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 929409549..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: v0.11.0-7-gb5113cf +_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..46a066d2b 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_` method, not a public setter. @@ -77,7 +80,7 @@ ## Testing - Every new module, class, or bug fix ships with tests. See - `docs/architecture/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//.py` → `tests/unit/easydiffraction//test_.py`. Verify with @@ -102,8 +105,11 @@ - 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/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. @@ -137,35 +146,51 @@ 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/architecture/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. +- 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/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 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_.md` (lowercase, - dash-separated, e.g. `plan_background-refactor.md`). Use the same - `` for the implementation branch - (`feature/`). Do not push the branch unless asked. +- Save plans as `docs/dev/plans/.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 `` for the implementation + branch (`feature/`). 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 @@ -185,6 +210,12 @@ 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. - 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/.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/docs.yml b/.github/workflows/docs.yml index 117416a3f..ea6bdad11 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: @@ -115,7 +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/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml index 31a69ad20..4fbf4fab1 100644 --- a/.github/workflows/issues-labels.yml +++ b/.github/workflows/issues-labels.yml @@ -1,6 +1,5 @@ -# 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 `[scope]` label and one +# `[priority]` label. If either is missing, the workflow adds a reminder label. name: Issue labels check @@ -13,8 +12,6 @@ permissions: jobs: check-labels: - if: github.actor != 'easyscience[bot]' - runs-on: ubuntu-latest concurrency: @@ -22,30 +19,22 @@ jobs: cancel-in-progress: true steps: - - name: Checkout repository - uses: actions/checkout@v6 - - - name: Setup easyscience[bot] - id: bot - uses: ./.github/actions/setup-easyscience-bot + - name: Ensure [scope] label + uses: Rindrics/expect-label-prefix@v1.2.1 with: - app-id: ${{ vars.EASYSCIENCE_APP_ID }} - private-key: ${{ secrets.EASYSCIENCE_APP_KEY }} - - - 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 + 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: - secret: ${{ steps.bot.outputs.token }} - prefix: '[priority]' - labelSeparator: ' ' - addLabel: true - defaultLabel: '[priority] ⚠️ label needed' + repository_full_name: ${{ github.repository }} + token: ${{ github.token }} + label_prefix: '[priority]' + label_separator: ' ' + add_label: 'true' + default_label: '[priority] ⚠️ label needed' diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml index 642cd3184..fcb8a78cb 100644 --- a/.github/workflows/pr-labels.yml +++ b/.github/workflows/pr-labels.yml @@ -1,14 +1,9 @@ # Verifies if a pull request has at least one label from a set of valid # labels before it can be merged. # -# NOTE: -# This workflow may be triggered twice in quick succession when a PR is -# created: -# 1) `opened` — when the pull request is initially created -# 2) `labeled` — if labels are added immediately after creation -# (e.g. by manual labeling, another workflow, or GitHub App). -# -# These are separate GitHub events, so two workflow runs can be started. +# The label validation is delegated to `mheap/github-action-required-labels`, +# which checks the current PR labels via the GitHub API and can add or update a +# PR comment when the required label set is missing. name: PR labels check @@ -17,40 +12,34 @@ on: types: [opened, labeled, unlabeled, synchronize] permissions: - pull-requests: read + issues: write + pull-requests: write jobs: check-labels: runs-on: ubuntu-latest steps: - - name: Check for valid labels - run: | - PR_LABELS=$(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/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 \`${{ env.DEFAULT_BRANCH }}\`. diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index d0298f95b..86bfa6063 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -248,10 +248,10 @@ jobs: exit 1 fi - whl_url="file://$(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/.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/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/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/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/accepted/category-owner-sections.md b/docs/dev/adrs/accepted/category-owner-sections.md new file mode 100644 index 000000000..46f9b3db0 --- /dev/null +++ b/docs/dev/adrs/accepted/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_` 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_` 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_` 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_` 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_` 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_` +- `experiment.as_cif` starts with `data_` +- `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/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/accepted/display-ux.md b/docs/dev/adrs/accepted/display-ux.md new file mode 100644 index 000000000..23a780812 --- /dev/null +++ b/docs/dev/adrs/accepted/display-ux.md @@ -0,0 +1,244 @@ +# ADR: Display UX Facade + +## Status + +Accepted and implemented. + +## Context + +The previous user-facing display API mixed 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 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 +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() +``` + +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='diffrn.ambient_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, while the implementation +may keep the existing helpers as internal delegation targets: + +| 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` 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 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 on the experiment +- uncertainty bands where posterior predictive data exists and the chart + engine supports them + +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 shows option name, description, availability for the +experiment, whether `include='auto'` includes it, and the reason an +option is unavailable. + +Pattern option names: + +- `auto` +- `measured` +- `calculated` +- `background` +- `residual` +- `bragg` +- `excluded` +- `uncertainty` + +`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 + +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, using a persisted `diffrn.*` + path for `versus`. +- `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='diffrn.ambient_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` 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/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/accepted/fit-mode-categories.md b/docs/dev/adrs/accepted/fit-mode-categories.md new file mode 100644 index 000000000..278d3752a --- /dev/null +++ b/docs/dev/adrs/accepted/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.` (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.` 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/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/accepted/help-discoverability.md b/docs/dev/adrs/accepted/help-discoverability.md new file mode 100644 index 000000000..8fd019f39 --- /dev/null +++ b/docs/dev/adrs/accepted/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/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/loop-category-key-identity.md b/docs/dev/adrs/accepted/loop-category-key-identity.md new file mode 100644 index 000000000..4334b4e13 --- /dev/null +++ b/docs/dev/adrs/accepted/loop-category-key-identity.md @@ -0,0 +1,163 @@ +# ADR: Loop Category Keys and Identity Naming + +**Status:** Accepted +**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 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 +.. +``` + +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 `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. + +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 + +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 has an explicit `id` field and uses it as the +collection key: + +```text +analysis.constraints[id].id -> _constraint.id +``` + +`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. +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. 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 + +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 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, 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`, +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 + +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/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/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 new file mode 100644 index 000000000..3b922f423 --- /dev/null +++ b/docs/dev/adrs/accepted/project-facade-and-persistence.md @@ -0,0 +1,80 @@ +# ADR: Project Facade and Persistence Layout + +## Status + +Accepted current design. + +## Date + +2026-05-17 + +## Group + +Persistence. + +## Context + +`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 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_` 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. 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/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 `_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__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..50a287db9 --- /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 | 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/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/ + _measured.csv + _calculated.csv + _residual.csv + _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-posterior-summary.md b/docs/dev/adrs/suggestions/parameter-posterior-summary.md new file mode 100644 index 000000000..36c0b979b --- /dev/null +++ b/docs/dev/adrs/suggestions/parameter-posterior-summary.md @@ -0,0 +1,337 @@ +# ADR: Parameter-Level Posterior Projection + +**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 + +`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 accepted +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 +`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`, +`best_sample_value`, `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 restore from persisted analysis state. + +The summary object currently provides the right level of detail: + +- `best_sample_value` +- `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: + +- `best_sample_value` -> `Best posterior sample` +- `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: + best_sample_value = param.posterior.best_sample_value + 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 best posterior sample to `parameter.value` after Bayesian fits + +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 +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. Rebuild posterior from analysis-level state + +Canonical Bayesian state is owned by `analysis.fit_results`, not by +individual parameters. The saved fit-state format and restore order are +defined in `analysis-cif-fit-state.md`. + +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 rebuilt from the analysis-level saved result projection when that +projection is available. + +Two restore levels matter for the parameter API: + +- summary-only restore can populate `parameter.posterior` and fit-result + tables +- full restore can also support posterior plots and predictive plots + +If the saved project has no analysis-level posterior summary for a +parameter, `parameter.posterior` remains `None`. + +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.best_sample_value) + print(posterior.uncertainty) + print(posterior.interval_68) + +project.analysis.display.fit_results() +``` + +Posterior plot availability after load follows the fit-state restore +level defined in `analysis-cif-fit-state.md`. + +### 11. Keep parameter posterior data rebuilt, not duplicated + +`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 +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 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. + +### 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. + +## 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 defines the parameter-level posterior projection. It defers: + +- 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 analysis-level + Bayesian data rather than serialized redundantly at parameter level. + +## Chosen Defaults + +- `parameter.value` remains committed to the best posterior sample after + posterior fits. +- If a project is loaded with only posterior summaries, restoring + `parameter.posterior` is acceptable for table display and parameter + inspection. 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..191f18e59 --- /dev/null +++ b/docs/dev/adrs/suggestions/python-cif-category-correspondence.md @@ -0,0 +1,338 @@ +# 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.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 +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` | `project.cif` | bare category | Project-owned fit-output verbosity category backed by `VerbosityEnum`. | +| `project.structures[name]` | `structures/.cif` | `data_` | Each structure is one CIF data block. | +| `project.experiments[name]` | `experiments/.cif` | `data_` | 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.fit` | `_verbosity.fit` | Yes | Direct category and field mapping for fitting process output verbosity. | + +### 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.. -> project.cif: _. +``` + +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. + +The accepted baseline is: + +```text +project.info. -> project.cif: _project. +``` + +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`: + +```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.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 +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 + +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. +- 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. + +## 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.rendering.chart_engine` and + `project.rendering.table_engine` remain separate, or should the public + API and CIF collapse to one `engine` field? +- Should `project.verbosity = 'short'` remain as a convenience alias for + `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 new file mode 100644 index 000000000..cd658aba9 --- /dev/null +++ b/docs/dev/adrs/suggestions/undo-fit.md @@ -0,0 +1,126 @@ +# ADR: Undo Fit + +**Status:** Proposed +**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 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 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. + +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`: + +```python +project.analysis.undo_fit() +``` + +`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 fit-state clear + +The first undo implementation restores each fitted parameter's saved +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` +- `analysis.fit_results` is cleared +- persisted fit-state summaries and Bayesian caches for the discarded + fit are cleared + +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. + +### 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 belong to analysis configuration, not fit output. + +### 4. Undo is single-level for now + +Only the latest saved pre-fit snapshot is addressable. Multi-level undo +and redo require a dedicated snapshot-history design and remain +deferred. + +### 5. CLI exposure follows the project-first command style + +The command-line surface should follow the current CLI style: + +```bash +python -m easydiffraction PROJECT_DIR undo +``` + +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 preview the rollback without overwriting files +- fail with a clear non-zero exit status when no usable undo snapshot is + available + +Compatibility aliases may remain if the CLI supports them, but the +project-first form is the canonical user-facing syntax. + +## Consequences + +### Positive + +- 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 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 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/architecture.md b/docs/dev/architecture.md deleted file mode 100644 index 6cb8c67e2..000000000 --- a/docs/dev/architecture.md +++ /dev/null @@ -1,1442 +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) -└── DatablockItem # 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_` 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_` -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_` 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 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 | - -When any `Parameter.value` is set, it propagates -`_need_categories_update = True` up to the owning `DatablockItem`. -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, constrained, symmetry_fixed -``` - -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 | - ---- - -## 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_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. - -### 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`. - -**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. - -**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 -type-neutral tensor components. - -**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. - -**Auto-conversion.** Setting `adp_type` triggers value conversion: B ↔ U -via `B = 8π²U`; iso → ani seeds the diagonal; ani → iso averages the -diagonal. - -**Collection sync.** `Structure._update_categories()` reconciles the two -collections: adds missing aniso entries, removes stale ones, and rekeys -on label rename. - -See [`adp_implementation.md`](adp_implementation.md) for the full -implementation plan. - ---- - -## 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` | -| `JointFitExperimentsFactory` | Joint-fit weights | `JointFitExperiments` | -| `CalculatorFactory` | Calculation engines | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator` | -| `MinimizerFactory` | Minimisers | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `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 (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` | -| `JointFitExperiments` | `JointFitExperimentsFactory` | - -#### 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` | - -#### 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: - -- 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). -- Joint-fit weights: `joint_fit_experiments` (`CategoryCollection` of - per-experiment weight entries); sibling of `fit`, not a child. -- 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. -- Aliases and constraints (single-type categories; no public `_type` - getter or setter) - ---- - -## 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.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) | - -### 7.1 Data Flow - -``` -Parameter.value set - → AttributeSpec validation (type + value) - → _need_categories_update = True (on parent DatablockItem) - -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 # ProjectInfo + Display preferences -├── 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` carries both the `_project.*` metadata and the -`_display.*` engine preferences (`plotter_type`, `tabler_type`), 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`. - -### 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()`) 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. - -```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 -``` - -**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.fit.minimizer_type = 'lmfit' - -# Plot before fitting -project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True) - -# 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.analysis.display.free_params() - -# Fit and show results -project.analysis.fit() -project.analysis.display.fit_results() - -# Plot after fitting -project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True) - -# Save -project.save() -``` - -### 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 | `` property (read-only) | `expt.background`, `expt.peak` | -| Active type tag | `_type` property (getter + setter) | `expt.background_type`, `expt.peak_profile_type` | -| Show types | `show__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 `` 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`. - -`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 -`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). - -**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 -`_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` | - -Backend selectors and semantic value selectors live on a dedicated -configuration category (`fit`, `calculation`, `display`). Switchable- -category implementation selectors are owned by the host (typically the -experiment) because switching them replaces the category instance, as -described in §9.3. - -### 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.fit.show_minimizer_types() -project.analysis.fit.show_modes() -project.display.show_plotter_types() -project.display.show_tabler_types() -``` - -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._fit.mode.value == FitModeEnum.JOINT: - -# ❌ Wrong — compare with raw string -if self._fit.mode.value == '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 (DatablockItem / Analysis) -├── 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_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: - -```python -# ✅ Correct — sibling categories on Analysis -project.analysis.fit.mode = 'joint' -project.analysis.joint_fit_experiments['npd'].weight = 0.7 - -# ❌ Wrong — joint_fit_experiments as a child of fit -project.analysis.fit.joint_fit_experiments['npd'].weight = 0.7 -``` - -In CIF output, sibling categories appear as independent blocks: - -``` -_fit.minimizer_type lmfit -_fit.mode joint - -loop_ -_joint_fit_experiment.id -_joint_fit_experiment.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//.py - → tests/unit/easydiffraction//test_.py -``` - -Two additional patterns are recognised: - -1. **Supplementary coverage files** — `test__coverage.py`, - `test__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_.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 (``, `_type` get/set, `show__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_open.md) — prioritised backlog. -- **Closed:** [`issues_closed.md`](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/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/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/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_closed.md b/docs/dev/issues/closed.md similarity index 81% rename from docs/dev/issues_closed.md rename to docs/dev/issues/closed.md index a5ae2621a..c2e60653a 100644 --- a/docs/dev/issues_closed.md +++ b/docs/dev/issues/closed.md @@ -4,6 +4,19 @@ 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 +[`help-discoverability.md`](../adrs/accepted/help-discoverability.md). + +--- + ## Restore Minimiser Variant Support Used thin subclasses (approach A) to restore lmfit algorithm variants. @@ -50,6 +63,19 @@ 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_` 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_open.md b/docs/dev/issues/open.md similarity index 66% rename from docs/dev/issues_open.md rename to docs/dev/issues/open.md index 014aa4675..04d97ab85 100644 --- a/docs/dev/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 @@ -14,31 +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`. - -**Depends on:** nothing. - ---- - -## 5. 