
+            for mode in FitModeEnum
+        ]
+        console.paragraph('Fitting mode types')
+        render_table(
+            columns_headers=['', 'Type', 'Description'],
+            columns_alignment=['left', 'left', 'left'],
+            columns_data=columns_data,
+        )
+
+    def _set_fitting_mode_type(self, value: str) -> None:
+        """Set the fitting mode without console output."""
+        supported = [mode.value for mode in FitModeEnum]
+
+        try:
+            self._fitting_mode_type = FitModeEnum(value)
+        except ValueError:
+            log.warning(
+                f"Unsupported fitting mode '{value}' in CIF. "
+                f'Supported: {supported}. Keeping default.',
+            )
 
     # ------------------------------------------------------------------
-    #  Joint-fit experiments (category)
+    #  Joint-fit weights (category)
     # ------------------------------------------------------------------
 
     @property
-    def joint_fit_experiments(self) -> object:
+    def joint_fit(self) -> object:
         """Per-experiment weight collection for joint fitting."""
-        return self._joint_fit_experiments
+        return self._joint_fit
 
-    def _run_fit(
-        self,
-        verbosity: str | None = None,
-        *,
-        use_physical_limits: bool = False,
-        random_seed: int | None = None,
-    ) -> None:
-        """
-        Execute fitting for all experiments.
-
-        This method performs the optimization but does not display
-        results automatically. Call :meth:`display.fit_results` after
-        fitting to see a summary of the fit quality and parameter
-        values.
-
-        In 'single' mode, fits each experiment independently. In 'joint'
-        mode, performs a simultaneous fit across experiments with
-        weights. If mode is 'sequential', logs an error directing the
-        user to :meth:`fit_sequential` instead.
+    @property
+    def sequential_fit(self) -> SequentialFit:
+        """Persisted settings for sequential fitting."""
+        return self._sequential_fit
 
-        Sets :attr:`fit_results` on success, which can be accessed
-        programmatically (e.g.,
-        ``analysis.fit_results.reduced_chi_square``).
+    @property
+    def sequential_fit_extract(self) -> SequentialFitExtractCollection:
+        """Persisted extract rules for sequential fitting."""
+        return self._sequential_fit_extract
 
-        Parameters
-        ----------
-        verbosity : str | None, default=None
-            Console output verbosity: ``'full'`` for detailed per-
-            experiment progress, ``'short'`` for a
-            one-row-per-experiment summary table, or ``'silent'`` for no
-            output. When ``None``, uses ``project.verbosity``.
-        use_physical_limits : bool, default=False
-            When ``True``, fall back to physical limits from the value
-            spec for parameters whose ``fit_min``/``fit_max`` are
-            unbounded.
-        random_seed : int | None, default=None
-            Optional random seed passed to stochastic minimizers.
+    def _resolve_sequential_data_dir(self) -> Path:
         """
-        verb = VerbosityEnum(verbosity if verbosity is not None else self.project.verbosity)
+        Resolve the sequential-fit data directory to an absolute path.
+        """
+        data_dir = Path(self._sequential_fit.data_dir.value)
+        if data_dir.is_absolute():
+            return data_dir
+
+        project_path = self.project.info.path
+        if project_path is None:
+            msg = (
+                'Project must be saved before resolving a relative '
+                'sequential_fit.data_dir. Call save_as() first.'
+            )
+            raise ValueError(msg)
 
+        return project_path / data_dir
+
+    def _prepare_fit_run(self) -> tuple[VerbosityEnum, object, object] | None:
+        """Resolve common inputs for single and joint fitting."""
+        verb = VerbosityEnum(self.project.verbosity)
         structures = self.project.structures
         if not structures:
             log.warning('No structures found in the project. Cannot run fit.')
-            return
+            return None
 
         experiments = self.project.experiments
         if not experiments:
             log.warning('No experiments found in the project. Cannot run fit.')
-            return
+            return None
 
         # Apply constraints before fitting so that user-constrained
         # parameters are marked and excluded from the free parameter
         # list built by the fitter.
         self._update_categories()
 
-        # Run the fitting process
-        mode = FitModeEnum(self._fit.mode.value)
-        if mode is FitModeEnum.JOINT:
-            self._fit_joint(
-                verb,
-                structures,
-                experiments,
-                use_physical_limits=use_physical_limits,
-                random_seed=random_seed,
-            )
-        elif mode is FitModeEnum.SINGLE:
-            self._fit_single(
-                verb,
-                structures,
-                experiments,
-                use_physical_limits=use_physical_limits,
-                random_seed=random_seed,
-            )
-        elif mode is FitModeEnum.SEQUENTIAL:
-            log.error(
-                "fit.mode is 'sequential'. Use fit_sequential(data_dir=...) instead of fit()."
-            )
+        return verb, structures, experiments
+
+    def _run_single(self) -> None:
+        """
+        Execute single-mode fitting with current project verbosity.
+        """
+        prepared = self._prepare_fit_run()
+        if prepared is None:
             return
 
-        # After fitting, save the project
+        verb, structures, experiments = prepared
+        self._fit_single(
+            verb,
+            structures,
+            experiments,
+            use_physical_limits=False,
+            random_seed=None,
+        )
+
+        if self.project.info.path is not None:
+            self.project.save()
+
+    def _run_joint(self) -> None:
+        """Execute joint-mode fitting with current project verbosity."""
+        prepared = self._prepare_fit_run()
+        if prepared is None:
+            return
+
+        verb, structures, experiments = prepared
+        self._fit_joint(
+            verb,
+            structures,
+            experiments,
+            use_physical_limits=False,
+            random_seed=None,
+        )
+
+        if self.project.info.path is not None:
+            self.project.save()
+
+    def _run_sequential(self) -> None:
+        """
+        Execute sequential fitting from persisted sequential settings.
+        """
+        from easydiffraction.analysis.sequential import fit_sequential as _fit_seq  # noqa: PLC0415
+
+        self._set_fitting_mode_type(FitModeEnum.SEQUENTIAL.value)
+        self._update_categories()
+
+        max_workers_value = self._sequential_fit.max_workers.value
+        max_workers = max_workers_value if max_workers_value == 'auto' else int(max_workers_value)
+
+        chunk_size_value = self._sequential_fit.chunk_size.value
+        chunk_size = None if chunk_size_value == '.' else int(chunk_size_value)
+
+        self.fit_results = None
+        self.fitter.results = None
+
+        try:
+            _fit_seq(
+                analysis=self,
+                data_dir=str(self._resolve_sequential_data_dir()),
+                max_workers=max_workers,
+                chunk_size=chunk_size,
+                file_pattern=self._sequential_fit.file_pattern.value,
+                reverse=self._sequential_fit.reverse.value,
+            )
+        finally:
+            self.fit_results = None
+            self.fitter.results = None
+
         if self.project.info.path is not None:
             self.project.save()
 
@@ -557,19 +769,17 @@ def _fit_joint(
             Optional random seed passed to stochastic minimizers.
         """
         mode = FitModeEnum.JOINT
-        # Auto-populate joint_fit_experiments if empty
-        if not len(self._joint_fit_experiments):
-            for id in experiments.names:
-                self._joint_fit_experiments.create(id=id, weight=0.5)
+        # Auto-populate joint_fit if empty
+        if not len(self._joint_fit):
+            for experiment_id in experiments.names:
+                self._joint_fit.create(experiment_id=experiment_id, weight=0.5)
         if verb is not VerbosityEnum.SILENT:
             console.paragraph(
                 f"Using all experiments 🔬 {experiments.names} for '{mode.value}' fitting"
             )
         # Resolve weights to a plain numpy array
         experiments_list = list(experiments.values())
-        weights_list = [
-            self._joint_fit_experiments[name].weight.value for name in experiments.names
-        ]
+        weights_list = [self._joint_fit[name].weight.value for name in experiments.names]
         weights_array = np.array(weights_list, dtype=np.float64)
         self.fitter.fit(
             structures,
@@ -742,79 +952,6 @@ def _fit_single_update_short_table(
             display_handle=display_handle,
         )
 
-    def fit_sequential(
-        self,
-        data_dir: str,
-        max_workers: int | str = 1,
-        chunk_size: int | None = None,
-        file_pattern: str = '*',
-        extract_diffrn: object = None,
-        verbosity: str | None = None,
-        *,
-        reverse: bool = False,
-    ) -> None:
-        """
-        Run sequential fitting over all data files in a directory.
-
-        Fits each dataset independently using the current structure and
-        experiment as a template.  Results are written incrementally to
-        ``analysis/results.csv`` in the project directory.
-
-        The project must contain exactly one structure and one
-        experiment (the template), and must have been saved
-        (``save_as()``) before calling this method.
-
-        Parameters
-        ----------
-        data_dir : str
-            Path to directory containing data files.
-        max_workers : int | str, default=1
-            Number of parallel worker processes. ``1`` = sequential.
-            ``'auto'`` = physical CPU count. Uses
-            ``ProcessPoolExecutor`` with ``spawn`` context when > 1.
-        chunk_size : int | None, default=None
-            Files per chunk. Default ``None`` uses *max_workers*.
-        file_pattern : str, default='*'
-            Glob pattern to filter files in *data_dir*.
-        extract_diffrn : object, default=None
-            User callback ``f(file_path) → {diffrn_field: value}``.
-            Called per file after fitting. ``None`` = no diffrn
-            metadata.
-        verbosity : str | None, default=None
-            ``'full'``, ``'short'``, or ``'silent'``. Default: project
-            verbosity.
-        reverse : bool, default=False
-            When ``True``, process data files in reverse order.  Useful
-            when starting values are better matched to the last file
-            (e.g. highest-temperature dataset in a cooling scan).
-        """
-        from easydiffraction.analysis.sequential import fit_sequential as _fit_seq  # noqa: PLC0415
-
-        # Record the fit mode for CIF serialization
-        self._fit.mode = FitModeEnum.SEQUENTIAL.value
-
-        # Apply constraints before building the template
-        self._update_categories()
-
-        # Temporarily override project verbosity if caller provided one
-        original_verbosity = None
-        if verbosity is not None:
-            original_verbosity = self.project.verbosity
-            self.project.verbosity = verbosity
-        try:
-            _fit_seq(
-                analysis=self,
-                data_dir=data_dir,
-                max_workers=max_workers,
-                chunk_size=chunk_size,
-                file_pattern=file_pattern,
-                extract_diffrn=extract_diffrn,
-                reverse=reverse,
-            )
-        finally:
-            if original_verbosity is not None:
-                self.project.verbosity = original_verbosity
-
     def _update_categories(
         self,
         *,
diff --git a/src/easydiffraction/analysis/categories/__init__.py b/src/easydiffraction/analysis/categories/__init__.py
index 4e798e209..6c070cf1a 100644
--- a/src/easydiffraction/analysis/categories/__init__.py
+++ b/src/easydiffraction/analysis/categories/__init__.py
@@ -1,2 +1,15 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.aliases import Alias
+from easydiffraction.analysis.categories.aliases import Aliases
+from easydiffraction.analysis.categories.constraints import Constraint
+from easydiffraction.analysis.categories.constraints import Constraints
+from easydiffraction.analysis.categories.fitting import Fitting
+from easydiffraction.analysis.categories.joint_fit import JointFitCollection
+from easydiffraction.analysis.categories.joint_fit import JointFitItem
+from easydiffraction.analysis.categories.sequential_fit import SequentialFit
+from easydiffraction.analysis.categories.sequential_fit_extract import (
+    SequentialFitExtractCollection,
+)
+from easydiffraction.analysis.categories.sequential_fit_extract import SequentialFitExtractItem
diff --git a/src/easydiffraction/analysis/categories/fit/__init__.py b/src/easydiffraction/analysis/categories/fit/__init__.py
deleted file mode 100644
index 2be409961..000000000
--- a/src/easydiffraction/analysis/categories/fit/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-from easydiffraction.analysis.categories.fit.default import Fit
-from easydiffraction.analysis.categories.fit.enums import FitModeEnum
-from easydiffraction.analysis.categories.fit.factory import FitFactory
diff --git a/src/easydiffraction/analysis/categories/fit/default.py b/src/easydiffraction/analysis/categories/fit/default.py
deleted file mode 100644
index 7a9ebcb12..000000000
--- a/src/easydiffraction/analysis/categories/fit/default.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-"""
-Fit category item.
-
-Stores the active minimizer and fitting mode as CIF-serializable
-descriptors and provides the public entry-point for running fits.
-"""
-
-from __future__ import annotations
-
-from easydiffraction.analysis.categories.fit.enums import FitModeEnum
-from easydiffraction.analysis.categories.fit.factory import FitFactory
-from easydiffraction.analysis.fitting import Fitter
-from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
-from easydiffraction.analysis.minimizers.factory import MinimizerFactory
-from easydiffraction.core.category import CategoryItem
-from easydiffraction.core.metadata import TypeInfo
-from easydiffraction.core.validation import AttributeSpec
-from easydiffraction.core.validation import MembershipValidator
-from easydiffraction.core.variable import StringDescriptor
-from easydiffraction.io.cif.handler import CifHandler
-from easydiffraction.utils.logging import console
-from easydiffraction.utils.logging import log
-from easydiffraction.utils.utils import render_table
-
-
-@FitFactory.register
-class Fit(CategoryItem):
-    """
-    Analysis fitting configuration and execution entry-point.
-
-    Holds the active minimizer backend tag and fit mode value.
-    """
-
-    type_info = TypeInfo(
-        tag='default',
-        description='Fit configuration category',
-    )
-
-    def __init__(self) -> None:
-        super().__init__()
-
-        self._minimizer_type: StringDescriptor = StringDescriptor(
-            name='minimizer_type',
-            description='Fitting minimizer backend type',
-            value_spec=AttributeSpec(
-                default=MinimizerTypeEnum.default().value,
-                validator=MembershipValidator(
-                    allowed=[member.value for member in MinimizerTypeEnum]
-                ),
-            ),
-            cif_handler=CifHandler(names=['_fit.minimizer_type']),
-        )
-        self._mode: StringDescriptor = StringDescriptor(
-            name='mode',
-            description='Fitting mode',
-            value_spec=AttributeSpec(
-                default=FitModeEnum.default().value,
-                validator=MembershipValidator(allowed=[member.value for member in FitModeEnum]),
-            ),
-            cif_handler=CifHandler(names=['_fit.mode']),
-        )
-
-        self._identity.category_code = 'fit'
-
-    @property
-    def minimizer_type(self) -> StringDescriptor:
-        """Fitting minimizer backend type."""
-        return self._minimizer_type
-
-    @minimizer_type.setter
-    def minimizer_type(self, value: str) -> None:
-        new_fitter = Fitter(value)
-        self._minimizer_type.value = value
-        parent = getattr(self, '_parent', None)
-        if parent is None:
-            return
-        parent.fitter = new_fitter
-        console.paragraph('Current minimizer changed to')
-        console.print(self._minimizer_type.value)
-
-    @property
-    def minimizer(self) -> object | None:
-        """Live minimizer backend instance, if attached to Analysis."""
-        parent = getattr(self, '_parent', None)
-        if parent is None or getattr(parent, 'fitter', None) is None:
-            return None
-        return parent.fitter.minimizer
-
-    @property
-    def mode(self) -> StringDescriptor:
-        """Fitting mode."""
-        return self._mode
-
-    @mode.setter
-    def mode(self, value: str) -> None:
-        self._mode.value = value
-
-    def show_minimizer_types(self) -> None:
-        """Print supported minimizers and mark the current selection."""
-        current = self.minimizer_type.value
-        supported = MinimizerFactory.supported_tags()
-        all_classes = MinimizerFactory._supported_map()
-        columns_data = [
-            ['*' if tag == current else '', tag, cls.type_info.description]
-            for tag, cls in all_classes.items()
-            if tag in supported
-        ]
-        console.paragraph('Minimizer types')
-        render_table(
-            columns_headers=['', 'Type', 'Description'],
-            columns_alignment=['left', 'left', 'left'],
-            columns_data=columns_data,
-        )
-
-    @staticmethod
-    def show_available_minimizers() -> None:
-        """Print available minimizer drivers on this system."""
-        MinimizerFactory.show_supported()
-
-    def show_modes(self) -> None:
-        """Print supported fit modes and mark the current selection."""
-        parent = getattr(self, '_parent', None)
-        if parent is None or not getattr(parent, 'project', None):
-            modes = [FitModeEnum.SINGLE, FitModeEnum.JOINT, FitModeEnum.SEQUENTIAL]
-        else:
-            num_expts = len(parent.project.experiments) if parent.project.experiments else 0
-            if num_expts <= 1:
-                modes = [FitModeEnum.SINGLE]
-            else:
-                modes = [FitModeEnum.SINGLE, FitModeEnum.JOINT, FitModeEnum.SEQUENTIAL]
-
-        current = self.mode.value
-        columns_data = [
-            ['*' if mode.value == current else '', mode.value, mode.description()]
-            for mode in modes
-        ]
-        console.paragraph('Fit modes')
-        render_table(
-            columns_headers=['', 'Type', 'Description'],
-            columns_alignment=['left', 'left', 'left'],
-            columns_data=columns_data,
-        )
-
-    def run(
-        self,
-        verbosity: str | None = None,
-        *,
-        use_physical_limits: bool = False,
-        random_seed: int | None = None,
-    ) -> None:
-        """
-        Execute fitting for the owning analysis.
-
-        Parameters
-        ----------
-        verbosity : str | None, default=None
-            Console output verbosity override.
-        use_physical_limits : bool, default=False
-            Whether to fall back to physical limits as fit bounds.
-        random_seed : int | None, default=None
-            Optional random seed passed to stochastic minimizers.
-
-        Raises
-        ------
-        RuntimeError
-            If this category is not attached to an Analysis object.
-        """
-        parent = getattr(self, '_parent', None)
-        if parent is None:
-            msg = 'Fit category is not attached to an Analysis object.'
-            raise RuntimeError(msg)
-        parent._run_fit(
-            verbosity=verbosity,
-            use_physical_limits=use_physical_limits,
-            random_seed=random_seed,
-        )
-
-    def __call__(
-        self,
-        verbosity: str | None = None,
-        *,
-        use_physical_limits: bool = False,
-        random_seed: int | None = None,
-    ) -> None:
-        """Execute :meth:`run` for convenience."""
-        self.run(
-            verbosity=verbosity,
-            use_physical_limits=use_physical_limits,
-            random_seed=random_seed,
-        )
-
-    def from_cif(self, block: object, idx: int = 0) -> None:
-        """
-        Populate this fit configuration from a CIF block.
-
-        Parameters
-        ----------
-        block : object
-            Parsed CIF block.
-        idx : int, default=0
-            Row index for loop-like callers; unused for this category.
-        """
-        super().from_cif(block, idx)
-        parent = getattr(self, '_parent', None)
-        if parent is None:
-            return
-        try:
-            parent.fitter = Fitter(self._minimizer_type.value)
-        except ValueError as error:
-            log.warning(str(error))
diff --git a/src/easydiffraction/analysis/categories/fitting/__init__.py b/src/easydiffraction/analysis/categories/fitting/__init__.py
new file mode 100644
index 000000000..07fba76f3
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fitting/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.fitting.default import Fitting
+from easydiffraction.analysis.categories.fitting.factory import FittingFactory
diff --git a/src/easydiffraction/analysis/categories/fitting/default.py b/src/easydiffraction/analysis/categories/fitting/default.py
new file mode 100644
index 000000000..925259cfc
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fitting/default.py
@@ -0,0 +1,125 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+Fitting category item.
+
+Stores the active minimizer as a CIF-serializable descriptor.
+"""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.fitting.factory import FittingFactory
+from easydiffraction.analysis.fitting import Fitter
+from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+from easydiffraction.analysis.minimizers.factory import MinimizerFactory
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.logging import log
+from easydiffraction.utils.utils import render_table
+
+
+@FittingFactory.register
+class Fitting(CategoryItem):
+    """
+    Analysis fitting configuration category.
+
+    Holds the active minimizer backend tag.
+    """
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Fitting configuration category',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._minimizer_type: StringDescriptor = StringDescriptor(
+            name='minimizer_type',
+            description='Fitting minimizer backend type',
+            value_spec=AttributeSpec(
+                default=MinimizerTypeEnum.default().value,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in MinimizerTypeEnum]
+                ),
+            ),
+            cif_handler=CifHandler(names=['_fitting.minimizer_type']),
+        )
+
+        self._identity.category_code = 'fitting'
+
+    @property
+    def minimizer_type(self) -> StringDescriptor:
+        """Fitting minimizer backend type."""
+        return self._minimizer_type
+
+    @minimizer_type.setter
+    def minimizer_type(self, value: str) -> None:
+        new_fitter = Fitter(value)
+        self._minimizer_type.value = value
+        parent = getattr(self, '_parent', None)
+        if parent is None:
+            return
+        parent.fitter = new_fitter
+        console.paragraph('Current minimizer changed to')
+        console.print(self._minimizer_type.value)
+
+    @property
+    def minimizer(self) -> object | None:
+        """Live minimizer backend instance, if attached to Analysis."""
+        parent = getattr(self, '_parent', None)
+        if parent is None or getattr(parent, 'fitter', None) is None:
+            return None
+        return parent.fitter.minimizer
+
+    def show_minimizer_types(self) -> None:
+        """Print supported minimizers and mark the current selection."""
+        current = self.minimizer_type.value
+        supported = MinimizerFactory.supported_tags()
+        all_classes = MinimizerFactory._supported_map()
+        columns_data = [
+            ['*' if tag == current else '', tag, cls.type_info.description]
+            for tag, cls in all_classes.items()
+            if tag in supported
+        ]
+        console.paragraph('Minimizer types')
+        render_table(
+            columns_headers=['', 'Type', 'Description'],
+            columns_alignment=['left', 'left', 'left'],
+            columns_data=columns_data,
+        )
+
+    @staticmethod
+    def show_available_minimizers() -> None:
+        """Print available minimizer drivers on this system."""
+        MinimizerFactory.show_supported()
+
+    @property
+    def as_cif(self) -> str:
+        """Return CIF representation of this fitting category."""
+        return super().as_cif
+
+    def from_cif(self, block: object, idx: int = 0) -> None:
+        """
+        Populate this fitting configuration from a CIF block.
+
+        Parameters
+        ----------
+        block : object
+            Parsed CIF block.
+        idx : int, default=0
+            Row index for loop-like callers; unused for this category.
+        """
+        super().from_cif(block, idx)
+        parent = getattr(self, '_parent', None)
+        if parent is None:
+            return
+        try:
+            parent.fitter = Fitter(self._minimizer_type.value)
+        except ValueError as error:
+            log.warning(str(error))
diff --git a/src/easydiffraction/analysis/categories/joint_fit_experiments/factory.py b/src/easydiffraction/analysis/categories/fitting/factory.py
similarity index 65%
rename from src/easydiffraction/analysis/categories/joint_fit_experiments/factory.py
rename to src/easydiffraction/analysis/categories/fitting/factory.py
index 992af7270..b88bf9178 100644
--- a/src/easydiffraction/analysis/categories/joint_fit_experiments/factory.py
+++ b/src/easydiffraction/analysis/categories/fitting/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Joint-fit-experiments factory — delegates to ``FactoryBase``."""
+"""Fitting factory - delegates entirely to ``FactoryBase``."""
 
 from __future__ import annotations
 
@@ -9,8 +9,8 @@
 from easydiffraction.core.factory import FactoryBase
 
 
-class JointFitExperimentsFactory(FactoryBase):
-    """Create joint-fit experiment collections by tag."""
+class FittingFactory(FactoryBase):
+    """Create fitting category items by tag."""
 
     _default_rules: ClassVar[dict] = {
         frozenset(): 'default',
diff --git a/src/easydiffraction/analysis/categories/joint_fit/__init__.py b/src/easydiffraction/analysis/categories/joint_fit/__init__.py
new file mode 100644
index 000000000..9c7c49f98
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/joint_fit/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.joint_fit.default import JointFitCollection
+from easydiffraction.analysis.categories.joint_fit.default import JointFitItem
+from easydiffraction.analysis.categories.joint_fit.factory import JointFitFactory
diff --git a/src/easydiffraction/analysis/categories/joint_fit_experiments/default.py b/src/easydiffraction/analysis/categories/joint_fit/default.py
similarity index 64%
rename from src/easydiffraction/analysis/categories/joint_fit_experiments/default.py
rename to src/easydiffraction/analysis/categories/joint_fit/default.py
index b94bd7de8..62f9e7e36 100644
--- a/src/easydiffraction/analysis/categories/joint_fit_experiments/default.py
+++ b/src/easydiffraction/analysis/categories/joint_fit/default.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 """
-Joint-fit experiment weighting configuration.
+Joint-fit weighting configuration.
 
 Stores per-experiment weights to be used when multiple experiments are
 fitted simultaneously.
@@ -9,9 +9,7 @@
 
 from __future__ import annotations
 
-from easydiffraction.analysis.categories.joint_fit_experiments.factory import (
-    JointFitExperimentsFactory,
-)
+from easydiffraction.analysis.categories.joint_fit.factory import JointFitFactory
 from easydiffraction.core.category import CategoryCollection
 from easydiffraction.core.category import CategoryItem
 from easydiffraction.core.metadata import TypeInfo
@@ -23,20 +21,20 @@
 from easydiffraction.io.cif.handler import CifHandler
 
 
-class JointFitExperiment(CategoryItem):
+class JointFitItem(CategoryItem):
     """A single joint-fit entry."""
 
     def __init__(self) -> None:
         super().__init__()
 
-        self._id: StringDescriptor = StringDescriptor(
-            name='id',  # TODO: need new name instead of id
+        self._experiment_id: StringDescriptor = StringDescriptor(
+            name='experiment_id',
             description='Experiment identifier',  # TODO
             value_spec=AttributeSpec(
                 default='_',
                 validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_]*$'),
             ),
-            cif_handler=CifHandler(names=['_joint_fit_experiment.id']),
+            cif_handler=CifHandler(names=['_joint_fit.experiment_id']),
         )
         self._weight: NumericDescriptor = NumericDescriptor(
             name='weight',
@@ -45,18 +43,14 @@ def __init__(self) -> None:
                 default=0.0,
                 validator=RangeValidator(),
             ),
-            cif_handler=CifHandler(names=['_joint_fit_experiment.weight']),
+            cif_handler=CifHandler(names=['_joint_fit.weight']),
         )
 
-        self._identity.category_code = 'joint_fit_experiment'
-        self._identity.category_entry_name = lambda: str(self.id.value)
-
-    # ------------------------------------------------------------------
-    #  Public properties
-    # ------------------------------------------------------------------
+        self._identity.category_code = 'joint_fit'
+        self._identity.category_entry_name = lambda: str(self.experiment_id.value)
 
     @property
-    def id(self) -> StringDescriptor:
+    def experiment_id(self) -> StringDescriptor:
         """
         Experiment identifier.
 
@@ -64,11 +58,11 @@ def id(self) -> StringDescriptor:
         ``StringDescriptor`` object. Assigning to it updates the
         parameter value.
         """
-        return self._id
+        return self._experiment_id
 
-    @id.setter
-    def id(self, value: str) -> None:
-        self._id.value = value
+    @experiment_id.setter
+    def experiment_id(self, value: str) -> None:
+        self._experiment_id.value = value
 
     @property
     def weight(self) -> NumericDescriptor:
@@ -86,9 +80,9 @@ def weight(self, value: float) -> None:
         self._weight.value = value
 
 
-@JointFitExperimentsFactory.register
-class JointFitExperiments(CategoryCollection):
-    """Collection of :class:`JointFitExperiment` items."""
+@JointFitFactory.register
+class JointFitCollection(CategoryCollection):
+    """Collection of :class:`JointFitItem` items."""
 
     type_info = TypeInfo(
         tag='default',
@@ -96,5 +90,5 @@ class JointFitExperiments(CategoryCollection):
     )
 
     def __init__(self) -> None:
-        """Create an empty joint-fit experiments collection."""
-        super().__init__(item_type=JointFitExperiment)
+        """Create an empty joint-fit collection."""
+        super().__init__(item_type=JointFitItem)
diff --git a/src/easydiffraction/analysis/categories/fit/factory.py b/src/easydiffraction/analysis/categories/joint_fit/factory.py
similarity index 70%
rename from src/easydiffraction/analysis/categories/fit/factory.py
rename to src/easydiffraction/analysis/categories/joint_fit/factory.py
index 37bef2bbc..fa15edc00 100644
--- a/src/easydiffraction/analysis/categories/fit/factory.py
+++ b/src/easydiffraction/analysis/categories/joint_fit/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Fit factory — delegates entirely to ``FactoryBase``."""
+"""Joint-fit factory - delegates to ``FactoryBase``."""
 
 from __future__ import annotations
 
@@ -9,8 +9,8 @@
 from easydiffraction.core.factory import FactoryBase
 
 
-class FitFactory(FactoryBase):
-    """Create fit category items by tag."""
+class JointFitFactory(FactoryBase):
+    """Create joint-fit collections by tag."""
 
     _default_rules: ClassVar[dict] = {
         frozenset(): 'default',
diff --git a/src/easydiffraction/analysis/categories/joint_fit_experiments/__init__.py b/src/easydiffraction/analysis/categories/joint_fit_experiments/__init__.py
deleted file mode 100644
index 1aa8f0ae3..000000000
--- a/src/easydiffraction/analysis/categories/joint_fit_experiments/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-from easydiffraction.analysis.categories.joint_fit_experiments.default import JointFitExperiment
-from easydiffraction.analysis.categories.joint_fit_experiments.default import JointFitExperiments
diff --git a/src/easydiffraction/analysis/categories/sequential_fit/__init__.py b/src/easydiffraction/analysis/categories/sequential_fit/__init__.py
new file mode 100644
index 000000000..5381b7e1e
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit/__init__.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.sequential_fit.default import SequentialFit
+from easydiffraction.analysis.categories.sequential_fit.factory import SequentialFitFactory
diff --git a/src/easydiffraction/analysis/categories/sequential_fit/default.py b/src/easydiffraction/analysis/categories/sequential_fit/default.py
new file mode 100644
index 000000000..aad826f18
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit/default.py
@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+Sequential-fit configuration category.
+
+Stores persisted settings for directory-based sequential fitting.
+"""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.sequential_fit.factory import SequentialFitFactory
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@SequentialFitFactory.register
+class SequentialFit(CategoryItem):
+    """Persisted settings for sequential fitting."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Sequential fitting settings',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._data_dir = StringDescriptor(
+            name='data_dir',
+            description='Directory containing sequential-fit data files.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_sequential_fit.data_dir']),
+        )
+        self._file_pattern = StringDescriptor(
+            name='file_pattern',
+            description='Glob pattern selecting sequential-fit files.',
+            value_spec=AttributeSpec(default='*'),
+            cif_handler=CifHandler(names=['_sequential_fit.file_pattern']),
+        )
+        self._max_workers = StringDescriptor(
+            name='max_workers',
+            description='Worker-count token for sequential fitting.',
+            value_spec=AttributeSpec(
+                default='1',
+                validator=RegexValidator(pattern=r'^(auto|[1-9]\d*)$'),
+            ),
+            cif_handler=CifHandler(names=['_sequential_fit.max_workers']),
+        )
+        self._chunk_size = StringDescriptor(
+            name='chunk_size',
+            description='Chunk-size token for sequential fitting.',
+            value_spec=AttributeSpec(
+                default='.',
+                validator=RegexValidator(pattern=r'^([1-9]\d*|\.)$'),
+            ),
+            cif_handler=CifHandler(names=['_sequential_fit.chunk_size']),
+        )
+        self._reverse = BoolDescriptor(
+            name='reverse',
+            description='Whether to process sequential-fit files in reverse.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_sequential_fit.reverse']),
+        )
+
+        self._identity.category_code = 'sequential_fit'
+
+    @property
+    def data_dir(self) -> StringDescriptor:
+        """Directory containing sequential-fit data files."""
+        return self._data_dir
+
+    @data_dir.setter
+    def data_dir(self, value: str) -> None:
+        self._data_dir.value = value
+
+    @property
+    def file_pattern(self) -> StringDescriptor:
+        """Glob pattern selecting sequential-fit files."""
+        return self._file_pattern
+
+    @file_pattern.setter
+    def file_pattern(self, value: str) -> None:
+        self._file_pattern.value = value
+
+    @property
+    def max_workers(self) -> StringDescriptor:
+        """Worker-count token for sequential fitting."""
+        return self._max_workers
+
+    @max_workers.setter
+    def max_workers(self, value: str) -> None:
+        self._max_workers.value = value
+
+    @property
+    def chunk_size(self) -> StringDescriptor:
+        """Chunk-size token for sequential fitting."""
+        return self._chunk_size
+
+    @chunk_size.setter
+    def chunk_size(self, value: str) -> None:
+        self._chunk_size.value = value
+
+    @property
+    def reverse(self) -> BoolDescriptor:
+        """Whether to process sequential-fit files in reverse."""
+        return self._reverse
+
+    @reverse.setter
+    def reverse(self, value: bool) -> None:
+        self._reverse.value = value
+
+    @property
+    def as_cif(self) -> str:
+        """Return CIF representation of this sequential-fit category."""
+        return super().as_cif
+
+    def from_cif(self, block: object, idx: int = 0) -> None:
+        """Populate this sequential-fit category from a CIF block."""
+        super().from_cif(block, idx)
diff --git a/src/easydiffraction/analysis/categories/sequential_fit/factory.py b/src/easydiffraction/analysis/categories/sequential_fit/factory.py
new file mode 100644
index 000000000..43c96f1ee
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Sequential-fit factory - delegates to ``FactoryBase``."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class SequentialFitFactory(FactoryBase):
+    """Create sequential-fit category items by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/sequential_fit_extract/__init__.py b/src/easydiffraction/analysis/categories/sequential_fit_extract/__init__.py
new file mode 100644
index 000000000..ff6ba0563
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit_extract/__init__.py
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+    SequentialFitExtractCollection,
+)
+from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+    SequentialFitExtractItem,
+)
+from easydiffraction.analysis.categories.sequential_fit_extract.factory import (
+    SequentialFitExtractFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py b/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
new file mode 100644
index 000000000..84bbf70c5
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
@@ -0,0 +1,162 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+Sequential-fit extract-rule configuration.
+
+Stores persisted rules for extracting diffrn metadata from sequential
+fit input files.
+"""
+
+from __future__ import annotations
+
+import re
+
+from easydiffraction.analysis.categories.sequential_fit_extract.factory import (
+    SequentialFitExtractFactory,
+)
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+_TARGET_SEGMENT_PATTERN = re.compile(r'^[A-Za-z_][A-Za-z0-9_]*$')
+_EXTRACT_TARGET_SEGMENTS = 2
+
+
+def _validate_extract_target_shape(value: str) -> None:
+    """Validate the supported two-segment extract target form."""
+    parts = value.split('.')
+    if (
+        len(parts) != _EXTRACT_TARGET_SEGMENTS
+        or parts[0] != 'diffrn'
+        or not _TARGET_SEGMENT_PATTERN.fullmatch(parts[1])
+    ):
+        msg = (
+            'sequential_fit_extract.target must use the form '
+            "'diffrn.<name>' with exactly two segments."
+        )
+        raise ValueError(msg)
+
+
+def _validate_extract_pattern(value: str) -> None:
+    """Validate that an extract pattern compiles and captures once."""
+    try:
+        compiled = re.compile(value)
+    except re.error as error:
+        msg = f'Invalid sequential_fit_extract.pattern {value!r}: {error}.'
+        raise ValueError(msg) from error
+
+    if compiled.groups != 1:
+        msg = 'sequential_fit_extract.pattern must define exactly one capture group.'
+        raise ValueError(msg)
+
+
+class SequentialFitExtractItem(CategoryItem):
+    """A single sequential-fit extract rule."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._id = StringDescriptor(
+            name='id',
+            description='Identifier for this extract rule.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_]*$'),
+            ),
+            cif_handler=CifHandler(names=['_sequential_fit_extract.id']),
+        )
+        self._target = StringDescriptor(
+            name='target',
+            description='diffrn attribute updated by this extract rule.',
+            value_spec=AttributeSpec(default='diffrn._'),
+            cif_handler=CifHandler(names=['_sequential_fit_extract.target']),
+        )
+        self._pattern = StringDescriptor(
+            name='pattern',
+            description='Regex used to extract one numeric capture group.',
+            value_spec=AttributeSpec(default='(.*)'),
+            cif_handler=CifHandler(names=['_sequential_fit_extract.pattern']),
+        )
+        self._required = BoolDescriptor(
+            name='required',
+            description='Whether this extract rule must match every file.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_sequential_fit_extract.required']),
+        )
+
+        self._identity.category_code = 'sequential_fit_extract'
+        self._identity.category_entry_name = lambda: str(self.id.value)
+
+    @property
+    def id(self) -> StringDescriptor:
+        """Identifier for this extract rule."""
+        return self._id
+
+    @id.setter
+    def id(self, value: str) -> None:
+        self._id.value = value
+
+    @property
+    def target(self) -> StringDescriptor:
+        """Diffrn attribute updated by this extract rule."""
+        return self._target
+
+    @target.setter
+    def target(self, value: str) -> None:
+        self._target.value = value
+
+    @property
+    def pattern(self) -> StringDescriptor:
+        """Regex used to extract one numeric capture group."""
+        return self._pattern
+
+    @pattern.setter
+    def pattern(self, value: str) -> None:
+        self._pattern.value = value
+
+    @property
+    def required(self) -> BoolDescriptor:
+        """Whether this extract rule must match every file."""
+        return self._required
+
+    @required.setter
+    def required(self, value: bool) -> None:
+        self._required.value = value
+
+
+@SequentialFitExtractFactory.register
+class SequentialFitExtractCollection(CategoryCollection):
+    """Collection of :class:`SequentialFitExtractItem` items."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Sequential-fit metadata extraction rules',
+    )
+
+    def __init__(self) -> None:
+        """Create an empty collection of extract rules."""
+        super().__init__(item_type=SequentialFitExtractItem)
+
+    def create(
+        self,
+        *,
+        id: str,
+        target: str,
+        pattern: str,
+        required: bool = False,
+    ) -> None:
+        """Create a validated sequential-fit extract rule."""
+        _validate_extract_target_shape(target)
+        _validate_extract_pattern(pattern)
+
+        item = SequentialFitExtractItem()
+        item.id = id
+        item.target = target
+        item.pattern = pattern
+        item.required = required
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/sequential_fit_extract/factory.py b/src/easydiffraction/analysis/categories/sequential_fit_extract/factory.py
new file mode 100644
index 000000000..6fe176d60
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit_extract/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Sequential-fit-extract factory - delegates to ``FactoryBase``."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class SequentialFitExtractFactory(FactoryBase):
+    """Create sequential-fit-extract collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/fit/enums.py b/src/easydiffraction/analysis/enums.py
similarity index 73%
rename from src/easydiffraction/analysis/categories/fit/enums.py
rename to src/easydiffraction/analysis/enums.py
index a5b87054e..c1ef93f7b 100644
--- a/src/easydiffraction/analysis/categories/fit/enums.py
+++ b/src/easydiffraction/analysis/enums.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Enumeration for fit-mode values."""
+"""Enumeration types used by analysis components."""
 
 from __future__ import annotations
 
@@ -22,9 +22,9 @@ def default(cls) -> FitModeEnum:
     def description(self) -> str:
         """Return a human-readable description of this fit mode."""
         if self is FitModeEnum.SINGLE:
-            return 'Independent fitting of each experiment'
+            return 'Fit one experiment at a time.'
         if self is FitModeEnum.JOINT:
-            return 'Simultaneous fitting of all experiments with weights'
+            return 'Fit several experiments together with shared parameters.'
         if self is FitModeEnum.SEQUENTIAL:
-            return 'Sequential fitting over data files in a directory'
+            return 'Fit one experiment against a series of data files.'
         return ''
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index e10d9c698..ca191c835 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -42,8 +42,8 @@ class PosteriorParameterSummary:
         Unique parameter name used across EasyDiffraction.
     display_name : str
         Human-readable label used in plots and tables.
-    map_value : float
-        Maximum-a-posteriori or best-sampled parameter value.
+    best_sample_value : float
+        Highest-posterior sampled parameter value.
     median : float
         Posterior median value.
     standard_deviation : float
@@ -60,7 +60,7 @@ class PosteriorParameterSummary:
 
     unique_name: str
     display_name: str
-    map_value: float
+    best_sample_value: float
     median: float
     standard_deviation: float
     interval_68: tuple[float, float]
@@ -82,7 +82,7 @@ class PosteriorPredictiveSummary:
         Name of the x-axis used for the predictive arrays.
     x : np.ndarray
         X-axis values for the predictive curves.
-    map_prediction : np.ndarray
+    best_sample_prediction : np.ndarray
         Prediction corresponding to the committed point estimate.
     lower_95 : np.ndarray | None, default=None
         Lower bound of the 95% credible interval.
@@ -99,7 +99,7 @@ class PosteriorPredictiveSummary:
     experiment_name: str
     x_axis_name: str
     x: np.ndarray
-    map_prediction: np.ndarray
+    best_sample_prediction: np.ndarray
     lower_95: np.ndarray | None = None
     upper_95: np.ndarray | None = None
     lower_68: np.ndarray | None = None
@@ -212,7 +212,7 @@ class BayesianFitResults(FitResults):
         Total fitting time in seconds.
     sampler_name : str, default='dream'
         Sampler identifier.
-    point_estimate_name : str, default='map'
+    point_estimate_name : str, default='best_sample'
         Name of the point estimate committed back to the project.
     posterior_samples : PosteriorSamples | None, default=None
         Stored posterior samples.
@@ -239,7 +239,7 @@ class BayesianFitResults(FitResults):
     starting_parameters: list[object] | None = None
     fitting_time: float | None = None
     sampler_name: str = 'dream'
-    point_estimate_name: str = 'map'
+    point_estimate_name: str = 'best_sample'
     posterior_samples: PosteriorSamples | None = None
     posterior_parameter_summaries: SummaryList = None
     posterior_predictive: PredictiveMap = None
@@ -412,7 +412,7 @@ def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict
 def summarize_posterior_parameters(
     parameter_names: list[str],
     posterior_samples: PosteriorSamples,
-    map_values: np.ndarray,
+    best_sample_values: np.ndarray,
     parameter_display_names: list[str] | None = None,
     convergence_diagnostics: dict[str, object] | None = None,
 ) -> list[PosteriorParameterSummary]:
@@ -425,8 +425,8 @@ def summarize_posterior_parameters(
         Sampled parameter names in EasyDiffraction order.
     posterior_samples : PosteriorSamples
         Posterior sample container.
-    map_values : np.ndarray
-        MAP or best-sampled parameter values in the same order.
+    best_sample_values : np.ndarray
+        Best posterior sample values in the same order.
     parameter_display_names : list[str] | None, default=None
         Human-readable parameter names in the same order.
     convergence_diagnostics : dict[str, object] | None, default=None
@@ -473,7 +473,7 @@ def summarize_posterior_parameters(
             PosteriorParameterSummary(
                 unique_name=parameter_name,
                 display_name=display_name,
-                map_value=float(map_values[index]),
+                best_sample_value=float(best_sample_values[index]),
                 median=float(np.median(values)),
                 standard_deviation=float(np.std(values, ddof=1)),
                 interval_68=(float(interval_68[0]), float(interval_68[1])),
@@ -574,8 +574,8 @@ def _print_fit_quality_metrics(metrics: dict[str, float | None]) -> None:
 def _format_point_estimate_name(point_estimate_name: str) -> str:
     """Return a user-facing label for the committed point estimate."""
     normalized_name = point_estimate_name.strip().lower().replace('_', ' ')
-    if normalized_name == 'map':
-        return 'Max posterior'
+    if normalized_name in {'best sample', 'map'}:
+        return 'Best posterior sample'
     return point_estimate_name.replace('_', ' ').title()
 
 
@@ -631,7 +631,7 @@ def _render_committed_parameter_table(parameters: list[object]) -> None:
         'parameter',
         'units',
         'start',
-        'max posterior',
+        'best posterior sample',
         'uncertainty',
         'change',
     ]
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 256ff9a55..1cb42fb51 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -874,7 +874,10 @@ def _build_success_result(
         label_to_index = {label: index for index, label in enumerate(raw_state.labels)}
         ordered_indices = [label_to_index[uid] for uid in context.parameter_uids]
         ordered_samples = np.asarray(parameter_samples_array, dtype=float)[:, :, ordered_indices]
-        map_values = np.array([best_by_name[uid] for uid in context.parameter_uids], dtype=float)
+        best_sample_values = np.array(
+            [best_by_name[uid] for uid in context.parameter_uids],
+            dtype=float,
+        )
         posterior_samples = PosteriorSamples(
             parameter_names=context.parameter_names,
             parameter_samples=ordered_samples,
@@ -885,7 +888,7 @@ def _build_success_result(
         posterior_parameter_summaries = summarize_posterior_parameters(
             parameter_names=context.parameter_names,
             posterior_samples=posterior_samples,
-            map_values=map_values,
+            best_sample_values=best_sample_values,
             parameter_display_names=context.parameter_display_names,
             convergence_diagnostics=convergence_diagnostics,
         )
@@ -897,7 +900,7 @@ def _build_success_result(
             log.warning('Convergence diagnostics indicate the posterior may be poorly mixed.')
 
         return OptimizeResult(
-            x=map_values,
+            x=best_sample_values,
             dx=posterior_standard_deviations,
             fun=float(best_nllf),
             success=True,
@@ -921,7 +924,7 @@ def _sync_result_to_parameters(
         raw_result: object,
     ) -> None:
         """
-        Commit MAP values on success and restore starts on failure.
+        Sync best posterior values or restore starts.
 
         Parameters
         ----------
@@ -985,7 +988,7 @@ def _build_fit_results(
             starting_parameters=parameters,
             fitting_time=self.tracker.fitting_time,
             sampler_name='dream',
-            point_estimate_name='map',
+            point_estimate_name='best_sample',
             posterior_samples=getattr(raw_result, 'posterior_samples', None),
             posterior_parameter_summaries=getattr(raw_result, 'posterior_parameter_summaries', []),
             posterior_predictive={},
diff --git a/src/easydiffraction/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index a52d38b1a..029486c79 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -9,12 +9,14 @@
 import contextlib
 import csv
 import multiprocessing as mp
+import os
+import re
 import sys
+import time
 from concurrent.futures import ProcessPoolExecutor
 from dataclasses import dataclass
 from dataclasses import replace
 from pathlib import Path
-from typing import TYPE_CHECKING
 from typing import Any
 
 from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
@@ -23,15 +25,23 @@
 from easydiffraction.utils.enums import VerbosityEnum
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
-
-if TYPE_CHECKING:
-    from collections.abc import Callable
+from easydiffraction.utils.utils import build_table_renderable
 
 # ------------------------------------------------------------------
 #  Template dataclass (picklable for ProcessPoolExecutor)
 # ------------------------------------------------------------------
 
 
+@dataclass(frozen=True)
+class SequentialFitExtractRule:
+    """Picklable sequential-fit extract rule for worker execution."""
+
+    id: str
+    field_name: str
+    pattern: str
+    required: bool
+
+
 @dataclass(frozen=True)
 class SequentialFitTemplate:
     """
@@ -50,6 +60,7 @@ class SequentialFitTemplate:
     constraints_enabled: bool
     minimizer_tag: str
     calculator_tag: str
+    diffrn_extract_rules: list[SequentialFitExtractRule]
     diffrn_field_names: list[str]
 
 
@@ -108,13 +119,16 @@ def _fit_worker(
         # 4. Replace data from the new data path
         expt._load_ascii_data_to_experiment(data_path)
 
-        # 5. Override parameter values from propagated starting values
+        # 5. Extract diffrn metadata from the data file
+        result.update(_extract_diffrn_values(expt, data_path, template.diffrn_extract_rules))
+
+        # 6. Override parameter values from propagated starting values
         _apply_param_overrides(project, template.initial_params)
 
-        # 6. Set free flags
+        # 7. Set free flags
         _set_free_params(project, template.free_param_unique_names)
 
-        # 7. Apply constraints
+        # 8. Apply constraints
         if template.constraints_enabled and template.alias_defs:
             _apply_constraints(
                 project,
@@ -122,17 +136,22 @@ def _fit_worker(
                 template.constraint_defs,
             )
 
-        # 8. Set calculator and minimizer
+        # 9. Set calculator and minimizer
         #    (internal, no console output)
         from easydiffraction.analysis.fitting import Fitter  # noqa: PLC0415
 
         expt._set_calculator_type(template.calculator_tag, announce=False)
         project.analysis.fitter = Fitter(template.minimizer_tag)
 
-        # 9. Fit
-        project.analysis.fit(verbosity='silent')
+        # 10. Fit
+        original_verbosity = project.verbosity
+        project.verbosity = 'silent'
+        try:
+            project.analysis.fit()
+        finally:
+            project.verbosity = original_verbosity
 
-        # 10. Collect results
+        # 11. Collect results
         result.update(_collect_results(project, template))
 
     except (
@@ -234,6 +253,82 @@ def _apply_constraints(
         project.analysis.constraints.create(expression=expr)
 
 
+def _extract_diffrn_values(
+    experiment: object,
+    data_path: str,
+    extract_rules: list[SequentialFitExtractRule],
+) -> dict[str, float]:
+    """
+    Extract diffrn metadata from a single data file.
+
+    Parameters
+    ----------
+    experiment : object
+        The worker experiment whose diffrn descriptors are updated.
+    data_path : str
+        Path to the data file being fitted.
+    extract_rules : list[SequentialFitExtractRule]
+        Persisted extract rules resolved from analysis settings.
+
+    Returns
+    -------
+    dict[str, float]
+        Extracted ``diffrn.<field>`` values for the CSV row.
+
+    Raises
+    ------
+    ValueError
+        If a required rule does not match or captures a non-numeric
+        value.
+    """
+    if not extract_rules:
+        return {}
+
+    compiled_rules = [(rule, re.compile(rule.pattern)) for rule in extract_rules]
+    matched_rule_ids: set[str] = set()
+    extracted_values: dict[str, float] = {}
+
+    with Path(data_path).open(encoding='utf-8', errors='ignore') as handle:
+        for line in handle:
+            for rule, pattern in compiled_rules:
+                if rule.id in matched_rule_ids:
+                    continue
+
+                match = pattern.search(line)
+                if match is None:
+                    continue
+
+                try:
+                    extracted_value = float(match.group(1))
+                except (TypeError, ValueError) as error:
+                    msg = (
+                        f"Sequential extract rule '{rule.id}' captured a non-numeric value "
+                        f"for 'diffrn.{rule.field_name}' in {data_path!r}."
+                    )
+                    raise ValueError(msg) from error
+
+                descriptor = getattr(experiment.diffrn, rule.field_name)
+                descriptor.value = extracted_value
+                extracted_values[f'diffrn.{rule.field_name}'] = extracted_value
+                matched_rule_ids.add(rule.id)
+
+            if len(matched_rule_ids) == len(extract_rules):
+                break
+
+    missing_required = [
+        f'{rule.id} (diffrn.{rule.field_name})'
+        for rule in extract_rules
+        if rule.required and rule.id not in matched_rule_ids
+    ]
+    if missing_required:
+        msg = (
+            f'Sequential extract rules did not match {data_path!r}: {", ".join(missing_required)}.'
+        )
+        raise ValueError(msg)
+
+    return extracted_values
+
+
 def _collect_results(
     project: object,
     template: SequentialFitTemplate,
@@ -357,7 +452,47 @@ def _append_to_csv(
     with csv_path.open('a', newline='', encoding='utf-8') as f:
         writer = csv.DictWriter(f, fieldnames=header, extrasaction='ignore')
         for result in results:
-            writer.writerow(result)
+            row = dict(result)
+            file_path = row.get('file_path')
+            if file_path:
+                row['file_path'] = _relative_file_path_for_csv(csv_path, str(file_path))
+            writer.writerow(row)
+
+
+def _relative_file_path_for_csv(
+    csv_path: Path,
+    file_path: str,
+) -> str:
+    """Return *file_path* relative to the CSV-owning project."""
+    project_path = csv_path.parent.parent.resolve()
+    resolved_path = _resolve_project_file_path(project_path, file_path)
+    relative_path = os.path.relpath(resolved_path, start=project_path)
+    return relative_path.replace('\\', '/')
+
+
+def _resolve_csv_file_path(
+    csv_path: Path,
+    file_path: str,
+) -> str:
+    """Resolve a stored CSV file path against the owning project."""
+    project_path = csv_path.parent.parent.resolve()
+    return str(_resolve_project_file_path(project_path, file_path))
+
+
+def _resolve_project_file_path(
+    project_path: Path,
+    file_path: str,
+) -> Path:
+    """Resolve a data file path to an absolute path near the project."""
+    path = Path(file_path)
+    if path.is_absolute():
+        return path.resolve()
+
+    cwd_relative_path = path.resolve()
+    if cwd_relative_path.is_relative_to(project_path):
+        return cwd_relative_path
+
+    return (project_path / path).resolve()
 
 
 def _extract_params_from_row(row: dict[str, str]) -> dict[str, float]:
@@ -415,7 +550,7 @@ def _read_csv_for_recovery(
         for row in reader:
             file_path = row.get('file_path', '')
             if file_path:
-                fitted.add(file_path)
+                fitted.add(_resolve_csv_file_path(csv_path, file_path))
             if row.get('fit_success', '').lower() == 'true':
                 params = _extract_params_from_row(row)
                 if params:
@@ -443,7 +578,14 @@ def _build_template(project: object) -> SequentialFitTemplate:
     -------
     SequentialFitTemplate
         A frozen, picklable snapshot.
+
+    Raises
+    ------
+    TypeError
+        If a sequential extract target does not reference an existing
+        numeric ``diffrn`` descriptor on the template experiment.
     """
+    from easydiffraction.core.variable import NumericDescriptor  # noqa: PLC0415
     from easydiffraction.core.variable import Parameter  # noqa: PLC0415
 
     structure = next(iter(project.structures.values()))
@@ -472,12 +614,31 @@ def _build_template(project: object) -> SequentialFitTemplate:
         constraint.expression.value for constraint in project.analysis.constraints
     ]
 
-    # Collect diffrn field names from the experiment
+    # Validate and collect sequential diffrn extract rules against the
+    # template experiment before worker execution starts.
+    diffrn_extract_rules: list[SequentialFitExtractRule] = []
     diffrn_field_names: list[str] = []
-    if hasattr(experiment, 'diffrn'):
-        diffrn_field_names.extend(
-            p.name for p in experiment.diffrn.parameters if hasattr(p, 'name') and p.name != 'type'
+    for extract_rule in project.analysis.sequential_fit_extract:
+        target = extract_rule.target.value
+        field_name = target.split('.', maxsplit=1)[1]
+        descriptor = getattr(experiment.diffrn, field_name, None)
+        if not isinstance(descriptor, NumericDescriptor):
+            msg = (
+                f"Sequential extract target '{target}' must reference an existing numeric "
+                'diffrn descriptor on the template experiment.'
+            )
+            raise TypeError(msg)
+
+        diffrn_extract_rules.append(
+            SequentialFitExtractRule(
+                id=extract_rule.id.value,
+                field_name=field_name,
+                pattern=extract_rule.pattern.value,
+                required=extract_rule.required.value,
+            )
         )
+        if field_name not in diffrn_field_names:
+            diffrn_field_names.append(field_name)
 
     return SequentialFitTemplate(
         structure_cif=structure.as_cif,
@@ -487,8 +648,9 @@ def _build_template(project: object) -> SequentialFitTemplate:
         alias_defs=alias_defs,
         constraint_defs=constraint_defs,
         constraints_enabled=project.analysis.constraints.enabled,
-        minimizer_tag=project.analysis.fit.minimizer_type.value or 'lmfit',
+        minimizer_tag=project.analysis.fitting.minimizer_type.value or 'lmfit',
         calculator_tag=experiment.calculation.calculator_type.value,
+        diffrn_extract_rules=diffrn_extract_rules,
         diffrn_field_names=diffrn_field_names,
     )
 
@@ -498,11 +660,315 @@ def _build_template(project: object) -> SequentialFitTemplate:
 # ------------------------------------------------------------------
 
 
-def _report_chunk_progress(
+_SEQUENTIAL_CHUNK_PROGRESS_HEADERS = [
+    'chunk',
+    'progress',
+    'time (s)',
+    'files',
+    'count',
+    'average χ²',
+    'status',
+]
+_SEQUENTIAL_CHUNK_PROGRESS_ALIGNMENTS = [
+    'right',
+    'right',
+    'right',
+    'left',
+    'right',
+    'right',
+    'center',
+]
+_SEQUENTIAL_FILE_PROGRESS_HEADERS = ['file', 'progress', 'time (s)', 'χ²', 'iterations', 'status']
+_SEQUENTIAL_FILE_PROGRESS_ALIGNMENTS = ['left', 'right', 'right', 'right', 'right', 'center']
+
+
+@dataclass
+class SequentialProgressState:
+    """Mutable live progress rows for sequential fitting."""
+
+    chunk_rows: list[list[str]]
+    file_rows: list[list[str]]
+
+
+@dataclass
+class SequentialProgressContext:
+    """Mutable sequential-fit progress handles and state."""
+
+    verbosity: VerbosityEnum
+    state: SequentialProgressState | None
+    indicator: ActivityIndicator | None = None
+
+
+@dataclass(frozen=True)
+class _ChunkProgressMetrics:
+    """File counts and elapsed time for a completed chunk."""
+
+    completed_files_before: int
+    total_files: int
+    elapsed_time: float
+
+
+@dataclass(frozen=True)
+class SequentialRunPlan:
+    """Resolved sequential-fit inputs and bookkeeping."""
+
+    verbosity: VerbosityEnum
+    template: SequentialFitTemplate
+    csv_path: Path
+    header: list[str]
+    remaining: list[str]
+    chunks: list[list[str]]
+    max_workers: int
+    processed_count: int
+
+
+def _summarize_chunk_results(results: list[dict[str, Any]]) -> tuple[str, str]:
+    """Return average reduced chi-square and status for a chunk."""
+    num_files = len(results)
+    successful = [r for r in results if r.get('fit_success')]
+    if successful:
+        avg_chi2 = sum(r['reduced_chi_squared'] for r in successful) / len(successful)
+        chi2_str = f'{avg_chi2:.2f}'
+    else:
+        chi2_str = '—'
+
+    if len(successful) == num_files:
+        status = '✅'
+    elif successful:
+        status = '⚠️'
+    else:
+        status = '❌'
+
+    return chi2_str, status
+
+
+def _chunk_file_range(chunk: list[str]) -> str:
+    """Return the inclusive file-name range for a chunk."""
+    first_name = Path(chunk[0]).name
+    last_name = Path(chunk[-1]).name
+    if first_name == last_name:
+        return first_name
+    return f'{first_name} - {last_name}'
+
+
+def _format_progress_percent(completed_items: int, total_items: int) -> str:
+    """Return overall progress as a percentage string."""
+    if total_items < 1:
+        return '0.0%'
+    clamped_completed = min(max(completed_items, 0), total_items)
+    return f'{100.0 * clamped_completed / total_items:.1f}%'
+
+
+def _format_elapsed_seconds(elapsed_time: float) -> str:
+    """Return elapsed time in seconds with two decimal places."""
+    return f'{max(elapsed_time, 0.0):.2f}'
+
+
+def _build_chunk_progress_row(
     chunk_idx: int,
     total_chunks: int,
+    chunk: list[str],
+    results: list[dict[str, Any]],
+    completed_files: int,
+    total_files: int,
+    elapsed_time: float,
+) -> list[str]:
+    """
+    Return one sequential-progress table row for a completed chunk.
+    """
+    chi2_str, status = _summarize_chunk_results(results)
+    return [
+        f'{chunk_idx}/{total_chunks}',
+        _format_progress_percent(completed_files, total_files),
+        _format_elapsed_seconds(elapsed_time),
+        _chunk_file_range(chunk),
+        str(len(results)),
+        chi2_str,
+        status,
+    ]
+
+
+def _build_file_progress_rows(
     results: list[dict[str, Any]],
+    completed_files_before: int,
+    total_files: int,
+    elapsed_time: float,
+) -> list[list[str]]:
+    """Return sequential-progress rows for individual file fits."""
+    rows: list[list[str]] = []
+    time_str = _format_elapsed_seconds(elapsed_time)
+    for index, result in enumerate(results, start=1):
+        reduced_chi2 = result.get('reduced_chi_squared')
+        chi2_str = f'{reduced_chi2:.2f}' if reduced_chi2 is not None else '—'
+        iterations = str(result.get('n_iterations') or 0)
+        status = '✅' if result.get('fit_success') else '❌'
+        rows.append([
+            Path(result['file_path']).name,
+            _format_progress_percent(completed_files_before + index, total_files),
+            time_str,
+            chi2_str,
+            iterations,
+            status,
+        ])
+    return rows
+
+
+def _build_progress_renderable(
+    verbosity: VerbosityEnum,
+    progress_state: SequentialProgressState,
+) -> object:
+    """Build the sequential progress table renderable."""
+    if verbosity is VerbosityEnum.FULL:
+        return build_table_renderable(
+            columns_headers=_SEQUENTIAL_FILE_PROGRESS_HEADERS,
+            columns_alignment=_SEQUENTIAL_FILE_PROGRESS_ALIGNMENTS,
+            columns_data=progress_state.file_rows,
+        )
+
+    return build_table_renderable(
+        columns_headers=_SEQUENTIAL_CHUNK_PROGRESS_HEADERS,
+        columns_alignment=_SEQUENTIAL_CHUNK_PROGRESS_ALIGNMENTS,
+        columns_data=progress_state.chunk_rows,
+    )
+
+
+def _create_progress_context(
+    verbosity: VerbosityEnum,
+) -> SequentialProgressContext:
+    """Return a mutable progress context for the given verbosity."""
+    if verbosity is VerbosityEnum.SILENT:
+        return SequentialProgressContext(verbosity=verbosity, state=None)
+
+    return SequentialProgressContext(
+        verbosity=verbosity,
+        state=SequentialProgressState(chunk_rows=[], file_rows=[]),
+    )
+
+
+def _start_progress_display(progress: SequentialProgressContext) -> None:
+    """
+    Start the live progress indicator with an empty bordered table.
+    """
+    if progress.verbosity is VerbosityEnum.SILENT or progress.state is None:
+        return
+
+    indicator = ActivityIndicator(
+        ACTIVITY_LABEL_FITTING,
+        verbosity=progress.verbosity,
+    )
+    indicator.start()
+    indicator.update(
+        content=_build_progress_renderable(progress.verbosity, progress.state),
+    )
+    progress.indicator = indicator
+
+
+def _stop_progress_display(progress: SequentialProgressContext) -> None:
+    """Stop any active sequential-fit progress display."""
+    if progress.indicator is not None:
+        progress.indicator.stop()
+        progress.indicator = None
+
+
+def _print_sequential_header(
+    analysis: object,
+    verbosity: VerbosityEnum,
+    remaining: list[str],
+    chunks: list[list[str]],
+    max_workers: int,
+) -> None:
+    """Print the user-facing sequential-fit header."""
+    if verbosity is VerbosityEnum.SILENT:
+        return
+
+    console.paragraph('Sequential fitting')
+    console.print(f"🚀 Starting fit process with '{analysis.fitter.selection}'...")
+    console.print(f'📋 {len(remaining)} files in {len(chunks)} chunks (max_workers={max_workers})')
+    console.print('📈 Goodness-of-fit progress:')
+
+
+def _print_sequential_completion(
     verbosity: VerbosityEnum,
+    processed_count: int,
+    csv_path: Path,
+) -> None:
+    """Print the final sequential-fit summary."""
+    if verbosity is VerbosityEnum.SILENT:
+        return
+
+    console.print(f'✅ Sequential fitting complete: {processed_count} files processed.')
+    console.print(f'📄 Results saved to: {csv_path}')
+
+
+def _prepare_sequential_run(
+    analysis: object,
+    data_dir: str,
+    max_workers: int | str,
+    chunk_size: int | None,
+    file_pattern: str,
+    *,
+    reverse: bool,
+) -> SequentialRunPlan | None:
+    """Resolve inputs and bookkeeping for one sequential-fit run."""
+    verbosity = VerbosityEnum(analysis.project.verbosity)
+
+    _check_seq_preconditions(analysis.project)
+
+    data_paths = extract_data_paths_from_dir(data_dir, file_pattern=file_pattern)
+    template = _build_template(analysis.project)
+    csv_path, header, already_fitted, template = _setup_csv_and_recovery(
+        analysis.project,
+        template,
+        verbosity,
+    )
+
+    remaining = [path for path in data_paths if path not in already_fitted]
+    if reverse:
+        remaining.reverse()
+    if not remaining:
+        if verbosity is not VerbosityEnum.SILENT:
+            console.print('✅ All files already fitted. Nothing to do.')
+        return None
+
+    resolved_workers, resolved_chunk_size = _resolve_workers(max_workers, chunk_size)
+    chunks = [
+        remaining[index : index + resolved_chunk_size]
+        for index in range(0, len(remaining), resolved_chunk_size)
+    ]
+    return SequentialRunPlan(
+        verbosity=verbosity,
+        template=template,
+        csv_path=csv_path,
+        header=header,
+        remaining=remaining,
+        chunks=chunks,
+        max_workers=resolved_workers,
+        processed_count=len(already_fitted) + len(remaining),
+    )
+
+
+def _run_fit_loop_with_pool(
+    max_workers: int,
+    chunks: list[list[str]],
+    template: SequentialFitTemplate,
+    csv_info: tuple[Path, list[str]],
+    progress: SequentialProgressContext,
+) -> None:
+    """Execute the fit loop inside a worker-pool context."""
+    pool_cm, main_mod, main_file_bak, main_spec_bak = _create_pool_context(max_workers)
+    try:
+        _run_fit_loop(pool_cm, chunks, template, csv_info, progress)
+    finally:
+        _restore_main_state(main_mod, main_file_bak, main_spec_bak)
+
+
+def _report_chunk_progress(
+    chunk_idx: int,
+    total_chunks: int,
+    chunk: list[str],
+    results: list[dict[str, Any]],
+    progress: SequentialProgressContext,
+    metrics: _ChunkProgressMetrics,
 ) -> None:
     """
     Report progress after a chunk completes.
@@ -513,65 +979,46 @@ def _report_chunk_progress(
         1-based index of the current chunk.
     total_chunks : int
         Total number of chunks.
+    chunk : list[str]
+        File paths in the current chunk.
     results : list[dict[str, Any]]
         Results from the chunk.
-    verbosity : VerbosityEnum
-        Output verbosity.
+    progress : SequentialProgressContext
+        Mutable progress handles and accumulated table rows.
+    metrics : _ChunkProgressMetrics
+        File counts and elapsed time for the completed chunk.
     """
-    if verbosity is VerbosityEnum.SILENT:
+    if progress.verbosity is VerbosityEnum.SILENT or progress.state is None:
         return
 
-    num_files = len(results)
-    successful = [r for r in results if r.get('fit_success')]
-    if successful:
-        avg_chi2 = sum(r['reduced_chi_squared'] for r in successful) / len(successful)
-        chi2_str = f'{avg_chi2:.2f}'
-    else:
-        chi2_str = '—'
+    completed_files = metrics.completed_files_before + len(results)
 
-    if verbosity is VerbosityEnum.SHORT:
-        status = '✅' if successful else '❌'
-        console.print(
-            f'{status} Chunk {chunk_idx}/{total_chunks}: {num_files} files, avg χ² = {chi2_str}'
+    if progress.verbosity is VerbosityEnum.FULL:
+        new_rows = _build_file_progress_rows(
+            results,
+            metrics.completed_files_before,
+            metrics.total_files,
+            metrics.elapsed_time,
         )
-    elif verbosity is VerbosityEnum.FULL:
-        console.print(
-            f'Chunk {chunk_idx}/{total_chunks}: '
-            f'{num_files} files, {len(successful)} succeeded, '
-            f'avg reduced χ² = {chi2_str}'
-        )
-        for r in results:
-            status = '✅' if r.get('fit_success') else '❌'
-            rchi2 = r.get('reduced_chi_squared')
-            rchi2_str = f'{rchi2:.2f}' if rchi2 is not None else '—'
-            console.print(f'  {status} {Path(r["file_path"]).name}: χ² = {rchi2_str}')
-
-
-def _apply_diffrn_metadata(
-    results: list[dict[str, Any]],
-    extract_diffrn: Callable,
-) -> None:
-    """
-    Enrich result dicts with diffrn metadata from a user callback.
-
-    Calls *extract_diffrn* for each result and merges the returned
-    key/value pairs into the result dict under ``diffrn.<key>`` keys.
-    Failures are logged as warnings and do not interrupt processing.
+        progress.state.file_rows.extend(new_rows)
+    else:
+        new_rows = [
+            _build_chunk_progress_row(
+                chunk_idx,
+                total_chunks,
+                chunk,
+                results,
+                completed_files,
+                metrics.total_files,
+                metrics.elapsed_time,
+            )
+        ]
+        progress.state.chunk_rows.extend(new_rows)
 
-    Parameters
-    ----------
-    results : list[dict[str, Any]]
-        Worker result dicts (mutated in place).
-    extract_diffrn : Callable
-        User callback: ``f(file_path) → {field: value}``.
-    """
-    for result in results:
-        try:
-            diffrn_values = extract_diffrn(result['file_path'])
-            for key, val in diffrn_values.items():
-                result[f'diffrn.{key}'] = val
-        except (RuntimeError, ValueError, TypeError, KeyError, AttributeError, OSError) as exc:
-            log.warning(f'extract_diffrn failed for {result["file_path"]}: {exc}')
+    if progress.indicator is not None:
+        progress.indicator.update(
+            content=_build_progress_renderable(progress.verbosity, progress.state),
+        )
 
 
 # ------------------------------------------------------------------
@@ -757,9 +1204,7 @@ def _run_fit_loop(
     chunks: list[list[str]],
     template: SequentialFitTemplate,
     csv_info: tuple[Path, list[str]],
-    extract_diffrn: Callable | None,
-    verb: VerbosityEnum,
-    indicator: ActivityIndicator | None,
+    progress: SequentialProgressContext,
 ) -> None:
     """
     Execute the chunk-based fitting loop.
@@ -774,15 +1219,14 @@ def _run_fit_loop(
         Starting template (updated via propagation).
     csv_info : tuple[Path, list[str]]
         Tuple of ``(csv_path, header)``.
-    extract_diffrn : Callable | None
-        User callback for diffrn metadata.
-    verb : VerbosityEnum
-        Output verbosity.
-    indicator : ActivityIndicator | None
-        Shared sequential-fit activity indicator.
+    progress : SequentialProgressContext
+        Mutable progress handles and accumulated table rows.
     """
     csv_path, header = csv_info
     total_chunks = len(chunks)
+    total_files = sum(len(chunk) for chunk in chunks)
+    completed_files = 0
+    started_at = time.perf_counter()
     with pool_cm as executor:
         for chunk_idx, chunk in enumerate(chunks, start=1):
             if executor is not None:
@@ -791,13 +1235,21 @@ def _run_fit_loop(
             else:
                 results = [_fit_worker(template, path) for path in chunk]
 
-            if extract_diffrn is not None:
-                _apply_diffrn_metadata(results, extract_diffrn)
-
             _append_to_csv(csv_path, header, results)
-            _report_chunk_progress(chunk_idx, total_chunks, results, verb)
-            if indicator is not None:
-                indicator.update()
+            elapsed_time = time.perf_counter() - started_at
+            _report_chunk_progress(
+                chunk_idx,
+                total_chunks,
+                chunk,
+                results,
+                progress,
+                _ChunkProgressMetrics(
+                    completed_files_before=completed_files,
+                    total_files=total_files,
+                    elapsed_time=elapsed_time,
+                ),
+            )
+            completed_files += len(results)
 
             # Propagate last successful params
             last_ok = _find_last_successful(results)
@@ -819,7 +1271,6 @@ def fit_sequential(
     max_workers: int | str = 1,
     chunk_size: int | None = None,
     file_pattern: str = '*',
-    extract_diffrn: Callable | None = None,
     *,
     reverse: bool = False,
 ) -> None:
@@ -840,8 +1291,6 @@ def fit_sequential(
         Files per chunk. Default ``None`` uses ``max_workers``.
     file_pattern : str, default='*'
         Glob pattern to filter files in *data_dir*.
-    extract_diffrn : Callable | None, default=None
-        User callback: ``f(file_path) → {diffrn_field: value}``.
     reverse : bool, default=False
         When ``True``, process data files in reverse order.  Useful when
         starting values are better matched to the last file (e.g.
@@ -850,62 +1299,40 @@ def fit_sequential(
     if mp.parent_process() is not None:
         return
 
-    verb = VerbosityEnum(analysis.project.verbosity)
-
-    _check_seq_preconditions(analysis.project)
-
-    data_paths = extract_data_paths_from_dir(data_dir, file_pattern=file_pattern)
-    template = _build_template(analysis.project)
-
-    csv_path, header, already_fitted, template = _setup_csv_and_recovery(
-        analysis.project,
-        template,
-        verb,
+    plan = _prepare_sequential_run(
+        analysis,
+        data_dir,
+        max_workers,
+        chunk_size,
+        file_pattern,
+        reverse=reverse,
     )
-
-    remaining = [p for p in data_paths if p not in already_fitted]
-    if reverse:
-        remaining.reverse()
-    if not remaining:
-        if verb is not VerbosityEnum.SILENT:
-            console.print('✅ All files already fitted. Nothing to do.')
+    if plan is None:
         return
 
-    max_workers, chunk_size = _resolve_workers(max_workers, chunk_size)
-    chunks = [remaining[i : i + chunk_size] for i in range(0, len(remaining), chunk_size)]
-
-    if verb is not VerbosityEnum.SILENT:
-        console.paragraph('Sequential fitting')
-        console.print(f"🚀 Starting fit process with '{analysis.fitter.selection}'...")
-        console.print(
-            f'📋 {len(remaining)} files in {len(chunks)} chunks (max_workers={max_workers})'
-        )
-        console.print('📈 Goodness-of-fit (reduced χ²):')
-
-    indicator = None
-    if verb is not VerbosityEnum.SILENT:
-        indicator = ActivityIndicator(ACTIVITY_LABEL_FITTING, verbosity=verb)
-        indicator.start()
+    _print_sequential_header(
+        analysis,
+        plan.verbosity,
+        plan.remaining,
+        plan.chunks,
+        plan.max_workers,
+    )
 
-    pool_cm, main_mod, main_file_bak, main_spec_bak = _create_pool_context(max_workers)
+    progress = _create_progress_context(plan.verbosity)
+    _start_progress_display(progress)
     try:
-        _run_fit_loop(
-            pool_cm,
-            chunks,
-            template,
-            (csv_path, header),
-            extract_diffrn,
-            verb,
-            indicator,
+        _run_fit_loop_with_pool(
+            plan.max_workers,
+            plan.chunks,
+            plan.template,
+            (plan.csv_path, plan.header),
+            progress,
         )
     finally:
-        if indicator is not None:
-            indicator.stop()
-        _restore_main_state(main_mod, main_file_bak, main_spec_bak)
+        _stop_progress_display(progress)
 
-    if verb is not VerbosityEnum.SILENT:
-        console.print(
-            f'✅ Sequential fitting complete: '
-            f'{len(already_fitted) + len(remaining)} files processed.'
-        )
-        console.print(f'📄 Results saved to: {csv_path}')
+    _print_sequential_completion(
+        plan.verbosity,
+        plan.processed_count,
+        plan.csv_path,
+    )
diff --git a/src/easydiffraction/core/__init__.py b/src/easydiffraction/core/__init__.py
index 4e798e209..72bd7f4d6 100644
--- a/src/easydiffraction/core/__init__.py
+++ b/src/easydiffraction/core/__init__.py
@@ -1,2 +1,4 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.core.variable import BoolDescriptor
diff --git a/src/easydiffraction/core/guard.py b/src/easydiffraction/core/guard.py
index 986b589b3..d1f8d4fd4 100644
--- a/src/easydiffraction/core/guard.py
+++ b/src/easydiffraction/core/guard.py
@@ -14,6 +14,33 @@
     from collections.abc import Generator
 
 
+def _apply_help_filter(
+    obj: object,
+    properties: list[str],
+    methods: list[str],
+) -> tuple[list[str], list[str]]:
+    """
+    Apply an optional instance help filter that may only hide members.
+    """
+    help_filter = getattr(obj, '_help_filter', None)
+    if not callable(help_filter):
+        return properties, methods
+
+    filtered_properties, filtered_methods = help_filter(list(properties), list(methods))
+    invalid_properties = sorted(set(filtered_properties) - set(properties))
+    invalid_methods = sorted(set(filtered_methods) - set(methods))
+    if invalid_properties or invalid_methods:
+        owner_name = type(obj).__name__
+        msg = f'{owner_name}._help_filter() may only hide discovered members.'
+        if invalid_properties:
+            msg += f' Invalid properties: {invalid_properties}.'
+        if invalid_methods:
+            msg += f' Invalid methods: {invalid_methods}.'
+        raise RuntimeError(msg)
+
+    return filtered_properties, filtered_methods
+
+
 class GuardedBase(ABC):
     """Base class enforcing controlled attribute access and linkage."""
 
@@ -208,8 +235,14 @@ def help(self) -> None:
             if key not in seen:
                 seen[key] = prop
 
+        property_names = sorted(seen)
+
+        methods = dict(cls._iter_methods())
+        method_names = sorted(methods)
+        property_names, method_names = _apply_help_filter(self, property_names, method_names)
+
         prop_rows = []
-        for i, key in enumerate(sorted(seen), 1):
+        for i, key in enumerate(property_names, 1):
             prop = seen[key]
             writable = '✓' if prop.fset else '✗'
             doc = self._first_sentence(prop.fget.__doc__ if prop.fget else None)
@@ -223,9 +256,8 @@ def help(self) -> None:
                 columns_data=prop_rows,
             )
 
-        methods = dict(cls._iter_methods())
         method_rows = []
-        for i, key in enumerate(sorted(methods), 1):
+        for i, key in enumerate(method_names, 1):
             doc = self._first_sentence(getattr(methods[key], '__doc__', None))
             method_rows.append([str(i), f'{key}()', doc])
 
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 55af6cba9..76554bdd1 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -223,6 +223,28 @@ def __init__(
 # ======================================================================
 
 
+class GenericBoolDescriptor(GenericDescriptorBase):
+    """Base descriptor that constrains values to booleans."""
+
+    _value_type = DataTypes.BOOL
+
+    def __init__(
+        self,
+        *,
+        value_spec: AttributeSpec | None = None,
+        **kwargs: object,
+    ) -> None:
+        if value_spec is None:
+            value_spec = AttributeSpec(
+                data_type=DataTypes.BOOL,
+                default=False,
+            )
+        super().__init__(value_spec=value_spec, **kwargs)
+
+
+# ======================================================================
+
+
 class GenericNumericDescriptor(GenericDescriptorBase):
     """Base descriptor that constrains values to numbers."""
 
@@ -536,6 +558,33 @@ def __init__(
 # ======================================================================
 
 
+class BoolDescriptor(GenericBoolDescriptor):
+    """Boolean descriptor bound to a CIF handler."""
+
+    def __init__(
+        self,
+        *,
+        cif_handler: CifHandler,
+        **kwargs: object,
+    ) -> None:
+        """
+        Initialize a boolean descriptor bound to a CIF handler.
+
+        Parameters
+        ----------
+        cif_handler : CifHandler
+            Object that tracks CIF identifiers.
+        **kwargs : object
+            Forwarded to GenericBoolDescriptor.
+        """
+        super().__init__(**kwargs)
+        self._cif_handler = cif_handler
+        self._cif_handler.attach(self)
+
+
+# ======================================================================
+
+
 class NumericDescriptor(GenericNumericDescriptor):
     """Numeric descriptor bound to a CIF handler."""
 
diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index 467467001..625b2e2fd 100644
--- a/src/easydiffraction/display/plotters/ascii.py
+++ b/src/easydiffraction/display/plotters/ascii.py
@@ -32,6 +32,7 @@
 ASCII_CHART_LEFT_PADDING = 15
 ASCII_CHART_FALLBACK_POINT_COUNT = 80
 ASCII_CHART_MIN_POINT_COUNT = 2
+ASCII_CHART_CROP_TRIGGER_MULTIPLIER = 2
 
 
 class AsciiPlotter(PlotterBase):
@@ -54,16 +55,24 @@ def _resample_series_for_chart(
         cls,
         y_series: object,
     ) -> list[list[float]]:
-        """Return y-series resampled to the available chart width."""
+        """Return y-series adapted to the available chart width."""
         target_point_count = cls._chart_point_count()
+        series_arrays = [np.ravel(np.asarray(series, dtype=float)) for series in y_series]
+        if not series_arrays:
+            return []
+
+        reference_array = series_arrays[0]
+        if cls._should_crop_to_peak_window(reference_array.size):
+            start, end = cls._peak_window_bounds(reference_array)
+            return [series_array[start:end].tolist() for series_array in series_arrays]
+
         resampled_series: list[list[float]] = []
-        for series in y_series:
-            series_array = np.ravel(np.asarray(series, dtype=float))
-            if (
-                series_array.size <= target_point_count
-                or series_array.size < ASCII_CHART_MIN_POINT_COUNT
-            ):
-                resampled_series.append(series_array.tolist())
+        for series_array in series_arrays:
+            if series_array.size == 0:
+                resampled_series.append([0.0] * target_point_count)
+                continue
+            if series_array.size == 1:
+                resampled_series.append([float(series_array[0])] * target_point_count)
                 continue
 
             source_positions = np.linspace(0.0, 1.0, series_array.size)
@@ -73,6 +82,30 @@ def _resample_series_for_chart(
             )
         return resampled_series
 
+    @classmethod
+    def _should_crop_to_peak_window(
+        cls,
+        point_count: int,
+    ) -> bool:
+        """Return whether a peak-centred viewport should be used."""
+        return point_count > cls._chart_point_count() * ASCII_CHART_CROP_TRIGGER_MULTIPLIER
+
+    @classmethod
+    def _peak_window_bounds(
+        cls,
+        y_array: np.ndarray,
+    ) -> tuple[int, int]:
+        """Return start/end indices for a peak-centred chart window."""
+        target_point_count = cls._chart_point_count()
+        if y_array.size <= target_point_count:
+            return 0, y_array.size
+
+        peak_index = int(np.argmax(np.nan_to_num(y_array, nan=float('-inf'))))
+        start = max(0, peak_index - target_point_count // 2)
+        end = min(y_array.size, start + target_point_count)
+        start = max(0, end - target_point_count)
+        return start, end
+
     @staticmethod
     def _get_legend_item(label: str) -> str:
         """
@@ -193,8 +226,8 @@ def plot_powder_meas_vs_calc(
         if plot_spec.bragg_tick_sets:
             console.print('Bragg peak subplot rows are available with the Plotly engine only.')
 
-    @staticmethod
     def plot_single_crystal(
+        self,
         x_calc: object,
         y_meas: object,
         y_meas_su: object,
@@ -229,7 +262,7 @@ def plot_single_crystal(
 
         if height is None:
             height = DEFAULT_HEIGHT
-        width = 60  # TODO: Make width configurable
+        width = self._chart_point_count()
 
         # Determine axis limits
         vmin = float(min(np.min(y_meas), np.min(x_calc)))
@@ -272,8 +305,8 @@ def plot_single_crystal(
         print(f'  {x_axis}')
         console.print(f'{" " * (width - 3)}{axes_labels[0]}')
 
-    @staticmethod
     def plot_scatter(
+        self,
         x: object,
         y: object,
         sy: object,
@@ -282,13 +315,20 @@ def plot_scatter(
         height: int | None = None,
     ) -> None:
         """Render a scatter plot with error bars in ASCII."""
-        _ = x, sy  # ASCII backend does not use x ticks or error bars
+        _ = sy  # ASCII backend does not use error bars
 
         if height is None:
             height = DEFAULT_HEIGHT
 
+        x_array = np.ravel(np.asarray(x, dtype=float))
+        y_array = np.ravel(np.asarray(y, dtype=float))
+        if x_array.size == y_array.size and x_array.size > 1:
+            order = np.argsort(x_array, kind='stable')
+            y_array = y_array[order]
+
+        y_series = self._resample_series_for_chart([y_array])
         config = {'height': height, 'colors': [asciichartpy.blue]}
-        chart = asciichartpy.plot([list(y)], config)
+        chart = asciichartpy.plot(y_series, config)
 
         console.paragraph(f'{title}')
         console.print(f'{axes_labels[1]} vs {axes_labels[0]}')
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index 6910321be..a080ba8e7 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -193,7 +193,7 @@ class XAxisType(StrEnum):
     },
     'posterior': {
         'mode': 'lines',
-        'name': 'Max posterior',
+        'name': 'Best posterior sample',
     },
     'density': {
         'mode': 'lines',
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 3380141d3..85cb1545a 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -99,7 +99,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.14)'
 POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
-POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Max posterior'
+POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Best posterior sample'
 POSTERIOR_POINT_ESTIMATE_LINE_DASH = 'dot'
 POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME = '95% credible interval'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
@@ -330,7 +330,12 @@ def _auto_x_range_for_ascii(
         tuple
             Tuple of ``(x_min, x_max)``, possibly narrowed.
         """
-        if self._engine == 'asciichartpy' and (x_min is None or x_max is None):
+        if (
+            self._engine == 'asciichartpy'
+            and x_min is None
+            and x_max is None
+            and AsciiPlotter._should_crop_to_peak_window(len(x_array))
+        ):
             max_intensity_pos = int(np.argmax(pattern.intensity_meas))
             target_point_count = min(len(x_array), AsciiPlotter._chart_point_count())
             start = max(0, max_intensity_pos - target_point_count // 2)
@@ -723,7 +728,7 @@ def _plot_meas_vs_calc_request(
     def plot_param_series(
         self,
         param: object,
-        versus: object | None = None,
+        versus: str | None = None,
     ) -> None:
         """
         Plot a parameter's value across sequential fit results.
@@ -738,11 +743,11 @@ def plot_param_series(
         param : object
             Parameter descriptor whose ``unique_name`` identifies the
             values to plot.
-        versus : object | None, default=None
-            A diffrn descriptor (e.g.
-            ``expt.diffrn.ambient_temperature``) whose value is used as
-            the x-axis for each experiment.  When ``None``, the
-            experiment sequence number is used instead.
+        versus : str | None, default=None
+            Persisted diffrn path (e.g.
+            ``'diffrn.ambient_temperature'``) whose sequential-results
+            column is used as the x-axis. When ``None``, the experiment
+            sequence number is used instead.
         """
         unique_name = param.unique_name
 
@@ -758,18 +763,133 @@ def plot_param_series(
                 csv_path=csv_path,
                 unique_name=unique_name,
                 param_descriptor=param,
-                versus_descriptor=versus,
+                versus_path=versus,
             )
         else:
             # Fallback: in-memory snapshots from fit() single mode
-            versus_name = versus.name if versus is not None else None
             self.plot_param_series_from_snapshots(
                 unique_name,
-                versus_name,
+                versus,
                 self._project.experiments,
                 self._project.analysis._parameter_snapshots,
             )
 
+    def plot_all_param_series(
+        self,
+        versus: str | None = None,
+    ) -> None:
+        """
+        Plot every fitted parameter across sequential fit results.
+
+        Iterates the fitted parameters recorded in ``results.csv`` (or,
+        when absent, in the in-memory parameter snapshots) and emits one
+        ``plot_param_series`` plot per parameter.
+
+        Parameters
+        ----------
+        versus : str | None, default=None
+            Persisted diffrn path (e.g.
+            ``'diffrn.ambient_temperature'``) whose sequential-results
+            column is used as the x-axis. When ``None``, the experiment
+            sequence number is used instead.
+        """
+        unique_names = self._collect_fitted_param_unique_names()
+        if not unique_names:
+            log.warning('No fitted parameters found to plot.')
+            return
+
+        descriptors_by_name = self._fitted_param_descriptors_by_unique_name()
+
+        for unique_name in unique_names:
+            descriptor = descriptors_by_name.get(unique_name)
+            if descriptor is None:
+                log.warning(f"Parameter '{unique_name}' not found in project; skipping plot.")
+                continue
+            self.plot_param_series(param=descriptor, versus=versus)
+
+    def _collect_fitted_param_unique_names(self) -> list[str]:
+        """
+        Return fitted parameter unique names from CSV or snapshots.
+        """
+        from easydiffraction.analysis.sequential import _META_COLUMNS  # noqa: PLC0415
+
+        meta = set(_META_COLUMNS)
+
+        csv_path = None
+        if self._project.info.path is not None:
+            candidate = pathlib.Path(self._project.info.path) / 'analysis' / 'results.csv'
+            if candidate.is_file():
+                csv_path = str(candidate)
+
+        if csv_path is not None:
+            df = pd.read_csv(csv_path)
+            return [
+                column
+                for column in df.columns
+                if column not in meta
+                and not column.startswith('diffrn.')
+                and not column.endswith('.uncertainty')
+            ]
+
+        snapshots = self._project.analysis._parameter_snapshots
+        if not snapshots:
+            return []
+        first_snapshot = next(iter(snapshots.values()))
+        return list(first_snapshot.keys())
+
+    def _fitted_param_descriptors_by_unique_name(self) -> dict[str, object]:
+        """Return descriptor map keyed by ``unique_name``."""
+        all_params = self._project.structures.parameters + self._project.experiments.parameters
+        return {p.unique_name: p for p in all_params if hasattr(p, 'unique_name')}
+
+    def _resolve_versus_descriptor_from_path(
+        self,
+        versus_path: str | None,
+    ) -> object | None:
+        """Return a template diffrn descriptor for a persisted path."""
+        field_name = self._versus_field_name(versus_path)
+        if field_name is None:
+            return None
+
+        project = getattr(self, '_project', None)
+        if project is None or getattr(project, 'experiments', None) is None:
+            return None
+
+        experiment = next(iter(project.experiments.values()), None)
+        if experiment is None:
+            return None
+
+        return self._resolve_diffrn_descriptor(experiment.diffrn, field_name)
+
+    @staticmethod
+    def _versus_field_name(versus_path: str | None) -> str | None:
+        """Return the diffrn field name from a persisted path."""
+        if versus_path is None:
+            return None
+        if versus_path.startswith('diffrn.'):
+            return versus_path.removeprefix('diffrn.')
+        return versus_path
+
+    @classmethod
+    def _versus_axis_label(
+        cls,
+        versus_path: str | None,
+        descriptor: object | None,
+    ) -> str:
+        """Return the x-axis label for a persisted diffrn path."""
+        if descriptor is not None:
+            label = getattr(descriptor, 'description', None) or getattr(descriptor, 'name', None)
+            units = getattr(descriptor, 'units', None)
+            if label is not None and units:
+                return f'{label} ({units})'
+            if label is not None:
+                return label
+
+        field_name = cls._versus_field_name(versus_path)
+        if field_name is None:
+            return 'Experiment No.'
+        return field_name.replace('_', ' ')
+
     def plot_param_correlations(
         self,
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
@@ -1205,7 +1325,7 @@ def _plot_single_crystal_posterior_predictive(
             log.warning(f'No measured data available for experiment {expt_name}.')
             return
         y_meas = np.asarray(y_meas_raw, dtype=float)
-        if y_meas.shape != np.asarray(summary.map_prediction).shape:
+        if y_meas.shape != np.asarray(summary.best_sample_prediction).shape:
             log.warning(
                 'Single-crystal posterior predictive values do not match the '
                 'measured reflection array shape.'
@@ -2729,7 +2849,7 @@ def _add_posterior_distribution_reference_traces(
 
         fig.add_trace(
             self._posterior_reference_line_trace(
-                x_value=summary.map_value,
+                x_value=summary.best_sample_value,
                 y_axis_range=y_axis_range,
                 trace_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
                 color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
@@ -3259,7 +3379,7 @@ def _build_posterior_predictive_summary(
         if predictive_data is None:
             return None
 
-        map_prediction, x_values, predictive_draw_array = predictive_data
+        best_sample_prediction, x_values, predictive_draw_array = predictive_data
         lower_68, upper_68 = np.quantile(predictive_draw_array, [0.16, 0.84], axis=0)
         lower_95, upper_95 = np.quantile(predictive_draw_array, [0.025, 0.975], axis=0)
         x_axis_name = getattr(x_axis, 'value', x_axis)
@@ -3268,7 +3388,7 @@ def _build_posterior_predictive_summary(
             experiment_name=expt_name,
             x_axis_name=str(x_axis_name),
             x=np.asarray(x_values, dtype=float),
-            map_prediction=np.asarray(map_prediction, dtype=float),
+            best_sample_prediction=np.asarray(best_sample_prediction, dtype=float),
             lower_95=np.asarray(lower_95, dtype=float),
             upper_95=np.asarray(upper_95, dtype=float),
             lower_68=np.asarray(lower_68, dtype=float),
@@ -3324,7 +3444,7 @@ def _evaluate_posterior_predictive_draws(
         expt_name: str,
         x_axis: object,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
-        """Return MAP and sampled predictive curves."""
+        """Return best-sample and sampled predictive curves."""
         original_values = np.array(
             [parameter.value for parameter in sampled_parameters],
             dtype=float,
@@ -3334,14 +3454,14 @@ def _evaluate_posterior_predictive_draws(
         draw_indices = self._posterior_predictive_draw_indices(flattened_samples.shape[0])
 
         try:
-            map_prediction, x_values = self._evaluate_posterior_predictive_state(
+            best_sample_prediction, x_values = self._evaluate_posterior_predictive_state(
                 sampled_parameters=sampled_parameters,
                 values=original_values,
                 experiment=experiment,
                 expt_name=expt_name,
                 x_axis=x_axis,
             )
-            if map_prediction is None or x_values is None:
+            if best_sample_prediction is None or x_values is None:
                 return None
 
             for index in draw_indices:
@@ -3354,7 +3474,10 @@ def _evaluate_posterior_predictive_draws(
                 )
                 if prediction is None or current_x is None:
                     return None
-                if prediction.shape != map_prediction.shape or current_x.shape != x_values.shape:
+                if (
+                    prediction.shape != best_sample_prediction.shape
+                    or current_x.shape != x_values.shape
+                ):
                     log.warning('Posterior predictive draws returned inconsistent array shapes.')
                     return None
                 predictive_draws.append(prediction)
@@ -3367,7 +3490,7 @@ def _evaluate_posterior_predictive_draws(
             )
 
         return (
-            np.asarray(map_prediction, dtype=float),
+            np.asarray(best_sample_prediction, dtype=float),
             np.asarray(x_values, dtype=float),
             np.asarray(predictive_draws, dtype=float),
         )
@@ -3511,7 +3634,7 @@ def _plot_posterior_predictive_summary(
                 expt_name=expt_name,
                 x=np.asarray(summary.x, dtype=float),
                 y_meas=np.asarray(y_meas, dtype=float),
-                y_calc=np.asarray(summary.map_prediction, dtype=float),
+                y_calc=np.asarray(summary.best_sample_prediction, dtype=float),
                 axes_labels=axes_labels,
                 excluded_ranges=excluded_ranges,
             )
@@ -3579,7 +3702,7 @@ def _plot_posterior_predictive_summary(
         fig.add_trace(
             go.Scatter(
                 x=summary.x,
-                y=summary.map_prediction,
+                y=summary.best_sample_prediction,
                 mode='lines',
                 line={
                     'color': POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
@@ -3673,23 +3796,26 @@ def _plot_single_crystal_posterior_predictive_summary(
             )
             return
 
-        map_prediction = np.asarray(summary.map_prediction, dtype=float)
+        best_sample_prediction = np.asarray(summary.best_sample_prediction, dtype=float)
         lower_95 = np.asarray(summary.lower_95, dtype=float)
         upper_95 = np.asarray(summary.upper_95, dtype=float)
-        if lower_95.shape != map_prediction.shape or upper_95.shape != map_prediction.shape:
+        if (
+            lower_95.shape != best_sample_prediction.shape
+            or upper_95.shape != best_sample_prediction.shape
+        ):
             log.warning('Single-crystal posterior predictive interval arrays have invalid shapes.')
             return
 
         go = __import__('plotly.graph_objects', fromlist=['Figure'])
         trace = PlotlyPlotter._get_single_crystal_trace(
-            x_calc=map_prediction,
+            x_calc=best_sample_prediction,
             y_meas=y_meas,
             y_meas_su=y_meas_su,
         )
         trace.error_x = {
             'type': 'data',
-            'array': np.maximum(0.0, upper_95 - map_prediction),
-            'arrayminus': np.maximum(0.0, map_prediction - lower_95),
+            'array': np.maximum(0.0, upper_95 - best_sample_prediction),
+            'arrayminus': np.maximum(0.0, best_sample_prediction - lower_95),
             'visible': True,
         }
         trace.customdata = np.column_stack((lower_95, upper_95, y_meas_su))
@@ -3734,7 +3860,12 @@ def _filtered_posterior_predictive_summary(
             experiment_name=summary.experiment_name,
             x_axis_name=summary.x_axis_name,
             x=x_filtered,
-            map_prediction=self._filtered_y_array(summary.map_prediction, summary.x, x_min, x_max),
+            best_sample_prediction=self._filtered_y_array(
+                summary.best_sample_prediction,
+                summary.x,
+                x_min,
+                x_max,
+            ),
             lower_95=(
                 None
                 if summary.lower_95 is None
@@ -3808,7 +3939,10 @@ def _plot_posterior_predictive_data(
         if not self._show_background_enabled(plot_options, background_available=y_bkg is not None):
             y_bkg = None
         y_calc = self._filtered_y_array(
-            summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max']
+            summary.best_sample_prediction,
+            summary.x,
+            ctx['x_min'],
+            ctx['x_max'],
         )
         excluded_ranges = (
             self._excluded_ranges(
@@ -5405,20 +5539,20 @@ def _plot_param_series_from_csv(
         csv_path: str,
         unique_name: str,
         param_descriptor: object,
-        versus_descriptor: object | None = None,
+        versus_path: str | None = None,
     ) -> None:
         """
         Plot a parameter's value across sequential fit results.
 
         Reads data from the CSV file at *csv_path*.  The y-axis values
         come from the column named *unique_name*, uncertainties from
-        ``{unique_name}.uncertainty``.  When *versus_descriptor* is
-        provided, the x-axis uses the corresponding ``diffrn.{name}``
-        column; otherwise the row index is used.
+        ``{unique_name}.uncertainty``. When *versus_path* is provided,
+        the x-axis uses the corresponding ``diffrn.*`` CSV column;
+        otherwise the row index is used.
 
-        Axis labels are derived from the live descriptor objects
-        (*param_descriptor* and *versus_descriptor*), which carry
-        ``.description`` and ``.units`` attributes.
+        Axis labels use the live parameter descriptor and, when
+        available, a template diffrn descriptor resolved from
+        *versus_path*.
 
         Parameters
         ----------
@@ -5428,9 +5562,9 @@ def _plot_param_series_from_csv(
             Unique name of the parameter to plot (CSV column key).
         param_descriptor : object
             The live parameter descriptor (for axis label / units).
-        versus_descriptor : object | None, default=None
-            A diffrn descriptor whose ``.name`` maps to a
-            ``diffrn.{name}`` CSV column.  ``None`` → use row index.
+        versus_path : str | None, default=None
+            Persisted diffrn path whose matching CSV column provides the
+            x-axis values. ``None`` uses row index.
         """
         df = pd.read_csv(csv_path)
 
@@ -5446,14 +5580,12 @@ def _plot_param_series_from_csv(
         sy = df[uncert_col].astype(float).tolist() if uncert_col in df.columns else [0.0] * len(y)
 
         # X-axis: diffrn column or row index
-        versus_name = versus_descriptor.name if versus_descriptor is not None else None
-        diffrn_col = f'diffrn.{versus_name}' if versus_name else None
+        diffrn_col = versus_path
+        versus_descriptor = self._resolve_versus_descriptor_from_path(versus_path)
 
         if diffrn_col and diffrn_col in df.columns:
             x = pd.to_numeric(df[diffrn_col], errors='coerce').tolist()
-            x_label = getattr(versus_descriptor, 'description', None) or versus_name
-            if hasattr(versus_descriptor, 'units') and versus_descriptor.units:
-                x_label = f'{x_label} ({versus_descriptor.units})'
+            x_label = self._versus_axis_label(versus_path, versus_descriptor)
         else:
             x = list(range(1, len(y) + 1))
             x_label = 'Experiment No.'
@@ -5476,7 +5608,7 @@ def _plot_param_series_from_csv(
     def plot_param_series_from_snapshots(
         self,
         unique_name: str,
-        versus_name: str | None,
+        versus_path: str | None,
         experiments: object,
         parameter_snapshots: dict[str, dict[str, dict]],
     ) -> None:
@@ -5491,8 +5623,8 @@ def plot_param_series_from_snapshots(
         ----------
         unique_name : str
             Unique name of the parameter to plot.
-        versus_name : str | None
-            Name of the diffrn descriptor for the x-axis.
+        versus_path : str | None
+            Persisted diffrn path for the x-axis.
         experiments : object
             Experiments collection for accessing diffrn conditions.
         parameter_snapshots : dict[str, dict[str, dict]]
@@ -5508,7 +5640,10 @@ def plot_param_series_from_snapshots(
             experiment = experiments[expt_name]
             diffrn = experiment.diffrn
 
-            x_axis_param = self._resolve_diffrn_descriptor(diffrn, versus_name)
+            x_axis_param = self._resolve_diffrn_descriptor(
+                diffrn,
+                self._versus_field_name(versus_path),
+            )
 
             if x_axis_param is not None and x_axis_param.value is not None:
                 value = x_axis_param.value
@@ -5522,7 +5657,7 @@ def plot_param_series_from_snapshots(
 
             if x_axis_param is not None:
                 axes_labels = [
-                    x_axis_param.description or x_axis_param.name,
+                    self._versus_axis_label(versus_path, x_axis_param),
                     f'Parameter value ({param_data["units"]})',
                 ]
             else:
diff --git a/src/easydiffraction/display/progress.py b/src/easydiffraction/display/progress.py
index 1cde4d1c2..ef381c505 100644
--- a/src/easydiffraction/display/progress.py
+++ b/src/easydiffraction/display/progress.py
@@ -35,6 +35,7 @@
 ACTIVITY_LABEL_SAMPLING = 'Sampling...'
 ACTIVITY_ACCENT_COLOR = '#d97706'
 ACTIVITY_TERMINAL_STYLE = ACTIVITY_ACCENT_COLOR
+ACTIVITY_TERMINAL_FALLBACK_STYLE = 'bold yellow'
 
 SPINNER_FRAMES: tuple[str, ...] = (
     '⠋',
@@ -52,6 +53,27 @@
 _JUPYTER_SPINNER_SECONDS = 1.0
 
 
+def resolve_activity_terminal_style(console: object | None = None) -> str:
+    """
+    Return a terminal-safe activity indicator style.
+
+    Parameters
+    ----------
+    console : object | None, default=None
+        Console-like object whose ``color_system`` determines whether
+        the accent color can be rendered directly.
+
+    Returns
+    -------
+    str
+        The preferred terminal style for the current console.
+    """
+    color_system = getattr(console, 'color_system', None)
+    if color_system in {'standard', 'windows'}:
+        return ACTIVITY_TERMINAL_FALLBACK_STYLE
+    return ACTIVITY_TERMINAL_STYLE
+
+
 class _TerminalLiveHandle:
     """
     Adapter exposing update()/close() for terminal live updates.
@@ -60,13 +82,14 @@ class _TerminalLiveHandle:
     and notebook handles through a single update-oriented interface.
     """
 
-    def __init__(self, *, console: object) -> None:
+    def __init__(self, *, console: object, auto_refresh: bool = True) -> None:
         self._renderable: object = Text('')
         self._live = Live(
             console=console,
-            auto_refresh=True,
+            auto_refresh=auto_refresh,
             refresh_per_second=1 / _SPINNER_FRAME_SECONDS,
             get_renderable=self._get_renderable,
+            vertical_overflow='visible',
         )
         self._live.start()
 
@@ -94,10 +117,15 @@ def close(self) -> None:
             self._live.stop()
 
 
-def make_display_handle() -> object | None:
+def make_display_handle(*, auto_refresh: bool = True) -> object | None:
     """
     Create a generic in-place display handle for the active environment.
 
+    Parameters
+    ----------
+    auto_refresh : bool, default=True
+        Whether a terminal live handle should refresh continuously.
+
     Returns
     -------
     object | None
@@ -110,7 +138,7 @@ def make_display_handle() -> object | None:
             handle.display(HTML(''))
         return handle
 
-    return _TerminalLiveHandle(console=ConsoleManager.get())
+    return _TerminalLiveHandle(console=ConsoleManager.get(), auto_refresh=auto_refresh)
 
 
 class ActivityIndicator:
@@ -125,6 +153,12 @@ class ActivityIndicator:
         Output verbosity controlling whether live display is shown.
     display_handle : object | None, default=None
         Optional existing live display handle to reuse.
+    animated : bool, default=True
+        Whether to animate the spinner label continuously.
+    refresh_per_second : float | None, default=None
+        Optional override for the Rich Live refresh rate. When ``None``,
+        defaults to one refresh per spinner frame. Lower values reduce
+        terminal flicker for multi-line live regions.
     """
 
     def __init__(
@@ -133,11 +167,17 @@ def __init__(
         *,
         verbosity: VerbosityEnum,
         display_handle: object | None = None,
+        animated: bool = True,
+        refresh_per_second: float | None = None,
     ) -> None:
         self._label = label
         self._verbosity = verbosity
         self._content: object | None = None
         self._provided_display_handle = display_handle
+        self._refresh_per_second = (
+            refresh_per_second if refresh_per_second is not None else 1 / _SPINNER_FRAME_SECONDS
+        )
+        self._animated = animated
         self._display_handle: object | None = None
         self._live: object | None = None
         self._running = False
@@ -174,9 +214,10 @@ def start(self) -> None:
 
         live = Live(
             console=ConsoleManager.get(),
-            auto_refresh=True,
-            refresh_per_second=1 / _SPINNER_FRAME_SECONDS,
+            auto_refresh=self._animated,
+            refresh_per_second=self._refresh_per_second,
             get_renderable=self._terminal_renderable,
+            vertical_overflow='visible',
         )
         live.start()
         self._live = live
@@ -286,11 +327,14 @@ def _terminal_content(self) -> object | None:
         return Text(str(self._content))
 
     def _terminal_indicator_line(self) -> Text | None:
+        style = resolve_activity_terminal_style(ConsoleManager.get())
         if self._running:
-            frame = self._current_frame()
-            return Text(f'{frame} {self._label}', style=ACTIVITY_TERMINAL_STYLE)
+            if self._animated:
+                frame = self._current_frame()
+                return Text(f'{frame} {self._label}', style=style)
+            return Text(self._label, style=style)
         if self._keep_stopped_label:
-            return Text(self._label, style=ACTIVITY_TERMINAL_STYLE)
+            return Text(self._label, style=style)
         return None
 
     def _current_frame(self) -> str:
@@ -326,6 +370,12 @@ def _html_indicator(self) -> str:
         safe_label = html.escape(self._label)
 
         if self._running:
+            if not self._animated:
+                return (
+                    '<div class="ed-activity">'
+                    f'<span class="ed-activity-label">{safe_label}</span>'
+                    '</div>'
+                )
             return (
                 '<div class="ed-activity">'
                 '<span class="ed-activity-spinner" aria-hidden="true"></span>'
diff --git a/src/easydiffraction/io/ascii.py b/src/easydiffraction/io/ascii.py
index 189926a16..faab76820 100644
--- a/src/easydiffraction/io/ascii.py
+++ b/src/easydiffraction/io/ascii.py
@@ -13,6 +13,20 @@
 import numpy as np
 
 
+def _resolve_extraction_destination(destination: str | Path | None) -> Path:
+    """Return an extraction directory for ZIP contents."""
+    if destination is None:
+        return Path(tempfile.mkdtemp(prefix='ed_zip_'))
+
+    extract_dir = Path(destination)
+    if not extract_dir.is_absolute():
+        extract_dir = Path.cwd() / extract_dir
+
+    extract_dir = extract_dir.resolve()
+    extract_dir.mkdir(parents=True, exist_ok=True)
+    return extract_dir
+
+
 def extract_project_from_zip(
     zip_path: str | Path,
     destination: str | Path | None = None,
@@ -92,7 +106,8 @@ def extract_data_paths_from_zip(
         Path to the ZIP archive.
     destination : str | Path | None, default=None
         Directory to extract files into.  When ``None``, a temporary
-        directory is created.
+        directory is created. Relative destinations are resolved against
+        the current working directory.
 
     Returns
     -------
@@ -111,12 +126,7 @@ def extract_data_paths_from_zip(
         msg = f'ZIP file not found: {zip_path}'
         raise FileNotFoundError(msg)
 
-    if destination is not None:
-        extract_dir = Path(destination)
-        extract_dir.mkdir(parents=True, exist_ok=True)
-    else:
-        # TODO: Unify mkdir with other uses in the code
-        extract_dir = Path(tempfile.mkdtemp(prefix='ed_zip_'))
+    extract_dir = _resolve_extraction_destination(destination)
 
     with zipfile.ZipFile(zip_path, 'r') as zf:
         zf.extractall(extract_dir)
@@ -163,7 +173,7 @@ def extract_data_paths_from_dir(
     ValueError
         If no matching data files are found.
     """
-    dir_path = Path(dir_path)
+    dir_path = Path(dir_path).resolve()
     if not dir_path.is_dir():
         msg = f'Directory not found: {dir_path}'
         raise FileNotFoundError(msg)
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index 0527ac690..ae5b59fa8 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -48,6 +48,9 @@ def format_value(value: object) -> str:
     # None → CIF unknown marker
     if value is None:
         value = '?'
+    # Booleans use CIF true/false tokens
+    elif isinstance(value, bool):
+        value = 'true' if value else 'false'
     # Convert ints to floats
     elif isinstance(value, int):
         value = float(value)
@@ -70,6 +73,22 @@ def format_value(value: object) -> str:
     return str(value)
 
 
+def _strip_optional_quotes(raw: str) -> str:
+    """Return an unquoted CIF token when it is wrapped in quotes."""
+    is_quoted = len(raw) >= _MIN_QUOTED_LEN and raw[0] == raw[-1] and raw[0] in {"'", '"'}
+    return raw[1:-1] if is_quoted else raw
+
+
+def _parse_bool_cif_value(raw: str) -> bool | str:
+    """Parse CIF boolean tokens, returning the raw token if invalid."""
+    normalized_value = _strip_optional_quotes(raw).lower()
+    if normalized_value == 'true':
+        return True
+    if normalized_value == 'false':
+        return False
+    return _strip_optional_quotes(raw)
+
+
 ##################
 # Serialize to CIF
 ##################
@@ -371,18 +390,34 @@ def experiment_to_cif(experiment: object) -> str:
 
 def analysis_to_cif(analysis: object) -> str:
     """Render analysis metadata, aliases, and constraints to CIF."""
-    lines: list[str] = []
-    lines.extend((
-        analysis.fit.as_cif,
-        '',
-        analysis.aliases.as_cif,
-        '',
-        analysis.constraints.as_cif,
-    ))
-    jfe_cif = analysis.joint_fit_experiments.as_cif
-    if jfe_cif:
-        lines.extend(('', jfe_cif))
-    return '\n'.join(lines)
+    parts: list[str] = [f'_fitting.mode_type {format_value(analysis.fitting_mode_type)}']
+
+    fitting_cif = analysis.fitting.as_cif
+    if fitting_cif:
+        parts.append(fitting_cif)
+
+    aliases_cif = analysis.aliases.as_cif
+    if aliases_cif:
+        parts.append(aliases_cif)
+
+    constraints_cif = analysis.constraints.as_cif
+    if constraints_cif:
+        parts.append(constraints_cif)
+
+    if analysis.fitting_mode_type == 'joint':
+        joint_fit_cif = analysis.joint_fit.as_cif
+        if joint_fit_cif:
+            parts.append(joint_fit_cif)
+    elif analysis.fitting_mode_type == 'sequential':
+        sequential_fit_cif = analysis.sequential_fit.as_cif
+        if sequential_fit_cif:
+            parts.append(sequential_fit_cif)
+
+        sequential_extract_cif = analysis.sequential_fit_extract.as_cif
+        if sequential_extract_cif:
+            parts.append(sequential_extract_cif)
+
+    return '\n\n'.join(parts)
 
 
 def summary_to_cif(_summary: object) -> str:
@@ -487,8 +522,12 @@ def analysis_from_cif(analysis: object, cif_text: str) -> None:
     doc = gemmi.cif.read_string(_wrap_in_data_block(cif_text, 'analysis'))
     block = doc.sole_block()
 
+    _raise_for_legacy_analysis_tags(block)
+    analysis._set_fitting_mode_type(_analysis_mode_from_cif_block(block))
+
     # Restore fit configuration
-    analysis.fit.from_cif(block)
+    analysis.fitting.from_cif(block)
+    _restore_mode_specific_analysis_sections(analysis, block)
 
     # Restore aliases (loop)
     analysis.aliases.from_cif(block)
@@ -498,8 +537,111 @@ def analysis_from_cif(analysis: object, cif_text: str) -> None:
     if analysis.constraints._items:
         analysis.constraints.enable()
 
-    # Restore joint-fit experiment weights (loop)
-    analysis._joint_fit_experiments.from_cif(block)
+
+def _collect_legacy_analysis_tags(block: object) -> list[str]:
+    """Return deprecated analysis CIF tags present in a block."""
+    legacy_tags: list[str] = []
+    if _has_cif_value(block, '_fit.minimizer_type'):
+        legacy_tags.append('_fit.minimizer_type')
+    if _has_cif_value(block, '_fit.mode'):
+        legacy_tags.append('_fit.mode')
+    if _has_cif_loop(block, '_joint_fit_experiment.id'):
+        legacy_tags.append('_joint_fit_experiment.id')
+    if _has_cif_loop(block, '_joint_fit_experiment.weight'):
+        legacy_tags.append('_joint_fit_experiment.weight')
+    return legacy_tags
+
+
+def _raise_for_legacy_analysis_tags(block: object) -> None:
+    """Raise when deprecated analysis CIF tags are present."""
+    legacy_tags = _collect_legacy_analysis_tags(block)
+    if not legacy_tags:
+        return
+
+    msg = (
+        'Legacy analysis CIF tags are no longer supported: '
+        f'{legacy_tags}. Use _fitting.minimizer_type, _fitting.mode_type, '
+        '_joint_fit.experiment_id, and _joint_fit.weight.'
+    )
+    raise ValueError(msg)
+
+
+def _analysis_mode_from_cif_block(block: object) -> str:
+    """Return the fitting mode stored in an analysis CIF block."""
+    read_cif_string = _make_cif_string_reader(block)
+    mode_value = read_cif_string('_fitting.mode_type')
+    if mode_value is not None:
+        return mode_value
+
+    from easydiffraction.analysis.enums import FitModeEnum  # noqa: PLC0415
+
+    return FitModeEnum.default().value
+
+
+def _has_joint_fit_rows(block: object) -> bool:
+    """Return True when joint-fit rows are present."""
+    return _has_cif_loop(block, '_joint_fit.experiment_id') or _has_cif_loop(
+        block,
+        '_joint_fit.weight',
+    )
+
+
+def _has_sequential_fit_settings(block: object) -> bool:
+    """Return True when sequential-fit scalar settings are present."""
+    return any(
+        _has_cif_value(block, tag)
+        for tag in (
+            '_sequential_fit.data_dir',
+            '_sequential_fit.file_pattern',
+            '_sequential_fit.max_workers',
+            '_sequential_fit.chunk_size',
+            '_sequential_fit.reverse',
+        )
+    )
+
+
+def _warn_inactive_analysis_sections(
+    *,
+    has_joint_rows: bool,
+    has_sequential_settings: bool,
+    has_sequential_extract_rows: bool,
+) -> None:
+    """Warn when inactive analysis sections are skipped."""
+    skipped_sections: list[str] = []
+    if has_joint_rows:
+        skipped_sections.append('joint_fit')
+    if has_sequential_settings or has_sequential_extract_rows:
+        skipped_sections.append('sequential_fit')
+    log.warning(
+        'Skipping inactive analysis CIF sections while fitting_mode_type is single: '
+        f'{skipped_sections}.'
+    )
+
+
+def _restore_mode_specific_analysis_sections(analysis: object, block: object) -> None:
+    """Restore only the active mode-specific analysis sections."""
+    has_joint_rows = _has_joint_fit_rows(block)
+    has_sequential_settings = _has_sequential_fit_settings(block)
+    has_sequential_extract_rows = _has_cif_loop(block, '_sequential_fit_extract.id')
+
+    if analysis.fitting_mode_type == 'joint':
+        if has_joint_rows:
+            analysis.joint_fit.from_cif(block)
+        return
+
+    if analysis.fitting_mode_type == 'sequential':
+        if has_sequential_settings:
+            analysis.sequential_fit.from_cif(block)
+        if has_sequential_extract_rows:
+            analysis.sequential_fit_extract.from_cif(block)
+        return
+
+    if has_joint_rows or has_sequential_settings or has_sequential_extract_rows:
+        _warn_inactive_analysis_sections(
+            has_joint_rows=has_joint_rows,
+            has_sequential_settings=has_sequential_settings,
+            has_sequential_extract_rows=has_sequential_extract_rows,
+        )
 
 
 def _make_cif_string_reader(block: gemmi.cif.Block) -> object:
@@ -534,6 +676,20 @@ def _read(tag: str) -> str | None:
     return _read
 
 
+def _has_cif_value(block: gemmi.cif.Block, tag: str) -> bool:
+    """Return True when a scalar CIF tag is present in the block."""
+    return block.find_value(tag) is not None
+
+
+def _has_cif_loop(block: gemmi.cif.Block, tag: str) -> bool:
+    """Return True when a CIF loop column is present in the block."""
+    loop_ref = block.find_loop(tag)
+    if loop_ref is None:
+        return False
+    loop = loop_ref.get_loop() if hasattr(loop_ref, 'get_loop') else loop_ref
+    return loop is not None
+
+
 # TODO: Check the following methods:
 
 ######################
@@ -591,10 +747,10 @@ def param_from_cif(
 
     # If string, strip quotes if present
     elif self._value_type == DataTypes.STRING:
-        if len(raw) >= _MIN_QUOTED_LEN and raw[0] == raw[-1] and raw[0] in {"'", '"'}:
-            self.value = raw[1:-1]
-        else:
-            self.value = raw
+        self.value = _strip_optional_quotes(raw)
+
+    elif self._value_type == DataTypes.BOOL:
+        self.value = _parse_bool_cif_value(raw)
 
     # Other types are not supported
     else:
@@ -642,10 +798,11 @@ def _set_param_from_raw_cif_value(
                 param.uncertainty = u.s  # type: ignore[attr-defined]
 
     # If string, strip quotes if present
-    # TODO: Make a helper function for this
     elif param._value_type == DataTypes.STRING:
-        is_quoted = len(raw) >= _MIN_QUOTED_LEN and raw[0] == raw[-1] and raw[0] in {"'", '"'}
-        param.value = raw[1:-1] if is_quoted else raw
+        param.value = _strip_optional_quotes(raw)
+
+    elif param._value_type == DataTypes.BOOL:
+        param.value = _parse_bool_cif_value(raw)
 
     else:
         log.debug(f'Unrecognized type: {param._value_type}')
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index c248c9ad3..83a9e40f2 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -106,11 +106,22 @@ def correlations(
 
     def series(
         self,
-        param: object,
-        versus: object | None = None,
+        param: object | None = None,
+        versus: str | None = None,
     ) -> None:
-        """Plot one fitted parameter across sequential results."""
-        self._project.rendering.plotter.plot_param_series(param=param, versus=versus)
+        """
+        Plot fitted parameter(s) across sequential results.
+
+            Use a persisted diffrn path such as
+            ``'diffrn.ambient_temperature'`` for *versus*. When *param*
+            is provided, plot that single parameter. When *param* is
+            ``None`` (default), plot every fitted parameter, one after
+            another.
+        """
+        if param is None:
+            self._project.rendering.plotter.plot_all_param_series(versus=versus)
+        else:
+            self._project.rendering.plotter.plot_param_series(param=param, versus=versus)
 
     def help(self) -> None:
         """Print available fit-display methods."""
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index e7508201f..df8972de9 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -6,6 +6,7 @@
 
 import pathlib
 import tempfile
+from typing import ClassVar
 
 from typeguard import typechecked
 from varname import varname
@@ -59,6 +60,51 @@ def _apply_csv_row_to_params(
             param_map[col_name].value = float(row[col_name])
 
 
+def _apply_csv_row_to_diffrn(
+    row: object,
+    columns: object,
+    experiment: object,
+) -> None:
+    """
+    Override ``experiment.diffrn`` values from a CSV row.
+
+    Parameters
+    ----------
+    row : object
+        A pandas Series representing one CSV row.
+    columns : object
+        The DataFrame column index.
+    experiment : object
+        Live experiment whose ``diffrn`` descriptors are updated.
+    """
+    import pandas as pd  # noqa: PLC0415
+
+    from easydiffraction.core.variable import NumericDescriptor  # noqa: PLC0415
+
+    for col_name in columns:
+        if not col_name.startswith('diffrn.') or pd.isna(row[col_name]):
+            continue
+
+        field_name = col_name.removeprefix('diffrn.')
+        descriptor = getattr(experiment.diffrn, field_name, None)
+        if isinstance(descriptor, NumericDescriptor):
+            descriptor.value = float(row[col_name])
+
+
+def _resolve_data_path_from_results_csv(
+    project_path: pathlib.Path,
+    file_path: object,
+) -> pathlib.Path | None:
+    """Resolve a CSV-stored data path against the project path."""
+    if not isinstance(file_path, str) or not file_path:
+        return None
+
+    path = pathlib.Path(file_path)
+    if path.is_absolute():
+        return path
+    return project_path / path
+
+
 class Project(GuardedBase):
     """
     Central API for managing a diffraction data analysis project.
@@ -71,6 +117,7 @@ class Project(GuardedBase):
     # ------------------------------------------------------------------
     # Class-level sentinel: True while load() is constructing a project.
     _loading: bool = False
+    _current_project: ClassVar[Project | None] = None
 
     def __init__(
         self,
@@ -91,6 +138,15 @@ def __init__(
         self._saved = False
         self._varname = 'project' if type(self)._loading else varname()
         self._verbosity: VerbosityEnum = VerbosityEnum.FULL
+        type(self)._current_project = self
+
+    @classmethod
+    def current_project_path(cls) -> pathlib.Path | None:
+        """Return the saved path of the current project, if any."""
+        current_project = cls._current_project
+        if current_project is None:
+            return None
+        return current_project.info.path
 
     # ------------------------------------------------------------------
     # Dunder methods
@@ -382,7 +438,13 @@ def save(self) -> None:
         with (analysis_dir / 'analysis.cif').open('w') as f:
             f.write(self.analysis.as_cif)
             console.print('├── 📁 analysis/')
-            console.print('│   └── 📄 analysis.cif')
+
+        analysis_file_names = sorted(
+            path.name for path in analysis_dir.iterdir() if path.is_file()
+        )
+        for index, file_name in enumerate(analysis_file_names):
+            branch = '└──' if index == len(analysis_file_names) - 1 else '├──'
+            console.print(f'│   {branch} 📄 {file_name}')
 
         # Save summary
         with (self._info.path / 'summary.cif').open('w') as f:
@@ -458,13 +520,18 @@ def apply_params_from_csv(self, row_index: int) -> None:
 
         row = df.iloc[row_index]
 
+        experiment = next(iter(self.experiments.values()))
+
         # 1. Reload data if file_path points to a real file
         file_path = row.get('file_path', '')
-        if file_path and pathlib.Path(file_path).is_file():
-            experiment = next(iter(self.experiments.values()))
-            experiment._load_ascii_data_to_experiment(file_path)
+        data_path = _resolve_data_path_from_results_csv(self.info.path, file_path)
+        if data_path is not None and data_path.is_file():
+            experiment._load_ascii_data_to_experiment(str(data_path))
+
+        # 2. Restore extracted diffrn metadata from the CSV row.
+        _apply_csv_row_to_diffrn(row, df.columns, experiment)
 
-        # 2. Override parameter values and uncertainties
+        # 3. Override parameter values and uncertainties
         all_params = self.structures.parameters + self.experiments.parameters
         param_map = {
             p.unique_name: p
diff --git a/src/easydiffraction/summary/summary.py b/src/easydiffraction/summary/summary.py
index 27826dab7..25977bfa9 100644
--- a/src/easydiffraction/summary/summary.py
+++ b/src/easydiffraction/summary/summary.py
@@ -219,7 +219,7 @@ def show_fitting_details(self) -> None:
         console.section('Fitting')
 
         console.paragraph('Minimization engine')
-        console.print(self.project.analysis.fit.minimizer_type.value)
+        console.print(self.project.analysis.fitting.minimizer_type.value)
 
         console.paragraph('Fit quality')
         columns_headers = ['metric', 'value']
diff --git a/src/easydiffraction/utils/utils.py b/src/easydiffraction/utils/utils.py
index 2fe66e739..cf461aeea 100644
--- a/src/easydiffraction/utils/utils.py
+++ b/src/easydiffraction/utils/utils.py
@@ -28,9 +28,9 @@
 _DATA_REPO = 'easyscience/diffraction'
 _DATA_ROOT = 'data'
 # commit SHA preferred
-_DATA_INDEX_REF = 'd5a1fddd0d3e3e919c7e4a19e83b94b4231b99b6'
+_DATA_INDEX_REF = '39dad256ba1faedf4b26fad3e44a361c802fd8e4'
 # macOS: sha256sum index.json
-_DATA_INDEX_HASH = 'sha256:8305fd55d5b0c7c63ffa2641c082c623a107c32af09343ba901e196f68fd9f73'
+_DATA_INDEX_HASH = 'sha256:301aaca0f35927cd63715b858a1f03164e4d05d1d39234325a3798d2b4a5f4ea'
 
 
 def _build_data_url(path: str) -> str:
diff --git a/tests/functional/test_fitting_workflow.py b/tests/functional/test_fitting_workflow.py
index 3fde95dde..0901269c6 100644
--- a/tests/functional/test_fitting_workflow.py
+++ b/tests/functional/test_fitting_workflow.py
@@ -139,13 +139,15 @@ def test_create_constraint(self):
 class TestFitting:
     def test_fit_produces_results(self):
         project = _make_fit_ready_project()
-        project.analysis.fit(verbosity='silent')
+        project.verbosity = 'silent'
+        project.analysis.fit()
         assert project.analysis.fit_results is not None
         assert project.analysis.fit_results.success is True
 
     def test_fit_improves_chi_squared(self):
         project = _make_fit_ready_project()
-        project.analysis.fit(verbosity='silent')
+        project.verbosity = 'silent'
+        project.analysis.fit()
         results = project.analysis.fit_results
         assert results.reduced_chi_square is not None
         # A well-configured fit should get reasonable chi-squared
@@ -154,7 +156,8 @@ def test_fit_improves_chi_squared(self):
     def test_fit_updates_parameter_values(self):
         project = _make_fit_ready_project()
         initial_a = project.structures['lbco'].cell.length_a.value
-        project.analysis.fit(verbosity='silent')
+        project.verbosity = 'silent'
+        project.analysis.fit()
         fitted_a = project.structures['lbco'].cell.length_a.value
         # Fitting should have adjusted the cell parameter
         assert fitted_a != pytest.approx(initial_a, abs=1e-6)
@@ -177,7 +180,8 @@ def test_fit_with_constraints(self):
             expression='biso_Ba = biso_La',
         )
 
-        project.analysis.fit(verbosity='silent')
+        project.verbosity = 'silent'
+        project.analysis.fit()
         assert project.analysis.fit_results.success is True
         # Constrained params should be equal after fitting
         la_biso = s.atom_sites['La'].adp_iso.value
diff --git a/tests/functional/test_switchable_categories.py b/tests/functional/test_switchable_categories.py
index 25cc6cf3c..52bc991bd 100644
--- a/tests/functional/test_switchable_categories.py
+++ b/tests/functional/test_switchable_categories.py
@@ -45,7 +45,7 @@ def test_fit_default(self):
 
     def test_minimizer_default(self):
         project = _make_project_with_experiment()
-        assert project.analysis.fit.minimizer_type is not None
+        assert project.analysis.fitting.minimizer_type is not None
 
 
 # ------------------------------------------------------------------
diff --git a/tests/integration/fitting/conftest.py b/tests/integration/fitting/conftest.py
index a4987ae7f..7c5113274 100644
--- a/tests/integration/fitting/conftest.py
+++ b/tests/integration/fitting/conftest.py
@@ -76,7 +76,7 @@ def lbco_fitted_project():
     project = Project()
     project.structures.add(model)
     project.experiments.add(expt)
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     model.cell.length_a.free = True
     expt.linked_phases['lbco'].scale.free = True
@@ -84,6 +84,7 @@ def lbco_fitted_project():
     expt.background['1'].y.free = True
     expt.background['2'].y.free = True
 
-    project.analysis.fit(verbosity='silent')
+    project.verbosity = 'silent'
+    project.analysis.fit()
 
     return project
diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
index 0ed65e4fc..5633cb6ad 100644
--- a/tests/integration/fitting/test_analysis_and_fit_category_support.py
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -78,7 +78,7 @@ class Project:
 
 
 def test_fit_mode_enum_members_default_and_descriptions():
-    from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+    from easydiffraction.analysis.enums import FitModeEnum
 
     assert FitModeEnum.SINGLE == 'single'
     assert FitModeEnum.JOINT == 'joint'
@@ -87,112 +87,84 @@ def test_fit_mode_enum_members_default_and_descriptions():
     assert all(member.description() for member in FitModeEnum)
 
 
-def test_fit_instantiation_defaults_and_run_paths():
-    from easydiffraction.analysis.categories.fit.default import Fit
-    import easydiffraction.analysis.categories.fit.default as fit_mod
+def test_fitting_instantiation_defaults_and_helpers():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+    import easydiffraction.analysis.categories.fitting.default as fitting_mod
 
-    fit = Fit()
+    fitting = Fitting()
 
-    assert fit._identity.category_code == 'fit'
-    assert fit.mode.value == 'single'
-    assert fit.minimizer_type.value == 'lmfit (leastsq)'
-    assert fit.minimizer is None
-
-    with pytest.raises(
-        RuntimeError,
-        match=r'Fit category is not attached to an Analysis object\.',
-    ):
-        fit.run()
-
-    calls: list[tuple[str | None, bool, int | None]] = []
-
-    class Parent:
-        fitter = object()
-
-        @staticmethod
-        def _run_fit(
-            verbosity: str | None = None,
-            *,
-            use_physical_limits: bool = False,
-            random_seed: int | None = None,
-        ) -> None:
-            calls.append((verbosity, use_physical_limits, random_seed))
-
-    fit._parent = Parent()
-    fit(verbosity='silent', use_physical_limits=True, random_seed=7)
-
-    assert calls == [('silent', True, 7)]
+    assert fitting._identity.category_code == 'fitting'
+    assert fitting.minimizer_type.value == 'lmfit (leastsq)'
+    assert fitting.minimizer is None
 
     class ParentWithMinimizer:
         fitter = type('FitterHolder', (), {'minimizer': 'MIN'})()
 
-    fit._parent = ParentWithMinimizer()
-    assert fit.minimizer == 'MIN'
+    fitting._parent = ParentWithMinimizer()
+    assert fitting.minimizer == 'MIN'
 
     shown: list[str] = []
     monkeypatch = pytest.MonkeyPatch()
-    monkeypatch.setattr(fit_mod.MinimizerFactory, 'show_supported', lambda: shown.append('shown'))
-    Fit.show_available_minimizers()
+    monkeypatch.setattr(
+        fitting_mod.MinimizerFactory,
+        'show_supported',
+        lambda: shown.append('shown'),
+    )
+    Fitting.show_available_minimizers()
     monkeypatch.undo()
 
     assert shown == ['shown']
 
 
 def test_fit_from_cif_warns_on_invalid_minimizer(monkeypatch):
-    import easydiffraction.analysis.categories.fit.default as fit_mod
-    from easydiffraction.analysis.categories.fit.default import Fit
+    import easydiffraction.analysis.categories.fitting.default as fitting_mod
+    from easydiffraction.analysis.categories.fitting.default import Fitting
 
-    fit = Fit()
-    fit._minimizer_type._value = 'bad-minimizer'
+    fitting = Fitting()
+    fitting._minimizer_type._value = 'bad-minimizer'
 
     class Parent:
         fitter = None
 
     warnings: list[str] = []
-    fit._parent = Parent()
-    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    fitting._parent = Parent()
+    monkeypatch.setattr(fitting_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
     monkeypatch.setattr(
-        fit_mod,
+        fitting_mod,
         'Fitter',
         lambda value: (_ for _ in ()).throw(ValueError('bad minimizer')),
     )
-    monkeypatch.setattr(fit_mod.log, 'warning', lambda message: warnings.append(message))
+    monkeypatch.setattr(fitting_mod.log, 'warning', lambda message: warnings.append(message))
 
-    fit.from_cif(object())
+    fitting.from_cif(object())
 
     assert warnings == ['bad minimizer']
 
 
-def test_fit_fallback_paths_without_parent_or_project(capsys, monkeypatch):
-    import easydiffraction.analysis.categories.fit.default as fit_mod
-    from easydiffraction.analysis.categories.fit.default import Fit
+def test_fitting_fallback_paths_without_parent(monkeypatch):
+    import easydiffraction.analysis.categories.fitting.default as fitting_mod
+    from easydiffraction.analysis.categories.fitting.default import Fitting
 
-    fit = Fit()
+    fitting = Fitting()
 
-    assert fit.minimizer is None
-
-    fit.show_modes()
-    out = capsys.readouterr().out
-    assert 'single' in out
-    assert 'joint' in out
-    assert 'sequential' in out
+    assert fitting.minimizer is None
 
-    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
-    fit.from_cif(object())
+    monkeypatch.setattr(fitting_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    fitting.from_cif(object())
 
 
-def test_fit_show_modes_for_single_and_multiple_experiments(capsys):
+def test_show_fitting_mode_types_for_single_and_multiple_experiments(capsys):
     from easydiffraction.analysis.analysis import Analysis
 
     single = Analysis(project=_make_project_with_names(['e1']))
-    single.fit.show_modes()
+    single.show_fitting_mode_types()
     out_single = capsys.readouterr().out
-    assert 'Fit modes' in out_single
+    assert 'Fitting mode types' in out_single
     assert 'single' in out_single
-    assert 'joint' not in out_single
+    assert 'joint' in out_single
 
     multi = Analysis(project=_make_project_with_names(['e1', 'e2']))
-    multi.fit.show_modes()
+    multi.show_fitting_mode_types()
     out_multi = capsys.readouterr().out
     assert 'joint' in out_multi
     assert 'sequential' in out_multi
@@ -202,7 +174,7 @@ def test_show_minimizer_types_prints(capsys):
     from easydiffraction.analysis.analysis import Analysis
 
     analysis = Analysis(project=_make_project_with_names([]))
-    analysis.fit.show_minimizer_types()
+    analysis.fitting.show_minimizer_types()
     out = capsys.readouterr().out
     assert 'Minimizer types' in out
     assert 'lmfit (leastsq)' in out
@@ -212,19 +184,19 @@ def test_analysis_help_and_mode_switching(capsys):
     from easydiffraction.analysis.analysis import Analysis
 
     analysis = Analysis(project=_make_project_with_names(['e1', 'e2']))
-    assert analysis.fit.mode.value == 'single'
-    analysis.fit.mode = 'joint'
-    assert analysis.fit.mode.value == 'joint'
-    assert len(analysis.joint_fit_experiments) == 0
+    assert analysis.fitting_mode_type == 'single'
+    analysis.fitting_mode_type = 'joint'
+    assert analysis.fitting_mode_type == 'joint'
+    assert len(analysis.joint_fit) == 0
 
     analysis.help()
     out = _unstyled_output(capsys.readouterr().out)
     assert "Help for 'Analysis'" in out
-    assert 'fit' in out
+    assert 'fitting' in out
     assert 'display' in out
     assert 'Properties' in out
     assert 'Methods' in out
-    assert 'fit_sequential()' in out
+    assert 'fit()' in out
 
 
 def test_display_fit_results_warns_when_no_results(capsys):
diff --git a/tests/integration/fitting/test_analysis_display.py b/tests/integration/fitting/test_analysis_display.py
index bc549ee42..3a139be26 100644
--- a/tests/integration/fitting/test_analysis_display.py
+++ b/tests/integration/fitting/test_analysis_display.py
@@ -59,12 +59,12 @@ def test_analysis_help(lbco_fitted_project):
 
 def test_show_minimizer_types_again(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.fit.show_minimizer_types()
+    project.analysis.fitting.show_minimizer_types()
 
 
 def test_show_minimizer_types(lbco_fitted_project):
     project = lbco_fitted_project
-    project.analysis.fit.show_minimizer_types()
+    project.analysis.fitting.show_minimizer_types()
 
 
 def test_fit_results_attributes(lbco_fitted_project):
diff --git a/tests/integration/fitting/test_aniso_adp_fitting.py b/tests/integration/fitting/test_aniso_adp_fitting.py
index 24fed45f9..ccfbef066 100644
--- a/tests/integration/fitting/test_aniso_adp_fitting.py
+++ b/tests/integration/fitting/test_aniso_adp_fitting.py
@@ -59,7 +59,8 @@ def test_iso_then_aniso_fit() -> None:
     e.extinction.radius.free = True
 
     # Fit isotropic
-    project.analysis.fit(verbosity='silent')
+    project.verbosity = 'silent'
+    project.analysis.fit()
     chi2_iso = project.analysis.fit_results.reduced_chi_square
     assert chi2_iso < 20.0
 
@@ -81,7 +82,7 @@ def test_iso_then_aniso_fit() -> None:
     s.atom_site_aniso['O1'].adp_23.free = True
 
     # Fit anisotropic
-    project.analysis.fit(verbosity='silent')
+    project.analysis.fit()
     chi2_aniso = project.analysis.fit_results.reduced_chi_square
 
     # Anisotropic fit should improve (or at least match) chi2
diff --git a/tests/integration/fitting/test_bayesian_dream.py b/tests/integration/fitting/test_bayesian_dream.py
index 23e0de589..d6695973b 100644
--- a/tests/integration/fitting/test_bayesian_dream.py
+++ b/tests/integration/fitting/test_bayesian_dream.py
@@ -88,8 +88,8 @@ def _dream_parameters(project: Project) -> tuple[object, object, object]:
 
 
 def _configure_small_dream(project: Project) -> None:
-    project.analysis.fit.minimizer_type = 'bumps (dream)'
-    minimizer = project.analysis.fit.minimizer
+    project.analysis.fitting.minimizer_type = 'bumps (dream)'
+    minimizer = project.analysis.fitting.minimizer
     minimizer.steps = 20
     minimizer.burn = 5
     minimizer.thin = 1
@@ -97,6 +97,20 @@ def _configure_small_dream(project: Project) -> None:
     minimizer.init = 'lhs'
 
 
+def _run_single_fit(project: Project, *, random_seed: int | None = None) -> None:
+    project.verbosity = 'silent'
+    prepared = project.analysis._prepare_fit_run()
+    assert prepared is not None
+    verb, structures, experiments = prepared
+    project.analysis._fit_single(
+        verb,
+        structures,
+        experiments,
+        use_physical_limits=False,
+        random_seed=random_seed,
+    )
+
+
 def test_small_bounded_dream_refinement_produces_posterior_results():
     project = _create_lbco_project()
     length_a, scale, offset = _dream_parameters(project)
@@ -111,7 +125,7 @@ def test_small_bounded_dream_refinement_produces_posterior_results():
     offset.fit_max = 1.0
 
     _configure_small_dream(project)
-    project.analysis.fit(verbosity='silent', random_seed=11)
+    _run_single_fit(project, random_seed=11)
 
     results = project.analysis.fit_results
     assert results.success is True
@@ -130,8 +144,8 @@ def test_lm_prefit_followed_by_dream_uses_uncertainty_based_bounds():
     for parameter in (length_a, scale, offset):
         parameter.free = True
 
-    project.analysis.fit.minimizer_type = 'bumps (lm)'
-    project.analysis.fit(verbosity='silent')
+    project.analysis.fitting.minimizer_type = 'bumps (lm)'
+    _run_single_fit(project)
 
     for parameter in (length_a, scale, offset):
         assert parameter.uncertainty is not None
@@ -140,7 +154,7 @@ def test_lm_prefit_followed_by_dream_uses_uncertainty_based_bounds():
         assert np.isfinite(parameter.fit_max)
 
     _configure_small_dream(project)
-    project.analysis.fit(verbosity='silent', random_seed=13)
+    _run_single_fit(project, random_seed=13)
 
     results = project.analysis.fit_results
     assert results.success is True
@@ -163,7 +177,7 @@ def test_bayesian_fit_results_are_runtime_only_after_save_load(tmp_path):
     offset.fit_max = 1.0
 
     _configure_small_dream(project)
-    project.analysis.fit(verbosity='silent', random_seed=17)
+    _run_single_fit(project, random_seed=17)
 
     assert project.analysis.fit_results.posterior_samples is not None
 
diff --git a/tests/integration/fitting/test_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py
index a4eef9007..b67d5db02 100644
--- a/tests/integration/fitting/test_bayesian_helper_support.py
+++ b/tests/integration/fitting/test_bayesian_helper_support.py
@@ -150,7 +150,7 @@ def test_summarize_posterior_parameters_preserves_order_and_display_names():
     summaries = summarize_posterior_parameters(
         parameter_names=['beta', 'alpha'],
         posterior_samples=posterior_samples,
-        map_values=np.array([2.05, 1.05]),
+        best_sample_values=np.array([2.05, 1.05]),
         parameter_display_names=['Beta width', 'Alpha shift'],
         convergence_diagnostics={
             'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
@@ -182,7 +182,7 @@ def test_summarize_posterior_parameters_validates_display_name_length():
         summarize_posterior_parameters(
             parameter_names=['alpha'],
             posterior_samples=posterior_samples,
-            map_values=np.array([1.0]),
+            best_sample_values=np.array([1.0]),
             parameter_display_names=['Alpha', 'Extra'],
         )
 
@@ -195,7 +195,7 @@ def test_standard_deviations_from_summaries_returns_float_array():
         PosteriorParameterSummary(
             unique_name='a',
             display_name='A',
-            map_value=1.0,
+            best_sample_value=1.0,
             median=1.0,
             standard_deviation=0.2,
             interval_68=(0.9, 1.1),
@@ -204,7 +204,7 @@ def test_standard_deviations_from_summaries_returns_float_array():
         PosteriorParameterSummary(
             unique_name='b',
             display_name='B',
-            map_value=2.0,
+            best_sample_value=2.0,
             median=2.0,
             standard_deviation=0.3,
             interval_68=(1.9, 2.1),
@@ -240,8 +240,8 @@ def test_bayesian_format_helpers_cover_edge_cases():
         _format_sampler_settings({'steps': 10, 'burn': 2, 'samples': 40})
         == 'steps=10, burn=2, samples=40'
     )
-    assert _format_point_estimate_name('map') == 'Max posterior'
-    assert _format_point_estimate_name('best_sample') == 'Best Sample'
+    assert _format_point_estimate_name('map') == 'Best posterior sample'
+    assert _format_point_estimate_name('best_sample') == 'Best posterior sample'
     assert _format_bayesian_overall_status(
         success=False,
         sampler_completed=False,
@@ -322,7 +322,7 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
             PosteriorParameterSummary(
                 unique_name='a',
                 display_name='a',
-                map_value=1.2,
+                best_sample_value=1.2,
                 median=1.15,
                 standard_deviation=0.05,
                 interval_68=(1.1, 1.2),
@@ -377,7 +377,7 @@ def test_build_posterior_summary_row_restores_identifier_columns():
     summary = PosteriorParameterSummary(
         unique_name='a',
         display_name='a',
-        map_value=1.2,
+        best_sample_value=1.2,
         median=1.15,
         standard_deviation=0.05,
         interval_68=(1.1, 1.2),
@@ -424,7 +424,7 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
         'parameter',
         'units',
         'start',
-        'max posterior',
+        'best posterior sample',
         'uncertainty',
         'change',
     ]
@@ -473,7 +473,7 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
             PosteriorParameterSummary(
                 unique_name='a',
                 display_name='a',
-                map_value=1.2,
+                best_sample_value=1.2,
                 median=1.15,
                 standard_deviation=0.05,
                 interval_68=(1.1, 1.2),
@@ -529,7 +529,7 @@ def test_posterior_table_notes_split_failed_diagnostics():
         PosteriorParameterSummary(
             unique_name='a',
             display_name='a',
-            map_value=1.0,
+            best_sample_value=1.0,
             median=1.0,
             standard_deviation=0.1,
             interval_68=(0.9, 1.1),
@@ -554,7 +554,7 @@ def test_bayesian_helpers_cover_non_warning_and_default_display_paths():
     summary = PosteriorParameterSummary(
         unique_name='missing',
         display_name='Missing',
-        map_value=1.0,
+        best_sample_value=1.0,
         median=1.0,
         standard_deviation=0.1,
         interval_68=(0.9, 1.1),
diff --git a/tests/integration/fitting/test_bumps_dream_support.py b/tests/integration/fitting/test_bumps_dream_support.py
index 4bcfed845..c09f0b92e 100644
--- a/tests/integration/fitting/test_bumps_dream_support.py
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -471,7 +471,7 @@ def best(self):
                 PosteriorParameterSummary(
                     unique_name='beta',
                     display_name='Beta',
-                    map_value=22.0,
+                    best_sample_value=22.0,
                     median=21.0,
                     standard_deviation=0.4,
                     interval_68=(20.5, 21.5),
@@ -480,7 +480,7 @@ def best(self):
                 PosteriorParameterSummary(
                     unique_name='alpha',
                     display_name='Alpha',
-                    map_value=11.0,
+                    best_sample_value=11.0,
                     median=10.5,
                     standard_deviation=0.3,
                     interval_68=(10.0, 11.0),
@@ -686,7 +686,7 @@ def best():
             PosteriorParameterSummary(
                 unique_name='alpha',
                 display_name='Alpha',
-                map_value=1.0,
+                best_sample_value=1.0,
                 median=1.0,
                 standard_deviation=0.2,
                 interval_68=(0.9, 1.1),
diff --git a/tests/integration/fitting/test_multi.py b/tests/integration/fitting/test_multi.py
index a955bf928..d75acf6c8 100644
--- a/tests/integration/fitting/test_multi.py
+++ b/tests/integration/fitting/test_multi.py
@@ -107,7 +107,7 @@ def test_single_fit_neutron_pd_tof_mcstas_lbco_si() -> None:
     project.experiments['mcstas'].excluded_regions.create(start=108000, end=200000)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # Select fitting parameters
     model_1.cell.length_a.free = True
@@ -200,8 +200,8 @@ def _test_joint_fit_bragg_pdf_neutron_pd_tof_si() -> None:
     project.experiments.add(pdf_expt)
 
     # Prepare for fitting
-    project.analysis.fit.mode = 'joint'
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting_mode_type = 'joint'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # Select fitting parameters — shared structure
     model.cell.length_a.free = True
diff --git a/tests/integration/fitting/test_powder-diffraction_constant-wavelength.py b/tests/integration/fitting/test_powder-diffraction_constant-wavelength.py
index 9e65996ac..0ffa95f14 100644
--- a/tests/integration/fitting/test_powder-diffraction_constant-wavelength.py
+++ b/tests/integration/fitting/test_powder-diffraction_constant-wavelength.py
@@ -86,7 +86,7 @@ def test_single_fit_neutron_pd_cwl_lbco() -> None:
     project.experiments.add(expt)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # ------------ 1st fitting ------------
 
@@ -234,7 +234,7 @@ def test_single_fit_neutron_pd_cwl_lbco_with_constraints() -> None:
     project.experiments.add(expt)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # ------------ 1st fitting ------------
 
@@ -400,7 +400,7 @@ def test_fit_neutron_pd_cwl_hs() -> None:
     project.experiments.add(expt)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # ------------ 1st fitting ------------
 
diff --git a/tests/integration/fitting/test_powder-diffraction_joint-fit.py b/tests/integration/fitting/test_powder-diffraction_joint-fit.py
index ec39c1de0..0e7e11898 100644
--- a/tests/integration/fitting/test_powder-diffraction_joint-fit.py
+++ b/tests/integration/fitting/test_powder-diffraction_joint-fit.py
@@ -122,8 +122,8 @@ def test_joint_fit_split_dataset_neutron_pd_cwl_pbso4() -> None:
     project.experiments.add(expt2)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
-    project.analysis.fit.mode = 'joint'
+    project.analysis.fitting.minimizer_type = 'lmfit'
+    project.analysis.fitting_mode_type = 'joint'
 
     # Select fitting parameters
     model.cell.length_a.free = True
@@ -255,7 +255,7 @@ def test_joint_fit_neutron_xray_pd_cwl_pbso4() -> None:
     project.experiments.add(expt2)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # Select fitting parameters
     model.cell.length_a.free = True
@@ -279,7 +279,7 @@ def test_joint_fit_neutron_xray_pd_cwl_pbso4() -> None:
     # ------------ 2nd fitting ------------
 
     # Perform fit
-    project.analysis.fit.mode = 'joint'
+    project.analysis.fitting_mode_type = 'joint'
     project.analysis.fit()
 
     # Compare fit quality
@@ -292,8 +292,8 @@ def test_joint_fit_neutron_xray_pd_cwl_pbso4() -> None:
     # ------------ 3rd fitting ------------
 
     # Perform fit
-    project.analysis.joint_fit_experiments['xrd'].weight = 0.5  # Default
-    project.analysis.joint_fit_experiments['npd'].weight = 0.5  # Default
+    project.analysis.joint_fit['xrd'].weight = 0.5  # Default
+    project.analysis.joint_fit['npd'].weight = 0.5  # Default
     project.analysis.fit()
 
     # Compare fit quality
@@ -306,8 +306,8 @@ def test_joint_fit_neutron_xray_pd_cwl_pbso4() -> None:
     # ------------ 4th fitting ------------
 
     # Perform fit
-    project.analysis.joint_fit_experiments['xrd'].weight = 0.3
-    project.analysis.joint_fit_experiments['npd'].weight = 0.7
+    project.analysis.joint_fit['xrd'].weight = 0.3
+    project.analysis.joint_fit['npd'].weight = 0.7
     project.analysis.fit()
 
     # Compare fit quality
diff --git a/tests/integration/fitting/test_powder-diffraction_time-of-flight.py b/tests/integration/fitting/test_powder-diffraction_time-of-flight.py
index 1885fb80f..496852e1d 100644
--- a/tests/integration/fitting/test_powder-diffraction_time-of-flight.py
+++ b/tests/integration/fitting/test_powder-diffraction_time-of-flight.py
@@ -58,7 +58,7 @@ def test_single_fit_neutron_pd_tof_si() -> None:
     project.experiments.add(expt)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # Select fitting parameters
     model.cell.length_a.free = True
@@ -200,7 +200,7 @@ def test_single_fit_neutron_pd_tof_ncaf() -> None:
     project.experiments.add(expt)
 
     # Prepare for fitting
-    project.analysis.fit.minimizer_type = 'lmfit'
+    project.analysis.fitting.minimizer_type = 'lmfit'
 
     # Select fitting parameters
     expt.linked_phases['ncaf'].scale.free = True
diff --git a/tests/integration/fitting/test_project_load.py b/tests/integration/fitting/test_project_load.py
index acbc9432a..f1f81c801 100644
--- a/tests/integration/fitting/test_project_load.py
+++ b/tests/integration/fitting/test_project_load.py
@@ -209,8 +209,11 @@ def test_save_load_round_trip_preserves_parameters(tmp_path) -> None:
     assert loaded.analysis.constraints.enabled is True
 
     # Compare analysis settings
-    assert loaded.analysis.fit.minimizer_type.value == original.analysis.fit.minimizer_type.value
-    assert loaded.analysis.fit.mode.value == original.analysis.fit.mode.value
+    assert (
+        loaded.analysis.fitting.minimizer_type.value
+        == original.analysis.fitting.minimizer_type.value
+    )
+    assert loaded.analysis.fitting_mode_type == original.analysis.fitting_mode_type
 
 
 # ------------------------------------------------------------------
@@ -227,7 +230,8 @@ def test_save_load_round_trip_preserves_fit_quality(tmp_path) -> None:
     """
     # Create and fit the original project
     original = _create_lbco_project()
-    original.analysis.fit(verbosity='silent')
+    original.verbosity = 'silent'
+    original.analysis.fit()
     original_chi2 = original.analysis.fit_results.reduced_chi_square
 
     # Save the fitted project
@@ -238,7 +242,8 @@ def test_save_load_round_trip_preserves_fit_quality(tmp_path) -> None:
     loaded = Project.load(proj_dir)
 
     # Fit the loaded project
-    loaded.analysis.fit(verbosity='silent')
+    loaded.verbosity = 'silent'
+    loaded.analysis.fit()
     loaded_chi2 = loaded.analysis.fit_results.reduced_chi_square
 
     # The χ² values should be very close (same starting point,
diff --git a/tests/integration/fitting/test_sequential.py b/tests/integration/fitting/test_sequential.py
index 2583b9e10..da4c28774 100644
--- a/tests/integration/fitting/test_sequential.py
+++ b/tests/integration/fitting/test_sequential.py
@@ -1,11 +1,10 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Integration tests for Analysis.fit_sequential()."""
+"""Integration tests for sequential fitting via Analysis.fit()."""
 
 from __future__ import annotations
 
 import csv
-import shutil
 import tempfile
 from pathlib import Path
 
@@ -20,7 +19,10 @@
 TEMP_DIR = tempfile.gettempdir()
 
 
-def _create_sequential_project(tmp_path: Path) -> tuple[Project, str]:
+def _create_sequential_project(
+    tmp_path: Path,
+    temperatures: dict[str, float] | None = None,
+) -> tuple[Project, str]:
     """
     Build a project for sequential fitting and save it.
 
