diff --git a/README.md b/README.md
index a569985..591e121 100644
--- a/README.md
+++ b/README.md
@@ -71,6 +71,15 @@ header adds no nested namespace, the class) the kernel lives in.
| `grm_comb.h` | `tap.5comb~` | GRM comb-bank recreation (`tap::tools::fivecomb`) |
| `grm_pitchaccum.h` | `tap.pitchaccum~` | GRM PitchAccum recreation (`tap::tools::pitchaccum`) |
+**Tape and loops**
+
+| Kernel | Max object | Contents |
+|---|---|---|
+| `tape_loop.h` | *(shared)* | Tape reel, wow/flutter transport, generation-loss wear (`tap::tools::tape`) |
+| `discreet.h` | `tap.discreet~` | *Discreet Music* two-machine regeneration loop (`tap::tools::discreet`) |
+| `airport.h` | `tap.airport~` | *Music for Airports* incommensurate loop bank (`tap::tools::airport`) |
+| `garden.h` | `tap.garden~` | Generative event loop on the Bloom principle (`tap::tools::garden`) |
+
`taptools.h` is the umbrella header that pulls in every kernel above. `stft.h`, `tune.h`,
`harmonizer.h` and `conv_engine.h` reach into `tap::dsp` (the pinned DspTap submodule) for the
real FFT and the pitch primitives; every other kernel is standard library only.
diff --git a/book/PLAN-eno-chapters.md b/book/PLAN-eno-chapters.md
new file mode 100644
index 0000000..be9fa8a
--- /dev/null
+++ b/book/PLAN-eno-chapters.md
@@ -0,0 +1,217 @@
+# Plan — the Eno-family chapters
+
+> **Status: drafted.** All six chapters are written and live in `src/` per the placement
+> below (2026-08-12). This file remains as the drafting record, the plans-directory way.
+
+Planning document for the *Tools on Tap* chapters covering the 2026-08 Eno-family work
+(`tap.discreet~`, `tap.airport~`, `tap.garden~`; `taptools/tape_loop.h`, `discreet.h`,
+`airport.h`, `garden.h`). Six chapters: three user-facing, three machine appendices. This
+file is the outline to draft from; it is not part of the built book.
+
+Every measured claim below already exists as an executed notebook cell or a pinned test —
+each section lists its evidence so the chapters keep the book's "measured, not remembered"
+promise without new lab work.
+
+The family's single thesis, which every chapter should serve and none should re-derive from
+scratch: three Brian Eno works are the same idea at three levels of abstraction —
+*Discreet Music* (1975) recirculates **audio**, *Music for Airports* "2/1" (1978) phases
+**loops**, and the Bloom principle (2008) recirculates **events** — and in all three,
+**degradation is the stability mechanism**. Where every other feedback loop in this library
+caps its gain below one (`delay.h`'s `k_fb_max`, the comb bank's calibrated ring), these
+kernels let regeneration reach exactly 1.0 and stay bounded because each pass is worn:
+darkened and saturated in the tape kernels, decayed and softened in the garden.
+
+## Placement in SUMMARY.md
+
+Insert a new part after Part III (Strings, rooms, and spirals); Parts IV–IX renumber to
+V–X. The three machine entries slot after `machine/overdrive.md`, keeping the file-by-file
+order chronological.
+
+```md
+# Part IV — Tape and time
+
+- [The tape that forgets slowly](discreet.md)
+- [Loops that never line up](airport.md)
+- [The garden that plays itself](garden.md)
+
+# Part IX — The machine, file by file
+ ...existing entries...
+- [The clipper in the loop: overdrive.h](machine/overdrive.md)
+- [Wear as the stabilizer: tape_loop.h and discreet.h](machine/tape.md)
+- [Free-running heads, one shared clock: airport.h](machine/airport.md)
+- [Events, not audio: garden.h](machine/garden.md)
+```
+
+The user chapters cross-reference `recipes/shimmer.md` (which already credits Eno/Lanois
+for the shimmer school) rather than re-telling that lineage.
+
+## Figures
+
+Hand-authored block diagrams in the house style (grey main path, colored emphasis paths,
+dashed rate regions): `images/discreet/block-diagram.svg` (the two-machine loop, wear path
+in red like the comb's feedback ring), `images/airport/block-diagram.svg` (seven reels, one
+free-running head each), `images/garden/block-diagram.svg` (event ring feeding the bell
+pool, the gardener dashed).
+
+Measured figures generated by `book/figures/eno.py` (the `overdrive.py` regeneration
+contract: drives the shipping kernels through the C ABI, never re-implementations):
+`images/discreet/generation-loss.svg` (per-pass two-tone decay vs. the analytic wear
+transfer), `images/airport/raster.svg` (return raster of two incommensurate loops, lcm
+marked), `images/garden/staircase.svg` (the decay-0.5 return staircase).
+
+---
+
+## Chapter 1 (user-facing) — *The tape that forgets slowly* (`src/discreet.md`)
+
+The image: a machine whose memory is the instrument — everything you play into it comes
+back five seconds later a little darker and a little softer, forever if you ask. The
+inversion to sell in one paragraph: this is the one delay in the house allowed to run at
+regeneration 1.0, *because* it forgets.
+
+1. Provenance: the schematic on the *Discreet Music* back cover; Fripp's rig; the AES
+ Echoplex-model literature for the tape path. *Evidence: header @details; no measurement.*
+2. The echo grid and what "one loop later" means. *Evidence: discreet.ipynb §1; scenario
+ "the loop echoes at exactly the loop period".*
+3. `regen` — and why 1.0 is legal here and illegal in tap.delay~. The wear path as the
+ stabilizer. *Evidence: discreet.ipynb §2 (20 s bounded RMS at regen 1.0); scenario
+ "regen 1.0 with drive engaged is bounded and does not grow".*
+4. `darken` and `drive` — generation loss, measured against the analytic per-pass transfer
+ (0.292 at 6 kHz, 0.890 at 300 Hz per pass, both matching prediction to three decimals).
+ *Evidence: discreet.ipynb §3; scenario "every pass through the loop is darker by the
+ wear filter".* Figure: `generation-loss.svg`.
+5. `wow`/`flutter` — the transport, in cents (10.9 measured vs 10.9 predicted), and the
+ determinism contract. *Evidence: discreet.ipynb §4; scenario "wow bends pitch by the set
+ depth, and two runs are bit-exact".*
+6. `loop` moves are tape speed — the doppler is honest, not a defect. *Evidence: scenario
+ "a loop-time change glides as tape speed, not a splice".*
+7. `input_level` — the performance move: fade the send, the piece continues. *Evidence:
+ discreet.ipynb §5; eno_render discreet_sustain.*
+8. Recipes: the Discreet Music bed; Frippertronics duo (regen 1.0, drive up); haunted
+ slapback (short loop, heavy wow); infinite pad sustainer.
+9. When it is not the right tool: rhythmic delays that must not bend pitch (tap.delay~);
+ multitap patterns (tap.multitap~); anything needing a dry-signal guarantee at regen 1.0.
+10. Checkpoint.
+
+## Chapter 2 (user-facing) — *Loops that never line up* (`src/airport.md`)
+
+The image: seven tape loops of awkward lengths, each holding one phrase, all turning at
+once — composition by coincidence. The chapter should make the reader feel that the
+*lengths are the score*.
+
+1. Provenance: Eno's published account of "2/1"; the machine keeps the loops turning, the
+ incommensurability does the composing. *Evidence: header @details.*
+2. Record and return: punch-in at the head, bit-exact freeze, no downbeat, no reset —
+ the free-run is the piece. *Evidence: airport.ipynb §1; scenarios "a recorded phrase
+ returns every loop period and no setter resets the phase", "record off freezes the tape
+ bit-exactly".*
+3. The composite period: 24000- and 30000-sample loops realign at exactly 2.5 s
+ (`composite_period_seconds`), and seven airport-scale loops overflow to infinity —
+ which is the point. *Evidence: airport.ipynb §2; scenario "two incommensurate loops
+ realign only at the lcm".* Figure: `raster.svg`.
+4. Level, pan, darken: placing phrases in the field; the shade is a playback tone, not
+ generation loss (a frozen loop replays the same imprint — the honest non-feature),
+ measured at 0.169 vs 0.169 predicted. *Evidence: airport.ipynb §3; scenarios
+ "a hard-panned loop is bitwise absent from the far bus", "darken shades one loop's
+ playback and only that loop's".*
+5. Splices: what a length change does and why it may click. *Evidence: scenario "a length
+ change is a splice: phase re-wraps and never rewinds".*
+6. Recipes: the "2/1" bed (seven loops, published-spirit ratios); two-loop phase study
+ (Reich-adjacent); one-loop sound-on-sound sketchpad; run sources through tap.discreet~
+ first for tape breath (cross-reference, wow is deliberately absent here).
+7. When it is not the right tool: synchronized loopers (this one never lines up by
+ design); beat-locked material; per-pass degradation (that is tap.discreet~'s job).
+8. Checkpoint.
+
+## Chapter 3 (user-facing) — *The garden that plays itself* (`src/garden.md`)
+
+The image: an instrument you tend rather than play — plant a note, it returns each pass a
+step quieter and purer until it fades; stop playing and the garden keeps itself. Name the
+IP posture plainly (the principle from published descriptions; no Bloom tables, timings, or
+sounds; "Bloom" is Opal's trademark — the tune.md history paragraph is the template for
+this kind of honesty).
+
+1. Provenance and the third abstraction level: audio → loops → events; per-pass decay is
+ the same stabilizer wearing its third costume. *Evidence: header @details.*
+2. Plant and return: the staircase (0.795, 0.399, 0.2, 0.1, 0.05, silence) and the
+ retirement arithmetic — the population converges by construction. *Evidence:
+ garden.ipynb §1; scenarios "a planted note blooms again every loop period", "each
+ return is quieter by the decay ratio and the bloom retires below the floor".* Figure:
+ `staircase.svg`.
+3. `soften` — returns get purer, not just quieter: the FM sideband fades while the
+ fundamental holds. *Evidence: garden.ipynb §2; scenario "each return is purer: the fm
+ partial fades by the soften ratio".*
+4. The scale contract: thirteen chromatic plants, every bloom on the pentatonic by the
+ YIN oracle; quantize-at-entry and why wrong notes are impossible. *Evidence:
+ garden.ipynb §3; scenario "every bloom lands on the scale".*
+5. The gardener: idle threshold, one plant per pass, and the seed triad (bit-exact /
+ different / cannot-matter) — the library's first randomized event source, with the
+ tr808 seed contract as the bridge back to reproducibility. *Evidence: garden.ipynb §4;
+ scenarios "the seeded garden is bit-exact per seed...", "left alone, the garden starts
+ playing after idle_seconds — and never when idle is disabled".*
+6. Bounds you can lean on: 64 events (oldest yields), 16 bells (quietest stolen,
+ envelopes re-aimed not reset). *Evidence: scenarios "when the garden is full the
+ oldest bloom yields to the newest", "the bell pool never exceeds its size...".*
+7. Recipes: the lobby garden (defaults, long idle); the music box (fast decay, no
+ gardener); the endless install (seeded, level low, walk away); duet mode (idle short,
+ trade phrases with the gardener).
+8. When it is not the right tool: melodies with wrong notes in them (quantization is
+ always on); rhythm outside the loop grid; any timbre that is not a soft bell.
+9. Checkpoint.
+
+## Chapter 4 (machine) — *Wear as the stabilizer: tape_loop.h and discreet.h*
+(`src/machine/tape.md`)
+
+The centerpiece of the family's engineering story. Sections in code order: the shared
+header decision (class-with-state → shared header, the swing_vca.h precedent; the ramp and
+Hermite read as cited copies); `reel` and the one wrap that serves two topologies;
+`wow_flutter` and the periodic-only decision (testability as a design force); `wear` and
+the boundedness argument (swing_shape bounded by 1/drive ⇒ BIBO at regen 1.0; the
+normalized DC blocker; what drive 0 promises and what it does not); the doppler decision
+told as a design choice (moving the read head IS the tape speed — no crossfade mode); the
+LLP64 head-wrap note. One section told as a finding: the notebook's per-pass measurement
+landing on the analytic transfer to three decimals — the moment the model and the
+arithmetic agreed. The engineering ledger: analytic-transfer oracle, two-window RMS
+non-growth (the grm_comb swell story inherited), YIN as transport oracle, bitwise endpoint
+laws. *Evidence: discreet_test.cpp scenarios (all), discreet.ipynb §§1–5.*
+
+## Chapter 5 (machine) — *Free-running heads, one shared clock: airport.h*
+(`src/machine/airport.md`)
+
+Sections: the loop_state shape (multitap's fixed-array idiom with a reel per slot); the
+phase discipline (never reset — enumerate what may and may not touch it, and the
+setter-storm test that pins it); record semantics (replace at the head, read-before-write,
+the two-sample Hermite blend at the punch, no overdub by provenance); the splice
+arithmetic; the darken bypass at the band ceiling (bit-transparency as a testable
+contract); composite_period_seconds (gcd/lcm in long long, overflow → +inf as a feature).
+Finding section: the raster plot making the 2.5 s lcm visible before the assertion pinned
+it. Ledger: bitwise structural assertions over spectral ones wherever the promise allows.
+*Evidence: airport_test.cpp scenarios (all), airport.ipynb §§1–4.*
+
+## Chapter 6 (machine) — *Events, not audio: garden.h* (`src/machine/garden.md`)
+
+Sections: the event ring (fixed 64, seq-numbered, oldest-yields — the musical argument for
+the overflow policy); the fire/bloom split and why note() does not sound the voice itself
+(the double-trigger it avoids); the bell (2-op FM at ratio 3, why harmonicity was a test
+requirement before it was an aesthetic; decay_env reuse; steal-by-re-aim); quantize-at-
+entry (the tune.h mask idiom, copied not included, and the coupling argument); the
+gardener (consumption discipline: rng touched only when idling — the cannot-matter leg of
+the triad depends on it); the population-convergence arithmetic as the header's stated
+theorem. Finding section: the onset-detector rewrite — exponential tails never reach zero,
+so "returns on the grid" had to be pinned by threshold, an honest lesson about testing
+envelopes. Ledger: the seed triad as contract, YIN for the scale promise, Goertzel for the
+softening trajectory. *Evidence: garden_test.cpp scenarios (all), garden.ipynb §§1–5.*
+
+## Notes for drafting
+
+- Voice: the person patching, not the person marketing. The family chapters may assume
+ the reader has met `delay.h`'s feedback story (Part III) — the inversion lands harder
+ against it.
+- Title alternates considered and rejected: "The two tape machines" (names the rig, not
+ the image), "Airport music" (flip), "Bloom, recreated" (trademark in a title — no).
+- Figures: regenerate rather than screenshot (`book/figures/eno.py`); notebook previews
+ are decimated for repo size, figures are full-rate measurements.
+- Cross-repo linking: none needed here (all four headers live in this repo); the
+ machine/spectral.md convention is not required.
+- The render tool (`eno_render`) is the listening companion; chapters may point at its
+ scenario names for "hear this" moments but must not cite it for numbers — numbers come
+ from the notebooks and tests only.
diff --git a/book/figures/eno.py b/book/figures/eno.py
new file mode 100644
index 0000000..d70afa3
--- /dev/null
+++ b/book/figures/eno.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+"""Generate the measured figures for the Eno-family book chapters.
+
+Drives the *shipping* kernels (discreet.h, airport.h, garden.h) through the
+C ABI via the notebooks' ctypes bridge — the same rule as the verification
+notebooks: figures are measurements of the real DSP, never illustrations of
+what it should do. The companion notebooks (notebooks/discreet.ipynb,
+airport.ipynb, garden.ipynb) carry the same measurements with commentary;
+this script renders the book-styled SVGs.
+
+Regenerate after a kernel behavior change:
+
+ python3 book/figures/eno.py # writes book/src/images/{discreet,airport,garden}/*.svg
+
+Colors: the house categorical hues (notebooks/taptools_py.py PALETTE), with
+the amber snapped darker (#efb118 -> #b8890f) so pairs pass the print/CVD
+lightness-band and separation checks on a light page. Every multi-series
+figure carries direct labels, so identity never rides on color alone.
+"""
+
+import pathlib
+import sys
+
+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[2] / "notebooks"))
+import taptools_py as tap
+
+IMAGES = pathlib.Path(__file__).resolve().parents[1] / "src" / "images"
+
+BLUE, AMBER, RED = "#4269d0", "#b8890f", "#ff725c"
+INK, MUTED = "#1a1a1a", "#666666"
+
+plt.rcParams.update({
+ "figure.dpi": 96, "figure.figsize": (7.2, 3.1),
+ "svg.fonttype": "none", "font.family": "sans-serif", "font.size": 9.5,
+ "axes.grid": True, "grid.alpha": 0.22, "grid.linewidth": 0.5,
+ "axes.spines.top": False, "axes.spines.right": False,
+ "axes.edgecolor": MUTED, "axes.labelcolor": INK,
+ "xtick.color": MUTED, "ytick.color": MUTED,
+ "axes.titlesize": 10, "axes.titlecolor": INK,
+ "lines.linewidth": 2.0, "legend.frameon": False, "legend.fontsize": 8.5,
+})
+
+fs = 48000.0
+
+
+def out_dir(name):
+ d = IMAGES / name
+ d.mkdir(parents=True, exist_ok=True)
+ return d
+
+
+def tone(x, f):
+ n = np.arange(x.size)
+ return 2.0 * np.abs(np.dot(x, np.exp(-2j * np.pi * f * n / fs))) / x.size
+
+
+def generation_loss():
+ """discreet: per-pass two-tone decay vs. the analytic wear transfer."""
+ m = tap.Discreet(fs, 8.0, smooth_ms=0, wow=(0, 0), flutter=(0, 0), mix=100,
+ input_level=1.0, loop_seconds=0.25, regen=0.9, drive=0.0,
+ darken_hz=2000.0)
+ f_hi, f_lo = 6000.0, 300.0
+ n_burst = int(0.1 * fs)
+ tt = np.arange(n_burst) / fs
+ x = np.zeros(int(1.6 * fs))
+ x[:n_burst] = 0.4 * np.sin(2 * np.pi * f_hi * tt) + 0.4 * np.sin(2 * np.pi * f_lo * tt)
+ y = m.process(x)
+
+ def wear_gain(f, cutoff):
+ w = 2 * np.pi * f / fs
+ a = 1.0 - np.exp(-2 * np.pi * cutoff / fs)
+ ejw = np.exp(-1j * w)
+ lp = np.abs(a / (1 - (1 - a) * ejw))
+ r, nm = 0.999, (1 + 0.999) / 2
+ return lp * np.abs(nm * (1 - ejw) / (1 - r * ejw))
+
+ loop = int(0.25 * fs)
+ passes = np.arange(1, 6)
+ hi = [tone(y[k * loop: k * loop + n_burst], f_hi) for k in passes]
+ lo = [tone(y[k * loop: k * loop + n_burst], f_lo) for k in passes]
+ pred_hi = hi[0] * (0.9 * wear_gain(f_hi, 2000.0)) ** (passes - 1)
+ pred_lo = lo[0] * (0.9 * wear_gain(f_lo, 2000.0)) ** (passes - 1)
+
+ fig, ax = plt.subplots()
+ ax.semilogy(passes, pred_lo, "-", color=AMBER, lw=1.2, alpha=0.7)
+ ax.semilogy(passes, lo, "s", color=AMBER, ms=6)
+ ax.semilogy(passes, pred_hi, "-", color=BLUE, lw=1.2, alpha=0.7)
+ ax.semilogy(passes, hi, "o", color=BLUE, ms=6)
+ ax.text(2.1, lo[1] * 1.45, "300 Hz — below the corner", color=AMBER)
+ ax.text(1.6, hi[1] * 0.5, "6 kHz — above it", color=BLUE)
+ ax.set_xticks(passes)
+ ax.set_xlabel("pass through the loop")
+ ax.set_ylabel("tone level")
+ ax.set_title("generation loss, measured (points) vs. regen · |H_wear| (lines)")
+ fig.savefig(out_dir("discreet") / "generation-loss.svg", bbox_inches="tight")
+ plt.close(fig)
+
+
+def raster():
+ """airport: return raster of two incommensurate loops; the lcm marked."""
+ b = tap.Airport(fs, 2.0, smooth_ms=0, lengths=[0.5, 0.625], pans=[-1.0, 1.0])
+ click = np.zeros(1)
+ click[0] = 1.0
+ for i in (0, 1):
+ b.record(i, True)
+ b.process(click)
+ b.record(i, False)
+ yl, yr = b.process(np.zeros(int(7.5 * fs)))
+ hits_a = np.flatnonzero(yl > 0.5) / fs
+ hits_b = np.flatnonzero(yr > 0.5) / fs
+
+ fig, ax = plt.subplots(figsize=(7.2, 2.3))
+ ax.eventplot([hits_a, hits_b, np.concatenate([hits_a, hits_b])],
+ colors=[BLUE, AMBER, MUTED], lineoffsets=[2, 1, 0], linelengths=0.75)
+ for k in (1, 2):
+ ax.axvline(2.5 * k, color=RED, lw=1.0, ls=":")
+ ax.text(2.5, 2.72, "composite period: 2.5 s (the lcm)", color=RED, ha="center")
+ ax.set_yticks([2, 1, 0])
+ ax.set_yticklabels(["loop A · 0.5 s", "loop B · 0.625 s", "the sum"])
+ ax.set_xlabel("time (s)")
+ ax.set_ylim(-0.6, 3.0)
+ ax.grid(axis="y", alpha=0)
+ fig.savefig(out_dir("airport") / "raster.svg", bbox_inches="tight")
+ plt.close(fig)
+
+
+def staircase():
+ """garden: the decay-0.5 return staircase, retiring below the floor."""
+ g = tap.Garden(fs, smooth_ms=0, idle_seconds=0, loop_seconds=0.5,
+ decay=0.5, floor=0.05, bell=(0.002, 0.05, 1.0), scale=0)
+ g.note(69, 0.8)
+ y = g.process(int(3.5 * fs))
+
+ t = np.arange(y.size) / fs
+ fig, ax = plt.subplots()
+ ax.plot(t, y, color=BLUE, lw=0.5)
+ for k in range(5):
+ v = 0.8 * 0.5 ** k
+ ax.plot([k * 0.5, k * 0.5 + 0.22], [v, v], color=AMBER, lw=1.6)
+ ax.text(k * 0.5 + 0.24, v, f"{v:g}", color=AMBER, va="center", fontsize=8.5)
+ ax.axhline(0.05, color=RED, lw=0.9, ls=":")
+ ax.text(3.44, 0.075, "floor 0.05 — retirement", color=RED, ha="right", fontsize=8.5)
+ ax.set_xlabel("time (s)")
+ ax.set_ylabel("output")
+ ax.set_title("decay 0.5: each return half as loud, then the bloom retires")
+ fig.savefig(out_dir("garden") / "staircase.svg", bbox_inches="tight")
+ plt.close(fig)
+
+
+if __name__ == "__main__":
+ generation_loss()
+ raster()
+ staircase()
+ print("wrote", *(str(p) for p in sorted(IMAGES.glob("*/generation-loss.svg"))),
+ str(IMAGES / "airport" / "raster.svg"), str(IMAGES / "garden" / "staircase.svg"))
diff --git a/book/src/SUMMARY.md b/book/src/SUMMARY.md
index 39d9fc7..2e0cd65 100644
--- a/book/src/SUMMARY.md
+++ b/book/src/SUMMARY.md
@@ -18,26 +18,32 @@
- [Five strings, no guitar](fivecomb.md)
- [The spiral staircase](pitchaccum.md)
-# Part IV — The spectral set
+# Part IV — Tape and time
+
+- [The tape that forgets slowly](discreet.md)
+- [Loops that never line up](airport.md)
+- [The garden that plays itself](garden.md)
+
+# Part V — The spectral set
- [Making the machine talk](vocoder.md)
- [A gate for every bin](nr.md)
- [The spectrum, re-plumbed](spectra.md)
-# Part V — The rhythm section
+# Part VI — The rhythm section
- [The acid machine](acid.md)
- [The drum machine](drums.md)
-# Part VI — Staying in tune
+# Part VII — Staying in tune
- [The note you meant](tune.md)
-# Part VII — The pedalboard
+# Part VIII — The pedalboard
- [Distortion with a memory](overdrive.md)
-# Part VIII — The machine, file by file
+# Part IX — The machine, file by file
- [Solving the filter on paper: svf.h](machine/svf.md)
- [The nonlinear loop: ladder.h](machine/ladder.md)
@@ -55,8 +61,11 @@
- [Three ways to move a pitch: yin.h, psola.h, pvoc.h](machine/pitch.md)
- [The nearest allowed note: tune.h](machine/tune.md)
- [The clipper in the loop: overdrive.h](machine/overdrive.md)
+- [Wear as the stabilizer: tape_loop.h and discreet.h](machine/tape.md)
+- [Free-running heads, one shared clock: airport.h](machine/airport.md)
+- [Events, not audio: garden.h](machine/garden.md)
-# Part IX — Recipes
+# Part X — Recipes
- [How to read a recipe](recipes/cookbook.md)
- [One machine, four decades](recipes/808-classics.md)
diff --git a/book/src/airport.md b/book/src/airport.md
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+# Loops that never line up
+
+Take seven tape loops of deliberately awkward lengths — none a multiple of
+another — put one soft phrase on each, and let them all turn at once. Each
+loop is trivial: it plays the same thing forever. The *system* is not: the
+phrases drift against each other, meet, part, and meet again differently,
+and the pattern of coincidences does not repeat within a human afternoon.
+That is "2/1" from Brian Eno's *Music for Airports* (Ambient 1, EG, 1978),
+as he described the rig in the album's liner notes and in *A Year with
+Swollen Appendices*: the lengths are the score, and the machine's whole job
+is to keep the loops turning without an opinion. `tap.airport~` is that
+machine — up to eight free-running loops, each with a single head that both
+plays and records, summed to stereo.
+
+The discipline that makes it the instrument it is: **nothing resets a
+phase.** Not recording, not a level move, not a pan, not even a length
+change. The free-run *is* the composition, and the kernel treats the heads
+as sacred; the test suite literally hammers every setter mid-run and then
+checks that the heads have advanced by exactly the samples processed.
+
+Companion material: the executed notebook `airport.ipynb`, which measured
+every claim below, and the `eno_render` tool's `airport_two_one` scenario —
+three stereo minutes of seven loops, the listening copy. The Max wrapper
+lands in the TapTools-Max package alongside the rest of the family.
+
+
+
+*One loop of eight. The head plays, then records, then advances; nobody ever tells it where to be.*
+
+## Record and return
+
+`record(loop, 1)` punches the input onto that loop's tape at wherever its
+head happens to be — there is no downbeat, no quantized punch-in, because
+Eno's rig had none. Recording *replaces* (each phrase was recorded once, not
+overdubbed), and playback reads just ahead of the write, so while recording
+you hear the previous generation under the head. `record(loop, 0)` freezes
+the tape, and freezes it bit-exactly: the pinned test compares two whole
+passes of a frozen loop and requires them identical to the bit. A loop is
+not a degrading medium here — it replays the *same* magnetic imprint every
+revolution, which is why this kernel deliberately has no per-pass
+generation loss (that is `tap.discreet~`'s physics, not a loop's).
+
+## The lengths are the score
+
+`length_seconds` per loop is where the composing happens. Two loops of
+24000 and 30000 samples realign only at their least common multiple —
+120000 samples, 2.5 seconds — and the kernel will tell you:
+`composite_period_seconds` reports exactly 2.5 for that pair, confirmed in
+the notebook by rendering the coincidence raster and watching it repeat at
+2.5 s and at no shorter lag.
+
+
+
+*Two awkward lengths and their coincidences. Stretch the lengths and the composite period leaves the room.*
+
+Then stretch toward the piece: give seven loops airport-scale lengths in
+awkward ratios and the composite period overflows a 64-bit sample count —
+the kernel reports infinity, which is not a failure mode. It is the point.
+
+Changing a length while running is a *splice*: the tape keeps its content
+and the head re-wraps modulo the new length — never rewinding — exactly as
+cutting a physical loop shorter would land you mid-phrase. It can click.
+Splices do.
+
+## Level, pan, shade
+
+Each loop has a slewed linear `level`, an equal-power `pan` with exact
+endpoints (a hard-panned loop is *bitwise* absent from the far bus — the
+same law as `tap.multitap~`), and a `darken` corner that shades that loop's
+playback tone. The shade is a static one-pole per loop, not wear: measured
+in the notebook, a 6 kHz phrase through a 1 kHz shade lands at 0.169 of its
+transparent twin, against an analytic prediction of 0.169. At the band
+ceiling — the default — the shade stage is bypassed entirely and playback
+is bit-transparent, which is what makes the freeze and hard-pan promises
+testable as bitwise facts rather than tolerances.
+
+There is deliberately no wow here: the phasing engine of "2/1" is the
+incommensurate lengths, not pitch drift. If a loop's source should breathe
+like tape, run it through `tap.discreet~` on the way in.
+
+## Recipes
+
+- **The terminal:** seven loops, `@lengths 17.8 19.1 21.3 23.9 26.2 28.7
+ 30.9`, one sustained tone phrase recorded onto each, levels around 0.45,
+ pans spread wide, a 4 kHz shade on two of them. Let it run. Come back in
+ an hour; it will not have repeated.
+- **Phase study:** two loops, lengths in a near ratio (say 8.0 and 8.1),
+ the same short phrase on both, panned hard left and right — the
+ Reich-adjacent version, where the drift itself is the melody.
+- **Sound-on-sound sketchpad:** one loop, `@lengths 12.`, record gate on a
+ footswitch. Punch in fragments as they occur to you; the head's
+ indifference to your downbeat is the charm.
+- **Breathing loops:** patch sources through `tap.discreet~` (gentle wow,
+ regen 0) before the record gate — tape transport on the way in, stable
+ free-run once captured.
+
+## When it is not the right tool
+
+- **Synchronized looping.** This machine never lines up *by design*. A
+ beat-locked looper wants a phase reset on the downbeat, which is the one
+ thing this kernel refuses to do.
+- **Degrading loops.** A frozen loop here is bit-eternal. For material that
+ should wear out as it circulates, `tap.discreet~` is the machine with
+ the forgetting built in.
+- **Dense delay textures.** Eight long loops is a composition system, not
+ an echo; `tap.multitap~` does a hundred taps without ceremony.
+
+## Checkpoint
+
+Up to eight free-running loops, one sacred head each: record replaces at
+wherever the head is, freeze is bitwise, splices re-wrap and never rewind,
+and no setter touches a phase. Level, exact-endpoint pan, and a bypassable
+playback shade place the phrases; the lengths do the composing, and
+`composite_period_seconds` tells you how long until the piece repeats —
+ideally, longer than you will be alive. Every number above lives twice: as
+an executed cell in `airport.ipynb` and as a pinned scenario in
+`tests/airport_test.cpp`, which CI runs on every push.
diff --git a/book/src/discreet.md b/book/src/discreet.md
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+# The tape that forgets slowly
+
+Every other delay in this house is kept honest by a cap: feedback stops just
+short of one, because a loop that gains nothing and loses nothing will pile
+up until it clips. `tap.discreet~` is built on the opposite bargain. Its
+regeneration goes all the way to 1.0 — legally, cleanly, forever — because
+the loop *forgets*: every pass through the tape comes back a little darker
+and a little softer than it went in. The memory loss is not a defect the
+kernel tolerates; it is the mechanism that keeps the machine stable. You are
+not patching a delay effect. You are renting a machine whose memory is the
+instrument.
+
+The rig it recreates is printed on the back cover of *Discreet Music*
+(Obscure/EG, 1975): Brian Eno's synthesizer feeding one Revox tape machine,
+the tape spooling for seconds across the room to a second machine, and the
+second machine's playback both sent to the speakers and folded back into the
+first machine's record head. It is the same two-machine system Robert Fripp
+ran for the *No Pussyfooting* loops. The tape path itself — the fractional
+read, the periodic wow and flutter, the in-loop coloration — follows the
+published tape-echo modeling literature (Arnardóttir, Abel, and Smith's AES
+model of the Echoplex, and Välimäki et al.'s tape-echo work). The schematic
+is the score; this kernel is a faithful performance of it.
+
+Companion material: the executed notebook `discreet.ipynb`, which measured
+every claim below, and the `eno_render` tool, whose `discreet_basic` and
+`discreet_sustain` scenarios are the listening copies. The Max wrapper lands
+in the TapTools-Max package alongside the rest of the family.
+
+
+
+*Two machines and a spool of tape; the red return is where the forgetting — and therefore the stability — lives.*
+
+## `loop` — the tape span
+
+`loop_seconds` is the distance between the machines: how long a phrase
+travels before it returns. The kernel test pins the grid to the sample — an
+impulse comes back at exactly one loop, bit-for-bit the first time, and
+every later return lands within a sample of its grid point.
+
+Changing the loop while audio runs is a tape-speed change, not a menu
+option: the read head physically glides to its new distance, and gliding a
+read head *is* doppler. Move from 0.5 s to 0.75 s over half a second and the
+playback drops an octave while the transport re-spools, then re-locks on
+pitch — the test measures 220 Hz mid-glide and 440 Hz within five cents
+after. There is no crossfading "digital" mode, on purpose. If a pitch bend
+on loop changes would ruin the patch, this is the wrong delay (see below).
+
+## `regen` — and why 1.0 is legal here
+
+`regen` is the return level into the record head, and unlike `tap.delay~`'s
+feedback (capped at 0.99), it reaches exactly 1.0. The notebook plays a
+one-second noise burst into the loop at regen 1.0 and lets it run for twenty
+seconds: the level settles and stays — no growth, no collapse — because the
+wear path bounds it. The saturator's output can never exceed 1/drive
+regardless of what the loop accumulates, the DC blocker keeps offsets from
+stacking, and the darkening lowpass decides *what* survives: lows sustain,
+highs surrender. The pinned scenario is blunt about the contract — it
+asserts *non-growth*, never decay, because at regen 1.0 sustain is the
+promise. Bring `regen` down, or darken harder, to end a piece; `clear` is
+the eject button, and regen-1.0 material is gone for good.
+
+## `darken` and `drive` — the wear
+
+`darken_hz` is the record/playback corner: every pass through the loop runs
+through a one-pole lowpass at this frequency, so a bright phrase sheds its
+treble generation by generation while its body lingers. This is measured,
+not vibes: with the corner at 2 kHz, a 6 kHz tone loses to 0.292 of itself
+per pass and a 300 Hz tone keeps 0.890 — and both numbers match the analytic
+transfer of the wear path to three decimals in the executed notebook.
+
+
+
+*Generation loss, measured against `regen · |H_wear|`. The tape forgets treble first.*
+
+`drive` is the record-head saturation — the guarantee. At any drive above
+zero the loop is absolutely bounded no matter the settings; at drive 0 the
+path is exactly linear (a real bit-for-bit passthrough, not "almost") and
+the loop leans on darkening alone. Drive around 0.5 is the tape sound;
+drive high is the loop slowly compressing itself into a wash.
+
+## `wow` and `flutter` — the transport
+
+Two sines, slow-deep and fast-shallow, breathing the play head's position.
+The pitch math is honest and checkable: depth times 2π times rate is the
+peak deviation, so 2 ms of wow at 0.5 Hz predicts ±10.9 cents — and the
+notebook's YIN pitch track measures 10.9. The transport is periodic and
+deterministic by design (no stochastic capstan drift): two renders of the
+same settings are bit-identical, which is also a pinned test. Set both
+depths to 0 for a perfectly still machine.
+
+## `input_level` — the performance move
+
+The fader Eno actually rode was not the output — it was the *send*. Play a
+few phrases into the machine, then bring `input_level` to zero: the loop
+keeps unrolling everything it holds, worn a shade further every pass, and
+the piece continues without you. That gesture — set up a system, feed it,
+step away — is the whole record, and it is one setter here. `mix` is the
+ordinary equal-power dry/wet with bitwise-exact endpoints.
+
+## Recipes
+
+- **The Discreet Music bed:** `@loop 5. @regen 0.95 @darken 3500 @drive
+ 0.4 @mix 60`. Play sparse, slow phrases; stop; listen to what the tape
+ decides to keep.
+- **Frippertronics:** `@loop 6.5 @regen 1. @drive 0.7 @darken 2200 @mix
+ 100`. Solo over yourself from a minute ago. The wash never clips and
+ never ends until you end it.
+- **Haunted slapback:** `@loop 0.15 @regen 0.85 @wow 4. 0.9 @flutter 0.15
+ 12.` — a short loop with a seasick transport; the doppler and the wear
+ turn a slap delay into a memory of one.
+- **The exit:** whatever is running, ride `@regen` from 1. to 0.7 over a
+ minute. The piece performs its own fade, oldest material first.
+
+## When it is not the right tool
+
+- **Rhythmic delays.** Loop changes bend pitch by design, and there is no
+ tempo sync. `tap.delay~` is the clean line; `tap.multitap~` is the
+ pattern.
+- **Anything that must not color the repeats.** Wear is always in the loop
+ (drive 0 removes only the saturation, not the darkening you set). If the
+ tenth echo must equal the first, this machine is philosophically opposed.
+- **Loops that should line up with other loops.** One machine, one spool.
+ For a bank of independent free-running loops, the next chapter's
+ `tap.airport~` is the instrument.
+
+## Checkpoint
+
+Seconds of tape between two machines; a worn return path — darken, saturate,
+DC-block — instead of a feedback cap; regeneration to exactly 1.0 because
+forgetting is the stabilizer. Loop moves are honest tape-speed doppler, the
+transport is two deterministic sines measured in cents, and the send fader
+is the performance. Every number above lives twice: as an executed cell in
+`discreet.ipynb` and as a pinned scenario in `tests/discreet_test.cpp`,
+which CI runs on every push.
diff --git a/book/src/garden.md b/book/src/garden.md
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+# The garden that plays itself
+
+The first two chapters of this part recirculate sound: tape that forgets,
+loops that never agree. This one recirculates *decisions*. Plant a note and
+it comes back every pass of the loop a step quieter and a step purer, until
+it fades below hearing and retires. Plant several and they braid. Stop
+planting altogether and, after a patient interval, the garden starts
+planting for itself — always on the scale, never in a hurry. You do not
+play this instrument so much as tend it, which is exactly the posture Eno
+kept asking for: the composer as gardener, not architect. The kernel is
+named for that metaphor.
+
+What it recreates is the *principle* behind Brian Eno and Peter Chilvers'
+generative apps (Bloom, 2008), as described in their published interviews
+and in Eno's 1996 "Generative Music" talk: touch becomes note, note repeats
+and fades, scale makes wrong notes impossible, idleness hands the piece to
+the system. The principle only — no scale tables, timings, or sounds are
+taken from the app, and its name is a live trademark of Opal Limited, which
+is why this object is a garden and not a bloom. (As with `tap.tune~`'s
+history paragraph, none of this is legal advice; the project's ship-gate is
+a freedom-to-operate review.)
+
+Companion material: the executed notebook `garden.ipynb`, which measured
+every claim below, and the `eno_render` tool's `garden_played` and
+`garden_idle` scenarios, the listening copies. The Max wrapper lands in the
+TapTools-Max package alongside the rest of the family.
+
+
+
+*Events on a loop instead of audio on a tape — the same recirculation, one level of abstraction up.*
+
+## Plant and return
+
+`note(pitch, velocity)` plants: the pitch snaps to the current root and
+scale *at entry*, a soft two-operator FM bell sounds on the next sample,
+and the event takes a seat at the loop's current position. Every pass, it
+fires again at `velocity × decay`, and below `floor` it retires. The
+notebook's staircase is the whole contract in one figure: a plant at 0.8
+with decay 0.5 returns at 0.795, 0.399, 0.2, 0.1, 0.05 — then silence, and
+`active_events` reads zero.
+
+
+
+*The return staircase: decay 0.5, floor 0.05, and a bloom that knows when it is finished.*
+
+That arithmetic is also the stability story. The family's inversion —
+degradation as the stabilizer — reaches its third form here: a bloom lives
+exactly `ceil(log(floor/velocity) / log(decay))` passes, so the population
+of live events *converges by construction* no matter how fast you plant.
+And beneath the arithmetic sits a hard bound: sixteen bells in a fixed
+pool, the quietest stolen when a seventeenth is needed, its envelope
+re-aimed rather than reset so a steal glides instead of clicking.
+
+## `soften` — returns get purer, not just quieter
+
+Each pass also multiplies the event's *brightness* by `soften`, and
+brightness is the bell's FM index: the upper partial fades while the
+fundamental holds, so a bloom collapses toward a sine as it recedes — the
+tape chapters' generation loss, restated in partials instead of passbands.
+The notebook measures the sideband-to-fundamental ratio shrinking every
+single return, and the pinned test requires it strictly.
+
+## The scale contract
+
+`root` and `scale` (chromatic, major, minor, and both pentatonics — plain
+public-domain scale theory) define where plants may land, and quantization
+happens at entry: the notebook plants all thirteen chromatic pitches from
+60 to 72 into a C major-pentatonic garden and the YIN oracle reads every
+sounded note on {C, D, E, G, A}. Wrong notes are not discouraged; they are
+unrepresentable, which is most of why instruments in this family feel
+effortless to strangers. Because quantization is at entry, changing the
+scale re-pitches nothing already planted — the field changes for future
+seeds only.
+
+## The gardener
+
+`idle_seconds` is the patience: that long after your last plant, the
+garden begins seeding itself, roughly one note per loop pass, uniformly
+placed, on the scale, within two octaves. The randomness is the family's
+seeded xorshift64* with the full tr808 contract, pinned as a triad: same
+seed, bit-identical garden; different seed, a different garden; gardener
+disabled (`idle_seconds 0`), the seed cannot matter at all, because the
+generator is never consumed. This is the library's first randomized event
+source — `step_seq.h` proudly promises "no randomness anywhere" — and the
+seed contract is what lets a generative instrument live in a test suite
+that demands reproducibility.
+
+## Recipes
+
+- **The lobby:** defaults, `@idle 30. @level 0.4`, plant four or five
+ notes, walk away. The garden holds the room indefinitely, bounded.
+- **The music box:** `@decay 0.5 @soften 0.7 @idle 0 @bell 0.005 0.8 1.` —
+ no gardener, fast decay: each phrase you play unwinds itself to silence
+ in a few passes, a wind-up toy running down.
+- **The endless install:** `@scale minorpentatonic @root 2 @idle 3.
+ @seed 2008 @level 0.35`, never touch it again. Same seed next year,
+ same garden.
+- **Duet:** `@idle 6.` and stay at the keyboard — every silence longer
+ than six seconds, the gardener answers you; every plant of yours resets
+ its patience.
+
+## When it is not the right tool
+
+- **Melodies with wrong notes in them.** Quantization is always on;
+ chromatic passing tones survive only in `@scale chromatic`, and
+ micro-tonal pitches not at all. This is a fence, and it is the product.
+- **Rhythm.** Events return on the loop grid, exactly, forever — no swing,
+ no humanization. For patterns as *rhythm*, `tap.808.seq~` is the
+ machine.
+- **Any other timbre.** One soft bell family, on purpose. It is an
+ instrument, not a polysynth; for FM as a playground, patch oscillators.
+
+## Checkpoint
+
+Notes become events; events recirculate on a loop, quieter by `decay` and
+purer by `soften` each pass, retiring below `floor`; a sixteen-bell pool
+bounds the sound and a sixty-four-seat ring bounds the score, oldest bloom
+yielding first. The scale makes wrong notes unrepresentable, and a seeded
+gardener keeps the piece alive exactly as long as you neglect it. Every
+number above lives twice: as an executed cell in `garden.ipynb` and as a
+pinned scenario in `tests/garden_test.cpp`, which CI runs on every push.
diff --git a/book/src/images/airport/block-diagram.svg b/book/src/images/airport/block-diagram.svg
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+
diff --git a/book/src/images/airport/raster.svg b/book/src/images/airport/raster.svg
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+
+
+
diff --git a/book/src/images/discreet/block-diagram.svg b/book/src/images/discreet/block-diagram.svg
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+
diff --git a/book/src/images/discreet/generation-loss.svg b/book/src/images/discreet/generation-loss.svg
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+
+
+
diff --git a/book/src/images/garden/block-diagram.svg b/book/src/images/garden/block-diagram.svg
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+
diff --git a/book/src/images/garden/staircase.svg b/book/src/images/garden/staircase.svg
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+
+
+
diff --git a/book/src/machine/airport.md b/book/src/machine/airport.md
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+# Free-running heads, one shared clock: `airport.h`
+
+`airport::loop_bank` is structurally the smallest kernel in the family — a
+fixed array of loops, a stereo sum, no feedback anywhere — and that is what
+makes it interesting to read: nearly every promise it makes is *structural*,
+so nearly every test on it is bitwise. This appendix walks the file in code
+order and dwells on the one discipline that defines it.
+
+## `loop_state`: the multitap idiom with a reel in each seat
+
+The bank is `std::array` with an active count —
+`delay.h`'s multitap shape, kept deliberately: per-index setters that
+silently no-op on a bad index, getters that return safe defaults, newly
+activated slots arriving at their stored settings. Each seat holds a
+`tape::reel` (its own worst-case buy — eight 30-second reels is ~92 MB of
+double tape, the family's largest allocation, stated in the header rather
+than discovered in production), a `tape::wear` used as a playback shade, a
+phase, a record flag, and three ramps (level, pan, darken).
+
+## The phase discipline
+
+The load-bearing sentence in the header is "the phase is NEVER reset":
+recording starts wherever the head is, `set_loops` activates a loop with
+its head wherever it last was, a splice re-wraps the head modulo the new
+length without rewinding, and only `prepare()`/`clear()` — DSP restarts —
+may rewind. The reason is musical: in "2/1" the free-run *is* the piece,
+and any convenience reset (snap to zero on record, realign on length
+change) would quietly delete the composition. The pinned scenario earns the
+promise the blunt way: it fires a setter storm mid-render — level, darken,
+record, length, count — and then requires the click grid unmoved and the
+head advanced by exactly the samples processed. `phase()` exists as
+introspection precisely so that test could be written.