🟡 Make `Analysis` a `DatablockItem` - -**Type:** Consistency - -`Analysis` owns categories (`Aliases`, `Constraints`, -`JointFitExperiments`) 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. +**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. @@ -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. --- @@ -161,6 +144,156 @@ 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 an ADR 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 @@ -180,6 +313,30 @@ 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 @@ -665,12 +822,13 @@ 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:** @@ -759,18 +917,18 @@ 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. @@ -1239,27 +1397,6 @@ 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 @@ -1315,8 +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. @@ -1420,6 +1557,40 @@ threaded because each step's output is the next step's input. --- +## 95. 🟡 Re-Enable DREAM Multiprocessing in Direct Python Scripts + +**Type:** Performance / Script runtime + +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 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 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. + +--- + ## 90. 🟢 Show Experiment Number/Total During Sequential Fitting **Type:** UX @@ -1438,7 +1609,7 @@ 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. @@ -1458,91 +1629,158 @@ 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 | -| --- | ------------------------------------------------ | -------- | ---------------- | -| 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 | Implement resetting `.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 | -| 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 | -| 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 | 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 deleted file mode 100644 index ae233b21d..000000000 --- a/docs/dev/package-structure-full.md +++ /dev/null @@ -1,412 +0,0 @@ -# Package Structure (full) - -``` -📦 easydiffraction -├── 📁 analysis -│ ├── 📁 calculators -│ │ ├── 📄 __init__.py -│ │ ├── 📄 base.py -│ │ │ └── 🏷️ 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 -│ │ │ │ ├── 📄 bragg_sc.py -│ │ │ │ │ ├── 🏷️ class Refln -│ │ │ │ │ └── 🏷️ class ReflnData -│ │ │ │ ├── 📄 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 -│ │ │ └── 📄 __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 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/dev/package-structure-short.md b/docs/dev/package-structure-short.md deleted file mode 100644 index 30b4daf72..000000000 --- a/docs/dev/package-structure-short.md +++ /dev/null @@ -1,220 +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 -│ │ │ │ ├── 📄 bragg_sc.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 -│ │ │ └── 📄 __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/architecture/package-structure-full.md b/docs/dev/package-structure/full.md similarity index 69% rename from docs/architecture/package-structure-full.md rename to docs/dev/package-structure/full.md index be35bb076..677f5851d 100644 --- a/docs/architecture/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 @@ -31,47 +79,98 @@ │ │ │ │ └── 🏷️ class Constraints │ │ │ └── 📄 factory.py │ │ │ └── 🏷️ class ConstraintsFactory -│ │ ├── 📁 fit +│ │ ├── 📁 deterministic_result │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py -│ │ │ │ └── 🏷️ class Fit -│ │ │ ├── 📄 enums.py -│ │ │ │ └── 🏷️ class FitModeEnum +│ │ │ │ └── 🏷️ class DeterministicResult │ │ │ └── 📄 factory.py -│ │ │ └── 🏷️ class FitFactory -│ │ ├── 📁 joint_fit_experiments +│ │ │ └── 🏷️ class DeterministicResultFactory +│ │ ├── 📁 fit_parameter_correlations │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py -│ │ │ │ ├── 🏷️ class JointFitExperiment -│ │ │ │ └── 🏷️ class JointFitExperiments +│ │ │ │ ├── 🏷️ class FitParameterCorrelationItem +│ │ │ │ └── 🏷️ class FitParameterCorrelations │ │ │ └── 📄 factory.py -│ │ │ └── 🏷️ class JointFitExperimentsFactory +│ │ │ └── 🏷️ 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 +│ │ │ │ └── 🏷️ class Fitting +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class FittingFactory +│ │ ├── 📁 joint_fit +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ │ ├── 🏷️ class JointFitItem +│ │ │ │ └── 🏷️ class JointFitCollection +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class JointFitFactory +│ │ ├── 📁 sequential_fit +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ │ └── 🏷️ class SequentialFit +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class SequentialFitFactory +│ │ ├── 📁 sequential_fit_extract +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ │ ├── 🏷️ class SequentialFitExtractItem +│ │ │ │ └── 🏷️ class SequentialFitExtractCollection +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class SequentialFitExtractFactory │ │ └── 📄 __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 _TerminalLiveHandle +│ │ ├── 🏷️ class SamplerProgressUpdate │ │ └── 🏷️ class FitProgressTracker │ ├── 📁 minimizers │ │ ├── 📄 __init__.py │ │ ├── 📄 base.py │ │ │ └── 🏷️ class MinimizerBase │ │ ├── 📄 bumps.py +│ │ │ ├── 🏷️ class _BumpsEvaluationLimitError │ │ │ ├── 🏷️ class _EasyDiffractionFitness +│ │ │ ├── 🏷️ class _BumpsProgressMonitor │ │ │ └── 🏷️ class BumpsMinimizer │ │ ├── 📄 bumps_amoeba.py │ │ │ └── 🏷️ 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 @@ -83,16 +182,29 @@ │ ├── 📄 __init__.py │ ├── 📄 analysis.py │ │ ├── 🏷️ class AnalysisDisplay +│ │ ├── 🏷️ class _AnalysisOwnerAccessorsMixin +│ │ ├── 🏷️ class _AnalysisPersistedCategoryAccessorsMixin │ │ └── 🏷️ class Analysis +│ ├── 📄 enums.py +│ │ ├── 🏷️ class FitModeEnum +│ │ ├── 🏷️ class FitResultKindEnum +│ │ └── 🏷️ class FitCorrelationSourceEnum │ ├── 📄 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 │ │ ├── 🏷️ class CategoryItem │ │ └── 🏷️ class CategoryCollection +│ ├── 📄 category_owner.py +│ │ └── 🏷️ class CategoryOwner │ ├── 📄 collection.py │ │ └── 🏷️ class CollectionBase │ ├── 📄 datablock.py @@ -126,10 +238,14 @@ │ └── 📄 variable.py │ ├── 🏷️ class GenericDescriptorBase │ ├── 🏷️ class GenericStringDescriptor +│ ├── 🏷️ class GenericBoolDescriptor │ ├── 🏷️ class GenericNumericDescriptor +│ ├── 🏷️ class GenericIntegerDescriptor │ ├── 🏷️ class GenericParameter │ ├── 🏷️ class StringDescriptor +│ ├── 🏷️ class BoolDescriptor │ ├── 🏷️ class NumericDescriptor +│ ├── 🏷️ class IntegerDescriptor │ └── 🏷️ class Parameter ├── 📁 crystallography │ ├── 📄 __init__.py @@ -364,10 +480,19 @@ │ │ └── 🏷️ class RendererFactoryBase │ ├── 📄 plotting.py │ │ ├── 🏷️ class PlotterEngineEnum +│ │ ├── 🏷️ class PosteriorPairPlotStyleEnum │ │ ├── 🏷️ class _MeasVsCalcPlotOptions │ │ ├── 🏷️ class _PowderMeasVsCalcSeries +│ │ ├── 🏷️ class _PosteriorDistributionContext +│ │ ├── 🏷️ class _PosteriorPairsContext +│ │ ├── 🏷️ class _CorrelationHeatmapContext +│ │ ├── 🏷️ class _PosteriorPairsLegendState │ │ ├── 🏷️ class Plotter │ │ └── 🏷️ class PlotterFactory +│ ├── 📄 progress.py +│ │ ├── 🏷️ class _TerminalLiveHandle +│ │ ├── 🏷️ class ActivityIndicator +│ │ └── 🏷️ class _ActivityIndicatorContext │ ├── 📄 tables.py │ │ ├── 🏷️ class TableEngineEnum │ │ ├── 🏷️ class TableRenderer @@ -382,21 +507,41 @@ │ │ ├── 📄 parse.py │ │ └── 📄 serialize.py │ ├── 📄 __init__.py -│ └── 📄 ascii.py +│ ├── 📄 ascii.py +│ └── 📄 results_sidecar.py ├── 📁 project │ ├── 📁 categories -│ │ ├── 📁 display +│ │ ├── 📁 info +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ │ └── 🏷️ class ProjectInfo +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class ProjectInfoFactory +│ │ ├── 📁 rendering +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ │ └── 🏷️ class Rendering +│ │ │ └── 📄 factory.py +│ │ │ └── 🏷️ class RenderingFactory +│ │ ├── 📁 verbosity │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py -│ │ │ │ └── 🏷️ class Display +│ │ │ │ └── 🏷️ class Verbosity │ │ │ └── 📄 factory.py -│ │ │ └── 🏷️ class DisplayFactory +│ │ │ └── 🏷️ class VerbosityFactory │ │ └── 📄 __init__.py │ ├── 📄 __init__.py +│ ├── 📄 display.py +│ │ ├── 🏷️ class PatternOptionStatus +│ │ ├── 🏷️ class ParameterDisplay +│ │ ├── 🏷️ class FitDisplay +│ │ ├── 🏷️ class PosteriorDisplay +│ │ └── 🏷️ 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/architecture/package-structure-short.md b/docs/dev/package-structure/short.md similarity index 73% rename from docs/architecture/package-structure-short.md rename to docs/dev/package-structure/short.md index ba2a0b33b..367a36a51 100644 --- a/docs/architecture/package-structure-short.md +++ b/docs/dev/package-structure/short.md @@ -15,22 +15,75 @@ │ │ │ ├── 📄 __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 -│ │ ├── 📁 fit +│ │ ├── 📁 deterministic_result │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py -│ │ │ ├── 📄 enums.py │ │ │ └── 📄 factory.py -│ │ ├── 📁 joint_fit_experiments +│ │ ├── 📁 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 +│ │ │ └── 📄 factory.py +│ │ ├── 📁 joint_fit +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ └── 📄 factory.py +│ │ ├── 📁 sequential_fit +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ └── 📄 factory.py +│ │ ├── 📁 sequential_fit_extract │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py │ │ │ └── 📄 factory.py │ │ └── 📄 __init__.py │ ├── 📁 fit_helpers │ │ ├── 📄 __init__.py +│ │ ├── 📄 bayesian.py │ │ ├── 📄 metrics.py │ │ ├── 📄 reporting.py │ │ └── 📄 tracking.py @@ -40,6 +93,7 @@ │ │ ├── 📄 bumps.py │ │ ├── 📄 bumps_amoeba.py │ │ ├── 📄 bumps_de.py +│ │ ├── 📄 bumps_dream.py │ │ ├── 📄 bumps_lm.py │ │ ├── 📄 dfols.py │ │ ├── 📄 enums.py @@ -49,11 +103,13 @@ │ │ └── 📄 lmfit_leastsq.py │ ├── 📄 __init__.py │ ├── 📄 analysis.py +│ ├── 📄 enums.py │ ├── 📄 fitting.py │ └── 📄 sequential.py ├── 📁 core │ ├── 📄 __init__.py │ ├── 📄 category.py +│ ├── 📄 category_owner.py │ ├── 📄 collection.py │ ├── 📄 datablock.py │ ├── 📄 diagnostic.py @@ -184,6 +240,7 @@ │ ├── 📄 __init__.py │ ├── 📄 base.py │ ├── 📄 plotting.py +│ ├── 📄 progress.py │ ├── 📄 tables.py │ └── 📄 utils.py ├── 📁 io @@ -193,16 +250,27 @@ │ │ ├── 📄 parse.py │ │ └── 📄 serialize.py │ ├── 📄 __init__.py -│ └── 📄 ascii.py +│ ├── 📄 ascii.py +│ └── 📄 results_sidecar.py ├── 📁 project │ ├── 📁 categories -│ │ ├── 📁 display +│ │ ├── 📁 info +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ └── 📄 factory.py +│ │ ├── 📁 rendering +│ │ │ ├── 📄 __init__.py +│ │ │ ├── 📄 default.py +│ │ │ └── 📄 factory.py +│ │ ├── 📁 verbosity │ │ │ ├── 📄 __init__.py │ │ │ ├── 📄 default.py │ │ │ └── 📄 factory.py │ │ └── 📄 __init__.py │ ├── 📄 __init__.py +│ ├── 📄 display.py │ ├── 📄 project.py +│ ├── 📄 project_config.py │ └── 📄 project_info.py ├── 📁 summary │ ├── 📄 __init__.py 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/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/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/installation-and-setup/index.md b/docs/docs/installation-and-setup/index.md index fdea87bfc..ee86679ba 100644 --- a/docs/docs/installation-and-setup/index.md +++ b/docs/docs/installation-and-setup/index.md @@ -244,17 +244,18 @@ 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: ```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/ ``` @@ -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 @@ -276,13 +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/quick-reference/index.md b/docs/docs/quick-reference/index.md new file mode 100644 index 000000000..9c4d715bd --- /dev/null +++ b/docs/docs/quick-reference/index.md @@ -0,0 +1,430 @@ +--- +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 +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 +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.show_fitting_mode_types() +project.analysis.fitting.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.fitting_mode_type = 'single' +project.analysis.fitting.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.show_fitting_mode_types() +project.analysis.fitting_mode_type = 'single' + +project.analysis.fitting.show_minimizer_types() +project.analysis.fitting.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') +``` + +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 +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 +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 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 + +```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 PROJECT_DIR fit +python -m easydiffraction PROJECT_DIR display +``` 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 4d605962c..ca636d82b 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": "tags,title,-all", + "cell_metadata_filter": "title,tags,-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..e79bdd876 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')" ] }, { @@ -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'" ] }, { @@ -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", @@ -257,18 +268,18 @@ { "cell_type": "code", "execution_count": null, - "id": "24", + "id": "25", "metadata": {}, "outputs": [], "source": [ "# Show fit results summary\n", - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "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": [ @@ -288,16 +299,16 @@ { "cell_type": "code", "execution_count": null, - "id": "27", + "id": "28", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='senju')" + "project.display.pattern(expt_name='senju')" ] }, { "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": [ @@ -340,17 +351,17 @@ { "cell_type": "code", "execution_count": null, - "id": "32", + "id": "33", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.free_params()" + "project.display.parameters.free()" ] }, { "cell_type": "code", "execution_count": null, - "id": "33", + "id": "34", "metadata": {}, "outputs": [], "source": [ @@ -360,37 +371,37 @@ { "cell_type": "code", "execution_count": null, - "id": "34", + "id": "35", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "cell_type": "code", "execution_count": null, - "id": "35", + "id": "36", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_param_correlations()" + "project.display.fit.correlations()" ] }, { "cell_type": "code", "execution_count": null, - "id": "36", + "id": "37", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='senju')" + "project.display.pattern(expt_name='senju')" ] }, { "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 c198fc234..03dc35fa7 100644 --- a/docs/docs/tutorials/ed-15.py +++ b/docs/docs/tutorials/ed-15.py @@ -67,17 +67,21 @@ # ## 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 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' + +# %% +# 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 @@ -86,7 +90,7 @@ # %% # Show fit results summary -project.analysis.display.fit_results() +project.display.fit.results() # %% structure.show_as_cif() @@ -95,7 +99,7 @@ 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 +118,19 @@ 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..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)" ] }, { @@ -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..e5520bfe3 100644 --- a/docs/docs/tutorials/ed-16.py +++ b/docs/docs/tutorials/ed-16.py @@ -182,18 +182,18 @@ # #### 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) # %% -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..e471d99a7 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." ] }, @@ -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('data/cosio_project', 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')" ] }, { @@ -264,8 +267,11 @@ "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", + ")" ] }, { @@ -424,28 +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" ] }, { @@ -485,13 +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", ")" ] }, @@ -510,7 +516,7 @@ "metadata": {}, "outputs": [], "source": [ - "project.analysis.constraints.create(expression='biso_Co2 = biso_Co1')" + "analysis.constraints.create(expression='biso_Co2 = biso_Co1')" ] }, { @@ -528,7 +534,7 @@ "metadata": {}, "outputs": [], "source": [ - "project.analysis.fit.minimizer_type = 'bumps (lm)'" + "analysis.fitting.minimizer_type = 'bumps (lm)'" ] }, { @@ -551,13 +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" ] @@ -565,16 +581,16 @@ { "cell_type": "code", "execution_count": null, - "id": "45", + "id": "46", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_param_correlations()" + "display.fit.correlations()" ] }, { "cell_type": "markdown", - "id": "46", + "id": "47", "metadata": {}, "source": [ "#### Compare measured and calculated patterns for the first fit." @@ -583,16 +599,16 @@ { "cell_type": "code", "execution_count": null, - "id": "47", + "id": "48", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='d20')" + "display.pattern(expt_name='d20')" ] }, { "cell_type": "markdown", - "id": "48", + "id": "49", "metadata": {}, "source": [ "#### Run Sequential Fitting\n", @@ -604,7 +620,7 @@ { "cell_type": "code", "execution_count": null, - "id": "49", + "id": "50", "metadata": {}, "outputs": [], "source": [ @@ -613,33 +629,65 @@ }, { "cell_type": "markdown", - "id": "50", + "id": "51", "metadata": { "lines_to_next_cell": 2 }, "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." ] }, { "cell_type": "code", "execution_count": null, - "id": "51", + "id": "52", + "metadata": {}, + "outputs": [], + "source": [ + "temperature = 'diffrn.ambient_temperature'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53", "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}" + "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": "52", + "id": "54", + "metadata": {}, + "source": [ + "Set the sequential fitting parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55", + "metadata": {}, + "outputs": [], + "source": [ + "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": "56", "metadata": {}, "source": [ "Run the sequential fit over all data files in the scan directory." @@ -648,21 +696,16 @@ { "cell_type": "code", "execution_count": null, - "id": "53", + "id": "57", "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", - ")" + "analysis.fit()" ] }, { "cell_type": "markdown", - "id": "54", + "id": "58", "metadata": {}, "source": [ "#### Replay a Dataset\n", @@ -673,17 +716,17 @@ { "cell_type": "code", "execution_count": null, - "id": "55", + "id": "59", "metadata": {}, "outputs": [], "source": [ "project.apply_params_from_csv(row_index=0)\n", - "project.display.plotter.plot_meas_vs_calc(expt_name='d20')" + "display.pattern(expt_name='d20')" ] }, { "cell_type": "markdown", - "id": "56", + "id": "60", "metadata": {}, "source": [ "\n", @@ -693,37 +736,47 @@ { "cell_type": "code", "execution_count": null, - "id": "57", + "id": "61", "metadata": {}, "outputs": [], "source": [ "project.apply_params_from_csv(row_index=-1)\n", - "project.display.plotter.plot_meas_vs_calc(expt_name='d20')" + "display.pattern(expt_name='d20')" ] }, { "cell_type": "markdown", - "id": "58", + "id": "62", "metadata": {}, "source": [ "#### Plot Parameter Evolution\n", "\n", - "Define the quantity to use as the x-axis in the following plots." + "Reuse the extracted diffrn path as the x-axis in the following plots." + ] + }, + { + "cell_type": "markdown", + "id": "63", + "metadata": {}, + "source": [ + "Plot fit quality metrics vs. temperature." ] }, { "cell_type": "code", "execution_count": null, - "id": "59", + "id": "64", "metadata": {}, "outputs": [], "source": [ - "temperature = expt.diffrn.ambient_temperature" + "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": "60", + "id": "65", "metadata": {}, "source": [ "Plot unit cell parameters vs. temperature." @@ -732,18 +785,18 @@ { "cell_type": "code", "execution_count": null, - "id": "61", + "id": "66", "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)" + "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": "62", + "id": "67", "metadata": {}, "source": [ "Plot isotropic displacement parameters vs. temperature." @@ -752,35 +805,20 @@ { "cell_type": "code", "execution_count": null, - "id": "63", + "id": "68", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['Co1'].adp_iso,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['Si'].adp_iso,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['O1'].adp_iso,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['O2'].adp_iso,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_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": "64", + "id": "69", "metadata": {}, "source": [ "Plot selected fractional coordinates vs. temperature." @@ -789,30 +827,15 @@ { "cell_type": "code", "execution_count": null, - "id": "65", + "id": "70", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['Co2'].fract_x,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['Co2'].fract_z,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['O1'].fract_z,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_series(\n", - " structure.atom_sites['O2'].fract_z,\n", - " versus=temperature,\n", - ")\n", - "project.display.plotter.plot_param_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 f929cd47c..93f691fb7 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] @@ -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('data/cosio_project', 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, @@ -125,14 +128,17 @@ # #### 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 # %% -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 @@ -202,28 +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 @@ -244,26 +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.fit.minimizer_type = 'bumps (lm)' +analysis.fitting.minimizer_type = 'bumps (lm)' # %% [markdown] # #### Run Single Fitting @@ -274,19 +280,22 @@ # if the initial parameters are far from optimal. # %% -project.analysis.fit() +analysis.fit() + +# %% +display.fit.results() # %% [markdown] # #### Show parameter correlations # %% -project.display.plotter.plot_param_correlations() +display.fit.correlations() # %% [markdown] # #### Compare measured and calculated patterns for the first fit. # %% -project.display.plotter.plot_meas_vs_calc(expt_name='d20') +display.pattern(expt_name='d20') # %% [markdown] # #### Run Sequential Fitting @@ -299,28 +308,35 @@ # %% [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' +# %% +analysis.sequential_fit_extract.create( + id='temperature', + target=temperature, + pattern=r'^TEMP\s+([0-9.]+)', + required=True, +) + +# %% [markdown] +# Set the sequential fitting parameters. + +# %% +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_sequential( - data_dir=data_dir, - extract_diffrn=extract_diffrn, - max_workers='auto', - reverse=True, -) +analysis.fit() # %% [markdown] # #### Replay a Dataset @@ -329,7 +345,7 @@ def extract_diffrn(file_path): # %% project.apply_params_from_csv(row_index=0) -project.display.plotter.plot_meas_vs_calc(expt_name='d20') +display.pattern(expt_name='d20') # %% [markdown] # @@ -337,70 +353,45 @@ def extract_diffrn(file_path): # %% project.apply_params_from_csv(row_index=-1) -project.display.plotter.plot_meas_vs_calc(expt_name='d20') +display.pattern(expt_name='d20') # %% [markdown] # #### Plot Parameter Evolution # -# Define the quantity to use as the x-axis in the following plots. +# Reuse the extracted diffrn path as the x-axis in the following plots. + +# %% [markdown] +# Plot fit quality metrics vs. temperature. # %% -temperature = expt.diffrn.ambient_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.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) +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.plotter.plot_param_series( - structure.atom_sites['Co1'].adp_iso, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['Si'].adp_iso, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['O1'].adp_iso, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['O2'].adp_iso, - versus=temperature, -) -project.display.plotter.plot_param_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.plotter.plot_param_series( - structure.atom_sites['Co2'].fract_x, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['Co2'].fract_z, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['O1'].fract_z, - versus=temperature, -) -project.display.plotter.plot_param_series( - structure.atom_sites['O2'].fract_z, - versus=temperature, -) -project.display.plotter.plot_param_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 47a25154a..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" @@ -140,31 +121,31 @@ { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "11", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "12", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_param_correlations()" + "project.display.fit.correlations()" ] }, { "cell_type": "code", "execution_count": null, - "id": "15", + "id": "13", "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..