@@ -100,7 +102,8 @@ def _create_sequential_project(tmp_path: Path) -> tuple[Project, str]:
     expt.background['2'].y.free = True
 
     # Initial fit on the template
-    project.analysis.fit(verbosity='silent')
+    project.verbosity = 'silent'
+    project.analysis.fit()
 
     # Save project
     proj_dir = str(tmp_path / 'seq_project')
@@ -109,12 +112,39 @@ def _create_sequential_project(tmp_path: Path) -> tuple[Project, str]:
     # Create a data directory with copies of the same data file
     data_dir = tmp_path / 'scan_data'
     data_dir.mkdir()
+    source_text = Path(data_path).read_text(encoding='utf-8')
     for i in range(3):
-        shutil.copy(data_path, data_dir / f'scan_{i + 1:03d}.xye')
+        file_name = f'scan_{i + 1:03d}.xye'
+        destination = data_dir / file_name
+        destination_text = source_text
+        if temperatures is not None:
+            temperature = temperatures[file_name]
+            destination_text = f'# ambient_temperature = {temperature}\n{source_text}'
+        destination.write_text(destination_text, encoding='utf-8')
 
     return project, str(data_dir)
 
 
+def _run_sequential_fit(
+    project: Project,
+    data_dir: str,
+    *,
+    max_workers: int | str = 1,
+    chunk_size: int | None = None,
+    file_pattern: str = '*',
+    reverse: bool = False,
+) -> None:
+    project.analysis.fitting_mode_type = 'sequential'
+    project.analysis.sequential_fit.data_dir = data_dir
+    project.analysis.sequential_fit.max_workers = (
+        'auto' if max_workers == 'auto' else str(max_workers)
+    )
+    project.analysis.sequential_fit.chunk_size = '.' if chunk_size is None else str(chunk_size)
+    project.analysis.sequential_fit.file_pattern = file_pattern
+    project.analysis.sequential_fit.reverse = reverse
+    project.analysis.fit()
+
+
 # ------------------------------------------------------------------
 #  Test 1: Basic sequential fit produces CSV
 # ------------------------------------------------------------------
@@ -124,10 +154,7 @@ def test_fit_sequential_produces_csv(tmp_path) -> None:
     """fit_sequential creates a results.csv with one row per file."""
     project, data_dir = _create_sequential_project(tmp_path)
 
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir)
 
     csv_path = project.info.path / 'analysis' / 'results.csv'
     assert csv_path.is_file(), 'results.csv was not created'
@@ -157,10 +184,7 @@ def test_fit_sequential_crash_recovery(tmp_path) -> None:
     project, data_dir = _create_sequential_project(tmp_path)
 
     # First run: fit all 3 files
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir)
 
     csv_path = project.info.path / 'analysis' / 'results.csv'
     with csv_path.open() as f:
@@ -168,10 +192,7 @@ def test_fit_sequential_crash_recovery(tmp_path) -> None:
     assert len(rows_first) == 3
 
     # Second run: should skip all 3 files
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir)
 
     with csv_path.open() as f:
         rows_second = list(csv.DictReader(f))
@@ -188,10 +209,7 @@ def test_fit_sequential_parameter_propagation(tmp_path) -> None:
     """Parameters from one fit propagate to the next."""
     project, data_dir = _create_sequential_project(tmp_path)
 
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir)
 
     csv_path = project.info.path / 'analysis' / 'results.csv'
     with csv_path.open() as f:
@@ -204,26 +222,24 @@ def test_fit_sequential_parameter_propagation(tmp_path) -> None:
 
 
 # ------------------------------------------------------------------
-#  Test 4: extract_diffrn callback
+#  Test 4: extract metadata rules
 # ------------------------------------------------------------------
 
 
-def test_fit_sequential_with_diffrn_callback(tmp_path) -> None:
-    """extract_diffrn callback populates diffrn columns in CSV."""
-    project, data_dir = _create_sequential_project(tmp_path)
-
+def test_fit_sequential_with_diffrn_extract_rules(tmp_path) -> None:
+    """Sequential extract rules populate diffrn columns in the CSV."""
     temperatures = {'scan_001.xye': 300.0, 'scan_002.xye': 350.0, 'scan_003.xye': 400.0}
+    project, data_dir = _create_sequential_project(tmp_path, temperatures=temperatures)
 
-    def extract_diffrn(file_path: str) -> dict[str, float]:
-        name = Path(file_path).name
-        return {'ambient_temperature': temperatures.get(name, 0.0)}
-
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        extract_diffrn=extract_diffrn,
-        verbosity='silent',
+    project.analysis.sequential_fit_extract.create(
+        id='temperature',
+        target='diffrn.ambient_temperature',
+        pattern=r'ambient_temperature\s*=\s*([0-9.]+)',
+        required=True,
     )
 
+    _run_sequential_fit(project, data_dir)
+
     csv_path = project.info.path / 'analysis' / 'results.csv'
     with csv_path.open() as f:
         rows = list(csv.DictReader(f))
@@ -231,9 +247,9 @@ def extract_diffrn(file_path: str) -> dict[str, float]:
     # Check that temperature column is present and populated
     for row in rows:
         name = Path(row['file_path']).name
-        if 'diffrn.ambient_temperature' in row:
-            expected = temperatures.get(name, 0.0)
-            assert_almost_equal(float(row['diffrn.ambient_temperature']), expected)
+        assert 'diffrn.ambient_temperature' in row
+        expected = temperatures[name]
+        assert_almost_equal(float(row['diffrn.ambient_temperature']), expected)
 
 
 # ------------------------------------------------------------------
@@ -256,7 +272,7 @@ def test_fit_sequential_requires_saved_project(tmp_path) -> None:
     project.experiments.add(expt)
 
     with pytest.raises(ValueError, match='must be saved'):
-        project.analysis.fit_sequential(data_dir=str(tmp_path))
+        _run_sequential_fit(project, str(tmp_path))
 
 
 def test_fit_sequential_requires_one_structure(tmp_path) -> None:
@@ -265,7 +281,7 @@ def test_fit_sequential_requires_one_structure(tmp_path) -> None:
     project.save_as(str(tmp_path / 'proj'))
 
     with pytest.raises(ValueError, match='exactly 1 structure'):
-        project.analysis.fit_sequential(data_dir=str(tmp_path))
+        _run_sequential_fit(project, str(tmp_path))
 
 
 def test_fit_sequential_requires_one_experiment(tmp_path) -> None:
@@ -276,7 +292,7 @@ def test_fit_sequential_requires_one_experiment(tmp_path) -> None:
     project.save_as(str(tmp_path / 'proj'))
 
     with pytest.raises(ValueError, match='exactly 1 experiment'):
-        project.analysis.fit_sequential(data_dir=str(tmp_path))
+        _run_sequential_fit(project, str(tmp_path))
 
 
 # ------------------------------------------------------------------
@@ -288,11 +304,7 @@ def test_fit_sequential_parallel(tmp_path) -> None:
     """fit_sequential with max_workers=2 produces correct CSV."""
     project, data_dir = _create_sequential_project(tmp_path)
 
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        max_workers=2,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir, max_workers=2)
 
     csv_path = project.info.path / 'analysis' / 'results.csv'
     assert csv_path.is_file(), 'results.csv was not created'
@@ -320,19 +332,27 @@ def test_fit_sequential_parallel(tmp_path) -> None:
 
 def test_apply_params_from_csv_loads_data_and_params(tmp_path) -> None:
     """apply_params_from_csv overrides params and reloads data."""
-    project, data_dir = _create_sequential_project(tmp_path)
-
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
+    temperatures = {'scan_001.xye': 300.0, 'scan_002.xye': 350.0, 'scan_003.xye': 400.0}
+    project, data_dir = _create_sequential_project(tmp_path, temperatures=temperatures)
+    project.analysis.sequential_fit_extract.create(
+        id='temperature',
+        target='diffrn.ambient_temperature',
+        pattern=r'ambient_temperature\s*=\s*([0-9.]+)',
+        required=True,
     )
 
+    _run_sequential_fit(project, data_dir)
+
     csv_path = project.info.path / 'analysis' / 'results.csv'
     with csv_path.open() as f:
         rows = list(csv.DictReader(f))
 
     # Read the expected cell_length_a from CSV row 1
     expected_a = float(rows[1]['lbco.cell.length_a'])
+    expected_temperature = float(rows[1]['diffrn.ambient_temperature'])
+
+    expt = next(iter(project.experiments.values()))
+    expt.diffrn.ambient_temperature.value = None
 
     # Apply params from row 1
     project.apply_params_from_csv(row_index=1)
@@ -343,8 +363,8 @@ def test_apply_params_from_csv_loads_data_and_params(tmp_path) -> None:
 
     # Verify that the experiment has measured data loaded
     # (from the file_path in that CSV row)
-    expt = next(iter(project.experiments.values()))
     assert expt.data.intensity_meas is not None
+    assert_almost_equal(expt.diffrn.ambient_temperature.value, expected_temperature)
 
 
 def test_apply_params_from_csv_raises_on_missing_csv(tmp_path) -> None:
@@ -360,10 +380,7 @@ def test_apply_params_from_csv_raises_on_bad_index(tmp_path) -> None:
     """apply_params_from_csv raises on out-of-range index."""
     project, data_dir = _create_sequential_project(tmp_path)
 
-    project.analysis.fit_sequential(
-        data_dir=data_dir,
-        verbosity='silent',
-    )
+    _run_sequential_fit(project, data_dir)
 
     with pytest.raises(IndexError, match='out of range'):
         project.apply_params_from_csv(row_index=99)
diff --git a/tests/unit/easydiffraction/analysis/categories/fitting/test_default.py b/tests/unit/easydiffraction/analysis/categories/fitting/test_default.py
new file mode 100644
index 000000000..094a033a3
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fitting/test_default.py
@@ -0,0 +1,37 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/fitting/default.py."""
+
+from types import SimpleNamespace
+
+
+def test_fitting_defaults():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+
+    fitting = Fitting()
+
+    assert fitting.minimizer_type.value == 'lmfit (leastsq)'
+    assert fitting._identity.category_code == 'fitting'
+    assert Fitting.type_info.tag == 'default'
+
+
+def test_fitting_setter_updates_parent_fitter():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+
+    fitting = Fitting()
+    parent = SimpleNamespace(fitter=None)
+    fitting._parent = parent
+
+    fitting.minimizer_type = 'lmfit'
+
+    assert fitting.minimizer_type.value == 'lmfit'
+    assert parent.fitter is not None
+
+
+def test_fitting_as_cif_uses_fitting_prefix():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+
+    fitting = Fitting()
+    fitting.minimizer_type = 'lmfit'
+
+    assert '_fitting.minimizer_type' in fitting.as_cif
diff --git a/tests/unit/easydiffraction/analysis/categories/fitting/test_factory.py b/tests/unit/easydiffraction/analysis/categories/fitting/test_factory.py
new file mode 100644
index 000000000..24d248f1e
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fitting/test_factory.py
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/fitting/factory.py."""
+
+
+def test_fitting_factory_supported_tags():
+    from easydiffraction.analysis.categories.fitting.factory import FittingFactory
+
+    assert 'default' in FittingFactory.supported_tags()
+
+
+def test_fitting_factory_default_tag():
+    from easydiffraction.analysis.categories.fitting.factory import FittingFactory
+
+    assert FittingFactory.default_tag() == 'default'
+
+
+def test_fitting_factory_create():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+    from easydiffraction.analysis.categories.fitting.factory import FittingFactory
+
+    fitting = FittingFactory.create('default')
+
+    assert isinstance(fitting, Fitting)
diff --git a/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_default.py b/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_default.py
new file mode 100644
index 000000000..79c1c1fea
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_default.py
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/sequential_fit/default.py."""
+
+
+def test_sequential_fit_defaults():
+    from easydiffraction.analysis.categories.sequential_fit.default import SequentialFit
+
+    sequential_fit = SequentialFit()
+
+    assert sequential_fit.data_dir.value == ''
+    assert sequential_fit.file_pattern.value == '*'
+    assert sequential_fit.max_workers.value == '1'
+    assert sequential_fit.chunk_size.value == '.'
+    assert sequential_fit.reverse.value is False
+    assert sequential_fit._identity.category_code == 'sequential_fit'
+
+
+def test_sequential_fit_as_cif_serializes_all_fields():
+    from easydiffraction.analysis.categories.sequential_fit.default import SequentialFit
+
+    sequential_fit = SequentialFit()
+    sequential_fit.data_dir = 'scans'
+    sequential_fit.file_pattern = '*.xye'
+    sequential_fit.max_workers = 'auto'
+    sequential_fit.chunk_size = '4'
+    sequential_fit.reverse = True
+
+    as_cif = sequential_fit.as_cif
+
+    assert '_sequential_fit.data_dir scans' in as_cif
+    assert '_sequential_fit.file_pattern *.xye' in as_cif
+    assert '_sequential_fit.max_workers auto' in as_cif
+    assert '_sequential_fit.chunk_size 4' in as_cif
+    assert '_sequential_fit.reverse true' in as_cif.lower()
diff --git a/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_factory.py b/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_factory.py
new file mode 100644
index 000000000..aa3e9c86f
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/sequential_fit/test_factory.py
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/sequential_fit/factory.py."""
+
+
+def test_sequential_fit_factory_supported_tags():
+    from easydiffraction.analysis.categories.sequential_fit.factory import SequentialFitFactory
+
+    assert 'default' in SequentialFitFactory.supported_tags()
+
+
+def test_sequential_fit_factory_default_tag():
+    from easydiffraction.analysis.categories.sequential_fit.factory import SequentialFitFactory
+
+    assert SequentialFitFactory.default_tag() == 'default'
+
+
+def test_sequential_fit_factory_create():
+    from easydiffraction.analysis.categories.sequential_fit.default import SequentialFit
+    from easydiffraction.analysis.categories.sequential_fit.factory import SequentialFitFactory
+
+    sequential_fit = SequentialFitFactory.create('default')
+
+    assert isinstance(sequential_fit, SequentialFit)
diff --git a/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_default.py b/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_default.py
new file mode 100644
index 000000000..ccf8e2f96
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_default.py
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/sequential_fit_extract/default.py."""
+
+import pytest
+
+
+def test_sequential_fit_extract_item_defaults():
+    from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+        SequentialFitExtractItem,
+    )
+
+    item = SequentialFitExtractItem()
+
+    assert item.id.value == '_'
+    assert item.target.value == 'diffrn._'
+    assert item.pattern.value == '(.*)'
+    assert item.required.value is False
+
+
+def test_sequential_fit_extract_collection_create():
+    from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+        SequentialFitExtractCollection,
+    )
+
+    collection = SequentialFitExtractCollection()
+    collection.create(
+        id='temperature',
+        target='diffrn.ambient_temperature',
+        pattern=r'temp_(\d+)',
+        required=True,
+    )
+
+    assert collection.names == ['temperature']
+    assert collection['temperature'].target.value == 'diffrn.ambient_temperature'
+    assert collection['temperature'].required.value is True
+
+
+def test_sequential_fit_extract_collection_rejects_invalid_target():
+    from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+        SequentialFitExtractCollection,
+    )
+
+    collection = SequentialFitExtractCollection()
+
+    with pytest.raises(ValueError, match='must use the form'):
+        collection.create(
+            id='temperature',
+            target='experiment.ambient_temperature',
+            pattern=r'temp_(\d+)',
+        )
+
+
+def test_sequential_fit_extract_collection_rejects_invalid_pattern():
+    from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+        SequentialFitExtractCollection,
+    )
+
+    collection = SequentialFitExtractCollection()
+
+    with pytest.raises(ValueError, match='must define exactly one capture group'):
+        collection.create(
+            id='temperature',
+            target='diffrn.ambient_temperature',
+            pattern=r'temp_\d+',
+        )
diff --git a/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_factory.py b/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_factory.py
new file mode 100644
index 000000000..61e626c74
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/sequential_fit_extract/test_factory.py
@@ -0,0 +1,32 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/sequential_fit_extract/factory.py."""
+
+
+def test_sequential_fit_extract_factory_supported_tags():
+    from easydiffraction.analysis.categories.sequential_fit_extract.factory import (
+        SequentialFitExtractFactory,
+    )
+
+    assert 'default' in SequentialFitExtractFactory.supported_tags()
+
+
+def test_sequential_fit_extract_factory_default_tag():
+    from easydiffraction.analysis.categories.sequential_fit_extract.factory import (
+        SequentialFitExtractFactory,
+    )
+
+    assert SequentialFitExtractFactory.default_tag() == 'default'
+
+
+def test_sequential_fit_extract_factory_create():
+    from easydiffraction.analysis.categories.sequential_fit_extract.default import (
+        SequentialFitExtractCollection,
+    )
+    from easydiffraction.analysis.categories.sequential_fit_extract.factory import (
+        SequentialFitExtractFactory,
+    )
+
+    collection = SequentialFitExtractFactory.create('default')
+
+    assert isinstance(collection, SequentialFitExtractCollection)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit.py b/tests/unit/easydiffraction/analysis/categories/test_fit.py
index aeac67736..8cbc25e12 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_fit.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit.py
@@ -1,31 +1,31 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Tests for the fit category."""
+"""Tests for the fitting category."""
 
 
 def test_module_import():
-    import easydiffraction.analysis.categories.fit as MUT
+    import easydiffraction.analysis.categories.fitting as MUT
 
-    expected_module_name = 'easydiffraction.analysis.categories.fit'
+    expected_module_name = 'easydiffraction.analysis.categories.fitting'
     actual_module_name = MUT.__name__
     assert expected_module_name == actual_module_name
 
 
 class TestFitModeEnum:
     def test_members(self):
-        from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+        from easydiffraction.analysis.enums import FitModeEnum
 
         assert FitModeEnum.SINGLE == 'single'
         assert FitModeEnum.JOINT == 'joint'
         assert FitModeEnum.SEQUENTIAL == 'sequential'
 
     def test_default(self):
-        from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+        from easydiffraction.analysis.enums import FitModeEnum
 
         assert FitModeEnum.default() is FitModeEnum.SINGLE
 
     def test_descriptions(self):
-        from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+        from easydiffraction.analysis.enums import FitModeEnum
 
         for member in FitModeEnum:
             desc = member.description()
@@ -33,60 +33,53 @@ def test_descriptions(self):
             assert len(desc) > 0
 
 
-class TestFitFactory:
+class TestFittingFactory:
     def test_supported_tags(self):
-        from easydiffraction.analysis.categories.fit.factory import FitFactory
+        from easydiffraction.analysis.categories.fitting.factory import FittingFactory
 
-        tags = FitFactory.supported_tags()
+        tags = FittingFactory.supported_tags()
         assert 'default' in tags
 
     def test_default_tag(self):
-        from easydiffraction.analysis.categories.fit.factory import FitFactory
+        from easydiffraction.analysis.categories.fitting.factory import FittingFactory
 
-        assert FitFactory.default_tag() == 'default'
+        assert FittingFactory.default_tag() == 'default'
 
     def test_create(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
-        from easydiffraction.analysis.categories.fit.factory import FitFactory
+        from easydiffraction.analysis.categories.fitting.default import Fitting
+        from easydiffraction.analysis.categories.fitting.factory import FittingFactory
 
-        obj = FitFactory.create('default')
-        assert isinstance(obj, Fit)
+        obj = FittingFactory.create('default')
+        assert isinstance(obj, Fitting)
 
 
-class TestFit:
+class TestFitting:
     def test_instantiation(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
+        from easydiffraction.analysis.categories.fitting.default import Fitting
 
-        fit = Fit()
-        assert fit is not None
+        fitting = Fitting()
+        assert fitting is not None
 
     def test_type_info(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
+        from easydiffraction.analysis.categories.fitting.default import Fitting
 
-        assert Fit.type_info.tag == 'default'
+        assert Fitting.type_info.tag == 'default'
 
     def test_identity_category_code(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
+        from easydiffraction.analysis.categories.fitting.default import Fitting
 
-        fit = Fit()
-        assert fit._identity.category_code == 'fit'
+        fitting = Fitting()
+        assert fitting._identity.category_code == 'fitting'
 
-    def test_mode_default(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
-        from easydiffraction.analysis.categories.fit.enums import FitModeEnum
-
-        fit = Fit()
-        assert fit.mode.value == FitModeEnum.default().value
-
-    def test_mode_setter(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
+    def test_minimizer_default(self):
+        from easydiffraction.analysis.categories.fitting.default import Fitting
 
-        fit = Fit()
-        fit.mode = 'joint'
-        assert fit.mode.value == 'joint'
+        fitting = Fitting()
+        assert fitting.minimizer_type.value == 'lmfit (leastsq)'
 
-    def test_minimizer_default(self):
-        from easydiffraction.analysis.categories.fit.default import Fit
+    def test_minimizer_type_setter(self):
+        from easydiffraction.analysis.categories.fitting.default import Fitting
 
-        fit = Fit()
-        assert fit.minimizer_type.value == 'lmfit (leastsq)'
+        fitting = Fitting()
+        fitting.minimizer_type = 'lmfit'
+        assert fitting.minimizer_type.value == 'lmfit'
diff --git a/tests/unit/easydiffraction/analysis/categories/test_joint_fit_experiments.py b/tests/unit/easydiffraction/analysis/categories/test_joint_fit_experiments.py
index 51777f118..587a17cc2 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_joint_fit_experiments.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_joint_fit_experiments.py
@@ -1,17 +1,17 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiment
-from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
+from easydiffraction.analysis.categories.joint_fit import JointFitCollection
+from easydiffraction.analysis.categories.joint_fit import JointFitItem
 
 
-def test_joint_fit_experiment_and_collection():
-    j = JointFitExperiment()
-    j.id = 'ex1'
+def test_joint_fit_item_and_collection():
+    j = JointFitItem()
+    j.experiment_id = 'ex1'
     j.weight = 0.5
-    assert j.id.value == 'ex1'
+    assert j.experiment_id.value == 'ex1'
     assert j.weight.value == 0.5
-    coll = JointFitExperiments()
-    coll.create(id='ex1', weight=0.5)
+    coll = JointFitCollection()
+    coll.create(experiment_id='ex1', weight=0.5)
     assert 'ex1' in coll.names
     assert coll['ex1'].weight.value == 0.5
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 3b641e6d6..348f380f3 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -121,7 +121,7 @@ def test_summarize_posterior_parameters_preserves_order_and_display_names():
     summaries = summarize_posterior_parameters(
         parameter_names=['beta', 'alpha'],
         posterior_samples=posterior_samples,
-        map_values=np.array([2.05, 1.05]),
+        best_sample_values=np.array([2.05, 1.05]),
         parameter_display_names=['Beta width', 'Alpha shift'],
         convergence_diagnostics={
             'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
@@ -171,7 +171,7 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
             PosteriorParameterSummary(
                 unique_name='a',
                 display_name='a',
-                map_value=1.2,
+                best_sample_value=1.2,
                 median=1.15,
                 standard_deviation=0.05,
                 interval_68=(1.1, 1.2),
@@ -218,7 +218,7 @@ def test_build_posterior_summary_row_restores_identifier_columns():
     summary = PosteriorParameterSummary(
         unique_name='a',
         display_name='a',
-        map_value=1.2,
+        best_sample_value=1.2,
         median=1.15,
         standard_deviation=0.05,
         interval_68=(1.1, 1.2),
@@ -265,7 +265,7 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
         'parameter',
         'units',
         'start',
-        'max posterior',
+        'best posterior sample',
         'uncertainty',
         'change',
     ]
@@ -314,7 +314,7 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
             PosteriorParameterSummary(
                 unique_name='a',
                 display_name='a',
-                map_value=1.2,
+                best_sample_value=1.2,
                 median=1.15,
                 standard_deviation=0.05,
                 interval_68=(1.1, 1.2),
@@ -370,7 +370,7 @@ def test_posterior_table_notes_split_failed_diagnostics():
         PosteriorParameterSummary(
             unique_name='a',
             display_name='a',
-            map_value=1.0,
+            best_sample_value=1.0,
             median=1.0,
             standard_deviation=0.1,
             interval_68=(0.9, 1.1),
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index eea9f0898..d17dce4fa 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -261,7 +261,7 @@ def best(self):
                 PosteriorParameterSummary(
                     unique_name='beta',
                     display_name='Beta',
-                    map_value=22.0,
+                    best_sample_value=22.0,
                     median=21.0,
                     standard_deviation=0.4,
                     interval_68=(20.5, 21.5),
@@ -270,7 +270,7 @@ def best(self):
                 PosteriorParameterSummary(
                     unique_name='alpha',
                     display_name='Alpha',
-                    map_value=11.0,
+                    best_sample_value=11.0,
                     median=10.5,
                     standard_deviation=0.3,
                     interval_68=(10.0, 11.0),
diff --git a/tests/unit/easydiffraction/analysis/test_analysis.py b/tests/unit/easydiffraction/analysis/test_analysis.py
index 637facc02..2af863797 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis.py
@@ -33,26 +33,26 @@ def test_show_minimizer_types_prints(capsys):
     from easydiffraction.analysis.analysis import Analysis
 
     a = Analysis(project=_make_project_with_names([]))
-    a.fit.show_minimizer_types()
+    a.fitting.show_minimizer_types()
     out = capsys.readouterr().out
     assert 'Minimizer types' in out
     assert 'lmfit (leastsq)' in out
 
 
-def test_fit_mode_category_and_joint_fit_experiments(monkeypatch, capsys):
+def test_fit_mode_category_and_joint_fit(monkeypatch, capsys):
     from easydiffraction.analysis.analysis import Analysis
 
     a = Analysis(project=_make_project_with_names(['e1', 'e2']))
 
     # Default fit mode is 'single'
-    assert a.fit.mode.value == 'single'
+    assert a.fitting_mode_type == 'single'
 
     # Switch to joint
-    a.fit.mode = 'joint'
-    assert a.fit.mode.value == 'joint'
+    a.fitting_mode_type = 'joint'
+    assert a.fitting_mode_type == 'joint'
 
-    # joint_fit_experiments exists but is empty until fit() populates it
-    assert len(a.joint_fit_experiments) == 0
+    # joint_fit exists but is empty until fit() populates it
+    assert len(a.joint_fit) == 0
 
 
 def test_analysis_help(capsys):
@@ -66,7 +66,8 @@ def test_analysis_help(capsys):
     assert 'display' in out
     assert 'Properties' in out
     assert 'Methods' in out
-    assert 'fit_sequential()' in out
+    assert 'fit()' in out
+    assert 'show_fitting_mode_types()' in out
 
 
 def test_analysis_display_help(capsys):
@@ -227,3 +228,74 @@ def fake_update_short_table(
     assert tracker.display_handles == [handle, None]
     assert short_display_handles == [handle]
     assert handle.closed is True
+
+
+def test_run_sequential_sets_mode_and_saves_project(monkeypatch, tmp_path):
+    from easydiffraction.analysis.analysis import Analysis
+
+    project = SimpleNamespace(
+        info=SimpleNamespace(path=tmp_path),
+        save_calls=0,
+        _varname='proj',
+    )
+
+    def save() -> None:
+        project.save_calls += 1
+
+    project.save = save
+
+    analysis = Analysis(project=project)
+    analysis.sequential_fit.data_dir.value = 'scans'
+    analysis.sequential_fit.file_pattern.value = '*.xye'
+    analysis.sequential_fit.max_workers.value = 'auto'
+    analysis.sequential_fit.chunk_size.value = '.'
+    analysis.sequential_fit.reverse.value = True
+
+    calls: list[tuple[str, object]] = []
+
+    def fake_fit_sequential(
+        *,
+        analysis: object,
+        data_dir: str,
+        max_workers: int | str,
+        chunk_size: int | None,
+        file_pattern: str,
+        reverse: bool,
+    ) -> None:
+        calls.append(('analysis', analysis))
+        calls.append(('data_dir', data_dir))
+        calls.append(('max_workers', max_workers))
+        calls.append(('chunk_size', chunk_size))
+        calls.append(('file_pattern', file_pattern))
+        calls.append(('reverse', reverse))
+
+    monkeypatch.setattr('easydiffraction.analysis.sequential.fit_sequential', fake_fit_sequential)
+    monkeypatch.setattr(
+        analysis, '_update_categories', lambda: calls.append(('update_categories', None))
+    )
+    monkeypatch.setattr(
+        analysis, '_resolve_sequential_data_dir', lambda: tmp_path / 'resolved-scans'
+    )
+    analysis.fit_results = object()
+    analysis.fitter.results = object()
+
+    analysis._run_sequential()
+
+    assert analysis.fitting_mode_type == 'sequential'
+    analysis_cif = analysis.as_cif
+    assert '_fitting.mode_type sequential' in analysis_cif
+    assert '_sequential_fit.data_dir scans' in analysis_cif
+    assert '_sequential_fit.file_pattern *.xye' in analysis_cif
+    assert calls == [
+        ('update_categories', None),
+        ('analysis', analysis),
+        ('data_dir', str(tmp_path / 'resolved-scans')),
+        ('max_workers', 'auto'),
+        ('chunk_size', None),
+        ('file_pattern', '*.xye'),
+        ('reverse', True),
+        ('update_categories', None),
+    ]
+    assert project.save_calls == 1
+    assert analysis.fit_results is None
+    assert analysis.fitter.results is None
diff --git a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
index 9ff2926ea..42ef01ace 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
@@ -164,8 +164,8 @@ def test_setter_changes_minimizer(self, capsys):
         from easydiffraction.analysis.analysis import Analysis
 
         a = Analysis(project=_make_project())
-        assert a.fit.minimizer_type.value == 'lmfit (leastsq)'
-        a.fit.minimizer_type = 'lmfit (leastsq)'
+        assert a.fitting.minimizer_type.value == 'lmfit (leastsq)'
+        a.fitting.minimizer_type = 'lmfit (leastsq)'
         out = capsys.readouterr().out
         assert 'Current minimizer changed to' in out
 
diff --git a/tests/unit/easydiffraction/analysis/test_enums.py b/tests/unit/easydiffraction/analysis/test_enums.py
new file mode 100644
index 000000000..fe854e536
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/test_enums.py
@@ -0,0 +1,22 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/enums.py."""
+
+from easydiffraction.analysis.enums import FitModeEnum
+
+
+def test_fit_mode_enum_members():
+    assert FitModeEnum.SINGLE == 'single'
+    assert FitModeEnum.JOINT == 'joint'
+    assert FitModeEnum.SEQUENTIAL == 'sequential'
+
+
+def test_fit_mode_enum_default():
+    assert FitModeEnum.default() is FitModeEnum.SINGLE
+
+
+def test_fit_mode_enum_descriptions():
+    for member in FitModeEnum:
+        description = member.description()
+        assert isinstance(description, str)
+        assert description
diff --git a/tests/unit/easydiffraction/analysis/test_sequential.py b/tests/unit/easydiffraction/analysis/test_sequential.py
index 5e01b586d..ab2327c94 100644
--- a/tests/unit/easydiffraction/analysis/test_sequential.py
+++ b/tests/unit/easydiffraction/analysis/test_sequential.py
@@ -14,11 +14,16 @@
 from easydiffraction.analysis.sequential import _META_COLUMNS
 from easydiffraction.analysis.sequential import _append_to_csv
 from easydiffraction.analysis.sequential import _build_csv_header
+from easydiffraction.analysis.sequential import _chunk_file_range
 from easydiffraction.analysis.sequential import _read_csv_for_recovery
+from easydiffraction.analysis.sequential import _relative_file_path_for_csv
 from easydiffraction.analysis.sequential import _write_csv_header
 from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
 from easydiffraction.utils.enums import VerbosityEnum
 
+_TEST_SCAN_001 = 'data/scan_001.xye'
+_TEST_SCAN_002 = 'data/scan_002.xye'
+
 
 # ------------------------------------------------------------------
 #  Fixture: a minimal template
@@ -43,10 +48,103 @@ def _minimal_template(
         constraints_enabled=False,
         minimizer_tag='lmfit',
         calculator_tag='cryspy',
+        diffrn_extract_rules=[],
         diffrn_field_names=diffrn_fields,
     )
 
 
+class _RecordingConsole:
+    def __init__(self, events):
+        self._events = events
+
+    def paragraph(self, text):
+        self._events.append(('paragraph', text))
+
+    def print(self, *args, **kwargs):
+        self._events.append(('console_print', args, kwargs))
+
+
+def _make_indicator(events):
+    class RecordingIndicator:
+        def __init__(self, label, *, verbosity, animated=True):
+            events.append(('init', label, verbosity, animated))
+
+        def start(self):
+            events.append(('start',))
+
+        def update(self, *, label=None, content=None):
+            events.append(('update', label, content))
+
+        def stop(self):
+            events.append(('stop',))
+
+    return RecordingIndicator
+
+
+def _make_run_fit_loop(events, template, verbosity):
+    def fake_run_fit_loop(pool_cm, chunks, template_arg, csv_info, progress):
+        del pool_cm, csv_info
+        assert chunks == [['scan_001.xye']]
+        assert template_arg == template
+        assert progress.verbosity is VerbosityEnum(verbosity)
+        assert progress.state is not None
+        assert progress.state.chunk_rows == []
+        assert progress.state.file_rows == []
+        events.append((
+            'run_loop',
+            progress.indicator is not None,
+        ))
+
+    return fake_run_fit_loop
+
+
+def _run_non_silent_fit(monkeypatch, tmp_path, *, verbosity, is_jupyter):
+    import easydiffraction.analysis.sequential as sequential_mod
+
+    events: list[tuple[object, ...]] = []
+    template = _minimal_template()
+
+    monkeypatch.setattr(sequential_mod.mp, 'parent_process', lambda: None)
+    monkeypatch.setattr(sequential_mod, '_check_seq_preconditions', lambda project: None)
+    monkeypatch.setattr(sequential_mod, 'console', _RecordingConsole(events))
+    monkeypatch.setattr(
+        sequential_mod,
+        'extract_data_paths_from_dir',
+        lambda data_dir, file_pattern='*': ['scan_001.xye'],
+    )
+    monkeypatch.setattr(sequential_mod, '_build_template', lambda project: template)
+    monkeypatch.setattr(
+        sequential_mod,
+        '_setup_csv_and_recovery',
+        lambda project, template_arg, verb: (
+            tmp_path / 'results.csv',
+            ['file_path'],
+            set(),
+            template_arg,
+        ),
+    )
+    monkeypatch.setattr(sequential_mod, '_resolve_workers', lambda max_workers, chunk_size: (1, 1))
+    monkeypatch.setattr(
+        sequential_mod,
+        '_run_fit_loop_with_pool',
+        lambda max_workers, chunks, template_arg, csv_info, progress: _make_run_fit_loop(
+            events,
+            template,
+            verbosity,
+        )(None, chunks, template_arg, csv_info, progress),
+    )
+    monkeypatch.setattr(sequential_mod, 'ActivityIndicator', _make_indicator(events))
+    del is_jupyter  # legacy parameter, no longer affects behavior
+
+    analysis = SimpleNamespace(
+        project=SimpleNamespace(verbosity=verbosity),
+        fitter=SimpleNamespace(selection='lmfit'),
+    )
+
+    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
+    return events
+
+
 # ------------------------------------------------------------------
 #  _build_csv_header
 # ------------------------------------------------------------------
@@ -128,6 +226,73 @@ def test_append_adds_rows(self, tmp_path):
         assert rows[0]['file_path'] == 'a.dat'
         assert rows[1]['value'] == '2.0'
 
+    def test_append_stores_file_paths_relative_to_project(self, tmp_path):
+        project_dir = tmp_path / 'project'
+        csv_path = project_dir / 'analysis' / 'results.csv'
+        csv_path.parent.mkdir(parents=True)
+        data_dir = project_dir / 'experiments' / 'scan'
+        data_dir.mkdir(parents=True)
+        data_path = data_dir / 'scan_001.dat'
+        data_path.write_text('1 2 3\n')
+        header = ['file_path', 'value']
+        _write_csv_header(csv_path, header)
+
+        _append_to_csv(
+            csv_path,
+            header,
+            [
+                {'file_path': str(data_path), 'value': 1.0},
+            ],
+        )
+
+        with csv_path.open() as f:
+            rows = list(csv.DictReader(f))
+        assert rows[0]['file_path'] == 'experiments/scan/scan_001.dat'
+
+    def test_append_normalizes_repo_relative_project_paths(self, tmp_path, monkeypatch):
+        workspace_dir = tmp_path / 'workspace'
+        project_dir = workspace_dir / 'projects' / 'cosio'
+        csv_path = project_dir / 'analysis' / 'results.csv'
+        csv_path.parent.mkdir(parents=True)
+        monkeypatch.chdir(workspace_dir)
+        header = ['file_path', 'value']
+        _write_csv_header(csv_path, header)
+
+        _append_to_csv(
+            csv_path,
+            header,
+            [
+                {
+                    'file_path': 'projects/cosio/experiments/d20_scan/scan_001.dat',
+                    'value': 1.0,
+                },
+            ],
+        )
+
+        with csv_path.open() as f:
+            rows = list(csv.DictReader(f))
+        assert rows[0]['file_path'] == 'experiments/d20_scan/scan_001.dat'
+
+    def test_relative_file_paths_use_posix_separators(self, tmp_path, monkeypatch):
+        import easydiffraction.analysis.sequential as sequential_mod
+
+        project_dir = tmp_path / 'project'
+        csv_path = project_dir / 'analysis' / 'results.csv'
+        csv_path.parent.mkdir(parents=True)
+        data_dir = project_dir / 'experiments' / 'scan'
+        data_dir.mkdir(parents=True)
+        data_path = data_dir / 'scan_001.dat'
+        data_path.write_text('1 2 3\n')
+        monkeypatch.setattr(
+            sequential_mod.os.path,
+            'relpath',
+            lambda _path, start: 'experiments\\scan\\scan_001.dat',
+        )
+
+        relative_path = _relative_file_path_for_csv(csv_path, str(data_path))
+
+        assert relative_path == 'experiments/scan/scan_001.dat'
+
     def test_append_ignores_extra_keys(self, tmp_path):
         csv_path = tmp_path / 'results.csv'
         header = ['file_path']
@@ -160,7 +325,9 @@ def test_returns_empty_when_no_file(self, tmp_path):
         assert params is None
 
     def test_returns_fitted_file_paths(self, tmp_path):
-        csv_path = tmp_path / 'results.csv'
+        project_dir = tmp_path / 'project'
+        csv_path = project_dir / 'analysis' / 'results.csv'
+        csv_path.parent.mkdir(parents=True)
         header = [*_META_COLUMNS, 'cell.a', 'cell.a.uncertainty']
         _write_csv_header(csv_path, header)
         _append_to_csv(
@@ -168,7 +335,7 @@ def test_returns_fitted_file_paths(self, tmp_path):
             header,
             [
                 {
-                    'file_path': '/data/a.dat',
+                    'file_path': str(project_dir / 'experiments' / 'a.dat'),
                     'fit_success': 'True',
                     'chi_squared': '5.0',
                     'reduced_chi_squared': '2.5',
@@ -177,7 +344,7 @@ def test_returns_fitted_file_paths(self, tmp_path):
                     'cell.a.uncertainty': '0.01',
                 },
                 {
-                    'file_path': '/data/b.dat',
+                    'file_path': str(project_dir / 'experiments' / 'b.dat'),
                     'fit_success': 'False',
                     'chi_squared': '',
                     'reduced_chi_squared': '',
@@ -189,7 +356,36 @@ def test_returns_fitted_file_paths(self, tmp_path):
         )
 
         fitted, _params = _read_csv_for_recovery(csv_path)
-        assert fitted == {'/data/a.dat', '/data/b.dat'}
+        assert fitted == {
+            str((project_dir / 'experiments' / 'a.dat').resolve()),
+            str((project_dir / 'experiments' / 'b.dat').resolve()),
+        }
+
+    def test_resolves_legacy_repo_relative_paths(self, tmp_path, monkeypatch):
+        workspace_dir = tmp_path / 'workspace'
+        project_dir = workspace_dir / 'projects' / 'cosio'
+        csv_path = project_dir / 'analysis' / 'results.csv'
+        csv_path.parent.mkdir(parents=True)
+        monkeypatch.chdir(workspace_dir)
+        header = [*_META_COLUMNS, 'cell.a', 'cell.a.uncertainty']
+
+        with csv_path.open('w', newline='', encoding='utf-8') as handle:
+            writer = csv.DictWriter(handle, fieldnames=header)
+            writer.writeheader()
+            writer.writerow({
+                'file_path': 'projects/cosio/experiments/d20_scan/scan_001.dat',
+                'fit_success': 'True',
+                'chi_squared': '5.0',
+                'reduced_chi_squared': '2.5',
+                'n_iterations': '10',
+                'cell.a': '3.89',
+                'cell.a.uncertainty': '0.01',
+            })
+
+        fitted, _params = _read_csv_for_recovery(csv_path)
+        assert fitted == {
+            str((project_dir / 'experiments' / 'd20_scan' / 'scan_001.dat').resolve())
+        }
 
     def test_returns_last_successful_params(self, tmp_path):
         csv_path = tmp_path / 'results.csv'
@@ -306,75 +502,176 @@ def test_fields_accessible(self):
         assert template.calculator_tag == 'cryspy'
 
 
-def test_fit_sequential_short_starts_and_stops_shared_indicator(monkeypatch, tmp_path):
+class TestChunkFileRange:
+    def test_formats_inclusive_range_with_spaced_dash(self):
+        assert _chunk_file_range([_TEST_SCAN_001, _TEST_SCAN_002]) == (
+            'scan_001.xye - scan_002.xye'
+        )
+
+    def test_returns_single_name_for_single_file_chunk(self):
+        assert _chunk_file_range([_TEST_SCAN_001]) == 'scan_001.xye'
+
+
+@pytest.mark.parametrize('verbosity', [VerbosityEnum.SHORT, VerbosityEnum.FULL])
+def test_report_chunk_progress_updates_indicator_with_renderable(monkeypatch, verbosity):
     import easydiffraction.analysis.sequential as sequential_mod
 
-    events: list[tuple[object, ...]] = []
-    template = _minimal_template()
+    update_calls: list[object] = []
 
-    class FakeIndicator:
-        def __init__(self, label, *, verbosity):
-            events.append(('init', label, verbosity))
+    class RecordingIndicator:
+        def update(self, *, label=None, content=None):
+            del label
+            update_calls.append(content)
 
-        def start(self):
-            events.append(('start',))
+    progress_state = sequential_mod.SequentialProgressState(chunk_rows=[], file_rows=[])
+    progress = sequential_mod.SequentialProgressContext(
+        verbosity=verbosity,
+        state=progress_state,
+        indicator=RecordingIndicator(),
+    )
 
-        def update(self):
-            events.append(('update',))
+    sequential_mod._report_chunk_progress(
+        1,
+        3,
+        [_TEST_SCAN_001, _TEST_SCAN_002],
+        [
+            {
+                'file_path': _TEST_SCAN_001,
+                'fit_success': True,
+                'reduced_chi_squared': 4.0,
+                'n_iterations': 11,
+            },
+            {
+                'file_path': _TEST_SCAN_002,
+                'fit_success': False,
+                'reduced_chi_squared': None,
+                'n_iterations': 0,
+            },
+        ],
+        progress,
+        sequential_mod._ChunkProgressMetrics(
+            completed_files_before=0,
+            total_files=3,
+            elapsed_time=19.76,
+        ),
+    )
 
-        def stop(self):
-            events.append(('stop',))
+    if verbosity is VerbosityEnum.SHORT:
+        expected_rows = [
+            ['1/3', '66.7%', '19.76', 'scan_001.xye - scan_002.xye', '2', '4.00', '⚠️']
+        ]
+        assert progress_state.chunk_rows == expected_rows
+        assert progress_state.file_rows == []
+    else:
+        expected_rows = [
+            ['scan_001.xye', '33.3%', '19.76', '4.00', '11', '✅'],
+            ['scan_002.xye', '66.7%', '19.76', '—', '0', '❌'],
+        ]
+        assert progress_state.chunk_rows == []
+        assert progress_state.file_rows == expected_rows
 
-    def fake_run_fit_loop(
-        pool_cm, chunks, template_arg, csv_info, extract_diffrn, verb, indicator
-    ):
-        del pool_cm, csv_info, extract_diffrn
-        assert chunks == [['scan_001.xye']]
-        assert template_arg == template
-        assert verb is VerbosityEnum.SHORT
-        assert indicator is not None
-        indicator.update()
+    assert len(update_calls) == 1
+    assert update_calls[0] is not None
 
-    monkeypatch.setattr(sequential_mod, 'ActivityIndicator', FakeIndicator)
-    monkeypatch.setattr(sequential_mod.mp, 'parent_process', lambda: None)
-    monkeypatch.setattr(sequential_mod, '_check_seq_preconditions', lambda project: None)
-    monkeypatch.setattr(
-        sequential_mod,
-        'extract_data_paths_from_dir',
-        lambda data_dir, file_pattern='*': ['scan_001.xye'],
+
+@pytest.mark.parametrize(
+    'verbosity',
+    ['short', 'full'],
+)
+def test_fit_sequential_non_silent_starts_indicator_with_progress_table(
+    monkeypatch,
+    tmp_path,
+    verbosity,
+):
+    events = _run_non_silent_fit(
+        monkeypatch,
+        tmp_path,
+        verbosity=verbosity,
+        is_jupyter=False,
     )
-    monkeypatch.setattr(sequential_mod, '_build_template', lambda project: template)
+
+    verb_enum = VerbosityEnum(verbosity)
+
+    assert events[:4] == [
+        ('paragraph', 'Sequential fitting'),
+        ('console_print', ("🚀 Starting fit process with 'lmfit'...",), {}),
+        ('console_print', ('📋 1 files in 1 chunks (max_workers=1)',), {}),
+        ('console_print', ('📈 Goodness-of-fit progress:',), {}),
+    ]
+    assert events[4] == ('init', ACTIVITY_LABEL_FITTING, verb_enum, True)
+    assert events[5] == ('start',)
+    # Initial empty table renderable is pushed before the fit loop runs.
+    assert events[6][0] == 'update'
+    assert events[6][1] is None
+    assert events[6][2] is not None
+    assert events[7] == ('run_loop', True)
+    assert events[8] == ('stop',)
+    assert events[9:] == [
+        ('console_print', ('✅ Sequential fitting complete: 1 files processed.',), {}),
+        ('console_print', (f'📄 Results saved to: {tmp_path / "results.csv"}',), {}),
+    ]
+
+
+def test_run_fit_loop_runs_chunks_sequentially_with_executor_map(monkeypatch, tmp_path):
+    import easydiffraction.analysis.sequential as sequential_mod
+
+    template = _minimal_template()
+    header = ['file_path']
+    events: list[tuple[object, ...]] = []
+    progress = sequential_mod.SequentialProgressContext(
+        verbosity=VerbosityEnum.SHORT,
+        state=sequential_mod.SequentialProgressState(chunk_rows=[], file_rows=[]),
+    )
+
+    class FakeExecutor:
+        def map(self, func, templates, paths):
+            assert func is sequential_mod._fit_worker
+            for template_arg in templates:
+                assert template_arg == template
+            for path in paths:
+                yield {
+                    'file_path': path,
+                    'fit_success': True,
+                    'reduced_chi_squared': 1.0,
+                    'n_iterations': 5,
+                    'params': {'cell.a': 4.0},
+                }
+
+    class FakePool:
+        def __enter__(self):
+            return FakeExecutor()
+
+        def __exit__(self, exc_type, exc, tb):
+            del exc_type, exc, tb
+            return False
+
     monkeypatch.setattr(
         sequential_mod,
-        '_setup_csv_and_recovery',
-        lambda project, template_arg, verb: (
-            tmp_path / 'results.csv',
-            ['file_path'],
-            set(),
-            template_arg,
-        ),
+        '_append_to_csv',
+        lambda csv_path, header_arg, results: events.append((
+            'append',
+            csv_path,
+            header_arg,
+            [result['file_path'] for result in results],
+        )),
     )
-    monkeypatch.setattr(sequential_mod, '_resolve_workers', lambda max_workers, chunk_size: (1, 1))
     monkeypatch.setattr(
         sequential_mod,
-        '_create_pool_context',
-        lambda max_workers: (contextlib.nullcontext(None), None, None, None),
+        '_report_chunk_progress',
+        lambda *args: events.append(('report', [result['file_path'] for result in args[3]])),
     )
-    monkeypatch.setattr(sequential_mod, '_run_fit_loop', fake_run_fit_loop)
-    monkeypatch.setattr(sequential_mod, '_restore_main_state', lambda *args: None)
 
-    analysis = SimpleNamespace(
-        project=SimpleNamespace(verbosity='short'),
-        fitter=SimpleNamespace(selection='lmfit'),
+    sequential_mod._run_fit_loop(
+        FakePool(),
+        [[_TEST_SCAN_001, _TEST_SCAN_002]],
+        template,
+        (tmp_path / 'results.csv', header),
+        progress,
     )
 
-    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
-
     assert events == [
-        ('init', ACTIVITY_LABEL_FITTING, VerbosityEnum.SHORT),
-        ('start',),
-        ('update',),
-        ('stop',),
+        ('append', tmp_path / 'results.csv', header, [_TEST_SCAN_001, _TEST_SCAN_002]),
+        ('report', [_TEST_SCAN_001, _TEST_SCAN_002]),
     ]
 
 
@@ -389,13 +686,18 @@ def __init__(self, *args, **kwargs):
             raise AssertionError(message)
 
     def fake_run_fit_loop(
-        pool_cm, chunks, template_arg, csv_info, extract_diffrn, verb, indicator
+        pool_cm,
+        chunks,
+        template_arg,
+        csv_info,
+        progress,
     ):
-        del pool_cm, csv_info, extract_diffrn
+        del pool_cm, csv_info
         assert chunks == [['scan_001.xye']]
         assert template_arg == template
-        assert verb is VerbosityEnum.SILENT
-        assert indicator is None
+        assert progress.verbosity is VerbosityEnum.SILENT
+        assert progress.state is None
+        assert progress.indicator is None
 
     monkeypatch.setattr(sequential_mod, 'ActivityIndicator', FailingIndicator)
     monkeypatch.setattr(sequential_mod.mp, 'parent_process', lambda: None)
diff --git a/tests/unit/easydiffraction/display/plotters/test_ascii.py b/tests/unit/easydiffraction/display/plotters/test_ascii.py
index a0e84dc42..54e533ea0 100644
--- a/tests/unit/easydiffraction/display/plotters/test_ascii.py
+++ b/tests/unit/easydiffraction/display/plotters/test_ascii.py
@@ -44,7 +44,31 @@ def test_ascii_plotter_plot_supports_max_posterior_legend(capsys):
 
     out = capsys.readouterr().out
     assert 'Measured (Imeas)' in out
-    assert 'Max posterior' in out
+    assert 'Best posterior sample' in out
+
+
+def test_ascii_plotter_plot_single_crystal_uses_detected_terminal_width(monkeypatch, capsys):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    monkeypatch.setattr(
+        ascii_mod.shutil,
+        'get_terminal_size',
+        lambda fallback: os.terminal_size((44, 24)),
+    )
+
+    p = AsciiPlotter()
+    p.plot_single_crystal(
+        x_calc=np.array([1.0, 2.0, 3.0]),
+        y_meas=np.array([1.1, 1.9, 3.2]),
+        y_meas_su=np.array([0.1, 0.1, 0.1]),
+        axes_labels=['F²calc', 'F²meas'],
+        title='SC width test',
+        height=6,
+    )
+
+    out = capsys.readouterr().out
+    assert f'└{"─" * 26}' in out
 
 
 def test_ascii_plotter_plot_single_crystal(capsys):
@@ -111,7 +135,7 @@ def test_ascii_plotter_plot_powder_meas_vs_calc_announces_plotly_only_bragg_row(
     assert 'Bragg peak subplot rows are available with the Plotly engine only.' in out
 
 
-def test_ascii_plotter_plot_resamples_to_detected_terminal_width(monkeypatch):
+def test_ascii_plotter_plot_limits_oversized_series_to_detected_terminal_width(monkeypatch):
     from easydiffraction.display.plotters import ascii as ascii_mod
     from easydiffraction.display.plotters.ascii import ASCII_CHART_LEFT_PADDING
     from easydiffraction.display.plotters.ascii import ASCII_CHART_MIN_POINT_COUNT
@@ -145,6 +169,46 @@ def fake_plot(series, config):
         ASCII_CHART_MIN_POINT_COUNT,
         44 - ASCII_CHART_OFFSET - ASCII_CHART_LEFT_PADDING,
     )
+    assert series[0][0] > 0.0
+    assert config['offset'] == ASCII_CHART_OFFSET
+
+
+def test_ascii_plotter_plot_interpolates_smaller_series_to_detected_terminal_width(monkeypatch):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_LEFT_PADDING
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_MIN_POINT_COUNT
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_OFFSET
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    captured: dict[str, object] = {}
+
+    def fake_plot(series, config):
+        captured['call'] = (series, config)
+        return 'chart'
+
+    monkeypatch.setattr(
+        ascii_mod.shutil,
+        'get_terminal_size',
+        lambda fallback: os.terminal_size((44, 24)),
+    )
+    monkeypatch.setattr(ascii_mod.asciichartpy, 'plot', fake_plot)
+
+    AsciiPlotter().plot_powder(
+        x=np.arange(4, dtype=float),
+        y_series=[np.array([0.0, 1.0, 0.0, 1.0])],
+        labels=['density'],
+        axes_labels=['x', 'y'],
+        title='Interpolation test',
+        height=5,
+    )
+
+    series, config = captured['call']
+    assert len(series[0]) == max(
+        ASCII_CHART_MIN_POINT_COUNT,
+        44 - ASCII_CHART_OFFSET - ASCII_CHART_LEFT_PADDING,
+    )
+    assert series[0][0] == 0.0
+    assert series[0][-1] == 1.0
     assert config['offset'] == ASCII_CHART_OFFSET
 
 
@@ -179,3 +243,66 @@ def fake_plot(series, config):
     series, config = captured['call']
     assert len(series[0]) == ASCII_CHART_FALLBACK_POINT_COUNT
     assert config['offset'] == ASCII_CHART_OFFSET
+
+
+def test_ascii_plotter_plot_scatter_uses_detected_terminal_width(monkeypatch):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_LEFT_PADDING
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_MIN_POINT_COUNT
+    from easydiffraction.display.plotters.ascii import ASCII_CHART_OFFSET
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    captured: dict[str, object] = {}
+
+    def fake_plot(series, config):
+        captured['call'] = (series, config)
+        return 'chart'
+
+    monkeypatch.setattr(
+        ascii_mod.shutil,
+        'get_terminal_size',
+        lambda fallback: os.terminal_size((44, 24)),
+    )
+    monkeypatch.setattr(ascii_mod.asciichartpy, 'plot', fake_plot)
+
+    AsciiPlotter().plot_scatter(
+        x=np.arange(4, dtype=float),
+        y=np.array([0.0, 1.0, 0.0, 1.0]),
+        sy=np.array([0.1, 0.1, 0.1, 0.1]),
+        axes_labels=['x', 'y'],
+        title='Scatter width test',
+        height=5,
+    )
+
+    series, config = captured['call']
+    assert len(series[0]) == max(
+        ASCII_CHART_MIN_POINT_COUNT,
+        44 - ASCII_CHART_OFFSET - ASCII_CHART_LEFT_PADDING,
+    )
+    assert config['colors'] == [ascii_mod.asciichartpy.blue]
+
+
+def test_ascii_plotter_plot_scatter_sorts_by_x_before_resampling(monkeypatch):
+    from easydiffraction.display.plotters import ascii as ascii_mod
+    from easydiffraction.display.plotters.ascii import AsciiPlotter
+
+    captured: dict[str, object] = {}
+
+    def fake_plot(series, config):
+        captured['call'] = (series, config)
+        return 'chart'
+
+    monkeypatch.setattr(AsciiPlotter, '_chart_point_count', lambda: 4)
+    monkeypatch.setattr(ascii_mod.asciichartpy, 'plot', fake_plot)
+
+    AsciiPlotter().plot_scatter(
+        x=np.array([400.0, 300.0, 200.0, 100.0]),
+        y=np.array([4.0, 3.0, 2.0, 1.0]),
+        sy=np.array([0.1, 0.1, 0.1, 0.1]),
+        axes_labels=['Temperature', 'Parameter value'],
+        title='Scatter order test',
+        height=5,
+    )
+
+    series, _config = captured['call']
+    assert series[0] == [1.0, 2.0, 3.0, 4.0]
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index 5b5836229..097d4321f 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -822,7 +822,7 @@ def fake_show_figure(self, fig):
             height=None,
             predictive_lower_95=np.array([8.0, 9.0, 10.0]),
             predictive_upper_95=np.array([10.0, 11.0, 12.0]),
-            y_calc_name='Max posterior',
+            y_calc_name='Best posterior sample',
             y_calc_line_dash='dot',
         ),
     )
@@ -831,7 +831,9 @@ def fake_show_figure(self, fig):
     predictive_band_trace = next(
         trace for trace in fig.data if trace.name == '95% credible interval'
     )
-    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+    max_posterior_trace = next(
+        trace for trace in fig.data if trace.name == 'Best posterior sample'
+    )
     residual_trace = next(trace for trace in fig.data if trace.name == 'Residual (Imeas - Icalc)')
 
     assert predictive_band_trace.fillcolor == pp.PREDICTIVE_BAND_COLOR
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index c32424044..461e85ce2 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -313,7 +313,7 @@ def _make_bayesian_plotter_fixture():
         PosteriorParameterSummary(
             unique_name=name,
             display_name=name,
-            map_value=float(samples[-1, -1, index]),
+            best_sample_value=float(samples[-1, -1, index]),
             median=float(np.median(samples[:, :, index])),
             standard_deviation=float(np.std(samples[:, :, index], ddof=1)),
             interval_68=tuple(np.quantile(samples[:, :, index], [0.16, 0.84]).tolist()),
@@ -627,7 +627,7 @@ def test_posterior_pair_diagonal_matches_standalone_distribution_when_thinned():
         PosteriorParameterSummary(
             unique_name=name,
             display_name=name,
-            map_value=float(samples[0, -1, index]),
+            best_sample_value=float(samples[0, -1, index]),
             median=float(np.median(samples[:, :, index])),
             standard_deviation=float(np.std(samples[:, :, index], ddof=1)),
             interval_68=tuple(np.quantile(samples[:, :, index], [0.16, 0.84]).tolist()),
@@ -693,12 +693,14 @@ def test_build_param_distribution_plot_returns_plotly_figure():
         'Marginal density',
         '95% credible interval',
         'Median',
-        'Max posterior',
+        'Best posterior sample',
     }
     marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
     histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
     interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
-    max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
+    max_posterior_trace = next(
+        trace for trace in figure.data if trace.name == 'Best posterior sample'
+    )
     assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
     assert marginal_trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
     assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
@@ -765,7 +767,7 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
             x=np.array([1.0, 2.0, 3.0]),
             lower_95=np.array([8.0, 9.0, 10.0]),
             upper_95=np.array([10.0, 11.0, 12.0]),
-            map_prediction=np.array([9.0, 10.0, 11.0]),
+            best_sample_prediction=np.array([9.0, 10.0, 11.0]),
         ),
         y_meas=np.array([9.5, 10.5, 11.5]),
         axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
@@ -776,7 +778,9 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     fig = captured['fig']
     upper_band_trace = fig.data[1]
     measured_trace = next(trace for trace in fig.data if trace.name == 'Measured')
-    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+    max_posterior_trace = next(
+        trace for trace in fig.data if trace.name == 'Best posterior sample'
+    )
 
     assert upper_band_trace.name == '95% credible interval'
     assert upper_band_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
@@ -868,7 +872,7 @@ class Experiment:
             x=np.array([1.0, 2.0, 3.0]),
             lower_95=np.array([8.0, 9.0, 10.0]),
             upper_95=np.array([10.0, 11.0, 12.0]),
-            map_prediction=np.array([9.0, 11.0, 10.5]),
+            best_sample_prediction=np.array([9.0, 11.0, 10.5]),
             draws=None,
         ),
     )
@@ -891,7 +895,7 @@ class Experiment:
     )
 
     plot_spec = captured['plot_spec']
-    assert plot_spec.y_calc_name == 'Max posterior'
+    assert plot_spec.y_calc_name == 'Best posterior sample'
     assert plot_spec.y_calc_line_dash == 'dot'
 
 
@@ -963,7 +967,7 @@ def test_plot_posterior_predictive_summary_routes_ascii_to_measured_and_map(monk
         expt_name='pdf',
         summary=SimpleNamespace(
             x=np.array([1.0, 2.0, 3.0]),
-            map_prediction=np.array([9.0, 10.0, 11.0]),
+            best_sample_prediction=np.array([9.0, 10.0, 11.0]),
             lower_95=np.array([8.0, 9.0, 10.0]),
             upper_95=np.array([10.0, 11.0, 12.0]),
             draws=np.array([[8.5, 9.5, 10.5]]),
@@ -1027,7 +1031,7 @@ class Experiment:
             x=np.array([1.0, 2.0, 3.0]),
             lower_95=np.array([8.0, 9.0, 10.0]),
             upper_95=np.array([10.0, 11.0, 12.0]),
-            map_prediction=np.array([9.0, 11.0, 10.5]),
+            best_sample_prediction=np.array([9.0, 11.0, 10.5]),
             draws=None,
         ),
     )
@@ -1168,7 +1172,7 @@ def test_resolve_posterior_parameter_names_warns_on_ambiguous_label(monkeypatch)
             PosteriorParameterSummary(
                 unique_name='phase_a.length_a',
                 display_name='length_a',
-                map_value=1.0,
+                best_sample_value=1.0,
                 median=1.0,
                 standard_deviation=0.1,
                 interval_68=(0.9, 1.1),
@@ -1177,7 +1181,7 @@ def test_resolve_posterior_parameter_names_warns_on_ambiguous_label(monkeypatch)
             PosteriorParameterSummary(
                 unique_name='phase_b.length_a',
                 display_name='length_a',
-                map_value=2.0,
+                best_sample_value=2.0,
                 median=2.0,
                 standard_deviation=0.1,
                 interval_68=(1.9, 2.1),
@@ -1262,7 +1266,7 @@ def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_
     assert summary.experiment_name == 'hrpt'
     assert summary.x_axis_name == 'two_theta'
     assert summary.draws.shape == (4, 2)
-    np.testing.assert_allclose(summary.map_prediction, np.array([3.0, -1.0]))
+    np.testing.assert_allclose(summary.best_sample_prediction, np.array([3.0, -1.0]))
     np.testing.assert_allclose([parameter.value for parameter in sampled_parameters], [1.0, 2.0])
     assert [parameter.uncertainty for parameter in sampled_parameters] == [0.1, 0.2]
 
@@ -1465,7 +1469,7 @@ class Project:
             experiment_name='pdf',
             x_axis_name='two_theta',
             x=np.array([1.0, 2.0, 3.0]),
-            map_prediction=np.array([9.0, 19.0, 29.0]),
+            best_sample_prediction=np.array([9.0, 19.0, 29.0]),
             lower_95=np.array([8.0, 18.0, 28.0]),
             upper_95=np.array([10.0, 20.0, 30.0]),
         ),
@@ -1497,7 +1501,10 @@ def fake_plot_summary(
 
     assert captured['expt_name'] == 'pdf'
     np.testing.assert_allclose(captured['summary'].x, np.array([2.0]))
-    np.testing.assert_allclose(captured['summary'].map_prediction, np.array([19.0]))
+    np.testing.assert_allclose(
+        captured['summary'].best_sample_prediction,
+        np.array([19.0]),
+    )
     np.testing.assert_allclose(captured['summary'].lower_95, np.array([18.0]))
     np.testing.assert_allclose(captured['summary'].upper_95, np.array([20.0]))
     np.testing.assert_allclose(captured['y_meas'], np.array([20.0]))
diff --git a/tests/unit/easydiffraction/display/test_plotting_coverage.py b/tests/unit/easydiffraction/display/test_plotting_coverage.py
index 542dfa4f5..008881bcf 100644
--- a/tests/unit/easydiffraction/display/test_plotting_coverage.py
+++ b/tests/unit/easydiffraction/display/test_plotting_coverage.py
@@ -271,6 +271,40 @@ class Ptn:
         assert x_min == 60.0
         assert x_max == 139.0
 
+    def test_keeps_full_range_when_series_is_within_crop_threshold(self, monkeypatch):
+        from easydiffraction.display.plotters.ascii import AsciiPlotter
+        from easydiffraction.display.plotting import Plotter
+
+        p = Plotter()
+        p.engine = 'asciichartpy'
+        monkeypatch.setattr(AsciiPlotter, '_chart_point_count', lambda: 80)
+
+        class Ptn:
+            intensity_meas = np.zeros(120)
+
+        Ptn.intensity_meas[60] = 10.0
+        x_array = np.arange(120, dtype=float)
+        x_min, x_max = p._auto_x_range_for_ascii(Ptn(), x_array, None, None)
+        assert x_min is None
+        assert x_max is None
+
+    def test_keeps_explicit_partial_limit_for_ascii(self, monkeypatch):
+        from easydiffraction.display.plotters.ascii import AsciiPlotter
+        from easydiffraction.display.plotting import Plotter
+
+        p = Plotter()
+        p.engine = 'asciichartpy'
+        monkeypatch.setattr(AsciiPlotter, '_chart_point_count', lambda: 80)
+
+        class Ptn:
+            intensity_meas = np.zeros(200)
+
+        Ptn.intensity_meas[100] = 10.0
+        x_array = np.arange(200, dtype=float)
+        x_min, x_max = p._auto_x_range_for_ascii(Ptn(), x_array, 20.0, None)
+        assert x_min == 20.0
+        assert x_max is None
+
     def test_no_narrowing_when_limits_provided(self):
         from easydiffraction.display.plotting import Plotter
 
@@ -331,7 +365,7 @@ def test_csv_plots_with_versus_descriptor(self, tmp_path, monkeypatch):
 
         csv = tmp_path / 'results.csv'
         csv.write_text(
-            'my_param,my_param.uncertainty,diffrn.temperature\n1.0,0.1,300\n2.0,0.2,400\n'
+            'my_param,my_param.uncertainty,diffrn.ambient_temperature\n1.0,0.1,300\n2.0,0.2,400\n'
         )
 
         plot_calls = []
@@ -348,12 +382,12 @@ class ParamDesc:
             description = 'A param'
             units = 'Å'
 
-        class VersusDesc:
-            name = 'temperature'
-            description = 'Temperature'
-            units = 'K'
-
-        p._plot_param_series_from_csv(str(csv), 'my_param', ParamDesc(), VersusDesc())
+        p._plot_param_series_from_csv(
+            str(csv),
+            'my_param',
+            ParamDesc(),
+            'diffrn.ambient_temperature',
+        )
         assert len(plot_calls) == 1
         assert plot_calls[0]['x'] == [300.0, 400.0]
         assert plot_calls[0]['y'] == [1.0, 2.0]
@@ -416,7 +450,7 @@ class Expt:
             },
         }
         p.plot_param_series_from_snapshots(
-            'param_a', 'ambient_temperature', experiments, snapshots
+            'param_a', 'diffrn.ambient_temperature', experiments, snapshots
         )
         assert len(plot_calls) == 1
         assert plot_calls[0]['y'] == [1.23]
diff --git a/tests/unit/easydiffraction/display/test_progress.py b/tests/unit/easydiffraction/display/test_progress.py
index f690159ed..b12147fa2 100644
--- a/tests/unit/easydiffraction/display/test_progress.py
+++ b/tests/unit/easydiffraction/display/test_progress.py
@@ -19,6 +19,7 @@ def __init__(
             auto_refresh,
             refresh_per_second,
             get_renderable=None,
+            vertical_overflow=None,
         ):
             self.renderable = renderable
             self.console = console
@@ -60,6 +61,42 @@ def refresh(self):
     assert handle._live.stopped is True
 
 
+def test_make_display_handle_passes_auto_refresh(monkeypatch):
+    import easydiffraction.display.progress as progress_mod
+
+    class FakeLive:
+        def __init__(
+            self,
+            renderable=None,
+            *,
+            console,
+            auto_refresh,
+            refresh_per_second,
+            get_renderable=None,
+            vertical_overflow=None,
+        ):
+            self.auto_refresh = auto_refresh
+            self.started = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.started = False
+
+        def refresh(self):
+            pass
+
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(progress_mod, 'Live', FakeLive)
+    monkeypatch.setattr(progress_mod.ConsoleManager, 'get', lambda: 'console')
+
+    handle = progress_mod.make_display_handle(auto_refresh=False)
+
+    assert isinstance(handle, progress_mod._TerminalLiveHandle)
+    assert handle._live.auto_refresh is False
+
+
 def test_activity_indicator_silent_does_not_create_handles():
     from easydiffraction.display.progress import ActivityIndicator
 
@@ -95,6 +132,22 @@ def test_activity_indicator_terminal_line_uses_accent_style():
     assert 'bold' not in str(line.style)
 
 
+def test_activity_indicator_terminal_line_is_static_when_not_animated():
+    import easydiffraction.display.progress as progress_mod
+
+    indicator = progress_mod.ActivityIndicator(
+        label='Fitting...',
+        verbosity=VerbosityEnum.FULL,
+        animated=False,
+    )
+    indicator._running = True
+
+    line = indicator._terminal_indicator_line()
+
+    assert line is not None
+    assert line.plain == 'Fitting...'
+
+
 def test_activity_indicator_terminal_live_uses_dynamic_renderable(monkeypatch):
     import easydiffraction.display.progress as progress_mod
 
@@ -107,6 +160,7 @@ def __init__(
             auto_refresh,
             refresh_per_second,
             get_renderable=None,
+            vertical_overflow=None,
         ):
             self.renderable = renderable
             self.console = console
@@ -144,6 +198,56 @@ def refresh(self):
     assert indicator._live.get_renderable().plain == 'X Sampling...'
 
 
+def test_activity_indicator_terminal_live_disables_auto_refresh_when_not_animated(
+    monkeypatch,
+):
+    import easydiffraction.display.progress as progress_mod
+
+    class FakeLive:
+        def __init__(
+            self,
+            renderable=None,
+            *,
+            console,
+            auto_refresh,
+            refresh_per_second,
+            get_renderable=None,
+            vertical_overflow=None,
+        ):
+            self.renderable = renderable
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.refresh_per_second = refresh_per_second
+            self.get_renderable = get_renderable
+            self.refresh_calls = 0
+            self.started = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.started = False
+
+        def refresh(self):
+            self.refresh_calls += 1
+
+    monkeypatch.setattr(progress_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(progress_mod, 'Live', FakeLive)
+    monkeypatch.setattr(progress_mod.ConsoleManager, 'get', lambda: 'console')
+
+    indicator = progress_mod.ActivityIndicator(
+        label='Fitting...',
+        verbosity=VerbosityEnum.FULL,
+        animated=False,
+    )
+    indicator.start()
+
+    assert indicator._live is not None
+    assert indicator._live.auto_refresh is False
+    assert indicator._live.get_renderable is not None
+    assert indicator._live.get_renderable().plain == 'Fitting...'
+
+
 def test_activity_indicator_render_html_uses_current_label():
     from easydiffraction.display.progress import ActivityIndicator
 
@@ -167,6 +271,7 @@ def __init__(
             auto_refresh,
             refresh_per_second,
             get_renderable=None,
+            vertical_overflow=None,
         ):
             self.renderable = renderable
             self.console = console
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize_more.py b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
index 1add7692d..f526f21a8 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize_more.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
@@ -164,7 +164,6 @@ def as_cif(self):
 
 def test_analysis_to_cif_renders_all_sections():
     import easydiffraction.io.cif.serialize as MUT
-    from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
 
     class Obj:
         def __init__(self, t):
@@ -175,16 +174,16 @@ def as_cif(self):
             return self._t
 
     class A:
-        fit = Obj('_fit.minimizer_type lmfit\n_fit.mode single')
-        joint_fit_experiments = JointFitExperiments()
+        fitting_mode_type = 'single'
+        fitting = Obj('_fitting.minimizer_type lmfit')
         aliases = Obj('ALIASES')
         constraints = Obj('CONSTRAINTS')
 
     out = MUT.analysis_to_cif(A())
-    lines = out.splitlines()
-    assert lines[0].startswith('_fit.minimizer_type')
-    assert 'lmfit' in lines[0]
-    assert lines[1].startswith('_fit.mode')
-    assert 'single' in lines[1]
+    lines = [line for line in out.splitlines() if line]
+    assert lines[0].startswith('_fitting.mode_type')
+    assert 'single' in lines[0]
+    assert lines[1].startswith('_fitting.minimizer_type')
+    assert 'lmfit' in lines[1]
     assert 'ALIASES' in out
     assert 'CONSTRAINTS' in out
diff --git a/tests/unit/easydiffraction/io/test_ascii.py b/tests/unit/easydiffraction/io/test_ascii.py
index 310542d15..c76e8fcf4 100644
--- a/tests/unit/easydiffraction/io/test_ascii.py
+++ b/tests/unit/easydiffraction/io/test_ascii.py
@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 import zipfile
+from pathlib import Path
 
 import numpy as np
 import pytest
@@ -13,6 +14,7 @@
 from easydiffraction.io.ascii import extract_data_paths_from_zip
 from easydiffraction.io.ascii import extract_project_from_zip
 from easydiffraction.io.ascii import load_numeric_block
+from easydiffraction.project.project import Project
 
 
 class TestLoadNumericBlock:
@@ -183,6 +185,34 @@ def test_destination_creates_directory(self, tmp_path):
         assert len(paths) == 1
         assert dest.is_dir()
 
+    def test_relative_destination_does_not_depend_on_current_project(self, tmp_path, monkeypatch):
+        """Relative destinations are resolved from cwd, not project state."""
+        zip_path = tmp_path / 'test.zip'
+        with zipfile.ZipFile(zip_path, 'w') as zf:
+            zf.writestr('scan_001.dat', '1 2 3\n')
+
+        workspace = tmp_path / 'workspace'
+        workspace.mkdir()
+        monkeypatch.chdir(workspace)
+
+        original_current_project = Project._current_project
+        try:
+            Project._loading = True
+            project_one = Project()
+            project_two = Project()
+        finally:
+            Project._loading = False
+
+        try:
+            project_one.save_as(str(tmp_path / 'project-one'))
+            project_two.save_as(str(tmp_path / 'project-two'))
+            paths = extract_data_paths_from_zip(zip_path, destination='data/d20_scan')
+        finally:
+            Project._current_project = original_current_project
+
+        assert len(paths) == 1
+        assert Path(paths[0]).parent == (workspace / 'data' / 'd20_scan').resolve()
+
     def test_raises_file_not_found(self, tmp_path):
         """Raises FileNotFoundError for missing ZIP path."""
         with pytest.raises(FileNotFoundError):
@@ -239,6 +269,21 @@ def test_lists_files_in_directory(self, tmp_path):
         assert 'scan_001.dat' in paths[0]
         assert 'scan_002.dat' in paths[1]
 
+    def test_lists_absolute_paths_for_relative_directory(self, tmp_path, monkeypatch):
+        """Returns absolute paths even when the input directory is relative."""
+        data_dir = tmp_path / 'scans'
+        data_dir.mkdir()
+        (data_dir / 'scan_002.dat').write_text('2\n')
+        (data_dir / 'scan_001.dat').write_text('1\n')
+        monkeypatch.chdir(tmp_path)
+
+        paths = extract_data_paths_from_dir('scans')
+
+        assert paths == [
+            str((data_dir / 'scan_001.dat').resolve()),
+            str((data_dir / 'scan_002.dat').resolve()),
+        ]
+
     def test_raises_for_missing_directory(self, tmp_path):
         """Raises FileNotFoundError for non-existent directory."""
         with pytest.raises(FileNotFoundError):
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index b56f8fce0..1a892bca0 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -38,6 +38,7 @@ def _recorder(*args, **kwargs):
     plotter = SimpleNamespace(
         plot_param_correlations=record('plot_param_correlations'),
         plot_param_series=record('plot_param_series'),
+        plot_all_param_series=record('plot_all_param_series'),
         plot_posterior_pairs=record('plot_posterior_pairs'),
         plot_param_distribution=record('plot_param_distribution'),
         plot_posterior_predictive=record('plot_posterior_predictive'),
@@ -192,7 +193,8 @@ def test_fit_display_delegates_to_analysis_and_rendering():
         max_parameters=4,
         show_diagonal=False,
     )
-    display.fit.series(param='scale', versus='temperature')
+    display.fit.series(param='scale', versus='diffrn.ambient_temperature')
+    display.fit.series(versus='diffrn.ambient_temperature')
 
     assert calls[0] == ('fit_results', (), {})
     assert calls[1] == (
@@ -208,7 +210,12 @@ def test_fit_display_delegates_to_analysis_and_rendering():
     assert calls[2] == (
         'plot_param_series',
         (),
-        {'param': 'scale', 'versus': 'temperature'},
+        {'param': 'scale', 'versus': 'diffrn.ambient_temperature'},
+    )
+    assert calls[3] == (
+        'plot_all_param_series',
+        (),
+        {'versus': 'diffrn.ambient_temperature'},
     )
 
 
diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py
index e0ff677dd..e3dcf7a19 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from collections import UserList
+import csv
 from types import SimpleNamespace
 
 
@@ -74,3 +76,48 @@ def test_project_exposes_rendering_and_display_facades():
 
     assert isinstance(project.rendering, Rendering)
     assert isinstance(project.display, ProjectDisplay)
+
+
+def test_apply_params_from_csv_resolves_relative_file_paths(tmp_path):
+    from easydiffraction.project.project import Project
+
+    project = Project()
+    project.info.path = tmp_path / 'project'
+    analysis_dir = project.info.path / 'analysis'
+    analysis_dir.mkdir(parents=True)
+    data_dir = project.info.path / 'experiments' / 'scan'
+    data_dir.mkdir(parents=True)
+    data_path = data_dir / 'scan_001.dat'
+    data_path.write_text('1 2 3\n')
+
+    csv_path = analysis_dir / 'results.csv'
+    with csv_path.open('w', newline='', encoding='utf-8') as handle:
+        writer = csv.DictWriter(handle, fieldnames=['file_path'])
+        writer.writeheader()
+        writer.writerow({'file_path': 'experiments/scan/scan_001.dat'})
+
+    loaded_paths: list[str] = []
+
+    class Experiment:
+        diffrn = SimpleNamespace()
+
+        def _load_ascii_data_to_experiment(self, file_path):
+            loaded_paths.append(file_path)
+
+    class Structures(UserList):
+        parameters = []
+
+    class Experiments:
+        parameters = []
+
+        @staticmethod
+        def values():
+            return [experiment]
+
+    experiment = Experiment()
+    project._structures = Structures()
+    project._experiments = Experiments()
+
+    project.apply_params_from_csv(0)
+
+    assert loaded_paths == [str(data_path)]
diff --git a/tests/unit/easydiffraction/project/test_project_load.py b/tests/unit/easydiffraction/project/test_project_load.py
index 448345777..4f32ffde4 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -70,16 +70,16 @@ def test_round_trips_minimizer(self, tmp_path):
 
         loaded = Project.load(str(tmp_path / 'proj'))
 
-        assert loaded.analysis.fit.minimizer_type.value == 'lmfit (leastsq)'
+        assert loaded.analysis.fitting.minimizer_type.value == 'lmfit (leastsq)'
 
     def test_round_trips_fit_mode(self, tmp_path):
         original = Project(name='a2')
-        original.analysis.fit.mode = 'joint'
+        original.analysis.fitting_mode_type = 'joint'
         original.save_as(str(tmp_path / 'proj'))
 
         loaded = Project.load(str(tmp_path / 'proj'))
 
-        assert loaded.analysis.fit.mode.value == 'joint'
+        assert loaded.analysis.fitting_mode_type == 'joint'
 
     def test_round_trips_rendering_configuration(self, tmp_path):
         original = Project(name='d1')
@@ -136,7 +136,7 @@ def test_loads_analysis_from_subdir(self, tmp_path):
         assert (tmp_path / 'proj' / 'analysis' / 'analysis.cif').is_file()
 
         loaded = Project.load(str(tmp_path / 'proj'))
-        assert loaded.analysis.fit.minimizer_type.value == 'lmfit (leastsq)'
+        assert loaded.analysis.fitting.minimizer_type.value == 'lmfit (leastsq)'
 
     def test_loads_analysis_from_root_fallback(self, tmp_path):
         """Old layout fallback: analysis.cif at project root."""
@@ -150,4 +150,4 @@ def test_loads_analysis_from_root_fallback(self, tmp_path):
         analysis_dir.rmdir()
 
         loaded = Project.load(str(proj_dir))
-        assert loaded.analysis.fit.minimizer_type.value == 'lmfit (leastsq)'
+        assert loaded.analysis.fitting.minimizer_type.value == 'lmfit (leastsq)'
diff --git a/tests/unit/easydiffraction/project/test_project_save.py b/tests/unit/easydiffraction/project/test_project_save.py
index 08292525d..27212251b 100644
--- a/tests/unit/easydiffraction/project/test_project_save.py
+++ b/tests/unit/easydiffraction/project/test_project_save.py
@@ -38,3 +38,27 @@ def test_project_save_as_writes_core_files(tmp_path, monkeypatch):
     assert (target / 'summary.cif').is_file()
     assert (target / 'structures').is_dir()
     assert (target / 'experiments').is_dir()
+
+
+def test_project_save_lists_existing_analysis_results_csv(tmp_path, monkeypatch, capsys):
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.project.project import Project
+    from easydiffraction.project.project_info import ProjectInfo
+    from easydiffraction.summary.summary import Summary
+
+    monkeypatch.setattr(ProjectInfo, 'as_cif', property(lambda self: 'info'))
+    monkeypatch.setattr(Analysis, 'as_cif', property(lambda self: 'analysis'))
+    monkeypatch.setattr(Summary, 'as_cif', lambda self: 'summary')
+
+    target = tmp_path / 'proj_dir'
+    analysis_dir = target / 'analysis'
+    analysis_dir.mkdir(parents=True)
+    (analysis_dir / 'results.csv').write_text('file_path\nscan_001.xye\n')
+
+    p = Project(name='p1')
+    p.info.path = target
+    p.save()
+
+    out = capsys.readouterr().out
+    assert 'analysis.cif' in out
+    assert 'results.csv' in out
diff --git a/tests/unit/easydiffraction/summary/test_summary.py b/tests/unit/easydiffraction/summary/test_summary.py
index 6c34baf11..0619aaf1c 100644
--- a/tests/unit/easydiffraction/summary/test_summary.py
+++ b/tests/unit/easydiffraction/summary/test_summary.py
@@ -28,10 +28,10 @@ def __init__(self):
             self.experiments = {}  # empty mapping to exercise loops safely
 
             class A:
-                class Fit:
+                class Fitting:
                     minimizer_type = type('V', (), {'value': 'lmfit'})()
 
-                fit = Fit()
+                fitting = Fitting()
 
                 class R:
                     reduced_chi_square = 0.0
diff --git a/tests/unit/easydiffraction/summary/test_summary_details.py b/tests/unit/easydiffraction/summary/test_summary_details.py
index 9e62dba2d..69158b7d6 100644
--- a/tests/unit/easydiffraction/summary/test_summary_details.py
+++ b/tests/unit/easydiffraction/summary/test_summary_details.py
@@ -112,10 +112,10 @@ def __init__(self):
         self.experiments = {'exp1': _Expt()}
 
         class A:
-            class Fit:
+            class Fitting:
                 minimizer_type = _Val('lmfit')
 
-            fit = Fit()
+            fitting = Fitting()
 
             class R:
                 reduced_chi_square = 1.23
diff --git a/tests/unit/easydiffraction/test___main__.py b/tests/unit/easydiffraction/test___main__.py
index 9edd2bc27..62ad8c68c 100644
--- a/tests/unit/easydiffraction/test___main__.py
+++ b/tests/unit/easydiffraction/test___main__.py
@@ -127,6 +127,63 @@ def pattern(expt_name, **kwargs):
     assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_False']
 
 
+def test_cli_fit_skips_fit_reports_for_sequential_mode(monkeypatch, tmp_path):
+    import easydiffraction.__main__ as main_mod
+    from easydiffraction.project.project import Project
+
+    calls = []
+
+    class FakeInfo:
+        _path = '/some/path'
+
+    class FakeExperiment:
+        name = 'exp1'
+
+    class FakeProject:
+        info = FakeInfo()
+        experiments = [FakeExperiment()]
+
+        class _analysis:
+            fitting_mode_type = 'sequential'
+
+            @staticmethod
+            def fit():
+                calls.append('FIT')
+
+        analysis = _analysis()
+
+        class _display:
+            class _fit:
+                @staticmethod
+                def results():
+                    calls.append('DISPLAY')
+
+                @staticmethod
+                def correlations():
+                    calls.append('PLOT_CORR')
+
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name, **kwargs):
+                del kwargs
+                calls.append(f'PLOT_{expt_name}_False')
+
+        display = _display()
+
+    fake_project = FakeProject()
+
+    proj_dir = tmp_path / 'proj'
+    proj_dir.mkdir()
+    (proj_dir / 'project.cif').write_text('_project.id test\n')
+
+    monkeypatch.setattr(Project, 'load', staticmethod(lambda dir_path: fake_project))
+
+    result = runner.invoke(main_mod.app, ['fit', str(proj_dir)])
+    assert result.exit_code == 0
+    assert calls == ['FIT', 'PLOT_exp1_False']
+
+
 def test_cli_fit_dry_clears_path(monkeypatch, tmp_path):
     import easydiffraction.__main__ as main_mod
     from easydiffraction.project.project import Project

From aa44c46a0a08c554ab49984e8016db768816ba32 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Sun, 17 May 2026 15:47:24 +0200
Subject: [PATCH 08/10] Separate category-owning sections from real CIF
 datablocks (#176)

* Add CategoryOwner ADR and migration plan

* Add category-owner baseline tests

* Add CategoryOwner base class

* Make DatablockItem inherit CategoryOwner

* Split category-owner CIF serialization

* Make Analysis inherit CategoryOwner

* Generalize dirty-flag lookup to CategoryOwner

* Document CategoryOwner architecture changes

* Move project info into ProjectConfig categories

* Plan Project-to-Workspace rename

* Finalize CategoryOwner refactor

* Standardize project.save_as usage in tutorials

* Ignore projects directory

* Mark verification steps as complete

* Accept CategoryOwner ADR and remove migration plan
---
 .gitignore                                    |    1 +
 .../adr_workspace-root-project-category.md    |  328 ++++++
 docs/dev/ADRs/adr_category-owner-sections.md  |  206 ++++
 docs/dev/Issues/issues_closed.md              |   13 +
 docs/dev/Issues/issues_open.md                |   19 +-
 docs/dev/architecture.md                      |   70 +-
 docs/dev/package-structure-full.md            |   11 +-
 docs/dev/package-structure-short.md           |    6 +
 .../plan_workspace-root-project-category.md   | 1041 +++++++++++++++++
 docs/docs/tutorials/ed-17.py                  |    2 +-
 docs/docs/tutorials/ed-20.py                  |    2 +-
 src/easydiffraction/analysis/analysis.py      |   77 +-
 src/easydiffraction/core/category_owner.py    |   76 ++
 src/easydiffraction/core/datablock.py         |   80 +-
 src/easydiffraction/core/variable.py          |   36 +-
 src/easydiffraction/io/cif/serialize.py       |  121 +-
 .../project/categories/info/__init__.py       |    8 +
 .../project/categories/info/default.py        |  175 +++
 .../project/categories/info/factory.py        |   17 +
 .../project/categories/rendering/default.py   |   11 +-
 src/easydiffraction/project/project.py        |   35 +-
 src/easydiffraction/project/project_config.py |   47 +
 src/easydiffraction/project/project_info.py   |  136 +--
 .../core/test_category_owner.py               |  134 +++
 .../test_serialize_category_owner_baseline.py |   97 ++
 .../project/categories/info/test_default.py   |   63 +
 .../project/categories/info/test_factory.py   |   32 +
 .../project/test_project_config.py            |   68 ++
 28 files changed, 2556 insertions(+), 356 deletions(-)
 create mode 100644 docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
 create mode 100644 docs/dev/ADRs/adr_category-owner-sections.md
 create mode 100644 docs/dev/plan_workspace-root-project-category.md
 create mode 100644 src/easydiffraction/core/category_owner.py
 create mode 100644 src/easydiffraction/project/categories/info/__init__.py
 create mode 100644 src/easydiffraction/project/categories/info/default.py
 create mode 100644 src/easydiffraction/project/categories/info/factory.py
 create mode 100644 src/easydiffraction/project/project_config.py
 create mode 100644 tests/unit/easydiffraction/core/test_category_owner.py
 create mode 100644 tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
 create mode 100644 tests/unit/easydiffraction/project/categories/info/test_default.py
 create mode 100644 tests/unit/easydiffraction/project/categories/info/test_factory.py
 create mode 100644 tests/unit/easydiffraction/project/test_project_config.py

diff --git a/.gitignore b/.gitignore
index 28df1a0ac..ec2e0d270 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,4 +49,5 @@ CMakeLists.txt.user*
 # Used to fetch tutorials data during their runtime. Need to have '/' at
 # the beginning to avoid ignoring 'data' module in the src/.
 /data/
+/projects/
 /tmp/
diff --git a/docs/dev/ADR-suggestions/adr_workspace-root-project-category.md b/docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
new file mode 100644
index 000000000..b53ead958
--- /dev/null
+++ b/docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
@@ -0,0 +1,328 @@
+# ADR: Workspace Root and Project Information Category
+
+**Status:** Proposed  
+**Date:** 2026-05-17
+
+## Context
+
+The current public root object is `Project`. It acts as the top-level
+facade for an EasyDiffraction working session:
+
+```python
+project = ed.Project(name='lbco_hrpt')
+project.structures
+project.experiments
+project.analysis
+project.display
+project.summary
+project.save()
+```
+
+The same word, "project", is also the natural CIF category name for
+information about the scientific project:
+
+```cif
+_project.id
+_project.title
+_project.description
+_project.created
+_project.last_modified
+```
+
+This creates a naming conflict. The root object is a broad runtime
+facade, while the `_project.*` category is only information about the
+scientific project. Using the same name for both makes category naming
+awkward:
+
+```python
+project.project_info.title
+project.config.project_info.title
+project.project.title
+```
+
+At the same time, replacing `_project.*` with a generic `_meta.*`
+category would weaken the CIF model:
+
+```cif
+_meta.project_id
+_meta.project_title
+```
+
+The category name `meta` is too generic. It forces each item name to
+repeat what the category should already communicate. The existing
+`_project.id` and `_project.title` names are more semantic and better
+aligned with the repository rule to follow CIF naming unless there is a
+clearly better API.
+
+The design question is therefore:
+
+- should the top-level runtime object remain `Project`, and project
+  information move to a different category such as `meta`;
+- or should the top-level runtime object be renamed to `Workspace`, so
+  `project` can be used cleanly for project information?
+
+## Decision
+
+Rename the top-level runtime facade from `Project` to `Workspace`.
+
+Use `project` as the public project-information category under the
+workspace:
+
+```python
+workspace = ed.Workspace(project_id='lbco_hrpt')
+workspace.project.id
+workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
+workspace.rendering.table_engine = 'rich'
+workspace.structures
+workspace.experiments
+workspace.analysis
+```
+
+Persist workspace-level singleton categories in `workspace.cif`:
+
+```cif
+_project.id
+_project.title
+_project.description
+_project.created
+_project.last_modified
+
+_rendering.chart_engine
+_rendering.table_engine
+```
+
+Do not introduce `_meta.*` CIF tags.
+
+The intended naming split is:
+
+```text
+Workspace
+|-- project      # information about the scientific project
+|-- rendering    # rendering preferences
+|-- structures   # real structure datablocks
+|-- experiments  # real experiment datablocks
+|-- analysis     # analysis section
+|-- display      # display facade
+`-- summary      # summary/report facade
+```
+
+## Rationale
+
+### `Workspace` better describes the top-level facade
+
+The top-level object is more than project metadata. It owns active
+collections, analysis state, display helpers, save/load behavior, and
+runtime orchestration. `Workspace` describes that broader role without
+consuming the domain word `project`.
+
+The name is also familiar in scientific software. It commonly means an
+active analysis environment, a data/model container, or a working area.
+That is close to the role of the current EasyDiffraction root object.
+
+### `project` is the right category name for project information
+
+Project information is not generic metadata. It is specifically the
+identity, title, description, and timestamps of the scientific project.
+
+This reads cleanly:
+
+```python
+workspace.project.title
+```
+
+and maps directly to clean CIF:
+
+```cif
+_project.title
+```
+
+### `_meta.project_title` is weaker than `_project.title`
+
+The `_meta` category would make the CIF less domain-oriented. It also
+creates longer and more repetitive item names:
+
+```cif
+_meta.project_id
+_meta.project_title
+_meta.project_description
+```
+
+The existing `_project.*` tags are clearer:
+
+```cif
+_project.id
+_project.title
+_project.description
+```
+
+### This keeps layer-specific consistency
+
+After this decision, each layer has a clear rule:
+
+| Layer           | Rule                           | Example             |
+| --------------- | ------------------------------ | ------------------- |
+| Runtime root    | working-session facade         | `Workspace`         |
+| Public category | semantic category name         | `workspace.project` |
+| CIF category    | semantic CIF category          | `_project.*`        |
+| Config file     | workspace singleton categories | `workspace.cif`     |
+
+This avoids one-off aliases such as `project.info` while preserving
+semantic CIF names.
+
+## Consequences
+
+### Positive
+
+- The root object and project-information category no longer share the
+  same conceptual name.
+- Public category access becomes uniform: `workspace.project`,
+  `workspace.rendering`, `workspace.analysis`.
+- CIF stays semantic and does not introduce `_meta.*`.
+- Project information can use short item names such as `id`, `title`,
+  and `description`.
+- The top-level facade name better reflects active runtime
+  orchestration.
+
+### Negative
+
+- This is a breaking public API change.
+- Tutorials, scripts, tests, docs, type hints, and imports must be
+  updated from `Project` to `Workspace`.
+- Existing saved directories using `project.cif` must be migrated to
+  `workspace.cif` if no compatibility loader is kept.
+- Users familiar with `Project` must learn the new root name.
+- `Workspace` can be confused with a filesystem workspace in some
+  ecosystems, so documentation must define it clearly as the active
+  EasyDiffraction working object.
+
+## Compatibility Policy
+
+EasyDiffraction is in beta, and repository instructions say not to keep
+legacy shims by default.
+
+Therefore the target implementation should not add a permanent
+`Project = Workspace` alias unless explicitly approved before
+implementation.
+
+The migration plan still includes a review gate before removing the old
+`Project` public symbol, because this is a user-facing breaking change.
+
+## Alternatives Considered
+
+### Keep `Project` root and rename project information to `meta`
+
+Rejected.
+
+Example:
+
+```python
+project.meta.project_title
+```
+
+```cif
+_meta.project_title
+```
+
+This keeps the root class stable, but it weakens the category model.
+`meta` is too broad, and the CIF item names become repetitive.
+
+### Keep `Project` root and use `project.config.project`
+
+Rejected for now.
+
+Example:
+
+```python
+project.config.project.title
+```
+
+This is technically consistent, but it still repeats `project` at
+different semantic layers. It also adds depth to common user workflows.
+It is a reasonable fallback if the public root rename is rejected.
+
+### Keep `Project` root and use `project.info`
+
+Rejected for the target design.
+
+Example:
+
+```python
+project.info.title
+```
+
+This is readable, but it preserves a special-case category alias. The
+current goal is stronger consistency between public categories and CIF
+category concepts.
+
+### Rename only internal files and keep public API unchanged
+
+Rejected for the target design.
+
+This improves implementation clarity but does not solve the public
+naming inconsistency.
+
+### Use `Study` instead of `Workspace`
+
+Rejected.
+
+`Study` is a plausible scientific term, but it is less established for
+an active computational container. It also does not map as naturally to
+save/load, display, and analysis orchestration.
+
+## Implementation Notes
+
+The implementation should follow:
+
+```text
+docs/dev/plan_workspace-root-project-category.md
+```
+
+The high-level migration is:
+
+1. Rename the root facade `Project` to `Workspace`.
+2. Rename the project package/module surface from `project` to
+   `workspace`.
+3. Rename `ProjectConfig` to `WorkspaceConfig`.
+4. Rename project-level category access from `info` to `project`.
+5. Rename project-information `name` access to `id`, matching
+   `_project.id`.
+6. Move the storage path to `Workspace.path`, because it describes the
+   saved workspace directory rather than project information.
+7. Keep the information category class named `ProjectInfo` unless a
+   later decision chooses `ProjectMetadata`.
+8. Keep CIF tags `_project.*` and `_rendering.*`.
+9. Rename saved singleton config file from `project.cif` to
+   `workspace.cif`.
+10. Update code, tests, scripts, tutorials, docs, and architecture
+    references.
+
+## Post-Implementation ADR Update
+
+This ADR must be updated after the migration plan is implemented.
+
+When implementation is complete:
+
+1. Change status from `Proposed` to `Accepted and implemented`.
+2. Record the final public API and saved file layout.
+3. Record whether a temporary or permanent `Project` compatibility alias
+   was approved.
+4. Record any deviations from the migration plan.
+5. Move this file from `docs/dev/ADR-suggestions/` to `docs/dev/ADRs/`
+   if that is the repository convention for accepted decisions.
+6. Update `docs/dev/architecture.md`.
+7. Update or close related items in `docs/dev/Issues/issues_open.md`.
+
+## Acceptance Criteria
+
+This ADR is satisfied when:
+
+- `ed.Workspace` is the public top-level facade.
+- `ed.Project` is removed unless explicitly approved as an alias.
+- the public project-information category is `workspace.project`.
+- project identity is exposed as `workspace.project.id`.
+- the saved directory path is exposed as `workspace.path`.
+- the public rendering category is `workspace.rendering`.
+- saved singleton configuration lives in `workspace.cif`.
+- `workspace.cif` uses `_project.*` and `_rendering.*` tags.
+- no `_meta.*` tags are introduced for project information.
+- tutorials and architecture documentation use `Workspace`.
diff --git a/docs/dev/ADRs/adr_category-owner-sections.md b/docs/dev/ADRs/adr_category-owner-sections.md
new file mode 100644
index 000000000..46f9b3db0
--- /dev/null
+++ b/docs/dev/ADRs/adr_category-owner-sections.md
@@ -0,0 +1,206 @@
+# ADR: Category Owners and Real Datablocks
+
+## Status
+
+Accepted and implemented.
+
+## Date
+
+2026-05-17
+
+## Context
+
+The library has two different kinds of objects that expose CIF-like
+categories:
+
+- real datablocks such as structures and experiments
+- singleton project sections such as analysis and project configuration
+
+Real datablocks map to CIF `data_<id>` blocks and therefore need a
+datablock identity plus a `data_` header. Singleton sections do not.
+
+Before this change, `DatablockItem` mixed two responsibilities:
+
+1. owning and updating flat categories
+2. representing a real CIF data block with a `data_<id>` header
+
+That made it tempting to move `Analysis` onto `DatablockItem` just to
+reuse category discovery, parameter enumeration, dirty tracking, and CIF
+serialization. Doing so would have weakened the meaning of "datablock"
+in the architecture and encouraged fake identities such as
+`datablock_entry_name = "analysis"`.
+
+## Decision
+
+Introduce `CategoryOwner` as the shared abstraction for objects that own
+flat CIF-like categories.
+
+### 1. Real datablocks remain `DatablockItem`-based
+
+`DatablockItem` now inherits from `CategoryOwner` and keeps only the
+behavior specific to real CIF data blocks:
+
+- `data_<id>` header serialization
+- datablock identity via `datablock_entry_name`
+- participation in `DatablockCollection`
+
+The real datablock families remain:
+
+- `Structure`
+- `ExperimentBase` subclasses
+
+They continue to serialize as independent CIF data blocks with
+`data_<name>` headers.
+
+### 2. `Analysis` is a category-owning singleton section
+
+`Analysis` inherits from `CategoryOwner`, not `DatablockItem`.
+
+It reuses shared behavior for:
+
+- category discovery
+- parameter aggregation
+- category update orchestration
+- dirty-flag tracking
+- help display
+- headerless CIF body serialization
+
+`Analysis` remains a singleton section without a fake `data_` header. It
+uses an owner-level `_serializable_categories()` policy so that only the
+active sibling categories are written for the current fitting mode.
+Inactive mode-specific categories remain accessible but are not
+serialized.
+
+### 3. Project configuration is a category-owning singleton section
+
+Project-level configuration follows the same pattern via a private
+`ProjectConfig(CategoryOwner)` object.
+
+Its current children are:
+
+- `ProjectInfo`
+- `Rendering`
+
+The public API stays flat and user-facing:
+
+- `project.info`
+- `project.rendering`
+
+Saved `project.cif` remains a section file without a `data_` header. It
+serializes the `_project.*` metadata category and the `_rendering.*`
+configuration category without pretending that the project config is a
+real datablock.
+
+### 4. CIF serialization is split by responsibility
+
+Serialization is separated into two layers:
+
+- `category_owner_to_cif(owner)` renders category bodies without a
+  `data_` header
+- `datablock_item_to_cif(datablock)` renders the `data_<id>` header and
+  then the category-owner body
+
+This keeps the meaning of `DatablockItem` precise while letting
+singleton sections reuse the same category serialization logic.
+
+### 5. Dirty-flag propagation is generalized to `CategoryOwner`
+
+Descriptor changes now mark the nearest `CategoryOwner` ancestor dirty
+instead of depending on `DatablockItem` specifically. Structures,
+experiments, analysis, and project configuration now share the same
+owner-level dirty/update contract.
+
+## Resulting Hierarchy
+
+```text
+GuardedBase
+|-- CategoryItem
+|-- CollectionBase
+|   |-- CategoryCollection
+|   `-- DatablockCollection
+`-- CategoryOwner
+    |-- DatablockItem
+    |   |-- Structure
+    |   `-- ExperimentBase
+    |-- Analysis
+    `-- ProjectConfig
+```
+
+## Consequences
+
+### Positive
+
+- The term "datablock" remains semantically precise.
+- `Analysis` and project configuration reuse the standard category-owner
+  behavior without becoming fake data blocks.
+- CIF serialization is clearer because category-body rendering is
+  separated from `data_` header rendering.
+- Dirty-flag handling is consistent across all category owners.
+- Project-level singleton sections now follow the same architectural
+  pattern as analysis.
+
+### Trade-offs
+
+- The core model gains a new abstraction that must be understood and
+  documented.
+- Owner-level serialization policy now lives in explicit hooks such as
+  `_serializable_categories()` instead of falling out of the raw object
+  layout.
+
+### Compatibility Outcomes
+
+The implemented design preserves these contracts:
+
+- `structure.as_cif` starts with `data_<structure_name>`
+- `experiment.as_cif` starts with `data_<experiment_name>`
+- `analysis.as_cif` does not start with `data_`
+- `project.cif` does not emit `data_project`
+- `project.parameters` remains fit-focused and does not include analysis
+  configuration parameters
+- saved project layout remains compatible
+
+## Alternatives Considered
+
+### Make `Analysis` inherit from `DatablockItem`
+
+Rejected.
+
+This would have been the smallest code change, but it would make
+"datablock" mean both real CIF data blocks and singleton project
+sections.
+
+### Add `emit_data_header = False` to `DatablockItem`
+
+Rejected.
+
+This would keep reuse through inheritance but encode two different
+concepts in one class and force datablock behavior to branch on whether
+the object is "real enough" to emit a header.
+
+### Keep `Analysis` fully ad hoc
+
+Rejected.
+
+That would preserve current behavior but keep duplicated logic for
+category discovery, category updates, parameter enumeration, and CIF
+section serialization.
+
+### Make `Project` itself a `CategoryOwner`
+
+Rejected.
+
+`Project` is a top-level facade that coordinates structures,
+experiments, analysis, display, summary, and file I/O. A smaller private
+`ProjectConfig(CategoryOwner)` keeps the category-owning concern local
+to the singleton project-section surface instead of mixing it into the
+facade itself.
+
+## Verification
+
+This decision is fully implemented and was verified with:
+
+- `pixi run fix`
+- `pixi run check`
+- `pixi run unit-tests`
+- `pixi run integration-tests`
+- `pixi run script-tests`
diff --git a/docs/dev/Issues/issues_closed.md b/docs/dev/Issues/issues_closed.md
index fee4f327b..0e6db4dc5 100644
--- a/docs/dev/Issues/issues_closed.md
+++ b/docs/dev/Issues/issues_closed.md
@@ -62,6 +62,19 @@ user-facing path and bypassed during fitting
 
 ---
 
+## Introduce CategoryOwner for Analysis and Datablocks
+
+Added `CategoryOwner` as the shared base class for flat CIF-like
+category owners. `DatablockItem` now extends `CategoryOwner`, keeping
+real `data_<id>` header behavior for structures and experiments.
+`Analysis` also extends `CategoryOwner`, reusing shared category
+discovery, parameter aggregation, and dirty tracking while remaining a
+singleton section without a `data_` header. CIF serialization now splits
+category-body rendering from datablock header rendering via
+`category_owner_to_cif()`.
+
+---
+
 ## Move Calculator from Global to Per-Experiment
 
 Each experiment owns its calculator, auto-resolved on first access from
diff --git a/docs/dev/Issues/issues_open.md b/docs/dev/Issues/issues_open.md
index 3e961f1cc..2e6142e8e 100644
--- a/docs/dev/Issues/issues_open.md
+++ b/docs/dev/Issues/issues_open.md
@@ -27,23 +27,6 @@ match `project.experiments.names`.
 
 ---
 
-## 5. 🟡 Make `Analysis` a `DatablockItem`
-
-**Type:** Consistency
-
-`Analysis` owns categories (`Aliases`, `Constraints`,
-`JointFitCollection`) but does not extend `DatablockItem`. Its ad-hoc
-`_update_categories()` iterates over a hard-coded list and does not
-participate in standard category discovery, parameter enumeration, or
-CIF serialisation.
-
-**Fix:** make `Analysis` extend `DatablockItem`, or extract a shared
-`_update_categories()` protocol.
-
-**Depends on:** nothing.
-
----
-
 ## 8. 🟡 Add Explicit `create()` Signatures on Collections
 
 **Type:** API safety
@@ -100,7 +83,7 @@ with joint-fit workflows.
 at minimum document the required update order. For joint fitting, all
 experiments should be updateable in a single call.
 
-**Depends on:** benefits from issue 5 (Analysis as DatablockItem).
+**Depends on:** benefits from the CategoryOwner migration.
 
 ---
 
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index d6a63b6d5..768531e72 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -53,7 +53,8 @@ GuardedBase                            # Controlled attribute access, parent lin
 ├── CollectionBase                     # Ordered name→item container
 │   ├── CategoryCollection             # CIF loop  (e.g. AtomSites, Background, Data)
 │   └── DatablockCollection            # Top-level container  (e.g. Structures, Experiments)
-└── DatablockItem                      # CIF data block  (e.g. Structure, Experiment)
+└── CategoryOwner                      # Flat category owner (e.g. Analysis, DatablockItem)
+  └── DatablockItem                  # Real CIF data block (e.g. Structure, Experiment)
 ```
 
 `CollectionBase` provides a unified dict-like API over an ordered item
@@ -185,7 +186,18 @@ design constraint. Categories are never nested inside other categories
 **Update priority:** lower values run first. This ensures correct
 execution order within a datablock (e.g. background before data).
 
-### 2.4 DatablockItem and DatablockCollection
+### 2.4 CategoryOwner, DatablockItem, and DatablockCollection
+
+`CategoryOwner` is the shared base class for objects that own flat
+category siblings. It provides category discovery, category sorting by
+`_update_priority`, parameter aggregation, `_need_categories_update`
+tracking, and category-body CIF serialization without a `data_` header.
+
+`DatablockItem` extends `CategoryOwner` for real CIF `data_<id>` blocks.
+`Structure` and `ExperimentBase` subclasses are real datablocks.
+`Analysis` is also a `CategoryOwner`, but it serializes as a singleton
+section body in `analysis/analysis.cif` and does not emit a fake
+`data_analysis` header.
 
 | Aspect             | `DatablockItem`                             | `DatablockCollection`                                    |
 | ------------------ | ------------------------------------------- | -------------------------------------------------------- |
@@ -199,7 +211,7 @@ execution order within a datablock (e.g. background before data).
 | Dirty flag         | `_need_categories_update`                   | N/A                                                      |
 
 When any `Parameter.value` is set, it propagates
-`_need_categories_update = True` up to the owning `DatablockItem`.
+`_need_categories_update = True` up to the owning `CategoryOwner`.
 Serialisation (`as_cif`) and plotting trigger `_update_categories()` if
 the flag is set.
 
@@ -847,14 +859,16 @@ workflow:
 
 `Analysis` is bound to a `Project` and provides the high-level API:
 
-- Fit configuration: `fit` (`CategoryItem` with `minimizer_type` and
-  `mode` descriptors). `fit.minimizer_type` selects the minimizer
-  backend. `fit.mode` stores whether fitting is `'single'`, `'joint'`,
-  or `'sequential'`. `fit.show_minimizer_types()` lists supported
-  minimizers; `fit.show_modes()` filters modes by experiment count (≤1 →
-  only `single`; >1 → all three).
+- Singleton section: `Analysis` is a `CategoryOwner`, not a
+  `DatablockItem`. It owns sibling categories and serializes as the body
+  of `analysis/analysis.cif` without a `data_` header.
+- Fit configuration: `fitting` (`CategoryItem` with `minimizer_type`).
+  `fitting.minimizer_type` selects the minimizer backend. The active
+  fitting mode lives on the owner as `analysis.fitting_mode_type`, not
+  as a nested child category field. `fitting.show_minimizer_types()`
+  lists supported minimizers.
 - Joint-fit weights: `joint_fit` (`CategoryCollection` of per-experiment
-  weight entries); sibling of `fit`, not a child.
+  weight entries); sibling of `fitting`, not a child.
 - Fit results: `analysis.fit_results` stores the latest runtime result
   object. This is `FitResults` for deterministic fits and
   `BayesianFitResults` for Bayesian DREAM runs.
@@ -922,12 +936,18 @@ It owns and coordinates all components:
 | `project.summary`     | `Summary`        | Report generation                        |
 | `project.verbosity`   | `str`            | Console output level (full/short/silent) |
 
+Internally, `Project` keeps project-scoped singleton categories under a
+private `ProjectConfig(CategoryOwner)` object. That owner currently
+holds `project.info` (`ProjectInfo`) and `project.rendering`
+(`Rendering`), while the public access paths stay flat on `Project` for
+user discoverability.
+
 ### 7.1 Data Flow
 
 ```
 Parameter.value set
     → AttributeSpec validation (type + value)
-    → _need_categories_update = True (on parent DatablockItem)
+    → _need_categories_update = True (on parent CategoryOwner)
 
 Plot / CIF export / fit objective evaluation
     → _update_categories()
@@ -944,7 +964,7 @@ Projects are saved as a directory of CIF files:
 
 ```shell
 project_dir/
-├── project.cif          # ProjectInfo + Display preferences
+├── project.cif          # ProjectConfig categories (info + rendering)
 ├── summary.cif          # Summary report
 ├── structures/
 │   └── lbco.cif         # One file per structure
@@ -954,14 +974,16 @@ project_dir/
     └── analysis.cif     # Analysis settings
 ```
 
-`project.cif` carries both the `_project.*` metadata and the
-`_rendering.*` engine preferences (`chart_engine`, `table_engine`), so a
-saved project re-opens with the same display backends. Per-experiment
-calculator selection (`_calculation.calculator_type`) lives in each
-experiment file, and fit configuration (`_fitting.minimizer_type`,
-`_fitting.mode_type`) lives in `analysis/analysis.cif`. Runtime fit
-outputs, including `analysis.fit_results`, posterior chains, posterior
-predictive summaries, and convergence diagnostics, are not serialized.
+`project.cif` serializes the private `ProjectConfig` owner without a
+`data_` header. It carries both the `_project.*` metadata category and
+the `_rendering.*` engine preferences (`chart_engine`, `table_engine`),
+so a saved project re-opens with the same display backends.
+Per-experiment calculator selection (`_calculation.calculator_type`)
+lives in each experiment file, and fit configuration
+(`_fitting.minimizer_type`, `_fitting.mode_type`) lives in
+`analysis/analysis.cif`. Runtime fit outputs, including
+`analysis.fit_results`, posterior chains, posterior predictive
+summaries, and convergence diagnostics, are not serialized.
 
 ### 7.3 Verbosity
 
@@ -1362,12 +1384,12 @@ if self._fitting_mode_type == 'joint':
 ### 9.7 Flat Category Structure — No Nested Categories
 