+
+## Record semantics
+
+`record` is a gate, not an action: while on, the input *replaces* the tape
+at the integer head position, after the read — so you hear the previous
+generation under the head while punching, and one Hermite support point
+(two samples) of the old generation blends across the punch, which the
+header files under honest limits instead of papering over with a crossfade.
+No overdub-sum, because the provenance had none: each Airports phrase was
+recorded once. Freeze is the strong promise — record off, and two
+successive passes of the loop are required bit-identical. That promise is
+only possible because of the next decision.
+
+## The shade and its bypass
+
+Per-loop `darken` reuses `tape::wear` with drive pinned at 0, as a *static
+playback tone* — deliberately not generation loss, because a frozen loop
+replays the same magnetic imprint every revolution and modeling wear on it
+would be dishonest physics. At the band ceiling (the default) the stage is
+bypassed entirely: not "flat enough", but not-in-the-signal-path, which is
+what upgrades the freeze test and the hard-pan test (a pan of −1 adds the
+loop's samples to the left bus unscaled) from tolerance checks to bitwise
+facts. Engaged, the shade is the exact one-pole from grm_comb.h, and the
+notebook measures a 6 kHz phrase through a 1 kHz shade at 0.169 of its
+transparent twin against 0.169 predicted.
+
+## `composite_period_seconds`
+
+The lcm of the active loop lengths in samples, folded pairwise with a
+`long long` gcd, overflow detected before each multiply and reported as
++inf. It is introspection, not DSP — but it is the piece's thesis as a
+number: 24000- and 30000-sample loops report exactly 2.5 s (and the pinned
+scenario also proves the rendered output repeats at 120000 samples and
+does *not* repeat at 60000), while seven airport-scale lengths overflow to
+infinity, which the header calls the point.
+
+## A finding: the raster before the assertion
+
+The lcm scenario existed as an assertion first — bitwise equality of two
+2.5-second windows — and it passed, which is exactly why it was worth
+plotting. The notebook's event raster (every return of loop A, loop B, and
+their sum on one timeline) made the same fact *visible*: the coincidence
+pattern audibly and graphically re-enters at 2.5 s and drifts everywhere
+short of it. The assertion pins the promise; the raster is what convinces a
+human the promise means something. The pair — one bitwise test, one
+executed figure — is this library's preferred way to hold a structural
+claim from both sides.
+
+## The engineering ledger
+
+Almost everything here is exact, so the suite asserts exactly: bit-equality
+for freeze and for the lcm window, bitwise silence on the far bus for hard
+pans, `phase()` continuity to 1e−9 through the setter storm, and the splice
+law (0.9 of a 1 s loop re-wraps to 0.8 of a 0.5 s loop, never zero). The
+one measured tolerance in the file is the shade's analytic transfer at 20%,
+and the equal-power pan law needs no scenario of its own because the
+multitap chapter already pinned the center at 1/√2 to 1e−12 — same code
+shape, same law, cited rather than re-proven. Long-run behavior needs no
+stability test at all: there is no feedback path to go wrong, which is
+itself a fact the file's structure makes obvious enough not to test.
+
+## Checkpoint
+
+A fixed bank of reels, one sacred free-running head each; record replaces
+and freeze is bitwise; splices re-wrap, never rewind; the shade bypasses to
+bit-transparency at the ceiling; and the composite period is the score's
+arithmetic made introspectable. The promises are structural, the tests are
+bitwise, and the executed raster in `airport.ipynb` is the human-readable
+proof that the structure composes.
diff --git a/book/src/machine/garden.md b/book/src/machine/garden.md
new file mode 100644
index 0000000..9264fec
--- /dev/null
+++ b/book/src/machine/garden.md
@@ -0,0 +1,119 @@
+# Events, not audio: `garden.h`
+
+`garden::bed` recirculates *events* where its siblings recirculate samples,
+which makes it the family's odd one out mechanically and its purest member
+conceptually: the wear-as-stabilizer inversion survives the abstraction jump
+intact, as arithmetic. This appendix walks the machinery — the ring, the
+split between planting and firing, the bell, the quantizer, the gardener —
+and the two contracts that had to be designed before they could be tested.
+
+## The event ring
+
+Sixty-four fixed seats (`std::array`, nothing allocated at `prepare()` —
+this kernel buys no tape at all), each event a pitch, a velocity, a
+brightness, a position on the loop, and a plant-order sequence number. The
+sequence number exists for one policy: when the garden is full, the *oldest
+live* bloom yields to a new plant. The musical argument is stated in the
+header — a touch must always speak (rejecting input makes an instrument
+feel dead), and the oldest bloom has survived the most decay passes, so it
+is the quietest thing on the table; retiring it is the least audible edit
+available. The pinned scenario plants a distinctive high note, floods the
+ring with sixty-four more, and requires the first note's pitch measurably
+gone from the following pass.
+
+## Fire is not plant
+
+`note()` does *not* sound a voice. It quantizes, seats the event at the
+loop's current position, and returns; the next `process()` sample finds the
+event's position under the playhead and fires it. The first draft did both
+— plant-and-fire in `note()` — and the loop fired it again one sample
+later, a double-trigger that fell out of the design the moment firing
+became the loop's exclusive job. One mechanism, two consequences: a plant
+sounds one sample late (inaudible, documented), and every sounding of every
+event goes through a single code path, which is what makes the return grid
+a testable promise. After each fire the event blooms: velocity times
+`decay`, brightness times `soften`, retire below `floor` — so a bloom lives
+exactly `ceil(log(floor/velocity)/log(decay))` passes and the population
+converges no matter the planting rate. That is the stability theorem, and
+it is three lines of arithmetic instead of a saturator.
+
+## The bell
+
+Two-operator FM at a fixed ratio of 3 (Chowning 1973), amplitude from the
+shared `tr808::decay_env`, modulation index `velocity · brightness ·
+k_index_max`. The ratio was a *test requirement* before it was an
+aesthetic: an integer ratio keeps the spectrum harmonic, harmonic means the
+YIN oracle reads the fundamental, and the scale-contract scenario — plant
+off-scale pitches, require every sounded note on the scale within 20 cents
+— only exists because the voice is honest to a pitch detector. Softening
+maps to the index, so "purer every pass" is measurable as a Goertzel
+trajectory: the 4f sideband fades return over return while the fundamental
+holds. Steals re-aim: the pool's quietest bell gets `trigger()`ed with new
+targets while its envelope and phases free-run, so a steal glides where a
+reset would click; the `decay_env` was built for exactly this non-resetting
+retrigger, one family over.
+
+## Quantize at entry
+
+The scale machinery is `tune.h`'s 12-bit pitch-class mask idiom — the
+`make_mask` builder, the nearest-allowed search that never travels more
+than a tritone — *copied with citation, not included*, because `tune.h`
+reaches into `tap::dsp` for its detector and a garden should not link a
+pitch tracker to hold five scale presets. The masks themselves are plain
+public-domain scale theory, deliberately not any app's preset list.
+Quantizing at entry (rather than at fire) is the semantic choice: a scale
+change re-pitches nothing already planted, which keeps running gardens
+stable under live tinkering and makes the contract easy to state.
+
+## The gardener and the seed
+
+Idle planting consumes the family RNG (`tr808::white_noise`, xorshift64*,
+the seed-folding and clear-reseeds contract) — and *only* idle planting
+does. That consumption discipline is load-bearing: the third leg of the
+seeded triad, "with the gardener disabled the seed cannot matter at all",
+is only true because a disabled gardener never touches the generator, so
+two beds with different seeds run bit-identical until the first idle draw.
+The suite pins all three legs, the way the tr808 voices taught: same seed
+bit-exact, different seed audibly different, seed irrelevant when the
+random feature is off. `step_seq.h` promises "no randomness anywhere"; this
+kernel is the deliberate counterpoint, and the triad is the bridge back to
+a reproducible test suite.
+
+## A finding: envelopes never reach zero
+
+The return-grid scenario was first written the obvious way — the percussive
+test bell surely dies between returns, so the first nonzero sample after
+silence is the onset. It failed, instructively: `decay_env`'s exponential
+tail crosses the 1e−12 hard-zero more than half a second after a "20 ms"
+decay, so there *is* no silence between returns, only −200 dB of not-quite.
+The fix was to stop pretending: an instant-attack bell, an amplitude
+threshold scaled to the expected return velocity, and a grid claim of
+"within 8 samples" — a sixth of a millisecond — with the comment explaining
+that a threshold on a sine sits a few samples into the cycle. The lesson is
+general for this library: exponential envelopes make "silence" a tolerance,
+and tests that assume literal zeros between notes are wrong even when they
+pass.
+
+## The engineering ledger
+
+The suite measures the output, never the internals: peak-per-window ratios
+for the decay staircase (0.5 ± 0.075 across four returns, then
+`active_events() == 0` and the render below 1e−6), a strictly-decreasing
+Goertzel sideband for softening, YIN for the scale contract, the seeded
+triad rendered three times over, and structural bounds exercised at their
+edges — sixty-five plants against sixty-four seats, thirty-two notes
+against sixteen bells, finiteness and the `k_voices` amplitude bound under
+sustained stealing. The two introspection counts (`active_events`,
+`active_voices`) exist, as `phase()` does next door, so those scenarios
+could be written against public surface.
+
+## Checkpoint
+
+A fixed ring of events fired by a loop counter into a fixed pool of FM
+bells: plant and fire kept strictly apart, wear as per-pass arithmetic
+(decay, soften, floor) with convergence as its theorem, scale masks copied
+from `tune.h` and applied at entry, and a gardener whose RNG discipline
+makes generative behavior compatible with a bit-exact test suite. Third
+costume, same inversion: the system stays bounded because everything in it
+is always fading. Every claim lives twice — `garden.ipynb` executed,
+`garden_test.cpp` pinned.
diff --git a/book/src/machine/tape.md b/book/src/machine/tape.md
new file mode 100644
index 0000000..4de70ba
--- /dev/null
+++ b/book/src/machine/tape.md
@@ -0,0 +1,120 @@
+# Wear as the stabilizer: `tape_loop.h` and `discreet.h`
+
+Every regenerating loop in this library before these files made the same
+promise the same way: the loop is strictly contractive because feedback is
+capped below one (`delay.h`'s `k_fb_max = 0.99`, the comb bank's calibrated
+ring time). `tape_loop.h` and `discreet.h` exist to make the opposite
+promise — regeneration at exactly 1.0, bounded anyway — and this appendix is
+the derivation of why that is allowed.
+
+## A shared header, by the house rule
+
+The family needed the same four pieces twice (`discreet.h` and `airport.h`
+are both tape machines), and the reuse rule sorted them cleanly. Classes
+with state went into a shared header the way `swing_vca.h` was created for
+the drum family: `tape::reel`, `tape::wow_flutter`, `tape::wear`, and a
+`tape::ramp` that is a cited copy of `delay.h`'s anti-zipper unit. Few-line
+expressions stayed copies-with-citation, as ever: the Hermite polynomial
+inside `reel` is *the same read as delay.h*, line for line, and says so; the
+saturator is not copied at all but included — `vca::swing_shape`, the shared
+swing-type stage, with the reason on the include line.
+
+## `reel`: one wrap, two topologies
+
+A reel is position-addressed circular storage whose reads and writes wrap
+modulo a *settable loop length*, not the buffer size. That one decision lets
+the same class serve both kernels. `discreet.h` runs it as a delay line:
+loop length equals capacity, an integer write head advances forever (wrapped
+into range each sample — a bare `long` head would overflow LLP64's 32-bit
+`long` in half a day of audio), and the play head trails it by the loop
+span. `airport.h` runs it as a true loop: length set per piece, one
+free-running head, positions handed in raw because the reel does all modular
+arithmetic itself. A length change is deliberately a *splice* — content
+kept, positions re-wrapped — because that is what cutting tape does.
+
+## `wow_flutter`: periodic on purpose
+
+The transport error is two sines — slow-deep wow, fast-shallow flutter —
+returning a read-position offset in samples, phases zeroed at `prepare()`.
+The periodic term is the dominant one in the tape-echo literature
+(Arnardóttir, Abel, Smith, AES 2008), but the deeper reason the stochastic
+term is a documented non-goal is testability: the wow promise is pinned by
+predicting peak pitch deviation in closed form (`depth · 2π · rate`, so 2 ms
+at 0.5 Hz ⇒ ±10.9 cents) and measuring it with the YIN oracle — 10.9
+measured — and that oracle test only exists because two renders are
+bit-identical. Determinism was a design force here, not an afterthought.
+
+## `wear`: the boundedness argument
+
+One pass of generation loss is three stages in fixed order: an exact
+one-pole darkening lowpass (`1 − e^(−2πf_c/sr)`, the grm_comb.h map), the
+shared saturator `swing_shape(v, d) = tanh(d·v)/d`, and the normalized DC
+blocker. Each carries one clause of the proof:
+
+- `tanh` is bounded, so for any drive `d > 0` the wear output can never
+ exceed `1/d` — whatever the loop has accumulated. That is BIBO stability
+ at regen 1.0, unconditionally, from the saturator alone.
+- The DC blocker (pole 0.999, peak gain normalized to exactly 1 — the
+ normalization grm_comb.h earned the hard way, chasing a +0.2 dB/s swell)
+ kills the one frequency the lowpass would happily sustain forever with an
+ offset attached.
+- The lowpass is strictly contractive above its corner and asymptotically
+ transparent below it — which is not a leak in the proof but the musical
+ contract: at drive 0 and regen 1.0 the sub-corner band sustains
+ indefinitely, cleanly. The header calls this the Frippertronics contract
+ and states it rather than hiding it.
+
+So where `delay.h` proves stability by gain, this family proves it by
+*shape*: each pass survives because it is degraded. The pinned test drives
+regen 1.0 for ten seconds of ring and asserts non-growth — never decay,
+because decay would betray the contract just as surely as growth.
+
+## The doppler decision
+
+`discreet::machine` gives `loop_seconds` an ordinary ramp and does nothing
+else, because nothing else is needed: moving a fractional read head *is*
+tape-speed doppler. A 0.5 → 0.75 s glide over half a second reads back an
+octave down mid-move (measured: 220 Hz, then re-lock within five cents) with
+no discontinuity, since position is continuous even where its slope is not.
+The rejected alternative — crossfading between two taps — would have hidden
+the machine, and hiding the machine is the one thing this kernel is for.
+The wow offset is clamped so the read can never cross the record head; at
+absurd depths on short loops the transport flattens against the clamp
+rather than wrapping, which the header files under honest limits.
+
+## A finding: the arithmetic agreed
+
+The per-pass wear transfer is fully analytic — `regen · |H_lp| · |H_dc|` on
+the unit circle — so the notebook measured it the direct way: a two-tone
+burst (300 Hz under the corner, 6 kHz over it) recirculated at drive 0, each
+generation's tones read by Goertzel. Measured per-pass ratios: 0.292 and
+0.890. Predicted: 0.292 and 0.890. Three decimals of agreement between a
+rendering kernel and a formula derived independently in the test is the
+cheapest kind of confidence this library knows how to buy, and both the test
+(with 15% and 5% tolerance bands it never needs) and the executed notebook
+carry the measurement.
+
+## The engineering ledger
+
+The suite leans on four instruments. Analytic transfers wherever the path
+is linear (the per-pass darkening scenario asserts against the exact
+formula, both tones, both directions — highs die faster *and* lows barely
+fade, so the test cannot pass vacuously). Two-window RMS for long-run
+claims, inherited from the comb bank's swell story: regen 1.0 rings ten
+seconds and the late window may not exceed the early one. The YIN oracle
+for anything with a pitch: wow depth in cents against the closed form, the
+doppler glide and its re-lock. And bitwise assertions where the law is
+exact: mix endpoints, the first echo returning as literally the recorded
+impulse, two wow renders identical to the bit. The DC-step scenario checks
+the blocker's actual job — a held offset at regen 1.0 does not accumulate
+and the tail's mean returns below 0.02 — rather than a decay the contract
+never promised.
+
+## Checkpoint
+
+One shared header, four blocks: a reel that wraps at the loop, a transport
+that is two deterministic sines, a wear stage whose `tanh` bound *is* the
+stability proof, and a cited copy of the house ramp. `discreet.h` composes
+them into the two-machine loop where regeneration legally reaches 1.0,
+loop moves are doppler because read heads are physical, and every claim is
+carried twice — `discreet.ipynb` executed, `discreet_test.cpp` pinned.
diff --git a/include/taptools/airport.h b/include/taptools/airport.h
new file mode 100644
index 0000000..cb8dc39
--- /dev/null
+++ b/include/taptools/airport.h
@@ -0,0 +1,298 @@
+/// @file
+/// @brief Portable incommensurate-loop-bank kernel for tap.airport~ — no Max/Min dependency.
+/// @details A recreation of the tape system behind "2/1" on Brian Eno's *Music for Airports*
+/// (Ambient 1, EG, 1978), as described in Eno's own published accounts (the album's
+/// liner notes and *A Year with Swollen Appendices*, Faber, 1996): a small number of
+/// long tape loops — around seven, each holding one recorded phrase — of unequal,
+/// incommensurate lengths, all free-running, so the phrases drift in and out of
+/// coincidence and the piece never repeats on a human timescale. The composition IS
+/// the phase system; the machine just keeps the loops turning.
+///
+/// Each of up to k_max_loops loops is a tape_loop.h reel with a single free-running
+/// head that both plays and records. `record(loop, true)` punches the input onto that
+/// loop's tape at wherever its head happens to be — the phase is NEVER reset, by
+/// record or by any setter, because the free-run is the piece — and `record(loop,
+/// false)` freezes the tape bit-exactly. Playback is read-before-write, so while
+/// recording you hear the previous generation under the head. Per-loop level and
+/// equal-power pan (exact endpoints, the delay.h multitap law) place each phrase in
+/// the stereo field; a per-loop `darken` corner shades its playback tone (a
+/// tape_loop.h wear stage with drive fixed at 0 — a real loop replays the *same*
+/// magnetic imprint every pass, so there is no per-pass generation loss to model, and
+/// pretending otherwise would be dishonest; at the band ceiling the stage is bypassed
+/// entirely and playback is bit-transparent).
+///
+/// Geometry: prepare(sr, max_loop_seconds) buys k_max_loops worst-case reels — the
+/// family's largest buy (8 loops x 30 s at 48 kHz is ~92 MB of double tape); size
+/// max_loop_seconds to the piece. No later call allocates.
+///
+/// Honest limits:
+/// - A length change is a splice: the tape keeps its content and the head re-wraps
+/// modulo the new length. It can land mid-phrase and click — that is what splicing
+/// tape does. It never rewinds.
+/// - Recording starts at the head's current position, not at a downbeat. There is no
+/// quantized punch-in; Eno's rig had none.
+/// - The Hermite playback read spans two samples ahead of the record head, so for ~2
+/// samples around the punch point one generation blends into the next.
+/// - No wow/flutter here: the phasing engine of "2/1" is the incommensurate lengths,
+/// not pitch drift. Run a loop's source through tap.discreet~ first if you want
+/// tape breath.
+/// - composite_period_seconds() is informational (long-long lcm of the active loop
+/// lengths in samples; +inf when it overflows — with incommensurate lengths it is
+/// astronomically long, which is the point).
+/// - No dry path and no master gain: gain staging is the caller's job.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include
+#include
+#include
+#include
+
+#include "tape_loop.h" // tap::tools::tape — reel / wear / ramp, the shared machinery
+
+namespace tap::tools {
+ namespace airport {
+
+ constexpr int k_max_loops = 8; // "2/1" used about seven; eight buys a spare
+ constexpr double k_min_loop_seconds = 0.5; // shorter is a delay effect, not a phrase loop
+ constexpr double k_default_max_seconds = 30.0; // worst case per loop (~92 MB total @ 48k)
+ constexpr double k_default_smooth_ms = 20.0; // anti-zipper ramp for level/pan/darken
+
+ /// Up to eight free-running tape loops of unequal lengths, summed to stereo.
+ class loop_bank {
+ public:
+ loop_bank() {
+ for (auto& l : m_loops) {
+ l.level.snap(1.0);
+ l.darken_hz.snap(tape::k_darken_ceil_hz); // transparent until asked to shade
+ }
+ }
+
+ // -- lifecycle -----------------------------------------------------------------------
+
+ /// Buy k_max_loops reels for `max_loop_seconds` at `sr`, apply the stored lengths,
+ /// snap all ramps, erase all tape, and rewind every head — a DSP restart is the one
+ /// thing allowed to touch the phases. Not real-time-safe.
+ void prepare(double sr, double max_loop_seconds = k_default_max_seconds) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ for (auto& l : m_loops) {
+ l.tape.prepare(m_sr, std::max(k_min_loop_seconds, max_loop_seconds));
+ l.tape.set_loop_samples(seconds_to_samples(l.length_seconds));
+ l.length_seconds = static_cast(l.tape.loop_samples()) / m_sr;
+ l.shade.prepare(m_sr);
+ l.level.snap(l.level.target());
+ l.pan.snap(l.pan.target());
+ l.darken_hz.snap(l.darken_hz.target());
+ l.shade.set_cutoff_hz(l.darken_hz.current());
+ }
+ clear();
+ }
+
+ /// Erase every tape and rewind every head; parameters (lengths, levels, pans, darken,
+ /// record gates) are untouched.
+ void clear() {
+ for (auto& l : m_loops) {
+ l.tape.clear();
+ l.shade.clear();
+ l.phase = 0.0;
+ }
+ }
+
+ bool prepared() const { return m_loops[0].tape.prepared(); }
+
+ // -- structure (instant; never touches a phase) --------------------------------------
+
+ /// Number of active loops, clamped to [0, k_max_loops]. Newly activated loops come in
+ /// at their stored settings, their heads wherever they last were.
+ void set_loops(int count) { m_num_loops = std::clamp(count, 0, k_max_loops); }
+
+ /// Per-loop length in seconds, clamped to [k_min_loop_seconds, the prepared max].
+ /// A splice: content kept, head re-wraps modulo the new length, never rewinds.
+ void set_length_seconds(int loop, double s) {
+ if (!valid_loop(loop)) {
+ return;
+ }
+ loop_state& l = m_loops[static_cast(loop)];
+ l.length_seconds = std::max(k_min_loop_seconds, s);
+ if (l.tape.prepared()) {
+ l.tape.set_loop_samples(seconds_to_samples(l.length_seconds));
+ l.length_seconds = static_cast(l.tape.loop_samples()) / m_sr;
+ const double n = static_cast(l.tape.loop_samples());
+ l.phase = l.phase - std::floor(l.phase / n) * n; // re-wrap, no rewind
+ }
+ }
+
+ /// Punch the input onto this loop's tape (true) or freeze it bit-exactly (false).
+ /// Recording replaces — no overdub sum; Eno recorded each phrase once.
+ void record(int loop, bool on) {
+ if (valid_loop(loop)) {
+ m_loops[static_cast(loop)].recording = on;
+ }
+ }
+
+ // -- parameter targets (click-free; safe while audio runs) ---------------------------
+
+ /// Per-loop linear playback level, slewed. Unclamped (negative flips polarity).
+ void set_level(int loop, double lin) {
+ if (valid_loop(loop)) {
+ m_loops[static_cast(loop)].level.to(lin, smooth_samples());
+ }
+ }
+
+ /// Per-loop equal-power pan, -1 (hard left) .. 1 (hard right), slewed. Endpoints are
+ /// exact: a hard-panned loop is bitwise absent from the far bus (delay.h law).
+ void set_pan(int loop, double pan) {
+ if (valid_loop(loop)) {
+ m_loops[static_cast(loop)].pan.to(std::clamp(pan, -1.0, 1.0), smooth_samples());
+ }
+ }
+
+ /// Per-loop playback darkening corner in Hz, slewed. At the band ceiling (the
+ /// default) the stage is bypassed and playback is bit-transparent.
+ void set_darken_hz(int loop, double hz) {
+ if (valid_loop(loop)) {
+ m_loops[static_cast(loop)].darken_hz.to(
+ std::clamp(hz, tape::k_darken_floor_hz, tape::k_darken_ceil_hz), smooth_samples());
+ }
+ }
+
+ void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+ // -- introspection -------------------------------------------------------------------
+
+ int loops() const { return m_num_loops; }
+ double length_seconds(int loop) const {
+ return valid_loop(loop) ? m_loops[static_cast(loop)].length_seconds : 0.0;
+ }
+ bool recording(int loop) const { return valid_loop(loop) && m_loops[static_cast(loop)].recording; }
+ double level(int loop) const {
+ return valid_loop(loop) ? m_loops[static_cast(loop)].level.target() : 0.0;
+ }
+ double pan(int loop) const {
+ return valid_loop(loop) ? m_loops[static_cast(loop)].pan.target() : 0.0;
+ }
+ double darken_hz(int loop) const {
+ return valid_loop(loop) ? m_loops[static_cast(loop)].darken_hz.target() : 0.0;
+ }
+ double smooth_ms() const { return m_smooth_ms; }
+ double samplerate() const { return m_sr; }
+ double max_loop_seconds() const {
+ return prepared() ? static_cast(m_loops[0].tape.capacity()) / m_sr : 0.0;
+ }
+
+ /// This loop's head position as a fraction of its length, 0..1 — read-only, so tests
+ /// can pin the promise that nothing but prepare()/clear() ever resets it.
+ double phase(int loop) const {
+ if (!valid_loop(loop) || !prepared()) {
+ return 0.0;
+ }
+ const loop_state& l = m_loops[static_cast(loop)];
+ return l.phase / static_cast(l.tape.loop_samples());
+ }
+
+ /// Least common multiple of the active loop lengths, in seconds — how long until the
+ /// whole system realigns. Informational; +inf on 64-bit overflow (incommensurate
+ /// lengths overflow fast, which is the point of the piece).
+ double composite_period_seconds() const {
+ if (!prepared() || m_num_loops < 1) {
+ return 0.0;
+ }
+ long long acc = 1;
+ for (int i = 0; i < m_num_loops; ++i) {
+ const long long n = static_cast(m_loops[static_cast(i)].tape.loop_samples());
+ const long long g = gcd_ll(acc, n);
+ if (acc / g > std::numeric_limits::max() / n) {
+ return std::numeric_limits::infinity();
+ }
+ acc = acc / g * n;
+ }
+ return static_cast(acc) / m_sr;
+ }
+
+ // -- audio ---------------------------------------------------------------------------
+
+ /// Sum the active loops to the stereo bus; punch `in` onto any recording loop.
+ void process(double in, double& out_left, double& out_right) {
+ out_left = 0.0;
+ out_right = 0.0;
+ if (!prepared()) {
+ return;
+ }
+ for (int i = 0; i < m_num_loops; ++i) {
+ loop_state& l = m_loops[static_cast(i)];
+ const double played = l.tape.read_hermite(l.phase);
+ const double shade_hz = l.darken_hz.tick();
+ double toned = played;
+ if (shade_hz < tape::k_darken_ceil_hz) { // ceiling = bypass, bit-transparent
+ if (shade_hz != l.shade.cutoff_hz()) {
+ l.shade.set_cutoff_hz(shade_hz);
+ }
+ toned = l.shade.process(played);
+ }
+ const double g = l.level.tick() * toned;
+ const double pan = l.pan.tick();
+ // Equal-power with exact endpoints — same law as delay.h multitap.
+ if (pan <= -1.0) {
+ out_left += g;
+ }
+ else if (pan >= 1.0) {
+ out_right += g;
+ }
+ else {
+ const double theta = (pan + 1.0) * 0.25 * tape::k_pi;
+ out_left += std::cos(theta) * g;
+ out_right += std::sin(theta) * g;
+ }
+ if (l.recording) { // read-before-write: you hear the old pass under the head
+ l.tape.write(static_cast(std::floor(l.phase)), in);
+ }
+ l.phase += 1.0;
+ if (l.phase >= static_cast(l.tape.loop_samples())) {
+ l.phase -= static_cast(l.tape.loop_samples());
+ }
+ }
+ }
+
+ /// Block form: the trivial loop over the scalar path.
+ void process(const double* in, double* out_left, double* out_right, size_t n) {
+ for (size_t i = 0; i < n; ++i) {
+ process(in[i], out_left[i], out_right[i]);
+ }
+ }
+
+ private:
+ struct loop_state {
+ tape::reel tape;
+ tape::wear shade; // playback tone only: drive stays 0, bypassed at ceiling
+ double phase{0.0}; // samples into the loop; the piece lives here
+ double length_seconds{k_min_loop_seconds};
+ bool recording{false};
+ tape::ramp level; // linear
+ tape::ramp pan; // -1..1
+ tape::ramp darken_hz; // Hz
+ };
+
+ static long long gcd_ll(long long a, long long b) {
+ while (b != 0) {
+ const long long t = a % b;
+ a = b;
+ b = t;
+ }
+ return a;
+ }
+
+ bool valid_loop(int loop) const { return loop >= 0 && loop < k_max_loops; }
+ long smooth_samples() const { return static_cast(m_smooth_ms * 0.001 * m_sr); }
+ long seconds_to_samples(double s) const { return static_cast(std::ceil(s * m_sr)); }
+
+ double m_sr{48000.0};
+ double m_smooth_ms{k_default_smooth_ms};
+ int m_num_loops{0};
+ std::array m_loops;
+ };
+
+ } // namespace airport
+} // namespace tap::tools
diff --git a/include/taptools/discreet.h b/include/taptools/discreet.h
new file mode 100644
index 0000000..6f4691f
--- /dev/null
+++ b/include/taptools/discreet.h
@@ -0,0 +1,242 @@
+/// @file
+/// @brief Portable long-loop tape regeneration kernel for tap.discreet~ — no Max/Min
+/// dependency.
+/// @details A recreation of the two-tape-machine long-delay system Brian Eno printed as a
+/// signal-flow schematic on the back cover of *Discreet Music* (Obscure/EG, 1975) —
+/// the same rig Robert Fripp ran for the *No Pussyfooting* loops: input is recorded
+/// onto tape by machine A, the tape spools for seconds to machine B, and machine B's
+/// playback is both the output and the signal folded back into machine A's record
+/// head. The tape-path DSP (fractional read, periodic wow/flutter, in-loop
+/// coloration) follows the published tape-echo modeling literature (Arnardottir,
+/// Abel, Smith, "A Digital Model of the Echoplex Tape Delay", AES 125, 2008;
+/// Valimaki et al.'s tape-echo work).
+///
+/// The design inversion this kernel exists to state: delay.h keeps its feedback loop
+/// stable by capping feedback strictly below 1; here regeneration deliberately
+/// reaches 1.0, and stability comes from the tape path itself (tape_loop.h `wear`:
+/// darkening lowpass -> bounded soft saturation -> DC blocker). Each pass survives
+/// *because* it is degraded — wear is the stabilizer, not an fb cap. With drive
+/// engaged the loop output is absolutely bounded at any regeneration in [0, 1]; at
+/// drive 0 and regen 1.0 the band below the darkening cutoff sustains indefinitely,
+/// cleanly — the Frippertronics contract.
+///
+/// The performance surface mirrors the rig: `input_level` is the send fader Eno rode
+/// (fade the input while the loop sustains and the piece keeps evolving without you),
+/// `regen` is the return level into the record head, `loop_seconds` is the tape span
+/// between the machines, and wow/flutter are the transport.
+///
+/// Geometry: prepare(sr, max_loop_seconds) buys the worst case once — the family's
+/// biggest single buy (30 s at 48 kHz is ~11.5 MB of double tape); size it to the
+/// piece. No later call allocates; setters only retarget ramps and are safe while
+/// audio runs. All processing is double-precision, per-sample, mono.
+///
+/// Honest limits:
+/// - `set_loop_seconds` glides as a tape-speed change and audibly bends pitch while
+/// moving — by design (moving the read head IS the doppler); use set_smooth_ms to
+/// choose how fast the transport re-spools. There is no crossfading "digital" mode.
+/// - regen 1.0 sustains forever by design. Bring `regen` down (or darken harder) to
+/// end a piece; clear() is the eject button.
+/// - The wow/flutter transport is periodic only (see tape_loop.h) — deterministic,
+/// reproducible, no stochastic capstan drift.
+/// - The read floor is 2.5 samples (Hermite support), and wow excursion is clamped so
+/// the read can never cross the record head; extreme wow depths at very short loops
+/// flatten against that clamp rather than wrapping.
+/// - Fresh input reaches the output only via the tape (the dry path is the input
+/// itself, mixed equal-power): you hear a new note un-recirculated once, a loop
+/// later it returns worn. No gain staging beyond input_level — that is the
+/// caller's job.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include
+#include
+
+#include "tape_loop.h" // tap::tools::tape — reel / wow_flutter / wear / ramp, the shared machinery
+
+namespace tap::tools {
+ namespace discreet {
+
+ constexpr double k_min_loop_seconds = 0.1; // below this it is a comb, not a loop
+ constexpr double k_regen_max = 1.0; // deliberately reaches unity: wear is the stabilizer
+ constexpr double k_default_loop_seconds = 5.0; // the order of Eno's machine-to-machine span
+ constexpr double k_default_max_seconds = 30.0; // default worst-case buy (~11.5 MB @ 48k)
+ constexpr double k_default_darken_hz = 3000.0; // gentle generation loss per pass
+ constexpr double k_default_drive = 0.5; // mild record-head saturation
+ constexpr double k_default_wow_ms = 1.0; // ~9 cents peak at 0.8 Hz (see the tests)
+ constexpr double k_default_wow_hz = 0.8;
+ constexpr double k_default_flutter_ms = 0.05;
+ constexpr double k_default_flutter_hz = 8.0;
+ constexpr double k_default_mix = 50.0; // half in the room, half on the tape
+ constexpr double k_default_smooth_ms = 20.0; // anti-zipper ramp for setters
+
+ /// The two-machine loop: record head, seconds of tape, playback head, worn return path.
+ class machine {
+ public:
+ /// Defaults are a tape machine, not a neutral bypass: a 5 s span, gentle wear, the
+ /// stock transport breathing. Regen starts at 0 — the loop recirculates when you send.
+ machine() {
+ m_loop_seconds.snap(k_default_loop_seconds);
+ m_darken_hz.snap(k_default_darken_hz);
+ m_drive.snap(k_default_drive);
+ m_input_level.snap(1.0);
+ m_mix.snap(k_default_mix);
+ m_transport.set_wow(k_default_wow_ms, k_default_wow_hz);
+ m_transport.set_flutter(k_default_flutter_ms, k_default_flutter_hz);
+ }
+
+ // -- lifecycle -----------------------------------------------------------------------
+
+ /// (Re)allocate tape for `max_loop_seconds` at `sr`, snap all ramps (a DSP restart is
+ /// not a parameter move), and erase the tape. Not real-time-safe.
+ void prepare(double sr, double max_loop_seconds = k_default_max_seconds) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ m_reel.prepare(m_sr, std::max(k_min_loop_seconds, max_loop_seconds));
+ m_transport.prepare(m_sr);
+ m_wear.prepare(m_sr);
+ m_loop_seconds.snap(std::min(m_loop_seconds.target(), static_cast(m_reel.capacity()) / m_sr));
+ m_regen.snap(m_regen.target());
+ m_darken_hz.snap(m_darken_hz.target());
+ m_drive.snap(m_drive.target());
+ m_input_level.snap(m_input_level.target());
+ m_mix.snap(m_mix.target());
+ m_wear.set_cutoff_hz(m_darken_hz.current());
+ m_wear.set_drive(m_drive.current());
+ clear();
+ }
+
+ /// Erase the tape and the wear/transport state; parameters are untouched. The eject
+ /// button: regen 1.0 material is gone for good.
+ void clear() {
+ m_reel.clear();
+ m_wear.clear();
+ m_transport.clear();
+ m_head = 0;
+ }
+
+ bool prepared() const { return m_reel.prepared(); }
+
+ // -- parameter targets (click-free; safe while audio runs) ---------------------------
+
+ /// Tape span between the machines, in seconds, clamped to [k_min_loop_seconds, the
+ /// prepared max]. Slewed — and the slew IS the tape-speed doppler (see Honest limits).
+ void set_loop_seconds(double s) {
+ m_loop_seconds.to(std::clamp(s, k_min_loop_seconds, max_loop_seconds()), smooth_samples());
+ }
+
+ /// Return level into the record head, clamped to [0, 1]. 1.0 is legal and sustains.
+ void set_regen(double r) { m_regen.to(std::clamp(r, 0.0, k_regen_max), smooth_samples()); }
+
+ /// Per-pass darkening corner in Hz (tape_loop.h wear band), slewed.
+ void set_darken_hz(double hz) {
+ m_darken_hz.to(std::clamp(hz, tape::k_darken_floor_hz, tape::k_darken_ceil_hz), smooth_samples());
+ }
+
+ /// Record-head saturation drive, >= 0, slewed. 0 is exactly linear (no wear boundedness
+ /// — the loop then relies on darkening alone; see the header banner).
+ void set_drive(double d) { m_drive.to(std::max(0.0, d), smooth_samples()); }
+
+ /// The send fader: input level into the record head, linear, slewed. Fading this while
+ /// the loop sustains is the Discreet Music performance move.
+ void set_input_level(double lin) { m_input_level.to(lin, smooth_samples()); }
+
+ /// Dry/wet mix 0..100, equal-power, slewed. 0 is bitwise dry, 100 bitwise wet.
+ void set_mix(double pct) { m_mix.to(std::clamp(pct, 0.0, 100.0), smooth_samples()); }
+
+ /// Wow: depth in ms of tape position, rate in Hz. Instant transport config, not ramped.
+ void set_wow(double depth_ms, double rate_hz) { m_transport.set_wow(depth_ms, rate_hz); }
+
+ /// Flutter: the faster, shallower partner. Instant transport config, not ramped.
+ void set_flutter(double depth_ms, double rate_hz) { m_transport.set_flutter(depth_ms, rate_hz); }
+
+ /// Anti-zipper ramp time for the setters, in ms. 0 = instant (useful for tests).
+ void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+ // -- introspection -------------------------------------------------------------------
+
+ double loop_seconds() const { return m_loop_seconds.target(); }
+ double regen() const { return m_regen.target(); }
+ double darken_hz() const { return m_darken_hz.target(); }
+ double drive() const { return m_drive.target(); }
+ double input_level() const { return m_input_level.target(); }
+ double mix() const { return m_mix.target(); }
+ double wow_depth_ms() const { return m_transport.wow_depth_ms(); }
+ double wow_rate_hz() const { return m_transport.wow_rate_hz(); }
+ double flutter_depth_ms() const { return m_transport.flutter_depth_ms(); }
+ double flutter_rate_hz() const { return m_transport.flutter_rate_hz(); }
+ double smooth_ms() const { return m_smooth_ms; }
+ double max_loop_seconds() const { return static_cast(m_reel.capacity()) / m_sr; }
+ double samplerate() const { return m_sr; }
+
+ // -- audio ---------------------------------------------------------------------------
+
+ double process(double in) {
+ if (!prepared()) {
+ return in;
+ }
+ const double loop_s = m_loop_seconds.tick();
+ const double regen = m_regen.tick();
+ const double darken = m_darken_hz.tick();
+ const double drive = m_drive.tick();
+ const double send = m_input_level.tick();
+ const double mix = m_mix.tick();
+
+ if (darken != m_wear.cutoff_hz()) {
+ m_wear.set_cutoff_hz(darken);
+ }
+ if (drive != m_wear.drive()) {
+ m_wear.set_drive(drive);
+ }
+
+ // Machine B's playback head: loop_s behind the record head, breathed by the
+ // transport, clamped so the Hermite support can never cross the record head.
+ const double span = loop_s * m_sr - m_transport.tick();
+ const double d_samples =
+ std::clamp(span, tape::k_min_frac_delay, static_cast(m_reel.capacity()) - 2.0);
+ const double played = m_reel.read_hermite(static_cast(m_head) - d_samples);
+
+ // The return path: playback -> wear (darken, saturate, DC block) -> record head.
+ m_reel.write(m_head, send * in + regen * m_wear.process(played));
+ if (++m_head >= m_reel.capacity()) { // keep the head in [0, capacity): a long can
+ m_head = 0; // overflow in half a day of audio on LLP64
+ }
+
+ // Equal-power dry/wet with exact endpoints (delay.h law: 0 bitwise dry, 100 wet).
+ if (mix <= 0.0) {
+ return in;
+ }
+ if (mix >= 100.0) {
+ return played;
+ }
+ const double theta = mix * 0.01 * (tape::k_pi * 0.5);
+ return std::cos(theta) * in + std::sin(theta) * played;
+ }
+
+ /// Block form: the trivial loop over the scalar path.
+ void process(const double* in, double* out, size_t n) {
+ for (size_t i = 0; i < n; ++i) {
+ out[i] = process(in[i]);
+ }
+ }
+
+ private:
+ long smooth_samples() const { return static_cast(m_smooth_ms * 0.001 * m_sr); }
+
+ double m_sr{48000.0};
+ double m_smooth_ms{k_default_smooth_ms};
+ long m_head{0};
+ tape::reel m_reel;
+ tape::wow_flutter m_transport;
+ tape::wear m_wear;
+ tape::ramp m_loop_seconds; // seconds
+ tape::ramp m_regen; // 0..1
+ tape::ramp m_darken_hz; // Hz
+ tape::ramp m_drive; // >= 0
+ tape::ramp m_input_level; // linear
+ tape::ramp m_mix; // 0..100
+ };
+
+ } // namespace discreet
+} // namespace tap::tools
diff --git a/include/taptools/garden.h b/include/taptools/garden.h
new file mode 100644
index 0000000..b30d729
--- /dev/null
+++ b/include/taptools/garden.h
@@ -0,0 +1,456 @@
+/// @file
+/// @brief Portable generative event-loop kernel for tap.garden~ — no Max/Min dependency.
+/// @details A recreation of the *principle* behind Brian Eno and Peter Chilvers' generative
+/// music apps (Bloom, 2008), as described in their published interviews and in Eno's
+/// "Generative Music" talk (In Motion Magazine, 1996): a touch becomes a note; the
+/// note repeats on a fixed loop, a little quieter and a little purer each pass, until
+/// it fades below hearing; pitches snap to a scale so anything you plant sounds
+/// consonant; and left alone past an idle threshold, the system starts planting notes
+/// itself. The principle only — no scale tables, timings, or sounds are copied from
+/// the app, whose name (Bloom) is a live trademark of Opal Limited. The kernel is
+/// named for Eno's own metaphor: the composer as gardener, not architect.
+///
+/// This is the family's third abstraction level: discreet.h recirculates audio,
+/// airport.h phases loops, garden.h recirculates *events*. The stability inversion
+/// carries over intact, one level up: per-pass decay is the stabilizer. An event's
+/// velocity is multiplied by `decay` on every recirculation and the event retires
+/// below `floor`, so the live-event population converges no matter how fast you
+/// plant — and a fixed bell pool (quietest-stolen) hard-bounds the audio regardless.
+///
+/// The voice is a two-operator FM bell (Chowning, "The Synthesis of Complex Audio
+/// Spectra by Means of Frequency Modulation", JAES 1973): carrier plus modulator at
+/// the fixed harmonic ratio 3 — odd-partial, bell-ish, and harmonic, so a pitch
+/// detector reads it at the fundamental — with modulation index scaled by velocity
+/// and per-event brightness (velocity-to-index is standard published FM practice).
+/// Each pass multiplies the event's brightness by `soften`, so a bloom does not just
+/// fade: it purifies toward a sine. Amplitude rides the shared tr808 decay_env; a
+/// steal re-aims the envelope without a reset, so stolen voices glide, not click.
+///
+/// Randomness: the idle gardener draws from the family's seeded xorshift64*
+/// (tr808::white_noise) — deterministic per seed, so renders and tests reproduce and
+/// instances decorrelate by seed. This is the library's first randomized *event*
+/// source (step_seq.h promises "no randomness anywhere"; this kernel is the deliberate
+/// counterpoint, and the seed contract is the bridge back to reproducibility).
+///
+/// Geometry: everything is fixed arrays — k_max_events events, k_voices bells —
+/// so prepare(sr) allocates nothing at all and no later call ever does.
+///
+/// Honest limits:
+/// - Pitch is quantized AT ENTRY: changing root or scale re-pitches nothing already
+/// planted, only future plants (replants pick up the new field).
+/// - Event timing is the loop grid: a plant returns at its own phase point every
+/// pass, exactly — there is no swing, no drift, no humanization.
+/// - A full garden (k_max_events live) retires its OLDEST bloom to make room for a
+/// new plant: a touch must always speak, and the oldest is the quietest.
+/// - The idle gardener is statistical (about one plant per loop pass, uniformly
+/// placed), not a transcription of any published piece or app behavior.