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 @@ -47,10 +40,10 @@ # ## 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..1212cbcc7 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')" ] }, { @@ -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'" ] }, { @@ -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 +517,25 @@ "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" + "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')" ] } ], diff --git a/docs/docs/tutorials/ed-2.py b/docs/docs/tutorials/ed-2.py index fa4918c05..220ebae9c 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) @@ -192,20 +192,20 @@ 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() # %% -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 +220,16 @@ 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 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 b9a47a281..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')" ] }, { @@ -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')" ] }, { @@ -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'" ] }, { @@ -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..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 @@ -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 @@ -259,16 +259,16 @@ # #### 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 # %% -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 new file mode 100644 index 000000000..689c3d57a --- /dev/null +++ b/docs/docs/tutorials/ed-21.ipynb @@ -0,0 +1,763 @@ +{ + "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.\n", + "\n", + "Save the project to a directory early on so that you can easily reload\n", + "it later if needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "project = ed.Project()" + ] + }, + { + "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", + "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": "8", + "metadata": {}, + "outputs": [], + "source": [ + "project.structures.create(name='lbco')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "structure = project.structures['lbco']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "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": "11", + "metadata": {}, + "outputs": [], + "source": [ + "structure.cell.length_a = 3.88" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "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": "13", + "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": "14", + "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": "15", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = ed.download_data(id=3, destination='data')" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "Create the experiment object and specify the sample form, beam mode,\n", + "and radiation probe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "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": "19", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = project.experiments['hrpt']" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + "Link the structural phase to the experiment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.linked_phases.create(id='lbco', scale=9.1351)" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "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": "23", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.instrument.setup_wavelength = 1.494\n", + "experiment.instrument.calib_twotheta_offset = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "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": "25", + "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": "26", + "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": "27", + "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": "28", + "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": "29", + "metadata": {}, + "outputs": [], + "source": [ + "structure.cell.length_a.free = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "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": "31", + "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": "32", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.minimizer_type = 'bumps (lm)'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.results()" + ] + }, + { + "cell_type": "markdown", + "id": "36", + "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": "37", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.pattern(expt_name='hrpt')" + ] + }, + { + "cell_type": "markdown", + "id": "39", + "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", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.parameters.free()" + ] + }, + { + "cell_type": "markdown", + "id": "41", + "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": "42", + "metadata": {}, + "outputs": [], + "source": [ + "for param in project.free_parameters:\n", + " param.set_fit_bounds_from_uncertainty()" + ] + }, + { + "cell_type": "markdown", + "id": "43", + "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": "44", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.parameters.free()" + ] + }, + { + "cell_type": "markdown", + "id": "45", + "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 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. 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": "46", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.minimizer_type = 'bumps (dream)'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48", + "metadata": {}, + "outputs": [], + "source": [ + "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": "49", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "50", + "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": "51", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.results()" + ] + }, + { + "cell_type": "markdown", + "id": "52", + "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 `±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": "53", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.posterior.pairs()" + ] + }, + { + "cell_type": "markdown", + "id": "55", + "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": "56", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.posterior.distribution()" + ] + }, + { + "cell_type": "markdown", + "id": "57", + "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": "58", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.posterior.predictive(expt_name='hrpt')" + ] + }, + { + "cell_type": "markdown", + "id": "59", + "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": "60", + "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-21.py b/docs/docs/tutorials/ed-21.py new file mode 100644 index 000000000..df32bcabe --- /dev/null +++ b/docs/docs/tutorials/ed-21.py @@ -0,0 +1,356 @@ +# %% [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. +# +# 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 +# +# 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.fitting.show_minimizer_types() + +# %% +project.analysis.fitting.minimizer_type = 'bumps (lm)' + +# %% +project.analysis.fit() + +# %% +project.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.fit.correlations() + +# %% +project.display.pattern(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.display.parameters.free() + +# %% [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.display.parameters.free() + +# %% [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.fitting.show_minimizer_types() + +# %% +project.analysis.fitting.minimizer_type = 'bumps (dream)' + +# %% +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() + +# %% [markdown] +# ## Step 7: Inspect Bayesian Results +# +# The fit-results display now includes sampler settings, convergence +# diagnostics, committed parameter values, and posterior summary +# statistics. + +# %% +project.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.fit.correlations() + +# %% +project.display.posterior.pairs() + +# %% [markdown] +# The one-dimensional posterior distributions below make it easier to +# inspect individual parameters in isolation, including asymmetry or +# multimodality. + +# %% +project.display.posterior.distribution() + +# %% [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.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.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..a8d511132 --- /dev/null +++ b/docs/docs/tutorials/ed-22.ipynb @@ -0,0 +1,607 @@ +{ + "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.\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?" + ] + }, + { + "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", + "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." + ] + }, + { + "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\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." + ] + }, + { + "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": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Link the crystal structure to the experiment and set its scale factor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.linked_crystal.id = 'tbti'\n", + "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": "17", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.instrument.setup_wavelength = 0.793" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.extinction.mosaicity = 35000\n", + "experiment.extinction.radius = 10" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "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 a small set of structural and extinction\n", + "parameters while keeping occupancies fixed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "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": "21", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.linked_crystal.scale.free = True\n", + "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": "23", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "26", + "metadata": {}, + "outputs": [], + "source": [ + "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": "28", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.pattern(expt_name='heidi')" + ] + }, + { + "cell_type": "markdown", + "id": "30", + "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", + "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": "31", + "metadata": {}, + "outputs": [], + "source": [ + "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": "33", + "metadata": {}, + "outputs": [], + "source": [ + "for param in project.free_parameters:\n", + " 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": "35", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.parameters.free()" + ] + }, + { + "cell_type": "markdown", + "id": "36", + "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 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": "37", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fitting.minimizer_type = 'bumps (dream)'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "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": "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": "42", + "metadata": {}, + "outputs": [], + "source": [ + "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": "44", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": {}, + "outputs": [], + "source": [ + "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": "47", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.posterior.distribution()" + ] + }, + { + "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": "49", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.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..cbc08f74e --- /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.fitting.show_minimizer_types() + +# %% +project.analysis.fit() + +# %% [markdown] +# The fit-results display summarizes the locally refined values and their +# estimated uncertainties. + +# %% +project.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.fit.correlations() + +# %% +project.display.pattern(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.display.parameters.free() + +# %% [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.display.parameters.free() + +# %% [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.fitting.show_minimizer_types() + +# %% +project.analysis.fitting.minimizer_type = 'bumps (dream)' + +# %% +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() + +# %% [markdown] +# ## Step 7: Inspect Bayesian Results +# +# The fit-results display now includes sampler settings, convergence +# diagnostics, committed parameter values, and posterior summary +# statistics. + +# %% +project.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.fit.correlations() + +# %% +project.display.posterior.pairs() + +# %% [markdown] +# The one-dimensional posterior distributions below make it easier to +# inspect individual parameters in isolation, including asymmetry or +# multimodality. + +# %% +project.display.posterior.distribution() + +# %% [markdown] +# Finally, the posterior predictive plot propagates the sampled +# parameter uncertainty into the calculated single-crystal reflection +# intensities. + +# %% +project.display.posterior.predictive(expt_name='heidi') diff --git a/docs/docs/tutorials/ed-23.ipynb b/docs/docs/tutorials/ed-23.ipynb new file mode 100644 index 000000000..11a28c791 --- /dev/null +++ b/docs/docs/tutorials/ed-23.ipynb @@ -0,0 +1,236 @@ +{ + "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\n", + "\n", + "The returned path points directly to the saved project directory with\n", + "a partially completed sequential fit, including\n", + "`analysis/results.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = ed.download_data(id=37, destination='projects')" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Load Saved Project" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "project = ed.Project.load(project_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "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": "9", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "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": "11", + "metadata": {}, + "outputs": [], + "source": [ + "project.apply_params_from_csv(row_index=0)\n", + "project.display.pattern(expt_name='d20')" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "\n", + "Apply fitted parameters from the last CSV row and plot the result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "project.apply_params_from_csv(row_index=-1)\n", + "project.display.pattern(expt_name='d20')" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "17", + "metadata": {}, + "outputs": [], + "source": [ + "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": "18", + "metadata": {}, + "source": [ + "Omitting `param` plots every fitted parameter one after another." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.fit.series(versus=temperature)" + ] + } + ], + "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..a4dbc8ed7 --- /dev/null +++ b/docs/docs/tutorials/ed-23.py @@ -0,0 +1,90 @@ +# %% [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 +# +# The returned path points directly to the saved project directory with +# a partially completed sequential fit, including +# `analysis/results.csv`. + +# %% +project_dir = ed.download_data(id=37, 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. + +# %% +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/ed-3.ipynb b/docs/docs/tutorials/ed-3.ipynb index f99298a2f..357607f26 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()" ] }, { @@ -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'" ] }, { @@ -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')" ] }, { @@ -1061,31 +1061,13 @@ "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)" ] }, { "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." @@ -1116,16 +1098,16 @@ { "cell_type": "code", "execution_count": null, - "id": "108", + "id": "106", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.free_params()" + "project.display.parameters.free()" ] }, { "cell_type": "markdown", - "id": "109", + "id": "107", "metadata": {}, "source": [ "#### Run Fitting" @@ -1134,17 +1116,17 @@ { "cell_type": "code", "execution_count": null, - "id": "110", + "id": "108", "metadata": {}, "outputs": [], "source": [ "project.analysis.fit()\n", - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "cell_type": "markdown", - "id": "111", + "id": "109", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" @@ -1153,26 +1135,26 @@ { "cell_type": "code", "execution_count": null, - "id": "112", + "id": "110", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" + "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "113", + "id": "111", "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)" ] }, { "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." @@ -1222,16 +1204,16 @@ { "cell_type": "code", "execution_count": null, - "id": "119", + "id": "117", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.free_params()" + "project.display.parameters.free()" ] }, { "cell_type": "markdown", - "id": "120", + "id": "118", "metadata": {}, "source": [ "#### Run Fitting" @@ -1240,17 +1222,17 @@ { "cell_type": "code", "execution_count": null, - "id": "121", + "id": "119", "metadata": {}, "outputs": [], "source": [ "project.analysis.fit()\n", - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "cell_type": "markdown", - "id": "122", + "id": "120", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" @@ -1259,44 +1241,26 @@ { "cell_type": "code", "execution_count": null, - "id": "123", - "metadata": {}, - "outputs": [], - "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "124", + "id": "121", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)" - ] - }, - { - "cell_type": "markdown", - "id": "125", - "metadata": {}, - "source": [ - "#### Save Project State" + "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "126", + "id": "122", "metadata": {}, "outputs": [], "source": [ - "project.save_as(dir_path='lbco_hrpt', temporary=True)" + "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)" ] }, { "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." @@ -1352,16 +1316,16 @@ { "cell_type": "code", "execution_count": null, - "id": "132", + "id": "128", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.constraints()" + "project.analysis.constraints.show()" ] }, { "cell_type": "markdown", - "id": "133", + "id": "129", "metadata": {}, "source": [ "Show free parameters." @@ -1370,16 +1334,16 @@ { "cell_type": "code", "execution_count": null, - "id": "134", + "id": "130", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.free_params()" + "project.display.parameters.free()" ] }, { "cell_type": "markdown", - "id": "135", + "id": "131", "metadata": {}, "source": [ "#### Run Fitting" @@ -1388,17 +1352,17 @@ { "cell_type": "code", "execution_count": null, - "id": "136", + "id": "132", "metadata": {}, "outputs": [], "source": [ "project.analysis.fit()\n", - "project.analysis.display.fit_results()" + "project.display.fit.results()" ] }, { "cell_type": "markdown", - "id": "137", + "id": "133", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" @@ -1407,44 +1371,26 @@ { "cell_type": "code", "execution_count": null, - "id": "138", - "metadata": {}, - "outputs": [], - "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "139", + "id": "134", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41)" - ] - }, - { - "cell_type": "markdown", - "id": "140", - "metadata": {}, - "source": [ - "#### Save Project State" + "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "141", + "id": "135", "metadata": {}, "outputs": [], "source": [ - "project.save_as(dir_path='lbco_hrpt', temporary=True)" + "project.display.pattern(expt_name='hrpt', x_min=38, x_max=41)" ] }, { "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." @@ -1502,18 +1448,18 @@ { "cell_type": "code", "execution_count": null, - "id": "147", + "id": "141", "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "project.analysis.display.constraints()" + "project.analysis.constraints.show()" ] }, { "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." @@ -1540,16 +1486,16 @@ { "cell_type": "code", "execution_count": null, - "id": "151", + "id": "145", "metadata": {}, "outputs": [], "source": [ - "project.analysis.display.free_params()" + "project.display.parameters.free()" ] }, { "cell_type": "markdown", - "id": "152", + "id": "146", "metadata": {}, "source": [ "#### Run Fitting" @@ -1558,18 +1504,18 @@ { "cell_type": "code", "execution_count": null, - "id": "153", + "id": "147", "metadata": {}, "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()" ] }, { "cell_type": "markdown", - "id": "154", + "id": "148", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" @@ -1578,26 +1524,26 @@ { "cell_type": "code", "execution_count": null, - "id": "155", + "id": "149", "metadata": {}, "outputs": [], "source": [ - "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" + "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "code", "execution_count": null, - "id": "156", + "id": "150", "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)" ] }, { "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 de1f74c63..7a194acd2 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 @@ -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 @@ -417,29 +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) - -# %% [markdown] -# #### Save Project State - -# %% -project.save_as(dir_path='lbco_hrpt', temporary=True) +project.display.pattern(expt_name='hrpt', x_min=38, x_max=41) # %% [markdown] # ### Perform Fit 2/5 @@ -456,29 +450,29 @@ # 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 # %% -project.save_as(dir_path='lbco_hrpt', temporary=True) +project.save() # %% [markdown] # ### Perform Fit 3/5 @@ -495,29 +489,23 @@ # 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.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=38, x_max=41) - -# %% [markdown] -# #### Save Project State +project.display.pattern(expt_name='hrpt') # %% -project.save_as(dir_path='lbco_hrpt', temporary=True) +project.display.pattern(expt_name='hrpt', x_min=38, x_max=41) # %% [markdown] # ### Perform Fit 4/5 @@ -546,35 +534,29 @@ # 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) - -# %% [markdown] -# #### Save Project State - -# %% -project.save_as(dir_path='lbco_hrpt', temporary=True) +project.display.pattern(expt_name='hrpt', x_min=38, x_max=41) # %% [markdown] # ### Perform Fit 5/5 @@ -605,7 +587,7 @@ # Show defined constraints. # %% -project.analysis.display.constraints() +project.analysis.constraints.show() # %% [markdown] @@ -618,30 +600,30 @@ # 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 # %% -project.save_as(dir_path='lbco_hrpt', temporary=True) +project.save() # %% [markdown] # ## Step 5: Summary diff --git a/docs/docs/tutorials/ed-4.ipynb b/docs/docs/tutorials/ed-4.ipynb index 3a77ab8bb..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'" ] }, { @@ -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..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 @@ -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..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'" ] }, { @@ -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..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 @@ -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/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..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 @@ -98,6 +107,22 @@ 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/docs/user-guide/analysis-workflow/analysis.md b/docs/docs/user-guide/analysis-workflow/analysis.md index bf1d4ee87..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 @@ -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: @@ -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/analysis-workflow/project.md b/docs/docs/user-guide/analysis-workflow/project.md index 7959f9f85..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. │ ├── 📄 hrpt.cif - Instrumental parameters, calculator selection and measured data from HRPT@PSI. │ └── ... -├── 📄 analysis.cif - Settings for data analysis (minimizer, fit mode, etc.). -└── 📁 summary - └── 📄 report.cif - Summary report after structure refinement. +├── 📁 analysis - Analysis settings and optional persisted Bayesian arrays. +│ ├── 📄 analysis.cif - Settings for data analysis (minimizer, fit mode, constraints, persisted fit state). +│ └── 📄 results.h5 - Optional Bayesian sidecar with posterior and predictive arrays. +└── 📄 summary.cif - Summary report after structure refinement. @@ -110,13 +112,16 @@ This file stores project-level metadata and display configuration. 
-data_La0.5Ba0.5CoO3
-
+_project.id          lbco_hrpt
 _project.title       "La0.5Ba0.5CoO3 from neutron diffraction at HRPT@PSI"
 _project.description "neutrons, powder, constant wavelength, HRPT@PSI"
 