 Following CIF conventions, categories are **flat siblings** within their
-owner (datablock or analysis object). A category must never be a child
-of another category of a different type. Categories can reference each
-other via IDs, but the ownership hierarchy is always:
+owner (`CategoryOwner`). A category must never be a child of another
+category of a different type. Categories can reference each other via
+IDs, but the ownership hierarchy is always:
 
 ```
-Owner (DatablockItem / Analysis)
+Owner (CategoryOwner)
 ├── CategoryA   (CategoryItem or CategoryCollection)
 ├── CategoryB   (CategoryItem or CategoryCollection)
 └── CategoryC   (CategoryItem or CategoryCollection)
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index c1728f1ba..720505baa 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -122,6 +122,8 @@
 │   ├── 📄 category.py
 │   │   ├── 🏷️ class CategoryItem
 │   │   └── 🏷️ class CategoryCollection
+│   ├── 📄 category_owner.py
+│   │   └── 🏷️ class CategoryOwner
 │   ├── 📄 collection.py
 │   │   └── 🏷️ class CollectionBase
 │   ├── 📄 datablock.py
@@ -425,6 +427,12 @@
 │   └── 📄 ascii.py
 ├── 📁 project
 │   ├── 📁 categories
+│   │   ├── 📁 info
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class ProjectInfo
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class ProjectInfoFactory
 │   │   ├── 📁 rendering
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
@@ -441,8 +449,9 @@
 │   │   └── 🏷️ class ProjectDisplay
 │   ├── 📄 project.py
 │   │   └── 🏷️ class Project
+│   ├── 📄 project_config.py
+│   │   └── 🏷️ class ProjectConfig
 │   └── 📄 project_info.py
-│       └── 🏷️ class ProjectInfo
 ├── 📁 summary
 │   ├── 📄 __init__.py
 │   └── 📄 summary.py
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index 4c1c62f8d..da42f7a57 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -64,6 +64,7 @@
 ├── 📁 core
 │   ├── 📄 __init__.py
 │   ├── 📄 category.py
+│   ├── 📄 category_owner.py
 │   ├── 📄 collection.py
 │   ├── 📄 datablock.py
 │   ├── 📄 diagnostic.py
@@ -207,6 +208,10 @@
 │   └── 📄 ascii.py
 ├── 📁 project
 │   ├── 📁 categories
+│   │   ├── 📁 info
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
 │   │   ├── 📁 rendering
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
@@ -215,6 +220,7 @@
 │   ├── 📄 __init__.py
 │   ├── 📄 display.py
 │   ├── 📄 project.py
+│   ├── 📄 project_config.py
 │   └── 📄 project_info.py
 ├── 📁 summary
 │   ├── 📄 __init__.py
diff --git a/docs/dev/plan_workspace-root-project-category.md b/docs/dev/plan_workspace-root-project-category.md
new file mode 100644
index 000000000..b57e318a6
--- /dev/null
+++ b/docs/dev/plan_workspace-root-project-category.md
@@ -0,0 +1,1041 @@
+# Workspace Root and Project Category Migration Plan
+
+## Status
+
+Branch: `feature/workspace-root-project-category`
+
+ADR suggestion:
+
+```text
+docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
+```
+
+Two-phase workflow from `.github/copilot-instructions.md`:
+
+- Phase 1 - Implementation. Code, docs, and architecture updates only.
+  Do not create or run tests unless the user explicitly asks.
+- Phase 2 - Verification. Add/update tests, then run the verification
+  commands listed near the end of this plan.
+
+Stop after Phase 1 and request review before starting Phase 2.
+
+Status checklist. Mark `[x]` only while implementing:
+
+```text
+Phase 1 - Implementation
+[ ] Phase 0: Confirm breaking-change approval.
+[ ] Phase 1: Rename root package and public facade to Workspace.
+[ ] Phase 2: Rename project-info access from info to project.
+[ ] Phase 3: Align project information fields with _project.* tags.
+[ ] Phase 4: Rename project config file to workspace.cif.
+[ ] Phase 5: Update root-object references across runtime code.
+[ ] Phase 6: Update docs, tutorials, and ADR references.
+[ ] Phase 7: Remove old public Project surface unless approved.
+[ ] Phase 1 review gate: present diff for approval.
+
+Phase 2 - Verification
+[ ] Move/update unit tests to workspace paths.
+[ ] Add workspace naming and CIF layout tests.
+[ ] pixi run test-structure-check
+[ ] pixi run fix
+[ ] pixi run check
+[ ] pixi run unit-tests
+[ ] pixi run integration-tests
+[ ] pixi run script-tests
+[ ] pixi run notebook-prepare
+[ ] pixi run notebook-tests
+```
+
+## Commit Discipline
+
+When an AI agent follows this plan, every completed Phase 1
+implementation step must be staged with explicit paths and committed
+locally before moving to the next implementation step or to the Phase 1
+review gate.
+
+Follow the **Commits** section of `.github/copilot-instructions.md`.
+
+Rules:
+
+- One commit per phase.
+- Keep each commit atomic and single-purpose.
+- Stage explicit paths only. Do not use `git add .`.
+- Use `git mv` for file and directory moves.
+- Do not stage unrelated user changes.
+- Do not stage generated artifacts unless the user explicitly asks.
+- If a serious uncovered design issue appears, stop and ask before
+  continuing.
+
+Suggested commit messages:
+
+```text
+Rename Project facade to Workspace
+Expose project information as workspace.project
+Align project metadata fields with CIF names
+Rename project config file to workspace.cif
+Update runtime references to Workspace
+Update docs for Workspace root API
+Remove old Project public API surface
+```
+
+## Goal
+
+Split the name "project" into two distinct concepts:
+
+1. `Workspace` - the top-level runtime facade, currently named
+   `Project`.
+2. `workspace.project` - the category that stores information about the
+   scientific project.
+
+Target public API:
+
+```python
+import easydiffraction as ed
+
+workspace = ed.Workspace(project_id='lbco_hrpt')
+workspace.project.id
+workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
+workspace.rendering.table_engine = 'rich'
+workspace.structures
+workspace.experiments
+workspace.analysis
+workspace.display
+workspace.summary
+workspace.save_as('lbco_hrpt')
+```
+
+Target workspace-level config file:
+
+```text
+workspace.cif
+```
+
+Target CIF tags inside `workspace.cif`:
+
+```cif
+_project.id
+_project.title
+_project.description
+_project.created
+_project.last_modified
+
+_rendering.chart_engine
+_rendering.table_engine
+```
+
+Do not introduce `_meta.*` tags.
+
+## Decisions Already Made
+
+Use these decisions unless the user explicitly changes the ADR before
+implementation:
+
+- The public root class becomes `Workspace`.
+- The public root import becomes
+  `from easydiffraction import Workspace`.
+- The public project-information category becomes `workspace.project`.
+- The public rendering category remains `workspace.rendering`.
+- The project-information category keeps semantic CIF tags `_project.*`.
+- The rendering category keeps semantic CIF tags `_rendering.*`.
+- The saved singleton config file becomes `workspace.cif`.
+- The storage directory path belongs to `Workspace.path`, not
+  `workspace.project.path`.
+- The old `Project` public API is removed unless the user explicitly
+  approves an alias before implementation.
+
+## Current Shape
+
+The current implementation already has a category-owner based project
+configuration layer:
+
+```text
+src/easydiffraction/project/
+|-- project.py             # class Project
+|-- project_config.py      # class ProjectConfig
+|-- project_info.py        # ProjectInfo export
+|-- display.py             # class ProjectDisplay
+`-- categories/
+    |-- info/              # ProjectInfo category
+    `-- rendering/         # Rendering category
+```
+
+Current public API:
+
+```python
+project = ed.Project(name='my_project')
+project.info.title
+project.rendering.table_engine
+```
+
+Current saved config file:
+
+```text
+project.cif
+```
+
+## Target Shape
+
+Target implementation:
+
+```text
+src/easydiffraction/workspace/
+|-- workspace.py           # class Workspace
+|-- workspace_config.py    # class WorkspaceConfig
+|-- project_info.py        # ProjectInfo export
+|-- display.py             # class WorkspaceDisplay
+`-- categories/
+    |-- project/           # ProjectInfo category
+    `-- rendering/         # Rendering category
+```
+
+Target public API:
+
+```python
+workspace = ed.Workspace(project_id='my_project')
+workspace.project.title
+workspace.rendering.table_engine
+```
+
+## Out Of Scope
+
+Do not do these in this migration:
+
+- Do not add `_meta.*` CIF tags.
+- Do not redesign structure or experiment datablocks.
+- Do not change analysis fit-mode semantics.
+- Do not change calculator behavior.
+- Do not edit generated package-structure docs by hand.
+- Do not edit generated notebooks directly. Edit tutorial `.py` sources
+  and regenerate notebooks during Phase 2.
+- Do not keep a `Project = Workspace` compatibility alias unless the
+  user explicitly approves it.
+
+## Phase 0: Confirm Breaking-Change Approval
+
+This migration removes or replaces the public `Project` API unless the
+user approves a compatibility alias.
+
+Before changing code, ask the user to confirm:
+
+```text
+This migration removes ed.Project and replaces it with ed.Workspace.
+Should implementation proceed without a Project compatibility alias?
+```
+
+If the user asks for a compatibility alias, record that decision in this
+plan and in the ADR before implementation.
+
+Do not implement code before this approval gate.
+
+Commit: no commit required for this phase unless the plan or ADR is
+updated.
+
+## Phase 1: Rename Root Package And Public Facade
+
+### Objective
+
+Rename the top-level runtime facade and package from `project` to
+`workspace`.
+
+### Files Likely To Change
+
+- `src/easydiffraction/project/`
+- `src/easydiffraction/__init__.py`
+- `src/easydiffraction/__main__.py`
+- `src/easydiffraction/analysis/analysis.py`
+- `src/easydiffraction/analysis/sequential.py`
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/summary/summary.py`
+- any source file importing `easydiffraction.project.*`
+
+### Steps
+
+1. Move the source package:
+
+   ```text
+   src/easydiffraction/project/
+   -> src/easydiffraction/workspace/
+   ```
+
+2. Rename files:
+
+   ```text
+   workspace/project.py
+   -> workspace/workspace.py
+
+   workspace/project_config.py
+   -> workspace/workspace_config.py
+   ```
+
+3. Rename classes:
+
+   ```text
+   Project -> Workspace
+   ProjectConfig -> WorkspaceConfig
+   ProjectDisplay -> WorkspaceDisplay
+   ```
+
+4. Update top-level import:
+
+   ```python
+   from easydiffraction.workspace.workspace import Workspace
+   ```
+
+5. Remove the old top-level `Project` import unless the user approved an
+   alias.
+
+6. Update type-checking imports:
+
+   ```python
+   from easydiffraction.workspace.workspace import Workspace
+   ```
+
+7. Update docstrings from "Project facade" to "Workspace facade" where
+   they describe the root object.
+
+8. Run a source-only grep. Do not run tests in Phase 1:
+
+   ```shell
+   rg -n "easydiffraction\\.project|\\bProject\\b|ProjectDisplay|ProjectConfig" src
+   ```
+
+   For every match, decide whether it refers to:
+   - the old root object, which should become `Workspace`;
+   - the project-information category, which should remain project;
+   - historical text that should be updated in docs later.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- another public class named `Workspace` already exists;
+- package moves break imports in a way that would require compatibility
+  shims;
+- a file has both root-object `project` and category `project` meanings
+  that cannot be separated clearly.
+
+### Commit
+
+Stage explicit moved and edited files, then commit:
+
+```text
+Rename Project facade to Workspace
+```
+
+## Phase 2: Rename Project-Info Access From `info` To `project`
+
+### Objective
+
+Make the project-information category public as `workspace.project`
+instead of `workspace.info`.
+
+### Files Likely To Change
+
+- `src/easydiffraction/workspace/workspace_config.py`
+- `src/easydiffraction/workspace/workspace.py`
+- `src/easydiffraction/workspace/categories/info/`
+- `src/easydiffraction/workspace/project_info.py`
+- `src/easydiffraction/io/cif/serialize.py`
+- all code using `.info` for project information
+
+### Steps
+
+1. Rename category package:
+
+   ```text
+   src/easydiffraction/workspace/categories/info/
+   -> src/easydiffraction/workspace/categories/project/
+   ```
+
+2. Keep the category class name `ProjectInfo` for now. The class name is
+   explicit and avoids a confusing `Project` class after the root class
+   is renamed to `Workspace`.
+
+3. Rename imports:
+
+   ```python
+   from easydiffraction.workspace.categories.project import ProjectInfo
+   from easydiffraction.workspace.categories.project import ProjectInfoFactory
+   ```
+
+4. In `WorkspaceConfig`, rename:
+
+   ```text
+   _info -> _project
+   info -> project
+   ```
+
+5. In `Workspace`, rename:
+
+   ```text
+   _info -> _project
+   info -> project
+   ```
+
+6. Remove the public `.info` property unless the user approved a
+   compatibility alias.
+
+7. Update all runtime references:
+
+   ```text
+   workspace.info.title -> workspace.project.title
+   workspace.info.description -> workspace.project.description
+   workspace.info.update_last_modified() -> workspace.project.update_last_modified()
+   ```
+
+8. Run grep:
+
+   ```shell
+   rg -n "\\.info\\b|categories/info|categories\\.info" src
+   ```
+
+9. For every match, update it if it refers to project information. Leave
+   unrelated uses of the word "info" alone.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- `info` appears as a different public concept unrelated to project
+  information;
+- removing `.info` would break a user-requested compatibility alias.
+
+### Commit
+
+```text
+Expose project information as workspace.project
+```
+
+## Phase 3: Align Project Information Fields With `_project.*`
+
+### Objective
+
+Expose project identity as `workspace.project.id`, matching CIF
+`_project.id`.
+
+Move the saved directory path to `workspace.path`, because it describes
+the workspace location and is not serialized project information.
+
+### Files Likely To Change
+
+- `src/easydiffraction/workspace/categories/project/default.py`
+- `src/easydiffraction/workspace/workspace.py`
+- `src/easydiffraction/io/cif/serialize.py`
+- `src/easydiffraction/summary/summary.py`
+- `src/easydiffraction/display/plotting.py`
+- any code using `.name` for project identity or `.project.path`
+
+### Steps
+
+1. In `ProjectInfo`, rename the public identity property:
+
+   ```text
+   name -> id
+   ```
+
+2. Keep the underlying CIF tag unchanged:
+
+   ```python
+   CifHandler(names=['_project.id'])
+   ```
+
+3. Update `ProjectInfo.unique_name` to return `self.id`.
+
+4. Update `project_info_to_cif()` and CIF loading helpers to use
+   `info.id`.
+
+5. Rename constructor arguments:
+
+   ```text
+   name -> project_id
+   ```
+
+   Apply this to:
+   - `Workspace.__init__`
+   - `WorkspaceConfig.__init__`
+   - `ProjectInfo.__init__`
+   - `ProjectInfoFactory.create(...)` call sites
+
+6. Add `Workspace.path` as the runtime storage path.
+
+   Suggested shape:
+
+   ```python
+   @property
+   def path(self) -> pathlib.Path | None:
+       """Saved workspace directory."""
+       return self._path
+
+   @path.setter
+   def path(self, value: object) -> None:
+       self._path = pathlib.Path(value)
+   ```
+
+7. Remove `ProjectInfo.path` unless explicitly approved as a
+   compatibility alias.
+
+8. Update save/load logic:
+
+   ```text
+   workspace.path
+   ```
+
+   should replace:
+
+   ```text
+   workspace.project.path
+   ```
+
+9. Update messages and string representations:
+
+   ```text
+   Workspace '<project_id>' (...)
+   Saving workspace '<project_id>' to ...
+   ```
+
+10. Run grep:
+
+```shell
+rg -n "\\.name\\b|\\.path\\b|project_id|Project identifier" src/easydiffraction/workspace src/easydiffraction/io src/easydiffraction/display src/easydiffraction/summary
+```
+
+Inspect each match manually. Do not blindly replace every `.name`;
+structures and experiments still use `.name`.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- a caller depends on `workspace.name` as a root-object property;
+- moving `path` out of `ProjectInfo` makes save/load unclear;
+- external saved fixtures require an approved compatibility path.
+
+### Commit
+
+```text
+Align project metadata fields with CIF names
+```
+
+## Phase 4: Rename Project Config File To `workspace.cif`
+
+### Objective
+
+Rename the saved singleton configuration file from `project.cif` to
+`workspace.cif`.
+
+### Files Likely To Change
+
+- `src/easydiffraction/workspace/workspace.py`
+- `src/easydiffraction/io/cif/serialize.py`
+- CLI entry points in `src/easydiffraction/__main__.py`
+- docs that describe saved project directories
+- test fixtures in Phase 2
+
+### Steps
+
+1. Rename serializer functions if they still use project-root naming:
+
+   ```text
+   project_config_to_cif -> workspace_config_to_cif
+   project_config_from_cif -> workspace_config_from_cif
+   project_to_cif -> workspace_to_cif
+   ```
+
+   Do not rename `project_info_to_cif`; it serializes the `_project`
+   category and that name remains correct.
+
+2. Update `Workspace.save()` to write:
+
+   ```text
+   workspace.cif
+   ```
+
+3. Update `Workspace.load()` to read:
+
+   ```text
+   workspace.cif
+   ```
+
+4. Do not add `project.cif` fallback unless the user approved a
+   compatibility loader.
+
+5. Keep the contents semantic:
+
+   ```cif
+   _project.id
+   _project.title
+   _rendering.table_engine
+   ```
+
+6. Update logging and console output from `project.cif` to
+   `workspace.cif`.
+
+7. Run grep:
+
+   ```shell
+   rg -n "project\\.cif|project_config_to_cif|project_config_from_cif|project_to_cif" src docs tests
+   ```
+
+   In Phase 1, update source and docs only. Test files are handled in
+   Phase 2 unless the user explicitly asks otherwise.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- repository fixtures or tutorials contain saved directories that must
+  remain loadable without conversion;
+- the user wants a one-release compatibility loader.
+
+### Commit
+
+```text
+Rename project config file to workspace.cif
+```
+
+## Phase 5: Update Root-Object References Across Runtime Code
+
+### Objective
+
+Replace root-object variables and attributes named `project` with
+`workspace` where they refer to the top-level facade.
+
+Keep the word `project` where it refers to the project-information
+category or the scientific project itself.
+
+### Files Likely To Change
+
+- `src/easydiffraction/analysis/analysis.py`
+- `src/easydiffraction/analysis/sequential.py`
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/project/display.py` after it has moved to
+  `workspace/display.py`
+- `src/easydiffraction/summary/summary.py`
+- `src/easydiffraction/__main__.py`
+- `src/easydiffraction/io/*`
+
+### Steps
+
+1. Rename root references in `Analysis`:
+
+   ```text
+   self.project -> self.workspace
+   analysis.project -> analysis.workspace
+   ```
+
+2. Rename display internals:
+
+   ```text
+   self._project -> self._workspace
+   _set_project(...) -> _set_workspace(...)
+   ```
+
+   Only do this when the object is the top-level runtime facade.
+
+3. Rename local variables in runtime code:
+
+   ```text
+   project = Workspace(...)
+   -> workspace = Workspace(...)
+   ```
+
+4. Keep scientific-project wording where appropriate:
+
+   ```text
+   workspace.project.title
+   project_id
+   _project.id
+   ```
+
+5. Update user-facing messages carefully. Good examples:
+
+   ```text
+   "Workspace directory not found"
+   "Saving workspace"
+   "Project title"
+   ```
+
+6. Run grep:
+
+   ```shell
+   rg -n "\\bproject\\b|\\bProject\\b|_project|ProjectDisplay" src/easydiffraction
+   ```
+
+7. Inspect each match. Do not replace `_project` CIF tags.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- a name has both root-workspace and project-information meanings in the
+  same function and cannot be made clear;
+- renaming a method such as `_set_project` would require updating public
+  plugin or user code.
+
+### Commit
+
+```text
+Update runtime references to Workspace
+```
+
+## Phase 6: Update Docs, Tutorials, And ADR References
+
+### Objective
+
+Update user-facing and developer-facing documentation to describe the
+new root object and project-information category.
+
+### Files Likely To Change
+
+- `docs/dev/architecture.md`
+- `docs/dev/Issues/issues_open.md`
+- `docs/dev/ADRs/*.md`
+- `docs/dev/ADR-suggestions/*.md`
+- `docs/docs/tutorials/*.py`
+- `README.md`
+- `CONTRIBUTING.md` only if it contains API examples
+
+Do not edit these by hand:
+
+- `docs/dev/package-structure-full.md`
+- `docs/dev/package-structure-short.md`
+- generated tutorial notebooks
+- generated `docs/site/` files
+
+### Steps
+
+1. Update architecture section 7:
+
+   ```text
+   Project - The Top-Level Facade
+   -> Workspace - The Top-Level Facade
+   ```
+
+2. Update the architecture table to use:
+
+   ```text
+   workspace.project      ProjectInfo
+   workspace.rendering    Rendering
+   workspace.display      WorkspaceDisplay
+   ```
+
+3. Update saved layout examples:
+
+   ```text
+   workspace.cif
+   structures/
+   experiments/
+   analysis/
+   summary.cif
+   ```
+
+4. Update public examples:
+
+   ```python
+   workspace = ed.Workspace(project_id='lbco_hrpt')
+   workspace.project.title = '...'
+   ```
+
+5. Update ADRs that describe current API. Historical reasoning can keep
+   old names only if it is clearly historical and not presented as
+   current usage.
+
+6. Update tutorial `.py` files, not notebooks. Phase 2 will run
+   `pixi run notebook-prepare`.
+
+7. Run grep:
+
+   ```shell
+   rg -n "ed\\.Project|from easydiffraction import Project|project\\.info|project\\.rendering|ProjectDisplay|project\\.cif" docs README.md CONTRIBUTING.md
+   ```
+
+8. Inspect each match manually.
+
+### Stop Conditions
+
+Stop and ask if:
+
+- a tutorial title uses "Project" as ordinary English rather than API
+  naming;
+- historical ADRs would become misleading if edited mechanically.
+
+### Commit
+
+```text
+Update docs for Workspace root API
+```
+
+## Phase 7: Remove Old Public `Project` Surface Unless Approved
+
+### Objective
+
+Finish the breaking rename by removing old public imports and module
+paths unless the user approved compatibility.
+
+### Steps Without Compatibility Alias
+
+1. Ensure top-level `easydiffraction.__init__` exports `Workspace`, not
+   `Project`.
+
+2. Ensure no source imports from:
+
+   ```text
+   easydiffraction.project
+   ```
+
+3. Ensure no public source package remains at:
+
+   ```text
+   src/easydiffraction/project
+   ```
+
+4. Run grep:
+
+   ```shell
+   rg -n "from easydiffraction import Project|ed\\.Project|easydiffraction\\.project|\\bProject\\(" src docs tests tools README.md CONTRIBUTING.md
+   ```
+
+5. Any remaining match must be:
+   - historical text that intentionally names the old API; or
+   - a test that will be updated in Phase 2; or
+   - a generated artifact that should not be edited manually.
+
+### Steps With Approved Compatibility Alias
+
+Only do this if the user explicitly approved it.
+
+1. Add a temporary alias in `src/easydiffraction/__init__.py`:
+
+   ```python
+   Project = Workspace
+   ```
+
+2. Keep the alias undocumented unless the user asks for a migration
+   note.
+
+3. Add tests in Phase 2 proving both `Workspace` and `Project` construct
+   the same root object.
+
+### Commit
+
+Without alias:
+
+```text
+Remove old Project public API surface
+```
+
+With alias:
+
+```text
+Add Project alias for Workspace migration
+```
+
+## Phase 1 Review Gate
+
+After Phase 1 commits are complete:
+
+1. Run `git status --short`.
+2. Confirm only intended files are changed.
+3. Summarize:
+   - whether `Project` was removed or aliased;
+   - whether `workspace.cif` replaced `project.cif`;
+   - any files intentionally left for Phase 2 test updates;
+   - any unresolved questions.
+
+4. Stop and ask the user to review before starting Phase 2.
+
+Do not run the full verification suite until the user approves moving to
+Phase 2.
+
+## Phase 2: Verification And Tests
+
+Only start this phase after the user approves the Phase 1
+implementation.
+
+### Test Updates
+
+Move or update tests to mirror the new source tree:
+
+```text
+tests/unit/easydiffraction/project/
+-> tests/unit/easydiffraction/workspace/
+```
+
+Update imports:
+
+```python
+from easydiffraction.workspace.workspace import Workspace
+from easydiffraction.workspace.display import WorkspaceDisplay
+```
+
+Update functional and integration tests:
+
+```python
+from easydiffraction import Workspace
+workspace = Workspace(project_id='...')
+```
+
+### New Tests To Add
+
+Add focused tests for:
+
+1. `from easydiffraction import Workspace`.
+2. `Workspace(project_id='p1').project.id == 'p1'`.
+3. `workspace.project.title` round-trips through `workspace.cif`.
+4. `workspace.rendering.table_engine` round-trips through
+   `workspace.cif`.
+5. `Workspace.save()` writes `workspace.cif`.
+6. `Workspace.load()` reads `workspace.cif`.
+7. `workspace.cif` contains `_project.id`, not `_meta.project_id`.
+8. `workspace.cif` contains `_rendering.table_engine`.
+9. `workspace.path` is set after `save_as()` and `load()`.
+10. `workspace.project` has no serialized path field.
+11. `project.cif` is not written unless compatibility was approved.
+12. `ed.Project` is absent unless compatibility was approved.
+
+If compatibility alias was approved, add tests for:
+
+1. `from easydiffraction import Project`.
+2. `Project is Workspace` or equivalent behavior.
+3. Any approved `project.cif` fallback behavior.
+
+### Verification Commands
+
+Run in this order:
+
+```shell
+pixi run test-structure-check
+pixi run fix
+pixi run check
+pixi run unit-tests
+pixi run integration-tests
+pixi run script-tests
+pixi run notebook-prepare
+pixi run notebook-tests
+```
+
+If `pixi run fix` regenerates package-structure docs, accept those
+generated changes and do not hand-edit them.
+
+### Phase 2 Commit Suggestions
+
+Use one or more commits, depending on size:
+
+```text
+Update workspace unit tests
+Update tutorials for Workspace API
+Regenerate tutorial notebooks for Workspace API
+```
+
+## Grep Checklist
+
+Use this checklist before final review.
+
+Runtime root object should use `Workspace`:
+
+```shell
+rg -n "\\bProject\\b|ed\\.Project|from easydiffraction import Project" src tests docs tools README.md CONTRIBUTING.md
+```
+
+Project-information category should use `workspace.project`:
+
+```shell
+rg -n "\\.info\\b|workspace\\.project|project\\.info" src tests docs tools README.md CONTRIBUTING.md
+```
+
+CIF project category should stay `_project`:
+
+```shell
+rg -n "_meta\\.|_project\\." src tests docs tools README.md CONTRIBUTING.md
+```
+
+Saved config file should be `workspace.cif`:
+
+```shell
+rg -n "project\\.cif|workspace\\.cif" src tests docs tools README.md CONTRIBUTING.md
+```
+
+Generated docs should not be manually edited:
+
+```shell
+git diff -- docs/site docs/dev/package-structure-full.md docs/dev/package-structure-short.md
+```
+
+If package-structure docs changed because of `pixi run fix`, that is
+expected. If `docs/site` changed, ask before staging.
+
+## Common Mistakes
+
+### Mistake: Renaming `_project.*` To `_workspace.*`
+
+Do not do this. The CIF category describes scientific project
+information, not the runtime facade.
+
+Correct:
+
+```cif
+_project.id
+_project.title
+```
+
+Incorrect:
+
+```cif
+_workspace.project_id
+_workspace.title
+```
+
+### Mistake: Introducing `_meta.*`
+
+Do not replace `_project.*` with `_meta.*`.
+
+Correct:
+
+```cif
+_project.title
+```
+
+Incorrect:
+
+```cif
+_meta.project_title
+```
+
+### Mistake: Blindly Replacing Every `project`
+
+Some uses of `project` should remain:
+
+- CIF tags such as `_project.id`
+- `workspace.project`
+- scientific project wording in prose
+- `ProjectInfo` class name, unless a later ADR changes it
+
+Only root-facade uses should become `workspace` or `Workspace`.
+
+### Mistake: Leaving Path On Project Information
+
+The saved directory path belongs to the workspace runtime state. It
+should be `workspace.path`, not `workspace.project.path`.
+
+### Mistake: Editing Generated Notebooks Directly
+
+Tutorial notebooks are generated artifacts. Edit tutorial `.py` files,
+then run `pixi run notebook-prepare` in Phase 2.
+
+## Suggested Pull Request
+
+Title:
+
+```text
+Rename Project root object to Workspace
+```
+
+Description:
+
+```text
+This change separates the working EasyDiffraction workspace from the
+scientific project information stored inside it. Users now create a
+Workspace, while project title and description live under
+workspace.project and continue to serialize with clear _project.* CIF
+names.
+```
diff --git a/docs/docs/tutorials/ed-17.py b/docs/docs/tutorials/ed-17.py
index 2fd4b0f64..24e648c56 100644
--- a/docs/docs/tutorials/ed-17.py
+++ b/docs/docs/tutorials/ed-17.py
@@ -26,7 +26,7 @@
 # results can be written to `analysis/results.csv`.
 
 # %%
-project.save_as('projects/cosio', temporary=False)
+project.save_as(dir_path='projects/cosio', temporary=False)
 
 # %% [markdown]
 # ## Step 2: Define Crystal Structure
diff --git a/docs/docs/tutorials/ed-20.py b/docs/docs/tutorials/ed-20.py
index 8c6a5b549..1fcc6d24a 100644
--- a/docs/docs/tutorials/ed-20.py
+++ b/docs/docs/tutorials/ed-20.py
@@ -225,7 +225,7 @@
 
 # %%
 project = Project(name='beer')
-project.save_as(dir_path='beer_mcstas')
+project.save_as(dir_path='projects/beer_mcstas')
 
 # %% [markdown]
 # #### Add Structures
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index 863352413..08303f65b 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -21,6 +21,7 @@
 )
 from easydiffraction.analysis.enums import FitModeEnum
 from easydiffraction.analysis.fitting import Fitter
+from easydiffraction.core.category_owner import CategoryOwner
 from easydiffraction.core.guard import _apply_help_filter
 from easydiffraction.core.singleton import ConstraintsHandler
 from easydiffraction.core.variable import NumericDescriptor
@@ -346,7 +347,7 @@ def as_cif(self) -> None:
         self._analysis.show_as_cif()
 
 
-class Analysis:
+class Analysis(CategoryOwner):
     """
     High-level orchestration of analysis tasks for a Project.
 
@@ -364,31 +365,63 @@ def __init__(self, project: object) -> None:
         project : object
             The project that owns models and experiments.
         """
-        self.project = project
+        super().__init__()
+        self._project = project
         self._aliases_type: str = AliasesFactory.default_tag()
-        self.aliases = AliasesFactory.create(self._aliases_type)
+        self._aliases = AliasesFactory.create(self._aliases_type)
         self._constraints_type: str = ConstraintsFactory.default_tag()
-        self.constraints = ConstraintsFactory.create(self._constraints_type)
-        self.constraints_handler = ConstraintsHandler.get()
+        self._constraints = ConstraintsFactory.create(self._constraints_type)
+        self._constraints_handler = ConstraintsHandler.get()
         self._fitting: Fitting = FittingFactory.create(FittingFactory.default_tag())
-        self._fitting._parent = self
         self._fitting_mode_type: FitModeEnum = FitModeEnum.default()
         self._joint_fit: JointFitCollection = JointFitCollection()
         self._sequential_fit: SequentialFit = SequentialFitFactory.create(
             SequentialFitFactory.default_tag()
         )
-        self._sequential_fit._parent = self
         self._sequential_fit_extract = SequentialFitExtractCollection()
-        self.fitter = Fitter(self._fitting.minimizer_type.value)
-        self.fit_results = None
+        self._fitter = Fitter(self._fitting.minimizer_type.value)
+        self._fit_results = None
         self._parameter_snapshots: dict[str, dict[str, dict]] = {}
         self._display = AnalysisDisplay(self)
 
+    @property
+    def project(self) -> object:
+        """Project that owns this analysis section."""
+        return self._project
+
+    @property
+    def aliases(self) -> object:
+        """Alias mappings used by symbolic constraints and displays."""
+        return self._aliases
+
+    @property
+    def constraints(self) -> object:
+        """Symbolic constraints owned by this analysis section."""
+        return self._constraints
+
     @property
     def display(self) -> AnalysisDisplay:
         """Display helper for parameter tables, CIF, and fit results."""
         return self._display
 
+    @property
+    def fitter(self) -> Fitter:
+        """Fitting engine used by this analysis object."""
+        return self._fitter
+
+    @fitter.setter
+    def fitter(self, value: Fitter) -> None:
+        self._fitter = value
+
+    @property
+    def fit_results(self) -> object | None:
+        """Results from the most recent fit, if any."""
+        return self._fit_results
+
+    @fit_results.setter
+    def fit_results(self, value: object | None) -> None:
+        self._fit_results = value
+
     def help(self) -> None:
         """Print a summary of analysis properties and methods."""
         cls = type(self)
@@ -453,6 +486,24 @@ def _help_filter(
         filtered_properties = [name for name in properties if name not in hidden_properties]
         return filtered_properties, methods
 
+    def _serializable_categories(self) -> list:
+        """Serializable analysis categories for the active fit mode."""
+        categories = [
+            self.fitting,
+            self.aliases,
+            self.constraints,
+        ]
+
+        if self._fitting_mode_type is FitModeEnum.JOINT:
+            categories.append(self.joint_fit)
+        elif self._fitting_mode_type is FitModeEnum.SEQUENTIAL:
+            categories.extend([
+                self.sequential_fit,
+                self.sequential_fit_extract,
+            ])
+
+        return categories
+
     # ------------------------------------------------------------------
     #  Parameter helpers
     # ------------------------------------------------------------------
@@ -968,13 +1019,13 @@ def _update_categories(
         called_by_minimizer : bool, default=False
             Whether this is called during fitting.
         """
-        del called_by_minimizer
+        super()._update_categories(called_by_minimizer=called_by_minimizer)
 
         # Apply constraints to sync dependent parameters
         if self.constraints.enabled and self.constraints._items:
-            self.constraints_handler.set_aliases(self.aliases)
-            self.constraints_handler.set_constraints(self.constraints)
-            self.constraints_handler.apply()
+            self._constraints_handler.set_aliases(self.aliases)
+            self._constraints_handler.set_constraints(self.constraints)
+            self._constraints_handler.apply()
 
     @property
     def as_cif(self) -> str:
diff --git a/src/easydiffraction/core/category_owner.py b/src/easydiffraction/core/category_owner.py
new file mode 100644
index 000000000..09e0cf68e
--- /dev/null
+++ b/src/easydiffraction/core/category_owner.py
@@ -0,0 +1,76 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.guard import GuardedBase
+
+
+class CategoryOwner(GuardedBase):
+    """Base class for objects that own flat CIF-like categories."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._need_categories_update = True
+
+    @property
+    def categories(self) -> list:
+        """
+        All category objects owned by this object, sorted by priority.
+        """
+        categories = [
+            value
+            for value in vars(self).values()
+            if isinstance(value, (CategoryItem, CategoryCollection))
+        ]
+        return sorted(categories, key=lambda category: type(category)._update_priority)
+
+    def _serializable_categories(self) -> list:
+        """Categories that should be serialized for this owner."""
+        return self.categories
+
+    @property
+    def parameters(self) -> list:
+        """All parameters from all owned categories."""
+        parameters = []
+        for category in self.categories:
+            parameters.extend(category.parameters)
+        return parameters
+
+    def _update_categories(
+        self,
+        *,
+        called_by_minimizer: bool = False,
+    ) -> None:
+        """Run update hooks on all owned categories."""
+        if not called_by_minimizer and not self._need_categories_update:
+            return
+
+        for category in self.categories:
+            category._update(called_by_minimizer=called_by_minimizer)
+
+        self._need_categories_update = False
+
+    def help(self) -> None:
+        """Print a summary of public attributes and categories."""
+        super().help()
+
+        from easydiffraction.utils.logging import console  # noqa: PLC0415
+        from easydiffraction.utils.utils import render_table  # noqa: PLC0415
+
+        categories = self.categories
+        if categories:
+            console.paragraph('Categories')
+            rows = []
+            for category in categories:
+                code = category._identity.category_code or type(category).__name__
+                type_name = type(category).__name__
+                num_params = len(category.parameters)
+                rows.append([code, type_name, str(num_params)])
+            render_table(
+                columns_headers=['Category', 'Type', '# Parameters'],
+                columns_alignment=['left', 'left', 'right'],
+                columns_data=rows,
+            )
diff --git a/src/easydiffraction/core/datablock.py b/src/easydiffraction/core/datablock.py
index 0ac201760..4eb18866a 100644
--- a/src/easydiffraction/core/datablock.py
+++ b/src/easydiffraction/core/datablock.py
@@ -3,20 +3,14 @@
 
 from __future__ import annotations
 
-from easydiffraction.core.category import CategoryCollection
-from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.category_owner import CategoryOwner
 from easydiffraction.core.collection import CollectionBase
-from easydiffraction.core.guard import GuardedBase
 from easydiffraction.core.variable import Parameter
 
 
-class DatablockItem(GuardedBase):
+class DatablockItem(CategoryOwner):
     """Base class for items in a datablock collection."""
 
-    def __init__(self) -> None:
-        super().__init__()
-        self._need_categories_update = True
-
     def __str__(self) -> str:
         """Human-readable representation of this component."""
         name = self.unique_name
@@ -31,59 +25,11 @@ def __repr__(self) -> str:
         num_categories = len(self.categories)
         return f'<{cls} datablock "{name}" ({num_categories} categories)>'
 
-    def _update_categories(
-        self,
-        *,
-        called_by_minimizer: bool = False,
-    ) -> None:
-        # TODO: Make abstract method and implement in subclasses.
-        # This should call apply_symmetry and apply_constraints in the
-        # case of structures. In the case of experiments, it should
-        # run calculations to update the "data" categories.
-        # Any parameter change should set _need_categories_update to
-        # True.
-        # Calling as_cif or data getter should first check this flag
-        # and call this method if True.
-        # Should this be also called when parameters are accessed? E.g.
-        # if one change background coefficients, then access the
-        # background points in the data category?
-        #
-        # Dirty-flag guard: skip if no parameter has changed since the
-        # last update.  Minimisers use _set_value_from_minimizer()
-        # which bypasses validation but still sets this flag.
-        # During fitting the guard is bypassed because experiment
-        # calculations depend on structure parameters owned by a
-        # different DatablockItem whose flag changes are invisible here.
-        if not called_by_minimizer and not self._need_categories_update:
-            return
-
-        for category in self.categories:
-            category._update(called_by_minimizer=called_by_minimizer)
-
-        self._need_categories_update = False
-
     @property
     def unique_name(self) -> str | None:
         """Unique name of this datablock item (from identity)."""
         return self._identity.datablock_entry_name
 
-    @property
-    def categories(self) -> list:
-        """All category objects in this datablock by priority."""
-        cats = [
-            v for v in vars(self).values() if isinstance(v, (CategoryItem, CategoryCollection))
-        ]
-        # Sort by _update_priority (lower values first)
-        return sorted(cats, key=lambda c: type(c)._update_priority)
-
-    @property
-    def parameters(self) -> list:
-        """All parameters from all categories in this datablock."""
-        params = []
-        for v in self.categories:
-            params.extend(v.parameters)
-        return params
-
     @property
     def as_cif(self) -> str:
         """Return CIF representation of this object."""
@@ -113,28 +59,6 @@ def _cif_for_display(self, max_loop_display: int = 20) -> str:
         self._update_categories()
         return datablock_item_to_cif(self, max_loop_display=max_loop_display)
 
-    def help(self) -> None:
-        """Print a summary of public attributes and categories."""
-        super().help()
-
-        from easydiffraction.utils.logging import console  # noqa: PLC0415
-        from easydiffraction.utils.utils import render_table  # noqa: PLC0415
-
-        cats = self.categories
-        if cats:
-            console.paragraph('Categories')
-            rows = []
-            for c in cats:
-                code = c._identity.category_code or type(c).__name__
-                type_name = type(c).__name__
-                num_params = len(c.parameters)
-                rows.append([code, type_name, str(num_params)])
-            render_table(
-                columns_headers=['Category', 'Type', '# Parameters'],
-                columns_alignment=['left', 'left', 'right'],
-                columns_data=rows,
-            )
-
 
 # ======================================================================
 
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 76554bdd1..257a5771f 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -128,11 +128,11 @@ def _parent_of_type(self, cls: type) -> object | None:
             obj = getattr(obj, '_parent', None)
         return None
 
-    def _datablock_item(self) -> object | None:
-        """Return the DatablockItem ancestor, if any."""
-        from easydiffraction.core.datablock import DatablockItem  # noqa: PLC0415
+    def _category_owner(self) -> object | None:
+        """Return the CategoryOwner ancestor, if any."""
+        from easydiffraction.core.category_owner import CategoryOwner  # noqa: PLC0415
 
-        return self._parent_of_type(DatablockItem)
+        return self._parent_of_type(CategoryOwner)
 
     @property
     def value(self) -> object:
@@ -153,18 +153,18 @@ def value(self, v: object) -> None:
             current=self._value,
         )
 
-        # Mark parent datablock as needing categories update
+        # Mark the owning category owner as needing an update
         # TODO: Check if it is actually in use?
-        parent_datablock = self._datablock_item()
-        if parent_datablock is not None:
-            parent_datablock._need_categories_update = True
+        parent_owner = self._category_owner()
+        if parent_owner is not None:
+            parent_owner._need_categories_update = True
 
     def _set_value_from_minimizer(self, v: object) -> None:
         """
         Set the value from a minimizer, bypassing validation.
 
         Writes ``_value`` directly — no type or range checks — but still
-        marks the owning :class:`DatablockItem` dirty so that
+        marks the owning category owner dirty so that
         ``_update_categories()`` knows work is needed.
 
         This exists because:
@@ -175,9 +175,9 @@ def _set_value_from_minimizer(self, v: object) -> None:
         evaluations.
         """
         self._value = v
-        parent_datablock = self._datablock_item()
-        if parent_datablock is not None:
-            parent_datablock._need_categories_update = True
+        parent_owner = self._category_owner()
+        if parent_owner is not None:
+            parent_owner._need_categories_update = True
 
     @property
     def description(self) -> str | None:
@@ -355,17 +355,17 @@ def _set_value_user_constrained(self, v: object) -> None:
         """
         Set the value from a constraint expression.
 
-        Bypasses validation and marks the parent datablock dirty, like
-        ``_set_value_from_minimizer``, because constraints are applied
-        inside the minimizer loop where trial values may exceed
+        Bypasses validation and marks the parent category owner dirty,
+        like ``_set_value_from_minimizer``, because constraints are
+        applied inside the minimizer loop where trial values may exceed
         physical-range validators. Flags the parameter as user
         constrained. Used exclusively by ``ConstraintsHandler.apply()``.
         """
         self._value = v
         self._user_constrained = True
-        parent_datablock = self._datablock_item()
-        if parent_datablock is not None:
-            parent_datablock._need_categories_update = True
+        parent_owner = self._category_owner()
+        if parent_owner is not None:
+            parent_owner._need_categories_update = True
 
     @property
     def free(self) -> bool:
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index ae5b59fa8..f7625330e 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -268,6 +268,39 @@ def _row(item: object) -> list[str]:
     return '\n'.join(lines)
 
 
+def category_owner_to_cif(
+    owner: object,
+    max_loop_display: int | None = None,
+) -> str:
+    """Render a category-owning object without a ``data_`` header."""
+    from easydiffraction.core.category import CategoryCollection  # noqa: PLC0415
+    from easydiffraction.core.category import CategoryItem  # noqa: PLC0415
+
+    categories_getter = getattr(owner, '_serializable_categories', None)
+    if callable(categories_getter):
+        categories = categories_getter()
+    else:
+        categories = [
+            value
+            for value in vars(owner).values()
+            if isinstance(value, (CategoryItem, CategoryCollection))
+        ]
+
+    item_parts = [
+        category.as_cif
+        for category in categories
+        if isinstance(category, CategoryItem) and category.as_cif
+    ]
+
+    collection_parts = [
+        category_collection_to_cif(category, max_display=max_loop_display)
+        for category in categories
+        if isinstance(category, CategoryCollection)
+    ]
+
+    return '\n\n'.join([part for part in item_parts + collection_parts if part])
+
+
 def datablock_item_to_cif(
     datablock: object,
     max_loop_display: int | None = None,
@@ -290,32 +323,11 @@ def datablock_item_to_cif(
     str
         CIF text representing the datablock as a loop.
     """
-    # Local imports to avoid import-time cycles
-    from easydiffraction.core.category import CategoryCollection  # noqa: PLC0415
-    from easydiffraction.core.category import CategoryItem  # noqa: PLC0415
-
     header = f'data_{datablock._identity.datablock_entry_name}'
-    parts: list[str] = [header]
-
-    # First categories
-    parts.extend(
-        cif_text
-        for cif_text in (v.as_cif for v in vars(datablock).values() if isinstance(v, CategoryItem))
-        if cif_text
-    )
-
-    # Then collections
-    parts.extend(
-        cif_text
-        for cif_text in (
-            category_collection_to_cif(v, max_display=max_loop_display)
-            for v in vars(datablock).values()
-            if isinstance(v, CategoryCollection)
-        )
-        if cif_text
-    )
-
-    return '\n\n'.join(parts)
+    body = category_owner_to_cif(datablock, max_loop_display=max_loop_display)
+    if not body:
+        return header
+    return f'{header}\n\n{body}'
 
 
 def datablock_collection_to_cif(collection: object) -> str:
@@ -340,8 +352,8 @@ def project_info_to_cif(info: object) -> str:
     else:
         description = '?'
 
-    created = f"'{info._created.strftime('%d %b %Y %H:%M:%S')}'"
-    last_modified = f"'{info._last_modified.strftime('%d %b %Y %H:%M:%S')}'"
+    created = f"'{info.created.strftime('%d %b %Y %H:%M:%S')}'"
+    last_modified = f"'{info.last_modified.strftime('%d %b %Y %H:%M:%S')}'"
 
     return (
         f'_project.id               {name}\n'
@@ -360,6 +372,10 @@ def _as_cif_text(section: object) -> str:
 
 def project_config_to_cif(project: object) -> str:
     """Render project-level configuration to ``project.cif`` text."""
+    config = getattr(project, '_config', None)
+    if config is not None:
+        return category_owner_to_cif(config)
+
     lines: list[str] = [_as_cif_text(project.info)]
     rendering = getattr(project, 'rendering', None)
     if rendering is not None:
@@ -392,30 +408,28 @@ def analysis_to_cif(analysis: object) -> str:
     """Render analysis metadata, aliases, and constraints to CIF."""
     parts: list[str] = [f'_fitting.mode_type {format_value(analysis.fitting_mode_type)}']
 
-    fitting_cif = analysis.fitting.as_cif
-    if fitting_cif:
-        parts.append(fitting_cif)
-
-    aliases_cif = analysis.aliases.as_cif
-    if aliases_cif:
-        parts.append(aliases_cif)
-
-    constraints_cif = analysis.constraints.as_cif
-    if constraints_cif:
-        parts.append(constraints_cif)
-
-    if analysis.fitting_mode_type == 'joint':
-        joint_fit_cif = analysis.joint_fit.as_cif
-        if joint_fit_cif:
-            parts.append(joint_fit_cif)
-    elif analysis.fitting_mode_type == 'sequential':
-        sequential_fit_cif = analysis.sequential_fit.as_cif
-        if sequential_fit_cif:
-            parts.append(sequential_fit_cif)
-
-        sequential_extract_cif = analysis.sequential_fit_extract.as_cif
-        if sequential_extract_cif:
-            parts.append(sequential_extract_cif)
+    body = category_owner_to_cif(analysis)
+    if not body:
+        fallback_sections = [
+            getattr(analysis, 'fitting', None),
+            getattr(analysis, 'aliases', None),
+            getattr(analysis, 'constraints', None),
+        ]
+
+        if analysis.fitting_mode_type == 'joint':
+            fallback_sections.append(getattr(analysis, 'joint_fit', None))
+        elif analysis.fitting_mode_type == 'sequential':
+            fallback_sections.extend([
+                getattr(analysis, 'sequential_fit', None),
+                getattr(analysis, 'sequential_fit_extract', None),
+            ])
+
+        body = '\n\n'.join([
+            _as_cif_text(section) for section in fallback_sections if section is not None
+        ])
+
+    if body:
+        parts.append(body)
 
     return '\n\n'.join(parts)
 
@@ -449,6 +463,11 @@ def _populate_project_info_from_block(
     block: gemmi.cif.Block,
 ) -> None:
     """Populate ProjectInfo fields from a parsed CIF block."""
+    from_cif = getattr(info, 'from_cif', None)
+    if callable(from_cif):
+        from_cif(block)
+        return
+
     read_cif_string = _make_cif_string_reader(block)
 
     name = read_cif_string('_project.id')
diff --git a/src/easydiffraction/project/categories/info/__init__.py b/src/easydiffraction/project/categories/info/__init__.py
new file mode 100644
index 000000000..5b464da22
--- /dev/null
+++ b/src/easydiffraction/project/categories/info/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project info category exports."""
+
+from __future__ import annotations
+
+from easydiffraction.project.categories.info.default import ProjectInfo
+from easydiffraction.project.categories.info.factory import ProjectInfoFactory
diff --git a/src/easydiffraction/project/categories/info/default.py b/src/easydiffraction/project/categories/info/default.py
new file mode 100644
index 000000000..1d7b8e318
--- /dev/null
+++ b/src/easydiffraction/project/categories/info/default.py
@@ -0,0 +1,175 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project info category."""
+
+from __future__ import annotations
+
+import datetime
+import pathlib
+
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.io.cif.serialize import project_info_to_cif
+from easydiffraction.project.categories.info.factory import ProjectInfoFactory
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.utils import render_cif
+
+_PROJECT_TIMESTAMP_FORMAT = '%d %b %Y %H:%M:%S'
+
+
+@ProjectInfoFactory.register
+class ProjectInfo(CategoryItem):
+    """Project metadata category."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Project metadata category',
+    )
+
+    def __init__(
+        self,
+        name: str = 'untitled_project',
+        title: str = 'Untitled Project',
+        description: str = '',
+    ) -> None:
+        super().__init__()
+
+        created = datetime.datetime.now(tz=datetime.UTC)
+        last_modified = datetime.datetime.now(tz=datetime.UTC)
+
+        self._project_id = StringDescriptor(
+            name='id',
+            description='Project identifier',
+            value_spec=AttributeSpec(default=name),
+            cif_handler=CifHandler(names=['_project.id']),
+        )
+        self._title_descriptor = StringDescriptor(
+            name='title',
+            description='Project title',
+            value_spec=AttributeSpec(default=title),
+            cif_handler=CifHandler(names=['_project.title']),
+        )
+        self._description_descriptor = StringDescriptor(
+            name='description',
+            description='Project description',
+            value_spec=AttributeSpec(default=' '.join(description.split())),
+            cif_handler=CifHandler(names=['_project.description']),
+        )
+        self._created_descriptor = StringDescriptor(
+            name='created',
+            description='Project creation timestamp',
+            value_spec=AttributeSpec(default=created.strftime(_PROJECT_TIMESTAMP_FORMAT)),
+            cif_handler=CifHandler(names=['_project.created']),
+        )
+        self._last_modified_descriptor = StringDescriptor(
+            name='last_modified',
+            description='Project last-modified timestamp',
+            value_spec=AttributeSpec(default=last_modified.strftime(_PROJECT_TIMESTAMP_FORMAT)),
+            cif_handler=CifHandler(names=['_project.last_modified']),
+        )
+        self._path: pathlib.Path | None = None
+
+        self._identity.category_code = 'project'
+
+    @staticmethod
+    def _parse_timestamp(value: str) -> datetime.datetime:
+        """Parse project timestamp text from CIF storage format."""
+        return datetime.datetime.strptime(value, _PROJECT_TIMESTAMP_FORMAT).replace(
+            tzinfo=datetime.UTC,
+        )
+
+    @staticmethod
+    def _normalize_timestamp(value: datetime.datetime) -> datetime.datetime:
+        """Return timestamps as UTC-aware datetimes."""
+        if value.tzinfo is None:
+            return value.replace(tzinfo=datetime.UTC)
+        return value.astimezone(datetime.UTC)
+
+    @staticmethod
+    def _format_timestamp(value: datetime.datetime) -> str:
+        """Format a project timestamp for CIF storage."""
+        return ProjectInfo._normalize_timestamp(value).strftime(_PROJECT_TIMESTAMP_FORMAT)
+
+    @property
+    def unique_name(self) -> str:
+        """Unique name for GuardedBase diagnostics."""
+        return self.name
+
+    @property
+    def name(self) -> str:
+        """Return the project name."""
+        return self._project_id.value
+
+    @name.setter
+    def name(self, value: str) -> None:
+        self._project_id.value = value
+
+    @property
+    def title(self) -> str:
+        """Return the project title."""
+        return self._title_descriptor.value
+
+    @title.setter
+    def title(self, value: str) -> None:
+        self._title_descriptor.value = value
+
+    @property
+    def description(self) -> str:
+        """Return sanitized description with single spaces."""
+        return ' '.join(self._description_descriptor.value.split())
+
+    @description.setter
+    def description(self, value: str) -> None:
+        self._description_descriptor.value = ' '.join(value.split())
+
+    @property
+    def path(self) -> pathlib.Path | None:
+        """Return the project path as a Path object."""
+        return self._path
+
+    @path.setter
+    def path(self, value: object) -> None:
+        """Set the project directory path."""
+        self._path = pathlib.Path(value)
+
+    @property
+    def created(self) -> datetime.datetime:
+        """Return the creation timestamp."""
+        return self._parse_timestamp(self._created_descriptor.value)
+
+    def _set_created(self, value: datetime.datetime | str) -> None:
+        """Set the creation timestamp from runtime or CIF input."""
+        if isinstance(value, datetime.datetime):
+            self._created_descriptor.value = self._format_timestamp(value)
+            return
+        self._created_descriptor.value = value
+
+    @property
+    def last_modified(self) -> datetime.datetime:
+        """Return the last modified timestamp."""
+        return self._parse_timestamp(self._last_modified_descriptor.value)
+
+    def _set_last_modified(self, value: datetime.datetime | str) -> None:
+        """Set the last-modified timestamp from runtime or CIF input."""
+        if isinstance(value, datetime.datetime):
+            self._last_modified_descriptor.value = self._format_timestamp(value)
+            return
+        self._last_modified_descriptor.value = value
+
+    def update_last_modified(self) -> None:
+        """Update the last modified timestamp."""
+        self._set_last_modified(datetime.datetime.now())
+
+    @property
+    def as_cif(self) -> str:
+        """Export project metadata to CIF."""
+        return project_info_to_cif(self)
+
+    def show_as_cif(self) -> None:
+        """Pretty-print CIF via shared utilities."""
+        paragraph_title = f"Project 📦 '{self.name}' info as CIF"
+        console.paragraph(paragraph_title)
+        render_cif(self.as_cif)
diff --git a/src/easydiffraction/project/categories/info/factory.py b/src/easydiffraction/project/categories/info/factory.py
new file mode 100644
index 000000000..1a6bccb4f
--- /dev/null
+++ b/src/easydiffraction/project/categories/info/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Factory for project info categories."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class ProjectInfoFactory(FactoryBase):
+    """Create project info category instances."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/project/categories/rendering/default.py b/src/easydiffraction/project/categories/rendering/default.py
index f8970cb55..b55a6f923 100644
--- a/src/easydiffraction/project/categories/rendering/default.py
+++ b/src/easydiffraction/project/categories/rendering/default.py
@@ -83,9 +83,14 @@ def table_engine(self, value: str) -> None:
     @property
     def plotter(self) -> Plotter:
         """Live plotting facade bound to the owning project."""
-        parent = getattr(self, '_parent', None)
-        if parent is not None:
-            self._plotter._set_project(parent)
+        direct_parent = getattr(self, '_parent', None)
+        owner = direct_parent
+        while owner is not None and not hasattr(owner, 'structures'):
+            owner = getattr(owner, '_parent', None)
+        if owner is None:
+            owner = direct_parent
+        if owner is not None:
+            self._plotter._set_project(owner)
         return self._plotter
 
     @property
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index df8972de9..5bbe8acba 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -6,6 +6,7 @@
 
 import pathlib
 import tempfile
+from typing import TYPE_CHECKING
 from typing import ClassVar
 
 from typeguard import typechecked
@@ -17,15 +18,17 @@
 from easydiffraction.datablocks.structure.collection import Structures
 from easydiffraction.io.cif.serialize import project_config_to_cif
 from easydiffraction.io.cif.serialize import project_to_cif
-from easydiffraction.project.categories.rendering import Rendering
-from easydiffraction.project.categories.rendering import RenderingFactory
 from easydiffraction.project.display import ProjectDisplay
-from easydiffraction.project.project_info import ProjectInfo
+from easydiffraction.project.project_config import ProjectConfig
 from easydiffraction.summary.summary import Summary
 from easydiffraction.utils.enums import VerbosityEnum
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
 
+if TYPE_CHECKING:
+    from easydiffraction.project.categories.rendering import Rendering
+    from easydiffraction.project.project_info import ProjectInfo
+
 
 def _apply_csv_row_to_params(
     row: object,
@@ -127,11 +130,11 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        self._info: ProjectInfo = ProjectInfo(name, title, description)
+        self._config = ProjectConfig(name, title, description)
+        object.__setattr__(self, '_info', self._config.info)
         self._structures = Structures()
         self._experiments = Experiments()
-        self._rendering = RenderingFactory.create('default')
-        self._rendering._parent = self
+        object.__setattr__(self, '_rendering', self._config.rendering)
         self._display = ProjectDisplay(self)
         self._analysis = Analysis(self)
         self._summary = Summary(self)
@@ -323,7 +326,7 @@ def load(cls, dir_path: str) -> Project:
             cif_text = project_cif_path.read_text()
             project_config_from_cif(project, cif_text)
 
-        project._info.path = project_path
+        project.info.path = project_path
 
         # 2. Load structures
         structures_dir = project_path / 'structures'
@@ -390,7 +393,7 @@ def _resolve_alias_references(self) -> None:
 
     def save(self) -> None:
         """Save the project into the existing project directory."""
-        if self._info.path is None:
+        if self.info.path is None:
             log.error('Project path not specified. Use save_as() to define the path first.')
             return
 
@@ -403,15 +406,15 @@ def save(self) -> None:
         self._analysis._update_categories()
 
         # Ensure project directory exists
-        self._info.path.mkdir(parents=True, exist_ok=True)
+        self.info.path.mkdir(parents=True, exist_ok=True)
 
         # Save project-level configuration
-        with (self._info.path / 'project.cif').open('w') as f:
+        with (self.info.path / 'project.cif').open('w') as f:
             f.write(project_config_to_cif(self))
             console.print('├── 📄 project.cif')
 
         # Save structures
-        sm_dir = self._info.path / 'structures'
+        sm_dir = self.info.path / 'structures'
         sm_dir.mkdir(parents=True, exist_ok=True)
         console.print('├── 📁 structures/')
         for structure in self.structures.values():
@@ -422,7 +425,7 @@ def save(self) -> None:
                 console.print(f'│   └── 📄 {file_name}')
 
         # Save experiments
-        expt_dir = self._info.path / 'experiments'
+        expt_dir = self.info.path / 'experiments'
         expt_dir.mkdir(parents=True, exist_ok=True)
         console.print('├── 📁 experiments/')
         for experiment in self.experiments.values():
@@ -433,7 +436,7 @@ def save(self) -> None:
                 console.print(f'│   └── 📄 {file_name}')
 
         # Save analysis
-        analysis_dir = self._info.path / 'analysis'
+        analysis_dir = self.info.path / 'analysis'
         analysis_dir.mkdir(parents=True, exist_ok=True)
         with (analysis_dir / 'analysis.cif').open('w') as f:
             f.write(self.analysis.as_cif)
@@ -447,11 +450,11 @@ def save(self) -> None:
             console.print(f'│   {branch} 📄 {file_name}')
 
         # Save summary
-        with (self._info.path / 'summary.cif').open('w') as f:
+        with (self.info.path / 'summary.cif').open('w') as f:
             f.write(self.summary.as_cif())
             console.print('└── 📄 summary.cif')
 
-        self._info.update_last_modified()
+        self.info.update_last_modified()
         self._saved = True
 
     def save_as(
@@ -464,7 +467,7 @@ def save_as(
         if temporary:
             tmp: str = tempfile.gettempdir()
             dir_path = pathlib.Path(tmp) / dir_path
-        self._info.path = dir_path
+        self.info.path = dir_path
         self.save()
 
     def apply_params_from_csv(self, row_index: int) -> None:
diff --git a/src/easydiffraction/project/project_config.py b/src/easydiffraction/project/project_config.py
new file mode 100644
index 000000000..32147b609
--- /dev/null
+++ b/src/easydiffraction/project/project_config.py
@@ -0,0 +1,47 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project configuration owner for singleton project categories."""
+
+from __future__ import annotations
+
+from easydiffraction.core.category_owner import CategoryOwner
+from easydiffraction.project.categories.info import ProjectInfo
+from easydiffraction.project.categories.info import ProjectInfoFactory
+from easydiffraction.project.categories.rendering import Rendering
+from easydiffraction.project.categories.rendering import RenderingFactory
+
+
+class ProjectConfig(CategoryOwner):
+    """Own singleton project configuration categories."""
+
+    def __init__(
+        self,
+        name: str = 'untitled_project',
+        title: str = 'Untitled Project',
+        description: str = '',
+    ) -> None:
+        super().__init__()
+        self._info = ProjectInfoFactory.create(
+            ProjectInfoFactory.default_tag(),
+            name=name,
+            title=title,
+            description=description,
+        )
+        self._rendering = RenderingFactory.create(RenderingFactory.default_tag())
+
+    @property
+    def info(self) -> ProjectInfo:
+        """Project metadata category."""
+        return self._info
+
+    @property
+    def rendering(self) -> Rendering:
+        """Rendering configuration category."""
+        return self._rendering
+
+    @property
+    def as_cif(self) -> str:
+        """Serialize singleton project categories to CIF."""
+        from easydiffraction.io.cif.serialize import category_owner_to_cif  # noqa: PLC0415
+
+        return category_owner_to_cif(self)
diff --git a/src/easydiffraction/project/project_info.py b/src/easydiffraction/project/project_info.py
index 9bdfac7b0..215e8ed54 100644
--- a/src/easydiffraction/project/project_info.py
+++ b/src/easydiffraction/project/project_info.py
@@ -1,137 +1,9 @@
-# SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Project metadata container used by Project."""
+"""Project metadata category export."""
 
 from __future__ import annotations
 
-import datetime
-import pathlib
+from easydiffraction.project.categories.info.default import ProjectInfo as _ProjectInfo
 
-from easydiffraction.core.guard import GuardedBase
-from easydiffraction.io.cif.serialize import project_info_to_cif
-from easydiffraction.utils.logging import console
-from easydiffraction.utils.utils import render_cif
-
-
-class ProjectInfo(GuardedBase):
-    """Store project metadata: name, title, description, paths."""
-
-    def __init__(
-        self,
-        name: str = 'untitled_project',
-        title: str = 'Untitled Project',
-        description: str = '',
-    ) -> None:
-        super().__init__()
-
-        self._name = name
-        self._title = title
-        self._description = description
-        self._path: pathlib.Path | None = None  # pathlib.Path.cwd()
-        self._created: datetime.datetime = datetime.datetime.now()
-        self._last_modified: datetime.datetime = datetime.datetime.now()
-
-    @property
-    def name(self) -> str:
-        """Return the project name."""
-        return self._name
-
-    @name.setter
-    def name(self, value: str) -> None:
-        """
-        Set the project name.
-
-        Parameters
-        ----------
-        value : str
-            New project name.
-        """
-        self._name = value
-
-    @property
-    def unique_name(self) -> str:
-        """Unique name for GuardedBase diagnostics."""
-        return self.name
-
-    @property
-    def title(self) -> str:
-        """Return the project title."""
-        return self._title
-
-    @title.setter
-    def title(self, value: str) -> None:
-        """
-        Set the project title.
-
-        Parameters
-        ----------
-        value : str
-            New project title.
-        """
-        self._title = value
-
-    @property
-    def description(self) -> str:
-        """Return sanitized description with single spaces."""
-        return ' '.join(self._description.split())
-
-    @description.setter
-    def description(self, value: str) -> None:
-        """
-        Set the project description (whitespace normalized).
-
-        Parameters
-        ----------
-        value : str
-            New description text.
-        """
-        self._description = ' '.join(value.split())
-
-    @property
-    def path(self) -> pathlib.Path | None:
-        """Return the project path as a Path object."""
-        return self._path
-
-    @path.setter
-    def path(self, value: object) -> None:
-        """
-        Set the project directory path.
-
-        Parameters
-        ----------
-        value : object
-            New path as a :class:`str` or :class:`pathlib.Path`.
-        """
-        # Accept str or Path; normalize to Path
-        self._path = pathlib.Path(value)
-
-    @property
-    def created(self) -> datetime.datetime:
-        """Return the creation timestamp."""
-        return self._created
-
-    @property
-    def last_modified(self) -> datetime.datetime:
-        """Return the last modified timestamp."""
-        return self._last_modified
-
-    def update_last_modified(self) -> None:
-        """Update the last modified timestamp."""
-        self._last_modified = datetime.datetime.now()
-
-    def parameters(self) -> None:
-        """List parameters (not implemented)."""
-
-    # TODO: Consider moving to io.cif.serialize
-    @property
-    def as_cif(self) -> str:
-        """Export project metadata to CIF."""
-        return project_info_to_cif(self)
-
-    # TODO: Consider moving to io.cif.serialize
-    def show_as_cif(self) -> None:
-        """Pretty-print CIF via shared utilities."""
-        paragraph_title: str = f"Project 📦 '{self.name}' info as CIF"
-        cif_text: str = self.as_cif
-        console.paragraph(paragraph_title)
-        render_cif(cif_text)
+ProjectInfo = _ProjectInfo
diff --git a/tests/unit/easydiffraction/core/test_category_owner.py b/tests/unit/easydiffraction/core/test_category_owner.py
new file mode 100644
index 000000000..264f01bb4
--- /dev/null
+++ b/tests/unit/easydiffraction/core/test_category_owner.py
@@ -0,0 +1,134 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.category_owner import CategoryOwner
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import Parameter
+from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.io.cif.serialize import category_owner_to_cif
+
+
+class _FastCategory(CategoryItem):
+    _update_priority = 1
+
+    def __init__(self, update_calls: list[tuple[str, bool]] | None = None) -> None:
+        super().__init__()
+        self._identity.category_code = 'fast'
+        self._update_calls = update_calls
+        self._param = Parameter(
+            name='param',
+            description='Fast category parameter',
+            value_spec=AttributeSpec(default=0.0),
+            units='',
+            cif_handler=CifHandler(names=['_fast.param']),
+        )
+
+    @property
+    def param(self) -> Parameter:
+        return self._param
+
+    def _update(self, *, called_by_minimizer: bool = False) -> None:
+        if self._update_calls is not None:
+            self._update_calls.append(('fast', called_by_minimizer))
+
+
+class _SlowCategory(CategoryItem):
+    _update_priority = 20
+
+    def __init__(self, update_calls: list[tuple[str, bool]] | None = None) -> None:
+        super().__init__()
+        self._identity.category_code = 'slow'
+        self._update_calls = update_calls
+        self._param = Parameter(
+            name='param',
+            description='Slow category parameter',
+            value_spec=AttributeSpec(default=0.0),
+            units='',
+            cif_handler=CifHandler(names=['_slow.param']),
+        )
+
+    @property
+    def param(self) -> Parameter:
+        return self._param
+
+    def _update(self, *, called_by_minimizer: bool = False) -> None:
+        if self._update_calls is not None:
+            self._update_calls.append(('slow', called_by_minimizer))
+
+
+class _Owner(CategoryOwner):
+    def __init__(self, update_calls: list[tuple[str, bool]] | None = None) -> None:
+        super().__init__()
+        self._slow = _SlowCategory(update_calls)
+        self._fast = _FastCategory(update_calls)
+
+    @property
+    def fast(self) -> _FastCategory:
+        return self._fast
+
+    @property
+    def slow(self) -> _SlowCategory:
+        return self._slow
+
+    @property
+    def as_cif(self) -> str:
+        return category_owner_to_cif(self)
+
+
+class _OwnerWithSerializableSubset(_Owner):
+    def _serializable_categories(self) -> list:
+        return [self.slow]
+
+
+def test_category_owner_sorts_categories_and_flattens_parameters():
+    owner = _Owner()
+
+    assert owner.fast._parent is owner
+    assert owner.slow._parent is owner
+    assert [category._identity.category_code for category in owner.categories] == ['fast', 'slow']
+    assert [parameter.unique_name for parameter in owner.parameters] == [
+        'fast.param',
+        'slow.param',
+    ]
+
+
+def test_category_owner_updates_only_when_needed_and_can_force_minimizer_updates():
+    update_calls: list[tuple[str, bool]] = []
+    owner = _Owner(update_calls)
+
+    owner._update_categories()
+
+    assert update_calls == [('fast', False), ('slow', False)]
+    assert owner._need_categories_update is False
+
+    update_calls.clear()
+    owner._update_categories()
+    assert update_calls == []
+
+    owner._update_categories(called_by_minimizer=True)
+    assert update_calls == [('fast', True), ('slow', True)]
+    assert owner._need_categories_update is False
+
+
+def test_category_owner_descriptor_changes_mark_owner_dirty():
+    owner = _Owner()
+    owner._need_categories_update = False
+
+    owner.fast.param.value = 1.5
+    assert owner._need_categories_update is True
+
+    owner._need_categories_update = False
+    owner.slow.param._set_value_from_minimizer(2.5)
+    assert owner._need_categories_update is True
+
+
+def test_category_owner_as_cif_respects_serializable_categories_override():
+    owner = _OwnerWithSerializableSubset()
+
+    cif_text = owner.as_cif
+
+    assert '_slow.param' in cif_text
+    assert '_fast.param' not in cif_text
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
new file mode 100644
index 000000000..badd72418
--- /dev/null
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
@@ -0,0 +1,97 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from easydiffraction.datablocks.experiment.item.factory import ExperimentFactory
+from easydiffraction.datablocks.structure.item.base import Structure
+from easydiffraction.project.project import Project
+
+
+def test_real_structure_as_cif_starts_with_data_header() -> None:
+    structure = Structure(name='nickelate')
+
+    assert structure.as_cif.startswith('data_nickelate')
+
+
+def test_real_experiment_as_cif_starts_with_data_header() -> None:
+    experiment = ExperimentFactory.from_scratch(
+        name='powder_scan',
+        sample_form='powder',
+        beam_mode='constant wavelength',
+        radiation_probe='neutron',
+        scattering_type='bragg',
+    )
+
+    assert experiment.as_cif.startswith('data_powder_scan')
+
+
+def test_real_analysis_as_cif_is_singleton_section_without_data_header() -> None:
+    project = Project(name='proj')
+
+    analysis_cif = project.analysis.as_cif
+
+    assert analysis_cif.startswith('_fitting.mode_type single')
+    assert not analysis_cif.startswith('data_')
+    assert '_fitting.minimizer_type' in analysis_cif
+    assert '_joint_fit.experiment_id' not in analysis_cif
+    assert '_sequential_fit.data_dir' not in analysis_cif
+    assert '_sequential_fit_extract.id' not in analysis_cif
+
+
+def test_real_analysis_as_cif_includes_aliases_and_constraints_when_present() -> None:
+    project = Project(name='proj')
+    project.structures.create(name='phase_1')
+    parameter = project.structures['phase_1'].cell.length_a
+
+    analysis = project.analysis
+    analysis.aliases.create(label='a_param', param=parameter)
+    analysis.constraints.create(expression='a_param = a_param')
+
+    analysis_cif = analysis.as_cif
+
+    assert '_alias.label' in analysis_cif
+    assert '_alias.param_unique_name' in analysis_cif
+    assert '_constraint.expression' in analysis_cif
+    assert 'a_param = a_param' in analysis_cif
+
+
+def test_real_analysis_as_cif_includes_joint_fit_only_in_joint_mode() -> None:
+    project = Project(name='proj')
+    analysis = project.analysis
+    analysis._set_fitting_mode_type('joint')
+    analysis.joint_fit.create(experiment_id='ex1', weight=0.5)
+
+    analysis_cif = analysis.as_cif
+
+    assert not analysis_cif.startswith('data_')
+    assert '_fitting.mode_type joint' in analysis_cif
+    assert '_joint_fit.experiment_id' in analysis_cif
+    assert '_joint_fit.weight' in analysis_cif
+    assert '_sequential_fit.data_dir' not in analysis_cif
+    assert '_sequential_fit_extract.id' not in analysis_cif
+
+
+def test_real_analysis_as_cif_includes_sequential_sections_only_in_sequential_mode() -> None:
+    project = Project(name='proj')
+    analysis = project.analysis
+    analysis._set_fitting_mode_type('sequential')
+    analysis.sequential_fit.data_dir.value = 'scans'
+    analysis.sequential_fit.file_pattern.value = '*.xye'
+    analysis.sequential_fit_extract.create(
+        id='temperature',
+        target='diffrn.ambient_temperature',
+        pattern=r'temp_(\d+)',
+        required=True,
+    )
+
+    analysis_cif = analysis.as_cif
+
+    assert not analysis_cif.startswith('data_')
+    assert '_fitting.mode_type sequential' in analysis_cif
+    assert '_sequential_fit.data_dir scans' in analysis_cif
+    assert '_sequential_fit.file_pattern *.xye' in analysis_cif
+    assert '_sequential_fit_extract.id' in analysis_cif
+    assert '_sequential_fit_extract.target' in analysis_cif
+    assert 'diffrn.ambient_temperature' in analysis_cif
+    assert '_joint_fit.experiment_id' not in analysis_cif
diff --git a/tests/unit/easydiffraction/project/categories/info/test_default.py b/tests/unit/easydiffraction/project/categories/info/test_default.py
new file mode 100644
index 000000000..b480ca16d
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/info/test_default.py
@@ -0,0 +1,63 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import datetime
+
+import gemmi
+
+
+def test_project_info_defaults_and_identity():
+    from easydiffraction.project.categories.info.default import ProjectInfo
+
+    info = ProjectInfo(name='beer', title='Beer title', description='Some description')
+
+    assert info.type_info.tag == 'default'
+    assert info._identity.category_code == 'project'
+    assert info.name == 'beer'
+    assert info.title == 'Beer title'
+    assert info.description == 'Some description'
+    assert info.path is None
+    assert isinstance(info.created, datetime.datetime)
+    assert isinstance(info.last_modified, datetime.datetime)
+    assert info.created.tzinfo is not None
+    assert info.last_modified.tzinfo is not None
+
+
+def test_project_info_setters_and_from_cif_restore_fields():
+    from easydiffraction.project.categories.info.default import ProjectInfo
+
+    info = ProjectInfo()
+    info.description = 'Some   spaced\n description'
+    info.path = 'project-dir'
+
+    assert info.description == 'Some spaced description'
+    assert info.path is not None
+    assert info.path.name == 'project-dir'
+
+    block = gemmi.cif.read_string(
+        """data_test
+_project.id beer
+_project.title 'Beer title'
+_project.description 'Some description'
+_project.created '17 May 2026 11:13:21'
+_project.last_modified '17 May 2026 11:13:51'
+""",
+    ).sole_block()
+
+    info.from_cif(block)
+
+    assert info.name == 'beer'
+    assert info.title == 'Beer title'
+    assert info.description == 'Some description'
+    assert info.created == datetime.datetime(2026, 5, 17, 11, 13, 21, tzinfo=datetime.UTC)
+    assert info.last_modified == datetime.datetime(
+        2026,
+        5,
+        17,
+        11,
+        13,
+        51,
+        tzinfo=datetime.UTC,
+    )
diff --git a/tests/unit/easydiffraction/project/categories/info/test_factory.py b/tests/unit/easydiffraction/project/categories/info/test_factory.py
new file mode 100644
index 000000000..9010e2f85
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/info/test_factory.py
@@ -0,0 +1,32 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import pytest
+
+
+def test_project_info_factory_default_and_create():
+    from easydiffraction.project.categories.info.default import ProjectInfo
+    from easydiffraction.project.categories.info.factory import ProjectInfoFactory
+
+    assert ProjectInfoFactory.default_tag() == 'default'
+    assert 'default' in ProjectInfoFactory.supported_tags()
+
+    info = ProjectInfoFactory.create(
+        'default',
+        name='beer',
+        title='Beer title',
+        description='Some description',
+    )
+
+    assert isinstance(info, ProjectInfo)
+    assert info.name == 'beer'
+    assert info.title == 'Beer title'
+
+
+def test_project_info_factory_rejects_unknown_tag():
+    from easydiffraction.project.categories.info.factory import ProjectInfoFactory
+
+    with pytest.raises(ValueError, match=r"Unsupported type: 'missing'"):
+        ProjectInfoFactory.create('missing')
diff --git a/tests/unit/easydiffraction/project/test_project_config.py b/tests/unit/easydiffraction/project/test_project_config.py
new file mode 100644
index 000000000..3ba20eea1
--- /dev/null
+++ b/tests/unit/easydiffraction/project/test_project_config.py
@@ -0,0 +1,68 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import datetime
+
+
+def test_project_config_exposes_project_info_and_rendering_categories():
+    from easydiffraction.core.category_owner import CategoryOwner
+    from easydiffraction.project.project_config import ProjectConfig
+    from easydiffraction.project.project_info import ProjectInfo
+
+    config = ProjectConfig(name='beer', title='Beer title', description='Some description')
+
+    assert isinstance(config, CategoryOwner)
+    assert isinstance(config.info, ProjectInfo)
+    assert config.info._parent is config
+    assert config.rendering._parent is config
+    assert config.info.name == 'beer'
+    assert config.info.title == 'Beer title'
+    assert config.info.description == 'Some description'
+    assert config.info.path is None
+    assert isinstance(config.info.created, datetime.datetime)
+    assert isinstance(config.info.last_modified, datetime.datetime)
+    assert config.categories == [config.info, config.rendering]
+    assert config.parameters == config.info.parameters + config.rendering.parameters
+
+
+def test_project_config_as_cif_has_project_and_rendering_sections_without_data_header():
+    from easydiffraction.project.project_config import ProjectConfig
+
+    config = ProjectConfig(name='beer', title='Beer title', description='Some description')
+
+    cif_text = config.as_cif
+
+    assert not cif_text.startswith('data_')
+    assert '_project.id               beer' in cif_text
+    assert '_project.title' in cif_text
+    assert '_project.description' in cif_text
+    assert '_project.created' in cif_text
+    assert '_project.last_modified' in cif_text
+    assert '_rendering.chart_engine' in cif_text
+    assert '_rendering.table_engine' in cif_text
+
+
+def test_project_save_and_load_keep_project_config_section_format(tmp_path):
+    from easydiffraction.project.project import Project
+
+    project = Project(name='beer', title='Beer title', description='Some description')
+    project.rendering.chart_engine = 'asciichartpy'
+    project.rendering.table_engine = 'rich'
+    project.save_as(str(tmp_path / 'proj'))
+
+    project_cif = (tmp_path / 'proj' / 'project.cif').read_text()
+    assert not project_cif.startswith('data_')
+    assert '_project.id               beer' in project_cif
+    assert '_rendering.chart_engine asciichartpy' in project_cif
+    assert '_rendering.table_engine rich' in project_cif
+
+    loaded = Project.load(str(tmp_path / 'proj'))
+    assert loaded.info.name == 'beer'
+    assert loaded.info.title == 'Beer title'
+    assert loaded.info.description == 'Some description'
+    assert isinstance(loaded.info.created, datetime.datetime)
+    assert isinstance(loaded.info.last_modified, datetime.datetime)
+    assert loaded.rendering.chart_engine.value == 'asciichartpy'
+    assert loaded.rendering.table_engine.value == 'rich'

From ab689796963c26dc4ac72aec0757ef97c4ae77e0 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Mon, 18 May 2026 14:04:21 +0200
Subject: [PATCH 09/10] Use declarative category identity metadata (#177)

* Document verbosity category and workspace.cif layout

* Reorganize development documentation structure

* Tighten workspace migration plan for autonomous implementation

* Consolidate ADR index into single table

* Add loop category key identity ADR and plan

* Add declarative category identity resolution

* Declare category identities on current items

* Persist explicit constraint identifiers

* Update ADR for declarative category identity

* Verify declarative category identity behavior
---
 .github/copilot-instructions.md               |   57 +-
 .../accepted/category-owner-sections.md}      |    0
 .../accepted/category-parameter-access.md     |   44 +
 .../accepted/development-docs-structure.md    |   66 +
 .../accepted/display-ux.md}                   |    0
 .../accepted/enum-backed-closed-values.md     |   34 +
 docs/dev/adrs/accepted/factory-contracts.md   |   44 +
 docs/dev/adrs/accepted/factory-tag-naming.md  |   48 +
 .../accepted/fit-mode-categories.md}          |    0
 .../adrs/accepted/free-flag-cif-encoding.md   |   39 +
 .../accepted/guarded-public-properties.md     |   44 +
 .../accepted/help-discoverability.md}         |    0
 .../accepted/immutable-experiment-type.md     |   35 +
 .../accepted/lint-complexity-thresholds.md    |   33 +
 docs/dev/adrs/accepted/notebook-generation.md |   34 +
 .../project-facade-and-persistence.md         |   47 +
 .../accepted/property-docstring-template.md   |   39 +
 docs/dev/adrs/accepted/runtime-fit-results.md |   36 +
 docs/dev/adrs/accepted/selector-families.md   |   40 +
 .../string-paths-and-live-descriptors.md      |   45 +
 .../adrs/accepted/switchable-category-api.md  |   45 +
 docs/dev/adrs/accepted/test-strategy.md       |   41 +
 .../accepted/type-neutral-adp-parameters.md   |   42 +
 docs/dev/adrs/index.md                        |   46 +
 .../suggestions/analysis-cif-fit-state.md}    |    8 +-
 .../suggestions/loop-category-key-identity.md |  158 ++
 .../parameter-correlation-persistence.md}     |    0
 .../parameter-posterior-summary.md}           |    4 +-
 .../python-cif-category-correspondence.md     |  314 ++++
 .../suggestions/undo-fit.md}                  |    2 +-
 .../workspace-root-project-category.md}       |   76 +-
 docs/dev/architecture.md                      | 1631 -----------------
 docs/dev/index.md                             |   29 +
 .../issues_closed.md => issues/closed.md}     |    3 +-
 .../{Issues/issues_open.md => issues/open.md} |   12 +-
 .../full.md}                                  |    0
 .../short.md}                                 |    0
 docs/dev/plans/loop-category-key-identity.md  |  620 +++++++
 .../workspace-root-project-category.md}       |  324 +++-
 docs/dev/{ => roadmap}/ROADMAP.md             |    0
 .../analysis/categories/aliases/default.py    |    6 +-
 .../categories/constraints/default.py         |   42 +-
 .../analysis/categories/fitting/default.py    |    4 +-
 .../analysis/categories/joint_fit/default.py  |    6 +-
 .../categories/sequential_fit/default.py      |    4 +-
 .../sequential_fit_extract/default.py         |    6 +-
 src/easydiffraction/core/category.py          |   18 +
 src/easydiffraction/core/identity.py          |    7 +
 .../categories/background/chebyshev.py        |    6 +-
 .../categories/background/line_segment.py     |    6 +-
 .../categories/calculation/default.py         |    4 +-
 .../experiment/categories/data/bragg_pd.py    |   10 +-
 .../experiment/categories/data/total_pd.py    |    6 +-
 .../experiment/categories/diffrn/default.py   |    4 +-
 .../categories/excluded_regions/default.py    |    5 +-
 .../categories/experiment_type/default.py     |    4 +-
 .../categories/extinction/becker_coppens.py   |    4 +-
 .../experiment/categories/instrument/base.py  |    5 +-
 .../categories/linked_crystal/default.py      |    4 +-
 .../categories/linked_phases/default.py       |    6 +-
 .../experiment/categories/peak/base.py        |    3 +-
 .../experiment/categories/refln/bragg_sc.py   |    6 +-
 .../categories/atom_site_aniso/default.py     |    6 +-
 .../categories/atom_sites/default.py          |    6 +-
 .../structure/categories/cell/default.py      |    4 +-
 .../categories/space_group/default.py         |    4 +-
 src/easydiffraction/io/cif/serialize.py       |    6 +
 .../project/categories/info/default.py        |    4 +-
 .../project/categories/rendering/default.py   |    4 +-
 .../test_analysis_and_fit_category_support.py |    7 +-
 .../integration/fitting/test_project_load.py  |    3 +
 .../analysis/categories/test_constraints.py   |   36 +
 .../analysis/test_analysis_coverage.py        |    7 +-
 .../easydiffraction/core/test_category.py     |  157 +-
 .../test_serialize_category_owner_baseline.py |    1 +
 .../project/test_project_load.py              |    2 +
 tools/generate_package_docs.py                |   12 +-
 tools/param_consistency.py                    |    4 +-
 78 files changed, 2648 insertions(+), 1821 deletions(-)
 rename docs/dev/{ADRs/adr_category-owner-sections.md => adrs/accepted/category-owner-sections.md} (100%)
 create mode 100644 docs/dev/adrs/accepted/category-parameter-access.md
 create mode 100644 docs/dev/adrs/accepted/development-docs-structure.md
 rename docs/dev/{ADRs/adr_display-ux.md => adrs/accepted/display-ux.md} (100%)
 create mode 100644 docs/dev/adrs/accepted/enum-backed-closed-values.md
 create mode 100644 docs/dev/adrs/accepted/factory-contracts.md
 create mode 100644 docs/dev/adrs/accepted/factory-tag-naming.md
 rename docs/dev/{ADRs/adr_fit-mode-categories.md => adrs/accepted/fit-mode-categories.md} (100%)
 create mode 100644 docs/dev/adrs/accepted/free-flag-cif-encoding.md
 create mode 100644 docs/dev/adrs/accepted/guarded-public-properties.md
 rename docs/dev/{ADRs/adr_help-discoverability.md => adrs/accepted/help-discoverability.md} (100%)
 create mode 100644 docs/dev/adrs/accepted/immutable-experiment-type.md
 create mode 100644 docs/dev/adrs/accepted/lint-complexity-thresholds.md
 create mode 100644 docs/dev/adrs/accepted/notebook-generation.md
 create mode 100644 docs/dev/adrs/accepted/project-facade-and-persistence.md
 create mode 100644 docs/dev/adrs/accepted/property-docstring-template.md
 create mode 100644 docs/dev/adrs/accepted/runtime-fit-results.md
 create mode 100644 docs/dev/adrs/accepted/selector-families.md
 create mode 100644 docs/dev/adrs/accepted/string-paths-and-live-descriptors.md
 create mode 100644 docs/dev/adrs/accepted/switchable-category-api.md
 create mode 100644 docs/dev/adrs/accepted/test-strategy.md
 create mode 100644 docs/dev/adrs/accepted/type-neutral-adp-parameters.md
 create mode 100644 docs/dev/adrs/index.md
 rename docs/dev/{ADR-suggestions/adr_analysis-cif-fit-state.md => adrs/suggestions/analysis-cif-fit-state.md} (96%)
 create mode 100644 docs/dev/adrs/suggestions/loop-category-key-identity.md
 rename docs/dev/{ADR-suggestions/adr_parameter-correlation-persistence.md => adrs/suggestions/parameter-correlation-persistence.md} (100%)
 rename docs/dev/{ADR-suggestions/adr_parameter-posterior-summary.md => adrs/suggestions/parameter-posterior-summary.md} (99%)
 create mode 100644 docs/dev/adrs/suggestions/python-cif-category-correspondence.md
 rename docs/dev/{ADR-suggestions/adr_undo-fit.md => adrs/suggestions/undo-fit.md} (99%)
 rename docs/dev/{ADR-suggestions/adr_workspace-root-project-category.md => adrs/suggestions/workspace-root-project-category.md} (76%)
 delete mode 100644 docs/dev/architecture.md
 create mode 100644 docs/dev/index.md
 rename docs/dev/{Issues/issues_closed.md => issues/closed.md} (98%)
 rename docs/dev/{Issues/issues_open.md => issues/open.md} (99%)
 rename docs/dev/{package-structure-full.md => package-structure/full.md} (100%)
 rename docs/dev/{package-structure-short.md => package-structure/short.md} (100%)
 create mode 100644 docs/dev/plans/loop-category-key-identity.md
 rename docs/dev/{plan_workspace-root-project-category.md => plans/workspace-root-project-category.md} (67%)
 rename docs/dev/{ => roadmap}/ROADMAP.md (100%)

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 0a54cc941..d5f96553d 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -34,7 +34,10 @@
 - One class per file when substantial; group small related classes.
 - No `**kwargs` — use explicit keyword arguments.
 - No string-based dispatch (e.g. `getattr(self, f'_{name}')`); write
-  named methods (`_set_sample_form`, `_set_beam_mode`).
+  named methods (`_set_sample_form`, `_set_beam_mode`). Narrow framework
+  metadata lookups are allowed when the attribute name is a class-level
+  declaration, is not user input, and is validated in one central place;
+  for example, `CategoryItem._category_entry_name`.
 - Public attrs are either editable (getter+setter property) or read-only
   (getter only). For internal mutation of read-only props, use a private
   `_set_<name>` method, not a public setter.
@@ -77,7 +80,7 @@
 ## Testing
 
 - Every new module, class, or bug fix ships with tests. See
-  `docs/dev/architecture.md` §10 for the full strategy.
+  `docs/dev/adrs/accepted/test-strategy.md` for the full strategy.
 - Unit tests mirror the source tree:
   `src/easydiffraction/<pkg>/<mod>.py` →
   `tests/unit/easydiffraction/<pkg>/test_<mod>.py`. Verify with
@@ -102,8 +105,11 @@
 
 - Before any structural/design change (new categories, factories,
   switchable-category wiring, datablocks, CIF serialisation), read
-  `docs/dev/architecture.md` and follow documented patterns. Localised
-  bug fixes or test updates need only this file.
+  `docs/dev/adrs/index.md` and the relevant accepted ADRs. Localised bug
+  fixes or test updates need only this file.
+- Development documentation lives under `docs/dev/`. Use
+  `docs/dev/adrs/index.md` as the architecture and decision navigation
+  surface; there is no separate `architecture.md` source of truth.
 - Project is in beta: no legacy shims, no deprecation warnings — update
   tests and tutorials to the current API.
 - Minimal diffs; don't reformat working code. Fix only what's asked;
@@ -112,7 +118,10 @@
   resolves them.
 - Never remove or replace existing functionality without explicit
   confirmation — highlight every removal and wait for approval.
-- When renaming, grep the entire project (code, tests, tutorials, docs).
+- When renaming or auditing usages, search the entire project (code,
+  tests, tutorials, docs). Use `git grep -n` because all contributors
+  have Git; do not assume `rg` is installed. If `git grep` is
+  unavailable, fall back to `find ... -type f` plus `grep -n`.
 - Each change is atomic and single-commit-sized: make one change,
   suggest the commit message, then stop and wait for confirmation.
 - When in doubt, ask.
@@ -137,34 +146,45 @@
 
 Non-trivial changes use a two-phase workflow:
 
-- **Phase 1 — Implementation.** Code, docs, and architecture updates
-  only. Do not create or run tests unless the user explicitly asks. When
-  done, present for review and iterate until approved.
+- **Phase 1 — Implementation.** Code and docs updates only. Update ADRs
+  when the change affects architecture or documented decisions. Do not
+  create or run tests unless the user explicitly asks. When done,
+  present for review and iterate until approved.
 - **Phase 2 — Verification.** Add/update tests, then run `pixi run fix`,
   `pixi run check`, `pixi run unit-tests`, `pixi run integration-tests`,
   `pixi run script-tests`.
 
 Notes:
 
-- `pixi run fix` regenerates `docs/dev/package-structure-*.md`
-  automatically — never edit those by hand. Don't review auto-fixes;
-  accept and move on. Then `pixi run check` until clean.
+- `pixi run fix` regenerates `docs/dev/package-structure/full.md` and
+  `docs/dev/package-structure/short.md` automatically — never edit those
+  by hand. Don't review auto-fixes; accept and move on. Then
+  `pixi run check` until clean.
 - Open issues / design questions / planned improvements live in
-  `docs/dev/issues_open.md` (priority-ordered). On resolution, move to
-  `docs/dev/issues_closed.md` and update `architecture.md` if affected.
+  `docs/dev/issues/open.md` (priority-ordered). On resolution, move to
+  `docs/dev/issues/closed.md` and update the relevant ADR or
+  `docs/dev/adrs/index.md` if affected.
 
 ### Planning
 
 When asked to create a plan:
 
+- Start the plan by referencing this file:
+  `.github/copilot-instructions.md`. State any deliberate exception to
+  these instructions in the plan itself.
 - First gather enough repository context to make the plan concrete. Ask
   all ambiguous or unclear questions in one concise batch; record
   unresolved questions in the plan if the user wants it saved before
   answering them.
-- Save plans as `docs/dev/plan_<feature-name>.md` (lowercase,
-  dash-separated, e.g. `plan_background-refactor.md`). Use the same
-  `<feature-name>` for the implementation branch
-  (`feature/<feature-name>`). Do not push the branch unless asked.
+- Save plans as `docs/dev/plans/<feature-name>.md` (lowercase,
+  dash-separated, e.g. `docs/dev/plans/background-refactor.md`). When a
+  plan implements one ADR, use the same slug as the ADR file; for
+  example, `docs/dev/adrs/suggestions/foo.md` maps to
+  `docs/dev/plans/foo.md`. If a plan has no corresponding ADR or spans
+  multiple ADRs, choose a concise feature slug and list all related ADRs
+  in the plan. Use the same `<feature-name>` for the implementation
+  branch (`feature/<feature-name>`). Do not push the branch unless
+  asked.
 - Include a status checklist with `[ ]` items; mark `[x]` as completed
   during implementation.
 - Apply the two-phase workflow (Phase 1 implementation, Phase 2
@@ -184,6 +204,9 @@ When asked to create a plan:
   files likely to change, decisions already made, open questions,
   verification commands for Phase 2, and a short suggested commit
   message or branch name when useful.
+- Before saving a plan, verify that referenced files, directories,
+  scripts, and task names exist locally when that is practical. If a
+  referenced tool is optional or missing, include an available fallback.
 - End every plan with a "Suggested Pull Request" section containing a
   short PR title and a brief end-user-oriented description. Keep this
   section non-technical enough for scientists and other users to
diff --git a/docs/dev/ADRs/adr_category-owner-sections.md b/docs/dev/adrs/accepted/category-owner-sections.md
similarity index 100%
rename from docs/dev/ADRs/adr_category-owner-sections.md
rename to docs/dev/adrs/accepted/category-owner-sections.md
diff --git a/docs/dev/adrs/accepted/category-parameter-access.md b/docs/dev/adrs/accepted/category-parameter-access.md
new file mode 100644
index 000000000..8c4b17d39
--- /dev/null
+++ b/docs/dev/adrs/accepted/category-parameter-access.md
@@ -0,0 +1,44 @@
+# ADR: Two-Level Category Parameter Access
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Core model.
+
+## Context
+
+EasyDiffraction models CIF concepts as datablocks, categories, category
+collections, and parameters. Users need predictable navigation paths
+from a structure or experiment to any parameter.
+
+## Decision
+
+Use at most two navigation levels from a datablock to a parameter:
+
+```python
+structure.cell.length_a = 3.88
+experiment.instrument.setup_wavelength = 1.494
+structure.atom_sites['Si'].adp_iso = 0.47
+experiment.background['10'].y = 170
+```
+
+The general forms are:
+
+- `DATABLOCK.CATEGORY_ITEM.PARAMETER`
+- `DATABLOCK.CATEGORY_COLLECTION[ITEM_ID].PARAMETER`
+
+Categories are flat siblings under their owner. A category must not own
+another category of a different type.
+
+## Consequences
+
+API paths remain short, regular, and close to CIF category structure.
+Nested category designs are rejected because they make parameter access
+depth depend on the domain area and obscure the CIF-like model.
diff --git a/docs/dev/adrs/accepted/development-docs-structure.md b/docs/dev/adrs/accepted/development-docs-structure.md
new file mode 100644
index 000000000..223fb71e3
--- /dev/null
+++ b/docs/dev/adrs/accepted/development-docs-structure.md
@@ -0,0 +1,66 @@
+# ADR: Development Documentation Structure
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Documentation.
+
+## Context
+
+Development notes, ADRs, roadmap material, package-structure snapshots,
+and issue backlogs were all kept directly under `docs/dev` or under
+mixed-case directories. That made the development documentation harder
+to scan and created several competing naming conventions.
+
+The repository also contains `docs/docs`, which is the MkDocs source for
+published user documentation. Development-only material should not be
+mixed into that tree unless it is intentionally published.
+
+## Decision
+
+Keep development-only documentation under `docs/dev`.
+
+Use this structure:
+
+```text
+docs/dev/
+|-- index.md
+|-- adrs/
+|   |-- index.md
+|   |-- accepted/
+|   `-- suggestions/
+|-- issues/
+|   |-- open.md
+|   `-- closed.md
+|-- package-structure/
+|   |-- full.md
+|   `-- short.md
+|-- plans/
+`-- roadmap/
+    `-- ROADMAP.md
+```
+
+Use lowercase directory names for new development-doc folders. Keep
+`ROADMAP.md` uppercase because it may later be copied into published
+user documentation where the conventional filename is useful.
+
+Use `docs/dev/adrs/index.md` as the architecture and decision navigation
+surface. Do not keep a separate architecture overview that duplicates
+ADR content.
+
+## Consequences
+
+- Development documentation remains under the documentation tree without
+  being part of the published MkDocs source by default.
+- ADRs have one entry point for architecture navigation and separate
+  accepted and proposed areas.
+- Package-structure snapshots have stable, script-friendly paths.
+- Roadmap publication can later be implemented as a build-time copy from
+  `docs/dev/roadmap/ROADMAP.md` into `docs/docs`.
diff --git a/docs/dev/ADRs/adr_display-ux.md b/docs/dev/adrs/accepted/display-ux.md
similarity index 100%
rename from docs/dev/ADRs/adr_display-ux.md
rename to docs/dev/adrs/accepted/display-ux.md
diff --git a/docs/dev/adrs/accepted/enum-backed-closed-values.md b/docs/dev/adrs/accepted/enum-backed-closed-values.md
new file mode 100644
index 000000000..1e89cc7cc
--- /dev/null
+++ b/docs/dev/adrs/accepted/enum-backed-closed-values.md
@@ -0,0 +1,34 @@
+# ADR: Enum-Backed Closed Value Sets
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Core model.
+
+## Context
+
+Many attributes accept a finite set of values: experiment axes, factory
+tags, fit modes, calculators, minimizers, and rendering engines.
+String-only dispatch is hard to grep and easy to mistype.
+
+## Decision
+
+Represent every finite closed set with a `(str, Enum)` class.
+
+Use enum members as the internal source of truth for validation,
+dispatch, default rules, and descriptions. User-facing setters may
+accept either enum members or their string values, but internal code
+compares against enum members.
+
+## Consequences
+
+Finite choices are discoverable, type-checkable, and greppable.
+Validation can use enum membership instead of hand-written string
+patterns.
diff --git a/docs/dev/adrs/accepted/factory-contracts.md b/docs/dev/adrs/accepted/factory-contracts.md
new file mode 100644
index 000000000..154f092c0
--- /dev/null
+++ b/docs/dev/adrs/accepted/factory-contracts.md
@@ -0,0 +1,44 @@
+# ADR: Factory Contracts and Metadata
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Factories.
+
+## Context
+
+Many domain categories have multiple implementations, and some currently
+have only one implementation but may gain more later. Construction,
+default selection, compatibility filtering, and supported-option display
+need a common contract.
+
+## Decision
+
+Every category that can be constructed by framework code is created
+through a factory, even when it currently has only one implementation.
+
+Concrete factory-created classes carry metadata appropriate to their
+role:
+
+- `type_info` for stable tag lookup and user descriptions.
+- `compatibility` for experiment-axis compatibility where relevant.
+- `calculator_support` for calculation-engine support where relevant.
+
+Child rows that only exist inside a collection do not carry factory
+metadata; the collection carries the metadata.
+
+Factory registration is triggered by explicit imports in package
+`__init__.py` files.
+
+## Consequences
+
+Construction, default resolution, support tables, and compatibility
+filtering stay uniform. Single-implementation categories are ready for
+future alternatives without changing the public construction pattern.
diff --git a/docs/dev/adrs/accepted/factory-tag-naming.md b/docs/dev/adrs/accepted/factory-tag-naming.md
new file mode 100644
index 000000000..edf8a724a
--- /dev/null
+++ b/docs/dev/adrs/accepted/factory-tag-naming.md
@@ -0,0 +1,48 @@
+# ADR: Factory Tag Naming
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Naming.
+
+## Context
+
+Factory tags are persisted and used for public selection. Inconsistent
+abbreviations or ordering would make saved files and user code harder to
+read.
+
+## Decision
+
+Canonical factory tags are:
+
+- lowercase
+- hyphen-separated
+- semantically ordered from general to specific
+- unique within a factory
+
+Use standard abbreviations:
+
+| Concept             | Abbreviation |
+| ------------------- | ------------ |
+| Powder              | `pd`         |
+| Single crystal      | `sc`         |
+| Constant wavelength | `cwl`        |
+| Time-of-flight      | `tof`        |
+| Bragg scattering    | `bragg`      |
+| Total scattering    | `total`      |
+
+Context-local aliases are allowed when the owning object already
+disambiguates the choice, for example `pseudo-voigt` inside a CWL
+experiment.
+
+## Consequences
+
+Saved tags are stable and greppable, while user-facing APIs can remain
+short where context makes the canonical prefix redundant.
diff --git a/docs/dev/ADRs/adr_fit-mode-categories.md b/docs/dev/adrs/accepted/fit-mode-categories.md
similarity index 100%
rename from docs/dev/ADRs/adr_fit-mode-categories.md
rename to docs/dev/adrs/accepted/fit-mode-categories.md
diff --git a/docs/dev/adrs/accepted/free-flag-cif-encoding.md b/docs/dev/adrs/accepted/free-flag-cif-encoding.md
new file mode 100644
index 000000000..e9e7509bf
--- /dev/null
+++ b/docs/dev/adrs/accepted/free-flag-cif-encoding.md
@@ -0,0 +1,39 @@
+# ADR: Free-Flag CIF Encoding
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Persistence.
+
+## Context
+
+Fitting needs to persist whether a parameter is fixed or free. A
+separate free-parameter list would duplicate state already attached to
+individual parameter values and could become stale.
+
+## Decision
+
+Encode a parameter's free/fixed status in the CIF value uncertainty
+syntax:
+
+```text
+3.89     fixed
+3.89(2)  free with estimated standard deviation
+3.89()   free without estimated standard deviation
+```
+
+Do not persist a separate list of free parameters as the source of
+truth.
+
+## Consequences
+
+Each parameter carries its own fit-state information in the same place
+as the value. CIF round-trips are simpler because there is no separate
+state table to reconcile.
diff --git a/docs/dev/adrs/accepted/guarded-public-properties.md b/docs/dev/adrs/accepted/guarded-public-properties.md
new file mode 100644
index 000000000..15fd6308d
--- /dev/null
+++ b/docs/dev/adrs/accepted/guarded-public-properties.md
@@ -0,0 +1,44 @@
+# ADR: Guarded Public Properties
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Core model.
+
+## Context
+
+Most EasyDiffraction domain objects inherit from `GuardedBase`.
+Descriptors and parameters are stored privately and exposed through
+public properties. The public API needs a clear rule for whether a user
+can assign to an attribute, and internal construction code still needs a
+way to populate read-only values.
+
+## Decision
+
+Treat the presence of a property setter as the public writability
+contract.
+
+- Public properties with setters are user-editable.
+- Public properties without setters are read-only.
+- Internal mutation of read-only properties uses explicit private
+  methods such as `_set_sample_form`.
+- Unknown public attributes are rejected by `GuardedBase.__setattr__`
+  with diagnostics.
+
+Do not add a public setter only for internal use, because that makes the
+attribute user-writable.
+
+## Consequences
+
+The public API stays predictable: reading a property returns the live
+descriptor or parameter object, and assigning to the property is allowed
+only when the class explicitly declares that assignment as part of the
+API. Internal loaders and factories remain greppable because private
+mutator methods are named directly.
diff --git a/docs/dev/ADRs/adr_help-discoverability.md b/docs/dev/adrs/accepted/help-discoverability.md
similarity index 100%
rename from docs/dev/ADRs/adr_help-discoverability.md
rename to docs/dev/adrs/accepted/help-discoverability.md
diff --git a/docs/dev/adrs/accepted/immutable-experiment-type.md b/docs/dev/adrs/accepted/immutable-experiment-type.md
new file mode 100644
index 000000000..9740b1107
--- /dev/null
+++ b/docs/dev/adrs/accepted/immutable-experiment-type.md
@@ -0,0 +1,35 @@
+# ADR: Immutable Experiment Type
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Experiment model.
+
+## Context
+
+An experiment is defined by four orthogonal axes: sample form,
+scattering type, beam mode, and radiation probe. Changing those axes
+after creation can require replacing categories, calculators, data
+collections, and validation rules.
+
+## Decision
+
+Experiment type is set at creation time and is immutable afterwards.
+
+Factory and CIF-loading code may set the type through private
+construction methods, but users cannot mutate the type axes on an
+existing experiment.
+
+## Consequences
+
+The model avoids partial transformations between fundamentally different
+experiment configurations. Runtime switching remains available for
+category implementations, such as background or peak profile, where the
+state replacement is bounded and explicit.
diff --git a/docs/dev/adrs/accepted/lint-complexity-thresholds.md b/docs/dev/adrs/accepted/lint-complexity-thresholds.md
new file mode 100644
index 000000000..d2ba16b87
--- /dev/null
+++ b/docs/dev/adrs/accepted/lint-complexity-thresholds.md
@@ -0,0 +1,33 @@
+# ADR: Lint Complexity Thresholds
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Quality.
+
+## Context
+
+The repository enforces ruff PLR complexity rules. Raising thresholds or
+adding local suppressions would make complex functions easier to merge
+without addressing maintainability.
+
+## Decision
+
+Keep ruff's default PLR thresholds, except set `max-args` and
+`max-positional-args` to 6 because ruff counts `self` and `cls`.
+
+Do not raise complexity thresholds or add `# noqa` comments to silence
+complexity rules. Refactor code instead.
+
+## Consequences
+
+Complexity failures are treated as design feedback. Large refactors that
+change public API should be planned explicitly instead of bypassing the
+guardrail.
diff --git a/docs/dev/adrs/accepted/notebook-generation.md b/docs/dev/adrs/accepted/notebook-generation.md
new file mode 100644
index 000000000..47faa3b67
--- /dev/null
+++ b/docs/dev/adrs/accepted/notebook-generation.md
@@ -0,0 +1,34 @@
+# ADR: Notebook Generation Source of Truth
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Documentation.
+
+## Context
+
+Tutorial notebooks are published as `.ipynb` files, but editing notebook
+JSON by hand creates noisy diffs and inconsistent formatting.
+
+## Decision
+
+Treat tutorial `.py` files under `docs/docs/tutorials/` as the editable
+source of truth. Regenerate notebooks with:
+
+```shell
+pixi run notebook-prepare
+```
+
+Do not edit generated `.ipynb` tutorial files by hand.
+
+## Consequences
+
+Tutorial diffs stay reviewable, and notebooks are regenerated through
+the same script path used by the documentation workflow.
diff --git a/docs/dev/adrs/accepted/project-facade-and-persistence.md b/docs/dev/adrs/accepted/project-facade-and-persistence.md
new file mode 100644
index 000000000..67492d8d4
--- /dev/null
+++ b/docs/dev/adrs/accepted/project-facade-and-persistence.md
@@ -0,0 +1,47 @@
+# ADR: Project Facade and Persistence Layout
+
+## Status
+
+Accepted current design.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Persistence.
+
+## Context
+
+`Project` is the current top-level user facade. It owns project
+metadata, structures, experiments, rendering preferences, display
+helpers, analysis, summaries, verbosity, and save/load behavior.
+
+The persisted project directory needs to separate real CIF datablocks
+from singleton project sections.
+
+## Decision
+
+Use `Project` as the current top-level facade and persist projects as a
+directory of CIF files:
+
+```text
+project_dir/
+|-- project.cif
+|-- summary.cif
+|-- structures/
+|-- experiments/
+`-- analysis/
+    `-- analysis.cif
+```
+
+Real structures and experiments serialize as `data_<id>` datablocks.
+Singleton sections such as project configuration, analysis, and summary
+serialize without fake `data_` headers.
+
+## Consequences
+
+The saved layout mirrors the current object graph while preserving the
+semantic difference between real datablocks and singleton sections. A
+proposed `Workspace` rename is tracked separately as an ADR suggestion.
diff --git a/docs/dev/adrs/accepted/property-docstring-template.md b/docs/dev/adrs/accepted/property-docstring-template.md
new file mode 100644
index 000000000..e78bd457a
--- /dev/null
+++ b/docs/dev/adrs/accepted/property-docstring-template.md
@@ -0,0 +1,39 @@
+# ADR: Descriptor Property Docstring Template
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Documentation.
+
+## Context
+
+Public properties backed by `Parameter`, `NumericDescriptor`, or
+`StringDescriptor` need consistent documentation and type hints.
+Duplicating free-form text across docstrings and descriptor descriptions
+creates drift.
+
+## Decision
+
+Use descriptor `description` fields as the source of truth for property
+docstrings. Public properties follow the architecture template for:
+
+- getter summary text
+- writable versus read-only getter body
+- getter return annotation
+- setter value annotation
+
+Setter docstrings are omitted because they are not rendered by the API
+documentation pipeline.
+
+## Consequences
+
+Generated API documentation stays consistent with descriptor metadata.
+The `param-consistency` tool can validate and repair property docstrings
+mechanically.
diff --git a/docs/dev/adrs/accepted/runtime-fit-results.md b/docs/dev/adrs/accepted/runtime-fit-results.md
new file mode 100644
index 000000000..f7ee8389e
--- /dev/null
+++ b/docs/dev/adrs/accepted/runtime-fit-results.md
@@ -0,0 +1,36 @@
+# ADR: Runtime Fit Results
+
+## Status
+
+Accepted current design.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Analysis and fitting.
+
+## Context
+
+Analysis settings are persisted in `analysis/analysis.cif`, but full fit
+outputs can include large arrays, posterior samples, predictive caches,
+and diagnostics. Persisting all of that by default would make project
+directories heavy and would require a broader result schema.
+
+## Decision
+
+Persist fit configuration, not full runtime fit results.
+
+Per-experiment calculator selection lives in experiment files. Common
+fit configuration and fit-mode settings live in `analysis/analysis.cif`.
+Runtime fit outputs such as `analysis.fit_results`, posterior samples,
+posterior predictive arrays, summaries, and diagnostics remain
+runtime-only unless a later ADR narrows the persisted projection.
+
+## Consequences
+
+Saved projects remain focused on reusable model and analysis settings.
+Result persistence can be added later through specific ADRs without
+making the initial project layout carry large runtime artifacts.
diff --git a/docs/dev/adrs/accepted/selector-families.md b/docs/dev/adrs/accepted/selector-families.md
new file mode 100644
index 000000000..698991649
--- /dev/null
+++ b/docs/dev/adrs/accepted/selector-families.md
@@ -0,0 +1,40 @@
+# ADR: Selector Families
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+User-facing API.
+
+## Context
+
+Several public names use a selector-like shape, but they do not all mean
+the same thing. Treating every `<name>_type` as a switchable category
+would blur distinct concepts.
+
+## Decision
+
+Recognize three selector families:
+
+| Family                       | User intent                     | Examples                                                                          |
+| ---------------------------- | ------------------------------- | --------------------------------------------------------------------------------- |
+| Backend selector             | Pick an execution backend       | `fitting.minimizer_type`, `calculation.calculator_type`, `rendering.chart_engine` |
+| Switchable-category selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                      |
+| Active-sibling selector      | Pick the active sibling surface | `analysis.fitting_mode_type`                                                      |
+
+Backend selectors live on dedicated configuration categories.
+Switchable-category selectors live on the host because they replace a
+category instance. Active-sibling selectors live on the owner and decide
+which sibling categories are visible, authoritative, and serialized.
+
+## Consequences
+
+Selector names stay familiar without forcing one implementation pattern
+onto different concepts. Future selectors should declare which family
+they belong to before adding API.
diff --git a/docs/dev/adrs/accepted/string-paths-and-live-descriptors.md b/docs/dev/adrs/accepted/string-paths-and-live-descriptors.md
new file mode 100644
index 000000000..fa8815a95
--- /dev/null
+++ b/docs/dev/adrs/accepted/string-paths-and-live-descriptors.md
@@ -0,0 +1,45 @@
+# ADR: String Paths and Live Descriptors
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+User-facing API.
+
+## Context
+
+Some APIs refer to persisted CIF-style fields, while other APIs refer to
+one concrete live model parameter. Accepting both forms everywhere would
+make call sites ambiguous and harder to validate.
+
+## Decision
+
+Use CIF-style string paths for setup-time, schema-level, and
+cross-experiment selectors:
+
+```python
+sequential_fit_extract.create(target='diffrn.ambient_temperature')
+project.display.fit.series(
+    param=structure.cell.length_a,
+    versus='diffrn.ambient_temperature',
+)
+```
+
+Use live descriptor or parameter objects when the call targets one exact
+model quantity and needs its `unique_name`, description, units, or
+runtime value.
+
+Do not add new APIs that accept both a string path and a live descriptor
+unless a later ADR defines a stronger reason.
+
+## Consequences
+
+Persisted fields and live model parameters stay distinct. APIs that
+operate across experiments can round-trip through CIF paths, while APIs
+that operate on one fitted parameter keep direct object references.
diff --git a/docs/dev/adrs/accepted/switchable-category-api.md b/docs/dev/adrs/accepted/switchable-category-api.md
new file mode 100644
index 000000000..629f01f9b
--- /dev/null
+++ b/docs/dev/adrs/accepted/switchable-category-api.md
@@ -0,0 +1,45 @@
+# ADR: Switchable Category API
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+User-facing API.
+
+## Context
+
+Some categories have multiple concrete implementations that users can
+switch at runtime, such as background, peak profile, and extinction.
+Other categories are fixed by experiment type or have only one current
+implementation.
+
+## Decision
+
+For multi-type switchable categories, expose the selector on the owner:
+
+```python
+experiment.background_type = 'chebyshev'
+experiment.peak_profile_type = 'pseudo-voigt'
+```
+
+The category object itself remains a read-only property. Switching the
+owner-level type replaces the underlying category object.
+
+Expose `show_<category>_types()` on the owner so supported choices can
+be filtered by the owner context.
+
+Do not expose public `_type` selectors for fixed-at-creation categories
+or single-implementation categories. Their factories and internal type
+attributes may still exist for consistency.
+
+## Consequences
+
+Users can tell when a change replaces a whole category implementation.
+The public API stays smaller for single-type categories while preserving
+the internal factory pattern.
diff --git a/docs/dev/adrs/accepted/test-strategy.md b/docs/dev/adrs/accepted/test-strategy.md
new file mode 100644
index 000000000..17194c51c
--- /dev/null
+++ b/docs/dev/adrs/accepted/test-strategy.md
@@ -0,0 +1,41 @@
+# ADR: Test Strategy
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Quality.
+
+## Context
+
+EasyDiffraction combines core model logic, factories, calculators,
+display helpers, tutorials, notebooks, and filesystem persistence.
+Regressions can appear at several levels.
+
+## Decision
+
+Use layered tests:
+
+- unit tests for isolated classes and functions
+- functional tests for multi-component workflows without heavy external
+  dependencies
+- integration tests for end-to-end behavior with real calculation
+  engines and data
+- script tests for tutorial `.py` files
+- notebook tests for generated tutorials
+
+The unit-test tree mirrors the source tree where practical. The
+`test-structure-check` script tracks expected test locations and known
+aliases.
+
+## Consequences
+
+New features should add focused tests at the lowest useful layer and
+broader tests when behavior crosses module boundaries. The mirrored
+structure makes missing coverage easier to spot.
diff --git a/docs/dev/adrs/accepted/type-neutral-adp-parameters.md b/docs/dev/adrs/accepted/type-neutral-adp-parameters.md
new file mode 100644
index 000000000..5609c7a29
--- /dev/null
+++ b/docs/dev/adrs/accepted/type-neutral-adp-parameters.md
@@ -0,0 +1,42 @@
+# ADR: Type-Neutral ADP Parameters
+
+## Status
+
+Accepted.
+
+## Date
+
+2026-05-17
+
+## Group
+
+Structure model.
+
+## Context
+
+Atomic displacement parameters support CIF-standard B/U and
+isotropic/anisotropic variants. Type-specific public names such as
+`b_iso` and `u_iso` would require replacing parameter objects when
+`adp_type` changes, breaking references, constraints, and free flags.
+
+## Decision
+
+Use type-neutral parameter names:
+
+- `atom_site.adp_iso`
+- `atom_site_aniso.adp_11`
+- `atom_site_aniso.adp_22`
+- `atom_site_aniso.adp_33`
+- `atom_site_aniso.adp_12`
+- `atom_site_aniso.adp_13`
+- `atom_site_aniso.adp_23`
+
+Use `atom_site.adp_type` to determine whether those values are B or U,
+isotropic or anisotropic. Keep `atom_site_aniso` as an always-present
+sibling collection and synchronize it from `atom_sites`.
+
+## Consequences
+
+Parameter object identity remains stable across ADP type switches.
+Serialization and calculator code can branch on `adp_type` without
+replacing public parameter objects.
diff --git a/docs/dev/adrs/index.md b/docs/dev/adrs/index.md
new file mode 100644
index 000000000..ed0940bf3
--- /dev/null
+++ b/docs/dev/adrs/index.md
@@ -0,0 +1,46 @@
+# Architecture Decision Records
+
+Project-specific ADRs are kept in this repository. Organization-wide ADR
+indexing may exist separately, but repository decisions should stay near
+the code they explain.
+
+The index follows the EasyScience discussion guidance from
+[discussion #47](https://github.com/orgs/easyscience/discussions/47):
+group decisions by topic first, then use titles and short descriptions
+to keep the list easy to scan. If a group becomes too broad, split it;
+if a group stays small, keep it as a group rather than adding deeper
+folders.
+
+## ADR Index
+
+| Group                | Status     | Title                                                         | Short description                                                                                                  | Link                                                                                         |
+| -------------------- | ---------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------- |
+| Analysis and fitting | Accepted   | Fit Mode Categories and Fit Execution API                     | Splits fitting configuration from execution and defines active sibling fit-mode categories.                        | [`fit-mode-categories.md`](accepted/fit-mode-categories.md)                                  |
+| Analysis and fitting | Accepted   | Runtime Fit Results                                           | Keeps full fit outputs runtime-only in the current design unless a narrower persistence ADR is accepted.           | [`runtime-fit-results.md`](accepted/runtime-fit-results.md)                                  |
+| Analysis and fitting | Suggestion | Analysis CIF Fit State                                        | Proposes a persisted scalar projection of fit state in `analysis.cif`.                                             | [`analysis-cif-fit-state.md`](suggestions/analysis-cif-fit-state.md)                         |
+| Analysis and fitting | Suggestion | Parameter Correlation Persistence                             | Proposes persisting deterministic and posterior correlation summaries.                                             | [`parameter-correlation-persistence.md`](suggestions/parameter-correlation-persistence.md)   |
+| Analysis and fitting | Suggestion | Parameter-Level Posterior Projection and Bayesian Persistence | Proposes saved Bayesian summaries and canonical posterior storage.                                                 | [`parameter-posterior-summary.md`](suggestions/parameter-posterior-summary.md)               |
+| Analysis and fitting | Suggestion | Undo Fit                                                      | Proposes an analysis-owned rollback operation for the latest pre-fit scalar state.                                 | [`undo-fit.md`](suggestions/undo-fit.md)                                                     |
+| Core model           | Accepted   | Category Owners and Real Datablocks                           | Introduces `CategoryOwner` so singleton sections do not pretend to be real CIF datablocks.                         | [`category-owner-sections.md`](accepted/category-owner-sections.md)                          |
+| Core model           | Accepted   | Enum-Backed Closed Value Sets                                 | Requires finite option sets to use `(str, Enum)` classes for validation and dispatch.                              | [`enum-backed-closed-values.md`](accepted/enum-backed-closed-values.md)                      |
+| Core model           | Accepted   | Guarded Public Properties                                     | Uses property setters as the public writability contract for guarded objects.                                      | [`guarded-public-properties.md`](accepted/guarded-public-properties.md)                      |
+| Core model           | Accepted   | Two-Level Category Parameter Access                           | Keeps parameter access to `datablock.category.parameter` or `datablock.collection[id].parameter`.                  | [`category-parameter-access.md`](accepted/category-parameter-access.md)                      |
+| Documentation        | Accepted   | Descriptor Property Docstring Template                        | Makes descriptor metadata the source of truth for public property docstrings and annotations.                      | [`property-docstring-template.md`](accepted/property-docstring-template.md)                  |
+| Documentation        | Accepted   | Development Documentation Structure                           | Defines the `docs/dev` layout for ADRs, issues, plans, package structure, and roadmap.                             | [`development-docs-structure.md`](accepted/development-docs-structure.md)                    |
+| Documentation        | Accepted   | Help Method Discoverability                                   | Requires primary public objects and facades to expose consistent `help()` output.                                  | [`help-discoverability.md`](accepted/help-discoverability.md)                                |
+| Documentation        | Accepted   | Notebook Generation Source of Truth                           | Treats tutorial `.py` files as editable sources and notebooks as generated artifacts.                              | [`notebook-generation.md`](accepted/notebook-generation.md)                                  |
+| Experiment model     | Accepted   | Immutable Experiment Type                                     | Makes experiment type axes creation-time state rather than mutable runtime state.                                  | [`immutable-experiment-type.md`](accepted/immutable-experiment-type.md)                      |
+| Factories            | Accepted   | Factory Contracts and Metadata                                | Standardizes factory construction, metadata, compatibility, and registration behavior.                             | [`factory-contracts.md`](accepted/factory-contracts.md)                                      |
+| Naming               | Accepted   | Factory Tag Naming                                            | Defines canonical factory tag style and standard abbreviations.                                                    | [`factory-tag-naming.md`](accepted/factory-tag-naming.md)                                    |
+| Persistence          | Accepted   | Free-Flag CIF Encoding                                        | Encodes fit free/fixed state through CIF uncertainty syntax instead of a separate free list.                       | [`free-flag-cif-encoding.md`](accepted/free-flag-cif-encoding.md)                            |
+| Persistence          | Accepted   | Project Facade and Persistence Layout                         | Documents the current `Project` facade and saved directory layout.                                                 | [`project-facade-and-persistence.md`](accepted/project-facade-and-persistence.md)            |
+| Persistence          | Suggestion | Loop Category Keys and Identity Naming                        | Documents current loop collection keys and proposes naming rules aligned with CIF category keys.                   | [`loop-category-key-identity.md`](suggestions/loop-category-key-identity.md)                 |
+| Persistence          | Suggestion | Python and CIF Category Correspondence                        | Compares current Python paths and CIF tags, then proposes scoped one-to-one mapping for project-level categories.  | [`python-cif-category-correspondence.md`](suggestions/python-cif-category-correspondence.md) |
+| Quality              | Accepted   | Lint Complexity Thresholds                                    | Treats ruff PLR complexity limits as design guardrails that should not be bypassed.                                | [`lint-complexity-thresholds.md`](accepted/lint-complexity-thresholds.md)                    |
+| Quality              | Accepted   | Test Strategy                                                 | Defines layered unit, functional, integration, script, and notebook testing.                                       | [`test-strategy.md`](accepted/test-strategy.md)                                              |
+| Structure model      | Accepted   | Type-Neutral ADP Parameters                                   | Keeps ADP parameter object identities stable across B/U and iso/ani switches.                                      | [`type-neutral-adp-parameters.md`](accepted/type-neutral-adp-parameters.md)                  |
+| User-facing API      | Accepted   | Display UX Facade                                             | Defines `project.display` and `project.rendering` responsibilities and display method names.                       | [`display-ux.md`](accepted/display-ux.md)                                                    |
+| User-facing API      | Accepted   | Selector Families                                             | Distinguishes backend selectors, switchable-category selectors, and active-sibling selectors.                      | [`selector-families.md`](accepted/selector-families.md)                                      |
+| User-facing API      | Accepted   | String Paths and Live Descriptors                             | Separates persisted field selectors from references to live model parameters.                                      | [`string-paths-and-live-descriptors.md`](accepted/string-paths-and-live-descriptors.md)      |
+| User-facing API      | Accepted   | Switchable Category API                                       | Places multi-type category selectors on the owner and omits public selectors for fixed or single-type categories.  | [`switchable-category-api.md`](accepted/switchable-category-api.md)                          |
+| Workspace model      | Suggestion | Workspace Root and Project Information Category               | Proposes renaming the top-level facade from `Project` to `Workspace` and reserving `project` for project metadata. | [`workspace-root-project-category.md`](suggestions/workspace-root-project-category.md)       |
diff --git a/docs/dev/ADR-suggestions/adr_analysis-cif-fit-state.md b/docs/dev/adrs/suggestions/analysis-cif-fit-state.md
similarity index 96%
rename from docs/dev/ADR-suggestions/adr_analysis-cif-fit-state.md
rename to docs/dev/adrs/suggestions/analysis-cif-fit-state.md
index c39fc192b..d471bb117 100644
--- a/docs/dev/ADR-suggestions/adr_analysis-cif-fit-state.md
+++ b/docs/dev/adrs/suggestions/analysis-cif-fit-state.md
@@ -33,7 +33,7 @@ This separation matters because:
 - `analysis.fit_results` already changes by fit type, but its persisted
   projection should have a stable analysis-owned home
 
-The current architecture document still describes fit results as
+The accepted runtime-fit-results ADR describes fit results as
 runtime-only. This ADR proposes a narrower persisted projection of the
 latest fit state, not a direct dump of backend runtime objects.
 
@@ -137,7 +137,7 @@ and Bayesian fitting.
 Fit-type-specific extensions are layered on top:
 
 - Bayesian persistence extends this with `_bayesian_*` categories and an
-  HDF5 sidecar, as described in `adr_parameter-posterior-summary.md`.
+  HDF5 sidecar, as described in `parameter-posterior-summary.md`.
 - Future fit-specific summaries should follow the same pattern: generic
   shared fields in `_fit_result`, specialized fields in separate
   categories.
@@ -204,7 +204,7 @@ _fit_result.reduced_chi_square 1.031
 
 ### Trade-offs
 
-- The architecture document must be updated because fit state is no
+- The runtime fit-results ADR must be updated because fit state is no
   longer entirely runtime-only.
 - Analysis persistence becomes more stateful and must be kept in sync
   with live parameter objects.
@@ -214,7 +214,7 @@ _fit_result.reduced_chi_square 1.031
 ## Deferred Work
 
 - Bayesian-specific categories and HDF5 sidecar details remain in
-  `adr_parameter-posterior-summary.md`.
+  `parameter-posterior-summary.md`.
 - Undo semantics for `start_value` and `start_uncertainty` are defined
   in a separate ADR.
 - Correlation-matrix persistence is defined in a separate ADR.
diff --git a/docs/dev/adrs/suggestions/loop-category-key-identity.md b/docs/dev/adrs/suggestions/loop-category-key-identity.md
new file mode 100644
index 000000000..f55ef4a89
--- /dev/null
+++ b/docs/dev/adrs/suggestions/loop-category-key-identity.md
@@ -0,0 +1,158 @@
+# ADR: Loop Category Keys and Identity Naming
+
+**Status:** Proposed  
+**Date:** 2026-05-18
+
+## Context
+
+CIF dictionaries can declare the key column for a loop category through
+`_category_key.name`. For example, crystallographic categories commonly
+use domain-specific identity tags such as `_atom_site.label`, while
+other loop categories may use an explicit `id` tag.
+
+EasyDiffraction currently models the same runtime concept with
+`item._identity.category_entry_name`. `CategoryCollection` uses this
+value as the collection key, and category items use it in their
+`unique_name` path:
+
+```text
+<datablock>.<category_code>.<category_entry_name>
+```
+
+The design question is whether the current `category_entry_name`
+approach is enough, and how closely Python-facing identity names should
+follow CIF key tags.
+
+## Assessment
+
+The current approach is directionally good. It gives every loop item a
+stable collection key without hard-coding the key field into
+`CategoryCollection`, and most current loop categories derive that key
+from the same field that is serialized into CIF.
+
+It is not explicit enough yet. The key field is encoded as a lambda on
+each item, not as declarative metadata on the category or descriptor.
+Nothing validates that the runtime key corresponds to a serialized CIF
+field. The main visible example is `Constraint`: the current collection
+key is derived from the left-hand side of `_constraint.expression`, but
+no separate `_constraint.id` field is persisted.
+
+## Decision
+
+Keep `category_entry_name` as the runtime analogue of CIF
+`_category_key.name`, and formalize the rule:
+
+1. Every concrete `CategoryCollection` should have a documented key
+   field.
+2. The key should normally be a public descriptor on the item.
+3. The key descriptor should normally be serialized to CIF.
+4. Standard CIF categories should keep CIF names in Python where those
+   names are already meaningful to scientists.
+5. Custom categories should prefer `id` only for opaque machine
+   identity. Use `label` or `*_id` when the value has clearer domain
+   meaning.
+
+The `constraint` category should add an explicit `id` field and use it
+as the collection key:
+
+```text
+analysis.constraints[id].id -> _constraint.id
+```
+
+The existing `lhs_alias` and `rhs_expr` properties should remain derived
+helpers from `_constraint.expression`, not the row identity.
+
+This argues against a blanket Python API change to use `id` everywhere.
+For scientists moving between notebooks and saved CIF, `atom_site.label`
+in Python and `_atom_site.label` in CIF is less surprising than
+`atom_site.id` in Python and `_atom_site.label` in CIF.
+
+## Loop Category Keys
+
+Rows are sorted by the chosen Python key style: `id`, then `*_id`, then
+`label`. This table lists fields that drive `category_entry_name`;
+identifier-like fields that are not collection keys are listed in the
+next section.
+
+| Python key      | Area       | Collection class                            | Category code            | CIF key tag                  | Source                      | Decision                                                                                             |
+| --------------- | ---------- | ------------------------------------------- | ------------------------ | ---------------------------- | --------------------------- | ---------------------------------------------------------------------------------------------------- |
+| `id`            | Analysis   | `Constraints`                               | `constraint`             | `_constraint.id`             | Custom category             | Add this key. Current implementation derives the key from the left side of `_constraint.expression`. |
+| `id`            | Analysis   | `SequentialFitExtractCollection`            | `sequential_fit_extract` | `_sequential_fit_extract.id` | Custom category             | Keep. It is an explicit row identifier for extraction rules.                                         |
+| `id`            | Experiment | `LineSegmentBackground`                     | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and already serialized.                                             |
+| `id`            | Experiment | `ChebyshevPolynomialBackground`             | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and shared with other background variants.                          |
+| `id`            | Experiment | `LinkedPhases`                              | `linked_phases`          | `_pd_phase_block.id`         | Powder CIF-style category   | Keep. Consider `phase_id` only if the public API later standardizes foreign-key names.               |
+| `id`            | Experiment | `ExcludedRegions`                           | `excluded_regions`       | `_excluded_region.id`        | Custom category             | Keep. It is a simple custom loop row identifier.                                                     |
+| `id`            | Experiment | `ReflnData`                                 | `refln`                  | `_refln.id`                  | CIF-style category          | Keep for the current reflection table shape.                                                         |
+| `id`            | Experiment | `PowderCwlReflnData` / `PowderTofReflnData` | `refln`                  | `_refln.id`                  | CIF-style category          | Keep; `phase_id` remains a separate field, not the row key.                                          |
+| `experiment_id` | Analysis   | `JointFitCollection`                        | `joint_fit`              | `_joint_fit.experiment_id`   | Custom category             | Keep. The key is a reference to an experiment, so `id` alone would lose context.                     |
+| `point_id`      | Experiment | `PdCwlData` / `PdTofData`                   | `pd_data`                | `_pd_data.point_id`          | Powder CIF-style category   | Keep. It is clearer than `id` for dense measured/calculated data points.                             |
+| `point_id`      | Experiment | `TotalData`                                 | `total_data`             | `_pd_data.point_id`          | Current powder-data mapping | Keep. Revisit the CIF tag only when total-scattering-specific CIF tags are introduced.               |
+| `label`         | Analysis   | `Aliases`                                   | `alias`                  | `_alias.label`               | Custom category             | Keep. It is the user-visible symbol referenced by expressions, not an opaque row id.                 |
+| `label`         | Structure  | `AtomSites`                                 | `atom_site`              | `_atom_site.label`           | CIF core category           | Keep. This is a well-known crystallographic identity field.                                          |
+| `label`         | Structure  | `AtomSiteAnisoCollection`                   | `atom_site_aniso`        | `_atom_site_aniso.label`     | CIF core category           | Keep. It intentionally matches and references the atom-site label.                                   |
+
+## Non-Key Identity And Reference Fields
+
+These fields are serialized in loop rows and look identity-like, but
+they do not define the collection key.
+
+| Python field        | Area       | Collection class                            | Category code | CIF tag                    | Role                                                                                  |
+| ------------------- | ---------- | ------------------------------------------- | ------------- | -------------------------- | ------------------------------------------------------------------------------------- |
+| `phase_id`          | Experiment | `PowderCwlReflnData` / `PowderTofReflnData` | `refln`       | `_refln.phase_id`          | References the linked phase for a calculated reflection. Row key remains `_refln.id`. |
+| `param_unique_name` | Analysis   | `Aliases`                                   | `alias`       | `_alias.param_unique_name` | References the target parameter. Row key remains `_alias.label`.                      |
+
+## Naming Guidance
+
+Use `label` when the identity is a user-visible, domain-specific symbol.
+This applies to atom sites and aliases. A label is not weaker than an
+`id` if the category defines it as the key.
+
+Use `id` when the identity is an opaque row identifier without a richer
+domain word. This applies to excluded regions, background terms,
+sequential-fit extraction rules, and current reflection rows.
+
+Use `*_id` when the identity is primarily a reference to another object.
+This applies to `experiment_id` and `point_id`. It keeps the public API
+clearer than a generic `id` while still expressing uniqueness within the
+collection.
+
+Avoid renaming standard CIF identity fields in Python unless the CIF
+name is actively hostile to the user-facing model. Each intentional
+Python/CIF mismatch adds translation cost for users who move between
+Jupyter, CLI output, and saved CIF files.
+
+## Implementation Notes
+
+The current `category_entry_name` mechanism can stay, but it should be
+made easier to audit. The implementation now uses class-level
+`_category_code` and `_category_entry_name` declarations on concrete
+`CategoryItem` subclasses. `CategoryItem` resolves the declared
+`_category_entry_name` lazily from the named public attribute, and
+`Identity` exposes the resolved value through
+`item._identity.category_entry_name`.
+
+For constraints, add a descriptor-backed `id` property serialized as
+`_constraint.id`, and change `category_entry_name` to resolve from that
+descriptor. Keep `_constraint.expression` for the full equation. Keep
+`lhs_alias` and `rhs_expr` as derived convenience properties.
+
+When reading older CIF files that only contain `_constraint.expression`,
+derive a deterministic fallback `id` from the old `lhs_alias` key after
+row values are loaded, then write `_constraint.id` on the next save.
+
+## Consequences
+
+Keeping CIF names where possible improves notebook-to-CIF continuity and
+makes saved files easier to inspect. It also reduces the amount of
+documentation needed to explain common crystallographic terms such as
+`atom_site.label`.
+
+Allowing custom categories to use `id`, `label`, or `*_id` means the API
+will not be mechanically uniform, but it will be semantically clearer.
+Uniformity should come from documented key metadata and predictable
+collection behavior, not from forcing every row key to be named `id`.
+
+## References
+
+- [COMCIFS `cif_core.dic`](https://github.com/COMCIFS/cif_core/blob/main/cif_core.dic)
+- [IUCr Core CIF dictionary browser](https://www.iucr.org/resources/cif/dictionaries/browse/cif_core)
diff --git a/docs/dev/ADR-suggestions/adr_parameter-correlation-persistence.md b/docs/dev/adrs/suggestions/parameter-correlation-persistence.md
similarity index 100%
rename from docs/dev/ADR-suggestions/adr_parameter-correlation-persistence.md
rename to docs/dev/adrs/suggestions/parameter-correlation-persistence.md
diff --git a/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md b/docs/dev/adrs/suggestions/parameter-posterior-summary.md
similarity index 99%
rename from docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
rename to docs/dev/adrs/suggestions/parameter-posterior-summary.md
index f81e64472..3de39bf37 100644
--- a/docs/dev/ADR-suggestions/adr_parameter-posterior-summary.md
+++ b/docs/dev/adrs/suggestions/parameter-posterior-summary.md
@@ -12,8 +12,8 @@ single scalar used by calculations.
 Bayesian DREAM currently keeps posterior state only on
 `analysis.fit_results` via `BayesianFitResults`, including
 `posterior_samples`, `posterior_parameter_summaries`,
-`posterior_predictive`, diagnostics, and sampler settings. The current
-architecture document describes this state as runtime-only and not
+`posterior_predictive`, diagnostics, and sampler settings. The accepted
+runtime-fit-results ADR describes this state as runtime-only and not
 serialized.
 
 `analysis.fit_results` already changes by analysis type: deterministic
diff --git a/docs/dev/adrs/suggestions/python-cif-category-correspondence.md b/docs/dev/adrs/suggestions/python-cif-category-correspondence.md
new file mode 100644
index 000000000..6f970a0e2
--- /dev/null
+++ b/docs/dev/adrs/suggestions/python-cif-category-correspondence.md
@@ -0,0 +1,314 @@
+# ADR: Python and CIF Category Correspondence
+
+**Status:** Proposed  
+**Date:** 2026-05-17
+
+## Context
+
+EasyDiffraction exposes a Python object graph and persists state in CIF
+files. The public Python API should be easy for scientists to predict,
+while CIF output should remain readable and semantically useful.
+
+The current public root is `Project`, and project-level configuration is
+saved in:
+
+```text
+project.cif
+```
+
+Inside that file, generic category names such as `_info.*`,
+`_rendering.*`, and `_verbosity.*` are less ambiguous than they would be
+in a single monolithic CIF file. This opens the option of a strict
+one-to-one correspondence for project-owned singleton categories:
+
+```text
+project.info.title        -> project.cif: _info.title
+project.rendering.engine  -> project.cif: _rendering.engine
+project.verbosity.level   -> project.cif: _verbosity.level
+```
+
+The design question is whether this rule should be applied only to
+project-level configuration, or more broadly across analysis,
+experiments, structures, and calculated data.
+
+## Scope Of Comparison
+
+The comparison below is category-level and public-API oriented. It lists
+all currently persisted Python category surfaces found in the source
+code, with complete field sets where the category is small and compact
+field groups where a CIF loop has many repeated parameters.
+
+Shorthand names such as `analysis`, `experiment`, and `structure` refer
+to objects reached from the current `Project` root, for example
+`project.analysis`, `project.experiments[name]`, and
+`project.structures[name]`.
+
+## Current Persistence Layout
+
+| Current Python surface              | Current saved location   | Current CIF block form | Notes                                                                               |
+| ----------------------------------- | ------------------------ | ---------------------- | ----------------------------------------------------------------------------------- |
+| `project.info`, `project.rendering` | `project.cif`            | bare categories        | Project-level singleton config.                                                     |
+| `project.verbosity`                 | not persisted            | none                   | Runtime-only string property backed by `VerbosityEnum`; no `_verbosity` category.   |
+| `project.structures[name]`          | `structures/<name>.cif`  | `data_<name>`          | Each structure is one CIF data block.                                               |
+| `project.experiments[name]`         | `experiments/<name>.cif` | `data_<name>`          | Each experiment is one CIF data block.                                              |
+| `project.analysis`                  | `analysis/analysis.cif`  | bare categories        | Loader also accepts legacy root-level `analysis.cif`.                               |
+| `project.summary`                   | `summary.cif`            | placeholder text       | Summary persistence exists as a file but `summary_to_cif()` is not implemented yet. |
+
+## Current Correspondence
+
+### Project-Level Configuration
+
+| Current Python path              | Current CIF path          | Match? | Notes                                                                                                                           |
+| -------------------------------- | ------------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------------- |
+| `project.info.name`              | `_project.id`             | No     | Python uses user-facing `name`; CIF uses `id`; category is `info` in Python but `_project` in CIF.                              |
+| `project.info.title`             | `_project.title`          | Partly | Field name matches, category name does not.                                                                                     |
+| `project.info.description`       | `_project.description`    | Partly | Field name matches, category name does not.                                                                                     |
+| `project.info.created`           | `_project.created`        | Partly | Field name matches, category name does not.                                                                                     |
+| `project.info.last_modified`     | `_project.last_modified`  | Partly | Field name matches, category name does not.                                                                                     |
+| `project.info.path`              | none                      | No     | Runtime storage path, not a CIF field.                                                                                          |
+| `project.rendering.chart_engine` | `_rendering.chart_engine` | Yes    | Direct category and field mapping.                                                                                              |
+| `project.rendering.table_engine` | `_rendering.table_engine` | Yes    | Direct category and field mapping.                                                                                              |
+| `project.verbosity`              | none                      | No     | Runtime-only string convenience property; current code has no `project.verbosity.level` category and no `_verbosity.level` tag. |
+
+### Analysis Configuration
+
+| Current Python path                               | Current CIF path                   | Match? | Notes                                                                                            |
+| ------------------------------------------------- | ---------------------------------- | ------ | ------------------------------------------------------------------------------------------------ |
+| `analysis.fitting.minimizer_type`                 | `_fitting.minimizer_type`          | Yes    | Direct category mapping.                                                                         |
+| `analysis.fitting_mode_type`                      | `_fitting.mode_type`               | No     | Public selector is owner-level state serialized into the `_fitting` category.                    |
+| `analysis.joint_fit[experiment_id].experiment_id` | `_joint_fit.experiment_id`         | Yes    | Collection key is also stored as a field.                                                        |
+| `analysis.joint_fit[experiment_id].weight`        | `_joint_fit.weight`                | Yes    | Direct field mapping.                                                                            |
+| `analysis.sequential_fit.data_dir`                | `_sequential_fit.data_dir`         | Yes    | Direct category mapping.                                                                         |
+| `analysis.sequential_fit.file_pattern`            | `_sequential_fit.file_pattern`     | Yes    | Direct category mapping.                                                                         |
+| `analysis.sequential_fit.max_workers`             | `_sequential_fit.max_workers`      | Yes    | Direct category mapping.                                                                         |
+| `analysis.sequential_fit.chunk_size`              | `_sequential_fit.chunk_size`       | Yes    | Direct category mapping.                                                                         |
+| `analysis.sequential_fit.reverse`                 | `_sequential_fit.reverse`          | Yes    | Direct category mapping.                                                                         |
+| `analysis.sequential_fit_extract[id].id`          | `_sequential_fit_extract.id`       | Yes    | Direct collection mapping.                                                                       |
+| `analysis.sequential_fit_extract[id].target`      | `_sequential_fit_extract.target`   | Yes    | Direct collection mapping.                                                                       |
+| `analysis.sequential_fit_extract[id].pattern`     | `_sequential_fit_extract.pattern`  | Yes    | Direct collection mapping.                                                                       |
+| `analysis.sequential_fit_extract[id].required`    | `_sequential_fit_extract.required` | Yes    | Direct collection mapping.                                                                       |
+| `analysis.aliases[label].label`                   | `_alias.label`                     | Partly | Python collection is plural; CIF row category is singular.                                       |
+| `analysis.aliases[label].param_unique_name`       | `_alias.param_unique_name`         | Partly | Python collection is plural; CIF row category is singular.                                       |
+| `analysis.constraints[lhs_alias].expression`      | `_constraint.expression`           | Partly | Collection key is derived from the expression; there is no separate `_constraint.lhs_alias` tag. |
+
+### Experiment Configuration
+
+| Current Python path                           | Current CIF path                                                                   | Match? | Notes                                                                                |
+| --------------------------------------------- | ---------------------------------------------------------------------------------- | ------ | ------------------------------------------------------------------------------------ |
+| `experiment.type.sample_form`                 | `_expt_type.sample_form`                                                           | Partly | Python uses the user-facing word `type`; CIF uses abbreviated `_expt_type`.          |
+| `experiment.type.beam_mode`                   | `_expt_type.beam_mode`                                                             | Partly | Python uses the user-facing word `type`; CIF uses abbreviated `_expt_type`.          |
+| `experiment.type.radiation_probe`             | `_expt_type.radiation_probe`                                                       | Partly | Python uses the user-facing word `type`; CIF uses abbreviated `_expt_type`.          |
+| `experiment.type.scattering_type`             | `_expt_type.scattering_type`                                                       | Partly | Python uses the user-facing word `type`; CIF uses abbreviated `_expt_type`.          |
+| `experiment.calculation.calculator_type`      | `_calculation.calculator_type`                                                     | Yes    | Direct category mapping.                                                             |
+| `experiment.diffrn.ambient_temperature`       | `_diffrn.ambient_temperature`                                                      | Yes    | Direct category mapping.                                                             |
+| `experiment.diffrn.ambient_pressure`          | `_diffrn.ambient_pressure`                                                         | Yes    | Direct category mapping.                                                             |
+| `experiment.diffrn.ambient_magnetic_field`    | `_diffrn.ambient_magnetic_field`                                                   | Yes    | Direct category mapping.                                                             |
+| `experiment.diffrn.ambient_electric_field`    | `_diffrn.ambient_electric_field`                                                   | Yes    | Direct category mapping.                                                             |
+| `experiment.instrument.setup_wavelength`      | `_instr.wavelength`                                                                | Partly | Python name exposes setup role; CIF tag uses compact instrument name.                |
+| `experiment.instrument.calib_twotheta_offset` | `_instr.2theta_offset`                                                             | Partly | Python name exposes calibration role; CIF tag uses compact instrument name.          |
+| `experiment.instrument.setup_twotheta_bank`   | `_instr.2theta_bank`                                                               | Partly | Python name exposes setup role; CIF tag uses compact instrument name.                |
+| `experiment.instrument.calib_d_to_tof_offset` | `_instr.d_to_tof_offset`                                                           | Partly | Python name exposes calibration role; CIF tag uses compact instrument name.          |
+| `experiment.instrument.calib_d_to_tof_linear` | `_instr.d_to_tof_linear`                                                           | Partly | Python name exposes calibration role; CIF tag uses compact instrument name.          |
+| `experiment.instrument.calib_d_to_tof_quad`   | `_instr.d_to_tof_quad`                                                             | Partly | Python name exposes calibration role; CIF tag uses compact instrument name.          |
+| `experiment.instrument.calib_d_to_tof_recip`  | `_instr.d_to_tof_recip`                                                            | Partly | Python name exposes calibration role; CIF tag uses compact instrument name.          |
+| `experiment.peak.profile_type`                | `_peak.profile_type`                                                               | Yes    | The active peak category stores its own type tag.                                    |
+| `experiment.peak_profile_type`                | `_peak.profile_type`                                                               | No     | Public selector is an owner-level convenience alias.                                 |
+| `experiment.peak.broad_gauss_u`               | `_peak.broad_gauss_u`                                                              | Yes    | CWL peak field.                                                                      |
+| `experiment.peak.broad_gauss_v`               | `_peak.broad_gauss_v`                                                              | Yes    | CWL peak field.                                                                      |
+| `experiment.peak.broad_gauss_w`               | `_peak.broad_gauss_w`                                                              | Yes    | CWL peak field.                                                                      |
+| `experiment.peak.broad_lorentz_x`             | `_peak.broad_lorentz_x`                                                            | Yes    | CWL peak field.                                                                      |
+| `experiment.peak.broad_lorentz_y`             | `_peak.broad_lorentz_y`                                                            | Yes    | CWL peak field.                                                                      |
+| `experiment.peak.asym_empir_1..4`             | `_peak.asym_empir_1..4`                                                            | Yes    | CWL peak field group.                                                                |
+| `experiment.peak.asym_fcj_1..2`               | `_peak.asym_fcj_1..2`                                                              | Yes    | CWL peak field group.                                                                |
+| `experiment.peak.broad_gauss_sigma_0..2`      | `_peak.gauss_sigma_0..2`                                                           | Partly | Python prefixes the family with `broad_`; CIF tags omit that grouping prefix.        |
+| `experiment.peak.broad_lorentz_gamma_0..2`    | `_peak.lorentz_gamma_0..2`                                                         | Partly | Python prefixes the family with `broad_`; CIF tags omit that grouping prefix.        |
+| `experiment.peak.exp_rise_alpha_0..1`         | `_peak.rise_alpha_0..1`                                                            | Partly | Python prefixes the family with `exp_`; CIF tags omit that grouping prefix.          |
+| `experiment.peak.exp_decay_beta_0..1`         | `_peak.decay_beta_0..1`                                                            | Partly | Python prefixes the family with `exp_`; CIF tags omit that grouping prefix.          |
+| `experiment.peak.dexp_*`                      | `_peak.dexp_*`                                                                     | Yes    | TOF double-exponential peak field group.                                             |
+| `experiment.peak.damp_q`                      | `_peak.damp_q`                                                                     | Yes    | Total-scattering peak field.                                                         |
+| `experiment.peak.broad_q`                     | `_peak.broad_q`                                                                    | Yes    | Total-scattering peak field.                                                         |
+| `experiment.peak.cutoff_q`                    | `_peak.cutoff_q`                                                                   | Yes    | Total-scattering peak field.                                                         |
+| `experiment.peak.sharp_delta_1`               | `_peak.sharp_delta_1`                                                              | Yes    | Total-scattering peak field.                                                         |
+| `experiment.peak.sharp_delta_2`               | `_peak.sharp_delta_2`                                                              | Yes    | Total-scattering peak field.                                                         |
+| `experiment.peak.damp_particle_diameter`      | `_peak.damp_particle_diameter`                                                     | Yes    | Total-scattering peak field.                                                         |
+| `experiment.background[id].id` line segment   | `_pd_background.id`                                                                | Partly | Python category is `background`; CIF uses powder-background category.                |
+| `experiment.background[id].x` line segment    | `_pd_background.line_segment_X` or `_pd_background_line_segment_X`                 | Partly | Python uses compact `x`; CIF tag encodes powder-background line-segment meaning.     |
+| `experiment.background[id].y` line segment    | `_pd_background.line_segment_intensity` or `_pd_background_line_segment_intensity` | Partly | Python uses compact `y`; CIF tag encodes powder-background line-segment meaning.     |
+| `experiment.background[id].id` Chebyshev      | `_pd_background.id`                                                                | Partly | Python category is `background`; CIF uses powder-background category.                |
+| `experiment.background[id].order` Chebyshev   | `_pd_background.Chebyshev_order`                                                   | Partly | CIF tag encodes polynomial type and uses CIF-style capitalization.                   |
+| `experiment.background[id].coef` Chebyshev    | `_pd_background.Chebyshev_coef`                                                    | Partly | CIF tag encodes polynomial type and uses CIF-style capitalization.                   |
+| `experiment.background_type`                  | implied by active background category                                              | No     | There is no standalone selector tag.                                                 |
+| `experiment.extinction.model`                 | `_extinction.model`                                                                | Yes    | Direct category mapping.                                                             |
+| `experiment.extinction.mosaicity`             | `_extinction.mosaicity`                                                            | Yes    | Direct category mapping.                                                             |
+| `experiment.extinction.radius`                | `_extinction.radius`                                                               | Yes    | Direct category mapping.                                                             |
+| `experiment.extinction_type`                  | `_extinction.model`                                                                | Partly | Public selector chooses the category; persisted model tag lives inside the category. |
+| `experiment.linked_phases[id].id`             | `_pd_phase_block.id`                                                               | Partly | Python name is user-facing; CIF tag follows powder phase-block convention.           |
+| `experiment.linked_phases[id].scale`          | `_pd_phase_block.scale`                                                            | Partly | Python name is user-facing; CIF tag follows powder phase-block convention.           |
+| `experiment.linked_crystal.id`                | `_sc_crystal_block.id`                                                             | Partly | Python name is user-facing; CIF tag follows single-crystal block convention.         |
+| `experiment.linked_crystal.scale`             | `_sc_crystal_block.scale`                                                          | Partly | Python name is user-facing; CIF tag follows single-crystal block convention.         |
+| `experiment.excluded_regions[id].id`          | `_excluded_region.id`                                                              | Partly | Python collection is plural; CIF row category is singular.                           |
+| `experiment.excluded_regions[id].start`       | `_excluded_region.start`                                                           | Partly | Python collection is plural; CIF row category is singular.                           |
+| `experiment.excluded_regions[id].end`         | `_excluded_region.end`                                                             | Partly | Python collection is plural; CIF row category is singular.                           |
+
+### Experiment Data And Calculated Results
+
+| Current Python path                                        | Current CIF path                                                                                                                 | Match? | Notes                                                                |
+| ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | ------ | -------------------------------------------------------------------- |
+| `experiment.data[point_id].point_id`                       | `_pd_data.point_id`                                                                                                              | Partly | Python row key maps to a powder-data CIF tag.                        |
+| `experiment.data[point_id].d_spacing` Bragg powder         | `_pd_proc.d_spacing`                                                                                                             | Partly | Python name is analysis-oriented; CIF tag is processed powder data.  |
+| `experiment.data[point_id].intensity_meas` Bragg powder    | `_pd_meas.intensity_total` or `_pd_proc.intensity_norm`                                                                          | Partly | CIF can use measured or processed intensity tags.                    |
+| `experiment.data[point_id].intensity_meas_su` Bragg powder | `_pd_meas.intensity_total_su` or `_pd_proc.intensity_norm_su`                                                                    | Partly | CIF can use measured or processed uncertainty tags.                  |
+| `experiment.data[point_id].intensity_calc` Bragg powder    | `_pd_calc.intensity_total`                                                                                                       | Partly | Python name is analysis-oriented; CIF tag is calculated powder data. |
+| `experiment.data[point_id].intensity_bkg` Bragg powder     | `_pd_calc.intensity_bkg`                                                                                                         | Partly | Python name is analysis-oriented; CIF tag is calculated background.  |
+| `experiment.data[point_id].calc_status` Bragg powder       | `_pd_data.refinement_status`                                                                                                     | Partly | Python says calculation status; CIF tag says refinement status.      |
+| `experiment.data[point_id].two_theta` Bragg powder         | `_pd_proc.2theta_scan` or `_pd_meas.2theta_scan`                                                                                 | Partly | CIF can use processed or measured x-coordinate tags.                 |
+| `experiment.data[point_id].time_of_flight` Bragg powder    | `_pd_meas.time_of_flight`                                                                                                        | Partly | CIF tag is measurement-specific.                                     |
+| `experiment.data[point_id].r` total powder                 | `_pd_proc.r`                                                                                                                     | Partly | Current code comments say PDF-specific CIF names are still needed.   |
+| `experiment.data[point_id].g_r_meas` total powder          | `_pd_meas.intensity_total`                                                                                                       | Partly | Current code comments say PDF-specific CIF names are still needed.   |
+| `experiment.data[point_id].g_r_meas_su` total powder       | `_pd_meas.intensity_total_su`                                                                                                    | Partly | Current code comments say PDF-specific CIF names are still needed.   |
+| `experiment.data[point_id].g_r_calc` total powder          | `_pd_calc.intensity_total`                                                                                                       | Partly | Current code comments say PDF-specific CIF names are still needed.   |
+| `experiment.data[point_id].calc_status` total powder       | `_pd_data.refinement_status`                                                                                                     | Partly | Current code comments say PDF-specific CIF names are still needed.   |
+| `experiment.refln[id]` single crystal                      | `_refln.{id,d_spacing,sin_theta_over_lambda,index_h,index_k,index_l,intensity_meas,intensity_meas_su,intensity_calc,wavelength}` | Yes    | Direct reflection-loop mapping.                                      |
+| `experiment.refln[id]` powder calculated reflections       | `_refln.{id,phase_id,d_spacing,sin_theta_over_lambda,index_h,index_k,index_l,f_calc,f_squared_calc,two_theta,time_of_flight}`    | Yes    | Direct calculated-reflection loop mapping.                           |
+
+### Structure Configuration
+
+| Current Python path                               | Current CIF path                                                                                                                                                    | Match? | Notes                                                                             |
+| ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | --------------------------------------------------------------------------------- |
+| `structure.cell.length_a`                         | `_cell.length_a`                                                                                                                                                    | Yes    | Direct category mapping.                                                          |
+| `structure.cell.length_b`                         | `_cell.length_b`                                                                                                                                                    | Yes    | Direct category mapping.                                                          |
+| `structure.cell.length_c`                         | `_cell.length_c`                                                                                                                                                    | Yes    | Direct category mapping.                                                          |
+| `structure.cell.angle_alpha`                      | `_cell.angle_alpha`                                                                                                                                                 | Yes    | Direct category mapping.                                                          |
+| `structure.cell.angle_beta`                       | `_cell.angle_beta`                                                                                                                                                  | Yes    | Direct category mapping.                                                          |
+| `structure.cell.angle_gamma`                      | `_cell.angle_gamma`                                                                                                                                                 | Yes    | Direct category mapping.                                                          |
+| `structure.space_group.name_h_m`                  | `_space_group.name_H-M_alt`, `_space_group_name_H-M_alt`, `_symmetry.space_group_name_H-M`, or `_symmetry_space_group_name_H-M`                                     | Partly | CIF naming follows crystallographic conventions and supports legacy alternatives. |
+| `structure.space_group.it_coordinate_system_code` | `_space_group.IT_coordinate_system_code`, `_space_group_IT_coordinate_system_code`, `_symmetry.IT_coordinate_system_code`, or `_symmetry_IT_coordinate_system_code` | Partly | CIF naming follows crystallographic conventions and supports legacy alternatives. |
+| `structure.atom_sites[label].label`               | `_atom_site.label`                                                                                                                                                  | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].type_symbol`         | `_atom_site.type_symbol`                                                                                                                                            | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].fract_x`             | `_atom_site.fract_x`                                                                                                                                                | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].fract_y`             | `_atom_site.fract_y`                                                                                                                                                | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].fract_z`             | `_atom_site.fract_z`                                                                                                                                                | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].wyckoff_letter`      | `_atom_site.Wyckoff_letter` or `_atom_site.Wyckoff_symbol`                                                                                                          | Partly | CIF uses capitalized/legacy Wyckoff tags.                                         |
+| `structure.atom_sites[label].occupancy`           | `_atom_site.occupancy`                                                                                                                                              | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_sites[label].adp_iso`             | `_atom_site.B_iso_or_equiv` or `_atom_site.U_iso_or_equiv`                                                                                                          | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_sites[label].adp_type`            | `_atom_site.adp_type`                                                                                                                                               | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_site_aniso[label].label`          | `_atom_site_aniso.label`                                                                                                                                            | Yes    | Direct row-field mapping.                                                         |
+| `structure.atom_site_aniso[label].adp_11`         | `_atom_site_aniso.B_11` or `_atom_site_aniso.U_11`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_site_aniso[label].adp_22`         | `_atom_site_aniso.B_22` or `_atom_site_aniso.U_22`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_site_aniso[label].adp_33`         | `_atom_site_aniso.B_33` or `_atom_site_aniso.U_33`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_site_aniso[label].adp_12`         | `_atom_site_aniso.B_12` or `_atom_site_aniso.U_12`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_site_aniso[label].adp_13`         | `_atom_site_aniso.B_13` or `_atom_site_aniso.U_13`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+| `structure.atom_site_aniso[label].adp_23`         | `_atom_site_aniso.B_23` or `_atom_site_aniso.U_23`                                                                                                                  | No     | Python uses type-neutral ADP name; CIF uses B/U-specific tags.                    |
+
+### Not Yet Mapped
+
+| Current Python path | Current CIF status    | Notes                                        |
+| ------------------- | --------------------- | -------------------------------------------- |
+| `project.summary`   | placeholder text only | `summary_to_cif()` currently returns a stub. |
+
+## Decision To Discuss
+
+Adopt a scoped one-to-one rule for project-level configuration:
+
+```text
+project.<category>.<field> -> project.cif: _<category>.<field>
+```
+
+This ADR does not propose renaming the public root object. The current
+root object is already `Project`; the proposal is about category and tag
+correspondence inside project-owned singleton configuration.
+
+Target project-level mappings if the current Python names are kept:
+
+| Python path                      | Target CIF path           | Current state                              |
+| -------------------------------- | ------------------------- | ------------------------------------------ |
+| `project.info.name`              | `_info.name`              | Currently `_project.id`.                   |
+| `project.info.title`             | `_info.title`             | Currently `_project.title`.                |
+| `project.info.description`       | `_info.description`       | Currently `_project.description`.          |
+| `project.info.created`           | `_info.created`           | Currently `_project.created`.              |
+| `project.info.last_modified`     | `_info.last_modified`     | Currently `_project.last_modified`.        |
+| `project.rendering.chart_engine` | `_rendering.chart_engine` | Already matches.                           |
+| `project.rendering.table_engine` | `_rendering.table_engine` | Already matches.                           |
+| `project.verbosity.level`        | `_verbosity.level`        | Currently no persisted verbosity category. |
+
+Alternative target if the project identity field should be called `id`
+rather than `name`:
+
+```text
+project.info.id -> _info.id
+```
+
+Do not force strict one-to-one correspondence globally where CIF-domain
+names are clearer or where the Python API intentionally abstracts over
+CIF details.
+
+## Rationale
+
+### Project-Level Categories Are Repository-Owned
+
+Project-level configuration categories are not external crystallographic
+CIF categories. They are EasyDiffraction project-file categories, so the
+repository can optimize them for API/persistence symmetry.
+
+### `project.cif` Scopes Generic Categories
+
+`_info.title` is generic in isolation, but inside `project.cif` it reads
+as project information. This is similar to `_verbosity.level`: the file
+scope tells the reader this is project-level verbosity.
+
+### The Current `Project` Root Already Matches User Language
+
+The current public root object is already `Project`. Keeping it avoids a
+user-facing `workspace.project.*` nesting and aligns with scientific
+workflows where a project is the container for structures, experiments,
+analysis, and saved files.
+
+### Scientific CIF/Domain Categories Should Stay Domain-Oriented
+
+For structures, experiments, measured data, and calculated results, many
+CIF names are standard or domain-specific. Exact Python mirroring would
+make those tags less meaningful to CIF readers and could weaken
+compatibility with scientific conventions.
+
+### Some Python Names Are Deliberate Abstractions
+
+Type-neutral ADP parameters, owner-level switchable selectors,
+active-sibling selectors, and analysis-friendly data names intentionally
+do not mirror individual CIF fields. These should remain exceptions
+unless a separate ADR changes the underlying API pattern.
+
+## Consequences
+
+### Positive
+
+- Project-level configuration becomes easier to explain and inspect.
+- Users can predict project-level CIF tags from Python paths.
+- The decision can focus on project-owned singleton config without
+  forcing scientific CIF categories to mirror Python convenience names.
+
+### Trade-Offs
+
+- `_info.*` is less self-describing if copied out of `project.cif`.
+- Existing `_project.*` project files would need migration or a
+  deliberate compatibility decision.
+- If verbosity is persisted, `project.verbosity` would either need to
+  become a category object or remain as a convenience alias for a new
+  `project.verbosity.level` field.
+- Collapsing rendering to `project.rendering.engine` would simplify the
+  API, but only if chart and table renderers are intended to share one
+  backend choice.
+
+## Open Questions
+
+- Should the project identity remain `project.info.name`, or should it
+  become `project.info.id` to mirror the saved identifier field?
+- Should project metadata move from `_project.*` to `_info.*`, or is
+  `_project.*` clearer even inside `project.cif`?
+- Should `project.rendering.chart_engine` and
+  `project.rendering.table_engine` remain separate, or should the public
+  API and CIF collapse to one `engine` field?
+- Should `_project.*` be accepted as a read-only legacy fallback when
+  loading older saved projects?
+- Should `project.verbosity = 'short'` remain as a convenience alias for
+  `project.verbosity.level = 'short'`, or should strict correspondence
+  remove the alias?
diff --git a/docs/dev/ADR-suggestions/adr_undo-fit.md b/docs/dev/adrs/suggestions/undo-fit.md
similarity index 99%
rename from docs/dev/ADR-suggestions/adr_undo-fit.md
rename to docs/dev/adrs/suggestions/undo-fit.md
index 299741308..578d43a38 100644
--- a/docs/dev/ADR-suggestions/adr_undo-fit.md
+++ b/docs/dev/adrs/suggestions/undo-fit.md
@@ -82,7 +82,7 @@ and is deferred.
 The minimum persisted state required for clean cross-session undo is the
 pair of `_fit_parameter.start_value` and
 `_fit_parameter.start_uncertainty` defined in
-`adr_analysis-cif-fit-state.md`.
+`analysis-cif-fit-state.md`.
 
 If a parameter has no saved `start_value`, `undo_fit()` leaves that
 parameter unchanged.
diff --git a/docs/dev/ADR-suggestions/adr_workspace-root-project-category.md b/docs/dev/adrs/suggestions/workspace-root-project-category.md
similarity index 76%
rename from docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
rename to docs/dev/adrs/suggestions/workspace-root-project-category.md
index b53ead958..a6c94cba6 100644
--- a/docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
+++ b/docs/dev/adrs/suggestions/workspace-root-project-category.md
@@ -73,6 +73,7 @@ workspace = ed.Workspace(project_id='lbco_hrpt')
 workspace.project.id
 workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
 workspace.rendering.table_engine = 'rich'
+workspace.verbosity = 'short'
 workspace.structures
 workspace.experiments
 workspace.analysis
@@ -89,9 +90,29 @@ _project.last_modified
 
 _rendering.chart_engine
 _rendering.table_engine
+
+_verbosity.level
+```
+
+The saved directory is a workspace directory whose filesystem name is
+chosen by the user. The canonical layout is:
+
+```text
+<workspace-dir>/
+|-- workspace.cif
+|-- structures/
+|   `-- cosio.cif
+|-- experiments/
+|   `-- d20.cif
+|-- analysis/
+|   `-- analysis.cif
+`-- summary/
+    `-- summary.cif
 ```
 
-Do not introduce `_meta.*` CIF tags.
+Do not introduce `_meta.*` CIF tags. Do not use `project.cif`,
+`config.cif`, or `meta.cif` as the primary singleton configuration file
+in the target layout.
 
 The intended naming split is:
 
@@ -99,6 +120,7 @@ The intended naming split is:
 Workspace
 |-- project      # information about the scientific project
 |-- rendering    # rendering preferences
+|-- verbosity    # console/output verbosity preference
 |-- structures   # real structure datablocks
 |-- experiments  # real experiment datablocks
 |-- analysis     # analysis section
@@ -169,15 +191,25 @@ After this decision, each layer has a clear rule:
 This avoids one-off aliases such as `project.info` while preserving
 semantic CIF names.
 
+### `workspace.cif` is clearer than `project.cif`, `config.cif`, or `meta.cif`
+
+The file stores singleton settings owned by the workspace: scientific
+project information, rendering preferences, and verbosity. `project.cif`
+overloads the project name again, while `config.cif` and `meta.cif` are
+generic. `workspace.cif` names the owning layer and lets each category
+inside the file keep its domain-specific name.
+
 ## Consequences
 
 ### Positive
 
 - The root object and project-information category no longer share the
   same conceptual name.
-- Public category access becomes uniform: `workspace.project`,
-  `workspace.rendering`, `workspace.analysis`.
+- Public access becomes uniform: `workspace.project`,
+  `workspace.rendering`, `workspace.verbosity`, `workspace.analysis`.
 - CIF stays semantic and does not introduce `_meta.*`.
+- Workspace-level preferences such as rendering and verbosity are saved
+  with the workspace instead of being hidden runtime-only state.
 - Project information can use short item names such as `id`, `title`,
   and `description`.
 - The top-level facade name better reflects active runtime
@@ -190,6 +222,8 @@ semantic CIF names.
   updated from `Project` to `Workspace`.
 - Existing saved directories using `project.cif` must be migrated to
   `workspace.cif` if no compatibility loader is kept.
+- Existing code that expected verbosity to be runtime-only must account
+  for it round-tripping through `workspace.cif`.
 - Users familiar with `Project` must learn the new root name.
 - `Workspace` can be confused with a filesystem workspace in some
   ecosystems, so documentation must define it clearly as the active
@@ -254,6 +288,16 @@ This is readable, but it preserves a special-case category alias. The
 current goal is stronger consistency between public categories and CIF
 category concepts.
 
+### Use `project.cif`, `config.cif`, or `meta.cif` for singleton settings
+
+Rejected for the target layout.
+
+`project.cif` repeats the overloaded term that this migration removes.
+`config.cif` and `meta.cif` are too generic and do not say which layer
+owns the settings. `workspace.cif` is more explicit while still allowing
+semantic categories such as `_project`, `_rendering`, and `_verbosity`
+inside the file.
+
 ### Rename only internal files and keep public API unchanged
 
 Rejected for the target design.
@@ -274,7 +318,7 @@ save/load, display, and analysis orchestration.
 The implementation should follow:
 
 ```text
-docs/dev/plan_workspace-root-project-category.md
+docs/dev/plans/workspace-root-project-category.md
 ```
 
 The high-level migration is:
@@ -293,8 +337,10 @@ The high-level migration is:
 8. Keep CIF tags `_project.*` and `_rendering.*`.
 9. Rename saved singleton config file from `project.cif` to
    `workspace.cif`.
-10. Update code, tests, scripts, tutorials, docs, and architecture
-    references.
+10. Persist workspace verbosity in `workspace.cif` as
+    `_verbosity.level`, owned by a first-class `Verbosity` category
+    under `WorkspaceConfig` (parallel to `Rendering`).
+11. Update code, tests, scripts, tutorials, docs, and ADR references.
 
 ## Post-Implementation ADR Update
 
@@ -307,10 +353,11 @@ When implementation is complete:
 3. Record whether a temporary or permanent `Project` compatibility alias
    was approved.
 4. Record any deviations from the migration plan.
-5. Move this file from `docs/dev/ADR-suggestions/` to `docs/dev/ADRs/`
-   if that is the repository convention for accepted decisions.
-6. Update `docs/dev/architecture.md`.
-7. Update or close related items in `docs/dev/Issues/issues_open.md`.
+5. Move this file from `docs/dev/adrs/suggestions/` to
+   `docs/dev/adrs/accepted/` if the decision is accepted.
+6. Update `docs/dev/adrs/index.md` and related accepted ADRs if the ADR
+   map changes.
+7. Update or close related items in `docs/dev/issues/open.md`.
 