+/// - Mono out; one bell timbre family. It is an instrument, not a polysynth.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include
+#include
+#include
+#include
+#include
+
+#include "swing_vca.h" // tap::tools::tr808 — decay_env (the bell's amplitude) + white_noise (the seeded gardener)
+
+namespace tap::tools {
+ namespace garden {
+
+ constexpr double k_pi = 3.14159265358979323846;
+
+ constexpr int k_max_events = 64; // live blooms; oldest yields when full
+ constexpr int k_voices = 16; // fixed bell pool; quietest-first steal
+ constexpr double k_fm_ratio = 3.0; // harmonic odd-partial bell (Chowning 1973)
+ constexpr double k_index_max = 2.0; // modulation index at velocity 1, brightness 1
+ constexpr double k_gain_epsilon = 1e-4; // below this a voice is "off" (harmonizer.h idiom)
+
+ constexpr double k_min_loop_seconds = 0.25; // beneath this it is a buzzer, not a garden
+ constexpr double k_max_loop_seconds = 120.0; // the loop is a counter — no tape is bought
+
+ constexpr double k_default_loop_seconds = 8.0;
+ constexpr double k_default_decay = 0.85; // velocity multiplier per pass
+ constexpr double k_default_soften = 0.9; // brightness multiplier per pass
+ constexpr double k_default_floor = 0.03; // retirement threshold
+ constexpr double k_default_idle_seconds = 30.0; // the gardener's patience; 0 disables
+ constexpr double k_default_attack_s = 0.15; // soft mallet, not a hammer
+ constexpr double k_default_decay_s = 4.0;
+ constexpr double k_default_brightness = 1.0;
+ constexpr double k_default_smooth_ms = 20.0; // one-pole slew for the master level
+
+ /// Build a 12-bit pitch-class mask from scale degrees — same idiom as tune.h (copied, not
+ /// included: tune.h reaches into tap::dsp).
+ constexpr unsigned make_mask(std::initializer_list degrees) {
+ unsigned mask = 0u;
+ for (const int d : degrees) {
+ mask |= 1u << (((d % 12) + 12) % 12);
+ }
+ return mask;
+ }
+
+ enum scale_index : int {
+ scale_chromatic = 0,
+ scale_major,
+ scale_minor,
+ scale_major_pentatonic,
+ scale_minor_pentatonic,
+ k_num_scales
+ };
+
+ // Scale presets relative to the root, addressed by scale_index — public-domain scale
+ // theory, deliberately NOT any app's preset list.
+ constexpr std::array k_scale_masks = {
+ 0xFFFu, // chromatic
+ make_mask({0, 2, 4, 5, 7, 9, 11}), // major
+ make_mask({0, 2, 3, 5, 7, 8, 10}), // minor
+ make_mask({0, 2, 4, 7, 9}), // major pentatonic
+ make_mask({0, 3, 5, 7, 10}), // minor pentatonic
+ };
+
+ /// One two-operator FM bell: carrier + modulator at k_fm_ratio, amplitude from the shared
+ /// decay_env. Phases free-run so a steal re-aims without a click.
+ class bell {
+ public:
+ void prepare(double sr) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ m_env.prepare(m_sr);
+ m_env.set_times(k_default_attack_s, k_default_decay_s);
+ }
+
+ void set_times(double attack_s, double decay_s) { m_env.set_times(attack_s, decay_s); }
+
+ void reset() {
+ m_env.reset();
+ m_carrier_phase = m_mod_phase = 0.0;
+ }
+
+ /// Fire at `freq_hz`, envelope target `level`, modulation index `index`.
+ void trigger(double freq_hz, double level, double index) {
+ m_carrier_inc = freq_hz / m_sr;
+ m_mod_inc = k_fm_ratio * freq_hz / m_sr;
+ m_index = index;
+ m_env.trigger(level);
+ }
+
+ double level() const { return m_env.value(); } // the quietest-first steal key
+
+ double process() {
+ m_mod_phase += m_mod_inc;
+ m_mod_phase -= std::floor(m_mod_phase);
+ m_carrier_phase += m_carrier_inc;
+ m_carrier_phase -= std::floor(m_carrier_phase);
+ const double mod = m_index * std::sin(2.0 * k_pi * m_mod_phase);
+ return m_env.process() * std::sin(2.0 * k_pi * m_carrier_phase + mod);
+ }
+
+ private:
+ double m_sr{48000.0};
+ double m_carrier_phase{0.0}, m_carrier_inc{0.0};
+ double m_mod_phase{0.0}, m_mod_inc{0.0};
+ double m_index{0.0};
+ tr808::decay_env m_env;
+ };
+
+ /// The garden bed: plant notes, they bloom on the loop, fade, and retire; left alone,
+ /// the gardener plants for you.
+ class bed {
+ public:
+ // -- lifecycle -----------------------------------------------------------------------
+
+ /// Set the rate everywhere and start an empty garden. Allocation-free by construction
+ /// (fixed arrays); still not real-time-safe by the house contract.
+ void prepare(double sr) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ for (auto& v : m_bells) {
+ v.prepare(m_sr);
+ v.set_times(m_attack_s, m_decay_s);
+ }
+ m_prepared = true;
+ clear();
+ }
+
+ /// Uproot everything: kill all events and voices, rewind the loop, re-seed the
+ /// gardener, restart the idle clock. Parameters are untouched.
+ void clear() {
+ for (auto& e : m_events) {
+ e.alive = false;
+ }
+ for (auto& v : m_bells) {
+ v.reset();
+ }
+ m_rng.reset();
+ m_pos = 0;
+ m_planted = 0;
+ m_since_note = 0;
+ m_level_current = m_level_target;
+ }
+
+ bool prepared() const { return m_prepared; }
+
+ // -- events --------------------------------------------------------------------------
+
+ /// Plant a note: MIDI pitch (semitones, fractional accepted), velocity in (0, 1].
+ /// The pitch snaps to the current root/scale, the bell sounds on the next processed
+ /// sample, and the bloom returns at this loop position every pass until it fades
+ /// below the floor. Resets the gardener's idle clock. A full garden retires its
+ /// oldest bloom to make room.
+ void note(double pitch, double velocity) {
+ if (!m_prepared || velocity <= 0.0) {
+ return;
+ }
+ event& e = allocate();
+ e.pitch = quantize(pitch);
+ e.velocity = std::min(velocity, 1.0);
+ e.brightness = m_brightness;
+ e.offset = m_pos; // process() fires it this coming sample, then every pass
+ e.alive = true;
+ e.seq = m_planted++;
+ m_since_note = 0;
+ }
+
+ // -- parameter targets (safe while audio runs) ---------------------------------------
+
+ /// Loop length in seconds, clamped to [k_min_loop_seconds, k_max_loop_seconds].
+ /// Instant (the loop is a counter): blooms keep their positions modulo the new length.
+ void set_loop_seconds(double s) {
+ m_loop_seconds = std::clamp(s, k_min_loop_seconds, k_max_loop_seconds);
+ const long n = loop_samples();
+ m_pos = m_pos % n;
+ for (auto& e : m_events) {
+ e.offset = e.offset % n;
+ }
+ }
+
+ /// Velocity multiplier per pass, [0, 1]. The stabilizer: with floor f and a plant at
+ /// velocity v, a bloom lives ceil(log(f/v)/log(decay)) passes, always.
+ void set_decay(double per_pass) { m_decay = std::clamp(per_pass, 0.0, 1.0); }
+
+ /// Brightness multiplier per pass, [0, 1]: each return is purer, collapsing to sine.
+ void set_soften(double per_pass) { m_soften = std::clamp(per_pass, 0.0, 1.0); }
+
+ /// Retirement threshold, [1e-4, 1].
+ void set_floor(double v) { m_floor = std::clamp(v, 1e-4, 1.0); }
+
+ /// The bell: envelope times in SECONDS (decay_env contract) and base brightness
+ /// (0..1 scale on the modulation index). Applies to future blooms; ringing voices
+ /// keep their envelope times until retriggered.
+ void set_bell(double attack_s, double decay_s, double brightness) {
+ m_attack_s = std::max(attack_s, 1e-6);
+ m_decay_s = std::max(decay_s, 1e-6);
+ m_brightness = std::clamp(brightness, 0.0, 1.0);
+ for (auto& v : m_bells) {
+ v.set_times(m_attack_s, m_decay_s);
+ }
+ }
+
+ /// Root pitch class, 0..11 (0 = C). A mode: instant, affects future plants only.
+ void set_root(int semitone) { m_root = ((semitone % 12) + 12) % 12; }
+
+ /// Scale preset (scale_index). A mode: instant, affects future plants only.
+ void set_scale(int scale) { m_scale = std::clamp(scale, 0, k_num_scales - 1); }
+
+ /// Seconds of silence before the gardener starts planting; 0 disables self-seeding
+ /// (and then the seed cannot matter at all — pinned by test).
+ void set_idle_seconds(double s) { m_idle_seconds = std::max(0.0, s); }
+
+ /// The gardener's seed — deterministic per seed, house triad contract. Instant.
+ void set_seed(uint64_t seed) { m_rng.set_seed(seed); }
+
+ /// Master linear output level, one-pole slewed over smooth_ms.
+ void set_level(double lin) { m_level_target = lin; }
+
+ void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+ // -- introspection -------------------------------------------------------------------
+
+ int active_events() const {
+ int n = 0;
+ for (const auto& e : m_events) {
+ n += e.alive ? 1 : 0;
+ }
+ return n;
+ }
+ int active_voices() const {
+ int n = 0;
+ for (const auto& v : m_bells) {
+ n += (v.level() > k_gain_epsilon) ? 1 : 0;
+ }
+ return n;
+ }
+ double loop_seconds() const { return m_loop_seconds; }
+ double decay() const { return m_decay; }
+ double soften() const { return m_soften; }
+ double floor_level() const { return m_floor; }
+ double attack_s() const { return m_attack_s; }
+ double decay_s() const { return m_decay_s; }
+ double brightness() const { return m_brightness; }
+ int root() const { return m_root; }
+ int scale() const { return m_scale; }
+ double idle_seconds() const { return m_idle_seconds; }
+ uint64_t seed() const { return m_rng.seed(); }
+ double level() const { return m_level_target; }
+ double smooth_ms() const { return m_smooth_ms; }
+ double samplerate() const { return m_sr; }
+
+ // -- audio ---------------------------------------------------------------------------
+
+ /// A source: no input. Advance the loop one sample, fire any blooms whose position
+ /// this is, let the gardener plant if the garden has been idle, and sum the bells.
+ double process() {
+ if (!m_prepared) {
+ return 0.0;
+ }
+ for (auto& e : m_events) {
+ if (e.alive && e.offset == m_pos) {
+ fire(e);
+ bloom(e);
+ }
+ }
+ tend();
+ if (++m_pos >= loop_samples()) {
+ m_pos = 0;
+ }
+
+ double sum = 0.0;
+ for (auto& v : m_bells) {
+ sum += v.process();
+ }
+ const double coeff = (m_smooth_ms > 0.0) ? 1.0 - std::exp(-1.0 / (m_smooth_ms * 0.001 * m_sr)) : 1.0;
+ m_level_current += coeff * (m_level_target - m_level_current);
+ return sum * m_level_current;
+ }
+
+ /// Block form: the trivial loop over the scalar path.
+ void process(double* out, size_t n) {
+ for (size_t i = 0; i < n; ++i) {
+ out[i] = process();
+ }
+ }
+
+ private:
+ struct event {
+ double pitch{0.0}; // MIDI semitones, already quantized
+ double velocity{0.0}; // decays per pass
+ double brightness{0.0}; // softens per pass
+ long offset{0}; // position on the loop, samples
+ uint32_t seq{0}; // plant order; lowest live seq = oldest
+ bool alive{false};
+ };
+
+ long loop_samples() const { return static_cast(m_loop_seconds * m_sr); }
+
+ /// Snap MIDI semitones to the nearest pitch in the current root/scale — the tune.h
+ /// nearest-allowed search (any non-empty mask has a note within a tritone).
+ double quantize(double pitch) const {
+ const unsigned mask = k_scale_masks[static_cast(m_scale)];
+ const int p = static_cast(std::lround(pitch));
+ for (int off = 0; off <= 6; ++off) {
+ for (const int cand : {p + off, p - off}) {
+ const int pc = (((cand - m_root) % 12) + 12) % 12;
+ if ((mask & (1u << pc)) != 0u) {
+ return static_cast(cand);
+ }
+ }
+ }
+ return static_cast(p); // unreachable for any non-empty mask
+ }
+
+ /// Find a slot for a new plant: a dead one if any, else the oldest live bloom yields.
+ event& allocate() {
+ event* oldest = &m_events[0];
+ for (auto& e : m_events) {
+ if (!e.alive) {
+ return e;
+ }
+ if (e.seq < oldest->seq) {
+ oldest = &e;
+ }
+ }
+ return *oldest;
+ }
+
+ /// Sound this event now on the pool: an idle voice if any, else steal the quietest.
+ void fire(event& e) {
+ bell* voice = &m_bells[0];
+ for (auto& v : m_bells) {
+ if (v.level() <= k_gain_epsilon) {
+ voice = &v;
+ break;
+ }
+ if (v.level() < voice->level()) {
+ voice = &v;
+ }
+ }
+ const double freq = 440.0 * std::exp2((e.pitch - 69.0) / 12.0);
+ voice->trigger(freq, e.velocity, e.brightness * k_index_max * e.velocity);
+ }
+
+ /// One pass of wear, one level up: quieter, purer, and gone below the floor.
+ void bloom(event& e) {
+ e.velocity *= m_decay;
+ e.brightness *= m_soften;
+ if (e.velocity < m_floor) {
+ e.alive = false;
+ }
+ }
+
+ /// The idle gardener: after idle_seconds without a caller plant, sow about one seed
+ /// per loop pass, uniformly placed, on the scale, within two octaves of middle root.
+ void tend() {
+ ++m_since_note;
+ if (m_idle_seconds <= 0.0) {
+ return; // disabled: the rng is never consumed, so the seed cannot matter
+ }
+ if (static_cast(m_since_note) < m_idle_seconds * m_sr) {
+ return;
+ }
+ const double u = 0.5 * (m_rng.process() + 1.0); // [0, 1)
+ if (u * static_cast(loop_samples()) >= 1.0) {
+ return; // ~one plant per pass
+ }
+ const double pitch = 60.0 + std::floor(12.0 * (m_rng.process() + 1.0)); // [60, 84)
+ const double velocity = 0.3 + 0.2 * (m_rng.process() + 1.0); // [0.3, 0.7)
+ event& e = allocate();
+ e.pitch = quantize(pitch);
+ e.velocity = velocity;
+ e.brightness = m_brightness;
+ e.offset = m_pos;
+ e.alive = true;
+ e.seq = m_planted++;
+ fire(e);
+ bloom(e);
+ // Deliberately does NOT reset m_since_note's gate below the threshold: once the
+ // gardener starts, it keeps tending until the caller plants again.
+ }
+
+ double m_sr{48000.0};
+ bool m_prepared{false};
+ double m_loop_seconds{k_default_loop_seconds};
+ double m_decay{k_default_decay};
+ double m_soften{k_default_soften};
+ double m_floor{k_default_floor};
+ double m_attack_s{k_default_attack_s};
+ double m_decay_s{k_default_decay_s};
+ double m_brightness{k_default_brightness};
+ int m_root{0};
+ int m_scale{scale_major_pentatonic}; // anything you plant sounds consonant
+ double m_idle_seconds{k_default_idle_seconds};
+ double m_level_target{1.0};
+ double m_level_current{1.0};
+ double m_smooth_ms{k_default_smooth_ms};
+
+ long m_pos{0};
+ uint32_t m_planted{0};
+ long long m_since_note{0};
+ tr808::white_noise m_rng;
+ std::array m_events;
+ std::array m_bells;
+ };
+
+ } // namespace garden
+} // namespace tap::tools
diff --git a/include/taptools/tape_loop.h b/include/taptools/tape_loop.h
new file mode 100644
index 0000000..40e30f7
--- /dev/null
+++ b/include/taptools/tape_loop.h
@@ -0,0 +1,260 @@
+/// @file
+/// @brief Shared tape-loop machinery for the Eno family (discreet.h, airport.h) — no Max/Min
+/// dependency.
+/// @details The building blocks both tape kernels compose, factored the way swing_vca.h holds
+/// the drum family's shared stages: a class with state is shared by include, a
+/// few-line expression is copied with a citation.
+///
+/// - `reel` — a circular span of tape. Storage is bought once at prepare() (the
+/// worst-case loop), and reads/writes wrap at a settable loop length, so the same
+/// class serves a delay-line topology (loop length == capacity, an advancing write
+/// head trailed by a read head — discreet.h) and a fixed-loop topology (loop length
+/// set per piece, one free-running head that both plays and records — airport.h).
+/// Fractional reads use the family's 4-point, 3rd-order Hermite.
+/// - `wow_flutter` — the tape-transport speed error as a deterministic pair of sines
+/// (slow/deep wow, fast/shallow flutter) returning a read-position offset in
+/// samples. Periodic-only by design: the periodic term is the dominant one in the
+/// tape-echo literature (Arnardottir, Abel, Smith, "A Digital Model of the Echoplex
+/// Tape Delay", AES 125, 2008), and a deterministic transport means renders and
+/// tests reproduce bit-exactly. Real capstan drift also has a stochastic term; that
+/// is a documented non-goal here.
+/// - `wear` — one pass of generation loss: a record/playback darkening one-pole
+/// lowpass, then the shared soft saturator (vca::swing_shape — tanh(d*v)/d, exact
+/// linear passthrough at drive 0), then the family's normalized DC blocker. This is
+/// the load-bearing inversion of delay.h's stability story: swing_shape is bounded
+/// by 1/drive for any drive > 0, so a regeneration loop built on wear is BIBO-
+/// bounded even at unity regeneration — degradation is the stability mechanism,
+/// where delay.h caps feedback below 1 instead. At drive 0 the saturator is exactly
+/// linear and only the lowpass and DC blocker contract the loop; a sub-cutoff band
+/// fed back at unity then sustains indefinitely — that is the Frippertronics
+/// contract, stated, not hidden.
+/// - `ramp` — the per-sample linear anti-zipper unit, same shape as delay.h, copied
+/// here so both tape kernels share one without dragging in a whole delay kernel.
+///
+/// All processing is double-precision, per-sample, allocation-free after prepare().
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include
+#include
+#include
+#include
+
+#include "vca.h" // tap::tools::vca::swing_shape — the shared soft saturator
+
+namespace tap::tools {
+ namespace tape {
+
+ constexpr double k_pi = 3.14159265358979323846;
+ constexpr long k_min_loop_samples = 8; // Hermite needs 4 support points; margin as delay_buffer
+ constexpr double k_min_frac_delay = 2.5; // same floor, same reason as delay.h
+ constexpr double k_dc_block_r = 0.999; // in-loop DC blocker pole (~7 Hz corner @ 48k)
+ // Normalized to unity peak gain so the blocker never amplifies the loop (grm_comb.h).
+ constexpr double k_dc_block_norm = (1.0 + k_dc_block_r) * 0.5;
+ constexpr double k_darken_floor_hz = 20.0; // wear cutoff range: the audible band
+ constexpr double k_darken_ceil_hz = 20000.0;
+ constexpr double k_wow_rate_max_hz = 5.0; // transport wow lives below a few Hz
+ constexpr double k_flutter_rate_max_hz = 30.0; // flutter sits above wow, below audio rate
+
+ /// Per-sample linear parameter ramp — the anti-zipper unit every setter retargets.
+ /// Same shape as delay.h's ramp.
+ class ramp {
+ public:
+ void snap(double v) {
+ m_current = m_target = v;
+ m_inc = 0.0;
+ m_remaining = 0;
+ }
+
+ void to(double tgt, long nsamples) {
+ if (nsamples < 1 || tgt == m_current) {
+ snap(tgt);
+ }
+ else {
+ m_target = tgt;
+ m_inc = (tgt - m_current) / static_cast(nsamples);
+ m_remaining = nsamples;
+ }
+ }
+
+ double tick() {
+ if (m_remaining > 0) {
+ m_current += m_inc;
+ if (--m_remaining == 0) {
+ m_current = m_target;
+ }
+ }
+ return m_current;
+ }
+
+ double current() const { return m_current; }
+ double target() const { return m_target; }
+
+ private:
+ double m_current{0.0};
+ double m_target{0.0};
+ double m_inc{0.0};
+ long m_remaining{0};
+ };
+
+ /// One spool of tape: position-addressed circular storage with fractional Hermite reads.
+ /// Positions may be any long/double — they wrap modulo the active loop length, so callers
+ /// keep monotonically advancing heads and never do their own modular arithmetic.
+ class reel {
+ public:
+ /// Buy the worst-case loop once. Loop length starts at full capacity. Not real-time-safe.
+ void prepare(double sr, double max_seconds) {
+ const double worst = std::ceil(std::max(0.0, max_seconds) * ((sr > 0.0) ? sr : 48000.0));
+ m_tape.assign(static_cast(std::max(k_min_loop_samples, worst)), 0.0);
+ m_loop_samples = static_cast(m_tape.size());
+ }
+
+ /// Erase the tape; loop length and caller-held heads are untouched.
+ void clear() { std::fill(m_tape.begin(), m_tape.end(), 0.0); }
+
+ bool prepared() const { return !m_tape.empty(); }
+ long capacity() const { return static_cast(m_tape.size()); }
+ long loop_samples() const { return m_loop_samples; }
+
+ /// Set the active loop length, clamped to [k_min_loop_samples, capacity]. A splice:
+ /// tape content is kept, positions simply re-wrap modulo the new length.
+ void set_loop_samples(long n) { m_loop_samples = std::clamp(n, k_min_loop_samples, capacity()); }
+
+ void write(long pos, double x) { m_tape[wrap(pos)] = x; }
+
+ /// 4-point, 3rd-order Hermite at fractional position `pos` (wrapped modulo the loop
+ /// length). Same read as delay.h / grm_comb.h.
+ double read_hermite(double pos) const {
+ const double fpos = std::floor(pos);
+ const double frac = pos - fpos;
+ const long base = static_cast(fpos);
+ const double xm1 = m_tape[wrap(base - 1)];
+ const double x0 = m_tape[wrap(base)];
+ const double x1 = m_tape[wrap(base + 1)];
+ const double x2 = m_tape[wrap(base + 2)];
+ const double c = (x1 - xm1) * 0.5;
+ const double v = x0 - x1;
+ const double w = c + v;
+ const double a = w + v + (x2 - x0) * 0.5;
+ const double b = w + a;
+ return (((a * frac - b) * frac + c) * frac + x0);
+ }
+
+ private:
+ // Same wrap as delay.h, against the loop length rather than the buffer size.
+ size_t wrap(long i) const {
+ return static_cast(((i % m_loop_samples) + m_loop_samples) % m_loop_samples);
+ }
+
+ std::vector m_tape;
+ long m_loop_samples{k_min_loop_samples};
+ };
+
+ /// Deterministic transport speed error: wow + flutter as two sines, returning a read-
+ /// position offset in samples. Phases start at zero at prepare()/clear(), so two runs of
+ /// the same settings are bit-exact.
+ class wow_flutter {
+ public:
+ void prepare(double sr) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ clear();
+ }
+
+ void clear() { m_wow_phase = m_flutter_phase = 0.0; }
+
+ /// Wow: excursion depth in ms of tape position, rate in Hz (clamped to the wow band).
+ void set_wow(double depth_ms, double rate_hz) {
+ m_wow_depth_ms = std::max(0.0, depth_ms);
+ m_wow_rate_hz = std::clamp(rate_hz, 0.0, k_wow_rate_max_hz);
+ }
+
+ /// Flutter: the faster, shallower partner (clamped to the flutter band).
+ void set_flutter(double depth_ms, double rate_hz) {
+ m_flutter_depth_ms = std::max(0.0, depth_ms);
+ m_flutter_rate_hz = std::clamp(rate_hz, 0.0, k_flutter_rate_max_hz);
+ }
+
+ double wow_depth_ms() const { return m_wow_depth_ms; }
+ double wow_rate_hz() const { return m_wow_rate_hz; }
+ double flutter_depth_ms() const { return m_flutter_depth_ms; }
+ double flutter_rate_hz() const { return m_flutter_rate_hz; }
+
+ /// Advance one sample; returns this sample's position offset in samples.
+ double tick() {
+ const double wow = m_wow_depth_ms * 0.001 * m_sr * std::sin(2.0 * k_pi * m_wow_phase);
+ const double flutter = m_flutter_depth_ms * 0.001 * m_sr * std::sin(2.0 * k_pi * m_flutter_phase);
+ m_wow_phase += m_wow_rate_hz / m_sr;
+ m_wow_phase -= std::floor(m_wow_phase);
+ m_flutter_phase += m_flutter_rate_hz / m_sr;
+ m_flutter_phase -= std::floor(m_flutter_phase);
+ return wow + flutter;
+ }
+
+ private:
+ double m_sr{48000.0};
+ double m_wow_depth_ms{0.0};
+ double m_wow_rate_hz{0.0};
+ double m_flutter_depth_ms{0.0};
+ double m_flutter_rate_hz{0.0};
+ double m_wow_phase{0.0};
+ double m_flutter_phase{0.0};
+ };
+
+ /// One pass of generation loss: darkening one-pole lowpass -> bounded soft saturation ->
+ /// normalized DC blocker. The stabilizer of the regeneration loops built on it (see the
+ /// file banner: bounded by 1/drive for any drive > 0, contractive above the cutoff).
+ class wear {
+ public:
+ void prepare(double sr) {
+ m_sr = (sr > 0.0) ? sr : 48000.0;
+ update_coeff();
+ clear();
+ }
+
+ void clear() {
+ m_lp = 0.0;
+ m_dc_x1 = 0.0;
+ m_dc_y1 = 0.0;
+ }
+
+ /// Record/playback darkening corner, clamped to the audible band. Exact one-pole
+ /// coefficient, same map as grm_comb.h (tap.comb~'s cruder hz*2/sr was rejected there).
+ void set_cutoff_hz(double hz) {
+ m_cutoff_hz = std::clamp(hz, k_darken_floor_hz, k_darken_ceil_hz);
+ update_coeff();
+ }
+
+ /// Saturation drive, >= 0. 0 is an exact linear passthrough (vca::swing_shape contract).
+ void set_drive(double d) { m_drive = std::max(0.0, d); }
+
+ double cutoff_hz() const { return m_cutoff_hz; }
+ double drive() const { return m_drive; }
+
+ double process(double x) {
+ m_lp += m_lp_a * (x - m_lp);
+ const double sat = vca::swing_shape(m_lp, m_drive);
+ const double dc_out = k_dc_block_norm * (sat - m_dc_x1) + k_dc_block_r * m_dc_y1;
+ m_dc_x1 = sat;
+ m_dc_y1 = anti_denormal(dc_out);
+ return m_dc_y1;
+ }
+
+ private:
+ void update_coeff() { m_lp_a = 1.0 - std::exp(-2.0 * k_pi * m_cutoff_hz / m_sr); }
+
+ static double anti_denormal(double x) { return (std::abs(x) < 1e-15) ? 0.0 : x; } // same guard as tap.comb~
+
+ double m_sr{48000.0};
+ double m_cutoff_hz{k_darken_ceil_hz};
+ double m_drive{0.0};
+ double m_lp_a{1.0};
+ double m_lp{0.0};
+ double m_dc_x1{0.0};
+ double m_dc_y1{0.0};
+ };
+
+ } // namespace tape
+} // namespace tap::tools
diff --git a/include/taptools/taptools.h b/include/taptools/taptools.h
index 4d4983c..13b2c37 100644
--- a/include/taptools/taptools.h
+++ b/include/taptools/taptools.h
@@ -7,11 +7,14 @@
#pragma once
+#include "airport.h"
#include "autowah.h"
#include "bridged_t.h"
#include "conv_engine.h"
#include "delay.h"
#include "diode_ladder.h"
+#include "discreet.h"
+#include "garden.h"
#include "grm_comb.h"
#include "grm_pitchaccum.h"
#include "ladder.h"
@@ -22,6 +25,7 @@
#include "stft.h"
#include "svf.h"
#include "swing_vca.h"
+#include "tape_loop.h"
#include "tb303_voice.h"
#include "tr808_clap.h"
#include "tr808_cowbell.h"
diff --git a/notebooks/airport.ipynb b/notebooks/airport.ipynb
new file mode 100644
index 0000000..997dd8d
--- /dev/null
+++ b/notebooks/airport.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "103df388",
+ "metadata": {},
+ "source": [
+ "# tap.airport~ — the loops, measured\n",
+ "\n",
+ "The \"2/1\" tape system from *Music for Airports* (`taptools/airport.h`): up to eight\n",
+ "free-running tape loops of unequal, incommensurate lengths, each holding one recorded phrase.\n",
+ "The phrases drift in and out of coincidence; the piece never repeats on a human timescale;\n",
+ "**no setter ever resets a phase**, because the free-run *is* the composition. Every trace\n",
+ "below drives the **shipping C++** through `tools/capi` via ctypes.\n",
+ "\n",
+ "Sections: **1** record and return · **2** incommensurate lengths and the composite period ·\n",
+ "**3** the per-loop shade, measured · **4** two minutes in the terminal."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4eea065d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:30:17.566485Z",
+ "iopub.status.busy": "2026-08-12T01:30:17.566295Z",
+ "iopub.status.idle": "2026-08-12T01:30:17.902742Z",
+ "shell.execute_reply": "2026-08-12T01:30:17.901310Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import taptools_py as tap\n",
+ "\n",
+ "plt.rcParams.update({\n",
+ " \"figure.dpi\": 96, \"figure.figsize\": (9, 3.2),\n",
+ " \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+ "})\n",
+ "C = tap.PALETTE\n",
+ "sr = 48000.0\n",
+ "\n",
+ "def punch(bank, loop, phrase):\n",
+ " \"\"\"Record a phrase onto one loop at wherever its head happens to be.\"\"\"\n",
+ " bank.record(loop, True)\n",
+ " bank.process(phrase)\n",
+ " bank.record(loop, False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bbcb2e90",
+ "metadata": {},
+ "source": [
+ "## 1 · Record and return\n",
+ "\n",
+ "A click planted on a 0.5 s loop returns every 0.5 s, bit-exactly — the playback path is\n",
+ "bit-transparent at the default (ceiling) darken, and the loop replays the *same* imprint\n",
+ "every pass, so there is no generation loss to model. The head's phase advances by exactly\n",
+ "the samples processed, through any setter traffic. (Kernel scenarios: *\"a recorded phrase\n",
+ "returns every loop period and no setter resets the phase\"*, *\"record off freezes the tape\n",
+ "bit-exactly\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d66f190d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:30:17.905454Z",
+ "iopub.status.busy": "2026-08-12T01:30:17.905188Z",
+ "iopub.status.idle": "2026-08-12T01:30:18.031537Z",
+ "shell.execute_reply": "2026-08-12T01:30:18.030403Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "bit-exact returns at samples: [23999 47999 71999 95999] — spacing: [24000 24000 24000]\n",
+ "phase after the render: 0.400042 of the loop\n"
+ ]
+ }
+ ],
+ "source": [
+ "b = tap.Airport(sr, 2.0, smooth_ms=0, lengths=[0.5], pans=[-1.0])\n",
+ "click = np.zeros(1); click[0] = 1.0\n",
+ "punch(b, 0, click)\n",
+ "yl, yr = b.process(np.zeros(int(2.2 * sr)))\n",
+ "\n",
+ "t = np.arange(yl.size) / sr\n",
+ "fig, ax = plt.subplots(figsize=(9, 2.4))\n",
+ "ax.plot(t, yl, color=C[0], lw=0.8)\n",
+ "for k in range(1, 5):\n",
+ " ax.axvline(k * 0.5 - 1 / sr, color=C[3], lw=0.8, ls=\":\")\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"left bus\")\n",
+ "ax.set_title(\"one planted click, returning on the 0.5 s grid (dotted)\")\n",
+ "plt.show()\n",
+ "\n",
+ "returns = np.flatnonzero(yl == 1.0)\n",
+ "print(\"bit-exact returns at samples:\", returns[:4], \"— spacing:\", np.diff(returns[:4]))\n",
+ "print(f\"phase after the render: {b.phase(0):.6f} of the loop\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db91e4bf",
+ "metadata": {},
+ "source": [
+ "## 2 · Incommensurate lengths and the composite period\n",
+ "\n",
+ "Two loops of 0.5 s and 0.625 s (24000 and 30000 samples, gcd 6000) realign only at their\n",
+ "lcm — 2.5 s — and the kernel's `composite_period_seconds` says exactly that. The raster\n",
+ "below marks every return of each loop: the coincidence pattern (bottom row) repeats at 2.5 s\n",
+ "and at no shorter lag. Stretch the lengths toward true incommensurability and the composite\n",