-_display.plotter_type  asciichartpy
-_display.tabler_type   rich
+_project.created     "18 May 2026 10:15:00"
+_project.last_modified "18 May 2026 10:20:00"
+
+_rendering.chart_engine auto
+_rendering.table_engine auto
+_verbosity.fit         full
@@ -233,7 +238,7 @@ loop_ -### 4. analysis.cif +### 4. analysis / analysis.cif 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 
-_fit.minimizer_type          "lmfit (leastsq)"
-_fit.mode                    single
+_fitting.mode_type              single
+_fitting.minimizer_type         lmfit
 
 loop_
 _alias.label
@@ -263,6 +268,9 @@ loop_
 
 
 
+When a Bayesian fit stores persisted posterior or predictive arrays, the
+same `analysis/` directory also contains `results.h5`.
+
 

 
 ---
diff --git a/docs/docs/user-guide/first-steps.md b/docs/docs/user-guide/first-steps.md
index b8f69070d..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
@@ -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/docs/mkdocs.yml b/docs/mkdocs.yml
index 45c2ce5fb..02f862023 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
@@ -191,10 +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
@@ -214,8 +219,13 @@ 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
+  - Command-Line:
+      - Command-Line: cli/index.md
   - API Reference:
       - API Reference: api-reference/index.md
       - analysis: api-reference/analysis.md
@@ -229,5 +239,5 @@ 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
+  - Quick Reference:
+      - Quick Reference: quick-reference/index.md
diff --git a/pixi.lock b/pixi.lock
index 36a2ecb00..974cbe77e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,53 +1,100 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-arm64
+- name: win-64
 environments:
   default:
     channels:
+    - url: https://conda.anaconda.org/nodefaults/
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
-    options:
-      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
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-  name: yarl
-  version: 1.23.0
-  sha256: 1c57676bdedc94cd3bc37724cf6f8cd2779f02f6aba48de45feca073e714fe52
+  - hyperscan>=0.7 ; extra == 'hyperscan'
+  - typing-extensions>=4 ; extra == 'optional'
+  - google-re2>=1.1 ; extra == 're2'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/f2/f2/728f041460f1b9739b85ee23b45fa5a505962ea11fd85bdbe2a02b021373/darkdetect-0.8.0-py3-none-any.whl
+  name: darkdetect
+  version: 0.8.0
+  sha256: a7509ccf517eaad92b31c214f593dbcf138ea8a43b2935406bbd565e15527a85
+  requires_dist:
+  - pyobjc-framework-cocoa ; sys_platform == 'darwin' and extra == 'macos-listener'
+  requires_python: '>=3.6'
+- pypi: https://files.pythonhosted.org/packages/f3/8d/5e5be3ced1d12966fefb5c4ea3b2a5b480afcea36406559442c6e31d4a48/multidict-6.7.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
+  name: multidict
+  version: 6.7.1
+  sha256: bfde23ef6ed9db7eaee6c37dcec08524cb43903c60b285b172b6c094711b3961
   requires_dist:
-  - idna>=2.0
-  - multidict>=4.0
-  - propcache>=0.2.1
-  requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/66/3e/868e5c3364b6cee19ff3e1a122194fa4ce51def02c61023970442162859e/yarl-1.23.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
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-  version: 1.23.0
-  sha256: a3d2bff8f37f8d0f96c7ec554d16945050d54462d6e95414babaa18bfafc7f51
+  - typing-extensions>=4.1.0 ; python_full_version < '3.11'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/f3/9b/4165a1d56ddc302a0e2d518fd9d412a4fd0b57562618c78c5f21c57194f5/coverage-7.14.0-cp314-cp314-macosx_11_0_arm64.whl
+  name: coverage
+  version: 7.14.0
+  sha256: ba3b8390db29296dbbf49e91b6fe08f990743a90c8f447ba4c2ffc29670dfa63
   requires_dist:
-  - idna>=2.0
-  - multidict>=4.0
-  - propcache>=0.2.1
+  - tomli ; python_full_version <= '3.11' and extra == 'toml'
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/a9/5b/9b92f54c784c26e2a422e55a8d2607ab15b7ea3349e28359282f84f01d43/yarl-1.23.0-cp314-cp314-win_amd64.whl
-  name: yarl
-  version: 1.23.0
-  sha256: 63e92247f383c85ab00dd0091e8c3fa331a96e865459f5ee80353c70a4a42d70
+- 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:
-  - idna>=2.0
-  - multidict>=4.0
-  - propcache>=0.2.1
-  requires_python: '>=3.10'
+  - 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
+  sha256: 27b3b67cf898684e646d569f017cb27046774ad23866cb0bdf51d5f76a46476b
+  requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/f5/57/2a154a69d6642860300bf8eb205d13131104991f2b1065bbb9075ac5c32e/tof-26.3.0-py3-none-any.whl
+  name: tof
+  version: 26.3.0
+  sha256: e89783a072b05fdb53d9e76fbf919dc8935e75e118fdaf17ca5cc33727ef002b
+  requires_dist:
+  - plopp>=23.10.0
+  - pooch>=1.5.0
+  - scipp>=25.1.0
+  - lazy-loader>=0.3
+  - pytest>=8.0 ; extra == 'test'
+  - scippneutron>=24.12.0 ; extra == 'test'
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/f5/be/25216a49daeeb7af2bec0db22d5e7df08ed1d7c9f65d78b14f3b74fd72fc/yarl-1.23.0-cp312-cp312-win_amd64.whl
   name: yarl
   version: 1.23.0
@@ -12002,91 +12974,134 @@ packages:
   - multidict>=4.0
   - propcache>=0.2.1
   requires_python: '>=3.10'
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h41580af_10.conda
-  sha256: 325d370b28e2b9cc1f765c5b4cdb394c91a5d958fbd15da1a14607a28fee09f6
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-  depends:
-  - libgcc >=14
-  - __glibc >=2.17,<3.0.a0
-  - libstdcxx >=14
-  - krb5 >=1.22.2,<1.23.0a0
-  - libsodium >=1.0.21,<1.0.22.0a0
-  license: MPL-2.0
-  license_family: MOZILLA
-  purls: []
-  size: 311150
-  timestamp: 1772476812121
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h4818236_10.conda
-  sha256: 2705360c72d4db8de34291493379ffd13b09fd594d0af20c9eefa8a3f060d868
-  md5: e85dcd3bde2b10081cdcaeae15797506
-  depends:
-  - __osx >=11.0
-  - libcxx >=19
-  - krb5 >=1.22.2,<1.23.0a0
-  - libsodium >=1.0.21,<1.0.22.0a0
-  license: MPL-2.0
-  license_family: MOZILLA
-  purls: []
-  size: 245246
-  timestamp: 1772476886668
-- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h507cc87_10.conda
-  sha256: b8568dfde46edf3455458912ea6ffb760e4456db8230a0cf34ecbc557d3c275f
-  md5: 1ab0237036bfb14e923d6107473b0021
-  depends:
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - libsodium >=1.0.21,<1.0.22.0a0
-  - krb5 >=1.22.2,<1.23.0a0
-  license: MPL-2.0
-  license_family: MOZILLA
-  purls: []
-  size: 265665
-  timestamp: 1772476832995
-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda
-  sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca
-  md5: e1c36c6121a7c9c76f2f148f1e83b983
-  depends:
-  - python >=3.10
-  - python
-  license: MIT
-  license_family: MIT
-  purls:
-  - pkg:pypi/zipp?source=compressed-mapping
-  size: 24461
-  timestamp: 1776131454755
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
-  sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
-  md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - libzlib >=1.3.1,<2.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 601375
-  timestamp: 1764777111296
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
-  sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9
-  md5: ab136e4c34e97f34fb621d2592a393d8
-  depends:
-  - __osx >=11.0
-  - libzlib >=1.3.1,<2.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 433413
-  timestamp: 1764777166076
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
-  sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
-  md5: 053b84beec00b71ea8ff7a4f84b55207
-  depends:
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - libzlib >=1.3.1,<2.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 388453
-  timestamp: 1764777142545
+- pypi: https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl
+  name: py
+  version: 1.11.0
+  sha256: 607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378
+  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*'
+- pypi: https://files.pythonhosted.org/packages/f7/00/bbca25f8a2372465cdf93138c1e1e38dff045fb0afef1488f395d0afcb3b/ncrystal-4.4.2-py3-none-any.whl
+  name: ncrystal
+  version: 4.4.2
+  sha256: e02fa7d743addc3fbea23287737a88b8d01192450fdca51554d3f9032fe4617c
+  requires_dist:
+  - ncrystal-core==4.4.2
+  - ncrystal-python==4.4.2
+  - spglib>=2.1.0 ; extra == 'composer'
+  - ase>=3.23.0 ; extra == 'cif'
+  - gemmi>=0.6.1 ; extra == 'cif'
+  - spglib>=2.1.0 ; extra == 'cif'
+  - endf-parserpy>=0.14.3 ; extra == 'endf'
+  - matplotlib>=3.6.0 ; extra == 'plot'
+  - ase>=3.23.0 ; extra == 'all'
+  - endf-parserpy>=0.14.3 ; extra == 'all'
+  - gemmi>=0.6.1 ; extra == 'all'
+  - matplotlib>=3.6.0 ; extra == 'all'
+  - spglib>=2.1.0 ; extra == 'all'
+  - pyyaml>=6.0.0 ; extra == 'devel'
+  - ase>=3.23.0 ; extra == 'devel'
+  - cppcheck ; extra == 'devel'
+  - endf-parserpy>=0.14.3 ; extra == 'devel'
+  - gemmi>=0.6.1 ; extra == 'devel'
+  - matplotlib>=3.6.0 ; extra == 'devel'
+  - mpmath>=1.3.0 ; extra == 'devel'
+  - numpy>=1.22 ; extra == 'devel'
+  - pybind11>=2.11.0 ; extra == 'devel'
+  - ruff>=0.8.1 ; extra == 'devel'
+  - simple-build-system>=1.6.0 ; extra == 'devel'
+  - 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/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl
+  name: ghp-import
+  version: 2.1.0
+  sha256: 8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619
+  requires_dist:
+  - python-dateutil>=2.8.1
+  - twine ; extra == 'dev'
+  - markdown ; extra == 'dev'
+  - flake8 ; extra == 'dev'
+  - wheel ; extra == 'dev'
+- pypi: https://files.pythonhosted.org/packages/f9/58/6e1b8f52fdc3184b47dc5037f5070d83a3d11042db1594b02d2a44d786c8/coverage-7.14.0-cp314-cp314-win_amd64.whl
+  name: coverage
+  version: 7.14.0
+  sha256: 45e0f79d8351fa76e256716df91eab12890d32678b9590df7ae1042e4bd4cf5d
+  requires_dist:
+  - tomli ; python_full_version <= '3.11' and extra == 'toml'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/fa/bc/8b8ec5a4bfc5b9cf3ce27a118339e994f88410be5677c96493e0ea28e76d/dunamai-1.26.1-py3-none-any.whl
+  name: dunamai
+  version: 1.26.1
+  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/pixi.toml b/pixi.toml
index 546620d4a..70a8a310e 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
@@ -104,8 +102,8 @@ default = { features = ['py-max'] }
 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 = [
@@ -186,10 +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 c0943384c..507a069d1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,10 +23,7 @@ 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
   'asciichartpy',   # ASCII charts for terminal output
   'pooch',          # Data downloader
   'typer',          # Command-line interface creation
@@ -44,15 +41,18 @@ 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
   'plotly',         # Interactive plots
+  'arviz',          # Bayesian analysis summaries and posterior plotting
   'py3Dmol',        # Visualisation of crystal structures
 ]
 
 [project.optional-dependencies]
 dev = [
+  'essdiffraction',                  # ESS-specific diffraction library
   'GitPython',                       # Interact with Git repositories
   'build',                           # Building the package
   'pre-commit',                      # Pre-commit hooks
@@ -63,7 +63,6 @@ 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
@@ -170,7 +169,7 @@ 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/__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 bd5ae9be5..a66d0d8f8 100644
--- a/src/easydiffraction/__main__.py
+++ b/src/easydiffraction/__main__.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors 
 # 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(
@@ -94,17 +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()
-    project.analysis.display.fit_results()
-    project.display.plotter.plot_param_correlations()
-    for expt in project.experiments:
-        project.display.plotter.plot_meas_vs_calc(expt_name=expt.name, show_residual=True)
-    # 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 78150ea54..f9d5e94de 100644
--- a/src/easydiffraction/analysis/__init__.py
+++ b/src/easydiffraction/analysis/__init__.py
@@ -1,2 +1,68 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors 
 # 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
+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 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 c132e409c..eccbf28d8 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -1,98 +1,89 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
 
+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.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.fit_helpers.tracking import _make_display_handle
+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
+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 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.core.guard import GuardedBase
+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
 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'})
+_POSTERIOR_SAMPLE_NDIM = 3
+_FLATTENED_POSTERIOR_SAMPLE_NDIM = 2
+_CREDIBLE_INTERVAL_LEVEL_COUNT = 2
 
-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:
@@ -102,9 +93,13 @@ class AnalysisDisplay:
     Accessed via ``analysis.display``.
     """
 
-    def __init__(self, analysis: 'Analysis') -> None:
+    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.
@@ -114,12 +109,23 @@ 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.')
@@ -136,15 +142,17 @@ 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."""
@@ -170,23 +178,27 @@ 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."""
         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.')
@@ -221,14 +233,14 @@ 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
 
@@ -287,14 +299,14 @@ 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
 
@@ -340,20 +352,7 @@ 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:
         """
@@ -379,13 +378,114 @@ 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 _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
 
-class Analysis:
+    @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.
 
@@ -403,49 +503,421 @@ 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._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.fit_results = None
+        self._constraints = ConstraintsFactory.create(self._constraints_type)
+        self._constraints_handler = ConstraintsHandler.get()
+        self._fitting: Fitting = FittingFactory.create(FittingFactory.default_tag())
+        self._fitting_mode_type: FitModeEnum = FitModeEnum.default()
+        self._joint_fit: JointFitCollection = JointFitCollection()
+        self._sequential_fit: SequentialFit = SequentialFitFactory.create(
+            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 display(self) -> AnalysisDisplay:
-        """Display helper for parameter tables, CIF, and fit results."""
-        return self._display
+    @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')
+        }
+
+    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
+
+            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
+
+        if self.fit_result.result_kind.value != FitResultKindEnum.BAYESIAN.value:
+            return
+
+        if self.fitting.minimizer_type.value != MinimizerTypeEnum.BUMPS_DREAM.value:
+            return
+
+        minimizer = self.fitting.minimizer
+        if minimizer is None:
+            return
+
+        steps = int(self.bayesian_sampler.steps.value)
+        if steps <= 0:
+            return
+
+        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."""
-        console.paragraph("Help for 'Analysis'")
-
         cls = type(self)
+        console.paragraph(f"Help for '{cls.__name__}'")
 
-        prop_rows = _discover_property_rows(cls)
-        if prop_rows:
+        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=prop_rows,
+                columns_data=filtered_property_rows,
             )
 
-        method_rows = _discover_method_rows(cls)
-        if method_rows:
+        if filtered_method_rows:
             console.paragraph('Methods')
             render_table(
                 columns_headers=['#', 'Name', 'Description'],
                 columns_alignment=['right', 'left', 'left'],
-                columns_data=method_rows,
+                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
+
+    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,
+            ])
+
+        if self._has_persisted_fit_state():
+            categories.extend(self._fit_state_categories())
+
+        return categories
+
     # ------------------------------------------------------------------
     #  Parameter helpers
     # ------------------------------------------------------------------
@@ -487,7 +959,8 @@ 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,
@@ -499,82 +972,895 @@ 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
+
+    @property
+    def sequential_fit(self) -> SequentialFit:
+        """Persisted settings for sequential fitting."""
+        return self._sequential_fit
+
+    @property
+    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 _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool = False) -> None:
+    def _selected_parameters_for_fit(self, experiments: list[object]) -> list[Parameter]:
+        """
+        Return unique live parameters involved in the current fit slice.
         """
-        Execute fitting for all experiments.
+        selected_parameters: list[Parameter] = []
+        seen_unique_names: set[str] = set()
 
-        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.
+        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)
 
-        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.
+        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)
 
-        Sets :attr:`fit_results` on success, which can be accessed
-        programmatically (e.g.,
-        ``analysis.fit_results.reduced_chi_square``).
+        return selected_parameters
 
-        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.
-        """
-        verb = VerbosityEnum(verbosity if verbosity is not None else self.project.verbosity)
+    @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.
+        """
+        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.fit.value)
         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 constrained
+        # 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()
 
-        # 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)
-        elif mode is FitModeEnum.SINGLE:
-            self._fit_single(
-                verb, structures, experiments, use_physical_limits=use_physical_limits
-            )
-        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
+
+        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
 
-        # After fitting, save the project
+        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()
+        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)
+
+        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
+            self._clear_persisted_fit_state()
+
         if self.project.info.path is not None:
             self.project.save()
 
@@ -585,6 +1871,7 @@ def _fit_joint(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run joint fitting across all experiments with weights.
@@ -599,21 +1886,21 @@ 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
-        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,
@@ -622,6 +1909,7 @@ def _fit_joint(
             analysis=self,
             verbosity=verb,
             use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
         )
 
         # After fitting, get the results
@@ -634,6 +1922,7 @@ def _fit_single(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run single-mode fitting for each experiment independently.
@@ -648,42 +1937,51 @@ 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
 
         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,
-            )
+                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(
@@ -719,7 +2017,7 @@ 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:
         """
@@ -775,79 +2073,6 @@ 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,
         *,
@@ -864,14 +2089,15 @@ 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:
         """
         Serialize the analysis section to a CIF string.
@@ -883,3 +2109,8 @@ 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/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/__init__.py b/src/easydiffraction/analysis/categories/__init__.py
index 4e798e209..743e0ff02 100644
--- a/src/easydiffraction/analysis/categories/__init__.py
+++ b/src/easydiffraction/analysis/categories/__init__.py
@@ -1,2 +1,46 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
+
+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
+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/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/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 
+# 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 
+# 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/joint_fit_experiments/factory.py b/src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
similarity index 65%
rename from src/easydiffraction/analysis/categories/joint_fit_experiments/factory.py
rename to src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
index 992af7270..fbe5da383 100644
--- a/src/easydiffraction/analysis/categories/joint_fit_experiments/factory.py
+++ b/src/easydiffraction/analysis/categories/bayesian_convergence/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
-"""Joint-fit-experiments factory — delegates to ``FactoryBase``."""
+"""Bayesian-convergence factory."""
 
 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 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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/project/categories/display/factory.py b/src/easydiffraction/analysis/categories/bayesian_result/factory.py
similarity index 70%
rename from src/easydiffraction/project/categories/display/factory.py
rename to src/easydiffraction/analysis/categories/bayesian_result/factory.py
index 0d1be80a9..3d437a0df 100644
--- a/src/easydiffraction/project/categories/display/factory.py
+++ b/src/easydiffraction/analysis/categories/bayesian_result/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
-"""Factory for project display categories."""
+"""Bayesian-result factory."""
 
 from __future__ import annotations
 
@@ -9,8 +9,8 @@
 from easydiffraction.core.factory import FactoryBase
 
 
-class DisplayFactory(FactoryBase):
-    """Create project display category instances."""
+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 
+# 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 
+# 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/fit/factory.py b/src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
similarity index 70%
rename from src/easydiffraction/analysis/categories/fit/factory.py
rename to src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
index 37bef2bbc..6f5d1033c 100644
--- a/src/easydiffraction/analysis/categories/fit/factory.py
+++ b/src/easydiffraction/analysis/categories/bayesian_sampler/factory.py
@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
-"""Fit factory — delegates entirely to ``FactoryBase``."""
+"""Bayesian-sampler factory."""
 
 from __future__ import annotations
 
@@ -9,8 +9,8 @@
 from easydiffraction.core.factory import FactoryBase
 
 
-class FitFactory(FactoryBase):
-    """Create fit category items by tag."""
+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 63a4264d4..0b622e183 100644
--- a/src/easydiffraction/analysis/categories/constraints/default.py
+++ b/src/easydiffraction/analysis/categories/constraints/default.py
@@ -18,14 +18,29 @@
 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):
     """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".',
@@ -36,13 +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:
         """
@@ -116,7 +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.
 