 ## Acceptance Criteria
 
@@ -323,6 +370,11 @@ This ADR is satisfied when:
 - the saved directory path is exposed as `workspace.path`.
 - the public rendering category is `workspace.rendering`.
 - saved singleton configuration lives in `workspace.cif`.
-- `workspace.cif` uses `_project.*` and `_rendering.*` tags.
+- `workspace.cif` uses `_project.*`, `_rendering.*`, and
+  `_verbosity.level` tags.
+- workspace verbosity is owned by a registered `Verbosity` category
+  alongside `Rendering`.
+- `ProjectInfo.path` is removed; the saved directory path is exposed
+  only as `workspace.path`.
 - no `_meta.*` tags are introduced for project information.
-- tutorials and architecture documentation use `Workspace`.
+- tutorials and accepted ADRs use `Workspace`.
diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
deleted file mode 100644
index 768531e72..000000000
--- a/docs/dev/architecture.md
+++ /dev/null
@@ -1,1631 +0,0 @@
-# EasyDiffraction Architecture
-
-**Version:** 1.0  
-**Date:** 2026-03-24  
-**Status:** Living document — updated as the project evolves
-
----
-
-## 1. Overview
-
-EasyDiffraction is a Python library for crystallographic diffraction
-analysis (Rietveld refinement, pair-distribution-function fitting,
-etc.). It models the domain using **CIF-inspired abstractions** —
-datablocks, categories, and parameters — while providing a high-level,
-user-friendly API through a single `Project` façade.
-
-### 1.1 Supported Experiment Dimensions
-
-Every experiment is fully described by four orthogonal axes:
-
-| Axis            | Options                             | Enum                 |
-| --------------- | ----------------------------------- | -------------------- |
-| Sample form     | powder, single crystal              | `SampleFormEnum`     |
-| Scattering type | Bragg, total (PDF)                  | `ScatteringTypeEnum` |
-| Beam mode       | constant wavelength, time-of-flight | `BeamModeEnum`       |
-| Radiation probe | neutron, X-ray                      | `RadiationProbeEnum` |
-
-> **Planned extensions:** 1D / 2D data dimensionality, polarised /
-> unpolarised neutron beam.
-
-### 1.2 Calculation Engines
-
-External libraries perform the heavy computation:
-
-| Engine    | Scope             |
-| --------- | ----------------- |
-| `cryspy`  | Bragg diffraction |
-| `crysfml` | Bragg diffraction |
-| `pdffit2` | Total scattering  |
-
----
-
-## 2. Core Abstractions
-
-All core types live in `core/` which contains **only** base classes and
-utilities — no domain logic.
-
-### 2.1 Object Hierarchy
-
-```shell
-GuardedBase                            # Controlled attribute access, parent linkage, identity
-├── CategoryItem                       # Single CIF category row  (e.g. Cell, Peak, Instrument)
-├── CollectionBase                     # Ordered name→item container
-│   ├── CategoryCollection             # CIF loop  (e.g. AtomSites, Background, Data)
-│   └── DatablockCollection            # Top-level container  (e.g. Structures, Experiments)
-└── CategoryOwner                      # Flat category owner (e.g. Analysis, DatablockItem)
-  └── DatablockItem                  # Real CIF data block (e.g. Structure, Experiment)
-```
-
-`CollectionBase` provides a unified dict-like API over an ordered item
-list with name-based indexing. All key operations — `__getitem__`,
-`__setitem__`, `__delitem__`, `__contains__`, `remove()` — resolve keys
-through a single `_key_for(item)` method that returns
-`category_entry_name` for category items or `datablock_entry_name` for
-datablock items. Subclasses `CategoryCollection` and
-`DatablockCollection` inherit this consistently.
-
-### 2.2 GuardedBase — Controlled Attribute Access
-
-`GuardedBase` is the root ABC. It enforces that only **declared
-`@property` attributes** are accessible publicly:
-
-- **`__getattr__`** rejects any attribute not declared as a `@property`
-  on the class hierarchy. Shows diagnostics with closest-match
-  suggestions on typos.
-- **`__setattr__`** distinguishes:
-  - **Private** (`_`-prefixed) — always allowed, no diagnostics.
-  - **Read-only public** (property without setter) — blocked with a
-    clear error.
-  - **Writable public** (property with setter) — goes through the
-    property setter, which is where validation happens.
-  - **Unknown** — blocked with diagnostics showing allowed writable
-    attrs.
-- **Parent linkage** — when a `GuardedBase` child is assigned to
-  another, the child's `_parent` is set automatically, forming an
-  implicit ownership tree.
-- **Identity** — every instance gets an `_identity: Identity` object for
-  lazy CIF-style name resolution (`datablock_entry_name`,
-  `category_code`, `category_entry_name`) by walking the `_parent`
-  chain.
-
-**Key design rule:** if a parameter has a public setter, it is writable
-for the user. If only a getter — it is read-only. If internal code needs
-to set it, a private method (underscore prefix) is used. See § 2.2.1
-below for the full pattern.
-
-#### 2.2.1 Public Property Convention — Editable vs Read-Only
-
-Every public parameter or descriptor exposed on a `GuardedBase` subclass
-follows one of two patterns:
-
-| Kind          | Getter | Setter | Internal mutation                  |
-| ------------- | ------ | ------ | ---------------------------------- |
-| **Editable**  | yes    | yes    | Via the public setter              |
-| **Read-only** | yes    | no     | Via a private `_set_<name>` method |
-
-**Editable property** — the user can both read and write the value. The
-setter runs through `GuardedBase.__setattr__` and into the property
-setter, where validation happens:
-
-```python
-@property
-def name(self) -> str:
-    """Human-readable name of the experiment."""
-    return self._name
-
-
-@name.setter
-def name(self, new: str) -> None:
-    self._name = new
-```
-
-**Read-only property** — the user can read but cannot assign. Any
-attempt to set the attribute is blocked by `GuardedBase.__setattr__`
-with a clear error message. If _internal_ code (factory builders, CIF
-loaders, etc.) needs to set the value, it calls a private `_set_<name>`
-method instead of exposing a public setter:
-
-```python
-@property
-def sample_form(self) -> StringDescriptor:
-    """Sample form descriptor (read-only for the user)."""
-    return self._sample_form
-
-
-def _set_sample_form(self, value: str) -> None:
-    """Internal setter used by factory/CIF code during construction."""
-    self._sample_form.value = value
-```
-
-**Why this matters:**
-
-- `GuardedBase.__setattr__` uses the presence of a setter to decide
-  writability. Adding a setter "just for internal use" would open the
-  attribute to users.
-- Private `_set_<name>` methods keep the public API surface minimal and
-  intention-clear, while remaining greppable and type-safe.
-- The pattern avoids string-based dispatch — every mutator has an
-  explicit named method.
-
-### 2.3 CategoryItem and CategoryCollection
-
-**Parameter access pattern.** Users reach any parameter through at most
-two levels of navigation from the datablock:
-
-```python
-# CategoryItem → Parameter
-structure.cell.length_a = 3.88
-experiment.instrument.setup_wavelength = 1.494
-
-# CategoryCollection[item_id] → Parameter
-structure.atom_sites['Si'].adp_iso = 0.47
-experiment.background['10'].y = 170
-```
-
-The general forms are:
-
-- `DATABLOCK.CATEGORY_ITEM.PARAMETER`
-- `DATABLOCK.CATEGORY_COLLECTION[ITEM_ID].PARAMETER`
-
-This two-level convention (category then parameter) is a deliberate
-design constraint. Categories are never nested inside other categories
-(see § 9.7), which keeps the path depth uniform and the API predictable.
-
-| Aspect          | `CategoryItem`                     | `CategoryCollection`                      |
-| --------------- | ---------------------------------- | ----------------------------------------- |
-| CIF analogy     | Single category row                | Loop (table) of rows                      |
-| Examples        | Cell, SpaceGroup, Instrument, Peak | AtomSites, Background, Data, LinkedPhases |
-| Parameters      | All `GenericDescriptorBase` attrs  | Aggregated from all child items           |
-| Serialisation   | `as_cif` / `from_cif`              | `as_cif` / `from_cif`                     |
-| Update hook     | `_update(called_by_minimizer=)`    | `_update(called_by_minimizer=)`           |
-| Update priority | `_update_priority` (default 10)    | `_update_priority` (default 10)           |
-| Display         | `show()` on concrete subclasses    | `show()` on concrete subclasses           |
-| Building items  | N/A                                | `add(item)`, `create(**kwargs)`           |
-
-**Update priority:** lower values run first. This ensures correct
-execution order within a datablock (e.g. background before data).
-
-### 2.4 CategoryOwner, DatablockItem, and DatablockCollection
-
-`CategoryOwner` is the shared base class for objects that own flat
-category siblings. It provides category discovery, category sorting by
-`_update_priority`, parameter aggregation, `_need_categories_update`
-tracking, and category-body CIF serialization without a `data_` header.
-
-`DatablockItem` extends `CategoryOwner` for real CIF `data_<id>` blocks.
-`Structure` and `ExperimentBase` subclasses are real datablocks.
-`Analysis` is also a `CategoryOwner`, but it serializes as a singleton
-section body in `analysis/analysis.cif` and does not emit a fake
-`data_analysis` header.
-
-| Aspect             | `DatablockItem`                             | `DatablockCollection`                                    |
-| ------------------ | ------------------------------------------- | -------------------------------------------------------- |
-| CIF analogy        | A single `data_` block                      | Collection of data blocks                                |
-| Examples           | Structure, BraggPdExperiment                | Structures, Experiments                                  |
-| Category discovery | Scans `vars(self)` for categories           | N/A                                                      |
-| Update cascade     | `_update_categories()` — sorted by priority | N/A                                                      |
-| Parameters         | Aggregated from all categories              | Aggregated from all datablocks                           |
-| Fittable params    | N/A                                         | `Parameter`s not blocked by user or symmetry constraints |
-| Free params        | N/A                                         | Fittable + `free == True`                                |
-| Dirty flag         | `_need_categories_update`                   | N/A                                                      |
-
-When any `Parameter.value` is set, it propagates
-`_need_categories_update = True` up to the owning `CategoryOwner`.
-Serialisation (`as_cif`) and plotting trigger `_update_categories()` if
-the flag is set.
-
-### 2.5 Variable System — Parameters and Descriptors
-
-```shell
-GuardedBase
-└── GenericDescriptorBase               # name, value (validated via AttributeSpec), description
-    ├── GenericStringDescriptor         # _value_type = DataTypes.STRING
-    └── GenericNumericDescriptor        # _value_type = DataTypes.NUMERIC, + units
-        └── GenericParameter            # + free, uncertainty, fit_min, fit_max, user_constrained, symmetry_constrained
-```
-
-CIF-bound concrete classes add a `CifHandler` for serialisation:
-
-| Class               | Base                       | Use case                     |
-| ------------------- | -------------------------- | ---------------------------- |
-| `StringDescriptor`  | `GenericStringDescriptor`  | Read-only or writable text   |
-| `NumericDescriptor` | `GenericNumericDescriptor` | Read-only or writable number |
-| `Parameter`         | `GenericParameter`         | Fittable numeric value       |
-
-**Initialisation rule:** all Parameters/Descriptors are initialised with
-their default values from `value_spec` (an `AttributeSpec`) **without
-any validation** — we trust internal definitions. Changes go through
-public property setters, which run both type and value validation.
-
-**Mixin safety:** Parameter/Descriptor classes must not have init
-arguments so they can be used as mixins safely (e.g.
-`PdTofDataPointMixin`).
-
-### 2.6 Validation
-
-`AttributeSpec` bundles `default`, `data_type`, `validator`,
-`allow_none`. Validators include:
-
-| Validator             | Purpose                                |
-| --------------------- | -------------------------------------- |
-| `TypeValidator`       | Checks Python type against `DataTypes` |
-| `RangeValidator`      | `ge`, `le`, `gt`, `lt` bounds checking |
-| `MembershipValidator` | Value must be in an allowed set        |
-| `RegexValidator`      | Value must match a pattern             |
-
-### 2.7 String Paths vs Live Descriptors in Public APIs
-
-Public APIs reference parameters in one of two ways. The choice is not
-stylistic — it follows the call site's role:
-
-- **Setup-time / schema-level APIs use string paths** (CIF-style
-  `'category.attribute'`). The targeted descriptor may not yet exist on
-  any concrete object (e.g. an extraction rule applies uniformly to
-  files about to be loaded), and the value must round-trip through CIF.
-  Examples:
-  `sequential_fit_extract.create(target='diffrn.ambient_temperature', ...)`,
-  alias/constraint definitions persisted in project CIF.
-- **Cross-experiment selectors use the same string paths at runtime.**
-  `project.display.fit.series(..., versus=...)` selects a persisted
-  `diffrn.*` column in `analysis/results.csv` and the matching field
-  across experiments; it does not use one experiment's current live
-  descriptor value. Example:
-  `project.display.fit.series(param=structure.cell.length_a, versus='diffrn.ambient_temperature')`.
-- **Concrete model parameters still use live descriptors.** The call
-  needs the parameter's `unique_name`, `description`, and `units`, and
-  it refers to one exact fitted quantity in the model. Example:
-  `param=structure.cell.length_a` in `project.display.fit.series(...)`.
-
-When adding a new public API, place it on one side of this rule rather
-than accepting both. Use string paths when the value names a persisted
-field or cross-experiment selector, and use live descriptors when the
-value names one concrete model parameter.
-
----
-
-## 3. Experiment System
-
-### 3.1 Experiment Type
-
-An experiment's type is defined by the four enum axes and is **immutable
-after creation**. This avoids the complexity of transforming all
-internal state when the experiment type changes. The type is stored in
-an `ExperimentType` category with four `StringDescriptor`s validated by
-`MembershipValidator`s. Public properties are read-only; factory and
-CIF-loading code use private setters (`_set_sample_form`,
-`_set_beam_mode`, `_set_radiation_probe`, `_set_scattering_type`) during
-construction only.
-
-### 3.2 Experiment Hierarchy
-
-```shell
-DatablockItem
-└── ExperimentBase                   # name, type: ExperimentType, as_cif
-    ├── PdExperimentBase             # + linked_phases, excluded_regions, peak, data
-    │   ├── BraggPdExperiment        # + instrument, background (both via factories)
-    │   └── TotalPdExperiment        # (no extra categories yet)
-    └── ScExperimentBase             # + linked_crystal, extinction, instrument, data
-        ├── CwlScExperiment
-        └── TofScExperiment
-```
-
-Each concrete experiment class carries:
-
-- `type_info: TypeInfo` — tag and description for factory lookup
-- `compatibility: Compatibility` — which enum axis values it supports
-
-### 3.3 Category Ownership
-
-Every experiment owns its categories as private attributes with public
-read-only or read-write properties:
-
-```python
-# Read-only — user cannot replace the object, only modify its contents
-experiment.linked_phases  # CategoryCollection
-experiment.excluded_regions  # CategoryCollection
-experiment.instrument  # CategoryItem
-experiment.peak  # CategoryItem
-experiment.data  # CategoryCollection (powder / total only)
-experiment.refln  # CategoryCollection (Bragg powder + single crystal)
-
-# Type-switchable — recreates the underlying object
-experiment.background_type = 'chebyshev'  # triggers BackgroundFactory.create(...)
-experiment.peak_profile_type = 'thompson-cox-hastings'  # triggers PeakFactory.create(...)
-experiment.extinction_type = 'becker-coppens'  # triggers ExtinctionFactory.create(...)
-```
-
-**Type switching pattern:** `expt.background_type = 'chebyshev'` rather
-than `expt.background.type = 'chebyshev'`. This keeps the API at the
-experiment level and makes it clear that the entire category object is
-being replaced.
-
----
-
-## 4. Structure System
-
-### 4.1 Structure Hierarchy
-
-```shell
-DatablockItem
-└── Structure                       # name, cell, space_group, atom_sites, atom_site_aniso
-```
-
-A `Structure` contains four categories:
-
-- `Cell` — unit cell parameters (`CategoryItem`)
-- `SpaceGroup` — symmetry information (`CategoryItem`)
-- `AtomSites` — atomic positions and isotropic ADP
-  (`CategoryCollection`)
-- `AtomSiteAniso` — anisotropic ADP tensor components
-  (`CategoryCollection`)
-
-Symmetry constraints (cell metric, atomic coordinates, ADPs) are applied
-via the `crystallography` module during `_update_categories()`.
-
-Parameters that are fully determined by symmetry (e.g. `lattice_b` in
-cubic, `fract_y` of an atom on a 4-fold axis, off-diagonal ADPs forced
-to zero by site symmetry) are flagged as `symmetry_constrained = True`
-on the `Parameter`. This forces `free = False`; any subsequent attempt
-to set `free = True` on such a parameter is ignored with a warning.
-Flags are recomputed on every `_update_categories()` so that changing
-the space group, Wyckoff letter, or ADP type re-evaluates which
-parameters are symmetry constrained. Surface helpers
-`cell_symmetry_constrained_flags(...)` and
-`atom_site_symmetry_constrained_flags(...)` in `crystallography` expose
-the per-key flags.
-
-### 4.2 Atomic Displacement Parameters (ADP)
-
-ADP support covers four CIF-standard types: **Biso**, **Uiso**,
-**Bani**, **Uani**. The design uses **type-neutral parameter names** so
-that switching type is a one-line operation on `adp_type`: parameter
-objects stay stable while their values and CIF output names change.
-
-**Two sibling collections.** Following CIF conventions (`_atom_site` and
-`_atom_site_aniso` are separate loops), isotropic and anisotropic data
-live in separate collections on `Structure`. Every atom always has an
-entry in both collections; when `adp_type` is isotropic, the aniso
-parameters hold `0.0` and are ignored by calculators.
-
-```shell
-Structure
-├── cell              (CategoryItem)
-├── space_group       (CategoryItem)
-├── atom_sites        (CategoryCollection of AtomSite)
-└── atom_site_aniso   (CategoryCollection of AtomSiteAniso)
-```
-
-**Type-neutral names.** `atom_site.adp_iso` is the isotropic ADP
-parameter. Its physical meaning (B or U) is determined by
-`atom_site.adp_type`. Similarly, `atom_site_aniso.adp_11`...`adp_23` are
-type-neutral tensor components.
-
-| Parameter  | Location        | CIF names                                                |
-| ---------- | --------------- | -------------------------------------------------------- |
-| `adp_type` | `AtomSite`      | `_atom_site.adp_type`                                    |
-| `adp_iso`  | `AtomSite`      | `_atom_site.B_iso_or_equiv`, `_atom_site.U_iso_or_equiv` |
-| `adp_11`   | `AtomSiteAniso` | `_atom_site_aniso.B_11`, `_atom_site_aniso.U_11`         |
-| `adp_22`   | `AtomSiteAniso` | `_atom_site_aniso.B_22`, `_atom_site_aniso.U_22`         |
-| `adp_33`   | `AtomSiteAniso` | `_atom_site_aniso.B_33`, `_atom_site_aniso.U_33`         |
-| `adp_12`   | `AtomSiteAniso` | `_atom_site_aniso.B_12`, `_atom_site_aniso.U_12`         |
-| `adp_13`   | `AtomSiteAniso` | `_atom_site_aniso.B_13`, `_atom_site_aniso.U_13`         |
-| `adp_23`   | `AtomSiteAniso` | `_atom_site_aniso.B_23`, `_atom_site_aniso.U_23`         |
-
-`adp_type` is a `StringDescriptor` validated against `AdpTypeEnum`
-(`Biso`, `Uiso`, `Bani`, `Uani`).
-
-**Dual CIF names.** Each parameter's `CifHandler` carries both CIF name
-variants (e.g.
-`['_atom_site.B_iso_or_equiv', '_atom_site.U_iso_or_equiv']`). Reading
-tries each name until a match is found; writing uses `names[0]`. The
-`adp_type` setter reorders the list so the correct tag is emitted.
-
-For example, switching from `Biso` to `Uiso` makes
-`_atom_site.U_iso_or_equiv` the first CIF name on `adp_iso`; switching
-from `Bani` to `Uani` does the same for all six `_atom_site_aniso.U_*`
-tensor tags. No dynamic CIF handler is needed.
-
-**Auto-conversion.** Setting `adp_type` triggers value conversion: B ↔ U
-via `B = 8π²U`; iso → ani seeds the diagonal; ani → iso averages the
-diagonal.
-
-| Transition | Conversion rule                                                       |
-| ---------- | --------------------------------------------------------------------- |
-| B ↔ U      | `B = 8π²U`                                                            |
-| Iso → Ani  | `adp_11 = adp_22 = adp_33 = adp_iso`; off-diagonal terms become `0.0` |
-| Ani → Iso  | `adp_iso = (adp_11 + adp_22 + adp_33) / 3`                            |
-
-**Collection sync.** `Structure._update_categories()` reconciles the two
-collections: adds missing aniso entries, removes stale ones, and rekeys
-on label rename. Sync follows the datablock dirty-flag pattern: category
-or parameter changes mark the structure as needing an update, and the
-next serialisation, plot, or fit call performs the reconciliation.
-
-| Event                              | Sync action                                               |
-| ---------------------------------- | --------------------------------------------------------- |
-| Atom added to `atom_sites`         | Create matching `AtomSiteAniso` entry with `0.0` defaults |
-| Atom removed from `atom_sites`     | Remove matching `AtomSiteAniso` entry                     |
-| Atom label renamed in `atom_sites` | Rekey the matching `AtomSiteAniso` entry                  |
-
-**User-facing access.** ADP parameters follow the same two-level access
-pattern as other category parameters:
-
-```python
-structure.atom_sites['Si'].adp_type = 'Biso'
-structure.atom_sites['Si'].adp_iso = 0.47
-structure.atom_site_aniso['Si'].adp_11 = 0.05
-```
-
-Creating an atom uses `adp_iso`; the matching `atom_site_aniso` entry is
-created by `_update_categories()`:
-
-```python
-structure.atom_sites.create(
-    label='Si',
-    type_symbol='Si',
-    fract_x=0.0,
-    fract_y=0.0,
-    fract_z=0.0,
-    adp_type='Biso',
-    adp_iso=0.47,
-)
-```
-
-**Design rule.** ADP parameter names are type-neutral because
-type-specific names (`b_iso`, `u_iso`, etc.) would require replacing
-parameter objects when the ADP type changes, which would break
-constraints, free flags, and existing references. The always-present
-`atom_site_aniso` collection avoids conditional branches in
-serialisation, calculators, parameter tables, constraint wiring, and UI.
-
----
-
-## 5. Factory System
-
-### 5.1 FactoryBase
-
-All factories inherit from `FactoryBase`, which provides:
-
-| Feature            | Method / Attribute           | Description                                       |
-| ------------------ | ---------------------------- | ------------------------------------------------- |
-| Registration       | `@Factory.register`          | Class decorator, appends to `_registry`           |
-| Supported map      | `_supported_map()`           | `{tag: class}` from all registered classes        |
-| Creation           | `create(tag)`                | Instantiate by tag string                         |
-| Default resolution | `default_tag(**conditions)`  | Largest-subset matching on `_default_rules`       |
-| Context creation   | `create_default_for(**cond)` | Resolve tag → create                              |
-| Filtered query     | `supported_for(**filters)`   | Filter by `Compatibility` and `CalculatorSupport` |
-| Display            | `show_supported(**filters)`  | Pretty-print table of type + description          |
-| Tag listing        | `supported_tags()`           | List of all registered tags                       |
-
-Each `__init_subclass__` gives every factory its own independent
-`_registry` and `_default_rules`.
-
-### 5.2 Default Rules
-
-`_default_rules` maps frozensets of `(axis_name, enum_value)` tuples to
-tag strings (preferably enum values for type safety):
-
-```python
-class PeakFactory(FactoryBase):
-    _default_rules = {
-        frozenset({
-            ('scattering_type', ScatteringTypeEnum.BRAGG),
-            ('beam_mode', BeamModeEnum.CONSTANT_WAVELENGTH),
-        }): PeakProfileTypeEnum.CWL_PSEUDO_VOIGT,
-        frozenset({
-            ('scattering_type', ScatteringTypeEnum.BRAGG),
-            ('beam_mode', BeamModeEnum.TIME_OF_FLIGHT),
-        }): PeakProfileTypeEnum.TOF_JORGENSEN,
-        frozenset({
-            ('scattering_type', ScatteringTypeEnum.TOTAL),
-        }): PeakProfileTypeEnum.TOTAL_GAUSSIAN_DAMPED_SINC,
-    }
-```
-
-Resolution uses **largest-subset matching**: the rule whose frozenset is
-the biggest subset of the given conditions wins. `frozenset()` acts as a
-universal fallback.
-
-### 5.3 Metadata on Registered Classes
-
-Every `@Factory.register`-ed class carries three frozen dataclass
-attributes:
-
-```python
-@PeakFactory.register
-class CwlPseudoVoigt(PeakBase, CwlBroadeningMixin):
-    type_info = TypeInfo(
-        tag=PeakProfileTypeEnum.CWL_PSEUDO_VOIGT.value,
-        description=PeakProfileTypeEnum.CWL_PSEUDO_VOIGT.description(),
-    )
-    compatibility = Compatibility(
-        scattering_type=frozenset({ScatteringTypeEnum.BRAGG}),
-        beam_mode=frozenset({BeamModeEnum.CONSTANT_WAVELENGTH}),
-    )
-    calculator_support = CalculatorSupport(
-        calculators=frozenset({CalculatorEnum.CRYSPY, CalculatorEnum.CRYSFML}),
-    )
-```
-
-| Metadata            | Purpose                                                 |
-| ------------------- | ------------------------------------------------------- |
-| `TypeInfo`          | Stable tag for lookup/serialisation + human description |
-| `Compatibility`     | Which enum axis values this class works with            |
-| `CalculatorSupport` | Which calculation engines support this class            |
-
-### 5.4 Registration Trigger
-
-Concrete classes use `@Factory.register` decorators. To trigger
-registration, each package's `__init__.py` must **explicitly import**
-every concrete class:
-
-```python
-# datablocks/experiment/categories/background/__init__.py
-from .chebyshev import ChebyshevPolynomialBackground
-from .line_segment import LineSegmentBackground
-```
-
-### 5.5 All Factories
-
-| Factory                  | Domain                 | Tags resolve to                                                                                                                                                                                    |
-| ------------------------ | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `BackgroundFactory`      | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                                           |
-| `PeakFactory`            | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                                       |
-| `InstrumentFactory`      | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                                            |
-| `DataFactory`            | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                                              |
-| `ReflnFactory`           | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                                            |
-| `ExtinctionFactory`      | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                                          |
-| `LinkedCrystalFactory`   | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                                                    |
-| `ExcludedRegionsFactory` | Excluded regions       | `ExcludedRegions`                                                                                                                                                                                  |
-| `LinkedPhasesFactory`    | Linked phases          | `LinkedPhases`                                                                                                                                                                                     |
-| `ExperimentTypeFactory`  | Experiment descriptors | `ExperimentType`                                                                                                                                                                                   |
-| `CellFactory`            | Unit cells             | `Cell`                                                                                                                                                                                             |
-| `SpaceGroupFactory`      | Space groups           | `SpaceGroup`                                                                                                                                                                                       |
-| `AtomSitesFactory`       | Atom sites             | `AtomSites`                                                                                                                                                                                        |
-| `AtomSiteAnisoFactory`   | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                                          |
-| `AliasesFactory`         | Parameter aliases      | `Aliases`                                                                                                                                                                                          |
-| `ConstraintsFactory`     | Parameter constraints  | `Constraints`                                                                                                                                                                                      |
-| `FitModeFactory`         | Fit-mode category      | `FitMode`                                                                                                                                                                                          |
-| `JointFitFactory`        | Joint-fit weights      | `JointFitCollection`                                                                                                                                                                               |
-| `CalculatorFactory`      | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                                        |
-| `MinimizerFactory`       | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
-
-> **Note:** `ExperimentFactory` and `StructureFactory` are _builder_
-> factories with `from_cif_path`, `from_cif_str`, `from_data_path`, and
-> `from_scratch` classmethods. `ExperimentFactory` inherits
-> `FactoryBase` and uses `@register` on all four concrete experiment
-> classes; `_resolve_class` looks up the registered class via
-> `default_tag()` + `_supported_map()`. `StructureFactory` is a plain
-> class without `FactoryBase` inheritance (only one structure type
-> exists today).
-
-### 5.6 Tag Naming Convention
-
-Canonical tags are the stable identifiers for factory lookup and
-serialisation. User-facing APIs may expose context-local aliases when
-the owner object already provides enough context to disambiguate the
-choice (for example, `experiment.peak_profile_type = 'pseudo-voigt'`
-inside a CWL or TOF experiment). Canonical tags must be:
-
-- **Consistent** — use the same abbreviations everywhere.
-- **Hyphen-separated** — all lowercase, words joined by hyphens.
-- **Semantically ordered** — from general to specific.
-- **Unique within a factory** — but may overlap across factories.
-
-#### Standard Abbreviations
-
-| Concept             | Abbreviation | Never use                   |
-| ------------------- | ------------ | --------------------------- |
-| Powder              | `pd`         | `powder`                    |
-| Single crystal      | `sc`         | `single-crystal`            |
-| Constant wavelength | `cwl`        | `cw`, `constant-wavelength` |
-| Time-of-flight      | `tof`        | `time-of-flight`            |
-| Bragg (scattering)  | `bragg`      |                             |
-| Total (scattering)  | `total`      |                             |
-
-#### Complete Tag Registry
-
-**Background tags**
-
-| Tag            | Class                           |
-| -------------- | ------------------------------- |
-| `line-segment` | `LineSegmentBackground`         |
-| `chebyshev`    | `ChebyshevPolynomialBackground` |
-
-**Peak tags**
-
-Canonical peak tags are globally unique within `PeakFactory`. The
-experiment-facing `peak_profile_type` getter, setter, and supported-type
-display use context-local aliases so users do not need to type `cwl-`,
-`tof-`, or `total-` when the experiment context already disambiguates
-the choice.
-
-| Canonical tag                          | Local alias                          | Class                              |
-| -------------------------------------- | ------------------------------------ | ---------------------------------- |
-| `cwl-pseudo-voigt`                     | `pseudo-voigt`                       | `CwlPseudoVoigt`                   |
-| `cwl-pseudo-voigt-empirical-asymmetry` | `pseudo-voigt + empirical asymmetry` | `CwlPseudoVoigtEmpiricalAsymmetry` |
-| `cwl-thompson-cox-hastings`            | `thompson-cox-hastings`              | `CwlThompsonCoxHastings`           |
-| `tof-pseudo-voigt`                     | `pseudo-voigt`                       | `TofPseudoVoigt`                   |
-| `tof-jorgensen`                        | `jorgensen`                          | `TofJorgensen`                     |
-| `tof-jorgensen-von-dreele`             | `jorgensen-von-dreele`               | `TofJorgensenVonDreele`            |
-| `tof-double-jorgensen-von-dreele`      | `double-jorgensen-von-dreele`        | `TofDoubleJorgensenVonDreele`      |
-| `total-gaussian-damped-sinc`           | `gaussian-damped-sinc`               | `TotalGaussianDampedSinc`          |
-
-**Instrument tags**
-
-| Tag      | Class             |
-| -------- | ----------------- |
-| `cwl-pd` | `CwlPdInstrument` |
-| `cwl-sc` | `CwlScInstrument` |
-| `tof-pd` | `TofPdInstrument` |
-| `tof-sc` | `TofScInstrument` |
-
-**Data tags**
-
-| Tag            | Class       |
-| -------------- | ----------- |
-| `bragg-pd`     | `PdCwlData` |
-| `bragg-pd-tof` | `PdTofData` |
-| `total-pd`     | `TotalData` |
-
-**Refln tags**
-
-| Tag                  | Class                |
-| -------------------- | -------------------- |
-| `bragg-sc`           | `ReflnData`          |
-| `bragg-pd-refln`     | `PowderCwlReflnData` |
-| `bragg-pd-tof-refln` | `PowderTofReflnData` |
-
-**Extinction tags**
-
-| Tag              | Class                     |
-| ---------------- | ------------------------- |
-| `becker-coppens` | `BeckerCoppensExtinction` |
-
-**Linked-crystal tags**
-
-| Tag       | Class           |
-| --------- | --------------- |
-| `default` | `LinkedCrystal` |
-
-**Experiment tags**
-
-| Tag            | Class               |
-| -------------- | ------------------- |
-| `bragg-pd`     | `BraggPdExperiment` |
-| `total-pd`     | `TotalPdExperiment` |
-| `bragg-sc-cwl` | `CwlScExperiment`   |
-| `bragg-sc-tof` | `TofScExperiment`   |
-
-**Calculator tags**
-
-| Tag       | Class               |
-| --------- | ------------------- |
-| `cryspy`  | `CryspyCalculator`  |
-| `crysfml` | `CrysfmlCalculator` |
-| `pdffit`  | `PdffitCalculator`  |
-
-**Minimizer tags**
-
-| Tag                     | Class                        |
-| ----------------------- | ---------------------------- |
-| `lmfit`                 | `LmfitMinimizer`             |
-| `lmfit (leastsq)`       | `LmfitLeastsqMinimizer`      |
-| `lmfit (least_squares)` | `LmfitLeastSquaresMinimizer` |
-| `dfols`                 | `DfolsMinimizer`             |
-| `bumps`                 | `BumpsMinimizer`             |
-| `bumps (lm)`            | `BumpsLmMinimizer`           |
-| `bumps (dream)`         | `BumpsDreamMinimizer`        |
-| `bumps (amoeba)`        | `BumpsAmoebaMinimizer`       |
-| `bumps (de)`            | `BumpsDEMinimizer`           |
-
-### 5.7 Metadata Classification — Which Classes Get What
-
-#### The Rule
-
-> **If a concrete class is created by a factory, it gets `type_info`,
-> `compatibility`, and `calculator_support`.**
->
-> **If a `CategoryItem` only exists as a child row inside a
-> `CategoryCollection`, it does NOT get these attributes — the
-> collection does.**
-
-#### Rationale
-
-A `LineSegment` item (a single background control point) is never
-selected, created, or queried by a factory. It is always instantiated
-internally by its parent `LineSegmentBackground` collection. The
-meaningful unit of selection is the _collection_, not the item. The user
-picks "line-segment background" (the collection type), not individual
-line-segment points.
-
-#### Singleton CategoryItems — factory-created (get all three)
-
-| Class                              | Factory                 |
-| ---------------------------------- | ----------------------- |
-| `CwlPdInstrument`                  | `InstrumentFactory`     |
-| `CwlScInstrument`                  | `InstrumentFactory`     |
-| `TofPdInstrument`                  | `InstrumentFactory`     |
-| `TofScInstrument`                  | `InstrumentFactory`     |
-| `CwlPseudoVoigt`                   | `PeakFactory`           |
-| `CwlPseudoVoigtEmpiricalAsymmetry` | `PeakFactory`           |
-| `CwlThompsonCoxHastings`           | `PeakFactory`           |
-| `TofJorgensen`                     | `PeakFactory`           |
-| `TofJorgensenVonDreele`            | `PeakFactory`           |
-| `TofDoubleJorgensenVonDreele`      | `PeakFactory`           |
-| `TotalGaussianDampedSinc`          | `PeakFactory`           |
-| `BeckerCoppensExtinction`          | `ExtinctionFactory`     |
-| `LinkedCrystal`                    | `LinkedCrystalFactory`  |
-| `Cell`                             | `CellFactory`           |
-| `SpaceGroup`                       | `SpaceGroupFactory`     |
-| `ExperimentType`                   | `ExperimentTypeFactory` |
-| `FitMode`                          | `FitModeFactory`        |
-
-#### CategoryCollections — factory-created (get all three)
-
-| Class                           | Factory                  |
-| ------------------------------- | ------------------------ |
-| `LineSegmentBackground`         | `BackgroundFactory`      |
-| `ChebyshevPolynomialBackground` | `BackgroundFactory`      |
-| `PdCwlData`                     | `DataFactory`            |
-| `PdTofData`                     | `DataFactory`            |
-| `TotalData`                     | `DataFactory`            |
-| `ReflnData`                     | `ReflnFactory`           |
-| `PowderCwlReflnData`            | `ReflnFactory`           |
-| `PowderTofReflnData`            | `ReflnFactory`           |
-| `ExcludedRegions`               | `ExcludedRegionsFactory` |
-| `LinkedPhases`                  | `LinkedPhasesFactory`    |
-| `AtomSites`                     | `AtomSitesFactory`       |
-| `AtomSiteAnisoCollection`       | `AtomSiteAnisoFactory`   |
-| `Aliases`                       | `AliasesFactory`         |
-| `Constraints`                   | `ConstraintsFactory`     |
-| `JointFitCollection`            | `JointFitFactory`        |
-
-#### CategoryItems that are ONLY children of collections (NO metadata)
-
-| Class            | Parent collection               |
-| ---------------- | ------------------------------- |
-| `LineSegment`    | `LineSegmentBackground`         |
-| `PolynomialTerm` | `ChebyshevPolynomialBackground` |
-| `AtomSite`       | `AtomSites`                     |
-| `AtomSiteAniso`  | `AtomSiteAnisoCollection`       |
-| `PdCwlDataPoint` | `PdCwlData`                     |
-| `PdTofDataPoint` | `PdTofData`                     |
-| `TotalDataPoint` | `TotalData`                     |
-| `Refln`          | `ReflnData`                     |
-| `LinkedPhase`    | `LinkedPhases`                  |
-| `ExcludedRegion` | `ExcludedRegions`               |
-| `Alias`          | `Aliases`                       |
-| `Constraint`     | `Constraints`                   |
-| `JointFitItem`   | `JointFitCollection`            |
-
-#### Non-category classes — factory-created (get `type_info` only)
-
-| Class               | Factory             | Notes                                                    |
-| ------------------- | ------------------- | -------------------------------------------------------- |
-| `CryspyCalculator`  | `CalculatorFactory` | No `compatibility` — limitations expressed on categories |
-| `CrysfmlCalculator` | `CalculatorFactory` | (same)                                                   |
-| `PdffitCalculator`  | `CalculatorFactory` | (same)                                                   |
-| `LmfitMinimizer`    | `MinimizerFactory`  | `type_info` only                                         |
-| `DfolsMinimizer`    | `MinimizerFactory`  | (same)                                                   |
-| `BumpsMinimizer`    | `MinimizerFactory`  | (same)                                                   |
-| `BraggPdExperiment` | `ExperimentFactory` | `type_info` + `compatibility` (no `calculator_support`)  |
-| `TotalPdExperiment` | `ExperimentFactory` | (same)                                                   |
-| `CwlScExperiment`   | `ExperimentFactory` | (same)                                                   |
-| `TofScExperiment`   | `ExperimentFactory` | (same)                                                   |
-
----
-
-## 6. Analysis
-
-### 6.1 Calculator
-
-The calculator performs the actual diffraction computation. It is
-attached per-experiment on the `ExperimentBase` object. Each experiment
-auto-resolves its calculator on first access based on the experiment's
-active support category (`data` for powder, `refln` for Bragg
-single-crystal) `calculator_support` metadata and
-`CalculatorFactory._default_rules`. The `CalculatorFactory` filters its
-registry by `engine_imported` (whether the third-party library is
-available in the environment).
-
-The experiment exposes a dedicated `calculation` category:
-
-- `calculation.calculator_type` — getter + setter for the calculator
-  backend tag
-- `calculation.calculator` — read-only access to the live backend
-  instance
-- `calculation.show_calculator_types()` — filtered by the active support
-  category and marks the current type
-
-### 6.2 Minimiser
-
-The minimiser drives the optimisation loop. `MinimizerFactory` creates
-instances by tag (e.g. `'lmfit'`, `'dfols'`, `'bumps'`).
-
-### 6.3 Fitter
-
-`Fitter` wraps a minimiser instance and orchestrates the fitting
-workflow:
-
-1. Collect `free_parameters` from structures + experiments.
-2. Record start values.
-3. Build an objective function that calls the calculator.
-4. Delegate to `minimizer.fit()`.
-5. Sync results (values + uncertainties) back to parameters.
-
-### 6.4 Analysis Object
-
-`Analysis` is bound to a `Project` and provides the high-level API:
-
-- Singleton section: `Analysis` is a `CategoryOwner`, not a
-  `DatablockItem`. It owns sibling categories and serializes as the body
-  of `analysis/analysis.cif` without a `data_` header.
-- Fit configuration: `fitting` (`CategoryItem` with `minimizer_type`).
-  `fitting.minimizer_type` selects the minimizer backend. The active
-  fitting mode lives on the owner as `analysis.fitting_mode_type`, not
-  as a nested child category field. `fitting.show_minimizer_types()`
-  lists supported minimizers.
-- Joint-fit weights: `joint_fit` (`CategoryCollection` of per-experiment
-  weight entries); sibling of `fitting`, not a child.
-- Fit results: `analysis.fit_results` stores the latest runtime result
-  object. This is `FitResults` for deterministic fits and
-  `BayesianFitResults` for Bayesian DREAM runs.
-- Parameter tables: `show_all_params()`, `show_fittable_params()`,
-  `show_free_params()`, `how_to_access_parameters()` Compact
-  summary-style parameter displays intentionally hide the large
-  loop-backed experiment categories `pd_data`, `total_data`, and `refln`
-  in `all()`, `access()`, and `cif_uids()` so the output stays readable.
-- Fitting: `fitting.minimizer_type` stores the shared minimizer
-  selection; `fitting_mode_type` stores the active mode on `Analysis`
-  itself; `fit()` dispatches to the current mode using the persisted
-  sibling categories `joint_fit`, `sequential_fit`, and
-  `sequential_fit_extract`. `display.fit_results()` dispatches through
-  the active runtime result object.
-- Aliases and constraints (single-type categories; no public `_type`
-  getter or setter)
-
-#### 6.4.1 Bayesian DREAM Runtime Results
-
-Bayesian sampling is integrated as a normal minimizer selection with tag
-`'bumps (dream)'`. It does not create a parallel `Analysis` stack or a
-new persisted results category.
-
-- `BayesianFitResults` extends `FitResults` with runtime-only posterior
-  state such as `posterior_samples`, `posterior_parameter_summaries`,
-  `posterior_predictive`, `diagnostics`, and `sampler_settings`.
-- Posterior arrays and predictive caches remain runtime-only; they are
-  not serialized into CIF or project directories.
-- `sampler_settings` records the resolved stochastic settings actually
-  used for the run, including `random_seed`, `steps`, `burn`, `thin`,
-  `pop`, and `parallel`.
-- The current user-facing DREAM controls live on the active minimizer
-  object, for example `project.analysis.fitting.minimizer.steps`,
-  `burn`, `thin`, `pop`, `parallel`, and `init`.
-- `plot_param_correlations()` uses posterior samples when available and
-  otherwise falls back to deterministic covariance or engine-derived
-  correlations.
-- Bayesian-only plotting methods are exposed explicitly rather than by
-  overloading deterministic plot calls: `plot_posterior_pairs()`,
-  `plot_param_distribution(param)`, and
-  `plot_posterior_predictive(expt_name, ...)`.
-
----
-
-## 7. Project — The Top-Level Façade
-
-`Project` is the single entry point for the user:
-
-```python
-import easydiffraction as ed
-
-project = ed.Project(name='my_project')
-```
-
-It owns and coordinates all components:
-
-| Property              | Type             | Description                              |
-| --------------------- | ---------------- | ---------------------------------------- |
-| `project.info`        | `ProjectInfo`    | Metadata: name, title, description, path |
-| `project.structures`  | `Structures`     | Collection of structure datablocks       |
-| `project.experiments` | `Experiments`    | Collection of experiment datablocks      |
-| `project.rendering`   | `Rendering`      | Plot/table engine selection              |
-| `project.display`     | `ProjectDisplay` | Pattern/report facade                    |
-| `project.analysis`    | `Analysis`       | Minimiser, fitting, aliases, constraints |
-| `project.summary`     | `Summary`        | Report generation                        |
-| `project.verbosity`   | `str`            | Console output level (full/short/silent) |
-
-Internally, `Project` keeps project-scoped singleton categories under a
-private `ProjectConfig(CategoryOwner)` object. That owner currently
-holds `project.info` (`ProjectInfo`) and `project.rendering`
-(`Rendering`), while the public access paths stay flat on `Project` for
-user discoverability.
-
-### 7.1 Data Flow
-
-```
-Parameter.value set
-    → AttributeSpec validation (type + value)
-    → _need_categories_update = True (on parent CategoryOwner)
-
-Plot / CIF export / fit objective evaluation
-    → _update_categories()
-        → categories sorted by _update_priority
-        → each category._update()
-            → background: interpolate/evaluate → write to data
-            → calculator: compute pattern → write to data
-    → _need_categories_update = False
-```
-
-### 7.2 Persistence
-
-Projects are saved as a directory of CIF files:
-
-```shell
-project_dir/
-├── project.cif          # ProjectConfig categories (info + rendering)
-├── summary.cif          # Summary report
-├── structures/
-│   └── lbco.cif         # One file per structure
-├── experiments/
-│   └── hrpt.cif         # One file per experiment
-└── analysis/
-    └── analysis.cif     # Analysis settings
-```
-
-`project.cif` serializes the private `ProjectConfig` owner without a
-`data_` header. It carries both the `_project.*` metadata category and
-the `_rendering.*` engine preferences (`chart_engine`, `table_engine`),
-so a saved project re-opens with the same display backends.
-Per-experiment calculator selection (`_calculation.calculator_type`)
-lives in each experiment file, and fit configuration
-(`_fitting.minimizer_type`, `_fitting.mode_type`) lives in
-`analysis/analysis.cif`. Runtime fit outputs, including
-`analysis.fit_results`, posterior chains, posterior predictive
-summaries, and convergence diagnostics, are not serialized.
-
-### 7.3 Verbosity
-
-`Project.verbosity` controls how much console output operations produce.
-It is backed by `VerbosityEnum` (in `utils/enums.py`) and accepts three
-values:
-
-| Level    | Enum member            | Behaviour                                          |
-| -------- | ---------------------- | -------------------------------------------------- |
-| `full`   | `VerbosityEnum.FULL`   | Multi-line output with headers, tables, and detail |
-| `short`  | `VerbosityEnum.SHORT`  | One-line status message per action                 |
-| `silent` | `VerbosityEnum.SILENT` | No console output                                  |
-
-The default is `'full'`.
-
-```python
-project.verbosity = 'short'
-```
-
-**Resolution order:** methods that produce console output (e.g.
-`analysis.fit()`, `experiments.add_from_data_path()`) read
-`project.verbosity`.
-
-```python
-# Use project-level default for all operations
-project.verbosity = 'short'
-project.analysis.fit()                         # → short mode
-
-# Override for one fit, then restore the project default
-original_verbosity = project.verbosity
-project.verbosity = 'silent'
-project.analysis.fit()                         # → silent
-project.verbosity = original_verbosity
-```
-
-**Output styles per level:**
-
-- **Data loading** — `full`: paragraph header + detail line; `short`:
-  `✅ Data loaded: Experiment 🔬 'name'. N points.`; `silent`: nothing.
-- **Fitting** — `full`: per-iteration progress table with improvement
-  percentages; `short`: one-row-per-experiment summary table; `silent`:
-  nothing.
-
----
-
-## 8. User-Facing API Patterns
-
-All examples below are drawn from the actual tutorials (`tutorials/`).
-
-> **Notebook workflow:** Jupyter notebooks (`*.ipynb`) in
-> `docs/docs/tutorials/` are generated artifacts. Edit only the
-> corresponding `*.py` script, then run `pixi run notebook-prepare` to
-> regenerate the notebook. Never edit `*.ipynb` files by hand.
-
-### 8.1 Project Setup
-
-```python
-import easydiffraction as ed
-
-project = ed.Project(name='lbco_hrpt')
-project.info.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
-project.save_as(dir_path='lbco_hrpt', temporary=True)
-```
-
-### 8.2 Define Structures
-
-```python
-# Create a structure datablock
-project.structures.create(name='lbco')
-
-# Set space group and unit cell
-project.structures['lbco'].space_group.name_h_m = 'P m -3 m'
-project.structures['lbco'].cell.length_a = 3.88
-
-# Add atom sites
-project.structures['lbco'].atom_sites.create(
-    label='La',
-    type_symbol='La',
-    fract_x=0,
-    fract_y=0,
-    fract_z=0,
-    wyckoff_letter='a',
-    adp_iso=0.5,
-    occupancy=0.5,
-)
-
-# Show as CIF
-project.structures['lbco'].show_as_cif()
-```
-
-### 8.3 Define Experiments
-
-```python
-# Download data and create experiment from a data file
-data_path = ed.download_data(id=3, destination='data')
-project.experiments.add_from_data_path(
-    name='hrpt',
-    data_path=data_path,
-    sample_form='powder',
-    beam_mode='constant wavelength',
-    radiation_probe='neutron',
-)
-
-# Set instrument parameters
-project.experiments['hrpt'].instrument.setup_wavelength = 1.494
-project.experiments['hrpt'].instrument.calib_twotheta_offset = 0.6
-
-# Browse and select peak profile type
-project.experiments['hrpt'].show_peak_profile_types()
-project.experiments['hrpt'].peak_profile_type = 'pseudo-voigt'
-
-# Set peak profile parameters
-project.experiments['hrpt'].peak.broad_gauss_u = 0.1
-project.experiments['hrpt'].peak.broad_gauss_v = -0.1
-
-# Browse and select background type
-project.experiments['hrpt'].show_background_types()
-project.experiments['hrpt'].background_type = 'line-segment'
-
-# Add background points
-project.experiments['hrpt'].background.create(id='10', x=10, y=170)
-project.experiments['hrpt'].background.create(id='50', x=50, y=170)
-
-# Link structure to experiment
-project.experiments['hrpt'].linked_phases.create(id='lbco', scale=10.0)
-```
-
-### 8.4 Analysis and Fitting
-
-```python
-# Calculator is auto-resolved per experiment; override if needed
-project.experiments['hrpt'].calculation.show_calculator_types()
-project.experiments['hrpt'].calculation.calculator_type = 'cryspy'
-project.analysis.fitting.minimizer_type = 'lmfit'
-
-# Plot before fitting
-project.display.pattern(expt_name='hrpt')
-
-# Select free parameters
-project.structures['lbco'].cell.length_a.free = True
-project.experiments['hrpt'].linked_phases['lbco'].scale.free = True
-project.experiments['hrpt'].instrument.calib_twotheta_offset.free = True
-project.experiments['hrpt'].background['10'].y.free = True
-
-# Inspect free parameters
-project.display.parameters.free()
-
-# Fit and show results
-project.analysis.fit()
-project.display.fit.results()
-
-# Plot after fitting
-project.display.pattern(expt_name='hrpt')
-
-# Save
-project.save()
-```
-
-### 8.4.1 Bayesian Refinement
-
-```python
-# Deterministic pre-fit remains explicit
-project.analysis.fitting.minimizer_type = 'bumps (lm)'
-project.analysis.fit()
-
-# Switch to Bayesian sampling using the same entry point
-project.analysis.fitting.minimizer_type = 'bumps (dream)'
-project.analysis.fitting.minimizer.steps = 1000
-project.analysis.fitting.minimizer.parallel = 0
-project.analysis.fit()
-
-# Runtime-only Bayesian summaries and plots
-project.display.fit.results()
-project.display.fit.correlations()
-project.display.posterior.pairs()
-project.display.posterior.distribution(param)
-project.display.posterior.predictive(expt_name='hrpt')
-```
-
-### 8.5 TOF Experiment (tutorial ed-7)
-
-```python
-expt = ExperimentFactory.from_data_path(
-    name='sepd',
-    data_path=data_path,
-    beam_mode='time-of-flight',
-)
-expt.instrument.calib_d_to_tof_offset = 0.0
-expt.instrument.calib_d_to_tof_linear = 7476.91
-expt.peak_profile_type = 'jorgensen'
-expt.peak.broad_gauss_sigma_0 = 3.0
-```
-
-### 8.6 Total Scattering / PDF (tutorial ed-12)
-
-```python
-project.experiments.add_from_data_path(
-    name='xray_pdf',
-    data_path=data_path,
-    sample_form='powder',
-    beam_mode='constant wavelength',
-    radiation_probe='xray',
-    scattering_type='total',
-)
-project.experiments['xray_pdf'].peak_profile_type = 'gaussian-damped-sinc'
-# Calculator is auto-resolved to 'pdffit' for total scattering experiments
-```
-
----
-
-## 9. Design Principles
-
-### 9.1 Naming and CIF Conventions
-
-- Follow CIF naming conventions where possible. Deviate for better API
-  design when necessary, but keep the spirit of CIF names.
-- Reuse the concept of datablocks and categories from CIF.
-- `DatablockItem` = one CIF `data_` block, `DatablockCollection` = set
-  of blocks.
-- `CategoryItem` = one CIF category, `CategoryCollection` = CIF loop.
-- **Free-flag encoding**: A parameter's free/fixed status is encoded in
-  CIF via uncertainty brackets. `3.89` = fixed, `3.89(2)` = free with
-  esd, `3.89()` = free without esd. There is no separate list of free
-  parameters; the brackets are the single source of truth.
-
-### 9.2 Immutability of Experiment Type
-
-The experiment type (the four enum axes) can only be set at creation
-time. It cannot be changed afterwards. This avoids the complexity of
-maintaining different state transformations when switching between
-fundamentally different experiment configurations.
-
-### 9.3 Category Type Switching
-
-In contrast to experiment type, categories that have multiple
-implementations (peak profiles, backgrounds, instruments) can be
-switched at runtime by the user. The API pattern uses a type property on
-the **experiment**, not on the category itself:
-
-```python
-# ✅ Correct — type property on the experiment
-expt.background_type = 'chebyshev'
-
-# ❌ Not used — type property on the category
-expt.background.type = 'chebyshev'
-```
-
-This makes it clear that the entire category object is being replaced
-and simplifies maintenance.
-
-### 9.4 Switchable-Category Convention
-
-Categories whose concrete implementation can be swapped at runtime
-(background, peak profile, etc.) are called **switchable categories**.
-**Every category must be factory-based** — even if only one
-implementation exists today. This ensures uniform construction,
-consistent metadata, and makes adding a second implementation trivial.
-
-For categories with **multiple implementations** (multi-type), the owner
-exposes the full switchable API:
-
-| Facet           | Naming pattern                               | Example                                          |
-| --------------- | -------------------------------------------- | ------------------------------------------------ |
-| Current object  | `<category>` property (read-only)            | `expt.background`, `expt.peak`                   |
-| Active type tag | `<category>_type` property (getter + setter) | `expt.background_type`, `expt.peak_profile_type` |
-| Show types      | `show_<category>_types()`                    | `expt.show_background_types()`                   |
-
-Multi-type categories:
-
-- **Experiment:** `background_type`, `peak_profile_type`,
-  `extinction_type`.
-
-Categories that are **fixed at creation** (determined by the experiment
-type and never changed) expose only a read-only `<category>` property
-with no `_type` getter, setter, or show methods:
-
-- **Experiment:** `instrument`, `data`, `refln`.
-
-For categories with **only one implementation** (single-type), the
-`_type` getter, setter, and show methods are omitted from the public API
-to avoid clutter. The factory and `_type` attribute still exist
-internally for consistency and future extensibility.
-
-Single-type categories (no public `_type` property):
-
-- **Experiment:** `diffrn`, `linked_crystal`, `excluded_regions`,
-  `linked_phases`.
-- **Structure:** `cell`, `space_group`, `atom_sites`, `atom_site_aniso`.
-- **Analysis:** `aliases`, `constraints`, `fitting`, `sequential_fit`.
-
-`fitting` is a dedicated analysis configuration category, but the fit
-mode selector lives on the owner as `analysis.fitting_mode_type`. This
-is the project's active-sibling selector pattern: the owner stores the
-authoritative mode and decides which sibling categories are active,
-shown in help, and serialized. `joint_fit`, `sequential_fit`, and
-`sequential_fit_extract` remain direct `Analysis` siblings even when
-inactive. See the fit-mode ADR for the full contract:
-[`adr_fit-mode-categories.md`](ADRs/adr_fit-mode-categories.md).
-Likewise, `calculation` is a dedicated experiment category that owns
-calculator selection — `experiment.calculation.calculator_type` and
-`experiment.calculation.show_calculator_types()` — instead of the
-selector being exposed at the experiment owner level. The same pattern
-applies to `display` on `Project`, which owns `chart_engine` and
-`table_engine` (see §9.4.1).
-
-**Design decisions:**
-
-- The **experiment owns** the `_type` setter because switching replaces
-  the entire category object
-  (`self._background = BackgroundFactory.create(...)`).
-- The **experiment owns** the `show_*` methods because they are
-  one-liners that delegate to `Factory.show_supported(...)` and can pass
-  experiment-specific context (e.g. `scattering_type`, `beam_mode` for
-  peak filtering).
-- Concrete category subclasses provide a public `show()` method for
-  displaying the current content (not on the base
-  `CategoryItem`/`CategoryCollection`).
-
-#### 9.4.1 Selector Families
-
-Not every `_type` attribute represents the same kind of choice. The API
-recognises three distinct selector families. They share a similar
-`<name>_type` shape so the user can inspect and set them uniformly, but
-their intent and ownership differ:
-
-| Family                             | User intent                     | Examples                                                                          | CIF                                                                                  |
-| ---------------------------------- | ------------------------------- | --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
-| Backend selector                   | Pick an execution backend       | `fitting.minimizer_type`, `calculation.calculator_type`, `rendering.chart_engine` | `_fitting.minimizer_type`, `_calculation.calculator_type`, `_rendering.chart_engine` |
-| Switchable-category impl. selector | Swap a category implementation  | `experiment.background_type`, `experiment.peak_profile_type`                      | category-owned type tag such as `_peak.profile_type`                                 |
-| Active-sibling selector            | Pick the active sibling surface | `analysis.fitting_mode_type`                                                      | owner-owned tag such as `_fitting.mode_type`                                         |
-
-Backend selectors live on a dedicated configuration category (`fitting`,
-`calculation`, `rendering`). Switchable-category implementation
-selectors are owned by the host (typically the experiment) because
-switching them replaces the category instance, as described in §9.3.
-Active-sibling selectors are also owner-level, but they do not swap one
-category implementation for another. Instead, they select which sibling
-category family is authoritative while the shared configuration category
-keeps a stable shape.
-
-### 9.5 Discoverable Supported Options
-
-The user can always discover what is supported for the current
-experiment:
-
-```python
-expt.show_peak_profile_types()
-expt.show_background_types()
-expt.calculation.show_calculator_types()
-expt.show_extinction_types()
-project.analysis.fitting.show_minimizer_types()
-project.analysis.show_fitting_mode_types()
-project.rendering.show_chart_engines()
-project.rendering.show_table_engines()
-```
-
-Available calculators are filtered by `engine_imported` (whether the
-library is installed) and by the experiment's active support category
-`calculator_support` metadata.
-
-### 9.6 Enums for Finite Value Sets
-
-Every attribute, descriptor, or configuration option that accepts a
-**finite, closed set of values** must be represented by a `(str, Enum)`
-class. This applies to:
-
-- Factory tags (§5.6) — e.g. `PeakProfileTypeEnum`, `CalculatorEnum`.
-- Experiment-axis values — e.g. `SampleFormEnum`, `BeamModeEnum`.
-- Category descriptors with enumerated choices — e.g. fit mode
-  (`FitModeEnum.SINGLE`, `FitModeEnum.JOINT`, `FitModeEnum.SEQUENTIAL`).
-
-The enum serves as the **single source of truth** for valid values,
-their user-facing string representations, and their descriptions.
-Benefits:
-
-- **Autocomplete and typo safety** — IDEs list valid members;
-  misspellings are caught at assignment time.
-- **Greppable** — searching for `FitModeEnum.JOINT` finds every code
-  path that handles joint fitting.
-- **Type-safe dispatch** — `if mode == FitModeEnum.JOINT:` is checked by
-  type checkers; `if mode == 'joint':` is not.
-- **Consistent validation** — use `MembershipValidator` with the enum
-  members instead of `RegexValidator` with hand-written patterns.
-
-**Rule:** internal code must compare against enum members, never raw
-strings. User-facing setters accept either the enum member or its string
-value (because `str(EnumMember) == EnumMember.value` for `(str, Enum)`),
-but internal dispatch always uses the enum:
-
-```python
-# ✅ Correct — compare with enum
-if self._fitting_mode_type is FitModeEnum.JOINT:
-
-# ❌ Wrong — compare with raw string
-if self._fitting_mode_type == 'joint':
-```
-
-### 9.7 Flat Category Structure — No Nested Categories
-
-Following CIF conventions, categories are **flat siblings** within their
-owner (`CategoryOwner`). A category must never be a child of another
-category of a different type. Categories can reference each other via
-IDs, but the ownership hierarchy is always:
-
-```
-Owner (CategoryOwner)
-├── CategoryA   (CategoryItem or CategoryCollection)
-├── CategoryB   (CategoryItem or CategoryCollection)
-└── CategoryC   (CategoryItem or CategoryCollection)
-```
-
-Never:
-
-```
-Owner
-└── CategoryA
-    └── CategoryB   ← WRONG: CategoryB is a child of CategoryA
-```
-
-**Example — `fit` and `joint_fit`:** `fit` is a `CategoryItem` holding
-the active minimizer and fitting mode. `joint_fit` is a separate
-`CategoryCollection` holding per-experiment weights. Both are direct
-children of `Analysis`, not nested:
-
-```python
-# ✅ Correct — sibling categories on Analysis
-project.analysis.fitting_mode_type = 'joint'
-project.analysis.joint_fit['npd'].weight = 0.7
-
-# ❌ Wrong — joint_fit as a child of fit
-project.analysis.fitting.joint_fit['npd'].weight = 0.7
-```
-
-In CIF output, sibling categories appear as independent blocks:
-
-```
-_fitting.mode_type       joint
-_fitting.minimizer_type  lmfit
-
-loop_
-_joint_fit.experiment_id
-_joint_fit.weight
-npd  0.7
-xrd  0.3
-```
-
-### 9.8 Property Docstring and Type-Hint Template
-
-Every public property backed by a private `Parameter`,
-`NumericDescriptor`, or `StringDescriptor` attribute must follow the
-template below. The `description` field on the descriptor is the
-**single source of truth**; docstrings and type hints are mechanically
-derived from it.
-
-**Definitions:**
-
-| Symbol    | Meaning                                                                                |
-| --------- | -------------------------------------------------------------------------------------- |
-| `{desc}`  | `description` string without trailing period                                           |
-| `{units}` | `units` string; omit the `({units})` parenthetical when absent/empty                   |
-| `{Type}`  | Descriptor class name: `Parameter`, `NumericDescriptor`, or `StringDescriptor`         |
-| `{ann}`   | Setter value annotation: `float` for numeric descriptors, `str` for string descriptors |
-
-**Template — writable property:**
-
-```python
-@property
-def length_a(self) -> Parameter:
-    """Length of the a axis of the unit cell (Å).
-
-    Reading this property returns the underlying ``Parameter``
-    object. Assigning to it updates the parameter value.
-    """
-    return self._length_a
-
-@length_a.setter
-def length_a(self, value: float) -> None:
-    self._length_a.value = value
-```
-
-**Template — read-only property:**
-
-```python
-@property
-def length_a(self) -> Parameter:
-    """Length of the a axis of the unit cell (Å).
-
-    Reading this property returns the underlying ``Parameter``
-    object.
-    """
-    return self._length_a
-```
-
-**Quick-reference table:**
-
-| Element                | Text                                                                                                           |
-| ---------------------- | -------------------------------------------------------------------------------------------------------------- |
-| Getter summary line    | `"""{desc} ({units}).` (or `"""{desc}.` when unitless)                                                         |
-| Getter body (writable) | `Reading this property returns the underlying ``{Type}`` object. Assigning to it updates the parameter value.` |
-| Getter body (readonly) | `Reading this property returns the underlying ``{Type}`` object.`                                              |
-| Setter docstring       | _(none — not rendered by griffe / MkDocs)_                                                                     |
-| Getter annotation      | `-> {Type}`                                                                                                    |
-| Setter annotation      | `value: {ann}` and `-> None`                                                                                   |
-
-**Notes:**
-
-- Getter docstrings have **no** `Args:` or `Returns:` sections.
-- Setters have **no** docstring.
-- Avoid markdown emphasis (`*a*`) in docstrings; use plain text to stay
-  in sync with the `description` field.
-- The CI tool `pixi run param-consistency-check` validates compliance;
-  `pixi run param-consistency-fix` auto-fixes violations.
-
-### 9.9 Lint Complexity Thresholds
-
-The Pylint-style complexity limits in `pyproject.toml` are **intentional
-code-quality guardrails**, not arbitrary numbers. A violation is a
-signal that the function or class needs refactoring — not that the
-threshold needs raising.
-
-The project uses **ruff's defaults** for all PLR thresholds, with one
-exception: `max-args` and `max-positional-args` are set to **6** instead
-of the ruff default of 5, because ruff counts `self`/`cls` while
-traditional pylint does not. Setting 6 in ruff matches pylint's standard
-limit of 5 real parameters per function.
-
-| Threshold             | Value | Rule    |
-| --------------------- | ----- | ------- |
-| `max-args`            | 6     | PLR0913 |
-| `max-positional-args` | 6     | PLR0917 |
-| `max-branches`        | 12    | PLR0912 |
-| `max-statements`      | 50    | PLR0915 |
-| `max-locals`          | 15    | PLR0914 |
-| `max-nested-blocks`   | 5     | PLR1702 |
-| `max-returns`         | 6     | PLR0911 |
-| `max-public-methods`  | 20    | PLR0904 |
-
-**Rules:**
-
-- **Do not raise thresholds.** The current values represent the
-  project's design intent for maximum acceptable complexity.
-- **Do not add `# noqa` comments** (or any other mechanism) to silence
-  complexity rules such as `PLR0912`, `PLR0913`, `PLR0914`, `PLR0915`,
-  `PLR0917`, `PLR1702`.
-- **Refactor the code instead:** extract helper functions, introduce
-  parameter objects, flatten nesting, use early returns, etc.
-- **For complex refactors** that touch many lines or change public API,
-  propose a refactoring plan and wait for approval before proceeding.
-
----
-
-## 10. Test Strategy
-
-Every new feature, category, factory, or bug fix must ship with tests.
-The project enforces a multi-layered testing approach that catches
-regressions at different levels of abstraction.
-
-### 10.1 Test Layers
-
-| Layer                 | Location                | Runner command               | Scope                                                                                                                |
-| --------------------- | ----------------------- | ---------------------------- | -------------------------------------------------------------------------------------------------------------------- |
-| **Unit**              | `tests/unit/`           | `pixi run unit-tests`        | Single class or function in isolation. Fast, no I/O, no external engines.                                            |
-| **Functional**        | `tests/functional/`     | `pixi run functional-tests`  | Multi-component workflows (e.g. create experiment → load data → fit). No external data files beyond tiny test stubs. |
-| **Integration**       | `tests/integration/`    | `pixi run integration-tests` | End-to-end pipelines using real calculation engines (cryspy, crysfml, pdffit2) and real data files from `data/`.     |
-| **Script (tutorial)** | `tools/test_scripts.py` | `pixi run script-tests`      | Runs each tutorial `*.py` script under `docs/docs/tutorials/` as a subprocess and checks for a zero exit code.       |
-| **Notebook**          | `docs/docs/tutorials/`  | `pixi run notebook-tests`    | Executes every Jupyter notebook end-to-end via `nbmake`.                                                             |
-
-### 10.2 Directory Structure Convention
-
-The unit-test tree **mirrors** the source tree:
-
-```
-src/easydiffraction/<pkg>/<module>.py
-    → tests/unit/easydiffraction/<pkg>/test_<module>.py
-```
-
-Two additional patterns are recognised:
-
-1. **Supplementary coverage files** — `test_<module>_coverage.py`,
-   `test_<module>_more.py`, etc. sit beside the main test file and add
-   extra scenarios.
-2. **Parent-level roll-up** — for category packages that contain only
-   `default.py` and `factory.py`, a single `test_<package_name>.py` one
-   directory up covers the whole package (e.g.
-   `categories/test_experiment_type.py` covers
-   `categories/experiment_type/default.py` and
-   `categories/experiment_type/factory.py`).
-
-The CI tool `pixi run test-structure-check` validates that every source
-module has a corresponding test file and reports any gaps. Explicit name
-aliases (e.g. `variable.py` tested by `test_parameters.py`) are declared
-in `KNOWN_ALIASES` inside the tool script.
-
-### 10.3 What to Test per Source Module Type
-
-| Source module type               | Required tests                                                                                                                       |
-| -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
-| **Core base class** (`core/`)    | Instantiation, public properties, validation edge cases, identity wiring.                                                            |
-| **Factory** (`factory.py`)       | Registration check, `supported_tags()`, `default_tag()`, `create()` for each tag, `show_supported()` output, invalid-tag handling.   |
-| **Category** (`default.py`)      | Instantiation, all public properties (read + write where applicable), CIF round-trip (`as_cif` → `from_cif`), parameter enumeration. |
-| **Enum** (`enums.py`)            | Membership of all members, `default()` method, `description()` for every member, `StrEnum` string equality.                          |
-| **Datablock item** (`base.py`)   | Construction, switchable-category full API (`<cat>`, `<cat>_type` get/set, `show_<cat>_types`), `show`/`show_as_cif`.                |
-| **Collection** (`collection.py`) | `create`, `add`, `remove`, `names`, `show_names`, `show_params`, iteration, duplicate-name handling.                                 |
-| **Calculator / Minimizer**       | `can_handle()` with compatible and incompatible experiment types, `_compute()` stub or mock.                                         |
-| **Display / IO**                 | Input → output for representative cases; file-not-found and malformed-input error paths.                                             |
-
-### 10.4 Test Conventions
-
-- **No test-ordering dependence.** Each test must be self-contained. Use
-  `monkeypatch` to set `Logger._reaction` when the test expects a raised
-  exception (another test may have leaked WARN mode via the global
-  `Logger` singleton).
-- **Error paths are tested explicitly.** Use `pytest.raises()` (with
-  `monkeypatch` for Logger RAISE mode) for `log.error()` calls that
-  specify `exc_type`.
-- **`@typechecked` setters raise `typeguard.TypeCheckError`**, not
-  `TypeError`. Tests must catch the correct exception.
-- **Use `capsys` / `capfd`** for asserting console output from `show_*`
-  methods.
-- **Prefer `tmp_path`** (pytest fixture) for file-system tests.
-- **No sleeping, no network calls, no real calculation engines** in unit
-  tests.
-- Test files carry the SPDX license header and a module-level docstring.
-  They are exempt from most lint rules (ANN, D, DOC, INP001, S101, etc.)
-  per `pyproject.toml`.
-
-### 10.5 Coverage Threshold
-
-The minimum line-coverage threshold is **70 %** (`fail_under = 70` in
-`pyproject.toml`). The project aspires to test every code path; the
-threshold is a safety net, not a target.
-
-Run `pixi run unit-tests-coverage` for a per-module report.
-
----
-
-## 11. Issues
-
-- **Open:** [`issues_open.md`](Issues/issues_open.md) — prioritised
-  backlog.
-- **Closed:** [`issues_closed.md`](Issues/issues_closed.md) — resolved
-  items for reference.
-
-When a resolution affects the architecture described above, the relevant
-sections of this document are updated accordingly.
diff --git a/docs/dev/index.md b/docs/dev/index.md
new file mode 100644
index 000000000..424140f2b
--- /dev/null
+++ b/docs/dev/index.md
@@ -0,0 +1,29 @@
+# Development Documentation
+
+This directory contains development-only project documentation. Keep it
+under `docs/dev` rather than a repository-root `dev/` directory so all
+documentation lives under one tree, while published user documentation
+remains isolated under `docs/docs`.
+
+## Structure
+
+| Path                                                       | Purpose                                                                                         |
+| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
+| [`adrs/index.md`](adrs/index.md)                           | Architecture and decision navigation, grouped by topic.                                         |
+| [`issues/open.md`](issues/open.md)                         | Prioritized open development issues and design questions.                                       |
+| [`issues/closed.md`](issues/closed.md)                     | Closed development issues retained for history.                                                 |
+| [`package-structure/short.md`](package-structure/short.md) | Generated compact package tree.                                                                 |
+| [`package-structure/full.md`](package-structure/full.md)   | Generated package tree with top-level classes.                                                  |
+| [`plans/`](plans/)                                         | Implementation plans for larger migrations.                                                     |
+| [`roadmap/ROADMAP.md`](roadmap/ROADMAP.md)                 | Development roadmap. This may later be copied into `docs/docs` during the published-docs build. |
+
+## Rules
+
+- Put architecture and decision history in ADR files.
+- Put proposed decisions in `adrs/suggestions/` until accepted.
+- Move accepted suggestions to `adrs/accepted/` and update
+  `adrs/index.md`.
+- Do not edit generated package-structure files by hand; run
+  `pixi run update-package-diagrams`.
+- Keep development documentation out of `docs/docs` unless it is meant
+  to become user-facing documentation.
diff --git a/docs/dev/Issues/issues_closed.md b/docs/dev/issues/closed.md
similarity index 98%
rename from docs/dev/Issues/issues_closed.md
rename to docs/dev/issues/closed.md
index 0e6db4dc5..c2e60653a 100644
--- a/docs/dev/Issues/issues_closed.md
+++ b/docs/dev/issues/closed.md
@@ -12,7 +12,8 @@ that do not inherit the guarded object hierarchy: `project.display`,
 `project.display.posterior`, `analysis.display`, and `project.summary`.
 Introduced `render_object_help()` so these helpers share the same
 property and method table style as `GuardedBase.help()`. Documented the
-convention in `docs/dev/ADRs/adr_help-discoverability.md`.
+convention in
+[`help-discoverability.md`](../adrs/accepted/help-discoverability.md).
 
 ---
 
diff --git a/docs/dev/Issues/issues_open.md b/docs/dev/issues/open.md
similarity index 99%
rename from docs/dev/Issues/issues_open.md
rename to docs/dev/issues/open.md
index 2e6142e8e..6c96f87ea 100644
--- a/docs/dev/Issues/issues_open.md
+++ b/docs/dev/issues/open.md
@@ -3,8 +3,8 @@
 Prioritised list of issues, improvements, and design questions to
 address. Items are ordered by a combination of user impact, blocking
 potential, and implementation readiness. When an item is fully
-implemented, remove it from this file and update `architecture.md` if
-needed.
+implemented, remove it from this file and update
+[`adrs/index.md`](../adrs/index.md) or the relevant ADR if needed.
 
 **Legend:** 🔴 High · 🟡 Medium · 🟢 Low
 
@@ -200,7 +200,7 @@ duplicate rules writing the same target, and how additional supported
 prefixes should be introduced when new environment categories appear.
 
 **Fix:** pin the allowed target grammar and duplicate-target behaviour
-in architecture and validation rules.
+in an ADR and validation rules.
 
 **Depends on:** nothing.
 
@@ -1452,8 +1452,8 @@ Two manual workflow steps are required between releases/changes:
 2. `pixi run notebook-prepare` — regenerate tutorial notebooks from
    scripts.
 
-Document these in `CONTRIBUTING.md` or the architecture doc so they are
-not forgotten.
+Document these in `CONTRIBUTING.md` or a relevant ADR so they are not
+forgotten.
 
 **Depends on:** nothing.
 
@@ -1600,7 +1600,7 @@ Should print:
 **Type:** CI / Tooling
 
 CodeFactor flags TODO comments as unresolved issues (rule C100) in PRs.
-Since TODOs are tracked in `issues_open.md`, the CodeFactor check adds
+Since TODOs are tracked in `issues/open.md`, the CodeFactor check adds
 noise. Disable the C100 rule or configure CodeFactor to ignore TODO
 comments.
 
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure/full.md
similarity index 100%
rename from docs/dev/package-structure-full.md
rename to docs/dev/package-structure/full.md
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure/short.md
similarity index 100%
rename from docs/dev/package-structure-short.md
rename to docs/dev/package-structure/short.md
diff --git a/docs/dev/plans/loop-category-key-identity.md b/docs/dev/plans/loop-category-key-identity.md
new file mode 100644
index 000000000..9917d1bae
--- /dev/null
+++ b/docs/dev/plans/loop-category-key-identity.md
@@ -0,0 +1,620 @@
+# Loop Category Key Identity Implementation Plan
+
+## Status
+
+Workflow instructions:
+
+```text
+.github/copilot-instructions.md
+```
+
+Related ADR suggestion:
+
+```text
+docs/dev/adrs/suggestions/loop-category-key-identity.md
+```
+
+This plan implements two related changes:
+
+1. Move category identity declarations from per-instance assignments to
+   class-level declarations.
+2. Add an explicit persisted `id` field to the constraints loop.
+
+Status checklist. Mark `[x]` only while implementing:
+
+```text
+Phase 1 - Implementation
+[x] Add class-level identity declarations to CategoryItem.
+[x] Teach Identity to resolve declared category entry names.
+[x] Rebuild collection indexes after CIF loop loading.
+[x] Add _category_code to all current CategoryItem subclasses.
+[x] Add _category_entry_name to all current loop CategoryItem subclasses.
+[x] Remove direct self._identity.category_code assignments.
+[x] Remove direct self._identity.category_entry_name lambda assignments.
+[x] Add Constraint.id descriptor serialized as _constraint.id.
+[x] Change constraints collection keys from lhs_alias to id.
+[x] Preserve default constraints.create(expression=...) behavior by using lhs_alias as the default id.
+[x] Add backward-compatible loading for old CIF loops without _constraint.id.
+[x] Update constraints display.
+[x] Update loop-category-key-identity.md if implementation details differ from the ADR.
+[ ] Phase 1 review gate: present diff for approval.
+
+Phase 2 - Verification
+[x] Add tests for the base declarative identity behavior.
+[x] Add parametrized tests for current loop category identity declarations.
+[x] Update constraints tests.
+[x] Update existing round-trip tests that compare constraints CIF.
+[x] Run formatting.
+[x] Run targeted unit tests.
+[x] Run broader checks.
+```
+
+## Commit Discipline
+
+When an AI agent follows this plan, every completed Phase 1
+implementation step must be staged with explicit paths and committed
+locally before moving to the next implementation step or to the Phase 1
+review gate.
+
+Follow the **Commits** section of `.github/copilot-instructions.md`.
+
+Rules:
+
+- One commit per implementation step.
+- Keep each commit atomic and single-purpose.
+- Stage explicit paths only. Do not use `git add .`.
+- Do not stage unrelated user changes.
+- Do not stage generated artifacts unless the user explicitly asks.
+- If a serious uncovered design issue appears, stop and ask before
+  continuing.
+
+Suggested branch:
+
+```text
+feature/loop-category-key-identity
+```
+
+Suggested commit messages:
+
+```text
+Add declarative category identity resolution
+Declare category identities on current items
+Persist explicit constraint identifiers
+Update ADR for declarative category identity
+Add declarative category identity tests
+```
+
+## Goal
+
+Replace repeated constructor code like this:
+
+```python
+self._identity.category_code = 'atom_site'
+self._identity.category_entry_name = lambda: str(self.label.value)
+```
+
+with class-level declarations:
+
+```python
+class AtomSite(CategoryItem):
+    _category_code = 'atom_site'
+    _category_entry_name = 'label'
+```
+
+The name `_category_entry_name` is intentionally kept because this is
+the preferred project terminology. In this plan it means "the name of
+the item attribute used to resolve the entry name". The resolved entry
+value is still obtained through:
+
+```python
+item._identity.category_entry_name
+```
+
+For example, `AtomSite._category_entry_name == 'label'`, while
+`atom_site._identity.category_entry_name == 'Ba1'`.
+
+## Non-Goals
+
+Do not change these in this migration:
+
+- Do not rename `Identity.category_entry_name`.
+- Do not rename `CategoryCollection`.
+- Do not change public collection access syntax.
+- Do not change CIF tags except adding `_constraint.id`.
+- Do not rename `label` to `id` for atom sites or aliases.
+- Do not make `phase_id` the row key for powder reflection loops.
+
+## Design
+
+This plan intentionally uses a narrow metadata lookup based on
+`_category_entry_name`. That is an allowed exception to the general "no
+string-based dispatch" rule because the attribute name is a class-level
+declaration, not user input, and resolution is centralized in
+`CategoryItem`.
+
+### CategoryItem Declarations
+
+Add these class attributes to `CategoryItem` in
+`src/easydiffraction/core/category.py`:
+
+```python
+class CategoryItem(GuardedBase):
+    _category_code: str | None = None
+    _category_entry_name: str | None = None
+```
+
+Update `CategoryItem.__init__()` so it assigns `_category_code` once:
+
+```python
+def __init__(self) -> None:
+    super().__init__()
+    if self._category_code is not None:
+        self._identity.category_code = self._category_code
+```
+
+Do not try to resolve `_category_entry_name` in `CategoryItem.__init__`.
+Many item classes create descriptors after `super().__init__()` returns,
+and some mixin-based classes run `CategoryItem.__init__()` before their
+descriptors are created.
+
+Add a resolver method to `CategoryItem`:
+
+```python
+def _resolve_category_entry_name(self) -> str | None:
+    attr_name = self._category_entry_name
+    if attr_name is None:
+        return None
+
+    value = getattr(self, attr_name)
+    if isinstance(value, GenericDescriptorBase):
+        value = value.value
+    return str(value)
+```
+
+Import `GenericDescriptorBase` from `easydiffraction.core.variable` in
+`category.py` if it is not already in scope.
+
+### Identity Resolution
+
+Update `Identity._resolve_up()` in
+`src/easydiffraction/core/identity.py` so that category entries can be
+resolved from the owning object before walking to the parent.
+
+Add this logic after checking direct callable/string values and before
+climbing to the parent:
+
+```python
+if attr == 'category_entry':
+    resolver = getattr(self._owner, '_resolve_category_entry_name', None)
+    if callable(resolver):
+        resolved = resolver()
+        if resolved is not None:
+            return resolved
+```
+
+Keep the existing `category_entry_name` setter. It remains useful as an
+escape hatch and keeps old code compatible during migration.
+
+### Collection Index Rebuild After CIF Loading
+
+Update `category_collection_from_cif()` in
+`src/easydiffraction/io/cif/serialize.py`.
+
+Currently `_adopt_items()` rebuilds the index before loop values are
+loaded into each item. After declarative keys are resolved from
+descriptor values, the index must be rebuilt after parameters are
+loaded.
+
+After the row population loop, run any collection hook and rebuild:
+
+```python
+after_from_cif = getattr(self, '_after_from_cif', None)
+if callable(after_from_cif):
+    after_from_cif()
+
+self._rebuild_index()
+```
+
+The hook is needed for constraints to backfill missing ids from old CIF
+files.
+
+## Category Migration Table
+
+Add `_category_code` to each listed class. Add `_category_entry_name`
+only when the class is a loop item and currently has a collection key.
+Then remove matching constructor assignments.
+
+| File                                                                                | Class                      | `_category_code`         | `_category_entry_name` | Notes                                        |
+| ----------------------------------------------------------------------------------- | -------------------------- | ------------------------ | ---------------------- | -------------------------------------------- |
+| `src/easydiffraction/project/categories/info/default.py`                            | `ProjectInfo`              | `project`                | none                   | Singleton category.                          |
+| `src/easydiffraction/project/categories/rendering/default.py`                       | `Rendering`                | `rendering`              | none                   | Singleton category.                          |
+| `src/easydiffraction/analysis/categories/fitting/default.py`                        | `Fitting`                  | `fitting`                | none                   | Singleton category.                          |
+| `src/easydiffraction/analysis/categories/sequential_fit/default.py`                 | `SequentialFit`            | `sequential_fit`         | none                   | Singleton category.                          |
+| `src/easydiffraction/analysis/categories/aliases/default.py`                        | `Alias`                    | `alias`                  | `label`                | Loop key stays `_alias.label`.               |
+| `src/easydiffraction/analysis/categories/constraints/default.py`                    | `Constraint`               | `constraint`             | `id`                   | Add the id descriptor first.                 |
+| `src/easydiffraction/analysis/categories/joint_fit/default.py`                      | `JointFitItem`             | `joint_fit`              | `experiment_id`        | Loop key stays `_joint_fit.experiment_id`.   |
+| `src/easydiffraction/analysis/categories/sequential_fit_extract/default.py`         | `SequentialFitExtractItem` | `sequential_fit_extract` | `id`                   | Loop key stays `_sequential_fit_extract.id`. |
+| `src/easydiffraction/datablocks/structure/categories/cell/default.py`               | `Cell`                     | `cell`                   | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/structure/categories/space_group/default.py`        | `SpaceGroup`               | `space_group`            | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/structure/categories/atom_sites/default.py`         | `AtomSite`                 | `atom_site`              | `label`                | Loop key stays `_atom_site.label`.           |
+| `src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py`    | `AtomSiteAniso`            | `atom_site_aniso`        | `label`                | Loop key stays `_atom_site_aniso.label`.     |
+| `src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py`   | `ExperimentType`           | `expt_type`              | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/experiment/categories/calculation/default.py`       | `Calculation`              | `calculation`            | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/experiment/categories/diffrn/default.py`            | `DefaultDiffrn`            | `diffrn`                 | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/experiment/categories/instrument/base.py`           | `InstrumentBase`           | `instrument`             | none                   | Subclasses inherit this code.                |
+| `src/easydiffraction/datablocks/experiment/categories/peak/base.py`                 | `PeakBase`                 | `peak`                   | none                   | Subclasses inherit this code.                |
+| `src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py` | `BeckerCoppensExtinction`  | `extinction`             | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py`    | `LinkedCrystal`            | `linked_crystal`         | none                   | Singleton category.                          |
+| `src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py`     | `LinkedPhase`              | `linked_phases`          | `id`                   | Loop key stays `_pd_phase_block.id`.         |
+| `src/easydiffraction/datablocks/experiment/categories/background/line_segment.py`   | `LineSegment`              | `background`             | `id`                   | Loop key stays `_pd_background.id`.          |
+| `src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py`      | `PolynomialTerm`           | `background`             | `id`                   | Loop key stays `_pd_background.id`.          |
+| `src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py`  | `ExcludedRegion`           | `excluded_regions`       | `id`                   | Loop key stays `_excluded_region.id`.        |
+| `src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py`            | `Refln`                    | `refln`                  | `id`                   | Powder reflection rows inherit this key.     |
+| `src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py`             | `PdCwlDataPoint`           | `pd_data`                | `point_id`             | CWL powder data row.                         |
+| `src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py`             | `PdTofDataPoint`           | `pd_data`                | `point_id`             | TOF powder data row.                         |
+| `src/easydiffraction/datablocks/experiment/categories/data/total_pd.py`             | `TotalDataPoint`           | `total_data`             | `point_id`             | Total-scattering data row.                   |
+
+Do not add `_category_code` or `_category_entry_name` to
+`PowderReflnBase`, `PowderCwlRefln`, or `PowderTofRefln`; they inherit
+the `refln` identity declarations from `Refln`.
+
+## Constraints Migration
+
+### Target Shape
+
+`Constraint` should become:
+
+```python
+class Constraint(CategoryItem):
+    _category_code = 'constraint'
+    _category_entry_name = 'id'
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._id = StringDescriptor(
+            name='id',
+            description='Identifier for this constraint.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_]*$'),
+            ),
+            cif_handler=CifHandler(names=['_constraint.id']),
+        )
+        self._expression = StringDescriptor(...)
+```
+
+Define the public property:
+
+```python
+@property
+def id(self) -> StringDescriptor:
+    """Identifier for this constraint."""
+    return self._id
+
+@id.setter
+def id(self, value: str) -> None:
+    self._id.value = value
+```
+
+Keep `lhs_alias` and `rhs_expr` as derived read-only properties.
+
+### Create API
+
+Change `Constraints.create()` from:
+
+```python
+def create(self, *, expression: str) -> None:
+```
+
+to:
+
+```python
+def create(self, *, expression: str, id: str | None = None) -> None:
+```
+
+Implementation order:
+
+```python
+item = Constraint()
+item.expression = expression
+item.id = id if id is not None else item.lhs_alias
+self.add(item)
+self._enabled = True
+```
+
+This preserves the current default user experience:
+
+```python
+analysis.constraints.create(expression='biso_Ba = biso_La')
+analysis.constraints['biso_Ba']
+```
+
+It also allows explicit ids:
+
+```python
+analysis.constraints.create(
+    id='constraint_1',
+    expression='biso_Ba = biso_La',
+)
+analysis.constraints['constraint_1']
+```
+
+### Backward-Compatible CIF Loading
+
+Old CIF files only contain:
+
+```cif
+loop_
+_constraint.expression
+biso_Ba = biso_La
+```
+
+New CIF files should contain:
+
+```cif
+loop_
+_constraint.id
+_constraint.expression
+biso_Ba "biso_Ba = biso_La"
+```
+
+Add a hook on `Constraints`:
+
+```python
+def _after_from_cif(self) -> None:
+    for index, item in enumerate(self._items, start=1):
+        if item.id.value in {'', '_'}:
+            fallback = item.lhs_alias or f'constraint_{index}'
+            item.id = fallback
+```
+
+The generic `category_collection_from_cif()` hook described above must
+call this before rebuilding the index.
+
+### Constraints Display
+
+Update `Constraints.show()` to include the id:
+
+```text
+id | expression
+```
+
+Keep the existing empty warning behavior.
+
+## Required Code Searches
+
+After migration, these searches should return no category-item
+constructor assignments:
+
+```shell
+git grep -n -E "self\\._identity\\.category_code =" -- src/easydiffraction
+git grep -n -E "self\\._identity\\.category_entry_name =" -- src/easydiffraction
+git grep -n -E "category_entry_name = lambda" -- src/easydiffraction
+```
+
+The following reads are expected to remain:
+
+```shell
+git grep -n "_identity\\.category_entry_name" -- src/easydiffraction
+```
+
+Those reads are used by collections, display, reporting, and parameter
+unique names.
+
+Do not use `rg` in this plan; it is not available in every contributor
+environment. Use `git grep` for repository searches.
+
+## Tests To Add Or Update
+
+### Base Identity Tests
+
+Add or update tests under `tests/unit/easydiffraction/core/`.
+
+Test a fake category item:
+
+```python
+class FakeItem(CategoryItem):
+    _category_code = 'fake'
+    _category_entry_name = 'id'
+
+    def __init__(self):
+        super().__init__()
+        self._id = StringDescriptor(...)
+
+    @property
+    def id(self):
+        return self._id
+```
+
+Assert:
+
+```python
+item._identity.category_code == 'fake'
+item._identity.category_entry_name == item.id.value
+item.id.unique_name.endswith('.fake.<id>.id')
+```
+
+Also test that a singleton-like item with `_category_code` and no
+`_category_entry_name` resolves category code but returns no entry name.
+
+### Current Category Parametrized Tests
+
+Add a parametrized test that instantiates representative loop item
+classes and verifies category code plus entry:
+
+```text
+Alias -> alias, label
+JointFitItem -> joint_fit, experiment_id
+SequentialFitExtractItem -> sequential_fit_extract, id
+AtomSite -> atom_site, label
+AtomSiteAniso -> atom_site_aniso, label
+LinkedPhase -> linked_phases, id
+LineSegment -> background, id
+PolynomialTerm -> background, id
+ExcludedRegion -> excluded_regions, id
+Refln -> refln, id
+PdCwlDataPoint -> pd_data, point_id
+PdTofDataPoint -> pd_data, point_id
+TotalDataPoint -> total_data, point_id
+```
+
+Do not instantiate heavy calculator-backed owner objects for this test;
+instantiate item classes directly.
+
+### Constraints Tests
+
+Update
+`tests/unit/easydiffraction/analysis/categories/test_constraints.py`.
+
+Required assertions:
+
+```python
+c = Constraint()
+c.expression = 'a = b + c'
+c.id = 'constraint_a'
+assert c.id.value == 'constraint_a'
+assert c.lhs_alias == 'a'
+assert c.rhs_expr == 'b + c'
+assert c._identity.category_entry_name == 'constraint_a'
+```
+
+Default id behavior:
+
+```python
+coll = Constraints()
+coll.create(expression='a = b + c')
+assert 'a' in coll.names
+assert coll['a'].id.value == 'a'
+assert coll['a'].rhs_expr == 'b + c'
+```
+
+Explicit id behavior:
+
+```python
+coll = Constraints()
+coll.create(id='c1', expression='a = b + c')
+assert 'c1' in coll.names
+assert coll['c1'].lhs_alias == 'a'
+```
+
+CIF serialization:
+
+```python
+cif = coll.as_cif
+assert '_constraint.id' in cif
+assert '_constraint.expression' in cif
+```
+
+Backward-compatible CIF loading:
+
+```python
+cif = '''
+data_analysis
+
+loop_
+_constraint.expression
+"a = b + c"
+'''
+```
+
+Load through the existing analysis/constraints loader and assert the
+loaded collection has key `a`.
+
+### Existing Round-Trip Tests
+
+Update tests that compare constraint CIF or constraint collection keys:
+
+- `tests/unit/easydiffraction/project/test_project_load.py`
+- `tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py`
+- `tests/integration/fitting/test_project_load.py`
+
+Expected changes:
+
+- New saved analysis CIF includes `_constraint.id`.
+- Old expression-only fixtures, if any, still load.
+- Existing constraint workflows still work when no explicit id is
+  supplied.
+
+## Verification Commands
+
+Run the smallest useful checks first:
+
+```shell
+pixi run python -m pytest tests/unit/easydiffraction/analysis/categories/test_constraints.py
+pixi run python -m pytest tests/unit/easydiffraction/core/
+pixi run python -m pytest tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
+```
+
+Then run broader checks:
+
+```shell
+pixi run unit-tests
+pixi run integration-tests
+pixi run check
+```
+
+If the modified-file Prettier helper is still missing, format changed
+Markdown directly:
+
+```shell
+npx prettier --write --config=prettierrc.toml docs/dev/plans/loop-category-key-identity.md docs/dev/adrs/suggestions/loop-category-key-identity.md
+```
+
+## Local Availability Check
+
+The plan was checked against the current repository state:
+
+- `.github/copilot-instructions.md` exists.
+- `src/easydiffraction/core/category.py`,
+  `src/easydiffraction/core/identity.py`, and
+  `src/easydiffraction/io/cif/serialize.py` exist.
+- `tests/unit/easydiffraction/analysis/categories/test_constraints.py`
+  exists.
+- `tests/unit/easydiffraction/core/` exists.
+- `tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py`
+  exists.
+- `pixi.toml` defines `fix`, `check`, `unit-tests`, `integration-tests`,
+  and `test-structure-check`.
+- `prettierrc.toml` and local Prettier are available.
+- `tools/nonpy_prettier_modified.py` is not present, so use the direct
+  `npx prettier --write --config=prettierrc.toml ...` fallback for
+  touched Markdown files.
+
+## Acceptance Criteria
+
+The implementation is done when all of these are true:
+
+- All current category codes are declared as `_category_code` on item
+  classes.
+- All current loop collection keys are declared as
+  `_category_entry_name` on item classes.
+- No current category item sets `self._identity.category_code` in its
+  constructor.
+- No current category item sets `self._identity.category_entry_name` in
+  its constructor.
+- `item._identity.category_entry_name` still works for all loop rows.
+- Descriptor `unique_name` values still include datablock, category,
+  entry, and descriptor name.
+- Constraints persist `_constraint.id`.
+- Constraints created without an explicit id still default to the left
+  hand alias.
+- Old constraints CIF without `_constraint.id` still loads and receives
+  deterministic ids.
+- Collection indexes are correct after CIF loading.
+
+## Suggested Pull Request
+
+Title:
+
+```text
+Use declarative category identity metadata
+```
+
+Description:
+
+```text
+This change makes category and loop-row identities easier to audit and
+keeps saved CIF identifiers explicit. Constraint rows gain a stable
+identifier while existing constraint expressions continue to work.
+```
diff --git a/docs/dev/plan_workspace-root-project-category.md b/docs/dev/plans/workspace-root-project-category.md
similarity index 67%
rename from docs/dev/plan_workspace-root-project-category.md
rename to docs/dev/plans/workspace-root-project-category.md
index b57e318a6..778fd7584 100644
--- a/docs/dev/plan_workspace-root-project-category.md
+++ b/docs/dev/plans/workspace-root-project-category.md
@@ -7,13 +7,13 @@ Branch: `feature/workspace-root-project-category`
 ADR suggestion:
 
 ```text
-docs/dev/ADR-suggestions/adr_workspace-root-project-category.md
+docs/dev/adrs/suggestions/workspace-root-project-category.md
 ```
 
 Two-phase workflow from `.github/copilot-instructions.md`:
 
-- Phase 1 - Implementation. Code, docs, and architecture updates only.
-  Do not create or run tests unless the user explicitly asks.
+- Phase 1 - Implementation. Code and docs updates only. Do not create or
+  run tests unless the user explicitly asks.
 - Phase 2 - Verification. Add/update tests, then run the verification
   commands listed near the end of this plan.
 
@@ -96,6 +96,7 @@ workspace = ed.Workspace(project_id='lbco_hrpt')
 workspace.project.id
 workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
 workspace.rendering.table_engine = 'rich'
+workspace.verbosity = 'short'
 workspace.structures
 workspace.experiments
 workspace.analysis
@@ -121,10 +122,27 @@ _project.last_modified
 
 _rendering.chart_engine
 _rendering.table_engine
+
+_verbosity.level
 ```
 
 Do not introduce `_meta.*` tags.
 
+Target saved layout:
+
+```text
+<workspace-dir>/
+|-- workspace.cif
+|-- structures/
+|   `-- cosio.cif
+|-- experiments/
+|   `-- d20.cif
+|-- analysis/
+|   `-- analysis.cif
+`-- summary/
+    `-- summary.cif
+```
+
 ## Decisions Already Made
 
 Use these decisions unless the user explicitly changes the ADR before
@@ -137,11 +155,28 @@ implementation:
 - The public rendering category remains `workspace.rendering`.
 - The project-information category keeps semantic CIF tags `_project.*`.
 - The rendering category keeps semantic CIF tags `_rendering.*`.
+- The verbosity preference is serialized as `_verbosity.level`.
 - The saved singleton config file becomes `workspace.cif`.
+- The saved root is a workspace directory with a user-chosen filesystem
+  name; do not use `project` as the conceptual root name in new docs.
+- Do not use `project.cif`, `config.cif`, or `meta.cif` as the primary
+  singleton config file in the target layout.
 - The storage directory path belongs to `Workspace.path`, not
   `workspace.project.path`.
 - The old `Project` public API is removed unless the user explicitly
   approves an alias before implementation.
+- The category class name `ProjectInfo` is kept; only the public
+  attribute name (`info` → `project`) changes.
+- `ProjectInfo.path` is removed; the saved directory path lives on
+  `Workspace.path` only.
+- A first-class `Verbosity` category under `WorkspaceConfig` is required
+  (not optional) so that `_verbosity.level` round-trips through
+  `workspace.cif` like other singleton categories.
+- Source-tree imports may be temporarily inconsistent between phases
+  (for example, Phase 1 leaves `project_config_to_cif` imports until
+  Phase 4 renames the serializer functions). This is acceptable because
+  tests are not run in Phase 1. Each phase must still leave the source
+  importable at the end of the phase.
 
 ## Current Shape
 
@@ -185,7 +220,8 @@ src/easydiffraction/workspace/
 |-- display.py             # class WorkspaceDisplay
 `-- categories/
     |-- project/           # ProjectInfo category
-    `-- rendering/         # Rendering category
+    |-- rendering/         # Rendering category
+    `-- verbosity/         # Verbosity category
 ```
 
 Target public API:
@@ -194,6 +230,7 @@ Target public API:
 workspace = ed.Workspace(project_id='my_project')
 workspace.project.title
 workspace.rendering.table_engine
+workspace.verbosity = 'short'
 ```
 
 ## Out Of Scope
@@ -201,6 +238,8 @@ workspace.rendering.table_engine
 Do not do these in this migration:
 
 - Do not add `_meta.*` CIF tags.
+- Do not use `project.cif`, `config.cif`, or `meta.cif` as the target
+  singleton settings file.
 - Do not redesign structure or experiment datablocks.
 - Do not change analysis fit-mode semantics.
 - Do not change calculator behavior.
@@ -250,7 +289,7 @@ Rename the top-level runtime facade and package from `project` to
 
 ### Steps
 
-1. Move the source package:
+1. Move the source package with `git mv` so history is preserved:
 
    ```text
    src/easydiffraction/project/
@@ -275,34 +314,67 @@ Rename the top-level runtime facade and package from `project` to
    ProjectDisplay -> WorkspaceDisplay
    ```
 
-4. Update top-level import:
+4. Rename class-level state inside the facade:
+
+   ```text
+   Project._current_project   -> Workspace._current_workspace
+   Project._loading           -> Workspace._loading      # name kept
+   Project.current_project_path() -> Workspace.current_workspace_path()
+   ```
+
+   Update the `ClassVar` annotation accordingly and update all internal
+   references (`type(self)._current_project = self`,
+   `cls._current_project`).
+
+5. Update the `varname()` fallback inside `__init__` so the default
+   variable name becomes `'workspace'` instead of `'project'`:
+
+   ```python
+   self._varname = 'workspace' if type(self)._loading else varname()
+   ```
+
+6. Update top-level import:
 
    ```python
    from easydiffraction.workspace.workspace import Workspace
    ```
 
-5. Remove the old top-level `Project` import unless the user approved an
+7. Remove the old top-level `Project` import unless the user approved an
    alias.
 
-6. Update type-checking imports:
+8. Update type-checking imports:
 
    ```python
    from easydiffraction.workspace.workspace import Workspace
    ```
 
-7. Update docstrings from "Project facade" to "Workspace facade" where
-   they describe the root object.
+9. Update docstrings from "Project facade" to "Workspace facade" where
+   they describe the root object. Keep wording that talks about the
+   scientific project (titles, descriptions, identity) unchanged.
 
-8. Run a source-only grep. Do not run tests in Phase 1:
+10. Rename `io/ascii.py::extract_project_from_zip` to
+    `extract_workspace_from_zip` and update its re-exports in
+    `src/easydiffraction/io/__init__.py` and
+    `src/easydiffraction/__init__.py`. The function extracts a saved
+    workspace directory, not scientific project information.
 
-   ```shell
-   rg -n "easydiffraction\\.project|\\bProject\\b|ProjectDisplay|ProjectConfig" src
-   ```
+11. Note: `src/easydiffraction/io/cif/serialize.py` still defines
+    `project_config_to_cif`, `project_config_from_cif`, and
+    `project_to_cif` at this point. Leave those imports as-is in
+    `workspace.py`; they are renamed in Phase 4. The function
+    `project_info_to_cif` keeps its name.
 
-   For every match, decide whether it refers to:
-   - the old root object, which should become `Workspace`;
-   - the project-information category, which should remain project;
-   - historical text that should be updated in docs later.
+12. Run a source-only grep. Do not run tests in Phase 1:
+
+```shell
+rg -n "easydiffraction\\.project|\\bProject\\b|ProjectDisplay|ProjectConfig" src
+```
+
+For every match, decide whether it refers to:
+
+- the old root object, which should become `Workspace`;
+- the project-information category, which should remain project;
+- historical text that should be updated in docs later.
 
 ### Stop Conditions
 
@@ -427,12 +499,16 @@ the workspace location and is not serialized project information.
 
 ### Steps
 
-1. In `ProjectInfo`, rename the public identity property:
+1. In `ProjectInfo`, rename the public identity property and its setter:
 
    ```text
-   name -> id
+   name (getter)  -> id (getter)
+   name (setter)  -> id (setter)
    ```
 
+   Do not also rename the internal descriptor attribute
+   `self._project_id`; it already matches the new public name.
+
 2. Keep the underlying CIF tag unchanged:
 
    ```python
@@ -441,10 +517,12 @@ the workspace location and is not serialized project information.
 
 3. Update `ProjectInfo.unique_name` to return `self.id`.
 
-4. Update `project_info_to_cif()` and CIF loading helpers to use
+4. Keep `ProjectInfo._identity.category_code = 'project'` as-is.
+
+5. Update `project_info_to_cif()` and CIF loading helpers to use
    `info.id`.
 
-5. Rename constructor arguments:
+6. Rename constructor arguments:
 
    ```text
    name -> project_id
@@ -455,10 +533,15 @@ the workspace location and is not serialized project information.
    - `WorkspaceConfig.__init__`
    - `ProjectInfo.__init__`
    - `ProjectInfoFactory.create(...)` call sites
+   - Default value: `'untitled_project'` (unchanged value, just the
+     parameter name changes)
 
-6. Add `Workspace.path` as the runtime storage path.
+7. Add `Workspace.path` as the runtime storage path. Initialize
+   `self._path: pathlib.Path | None = None` in `Workspace.__init__`.
 
-   Suggested shape:
+   Suggested shape (match the surrounding `GuardedBase` pattern; do not
+   add `@typechecked` here because the setter accepts both `str` and
+   `pathlib.Path`):
 
    ```python
    @property
@@ -468,13 +551,14 @@ the workspace location and is not serialized project information.
 
    @path.setter
    def path(self, value: object) -> None:
-       self._path = pathlib.Path(value)
+       self._path = pathlib.Path(value) if value is not None else None
    ```
 
-7. Remove `ProjectInfo.path` unless explicitly approved as a
-   compatibility alias.
+8. Remove `ProjectInfo.path` (property, setter, and the `self._path`
+   attribute inside `ProjectInfo.__init__`) unless explicitly approved
+   as a compatibility alias.
 
-8. Update save/load logic:
+9. Update save/load logic across the codebase:
 
    ```text
    workspace.path
@@ -483,17 +567,22 @@ the workspace location and is not serialized project information.
    should replace:
 
    ```text
-   workspace.project.path
+   project.info.path        # old
+   workspace.project.path   # never used; do not introduce
    ```
 
-9. Update messages and string representations:
+   Concrete call sites include `Workspace.save`, `Workspace.load`,
+   `Workspace.current_workspace_path`, and any consumers in `analysis/`,
+   `display/`, `summary/`, and `io/`.
 
-   ```text
-   Workspace '<project_id>' (...)
-   Saving workspace '<project_id>' to ...
-   ```
+10. Update messages and string representations:
+
+```text
+Workspace '<project_id>' (...)
+Saving workspace '<project_id>' to ...
+```
 
-10. Run grep:
+11. Run grep:
 
 ```shell
 rg -n "\\.name\\b|\\.path\\b|project_id|Project identifier" src/easydiffraction/workspace src/easydiffraction/io src/easydiffraction/display src/easydiffraction/summary
@@ -527,20 +616,29 @@ Rename the saved singleton configuration file from `project.cif` to
 
 - `src/easydiffraction/workspace/workspace.py`
 - `src/easydiffraction/io/cif/serialize.py`
+- `src/easydiffraction/workspace/workspace_config.py`
+- `src/easydiffraction/workspace/categories/verbosity/`
 - CLI entry points in `src/easydiffraction/__main__.py`
 - docs that describe saved project directories
 - test fixtures in Phase 2
 
 ### Steps
 
-1. Rename serializer functions if they still use project-root naming:
+1. Rename serializer functions in
+   `src/easydiffraction/io/cif/serialize.py` and every call site:
 
    ```text
-   project_config_to_cif -> workspace_config_to_cif
+   project_config_to_cif   -> workspace_config_to_cif
    project_config_from_cif -> workspace_config_from_cif
-   project_to_cif -> workspace_to_cif
+   project_to_cif          -> workspace_to_cif
    ```
 
+   Call sites include `workspace.py` (formerly `project.py`),
+   `workspace_config.py`, and the serializer itself (the
+   `project_to_cif` body calls `project_config_to_cif`). After this step
+   the imports that were intentionally left stale in Phase 1 must
+   compile cleanly.
+
    Do not rename `project_info_to_cif`; it serializes the `_project`
    category and that name remains correct.
 
@@ -556,28 +654,68 @@ Rename the saved singleton configuration file from `project.cif` to
    workspace.cif
    ```
 
-4. Do not add `project.cif` fallback unless the user approved a
-   compatibility loader.
+4. Do not add `project.cif`, `config.cif`, or `meta.cif` fallbacks
+   unless the user approved a compatibility loader.
+
+5. Add a `Verbosity` category under
+   `src/easydiffraction/workspace/categories/verbosity/` following the
+   same shape as `rendering/` (a `default.py` with a `Verbosity`
+   `CategoryItem`, a `factory.py` with `VerbosityFactory`, and an
+   `__init__.py` that imports both to trigger registration).
+
+   The category owns one descriptor:
+
+   ```python
+   CifHandler(names=['_verbosity.level'])
+   ```
+
+   Bind it in `WorkspaceConfig.__init__` next to `_rendering`, and
+   expose it from `Workspace` so that the public access path remains:
+
+   ```python
+   workspace.verbosity = 'short'
+   workspace.verbosity              # -> 'short'
+   ```
+
+   The public `verbosity` getter/setter on `Workspace` reads and writes
+   the category's `level` descriptor (validated against `VerbosityEnum`)
+   and replaces the current `self._verbosity: VerbosityEnum`
+   runtime-only attribute. Remove that attribute.
 
-5. Keep the contents semantic:
+   Serialize it as:
+
+   ```cif
+   _verbosity.level short
+   ```
+
+6. Keep the contents semantic:
 
    ```cif
    _project.id
    _project.title
    _rendering.table_engine
+   _verbosity.level
    ```
 
-6. Update logging and console output from `project.cif` to
+7. Update logging and console output from `project.cif` to
    `workspace.cif`.
 
-7. Run grep:
+8. Run grep:
 
    ```shell
-   rg -n "project\\.cif|project_config_to_cif|project_config_from_cif|project_to_cif" src docs tests
+   rg -n "project\\.cif|config\\.cif|meta\\.cif|project_config_to_cif|project_config_from_cif|project_to_cif|verbosity" src docs tests
    ```
 
-   In Phase 1, update source and docs only. Test files are handled in
-   Phase 2 unless the user explicitly asks otherwise.
+   Update source and docs only. Test files are handled in Phase 2 unless
+   the user explicitly asks otherwise.
+
+9. Saved on-disk fixtures under `data/` and `projects/` still contain
+   `project.cif` files (for example `data/lbco_project/project.cif`,
+   `projects/cosio/project.cif`). Do **not** edit or regenerate them in
+   Phase 1. They are inputs to integration/script tests and will either
+   be regenerated in Phase 2 or the relevant tests will be updated to
+   write fresh workspace directories. Flag any that block Phase 2 in the
+   review gate.
 
 ### Stop Conditions
 
@@ -586,6 +724,8 @@ Stop and ask if:
 - repository fixtures or tutorials contain saved directories that must
   remain loadable without conversion;
 - the user wants a one-release compatibility loader.
+- the verbosity setting cannot be represented as a category without
+  weakening the public `workspace.verbosity` API.
 
 ### Commit
 
@@ -623,14 +763,21 @@ category or the scientific project itself.
    analysis.project -> analysis.workspace
    ```
 
-2. Rename display internals:
+   This includes the constructor argument, the stored attribute, and any
+   public read-only property exposing the parent workspace.
+
+2. Rename display internals in `WorkspaceDisplay` (formerly
+   `ProjectDisplay`) and in any other class that stores a back-
+   reference to the root facade:
 
    ```text
    self._project -> self._workspace
    _set_project(...) -> _set_workspace(...)
    ```
 
-   Only do this when the object is the top-level runtime facade.
+   Only do this when the stored object is the top-level runtime facade.
+   Do not touch `self._project_id` inside `ProjectInfo` or `_project.*`
+   CIF tags.
 
 3. Rename local variables in runtime code:
 
@@ -687,35 +834,36 @@ new root object and project-information category.
 
 ### Files Likely To Change
 
-- `docs/dev/architecture.md`
-- `docs/dev/Issues/issues_open.md`
-- `docs/dev/ADRs/*.md`
-- `docs/dev/ADR-suggestions/*.md`
+- `docs/dev/adrs/index.md`
+- `docs/dev/issues/open.md`
+- `docs/dev/adrs/accepted/*.md`
+- `docs/dev/adrs/suggestions/*.md`
 - `docs/docs/tutorials/*.py`
 - `README.md`
 - `CONTRIBUTING.md` only if it contains API examples
 
 Do not edit these by hand:
 
-- `docs/dev/package-structure-full.md`
-- `docs/dev/package-structure-short.md`
+- `docs/dev/package-structure/full.md`
+- `docs/dev/package-structure/short.md`
 - generated tutorial notebooks
 - generated `docs/site/` files
 
 ### Steps
 
-1. Update architecture section 7:
+1. Update the relevant accepted ADRs:
 
    ```text
-   Project - The Top-Level Facade
-   -> Workspace - The Top-Level Facade
+   Project Facade and Persistence Layout
+   -> Workspace Facade and Persistence Layout
    ```
 
-2. Update the architecture table to use:
+2. Update the affected ADR examples to use:
 
    ```text
    workspace.project      ProjectInfo
    workspace.rendering    Rendering
+   workspace.verbosity    str
    workspace.display      WorkspaceDisplay
    ```
 
@@ -884,14 +1032,22 @@ Add focused tests for:
 3. `workspace.project.title` round-trips through `workspace.cif`.
 4. `workspace.rendering.table_engine` round-trips through
    `workspace.cif`.
-5. `Workspace.save()` writes `workspace.cif`.
-6. `Workspace.load()` reads `workspace.cif`.
-7. `workspace.cif` contains `_project.id`, not `_meta.project_id`.
-8. `workspace.cif` contains `_rendering.table_engine`.
-9. `workspace.path` is set after `save_as()` and `load()`.
-10. `workspace.project` has no serialized path field.
-11. `project.cif` is not written unless compatibility was approved.
-12. `ed.Project` is absent unless compatibility was approved.
+5. `workspace.verbosity` round-trips through `workspace.cif`.
+6. `Workspace.save()` writes `workspace.cif`.
+7. `Workspace.load()` reads `workspace.cif`.
+8. `workspace.cif` contains `_project.id`, not `_meta.project_id`.
+9. `workspace.cif` contains `_rendering.table_engine`.
+10. `workspace.cif` contains `_verbosity.level`.
+11. `workspace.path` is set after `save_as()` and `load()`.
+12. `workspace.project` has no serialized path field.
+13. `project.cif`, `config.cif`, and `meta.cif` are not written unless
+    compatibility was approved.
+14. `ed.Project` is absent unless compatibility was approved.
+15. `Verbosity` category is registered via its factory and reachable
+    through `WorkspaceConfig` (parallel to `Rendering`).
+16. `ed.extract_workspace_from_zip` is importable and
+    `ed.extract_project_from_zip` is not (unless compatibility
+    approved).
 
 If compatibility alias was approved, add tests for:
 
@@ -952,13 +1108,19 @@ rg -n "_meta\\.|_project\\." src tests docs tools README.md CONTRIBUTING.md
 Saved config file should be `workspace.cif`:
 
 ```shell
-rg -n "project\\.cif|workspace\\.cif" src tests docs tools README.md CONTRIBUTING.md
+rg -n "project\\.cif|config\\.cif|meta\\.cif|workspace\\.cif" src tests docs tools README.md CONTRIBUTING.md
+```
+
+Workspace verbosity should serialize as a workspace-level category:
+
+```shell
+rg -n "_verbosity|verbosity" src tests docs tools README.md CONTRIBUTING.md
 ```
 
 Generated docs should not be manually edited:
 
 ```shell
-git diff -- docs/site docs/dev/package-structure-full.md docs/dev/package-structure-short.md
+git diff -- docs/site docs/dev/package-structure/full.md docs/dev/package-structure/short.md
 ```
 
 If package-structure docs changed because of `pixi run fix`, that is
@@ -1001,6 +1163,17 @@ Incorrect:
 _meta.project_title
 ```
 
+### Mistake: Keeping `project.cif` Or Switching To Generic File Names
+
+Do not use `project.cif`, `config.cif`, or `meta.cif` as the target
+singleton settings file. The file belongs to the workspace layer.
+
+Correct:
+
+```text
+workspace.cif
+```
+
 ### Mistake: Blindly Replacing Every `project`
 
 Some uses of `project` should remain:
@@ -1017,6 +1190,21 @@ Only root-facade uses should become `workspace` or `Workspace`.
 The saved directory path belongs to the workspace runtime state. It
 should be `workspace.path`, not `workspace.project.path`.
 
+### Mistake: Forgetting Facade Class-Level State
+
+When renaming `Project` to `Workspace`, the `ClassVar`
+`_current_project`, the `current_project_path()` classmethod, and the
+`varname()` fallback string `'project'` all live on the class itself and
+are easy to miss with a single search-and-replace. Rename them to
+`_current_workspace`, `current_workspace_path()`, and `'workspace'`
+respectively.
+
+### Mistake: Renaming `ProjectInfo._project_id`
+
+The internal descriptor attribute `self._project_id` inside
+`ProjectInfo` already matches the new public name `id` and stays as is.
+Only the public `name` property/setter becomes `id`.
+
 ### Mistake: Editing Generated Notebooks Directly
 
 Tutorial notebooks are generated artifacts. Edit tutorial `.py` files,
diff --git a/docs/dev/ROADMAP.md b/docs/dev/roadmap/ROADMAP.md
similarity index 100%
rename from docs/dev/ROADMAP.md
rename to docs/dev/roadmap/ROADMAP.md
diff --git a/src/easydiffraction/analysis/categories/aliases/default.py b/src/easydiffraction/analysis/categories/aliases/default.py
index eef6201a8..5dd764ce5 100644
--- a/src/easydiffraction/analysis/categories/aliases/default.py
+++ b/src/easydiffraction/analysis/categories/aliases/default.py
@@ -30,6 +30,9 @@ class Alias(CategoryItem):
     ``unique_name`` for CIF serialization.
     """
 
+    _category_code = 'alias'
+    _category_entry_name = 'label'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -56,9 +59,6 @@ def __init__(self) -> None:
         # Stored via object.__setattr__ to avoid parent-chain mutation.
         object.__setattr__(self, '_param_ref', None)
 
-        self._identity.category_code = 'alias'
-        self._identity.category_entry_name = lambda: str(self.label.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/analysis/categories/constraints/default.py b/src/easydiffraction/analysis/categories/constraints/default.py
index fc1d74fde..d6f026221 100644
--- a/src/easydiffraction/analysis/categories/constraints/default.py
+++ b/src/easydiffraction/analysis/categories/constraints/default.py
@@ -26,9 +26,21 @@
 class Constraint(CategoryItem):
     """Single constraint item stored as ``lhs = rhs`` expression."""
 
+    _category_code = 'constraint'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
+        self._id = StringDescriptor(
+            name='id',
+            description='Explicit identifier for this constraint row.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z0-9_]*$'),
+            ),
+            cif_handler=CifHandler(names=['_constraint.id']),
+        )
         self._expression = StringDescriptor(
             name='expression',
             description='Constraint equation, e.g. "occ_Ba = 1 - occ_La".',
@@ -39,13 +51,19 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_constraint.expression']),
         )
 
-        self._identity.category_code = 'constraint'
-        self._identity.category_entry_name = lambda: self.lhs_alias
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
 
+    @property
+    def id(self) -> StringDescriptor:
+        """Explicit identifier for this constraint row."""
+        return self._id
+
+    @id.setter
+    def id(self, value: str) -> None:
+        self._id.value = value
+
     @property
     def expression(self) -> StringDescriptor:
         """
@@ -133,21 +151,33 @@ def create(self, *, expression: str) -> None:
         """
         item = Constraint()
         item.expression = expression
+        if item.lhs_alias:
+            item.id = item.lhs_alias
         self.add(item)
         self._enabled = True
 
+    def _after_from_cif(self) -> None:
+        """
+        Backfill explicit ids when loading older CIF constraint loops.
+        """
+        for item in self:
+            constraint_id = item.id.value.strip()
+            if constraint_id not in {'', '_', '?'} or not item.lhs_alias:
+                continue
+            item.id = item.lhs_alias
+
     def show(self) -> None:
         """Print a table of all user-defined symbolic constraints."""
         if not self._items:
             log.warning('No constraints defined.')
             return
 
-        rows = [[constraint.expression.value] for constraint in self]
+        rows = [[constraint.id.value, constraint.expression.value] for constraint in self]
 
         console.paragraph('User defined constraints')
         render_table(
-            columns_headers=['expression'],
-            columns_alignment=['left'],
+            columns_headers=['id', 'expression'],
+            columns_alignment=['left', 'left'],
             columns_data=rows,
         )
         console.print(f'Constraints enabled: {self.enabled}')
diff --git a/src/easydiffraction/analysis/categories/fitting/default.py b/src/easydiffraction/analysis/categories/fitting/default.py
index 925259cfc..141e22365 100644
--- a/src/easydiffraction/analysis/categories/fitting/default.py
+++ b/src/easydiffraction/analysis/categories/fitting/default.py
@@ -31,6 +31,8 @@ class Fitting(CategoryItem):
     Holds the active minimizer backend tag.
     """
 
+    _category_code = 'fitting'
+
     type_info = TypeInfo(
         tag='default',
         description='Fitting configuration category',
@@ -51,8 +53,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_fitting.minimizer_type']),
         )
 
-        self._identity.category_code = 'fitting'
-
     @property
     def minimizer_type(self) -> StringDescriptor:
         """Fitting minimizer backend type."""
diff --git a/src/easydiffraction/analysis/categories/joint_fit/default.py b/src/easydiffraction/analysis/categories/joint_fit/default.py
index 62f9e7e36..2821f2e8f 100644
--- a/src/easydiffraction/analysis/categories/joint_fit/default.py
+++ b/src/easydiffraction/analysis/categories/joint_fit/default.py
@@ -24,6 +24,9 @@
 class JointFitItem(CategoryItem):
     """A single joint-fit entry."""
 
+    _category_code = 'joint_fit'
+    _category_entry_name = 'experiment_id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -46,9 +49,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_joint_fit.weight']),
         )
 
-        self._identity.category_code = 'joint_fit'
-        self._identity.category_entry_name = lambda: str(self.experiment_id.value)
-
     @property
     def experiment_id(self) -> StringDescriptor:
         """
diff --git a/src/easydiffraction/analysis/categories/sequential_fit/default.py b/src/easydiffraction/analysis/categories/sequential_fit/default.py
index aad826f18..52519f56d 100644
--- a/src/easydiffraction/analysis/categories/sequential_fit/default.py
+++ b/src/easydiffraction/analysis/categories/sequential_fit/default.py
@@ -22,6 +22,8 @@
 class SequentialFit(CategoryItem):
     """Persisted settings for sequential fitting."""
 
+    _category_code = 'sequential_fit'
+
     type_info = TypeInfo(
         tag='default',
         description='Sequential fitting settings',
@@ -67,8 +69,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_sequential_fit.reverse']),
         )
 
-        self._identity.category_code = 'sequential_fit'
-
     @property
     def data_dir(self) -> StringDescriptor:
         """Directory containing sequential-fit data files."""
diff --git a/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py b/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
index 84bbf70c5..a724692c3 100644
--- a/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
+++ b/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
@@ -58,6 +58,9 @@ def _validate_extract_pattern(value: str) -> None:
 class SequentialFitExtractItem(CategoryItem):
     """A single sequential-fit extract rule."""
 
+    _category_code = 'sequential_fit_extract'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -89,9 +92,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_sequential_fit_extract.required']),
         )
 
-        self._identity.category_code = 'sequential_fit_extract'
-        self._identity.category_entry_name = lambda: str(self.id.value)
-
     @property
     def id(self) -> StringDescriptor:
         """Identifier for this extract rule."""
diff --git a/src/easydiffraction/core/category.py b/src/easydiffraction/core/category.py
index 0804d7d40..d2d95fb80 100644
--- a/src/easydiffraction/core/category.py
+++ b/src/easydiffraction/core/category.py
@@ -22,6 +22,13 @@ class CategoryItem(GuardedBase):
     #  CategoryCollection and use them when serializing to CIF!
     # TODO: Common for all categories
     _update_priority = 10  # Default. Lower values run first.
+    _category_code: str | None = None
+    _category_entry_name: str | None = None
+
+    def __init__(self) -> None:
+        super().__init__()
+        if self._category_code is not None:
+            self._identity.category_code = self._category_code
 
     def __str__(self) -> str:
         """Human-readable representation of this component."""
@@ -37,6 +44,17 @@ def _update(  # noqa: PLR6301
     ) -> None:
         del called_by_minimizer
 
+    def _resolve_category_entry_name(self) -> str | None:
+        """Resolve the declared category entry value for this item."""
+        attr_name = self._category_entry_name
+        if attr_name is None:
+            return None
+
+        value = getattr(self, attr_name)
+        if isinstance(value, GenericDescriptorBase):
+            value = value.value
+        return str(value)
+
     @property
     def unique_name(self) -> str:
         """Fully qualified name: datablock, category, entry."""
diff --git a/src/easydiffraction/core/identity.py b/src/easydiffraction/core/identity.py
index f6aca8cb2..b6b9e57a2 100644
--- a/src/easydiffraction/core/identity.py
+++ b/src/easydiffraction/core/identity.py
@@ -41,6 +41,13 @@ def _resolve_up(self, attr: str, visited: set[int] | None = None) -> str | None:
         if isinstance(value, str):
             return value
 
+        if attr == 'category_entry':
+            resolver = getattr(self._owner, '_resolve_category_entry_name', None)
+            if callable(resolver):
+                resolved = resolver()
+                if resolved is not None:
+                    return resolved
+
         # Climb to parent if available
         parent = getattr(self._owner, '__dict__', {}).get('_parent')
         if parent and hasattr(parent, '_identity'):
diff --git a/src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py b/src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py
index 5f90094dc..77e109c8d 100644
--- a/src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py
+++ b/src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py
@@ -41,6 +41,9 @@ class PolynomialTerm(CategoryItem):
     not break immediately. Tests should migrate to the short names.
     """
 
+    _category_code = 'background'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -75,9 +78,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_pd_background.Chebyshev_coef']),
         )
 
-        self._identity.category_code = 'background'
-        self._identity.category_entry_name = lambda: str(self._id.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/background/line_segment.py b/src/easydiffraction/datablocks/experiment/categories/background/line_segment.py
index 83e202b43..431abb317 100644
--- a/src/easydiffraction/datablocks/experiment/categories/background/line_segment.py
+++ b/src/easydiffraction/datablocks/experiment/categories/background/line_segment.py
@@ -34,6 +34,9 @@
 class LineSegment(CategoryItem):
     """Single background control point for interpolation."""
 
+    _category_code = 'background'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -78,9 +81,6 @@ def __init__(self) -> None:
             ),
         )
 
-        self._identity.category_code = 'background'
-        self._identity.category_entry_name = lambda: str(self._id.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/calculation/default.py b/src/easydiffraction/datablocks/experiment/categories/calculation/default.py
index 1574c58b6..ae54663a1 100644
--- a/src/easydiffraction/datablocks/experiment/categories/calculation/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/calculation/default.py
@@ -21,6 +21,8 @@
 class Calculation(CategoryItem):
     """Calculator selection and access for an experiment."""
 
+    _category_code = 'calculation'
+
     type_info = TypeInfo(
         tag='default',
         description='Experiment calculation category',
@@ -45,8 +47,6 @@ def __init__(
             cif_handler=CifHandler(names=['_calculation.calculator_type']),
         )
 
-        self._identity.category_code = 'calculation'
-
     @property
     def calculator_type(self) -> StringDescriptor:
         """Calculation backend type."""
diff --git a/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py b/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
index b1e30aa0a..1d5c866b8 100644
--- a/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
+++ b/src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py
@@ -285,10 +285,11 @@ class PdCwlDataPoint(
 ):
     """Powder diffraction data point for CWL experiments."""
 
+    _category_code = 'pd_data'
+    _category_entry_name = 'point_id'
+
     def __init__(self) -> None:
         super().__init__()
-        self._identity.category_code = 'pd_data'
-        self._identity.category_entry_name = lambda: str(self.point_id.value)
 
 
 class PdTofDataPoint(
@@ -298,10 +299,11 @@ class PdTofDataPoint(
 ):
     """Powder diffraction data point for time-of-flight experiments."""
 
+    _category_code = 'pd_data'
+    _category_entry_name = 'point_id'
+
     def __init__(self) -> None:
         super().__init__()
-        self._identity.category_code = 'pd_data'
-        self._identity.category_entry_name = lambda: str(self.point_id.value)
 
 
 class PdDataBase(CategoryCollection):
diff --git a/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py b/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
index 31ac92e76..58ef0c74b 100644
--- a/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
+++ b/src/easydiffraction/datablocks/experiment/categories/data/total_pd.py
@@ -33,6 +33,9 @@ class TotalDataPoint(CategoryItem):
     original measurement was CWL or TOF.
     """
 
+    _category_code = 'total_data'
+    _category_entry_name = 'point_id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -114,9 +117,6 @@ def __init__(self) -> None:
             ),
         )
 
-        self._identity.category_code = 'total_data'
-        self._identity.category_entry_name = lambda: str(self.point_id.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/diffrn/default.py b/src/easydiffraction/datablocks/experiment/categories/diffrn/default.py
index 9635fc2ef..92b4290de 100644
--- a/src/easydiffraction/datablocks/experiment/categories/diffrn/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/diffrn/default.py
@@ -17,6 +17,8 @@
 class DefaultDiffrn(CategoryItem):
     """Ambient conditions recorded during diffraction measurement."""
 
+    _category_code = 'diffrn'
+
     type_info = TypeInfo(
         tag='default',
         description='Diffraction ambient conditions',
@@ -73,8 +75,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_diffrn.ambient_electric_field']),
         )
 
-        self._identity.category_code = 'diffrn'
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py b/src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py
index 2f4170bbd..36df8e468 100644
--- a/src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py
@@ -27,6 +27,9 @@
 class ExcludedRegion(CategoryItem):
     """Closed interval [start, end] to be excluded."""
 
+    _category_code = 'excluded_regions'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -61,8 +64,6 @@ def __init__(self) -> None:
             ),
             cif_handler=CifHandler(names=['_excluded_region.end']),
         )
-        self._identity.category_code = 'excluded_regions'
-        self._identity.category_entry_name = lambda: str(self._id.value)
 
     # ------------------------------------------------------------------
     #  Public properties
diff --git a/src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py b/src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py
index 1b9d38113..83348086b 100644
--- a/src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py
@@ -29,6 +29,8 @@
 class ExperimentType(CategoryItem):
     """Container of attributes defining the experiment type."""
 
+    _category_code = 'expt_type'
+
     type_info = TypeInfo(
         tag='default',
         description='Experiment type descriptor',
@@ -79,8 +81,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_expt_type.scattering_type']),
         )
 
-        self._identity.category_code = 'expt_type'
-
     # ------------------------------------------------------------------
     #  Private setters (used by factories and loaders only)
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py b/src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py
index 0555d7d02..d5ffa1578 100644
--- a/src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py
+++ b/src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py
@@ -36,6 +36,8 @@ class BeckerCoppensExtinction(CategoryItem):
     (in arc-minutes, as expected by CrysPy).
     """
 
+    _category_code = 'extinction'
+
     type_info = TypeInfo(
         tag='becker-coppens',
         description='Becker-Coppens isotropic extinction correction',
@@ -83,8 +85,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_extinction.radius']),
         )
 
-        self._identity.category_code = 'extinction'
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/instrument/base.py b/src/easydiffraction/datablocks/experiment/categories/instrument/base.py
index a2568884b..c8e209744 100644
--- a/src/easydiffraction/datablocks/experiment/categories/instrument/base.py
+++ b/src/easydiffraction/datablocks/experiment/categories/instrument/base.py
@@ -20,7 +20,8 @@ class InstrumentBase(CategoryItem):
     for concrete CWL/TOF instrument definitions.
     """
 
+    _category_code = 'instrument'
+
     def __init__(self) -> None:
-        """Initialize instrument base and set category code."""
+        """Initialize instrument base."""
         super().__init__()
-        self._identity.category_code = 'instrument'
diff --git a/src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py b/src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py
index d441aa9ca..b1cbf2d85 100644
--- a/src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py
@@ -23,6 +23,8 @@
 class LinkedCrystal(CategoryItem):
     """Linked crystal reference for single-crystal diffraction."""
 
+    _category_code = 'linked_crystal'
+
     type_info = TypeInfo(
         tag='default',
         description='Crystal reference with id and scale factor',
@@ -53,8 +55,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_sc_crystal_block.scale']),
         )
 
-        self._identity.category_code = 'linked_crystal'
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py b/src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py
index eb6047a79..fca0fb3b2 100644
--- a/src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py
+++ b/src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py
@@ -23,6 +23,9 @@
 class LinkedPhase(CategoryItem):
     """Link to a phase by id with a scale factor."""
 
+    _category_code = 'linked_phases'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -45,9 +48,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_pd_phase_block.scale']),
         )
 
-        self._identity.category_code = 'linked_phases'
-        self._identity.category_entry_name = lambda: str(self.id.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/experiment/categories/peak/base.py b/src/easydiffraction/datablocks/experiment/categories/peak/base.py
index c8270ed1c..330d34624 100644
--- a/src/easydiffraction/datablocks/experiment/categories/peak/base.py
+++ b/src/easydiffraction/datablocks/experiment/categories/peak/base.py
@@ -13,9 +13,10 @@
 class PeakBase(CategoryItem):
     """Base class for peak profile categories."""
 
+    _category_code = 'peak'
+
     def __init__(self) -> None:
         super().__init__()
-        self._identity.category_code = 'peak'
 
         type_info = getattr(type(self), 'type_info', None)
         default_tag = type_info.tag if type_info is not None else ''
diff --git a/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py b/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
index ada32c70f..8b29900d4 100644
--- a/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
+++ b/src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py
@@ -28,6 +28,9 @@
 class Refln(CategoryItem):
     """Single reflection for single-crystal diffraction data."""
 
+    _category_code = 'refln'
+    _category_entry_name = 'id'
+
     def __init__(self) -> None:
         super().__init__()
 
@@ -128,9 +131,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_refln.wavelength']),
         )
 
-        self._identity.category_code = 'refln'
-        self._identity.category_entry_name = lambda: str(self.id.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py b/src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py
index fbb7dedf9..acc422b02 100644
--- a/src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py
@@ -32,6 +32,9 @@ class AtomSiteAniso(CategoryItem):
     ``atom_site.adp_type``.
     """
 
+    _category_code = 'atom_site_aniso'
+    _category_entry_name = 'label'
+
     def __init__(self) -> None:
         """Initialise with default zero-valued tensor components."""
         super().__init__()
@@ -134,9 +137,6 @@ def __init__(self) -> None:
             ),
         )
 
-        self._identity.category_code = 'atom_site_aniso'
-        self._identity.category_entry_name = lambda: str(self.label.value)
-
     # ------------------------------------------------------------------
     #  Public properties
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py b/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
index 6c91a8561..2ac4b1d3e 100644
--- a/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/atom_sites/default.py
@@ -36,6 +36,9 @@ class AtomSite(CategoryItem):
     CIF serialization.
     """
 
+    _category_code = 'atom_site'
+    _category_entry_name = 'label'
+
     def __init__(self) -> None:
         """Initialise the atom site with default descriptor values."""
         super().__init__()
@@ -139,9 +142,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_atom_site.adp_type']),
         )
 
-        self._identity.category_code = 'atom_site'
-        self._identity.category_entry_name = lambda: str(self.label.value)
-
     # ------------------------------------------------------------------
     #  Private helper methods
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/structure/categories/cell/default.py b/src/easydiffraction/datablocks/structure/categories/cell/default.py
index 712629501..9b2980082 100644
--- a/src/easydiffraction/datablocks/structure/categories/cell/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/cell/default.py
@@ -23,6 +23,8 @@ class Cell(CategoryItem):
     descriptors supporting validation, fitting and CIF serialization.
     """
 
+    _category_code = 'cell'
+
     type_info = TypeInfo(
         tag='default',
         description='Unit cell parameters',
@@ -93,8 +95,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_cell.angle_gamma']),
         )
 
-        self._identity.category_code = 'cell'
-
     # ------------------------------------------------------------------
     #  Private helper methods
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/datablocks/structure/categories/space_group/default.py b/src/easydiffraction/datablocks/structure/categories/space_group/default.py
index e04015f24..bb727ac43 100644
--- a/src/easydiffraction/datablocks/structure/categories/space_group/default.py
+++ b/src/easydiffraction/datablocks/structure/categories/space_group/default.py
@@ -30,6 +30,8 @@ class SpaceGroup(CategoryItem):
     to the first allowed value for the new group.
     """
 
+    _category_code = 'space_group'
+
     type_info = TypeInfo(
         tag='default',
         description='Space group symmetry',
@@ -77,8 +79,6 @@ def __init__(self) -> None:
             ),
         )
 
-        self._identity.category_code = 'space_group'
-
     # ------------------------------------------------------------------
     #  Private helper methods
     # ------------------------------------------------------------------
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index f7625330e..5c3a29c14 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -912,3 +912,9 @@ def category_collection_from_cif(
                     #  param_from_cif
                     _set_param_from_raw_cif_value(param, array[row_idx][col_idx])
                     break
+
+    after_from_cif = getattr(self, '_after_from_cif', None)
+    if callable(after_from_cif):
+        after_from_cif()
+
+    self._rebuild_index()
diff --git a/src/easydiffraction/project/categories/info/default.py b/src/easydiffraction/project/categories/info/default.py
index 1d7b8e318..ff730dcee 100644
--- a/src/easydiffraction/project/categories/info/default.py
+++ b/src/easydiffraction/project/categories/info/default.py
@@ -24,6 +24,8 @@
 class ProjectInfo(CategoryItem):
     """Project metadata category."""
 
+    _category_code = 'project'
+
     type_info = TypeInfo(
         tag='default',
         description='Project metadata category',
@@ -72,8 +74,6 @@ def __init__(
         )
         self._path: pathlib.Path | None = None
 
-        self._identity.category_code = 'project'
-
     @staticmethod
     def _parse_timestamp(value: str) -> datetime.datetime:
         """Parse project timestamp text from CIF storage format."""
diff --git a/src/easydiffraction/project/categories/rendering/default.py b/src/easydiffraction/project/categories/rendering/default.py
index b55a6f923..d3e2fad3e 100644
--- a/src/easydiffraction/project/categories/rendering/default.py
+++ b/src/easydiffraction/project/categories/rendering/default.py
@@ -24,6 +24,8 @@
 class Rendering(CategoryItem):
     """Chart and table engine selection for a project."""
 
+    _category_code = 'rendering'
+
     type_info = TypeInfo(
         tag='default',
         description='Project rendering category',
@@ -58,8 +60,6 @@ def __init__(self) -> None:
             cif_handler=CifHandler(names=['_rendering.table_engine']),
         )
 
-        self._identity.category_code = 'rendering'
-
     @property
     def chart_engine(self) -> StringDescriptor:
         """Chart renderer backend type."""
diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
index 5633cb6ad..89c0fd931 100644
--- a/tests/integration/fitting/test_analysis_and_fit_category_support.py
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -267,10 +267,14 @@ def test_analysis_display_as_cif_and_constraints(monkeypatch, capsys):
     analysis.display.constraints()
     assert 'No constraints' in capsys.readouterr().out
 
+    class FakeId:
+        value = 'constraint_1'
+
     class FakeExpr:
         value = 'x = y + 1'
 
     class FakeConstraint:
+        id = FakeId()
         expression = FakeExpr()
 
     analysis.constraints._items = [FakeConstraint()]
@@ -279,7 +283,8 @@ class FakeConstraint:
     analysis.display.constraints()
     out = capsys.readouterr().out
     assert 'User defined constraints' in out
-    assert captured['columns_data'][0][0] == 'x = y + 1'
+    assert captured['columns_headers'] == ['id', 'expression']
+    assert captured['columns_data'][0] == ['constraint_1', 'x = y + 1']
 
 
 def test_discover_helpers_and_snapshot_params():
diff --git a/tests/integration/fitting/test_project_load.py b/tests/integration/fitting/test_project_load.py
index f1f81c801..f908789a6 100644
--- a/tests/integration/fitting/test_project_load.py
+++ b/tests/integration/fitting/test_project_load.py
@@ -205,6 +205,9 @@ def test_save_load_round_trip_preserves_parameters(tmp_path) -> None:
     # Compare constraints
     assert len(loaded.analysis.constraints) == len(original.analysis.constraints)
     for i, orig_c in enumerate(original.analysis.constraints):
+        loaded_constraint = loaded.analysis.constraints[orig_c.id.value]
+        assert loaded_constraint.id.value == orig_c.id.value
+        assert loaded_constraint.expression.value == orig_c.expression.value
         assert loaded.analysis.constraints[i].expression.value == orig_c.expression.value
     assert loaded.analysis.constraints.enabled is True
 
diff --git a/tests/unit/easydiffraction/analysis/categories/test_constraints.py b/tests/unit/easydiffraction/analysis/categories/test_constraints.py
index 15dddc4f2..b887408ea 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_constraints.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_constraints.py
@@ -1,16 +1,52 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+import gemmi
+
 from easydiffraction.analysis.categories.constraints import Constraint
 from easydiffraction.analysis.categories.constraints import Constraints
 
 
 def test_constraint_creation_and_collection():
     c = Constraint()
+    c.id = 'constraint_1'
     c.expression = 'a = b + c'
+    assert c.id.value == 'constraint_1'
     assert c.lhs_alias == 'a'
     assert c.rhs_expr == 'b + c'
+
     coll = Constraints()
     coll.create(expression='a = b + c')
     assert 'a' in coll.names
+    assert coll['a'].id.value == 'a'
     assert coll['a'].rhs_expr == 'b + c'
+
+
+def test_constraints_from_cif_preserves_explicit_id_keys():
+    doc = gemmi.cif.read_string(
+        'data_constraints\n\n'
+        'loop_\n'
+        '_constraint.id\n'
+        '_constraint.expression\n'
+        'constraint_1 "a = b + c"\n',
+    )
+    coll = Constraints()
+
+    coll.from_cif(doc.sole_block())
+
+    assert coll.names == ['constraint_1']
+    assert coll['constraint_1'].id.value == 'constraint_1'
+    assert coll['constraint_1'].lhs_alias == 'a'
+
+
+def test_constraints_from_cif_backfills_missing_id_from_lhs_alias():
+    doc = gemmi.cif.read_string(
+        'data_constraints\n\nloop_\n_constraint.expression\n"a = b + c"\n',
+    )
+    coll = Constraints()
+
+    coll.from_cif(doc.sole_block())
+
+    assert coll.names == ['a']
+    assert coll['a'].id.value == 'a'
+    assert coll['a'].expression.value == 'a = b + c'
diff --git a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
index 42ef01ace..13072544a 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
@@ -78,10 +78,14 @@ def test_constraints_with_items(self, capsys, monkeypatch):
         a = Analysis(project=_make_project())
 
         # Create a fake constraint with expression
+        class FakeId:
+            value = 'constraint_1'
+
         class FakeExpr:
             value = 'x = y + 1'
 
         class FakeConstraint:
+            id = FakeId()
             expression = FakeExpr()
 
         a.constraints._items = [FakeConstraint()]
@@ -95,8 +99,9 @@ def fake_render_table(**kwargs):
         a.display.constraints()
         out = capsys.readouterr().out
         assert 'User defined constraints' in out
+        assert captured['columns_headers'] == ['id', 'expression']
         assert 'columns_data' in captured
-        assert captured['columns_data'][0][0] == 'x = y + 1'
+        assert captured['columns_data'][0] == ['constraint_1', 'x = y + 1']
 
 
 # ------------------------------------------------------------------
diff --git a/tests/unit/easydiffraction/core/test_category.py b/tests/unit/easydiffraction/core/test_category.py
index 3d27b5012..c1a4ffbe2 100644
--- a/tests/unit/easydiffraction/core/test_category.py
+++ b/tests/unit/easydiffraction/core/test_category.py
@@ -1,6 +1,10 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+import importlib
+
+import pytest
+
 from easydiffraction.core.category import CategoryCollection
 from easydiffraction.core.category import CategoryItem
 from easydiffraction.core.validation import AttributeSpec
@@ -9,9 +13,11 @@
 
 
 class SimpleItem(CategoryItem):
+    _category_code = 'simple'
+    _category_entry_name = 'a'
+
     def __init__(self):
         super().__init__()
-        self._identity.category_code = 'simple'
         object.__setattr__(
             self,
             '_a',
@@ -32,7 +38,6 @@ def __init__(self):
                 cif_handler=CifHandler(names=['_simple.b']),
             ),
         )
-        self._identity.category_entry_name = lambda: str(self._a.value)
 
     @property
     def a(self):
@@ -56,6 +61,154 @@ def __init__(self):
         super().__init__(item_type=SimpleItem)
 
 
+def test_category_item_uses_declared_identity_metadata():
+    it = SimpleItem()
+
+    assert type(it)._category_code == 'simple'
+    assert type(it)._category_entry_name == 'a'
+    assert it._identity.category_code == 'simple'
+
+    it.a = 'name1'
+
+    assert it._identity.category_entry_name == 'name1'
+
+
+@pytest.mark.parametrize(
+    ('module_name', 'class_name', 'attr_name', 'value', 'category_code'),
+    [
+        pytest.param(
+            'easydiffraction.analysis.categories.aliases.default',
+            'Alias',
+            'label',
+            'alias_1',
+            'alias',
+            id='alias',
+        ),
+        pytest.param(
+            'easydiffraction.analysis.categories.constraints.default',
+            'Constraint',
+            'id',
+            'constraint_1',
+            'constraint',
+            id='constraint',
+        ),
+        pytest.param(
+            'easydiffraction.analysis.categories.joint_fit.default',
+            'JointFitItem',
+            'experiment_id',
+            'exp_1',
+            'joint_fit',
+            id='joint_fit',
+        ),
+        pytest.param(
+            'easydiffraction.analysis.categories.sequential_fit_extract.default',
+            'SequentialFitExtractItem',
+            'id',
+            'rule_1',
+            'sequential_fit_extract',
+            id='sequential_fit_extract',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.structure.categories.atom_sites.default',
+            'AtomSite',
+            'label',
+            'Fe1',
+            'atom_site',
+            id='atom_site',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.structure.categories.atom_site_aniso.default',
+            'AtomSiteAniso',
+            'label',
+            'Fe1',
+            'atom_site_aniso',
+            id='atom_site_aniso',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.linked_phases.default',
+            'LinkedPhase',
+            'id',
+            'phase_1',
+            'linked_phases',
+            id='linked_phases',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.background.line_segment',
+            'LineSegment',
+            'id',
+            'bg_1',
+            'background',
+            id='background_line_segment',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.background.chebyshev',
+            'PolynomialTerm',
+            'id',
+            'poly_1',
+            'background',
+            id='background_chebyshev',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.excluded_regions.default',
+            'ExcludedRegion',
+            'id',
+            'mask_1',
+            'excluded_regions',
+            id='excluded_region',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.refln.bragg_sc',
+            'Refln',
+            'id',
+            'refl_1',
+            'refln',
+            id='refln',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.data.bragg_pd',
+            'PdCwlDataPoint',
+            'point_id',
+            '1',
+            'pd_data',
+            id='pd_cwl_data',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.data.bragg_pd',
+            'PdTofDataPoint',
+            'point_id',
+            '2',
+            'pd_data',
+            id='pd_tof_data',
+        ),
+        pytest.param(
+            'easydiffraction.datablocks.experiment.categories.data.total_pd',
+            'TotalDataPoint',
+            'point_id',
+            '3',
+            'total_data',
+            id='total_data',
+        ),
+    ],
+)
+def test_loop_items_resolve_declared_category_identity(
+    module_name,
+    class_name,
+    attr_name,
+    value,
+    category_code,
+):
+    module = importlib.import_module(module_name)
+    item_cls = getattr(module, class_name)
+    item = item_cls()
+
+    getattr(item, attr_name).value = value
+
+    assert item_cls._category_code == category_code
+    assert item_cls._category_entry_name == attr_name
+    assert item._identity.category_code == category_code
+    assert item._identity.category_entry_name == value
+
+
 def test_category_item_str_and_properties():
     it = SimpleItem()
     it.a = 'name1'
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
index badd72418..7d45dfbd3 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
@@ -52,6 +52,7 @@ def test_real_analysis_as_cif_includes_aliases_and_constraints_when_present() ->
 
     assert '_alias.label' in analysis_cif
     assert '_alias.param_unique_name' in analysis_cif
+    assert '_constraint.id' in analysis_cif
     assert '_constraint.expression' in analysis_cif
     assert 'a_param = a_param' in analysis_cif
 
diff --git a/tests/unit/easydiffraction/project/test_project_load.py b/tests/unit/easydiffraction/project/test_project_load.py
index 4f32ffde4..e44c12608 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -120,6 +120,8 @@ def test_round_trips_constraints(self, tmp_path):
         assert loaded.analysis.aliases['b_param'].param is not None
 
         assert len(loaded.analysis.constraints) == 1
+        assert loaded.analysis.constraints['b_param'].id.value == 'b_param'
+        assert loaded.analysis.constraints['b_param'].expression.value == 'b_param = a_param'
         assert loaded.analysis.constraints[0].expression.value == 'b_param = a_param'
         assert loaded.analysis.constraints.enabled is True
 
diff --git a/tools/generate_package_docs.py b/tools/generate_package_docs.py
index 1d860557e..78915c2f7 100644
--- a/tools/generate_package_docs.py
+++ b/tools/generate_package_docs.py
@@ -3,9 +3,9 @@
 
 """Generate project package structure markdown files.
 
-Outputs two docs under docs/dev/:
- - package-structure-short.md  (folders/files only)
- - package-structure-full.md   (folders/files and classes)
+Outputs two docs under docs/dev/package-structure/:
+ - short.md  (folders/files only)
+ - full.md   (folders/files and classes)
 
 Run (from repo root):
     pixi run python tools/generate_package_docs.py
@@ -21,7 +21,7 @@
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
 SRC_ROOT = REPO_ROOT / 'src' / 'easydiffraction'
-DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'dev'
+DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'dev' / 'package-structure'
 
 
 IGNORE_DIRS = {
@@ -132,8 +132,8 @@ def _render(node: Node, prefix: str = '') -> None:
 def write_markdown(short_lines: List[str], full_lines: List[str]) -> None:
     DOCS_OUT_DIR.mkdir(parents=True, exist_ok=True)
 
-    short_md = DOCS_OUT_DIR / 'package-structure-short.md'
-    full_md = DOCS_OUT_DIR / 'package-structure-full.md'
+    short_md = DOCS_OUT_DIR / 'short.md'
+    full_md = DOCS_OUT_DIR / 'full.md'
 
     short_content = [
         '# Package Structure (short)',
diff --git a/tools/param_consistency.py b/tools/param_consistency.py
index 1c459e055..a9d266bae 100644
--- a/tools/param_consistency.py
+++ b/tools/param_consistency.py
@@ -8,8 +8,8 @@
     python param_consistency.py --fix
     python param_consistency.py src/mypackage/ --check
 
-Template (see architecture.md §9.8 for the full spec)
-------------------------------------------------------
+Template (see docs/dev/adrs/accepted/property-docstring-template.md)
+-----------------------------------------------------------------------
 Given ``description='Length of the a axis of the unit
 cell.'``, ``units='Å'``, and type ``Parameter``:
 

From 57f00e1cb92565c699262ef0932f19ccd80d04c6 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <AndrewSazonov@users.noreply.github.com>
Date: Tue, 19 May 2026 15:09:45 +0200
Subject: [PATCH 10/10] Add persistent fit results and project-centric
 workflows (#178)

* Split persistence ADR and add fit output ADR

* Add artifact root for reproducible tutorials

* Finalize Project facade decision

* Store auto rendering defaults in project config

* Accept loop category key identity ADR

* Add project save to ed-2 tutorial

* Remove extraneous blank line

* Add project fit verbosity category

* Add analysis fit state implementation plan

* Reformat documentation tables and line breaks

* Add project fit verbosity verification

* Clarify analysis fit-state ADR schema

* Add common analysis fit-state categories

* Add deterministic fit-result categories

* Add Bayesian fit-result metadata categories

* Add Bayesian fit-cache manifest categories

* Add project save step to tutorial

* Wire analysis fit-state CIF restore

* Linting and formatting

* Add h5py dependency

* Capture persisted fit-state projections

* Persist Bayesian fit arrays in results sidecar

* Restore fit results from saved analysis state

* Align persisted fit-state schema with review feedback

* Persist Bayesian plot caches in sidecar

* Add integer type and skip redundant processing

* Update tutorial notebooks with consistent IDs and saves

* Add pre/post-processing tracking to Bayesian fits

* Add project-first CLI commands for saved projects

* Fix missing pre-processing row in DREAM progress table

* Add CLI commands for example data downloads

* Remove pattern display from tutorial

* Group saved-project tutorials under Load Project

* Consolidate download and extract in tutorials

* Refactor Bayesian fit-state manifest create APIs

* Remove deterministic parameter-result persistence

* Update data index reference and hash

* Consolidate saved project tutorials to download_data

* Remove deterministic_parameter_results from docs

* Revise docs for CLI syntax and project loading

* Update undo command to show it's a placeholder

* Harden fit-state sidecar paths and finalize ADR

* Reorder CLI command definitions

* Restore saved DREAM sampler settings on project load

* Restore legacy DREAM sampler settings before CLI fit

* Clarify DREAM multiprocessing issue for direct scripts

* Update data index ref and hash

* Add DREAM multiprocessing issue #95

* Remove obsolete package references from docs

* Update CIF serializer test for long descriptions

* Update ADR suggestions for implemented fit-state work

* Accept parameter correlation persistence ADR

* Rename sequential fit result CSV columns

* Add overwrite option to Project.save_as

* Improve code formatting and docstrings

* Update data index reference and hash

* Simplify tutorial with API shortcuts

* Improve tutorial index descriptions

* Accept str or list for column_names

* Fix fit tracking finalization cleanup

* Add tutorial benchmark runner and pixi task

* Write benchmark results incrementally

* Reduce tutorial computation time and fix data ID

* Simplify tutorial titles for consistency

* Honor max_iterations in BUMPS fits

* Add tutorial benchmark results

* Refactor BumpsMinimizer evaluation limit handling

* Plot fit quality vs temperature

* Improve output formatting and add logging

* Optimize tutorial execution time

* Add newline before saved path

* Remove notebook timeout, add CI artifact root
---
 .github/copilot-instructions.md               |    9 +
 .github/workflows/docs.yml                    |    2 +-
 .../adrs/accepted/analysis-cif-fit-state.md   |  193 +++
 .../loop-category-key-identity.md             |   95 +-
 .../parameter-correlation-persistence.md      |  104 ++
 .../project-facade-and-persistence.md         |   45 +-
 docs/dev/adrs/index.md                        |   62 +-
 .../suggestions/analysis-cif-fit-state.md     |  220 ---
 .../fit-output-files-and-data-exports.md      |  172 +++
 .../parameter-correlation-persistence.md      |  114 --
 .../parameter-posterior-summary.md            |  437 +-----
 .../python-cif-category-correspondence.md     |   98 +-
 docs/dev/adrs/suggestions/undo-fit.md         |  146 +-
 .../workspace-root-project-category.md        |  380 -----
 ...darwin-arm64_py314_tutorial-benchmarks.csv |   24 +
 ...darwin-arm64_py314_tutorial-benchmarks.csv |   24 +
 ...darwin-arm64_py314_tutorial-benchmarks.csv |   24 +
 docs/dev/issues/open.md                       |  205 +--
 docs/dev/package-structure/full.md            |   94 +-
 docs/dev/package-structure/short.md           |   52 +-
 docs/dev/plans/loop-category-key-identity.md  |  620 ---------
 .../plans/workspace-root-project-category.md  | 1229 -----------------
 docs/docs/cli/index.md                        |   73 +-
 docs/docs/quick-reference/index.md            |   22 +-
 docs/docs/tutorials/ed-13.ipynb               |    2 +-
 docs/docs/tutorials/ed-15.ipynb               |   39 +-
 docs/docs/tutorials/ed-15.py                  |    4 +
 docs/docs/tutorials/ed-17.ipynb               |  243 ++--
 docs/docs/tutorials/ed-17.py                  |  176 ++-
 docs/docs/tutorials/ed-18.ipynb               |   39 +-
 docs/docs/tutorials/ed-18.py                  |   11 +-
 docs/docs/tutorials/ed-2.ipynb                |   18 +
 docs/docs/tutorials/ed-2.py                   |    6 +
 docs/docs/tutorials/ed-20.ipynb               |    2 +-
 docs/docs/tutorials/ed-21.ipynb               |  126 +-
 docs/docs/tutorials/ed-21.py                  |    9 +-
 docs/docs/tutorials/ed-22.ipynb               |    3 +-
 docs/docs/tutorials/ed-22.py                  |    3 +-
 docs/docs/tutorials/ed-23.ipynb               |   85 +-
 docs/docs/tutorials/ed-23.py                  |   41 +-
 docs/docs/tutorials/ed-24.ipynb               |  223 +++
 docs/docs/tutorials/ed-24.py                  |   82 ++
 docs/docs/tutorials/index.md                  |   20 +-
 .../user-guide/analysis-workflow/project.md   |   40 +-
 docs/mkdocs.yml                               |    6 +-
 pixi.lock                                     |    1 +
 pixi.toml                                     |   12 +-
 pyproject.toml                                |    1 +
 src/easydiffraction/__init__.py               |    1 +
 src/easydiffraction/__main__.py               |  152 +-
 src/easydiffraction/analysis/__init__.py      |   52 +
 src/easydiffraction/analysis/analysis.py      | 1134 ++++++++++++++-
 .../analysis/categories/__init__.py           |   31 +
 .../bayesian_convergence/__init__.py          |    7 +
 .../bayesian_convergence/default.py           |  121 ++
 .../bayesian_convergence/factory.py           |   17 +
 .../bayesian_distribution_caches/__init__.py  |   12 +
 .../bayesian_distribution_caches/default.py   |  151 ++
 .../bayesian_distribution_caches/factory.py   |   17 +
 .../bayesian_pair_caches/__init__.py          |    8 +
 .../bayesian_pair_caches/default.py           |  267 ++++
 .../bayesian_pair_caches/factory.py           |   17 +
 .../bayesian_parameter_posteriors/__init__.py |   12 +
 .../bayesian_parameter_posteriors/default.py  |  242 ++++
 .../bayesian_parameter_posteriors/factory.py  |   17 +
 .../bayesian_predictive_datasets/__init__.py  |   12 +
 .../bayesian_predictive_datasets/default.py   |  260 ++++
 .../bayesian_predictive_datasets/factory.py   |   17 +
 .../categories/bayesian_result/__init__.py    |    5 +
 .../categories/bayesian_result/default.py     |  215 +++
 .../categories/bayesian_result/factory.py     |   17 +
 .../categories/bayesian_sampler/__init__.py   |    5 +
 .../categories/bayesian_sampler/default.py    |  133 ++
 .../categories/bayesian_sampler/factory.py    |   17 +
 .../categories/constraints/default.py         |    9 +-
 .../deterministic_result/__init__.py          |    7 +
 .../deterministic_result/default.py           |  187 +++
 .../deterministic_result/factory.py           |   17 +
 .../fit_parameter_correlations/__init__.py    |   12 +
 .../fit_parameter_correlations/default.py     |  184 +++
 .../fit_parameter_correlations/factory.py     |   17 +
 .../categories/fit_parameters/__init__.py     |    6 +
 .../categories/fit_parameters/default.py      |  177 +++
 .../categories/fit_parameters/factory.py      |   17 +
 .../categories/fit_result/__init__.py         |    5 +
 .../analysis/categories/fit_result/default.py |  135 ++
 .../analysis/categories/fit_result/factory.py |   17 +
 src/easydiffraction/analysis/enums.py         |   24 +
 .../analysis/fit_helpers/tracking.py          |  107 +-
 src/easydiffraction/analysis/fitting.py       |  181 ++-
 .../analysis/minimizers/base.py               |   50 +-
 .../analysis/minimizers/bumps.py              |  271 +++-
 .../analysis/minimizers/bumps_dream.py        |   37 +-
 src/easydiffraction/analysis/sequential.py    |   61 +-
 src/easydiffraction/core/validation.py        |    2 +
 src/easydiffraction/core/variable.py          |   40 +
 .../datablocks/experiment/collection.py       |    2 +-
 src/easydiffraction/display/plotting.py       |  337 ++++-
 src/easydiffraction/display/progress.py       |    2 +
 src/easydiffraction/io/ascii.py               |   21 +-
 src/easydiffraction/io/cif/serialize.py       |  206 ++-
 src/easydiffraction/io/results_sidecar.py     |  652 +++++++++
 .../project/categories/rendering/default.py   |   61 +-
 .../project/categories/verbosity/__init__.py  |    8 +
 .../project/categories/verbosity/default.py   |   55 +
 .../project/categories/verbosity/factory.py   |   17 +
 src/easydiffraction/project/display.py        |  137 +-
 src/easydiffraction/project/project.py        |  190 ++-
 src/easydiffraction/project/project_config.py |    8 +
 src/easydiffraction/utils/environment.py      |   94 ++
 src/easydiffraction/utils/utils.py            |  124 +-
 tests/functional/test_project_lifecycle.py    |    6 +-
 .../fitting/test_bayesian_dream.py            |   11 +-
 .../fitting/test_bumps_dream_support.py       |    5 +-
 tests/integration/fitting/test_sequential.py  |   18 +-
 .../categories/test_bayesian_convergence.py   |   17 +
 .../test_bayesian_distribution_caches.py      |   17 +
 .../categories/test_bayesian_pair_caches.py   |   15 +
 .../test_bayesian_parameter_posteriors.py     |   17 +
 .../test_bayesian_predictive_datasets.py      |   17 +
 .../categories/test_bayesian_result.py        |   13 +
 .../categories/test_bayesian_sampler.py       |   13 +
 .../analysis/categories/test_constraints.py   |   11 +
 .../categories/test_deterministic_result.py   |   17 +
 .../test_fit_parameter_correlations.py        |   17 +
 .../categories/test_fit_parameters.py         |   13 +
 .../analysis/categories/test_fit_result.py    |   13 +
 .../analysis/categories/test_fit_state.py     |  207 +++
 .../analysis/fit_helpers/test_tracking.py     |   47 +
 .../analysis/minimizers/test_base.py          |   90 +-
 .../analysis/minimizers/test_bumps.py         |  121 ++
 .../analysis/minimizers/test_bumps_dream.py   |   22 +
 .../analysis/minimizers/test_dfols.py         |    4 +
 .../analysis/minimizers/test_lmfit.py         |   35 +
 .../analysis/test_analysis_coverage.py        |  105 ++
 .../easydiffraction/analysis/test_enums.py    |   14 +
 .../easydiffraction/analysis/test_fitting.py  |  184 ++-
 .../analysis/test_sequential.py               |   77 +-
 .../easydiffraction/display/test_plotting.py  |   73 +
 .../easydiffraction/io/cif/test_serialize.py  |    2 +-
 .../io/cif/test_serialize_more.py             |   29 +-
 .../io/test_results_sidecar.py                |  138 ++
 .../categories/rendering/test_default.py      |   18 +-
 .../categories/verbosity/test_default.py      |   44 +
 .../categories/verbosity/test_factory.py      |   25 +
 .../easydiffraction/project/test_display.py   |   78 +-
 .../easydiffraction/project/test_project.py   |    8 +-
 .../project/test_project_config.py            |   65 +-
 .../project/test_project_load.py              |  163 +++
 .../project/test_project_save.py              |   59 +
 tests/unit/easydiffraction/test___main__.py   |   27 +-
 .../easydiffraction/utils/test_environment.py |   41 +
 .../utils/test_utils_coverage.py              |   36 +
 tests/unit/test_benchmark_tutorials.py        |  133 ++
 tools/benchmark_tutorials.py                  |  216 +++
 155 files changed, 10177 insertions(+), 4156 deletions(-)
 create mode 100644 docs/dev/adrs/accepted/analysis-cif-fit-state.md
 rename docs/dev/adrs/{suggestions => accepted}/loop-category-key-identity.md (78%)
 create mode 100644 docs/dev/adrs/accepted/parameter-correlation-persistence.md
 delete mode 100644 docs/dev/adrs/suggestions/analysis-cif-fit-state.md
 create mode 100644 docs/dev/adrs/suggestions/fit-output-files-and-data-exports.md
 delete mode 100644 docs/dev/adrs/suggestions/parameter-correlation-persistence.md
 delete mode 100644 docs/dev/adrs/suggestions/workspace-root-project-category.md
 create mode 100644 docs/dev/benchmarking/20260519-103251_darwin-arm64_py314_tutorial-benchmarks.csv
 create mode 100644 docs/dev/benchmarking/20260519-103500_darwin-arm64_py314_tutorial-benchmarks.csv
 create mode 100644 docs/dev/benchmarking/20260519-121524_darwin-arm64_py314_tutorial-benchmarks.csv
 delete mode 100644 docs/dev/plans/loop-category-key-identity.md
 delete mode 100644 docs/dev/plans/workspace-root-project-category.md
 create mode 100644 docs/docs/tutorials/ed-24.ipynb
 create mode 100644 docs/docs/tutorials/ed-24.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_convergence/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_convergence/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_distribution_caches/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_distribution_caches/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_distribution_caches/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_pair_caches/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_pair_caches/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_pair_caches/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_predictive_datasets/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_predictive_datasets/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_predictive_datasets/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_result/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_result/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_result/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_sampler/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_sampler/default.py
 create mode 100644 src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/deterministic_result/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/deterministic_result/default.py
 create mode 100644 src/easydiffraction/analysis/categories/deterministic_result/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameter_correlations/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameter_correlations/default.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameter_correlations/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameters/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameters/default.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_parameters/factory.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_result/__init__.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_result/default.py
 create mode 100644 src/easydiffraction/analysis/categories/fit_result/factory.py
 create mode 100644 src/easydiffraction/io/results_sidecar.py
 create mode 100644 src/easydiffraction/project/categories/verbosity/__init__.py
 create mode 100644 src/easydiffraction/project/categories/verbosity/default.py
 create mode 100644 src/easydiffraction/project/categories/verbosity/factory.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_convergence.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_distribution_caches.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_pair_caches.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_parameter_posteriors.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_predictive_datasets.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_result.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_bayesian_sampler.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_deterministic_result.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_fit_parameter_correlations.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_fit_parameters.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_fit_result.py
 create mode 100644 tests/unit/easydiffraction/analysis/categories/test_fit_state.py
 create mode 100644 tests/unit/easydiffraction/io/test_results_sidecar.py
 create mode 100644 tests/unit/easydiffraction/project/categories/verbosity/test_default.py
 create mode 100644 tests/unit/easydiffraction/project/categories/verbosity/test_factory.py
 create mode 100644 tests/unit/test_benchmark_tutorials.py
 create mode 100644 tools/benchmark_tutorials.py

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index d5f96553d..46a066d2b 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -160,6 +160,12 @@ Notes:
   `docs/dev/package-structure/short.md` automatically — never edit those
   by hand. Don't review auto-fixes; accept and move on. Then
   `pixi run check` until clean.
+- When a check command needs saved output for analysis, capture the log
+  and preserve the command exit code with a zsh-safe variable name:
+  `pixi run check > /tmp/easydiffraction-check.log 2>&1; check_exit_code=$?; tail -n 200 /tmp/easydiffraction-check.log; exit $check_exit_code`.
+  Never assign to `status` in zsh; it is readonly. Use task-specific
+  names such as `check_exit_code`, `unit_tests_exit_code`, or
+  `script_tests_exit_code`.
 - Open issues / design questions / planned improvements live in
   `docs/dev/issues/open.md` (priority-ordered). On resolution, move to
   `docs/dev/issues/closed.md` and update the relevant ADR or
@@ -204,6 +210,9 @@ When asked to create a plan:
   files likely to change, decisions already made, open questions,
   verification commands for Phase 2, and a short suggested commit
   message or branch name when useful.
+- Verification commands in plans must include the zsh-safe log-capture
+  pattern from **Workflow** whenever saved output is needed for later
+  analysis.
 - Before saving a plan, verify that referenced files, directories,
   scripts, and task names exist locally when that is practical. If a
   referenced tool is optional or missing, include an available fallback.
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index def30827c..ea6bdad11 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -119,7 +119,7 @@ jobs:
       # Uses multiple cores for parallel execution to speed up the process.
       - name: Run notebooks
         # if: false # Temporarily disabled to speed up the docs build
-        run: pixi run notebook-exec
+        run: pixi run notebook-exec-ci
 
       # Build the static files for the documentation site for local inspection
       # Input: docs/ directory containing the Markdown files
diff --git a/docs/dev/adrs/accepted/analysis-cif-fit-state.md b/docs/dev/adrs/accepted/analysis-cif-fit-state.md
new file mode 100644
index 000000000..a38f0e7c7
--- /dev/null
+++ b/docs/dev/adrs/accepted/analysis-cif-fit-state.md
@@ -0,0 +1,193 @@
+# ADR: Analysis CIF Fit State
+
+## Status
+
+Accepted current design.
+
+## Date
+
+2026-05-18
+
+## Group
+
+Analysis and fitting.
+
+## Context
+
+`analysis/analysis.cif` already persists analysis configuration such as
+`_fitting.minimizer_type`, `_fitting.mode_type`, aliases, constraints,
+and active fit-mode settings. That configuration alone is not enough to
+reopen a saved project and continue the same fit-result, plotting, and
+command-line workflow.
+
+Analysis-owned fit state needs to persist:
+
+- fit bounds and bound provenance
+- pre-fit scalar snapshots for recovery workflows
+- compact status metadata for the latest saved fit projection
+- deterministic correlation summaries
+- Bayesian summary metadata and manifests for bulk array sidecars
+- plot-ready Bayesian caches so restored posterior displays do not need
+  to recompute on first use
+
+Committed model parameter values and uncertainties already persist in
+structure and experiment CIF files through the accepted free-flag CIF
+encoding. Those committed values must remain the source of truth for the
+current model state.
+
+The accepted runtime fit-results ADR keeps backend runtime objects
+runtime-only unless a narrower persistence ADR defines a saved
+projection. This ADR defines that narrower saved projection.
+
+## Decision
+
+Persist analysis-owned fit state as explicit sibling categories in
+`analysis/analysis.cif`, with large Bayesian arrays stored in
+`analysis/results.h5`.
+
+Do not add a dedicated `_fit_state` category or
+`_fit_state.schema_version`. Persisted fit state is detected from
+`_fit_result` and the related fit-state categories.
+
+### Common fit-state categories
+
+Persist these common categories for any saved fit projection:
+
+- `_fit_parameter`
+- `_fit_result`
+- `_fit_parameter_correlation`
+
+`_fit_parameter` stores analysis-owned per-parameter fit controls and
+pre-fit scalar snapshots:
+
+- `param_unique_name`
+- `fit_min`
+- `fit_max`
+- `fit_bounds_uncertainty_multiplier`
+- `start_value`
+- `start_uncertainty`
+
+`_fit_result` stores the latest saved fit header:
+
+- `result_kind`
+- `success`
+- `message`
+- `iterations`
+- `fitting_time`
+- `reduced_chi_square`
+
+`_fit_parameter_correlation` stores pairwise deterministic or posterior
+correlation summaries keyed by a persisted `id`. Only unique parameter
+pairs are stored.
+
+### Deterministic fit projection
+
+Deterministic fits persist `_deterministic_result` in addition to the
+common categories above.
+
+`_deterministic_result` stores compact optimizer metadata and counts:
+
+- `optimizer_name`
+- `method_name`
+- `objective_name`
+- `objective_value`
+- `n_data_points`
+- `n_parameters`
+- `n_free_parameters`
+- `degrees_of_freedom`
+- `covariance_available`
+- `correlation_available`
+
+Do not persist a `_deterministic_parameter_result` category. Final
+deterministic parameter values and uncertainties already persist in the
+model CIF files, and restored deterministic ordering comes from
+`_fit_parameter`.
+
+### Bayesian fit projection
+
+Bayesian fits persist these additional categories:
+
+- `_bayesian_result`
+- `_bayesian_sampler`
+- `_bayesian_convergence`
+- `_bayesian_parameter_posterior`
+- `_bayesian_distribution_cache`
+- `_bayesian_pair_cache`
+- `_bayesian_predictive_dataset`
+
+`_bayesian_result` stores the saved Bayesian header and sidecar flags,
+including `sidecar_file`, `has_posterior_samples`,
+`has_distribution_cache`, `has_pair_cache`, and
+`has_posterior_predictive`.
+
+`_bayesian_sampler` stores the resolved sampler settings used for the
+run. `parallel` persists the resolved non-negative worker count as an
+integer.
+
+`_bayesian_convergence` stores convergence metadata and posterior array
+shape counts.
+
+`_bayesian_parameter_posterior` stores one summary row per sampled
+parameter, including credible intervals, uncertainty, ESS, and R-hat.
+Its row order defines the saved posterior parameter order.
+
+`_bayesian_distribution_cache`, `_bayesian_pair_cache`, and
+`_bayesian_predictive_dataset` store manifest rows for plot-ready
+posterior caches. Distribution and predictive caches are persisted for
+any Bayesian fit with posterior samples, including single-parameter
+fits. Pair caches and posterior correlation summaries are only persisted
+when more than one parameter was sampled.
+
+`parameter.posterior` is not part of this accepted design. This ADR
+persists analysis-level posterior summaries and caches only. Any future
+parameter-level posterior API remains a separate decision.
+
+### Bayesian sidecar
+
+Persist large Bayesian arrays in `analysis/results.h5` using `h5py`.
+This includes canonical posterior arrays and any saved distribution,
+pair, and predictive cache arrays referenced by the CIF manifests.
+
+The persisted `sidecar_file` value is a local file name only. It must
+resolve to a basename inside the project `analysis/` directory. Absolute
+paths and traversal paths are rejected and fall back to `results.h5`.
+
+If the sidecar is missing on load, summary rows in
+`analysis/analysis.cif` still restore fit tables and metadata. Features
+that require missing bulk arrays must warn clearly instead of failing
+silently.
+
+### Save and restore behavior
+
+After a fit completes, project save writes the fit-state projection
+before the project is considered fully persisted. For Bayesian fits,
+that includes the prepared summaries and saved plot caches used by
+posterior displays.
+
+Load order is:
+
+1. standard analysis configuration
+2. common fit-state categories
+3. deterministic or Bayesian fit-specific categories according to
+   `_fit_result.result_kind`
+4. Bayesian sidecar arrays when a Bayesian sidecar is expected
+
+Persist backend runtime objects, optimizer instances, and raw driver
+payloads nowhere in this design.
+
+## Consequences
+
+Saved projects reopen with enough fit-state context to display the last
+saved result and rerun fits without rebuilding analysis-owned bounds by
+hand.
+
+Deterministic persistence stays compact because committed parameter
+values remain in the model CIF files instead of being duplicated in a
+second deterministic per-parameter result loop.
+
+Bayesian persistence spans CIF metadata and an HDF5 sidecar, so save and
+load must validate consistency between manifest rows and bulk datasets.
+
+The accepted runtime fit-results ADR should now be read as runtime-only
+except where this narrower projection explicitly persists fit-state
+metadata, summaries, and cache arrays.
diff --git a/docs/dev/adrs/suggestions/loop-category-key-identity.md b/docs/dev/adrs/accepted/loop-category-key-identity.md
similarity index 78%
rename from docs/dev/adrs/suggestions/loop-category-key-identity.md
rename to docs/dev/adrs/accepted/loop-category-key-identity.md
index f55ef4a89..4334b4e13 100644
--- a/docs/dev/adrs/suggestions/loop-category-key-identity.md
+++ b/docs/dev/adrs/accepted/loop-category-key-identity.md
@@ -1,6 +1,6 @@
 # ADR: Loop Category Keys and Identity Naming
 
-**Status:** Proposed  
+**Status:** Accepted  
 **Date:** 2026-05-18
 
 ## Context
@@ -10,7 +10,7 @@ CIF dictionaries can declare the key column for a loop category through
 use domain-specific identity tags such as `_atom_site.label`, while
 other loop categories may use an explicit `id` tag.
 
-EasyDiffraction currently models the same runtime concept with
+EasyDiffraction models the same runtime concept with
 `item._identity.category_entry_name`. `CategoryCollection` uses this
 value as the collection key, and category items use it in their
 `unique_name` path:
@@ -19,23 +19,24 @@ value as the collection key, and category items use it in their
 <datablock>.<category_code>.<category_entry_name>
 ```
 
-The design question is whether the current `category_entry_name`
-approach is enough, and how closely Python-facing identity names should
-follow CIF key tags.
+The design question was whether the `category_entry_name` approach was
+enough, and how closely Python-facing identity names should follow CIF
+key tags.
 
 ## Assessment
 
-The current approach is directionally good. It gives every loop item a
-stable collection key without hard-coding the key field into
+The `category_entry_name` approach is directionally good. It gives every
+loop item a stable collection key without hard-coding the key field into
 `CategoryCollection`, and most current loop categories derive that key
 from the same field that is serialized into CIF.
 
-It is not explicit enough yet. The key field is encoded as a lambda on
-each item, not as declarative metadata on the category or descriptor.
-Nothing validates that the runtime key corresponds to a serialized CIF
-field. The main visible example is `Constraint`: the current collection
-key is derived from the left-hand side of `_constraint.expression`, but
-no separate `_constraint.id` field is persisted.
+Before this decision it was not explicit enough. The key field was
+encoded as a lambda on each item, not as declarative metadata on the
+category or descriptor. Nothing validated that the runtime key
+corresponded to a serialized CIF field. The main visible example was
+`Constraint`: the collection key was derived from the left-hand side of
+`_constraint.expression`, but no separate `_constraint.id` field was
+persisted.
 
 ## Decision
 
@@ -52,14 +53,19 @@ Keep `category_entry_name` as the runtime analogue of CIF
    identity. Use `label` or `*_id` when the value has clearer domain
    meaning.
 
-The `constraint` category should add an explicit `id` field and use it
-as the collection key:
+The `constraint` category has an explicit `id` field and uses it as the
+collection key:
 
 ```text
 analysis.constraints[id].id -> _constraint.id
 ```
 
-The existing `lhs_alias` and `rhs_expr` properties should remain derived
+`Constraints.create()` accepts an optional `id` argument. A
+user-provided `id` is used whenever it is not `None`; otherwise the
+method derives the default `id` from `lhs_alias`, preserving the old
+`create(expression=...)` behavior.
+
+The existing `lhs_alias` and `rhs_expr` properties remain derived
 helpers from `_constraint.expression`, not the row identity.
 
 This argues against a blanket Python API change to use `id` everywhere.
@@ -74,22 +80,22 @@ Rows are sorted by the chosen Python key style: `id`, then `*_id`, then
 identifier-like fields that are not collection keys are listed in the
 next section.
 
-| Python key      | Area       | Collection class                            | Category code            | CIF key tag                  | Source                      | Decision                                                                                             |
-| --------------- | ---------- | ------------------------------------------- | ------------------------ | ---------------------------- | --------------------------- | ---------------------------------------------------------------------------------------------------- |
-| `id`            | Analysis   | `Constraints`                               | `constraint`             | `_constraint.id`             | Custom category             | Add this key. Current implementation derives the key from the left side of `_constraint.expression`. |
-| `id`            | Analysis   | `SequentialFitExtractCollection`            | `sequential_fit_extract` | `_sequential_fit_extract.id` | Custom category             | Keep. It is an explicit row identifier for extraction rules.                                         |
-| `id`            | Experiment | `LineSegmentBackground`                     | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and already serialized.                                             |
-| `id`            | Experiment | `ChebyshevPolynomialBackground`             | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and shared with other background variants.                          |
-| `id`            | Experiment | `LinkedPhases`                              | `linked_phases`          | `_pd_phase_block.id`         | Powder CIF-style category   | Keep. Consider `phase_id` only if the public API later standardizes foreign-key names.               |
-| `id`            | Experiment | `ExcludedRegions`                           | `excluded_regions`       | `_excluded_region.id`        | Custom category             | Keep. It is a simple custom loop row identifier.                                                     |
-| `id`            | Experiment | `ReflnData`                                 | `refln`                  | `_refln.id`                  | CIF-style category          | Keep for the current reflection table shape.                                                         |
-| `id`            | Experiment | `PowderCwlReflnData` / `PowderTofReflnData` | `refln`                  | `_refln.id`                  | CIF-style category          | Keep; `phase_id` remains a separate field, not the row key.                                          |
-| `experiment_id` | Analysis   | `JointFitCollection`                        | `joint_fit`              | `_joint_fit.experiment_id`   | Custom category             | Keep. The key is a reference to an experiment, so `id` alone would lose context.                     |
-| `point_id`      | Experiment | `PdCwlData` / `PdTofData`                   | `pd_data`                | `_pd_data.point_id`          | Powder CIF-style category   | Keep. It is clearer than `id` for dense measured/calculated data points.                             |
-| `point_id`      | Experiment | `TotalData`                                 | `total_data`             | `_pd_data.point_id`          | Current powder-data mapping | Keep. Revisit the CIF tag only when total-scattering-specific CIF tags are introduced.               |
-| `label`         | Analysis   | `Aliases`                                   | `alias`                  | `_alias.label`               | Custom category             | Keep. It is the user-visible symbol referenced by expressions, not an opaque row id.                 |
-| `label`         | Structure  | `AtomSites`                                 | `atom_site`              | `_atom_site.label`           | CIF core category           | Keep. This is a well-known crystallographic identity field.                                          |
-| `label`         | Structure  | `AtomSiteAnisoCollection`                   | `atom_site_aniso`        | `_atom_site_aniso.label`     | CIF core category           | Keep. It intentionally matches and references the atom-site label.                                   |
+| Python key      | Area       | Collection class                            | Category code            | CIF key tag                  | Source                      | Decision                                                                                               |
+| --------------- | ---------- | ------------------------------------------- | ------------------------ | ---------------------------- | --------------------------- | ------------------------------------------------------------------------------------------------------ |
+| `id`            | Analysis   | `Constraints`                               | `constraint`             | `_constraint.id`             | Custom category             | Add this key. Previous implementations derived the key from the left side of `_constraint.expression`. |
+| `id`            | Analysis   | `SequentialFitExtractCollection`            | `sequential_fit_extract` | `_sequential_fit_extract.id` | Custom category             | Keep. It is an explicit row identifier for extraction rules.                                           |
+| `id`            | Experiment | `LineSegmentBackground`                     | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and already serialized.                                               |
+| `id`            | Experiment | `ChebyshevPolynomialBackground`             | `background`             | `_pd_background.id`          | Powder CIF-style category   | Keep. The row identity is opaque and shared with other background variants.                            |
+| `id`            | Experiment | `LinkedPhases`                              | `linked_phases`          | `_pd_phase_block.id`         | Powder CIF-style category   | Keep. Consider `phase_id` only if the public API later standardizes foreign-key names.                 |
+| `id`            | Experiment | `ExcludedRegions`                           | `excluded_regions`       | `_excluded_region.id`        | Custom category             | Keep. It is a simple custom loop row identifier.                                                       |
+| `id`            | Experiment | `ReflnData`                                 | `refln`                  | `_refln.id`                  | CIF-style category          | Keep for the current reflection table shape.                                                           |
+| `id`            | Experiment | `PowderCwlReflnData` / `PowderTofReflnData` | `refln`                  | `_refln.id`                  | CIF-style category          | Keep; `phase_id` remains a separate field, not the row key.                                            |
+| `experiment_id` | Analysis   | `JointFitCollection`                        | `joint_fit`              | `_joint_fit.experiment_id`   | Custom category             | Keep. The key is a reference to an experiment, so `id` alone would lose context.                       |
+| `point_id`      | Experiment | `PdCwlData` / `PdTofData`                   | `pd_data`                | `_pd_data.point_id`          | Powder CIF-style category   | Keep. It is clearer than `id` for dense measured/calculated data points.                               |
+| `point_id`      | Experiment | `TotalData`                                 | `total_data`             | `_pd_data.point_id`          | Current powder-data mapping | Keep. Revisit the CIF tag only when total-scattering-specific CIF tags are introduced.                 |
+| `label`         | Analysis   | `Aliases`                                   | `alias`                  | `_alias.label`               | Custom category             | Keep. It is the user-visible symbol referenced by expressions, not an opaque row id.                   |
+| `label`         | Structure  | `AtomSites`                                 | `atom_site`              | `_atom_site.label`           | CIF core category           | Keep. This is a well-known crystallographic identity field.                                            |
+| `label`         | Structure  | `AtomSiteAnisoCollection`                   | `atom_site_aniso`        | `_atom_site_aniso.label`     | CIF core category           | Keep. It intentionally matches and references the atom-site label.                                     |
 
 ## Non-Key Identity And Reference Fields
 
@@ -123,22 +129,21 @@ Jupyter, CLI output, and saved CIF files.
 
 ## Implementation Notes
 
-The current `category_entry_name` mechanism can stay, but it should be
-made easier to audit. The implementation now uses class-level
-`_category_code` and `_category_entry_name` declarations on concrete
-`CategoryItem` subclasses. `CategoryItem` resolves the declared
-`_category_entry_name` lazily from the named public attribute, and
-`Identity` exposes the resolved value through
-`item._identity.category_entry_name`.
+The implementation uses class-level `_category_code` and
+`_category_entry_name` declarations on concrete `CategoryItem`
+subclasses. `CategoryItem` resolves the declared `_category_entry_name`
+lazily from the named public attribute, and `Identity` exposes the
+resolved value through `item._identity.category_entry_name`.
 
-For constraints, add a descriptor-backed `id` property serialized as
-`_constraint.id`, and change `category_entry_name` to resolve from that
-descriptor. Keep `_constraint.expression` for the full equation. Keep
-`lhs_alias` and `rhs_expr` as derived convenience properties.
+For constraints, a descriptor-backed `id` property is serialized as
+`_constraint.id`, and `category_entry_name` resolves from that
+descriptor. `_constraint.expression` stores the full equation.
+`lhs_alias` and `rhs_expr` remain derived convenience properties.
 
 When reading older CIF files that only contain `_constraint.expression`,
-derive a deterministic fallback `id` from the old `lhs_alias` key after
-row values are loaded, then write `_constraint.id` on the next save.
+the loader derives a deterministic fallback `id` from the old
+`lhs_alias` key after row values are loaded, then writes
+`_constraint.id` on the next save.
 
 ## Consequences
 
diff --git a/docs/dev/adrs/accepted/parameter-correlation-persistence.md b/docs/dev/adrs/accepted/parameter-correlation-persistence.md
new file mode 100644
index 000000000..9b51c0362
--- /dev/null
+++ b/docs/dev/adrs/accepted/parameter-correlation-persistence.md
@@ -0,0 +1,104 @@
+# ADR: Parameter Correlation Persistence
+
+## Status
+
+Accepted current design.
+
+## Date
+
+2026-05-19
+
+## Group
+
+Analysis and fitting.
+
+## Context
+
+`plot_param_correlations()` can visualize either deterministic parameter
+correlations derived from engine covariance or Bayesian correlations
+derived from posterior samples.
+
+Reloaded projects still need correlation heatmaps even when the raw
+runtime covariance or posterior arrays are unavailable. The broader
+fit-state layout is defined in `analysis-cif-fit-state.md`; this ADR
+records the narrower persisted correlation-summary projection within
+that accepted design.
+
+Correlation data is analysis-owned derived state, not model state. It
+therefore belongs in `analysis/analysis.cif`, not in structure or
+experiment CIF files.
+
+## Decision
+
+Persist pairwise parameter correlations in `_fit_parameter_correlation`
+rows inside `analysis/analysis.cif`.
+
+### Correlation summary schema
+
+Store one row per unique parameter pair with these fields:
+
+- `id`
+- `source_kind`
+- `param_unique_name_i`
+- `param_unique_name_j`
+- `correlation`
+
+Example:
+
+```cif
+loop_
+_fit_parameter_correlation.id
+_fit_parameter_correlation.source_kind
+_fit_parameter_correlation.param_unique_name_i
+_fit_parameter_correlation.param_unique_name_j
+_fit_parameter_correlation.correlation
+1 posterior cosio.atom_site.Co1.adp_iso cosio.atom_site.Co2.adp_iso 0.87
+```
+
+Normalize each row to the upper triangle excluding the diagonal.
+`param_unique_name_i` and `param_unique_name_j` use a stable ordering so
+only one unordered pair is stored. The diagonal is omitted because it is
+always `1.0` and can be rebuilt on load.
+
+Use the same loop for deterministic and Bayesian projections. The source
+is carried by `source_kind`, currently `deterministic` or `posterior`.
+
+### Summary-only role
+
+`_fit_parameter_correlation` is a persisted summary. It does not replace
+posterior samples, posterior pair densities, or covariance matrices.
+
+This summary is enough to restore correlation heatmaps. It is not enough
+to restore richer pair-plot density surfaces or covariance-specific
+workflows.
+
+### Restore behavior
+
+On load, restore `_fit_parameter_correlation` rows into an analysis-
+owned correlation collection.
+
+When runtime covariance or posterior samples are unavailable,
+`project.display.plotter.plot_param_correlations()` may rebuild a square
+correlation matrix from the persisted rows and the restored fit-result
+parameter ordering.
+
+### Relation to the broader fit-state ADR
+
+`analysis-cif-fit-state.md` remains the source of truth for the full
+fit-state projection, save/load ordering, and Bayesian sidecar layout.
+This ADR records the implemented correlation-specific piece of that
+accepted design.
+
+## Consequences
+
+- Deterministic and Bayesian correlation summaries survive reload.
+- Correlation heatmaps no longer depend entirely on runtime-only data.
+- The schema stays compact and fit-type-agnostic.
+- Posterior pair plots and covariance-specific workflows still need
+  richer runtime or persisted data.
+
+## Deferred Work
+
+- optional storage of covariance matrices in addition to correlations
+- multiple named correlation sources for the same saved project
+- full correlation restoration for pair-plot density surfaces
diff --git a/docs/dev/adrs/accepted/project-facade-and-persistence.md b/docs/dev/adrs/accepted/project-facade-and-persistence.md
index 67492d8d4..3b922f423 100644
--- a/docs/dev/adrs/accepted/project-facade-and-persistence.md
+++ b/docs/dev/adrs/accepted/project-facade-and-persistence.md
@@ -14,16 +14,22 @@ Persistence.
 
 ## Context
 
-`Project` is the current top-level user facade. It owns project
-metadata, structures, experiments, rendering preferences, display
-helpers, analysis, summaries, verbosity, and save/load behavior.
+`Project` is the top-level user facade. It owns project metadata,
+structures, experiments, rendering preferences, display helpers,
+analysis, summaries, verbosity, and save/load behavior.
+
+A later proposal considered renaming this facade to `Workspace` so that
+`project` could be reserved for the scientific project information
+category. The final naming decision is to keep `Project` as the public
+root because it matches scientific user language and the saved project
+container.
 
 The persisted project directory needs to separate real CIF datablocks
 from singleton project sections.
 
 ## Decision
 
-Use `Project` as the current top-level facade and persist projects as a
+Use `Project` as the top-level facade and persist projects as a
 directory of CIF files:
 
 ```text
@@ -40,8 +46,35 @@ Real structures and experiments serialize as `data_<id>` datablocks.
 Singleton sections such as project configuration, analysis, and summary
 serialize without fake `data_` headers.
 
+Keep project information available as `project.info`. The Python name
+avoids a confusing `project.project` access path, while the persisted
+CIF category remains the semantic `_project.*` category:
+
+```cif
+_project.id
+_project.title
+_project.description
+_project.created
+_project.last_modified
+```
+
+Do not introduce `_meta.*` tags for project information. The category is
+scientific project information, not generic metadata. Any future change
+from `_project.*` to another category name must be a separate explicit
+persistence decision.
+
+Keep `project.cif` as the primary singleton project configuration file
+while `Project` remains the root facade. Do not rename it to
+`workspace.cif` as a side effect of category cleanup.
+
+The saved project directory path is runtime file-I/O state, not a
+serialized project-information field. If the path is exposed in Python,
+it must not emit a `_project.path` CIF item.
+
 ## Consequences
 
 The saved layout mirrors the current object graph while preserving the
-semantic difference between real datablocks and singleton sections. A
-proposed `Workspace` rename is tracked separately as an ADR suggestion.
+semantic difference between real datablocks and singleton sections. The
+`Workspace` rename proposal is rejected; ADR examples should continue to
+use `Project`, `project.info`, and `project.cif` unless a later accepted
+ADR changes a narrower part of this design.
diff --git a/docs/dev/adrs/index.md b/docs/dev/adrs/index.md
index ed0940bf3..50a287db9 100644
--- a/docs/dev/adrs/index.md
+++ b/docs/dev/adrs/index.md
@@ -13,34 +13,34 @@ folders.
 
 ## ADR Index
 
-| Group                | Status     | Title                                                         | Short description                                                                                                  | Link                                                                                         |
-| -------------------- | ---------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------- |
-| Analysis and fitting | Accepted   | Fit Mode Categories and Fit Execution API                     | Splits fitting configuration from execution and defines active sibling fit-mode categories.                        | [`fit-mode-categories.md`](accepted/fit-mode-categories.md)                                  |
-| Analysis and fitting | Accepted   | Runtime Fit Results                                           | Keeps full fit outputs runtime-only in the current design unless a narrower persistence ADR is accepted.           | [`runtime-fit-results.md`](accepted/runtime-fit-results.md)                                  |
-| Analysis and fitting | Suggestion | Analysis CIF Fit State                                        | Proposes a persisted scalar projection of fit state in `analysis.cif`.                                             | [`analysis-cif-fit-state.md`](suggestions/analysis-cif-fit-state.md)                         |
-| Analysis and fitting | Suggestion | Parameter Correlation Persistence                             | Proposes persisting deterministic and posterior correlation summaries.                                             | [`parameter-correlation-persistence.md`](suggestions/parameter-correlation-persistence.md)   |
-| Analysis and fitting | Suggestion | Parameter-Level Posterior Projection and Bayesian Persistence | Proposes saved Bayesian summaries and canonical posterior storage.                                                 | [`parameter-posterior-summary.md`](suggestions/parameter-posterior-summary.md)               |
-| Analysis and fitting | Suggestion | Undo Fit                                                      | Proposes an analysis-owned rollback operation for the latest pre-fit scalar state.                                 | [`undo-fit.md`](suggestions/undo-fit.md)                                                     |
-| Core model           | Accepted   | Category Owners and Real Datablocks                           | Introduces `CategoryOwner` so singleton sections do not pretend to be real CIF datablocks.                         | [`category-owner-sections.md`](accepted/category-owner-sections.md)                          |
-| Core model           | Accepted   | Enum-Backed Closed Value Sets                                 | Requires finite option sets to use `(str, Enum)` classes for validation and dispatch.                              | [`enum-backed-closed-values.md`](accepted/enum-backed-closed-values.md)                      |
-| Core model           | Accepted   | Guarded Public Properties                                     | Uses property setters as the public writability contract for guarded objects.                                      | [`guarded-public-properties.md`](accepted/guarded-public-properties.md)                      |
-| Core model           | Accepted   | Two-Level Category Parameter Access                           | Keeps parameter access to `datablock.category.parameter` or `datablock.collection[id].parameter`.                  | [`category-parameter-access.md`](accepted/category-parameter-access.md)                      |
-| Documentation        | Accepted   | Descriptor Property Docstring Template                        | Makes descriptor metadata the source of truth for public property docstrings and annotations.                      | [`property-docstring-template.md`](accepted/property-docstring-template.md)                  |
-| Documentation        | Accepted   | Development Documentation Structure                           | Defines the `docs/dev` layout for ADRs, issues, plans, package structure, and roadmap.                             | [`development-docs-structure.md`](accepted/development-docs-structure.md)                    |
-| Documentation        | Accepted   | Help Method Discoverability                                   | Requires primary public objects and facades to expose consistent `help()` output.                                  | [`help-discoverability.md`](accepted/help-discoverability.md)                                |
-| Documentation        | Accepted   | Notebook Generation Source of Truth                           | Treats tutorial `.py` files as editable sources and notebooks as generated artifacts.                              | [`notebook-generation.md`](accepted/notebook-generation.md)                                  |
-| Experiment model     | Accepted   | Immutable Experiment Type                                     | Makes experiment type axes creation-time state rather than mutable runtime state.                                  | [`immutable-experiment-type.md`](accepted/immutable-experiment-type.md)                      |
-| Factories            | Accepted   | Factory Contracts and Metadata                                | Standardizes factory construction, metadata, compatibility, and registration behavior.                             | [`factory-contracts.md`](accepted/factory-contracts.md)                                      |
-| Naming               | Accepted   | Factory Tag Naming                                            | Defines canonical factory tag style and standard abbreviations.                                                    | [`factory-tag-naming.md`](accepted/factory-tag-naming.md)                                    |
-| Persistence          | Accepted   | Free-Flag CIF Encoding                                        | Encodes fit free/fixed state through CIF uncertainty syntax instead of a separate free list.                       | [`free-flag-cif-encoding.md`](accepted/free-flag-cif-encoding.md)                            |
-| Persistence          | Accepted   | Project Facade and Persistence Layout                         | Documents the current `Project` facade and saved directory layout.                                                 | [`project-facade-and-persistence.md`](accepted/project-facade-and-persistence.md)            |
-| Persistence          | Suggestion | Loop Category Keys and Identity Naming                        | Documents current loop collection keys and proposes naming rules aligned with CIF category keys.                   | [`loop-category-key-identity.md`](suggestions/loop-category-key-identity.md)                 |
-| Persistence          | Suggestion | Python and CIF Category Correspondence                        | Compares current Python paths and CIF tags, then proposes scoped one-to-one mapping for project-level categories.  | [`python-cif-category-correspondence.md`](suggestions/python-cif-category-correspondence.md) |
-| Quality              | Accepted   | Lint Complexity Thresholds                                    | Treats ruff PLR complexity limits as design guardrails that should not be bypassed.                                | [`lint-complexity-thresholds.md`](accepted/lint-complexity-thresholds.md)                    |
-| Quality              | Accepted   | Test Strategy                                                 | Defines layered unit, functional, integration, script, and notebook testing.                                       | [`test-strategy.md`](accepted/test-strategy.md)                                              |
-| Structure model      | Accepted   | Type-Neutral ADP Parameters                                   | Keeps ADP parameter object identities stable across B/U and iso/ani switches.                                      | [`type-neutral-adp-parameters.md`](accepted/type-neutral-adp-parameters.md)                  |
-| User-facing API      | Accepted   | Display UX Facade                                             | Defines `project.display` and `project.rendering` responsibilities and display method names.                       | [`display-ux.md`](accepted/display-ux.md)                                                    |
-| User-facing API      | Accepted   | Selector Families                                             | Distinguishes backend selectors, switchable-category selectors, and active-sibling selectors.                      | [`selector-families.md`](accepted/selector-families.md)                                      |
-| User-facing API      | Accepted   | String Paths and Live Descriptors                             | Separates persisted field selectors from references to live model parameters.                                      | [`string-paths-and-live-descriptors.md`](accepted/string-paths-and-live-descriptors.md)      |
-| User-facing API      | Accepted   | Switchable Category API                                       | Places multi-type category selectors on the owner and omits public selectors for fixed or single-type categories.  | [`switchable-category-api.md`](accepted/switchable-category-api.md)                          |
-| Workspace model      | Suggestion | Workspace Root and Project Information Category               | Proposes renaming the top-level facade from `Project` to `Workspace` and reserving `project` for project metadata. | [`workspace-root-project-category.md`](suggestions/workspace-root-project-category.md)       |
+| Group                | Status     | Title                                     | Short description                                                                                                 | Link                                                                                         |
+| -------------------- | ---------- | ----------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
+| Analysis and fitting | Accepted   | Fit Mode Categories and Fit Execution API | Splits fitting configuration from execution and defines active sibling fit-mode categories.                       | [`fit-mode-categories.md`](accepted/fit-mode-categories.md)                                  |
+| Analysis and fitting | Accepted   | Runtime Fit Results                       | Keeps full fit outputs runtime-only in the current design unless a narrower persistence ADR is accepted.          | [`runtime-fit-results.md`](accepted/runtime-fit-results.md)                                  |
+| Analysis and fitting | Accepted   | Analysis CIF Fit State                    | Defines the persisted fit-state projection in `analysis/analysis.cif` and `analysis/results.h5`.                  | [`analysis-cif-fit-state.md`](accepted/analysis-cif-fit-state.md)                            |
+| Analysis and fitting | Accepted   | Parameter Correlation Persistence         | Persists deterministic and posterior correlation summaries in `_fit_parameter_correlation`                        | [`parameter-correlation-persistence.md`](accepted/parameter-correlation-persistence.md)      |
+| Analysis and fitting | Suggestion | Fit Output Files and Data Exports         | Narrows remaining archive/export questions after adopting `results.csv` and `results.h5`.                         | [`fit-output-files-and-data-exports.md`](suggestions/fit-output-files-and-data-exports.md)   |
+| Analysis and fitting | Suggestion | Parameter-Level Posterior Projection      | Narrows the still-open `parameter.posterior` API after analysis-level posterior summaries were accepted.          | [`parameter-posterior-summary.md`](suggestions/parameter-posterior-summary.md)               |
+| Analysis and fitting | Suggestion | Undo Fit                                  | Builds rollback semantics and CLI behavior on already-persisted pre-fit scalar snapshots.                         | [`undo-fit.md`](suggestions/undo-fit.md)                                                     |
+| Core model           | Accepted   | Category Owners and Real Datablocks       | Introduces `CategoryOwner` so singleton sections do not pretend to be real CIF datablocks.                        | [`category-owner-sections.md`](accepted/category-owner-sections.md)                          |
+| Core model           | Accepted   | Enum-Backed Closed Value Sets             | Requires finite option sets to use `(str, Enum)` classes for validation and dispatch.                             | [`enum-backed-closed-values.md`](accepted/enum-backed-closed-values.md)                      |
+| Core model           | Accepted   | Guarded Public Properties                 | Uses property setters as the public writability contract for guarded objects.                                     | [`guarded-public-properties.md`](accepted/guarded-public-properties.md)                      |
+| Core model           | Accepted   | Two-Level Category Parameter Access       | Keeps parameter access to `datablock.category.parameter` or `datablock.collection[id].parameter`.                 | [`category-parameter-access.md`](accepted/category-parameter-access.md)                      |
+| Documentation        | Accepted   | Descriptor Property Docstring Template    | Makes descriptor metadata the source of truth for public property docstrings and annotations.                     | [`property-docstring-template.md`](accepted/property-docstring-template.md)                  |
+| Documentation        | Accepted   | Development Documentation Structure       | Defines the `docs/dev` layout for ADRs, issues, plans, package structure, and roadmap.                            | [`development-docs-structure.md`](accepted/development-docs-structure.md)                    |
+| Documentation        | Accepted   | Help Method Discoverability               | Requires primary public objects and facades to expose consistent `help()` output.                                 | [`help-discoverability.md`](accepted/help-discoverability.md)                                |
+| Documentation        | Accepted   | Notebook Generation Source of Truth       | Treats tutorial `.py` files as editable sources and notebooks as generated artifacts.                             | [`notebook-generation.md`](accepted/notebook-generation.md)                                  |
+| Experiment model     | Accepted   | Immutable Experiment Type                 | Makes experiment type axes creation-time state rather than mutable runtime state.                                 | [`immutable-experiment-type.md`](accepted/immutable-experiment-type.md)                      |
+| Factories            | Accepted   | Factory Contracts and Metadata            | Standardizes factory construction, metadata, compatibility, and registration behavior.                            | [`factory-contracts.md`](accepted/factory-contracts.md)                                      |
+| Naming               | Accepted   | Factory Tag Naming                        | Defines canonical factory tag style and standard abbreviations.                                                   | [`factory-tag-naming.md`](accepted/factory-tag-naming.md)                                    |
+| Persistence          | Accepted   | Free-Flag CIF Encoding                    | Encodes fit free/fixed state through CIF uncertainty syntax instead of a separate free list.                      | [`free-flag-cif-encoding.md`](accepted/free-flag-cif-encoding.md)                            |
+| Persistence          | Accepted   | Loop Category Keys and Identity Naming    | Documents loop collection keys and naming rules aligned with CIF category keys.                                   | [`loop-category-key-identity.md`](accepted/loop-category-key-identity.md)                    |
+| Persistence          | Accepted   | Project Facade and Persistence Layout     | Documents the current `Project` facade and saved directory layout.                                                | [`project-facade-and-persistence.md`](accepted/project-facade-and-persistence.md)            |
+| Persistence          | Suggestion | Python and CIF Category Correspondence    | Compares current Python paths and CIF tags, then proposes scoped one-to-one mapping for project-level categories. | [`python-cif-category-correspondence.md`](suggestions/python-cif-category-correspondence.md) |
+| Quality              | Accepted   | Lint Complexity Thresholds                | Treats ruff PLR complexity limits as design guardrails that should not be bypassed.                               | [`lint-complexity-thresholds.md`](accepted/lint-complexity-thresholds.md)                    |
+| Quality              | Accepted   | Test Strategy                             | Defines layered unit, functional, integration, script, and notebook testing.                                      | [`test-strategy.md`](accepted/test-strategy.md)                                              |
+| Structure model      | Accepted   | Type-Neutral ADP Parameters               | Keeps ADP parameter object identities stable across B/U and iso/ani switches.                                     | [`type-neutral-adp-parameters.md`](accepted/type-neutral-adp-parameters.md)                  |
+| User-facing API      | Accepted   | Display UX Facade                         | Defines `project.display` and `project.rendering` responsibilities and display method names.                      | [`display-ux.md`](accepted/display-ux.md)                                                    |
+| User-facing API      | Accepted   | Selector Families                         | Distinguishes backend selectors, switchable-category selectors, and active-sibling selectors.                     | [`selector-families.md`](accepted/selector-families.md)                                      |
+| User-facing API      | Accepted   | String Paths and Live Descriptors         | Separates persisted field selectors from references to live model parameters.                                     | [`string-paths-and-live-descriptors.md`](accepted/string-paths-and-live-descriptors.md)      |
+| User-facing API      | Accepted   | Switchable Category API                   | Places multi-type category selectors on the owner and omits public selectors for fixed or single-type categories. | [`switchable-category-api.md`](accepted/switchable-category-api.md)                          |
diff --git a/docs/dev/adrs/suggestions/analysis-cif-fit-state.md b/docs/dev/adrs/suggestions/analysis-cif-fit-state.md
deleted file mode 100644
index d471bb117..000000000
--- a/docs/dev/adrs/suggestions/analysis-cif-fit-state.md
+++ /dev/null
@@ -1,220 +0,0 @@
-# ADR: Analysis CIF Fit State
-
-**Status:** Proposed  
-**Date:** 2026-05-13
-
-## Context
-
-`analysis/analysis.cif` currently persists analysis configuration such
-as `_fit.minimizer_type`, `_fit.mode`, aliases, constraints, and
-joint-fit weights. It does not persist analysis-owned fit state such as
-fit bounds, bound provenance, pre-fit scalar snapshots, or latest
-fit-status metadata.
-
-At the same time, parameter CIF serialization already carries the
-committed parameter `value`, the current `free` state, and the current
-`uncertainty` via CIF bracket notation. That data belongs to the model
-and should remain in structure or experiment CIF files.
-
-The missing piece is analysis-owned fit state:
-
-- fit controls that apply to parameters during fitting but are not part
-  of the model itself
-- latest fit-status metadata shown by `display.fit_results()`
-- deterministic and Bayesian fit metadata that should survive project
-  reloads and command-line workflows
-
-This separation matters because:
-
-- Bayesian plotting after reload needs `fit_min`, `fit_max`, and bound
-  provenance even when raw posterior arrays are absent
-- command-line users need a saved pre-fit starting state to recover from
-  a poor minimization run
-- `analysis.fit_results` already changes by fit type, but its persisted
-  projection should have a stable analysis-owned home
-
-The accepted runtime-fit-results ADR describes fit results as
-runtime-only. This ADR proposes a narrower persisted projection of the
-latest fit state, not a direct dump of backend runtime objects.
-
-## Decision
-
-### 1. `analysis/analysis.cif` becomes the home of analysis-owned fit state
-
-The analysis CIF file will persist:
-
-- fit configuration
-- aliases and constraints
-- joint-fit weights
-- per-parameter fit controls owned by analysis
-- latest fit-status metadata common to deterministic and Bayesian fits
-- fit-type-specific extensions defined in separate ADRs
-
-Committed parameter values remain in structure or experiment CIF files.
-They are not duplicated into `analysis/analysis.cif`.
-
-### 2. Add a real `_fit_parameter` loop
-
-Introduce a new analysis-owned loop category:
-
-```cif
-loop_
-_fit_parameter.param_unique_name
-_fit_parameter.fit_min
-_fit_parameter.fit_max
-_fit_parameter.fit_bounds_uncertainty_multiplier
-_fit_parameter.start_value
-_fit_parameter.start_uncertainty
-cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-```
-
-Fields:
-
-- `param_unique_name`
-- `fit_min`
-- `fit_max`
-- `fit_bounds_uncertainty_multiplier`
-- `start_value`
-- `start_uncertainty`
-
-Rationale:
-
-- `fit_min` and `fit_max` are required to restore deterministic and
-  Bayesian fit controls faithfully.
-- `fit_bounds_uncertainty_multiplier` preserves the provenance of
-  uncertainty-derived bounds for restored Bayesian plot annotations.
-- `start_value` and `start_uncertainty` capture the last committed
-  pre-fit scalar state and enable fit recovery workflows, especially in
-  command-line usage.
-- `start_uncertainty` preserves a user-visible pre-fit uncertainty
-  instead of treating it as disposable fit residue.
-
-### 3. Add a generic `_fit_result` single-item category
-
-Persist the latest fit-status metadata shared across fit types in a
-single analysis-owned category:
-
-- `result_kind`
-- `success`
-- `message`
-- `iterations`
-- `fitting_time`
-- `reduced_chi_square`
-
-Suggested CIF fragment:
-
-```cif
-_fit_result.result_kind deterministic
-_fit_result.success yes
-_fit_result.message "Fit converged"
-_fit_result.iterations 37
-_fit_result.fitting_time 1.82
-_fit_result.reduced_chi_square 1.031
-```
-
-`result_kind` distinguishes the latest persisted fit projection, for
-example `deterministic` or `bayesian`.
-
-### 4. Persist only stable fit-status fields here
-
-The `_fit_result` category is for generic status fields that are stable
-across engines and already belong to the result model.
-
-It should not persist backend runtime objects or arbitrary engine
-payloads.
-
-Metrics such as R-factors shown by `display.fit_results()` are derived
-from observed and calculated data and can be recomputed after load when
-needed. They do not need to be part of the first persisted fit-state
-schema.
-
-### 5. Fit-type-specific extensions build on this ADR
-
-This ADR defines the common `analysis.cif` contract for deterministic
-and Bayesian fitting.
-
-Fit-type-specific extensions are layered on top:
-
-- Bayesian persistence extends this with `_bayesian_*` categories and an
-  HDF5 sidecar, as described in `parameter-posterior-summary.md`.
-- Future fit-specific summaries should follow the same pattern: generic
-  shared fields in `_fit_result`, specialized fields in separate
-  categories.
-
-### 6. Restore order is analysis-first, fit-type-second
-
-Load order should be:
-
-1. standard analysis configuration
-2. aliases and constraints
-3. joint-fit weights
-4. `_fit_parameter`
-5. `_fit_result`
-6. fit-type-specific extensions such as `_bayesian_*`
-
-This ensures that generic fit controls are available before restoring
-specialized fit summaries.
-
-### 7. Suggested full `analysis.cif` example
-
-```cif
-_fit.minimizer_type "bumps (dream)"
-_fit.mode single
-
-loop_
-_alias.label
-_alias.param_unique_name
-biso_Co1 cosio.atom_site.Co1.adp_iso
-biso_Co2 cosio.atom_site.Co2.adp_iso
-
-loop_
-_constraint.expression
-"biso_Co2 = biso_Co1"
-
-loop_
-_fit_parameter.param_unique_name
-_fit_parameter.fit_min
-_fit_parameter.fit_max
-_fit_parameter.fit_bounds_uncertainty_multiplier
-_fit_parameter.start_value
-_fit_parameter.start_uncertainty
-cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-
-_fit_result.result_kind bayesian
-_fit_result.success yes
-_fit_result.message "Sampler converged"
-_fit_result.iterations 3000
-_fit_result.fitting_time 82.4
-_fit_result.reduced_chi_square 1.031
-
-# Bayesian-specific extension categories follow here.
-```
-
-## Consequences
-
-### Positive
-
-- `analysis.cif` becomes the single analysis-owned source of fit state.
-- Deterministic and Bayesian persistence share one common base schema.
-- Fit bounds, bound provenance, and start values survive project
-  reloads.
-- Command-line workflows gain a persisted pre-fit starting state.
-
-### Trade-offs
-
-- The runtime fit-results ADR must be updated because fit state is no
-  longer entirely runtime-only.
-- Analysis persistence becomes more stateful and must be kept in sync
-  with live parameter objects.
-- Some existing serializer assumptions will need refactoring so that
-  `analysis.cif` owns fit metadata rather than individual parameters.
-
-## Deferred Work
-
-- Bayesian-specific categories and HDF5 sidecar details remain in
-  `parameter-posterior-summary.md`.
-- Undo semantics for `start_value` and `start_uncertainty` are defined
-  in a separate ADR.
-- Correlation-matrix persistence is defined in a separate ADR.
diff --git a/docs/dev/adrs/suggestions/fit-output-files-and-data-exports.md b/docs/dev/adrs/suggestions/fit-output-files-and-data-exports.md
new file mode 100644
index 000000000..8705fe355
--- /dev/null
+++ b/docs/dev/adrs/suggestions/fit-output-files-and-data-exports.md
@@ -0,0 +1,172 @@
+# ADR: Fit Output Files and Data Exports
+
+**Status:** Proposed **Date:** 2026-05-18
+
+## Status Note
+
+The current branch already adopts two pieces of this naming scheme:
+
+- sequential deterministic results stay in `analysis/results.csv`
+- Bayesian arrays and plot caches use `analysis/results.h5`
+
+Those decisions now live in
+[Analysis CIF Fit State](../accepted/analysis-cif-fit-state.md). This
+proposal is therefore narrowed to the still-open roles for
+`analysis/data.h5`, `analysis/exports/`, and any extra deterministic
+convenience exports.
+
+## Context
+
+Different fit modes still produce different kinds of reusable output:
+
+- sequential deterministic fits produce a rectangular parameter
+  evolution table, already saved as `analysis/results.csv`
+- Bayesian fits produce posterior samples, diagnostics, predictive
+  arrays, and plot caches, which are too large and structured for CIF or
+  CSV
+- deterministic single and joint fits produce fitted model state,
+  calculated data, reflection tables, residuals, and optional
+  covariance/correlation summaries
+
+The accepted fit-state ADR already standardizes the canonical saved fit
+projection in `analysis/analysis.cif` plus `analysis/results.h5` for
+Bayesian sidecars. What remains open here is whether project save should
+also produce optional archives or user-facing export files beyond that
+accepted baseline.
+
+The project should keep naming consistent and avoid making users extract
+ordinary plotting data from CIF when a clearer CSV export is possible.
+At the same time, CIF remains the canonical model/configuration format,
+and large numerical arrays should not be embedded in
+`analysis/analysis.cif`.
+
+## Decision
+
+### 1. Keep the implemented results baseline
+
+The accepted baseline is:
+
+- `analysis/results.csv` for sequential deterministic fit tables
+- `analysis/results.h5` for large Bayesian arrays and result-derived
+  caches
+
+Any future change to those canonical filenames would need a follow-up
+ADR.
+
+### 2. Reserve separate roles for archives and exports
+
+If extra persisted files are added under `analysis/`, keep their roles
+separate:
+
+- `analysis/data.h5` for optional archived input or measured data.
+- `analysis/exports/` for optional user-facing CSV files intended for
+  external plotting and inspection.
+
+The fit type and saved fit-state manifests stay recorded in
+`analysis/analysis.cif`, principally through `_fit_result.result_kind`
+and the related fit-state categories.
+
+### 3. Sequential deterministic results stay CSV
+
+Sequential deterministic fitting should keep `analysis/results.csv` as
+the canonical table for parameter evolution and extracted metadata.
+
+This file is intentionally CSV because:
+
+- each row naturally corresponds to one sequential fit step
+- users often inspect, filter, and plot it outside EasyDiffraction
+- it should remain easy to diff, copy, and load in spreadsheets
+
+Sequential measured input data may optionally be archived in
+`analysis/data.h5`, but that archive is data, not results. It must not
+replace `analysis/results.csv`.
+
+### 4. Bayesian arrays use `analysis/results.h5`
+
+Single Bayesian fits should store posterior samples, log posterior
+arrays, predictive arrays, and prepared plot caches in
+`analysis/results.h5`.
+
+The previous candidate name `analysis/bayesian_data.h5` remains rejected
+because it mixes fit type with file role and blurs result arrays with
+input data. Bayesian-specific meaning belongs in the CIF manifest and
+HDF5 groups, not the sidecar filename.
+
+### 5. Deterministic single and joint fits may gain CSV exports
+
+For single, joint, and sequential deterministic fits, EasyDiffraction
+should consider optional CSV exports for ordinary plotting data:
+
+- measured data
+- calculated data
+- residuals
+- reflection tables / `refln` categories
+
+These exports are not canonical persistence. They are convenience files
+for users who want to plot or analyze results in external software
+without parsing CIF.
+
+Suggested first layout:
+
+```text
+analysis/
+  analysis.cif
+  results.csv        # sequential deterministic only, when applicable
+  results.h5         # Bayesian and other structured result arrays
+  data.h5            # optional archived measured/input data
+  exports/
+    <experiment>_measured.csv
+    <experiment>_calculated.csv
+    <experiment>_residual.csv
+    <experiment>_reflections.csv
+```
+
+## Fit-Type Mapping
+
+| Fit type                 | Canonical fit state                              | Tabular results              | Large arrays / caches | Optional data archive | Optional exports                |
+| ------------------------ | ------------------------------------------------ | ---------------------------- | --------------------- | --------------------- | ------------------------------- |
+| single deterministic     | `analysis/analysis.cif`                          | open question                | none initially        | none initially        | `analysis/exports/*.csv`        |
+| joint deterministic      | `analysis/analysis.cif`                          | open question                | none initially        | none initially        | `analysis/exports/*.csv`        |
+| sequential deterministic | `analysis/analysis.cif` + `analysis/results.csv` | `analysis/results.csv`       | none initially        | `analysis/data.h5`    | `analysis/exports/*.csv`        |
+| single Bayesian          | `analysis/analysis.cif` + `analysis/results.h5`  | optional summary export only | `analysis/results.h5` | none initially        | optional summary/predictive CSV |
+
+## Open Questions
+
+- Should single and joint deterministic fits write a one-row
+  `analysis/results.csv`, or is their result projection in
+  `analysis/analysis.cif` enough?
+- Should CSV exports be written automatically after fit/save, or only by
+  an explicit export command?
+- What exact CSV column schemas should be used for measured, calculated,
+  residual, and reflection exports?
+- Should exported `refln` CSVs mirror CIF tag names exactly, or use
+  shorter user-facing column names?
+- Should sequential measured data archival in `analysis/data.h5` be
+  opt-in, automatic below a size threshold, or always disabled unless
+  requested?
+- What size threshold and compression policy should control the optional
+  `analysis/data.h5`, and does `analysis/results.h5` need a matching
+  convention?
+- Should external CSV exports be regenerated from canonical CIF/HDF5 on
+  demand rather than stored persistently?
+
+## Consequences
+
+### Positive
+
+- Fit output filenames become role-based and consistent across fit
+  types.
+- Sequential parameter evolution keeps its simple CSV workflow.
+- Bayesian arrays get a generic result sidecar name that can also serve
+  future structured result types.
+- External plotting data can be exposed as plain CSV without weakening
+  CIF as the canonical model format.
+
+### Trade-offs
+
+- The project gains more output-file roles under `analysis/`, so save
+  and cleanup behavior must be explicit.
+- Export CSVs are derived data and must be invalidated or regenerated
+  when the project changes.
+- HDF5 archives introduce size and compression choices that should be
+  resolved before implementation.
diff --git a/docs/dev/adrs/suggestions/parameter-correlation-persistence.md b/docs/dev/adrs/suggestions/parameter-correlation-persistence.md
deleted file mode 100644
index 9c494908b..000000000
--- a/docs/dev/adrs/suggestions/parameter-correlation-persistence.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# ADR: Parameter Correlation Persistence
-
-**Status:** Proposed  
-**Date:** 2026-05-13
-
-## Context
-
-`plot_param_correlations()` can currently visualize either:
-
-- deterministic parameter correlations derived from engine covariance
-- Bayesian correlations derived from posterior samples
-
-After project reload, this correlation information is not available
-unless the underlying runtime objects are rebuilt. For Bayesian fits,
-full posterior samples may not always be restored. For deterministic
-fits, engine covariance is typically not persisted at all.
-
-The correlation matrix is an analysis-owned summary, not model state. It
-therefore belongs in `analysis/analysis.cif`, not in structure or
-experiment CIF files.
-
-## Decision
-
-### 1. Add a `_fit_parameter_correlation` loop category
-
-Persist pairwise parameter correlations in a new analysis-owned loop:
-
-- `source_kind`
-- `param_unique_name_i`
-- `param_unique_name_j`
-- `correlation`
-
-Suggested example:
-
-```cif
-loop_
-_fit_parameter_correlation.source_kind
-_fit_parameter_correlation.param_unique_name_i
-_fit_parameter_correlation.param_unique_name_j
-_fit_parameter_correlation.correlation
-posterior cosio.atom_site.Co1.adp_iso cosio.atom_site.Co2.adp_iso 0.87
-```
-
-`source_kind` records how the correlation was obtained, for example:
-
-- `deterministic`
-- `posterior`
-
-### 2. Store only the upper triangle excluding the diagonal
-
-Each row stores one unordered parameter pair with
-`param_unique_name_i < param_unique_name_j` in a stable ordering.
-
-The diagonal is omitted because it is always 1.0 and can be rebuilt on
-load.
-
-This keeps the CIF loop compact while remaining lossless for the
-correlation matrix.
-
-### 3. Treat the loop as a summary, not a replacement for raw samples
-
-For Bayesian fits, `_fit_parameter_correlation` is a persisted summary.
-It does not replace posterior samples or posterior pair data.
-
-This means:
-
-- correlation heatmaps can be restored from the loop alone
-- posterior pair plots still require posterior samples
-
-### 4. Deterministic and Bayesian fits share the same loop schema
-
-The same loop category is used for both deterministic and Bayesian fit
-results. The distinction is carried by `source_kind`, not by separate
-loop names.
-
-### 5. Suggested restore behavior
-
-On load:
-
-- if `_fit_parameter_correlation` is present, correlation summaries are
-  restored into a lightweight analysis-owned correlation structure
-- if it is absent, correlation plots fall back to whatever live runtime
-  information is available
-
-### 6. Suggested user-facing behavior
-
-```python
-# Restored from analysis.cif when available
-project.display.plotter.plot_param_correlations()
-```
-
-If only the correlation loop is restored, the user still gets the
-correlation heatmap without needing raw posterior samples.
-
-## Consequences
-
-### Positive
-
-- Deterministic and Bayesian correlation summaries survive reload.
-- Correlation heatmaps no longer depend entirely on runtime-only data.
-- The schema is compact and fit-type-agnostic.
-
-### Trade-offs
-
-- The correlation loop is a derived summary, so it must be kept in sync
-  with the latest fit result.
-- Restored correlation data is not enough for posterior pair plots or
-  predictive summaries.
-
-## Deferred Work
-
-- optional storage of covariance matrices in addition to correlations
-- multiple named correlation sources for the same saved project
-- full correlation restoration for pair-plot density surfaces
diff --git a/docs/dev/adrs/suggestions/parameter-posterior-summary.md b/docs/dev/adrs/suggestions/parameter-posterior-summary.md
index 3de39bf37..36c0b979b 100644
--- a/docs/dev/adrs/suggestions/parameter-posterior-summary.md
+++ b/docs/dev/adrs/suggestions/parameter-posterior-summary.md
@@ -1,7 +1,17 @@
-# ADR: Parameter-Level Posterior Projection and Bayesian Persistence
+# ADR: Parameter-Level Posterior Projection
 
-**Status:** Proposed  
-**Date:** 2026-05-13
+**Status:** Proposed **Date:** 2026-05-13
+
+## Status Note
+
+This proposal is narrower than its original draft. The accepted
+[Analysis CIF Fit State](../accepted/analysis-cif-fit-state.md) ADR now
+persists analysis-level posterior summaries in
+`_bayesian_parameter_posterior`, and current Bayesian fits already
+commit the best posterior sample to `parameter.value`. What remains
+undecided is whether parameters should also expose a convenience
+`parameter.posterior` projection and dedicated internal helpers for
+updating fit-derived metadata atomically.
 
 ## Context
 
@@ -13,8 +23,9 @@ Bayesian DREAM currently keeps posterior state only on
 `analysis.fit_results` via `BayesianFitResults`, including
 `posterior_samples`, `posterior_parameter_summaries`,
 `posterior_predictive`, diagnostics, and sampler settings. The accepted
-runtime-fit-results ADR describes this state as runtime-only and not
-serialized.
+runtime-fit-results ADR originally described this state as runtime-only,
+but the accepted fit-state ADR now persists analysis-level posterior
+summaries and cache manifests as a narrower saved projection.
 
 `analysis.fit_results` already changes by analysis type: deterministic
 fits use `FitResults`, while posterior-capable fits such as DREAM use
@@ -59,7 +70,7 @@ Do not add separate flat parameter attributes such as `median`,
 Instead, the parameter-level projection reuses the existing
 `PosteriorParameterSummary` object already produced for
 `BayesianFitResults`. This keeps one grouped summary shape for display,
-inspection, and later persistence.
+inspection, and restore from persisted analysis state.
 
 The summary object currently provides the right level of detail:
 
@@ -192,6 +203,8 @@ while internal fit application installs fresh metadata atomically.
 After a posterior-capable fit, `parameter.value` is committed from the
 best posterior sample.
 
+Current DREAM support already follows this rule.
+
 The best posterior sample is chosen because it is a coherent joint point
 estimate across all free parameters. Marginal medians remain available
 on `parameter.posterior`, but they are summary data rather than the
@@ -211,358 +224,28 @@ Asymmetric interval information is not squeezed into
 `parameter.uncertainty`; it remains available only via
 `parameter.posterior`.
 
-### 10. Persist canonical Bayesian state at analysis level
+### 10. Rebuild posterior from analysis-level state
 
 Canonical Bayesian state is owned by `analysis.fit_results`, not by
-individual parameters.
+individual parameters. The saved fit-state format and restore order are
+defined in `analysis-cif-fit-state.md`.
 
-When the active result is a `BayesianFitResults` instance, persistence
-must save enough data to restore two distinct capability levels:
-
-- summary-only restore for parameter inspection and tables
-- full restore for posterior plots and predictive plots
+The accepted fit-state ADR currently rebuilds analysis-level posterior
+state only. This proposal would add a parameter-level convenience
+projection on top of that restored analysis state.
 
 `parameter.posterior` is never serialized as a per-parameter property.
-It is always rebuilt from the canonical analysis-level persisted data.
-
-### 11. Persist fit-control and Bayesian metadata in `analysis/analysis.cif`
-
-The existing `analysis/analysis.cif` file remains the text metadata
-entry point for analysis persistence.
-
-The persisted fit-control and Bayesian metadata is split into explicit
-CIF categories.
-
-#### 11.1 `_fit_parameter` loop
-
-Stores analysis-owned per-parameter fit metadata that is not currently
-covered by parameter CIF serialization.
-
-This loop exists because the committed parameter CIF representation
-already carries the active `value`, current `free` state, and current
-`uncertainty`, but it does not carry fit bounds, bound provenance, or
-the pre-fit uncertainty snapshot needed by undo. Those fields are
-required for Bayesian plot ranges, pair-plot bound annotations, and
-clean fit rollback after project reload.
-
-Fields:
-
-- `param_unique_name`
-- `fit_min`
-- `fit_max`
-- `fit_bounds_uncertainty_multiplier`
-- `start_value`
-- `start_uncertainty`
-
-`fit_min` and `fit_max` are required for restored Bayesian plotting.
-`fit_bounds_uncertainty_multiplier` is required if restored plots should
-preserve the uncertainty-derived bound annotation exactly. `start_value`
-and `start_uncertainty` are required for clean cross-session undo. If
-omitted, restored fit reports may show `N/A` for `start` and `change`,
-and undo may need to clear uncertainty as a compatibility fallback.
-
-#### 11.2 `_bayesian_result` single item
-
-Stores one saved Bayesian result header with these fields:
-
-- `schema_version`
-- `sampler_name`
-- `point_estimate_name`
-- `success`
-- `sampler_completed`
-- `reduced_chi_square`
-- `fitting_time`
-- `best_log_posterior`
-- `credible_interval_inner`
-- `credible_interval_outer`
-- `has_posterior_samples`
-- `has_posterior_predictive`
-- `sidecar_file`
-
-For the current design, `point_estimate_name` is always `best_sample`.
-
-#### 11.3 `_bayesian_sampler` single item
-
-Stores resolved sampler settings actually used for the run:
-
-- `steps`
-- `burn`
-- `thin`
-- `pop`
-- `parallel`
-- `init`
-- `random_seed`
-
-This persists the existing runtime `sampler_settings` in an explicit,
-schema-driven form rather than as an open-ended key/value mapping.
+It is rebuilt from the analysis-level saved result projection when that
+projection is available.
 
-#### 11.4 `_bayesian_convergence` single item
+Two restore levels matter for the parameter API:
 
-Stores top-level convergence and shape metadata:
+- summary-only restore can populate `parameter.posterior` and fit-result
+  tables
+- full restore can also support posterior plots and predictive plots
 
-- `converged`
-- `max_r_hat`
-- `min_ess_bulk`
-- `n_draws`
-- `n_chains`
-- `n_parameters`
-
-Per-parameter `r_hat` and `ess_bulk` remain in the parameter summary
-loop described below.
-
-#### 11.5 `_bayesian_parameter_posterior` loop
-
-Stores one canonical posterior summary row per sampled parameter. These
-rows are the source used to rebuild `parameter.posterior` on load.
-
-Fields:
-
-- `order_index`
-- `unique_name`
-- `display_name`
-- `best_sample_value`
-- `median`
-- `uncertainty`
-- `interval_68_lower`
-- `interval_68_upper`
-- `interval_95_lower`
-- `interval_95_upper`
-- `ess_bulk`
-- `r_hat`
-
-`order_index` defines the parameter order used by posterior sample
-columns in the sidecar arrays.
-
-#### 11.6 `_bayesian_predictive_dataset` loop
-
-Stores one manifest row per persisted posterior predictive summary.
-
-Fields:
-
-- `experiment_name`
-- `x_axis_name`
-- `x_path`
-- `best_sample_prediction_path`
-- `lower_95_path`
-- `upper_95_path`
-- `lower_68_path`
-- `upper_68_path`
-- `draws_path`
-- `n_x`
-- `n_draws_cached`
-
-This loop tells the loader which arrays to read from the sidecar file
-for each experiment-level predictive summary.
-
-#### 11.7 Suggested CIF fragments
-
-The active parameter value remains in the structure or experiment CIF as
-it does today, for example:
-
-```cif
-_atom_site_U_iso_or_equiv 0.0319(21)
-```
-
-Analysis-owned fit-control and Bayesian metadata then lives in
-`analysis/analysis.cif`, for example:
-
-```cif
-_fit.minimizer_type "bumps (dream)"
-_fit.mode single
-
-loop_
-_fit_parameter.param_unique_name
-_fit_parameter.fit_min
-_fit_parameter.fit_max
-_fit_parameter.fit_bounds_uncertainty_multiplier
-_fit_parameter.start_value
-_fit_parameter.start_uncertainty
-cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
-
-_bayesian_result.schema_version 1
-_bayesian_result.sampler_name dream
-_bayesian_result.point_estimate_name best_sample
-_bayesian_result.success yes
-_bayesian_result.sampler_completed yes
-_bayesian_result.reduced_chi_square 1.031
-_bayesian_result.fitting_time 82.4
-_bayesian_result.best_log_posterior -1542.77
-_bayesian_result.credible_interval_inner 0.68
-_bayesian_result.credible_interval_outer 0.95
-_bayesian_result.has_posterior_samples yes
-_bayesian_result.has_posterior_predictive yes
-_bayesian_result.sidecar_file "bayesian_data.h5"
-
-_bayesian_sampler.steps 3000
-_bayesian_sampler.burn 600
-_bayesian_sampler.thin 1
-_bayesian_sampler.pop 20
-_bayesian_sampler.parallel 0
-_bayesian_sampler.init lhs
-_bayesian_sampler.random_seed 12345
-
-_bayesian_convergence.converged yes
-_bayesian_convergence.max_r_hat 1.01
-_bayesian_convergence.min_ess_bulk 812.4
-_bayesian_convergence.n_draws 2400
-_bayesian_convergence.n_chains 20
-_bayesian_convergence.n_parameters 2
-
-loop_
-_bayesian_parameter_posterior.order_index
-_bayesian_parameter_posterior.unique_name
-_bayesian_parameter_posterior.display_name
-_bayesian_parameter_posterior.best_sample_value
-_bayesian_parameter_posterior.median
-_bayesian_parameter_posterior.uncertainty
-_bayesian_parameter_posterior.interval_68_lower
-_bayesian_parameter_posterior.interval_68_upper
-_bayesian_parameter_posterior.interval_95_lower
-_bayesian_parameter_posterior.interval_95_upper
-_bayesian_parameter_posterior.ess_bulk
-_bayesian_parameter_posterior.r_hat
-0 cosio.atom_site.Co1.adp_iso "Co1 ADP" 0.0319 0.0317 0.0021 0.0298 0.0339 0.0278 0.0361 812.4 1.01
-1 cosio.atom_site.Co2.adp_iso "Co2 ADP" 0.0320 0.0318 0.0020 0.0300 0.0338 0.0281 0.0359 830.7 1.00
-
-loop_
-_bayesian_predictive_dataset.experiment_name
-_bayesian_predictive_dataset.x_axis_name
-_bayesian_predictive_dataset.x_path
-_bayesian_predictive_dataset.best_sample_prediction_path
-_bayesian_predictive_dataset.lower_95_path
-_bayesian_predictive_dataset.upper_95_path
-_bayesian_predictive_dataset.lower_68_path
-_bayesian_predictive_dataset.upper_68_path
-_bayesian_predictive_dataset.draws_path
-_bayesian_predictive_dataset.n_x
-_bayesian_predictive_dataset.n_draws_cached
-hrpt ttheta /predictive/hrpt/x /predictive/hrpt/best_sample_prediction /predictive/hrpt/lower_95 /predictive/hrpt/upper_95 /predictive/hrpt/lower_68 /predictive/hrpt/upper_68 /predictive/hrpt/draws 2500 200
-```
-
-### 12. Persist bulk arrays in `analysis/bayesian_data.h5`
-
-Large numerical arrays are stored outside CIF text in a single sidecar
-file:
-
-- `analysis/bayesian_data.h5`
-
-The sidecar is optional. Summary-only restore remains valid without it.
-
-HDF5 is selected instead of NPZ because the persisted Bayesian payload
-is a structured collection of named datasets with heterogeneous shapes,
-optional groups, and potentially large predictive arrays. HDF5 provides
-explicit hierarchical storage, dataset metadata, selective reads, and a
-better long-term path for compression or chunking. NPZ is simpler, but
-it is flatter and less suitable once the saved Bayesian state grows
-beyond a small set of arrays.
-
-#### 12.1 Required core HDF5 dataset paths when posterior samples are saved
-
-- `posterior_parameter_samples`
-- `posterior_log_posterior`
-- `posterior_draw_index`
-
-Expected array shapes:
-
-- `posterior_parameter_samples`: `(n_draws, n_chains, n_parameters)`
-- `posterior_log_posterior`: `(n_draws, n_chains)` when available
-- `posterior_draw_index`: `(n_draws,)` when available
-
-If `posterior_log_posterior` or `posterior_draw_index` are unavailable,
-their corresponding header flags remain false and the arrays may be
-omitted.
-
-#### 12.2 Predictive dataset keys
-
-Posterior predictive arrays are addressed through the
-`_bayesian_predictive_dataset` manifest rather than inferred from file
-ordering.
-
-Recommended HDF5 dataset naming is:
-
-- `predictive__<experiment>__x`
-- `predictive__<experiment>__best_sample_prediction`
-- `predictive__<experiment>__lower_95`
-- `predictive__<experiment>__upper_95`
-- `predictive__<experiment>__lower_68`
-- `predictive__<experiment>__upper_68`
-- `predictive__<experiment>__draws`
-
-The manifest, not the naming convention, is the source of truth.
-
-Recommended HDF5 group layout is:
-
-- `/posterior/parameter_samples`
-- `/posterior/log_posterior`
-- `/posterior/draw_index`
-- `/predictive/<experiment>/x`
-- `/predictive/<experiment>/best_sample_prediction`
-- `/predictive/<experiment>/lower_95`
-- `/predictive/<experiment>/upper_95`
-- `/predictive/<experiment>/lower_68`
-- `/predictive/<experiment>/upper_68`
-- `/predictive/<experiment>/draws`
-
-The manifest remains the canonical mapping used by the loader, so this
-layout is recommended rather than mandatory.
-
-#### 12.3 What is not persisted in the sidecar
-
-Do not persist backend-specific runtime objects such as `engine_result`,
-the DREAM driver, or ArviZ `InferenceData`.
-
-Those objects can be rebuilt from canonical saved arrays when needed, or
-left unavailable after load.
-
-### 13. Restore flow and partial-availability policy
-
-Persistence must support both full and partial restore.
-
-#### 13.1 Save flow
-
-When `Project.save()` sees `analysis.fit_results` as a
-`BayesianFitResults` instance:
-
-1. It writes standard analysis configuration to `analysis/analysis.cif`.
-2. It appends `_fit_parameter`, `_bayesian_result`, `_bayesian_sampler`,
-   `_bayesian_convergence`, and `_bayesian_parameter_posterior`.
-3. If posterior predictive summaries are available, it also writes the
-   `_bayesian_predictive_dataset` manifest.
-4. If posterior sample arrays or predictive arrays are available, it
-   writes `analysis/bayesian_data.h5`.
-
-#### 13.2 Load flow
-
-When `Project.load()` restores `analysis/analysis.cif`:
-
-1. Standard analysis configuration is restored first.
-2. If `_fit_parameter` is present, fit bounds, bound provenance, and
-   optional pre-fit scalar snapshots are restored by matching
-   `param_unique_name` to live parameters.
-3. If Bayesian categories are present, a lightweight
-   `BayesianFitResults` instance is rebuilt from persisted metadata and
-   parameter summary rows.
-4. `parameter.posterior` is rebuilt by matching summary rows to live
-   parameters via `unique_name`.
-5. `parameter.value` and `parameter.uncertainty` continue to come from
-   the normal project serialization path; Bayesian restore does not
-   overwrite them.
-6. If `analysis/bayesian_data.h5` exists and matches the manifest,
-   `posterior_samples` and `posterior_predictive` are restored.
-7. If the sidecar is missing or incomplete, the restore degrades to
-   summary-only mode without failing the whole project load.
-
-#### 13.3 Partial restore behavior
-
-The chosen partial-restore policy is:
-
-- `parameter.posterior` and `display.fit_results()` remain available
-  from saved metadata and summaries.
-- Bayesian-only plots requiring canonical posterior arrays remain
-  unavailable unless those arrays were restored successfully.
-- Missing sidecar data should produce a clear warning, not a hard load
-  failure.
+If the saved project has no analysis-level posterior summary for a
+parameter, `parameter.posterior` remains `None`.
 
 Example user access after load:
 
@@ -574,28 +257,18 @@ posterior = param.posterior
 
 if posterior is not None:
   print(posterior.best_sample_value)
-    print(posterior.uncertainty)
-    print(posterior.interval_68)
+  print(posterior.uncertainty)
+  print(posterior.interval_68)
 
 project.analysis.display.fit_results()
 ```
 
-Example plotting behavior after load:
-
-```python
-# Works with summary-only restore
-project.analysis.display.fit_results()
+Posterior plot availability after load follows the fit-state restore
+level defined in `analysis-cif-fit-state.md`.
 
-# Requires canonical posterior arrays from analysis/bayesian_data.h5
-project.display.plotter.plot_posterior_pairs()
-project.display.plotter.plot_param_distribution(param)
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
-```
+### 11. Keep parameter posterior data rebuilt, not duplicated
 
-### 14. Keep parameter posterior data rebuilt, not duplicated
-
-`parameter.posterior` is always rebuilt from the canonical
-`_bayesian_parameter_posterior` loop in `analysis/analysis.cif`.
+`parameter.posterior` is always rebuilt from analysis-level fit state.
 
 Do not serialize posterior summaries again inside each parameter's own
 CIF representation. Duplicating the same posterior summary data across
@@ -610,15 +283,10 @@ structure and experiment files would create multiple sources of truth.
   into one optional object rather than many flat attributes.
 - `uncertainty` and posterior metadata become clearly fit-owned rather
   than mixed user-editable and fit-editable state.
-- `analysis.fit_results` remains the canonical source for full Bayesian
-  state.
-- `analysis/analysis.cif` becomes the home for fit-control metadata that
-  does not belong in structure or experiment CIF files.
-- Bayesian save/load gains a clear split between text metadata in
-  `analysis/analysis.cif` and bulk numerical arrays in
-  `analysis/bayesian_data.h5`.
-- Partial restore works for summaries even when full posterior arrays
-  are absent.
+- `analysis.fit_results` remains the canonical runtime source for full
+  Bayesian state.
+- Partial restore can still expose parameter summaries when
+  analysis-level saved state contains them.
 - The design matches the current rule that `value` is the only active
   scalar used for calculations.
 
@@ -632,8 +300,6 @@ structure and experiment files would create multiple sources of truth.
   application code must be updated to use dedicated internal helpers
   rather than a mix of `_set_value_from_minimizer(...)` and public
   uncertainty assignment.
-- Bayesian persistence now spans both CIF metadata and a binary sidecar,
-  so save/load code must validate consistency between the two.
 
 ## Layering and Ownership
 
@@ -647,14 +313,8 @@ type-only import is acceptable.
 
 ## Deferred Work
 
-This ADR now defines persistence for one canonical saved Bayesian run.
-
-It still defers:
+This ADR defines the parameter-level posterior projection. It defers:
 
-- support for multiple saved Bayesian runs per project
-- plot-ready cache layers beyond canonical posterior and predictive data
-- persistence-time compression or chunking strategies beyond the first
-  HDF5 sidecar implementation
 - persistence for future posterior-capable minimizers beyond DREAM
 - enabling currently unsupported single-crystal predictive draw plots
 
@@ -665,18 +325,13 @@ It still defers:
 - Existing `PosteriorParameterSummary` instances should be reused rather
   than copied into a second summary type unless implementation reveals a
   concrete layering problem that cannot be resolved cleanly.
-- `parameter.posterior` should always be rebuilt from restored canonical
+- `parameter.posterior` should always be rebuilt from analysis-level
   Bayesian data rather than serialized redundantly at parameter level.
-- The sidecar reader and writer should be isolated behind explicit
-  serializer helpers instead of being implemented inline in
-  `Project.save()` and `Project.load()`.
 
 ## Chosen Defaults
 
 - `parameter.value` remains committed to the best posterior sample after
   posterior fits.
-- If a project is loaded without full posterior arrays, restoring only
+- If a project is loaded with only posterior summaries, restoring
   `parameter.posterior` is acceptable for table display and parameter
   inspection.
-- Posterior plotting remains unavailable unless the canonical Bayesian
-  containers needed by those plots are also restored.
diff --git a/docs/dev/adrs/suggestions/python-cif-category-correspondence.md b/docs/dev/adrs/suggestions/python-cif-category-correspondence.md
index 6f970a0e2..191f18e59 100644
--- a/docs/dev/adrs/suggestions/python-cif-category-correspondence.md
+++ b/docs/dev/adrs/suggestions/python-cif-category-correspondence.md
@@ -24,13 +24,20 @@ one-to-one correspondence for project-owned singleton categories:
 ```text
 project.info.title        -> project.cif: _info.title
 project.rendering.engine  -> project.cif: _rendering.engine
-project.verbosity.level   -> project.cif: _verbosity.level
+project.verbosity.fit     -> project.cif: _verbosity.fit
 ```
 
 The design question is whether this rule should be applied only to
 project-level configuration, or more broadly across analysis,
 experiments, structures, and calculated data.
 
+The accepted project-facade decision keeps `Project` as the public root
+and keeps `project.cif` as the singleton project configuration file. It
+also keeps `_project.*` as the semantic CIF category for scientific
+project information and rejects `_meta.*` for that purpose. This ADR
+therefore must not reintroduce the rejected `Workspace` rename,
+`workspace.cif`, or `_meta.project_*` tags as incidental cleanup.
+
 ## Scope Of Comparison
 
 The comparison below is category-level and public-API oriented. It lists
@@ -48,7 +55,7 @@ to objects reached from the current `Project` root, for example
 | Current Python surface              | Current saved location   | Current CIF block form | Notes                                                                               |
 | ----------------------------------- | ------------------------ | ---------------------- | ----------------------------------------------------------------------------------- |
 | `project.info`, `project.rendering` | `project.cif`            | bare categories        | Project-level singleton config.                                                     |
-| `project.verbosity`                 | not persisted            | none                   | Runtime-only string property backed by `VerbosityEnum`; no `_verbosity` category.   |
+| `project.verbosity`                 | `project.cif`            | bare category          | Project-owned fit-output verbosity category backed by `VerbosityEnum`.              |
 | `project.structures[name]`          | `structures/<name>.cif`  | `data_<name>`          | Each structure is one CIF data block.                                               |
 | `project.experiments[name]`         | `experiments/<name>.cif` | `data_<name>`          | Each experiment is one CIF data block.                                              |
 | `project.analysis`                  | `analysis/analysis.cif`  | bare categories        | Loader also accepts legacy root-level `analysis.cif`.                               |
@@ -58,17 +65,17 @@ to objects reached from the current `Project` root, for example
 
 ### Project-Level Configuration
 
-| Current Python path              | Current CIF path          | Match? | Notes                                                                                                                           |
-| -------------------------------- | ------------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------------- |
-| `project.info.name`              | `_project.id`             | No     | Python uses user-facing `name`; CIF uses `id`; category is `info` in Python but `_project` in CIF.                              |
-| `project.info.title`             | `_project.title`          | Partly | Field name matches, category name does not.                                                                                     |
-| `project.info.description`       | `_project.description`    | Partly | Field name matches, category name does not.                                                                                     |
-| `project.info.created`           | `_project.created`        | Partly | Field name matches, category name does not.                                                                                     |
-| `project.info.last_modified`     | `_project.last_modified`  | Partly | Field name matches, category name does not.                                                                                     |
-| `project.info.path`              | none                      | No     | Runtime storage path, not a CIF field.                                                                                          |
-| `project.rendering.chart_engine` | `_rendering.chart_engine` | Yes    | Direct category and field mapping.                                                                                              |
-| `project.rendering.table_engine` | `_rendering.table_engine` | Yes    | Direct category and field mapping.                                                                                              |
-| `project.verbosity`              | none                      | No     | Runtime-only string convenience property; current code has no `project.verbosity.level` category and no `_verbosity.level` tag. |
+| Current Python path              | Current CIF path          | Match? | Notes                                                                                              |
+| -------------------------------- | ------------------------- | ------ | -------------------------------------------------------------------------------------------------- |
+| `project.info.name`              | `_project.id`             | No     | Python uses user-facing `name`; CIF uses `id`; category is `info` in Python but `_project` in CIF. |
+| `project.info.title`             | `_project.title`          | Partly | Field name matches, category name does not.                                                        |
+| `project.info.description`       | `_project.description`    | Partly | Field name matches, category name does not.                                                        |
+| `project.info.created`           | `_project.created`        | Partly | Field name matches, category name does not.                                                        |
+| `project.info.last_modified`     | `_project.last_modified`  | Partly | Field name matches, category name does not.                                                        |
+| `project.info.path`              | none                      | No     | Runtime storage path, not a CIF field.                                                             |
+| `project.rendering.chart_engine` | `_rendering.chart_engine` | Yes    | Direct category and field mapping.                                                                 |
+| `project.rendering.table_engine` | `_rendering.table_engine` | Yes    | Direct category and field mapping.                                                                 |
+| `project.verbosity.fit`          | `_verbosity.fit`          | Yes    | Direct category and field mapping for fitting process output verbosity.                            |
 
 ### Analysis Configuration
 
@@ -218,18 +225,30 @@ This ADR does not propose renaming the public root object. The current
 root object is already `Project`; the proposal is about category and tag
 correspondence inside project-owned singleton configuration.
 
-Target project-level mappings if the current Python names are kept:
+The accepted baseline is:
+
+```text
+project.info.<field> -> project.cif: _project.<field>
+```
 
-| Python path                      | Target CIF path           | Current state                              |
-| -------------------------------- | ------------------------- | ------------------------------------------ |
-| `project.info.name`              | `_info.name`              | Currently `_project.id`.                   |
-| `project.info.title`             | `_info.title`             | Currently `_project.title`.                |
-| `project.info.description`       | `_info.description`       | Currently `_project.description`.          |
-| `project.info.created`           | `_info.created`           | Currently `_project.created`.              |
-| `project.info.last_modified`     | `_info.last_modified`     | Currently `_project.last_modified`.        |
-| `project.rendering.chart_engine` | `_rendering.chart_engine` | Already matches.                           |
-| `project.rendering.table_engine` | `_rendering.table_engine` | Already matches.                           |
-| `project.verbosity.level`        | `_verbosity.level`        | Currently no persisted verbosity category. |
+Future one-to-one correspondence work may still discuss whether the
+public identity field should be `name` or `id`, whether verbosity should
+gain additional coverage-specific fields, and whether rendering should
+keep separate chart and table engine fields.
+
+Possible strict-correspondence target if a future ADR explicitly changes
+the accepted `_project.*` baseline:
+
+| Python path                      | Target CIF path           | Current state                                    |
+| -------------------------------- | ------------------------- | ------------------------------------------------ |
+| `project.info.name`              | `_info.name`              | Currently `_project.id`.                         |
+| `project.info.title`             | `_info.title`             | Currently `_project.title`.                      |
+| `project.info.description`       | `_info.description`       | Currently `_project.description`.                |
+| `project.info.created`           | `_info.created`           | Currently `_project.created`.                    |
+| `project.info.last_modified`     | `_info.last_modified`     | Currently `_project.last_modified`.              |
+| `project.rendering.chart_engine` | `_rendering.chart_engine` | Already matches.                                 |
+| `project.rendering.table_engine` | `_rendering.table_engine` | Already matches.                                 |
+| `project.verbosity.fit`          | `_verbosity.fit`          | Implemented direct fit-output verbosity mapping. |
 
 Alternative target if the project identity field should be called `id`
 rather than `name`:
@@ -253,15 +272,24 @@ repository can optimize them for API/persistence symmetry.
 ### `project.cif` Scopes Generic Categories
 
 `_info.title` is generic in isolation, but inside `project.cif` it reads
-as project information. This is similar to `_verbosity.level`: the file
-scope tells the reader this is project-level verbosity.
+as project information. This is similar to `_verbosity.fit`: the file
+scope tells the reader this is project-level verbosity, and the field
+name identifies the fitting-process coverage.
 
 ### The Current `Project` Root Already Matches User Language
 
 The current public root object is already `Project`. Keeping it avoids a
-user-facing `workspace.project.*` nesting and aligns with scientific
-workflows where a project is the container for structures, experiments,
-analysis, and saved files.
+broad user-facing root rename and aligns with scientific workflows where
+a project is the container for structures, experiments, analysis, and
+saved files.
+
+### `_project.*` Is More Semantic Than `_meta.*`
+
+The project-information category stores the scientific project identity,
+title, description, and timestamps. `_project.id` and `_project.title`
+say that directly, while `_meta.project_id` and `_meta.project_title`
+make the CIF less domain-oriented and repeat the concept in every item
+name.
 
 ### Scientific CIF/Domain Categories Should Stay Domain-Oriented
 
@@ -291,9 +319,9 @@ unless a separate ADR changes the underlying API pattern.
 - `_info.*` is less self-describing if copied out of `project.cif`.
 - Existing `_project.*` project files would need migration or a
   deliberate compatibility decision.
-- If verbosity is persisted, `project.verbosity` would either need to
-  become a category object or remain as a convenience alias for a new
-  `project.verbosity.level` field.
+- Persisted verbosity is now a category object. The initial field is
+  `project.verbosity.fit`, leaving room for future coverage-specific
+  verbosity fields.
 - Collapsing rendering to `project.rendering.engine` would simplify the
   API, but only if chart and table renderers are intended to share one
   backend choice.
@@ -302,13 +330,9 @@ unless a separate ADR changes the underlying API pattern.
 
 - Should the project identity remain `project.info.name`, or should it
   become `project.info.id` to mirror the saved identifier field?
-- Should project metadata move from `_project.*` to `_info.*`, or is
-  `_project.*` clearer even inside `project.cif`?
 - Should `project.rendering.chart_engine` and
   `project.rendering.table_engine` remain separate, or should the public
   API and CIF collapse to one `engine` field?
-- Should `_project.*` be accepted as a read-only legacy fallback when
-  loading older saved projects?
 - Should `project.verbosity = 'short'` remain as a convenience alias for
-  `project.verbosity.level = 'short'`, or should strict correspondence
+  `project.verbosity.fit = 'short'`, or should strict correspondence
   remove the alias?
diff --git a/docs/dev/adrs/suggestions/undo-fit.md b/docs/dev/adrs/suggestions/undo-fit.md
index 578d43a38..cd658aba9 100644
--- a/docs/dev/adrs/suggestions/undo-fit.md
+++ b/docs/dev/adrs/suggestions/undo-fit.md
@@ -1,57 +1,61 @@
 # ADR: Undo Fit
 
 **Status:** Proposed  
-**Date:** 2026-05-13
+**Date:** 2026-05-18
+
+## Status Note
+
+The rollback anchors described here are already persisted and restored.
+Current code saves `_fit_parameter.start_value` and
+`_fit_parameter.start_uncertainty` in `analysis/analysis.cif`, and the
+CLI already reserves `PROJECT_DIR undo`, but no rollback operation is
+implemented yet.
 
 ## Context
 
-The new `_fit_parameter.start_value` and
-`_fit_parameter.start_uncertainty` fields in `analysis/analysis.cif`
-capture the last committed pre-fit scalar state for each fitted
-parameter. This is useful when a minimization run produces a poor result
-and the user wants to return to the state from immediately before the
-fit.
+The accepted fit-state persistence design now stores
+`_fit_parameter.start_value` and `_fit_parameter.start_uncertainty` in
+`analysis/analysis.cif`. Those fields capture the last committed pre-fit
+scalar state for each fitted parameter and are the essential rollback
+anchors for any undo feature.
 
-This need is especially important in command-line workflows, where the
-user may save a project after a bad fit and reopen it later expecting a
-simple way to roll back to the pre-fit state.
+This branch also introduced project-first CLI routing and reserved a
+top-level `undo` command shape, but the command is still only a
+placeholder. The actual rollback semantics are still undecided.
 
-However, these snapshots alone do not define undo semantics. The API
-owner, rollback scope, and interaction with fit-derived metadata must be
-explicit.
+Parameter-level posterior access remains a separate proposal. Undo must
+not depend on `parameter.posterior` existing.
 
 ## Decision
 
 ### 1. Add an analysis-owned `undo_fit()` operation
 
-The rollback operation belongs on `Analysis`, for example:
+The rollback operation belongs on `Analysis`:
 
 ```python
 project.analysis.undo_fit()
 ```
 
-`Analysis` owns the fit lifecycle, fit metadata, and persisted
-`analysis.cif` state, so it is the correct owner for this operation.
+`Analysis` owns fit execution, fit metadata, and the persisted fit-state
+projection, so it is the correct public owner.
 
-### 2. Initial undo scope is scalar rollback plus posterior clear
+### 2. Initial undo scope is scalar rollback plus fit-state clear
 
 The first undo implementation restores each fitted parameter's saved
-pre-fit scalar state and clears fit state that belongs only to the
-discarded fit.
-
-It does not attempt to restore every possible runtime detail of the
-previous fit result.
+pre-fit scalar state and clears fit-derived state that belongs only to
+the discarded fit.
 
 After `undo_fit()`:
 
 - `parameter.value` is restored from `_fit_parameter.start_value`
 - `parameter.uncertainty` is restored from
   `_fit_parameter.start_uncertainty`
-- `parameter.posterior` is cleared
 - `analysis.fit_results` is cleared
+- persisted fit-state summaries and Bayesian caches for the discarded
+  fit are cleared
 
-This gives the user a safe, predictable return to the pre-fit visible
-parameter state without pretending to restore a full historical result.
+If a future `parameter.posterior` API exists, undo should clear that
+projection too. It is not a prerequisite for the initial implementation.
 
 If an older saved project lacks `start_uncertainty`, clearing
 `parameter.uncertainty` remains an acceptable compatibility fallback.
@@ -67,51 +71,20 @@ The initial undo operation does not revert:
 - fit mode
 - joint-fit weights
 
-These are analysis configuration, not fit output.
+These belong to analysis configuration, not fit output.
 
 ### 4. Undo is single-level for now
 
-Only the latest saved pre-fit state is addressable.
-
-The initial API does not create a stack of historical fits. Supporting
-multiple undo levels would require a dedicated snapshot history design
-and is deferred.
-
-### 5. Persisted scalar snapshots are the rollback anchors
-
-The minimum persisted state required for clean cross-session undo is the
-pair of `_fit_parameter.start_value` and
-`_fit_parameter.start_uncertainty` defined in
-`analysis-cif-fit-state.md`.
-
-If a parameter has no saved `start_value`, `undo_fit()` leaves that
-parameter unchanged.
-
-If a parameter has no saved `start_uncertainty`, `undo_fit()` may clear
-that parameter's uncertainty as a compatibility fallback for older saved
-projects.
-
-### 6. Suggested user flow
-
-```python
-project.analysis.fit()
-
-# Decide that the latest fit should be discarded.
-project.analysis.undo_fit()
-
-# Save the recovered state if desired.
-project.save()
-```
+Only the latest saved pre-fit snapshot is addressable. Multi-level undo
+and redo require a dedicated snapshot-history design and remain
+deferred.
 
-### 7. Add a top-level `undo-fit` CLI command
+### 5. CLI exposure follows the project-first command style
 
-Because command-line recovery is one of the main motivations for this
-feature, undo must also be exposed through the existing top-level CLI.
-
-Suggested command:
+The command-line surface should follow the current CLI style:
 
 ```bash
-python -m easydiffraction undo-fit PROJECT_DIR
+python -m easydiffraction PROJECT_DIR undo
 ```
 
 This command should:
@@ -119,44 +92,35 @@ This command should:
 - load the saved project from `PROJECT_DIR`
 - execute `project.analysis.undo_fit()`
 - save the recovered state back to the same project directory by default
-- support `--dry` to perform the rollback in memory without overwriting
-  project files
-- emit a clear message describing whether the latest fit snapshot was
-  successfully discarded
-
-Suggested dry-run form:
-
-```bash
-python -m easydiffraction undo-fit PROJECT_DIR --dry
-```
+- support `--dry` to preview the rollback without overwriting files
+- fail with a clear non-zero exit status when no usable undo snapshot is
+  available
 
-If the project does not contain a usable undo snapshot, the command
-should fail with a clear non-zero exit status instead of silently doing
-nothing.
+Compatibility aliases may remain if the CLI supports them, but the
+project-first form is the canonical user-facing syntax.
 
 ## Consequences
 
 ### Positive
 
-- Users gain a simple recovery path after a poor fit.
-- The feature works naturally with saved projects and both Python and
-  command-line workflows.
-- The initial scope stays small and does not require full historical fit
-  snapshots.
+- The accepted fit-state persistence already provides the minimum saved
+  anchors required for cross-session undo.
+- Users gain a predictable recovery path after a poor fit without
+  needing full historical fit snapshots.
+- The feature aligns naturally with saved-project workflows in both
+  Python and the CLI.
 
 ### Trade-offs
 
-- Undo restores pre-fit scalar parameter state, not a full historical
-  `fit_results` object.
-- Older saved projects that do not carry `start_uncertainty` may still
-  fall back to clearing uncertainty.
+- Undo restores visible scalar parameter state, not a full historical
+  runtime result object.
+- Older saved projects may still need the uncertainty-clearing fallback.
 - Multi-level undo remains unsupported.
 
 ## Deferred Work
 
-- exact restoration of previous posterior-derived projections beyond the
-  scalar parameter snapshot
-- multi-level undo / redo
-- user-facing confirmation or preview APIs
-- rollback of fit-type-specific persisted summaries beyond parameter
-  values
+- exact restoration of previous posterior-derived displays beyond the
+  scalar rollback anchors
+- multi-level undo and redo
+- confirmation or preview UX beyond `--dry`
+- any dependency on a future `parameter.posterior` API
diff --git a/docs/dev/adrs/suggestions/workspace-root-project-category.md b/docs/dev/adrs/suggestions/workspace-root-project-category.md
deleted file mode 100644
index a6c94cba6..000000000
--- a/docs/dev/adrs/suggestions/workspace-root-project-category.md
+++ /dev/null
@@ -1,380 +0,0 @@
-# ADR: Workspace Root and Project Information Category
-
-**Status:** Proposed  
-**Date:** 2026-05-17
-
-## Context
-
-The current public root object is `Project`. It acts as the top-level
-facade for an EasyDiffraction working session:
-
-```python
-project = ed.Project(name='lbco_hrpt')
-project.structures
-project.experiments
-project.analysis
-project.display
-project.summary
-project.save()
-```
-
-The same word, "project", is also the natural CIF category name for
-information about the scientific project:
-
-```cif
-_project.id
-_project.title
-_project.description
-_project.created
-_project.last_modified
-```
-
-This creates a naming conflict. The root object is a broad runtime
-facade, while the `_project.*` category is only information about the
-scientific project. Using the same name for both makes category naming
-awkward:
-
-```python
-project.project_info.title
-project.config.project_info.title
-project.project.title
-```
-
-At the same time, replacing `_project.*` with a generic `_meta.*`
-category would weaken the CIF model:
-
-```cif
-_meta.project_id
-_meta.project_title
-```
-
-The category name `meta` is too generic. It forces each item name to
-repeat what the category should already communicate. The existing
-`_project.id` and `_project.title` names are more semantic and better
-aligned with the repository rule to follow CIF naming unless there is a
-clearly better API.
-
-The design question is therefore:
-
-- should the top-level runtime object remain `Project`, and project
-  information move to a different category such as `meta`;
-- or should the top-level runtime object be renamed to `Workspace`, so
-  `project` can be used cleanly for project information?
-
-## Decision
-
-Rename the top-level runtime facade from `Project` to `Workspace`.
-
-Use `project` as the public project-information category under the
-workspace:
-
-```python
-workspace = ed.Workspace(project_id='lbco_hrpt')
-workspace.project.id
-workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
-workspace.rendering.table_engine = 'rich'
-workspace.verbosity = 'short'
-workspace.structures
-workspace.experiments
-workspace.analysis
-```
-
-Persist workspace-level singleton categories in `workspace.cif`:
-
-```cif
-_project.id
-_project.title
-_project.description
-_project.created
-_project.last_modified
-
-_rendering.chart_engine
-_rendering.table_engine
-
-_verbosity.level
-```
-
-The saved directory is a workspace directory whose filesystem name is
-chosen by the user. The canonical layout is:
-
-```text
-<workspace-dir>/
-|-- workspace.cif
-|-- structures/
-|   `-- cosio.cif
-|-- experiments/
-|   `-- d20.cif
-|-- analysis/
-|   `-- analysis.cif
-`-- summary/
-    `-- summary.cif
-```
-
-Do not introduce `_meta.*` CIF tags. Do not use `project.cif`,
-`config.cif`, or `meta.cif` as the primary singleton configuration file
-in the target layout.
-
-The intended naming split is:
-
-```text
-Workspace
-|-- project      # information about the scientific project
-|-- rendering    # rendering preferences
-|-- verbosity    # console/output verbosity preference
-|-- structures   # real structure datablocks
-|-- experiments  # real experiment datablocks
-|-- analysis     # analysis section
-|-- display      # display facade
-`-- summary      # summary/report facade
-```
-
-## Rationale
-
-### `Workspace` better describes the top-level facade
-
-The top-level object is more than project metadata. It owns active
-collections, analysis state, display helpers, save/load behavior, and
-runtime orchestration. `Workspace` describes that broader role without
-consuming the domain word `project`.
-
-The name is also familiar in scientific software. It commonly means an
-active analysis environment, a data/model container, or a working area.
-That is close to the role of the current EasyDiffraction root object.
-
-### `project` is the right category name for project information
-
-Project information is not generic metadata. It is specifically the
-identity, title, description, and timestamps of the scientific project.
-
-This reads cleanly:
-
-```python
-workspace.project.title
-```
-
-and maps directly to clean CIF:
-
-```cif
-_project.title
-```
-
-### `_meta.project_title` is weaker than `_project.title`
-
-The `_meta` category would make the CIF less domain-oriented. It also
-creates longer and more repetitive item names:
-
-```cif
-_meta.project_id
-_meta.project_title
-_meta.project_description
-```
-
-The existing `_project.*` tags are clearer:
-
-```cif
-_project.id
-_project.title
-_project.description
-```
-
-### This keeps layer-specific consistency
-
-After this decision, each layer has a clear rule:
-
-| Layer           | Rule                           | Example             |
-| --------------- | ------------------------------ | ------------------- |
-| Runtime root    | working-session facade         | `Workspace`         |
-| Public category | semantic category name         | `workspace.project` |
-| CIF category    | semantic CIF category          | `_project.*`        |
-| Config file     | workspace singleton categories | `workspace.cif`     |
-
-This avoids one-off aliases such as `project.info` while preserving
-semantic CIF names.
-
-### `workspace.cif` is clearer than `project.cif`, `config.cif`, or `meta.cif`
-
-The file stores singleton settings owned by the workspace: scientific
-project information, rendering preferences, and verbosity. `project.cif`
-overloads the project name again, while `config.cif` and `meta.cif` are
-generic. `workspace.cif` names the owning layer and lets each category
-inside the file keep its domain-specific name.
-
-## Consequences
-
-### Positive
-
-- The root object and project-information category no longer share the
-  same conceptual name.
-- Public access becomes uniform: `workspace.project`,
-  `workspace.rendering`, `workspace.verbosity`, `workspace.analysis`.
-- CIF stays semantic and does not introduce `_meta.*`.
-- Workspace-level preferences such as rendering and verbosity are saved
-  with the workspace instead of being hidden runtime-only state.
-- Project information can use short item names such as `id`, `title`,
-  and `description`.
-- The top-level facade name better reflects active runtime
-  orchestration.
-
-### Negative
-
-- This is a breaking public API change.
-- Tutorials, scripts, tests, docs, type hints, and imports must be
-  updated from `Project` to `Workspace`.
-- Existing saved directories using `project.cif` must be migrated to
-  `workspace.cif` if no compatibility loader is kept.
-- Existing code that expected verbosity to be runtime-only must account
-  for it round-tripping through `workspace.cif`.
-- Users familiar with `Project` must learn the new root name.
-- `Workspace` can be confused with a filesystem workspace in some
-  ecosystems, so documentation must define it clearly as the active
-  EasyDiffraction working object.
-
-## Compatibility Policy
-
-EasyDiffraction is in beta, and repository instructions say not to keep
-legacy shims by default.
-
-Therefore the target implementation should not add a permanent
-`Project = Workspace` alias unless explicitly approved before
-implementation.
-
-The migration plan still includes a review gate before removing the old
-`Project` public symbol, because this is a user-facing breaking change.
-
-## Alternatives Considered
-
-### Keep `Project` root and rename project information to `meta`
-
-Rejected.
-
-Example:
-
-```python
-project.meta.project_title
-```
-
-```cif
-_meta.project_title
-```
-
-This keeps the root class stable, but it weakens the category model.
-`meta` is too broad, and the CIF item names become repetitive.
-
-### Keep `Project` root and use `project.config.project`
-
-Rejected for now.
-
-Example:
-
-```python
-project.config.project.title
-```
-
-This is technically consistent, but it still repeats `project` at
-different semantic layers. It also adds depth to common user workflows.
-It is a reasonable fallback if the public root rename is rejected.
-
-### Keep `Project` root and use `project.info`
-
-Rejected for the target design.
-
-Example:
-
-```python
-project.info.title
-```
-
-This is readable, but it preserves a special-case category alias. The
-current goal is stronger consistency between public categories and CIF
-category concepts.
-
-### Use `project.cif`, `config.cif`, or `meta.cif` for singleton settings
-
-Rejected for the target layout.
-
-`project.cif` repeats the overloaded term that this migration removes.
-`config.cif` and `meta.cif` are too generic and do not say which layer
-owns the settings. `workspace.cif` is more explicit while still allowing
-semantic categories such as `_project`, `_rendering`, and `_verbosity`
-inside the file.
-
-### Rename only internal files and keep public API unchanged
-
-Rejected for the target design.
-
-This improves implementation clarity but does not solve the public
-naming inconsistency.
-
-### Use `Study` instead of `Workspace`
-
-Rejected.
-
-`Study` is a plausible scientific term, but it is less established for
-an active computational container. It also does not map as naturally to
-save/load, display, and analysis orchestration.
-
-## Implementation Notes
-
-The implementation should follow:
-
-```text
-docs/dev/plans/workspace-root-project-category.md
-```
-
-The high-level migration is:
-
-1. Rename the root facade `Project` to `Workspace`.
-2. Rename the project package/module surface from `project` to
-   `workspace`.
-3. Rename `ProjectConfig` to `WorkspaceConfig`.
-4. Rename project-level category access from `info` to `project`.
-5. Rename project-information `name` access to `id`, matching
-   `_project.id`.
-6. Move the storage path to `Workspace.path`, because it describes the
-   saved workspace directory rather than project information.
-7. Keep the information category class named `ProjectInfo` unless a
-   later decision chooses `ProjectMetadata`.
-8. Keep CIF tags `_project.*` and `_rendering.*`.
-9. Rename saved singleton config file from `project.cif` to
-   `workspace.cif`.
-10. Persist workspace verbosity in `workspace.cif` as
-    `_verbosity.level`, owned by a first-class `Verbosity` category
-    under `WorkspaceConfig` (parallel to `Rendering`).
-11. Update code, tests, scripts, tutorials, docs, and ADR references.
-
-## Post-Implementation ADR Update
-
-This ADR must be updated after the migration plan is implemented.
-
-When implementation is complete:
-
-1. Change status from `Proposed` to `Accepted and implemented`.
-2. Record the final public API and saved file layout.
-3. Record whether a temporary or permanent `Project` compatibility alias
-   was approved.
-4. Record any deviations from the migration plan.
-5. Move this file from `docs/dev/adrs/suggestions/` to
-   `docs/dev/adrs/accepted/` if the decision is accepted.
-6. Update `docs/dev/adrs/index.md` and related accepted ADRs if the ADR
-   map changes.
-7. Update or close related items in `docs/dev/issues/open.md`.
-
-## Acceptance Criteria
-
-This ADR is satisfied when:
-
-- `ed.Workspace` is the public top-level facade.
-- `ed.Project` is removed unless explicitly approved as an alias.
-- the public project-information category is `workspace.project`.
-- project identity is exposed as `workspace.project.id`.
-- the saved directory path is exposed as `workspace.path`.
-- the public rendering category is `workspace.rendering`.
-- saved singleton configuration lives in `workspace.cif`.
-- `workspace.cif` uses `_project.*`, `_rendering.*`, and
-  `_verbosity.level` tags.
-- workspace verbosity is owned by a registered `Verbosity` category
-  alongside `Rendering`.
-- `ProjectInfo.path` is removed; the saved directory path is exposed
-  only as `workspace.path`.
-- no `_meta.*` tags are introduced for project information.
-- tutorials and accepted ADRs use `Workspace`.
diff --git a/docs/dev/benchmarking/20260519-103251_darwin-arm64_py314_tutorial-benchmarks.csv b/docs/dev/benchmarking/20260519-103251_darwin-arm64_py314_tutorial-benchmarks.csv
new file mode 100644
index 000000000..edb8e3f63
--- /dev/null
+++ b/docs/dev/benchmarking/20260519-103251_darwin-arm64_py314_tutorial-benchmarks.csv
@@ -0,0 +1,24 @@
+tutorial_name,elapsed_seconds,status,return_code
+ed-1.py,13.789,ok,0
+ed-10.py,39.098,ok,0
+ed-11.py,10.343,ok,0
+ed-12.py,8.331,ok,0
+ed-13.py,21.745,ok,0
+ed-14.py,6.158,ok,0
+ed-15.py,240.151,ok,0
+ed-16.py,58.481,ok,0
+ed-17.py,152.214,ok,0
+ed-18.py,6.114,ok,0
+ed-2.py,18.322,ok,0
+ed-20.py,36.422,ok,0
+ed-21.py,197.610,ok,0
+ed-22.py,194.351,ok,0
+ed-23.py,6.060,ok,0
+ed-24.py,4.749,ok,0
+ed-3.py,19.050,ok,0
+ed-4.py,4.480,ok,0
+ed-5.py,36.605,ok,0
+ed-6.py,61.846,ok,0
+ed-7.py,115.147,ok,0
+ed-8.py,101.442,ok,0
+ed-9.py,9.214,ok,0
diff --git a/docs/dev/benchmarking/20260519-103500_darwin-arm64_py314_tutorial-benchmarks.csv b/docs/dev/benchmarking/20260519-103500_darwin-arm64_py314_tutorial-benchmarks.csv
new file mode 100644
index 000000000..608944cb5
--- /dev/null
+++ b/docs/dev/benchmarking/20260519-103500_darwin-arm64_py314_tutorial-benchmarks.csv
@@ -0,0 +1,24 @@
+tutorial_name,elapsed_seconds,status,return_code
+ed-1.py,13.979,ok,0
+ed-10.py,38.764,ok,0
+ed-11.py,10.606,ok,0
+ed-12.py,9.044,ok,0
+ed-13.py,23.157,ok,0
+ed-14.py,6.585,ok,0
+ed-15.py,258.188,ok,0
+ed-16.py,60.097,ok,0
+ed-17.py,69.418,ok,0
+ed-18.py,6.181,ok,0
+ed-2.py,18.844,ok,0
+ed-20.py,39.157,ok,0
+ed-21.py,96.730,ok,0
+ed-22.py,73.480,ok,0
+ed-23.py,5.984,ok,0
+ed-24.py,4.942,ok,0
+ed-3.py,20.782,ok,0
+ed-4.py,5.780,ok,0
+ed-5.py,37.716,ok,0
+ed-6.py,66.911,ok,0
+ed-7.py,119.645,ok,0
+ed-8.py,103.887,ok,0
+ed-9.py,8.891,ok,0
diff --git a/docs/dev/benchmarking/20260519-121524_darwin-arm64_py314_tutorial-benchmarks.csv b/docs/dev/benchmarking/20260519-121524_darwin-arm64_py314_tutorial-benchmarks.csv
new file mode 100644
index 000000000..ff8eda19a
--- /dev/null
+++ b/docs/dev/benchmarking/20260519-121524_darwin-arm64_py314_tutorial-benchmarks.csv
@@ -0,0 +1,24 @@
+tutorial_name,elapsed_seconds,status,return_code
+ed-1.py,15.557,ok,0
+ed-10.py,40.860,ok,0
+ed-11.py,10.823,ok,0
+ed-12.py,8.861,ok,0
+ed-13.py,24.128,ok,0
+ed-14.py,6.722,ok,0
+ed-15.py,28.243,ok,0
+ed-16.py,59.218,ok,0
+ed-17.py,70.816,ok,0
+ed-18.py,6.944,ok,0
+ed-2.py,20.385,ok,0
+ed-20.py,39.513,ok,0
+ed-21.py,96.953,ok,0
+ed-22.py,75.390,ok,0
+ed-23.py,6.115,ok,0
+ed-24.py,5.159,ok,0
+ed-3.py,34.082,ok,0
+ed-4.py,8.215,ok,0
+ed-5.py,61.949,ok,0
+ed-6.py,83.857,ok,0
+ed-7.py,120.332,ok,0
+ed-8.py,103.831,ok,0
+ed-9.py,9.270,ok,0
diff --git a/docs/dev/issues/open.md b/docs/dev/issues/open.md
index 6c96f87ea..04d97ab85 100644
--- a/docs/dev/issues/open.md
+++ b/docs/dev/issues/open.md
@@ -1557,26 +1557,35 @@ threaded because each step's output is the next step's input.
 
 ---
 
-## 95. 🟡 Re-Enable DREAM Multiprocessing in CLI Workflows
+## 95. 🟡 Re-Enable DREAM Multiprocessing in Direct Python Scripts
 
-**Type:** Performance / CLI robustness
+**Type:** Performance / Script runtime
 
-On macOS and other spawn-based platforms, running Bayesian scripts via
-direct CLI entry points such as `python script.py` can fail during BUMPS
+On macOS and other spawn-based platforms, direct Bayesian tutorial
+execution via `python script.py` or wrappers such as
+`pixi run tutorial docs/docs/tutorials/ed-21.py` can fail during BUMPS
 `MPMapper` startup because worker processes re-import `__main__` and
 re-execute top-level tutorial code. The current defensive workaround is
 to fall back to serial execution for these direct-script entry points,
-which avoids the crash but disables DREAM multiprocessing in terminal
-workflows.
+which avoids the crash but disables DREAM multiprocessing and causes a
+large performance drop.
+
+Observed behavior for `ed-21` today:
+
+- Jupyter execution and `easydiffraction PROJECT_DIR fit` both appear to
+  use working parallel DREAM and complete `361/361` in about 40 seconds.
+- Direct Python-script execution of the same tutorial runs `361/361` in
+  about 220 seconds, consistent with the serial fallback path.
 
 **Possible solution:** keep the existing tracker-state cleanup before
 pickling and mapper startup, but replace the blanket serial fallback
 with an EasyDiffraction-controlled multiprocessing context policy. For
-direct CLI script entry points, prefer a `fork` context when available
-so workers do not re-import the tutorial top level. Keep the existing
-behavior for import-safe module entry points and for platforms where
-`fork` is unavailable. Document the tradeoff clearly because `fork` on
-macOS is less conservative than `spawn`.
+direct Python script entry points, prefer a `fork` context when
+available so workers do not re-import the tutorial top level. Keep the
+existing behavior for import-safe module entry points such as
+`easydiffraction PROJECT_DIR fit` and for platforms where `fork` is
+unavailable. Document the tradeoff clearly because `fork` on macOS is
+less conservative than `spawn`.
 
 **Depends on:** related to issue 89, but independent.
 
@@ -1688,90 +1697,90 @@ sampler progress displays — any fix should keep their visuals consistent
 
 ## Summary
 
-| #   | Issue                                            | Severity | Type                         |
-| --- | ------------------------------------------------ | -------- | ---------------------------- |
-| 3   | Rebuild joint-fit weights                        | 🟡 Med   | Fragility                    |
-| 5   | `Analysis` as `DatablockItem`                    | 🟡 Med   | Consistency                  |
-| 8   | Explicit `create()` signatures                   | 🟡 Med   | API safety                   |
-| 9   | Future enum extensions                           | 🟢 Low   | Design                       |
-| 10  | Unify update orchestration                       | 🟢 Low   | Maintainability              |
-| 11  | Document `_update` contract                      | 🟢 Low   | Maintainability              |
-| 13  | Suppress redundant dirty-flag sets               | 🟢 Low   | Performance                  |
-| 14  | Finer-grained change tracking                    | 🟢 Low   | Performance                  |
-| 15  | Validate joint-fit weights                       | 🟡 Med   | Correctness                  |
-| 17  | Use PDF-specific CIF names                       | 🟢 Low   | Naming                       |
-| 18  | Move CIF v2→v1 conversion out of calculator      | 🟢 Low   | Maintainability              |
-| 19  | Debug-mode logging for calculator imports        | 🟢 Low   | Diagnostics                  |
-| 20  | Redirect/suppress CrysPy stderr                  | 🟢 Low   | UX                           |
-| 21  | Clarify CrysPy TOF background CIF tags           | 🟡 Med   | Correctness                  |
-| 22  | Check SC instrument mapping in CrysPy            | 🟢 Low   | Correctness                  |
-| 23  | Investigate PyCrysFML pattern length discrepancy | 🟢 Low   | Correctness                  |
-| 24  | Process defaults on experiment creation          | 🟢 Low   | Design                       |
-| 25  | Refactor data `_update` methods                  | 🟡 Med   | Maintainability              |
-| 26  | Clarify `dtype` usage in data arrays             | 🟢 Low   | Cleanup                      |
-| 27  | Handle zero uncertainty in Bragg PD              | 🟢 Low   | Correctness                  |
-| 28  | Clarify Bragg PD data collection description     | 🟢 Low   | Cleanup                      |
-| 29  | Standardise CIF ID validator pattern             | 🟡 Med   | Consistency                  |
-| 30  | Make `refinement_status` default an Enum         | 🟢 Low   | Design                       |
-| 31  | Rename PD data point mixins                      | 🟢 Low   | Naming                       |
-| 32  | Move common methods to `DatablockCollection`     | 🟡 Med   | Maintainability              |
-| 33  | Make `_update_categories` abstract               | 🟡 Med   | Design                       |
-| 34  | Auto-extract `PeakProfileTypeEnum`               | 🟢 Low   | Design                       |
-| 35  | Rename `BeamModeEnum` members to CWL/TOF         | 🟢 Low   | Naming                       |
-| 36  | Common `EnumBase` class                          | 🟢 Low   | Design                       |
-| 37  | Rename experiment `.type` property               | 🟢 Low   | Naming                       |
-| 38  | Fix `@typechecked`/gemmi in factories            | 🟡 Med   | Bug                          |
-| 39  | Improve `_update_priority` handling              | 🟢 Low   | Design                       |
-| 40  | Reset `.user_constrained` to `False`             | 🟢 Low   | Feature                      |
-| 41  | Check `_mark_dirty` in `_set_value`              | 🟢 Low   | Cleanup                      |
-| 42  | MkDocs type unpacking in validation              | 🟢 Low   | Docs                         |
-| 43  | Fix summary display inconsistencies              | 🟢 Low   | UX                           |
-| 44  | Merge parameter record construction              | 🟢 Low   | Cleanup                      |
-| 45  | Decide alias/constraint descriptor default       | 🟢 Low   | Design                       |
-| 46  | Improve `JointFitItem` descriptions              | 🟢 Low   | Naming                       |
-| 47  | Improve error handling in crystallography        | 🟢 Low   | Diagnostics                  |
-| 48  | Fix CrysPy TOF instrument default                | 🟢 Low   | Bug workaround               |
-| 49  | Automate space group CIF name variants           | 🟢 Low   | Maintainability              |
-| 50  | Clarify `Cell._update` minimizer param           | 🟢 Low   | Cleanup                      |
-| 51  | Access space group for Wyckoff letters           | 🟢 Low   | Design                       |
-| 52  | Rename line-segment `y` to `intensity`           | 🟢 Low   | Naming                       |
-| 53  | Move `show()` to `CategoryCollection`            | 🟢 Low   | Maintainability              |
-| 54  | Add `point_id` to excluded regions               | 🟢 Low   | Completeness                 |
-| 55  | Fix Jupyter scroll disabling for MkDocs          | 🟢 Low   | Docs / UX                    |
-| 56  | Make ASCII plot width configurable               | 🟢 Low   | UX                           |
-| 57  | Clean up CIF deserialisation helpers             | 🟢 Low   | Maintainability              |
-| 58  | Move `ProjectInfo` CIF methods to `serialize`    | 🟢 Low   | Maintainability              |
-| 59  | Add CIF name validation in parse                 | 🟢 Low   | Robustness                   |
-| 60  | Unify `mkdir` usage                              | 🟢 Low   | Cleanup                      |
-| 61  | Clarify logger default reaction mode             | 🟢 Low   | Design                       |
-| 62  | Complete `render_table` → `TableRenderer`        | 🟢 Low   | Cleanup                      |
-| 63  | Fix calculator `calculate_pattern` signature     | 🟢 Low   | Design                       |
-| 64  | Check unused-if-loading-from-CIF code            | 🟢 Low   | Cleanup                      |
-| 65  | Replace all bare `print()` with logging          | 🟡 Med   | Code quality                 |
-| 66  | Error-handling strategy: `log.error` vs `raise`  | 🟡 Med   | Design                       |
-| 67  | Custom validation for params and category types  | 🟡 Med   | Design                       |
-| 68  | `@typechecked` on all public methods?            | 🟢 Low   | Design                       |
-| 69  | Shorter public API names via `__init__`          | 🟢 Low   | API ergonomics               |
-| 70  | Standardise class member ordering + headers      | 🟡 Med   | Code style                   |
-| 71  | `_update_priority` reference table               | 🟢 Low   | Documentation                |
-| 72  | Warn on all switchable-category type changes     | 🟡 Med   | UX                           |
-| 73  | Unify setter parameter naming                    | 🟢 Low   | Code style                   |
-| 74  | Sync property type hints + custom lint rules     | 🟡 Med   | Tooling                      |
-| 75  | `show_supported_calculators()` on Analysis       | 🟢 Low   | API completeness             |
-| 76  | Consistent `_type` suffix in switchable APIs     | 🟡 Med   | Naming                       |
-| 79  | Verify analysis CIF serialisation completeness   | 🟢 Low   | Correctness                  |
-| 80  | Resolve `Any` vs `object` annotation policy      | 🟢 Low   | Code style                   |
-| 81  | Enforce docstrings on all public methods         | 🟡 Med   | Code quality                 |
-| 82  | Document `param-docstring-fix` workflow          | 🟢 Low   | Documentation                |
-| 83  | Remove redundant parameter listing               | 🟢 Low   | Cleanup                      |
-| 84  | Serialise `None` as `.` in CIF output            | 🟡 Med   | Correctness                  |
-| 85  | Retain per-experiment fitted params for plotting | 🟡 Med   | Correctness                  |
-| 86  | Auto-resolve `plot_param` x-axis + add units     | 🟢 Low   | UX                           |
-| 87  | Redesign tutorial grouping/categorisation        | 🟢 Low   | Documentation                |
-| 88  | Fix Dataset 26 description (47 not 57)           | 🟢 Low   | Data                         |
-| 89  | Parallel independent fits for single mode        | 🟡 Med   | Performance                  |
-| 95  | Re-enable DREAM multiprocessing in CLI workflows | 🟡 Med   | Performance / CLI robustness |
-| 90  | Show experiment number during sequential fitting | 🟢 Low   | UX                           |
-| 91  | Disable TODO checks in CodeFactor PRs            | 🟢 Low   | CI / Tooling                 |
-| 92  | Make `save()` respect verbosity                  | 🟢 Low   | UX                           |
-| 93  | Eliminate flicker in live progress tables        | 🟡 Med   | UX                           |
+| #   | Issue                                             | Severity | Type                         |
+| --- | ------------------------------------------------- | -------- | ---------------------------- |
+| 3   | Rebuild joint-fit weights                         | 🟡 Med   | Fragility                    |
+| 5   | `Analysis` as `DatablockItem`                     | 🟡 Med   | Consistency                  |
+| 8   | Explicit `create()` signatures                    | 🟡 Med   | API safety                   |
+| 9   | Future enum extensions                            | 🟢 Low   | Design                       |
+| 10  | Unify update orchestration                        | 🟢 Low   | Maintainability              |
+| 11  | Document `_update` contract                       | 🟢 Low   | Maintainability              |
+| 13  | Suppress redundant dirty-flag sets                | 🟢 Low   | Performance                  |
+| 14  | Finer-grained change tracking                     | 🟢 Low   | Performance                  |
+| 15  | Validate joint-fit weights                        | 🟡 Med   | Correctness                  |
+| 17  | Use PDF-specific CIF names                        | 🟢 Low   | Naming                       |
+| 18  | Move CIF v2→v1 conversion out of calculator       | 🟢 Low   | Maintainability              |
+| 19  | Debug-mode logging for calculator imports         | 🟢 Low   | Diagnostics                  |
+| 20  | Redirect/suppress CrysPy stderr                   | 🟢 Low   | UX                           |
+| 21  | Clarify CrysPy TOF background CIF tags            | 🟡 Med   | Correctness                  |
+| 22  | Check SC instrument mapping in CrysPy             | 🟢 Low   | Correctness                  |
+| 23  | Investigate PyCrysFML pattern length discrepancy  | 🟢 Low   | Correctness                  |
+| 24  | Process defaults on experiment creation           | 🟢 Low   | Design                       |
+| 25  | Refactor data `_update` methods                   | 🟡 Med   | Maintainability              |
+| 26  | Clarify `dtype` usage in data arrays              | 🟢 Low   | Cleanup                      |
+| 27  | Handle zero uncertainty in Bragg PD               | 🟢 Low   | Correctness                  |
+| 28  | Clarify Bragg PD data collection description      | 🟢 Low   | Cleanup                      |
+| 29  | Standardise CIF ID validator pattern              | 🟡 Med   | Consistency                  |
+| 30  | Make `refinement_status` default an Enum          | 🟢 Low   | Design                       |
+| 31  | Rename PD data point mixins                       | 🟢 Low   | Naming                       |
+| 32  | Move common methods to `DatablockCollection`      | 🟡 Med   | Maintainability              |
+| 33  | Make `_update_categories` abstract                | 🟡 Med   | Design                       |
+| 34  | Auto-extract `PeakProfileTypeEnum`                | 🟢 Low   | Design                       |
+| 35  | Rename `BeamModeEnum` members to CWL/TOF          | 🟢 Low   | Naming                       |
+| 36  | Common `EnumBase` class                           | 🟢 Low   | Design                       |
+| 37  | Rename experiment `.type` property                | 🟢 Low   | Naming                       |
+| 38  | Fix `@typechecked`/gemmi in factories             | 🟡 Med   | Bug                          |
+| 39  | Improve `_update_priority` handling               | 🟢 Low   | Design                       |
+| 40  | Reset `.user_constrained` to `False`              | 🟢 Low   | Feature                      |
+| 41  | Check `_mark_dirty` in `_set_value`               | 🟢 Low   | Cleanup                      |
+| 42  | MkDocs type unpacking in validation               | 🟢 Low   | Docs                         |
+| 43  | Fix summary display inconsistencies               | 🟢 Low   | UX                           |
+| 44  | Merge parameter record construction               | 🟢 Low   | Cleanup                      |
+| 45  | Decide alias/constraint descriptor default        | 🟢 Low   | Design                       |
+| 46  | Improve `JointFitItem` descriptions               | 🟢 Low   | Naming                       |
+| 47  | Improve error handling in crystallography         | 🟢 Low   | Diagnostics                  |
+| 48  | Fix CrysPy TOF instrument default                 | 🟢 Low   | Bug workaround               |
+| 49  | Automate space group CIF name variants            | 🟢 Low   | Maintainability              |
+| 50  | Clarify `Cell._update` minimizer param            | 🟢 Low   | Cleanup                      |
+| 51  | Access space group for Wyckoff letters            | 🟢 Low   | Design                       |
+| 52  | Rename line-segment `y` to `intensity`            | 🟢 Low   | Naming                       |
+| 53  | Move `show()` to `CategoryCollection`             | 🟢 Low   | Maintainability              |
+| 54  | Add `point_id` to excluded regions                | 🟢 Low   | Completeness                 |
+| 55  | Fix Jupyter scroll disabling for MkDocs           | 🟢 Low   | Docs / UX                    |
+| 56  | Make ASCII plot width configurable                | 🟢 Low   | UX                           |
+| 57  | Clean up CIF deserialisation helpers              | 🟢 Low   | Maintainability              |
+| 58  | Move `ProjectInfo` CIF methods to `serialize`     | 🟢 Low   | Maintainability              |
+| 59  | Add CIF name validation in parse                  | 🟢 Low   | Robustness                   |
+| 60  | Unify `mkdir` usage                               | 🟢 Low   | Cleanup                      |
+| 61  | Clarify logger default reaction mode              | 🟢 Low   | Design                       |
+| 62  | Complete `render_table` → `TableRenderer`         | 🟢 Low   | Cleanup                      |
+| 63  | Fix calculator `calculate_pattern` signature      | 🟢 Low   | Design                       |
+| 64  | Check unused-if-loading-from-CIF code             | 🟢 Low   | Cleanup                      |
+| 65  | Replace all bare `print()` with logging           | 🟡 Med   | Code quality                 |
+| 66  | Error-handling strategy: `log.error` vs `raise`   | 🟡 Med   | Design                       |
+| 67  | Custom validation for params and category types   | 🟡 Med   | Design                       |
+| 68  | `@typechecked` on all public methods?             | 🟢 Low   | Design                       |
+| 69  | Shorter public API names via `__init__`           | 🟢 Low   | API ergonomics               |
+| 70  | Standardise class member ordering + headers       | 🟡 Med   | Code style                   |
+| 71  | `_update_priority` reference table                | 🟢 Low   | Documentation                |
+| 72  | Warn on all switchable-category type changes      | 🟡 Med   | UX                           |
+| 73  | Unify setter parameter naming                     | 🟢 Low   | Code style                   |
+| 74  | Sync property type hints + custom lint rules      | 🟡 Med   | Tooling                      |
+| 75  | `show_supported_calculators()` on Analysis        | 🟢 Low   | API completeness             |
+| 76  | Consistent `_type` suffix in switchable APIs      | 🟡 Med   | Naming                       |
+| 79  | Verify analysis CIF serialisation completeness    | 🟢 Low   | Correctness                  |
+| 80  | Resolve `Any` vs `object` annotation policy       | 🟢 Low   | Code style                   |
+| 81  | Enforce docstrings on all public methods          | 🟡 Med   | Code quality                 |
+| 82  | Document `param-docstring-fix` workflow           | 🟢 Low   | Documentation                |
+| 83  | Remove redundant parameter listing                | 🟢 Low   | Cleanup                      |
+| 84  | Serialise `None` as `.` in CIF output             | 🟡 Med   | Correctness                  |
+| 85  | Retain per-experiment fitted params for plotting  | 🟡 Med   | Correctness                  |
+| 86  | Auto-resolve `plot_param` x-axis + add units      | 🟢 Low   | UX                           |
+| 87  | Redesign tutorial grouping/categorisation         | 🟢 Low   | Documentation                |
+| 88  | Fix Dataset 26 description (47 not 57)            | 🟢 Low   | Data                         |
+| 89  | Parallel independent fits for single mode         | 🟡 Med   | Performance                  |
+| 95  | Re-enable DREAM multiprocessing in direct scripts | 🟡 Med   | Performance / Script runtime |
+| 90  | Show experiment number during sequential fitting  | 🟢 Low   | UX                           |
+| 91  | Disable TODO checks in CodeFactor PRs             | 🟢 Low   | CI / Tooling                 |
+| 92  | Make `save()` respect verbosity                   | 🟢 Low   | UX                           |
+| 93  | Eliminate flicker in live progress tables         | 🟡 Med   | UX                           |
diff --git a/docs/dev/package-structure/full.md b/docs/dev/package-structure/full.md
index 720505baa..677f5851d 100644
--- a/docs/dev/package-structure/full.md
+++ b/docs/dev/package-structure/full.md
@@ -24,6 +24,54 @@
 │   │   │   │   └── 🏷️ class Aliases
 │   │   │   └── 📄 factory.py
 │   │   │       └── 🏷️ class AliasesFactory
+│   │   ├── 📁 bayesian_convergence
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class BayesianConvergence
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianConvergenceFactory
+│   │   ├── 📁 bayesian_distribution_caches
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class BayesianDistributionCacheItem
+│   │   │   │   └── 🏷️ class BayesianDistributionCaches
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianDistributionCachesFactory
+│   │   ├── 📁 bayesian_pair_caches
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class BayesianPairCachePaths
+│   │   │   │   ├── 🏷️ class BayesianPairCacheItem
+│   │   │   │   └── 🏷️ class BayesianPairCaches
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianPairCachesFactory
+│   │   ├── 📁 bayesian_parameter_posteriors
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class BayesianParameterPosteriorItem
+│   │   │   │   └── 🏷️ class BayesianParameterPosteriors
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianParameterPosteriorsFactory
+│   │   ├── 📁 bayesian_predictive_datasets
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class BayesianPredictiveDatasetPaths
+│   │   │   │   ├── 🏷️ class BayesianPredictiveDatasetItem
+│   │   │   │   └── 🏷️ class BayesianPredictiveDatasets
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianPredictiveDatasetsFactory
+│   │   ├── 📁 bayesian_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class BayesianResult
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianResultFactory
+│   │   ├── 📁 bayesian_sampler
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class BayesianSampler
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class BayesianSamplerFactory
 │   │   ├── 📁 constraints
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
@@ -31,6 +79,33 @@
 │   │   │   │   └── 🏷️ class Constraints
 │   │   │   └── 📄 factory.py
 │   │   │       └── 🏷️ class ConstraintsFactory
+│   │   ├── 📁 deterministic_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class DeterministicResult
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class DeterministicResultFactory
+│   │   ├── 📁 fit_parameter_correlations
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class FitParameterCorrelationItem
+│   │   │   │   └── 🏷️ class FitParameterCorrelations
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class FitParameterCorrelationsFactory
+│   │   ├── 📁 fit_parameters
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   ├── 🏷️ class FitParameterItem
+│   │   │   │   └── 🏷️ class FitParameters
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class FitParametersFactory
+│   │   ├── 📁 fit_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class FitResult
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class FitResultFactory
+│   │   ├── 📁 fit_state
 │   │   ├── 📁 fitting
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
@@ -76,7 +151,9 @@
 │   │   ├── 📄 base.py
 │   │   │   └── 🏷️ class MinimizerBase
 │   │   ├── 📄 bumps.py
+│   │   │   ├── 🏷️ class _BumpsEvaluationLimitError
 │   │   │   ├── 🏷️ class _EasyDiffractionFitness
+│   │   │   ├── 🏷️ class _BumpsProgressMonitor
 │   │   │   └── 🏷️ class BumpsMinimizer
 │   │   ├── 📄 bumps_amoeba.py
 │   │   │   └── 🏷️ class BumpsAmoebaMinimizer
@@ -105,9 +182,13 @@
 │   ├── 📄 __init__.py
 │   ├── 📄 analysis.py
 │   │   ├── 🏷️ class AnalysisDisplay
+│   │   ├── 🏷️ class _AnalysisOwnerAccessorsMixin
+│   │   ├── 🏷️ class _AnalysisPersistedCategoryAccessorsMixin
 │   │   └── 🏷️ class Analysis
 │   ├── 📄 enums.py
-│   │   └── 🏷️ class FitModeEnum
+│   │   ├── 🏷️ class FitModeEnum
+│   │   ├── 🏷️ class FitResultKindEnum
+│   │   └── 🏷️ class FitCorrelationSourceEnum
 │   ├── 📄 fitting.py
 │   │   └── 🏷️ class Fitter
 │   └── 📄 sequential.py
@@ -159,10 +240,12 @@
 │       ├── 🏷️ class GenericStringDescriptor
 │       ├── 🏷️ class GenericBoolDescriptor
 │       ├── 🏷️ class GenericNumericDescriptor
+│       ├── 🏷️ class GenericIntegerDescriptor
 │       ├── 🏷️ class GenericParameter
 │       ├── 🏷️ class StringDescriptor
 │       ├── 🏷️ class BoolDescriptor
 │       ├── 🏷️ class NumericDescriptor
+│       ├── 🏷️ class IntegerDescriptor
 │       └── 🏷️ class Parameter
 ├── 📁 crystallography
 │   ├── 📄 __init__.py
@@ -424,7 +507,8 @@
 │   │   ├── 📄 parse.py
 │   │   └── 📄 serialize.py
 │   ├── 📄 __init__.py
-│   └── 📄 ascii.py
+│   ├── 📄 ascii.py
+│   └── 📄 results_sidecar.py
 ├── 📁 project
 │   ├── 📁 categories
 │   │   ├── 📁 info
@@ -439,6 +523,12 @@
 │   │   │   │   └── 🏷️ class Rendering
 │   │   │   └── 📄 factory.py
 │   │   │       └── 🏷️ class RenderingFactory
+│   │   ├── 📁 verbosity
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   │   └── 🏷️ class Verbosity
+│   │   │   └── 📄 factory.py
+│   │   │       └── 🏷️ class VerbosityFactory
 │   │   └── 📄 __init__.py
 │   ├── 📄 __init__.py
 │   ├── 📄 display.py
diff --git a/docs/dev/package-structure/short.md b/docs/dev/package-structure/short.md
index da42f7a57..367a36a51 100644
--- a/docs/dev/package-structure/short.md
+++ b/docs/dev/package-structure/short.md
@@ -15,10 +15,55 @@
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_convergence
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_distribution_caches
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_pair_caches
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_parameter_posteriors
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_predictive_datasets
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 bayesian_sampler
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
 │   │   ├── 📁 constraints
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
+│   │   ├── 📁 deterministic_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 fit_parameter_correlations
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 fit_parameters
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 fit_result
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
+│   │   ├── 📁 fit_state
 │   │   ├── 📁 fitting
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
@@ -205,7 +250,8 @@
 │   │   ├── 📄 parse.py
 │   │   └── 📄 serialize.py
 │   ├── 📄 __init__.py
-│   └── 📄 ascii.py
+│   ├── 📄 ascii.py
+│   └── 📄 results_sidecar.py
 ├── 📁 project
 │   ├── 📁 categories
 │   │   ├── 📁 info
@@ -216,6 +262,10 @@
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
 │   │   │   └── 📄 factory.py
+│   │   ├── 📁 verbosity
+│   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 default.py
+│   │   │   └── 📄 factory.py
 │   │   └── 📄 __init__.py
 │   ├── 📄 __init__.py
 │   ├── 📄 display.py
diff --git a/docs/dev/plans/loop-category-key-identity.md b/docs/dev/plans/loop-category-key-identity.md
deleted file mode 100644
index 9917d1bae..000000000
--- a/docs/dev/plans/loop-category-key-identity.md
+++ /dev/null
@@ -1,620 +0,0 @@
-# Loop Category Key Identity Implementation Plan
-
-## Status
-
-Workflow instructions:
-
-```text
-.github/copilot-instructions.md
-```
-
-Related ADR suggestion:
-
-```text
-docs/dev/adrs/suggestions/loop-category-key-identity.md
-```
-
-This plan implements two related changes:
-
-1. Move category identity declarations from per-instance assignments to
-   class-level declarations.
-2. Add an explicit persisted `id` field to the constraints loop.
-
-Status checklist. Mark `[x]` only while implementing:
-
-```text
-Phase 1 - Implementation
-[x] Add class-level identity declarations to CategoryItem.
-[x] Teach Identity to resolve declared category entry names.
-[x] Rebuild collection indexes after CIF loop loading.
-[x] Add _category_code to all current CategoryItem subclasses.
-[x] Add _category_entry_name to all current loop CategoryItem subclasses.
-[x] Remove direct self._identity.category_code assignments.
-[x] Remove direct self._identity.category_entry_name lambda assignments.
-[x] Add Constraint.id descriptor serialized as _constraint.id.
-[x] Change constraints collection keys from lhs_alias to id.
-[x] Preserve default constraints.create(expression=...) behavior by using lhs_alias as the default id.
-[x] Add backward-compatible loading for old CIF loops without _constraint.id.
-[x] Update constraints display.
-[x] Update loop-category-key-identity.md if implementation details differ from the ADR.
-[ ] Phase 1 review gate: present diff for approval.
-
-Phase 2 - Verification
-[x] Add tests for the base declarative identity behavior.
-[x] Add parametrized tests for current loop category identity declarations.
-[x] Update constraints tests.
-[x] Update existing round-trip tests that compare constraints CIF.
-[x] Run formatting.
-[x] Run targeted unit tests.
-[x] Run broader checks.
-```
-
-## Commit Discipline
-
-When an AI agent follows this plan, every completed Phase 1
-implementation step must be staged with explicit paths and committed
-locally before moving to the next implementation step or to the Phase 1
-review gate.
-
-Follow the **Commits** section of `.github/copilot-instructions.md`.
-
-Rules:
-
-- One commit per implementation step.
-- Keep each commit atomic and single-purpose.
-- Stage explicit paths only. Do not use `git add .`.
-- Do not stage unrelated user changes.
-- Do not stage generated artifacts unless the user explicitly asks.
-- If a serious uncovered design issue appears, stop and ask before
-  continuing.
-
-Suggested branch:
-
-```text
-feature/loop-category-key-identity
-```
-
-Suggested commit messages:
-
-```text
-Add declarative category identity resolution
-Declare category identities on current items
-Persist explicit constraint identifiers
-Update ADR for declarative category identity
-Add declarative category identity tests
-```
-
-## Goal
-
-Replace repeated constructor code like this:
-
-```python
-self._identity.category_code = 'atom_site'
-self._identity.category_entry_name = lambda: str(self.label.value)
-```
-
-with class-level declarations:
-
-```python
-class AtomSite(CategoryItem):
-    _category_code = 'atom_site'
-    _category_entry_name = 'label'
-```
-
-The name `_category_entry_name` is intentionally kept because this is
-the preferred project terminology. In this plan it means "the name of
-the item attribute used to resolve the entry name". The resolved entry
-value is still obtained through:
-
-```python
-item._identity.category_entry_name
-```
-
-For example, `AtomSite._category_entry_name == 'label'`, while
-`atom_site._identity.category_entry_name == 'Ba1'`.
-
-## Non-Goals
-
-Do not change these in this migration:
-
-- Do not rename `Identity.category_entry_name`.
-- Do not rename `CategoryCollection`.
-- Do not change public collection access syntax.
-- Do not change CIF tags except adding `_constraint.id`.
-- Do not rename `label` to `id` for atom sites or aliases.
-- Do not make `phase_id` the row key for powder reflection loops.
-
-## Design
-
-This plan intentionally uses a narrow metadata lookup based on
-`_category_entry_name`. That is an allowed exception to the general "no
-string-based dispatch" rule because the attribute name is a class-level
-declaration, not user input, and resolution is centralized in
-`CategoryItem`.
-
-### CategoryItem Declarations
-
-Add these class attributes to `CategoryItem` in
-`src/easydiffraction/core/category.py`:
-
-```python
-class CategoryItem(GuardedBase):
-    _category_code: str | None = None
-    _category_entry_name: str | None = None
-```
-
-Update `CategoryItem.__init__()` so it assigns `_category_code` once:
-
-```python
-def __init__(self) -> None:
-    super().__init__()
-    if self._category_code is not None:
-        self._identity.category_code = self._category_code
-```
-
-Do not try to resolve `_category_entry_name` in `CategoryItem.__init__`.
-Many item classes create descriptors after `super().__init__()` returns,
-and some mixin-based classes run `CategoryItem.__init__()` before their
-descriptors are created.
-
-Add a resolver method to `CategoryItem`:
-
-```python
-def _resolve_category_entry_name(self) -> str | None:
-    attr_name = self._category_entry_name
-    if attr_name is None:
-        return None
-
-    value = getattr(self, attr_name)
-    if isinstance(value, GenericDescriptorBase):
-        value = value.value
-    return str(value)
-```
-
-Import `GenericDescriptorBase` from `easydiffraction.core.variable` in
-`category.py` if it is not already in scope.
-
-### Identity Resolution
-
-Update `Identity._resolve_up()` in
-`src/easydiffraction/core/identity.py` so that category entries can be
-resolved from the owning object before walking to the parent.
-
-Add this logic after checking direct callable/string values and before
-climbing to the parent:
-
-```python
-if attr == 'category_entry':
-    resolver = getattr(self._owner, '_resolve_category_entry_name', None)
-    if callable(resolver):
-        resolved = resolver()
-        if resolved is not None:
-            return resolved
-```
-
-Keep the existing `category_entry_name` setter. It remains useful as an
-escape hatch and keeps old code compatible during migration.
-
-### Collection Index Rebuild After CIF Loading
-
-Update `category_collection_from_cif()` in
-`src/easydiffraction/io/cif/serialize.py`.
-
-Currently `_adopt_items()` rebuilds the index before loop values are
-loaded into each item. After declarative keys are resolved from
-descriptor values, the index must be rebuilt after parameters are
-loaded.
-
-After the row population loop, run any collection hook and rebuild:
-
-```python
-after_from_cif = getattr(self, '_after_from_cif', None)
-if callable(after_from_cif):
-    after_from_cif()
-
-self._rebuild_index()
-```
-
-The hook is needed for constraints to backfill missing ids from old CIF
-files.
-
-## Category Migration Table
-
-Add `_category_code` to each listed class. Add `_category_entry_name`
-only when the class is a loop item and currently has a collection key.
-Then remove matching constructor assignments.
-
-| File                                                                                | Class                      | `_category_code`         | `_category_entry_name` | Notes                                        |
-| ----------------------------------------------------------------------------------- | -------------------------- | ------------------------ | ---------------------- | -------------------------------------------- |
-| `src/easydiffraction/project/categories/info/default.py`                            | `ProjectInfo`              | `project`                | none                   | Singleton category.                          |
-| `src/easydiffraction/project/categories/rendering/default.py`                       | `Rendering`                | `rendering`              | none                   | Singleton category.                          |
-| `src/easydiffraction/analysis/categories/fitting/default.py`                        | `Fitting`                  | `fitting`                | none                   | Singleton category.                          |
-| `src/easydiffraction/analysis/categories/sequential_fit/default.py`                 | `SequentialFit`            | `sequential_fit`         | none                   | Singleton category.                          |
-| `src/easydiffraction/analysis/categories/aliases/default.py`                        | `Alias`                    | `alias`                  | `label`                | Loop key stays `_alias.label`.               |
-| `src/easydiffraction/analysis/categories/constraints/default.py`                    | `Constraint`               | `constraint`             | `id`                   | Add the id descriptor first.                 |
-| `src/easydiffraction/analysis/categories/joint_fit/default.py`                      | `JointFitItem`             | `joint_fit`              | `experiment_id`        | Loop key stays `_joint_fit.experiment_id`.   |
-| `src/easydiffraction/analysis/categories/sequential_fit_extract/default.py`         | `SequentialFitExtractItem` | `sequential_fit_extract` | `id`                   | Loop key stays `_sequential_fit_extract.id`. |
-| `src/easydiffraction/datablocks/structure/categories/cell/default.py`               | `Cell`                     | `cell`                   | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/structure/categories/space_group/default.py`        | `SpaceGroup`               | `space_group`            | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/structure/categories/atom_sites/default.py`         | `AtomSite`                 | `atom_site`              | `label`                | Loop key stays `_atom_site.label`.           |
-| `src/easydiffraction/datablocks/structure/categories/atom_site_aniso/default.py`    | `AtomSiteAniso`            | `atom_site_aniso`        | `label`                | Loop key stays `_atom_site_aniso.label`.     |
-| `src/easydiffraction/datablocks/experiment/categories/experiment_type/default.py`   | `ExperimentType`           | `expt_type`              | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/experiment/categories/calculation/default.py`       | `Calculation`              | `calculation`            | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/experiment/categories/diffrn/default.py`            | `DefaultDiffrn`            | `diffrn`                 | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/experiment/categories/instrument/base.py`           | `InstrumentBase`           | `instrument`             | none                   | Subclasses inherit this code.                |
-| `src/easydiffraction/datablocks/experiment/categories/peak/base.py`                 | `PeakBase`                 | `peak`                   | none                   | Subclasses inherit this code.                |
-| `src/easydiffraction/datablocks/experiment/categories/extinction/becker_coppens.py` | `BeckerCoppensExtinction`  | `extinction`             | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/experiment/categories/linked_crystal/default.py`    | `LinkedCrystal`            | `linked_crystal`         | none                   | Singleton category.                          |
-| `src/easydiffraction/datablocks/experiment/categories/linked_phases/default.py`     | `LinkedPhase`              | `linked_phases`          | `id`                   | Loop key stays `_pd_phase_block.id`.         |
-| `src/easydiffraction/datablocks/experiment/categories/background/line_segment.py`   | `LineSegment`              | `background`             | `id`                   | Loop key stays `_pd_background.id`.          |
-| `src/easydiffraction/datablocks/experiment/categories/background/chebyshev.py`      | `PolynomialTerm`           | `background`             | `id`                   | Loop key stays `_pd_background.id`.          |
-| `src/easydiffraction/datablocks/experiment/categories/excluded_regions/default.py`  | `ExcludedRegion`           | `excluded_regions`       | `id`                   | Loop key stays `_excluded_region.id`.        |
-| `src/easydiffraction/datablocks/experiment/categories/refln/bragg_sc.py`            | `Refln`                    | `refln`                  | `id`                   | Powder reflection rows inherit this key.     |
-| `src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py`             | `PdCwlDataPoint`           | `pd_data`                | `point_id`             | CWL powder data row.                         |
-| `src/easydiffraction/datablocks/experiment/categories/data/bragg_pd.py`             | `PdTofDataPoint`           | `pd_data`                | `point_id`             | TOF powder data row.                         |
-| `src/easydiffraction/datablocks/experiment/categories/data/total_pd.py`             | `TotalDataPoint`           | `total_data`             | `point_id`             | Total-scattering data row.                   |
-
-Do not add `_category_code` or `_category_entry_name` to
-`PowderReflnBase`, `PowderCwlRefln`, or `PowderTofRefln`; they inherit
-the `refln` identity declarations from `Refln`.
-
-## Constraints Migration
-
-### Target Shape
-
-`Constraint` should become:
-
-```python
-class Constraint(CategoryItem):
-    _category_code = 'constraint'
-    _category_entry_name = 'id'
-
-    def __init__(self) -> None:
-        super().__init__()
-
-        self._id = StringDescriptor(
-            name='id',
-            description='Identifier for this constraint.',
-            value_spec=AttributeSpec(
-                default='_',
-                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_]*$'),
-            ),
-            cif_handler=CifHandler(names=['_constraint.id']),
-        )
-        self._expression = StringDescriptor(...)
-```
-
-Define the public property:
-
-```python
-@property
-def id(self) -> StringDescriptor:
-    """Identifier for this constraint."""
-    return self._id
-
-@id.setter
-def id(self, value: str) -> None:
-    self._id.value = value
-```
-
-Keep `lhs_alias` and `rhs_expr` as derived read-only properties.
-
-### Create API
-
-Change `Constraints.create()` from:
-
-```python
-def create(self, *, expression: str) -> None:
-```
-
-to:
-
-```python
-def create(self, *, expression: str, id: str | None = None) -> None:
-```
-
-Implementation order:
-
-```python
-item = Constraint()
-item.expression = expression
-item.id = id if id is not None else item.lhs_alias
-self.add(item)
-self._enabled = True
-```
-
-This preserves the current default user experience:
-
-```python
-analysis.constraints.create(expression='biso_Ba = biso_La')
-analysis.constraints['biso_Ba']
-```
-
-It also allows explicit ids:
-
-```python
-analysis.constraints.create(
-    id='constraint_1',
-    expression='biso_Ba = biso_La',
-)
-analysis.constraints['constraint_1']
-```
-
-### Backward-Compatible CIF Loading
-
-Old CIF files only contain:
-
-```cif
-loop_
-_constraint.expression
-biso_Ba = biso_La
-```
-
-New CIF files should contain:
-
-```cif
-loop_
-_constraint.id
-_constraint.expression
-biso_Ba "biso_Ba = biso_La"
-```
-
-Add a hook on `Constraints`:
-
-```python
-def _after_from_cif(self) -> None:
-    for index, item in enumerate(self._items, start=1):
-        if item.id.value in {'', '_'}:
-            fallback = item.lhs_alias or f'constraint_{index}'
-            item.id = fallback
-```
-
-The generic `category_collection_from_cif()` hook described above must
-call this before rebuilding the index.
-
-### Constraints Display
-
-Update `Constraints.show()` to include the id:
-
-```text
-id | expression
-```
-
-Keep the existing empty warning behavior.
-
-## Required Code Searches
-
-After migration, these searches should return no category-item
-constructor assignments:
-
-```shell
-git grep -n -E "self\\._identity\\.category_code =" -- src/easydiffraction
-git grep -n -E "self\\._identity\\.category_entry_name =" -- src/easydiffraction
-git grep -n -E "category_entry_name = lambda" -- src/easydiffraction
-```
-
-The following reads are expected to remain:
-
-```shell
-git grep -n "_identity\\.category_entry_name" -- src/easydiffraction
-```
-
-Those reads are used by collections, display, reporting, and parameter
-unique names.
-
-Do not use `rg` in this plan; it is not available in every contributor
-environment. Use `git grep` for repository searches.
-
-## Tests To Add Or Update
-
-### Base Identity Tests
-
-Add or update tests under `tests/unit/easydiffraction/core/`.
-
-Test a fake category item:
-
-```python
-class FakeItem(CategoryItem):
-    _category_code = 'fake'
-    _category_entry_name = 'id'
-
-    def __init__(self):
-        super().__init__()
-        self._id = StringDescriptor(...)
-
-    @property
-    def id(self):
-        return self._id
-```
-
-Assert:
-
-```python
-item._identity.category_code == 'fake'
-item._identity.category_entry_name == item.id.value
-item.id.unique_name.endswith('.fake.<id>.id')
-```
-
-Also test that a singleton-like item with `_category_code` and no
-`_category_entry_name` resolves category code but returns no entry name.
-
-### Current Category Parametrized Tests
-
-Add a parametrized test that instantiates representative loop item
-classes and verifies category code plus entry:
-
-```text
-Alias -> alias, label
-JointFitItem -> joint_fit, experiment_id
-SequentialFitExtractItem -> sequential_fit_extract, id
-AtomSite -> atom_site, label
-AtomSiteAniso -> atom_site_aniso, label
-LinkedPhase -> linked_phases, id
-LineSegment -> background, id
-PolynomialTerm -> background, id
-ExcludedRegion -> excluded_regions, id
-Refln -> refln, id
-PdCwlDataPoint -> pd_data, point_id
-PdTofDataPoint -> pd_data, point_id
-TotalDataPoint -> total_data, point_id
-```
-
-Do not instantiate heavy calculator-backed owner objects for this test;
-instantiate item classes directly.
-
-### Constraints Tests
-
-Update
-`tests/unit/easydiffraction/analysis/categories/test_constraints.py`.
-
-Required assertions:
-
-```python
-c = Constraint()
-c.expression = 'a = b + c'
-c.id = 'constraint_a'
-assert c.id.value == 'constraint_a'
-assert c.lhs_alias == 'a'
-assert c.rhs_expr == 'b + c'
-assert c._identity.category_entry_name == 'constraint_a'
-```
-
-Default id behavior:
-
-```python
-coll = Constraints()
-coll.create(expression='a = b + c')
-assert 'a' in coll.names
-assert coll['a'].id.value == 'a'
-assert coll['a'].rhs_expr == 'b + c'
-```
-
-Explicit id behavior:
-
-```python
-coll = Constraints()
-coll.create(id='c1', expression='a = b + c')
-assert 'c1' in coll.names
-assert coll['c1'].lhs_alias == 'a'
-```
-
-CIF serialization:
-
-```python
-cif = coll.as_cif
-assert '_constraint.id' in cif
-assert '_constraint.expression' in cif
-```
-
-Backward-compatible CIF loading:
-
-```python
-cif = '''
-data_analysis
-
-loop_
-_constraint.expression
-"a = b + c"
-'''
-```
-
-Load through the existing analysis/constraints loader and assert the
-loaded collection has key `a`.
-
-### Existing Round-Trip Tests
-
-Update tests that compare constraint CIF or constraint collection keys:
-
-- `tests/unit/easydiffraction/project/test_project_load.py`
-- `tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py`
-- `tests/integration/fitting/test_project_load.py`
-
-Expected changes:
-
-- New saved analysis CIF includes `_constraint.id`.
-- Old expression-only fixtures, if any, still load.
-- Existing constraint workflows still work when no explicit id is
-  supplied.
-
-## Verification Commands
-
-Run the smallest useful checks first:
-
-```shell
-pixi run python -m pytest tests/unit/easydiffraction/analysis/categories/test_constraints.py
-pixi run python -m pytest tests/unit/easydiffraction/core/
-pixi run python -m pytest tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
-```
-
-Then run broader checks:
-
-```shell
-pixi run unit-tests
-pixi run integration-tests
-pixi run check
-```
-
-If the modified-file Prettier helper is still missing, format changed
-Markdown directly:
-
-```shell
-npx prettier --write --config=prettierrc.toml docs/dev/plans/loop-category-key-identity.md docs/dev/adrs/suggestions/loop-category-key-identity.md
-```
-
-## Local Availability Check
-
-The plan was checked against the current repository state:
-
-- `.github/copilot-instructions.md` exists.
-- `src/easydiffraction/core/category.py`,
-  `src/easydiffraction/core/identity.py`, and
-  `src/easydiffraction/io/cif/serialize.py` exist.
-- `tests/unit/easydiffraction/analysis/categories/test_constraints.py`
-  exists.
-- `tests/unit/easydiffraction/core/` exists.
-- `tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py`
-  exists.
-- `pixi.toml` defines `fix`, `check`, `unit-tests`, `integration-tests`,
-  and `test-structure-check`.
-- `prettierrc.toml` and local Prettier are available.
-- `tools/nonpy_prettier_modified.py` is not present, so use the direct
-  `npx prettier --write --config=prettierrc.toml ...` fallback for
-  touched Markdown files.
-
-## Acceptance Criteria
-
-The implementation is done when all of these are true:
-
-- All current category codes are declared as `_category_code` on item
-  classes.
-- All current loop collection keys are declared as
-  `_category_entry_name` on item classes.
-- No current category item sets `self._identity.category_code` in its
-  constructor.
-- No current category item sets `self._identity.category_entry_name` in
-  its constructor.
-- `item._identity.category_entry_name` still works for all loop rows.
-- Descriptor `unique_name` values still include datablock, category,
-  entry, and descriptor name.
-- Constraints persist `_constraint.id`.
-- Constraints created without an explicit id still default to the left
-  hand alias.
-- Old constraints CIF without `_constraint.id` still loads and receives
-  deterministic ids.
-- Collection indexes are correct after CIF loading.
-
-## Suggested Pull Request
-
-Title:
-
-```text
-Use declarative category identity metadata
-```
-
-Description:
-
-```text
-This change makes category and loop-row identities easier to audit and
-keeps saved CIF identifiers explicit. Constraint rows gain a stable
-identifier while existing constraint expressions continue to work.
-```
diff --git a/docs/dev/plans/workspace-root-project-category.md b/docs/dev/plans/workspace-root-project-category.md
deleted file mode 100644
index 778fd7584..000000000
--- a/docs/dev/plans/workspace-root-project-category.md
+++ /dev/null
@@ -1,1229 +0,0 @@
-# Workspace Root and Project Category Migration Plan
-
-## Status
-
-Branch: `feature/workspace-root-project-category`
-
-ADR suggestion:
-
-```text
-docs/dev/adrs/suggestions/workspace-root-project-category.md
-```
-
-Two-phase workflow from `.github/copilot-instructions.md`:
-
-- Phase 1 - Implementation. Code and docs updates only. Do not create or
-  run tests unless the user explicitly asks.
-- Phase 2 - Verification. Add/update tests, then run the verification
-  commands listed near the end of this plan.
-
-Stop after Phase 1 and request review before starting Phase 2.
-
-Status checklist. Mark `[x]` only while implementing:
-
-```text
-Phase 1 - Implementation
-[ ] Phase 0: Confirm breaking-change approval.
-[ ] Phase 1: Rename root package and public facade to Workspace.
-[ ] Phase 2: Rename project-info access from info to project.
-[ ] Phase 3: Align project information fields with _project.* tags.
-[ ] Phase 4: Rename project config file to workspace.cif.
-[ ] Phase 5: Update root-object references across runtime code.
-[ ] Phase 6: Update docs, tutorials, and ADR references.
-[ ] Phase 7: Remove old public Project surface unless approved.
-[ ] Phase 1 review gate: present diff for approval.
-
-Phase 2 - Verification
-[ ] Move/update unit tests to workspace paths.
-[ ] Add workspace naming and CIF layout tests.
-[ ] pixi run test-structure-check
-[ ] pixi run fix
-[ ] pixi run check
-[ ] pixi run unit-tests
-[ ] pixi run integration-tests
-[ ] pixi run script-tests
-[ ] pixi run notebook-prepare
-[ ] pixi run notebook-tests
-```
-
-## Commit Discipline
-
-When an AI agent follows this plan, every completed Phase 1
-implementation step must be staged with explicit paths and committed
-locally before moving to the next implementation step or to the Phase 1
-review gate.
-
-Follow the **Commits** section of `.github/copilot-instructions.md`.
-
-Rules:
-
-- One commit per phase.
-- Keep each commit atomic and single-purpose.
-- Stage explicit paths only. Do not use `git add .`.
-- Use `git mv` for file and directory moves.
-- Do not stage unrelated user changes.
-- Do not stage generated artifacts unless the user explicitly asks.
-- If a serious uncovered design issue appears, stop and ask before
-  continuing.
-
-Suggested commit messages:
-
-```text
-Rename Project facade to Workspace
-Expose project information as workspace.project
-Align project metadata fields with CIF names
-Rename project config file to workspace.cif
-Update runtime references to Workspace
-Update docs for Workspace root API
-Remove old Project public API surface
-```
-
-## Goal
-
-Split the name "project" into two distinct concepts:
-
-1. `Workspace` - the top-level runtime facade, currently named
-   `Project`.
-2. `workspace.project` - the category that stores information about the
-   scientific project.
-
-Target public API:
-
-```python
-import easydiffraction as ed
-
-workspace = ed.Workspace(project_id='lbco_hrpt')
-workspace.project.id
-workspace.project.title = 'La0.5Ba0.5CoO3 at HRPT@PSI'
-workspace.rendering.table_engine = 'rich'
-workspace.verbosity = 'short'
-workspace.structures
-workspace.experiments
-workspace.analysis
-workspace.display
-workspace.summary
-workspace.save_as('lbco_hrpt')
-```
-
-Target workspace-level config file:
-
-```text
-workspace.cif
-```
-
-Target CIF tags inside `workspace.cif`:
-
-```cif
-_project.id
-_project.title
-_project.description
-_project.created
-_project.last_modified
-
-_rendering.chart_engine
-_rendering.table_engine
-
-_verbosity.level
-```
-
-Do not introduce `_meta.*` tags.
-
-Target saved layout:
-
-```text
-<workspace-dir>/
-|-- workspace.cif
-|-- structures/
-|   `-- cosio.cif
-|-- experiments/
-|   `-- d20.cif
-|-- analysis/
-|   `-- analysis.cif
-`-- summary/
-    `-- summary.cif
-```
-
-## Decisions Already Made
-
-Use these decisions unless the user explicitly changes the ADR before
-implementation:
-
-- The public root class becomes `Workspace`.
-- The public root import becomes
-  `from easydiffraction import Workspace`.
-- The public project-information category becomes `workspace.project`.
-- The public rendering category remains `workspace.rendering`.
-- The project-information category keeps semantic CIF tags `_project.*`.
-- The rendering category keeps semantic CIF tags `_rendering.*`.
-- The verbosity preference is serialized as `_verbosity.level`.
-- The saved singleton config file becomes `workspace.cif`.
-- The saved root is a workspace directory with a user-chosen filesystem
-  name; do not use `project` as the conceptual root name in new docs.
-- Do not use `project.cif`, `config.cif`, or `meta.cif` as the primary
-  singleton config file in the target layout.
-- The storage directory path belongs to `Workspace.path`, not
-  `workspace.project.path`.
-- The old `Project` public API is removed unless the user explicitly
-  approves an alias before implementation.
-- The category class name `ProjectInfo` is kept; only the public
-  attribute name (`info` → `project`) changes.
-- `ProjectInfo.path` is removed; the saved directory path lives on
-  `Workspace.path` only.
-- A first-class `Verbosity` category under `WorkspaceConfig` is required
-  (not optional) so that `_verbosity.level` round-trips through
-  `workspace.cif` like other singleton categories.
-- Source-tree imports may be temporarily inconsistent between phases
-  (for example, Phase 1 leaves `project_config_to_cif` imports until
-  Phase 4 renames the serializer functions). This is acceptable because
-  tests are not run in Phase 1. Each phase must still leave the source
-  importable at the end of the phase.
-
-## Current Shape
-
-The current implementation already has a category-owner based project
-configuration layer:
-
-```text
-src/easydiffraction/project/
-|-- project.py             # class Project
-|-- project_config.py      # class ProjectConfig
-|-- project_info.py        # ProjectInfo export
-|-- display.py             # class ProjectDisplay
-`-- categories/
-    |-- info/              # ProjectInfo category
-    `-- rendering/         # Rendering category
-```
-
-Current public API:
-
-```python
-project = ed.Project(name='my_project')
-project.info.title
-project.rendering.table_engine
-```
-
-Current saved config file:
-
-```text
-project.cif
-```
-
-## Target Shape
-
-Target implementation:
-
-```text
-src/easydiffraction/workspace/
-|-- workspace.py           # class Workspace
-|-- workspace_config.py    # class WorkspaceConfig
-|-- project_info.py        # ProjectInfo export
-|-- display.py             # class WorkspaceDisplay
-`-- categories/
-    |-- project/           # ProjectInfo category
-    |-- rendering/         # Rendering category
-    `-- verbosity/         # Verbosity category
-```
-
-Target public API:
-
-```python
-workspace = ed.Workspace(project_id='my_project')
-workspace.project.title
-workspace.rendering.table_engine
-workspace.verbosity = 'short'
-```
-
-## Out Of Scope
-
-Do not do these in this migration:
-
-- Do not add `_meta.*` CIF tags.
-- Do not use `project.cif`, `config.cif`, or `meta.cif` as the target
-  singleton settings file.
-- Do not redesign structure or experiment datablocks.
-- Do not change analysis fit-mode semantics.
-- Do not change calculator behavior.
-- Do not edit generated package-structure docs by hand.
-- Do not edit generated notebooks directly. Edit tutorial `.py` sources
-  and regenerate notebooks during Phase 2.
-- Do not keep a `Project = Workspace` compatibility alias unless the
-  user explicitly approves it.
-
-## Phase 0: Confirm Breaking-Change Approval
-
-This migration removes or replaces the public `Project` API unless the
-user approves a compatibility alias.
-
-Before changing code, ask the user to confirm:
-
-```text
-This migration removes ed.Project and replaces it with ed.Workspace.
-Should implementation proceed without a Project compatibility alias?
-```
-
-If the user asks for a compatibility alias, record that decision in this
-plan and in the ADR before implementation.
-
-Do not implement code before this approval gate.
-
-Commit: no commit required for this phase unless the plan or ADR is
-updated.
-
-## Phase 1: Rename Root Package And Public Facade
-
-### Objective
-
-Rename the top-level runtime facade and package from `project` to
-`workspace`.
-
-### Files Likely To Change
-
-- `src/easydiffraction/project/`
-- `src/easydiffraction/__init__.py`
-- `src/easydiffraction/__main__.py`
-- `src/easydiffraction/analysis/analysis.py`
-- `src/easydiffraction/analysis/sequential.py`
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/summary/summary.py`
-- any source file importing `easydiffraction.project.*`
-
-### Steps
-
-1. Move the source package with `git mv` so history is preserved:
-
-   ```text
-   src/easydiffraction/project/
-   -> src/easydiffraction/workspace/
-   ```
-
-2. Rename files:
-
-   ```text
-   workspace/project.py
-   -> workspace/workspace.py
-
-   workspace/project_config.py
-   -> workspace/workspace_config.py
-   ```
-
-3. Rename classes:
-
-   ```text
-   Project -> Workspace
-   ProjectConfig -> WorkspaceConfig
-   ProjectDisplay -> WorkspaceDisplay
-   ```
-
-4. Rename class-level state inside the facade:
-
-   ```text
-   Project._current_project   -> Workspace._current_workspace
-   Project._loading           -> Workspace._loading      # name kept
-   Project.current_project_path() -> Workspace.current_workspace_path()
-   ```
-
-   Update the `ClassVar` annotation accordingly and update all internal
-   references (`type(self)._current_project = self`,
-   `cls._current_project`).
-
-5. Update the `varname()` fallback inside `__init__` so the default
-   variable name becomes `'workspace'` instead of `'project'`:
-
-   ```python
-   self._varname = 'workspace' if type(self)._loading else varname()
-   ```
-
-6. Update top-level import:
-
-   ```python
-   from easydiffraction.workspace.workspace import Workspace
-   ```
-
-7. Remove the old top-level `Project` import unless the user approved an
-   alias.
-
-8. Update type-checking imports:
-
-   ```python
-   from easydiffraction.workspace.workspace import Workspace
-   ```
-
-9. Update docstrings from "Project facade" to "Workspace facade" where
-   they describe the root object. Keep wording that talks about the
-   scientific project (titles, descriptions, identity) unchanged.
-
-10. Rename `io/ascii.py::extract_project_from_zip` to
-    `extract_workspace_from_zip` and update its re-exports in
-    `src/easydiffraction/io/__init__.py` and
-    `src/easydiffraction/__init__.py`. The function extracts a saved
-    workspace directory, not scientific project information.
-
-11. Note: `src/easydiffraction/io/cif/serialize.py` still defines
-    `project_config_to_cif`, `project_config_from_cif`, and
-    `project_to_cif` at this point. Leave those imports as-is in
-    `workspace.py`; they are renamed in Phase 4. The function
-    `project_info_to_cif` keeps its name.
-
-12. Run a source-only grep. Do not run tests in Phase 1:
-
-```shell
-rg -n "easydiffraction\\.project|\\bProject\\b|ProjectDisplay|ProjectConfig" src
-```
-
-For every match, decide whether it refers to:
-
-- the old root object, which should become `Workspace`;
-- the project-information category, which should remain project;
-- historical text that should be updated in docs later.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- another public class named `Workspace` already exists;
-- package moves break imports in a way that would require compatibility
-  shims;
-- a file has both root-object `project` and category `project` meanings
-  that cannot be separated clearly.
-
-### Commit
-
-Stage explicit moved and edited files, then commit:
-
-```text
-Rename Project facade to Workspace
-```
-
-## Phase 2: Rename Project-Info Access From `info` To `project`
-
-### Objective
-
-Make the project-information category public as `workspace.project`
-instead of `workspace.info`.
-
-### Files Likely To Change
-
-- `src/easydiffraction/workspace/workspace_config.py`
-- `src/easydiffraction/workspace/workspace.py`
-- `src/easydiffraction/workspace/categories/info/`
-- `src/easydiffraction/workspace/project_info.py`
-- `src/easydiffraction/io/cif/serialize.py`
-- all code using `.info` for project information
-
-### Steps
-
-1. Rename category package:
-
-   ```text
-   src/easydiffraction/workspace/categories/info/
-   -> src/easydiffraction/workspace/categories/project/
-   ```
-
-2. Keep the category class name `ProjectInfo` for now. The class name is
-   explicit and avoids a confusing `Project` class after the root class
-   is renamed to `Workspace`.
-
-3. Rename imports:
-
-   ```python
-   from easydiffraction.workspace.categories.project import ProjectInfo
-   from easydiffraction.workspace.categories.project import ProjectInfoFactory
-   ```
-
-4. In `WorkspaceConfig`, rename:
-
-   ```text
-   _info -> _project
-   info -> project
-   ```
-
-5. In `Workspace`, rename:
-
-   ```text
-   _info -> _project
-   info -> project
-   ```
-
-6. Remove the public `.info` property unless the user approved a
-   compatibility alias.
-
-7. Update all runtime references:
-
-   ```text
-   workspace.info.title -> workspace.project.title
-   workspace.info.description -> workspace.project.description
-   workspace.info.update_last_modified() -> workspace.project.update_last_modified()
-   ```
-
-8. Run grep:
-
-   ```shell
-   rg -n "\\.info\\b|categories/info|categories\\.info" src
-   ```
-
-9. For every match, update it if it refers to project information. Leave
-   unrelated uses of the word "info" alone.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- `info` appears as a different public concept unrelated to project
-  information;
-- removing `.info` would break a user-requested compatibility alias.
-
-### Commit
-
-```text
-Expose project information as workspace.project
-```
-
-## Phase 3: Align Project Information Fields With `_project.*`
-
-### Objective
-
-Expose project identity as `workspace.project.id`, matching CIF
-`_project.id`.
-
-Move the saved directory path to `workspace.path`, because it describes
-the workspace location and is not serialized project information.
-
-### Files Likely To Change
-
-- `src/easydiffraction/workspace/categories/project/default.py`
-- `src/easydiffraction/workspace/workspace.py`
-- `src/easydiffraction/io/cif/serialize.py`
-- `src/easydiffraction/summary/summary.py`
-- `src/easydiffraction/display/plotting.py`
-- any code using `.name` for project identity or `.project.path`
-
-### Steps
-
-1. In `ProjectInfo`, rename the public identity property and its setter:
-
-   ```text
-   name (getter)  -> id (getter)
-   name (setter)  -> id (setter)
-   ```
-
-   Do not also rename the internal descriptor attribute
-   `self._project_id`; it already matches the new public name.
-
-2. Keep the underlying CIF tag unchanged:
-
-   ```python
-   CifHandler(names=['_project.id'])
-   ```
-
-3. Update `ProjectInfo.unique_name` to return `self.id`.
-
-4. Keep `ProjectInfo._identity.category_code = 'project'` as-is.
-
-5. Update `project_info_to_cif()` and CIF loading helpers to use
-   `info.id`.
-
-6. Rename constructor arguments:
-
-   ```text
-   name -> project_id
-   ```
-
-   Apply this to:
-   - `Workspace.__init__`
-   - `WorkspaceConfig.__init__`
-   - `ProjectInfo.__init__`
-   - `ProjectInfoFactory.create(...)` call sites
-   - Default value: `'untitled_project'` (unchanged value, just the
-     parameter name changes)
-
-7. Add `Workspace.path` as the runtime storage path. Initialize
-   `self._path: pathlib.Path | None = None` in `Workspace.__init__`.
-
-   Suggested shape (match the surrounding `GuardedBase` pattern; do not
-   add `@typechecked` here because the setter accepts both `str` and
-   `pathlib.Path`):
-
-   ```python
-   @property
-   def path(self) -> pathlib.Path | None:
-       """Saved workspace directory."""
-       return self._path
-
-   @path.setter
-   def path(self, value: object) -> None:
-       self._path = pathlib.Path(value) if value is not None else None
-   ```
-
-8. Remove `ProjectInfo.path` (property, setter, and the `self._path`
-   attribute inside `ProjectInfo.__init__`) unless explicitly approved
-   as a compatibility alias.
-
-9. Update save/load logic across the codebase:
-
-   ```text
-   workspace.path
-   ```
-
-   should replace:
-
-   ```text
-   project.info.path        # old
-   workspace.project.path   # never used; do not introduce
-   ```
-
-   Concrete call sites include `Workspace.save`, `Workspace.load`,
-   `Workspace.current_workspace_path`, and any consumers in `analysis/`,
-   `display/`, `summary/`, and `io/`.
-
-10. Update messages and string representations:
-
-```text
-Workspace '<project_id>' (...)
-Saving workspace '<project_id>' to ...
-```
-
-11. Run grep:
-
-```shell
-rg -n "\\.name\\b|\\.path\\b|project_id|Project identifier" src/easydiffraction/workspace src/easydiffraction/io src/easydiffraction/display src/easydiffraction/summary
-```
-
-Inspect each match manually. Do not blindly replace every `.name`;
-structures and experiments still use `.name`.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- a caller depends on `workspace.name` as a root-object property;
-- moving `path` out of `ProjectInfo` makes save/load unclear;
-- external saved fixtures require an approved compatibility path.
-
-### Commit
-
-```text
-Align project metadata fields with CIF names
-```
-
-## Phase 4: Rename Project Config File To `workspace.cif`
-
-### Objective
-
-Rename the saved singleton configuration file from `project.cif` to
-`workspace.cif`.
-
-### Files Likely To Change
-
-- `src/easydiffraction/workspace/workspace.py`
-- `src/easydiffraction/io/cif/serialize.py`
-- `src/easydiffraction/workspace/workspace_config.py`
-- `src/easydiffraction/workspace/categories/verbosity/`
-- CLI entry points in `src/easydiffraction/__main__.py`
-- docs that describe saved project directories
-- test fixtures in Phase 2
-
-### Steps
-
-1. Rename serializer functions in
-   `src/easydiffraction/io/cif/serialize.py` and every call site:
-
-   ```text
-   project_config_to_cif   -> workspace_config_to_cif
-   project_config_from_cif -> workspace_config_from_cif
-   project_to_cif          -> workspace_to_cif
-   ```
-
-   Call sites include `workspace.py` (formerly `project.py`),
-   `workspace_config.py`, and the serializer itself (the
-   `project_to_cif` body calls `project_config_to_cif`). After this step
-   the imports that were intentionally left stale in Phase 1 must
-   compile cleanly.
-
-   Do not rename `project_info_to_cif`; it serializes the `_project`
-   category and that name remains correct.
-
-2. Update `Workspace.save()` to write:
-
-   ```text
-   workspace.cif
-   ```
-
-3. Update `Workspace.load()` to read:
-
-   ```text
-   workspace.cif
-   ```
-
-4. Do not add `project.cif`, `config.cif`, or `meta.cif` fallbacks
-   unless the user approved a compatibility loader.
-
-5. Add a `Verbosity` category under
-   `src/easydiffraction/workspace/categories/verbosity/` following the
-   same shape as `rendering/` (a `default.py` with a `Verbosity`
-   `CategoryItem`, a `factory.py` with `VerbosityFactory`, and an
-   `__init__.py` that imports both to trigger registration).
-
-   The category owns one descriptor:
-
-   ```python
-   CifHandler(names=['_verbosity.level'])
-   ```
-
-   Bind it in `WorkspaceConfig.__init__` next to `_rendering`, and
-   expose it from `Workspace` so that the public access path remains:
-
-   ```python
-   workspace.verbosity = 'short'
-   workspace.verbosity              # -> 'short'
-   ```
-
-   The public `verbosity` getter/setter on `Workspace` reads and writes
-   the category's `level` descriptor (validated against `VerbosityEnum`)
-   and replaces the current `self._verbosity: VerbosityEnum`
-   runtime-only attribute. Remove that attribute.
-
-   Serialize it as:
-
-   ```cif
-   _verbosity.level short
-   ```
-
-6. Keep the contents semantic:
-
-   ```cif
-   _project.id
-   _project.title
-   _rendering.table_engine
-   _verbosity.level
-   ```
-
-7. Update logging and console output from `project.cif` to
-   `workspace.cif`.
-
-8. Run grep:
-
-   ```shell
-   rg -n "project\\.cif|config\\.cif|meta\\.cif|project_config_to_cif|project_config_from_cif|project_to_cif|verbosity" src docs tests
-   ```
-
-   Update source and docs only. Test files are handled in Phase 2 unless
-   the user explicitly asks otherwise.
-
-9. Saved on-disk fixtures under `data/` and `projects/` still contain
-   `project.cif` files (for example `data/lbco_project/project.cif`,
-   `projects/cosio/project.cif`). Do **not** edit or regenerate them in
-   Phase 1. They are inputs to integration/script tests and will either
-   be regenerated in Phase 2 or the relevant tests will be updated to
-   write fresh workspace directories. Flag any that block Phase 2 in the
-   review gate.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- repository fixtures or tutorials contain saved directories that must
-  remain loadable without conversion;
-- the user wants a one-release compatibility loader.
-- the verbosity setting cannot be represented as a category without
-  weakening the public `workspace.verbosity` API.
-
-### Commit
-
-```text
-Rename project config file to workspace.cif
-```
-
-## Phase 5: Update Root-Object References Across Runtime Code
-
-### Objective
-
-Replace root-object variables and attributes named `project` with
-`workspace` where they refer to the top-level facade.
-
-Keep the word `project` where it refers to the project-information
-category or the scientific project itself.
-
-### Files Likely To Change
-
-- `src/easydiffraction/analysis/analysis.py`
-- `src/easydiffraction/analysis/sequential.py`
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/project/display.py` after it has moved to
-  `workspace/display.py`
-- `src/easydiffraction/summary/summary.py`
-- `src/easydiffraction/__main__.py`
-- `src/easydiffraction/io/*`
-
-### Steps
-
-1. Rename root references in `Analysis`:
-
-   ```text
-   self.project -> self.workspace
-   analysis.project -> analysis.workspace
-   ```
-
-   This includes the constructor argument, the stored attribute, and any
-   public read-only property exposing the parent workspace.
-
-2. Rename display internals in `WorkspaceDisplay` (formerly
-   `ProjectDisplay`) and in any other class that stores a back-
-   reference to the root facade:
-
-   ```text
-   self._project -> self._workspace
-   _set_project(...) -> _set_workspace(...)
-   ```
-
-   Only do this when the stored object is the top-level runtime facade.
-   Do not touch `self._project_id` inside `ProjectInfo` or `_project.*`
-   CIF tags.
-
-3. Rename local variables in runtime code:
-
-   ```text
-   project = Workspace(...)
-   -> workspace = Workspace(...)
-   ```
-
-4. Keep scientific-project wording where appropriate:
-
-   ```text
-   workspace.project.title
-   project_id
-   _project.id
-   ```
-
-5. Update user-facing messages carefully. Good examples:
-
-   ```text
-   "Workspace directory not found"
-   "Saving workspace"
-   "Project title"
-   ```
-
-6. Run grep:
-
-   ```shell
-   rg -n "\\bproject\\b|\\bProject\\b|_project|ProjectDisplay" src/easydiffraction
-   ```
-
-7. Inspect each match. Do not replace `_project` CIF tags.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- a name has both root-workspace and project-information meanings in the
-  same function and cannot be made clear;
-- renaming a method such as `_set_project` would require updating public
-  plugin or user code.
-
-### Commit
-
-```text
-Update runtime references to Workspace
-```
-
-## Phase 6: Update Docs, Tutorials, And ADR References
-
-### Objective
-
-Update user-facing and developer-facing documentation to describe the
-new root object and project-information category.
-
-### Files Likely To Change
-
-- `docs/dev/adrs/index.md`
-- `docs/dev/issues/open.md`
-- `docs/dev/adrs/accepted/*.md`
-- `docs/dev/adrs/suggestions/*.md`
-- `docs/docs/tutorials/*.py`
-- `README.md`
-- `CONTRIBUTING.md` only if it contains API examples
-
-Do not edit these by hand:
-
-- `docs/dev/package-structure/full.md`
-- `docs/dev/package-structure/short.md`
-- generated tutorial notebooks
-- generated `docs/site/` files
-
-### Steps
-
-1. Update the relevant accepted ADRs:
-
-   ```text
-   Project Facade and Persistence Layout
-   -> Workspace Facade and Persistence Layout
-   ```
-
-2. Update the affected ADR examples to use:
-
-   ```text
-   workspace.project      ProjectInfo
-   workspace.rendering    Rendering
-   workspace.verbosity    str
-   workspace.display      WorkspaceDisplay
-   ```
-
-3. Update saved layout examples:
-
-   ```text
-   workspace.cif
-   structures/
-   experiments/
-   analysis/
-   summary.cif
-   ```
-
-4. Update public examples:
-
-   ```python
-   workspace = ed.Workspace(project_id='lbco_hrpt')
-   workspace.project.title = '...'
-   ```
-
-5. Update ADRs that describe current API. Historical reasoning can keep
-   old names only if it is clearly historical and not presented as
-   current usage.
-
-6. Update tutorial `.py` files, not notebooks. Phase 2 will run
-   `pixi run notebook-prepare`.
-
-7. Run grep:
-
-   ```shell
-   rg -n "ed\\.Project|from easydiffraction import Project|project\\.info|project\\.rendering|ProjectDisplay|project\\.cif" docs README.md CONTRIBUTING.md
-   ```
-
-8. Inspect each match manually.
-
-### Stop Conditions
-
-Stop and ask if:
-
-- a tutorial title uses "Project" as ordinary English rather than API
-  naming;
-- historical ADRs would become misleading if edited mechanically.
-
-### Commit
-
-```text
-Update docs for Workspace root API
-```
-
-## Phase 7: Remove Old Public `Project` Surface Unless Approved
-
-### Objective
-
-Finish the breaking rename by removing old public imports and module
-paths unless the user approved compatibility.
-
-### Steps Without Compatibility Alias
-
-1. Ensure top-level `easydiffraction.__init__` exports `Workspace`, not
-   `Project`.
-
-2. Ensure no source imports from:
-
-   ```text
-   easydiffraction.project
-   ```
-
-3. Ensure no public source package remains at:
-
-   ```text
-   src/easydiffraction/project
-   ```
-
-4. Run grep:
-
-   ```shell
-   rg -n "from easydiffraction import Project|ed\\.Project|easydiffraction\\.project|\\bProject\\(" src docs tests tools README.md CONTRIBUTING.md
-   ```
-
-5. Any remaining match must be:
-   - historical text that intentionally names the old API; or
-   - a test that will be updated in Phase 2; or
-   - a generated artifact that should not be edited manually.
-
-### Steps With Approved Compatibility Alias
-
-Only do this if the user explicitly approved it.
-
-1. Add a temporary alias in `src/easydiffraction/__init__.py`:
-
-   ```python
-   Project = Workspace
-   ```
-
-2. Keep the alias undocumented unless the user asks for a migration
-   note.
-
-3. Add tests in Phase 2 proving both `Workspace` and `Project` construct
-   the same root object.
-
-### Commit
-
-Without alias:
-
-```text
-Remove old Project public API surface
-```
-
-With alias:
-
-```text
-Add Project alias for Workspace migration
-```
-
-## Phase 1 Review Gate
-
-After Phase 1 commits are complete:
-
-1. Run `git status --short`.
-2. Confirm only intended files are changed.
-3. Summarize:
-   - whether `Project` was removed or aliased;
-   - whether `workspace.cif` replaced `project.cif`;
-   - any files intentionally left for Phase 2 test updates;
-   - any unresolved questions.
-
-4. Stop and ask the user to review before starting Phase 2.
-
-Do not run the full verification suite until the user approves moving to
-Phase 2.
-
-## Phase 2: Verification And Tests
-
-Only start this phase after the user approves the Phase 1
-implementation.
-
-### Test Updates
-
-Move or update tests to mirror the new source tree:
-
-```text
-tests/unit/easydiffraction/project/
--> tests/unit/easydiffraction/workspace/
-```
-
-Update imports:
-
-```python
-from easydiffraction.workspace.workspace import Workspace
-from easydiffraction.workspace.display import WorkspaceDisplay
-```
-
-Update functional and integration tests:
-
-```python
-from easydiffraction import Workspace
-workspace = Workspace(project_id='...')
-```
-
-### New Tests To Add
-
-Add focused tests for:
-
-1. `from easydiffraction import Workspace`.
-2. `Workspace(project_id='p1').project.id == 'p1'`.
-3. `workspace.project.title` round-trips through `workspace.cif`.
-4. `workspace.rendering.table_engine` round-trips through
-   `workspace.cif`.
-5. `workspace.verbosity` round-trips through `workspace.cif`.
-6. `Workspace.save()` writes `workspace.cif`.
-7. `Workspace.load()` reads `workspace.cif`.
-8. `workspace.cif` contains `_project.id`, not `_meta.project_id`.
-9. `workspace.cif` contains `_rendering.table_engine`.
-10. `workspace.cif` contains `_verbosity.level`.
-11. `workspace.path` is set after `save_as()` and `load()`.
-12. `workspace.project` has no serialized path field.
-13. `project.cif`, `config.cif`, and `meta.cif` are not written unless
-    compatibility was approved.
-14. `ed.Project` is absent unless compatibility was approved.
-15. `Verbosity` category is registered via its factory and reachable
-    through `WorkspaceConfig` (parallel to `Rendering`).
-16. `ed.extract_workspace_from_zip` is importable and
-    `ed.extract_project_from_zip` is not (unless compatibility
-    approved).
-
-If compatibility alias was approved, add tests for:
-
-1. `from easydiffraction import Project`.
-2. `Project is Workspace` or equivalent behavior.
-3. Any approved `project.cif` fallback behavior.
-
-### Verification Commands
-
-Run in this order:
-
-```shell
-pixi run test-structure-check
-pixi run fix
-pixi run check
-pixi run unit-tests
-pixi run integration-tests
-pixi run script-tests
-pixi run notebook-prepare
-pixi run notebook-tests
-```
-
-If `pixi run fix` regenerates package-structure docs, accept those
-generated changes and do not hand-edit them.
-
-### Phase 2 Commit Suggestions
-
-Use one or more commits, depending on size:
-
-```text
-Update workspace unit tests
-Update tutorials for Workspace API
-Regenerate tutorial notebooks for Workspace API
-```
-
-## Grep Checklist
-
-Use this checklist before final review.
-
-Runtime root object should use `Workspace`:
-
-```shell
-rg -n "\\bProject\\b|ed\\.Project|from easydiffraction import Project" src tests docs tools README.md CONTRIBUTING.md
-```
-
-Project-information category should use `workspace.project`:
-
-```shell
-rg -n "\\.info\\b|workspace\\.project|project\\.info" src tests docs tools README.md CONTRIBUTING.md
-```
-
-CIF project category should stay `_project`:
-
-```shell
-rg -n "_meta\\.|_project\\." src tests docs tools README.md CONTRIBUTING.md
-```
-
-Saved config file should be `workspace.cif`:
-
-```shell
-rg -n "project\\.cif|config\\.cif|meta\\.cif|workspace\\.cif" src tests docs tools README.md CONTRIBUTING.md
-```
-
-Workspace verbosity should serialize as a workspace-level category:
-
-```shell
-rg -n "_verbosity|verbosity" src tests docs tools README.md CONTRIBUTING.md
-```
-
-Generated docs should not be manually edited:
-
-```shell
-git diff -- docs/site docs/dev/package-structure/full.md docs/dev/package-structure/short.md
-```
-
-If package-structure docs changed because of `pixi run fix`, that is
-expected. If `docs/site` changed, ask before staging.
-
-## Common Mistakes
-
-### Mistake: Renaming `_project.*` To `_workspace.*`
-
-Do not do this. The CIF category describes scientific project
-information, not the runtime facade.
-
-Correct:
-
-```cif
-_project.id
-_project.title
-```
-
-Incorrect:
-
-```cif
-_workspace.project_id
-_workspace.title
-```
-
-### Mistake: Introducing `_meta.*`
-
-Do not replace `_project.*` with `_meta.*`.
-
-Correct:
-
-```cif
-_project.title
-```
-
-Incorrect:
-
-```cif
-_meta.project_title
-```
-
-### Mistake: Keeping `project.cif` Or Switching To Generic File Names
-
-Do not use `project.cif`, `config.cif`, or `meta.cif` as the target
-singleton settings file. The file belongs to the workspace layer.
-
-Correct:
-
-```text
-workspace.cif
-```
-
-### Mistake: Blindly Replacing Every `project`
-
-Some uses of `project` should remain:
-
-- CIF tags such as `_project.id`
-- `workspace.project`
-- scientific project wording in prose
-- `ProjectInfo` class name, unless a later ADR changes it
-
-Only root-facade uses should become `workspace` or `Workspace`.
-
-### Mistake: Leaving Path On Project Information
-
-The saved directory path belongs to the workspace runtime state. It
-should be `workspace.path`, not `workspace.project.path`.
-
-### Mistake: Forgetting Facade Class-Level State
-
-When renaming `Project` to `Workspace`, the `ClassVar`
-`_current_project`, the `current_project_path()` classmethod, and the
-`varname()` fallback string `'project'` all live on the class itself and
-are easy to miss with a single search-and-replace. Rename them to
-`_current_workspace`, `current_workspace_path()`, and `'workspace'`
-respectively.
-
-### Mistake: Renaming `ProjectInfo._project_id`
-
-The internal descriptor attribute `self._project_id` inside
-`ProjectInfo` already matches the new public name `id` and stays as is.
-Only the public `name` property/setter becomes `id`.
-
-### Mistake: Editing Generated Notebooks Directly
-
-Tutorial notebooks are generated artifacts. Edit tutorial `.py` files,
-then run `pixi run notebook-prepare` in Phase 2.
-
-## Suggested Pull Request
-
-Title:
-
-```text
-Rename Project root object to Workspace
-```
-
-Description:
-
-```text
-This change separates the working EasyDiffraction workspace from the
-scientific project information stored inside it. Users now create a
-Workspace, while project title and description live under
-workspace.project and continue to serialize with clear _project.* CIF
-names.
-```
diff --git a/docs/docs/cli/index.md b/docs/docs/cli/index.md
index 5fc13c771..46c283879 100644
--- a/docs/docs/cli/index.md
+++ b/docs/docs/cli/index.md
@@ -48,6 +48,41 @@ List all available tutorial notebooks:
 python -m easydiffraction list-tutorials
 ```
 
+### List Example Data
+
+List all available example data files and downloadable project archives:
+
+```bash
+python -m easydiffraction list-data
+```
+
+The table includes the data ID, file name, record kind, and description.
+
+### Download Example Data
+
+Download a specific example data record by ID:
+
+```bash
+python -m easydiffraction download-data 3
+```
+
+For downloadable saved projects, the ZIP archive is extracted
+automatically, the ZIP file is removed, and the extracted project path
+is reported:
+
+```bash
+python -m easydiffraction download-data 30
+```
+
+This makes it possible to go straight from download to a project-first
+CLI command such as:
+
+```bash
+python -m easydiffraction EXTRACTED_PROJECT_DIR display
+```
+
+Use the extracted path printed by `download-data`.
+
 ### Download Tutorials
 
 Download a specific tutorial by ID:
@@ -66,12 +101,16 @@ Both commands accept `--destination` (`-d`) to specify the output
 directory (default: `tutorials/`) and `--overwrite` (`-o`) to replace
 existing files.
 
+`download-data` also accepts `--destination` (`-d`) and `--overwrite`
+(`-o`). For project archives, `--overwrite` replaces the extracted
+project directory before downloading and unpacking a fresh copy.
+
 ### Fit a Project
 
 Load a saved project and run structural refinement:
 
 ```bash
-python -m easydiffraction fit PROJECT_DIR
+python -m easydiffraction PROJECT_DIR fit
 ```
 
 `PROJECT_DIR` is the path to a project directory previously created by
@@ -86,5 +125,35 @@ Use the `--dry` flag to run the fit **without overwriting** the project
 files:
 
 ```bash
-python -m easydiffraction fit PROJECT_DIR --dry
+python -m easydiffraction PROJECT_DIR fit --dry
 ```
+
+EasyDiffraction also accepts the legacy subcommand-first form
+`python -m easydiffraction fit PROJECT_DIR`, but the project-first form
+is recommended because it makes it easy to rerun the same command and
+swap only the action.
+
+### Display a Project
+
+Load a saved project and show the outputs that match its current fit
+state and rendering backend:
+
+```bash
+python -m easydiffraction PROJECT_DIR display
+```
+
+For typical non-sequential projects this includes the latest fit
+results, parameter correlations, default pattern views, and when the
+saved state is Bayesian also posterior distributions and predictive
+checks. Plotly-only views such as posterior pair plots are shown only
+when the active chart engine is Plotly.
+
+### Undo the Last Fit
+
+The CLI already reserves the project-first undo command shape:
+
+```bash
+python -m easydiffraction PROJECT_DIR undo
+```
+
+This command currently reports that undo support is not implemented yet.
diff --git a/docs/docs/quick-reference/index.md b/docs/docs/quick-reference/index.md
index c035d3fe3..9c4d715bd 100644
--- a/docs/docs/quick-reference/index.md
+++ b/docs/docs/quick-reference/index.md
@@ -39,10 +39,15 @@ ed.show_version()
 Download a dataset by ID into a local directory:
 
 ```python
+ed.list_data()
+
 structure_path = ed.download_data(id=1, destination='data')
 data_path = ed.download_data(id=3, destination='data')
 ```
 
+Project archives are extracted automatically, and `download_data()`
+returns the extracted project directory path.
+
 For tutorial notebooks:
 
 ```python
@@ -398,8 +403,16 @@ project = ed.Project.load('lbco_hrpt')
 Run a saved project from the command line:
 
 ```bash
-python -m easydiffraction fit lbco_hrpt
-python -m easydiffraction fit lbco_hrpt --dry
+python -m easydiffraction lbco_hrpt fit
+python -m easydiffraction lbco_hrpt fit --dry
+python -m easydiffraction lbco_hrpt display
+```
+
+Load a saved example project straight from `download_data()`:
+
+```python
+saved_project_dir = ed.download_data(id=30, destination='projects')
+project = ed.Project.load(saved_project_dir)
 ```
 
 ## Command-Line Reminders
@@ -407,8 +420,11 @@ python -m easydiffraction fit lbco_hrpt --dry
 ```bash
 python -m easydiffraction --help
 python -m easydiffraction --version
+python -m easydiffraction list-data
+python -m easydiffraction download-data 30 --destination projects
 python -m easydiffraction list-tutorials
 python -m easydiffraction download-tutorial 1 --destination tutorials
 python -m easydiffraction download-all-tutorials --destination tutorials
-python -m easydiffraction fit PROJECT_DIR
+python -m easydiffraction PROJECT_DIR fit
+python -m easydiffraction PROJECT_DIR display
 ```
diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 616c536c5..ca636d82b 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -2657,7 +2657,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "tags,title,-all",
+   "cell_metadata_filter": "title,tags,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-15.ipynb b/docs/docs/tutorials/ed-15.ipynb
index b6d82a319..e79bdd876 100644
--- a/docs/docs/tutorials/ed-15.ipynb
+++ b/docs/docs/tutorials/ed-15.ipynb
@@ -248,6 +248,17 @@
    "id": "23",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Limit number of iterations to prevent long calculation time in this tutorial.\n",
+    "project.analysis.fitting.minimizer.max_iterations = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Start refinement. All parameters, which have standard uncertainties\n",
     "# in the input CIF files, are refined by default.\n",
@@ -257,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +279,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27",
+   "id": "28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28",
+   "id": "29",
    "metadata": {},
    "source": [
     "## Step 5: Perform Analysis (ADP aniso)"
@@ -306,7 +317,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36",
+   "id": "37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -390,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/docs/tutorials/ed-15.py b/docs/docs/tutorials/ed-15.py
index 7359b712a..03dc35fa7 100644
--- a/docs/docs/tutorials/ed-15.py
+++ b/docs/docs/tutorials/ed-15.py
@@ -79,6 +79,10 @@
 # %%
 project.analysis.fitting.minimizer_type = 'bumps'
 
+# %%
+# Limit number of iterations to prevent long calculation time in this tutorial.
+project.analysis.fitting.minimizer.max_iterations = 500
+
 # %%
 # Start refinement. All parameters, which have standard uncertainties
 # in the input CIF files, are refined by default.
diff --git a/docs/docs/tutorials/ed-17.ipynb b/docs/docs/tutorials/ed-17.ipynb
index f32b0371f..e471d99a7 100644
--- a/docs/docs/tutorials/ed-17.ipynb
+++ b/docs/docs/tutorials/ed-17.ipynb
@@ -58,7 +58,8 @@
    "source": [
     "## Step 1: Define Project\n",
     "\n",
-    "The project object manages structures, experiments, and analysis."
+    "The project object manages structures, experiments, analysis, display,\n",
+    "and other related components."
    ]
   },
   {
@@ -68,7 +69,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project = ed.Project()"
+    "project = ed.Project(name='cosio_d20')\n",
+    "analysis = project.analysis\n",
+    "display = project.display"
    ]
   },
   {
@@ -87,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.save_as('projects/cosio', temporary=False)"
+    "project.save_as(dir_path='projects/cosio_d20')"
    ]
   },
   {
@@ -111,7 +114,7 @@
    "outputs": [],
    "source": [
     "project.structures.create(name='cosio')\n",
-    "structure = project.structures['cosio']"
+    "struct = project.structures['cosio']"
    ]
   },
   {
@@ -129,8 +132,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.space_group.name_h_m = 'P n m a'\n",
-    "structure.space_group.it_coordinate_system_code = 'abc'"
+    "struct.space_group.name_h_m = 'P n m a'\n",
+    "struct.space_group.it_coordinate_system_code = 'abc'"
    ]
   },
   {
@@ -148,9 +151,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.cell.length_a = 10.31\n",
-    "structure.cell.length_b = 6.0\n",
-    "structure.cell.length_c = 4.79"
+    "struct.cell.length_a = 10.31\n",
+    "struct.cell.length_b = 6.0\n",
+    "struct.cell.length_c = 4.79"
    ]
   },
   {
@@ -168,7 +171,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='Co1',\n",
     "    type_symbol='Co',\n",
     "    fract_x=0,\n",
@@ -177,7 +180,7 @@
     "    wyckoff_letter='a',\n",
     "    adp_iso=0.3,\n",
     ")\n",
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='Co2',\n",
     "    type_symbol='Co',\n",
     "    fract_x=0.279,\n",
@@ -186,7 +189,7 @@
     "    wyckoff_letter='c',\n",
     "    adp_iso=0.3,\n",
     ")\n",
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='Si',\n",
     "    type_symbol='Si',\n",
     "    fract_x=0.094,\n",
@@ -195,7 +198,7 @@
     "    wyckoff_letter='c',\n",
     "    adp_iso=0.34,\n",
     ")\n",
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='O1',\n",
     "    type_symbol='O',\n",
     "    fract_x=0.091,\n",
@@ -204,7 +207,7 @@
     "    wyckoff_letter='c',\n",
     "    adp_iso=0.63,\n",
     ")\n",
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='O2',\n",
     "    type_symbol='O',\n",
     "    fract_x=0.448,\n",
@@ -213,7 +216,7 @@
     "    wyckoff_letter='c',\n",
     "    adp_iso=0.59,\n",
     ")\n",
-    "structure.atom_sites.create(\n",
+    "struct.atom_sites.create(\n",
     "    label='O3',\n",
     "    type_symbol='O',\n",
     "    fract_x=0.164,\n",
@@ -246,7 +249,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zip_path = ed.download_data(id=27, destination='data')"
+    "zip_path = ed.download_data(id=25, destination='data')"
    ]
   },
   {
@@ -427,28 +430,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.cell.length_a.free = True\n",
-    "structure.cell.length_b.free = True\n",
-    "structure.cell.length_c.free = True\n",
+    "struct.cell.length_a.free = True\n",
+    "struct.cell.length_b.free = True\n",
+    "struct.cell.length_c.free = True\n",
     "\n",
-    "structure.atom_sites['Co2'].fract_x.free = True\n",
-    "structure.atom_sites['Co2'].fract_z.free = True\n",
-    "structure.atom_sites['Si'].fract_x.free = True\n",
-    "structure.atom_sites['Si'].fract_z.free = True\n",
-    "structure.atom_sites['O1'].fract_x.free = True\n",
-    "structure.atom_sites['O1'].fract_z.free = True\n",
-    "structure.atom_sites['O2'].fract_x.free = True\n",
-    "structure.atom_sites['O2'].fract_z.free = True\n",
-    "structure.atom_sites['O3'].fract_x.free = True\n",
-    "structure.atom_sites['O3'].fract_y.free = True\n",
-    "structure.atom_sites['O3'].fract_z.free = True\n",
+    "struct.atom_sites['Co2'].fract_x.free = True\n",
+    "struct.atom_sites['Co2'].fract_z.free = True\n",
+    "struct.atom_sites['Si'].fract_x.free = True\n",
+    "struct.atom_sites['Si'].fract_z.free = True\n",
+    "struct.atom_sites['O1'].fract_x.free = True\n",
+    "struct.atom_sites['O1'].fract_z.free = True\n",
+    "struct.atom_sites['O2'].fract_x.free = True\n",
+    "struct.atom_sites['O2'].fract_z.free = True\n",
+    "struct.atom_sites['O3'].fract_x.free = True\n",
+    "struct.atom_sites['O3'].fract_y.free = True\n",
+    "struct.atom_sites['O3'].fract_z.free = True\n",
     "\n",
-    "structure.atom_sites['Co1'].adp_iso.free = True\n",
-    "structure.atom_sites['Co2'].adp_iso.free = True\n",
-    "structure.atom_sites['Si'].adp_iso.free = True\n",
-    "structure.atom_sites['O1'].adp_iso.free = True\n",
-    "structure.atom_sites['O2'].adp_iso.free = True\n",
-    "structure.atom_sites['O3'].adp_iso.free = True"
+    "struct.atom_sites['Co1'].adp_iso.free = True\n",
+    "struct.atom_sites['Co2'].adp_iso.free = True\n",
+    "struct.atom_sites['Si'].adp_iso.free = True\n",
+    "struct.atom_sites['O1'].adp_iso.free = True\n",
+    "struct.atom_sites['O2'].adp_iso.free = True\n",
+    "struct.atom_sites['O3'].adp_iso.free = True"
    ]
   },
   {
@@ -488,13 +491,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.aliases.create(\n",
+    "analysis.aliases.create(\n",
     "    label='biso_Co1',\n",
-    "    param=structure.atom_sites['Co1'].adp_iso,\n",
+    "    param=struct.atom_sites['Co1'].adp_iso,\n",
     ")\n",
-    "project.analysis.aliases.create(\n",
+    "analysis.aliases.create(\n",
     "    label='biso_Co2',\n",
-    "    param=structure.atom_sites['Co2'].adp_iso,\n",
+    "    param=struct.atom_sites['Co2'].adp_iso,\n",
     ")"
    ]
   },
@@ -513,7 +516,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.constraints.create(expression='biso_Co2 = biso_Co1')"
+    "analysis.constraints.create(expression='biso_Co2 = biso_Co1')"
    ]
   },
   {
@@ -531,7 +534,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fitting.minimizer_type = 'bumps (lm)'"
+    "analysis.fitting.minimizer_type = 'bumps (lm)'"
    ]
   },
   {
@@ -554,13 +557,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit()"
+    "analysis.fit()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "44",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "display.fit.results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45",
+   "metadata": {},
    "source": [
     "#### Show parameter correlations"
    ]
@@ -568,16 +581,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45",
+   "id": "46",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.fit.correlations()"
+    "display.fit.correlations()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "46",
+   "id": "47",
    "metadata": {},
    "source": [
     "#### Compare measured and calculated patterns for the first fit."
@@ -586,16 +599,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47",
+   "id": "48",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.pattern(expt_name='d20')"
+    "display.pattern(expt_name='d20')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "48",
+   "id": "49",
    "metadata": {},
    "source": [
     "#### Run Sequential Fitting\n",
@@ -607,7 +620,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "49",
+   "id": "50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -616,7 +629,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50",
+   "id": "51",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -629,7 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51",
+   "id": "52",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,11 +652,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "52",
+   "id": "53",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.sequential_fit_extract.create(\n",
+    "analysis.sequential_fit_extract.create(\n",
     "    id='temperature',\n",
     "    target=temperature,\n",
     "    pattern=r'^TEMP\\s+([0-9.]+)',\n",
@@ -653,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53",
+   "id": "54",
    "metadata": {},
    "source": [
     "Set the sequential fitting parameters."
@@ -662,19 +675,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54",
+   "id": "55",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fitting_mode_type = 'sequential'\n",
-    "project.analysis.sequential_fit.data_dir = scan_data_dir\n",
-    "project.analysis.sequential_fit.max_workers = 'auto'\n",
-    "project.analysis.sequential_fit.reverse = True"
+    "analysis.fitting_mode_type = 'sequential'\n",
+    "analysis.sequential_fit.data_dir = scan_data_dir\n",
+    "analysis.sequential_fit.max_workers = 'auto'\n",
+    "analysis.sequential_fit.reverse = True"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55",
+   "id": "56",
    "metadata": {},
    "source": [
     "Run the sequential fit over all data files in the scan directory."
@@ -683,16 +696,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "56",
+   "id": "57",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit()"
+    "analysis.fit()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "57",
+   "id": "58",
    "metadata": {},
    "source": [
     "#### Replay a Dataset\n",
@@ -703,17 +716,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "58",
+   "id": "59",
    "metadata": {},
    "outputs": [],
    "source": [
     "project.apply_params_from_csv(row_index=0)\n",
-    "project.display.pattern(expt_name='d20')"
+    "display.pattern(expt_name='d20')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "59",
+   "id": "60",
    "metadata": {},
    "source": [
     "\n",
@@ -723,17 +736,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "60",
+   "id": "61",
    "metadata": {},
    "outputs": [],
    "source": [
     "project.apply_params_from_csv(row_index=-1)\n",
-    "project.display.pattern(expt_name='d20')"
+    "display.pattern(expt_name='d20')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "61",
+   "id": "62",
    "metadata": {},
    "source": [
     "#### Plot Parameter Evolution\n",
@@ -743,7 +756,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62",
+   "id": "63",
+   "metadata": {},
+   "source": [
+    "Plot fit quality metrics vs. temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display.fit.series(analysis.fit_result.success, versus=temperature)\n",
+    "display.fit.series(analysis.fit_result.reduced_chi_square, versus=temperature)\n",
+    "display.fit.series(analysis.fit_result.iterations, versus=temperature)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65",
    "metadata": {},
    "source": [
     "Plot unit cell parameters vs. temperature."
@@ -752,18 +785,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63",
+   "id": "66",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.fit.series(structure.cell.length_a, versus=temperature)\n",
-    "project.display.fit.series(structure.cell.length_b, versus=temperature)\n",
-    "project.display.fit.series(structure.cell.length_c, versus=temperature)"
+    "display.fit.series(struct.cell.length_a, versus=temperature)\n",
+    "display.fit.series(struct.cell.length_b, versus=temperature)\n",
+    "display.fit.series(struct.cell.length_c, versus=temperature)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "64",
+   "id": "67",
    "metadata": {},
    "source": [
     "Plot isotropic displacement parameters vs. temperature."
@@ -772,35 +805,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65",
+   "id": "68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['Co1'].adp_iso,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['Si'].adp_iso,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O1'].adp_iso,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O2'].adp_iso,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O3'].adp_iso,\n",
-    "    versus=temperature,\n",
-    ")"
+    "display.fit.series(struct.atom_sites['Co1'].adp_iso, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['Si'].adp_iso, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O1'].adp_iso, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O2'].adp_iso, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O3'].adp_iso, versus=temperature)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66",
+   "id": "69",
    "metadata": {},
    "source": [
     "Plot selected fractional coordinates vs. temperature."
@@ -809,30 +827,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67",
+   "id": "70",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['Co2'].fract_x,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['Co2'].fract_z,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O1'].fract_z,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O2'].fract_z,\n",
-    "    versus=temperature,\n",
-    ")\n",
-    "project.display.fit.series(\n",
-    "    structure.atom_sites['O3'].fract_z,\n",
-    "    versus=temperature,\n",
-    ")"
+    "display.fit.series(struct.atom_sites['Co2'].fract_x, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['Co2'].fract_z, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O1'].fract_z, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O2'].fract_z, versus=temperature)\n",
+    "display.fit.series(struct.atom_sites['O3'].fract_z, versus=temperature)"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-17.py b/docs/docs/tutorials/ed-17.py
index 24e648c56..93f691fb7 100644
--- a/docs/docs/tutorials/ed-17.py
+++ b/docs/docs/tutorials/ed-17.py
@@ -16,17 +16,20 @@
 # %% [markdown]
 # ## Step 1: Define Project
 #
-# The project object manages structures, experiments, and analysis.
+# The project object manages structures, experiments, analysis, display,
+# and other related components.
 
 # %%
-project = ed.Project()
+project = ed.Project(name='cosio_d20')
+analysis = project.analysis
+display = project.display
 
 # %% [markdown]
 # The project must be saved before running sequential fitting, so that
 # results can be written to `analysis/results.csv`.
 
 # %%
-project.save_as(dir_path='projects/cosio', temporary=False)
+project.save_as(dir_path='projects/cosio_d20')
 
 # %% [markdown]
 # ## Step 2: Define Crystal Structure
@@ -38,28 +41,28 @@
 
 # %%
 project.structures.create(name='cosio')
-structure = project.structures['cosio']
+struct = project.structures['cosio']
 
 # %% [markdown]
 # #### Set Space Group
 
 # %%
-structure.space_group.name_h_m = 'P n m a'
-structure.space_group.it_coordinate_system_code = 'abc'
+struct.space_group.name_h_m = 'P n m a'
+struct.space_group.it_coordinate_system_code = 'abc'
 
 # %% [markdown]
 # #### Set Unit Cell
 
 # %%
-structure.cell.length_a = 10.31
-structure.cell.length_b = 6.0
-structure.cell.length_c = 4.79
+struct.cell.length_a = 10.31
+struct.cell.length_b = 6.0
+struct.cell.length_c = 4.79
 
 # %% [markdown]
 # #### Set Atom Sites
 
 # %%
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='Co1',
     type_symbol='Co',
     fract_x=0,
@@ -68,7 +71,7 @@
     wyckoff_letter='a',
     adp_iso=0.3,
 )
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='Co2',
     type_symbol='Co',
     fract_x=0.279,
@@ -77,7 +80,7 @@
     wyckoff_letter='c',
     adp_iso=0.3,
 )
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='Si',
     type_symbol='Si',
     fract_x=0.094,
@@ -86,7 +89,7 @@
     wyckoff_letter='c',
     adp_iso=0.34,
 )
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='O1',
     type_symbol='O',
     fract_x=0.091,
@@ -95,7 +98,7 @@
     wyckoff_letter='c',
     adp_iso=0.63,
 )
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='O2',
     type_symbol='O',
     fract_x=0.448,
@@ -104,7 +107,7 @@
     wyckoff_letter='c',
     adp_iso=0.59,
 )
-structure.atom_sites.create(
+struct.atom_sites.create(
     label='O3',
     type_symbol='O',
     fract_x=0.164,
@@ -125,7 +128,7 @@
 # #### Download Measured Data
 
 # %%
-zip_path = ed.download_data(id=27, destination='data')
+zip_path = ed.download_data(id=25, destination='data')
 
 # %% [markdown]
 # #### Extract Data Files
@@ -205,28 +208,28 @@
 # #### Set Free Parameters
 
 # %%
-structure.cell.length_a.free = True
-structure.cell.length_b.free = True
-structure.cell.length_c.free = True
+struct.cell.length_a.free = True
+struct.cell.length_b.free = True
+struct.cell.length_c.free = True
 
-structure.atom_sites['Co2'].fract_x.free = True
-structure.atom_sites['Co2'].fract_z.free = True
-structure.atom_sites['Si'].fract_x.free = True
-structure.atom_sites['Si'].fract_z.free = True
-structure.atom_sites['O1'].fract_x.free = True
-structure.atom_sites['O1'].fract_z.free = True
-structure.atom_sites['O2'].fract_x.free = True
-structure.atom_sites['O2'].fract_z.free = True
-structure.atom_sites['O3'].fract_x.free = True
-structure.atom_sites['O3'].fract_y.free = True
-structure.atom_sites['O3'].fract_z.free = True
+struct.atom_sites['Co2'].fract_x.free = True
+struct.atom_sites['Co2'].fract_z.free = True
+struct.atom_sites['Si'].fract_x.free = True
+struct.atom_sites['Si'].fract_z.free = True
+struct.atom_sites['O1'].fract_x.free = True
+struct.atom_sites['O1'].fract_z.free = True
+struct.atom_sites['O2'].fract_x.free = True
+struct.atom_sites['O2'].fract_z.free = True
+struct.atom_sites['O3'].fract_x.free = True
+struct.atom_sites['O3'].fract_y.free = True
+struct.atom_sites['O3'].fract_z.free = True
 
-structure.atom_sites['Co1'].adp_iso.free = True
-structure.atom_sites['Co2'].adp_iso.free = True
-structure.atom_sites['Si'].adp_iso.free = True
-structure.atom_sites['O1'].adp_iso.free = True
-structure.atom_sites['O2'].adp_iso.free = True
-structure.atom_sites['O3'].adp_iso.free = True
+struct.atom_sites['Co1'].adp_iso.free = True
+struct.atom_sites['Co2'].adp_iso.free = True
+struct.atom_sites['Si'].adp_iso.free = True
+struct.atom_sites['O1'].adp_iso.free = True
+struct.atom_sites['O2'].adp_iso.free = True
+struct.atom_sites['O3'].adp_iso.free = True
 
 # %%
 expt.linked_phases['cosio'].scale.free = True
@@ -247,26 +250,26 @@
 # Set aliases for parameters.
 
 # %%
-project.analysis.aliases.create(
+analysis.aliases.create(
     label='biso_Co1',
-    param=structure.atom_sites['Co1'].adp_iso,
+    param=struct.atom_sites['Co1'].adp_iso,
 )
-project.analysis.aliases.create(
+analysis.aliases.create(
     label='biso_Co2',
-    param=structure.atom_sites['Co2'].adp_iso,
+    param=struct.atom_sites['Co2'].adp_iso,
 )
 
 # %% [markdown]
 # Set constraints.
 
 # %%
-project.analysis.constraints.create(expression='biso_Co2 = biso_Co1')
+analysis.constraints.create(expression='biso_Co2 = biso_Co1')
 
 # %% [markdown]
 # #### Set Minimizer
 
 # %%
-project.analysis.fitting.minimizer_type = 'bumps (lm)'
+analysis.fitting.minimizer_type = 'bumps (lm)'
 
 # %% [markdown]
 # #### Run Single Fitting
@@ -277,19 +280,22 @@
 # if the initial parameters are far from optimal.
 
 # %%
-project.analysis.fit()
+analysis.fit()
+
+# %%
+display.fit.results()
 
 # %% [markdown]
 # #### Show parameter correlations
 
 # %%
-project.display.fit.correlations()
+display.fit.correlations()
 
 # %% [markdown]
 # #### Compare measured and calculated patterns for the first fit.
 
 # %%
-project.display.pattern(expt_name='d20')
+display.pattern(expt_name='d20')
 
 # %% [markdown]
 # #### Run Sequential Fitting
@@ -310,7 +316,7 @@
 temperature = 'diffrn.ambient_temperature'
 
 # %%
-project.analysis.sequential_fit_extract.create(
+analysis.sequential_fit_extract.create(
     id='temperature',
     target=temperature,
     pattern=r'^TEMP\s+([0-9.]+)',
@@ -321,16 +327,16 @@
 # Set the sequential fitting parameters.
 
 # %%
-project.analysis.fitting_mode_type = 'sequential'
-project.analysis.sequential_fit.data_dir = scan_data_dir
-project.analysis.sequential_fit.max_workers = 'auto'
-project.analysis.sequential_fit.reverse = True
+analysis.fitting_mode_type = 'sequential'
+analysis.sequential_fit.data_dir = scan_data_dir
+analysis.sequential_fit.max_workers = 'auto'
+analysis.sequential_fit.reverse = True
 
 # %% [markdown]
 # Run the sequential fit over all data files in the scan directory.
 
 # %%
-project.analysis.fit()
+analysis.fit()
 
 # %% [markdown]
 # #### Replay a Dataset
@@ -339,7 +345,7 @@
 
 # %%
 project.apply_params_from_csv(row_index=0)
-project.display.pattern(expt_name='d20')
+display.pattern(expt_name='d20')
 
 # %% [markdown]
 #
@@ -347,67 +353,45 @@
 
 # %%
 project.apply_params_from_csv(row_index=-1)
-project.display.pattern(expt_name='d20')
+display.pattern(expt_name='d20')
 
 # %% [markdown]
 # #### Plot Parameter Evolution
 #
 # Reuse the extracted diffrn path as the x-axis in the following plots.
 
+# %% [markdown]
+# Plot fit quality metrics vs. temperature.
+
+# %%
+display.fit.series(analysis.fit_result.success, versus=temperature)
+display.fit.series(analysis.fit_result.reduced_chi_square, versus=temperature)
+display.fit.series(analysis.fit_result.iterations, versus=temperature)
+
 # %% [markdown]
 # Plot unit cell parameters vs. temperature.
 
 # %%
-project.display.fit.series(structure.cell.length_a, versus=temperature)
-project.display.fit.series(structure.cell.length_b, versus=temperature)
-project.display.fit.series(structure.cell.length_c, versus=temperature)
+display.fit.series(struct.cell.length_a, versus=temperature)
+display.fit.series(struct.cell.length_b, versus=temperature)
+display.fit.series(struct.cell.length_c, versus=temperature)
 
 # %% [markdown]
 # Plot isotropic displacement parameters vs. temperature.
 
 # %%
-project.display.fit.series(
-    structure.atom_sites['Co1'].adp_iso,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['Si'].adp_iso,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O1'].adp_iso,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O2'].adp_iso,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O3'].adp_iso,
-    versus=temperature,
-)
+display.fit.series(struct.atom_sites['Co1'].adp_iso, versus=temperature)
+display.fit.series(struct.atom_sites['Si'].adp_iso, versus=temperature)
+display.fit.series(struct.atom_sites['O1'].adp_iso, versus=temperature)
+display.fit.series(struct.atom_sites['O2'].adp_iso, versus=temperature)
+display.fit.series(struct.atom_sites['O3'].adp_iso, versus=temperature)
 
 # %% [markdown]
 # Plot selected fractional coordinates vs. temperature.
 
 # %%
-project.display.fit.series(
-    structure.atom_sites['Co2'].fract_x,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['Co2'].fract_z,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O1'].fract_z,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O2'].fract_z,
-    versus=temperature,
-)
-project.display.fit.series(
-    structure.atom_sites['O3'].fract_z,
-    versus=temperature,
-)
+display.fit.series(struct.atom_sites['Co2'].fract_x, versus=temperature)
+display.fit.series(struct.atom_sites['Co2'].fract_z, versus=temperature)
+display.fit.series(struct.atom_sites['O1'].fract_z, versus=temperature)
+display.fit.series(struct.atom_sites['O2'].fract_z, versus=temperature)
+display.fit.series(struct.atom_sites['O3'].fract_z, versus=temperature)
diff --git a/docs/docs/tutorials/ed-18.ipynb b/docs/docs/tutorials/ed-18.ipynb
index c1200010a..7473bc6e5 100644
--- a/docs/docs/tutorials/ed-18.ipynb
+++ b/docs/docs/tutorials/ed-18.ipynb
@@ -53,8 +53,7 @@
    "outputs": [],
    "source": [
     "from easydiffraction import Project\n",
-    "from easydiffraction import download_data\n",
-    "from easydiffraction import extract_project_from_zip"
+    "from easydiffraction import download_data"
    ]
   },
   {
@@ -62,7 +61,7 @@
    "id": "4",
    "metadata": {},
    "source": [
-    "## Download Project Archive"
+    "## Download Saved Project"
    ]
   },
   {
@@ -72,31 +71,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zip_path = download_data(id=30, destination='data')"
+    "project_dir = download_data(id=36, destination='projects')"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "6",
    "metadata": {},
-   "source": [
-    "## Extract Project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project_dir = extract_project_from_zip(zip_path, destination='data')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8",
-   "metadata": {},
    "source": [
     "## Load Project"
    ]
@@ -104,7 +85,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Perform Analysis"
@@ -122,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12",
+   "id": "10",
    "metadata": {},
    "source": [
     "## Show Results"
@@ -140,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "13",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/docs/tutorials/ed-18.py b/docs/docs/tutorials/ed-18.py
index 7c4fff9fc..31e903c04 100644
--- a/docs/docs/tutorials/ed-18.py
+++ b/docs/docs/tutorials/ed-18.py
@@ -17,19 +17,12 @@
 # %%
 from easydiffraction import Project
 from easydiffraction import download_data
-from easydiffraction import extract_project_from_zip
 
 # %% [markdown]
-# ## Download Project Archive
+# ## Download Saved Project
 
 # %%
-zip_path = download_data(id=30, destination='data')
-
-# %% [markdown]
-# ## Extract Project
-
-# %%
-project_dir = extract_project_from_zip(zip_path, destination='data')
+project_dir = download_data(id=36, destination='projects')
 
 # %% [markdown]
 # ## Load Project
diff --git a/docs/docs/tutorials/ed-2.ipynb b/docs/docs/tutorials/ed-2.ipynb
index e3338eec8..1212cbcc7 100644
--- a/docs/docs/tutorials/ed-2.ipynb
+++ b/docs/docs/tutorials/ed-2.ipynb
@@ -519,6 +519,24 @@
    "source": [
     "project.display.pattern(expt_name='hrpt')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42",
+   "metadata": {},
+   "source": [
+    "## Step 7: Save Project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.save_as('projects/lbco_hrpt')"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/docs/tutorials/ed-2.py b/docs/docs/tutorials/ed-2.py
index a8e9e3d22..220ebae9c 100644
--- a/docs/docs/tutorials/ed-2.py
+++ b/docs/docs/tutorials/ed-2.py
@@ -227,3 +227,9 @@
 
 # %%
 project.display.pattern(expt_name='hrpt')
+
+# %% [markdown]
+# ## Step 7: Save Project
+
+# %%
+project.save_as('projects/lbco_hrpt')
diff --git a/docs/docs/tutorials/ed-20.ipynb b/docs/docs/tutorials/ed-20.ipynb
index 688fc593a..086d7d185 100644
--- a/docs/docs/tutorials/ed-20.ipynb
+++ b/docs/docs/tutorials/ed-20.ipynb
@@ -459,7 +459,7 @@
    "outputs": [],
    "source": [
     "project = Project(name='beer')\n",
-    "project.save_as(dir_path='beer_mcstas')"
+    "project.save_as(dir_path='projects/beer_mcstas')"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 4905c6f09..689c3d57a 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -74,7 +74,10 @@
     "\n",
     "The project object keeps structures, experiments, fit settings, and\n",
     "plotting utilities together in a single place. We will build the full\n",
-    "workflow inside this object."
+    "workflow inside this object.\n",
+    "\n",
+    "Save the project to a directory early on so that you can easily reload\n",
+    "it later if needed."
    ]
   },
   {
@@ -88,9 +91,19 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "6",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.save_as('projects/lbco_hrpt_bayesian')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
    "source": [
     "## Step 2: Build the Structural Model\n",
     "\n",
@@ -102,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11",
+   "id": "12",
    "metadata": {},
    "source": [
     "The atom-site definitions below form the starting structural model. The\n",
@@ -153,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Step 3: Define the Diffraction Experiment\n",
@@ -215,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14",
+   "id": "15",
    "metadata": {},
    "source": [
     "Download the measured data from the repository. Alternatively, you\n",
@@ -226,7 +239,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16",
+   "id": "17",
    "metadata": {},
    "source": [
     "Create the experiment object and specify the sample form, beam mode,\n",
@@ -245,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19",
+   "id": "20",
    "metadata": {},
    "source": [
     "Link the structural phase to the experiment."
@@ -279,7 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +301,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21",
+   "id": "22",
    "metadata": {},
    "source": [
     "Set instrument and peak profile parameters.\n",
@@ -300,7 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +324,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "23",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24",
+   "id": "25",
    "metadata": {},
    "source": [
     "Add background points and excluded regions.\n",
@@ -335,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27",
+   "id": "28",
    "metadata": {},
    "source": [
     "## Step 4: Run an Initial Local Refinement\n",
@@ -378,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30",
+   "id": "31",
    "metadata": {},
    "source": [
     "We choose the BUMPS Levenberg-Marquardt minimizer as a fast local\n",
@@ -411,7 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31",
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +434,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32",
+   "id": "33",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,7 +444,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "33",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,7 +454,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35",
+   "id": "36",
    "metadata": {},
    "source": [
     "The correlation plot shows how strongly the fitted parameters move\n",
@@ -462,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36",
+   "id": "37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +485,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,7 +494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38",
+   "id": "39",
    "metadata": {},
    "source": [
     "## Step 5: Prepare for Bayesian Sampling\n",
@@ -503,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "39",
+   "id": "40",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40",
+   "id": "41",
    "metadata": {},
    "source": [
     "Set fit bounds for all free parameters using the default multiplier of\n",
@@ -524,7 +537,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41",
+   "id": "42",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42",
+   "id": "43",
    "metadata": {},
    "source": [
     "Displaying the free parameters again is a convenient way to confirm\n",
@@ -545,7 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43",
+   "id": "44",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44",
+   "id": "45",
    "metadata": {},
    "source": [
     "## Step 6: Configure and Run DREAM\n",
@@ -580,7 +593,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45",
+   "id": "46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,7 +603,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "46",
+   "id": "47",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,17 +613,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47",
+   "id": "48",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fitting.minimizer.steps = 300  # lower than the default 3000"
+    "project.analysis.fitting.minimizer.steps = 100  # lower than the default 3000\n",
+    "project.analysis.fitting.minimizer.burn = 20  # lower than the default 600"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "48",
+   "id": "49",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -619,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49",
+   "id": "50",
    "metadata": {},
    "source": [
     "## Step 7: Inspect Bayesian Results\n",
@@ -632,7 +646,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "50",
+   "id": "51",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,7 +655,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51",
+   "id": "52",
    "metadata": {},
    "source": [
     "The correlation and posterior-pair plots are complementary:\n",
@@ -658,7 +672,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "52",
+   "id": "53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +682,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53",
+   "id": "54",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +691,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54",
+   "id": "55",
    "metadata": {},
    "source": [
     "The one-dimensional posterior distributions below make it easier to\n",
@@ -688,7 +702,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "55",
+   "id": "56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -697,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56",
+   "id": "57",
    "metadata": {},
    "source": [
     "Finally, the posterior predictive plot propagates the sampled parameter\n",
@@ -709,7 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57",
+   "id": "58",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58",
+   "id": "59",
    "metadata": {},
    "source": [
     "A final zoomed measured-vs-calculated plot is useful for checking how\n",
@@ -729,7 +743,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "59",
+   "id": "60",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 87f003e46..df32bcabe 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -33,10 +33,16 @@
 # The project object keeps structures, experiments, fit settings, and
 # plotting utilities together in a single place. We will build the full
 # workflow inside this object.
+#
+# Save the project to a directory early on so that you can easily reload
+# it later if needed.
 
 # %%
 project = ed.Project()
 
+# %%
+project.save_as('projects/lbco_hrpt_bayesian')
+
 # %% [markdown]
 # ## Step 2: Build the Structural Model
 #
@@ -291,7 +297,8 @@
 project.analysis.fitting.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fitting.minimizer.steps = 300  # lower than the default 3000
+project.analysis.fitting.minimizer.steps = 100  # lower than the default 3000
+project.analysis.fitting.minimizer.burn = 20  # lower than the default 600
 
 # %%
 project.analysis.fit()
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
index bacb7c777..a8d511132 100644
--- a/docs/docs/tutorials/ed-22.ipynb
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -482,7 +482,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fitting.minimizer.steps = 500  # lower than the default 3000"
+    "project.analysis.fitting.minimizer.steps = 100  # lower than the default 3000\n",
+    "project.analysis.fitting.minimizer.burn = 20  # lower than the default 600"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index da43c6d52..cbc08f74e 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -219,7 +219,8 @@
 project.analysis.fitting.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fitting.minimizer.steps = 500  # lower than the default 3000
+project.analysis.fitting.minimizer.steps = 100  # lower than the default 3000
+project.analysis.fitting.minimizer.burn = 20  # lower than the default 600
 
 # %%
 project.analysis.fit()
diff --git a/docs/docs/tutorials/ed-23.ipynb b/docs/docs/tutorials/ed-23.ipynb
index d20045c6c..11a28c791 100644
--- a/docs/docs/tutorials/ed-23.ipynb
+++ b/docs/docs/tutorials/ed-23.ipynb
@@ -56,10 +56,11 @@
    "id": "4",
    "metadata": {},
    "source": [
-    "## Download Saved Project Archive\n",
+    "## Download Saved Project\n",
     "\n",
-    "The archive should contain a saved project directory with a partially\n",
-    "completed sequential fit, including `analysis/results.csv`."
+    "The returned path points directly to the saved project directory with\n",
+    "a partially completed sequential fit, including\n",
+    "`analysis/results.csv`."
    ]
   },
   {
@@ -69,34 +70,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zip_path = ed.download_data(id=34, destination='data')"
+    "project_dir = ed.download_data(id=37, destination='projects')"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "6",
    "metadata": {},
-   "source": [
-    "## Extract Project\n",
-    "\n",
-    "Extract the saved project directory locally. For a project you\n",
-    "already have on disk, set `project_dir` directly instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project_dir = ed.extract_project_from_zip(zip_path, destination='projects')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8",
-   "metadata": {},
    "source": [
     "## Load Saved Project"
    ]
@@ -104,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10",
+   "id": "8",
    "metadata": {},
    "source": [
     "## Resume Sequential Analysis\n",
@@ -127,7 +107,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12",
+   "id": "10",
    "metadata": {},
    "source": [
     "## Replay Fitted Datasets\n",
@@ -147,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "13",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14",
+   "id": "12",
    "metadata": {},
    "source": [
     "\n",
@@ -167,7 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "15",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,55 +157,70 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Plot Parameter Evolution\n",
     "\n",
     "Use the same persisted diffrn path stored in `analysis/results.csv`\n",
-    "for the x-axis. Omitting `param` plots every fitted parameter one\n",
-    "after another."
+    "for the x-axis."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
     "temperature = 'diffrn.ambient_temperature'"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "Plot fit quality metrics vs. temperature."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.fit.series(versus=temperature)"
+    "project.display.fit.series(\n",
+    "    project.analysis.fit_result.success,\n",
+    "    versus=temperature,\n",
+    ")\n",
+    "project.display.fit.series(\n",
+    "    project.analysis.fit_result.reduced_chi_square,\n",
+    "    versus=temperature,\n",
+    ")\n",
+    "project.display.fit.series(\n",
+    "    project.analysis.fit_result.iterations,\n",
+    "    versus=temperature,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "19",
+   "id": "18",
    "metadata": {},
    "source": [
-    "## Save Project\n",
-    "\n",
-    "Save the updated project so the appended `analysis/results.csv` and\n",
-    "refreshed summary files remain on disk."
+    "Omitting `param` plots every fitted parameter one after another."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.save()"
+    "project.display.fit.series(versus=temperature)"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-23.py b/docs/docs/tutorials/ed-23.py
index 659cbcbb1..a4dbc8ed7 100644
--- a/docs/docs/tutorials/ed-23.py
+++ b/docs/docs/tutorials/ed-23.py
@@ -14,22 +14,14 @@
 import easydiffraction as ed
 
 # %% [markdown]
-# ## Download Saved Project Archive
+# ## Download Saved Project
 #
-# The archive should contain a saved project directory with a partially
-# completed sequential fit, including `analysis/results.csv`.
+# The returned path points directly to the saved project directory with
+# a partially completed sequential fit, including
+# `analysis/results.csv`.
 
 # %%
-zip_path = ed.download_data(id=34, destination='data')
-
-# %% [markdown]
-# ## Extract Project
-#
-# Extract the saved project directory locally. For a project you
-# already have on disk, set `project_dir` directly instead.
-
-# %%
-project_dir = ed.extract_project_from_zip(zip_path, destination='projects')
+project_dir = ed.download_data(id=37, destination='projects')
 
 # %% [markdown]
 # ## Load Saved Project
@@ -69,11 +61,30 @@
 # ## Plot Parameter Evolution
 #
 # Use the same persisted diffrn path stored in `analysis/results.csv`
-# for the x-axis. Omitting `param` plots every fitted parameter one
-# after another.
+# for the x-axis.
 
 # %%
 temperature = 'diffrn.ambient_temperature'
 
+# %% [markdown]
+# Plot fit quality metrics vs. temperature.
+
+# %%
+project.display.fit.series(
+    project.analysis.fit_result.success,
+    versus=temperature,
+)
+project.display.fit.series(
+    project.analysis.fit_result.reduced_chi_square,
+    versus=temperature,
+)
+project.display.fit.series(
+    project.analysis.fit_result.iterations,
+    versus=temperature,
+)
+
+# %% [markdown]
+# Omitting `param` plots every fitted parameter one after another.
+
 # %%
 project.display.fit.series(versus=temperature)
diff --git a/docs/docs/tutorials/ed-24.ipynb b/docs/docs/tutorials/ed-24.ipynb
new file mode 100644
index 000000000..745d962f8
--- /dev/null
+++ b/docs/docs/tutorials/ed-24.ipynb
@@ -0,0 +1,223 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Load Saved Bayesian Project: LBCO, HRPT\n",
+    "\n",
+    "This tutorial shows how to reopen the Bayesian project created in\n",
+    "`ed-21.py` and inspect the saved fit results without rerunning DREAM.\n",
+    "\n",
+    "The project already contains posterior samples together with cached\n",
+    "posterior density, pair, and predictive data, so the plots below are\n",
+    "restored directly from disk."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Download Saved Project\n",
+    "\n",
+    "The returned path points directly to the saved project directory with\n",
+    "the completed Bayesian fit and persisted posterior samples and plot\n",
+    "caches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_dir = ed.download_data(id=35, destination='projects')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Load the Saved Bayesian Project\n",
+    "\n",
+    "Loading restores the persisted fit state, posterior samples, and plot\n",
+    "caches. No new fit is launched in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project.load(project_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "## Review the Saved Fit Summary\n",
+    "\n",
+    "The fit summary reports the committed point estimate, sampler\n",
+    "settings, convergence diagnostics, and posterior parameter summaries\n",
+    "from the saved Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.fit.results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "## Show Correlations\n",
+    "\n",
+    "The correlation matrix is restored from the saved project state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.fit.correlations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "## Inspect Posterior Densities and Pair Structure\n",
+    "\n",
+    "The pair plot and one-dimensional posterior distributions now load\n",
+    "from the persisted caches generated when the Bayesian fit was saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.posterior.pairs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.posterior.distribution()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "## Plot Posterior Predictive Checks\n",
+    "\n",
+    "The posterior predictive view reuses the cached predictive summary\n",
+    "stored in the project rather than recalculating it on first display.\n",
+    "It overlays the 95% credible interval propagated from the posterior\n",
+    "samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.posterior.predictive(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "A zoomed view is useful for checking the propagated uncertainty in a\n",
+    "narrow region of the diffraction pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.posterior.predictive(expt_name='hrpt', x_min=92, x_max=93)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-24.py b/docs/docs/tutorials/ed-24.py
new file mode 100644
index 000000000..9e5d40f87
--- /dev/null
+++ b/docs/docs/tutorials/ed-24.py
@@ -0,0 +1,82 @@
+# %% [markdown]
+# # Load Saved Bayesian Project: LBCO, HRPT
+#
+# This tutorial shows how to reopen the Bayesian project created in
+# `ed-21.py` and inspect the saved fit results without rerunning DREAM.
+#
+# The project already contains posterior samples together with cached
+# posterior density, pair, and predictive data, so the plots below are
+# restored directly from disk.
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Download Saved Project
+#
+# The returned path points directly to the saved project directory with
+# the completed Bayesian fit and persisted posterior samples and plot
+# caches.
+
+# %%
+project_dir = ed.download_data(id=35, destination='projects')
+
+# %% [markdown]
+# ## Load the Saved Bayesian Project
+#
+# Loading restores the persisted fit state, posterior samples, and plot
+# caches. No new fit is launched in this tutorial.
+
+# %%
+project = ed.Project.load(project_dir)
+
+# %% [markdown]
+# ## Review the Saved Fit Summary
+#
+# The fit summary reports the committed point estimate, sampler
+# settings, convergence diagnostics, and posterior parameter summaries
+# from the saved Bayesian run.
+
+# %%
+project.display.fit.results()
+
+# %% [markdown]
+# ## Show Correlations
+#
+# The correlation matrix is restored from the saved project state.
+
+# %%
+project.display.fit.correlations()
+
+# %% [markdown]
+# ## Inspect Posterior Densities and Pair Structure
+#
+# The pair plot and one-dimensional posterior distributions now load
+# from the persisted caches generated when the Bayesian fit was saved.
+
+# %%
+project.display.posterior.pairs()
+
+# %%
+project.display.posterior.distribution()
+
+# %% [markdown]
+# ## Plot Posterior Predictive Checks
+#
+# The posterior predictive view reuses the cached predictive summary
+# stored in the project rather than recalculating it on first display.
+# It overlays the 95% credible interval propagated from the posterior
+# samples.
+
+# %%
+project.display.posterior.predictive(expt_name='hrpt')
+
+# %% [markdown]
+# A zoomed view is useful for checking the propagated uncertainty in a
+# narrow region of the diffraction pattern.
+
+# %%
+project.display.posterior.predictive(expt_name='hrpt', x_min=92, x_max=93)
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index a89601095..b789645d0 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -17,10 +17,6 @@ The tutorials are organized into the following categories:
 
 ## Getting Started
 
-- [LBCO `quick` `load`](ed-18.ipynb) – The most minimal example showing
-  how to load a previously saved project from a directory and run
-  refinement. Useful when a project has already been set up and saved in
-  a prior session.
 - [LBCO `quick` `code`](ed-2.ipynb) – A minimal example intended as a
   quick reference for users already familiar with the EasyDiffraction
   API or who want to see an example refinement when both the structure
@@ -42,6 +38,19 @@ The tutorials are organized into the following categories:
   descriptions of every step, making it suitable for users who are new
   to EasyDiffraction or those who prefer a more guided approach.
 
+## Load Project
+
+- [LBCO Single Fit](ed-18.ipynb) – The most minimal example showing how
+  to load a previously saved project from a directory and continue
+  working with it.
+- [Co2SiO4 Sequential Fit](ed-23.ipynb) – Resumes a sequential
+  refinement from an existing `analysis/results.csv` after an incomplete
+  previous run.
+- [LBCO Bayesian Display](ed-24.ipynb) – Shows how to load the saved
+  project after a Bayesian analysis and inspect the persisted fit
+  summary, correlation matrix, posterior plots, and predictive checks
+  without rerunning MCMC sampling.
+
 ## Powder Diffraction
 
 - [Co2SiO4 `pd-neut-cwl`](ed-5.ipynb) – Demonstrates a Rietveld
@@ -89,9 +98,6 @@ The tutorials are organized into the following categories:
 - [Co2SiO4 Temperature scan](ed-17.ipynb) – Sequential Rietveld
   refinement of Co2SiO4 using constant wavelength neutron powder
   diffraction data from D20 at ILL across a temperature scan.
-- [Co2SiO4 Temperature scan, resumed](ed-23.ipynb) – Continue a saved
-  sequential refinement of Co2SiO4 from an existing
-  `analysis/results.csv` after an incomplete previous run.
 
 ## Simulated Data
 
diff --git a/docs/docs/user-guide/analysis-workflow/project.md b/docs/docs/user-guide/analysis-workflow/project.md
index 5e8e8b417..72f77dc18 100644
--- a/docs/docs/user-guide/analysis-workflow/project.md
+++ b/docs/docs/user-guide/analysis-workflow/project.md
@@ -14,7 +14,7 @@ contribution from multiple **structures**.
 EasyDiffraction allows you to:
 
 - **Manually create** a new project by specifying its metadata.
-- **Load an existing project** from a file (**CIF** format).
+- **Load an existing saved project** from its project directory.
 
 Below are instructions on how to set up a project in EasyDiffraction. It
 is assumed that you have already imported the `easydiffraction` package,
@@ -55,14 +55,15 @@ you can just call the `save`:
 project.save()
 ```
 
-## Loading a Project from CIF
+## Loading a Saved Project
 
-If you have an existing project, you can load it directly from a CIF
-file. This is useful for reusing previously defined projects or sharing
-them with others.
+If you have an existing saved project, load it from the project
+directory created by `project.save_as()` or `project.save()`. This is
+useful for continuing a previous session or reusing a downloaded saved
+project.
 
 ```python
-project.load('data/lbco_hrpt.cif')
+project = ed.Project.load('lbco_hrpt')
 ```
 
 ## Project Structure
@@ -82,9 +83,10 @@ The example below illustrates a typical **project structure** for a
 ├── 📁 experiments    - Folder with experiment settings and measured data.
 │   ├── 📄 <span class="orange"><b>hrpt.cif</b></span>   - Instrumental parameters, calculator selection and measured data from HRPT@PSI.
 │   └── ...
-├── 📄 <span class="orange"><b>analysis.cif</b></span>   - Settings for data analysis (minimizer, fit mode, etc.).
-└── 📁 summary
-    └── 📄 report.cif - Summary report after structure refinement.
+├── 📁 analysis       - Analysis settings and optional persisted Bayesian arrays.
+│   ├── 📄 <span class="orange"><b>analysis.cif</b></span> - Settings for data analysis (minimizer, fit mode, constraints, persisted fit state).
+│   └── 📄 <span class="orange"><b>results.h5</b></span>   - Optional Bayesian sidecar with posterior and predictive arrays.
+└── 📄 <span class="orange"><b>summary.cif</b></span>    - Summary report after structure refinement.
 </pre>
 </div>
 
@@ -110,13 +112,16 @@ This file stores project-level metadata and display configuration.
 
 <div class="cif">
 <pre>
-data_<span class="red"><b>La0.5Ba0.5CoO3</b></span>
-
+<span class="blue"><b>_project</b>.id</span>          lbco_hrpt
 <span class="blue"><b>_project</b>.title</span>       "La0.5Ba0.5CoO3 from neutron diffraction at HRPT@PSI"
 <span class="blue"><b>_project</b>.description</span> "neutrons, powder, constant wavelength, HRPT@PSI"
 
-<span class="blue"><b>_display</b>.chart_engine</span>  asciichartpy
-<span class="blue"><b>_display</b>.table_engine</span>   rich
+<span class="blue"><b>_project</b>.created</span>     "18 May 2026 10:15:00"
+<span class="blue"><b>_project</b>.last_modified</span> "18 May 2026 10:20:00"
+
+<span class="blue"><b>_rendering</b>.chart_engine</span> auto
+<span class="blue"><b>_rendering</b>.table_engine</span> auto
+<span class="blue"><b>_verbosity</b>.fit</span>         full
 </pre>
 </div>
 
@@ -233,7 +238,7 @@ loop_
 
 <!-- prettier-ignore-end -->
 
-### 4. <span class="orange">analysis.cif</span>
+### 4. analysis / <span class="orange">analysis.cif</span>
 
 This file contains settings used for data analysis, including the choice
 of **calculation** and **fitting** engines, as well as user defined
@@ -243,8 +248,8 @@ of **calculation** and **fitting** engines, as well as user defined
 
 <div class="cif">
 <pre>
-<span class="blue"><b>_fit</b>.minimizer_type</span>          "lmfit (leastsq)"
-<span class="blue"><b>_fit</b>.mode</span>                    single
+<span class="blue"><b>_fitting</b>.mode_type</span>              single
+<span class="blue"><b>_fitting</b>.minimizer_type</span>         lmfit
 
 loop_
 <span class="green"><b>_alias</b>.label</span>
@@ -263,6 +268,9 @@ loop_
 
 <!-- prettier-ignore-end -->
 
+When a Bayesian fit stores persisted posterior or predictive arrays, the
+same `analysis/` directory also contains `results.h5`.
+
 <br>
 
 ---
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index c54ff9353..02f862023 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -193,10 +193,13 @@ nav:
   - Tutorials:
       - Tutorials: tutorials/index.md
       - Getting Started:
-          - LBCO quick load: tutorials/ed-18.ipynb
           - LBCO quick code: tutorials/ed-2.ipynb
           - LBCO basic load: tutorials/ed-1.ipynb
           - LBCO complete: tutorials/ed-3.ipynb
+      - Load Project:
+          - LBCO Single: tutorials/ed-18.ipynb
+          - Co2SiO4 Sequential: tutorials/ed-23.ipynb
+          - LBCO Bayesian: tutorials/ed-24.ipynb
       - Powder Diffraction:
           - Co2SiO4 pd-neut-cwl: tutorials/ed-5.ipynb
           - HS pd-neut-cwl: tutorials/ed-6.ipynb
@@ -213,7 +216,6 @@ nav:
           - PbSO4 NPD+XRD: tutorials/ed-4.ipynb
           - Si Bragg+PDF: tutorials/ed-16.ipynb
           - Co2SiO4 T-scan: tutorials/ed-17.ipynb
-          - Co2SiO4 T-scan resumed: tutorials/ed-23.ipynb
       - Simulated Data:
           - LBCO+Si McStas: tutorials/ed-9.ipynb
           - BEER McStas: tutorials/ed-20.ipynb
diff --git a/pixi.lock b/pixi.lock
index 927dd5686..974cbe77e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -8150,6 +8150,7 @@ packages:
   - diffpy-pdffit2
   - diffpy-utils
   - gemmi
+  - h5py
   - lmfit
   - numpy
   - pandas
diff --git a/pixi.toml b/pixi.toml
index 958851cf2..70a8a310e 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -102,8 +102,8 @@ user = { features = ['py-max', 'user'] }
 unit-tests = 'python -m pytest tests/unit/ --color=yes -v'
 functional-tests = 'python -m pytest tests/functional/ --color=yes -v'
 integration-tests = 'python -m pytest tests/integration/ --color=yes -n auto -v'
-script-tests = 'python -m pytest tools/test_scripts.py --color=yes -n auto -v'
-notebook-tests = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --color=yes -n auto -v'
+script-tests = { cmd = 'python -m pytest tools/test_scripts.py --color=yes -n auto -v', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+notebook-tests = { cmd = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --color=yes -n auto -v', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
 
 test = { depends-on = ['unit-tests', 'functional-tests'] }
 test-all = { depends-on = [
@@ -184,10 +184,16 @@ cov = { depends-on = [
 # 📓 Notebook Management
 ########################
 
+python = { cmd = 'python', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+tutorial = { cmd = 'python', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+tutorial-benchmarks = { cmd = 'python tools/benchmark_tutorials.py', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+jupyter = { cmd = 'jupyter', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+
 notebook-convert = 'jupytext docs/docs/tutorials/*.py --from py:percent --to ipynb'
 notebook-strip = 'nbstripout docs/docs/tutorials/*.ipynb'
 notebook-tweak = 'python tools/tweak_notebooks.py docs/docs/tutorials/'
-notebook-exec = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --overwrite --color=yes -n auto -v'
+notebook-exec = { cmd = 'python -m pytest --nbmake docs/docs/tutorials/ --overwrite --color=yes -n auto -v', env = { EASYDIFFRACTION_ARTIFACT_ROOT = 'tmp/tutorials' } }
+notebook-exec-ci = { cmd = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --overwrite --color=yes -n auto -v', env = { EASYDIFFRACTION_ARTIFACT_ROOT = '.' } }
 
 notebook-prepare = { depends-on = [
   'notebook-convert',
diff --git a/pyproject.toml b/pyproject.toml
index 3a4b78a01..507a069d1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,7 @@ dependencies = [
   'diffpy.pdffit2', # Calculations of Pair Distribution Function (PDF)
   'diffpy.utils',   # Utilities for PDF calculations
   'uncertainties',  # Propagation of uncertainties
+  'h5py',           # HDF5 file handling
   'typeguard',      # Runtime type checking
   'darkdetect',     # Detecting dark mode (system-level)
   'pandas',         # Displaying tables in Jupyter notebooks
diff --git a/src/easydiffraction/__init__.py b/src/easydiffraction/__init__.py
index 11ea117c8..0e69a9c7e 100644
--- a/src/easydiffraction/__init__.py
+++ b/src/easydiffraction/__init__.py
@@ -14,5 +14,6 @@
 from easydiffraction.utils.utils import download_all_tutorials
 from easydiffraction.utils.utils import download_data
 from easydiffraction.utils.utils import download_tutorial
+from easydiffraction.utils.utils import list_data
 from easydiffraction.utils.utils import list_tutorials
 from easydiffraction.utils.utils import show_version
diff --git a/src/easydiffraction/__main__.py b/src/easydiffraction/__main__.py
index ca1d3f8c4..a66d0d8f8 100644
--- a/src/easydiffraction/__main__.py
+++ b/src/easydiffraction/__main__.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from __future__ import annotations
+
 import sys
 
 # Ensure UTF-8 output on all platforms (e.g. Windows with cp1252)
@@ -14,6 +16,90 @@
 
 app = typer.Typer(add_completion=False)
 
+_MIN_PROJECT_FIRST_ARG_COUNT = 2
+_PROJECT_COMMAND_NAMES = frozenset({'fit', 'display', 'undo'})
+_GLOBAL_COMMAND_NAMES = frozenset({
+    'list-data',
+    'download-data',
+    'list-tutorials',
+    'download-tutorial',
+    'download-all-tutorials',
+    *_PROJECT_COMMAND_NAMES,
+})
+
+
+def _normalized_cli_args(args: list[str]) -> list[str]:
+    """Return CLI args rewritten to support project-first commands."""
+    if len(args) < _MIN_PROJECT_FIRST_ARG_COUNT:
+        return args
+
+    first_arg = args[0]
+    if first_arg.startswith('-') or first_arg in _GLOBAL_COMMAND_NAMES:
+        return args
+
+    if args[1] not in _PROJECT_COMMAND_NAMES:
+        return args
+
+    return [args[1], first_arg, *args[2:]]
+
+
+def _load_project(project_dir: str) -> object:
+    """Load one saved project directory."""
+    return ed.Project.load(project_dir)
+
+
+def _display_project_patterns(project: object) -> None:
+    """Render default pattern views for all experiments."""
+    for experiment in project.experiments:
+        project.display.pattern(expt_name=experiment.name)
+
+
+def _project_fit_mode(project: object) -> str | None:
+    """Return the resolved fitting mode type for one project."""
+    return getattr(project.analysis, 'fitting_mode_type', None)
+
+
+def _project_result_kind(project: object) -> str | None:
+    """Return the resolved fit result kind for one project."""
+    result_kind = getattr(getattr(project.analysis, 'fit_result', None), 'result_kind', None)
+    return getattr(result_kind, 'value', None)
+
+
+def _display_fit_outputs(project: object) -> None:
+    """Render the standard post-fit CLI outputs."""
+    if _project_fit_mode(project) != 'sequential':
+        project.display.fit.results()
+        project.display.fit.correlations()
+    _display_project_patterns(project)
+
+
+def _display_project_outputs(project: object) -> None:
+    """Render the typical displays for the loaded project state."""
+    if _project_fit_mode(project) == 'sequential':
+        project.display.fit.series()
+        _display_project_patterns(project)
+        return
+
+    project.display.fit.results()
+    project.display.fit.correlations()
+
+    if _project_result_kind(project) == 'bayesian':
+        if project.rendering.plotter.engine == 'plotly':
+            project.display.posterior.pairs()
+        project.display.posterior.distribution()
+        for experiment in project.experiments:
+            project.display.posterior.predictive(expt_name=experiment.name)
+
+    _display_project_patterns(project)
+
+
+def run_cli(args: list[str] | None = None) -> None:
+    """
+    Run the EasyDiffraction CLI with project-first argument support.
+    """
+    cli_args = list(sys.argv[1:] if args is None else args)
+    app(args=_normalized_cli_args(cli_args))
+
 
 @app.callback(invoke_without_command=True)
 def main(
@@ -37,12 +123,38 @@ def main(
     # Otherwise, let the chosen subcommand execute.
 
 
+@app.command('list-data')
+def list_data() -> None:
+    """List available example data and project archives."""
+    ed.list_data()
+
+
 @app.command('list-tutorials')
 def list_tutorials() -> None:
     """List available tutorial notebooks."""
     ed.list_tutorials()
 
 
+@app.command('download-data')
+def download_data(
+    id: int = typer.Argument(..., help='Data ID to download.'),
+    destination: str = typer.Option(
+        'data',
+        '--destination',
+        '-d',
+        help='Directory to save the data or extracted project into.',
+    ),
+    overwrite: bool = typer.Option(  # noqa: FBT001
+        False,  # noqa: FBT003
+        '--overwrite',
+        '-o',
+        help='Overwrite an existing file or extracted project if present.',
+    ),
+) -> None:
+    """Download one example data record by ID."""
+    ed.download_data(id=id, destination=destination, overwrite=overwrite)
+
+
 @app.command('download-tutorial')
 def download_tutorial(
     id: int = typer.Argument(..., help='Tutorial ID to download.'),
@@ -82,6 +194,18 @@ def download_all_tutorials(
     ed.download_all_tutorials(destination=destination, overwrite=overwrite)
 
 
+@app.command('display')
+def display(
+    project_dir: str = typer.Argument(
+        ...,
+        help='Path to the project directory (must contain project.cif).',
+    ),
+) -> None:
+    """Display the typical outputs for a saved project state."""
+    project = _load_project(project_dir)
+    _display_project_outputs(project)
+
+
 @app.command('fit')
 def fit(
     project_dir: str = typer.Argument(
@@ -94,18 +218,28 @@ def fit(
         help='Run fitting without saving results back to the project directory.',
     ),
 ) -> None:
-    """Fit a saved project: easydiffraction fit PROJECT_DIR [--dry]."""
-    project = ed.Project.load(project_dir)
+    """Fit a saved project: easydiffraction PROJECT_DIR fit [--dry]."""
+    project = _load_project(project_dir)
     if dry:
         project.info._path = None
     project.analysis.fit()
-    if getattr(project.analysis, 'fitting_mode_type', None) != 'sequential':
-        project.display.fit.results()
-        project.display.fit.correlations()
-    for expt in project.experiments:
-        project.display.pattern(expt_name=expt.name)
-    # project.summary.show_report()
+    _display_fit_outputs(project)
+
+
+@app.command('undo')
+def undo(
+    project_dir: str = typer.Argument(
+        ...,
+        help='Path to the project directory (must contain project.cif).',
+    ),
+) -> None:
+    """
+    Undo the last fit when fit-history support exists (not implemented).
+    """
+    _load_project(project_dir)
+    typer.echo('Undo is not yet implemented.')
+    raise typer.Exit(code=1)
 
 
 if __name__ == '__main__':
-    app()
+    run_cli()
diff --git a/src/easydiffraction/analysis/__init__.py b/src/easydiffraction/analysis/__init__.py
index 0fe4386c4..f9d5e94de 100644
--- a/src/easydiffraction/analysis/__init__.py
+++ b/src/easydiffraction/analysis/__init__.py
@@ -1,6 +1,56 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from easydiffraction.analysis.categories.bayesian_convergence import BayesianConvergence
+from easydiffraction.analysis.categories.bayesian_convergence import BayesianConvergenceFactory
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCacheItem,
+)
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCaches,
+)
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCachesFactory,
+)
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCacheItem
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCaches
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCachesFactory
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriorItem,
+)
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriors,
+)
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriorsFactory,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasetItem,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasets,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasetsFactory,
+)
+from easydiffraction.analysis.categories.bayesian_result import BayesianResult
+from easydiffraction.analysis.categories.bayesian_result import BayesianResultFactory
+from easydiffraction.analysis.categories.bayesian_sampler import BayesianSampler
+from easydiffraction.analysis.categories.bayesian_sampler import BayesianSamplerFactory
+from easydiffraction.analysis.categories.deterministic_result import DeterministicResult
+from easydiffraction.analysis.categories.deterministic_result import DeterministicResultFactory
+from easydiffraction.analysis.categories.fit_parameter_correlations import (
+    FitParameterCorrelationItem,
+)
+from easydiffraction.analysis.categories.fit_parameter_correlations import FitParameterCorrelations
+from easydiffraction.analysis.categories.fit_parameter_correlations import (
+    FitParameterCorrelationsFactory,
+)
+from easydiffraction.analysis.categories.fit_parameters import FitParameterItem
+from easydiffraction.analysis.categories.fit_parameters import FitParameters
+from easydiffraction.analysis.categories.fit_parameters import FitParametersFactory
+from easydiffraction.analysis.categories.fit_result import FitResult
+from easydiffraction.analysis.categories.fit_result import FitResultFactory
 from easydiffraction.analysis.categories.fitting import Fitting
 from easydiffraction.analysis.categories.fitting import FittingFactory
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
@@ -13,4 +63,6 @@
 )
 from easydiffraction.analysis.categories.sequential_fit_extract import SequentialFitExtractFactory
 from easydiffraction.analysis.categories.sequential_fit_extract import SequentialFitExtractItem
+from easydiffraction.analysis.enums import FitCorrelationSourceEnum
 from easydiffraction.analysis.enums import FitModeEnum
+from easydiffraction.analysis.enums import FitResultKindEnum
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index 08303f65b..eccbf28d8 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -4,13 +4,35 @@
 from __future__ import annotations
 
 from contextlib import suppress
+from itertools import combinations
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
 
 from easydiffraction.analysis.categories.aliases.factory import AliasesFactory
+from easydiffraction.analysis.categories.bayesian_convergence import BayesianConvergence
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCaches,
+)
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCaches
+from easydiffraction.analysis.categories.bayesian_pair_caches.default import BayesianPairCachePaths
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriors,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasets,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+    BayesianPredictiveDatasetPaths,
+)
+from easydiffraction.analysis.categories.bayesian_result import BayesianResult
+from easydiffraction.analysis.categories.bayesian_sampler import BayesianSampler
 from easydiffraction.analysis.categories.constraints.factory import ConstraintsFactory
+from easydiffraction.analysis.categories.deterministic_result import DeterministicResult
+from easydiffraction.analysis.categories.fit_parameter_correlations import FitParameterCorrelations
+from easydiffraction.analysis.categories.fit_parameters import FitParameters
+from easydiffraction.analysis.categories.fit_result import FitResult
 from easydiffraction.analysis.categories.fitting import Fitting
 from easydiffraction.analysis.categories.fitting import FittingFactory
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
@@ -19,14 +41,23 @@
 from easydiffraction.analysis.categories.sequential_fit_extract import (
     SequentialFitExtractCollection,
 )
+from easydiffraction.analysis.enums import FitCorrelationSourceEnum
 from easydiffraction.analysis.enums import FitModeEnum
+from easydiffraction.analysis.enums import FitResultKindEnum
+from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
 from easydiffraction.analysis.fitting import Fitter
+from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.core.category_owner import CategoryOwner
 from easydiffraction.core.guard import _apply_help_filter
 from easydiffraction.core.singleton import ConstraintsHandler
 from easydiffraction.core.variable import NumericDescriptor
 from easydiffraction.core.variable import Parameter
 from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.datablocks.experiment.item.base import intensity_category_for
 from easydiffraction.display.progress import make_display_handle
 from easydiffraction.display.tables import TableRenderer
 from easydiffraction.io.cif.serialize import analysis_to_cif
@@ -40,6 +71,9 @@
 from easydiffraction.utils.utils import render_table
 
 _SUMMARY_HIDDEN_PARAMETER_CATEGORIES = frozenset({'pd_data', 'total_data', 'refln'})
+_POSTERIOR_SAMPLE_NDIM = 3
+_FLATTENED_POSTERIOR_SAMPLE_NDIM = 2
+_CREDIBLE_INTERVAL_LEVEL_COUNT = 2
 
 
 def _discover_property_rows(cls: type) -> list[list[str]]:
@@ -347,7 +381,111 @@ def as_cif(self) -> None:
         self._analysis.show_as_cif()
 
 
-class Analysis(CategoryOwner):
+class _AnalysisOwnerAccessorsMixin:
+    @property
+    def project(self) -> object:
+        """Project that owns this analysis section."""
+        return self._project
+
+    @property
+    def aliases(self) -> object:
+        """Alias mappings used by symbolic constraints and displays."""
+        return self._aliases
+
+    @property
+    def constraints(self) -> object:
+        """Symbolic constraints owned by this analysis section."""
+        return self._constraints
+
+    @property
+    def display(self) -> AnalysisDisplay:
+        """Display helper for parameter tables, CIF, and fit results."""
+        return self._display
+
+    @property
+    def fitter(self) -> Fitter:
+        """Fitting engine used by this analysis object."""
+        return self._fitter
+
+    @fitter.setter
+    def fitter(self, value: Fitter) -> None:
+        self._fitter = value
+
+    @property
+    def fit_results(self) -> object | None:
+        """Results from the most recent fit, if any."""
+        if self._fit_results is None and self._has_persisted_fit_state():
+            self._restore_fit_results_from_projection()
+        return self._fit_results
+
+    @fit_results.setter
+    def fit_results(self, value: object | None) -> None:
+        self._fit_results = value
+        self._fitter.results = value
+
+
+class _AnalysisPersistedCategoryAccessorsMixin:
+    @property
+    def fit_parameters(self) -> FitParameters:
+        """Persisted fit-parameter control snapshots."""
+        return self._fit_parameters
+
+    @property
+    def fit_result(self) -> FitResult:
+        """Persisted common fit-result status metadata."""
+        return self._fit_result
+
+    @property
+    def fit_parameter_correlations(self) -> FitParameterCorrelations:
+        """Persisted fit-parameter correlation summaries."""
+        return self._fit_parameter_correlations
+
+    @property
+    def deterministic_result(self) -> DeterministicResult:
+        """Persisted deterministic fit-result metadata."""
+        return self._deterministic_result
+
+    @property
+    def bayesian_result(self) -> BayesianResult:
+        """Persisted Bayesian fit-result metadata."""
+        return self._bayesian_result
+
+    @property
+    def bayesian_sampler(self) -> BayesianSampler:
+        """Persisted Bayesian sampler settings."""
+        return self._bayesian_sampler
+
+    @property
+    def bayesian_convergence(self) -> BayesianConvergence:
+        """Persisted Bayesian convergence diagnostics."""
+        return self._bayesian_convergence
+
+    @property
+    def bayesian_parameter_posteriors(self) -> BayesianParameterPosteriors:
+        """Persisted Bayesian parameter posterior summaries."""
+        return self._bayesian_parameter_posteriors
+
+    @property
+    def bayesian_distribution_caches(self) -> BayesianDistributionCaches:
+        """Persisted Bayesian distribution-cache manifests."""
+        return self._bayesian_distribution_caches
+
+    @property
+    def bayesian_pair_caches(self) -> BayesianPairCaches:
+        """Persisted Bayesian pair-cache manifests."""
+        return self._bayesian_pair_caches
+
+    @property
+    def bayesian_predictive_datasets(self) -> BayesianPredictiveDatasets:
+        """Persisted Bayesian predictive-dataset manifests."""
+        return self._bayesian_predictive_datasets
+
+
+class Analysis(
+    _AnalysisOwnerAccessorsMixin,
+    _AnalysisPersistedCategoryAccessorsMixin,
+    CategoryOwner,
+):
     """
     High-level orchestration of analysis tasks for a Project.
 
@@ -379,48 +517,321 @@ def __init__(self, project: object) -> None:
             SequentialFitFactory.default_tag()
         )
         self._sequential_fit_extract = SequentialFitExtractCollection()
+        self._fit_parameters = FitParameters()
+        self._fit_result = FitResult()
+        self._fit_parameter_correlations = FitParameterCorrelations()
+        self._deterministic_result = DeterministicResult()
+        self._bayesian_result = BayesianResult()
+        self._bayesian_sampler = BayesianSampler()
+        self._bayesian_convergence = BayesianConvergence()
+        self._bayesian_parameter_posteriors = BayesianParameterPosteriors()
+        self._bayesian_distribution_caches = BayesianDistributionCaches()
+        self._bayesian_pair_caches = BayesianPairCaches()
+        self._bayesian_predictive_datasets = BayesianPredictiveDatasets()
+        self._has_persisted_fit_state_data = False
+        self._persisted_fit_state_sidecar: dict[str, object] = {}
         self._fitter = Fitter(self._fitting.minimizer_type.value)
         self._fit_results = None
         self._parameter_snapshots: dict[str, dict[str, dict]] = {}
         self._display = AnalysisDisplay(self)
 
-    @property
-    def project(self) -> object:
-        """Project that owns this analysis section."""
-        return self._project
+    @staticmethod
+    def _predictive_cache_key(
+        experiment_name: str,
+        x_axis_name: str,
+        *,
+        include_draws: bool = True,
+    ) -> str:
+        """Return the runtime cache key for one predictive summary."""
+        key_suffix = 'draws' if include_draws else 'band'
+        return f'{experiment_name}:{x_axis_name}:{key_suffix}'
+
+    def _live_parameter_map(self) -> dict[str, Parameter]:
+        """Return live parameters keyed by unique name."""
+        all_parameters = self.project.structures.parameters + self.project.experiments.parameters
+        return {
+            param.unique_name: param
+            for param in all_parameters
+            if isinstance(param, Parameter) and hasattr(param, 'unique_name')
+        }
 
-    @property
-    def aliases(self) -> object:
-        """Alias mappings used by symbolic constraints and displays."""
-        return self._aliases
+    def _ordered_restored_parameter_names(self) -> list[str]:
+        """
+        Return persisted parameter names in display and array order.
+        """
+        if self.fit_result.result_kind.value == FitResultKindEnum.BAYESIAN.value:
+            posterior_rows = list(self.bayesian_parameter_posteriors)
+            if posterior_rows:
+                return [row.unique_name.value for row in posterior_rows]
+
+        return [row.param_unique_name.value for row in self.fit_parameters]
+
+    def _restore_live_parameter_state(self, param_map: dict[str, Parameter]) -> None:
+        """Restore saved fit metadata onto live parameter objects."""
+        for row in self.fit_parameters:
+            parameter = param_map.get(row.param_unique_name.value)
+            if parameter is None:
+                log.warning(
+                    'Persisted fit-state references unknown parameter '
+                    f'{row.param_unique_name.value!r}.'
+                )
+                continue
 
-    @property
-    def constraints(self) -> object:
-        """Symbolic constraints owned by this analysis section."""
-        return self._constraints
+            parameter.fit_min = row.fit_min.value
+            parameter.fit_max = row.fit_max.value
+            parameter._set_fit_bounds_uncertainty_multiplier(
+                row.fit_bounds_uncertainty_multiplier.value
+            )
+            parameter._fit_start_value = row.start_value.value
+            parameter._fit_start_uncertainty = row.start_uncertainty.value
+
+        for row in self.bayesian_parameter_posteriors:
+            parameter = param_map.get(row.unique_name.value)
+            if parameter is None or row.uncertainty.value is None:
+                continue
+            parameter.uncertainty = float(row.uncertainty.value)
+
+    def _sync_live_minimizer_from_persisted_fit_state(self) -> None:
+        """Apply saved sampler settings to the live minimizer."""
+        if not self._has_persisted_fit_state():
+            return
 
-    @property
-    def display(self) -> AnalysisDisplay:
-        """Display helper for parameter tables, CIF, and fit results."""
-        return self._display
+        if self.fit_result.result_kind.value != FitResultKindEnum.BAYESIAN.value:
+            return
 
-    @property
-    def fitter(self) -> Fitter:
-        """Fitting engine used by this analysis object."""
-        return self._fitter
+        if self.fitting.minimizer_type.value != MinimizerTypeEnum.BUMPS_DREAM.value:
+            return
 
-    @fitter.setter
-    def fitter(self, value: Fitter) -> None:
-        self._fitter = value
+        minimizer = self.fitting.minimizer
+        if minimizer is None:
+            return
 
-    @property
-    def fit_results(self) -> object | None:
-        """Results from the most recent fit, if any."""
-        return self._fit_results
+        steps = int(self.bayesian_sampler.steps.value)
+        if steps <= 0:
+            return
 
-    @fit_results.setter
-    def fit_results(self, value: object | None) -> None:
-        self._fit_results = value
+        minimizer.steps = steps
+        minimizer.burn = int(self.bayesian_sampler.burn.value)
+
+        thin = int(self.bayesian_sampler.thin.value)
+        if thin > 0:
+            minimizer.thin = thin
+
+        pop = int(self.bayesian_sampler.pop.value)
+        if pop > 0:
+            minimizer.pop = pop
+
+        minimizer.parallel = int(self.bayesian_sampler.parallel.value)
+
+        init_value = str(self.bayesian_sampler.init.value)
+        if init_value:
+            minimizer.init = init_value
+
+    def _restored_fit_parameters(self, param_map: dict[str, Parameter]) -> list[Parameter]:
+        """Return live parameters in the persisted fit-result order."""
+        restored_parameters: list[Parameter] = []
+        for unique_name in self._ordered_restored_parameter_names():
+            parameter = param_map.get(unique_name)
+            if parameter is not None:
+                restored_parameters.append(parameter)
+        return restored_parameters
+
+    def _restored_posterior_samples(self) -> PosteriorSamples | None:
+        """Return restored posterior samples from the HDF5 sidecar."""
+        if not self.bayesian_result.has_posterior_samples.value:
+            return None
+
+        posterior_data = self._persisted_fit_state_sidecar.get('posterior', {})
+        parameter_samples = posterior_data.get('parameter_samples')
+        if parameter_samples is None:
+            return None
+
+        posterior_rows = list(self.bayesian_parameter_posteriors)
+        parameter_names = [row.unique_name.value for row in posterior_rows]
+        if not parameter_names:
+            parameter_names = [row.param_unique_name.value for row in self.fit_parameters]
+
+        parameter_sample_array = np.asarray(parameter_samples, dtype=float)
+        if parameter_sample_array.ndim != _POSTERIOR_SAMPLE_NDIM:
+            log.warning('Persisted posterior samples have an invalid shape for restore.')
+            return None
+        if parameter_sample_array.shape[2] != len(parameter_names):
+            log.warning(
+                'Persisted posterior samples do not match restored posterior parameter names.'
+            )
+            return None
+
+        log_posterior = posterior_data.get('log_posterior')
+        draw_index = posterior_data.get('draw_index')
+        return PosteriorSamples(
+            parameter_names=parameter_names,
+            parameter_samples=parameter_sample_array,
+            log_posterior=(
+                None if log_posterior is None else np.asarray(log_posterior, dtype=float)
+            ),
+            draw_index=None if draw_index is None else np.asarray(draw_index),
+        )
+
+    def _restored_posterior_summaries(self) -> list[PosteriorParameterSummary]:
+        """Return posterior summary rows as runtime summary objects."""
+        return [
+            PosteriorParameterSummary(
+                unique_name=row.unique_name.value,
+                display_name=row.display_name.value,
+                best_sample_value=float(row.best_sample_value.value),
+                median=float(row.median.value),
+                standard_deviation=float(row.uncertainty.value),
+                interval_68=(
+                    float(row.interval_68_lower.value),
+                    float(row.interval_68_upper.value),
+                ),
+                interval_95=(
+                    float(row.interval_95_lower.value),
+                    float(row.interval_95_upper.value),
+                ),
+                ess_bulk=row.ess_bulk.value,
+                r_hat=row.r_hat.value,
+            )
+            for row in self.bayesian_parameter_posteriors
+        ]
+
+    def _restored_predictive_summaries(self) -> dict[str, PosteriorPredictiveSummary]:
+        """Return restored predictive summaries for runtime reuse."""
+        restored_predictive: dict[str, PosteriorPredictiveSummary] = {}
+        predictive_data = self._persisted_fit_state_sidecar.get('predictive_datasets', {})
+        for row in self.bayesian_predictive_datasets:
+            experiment_name = str(row.experiment_name.value)
+            x_axis_name = str(row.x_axis_name.value)
+            dataset = predictive_data.get(experiment_name)
+            if dataset is None:
+                continue
+
+            summary = PosteriorPredictiveSummary(
+                experiment_name=experiment_name,
+                x_axis_name=x_axis_name,
+                x=np.asarray(dataset['x'], dtype=float),
+                best_sample_prediction=np.asarray(
+                    dataset['best_sample_prediction'],
+                    dtype=float,
+                ),
+                lower_95=(
+                    None
+                    if dataset.get('lower_95') is None
+                    else np.asarray(dataset['lower_95'], dtype=float)
+                ),
+                upper_95=(
+                    None
+                    if dataset.get('upper_95') is None
+                    else np.asarray(dataset['upper_95'], dtype=float)
+                ),
+                lower_68=(
+                    None
+                    if dataset.get('lower_68') is None
+                    else np.asarray(dataset['lower_68'], dtype=float)
+                ),
+                upper_68=(
+                    None
+                    if dataset.get('upper_68') is None
+                    else np.asarray(dataset['upper_68'], dtype=float)
+                ),
+                draws=(
+                    None
+                    if dataset.get('draws') is None
+                    else np.asarray(dataset['draws'], dtype=float)
+                ),
+            )
+            restored_predictive[experiment_name] = summary
+            restored_predictive[
+                self._predictive_cache_key(
+                    experiment_name,
+                    x_axis_name,
+                    include_draws=False,
+                )
+            ] = summary
+            if summary.draws is not None:
+                restored_predictive[
+                    self._predictive_cache_key(
+                        experiment_name,
+                        x_axis_name,
+                        include_draws=True,
+                    )
+                ] = summary
+        return restored_predictive
+
+    def _restore_fit_results_from_projection(self) -> object | None:
+        """Rebuild a runtime fit-result object from saved state."""
+        if not self._has_persisted_fit_state():
+            return None
+
+        param_map = self._live_parameter_map()
+        self._restore_live_parameter_state(param_map)
+        restored_parameters = self._restored_fit_parameters(param_map)
+        fitting_time = self.fit_result.fitting_time.value
+        reduced_chi_square = self.fit_result.reduced_chi_square.value
+
+        if self.fit_result.result_kind.value == FitResultKindEnum.BAYESIAN.value:
+            restored_results = BayesianFitResults(
+                success=bool(self.fit_result.success.value),
+                parameters=restored_parameters,
+                reduced_chi_square=reduced_chi_square,
+                starting_parameters=list(restored_parameters),
+                fitting_time=fitting_time,
+                sampler_name=self.bayesian_result.sampler_name.value,
+                point_estimate_name=self.bayesian_result.point_estimate_name.value,
+                posterior_samples=self._restored_posterior_samples(),
+                posterior_parameter_summaries=self._restored_posterior_summaries(),
+                posterior_predictive=self._restored_predictive_summaries(),
+                credible_interval_levels=(
+                    float(self.bayesian_result.credible_interval_inner.value),
+                    float(self.bayesian_result.credible_interval_outer.value),
+                ),
+                sampler_settings={
+                    'steps': int(self.bayesian_sampler.steps.value),
+                    'burn': int(self.bayesian_sampler.burn.value),
+                    'thin': int(self.bayesian_sampler.thin.value),
+                    'pop': int(self.bayesian_sampler.pop.value),
+                    'parallel': int(self.bayesian_sampler.parallel.value),
+                    'init': self.bayesian_sampler.init.value,
+                    'random_seed': self.bayesian_sampler.random_seed.value,
+                },
+                convergence_diagnostics={
+                    'converged': bool(self.bayesian_convergence.converged.value),
+                    'max_r_hat': self.bayesian_convergence.max_r_hat.value,
+                    'min_ess_bulk': self.bayesian_convergence.min_ess_bulk.value,
+                    'n_draws': int(self.bayesian_convergence.n_draws.value),
+                    'n_chains': int(self.bayesian_convergence.n_chains.value),
+                    'n_parameters': int(self.bayesian_convergence.n_parameters.value),
+                },
+                sampler_completed=bool(self.bayesian_result.sampler_completed.value),
+                best_log_posterior=self.bayesian_result.best_log_posterior.value,
+            )
+            restored_results.message = self.fit_result.message.value
+            restored_results.iterations = int(self.fit_result.iterations.value)
+            self.fit_results = restored_results
+            return restored_results
+
+        restored_results = FitResults(
+            success=bool(self.fit_result.success.value),
+            parameters=restored_parameters,
+            reduced_chi_square=reduced_chi_square,
+            starting_parameters=list(restored_parameters),
+            fitting_time=fitting_time,
+            optimizer_name=self.deterministic_result.optimizer_name.value,
+            method_name=self.deterministic_result.method_name.value,
+            objective_name=self.deterministic_result.objective_name.value,
+            objective_value=self.deterministic_result.objective_value.value,
+            n_data_points=int(self.deterministic_result.n_data_points.value),
+            n_parameters=int(self.deterministic_result.n_parameters.value),
+            n_free_parameters=int(self.deterministic_result.n_free_parameters.value),
+            degrees_of_freedom=int(self.deterministic_result.degrees_of_freedom.value),
+            covariance_available=bool(self.deterministic_result.covariance_available.value),
+            correlation_available=bool(self.deterministic_result.correlation_available.value),
+        )
+        restored_results.message = self.fit_result.message.value
+        restored_results.iterations = int(self.fit_result.iterations.value)
+        restored_results.chi_square = self.deterministic_result.objective_value.value
+        self.fit_results = restored_results
+        return restored_results
 
     def help(self) -> None:
         """Print a summary of analysis properties and methods."""
@@ -502,6 +913,9 @@ def _serializable_categories(self) -> list:
                 self.sequential_fit_extract,
             ])
 
+        if self._has_persisted_fit_state():
+            categories.extend(self._fit_state_categories())
+
         return categories
 
     # ------------------------------------------------------------------
@@ -684,6 +1098,659 @@ def sequential_fit_extract(self) -> SequentialFitExtractCollection:
         """Persisted extract rules for sequential fitting."""
         return self._sequential_fit_extract
 
+    def _has_persisted_fit_state(self) -> bool:
+        """
+        Return whether a persisted fit-state projection is present.
+        """
+        return self._has_persisted_fit_state_data
+
+    def _set_has_persisted_fit_state(self, *, value: bool) -> None:
+        """Set the persisted fit-state presence flag."""
+        self._has_persisted_fit_state_data = value
+
+    def _fit_state_categories(self) -> list[object]:
+        """Return fit-state categories for the current result kind."""
+        categories: list[object] = [
+            self.fit_parameters,
+            self.fit_result,
+            self.fit_parameter_correlations,
+        ]
+
+        try:
+            result_kind = FitResultKindEnum(self.fit_result.result_kind.value)
+        except ValueError:
+            log.warning(
+                'Unsupported fit_result.result_kind while serializing analysis CIF: '
+                f'{self.fit_result.result_kind.value!r}. '
+                'Saving only common fit-state categories.',
+            )
+            return categories
+
+        if result_kind is FitResultKindEnum.DETERMINISTIC:
+            categories.append(self.deterministic_result)
+            return categories
+
+        categories.extend([
+            self.bayesian_result,
+            self.bayesian_sampler,
+            self.bayesian_convergence,
+            self.bayesian_parameter_posteriors,
+            self.bayesian_distribution_caches,
+            self.bayesian_pair_caches,
+            self.bayesian_predictive_datasets,
+        ])
+        return categories
+
+    def _clear_persisted_fit_state(self) -> None:
+        """Reset all persisted fit-state categories before a new fit."""
+        self._fit_parameters = FitParameters()
+        self._fit_result = FitResult()
+        self._fit_parameter_correlations = FitParameterCorrelations()
+        self._deterministic_result = DeterministicResult()
+        self._bayesian_result = BayesianResult()
+        self._bayesian_sampler = BayesianSampler()
+        self._bayesian_convergence = BayesianConvergence()
+        self._bayesian_parameter_posteriors = BayesianParameterPosteriors()
+        self._bayesian_distribution_caches = BayesianDistributionCaches()
+        self._bayesian_pair_caches = BayesianPairCaches()
+        self._bayesian_predictive_datasets = BayesianPredictiveDatasets()
+        self._set_has_persisted_fit_state(value=False)
+        self._persisted_fit_state_sidecar = {}
+
+    def _capture_fit_parameter_state(self, parameters: list[Parameter]) -> None:
+        """Capture pre-fit parameter state."""
+        self._clear_persisted_fit_state()
+
+        for param in parameters:
+            self.fit_parameters.create(
+                param_unique_name=param.unique_name,
+                fit_min=param.fit_min,
+                fit_max=param.fit_max,
+                fit_bounds_uncertainty_multiplier=param.fit_bounds_uncertainty_multiplier,
+                start_value=param.value,
+                start_uncertainty=param.uncertainty,
+            )
+
+        self._set_has_persisted_fit_state(value=True)
+
+    def _selected_parameters_for_fit(self, experiments: list[object]) -> list[Parameter]:
+        """
+        Return unique live parameters involved in the current fit slice.
+        """
+        selected_parameters: list[Parameter] = []
+        seen_unique_names: set[str] = set()
+
+        for param in self.project.structures.parameters:
+            if not isinstance(param, Parameter):
+                continue
+            if param.unique_name in seen_unique_names:
+                continue
+            selected_parameters.append(param)
+            seen_unique_names.add(param.unique_name)
+
+        for experiment in experiments:
+            for param in experiment.parameters:
+                if not isinstance(param, Parameter):
+                    continue
+                if param.unique_name in seen_unique_names:
+                    continue
+                selected_parameters.append(param)
+                seen_unique_names.add(param.unique_name)
+
+        return selected_parameters
+
+    @staticmethod
+    def _fit_data_point_count(experiments: list[object]) -> int:
+        """Return observed data-point count for one fit slice."""
+        total = 0
+        for experiment in experiments:
+            intensity_category = intensity_category_for(experiment)
+            total += int(np.asarray(intensity_category.intensity_meas).size)
+        return total
+
+    @staticmethod
+    def _resolve_covariance_matrix(results: FitResults) -> np.ndarray | None:
+        """
+        Return a covariance matrix when the raw fit result exposes one.
+        """
+        raw_result = results.engine_result
+        for attribute_name in ('covar', 'covariance_matrix'):
+            covariance = getattr(raw_result, attribute_name, None)
+            if covariance is None:
+                continue
+
+            covariance_array = np.asarray(covariance, dtype=float)
+            if covariance_array.ndim != _FLATTENED_POSTERIOR_SAMPLE_NDIM:
+                continue
+            if covariance_array.shape[0] != covariance_array.shape[1]:
+                continue
+            return covariance_array
+
+        return None
+
+    @staticmethod
+    def _correlation_matrix_from_covariance(covariance: np.ndarray) -> np.ndarray | None:
+        """
+        Return a correlation matrix derived from a covariance matrix.
+        """
+        diagonal = np.diag(covariance)
+        if np.any(diagonal <= 0):
+            return None
+
+        scales = np.sqrt(diagonal)
+        denominator = np.outer(scales, scales)
+        with np.errstate(invalid='ignore', divide='ignore'):
+            correlation = covariance / denominator
+
+        if not np.all(np.isfinite(correlation)):
+            return None
+        return correlation
+
+    @staticmethod
+    def _resolve_objective_value(results: FitResults) -> float | None:
+        """Return the objective value stored for a fit result."""
+        if results.chi_square is None:
+            return None
+        return float(results.chi_square)
+
+    def _store_common_fit_result_projection(
+        self,
+        results: FitResults,
+        *,
+        result_kind: FitResultKindEnum,
+    ) -> None:
+        """
+        Store fields shared by deterministic and Bayesian fit results.
+        """
+        self.fit_result._set_result_kind(result_kind.value)
+        self.fit_result._set_success(value=results.success)
+        self.fit_result._set_message(results.message)
+        self.fit_result._set_iterations(results.iterations)
+        self.fit_result._set_fitting_time(results.fitting_time)
+        self.fit_result._set_reduced_chi_square(results.reduced_chi_square)
+        self._set_has_persisted_fit_state(value=True)
+
+    def _store_correlation_projection(
+        self,
+        *,
+        unique_names: list[str],
+        correlation_matrix: np.ndarray,
+        source_kind: FitCorrelationSourceEnum,
+    ) -> None:
+        """Store upper-triangle correlations from one matrix."""
+        if len(unique_names) <= 1:
+            return
+        if correlation_matrix.shape != (len(unique_names), len(unique_names)):
+            return
+
+        for row_index, unique_name_i in enumerate(unique_names[:-1]):
+            for column_index in range(row_index + 1, len(unique_names)):
+                correlation = correlation_matrix[row_index, column_index]
+                if not np.isfinite(correlation):
+                    continue
+                self.fit_parameter_correlations.create(
+                    source_kind=source_kind.value,
+                    param_unique_name_i=unique_name_i,
+                    param_unique_name_j=unique_names[column_index],
+                    correlation=float(np.clip(correlation, -1.0, 1.0)),
+                )
+
+    def _store_deterministic_result_projection(
+        self,
+        results: FitResults,
+        *,
+        experiments: list[object],
+        fitted_parameters: list[Parameter],
+    ) -> None:
+        """Store deterministic fit results in persisted categories."""
+        selected_parameters = self._selected_parameters_for_fit(experiments)
+        n_parameters = len(selected_parameters)
+        n_free_parameters = len(fitted_parameters)
+        n_data_points = self._fit_data_point_count(experiments)
+        degrees_of_freedom = max(n_data_points - n_free_parameters, 0)
+        covariance = self._resolve_covariance_matrix(results)
+        correlation_matrix = (
+            self._correlation_matrix_from_covariance(covariance)
+            if covariance is not None
+            else None
+        )
+
+        self.deterministic_result._set_optimizer_name(
+            str(self.fitter.minimizer.name or self.fitter.selection)
+        )
+        self.deterministic_result._set_method_name(str(self.fitter.minimizer.method or ''))
+        self.deterministic_result._set_objective_name('chi_square')
+        self.deterministic_result._set_objective_value(self._resolve_objective_value(results))
+        self.deterministic_result._set_n_data_points(n_data_points)
+        self.deterministic_result._set_n_parameters(n_parameters)
+        self.deterministic_result._set_n_free_parameters(n_free_parameters)
+        self.deterministic_result._set_degrees_of_freedom(degrees_of_freedom)
+        self.deterministic_result._set_covariance_available(value=covariance is not None)
+        self.deterministic_result._set_correlation_available(value=correlation_matrix is not None)
+
+        if correlation_matrix is not None:
+            self._store_correlation_projection(
+                unique_names=[param.unique_name for param in fitted_parameters],
+                correlation_matrix=correlation_matrix,
+                source_kind=FitCorrelationSourceEnum.DETERMINISTIC,
+            )
+
+    def _store_bayesian_distribution_cache_projection(
+        self,
+        *,
+        plotter: object,
+        results: BayesianFitResults,
+        flattened_samples: np.ndarray,
+        parameter_names: list[str],
+    ) -> dict[str, dict[str, np.ndarray]]:
+        """
+        Store cached posterior density curves into persisted manifests.
+        """
+        payload: dict[str, dict[str, np.ndarray]] = {}
+        for parameter_index, parameter_name in enumerate(parameter_names):
+            lower_bound, upper_bound = plotter._posterior_parameter_bounds(
+                fit_results=results,
+                parameter_name=parameter_name,
+            )
+            density_curve = plotter._posterior_density_curve(
+                flattened_samples[:, parameter_index],
+                lower_bound=lower_bound,
+                upper_bound=upper_bound,
+            )
+            if density_curve is None:
+                continue
+
+            x_values, density_values = density_curve
+            x_array = np.asarray(x_values, dtype=float)
+            density_array = np.asarray(density_values, dtype=float)
+            cache_index = len(payload)
+            self.bayesian_distribution_caches.create(
+                param_unique_name=parameter_name,
+                x_path=f'/posterior/distribution/{cache_index}/x',
+                density_path=f'/posterior/distribution/{cache_index}/density',
+                n_grid=float(x_array.size),
+                n_draws_cached=float(np.isfinite(flattened_samples[:, parameter_index]).sum()),
+            )
+            payload[parameter_name] = {
+                'x': x_array,
+                'density': density_array,
+            }
+        return payload
+
+    @staticmethod
+    def _posterior_pair_contour_levels(density: np.ndarray) -> np.ndarray:
+        """Return default contour levels for one cached pair."""
+        density_max = float(np.max(density))
+        if not np.isfinite(density_max) or density_max <= 0:
+            return np.asarray([], dtype=float)
+        return density_max * np.asarray([0.20, 0.35, 0.50, 0.65, 0.80, 0.95], dtype=float)
+
+    @staticmethod
+    def _ordered_pair_metadata(
+        parameter_names: list[str],
+        first_index: int,
+        second_index: int,
+    ) -> tuple[int, int, str, str]:
+        """Return ordered pair indices and parameter names."""
+        x_index = first_index
+        y_index = second_index
+        x_name = parameter_names[x_index]
+        y_name = parameter_names[y_index]
+        if x_name > y_name:
+            x_index, y_index = y_index, x_index
+            x_name, y_name = y_name, x_name
+        return x_index, y_index, x_name, y_name
+
+    def _store_one_bayesian_pair_cache_projection(
+        self,
+        *,
+        plotter: object,
+        results: BayesianFitResults,
+        density_samples: np.ndarray,
+        pair_metadata: tuple[int, int, str, str],
+        contour_grid_size: int,
+        pair_id: str,
+    ) -> tuple[str, dict[str, np.ndarray]] | None:
+        """Store one cached pair surface and return its payload."""
+        x_index, y_index, x_name, y_name = pair_metadata
+
+        x_values = density_samples[:, x_index]
+        y_values = density_samples[:, y_index]
+        x_bounds, y_bounds = plotter._posterior_pair_bounds(
+            fit_results=results,
+            x_parameter_name=x_name,
+            y_parameter_name=y_name,
+            x_values=x_values,
+            y_values=y_values,
+        )
+        density_surface = plotter._posterior_pair_density_surface(
+            x_values=x_values,
+            y_values=y_values,
+            x_bounds=x_bounds,
+            y_bounds=y_bounds,
+            grid_size=contour_grid_size,
+        )
+        if density_surface is None:
+            return None
+
+        x_grid_array = np.asarray(density_surface[0], dtype=float)
+        y_grid_array = np.asarray(density_surface[1], dtype=float)
+        density_array = np.asarray(density_surface[2], dtype=float)
+        contour_levels = self._posterior_pair_contour_levels(density_array)
+        self.bayesian_pair_caches.create(
+            id=pair_id,
+            parameter_names=(x_name, y_name),
+            paths=BayesianPairCachePaths(
+                x_path=f'/posterior/pairs/{pair_id}/x',
+                y_path=f'/posterior/pairs/{pair_id}/y',
+                density_path=f'/posterior/pairs/{pair_id}/density',
+                contour_level_path=f'/posterior/pairs/{pair_id}/contour_levels',
+            ),
+            grid_shape=(float(x_grid_array.size), float(y_grid_array.size)),
+            n_draws_cached=float(density_samples.shape[0]),
+        )
+        return pair_id, {
+            'x': x_grid_array,
+            'y': y_grid_array,
+            'density': density_array,
+            'contour_levels': contour_levels,
+        }
+
+    def _store_bayesian_pair_cache_projection(
+        self,
+        *,
+        plotter: object,
+        results: BayesianFitResults,
+        flattened_samples: np.ndarray,
+        parameter_names: list[str],
+    ) -> dict[str, dict[str, np.ndarray]]:
+        """Store cached pair-density surfaces in manifests."""
+        n_parameters = len(parameter_names)
+        if n_parameters <= 1:
+            return {}
+
+        density_samples = plotter._thin_posterior_samples(
+            flattened_samples,
+            max_points=plotter._posterior_pair_density_max_points(n_parameters),
+        )
+        contour_grid_size = plotter._posterior_pair_contour_grid_size(n_parameters)
+        payload: dict[str, dict[str, np.ndarray]] = {}
+        for first_index, second_index in combinations(range(n_parameters), 2):
+            pair_id = str(len(payload) + 1)
+            cache_projection = self._store_one_bayesian_pair_cache_projection(
+                plotter=plotter,
+                results=results,
+                density_samples=density_samples,
+                pair_metadata=self._ordered_pair_metadata(
+                    parameter_names,
+                    first_index,
+                    second_index,
+                ),
+                contour_grid_size=contour_grid_size,
+                pair_id=pair_id,
+            )
+            if cache_projection is None:
+                continue
+
+            pair_id, pair_payload = cache_projection
+            payload[pair_id] = pair_payload
+        return payload
+
+    @staticmethod
+    def _predictive_dataset_payload(
+        summary: PosteriorPredictiveSummary,
+    ) -> dict[str, np.ndarray]:
+        """Return persisted predictive arrays for one summary."""
+        payload: dict[str, np.ndarray] = {
+            'x': np.asarray(summary.x, dtype=float),
+            'best_sample_prediction': np.asarray(summary.best_sample_prediction, dtype=float),
+        }
+        if summary.lower_95 is not None:
+            payload['lower_95'] = np.asarray(summary.lower_95, dtype=float)
+        if summary.upper_95 is not None:
+            payload['upper_95'] = np.asarray(summary.upper_95, dtype=float)
+        if summary.lower_68 is not None:
+            payload['lower_68'] = np.asarray(summary.lower_68, dtype=float)
+        if summary.upper_68 is not None:
+            payload['upper_68'] = np.asarray(summary.upper_68, dtype=float)
+        if summary.draws is not None:
+            payload['draws'] = np.asarray(summary.draws, dtype=float)
+        return payload
+
+    def _store_bayesian_predictive_projection(
+        self,
+        *,
+        plotter: object,
+        results: BayesianFitResults,
+    ) -> dict[str, dict[str, np.ndarray]]:
+        """
+        Store posterior predictive summaries into persisted manifests.
+        """
+        predictive_payload: dict[str, dict[str, np.ndarray]] = {}
+        for experiment_name in self.project.experiments.names:
+            experiment = self.project.experiments[experiment_name]
+            x_axis, x_axis_name, _, _, _ = plotter._resolve_x_axis(experiment.type, None)
+            summary = plotter._build_posterior_predictive_summary(
+                fit_results=results,
+                experiment=experiment,
+                expt_name=experiment_name,
+                x_axis=x_axis,
+                include_draws=False,
+            )
+            if summary is None:
+                continue
+
+            results.posterior_predictive[summary.experiment_name] = summary
+            results.posterior_predictive[
+                self._predictive_cache_key(
+                    summary.experiment_name,
+                    str(x_axis_name),
+                    include_draws=False,
+                )
+            ] = summary
+            predictive_payload[summary.experiment_name] = self._predictive_dataset_payload(
+                summary,
+            )
+            predictive_root = f'/predictive/{summary.experiment_name}'
+            self.bayesian_predictive_datasets.create(
+                experiment_name=summary.experiment_name,
+                x_axis_name=str(x_axis_name),
+                paths=BayesianPredictiveDatasetPaths(
+                    x_path=f'{predictive_root}/x',
+                    best_sample_prediction_path=(f'{predictive_root}/best_sample_prediction'),
+                    lower_95_path=(
+                        None if summary.lower_95 is None else f'{predictive_root}/lower_95'
+                    ),
+                    upper_95_path=(
+                        None if summary.upper_95 is None else f'{predictive_root}/upper_95'
+                    ),
+                    lower_68_path=(
+                        None if summary.lower_68 is None else f'{predictive_root}/lower_68'
+                    ),
+                    upper_68_path=(
+                        None if summary.upper_68 is None else f'{predictive_root}/upper_68'
+                    ),
+                    draws_path=(None if summary.draws is None else f'{predictive_root}/draws'),
+                ),
+                n_x=float(np.asarray(summary.x).size),
+                n_draws_cached=(
+                    0.0 if summary.draws is None else float(np.asarray(summary.draws).shape[0])
+                ),
+            )
+        return predictive_payload
+
+    def _store_bayesian_plot_cache_projection(self, results: BayesianFitResults) -> None:
+        """Populate persisted Bayesian plot caches."""
+        posterior_samples = results.posterior_samples
+        if posterior_samples is None:
+            self._persisted_fit_state_sidecar['distribution_caches'] = {}
+            self._persisted_fit_state_sidecar['pair_caches'] = {}
+            self._persisted_fit_state_sidecar['predictive_datasets'] = {}
+            self.bayesian_result._set_has_distribution_cache(value=False)
+            self.bayesian_result._set_has_pair_cache(value=False)
+            self.bayesian_result._set_has_posterior_predictive(value=False)
+            return
+
+        flattened_samples = np.asarray(posterior_samples.flattened(), dtype=float)
+        parameter_names = list(posterior_samples.parameter_names)
+        if (
+            flattened_samples.ndim != _FLATTENED_POSTERIOR_SAMPLE_NDIM
+            or not parameter_names
+            or flattened_samples.shape[1] != len(parameter_names)
+        ):
+            self._persisted_fit_state_sidecar['distribution_caches'] = {}
+            self._persisted_fit_state_sidecar['pair_caches'] = {}
+            self._persisted_fit_state_sidecar['predictive_datasets'] = {}
+            self.bayesian_result._set_has_distribution_cache(value=False)
+            self.bayesian_result._set_has_pair_cache(value=False)
+            self.bayesian_result._set_has_posterior_predictive(value=False)
+            return
+
+        plotter = self.project.rendering.plotter
+        distribution_payload = self._store_bayesian_distribution_cache_projection(
+            plotter=plotter,
+            results=results,
+            flattened_samples=flattened_samples,
+            parameter_names=parameter_names,
+        )
+        pair_payload = self._store_bayesian_pair_cache_projection(
+            plotter=plotter,
+            results=results,
+            flattened_samples=flattened_samples,
+            parameter_names=parameter_names,
+        )
+        predictive_payload = self._store_bayesian_predictive_projection(
+            plotter=plotter,
+            results=results,
+        )
+
+        self._persisted_fit_state_sidecar['distribution_caches'] = distribution_payload
+        self._persisted_fit_state_sidecar['pair_caches'] = pair_payload
+        self._persisted_fit_state_sidecar['predictive_datasets'] = predictive_payload
+        self.bayesian_result._set_has_distribution_cache(value=bool(distribution_payload))
+        self.bayesian_result._set_has_pair_cache(value=bool(pair_payload))
+        self.bayesian_result._set_has_posterior_predictive(value=bool(predictive_payload))
+
+    def _store_bayesian_posterior_sidecar_projection(
+        self,
+        results: BayesianFitResults,
+    ) -> None:
+        """Persist posterior arrays while live samples exist."""
+        posterior_samples = results.posterior_samples
+        if posterior_samples is None:
+            self._persisted_fit_state_sidecar['posterior'] = {}
+            return
+
+        self._persisted_fit_state_sidecar['posterior'] = {
+            'parameter_samples': np.asarray(
+                posterior_samples.parameter_samples,
+                dtype=float,
+            ),
+            'log_posterior': (
+                None
+                if posterior_samples.log_posterior is None
+                else np.asarray(posterior_samples.log_posterior, dtype=float)
+            ),
+            'draw_index': (
+                None
+                if posterior_samples.draw_index is None
+                else np.asarray(posterior_samples.draw_index)
+            ),
+        }
+
+    def _store_bayesian_result_projection(self, results: BayesianFitResults) -> None:
+        """
+        Store Bayesian fit-result projections into persisted categories.
+        """
+        credible_interval_inner = 0.68
+        credible_interval_outer = 0.95
+        if len(results.credible_interval_levels) >= _CREDIBLE_INTERVAL_LEVEL_COUNT:
+            credible_interval_inner = float(results.credible_interval_levels[0])
+            credible_interval_outer = float(results.credible_interval_levels[1])
+
+        point_estimate_name = results.point_estimate_name or 'best_sample'
+        sampler_settings = results.sampler_settings
+        convergence = results.convergence_diagnostics
+
+        self.bayesian_result._set_sampler_name(results.sampler_name)
+        self.bayesian_result._set_point_estimate_name(point_estimate_name)
+        self.bayesian_result._set_success(value=results.success)
+        self.bayesian_result._set_sampler_completed(value=results.sampler_completed)
+        self.bayesian_result._set_best_log_posterior(results.best_log_posterior)
+        self.bayesian_result._set_credible_interval_inner(credible_interval_inner)
+        self.bayesian_result._set_credible_interval_outer(credible_interval_outer)
+        self.bayesian_result._set_has_posterior_samples(
+            value=results.posterior_samples is not None
+        )
+        self.bayesian_result._set_has_distribution_cache(value=False)
+        self.bayesian_result._set_has_pair_cache(value=False)
+        self.bayesian_result._set_has_posterior_predictive(value=False)
+        self.bayesian_result._set_sidecar_file('results.h5')
+        self._store_bayesian_posterior_sidecar_projection(results)
+
+        self.bayesian_sampler._set_steps(int(sampler_settings.get('steps', 0)))
+        self.bayesian_sampler._set_burn(int(sampler_settings.get('burn', 0)))
+        self.bayesian_sampler._set_thin(int(sampler_settings.get('thin', 0)))
+        self.bayesian_sampler._set_pop(int(sampler_settings.get('pop', 0)))
+        self.bayesian_sampler._set_parallel(int(sampler_settings.get('parallel', 0)))
+        self.bayesian_sampler._set_init(str(sampler_settings.get('init', '')))
+        random_seed = sampler_settings.get('random_seed')
+        self.bayesian_sampler._set_random_seed(None if random_seed is None else int(random_seed))
+
+        self.bayesian_convergence._set_converged(value=bool(convergence.get('converged', False)))
+        self.bayesian_convergence._set_max_r_hat(convergence.get('max_r_hat'))
+        self.bayesian_convergence._set_min_ess_bulk(convergence.get('min_ess_bulk'))
+        self.bayesian_convergence._set_n_draws(int(convergence.get('n_draws', 0)))
+        self.bayesian_convergence._set_n_chains(int(convergence.get('n_chains', 0)))
+        self.bayesian_convergence._set_n_parameters(int(convergence.get('n_parameters', 0)))
+
+        for summary in results.posterior_parameter_summaries:
+            self.bayesian_parameter_posteriors.create(summary=summary)
+
+        posterior_samples = results.posterior_samples
+        if posterior_samples is None:
+            return
+
+        self._store_bayesian_plot_cache_projection(results)
+        if len(posterior_samples.parameter_names) <= 1:
+            return
+
+        flattened = posterior_samples.flattened()
+        correlation_matrix = np.corrcoef(flattened, rowvar=False)
+        self._store_correlation_projection(
+            unique_names=list(posterior_samples.parameter_names),
+            correlation_matrix=correlation_matrix,
+            source_kind=FitCorrelationSourceEnum.POSTERIOR,
+        )
+
+    def _store_fit_result_projection(
+        self,
+        results: FitResults,
+        *,
+        experiments: list[object],
+        fitted_parameters: list[Parameter],
+    ) -> None:
+        """
+        Store the latest fit result into persisted fit-state categories.
+        """
+        if isinstance(results, BayesianFitResults):
+            self._store_common_fit_result_projection(
+                results,
+                result_kind=FitResultKindEnum.BAYESIAN,
+            )
+            self._store_bayesian_result_projection(results)
+            return
+
+        self._store_common_fit_result_projection(
+            results,
+            result_kind=FitResultKindEnum.DETERMINISTIC,
+        )
+        self._store_deterministic_result_projection(
+            results,
+            experiments=experiments,
+            fitted_parameters=fitted_parameters,
+        )
+
     def _resolve_sequential_data_dir(self) -> Path:
         """
         Resolve the sequential-fit data directory to an absolute path.
@@ -704,7 +1771,7 @@ def _resolve_sequential_data_dir(self) -> Path:
 
     def _prepare_fit_run(self) -> tuple[VerbosityEnum, object, object] | None:
         """Resolve common inputs for single and joint fitting."""
-        verb = VerbosityEnum(self.project.verbosity)
+        verb = VerbosityEnum(self.project.verbosity.fit.value)
         structures = self.project.structures
         if not structures:
             log.warning('No structures found in the project. Cannot run fit.')
@@ -718,6 +1785,7 @@ def _prepare_fit_run(self) -> tuple[VerbosityEnum, object, object] | None:
         # Apply constraints before fitting so that user-constrained
         # parameters are marked and excluded from the free parameter
         # list built by the fitter.
+        self._sync_live_minimizer_from_persisted_fit_state()
         self._update_categories()
 
         return verb, structures, experiments
@@ -768,6 +1836,7 @@ def _run_sequential(self) -> None:
 
         self._set_fitting_mode_type(FitModeEnum.SEQUENTIAL.value)
         self._update_categories()
+        self._clear_persisted_fit_state()
 
         max_workers_value = self._sequential_fit.max_workers.value
         max_workers = max_workers_value if max_workers_value == 'auto' else int(max_workers_value)
@@ -790,6 +1859,7 @@ def _run_sequential(self) -> None:
         finally:
             self.fit_results = None
             self.fitter.results = None
+            self._clear_persisted_fit_state()
 
         if self.project.info.path is not None:
             self.project.save()
diff --git a/src/easydiffraction/analysis/categories/__init__.py b/src/easydiffraction/analysis/categories/__init__.py
index 6c070cf1a..743e0ff02 100644
--- a/src/easydiffraction/analysis/categories/__init__.py
+++ b/src/easydiffraction/analysis/categories/__init__.py
@@ -3,8 +3,39 @@
 
 from easydiffraction.analysis.categories.aliases import Alias
 from easydiffraction.analysis.categories.aliases import Aliases
+from easydiffraction.analysis.categories.bayesian_convergence import BayesianConvergence
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCacheItem,
+)
+from easydiffraction.analysis.categories.bayesian_distribution_caches import (
+    BayesianDistributionCaches,
+)
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCacheItem
+from easydiffraction.analysis.categories.bayesian_pair_caches import BayesianPairCaches
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriorItem,
+)
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors import (
+    BayesianParameterPosteriors,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasetItem,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets import (
+    BayesianPredictiveDatasets,
+)
+from easydiffraction.analysis.categories.bayesian_result import BayesianResult
+from easydiffraction.analysis.categories.bayesian_sampler import BayesianSampler
 from easydiffraction.analysis.categories.constraints import Constraint
 from easydiffraction.analysis.categories.constraints import Constraints
+from easydiffraction.analysis.categories.deterministic_result import DeterministicResult
+from easydiffraction.analysis.categories.fit_parameter_correlations import (
+    FitParameterCorrelationItem,
+)
+from easydiffraction.analysis.categories.fit_parameter_correlations import FitParameterCorrelations
+from easydiffraction.analysis.categories.fit_parameters import FitParameterItem
+from easydiffraction.analysis.categories.fit_parameters import FitParameters
+from easydiffraction.analysis.categories.fit_result import FitResult
 from easydiffraction.analysis.categories.fitting import Fitting
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
 from easydiffraction.analysis.categories.joint_fit import JointFitItem
diff --git a/src/easydiffraction/analysis/categories/bayesian_convergence/__init__.py b/src/easydiffraction/analysis/categories/bayesian_convergence/__init__.py
new file mode 100644
index 000000000..e9acbd717
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_convergence/__init__.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_convergence.default import BayesianConvergence
+from easydiffraction.analysis.categories.bayesian_convergence.factory import (
+    BayesianConvergenceFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/bayesian_convergence/default.py b/src/easydiffraction/analysis/categories/bayesian_convergence/default.py
new file mode 100644
index 000000000..48d00fa41
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_convergence/default.py
@@ -0,0 +1,121 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian convergence diagnostics category."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.bayesian_convergence.factory import (
+    BayesianConvergenceFactory,
+)
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import IntegerDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@BayesianConvergenceFactory.register
+class BayesianConvergence(CategoryItem):
+    """Persisted Bayesian convergence diagnostics."""
+
+    _category_code = 'bayesian_convergence'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian convergence diagnostics',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._converged = BoolDescriptor(
+            name='converged',
+            description='Whether the Bayesian fit met convergence criteria.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_convergence.converged']),
+        )
+        self._max_r_hat = NumericDescriptor(
+            name='max_r_hat',
+            description='Maximum rank-normalized split-R-hat across parameters.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_convergence.max_r_hat']),
+        )
+        self._min_ess_bulk = NumericDescriptor(
+            name='min_ess_bulk',
+            description='Minimum bulk effective sample size across parameters.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_convergence.min_ess_bulk']),
+        )
+        self._n_draws = IntegerDescriptor(
+            name='n_draws',
+            description='Number of stored posterior draws.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_convergence.n_draws']),
+        )
+        self._n_chains = IntegerDescriptor(
+            name='n_chains',
+            description='Number of stored posterior chains.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_convergence.n_chains']),
+        )
+        self._n_parameters = IntegerDescriptor(
+            name='n_parameters',
+            description='Number of sampled parameters.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_convergence.n_parameters']),
+        )
+
+    @property
+    def converged(self) -> BoolDescriptor:
+        """Whether the Bayesian fit met convergence criteria."""
+        return self._converged
+
+    def _set_converged(self, *, value: bool) -> None:
+        """Set the convergence flag for internal callers."""
+        self._converged.value = value
+
+    @property
+    def max_r_hat(self) -> NumericDescriptor:
+        """Maximum rank-normalized split-R-hat across parameters."""
+        return self._max_r_hat
+
+    def _set_max_r_hat(self, value: float | None) -> None:
+        """Set the maximum R-hat for internal callers."""
+        self._max_r_hat.value = value
+
+    @property
+    def min_ess_bulk(self) -> NumericDescriptor:
+        """Minimum bulk effective sample size across parameters."""
+        return self._min_ess_bulk
+
+    def _set_min_ess_bulk(self, value: float | None) -> None:
+        """Set the minimum ESS bulk for internal callers."""
+        self._min_ess_bulk.value = value
+
+    @property
+    def n_draws(self) -> IntegerDescriptor:
+        """Number of stored posterior draws."""
+        return self._n_draws
+
+    def _set_n_draws(self, value: int) -> None:
+        """Set the draw count for internal callers."""
+        self._n_draws.value = value
+
+    @property
+    def n_chains(self) -> IntegerDescriptor:
+        """Number of stored posterior chains."""
+        return self._n_chains
+
+    def _set_n_chains(self, value: int) -> None:
+        """Set the chain count for internal callers."""
+        self._n_chains.value = value
+
+    @property
+    def n_parameters(self) -> IntegerDescriptor:
+        """Number of sampled parameters."""
+        return self._n_parameters
+
+    def _set_n_parameters(self, value: int) -> None:
+        """Set the sampled-parameter count for internal callers."""
+        self._n_parameters.value = value
diff --git a/src/easydiffraction/analysis/categories/bayesian_convergence/factory.py b/src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
new file mode 100644
index 000000000..fbe5da383
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-convergence factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianConvergenceFactory(FactoryBase):
+    """Create Bayesian-convergence categories by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_distribution_caches/__init__.py b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/__init__.py
new file mode 100644
index 000000000..4ecf63f0b
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_distribution_caches.default import (
+    BayesianDistributionCacheItem,
+)
+from easydiffraction.analysis.categories.bayesian_distribution_caches.default import (
+    BayesianDistributionCaches,
+)
+from easydiffraction.analysis.categories.bayesian_distribution_caches.factory import (
+    BayesianDistributionCachesFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/bayesian_distribution_caches/default.py b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/default.py
new file mode 100644
index 000000000..41183dc39
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/default.py
@@ -0,0 +1,151 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian distribution-cache manifest rows."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.bayesian_distribution_caches.factory import (
+    BayesianDistributionCachesFactory,
+)
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+class BayesianDistributionCacheItem(CategoryItem):
+    """Single persisted Bayesian distribution-cache manifest row."""
+
+    _category_code = 'bayesian_distribution_cache'
+    _category_entry_name = 'param_unique_name'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._param_unique_name = StringDescriptor(
+            name='param_unique_name',
+            description='Unique parameter name for the cached distribution.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_bayesian_distribution_cache.param_unique_name']),
+        )
+        self._x_path = StringDescriptor(
+            name='x_path',
+            description='HDF5 dataset path for the distribution x-grid.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_distribution_cache.x_path']),
+        )
+        self._density_path = StringDescriptor(
+            name='density_path',
+            description='HDF5 dataset path for the cached density values.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_distribution_cache.density_path']),
+        )
+        self._n_grid = NumericDescriptor(
+            name='n_grid',
+            description='Number of grid points in the cached distribution.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_distribution_cache.n_grid']),
+        )
+        self._n_draws_cached = NumericDescriptor(
+            name='n_draws_cached',
+            description='Number of draws summarized into the cached distribution.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_distribution_cache.n_draws_cached']),
+        )
+
+    @property
+    def param_unique_name(self) -> StringDescriptor:
+        """Unique parameter name for the cached distribution."""
+        return self._param_unique_name
+
+    def _set_param_unique_name(self, value: str) -> None:
+        """Set the unique parameter name for internal callers."""
+        self._param_unique_name.value = value
+
+    @property
+    def x_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the distribution x-grid."""
+        return self._x_path
+
+    def _set_x_path(self, value: str) -> None:
+        """Set the x-grid dataset path for internal callers."""
+        self._x_path.value = value
+
+    @property
+    def density_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the cached density values."""
+        return self._density_path
+
+    def _set_density_path(self, value: str) -> None:
+        """Set the density dataset path for internal callers."""
+        self._density_path.value = value
+
+    @property
+    def n_grid(self) -> NumericDescriptor:
+        """Number of grid points in the cached distribution."""
+        return self._n_grid
+
+    def _set_n_grid(self, value: float) -> None:
+        """Set the grid-size count for internal callers."""
+        self._n_grid.value = value
+
+    @property
+    def n_draws_cached(self) -> NumericDescriptor:
+        """Number of draws summarized into the cached distribution."""
+        return self._n_draws_cached
+
+    def _set_n_draws_cached(self, value: float) -> None:
+        """Set the cached-draw count for internal callers."""
+        self._n_draws_cached.value = value
+
+
+@BayesianDistributionCachesFactory.register
+class BayesianDistributionCaches(CategoryCollection):
+    """Collection of persisted Bayesian distribution-cache manifests."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian distribution-cache manifests',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=BayesianDistributionCacheItem)
+
+    def create(
+        self,
+        *,
+        param_unique_name: str,
+        x_path: str,
+        density_path: str,
+        n_grid: float,
+        n_draws_cached: float,
+    ) -> None:
+        """
+        Create a persisted Bayesian distribution-cache manifest row.
+
+        Parameters
+        ----------
+        param_unique_name : str
+            Unique parameter name for the cached distribution.
+        x_path : str
+            HDF5 dataset path for the distribution x-grid.
+        density_path : str
+            HDF5 dataset path for the cached density values.
+        n_grid : float
+            Number of grid points in the cached distribution.
+        n_draws_cached : float
+            Number of draws summarized into the cached distribution.
+        """
+        item = BayesianDistributionCacheItem()
+        item._set_param_unique_name(param_unique_name)
+        item._set_x_path(x_path)
+        item._set_density_path(density_path)
+        item._set_n_grid(n_grid)
+        item._set_n_draws_cached(n_draws_cached)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/bayesian_distribution_caches/factory.py b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/factory.py
new file mode 100644
index 000000000..a45016f5b
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_distribution_caches/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-distribution-caches factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianDistributionCachesFactory(FactoryBase):
+    """Create Bayesian-distribution-cache collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_pair_caches/__init__.py b/src/easydiffraction/analysis/categories/bayesian_pair_caches/__init__.py
new file mode 100644
index 000000000..edc955fa8
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_pair_caches/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_pair_caches.default import BayesianPairCacheItem
+from easydiffraction.analysis.categories.bayesian_pair_caches.default import BayesianPairCaches
+from easydiffraction.analysis.categories.bayesian_pair_caches.factory import (
+    BayesianPairCachesFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/bayesian_pair_caches/default.py b/src/easydiffraction/analysis/categories/bayesian_pair_caches/default.py
new file mode 100644
index 000000000..937c77a5e
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_pair_caches/default.py
@@ -0,0 +1,267 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian pair-cache manifest rows."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from easydiffraction.analysis.categories.bayesian_pair_caches.factory import (
+    BayesianPairCachesFactory,
+)
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+def _normalized_parameter_pair(
+    param_unique_name_x: str,
+    param_unique_name_y: str,
+) -> tuple[str, str]:
+    """Return a stable ordering for a cached parameter pair."""
+    if param_unique_name_x <= param_unique_name_y:
+        return param_unique_name_x, param_unique_name_y
+    return param_unique_name_y, param_unique_name_x
+
+
+@dataclass(frozen=True, slots=True)
+class BayesianPairCachePaths:
+    """HDF5 dataset paths for one persisted pair cache."""
+
+    x_path: str
+    y_path: str
+    density_path: str
+    contour_level_path: str
+
+
+class BayesianPairCacheItem(CategoryItem):
+    """Single persisted Bayesian pair-cache manifest row."""
+
+    _category_code = 'bayesian_pair_cache'
+    _category_entry_name = 'id'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._param_unique_name_x = StringDescriptor(
+            name='param_unique_name_x',
+            description='First unique parameter name in the cached pair.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.param_unique_name_x']),
+        )
+        self._param_unique_name_y = StringDescriptor(
+            name='param_unique_name_y',
+            description='Second unique parameter name in the cached pair.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.param_unique_name_y']),
+        )
+        self._id = StringDescriptor(
+            name='id',
+            description='Stable identifier for the cached parameter pair.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z0-9_.:-]+$'),
+            ),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.id']),
+        )
+        self._x_path = StringDescriptor(
+            name='x_path',
+            description='HDF5 dataset path for the pair-cache x-grid.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.x_path']),
+        )
+        self._y_path = StringDescriptor(
+            name='y_path',
+            description='HDF5 dataset path for the pair-cache y-grid.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.y_path']),
+        )
+        self._density_path = StringDescriptor(
+            name='density_path',
+            description='HDF5 dataset path for the pair-cache density grid.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.density_path']),
+        )
+        self._contour_level_path = StringDescriptor(
+            name='contour_level_path',
+            description='HDF5 dataset path for cached contour levels.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.contour_level_path']),
+        )
+        self._n_grid_x = NumericDescriptor(
+            name='n_grid_x',
+            description='Number of x-grid points in the cached pair.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.n_grid_x']),
+        )
+        self._n_grid_y = NumericDescriptor(
+            name='n_grid_y',
+            description='Number of y-grid points in the cached pair.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.n_grid_y']),
+        )
+        self._n_draws_cached = NumericDescriptor(
+            name='n_draws_cached',
+            description='Number of draws summarized into the cached pair.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_pair_cache.n_draws_cached']),
+        )
+
+    @property
+    def param_unique_name_x(self) -> StringDescriptor:
+        """First unique parameter name in the cached pair."""
+        return self._param_unique_name_x
+
+    def _set_param_unique_name_x(self, value: str) -> None:
+        """Set the first unique parameter name for internal callers."""
+        self._param_unique_name_x.value = value
+
+    @property
+    def param_unique_name_y(self) -> StringDescriptor:
+        """Second unique parameter name in the cached pair."""
+        return self._param_unique_name_y
+
+    def _set_param_unique_name_y(self, value: str) -> None:
+        """Set the second unique parameter name for internal callers."""
+        self._param_unique_name_y.value = value
+
+    @property
+    def id(self) -> StringDescriptor:
+        """Stable identifier for the cached parameter pair."""
+        return self._id
+
+    def _set_id(self, value: str) -> None:
+        """Set the pair-cache id for internal callers."""
+        self._id.value = value
+
+    @property
+    def x_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the pair-cache x-grid."""
+        return self._x_path
+
+    def _set_x_path(self, value: str) -> None:
+        """Set the pair-cache x-grid path for internal callers."""
+        self._x_path.value = value
+
+    @property
+    def y_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the pair-cache y-grid."""
+        return self._y_path
+
+    def _set_y_path(self, value: str) -> None:
+        """Set the pair-cache y-grid path for internal callers."""
+        self._y_path.value = value
+
+    @property
+    def density_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the pair-cache density grid."""
+        return self._density_path
+
+    def _set_density_path(self, value: str) -> None:
+        """Set the pair-cache density path for internal callers."""
+        self._density_path.value = value
+
+    @property
+    def contour_level_path(self) -> StringDescriptor:
+        """HDF5 dataset path for cached contour levels."""
+        return self._contour_level_path
+
+    def _set_contour_level_path(self, value: str) -> None:
+        """Set the contour-level path for internal callers."""
+        self._contour_level_path.value = value
+
+    @property
+    def n_grid_x(self) -> NumericDescriptor:
+        """Number of x-grid points in the cached pair."""
+        return self._n_grid_x
+
+    def _set_n_grid_x(self, value: float) -> None:
+        """Set the x-grid size for internal callers."""
+        self._n_grid_x.value = value
+
+    @property
+    def n_grid_y(self) -> NumericDescriptor:
+        """Number of y-grid points in the cached pair."""
+        return self._n_grid_y
+
+    def _set_n_grid_y(self, value: float) -> None:
+        """Set the y-grid size for internal callers."""
+        self._n_grid_y.value = value
+
+    @property
+    def n_draws_cached(self) -> NumericDescriptor:
+        """Number of draws summarized into the cached pair."""
+        return self._n_draws_cached
+
+    def _set_n_draws_cached(self, value: float) -> None:
+        """Set the cached-draw count for internal callers."""
+        self._n_draws_cached.value = value
+
+
+@BayesianPairCachesFactory.register
+class BayesianPairCaches(CategoryCollection):
+    """Collection of persisted Bayesian pair-cache manifests."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian pair-cache manifests',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=BayesianPairCacheItem)
+
+    def create(
+        self,
+        *,
+        parameter_names: tuple[str, str],
+        paths: BayesianPairCachePaths,
+        grid_shape: tuple[float, float],
+        n_draws_cached: float,
+        id: str | None = None,
+    ) -> None:
+        """
+        Create a persisted Bayesian pair-cache manifest row.
+
+        Parameters
+        ----------
+        parameter_names : tuple[str, str]
+            Unique parameter names for the cached pair.
+        paths : BayesianPairCachePaths
+            HDF5 dataset paths for the cached pair payloads.
+        grid_shape : tuple[float, float]
+            Number of x-grid and y-grid points in the cached pair.
+        n_draws_cached : float
+            Number of draws summarized into the cached pair.
+        id : str | None, default=None
+            Explicit persisted row id. When omitted, a simple sequential
+            identifier is generated.
+        """
+        param_unique_name_x, param_unique_name_y = parameter_names
+        normalized_x, normalized_y = _normalized_parameter_pair(
+            param_unique_name_x,
+            param_unique_name_y,
+        )
+        n_grid_x, n_grid_y = grid_shape
+        item = BayesianPairCacheItem()
+        item._set_param_unique_name_x(normalized_x)
+        item._set_param_unique_name_y(normalized_y)
+        item._set_x_path(paths.x_path)
+        item._set_y_path(paths.y_path)
+        item._set_density_path(paths.density_path)
+        item._set_contour_level_path(paths.contour_level_path)
+        item._set_n_grid_x(n_grid_x)
+        item._set_n_grid_y(n_grid_y)
+        item._set_n_draws_cached(n_draws_cached)
+        resolved_id = id or str(len(self) + 1)
+        item._set_id(resolved_id)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/bayesian_pair_caches/factory.py b/src/easydiffraction/analysis/categories/bayesian_pair_caches/factory.py
new file mode 100644
index 000000000..5c6df89b1
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_pair_caches/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-pair-caches factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianPairCachesFactory(FactoryBase):
+    """Create Bayesian-pair-cache collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/__init__.py b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/__init__.py
new file mode 100644
index 000000000..10ec76953
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors.default import (
+    BayesianParameterPosteriorItem,
+)
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors.default import (
+    BayesianParameterPosteriors,
+)
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors.factory import (
+    BayesianParameterPosteriorsFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/default.py b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/default.py
new file mode 100644
index 000000000..ea234f0ec
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/default.py
@@ -0,0 +1,242 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian parameter posterior summary rows."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from easydiffraction.analysis.categories.bayesian_parameter_posteriors.factory import (
+    BayesianParameterPosteriorsFactory,
+)
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+if TYPE_CHECKING:
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+
+
+class BayesianParameterPosteriorItem(CategoryItem):
+    """Single persisted Bayesian parameter posterior summary row."""
+
+    _category_code = 'bayesian_parameter_posterior'
+    _category_entry_name = 'unique_name'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._unique_name = StringDescriptor(
+            name='unique_name',
+            description='Unique EasyDiffraction parameter name.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.unique_name']),
+        )
+        self._display_name = StringDescriptor(
+            name='display_name',
+            description='Human-readable parameter label.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.display_name']),
+        )
+        self._best_sample_value = NumericDescriptor(
+            name='best_sample_value',
+            description='Committed sampled parameter value.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.best_sample_value']),
+        )
+        self._median = NumericDescriptor(
+            name='median',
+            description='Posterior median value.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.median']),
+        )
+        self._uncertainty = NumericDescriptor(
+            name='uncertainty',
+            description='Posterior standard deviation.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.uncertainty']),
+        )
+        self._interval_68_lower = NumericDescriptor(
+            name='interval_68_lower',
+            description='Lower bound of the 68% credible interval.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.interval_68_lower']),
+        )
+        self._interval_68_upper = NumericDescriptor(
+            name='interval_68_upper',
+            description='Upper bound of the 68% credible interval.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.interval_68_upper']),
+        )
+        self._interval_95_lower = NumericDescriptor(
+            name='interval_95_lower',
+            description='Lower bound of the 95% credible interval.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.interval_95_lower']),
+        )
+        self._interval_95_upper = NumericDescriptor(
+            name='interval_95_upper',
+            description='Upper bound of the 95% credible interval.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.interval_95_upper']),
+        )
+        self._ess_bulk = NumericDescriptor(
+            name='ess_bulk',
+            description='Bulk effective sample size when available.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.ess_bulk']),
+        )
+        self._r_hat = NumericDescriptor(
+            name='r_hat',
+            description='Rank-normalized split-R-hat when available.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_parameter_posterior.r_hat']),
+        )
+
+    @property
+    def unique_name(self) -> StringDescriptor:
+        """Unique EasyDiffraction parameter name."""
+        return self._unique_name
+
+    def _set_unique_name(self, value: str) -> None:
+        """Set the unique parameter name for internal callers."""
+        self._unique_name.value = value
+
+    @property
+    def display_name(self) -> StringDescriptor:
+        """Human-readable parameter label."""
+        return self._display_name
+
+    def _set_display_name(self, value: str) -> None:
+        """Set the display name for internal callers."""
+        self._display_name.value = value
+
+    @property
+    def best_sample_value(self) -> NumericDescriptor:
+        """Committed sampled parameter value."""
+        return self._best_sample_value
+
+    def _set_best_sample_value(self, value: float | None) -> None:
+        """Set the best sampled parameter value for internal callers."""
+        self._best_sample_value.value = value
+
+    @property
+    def median(self) -> NumericDescriptor:
+        """Posterior median value."""
+        return self._median
+
+    def _set_median(self, value: float | None) -> None:
+        """Set the posterior median for internal callers."""
+        self._median.value = value
+
+    @property
+    def uncertainty(self) -> NumericDescriptor:
+        """Posterior standard deviation."""
+        return self._uncertainty
+
+    def _set_uncertainty(self, value: float | None) -> None:
+        """Set the posterior uncertainty for internal callers."""
+        self._uncertainty.value = value
+
+    @property
+    def interval_68_lower(self) -> NumericDescriptor:
+        """Lower bound of the 68% credible interval."""
+        return self._interval_68_lower
+
+    def _set_interval_68_lower(self, value: float | None) -> None:
+        """Set the 68% interval lower bound for internal callers."""
+        self._interval_68_lower.value = value
+
+    @property
+    def interval_68_upper(self) -> NumericDescriptor:
+        """Upper bound of the 68% credible interval."""
+        return self._interval_68_upper
+
+    def _set_interval_68_upper(self, value: float | None) -> None:
+        """Set the 68% interval upper bound for internal callers."""
+        self._interval_68_upper.value = value
+
+    @property
+    def interval_95_lower(self) -> NumericDescriptor:
+        """Lower bound of the 95% credible interval."""
+        return self._interval_95_lower
+
+    def _set_interval_95_lower(self, value: float | None) -> None:
+        """Set the 95% interval lower bound for internal callers."""
+        self._interval_95_lower.value = value
+
+    @property
+    def interval_95_upper(self) -> NumericDescriptor:
+        """Upper bound of the 95% credible interval."""
+        return self._interval_95_upper
+
+    def _set_interval_95_upper(self, value: float | None) -> None:
+        """Set the 95% interval upper bound for internal callers."""
+        self._interval_95_upper.value = value
+
+    @property
+    def ess_bulk(self) -> NumericDescriptor:
+        """Bulk effective sample size when available."""
+        return self._ess_bulk
+
+    def _set_ess_bulk(self, value: float | None) -> None:
+        """Set the ESS bulk value for internal callers."""
+        self._ess_bulk.value = value
+
+    @property
+    def r_hat(self) -> NumericDescriptor:
+        """Rank-normalized split-R-hat when available."""
+        return self._r_hat
+
+    def _set_r_hat(self, value: float | None) -> None:
+        """Set the R-hat value for internal callers."""
+        self._r_hat.value = value
+
+
+@BayesianParameterPosteriorsFactory.register
+class BayesianParameterPosteriors(CategoryCollection):
+    """
+    Collection of persisted Bayesian parameter posterior summaries.
+    """
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian parameter posterior summaries',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=BayesianParameterPosteriorItem)
+
+    def create(
+        self,
+        *,
+        summary: PosteriorParameterSummary,
+    ) -> None:
+        """
+        Create a persisted Bayesian parameter posterior summary row.
+
+        Parameters
+        ----------
+        summary : PosteriorParameterSummary
+            Runtime posterior summary to persist.
+        """
+        item = BayesianParameterPosteriorItem()
+        item._set_unique_name(summary.unique_name)
+        item._set_display_name(summary.display_name)
+        item._set_best_sample_value(summary.best_sample_value)
+        item._set_median(summary.median)
+        item._set_uncertainty(summary.standard_deviation)
+        item._set_interval_68_lower(summary.interval_68[0])
+        item._set_interval_68_upper(summary.interval_68[1])
+        item._set_interval_95_lower(summary.interval_95[0])
+        item._set_interval_95_upper(summary.interval_95[1])
+        item._set_ess_bulk(summary.ess_bulk)
+        item._set_r_hat(summary.r_hat)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/factory.py b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/factory.py
new file mode 100644
index 000000000..54eef2b83
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_parameter_posteriors/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-parameter-posteriors factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianParameterPosteriorsFactory(FactoryBase):
+    """Create Bayesian-parameter-posterior collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/__init__.py b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/__init__.py
new file mode 100644
index 000000000..f706de4fc
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+    BayesianPredictiveDatasetItem,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+    BayesianPredictiveDatasets,
+)
+from easydiffraction.analysis.categories.bayesian_predictive_datasets.factory import (
+    BayesianPredictiveDatasetsFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/default.py b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/default.py
new file mode 100644
index 000000000..9bb8acf00
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/default.py
@@ -0,0 +1,260 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian predictive-dataset manifest rows."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from easydiffraction.analysis.categories.bayesian_predictive_datasets.factory import (
+    BayesianPredictiveDatasetsFactory,
+)
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@dataclass(frozen=True, slots=True)
+class BayesianPredictiveDatasetPaths:
+    """HDF5 dataset paths for one predictive dataset."""
+
+    x_path: str
+    best_sample_prediction_path: str
+    lower_95_path: str | None = None
+    upper_95_path: str | None = None
+    lower_68_path: str | None = None
+    upper_68_path: str | None = None
+    draws_path: str | None = None
+
+
+class BayesianPredictiveDatasetItem(CategoryItem):
+    """Single persisted Bayesian predictive-dataset manifest row."""
+
+    _category_code = 'bayesian_predictive_dataset'
+    _category_entry_name = 'experiment_name'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._experiment_name = StringDescriptor(
+            name='experiment_name',
+            description='Experiment name for the cached predictive dataset.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.experiment_name']),
+        )
+        self._x_axis_name = StringDescriptor(
+            name='x_axis_name',
+            description='Name of the predictive dataset x-axis.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.x_axis_name']),
+        )
+        self._x_path = StringDescriptor(
+            name='x_path',
+            description='HDF5 dataset path for the predictive x-axis values.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.x_path']),
+        )
+        self._best_sample_prediction_path = StringDescriptor(
+            name='best_sample_prediction_path',
+            description='HDF5 dataset path for the committed predictive curve.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(
+                names=['_bayesian_predictive_dataset.best_sample_prediction_path']
+            ),
+        )
+        self._lower_95_path = StringDescriptor(
+            name='lower_95_path',
+            description='HDF5 dataset path for the lower 95% predictive band.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.lower_95_path']),
+        )
+        self._upper_95_path = StringDescriptor(
+            name='upper_95_path',
+            description='HDF5 dataset path for the upper 95% predictive band.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.upper_95_path']),
+        )
+        self._lower_68_path = StringDescriptor(
+            name='lower_68_path',
+            description='HDF5 dataset path for the lower 68% predictive band.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.lower_68_path']),
+        )
+        self._upper_68_path = StringDescriptor(
+            name='upper_68_path',
+            description='HDF5 dataset path for the upper 68% predictive band.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.upper_68_path']),
+        )
+        self._draws_path = StringDescriptor(
+            name='draws_path',
+            description='HDF5 dataset path for cached predictive draws.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.draws_path']),
+        )
+        self._n_x = NumericDescriptor(
+            name='n_x',
+            description='Number of x-axis points in the cached predictive dataset.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.n_x']),
+        )
+        self._n_draws_cached = NumericDescriptor(
+            name='n_draws_cached',
+            description='Number of cached predictive draws.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_predictive_dataset.n_draws_cached']),
+        )
+
+    @property
+    def experiment_name(self) -> StringDescriptor:
+        """Experiment name for the cached predictive dataset."""
+        return self._experiment_name
+
+    def _set_experiment_name(self, value: str) -> None:
+        """Set the experiment name for internal callers."""
+        self._experiment_name.value = value
+
+    @property
+    def x_axis_name(self) -> StringDescriptor:
+        """Name of the predictive dataset x-axis."""
+        return self._x_axis_name
+
+    def _set_x_axis_name(self, value: str) -> None:
+        """Set the x-axis name for internal callers."""
+        self._x_axis_name.value = value
+
+    @property
+    def x_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the predictive x-axis values."""
+        return self._x_path
+
+    def _set_x_path(self, value: str) -> None:
+        """Set the predictive x-axis path for internal callers."""
+        self._x_path.value = value
+
+    @property
+    def best_sample_prediction_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the committed predictive curve."""
+        return self._best_sample_prediction_path
+
+    def _set_best_sample_prediction_path(self, value: str) -> None:
+        """Set the best-sample prediction path for internal callers."""
+        self._best_sample_prediction_path.value = value
+
+    @property
+    def lower_95_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the lower 95% predictive band."""
+        return self._lower_95_path
+
+    def _set_lower_95_path(self, value: str | None) -> None:
+        """Set the lower-95 path for internal callers."""
+        self._lower_95_path.value = value
+
+    @property
+    def upper_95_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the upper 95% predictive band."""
+        return self._upper_95_path
+
+    def _set_upper_95_path(self, value: str | None) -> None:
+        """Set the upper-95 path for internal callers."""
+        self._upper_95_path.value = value
+
+    @property
+    def lower_68_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the lower 68% predictive band."""
+        return self._lower_68_path
+
+    def _set_lower_68_path(self, value: str | None) -> None:
+        """Set the lower-68 path for internal callers."""
+        self._lower_68_path.value = value
+
+    @property
+    def upper_68_path(self) -> StringDescriptor:
+        """HDF5 dataset path for the upper 68% predictive band."""
+        return self._upper_68_path
+
+    def _set_upper_68_path(self, value: str | None) -> None:
+        """Set the upper-68 path for internal callers."""
+        self._upper_68_path.value = value
+
+    @property
+    def draws_path(self) -> StringDescriptor:
+        """HDF5 dataset path for cached predictive draws."""
+        return self._draws_path
+
+    def _set_draws_path(self, value: str | None) -> None:
+        """Set the predictive-draws path for internal callers."""
+        self._draws_path.value = value
+
+    @property
+    def n_x(self) -> NumericDescriptor:
+        """Number of x-axis points in the cached predictive dataset."""
+        return self._n_x
+
+    def _set_n_x(self, value: float) -> None:
+        """Set the predictive x-axis size for internal callers."""
+        self._n_x.value = value
+
+    @property
+    def n_draws_cached(self) -> NumericDescriptor:
+        """Number of cached predictive draws."""
+        return self._n_draws_cached
+
+    def _set_n_draws_cached(self, value: float) -> None:
+        """Set the cached predictive-draw count for internal callers."""
+        self._n_draws_cached.value = value
+
+
+@BayesianPredictiveDatasetsFactory.register
+class BayesianPredictiveDatasets(CategoryCollection):
+    """Collection of persisted Bayesian predictive-dataset manifests."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian predictive-dataset manifests',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=BayesianPredictiveDatasetItem)
+
+    def create(
+        self,
+        *,
+        experiment_name: str,
+        x_axis_name: str,
+        paths: BayesianPredictiveDatasetPaths,
+        n_x: float,
+        n_draws_cached: float,
+    ) -> None:
+        """
+        Create a persisted Bayesian predictive-dataset manifest row.
+
+        Parameters
+        ----------
+        experiment_name : str
+            Experiment name for the cached predictive dataset.
+        x_axis_name : str
+            Name of the predictive dataset x-axis.
+        paths : BayesianPredictiveDatasetPaths
+            HDF5 dataset paths for the predictive dataset payloads.
+        n_x : float
+            Number of x-axis points in the cached predictive dataset.
+        n_draws_cached : float
+            Number of cached predictive draws.
+        """
+        item = BayesianPredictiveDatasetItem()
+        item._set_experiment_name(experiment_name)
+        item._set_x_axis_name(x_axis_name)
+        item._set_x_path(paths.x_path)
+        item._set_best_sample_prediction_path(paths.best_sample_prediction_path)
+        item._set_lower_95_path(paths.lower_95_path)
+        item._set_upper_95_path(paths.upper_95_path)
+        item._set_lower_68_path(paths.lower_68_path)
+        item._set_upper_68_path(paths.upper_68_path)
+        item._set_draws_path(paths.draws_path)
+        item._set_n_x(n_x)
+        item._set_n_draws_cached(n_draws_cached)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/factory.py b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/factory.py
new file mode 100644
index 000000000..a88449263
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_predictive_datasets/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-predictive-datasets factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianPredictiveDatasetsFactory(FactoryBase):
+    """Create Bayesian-predictive-dataset collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_result/__init__.py b/src/easydiffraction/analysis/categories/bayesian_result/__init__.py
new file mode 100644
index 000000000..eab2a07f3
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_result/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_result.default import BayesianResult
+from easydiffraction.analysis.categories.bayesian_result.factory import BayesianResultFactory
diff --git a/src/easydiffraction/analysis/categories/bayesian_result/default.py b/src/easydiffraction/analysis/categories/bayesian_result/default.py
new file mode 100644
index 000000000..dd4210711
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_result/default.py
@@ -0,0 +1,215 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian fit-result metadata category."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.bayesian_result.factory import BayesianResultFactory
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@BayesianResultFactory.register
+class BayesianResult(CategoryItem):
+    """Persisted Bayesian fit-result metadata."""
+
+    _category_code = 'bayesian_result'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted Bayesian fit-result metadata',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._sampler_name = StringDescriptor(
+            name='sampler_name',
+            description='Name of the persisted Bayesian sampler.',
+            value_spec=AttributeSpec(default='dream'),
+            cif_handler=CifHandler(names=['_bayesian_result.sampler_name']),
+        )
+        self._point_estimate_name = StringDescriptor(
+            name='point_estimate_name',
+            description='Committed sampled point estimate name.',
+            value_spec=AttributeSpec(default='best_sample'),
+            cif_handler=CifHandler(names=['_bayesian_result.point_estimate_name']),
+        )
+        self._success = BoolDescriptor(
+            name='success',
+            description='Whether the persisted Bayesian fit produced usable results.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.success']),
+        )
+        self._sampler_completed = BoolDescriptor(
+            name='sampler_completed',
+            description='Whether the sampler completed and returned posterior data.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.sampler_completed']),
+        )
+        self._best_log_posterior = NumericDescriptor(
+            name='best_log_posterior',
+            description='Best log-posterior value reported by the sampler.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_result.best_log_posterior']),
+        )
+        self._credible_interval_inner = NumericDescriptor(
+            name='credible_interval_inner',
+            description='Inner credible-interval level used in summaries.',
+            value_spec=AttributeSpec(default=0.68),
+            cif_handler=CifHandler(names=['_bayesian_result.credible_interval_inner']),
+        )
+        self._credible_interval_outer = NumericDescriptor(
+            name='credible_interval_outer',
+            description='Outer credible-interval level used in summaries.',
+            value_spec=AttributeSpec(default=0.95),
+            cif_handler=CifHandler(names=['_bayesian_result.credible_interval_outer']),
+        )
+        self._has_posterior_samples = BoolDescriptor(
+            name='has_posterior_samples',
+            description='Whether posterior samples were persisted.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.has_posterior_samples']),
+        )
+        self._has_distribution_cache = BoolDescriptor(
+            name='has_distribution_cache',
+            description='Whether distribution-cache manifests were persisted.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.has_distribution_cache']),
+        )
+        self._has_pair_cache = BoolDescriptor(
+            name='has_pair_cache',
+            description='Whether pair-cache manifests were persisted.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.has_pair_cache']),
+        )
+        self._has_posterior_predictive = BoolDescriptor(
+            name='has_posterior_predictive',
+            description='Whether posterior predictive manifests were persisted.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_bayesian_result.has_posterior_predictive']),
+        )
+        self._sidecar_file = StringDescriptor(
+            name='sidecar_file',
+            description='Relative path to the persisted Bayesian HDF5 sidecar.',
+            value_spec=AttributeSpec(default='results.h5'),
+            cif_handler=CifHandler(names=['_bayesian_result.sidecar_file']),
+        )
+
+    @property
+    def sampler_name(self) -> StringDescriptor:
+        """Name of the persisted Bayesian sampler."""
+        return self._sampler_name
+
+    def _set_sampler_name(self, value: str) -> None:
+        """Set the sampler name for internal callers."""
+        self._sampler_name.value = value
+
+    @property
+    def point_estimate_name(self) -> StringDescriptor:
+        """Committed sampled point estimate name."""
+        return self._point_estimate_name
+
+    def _set_point_estimate_name(self, value: str) -> None:
+        """Set the point-estimate name for internal callers."""
+        self._point_estimate_name.value = value
+
+    @property
+    def success(self) -> BoolDescriptor:
+        """
+        Whether the persisted Bayesian fit produced usable results.
+        """
+        return self._success
+
+    def _set_success(self, *, value: bool) -> None:
+        """Set the success flag for internal callers."""
+        self._success.value = value
+
+    @property
+    def sampler_completed(self) -> BoolDescriptor:
+        """Whether the sampler completed and returned posterior data."""
+        return self._sampler_completed
+
+    def _set_sampler_completed(self, *, value: bool) -> None:
+        """Set the sampler-completed flag for internal callers."""
+        self._sampler_completed.value = value
+
+    @property
+    def best_log_posterior(self) -> NumericDescriptor:
+        """Best log-posterior value reported by the sampler."""
+        return self._best_log_posterior
+
+    def _set_best_log_posterior(self, value: float | None) -> None:
+        """Set the best log-posterior for internal callers."""
+        self._best_log_posterior.value = value
+
+    @property
+    def credible_interval_inner(self) -> NumericDescriptor:
+        """Inner credible-interval level used in summaries."""
+        return self._credible_interval_inner
+
+    def _set_credible_interval_inner(self, value: float) -> None:
+        """
+        Set the inner credible-interval level for internal callers.
+        """
+        self._credible_interval_inner.value = value
+
+    @property
+    def credible_interval_outer(self) -> NumericDescriptor:
+        """Outer credible-interval level used in summaries."""
+        return self._credible_interval_outer
+
+    def _set_credible_interval_outer(self, value: float) -> None:
+        """
+        Set the outer credible-interval level for internal callers.
+        """
+        self._credible_interval_outer.value = value
+
+    @property
+    def has_posterior_samples(self) -> BoolDescriptor:
+        """Whether posterior samples were persisted."""
+        return self._has_posterior_samples
+
+    def _set_has_posterior_samples(self, *, value: bool) -> None:
+        """Set the posterior-samples flag for internal callers."""
+        self._has_posterior_samples.value = value
+
+    @property
+    def has_distribution_cache(self) -> BoolDescriptor:
+        """Whether distribution-cache manifests were persisted."""
+        return self._has_distribution_cache
+
+    def _set_has_distribution_cache(self, *, value: bool) -> None:
+        """Set the distribution-cache flag for internal callers."""
+        self._has_distribution_cache.value = value
+
+    @property
+    def has_pair_cache(self) -> BoolDescriptor:
+        """Whether pair-cache manifests were persisted."""
+        return self._has_pair_cache
+
+    def _set_has_pair_cache(self, *, value: bool) -> None:
+        """Set the pair-cache flag for internal callers."""
+        self._has_pair_cache.value = value
+
+    @property
+    def has_posterior_predictive(self) -> BoolDescriptor:
+        """Whether posterior predictive manifests were persisted."""
+        return self._has_posterior_predictive
+
+    def _set_has_posterior_predictive(self, *, value: bool) -> None:
+        """Set the posterior-predictive flag for internal callers."""
+        self._has_posterior_predictive.value = value
+
+    @property
+    def sidecar_file(self) -> StringDescriptor:
+        """Relative path to the persisted Bayesian HDF5 sidecar."""
+        return self._sidecar_file
+
+    def _set_sidecar_file(self, value: str) -> None:
+        """Set the sidecar-file path for internal callers."""
+        self._sidecar_file.value = value
diff --git a/src/easydiffraction/analysis/categories/bayesian_result/factory.py b/src/easydiffraction/analysis/categories/bayesian_result/factory.py
new file mode 100644
index 000000000..3d437a0df
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_result/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-result factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianResultFactory(FactoryBase):
+    """Create Bayesian-result categories by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/bayesian_sampler/__init__.py b/src/easydiffraction/analysis/categories/bayesian_sampler/__init__.py
new file mode 100644
index 000000000..962e3fbae
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_sampler/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.bayesian_sampler.default import BayesianSampler
+from easydiffraction.analysis.categories.bayesian_sampler.factory import BayesianSamplerFactory
diff --git a/src/easydiffraction/analysis/categories/bayesian_sampler/default.py b/src/easydiffraction/analysis/categories/bayesian_sampler/default.py
new file mode 100644
index 000000000..c75ffa156
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_sampler/default.py
@@ -0,0 +1,133 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Resolved Bayesian sampler settings category."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.bayesian_sampler.factory import BayesianSamplerFactory
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import IntegerDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@BayesianSamplerFactory.register
+class BayesianSampler(CategoryItem):
+    """Persisted resolved Bayesian sampler settings."""
+
+    _category_code = 'bayesian_sampler'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted resolved Bayesian sampler settings',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._steps = IntegerDescriptor(
+            name='steps',
+            description='Resolved number of sampler steps.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_sampler.steps']),
+        )
+        self._burn = IntegerDescriptor(
+            name='burn',
+            description='Resolved burn-in count.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_sampler.burn']),
+        )
+        self._thin = IntegerDescriptor(
+            name='thin',
+            description='Resolved thinning interval.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_sampler.thin']),
+        )
+        self._pop = IntegerDescriptor(
+            name='pop',
+            description='Resolved population size.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_sampler.pop']),
+        )
+        self._parallel = IntegerDescriptor(
+            name='parallel',
+            description='Resolved DREAM worker count; 0 uses all CPUs.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_bayesian_sampler.parallel']),
+        )
+        self._init = StringDescriptor(
+            name='init',
+            description='Resolved DREAM initialization mode.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_bayesian_sampler.init']),
+        )
+        self._random_seed = IntegerDescriptor(
+            name='random_seed',
+            description='Resolved random seed used by the sampler.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_bayesian_sampler.random_seed']),
+        )
+
+    @property
+    def steps(self) -> IntegerDescriptor:
+        """Resolved number of sampler steps."""
+        return self._steps
+
+    def _set_steps(self, value: int) -> None:
+        """Set the step count for internal callers."""
+        self._steps.value = value
+
+    @property
+    def burn(self) -> IntegerDescriptor:
+        """Resolved burn-in count."""
+        return self._burn
+
+    def _set_burn(self, value: int) -> None:
+        """Set the burn-in count for internal callers."""
+        self._burn.value = value
+
+    @property
+    def thin(self) -> IntegerDescriptor:
+        """Resolved thinning interval."""
+        return self._thin
+
+    def _set_thin(self, value: int) -> None:
+        """Set the thinning interval for internal callers."""
+        self._thin.value = value
+
+    @property
+    def pop(self) -> IntegerDescriptor:
+        """Resolved population size."""
+        return self._pop
+
+    def _set_pop(self, value: int) -> None:
+        """Set the population size for internal callers."""
+        self._pop.value = value
+
+    @property
+    def parallel(self) -> IntegerDescriptor:
+        """Resolved DREAM worker count; 0 uses all CPUs."""
+        return self._parallel
+
+    def _set_parallel(self, value: int) -> None:
+        """Set the DREAM worker count for internal callers."""
+        self._parallel.value = value
+
+    @property
+    def init(self) -> StringDescriptor:
+        """Resolved DREAM initialization mode."""
+        return self._init
+
+    def _set_init(self, value: str) -> None:
+        """Set the initialization mode for internal callers."""
+        self._init.value = value
+
+    @property
+    def random_seed(self) -> IntegerDescriptor:
+        """Resolved random seed used by the sampler."""
+        return self._random_seed
+
+    def _set_random_seed(self, value: int | None) -> None:
+        """Set the random seed for internal callers."""
+        self._random_seed.value = value
diff --git a/src/easydiffraction/analysis/categories/bayesian_sampler/factory.py b/src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
new file mode 100644
index 000000000..6f5d1033c
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian-sampler factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class BayesianSamplerFactory(FactoryBase):
+    """Create Bayesian-sampler categories by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/constraints/default.py b/src/easydiffraction/analysis/categories/constraints/default.py
index d6f026221..0b622e183 100644
--- a/src/easydiffraction/analysis/categories/constraints/default.py
+++ b/src/easydiffraction/analysis/categories/constraints/default.py
@@ -137,7 +137,7 @@ def disable(self) -> None:
         """Deactivate constraints without deleting them."""
         self._enabled = False
 
-    def create(self, *, expression: str) -> None:
+    def create(self, *, expression: str, id: str | None = None) -> None:
         """
         Create a constraint from an expression string.
 
@@ -148,10 +148,15 @@ def create(self, *, expression: str) -> None:
         expression : str
             Constraint equation, e.g. ``'biso_Co2 = biso_Co1'`` or
             ``'occ_Ba = 1 - occ_La'``.
+        id : str | None, default=None
+            Explicit row identifier. When not ``None``, this value is
+            used as the collection key instead of the left-hand alias.
         """
         item = Constraint()
         item.expression = expression
-        if item.lhs_alias:
+        if id is not None:
+            item.id = id
+        elif item.lhs_alias:
             item.id = item.lhs_alias
         self.add(item)
         self._enabled = True
diff --git a/src/easydiffraction/analysis/categories/deterministic_result/__init__.py b/src/easydiffraction/analysis/categories/deterministic_result/__init__.py
new file mode 100644
index 000000000..18ed7af8f
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/deterministic_result/__init__.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.deterministic_result.default import DeterministicResult
+from easydiffraction.analysis.categories.deterministic_result.factory import (
+    DeterministicResultFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/deterministic_result/default.py b/src/easydiffraction/analysis/categories/deterministic_result/default.py
new file mode 100644
index 000000000..f66f2018d
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/deterministic_result/default.py
@@ -0,0 +1,187 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Deterministic fit-result metadata category."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.deterministic_result.factory import (
+    DeterministicResultFactory,
+)
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@DeterministicResultFactory.register
+class DeterministicResult(CategoryItem):
+    """Persisted deterministic fit-result metadata."""
+
+    _category_code = 'deterministic_result'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted deterministic fit-result metadata',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._optimizer_name = StringDescriptor(
+            name='optimizer_name',
+            description='Name of the persisted deterministic optimizer.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_deterministic_result.optimizer_name']),
+        )
+        self._method_name = StringDescriptor(
+            name='method_name',
+            description='Method name of the persisted deterministic optimizer.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_deterministic_result.method_name']),
+        )
+        self._objective_name = StringDescriptor(
+            name='objective_name',
+            description='Objective function name for the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_deterministic_result.objective_name']),
+        )
+        self._objective_value = NumericDescriptor(
+            name='objective_value',
+            description='Objective value for the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_deterministic_result.objective_value']),
+        )
+        self._n_data_points = NumericDescriptor(
+            name='n_data_points',
+            description='Number of data points used in the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_deterministic_result.n_data_points']),
+        )
+        self._n_parameters = NumericDescriptor(
+            name='n_parameters',
+            description='Number of parameters considered in the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_deterministic_result.n_parameters']),
+        )
+        self._n_free_parameters = NumericDescriptor(
+            name='n_free_parameters',
+            description='Number of free parameters in the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_deterministic_result.n_free_parameters']),
+        )
+        self._degrees_of_freedom = NumericDescriptor(
+            name='degrees_of_freedom',
+            description='Degrees of freedom for the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_deterministic_result.degrees_of_freedom']),
+        )
+        self._covariance_available = BoolDescriptor(
+            name='covariance_available',
+            description='Whether covariance was available for the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_deterministic_result.covariance_available']),
+        )
+        self._correlation_available = BoolDescriptor(
+            name='correlation_available',
+            description='Whether correlations were available for the persisted deterministic fit.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_deterministic_result.correlation_available']),
+        )
+
+    @property
+    def optimizer_name(self) -> StringDescriptor:
+        """Name of the persisted deterministic optimizer."""
+        return self._optimizer_name
+
+    def _set_optimizer_name(self, value: str) -> None:
+        """Set the optimizer name for internal callers."""
+        self._optimizer_name.value = value
+
+    @property
+    def method_name(self) -> StringDescriptor:
+        """Method name of the persisted deterministic optimizer."""
+        return self._method_name
+
+    def _set_method_name(self, value: str) -> None:
+        """Set the method name for internal callers."""
+        self._method_name.value = value
+
+    @property
+    def objective_name(self) -> StringDescriptor:
+        """
+        Objective function name for the persisted deterministic fit.
+        """
+        return self._objective_name
+
+    def _set_objective_name(self, value: str) -> None:
+        """Set the objective name for internal callers."""
+        self._objective_name.value = value
+
+    @property
+    def objective_value(self) -> NumericDescriptor:
+        """Objective value for the persisted deterministic fit."""
+        return self._objective_value
+
+    def _set_objective_value(self, value: float | None) -> None:
+        """Set the objective value for internal callers."""
+        self._objective_value.value = value
+
+    @property
+    def n_data_points(self) -> NumericDescriptor:
+        """
+        Number of data points used in the persisted deterministic fit.
+        """
+        return self._n_data_points
+
+    def _set_n_data_points(self, value: float) -> None:
+        """Set the data-point count for internal callers."""
+        self._n_data_points.value = value
+
+    @property
+    def n_parameters(self) -> NumericDescriptor:
+        """Number of parameters considered in the persisted fit."""
+        return self._n_parameters
+
+    def _set_n_parameters(self, value: float) -> None:
+        """Set the parameter count for internal callers."""
+        self._n_parameters.value = value
+
+    @property
+    def n_free_parameters(self) -> NumericDescriptor:
+        """
+        Number of free parameters in the persisted deterministic fit.
+        """
+        return self._n_free_parameters
+
+    def _set_n_free_parameters(self, value: float) -> None:
+        """Set the free-parameter count for internal callers."""
+        self._n_free_parameters.value = value
+
+    @property
+    def degrees_of_freedom(self) -> NumericDescriptor:
+        """Degrees of freedom for the persisted deterministic fit."""
+        return self._degrees_of_freedom
+
+    def _set_degrees_of_freedom(self, value: float) -> None:
+        """Set the degrees of freedom for internal callers."""
+        self._degrees_of_freedom.value = value
+
+    @property
+    def covariance_available(self) -> BoolDescriptor:
+        """Whether covariance was available for the persisted fit."""
+        return self._covariance_available
+
+    def _set_covariance_available(self, *, value: bool) -> None:
+        """Set the covariance-available flag for internal callers."""
+        self._covariance_available.value = value
+
+    @property
+    def correlation_available(self) -> BoolDescriptor:
+        """Whether correlations were available for the persisted fit."""
+        return self._correlation_available
+
+    def _set_correlation_available(self, *, value: bool) -> None:
+        """Set the correlation-available flag for internal callers."""
+        self._correlation_available.value = value
diff --git a/src/easydiffraction/analysis/categories/deterministic_result/factory.py b/src/easydiffraction/analysis/categories/deterministic_result/factory.py
new file mode 100644
index 000000000..1ee96acf9
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/deterministic_result/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Deterministic-result factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class DeterministicResultFactory(FactoryBase):
+    """Create deterministic-result categories by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/fit_parameter_correlations/__init__.py b/src/easydiffraction/analysis/categories/fit_parameter_correlations/__init__.py
new file mode 100644
index 000000000..bbb74736d
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameter_correlations/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.fit_parameter_correlations.default import (
+    FitParameterCorrelationItem,
+)
+from easydiffraction.analysis.categories.fit_parameter_correlations.default import (
+    FitParameterCorrelations,
+)
+from easydiffraction.analysis.categories.fit_parameter_correlations.factory import (
+    FitParameterCorrelationsFactory,
+)
diff --git a/src/easydiffraction/analysis/categories/fit_parameter_correlations/default.py b/src/easydiffraction/analysis/categories/fit_parameter_correlations/default.py
new file mode 100644
index 000000000..4b08057e3
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameter_correlations/default.py
@@ -0,0 +1,184 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Persisted fit-parameter correlation summaries."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.fit_parameter_correlations.factory import (
+    FitParameterCorrelationsFactory,
+)
+from easydiffraction.analysis.enums import FitCorrelationSourceEnum
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.validation import RangeValidator
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+def _normalized_parameter_pair(
+    param_unique_name_i: str,
+    param_unique_name_j: str,
+) -> tuple[str, str]:
+    """Return a stable ordering for a parameter pair."""
+    if param_unique_name_i <= param_unique_name_j:
+        return param_unique_name_i, param_unique_name_j
+    return param_unique_name_j, param_unique_name_i
+
+
+class FitParameterCorrelationItem(CategoryItem):
+    """Single persisted fit-parameter correlation row."""
+
+    _category_code = 'fit_parameter_correlation'
+    _category_entry_name = 'id'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._id = StringDescriptor(
+            name='id',
+            description='Stable identifier for the persisted correlation row.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z0-9_.:-]+$'),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter_correlation.id']),
+        )
+        self._source_kind = StringDescriptor(
+            name='source_kind',
+            description='Origin of the persisted correlation summary.',
+            value_spec=AttributeSpec(
+                default=FitCorrelationSourceEnum.default().value,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in FitCorrelationSourceEnum]
+                ),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter_correlation.source_kind']),
+        )
+        self._param_unique_name_i = StringDescriptor(
+            name='param_unique_name_i',
+            description='First unique parameter name in the persisted pair.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter_correlation.param_unique_name_i']),
+        )
+        self._param_unique_name_j = StringDescriptor(
+            name='param_unique_name_j',
+            description='Second unique parameter name in the persisted pair.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter_correlation.param_unique_name_j']),
+        )
+        self._correlation = NumericDescriptor(
+            name='correlation',
+            description='Persisted correlation coefficient for the parameter pair.',
+            value_spec=AttributeSpec(
+                default=0.0,
+                validator=RangeValidator(ge=-1.0, le=1.0),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter_correlation.correlation']),
+        )
+
+    @property
+    def id(self) -> StringDescriptor:
+        """Stable identifier for the persisted correlation row."""
+        return self._id
+
+    def _set_id(self, value: str) -> None:
+        """Set the persisted correlation-row id for internal callers."""
+        self._id.value = value
+
+    @property
+    def source_kind(self) -> StringDescriptor:
+        """Origin of the persisted correlation summary."""
+        return self._source_kind
+
+    def _set_source_kind(self, value: str) -> None:
+        """Set the correlation source kind for internal callers."""
+        self._source_kind.value = value
+
+    @property
+    def param_unique_name_i(self) -> StringDescriptor:
+        """First unique parameter name in the persisted pair."""
+        return self._param_unique_name_i
+
+    def _set_param_unique_name_i(self, value: str) -> None:
+        """Set the first parameter name for internal callers."""
+        self._param_unique_name_i.value = value
+
+    @property
+    def param_unique_name_j(self) -> StringDescriptor:
+        """Second unique parameter name in the persisted pair."""
+        return self._param_unique_name_j
+
+    def _set_param_unique_name_j(self, value: str) -> None:
+        """Set the second parameter name for internal callers."""
+        self._param_unique_name_j.value = value
+
+    @property
+    def correlation(self) -> NumericDescriptor:
+        """Persisted correlation coefficient for the parameter pair."""
+        return self._correlation
+
+    def _set_correlation(self, value: float) -> None:
+        """Set the correlation coefficient for internal callers."""
+        self._correlation.value = value
+
+
+@FitParameterCorrelationsFactory.register
+class FitParameterCorrelations(CategoryCollection):
+    """Collection of persisted fit-parameter correlation summaries."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted fit-parameter correlation summaries',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=FitParameterCorrelationItem)
+
+    def create(
+        self,
+        *,
+        source_kind: str,
+        param_unique_name_i: str,
+        param_unique_name_j: str,
+        correlation: float,
+        id: str | None = None,
+    ) -> None:
+        """
+        Create a persisted fit-parameter correlation row.
+
+        Parameters
+        ----------
+        source_kind : str
+            Origin of the persisted correlation summary.
+        param_unique_name_i : str
+            First unique parameter name in the pair.
+        param_unique_name_j : str
+            Second unique parameter name in the pair.
+        correlation : float
+            Correlation coefficient for the parameter pair.
+        id : str | None, default=None
+            Explicit persisted row identifier. When omitted, a simple
+            sequential identifier is generated.
+        """
+        normalized_i, normalized_j = _normalized_parameter_pair(
+            param_unique_name_i,
+            param_unique_name_j,
+        )
+        item = FitParameterCorrelationItem()
+        item._set_source_kind(source_kind)
+        item._set_param_unique_name_i(normalized_i)
+        item._set_param_unique_name_j(normalized_j)
+        item._set_correlation(correlation)
+        resolved_id = id or str(len(self) + 1)
+        item._set_id(resolved_id)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/fit_parameter_correlations/factory.py b/src/easydiffraction/analysis/categories/fit_parameter_correlations/factory.py
new file mode 100644
index 000000000..095411086
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameter_correlations/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Fit-parameter-correlation factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class FitParameterCorrelationsFactory(FactoryBase):
+    """Create fit-parameter correlation collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/fit_parameters/__init__.py b/src/easydiffraction/analysis/categories/fit_parameters/__init__.py
new file mode 100644
index 000000000..64e72b416
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameters/__init__.py
@@ -0,0 +1,6 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.fit_parameters.default import FitParameterItem
+from easydiffraction.analysis.categories.fit_parameters.default import FitParameters
+from easydiffraction.analysis.categories.fit_parameters.factory import FitParametersFactory
diff --git a/src/easydiffraction/analysis/categories/fit_parameters/default.py b/src/easydiffraction/analysis/categories/fit_parameters/default.py
new file mode 100644
index 000000000..a5e1f94f5
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameters/default.py
@@ -0,0 +1,177 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Fit-parameter control snapshots."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from easydiffraction.analysis.categories.fit_parameters.factory import FitParametersFactory
+from easydiffraction.core.category import CategoryCollection
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import RegexValidator
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+class FitParameterItem(CategoryItem):
+    """Single persisted fit-parameter control row."""
+
+    _category_code = 'fit_parameter'
+    _category_entry_name = 'param_unique_name'
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._param_unique_name = StringDescriptor(
+            name='param_unique_name',
+            description='Unique name of the referenced live parameter.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_.]*$'),
+            ),
+            cif_handler=CifHandler(names=['_fit_parameter.param_unique_name']),
+        )
+        self._fit_min = NumericDescriptor(
+            name='fit_min',
+            description='Persisted lower fit bound.',
+            value_spec=AttributeSpec(default=-np.inf),
+            cif_handler=CifHandler(names=['_fit_parameter.fit_min']),
+        )
+        self._fit_max = NumericDescriptor(
+            name='fit_max',
+            description='Persisted upper fit bound.',
+            value_spec=AttributeSpec(default=np.inf),
+            cif_handler=CifHandler(names=['_fit_parameter.fit_max']),
+        )
+        self._fit_bounds_uncertainty_multiplier = NumericDescriptor(
+            name='fit_bounds_uncertainty_multiplier',
+            description='Multiplier used to derive fit bounds from uncertainty.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_parameter.fit_bounds_uncertainty_multiplier']),
+        )
+        self._start_value = NumericDescriptor(
+            name='start_value',
+            description='Persisted pre-fit value snapshot.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_parameter.start_value']),
+        )
+        self._start_uncertainty = NumericDescriptor(
+            name='start_uncertainty',
+            description='Persisted pre-fit uncertainty snapshot.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_parameter.start_uncertainty']),
+        )
+
+    @property
+    def param_unique_name(self) -> StringDescriptor:
+        """Unique name of the referenced live parameter."""
+        return self._param_unique_name
+
+    def _set_param_unique_name(self, value: str) -> None:
+        """
+        Set the referenced parameter unique name for internal callers.
+        """
+        self._param_unique_name.value = value
+
+    @property
+    def fit_min(self) -> NumericDescriptor:
+        """Persisted lower fit bound."""
+        return self._fit_min
+
+    def _set_fit_min(self, value: float) -> None:
+        """Set the persisted lower fit bound for internal callers."""
+        self._fit_min.value = value
+
+    @property
+    def fit_max(self) -> NumericDescriptor:
+        """Persisted upper fit bound."""
+        return self._fit_max
+
+    def _set_fit_max(self, value: float) -> None:
+        """Set the persisted upper fit bound for internal callers."""
+        self._fit_max.value = value
+
+    @property
+    def fit_bounds_uncertainty_multiplier(self) -> NumericDescriptor:
+        """Multiplier used to derive fit bounds from uncertainty."""
+        return self._fit_bounds_uncertainty_multiplier
+
+    def _set_fit_bounds_uncertainty_multiplier(
+        self,
+        value: float | None,
+    ) -> None:
+        """
+        Set the fit-bounds uncertainty multiplier for internal callers.
+        """
+        self._fit_bounds_uncertainty_multiplier.value = value
+
+    @property
+    def start_value(self) -> NumericDescriptor:
+        """Persisted pre-fit value snapshot."""
+        return self._start_value
+
+    def _set_start_value(self, value: float | None) -> None:
+        """Set the pre-fit value snapshot for internal callers."""
+        self._start_value.value = value
+
+    @property
+    def start_uncertainty(self) -> NumericDescriptor:
+        """Persisted pre-fit uncertainty snapshot."""
+        return self._start_uncertainty
+
+    def _set_start_uncertainty(self, value: float | None) -> None:
+        """Set the pre-fit uncertainty snapshot for internal callers."""
+        self._start_uncertainty.value = value
+
+
+@FitParametersFactory.register
+class FitParameters(CategoryCollection):
+    """Collection of persisted fit-parameter control snapshots."""
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Persisted fit-parameter control snapshots',
+    )
+
+    def __init__(self) -> None:
+        super().__init__(item_type=FitParameterItem)
+
+    def create(
+        self,
+        *,
+        param_unique_name: str,
+        fit_min: float,
+        fit_max: float,
+        fit_bounds_uncertainty_multiplier: float | None = None,
+        start_value: float | None = None,
+        start_uncertainty: float | None = None,
+    ) -> None:
+        """
+        Create a persisted fit-parameter control snapshot row.
+
+        Parameters
+        ----------
+        param_unique_name : str
+            Unique name of the referenced live parameter.
+        fit_min : float
+            Persisted lower fit bound.
+        fit_max : float
+            Persisted upper fit bound.
+        fit_bounds_uncertainty_multiplier : float | None, default=None
+            Multiplier used to derive fit bounds from uncertainty.
+        start_value : float | None, default=None
+            Persisted pre-fit value snapshot.
+        start_uncertainty : float | None, default=None
+            Persisted pre-fit uncertainty snapshot.
+        """
+        item = FitParameterItem()
+        item._set_param_unique_name(param_unique_name)
+        item._set_fit_min(fit_min)
+        item._set_fit_max(fit_max)
+        item._set_fit_bounds_uncertainty_multiplier(fit_bounds_uncertainty_multiplier)
+        item._set_start_value(start_value)
+        item._set_start_uncertainty(start_uncertainty)
+        self.add(item)
diff --git a/src/easydiffraction/analysis/categories/fit_parameters/factory.py b/src/easydiffraction/analysis/categories/fit_parameters/factory.py
new file mode 100644
index 000000000..4b73be48a
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_parameters/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Fit-parameter factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class FitParametersFactory(FactoryBase):
+    """Create fit-parameter collections by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/categories/fit_result/__init__.py b/src/easydiffraction/analysis/categories/fit_result/__init__.py
new file mode 100644
index 000000000..8578ab47b
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.categories.fit_result.default import FitResult
+from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
diff --git a/src/easydiffraction/analysis/categories/fit_result/default.py b/src/easydiffraction/analysis/categories/fit_result/default.py
new file mode 100644
index 000000000..564df008e
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/default.py
@@ -0,0 +1,135 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Common fit-result status category."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+from easydiffraction.analysis.enums import FitResultKindEnum
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import IntegerDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@FitResultFactory.register
+class FitResult(CategoryItem):
+    """Common persisted fit-result status metadata."""
+
+    _category_code = 'fit_result'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Common persisted fit-result status metadata',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._result_kind = StringDescriptor(
+            name='result_kind',
+            description='Kind of the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(
+                default=FitResultKindEnum.default().value,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in FitResultKindEnum]
+                ),
+            ),
+            cif_handler=CifHandler(names=['_fit_result.result_kind']),
+        )
+        self._success = BoolDescriptor(
+            name='success',
+            description='Whether the latest persisted fit-result projection succeeded.',
+            value_spec=AttributeSpec(default=False),
+            cif_handler=CifHandler(names=['_fit_result.success']),
+        )
+        self._message = StringDescriptor(
+            name='message',
+            description='Status message for the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(default=''),
+            cif_handler=CifHandler(names=['_fit_result.message']),
+        )
+        self._iterations = IntegerDescriptor(
+            name='iterations',
+            description='Iteration count for the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(default=0),
+            cif_handler=CifHandler(names=['_fit_result.iterations']),
+        )
+        self._fitting_time = NumericDescriptor(
+            name='fitting_time',
+            description='Fitting time in seconds for the latest persisted projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.fitting_time']),
+        )
+        self._reduced_chi_square = NumericDescriptor(
+            name='reduced_chi_square',
+            description='Reduced chi-square for the latest persisted projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.reduced_chi_square']),
+        )
+
+    @property
+    def result_kind(self) -> StringDescriptor:
+        """Kind of the latest persisted fit-result projection."""
+        return self._result_kind
+
+    def _set_result_kind(self, value: str) -> None:
+        """Set the result kind for internal callers."""
+        self._result_kind.value = value
+
+    @property
+    def success(self) -> BoolDescriptor:
+        """
+        Whether the latest persisted fit-result projection succeeded.
+        """
+        return self._success
+
+    def _set_success(self, *, value: bool) -> None:
+        """Set the success flag for internal callers."""
+        self._success.value = value
+
+    @property
+    def message(self) -> StringDescriptor:
+        """
+        Status message for the latest persisted fit-result projection.
+        """
+        return self._message
+
+    def _set_message(self, value: str) -> None:
+        """Set the fit-result message for internal callers."""
+        self._message.value = value
+
+    @property
+    def iterations(self) -> IntegerDescriptor:
+        """
+        Iteration count for the latest persisted fit-result projection.
+        """
+        return self._iterations
+
+    def _set_iterations(self, value: int) -> None:
+        """Set the iteration count for internal callers."""
+        self._iterations.value = value
+
+    @property
+    def fitting_time(self) -> NumericDescriptor:
+        """
+        Fitting time in seconds for the latest persisted projection.
+        """
+        return self._fitting_time
+
+    def _set_fitting_time(self, value: float | None) -> None:
+        """Set the fitting time for internal callers."""
+        self._fitting_time.value = value
+
+    @property
+    def reduced_chi_square(self) -> NumericDescriptor:
+        """Reduced chi-square for the latest persisted projection."""
+        return self._reduced_chi_square
+
+    def _set_reduced_chi_square(self, value: float | None) -> None:
+        """Set the reduced chi-square for internal callers."""
+        self._reduced_chi_square.value = value
diff --git a/src/easydiffraction/analysis/categories/fit_result/factory.py b/src/easydiffraction/analysis/categories/fit_result/factory.py
new file mode 100644
index 000000000..66cbd32bc
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Fit-result factory."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class FitResultFactory(FactoryBase):
+    """Create fit-result categories by tag."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/analysis/enums.py b/src/easydiffraction/analysis/enums.py
index c1ef93f7b..94d78cc64 100644
--- a/src/easydiffraction/analysis/enums.py
+++ b/src/easydiffraction/analysis/enums.py
@@ -28,3 +28,27 @@ def description(self) -> str:
         if self is FitModeEnum.SEQUENTIAL:
             return 'Fit one experiment against a series of data files.'
         return ''
+
+
+class FitResultKindEnum(StrEnum):
+    """Persisted kind of the latest fit-result projection."""
+
+    DETERMINISTIC = 'deterministic'
+    BAYESIAN = 'bayesian'
+
+    @classmethod
+    def default(cls) -> FitResultKindEnum:
+        """Return the default persisted fit-result kind."""
+        return cls.DETERMINISTIC
+
+
+class FitCorrelationSourceEnum(StrEnum):
+    """Source of a persisted fit-parameter correlation summary."""
+
+    DETERMINISTIC = 'deterministic'
+    POSTERIOR = 'posterior'
+
+    @classmethod
+    def default(cls) -> FitCorrelationSourceEnum:
+        """Return the default persisted correlation source."""
+        return cls.DETERMINISTIC
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 0f8e4c970..c24f94359 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -11,6 +11,8 @@
 from easydiffraction.analysis.fit_helpers.metrics import calculate_reduced_chi_square
 from easydiffraction.display.progress import ACTIVITY_LABEL_BURN_IN
 from easydiffraction.display.progress import ACTIVITY_LABEL_FITTING
+from easydiffraction.display.progress import ACTIVITY_LABEL_POST_PROCESSING
+from easydiffraction.display.progress import ACTIVITY_LABEL_PRE_PROCESSING
 from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
 from easydiffraction.display.progress import ACTIVITY_LABEL_SAMPLING
 from easydiffraction.display.progress import ActivityIndicator
@@ -28,6 +30,8 @@
 SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
 TRACKING_MODE_FIT = 'fit'
 TRACKING_MODE_SAMPLER = 'sampling'
+SAMPLER_PHASE_POST_PROCESSING = 'post-processing'
+SAMPLER_PHASE_PRE_PROCESSING = 'pre-processing'
 DEFAULT_HEADERS = ['iteration', 'time (s)', 'χ²', 'change / status']
 DEFAULT_ALIGNMENTS = ['center', 'center', 'center', 'center']
 SAMPLER_HEADERS = ['iteration', 'progress', 'time (s)', 'log posterior', 'phase']
@@ -85,6 +89,7 @@ def __init__(self) -> None:
         self._last_sampler_progress_percent: float | None = None
         self._last_sampler_log_posterior: float | None = None
         self._last_sampler_elapsed_time: float | None = None
+        self._sampler_pre_processing_pending: bool = False
 
         self._df_rows: list[list[str]] = []
         self._activity_indicator: ActivityIndicator | None = None
@@ -111,6 +116,7 @@ def reset(self) -> None:
         self._last_sampler_progress_percent = None
         self._last_sampler_log_posterior = None
         self._last_sampler_elapsed_time = None
+        self._sampler_pre_processing_pending = False
         self._df_rows = []
         self._activity_label = ACTIVITY_LABEL_FITTING
 
@@ -148,8 +154,25 @@ def track(
             self._last_chi2 = reduced_chi2
             return residuals
 
+        self.track_fit_progress(
+            iteration=self._iteration,
+            reduced_chi2=reduced_chi2,
+            elapsed_time=self._current_elapsed_time(),
+        )
+
+        return residuals
+
+    def track_fit_progress(
+        self,
+        *,
+        iteration: int,
+        reduced_chi2: float,
+        elapsed_time: float,
+    ) -> None:
+        """Update fit progress from a backend iteration callback."""
+        self._iteration = max(1, iteration)
+
         row: list[str] = []
-        elapsed_time = self._current_elapsed_time()
 
         if self._previous_chi2 is None:
             self._previous_chi2 = reduced_chi2
@@ -197,8 +220,6 @@ def track(
         self._last_chi2 = reduced_chi2
         self._last_iteration = self._iteration
 
-        return residuals
-
     def track_sampler_progress(self, update: SamplerProgressUpdate) -> None:
         """
         Update progress from a sampler monitor.
@@ -240,6 +261,37 @@ def track_sampler_progress(self, update: SamplerProgressUpdate) -> None:
         self._last_chi2 = update.reduced_chi2
         self._last_iteration = update.iteration
 
+    def start_sampler_pre_processing(self, *, total_iterations: int) -> None:
+        """Mark sampler setup so a status row appears on update."""
+        self._tracking_mode = TRACKING_MODE_SAMPLER
+        self._sampler_total_iterations = max(1, total_iterations)
+        self._last_sampler_phase = SAMPLER_PHASE_PRE_PROCESSING
+        self._last_sampler_progress_percent = None
+        self._last_sampler_log_posterior = None
+        self._last_sampler_elapsed_time = None
+        self._sampler_pre_processing_pending = True
+        self._set_activity_label(ACTIVITY_LABEL_PRE_PROCESSING)
+
+    def start_sampler_post_processing(
+        self,
+        *,
+        log_posterior: float | None = None,
+    ) -> None:
+        """Switch the activity indicator to post-processing."""
+        if self._tracking_mode != TRACKING_MODE_SAMPLER:
+            return
+
+        if self._sampler_total_iterations is None:
+            self._sampler_total_iterations = max(1, self._last_iteration or 1)
+
+        elapsed_time = self._elapsed_since_start()
+        self._last_sampler_phase = SAMPLER_PHASE_POST_PROCESSING
+        self._last_sampler_progress_percent = 100.0
+        if log_posterior is not None:
+            self._last_sampler_log_posterior = float(log_posterior)
+        self._last_sampler_elapsed_time = elapsed_time
+        self._set_activity_label(ACTIVITY_LABEL_POST_PROCESSING)
+
     @property
     def best_chi2(self) -> float | None:
         """Best recorded reduced chi-square value or None."""
@@ -273,6 +325,16 @@ def stop_timer(self) -> None:
         self._end_time = time.perf_counter()
         self._fitting_time = self._end_time - self._start_time
 
+    def _elapsed_since_start(self) -> float | None:
+        """
+        Return elapsed wall time using the active timer when available.
+        """
+        if self._start_time is None:
+            return None
+        if self._end_time is not None:
+            return self._end_time - self._start_time
+        return time.perf_counter() - self._start_time
+
     def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT) -> None:
         """
         Initialize display and headers and announce the minimizer.
@@ -347,13 +409,21 @@ def _initial_sampler_progress_row(
         clamped_iteration: int,
         clamped_progress: float,
     ) -> list[str]:
-        if self._previous_chi2 is not None and self._best_chi2 is not None:
+        if self._df_rows:
             return []
 
         self._previous_chi2 = update.reduced_chi2
         self._best_chi2 = update.reduced_chi2
         self._best_iteration = update.iteration
         self._last_progress_time = update.elapsed_time
+        if self._sampler_pre_processing_pending:
+            self._sampler_pre_processing_pending = False
+            return self._sampler_status_row(
+                iteration_label=self._sampler_iteration_label(clamped_iteration),
+                phase=SAMPLER_PHASE_PRE_PROCESSING,
+                elapsed_time=update.elapsed_time,
+                log_posterior=update.log_posterior,
+            )
         return self._sampler_progress_row(
             clamped_iteration=clamped_iteration,
             clamped_progress=clamped_progress,
@@ -431,6 +501,25 @@ def _sampler_progress_row(
             phase,
         ]
 
+    def _sampler_status_row(
+        self,
+        *,
+        iteration_label: str = '',
+        phase: str,
+        elapsed_time: float | None,
+        log_posterior: float | None = None,
+    ) -> list[str]:
+        """
+        Return a status-only sampler row without iteration metrics.
+        """
+        return [
+            iteration_label,
+            '',
+            self._format_elapsed_time(elapsed_time),
+            '' if log_posterior is None else f'{log_posterior:.2f}',
+            phase,
+        ]
+
     def _finalize_sampler_tracking_row(self) -> None:
         row = self._final_sampler_tracking_row()
         if row is None:
@@ -453,6 +542,12 @@ def _final_sampler_tracking_row(self) -> list[str] | None:
 
         final_progress = self._resolved_final_sampler_progress()
         elapsed_time = self._resolved_final_sampler_elapsed_time()
+        if self._last_sampler_phase == SAMPLER_PHASE_POST_PROCESSING:
+            return self._sampler_status_row(
+                phase=SAMPLER_PHASE_POST_PROCESSING,
+                elapsed_time=elapsed_time,
+            )
+
         log_posterior = (
             f'{self._last_sampler_log_posterior:.2f}'
             if self._last_sampler_log_posterior is not None
@@ -602,8 +697,12 @@ def _default_activity_label(self) -> str:
     @staticmethod
     def _activity_label_for_sampler_phase(phase: str) -> str:
         normalized_phase = phase.strip().lower()
+        if normalized_phase == SAMPLER_PHASE_PRE_PROCESSING:
+            return ACTIVITY_LABEL_PRE_PROCESSING
         if normalized_phase == 'burn-in':
             return ACTIVITY_LABEL_BURN_IN
+        if normalized_phase == SAMPLER_PHASE_POST_PROCESSING:
+            return ACTIVITY_LABEL_POST_PROCESSING
         if normalized_phase == 'sampling':
             return ACTIVITY_LABEL_SAMPLING
         if normalized_phase:
diff --git a/src/easydiffraction/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py
index 343c15742..72d89a6ce 100644
--- a/src/easydiffraction/analysis/fitting.py
+++ b/src/easydiffraction/analysis/fitting.py
@@ -21,6 +21,50 @@
     from easydiffraction.datablocks.structure.collection import Structures
 
 
+def _resolve_fit_result_message(results: FitResults) -> str:
+    """Return a normalized fit-result message."""
+    if results.message:
+        return results.message
+
+    raw_result = results.engine_result
+    message = getattr(raw_result, 'message', '')
+    return str(message) if message is not None else ''
+
+
+def _resolve_fit_result_iterations(results: FitResults) -> int:
+    """Return a normalized iteration or evaluation count."""
+    if results.iterations:
+        return int(results.iterations)
+
+    raw_result = results.engine_result
+    for attribute_name in ('nfev', 'nit', 'iterations', 'niter'):
+        value = getattr(raw_result, attribute_name, None)
+        if value is not None:
+            return int(value)
+    return 0
+
+
+def _resolve_fit_result_chi_square(results: FitResults) -> float | None:
+    """Return a normalized chi-square-like objective value."""
+    if results.chi_square is not None:
+        return float(results.chi_square)
+
+    raw_result = results.engine_result
+    chisqr = getattr(raw_result, 'chisqr', None)
+    if chisqr is not None:
+        return float(chisqr)
+
+    fun = getattr(raw_result, 'fun', None)
+    if fun is None:
+        return None
+
+    if np.isscalar(fun):
+        return float(fun)
+
+    fun_array = np.asarray(fun, dtype=float)
+    return float(np.sum(fun_array**2))
+
+
 class Fitter:
     """Handles the fitting workflow using a pluggable minimizer."""
 
@@ -30,6 +74,69 @@ def __init__(self, selection: str = MinimizerTypeEnum.default()) -> None:
         self.minimizer = MinimizerFactory.create(selection)
         self.results: FitResults | None = None
 
+    @staticmethod
+    def _collect_fit_parameters(
+        structures: Structures,
+        experiments: list[ExperimentBase],
+    ) -> list[Parameter]:
+        """Return free parameters from structures and experiments."""
+        expt_free_params: list[Parameter] = []
+        for expt in experiments:
+            expt_free_params.extend(
+                p
+                for p in expt.parameters
+                if isinstance(p, Parameter) and not p.user_constrained and p.free
+            )
+        return structures.free_parameters + expt_free_params
+
+    def _build_objective_function(
+        self,
+        *,
+        params: list[Parameter],
+        structures: Structures,
+        experiments: list[ExperimentBase],
+        weights: np.ndarray | None,
+        analysis: object,
+    ) -> object:
+        """Return the residual function for the current fit context."""
+
+        def objective_function(engine_params: dict[str, Any]) -> np.ndarray:
+            """Evaluate residuals for the current minimizer state."""
+            return self._residual_function(
+                engine_params=engine_params,
+                parameters=params,
+                structures=structures,
+                experiments=experiments,
+                weights=weights,
+                analysis=analysis,
+            )
+
+        return objective_function
+
+    def _postprocess_fit_results(
+        self,
+        *,
+        analysis: object,
+        experiments: list[ExperimentBase],
+        fitted_parameters: list[Parameter],
+    ) -> None:
+        """Populate result fields and persist fit projections."""
+        if self.results is None:
+            return
+
+        self.results.message = _resolve_fit_result_message(self.results)
+        self.results.iterations = _resolve_fit_result_iterations(self.results)
+        self.results.chi_square = _resolve_fit_result_chi_square(self.results)
+
+        if analysis is None:
+            return
+
+        analysis._store_fit_result_projection(
+            self.results,
+            experiments=experiments,
+            fitted_parameters=fitted_parameters,
+        )
+
     def fit(
         self,
         structures: Structures,
@@ -76,53 +183,48 @@ def fit(
             structure._need_categories_update = True
             structure._update_categories()
 
-        expt_free_params: list[Parameter] = []
-        for expt in experiments:
-            expt_free_params.extend(
-                p
-                for p in expt.parameters
-                if isinstance(p, Parameter) and not p.user_constrained and p.free
-            )
-        params = structures.free_parameters + expt_free_params
+        params = self._collect_fit_parameters(structures, experiments)
 
         if not params:
+            if analysis is not None:
+                analysis._clear_persisted_fit_state()
+                analysis.fit_results = None
+            self.results = None
             print('⚠️ No parameters selected for fitting.')
             return
 
+        if analysis is not None:
+            analysis._capture_fit_parameter_state(params)
+
         for param in params:
             param._fit_start_value = param.value
 
-        def objective_function(engine_params: dict[str, Any]) -> np.ndarray:
-            """
-            Evaluate the residual for the current minimizer parameters.
-
-            Parameters
-            ----------
-            engine_params : dict[str, Any]
-                Parameter values provided by the minimizer engine.
-
-            Returns
-            -------
-            np.ndarray
-                Residual array passed back to the minimizer.
-            """
-            return self._residual_function(
-                engine_params=engine_params,
-                parameters=params,
-                structures=structures,
-                experiments=experiments,
-                weights=weights,
+        objective_function = self._build_objective_function(
+            params=params,
+            structures=structures,
+            experiments=experiments,
+            weights=weights,
+            analysis=analysis,
+        )
+
+        try:
+            # Keep tracker finalization in this layer so post-processing
+            # can run before the live display is closed.
+            self.results = self.minimizer.fit(
+                params,
+                objective_function,
+                verbosity=verbosity,
+                finalize_tracking=False,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
+            )
+            self._postprocess_fit_results(
                 analysis=analysis,
+                experiments=experiments,
+                fitted_parameters=params,
             )
-
-        # Perform fitting
-        self.results = self.minimizer.fit(
-            params,
-            objective_function,
-            verbosity=verbosity,
-            use_physical_limits=use_physical_limits,
-            random_seed=random_seed,
-        )
+        finally:
+            self.minimizer._stop_tracking()
 
     def _process_fit_results(
         self,
@@ -243,4 +345,7 @@ def _residual_function(
             # Append the residuals for this experiment
             residuals.extend(diff)
 
-        return self.minimizer.tracker.track(np.array(residuals), parameters)
+        residual_array = np.array(residuals)
+        if getattr(self.minimizer, '_tracks_progress_via_solver_monitor', lambda: False)():
+            return residual_array
+        return self.minimizer.tracker.track(residual_array, parameters)
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index bbc639f10..2d8bc83f9 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -34,7 +34,7 @@ def __init__(
     ) -> None:
         self.name: str | None = name
         self.method: str | None = method
-        self.max_iterations: int | None = max_iterations
+        self._max_iterations: int | None = max_iterations
         self.result: FitResults | None = None
         self._previous_chi2: float | None = None
         self._iteration: int | None = None
@@ -42,8 +42,19 @@ def __init__(
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
         self._resolved_random_seed: int | None = None
+        self._tracking_active: bool = False
+        self._deferred_warning_messages: list[str] = []
         self.tracker: FitProgressTracker = FitProgressTracker()
 
+    @property
+    def max_iterations(self) -> int | None:
+        """User-facing iteration limit for the current minimizer."""
+        return self._max_iterations
+
+    @max_iterations.setter
+    def max_iterations(self, value: int | None) -> None:
+        self._max_iterations = value
+
     def _start_tracking(
         self,
         minimizer_name: str,
@@ -61,19 +72,47 @@ def _start_tracking(
         """
         self.tracker.reset()
         self.tracker._verbosity = verbosity
+        self._tracking_active = True
+        self._deferred_warning_messages = []
         self.tracker.start_tracking(minimizer_name, mode=self._tracking_mode())
         self.tracker.start_timer()
 
     def _stop_tracking(self) -> None:
         """Stop timer and finalize tracking."""
+        if not self._tracking_active:
+            self._emit_deferred_warnings()
+            return
+
+        self._tracking_active = False
         self.tracker.stop_timer()
         self.tracker.finish_tracking()
+        if self.result is not None:
+            self.result.fitting_time = self.tracker.fitting_time
+        self._emit_deferred_warnings()
+
+    def _warn_after_tracking(self, message: str) -> None:
+        """Log immediately or defer a warning until tracking stops."""
+        if self._tracking_active:
+            self._deferred_warning_messages.append(message)
+            return
+
+        log.warning(message)
+
+    def _emit_deferred_warnings(self) -> None:
+        """Flush warnings deferred during live progress display."""
+        while self._deferred_warning_messages:
+            log.warning(self._deferred_warning_messages.pop(0))
 
     @staticmethod
     def _tracking_mode() -> str:
         """Return the tracker mode for the current minimizer."""
         return 'fit'
 
+    @staticmethod
+    def _tracks_progress_via_solver_monitor() -> bool:
+        """Return whether live progress comes from solver callbacks."""
+        return False
+
     @abstractmethod
     def _prepare_solver_args(self, parameters: list[Any]) -> dict[str, Any]:
         """
@@ -300,6 +339,7 @@ def fit(
         objective_function: Callable[..., object],
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
+        finalize_tracking: bool = True,
         use_physical_limits: bool = False,
         random_seed: int | None = None,
     ) -> FitResults:
@@ -315,6 +355,8 @@ def fit(
             arguments.
         verbosity : VerbosityEnum, default=VerbosityEnum.FULL
             Console output verbosity.
+        finalize_tracking : bool, default=True
+            Whether to stop and finalize live tracking before returning.
         use_physical_limits : bool, default=False
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
@@ -343,10 +385,10 @@ def fit(
             if resolved_random_seed is not None:
                 solver_args['random_seed'] = resolved_random_seed
             raw_result = self._run_solver(objective_function, **solver_args)
+            return self._finalize_fit(parameters, raw_result)
         finally:
-            self._stop_tracking()
-
-        return self._finalize_fit(parameters, raw_result)
+            if finalize_tracking:
+                self._stop_tracking()
 
     def _objective_function(
         self,
diff --git a/src/easydiffraction/analysis/minimizers/bumps.py b/src/easydiffraction/analysis/minimizers/bumps.py
index 109262343..f80446cfc 100644
--- a/src/easydiffraction/analysis/minimizers/bumps.py
+++ b/src/easydiffraction/analysis/minimizers/bumps.py
@@ -8,6 +8,7 @@
 from bumps.fitproblem import FitProblem
 from bumps.fitters import FITTERS
 from bumps.fitters import FitDriver
+from bumps.fitters import monitor as bumps_monitor
 from bumps.parameter import Parameter as BumpsParameter
 from scipy.optimize import OptimizeResult
 
@@ -20,38 +21,74 @@
 DEFAULT_MAX_ITERATIONS = 1000
 
 
+class _BumpsEvaluationLimitError(RuntimeError):
+    """Raised when the BUMPS residual-evaluation budget is exhausted."""
+
+    def __init__(
+        self,
+        *,
+        evaluation_count: int,
+        parameter_values: np.ndarray,
+        residuals: np.ndarray | None,
+    ) -> None:
+        super().__init__('maximum number of residual evaluations reached')
+        self.evaluation_count = evaluation_count
+        self.parameter_values = parameter_values
+        self.residuals = residuals
+
+
 class _EasyDiffractionFitness:
-    """
-    Adaptor wrapping an EasyDiffraction objective into bumps Fitness.
-    """
+    """Wrap an EasyDiffraction objective in the BUMPS fitness API."""
 
     def __init__(
         self,
         bumps_params: list[BumpsParameter],
         objective_function: object,
+        max_evaluations: int | None = None,
     ) -> None:
         self._bumps_params = bumps_params
         self._objective_function = objective_function
+        self._max_evaluations = max_evaluations
         self._numpoints = 0
+        self._evaluation_count = 0
+        self._count_evaluations = True
+        self._last_parameter_values: np.ndarray | None = None
+        self._last_residuals: np.ndarray | None = None
 
     def parameters(self) -> dict[str, BumpsParameter]:
-        """Return bumps parameters as a name-keyed dictionary."""
+        """Return BUMPS parameters as a name-keyed dictionary."""
         return {p.name: p for p in self._bumps_params}
 
     def update(self) -> None:
-        """Signal that parameters have changed (no-op)."""
+        """Signal that parameters have changed."""
 
     def residuals(self) -> np.ndarray:
-        """Compute residuals using current bumps parameter values."""
+        """Compute residuals for the current BUMPS parameter values."""
+        if (
+            self._count_evaluations
+            and self._max_evaluations is not None
+            and self._evaluation_count >= self._max_evaluations
+        ):
+            last_parameter_values = self._last_parameter_values
+            if last_parameter_values is None:
+                last_parameter_values = np.array([p.value for p in self._bumps_params])
+            raise _BumpsEvaluationLimitError(
+                evaluation_count=self._evaluation_count,
+                parameter_values=last_parameter_values,
+                residuals=self._last_residuals,
+            )
+
         values = np.array([p.value for p in self._bumps_params])
-        r = self._objective_function(values)
+        r = np.asarray(self._objective_function(values), dtype=float)
         self._numpoints = len(r)
+        self._last_parameter_values = values.copy()
+        self._last_residuals = r.copy()
+        if self._count_evaluations:
+            self._evaluation_count += 1
         return r
 
     def nllf(self) -> float:
-        """
-        Negative log-likelihood as half the sum of squared residuals.
-        """
+        """Return half the sum of squared residuals."""
         r = self.residuals()
         return 0.5 * np.sum(r**2)
 
@@ -59,10 +96,101 @@ def numpoints(self) -> int:
         """Return the number of data points."""
         return self._numpoints
 
+    @property
+    def evaluation_count(self) -> int:
+        """Return the live residual-evaluation count."""
+        return self._evaluation_count
+
+    def reset_evaluation_count(self) -> None:
+        """Reset the residual-evaluation counter."""
+        self._evaluation_count = 0
+
+    def stop_counting_evaluations(self) -> None:
+        """Freeze residual-evaluation counting."""
+        self._count_evaluations = False
+
+    @property
+    def last_residuals(self) -> np.ndarray | None:
+        """Return the last successful residual vector."""
+        return self._last_residuals
+
+    def last_reduced_chi_square(self, *, n_parameters: int) -> float | None:
+        """Return reduced chi-square for the last residual vector."""
+        if self._last_residuals is None:
+            return None
+
+        chi_square = float(np.sum(self._last_residuals**2))
+        dof = len(self._last_residuals) - n_parameters
+        if dof <= 0:
+            return chi_square
+        return chi_square / dof
+
+
+class _BumpsProgressMonitor(bumps_monitor.Monitor):
+    """Report live BUMPS fit evaluation counts."""
+
+    def __init__(
+        self,
+        *,
+        tracker: object,
+        fitness: _EasyDiffractionFitness,
+        n_points: int,
+        n_parameters: int,
+    ) -> None:
+        self._tracker = tracker
+        self._fitness = fitness
+        self._n_points = n_points
+        self._n_parameters = n_parameters
+
+    @staticmethod
+    def config_history(history: object) -> None:
+        """
+        Declare the history fields needed for deterministic progress.
+        """
+        history.requires(time=1, step=1, value=1)
+
+    def __call__(self, history: object) -> None:
+        """Forward deterministic BUMPS progress to the fit tracker."""
+        if not history.time or not history.value:
+            return
+
+        self._tracker.track_fit_progress(
+            iteration=self._reported_iteration(history),
+            reduced_chi2=self._reduced_chi_square_from_nllf(float(history.value[0])),
+            elapsed_time=float(history.time[0]),
+        )
+
+    def final(self, history: object, best: dict[str, object]) -> None:
+        """Record the final BUMPS state in the fit tracker."""
+        if not history.time or best.get('value') is None:
+            return
+
+        self._tracker.track_fit_progress(
+            iteration=self._reported_iteration(history),
+            reduced_chi2=self._reduced_chi_square_from_nllf(float(best['value'])),
+            elapsed_time=float(history.time[0]),
+        )
+
+    def _reported_iteration(self, history: object) -> int:
+        """Return the live fit evaluation count shown in progress."""
+        if self._fitness.evaluation_count > 0:
+            return self._fitness.evaluation_count
+
+        step = int(history.step[0]) if history.step else 0
+        return max(1, step)
+
+    def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
+        """Convert negative log-likelihood to reduced chi-square."""
+        dof = self._n_points - self._n_parameters
+        chi_square = 2.0 * nllf
+        if dof <= 0:
+            return chi_square
+        return chi_square / dof
+
 
 @MinimizerFactory.register
 class BumpsMinimizer(MinimizerBase):
-    """Minimizer using the bumps package."""
+    """Minimizer using the BUMPS package."""
 
     type_info = TypeInfo(
         tag=MinimizerTypeEnum.BUMPS,
@@ -81,12 +209,19 @@ def __init__(
             max_iterations=max_iterations,
         )
 
+    @staticmethod
+    def _tracks_progress_via_solver_monitor() -> bool:
+        """
+        Use BUMPS monitor callbacks for live deterministic progress.
+        """
+        return True
+
     def _prepare_solver_args(  # noqa: PLR6301
         self,
         parameters: list[object],
     ) -> dict[str, object]:
         """
-        Prepare bumps parameters from EasyDiffraction parameters.
+        Prepare BUMPS parameters from EasyDiffraction parameters.
 
         Parameters
         ----------
@@ -116,7 +251,7 @@ def _run_solver(
         **kwargs: object,
     ) -> object:
         """
-        Run the bumps solver.
+        Run the BUMPS solver.
 
         Uses FitDriver directly instead of bumps.fitters.fit() to skip
         the expensive post-fit stderr/Jacobian computation that would
@@ -135,43 +270,123 @@ def _run_solver(
             A scipy OptimizeResult with the optimized values.
         """
         bumps_params = kwargs.get('bumps_params')
-        fitness = _EasyDiffractionFitness(bumps_params, objective_function)
+        fitness = _EasyDiffractionFitness(
+            bumps_params,
+            objective_function,
+            max_evaluations=self.max_iterations,
+        )
         fitness.nllf()  # pre-compute so numpoints() is valid
+        fitness.reset_evaluation_count()
         problem = FitProblem(fitness)
+        progress_monitor = _BumpsProgressMonitor(
+            tracker=self.tracker,
+            fitness=fitness,
+            n_points=fitness.numpoints(),
+            n_parameters=len(bumps_params),
+        )
 
         fitclass = next(cls for cls in FITTERS if cls.id == self.method)
         driver = FitDriver(
             fitclass=fitclass,
             problem=problem,
-            monitors=[],
+            monitors=[progress_monitor],
             steps=self.max_iterations,
         )
         driver.clip()
-        x, fx = driver.fit()
+        try:
+            x, fx = driver.fit()
+            evaluation_limit_reached = False
+            evaluation_limit_message = 'successful termination'
+        except _BumpsEvaluationLimitError as exc:
+            x, fx, evaluation_limit_message = self._handle_evaluation_limit(
+                exc=exc,
+                fitness=fitness,
+                n_parameters=len(bumps_params),
+            )
+            evaluation_limit_reached = True
+        finally:
+            fitness.stop_counting_evaluations()
 
-        success = x is not None
+        success = x is not None and not evaluation_limit_reached
         if success:
             problem.setp(x)
 
+        return self._build_optimize_result(
+            bumps_params=bumps_params,
+            fitness=fitness,
+            success=success,
+            evaluation_limit_reached=evaluation_limit_reached,
+            evaluation_limit_message=evaluation_limit_message,
+            function_value=fx,
+        )
+
+    def _handle_evaluation_limit(
+        self,
+        *,
+        exc: _BumpsEvaluationLimitError,
+        fitness: _EasyDiffractionFitness,
+        n_parameters: int,
+    ) -> tuple[np.ndarray, None, str]:
+        """
+        Build a partial result when the evaluation budget is exhausted.
+        """
+        reduced_chi2 = self._reduced_chi_square_from_limit(
+            exc=exc,
+            fitness=fitness,
+            n_parameters=n_parameters,
+        )
+        if reduced_chi2 is not None:
+            elapsed_time = self.tracker._current_elapsed_time()
+            self.tracker.track_fit_progress(
+                iteration=exc.evaluation_count,
+                reduced_chi2=reduced_chi2,
+                elapsed_time=0.0 if elapsed_time is None else elapsed_time,
+            )
+        return exc.parameter_values.copy(), None, str(exc)
+
+    @staticmethod
+    def _reduced_chi_square_from_limit(
+        *,
+        exc: _BumpsEvaluationLimitError,
+        fitness: _EasyDiffractionFitness,
+        n_parameters: int,
+    ) -> float | None:
+        """Return reduced chi-square at the evaluation cutoff."""
+        if exc.residuals is not None:
+            chi_square = float(np.sum(exc.residuals**2))
+            dof = len(exc.residuals) - n_parameters
+            return chi_square if dof <= 0 else chi_square / dof
+        if fitness.last_residuals is None:
+            return None
+        return fitness.last_reduced_chi_square(n_parameters=n_parameters)
+
+    def _build_optimize_result(
+        self,
+        *,
+        bumps_params: list[BumpsParameter],
+        fitness: _EasyDiffractionFitness,
+        success: bool,
+        evaluation_limit_reached: bool,
+        evaluation_limit_message: str,
+        function_value: float | None,
+    ) -> OptimizeResult:
+        """Convert the BUMPS solver outcome into an OptimizeResult."""
         # Read values back from bumps Parameters in our original order.
         # FitProblem sorts parameters alphabetically, so x from
         # driver.fit() uses that sorted order — not ours.
         result_x = np.array([p.value for p in bumps_params])
-
-        covar, stderr = (
+        covariance, stderr = (
             self._compute_covariance(bumps_params, fitness) if success else (None, None)
         )
-        var_names = [p.name for p in bumps_params]
-
         return OptimizeResult(
             x=result_x,
             dx=stderr,
-            fun=fx,
+            fun=function_value,
             success=success,
-            status=0 if success else -1,
-            message='successful termination' if success else 'fit failed',
-            covar=covar,
-            var_names=var_names,
+            status=0 if success else 5 if evaluation_limit_reached else -1,
+            message='successful termination' if success else evaluation_limit_message,
+            covar=covariance,
+            var_names=[p.name for p in bumps_params],
         )
 
     def _compute_covariance(  # noqa: PLR6301
@@ -225,7 +440,7 @@ def _sync_result_to_parameters(  # noqa: PLR6301
         raw_result: object,
     ) -> None:
         """
-        Synchronize the result from the solver to the parameters.
+        Synchronize the solver result back to the parameters.
 
         Parameters
         ----------
@@ -251,7 +466,7 @@ def _sync_result_to_parameters(  # noqa: PLR6301
 
     def _check_success(self, raw_result: object) -> bool:  # noqa: PLR6301
         """
-        Determine success from bumps OptimizeResult.
+        Determine success from a BUMPS OptimizeResult.
 
         Parameters
         ----------
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 1cb42fb51..225c7ea0d 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -32,6 +32,8 @@
 from easydiffraction.core.metadata import TypeInfo
 from easydiffraction.utils.logging import log
 
+_BUMPS_DREAM_LOG = log
+
 DEFAULT_METHOD = 'dream'
 DEFAULT_MAX_ITERATIONS = 3000
 DEFAULT_BURN_FRACTION = 0.2
@@ -304,14 +306,28 @@ def __init__(
         self._parallel: int = DEFAULT_PARALLEL
         self._init: DreamPopulationInitializationEnum = DEFAULT_INIT
 
+    @property
+    def max_iterations(self) -> int:
+        """DREAM exposes sampler length through ``steps`` instead."""
+        sampler_name = self.type_info.description.partition('with ')[2].split()[0]
+        msg = f"{sampler_name} sampler uses 'steps' instead of 'max_iterations'."
+        raise AttributeError(msg)
+
+    @max_iterations.setter
+    def max_iterations(self, value: int) -> None:
+        del value
+        sampler_name = self.type_info.description.partition('with ')[2].split()[0]
+        msg = f"{sampler_name} sampler uses 'steps' instead of 'max_iterations'."
+        raise AttributeError(msg)
+
     @property
     def steps(self) -> int:
         """Number of DREAM generations retained after burn-in."""
-        return self._validated_positive_integer('steps', self.max_iterations)
+        return self._validated_positive_integer('steps', self._max_iterations)
 
     @steps.setter
     def steps(self, value: int) -> None:
-        self.max_iterations = self._validated_positive_integer('steps', value)
+        self._max_iterations = self._validated_positive_integer('steps', value)
 
     @property
     def burn(self) -> int | None:
@@ -601,6 +617,8 @@ def _run_solver(
         object
             Normalized DREAM result stored in an ``OptimizeResult``.
         """
+        total_iterations = int(self.steps + self._resolved_burn(self.steps) + 1)
+        self.tracker.start_sampler_pre_processing(total_iterations=total_iterations)
         context = self._prepare_run_context(objective_function=objective_function, kwargs=kwargs)
         driver_result = self._execute_driver(
             driver=context.driver,
@@ -621,6 +639,8 @@ def _run_solver(
                 sampler_completed=False,
             )
 
+        self.tracker.start_sampler_post_processing()
+
         return self._build_success_result(
             context=context,
             raw_state=driver_result.raw_state,
@@ -729,7 +749,7 @@ def _build_mapper(self, problem: FitProblem) -> object | None:
             return None
 
         if self._requires_serial_mapper_for_spawn_main_module():
-            log.warning(
+            self._warn_after_tracking(
                 'DREAM parallel evaluation requires an import-safe main '
                 'module on spawn-based multiprocessing; falling back to '
                 'serial execution.'
@@ -745,7 +765,7 @@ def _build_mapper(self, problem: FitProblem) -> object | None:
 
         try:
             if not can_pickle(problem):
-                log.warning(
+                self._warn_after_tracking(
                     'DREAM parallel evaluation requires a picklable '
                     'problem; falling back to serial execution.'
                 )
@@ -756,7 +776,7 @@ def _build_mapper(self, problem: FitProblem) -> object | None:
             message = str(error)
             if 'bootstrapping phase' not in message:
                 raise
-            log.warning(
+            self._warn_after_tracking(
                 'DREAM parallel evaluation requires an import-safe main '
                 'module on spawn-based multiprocessing; falling back to '
                 'serial execution.'
@@ -885,6 +905,10 @@ def _build_success_result(
             draw_index=np.asarray(draw_index, dtype=float),
         )
         convergence_diagnostics = compute_convergence_diagnostics(posterior_samples)
+        if not convergence_diagnostics.get('converged', True):
+            self._warn_after_tracking(
+                'Convergence diagnostics indicate the posterior may be poorly mixed.'
+            )
         posterior_parameter_summaries = summarize_posterior_parameters(
             parameter_names=context.parameter_names,
             posterior_samples=posterior_samples,
@@ -896,9 +920,6 @@ def _build_success_result(
             posterior_parameter_summaries
         )
 
-        if not convergence_diagnostics.get('converged', True):
-            log.warning('Convergence diagnostics indicate the posterior may be poorly mixed.')
-
         return OptimizeResult(
             x=best_sample_values,
             dx=posterior_standard_deviations,
diff --git a/src/easydiffraction/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index 029486c79..a43b537a3 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -90,9 +90,9 @@ def _fit_worker(
     Returns
     -------
     dict[str, Any]
-        Result dict with keys: ``file_path``, ``fit_success``,
-        ``chi_squared``, ``reduced_chi_squared``, ``n_iterations``, and
-        per-parameter ``{unique_name}`` / ``{unique_name}.uncertainty``.
+        Result dict with keys: ``file_path``, ``success``,
+        ``reduced_chi_square``, ``iterations``, and per-parameter
+        ``{unique_name}`` / ``{unique_name}.uncertainty``.
     """
     # Lazy import to avoid circular dependencies and keep the module
     # importable without heavy imports at top level.
@@ -144,12 +144,12 @@ def _fit_worker(
         project.analysis.fitter = Fitter(template.minimizer_tag)
 
         # 10. Fit
-        original_verbosity = project.verbosity
-        project.verbosity = 'silent'
+        original_verbosity = project.verbosity.fit.value
+        project.verbosity.fit = 'silent'
         try:
             project.analysis.fit()
         finally:
-            project.verbosity = original_verbosity
+            project.verbosity.fit = original_verbosity
 
         # 11. Collect results
         result.update(_collect_results(project, template))
@@ -163,10 +163,9 @@ def _fit_worker(
         IndexError,
         OSError,
     ) as exc:
-        result['fit_success'] = False
-        result['chi_squared'] = None
-        result['reduced_chi_squared'] = None
-        result['n_iterations'] = 0
+        result['success'] = False
+        result['reduced_chi_square'] = None
+        result['iterations'] = 0
         result['error'] = str(exc)
 
     return result
@@ -352,17 +351,17 @@ def _collect_results(
 
     result: dict[str, Any] = {}
     fit_results = project.analysis.fit_results
+    tracker = getattr(project.analysis.fitter.minimizer, 'tracker', None)
+    best_iteration = getattr(tracker, 'best_iteration', None)
 
     if fit_results is not None:
-        result['fit_success'] = fit_results.success
-        result['chi_squared'] = fit_results.chi_square
-        result['reduced_chi_squared'] = fit_results.reduced_chi_square
-        result['n_iterations'] = project.analysis.fitter.minimizer.tracker.best_iteration or 0
+        result['fit_result.success'] = fit_results.success
+        result['fit_result.reduced_chi_square'] = fit_results.reduced_chi_square
+        result['fit_result.iterations'] = fit_results.iterations or best_iteration or 0
     else:
-        result['fit_success'] = False
-        result['chi_squared'] = None
-        result['reduced_chi_squared'] = None
-        result['n_iterations'] = 0
+        result['fit_result.success'] = False
+        result['fit_result.reduced_chi_square'] = None
+        result['fit_result.iterations'] = best_iteration or 0
 
     # Collect all free parameter values and uncertainties
     all_params = project.structures.parameters + project.experiments.parameters
@@ -383,10 +382,9 @@ def _collect_results(
 
 _META_COLUMNS = [
     'file_path',
-    'chi_squared',
-    'reduced_chi_squared',
-    'fit_success',
-    'n_iterations',
+    'fit_result.reduced_chi_square',
+    'fit_result.success',
+    'fit_result.iterations',
 ]
 
 
@@ -551,7 +549,8 @@ def _read_csv_for_recovery(
             file_path = row.get('file_path', '')
             if file_path:
                 fitted.add(_resolve_csv_file_path(csv_path, file_path))
-            if row.get('fit_success', '').lower() == 'true':
+            success_value = row.get('fit_result.success', row.get('success', ''))
+            if success_value.lower() == 'true':
                 params = _extract_params_from_row(row)
                 if params:
                     last_params = params
@@ -725,9 +724,9 @@ class SequentialRunPlan:
 def _summarize_chunk_results(results: list[dict[str, Any]]) -> tuple[str, str]:
     """Return average reduced chi-square and status for a chunk."""
     num_files = len(results)
-    successful = [r for r in results if r.get('fit_success')]
+    successful = [r for r in results if r.get('fit_result.success')]
     if successful:
-        avg_chi2 = sum(r['reduced_chi_squared'] for r in successful) / len(successful)
+        avg_chi2 = sum(r['fit_result.reduced_chi_square'] for r in successful) / len(successful)
         chi2_str = f'{avg_chi2:.2f}'
     else:
         chi2_str = '—'
@@ -798,10 +797,10 @@ def _build_file_progress_rows(
     rows: list[list[str]] = []
     time_str = _format_elapsed_seconds(elapsed_time)
     for index, result in enumerate(results, start=1):
-        reduced_chi2 = result.get('reduced_chi_squared')
+        reduced_chi2 = result.get('fit_result.reduced_chi_square')
         chi2_str = f'{reduced_chi2:.2f}' if reduced_chi2 is not None else '—'
-        iterations = str(result.get('n_iterations') or 0)
-        status = '✅' if result.get('fit_success') else '❌'
+        iterations = str(result.get('fit_result.iterations') or 0)
+        status = '✅' if result.get('fit_result.success') else '❌'
         rows.append([
             Path(result['file_path']).name,
             _format_progress_percent(completed_files_before + index, total_files),
@@ -897,7 +896,7 @@ def _print_sequential_completion(
         return
 
     console.print(f'✅ Sequential fitting complete: {processed_count} files processed.')
-    console.print(f'📄 Results saved to: {csv_path}')
+    console.print(f'📄 Results saved to:\n{csv_path}')
 
 
 def _prepare_sequential_run(
@@ -910,7 +909,7 @@ def _prepare_sequential_run(
     reverse: bool,
 ) -> SequentialRunPlan | None:
     """Resolve inputs and bookkeeping for one sequential-fit run."""
-    verbosity = VerbosityEnum(analysis.project.verbosity)
+    verbosity = VerbosityEnum(analysis.project.verbosity.fit.value)
 
     _check_seq_preconditions(analysis.project)
 
@@ -1260,7 +1259,7 @@ def _run_fit_loop(
 def _find_last_successful(results: list[dict[str, Any]]) -> dict[str, Any] | None:
     """Return the last successful result dict, or None."""
     for r in reversed(results):
-        if r.get('fit_success') and r.get('params'):
+        if r.get('fit_result.success') and r.get('params'):
             return r
     return None
 
diff --git a/src/easydiffraction/core/validation.py b/src/easydiffraction/core/validation.py
index 3fd882084..314d7e94c 100644
--- a/src/easydiffraction/core/validation.py
+++ b/src/easydiffraction/core/validation.py
@@ -27,6 +27,7 @@ class DataTypeHints:
     """Type hint aliases for numeric, string, and boolean types."""
 
     Numeric = int | float | np.integer | np.floating
+    Integer = int | np.integer
     String = str
     Bool = bool
 
@@ -38,6 +39,7 @@ class DataTypes(Enum):
     """Enumeration of supported data types for descriptors."""
 
     NUMERIC = (int, float, np.integer, np.floating)
+    INTEGER = (int, np.integer)
     STRING = (str,)
     BOOL = (bool,)
     ANY = (object,)  # fallback for unconstrained
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 257a5771f..074afb714 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -276,6 +276,15 @@ def units(self) -> str:
 # ======================================================================
 
 
+class GenericIntegerDescriptor(GenericNumericDescriptor):
+    """Base descriptor that constrains values to integers."""
+
+    _value_type = DataTypes.INTEGER
+
+
+# ======================================================================
+
+
 class GenericParameter(GenericNumericDescriptor):
     """
     Numeric descriptor extended with fitting-related attributes.
@@ -461,6 +470,10 @@ def fit_bounds_uncertainty_multiplier(self) -> float | None:
         """
         return self._fit_bounds_uncertainty_multiplier
 
+    def _set_fit_bounds_uncertainty_multiplier(self, value: float | None) -> None:
+        """Set the cached uncertainty-derived fit-bounds multiplier."""
+        self._fit_bounds_uncertainty_multiplier = value
+
     def set_fit_bounds_from_uncertainty(
         self,
         multiplier: float = DEFAULT_FIT_BOUNDS_MULTIPLIER,
@@ -612,6 +625,33 @@ def __init__(
 # ======================================================================
 
 
+class IntegerDescriptor(GenericIntegerDescriptor):
+    """Integer descriptor bound to a CIF handler."""
+
+    def __init__(
+        self,
+        *,
+        cif_handler: CifHandler,
+        **kwargs: object,
+    ) -> None:
+        """
+        Integer descriptor bound to a CIF handler.
+
+        Parameters
+        ----------
+        cif_handler : CifHandler
+            Object that tracks CIF identifiers.
+        **kwargs : object
+            Forwarded to GenericIntegerDescriptor.
+        """
+        super().__init__(**kwargs)
+        self._cif_handler = cif_handler
+        self._cif_handler.attach(self)
+
+
+# ======================================================================
+
+
 class Parameter(GenericParameter):
     """Fittable parameter bound to a CIF handler."""
 
diff --git a/src/easydiffraction/datablocks/experiment/collection.py b/src/easydiffraction/datablocks/experiment/collection.py
index 16bfe5a49..65646d506 100644
--- a/src/easydiffraction/datablocks/experiment/collection.py
+++ b/src/easydiffraction/datablocks/experiment/collection.py
@@ -125,7 +125,7 @@ def add_from_data_path(
         scattering_type : str | None, default=None
             Scattering type (e.g. ``'bragg'``).
         """
-        verbosity = self._parent.verbosity if self._parent is not None else None
+        verbosity = self._parent.verbosity.fit.value if self._parent is not None else None
         verb = VerbosityEnum(verbosity) if verbosity is not None else VerbosityEnum.FULL
         experiment = ExperimentFactory.from_scratch(
             name=name,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 85cb1545a..3bfbd5ec5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -16,6 +16,8 @@
 import numpy as np
 import pandas as pd
 
+from easydiffraction.analysis.enums import FitCorrelationSourceEnum
+from easydiffraction.analysis.enums import FitResultKindEnum
 from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
 from easydiffraction.datablocks.experiment.item.base import intensity_category_for
 from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
@@ -89,6 +91,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_FLATTENED_SAMPLE_NDIM = 2
 MIN_POSTERIOR_PARAMETER_COUNT = 2
 MIN_POSTERIOR_SAMPLE_COUNT = 2
+PAIR_DENSITY_SURFACE_NDIM = 2
 POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
 POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
 POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR = 'rgb(44, 160, 44)'
@@ -741,7 +744,7 @@ def plot_param_series(
         Parameters
         ----------
         param : object
-            Parameter descriptor whose ``unique_name`` identifies the
+            Descriptor whose ``unique_name`` or ``name`` identifies the
             values to plot.
         versus : str | None, default=None
             Persisted diffrn path (e.g.
@@ -749,7 +752,10 @@ def plot_param_series(
             column is used as the x-axis. When ``None``, the experiment
             sequence number is used instead.
         """
-        unique_name = param.unique_name
+        column_names = self._series_column_names(param)
+        if not column_names:
+            log.warning('Series plot target does not expose a CSV column name.')
+            return
 
         # Try CSV first (produced by fit_sequential or future fit)
         csv_path = None
@@ -761,19 +767,49 @@ def plot_param_series(
         if csv_path is not None:
             self._plot_param_series_from_csv(
                 csv_path=csv_path,
-                unique_name=unique_name,
+                column_names=column_names,
                 param_descriptor=param,
                 versus_path=versus,
             )
         else:
             # Fallback: in-memory snapshots from fit() single mode
             self.plot_param_series_from_snapshots(
-                unique_name,
+                column_names[0],
                 versus,
                 self._project.experiments,
                 self._project.analysis._parameter_snapshots,
             )
 
+    @staticmethod
+    def _series_column_names(param: object) -> list[str]:
+        """Return candidate CSV column names for one plotted series."""
+        names: list[str] = []
+
+        unique_name = getattr(param, 'unique_name', None)
+        if isinstance(unique_name, str) and unique_name:
+            names.append(unique_name)
+
+        name = getattr(param, 'name', None)
+        if isinstance(name, str) and name and name not in names:
+            names.append(name)
+
+        return names
+
+    @staticmethod
+    def _numeric_series_values(values: object) -> list[float]:
+        """Return one CSV column normalized to numeric plot values."""
+        series = pd.Series(values)
+        if series.dtype == bool:
+            return series.astype(float).tolist()
+
+        normalized = series.replace({
+            'True': 1.0,
+            'False': 0.0,
+            'true': 1.0,
+            'false': 0.0,
+        })
+        return pd.to_numeric(normalized, errors='raise').tolist()
+
     def plot_all_param_series(
         self,
         versus: str | None = None,
@@ -1573,13 +1609,15 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
             return corr_df
 
         raw_result = self._raw_fit_result_for_correlation(fit_results)
-        if raw_result is None:
-            return None
+        if raw_result is not None:
+            corr_df = self._correlation_dataframe_from_engine_result(
+                raw_result=raw_result,
+                parameters=fit_results.parameters,
+            )
+            if corr_df is not None:
+                return corr_df
 
-        corr_df = self._correlation_dataframe_from_engine_result(
-            raw_result=raw_result,
-            parameters=fit_results.parameters,
-        )
+        corr_df = self._correlation_dataframe_from_persisted_projection(fit_results)
         if corr_df is not None:
             return corr_df
 
@@ -1606,15 +1644,71 @@ def _raw_fit_result_for_correlation(fit_results: object) -> object | None:
         if raw_result is None:
             raw_result = getattr(fit_results, 'engine_result', None)
         if raw_result is None:
-            log.warning('No raw fit result available. Correlation matrix cannot be plotted.')
             return None
 
         var_names = getattr(raw_result, 'var_names', None)
         if not var_names:
-            log.warning('Fit result does not expose variable names for a correlation matrix.')
             return None
         return raw_result
 
+    def _correlation_dataframe_from_persisted_projection(
+        self,
+        fit_results: object,
+    ) -> pd.DataFrame | None:
+        """
+        Return correlations restored from persisted fit-state rows.
+        """
+        if self._project is None:
+            return None
+
+        analysis = self._project.analysis
+        source_kind = (
+            FitCorrelationSourceEnum.POSTERIOR.value
+            if analysis.fit_result.result_kind.value == FitResultKindEnum.BAYESIAN.value
+            else FitCorrelationSourceEnum.DETERMINISTIC.value
+        )
+        correlation_rows = [
+            row
+            for row in analysis.fit_parameter_correlations
+            if row.source_kind.value == source_kind
+        ]
+        if not correlation_rows:
+            return None
+
+        parameter_names = [
+            getattr(parameter, 'unique_name', '')
+            for parameter in getattr(fit_results, 'parameters', [])
+            if getattr(parameter, 'unique_name', None)
+        ]
+        if not parameter_names:
+            parameter_names = [
+                summary.unique_name
+                for summary in getattr(fit_results, 'posterior_parameter_summaries', [])
+            ]
+
+        for row in correlation_rows:
+            parameter_names.extend([row.param_unique_name_i.value, row.param_unique_name_j.value])
+        parameter_names = list(dict.fromkeys(parameter_names))
+        if len(parameter_names) < MIN_POSTERIOR_PARAMETER_COUNT:
+            return None
+
+        correlation_values = np.eye(len(parameter_names), dtype=float)
+        corr_df = pd.DataFrame(
+            correlation_values,
+            index=parameter_names,
+            columns=parameter_names,
+        )
+        wrote_any = False
+        for row in correlation_rows:
+            i_name = row.param_unique_name_i.value
+            j_name = row.param_unique_name_j.value
+            if i_name not in corr_df.index or j_name not in corr_df.index:
+                continue
+            corr_df.loc[i_name, j_name] = float(row.correlation.value)
+            corr_df.loc[j_name, i_name] = float(row.correlation.value)
+            wrote_any = True
+        return corr_df if wrote_any else None
+
     def _correlation_dataframe_from_engine_result(
         self,
         *,
@@ -2431,7 +2525,7 @@ def _plot_legend_background_color(self) -> str:
             return legend_background_color()
         return PlotlyPlotter._legend_background_color()
 
-    def _posterior_contour_traces(
+    def _resolved_posterior_contour_surface(
         self,
         *,
         fit_results: object,
@@ -2440,11 +2534,14 @@ def _posterior_contour_traces(
         x_values: np.ndarray,
         y_values: np.ndarray,
         grid_size: int,
-    ) -> tuple[object, object] | None:
-        """
-        Return filled and line contour traces for posterior pair plots.
-        """
-        go = __import__('plotly.graph_objects', fromlist=['Contour'])
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray | None] | None:
+        """Return cached or computed posterior contour surface data."""
+        cached_surface = self._cached_posterior_pair_surface(
+            x_parameter_name=x_parameter_name,
+            y_parameter_name=y_parameter_name,
+        )
+        if cached_surface is not None:
+            return cached_surface
 
         bounds = self._posterior_pair_bounds(
             fit_results=fit_results,
@@ -2464,13 +2561,44 @@ def _posterior_contour_traces(
             return None
 
         x_grid, y_grid, density = density_surface
+        return x_grid, y_grid, density, None
+
+    def _posterior_contour_traces(
+        self,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+        grid_size: int,
+    ) -> tuple[object, object] | None:
+        """
+        Return filled and line contour traces for posterior pair plots.
+        """
+        go = __import__('plotly.graph_objects', fromlist=['Contour'])
+
+        surface = self._resolved_posterior_contour_surface(
+            fit_results=fit_results,
+            x_parameter_name=x_parameter_name,
+            y_parameter_name=y_parameter_name,
+            x_values=x_values,
+            y_values=y_values,
+            grid_size=grid_size,
+        )
+        if surface is None:
+            return None
+
+        x_grid, y_grid, density, contour_levels = surface
+
         fill_colorscale, line_colorscale = self._posterior_pair_contour_colorscales(
             x_values,
             y_values,
         )
-        contour_start = float(np.max(density) * 0.20)
-        contour_end = float(np.max(density) * 0.95)
-        contour_size = float(np.max(density) * 0.15)
+        contour_start, contour_end, contour_size = self._posterior_contour_levels(
+            density=density,
+            contour_levels=contour_levels,
+        )
         fill_density = np.array(density, copy=True)
         fill_density[fill_density < contour_start] = np.nan
         fill_trace = go.Contour(
@@ -2516,6 +2644,80 @@ def _posterior_contour_traces(
         )
         return fill_trace, line_trace
 
+    def _cached_posterior_pair_surface(
+        self,
+        *,
+        x_parameter_name: str,
+        y_parameter_name: str,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray | None] | None:
+        """
+        Return a restored posterior pair-density surface when available.
+        """
+        if self._project is None:
+            return None
+
+        analysis = self._project.analysis
+        sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+        pair_caches = sidecar_data.get('pair_caches', {})
+        for cache in analysis.bayesian_pair_caches:
+            cache_x = cache.param_unique_name_x.value
+            cache_y = cache.param_unique_name_y.value
+            if {cache_x, cache_y} != {x_parameter_name, y_parameter_name}:
+                continue
+
+            cache_data = pair_caches.get(cache.id.value)
+            if cache_data is None:
+                return None
+
+            x_grid = np.asarray(cache_data.get('x'), dtype=float)
+            y_grid = np.asarray(cache_data.get('y'), dtype=float)
+            density = np.asarray(cache_data.get('density'), dtype=float)
+            contour_levels = cache_data.get('contour_levels')
+            if contour_levels is not None:
+                contour_levels = np.asarray(contour_levels, dtype=float)
+
+            if x_parameter_name != cache_x or y_parameter_name != cache_y:
+                x_grid, y_grid = y_grid, x_grid
+                if density.ndim == PAIR_DENSITY_SURFACE_NDIM:
+                    density = density.T
+
+            expected_shape = (y_grid.size, x_grid.size)
+            if x_grid.ndim != 1 or y_grid.ndim != 1 or density.shape != expected_shape:
+                log.warning(
+                    'Persisted posterior pair cache is invalid for '
+                    f'{x_parameter_name!r} and {y_parameter_name!r}.'
+                )
+                return None
+            return x_grid, y_grid, density, contour_levels
+
+        return None
+
+    @staticmethod
+    def _posterior_contour_levels(
+        *,
+        density: np.ndarray,
+        contour_levels: np.ndarray | None,
+    ) -> tuple[float, float, float]:
+        """Return contour start, end, and step for pair density."""
+        if contour_levels is not None and contour_levels.ndim == 1 and contour_levels.size > 0:
+            finite_levels = contour_levels[np.isfinite(contour_levels)]
+            if finite_levels.size > 0:
+                start = float(finite_levels[0])
+                end = float(finite_levels[-1])
+                if finite_levels.size > 1:
+                    size = float(np.min(np.diff(finite_levels)))
+                else:
+                    size = max(end - start, abs(end) * 0.15, 1e-6)
+                if end > start and size > 0:
+                    return start, end, size
+
+        density_max = float(np.max(density))
+        return (
+            density_max * 0.20,
+            density_max * 0.95,
+            density_max * 0.15,
+        )
+
     def _build_param_distribution_plot(
         self,
         param: object,
@@ -2608,11 +2810,13 @@ def _plot_ascii_param_distribution(
             fit_results=context.fit_results,
             parameter_name=context.parameter_name,
         )
-        density_curve = self._posterior_density_curve(
-            context.values,
-            lower_bound=lower_bound,
-            upper_bound=upper_bound,
-        )
+        density_curve = self._cached_posterior_density_curve(context.parameter_name)
+        if density_curve is None:
+            density_curve = self._posterior_density_curve(
+                context.values,
+                lower_bound=lower_bound,
+                upper_bound=upper_bound,
+            )
         if density_curve is None:
             log.warning(
                 f'Posterior distribution is unavailable for parameter {context.parameter_name}.'
@@ -2629,6 +2833,30 @@ def _plot_ascii_param_distribution(
             height=self.height,
         )
 
+    def _cached_posterior_density_curve(
+        self,
+        parameter_name: str,
+    ) -> tuple[np.ndarray, np.ndarray] | None:
+        """
+        Return a restored posterior density curve for one parameter.
+        """
+        if self._project is None:
+            return None
+
+        sidecar_data = getattr(self._project.analysis, '_persisted_fit_state_sidecar', {})
+        cache_data = sidecar_data.get('distribution_caches', {}).get(parameter_name)
+        if cache_data is None:
+            return None
+
+        x_values = np.asarray(cache_data.get('x'), dtype=float)
+        density_values = np.asarray(cache_data.get('density'), dtype=float)
+        if x_values.ndim != 1 or density_values.shape != x_values.shape:
+            log.warning(
+                f'Persisted posterior distribution cache is invalid for {parameter_name!r}.'
+            )
+            return None
+        return x_values, density_values
+
     def _posterior_distribution_context(
         self,
         param: object,
@@ -2974,11 +3202,13 @@ def _posterior_density_trace(
             fit_results=fit_results,
             parameter_name=parameter_name,
         )
-        density_curve = self._posterior_density_curve(
-            values,
-            lower_bound=lower_bound,
-            upper_bound=upper_bound,
-        )
+        density_curve = self._cached_posterior_density_curve(parameter_name)
+        if density_curve is None:
+            density_curve = self._posterior_density_curve(
+                values,
+                lower_bound=lower_bound,
+                upper_bound=upper_bound,
+            )
         if density_curve is None:
             return None
 
@@ -3312,8 +3542,7 @@ def _get_or_build_posterior_predictive_summary(
             return None
 
         posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
-        posterior_samples = getattr(fit_results, 'posterior_samples', None)
-        if posterior_predictive is None or posterior_samples is None:
+        if posterior_predictive is None:
             return None
 
         x_axis_name = getattr(x_axis, 'value', x_axis)
@@ -3331,9 +3560,18 @@ def _get_or_build_posterior_predictive_summary(
         summary = posterior_predictive.get(cache_key)
         if summary is None and not include_draws:
             summary = posterior_predictive.get(draw_cache_key)
+        if summary is None:
+            summary = posterior_predictive.get(expt_name)
+            summary_x_axis = getattr(summary, 'x_axis_name', None)
+            if summary is not None and str(summary_x_axis) != str(x_axis_name):
+                summary = None
         if summary is not None:
             return summary
 
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            return None
+
         summary = self._build_posterior_predictive_summary(
             fit_results=fit_results,
             experiment=experiment,
@@ -5537,7 +5775,7 @@ def _bragg_tick_d_spacing(
     def _plot_param_series_from_csv(
         self,
         csv_path: str,
-        unique_name: str,
+        column_names: str | list[str],
         param_descriptor: object,
         versus_path: str | None = None,
     ) -> None:
@@ -5545,10 +5783,10 @@ def _plot_param_series_from_csv(
         Plot a parameter's value across sequential fit results.
 
         Reads data from the CSV file at *csv_path*.  The y-axis values
-        come from the column named *unique_name*, uncertainties from
-        ``{unique_name}.uncertainty``. When *versus_path* is provided,
-        the x-axis uses the corresponding ``diffrn.*`` CSV column;
-        otherwise the row index is used.
+        come from the first matching column named in *column_names*,
+        with uncertainties from ``{column_name}.uncertainty``. When
+        *versus_path* is provided, the x-axis uses the corresponding
+        ``diffrn.*`` CSV column; otherwise the row index is used.
 
         Axis labels use the live parameter descriptor and, when
         available, a template diffrn descriptor resolved from
@@ -5558,8 +5796,8 @@ def _plot_param_series_from_csv(
         ----------
         csv_path : str
             Path to the ``results.csv`` file.
-        unique_name : str
-            Unique name of the parameter to plot (CSV column key).
+        column_names : str | list[str]
+            Candidate CSV column keys to plot.
         param_descriptor : object
             The live parameter descriptor (for axis label / units).
         versus_path : str | None, default=None
@@ -5568,16 +5806,23 @@ def _plot_param_series_from_csv(
         """
         df = pd.read_csv(csv_path)
 
-        if unique_name not in df.columns:
+        column_candidates = [column_names] if isinstance(column_names, str) else column_names
+
+        column_name = next((name for name in column_candidates if name in df.columns), None)
+        if column_name is None:
             log.warning(
-                f"Parameter '{unique_name}' not found in CSV columns. "
+                f"Parameter '{column_candidates[0]}' not found in CSV columns. "
                 f'Available: {list(df.columns)}'
             )
             return
 
-        y = df[unique_name].astype(float).tolist()
-        uncert_col = f'{unique_name}.uncertainty'
-        sy = df[uncert_col].astype(float).tolist() if uncert_col in df.columns else [0.0] * len(y)
+        y = self._numeric_series_values(df[column_name])
+        uncert_col = f'{column_name}.uncertainty'
+        sy = (
+            self._numeric_series_values(df[uncert_col])
+            if uncert_col in df.columns
+            else [0.0] * len(y)
+        )
 
         # X-axis: diffrn column or row index
         diffrn_col = versus_path
@@ -5594,7 +5839,7 @@ def _plot_param_series_from_csv(
         param_units = getattr(param_descriptor, 'units', '')
         y_label = f'Parameter value ({param_units})' if param_units else 'Parameter value'
 
-        title = f"Parameter '{unique_name}' across fit results"
+        title = f"Parameter '{column_name}' across fit results"
 
         self._backend.plot_scatter(
             x=x,
diff --git a/src/easydiffraction/display/progress.py b/src/easydiffraction/display/progress.py
index ef381c505..6123e626e 100644
--- a/src/easydiffraction/display/progress.py
+++ b/src/easydiffraction/display/progress.py
@@ -31,6 +31,8 @@
 
 ACTIVITY_LABEL_BURN_IN = 'Burn-in...'
 ACTIVITY_LABEL_FITTING = 'Fitting...'
+ACTIVITY_LABEL_POST_PROCESSING = 'Post-processing...'
+ACTIVITY_LABEL_PRE_PROCESSING = 'Pre-processing...'
 ACTIVITY_LABEL_PROCESSING = 'Processing...'
 ACTIVITY_LABEL_SAMPLING = 'Sampling...'
 ACTIVITY_ACCENT_COLOR = '#d97706'
diff --git a/src/easydiffraction/io/ascii.py b/src/easydiffraction/io/ascii.py
index faab76820..dcbea91b3 100644
--- a/src/easydiffraction/io/ascii.py
+++ b/src/easydiffraction/io/ascii.py
@@ -5,20 +5,22 @@
 from __future__ import annotations
 
 import re
-import tempfile
 import zipfile
 from io import StringIO
 from pathlib import Path
 
 import numpy as np
 
+from easydiffraction.utils.environment import create_artifact_temp_dir
+from easydiffraction.utils.environment import resolve_artifact_path
+
 
 def _resolve_extraction_destination(destination: str | Path | None) -> Path:
     """Return an extraction directory for ZIP contents."""
     if destination is None:
-        return Path(tempfile.mkdtemp(prefix='ed_zip_'))
+        return create_artifact_temp_dir(prefix='ed_zip_')
 
-    extract_dir = Path(destination)
+    extract_dir = resolve_artifact_path(destination)
     if not extract_dir.is_absolute():
         extract_dir = Path.cwd() / extract_dir
 
@@ -45,7 +47,9 @@ def extract_project_from_zip(
         Path to the ZIP archive containing the project.
     destination : str | Path | None, default=None
         Directory to extract into.  When ``None``, a temporary directory
-        is created.
+        is created. Relative destinations are resolved against the
+        configured artifact root when ``EASYDIFFRACTION_ARTIFACT_ROOT``
+        is set.
 
     Returns
     -------
@@ -65,11 +69,7 @@ def extract_project_from_zip(
         msg = f'ZIP file not found: {zip_path}'
         raise FileNotFoundError(msg)
 
-    if destination is not None:
-        extract_dir = Path(destination)
-        extract_dir.mkdir(parents=True, exist_ok=True)
-    else:
-        extract_dir = Path(tempfile.mkdtemp(prefix='ed_zip_'))
+    extract_dir = _resolve_extraction_destination(destination)
 
     with zipfile.ZipFile(zip_path, 'r') as zf:
         # Determine the project directory from the archive contents
@@ -107,7 +107,8 @@ def extract_data_paths_from_zip(
     destination : str | Path | None, default=None
         Directory to extract files into.  When ``None``, a temporary
         directory is created. Relative destinations are resolved against
-        the current working directory.
+        the current working directory, or against the configured
+        artifact root when ``EASYDIFFRACTION_ARTIFACT_ROOT`` is set.
 
     Returns
     -------
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index 5c3a29c14..ef9fa277d 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import textwrap
 from typing import TYPE_CHECKING
 from typing import Any
 
@@ -21,15 +22,15 @@
     from easydiffraction.core.category import CategoryItem
     from easydiffraction.core.variable import GenericDescriptorBase
 
-# Maximum CIF description length before using semicolon-delimited block
-_CIF_DESCRIPTION_WRAP_LEN = 60
-
 # Minimum string length to check for surrounding quotes
 _MIN_QUOTED_LEN = 2
 
 # Number of significant digits kept for CIF uncertainty notation
 _CIF_UNCERTAINTY_SIG_DIGITS = 2
 
+# Maximum CIF description length before using semicolon-delimited block
+_CIF_DESCRIPTION_WRAP_LEN = 60
+
 
 def format_value(value: object) -> str:
     """
@@ -51,9 +52,9 @@ def format_value(value: object) -> str:
     # Booleans use CIF true/false tokens
     elif isinstance(value, bool):
         value = 'true' if value else 'false'
-    # Convert ints to floats
-    elif isinstance(value, int):
-        value = float(value)
+    # Preserve integers as integers in CIF output
+    elif isinstance(value, (int, np.integer)):
+        value = str(int(value))
     # Empty strings → CIF unknown marker
     elif isinstance(value, str) and not value.strip():
         value = '?'
@@ -79,6 +80,13 @@ def _strip_optional_quotes(raw: str) -> str:
     return raw[1:-1] if is_quoted else raw
 
 
+def _strip_cif_text_field_delimiters(raw: str) -> str:
+    """Return CIF text-field content without delimiter lines."""
+    if raw.startswith(';\n') and raw.endswith('\n;'):
+        return raw[2:-2].strip()
+    return raw
+
+
 def _parse_bool_cif_value(raw: str) -> bool | str:
     """Parse CIF boolean tokens, returning the raw token if invalid."""
     normalized_value = _strip_optional_quotes(raw).lower()
@@ -335,25 +343,38 @@ def datablock_collection_to_cif(collection: object) -> str:
     return '\n\n'.join([block.as_cif for block in collection.values()])
 
 
+def _format_project_description(description: str) -> str:
+    """Format project descriptions as CIF text."""
+    normalized_description = ' '.join(description.split())
+    if not normalized_description:
+        return '?'
+
+    if len(normalized_description) > _CIF_DESCRIPTION_WRAP_LEN:
+        wrapped_description = '\n'.join(
+            textwrap.wrap(
+                normalized_description,
+                width=_CIF_DESCRIPTION_WRAP_LEN,
+                break_long_words=False,
+                break_on_hyphens=False,
+            )
+        )
+        return f'\n;\n{wrapped_description}\n;'
+
+    return format_value(normalized_description)
+
+
 def project_info_to_cif(info: object) -> str:
     """Render ProjectInfo to CIF text (id, title, description)."""
     name = f'{info.name}'
 
     title = f'{info.title}'
     if ' ' in title:
-        title = f"'{title}'"
-
-    if len(info.description) > _CIF_DESCRIPTION_WRAP_LEN:
-        description = f'\n;\n{info.description}\n;'
-    elif info.description:
-        description = f'{info.description}'
-        if ' ' in description:
-            description = f"'{description}'"
-    else:
-        description = '?'
+        title = format_value(info.title)
+
+    description = _format_project_description(info.description)
 
-    created = f"'{info.created.strftime('%d %b %Y %H:%M:%S')}'"
-    last_modified = f"'{info.last_modified.strftime('%d %b %Y %H:%M:%S')}'"
+    created = format_value(info.created.strftime('%d %b %Y %H:%M:%S'))
+    last_modified = format_value(info.last_modified.strftime('%d %b %Y %H:%M:%S'))
 
     return (
         f'_project.id               {name}\n'
@@ -458,6 +479,13 @@ def _wrap_in_data_block(cif_text: str, block_name: str = '_') -> str:
     return f'data_{block_name}\n\n{cif_text}'
 
 
+def _project_block_from_cif_text(cif_text: str) -> gemmi.cif.Block:
+    """Parse project CIF text."""
+    import gemmi  # noqa: PLC0415
+
+    return gemmi.cif.read_string(_wrap_in_data_block(cif_text, 'project')).sole_block()
+
+
 def _populate_project_info_from_block(
     info: object,
     block: gemmi.cif.Block,
@@ -487,9 +515,7 @@ def project_info_from_cif(info: object, cif_text: str) -> None:
     """
     Populate a ProjectInfo instance from CIF text.
 
-    Reads ``_project.id``, ``_project.title``, and
-    ``_project.description`` from the given CIF string and sets them on
-    the *info* object.
+    Reads the core project metadata fields from CIF text.
 
     Parameters
     ----------
@@ -498,10 +524,7 @@ def project_info_from_cif(info: object, cif_text: str) -> None:
     cif_text : str
         CIF text content of ``project.cif``.
     """
-    import gemmi  # noqa: PLC0415
-
-    doc = gemmi.cif.read_string(_wrap_in_data_block(cif_text, 'project'))
-    block = doc.sole_block()
+    block = _project_block_from_cif_text(cif_text)
 
     _populate_project_info_from_block(info, block)
 
@@ -510,10 +533,7 @@ def project_config_from_cif(project: object, cif_text: str) -> None:
     """
     Populate project-level configuration from ``project.cif`` text.
     """
-    import gemmi  # noqa: PLC0415
-
-    doc = gemmi.cif.read_string(_wrap_in_data_block(cif_text, 'project'))
-    block = doc.sole_block()
+    block = _project_block_from_cif_text(cif_text)
 
     _populate_project_info_from_block(project.info, block)
 
@@ -521,6 +541,10 @@ def project_config_from_cif(project: object, cif_text: str) -> None:
     if rendering is not None:
         rendering.from_cif(block)
 
+    verbosity = getattr(project, 'verbosity', None)
+    if verbosity is not None:
+        verbosity.from_cif(block)
+
 
 def analysis_from_cif(analysis: object, cif_text: str) -> None:
     """
@@ -556,6 +580,82 @@ def analysis_from_cif(analysis: object, cif_text: str) -> None:
     if analysis.constraints._items:
         analysis.constraints.enable()
 
+    if _has_persisted_fit_state_sections(block):
+        _restore_persisted_fit_state(analysis, block)
+
+
+def _has_persisted_fit_state_sections(block: object) -> bool:
+    """Return True when any persisted fit-state section is present."""
+    scalar_tags = (
+        '_fit_result.result_kind',
+        '_deterministic_result.optimizer_name',
+        '_bayesian_result.sampler_name',
+        '_bayesian_sampler.steps',
+        '_bayesian_convergence.converged',
+    )
+    loop_tags = (
+        '_fit_parameter.param_unique_name',
+        '_fit_parameter_correlation.param_unique_name_i',
+        '_bayesian_parameter_posterior.unique_name',
+        '_bayesian_distribution_cache.param_unique_name',
+        '_bayesian_pair_cache.param_unique_name_x',
+        '_bayesian_predictive_dataset.experiment_name',
+    )
+
+    return any(_has_cif_value(block, tag) for tag in scalar_tags) or any(
+        _has_cif_loop(block, tag) for tag in loop_tags
+    )
+
+
+def _restore_common_fit_state(analysis: object, block: object) -> None:
+    """Restore fit-state categories shared by both fit kinds."""
+    analysis.fit_parameters.from_cif(block)
+    analysis.fit_result.from_cif(block)
+    analysis.fit_parameter_correlations.from_cif(block)
+
+
+def _restore_deterministic_fit_state(analysis: object, block: object) -> None:
+    """Restore deterministic-only persisted fit-state categories."""
+    analysis.deterministic_result.from_cif(block)
+
+
+def _restore_bayesian_fit_state(analysis: object, block: object) -> None:
+    """Restore Bayesian-only persisted fit-state categories."""
+    analysis.bayesian_result.from_cif(block)
+    analysis.bayesian_sampler.from_cif(block)
+    analysis.bayesian_convergence.from_cif(block)
+    analysis.bayesian_parameter_posteriors.from_cif(block)
+    analysis.bayesian_distribution_caches.from_cif(block)
+    analysis.bayesian_pair_caches.from_cif(block)
+    analysis.bayesian_predictive_datasets.from_cif(block)
+    analysis._sync_live_minimizer_from_persisted_fit_state()
+
+
+def _restore_persisted_fit_state(analysis: object, block: object) -> None:
+    """
+    Restore persisted fit-state categories after analysis configuration.
+    """
+    from easydiffraction.analysis.enums import FitResultKindEnum  # noqa: PLC0415
+
+    analysis._set_has_persisted_fit_state(value=True)
+    _restore_common_fit_state(analysis, block)
+
+    result_kind_value = analysis.fit_result.result_kind.value
+    try:
+        result_kind = FitResultKindEnum(result_kind_value)
+    except ValueError:
+        log.warning(
+            'Unsupported _fit_result.result_kind in analysis CIF: '
+            f'{result_kind_value!r}. Skipping kind-specific fit-state categories.',
+        )
+        return
+
+    if result_kind is FitResultKindEnum.DETERMINISTIC:
+        _restore_deterministic_fit_state(analysis, block)
+        return
+
+    _restore_bayesian_fit_state(analysis, block)
+
 
 def _collect_legacy_analysis_tags(block: object) -> list[str]:
     """Return deprecated analysis CIF tags present in a block."""
@@ -687,10 +787,8 @@ def _read(tag: str) -> str | None:
         # CIF unknown / inapplicable markers
         if raw in {'?', '.'}:
             return None
-        # Strip surrounding quotes
-        if len(raw) >= _MIN_QUOTED_LEN and raw[0] == raw[-1] and raw[0] in {"'", '"'}:
-            raw = raw[1:-1]
-        return raw
+        raw = _strip_cif_text_field_delimiters(raw)
+        return _strip_optional_quotes(raw)
 
     return _read
 
@@ -747,33 +845,9 @@ def param_from_cif(
     if not found_values:
         return
 
-    # If found, pick the one at the given index
+    # If found, pick the one at the given index.
     raw = found_values[idx]
-
-    # CIF unknown / inapplicable markers → keep default
-    if raw in {'?', '.'}:
-        return
-
-    # If numeric, parse with uncertainty if present
-    if self._value_type == DataTypes.NUMERIC:
-        has_brackets = '(' in raw
-        u = str_to_ufloat(raw)
-        self.value = u.n
-        if has_brackets and hasattr(self, 'free'):
-            self.free = True  # type: ignore[attr-defined]
-            if not np.isnan(u.s) and hasattr(self, 'uncertainty'):
-                self.uncertainty = u.s  # type: ignore[attr-defined]
-
-    # If string, strip quotes if present
-    elif self._value_type == DataTypes.STRING:
-        self.value = _strip_optional_quotes(raw)
-
-    elif self._value_type == DataTypes.BOOL:
-        self.value = _parse_bool_cif_value(raw)
-
-    # Other types are not supported
-    else:
-        log.debug(f'Unrecognized type: {self._value_type}')
+    _set_param_from_raw_cif_value(self, raw)
 
 
 def category_item_from_cif(
@@ -803,11 +877,23 @@ def _set_param_from_raw_cif_value(
     raw : str
         The raw string from the CIF loop cell.
     """
+    raw = _strip_cif_text_field_delimiters(raw)
+
     # CIF unknown / inapplicable markers → keep default
     if raw in {'?', '.'}:
         return
 
-    if param._value_type == DataTypes.NUMERIC:
+    if param._value_type == DataTypes.INTEGER:
+        numeric_value = str_to_ufloat(raw).n
+        integer_value = round(numeric_value)
+        if not np.isclose(numeric_value, integer_value):
+            log.warning(
+                f'Ignoring non-integer CIF value {raw!r} for integer field {param.unique_name}.'
+            )
+            return
+        param.value = integer_value
+
+    elif param._value_type == DataTypes.NUMERIC:
         has_brackets = '(' in raw
         u = str_to_ufloat(raw)
         param.value = u.n
diff --git a/src/easydiffraction/io/results_sidecar.py b/src/easydiffraction/io/results_sidecar.py
new file mode 100644
index 000000000..36d442c77
--- /dev/null
+++ b/src/easydiffraction/io/results_sidecar.py
@@ -0,0 +1,652 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian fit sidecar read/write helpers."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+
+from easydiffraction.utils.logging import log
+
+_DEFAULT_SIDECAR_FILE_NAME = 'results.h5'
+_POSTERIOR_PARAMETER_SAMPLES_PATH = '/posterior/parameter_samples'
+_POSTERIOR_LOG_POSTERIOR_PATH = '/posterior/log_posterior'
+_POSTERIOR_DRAW_INDEX_PATH = '/posterior/draw_index'
+_POSTERIOR_SAMPLE_NDIM = 3
+_PREDICTIVE_DRAWS_NDIM = 2
+
+
+def _normalized_hdf5_path(path: str) -> str:
+    """Return an HDF5 path without a leading slash."""
+    return path.lstrip('/')
+
+
+def _sidecar_file_name(analysis: object) -> str:
+    """Return the configured sidecar file name for an analysis."""
+    bayesian_result = getattr(analysis, 'bayesian_result', None)
+    if bayesian_result is None:
+        return _DEFAULT_SIDECAR_FILE_NAME
+
+    file_name = bayesian_result.sidecar_file.value
+    if not isinstance(file_name, str) or not file_name.strip():
+        return _DEFAULT_SIDECAR_FILE_NAME
+
+    normalized_name = file_name.strip()
+    normalized_path = Path(normalized_name)
+    if (
+        normalized_path.is_absolute()
+        or normalized_path.name in {'', '.', '..'}
+        or normalized_path.name != normalized_name
+    ):
+        log.warning(
+            'Ignoring Bayesian sidecar file path outside the analysis directory: '
+            f'{normalized_name!r}. Using {_DEFAULT_SIDECAR_FILE_NAME!r} instead.'
+        )
+        return _DEFAULT_SIDECAR_FILE_NAME
+
+    return normalized_path.name
+
+
+def _sidecar_path(*, analysis: object, analysis_dir: Path) -> Path:
+    """Return the results sidecar path inside the analysis directory."""
+    resolved_analysis_dir = analysis_dir.resolve()
+    sidecar_path = (resolved_analysis_dir / _sidecar_file_name(analysis)).resolve()
+    if sidecar_path.parent != resolved_analysis_dir:
+        log.warning(
+            'Resolved Bayesian sidecar file path escaped the analysis directory. '
+            f'Using {_DEFAULT_SIDECAR_FILE_NAME!r} instead.'
+        )
+        return resolved_analysis_dir / _DEFAULT_SIDECAR_FILE_NAME
+    return sidecar_path
+
+
+def _should_use_sidecar(analysis: object) -> bool:
+    """
+    Return whether the analysis currently expects a Bayesian sidecar.
+    """
+    has_fit_state = getattr(analysis, '_has_persisted_fit_state', None)
+    if not callable(has_fit_state) or not has_fit_state():
+        return False
+
+    if analysis.fit_result.result_kind.value != 'bayesian':
+        return False
+
+    return any((
+        analysis.bayesian_result.has_posterior_samples.value,
+        len(analysis.bayesian_distribution_caches) > 0,
+        len(analysis.bayesian_pair_caches) > 0,
+        len(analysis.bayesian_predictive_datasets) > 0,
+    ))
+
+
+def _delete_stale_sidecar(sidecar_path: Path) -> None:
+    """
+    Delete an existing sidecar when no persisted arrays should remain.
+    """
+    if sidecar_path.is_file():
+        sidecar_path.unlink()
+
+
+def _create_dataset(handle: object, path: str, data: np.ndarray) -> None:
+    """Create or replace one dataset in an open HDF5 file."""
+    normalized_path = _normalized_hdf5_path(path)
+    group_name, _, dataset_name = normalized_path.rpartition('/')
+    group = handle.require_group(group_name) if group_name else handle
+    if dataset_name in group:
+        del group[dataset_name]
+    group.create_dataset(dataset_name, data=data)
+
+
+def _read_dataset(handle: object, path: str) -> np.ndarray | None:
+    """Read one dataset from an open HDF5 file when it exists."""
+    normalized_path = _normalized_hdf5_path(path)
+    if normalized_path not in handle:
+        return None
+    return np.asarray(handle[normalized_path])
+
+
+def _posterior_payload_from_analysis(analysis: object) -> dict[str, np.ndarray | None]:
+    """Return posterior arrays from runtime or restored data."""
+    fit_results = getattr(analysis, 'fit_results', None)
+    posterior_samples = getattr(fit_results, 'posterior_samples', None)
+    if posterior_samples is not None:
+        return {
+            'parameter_samples': np.asarray(posterior_samples.parameter_samples, dtype=float),
+            'log_posterior': (
+                None
+                if posterior_samples.log_posterior is None
+                else np.asarray(posterior_samples.log_posterior, dtype=float)
+            ),
+            'draw_index': (
+                None
+                if posterior_samples.draw_index is None
+                else np.asarray(posterior_samples.draw_index)
+            ),
+        }
+
+    sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+    return dict(sidecar_data.get('posterior', {}))
+
+
+def _distribution_cache_payload(analysis: object) -> dict[str, dict[str, np.ndarray]]:
+    """Return persisted distribution caches keyed by parameter name."""
+    sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+    return dict(sidecar_data.get('distribution_caches', {}))
+
+
+def _pair_cache_payload(analysis: object) -> dict[str, dict[str, np.ndarray]]:
+    """Return persisted pair-cache arrays keyed by cache id."""
+    sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+    return dict(sidecar_data.get('pair_caches', {}))
+
+
+def _predictive_payload(analysis: object) -> dict[str, dict[str, np.ndarray]]:
+    """Return persisted predictive arrays keyed by experiment name."""
+    fit_results = getattr(analysis, 'fit_results', None)
+    posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
+    if posterior_predictive:
+        payload: dict[str, dict[str, np.ndarray]] = {}
+        for runtime_key, summary in posterior_predictive.items():
+            experiment_name = getattr(summary, 'experiment_name', None)
+            if not isinstance(experiment_name, str) or not experiment_name.strip():
+                experiment_name = runtime_key
+
+            dataset_payload = payload.setdefault(experiment_name, {})
+            dataset_payload['x'] = np.asarray(summary.x, dtype=float)
+            dataset_payload['best_sample_prediction'] = np.asarray(
+                summary.best_sample_prediction,
+                dtype=float,
+            )
+            if summary.lower_95 is not None:
+                dataset_payload['lower_95'] = np.asarray(summary.lower_95, dtype=float)
+            if summary.upper_95 is not None:
+                dataset_payload['upper_95'] = np.asarray(summary.upper_95, dtype=float)
+            if summary.lower_68 is not None:
+                dataset_payload['lower_68'] = np.asarray(summary.lower_68, dtype=float)
+            if summary.upper_68 is not None:
+                dataset_payload['upper_68'] = np.asarray(summary.upper_68, dtype=float)
+            if summary.draws is not None:
+                dataset_payload['draws'] = np.asarray(summary.draws, dtype=float)
+        return payload
+
+    sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+    return dict(sidecar_data.get('predictive_datasets', {}))
+
+
+def _validate_posterior_payload(
+    analysis: object,
+    payload: dict[str, np.ndarray | None],
+) -> bool:
+    """Return whether posterior arrays match stored metadata."""
+    parameter_samples = payload.get('parameter_samples')
+    if parameter_samples is None:
+        if analysis.bayesian_result.has_posterior_samples.value:
+            log.warning('Bayesian fit-state expects posterior samples, but none are available.')
+        return False
+
+    parameter_samples = np.asarray(parameter_samples, dtype=float)
+    if parameter_samples.ndim != _POSTERIOR_SAMPLE_NDIM:
+        log.warning(
+            'Posterior parameter samples must have shape (n_draws, n_chains, n_parameters).'
+        )
+        return False
+
+    n_draws, n_chains, n_parameters = parameter_samples.shape
+    if not _posterior_manifest_counts_match(
+        analysis,
+        n_draws=n_draws,
+        n_chains=n_chains,
+        n_parameters=n_parameters,
+    ):
+        return False
+
+    return _posterior_aux_shapes_match(
+        payload,
+        n_draws=n_draws,
+        n_chains=n_chains,
+    )
+
+
+def _posterior_manifest_counts_match(
+    analysis: object,
+    *,
+    n_draws: int,
+    n_chains: int,
+    n_parameters: int,
+) -> bool:
+    """Return whether manifest counts match the sample shape."""
+    if analysis.bayesian_convergence.n_draws.value not in {0, n_draws}:
+        log.warning('Posterior sample draw count does not match bayesian_convergence.n_draws.')
+        return False
+    if analysis.bayesian_convergence.n_chains.value not in {0, n_chains}:
+        log.warning('Posterior sample chain count does not match bayesian_convergence.n_chains.')
+        return False
+    if analysis.bayesian_convergence.n_parameters.value not in {0, n_parameters}:
+        log.warning(
+            'Posterior sample parameter count does not match bayesian_convergence.n_parameters.'
+        )
+        return False
+
+    return True
+
+
+def _posterior_aux_shapes_match(
+    payload: dict[str, np.ndarray | None],
+    *,
+    n_draws: int,
+    n_chains: int,
+) -> bool:
+    """Return whether auxiliary arrays match the sample shape."""
+    log_posterior = payload.get('log_posterior')
+    if log_posterior is not None and np.asarray(log_posterior).shape != (n_draws, n_chains):
+        log.warning(
+            'Posterior log-posterior array does not match posterior sample draw and chain axes.'
+        )
+        return False
+
+    draw_index = payload.get('draw_index')
+    if draw_index is not None and np.asarray(draw_index).shape != (n_draws,):
+        log.warning('Posterior draw-index array does not match posterior sample draw count.')
+        return False
+
+    return True
+
+
+def _write_posterior_payload(handle: object, analysis: object) -> bool:
+    """Write canonical posterior arrays when they are available."""
+    payload = _posterior_payload_from_analysis(analysis)
+    if not _validate_posterior_payload(analysis, payload):
+        return False
+
+    parameter_samples = np.asarray(payload['parameter_samples'], dtype=float)
+    _create_dataset(handle, _POSTERIOR_PARAMETER_SAMPLES_PATH, parameter_samples)
+
+    log_posterior = payload.get('log_posterior')
+    if log_posterior is not None:
+        _create_dataset(handle, _POSTERIOR_LOG_POSTERIOR_PATH, np.asarray(log_posterior))
+
+    draw_index = payload.get('draw_index')
+    if draw_index is not None:
+        _create_dataset(handle, _POSTERIOR_DRAW_INDEX_PATH, np.asarray(draw_index))
+
+    return True
+
+
+def _write_distribution_caches(handle: object, analysis: object) -> bool:
+    """Write cached posterior distribution arrays for manifest rows."""
+    payload = _distribution_cache_payload(analysis)
+    wrote_any = False
+    for cache in analysis.bayesian_distribution_caches:
+        cache_data = payload.get(cache.param_unique_name.value)
+        if cache_data is None:
+            continue
+
+        x_values = np.asarray(cache_data.get('x'))
+        density_values = np.asarray(cache_data.get('density'))
+        n_grid = int(cache.n_grid.value)
+        if x_values.shape != (n_grid,) or density_values.shape != (n_grid,):
+            log.warning(
+                'Skipping Bayesian distribution cache with shape mismatch for '
+                f'{cache.param_unique_name.value!r}.'
+            )
+            continue
+
+        _create_dataset(handle, cache.x_path.value, x_values)
+        _create_dataset(handle, cache.density_path.value, density_values)
+        wrote_any = True
+
+    return wrote_any
+
+
+def _write_pair_caches(handle: object, analysis: object) -> bool:
+    """Write cached posterior pair-density arrays for manifest rows."""
+    payload = _pair_cache_payload(analysis)
+    wrote_any = False
+    for cache in analysis.bayesian_pair_caches:
+        cache_data = payload.get(cache.id.value)
+        if cache_data is None:
+            continue
+
+        x_values = np.asarray(cache_data.get('x'))
+        y_values = np.asarray(cache_data.get('y'))
+        density_values = np.asarray(cache_data.get('density'))
+        contour_levels = np.asarray(cache_data.get('contour_levels'))
+        n_grid_x = int(cache.n_grid_x.value)
+        n_grid_y = int(cache.n_grid_y.value)
+
+        valid_density_shape = density_values.shape in {
+            (n_grid_y, n_grid_x),
+            (n_grid_x, n_grid_y),
+        }
+        if (
+            x_values.shape != (n_grid_x,)
+            or y_values.shape != (n_grid_y,)
+            or not valid_density_shape
+        ):
+            log.warning(
+                f'Skipping Bayesian pair cache with shape mismatch for {cache.id.value!r}.'
+            )
+            continue
+
+        _create_dataset(handle, cache.x_path.value, x_values)
+        _create_dataset(handle, cache.y_path.value, y_values)
+        _create_dataset(handle, cache.density_path.value, density_values)
+        _create_dataset(handle, cache.contour_level_path.value, contour_levels)
+        wrote_any = True
+
+    return wrote_any
+
+
+def _write_predictive_datasets(handle: object, analysis: object) -> bool:
+    """Write cached posterior predictive arrays for manifest rows."""
+    payload = _predictive_payload(analysis)
+    wrote_any = False
+    for dataset in analysis.bayesian_predictive_datasets:
+        dataset_data = payload.get(dataset.experiment_name.value)
+        if dataset_data is None:
+            continue
+
+        x_values = np.asarray(dataset_data.get('x'))
+        best_sample_prediction = np.asarray(dataset_data.get('best_sample_prediction'))
+        n_x = int(dataset.n_x.value)
+        if x_values.shape != (n_x,) or best_sample_prediction.shape != (n_x,):
+            log.warning(
+                'Skipping Bayesian predictive dataset with shape mismatch for '
+                f'{dataset.experiment_name.value!r}.'
+            )
+            continue
+
+        _create_dataset(handle, dataset.x_path.value, x_values)
+        _create_dataset(
+            handle,
+            dataset.best_sample_prediction_path.value,
+            best_sample_prediction,
+        )
+
+        for field_name, path_value in (
+            ('lower_95', dataset.lower_95_path.value),
+            ('upper_95', dataset.upper_95_path.value),
+            ('lower_68', dataset.lower_68_path.value),
+            ('upper_68', dataset.upper_68_path.value),
+        ):
+            values = dataset_data.get(field_name)
+            if values is None or path_value is None:
+                continue
+            values_array = np.asarray(values)
+            if values_array.shape != (n_x,):
+                log.warning(
+                    'Skipping Bayesian predictive band with shape mismatch for '
+                    f'{dataset.experiment_name.value!r}:{field_name}.'
+                )
+                continue
+            _create_dataset(handle, path_value, values_array)
+
+        draws = dataset_data.get('draws')
+        if draws is not None and dataset.draws_path.value is not None:
+            draws_array = np.asarray(draws)
+            if draws_array.ndim != _PREDICTIVE_DRAWS_NDIM or draws_array.shape[1] != n_x:
+                log.warning(
+                    'Skipping Bayesian predictive draws with shape mismatch for '
+                    f'{dataset.experiment_name.value!r}.'
+                )
+            elif dataset.n_draws_cached.value not in {0, draws_array.shape[0]}:
+                log.warning(
+                    'Skipping Bayesian predictive draws whose draw count does not match '
+                    'the manifest metadata.'
+                )
+            else:
+                _create_dataset(handle, dataset.draws_path.value, draws_array)
+
+        wrote_any = True
+
+    return wrote_any
+
+
+def write_analysis_results_sidecar(
+    *,
+    analysis: object,
+    analysis_dir: Path,
+) -> None:
+    """
+    Write persisted Bayesian arrays to ``analysis/results.h5``.
+
+    Parameters
+    ----------
+    analysis : object
+        Analysis instance that owns fit-state categories and runtime fit
+        results.
+    analysis_dir : Path
+        The project ``analysis/`` directory.
+
+    Raises
+    ------
+    Exception
+        Propagated when sidecar writing fails after temporary-file
+        cleanup.
+    """
+    sidecar_path = _sidecar_path(analysis=analysis, analysis_dir=analysis_dir)
+    if not _should_use_sidecar(analysis):
+        _delete_stale_sidecar(sidecar_path)
+        return
+
+    import h5py  # noqa: PLC0415
+
+    analysis_dir.mkdir(parents=True, exist_ok=True)
+    with NamedTemporaryFile(
+        delete=False,
+        dir=analysis_dir,
+        prefix=f'{sidecar_path.stem}.',
+        suffix=sidecar_path.suffix,
+    ) as temporary_file:
+        temporary_path = Path(temporary_file.name)
+
+    try:
+        with h5py.File(temporary_path, 'w') as handle:
+            wrote_any = _write_posterior_payload(handle, analysis)
+            wrote_any = _write_distribution_caches(handle, analysis) or wrote_any
+            wrote_any = _write_pair_caches(handle, analysis) or wrote_any
+            wrote_any = _write_predictive_datasets(handle, analysis) or wrote_any
+    except Exception:
+        if temporary_path.exists():
+            temporary_path.unlink()
+        raise
+
+    if not wrote_any:
+        temporary_path.unlink()
+        _delete_stale_sidecar(sidecar_path)
+        return
+
+    temporary_path.replace(sidecar_path)
+
+
+def _read_posterior_payload(handle: object, analysis: object) -> dict[str, np.ndarray]:
+    """Read canonical posterior arrays from a sidecar file."""
+    parameter_samples = _read_dataset(handle, _POSTERIOR_PARAMETER_SAMPLES_PATH)
+    if parameter_samples is None:
+        return {}
+
+    payload: dict[str, np.ndarray] = {
+        'parameter_samples': np.asarray(parameter_samples, dtype=float),
+    }
+    log_posterior = _read_dataset(handle, _POSTERIOR_LOG_POSTERIOR_PATH)
+    if log_posterior is not None:
+        payload['log_posterior'] = np.asarray(log_posterior, dtype=float)
+    draw_index = _read_dataset(handle, _POSTERIOR_DRAW_INDEX_PATH)
+    if draw_index is not None:
+        payload['draw_index'] = np.asarray(draw_index)
+
+    if not _validate_posterior_payload(analysis, payload):
+        return {}
+    return payload
+
+
+def _read_distribution_caches(
+    handle: object, analysis: object
+) -> dict[str, dict[str, np.ndarray]]:
+    """Read cached posterior distribution arrays for manifest rows."""
+    payload: dict[str, dict[str, np.ndarray]] = {}
+    for cache in analysis.bayesian_distribution_caches:
+        x_values = _read_dataset(handle, cache.x_path.value)
+        density_values = _read_dataset(handle, cache.density_path.value)
+        if x_values is None or density_values is None:
+            continue
+        if x_values.shape != (int(cache.n_grid.value),) or density_values.shape != (
+            int(cache.n_grid.value),
+        ):
+            log.warning(
+                'Skipping restored Bayesian distribution cache with shape mismatch for '
+                f'{cache.param_unique_name.value!r}.'
+            )
+            continue
+        payload[cache.param_unique_name.value] = {
+            'x': np.asarray(x_values),
+            'density': np.asarray(density_values),
+        }
+    return payload
+
+
+def _read_pair_caches(handle: object, analysis: object) -> dict[str, dict[str, np.ndarray]]:
+    """Read cached posterior pair-density arrays for manifest rows."""
+    payload: dict[str, dict[str, np.ndarray]] = {}
+    for cache in analysis.bayesian_pair_caches:
+        x_values = _read_dataset(handle, cache.x_path.value)
+        y_values = _read_dataset(handle, cache.y_path.value)
+        density_values = _read_dataset(handle, cache.density_path.value)
+        contour_levels = _read_dataset(handle, cache.contour_level_path.value)
+        if any(value is None for value in (x_values, y_values, density_values, contour_levels)):
+            continue
+
+        n_grid_x = int(cache.n_grid_x.value)
+        n_grid_y = int(cache.n_grid_y.value)
+        valid_density_shape = density_values.shape in {
+            (n_grid_y, n_grid_x),
+            (n_grid_x, n_grid_y),
+        }
+        if (
+            x_values.shape != (n_grid_x,)
+            or y_values.shape != (n_grid_y,)
+            or not valid_density_shape
+        ):
+            log.warning(
+                'Skipping restored Bayesian pair cache with shape mismatch for '
+                f'{cache.id.value!r}.'
+            )
+            continue
+
+        payload[cache.id.value] = {
+            'x': np.asarray(x_values),
+            'y': np.asarray(y_values),
+            'density': np.asarray(density_values),
+            'contour_levels': np.asarray(contour_levels),
+        }
+    return payload
+
+
+def _read_predictive_datasets(
+    handle: object, analysis: object
+) -> dict[str, dict[str, np.ndarray]]:
+    """Read cached posterior predictive arrays for manifest rows."""
+    payload: dict[str, dict[str, np.ndarray]] = {}
+    for dataset in analysis.bayesian_predictive_datasets:
+        x_values = _read_dataset(handle, dataset.x_path.value)
+        best_sample_prediction = _read_dataset(handle, dataset.best_sample_prediction_path.value)
+        if x_values is None or best_sample_prediction is None:
+            continue
+
+        n_x = int(dataset.n_x.value)
+        if x_values.shape != (n_x,) or best_sample_prediction.shape != (n_x,):
+            log.warning(
+                'Skipping restored Bayesian predictive dataset with shape mismatch for '
+                f'{dataset.experiment_name.value!r}.'
+            )
+            continue
+
+        dataset_payload: dict[str, np.ndarray] = {
+            'x': np.asarray(x_values),
+            'best_sample_prediction': np.asarray(best_sample_prediction),
+        }
+
+        for field_name, path_value in (
+            ('lower_95', dataset.lower_95_path.value),
+            ('upper_95', dataset.upper_95_path.value),
+            ('lower_68', dataset.lower_68_path.value),
+            ('upper_68', dataset.upper_68_path.value),
+            ('draws', dataset.draws_path.value),
+        ):
+            if path_value is None:
+                continue
+            values = _read_dataset(handle, path_value)
+            if values is None:
+                continue
+            values_array = np.asarray(values)
+            if field_name == 'draws':
+                if values_array.ndim != _PREDICTIVE_DRAWS_NDIM or values_array.shape[1] != n_x:
+                    log.warning(
+                        'Skipping restored Bayesian predictive draws with shape mismatch for '
+                        f'{dataset.experiment_name.value!r}.'
+                    )
+                    continue
+            elif values_array.shape != (n_x,):
+                log.warning(
+                    'Skipping restored Bayesian predictive band with shape mismatch for '
+                    f'{dataset.experiment_name.value!r}:{field_name}.'
+                )
+                continue
+            dataset_payload[field_name] = values_array
+
+        payload[dataset.experiment_name.value] = dataset_payload
+    return payload
+
+
+def read_analysis_results_sidecar(
+    *,
+    analysis: object,
+    analysis_dir: Path,
+) -> None:
+    """
+    Read persisted Bayesian arrays from ``analysis/results.h5``.
+
+    Parameters
+    ----------
+    analysis : object
+        Analysis instance that owns fit-state categories.
+    analysis_dir : Path
+        The project ``analysis/`` directory.
+    """
+    analysis._persisted_fit_state_sidecar = {}
+    if not _should_use_sidecar(analysis):
+        return
+
+    sidecar_path = _sidecar_path(analysis=analysis, analysis_dir=analysis_dir)
+    if not sidecar_path.is_file():
+        log.warning(
+            'Expected Bayesian results sidecar is missing: '
+            f"'{sidecar_path}'. Restoring available CIF summaries only."
+        )
+        return
+
+    import h5py  # noqa: PLC0415
+
+    with h5py.File(sidecar_path, 'r') as handle:
+        sidecar_data: dict[str, object] = {}
+
+        posterior_payload = _read_posterior_payload(handle, analysis)
+        if posterior_payload:
+            sidecar_data['posterior'] = posterior_payload
+
+        distribution_caches = _read_distribution_caches(handle, analysis)
+        if distribution_caches:
+            sidecar_data['distribution_caches'] = distribution_caches
+
+        pair_caches = _read_pair_caches(handle, analysis)
+        if pair_caches:
+            sidecar_data['pair_caches'] = pair_caches
+
+        predictive_datasets = _read_predictive_datasets(handle, analysis)
+        if predictive_datasets:
+            sidecar_data['predictive_datasets'] = predictive_datasets
+
+    analysis._persisted_fit_state_sidecar = sidecar_data
diff --git a/src/easydiffraction/project/categories/rendering/default.py b/src/easydiffraction/project/categories/rendering/default.py
index d3e2fad3e..179162a08 100644
--- a/src/easydiffraction/project/categories/rendering/default.py
+++ b/src/easydiffraction/project/categories/rendering/default.py
@@ -19,6 +19,10 @@
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.utils import render_table
 
+AUTO_ENGINE = 'auto'
+CHART_ENGINE_OPTIONS = [AUTO_ENGINE, *[member.value for member in PlotterEngineEnum]]
+TABLE_ENGINE_OPTIONS = [AUTO_ENGINE, *[member.value for member in TableEngineEnum]]
+
 
 @RenderingFactory.register
 class Rendering(CategoryItem):
@@ -37,13 +41,14 @@ def __init__(self) -> None:
         self._plotter = Plotter()
         self._tabler = TableRenderer.get()
 
+        # Persist symbolic "auto" so project.cif stays portable.
         self._chart_engine = StringDescriptor(
             name='chart_engine',
             description='Chart renderer backend type',
             value_spec=AttributeSpec(
-                default=self._plotter.engine,
+                default=AUTO_ENGINE,
                 validator=MembershipValidator(
-                    allowed=[member.value for member in PlotterEngineEnum],
+                    allowed=CHART_ENGINE_OPTIONS,
                 ),
             ),
             cif_handler=CifHandler(names=['_rendering.chart_engine']),
@@ -52,14 +57,46 @@ def __init__(self) -> None:
             name='table_engine',
             description='Table renderer backend type',
             value_spec=AttributeSpec(
-                default=self._tabler.engine,
+                default=AUTO_ENGINE,
                 validator=MembershipValidator(
-                    allowed=[member.value for member in TableEngineEnum],
+                    allowed=TABLE_ENGINE_OPTIONS,
                 ),
             ),
             cif_handler=CifHandler(names=['_rendering.table_engine']),
         )
 
+    @staticmethod
+    def _resolved_chart_engine(value: str) -> str:
+        if value == AUTO_ENGINE:
+            return PlotterEngineEnum.default().value
+        return value
+
+    @staticmethod
+    def _resolved_table_engine(value: str) -> str:
+        if value == AUTO_ENGINE:
+            return TableEngineEnum.default().value
+        return value
+
+    def _set_chart_engine(self, value: str) -> None:
+        if value not in CHART_ENGINE_OPTIONS:
+            self._plotter.engine = value
+            return
+
+        resolved_engine = self._resolved_chart_engine(value)
+        if self._plotter.engine != resolved_engine:
+            self._plotter.engine = resolved_engine
+        self._chart_engine.value = value
+
+    def _set_table_engine(self, value: str) -> None:
+        if value not in TABLE_ENGINE_OPTIONS:
+            self._tabler.engine = value
+            return
+
+        resolved_engine = self._resolved_table_engine(value)
+        if self._tabler.engine != resolved_engine:
+            self._tabler.engine = resolved_engine
+        self._table_engine.value = value
+
     @property
     def chart_engine(self) -> StringDescriptor:
         """Chart renderer backend type."""
@@ -67,8 +104,7 @@ def chart_engine(self) -> StringDescriptor:
 
     @chart_engine.setter
     def chart_engine(self, value: str) -> None:
-        self._plotter.engine = value
-        self._chart_engine.value = self._plotter.engine
+        self._set_chart_engine(value)
 
     @property
     def table_engine(self) -> StringDescriptor:
@@ -77,8 +113,7 @@ def table_engine(self) -> StringDescriptor:
 
     @table_engine.setter
     def table_engine(self, value: str) -> None:
-        self._tabler.engine = value
-        self._table_engine.value = self._tabler.engine
+        self._set_table_engine(value)
 
     @property
     def plotter(self) -> Plotter:
@@ -123,14 +158,8 @@ def from_cif(self, block: object, idx: int = 0) -> None:
         del idx
         chart_engine = read_cif_str(block, '_rendering.chart_engine')
         if chart_engine is not None:
-            if chart_engine == self._plotter.engine:
-                self._chart_engine.value = chart_engine
-            else:
-                self.chart_engine = chart_engine
+            self._set_chart_engine(chart_engine)
 
         table_engine = read_cif_str(block, '_rendering.table_engine')
         if table_engine is not None:
-            if table_engine == self._tabler.engine:
-                self._table_engine.value = table_engine
-            else:
-                self.table_engine = table_engine
+            self._set_table_engine(table_engine)
diff --git a/src/easydiffraction/project/categories/verbosity/__init__.py b/src/easydiffraction/project/categories/verbosity/__init__.py
new file mode 100644
index 000000000..a7bfce5b3
--- /dev/null
+++ b/src/easydiffraction/project/categories/verbosity/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project verbosity category exports."""
+
+from __future__ import annotations
+
+from easydiffraction.project.categories.verbosity.default import Verbosity
+from easydiffraction.project.categories.verbosity.factory import VerbosityFactory
diff --git a/src/easydiffraction/project/categories/verbosity/default.py b/src/easydiffraction/project/categories/verbosity/default.py
new file mode 100644
index 000000000..09b3821c6
--- /dev/null
+++ b/src/easydiffraction/project/categories/verbosity/default.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project fit-output verbosity category."""
+
+from __future__ import annotations
+
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.project.categories.verbosity.factory import VerbosityFactory
+from easydiffraction.utils.enums import VerbosityEnum
+
+
+@VerbosityFactory.register
+class Verbosity(CategoryItem):
+    """Fit-output verbosity selection for a project."""
+
+    _category_code = 'verbosity'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Project verbosity category',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._fit = StringDescriptor(
+            name='fit',
+            description='Fitting process output verbosity',
+            value_spec=AttributeSpec(
+                default=VerbosityEnum.default().value,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in VerbosityEnum],
+                ),
+            ),
+            cif_handler=CifHandler(names=['_verbosity.fit']),
+        )
+
+    @property
+    def fit(self) -> StringDescriptor:
+        """Fitting process output verbosity."""
+        return self._fit
+
+    @fit.setter
+    def fit(self, value: str) -> None:
+        self._fit.value = VerbosityEnum(value).value
+
+    @property
+    def as_cif(self) -> str:
+        """Return CIF representation of this verbosity category."""
+        return super().as_cif
diff --git a/src/easydiffraction/project/categories/verbosity/factory.py b/src/easydiffraction/project/categories/verbosity/factory.py
new file mode 100644
index 000000000..28b26bb97
--- /dev/null
+++ b/src/easydiffraction/project/categories/verbosity/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Factory for project verbosity categories."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class VerbosityFactory(FactoryBase):
+    """Create project verbosity category instances."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index 83a9e40f2..5b760d997 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -4,6 +4,7 @@
 
 from __future__ import annotations
 
+from contextlib import nullcontext
 from dataclasses import dataclass
 from typing import TYPE_CHECKING
 
@@ -134,6 +135,68 @@ class PosteriorDisplay:
     def __init__(self, project: Project) -> None:
         self._project = project
 
+    def _pairs_need_processing_indicator(
+        self,
+        *,
+        parameters: list[object] | None,
+    ) -> bool:
+        """
+        Return whether posterior pair plotting still needs processing.
+        """
+        if parameters is not None:
+            return True
+
+        analysis = self._project.analysis
+        sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+        pair_caches = sidecar_data.get('pair_caches', {})
+        return not (
+            analysis.bayesian_result.has_pair_cache.value
+            and len(analysis.bayesian_pair_caches) > 0
+            and bool(pair_caches)
+        )
+
+    def _predictive_needs_processing_indicator(
+        self,
+        *,
+        expt_name: str,
+        style: str,
+        x: object | None,
+    ) -> bool:
+        """Return whether predictive plotting still needs processing."""
+        analysis = self._project.analysis
+        sidecar_data = getattr(analysis, '_persisted_fit_state_sidecar', {})
+        predictive_datasets = sidecar_data.get('predictive_datasets', {})
+        if not (
+            analysis.bayesian_result.has_posterior_predictive.value
+            and bool(predictive_datasets)
+            and expt_name in predictive_datasets
+        ):
+            return True
+
+        experiment = self._project.experiments[expt_name]
+        plotter = self._project.rendering.plotter
+        _, x_axis_name, _, _, _ = plotter._resolve_x_axis(experiment.type, x)
+        require_draws = plotter.engine == PlotterEngineEnum.PLOTLY.value and style in {
+            'draws',
+            'band+draws',
+        }
+
+        matching_rows = [
+            row
+            for row in analysis.bayesian_predictive_datasets
+            if row.experiment_name.value == expt_name
+            and str(row.x_axis_name.value) == str(x_axis_name)
+        ]
+        if not matching_rows:
+            return True
+        if not require_draws:
+            return False
+        return not any(
+            row.draws_path.value is not None
+            and predictive_datasets[expt_name].get('draws') is not None
+            for row in matching_rows
+        )
+
     def pairs(
         self,
         parameters: list[object] | None = None,
@@ -143,10 +206,15 @@ def pairs(
         max_parameters: int = 6,
     ) -> None:
         """Plot posterior pair relationships for sampled parameters."""
-        with activity_indicator(
-            ACTIVITY_LABEL_PROCESSING,
-            verbosity=VerbosityEnum(self._project.verbosity),
-        ):
+        indicator_context = (
+            activity_indicator(
+                ACTIVITY_LABEL_PROCESSING,
+                verbosity=VerbosityEnum(self._project.verbosity.fit.value),
+            )
+            if self._pairs_need_processing_indicator(parameters=parameters)
+            else nullcontext()
+        )
+        with indicator_context:
             self._project.rendering.plotter.plot_posterior_pairs(
                 parameters=parameters,
                 style=style,
@@ -182,10 +250,19 @@ def predictive(
         x: object | None = None,
     ) -> None:
         """Plot posterior predictive summaries for one experiment."""
-        with activity_indicator(
-            ACTIVITY_LABEL_PROCESSING,
-            verbosity=VerbosityEnum(self._project.verbosity),
-        ):
+        indicator_context = (
+            activity_indicator(
+                ACTIVITY_LABEL_PROCESSING,
+                verbosity=VerbosityEnum(self._project.verbosity.fit.value),
+            )
+            if self._predictive_needs_processing_indicator(
+                expt_name=expt_name,
+                style=style,
+                x=x,
+            )
+            else nullcontext()
+        )
+        with indicator_context:
             self._project.rendering.plotter.plot_posterior_predictive(
                 expt_name=expt_name,
                 style=style,
@@ -249,10 +326,19 @@ def pattern(
                 msg = self._status_by_name(statuses, 'auto').reason
                 raise ValueError(msg)
             if 'uncertainty' in auto_include:
-                with activity_indicator(
-                    ACTIVITY_LABEL_PROCESSING,
-                    verbosity=VerbosityEnum(self._project.verbosity),
-                ):
+                indicator_context = (
+                    activity_indicator(
+                        ACTIVITY_LABEL_PROCESSING,
+                        verbosity=VerbosityEnum(self._project.verbosity.fit.value),
+                    )
+                    if self._posterior._predictive_needs_processing_indicator(
+                        expt_name=expt_name,
+                        style='band',
+                        x=x,
+                    )
+                    else nullcontext()
+                )
+                with indicator_context:
                     self._project.rendering.plotter._plot_posterior_predictive_request(
                         expt_name=expt_name,
                         style='band',
@@ -283,10 +369,19 @@ def pattern(
             raise ValueError(msg)
 
         if 'uncertainty' in normalized_include:
-            with activity_indicator(
-                ACTIVITY_LABEL_PROCESSING,
-                verbosity=VerbosityEnum(self._project.verbosity),
-            ):
+            indicator_context = (
+                activity_indicator(
+                    ACTIVITY_LABEL_PROCESSING,
+                    verbosity=VerbosityEnum(self._project.verbosity.fit.value),
+                )
+                if self._posterior._predictive_needs_processing_indicator(
+                    expt_name=expt_name,
+                    style='band',
+                    x=x,
+                )
+                else nullcontext()
+            )
+            with indicator_context:
                 self._project.rendering.plotter._plot_posterior_predictive_request(
                     expt_name=expt_name,
                     style='band',
@@ -739,12 +834,16 @@ def _uncertainty_status(
         if fit_results is None:
             return False, 'No fit results are available.'
 
-        posterior_samples = getattr(fit_results, 'posterior_samples', None)
         posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
-        if posterior_samples is None or posterior_predictive is None:
+        if not posterior_predictive:
             return False, 'Posterior predictive data is unavailable.'
 
-        if self._project.rendering.chart_engine.value != PlotterEngineEnum.PLOTLY.value:
+        active_chart_engine = getattr(self._project.rendering.plotter, 'engine', None)
+        if active_chart_engine is None:
+            chart_engine = getattr(self._project.rendering, 'chart_engine', None)
+            active_chart_engine = getattr(chart_engine, 'value', None)
+
+        if active_chart_engine != PlotterEngineEnum.PLOTLY.value:
             return False, 'Uncertainty bands currently require the Plotly chart engine.'
 
         return True, ''
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index 5bbe8acba..9b89eb500 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 import pathlib
+import shutil
 import tempfile
 from typing import TYPE_CHECKING
 from typing import ClassVar
@@ -16,17 +17,25 @@
 from easydiffraction.core.guard import GuardedBase
 from easydiffraction.datablocks.experiment.collection import Experiments
 from easydiffraction.datablocks.structure.collection import Structures
+from easydiffraction.io.cif.serialize import analysis_from_cif
+from easydiffraction.io.cif.serialize import project_config_from_cif
 from easydiffraction.io.cif.serialize import project_config_to_cif
 from easydiffraction.io.cif.serialize import project_to_cif
+from easydiffraction.io.results_sidecar import read_analysis_results_sidecar
+from easydiffraction.io.results_sidecar import write_analysis_results_sidecar
 from easydiffraction.project.display import ProjectDisplay
 from easydiffraction.project.project_config import ProjectConfig
 from easydiffraction.summary.summary import Summary
 from easydiffraction.utils.enums import VerbosityEnum
+from easydiffraction.utils.environment import resolve_artifact_path
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from easydiffraction.project.categories.rendering import Rendering
+    from easydiffraction.project.categories.verbosity import Verbosity
     from easydiffraction.project.project_info import ProjectInfo
 
 
@@ -108,6 +117,63 @@ def _resolve_data_path_from_results_csv(
     return project_path / path
 
 
+def _load_cif_directory(
+    cif_dir: pathlib.Path,
+    add_from_cif_path: Callable[[str], None],
+) -> None:
+    """Load all CIF files from one directory using the given loader."""
+    if not cif_dir.is_dir():
+        return
+
+    for cif_file in sorted(cif_dir.glob('*.cif')):
+        add_from_cif_path(str(cif_file))
+
+
+def _create_loading_project(project_cls: type[Project]) -> Project:
+    """Create a project instance while suppressing varname lookup."""
+    project_cls._loading = True
+    try:
+        return project_cls()
+    finally:
+        project_cls._loading = False
+
+
+def _load_project_info(project: Project, project_path: pathlib.Path) -> None:
+    """
+    Restore project configuration from ``project.cif`` when present.
+    """
+    project_cif_path = project_path / 'project.cif'
+    if project_cif_path.is_file():
+        project_config_from_cif(project, project_cif_path.read_text())
+
+
+def _resolved_analysis_cif_path(project_path: pathlib.Path) -> pathlib.Path | None:
+    """Return the preferred analysis CIF path for a saved project."""
+    analysis_cif_path = project_path / 'analysis' / 'analysis.cif'
+    if analysis_cif_path.is_file():
+        return analysis_cif_path
+
+    analysis_cif_path = project_path / 'analysis.cif'
+    if analysis_cif_path.is_file():
+        return analysis_cif_path
+    return None
+
+
+def _load_project_analysis(project: Project, project_path: pathlib.Path) -> None:
+    """Restore analysis categories and sidecar state from disk."""
+    analysis_cif_path = _resolved_analysis_cif_path(project_path)
+    if analysis_cif_path is None:
+        return
+
+    analysis_from_cif(project._analysis, analysis_cif_path.read_text())
+    read_analysis_results_sidecar(
+        analysis=project._analysis,
+        analysis_dir=analysis_cif_path.parent,
+    )
+    if project._analysis._has_persisted_fit_state():
+        project._analysis._restore_live_parameter_state(project._build_parameter_map())
+
+
 class Project(GuardedBase):
     """
     Central API for managing a diffraction data analysis project.
@@ -135,12 +201,12 @@ def __init__(
         self._structures = Structures()
         self._experiments = Experiments()
         object.__setattr__(self, '_rendering', self._config.rendering)
+        object.__setattr__(self, '_verbosity', self._config.verbosity)
         self._display = ProjectDisplay(self)
         self._analysis = Analysis(self)
         self._summary = Summary(self)
         self._saved = False
         self._varname = 'project' if type(self)._loading else varname()
-        self._verbosity: VerbosityEnum = VerbosityEnum.FULL
         type(self)._current_project = self
 
     @classmethod
@@ -249,21 +315,14 @@ def as_cif(self) -> str:
         return project_to_cif(self)
 
     @property
-    def verbosity(self) -> str:
-        """
-        Project-wide console output verbosity.
-
-        Returns
-        -------
-        str
-            One of ``'full'``, ``'short'``, or ``'silent'``.
-        """
-        return self._verbosity.value
+    def verbosity(self) -> Verbosity:
+        """Verbosity configuration bound to the project."""
+        return self._verbosity
 
     @verbosity.setter
     def verbosity(self, value: str) -> None:
         """
-        Set project-wide console output verbosity.
+        Set fitting process output verbosity.
 
         Parameters
         ----------
@@ -271,7 +330,7 @@ def verbosity(self, value: str) -> None:
             ``'full'`` for multi-line output, ``'short'`` for one-line
             status messages, or ``'silent'`` for no output.
         """
-        self._verbosity = VerbosityEnum(value)
+        self._verbosity.fit = VerbosityEnum(value).value
 
     # ------------------------------------------
     #  Project File I/O
@@ -303,52 +362,19 @@ def load(cls, dir_path: str) -> Project:
         FileNotFoundError
             If *dir_path* does not exist.
         """
-        from easydiffraction.io.cif.serialize import analysis_from_cif  # noqa: PLC0415
-        from easydiffraction.io.cif.serialize import project_config_from_cif  # noqa: PLC0415
-
         project_path = pathlib.Path(dir_path)
         if not project_path.is_dir():
             msg = f"Project directory not found: '{dir_path}'"
             raise FileNotFoundError(msg)
 
-        # Create a minimal project.
-        # Use _loading sentinel to skip varname() inside __init__.
-        cls._loading = True
-        try:
-            project = cls()
-        finally:
-            cls._loading = False
+        project = _create_loading_project(cls)
         project._saved = True
 
-        # 1. Load project info
-        project_cif_path = project_path / 'project.cif'
-        if project_cif_path.is_file():
-            cif_text = project_cif_path.read_text()
-            project_config_from_cif(project, cif_text)
-
+        _load_project_info(project, project_path)
         project.info.path = project_path
-
-        # 2. Load structures
-        structures_dir = project_path / 'structures'
-        if structures_dir.is_dir():
-            for cif_file in sorted(structures_dir.glob('*.cif')):
-                project._structures.add_from_cif_path(str(cif_file))
-
-        # 3. Load experiments
-        experiments_dir = project_path / 'experiments'
-        if experiments_dir.is_dir():
-            for cif_file in sorted(experiments_dir.glob('*.cif')):
-                project._experiments.add_from_cif_path(str(cif_file))
-
-        # 4. Load analysis
-        #    Check analysis/analysis.cif first (future layout), then
-        #    fall back to analysis.cif at root (current layout).
-        analysis_cif_path = project_path / 'analysis' / 'analysis.cif'
-        if not analysis_cif_path.is_file():
-            analysis_cif_path = project_path / 'analysis.cif'
-        if analysis_cif_path.is_file():
-            cif_text = analysis_cif_path.read_text()
-            analysis_from_cif(project._analysis, cif_text)
+        _load_cif_directory(project_path / 'structures', project._structures.add_from_cif_path)
+        _load_cif_directory(project_path / 'experiments', project._experiments.add_from_cif_path)
+        _load_project_analysis(project, project_path)
 
         # 5. Resolve alias param references
         project._resolve_alias_references()
@@ -373,13 +399,7 @@ def _resolve_alias_references(self) -> None:
         if not aliases._items:
             return
 
-        # Build unique_name → parameter map
-        all_params = self._structures.parameters + self._experiments.parameters
-        param_map: dict[str, object] = {}
-        for p in all_params:
-            uname = getattr(p, 'unique_name', None)
-            if uname is not None:
-                param_map[uname] = p
+        param_map = self._build_parameter_map()
 
         for alias in aliases:
             uname = alias.param_unique_name.value
@@ -391,6 +411,22 @@ def _resolve_alias_references(self) -> None:
                     f"parameter '{uname}'. Reference not resolved."
                 )
 
+    def _build_parameter_map(self) -> dict[str, object]:
+        """
+        Return a ``unique_name`` to live parameter mapping.
+
+        The map combines structure and experiment parameters and is
+        reused by CIF restore steps that need to reconnect persisted
+        names to live parameter objects.
+        """
+        all_params = self._structures.parameters + self._experiments.parameters
+        param_map: dict[str, object] = {}
+        for param in all_params:
+            unique_name = getattr(param, 'unique_name', None)
+            if unique_name is not None:
+                param_map[unique_name] = param
+        return param_map
+
     def save(self) -> None:
         """Save the project into the existing project directory."""
         if self.info.path is None:
@@ -441,6 +477,10 @@ def save(self) -> None:
         with (analysis_dir / 'analysis.cif').open('w') as f:
             f.write(self.analysis.as_cif)
             console.print('├── 📁 analysis/')
+        write_analysis_results_sidecar(
+            analysis=self.analysis,
+            analysis_dir=analysis_dir,
+        )
 
         analysis_file_names = sorted(
             path.name for path in analysis_dir.iterdir() if path.is_file()
@@ -462,12 +502,40 @@ def save_as(
         dir_path: str,
         *,
         temporary: bool = False,
+        overwrite: bool = True,
     ) -> None:
-        """Save the project into a new directory."""
+        """
+        Save the project into a directory.
+
+        Parameters
+        ----------
+        dir_path : str
+            Destination directory for the saved project.
+        temporary : bool, default=False
+            Whether to save beneath the system temporary directory.
+        overwrite : bool, default=True
+            Whether to remove an existing target directory before
+            saving.
+        """
         if temporary:
             tmp: str = tempfile.gettempdir()
-            dir_path = pathlib.Path(tmp) / dir_path
-        self.info.path = dir_path
+            project_dir = pathlib.Path(tmp) / dir_path
+        else:
+            project_dir = resolve_artifact_path(dir_path)
+
+        if overwrite and project_dir.is_dir():
+            current_working_directory = pathlib.Path.cwd().resolve()
+            resolved_project_dir = project_dir.resolve()
+            if resolved_project_dir == current_working_directory:
+                for child_path in resolved_project_dir.iterdir():
+                    if child_path.is_dir():
+                        shutil.rmtree(child_path)
+                    else:
+                        child_path.unlink()
+            else:
+                shutil.rmtree(project_dir)
+
+        self.info.path = project_dir
         self.save()
 
     def apply_params_from_csv(self, row_index: int) -> None:
diff --git a/src/easydiffraction/project/project_config.py b/src/easydiffraction/project/project_config.py
index 32147b609..dd92137ab 100644
--- a/src/easydiffraction/project/project_config.py
+++ b/src/easydiffraction/project/project_config.py
@@ -9,6 +9,8 @@
 from easydiffraction.project.categories.info import ProjectInfoFactory
 from easydiffraction.project.categories.rendering import Rendering
 from easydiffraction.project.categories.rendering import RenderingFactory
+from easydiffraction.project.categories.verbosity import Verbosity
+from easydiffraction.project.categories.verbosity import VerbosityFactory
 
 
 class ProjectConfig(CategoryOwner):
@@ -28,6 +30,7 @@ def __init__(
             description=description,
         )
         self._rendering = RenderingFactory.create(RenderingFactory.default_tag())
+        self._verbosity = VerbosityFactory.create(VerbosityFactory.default_tag())
 
     @property
     def info(self) -> ProjectInfo:
@@ -39,6 +42,11 @@ def rendering(self) -> Rendering:
         """Rendering configuration category."""
         return self._rendering
 
+    @property
+    def verbosity(self) -> Verbosity:
+        """Verbosity configuration category."""
+        return self._verbosity
+
     @property
     def as_cif(self) -> str:
         """Serialize singleton project categories to CIF."""
diff --git a/src/easydiffraction/utils/environment.py b/src/easydiffraction/utils/environment.py
index e2df97d48..0fd35ce0c 100644
--- a/src/easydiffraction/utils/environment.py
+++ b/src/easydiffraction/utils/environment.py
@@ -5,7 +5,55 @@
 
 import os
 import sys
+import tempfile
 from importlib.util import find_spec
+from pathlib import Path
+
+_ARTIFACT_ROOT_ENV_VAR = 'EASYDIFFRACTION_ARTIFACT_ROOT'
+_PIXI_PROJECT_ROOT_ENV_VAR = 'PIXI_PROJECT_ROOT'
+_TUTORIALS_DIR = Path('docs') / 'docs' / 'tutorials'
+_TUTORIAL_ARTIFACT_ROOT = Path('tmp') / 'tutorials'
+
+
+def _repo_root() -> Path | None:
+    project_root = os.environ.get(_PIXI_PROJECT_ROOT_ENV_VAR)
+    if project_root:
+        return Path(project_root).resolve()
+
+    for parent in Path(__file__).resolve().parents:
+        if (parent / 'pixi.toml').is_file() and (parent / _TUTORIALS_DIR).is_dir():
+            return parent
+
+    return None
+
+
+def _tutorial_artifact_root() -> Path | None:
+    repo_root = _repo_root()
+    if repo_root is None:
+        return None
+
+    tutorials_dir = (repo_root / _TUTORIALS_DIR).resolve()
+    cwd = Path.cwd().resolve()
+    if not cwd.is_relative_to(tutorials_dir):
+        return None
+
+    return (repo_root / _TUTORIAL_ARTIFACT_ROOT).resolve()
+
+
+def _artifact_root() -> Path | None:
+    artifact_root = os.environ.get(_ARTIFACT_ROOT_ENV_VAR)
+    if not artifact_root:
+        return _tutorial_artifact_root()
+
+    root = Path(artifact_root)
+    if root.is_absolute():
+        return root.resolve()
+
+    project_root = os.environ.get(_PIXI_PROJECT_ROOT_ENV_VAR)
+    if project_root:
+        return (Path(project_root) / root).resolve()
+
+    return (Path.cwd() / root).resolve()
 
 
 def in_pytest() -> bool:
@@ -107,6 +155,52 @@ def in_github_ci() -> bool:
     return os.environ.get('GITHUB_ACTIONS') is not None
 
 
+def resolve_artifact_path(path: str | Path) -> Path:
+    """
+    Resolve a path against the configured artifact root.
+
+    Parameters
+    ----------
+    path : str | Path
+        Path to resolve.
+
+    Returns
+    -------
+    Path
+        The original path when no artifact root is configured or when
+        *path* is absolute. Otherwise, the absolute path under the
+        configured artifact root.
+    """
+    resolved_path = Path(path)
+    artifact_root = _artifact_root()
+    if artifact_root is None or resolved_path.is_absolute():
+        return resolved_path
+
+    return (artifact_root / resolved_path).resolve()
+
+
+def create_artifact_temp_dir(prefix: str) -> Path:
+    """
+    Create a temporary directory under the artifact root when set.
+
+    Parameters
+    ----------
+    prefix : str
+        Prefix for the temporary directory name.
+
+    Returns
+    -------
+    Path
+        Path to the created temporary directory.
+    """
+    artifact_root = _artifact_root()
+    if artifact_root is None:
+        return Path(tempfile.mkdtemp(prefix=prefix))
+
+    artifact_root.mkdir(parents=True, exist_ok=True)
+    return Path(tempfile.mkdtemp(prefix=prefix, dir=artifact_root)).resolve()
+
+
 # ----------------------------------------------------------------------
 # IPython/Jupyter helpers
 # ----------------------------------------------------------------------
diff --git a/src/easydiffraction/utils/utils.py b/src/easydiffraction/utils/utils.py
index cf461aeea..6dba4301d 100644
--- a/src/easydiffraction/utils/utils.py
+++ b/src/easydiffraction/utils/utils.py
@@ -6,6 +6,7 @@
 import functools
 import json
 import pathlib
+import shutil
 import urllib.request
 from importlib.metadata import PackageNotFoundError
 from importlib.metadata import version
@@ -20,6 +21,8 @@
 from uncertainties import ufloat_fromstr
 
 from easydiffraction.display.tables import TableRenderer
+from easydiffraction.io.ascii import extract_project_from_zip
+from easydiffraction.utils.environment import resolve_artifact_path
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
 
@@ -28,9 +31,9 @@
 _DATA_REPO = 'easyscience/diffraction'
 _DATA_ROOT = 'data'
 # commit SHA preferred
-_DATA_INDEX_REF = '39dad256ba1faedf4b26fad3e44a361c802fd8e4'
+_DATA_INDEX_REF = 'dbe92a87e0106c4742eee0ff9a8e32bdb8b483cb'
 # macOS: sha256sum index.json
-_DATA_INDEX_HASH = 'sha256:301aaca0f35927cd63715b858a1f03164e4d05d1d39234325a3798d2b4a5f4ea'
+_DATA_INDEX_HASH = 'sha256:9e7bbaf2cb650f4126572e85157c63bc76f201408856fe4af566bee55dcdfbb4'
 
 
 def _build_data_url(path: str) -> str:
@@ -112,6 +115,42 @@ def _fetch_data_index() -> dict:
         return json.load(f)
 
 
+def _existing_project_dir(extraction_dir: pathlib.Path) -> pathlib.Path | None:
+    """Return one extracted project directory from a destination."""
+    project_files = sorted(extraction_dir.rglob('project.cif'))
+    if not project_files:
+        return None
+    return project_files[0].parent.resolve()
+
+
+def _download_data_message(data_id: int | str, record: dict) -> str:
+    """Return the console message for one downloadable data record."""
+    description = record.get('description', '')
+    message = f'Data #{data_id}'
+    if description:
+        message += f': {description}'
+    return message
+
+
+def _download_data_targets(
+    data_id: int | str,
+    destination: str,
+    record: dict,
+) -> tuple[str, bool, pathlib.Path, pathlib.Path, pathlib.Path, str]:
+    """Return URL and filesystem targets for one download request."""
+    record_path = _record_path(record)
+    url = _build_data_url(record_path)
+    _validate_url(url)
+
+    fname = _filename_for_id_from_path(data_id, record_path)
+    is_project_archive = record.get('kind') == 'project' and fname.endswith('.zip')
+    dest_path = resolve_artifact_path(destination)
+    dest_path.mkdir(parents=True, exist_ok=True)
+    file_path = dest_path / fname
+    extraction_dir = dest_path / pathlib.Path(fname).stem
+    return url, is_project_archive, dest_path, file_path, extraction_dir, fname
+
+
 @functools.lru_cache(maxsize=1)
 def _fetch_tutorials_index() -> dict:
     """
@@ -160,14 +199,18 @@ def download_data(
     id : int | str
         Numeric dataset id (e.g. 12).
     destination : str, default='data'
-        Directory to save the file into (created if missing).
+        Directory to save the downloaded file or extracted project into
+        (created if missing). Relative destinations are resolved against
+        the configured artifact root when
+        ``EASYDIFFRACTION_ARTIFACT_ROOT`` is set.
     overwrite : bool, default=False
         Whether to overwrite the file if it already exists.
 
     Returns
     -------
     str
-        Full path to the downloaded file as string.
+        Full path to the downloaded file, or to the extracted project
+        directory for project ZIP archives, as string.
 
     Raises
     ------
@@ -186,24 +229,29 @@ def download_data(
         raise KeyError(msg)
 
     record = index[key]
-    record_path = _record_path(record)
-    url = _build_data_url(record_path)
-    _validate_url(url)
-    fname = _filename_for_id_from_path(id, record_path)
-
-    dest_path = pathlib.Path(destination)
-    dest_path.mkdir(parents=True, exist_ok=True)
-    file_path = dest_path / fname
-
-    description = record.get('description', '')
-    message = f'Data #{id}'
-    if description:
-        message += f': {description}'
+    url, is_project_archive, dest_path, file_path, extraction_dir, fname = _download_data_targets(
+        id, destination, record
+    )
+    message = _download_data_message(id, record)
 
     console.paragraph('Getting data...')
     console.print(f'{message}')
 
+    if is_project_archive and extraction_dir.exists() and not overwrite:
+        existing_project_dir = _existing_project_dir(extraction_dir)
+        if existing_project_dir is not None:
+            console.print(
+                f"✅ Data #{id} already extracted at '{existing_project_dir}'. "
+                'Keeping existing project.'
+            )
+            return str(existing_project_dir)
+
     if file_path.exists():
+        if is_project_archive and not overwrite:
+            project_dir = extract_project_from_zip(file_path, destination=extraction_dir)
+            file_path.unlink()
+            console.print(f"✅ Data #{id} extracted to '{project_dir}'")
+            return str(project_dir)
         if not overwrite:
             console.print(
                 f"✅ Data #{id} already present at '{file_path}'. Keeping existing file."
@@ -214,6 +262,9 @@ def download_data(
 
     known_hash = _normalize_known_hash(record.get('hash'))
 
+    if is_project_archive and extraction_dir.exists() and overwrite:
+        shutil.rmtree(extraction_dir)
+
     # Pooch downloads to destination with our controlled filename.
     pooch.retrieve(
         url=url,
@@ -222,10 +273,45 @@ def download_data(
         path=str(dest_path),
     )
 
-    console.print(f"✅ Data #{id} downloaded to '{file_path}'")
+    if is_project_archive:
+        project_dir = extract_project_from_zip(file_path, destination=extraction_dir)
+        file_path.unlink()
+        console.print(f"✅ Data #{id} downloaded and extracted to\n'{project_dir}'")
+        return str(project_dir)
+
+    console.print(f"✅ Data #{id} downloaded to:\n'{file_path}'")
     return str(file_path)
 
 
+def list_data() -> None:
+    """Display a table of available example data records."""
+    index = _fetch_data_index()
+    if not index:
+        console.print('❌ No example data available.')
+        return
+
+    console.paragraph('Example data available for download:')
+
+    columns_headers = ['id', 'file', 'kind', 'description']
+    columns_alignment = ['right', 'left', 'left', 'left']
+    columns_data = []
+
+    for data_id in sorted(index, key=lambda value: int(value) if value.isdigit() else value):
+        record = index[data_id]
+        columns_data.append([
+            data_id,
+            pathlib.PurePosixPath(_record_path(record)).name,
+            record.get('kind', ''),
+            record.get('description', ''),
+        ])
+
+    render_table(
+        columns_headers=columns_headers,
+        columns_data=columns_data,
+        columns_alignment=columns_alignment,
+    )
+
+
 def package_version(package_name: str) -> str | None:
     """
     Get the installed version string of the specified package.
@@ -474,7 +560,7 @@ def download_tutorial(
     with _safe_urlopen(url) as resp:
         file_path.write_bytes(resp.read())
 
-    console.print(f"✅ Tutorial #{id} downloaded to '{file_path}'")
+    console.print(f"✅ Tutorial #{id} downloaded to:\n'{file_path}'")
     return str(file_path)
 
 
diff --git a/tests/functional/test_project_lifecycle.py b/tests/functional/test_project_lifecycle.py
index 7d9c5a190..850f040e9 100644
--- a/tests/functional/test_project_lifecycle.py
+++ b/tests/functional/test_project_lifecycle.py
@@ -88,7 +88,7 @@ def test_default_verbosity_is_full(self):
             project = Project()
         finally:
             Project._loading = False
-        assert project.verbosity == 'full'
+        assert project.verbosity.fit.value == 'full'
 
     def test_set_verbosity_short(self):
         Project._loading = True
@@ -97,7 +97,7 @@ def test_set_verbosity_short(self):
         finally:
             Project._loading = False
         project.verbosity = 'short'
-        assert project.verbosity == 'short'
+        assert project.verbosity.fit.value == 'short'
 
     def test_set_verbosity_silent(self):
         Project._loading = True
@@ -106,7 +106,7 @@ def test_set_verbosity_silent(self):
         finally:
             Project._loading = False
         project.verbosity = 'silent'
-        assert project.verbosity == 'silent'
+        assert project.verbosity.fit.value == 'silent'
 
     def test_invalid_verbosity_raises(self):
         Project._loading = True
diff --git a/tests/integration/fitting/test_bayesian_dream.py b/tests/integration/fitting/test_bayesian_dream.py
index d6695973b..ef52c1f1a 100644
--- a/tests/integration/fitting/test_bayesian_dream.py
+++ b/tests/integration/fitting/test_bayesian_dream.py
@@ -163,7 +163,7 @@ def test_lm_prefit_followed_by_dream_uses_uncertainty_based_bounds():
     assert len(results.posterior_parameter_summaries) == 3
 
 
-def test_bayesian_fit_results_are_runtime_only_after_save_load(tmp_path):
+def test_bayesian_fit_results_reload_from_persisted_fit_state(tmp_path):
     project = _create_lbco_project()
     length_a, scale, offset = _dream_parameters(project)
     for parameter in (length_a, scale, offset):
@@ -185,8 +185,13 @@ def test_bayesian_fit_results_are_runtime_only_after_save_load(tmp_path):
     project.save_as(str(proj_dir))
 
     analysis_cif = proj_dir / 'analysis' / 'analysis.cif'
+    results_sidecar = proj_dir / 'analysis' / 'results.h5'
     assert analysis_cif.is_file()
-    assert 'posterior' not in analysis_cif.read_text().lower()
+    assert results_sidecar.is_file()
 
     loaded = Project.load(str(proj_dir))
-    assert loaded.analysis.fit_results is None
+    loaded_results = loaded.analysis.fit_results
+    assert loaded_results is not None
+    assert loaded_results.sampler_completed is True
+    assert loaded_results.posterior_samples is not None
+    assert loaded_results.posterior_samples.parameter_samples.ndim == 3
diff --git a/tests/integration/fitting/test_bumps_dream_support.py b/tests/integration/fitting/test_bumps_dream_support.py
index c09f0b92e..a824347e1 100644
--- a/tests/integration/fitting/test_bumps_dream_support.py
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -277,10 +277,7 @@ def test_build_mapper_falls_back_for_serial_and_unpicklable(monkeypatch):
     monkeypatch.setattr(
         'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: False
     )
-    monkeypatch.setattr(
-        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
-        lambda message: warnings.append(message),
-    )
+    monkeypatch.setattr(minimizer, '_warn_after_tracking', warnings.append)
 
     assert minimizer._build_mapper('problem') is None
     assert warnings == [
diff --git a/tests/integration/fitting/test_sequential.py b/tests/integration/fitting/test_sequential.py
index da4c28774..73c068b3b 100644
--- a/tests/integration/fitting/test_sequential.py
+++ b/tests/integration/fitting/test_sequential.py
@@ -165,9 +165,18 @@ def test_fit_sequential_produces_csv(tmp_path) -> None:
 
     assert len(rows) == 3, f'Expected 3 rows, got {len(rows)}'
 
-    # Each row should have fit_success
+    # Each row should have fit_result.success
     for row in rows:
-        assert row['fit_success'] == 'True', f'Fit failed for {row["file_path"]}'
+        assert row['fit_result.success'] == 'True', f'Fit failed for {row["file_path"]}'
+        assert int(row['fit_result.iterations']) > 0, (
+            f'Expected non-zero iterations for {row["file_path"]}'
+        )
+
+    assert 'fit_result.reduced_chi_square' in rows[0]
+    assert 'fit_result.iterations' in rows[0]
+    assert 'success' not in rows[0]
+    assert 'reduced_chi_square' not in rows[0]
+    assert 'iterations' not in rows[0]
 
     # Each row should have parameter values
     assert 'lbco.cell.length_a' in rows[0]
@@ -316,7 +325,10 @@ def test_fit_sequential_parallel(tmp_path) -> None:
     assert len(rows) == 3, f'Expected 3 rows, got {len(rows)}'
 
     for row in rows:
-        assert row['fit_success'] == 'True', f'Fit failed for {row["file_path"]}'
+        assert row['fit_result.success'] == 'True', f'Fit failed for {row["file_path"]}'
+        assert int(row['fit_result.iterations']) > 0, (
+            f'Expected non-zero iterations for {row["file_path"]}'
+        )
 
     # Parameter values should be present and reasonable
     assert 'lbco.cell.length_a' in rows[0]
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_convergence.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_convergence.py
new file mode 100644
index 000000000..dbcaf7299
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_convergence.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_convergence/."""
+
+
+def test_bayesian_convergence_factory_create():
+    from easydiffraction.analysis.categories.bayesian_convergence.default import (
+        BayesianConvergence,
+    )
+    from easydiffraction.analysis.categories.bayesian_convergence.factory import (
+        BayesianConvergenceFactory,
+    )
+
+    convergence = BayesianConvergenceFactory.create('default')
+
+    assert BayesianConvergenceFactory.default_tag() == 'default'
+    assert isinstance(convergence, BayesianConvergence)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_distribution_caches.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_distribution_caches.py
new file mode 100644
index 000000000..26e91f732
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_distribution_caches.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_distribution_caches/."""
+
+
+def test_bayesian_distribution_caches_factory_create():
+    from easydiffraction.analysis.categories.bayesian_distribution_caches.default import (
+        BayesianDistributionCaches,
+    )
+    from easydiffraction.analysis.categories.bayesian_distribution_caches.factory import (
+        BayesianDistributionCachesFactory,
+    )
+
+    caches = BayesianDistributionCachesFactory.create('default')
+
+    assert BayesianDistributionCachesFactory.default_tag() == 'default'
+    assert isinstance(caches, BayesianDistributionCaches)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_pair_caches.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_pair_caches.py
new file mode 100644
index 000000000..f4307f2c4
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_pair_caches.py
@@ -0,0 +1,15 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_pair_caches/."""
+
+
+def test_bayesian_pair_caches_factory_create():
+    from easydiffraction.analysis.categories.bayesian_pair_caches.default import BayesianPairCaches
+    from easydiffraction.analysis.categories.bayesian_pair_caches.factory import (
+        BayesianPairCachesFactory,
+    )
+
+    caches = BayesianPairCachesFactory.create('default')
+
+    assert BayesianPairCachesFactory.default_tag() == 'default'
+    assert isinstance(caches, BayesianPairCaches)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_parameter_posteriors.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_parameter_posteriors.py
new file mode 100644
index 000000000..18aa5f019
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_parameter_posteriors.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_parameter_posteriors/."""
+
+
+def test_bayesian_parameter_posteriors_factory_create():
+    from easydiffraction.analysis.categories.bayesian_parameter_posteriors.default import (
+        BayesianParameterPosteriors,
+    )
+    from easydiffraction.analysis.categories.bayesian_parameter_posteriors.factory import (
+        BayesianParameterPosteriorsFactory,
+    )
+
+    posteriors = BayesianParameterPosteriorsFactory.create('default')
+
+    assert BayesianParameterPosteriorsFactory.default_tag() == 'default'
+    assert isinstance(posteriors, BayesianParameterPosteriors)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_predictive_datasets.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_predictive_datasets.py
new file mode 100644
index 000000000..93746fb41
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_predictive_datasets.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_predictive_datasets/."""
+
+
+def test_bayesian_predictive_datasets_factory_create():
+    from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+        BayesianPredictiveDatasets,
+    )
+    from easydiffraction.analysis.categories.bayesian_predictive_datasets.factory import (
+        BayesianPredictiveDatasetsFactory,
+    )
+
+    datasets = BayesianPredictiveDatasetsFactory.create('default')
+
+    assert BayesianPredictiveDatasetsFactory.default_tag() == 'default'
+    assert isinstance(datasets, BayesianPredictiveDatasets)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_result.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_result.py
new file mode 100644
index 000000000..e1d985385
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_result.py
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_result/."""
+
+
+def test_bayesian_result_factory_create():
+    from easydiffraction.analysis.categories.bayesian_result.default import BayesianResult
+    from easydiffraction.analysis.categories.bayesian_result.factory import BayesianResultFactory
+
+    result = BayesianResultFactory.create('default')
+
+    assert BayesianResultFactory.default_tag() == 'default'
+    assert isinstance(result, BayesianResult)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_bayesian_sampler.py b/tests/unit/easydiffraction/analysis/categories/test_bayesian_sampler.py
new file mode 100644
index 000000000..976341765
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_bayesian_sampler.py
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/bayesian_sampler/."""
+
+
+def test_bayesian_sampler_factory_create():
+    from easydiffraction.analysis.categories.bayesian_sampler.default import BayesianSampler
+    from easydiffraction.analysis.categories.bayesian_sampler.factory import BayesianSamplerFactory
+
+    sampler = BayesianSamplerFactory.create('default')
+
+    assert BayesianSamplerFactory.default_tag() == 'default'
+    assert isinstance(sampler, BayesianSampler)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_constraints.py b/tests/unit/easydiffraction/analysis/categories/test_constraints.py
index b887408ea..07016b298 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_constraints.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_constraints.py
@@ -22,6 +22,17 @@ def test_constraint_creation_and_collection():
     assert coll['a'].rhs_expr == 'b + c'
 
 
+def test_constraints_create_uses_explicit_id():
+    coll = Constraints()
+
+    coll.create(id='constraint_1', expression='a = b + c')
+
+    assert coll.names == ['constraint_1']
+    assert coll['constraint_1'].id.value == 'constraint_1'
+    assert coll['constraint_1'].lhs_alias == 'a'
+    assert coll['constraint_1'].rhs_expr == 'b + c'
+
+
 def test_constraints_from_cif_preserves_explicit_id_keys():
     doc = gemmi.cif.read_string(
         'data_constraints\n\n'
diff --git a/tests/unit/easydiffraction/analysis/categories/test_deterministic_result.py b/tests/unit/easydiffraction/analysis/categories/test_deterministic_result.py
new file mode 100644
index 000000000..ed764146d
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_deterministic_result.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/deterministic_result/."""
+
+
+def test_deterministic_result_factory_create():
+    from easydiffraction.analysis.categories.deterministic_result.default import (
+        DeterministicResult,
+    )
+    from easydiffraction.analysis.categories.deterministic_result.factory import (
+        DeterministicResultFactory,
+    )
+
+    result = DeterministicResultFactory.create('default')
+
+    assert DeterministicResultFactory.default_tag() == 'default'
+    assert isinstance(result, DeterministicResult)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_parameter_correlations.py b/tests/unit/easydiffraction/analysis/categories/test_fit_parameter_correlations.py
new file mode 100644
index 000000000..8cb72d57b
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_parameter_correlations.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/fit_parameter_correlations/."""
+
+
+def test_fit_parameter_correlations_factory_create():
+    from easydiffraction.analysis.categories.fit_parameter_correlations.default import (
+        FitParameterCorrelations,
+    )
+    from easydiffraction.analysis.categories.fit_parameter_correlations.factory import (
+        FitParameterCorrelationsFactory,
+    )
+
+    correlations = FitParameterCorrelationsFactory.create('default')
+
+    assert FitParameterCorrelationsFactory.default_tag() == 'default'
+    assert isinstance(correlations, FitParameterCorrelations)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_parameters.py b/tests/unit/easydiffraction/analysis/categories/test_fit_parameters.py
new file mode 100644
index 000000000..e3d0cedde
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_parameters.py
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/fit_parameters/."""
+
+
+def test_fit_parameters_factory_create():
+    from easydiffraction.analysis.categories.fit_parameters.default import FitParameters
+    from easydiffraction.analysis.categories.fit_parameters.factory import FitParametersFactory
+
+    collection = FitParametersFactory.create('default')
+
+    assert FitParametersFactory.default_tag() == 'default'
+    assert isinstance(collection, FitParameters)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_result.py b/tests/unit/easydiffraction/analysis/categories/test_fit_result.py
new file mode 100644
index 000000000..4c9d869dc
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_result.py
@@ -0,0 +1,13 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for analysis/categories/fit_result/."""
+
+
+def test_fit_result_factory_create():
+    from easydiffraction.analysis.categories.fit_result.default import FitResult
+    from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+
+    fit_result = FitResultFactory.create('default')
+
+    assert FitResultFactory.default_tag() == 'default'
+    assert isinstance(fit_result, FitResult)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_state.py b/tests/unit/easydiffraction/analysis/categories/test_fit_state.py
new file mode 100644
index 000000000..236c798a5
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_state.py
@@ -0,0 +1,207 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for persisted fit-state analysis categories."""
+
+from __future__ import annotations
+
+import gemmi
+
+
+def test_fit_parameter_collection_is_empty_until_rows_exist():
+    from easydiffraction.analysis.categories.fit_parameters.default import FitParameters
+
+    collection = FitParameters()
+
+    assert collection.as_cif == ''
+
+
+def test_fit_parameter_collection_serializes_expected_tags_and_values():
+    from easydiffraction.analysis.categories.fit_parameters.default import FitParameters
+
+    collection = FitParameters()
+    collection.create(
+        param_unique_name='lbco.cell.length_a',
+        fit_min=3.88,
+        fit_max=3.90,
+        fit_bounds_uncertainty_multiplier=4.0,
+        start_value=3.89,
+        start_uncertainty=0.01,
+    )
+
+    cif_text = collection.as_cif
+
+    assert '_fit_parameter.param_unique_name' in cif_text
+    assert '_fit_parameter.fit_bounds_uncertainty_multiplier' in cif_text
+    assert 'lbco.cell.length_a' in cif_text
+
+
+def test_fit_result_serializes_expected_tags_and_enum_value():
+    from easydiffraction.analysis.categories.fit_result.default import FitResult
+
+    fit_result = FitResult()
+    fit_result._set_result_kind('bayesian')
+    fit_result._set_success(value=True)
+    fit_result._set_message('Sampler completed')
+    fit_result._set_iterations(3000)
+    fit_result._set_fitting_time(82.4)
+    fit_result._set_reduced_chi_square(1.031)
+
+    cif_text = fit_result.as_cif
+
+    assert '_fit_result.result_kind bayesian' in cif_text
+    assert '_fit_result.iterations 3000' in cif_text
+    assert '_fit_result.reduced_chi_square' in cif_text
+
+
+def test_fit_parameter_correlations_normalize_pair_order_and_replace_duplicate_ids():
+    from easydiffraction.analysis.categories.fit_parameter_correlations.default import (
+        FitParameterCorrelations,
+    )
+
+    correlations = FitParameterCorrelations()
+    correlations.create(
+        source_kind='posterior',
+        param_unique_name_i='z.param',
+        param_unique_name_j='a.param',
+        correlation=0.87,
+        id='1',
+    )
+    correlations.create(
+        source_kind='posterior',
+        param_unique_name_i='b.param',
+        param_unique_name_j='c.param',
+        correlation=0.55,
+        id='1',
+    )
+
+    assert len(correlations) == 1
+    assert correlations['1'].param_unique_name_i.value == 'b.param'
+    assert correlations['1'].param_unique_name_j.value == 'c.param'
+
+
+def test_fit_parameter_correlations_rebuild_index_from_cif():
+    from easydiffraction.analysis.categories.fit_parameter_correlations.default import (
+        FitParameterCorrelations,
+    )
+
+    cif_text = """data_fit_state
+loop_
+_fit_parameter_correlation.id
+_fit_parameter_correlation.source_kind
+_fit_parameter_correlation.param_unique_name_i
+_fit_parameter_correlation.param_unique_name_j
+_fit_parameter_correlation.correlation
+2 posterior hrpt.scale lbco.cell.length_a 0.42
+"""
+    document = gemmi.cif.read_string(cif_text)
+
+    correlations = FitParameterCorrelations()
+    correlations.from_cif(document.sole_block())
+
+    assert correlations.names == ['2']
+    assert correlations['2'].correlation.value == 0.42
+
+
+def test_bayesian_cache_manifest_collections_serialize_expected_keys():
+    from easydiffraction.analysis.categories.bayesian_distribution_caches.default import (
+        BayesianDistributionCaches,
+    )
+    from easydiffraction.analysis.categories.bayesian_pair_caches.default import (
+        BayesianPairCachePaths,
+        BayesianPairCaches,
+    )
+    from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+        BayesianPredictiveDatasetPaths,
+        BayesianPredictiveDatasets,
+    )
+
+    distributions = BayesianDistributionCaches()
+    distributions.create(
+        param_unique_name='lbco.cell.length_a',
+        x_path='/posterior/distribution/0/x',
+        density_path='/posterior/distribution/0/density',
+        n_grid=256,
+        n_draws_cached=48000,
+    )
+    pairs = BayesianPairCaches()
+    pairs.create(
+        parameter_names=('z.param', 'a.param'),
+        paths=BayesianPairCachePaths(
+            x_path='/posterior/pairs/0/x',
+            y_path='/posterior/pairs/0/y',
+            density_path='/posterior/pairs/0/density',
+            contour_level_path='/posterior/pairs/0/contour_levels',
+        ),
+        grid_shape=(64, 64),
+        n_draws_cached=4000,
+        id='7',
+    )
+    predictive = BayesianPredictiveDatasets()
+    predictive.create(
+        experiment_name='hrpt',
+        x_axis_name='two_theta',
+        paths=BayesianPredictiveDatasetPaths(
+            x_path='/predictive/hrpt/x',
+            best_sample_prediction_path='/predictive/hrpt/best_sample_prediction',
+            lower_95_path='/predictive/hrpt/lower_95',
+            upper_95_path='/predictive/hrpt/upper_95',
+        ),
+        n_x=2500,
+        n_draws_cached=0,
+    )
+
+    assert '_bayesian_distribution_cache.param_unique_name' in distributions.as_cif
+    assert pairs['7'].param_unique_name_x.value == 'a.param'
+    assert pairs['7'].param_unique_name_y.value == 'z.param'
+    assert '_bayesian_predictive_dataset.experiment_name' in predictive.as_cif
+
+
+def test_bayesian_sampler_and_convergence_use_integer_fields_in_cif():
+    from easydiffraction.analysis.categories.bayesian_convergence.default import (
+        BayesianConvergence,
+    )
+    from easydiffraction.analysis.categories.bayesian_sampler.default import BayesianSampler
+
+    sampler = BayesianSampler()
+    sampler._set_steps(100)
+    sampler._set_burn(20)
+    sampler._set_parallel(0)
+    sampler._set_random_seed(123)
+
+    convergence = BayesianConvergence()
+    convergence._set_n_draws(80)
+    convergence._set_n_chains(4)
+    convergence._set_n_parameters(3)
+
+    assert '_bayesian_sampler.steps 100' in sampler.as_cif
+    assert '_bayesian_sampler.parallel 0' in sampler.as_cif
+    assert '_bayesian_convergence.n_draws 80' in convergence.as_cif
+
+
+def test_bayesian_parameter_posteriors_preserve_row_order_from_cif():
+    from easydiffraction.analysis.categories.bayesian_parameter_posteriors.default import (
+        BayesianParameterPosteriors,
+    )
+
+    cif_text = """data_fit_state
+loop_
+_bayesian_parameter_posterior.unique_name
+_bayesian_parameter_posterior.display_name
+_bayesian_parameter_posterior.best_sample_value
+_bayesian_parameter_posterior.median
+_bayesian_parameter_posterior.uncertainty
+_bayesian_parameter_posterior.interval_68_lower
+_bayesian_parameter_posterior.interval_68_upper
+_bayesian_parameter_posterior.interval_95_lower
+_bayesian_parameter_posterior.interval_95_upper
+_bayesian_parameter_posterior.ess_bulk
+_bayesian_parameter_posterior.r_hat
+second.param second 2.0 2.1 0.2 1.9 2.3 1.8 2.4 20 1.01
+first.param first 1.0 1.1 0.1 0.9 1.3 0.8 1.4 10 1.00
+"""
+    document = gemmi.cif.read_string(cif_text)
+
+    posteriors = BayesianParameterPosteriors()
+    posteriors.from_cif(document.sole_block())
+
+    assert [row.unique_name.value for row in posteriors] == ['second.param', 'first.param']
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
index 877a46b1a..ff9df3288 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
@@ -77,3 +77,50 @@ def test_tracker_fit_adds_timed_rows_and_resets_counter(monkeypatch):
     ]
     assert tracker._last_progress_time == 12.2
     assert tracker._previous_chi2 == 4.0
+
+
+def test_tracker_fit_progress_uses_backend_iterations_for_display():
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    tracker = FitProgressTracker()
+
+    tracker.track_fit_progress(iteration=1, reduced_chi2=10.0, elapsed_time=0.1)
+    tracker.track_fit_progress(iteration=63, reduced_chi2=5.0, elapsed_time=1.0)
+    tracker.track_fit_progress(iteration=122, reduced_chi2=4.0, elapsed_time=2.0)
+
+    assert tracker._df_rows == [
+        ['1', '0.10', '10.00', ''],
+        ['63', '1.00', '5.00', '50.0% ↓'],
+        ['122', '2.00', '4.00', '20.0% ↓'],
+    ]
+    assert tracker.best_iteration == 122
+
+
+def test_tracker_sampler_post_processing_adds_final_status_row():
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
+
+    tracker = FitProgressTracker()
+    tracker.start_tracking('dream', mode='sampling')
+    tracker.start_timer()
+    tracker.track_sampler_progress(
+        SamplerProgressUpdate(
+            iteration=10,
+            total_iterations=10,
+            phase='sampling',
+            progress_percent=100.0,
+            log_posterior=-3.0,
+            reduced_chi2=1.0,
+            elapsed_time=5.0,
+            force_report=True,
+        )
+    )
+
+    tracker.start_sampler_post_processing()
+    tracker.stop_timer()
+    tracker.finish_tracking()
+
+    assert tracker._df_rows[-1][0] == ''
+    assert tracker._df_rows[-1][1] == ''
+    assert tracker._df_rows[-1][3] == ''
+    assert tracker._df_rows[-1][4] == 'post-processing'
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_base.py b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
index 3b18f5354..4ee99f1d1 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_base.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
@@ -87,7 +87,8 @@ def _compute_residuals(
     assert minim.synced is True
     assert isinstance(result.parameters, list)
     assert result.parameters[0].value == 42
-    # Fitting time should be a positive float
+    assert result.fitting_time is not None
+    assert result.fitting_time >= 0.0
     assert minim.tracker.fitting_time is not None
     assert minim.tracker.fitting_time >= 0.0
 
@@ -122,6 +123,37 @@ def _compute_residuals(
     assert np.allclose(out, np.array([1.0, 2.0, 3.0]))
 
 
+def test_max_iterations_property_updates_internal_value():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class M(MinimizerBase):
+        def __init__(self):
+            super().__init__(name='dummy', method='m', max_iterations=5)
+
+        def _prepare_solver_args(self, parameters):
+            del parameters
+            return {}
+
+        def _run_solver(self, objective_function, **kwargs):
+            del objective_function, kwargs
+            return
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            del parameters, raw_result
+
+        def _check_success(self, raw_result):
+            del raw_result
+            return True
+
+    minimizer = M()
+
+    assert minimizer.max_iterations == 5
+
+    minimizer.max_iterations = 200
+
+    assert minimizer.max_iterations == 200
+
+
 def test_minimizer_base_fit_stops_tracking_when_solver_prep_fails():
     from easydiffraction.analysis.minimizers.base import MinimizerBase
 
@@ -201,3 +233,59 @@ def _check_success(self, raw_result):
 
     with pytest.raises(ValueError, match='prep failed'):
         minimizer.fit(parameters=[_DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
+
+
+def test_minimizer_base_stop_tracking_backfills_result_fitting_time():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class DummyResult:
+        success = True
+
+    class DummyMinimizer(MinimizerBase):
+        def __init__(self):
+            super().__init__(name='dummy', method='m', max_iterations=5)
+
+        def _prepare_solver_args(self, parameters):
+            del parameters
+            return {'engine_parameters': {'ok': True}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            residuals = objective_function(kwargs.get('engine_parameters'))
+            self.tracker.track(residuals=np.array(residuals), parameters=[1])
+            return DummyResult()
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            del parameters, raw_result
+
+        def _check_success(self, raw_result):
+            del raw_result
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            del parameters, structures, experiments, calculator
+            assert engine_params == {'ok': True}
+            return np.array([0.0])
+
+    minimizer = DummyMinimizer()
+    params = [_DummyParam(1.0)]
+    objective = minimizer._create_objective_function(
+        parameters=params,
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    result = minimizer.fit(
+        parameters=params,
+        objective_function=objective,
+        finalize_tracking=False,
+    )
+
+    assert result.fitting_time is None
+
+    minimizer._stop_tracking()
+
+    assert result.fitting_time is not None
+    assert result.fitting_time >= 0.0
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
index 1a73fdbcd..830a4f64f 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
@@ -189,6 +189,80 @@ def test_fitness_numpoints_after_nllf():
     assert fitness.numpoints() == 3
 
 
+def test_fitness_evaluation_count_can_be_reset_and_stopped():
+    from bumps.parameter import Parameter as BumpsParameter
+
+    from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+
+    bp = BumpsParameter(value=0.0, name='a')
+    fitness = _EasyDiffractionFitness([bp], lambda values: np.array([values[0]]))
+
+    fitness.residuals()
+    fitness.residuals()
+    assert fitness.evaluation_count == 2
+
+    fitness.reset_evaluation_count()
+    assert fitness.evaluation_count == 0
+
+    fitness.stop_counting_evaluations()
+    fitness.residuals()
+    assert fitness.evaluation_count == 0
+
+
+def test_fitness_raises_when_max_evaluations_is_reached():
+    from bumps.parameter import Parameter as BumpsParameter
+
+    from easydiffraction.analysis.minimizers.bumps import _BumpsEvaluationLimitError
+    from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+
+    bp = BumpsParameter(value=2.0, name='a')
+    fitness = _EasyDiffractionFitness(
+        [bp],
+        lambda values: np.array([values[0]]),
+        max_evaluations=2,
+    )
+
+    fitness.residuals()
+    fitness.residuals()
+
+    with pytest.raises(_BumpsEvaluationLimitError) as exc_info:
+        fitness.residuals()
+
+    assert exc_info.value.evaluation_count == 2
+    np.testing.assert_array_equal(exc_info.value.parameter_values, np.array([2.0]))
+    np.testing.assert_array_equal(exc_info.value.residuals, np.array([2.0]))
+
+
+def test_bumps_progress_monitor_reports_evaluation_count():
+    from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+    from easydiffraction.analysis.minimizers.bumps import _BumpsProgressMonitor
+
+    tracker = MagicMock()
+    fitness = _EasyDiffractionFitness([], lambda values: np.array([]))
+    fitness.reset_evaluation_count()
+    fitness._evaluation_count = 63
+    monitor = _BumpsProgressMonitor(
+        tracker=tracker,
+        fitness=fitness,
+        n_points=20,
+        n_parameters=4,
+    )
+
+    monitor(
+        types.SimpleNamespace(
+            step=[7],
+            value=[8.0],
+            time=[1.5],
+        )
+    )
+
+    tracker.track_fit_progress.assert_called_once_with(
+        iteration=63,
+        reduced_chi2=pytest.approx(1.0),
+        elapsed_time=1.5,
+    )
+
+
 def test_fitness_update_is_noop():
     from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
 
@@ -232,6 +306,8 @@ def test_run_solver_returns_optimize_result():
             bumps_params=[bp1, bp2],
         )
 
+    assert len(mock_driver_cls.call_args.kwargs['monitors']) == 1
+
     assert isinstance(res, OptimizeResult)
     assert res.success is True
     np.testing.assert_array_almost_equal(res.x, [1.5, 2.5])
@@ -265,6 +341,51 @@ def test_run_solver_failure():
     assert res.status == -1
 
 
+def test_run_solver_stops_at_max_evaluations():
+    from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+    from easydiffraction.analysis.minimizers.bumps import _BumpsEvaluationLimitError
+
+    m = BumpsMinimizer(max_iterations=50)
+    m.tracker = MagicMock()
+    m.tracker._current_elapsed_time.return_value = 1.25
+
+    fake_fitter = types.SimpleNamespace(id='lm')
+    limit_error = _BumpsEvaluationLimitError(
+        evaluation_count=50,
+        parameter_values=np.array([1.5]),
+        residuals=np.array([2.0, 2.0]),
+    )
+
+    with (
+        patch('easydiffraction.analysis.minimizers.bumps.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps.FitProblem'),
+        patch('easydiffraction.analysis.minimizers.bumps.FITTERS', [fake_fitter]),
+        patch.object(m, '_compute_covariance', return_value=(None, None)) as mock_covariance,
+    ):
+        driver_instance = mock_driver_cls.return_value
+        driver_instance.fit.side_effect = limit_error
+        driver_instance.clip = MagicMock()
+
+        from bumps.parameter import Parameter as BumpsParameter
+
+        bp = BumpsParameter(value=1.5, name='a')
+        result = m._run_solver(
+            lambda values: np.array([values[0], values[0]]),
+            bumps_params=[bp],
+        )
+
+    assert result.success is False
+    assert result.status == 5
+    assert result.message == 'maximum number of residual evaluations reached'
+    np.testing.assert_array_equal(result.x, np.array([1.5]))
+    mock_covariance.assert_not_called()
+    m.tracker.track_fit_progress.assert_called_once_with(
+        iteration=50,
+        reduced_chi2=pytest.approx(8.0),
+        elapsed_time=1.25,
+    )
+
+
 # -- _sync_result_to_parameters tests -----------------------------------------
 
 
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index d17dce4fa..3a23f5577 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -53,6 +53,28 @@ def test_type_info_and_default_init():
     assert minimizer.steps == 3000
 
 
+def test_dream_uses_steps_instead_of_max_iterations():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        AttributeError,
+        match=r"DREAM sampler uses 'steps' instead of 'max_iterations'\.",
+    ):
+        _ = minimizer.max_iterations
+
+    with pytest.raises(
+        AttributeError,
+        match=r"DREAM sampler uses 'steps' instead of 'max_iterations'\.",
+    ):
+        minimizer.max_iterations = 300
+
+    minimizer.steps = 300
+
+    assert minimizer.steps == 300
+
+
 def test_dream_progress_monitor_allocates_rows_by_phase_ratio():
     from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
 
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_dfols.py b/tests/unit/easydiffraction/analysis/minimizers/test_dfols.py
index 7d94bd04e..68437dd2a 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_dfols.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_dfols.py
@@ -43,10 +43,12 @@ def __init__(self):
 
     def fake_solve(fun, x0, bounds, maxfun):
         # Verify we pass reasonable arguments
+        del fun
         assert isinstance(x0, np.ndarray)
         assert x0.shape[0] == 2
         assert isinstance(bounds, tuple)
         assert all(isinstance(b, np.ndarray) for b in bounds)
+        assert maxfun == 10
         return FakeRes()
 
     monkeypatch.setattr(mod, 'solve', fake_solve)
@@ -63,3 +65,5 @@ def fake_solve(fun, x0, bounds, maxfun):
     assert params[0].uncertainty is None
     assert params[1].uncertainty is None
     assert minim._check_success(res) is True
+    assert minim.max_iterations == 10
+    assert not hasattr(minim, 'steps')
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_lmfit.py b/tests/unit/easydiffraction/analysis/minimizers/test_lmfit.py
index f41e98965..7091fdcd4 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_lmfit.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_lmfit.py
@@ -77,3 +77,38 @@ def __init__(self):
     assert params[1].value == 20.0
     assert params[1].uncertainty == 1.0
     assert minim._check_success(res) is True
+
+
+def test_lmfit_max_iterations_is_user_facing_iteration_setting(monkeypatch):
+    from easydiffraction.analysis.minimizers.lmfit import LmfitMinimizer
+
+    import easydiffraction.analysis.minimizers.lmfit as lm
+
+    observed_max_nfev = {}
+
+    def fake_minimize(
+        objective_function,
+        *,
+        params,
+        method,
+        nan_policy,
+        max_nfev,
+    ):
+        del objective_function, params, method, nan_policy
+        observed_max_nfev['value'] = max_nfev
+        return types.SimpleNamespace(success=True, params={})
+
+    monkeypatch.setattr(
+        lm,
+        'lmfit',
+        types.SimpleNamespace(Parameters=lm.lmfit.Parameters, minimize=fake_minimize),
+    )
+
+    minimizer = LmfitMinimizer()
+
+    minimizer.max_iterations = 300
+    minimizer._run_solver(lambda *args, **kwargs: np.array([0.0]), engine_parameters=object())
+
+    assert minimizer.max_iterations == 300
+    assert observed_max_nfev['value'] == 300
+    assert not hasattr(minimizer, 'steps')
diff --git a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
index 13072544a..8a741fef7 100644
--- a/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
+++ b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
@@ -2,6 +2,10 @@
 # SPDX-License-Identifier: BSD-3-Clause
 """Additional unit tests for analysis.py to cover patch gaps."""
 
+from types import SimpleNamespace
+
+import numpy as np
+
 
 def _make_project():
     class ExpCol:
@@ -199,3 +203,104 @@ class FakeResults:
         assert 'expt1' in a._parameter_snapshots
         assert a._parameter_snapshots['expt1']['p1']['value'] == 1.23
         assert a._parameter_snapshots['expt1']['p1']['uncertainty'] == 0.01
+
+
+class TestBayesianProjection:
+    def test_single_parameter_projection_persists_distribution_and_predictive_caches(self):
+        from easydiffraction.analysis.analysis import Analysis
+        from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+        from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+        from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+        from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+        class Plotter:
+            @staticmethod
+            def _posterior_parameter_bounds(*, fit_results, parameter_name):
+                del fit_results, parameter_name
+                return 0.5, 1.5
+
+            @staticmethod
+            def _posterior_density_curve(values, *, lower_bound, upper_bound):
+                del values
+                return (
+                    np.asarray([lower_bound, upper_bound], dtype=float),
+                    np.asarray([0.25, 0.75], dtype=float),
+                )
+
+            @staticmethod
+            def _resolve_x_axis(experiment_type, _axis_name):
+                del experiment_type
+                return np.asarray([1.0, 2.0], dtype=float), 'two_theta', None, None, None
+
+            @staticmethod
+            def _build_posterior_predictive_summary(
+                *,
+                fit_results,
+                experiment,
+                expt_name,
+                x_axis,
+                include_draws,
+            ):
+                del fit_results, experiment, x_axis, include_draws
+                return PosteriorPredictiveSummary(
+                    experiment_name=expt_name,
+                    x_axis_name='two_theta',
+                    x=np.asarray([1.0, 2.0], dtype=float),
+                    best_sample_prediction=np.asarray([3.0, 4.0], dtype=float),
+                    lower_95=np.asarray([2.5, 3.5], dtype=float),
+                    upper_95=np.asarray([3.5, 4.5], dtype=float),
+                )
+
+        class Experiments:
+            names = ['hrpt']
+
+            def __getitem__(self, name):
+                del name
+                return SimpleNamespace(type='powder')
+
+        project = SimpleNamespace(
+            experiments=Experiments(),
+            structures=object(),
+            rendering=SimpleNamespace(plotter=Plotter()),
+            _varname='proj',
+        )
+        analysis = Analysis(project=project)
+
+        results = BayesianFitResults(
+            success=True,
+            parameters=[],
+            posterior_samples=PosteriorSamples(
+                parameter_names=['alpha'],
+                parameter_samples=np.asarray([[[1.0]], [[1.2]]], dtype=float),
+            ),
+            posterior_parameter_summaries=[
+                PosteriorParameterSummary(
+                    unique_name='alpha',
+                    display_name='Alpha',
+                    best_sample_value=1.2,
+                    median=1.1,
+                    standard_deviation=0.1,
+                    interval_68=(1.0, 1.2),
+                    interval_95=(0.9, 1.3),
+                )
+            ],
+            posterior_predictive={},
+            sampler_settings={},
+            convergence_diagnostics={},
+        )
+
+        analysis._store_bayesian_result_projection(results)
+
+        assert analysis.bayesian_result.has_distribution_cache.value is True
+        assert analysis.bayesian_result.has_pair_cache.value is False
+        assert analysis.bayesian_result.has_posterior_predictive.value is True
+        assert np.allclose(
+            analysis._persisted_fit_state_sidecar['distribution_caches']['alpha']['x'],
+            np.asarray([0.5, 1.5], dtype=float),
+        )
+        assert np.allclose(
+            analysis._persisted_fit_state_sidecar['predictive_datasets']['hrpt'][
+                'best_sample_prediction'
+            ],
+            np.asarray([3.0, 4.0], dtype=float),
+        )
diff --git a/tests/unit/easydiffraction/analysis/test_enums.py b/tests/unit/easydiffraction/analysis/test_enums.py
index fe854e536..75e1ca3ec 100644
--- a/tests/unit/easydiffraction/analysis/test_enums.py
+++ b/tests/unit/easydiffraction/analysis/test_enums.py
@@ -2,6 +2,8 @@
 # SPDX-License-Identifier: BSD-3-Clause
 """Tests for analysis/enums.py."""
 
+from easydiffraction.analysis.enums import FitCorrelationSourceEnum
+from easydiffraction.analysis.enums import FitResultKindEnum
 from easydiffraction.analysis.enums import FitModeEnum
 
 
@@ -20,3 +22,15 @@ def test_fit_mode_enum_descriptions():
         description = member.description()
         assert isinstance(description, str)
         assert description
+
+
+def test_fit_result_kind_enum_members_and_default():
+    assert FitResultKindEnum.DETERMINISTIC == 'deterministic'
+    assert FitResultKindEnum.BAYESIAN == 'bayesian'
+    assert FitResultKindEnum.default() is FitResultKindEnum.DETERMINISTIC
+
+
+def test_fit_correlation_source_enum_members_and_default():
+    assert FitCorrelationSourceEnum.DETERMINISTIC == 'deterministic'
+    assert FitCorrelationSourceEnum.POSTERIOR == 'posterior'
+    assert FitCorrelationSourceEnum.default() is FitCorrelationSourceEnum.DETERMINISTIC
diff --git a/tests/unit/easydiffraction/analysis/test_fitting.py b/tests/unit/easydiffraction/analysis/test_fitting.py
index 0f7ef88b1..83272f9fa 100644
--- a/tests/unit/easydiffraction/analysis/test_fitting.py
+++ b/tests/unit/easydiffraction/analysis/test_fitting.py
@@ -1,6 +1,10 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from types import SimpleNamespace
+
+from easydiffraction.utils.enums import VerbosityEnum
+
 
 def test_module_import():
     import easydiffraction.analysis.fitting as MUT
@@ -71,7 +75,11 @@ class DummyExperiment:
         parameters = []
 
     class MockFitResults:
-        pass
+        def __init__(self):
+            self.message = ''
+            self.iterations = 0
+            self.chi_square = None
+            self.engine_result = object()
 
     class DummyMin:
         tracker = type('T', (), {'track': staticmethod(lambda a, b: a)})()
@@ -82,6 +90,9 @@ def fit(self, params, obj, verbosity=None, **kwargs):
         def _sync_result_to_parameters(self, params, engine_params):
             pass
 
+        def _stop_tracking(self):
+            return None
+
     f = Fitter()
     f.minimizer = DummyMin()
 
@@ -101,3 +112,174 @@ def mock_process(*args, **kwargs):
         'Use Analysis.show_fit_results() instead.'
     )
     assert f.results is not None, 'Fitter.fit() should still set results'
+
+
+def test_fitter_fit_defers_minimizer_tracking_until_postprocessing(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+    from easydiffraction.analysis.fitting import Fitter
+
+    class DummyParam:
+        value = 1.0
+        uncertainty = 0.1
+        _fit_start_value = None
+
+    class DummyStructure:
+        _need_categories_update = False
+
+        def _update_categories(self):
+            return None
+
+    class DummyStructures:
+        def __iter__(self):
+            return iter([DummyStructure()])
+
+    class DummyExperiment:
+        parameters = []
+
+    class DummyMin:
+        def __init__(self):
+            self.fit_calls: list[dict[str, object]] = []
+            self.stop_calls = 0
+            self.tracker = SimpleNamespace(track=lambda residuals, parameters: residuals)
+
+        def fit(self, params, obj, verbosity=None, **kwargs):
+            del params, obj
+            self.fit_calls.append({'verbosity': verbosity, **kwargs})
+            return BayesianFitResults(
+                success=True,
+                reduced_chi_square=1.2,
+                convergence_diagnostics={'converged': False},
+                sampler_settings={'steps': 300},
+                best_log_posterior=-10.0,
+            )
+
+        def _stop_tracking(self):
+            self.stop_calls += 1
+
+    analysis_events: list[str] = []
+    analysis = SimpleNamespace(
+        _capture_fit_parameter_state=lambda params: analysis_events.append('capture'),
+        _store_fit_result_projection=lambda results, experiments, fitted_parameters: (
+            analysis_events.append('store')
+        ),
+    )
+
+    fitter = Fitter()
+    fitter.minimizer = DummyMin()
+    monkeypatch.setattr(
+        fitter,
+        '_collect_fit_parameters',
+        lambda structures, experiments: [DummyParam()],
+    )
+
+    fitter.fit(
+        structures=DummyStructures(),
+        experiments=[DummyExperiment()],
+        analysis=analysis,
+        verbosity=VerbosityEnum.FULL,
+    )
+
+    assert fitter.minimizer.fit_calls[0]['finalize_tracking'] is False
+    assert fitter.minimizer.stop_calls == 1
+    assert analysis_events == ['capture', 'store']
+
+
+def test_fitter_fit_stops_tracking_when_minimizer_fit_raises(monkeypatch):
+    import pytest
+
+    from easydiffraction.analysis.fitting import Fitter
+
+    class DummyParam:
+        value = 1.0
+        _fit_start_value = None
+
+    class DummyStructure:
+        _need_categories_update = False
+
+        def _update_categories(self):
+            return None
+
+    class DummyStructures:
+        def __iter__(self):
+            return iter([DummyStructure()])
+
+    class DummyExperiment:
+        parameters = []
+
+    class DummyMin:
+        def __init__(self):
+            self.stop_calls = 0
+            self.tracker = SimpleNamespace(track=lambda residuals, parameters: residuals)
+
+        def fit(self, params, obj, verbosity=None, **kwargs):
+            del params, obj, verbosity, kwargs
+            msg = 'fit failed'
+            raise RuntimeError(msg)
+
+        def _stop_tracking(self):
+            self.stop_calls += 1
+
+    fitter = Fitter()
+    fitter.minimizer = DummyMin()
+    monkeypatch.setattr(
+        fitter,
+        '_collect_fit_parameters',
+        lambda structures, experiments: [DummyParam()],
+    )
+
+    with pytest.raises(RuntimeError, match='fit failed'):
+        fitter.fit(
+            structures=DummyStructures(),
+            experiments=[DummyExperiment()],
+            verbosity=VerbosityEnum.FULL,
+        )
+
+    assert fitter.minimizer.stop_calls == 1
+
+
+def test_residual_function_skips_tracker_for_solver_monitored_minimizer(monkeypatch):
+    import numpy as np
+
+    from easydiffraction.analysis.fitting import Fitter
+
+    class DummyExperiment:
+        def _update_categories(self, *, called_by_minimizer=False):
+            del called_by_minimizer
+            return
+
+    class DummyMin:
+        def __init__(self):
+            self.tracker = SimpleNamespace(
+                track=lambda residuals, parameters: (_ for _ in ()).throw(
+                    AssertionError('tracker.track should not be called')
+                )
+            )
+
+        def _sync_result_to_parameters(self, parameters, engine_params):
+            del parameters, engine_params
+
+        def _tracks_progress_via_solver_monitor(self):
+            return True
+
+    fitter = Fitter()
+    fitter.minimizer = DummyMin()
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fitting.intensity_category_for',
+        lambda experiment: SimpleNamespace(
+            intensity_calc=np.array([1.0]),
+            intensity_meas=np.array([2.0]),
+            intensity_meas_su=np.array([1.0]),
+        ),
+    )
+
+    residuals = fitter._residual_function(
+        engine_params={},
+        parameters=[],
+        structures=[],
+        experiments=[DummyExperiment()],
+        weights=None,
+        analysis=None,
+    )
+
+    np.testing.assert_allclose(residuals, np.array([1.0]))
diff --git a/tests/unit/easydiffraction/analysis/test_sequential.py b/tests/unit/easydiffraction/analysis/test_sequential.py
index ab2327c94..2be4c19f3 100644
--- a/tests/unit/easydiffraction/analysis/test_sequential.py
+++ b/tests/unit/easydiffraction/analysis/test_sequential.py
@@ -137,7 +137,7 @@ def _run_non_silent_fit(monkeypatch, tmp_path, *, verbosity, is_jupyter):
     del is_jupyter  # legacy parameter, no longer affects behavior
 
     analysis = SimpleNamespace(
-        project=SimpleNamespace(verbosity=verbosity),
+        project=SimpleNamespace(verbosity=SimpleNamespace(fit=SimpleNamespace(value=verbosity))),
         fitter=SimpleNamespace(selection='lmfit'),
     )
 
@@ -198,7 +198,7 @@ def test_full_header_order(self):
 class TestCsvWriteAndAppend:
     def test_write_creates_file_with_header(self, tmp_path):
         csv_path = tmp_path / 'results.csv'
-        header = ['file_path', 'chi_squared', 'param_a']
+        header = ['file_path', 'fit_result.reduced_chi_square', 'param_a']
         _write_csv_header(csv_path, header)
 
         with csv_path.open() as f:
@@ -336,19 +336,17 @@ def test_returns_fitted_file_paths(self, tmp_path):
             [
                 {
                     'file_path': str(project_dir / 'experiments' / 'a.dat'),
-                    'fit_success': 'True',
-                    'chi_squared': '5.0',
-                    'reduced_chi_squared': '2.5',
-                    'n_iterations': '10',
+                    'fit_result.success': 'True',
+                    'fit_result.reduced_chi_square': '2.5',
+                    'fit_result.iterations': '10',
                     'cell.a': '3.89',
                     'cell.a.uncertainty': '0.01',
                 },
                 {
                     'file_path': str(project_dir / 'experiments' / 'b.dat'),
-                    'fit_success': 'False',
-                    'chi_squared': '',
-                    'reduced_chi_squared': '',
-                    'n_iterations': '0',
+                    'fit_result.success': 'False',
+                    'fit_result.reduced_chi_square': '',
+                    'fit_result.iterations': '0',
                     'cell.a': '',
                     'cell.a.uncertainty': '',
                 },
@@ -374,10 +372,9 @@ def test_resolves_legacy_repo_relative_paths(self, tmp_path, monkeypatch):
             writer.writeheader()
             writer.writerow({
                 'file_path': 'projects/cosio/experiments/d20_scan/scan_001.dat',
-                'fit_success': 'True',
-                'chi_squared': '5.0',
-                'reduced_chi_squared': '2.5',
-                'n_iterations': '10',
+                'fit_result.success': 'True',
+                'fit_result.reduced_chi_square': '2.5',
+                'fit_result.iterations': '10',
                 'cell.a': '3.89',
                 'cell.a.uncertainty': '0.01',
             })
@@ -397,19 +394,17 @@ def test_returns_last_successful_params(self, tmp_path):
             [
                 {
                     'file_path': 'a.dat',
-                    'fit_success': 'True',
-                    'chi_squared': '5.0',
-                    'reduced_chi_squared': '2.5',
-                    'n_iterations': '10',
+                    'fit_result.success': 'True',
+                    'fit_result.reduced_chi_square': '2.5',
+                    'fit_result.iterations': '10',
                     'cell.a': '3.89',
                     'cell.a.uncertainty': '0.01',
                 },
                 {
                     'file_path': 'b.dat',
-                    'fit_success': 'True',
-                    'chi_squared': '4.0',
-                    'reduced_chi_squared': '2.0',
-                    'n_iterations': '8',
+                    'fit_result.success': 'True',
+                    'fit_result.reduced_chi_square': '2.0',
+                    'fit_result.iterations': '8',
                     'cell.a': '3.90',
                     'cell.a.uncertainty': '0.02',
                 },
@@ -436,10 +431,9 @@ def test_skips_meta_columns_and_diffrn_and_uncertainty(self, tmp_path):
             [
                 {
                     'file_path': 'a.dat',
-                    'fit_success': 'True',
-                    'chi_squared': '5.0',
-                    'reduced_chi_squared': '2.5',
-                    'n_iterations': '10',
+                    'fit_result.success': 'True',
+                    'fit_result.reduced_chi_square': '2.5',
+                    'fit_result.iterations': '10',
                     'diffrn.temp': '300',
                     'cell.a': '3.89',
                     'cell.a.uncertainty': '0.01',
@@ -452,7 +446,7 @@ def test_skips_meta_columns_and_diffrn_and_uncertainty(self, tmp_path):
         assert 'cell.a' in params
         # Meta columns, diffrn, and uncertainty should be excluded
         assert 'file_path' not in params
-        assert 'fit_success' not in params
+        assert 'fit_result.success' not in params
         assert 'diffrn.temp' not in params
         assert 'cell.a.uncertainty' not in params
 
@@ -466,10 +460,9 @@ def test_returns_none_params_when_no_successful_rows(self, tmp_path):
             [
                 {
                     'file_path': 'a.dat',
-                    'fit_success': 'False',
-                    'chi_squared': '',
-                    'reduced_chi_squared': '',
-                    'n_iterations': '0',
+                    'fit_result.success': 'False',
+                    'fit_result.reduced_chi_square': '',
+                    'fit_result.iterations': '0',
                     'cell.a': '',
                     'cell.a.uncertainty': '',
                 },
@@ -537,15 +530,15 @@ def update(self, *, label=None, content=None):
         [
             {
                 'file_path': _TEST_SCAN_001,
-                'fit_success': True,
-                'reduced_chi_squared': 4.0,
-                'n_iterations': 11,
+                'fit_result.success': True,
+                'fit_result.reduced_chi_square': 4.0,
+                'fit_result.iterations': 11,
             },
             {
                 'file_path': _TEST_SCAN_002,
-                'fit_success': False,
-                'reduced_chi_squared': None,
-                'n_iterations': 0,
+                'fit_result.success': False,
+                'fit_result.reduced_chi_square': None,
+                'fit_result.iterations': 0,
             },
         ],
         progress,
@@ -608,7 +601,7 @@ def test_fit_sequential_non_silent_starts_indicator_with_progress_table(
     assert events[8] == ('stop',)
     assert events[9:] == [
         ('console_print', ('✅ Sequential fitting complete: 1 files processed.',), {}),
-        ('console_print', (f'📄 Results saved to: {tmp_path / "results.csv"}',), {}),
+        ('console_print', (f'📄 Results saved to:\n{tmp_path / "results.csv"}',), {}),
     ]
 
 
@@ -631,9 +624,9 @@ def map(self, func, templates, paths):
             for path in paths:
                 yield {
                     'file_path': path,
-                    'fit_success': True,
-                    'reduced_chi_squared': 1.0,
-                    'n_iterations': 5,
+                    'fit_result.success': True,
+                    'fit_result.reduced_chi_square': 1.0,
+                    'fit_result.iterations': 5,
                     'params': {'cell.a': 4.0},
                 }
 
@@ -728,7 +721,7 @@ def fake_run_fit_loop(
     monkeypatch.setattr(sequential_mod, '_restore_main_state', lambda *args: None)
 
     analysis = SimpleNamespace(
-        project=SimpleNamespace(verbosity='silent'),
+        project=SimpleNamespace(verbosity=SimpleNamespace(fit=SimpleNamespace(value='silent'))),
         fitter=SimpleNamespace(selection='lmfit'),
     )
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 461e85ce2..d5b6ae76c 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -1,6 +1,7 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+import csv
 import re
 from types import MethodType
 from types import SimpleNamespace
@@ -66,6 +67,78 @@ def test_plotter_factory_supported_and_unsupported():
         PlotterFactory.create('nope')
 
 
+@pytest.mark.parametrize(
+    ('descriptor_name', 'column_values', 'expected_y'),
+    [
+        ('reduced_chi_square', ['1.5', '2.5'], [1.5, 2.5]),
+        ('iterations', ['5', '8'], [5.0, 8.0]),
+        ('success', ['True', 'False'], [1.0, 0.0]),
+    ],
+)
+def test_plot_param_series_reads_fit_result_columns_from_csv(
+    monkeypatch,
+    tmp_path,
+    descriptor_name,
+    column_values,
+    expected_y,
+):
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.project.project import Project
+
+    project = Project(name='series')
+    project.info.path = tmp_path
+
+    analysis_dir = tmp_path / 'analysis'
+    analysis_dir.mkdir(parents=True)
+    csv_path = analysis_dir / 'results.csv'
+    with csv_path.open('w', newline='', encoding='utf-8') as handle:
+        writer = csv.DictWriter(
+            handle,
+            fieldnames=[
+                'file_path',
+                'fit_result.reduced_chi_square',
+                'fit_result.success',
+                'fit_result.iterations',
+            ],
+        )
+        writer.writeheader()
+        writer.writerow({
+            'file_path': 'a.dat',
+            'fit_result.reduced_chi_square': column_values[0],
+            'fit_result.success': column_values[0] if descriptor_name == 'success' else 'True',
+            'fit_result.iterations': column_values[0] if descriptor_name == 'iterations' else '5',
+        })
+        writer.writerow({
+            'file_path': 'b.dat',
+            'fit_result.reduced_chi_square': column_values[1],
+            'fit_result.success': column_values[1] if descriptor_name == 'success' else 'False',
+            'fit_result.iterations': column_values[1] if descriptor_name == 'iterations' else '8',
+        })
+
+    captured: dict[str, object] = {}
+
+    plotter = Plotter()
+    plotter._set_project(project)
+
+    def fake_plot_scatter(*, x, y, sy, axes_labels, title, height):
+        captured['x'] = x
+        captured['y'] = y
+        captured['sy'] = sy
+        captured['axes_labels'] = axes_labels
+        captured['title'] = title
+        captured['height'] = height
+
+    monkeypatch.setattr(plotter._backend, 'plot_scatter', fake_plot_scatter)
+
+    plotter.plot_param_series(getattr(project.analysis.fit_result, descriptor_name))
+
+    assert captured['x'] == [1, 2]
+    assert captured['y'] == expected_y
+    assert captured['sy'] == [0.0, 0.0]
+    assert captured['axes_labels'] == ['Experiment No.', 'Parameter value']
+    assert captured['title'] == f"Parameter 'fit_result.{descriptor_name}' across fit results"
+
+
 def test_plotter_error_paths_and_filtering(capsys, monkeypatch):
     from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
     from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize.py b/tests/unit/easydiffraction/io/cif/test_serialize.py
index 3a24b6ce2..375e5aa86 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize.py
@@ -27,7 +27,7 @@ def __init__(self):
             self.value = 3
 
     p = P()
-    assert MUT.param_to_cif(p) == '_x.y 3.'
+    assert MUT.param_to_cif(p) == '_x.y 3'
 
 
 def test_format_param_value_with_uncertainty_uses_two_sig_digits():
diff --git a/tests/unit/easydiffraction/io/cif/test_serialize_more.py b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
index f526f21a8..3c9db1bc1 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize_more.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
@@ -34,7 +34,7 @@ def __init__(self):
 
     out = MUT.datablock_item_to_cif(DB())
     assert out.startswith('data_block1')
-    assert '_aa 42.' in out
+    assert '_aa 42' in out
     assert 'loop_' in out
     assert '_aa' in out
     assert '7' in out
@@ -80,7 +80,7 @@ def __init__(self):
             self.empty_coll = CategoryCollection(item_type=Item)
 
     out = MUT.datablock_item_to_cif(DB())
-    assert out == 'data_block1\n\n_aa 42.\n\nloop_\n_aa\n7.'
+    assert out == 'data_block1\n\n_aa 42\n\nloop_\n_aa\n7'
     assert '\n\n\n' not in out
 
 
@@ -107,11 +107,24 @@ def test_project_info_to_cif_contains_core_fields():
     info = ProjectInfo(name='p1', title='My Title', description='Some description text')
     out = MUT.project_info_to_cif(info)
     assert '_project.id               p1' in out
-    assert '_project.title' in out
-    assert 'My Title' in out
-    assert '_project.description' in out
-    assert '_project.created' in out
-    assert '_project.last_modified' in out
+    assert '_project.title            "My Title"' in out
+    assert '_project.description      "Some description text"' in out
+    assert '_project.created          "' in out
+    assert '_project.last_modified    "' in out
+
+
+def test_project_info_to_cif_wraps_long_description_as_text_field():
+    import easydiffraction.io.cif.serialize as MUT
+    from easydiffraction.project.project_info import ProjectInfo
+
+    description = ' '.join(['long'] * 20)
+    info = ProjectInfo(name='p1', title='My Title', description=description)
+
+    out = MUT.project_info_to_cif(info)
+
+    assert '_project.description      ' in out
+    assert '\n;\n' in out
+    assert 'long long long long long long long long long long long long' in out
 
 
 def test_experiment_to_cif_with_and_without_data():
@@ -159,7 +172,7 @@ def as_cif(self):
 
     out_without = MUT.experiment_to_cif(Exp(''))
     assert out_without.startswith('data_expA')
-    assert out_without.endswith('1.')
+    assert out_without.endswith('1')
 
 
 def test_analysis_to_cif_renders_all_sections():
diff --git a/tests/unit/easydiffraction/io/test_results_sidecar.py b/tests/unit/easydiffraction/io/test_results_sidecar.py
new file mode 100644
index 000000000..556e21e5a
--- /dev/null
+++ b/tests/unit/easydiffraction/io/test_results_sidecar.py
@@ -0,0 +1,138 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for persisted Bayesian results sidecars."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+
+
+def _analysis_with_predictive_sidecar() -> object:
+    from easydiffraction.analysis.categories.bayesian_predictive_datasets.default import (
+        BayesianPredictiveDatasetPaths,
+        BayesianPredictiveDatasets,
+    )
+    from easydiffraction.analysis.categories.bayesian_result.default import BayesianResult
+    from easydiffraction.analysis.categories.fit_result.default import FitResult
+
+    fit_result = FitResult()
+    fit_result._set_result_kind('bayesian')
+    bayesian_result = BayesianResult()
+    bayesian_result._set_has_posterior_predictive(value=True)
+    predictive = BayesianPredictiveDatasets()
+    predictive.create(
+        experiment_name='hrpt',
+        x_axis_name='two_theta',
+        paths=BayesianPredictiveDatasetPaths(
+            x_path='/predictive/hrpt/x',
+            best_sample_prediction_path='/predictive/hrpt/best_sample_prediction',
+            lower_95_path='/predictive/hrpt/lower_95',
+            upper_95_path='/predictive/hrpt/upper_95',
+        ),
+        n_x=2,
+        n_draws_cached=0,
+    )
+    return SimpleNamespace(
+        fit_result=fit_result,
+        bayesian_result=bayesian_result,
+        bayesian_convergence=SimpleNamespace(
+            n_draws=SimpleNamespace(value=0),
+            n_chains=SimpleNamespace(value=0),
+            n_parameters=SimpleNamespace(value=0),
+        ),
+        bayesian_distribution_caches=[],
+        bayesian_pair_caches=[],
+        bayesian_predictive_datasets=predictive,
+        fit_results=SimpleNamespace(
+            posterior_predictive={
+                'hrpt': SimpleNamespace(
+                    experiment_name='hrpt',
+                    x_axis_name='two_theta',
+                    x=np.asarray([1.0, 2.0]),
+                    best_sample_prediction=np.asarray([3.0, 4.0]),
+                    lower_95=np.asarray([2.5, 3.5]),
+                    upper_95=np.asarray([3.5, 4.5]),
+                    lower_68=None,
+                    upper_68=None,
+                    draws=None,
+                )
+            }
+        ),
+        _persisted_fit_state_sidecar={},
+        _has_persisted_fit_state=lambda: True,
+    )
+
+
+def test_write_and_read_analysis_results_sidecar_round_trip_predictive(tmp_path):
+    from easydiffraction.io.results_sidecar import read_analysis_results_sidecar
+    from easydiffraction.io.results_sidecar import write_analysis_results_sidecar
+
+    analysis_dir = Path(tmp_path)
+    analysis = _analysis_with_predictive_sidecar()
+
+    write_analysis_results_sidecar(analysis=analysis, analysis_dir=analysis_dir)
+
+    sidecar_path = analysis_dir / 'results.h5'
+    assert sidecar_path.is_file()
+
+    restored = _analysis_with_predictive_sidecar()
+    restored.fit_results = None
+    read_analysis_results_sidecar(analysis=restored, analysis_dir=analysis_dir)
+
+    assert 'predictive_datasets' in restored._persisted_fit_state_sidecar
+    dataset = restored._persisted_fit_state_sidecar['predictive_datasets']['hrpt']
+    assert np.allclose(dataset['x'], np.asarray([1.0, 2.0]))
+    assert np.allclose(dataset['best_sample_prediction'], np.asarray([3.0, 4.0]))
+
+
+def test_read_analysis_results_sidecar_warns_when_expected_file_is_missing(tmp_path, monkeypatch):
+    from easydiffraction.io import results_sidecar as results_sidecar_mod
+
+    analysis = _analysis_with_predictive_sidecar()
+    warnings: list[str] = []
+    monkeypatch.setattr(results_sidecar_mod.log, 'warning', warnings.append)
+
+    results_sidecar_mod.read_analysis_results_sidecar(
+        analysis=analysis,
+        analysis_dir=Path(tmp_path),
+    )
+
+    assert analysis._persisted_fit_state_sidecar == {}
+    assert any('Expected Bayesian results sidecar is missing' in warning for warning in warnings)
+
+
+def test_sidecar_path_traversal_falls_back_to_local_results_file(tmp_path, monkeypatch):
+    from easydiffraction.io import results_sidecar as results_sidecar_mod
+
+    analysis_dir = Path(tmp_path) / 'analysis'
+    external_sidecar = Path(tmp_path) / 'outside.h5'
+    analysis = _analysis_with_predictive_sidecar()
+    analysis.bayesian_result._set_sidecar_file('../outside.h5')
+
+    warnings: list[str] = []
+    monkeypatch.setattr(results_sidecar_mod.log, 'warning', warnings.append)
+
+    results_sidecar_mod.write_analysis_results_sidecar(
+        analysis=analysis,
+        analysis_dir=analysis_dir,
+    )
+
+    assert (analysis_dir / 'results.h5').is_file()
+    assert not external_sidecar.exists()
+
+    restored = _analysis_with_predictive_sidecar()
+    restored.fit_results = None
+    restored.bayesian_result._set_sidecar_file('../outside.h5')
+    results_sidecar_mod.read_analysis_results_sidecar(
+        analysis=restored,
+        analysis_dir=analysis_dir,
+    )
+
+    assert 'predictive_datasets' in restored._persisted_fit_state_sidecar
+    assert any(
+        'Ignoring Bayesian sidecar file path outside the analysis directory' in warning
+        for warning in warnings
+    )
diff --git a/tests/unit/easydiffraction/project/categories/rendering/test_default.py b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
index edb884272..07a168ae2 100644
--- a/tests/unit/easydiffraction/project/categories/rendering/test_default.py
+++ b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
@@ -5,14 +5,18 @@
 
 
 def test_rendering_defaults():
+    from easydiffraction.display.plotting import PlotterEngineEnum
+    from easydiffraction.display.tables import TableEngineEnum
     from easydiffraction.project.categories.rendering.default import Rendering
 
     rendering = Rendering()
 
     assert rendering.type_info.tag == 'default'
     assert rendering._identity.category_code == 'rendering'
-    assert rendering.chart_engine.value == rendering.plotter.engine
-    assert rendering.table_engine.value == rendering.tabler.engine
+    assert rendering.chart_engine.value == 'auto'
+    assert rendering.table_engine.value == 'auto'
+    assert rendering.plotter.engine in [member.value for member in PlotterEngineEnum]
+    assert rendering.tabler.engine in [member.value for member in TableEngineEnum]
 
 
 def test_rendering_plotter_binds_parent():
@@ -28,6 +32,8 @@ def test_rendering_plotter_binds_parent():
 
 
 def test_rendering_setters_update_engines():
+    from easydiffraction.display.plotting import PlotterEngineEnum
+    from easydiffraction.display.tables import TableEngineEnum
     from easydiffraction.project.categories.rendering.default import Rendering
 
     rendering = Rendering()
@@ -40,6 +46,14 @@ def test_rendering_setters_update_engines():
     assert rendering.table_engine.value == 'rich'
     assert rendering.tabler.engine == 'rich'
 
+    rendering.chart_engine = 'auto'
+    rendering.table_engine = 'auto'
+
+    assert rendering.chart_engine.value == 'auto'
+    assert rendering.table_engine.value == 'auto'
+    assert rendering.plotter.engine == PlotterEngineEnum.default().value
+    assert rendering.tabler.engine == TableEngineEnum.default().value
+
 
 def test_rendering_from_cif_restores_types():
     from easydiffraction.project.categories.rendering.default import Rendering
diff --git a/tests/unit/easydiffraction/project/categories/verbosity/test_default.py b/tests/unit/easydiffraction/project/categories/verbosity/test_default.py
new file mode 100644
index 000000000..782bdbbf8
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/verbosity/test_default.py
@@ -0,0 +1,44 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import gemmi
+import pytest
+
+
+def test_verbosity_defaults_and_cif_output():
+    from easydiffraction.project.categories.verbosity.default import Verbosity
+
+    verbosity = Verbosity()
+
+    assert verbosity.type_info.tag == 'default'
+    assert verbosity._identity.category_code == 'verbosity'
+    assert verbosity.fit.value == 'full'
+    assert '_verbosity.fit full' in verbosity.as_cif
+
+
+def test_verbosity_setter_validates_enum_values():
+    from easydiffraction.project.categories.verbosity.default import Verbosity
+
+    verbosity = Verbosity()
+
+    verbosity.fit = 'short'
+    assert verbosity.fit.value == 'short'
+
+    verbosity.fit = 'silent'
+    assert verbosity.fit.value == 'silent'
+
+    with pytest.raises(ValueError, match="'verbose' is not a valid VerbosityEnum"):
+        verbosity.fit = 'verbose'
+
+
+def test_verbosity_from_cif_restores_fit_value():
+    from easydiffraction.project.categories.verbosity.default import Verbosity
+
+    verbosity = Verbosity()
+    block = gemmi.cif.read_string('data_test\n_verbosity.fit short\n').sole_block()
+
+    verbosity.from_cif(block)
+
+    assert verbosity.fit.value == 'short'
diff --git a/tests/unit/easydiffraction/project/categories/verbosity/test_factory.py b/tests/unit/easydiffraction/project/categories/verbosity/test_factory.py
new file mode 100644
index 000000000..3d3d6941b
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/verbosity/test_factory.py
@@ -0,0 +1,25 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import pytest
+
+
+def test_verbosity_factory_default_and_create():
+    from easydiffraction.project.categories.verbosity.default import Verbosity
+    from easydiffraction.project.categories.verbosity.factory import VerbosityFactory
+
+    assert VerbosityFactory.default_tag() == 'default'
+    assert 'default' in VerbosityFactory.supported_tags()
+
+    verbosity = VerbosityFactory.create('default')
+
+    assert isinstance(verbosity, Verbosity)
+
+
+def test_verbosity_factory_rejects_unknown_tag():
+    from easydiffraction.project.categories.verbosity.factory import VerbosityFactory
+
+    with pytest.raises(ValueError, match=r"Unsupported type: 'missing'"):
+        VerbosityFactory.create('missing')
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index 1a892bca0..f534da493 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -47,12 +47,25 @@ def _recorder(*args, **kwargs):
         plot_calc=record('plot_calc'),
         plot_meas_vs_calc=record('plot_meas_vs_calc'),
         _plot_meas_vs_calc_request=record('_plot_meas_vs_calc_request'),
+        engine='plotly',
+        _resolve_x_axis=lambda expt_type, x: ('two_theta', 'two_theta', None, None, None),
     )
     project = SimpleNamespace(
-        analysis=SimpleNamespace(display=analysis_display),
+        analysis=SimpleNamespace(
+            display=analysis_display,
+            fit_results=SimpleNamespace(posterior_predictive={}),
+            bayesian_result=SimpleNamespace(
+                has_pair_cache=SimpleNamespace(value=False),
+                has_posterior_predictive=SimpleNamespace(value=False),
+            ),
+            bayesian_pair_caches=[],
+            bayesian_predictive_datasets=[],
+            _persisted_fit_state_sidecar={},
+        ),
         rendering=SimpleNamespace(plotter=plotter),
+        experiments={'hrpt': SimpleNamespace(type=SimpleNamespace())},
         free_parameters=[],
-        verbosity='full',
+        verbosity=SimpleNamespace(fit=SimpleNamespace(value='full')),
     )
     return project, calls
 
@@ -267,6 +280,67 @@ def fake_activity_indicator(label, *, verbosity):
     ]
 
 
+def test_posterior_predictive_skips_processing_indicator_for_restored_cache(monkeypatch):
+    import easydiffraction.project.display as display_mod
+
+    project, calls = _make_project_stub()
+    project.analysis = SimpleNamespace(
+        fit_results=object(),
+        bayesian_result=SimpleNamespace(has_posterior_predictive=SimpleNamespace(value=True)),
+        bayesian_predictive_datasets=[
+            SimpleNamespace(
+                experiment_name=SimpleNamespace(value='hrpt'),
+                x_axis_name=SimpleNamespace(value='two_theta'),
+                draws_path=SimpleNamespace(value=None),
+            )
+        ],
+        _persisted_fit_state_sidecar={
+            'predictive_datasets': {
+                'hrpt': {
+                    'x': [1.0, 2.0],
+                    'best_sample_prediction': [3.0, 4.0],
+                }
+            }
+        },
+    )
+    project.experiments = {'hrpt': SimpleNamespace(type=SimpleNamespace())}
+    project.rendering.plotter.engine = 'plotly'
+    project.rendering.plotter._resolve_x_axis = lambda expt_type, x: (
+        'two_theta',
+        'two_theta',
+        None,
+        None,
+        None,
+    )
+    display = ProjectDisplay(project)
+    indicator_calls: list[tuple[str, VerbosityEnum]] = []
+
+    @contextmanager
+    def fake_activity_indicator(label, *, verbosity):
+        indicator_calls.append((label, verbosity))
+        yield object()
+
+    monkeypatch.setattr(display_mod, 'activity_indicator', fake_activity_indicator)
+
+    display.posterior.predictive('hrpt')
+
+    assert calls == [
+        (
+            'plot_posterior_predictive',
+            (),
+            {
+                'expt_name': 'hrpt',
+                'style': 'band',
+                'x_min': None,
+                'x_max': None,
+                'show_residual': None,
+                'x': None,
+            },
+        )
+    ]
+    assert indicator_calls == []
+
+
 def test_posterior_distribution_without_param_plots_all_free_parameters():
     project, calls = _make_project_stub()
     project.free_parameters = ['a', 'b']
diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py
index e3dcf7a19..3afbd363e 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -30,7 +30,7 @@ def test_project_verbosity_default():
     from easydiffraction.project.project import Project
 
     p = Project()
-    assert p.verbosity == 'full'
+    assert p.verbosity.fit.value == 'full'
 
 
 def test_project_verbosity_setter():
@@ -38,11 +38,11 @@ def test_project_verbosity_setter():
 
     p = Project()
     p.verbosity = 'short'
-    assert p.verbosity == 'short'
+    assert p.verbosity.fit.value == 'short'
     p.verbosity = 'silent'
-    assert p.verbosity == 'silent'
+    assert p.verbosity.fit.value == 'silent'
     p.verbosity = 'full'
-    assert p.verbosity == 'full'
+    assert p.verbosity.fit.value == 'full'
 
 
 def test_project_verbosity_invalid():
diff --git a/tests/unit/easydiffraction/project/test_project_config.py b/tests/unit/easydiffraction/project/test_project_config.py
index 3ba20eea1..be6576e94 100644
--- a/tests/unit/easydiffraction/project/test_project_config.py
+++ b/tests/unit/easydiffraction/project/test_project_config.py
@@ -23,8 +23,12 @@ def test_project_config_exposes_project_info_and_rendering_categories():
     assert config.info.path is None
     assert isinstance(config.info.created, datetime.datetime)
     assert isinstance(config.info.last_modified, datetime.datetime)
-    assert config.categories == [config.info, config.rendering]
-    assert config.parameters == config.info.parameters + config.rendering.parameters
+    assert config.verbosity._parent is config
+    assert config.verbosity.fit.value == 'full'
+    assert config.categories == [config.info, config.rendering, config.verbosity]
+    assert config.parameters == (
+        config.info.parameters + config.rendering.parameters + config.verbosity.parameters
+    )
 
 
 def test_project_config_as_cif_has_project_and_rendering_sections_without_data_header():
@@ -42,6 +46,29 @@ def test_project_config_as_cif_has_project_and_rendering_sections_without_data_h
     assert '_project.last_modified' in cif_text
     assert '_rendering.chart_engine' in cif_text
     assert '_rendering.table_engine' in cif_text
+    assert '_rendering.chart_engine auto' in cif_text
+    assert '_rendering.table_engine auto' in cif_text
+    assert '_verbosity.fit full' in cif_text
+
+
+def test_project_save_and_load_use_auto_rendering_defaults_when_unset(tmp_path):
+    from easydiffraction.project.project import Project
+
+    project = Project(name='beer', title='Beer title', description='Some description')
+    project.save_as(str(tmp_path / 'proj'))
+
+    project_cif = (tmp_path / 'proj' / 'project.cif').read_text()
+
+    assert not project_cif.startswith('data_')
+    assert '_rendering.chart_engine auto' in project_cif
+    assert '_rendering.table_engine auto' in project_cif
+    assert '_verbosity.fit full' in project_cif
+
+    loaded = Project.load(str(tmp_path / 'proj'))
+
+    assert loaded.rendering.chart_engine.value == 'auto'
+    assert loaded.rendering.table_engine.value == 'auto'
+    assert loaded.verbosity.fit.value == 'full'
 
 
 def test_project_save_and_load_keep_project_config_section_format(tmp_path):
@@ -57,6 +84,7 @@ def test_project_save_and_load_keep_project_config_section_format(tmp_path):
     assert '_project.id               beer' in project_cif
     assert '_rendering.chart_engine asciichartpy' in project_cif
     assert '_rendering.table_engine rich' in project_cif
+    assert '_verbosity.fit full' in project_cif
 
     loaded = Project.load(str(tmp_path / 'proj'))
     assert loaded.info.name == 'beer'
@@ -66,3 +94,36 @@ def test_project_save_and_load_keep_project_config_section_format(tmp_path):
     assert isinstance(loaded.info.last_modified, datetime.datetime)
     assert loaded.rendering.chart_engine.value == 'asciichartpy'
     assert loaded.rendering.table_engine.value == 'rich'
+    assert loaded.verbosity.fit.value == 'full'
+
+
+def test_project_save_wraps_long_description_as_cif_text_field(tmp_path):
+    from easydiffraction.project.project import Project
+
+    description = (
+        'This is the most minimal example of using EasyDiffraction. '
+        'It shows how to load a previously saved project from a directory '
+        'and run refinement in just a few lines of code.'
+    )
+    project = Project(name='beer', title='Beer title', description=description)
+    project.save_as(str(tmp_path / 'proj'))
+
+    project_cif = (tmp_path / 'proj' / 'project.cif').read_text()
+
+    assert '_project.description' in project_cif
+    description_tail = project_cif.split('_project.description', maxsplit=1)[1].lstrip(' ')
+    assert description_tail.startswith('\n;\n')
+    assert '\n;\n_project.created' in project_cif
+    description_block = description_tail.split('\n;\n', maxsplit=1)[1]
+    description_block = description_block.split('\n;\n_project.created', maxsplit=1)[0]
+    description_lines = description_block.splitlines()
+
+    assert len(description_lines) > 1
+    assert all(not line.startswith(';') for line in description_lines)
+    assert all(not line.endswith(';') for line in description_lines)
+    assert description_lines[0].startswith('This is the most minimal example')
+    assert description_lines[-1].endswith('lines of code.')
+
+    loaded = Project.load(str(tmp_path / 'proj'))
+
+    assert loaded.info.description == description
diff --git a/tests/unit/easydiffraction/project/test_project_load.py b/tests/unit/easydiffraction/project/test_project_load.py
index e44c12608..f19b4392d 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -125,6 +125,169 @@ def test_round_trips_constraints(self, tmp_path):
         assert loaded.analysis.constraints[0].expression.value == 'b_param = a_param'
         assert loaded.analysis.constraints.enabled is True
 
+    def test_round_trips_deterministic_fit_state_and_keeps_live_parameter_values(self, tmp_path):
+        original = Project(name='fit_state')
+        original.structures.create(name='lbco')
+        structure = original.structures['lbco']
+        structure.space_group.name_h_m = 'P m -3 m'
+        structure.cell.length_a = 3.88
+        parameter = structure.cell.length_a
+        parameter.free = True
+        parameter.uncertainty = 0.07
+        parameter.fit_min = 3.8
+        parameter.fit_max = 3.9
+        parameter._set_fit_bounds_uncertainty_multiplier(4.0)
+        parameter._fit_start_value = 3.87
+        parameter._fit_start_uncertainty = 0.02
+
+        original.analysis.fit_parameters.create(
+            param_unique_name=parameter.unique_name,
+            fit_min=parameter.fit_min,
+            fit_max=parameter.fit_max,
+            fit_bounds_uncertainty_multiplier=4.0,
+            start_value=3.87,
+            start_uncertainty=0.02,
+        )
+        original.analysis.fit_result._set_result_kind('deterministic')
+        original.analysis.fit_result._set_success(value=True)
+        original.analysis.fit_result._set_message('Fit converged')
+        original.analysis.fit_result._set_iterations(37)
+        original.analysis.fit_result._set_fitting_time(1.82)
+        original.analysis.fit_result._set_reduced_chi_square(1.031)
+        original.analysis.deterministic_result._set_optimizer_name('lmfit')
+        original.analysis.deterministic_result._set_method_name('leastsq')
+        original.analysis._set_has_persisted_fit_state(value=True)
+        original.save_as(str(tmp_path / 'proj'))
+
+        loaded = Project.load(str(tmp_path / 'proj'))
+        loaded_parameter = loaded.structures['lbco'].cell.length_a
+
+        assert loaded.analysis.fit_result.result_kind.value == 'deterministic'
+        assert loaded.analysis.fit_parameters[parameter.unique_name].fit_min.value == 3.8
+        assert loaded_parameter.value == 3.88
+        assert loaded_parameter.fit_min == 3.8
+        assert loaded_parameter.fit_max == 3.9
+        assert loaded_parameter.fit_bounds_uncertainty_multiplier == 4.0
+        assert loaded_parameter._fit_start_value == 3.87
+        assert loaded_parameter._fit_start_uncertainty == 0.02
+        assert loaded_parameter.uncertainty == 0.07
+
+    def test_round_trips_persisted_deterministic_correlation_summary_for_reloaded_display(
+        self,
+        tmp_path,
+    ):
+        from easydiffraction.display.plotting import Plotter
+
+        original = Project(name='fit_correlation_state')
+        original.structures.create(name='lbco')
+        structure = original.structures['lbco']
+        structure.space_group.name_h_m = 'P m -3 m'
+        structure.cell.length_a = 3.88
+        structure.cell.length_b = 3.89
+
+        parameter_a = structure.cell.length_a
+        parameter_b = structure.cell.length_b
+        for parameter, start_value in (
+            (parameter_a, 3.87),
+            (parameter_b, 3.88),
+        ):
+            parameter.free = True
+            parameter.uncertainty = 0.05
+            parameter.fit_min = 3.8
+            parameter.fit_max = 3.9
+            parameter._set_fit_bounds_uncertainty_multiplier(4.0)
+            parameter._fit_start_value = start_value
+            parameter._fit_start_uncertainty = 0.02
+            original.analysis.fit_parameters.create(
+                param_unique_name=parameter.unique_name,
+                fit_min=parameter.fit_min,
+                fit_max=parameter.fit_max,
+                fit_bounds_uncertainty_multiplier=4.0,
+                start_value=start_value,
+                start_uncertainty=0.02,
+            )
+
+        original.analysis.fit_result._set_result_kind('deterministic')
+        original.analysis.fit_result._set_success(value=True)
+        original.analysis.fit_result._set_message('Fit converged')
+        original.analysis.fit_result._set_iterations(21)
+        original.analysis.fit_result._set_fitting_time(0.74)
+        original.analysis.fit_result._set_reduced_chi_square(1.031)
+        original.analysis.deterministic_result._set_optimizer_name('lmfit')
+        original.analysis.deterministic_result._set_method_name('leastsq')
+        original.analysis.deterministic_result._set_objective_name('chi-square')
+        original.analysis.deterministic_result._set_objective_value(1.031)
+        original.analysis.deterministic_result._set_n_data_points(120)
+        original.analysis.deterministic_result._set_n_parameters(2)
+        original.analysis.deterministic_result._set_n_free_parameters(2)
+        original.analysis.deterministic_result._set_degrees_of_freedom(118)
+        original.analysis.deterministic_result._set_covariance_available(value=False)
+        original.analysis.deterministic_result._set_correlation_available(value=True)
+        original.analysis.fit_parameter_correlations.create(
+            source_kind='deterministic',
+            param_unique_name_i=parameter_b.unique_name,
+            param_unique_name_j=parameter_a.unique_name,
+            correlation=0.42,
+        )
+        original.analysis._set_has_persisted_fit_state(value=True)
+        original.save_as(str(tmp_path / 'proj'))
+
+        loaded = Project.load(str(tmp_path / 'proj'))
+        plotter = Plotter()
+        plotter._set_project(loaded)
+
+        corr_df = plotter._get_param_correlation_dataframe()
+
+        assert corr_df is not None
+        assert list(corr_df.index) == [parameter_a.unique_name, parameter_b.unique_name]
+        assert list(corr_df.columns) == [parameter_a.unique_name, parameter_b.unique_name]
+        assert corr_df.loc[parameter_a.unique_name, parameter_a.unique_name] == pytest.approx(1.0)
+        assert corr_df.loc[parameter_b.unique_name, parameter_b.unique_name] == pytest.approx(1.0)
+        assert corr_df.loc[parameter_a.unique_name, parameter_b.unique_name] == pytest.approx(0.42)
+        assert corr_df.loc[parameter_b.unique_name, parameter_a.unique_name] == pytest.approx(0.42)
+
+    def test_round_trips_bayesian_sampler_settings_to_live_dream_minimizer(self, tmp_path):
+        original = Project(name='bayes_state')
+        original.analysis.fitting.minimizer_type = 'bumps (dream)'
+        original.analysis.fit_result._set_result_kind('bayesian')
+        original.analysis.bayesian_sampler._set_steps(300)
+        original.analysis.bayesian_sampler._set_burn(60)
+        original.analysis.bayesian_sampler._set_thin(2)
+        original.analysis.bayesian_sampler._set_pop(8)
+        original.analysis.bayesian_sampler._set_parallel(0)
+        original.analysis.bayesian_sampler._set_init('lhs')
+        original.analysis._set_has_persisted_fit_state(value=True)
+        original.save_as(str(tmp_path / 'proj'))
+
+        loaded = Project.load(str(tmp_path / 'proj'))
+        minimizer = loaded.analysis.fitting.minimizer
+
+        assert minimizer is not None
+        assert minimizer.steps == 300
+        assert minimizer.burn == 60
+        assert minimizer.thin == 2
+        assert minimizer.pop == 8
+        assert minimizer.parallel == 0
+        assert minimizer.init.value == 'lhs'
+
+    def test_round_trips_legacy_bayesian_steps_and_burn_to_live_dream_minimizer(self, tmp_path):
+        original = Project(name='legacy_bayes_state')
+        original.analysis.fitting.minimizer_type = 'bumps (dream)'
+        original.analysis.fit_result._set_result_kind('bayesian')
+        original.analysis.bayesian_sampler._set_steps(300)
+        original.analysis.bayesian_sampler._set_burn(60)
+        original.analysis._set_has_persisted_fit_state(value=True)
+        original.save_as(str(tmp_path / 'proj'))
+
+        loaded = Project.load(str(tmp_path / 'proj'))
+        minimizer = loaded.analysis.fitting.minimizer
+
+        assert minimizer is not None
+        assert minimizer.steps == 300
+        assert minimizer.burn == 60
+        assert minimizer.thin == 1
+        assert minimizer.pop == 4
+
 
 class TestLoadAnalysisCifFallback:
     """Load falls back from analysis/analysis.cif to analysis.cif at root."""
diff --git a/tests/unit/easydiffraction/project/test_project_save.py b/tests/unit/easydiffraction/project/test_project_save.py
index 27212251b..ff8a9b9be 100644
--- a/tests/unit/easydiffraction/project/test_project_save.py
+++ b/tests/unit/easydiffraction/project/test_project_save.py
@@ -62,3 +62,62 @@ def test_project_save_lists_existing_analysis_results_csv(tmp_path, monkeypatch,
     out = capsys.readouterr().out
     assert 'analysis.cif' in out
     assert 'results.csv' in out
+
+
+def test_project_save_as_overwrites_existing_directory_by_default(tmp_path, monkeypatch):
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.project.project import Project
+    from easydiffraction.project.project_info import ProjectInfo
+    from easydiffraction.summary.summary import Summary
+
+    monkeypatch.setattr(ProjectInfo, 'as_cif', property(lambda self: 'info'))
+    monkeypatch.setattr(Analysis, 'as_cif', property(lambda self: 'analysis'))
+    monkeypatch.setattr(Summary, 'as_cif', lambda self: 'summary')
+
+    target = tmp_path / 'proj_dir'
+    stale_file = target / 'stale.txt'
+    target.mkdir()
+    stale_file.write_text('stale')
+
+    project = Project(name='p1')
+    project.save_as(str(target))
+
+    assert not stale_file.exists()
+    assert (target / 'project.cif').is_file()
+
+
+def test_project_save_as_preserves_existing_directory_when_disabled(tmp_path, monkeypatch):
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.project.project import Project
+    from easydiffraction.project.project_info import ProjectInfo
+    from easydiffraction.summary.summary import Summary
+
+    monkeypatch.setattr(ProjectInfo, 'as_cif', property(lambda self: 'info'))
+    monkeypatch.setattr(Analysis, 'as_cif', property(lambda self: 'analysis'))
+    monkeypatch.setattr(Summary, 'as_cif', lambda self: 'summary')
+
+    target = tmp_path / 'proj_dir'
+    stale_file = target / 'stale.txt'
+    target.mkdir()
+    stale_file.write_text('stale')
+
+    project = Project(name='p1')
+    project.save_as(
+        str(target),
+        overwrite=False,
+    )
+
+    assert stale_file.exists()
+    assert (target / 'project.cif').is_file()
+
+
+def test_project_save_omits_empty_fit_state_sections(tmp_path):
+    from easydiffraction.project.project import Project
+
+    project = Project(name='no_fit_state')
+    project.save_as(str(tmp_path / 'proj'))
+
+    analysis_cif = (tmp_path / 'proj' / 'analysis' / 'analysis.cif').read_text()
+
+    assert '_fit_parameter.param_unique_name' not in analysis_cif
+    assert '_fit_result.result_kind' not in analysis_cif
diff --git a/tests/unit/easydiffraction/test___main__.py b/tests/unit/easydiffraction/test___main__.py
index 62ad8c68c..a78710b35 100644
--- a/tests/unit/easydiffraction/test___main__.py
+++ b/tests/unit/easydiffraction/test___main__.py
@@ -44,6 +44,14 @@ def test_cli_subcommands_call_utils(monkeypatch):
     import easydiffraction.__main__ as main_mod
 
     logs = []
+    monkeypatch.setattr(ed, 'list_data', lambda: logs.append('LIST_DATA'))
+    monkeypatch.setattr(
+        ed,
+        'download_data',
+        lambda id, destination='data', overwrite=False: logs.append(
+            f'DATA_{id}_{destination}_{overwrite}'
+        ),
+    )
     monkeypatch.setattr(ed, 'list_tutorials', lambda: logs.append('LIST'))
     monkeypatch.setattr(
         ed,
@@ -56,14 +64,25 @@ def test_cli_subcommands_call_utils(monkeypatch):
         lambda id, destination='tutorials', overwrite=False: logs.append(f'DOWNLOAD_{id}'),
     )
 
-    res1 = runner.invoke(main_mod.app, ['list-tutorials'])
-    res2 = runner.invoke(main_mod.app, ['download-all-tutorials'])
-    res3 = runner.invoke(main_mod.app, ['download-tutorial', '1'])
+    res0 = runner.invoke(main_mod.app, ['list-data'])
+    res1 = runner.invoke(main_mod.app, ['download-data', '30', '--destination', 'projects'])
+    res2 = runner.invoke(main_mod.app, ['list-tutorials'])
+    res3 = runner.invoke(main_mod.app, ['download-all-tutorials'])
+    res4 = runner.invoke(main_mod.app, ['download-tutorial', '1'])
 
+    assert res0.exit_code == 0
     assert res1.exit_code == 0
     assert res2.exit_code == 0
     assert res3.exit_code == 0
-    assert logs == ['LIST', 'DOWNLOAD_ALL', 'DOWNLOAD_1']
+    assert res4.exit_code == 0
+    assert logs == ['LIST_DATA', 'DATA_30_projects_False', 'LIST', 'DOWNLOAD_ALL', 'DOWNLOAD_1']
+
+
+def test_cli_project_first_argument_normalization_supports_global_data_commands():
+    import easydiffraction.__main__ as main_mod
+
+    assert main_mod._normalized_cli_args(['list-data']) == ['list-data']
+    assert main_mod._normalized_cli_args(['download-data', '30']) == ['download-data', '30']
 
 
 def test_cli_fit_loads_and_fits(monkeypatch, tmp_path):
diff --git a/tests/unit/easydiffraction/utils/test_environment.py b/tests/unit/easydiffraction/utils/test_environment.py
index 691b0a9c5..fab45e723 100644
--- a/tests/unit/easydiffraction/utils/test_environment.py
+++ b/tests/unit/easydiffraction/utils/test_environment.py
@@ -84,3 +84,44 @@ def test_can_use_ipython_display_with_none(self):
         from easydiffraction.utils.environment import can_use_ipython_display
 
         assert can_use_ipython_display(None) is False
+
+
+class TestArtifactPaths:
+    def test_resolve_artifact_path_uses_env_root(self, monkeypatch, tmp_path):
+        from easydiffraction.utils.environment import resolve_artifact_path
+
+        monkeypatch.setenv('EASYDIFFRACTION_ARTIFACT_ROOT', 'tmp/tutorials')
+        monkeypatch.setenv('PIXI_PROJECT_ROOT', str(tmp_path))
+
+        assert resolve_artifact_path('data') == tmp_path / 'tmp' / 'tutorials' / 'data'
+
+    def test_resolve_artifact_path_uses_tutorial_fallback(self, monkeypatch, tmp_path):
+        import easydiffraction.utils.environment as env
+
+        repo_root = tmp_path / 'repo'
+        tutorials_dir = repo_root / 'docs' / 'docs' / 'tutorials'
+        tutorials_dir.mkdir(parents=True)
+
+        monkeypatch.delenv('EASYDIFFRACTION_ARTIFACT_ROOT', raising=False)
+        monkeypatch.delenv('PIXI_PROJECT_ROOT', raising=False)
+        monkeypatch.chdir(tutorials_dir)
+        monkeypatch.setattr(env, '_repo_root', lambda: repo_root)
+
+        assert env.resolve_artifact_path('data') == repo_root / 'tmp' / 'tutorials' / 'data'
+
+    def test_create_artifact_temp_dir_uses_tutorial_fallback(self, monkeypatch, tmp_path):
+        import easydiffraction.utils.environment as env
+
+        repo_root = tmp_path / 'repo'
+        tutorials_dir = repo_root / 'docs' / 'docs' / 'tutorials'
+        tutorials_dir.mkdir(parents=True)
+
+        monkeypatch.delenv('EASYDIFFRACTION_ARTIFACT_ROOT', raising=False)
+        monkeypatch.delenv('PIXI_PROJECT_ROOT', raising=False)
+        monkeypatch.chdir(tutorials_dir)
+        monkeypatch.setattr(env, '_repo_root', lambda: repo_root)
+
+        created_dir = env.create_artifact_temp_dir('ed_zip_')
+
+        assert created_dir.is_dir()
+        assert created_dir.parent == repo_root / 'tmp' / 'tutorials'
diff --git a/tests/unit/easydiffraction/utils/test_utils_coverage.py b/tests/unit/easydiffraction/utils/test_utils_coverage.py
index 9357e2725..d2d07159e 100644
--- a/tests/unit/easydiffraction/utils/test_utils_coverage.py
+++ b/tests/unit/easydiffraction/utils/test_utils_coverage.py
@@ -433,6 +433,42 @@ def test_download_data_no_description(monkeypatch, tmp_path, capsys):
     assert 'Data #1' in out
 
 
+def test_download_data_uses_tutorial_artifact_root_fallback(monkeypatch, tmp_path):
+    import easydiffraction.utils.environment as env
+    import easydiffraction.utils.utils as MUT
+
+    repo_root = tmp_path / 'repo'
+    tutorials_dir = repo_root / 'docs' / 'docs' / 'tutorials'
+    tutorials_dir.mkdir(parents=True)
+
+    fake_index = {
+        '1': {
+            'path': 'data.xye',
+            'hash': None,
+            'description': 'Test data',
+        }
+    }
+    monkeypatch.setattr(MUT, '_fetch_data_index', lambda: fake_index)
+    monkeypatch.setattr(env, '_repo_root', lambda: repo_root)
+    monkeypatch.delenv('EASYDIFFRACTION_ARTIFACT_ROOT', raising=False)
+    monkeypatch.delenv('PIXI_PROJECT_ROOT', raising=False)
+    monkeypatch.chdir(tutorials_dir)
+
+    def fake_retrieve(url, known_hash, fname, path):
+        import pathlib
+
+        pathlib.Path(path, fname).write_text('x y e')
+        return str(pathlib.Path(path, fname))
+
+    monkeypatch.setattr(MUT.pooch, 'retrieve', fake_retrieve)
+
+    result = MUT.download_data(id=1, destination='data')
+
+    expected_path = repo_root / 'tmp' / 'tutorials' / 'data' / 'ed-1.xye'
+    assert result == str(expected_path)
+    assert expected_path.exists()
+
+
 # --- download_tutorial with overwrite=True ------------------------------------
 
 
diff --git a/tests/unit/test_benchmark_tutorials.py b/tests/unit/test_benchmark_tutorials.py
new file mode 100644
index 000000000..c0b390ef7
--- /dev/null
+++ b/tests/unit/test_benchmark_tutorials.py
@@ -0,0 +1,133 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Unit tests for tutorial benchmark CSV persistence."""
+
+from __future__ import annotations
+
+import csv
+import importlib.util
+import sys
+from argparse import Namespace
+from pathlib import Path
+from types import SimpleNamespace
+
+
+def _load_module():
+    module_path = Path(__file__).resolve().parents[2] / 'tools' / 'benchmark_tutorials.py'
+    spec = importlib.util.spec_from_file_location('benchmark_tutorials', module_path)
+    assert spec is not None
+    assert spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+MUT = _load_module()
+
+
+def test_append_result_writes_one_row(tmp_path):
+    output_path = tmp_path / 'benchmark.csv'
+
+    MUT._write_csv_header(output_path)
+    MUT._append_result(
+        output_path,
+        MUT.TutorialBenchmarkResult(
+            tutorial_name='ed-21.py',
+            elapsed_seconds=12.3456,
+            status='ok',
+            return_code=0,
+        ),
+    )
+
+    with output_path.open(encoding='utf-8', newline='') as handle:
+        rows = list(csv.DictReader(handle))
+
+    assert rows == [
+        {
+            'tutorial_name': 'ed-21.py',
+            'elapsed_seconds': '12.346',
+            'status': 'ok',
+            'return_code': '0',
+        }
+    ]
+
+
+def test_main_appends_first_result_before_second_tutorial_starts(monkeypatch, tmp_path):
+    tutorial_dir = tmp_path / 'tutorials'
+    tutorial_dir.mkdir()
+    first_tutorial = tutorial_dir / 'ed-01.py'
+    second_tutorial = tutorial_dir / 'ed-02.py'
+    first_tutorial.write_text('print("first")\n', encoding='utf-8')
+    second_tutorial.write_text('print("second")\n', encoding='utf-8')
+
+    output_path = tmp_path / 'benchmarking' / 'results.csv'
+    args = Namespace(
+        tutorial_dir=tutorial_dir,
+        output_dir=tmp_path / 'benchmarking',
+        pattern=[],
+    )
+
+    monkeypatch.setattr(
+        MUT,
+        'build_parser',
+        lambda: SimpleNamespace(parse_args=lambda: args),
+    )
+    monkeypatch.setattr(MUT, '_build_output_path', lambda output_dir: output_path)
+    monkeypatch.setattr(MUT, '_build_env', dict)
+
+    def fake_run_tutorial(
+        script_path: Path,
+        tutorial_dir_path: Path,
+        env: dict[str, str],
+    ) -> MUT.TutorialBenchmarkResult:
+        del tutorial_dir_path, env
+        if script_path == first_tutorial:
+            with output_path.open(encoding='utf-8', newline='') as handle:
+                rows = list(csv.reader(handle))
+            assert rows == [MUT.CSV_HEADER]
+            return MUT.TutorialBenchmarkResult(
+                tutorial_name='ed-01.py',
+                elapsed_seconds=1.0,
+                status='ok',
+                return_code=0,
+            )
+
+        with output_path.open(encoding='utf-8', newline='') as handle:
+            rows = list(csv.DictReader(handle))
+        assert rows == [
+            {
+                'tutorial_name': 'ed-01.py',
+                'elapsed_seconds': '1.000',
+                'status': 'ok',
+                'return_code': '0',
+            }
+        ]
+        return MUT.TutorialBenchmarkResult(
+            tutorial_name='ed-02.py',
+            elapsed_seconds=2.0,
+            status='ok',
+            return_code=0,
+        )
+
+    monkeypatch.setattr(MUT, '_run_tutorial', fake_run_tutorial)
+
+    exit_code = MUT.main()
+
+    assert exit_code == 0
+    with output_path.open(encoding='utf-8', newline='') as handle:
+        rows = list(csv.DictReader(handle))
+    assert rows == [
+        {
+            'tutorial_name': 'ed-01.py',
+            'elapsed_seconds': '1.000',
+            'status': 'ok',
+            'return_code': '0',
+        },
+        {
+            'tutorial_name': 'ed-02.py',
+            'elapsed_seconds': '2.000',
+            'status': 'ok',
+            'return_code': '0',
+        },
+    ]
diff --git a/tools/benchmark_tutorials.py b/tools/benchmark_tutorials.py
new file mode 100644
index 000000000..e28d77e98
--- /dev/null
+++ b/tools/benchmark_tutorials.py
@@ -0,0 +1,216 @@
+"""Benchmark tutorial scripts and save timing results."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import os
+import platform
+import subprocess  # noqa: S404
+import sys
+import time
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from pathlib import PurePosixPath
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC_ROOT = ROOT / 'src'
+DEFAULT_TUTORIAL_DIR = ROOT / 'docs' / 'docs' / 'tutorials'
+DEFAULT_OUTPUT_DIR = ROOT / 'docs' / 'dev' / 'benchmarking'
+CHECKPOINT_DIR_NAME = '.ipynb_checkpoints'
+CSV_HEADER = ['tutorial_name', 'elapsed_seconds', 'status', 'return_code']
+
+
+@dataclass(frozen=True)
+class TutorialBenchmarkResult:
+    """Store timing data for one tutorial run."""
+
+    tutorial_name: str
+    elapsed_seconds: float
+    status: str
+    return_code: int
+
+
+def _relative_display_path(path: Path, start_path: Path) -> str:
+    try:
+        return path.relative_to(start_path).as_posix()
+    except ValueError:
+        return path.as_posix()
+
+
+def _slugify(value: str) -> str:
+    return value.lower().replace(' ', '-').replace('/', '-')
+
+
+def _build_env() -> dict[str, str]:
+    env = os.environ.copy()
+    if SRC_ROOT.exists():
+        existing_pythonpath = env.get('PYTHONPATH', '')
+        env['PYTHONPATH'] = (
+            str(SRC_ROOT)
+            if not existing_pythonpath
+            else str(SRC_ROOT) + os.pathsep + existing_pythonpath
+        )
+    return env
+
+
+def _discover_tutorials(tutorial_dir: Path) -> list[Path]:
+    return [
+        path
+        for path in sorted(tutorial_dir.rglob('*.py'))
+        if CHECKPOINT_DIR_NAME not in path.parts
+    ]
+
+
+def _matches_requested_patterns(
+    script_path: Path,
+    tutorial_dir: Path,
+    patterns: list[str],
+) -> bool:
+    if not patterns:
+        return True
+
+    rel_path = PurePosixPath(_relative_display_path(script_path, tutorial_dir))
+    return any(rel_path.match(pattern) or script_path.name == pattern for pattern in patterns)
+
+
+def _run_tutorial(
+    script_path: Path,
+    tutorial_dir: Path,
+    env: dict[str, str],
+) -> TutorialBenchmarkResult:
+    tutorial_name = _relative_display_path(script_path, tutorial_dir)
+    start_time = time.perf_counter()
+    result = subprocess.run(  # noqa: S603
+        [sys.executable, str(script_path)],
+        cwd=str(ROOT),
+        env=env,
+        capture_output=True,
+        text=True,
+        encoding='utf-8',
+    )
+    elapsed_seconds = time.perf_counter() - start_time
+    status = 'ok' if result.returncode == 0 else 'failed'
+
+    if result.returncode == 0:
+        print(f'        OK      {elapsed_seconds:.1f}s')
+    else:
+        print(f'        FAILED  {elapsed_seconds:.1f}s', file=sys.stderr)
+        details = ((result.stdout or '') + (result.stderr or '')).strip()
+        if details:
+            print(details, file=sys.stderr)
+
+    return TutorialBenchmarkResult(
+        tutorial_name=tutorial_name,
+        elapsed_seconds=elapsed_seconds,
+        status=status,
+        return_code=result.returncode,
+    )
+
+
+def _build_output_path(output_dir: Path) -> Path:
+    timestamp = datetime.now().astimezone().strftime('%Y%m%d-%H%M%S')
+    system_name = _slugify(platform.system())
+    machine_name = _slugify(platform.machine())
+    python_name = f'py{sys.version_info.major}{sys.version_info.minor}'
+    file_name = (
+        f'{timestamp}_{system_name}-{machine_name}_'
+        f'{python_name}_tutorial-benchmarks.csv'
+    )
+    return output_dir / file_name
+
+
+def _write_csv_header(output_path: Path) -> None:
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with output_path.open('w', encoding='utf-8', newline='') as handle:
+        writer = csv.writer(handle)
+        writer.writerow(CSV_HEADER)
+
+
+def _append_result(output_path: Path, result: TutorialBenchmarkResult) -> None:
+    with output_path.open('a', encoding='utf-8', newline='') as handle:
+        writer = csv.writer(handle)
+        writer.writerow(
+            [
+                result.tutorial_name,
+                f'{result.elapsed_seconds:.3f}',
+                result.status,
+                result.return_code,
+            ]
+        )
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description='Run tutorial scripts sequentially and record timings.',
+    )
+    parser.add_argument(
+        '--tutorial-dir',
+        type=Path,
+        default=DEFAULT_TUTORIAL_DIR,
+        help='Directory containing tutorial scripts to benchmark.',
+    )
+    parser.add_argument(
+        '--output-dir',
+        type=Path,
+        default=DEFAULT_OUTPUT_DIR,
+        help='Directory where the benchmark CSV should be written.',
+    )
+    parser.add_argument(
+        '--pattern',
+        action='append',
+        default=[],
+        help=(
+            'Glob for tutorial paths relative to the tutorial directory. '
+            'Pass multiple times to benchmark a subset.'
+        ),
+    )
+    return parser
+
+
+def main() -> int:
+    args = build_parser().parse_args()
+    tutorial_dir = args.tutorial_dir.resolve()
+    output_dir = args.output_dir.resolve()
+
+    if not tutorial_dir.is_dir():
+        print(f'Tutorial directory not found: {tutorial_dir}', file=sys.stderr)
+        return 1
+
+    tutorials = [
+        path
+        for path in _discover_tutorials(tutorial_dir)
+        if _matches_requested_patterns(path, tutorial_dir, args.pattern)
+    ]
+    if not tutorials:
+        print('No tutorial scripts matched the requested pattern(s).', file=sys.stderr)
+        return 1
+
+    output_path = _build_output_path(output_dir)
+    _write_csv_header(output_path)
+
+    env = _build_env()
+    results: list[TutorialBenchmarkResult] = []
+    for index, tutorial_path in enumerate(tutorials, start=1):
+        tutorial_name = _relative_display_path(tutorial_path, tutorial_dir)
+        print(f'[{index:2}/{len(tutorials)}] Running {tutorial_name}')
+        result = _run_tutorial(tutorial_path, tutorial_dir, env)
+        results.append(result)
+        _append_result(output_path, result)
+
+    total_elapsed = sum(result.elapsed_seconds for result in results)
+    failure_count = sum(result.status == 'failed' for result in results)
+
+    print(f'Wrote benchmark results to {_relative_display_path(output_path, ROOT)}')
+    print(f'Total elapsed time: {total_elapsed:.3f}s')
+
+    if failure_count:
+        print(f'Failed tutorials: {failure_count}', file=sys.stderr)
+        return 1
+
+    return 0
+
+
+if __name__ == '__main__':
+    raise SystemExit(main())
\ No newline at end of file