+ "period leaves the human timescale — that is the piece."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b29fba37",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:30:18.033695Z",
+ "iopub.status.busy": "2026-08-12T01:30:18.033504Z",
+ "iopub.status.idle": "2026-08-12T01:30:18.222863Z",
+ "shell.execute_reply": "2026-08-12T01:30:18.221775Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "composite period: 2.5 s (lcm of 24000 and 30000 samples)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "seven airport-scale loops -> composite period: inf hours\n"
+ ]
+ }
+ ],
+ "source": [
+ "b = tap.Airport(sr, 2.0, smooth_ms=0, lengths=[0.5, 0.625], pans=[-1.0, 1.0])\n",
+ "click = np.zeros(1); click[0] = 1.0\n",
+ "punch(b, 0, click)\n",
+ "punch(b, 1, click)\n",
+ "print(f\"composite period: {b.composite_period_seconds} s (lcm of 24000 and 30000 samples)\")\n",
+ "\n",
+ "yl, yr = b.process(np.zeros(int(7.5 * sr))) # loop A on the left bus, loop B on the right\n",
+ "hits_a = np.flatnonzero(yl > 0.5) / sr\n",
+ "hits_b = np.flatnonzero(yr > 0.5) / sr\n",
+ "fig, ax = plt.subplots(figsize=(9, 2.2))\n",
+ "ax.eventplot([hits_a, hits_b, np.concatenate([hits_a, hits_b])], colors=[C[0], C[1], C[3]],\n",
+ " lineoffsets=[2, 1, 0], linelengths=0.8)\n",
+ "for k in range(1, 3):\n",
+ " ax.axvline(2.5 * k, color=\"gray\", lw=0.8, ls=\":\")\n",
+ "ax.set_yticks([2, 1, 0], [\"loop A\", \"loop B\", \"sum\"])\n",
+ "ax.set_xlabel(\"time (s)\")\n",
+ "ax.set_title(\"returns of two incommensurate loops: the pattern repeats at the 2.5 s lcm\")\n",
+ "plt.show()\n",
+ "\n",
+ "# The published-scale version: seven loops in the 17..31 s range, mutually coprime in samples.\n",
+ "b7 = tap.Airport(sr, 32.0, lengths=[17.8, 19.1, 21.3, 23.9, 26.2, 28.7, 30.9])\n",
+ "print(f\"seven airport-scale loops -> composite period: \"\n",
+ " f\"{b7.composite_period_seconds / 3600.0:.1f} hours\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "812af78b",
+ "metadata": {},
+ "source": [
+ "## 3 · The per-loop shade, measured\n",
+ "\n",
+ "`darken` is a playback tone per loop — not generation loss (a frozen loop replays the same\n",
+ "imprint forever). The same 6 kHz phrase on two loops, one shaded at 1 kHz and one left\n",
+ "transparent, panned to opposite buses: the level ratio between the buses lands on the\n",
+ "analytic one-pole transfer. (Kernel scenario: *\"darken shades one loop's playback and only\n",
+ "that loop's\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "dff92d50",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:30:18.225226Z",
+ "iopub.status.busy": "2026-08-12T01:30:18.225028Z",
+ "iopub.status.idle": "2026-08-12T01:30:18.419755Z",
+ "shell.execute_reply": "2026-08-12T01:30:18.418607Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "6 kHz through the 1 kHz shade: measured 0.169, predicted 0.169\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "b = tap.Airport(sr, 2.0, smooth_ms=0, lengths=[0.5, 0.5],\n",
+ " pans=[-1.0, 1.0], darkens=[1000.0, 20000.0])\n",
+ "tt = np.arange(int(0.5 * sr)) / sr\n",
+ "phrase = 0.6 * np.sin(2 * np.pi * 6000.0 * tt)\n",
+ "b.record(0, True); b.record(1, True)\n",
+ "b.process(phrase)\n",
+ "b.record(0, False); b.record(1, False)\n",
+ "\n",
+ "yl, yr = b.process(np.zeros(int(1.0 * sr)))\n",
+ "\n",
+ "def tone(x, f):\n",
+ " n = np.arange(x.size)\n",
+ " return 2.0 * np.abs(np.dot(x, np.exp(-2j * np.pi * f * n / sr))) / x.size\n",
+ "\n",
+ "a = 1.0 - np.exp(-2 * np.pi * 1000.0 / sr)\n",
+ "w = 2 * np.pi * 6000.0 / sr\n",
+ "predicted = a / np.abs(1 - (1 - a) * np.exp(-1j * w))\n",
+ "measured = tone(yl, 6000.0) / tone(yr, 6000.0)\n",
+ "print(f\"6 kHz through the 1 kHz shade: measured {measured:.3f}, predicted {predicted:.3f}\")\n",
+ "\n",
+ "freqs = np.geomspace(100, 20000, 200)\n",
+ "H = a / np.abs(1 - (1 - a) * np.exp(-1j * 2 * np.pi * freqs / sr))\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.semilogx(freqs, 20 * np.log10(H), color=C[0], label=\"analytic 1 kHz shade\")\n",
+ "ax.plot([6000], [20 * np.log10(measured)], \"o\", color=C[1], ms=7, label=\"measured, 6 kHz\")\n",
+ "ax.set_xlabel(\"frequency (Hz)\"); ax.set_ylabel(\"gain (dB)\")\n",
+ "ax.set_title(\"the shade is a one-pole playback tone, nothing more\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17206ba1",
+ "metadata": {},
+ "source": [
+ "## 4 · Two minutes in the terminal\n",
+ "\n",
+ "Seven loops, lengths mutually drifting, each holding one soft chord-tone phrase (an \"aah\"\n",
+ "of a few harmonics with a slow envelope) recorded once — then the machine simply runs. Levels\n",
+ "and pans place the phrases across the field; two loops carry a gentle shade. Nothing in this\n",
+ "render repeats: the composite period of these lengths is measured above in hours."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "1f9000b2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:30:18.422239Z",
+ "iopub.status.busy": "2026-08-12T01:30:18.422057Z",
+ "iopub.status.idle": "2026-08-12T01:30:22.719861Z",
+ "shell.execute_reply": "2026-08-12T01:30:22.715630Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AYDabE86v2tpabNq0CV/5ylek19asWRPzGZ1Oh1WrVmHz5s1ZL+/RRx/FVVddBYfDgc7OTuj1egwMDMDpdGb8LevXr4cgCFizZg0uvPBCHHfccViyZImS3TBtYQH4NGTx4sVoamrCyy+/jHA4DKPRiEMPPRTr16/HX//6V3i9XmzcuBG33HKL9B2Px5O0sLCqqgqAWPiQKWCsqamJ+dtgMCR9DYAiz+/4INdoNMZ8L1WBTigUyrhsQPQfd7lcSW+68kHvxhtvRFdXFx544AGcd955CAQCOOWUU3D33XejoaEh6bLV2J/Zkml/Kf292ZDtMhsbG5MuJ/48AfI7f5I9IMXvj6997Wvw+XwYGhqS1vPRRx9hxYoVEAQh7fIz4Xa7ASCm6C4ej8cDACnPkz179qRdR6bfqGQb4pmYmAAAWK3WpNtEmZycBAA8++yzeO+99xI+G3/TV2Nbs1nnSy+9hFtvvRVPPPEEfvWrX8FsNuM73/kOfvaznyUdNwpxvHI9p7Pdt/Fkc15nOi5Kz4dk0O8m21/y1/K9DlKR6beptV4lY00u31O6/9KRbLyl59fPf/7zhPfmzp2bcE9LtX92796d1fJ2796Nc845B1dffTV+8pOfSMm/a665BnfccUfG37JkyRJs3LgRt99+O37xi1/giiuuwLx58/D73/8en//85zN+fzrCAvAyJl2F+Lp16/DKK69AEAQcccQR0Gq1OOaYY3DVVVfhiSeegN/vxzHHHCN9vrW1NekTe29vL7RabcrAqZTQzPrExETMoBH/O1LtJ5qNf/3119Ouh+d5XHTRRbjooovg8/nw1FNP4fzzz8e1116LP/3pT0m/U4j9masjAEXp781m3dkuM5mjRCnw+Xx46623cMcdd8QERD09Paosv6WlBRqNRrpJJaO5uRkcx6U8T2jVfyG3IZ62tjZoNJqEmSZCCPr7+7F8+fKYZV9wwQW49tpr89pOpduazTqrqqpw44034sYbb8To6Chuu+023HTTTVi5ciXOOOOMnNZf6OMVvy257Fu1z2ul50M23wWA/fv3S98t1n6NJ5v15jv25oLS/ZeOZOMtHbMfeOCBhAx8MpLtn76+PnR1dWW1vA0bNsDv9+O73/1uzHYlOzdT7e+VK1fivvvuAwBs2bIF3/rWt3DGGWdgZGQkYbZ5JlAed0tGUmgGgj7Jy1m/fj0+/PBDPPHEE1i/fj0AYNmyZaitrcUNN9yA+vp6LF26VPo8ne6UXwx+vx///Oc/ceyxx5blyU0zQe+884702vj4OF544YWYz6XaT1/+8pexceNGaSpNDrVKiv+/0WjEmWeeiZUrV2JgYCDlthVif6Y73kpQ+nuzWXc+yywlWq0WGo1GyjIBYlCR6oFKjsfjwcaNG7F///6Un7FYLDj++OPx17/+NWGfyTNvRxxxBB544AGEw2Hp/Q8//BBbt27FSSedlO3Pynob4tHpdDjuuOPwz3/+E4QQ6fWnnnoq5nharVaccMIJuP/++5M2Z8r22CvZVqXr9Pl8MbNg9fX1UiCb6poth+NFyWff5nNeJ0Pp+ZDqu8ceeywefPDBmNefe+45KWsKZLdflVx7SslmvVVVVTmPu7midP9ly8knn4yKigrcddddCe8JgpDwO5999tkYicjevXuxYcMGnHjiiVktj870yM/Nvr4+PPvsswnfS7a/48+3pUuX4vzzz4fL5cprf5QzLANexhxwwAGwWCy4/fbbcfHFF0On00nezMccc4zkIUsDcI7jsG7dOvz73//GmWeeGfOUecUVV+DBBx/E8ccfjxtvvBGVlZX4zW9+A7fbjf/93/8t1U9My7Jly3D00UfjO9/5DgKBAMLhMP7yl7/g9NNPx9NPPy19LtV+uuyyy/D888/juOOOw9VXX43ly5djbGwMGzZswLvvvou3334bAHDUUUfh6KOPxtq1a1FfX48333wTb7/9Nv7+97+n3LZC7M9Uv0MpSn9vNuvOZ5mlRKvV4txzz8Wvf/1r2Gw2NDU14d5778WKFSsSHuDi2bFjB9asWYObb74Z11xzTcrP3XHHHTjyyCOxZs0afP/734fNZsOGDRvw2WefSTfV2267DevWrcMpp5yCb37zmxgbG8O1116L1atX4/LLL8/7dyrZhnh+/vOfY+3atfjSl76Eiy66CL29vfjPf/6DZcuWxXzuD3/4g3RdXHHFFWhra8PevXvx+OOPY+HChTESN7W2Vck69+3bh1NOOQUXXnghli9fDo7jcM8996Curg6nnnpqXusv9PGi5Lpv8zmvU6H0fEjGzTffjLVr1+Kss87C+eefj/7+fjzzzDNYtmxZzP1H6X5Veu0pRel6DzvsMNx000244447sGrVKpjN5pQ1ImqidP9lQ2NjI+6++25ceOGFGB0dxamnngqj0YitW7finnvuwV//+teYeq5TTz0VZ555Ji6//HIEg0H8+Mc/xuLFi3HxxRdntbzjjz8ezc3NOO+883DttddibGwMt99+O84991w88MADMdt42GGH4cknn8QJJ5yA2tpatLW14b777sPrr7+OM888E/PmzcPg4CBuueUWnHTSSWU5Q68GLAAvY2pqavDoo4/ij3/8I6655hqEw2E8+eSTaGhowLx58/CFL3wBHo8HK1askL5zxhlnoL+/P2Ea1mw2480338Qdd9yB//u//4Pf78fy5cvx17/+NWVnRkpdXR1Wr16d0CRi/vz5CSb7gKgNlheRxHfCTLW85ubmBF3xww8/jJ/+9Ke4/fbbMXfuXPzhD3/As88+i6GhIUX76bHHHsODDz6Ixx9/HE888QRaWlpwxBFH4Oabb5a+/8Ybb+Dee+/Fgw8+iPHxcXR2duLll1/GkUcemXKfKN2fPM9j9erVihrApPodSveXVqtV9HuzWbfSfZhqGwFRJ5gsm3bIIYfENI0ARI1ufNfU+C5zqZa3ePHimL/vvPNOLFiwAA8++CAMBgPOO+88HHbYYXjttddipu/jl282m7F69eqMU+NdXV346KOPpHNAq9Vi7dq1uOeee6TPrFy5Eps2bcJtt92GX//61zAajbjyyivxrW99K2Zf5foblWxD/LJXrlyJt956C7feeituueUWrFq1Cvfddx+++93vxpy77e3t+Oijj3DXXXfhoYcegtvtxty5c3HRRRfh5JNPLsi2KlnnwoUL8eKLL+Luu+/Gb3/7WxBCpKK6zs7OxAOVxfqVHq98z2ml+zYZSs9rpcdF6fmQjJUrV2LDhg249dZb8ctf/hIHH3ww7r33Xhx88MExzXyU7teRkREAwIEHHph2vdn8NiXrPfnkk3Hbbbfhqaeewt///nd0d3fj73//u+L15Hr9Kt1/2ewDADj33HNx0EEH4U9/+hNuv/12mEwmHHDAAXj00UexYMGChG3/7ne/i1tuuQUDAwM49dRT8YMf/CCmqFnJ8qqqqrBhwwb84he/wC9/+Ut0d3fjb3/7G95++23s3LkzZp233norfvazn+GWW26B3+/HZZddJj0YPfjgg3jggQdQU1ODK664AhdeeGHa/TCd4Yh83onBYDAYDAZDAcFgEBqNJkHzu3jxYtx555245JJLslreD3/4Qzz11FP46KOPVN7S8kTt/ZctFosF3//+93H99dcXdD2M5LAMOIPBYDAYjKwZGRnBmWeeiYsvvhhdXV3o6enBL37xCyxYsCDG6k4pk5OTuOmmmwqwpeWJ2vuPMb1gGXAGg8FgMBg58f777+PPf/4ztm/fDpPJhLVr1+Lb3/52RhtDhkgp99/69evxla98RdJ7M4oLC8AZDAaDwWAwGIwiwmwIGQwGg8FgMBiMIsICcAaDwWAwGAwGo4iwAJzBYDAYDAaDwSgis8IFRRAEOBwOGI3GkrScZTAYDAaDwWDMfAgh8Pl8qK6ujrGYjGdWBOAOhwN1dXWl3gwGg8FgMBgMxixgbGwMtbW1Kd+fFQG40WgEIO4Mk8lU0HURQmC322Gz2Vi2vcxgx6Y8YcelPGHHpXxhx6Y8YcelPCn2cfF6vairq5Niz1TMigCc7nCTyVSUAJyuh12A5QU7NuUJOy7lCTsu5Qs7NuUJOy7lSamOS6Z1sSJMBoPBYDAYDAajiLAAnMFgMBgMBoPBKCIsAGcwGAwGg8FgMIoIC8AZDAZjFuDyhku9CQwGg8GIwALwacSOXg++95ud2NnnLfWmMBiMacTmnS6c/v2tePCF4VJvCoPBYDDAAvBpxWsfOvBxjxuvfego9aYwGIxpxK4+LwQCbO5xl3pTGAwGgwEWgE8rptziFDKbSmYwGNng8QkAAPt4oMRbwmAwGAyABeDTiimPGHi7WQDOYDCywO0Txwz7eACEkBJvDYOhLmGB4M5/78f7n06VelMYDMWwAHwaQQNwl4cF4AwGQzk0A+7xCdI4wmDMFLbt8eDhl0Zw+4P9pd4UBkMxLACfRky5QwCYBIVRfKY8IYw6gqXeDEaOeHzRMWNojMlQGDMLOsOzfyTAzm/GtIEF4NMIKQPOAnBGkfn+7btx0U+3Y3ySBeHTEZoBB4DhcXYMGTMLnz96fn/wGZOhMKYHLACfRkQ14EKGTzIY6kEIwZ4BL1zeMJ5+c7zUm8PIAZYBZ8xk/IHoPfHDz1wl3BIGQzksAJ8mhMJEymKxDDijmPiDBOHI/e3JN0YRDLEHwOmGW5YBZ04ojJmGPADftH0KgsAKjRnlDwvApwlTnpD0f59fQCjMBhhGcZAX/Y45Q3jjI2cJt4aRC16WAWfMYHzB6P3Q6Qpj1wBrVscof1gAPk2Idz5hVoSFZ8QRxM/v3Ydd/bN7MI+fcXn0ldESbQkjV1gGnDGTkWfAASZDYUwPWAA+TZh0xwZBzIqw8DyzYQwvvefAU2+MlXpTSgp92OtsNsJi0uDTPR5s3+cp8VYxskGuAbePsSJMxszCFwnAu1qMAIAPts2OQsxQmLBYYBrDAvBpwlR8AM4y4AXns71ikDk1y/c1Pdfqq7X4wuG1AIBHX2VZ8OlCKEwQCBLwPGDQcXB5w2z8YMwoaAb8sKWVAIAtu9wIBGd+rcqP/rgHX/nhpywIn6awAHyaEN88g91ACwshBNv3idKT2S73ob/fbNLgtKPrwHHAqx84MDHFMqnTAZr9Nhs1sNXpAQB2pgNnzCBoBry5To+5c4wIBAk+2e0u8VYVnu37PHD7BPQP+0u9KYwcYAH4NEFehAkwCUqhGR4PwuES93kpAvAX3hnHf9++qywetNyRc81i0qC53oA1SysRDBFmSZgDH+1wFb2mgLonmYw8mmgAznTgjBkEzYAb9DxWLrQCAD7YNrN14OEwkaSpTncow6cZ5QgLwKcJTIJSXD6TaZxL4bv++Otj+HC7Cx9tL/1NxCXLgAPA6evqAQBPvD7K3HiyYMoTwtW/240f3rmnqOuVZjCMGthqxQCcOaEwZhI0A27U81i5yAIA+HCGN+RxukIgkeHXOcUC8OlIyQLwnTt34tvf/jZOO+00/PCHP8TExISi7z3zzDM44YQT8O9//7vAW1heUAmKJnLE3CwDXlDkRYaleNgZc4ryjlFn6WUe9AHEEgnAVyy0oKPZgDFnCK9vcpRwy6YXg6MBhMIEwxPBop5TVIJSYeSjEhTWDZMxg4hmwDksnWeGTsthR58XTtfMDUwnZEG3Ywb/zplMSQLwgYEBHHbYYfB6vTj77LPx9ttvY/369QiF0p9EY2Nj+M53voNNmzZhz57iZpFKDQ3A6Q2UZcALC9V/A8WXoAgCwbhTvBZGHaUPlKQMeIUYgHMch9OPFrPgj7FiTMXIW8DvHymeZpNKUCqMGthqdQCYBpwxs/AFxFSwQc/DZNBgSVcFCBElXzMVeQA+kx80ZjIlCcBvv/12dHd34y9/+QvOOeccPPHEE9i5cyceeeSRtN/75je/iSuvvBINDQ1F2tLyYSqi8WqOBOCFDArveWIQN/+1F4TMTnmBIBD09EYz4F6/gHARpRaT7rAk7SinAJxmwAHguNU1MJt4fLLbgwFWAKQIue56cLR4AbA8A0414EyCwphJ+GUSFABYtVjUgc9kP/CJSVkGfIol5KYj2lKs9M0338TnP/956W+z2YzDDz8cGzZswJe//OWk3/nnP/+JoaEhfPOb38Sdd96ZdvnBYDAmm+71itlMQkjBg0q6DrXXMxnJgNMbqMsTLshvCQsED74wglCY4OLTmlBfrVN9HaVC6bHps/vg9gmoq9LCFxDg9gpweUOoNBfnchl1RIOj0YlgyR+EqNzJbOSlbTHqeXS1GLF1lwcjjgBaGvQ5L79Q10y5MSwLwAdG/EX7vW65BKUmkgEfD2Rc/2w5LtMRdmxioQG4XseBEIKVCy24B8AHn00VdR/RYzIyEcBfnhjCl45twNw5poKsa2Iympxxukp/nyhnin29KF1PSQLw4eFhNDY2xrzW2NiI4eHhpJ8fGhrCf//3f+Pll18Gx3EZl3/TTTfhhhtuSHjdbrfDZCrMxUAhhMDhcACAom1VimNSzDJajeJNfMzhgd1uV235lDFnNPu6p9eOsH9mBeBKjs27W8QHts4mDfYOCXB7gX19w2is0aT8jprs3BvNKA+NeQtynLNhYtIHAAj4nLDbo9IcvUYM7Pr2j6GpMnfLr0JdM+VG72C0KGxXrxN2e3FuBvaRyLEJ++H3jkGnEWdZ9vUNShnDZMyW4zIdYccmFrdXDEZdk+OwcxpUGQgqjBwGRwPY8tkAGmuKE+rQ4/LqFjdefNcNIezHRSdVFmRdA/boeDIy7iv5faKcKfb1QpO+mShJAK7X6+Hz+WJe8/l8qKioSPr5Sy65BFdeeSXmz5+vaPnXXXcdrr76aulvr9eLuro62Gy2ogTgAGCz2VQ90F6/qLWd11ELwIWgoIXNZlNt+RT7pAuAuC69qRo2m0X1dZQKpcdmaGIAALBsQQ0mXA6MOX0wmWtgsxX23KGEd44BcAAAJqYIGhsbS3qT9YfEbWlrbYDNZpReb6gNAPBDo7PCZqvNefmFumbKjUnPpPR/h5svyPWbDI12CIAL9XUWNDc1wVbnQP9wAERTE3M845ktx2U6wo5NLMGweM+a09KIKosY1qxc5MObH02id8yIpYvqirId9LgMO8UkijdQmPs0AATCAQCiVNLj54o2nkxHin29lHUAPm/ePPT09MS81tPTgzPOOCPhs3a7HU899RT8fj9eeOEFAMC+ffvw5z//GXv27MHvf//7hO/odDrodImZW47jirLz6XrUWhchRJKgNNcbAIjOFIX4LUOyQrEpd3jGDe5Kjs32XvHiWdhZgfciLY3dvsLs72SMybR9voAAj5/AYiqdY6hbpgGX7wNLpCjT5cn/PFH7milHhidkRZijgaL9Vo8/WoTJcRya6vToHw7APh5EV2v6h8rZcFymK+zYRJE04IboGLVqkRVvfjSJDz9z4eQj6ou2LRzHYc9+McE47gwV7PjInU+c7sKtZ6ZQzOtF6TpKEoB/6UtfwpVXXokf//jHaGtrw6uvvootW7bggQceAAC89tpruPnmm/Hss8+ipqYGzzzzTMz3P/vsMxx55JE499xzS7H5RcfrFyAIgNHAo6ZSPGSFasQzJCsOm3TPvsKOUJhIjVIWdphgNopBZjGdUMYcsRXto45gTAFksZFsCCtit4Fq4uO7tDISCQQFjE+GwPMACDAyEUQgKECvK/yDFXVBoeey5AXOmvEwZgCCQOAPihlOvTYa+CxfIM7ebtvrSfq9QhEIEqkwXZ5MURt5EabbKyAYEqDTstYu04mSBODnnnsuXnjhBSxduhSLFi3Cli1b8Mtf/hJLliwBAAwODuK5554DIMpVTjjhhJjvWywWLFy4EIcffnjRt70U0CY8VpNGCsQKZUM4KHNHmJyFgdWe/V4EggStDXpYK7RS0FnMADze+WTUEURnc2qpQCEJhQl8AQE8B5gMsYM73TcsAM8MPab11TrwHIehsQDs4wG0pZGAqIXcBQUAa0fPmFEEQhELQh0Hno8G4A0RA4FiN6kZGAlBiJR3TEwGIQgkZrvUgtoQchxACOB0hVFfzQLw6URJAnCe53H//fejp6cH/f39WLx4MZqamqT3161bl5D1lnPXXXehtbW1GJtaFkxG2tBbzRqYDDx4LmqNp9Goe2HL7ckmi+wtOuUJ4X9+uxvHHlKDM48tjdUk9f9e2CHWI5gj0o+iZsAjzXfmNBrQP+wvqRWhW9YFM35arZIG4KwNckao/KSxRg+9VgzA948UKwCPSFBMsRlw1o6eMROQt6GXYzTw0Go4+IME/oCQ8H6h6B2OjodhQZxJrraqG2oRQuCIBODN9XrsHwnA6QrNKNey2UBJH5fmz5+PY445Jib4BoCmpqaErLectWvXorOzs8BbVz5QuYm1QgOe56SW4IXIgpdSgvLBNhd29HrxwrvKuqIWAtoBUwrAjXRfF68dPQ3AF3WK21DKAJyee8kkMCwDrhxqQWir1aE5YtlYrGY8UgY8MoPBvMAZMwmfP9YDnMJxHCrNxR+j+uyxCYmxAnQzdnlFt7IKI4/GGvF6Zt0wpx9svmIaQANha4X4FE2DIbWzsoGgENP6fLLImc2ePjH4lfubFptoAC4Wp5kLtK9TEQ4TTEyFwHHA/DZxG0qaAffRDHjiUME04MqRZ8BbG8RC6oGR4gTA8k6YAFg7+iJACMHuAS8CweI9uM9WfIHkATggzhoDxb2X9UUy4Hxkc8YLcD+j2e8aqxZVFvE3Fltqw8gfFoBPA2iAQwcT2hJc7Qz48EQQcv/4YgdWPRH3EcdUqCRNBXwBAXv2+8BzwLxI8CtpwH3F2Rfjk+IxqLFqpbbhIxOlz4CbWQY8L2gGvLFWh5b6SAZ8tDgZ8OhDlHi86iq10Go4OKZCUvDCUI+RiQB+eOdeXHLTDvzh4f2l3pwZjz+YXIICRJNWU0WczaUB+JIuMwBgzKl+YEwLMKutWsl2kWXApx8sAJ8GUI2tNRLwFCoDTuUn9Im6mFkDQgh6+sQAPCyUJqjb1eeFIAAdzUaYDJGHnUjhWqFcZ+Khg3V9tQ4NkanF0QJMYSpFakNfkRiAWyUNOAvAMyHPgLdEMuCDxcqAe2kGXDyXeZ5DY+ThbpjpwFVDEAiefH0UF/50OzZuFT3f++zFeciazfhSaMABSBKUYt3LJqZCcLoFVBh5LOkSJYTjBRi/J6QMuA7VkQDc6WLj8HSDBeDTABqM0sGEygHUDgoHIxm5Be3iwFFMDfjwRDBmfY4STKd9Fqf/BoovQaFyk7oqHeoiBTXlUoQZj3x2ICyUvg3yQy8M4+GXRkq9GUmxyzLgzZEM+OBYAEKB9xshROaCEj2GTbVMB64mfXY/rrptF2775wA8PkGSsE15WFay0Ege4PpEQwIpAC9SAmXPgJhE6moxoq5KHL/HC2BFSAPwaqsWVVYagLNzbbrBAvBpAA3ALXEacLUlKNSCcEF75ObhDhdNCrKzL7Zz1EQB/VNTQfXfizqizUmiQWZxpuppwU5dlQ61lVrwvPgwUiotKS0+TVaEqeE5mE08CCmuS0wyRh1B3PXoIO789354iyQXUgohJJoBr9WjwqhBtVWLYIgU/OHKHyQQCKDXcdDKHJMaa5kVoVrs6vfikpu2Y/NON6qtWvz44g78+OJOAGx2qBj4AxEbwqQZcPGeWaxk0u5IA56uFiNqIz07ClGESe+PNZVaKQPOJCjTDxaATwPoIE6f5qUOhCo7c9BsWHuTEUY9j1CYwOsvTuDXExeAl2IwkSwIO2UZcOqCUjQJSiQAr9ZCw3OorSxcFkUJ6VxQAKCyorCNoZSyuccl/X//aHkFlVOeMHx+AWYjL+3HqA68sNvq8SZmv4GoEworxMyfDR87EQwRHLzYgnt/vBBHr6wueuZVzmd7Pfj5vfuKXkRfKtIWYRbZKnXPQCQAbzWitoAZ8NgizEgGnBVhTjtYAD4NmJLZEAKyDLjKgzvVgDfX6WXaueLcQGgBJtWfFzsD7vKE0T/sh07Loasl6s0sSVCKlFWVGrZEBu/6EstQ0klQgOjDYKm7pn4kC8AHyywAHx6PZr8pVAdeaCtCt9QFM3aopwW+rBtm/tCZw6NWVksZV5OBh4YXLfKKPXt175ODeOk9B57fWDo712KSVgNuKW4GnLag724xoa5KXHchXFAmpsRlVlu1qLaKYzDLgE8/WAA+DaA6QhrsFEqXTG8kTfX6ots3UQvCgxdbAUQ1bsViR6+4/rlzTDHtfIvdCTOaARcDJNrNbWSiNIFSuiJMIOrMU2qt68c73NL/B4rkr62U4cixa6yJNskoWgY8if4bkGXAy1yCMjEVxPdv34WNWyZLvSkpofuwuS76gMVxHKwlsOkMhgRs2SleCzQYnOn4FWXAC38MwgLB3kG5BCWSAXeq7+oll6BUmctbA/6vl0Zw57/3l8TZrNxhAfg0ICpBiWjAK9TPgHt8YUy6w9DrONRWaouqnRt3BjHmDImV492idRN9wi8GhBC8+oEDQNT/m2LQcdDwQCBIipLJklxQ4jPgBbCyUgJ98EglQbGWgRXhqCOI/uFo0L2/SO4iSkmeAY8UYhb4YSHqAR6XAa+bHt0wN3w8iU3bXXjoxeFSb0pKpMSFLAAH5J1ii3dtbNvjgT8oBjo0GJzppHdBofexwo+f+0f8CAQJ6ip5WCo0qDDyMOp5+AKCdB2qBc1211i1MVn+ciiGl0MIwb1PDOLhl0Yyno+CQKSHqdkCC8CnASklKCpmZem0fVOtPqaDWDEGrp5+UX4yb45JKlwplgQlLBD85h/9eHrDOHgeWLeyOuZ9juOKKkMZlRVhAii5E0pUgpJ8qCiFz248VP9tjHR6LJa/tlIkBxR5BpxKUEqUAa+v0oHnxQe+cm4WQ2d+evq8BXeMyYVQmGB0IgieAxpqYtuAV1I71yLODm3aHpVi7R30leU+U5t0GfBidsLcHdF/t9nEMZHjONQWSIYiZcCtOmg1HKwVGhBSfkW/bp8gPRBu7nGn/exfnhjEqVdtRZ99djw4AiwAL3uCIQFevwCej2axChGAD8nkJ0Bxq8epA8r8dhOqI5ZKxbAhDAQF3PinfXj6zXHodRxuuLQTB823JHwuKvkpbKDiDwiYcoeh1UQfgKgEZbTEEpRUGnAqQSllESbVf69bWQWgDDPgE6kz4AMj/oJOzUYD8NihXqPhpBbWwyVs9JQJ2oTK4xPK0lN7eCIAgYgzVXLpGlCah9MPZQG4zy+U/QyHGpSLDziV/LQ1aqXXqAxFzWY8voAYE+i0nJQYkQoxy0yGIk+kbd6ZOgAnhOD5jRMIhUnaz800WABe5siz3xwn2ogVQoIyFDeNaq0oYgacBuBtJtRYxQGr0BpwlzeMa+7YjTc/dsJs4vHL/9eNww+qSvrZQjU+imdskma/teB58VhHJSilCZIyuaBI50kJA3Cq/z7+sFrwnNhcJhgqn6yuvAsmpdqihcnAw+0VCvqQGy3CTDx+UiFmGevA5V1gqU1oOUEL1+PlJ4C8PqI414bXF8a2PW7wXFRKNxt04NSG0KhL9AG3yhJJhdYg7454gMsDcKkQU8Xxmyanqi1aKSaghZjlF4BHf/eWna6Ux2DfoF9yi5lNzcFYAF7m0OwJzaYAhQkIB0djC4morqwY2ZudEQeUeW0m1BQhAz4xGcRVv9mJj3vcqKvS4rbvzcPSeYmZb4q5QL7r8Yw5YuUngNwFpVQa8EgAlyEAd5WoCJPqv00GsX6goUYHgZSXvR7NMNtqYov0JB14AWUo1BPdZEwc6m3ToBBzxFHmAXhk39mSBeBFTGIAwJZdboQFYEFHBQ6cK9bS7C1yAL5puwtvbHIUdZ3pMuBGPQ+9jkMwRCQpRKGgDzvttuj4LWXAVZRU0qC2pjIaE2RqR99n9+HeJ4ekGbFiIU+kjTlDKce6D7dPSf8vp7G70LAAvMyhGUiaTQEAcwGcORIlKMWxl5vyhDA4FoBex6HdZoTZxEOn5eD1C9LAqjb3/2cYO/t9aG3U47ffn4/uVlPaz9NpvoJnwCPTlFT3DcTaEBZbzykIRNK9Z8yAl0h7SPXfB841Q6vh0BrRVpeLE0ooTDDmDILjYo8rALTUF96KMF0GnHbDLFeZAiEkLgD3pvl0aRhK4oBCKXYTGKr/XrHAIlmpFrMQUxAIrr97L278y76iZmL9aQJwQDZGFXCbvL4w9o8EoNVwaK6LXmu0pknNDLi8CyalOoMX+N+eGcbfnrHjlfcdqm2HEuJ/dyp5ibx2gWXAGWXDZFwBJgBUGHhwnHhzVavqOV6CUqzqcar/nttqgkbDgeO4gmfBacDzrTNbk04dx1MsCUq8BzggZnCsFRqEwgTOIjfW8PoFECIWN2o0idO7QHSKt1QacKr/XrZAnMFoltxFymMQH3UEQYg4q6GN24fNRbAiTKUBB6JZ23KVoLi8YgMjnVbcbzv7vWUlLQISExdyimmBB8gC8EUWdLWISQVaGFgMhsYCcHnDEITMszr/fnkEZ1/7qSqzL1IjHkPycKayCHaQ9EGnvckQc50XohnPhKwJD6VSyoAn/419Q+L2yd2iigH93bS/h7xhGiUcJvh4R/R1lgFnlA20g5c8AOd5TrqhqjGlRAiRrLRoVq5YFlq0Ac/8tmgWmj7ZTxSggQEQzUjRabtMRCUohb35x3uAU0rVjMeVwYIQKL0NIdV/L58vTrnT87dcMuDJPMApxWjG44mcsxVJjqGttrwDcKr/bq7TY06jAcEQKTtNsxSA1ybLgBfPI3/SHcLOfi90Wg4HdJvR0SSeW312P0Lh4sycyYP9TLMqr29yYMQRxKYdiQFZtkguKLpUAXjhpUD0t8ubuAEF0oDLPMAp1WmKMAkhUuBd7HGRPiwcsVysr0qWAd/R64HbJ0g1MiMTgbKzUywULAAvc2hgQ5/iKWp2w3S6xEyTxaSRCjwlC61CZ8CpBWF7NACnT/aFKsSkgxR9Ks9EoRofxUMD8Pqq2GNdLzXjKXIAnqEAEyi+zlWOXP89v70CANAayYCXixNKMg9wCtWAFzIDTiVEFUmyg8XQoOcDlZ801OikosJyk6GkzYCbi1dH8/EOFwgBlnRVwKjnYTJq0FynRyhMipb13L0/emwyBeBDY+KxVSMDnlmCUngpkNQBszU2AI9qwAshQYk+1KfTgDtcIUmKVuxxkdovHrLYigojj8HRQEJTOercc+iSStRUahEWovVQMx0WgBcQQSB5F+7RADy+E6HkhKJCUJjsJqKmfnFiKoi7Htmf1J85bQa8QAH4pDv5Q00qihWAjyYpwgSiAXixByUavKXyAAfkRZjFz4DH678Bub92mWTAk3iAU6RumAXMSnn9qTPg9dWiLGZ8MlSweot8oA+cYgAuPmCVUyFmIChgzBmCho9eo3KkzGsRrg0axKxYaJVe66Q68CLNGsgz4MNpZATifhPfH1JB7+sLpvYBB+TJpMIdB+qAkpgBj3bDVItkEhTJBSXJPXNgOLqP9xfY9jQemq2vq9ZJhcFb4rLgVDq1cpFFmpUr17oUtWEBeIEQBIITvrMZp121FeE8pgCTSVAAeQY8/xsnDVbkhURyr/F8p4Mef20MD704guv+sCfmRu/1hdE37IeGBzqbowMXnVorhAY8ECLwBQRo+OS62GRYIgFooV1Q4pvwUBpKZEWoJANuNPDQajj4i9QpVE68/huI6qoHRwNl0YREckBJkgFvrNFDq+Ew5gwVrAOch/q4JynC1PAcbHURK8IyzIJHA3B9WQbgUoOlWj00fBILPEnGV/jZoY9oELMwei3QALxYsh0ahALpA6iRCbEuAlArAy4uzKBPUadS4ONACMFuKQMeW9BfadZAw4uJNLXGR0eSIkzJBzzJb5TPgPiDRFVP8kxQDXhtpQ4HzRMDcLkMxR8QsHWXGxwHLF9gkaxRZ4sOnAXgBYLnOZgM+WepoxKU2BuomlnZZF62Gg0Hi0nsrpVvdnNHr3jT7B3y4w//GpBe3zXgAyFi1kAv0+/RqbVCdMOkDyxVMg/VTBQjA04ISeqCApROgiK1oa9IHYBzHFd0v2NKvP4bEDs+1lRqEQyRknUPlZPMA5yi0UQD4ELJQNwpWtFTJCeWMpkxkEOnqhuqdZjXZgLPi8Vu5ZKtj7dujceqsgvKJ7vd+M+GsYQHyxFHEL12UYq1sLNCer2riBlwrz8cI29I52Qhz3oPqlGE6U8vQSm0G82YM4QpdxjWCo2k+abwPCfJUNQqxJyYitgQJnFBSZa0ipcgFetaJ4TEZOsPilj9yjPgn+x2IxgimDvHhCqLlmXAGephUcEuMNqIJ04DXmAJCqDe1B11OuF54OkN43jl/QkAQE8kMJ/XVhHzeckFpQC2UbSQUqn8BChOJ0yPT4DPL8Bo4GGOC5ZKVYQpeYAnyZ7KsZqK6/YAJNd/U6i0Y6AMdOBSF8ya5EFaoYtGU7Wip8hnDMoNuQbcqOfR2WyEIETHk1JjT+MBDgBmIw+eF2VAari33HJfL279ez/++Mj+GBkBzX4fNM8c48BBA/A9g4XfX/sG/SAkOntpT5MskBf9jk4E8y4SpRIUUyoNeIGLYWnmv7vVmDSpQ9vRj6k0gzmRpAgz2gkzseHQQCQA10caFRVLBz7lCSMUJjAbeRj0PBZ0mKDXcdg76JPqsD6Mm7mhUj0WgDPyRo0gOdqIJ5UEJf+gZzCFl60aVoTjziDGnCGYjTy+9aVWAMBvHujH4Kg/2gGzPXbaLuqCov6AOeURByelBZhAcWwIJQeUqsTMfKm6YUpt6NNkwIHC3+CSkUz/TSknHXi6DDhQ+EJMjz99BjyfAPwX9/Xiilt7CiY9GpVpwAGUnQwl1bhJ4ThONZcgrz8sPVD+++VR/O2ZYem9TZEmJstl8hMAaLMZoOHFgKtQEicKDUJXLLBAp+Uw5Q5LTaDikWfHBYKEorxsIIRIv02fIgCvKnAGnEp84vXfFCkDrsL4HQ4TTLrD4Ljo7wLE7L/RwCMUJtKsF4U+3C+bL54fAyoU5RJC8O+XR2L8u+OhGf/qyIOCTstjcWSGhmbBP/xMPHdXRM5d+jCbroZgJsEC8AKiRpA8GQlqrOYUAbiaGfC4G4kaPrY9/dEul6cdVYcjllXB7RNw0z29+CxyI50/JzYAL6QPeD4Z8EJqwKMOKImBmhSAl6ELChB90CymBCWZ/pvSUiZe4C5vGG6fOKsR/wBNKWQznrBA4PML4LjUBWr5rP+1Dx3YusuD9z6dyvzhLCGEYJhmwKvF41luAXimDDgg86DOM/jrs4vHx2zkwXPAfU/b8ew7HhBCJCu/lbICTEAMeObYDCAE2DdUWBkKLcCcO8ckPWym0vFSB5To37lfp8EQASGATssl1eEDhW8WRiUe7U0pAnBqRahCQonOCleZtQm9GZI14yGESA9uhx4gnh9qPOzvG/TjDw/vx+3/7E/5mQmZ/ptyUOQhYPNOF1yeMHp6vdBqOCyN6MMlCUqZWqOqjfIohJE1NHDJJzBJ1gkTkGXX8wx6wgKRnjbjA3A1/FPlbeY5jsNVX52DHb0ebNsr3kQ5DuieEztwRW0I1Q84pzw0AFeeAS9GJ8xUDiiAuK06LQe3T4DXF4YpgyRELaIuKOnXV1lR2EYXb212YswZgk7LQafloNVw+GCbGHTI9d+UcvEClzugpKo3KKQVoJfqvw08+BTBSa4Z8EBQQCDS2vvl9x1Yu6wqjy1NxO0VJVkmAy9df9SK8LMyCcAzZcAB9Xzye4fEc3nlYitWH2DFr//Wj/97bgrg7RgeD6LSrEmwwAOArmYj9g36sWe/DwvipFpqIrmAtBphq9FjYDgA+0RAKgSVQwPu+modRh3BvAJwqQlPigdMoPCWurSGqjmJFSUA1Eka8PzvZ8kKMClVFg2GxsQgvbVRHAPHnCH4/AIqzRrpAVaNh/0+u/jARYvdk40vtI9HrUwqQwsxt+x046MdLggEOKCrQqqXk2vACSGK67SmKywALyD5SlAEgcgkKLGHStIl59mIZ9QhavBqK7UJRSxqFK9IMpOIzWClWYtrL2jH936zCwIRp0npxUcRCyTF9YbDJGUXxlyQAnCFTXiAqAba7Q0XbFAYjRRgJrMz4zgODTU67B8JYNQZRFuRAvBoBjz9RJmlgE2bdvR68KM/7k36XjL9NyDzAi+xrpk+2CZzQKEUMgPuzqD/BmQB+FjqG2m6ZQPA25udqj8YDssKMOn11t1qgk7LYWA4AJcnnLY4uBgMKciAW1VqAtMbyWB3NBnwhcPr4PKG8cd/D+L/IlKU5QssSY9dZ4sJ+NBZ0ELMeBcQ6nk/nCKwptnN5QssePHdiYSMeDZk8gAHovfOQiUIBlPMIFNoN0w13EeS6b8pyZrx0Ox8a6NBetgfiFgR5nMfo2NrKEwwNhmSnLrk0Iy/vFh0cVcFNLxYx7FhsxNArHOPpUIDs5GH2yfA6QonfdCYSZRMgvLggw9i0aJFMJvNOOyww/Duu++m/KzL5cJPfvITLFiwALW1tTjmmGPw1ltvFXFrc0Mqwszxwvf4BQhEDDTida5qacCTOaBQ1Mgc0IKpeTKf76XzLPj6yU2R/ydmMDUaDpVm0YElWWevfHB5xaxdNhlwvY6HXschLKBgDgxjaTLgQFSaUkwnFCUuKEBhO/5tj8yUtDcZ8PnDanDMwdU4cnkVDjuwEv/vrNaE6wKIasAHi+x5G0+6LpgUGgAPjQXysitNBs2Am9LYbVYYNai2iq4x2RSJye1P/UGCt7ZM5r6hSZAXYFK0Gg7zInK17b3qZsF39Hrw+Gujis8Xry8MpysMnZaLyfDFU6lSExiaAacyhzPXN+D0I6Nj54qFiVIsQFaIWcAAfJS6gJg1qK/SSs4+ySQowZCAUWcQPAfJFzofuYFPCsBTB5PS+OROLFDMF3EGOX0ArmY3zGQOKJQqa2IATvXecxoNqLZoUWHk4fYKeZ+P8kLOVMePbqtcgmIyaLCgvQICAV58VzRjiD93pQe4PGoDpgslCcA3btyI8847Dz/+8Y+xd+9efO5zn8MJJ5yAsbGxpJ//5z//iaqqKjz33HPo6enB4Ycfnvbz5UK+EpRU8hNAPWs8aRo1yfSZJC3I8WKd8oQwOBaAXseh3RY7FXnuCY34zXfn4tLTW5J+t4ZaEaqsA5+S2RBmQ6GdUORFmMkohROKVIRZQg343kExcDhhTS2uPq8dP7ywA9df2ombvtmF4w+rTfqdSrMGZlM0i1Iq0nXBpBj0PGpp9zeVi2wlCVGGzHQuMpT4cefl9xzZbVwG4gswKVJHzL2xAXggKOCP/96P5zeO57S+3z44gN8+OICPdiS2yk7GkCzoSjdroJZFJ9Vwt0dazAPAGevMOPeERsybY8SRK5JLgIrRjGcPdQFpEV1AqONPsgCKeoDX1+gwpzE6+5IrSiQoeh0Po14sUKSNqdRi1BFEWBDHbbmVrpxoN0wVMuBpJCiSFaFszOuXBeAcx0UL1POccZMXuKdyLIkvwqTQpJsgiH0kFnXGzmLOJh14SQLwu+++G8cffzy+8pWvoKGhATfccAMqKirw4IMPJv38xRdfjO9973vo6upCXV0drrnmGkxNTWHbtm1F3vLssOYpQaGZ58okGUhLhTrNYVIVYAL5a8Bp9ntuqylBRsJxHA6ab0lZ4Feobpi5FGECsTKUQiBpwJNM5QGlDcAzFWFa83xQS8feQXGglzdqygTHcWXhb60kAw7IMz7qHtuoBWH6YV7qyJlFAE6TA92tRvA88N6nk6rOVtF9UV8dOy5RHeuO3qi1Xlgg+Plfe/Gvl0bwh4f355Tl7I8UOW7bozAAH0teNxOPFIDnMYsYChMMDPvBcUCbLJHBcRwuOKUJd127UEpYxNNcr4dBx2HEESxYt1pagEmb0KTzcpbuN7V6SbqTT6BFm/CkC8ABdQwFkkFnkNPJzFTNgCeRdVCqkhRh0joYKsvL5VpPhrzAPZWGP1kRJhAtxARETbhOG3vs0s2gzDRKIrDZtGkTzjjjDOlvnuexatUqfPTRRxm/6/F48Ic//AFtbW1Yvnx50s8Eg0GEQtGT0OsVB2tCSMGnpOk6CCFS8ZDLk9vUFw18LRWahO/TgDDXZVMGIwFKU50+YTn05uHMceqO3iTnzjFm/f2aSGvdicmgaseMECIrwuSzWq7UDdMTKsg5JGXAK7VJly8143Gotz8yQbP9Fcb0+8oaeRicyvFclF8z8dAMeEeTIatlN9fr0dPnxcCwX7K+KjY0AGmo0aXd9sYaHT7bKxZtkm71tpU+LFYYE8cPOTQDHt+mOt1xoXKjOY0G1FRq8cE2F17f5MDJR9Spsu1SE56a2OthgawQk27b7f8YwBubnJHtCmPUEUxaS5EKlycsZai3R5abCTpu2mrTH9tKmQNHrtftwLAPYQFoqtPBoONijkumZfIc0NFsxI5eL/bs90qyDzWJtmEXr9HG2ogX+FjiWCXXzTdU68Bzor1qIBhOCMSU4POLx82gTz9GVZo1GHEE4XQFU1qC5kL8/TPZcam2ijVNjqkQQmEhpVuLEuRFmPG/lybMHK7oPYo+WLY2iNsn6cCHc5fnhcIE9onYADzZsmjRaY01dvw5oNsEjgMIEW0r478r9wJX895fjPhPvj4llCQAn5ycRFVV7JRZdXU1JidT6wgHBgbQ0dGBcDiM2tpaPPzww7BYkuvebrrpJtxwww0Jr9vtdphMpiTfUA9CCBwOBwAg6BdP0jGHB3a7Petl9UemDXWaUML3vZHgaMqd+F429A2KGR8D74bdHvvEGfSJf487fTmtY2uPeFO0VQez/r5BK667b/8E7HZ1pqIIIZiMZOmCXgfs9tQepvFoNeJA3z84hjqz8u8p3S6a2Q77J2C3Jw7QWojnwoDdldfxzmabXJEgyzM1hpAv9U0j6BOPz7jDm9O2ya8ZeWHQpFuAYyoEo54DCY4n3S+pqK4Q9+eOveNY2lGaqczBUfGYaQQn7PbUmVWzXtzWXb3jWNKmXsZ+aFgMjDjiT3tcKnTidu7pn4zZx6mOCwAM2kUJCI8AVs3X4YNtwHNvDeOQ+epkwQci+0tL3LDbo8vUg8Co5zAyEcSOXfvx3LtePL3BDZ0WqKzgMTYp4KNPB7F0riHVohPYOxgd9z7do+z62t0rWi9aDIG0nxdC4jEYGc/9ut28XTw+thpOWka6YxOPrYZgRy/w8WfDaLCo/zC6Y584HlabxOufhAk4iIH1wP6hmDqN3b3iZy2GAMZGh1FTyWPMKWBbzyBstdmHI0MjEWmNkP4eY9CJ43fvwCgqDcrPjUzsivweq1Fcf6rjYjVxmPQQ9OwelJJLuTA0GhlHwq6E+zVC4tgxPOaG3W6HQIgkNdHBCbt9ClaD+PeuPidyvY0MjYcgyJQ8vYPJz+0xhzjuhv0O2O2xVqXzWnXYORBEd1Pi9WPUiMe0d/+Uave6bK4XNaBJ30yUJAC3Wq1wOp0xrzkcDjQ2Nqb8TmtrK3w+H6ampvCvf/0LJ510Et58802sXLky4bPXXXcdrr76aulvr9eLuro62Gy2ogTgAGCz2dDm9gBwIBjWwmazZb0szY4xAE401JoTvh8WCIAReAMEDQ2Nit0L4hmbEjWTS+bbEqr5BU0AwDi8fi6n7e8fcQAAVh1gg82W3cDf0sgB8CAEU07rTgYhBG7/CACCrg5bVjKU2iofgAB0xkrYbNWqbA/F6QohLAzDWqFB25ympJ+Z53EDcGLKy6u2P9LhDwgIhYeh03KY02pLO2gF4AMwAV8ot22TXzPy9QzucAEYQWeLCU1NyfdLKuZ36oANHkx6dUXZX/GEwwQTU3ZwHLBwbnNKfSgAdM7hgXc88AYNqm6rVj8CYBJ1NYnjh5xF3S4Ak5hwxx6/VMcFAHjdMIApNNSaceJRNtz7n0/xWW8QGkNtVtnnVEx6HACABd2NsMXVjyzscOPjHjf+7wU/3t7iBs8DP764E+9sncRTb47D6TPBZmtQvK7t+50AxHFwfFKAzlgrOVekYsoXKS7vqE07HswZmwIwiWA49/NwctMwACfmt1dKy0h3bOJZ3M3jjY8HMe7Sq34tBEMC9o+K5/mKA1pgMojneW3VOMacIWgMtTH3lSm/GADOba+BzVaL1gYXxpxuBLlK2GzWpOtIh6lvAoATVVZj2t9WX+MD9jrB69Udv6f8AQBuzOsQf0+q41JX7cSkxweNvjrre6EcT0CMm7ra6xOW0+FxA3DAG9DAZrPBPhZAMDyM2kotOtubAQAL6bXuyv0+0js2BWAM1VYtHFMhOFxIGp9MesTgeV5XU8L499PLazHmDEqSMjkLPB4ATjg8ucUdycjmelEDpQF4STTgy5YtwwcffCD9TQjBpk2bsGzZsrTf02q1qKmpwaWXXor29na8+uqrST+n0+lgMpli/gHik08x/1FtrMsbzun70Tb0moT3tBqxZTkhgNdPclq+2ytgzBkEz4utsuPfp5qySU/22+/zC+gb9kPDA10tpqy/T22WHFMh1Y5HWBD3Fc+JuuVsvksLDT0+QfXzhNpT1VXpUn6GNiMZdQRVX3+yfx5ftA09z/NpP2uVNRtRcxv2DUX139l+t1UqNgoUZX/F/xufFLNENVYtDPrE61f+zybTgKt6DCMFZ2ZT+vW3NIoB7mAW+4rWUlgrtLBWaLH6gEoQArz2oTPv7QYgNeFJNi7Rm/bbW8Ss2n9/tQ2HH1Ql1QnsG/Jntb54DeuOPp/i7zTXG9J+Tm7lmuv+6I3ICNpzuA44jpOcUPYOZv5d2f7rHw4gLIh+9hXG6HkmndPjsee0Xeo5Ie43SQc+ntu5TzXgma4xKqd0ZXEve+UDB266txfBUOr7q7yGKt2yqA58Yiq/MdIxJcYEtdbEewWtA3C6xHvmQETn3doYPUfnNNIeCbmPi7RYm7qX2MfFwlr5Z6Y8YQiCGLskOzYNNXos6jQnXX5TnudEufxTQkkC8EsuuQT/+c9/8Mgjj8DlcuHnP/85JicncdZZZwEAHnroIWi10ezk9773PbzyyivweDyYnJzE3XffjT179mDt2rWl2HzFmPNsliMF4CkyteY8izxfeGcchIh+rMm8tiuMPDQ84PMLWbeb3j3gAyFiFX667F8qClGESQuhrGZN1jMGcs292oxmcEABRC9ZnhP3R0hlu7pkRNvQZz520oOmJwxBUG/b9kUkWMmaeWSi0C3eM0GLCNM5oFDoZ/JpyZ0Mj6ThTz/lXVephU7LweEKSYWbmaDWqnQMWn9INQDg5fcnctxa2bIjTXiMsiY8cuSuCZed0YLPR9xwOnO03KOFdLRYVUmnTaVFmJUquKBEPcCzvw6AWCtCtTWw8QWYlGhgHXtOR7uHisFiU56FmFEXlPTjeS49LR5+cQSvvO9I226dbndTiiY8lKgXeO6FhYSQDI14qA1hRC4p6b+jkpu6Kh30Og6OKeXXejx0TO1uMaLKokEwRBLu0+n8yjNRbRXHoyl3GN48+5yUOyUJwI844gjcfffd+O53v4vq6mo89NBDeOqpp9DQIE4bCoKAcDi64y+44AL87//+L5qamtDW1oa//vWveOihh7B69epSbL5i8nVBoYN2MhcUID8vcEIInt4gTrumKpwSMwe5NTGIb8CTLbRyWs129HRgqsrSAQVQz/YxGdQDPN3UvVYjzgoQok41fSaUOqAA4raZDDwEAinrqgZ7IgWYXVk4oFDqqnRiUJnHjSYZgaCAx14bzdillQYeNgUFX1Lr7kK5oBjSD/M8z0ndHJVaEcafH4cdWAmTgcf2fV7JezhXRiLa0cbq5B1ED1lixcGLLbj09GZ86dio1IQG4L2D2QWa1Abv8IPEuqRMAbjLE4bLG4bRwKPKksEhKE8XFEEg0Qx4U27a5fpqHcwmHpPusBQYqcVumQWhHFpIJ3f2CYYEjDpED3DauIUG4Ll2w1TSiAeQF8Mq//2TkXseLQSPJxgSMBL5PdR6MRXRbpi573+XN4xQmKDCyCf9vRVGHjotB19AgC8gSA4o1O4RiFzrUtF1bvuc6spbGgxoSuF4M57CAUUJPM9Fx8QZ7oRSskY8X//617Fv3z6EQiF8/PHHOOKII6T3zjrrLASD0R2/dOlSPPnkk5icnITT6cRbb72F0047rRSbnRUGndg2OxAkWWeQgahlUqpGKJY8gsJPd3uwZ78P1VYtDj+oMuXncrUi7OkTb2K0cUa2SBlwFW8Yk7IMeLZQF5R8O48mQy5BSYfUjKcIVoRuhR7gFDXs1uQQQqIOKDlkwHmei2bBc7zRJOPvzw7jdw8O4B/PDaf9nJTpU5ABr7YUJuNDH4YqFBzD5ob8AnCDnpfa0b/ygSPbTY1BsiBMYd9YYdTglv83F2d9LrZmqMaqQ5VFA7dPyKphFbWqXLeqGkBmJ5TBsYjzRa0+41Sz2agBzwFun5DTzNWIIwifX0C1VZu1dSqF46IylD0pgkn5+s64+hPc//SQomWnzIAnCcxGHEEIRHwgoI4n+QbgPmpDmOEhk3Y+ziYDTs/xVDMqw+NRT/NkDcHk1EZmN/PJgKezIATE41wl64Yp9wCXQzPiAzl6gdMxoqVBj8YUxy+fDDiQ3spyJlGyADwdHMfFSFCmKxzHRbPUOQTJ1OorVdfGfFrdP/mm2MToC2tq09o/SVN3WTY0oR7gyVqFK4EG4HJLpXxxRgbfXG5kZpU6jyZDsiDMULxGA5JieIFH29ArC8ArVW7GMzEpdtczm3jUp5HmpENtL/CwQPDc2+KsEe1MmIqhceUBOMdxSTOG+eKR2RBmItt9FX1Ai44d6w+uBgC89N5EXtcsbcKTyT89GVQHniprGU9YILBH5CTLF5hRadbA6QpLTZSSYVcoPwHEB0FLHlJEeQv6fOhsFgPkTA15PtnlhmMqhOffUSYlkjLgrbEPycmaqdhlFoQUtTLgxgwyx2x9wAWBSDKrVPuMXuPNCs4DmlwZz6MdvZKgls7IOF0hDAxHNeBy8mnGQwiRZoxa6mUZ8IQAnHbBzG3spjMKLABn5AXV0OYy+EaLMFNowHMM7qc8Ibz2oQMAcOLa5N0EKblkwANBAXv2+8BxwNzW3HSLRj0Pk4FHMERU6z5Jf0OmaeNkFLITJg2o6zNkwOnNKt8pfiXQIrtMbegplorcpEqpoJk6WoCZC5LnrUoZ8A+2TUmzD5kChmgXTGVBZFQHrl4A7pYKaTMP89l2w5Qy4LLzY9ViKyrNGvQO+fHP54dzDsJHJElW5sAmno4sA/AxRxChMEFNpRYmg0Yq8PwsjQxlUCq8U3Zso90wsw++4lvQ5wq9FjIFNDQZMDgayNhYyekKYcwZgtHAJzyMUI23/IFS3oSH0lCtA8+Ls4C5zBIrlqBkeR/z+gXQcpbeIV/EdSwWWjug5EGMBqJjk7lf347I8ai2pD7vaAZ8zBmSPMpbGuIC8Dya8UxMhuDzC7CaNbBUaGIKJuWMZ8jWZyLajIcF4Iw8kDuhZAt9Wk8lmchVA/7COxMIBAlWLbIkXJzxSANXFuvYOyg2jpjTaIBJQfYtFfRJX61CTKoBzycDXhgJSuYiTAAxbgaFRspwKjx+cpcBNdibRwEmhZ7bg3m2XaY881a0zXmmJhH2NB1mkyE1n1CxEDPaCVOBBCXbADzJDIlWw+EbX2wBxwF/fnwIdz0ymFNRbrQJT+4Z8H0KrxH6e2kWkwbg6XTg0cI7ZVnpyorcZhGB5C3oc6FOKgJMP5bKJX/pHkKAqDSjq8WYUNQutaOXXSc0SJNnwDUaTpbtzD449QWVBeDZ1jLJxzF/kEjBtpzBLK7xaAZcBQlKmqwybUe/Y58HYUF8wInvEipJUHJI5NCgnQbxNMGQIEGZotuamyWp3EVnJsMC8AJjMamRAU9+A82lMJAQgqci8pOTj8zctS6X6vF8CzApdDDJVPCmlKk8MuD5FLymgxAiafHifdjj6WoR9yfVXRaSZBnOdFhlHf/UYJ8sA54r9CahRgbc6Qrhrc2T4DlxdiYQJCkLqgghsiJMZQF4Q436NxyvL9rJNBPZS1AiMyRxEqUT1tTiugvaodVw+NdLI/jV3/qy1j7TWYBcAvCuLJ1QBiU7QRqAi9fY9n2pfXzjg/ZMWHNIYlDUyoArbYcuz9Bu35s+AE9VgAmI9yaLSQN/kEiZ26EUMwe0UDkXJxSfn7qgKMuAK5WgxCfMkp1P2Txk06B5fDJ3SeVEGgcUCs2Af7JbbNgTLz8BgJbG3Gtj5AWYQGoXm/E8JShMA85QhVwDt0BQrGTW8JCaGyQsOwcN+NZdbuwb9KOmUitV/adDyoBnmI6Us7NXnQBc7gWuBpN5acALU4Q5PBGE2yugyqLJOF1HdaB9dn/BrQjdWbigADLHnxym2ZOxV4UAXPICV0ED/uK7EwiFCQ5ZYpWOQ7KsGCDOtPiDBBaTRnERK80kDauYAXdnkQGnNmr2sWDS6XY5wZA4NvF88uK3Yw6uwU2Xd8Go5/H8xgn85K69klRACVIAnkNDnw6ZF7iS7Dudpm+OPIDQDHhPryfl94firPQyEdUf5yJBUUcDTt0oMkkg5AH6ZxkD8OQFmBRJhhJ5qEwVsEo68ByCLaUSFClB4FEWAMdnypPNOg4qtCAEAJNBgwqjKKnM1RWNJqLS3SdocL4tcuziCzABwFajh4YXpV7ZXJdAYgZcHijL92v+RZjMBUUxanuLziQsOUpQXDIP8FQa2Fyy60+9KU6jn7CmNmPlNl0/kJ22l2bA5+WbAVfZCYVqGlMVtaajUDaEewboNK4po9bZZNSguV6PUJhIFe6FIlmRXTrUzIATQqISlDwCcFudHjwvBnS56Evl20PlJyccXivdcAdTZOyk7LfCAA0Qb4qAehlwQojUTElJBtyo51FXpUUoTKQiyFTIs9+pztmDl1jx6yu6YTVrsHHrJP7nd7sV2UESQmKa8GRLlUWLmkotfH5BUUGrlM2OHNO6Kh3qq3Vw+4SkLhGEEFnxnUIJSo5Wrk5XCE5XGBVGPu/uokqLAMfjJCjp7u2pCjAp8YV0qQqT6fWUSyGmUh9wnVasKRKEaG1EOuLvqcky4Mk07emQHoJyLMRUVoQpvkev/dbGxG3TaKLNblKNYamIz4CbTRpYzbEzHfJtzcWGEBBnBHlOlGcGQ+rXXZULWQXgW7duxfPPPy/9/fbbb2Pu3LmwWCy49NJLEQqp6zE6E7DkWIQ5mUF+AiBrh5VJd7T48qQMxZeUbItXwmEiDcz5BuC0s1dZZMCN0U6Yajab2bM//U0sHmmKfUBZq9tcoeerchvC3Gsd4hl1BOH2Cag0a3LOoACiJrml3gBCsm/OImf7Pi/27PehyqLBmqWVGZ0bspWfANEMuFrNeIIhglCYQKflFDfCalZYnKVUnrS4y4zbvjcPDdU6bN3lxs1/7c147bh96ZvwKEFyQlFwzJPJSagM5bO9ideY0xWGzy/AbOIVy7OiY2h21wbVf7fZDDkXIlPMJh56HQevX0j7IEQDdJ2Wg9MVTnmOh4XoQ3JXirFLnsWkD3YclygtogFsLgG4PxixIcyQAQeyu5fRc7yjWQw042sKfAEBE5MhaDVcRvcqilIZUCrSNeGhVMfJK5NlwIHcnVDiM+CA3PFG/F3hMIHTHQLHRWWk2aLVcKir0oGQ4tjuloqsRrjLLrsMlZVRz+jzzjsPxxxzDG6//XY8/vjj+Ne//qX6Bk53aJCcbfZD6tqYLgDP0t7qhXcmEAwRHLzYIk25ZiJbDXif3Q9/kKCpTp+zby1F9SLMyD6tzEEDrok0myFqN5uRFTIpIVubtVyh8oVsJShKNZbp2KuCAwrlwLlmAMDHPam72WWCZr+PO7QGOi2vIACPFJtlEYA3yGwI1XjAo8cvlXwtGTSjO5hBspONRWVnsxG/vnIurBUavLV5Evc8kd5fmj6ApGrCo4RsrpF4DTiQvhBTakGvUP8NyK6NLOVZaum/AdHqsi5DBjYUFrOYPCd2RwZSy1C27fHAHyRobdSndOmKFtIFMDIRED3Aq3QJtrf5WBEqlaAAstlcBWMUPccP6DaD40QpkDwTG/X510GjsKtyrcJC2FRIhY3WNC4occF5awqThVx7JAxGPt8sW278A5RoHSw2vEvWYVspkhPKGAvA0dvbi/379+Owww4DIGbD3W437rrrLlx88cX4wQ9+EJMdZ4hQCUq20oVoG/rUN7lsZBGEEDz1RqT4MkXny2RkmwHPtwGPHKp1UzsDnksnTKAwMpRMOsp4aLYpn4yuElyeiIVdlgF4tg2bkrF3UAw88pGfUJYviATgO9xpP0cISTrV6QsIeCXSXv3Ew8XrJmMAHnldqQUhIGpErWaxrbMji3qLVHglC0LlD5vSTVlpBlzhuTGn0YAfX9wBngf+8fwwXkjjMT2SoQmPEqhzTqYMuNcvdobUaWOzmEoC8CaF8hMgGvhlmwFXS/9Nqc2QgZUX+S3ponaMyWfaNm6ZBCB2QE1Fo0wfPDSW6IBCyacdfVSCom4GnN5/66t0aKrTIyxA8tUG5OeB8gexWqkQM78MeDoNuPzexnOxD5ZycmnG4/GF4XCFoNdxqJPNTMZbBtLfl8/sJTA7CjEVB+D9/f1obm6W/n7//fexevVqaDTiST1v3jyMjo6qv4XTHEmnnWMAXpkiuwDIizAzZ2T7hwPotftRbdFijYLiS0o2WQMA2NUv3jTylZ8A0YFGDReUsECiunqFU8fxSIWYKgXgoTBBn90PjotOdWYiW5eHXMnaBcWcnRwqHXsjspxcOmDGc9B8MZO3ZZcrbXHh358dxheu2IJf3t8bM+X5xiYH3D4BizsrpMCO3tRSFWHmIkEBAFtNbNFaPkgFmFnIOJRaEUr1AVlcRysXWfHtL7UCAG79ex8+3ZP8gSifAkyK0gw4zaw1xmUxF7SLY9fOfm9CsTO1bstG359rl1g1M+CALAObIgCkgXltpRYLOyMBeIoM+NuRAHzN0tQBuFyCQq+JZAFrXbUOGl7Un2dbFJhNBrwyi1k6t2z8i4650YeRbCwIKVEryOyv72BIgNcvFj6nk2bJ5Sm2Wn1K+VkuEhSaLW+q08fYTtriMuD5FmBSGmUzKDMVxaNze3s7tmzZAofDAQB4/vnnpWw4AOzbtw8dHR2qb+B0x5Jjh8BMbeiB7FrR08F1js2gqPiSUiXTLyoptqVPv7Yssn+pkIowVciAuzxhEAJUGLmcp8Xy6WqajD67D6EwQXO9HiaD8myihheDJK9ffU9yStat6Cuye1BLB82Ad6mQAbfV6tFUp4fbK0i1CfEQQvDkG2MgBHhu4wTOv/4z3P/0ELz+cEzxpXyZgOjZHU7iRjOcYwAebcaT/w3H46UFmMqDZCpLUypBySa7DgCnHV2PU4+sQzBE8OO79ia9sdKHn4YcCjApNADvHfKllfNQd5yWODlepVmLlgY9AkESo/3ds9+Lf74wDABYkEWH32wCPzlRD3B1AnCatUxViEkDw9oqHRbJ3GDiz/HBUT/2DvpgNvKSxCsZcgmKPYUFIQBoeC4mW54NvmwC8CxmIuj92lKhSfpAl63PP5BfEeaUTPaVTpplrdCAxsbJCjApuTTjSXW9xM9gjOdZgElhGXAZc+bMwdFHH41Vq1bhC1/4Ah5//HGcc8450vtPPPEETj/99IJs5HSGBibuNAH4ho+d+J/f7sLt/+zHU2+OYdset9QdMZ1jB70Bur3hjLrRaBet7J5K9ToeRj2PUJjAq0D7nE+hYzySDaEKLiiTkqY+d+MftbthRh1QlN9gdVoe7U1GEJK5HXquhCPHmuOACoUaYmuOD5rxEBINevJpwiPnoPlikLC5J3nWdWe/F6OOIGortThyRRV8AQH3PW3H16//DB/3uGHU8zhmVbX0eb1OdAwRhOQFQtHp9uxuQGq2o5cy4FlowJXelN1Zzo7I+daXW7F8gQUTkyH86K69CQ+R+XiAUywVGtRVacUGKmlkDen8vOM7Yo47g7j2D3vg8QlYt7IK6w+uVrw9kgQli2vD6wtjeDwInZaLKXjLh0wZWLlzRZVFi+Z6PfxBkjCT8M7WKQCi0028nltOtVULnZbDpDssLSPVQ2kuOnBCSFYSlKgfu4IiTNlsqdQAbX90vM3GgpAyJxIQyzPpSlE6e8vznHTvTeYBTmmu14PjxP2t1NKWZsCpVI0S3w2TtqHPtQsmZTZYEWYVjTz44IO4+OKL0d3djZdeegnt7e0AgPHxcaxYsQLHHntsQTZyOqOkXfxDLw7jg89ceOL1MfzmgX58+1c78a+XRgCkv+A0Gg4VRh4CQcbgWEkFdSqyqeKnfuG5FDrGYzFpoNVwcPuEvGzkgGgXTEuOzgqA+hrw3VIBZnZync4Cy1Bcsi6Y8R3uUlFh5MHz4nmYj0f58HgQXr+AaqtWstTKl2URGUqqQsyNW8SAYs1Blbj+kk785ntzsbDdJGWqjlpZlTATkCpgcHvDcHnDMOi4rB92G2rUy/jQ8aBC4QwGID7wGnQcptzhtIXd2WrA5Wg1HH58cQdaGvTY2efFbf8YiJlZy6cLphwlXWOHkhRgUuQ6cF9AwI/u2ovh8SAWd1Xgf85rV3xdANk3gQGAvojUpbXBkFchmxwqQUnVQGosrnnKohQylI1bM+u/ATEYpA+Vm3eKD7+pmo3logMPhQkEAdDwUDSrK2XAFXQkdXnFfWQxaSRv+T2D0cA5mzb0lLlzTNBqOPQO+RVZcsqRZ+QzUWUVP5OqABMQkwgNNToIgvJ9Hs2Ax/5mSYIS8QKnM9a5NuGJXy7LgEeoqKjAD37wA/z+97+PkZ/U1tbiF7/4heobNxOQbAjTBG1jDvGEPfvzDTju0Bp0tRihiRyZBR3ppzppFjyTLCKfANyaRfFKvoWOcjiOU02GEs2A534zU1uCskcqwMwu00ulGYUKwGn2NBsbOI7jcnZ7kLNHBf/veKQM+E530pmit7c4AUQDioPmWXDH/8zHNV9vw/qDq/H1k5oSvpMqAJfrv7N18Yg248k/4+Px0iY82R1DSYYylnp2JRqA5/YwW2XR4qffEBv1vPjuBJ7eMC69J0lQ8vS97lBgRUgz4MmymFErQg9uua8Xn+31oKlOjxu/0alI7iDHbNKA48T9lqnJEWXfINV/q1OACWS2wZuIPHDSTPmiuFkAQMzMf7TDBY4DDj0gfQAORIMoev9J5Zmdiy+1PxCxIFQ6S2dWPktHi9AtFRq02QzgeTEDTDXnQ2k07anQ63jMbRVnL3f0ZpcFz6Z+qT5y/DoySJey7X47KGXAY89JS4UGZhMPn1/ApDssPeDlrwGn1qzqOEOVI4r3UG9vLx566KG0n+no6MCXvvSlvDdqJiG3ISSEJNyUCSFS5uGrJ9hgigTUgaCAQJBkfOK1VGgw4gjC5QnDlsbaOypByT5rlY12Lh+rv2RUW7UYdQQxMRnKWlMbs12R32/NKwOubhEmnYrMRoICRJ1Q9uYwlakEVxbZFjmWCg2crjCm3OG0VlnpoBnLbPdJOprr9Gio1mHEEcTeQV+M48y4M4jt+7zQ6zisXGSVXud5Dp9bXYvPrU5+UaUqWJQsCLO4MVNoM54RVSQo2bugAOLv2jvow/6RQEono1zPDzmdLUZ89ytzcPNfe3HHQwNY0G7CgvYKSXqXbwZcSSFmfBMeOfPnmMBzYlH5rn5R73zTN7ty0rVqeA4WkwZTHnFmQcnMTq/K+m8gczfMsTj3imQZ8A8+cyEYIjigu0JRMqdRNmYn8wCnRL2klQfg2chPAPl9TIkLSjQDrtfxmNNoQO+QH/uGfGhtMGDKLc5yZSuzWNhRge29Xny21yNZPSohmgHPvL6L/6sZKz5zYcWi9MtvbdDjox2iu8shSzJvg+QB3pB4vTTV6bGr34ehsYCsCDO/a9hk0KDKIt5TJqZC0oPhTELx2bNjxw5cffXVWLp0KbTa5F9bvXo1C8Dj0GlFDbUvIDaYMMXdEN1eMdA2GfiY9/Q6HnoF55tSWYQ6EpT0A1cwJMDjE8Bz2d/4U6GWFeGkVNSaRwBuVE+C4vaGYY9oPNNNFSaj0BKUbNvQUyortBhAIC8dOA2YOlTMgHMch4Pmm/HSew5s7nHHBOB0On3FQoviGzmQOQOejQUhRcqAqzDl6smiDb0cenNN54TiyrJANxXHHVqDrbvcePKNMdz4p3249cq58PjEJjy5yFvkdGaQoBBCZBKUxOvPZNSgvcmIvYM+8Dzw40s685qVsZrFAHzSrTQAF7OSalkQApm7YY7HZcDntZnA8+IsgtcfhsmgkdxPMslPKPJi/LoqXUpXDslZKAu9bzYOKEC2LijismnGuavFKAbggz7JMcdWl/0s18LOCuCNsaQWl+mg15ySDPiC9gpFRcL04W6XgqZuwZCA4fEAOC65jt9WKwbg9vGAZMSQrwQFELupOl1e2McDszsA7+rqwsEHH4yBgQFccskluOSSS9DS0lLIbZsxmE1iAO7yhhMC8GjleW4nq1JZRF4SFIUOF/R9q1m5djgT1SpZEUoSlDwy4DTjp0YRJp0a72g2Zq3xbKrVw6jnMeYMwekKJdzQCSH4ZLcHc+cYFburyKG/L9sAy5Kj24McqQBToS2jUpbNt+Cl9xz4qMeF/1pXL71OA/A1S5VbcwLyADx2+jbaoCP7DHhtlQ58xI4tEBQUd7BMRjZt6OXQgsR009L5FGHG880zW/DZXg96+rz40V17AIjyk3wbMNHp994hP8ICSWiWMjEVgi8gwGrWpAz2Dz3Ain1DPlxx9hwcvNia9DNKiZVnZT63C5EBrzRroOHFbKo/ICQErlLgFLkXGfU8uluM2NnvQ0+fFwd2m/HOJ5EAPI39oBx5BjyZAwrFlkMRpuSAovA6obOymYowgyEBvoBo+UflLZ0tRrz2oRN79vukh9psmjFRksl6lKDGrFM8iyNe75/uzrwt9vEgBCImCZKNS3IrQrVsCOlye/q8+O0/B9DSoIfFpIHZpEGNVYsvrK1N2QRquqB4dJ47dy7eeecdPPHEE+jv78eSJUtw5pln4uWXXy7k9s0I0lkR0gC8PsenO6XdMCUJSgGLMNV0QKGolQGXijDz0IArKahVyu4sO2DK4XkuZYtkQLTSu+LWnbjvKXtO25ZrhlMqNstRAy4I6jugUJZFpnu39Lilor9AUMAH28TCzMMOzC7AigbgsQ+GuVoQAqJUoT6ifR7Ns/1yNAOeZQAuWRGmyYBTfWyeWWpAnOn7ySUdsJg0Ug+BfOUngHjuNtbqEAyRpN3+0jmgUC7+r2Y8+PMlWTUuS0VlFv0UCCGSFjqdlVy28DwnyVDim8EQQpLax8n9wHf0ejExGUJjrU7xuCW/DtJdE3WVWmg1HBxTIcX2qtlKUGiwluk+Js820wfBTllNwVAODiiUtiYDTAYew+PBrBryTOXZwyIZ89tM0Gk57B30ZYwfqF94vAUhhY6H/cN+THnC4Hl14oBFkYeEnj4vXvvQiac3jOOhF0dw16ODOd/fyomsUywrV67En/70J+zbtw9HHXUULrnkEpx99tmF2LYZA71okgVu1GmhNtcAXGGjHxrA5uIsUWlRpp2j71eppP8G5M141CrCLA8JCtV/d+cYaFLnlGQylMdfExtibdo+ldOyc5Wg5GtF2Dfshz9IUFelVT2z0dqgR12VFg5XCPsi0/sf7XDBFxAwb44xa9/pxho9eF58gJY79ETb0Od2PTdGtiPfQsxoBjx7DTiQPhOZjwtK8nUacPXX26S/8y3ApNCgKdlDajr9N0XDc6pNe2fjJOUPEgRDBHodl9MMVjqi3TBjx9MpTxjBEIHZyMcEtDRju32vJ8b9ROkMhdyKM13BIs9zWdvOZStBscqSVemK+lyexPNbasYz6IsW7+aQAdfwnNToaXuKLqPJtymiSVcxANfreMyPNMzbtjd9p+B0+m8gui+2R+oFqi3ahFmnXDj7cw344w/m45Zvd+NHF3XgynPm4OzPNwAAXt/kmPbFmTlHI1u3bsU777yDsbExLFq0SM1tmnFImdMkgUm+eilLmuCeEg4TTHnC4Ljc3ElKmgGvVDkDXiZFmJIHeJYOKJRUHTF39HqkCvvd+31SligbpEYrWUtQlGf57GMBXP+nvfjmL3bg3B9twwU/t+Oin+4AoK4DCkXUgYtZ8M0RO0IpoMhSfgKIFqCNNXoQEhsw5NoFk2JTSQcuOdlkGYDTDPy4M5Sy8Va2XVKVcPhBVTjn+EYA2TW5SUdnGrcgJQG4mmTjEJQsAFSLuhTdMKXsd9wDh1SIuc8bbT+vUH4CUDmR+P9M10S2VoTRJjzKAj2NhoPZxIOQ9PfLZHKPlnoDdFoOw+NBqaFXLgE4EGtxqZRCZMAB4IBu0SHqkwwylGgGPPlvpg9adGZXDf03II7b89sqcPASK9atqsYpR9bh4tOa0Virw5gzhG17spPylBtZRSNTU1O48847cdBBB+HrX/86li9fjl27duH6668v0ObNDCxpAnAqQck105IuuKc43SEQEtEA5uApK908MmTAJQeUNM2DskXSgOfZjEeNDLiShx0lEEKkoCBbD3BKqkLM/8gs3QQB2NWXPsvyyW53gs9qrgFWZRY2Xy++O4E3NjmxvdcL+3gQgRCg03JorNXh5CPzn/JPxrJ54s3m44gMRUk77XTQYJnqwANBAeOTIWh4sb12LqidATdlKUGpMEaLxqmTipxQmMDnFwutTVk0+VHCxac14/7rF+GUo9Q5/h1pnFDoMUtWgFkIrFk4Sak9wyAnVSGmvA29nI5mI4wGHkNjAfT0eWHU81iRhXuHTstLy8zkDGSTGrooC8BpBtyURfF0pYIkwVSSByCNhpMKYrfuErPFuQbgqfzV05GNDWE2LOmmOvD0GfBBKQOe/HqhD1dCZMjI1wElHRzH4cjlYtLk9Y+cBVtPMVB85n744Ydoa2vDk08+iZ///OfYtm0bvvvd76KqqgqhUAihUAiCkH9x2kwknQQl37atSqb9qQVfro1NqhTePKQmPKpqwMX9kr8EhQ6qeWjAs5SgPPjCMJ59ezzh9VFnCFOeMKxmjeTNmy3dLVFNIs1Uev1hvPTeBADggO7MxT79w35ceetOfPOWnpjueO4cfZ7T1TrEQ2d+vnxcA+6/YSH+fHUD/nPbgfjHz5bgqBXVWa1XKfIM+O4BH4bHg6ip1EpTwtkiyTUiNycaNDfW6HOefqX653wz4J4cM+Acx6X1i3bL6gPyLZRMRmujQZWpayA6S5ROgpJrEJUtSpMYQLRrslnlYAuISlDiu2GmkkJqeA4L2qLXx8pFlqyLg1cussJaoZHkDqmgjaucChrlAKJUB1AuQQGUOXqlchyhD3ThSJiT6+yJvBAz1SxTPNk04smGJV1iUmLbXk9aj/pMM0bWCk1MvYkaBZjpoPeINzY5FO/DckTxmTs+Pg6n04lnnnkGp5xyCvR6PXQ6Xcy/z3/+84Xc1mlLuszpmINmwHM7YemAOZamaIvKN3JtDStVj2fUgFMJinqDBA1I9o/4EQzl9oBHCFG3FX2SzGA845NB3P3oIG79W19CALBnIKr/zjWIqanUotKsgcsblgr2XvvQCY9PwJKuCnz+MNG/Ol2W5YNtUxCIeH78/N5eaQDOtQgzm2l2R+QmO6/NhJZ6sTCpEAGdnPYmA6qtWoxPhvDvl8VOs6sPqMzZsaepTswG0aYc+VgQUqhrhHoa8OzP92jL8sTjmGt9QClobzKA54B9Q76ELLiU0SuSBCWb2SGXr4AZ8BRFmBNppJA0Ywsotx+Uc/V5bXjo5iUZE0DWLIu4fVlqwOXrmEzX6TXFA5C88NRszN0qs7FWh2qrFlPusOLGQ9EMuLqBbX21DrZaHTw+IWXTKkJIxgw4x3ExEiO1JCipWNJVgboqLezjQfRkmOUtZxSfuYceeijefvvttP9+97vfFXJbpy3pZCJUi5erBIW6p4ym6G4GRLPHuWbAperxDDePSbf6GfAqixbtNgP8QZKxe9hDLwzj13/rS3iSd3sFCIIYjChpWZwKk4EHzwE+Be3W6UOPQID7nh6KeW9PHg4oFI7jEnTgT705BgA46Yi6aPFUmgy4vDX7Rztc+PszYlV5rhrUaJYvc5CRjy1mrnAch4MiMpTn3xFnCnKVnwAyJ5TIzYkWLubShIdCW3cPT6iTAc+mFT2Fymfis6SA7OGsANlZtTEZNPjC4bUQBOCX9/ciHI6634w4guC5WJu8QqLUgQOQZcALEIDXpni4GkszEysPwA/NIQDnOE5R1jybhm+AOA4Dyl1QYtaRJsueSu7RKZMLNtVn7wFO4TguptOqEgqVAQeiOvBPU+ipxydFy87KNJadQKzGP9dGbErheQ5HUBnKh9NXhqL4zK2srMRhhx2W9t/cuXMLua3TFmsaq8D45gfZosS2zJlnsCPf/nTTVFIb+hwD/VRI0oGdqXVqXn8Y9zw5hGfeGseOuKDT6VJHm87znOQqkUmGIr+JvPahEz190W2KFmDmJn2gdMlkKHv2e7FtjwdmI4+jV1ahs8UIg47DwEgg6cwFIQQf7xD353fOagXHAff/x45N26eiXRSzDcAjN7dMllaALABX+VzJxLLIuUSIqDlflaFbXDqkbpiRwDsfC0IK/e7weDDnqVVBIFENeA46bZq9ShqAF7BAsBB844staKzRYfs+Lx58YRiAOLtAiBh85/NAng1ZtUEvqAY8Ii9KkQFPNhO7dJ4ZZiOPVYssqjnUJENpwzdKti4ogLIsezINOBCbMMlXukQfapQ4oYTCBN5I3UWFynUXgLwQM/n9lTaFSpX9psh93gudAQeAI5dXAwBe/2j6ylBUOZojIyO4+uqr8eUvfzmr7+3ZswdvvPEGRkZGMn5WEAR88skn2LRpEzye6VX5mqpZjtcXhtcvQK/jJIeNbKk0a6DTcnB7hZT+qdE29LldFBqN2EqZkPTB1WQBijAB4KD54gCxRZaxjWfLTjeCIfEi3LIrdiBRMzMvOaH4lM0GUO59MpoFz7UFfTw0I7N7vw9PvylqzY89pAYmgwZaDYd5bdTuKvF62Tfkh8MVQl2VFqceVYdzT7CBEOCme3sxGpE/ZN8JM/P0LiUfX/p8WLbALP1/+QJLQmOsbIhvHmIfy8+CEBDPL5OBh9cv5FzsK/kjG/ic9NT1aSQohQwOC4HZpMFVXxUtDu//jx179nsxGGkyVCz9NyD3Ac/CBSUPuVwqpHb08RlwZ+r24bWVOvzfjYtx42Vdqm+PnGxm0IDcJChKsuzRRlOxY1NjjU5qzJPvuUOdUJRkwOWSGLUa3MmRCjH3JA/AX/3AASBaV5QK+cxfIYswKQfNM6PKosHAcKBgXaELTVZX+FtvvYV169Zh8eLF+N///V+Ew2HcdNNN6OrqwtNPP42LL75Y0XIIIbjooouwbNkyXHnllejs7MTtt9+e8vOPPPIIFi5ciLPOOgvnnXce5syZgwcffDCbTS8pqZrl0Gm/usrcO79xHBeVoTiSD+6OKXG9+QQ7UR14mgDcpb4NIQAsnSsGTVt3uVNm4D/4LBqcf7IrPgOunjY92g1TWQb8sAMrYTTweGfrFD7Z7UY4TCQf6nwDcPr9Hb0evPiuKKk48Yha6f10WZaPd4j7a9l8CziOw3kn2bBsvhkTkyEpOM52utMiKzRLl5EIC0Qq2FV7tiQTHU1G6TzIxk4tGXWVWui0HJyuMLy+cN4WhIB4PUdb0uemA5dmMHLQfwMZJCgFDA4LxcGLrTj5iDoEQwS/vL8PfXbqgFLMAFy5D3ghH3JqrFpwnDgrKJfRjTvT1yJVWbRZST1yIVsJij/iv2/UKb93KimGjWbAY38vz3OStWXeGfBIAL6zzytJozJtj9oOKJTuVhOMeh4Dw4EEu1+vL4yX34/cW9amdydqignACz+uazQc1i4TZShvbJqeMhTFV1QgEMCZZ56J6upqnHHGGfjZz36GE088EXfffTf+9Kc/YfPmzTj55JMVLeuBBx7Ao48+ii1btuCDDz7Ak08+iauuugrbtm1L+vnx8XG89NJL2Lp1K7Zs2YIf//jH+PrXvz5tMuGpOijSwslc29BT6jLIUNTQ29JAyZGmJXwhbAgBcaq4uU4Pt0+QPFjj+fCzaNOZrbvcMQGgqhlwI32YSl+ISQPM9iYDvhhpfX7vk0PoH/YjGCJoqtNn3SQlnmijEbH72IJ2E+a3RbMUi9JkWaj+m3aI1PAcrr2gI2aWJFsJil4nWtiFhag+MxlT7jAEIt5QiiUBoPA8h9PX1aOz2Yh1K6vzXpbUgnk8oEoADuRvRejx0i6YuZ1f0SLMNBrwaZIBp3zji82w1eqwo9eLvz8jSlGKGYDLZ0HTyfjoZ+TfURONhkO1VQtCorITIH83LjXItgjTL5vpUYqSID/qgpJ4v1h7UCU0vDh7lg9VFi2a6/XwBYSkNpkx2xPZH9YCXXNaTVSTHp8Ff/VDBzw+AQd0V2Tsz1DMIkzKUSsiOvCZHoBv374dBoMBjz76KH72s5/hpz/9Kd5991188MEHOOecc8Dzyi+Chx56CF/84hfR0dEBAFi/fj0OPPBA/Pvf/076+Ysvvhjt7e3S38cccwz8fr8i6Uo5kEoDTgswc+2CSalPk7EC8pegAPLq+eSDoyAQaerQqnIADkRlKJt7EqfJxieD2D3gg0HHodoidjvsH44WsUW16flvl+SEojADXmnR4sufa4DZxGPTdhceeUXsUplv9hsQM86NsrbdJ8W1zI420Yi1u5Lrv6kmGhDPox+c3w6OEx/WcgmOLQpkKKWSn1DOO6kJf/nRQlXWT4O4geEARiIPwPm2Um/MohnP0FgA72+bignqpALMHPWidZJVXRoXlGlQhCmnwqjB9yNSFHr+FTMAlzeByTR2uL251WAopa4y9vj6A6LcSavhVE+eZINcgqKkw6EvkLsNYTotfrJGPJRzjm/E07ctRXee9TuATIaSoSFPIQswKUtoIWZcQ56nI30lMmW/gWgGXKflCpatj2f5AgssJg32DvrQZ59+MhTFd6CRkRF0d3dLUokFCxZg2bJlqK+vz3qlPT09WLt2bcxr8+fPx86dOxV9/5577sEBBxwgBfDxBINBhELRm4fXK2ZNCSEFF+vTdcjXY5a1i5e/LjXhqdTmtV31kRvmqCOQdDk0a11l1eS8njppHcmLw1zeEAQSdRpRez8vnWfGcxsnsLnHhS8eE3vOfbhtSvqMyaDBGx85sXWXC3MaxQGBFmFazZq8zwE6re/2ppdZSFn3CtGu6svHNeDeJ+2SU0lXi1GVfdTZbMTwRBBGPY9jVlXFLLOpTgerWYOJyRCGJwJSZnXfoE/Sf7c26GK+s2qxBb++ohsGPZ/T9lkrNBh1BDHlDsU8HMihmbdqizbmepmOhTRU7711lwuCIF4nOm1+5z8tdLOPJ17PYYFg2x6xLfjGrVOSddiapZW49vw2mIwaqT6hwpjb9U6zV+POIARBiFmGFAzkuOxSsmKhBaceVYcnXhevwaZafVF/g7VCC7c3gElXKG2AQjOeYsCefPvyuWZqq3RAvw9jzgAIMUn3ISobKNVx1Wo4VBh5eHwC3N5wxoDTF6l5MuiUX290v0+mkclFOwEn3//p7m/ZHJdFHSa8+oED2/d6cOLhtSk/NyVrQ1+oY7OkS3wY+GRPdPZ4z34ftu3xoMLI46gVVRnXXWnW4KLTmqRZ4mKcR1oNhzUHVeKFdybw+odOfOWE5IWixb7HKF2P4gBcEASEw2E4HA4AkOQf9G8A0Ol0MJvNSb4dSyAQgNEYmwE0Go3w+/0Zv3vnnXfib3/7G1577bWUn7nppptwww03JLxut9thMuX/5JoOQoi0T+jDCn2ad3sFDA4OSYUUffvFwFGv8cNut+e8ToNGvAn37nfCbk888LTiPeidgN2eW1bMoBGPTd9+B+z2xOzB0HjkpmHk8votqWipFpf/0Y4pDA0NxWjmN0S6YS1oBXg+jDcAvLd1FCu6xd89NCruZ07wwOEQs4q5au55iPthcNgJuz21RGBkXLw+hJALdnsIa5cIePglDlMe8fjUmvM75pSmmojW/AA9ppyjmIqbietq0mDzrjDe+XgQhy4Wr7k3PhC3bUGbFsPDw4nLjEij7faphPcyYdCK27OvfxQWXfIM475+8Xw16kKw2+1Jr5npgsUgnk8fbHMAAGos+Z//Ro2YMOgdnJKu17BA8MSbbjz7jgcub/QaN+o5cBzw9pZJfOuX23HV2dUYHBLPSw0XzHlbjHoOvgDB4LADHMdJx2V0XJw5CQddSceBcue0w3m894kGTpcAo8aZ0zmeKya9mNne1z8CLUk9SzIxGems6nXCbk8uucvnmqnQi+fH3v5xzLX5sKtPPIcrTaQgY3c2VBgAjw/Y0zuExpr04cmUS9xPXrcTdoXZz4BXvI9MTAZS/tZJt7h/vK5x2El298tsjkuDVdzvW3dNpt3v+4fE8VrDpd7mfGmwiOfmZ3vcGNg/BK2Gw8MvRDoFH2DApGMEkwqWs34ZAISKeh4d1EnwwjvAy++P4tgVqR+MinmPoUnfTGQ1B/vaa6+hpqYm5jX538ceeyxefPHFjMtpaGhIkI+MjIxgwYIFab9322234eabb8ZLL72EAw88MOXnrrvuOlx99dXS316vF3V1dbDZbEUJwAHAZrPFHGizaRRurwBLVb2kLfOH/AA86Giphs2W+gk4E51tDgAueAJ62Gy2mPeCIQEenx08B3R3NOVcRd3eOg7ABW8ocR0AMO7xABhDTWXy9/OlsZGgvnoSo44g/KQaHU1iMEkIwaf7xGmyow9pRjBM8Lfnd2LXfkHajmDYB8CLOU01qK72JxybbKivIwC84LUVaX+nPyTe2Ntb6mCziTKPc0/Q4o+PDAIAli+2wWbLX4Zy3il1qK4aw2lH1yUtaDxoAcHmXcMYckSPy+6hfQCA1UvrYLOp2/a9vtYL9AbB66yw2aqTf+izUQBO2OrNsNlsKa+Z6cC8DgcAF/YOijf2tqb054WiZXa6AExiysvDZrNhaCyAW/7ai08i08Mt9XoctrQShx1oxdJ5ZtjHg/jhnXvQaw/g+nsckj9uTXXu21JXPYGB4QAIb405LmHiAeBDs60GNltVXr+zVNx1bSP8QZJzY7Jcqa12Y8+gC1pDFWw2a8rPBUIOAEDbnIaUY0Q+10yrjQDwIUhMsNls2DHoBDCBxrr8z918qamcxKjTC52pBjZbetcNgZsCEECTrQ42W+bEHwAYzSEAY/D4kPS3EkLg8YnBY1dHU9YyvGyOS2W1AJ6bQN9wCNU1DSmlNJzWDmAKjXWWgh0fG4DWRicGhgNwhyrR2WDEW1tFueSZx7VmPBal5NhaAX94bBJ7B0MQNDVJpWXFvseoHoDTRjzpqKpSNiCvXbsWzz//PH76058CELPpGzZswAUXXABAzFRv2bIFxx13nPSdn/3sZ7jzzjvx6quvYvHixWmXTztzxiPP5BQSuh75uiwmDdxeAW6vgEqz+LrkglKduwsKIPMCdwYTljPpFp9sKy1aaDS5V7HTdYw7Q0m3lU5NV1m0BdnHtInKy+87sHmnW7Lg6x3yY9QRRLVFi+5WEwQiNmboHw7A6Qqj2qqNykEsWnBcIK/zwCLrhpluGVOS7jx6bE89qh5PbxhHICigrSn3Lphyaip1+PrJTSnfX9Qp3pg+2+sBx4lTp1RHv3yBVfVjRR15xieTnydAtAtmtTW6b5JdM9OB5npxypNKVm11uTfooFA7r+GJIF5534Hb/tEPt09AfbUOV5/XhhULLTHraLNpcMf/zMeNf9qHD7e78OQbosTCbMy9u2h9lU50RXAJMcfFFdEnWysKc50XA7NJC3Nh8zBJkawIPeG0+05eBJjuc7leM3Vx1yit66mryu8+pAa0fsiVYR8BsiJMvUbxdlvNWvC8uI+DIZLQIMjjE8R7iIGHTpvb/VLpcakwatDRbMSe/T7s6vfhgLnJHyJowX+hr7kDus0YGA7g090e9A8HMOUJY36bCQs6lD3clAqDXoPDllbilfcdePMjJ778ucaknyvmPUbpOhQH4LQRjxpcccUVuOeee3DppZfi85//PO6++250d3fji1/8IgDglVdewTnnnCM9tVx//fW4+eab8dvf/hYDAwMYGBgAABxyyCGKg/5SY6nQwD4exJQnjObIa1R7l2/luVSEmcQFheqf8832SM4Ik8llF4XyAJcjD8BPPUrUgVP3k5WLLOB5DjzE4sOPdriwdZcbRyyvihZhquCCYknT1VSOVIQp2x8GPY87r5kPjuOK5v5Bq9t7+rwICwR9Mv9vqpFXk7qq9I48QLQxVLEzkIUg3o4sXwcUQAx+OU60Ibzp3l4AwBHLqvC9c+ektG20Vmhx87e78bsHB6Q6g3w8zulxnHDFutlM1yLMckCJBR4Q3ccFK8KsiiZTgPRNeIpNZRZe4Lk04tHwom3v8EQQo84gWupjNcPSw0+RXH4WdVZgz34fPtvnSRmAF9qGkLKky4znN07g0z0eKTY5aW3uM/PFZN3KarzyvgNPbxjHmcc2FMQvvRCUxMy1ra0Nb7/9NjiOw7333osVK1bg5ZdflrLWTU1NOPbYY6XPj4+P48gjj8S//vUv/OIXv5D+9fX1lWLzc8KSxD0j3y6YlHqZbVh89fiESi2/pQA8hde4s0Ae4HKW0o6YPS7p4Yz6f69aHJ3SPVDmGw7IHg6K5IJCCMGkJ1r4Kcdk0BTcT1dObaUOjbU6eHwC+ux+fETtB+dbCpIJyOTIAwATJXZBUZMqiybGBk2NAFyv46WHE4OOw3e/MgfXX9qR0TNdq+Fw5Tmt+NaXWtBcp8chi1PLHDJBr3fHVIoAfJrZEJYDShw4AkEBgSCBVsPBkIW/dTZQ21vq/Z2uCU+xycYL3JeDDSGAtD776RxQCkG0V0NqJ5RibRNttPPuJ1P4uMcNo57H+kNqMnyrPFiztBK2Wh36h/14a7MStXp5ULI74MKFC3HXXXclfW/dunVYt26d9Pdvf/vbIm1V4YhvxqOm9ZNex6PSrMGkOwzHVCjG1tCpUsOTSrPo2ezyhuELCAlBZDEy4B1NBlRZNBhzhjA4GkBjrV7ys165MGqnJzXu2S1WdEez0Vo4Mzu7pYVaGdL9mgy3V4AgiI4wuU5jqsmijgoMjzvx2V6P1IAnXx/bVGTypAeivvTFbsJTCDiOQ3OdXurEpkYADgBfXN+Aj3e4cPkZLejI4L8bvz1fPKYBXzymIa/10yBtYio2EJJ8wFkGPGto7Y8SD2qLSbmsIluopSyVQI6XUQacJiwmFXiB+yM2hEZ9dvupoUYPwIORicSbQbED8PmRbsW7B1IXkcpdUApJR7MRFUZeOgfXraqaNn7/Gg2HM9Y34A8P78dDLw5LdTDlTumjg1kCzRjR7Acd9GortapMl8h14HIcKk33cxwnDdDjSbKbct/rQiHqwCNZ8J1ufLbXA49PQJvNgEZZ4LO4qwI8B/T0euFwhRAMERh0nCqZ5xprZGp+KvUNQs3GP2pA/Wa37/UkNOBRm7o0bcwpM0mCAsTKUPJpQy/nnM834hff7s4q+FYTOqvmkElQwmECj08Ax+XuMT6bsSrIgEvykwJ2GqV2gxOT4owpnYktZRMeipQBdynPgBt0WWbAa1JnwKNdMIsTeNKeAelmDKM1AYXdJg3PYXFntNhSifd3OXHi4bWwVmjwyW4PPtmV2C+kHGGjaJGQMuCRi4kGKPk24aHUp8g8TqiYbaxN0yGvGBlwQPT6BsROjh9si+q/5ZhNGnS3GhEKE7z7ifgZtbKt0ZtXugA8Uf9dSug056sfOuB0hSP+34VpQiJ/EEzlhTqTJChANAC3mjV56a7LCUkDLpOgUH9xs1EzbTSW5YRUhJlGA+4qQgCo1/GwmjUIC2L3YnkyqNQoeUgBgFCYIBQm4Dmx8Us20GSNPVkGvMg1DtUWsSh00h1GIJi8e3AxGvFQaEOezmaj5A0+XTAZNTjlSPGh4cEXE+11yxEWgBcJevG4pQBc3UEvVSGmU8Vgpz5NdlMu8ygktCPmlh43PtwuBterFiVqXakO/K2PJyPbpc7gZa3QSFX0qQZMZ5EeRpSyoN0Ejoseo+ULCqP/BkTnDaOeh88vwONL3D+hsNgxleMK0zG1FNAAXC35STlApUTyALzYwclMo1JqAqNMglJI6Fg+MhGUkjQ15RCAKyxUlRdgZjuW0Qz4yEQaDXiRMuA8z2XsMu0qUhEmAJywphYHdFfgG19sLrkjTi6cvq4eOi2HtzZPos+eua9MqWEBeJGId8+gMo58CzApdEBNJUGpVqEAMdqiunQZ8O5WE8wmHoNjol0SzyeXU9CK8vcjWXK1Hgx4npOkE6lkKJNFKEjNhgqjRvJNB2Lbz6sNx3Goq452TY1nktYkmLXQzJAsaleruG87SyQXKQS0XbljKtq9NxqcsNtGLtAicGea4LLQbegpNPGzZ78PgiCO2+VQr1KlsAgzFwcUCu0InLQIs0hyDzl1aVzM5LIvcxFm15rq9Pjt9+fj0AMqC76uQlBbpcPnVteAEOBfL5V/Frz0V9wsIV4DPibzXlWDVMVvDpVcUID0EhTqgqKG1V86NDwnZbcFIhYYJstW0EJMqhNUU5suBeApMhbFehjJBmpHCBRO/02pT3OeqOXKU06sXGjBzy7vwmVnNGf+8DTBZNSgwsgjGI4GJYW2x5vp0HPekaZ+pFizDHQs7+kVG4aodR/KF6VFmL4g9QDPIQCvpRnw0hdhArLEVhKLX6nomcm+FPOlY8UC9Oc3TkjyqnKFBeBFIl4DTjPgtSpVnten8F92SBKU/AfYdAV2xQw6aSEmEGs/KKehRi8NtGpvF7XrSpkBj2RvrGWSAQeiOvBC6r8pqeoRgOj5WKXCjEy5wHEc1iytlAp0Zwrx1qM0OGEBeG5YTBpoeLHZSyr5WrEkEDTo6+kTA/By0H8Dym0Io014sg9hrBWiFazbJ0j3Y0ppAvDUGfBiyk9mCu1NRhx+UCWCIYLHXxst9eakhQXgRSKVBrxOpcrzaNATGxQ6pYxj/hdwqsymLyB61+q0XNaerLmwbH60YcGqRamzuUtljQ3UtLyTuwgkoxwz4IcdWIm6Ki1OPqKu4Nq+unQzJTPMAWUmQ4MyehyZBjw/OI6TsuCpbEyLpQGnjie7+r0xf5caq8yuN76nhRyfn0pQsh/LOI6TOaHEZsGL7YICpE9sFbMAcybx5ePELPjjr4/B68/sqFMqWABeJBIkKJILirpFmPKsYyAowO0ToOHVGVCotjc+sKK63kpz4bxr5cxvr0BjjQ6NNTos7krdJvdAWQCuagY8kwa8SAWp2dBYq8dDNx+A805K3bZeLaLNeBL3z0yUoMxUot1vxWNG9cmsCU/u0ERAqrHDTWcZChxw0WPrjQSyat2H8kWj4WA28SAk6rqTDJ/kAZ5bCNOQohCzJBrwyL6Pr98CWAY8Vw6ca8bizgpMucN49q3xUm9OSlgAXiSscY14os0P1Mk8VFk00GmjjXKAWAcUNQJjmiVJCMCLHHBqNRzuvGYB7rxmQdqW7gd0ywNwNTPgEQlKCg34VBlmwItJunb0UhMeFoCXPfFF18XKzs5kqi3KMuCFLriLD7jLRQMOAJW0YVEaL/B8ijCBqBVhfAa8FDKrdDOGxWrCM9PgOA5f/pyYBX/45VGE08ymlBIWgBeJaCt6AcGQAKcrDJ5TLxMoNsqJ1ZKpnW0UK+U5uL1CzLROKSQX1VZtxt/V2WKEOeLYoKbmeDpmwItJuowODTxqZkAXzJlOvDtDKfSxM41MhZjuIsl84qWP5WBBSFHiBe4P5huAi7/fXhYZ8NQBOMuA587aZVU45cg6XHt+e9k6brEAvEgYDTx4XtRL02mvmkp1rdjq4+zf1G75LQ/yx2XygmJ0wcwFDc/h2ENqYDbxmDvHlPkLClGsAZ9BhYbZkMqTHpA1hmIZ8LJHalkeudZdzAUlb+hY7EiR3Y3OMhT21pyQAS8TDTgQTeSks2uks7zGLLtgUqgVYYIEpQQPmdHxkmnA1UTDc7jynDmSJXE5wgLwIsFxnPQUu3fQB0D9wpe6OC9wKduoYrAjFWbJgk+nq3wlF//vy6147FcHqrqvM9sQzvYMeDSjE19IpaYtJqOw0JkMKpdzMwlK3khFmCky4MUqdDUZNDAbo7f/ctGAA1H3qKk0Tih5S1CSFGEGQwJ8AQE8D5iKYCZAqTRroNXEykcpUywDPqNhAXgRoTeu3iGxQ5Pag158IWYhCt6SNQ2gAWehPcBzgec51f1TpQx4kptoMCTA6xcLX+U3uNmEXsejMtLq2hGndS3EQyGjMMRPjTMJSv5UZ5CvFbMTY61M910uLihANJGTrhumlAHPMVCWNOCyDLi8xqGYXSDFmeXIw64zuSTGUsHGy5nI7IwQSkQ0ABcz4GoXvsR7gTtd6kpQ5OsYi5GgUMnF7BgkKs1a8LyYnQiGYjMWcg/w6djKVy1S6RrVlkUxCket7FonhLAiTBXIVIRZTKcZOptp1POoKKNkgZUWYabJgEsBeA42hADQIEtW0QI9l0eIrL/453eq8ZJlwGc25XPVzQJo5kjKgKucdYjPgBdiuj/eGxiQSy5mxyCh4TnpRhpfTFWOHuClIJkvvdq2mIzCYjLwMBk4BEMEk+4w8wFXAakIM0kAHgoTSQJRjH4KNOirqSyvZEGlkiJMKkHJUQNu0POotmgRChNJSuiijiMlGJtSeYGzWaeZDQvAiwi9iPZJGfDCSlAcU+LFW61itlGSoMQE4DTonD1ZzVQyFGqdNZv2RTKSZXTktpisrfL0oMYq3iLGJ4NRj+oCFwjOZKpSPLgDUY29uUgSCDrDofZ9KF9oAD6ZRoLij/iA56oBB6JOKMORlvSlfMCMt/yksAz4zIaNpEWEPll7fOLTu+oSlOrYIkyHqwAa8KQSlNmVAQdSF2KyDLhIMieUCSY/mXbURJx8hieCcEfGrYoCe1TPZGhH4mQSlGLqvwGgLpJEKCf9N5CtBCX3ECa+GU8pHUdS9U5gNoQzGxaAF5H4C1vtAFzuAy4IJCpBUTMDnqRYJOqCMnsCqxprxI4xzopwtjugUOqTeIE7C/BAyCgs1ZEMeL9dlM2ZjXzZeupOBywmDTS8mIQJBGPrR4qtsV86zwyDjsOKhZairE8p1L5VkQQlnwx4xIrQHnFCcZcyAE8yswywRjwzHXYnLCLxA2utys0PDHoeVrMGU+4wnO5QgTPgiRIUNZvdlDtRL3CWAU9GMrcc+kDIHFCmD9UWMcChsrlCt0if6XAch2qrFmPOEBxTIcmNAyi+BGJxlxlP/O/StN2ES0E0A67ABUUNCcp4XAa8TDTgYYFIRbnMe39mwjLgRSR+YC1E9zHqUjIwHIDPL0Cn5VStcLdWRLph+sRumOGwOEjw3OwqrEvVDbNcmxIVm3g5FMAkKNMRqgHfNyhmwGfTNV4oos14khfcFboNvZxyC74BuQ1h5gx4XgF4XDOeUnTBpNQnSWxFawLYrNNMhQXgRUR+86qyaKDTqr/7aeCzs98LQJSfqFnQE98Nc1I2RTabCutqIrrJxACcZcABuRwqun+YBGX6UWONLRxnAXj+pGpHH21DP7tvyzRR5fKGJYvAeHySBCX3e05CEWYpM+DViUWYUf03Gy9nKrP7Si8y8gx4oQpfaOCziwbgBQh25BXbs9X1I7UEpXybEhUT0elEzPJRrSvrgjn9oBIUmo1kWtT8SeUF7pK5oMxmNDwHi0kDQqJBaDxqasCpBKWULigWkwZ6HQePT4DXJ24Ha0M/82EBeBGRX0g0U602Uga8r5ABeFReMFszvlEJSqoizNm1P+LR8Jz0kEkfUiYKUBTMKCxUgkKZ7cGhGkS9wGODSzfzWZfIVIjpi9gQ5iNBqanUQhNJEvgDQkkDXvnM8lhkvGQWhDMfFoAXEfnUltoFmBQagO/ZL04ZFyLYkReMSBnfWRZUZSrCtM7yABxI9KV3RgIOlgGfPlDbPAqToORPtIlXcss5to+jQWeqQkw1MuAankMD1YE7oj73pZJ8SDPLkfGSNeGZ+bAAvIjESFBUtiCk0KAnGBIzBFUFlKCMO4MyC8LZNUhUWbTgOTHjHQpHdYrMhjBKXZwVYSFsMRmFxaDjYgJCFgzkT5WkAY/N7rqK2Ia+3KHjZ6pCTF8w/yJMIOoFPjwewFSJJUDxDmMsAz7zKdmd0O124x//+Af6+vqwdOlSfPGLXwTPJ7+YPv30U9x///3S35deeim6u7uLtamqIR9Y6wqWAY9dbiElKGPOoJT5nm0Bp4bnUGnRwjEVwsRUCA3VOhBCMDVLJTnJqI/ztmUa8OlJbZWW6ZNVJKUG3MP2MSVTN0w1XFAAoJEG4BPBkje9kUs7AcBFDQ7Y+TBjKUkGfGpqCoceeij+8pe/wOfz4ZprrsGZZ56Z8vM6nQ7V1dWwWCy45ZZb0NvbW8StVQ+9jodeJ1ZtFyoDHt/cpxCey3UyacFs1YAD8m6Y4oDp9gkIC4DJwBfE4Wa6Ie/u5vWH4Quob4vJKDzyMcXC2tDnTUoXFB+ToFCiEpTEDHhYIAgExVlHej/NFerDPjweKGkRJpDoBc4y4DOfkoymd9xxB/x+P1599VXccsstePXVV/Hkk0/ipZdeSvr5+fPn45prrsH3v//9Im+p+tCLqb5AAXi1RRvj7VoIbXZdJe0CGZrVvteSDjxyI53NDyPJkN9QJP23yraYjMJDpUQACw7VIJMPOJP5yCQoSYowA7Lsd75jCc2A7xvyQxAAo4EvmTd6vASl1Bl5RuEpSdT0wgsv4L/+679gMBgAAHPmzMHhhx+O5557Dscee2zeyw8GgwiFooOb1ys6ghBCQEhyX1G1oOtItZ7OZiNcHjdaG/UF2RaOEws8hyPNBaotGtXXU1slDghjMhcUa4X661GbTMcmW2ppBtwZBCFEpofXlv2+KAZSO3pHQJolqLYm7hu1jwtDHegxkReMm03lf52XO9URhw+nKxSzLyWZj5HPuI9n+jUjZcDj9hEAeGUe4Pn+fqoB3x2x7bXkeX7nc1zkRZiEEOnhwzwN7q3lTrGvF6XrKUkA3t/fj1NOOSXmtdbWVgwMDKiy/Jtuugk33HBDwut2ux0mk0mVdaSCEAKHwwEASZ/Ov316Bbx+I3zuMfjchdmGKjMwPCH+P+hzwG6fUnX5hBDotIDHJ2DA7gEACIFJ2O0+VdejNpmOTbboNWIDh75BB+z2IHr7xW6BBl0Ydrs97+VPeyIPwfYxH/b2jQIATPrEfaP2cWGoAz0uej7aLt3vdcBuL9DANUsghEDDi+Nn38AQ9FrxnKf1Ix7XGOyh9JPTM/2aEUJiQDw87k4YL0YcYmCq1ZC8x1mNICYG+obFsduoz2+ZeR0XOl6O+2C32zHmEPdByD8Ju92f8zYxin+90KRvJkoSgHNc4pMrISRlEWa2XHfddbj66qulv71eL+rq6mCz2YoSgAOAzWYr2cDY3OBDT78TADCvqwkmg/pKo7oqB4bGAhgYFQfDjrYG2GxG1dejJmofm9YmDoAHQWKEzWaDZt8EAAcaakyw2Wx5L3+6Y7aGAYzB4SKA1gLAgca6ioR9Uw7XDCMRelw65xgBuAAA7XMaYKs3lHCrZgbV1nGMOUMwVNSisUaPcJjAF7CD44COtqaMXYVn+jXT1jIJYBKBsDZhvPAJPgCjMBt1eY+z4hg1DhqO1FgNeS0zn+NiqRTHS6dLQGNjI4LhSQBAW2s9bLaKnLeJUfzrpawD8La2toRCyr6+Phx55JGqLF+n00GnS9RYcxxXlJ1P11OqgZG6Txh0HCqMhdGP1VdrMTQWiNodThNtr5rHhjaacUyFwHEcJj1RPfx02BeFxlKhgUHHwesXMDgqzhbUWHVJ902prxlGcjiOi2kaZq1g57YaVFu1Um2ErZaDxy+OHRVGHhqNsoTJTL5mqAbc5Q4n/D5/pADToIIG3FKhQYWRh8cnSH/nu8xcj4vZpIFRz8MXEOD1RyUoleyaU4ViXi9K11GSIswTTzwRjz32GNxucSpz586d2LhxI0488UQAwObNm3HNNdeUYtNmBPSGWUi7t3i3ldlYeEiLMMcnaREm8wCXIw/edvYXrjMro7DUyoowzQV6oJ9tSIWYkQJuqQsmK3IFEB1DJ5MUYUoWhCrM7HIcJ7WkB0pbACt2w4zowJ1BVpQ7CyhJAH755ZejoaEBa9aswWWXXYajjz4aX/3qV6UM+KeffopbbrlF+vzExASuueYa/OhHPwIA3H333bjmmmuwefPmUmx+2VOMAJxmf4HZa7tXY41ttc5cUBKhD2q7+iOdWVkAPu2or9LBWqFBc50emhI5RMw0qIUpLdwutQVeuWFN4wPuVckDnEILMYHSPwDR8XLEEWTnxCygJHdDk8mEDRs24PHHH0d/fz/OOOMMfO5zn5PeX7ZsGW6++Wbpb57nUV1dDQAxryeTmTCA+W0m8Bwwr61wevc62bT0bA04ozaEYiEPy4AnEt+OnnXBnH7odTz++IMFJbNnm4nQDDi1MHV5WBdMOWaTBhwHuL0CwmES8+AntaHXqROAN9bKJVYlDsAj42XfkB+EiI44mgz1AIzpS8nuhnq9Hl/60peSvrd48WIsXrxY+ruqqopJUrKgvcmIB29eUhAPcEq9bFq6kOspZ0RPazHwDocJJl0sAx5PvFSJZcCnJ011+swfYigmvhumm3UajUHDc7BUaDDlDmPKE44ZN0YiFrtWlcZZW5lIUIDoeLl3UJwxLPX2MArL7NMNzBJqK3UFfXKWd/KcrQGnRsOhyqwFIWJTDalohmXAJeQzJQALwBkMQN4NUxwzXCwAT6AyEnxOeWJlKB/3iI48B841q7KeGAlKyQNw8bygAXipM/KMwsICcEZO1MUE4LM3qJJkKDFdQdmgSZHPlABAFds3DEZCEaZUcMcCcAmpEFPWjl4QCDb3iOYNy+arE4DTdvRA6fd/fUIGfPbeW2cDLABn5EQ9y4ADiBZTTUwFZUWYbNCkyDPgRj0Pk2H2nisMBiW+CNPNCu4SiBZiRgPwPft9mPKEYavVoVklP/qy0oBH7qtTbtaGfjbAAnBGTphNPAw6UeIymwNOmgEfngjC4xOg4cXCGYaI3EOayU8YDJEqKkGJd0FhGXAJGnxOyZxQPtohyk+WLbCoth55MqnUEqDauJqZUm8Po7CwSIGRExzHSYPFbM6A06Cyd0hsFWw1s6YJcuoqWQDOYMRTHS9BYRnwBJJJUKQAfL56Abhex0tZcJpQKRV1cZI9lgGf2bA7IiNn6qp0GBwNzFoXFACoiQSY+yKavUo2YMZg0POwmkU3A6b/ZjBEzCYeWo3YJdYfECQNOJs9i0ITO7QIUxAItuwU9d/LVcyAA8C157djeCIY09+iFFQYYztzsgB8ZsOudkbOrFpsgdHAY0FHRak3pWTURjIm+4YiATgLMhOgU7w1LAPOYAAQZxDpA6nDFWIa8CTEZ8B3D0T132rbYi6dZ8Gxh9SousxckRscsPNhZsMCcEbOnHdiEx7/9YGY06hOMcx0hAaVw+OiN+1s1sOnohidWRmM6Ua1rBCT+YAnEl+ESe0H1dR/lyPyAJxlwGc2LABn5MVs744XrxmczXr4VNAbSrWVda5lMChyHbjLG+mEyQIuifgizELov8sRuQ6cnQ8zG5aSYjDyoCYuqGQZ8EROOqIWk+4QjlpeVepNYTDKhmgznhDzAU+CXIJSSP13ucEy4LMHFi0wGHkQL6tgGfBElnSZ8dPLukq9GQxGWVEt9RAIwe2jRZhs/KBEizDDkv67qU6vuv673JBbt7JGPDMbJkFhMPJAq+Figm6WAWcwGEqg7lGDowEQApgMPDSzXNInh46lU+6QTH6iTvfLcqZWJmtkGfCZDQvAGYw8kevAWQacwWAogWrAB4bFHgJMfhJLhZEHzwFun4APPpsCMPMLMIHY7sFMAz6zYQE4g5Ench04y4AzGAwlUAlK/0gkAGfBVgw8z0n7ZNP22VGACURtW00GftabHMx0WLTAYOSJfMqQ+YAzGAwl0Az4yIRoYcosCBOpNGsx6Q4jGCKzQv8NALY6PVYstKC1Yfba+84WWADOYORJrASFXVIMBiMzVZEMOCHi30yCkohVJumbDfpvANDwHH59xdxSbwajCDAJCoORJ/IOj0wDzmAwlBDvoGQ2sdtxPPLxdDbovxmzC3bFMxh5UlMZ1ezptOySYjAYmTEbYzW+TAOeSKXMhm/5LNB/M2YXLFpgMPKEZsBZ9pvBYCiF47iYLDiToCRCa2qa6/SwzQL9N2N2wQJwBiNPmuvFG0NjLbtBMBgM5VTLirZZAJ4I9Uo/aJbovxmzC1YxxmDkSXuTET+9rBMdTcZSbwqDwZhG0AATAMxMgpLACWtqMTgawLknNJZ6UxgM1WEBOIOhAocfVFXqTWAwGNMMJkFJT12VDt//alupN4PBKAhMgsJgMBgMRgmQB+DMB5zBmF2wAJzBYDAYjBIgl6CwDDiDMbtgATiDwWAwGCUgRoLCNOAMxqyiZAH4hx9+iLPPPhtr167FZZddhv3796v6eQaDwWAwyplqlgFnMGYtJQnAd+3ahaOPPhrt7e24/vrrMTw8jHXr1sHn86nyeQaDwWAwyp1YDTibkGYwZhMlcUH57W9/i+XLl+OXv/wlAOCoo46CzWbDww8/jK9+9at5f57BYDAYjHKHZsANOo510WUwZhklCcA3btyIE044QfrbYDDg8MMPx8aNG5MG1Nl+PhgMIhQKSX97vV4AACEEhBA1f0oCdB2FXg8je9ixKU/YcSlP2HEpPPXVWjRU69DcoM9qP7NjU56w41KeFPu4KF1PSQLwsbEx1NfXx7xWX1+PsbExVT5/00034YYbbkh43W63w2Qy5bjVyiCEwOFwABBbDTPKB3ZsyhN2XMoTdlyKwy8vr4FGI96flMKOTXnCjkt5UuzjQpO+mShJAG4wGOB2u2Nec7vdqKysVOXz1113Ha6++mrpb6/Xi7q6OthstqIE4ABgs9nYBVhmsGNTnrDjUp6w41K+sGNTnrDjUp4U+7iUdQC+aNEibNu2Lea1zz77DF/72tdU+bxOp4NOp0t4neO4oux8uh52AZYf7NiUJ+y4lCfsuJQv7NiUJ+y4lCfFPC5K11GSqo9zzjkHjz76KLZv3w4AeOKJJ7Bjxw6ceeaZAIDnn38eBx98sOLPMxgMBoPBYDAY04WSZMDPPPNMvPXWW1i+fDlaW1tht9tx9913Y968eQCA8fFxfPDBB4o/z2AwGAwGg8FgTBc4UsJy3dHRUQwODqK7uxtms1l6fWJiArt27YrJgqf7fCa8Xi8qKirg8XiKogG32+1MA1aGsGNTnrDjUp6w41K+sGNTnrDjUp4U+7gojTlLkgGn1NfXJ7ibAEBNTU1C8J3u85mgzxhKhfH5QAiB1+uF1+tlF2CZwY5NecKOS3nCjkv5wo5NecKOS3lS7OMit75OR0kD8GJBO2bW1dWVeEsYDAaDwWAwGDMdn8+HioqKlO+XVIJSLARBgMPhgNFoLPjTD7U8HBsbK7jchZEd7NiUJ+y4lCfsuJQv7NiUJ+y4lCfFPi6EEPh8PlRXV4PnU3udzIoMOM/zqK2tLeo6TSYTuwDLFHZsyhN2XMoTdlzKF3ZsyhN2XMqTYh6XdJlvSklsCBkMBoPBYDAYjNkKC8AZDAaDwWAwGIwiwgJwldFqtfjJT34CrXZWqHumFezYlCfsuJQn7LiUL+zYlCfsuJQn5XpcZkURJoPBYDAYDAaDUS6wDDiDwWAwGAwGg1FEWADOYDAYDAaDwWAUERaAMxgMBoPBYDAYRaS8FOkzgJ6eHmzduhVz5szBIYccUurNmbXs3r0bn3zyCVpaWrBy5cqEBkwTExPYsGEDDAYDjjjiCObZWmS2b9+OTZs2Yd26dWhqapJeD4VCeOuttzAxMYFDDz0Uzc3NJdzK2YXP58Nbb70FQRBwxBFHwGg0xrz/6aefYvv27ejq6sLy5ctLs5GzkH379mHLli2oqKjAIYccAqvVGvP+8PAwNm7cCKvVirVr10Kv15doS2cumzZtwvbt26W/zz777ITPBAIBvPnmm3C73VizZg3q6+uzep+RPcPDw3j55Zelv4888ki0trbGfMbn8+H999+H2+3GsmXLYu43lI8++gh79uzBwoULsWTJkoJvtwRhqMYNN9xArFYr+fznP0+amprIySefTAKBQKk3a1YxMjJCTjrpJDJv3jxy8sknk7a2NrJixQoyMjIifeaVV14hVVVV5IgjjiAHHXQQ6ejoIDt37izhVs8u3G43WbJkCQFAnnnmGen1sbExsmLFCjJ37lyyfv16YrFYyN/+9rcSbuns4ZVXXiFNTU1kxYoV5KSTTiIrVqwg/f390vvf+c53SHV1NTn++ONJXV0d+epXv0oEQSjhFs8OfvSjHxGz2UxOPPFEcuihh5K6ujry5ptvSu8/9thjxGq1knXr1pGFCxeSRYsWkcHBwRJu8czkvvvuI2eddRY5+uijSbKwqb+/nyxYsIAsXryYHH300cRqtZKnnnpK8fuM3Pjkk0/IWWedRc466yzC8zx59NFHY95/6KGHSHd3N1m7di057rjjiMlkIr/61a+k9wVBIF/96ldJfX09Of7440lVVRW54oorirb9LABXic2bNxOO48jbb79NCCFkeHiYNDU1kT/+8Y8l3rLZxd69e8l//vMf6W+v10uWLFlCrrrqKkKIeMF1dXWR6667Tvr7v/7rv8jJJ59cku2djXzzm98k119/fUIAfsUVV5BVq1YRn89HCCHk3nvvJRaLhUxMTJRoS2cHdrudVFdXx4xV27dvJz09PYQQMTjXarXk008/JYQQsm/fPmK1WsnDDz9cku2dLYyMjBAA5LnnnpNeu+iii8i6desIIeLYVldXR37zm98QQggJBoPk6KOPJueff34pNndW8MwzzyQNwM8991yyfv16EgqFCCGE/OpXvyKNjY3E7/crep+RPwaDISEAf+SRR8jQ0FDM3xzHkYGBAUIIIf/617+I1Wol+/btI4QQ8umnnxKtVkteffXVomwz04CrxGOPPYZly5bhsMMOAwA0NDTgzDPPxKOPPlriLZtddHR04Atf+IL0t9FoxMKFCzE6OgogOtV0+eWXAwA4jsNll12GZ599Fj6fryTbPJt46aWXsGHDBlx77bUJ7z366KO44IILYDAYAADnnnsuOI7Diy++WOzNnFXcf//9aGpqwllnnYUnn3wSb731Fjo7OzFv3jwA4nFZt24dFi9eDABob2/HSSedxMa2AhMIBACIYxqls7NTev2NN96A0+nExRdfDED0Or7kkkvYcSkygiDg8ccfx6WXXgqNRgMAuPTSSzE6OipJutK9zygcp59+Omw2m/T3smXLQAjB+Pg4AHFsO+mkk9De3g4AWLx4MdatW1e0a4gF4Cqxe/dudHd3x7zW1dWF3bt3l2iLGICoyX/++efxxS9+EYB4nPR6fYxOrKurC6FQCL29vaXazFnB5OQkLr30Utxzzz3Q6XQx79H9L7+GdDod5syZw66hAvPRRx/BaDRi9erVuOuuu3DBBRdg+fLlGBgYAMDGtlLR0tKC6667Dueffz7+/Oc/45ZbbsF9992Hm2++GYB4XBobG2GxWKTvdHV1wel0SgEGo/CMjIzA5XLFXCOVlZWoq6vD7t27M77PKB533HEHFi9eLCUTSj22sSJMlQiFQgnFLwaDAaFQqERbxBgYGMAXvvAFXH755Tj11FMBpD5O9D1G4bjiiitw1llnYeXKlQnvhcNhAGDXUAnw+/3YsmULNm3ahKVLlyIUCmH9+vX44Q9/iHvvvZeNbSWksbERExMTePzxx+FwOFBXVycVx7KxrDyg+zrVNZLpfUZx+N3vfof7778fr732mjQTUeqxjWXAVcJms2FoaCjmtaGhoZjpD0bx2LNnD4488kicccYZ+NWvfiW9brPZ4HK54Ha7pdfocWPHqnB8+OGHeOihh7Bw4UL885//xD//f3t3HBN1/cdx/AkkFxQSKVKXMwcWAVIidzUv4LpyRaFoszExhaRWC5Zz4myas43ZImKxlhPZapabk2hOZqZmCh6yWTjaIaihFblokefAayWRcff7g/ld96N+1E84PH09/uL7+Xw/33vf3e543fc+3/vU1gLgdDr5/PPPMZlMREdH6zU0DuLi4pg2bRqpqanA0FSG7OxsXC6X0a/nJfAaGxtZvXo1Bw4c4OOPP+bIkSPk5+ezYMECfD4fcXFxuN1uvF6vMaanp4cJEyYQExMzjpVfXyZPnkxYWJjfa2RwcBC3201cXNyI/TL2Kisree2112hsbCQlJcVoH+/3NgXwUWK32zl69Kjx1Z/P52Pv3r3Y7fZxruz689VXX5GZmUlhYSFvvPGGX5/VaiUyMpJPPvnEaNuzZw8pKSlMmjQp0KVeN8LDw5k/fz779u2jvr6e+vp6AI4ePUpraysw9Br68/PS2tpKT08PWVlZ41HydcPhcPDTTz/x888/G21nzpwxpmnZ7XYaGhro7+8Hhs4a7d+/X+9tY+zHH38kIiICs9lstM2YMYPz589z6dIlMjIy+O233zh8+LDRv2fPHmw227ApXjJ2TCYTDzzwgN9718GDBxkcHMRms43YL2Pr1Vdf5e2338bpdBonGS6z2+18+umnxhnv/v5+GhoaAvbeFuLz+XwBuaVrnNfrxW638/vvv7N8+XIaGhpoamrC5XL95e9Oytg4e/YsVquVpKQk40JLGPoq9+GHHwbg9ddfp7KyknXr1uHxeKioqKCurs6YpiKBERISwr59+8jOzgaGzpJnZGTw7LPPkpiYSFVVFQ6Hg3fffXecK722DQ4OkpWVRVhYGAUFBZw6dYrq6moOHjyIzWZjYGAAq9XKpEmTWLx4Mbt37+bUqVO4XC4mTpw43uVfs9xuN7NmzSI1NZW8vDw8Hg9VVVU89NBDbNu2DYDS0lI+/PBD1qxZw/fff88777zDgQMH9KF1lHV1dfHFF1/Q1tZGeXk5O3bsAGDhwoXceOONNDY2kp2dzcqVKzGbzZSXl1NYWEh5eTnAiP3y/7l06RI7d+4EoKCggBUrVmCxWLBarSQkJFBWVkZZWRkbN25k+vTpxrisrCzMZjMej4e0tDSSkpLIzc1lx44d9PX10dLSYkznGksK4KPo4sWLbN68mfb2du644w6Ki4uZOnXqeJd1Xeno6GDjxo3D2pOTk9mwYYOxvXPnTvbu3YvJZGLJkiVkZGQEskxhaDGLtWvXct999xltHR0dvPfee/T19ZGZmckzzzxjzNeTsdPf309NTQ0ul4vbb7+dgoIC40IlAI/Hw6ZNm+js7CQ+Pp6SkhJiY2PHseLrw7lz59i6dSunT58mIiICm81GXl4eN9wwdPmWz+dj+/btHDp0iKioKAoLC0lPTx/nqq89TqeT6urqYe01NTVER0cDcOzYMbZt28avv/7K3Llzyc/P91sAbqR++fcuXrxIUVHRsPbnnnuOuXPnUl5ebkyl+7OXX36ZtLQ0YOiD7qZNm+jq6uKee+6hpKTEeE7HmgK4iIiIiEgAaQ64iIiIiEgAKYCLiIiIiASQAriIiIiISAApgIuIiIiIBJACuIiIiIhIACmAi4iIiIgEkAK4iIiIiEgAKYCLiMgVa25u9lt99n9ZtGgRZ86cGeOKRESuXgrgIiLXkNzcXJqbmwN+u2vWrOHxxx//R/s6HA5eeeWVMa5IROTqpZUwRUSCVE5ODuvXr2fOnDlGW3t7O9OmTQvYcsoALS0t5Obm8sMPPxAWFjbi/hcuXMBsNnP69GmmTp0agApFRK4uOgMuIhKk2tra8Hg8fm2pqakBDd8AtbW15OTk/KPwDXDLLbdgs9n46KOPxrgyEZGrkwK4iEgQeuGFFzh37hwrVqzAYrGwevVqYPgUlOzsbOrq6iguLsZut/P888/T29vLrl27yMnJ4dFHH6Wurs7v2F6vl82bN7NgwQIee+wxKisr+eOPP/62lubmZiwWi1/b7t27WbhwIQ6Hg3Xr1vHLL7/49VutVo4cOXKlD4OISFBSABcRCUKrVq0iJiaGl156iS1bthgXQB4/fpwLFy4Y+7lcLkpLS5kzZw4bNmzg2LFjPPjgg7z//vusWrWKvLw8li5dysmTJ40xy5cvZ+vWrRQVFVFaWsquXbsoKSn521q+++47zGazsd3e3s7ixYuZP38+ZWVlxMTEsHbtWr8xZrOZs2fPjtKjISISXG4Y7wJEROTfS0xMZMKECdx1113Dzj7/t/Xr17Ns2TIAVq5cyYsvvsiXX35JREQEAB988AFOp5Pk5GQ6OzvZvn073d3d3HbbbQAkJydz55138uabbzJx4sRhxx8YGCA8PNzY7unpYfLkySxbtozw8HAyMzMZGBjwG2MymYa1iYhcLxTARUSucQkJCcbf0dHRmM1mI3xfbrs8l/zEiROEhoYyb948v2P4fD6++eYb0tLShh1/ypQp9Pb2GtuPPPIIixYtYtasWVgsFux2O0uWLPEb09vbS2xs7KjcPxGRYKMALiISpEJCQkb9mLfeeismk4ktW7YM67v77rv/cszs2bM5ceKEsR0aGkpVVRVer5eTJ09SVVVFTU0NLS0txj7Hjx8nPT191OsXEQkGmgMuIhKkYmJicLvdo3rM+++/n9jYWJxOJ+np6VgsFhITE9m/fz833XTTX47Jycnh8OHDxvahQ4f47LPPCA0NZebMmTz55JN0dHTg9XqBobPpTU1NPPHEE6Nau4hIsFAAFxEJUkVFRRQXF5OWlmb8CsqVioyMpL6+ntraWuLi4khKSiI+Pv5vwzfAU089RWdnJ99++y0AM2bM4K233mLKlCmkpKSwdOlSKioqCA0d+pfT1NREZGQkDodjVGoWEQk2WohHRCSInT9/nu7ubqKiokhISBi2EE9bWxvx8fFERUUBQ4vgdHd3M3PmTOMYX3/9NTfffLNx0eVlbrebvr4+EhISRvyN74qKCrq6uqiurvarze12M336dL855/PmzePpp58mPz//iu+/iEgwUgAXEZErNjAwQGdnJ/fee++I+7a2tjJ79uwxmcMuIhIMFMBFRERERAJIc8BFRERERAJIAVxEREREJIAUwEVEREREAkgBXEREREQkgBTARUREREQCSAFcRERERCSAFMBFRERERAJIAVxEREREJIAUwEVEREREAkgBXEREREQkgP4DAdC1C21T+6oAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import Audio\n",
+ "\n",
+ "rng = np.random.default_rng(1978)\n",
+ "lengths = [4.45, 4.775, 5.325, 5.975, 6.55, 7.175, 7.725] # the \"2/1\" ratios, scaled down\n",
+ "midis = [57, 60, 62, 64, 65, 69, 72] # a soft modal cluster on A\n",
+ "b = tap.Airport(sr, 8.0, smooth_ms=0, lengths=lengths,\n",
+ " pans=[-0.8, 0.8, -0.45, 0.45, -0.15, 0.15, 0.0],\n",
+ " levels=[0.5] * 7, darkens=[20000, 20000, 4000, 20000, 4000, 20000, 20000])\n",
+ "\n",
+ "for i, (L, midi) in enumerate(zip(lengths, midis)):\n",
+ " f = 440.0 * 2 ** ((midi - 69) / 12)\n",
+ " n = int(0.55 * L * sr) # each phrase fills about half its loop\n",
+ " t = np.arange(n) / sr\n",
+ " env = np.sin(np.pi * np.minimum(t / (0.4 * L), 1.0)) ** 2\n",
+ " phrase = env * (0.5 * np.sin(2 * np.pi * f * t)\n",
+ " + 0.25 * np.sin(2 * np.pi * 2 * f * t + rng.uniform(0, 2 * np.pi))\n",
+ " + 0.12 * np.sin(2 * np.pi * 3 * f * t + rng.uniform(0, 2 * np.pi)))\n",
+ " punch(b, i, phrase)\n",
+ "\n",
+ "yl, yr = b.process(np.zeros(int(120.0 * sr)))\n",
+ "\n",
+ "win = int(1.0 * sr)\n",
+ "frames = (yl + yr)[: (yl.size // win) * win].reshape(-1, win)\n",
+ "fig, ax = plt.subplots(figsize=(9, 2.4))\n",
+ "ax.plot(np.arange(frames.shape[0]) + 0.5, np.sqrt((frames ** 2).mean(axis=1)), color=C[0])\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"RMS\")\n",
+ "ax.set_title(\"two minutes of the terminal: coincidences come and go, nothing repeats\")\n",
+ "plt.show()\n",
+ "\n",
+ "# Preview: first 30 s, embedded at 16 kHz to keep the executed notebook small (the phrases\n",
+ "# live below 2 kHz); tools/render writes the full-rate, full-length WAVs.\n",
+ "mix = np.vstack([yl[: int(30 * sr) : 3], yr[: int(30 * sr) : 3]])\n",
+ "Audio(np.clip(mix / max(1e-9, np.abs(mix).max()) * 0.9, -1, 1), rate=int(sr / 3))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/discreet.ipynb b/notebooks/discreet.ipynb
new file mode 100644
index 0000000..28b9ba0
--- /dev/null
+++ b/notebooks/discreet.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9f27f5d9",
+ "metadata": {},
+ "source": [
+ "# tap.discreet~ — the loop, measured\n",
+ "\n",
+ "The *Discreet Music* two-tape-machine system (`taptools/discreet.h` + `taptools/tape_loop.h`):\n",
+ "input is recorded onto tape, spools for seconds to a second machine, and the playback is both\n",
+ "the output and the signal folded back into the record head. The kernel's headline claim is an\n",
+ "inversion of the usual delay-stability story: **regeneration legally reaches 1.0**, and\n",
+ "boundedness comes from the wear path (darkening lowpass → bounded soft saturation → DC\n",
+ "blocker), not from a feedback cap. Every trace below drives the **shipping C++** through\n",
+ "`tools/capi` via ctypes.\n",
+ "\n",
+ "Sections: **1** the echo grid · **2** wear as the stabilizer (regen 1.0, bounded) ·\n",
+ "**3** per-pass darkening vs. the analytic wear transfer · **4** the transport, in cents ·\n",
+ "**5** the performance move: fade the send, the loop carries on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b19c9825",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:41.566251Z",
+ "iopub.status.busy": "2026-08-12T01:29:41.566021Z",
+ "iopub.status.idle": "2026-08-12T01:29:41.922596Z",
+ "shell.execute_reply": "2026-08-12T01:29:41.921161Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import taptools_py as tap\n",
+ "\n",
+ "plt.rcParams.update({\n",
+ " \"figure.dpi\": 96, \"figure.figsize\": (9, 3.2),\n",
+ " \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+ "})\n",
+ "C = tap.PALETTE\n",
+ "sr = 48000.0\n",
+ "\n",
+ "def machine(**params):\n",
+ " base = dict(smooth_ms=0, wow=(0, 0), flutter=(0, 0), input_level=1.0, mix=100)\n",
+ " base.update(params)\n",
+ " return tap.Discreet(sr, 8.0, **base)\n",
+ "\n",
+ "def goertzel(x, f):\n",
+ " n = np.arange(x.size)\n",
+ " return 2.0 * np.abs(np.dot(x, np.exp(-2j * np.pi * f * n / sr))) / x.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0aa23863",
+ "metadata": {},
+ "source": [
+ "## 1 · The echo grid\n",
+ "\n",
+ "An impulse into a 0.5 s loop at regen 0.7. The first return is the recorded impulse itself —\n",
+ "bit-exact at one loop, because the span is an integer number of samples and the Hermite read\n",
+ "at fraction 0 is exact — and every later return lands on the same grid, one wear pass darker.\n",
+ "(The kernel test pins the grid to ±1 sample: scenario *\"the loop echoes at exactly the loop\n",
+ "period\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "96dbe5ae",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:41.925411Z",
+ "iopub.status.busy": "2026-08-12T01:29:41.925120Z",
+ "iopub.status.idle": "2026-08-12T01:29:42.068944Z",
+ "shell.execute_reply": "2026-08-12T01:29:42.067797Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "first return, bit-exact: y[24000] = 1.0\n",
+ "echo 2: peak at grid +0 samples, level 0.4541\n",
+ "echo 3: peak at grid +0 samples, level 0.2062\n",
+ "echo 4: peak at grid +1 samples, level 0.0983\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = machine(loop_seconds=0.5, regen=0.7, drive=0.0, darken_hz=8000)\n",
+ "x = np.zeros(int(2.5 * sr)); x[0] = 1.0\n",
+ "y = m.process(x)\n",
+ "\n",
+ "t = np.arange(y.size) / sr\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(t, y, color=C[0], lw=0.8)\n",
+ "for k in range(1, 5):\n",
+ " ax.axvline(k * 0.5, color=C[3], lw=0.8, ls=\":\")\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"output\")\n",
+ "ax.set_title(\"impulse → echoes on the 0.5 s grid (dotted)\")\n",
+ "plt.show()\n",
+ "\n",
+ "loop = int(0.5 * sr)\n",
+ "print(f\"first return, bit-exact: y[{loop}] = {y[loop]}\")\n",
+ "for k in range(2, 5):\n",
+ " w = np.abs(y[k * loop - 16 : k * loop + 16])\n",
+ " print(f\"echo {k}: peak at grid {int(np.argmax(w)) - 16:+d} samples, \"\n",
+ " f\"level {w.max():.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "660f24f4",
+ "metadata": {},
+ "source": [
+ "## 2 · Wear as the stabilizer\n",
+ "\n",
+ "Regen at exactly 1.0, drive 0.5, darken 3 kHz: a one-second noise burst, then twenty seconds\n",
+ "of free run. The windowed RMS settles and stays — no growth, no collapse. This is the regime\n",
+ "delay.h forbids (its feedback caps at 0.99) and this kernel exists for: the saturator bounds\n",
+ "the loop (|out| ≤ 1/drive), the DC blocker stops offset accumulation, and the darkening\n",
+ "lowpass decides *what* survives. (Pinned in the kernel test *\"regen 1.0 with drive engaged is\n",
+ "bounded and does not grow\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2b11459c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:42.071426Z",
+ "iopub.status.busy": "2026-08-12T01:29:42.071211Z",
+ "iopub.status.idle": "2026-08-12T01:29:42.388745Z",
+ "shell.execute_reply": "2026-08-12T01:29:42.387440Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RMS at 3 s: -20.02 dB at 19 s: -26.60 dB peak |y|: 1.074 (saturation bound 1/drive = 2.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "rng = np.random.default_rng(2463534242)\n",
+ "m = machine(loop_seconds=0.25, regen=1.0, drive=0.5, darken_hz=3000)\n",
+ "x = np.zeros(int(20.0 * sr))\n",
+ "x[: int(1.0 * sr)] = 0.5 * rng.uniform(-1, 1, int(1.0 * sr))\n",
+ "y = m.process(x)\n",
+ "\n",
+ "win = int(0.5 * sr)\n",
+ "frames = y[: y.size // win * win].reshape(-1, win)\n",
+ "rms_db = 20 * np.log10(np.sqrt((frames ** 2).mean(axis=1)) + 1e-12)\n",
+ "tw = (np.arange(rms_db.size) + 0.5) * 0.5\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(tw, rms_db, color=C[0], marker=\"o\", ms=3)\n",
+ "ax.axvspan(0, 1, color=C[3], alpha=0.15, label=\"noise burst in\")\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"RMS (dB)\")\n",
+ "ax.set_title(\"regen 1.0: the loop sustains, bounded — it does not grow\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"RMS at 3 s: {rms_db[6]:.2f} dB at 19 s: {rms_db[-2]:.2f} dB \"\n",
+ " f\"peak |y|: {np.abs(y).max():.3f} (saturation bound 1/drive = 2.0)\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17620a7f",
+ "metadata": {},
+ "source": [
+ "## 3 · Per-pass darkening, against the analytic wear transfer\n",
+ "\n",
+ "A two-tone burst — 6 kHz above the 2 kHz darkening corner, 300 Hz below it — recirculated at\n",
+ "regen 0.9 with drive 0 (linear wear, so the per-pass ratio is exactly\n",
+ "`regen · |H_lowpass| · |H_dcblock|`). Measured level per pass, with the analytic prediction\n",
+ "drawn through it: the highs die generation by generation, the lows barely fade. The tape\n",
+ "forgets treble first."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0ee0114f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:42.390993Z",
+ "iopub.status.busy": "2026-08-12T01:29:42.390705Z",
+ "iopub.status.idle": "2026-08-12T01:29:42.596417Z",
+ "shell.execute_reply": "2026-08-12T01:29:42.595184Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "per-pass ratio, 6 kHz: measured 0.292, predicted 0.292\n",
+ "per-pass ratio, 300 Hz: measured 0.890, predicted 0.890\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = machine(loop_seconds=0.25, regen=0.9, drive=0.0, darken_hz=2000)\n",
+ "f_hi, f_lo = 6000.0, 300.0\n",
+ "n_burst = int(0.1 * sr)\n",
+ "tt = np.arange(n_burst) / sr\n",
+ "x = np.zeros(int(1.6 * sr))\n",
+ "x[:n_burst] = 0.4 * np.sin(2 * np.pi * f_hi * tt) + 0.4 * np.sin(2 * np.pi * f_lo * tt)\n",
+ "y = m.process(x)\n",
+ "\n",
+ "def wear_gain(f, cutoff):\n",
+ " w = 2 * np.pi * f / sr\n",
+ " a = 1.0 - np.exp(-2 * np.pi * cutoff / sr)\n",
+ " ejw = np.exp(-1j * w)\n",
+ " lp = np.abs(a / (1 - (1 - a) * ejw))\n",
+ " r, nm = 0.999, (1 + 0.999) / 2\n",
+ " return lp * np.abs(nm * (1 - ejw) / (1 - r * ejw))\n",
+ "\n",
+ "loop = int(0.25 * sr)\n",
+ "passes = np.arange(1, 6)\n",
+ "hi = [goertzel(y[k * loop : k * loop + n_burst], f_hi) for k in passes]\n",
+ "lo = [goertzel(y[k * loop : k * loop + n_burst], f_lo) for k in passes]\n",
+ "pred_hi = hi[0] * (0.9 * wear_gain(f_hi, 2000.0)) ** (passes - 1)\n",
+ "pred_lo = lo[0] * (0.9 * wear_gain(f_lo, 2000.0)) ** (passes - 1)\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.semilogy(passes, hi, \"o\", color=C[0], label=\"6 kHz measured\")\n",
+ "ax.semilogy(passes, pred_hi, \"-\", color=C[0], lw=1, alpha=0.6, label=\"6 kHz predicted\")\n",
+ "ax.semilogy(passes, lo, \"s\", color=C[1], label=\"300 Hz measured\")\n",
+ "ax.semilogy(passes, pred_lo, \"-\", color=C[1], lw=1, alpha=0.6, label=\"300 Hz predicted\")\n",
+ "ax.set_xticks(passes)\n",
+ "ax.set_xlabel(\"pass through the loop\"); ax.set_ylabel(\"tone level\")\n",
+ "ax.set_title(\"generation loss: measured vs. regen · |H_wear| per pass\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"per-pass ratio, 6 kHz: measured {hi[1]/hi[0]:.3f}, \"\n",
+ " f\"predicted {0.9 * wear_gain(f_hi, 2000.0):.3f}\")\n",
+ "print(f\"per-pass ratio, 300 Hz: measured {lo[1]/lo[0]:.3f}, \"\n",
+ " f\"predicted {0.9 * wear_gain(f_lo, 2000.0):.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3b58f16a",
+ "metadata": {},
+ "source": [
+ "## 4 · The transport, in cents\n",
+ "\n",
+ "Wow at 2 ms / 0.5 Hz on a 440 Hz sine, pitch-tracked with the same DspTap YIN detector the\n",
+ "tune kernel uses. The peak deviation of a sinusoidally modulated read is\n",
+ "`depth · 2π · rate` in pitch ratio — 2 ms at 0.5 Hz predicts ±10.9 cents — and the track\n",
+ "should breathe at exactly the wow rate. The transport is periodic and deterministic by\n",
+ "design: two renders are bit-identical (pinned in *\"wow bends pitch by the set depth, and two\n",
+ "runs are bit-exact\"*)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c7c15082",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:42.598751Z",
+ "iopub.status.busy": "2026-08-12T01:29:42.598525Z",
+ "iopub.status.idle": "2026-08-12T01:29:43.160711Z",
+ "shell.execute_reply": "2026-08-12T01:29:43.159336Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "measured peak deviation: 10.9 cents (predicted 10.9)\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = machine(loop_seconds=1.0, regen=0.0, wow=(2.0, 0.5))\n",
+ "tt = np.arange(int(6.0 * sr)) / sr\n",
+ "y = m.process(0.8 * np.sin(2 * np.pi * 440.0 * tt))\n",
+ "\n",
+ "hop = 1024\n",
+ "periods = tap.Yin().track(y[int(1.5 * sr):], hop=hop)\n",
+ "voiced = periods > 0\n",
+ "cents = np.full(periods.size, np.nan)\n",
+ "cents[voiced] = 1200 * np.log2((sr / periods[voiced]) / 440.0)\n",
+ "tt_track = np.arange(periods.size) * hop / sr\n",
+ "\n",
+ "predicted = 1200 / np.log(2) * 0.002 * 2 * np.pi * 0.5\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(tt_track, cents, color=C[0], lw=1)\n",
+ "ax.axhline(+predicted, color=C[3], lw=0.8, ls=\":\", label=f\"predicted ±{predicted:.1f} c\")\n",
+ "ax.axhline(-predicted, color=C[3], lw=0.8, ls=\":\")\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"deviation (cents)\")\n",
+ "ax.set_title(\"wow 2 ms @ 0.5 Hz on a 440 Hz sine: the playback breathes\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "print(f\"measured peak deviation: {np.nanmax(np.abs(cents)):.1f} cents \"\n",
+ " f\"(predicted {predicted:.1f})\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2763c554",
+ "metadata": {},
+ "source": [
+ "## 5 · The performance move\n",
+ "\n",
+ "The rig's fader was the *input send*, not the loop: play into the machine, then fade the\n",
+ "send to zero and the piece keeps unrolling on the tape alone. Four slow notes into a 2 s\n",
+ "loop at regen 1.0; the send fades out at t = 10 s; the loop carries the material on,\n",
+ "bounded, worn a shade darker every pass. This is the whole record in one gesture."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "43061af5",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:29:43.163070Z",
+ "iopub.status.busy": "2026-08-12T01:29:43.162837Z",
+ "iopub.status.idle": "2026-08-12T01:29:43.536556Z",
+ "shell.execute_reply": "2026-08-12T01:29:43.533842Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import Audio\n",
+ "\n",
+ "m = machine(loop_seconds=2.0, regen=1.0, drive=0.6, darken_hz=2500, mix=100,\n",
+ " smooth_ms=50)\n",
+ "dur = 30.0\n",
+ "x = np.zeros(int(dur * sr))\n",
+ "for i, midi in enumerate([57, 64, 62, 69]): # A3 E4 D4 A4\n",
+ " f = 440.0 * 2 ** ((midi - 69) / 12)\n",
+ " n0 = int((1.0 + 2.2 * i) * sr)\n",
+ " seg = np.arange(int(1.8 * sr))\n",
+ " env = np.minimum(1, seg / (0.3 * sr)) * np.exp(-seg / (0.9 * sr))\n",
+ " x[n0 : n0 + seg.size] += 0.35 * env * np.sin(2 * np.pi * f * seg / sr)\n",
+ "\n",
+ "y = np.empty_like(x)\n",
+ "n_fade = int(10.0 * sr)\n",
+ "y[:n_fade] = m.process(x[:n_fade])\n",
+ "m.set(input_level=0.0) # the fade: smooth_ms=50 glides the send down\n",
+ "y[n_fade:] = m.process(x[n_fade:])\n",
+ "\n",
+ "win = int(0.5 * sr)\n",
+ "frames = y[: y.size // win * win].reshape(-1, win)\n",
+ "rms = np.sqrt((frames ** 2).mean(axis=1))\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot((np.arange(rms.size) + 0.5) * 0.5, 20 * np.log10(rms + 1e-12), color=C[0])\n",
+ "ax.axvline(10.0, color=C[3], ls=\":\", label=\"send faded to 0\")\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"RMS (dB)\")\n",
+ "ax.set_title(\"fade the send at 10 s: the loop keeps playing the piece\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "# Preview embedded at 16 kHz to keep the executed notebook small (the material lives well\n",
+ "# below 2 kHz); tools/render writes the full-rate WAVs.\n",
+ "Audio(np.clip(y[::3], -1, 1), rate=int(sr / 3))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/garden.ipynb b/notebooks/garden.ipynb
new file mode 100644
index 0000000..3c827d2
--- /dev/null
+++ b/notebooks/garden.ipynb
@@ -0,0 +1,448 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "323f9358",
+ "metadata": {},
+ "source": [
+ "# tap.garden~ — the garden, measured\n",
+ "\n",
+ "The generative event loop (`taptools/garden.h`), a recreation of the *principle* behind\n",
+ "Eno/Chilvers' Bloom: a planted note snaps to the scale, blooms on a two-operator FM bell,\n",
+ "and returns every loop pass a step quieter (`decay`) and purer (`soften`) until it retires\n",
+ "below the `floor`; left idle, a seeded gardener plants for you. The family's stability\n",
+ "inversion, one level up: **per-pass decay is the stabilizer** — the event population\n",
+ "converges by construction, and a fixed sixteen-bell pool hard-bounds the audio. Every trace\n",
+ "drives the **shipping C++** through `tools/capi` via ctypes.\n",
+ "\n",
+ "Sections: **1** the return staircase · **2** softening, in partials · **3** the scale\n",
+ "contract, by the pitch oracle · **4** the seeded gardener · **5** an hour of garden,\n",
+ "in two minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d1468886",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:36.898478Z",
+ "iopub.status.busy": "2026-08-12T01:40:36.898279Z",
+ "iopub.status.idle": "2026-08-12T01:40:37.332788Z",
+ "shell.execute_reply": "2026-08-12T01:40:37.331442Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import taptools_py as tap\n",
+ "\n",
+ "plt.rcParams.update({\n",
+ " \"figure.dpi\": 96, \"figure.figsize\": (9, 3.2),\n",
+ " \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+ "})\n",
+ "C = tap.PALETTE\n",
+ "sr = 48000.0\n",
+ "\n",
+ "def tone(x, f):\n",
+ " n = np.arange(x.size)\n",
+ " return 2.0 * np.abs(np.dot(x, np.exp(-2j * np.pi * f * n / sr))) / x.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e0d35c8",
+ "metadata": {},
+ "source": [
+ "## 1 · The return staircase\n",
+ "\n",
+ "One note planted at velocity 0.8 into a 0.5 s loop, `decay` 0.5, `floor` 0.05: the bloom\n",
+ "returns at 0.8, 0.4, 0.2, 0.1, 0.05 — a measured staircase of halvings — and then retires;\n",
+ "`active_events` drops to zero and the garden is silent. That retirement arithmetic\n",
+ "(`ceil(log(floor/velocity)/log(decay))` passes, always) is the population-convergence\n",
+ "theorem in one plant. (Kernel scenarios: *\"a planted note blooms again every loop period\"*,\n",
+ "*\"each return is quieter by the decay ratio and the bloom retires below the floor\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "77aeb815",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:37.336263Z",
+ "iopub.status.busy": "2026-08-12T01:40:37.335983Z",
+ "iopub.status.idle": "2026-08-12T01:40:37.551063Z",
+ "shell.execute_reply": "2026-08-12T01:40:37.549876Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "per-return peaks: [np.float64(0.795), np.float64(0.399), np.float64(0.2), np.float64(0.1), np.float64(0.05), np.float64(0.0)]\n",
+ "live events after the render: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "g = tap.Garden(sr, smooth_ms=0, idle_seconds=0, loop_seconds=0.5,\n",
+ " decay=0.5, floor=0.05, bell=(0.002, 0.05, 1.0), scale=0)\n",
+ "g.note(69, 0.8)\n",
+ "y = g.process(int(3.5 * sr))\n",
+ "\n",
+ "t = np.arange(y.size) / sr\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(t, y, color=C[0], lw=0.6)\n",
+ "for k, v in enumerate([0.8 * 0.5 ** i for i in range(5)]):\n",
+ " ax.plot([k * 0.5, k * 0.5 + 0.25], [v, v], color=C[3], lw=1.2)\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"output\")\n",
+ "ax.set_title(\"decay 0.5: returns at 0.8, 0.4, 0.2, 0.1, 0.05 — then retirement\")\n",
+ "plt.show()\n",
+ "\n",
+ "peaks = [np.abs(y[int(k * 0.5 * sr) : int((k + 1) * 0.5 * sr)]).max() for k in range(6)]\n",
+ "print(\"per-return peaks:\", [round(p, 3) for p in peaks])\n",
+ "print(\"live events after the render:\", g.active_events)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1b7fbcd",
+ "metadata": {},
+ "source": [
+ "## 2 · Softening, in partials\n",
+ "\n",
+ "With `decay` held at 1.0 (velocity still) and `soften` 0.6, only the timbre moves: the FM\n",
+ "bell's first upper sideband (4× the fundamental, carrier + ratio-3 modulator) fades pass by\n",
+ "pass while the fundamental holds — each return is *purer*, collapsing toward a sine. The\n",
+ "tape family's generation loss, restated in partials instead of passbands."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "58b22404",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:37.553851Z",
+ "iopub.status.busy": "2026-08-12T01:40:37.553571Z",
+ "iopub.status.idle": "2026-08-12T01:40:37.694273Z",
+ "shell.execute_reply": "2026-08-12T01:40:37.693228Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sideband/fundamental per return: [np.float64(1.252), np.float64(0.546), np.float64(0.301), np.float64(0.175)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "g = tap.Garden(sr, smooth_ms=0, idle_seconds=0, loop_seconds=0.5,\n",
+ " decay=1.0, floor=0.001, soften=0.6, bell=(0.005, 0.06, 1.0), scale=0)\n",
+ "g.note(69, 0.8) # 440 Hz carrier -> first upper sideband at 1760 Hz\n",
+ "y = g.process(int(2.5 * sr))\n",
+ "\n",
+ "loop = int(0.5 * sr)\n",
+ "passes = np.arange(4)\n",
+ "fund = [tone(y[k * loop : k * loop + int(0.2 * sr)], 440.0) for k in passes]\n",
+ "side = [tone(y[k * loop : k * loop + int(0.2 * sr)], 1760.0) for k in passes]\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(passes, fund, \"o-\", color=C[0], label=\"fundamental (440 Hz)\")\n",
+ "ax.plot(passes, side, \"s-\", color=C[1], label=\"FM sideband (1760 Hz)\")\n",
+ "ax.set_xticks(passes)\n",
+ "ax.set_xlabel(\"return\"); ax.set_ylabel(\"partial level\")\n",
+ "ax.set_title(\"soften 0.6: the sideband fades, the fundamental holds — purer every pass\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"sideband/fundamental per return:\",\n",
+ " [round(s / f, 3) for s, f in zip(side, fund)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8144c147",
+ "metadata": {},
+ "source": [
+ "## 3 · The scale contract, by the pitch oracle\n",
+ "\n",
+ "Plant every chromatic pitch from 60 to 72 into a C major-pentatonic garden and measure what\n",
+ "actually sounds with the DspTap YIN detector: every bloom lands on {C, D, E, G, A}, whatever\n",
+ "you planted. Quantization happens at entry — the instrument makes wrong notes impossible,\n",
+ "which is most of why Bloom-style instruments feel effortless. (Kernel scenario: *\"every\n",
+ "bloom lands on the scale\"*.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "26c3b65b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:37.697003Z",
+ "iopub.status.busy": "2026-08-12T01:40:37.696639Z",
+ "iopub.status.idle": "2026-08-12T01:40:38.783825Z",
+ "shell.execute_reply": "2026-08-12T01:40:38.782751Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "all sounded pitches on the scale: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "pent = {0, 2, 4, 7, 9}\n",
+ "planted = np.arange(60, 73)\n",
+ "sounded = []\n",
+ "for p in planted:\n",
+ " g = tap.Garden(sr, smooth_ms=0, idle_seconds=0, loop_seconds=2.0,\n",
+ " scale=3, root=0, bell=(0.01, 0.5, 0.4))\n",
+ " g.note(float(p), 0.8)\n",
+ " y = g.process(int(0.5 * sr))\n",
+ " periods = tap.Yin().track(y[int(0.1 * sr):], hop=512)\n",
+ " hz = sr / np.median(periods[periods > 0])\n",
+ " sounded.append(69 + 12 * np.log2(hz / 440.0))\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(planted, planted, \":\", color=\"gray\", lw=0.8, label=\"planted = sounded\")\n",
+ "ax.plot(planted, sounded, \"o\", color=C[0], label=\"sounded (YIN)\")\n",
+ "for m in range(60, 73):\n",
+ " if m % 12 in pent:\n",
+ " ax.axhline(m, color=C[2], lw=0.5, alpha=0.4)\n",
+ "ax.set_xlabel(\"planted MIDI pitch\"); ax.set_ylabel(\"sounded MIDI pitch\")\n",
+ "ax.set_title(\"C major pentatonic: every plant snaps to a scale tone (horizontal lines)\")\n",
+ "ax.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "ok = all(round(s) % 12 in pent for s in sounded)\n",
+ "print(\"all sounded pitches on the scale:\", ok)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "439fe0e1",
+ "metadata": {},
+ "source": [
+ "## 4 · The seeded gardener\n",
+ "\n",
+ "Idle for half a second, then the garden plays itself: roughly one plant per loop pass,\n",
+ "uniformly placed, on the scale, within two octaves. The randomness rides the family's\n",
+ "seeded xorshift64* — same seed, bit-identical garden; different seed, different garden;\n",
+ "gardener disabled, the seed cannot matter at all (the rng is never consumed). The library's\n",
+ "first randomized event source, with the tr808 seed triad as its contract."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "2a44936a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:38.786156Z",
+ "iopub.status.busy": "2026-08-12T01:40:38.785963Z",
+ "iopub.status.idle": "2026-08-12T01:40:39.862934Z",
+ "shell.execute_reply": "2026-08-12T01:40:39.861747Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "same seed bit-exact: True different seed differs: True\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def render_idle(seed, seconds=8.0):\n",
+ " g = tap.Garden(sr, smooth_ms=0, idle_seconds=0.5, loop_seconds=1.0,\n",
+ " seed=seed, bell=(0.01, 0.6, 0.8), decay=0.7)\n",
+ " return g.process(int(seconds * sr))\n",
+ "\n",
+ "a = render_idle(1111)\n",
+ "b = render_idle(1111)\n",
+ "c = render_idle(2222)\n",
+ "print(\"same seed bit-exact:\", bool(np.all(a == b)),\n",
+ " \" different seed differs:\", bool(np.any(a != c)))\n",
+ "\n",
+ "t = np.arange(a.size) / sr\n",
+ "fig, axes = plt.subplots(2, 1, figsize=(9, 4.2), sharex=True)\n",
+ "for ax, y, seed, color in [(axes[0], a, 1111, C[0]), (axes[1], c, 2222, C[1])]:\n",
+ " ax.plot(t, y, color=color, lw=0.5)\n",
+ " ax.axvline(0.5, color=C[3], ls=\":\", lw=0.8)\n",
+ " ax.set_ylabel(f\"seed {seed}\")\n",
+ "axes[1].set_xlabel(\"time (s)\")\n",
+ "axes[0].set_title(\"two gardeners: patient to the idle threshold (dotted), then their own gardens\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6ed0434",
+ "metadata": {},
+ "source": [
+ "## 5 · An hour of garden, in two minutes\n",
+ "\n",
+ "A handful of hand-planted notes to start, then the gardener takes over: `decay` 0.85 and\n",
+ "`soften` 0.9 keep each bloom returning for a dozen passes, softening as it goes; the\n",
+ "population breathes around its converged size instead of piling up. This render would go on\n",
+ "— unrepeating, bounded, self-tending — for exactly as long as you let it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6de300a2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-12T01:40:39.866122Z",
+ "iopub.status.busy": "2026-08-12T01:40:39.865857Z",
+ "iopub.status.idle": "2026-08-12T01:40:46.003609Z",
+ "shell.execute_reply": "2026-08-12T01:40:46.002440Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "live events at the end: 20 ringing bells: 16\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import Audio\n",
+ "\n",
+ "g = tap.Garden(sr, smooth_ms=0, loop_seconds=6.0, decay=0.85, soften=0.9, floor=0.02,\n",
+ " bell=(0.12, 3.0, 0.9), scale=3, root=9, idle_seconds=4.0, seed=2008,\n",
+ " level=0.35)\n",
+ "\n",
+ "chunks = []\n",
+ "plants = [(0.0, 69, 0.7), (1.2, 76, 0.55), (2.6, 64, 0.6), (4.0, 81, 0.4)]\n",
+ "cursor = 0.0\n",
+ "for when, pitch, vel in plants:\n",
+ " n = int((when - cursor) * sr)\n",
+ " if n > 0:\n",
+ " chunks.append(g.process(n))\n",
+ " g.note(pitch, vel)\n",
+ " cursor = when\n",
+ "chunks.append(g.process(int((120.0 - cursor) * sr)))\n",
+ "y = np.concatenate(chunks)\n",
+ "\n",
+ "win = int(2.0 * sr)\n",
+ "frames = y[: y.size // win * win].reshape(-1, win)\n",
+ "fig, ax = plt.subplots(figsize=(9, 2.4))\n",
+ "ax.plot((np.arange(frames.shape[0]) + 0.5) * 2.0, np.sqrt((frames ** 2).mean(axis=1)),\n",
+ " color=C[0])\n",
+ "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"RMS\")\n",
+ "ax.set_title(\"four planted notes, then the gardener: the population breathes, bounded\")\n",
+ "plt.show()\n",
+ "print(\"live events at the end:\", g.active_events, \" ringing bells:\", g.active_voices)\n",
+ "\n",
+ "# Preview: first 60 s, embedded at 16 kHz to keep the executed notebook small (the bells\n",
+ "# live below 4 kHz); tools/render writes the full-rate, full-length WAVs.\n",
+ "Audio(np.clip(y[: int(60 * sr) : 3], -1, 1), rate=int(sr / 3))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/taptools_py.py b/notebooks/taptools_py.py
index 15b07fd..85e4d3e 100644
--- a/notebooks/taptools_py.py
+++ b/notebooks/taptools_py.py
@@ -14,9 +14,12 @@
(`Diode`), tap.303~ (`TB303`), tap.vco~ (`Vco`), tap.autowah~ (`Wah`),
tap.overdrive~ (`Overdrive`), the step-sequencer rows behind tap.808.seq~ /
tap.303.seq~ (`TriggerRow`, `NoteRow`), tap.808.kick~ (`Kick`),
-tap.delay~ (`Delay`), tap.multitap~ (`Multitap`), and tap.tune~'s pitch
-corrector (`Tune`, with the shared DspTap detector passed through as `Yin`
-for the notebooks' pitch tracking). Parameter names on the
+tap.delay~ (`Delay`), tap.multitap~ (`Multitap`), the Discreet Music
+two-machine tape loop tap.discreet~ (`Discreet`), the Music for Airports
+incommensurate loop bank tap.airport~ (`Airport`), the generative event
+loop tap.garden~ (`Garden`), and tap.tune~'s pitch corrector (`Tune`, with
+the shared DspTap detector passed through as `Yin` for the notebooks'
+pitch tracking). Parameter names on the
kernel classes mirror each kernel header's param_index enum.
Copyright 2003-2026 Timothy Place. MIT License.
@@ -262,6 +265,54 @@ def load() -> ctypes.CDLL:
"taptools_multitap_set_smooth_ms": ([vp, ctypes.c_double], ctypes.c_int),
"taptools_multitap_clear": ([vp], ctypes.c_int),
"taptools_multitap_process": ([vp, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+ "taptools_discreet_create": ([], vp),
+ "taptools_discreet_destroy": ([vp], None),
+ "taptools_discreet_prepare": ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_loop_seconds": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_regen": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_darken_hz": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_drive": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_input_level": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_mix": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_wow": ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_flutter": ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_set_smooth_ms": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_discreet_clear": ([vp], ctypes.c_int),
+ "taptools_discreet_process": ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+ "taptools_airport_create": ([], vp),
+ "taptools_airport_destroy": ([vp], None),
+ "taptools_airport_prepare": ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_set_loops": ([vp, ctypes.c_int], ctypes.c_int),
+ "taptools_airport_set_length_seconds": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_record": ([vp, ctypes.c_int, ctypes.c_int], ctypes.c_int),
+ "taptools_airport_set_level": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_set_pan": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_set_darken_hz": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_set_smooth_ms": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_airport_clear": ([vp], ctypes.c_int),
+ "taptools_airport_phase": ([vp, ctypes.c_int], ctypes.c_double),
+ "taptools_airport_composite_period_seconds": ([vp], ctypes.c_double),
+ "taptools_airport_process": ([vp, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+ "taptools_garden_create": ([], vp),
+ "taptools_garden_destroy": ([vp], None),
+ "taptools_garden_prepare": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_note": ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_loop_seconds": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_decay": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_soften": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_floor": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_bell": ([vp, ctypes.c_double, ctypes.c_double, ctypes.c_double],
+ ctypes.c_int),
+ "taptools_garden_set_root": ([vp, ctypes.c_int], ctypes.c_int),
+ "taptools_garden_set_scale": ([vp, ctypes.c_int], ctypes.c_int),
+ "taptools_garden_set_idle_seconds": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_seed": ([vp, ctypes.c_ulonglong], ctypes.c_int),
+ "taptools_garden_set_level": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_set_smooth_ms": ([vp, ctypes.c_double], ctypes.c_int),
+ "taptools_garden_clear": ([vp], ctypes.c_int),
+ "taptools_garden_active_events": ([vp], ctypes.c_int),
+ "taptools_garden_active_voices": ([vp], ctypes.c_int),
+ "taptools_garden_process": ([vp, f64p, ctypes.c_int], ctypes.c_int),
"taptools_yin_create": ([ctypes.c_int, ctypes.c_int, ctypes.c_int], vp),
"taptools_yin_destroy": ([vp], None),
"taptools_yin_frame_size": ([vp], ctypes.c_int),
@@ -1064,6 +1115,214 @@ def __del__(self):
self._h = None
+class Discreet:
+ """tap.discreet~'s kernel (tap::tools::discreet::machine): the Discreet
+ Music two-tape-machine regeneration loop. Regen legally reaches 1.0 —
+ stability comes from the wear path (darkening lowpass, bounded soft
+ saturation, DC blocker), not a feedback cap. Loop-time changes glide as
+ tape-speed doppler; wow/flutter are a deterministic periodic transport."""
+
+ def __init__(self, sr: float = 48000.0, max_loop_seconds: float = 30.0, **params):
+ self._h = _LIB.taptools_discreet_create()
+ _check(_LIB.taptools_discreet_prepare(self._h, float(sr), float(max_loop_seconds)),
+ "prepare")
+ self.set(**params)
+
+ def set(self, *, loop_seconds=None, regen=None, darken_hz=None, drive=None,
+ input_level=None, mix=None, wow=None, flutter=None, smooth_ms=None) -> "Discreet":
+ """`wow` and `flutter` take (depth_ms, rate_hz) pairs."""
+ # configuration first, so ramped targets in the same call honor the new slew
+ if smooth_ms is not None:
+ _check(_LIB.taptools_discreet_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+ if wow is not None:
+ depth, rate = wow
+ _check(_LIB.taptools_discreet_set_wow(self._h, float(depth), float(rate)), "wow")
+ if flutter is not None:
+ depth, rate = flutter
+ _check(_LIB.taptools_discreet_set_flutter(self._h, float(depth), float(rate)),
+ "flutter")
+ if loop_seconds is not None:
+ _check(_LIB.taptools_discreet_set_loop_seconds(self._h, float(loop_seconds)),
+ "loop_seconds")
+ if regen is not None:
+ _check(_LIB.taptools_discreet_set_regen(self._h, float(regen)), "regen")
+ if darken_hz is not None:
+ _check(_LIB.taptools_discreet_set_darken_hz(self._h, float(darken_hz)), "darken_hz")
+ if drive is not None:
+ _check(_LIB.taptools_discreet_set_drive(self._h, float(drive)), "drive")
+ if input_level is not None:
+ _check(_LIB.taptools_discreet_set_input_level(self._h, float(input_level)),
+ "input_level")
+ if mix is not None:
+ _check(_LIB.taptools_discreet_set_mix(self._h, float(mix)), "mix")
+ return self
+
+ def process(self, x) -> np.ndarray:
+ x = _f64(x)
+ out = np.zeros_like(x)
+ _check(_LIB.taptools_discreet_process(self._h, _p64(x), _p64(out), x.size), "process")
+ return out
+
+ def clear(self) -> None:
+ """The eject button: erases the tape, keeps the parameters."""
+ _check(_LIB.taptools_discreet_clear(self._h), "clear")
+
+ def __del__(self):
+ h = getattr(self, "_h", None)
+ if h:
+ _LIB.taptools_discreet_destroy(h)
+ self._h = None
+
+
+class Airport:
+ """tap.airport~'s kernel (tap::tools::airport::loop_bank): up to eight
+ free-running tape loops of unequal, incommensurate lengths, each with a
+ single head that both plays and records. No setter ever resets a phase —
+ the free-run is the piece. Per-loop level / equal-power pan / darken;
+ stereo sum, no dry path."""
+
+ def __init__(self, sr: float = 48000.0, max_loop_seconds: float = 30.0, **params):
+ self._h = _LIB.taptools_airport_create()
+ _check(_LIB.taptools_airport_prepare(self._h, float(sr), float(max_loop_seconds)),
+ "prepare")
+ self.set(**params)
+
+ def set(self, *, loops=None, lengths=None, levels=None, pans=None, darkens=None,
+ smooth_ms=None) -> "Airport":
+ """`lengths`/`levels`/`pans`/`darkens` are per-loop sequences (loop i
+ gets element i)."""
+ # configuration first, so ramped targets in the same call honor the new slew
+ if smooth_ms is not None:
+ _check(_LIB.taptools_airport_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+ if lengths is not None:
+ for i, s in enumerate(lengths):
+ _check(_LIB.taptools_airport_set_length_seconds(self._h, i, float(s)), "length")
+ if loops is None:
+ loops = len(list(lengths))
+ if loops is not None:
+ _check(_LIB.taptools_airport_set_loops(self._h, int(loops)), "loops")
+ if levels is not None:
+ for i, v in enumerate(levels):
+ _check(_LIB.taptools_airport_set_level(self._h, i, float(v)), "level")
+ if pans is not None:
+ for i, p in enumerate(pans):
+ _check(_LIB.taptools_airport_set_pan(self._h, i, float(p)), "pan")
+ if darkens is not None:
+ for i, hz in enumerate(darkens):
+ _check(_LIB.taptools_airport_set_darken_hz(self._h, i, float(hz)), "darken")
+ return self
+
+ def record(self, loop: int, on: bool) -> "Airport":
+ """Punch the process() input onto this loop's tape (True) or freeze it
+ bit-exactly (False). Recording starts wherever the head happens to be."""
+ _check(_LIB.taptools_airport_record(self._h, int(loop), 1 if on else 0), "record")
+ return self
+
+ def phase(self, loop: int) -> float:
+ """This loop's head position as a fraction of its length, 0..1."""
+ return float(_LIB.taptools_airport_phase(self._h, int(loop)))
+
+ @property
+ def composite_period_seconds(self) -> float:
+ """lcm of the active loop lengths (seconds); inf once it overflows."""
+ return float(_LIB.taptools_airport_composite_period_seconds(self._h))
+
+ def process(self, x):
+ x = _f64(x)
+ out_l = np.zeros_like(x)
+ out_r = np.zeros_like(x)
+ _check(_LIB.taptools_airport_process(self._h, _p64(x), _p64(out_l), _p64(out_r), x.size),
+ "process")
+ return out_l, out_r
+
+ def clear(self) -> None:
+ """Erase every tape and rewind every head; parameters are untouched."""
+ _check(_LIB.taptools_airport_clear(self._h), "clear")
+
+ def __del__(self):
+ h = getattr(self, "_h", None)
+ if h:
+ _LIB.taptools_airport_destroy(h)
+ self._h = None
+
+
+class Garden:
+ """tap.garden~'s kernel (tap::tools::garden::bed): a generative event
+ loop on the Bloom principle. Planted notes snap to the scale, bloom on a
+ two-operator FM bell, and return every loop pass a step quieter (decay)
+ and purer (soften) until they retire below the floor; left idle, a
+ seeded gardener plants for you. Scales: 0 chromatic, 1 major, 2 minor,
+ 3 major pentatonic, 4 minor pentatonic."""
+
+ def __init__(self, sr: float = 48000.0, **params):
+ self._h = _LIB.taptools_garden_create()
+ _check(_LIB.taptools_garden_prepare(self._h, float(sr)), "prepare")
+ self.set(**params)
+
+ def set(self, *, loop_seconds=None, decay=None, soften=None, floor=None, bell=None,
+ root=None, scale=None, idle_seconds=None, seed=None, level=None,
+ smooth_ms=None) -> "Garden":
+ """`bell` takes an (attack_s, decay_s, brightness) triple."""
+ # configuration first, so ramped targets in the same call honor the new slew
+ if smooth_ms is not None:
+ _check(_LIB.taptools_garden_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+ if seed is not None:
+ _check(_LIB.taptools_garden_set_seed(self._h, int(seed)), "seed")
+ if root is not None:
+ _check(_LIB.taptools_garden_set_root(self._h, int(root)), "root")
+ if scale is not None:
+ _check(_LIB.taptools_garden_set_scale(self._h, int(scale)), "scale")
+ if bell is not None:
+ attack_s, decay_s, brightness = bell
+ _check(_LIB.taptools_garden_set_bell(self._h, float(attack_s), float(decay_s),
+ float(brightness)), "bell")
+ if loop_seconds is not None:
+ _check(_LIB.taptools_garden_set_loop_seconds(self._h, float(loop_seconds)),
+ "loop_seconds")
+ if decay is not None:
+ _check(_LIB.taptools_garden_set_decay(self._h, float(decay)), "decay")
+ if soften is not None:
+ _check(_LIB.taptools_garden_set_soften(self._h, float(soften)), "soften")
+ if floor is not None:
+ _check(_LIB.taptools_garden_set_floor(self._h, float(floor)), "floor")
+ if idle_seconds is not None:
+ _check(_LIB.taptools_garden_set_idle_seconds(self._h, float(idle_seconds)),
+ "idle_seconds")
+ if level is not None:
+ _check(_LIB.taptools_garden_set_level(self._h, float(level)), "level")
+ return self
+
+ def note(self, pitch: float, velocity: float) -> "Garden":
+ """Plant: MIDI pitch (fractional ok, snaps to root/scale at entry),
+ velocity (0, 1]. Sounds on the next processed sample."""
+ _check(_LIB.taptools_garden_note(self._h, float(pitch), float(velocity)), "note")
+ return self
+
+ @property
+ def active_events(self) -> int:
+ return int(_LIB.taptools_garden_active_events(self._h))
+
+ @property
+ def active_voices(self) -> int:
+ return int(_LIB.taptools_garden_active_voices(self._h))
+
+ def process(self, n: int) -> np.ndarray:
+ """Render n samples (a source: no input)."""
+ out = np.zeros(int(n))
+ _check(_LIB.taptools_garden_process(self._h, _p64(out), out.size), "process")
+ return out
+
+ def clear(self) -> None:
+ """Uproot everything; parameters are untouched."""
+ _check(_LIB.taptools_garden_clear(self._h), "clear")
+
+ def __del__(self):
+ h = getattr(self, "_h", None)
+ if h:
+ _LIB.taptools_garden_destroy(h)
+ self._h = None
+
+
class Yin:
"""The shared DspTap pitch detector (tap::dsp::yin), passed through the C ABI
so the notebooks can track pitch with the same detector the corrector uses."""
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index c6cc13f..3421e67 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -13,9 +13,12 @@ FetchContent_MakeAvailable(Catch2)
add_executable(taptools_kernel_tests
adsr_test.cpp
+ airport_test.cpp
autowah_test.cpp
delay_test.cpp
diode_ladder_test.cpp
+ discreet_test.cpp
+ garden_test.cpp
grm_comb_test.cpp
harmonizer_test.cpp
nr_test.cpp
diff --git a/tests/airport_test.cpp b/tests/airport_test.cpp
new file mode 100644
index 0000000..f84e803
--- /dev/null
+++ b/tests/airport_test.cpp
@@ -0,0 +1,241 @@
+/// @file
+/// @brief Catch2 scenarios pinning the tap.airport~ kernel (airport.h).
+/// @details The promises are structural, so the measurements are exact: recorded phrases must
+/// return on their loop's grid bit-for-bit, no setter may touch a phase (the free-run
+/// IS the piece), hard pans must be bitwise absent from the far bus, and the
+/// composite period of coprime loop lengths must be their lcm to the sample.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+namespace {
+
+ constexpr double k_sr = 48000.0;
+
+ using tap::tools::airport::loop_bank;
+
+ loop_bank make(double max_loop_seconds = 2.0) {
+ loop_bank b;
+ b.prepare(k_sr, max_loop_seconds);
+ b.set_smooth_ms(0.0);
+ return b;
+ }
+
+ size_t at(double seconds) {
+ return static_cast(seconds * k_sr);
+ }
+
+ /// Punch a single unit impulse onto `loop` at its current head position.
+ void plant_click(loop_bank& b, int loop) {
+ double l = 0.0, r = 0.0;
+ b.record(loop, true);
+ b.process(1.0, l, r);
+ b.record(loop, false);
+ }
+
+ double goertzel(const std::vector& x, double f, size_t begin, size_t end) {
+ const double w = 2.0 * 3.14159265358979323846 * f / k_sr;
+ const double coef = 2.0 * std::cos(w);
+ double s1 = 0.0, s2 = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ const double s = x[i] + coef * s1 - s2;
+ s2 = s1;
+ s1 = s;
+ }
+ const double n = static_cast(end - begin);
+ const double re = s1 - s2 * std::cos(w);
+ const double im = s2 * std::sin(w);
+ return 2.0 * std::sqrt(re * re + im * im) / n;
+ }
+
+} // namespace
+
+SCENARIO("a recorded phrase returns every loop period and no setter resets the phase") {
+ loop_bank b = make();
+ b.set_loops(1);
+ b.set_length_seconds(0, 0.5);
+ b.set_pan(0, -1.0); // hard left: the left bus carries the loop bitwise
+
+ plant_click(b, 0);
+
+ const size_t loop = at(0.5);
+ std::vector yl(4 * loop, 0.0);
+ double r = 0.0;
+ for (size_t i = 0; i < yl.size(); ++i) {
+ if (i == loop + 100) { // mid-run setter storm: none of these may touch the head
+ b.set_level(0, 1.0);
+ b.set_darken_hz(0, tap::tools::tape::k_darken_ceil_hz);
+ b.record(0, false);
+ b.set_length_seconds(0, 0.5);
+ b.set_loops(1);
+ }
+ b.process(0.0, yl[i], r);
+ }
+
+ // The click was planted one sample into the run, so it returns at loop - 1, 2*loop - 1, ...
+ for (size_t k = 1; k <= 3; ++k) {
+ INFO("return " << k);
+ CHECK(yl[k * loop - 1] == 1.0); // bitwise: transparent playback of the same imprint
+ }
+
+ // And the head advances by exactly the samples processed, storm or no storm.
+ const double ph = b.phase(0);
+ INFO("phase after 4 loops + 1 planted sample: " << ph);
+ CHECK(std::abs(ph - 1.0 / static_cast(loop)) < 1e-9);
+}
+
+SCENARIO("record off freezes the tape bit-exactly") {
+ loop_bank b = make();
+ b.set_loops(1);
+ b.set_length_seconds(0, 0.5);
+ b.set_pan(0, -1.0);
+
+ const size_t loop = at(0.5);
+ double l = 0.0, r = 0.0;
+ b.record(0, true);
+ for (size_t i = 0; i < loop; ++i) { // one full pass of a phrase, then freeze
+ const double t = static_cast(i) / k_sr;
+ b.process(0.7 * std::sin(2.0 * 3.14159265358979323846 * 440.0 * t), l, r);
+ }
+ b.record(0, false);
+
+ std::vector pass_a(loop, 0.0), pass_b(loop, 0.0);
+ for (size_t i = 0; i < loop; ++i) {
+ b.process(0.0, pass_a[i], r);
+ }
+ for (size_t i = 0; i < loop; ++i) {
+ b.process(0.0, pass_b[i], r);
+ }
+ bool exact = true;
+ for (size_t i = 0; i < loop; ++i) {
+ exact = exact && (pass_a[i] == pass_b[i]); // bitwise, not approximately
+ }
+ REQUIRE(exact);
+ REQUIRE(*std::max_element(pass_a.begin(), pass_a.end()) > 0.5); // and it is the phrase, not silence
+}
+
+SCENARIO("two incommensurate loops realign only at the lcm") {
+ loop_bank b = make();
+ b.set_loops(2);
+ b.set_length_seconds(0, 0.5); // 24000 samples
+ b.set_length_seconds(1, 0.625); // 30000 samples; gcd 6000 -> lcm 120000 samples = 2.5 s
+ b.set_pan(0, -1.0);
+ b.set_pan(1, -1.0); // both on the left bus: the sum is where coincidence lives
+
+ REQUIRE(b.composite_period_seconds() == 2.5);
+
+ plant_click(b, 0);
+ plant_click(b, 1);
+
+ const size_t period = at(2.5);
+ std::vector yl(2 * period, 0.0);
+ double r = 0.0;
+ for (size_t i = 0; i < yl.size(); ++i) {
+ b.process(0.0, yl[i], r);
+ }
+
+ bool repeats_at_lcm = true;
+ for (size_t i = 0; i < period; ++i) {
+ repeats_at_lcm = repeats_at_lcm && (yl[i] == yl[i + period]);
+ }
+ REQUIRE(repeats_at_lcm);
+
+ bool differs_at_half = false;
+ for (size_t i = 0; i < period / 2; ++i) {
+ differs_at_half = differs_at_half || (yl[i] != yl[i + period / 2]);
+ }
+ REQUIRE(differs_at_half); // half the lcm is not a period: the pattern is still drifting
+}
+
+SCENARIO("a hard-panned loop is bitwise absent from the far bus") {
+ loop_bank b = make();
+ b.set_loops(1);
+ b.set_length_seconds(0, 0.5);
+ b.set_pan(0, -1.0);
+
+ plant_click(b, 0);
+
+ double l = 0.0, r = 0.0;
+ bool right_silent = true;
+ double left_peak = 0.0;
+ for (size_t i = 0; i < at(1.5); ++i) {
+ b.process(0.0, l, r);
+ right_silent = right_silent && (r == 0.0);
+ left_peak = std::max(left_peak, std::abs(l));
+ }
+ REQUIRE(right_silent);
+ REQUIRE(left_peak == 1.0);
+}
+
+SCENARIO("darken shades one loop's playback and only that loop's") {
+ loop_bank b = make();
+ b.set_loops(2);
+ b.set_length_seconds(0, 0.5);
+ b.set_length_seconds(1, 0.5);
+ b.set_pan(0, -1.0); // shaded loop on the left bus
+ b.set_pan(1, 1.0); // transparent loop on the right
+ b.set_darken_hz(0, 1000.0);
+
+ const double f = 6000.0;
+ double l = 0.0, r = 0.0;
+ b.record(0, true);
+ b.record(1, true);
+ for (size_t i = 0; i < at(0.5); ++i) { // the same phrase onto both tapes
+ const double t = static_cast(i) / k_sr;
+ b.process(0.6 * std::sin(2.0 * 3.14159265358979323846 * f * t), l, r);
+ }
+ b.record(0, false);
+ b.record(1, false);
+
+ std::vector yl(at(1.0), 0.0), yr(at(1.0), 0.0);
+ for (size_t i = 0; i < yl.size(); ++i) {
+ b.process(0.0, yl[i], yr[i]);
+ }
+
+ // Predicted shade at 6 kHz for a 1 kHz one-pole (+ the wear DC blocker, ~1 up there).
+ const double a = 1.0 - std::exp(-2.0 * 3.14159265358979323846 * 1000.0 / k_sr);
+ const double w = 2.0 * 3.14159265358979323846 * f / k_sr;
+ const double re = 1.0 - (1.0 - a) * std::cos(w);
+ const double im = (1.0 - a) * std::sin(w);
+ const double predicted = a / std::sqrt(re * re + im * im);
+
+ const double shaded = goertzel(yl, f, at(0.25), at(0.75));
+ const double transparent = goertzel(yr, f, at(0.25), at(0.75));
+ const double measured = shaded / transparent;
+ INFO("6 kHz through the 1 kHz shade: measured " << measured << ", predicted " << predicted);
+ CHECK(std::abs(measured - predicted) < 0.2 * predicted);
+ CHECK(transparent > 0.5); // and the transparent loop really is untouched
+}
+
+SCENARIO("a length change is a splice: phase re-wraps and never rewinds") {
+ loop_bank b = make();
+ b.set_loops(1);
+ b.set_length_seconds(0, 1.0);
+
+ double l = 0.0, r = 0.0;
+ for (size_t i = 0; i < at(0.9); ++i) {
+ b.process(0.0, l, r);
+ }
+ REQUIRE(std::abs(b.phase(0) - 0.9) < 1e-9);
+
+ b.set_length_seconds(0, 0.5); // head at 43200 of 24000: re-wraps to 19200, not to zero
+ INFO("phase after the splice: " << b.phase(0));
+ REQUIRE(std::abs(b.phase(0) - 0.8) < 1e-9);
+}
+
+SCENARIO("unprepared, the bank emits silence") {
+ loop_bank b;
+ b.set_loops(2);
+ double l = 1.0, r = 1.0;
+ b.process(0.7, l, r);
+ REQUIRE(l == 0.0);
+ REQUIRE(r == 0.0);
+}
diff --git a/tests/discreet_test.cpp b/tests/discreet_test.cpp
new file mode 100644
index 0000000..c62062e
--- /dev/null
+++ b/tests/discreet_test.cpp
@@ -0,0 +1,340 @@
+/// @file
+/// @brief Catch2 scenarios pinning the tap.discreet~ kernel (discreet.h + tape_loop.h).
+/// @details Oracle-based where the promise is musical: pitch claims (wow depth, the tape-speed
+/// doppler of a loop-time change) are measured with the DspTap YIN detector, spectral
+/// claims (per-pass darkening) with a local Goertzel against the analytically
+/// predicted per-pass transfer, and the headline stability claim — regeneration at
+/// exactly 1.0 stays bounded because the wear path is the stabilizer — with the
+/// two-window RMS pattern from grm_comb_test.cpp.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+
+namespace {
+
+ constexpr double k_sr = 48000.0;
+
+ using tap::tools::discreet::machine;
+
+ /// A machine with the transport parked and instant setters: tests opt into wow explicitly.
+ machine make(double max_loop_seconds = 4.0) {
+ machine m;
+ m.prepare(k_sr, max_loop_seconds);
+ m.set_smooth_ms(0.0);
+ m.set_wow(0.0, 0.0);
+ m.set_flutter(0.0, 0.0);
+ m.set_input_level(1.0);
+ m.set_mix(100.0); // wet only: the tape is what these tests measure
+ return m;
+ }
+
+ size_t at(double seconds) {
+ return static_cast(seconds * k_sr);
+ }
+
+ double rms(const std::vector& x, size_t begin, size_t end) {
+ double acc = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ acc += x[i] * x[i];
+ }
+ return std::sqrt(acc / static_cast(end - begin));
+ }
+
+ double mean(const std::vector& x, size_t begin, size_t end) {
+ double acc = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ acc += x[i];
+ }
+ return acc / static_cast(end - begin);
+ }
+
+ double peak(const std::vector& x, size_t begin, size_t end) {
+ double p = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ p = std::max(p, std::abs(x[i]));
+ }
+ return p;
+ }
+
+ /// Single-bin magnitude, 2|X(f)|/N — same probe as the tr808 tests.
+ double goertzel(const std::vector& x, double f, size_t begin, size_t end) {
+ const double w = 2.0 * 3.14159265358979323846 * f / k_sr;
+ const double coef = 2.0 * std::cos(w);
+ double s1 = 0.0, s2 = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ const double s = x[i] + coef * s1 - s2;
+ s2 = s1;
+ s1 = s;
+ }
+ const double n = static_cast(end - begin);
+ const double re = s1 - s2 * std::cos(w);
+ const double im = s2 * std::sin(w);
+ return 2.0 * std::sqrt(re * re + im * im) / n;
+ }
+
+ /// YIN oracle at an offset — same detector setup as tune_test.cpp / harmonizer_test.cpp.
+ double measure_hz(const std::vector& x, size_t offset) {
+ const size_t tau_min = static_cast(k_sr / 2000.0);
+ const size_t tau_max = static_cast(std::ceil(k_sr / 55.0));
+ tap::dsp::yin det(tau_max, tau_min, tau_max);
+ REQUIRE(x.size() >= offset + det.frame_size());
+ const auto r = det.analyze(x.data() + offset);
+ REQUIRE(r.voiced());
+ return k_sr / r.period;
+ }
+
+ double cents(double f, double ref) {
+ return 1200.0 * std::log2(f / ref);
+ }
+
+ /// Predicted per-pass magnitude of the wear path at drive 0: the exact one-pole lowpass times
+ /// the normalized DC blocker, evaluated on the unit circle — the same formulas the kernel
+ /// applies, computed independently here.
+ double wear_gain(double f, double cutoff_hz) {
+ const double w = 2.0 * 3.14159265358979323846 * f / k_sr;
+ const double a = 1.0 - std::exp(-2.0 * 3.14159265358979323846 * cutoff_hz / k_sr);
+ const auto ejw = std::exp(std::complex(0.0, -w));
+ const double lp = std::abs(a / (1.0 - (1.0 - a) * ejw));
+ const double r = tap::tools::tape::k_dc_block_r;
+ const double nm = tap::tools::tape::k_dc_block_norm;
+ const double dc = std::abs(nm * (1.0 - ejw) / (1.0 - r * ejw));
+ return lp * dc;
+ }
+
+ /// Deterministic noise, never denormal-small — same LCG as delay_test.cpp.
+ struct noise {
+ uint32_t state{2463534242u};
+ double operator()() {
+ state = state * 1664525u + 1013904223u;
+ return (static_cast(state) / 2147483648.0) - 1.0;
+ }
+ };
+
+} // namespace
+
+SCENARIO("the loop echoes at exactly the loop period") {
+ machine m = make();
+ m.set_loop_seconds(0.5);
+ m.set_regen(0.7);
+ m.set_drive(0.0);
+ m.set_darken_hz(20000.0);
+
+ const size_t loop = at(0.5);
+ std::vector y(at(1.8), 0.0);
+ for (size_t i = 0; i < y.size(); ++i) {
+ y[i] = m.process(i == 0 ? 1.0 : 0.0);
+ }
+
+ // The first return is the recorded impulse itself, bit-exact at one loop (integer span,
+ // Hermite frac 0 reads x0 exactly; wear touches only the return path, not the first read).
+ REQUIRE(y[loop] == 1.0);
+
+ // Later returns have passed the wear filter — smeared, but the peak stays on the grid.
+ for (size_t k = 2; k <= 3; ++k) {
+ const size_t lo = k * loop - 16;
+ const size_t hi = k * loop + 16;
+ size_t argmx = lo;
+ for (size_t i = lo; i < hi; ++i) {
+ if (std::abs(y[i]) > std::abs(y[argmx])) {
+ argmx = i;
+ }
+ }
+ INFO("echo " << k << " peak at " << argmx << ", grid " << k * loop);
+ CHECK(argmx >= k * loop - 1);
+ CHECK(argmx <= k * loop + 1);
+ }
+}
+
+// Regeneration at exactly 1.0 is a legal, sustaining regime: boundedness comes from the wear
+// path (bounded saturator + darkening + DC blocker), not from a feedback cap. Ten seconds of
+// ring covers ~36 passes of a 0.25 s loop — long enough that the +0.2 dB/s class of swell the
+// comb bank once had (grm_comb_test.cpp) would show clearly.
+SCENARIO("regen 1.0 with drive engaged is bounded and does not grow") {
+ machine m = make();
+ m.set_loop_seconds(0.25);
+ m.set_regen(1.0);
+ m.set_drive(0.5);
+ m.set_darken_hz(3000.0);
+
+ noise rng;
+ std::vector y(at(10.0), 0.0);
+ for (size_t i = 0; i < y.size(); ++i) {
+ const double in = (i < at(1.0)) ? 0.5 * rng() : 0.0;
+ y[i] = m.process(in);
+ }
+
+ const double early = rms(y, at(2.0), at(5.0));
+ const double late = rms(y, at(6.0), at(9.0));
+ INFO("ring RMS: [2,5)s = " << early << ", [6,9)s = " << late);
+ REQUIRE(std::isfinite(late));
+ REQUIRE(late <= early * 1.02); // sustain is the contract: no growth, decay not required
+ REQUIRE(peak(y, 0, y.size()) < 3.0); // |wear out| <= 1/drive = 2, plus the direct send
+}
+
+SCENARIO("every pass through the loop is darker by the wear filter") {
+ machine m = make();
+ m.set_loop_seconds(0.25);
+ m.set_regen(0.9);
+ m.set_drive(0.0); // linear wear: the per-pass ratio is exactly regen * |H_wear|
+ m.set_darken_hz(2000.0);
+
+ // A two-tone burst, one tone well above the darkening corner and one well below, so the
+ // test can assert both sides: highs die fast, lows barely fade (the honest tape story).
+ const double f_hi = 6000.0;
+ const double f_lo = 300.0;
+ const size_t loop = at(0.25);
+ std::vector y(at(1.5), 0.0);
+ for (size_t i = 0; i < y.size(); ++i) {
+ double in = 0.0;
+ if (i < at(0.1)) {
+ const double t = static_cast(i) / k_sr;
+ in = 0.4 * std::sin(2.0 * 3.14159265358979323846 * f_hi * t)
+ + 0.4 * std::sin(2.0 * 3.14159265358979323846 * f_lo * t);
+ }
+ y[i] = m.process(in);
+ }
+
+ const double expect_hi = 0.9 * wear_gain(f_hi, 2000.0);
+ const double expect_lo = 0.9 * wear_gain(f_lo, 2000.0);
+ for (size_t k = 1; k <= 3; ++k) {
+ const double hi_a = goertzel(y, f_hi, k * loop, k * loop + at(0.1));
+ const double hi_b = goertzel(y, f_hi, (k + 1) * loop, (k + 1) * loop + at(0.1));
+ const double lo_a = goertzel(y, f_lo, k * loop, k * loop + at(0.1));
+ const double lo_b = goertzel(y, f_lo, (k + 1) * loop, (k + 1) * loop + at(0.1));
+ const double hi_ratio = hi_b / hi_a;
+ const double lo_ratio = lo_b / lo_a;
+ INFO("pass " << k << " -> " << k + 1 << ": hi ratio " << hi_ratio << " (predicted " << expect_hi
+ << "), lo ratio " << lo_ratio << " (predicted " << expect_lo << ")");
+ CHECK(std::abs(hi_ratio - expect_hi) < 0.15 * expect_hi);
+ CHECK(std::abs(lo_ratio - expect_lo) < 0.05 * expect_lo);
+ CHECK(hi_ratio < lo_ratio); // both sides: the wear is a tilt, not a fader
+ }
+}
+
+SCENARIO("a dc step does not accumulate, even at regen 1.0") {
+ machine m = make();
+ m.set_loop_seconds(0.25);
+ m.set_regen(1.0);
+ m.set_drive(0.0);
+ m.set_darken_hz(3000.0);
+
+ // Without the in-loop DC blocker, a held 0.5 input at regen 1.0 would add 0.5 every pass,
+ // without bound. With it, the running mean stays put and the tail's mean returns to zero.
+ std::vector y(at(4.0), 0.0);
+ for (size_t i = 0; i < y.size(); ++i) {
+ y[i] = m.process(i < at(2.0) ? 0.5 : 0.0);
+ }
+
+ const double driven = peak(y, 0, at(2.0));
+ const double tail = mean(y, at(3.5), at(4.0)); // 2 whole loops: an unbiased DC estimate
+ INFO("driven peak " << driven << ", tail mean " << tail);
+ REQUIRE(driven < 3.0);
+ REQUIRE(std::abs(tail) < 0.02);
+}
+
+SCENARIO("wow bends pitch by the set depth, and two runs are bit-exact") {
+ const double depth_ms = 2.0;
+ const double rate_hz = 0.5;
+ // Peak deviation of a sinusoidally modulated read: ratio swings by depth * 2*pi*rate.
+ const double predicted = 1200.0 / std::log(2.0) * depth_ms * 0.001 * 2.0 * 3.14159265358979323846 * rate_hz;
+
+ auto render = [&] {
+ machine m = make();
+ m.set_loop_seconds(1.0);
+ m.set_regen(0.0);
+ m.set_wow(depth_ms, rate_hz);
+ std::vector y(at(4.5), 0.0);
+ for (size_t i = 0; i < y.size(); ++i) {
+ const double t = static_cast(i) / k_sr;
+ y[i] = m.process(0.8 * std::sin(2.0 * 3.14159265358979323846 * 440.0 * t));
+ }
+ return y;
+ };
+
+ const std::vector y = render();
+
+ // Track the wet pitch across one full wow cycle (2 s), after the tape has filled.
+ double worst = 0.0;
+ for (size_t off = at(1.5); off + at(0.05) < at(3.5); off += 2048) {
+ worst = std::max(worst, std::abs(cents(measure_hz(y, off), 440.0)));
+ }
+ INFO("peak deviation " << worst << " cents, predicted " << predicted);
+ CHECK(worst > 0.6 * predicted);
+ CHECK(worst < 1.4 * predicted);
+
+ const std::vector z = render();
+ bool exact = true;
+ for (size_t i = 0; i < y.size(); ++i) {
+ exact = exact && (y[i] == z[i]); // bitwise: the transport is deterministic
+ }
+ REQUIRE(exact);
+}
+
+SCENARIO("mix endpoints are bitwise exact") {
+ machine m = make();
+ m.set_loop_seconds(0.5);
+ m.set_regen(0.5);
+
+ m.set_mix(0.0);
+ noise rng;
+ bool exact = true;
+ for (int i = 0; i < 4800; ++i) {
+ const double in = rng();
+ exact = exact && (m.process(in) == in); // bitwise, not approximately
+ }
+ REQUIRE(exact);
+}
+
+SCENARIO("a loop-time change glides as tape speed, not a splice") {
+ machine m = make();
+ m.set_loop_seconds(0.5);
+ m.set_regen(0.0);
+
+ std::vector y;
+ y.reserve(at(4.5));
+ auto run = [&](double seconds) {
+ for (size_t i = 0; i < at(seconds); ++i) {
+ const double t = static_cast(y.size()) / k_sr;
+ y.push_back(m.process(0.8 * std::sin(2.0 * 3.14159265358979323846 * 440.0 * t)));
+ }
+ };
+
+ run(2.0); // fill the tape at the short span
+ m.set_smooth_ms(500.0);
+ m.set_loop_seconds(0.75); // respool +0.25 s of span over 0.5 s: tape speed halves
+ run(0.5);
+ m.set_smooth_ms(0.0);
+ run(2.0);
+
+ // Mid-glide the playback sits an octave down; after the ramp lands it re-locks to pitch.
+ const double gliding = measure_hz(y, at(2.2));
+ const double settled = measure_hz(y, at(3.5));
+ INFO("mid-glide " << gliding << " Hz, settled " << settled << " Hz");
+ CHECK(std::abs(cents(gliding, 220.0)) < 60.0);
+ CHECK(std::abs(cents(settled, 440.0)) < 5.0);
+
+ // And it is a glide: no splice discontinuity anywhere in the move.
+ double worst_step = 0.0;
+ for (size_t i = at(2.0) + 1; i < at(2.5); ++i) {
+ worst_step = std::max(worst_step, std::abs(y[i] - y[i - 1]));
+ }
+ INFO("largest sample step during the glide: " << worst_step);
+ CHECK(worst_step < 0.1); // a 440 Hz sine at 0.8 moves ~0.046/sample; a splice would jump ~1.6
+}
+
+SCENARIO("unprepared, the machine passes input through") {
+ machine m;
+ REQUIRE(m.process(0.7) == 0.7);
+ REQUIRE(m.process(-0.3) == -0.3);
+}
diff --git a/tests/garden_test.cpp b/tests/garden_test.cpp
new file mode 100644
index 0000000..9b40f08
--- /dev/null
+++ b/tests/garden_test.cpp
@@ -0,0 +1,328 @@
+/// @file
+/// @brief Catch2 scenarios pinning the tap.garden~ kernel (garden.h).
+/// @details The event-level restatement of the family's stability story, pinned the house
+/// way: the return grid to the sample, per-pass decay and softening measured on the
+/// output (Goertzel partials, YIN pitch for the scale contract), the population
+/// bounds (event ring and voice pool), and the full seeded-RNG triad the tr808
+/// voices established — same seed bit-exact, different seed different, seed
+/// irrelevant while the idle gardener is disabled.
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+
+namespace {
+
+ constexpr double k_sr = 48000.0;
+
+ using tap::tools::garden::bed;
+
+ /// A quiet, instrument-neutral bed: idle gardener off, instant level, percussive bell so
+ /// grid promises are sharp. Tests opt into slow bells and idling explicitly.
+ bed make() {
+ bed g;
+ g.prepare(k_sr);
+ g.set_smooth_ms(0.0);
+ g.set_idle_seconds(0.0);
+ g.set_bell(0.001, 0.02, 1.0);
+ g.set_scale(tap::tools::garden::scale_chromatic);
+ return g;
+ }
+
+ size_t at(double seconds) {
+ return static_cast(seconds * k_sr);
+ }
+
+ void render(bed& g, std::vector& y) {
+ for (auto& s : y) {
+ s = g.process();
+ }
+ }
+
+ double peak(const std::vector& x, size_t begin, size_t end) {
+ double p = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ p = std::max(p, std::abs(x[i]));
+ }
+ return p;
+ }
+
+ double goertzel(const std::vector& x, double f, size_t begin, size_t end) {
+ const double w = 2.0 * 3.14159265358979323846 * f / k_sr;
+ const double coef = 2.0 * std::cos(w);
+ double s1 = 0.0, s2 = 0.0;
+ for (size_t i = begin; i < end; ++i) {
+ const double s = x[i] + coef * s1 - s2;
+ s2 = s1;
+ s1 = s;
+ }
+ const double n = static_cast(end - begin);
+ const double re = s1 - s2 * std::cos(w);
+ const double im = s2 * std::sin(w);
+ return 2.0 * std::sqrt(re * re + im * im) / n;
+ }
+
+ /// YIN oracle at an offset — same detector setup as tune_test.cpp / harmonizer_test.cpp.
+ double measure_hz(const std::vector& x, size_t offset) {
+ const size_t tau_min = static_cast(k_sr / 2000.0);
+ const size_t tau_max = static_cast(std::ceil(k_sr / 55.0));
+ tap::dsp::yin det(tau_max, tau_min, tau_max);
+ REQUIRE(x.size() >= offset + det.frame_size());
+ const auto r = det.analyze(x.data() + offset);
+ REQUIRE(r.voiced());
+ return k_sr / r.period;
+ }
+
+ double cents(double f, double ref) {
+ return 1200.0 * std::log2(f / ref);
+ }
+
+ double midi_hz(double pitch) {
+ return 440.0 * std::exp2((pitch - 69.0) / 12.0);
+ }
+
+} // namespace
+
+SCENARIO("a planted note blooms again every loop period") {
+ bed g = make();
+ g.set_loop_seconds(0.5);
+ g.set_decay(0.9);
+ g.set_floor(0.001);
+ g.set_bell(1e-6, 0.02, 1.0); // instant attack: the envelope is at target one sample in
+
+ g.note(81.0, 0.8); // 880 Hz: the sine leaves the threshold within a few samples
+ std::vector y(at(2.0), 0.0);
+ render(g, y);
+
+ // The 20 ms bell is ~1e-11 by the next return, so an amplitude threshold separates the
+ // return from the previous tail cleanly. The onset detector is a threshold on a sine, so
+ // it can sit a few samples into the cycle — the grid claim is "within 8 samples", which at
+ // 48 kHz is a sixth of a millisecond.
+ const size_t loop = at(0.5);
+ REQUIRE(y[0] != 0.0); // the plant sounds on the next processed sample
+ for (size_t k = 1; k <= 3; ++k) {
+ const double vel = 0.8 * std::pow(0.9, static_cast(k));
+ size_t onset = 0;
+ for (size_t i = k * loop - 1000; i < k * loop + 1000; ++i) {
+ if (std::abs(y[i]) > 0.05 * vel) {
+ onset = i;
+ break;
+ }
+ }
+ INFO("return " << k << " onset at " << onset << ", grid " << k * loop);
+ CHECK(onset >= k * loop);
+ CHECK(onset < k * loop + 8);
+ }
+}
+
+SCENARIO("each return is quieter by the decay ratio and the bloom retires below the floor") {
+ bed g = make();
+ g.set_loop_seconds(0.25);
+ g.set_decay(0.5);
+ g.set_floor(0.05);
+
+ g.note(69.0, 0.8);
+ std::vector y(at(2.5), 0.0);
+ render(g, y);
+
+ // Velocity walks 0.8, 0.4, 0.2, 0.1, 0.05 and then retires: five audible returns.
+ const size_t loop = at(0.25);
+ double prev = peak(y, 0, loop);
+ for (size_t k = 1; k <= 4; ++k) {
+ const double p = peak(y, k * loop, (k + 1) * loop);
+ const double ratio = p / prev;
+ INFO("return " << k << ": peak " << p << ", ratio " << ratio);
+ CHECK(std::abs(ratio - 0.5) < 0.075);
+ prev = p;
+ }
+ REQUIRE(g.active_events() == 0); // retired below the floor
+ REQUIRE(peak(y, 6 * loop, y.size()) < 1e-6); // and audibly gone
+}
+
+SCENARIO("each return is purer: the fm partial fades by the soften ratio") {
+ bed g = make();
+ g.set_loop_seconds(0.5);
+ g.set_decay(1.0); // hold velocity still so only brightness moves
+ g.set_floor(0.001);
+ g.set_soften(0.6);
+ g.set_bell(0.005, 0.06, 1.0);
+
+ g.note(69.0, 0.8); // 440 Hz carrier; first upper FM sideband at 4f = 1760 Hz
+ std::vector y(at(2.5), 0.0);
+ render(g, y);
+
+ const size_t loop = at(0.5);
+ std::vector tilt;
+ for (size_t k = 0; k <= 3; ++k) {
+ const double fund = goertzel(y, 440.0, k * loop, k * loop + at(0.2));
+ const double side = goertzel(y, 1760.0, k * loop, k * loop + at(0.2));
+ tilt.push_back(side / fund);
+ INFO("return " << k << ": sideband/fundamental = " << side / fund);
+ }
+ for (size_t k = 1; k < tilt.size(); ++k) {
+ CHECK(tilt[k] < tilt[k - 1]); // strictly purer every pass
+ }
+ CHECK(tilt.back() < 0.3 * tilt.front()); // and substantially so over three passes
+}
+
+SCENARIO("every bloom lands on the scale") {
+ // Off-scale and fractional plants, C major pentatonic: each must sound a scale member.
+ const double planted[] = {61.0, 63.4, 66.0, 70.6};
+ for (const double pitch : planted) {
+ bed g = make();
+ g.set_scale(tap::tools::garden::scale_major_pentatonic);
+ g.set_root(0);
+ g.set_loop_seconds(2.0);
+ g.set_bell(0.01, 0.5, 0.4); // gentle index keeps the fundamental dominant for yin
+
+ g.note(pitch, 0.8);
+ std::vector y(at(0.5), 0.0);
+ render(g, y);
+
+ const double hz = measure_hz(y, at(0.1));
+ const double midi = 69.0 + 12.0 * std::log2(hz / 440.0);
+ const int pc = ((static_cast(std::lround(midi)) % 12) + 12) % 12;
+ const bool in_scale =
+ (tap::tools::garden::k_scale_masks[tap::tools::garden::scale_major_pentatonic] & (1u << pc)) != 0u;
+ INFO("planted " << pitch << " -> sounded " << midi << " (pc " << pc << ")");
+ CHECK(in_scale);
+ CHECK(std::abs(cents(hz, midi_hz(static_cast(std::lround(midi))))) < 20.0);
+ }
+}
+
+SCENARIO(
+ "the seeded garden is bit-exact per seed, differs across seeds, and the seed cannot matter while idle seeding is off") {
+ auto render_idle = [](uint64_t seed) {
+ bed g = make();
+ g.set_loop_seconds(0.25);
+ g.set_idle_seconds(0.5);
+ g.set_seed(seed);
+ std::vector y(at(3.0), 0.0);
+ render(g, y);
+ return y;
+ };
+
+ const std::vector a = render_idle(1111);
+ const std::vector b = render_idle(1111);
+ const std::vector c = render_idle(2222);
+
+ bool same = true, differ = false;
+ for (size_t i = 0; i < a.size(); ++i) {
+ same = same && (a[i] == b[i]);
+ differ = differ || (a[i] != c[i]);
+ }
+ REQUIRE(same); // same seed: bit-exact
+ REQUIRE(differ); // different seed: a different garden
+
+ // Idle seeding off: the rng is never consumed, so the seed cannot matter at all.
+ auto render_planted = [](uint64_t seed) {
+ bed g = make();
+ g.set_seed(seed);
+ g.note(60.0, 0.8);
+ std::vector y(at(1.0), 0.0);
+ render(g, y);
+ return y;
+ };
+ const std::vector p = render_planted(1111);
+ const std::vector q = render_planted(2222);
+ bool exact = true;
+ for (size_t i = 0; i < p.size(); ++i) {
+ exact = exact && (p[i] == q[i]);
+ }
+ REQUIRE(exact);
+ REQUIRE(peak(p, 0, p.size()) > 0.1); // a real render, not silence agreeing with silence
+}
+
+SCENARIO("left alone, the garden starts playing after idle_seconds — and never when idle is disabled") {
+ bed g = make();
+ g.set_loop_seconds(0.25);
+ g.set_idle_seconds(0.5);
+ g.set_seed(1111);
+
+ std::vector y(at(4.0), 0.0);
+ render(g, y);
+
+ // Deterministic per seed: with seed 1111 the gardener's first plant is a fixed fact.
+ REQUIRE(peak(y, 0, at(0.5)) == 0.0); // patient until the threshold
+ REQUIRE(peak(y, at(0.5), y.size()) > 0.05);
+
+ bed quiet = make(); // idle 0: disabled
+ std::vector z(at(4.0), 0.0);
+ render(quiet, z);
+ REQUIRE(peak(z, 0, z.size()) == 0.0);
+}
+
+SCENARIO("when the garden is full the oldest bloom yields to the newest") {
+ bed g = make();
+ g.set_loop_seconds(1.0);
+ g.set_decay(0.99);
+ g.set_floor(0.001);
+
+ // The first plant: a high, distinctive bell.
+ g.note(96.0, 0.8);
+ REQUIRE(g.active_events() == 1);
+
+ // Fill the garden and one more: 64 quiet low plants push the first bloom out.
+ std::vector scratch(200, 0.0);
+ for (int i = 0; i < tap::tools::garden::k_max_events; ++i) {
+ render(g, scratch);
+ g.note(45.0, 0.1);
+ }
+ REQUIRE(g.active_events() == tap::tools::garden::k_max_events);
+
+ // Render across where the first bloom would have returned: its pitch is gone.
+ std::vector y(at(1.2), 0.0);
+ render(g, y);
+ const double high = goertzel(y, midi_hz(96.0), at(0.95), at(1.15));
+ INFO("energy at the dropped bloom's pitch: " << high);
+ CHECK(high < 0.01);
+}
+
+SCENARIO("the bell pool never exceeds its size and stays finite and bounded under heavy stealing") {
+ bed g = make();
+ g.set_loop_seconds(0.5);
+ g.set_decay(0.9);
+ g.set_floor(0.001);
+ g.set_bell(0.01, 1.5, 1.0); // long ringing bells force constant stealing
+
+ // Plant twice the pool size in quick succession, then let everything recirculate.
+ std::vector y;
+ y.reserve(at(2.0));
+ for (int i = 0; i < 2 * tap::tools::garden::k_voices; ++i) {
+ g.note(48.0 + i, 0.9);
+ for (int s = 0; s < 400; ++s) {
+ y.push_back(g.process());
+ }
+ REQUIRE(g.active_voices() <= tap::tools::garden::k_voices);
+ }
+ while (y.size() < at(2.0)) {
+ y.push_back(g.process());
+ }
+
+ // The hard bound is structural: k_voices bells, each |env * sin| <= 1, level 1.
+ double worst = 0.0;
+ bool finite = true;
+ for (const double v : y) {
+ worst = std::max(worst, std::abs(v));
+ finite = finite && std::isfinite(v);
+ }
+ INFO("peak under heavy stealing: " << worst);
+ REQUIRE(finite);
+ REQUIRE(worst <= static_cast(tap::tools::garden::k_voices));
+ REQUIRE(g.active_voices() <= tap::tools::garden::k_voices);
+}
+
+SCENARIO("unprepared, the garden is silent") {
+ bed g;
+ g.note(60.0, 1.0); // a safe no-op before prepare
+ REQUIRE(g.process() == 0.0);
+ REQUIRE(g.active_events() == 0);
+}
diff --git a/tools/capi/taptools_capi.cpp b/tools/capi/taptools_capi.cpp
index 29d3a62..2c6f165 100644
--- a/tools/capi/taptools_capi.cpp
+++ b/tools/capi/taptools_capi.cpp
@@ -7,10 +7,13 @@
// The DSP cores are the same headers the Max externals compile — no Max/Min dependency.
#include
+#include
#include
#include
#include
#include
+#include
+#include
#include
#include
#include
@@ -1071,4 +1074,231 @@ int taptools_od_process(taptools_od h, const double* in, double* out, int n) {
return with(h, [&](overdrive& o) { o.process(in, out, static_cast(n)); });
}
+// ---- tap.discreet~ -------------------------------------------------------------------------------
+
+using discreet_machine = tap::tools::discreet::machine;
+
+taptools_discreet taptools_discreet_create(void) {
+ return static_cast(new discreet_machine());
+}
+
+void taptools_discreet_destroy(taptools_discreet h) {
+ delete static_cast(h);
+}
+
+int taptools_discreet_prepare(taptools_discreet h, double sr, double max_loop_seconds) {
+ if (max_loop_seconds <= 0.0) {
+ return -1;
+ }
+ return with(h, [&](discreet_machine& m) { m.prepare(sr, max_loop_seconds); });
+}
+
+int taptools_discreet_set_loop_seconds(taptools_discreet h, double s) {
+ return with(h, [&](discreet_machine& m) { m.set_loop_seconds(s); });
+}
+
+int taptools_discreet_set_regen(taptools_discreet h, double r) {
+ return with(h, [&](discreet_machine& m) { m.set_regen(r); });
+}
+
+int taptools_discreet_set_darken_hz(taptools_discreet h, double hz) {
+ return with(h, [&](discreet_machine& m) { m.set_darken_hz(hz); });
+}
+
+int taptools_discreet_set_drive(taptools_discreet h, double d) {
+ return with(h, [&](discreet_machine& m) { m.set_drive(d); });
+}
+
+int taptools_discreet_set_input_level(taptools_discreet h, double lin) {
+ return with(h, [&](discreet_machine& m) { m.set_input_level(lin); });
+}
+
+int taptools_discreet_set_mix(taptools_discreet h, double pct) {
+ return with(h, [&](discreet_machine& m) { m.set_mix(pct); });
+}
+
+int taptools_discreet_set_wow(taptools_discreet h, double depth_ms, double rate_hz) {
+ return with(h, [&](discreet_machine& m) { m.set_wow(depth_ms, rate_hz); });
+}
+
+int taptools_discreet_set_flutter(taptools_discreet h, double depth_ms, double rate_hz) {
+ return with(h, [&](discreet_machine& m) { m.set_flutter(depth_ms, rate_hz); });
+}
+
+int taptools_discreet_set_smooth_ms(taptools_discreet h, double ms) {
+ return with(h, [&](discreet_machine& m) { m.set_smooth_ms(ms); });
+}
+
+int taptools_discreet_clear(taptools_discreet h) {
+ return with(h, [&](discreet_machine& m) { m.clear(); });
+}
+
+int taptools_discreet_process(taptools_discreet h, const double* in, double* out, int n) {
+ if (!in || !out || n < 0) {
+ return -1;
+ }
+ return with(h, [&](discreet_machine& m) { m.process(in, out, static_cast(n)); });
+}
+
+// ---- tap.airport~ --------------------------------------------------------------------------------
+
+using airport_bank = tap::tools::airport::loop_bank;
+
+taptools_airport taptools_airport_create(void) {
+ return static_cast(new airport_bank());
+}
+
+void taptools_airport_destroy(taptools_airport h) {
+ delete static_cast(h);
+}
+
+int taptools_airport_prepare(taptools_airport h, double sr, double max_loop_seconds) {
+ if (max_loop_seconds <= 0.0) {
+ return -1;
+ }
+ return with(h, [&](airport_bank& b) { b.prepare(sr, max_loop_seconds); });
+}
+
+int taptools_airport_set_loops(taptools_airport h, int count) {
+ return with(h, [&](airport_bank& b) { b.set_loops(count); });
+}
+
+int taptools_airport_set_length_seconds(taptools_airport h, int loop, double s) {
+ return with(h, [&](airport_bank& b) { b.set_length_seconds(loop, s); });
+}
+
+int taptools_airport_record(taptools_airport h, int loop, int on) {
+ return with(h, [&](airport_bank& b) { b.record(loop, on != 0); });
+}
+
+int taptools_airport_set_level(taptools_airport h, int loop, double lin) {
+ return with(h, [&](airport_bank& b) { b.set_level(loop, lin); });
+}
+
+int taptools_airport_set_pan(taptools_airport h, int loop, double pan) {
+ return with(h, [&](airport_bank& b) { b.set_pan(loop, pan); });
+}
+
+int taptools_airport_set_darken_hz(taptools_airport h, int loop, double hz) {
+ return with(h, [&](airport_bank& b) { b.set_darken_hz(loop, hz); });
+}
+
+int taptools_airport_set_smooth_ms(taptools_airport h, double ms) {
+ return with(h, [&](airport_bank& b) { b.set_smooth_ms(ms); });
+}
+
+int taptools_airport_clear(taptools_airport h) {
+ return with(h, [&](airport_bank& b) { b.clear(); });
+}
+
+double taptools_airport_phase(taptools_airport h, int loop) {
+ if (!h) {
+ return -1.0;
+ }
+ return static_cast(h)->phase(loop);
+}
+
+double taptools_airport_composite_period_seconds(taptools_airport h) {
+ if (!h) {
+ return -1.0;
+ }
+ return static_cast(h)->composite_period_seconds();
+}
+
+int taptools_airport_process(taptools_airport h, const double* in, double* outL, double* outR, int n) {
+ if (!in || !outL || !outR || n < 0) {
+ return -1;
+ }
+ return with(h, [&](airport_bank& b) { b.process(in, outL, outR, static_cast(n)); });
+}
+
+// ---- tap.garden~ ---------------------------------------------------------------------------------
+
+using garden_bed = tap::tools::garden::bed;
+
+taptools_garden taptools_garden_create(void) {
+ return static_cast(new garden_bed());
+}
+
+void taptools_garden_destroy(taptools_garden h) {
+ delete static_cast(h);
+}
+
+int taptools_garden_prepare(taptools_garden h, double sr) {
+ return with(h, [&](garden_bed& g) { g.prepare(sr); });
+}
+
+int taptools_garden_note(taptools_garden h, double pitch, double velocity) {
+ return with(h, [&](garden_bed& g) { g.note(pitch, velocity); });
+}
+
+int taptools_garden_set_loop_seconds(taptools_garden h, double s) {
+ return with(h, [&](garden_bed& g) { g.set_loop_seconds(s); });
+}
+
+int taptools_garden_set_decay(taptools_garden h, double per_pass) {
+ return with(h, [&](garden_bed& g) { g.set_decay(per_pass); });
+}
+
+int taptools_garden_set_soften(taptools_garden h, double per_pass) {
+ return with(h, [&](garden_bed& g) { g.set_soften(per_pass); });
+}
+
+int taptools_garden_set_floor(taptools_garden h, double v) {
+ return with(h, [&](garden_bed& g) { g.set_floor(v); });
+}
+
+int taptools_garden_set_bell(taptools_garden h, double attack_s, double decay_s, double brightness) {
+ return with(h, [&](garden_bed& g) { g.set_bell(attack_s, decay_s, brightness); });
+}
+
+int taptools_garden_set_root(taptools_garden h, int semitone) {
+ return with(h, [&](garden_bed& g) { g.set_root(semitone); });
+}
+
+int taptools_garden_set_scale(taptools_garden h, int scale) {
+ return with(h, [&](garden_bed& g) { g.set_scale(scale); });
+}
+
+int taptools_garden_set_idle_seconds(taptools_garden h, double s) {
+ return with(h, [&](garden_bed& g) { g.set_idle_seconds(s); });
+}
+
+int taptools_garden_set_seed(taptools_garden h, unsigned long long seed) {
+ return with(h, [&](garden_bed& g) { g.set_seed(static_cast(seed)); });
+}
+
+int taptools_garden_set_level(taptools_garden h, double lin) {
+ return with(h, [&](garden_bed& g) { g.set_level(lin); });
+}
+
+int taptools_garden_set_smooth_ms(taptools_garden h, double ms) {
+ return with(h, [&](garden_bed& g) { g.set_smooth_ms(ms); });
+}
+
+int taptools_garden_clear(taptools_garden h) {
+ return with(h, [&](garden_bed& g) { g.clear(); });
+}
+
+int taptools_garden_active_events(taptools_garden h) {
+ if (!h) {
+ return -1;
+ }
+ return static_cast(h)->active_events();
+}
+
+int taptools_garden_active_voices(taptools_garden h) {
+ if (!h) {
+ return -1;
+ }
+ return static_cast(h)->active_voices();
+}
+
+int taptools_garden_process(taptools_garden h, double* out, int n) {
+ if (!out || n < 0) {
+ return -1;
+ }
+ return with(h, [&](garden_bed& g) { g.process(out, static_cast(n)); });
+}
+
} // extern "C"
diff --git a/tools/capi/taptools_capi.h b/tools/capi/taptools_capi.h
index 40c147a..c820714 100644
--- a/tools/capi/taptools_capi.h
+++ b/tools/capi/taptools_capi.h
@@ -346,6 +346,77 @@ TAPTOOLS_API int taptools_od_set_smooth_ms(taptools_od h, double ms);
TAPTOOLS_API int taptools_od_clear(taptools_od h);
TAPTOOLS_API int taptools_od_process(taptools_od h, const double* in, double* out, int n);
+// ---- tap.discreet~ (tap::tools::discreet::machine) -----------------------------------------------
+
+typedef void* taptools_discreet;
+
+TAPTOOLS_API taptools_discreet taptools_discreet_create(void);
+TAPTOOLS_API void taptools_discreet_destroy(taptools_discreet h);
+/// Buy tape for `max_loop_seconds` at `sr`; snaps ramps and erases the tape.
+TAPTOOLS_API int taptools_discreet_prepare(taptools_discreet h, double sr, double max_loop_seconds);
+TAPTOOLS_API int taptools_discreet_set_loop_seconds(taptools_discreet h, double s); // slewed: tape-speed doppler
+TAPTOOLS_API int taptools_discreet_set_regen(taptools_discreet h, double r); // 0..1; 1.0 legally sustains
+TAPTOOLS_API int taptools_discreet_set_darken_hz(taptools_discreet h, double hz); // per-pass wear corner
+TAPTOOLS_API int taptools_discreet_set_drive(taptools_discreet h, double d); // >= 0; 0 exactly linear
+TAPTOOLS_API int taptools_discreet_set_input_level(taptools_discreet h, double lin); // the send fader
+TAPTOOLS_API int taptools_discreet_set_mix(taptools_discreet h, double pct); // 0..100, equal-power
+TAPTOOLS_API int taptools_discreet_set_wow(taptools_discreet h, double depth_ms, double rate_hz);
+TAPTOOLS_API int taptools_discreet_set_flutter(taptools_discreet h, double depth_ms, double rate_hz);
+TAPTOOLS_API int taptools_discreet_set_smooth_ms(taptools_discreet h, double ms);
+TAPTOOLS_API int taptools_discreet_clear(taptools_discreet h);
+TAPTOOLS_API int taptools_discreet_process(taptools_discreet h, const double* in, double* out, int n);
+
+// ---- tap.airport~ (tap::tools::airport::loop_bank) -----------------------------------------------
+
+typedef void* taptools_airport;
+
+TAPTOOLS_API taptools_airport taptools_airport_create(void);
+TAPTOOLS_API void taptools_airport_destroy(taptools_airport h);
+/// Buy k_max_loops (8) reels for `max_loop_seconds` at `sr`; erases tape and rewinds heads.
+TAPTOOLS_API int taptools_airport_prepare(taptools_airport h, double sr, double max_loop_seconds);
+TAPTOOLS_API int taptools_airport_set_loops(taptools_airport h, int count); // 0..8 active loops
+/// Per-loop setters; `loop` is 0-based. A length change is a splice (head re-wraps, no rewind).
+TAPTOOLS_API int taptools_airport_set_length_seconds(taptools_airport h, int loop, double s);
+TAPTOOLS_API int taptools_airport_record(taptools_airport h, int loop, int on); // 1 punch, 0 freeze
+TAPTOOLS_API int taptools_airport_set_level(taptools_airport h, int loop, double lin);
+TAPTOOLS_API int taptools_airport_set_pan(taptools_airport h, int loop, double pan); // -1..1 equal-power
+TAPTOOLS_API int taptools_airport_set_darken_hz(taptools_airport h, int loop, double hz);
+TAPTOOLS_API int taptools_airport_set_smooth_ms(taptools_airport h, double ms);
+TAPTOOLS_API int taptools_airport_clear(taptools_airport h);
+/// This loop's head position as a fraction of its length, 0..1 (-1 on a bad handle/index).
+TAPTOOLS_API double taptools_airport_phase(taptools_airport h, int loop);
+/// lcm of the active loop lengths in seconds; +inf on 64-bit overflow; 0 if unprepared.
+TAPTOOLS_API double taptools_airport_composite_period_seconds(taptools_airport h);
+/// Process n samples; the stereo loop sum lands in outL/outR (no dry path).
+TAPTOOLS_API int taptools_airport_process(taptools_airport h, const double* in, double* outL, double* outR, int n);
+
+// ---- tap.garden~ (tap::tools::garden::bed) -------------------------------------------------------
+
+typedef void* taptools_garden;
+
+TAPTOOLS_API taptools_garden taptools_garden_create(void);
+TAPTOOLS_API void taptools_garden_destroy(taptools_garden h);
+TAPTOOLS_API int taptools_garden_prepare(taptools_garden h, double sr);
+/// Plant a note: MIDI pitch (fractional ok, snaps to root/scale at entry), velocity (0, 1].
+TAPTOOLS_API int taptools_garden_note(taptools_garden h, double pitch, double velocity);
+TAPTOOLS_API int taptools_garden_set_loop_seconds(taptools_garden h, double s);
+TAPTOOLS_API int taptools_garden_set_decay(taptools_garden h, double per_pass); // velocity/pass, 0..1
+TAPTOOLS_API int taptools_garden_set_soften(taptools_garden h, double per_pass); // brightness/pass, 0..1
+TAPTOOLS_API int taptools_garden_set_floor(taptools_garden h, double v); // retirement threshold
+/// Bell envelope times in SECONDS + base brightness 0..1 (scales the FM index).
+TAPTOOLS_API int taptools_garden_set_bell(taptools_garden h, double attack_s, double decay_s, double brightness);
+TAPTOOLS_API int taptools_garden_set_root(taptools_garden h, int semitone); // 0..11, 0 = C
+TAPTOOLS_API int taptools_garden_set_scale(taptools_garden h, int scale); // garden::scale_index
+TAPTOOLS_API int taptools_garden_set_idle_seconds(taptools_garden h, double s); // 0 disables the gardener
+TAPTOOLS_API int taptools_garden_set_seed(taptools_garden h, unsigned long long seed);
+TAPTOOLS_API int taptools_garden_set_level(taptools_garden h, double lin);
+TAPTOOLS_API int taptools_garden_set_smooth_ms(taptools_garden h, double ms);
+TAPTOOLS_API int taptools_garden_clear(taptools_garden h);
+TAPTOOLS_API int taptools_garden_active_events(taptools_garden h); // live blooms (-1 on bad handle)
+TAPTOOLS_API int taptools_garden_active_voices(taptools_garden h); // ringing bells (-1 on bad handle)
+/// A source: renders n samples into out (mono).
+TAPTOOLS_API int taptools_garden_process(taptools_garden h, double* out, int n);
+
#ifdef __cplusplus
}
#endif
diff --git a/tools/render/CMakeLists.txt b/tools/render/CMakeLists.txt
index 2291633..c4d95ea 100644
--- a/tools/render/CMakeLists.txt
+++ b/tools/render/CMakeLists.txt
@@ -2,7 +2,7 @@
# through its kernel header to WAV files for listening checks — the kernels' reusability
# outside Max, demonstrated in ~150 lines apiece.
-foreach (tool diode_render tb303_render ladder_render vco_render grm_comb_render grm_pitchaccum_render autowah_render tr808_render)
+foreach (tool diode_render tb303_render ladder_render vco_render grm_comb_render grm_pitchaccum_render autowah_render tr808_render eno_render)
add_executable(${tool} ${tool}.cpp)
target_link_libraries(${tool} PRIVATE TapTools::taptools)
set_target_properties(${tool} PROPERTIES
diff --git a/tools/render/eno_render.cpp b/tools/render/eno_render.cpp
new file mode 100644
index 0000000..3d8e4b8
--- /dev/null
+++ b/tools/render/eno_render.cpp
@@ -0,0 +1,233 @@
+/// @file
+/// @brief Offline renderer for the Eno family — writes demo WAVs for listening checks.
+/// @details Exercises discreet.h, airport.h, and garden.h with no Max involved (the kernels'
+/// portability, demonstrated) — and, for this family, the only practical audition:
+/// these are long-timescale systems, so the scenarios run minutes, not seconds.
+///
+/// Scenarios: `discreet_basic` (a phrase into the two-machine loop at regen 0.95),
+/// `discreet_sustain` (regen 1.0 with drive — the Frippertronics wash, input faded
+/// out at the halfway mark), `airport_two_one` (seven incommensurate loops, stereo,
+/// three minutes), `garden_played` (four planted notes recirculating to silence),
+/// and `garden_idle` (the seeded gardener left alone for two minutes).
+///
+/// Usage: eno_render [output-directory] (default: current directory)
+/// @author Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+
+namespace {
+
+ constexpr double k_g_sr = 48000.0;
+ constexpr double k_g_pi = 3.14159265358979323846;
+
+ /// 48 kHz float32 WAV, 1 or 2 channels (interleaved) — same writer as the other render tools.
+ bool write_wav(const std::string& path, const std::vector& samples, double sr, uint16_t channels = 1) {
+ std::FILE* f = std::fopen(path.c_str(), "wb");
+ if (!f) {
+ std::fprintf(stderr, "cannot open %s\n", path.c_str());
+ return false;
+ }
+ const uint32_t n = static_cast(samples.size());
+ const uint32_t data_bytes = n * 4;
+ const uint32_t rate = static_cast(sr);
+
+ auto u16 = [&](uint16_t v) { std::fwrite(&v, 2, 1, f); };
+ auto u32 = [&](uint32_t v) { std::fwrite(&v, 4, 1, f); };
+
+ std::fwrite("RIFF", 1, 4, f);
+ u32(36 + data_bytes);
+ std::fwrite("WAVE", 1, 4, f);
+ std::fwrite("fmt ", 1, 4, f);
+ u32(16);
+ u16(3); // IEEE float
+ u16(channels);
+ u32(rate);
+ u32(rate * 4 * channels);
+ u16(static_cast(4 * channels)); // block align
+ u16(32); // bits
+ std::fwrite("data", 1, 4, f);
+ u32(data_bytes);
+ for (double s : samples) {
+ const float v = static_cast(s);
+ std::fwrite(&v, 4, 1, f);
+ }
+ std::fclose(f);
+ std::printf("wrote %s (%.1f s)\n", path.c_str(), n / (sr * channels));
+ return true;
+ }
+
+ /// A soft additive phrase tone: fundamental + two harmonics under a sine^2 swell.
+ double phrase_tone(double t, double dur, double hz) {
+ if (t < 0.0 || t >= dur) {
+ return 0.0;
+ }
+ const double env = std::pow(std::sin(k_g_pi * std::min(t / (0.66 * dur), 1.0)), 2.0);
+ return env
+ * (0.5 * std::sin(2.0 * k_g_pi * hz * t) + 0.22 * std::sin(2.0 * k_g_pi * 2.0 * hz * t)
+ + 0.1 * std::sin(2.0 * k_g_pi * 3.0 * hz * t));
+ }
+
+ double midi_hz(double pitch) {
+ return 440.0 * std::exp2((pitch - 69.0) / 12.0);
+ }
+
+ void discreet_basic(const std::string& dir) {
+ tap::tools::discreet::machine m;
+ m.prepare(k_g_sr, 10.0);
+ m.set_loop_seconds(5.0);
+ m.set_regen(0.95);
+ m.set_drive(0.4);
+ m.set_darken_hz(3500.0);
+ m.set_mix(60.0);
+
+ // Four slow notes in the first fifteen seconds, then the machine on its own.
+ const double notes[][2] = {{57, 0.5}, {64, 8.0}, {62, 15.0}, {69, 21.0}};
+ std::vector y(static_cast(90.0 * k_g_sr));
+ for (size_t i = 0; i < y.size(); ++i) {
+ const double t = static_cast(i) / k_g_sr;
+ double in = 0.0;
+ for (const auto& n : notes) {
+ in += 0.5 * phrase_tone(t - n[1], 4.0, midi_hz(n[0]));
+ }
+ y[i] = m.process(in);
+ }
+ write_wav(dir + "/discreet_basic.wav", y, k_g_sr);
+ }
+
+ void discreet_sustain(const std::string& dir) {
+ tap::tools::discreet::machine m;
+ m.prepare(k_g_sr, 10.0);
+ m.set_loop_seconds(6.5);
+ m.set_regen(1.0); // the point of the kernel: wear is the stabilizer
+ m.set_drive(0.7);
+ m.set_darken_hz(2200.0);
+ m.set_mix(100.0);
+ m.set_smooth_ms(2000.0);
+
+ const double notes[][2] = {{45, 0.5}, {57, 5.0}, {60, 11.0}, {64, 17.0}, {67, 24.0}};
+ std::vector y(static_cast(120.0 * k_g_sr));
+ bool faded = false;
+ for (size_t i = 0; i < y.size(); ++i) {
+ const double t = static_cast(i) / k_g_sr;
+ double in = 0.0;
+ for (const auto& n : notes) {
+ in += 0.45 * phrase_tone(t - n[1], 5.0, midi_hz(n[0]));
+ }
+ if (!faded && t >= 60.0) { // the performance move: fade the send, the wash remains
+ m.set_input_level(0.0);
+ faded = true;
+ }
+ y[i] = m.process(in);
+ }
+ write_wav(dir + "/discreet_sustain.wav", y, k_g_sr);
+ }
+
+ void airport_two_one(const std::string& dir) {
+ tap::tools::airport::loop_bank b;
+ b.prepare(k_g_sr, 32.0);
+
+ // Seven loops in the spirit of the published description: long, mutually incommensurate,
+ // one soft phrase each. Lengths are deliberately awkward ratios of one another.
+ const double lengths[7] = {17.8, 19.1, 21.3, 23.9, 26.2, 28.7, 30.9};
+ const double pitches[7] = {57, 60, 62, 64, 65, 69, 72};
+ const double pans[7] = {-0.8, 0.8, -0.45, 0.45, -0.15, 0.15, 0.0};
+ b.set_loops(7);
+ for (int i = 0; i < 7; ++i) {
+ b.set_length_seconds(i, lengths[i]);
+ b.set_level(i, 0.45);
+ b.set_pan(i, pans[i]);
+ }
+ b.set_darken_hz(2, 4000.0);
+ b.set_darken_hz(4, 4000.0);
+
+ std::vector stereo;
+ stereo.reserve(static_cast