@@ -127,8 +148,41 @@ 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 id is not None:
+            item.id = id
+        elif 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.id.value, constraint.expression.value] for constraint in self]
+
+        console.paragraph('User defined constraints')
+        render_table(
+            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/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 
+# 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 
+# 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 
+# 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/__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 
-# 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/enums.py b/src/easydiffraction/analysis/categories/fit/enums.py
deleted file mode 100644
index a5b87054e..000000000
--- a/src/easydiffraction/analysis/categories/fit/enums.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors 
-# SPDX-License-Identifier: BSD-3-Clause
-"""Enumeration for fit-mode values."""
-
-from __future__ import annotations
-
-from enum import StrEnum
-
-
-class FitModeEnum(StrEnum):
-    """Fitting mode for the analysis."""
-
-    SINGLE = 'single'
-    JOINT = 'joint'
-    SEQUENTIAL = 'sequential'
-
-    @classmethod
-    def default(cls) -> FitModeEnum:
-        """Return the default fit mode (SINGLE)."""
-        return cls.SINGLE
-
-    def description(self) -> str:
-        """Return a human-readable description of this fit mode."""
-        if self is FitModeEnum.SINGLE:
-            return 'Independent fitting of each experiment'
-        if self is FitModeEnum.JOINT:
-            return 'Simultaneous fitting of all experiments with weights'
-        if self is FitModeEnum.SEQUENTIAL:
-            return 'Sequential fitting over data files in a directory'
-        return ''
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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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/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 
+# 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/fit/default.py b/src/easydiffraction/analysis/categories/fitting/default.py
similarity index 52%
rename from src/easydiffraction/analysis/categories/fit/default.py
rename to src/easydiffraction/analysis/categories/fitting/default.py
index c002fe399..141e22365 100644
--- a/src/easydiffraction/analysis/categories/fit/default.py
+++ b/src/easydiffraction/analysis/categories/fitting/default.py
@@ -1,16 +1,14 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
 """
-Fit category item.
+Fitting category item.
 
-Stores the active minimizer and fitting mode as CIF-serializable
-descriptors and provides the public entry-point for running fits.
+Stores the active minimizer as a CIF-serializable descriptor.
 """
 
 from __future__ import annotations
 
-from easydiffraction.analysis.categories.fit.enums import FitModeEnum
-from easydiffraction.analysis.categories.fit.factory import FitFactory
+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
@@ -25,17 +23,19 @@
 from easydiffraction.utils.utils import render_table
 
 
-@FitFactory.register
-class Fit(CategoryItem):
+@FittingFactory.register
+class Fitting(CategoryItem):
     """
-    Analysis fitting configuration and execution entry-point.
+    Analysis fitting configuration category.
 
-    Holds the active minimizer backend tag and fit mode value.
+    Holds the active minimizer backend tag.
     """
 
+    _category_code = 'fitting'
+
     type_info = TypeInfo(
         tag='default',
-        description='Fit configuration category',
+        description='Fitting configuration category',
     )
 
     def __init__(self) -> None:
@@ -50,20 +50,9 @@ def __init__(self) -> None:
                     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']),
+            cif_handler=CifHandler(names=['_fitting.minimizer_type']),
         )
 
-        self._identity.category_code = 'fit'
-
     @property
     def minimizer_type(self) -> StringDescriptor:
         """Fitting minimizer backend type."""
@@ -88,15 +77,6 @@ def minimizer(self) -> object | 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
@@ -119,69 +99,14 @@ 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,
-    ) -> 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.
-
-        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)
-
-    def __call__(
-        self,
-        verbosity: str | None = None,
-        *,
-        use_physical_limits: bool = False,
-    ) -> None:
-        """Execute :meth:`run` for convenience."""
-        self.run(verbosity=verbosity, use_physical_limits=use_physical_limits)
+    @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 fit configuration from a CIF block.
+        Populate this fitting configuration from a CIF block.
 
         Parameters
         ----------
diff --git a/src/easydiffraction/analysis/categories/fitting/factory.py b/src/easydiffraction/analysis/categories/fitting/factory.py
new file mode 100644
index 000000000..b88bf9178
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fitting/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Fitting factory - delegates entirely to ``FactoryBase``."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+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 
+# 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..2821f2e8f 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 
 # 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 +21,23 @@
 from easydiffraction.io.cif.handler import CifHandler
 
 
-class JointFitExperiment(CategoryItem):
+class JointFitItem(CategoryItem):
     """A single joint-fit entry."""
 
+    _category_code = 'joint_fit'
+    _category_entry_name = 'experiment_id'
+
     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 +46,11 @@ 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
-    # ------------------------------------------------------------------
-
     @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/joint_fit/factory.py b/src/easydiffraction/analysis/categories/joint_fit/factory.py
new file mode 100644
index 000000000..fa15edc00
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/joint_fit/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Joint-fit factory - delegates to ``FactoryBase``."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+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 
-# 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 
+# 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..52519f56d
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit/default.py
@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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."""
+
+    _category_code = 'sequential_fit'
+
+    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']),
+        )
+
+    @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 
+# 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 
+# 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..a724692c3
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/sequential_fit_extract/default.py
@@ -0,0 +1,162 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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_]*$')
+_EXTRACT_TARGET_SEGMENTS = 2
+
+
+def _validate_extract_target_shape(value: str) -> None:
+    """Validate the supported two-segment extract target form."""
+    parts = value.split('.')
+    if (
+        len(parts) != _EXTRACT_TARGET_SEGMENTS
+        or parts[0] != 'diffrn'
+        or not _TARGET_SEGMENT_PATTERN.fullmatch(parts[1])
+    ):
+        msg = (
+            'sequential_fit_extract.target must use the form '
+            "'diffrn.' with exactly two segments."
+        )
+        raise ValueError(msg)
+
+
+def _validate_extract_pattern(value: str) -> None:
+    """Validate that an extract pattern compiles and captures once."""
+    try:
+        compiled = re.compile(value)
+    except re.error as error:
+        msg = f'Invalid sequential_fit_extract.pattern {value!r}: {error}.'
+        raise ValueError(msg) from error
+
+    if compiled.groups != 1:
+        msg = 'sequential_fit_extract.pattern must define exactly one capture group.'
+        raise ValueError(msg)
+
+
+class SequentialFitExtractItem(CategoryItem):
+    """A single sequential-fit extract rule."""
+
+    _category_code = 'sequential_fit_extract'
+    _category_entry_name = 'id'
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self._id = StringDescriptor(
+            name='id',
+            description='Identifier for this extract rule.',
+            value_spec=AttributeSpec(
+                default='_',
+                validator=RegexValidator(pattern=r'^[A-Za-z_][A-Za-z0-9_]*$'),
+            ),
+            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']),
+        )
+
+    @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 
+# 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/enums.py b/src/easydiffraction/analysis/enums.py
new file mode 100644
index 000000000..94d78cc64
--- /dev/null
+++ b/src/easydiffraction/analysis/enums.py
@@ -0,0 +1,54 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Enumeration types used by analysis components."""
+
+from __future__ import annotations
+
+from enum import StrEnum
+
+
+class FitModeEnum(StrEnum):
+    """Fitting mode for the analysis."""
+
+    SINGLE = 'single'
+    JOINT = 'joint'
+    SEQUENTIAL = 'sequential'
+
+    @classmethod
+    def default(cls) -> FitModeEnum:
+        """Return the default fit mode (SINGLE)."""
+        return cls.SINGLE
+
+    def description(self) -> str:
+        """Return a human-readable description of this fit mode."""
+        if self is FitModeEnum.SINGLE:
+            return 'Fit one experiment at a time.'
+        if self is FitModeEnum.JOINT:
+            return 'Fit several experiments together with shared parameters.'
+        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/__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 
 # 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..ca191c835
--- /dev/null
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -0,0 +1,777 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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.
+    best_sample_value : float
+        Highest-posterior 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
+    best_sample_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.
+    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.
+    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
+    best_sample_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='best_sample'
+        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 = 'best_sample'
+    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,
+    best_sample_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.
+    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
+        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,
+                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])),
+                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 in {'best sample', 'map'}:
+        return 'Best posterior sample'
+    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',
+        'best posterior sample',
+        '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..c24f94359 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -1,86 +1,64 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
 
-import time
-from contextlib import suppress
-
-import numpy as np
+from __future__ import annotations
 
-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
+import sys
+import time
+from dataclasses import dataclass
+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_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
+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
-DEFAULT_HEADERS = ['iteration', 'χ²', 'improvement [%]']
-DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
-
-
-class _TerminalLiveHandle:
-    """
-    Adapter that exposes update()/close() for terminal live updates.
+FIT_PROGRESS_UPDATE_SECONDS = 5.0
+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']
+SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center', 'center']
 
-    Wraps a rich.live.Live instance but keeps the tracker decoupled from
-    the underlying UI mechanism.
-    """
+_TerminalLiveHandle = _SharedTerminalLiveHandle
 
-    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:
+    """Return a backward-compatible generic live display handle."""
+    return make_display_handle()
 
 
-def _make_display_handle() -> object | None:
+@dataclass(frozen=True, slots=True)
+class SamplerProgressUpdate:
     """
-    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.
+    Normalized sampler progress payload forwarded by monitor hooks.
     """
-    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
+
+    iteration: int
+    total_iterations: int
+    phase: str
+    progress_percent: float
+    log_posterior: float
+    reduced_chi2: float
+    elapsed_time: float
+    force_report: bool = False
 
 
 class FitProgressTracker:
@@ -97,24 +75,50 @@ 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._sampler_pre_processing_pending: bool = False
 
         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
         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
+        self._sampler_pre_processing_pending = False
+        self._df_rows = []
+        self._activity_label = ACTIVITY_LABEL_FITTING
 
     def track(
         self,
@@ -140,50 +144,153 @@ 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
+
+        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] = []
 
-        # 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(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(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 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
 
-        # Store last chi-square and iteration
         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.
+
+        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
+        self._set_activity_label(self._activity_label_for_sampler_phase(update.phase))
+
+        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
+
+    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:
@@ -208,13 +315,27 @@ 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 _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.
 
@@ -222,26 +343,26 @@ 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
+        )
+        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}'...")
-        console.print('📈 Goodness-of-fit (reduced χ²) change:')
+        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:')
 
-        # Reset rows and create an environment-appropriate handle
-        self._df_rows = []
-        self._display_handle = _make_display_handle()
-
-        # Initial empty table; subsequent updates will reuse the handle
-        render_table(
-            columns_headers=DEFAULT_HEADERS,
-            columns_alignment=DEFAULT_ALIGNMENTS,
-            columns_data=self._df_rows,
-            display_handle=self._display_handle,
-        )
+        self._start_activity_indicator()
 
     def add_tracking_info(self, row: list[str]) -> None:
         """
@@ -250,41 +371,386 @@ 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_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."""
-        # 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 VerbosityEnum.SILENT:
+            return
+
+        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,
+        *,
+        update: SamplerProgressUpdate,
+        clamped_iteration: int,
+        clamped_progress: float,
+    ) -> list[str]:
+        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,
+            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 _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:
+            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()
+        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
+            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 TRACKING_MODE_SAMPLER,
+        ]
+
+    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:
+    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}'
+
+    def _print_completion_summary(self) -> None:
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            console.print('✅ Bayesian sampling complete.')
             return
 
-        # Close terminal live if used
-        if self._display_handle is not None and hasattr(self._display_handle, 'close'):
-            with suppress(Exception):
-                self._display_handle.close()
+        if self._best_chi2 is None or self._best_iteration is None:
+            return
 
-        # Print best result
         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}'
+
+    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],
+        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 VerbosityEnum.FULL:
+            self._refresh_activity_indicator()
+
+    def _default_activity_label(self) -> str:
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return ACTIVITY_LABEL_PROCESSING
+        return ACTIVITY_LABEL_FITTING
+
+    @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:
+            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
+
+        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,
+        )
diff --git a/src/easydiffraction/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py
index 9b0360143..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,
@@ -39,6 +146,7 @@ def fit(
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """
         Run the fitting process.
@@ -65,6 +173,8 @@ 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
@@ -73,52 +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.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,
-        )
+        finally:
+            self.minimizer._stop_tracking()
 
     def _process_fit_results(
         self,
@@ -239,4 +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/__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..2d8bc83f9 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -34,15 +34,27 @@ 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
         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._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,
@@ -60,13 +72,46 @@ def _start_tracking(
         """
         self.tracker.reset()
         self.tracker._verbosity = verbosity
-        self.tracker.start_tracking(minimizer_name)
+        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]:
@@ -124,7 +169,38 @@ 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 +208,6 @@ def _finalize_fit(
             starting_parameters=parameters,
             fitting_time=self.tracker.fitting_time,
         )
-        return self.result
 
     @staticmethod
     def _warn_boundary_parameters(parameters: list[object]) -> None:
@@ -231,13 +306,42 @@ 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],
         objective_function: Callable[..., object],
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
+        finalize_tracking: bool = True,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> FitResults:
         """
         Run the full minimization workflow.
@@ -251,10 +355,14 @@ 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
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
 
         Returns
         -------
@@ -264,18 +372,23 @@ 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()
-
-        return self._finalize_fit(parameters, raw_result)
+        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)
+            return self._finalize_fit(parameters, raw_result)
+        finally:
+            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
new file mode 100644
index 000000000..225c7ea0d
--- /dev/null
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -0,0 +1,1025 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bumps minimizer variant using the DREAM sampler."""
+
+from __future__ import annotations
+
+import multiprocessing
+import random
+import sys
+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
+
+_BUMPS_DREAM_LOG = 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 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)
+
+    @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 ''
+
+    @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``.
+        """
+        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,
+            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,
+            )
+
+        self.tracker.start_sampler_post_processing()
+
+        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 self._requires_serial_mapper_for_spawn_main_module():
+            self._warn_after_tracking(
+                'DREAM parallel evaluation requires an import-safe main '
+                'module on spawn-based multiprocessing; falling back to '
+                'serial execution.'
+            )
+            return None
+
+        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):
+                self._warn_after_tracking(
+                    '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
+            self._warn_after_tracking(
+                '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:
+        """
+        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]
+        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,
+            log_posterior=np.asarray(log_posterior, dtype=float),
+            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,
+            best_sample_values=best_sample_values,
+            parameter_display_names=context.parameter_display_names,
+            convergence_diagnostics=convergence_diagnostics,
+        )
+        posterior_standard_deviations = standard_deviations_from_summaries(
+            posterior_parameter_summaries
+        )
+
+        return OptimizeResult(
+            x=best_sample_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:
+        """
+        Sync best posterior values or restore starts.
+
+        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='best_sample',
+            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/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index 220179f89..a43b537a3 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -9,27 +9,39 @@
 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
+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
 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:
     """
@@ -48,6 +60,7 @@ class SequentialFitTemplate:
     constraints_enabled: bool
     minimizer_tag: str
     calculator_tag: str
+    diffrn_extract_rules: list[SequentialFitExtractRule]
     diffrn_field_names: list[str]
 
 
@@ -77,9 +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.
@@ -106,13 +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,
@@ -120,17 +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.fit.value
+        project.verbosity.fit = 'silent'
+        try:
+            project.analysis.fit()
+        finally:
+            project.verbosity.fit = original_verbosity
 
-        # 10. Collect results
+        # 11. Collect results
         result.update(_collect_results(project, template))
 
     except (
@@ -142,10 +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
@@ -232,6 +252,82 @@ 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.`` 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,
@@ -255,17 +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
@@ -286,10 +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',
 ]
 
 
@@ -355,7 +450,47 @@ 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]:
@@ -413,8 +548,9 @@ def _read_csv_for_recovery(
         for row in reader:
             file_path = row.get('file_path', '')
             if file_path:
-                fitted.add(file_path)
-            if row.get('fit_success', '').lower() == 'true':
+                fitted.add(_resolve_csv_file_path(csv_path, file_path))
+            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
@@ -441,7 +577,14 @@ 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()))
@@ -452,7 +595,7 @@ 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
 
@@ -470,12 +613,31 @@ 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,
@@ -485,8 +647,9 @@ 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,
     )
 
@@ -496,11 +659,315 @@ 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_result.success')]
+    if 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 = '—'
+
+    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('fit_result.reduced_chi_square')
+        chi2_str = f'{reduced_chi2:.2f}' if reduced_chi2 is not None 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),
+            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:\n{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.fit.value)
+
+    _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.
@@ -511,63 +978,46 @@ 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 '❌'
-        print(f'{status} Chunk {chunk_idx}/{total_chunks}: {num_files} files, avg χ² = {chi2_str}')
-    elif verbosity is VerbosityEnum.FULL:
-        print(
-            f'Chunk {chunk_idx}/{total_chunks}: '
-            f'{num_files} files, {len(successful)} succeeded, '
-            f'avg reduced 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,
         )
-        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 '—'
-            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.`` 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),
+        )
 
 
 # ------------------------------------------------------------------
@@ -612,7 +1062,9 @@ 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.'
@@ -651,7 +1103,7 @@ 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:
@@ -751,8 +1203,7 @@ def _run_fit_loop(
     chunks: list[list[str]],
     template: SequentialFitTemplate,
     csv_info: tuple[Path, list[str]],
-    extract_diffrn: Callable | None,
-    verb: VerbosityEnum,
+    progress: SequentialProgressContext,
 ) -> None:
     """
     Execute the chunk-based fitting loop.
@@ -767,13 +1218,14 @@ 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.
+    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:
@@ -782,11 +1234,21 @@ 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)
+            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)
@@ -797,7 +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
 
@@ -808,7 +1270,6 @@ 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:
@@ -829,8 +1290,6 @@ 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.
@@ -839,48 +1298,40 @@ def fit_sequential(
     if mp.parent_process() is not None:
         return
 
-    project = analysis.project
-    verb = VerbosityEnum(project.verbosity)
-
-    _check_seq_preconditions(project)
-
-    data_paths = extract_data_paths_from_dir(data_dir, file_pattern=file_pattern)
-    template = _build_template(project)
-
-    csv_path, header, already_fitted, template = _setup_csv_and_recovery(
-        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:
-            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):')
+    _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)
+        _run_fit_loop_with_pool(
+            plan.max_workers,
+            plan.chunks,
+            plan.template,
+            (plan.csv_path, plan.header),
+            progress,
+        )
     finally:
-        _restore_main_state(main_mod, main_file_bak, main_spec_bak)
+        _stop_progress_display(progress)
 
-    if verb is not VerbosityEnum.SILENT:
-        print(
-            f'✅ Sequential fitting complete: '
-            f'{len(already_fitted) + len(remaining)} files processed.'
-        )
-        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 
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.core.variable import BoolDescriptor
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/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 
+# 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/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..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,
-            )
-
 
 # ======================================================================
 
@@ -187,8 +111,12 @@ 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/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/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/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/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 84bbd2f42..074afb714 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):
     """
@@ -126,11 +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:
@@ -151,18 +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:
@@ -173,9 +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:
@@ -221,6 +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."""
 
@@ -252,6 +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.
@@ -280,12 +313,13 @@ 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
-        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:
         """
@@ -322,25 +356,25 @@ 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()``.
+        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._constrained = True
-        parent_datablock = self._datablock_item()
-        if parent_datablock is not None:
-            parent_datablock._need_categories_update = True
+        self._user_constrained = True
+        parent_owner = self._category_owner()
+        if parent_owner is not None:
+            parent_owner._need_categories_update = True
 
     @property
     def free(self) -> bool:
@@ -353,24 +387,24 @@ 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
@@ -380,14 +414,14 @@ 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
 
@@ -414,6 +448,7 @@ 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 +461,84 @@ 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_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,
+        *,
+        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
 
 
 # ======================================================================
@@ -458,6 +571,33 @@ 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."""
 
@@ -485,6 +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/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/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 0f8633010..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):
@@ -572,7 +574,7 @@ 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 +653,7 @@ 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..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
     # ------------------------------------------------------------------
@@ -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/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 52329abb0..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
     # ------------------------------------------------------------------
@@ -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/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/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 562ad18d6..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
     # ------------------------------------------------------------------
@@ -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..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
     # ------------------------------------------------------------------
@@ -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/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/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index a0c95fe11..625b2e2fd 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 +24,88 @@
 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
+ASCII_CHART_CROP_TRIGGER_MULTIPLIER = 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 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_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)
+            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
+
+    @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:
         """
@@ -61,6 +138,7 @@ 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 +161,8 @@ 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
@@ -91,8 +171,12 @@ 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)
 
@@ -100,6 +184,11 @@ 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())
@@ -130,12 +219,15 @@ 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.')
         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,
@@ -170,7 +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)))
@@ -213,8 +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,
@@ -223,13 +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 b1a19a274..a080ba8e7 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -60,6 +60,12 @@ 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
+    excluded_ranges: tuple[tuple[float, float], ...] = ()
 
 
 class XAxisType(StrEnum):
@@ -185,6 +191,14 @@ class XAxisType(StrEnum):
         'mode': 'lines',
         'name': 'Total calculated (Icalc)',
     },
+    'posterior': {
+        'mode': 'lines',
+        'name': 'Best posterior sample',
+    },
+    'density': {
+        'mode': 'lines',
+        'name': 'Marginal density',
+    },
     'bkg': {
         'mode': 'lines',
         'name': 'Background (Ibkg)',
@@ -224,6 +238,7 @@ 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.
@@ -245,6 +260,8 @@ 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 887879dd3..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)',
 }
 
@@ -65,6 +66,16 @@
 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)'
+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'
+FIXED_ASPECT_WRAPPER_CLASS_NAME = 'ed-fixed-aspect-plotly-wrapper'
 
 
 @dataclass(frozen=True)
@@ -157,6 +168,11 @@ 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 +224,7 @@ def plot_correlation_heatmap(
                 'yanchor': 'middle',
             },
             hoverongaps=False,
-            hovertemplate=f'x: %{{x}}
y: %{{y}}
corr: %{{z:.{precision}f}}',
+            hovertemplate=f'%{{x}}
%{{y}}
correlation: %{{z:.{precision}f}}',
         )
         label_trace = self._get_correlation_label_trace(
             corr_df,
@@ -461,12 +477,13 @@ 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}
'
                 'Imeas: %{customdata[0]:,.2f}
'
-                'Icalc: %{customdata[1]:,.2f}
'
-                'Imeas - Icalc: %{customdata[2]:,.2f}'
+                f'{calc_label}: %{{customdata[1]:,.2f}}
'
+                f'Imeas - {calc_label}: %{{customdata[2]:,.2f}}'
                 ''
             )
 
@@ -474,8 +491,8 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str:
             'x: %{x:,.2f}
'
             'Imeas: %{customdata[0]:,.2f}
'
             'Ibkg: %{customdata[1]:,.2f}
'
-            'Icalc: %{customdata[2]:,.2f}
'
-            'Imeas - Icalc: %{customdata[3]:,.2f}'
+            f'{calc_label}: %{{customdata[2]:,.2f}}
'
+            f'Imeas - {calc_label}: %{{customdata[3]:,.2f}}'
             ''
         )
 
@@ -778,6 +795,90 @@ 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 (
+            '\n\n'
+            f'
\n'
+            f'{html_fig}\n'
+            '
'
+        )
+
     @staticmethod
     def _get_figure(
         data: object,
@@ -855,9 +956,7 @@ 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 +964,7 @@ 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 +1000,7 @@ def _get_layout(
             },
             title={
                 'text': title,
+                'font': {'size': TITLE_FONT_SIZE},
             },
             legend={
                 'bgcolor': cls._legend_background_color(),
@@ -909,14 +1010,22 @@ 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,
             },
@@ -931,6 +1040,7 @@ 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.
@@ -952,6 +1062,8 @@ 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
@@ -968,8 +1080,33 @@ 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,
@@ -1165,11 +1302,7 @@ 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 +1313,49 @@ 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 +1397,46 @@ 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._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,
+            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 +1444,146 @@ 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
x: %{x:,.2f}
y: %{y:,.2f}'
+                    ),
                 ),
                 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 +1592,10 @@ 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 +1605,64 @@ 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 +1673,7 @@ 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 +1682,86 @@ 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% credible 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..3bfbd5ec5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -16,6 +16,9 @@
 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
 from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
@@ -30,6 +33,10 @@
 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 +70,108 @@ 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
+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)'
+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_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
+POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
+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)'
+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)
@@ -78,6 +181,9 @@ 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
 
 
@@ -90,6 +196,71 @@ 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
+    marginal_density_samples: np.ndarray
+    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."""
 
@@ -162,11 +333,17 @@ 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):
-            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)
+        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)
+            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
@@ -208,6 +385,18 @@ 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,
@@ -409,6 +598,8 @@ 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.
@@ -423,16 +614,23 @@ 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(
@@ -441,6 +639,8 @@ 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.
@@ -455,16 +655,23 @@ 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(
@@ -474,6 +681,7 @@ def plot_meas_vs_calc(
         x_max: float | None = None,
         *,
         show_residual: bool | None = None,
+        show_excluded: bool = False,
         x: object | None = None,
     ) -> None:
         """
@@ -491,17 +699,29 @@ 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,
@@ -511,7 +731,7 @@ def plot_meas_vs_calc(
     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.
@@ -524,15 +744,18 @@ 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 : 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
+        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
@@ -544,24 +767,172 @@ 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_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,
+                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,
+    ) -> 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,
         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 +941,57 @@ 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
@@ -618,160 +1002,3527 @@ def plot_param_correlations(
                 display_corr_df,
                 row_numbers=row_numbers,
                 col_numbers=col_numbers,
-                threshold=threshold,
+                threshold=resolved_threshold,
                 precision=precision,
             )
         )
 
-    @staticmethod
-    def _filter_correlation_dataframe(
+    @classmethod
+    def _resolve_correlation_filter(
+        cls,
         corr_df: pd.DataFrame,
+        *,
         threshold: float | None,
-    ) -> pd.DataFrame | 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:
         """
-        Filter a correlation matrix to only strongly correlated params.
+        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
         ----------
-        corr_df : pd.DataFrame
-            Square correlation matrix.
-        threshold : float | None
-            Absolute-correlation cutoff. ``None`` or ``0`` keeps all
+        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``.
+        """
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            console.paragraph(self._posterior_pair_title(None))
 
-        Returns
-        -------
-        pd.DataFrame | None
-            Filtered square matrix, or ``None`` if no off-diagonal
-            correlations meet the cutoff.
+        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.
+        """
+        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
+        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,
+        show_excluded: bool = False,
+        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. 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
+            Upper bound for the x-axis range.
+        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.
 
         Raises
         ------
         ValueError
-            If *threshold* is outside ``[0, 1]``.
+            If ``style`` is not one of ``'band'``, ``'draws'``, or
+            ``'band+draws'``.
         """
-        if threshold is None or threshold <= 0:
-            return corr_df
-        if threshold > 1:
-            msg = 'Correlation threshold must be between 0 and 1.'
+        if style not in {'band', 'draws', 'band+draws'}:
+            msg = "style must be 'band', 'draws', or 'band+draws'."
             raise ValueError(msg)
 
-        abs_corr = np.abs(corr_df.to_numpy(copy=True))
-        np.fill_diagonal(abs_corr, 0.0)
-        keep_mask = (abs_corr >= threshold).any(axis=0)
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_residual=show_residual,
+            show_excluded=show_excluded,
+            x=x,
+        )
 
-        if not keep_mask.any():
-            log.warning(f'No parameter pairs with |correlation| >= {threshold:.2f} were found.')
-            return None
+        self._plot_posterior_predictive_request(
+            expt_name=expt_name,
+            style=style,
+            plot_options=plot_options,
+        )
 
-        labels = corr_df.index[keep_mask]
-        return corr_df.loc[labels, labels]
+    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
 
-    @staticmethod
-    def _mask_correlation_lower_triangle(
-        corr_df: pd.DataFrame,
-    ) -> pd.DataFrame:
-        """
-        Mask the upper triangle and diagonal of a correlation matrix.
+        self._update_project_categories(expt_name)
+        experiment = self._project.experiments[expt_name]
+        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(
+            experiment.type,
+            plot_options.x,
+        )
 
-        Only the lower triangle is kept, since the matrix is symmetric
-        and diagonal values are always ``1``.
+        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,
+                x_axis=x_axis,
+                scattering_type=scattering_type,
+                plot_options=plot_options,
+                style=style,
+            )
+            return
 
-        Parameters
+        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% credible 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.best_sample_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."""
+        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:
+            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=show_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=show_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'],
+        )
+        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(
+            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'},
+            excluded_ranges=excluded_ranges,
+        )
+
+    @staticmethod
+    def _filter_correlation_dataframe(
+        corr_df: pd.DataFrame,
+        threshold: float | None,
+    ) -> pd.DataFrame | None:
+        """
+        Filter a correlation matrix to only strongly correlated params.
+
+        Parameters
+        ----------
+        corr_df : pd.DataFrame
+            Square correlation matrix.
+        threshold : float | None
+            Absolute-correlation cutoff. ``None`` or ``0`` keeps all
+            parameters.
+
+        Returns
+        -------
+        pd.DataFrame | None
+            Filtered square matrix, or ``None`` if no off-diagonal
+            correlations meet the cutoff.
+
+        Raises
+        ------
+        ValueError
+            If *threshold* is outside ``[0, 1]``.
+        """
+        if threshold is None or threshold <= 0:
+            return corr_df
+        if threshold > 1:
+            msg = 'Correlation threshold must be between 0 and 1.'
+            raise ValueError(msg)
+
+        abs_corr = np.abs(corr_df.to_numpy(copy=True))
+        np.fill_diagonal(abs_corr, 0.0)
+        keep_mask = (abs_corr >= threshold).any(axis=0)
+
+        if not keep_mask.any():
+            log.warning(f'No parameter pairs with |correlation| >= {threshold:.2f} were found.')
+            return None
+
+        labels = corr_df.index[keep_mask]
+        return corr_df.loc[labels, labels]
+
+    @staticmethod
+    def _mask_correlation_lower_triangle(
+        corr_df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """
+        Mask the upper triangle and diagonal of a correlation matrix.
+
+        Only the lower triangle is kept, since the matrix is symmetric
+        and diagonal values are always ``1``.
+
+        Parameters
         ----------
         corr_df : pd.DataFrame
             Square correlation matrix.
 
-        Returns
-        -------
-        pd.DataFrame
-            Correlation matrix with upper triangle and diagonal masked.
-        """
-        masked_values = corr_df.to_numpy(copy=True)
-        mask = np.triu(np.ones_like(masked_values, dtype=bool), k=0)
-        masked_values[mask] = np.nan
-        return pd.DataFrame(masked_values, index=corr_df.index, columns=corr_df.columns)
+        Returns
+        -------
+        pd.DataFrame
+            Correlation matrix with upper triangle and diagonal masked.
+        """
+        masked_values = corr_df.to_numpy(copy=True)
+        mask = np.triu(np.ones_like(masked_values, dtype=bool), k=0)
+        masked_values[mask] = np.nan
+        return pd.DataFrame(masked_values, index=corr_df.index, columns=corr_df.columns)
+
+    @staticmethod
+    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.
+
+        For the lower triangle without diagonal, the last column and
+        first row are always empty and can be trimmed.
+
+        Parameters
+        ----------
+        corr_df : pd.DataFrame
+            Masked correlation matrix.
+        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
+        -------
+        tuple[pd.DataFrame, list[int], list[int]]
+            Display matrix plus 1-based parameter numbers for the kept
+            rows and columns.
+        """
+        num_rows, num_cols = corr_df.shape
+        row_numbers = list(range(1, num_rows + 1))
+        col_numbers = list(range(1, num_cols + 1))
+
+        if show_diagonal or min(num_rows, num_cols) <= 1:
+            return corr_df, row_numbers, col_numbers
+
+        if preserve_all_rows:
+            return corr_df.iloc[:, :-1], row_numbers, col_numbers[:-1]
+        return corr_df.iloc[1:, :-1], row_numbers[1:], col_numbers[:-1]
+
+    def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
+        """
+        Return the correlation matrix for the latest fit.
+
+        Returns
+        -------
+        pd.DataFrame | None
+            Square correlation matrix labeled by parameter unique names,
+            or ``None`` if unavailable.
+        """
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None
+
+        corr_df = self._posterior_correlation_dataframe(fit_results)
+        if corr_df is not None:
+            return corr_df
+
+        raw_result = self._raw_fit_result_for_correlation(fit_results)
+        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_persisted_projection(fit_results)
+        if corr_df is not None:
+            return corr_df
+
+        log.warning(
+            'Correlation matrix is unavailable for this fit. '
+            'Use a minimizer that returns covariance information or posterior samples.'
+        )
+        return None
+
+    def _posterior_correlation_dataframe(
+        self,
+        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)
+
+    @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:
+            return None
+
+        var_names = getattr(raw_result, 'var_names', None)
+        if not var_names:
+            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,
+        *,
+        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,
+            ),
+            marginal_density_samples=selected_samples,
+            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,
+                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],
+        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(
+                    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.marginal_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('
') + 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 _resolved_posterior_contour_surface(
+        self,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+        grid_size: int,
+    ) -> 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,
+            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
+        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, 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(
+            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 _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,
+    ) -> 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 _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._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}.'
+            )
+            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 _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,
+    ) -> _PosteriorDistributionContext | None:
+        """Return the context for a posterior distribution plot."""
+        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(
+            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,
+            )
+        )
+
+    @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}
density: %{y:.2f}',
+                **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.best_sample_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}}
density: %{{y:.4f}}'
+
+    @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}}
{y_parameter_name}: %{{y:.4f}}'
+
+    @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._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
+
+        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}
density: %{y:.2f}',
+        )
+
+    @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}}',
+        )
+
+    @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)
+        if posterior_predictive 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 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,
+            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
+
+        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)
+
+        return PosteriorPredictiveSummary(
+            experiment_name=expt_name,
+            x_axis_name=str(x_axis_name),
+            x=np.asarray(x_values, 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),
+            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 best-sample 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:
+            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 best_sample_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 != 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)
+        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(best_sample_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,
+        excluded_ranges: tuple[tuple[float, float], ...] = (),
+    ) -> None:
+        """
+        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.best_sample_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()
+
+        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.best_sample_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,
+            )
+        )
+        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}'",
+                '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_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,
+        *,
+        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
+
+        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 != 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=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 - 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))
+        trace.hovertemplate = (
+            'Predicted I²: %{x:,.2f}
'
+            '95% credible interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]
'
+            'Measured I²: %{y:,.2f}
'
+            'su(I²meas): %{customdata[2]:,.2f}'
+        )
+
+        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,
+            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
+                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."""
+        show_draws = self.engine == PlotterEngineEnum.PLOTLY.value and style in {
+            'draws',
+            'band+draws',
+        }
+        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=show_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 = 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.best_sample_prediction,
+            summary.x,
+            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 ()
+        )
+        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
+        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 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 summary.draws
+                ],
+                dtype=float,
+            )
+
+        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(
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=ctx['x_axis'],
+                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,
+            )
+        )
 
     @staticmethod
-    def _trim_correlation_display_dataframe(
-        corr_df: pd.DataFrame,
+    def _resolve_posterior_parameter_names(
         *,
-        preserve_all_rows: bool,
-    ) -> tuple[pd.DataFrame, list[int], list[int]]:
+        fit_results: object,
+        parameters: list[object] | None,
+    ) -> list[str] | None:
         """
-        Trim empty outer rows/columns from the lower-triangle view.
-
-        For the lower triangle without diagonal, the last column and
-        first row are always empty and can be trimmed.
+        Resolve posterior parameter names from descriptors.
 
         Parameters
         ----------
-        corr_df : pd.DataFrame
-            Masked correlation matrix.
-        preserve_all_rows : bool
-            Whether to keep the full row list so row labels continue to
-            identify all numeric column headers in tabular output.
+        fit_results : object
+            Bayesian fit result exposing posterior samples.
+        parameters : list[object] | None
+            Optional parameter subset.
 
         Returns
         -------
-        tuple[pd.DataFrame, list[int], list[int]]
-            Display matrix plus 1-based parameter numbers for the kept
-            rows and columns.
+        list[str] | None
+            Posterior parameter names in plotting order, or ``None`` if
+            the selection cannot be resolved.
         """
-        num_rows, num_cols = corr_df.shape
-        row_numbers = list(range(1, num_rows + 1))
-        col_numbers = list(range(1, num_cols + 1))
+        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 min(num_rows, num_cols) <= 1:
-            return corr_df, row_numbers, col_numbers
+        if parameters is None:
+            return list(available_names)
 
-        if preserve_all_rows:
-            return corr_df.iloc[:, :-1], row_numbers, col_numbers[:-1]
-        return corr_df.iloc[1:, :-1], row_numbers[1:], col_numbers[:-1]
+        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
 
-    def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
+    @staticmethod
+    def _resolve_posterior_parameter_name(
+        *,
+        fit_results: object,
+        available_names: list[str],
+        parameter: object,
+    ) -> str | None:
         """
-        Return the correlation matrix for the latest fit.
-
-        Returns
-        -------
-        pd.DataFrame | None
-            Square correlation matrix labeled by parameter unique names,
-            or ``None`` if unavailable.
+        Resolve one posterior parameter selection into a unique name.
         """
-        result = self._get_fit_result_for_correlation()
-        if result is None:
+        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
-        raw_result, var_names, fit_results = result
+        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)
 
-        covar = getattr(raw_result, 'covar', None)
-        if covar is not None:
-            return self._correlation_from_covariance(covar, var_names, fit_results.parameters)
+    @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
 
-        corr_df = self._get_param_correlation_dataframe_from_engine_params(
-            raw_result=raw_result,
-            parameters=fit_results.parameters,
+        selection_candidates = Plotter._posterior_parameter_selection_candidates(
+            fit_results=fit_results,
+            available_names=available_names,
         )
-        if corr_df is not None:
-            return corr_df
+        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(
-            'Correlation matrix is unavailable for this fit. '
-            'Use the lmfit minimizer and ensure covariance estimation succeeds.'
+            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 '
'.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,
-    ) -> tuple[object, list[str], object] | None:
+    ) -> object | None:
         """
-        Validate and return the raw fit result for correlation.
+        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 +4532,46 @@ 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 +4717,437 @@ 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]}
'
+                '%{customdata[1]}
'
+                f'correlation: %{{z:.{context.precision}f}}'
+            ),
+        )
+        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]}
'
+            f'{context.corr_df.index[row_index]}
'
+            f'correlation: %{{z:.{context.precision}f}}'
+        )
+        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(
@@ -1012,18 +5216,19 @@ 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.
@@ -1031,20 +5236,16 @@ 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
@@ -1055,6 +5256,15 @@ 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'],
@@ -1063,22 +5273,24 @@ 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.
@@ -1086,20 +5298,16 @@ 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
@@ -1110,6 +5318,15 @@ 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'],
@@ -1118,6 +5335,7 @@ 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(
@@ -1203,12 +5421,23 @@ 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(
@@ -1218,6 +5447,7 @@ def _plot_meas_vs_calc_data(
                 series=powder_series,
                 plot_options=plot_options,
                 title=title,
+                excluded_ranges=excluded_ranges,
             )
             return
 
@@ -1229,6 +5459,7 @@ 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(
@@ -1267,13 +5498,14 @@ 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(
@@ -1283,6 +5515,7 @@ 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,
@@ -1295,9 +5528,30 @@ 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],
@@ -1306,6 +5560,7 @@ 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.
@@ -1323,8 +5578,36 @@ 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,
@@ -1492,57 +5775,62 @@ 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_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.
+        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 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
         ----------
         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_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)
 
-        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
-        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.'
@@ -1551,7 +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,
@@ -1565,7 +5853,7 @@ 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:
@@ -1580,8 +5868,8 @@ 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]]
@@ -1597,7 +5885,10 @@ 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
@@ -1611,7 +5902,7 @@ 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
new file mode 100644
index 000000000..6123e626e
--- /dev/null
+++ b/src/easydiffraction/display/progress.py
@@ -0,0 +1,491 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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_POST_PROCESSING = 'Post-processing...'
+ACTIVITY_LABEL_PRE_PROCESSING = 'Pre-processing...'
+ACTIVITY_LABEL_PROCESSING = 'Processing...'
+ACTIVITY_LABEL_SAMPLING = 'Sampling...'
+ACTIVITY_ACCENT_COLOR = '#d97706'
+ACTIVITY_TERMINAL_STYLE = ACTIVITY_ACCENT_COLOR
+ACTIVITY_TERMINAL_FALLBACK_STYLE = 'bold yellow'
+
+SPINNER_FRAMES: tuple[str, ...] = (
+    '⠋',
+    '⠙',
+    '⠹',
+    '⠸',
+    '⠼',
+    '⠴',
+    '⠦',
+    '⠧',
+    '⠇',
+    '⠏',
+)
+_SPINNER_FRAME_SECONDS = 0.1
+_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.
+
+    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, auto_refresh: bool = True) -> None:
+        self._renderable: object = Text('')
+        self._live = Live(
+            console=console,
+            auto_refresh=auto_refresh,
+            refresh_per_second=1 / _SPINNER_FRAME_SECONDS,
+            get_renderable=self._get_renderable,
+            vertical_overflow='visible',
+        )
+        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(*, 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
+        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(), auto_refresh=auto_refresh)
+
+
+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.
+    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__(
+        self,
+        label: str = ACTIVITY_LABEL_PROCESSING,
+        *,
+        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
+        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=self._animated,
+            refresh_per_second=self._refresh_per_second,
+            get_renderable=self._terminal_renderable,
+            vertical_overflow='visible',
+        )
+        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:
+        style = resolve_activity_terminal_style(ConsoleManager.get())
+        if self._running:
+            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=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()}
{body}
'
+
+    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'
{text}
'
+
+    def _html_indicator(self) -> str:
+        safe_label = html.escape(self._label)
+
+        if self._running:
+            if not self._animated:
+                return (
+                    '
'
+                    f'{safe_label}'
+                    '
'
+                )
+            return (
+                '
'
+                ''
+                f'{safe_label}'
+                '
'
+            )
+
+        if self._keep_stopped_label:
+            return (
+                '
'
+                f'{safe_label}'
+                '
'
+            )
+
+        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 (
+            ''
+        )
+
+
+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:
             "
 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/io/ascii.py b/src/easydiffraction/io/ascii.py
index 189926a16..dcbea91b3 100644
--- a/src/easydiffraction/io/ascii.py
+++ b/src/easydiffraction/io/ascii.py
@@ -5,13 +5,29 @@
 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 create_artifact_temp_dir(prefix='ed_zip_')
+
+    extract_dir = resolve_artifact_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,
@@ -31,7 +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
     -------
@@ -51,11 +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
@@ -92,7 +106,9 @@ 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, or against the configured
+        artifact root when ``EASYDIFFRACTION_ARTIFACT_ROOT`` is set.
 
     Returns
     -------
@@ -111,12 +127,7 @@ 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 +174,7 @@ 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 0b868b6c1..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:
     """
@@ -48,9 +49,12 @@ def format_value(value: object) -> str:
     # None → CIF unknown marker
     if value is None:
         value = '?'
-    # Convert ints to floats
-    elif isinstance(value, int):
-        value = float(value)
+    # Booleans use CIF true/false tokens
+    elif isinstance(value, bool):
+        value = 'true' if value else 'false'
+    # 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 = '?'
@@ -70,6 +74,29 @@ 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 _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()
+    if normalized_value == 'true':
+        return True
+    if normalized_value == 'false':
+        return False
+    return _strip_optional_quotes(raw)
+
+
 ##################
 # Serialize to CIF
 ##################
@@ -87,7 +114,7 @@ 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 +125,7 @@ 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 +135,10 @@ 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
@@ -249,6 +276,39 @@ 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,
@@ -271,24 +331,11 @@ 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(v.as_cif for v in vars(datablock).values() if isinstance(v, CategoryItem))
-
-    # Then collections
-    parts.extend(
-        category_collection_to_cif(v, max_display=max_loop_display)
-        for v in vars(datablock).values()
-        if isinstance(v, CategoryCollection)
-    )
-
-    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:
@@ -296,25 +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'
@@ -333,10 +393,14 @@ 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)]
-    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 +414,7 @@ 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])
@@ -363,18 +427,32 @@ 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)}']
+
+    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)
 
 
 def summary_to_cif(_summary: object) -> str:
@@ -401,11 +479,23 @@ 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,
 ) -> 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')
@@ -425,9 +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
     ----------
@@ -436,10 +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)
 
@@ -448,16 +533,17 @@ 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)
 
-    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)
+
+    verbosity = getattr(project, 'verbosity', None)
+    if verbosity is not None:
+        verbosity.from_cif(block)
 
 
 def analysis_from_cif(analysis: object, cif_text: str) -> None:
@@ -479,8 +565,12 @@ 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)
@@ -490,8 +580,187 @@ 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)
+    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."""
+    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:
@@ -518,14 +787,26 @@ 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
 
 
+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:
 
 ######################
@@ -564,33 +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:
-        if len(raw) >= _MIN_QUOTED_LEN and raw[0] == raw[-1] and raw[0] in {"'", '"'}:
-            self.value = raw[1:-1]
-        else:
-            self.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(
@@ -620,11 +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
@@ -634,10 +903,11 @@ 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}')
@@ -715,11 +985,7 @@ 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):
@@ -732,3 +998,9 @@ 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/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 
+# 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/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 
-# 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 
-# 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/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 
+# 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..ff730dcee
--- /dev/null
+++ b/src/easydiffraction/project/categories/info/default.py
@@ -0,0 +1,175 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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."""
+
+    _category_code = 'project'
+
+    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
+
+    @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 
+# 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/__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 
+# 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..179162a08
--- /dev/null
+++ b/src/easydiffraction/project/categories/rendering/default.py
@@ -0,0 +1,165 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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
+
+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):
+    """Chart and table engine selection for a project."""
+
+    _category_code = 'rendering'
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Project rendering category',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        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=AUTO_ENGINE,
+                validator=MembershipValidator(
+                    allowed=CHART_ENGINE_OPTIONS,
+                ),
+            ),
+            cif_handler=CifHandler(names=['_rendering.chart_engine']),
+        )
+        self._table_engine = StringDescriptor(
+            name='table_engine',
+            description='Table renderer backend type',
+            value_spec=AttributeSpec(
+                default=AUTO_ENGINE,
+                validator=MembershipValidator(
+                    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."""
+        return self._chart_engine
+
+    @chart_engine.setter
+    def chart_engine(self, value: str) -> None:
+        self._set_chart_engine(value)
+
+    @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._set_table_engine(value)
+
+    @property
+    def plotter(self) -> Plotter:
+        """Live plotting facade bound to the owning project."""
+        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
+    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:
+            self._set_chart_engine(chart_engine)
+
+        table_engine = read_cif_str(block, '_rendering.table_engine')
+        if table_engine is not None:
+            self._set_table_engine(table_engine)
diff --git a/src/easydiffraction/project/categories/rendering/factory.py b/src/easydiffraction/project/categories/rendering/factory.py
new file mode 100644
index 000000000..c2bdf7c5f
--- /dev/null
+++ b/src/easydiffraction/project/categories/rendering/factory.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Factory for project rendering categories."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.core.factory import FactoryBase
+
+
+class RenderingFactory(FactoryBase):
+    """Create project rendering category instances."""
+
+    _default_rules: ClassVar[dict] = {
+        frozenset(): 'default',
+    }
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 
+# 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 
+# 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 
+# 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
new file mode 100644
index 000000000..5b760d997
--- /dev/null
+++ b/src/easydiffraction/project/display.py
@@ -0,0 +1,849 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Project display facade grouping charts and reports."""
+
+from __future__ import annotations
+
+from contextlib import nullcontext
+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.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
+
+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()
+
+    def help(self) -> None:
+        """Print available parameter-display methods."""
+        render_object_help(self)
+
+
+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 | None = None,
+        versus: str | None = None,
+    ) -> None:
+        """
+        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."""
+        render_object_help(self)
+
+
+class PosteriorDisplay:
+    """Posterior-plot namespace under ``project.display``."""
+
+    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,
+        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
+        *,
+        threshold: float | None = None,
+        max_parameters: int = 6,
+    ) -> None:
+        """Plot posterior pair relationships for sampled parameters."""
+        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,
+                threshold=threshold,
+                max_parameters=max_parameters,
+            )
+
+    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,
+        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."""
+        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,
+                x_min=x_min,
+                x_max=x_max,
+                show_residual=show_residual,
+                x=x,
+            )
+
+    def help(self) -> None:
+        """Print available posterior-display methods."""
+        render_object_help(self)
+
+
+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 help(self) -> None:
+        """Print display namespaces and methods."""
+        render_object_help(self)
+
+    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:
+                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',
+                        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:
+            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',
+                    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_predictive = getattr(fit_results, 'posterior_predictive', None)
+        if not posterior_predictive:
+            return False, 'Posterior predictive data is unavailable.'
+
+        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 7dc42cf6d..9b89eb500 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -5,7 +5,10 @@
 from __future__ import annotations
 
 import pathlib
+import shutil
 import tempfile
+from typing import TYPE_CHECKING
+from typing import ClassVar
 
 from typeguard import typechecked
 from varname import varname
@@ -14,16 +17,27 @@
 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.project.categories.display import Display
-from easydiffraction.project.categories.display import DisplayFactory
-from easydiffraction.project.project_info import ProjectInfo
+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
+
 
 def _apply_csv_row_to_params(
     row: object,
@@ -58,6 +72,108 @@ 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
+
+
+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.
@@ -70,6 +186,7 @@ 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,
@@ -79,16 +196,26 @@ 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._display = DisplayFactory.create('default')
-        self._display._parent = self
+        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
+    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
@@ -152,8 +279,13 @@ 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
@@ -171,6 +303,11 @@ 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."""
@@ -178,21 +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
         ----------
@@ -200,7 +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
@@ -232,52 +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)
-
-        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_project_info(project, project_path)
+        project.info.path = project_path
+        _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()
@@ -302,13 +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
@@ -320,9 +411,25 @@ 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:
+        if self.info.path is None:
             log.error('Project path not specified. Use save_as() to define the path first.')
             return
 
@@ -330,20 +437,20 @@ 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()
 
         # 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():
@@ -354,7 +461,7 @@ 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():
@@ -365,19 +472,29 @@ 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())
+            f.write(self.analysis.as_cif)
             console.print('├── 📁 analysis/')
-            console.print('│   └── 📄 analysis.cif')
+        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()
+        )
+        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:
+        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(
@@ -385,12 +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:
@@ -446,13 +591,18 @@ 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/project/project_config.py b/src/easydiffraction/project/project_config.py
new file mode 100644
index 000000000..dd92137ab
--- /dev/null
+++ b/src/easydiffraction/project/project_config.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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
+from easydiffraction.project.categories.verbosity import Verbosity
+from easydiffraction.project.categories.verbosity import VerbosityFactory
+
+
+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())
+        self._verbosity = VerbosityFactory.create(VerbosityFactory.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 verbosity(self) -> Verbosity:
+        """Verbosity configuration category."""
+        return self._verbosity
+
+    @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 94247f331..215e8ed54 100644
--- a/src/easydiffraction/project/project_info.py
+++ b/src/easydiffraction/project/project_info.py
@@ -1,134 +1,9 @@
-# SPDX-FileCopyrightText: 2025 EasyScience contributors 
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
-"""Project metadata container used by Project."""
+"""Project metadata category export."""
 
-import datetime
-import pathlib
+from __future__ import annotations
 
-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
+from easydiffraction.project.categories.info.default import ProjectInfo as _ProjectInfo
 
-
-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
-    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/src/easydiffraction/summary/summary.py b/src/easydiffraction/summary/summary.py
index 768abb4b2..25977bfa9 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,
@@ -214,7 +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/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/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/src/easydiffraction/utils/utils.py b/src/easydiffraction/utils/utils.py
index 0e301e30c..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 = 'd5a1fddd0d3e3e919c7e4a19e83b94b4231b99b6'
+_DATA_INDEX_REF = 'dbe92a87e0106c4742eee0ff9a8e32bdb8b483cb'
 # macOS: sha256sum index.json
-_DATA_INDEX_HASH = 'sha256:8305fd55d5b0c7c63ffa2641c082c623a107c32af09343ba901e196f68fd9f73'
+_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)
 
 
@@ -563,6 +649,123 @@ 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:
+        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_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_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/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/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
new file mode 100644
index 000000000..89c0fd931
--- /dev/null
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -0,0 +1,338 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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.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_fitting_instantiation_defaults_and_helpers():
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+    import easydiffraction.analysis.categories.fitting.default as fitting_mod
+
+    fitting = Fitting()
+
+    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'})()
+
+    fitting._parent = ParentWithMinimizer()
+    assert fitting.minimizer == 'MIN'
+
+    shown: list[str] = []
+    monkeypatch = pytest.MonkeyPatch()
+    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.fitting.default as fitting_mod
+    from easydiffraction.analysis.categories.fitting.default import Fitting
+
+    fitting = Fitting()
+    fitting._minimizer_type._value = 'bad-minimizer'
+
+    class Parent:
+        fitter = None
+
+    warnings: list[str] = []
+    fitting._parent = Parent()
+    monkeypatch.setattr(fitting_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    monkeypatch.setattr(
+        fitting_mod,
+        'Fitter',
+        lambda value: (_ for _ in ()).throw(ValueError('bad minimizer')),
+    )
+    monkeypatch.setattr(fitting_mod.log, 'warning', lambda message: warnings.append(message))
+
+    fitting.from_cif(object())
+
+    assert warnings == ['bad minimizer']
+
+
+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
+
+    fitting = Fitting()
+
+    assert fitting.minimizer is None
+
+    monkeypatch.setattr(fitting_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    fitting.from_cif(object())
+
+
+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.show_fitting_mode_types()
+    out_single = capsys.readouterr().out
+    assert 'Fitting mode types' in out_single
+    assert 'single' in out_single
+    assert 'joint' in out_single
+
+    multi = Analysis(project=_make_project_with_names(['e1', 'e2']))
+    multi.show_fitting_mode_types()
+    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.fitting.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.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 'fitting' in out
+    assert 'display' in out
+    assert 'Properties' in out
+    assert 'Methods' in out
+    assert 'fit()' 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
+    import easydiffraction.analysis.categories.constraints.default as constraints_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 FakeId:
+        value = 'constraint_1'
+
+    class FakeExpr:
+        value = 'x = y + 1'
+
+    class FakeConstraint:
+        id = FakeId()
+        expression = FakeExpr()
+
+    analysis.constraints._items = [FakeConstraint()]
+    captured: dict[str, object] = {}
+    monkeypatch.setattr(constraints_mod, 'render_table', lambda **kwargs: captured.update(kwargs))
+    analysis.display.constraints()
+    out = capsys.readouterr().out
+    assert 'User defined constraints' in out
+    assert captured['columns_headers'] == ['id', 'expression']
+    assert captured['columns_data'][0] == ['constraint_1', '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_analysis_display.py b/tests/integration/fitting/test_analysis_display.py
index 2f5b0e450..3a139be26 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 
 # 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
 
@@ -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
new file mode 100644
index 000000000..ef52c1f1a
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_dream.py
@@ -0,0 +1,197 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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.fitting.minimizer_type = 'bumps (dream)'
+    minimizer = project.analysis.fitting.minimizer
+    minimizer.steps = 20
+    minimizer.burn = 5
+    minimizer.thin = 1
+    minimizer.pop = 4
+    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)
+    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)
+    _run_single_fit(project, 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.fitting.minimizer_type = 'bumps (lm)'
+    _run_single_fit(project)
+
+    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)
+    _run_single_fit(project, 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_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):
+        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)
+    _run_single_fit(project, 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'
+    results_sidecar = proj_dir / 'analysis' / 'results.h5'
+    assert analysis_cif.is_file()
+    assert results_sidecar.is_file()
+
+    loaded = Project.load(str(proj_dir))
+    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_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py
new file mode 100644
index 000000000..b67d5db02
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_helper_support.py
@@ -0,0 +1,670 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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,
+        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},
+            '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,
+            best_sample_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',
+            best_sample_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',
+            best_sample_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') == 'Best posterior sample'
+    assert _format_point_estimate_name('best_sample') == 'Best posterior 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',
+                best_sample_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',
+        best_sample_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',
+        'best posterior sample',
+        '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',
+                best_sample_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',
+            best_sample_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',
+        best_sample_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..91ec0e572
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py
@@ -0,0 +1,511 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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
+
+    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')
+    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
+    assert ('init', tracking_mod.ACTIVITY_LABEL_FITTING, tracker._verbosity) in events
+
+
+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
+
+    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')
+    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
+    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
+
+
+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
+
+    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
+    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
+    assert events == [
+        ('init', tracking_mod.ACTIVITY_LABEL_PROCESSING, VerbosityEnum.SHORT),
+        ('start', None),
+        ('update', tracking_mod.ACTIVITY_LABEL_PROCESSING),
+        ('stop', None),
+    ]
+
+
+def test_tracker_final_sampler_row_replaces_last_row():
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    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
+
+    sentinel = object()
+
+    monkeypatch.setattr(tracking_mod, 'make_display_handle', lambda: sentinel)
+
+    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]] = []
+    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
+    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._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_activity_labels():
+    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', '', '']
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    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 (
+        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():
+    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..a824347e1
--- /dev/null
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -0,0 +1,711 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import threading
+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 _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
+    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
+    _simulate_import_safe_spawn_main_module(monkeypatch)
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: False
+    )
+    monkeypatch.setattr(minimizer, '_warn_after_tracking', warnings.append)
+
+    assert minimizer._build_mapper('problem') is None
+    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():
+    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',
+                    best_sample_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',
+                    best_sample_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',
+                best_sample_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..34cc85ad8
--- /dev/null
+++ b/tests/integration/fitting/test_cli_entrypoints.py
@@ -0,0 +1,182 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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 _fit:
+                @staticmethod
+                def results() -> None:
+                    calls.append('DISPLAY')
+
+                @staticmethod
+                def correlations() -> None:
+                    calls.append('PLOT_CORR')
+
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name: str, **kwargs) -> None:
+                del kwargs
+                calls.append(f'PLOT_{expt_name}_False')
+
+        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_False']
+
+
+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 _fit:
+                @staticmethod
+                def results() -> None:
+                    return None
+
+                @staticmethod
+                def correlations() -> None:
+                    return None
+
+            fit = _fit()
+
+            @staticmethod
+            def pattern(expt_name: str, **kwargs) -> None:
+                del expt_name, kwargs
+
+        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/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/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_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 
 # 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/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..f908789a6 100644
--- a/tests/integration/fitting/test_project_load.py
+++ b/tests/integration/fitting/test_project_load.py
@@ -205,12 +205,18 @@ 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
 
     # 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 +233,8 @@ 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 +245,8 @@ 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..73c068b3b 100644
--- a/tests/integration/fitting/test_sequential.py
+++ b/tests/integration/fitting/test_sequential.py
@@ -1,11 +1,10 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors 
 # 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'
@@ -138,9 +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]
@@ -157,10 +193,7 @@ 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 +201,7 @@ 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 +218,7 @@ 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 +231,24 @@ 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 +256,9 @@ 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 +281,7 @@ 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 +290,7 @@ 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 +301,7 @@ 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 +313,7 @@ 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'
@@ -304,7 +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]
@@ -320,19 +344,27 @@ 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 +375,8 @@ 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 +392,7 @@ 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/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}'
-    )
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/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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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_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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 
+# 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 15dddc4f2..07016b298 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_constraints.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_constraints.py
@@ -1,16 +1,63 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors 
 # 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_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'
+        '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/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 
+# 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.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 
 # 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_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 
+# 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 
+# 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 
+# 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 
+# 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/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 
 # 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
new file mode 100644
index 000000000..348f380f3
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -0,0 +1,385 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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,
+        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},
+            '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',
+                best_sample_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',
+        best_sample_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',
+        'best posterior sample',
+        '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',
+                best_sample_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',
+            best_sample_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..9758f70f8 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
@@ -57,5 +57,71 @@ 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 ('╒', '┌', '+', '─'))
+
+
+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/fit_helpers/test_tracking.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_tracking.py
index 561eb54c3..ff9df3288 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 +43,84 @@ 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
+
+
+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 23d385e7c..4ee99f1d1 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:
@@ -86,7 +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
 
@@ -119,3 +121,171 @@ def _compute_residuals(
     )
     out = f({})
     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
+
+    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
+
+
+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]))
+
+
+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 668524eaf..830a4f64f 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
@@ -177,6 +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
 
@@ -220,6 +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])
@@ -253,6 +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
new file mode 100644
index 000000000..3a23f5577
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -0,0 +1,384 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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_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
+
+    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',
+                    best_sample_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',
+                    best_sample_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_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_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/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.py b/tests/unit/easydiffraction/analysis/test_analysis.py
index a4d2c7faa..2af863797 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 
 # SPDX-License-Identifier: BSD-3-Clause
 
+from types import SimpleNamespace
+
 
 def test_module_import():
     import easydiffraction.analysis.analysis as MUT
@@ -31,26 +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):
@@ -64,7 +66,20 @@ 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):
+    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):
@@ -120,3 +135,167 @@ 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
+
+
+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_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/analysis/test_analysis_coverage.py b/tests/unit/easydiffraction/analysis/test_analysis_coverage.py
index 089e1f6b8..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:
@@ -72,16 +76,20 @@ 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())
 
         # 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()]
@@ -91,12 +99,13 @@ 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
+        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']
 
 
 # ------------------------------------------------------------------
@@ -164,8 +173,8 @@ 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
 
@@ -194,3 +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
new file mode 100644
index 000000000..75e1ca3ec
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/test_enums.py
@@ -0,0 +1,36 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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
+
+
+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
+
+
+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 
 # 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 3179a0b13..2be4c19f3 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
 
@@ -12,8 +14,15 @@
 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'
 
 
 # ------------------------------------------------------------------
@@ -39,10 +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=SimpleNamespace(fit=SimpleNamespace(value=verbosity))),
+        fitter=SimpleNamespace(selection='lmfit'),
+    )
+
+    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
+    return events
+
+
 # ------------------------------------------------------------------
 #  _build_csv_header
 # ------------------------------------------------------------------
@@ -96,7 +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:
@@ -124,6 +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']
@@ -156,7 +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(
@@ -164,20 +335,18 @@ def test_returns_fitted_file_paths(self, tmp_path):
             header,
             [
                 {
-                    'file_path': '/data/a.dat',
-                    'fit_success': 'True',
-                    'chi_squared': '5.0',
-                    'reduced_chi_squared': '2.5',
-                    'n_iterations': '10',
+                    'file_path': str(project_dir / 'experiments' / 'a.dat'),
+                    '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': '/data/b.dat',
-                    'fit_success': 'False',
-                    'chi_squared': '',
-                    'reduced_chi_squared': '',
-                    'n_iterations': '0',
+                    'file_path': str(project_dir / 'experiments' / 'b.dat'),
+                    'fit_result.success': 'False',
+                    'fit_result.reduced_chi_square': '',
+                    'fit_result.iterations': '0',
                     'cell.a': '',
                     'cell.a.uncertainty': '',
                 },
@@ -185,7 +354,35 @@ 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_result.success': 'True',
+                'fit_result.reduced_chi_square': '2.5',
+                'fit_result.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'
@@ -197,19 +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',
                 },
@@ -236,10 +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',
@@ -252,7 +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
 
@@ -266,10 +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': '',
                 },
@@ -300,3 +493,236 @@ def test_fields_accessible(self):
         assert template.diffrn_field_names == ['temp']
         assert template.minimizer_tag == 'lmfit'
         assert template.calculator_tag == 'cryspy'
+
+
+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
+
+    update_calls: list[object] = []
+
+    class RecordingIndicator:
+        def update(self, *, label=None, content=None):
+            del label
+            update_calls.append(content)
+
+    progress_state = sequential_mod.SequentialProgressState(chunk_rows=[], file_rows=[])
+    progress = sequential_mod.SequentialProgressContext(
+        verbosity=verbosity,
+        state=progress_state,
+        indicator=RecordingIndicator(),
+    )
+
+    sequential_mod._report_chunk_progress(
+        1,
+        3,
+        [_TEST_SCAN_001, _TEST_SCAN_002],
+        [
+            {
+                'file_path': _TEST_SCAN_001,
+                'fit_result.success': True,
+                'fit_result.reduced_chi_square': 4.0,
+                'fit_result.iterations': 11,
+            },
+            {
+                'file_path': _TEST_SCAN_002,
+                'fit_result.success': False,
+                'fit_result.reduced_chi_square': None,
+                'fit_result.iterations': 0,
+            },
+        ],
+        progress,
+        sequential_mod._ChunkProgressMetrics(
+            completed_files_before=0,
+            total_files=3,
+            elapsed_time=19.76,
+        ),
+    )
+
+    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
+
+    assert len(update_calls) == 1
+    assert update_calls[0] is not None
+
+
+@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,
+    )
+
+    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:\n{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_result.success': True,
+                    'fit_result.reduced_chi_square': 1.0,
+                    'fit_result.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,
+        '_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,
+        '_report_chunk_progress',
+        lambda *args: events.append(('report', [result['file_path'] for result in args[3]])),
+    )
+
+    sequential_mod._run_fit_loop(
+        FakePool(),
+        [[_TEST_SCAN_001, _TEST_SCAN_002]],
+        template,
+        (tmp_path / 'results.csv', header),
+        progress,
+    )
+
+    assert events == [
+        ('append', tmp_path / 'results.csv', header, [_TEST_SCAN_001, _TEST_SCAN_002]),
+        ('report', [_TEST_SCAN_001, _TEST_SCAN_002]),
+    ]
+
+
+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,
+        progress,
+    ):
+        del pool_cm, csv_info
+        assert chunks == [['scan_001.xye']]
+        assert template_arg == template
+        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)
+    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=SimpleNamespace(fit=SimpleNamespace(value='silent'))),
+        fitter=SimpleNamespace(selection='lmfit'),
+    )
+
+    sequential_mod.fit_sequential(analysis, data_dir=str(tmp_path))
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 
 # 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/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 
+# 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/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 e7d102d9d..f81e35e87 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
@@ -115,43 +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/display/plotters/test_ascii.py b/tests/unit/easydiffraction/display/plotters/test_ascii.py
index 49dd76537..54e533ea0 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 
 # SPDX-License-Identifier: BSD-3-Clause
 
+import os
+
 import numpy as np
 
 
@@ -23,6 +25,52 @@ 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 '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):
     from easydiffraction.display.plotters.ascii import AsciiPlotter
 
@@ -85,3 +133,176 @@ 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_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
+    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 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
+
+
+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
+
+
+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 3905ee278..097d4321f 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'] == '
plot
'
 
 
+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 '
plot
'
+
+    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 '
plot
' 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 +795,53 @@ 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='Best posterior sample',
+            y_calc_line_dash='dot',
+        ),
+    )
+
+    fig = captured['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 == '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
+    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/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 '
 # SPDX-License-Identifier: BSD-3-Clause
 
+import csv
 import re
+from types import MethodType
+from types import SimpleNamespace
+
+import numpy as np
 
 import pytest
 
@@ -62,6 +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
@@ -90,19 +167,43 @@ 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
 
@@ -150,13 +251,24 @@ 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)
 
@@ -174,9 +286,16 @@ 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():
@@ -233,6 +352,1241 @@ 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,
+            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()),
+            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.
peak.
broad_gauss_u' in annotation_texts
+    assert 'hrpt.
instrument.
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}
hrpt.peak.broad_gauss_u: %{y:.4f}'
+    ) in hovertemplates
+    assert (
+        'hrpt.instrument.twotheta_offset: %{x:.4f}
density: %{y:.4f}'
+    ) 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.marginal_density_samples.shape == (sample_count, parameter_count)
+    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_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,
+            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()),
+            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}
density: %{y:.4f}'
+    )
+    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()
+
+    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_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',
+        '95% credible interval',
+        'Median',
+        '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 == '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
+    assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}
density: %{y:.4f}'
+    assert histogram_trace.xbins.size is not None
+    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
+    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_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
+
+    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.engine = 'plotly'
+    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]),
+            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)'],
+        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 == 'Best posterior sample'
+    )
+
+    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
+    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]),
+            best_sample_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,
+            show_background=None,
+            show_bragg=None,
+            show_excluded=False,
+            x=None,
+        ),
+        x_axis=XAxisType.TWO_THETA,
+        style='band',
+    )
+
+    plot_spec = captured['plot_spec']
+    assert plot_spec.y_calc_name == 'Best posterior sample'
+    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]),
+            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]]),
+        ),
+        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]),
+            best_sample_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
+    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'
+    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',
+                best_sample_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',
+                best_sample_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.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]
+
+
+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]),
+            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]),
+        ),
+    )
+    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,
+        excluded_ranges,
+    ):
+        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
+        captured['excluded_ranges'] = excluded_ranges
+
+    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'].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]))
+    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)
+
+
 def test_extract_bragg_tick_sets_uses_derived_d_spacing_for_cwl_ticks():
     import numpy as np
 
@@ -641,7 +1995,7 @@ 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 +2014,12 @@ 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 +2053,70 @@ 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}
y: %{y}
corr: %{z:.2f}'
-    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]}
%{customdata[1]}
correlation: %{z:.2f}'
+    )
+    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.
scale',
+        'phase.
cell.
length_c',
+        'phase.
scale',
+        'phase.
cell.
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.
scale',
+        'phase.
cell.
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.
scale',
+                'phase.
cell.
length_c',
+                'phase.
scale',
+                'phase.
cell.
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 +2170,152 @@ 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_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_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))
+    )
 
-def test_plot_param_correlations_filters_by_default_threshold(monkeypatch):
+    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
 
@@ -843,18 +2363,28 @@ 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 +2439,7 @@ 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/display/test_plotting_coverage.py b/tests/unit/easydiffraction/display/test_plotting_coverage.py
index 806e68da2..008881bcf 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 +268,42 @@ 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_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
@@ -329,7 +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 = []
@@ -346,12 +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]
@@ -414,7 +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
new file mode 100644
index 000000000..b12147fa2
--- /dev/null
+++ b/tests/unit/easydiffraction/display/test_progress.py
@@ -0,0 +1,315 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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,
+            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.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_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
+
+    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_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
+
+    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)
+    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_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
+
+    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,
+            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, '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/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_category_owner_baseline.py b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
new file mode 100644
index 000000000..7d45dfbd3
--- /dev/null
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_category_owner_baseline.py
@@ -0,0 +1,98 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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.id' 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/io/cif/test_serialize_more.py b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
index 0f98fe1a7..3c9db1bc1 100644
--- a/tests/unit/easydiffraction/io/cif/test_serialize_more.py
+++ b/tests/unit/easydiffraction/io/cif/test_serialize_more.py
@@ -34,12 +34,56 @@ 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
 
 
+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
 
@@ -63,11 +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():
@@ -115,12 +172,11 @@ 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():
     import easydiffraction.io.cif.serialize as MUT
-    from easydiffraction.analysis.categories.joint_fit_experiments import JointFitExperiments
 
     class Obj:
         def __init__(self, t):
@@ -131,16 +187,16 @@ 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/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 
+# 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/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 
-# 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 
-# 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/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 
+# 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 
+# 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/categories/rendering/test_default.py b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
new file mode 100644
index 000000000..07a168ae2
--- /dev/null
+++ b/tests/unit/easydiffraction/project/categories/rendering/test_default.py
@@ -0,0 +1,69 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+
+import gemmi
+
+
+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 == '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():
+    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.display.plotting import PlotterEngineEnum
+    from easydiffraction.display.tables import TableEngineEnum
+    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'
+
+    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
+
+    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 
+# 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/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 
+# 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 
+# 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
new file mode 100644
index 000000000..f534da493
--- /dev/null
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -0,0 +1,668 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# SPDX-License-Identifier: BSD-3-Clause
+"""Unit tests for project/display.py."""
+
+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]]]:
+    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_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'),
+        _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'),
+        engine='plotly',
+        _resolve_x_axis=lambda expt_type, x: ('two_theta', 'two_theta', None, None, None),
+    )
+    project = SimpleNamespace(
+        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=SimpleNamespace(fit=SimpleNamespace(value='full')),
+    )
+    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_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)
+
+    display.fit.results()
+    display.fit.correlations(
+        threshold=0.75,
+        precision=3,
+        max_parameters=4,
+        show_diagonal=False,
+    )
+    display.fit.series(param='scale', versus='diffrn.ambient_temperature')
+    display.fit.series(versus='diffrn.ambient_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': 'diffrn.ambient_temperature'},
+    )
+    assert calls[3] == (
+        'plot_all_param_series',
+        (),
+        {'versus': 'diffrn.ambient_temperature'},
+    )
+
+
+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')
+    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',
+        },
+    )
+    assert indicator_calls == [
+        (ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL),
+        (ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL),
+    ]
+
+
+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']
+    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)
+    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(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(
+        measured=True,
+        calculated=True,
+        residual=True,
+        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',
+        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,
+                ),
+            },
+        )
+    ]
+    assert indicator_calls == [(ACTIVITY_LABEL_PROCESSING, VerbosityEnum.FULL)]
+
+
+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 92907f66c..3afbd363e 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -1,6 +1,10 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors 
 # SPDX-License-Identifier: BSD-3-Clause
 
+from collections import UserList
+import csv
+from types import SimpleNamespace
+
 
 def test_module_import():
     import easydiffraction.project.project as MUT
@@ -26,7 +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():
@@ -34,11 +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():
@@ -49,3 +53,71 @@ 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]
+
+
+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)
+
+
+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_config.py b/tests/unit/easydiffraction/project/test_project_config.py
new file mode 100644
index 000000000..be6576e94
--- /dev/null
+++ b/tests/unit/easydiffraction/project/test_project_config.py
@@ -0,0 +1,129 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors 
+# 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.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():
+    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
+    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):
+    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
+    assert '_verbosity.fit full' 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'
+    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 c788f0a74..f19b4392d 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -70,27 +70,27 @@ 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_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')
@@ -120,9 +120,174 @@ 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
 
+    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."""
@@ -136,7 +301,7 @@ 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 +315,4 @@ 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 bf632e11c..ff8a9b9be 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')
@@ -38,3 +38,86 @@ 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
+
+
+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/summary/test_summary.py b/tests/unit/easydiffraction/summary/test_summary.py
index b8af02667..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
@@ -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/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 76d150c6d..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):
@@ -95,16 +114,21 @@ 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 +143,64 @@ 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_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):
@@ -149,16 +230,20 @@ 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()
 
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.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
 
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 
+# 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
diff --git a/tools/generate_package_docs.py b/tools/generate_package_docs.py
index 671a4f0ec..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/architecture/:
- - 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' / 'architecture'
+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``: