diff --git a/README.md b/README.md
index acd99a9..18a95df 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,10 @@ header adds no nested namespace, the class) the kernel lives in.
 | `overdrive.h` | `tap.overdrive~` | LGW-voiced feedback overdrive (`tap::tools::od`) |
 | `vca.h` | `tap.vca~` | Voltage-controlled amplifier (`tap::tools::vca`) |
 | `adsr.h` | `tap.adsr~` | Virtual-analog ADSR envelope, legacy Jamoma curves as modes (`tap::tools::adsr`) |
+| `fuzz.h` | `tap.fuzz~` | Two-stage tone-stacked fuzz on the DAFx-07 cascade (`tap::tools::fuzz`) |
+| `touche.h` | `tap.touche~` | The Ondes Martenot intensity key as a published gain law (`tap::tools::touche`) |
+| `diffuseur.h` | `tap.metallique~`, `tap.palme~` | The Ondes diffuseurs as driven resonators (`tap::tools::diffuseur`) |
+| `ondes.h` | `tap.ondes~`, `tap.triode~` | The Ondes Martenot voice: heterodyne detector and load-line triode stages (`tap::tools::ondes`) |
 
 **Voices, drums, and sequencing**
 
@@ -82,6 +86,9 @@ header adds no nested namespace, the class) the kernel lives in.
 | `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`) |
+| `tapecho.h` | `tap.tapecho~` | Multi-head tape echo (`tap::tools::tapecho`) |
+| `stammer.h` | `tap.stammer~` | Live buffer-stutter rig (`tap::tools::stammer`) |
+| `scrub.h` | `tap.scrub~` | Granular scrub over live capture (`tap::tools::scrub`) |
 
 `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
@@ -92,7 +99,7 @@ Plus, all Max-free:
 - **`tests/`** — Catch2 unit tests for the kernels (fetched via FetchContent; run with `ctest`).
   Wrapper-level tests (attributes, Min plumbing) stay with the externals on the min-api harness
   in the Max package.
-- **`tools/render/`** — offline WAV renderers (`diode_render`, `tb303_render`, `ladder_render`, `vco_render`,
+- **`tools/render/`** — offline WAV renderers (`diode_render`, `tb303_render`, `ladder_render`, `vco_render`, `radiohead_render`,
   `grm_comb_render`, `grm_pitchaccum_render`, `autowah_render`) for listening checks outside Max.
 - **`tools/capi/`** — a small C ABI (`taptools_capi`) over a *subset* of the kernels, for the
   notebooks and other non-C++ consumers. `tools/capi/taptools_capi.h` is the authoritative list of
@@ -106,10 +113,13 @@ Plus, all Max-free:
 - **`bench/`** — CPU benchmarks and the per-machine regression ratchet (see `bench/README.md`).
 - **`book/`** — *Tools on Tap*, the mdBook field guide (the AmbiTap/SampleRateTap/MuTap book
   pattern): one chapter per object family, every claim measured by the notebooks/tests. Built
-  and published to Pages by `.github/workflows/docs.yml`. Fourteen user-facing chapters across seven
-  parts — sources (`vco`), filters (`svf`, `ladder`, `autowah`), strings/rooms/spirals
-  (`convolve`, `5comb`, `pitchaccum`), the spectral set (`vocoder`, `nr`, `spectra`), the rhythm
-  section (`acid`, `drums`), staying in tune (`tune`), the pedalboard (`overdrive`) — plus
+  and published to Pages by `.github/workflows/docs.yml`. Twenty-four user-facing chapters across
+  nine parts — sources (`vco`), filters (`svf`, `ladder`, `autowah`), strings/rooms/spirals
+  (`convolve`, `5comb`, `pitchaccum`), tape and time (`discreet`, `airport`, `garden`,
+  `components`), the machines you ride (`tapecho`, `stammer`, `fuzz`, `scrub`, `diffuseurs`,
+  `ondes`), the spectral set (`vocoder`, `nr`, `spectra`), the rhythm
+  section (`acid`, `drums`), staying in tune (`tune`), the pedalboard (`overdrive`) — plus a
+  recipes part of whole patches, and
   **"The machine, file by file"**: one deep-dive appendix per kernel header
   (SampleRateTap-style) deriving the math, reviewing the code, and recording why each
   algorithm is built the way it is.
diff --git a/book/PLAN-ondes.md b/book/PLAN-ondes.md
new file mode 100644
index 0000000..d1709f0
--- /dev/null
+++ b/book/PLAN-ondes.md
@@ -0,0 +1,263 @@
+# Plan — `tap.ondes~`, after reading the sources
+
+> **Status: complete as an object — every piece has shipped.** `touche` 2026-08-15 as
+> `tap.touche~`; both diffuseurs 2026-08-17 as `tap.metallique~` and `tap.palme~`; the `triode`
+> and the heterodyne source 2026-08-17 as `tap.triode~` and `tap.ondes~`. The book chapters
+> followed the same day — `book/src/ondes.md` (voice, triode and intensity key together) and
+> `book/src/diffuseurs.md`, plus `machine/ondes.md` and `machine/diffuseur.md`. What remains is
+> the waveform registers, which are still unsourced — a second hunt on 2026-08-17 failed to
+> close the gate but left two concrete leads (see the last section).
+> The source gate is closed —
+> `PLAN-radiohead-family.md` §3 records what was found and how far each paper was read. This
+> file is the design pass those findings forced, written before any code, because what the
+> papers describe is **not the object the family plan sketched**.
+
+## What changed, in one paragraph
+
+The family plan assumed an Ondes Martenot voice built on `vco.h`: an oscillator with waveform
+registers, switched timbres, the usual synthesis shape. The circuit paper says otherwise. The
+instrument is a **heterodyne** design whose oscillators are, measured, essentially pure — about
+0.03 % second-harmonic distortion even coupled to the rest of the circuit, which is precisely
+the simplification Najnudel et al. use to reach real time. The character comes from what
+happens *after* the difference tone: two triode stages, the intensity key, and the diffuseur.
+So the kernel is a clean source into a nonlinearity into a gain law into a resonator — much
+closer to `fuzz.h` and `garden.h` than to `vco.h`.
+
+## The shape
+
+```
+ribbon/keyboard ─▶ heterodyne source ─▶ triode stages ─▶ touche ─▶ diffuseur ─▶ out
+                     (near-sinusoidal)   (the timbre)   (the gain)  (the voice)
+```
+
+Four components under a thin composition, the family's habit — and unlike the tape echo's
+`head` or the stammer's `slicer`, **three of these are independently useful** and should be
+standalone externals from the start:
+
+| Component | What it is | Standalone? |
+|-----------|------------|-------------|
+| `source` | the heterodyne difference tone, near-sinusoidal | no — it is an oscillator, `tap.vco~` territory if anyone wants one |
+| `triode` | the two post-demodulator gain stages, the timbre | maybe — a triode-flavoured saturator has uses beyond this instrument |
+| `touche` | the intensity-key gain law | **yes** — a measured, published expressive gain curve is useful on *anything* |
+| `palme` / `metallique` | the diffuseurs as driven resonators | **yes** — run a guitar through the Palme; that is the killer feature |
+
+`touche` and the diffuseurs are the reason to build this object even for someone who never
+wants an ondes. That is worth designing for rather than discovering later, per the components
+chapter's lesson.
+
+## `touche` — ✅ shipped
+
+> **Shipped 2026-08-15**: `include/taptools/touche.h`, `tests/touche_test.cpp` (11 scenarios),
+> the C ABI + ctypes surface (`Touche`), the executed `notebooks/touche.ipynb`, and a
+> `radiohead_render` scenario putting the curve against the two laws you would otherwise reach
+> for. The seven published points come back to within 6e-5 dB.
+>
+> Two implementation notes worth carrying. **The normalized domain is the physical travel, not
+> the measured band** — an early cut mapped 0..1 onto 4.3–8.8 mm, which put position 0 exactly
+> on the first published point *and* returned silence there, contradicting the measurement. The
+> paper puts playable gestures at roughly 3–9.5 mm with the measured band inside, so position
+> now spans 9.5 mm and the bottom 45 % is genuinely silent (the key's first phase is bending
+> before it reaches the powder bag). **And the dead zone belongs in the lookup, not the table**:
+> zeroing dense-table entries below the floor put a cliff next to it, so a query landing on the
+> floor lerped toward zero and read 4.2 dB low. Both were caught by the reproduce-the-table
+> test, which is exactly what that test is for.
+>
+> Still to come for this piece: the Max vertical slice and a chapter (probably folded into an
+> Ondes-family chapter once more of the instrument exists, rather than one chapter per part).
+
+## The design (as written before implementation)
+
+The measurement is in `PLAN-radiohead-family.md`; the design consequences:
+
+- **Input is a position**, normalized 0..1 over the playable travel, not a force and not a
+  velocity. The paper is explicit that the result does not depend on gesture speed, so a
+  static curve is the finding, not a shortcut.
+- **The curve is the published table, interpolated** — 45.0 / 53.3 / 61.6 / 70.0 / 78.3 / 86.6
+  / 95.0 dB_SPL at 4.3 / 5.3 / 5.9 / 6.4 / 6.8 / 7.3 / 8.8 mm. Monotone cubic (PCHIP-style)
+  through seven points, precomputed at `prepare()` into a table; no fitting, no analytic
+  approximation, because the shape is the whole point and a straight line in dB-vs-mm is
+  visibly wrong (the displacement steps are 1.0, 0.6, 0.5, 0.4, 0.5, 1.5 mm for equal dB
+  steps).
+- **50 dB of range** over ~4.5 mm of travel, with the bottom of the table at the noise floor.
+  Below the first point the object should go to true silence rather than extrapolating.
+- Expose the raw millimetre domain too, not just 0..1 — the numbers are published and someone
+  will want to drive it from a real sensor.
+- An optional **force** input as a second mode: dB is linear in log(force) below ~85 dB_SPL /
+  1.3 N. Document it as the secondary map; displacement is primary.
+- The curve is **pitch-independent** (all notes share the shape), which the tests should pin:
+  the same `touche` position gives the same gain at any source frequency.
+
+Honest limit to state in the header: the table is one instrument (No. 320) and the paper notes
+variation between units can exceed 10 %.
+
+## `triode` — where the timbre actually is — ✅ shipped
+
+> **Shipped 2026-08-17** as `tap.triode~`, in `include/taptools/ondes.h`.
+>
+> **The open question resolved itself on a closer read, and in the best possible direction.** The
+> plan framed it as a choice between "a published grid-conduction curve" and "the tanh family with
+> an asymmetry bias". It is neither, because the circuit paper does not merely *mention* a tube
+> model — it names one (the **enhanced Norman Koren** model: Koren, *Glass Audio* 8(5), 1996, with
+> Cohen & Hélie's grid-current extension, AES 129, 2010), writes out its equations, and publishes
+> parameter sets **fitted to the actual valves in ondes No. 169** in its Table II, alongside the
+> supply voltages and cathode resistors of every stage. So there was nothing to voice by ear and
+> nothing to choose: the whole stage is a citation.
+>
+> A stage is then the static solution of `ipc(vpc, vgc) = (Vbias − Vk − vpc)/Rp` on its load line
+> — a memoryless nonlinearity in the DAFx-07 sense, so tabulating it is not an approximation of
+> the model, it *is* the model. The published operating points bias sanely (6C5 demodulator:
+> Vk 2.70 V, Vp 86.5 V, Ip 2.70 mA, gain 4.86) and the curve is strongly asymmetric — a 2.17:1
+> ratio between the two directions at ±4 units — which is where a triode's even harmonics live.
+>
+> Two things the build added to the plan. **The stage must invert**, and the sign is load-bearing
+> rather than cosmetic: the tube's asymmetry acts on whichever side of the waveform actually
+> reaches its grid, and a stage that quietly un-inverted itself applied the curve to the wrong
+> side. An early cut did exactly that, and its drive knob *reduced* harmonics as it was turned up.
+> **And the demodulator's grid-leak detection inverts too** — a growing envelope drives that grid
+> toward cutoff — so the two inversions put the demodulator's plate in phase with the envelope
+> while its curve has meanwhile acted on the underside.
+>
+> The gain-staging lesson from `fuzz.h` was applied from the start and held: output is normalized
+> by each stage's own small-signal gain, so `drive` sweeps total harmonic content 0.221 → 0.344
+> monotonically without the level running away.
+
+## `source` — cheap, and deliberately so — ✅ shipped, and the plan was wrong about how
+
+> **Shipped 2026-08-17** as `detector` inside `include/taptools/ondes.h`, and composed into
+> `tap.ondes~`.
+>
+> **The plan's central instruction here was a mistake, and catching it is the most valuable thing
+> this build did.** "Synthesize the difference tone **directly** as a sinusoid" would have thrown
+> away the instrument's single largest source of harmonics. The paper's 0.03 % figure and its
+> "replace with a sinewave generator" licence apply to the **oscillators**, not to the
+> demodulator — and the demodulator is not a mixer that hands you a difference tone. It is an
+> envelope detector, and the envelope of `cos(Φ) + cos(Φ − φ)` is `2|cos(φ/2)|`, whose Fourier
+> series puts H2 at −14.0 dB, H3 at −21.3 dB and H4 at −26.4 dB. All of that exists before any
+> valve touches the signal.
+>
+> **What replaces the carrier is better than a simplification: it is an identity.** For amplitudes
+> 1 and `depth` the envelope is exactly `sqrt(1 + depth² + 2·depth·cos φ)`, so the 80 kHz carrier
+> drops out of the arithmetic rather than being approximated away. Running the published RC
+> detector (200 µs, from R4·C21) on that closed form reproduces the full heterodyne-plus-diode-
+> plus-RC simulation to **within 0.10 dB on every harmonic at every pitch tried**, with one
+> systematic difference — a uniform 3.0–3.2 % level offset, because a follower chasing real
+> carrier half-cycles never quite reaches the peak between them. The detector's characteristic
+> pitch dependence comes along free: H2 runs −14.0 dB at A2 to −19.3 dB at A6 and the level falls
+> 2.0 dB across those five octaves, all out of the same 200 µs.
+>
+> A bonus the plan did not anticipate: because the closed form is parameterized by the two
+> oscillator amplitudes, **oscillator balance becomes a real physical timbre control**. At `depth`
+> 1 the envelope closes and the series is full; below that it never closes and the harmonics thin
+> out. That is a mismatch between two real oscillators, not an invented knob.
+>
+> The ribbon law is the circuit paper's Eq. 7, and it is simple: `f = A1 · 2^(d/12·d0)` with
+> A1 = 55 Hz. **The ribbon is linear in semitones**, which is exactly why an ondes glissando
+> sounds the way it does, and why `set_ribbon` takes semitones rather than Hz.
+
+## The diffuseurs — driven, not struck — ✅ shipped
+
+> **Shipped 2026-08-17**: `include/taptools/diffuseur.h`, `tests/diffuseur_test.cpp` (18
+> scenarios), the C ABI + ctypes surface (`Metallique`, `Palme`, plus the bare `Plate` and
+> `Transducer` components), the executed `notebooks/diffuseur.ipynb`, the `metallique_stages`
+> and `palme_halo` render scenarios, and both Max vertical slices.
+>
+> Everything below survived contact with the code. The one thing the design pass did not say,
+> and the build made explicit, is that **the order is a claim worth a null test**: the
+> transducer drives the body, so the cabinet must be exactly `transducer -> plate`, bitwise,
+> and the reversed wiring must measurably differ (it does — 28 % of peak). Two smaller findings:
+> the modal bank needs no limiter and no DC blocker at all, because Steiglitz's
+> constant-peak-gain resonator has unit peak gain at any Q and its zeros at ±1 null DC and
+> Nyquist exactly; and the transducer's output bound is **2/saturation, not 1/saturation**,
+> because taking the DC out of a hard-driven squared law doubles the worst-case swing.
+>
+> The transducer question the plan left open — model the nonlinearity or state its absence —
+> was answered by modelling it: the squared law is defensible from the moving-iron principle
+> alone, and it is measured against its own prediction (second harmonic at exactly
+> asymmetry × amplitude / 2, to 1.4e-4, with nothing at the third). The bounding saturator
+> after it is labelled what it is: a modelling necessity, not a measured stage.
+>
+> Still open, and stated in the header rather than hidden: no radiation or cabinet model, no
+> soundboard resonance, and no string stiffness (a real steel string's partials stretch sharp;
+> a delay loop's are exactly harmonic).
+
+The correction that matters most for reusing `garden.h`'s idiom. Wijnand et al.:
+
+- **Métallique** (1944–45, patented 1947) — a gong excited by a **motor**.
+- **Palme** (1949–50) — an electromagnet driving **12** metal strings on a soundboard.
+- **Résonance** (1970s) — motor-excited metal springs.
+
+So the mode banks carry over from `garden.h` but the excitation does not: these are
+continuously driven resonators, not struck ones. No `decay_env` per mode; instead the input
+signal drives the bank and the modes ring at their own decay rates — closer to `grm_comb.h`'s
+sustained resonance than to the chime's strike envelopes. Mode ratios still come from Fletcher
+& Rossing (plates/gongs for the métallique, strings for the palme), because there is no
+ondes-specific modal measurement in any of the four sources.
+
+The transducer is its own stage: early diffuseurs used a **moving-iron loudspeaker** whose
+operating principle is *inherently nonlinear* — the Forum Acusticum paper's whole point is that
+Thiele–Small does not apply, and it quantifies the nonlinearity on a heritage instrument. A
+diffuseur that is only a resonator is missing a documented stage. Whether to model it is a
+scope decision; **not** modelling it should be a stated limit rather than an omission.
+
+Note for the header: hobbyist build pages give the palme 24 strings (two sets of 12). The
+peer-reviewed source says 12. Prefer 12 and say why.
+
+## Order of work
+
+1. **`touche`** — fully specified, small, independently useful, and it can ship as
+   `tap.touche~` before the rest of the instrument exists. Do this first.
+2. ~~**The diffuseurs**~~ — ✅ shipped 2026-08-17 as `tap.metallique~` and `tap.palme~`.
+3. ~~**`triode`**~~ — ✅ shipped 2026-08-17 as `tap.triode~`. No listening comparison was needed:
+   the circuit paper publishes the tube model *and* parameter sets fitted to the instrument's own
+   valves, so there was no curve to choose.
+4. ~~**`source`** and the composition~~ — ✅ shipped 2026-08-17 as `tap.ondes~`.
+
+That order deliberately front-loads the parts that are useful on their own, so the object
+delivers value before the flagship is finished.
+
+## What is still unsourced
+
+- Modal data for either diffuseur specific to the instrument — falling back to Fletcher &
+  Rossing for the general plate/string physics, which is a recreation rather than a model, and
+  must be labelled as such.
+- The waveform-register filter shapes. **This is the only thing standing between `tap.ondes~`
+  and a complete instrument**, and the header states its absence rather than filling it with
+  invention. A second source hunt ran 2026-08-17 and **did not close the gate**; what it found
+  is below, so the next attempt starts further along rather than repeating it.
+
+  *Confirmed to exist, not obtainable from here:*
+  - **Leipp, "Les ondes Martenot", *Bulletin du GAM* n°60, April 1972.** The citation is real —
+    it appears in an academic bibliography, and the Catgut Acoustical Society Library holds 45
+    GAM issues (1963–1978) as a physical archive. Nothing is digitized anywhere reachable. This
+    needs a library request, not a search.
+  - **Laurendeau, *Maurice Martenot, luthier de l'électronique* (1990).** A print monograph; not
+    obtained.
+
+  *Ruled out as sources for this:*
+  - The TASLP circuit paper, read in full: five stages, and the registers are not among them.
+  - Its companion, "Simulation of the Ondes Martenot **Ribbon-Controlled Oscillator**"
+    (HAL hal-02425249) — the title is the scope.
+
+  *New lead, and the best one: the patents.* Martenot's 1928 "Perfectionnements aux instruments
+  de musique électriques" and **FR 841.128 (February 1939)**. Patents are exactly the right class
+  of source here — published, long expired, schematic-bearing, and IP-clean in a way a blog never
+  is. Google Patents and Espacenet both refused to serve this environment (503 / access denied),
+  so they remain unread. **Try these first next time, from a machine that can reach them.**
+
+  *And the trap to avoid.* Hobbyist and encyclopedic descriptions of the register *waveforms* are
+  abundant and broadly consistent — creux as a peak-limited triangle, gambe as a pulse at roughly
+  35/65 duty, nasillard as a very narrow pulse, octaviant as an added octave, petit gambe as
+  gambe lowpassed, souffle as noise, feutré as a softening filter. Every one of those is a blog,
+  a forum, a retailer's history page, a performer's site or a replica manual; none is published
+  literature and **none gives a filter shape, a corner, or a component value**. Implementing from
+  them would break the published-literature-only policy *and* the promise the header makes, to
+  buy a register set that would be guesswork wearing the instrument's vocabulary. Not done, on
+  purpose.
+- **Where the intensity key sits in the chain.** The paper's five stages do not include it, so
+  `tap.ondes~` offers both readings as a switch (`key_placement`): after the valves it is a clean
+  output law, before them the dirt comes up with the pressure. Measured, the difference is real
+  (THD 0.311 against 0.222 at a half-press with drive 6), so it is a choice worth exposing rather
+  than a detail to guess at.
+- **The winding sense of the transformer between the two triode stages.** It decides which side of
+  the waveform the preamplifier's asymmetry acts on, and it is audible — THD 0.274 against 0.394 —
+  so it is a switch too (`polarity`).
diff --git a/book/PLAN-radiohead-chapters.md b/book/PLAN-radiohead-chapters.md
new file mode 100644
index 0000000..b68da98
--- /dev/null
+++ b/book/PLAN-radiohead-chapters.md
@@ -0,0 +1,329 @@
+# Plan — the Radiohead-family chapters
+
+> **Status: drafted.** Twelve chapters now — the original four, `tap.fuzz~`'s pair
+> (2026-08-15), and the six added when the Ondes family and the scrub landed (2026-08-17).
+> This file remains as the drafting record, the plans-directory way. The
+> object-level plan is `PLAN-radiohead-family.md`; this one covers only the book.
+
+Planning document for the *Tools on Tap* chapters covering the shipped Radiohead-family
+objects (`tap.tapecho~`, `tap.stammer~`, `tap.fuzz~`, `tap.scrub~`, `tap.metallique~`,
+`tap.palme~`, `tap.ondes~`, `tap.triode~`, `tap.touche~`; `taptools/tapecho.h`, `stammer.h`,
+`fuzz.h`, `scrub.h`, `diffuseur.h`, `ondes.h`, `touche.h`). Twelve chapters: six user-facing,
+six machine appendices. 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 thesis, which every chapter serves and none re-derives: where the Eno family's
+spine was *degradation is the stability mechanism*, this family's is **the control is the
+instrument**. These are objects you ride, not systems you set up and walk away from — which
+is what the new part is named for, and what justifies a part boundary rather than filing the
+tape echo next to `tap.discreet~` on topology grounds.
+
+## Placement in SUMMARY.md
+
+A new part after Part IV (Tape and time); Parts V–X renumber to VI–XI. The two machine
+entries append to the file-by-file part, keeping its chronological order.
+
+```md
+# Part V — The machines you ride
+
+- [Four heads and a motor](tapecho.md)
+- [The part that comes apart](stammer.md)
+- [The dirt with two stages](fuzz.md)
+
+# Part X — The machine, file by file
+  ...existing entries...
+- [Events, not audio: garden.h](machine/garden.md)
+- [Composition, not construction: tapecho.h](machine/tapecho.md)
+- [Dice you can replay: stammer.h](machine/stammer.md)
+- [Two stages and a knee: fuzz.h](machine/fuzz.md)
+```
+
+**Found while renumbering:** `introduction.md`'s part list had been stale since the Eno wave
+landed — it still described Part IV as "The spectral set" and stopped at Part IX. It is now
+rewritten to match the eleven parts that actually exist. Worth a standing note: the
+introduction's list is a second copy of SUMMARY's structure and nothing checks the two
+against each other, so any future part insertion has to touch both by hand.
+
+## Figures
+
+Measured figures generated by `book/figures/radiohead.py` — the `eno.py` regeneration
+contract, driving the shipping kernels through the C ABI rather than illustrating them:
+
+- `images/tapecho/head-layout.svg` — one impulse, four heads, returns on the `span × ratio`
+  grid.
+- `images/tapecho/self-oscillation.svg` — measured peak against drive at regen 1.4, under
+  the analytic ceiling at every point.
+- `images/stammer/occupancy.svg` — when a slice is in flight, four density/repeat pairs.
+- `images/stammer/material.svg` — slice similarity for a sustained sine vs a played phrase.
+- `images/fuzz/curve.svg` — the clipping family at four knees, all through the same full-scale
+  point.
+- `images/fuzz/gain-and-bite.svg` — the gain knob's sweep and asymmetry's even/odd ratio.
+
+No hand-authored block diagrams this round. The Eno chapters needed them because their
+signal flow is a rig with named machines; these two are a tape line with extra read points
+and a buffer with dice, and the fuzz is a two-box chain; the measured figures carry more than
+a block diagram would. If a diagram is ever added, the obvious one is the stammer's
+grid/slice/repeat timeline.
+
+One thing the generator learned the hard way, recorded so it is not re-learned: the
+occupancy figure's first draft used `fill_between` over a per-sample boolean at 48 kHz and
+emitted a **117 MB SVG**. Busy stretches are now drawn as `broken_barh` spans computed from
+the run boundaries — same picture, 44 KB. Any future per-sample state figure should do the
+same.
+
+## Evidence the chapters are allowed to cite
+
+Everything traces to an executed notebook cell or a pinned scenario.
+
+`tap.tapecho~` — `notebooks/tapecho.ipynb`, `tests/tapecho_test.cpp`:
+
+- head returns land exactly on `span × ratio`, at the centre-pan level `cos(π/4)`
+  (§1; scenario *"each head echoes at its own position along the tape path"*)
+- **bitwise** equality with `delay.h`'s multitap when the tape path is neutral, in the kernel
+  suite and again across the C ABI (§2; *"with the tape path neutral, a one-head echo is
+  bitwise the multitap of delay.h"*)
+- per-pass generation loss 0.2915 measured vs 0.2920 predicted at 6 kHz, 0.8895 vs 0.8898 at
+  300 Hz (§3)
+- past-unity regeneration plateaus under `|in|max + regen/drive` at every drive measured;
+  growth ratio 1.007 between late windows; drive 0 caps back to 1.0 (§4; *"regeneration past
+  unity self-oscillates but stays bounded"*, *"at drive 0 the regeneration cap falls back to
+  unity"*)
+- wow 10.91 cents measured against 10.88 predicted, two runs bit-identical (§5)
+- the varispeed glide: 220 Hz mid-glide, 440 Hz within 5 cents settled (*"a span change
+  glides as tape speed, not a splice"*)
+
+`tap.stammer~` — `notebooks/stammer.ipynb`, `tests/stammer_test.cpp`:
+
+- the pinned-dice one-step-delay identity, bitwise (§1; *"with the dice pinned, the machine
+  is exactly a one-step delay"*)
+- occupancy 41 / 76 / 90 / 96 % across the four density/repeat pairs, 100 grid points each
+  (§2)
+- same seed bit-identical, different seed changes 89.4 % of samples (§3)
+- density 0: two seeds identical, bitwise bypass, still bitwise at mix 50 (§4)
+- flanks exactly 0 at both edges and exactly 1 on the plateau (§5)
+- slice similarity 1.000 (sine) vs 0.286 (played phrase) (§6)
+
+**A measurement that did not survive drafting**, recorded because the lesson generalizes: the
+material contract was first measured as the best lag-correlation between output and input,
+on the theory that a self-similar material would leave the output looking like a delayed
+copy. It measured the opposite of the claim (sine 0.279, plucks 0.450) because the test
+material was itself periodic, so aligning envelopes at a matching lag flattered the plucks.
+The metric was replaced with slice similarity — which measures the contract's *premise*
+(are two arbitrary slices interchangeable?) rather than a downstream consequence — and the
+test material was given real pitch variety, since a single repeated note also flatters a
+stutter. Prose follows numbers, never the reverse.
+
+## Structure
+
+### `src/tapecho.md` — *Four heads and a motor*
+
+Opens on the posture difference from `tap.discreet~` (a machine you walk away from vs one you
+keep your hands on), which is also the part's thesis. Then: the motor and why `span` is
+defined at the ratio-1.0 head; the layout and the nominal-not-measured spacing disclaimer;
+past-unity regeneration with the drive-dependent cap; the transport; and the null test framed
+as the design claim rather than a curiosity. Recipes, when-not-to, checkpoint.
+
+### `src/stammer.md` — *The part that comes apart*
+
+Opens on "Go To Sleep" and the odd fact that this is a Max technique arriving as a Max
+object — immediately followed by the IP posture (original design; the rig informs what the
+object is for, not what the code does). Then the one thing to internalize before patching:
+`density` grabs, `repeats` holds, and holding is what fills the timeline. Character
+parameters, the seed as a contract, and the material contract as a measurement.
+
+### `src/machine/tapecho.md` — *Composition, not construction*
+
+Deliberately the shortest appendix in the book, because its content is that there was almost
+nothing to write: `tape_loop.h` needed **no changes** to serve a second topology. Covers the
+one-motor geometry, why the regeneration cap lives in the audio path rather than the setter,
+and why the null test had to be bitwise to prove anything. Also records the one-ulp
+`cos(π/4)` ≠ `sin(π/4)` fact, since it explains the shape of the test.
+
+### `src/machine/stammer.md` — *Dice you can replay*
+
+The interesting content is not the DSP but how to pin three interacting integer clocks at
+once, so the pinned-dice identity gets the space. Then: the draw order as part of the
+contract, the early return at density 0, ring reads vs a burst memcpy (with the failure mode
+stated), the deliberate envelope dip, and the corollary to the components chapter — not every
+class boundary is a seam.
+
+### `src/fuzz.md` — *The dirt with two stages* and `src/machine/fuzz.md` — *Two stages and a knee*
+
+Added with the object. The user-facing chapter opens by placing it against `tap.overdrive~`
+(two dirt objects, not competing) and spends its length on the three things a patcher can act
+on: the knee as a character control, why the gain floor sits below unity, and what
+`oversample` actually buys. The appendix is deliberately **about mistakes**, because the DSP
+is a published recipe followed closely and the failures are the reusable part:
+
+- *Small-signal gain compounds across a cascade.* The tanh family's slope is `k/tanh(k)`, so a
+  fixed ×2.2 into a knee-3 curve gave the second stage an effective ×6.6 and left it saturated
+  at gain 0. Harmonic ratio measured 0.401 → 0.408 across the whole knob. The point worth
+  keeping is that it was **inaudible** — it sounded like a distortion at every setting because
+  it was one — so only a swept measurement found it.
+- *The house oversampler measured wrong here, and so did the first explanation.* 4th order made
+  4× worse than 2×; 8th order improves 4× ~6× but did not restore an ordering. An earlier
+  draft of both the appendix and the plan claimed it did; that was corrected, and the
+  ruled-out hypothesis (biquad conditioning, disproved by an impulse-response check) was
+  recorded alongside the surviving one (imaging) rather than left as a vague "needs
+  investigation".
+- *And then the surviving hypothesis was acted on, 2026-08-17.* Cascaded 2× resampling removed
+  the reversal; the appendix's "Mistake two" section is rewritten from *a hypothesis that died*
+  to *a hypothesis that was right*, with a before/after table. **A third mistake was found
+  doing it and got its own section**: every number in the original write-up came from one test
+  tone, and swept properly the shipped default of 2× collapses above 6 kHz. The default is now
+  4×. The section is called "one tone is not a sweep" and it is the reusable part.
+
+Two aliasing test-design errors are also written up in the appendix — a tone dividing the
+sample rate, and probes near enough the fundamental to read window leakage — since both passed
+review the first time.
+
+## Deliberately not covered
+
+- **The Max-side surface.** The reference pages and help patchers carry it; the book's
+  machine appendices are about the kernel.
+- **A recipes entry.** The family will earn one once the fuzz and the Ondes land and there is
+  a signal chain to describe. `recipes/` is for whole patches, and two objects is not a rig.
+- **Comparative listening claims** ("sounds like the record"). Not measurable, not the book's
+  business.
+
+
+---
+
+# The 2026-08-17 wave — six more chapters
+
+Nine objects shipped without chapters between 2026-08-15 and 2026-08-17 (`tap.touche~`, the
+two diffuseurs, `tap.scrub~`, `tap.triode~`, `tap.ondes~`). They became **six** chapters, not
+nine, and the grouping decisions are the interesting part of this record.
+
+## Grouping
+
+- **`src/scrub.md` — *Two hands on the same tape*.** Its own chapter, in Part V, filed
+  immediately after `stammer.md` because the two share a tape *literally* — `scrub.h`
+  includes `stammer.h` and uses `stammer::capture` itself. The chapter opens on that, since
+  "same recorder, different read pattern" is the cleanest way to say what the object is.
+- **`src/diffuseurs.md` — *Loudspeakers you can play*.** `tap.metallique~` and `tap.palme~`
+  together. They are one file, one idea and one set of caveats; two chapters would have
+  duplicated the provenance section twice over.
+- **`src/ondes.md` — *The instrument that is not a synthesizer*.** `tap.ondes~`,
+  `tap.triode~` **and** `tap.touche~`. The triode does not carry a chapter alone — it is one
+  stage of the instrument, and its interest (the model is a citation, the stage inverts) only
+  lands next to the thing it is a stage of. The touche shipped two days earlier and could have
+  had its own chapter then; holding it for this one was the right call, because "50 dB in
+  4.5 mm" means more when the reader can see what it is the dynamic range *of*.
+
+That is the one structural rule this wave establishes: **chapters follow instruments, not
+externals.** The diffuseurs get a separate chapter from the voice despite being part of the
+same instrument, because they are usable on anything and the audience is different.
+
+Machine appendices are one per header, as always: `machine/scrub.md`, `machine/diffuseur.md`,
+`machine/ondes.md`. No appendix for `touche.h` — it is a published table with a PCHIP
+interpolator over it, and the user chapter already carries everything true about it.
+
+## Placement
+
+Three entries appended to **Part V — The machines you ride** (after `fuzz.md`) and three to
+**Part X — The machine, file by file** (after `machine/fuzz.md`), keeping Part X's
+chronological order. No renumbering this time, so `introduction.md` needed only its Part V
+sentence extended — but it *did* need that, which is the second time the standing note below
+has earned its keep.
+
+> **Standing note, restated:** `introduction.md`'s part list is a second copy of SUMMARY's
+> structure and nothing checks the two against each other. Any part insertion *or* any change
+> to what a part contains has to touch both by hand.
+
+## Figures
+
+Eight more measured SVGs, appended to `book/figures/radiohead.py` under the same contract —
+driving the shipping kernels through the C ABI, never illustrating them:
+
+- `images/scrub/null.svg` — the delay identity, and the Hann window sum at overlaps 1/2/4.
+- `images/scrub/two-hands.svg` — band energy and concentration across ±19 semitones.
+- `images/ondes/envelope.svg` — the envelope at two depths, and `|cos|`'s harmonic series.
+- `images/ondes/tube.svg` — the 6C5's plate characteristics with the load line and quiescent
+  point, and the three stages' transfer curves.
+- `images/ondes/drive.svg` — THD and level against drive, with the demodulator's floor marked.
+- `images/touche/curve.svg` — the published law, the seven measured points, a straight line
+  for comparison, and the silent dead zone shaded.
+- `images/diffuseur/plate.svg` — the eight modes, and the body answering a sweep.
+- `images/diffuseur/selectivity.svg` — the palme's ring after a **faded** drive tone.
+
+Two rendering lessons, recorded so they are not re-learned:
+
+- **A label outside the axes stretches the layout.** `ondes_tube`'s first draft labelled each
+  plate-characteristic curve at its rightmost point; the Vg 0 curve is off the top of the box
+  by an order of magnitude, and `tight_layout` collapsed the axes to zero trying to fit the
+  text. Labels are now placed where each curve leaves the visible box.
+- **Two curves that are the same line need to look like two curves.** Overlaps 2 and 4 both
+  sum to exactly 1, so `scrub_null`'s right panel drew one line with two labels on top of each
+  other. The second is dashed and labelled further along.
+
+## Evidence the new chapters cite
+
+`tap.scrub~` — `notebooks/scrub.ipynb`, `tests/scrub_test.cpp`:
+
+- the delay identity at unity pitch, 4.4e-16 (*"held still at unity pitch, the scrub is the
+  input delayed"*)
+- band energy retained 0.988 mean / 0.917 worst over 7 fundamentals × 7 intervals;
+  concentration 0.920 / 0.750
+- the wander sweep: 0.933 / 0.958 / 0.965 / 0.990 / 0.993 mean at ±0.5 / ±1 / ±2 / ±3 / ±4
+  grains, worst 0.716 / 0.820 / 0.874 / 0.918 / 0.940
+- spray 0 ⇒ seed cannot matter, bitwise; mix 0 ⇒ bitwise passthrough
+
+`tap.metallique~` / `tap.palme~` — `notebooks/diffuseur.ipynb`, `tests/diffuseur_test.cpp`:
+
+- the cabinet is **bitwise** `transducer → body`; the reverse wiring differs by 28 % of peak
+- plate weights sum to exactly 1, unit peak gain per mode
+- transducer bound `2/saturation` (measured 1.49, naive bound 1.25)
+- every one of the twelve strings ≥ 4.4× selective, with the drive faded 250 ms in and out
+
+`tap.ondes~` / `tap.triode~` / `tap.touche~` — `notebooks/ondes.ipynb`, `notebooks/touche.ipynb`,
+`tests/ondes_test.cpp`, `tests/touche_test.cpp`:
+
+- `|cos|` harmonics −14.0 / −21.3 / −26.4 dB, before any valve
+- closed form vs full 80 kHz simulation: within 0.10 dB on every harmonic, uniform 3.0–3.2 %
+  level offset
+- detector pitch dependence: H2 −14.0 dB at A2 → −19.3 dB at A6, level down 2.0 dB
+- 6C5 demodulator bias Vk 2.70 V, Vp 86.5 V, Ip 2.70 mA, gain 4.86; asymmetry 2.17 : 1
+- drive sweeps THD 0.221 → 0.344 monotonically; `keyplacement` worth 0.09, `polarity` 0.12,
+  `power` 0.248 → 0.251
+- the oversampling table at 587 / 1175 / 1760 / 2637 / 3520 Hz
+- the published key table, 50 dB from 4.3 mm to 8.8 mm, silent below
+
+## What the appendices are *about*
+
+Same rule as `machine/fuzz.md`: an appendix earns its length from what went wrong, not from
+restating the header.
+
+- **`machine/scrub.md`** — the anchoring defect (grains anchored at the position cancel the
+  transposition, and no listening test can see it), and the measurement trap that nearly
+  inverted the conclusion (a single-bin probe reads the *fixed* kernel as broken, 0.02 against
+  a band figure of 0.43).
+- **`machine/diffuseur.md`** — unit peak gain removing the limiter, the DC blocker and the
+  decay/level coupling in one choice; the bitwise order null and the `cos(π/2)` endpoint that
+  had to be short-circuited to get it; the `2/saturation` correction; and the selectivity test
+  that was measuring its own on/off step.
+- **`machine/ondes.md`** — the stage that needed no design because the paper published it; the
+  detector that is an identity rather than an approximation; the sign error that made a
+  distortion knob run backwards; three measurements that lied in three different ways; the
+  oversampling evidence for `fuzz.h`'s open question; and the wrapper test that found a kernel
+  bug.
+
+## Still deliberately not covered
+
+- ~~**A recipes entry.**~~ — ✅ written 2026-08-17, three of them, appended to Part XI:
+  `recipes/tape-and-stutter.md` (the fuzz → stutter → echo rig, and why that order),
+  `recipes/scrub-pad.md` (the two-axis controller, freeze as a performance, and the grain/live-edge
+  constraint), and `recipes/ondes-rig.md` (the assembled instrument, where most of the length goes
+  to the two hands rather than the settings, because the ribbon being linear in semitones and the
+  key's bottom 45 % being silent are the two facts that decide whether it sounds like an ondes).
+  Between them they name all nine family objects.
+
+  One process note worth keeping: writing them **caught a documentation error the chapters had
+  missed**. A first draft set the stutter's `jump` to 0.15, reading it as a probability like
+  `reverse`; it is milliseconds. Recipes are the only part of the book that has to name every
+  control with a legal value, so they check the reference pages in a way prose never does. Verify
+  a recipe's controls against `docs/*.maxref.xml` before committing it.
+- **The Max-side surface**, and **comparative listening claims**. Unchanged.
diff --git a/book/PLAN-radiohead-family.md b/book/PLAN-radiohead-family.md
new file mode 100644
index 0000000..d679283
--- /dev/null
+++ b/book/PLAN-radiohead-family.md
@@ -0,0 +1,475 @@
+# Plan — the Radiohead family
+
+> **Status: every object in the plan has shipped.** `tap.tapecho~`, `tap.stammer~` and
+> `tap.fuzz~` end-to-end with chapters (2026-08-15); `tap.touche~`, `tap.metallique~`,
+> `tap.palme~`, `tap.scrub~`, `tap.triode~` and `tap.ondes~` as kernels plus Max slices
+> (2026-08-15/17), chapters still to come for those six. The one piece of the Ondes Martenot
+> still missing is its waveform registers, which no source obtained describes — see
+> `PLAN-ondes.md`. This is the drafting record of the
+> 2026-08-15 survey ("are there Radiohead-inspired objects we should consider?"), amended the
+> same day against the Eno components wave (`d4cf28a`) before any code was written. It stays
+> after the objects ship, the plans-directory way; the chapters have their own drafting
+> record in `PLAN-radiohead-chapters.md`. Per-object status lives in the table below.
+
+Planning document for a family of kernels drawn from Radiohead's performed electronics:
+the Ondes Martenot, the live Max/MSP mangling rigs, the tape echoes, the Kaoss-pad vocal
+scrubbing, the ShredMaster-era fuzz. Kernel-first in this repo, Max wrappers and pin bumps
+in TapTools-Max afterward, per the release flow.
+
+The family's single thesis, the Eno-family way — one sentence every object serves:
+**these are instruments, not processes.** Radiohead's electronics are played on stage in
+real time — Jonny Greenwood performs the Ondes and his own Max patches live; the Kaoss pad
+scrubs Thom's voice mid-song; the echo's regeneration is ridden like a fader. So in this
+family the *performance surface* is the point: every parameter is a hand on the machine,
+which makes the house no-zipper rule (per-sample ramps, allocation-free setters, safe while
+audio runs) not just hygiene here but the actual feature. Where the Eno family's spine was
+"degradation is the stability mechanism," this family's spine is "the control is the
+instrument."
+
+There is also a lineage claim that makes this family belong in a Max package specifically:
+Greenwood's stutter rigs *are* Max patches — the band's documented live setup runs
+Max/MSP. A Radiohead family in a Max package is not a tribute; it is a return.
+
+## The family at a glance
+
+| Object | Kernel | Recreates | Standing on | Status |
+|--------|--------|-----------|-------------|--------|
+| `tap.tapecho~` | `tapecho.h` | Multi-head tape echo (Copicat / Space Echo school) | `tape_loop.h` — almost pure composition | ✅ shipped 2026-08-15 (kernel, Max slice, chapters) |
+| `tap.stammer~` | `stammer.h` | The live buffer-stutter rig (*Go To Sleep*, *The Gloaming*) | Original design; `tape::reel`, seeded rng | ✅ shipped 2026-08-15 (kernel, Max slice, chapters) |
+| `tap.ondes~` | `ondes.h` | The Ondes Martenot voice: heterodyne detector, two triode stages, the intensity key | The published circuit's own reductions; `fuzz.h`'s DAFx-07 architecture with a fitted tube model in place of a tanh | ✅ shipped 2026-08-17 (kernel, notebook, Max slice) |
+| `tap.triode~` | `ondes.h` | One triode stage on its load line, from a published tube model | The enhanced Norman Koren model with the circuit paper's fitted parameters | ✅ shipped 2026-08-17 (kernel, notebook, Max slice) |
+| `tap.fuzz~` | `fuzz.h` | Two-stage tone-stacked fuzz (the OK Computer-era dirt) | `overdrive.h` sibling; the DAFx-07 cascade | ✅ shipped 2026-08-15 (kernel, Max slice, chapters) |
+| `tap.scrub~` | `scrub.h` | Kaoss-school granular scrub of live capture | `stammer::capture` (shared, not copied) + a Hann grain scheduler | ✅ shipped 2026-08-17 (kernel, notebook, Max slice) |
+| `tap.metallique~` / `tap.palme~` | `diffuseur.h` | The Ondes diffuseurs as driven resonators | `garden.h`'s modal maths without its strike envelopes; `grm_comb.h`'s sustained resonance | ✅ shipped 2026-08-17 (kernel, notebook, Max slices) |
+
+Parked (surveyed, deliberately not planned): a spectral freeze on the `stft.h` scaffold
+(`tap.sustain~` and `tap.discreet~` now cover most of that ground between them), a Klatt
+formant synthesizer (complete published spec — Klatt 1980, JASA — but a project of its
+own), and a signal-rate bitcrush (trivial; nothing blocks it, nothing urgent about it).
+
+## What the Eno wave changed (the amendments this plan bakes in)
+
+The survey predated the Eno components wave by hours. Five amendments, now assumptions:
+
+1. **`tap.tapecho~` is composition, not construction.** `tape_loop.h` already ships the
+   reel (delay-line topology explicitly supported), the periodic wow/flutter transport
+   citing the same tape-echo literature the survey cited, and `wear` (darken → bounded
+   saturation → DC block) as an in-loop stage. The echo is a multi-head sibling of
+   `discreet.h`, not a new machine.
+2. **The Ondes diffuseurs inherit the garden's modal pattern.** Mode banks with published
+   ratio tables (Fletcher & Rossing), doublet splitting, per-index deterministic scatter,
+   per-mode decay — the scary half of `tap.ondes~` now has a shipped house idiom and a
+   standalone component (`tap.chime~`) to model the shape on. *(Amended twice by the source
+   hunt below: the resonator maths stands, but the diffuseurs are driven rather than struck,
+   so the garden's strike envelopes do not carry over — and the voice is not a `vco.h`
+   descendant at all.)*
+3. **Components from day one.** The Eno components chapter's lesson — "the monoliths were
+   monoliths by accident" — is prospective here: every kernel below is planned as parts
+   with a thin composition, and the parts get C ABI + wrapper reachability from the start.
+4. **The family randomness convention.** Anything stochastic (the stammer's dice, scrub
+   jitter if any) draws from the seeded xorshift64* idiom so renders and tests reproduce
+   bit-exactly and instances decorrelate by seed. The stammer inherits this wholesale.
+5. **The delivery template exists.** Shared-machinery header where earned, kernels + C ABI
+   + ctypes + executed notebooks, a `tools/render` listening binary (`radiohead_render`,
+   the `eno_render` shape), book chapters with drafting records, full Max vertical slices.
+   This plan mirrors that template rather than inventing one.
+
+## Per-object plans
+
+### 1. `tap.tapecho~` — the multi-head tape echo *(first; small)* — ✅ shipped
+
+> **Shipped 2026-08-15**: `include/taptools/tapecho.h`, `tests/tapecho_test.cpp` (11
+> scenarios), the C ABI + ctypes surface (`TapEcho`), the executed `notebooks/tapecho.ipynb`,
+> and `tools/render/radiohead_render.cpp` with four performed scenarios; then the Max
+> vertical slice in TapTools-Max (wrapper, six min-api scenarios, maxref, help patcher,
+> pin bump — REVIVAL.md entry 18). What the plan predicted held: the kernel is composition
+> — the null test below is *bitwise*, and `tape_loop.h` needed no changes at all to serve a
+> second topology. The chapters shipped the same day (see `PLAN-radiohead-chapters.md`).
+> Still to come: the on-Mac validation pass.
+>
+> Design decisions taken during implementation that this record should carry:
+> the head layout is `span_ms` (the motor, = a ratio-1.0 head) times a per-head ratio, so
+> four evenly spaced heads is the default and a three-head Copicat layout is set explicitly
+> (the "preset + free" open question, resolved toward *free with an even-spacing default* —
+> no spacings are claimed as measured from any unit); regeneration reaches
+> `k_regen_max_driven` = 1.5 but is capped **per sample** back to 1.0 whenever drive is 0,
+> since the saturator is the only thing bounding the past-unity regime.
+
+The Watkins Copicat sits in Ed O'Brien's rig; the Space Echo school is all over the
+catalog. The house delay family (`delay.h`, multitap, procrastinate) is clean digital;
+this is the dirty one, and nearly all of it exists:
+
+- One `tape::reel` in delay-line topology: advancing record head, **N read heads** at
+  settable spacings (per-head level and pan, the multitap law). Motor speed is the master
+  delay control, and a speed change glides as varispeed — the `discreet.h` contract:
+  moving the head IS the doppler, no crossfading "digital" mode.
+- `tape::wow_flutter` on the transport — periodic-only and deterministic, inherited as a
+  *decision*, not an accident: the family keeps bit-exact renders; stochastic capstan
+  drift stays a documented non-goal unless listening says otherwise.
+- `tape::wear` in the regeneration path. The self-oscillation story must be stated in the
+  header, `discreet.h`-style: with drive engaged, `swing_shape`'s 1/drive bound holds the
+  loop absolutely bounded, so regeneration is *allowed* past unity into controlled
+  sound-on-sound howl — the dub move, made safe by the same inversion `discreet.h` runs
+  on. At drive 0 the fb cap falls back to the `delay.h` rule.
+
+Head layout: four evenly spaced heads by default, every ratio freely settable underneath.
+Tests, house patterns: an oracle measurement of wow — drive a sine through one head and read
+the pitch deviation with `tap::dsp::yin` against the analytic transport excursion; and a null
+test — wow 0, drive 0, regen 0 should collapse the echo to a plain multitap delay. *Both
+landed, and the null is bitwise* (the interpolators do align — same family Hermite, same
+fractional position, same equal-power pan law), measured in the kernel test and again across
+the C ABI in the notebook. Measured at ship: per-pass generation loss within 0.2% of the
+analytic wear transfer on both sides of the corner; wow 10.91 cents measured against 10.88
+predicted; every past-unity drive setting plateaus under its analytic ceiling.
+
+### 2. `tap.stammer~` — the live stutter rig *(second; the most "us")* — ✅ shipped
+
+> **Shipped 2026-08-15**: `include/taptools/stammer.h` (the planned `capture` + `slicer` split
+> under a thin `machine`), `tests/stammer_test.cpp` (9 scenarios), the C ABI + ctypes surface
+> (`Stammer`), the executed `notebooks/stammer.ipynb`, and three more `radiohead_render`
+> scenarios. The design decision worth carrying: the suite and the notebook both lean on a
+> **pinned-dice identity** — with density 1, whole-step slices, one forward pass and no flank,
+> the machine must reduce to *exactly* a one-step delay, bitwise. One identity pins the grid
+> countdown, the slice origin and the playback head together, which is far stronger than
+> testing three off-by-ones separately. The capture/slicer split is a component boundary for
+> composition and testing only: a slicer needs a capture, so (like tapecho.h's `head`) it is
+> honestly documented as not standalone-external material. Measured at ship: the identity holds
+> bitwise; a seed replays bit-identically while a different seed changes 89% of samples; at
+> density 0 the rng is provably untouched and the object is a bitwise bypass at any mix; and the
+> material contract is measured at its premise — slices of a sustained sine are 1.000 alike by
+> magnitude spectrum, slices of a plucked phrase 0.286. The Max vertical slice followed the
+> same day (wrapper, five min-api scenarios, maxref, help patcher, pin bump — REVIVAL.md entry
+> 19), and the chapters with it (see `PLAN-radiohead-chapters.md`). Still to come: the
+> on-Mac validation pass.
+
+The disintegrating guitar at the end of *Go To Sleep* and the mangling in *The Gloaming*
+come from Greenwood's own Max patches: capture the live input, re-fire randomized slices
+of it. An **original design** in the brassage tradition (Roads, *Microsound*) — no port,
+no IP entanglement; the published record of the band's rig (interviews, the *From the
+Basement* films) is behavioral reference only, nothing copied.
+
+Two components and a thin composition:
+
+- **capture** — a `tape::reel` recording the live input (shared shape with `tap.scrub~`
+  below; whether it is literally one shared class is an open question).
+- **slicer** — the dice and the playback: fire probability per quantum, quantized slice
+  lengths (musical divisions of a settable base), repeat-count distribution, reverse
+  probability, per-slice equal-power envelope width. Every continuous parameter rides a
+  ramp; every random draw comes from the seeded xorshift64* so a seed is a performance you
+  can replay. Dry/slice balance is the performance fader.
+
+Tests: determinism per seed (bit-exact renders), the envelope's constant-power promise,
+and a material contract stated in the header — the object is *for* transient material
+(drums, struck guitar); on static pads it is just a tremolo, and the header says so.
+
+### 3. `tap.ondes~` — the Ondes Martenot *(the flagship; gated)*
+
+*How to Disappear Completely*, *The National Anthem*, *Where I End and You Begin*. The
+instrument decomposes on exactly the family's seams, and the decomposition is the plan:
+
+- **The voice**: a continuous-pitch oscillator (`vco.h` groundwork) with the waveform mix
+  registers, driven by two performance signals — pitch (the ribbon; continuous, no
+  quantization, glide is the playing technique not a parameter) and the **touche
+  d'intensité**, the pressure key: a fast nonlinear-taper VCA whose response curve is the
+  expressive heart of the instrument and must come from published measurement, not vibes.
+- **The diffuseurs**, as standalone resonator components usable on *any* input — the
+  killer feature is running a guitar through the Palme: **Métallique** (gong plate) as a
+  modal bank per Fletcher & Rossing's plates/gongs chapters in the `garden.h` doublet
+  idiom; **Palme** (sympathetic strings) as a bank of tuned string resonators
+  (comb/waveguide school, `grm_comb.h` experience). Principal is the dry path.
+
+**Gate: source collection before implementation.** The provenance rule is the whole
+ballgame here. If a needed number has no published source, the honest fallback is a
+*recreation* voiced by ear against published recordings and documented as such — decided
+per-number, in the header, when we get there.
+
+#### Source hunt, 2026-08-15 — findings, and the gate
+
+**All three full texts are now in hand** (supplied manually — HAL's Anubis wall blocks
+automated retrieval, and Unpaywall confirms HAL is the only OA host for all three, so there is
+no mirror; the papers are open access, the wall is anti-automation, and it was not circumvented).
+
+| Source | For | Status |
+|--------|-----|--------|
+| Quartier, Meurisse, Colmars, Frelat, Vaiedelich, "Intensity Key of the Ondes Martenot: An Early Mechanical Haptic Device", *Acta Acustica united with Acustica* **101**(2), 421–428, 2015, doi:10.3813/AAA.918837 | the touche d'intensité | **read in full** |
+| Najnudel, Hélie, Roze, Boutin, "Simulation of an ondes Martenot circuit", *IEEE/ACM TASLP* **28**, 2651–2660, 2020 (HAL hal-02920526) | the circuit | **read in full** |
+| Najnudel, Hélie, Roze, "Simulation of the Ondes Martenot Ribbon-Controlled Oscillator…", *JAES* **67**(12), 961–971, 2019, doi:10.17743/jaes.2019.0040 (HAL hal-02425249) | the variable oscillator | **read in full** |
+| Wijnand, Boutin, Jossic, Maniguet, Forum Acusticum 2023 | the diffuseur transducer | read in full |
+
+**The touche d'intensité is fully specified — this subsystem's gate is open.** The key is a
+rheostat: a graphite/mica powder bag compressed by the key, resistance dropping as the number
+of conducting bead paths rises (the carbon-microphone principle). Quartier et al. measured
+force, displacement and sound simultaneously on instrument No. 320 and give the taper as a
+table, reproduced here because it *is* the specification:
+
+| dB_SPL | mean displacement (mm) | mean finger force (N) |
+|--------|------------------------|------------------------|
+| 45.0 | 4.3 (s.d. 0.15) | 0.39 (s.d. 0.06) |
+| 53.3 | 5.3 (s.d. 0.19) | 0.47 (s.d. 0.07) |
+| 61.6 | 5.9 (s.d. 0.15) | 0.52 (s.d. 0.07) |
+| 70.0 | 6.4 (s.d. 0.15) | 0.62 (s.d. 0.08) |
+| 78.3 | 6.8 (s.d. 0.12) | 0.82 (s.d. 0.11) |
+| 86.6 | 7.3 (s.d. 0.08) | 1.34 (s.d. 0.11) |
+| 95.0 | 8.8 (s.d. 0.05) | 9.60 (s.d. 0.30) |
+
+Five things follow directly, and together they are the `touche` component's contract:
+
+1. **4.5 mm of travel carries the whole 50 dB** (4.3 → 8.8 mm, background to maximum);
+   playable gestures span roughly 3–9.5 mm.
+2. **The map is memoryless.** The paper states explicitly that the change in sound intensity
+   depends only on displacement and the force it implies, and *not on the velocity of the
+   gesture* — so a static displacement→gain curve is not a simplification, it is the finding.
+3. **The taper is not linear-in-dB against displacement.** The table's dB steps are equal by
+   construction (six nuances over 50 dB) and the displacement steps are not: 1.0, 0.6, 0.5,
+   0.4, 0.5, 1.5 mm. Interpolate the table; do not fit a straight line.
+4. **Linear in dB against log(force)** over the quasi-linear region (below ~85 dB_SPL and
+   1.3 N) — Weber–Fechner, and the paper's argument for why the key feels the way it does.
+   Useful if a force-sensing controller is ever the input; displacement is the primary map.
+5. **Pitch-independent.** Resistance curves for different notes have the same shape, so the
+   key's law separates cleanly from the oscillator — one gain curve serves the whole range.
+   (Amplitude does fall ~8 dB as frequency rises, but that is the oscillator and speaker, not
+   the key.)
+
+**The voice is a heterodyne pair, not a waveform-register VCO — amendment needed.** The plan
+assumed an oscillator with timbre switches built on `vco.h`. Najnudel et al. model instrument
+No. 169 as **five coupled stages**: fixed-frequency oscillator (80 kHz), variable-frequency
+oscillator, demodulator, preamplifier, power amplifier, coupled through transformers. Two
+findings matter more than the topology:
+
+- **The oscillators are essentially pure.** Even coupled to the rest of the circuit, oscillator
+  output measures about **0.03 % second-harmonic distortion** — which is precisely the
+  simplification the authors use to reach real time. So the tone does *not* come from an
+  interesting oscillator waveform.
+- **The timbre comes from downstream.** Harmonics are generated by the **two successive triode
+  stages** after the demodulator, and then the **diffuseur** "converts the electrical waveform
+  into sound and in turn modifies its spectral content". Their plugin even exposes demodulator
+  input gain as a harmonics control — a knob the real instrument does not have.
+
+So the kernel's shape should be: a clean sinusoidal source (heterodyne difference tone, and the
+ribbon paper is the reference for the variable oscillator's tuning behaviour), into a
+**triode-flavoured nonlinearity**, into the **touche** gain curve, into the diffuseurs. That is
+much closer to `overdrive.h`/`fuzz.h` territory than to `vco.h`, and it is a different object
+than the one this plan sketched. Note also that their full PHS simulation runs at 768 kHz and
+their plugin consumes 85 % of a laptop CPU core — a faithful circuit solve is *not* the route
+for a TapTools kernel; the documented simplifications are.
+
+**Diffuseurs, unchanged from the earlier note:** driven, not struck — *métallique* a
+motor-excited gong (1944–45, patented 1947), *palme* an electromagnet driving **12** strings on
+a soundboard (1949–50), *résonance* motor-excited springs (1970s). The early transducer is a
+moving-iron loudspeaker and inherently nonlinear (Thiele–Small does not apply). Widely
+circulated DIY pages say the palme has 24 strings; the peer-reviewed source says 12 — prefer
+the peer-reviewed number and treat hobbyist build documentation as unciteable here.
+
+**Where the gate stands: open enough to build.** The touche is fully specified. The voice has a
+published decomposition and, more usefully, a published *justification for simplifying it*. The
+diffuseurs have their excitation and their transducer characterized, though the resonator mode
+data still has to come from Fletcher & Rossing rather than from an ondes-specific source. The
+remaining work is design, not sourcing — and the object it points at is not the one originally
+sketched, so `tap.ondes~` needs a design pass against these findings before implementation.
+
+### 4. `tap.fuzz~` — the two-stage fuzz *(small, parallel-friendly)* — ✅ shipped
+
+> **Shipped 2026-08-15**: `include/taptools/fuzz.h` (`stage` + `tone` under a thin `pedal`),
+> `tests/fuzz_test.cpp` (9 scenarios), and the C ABI + ctypes surface (`Fuzz`). The name
+> question closed: **`tap.fuzz~`**, generic, per the trademark posture below.
+>
+> The method is Yeh, Abel & Smith's DAFx-07 *simplified cascade* — conditioning filter →
+> memoryless nonlinearity → equalization filter, twice — which is a stronger footing than
+> the plan assumed: it supplies the architecture, the justification for a static curve
+> (the diode limiter's exact ODE is a lowpass whose pole moves with voltage), the curve
+> family (tanh), and the reason `asymmetry` exists (a real op-amp stage clips
+> asymmetrically, producing the even harmonics an odd-only model cannot).
+>
+> **Two defects found by measurement, both worth carrying:**
+> 1. *Gain staging.* The first cut had the second stage fully clipped at `gain` 0 — the
+>    knob did nothing above about 0.2 — because the tanh family's small-signal slope is
+>    `knee/tanh(knee)` (~3 at the original knee) and that multiplied into a fixed ×2.2.
+>    Retuned; the harmonic ratio now sweeps 0.010 → 0.358 across the knob.
+> 2. *The house oversampler is not steep enough here — and steepening it was not the whole
+>    story.* With the 4th-order Butterworth that `tap.ladder~` / `overdrive.h` use, alias
+>    energy at 4× came out worse than at 2× (1.7e-2 vs 2.8e-3). Eighth order improves 4× by
+>    ~6× but did **not** make the sequence monotone: measured, 1×/2×/4×/8× ran
+>    1.2e-1 / 2.7e-5 / 7.4e-4 / 1.8e-3, so 2× shipped as the default. An earlier draft of
+>    this record claimed 8th order "restored monotonicity" — it did not, and the notebook
+>    plot was the correction. The cause was recorded open, with ill-conditioned biquads
+>    **ruled out** by an impulse-response check and imaging left as the untested hypothesis.
+>    **Both halves of that were later closed** (see the open-questions list): imaging was
+>    right, the chain now cascades 2× stages and the reversal is gone — and the whole
+>    conclusion turned out to rest on a single test tone, so the default is now 4×.
+>    Whether `overdrive.h` is owed the 8th-order change is still a separate live question
+>    needing its own measurement.
+>
+> Two test-design errors were also caught and are recorded in the suite itself, since both
+> are easy to repeat: an alias test whose tone divided the sample rate (every fold lands on
+> a harmonic and is invisible), and probe frequencies close enough to the fundamental to
+> measure window leakage rather than aliasing. Still to come: the notebook, a render
+> scenario, the Max vertical slice, and the chapter.
+
+The OK Computer-era dirt (*Paranoid Android*, *My Iron Lung*). A circuit-informed
+recreation from the widely published schematic — cascaded clipping stages plus its
+characteristic tone stack — with the diode-clipper modeling literature (the Yeh/Abel/Smith
+DAFx line) behind the solver choices. Direct sibling of `overdrive.h`; the TR-808 kernels
+are the house precedent for schematic-based recreation. **Naming is an open question**
+(below): the garden precedent (Bloom → garden) says don't ship a live trademark;
+"ShredMaster" is Marshall's mark, and the header will cite it as provenance either way.
+
+### 5. `tap.scrub~` — the Kaoss school *(after stammer; shares its capture)* — ✅ shipped
+
+> **Shipped 2026-08-17**: `include/taptools/scrub.h` (`head` + `machine`),
+> `tests/scrub_test.cpp` (13 scenarios), the C ABI + ctypes surface (`Scrub`), the executed
+> `notebooks/scrub.ipynb`, two `radiohead_render` scenarios, and the Max slice.
+>
+> **The sharing is literal**, as planned: the tape is `stammer::capture` itself, not a second
+> copy of it, and the only addition the stutter needed was `read_frac` — the fractional Hermite
+> read its ±1-rate slices never used. The plan's "position and speed as signals" became
+> **position and pitch**, which is the stronger claim: on tape, moving the head *is* the pitch
+> change, and decoupling them is what makes this an instrument rather than a varispeed.
+>
+> **The load-bearing null**: Hann overlap-adds to exactly 1 at hop = size/2, so held still at
+> unity pitch with no spray the scrub is the input delayed, to 4.4e-16.
+>
+> **One real defect, found by measurement and worth carrying.** The first cut anchored every
+> grain at the position. Origins then advance at the *write head's* speed while each grain plays
+> at `rate`, so the transposition applies only inside a grain, the average read rate returns to
+> 1, and a steady tone comes out at its **original** pitch with a comb of grain-rate sidebands.
+> The pitch knob did nothing but add texture — and no other test on the page could see it. The
+> fix is a phase-continuous read head wrapped back toward the position only after it has
+> wandered ±1.5 grains, a bound chosen by sweep (band energy retained 0.933 / 0.958 / 0.965 /
+> 0.990 / 0.993 at ±0.5 / ±1 / ±2 / ±3 / ±4 grains; flat past 3, and every extra grain is a
+> grain of position error).
+>
+> **And a measurement warning.** A single-bin probe reads the fixed kernel as badly broken: the
+> wraps spread the transposed partial into a comb a few Hz wide, and a rectangular-window
+> Goertzel on one line saw 0.02 where the band figure was 0.43. Measured properly, 98.8 % of a
+> perfect shifter's energy lands within ±15 Hz of the transposed pitch (worst 91.7 %); what the
+> wraps cost is concentration — 92.0 % as focused as a clean shift, 75.0 % at worst. Measure the
+> band, not the bin.
+
+### 6. The diffuseurs — `tap.metallique~` and `tap.palme~` — ✅ shipped
+
+> **Shipped 2026-08-17**: `include/taptools/diffuseur.h` (`mode`, `plate`, `sympathetic`,
+> `harp`, `transducer`, and the two cabinets over a shared `cabinet` base),
+> `tests/diffuseur_test.cpp` (18 scenarios), the C ABI + ctypes surface (`Metallique`, `Palme`,
+> and the bare `Plate` / `Transducer` components), the executed `notebooks/diffuseur.ipynb`,
+> two `radiohead_render` scenarios, and both Max slices. Design record in `PLAN-ondes.md`.
+>
+> Three things the build settled. **The order is the argument**: the transducer drives the body,
+> so the nonlinearity is upstream of the resonator, and a null test pins that the cabinet is
+> exactly `transducer -> body` (bitwise) while the reverse wiring differs by 28 % of peak.
+> **Unit peak gain per mode** (Steiglitz's constant-peak-gain resonator) plus weights that sum
+> to exactly 1 makes the body bounded by its input with no limiter and no DC blocker — the
+> zeros at ±1 handle DC and Nyquist. **The transducer's bound is 2/saturation, not
+> 1/saturation**: a hard-driven squared law is a nearly-constant positive waveform, and removing
+> its DC doubles the worst-case swing (measured 1.49 against the naive 1.25).
+>
+> Honest about what it is: the bodies are **recreations of the general physics** (Fletcher &
+> Rossing's free circular plate, and the harmonic series), because no ondes-specific modal
+> measurement exists in any of the four sources; the string tuning is a design choice; and both
+> nonlinear coefficients are voiced by ear, since the source establishes *that* the moving-iron
+> driver is nonlinear without handing over a curve.
+
+## Cross-cutting commitments
+
+- **Kernel-first, components-first.** Every object lands as kernel parts + composition in
+  this repo with C ABI and ctypes bindings extended alongside (`tools/capi`,
+  `notebooks/taptools_py.py`), executed notebooks for every measured claim, then the Max
+  vertical slices (wrapper, `docs/` maxref, `help/` patcher, runtime maxtest) and the
+  submodule pin bump in TapTools-Max.
+- **`radiohead_render`** in `tools/render`: minutes-long listening checks per object, the
+  `eno_render` shape — the echo into self-oscillation and back, a seeded stammer
+  performance, the diffuseurs rung by a struck string.
+- **Oracle-based measurement** where a promise is audible: yin on the echo's wow, yin on
+  the ondes ribbon glide, envelope-power measurement on the stammer slices.
+- **Book**: a family part — shipped as **Part V, *The machines you ride*** (the title the
+  family thesis earned), with *Four heads and a motor* and *The part that comes apart* plus
+  their machine appendices; drafting record in `PLAN-radiohead-chapters.md`. Later objects
+  join the same part. Every number cites an executed cell or pinned test.
+
+## Provenance and naming (the IP posture, applied)
+
+- **Published-literature-only for new DSP**, per the house policy: the echo stands on the
+  tape-echo modeling literature already cited in `tape_loop.h`; the fuzz on a published
+  schematic plus the clipper-modeling literature; the diffuseurs on Fletcher & Rossing;
+  the ondes voice is gated on locating its published measurements. The stammer and scrub
+  are original designs in the granular literature's tradition.
+- **Nothing is copied from anyone's patch, preset, or firmware.** Greenwood's Max rigs
+  are known from published interviews and broadcast films; they inform *what the object
+  is for*, never what the code says. Same posture as `garden.h` toward Bloom.
+- **Trademark care in names**, the Bloom → garden precedent: no `kaoss` (Korg), no
+  `shredmaster` (Marshall), no song-title names that read as endorsement. "Ondes" is the
+  generic French word and the instrument's common name; "tapecho", "stammer", "scrub",
+  "fuzz" are generic English.
+
+## Order, and why
+
+1. **`tap.tapecho~`** — smallest distance from shipped code, and it stress-tests
+   `tape_loop.h` as the library the components chapter claims it is. ✅ *Kernel done; the
+   stress test passed — the shared machinery needed no changes to serve a second topology.*
+2. **`tap.stammer~`** — original design (no sourcing gate), highest Max-lineage
+   resonance, establishes the family's capture + seeded-performance conventions. ✅ *Done; both
+   conventions are now in place for `tap.scrub~` to inherit.*
+3. **`tap.ondes~`** — the flagship; source collection starts immediately (it
+   parallelizes with 1–2), implementation begins when the gate clears.
+4. **`tap.fuzz~`** — small and independent; slots into any gap.
+5. **`tap.scrub~`** — after the stammer, so the capture component is designed once with
+   both consumers in view.
+
+## Open questions
+
+- **The fuzz's name.** `tap.fuzz~` (generic, safe, dull) vs `tap.shred~` (generic English
+  word, but adjacent to the mark) vs something from the family's own metaphor. Decide
+  before the vertical slice; the kernel header name can follow.
+- ~~**Echo head layout.**~~ Resolved at ship: free ratios with a nominal even-spacing
+  default (0.25 / 0.5 / 0.75 / 1.0), rather than a named-machine preset — no head spacings
+  are claimed as measured from any unit, and a Copicat-style three is two lines to set.
+- ~~**One capture component or two.**~~ Resolved at the scrub's ship: **one**. `scrub.h`
+  includes `stammer.h` and uses `stammer::capture` directly; the only change the stutter needed
+  was a fractional read it does not itself call.
+- **`tap.pitchaccum~` has the same warble, and worse** — now filed as
+  [taptools#33](https://github.com/tap/TapTools/issues/33), **and partially retracted on the way
+  there.** The original entry here recorded a "near-total cancellation" at 311 Hz +19 semitones,
+  band energy 0.004. That number was an artifact of the metric: the sweep integrated a fixed
+  **±15 Hz** band, which is about 115 cents wide at 220 Hz but only 26 cents at 932 Hz, so at
+  high transposed pitches the probe was narrower than the shifter's own spread and missed the
+  energy. Widened to a constant 3 % — the same width in cents everywhere — the two "cancellations"
+  read 0.63 and 0.85, and none exists anywhere on the sweep.
+
+  What survives: on 5 fundamentals × 7 intervals, `tap.pitchaccum~` retains mean 0.907 / worst
+  0.633 against the scrub's 0.988 / 0.917; its strongest spectral line sits 5–20 Hz *beside* the
+  intended pitch (consistent with two-tap crossfade sidebands); and `tap::dsp::yin` reads it up to
+  +35 cents sharp at 110 Hz for the +7 and +19 intervals, falling to −5 cents at 440 Hz — which is
+  ambiguous between a real error and a detector artifact and is written up as such in the issue.
+
+  **The lesson is the one `machine/scrub.md` had just finished writing**, committed one section
+  later: if a process can smear or shift a partial, the probe must be wide enough *in the units
+  the process works in*. For a pitch shifter that unit is cents, never hertz.
+- ~~**Chapters for the six newest objects.**~~ — ✅ shipped 2026-08-17 as six chapters, three
+  user-facing and three machine appendices: `book/src/scrub.md`, `diffuseurs.md`, `ondes.md`
+  (which carries `tap.ondes~`, `tap.triode~` and `tap.touche~` together, since the triode and
+  the key only make sense next to the instrument they are stages of), plus
+  `machine/scrub.md`, `machine/diffuseur.md` and `machine/ondes.md`. Eight new measured
+  figures in `book/figures/radiohead.py`. Drafting record in `PLAN-radiohead-chapters.md`.
+- ~~**The oversampler's non-monotone sequence** (`fuzz.h`'s open question)~~ — ✅ **resolved
+  2026-08-17, and the imaging hypothesis was right.** `ondes.h` supplied the evidence by
+  accident: same 8th-order chain, comparably hard nonlinearity, but a **source** with nothing
+  zero-stuffed, and no reversal. Acting on it, `fuzz.h` now cascades one 2× stage per doubling
+  — each filtering at 0.225 of its own operating rate, a corner that never tightens however deep
+  the cascade goes — instead of zero-stuffing by N once at 0.45/N (0.056 at 8×). The reversal is
+  gone: worst step-up past 2× is a ratio of 1.017, and 4× / 8× improved by two to four orders of
+  magnitude (5171 Hz at 8×: 2.3e-3 → 1.9e-7). Cost: 3.16 % of a core at 8× against 3.02 %.
+
+  **And a second, larger error surfaced doing it.** Every number in the original write-up came
+  from a *single* test tone at 3733 Hz, where 2× happens to look best. Swept across nine tones,
+  2× collapses above about 6 kHz and at 10499 Hz measures worse than no oversampling at all. The
+  shipped default of 2× was safe only for material below 6 kHz; it is now **4×**. One probe is
+  not a sweep — the same lesson as the pitchaccum retraction above, from the other direction:
+  there, one metric applied everywhere; here, one point of the input domain.
+
+  Still open, unchanged: whether `overdrive.h` is owed the same change. Different nonlinearity,
+  different gain structure, so it needs its own measurement rather than this one's conclusion.
+- **Diffuseur delivery.** Ship the resonators inside `tap.ondes~` only, or as standalone
+  externals (`tap.palme~` / `tap.metallique~`) from day one? The components chapter's
+  lesson leans standalone-from-day-one.
+- **Stochastic transport, ever?** The family inherits periodic-only wow/flutter. If the
+  echo's listening checks say the grot is missing, the amendment is a *family* decision
+  (it breaks bit-exact renders) — flagged now so it is never a quiet local hack.
diff --git a/book/figures/radiohead.py b/book/figures/radiohead.py
new file mode 100644
index 0000000..22eb692
--- /dev/null
+++ b/book/figures/radiohead.py
@@ -0,0 +1,583 @@
+#!/usr/bin/env python3
+"""Generate the measured figures for the Radiohead-family book chapters.
+
+Drives the *shipping* kernels (tapecho.h, stammer.h, fuzz.h, scrub.h, touche.h, diffuseur.h,
+ondes.h) through the C ABI via the
+notebooks' ctypes bridge — the same rule as eno.py and the verification
+notebooks: figures are measurements of the real DSP, never illustrations of
+what it should do. The companion notebooks (notebooks/tapecho.ipynb,
+stammer.ipynb, fuzz.ipynb) carry the same measurements with commentary; this script renders
+the book-styled SVGs.
+
+Regenerate after a kernel behavior change:
+
+    python3 book/figures/radiohead.py
+    # writes book/src/images/{tapecho,stammer,fuzz,scrub,ondes,diffuseur}/*.svg
+
+Colors and rcParams are eno.py's, verbatim in intent: the house categorical
+hues with the amber snapped darker so pairs pass the print/CVD lightness-band
+checks on a light page, and direct labels everywhere 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 pluck_train(seconds, period=0.31):
+    """The family's documented material: transients, and a phrase rather than one
+    note repeating (a single note would flatter a stutter)."""
+    pitches = np.array([196.0, 233.1, 261.6, 349.2, 293.7])
+    t = np.arange(int(seconds * fs)) / fs
+    phi = np.mod(t, period)
+    hz = pitches[(t // period).astype(int) % pitches.size]
+    out = np.zeros_like(t)
+    for k in range(1, 6):
+        out += (1.0 / k) * np.exp(-phi * (4.0 + 3.0 * k)) * np.sin(2 * np.pi * hz * k * phi)
+    return 0.5 * out
+
+
+def head_layout():
+    """tapecho: an impulse returning once per head, on the motor's grid."""
+    span = 0.4
+    m = tap.TapEcho(fs, 2.0, smooth_ms=0, wow=(0, 0), flutter=(0, 0), regen=0.0,
+                    drive=0.0, darken_hz=20000.0, mix=100, span_ms=span * 1000, heads=4)
+    x = np.zeros(int(0.55 * fs))
+    x[0] = 1.0
+    left, _ = m.process(x)
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.5))
+    t = np.arange(left.size) / fs * 1000.0
+    ax.plot(t, left, color=BLUE, lw=1.0)
+    for i in range(4):
+        ms = span * (i + 1) / 4 * 1000.0
+        ax.axvline(ms, color=MUTED, lw=0.8, ls=":")
+        ax.text(ms + 4, 0.60, f"{(i + 1) / 4:.2f}", color=MUTED, fontsize=8.5)
+    ax.text(150, 0.60, "head position, as a fraction of span", color=MUTED, fontsize=8.5)
+    ax.set_xlabel("time (ms) — motor span 400 ms")
+    ax.set_ylabel("output")
+    ax.set_title("one impulse, four heads: each returns at span × its ratio")
+    fig.savefig(out_dir("tapecho") / "head-layout.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def self_oscillation():
+    """tapecho: past-unity regeneration plateaus under the saturator's ceiling."""
+    regen, burst = 1.4, 0.5
+    drives = np.array([0.3, 0.5, 0.8, 1.2, 2.0])
+    peaks = []
+    for d in drives:
+        m = tap.TapEcho(fs, 2.0, smooth_ms=0, wow=(0, 0), flutter=(0, 0), mix=100,
+                        heads=1, ratios=[1.0], span_ms=250.0, regen=regen,
+                        drive=float(d), darken_hz=6000.0)
+        x = np.zeros(int(14.0 * fs))
+        n = int(burst * fs)
+        x[:n] = 0.5 * np.random.default_rng(7).uniform(-1, 1, n)
+        y, _ = m.process(x)
+        peaks.append(float(np.max(np.abs(y))))
+
+    # The analytic ceiling on the tape is |in|max + regen/drive (the saturator bounds the
+    # returned signal by 1/drive; the record head adds the direct send), scaled by the single
+    # centre-panned head's cos(pi/4).
+    ceiling = np.cos(np.pi / 4) * (0.5 + regen / drives)
+
+    fig, ax = plt.subplots()
+    ax.plot(drives, ceiling, "-", color=AMBER, lw=1.2, alpha=0.8)
+    ax.plot(drives, peaks, "o", color=BLUE, ms=6)
+    ax.text(0.55, ceiling[1] * 1.06, "ceiling: (|in|max + regen/drive)", color=AMBER)
+    ax.text(0.55, peaks[1] * 0.72, "measured peak", color=BLUE)
+    ax.set_xlabel("drive")
+    ax.set_ylabel("peak |output|")
+    ax.set_title(f"regen {regen}: self-oscillating, and bounded by the saturator")
+    fig.savefig(out_dir("tapecho") / "self-oscillation.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def occupancy():
+    """stammer: repeats, not density, is what holds the machine busy."""
+    step_ms, seconds = 60.0, 6.0
+    runs = [
+        ("density 0.3, repeats 1", dict(density=0.3, repeats=1), BLUE),
+        ("density 0.3, repeats 6", dict(density=0.3, repeats=6), AMBER),
+        ("density 0.9, repeats 1", dict(density=0.9, repeats=1), RED),
+        ("density 0.9, repeats 6", dict(density=0.9, repeats=6), INK),
+    ]
+    src = pluck_train(seconds)
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.6))
+    for row, (label, params, color) in enumerate(runs):
+        m = tap.Stammer(fs, 4000.0, smooth_ms=0, mix=100, step_ms=step_ms,
+                        divisions=1, seed=7, **params)
+        flags = np.zeros(src.size, dtype=bool)
+        for i, v in enumerate(src):
+            m.process(np.array([v]))
+            flags[i] = m.playing
+        # Draw the busy stretches as spans, not a per-sample fill: at 48 kHz a fill_between
+        # over six seconds emits a path with 288k vertices per row (a 117 MB SVG, measured).
+        edges = np.flatnonzero(np.diff(flags.astype(np.int8)))
+        bounds = np.concatenate(([0], edges + 1, [flags.size]))
+        spans = [(lo / fs, (hi - lo) / fs)
+                 for lo, hi in zip(bounds[:-1], bounds[1:]) if flags[lo]]
+        ax.broken_barh(spans, (row, 0.78), facecolors=color, linewidth=0)
+        ax.text(seconds + 0.05, row + 0.3, f"{100.0 * flags.mean():.0f}% busy",
+                color=color, fontsize=8.5)
+    ax.set_yticks([r + 0.39 for r in range(len(runs))])
+    ax.set_yticklabels([r[0] for r in runs], fontsize=8.5)
+    ax.set_xlim(0, seconds + 1.0)
+    ax.set_xlabel("time (s)")
+    ax.set_title("when a slice is in flight — repeats is the hold, not density")
+    fig.savefig(out_dir("stammer") / "occupancy.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def material():
+    """stammer: the material contract, measured at its premise."""
+    t = np.arange(int(3.0 * fs)) / fs
+    materials = [("sustained sine", 0.5 * np.sin(2 * np.pi * 196.0 * t), AMBER),
+                 ("plucked phrase", pluck_train(3.0), BLUE)]
+
+    def similarity(x, win_ms=60.0, n=96, seed=0):
+        rng = np.random.default_rng(seed)
+        w = int(win_ms * 0.001 * fs)
+        window = np.hanning(w)
+        specs = []
+        for start in rng.integers(0, x.size - w, n):
+            s = np.abs(np.fft.rfft(x[start:start + w] * window))
+            norm = np.linalg.norm(s)
+            if norm > 0:
+                specs.append(s / norm)
+        specs = np.array(specs)
+        gram = specs @ specs.T
+        return float(gram[np.triu_indices(specs.shape[0], 1)].mean())
+
+    scores = [similarity(x) for _, x, _ in materials]
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.2))
+    ypos = np.arange(len(materials))
+    for y, (label, _, color), score in zip(ypos, materials, scores):
+        ax.barh(y, score, height=0.5, color=color)
+        ax.text(score + 0.015, y, f"{score:.3f}", color=color, va="center", fontsize=9)
+    ax.set_yticks(ypos)
+    ax.set_yticklabels([m[0] for m in materials])
+    ax.set_xlim(0, 1.12)
+    ax.set_xlabel("how alike two arbitrary slices are (1 = interchangeable)")
+    ax.set_title("the material contract: re-ordering interchangeable things does nothing")
+    ax.grid(axis="y", visible=False)
+    fig.savefig(out_dir("stammer") / "material.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def fuzz_curve():
+    """fuzz: the one clipping family, and its knee as a character control."""
+    x = np.linspace(-3, 3, 1201)
+    fig, ax = plt.subplots(figsize=(7.2, 2.8))
+    for k, color, label in [(0.5, INK, "0.5"), (1.6, BLUE, "1.6 — first stage"),
+                            (2.0, AMBER, "2.0 — second stage, edge 0"), (12.0, RED, "12 — edge 1")]:
+        ax.plot(x, np.tanh(k * x) / np.tanh(k), color=color, lw=1.4)
+        ax.text(3.05, np.tanh(k * 3.0) / np.tanh(k), label, color=color, fontsize=8.5, va="center")
+    ax.plot(x, x, color="0.75", lw=0.8, ls="--")
+    ax.set_xlim(-3, 4.4); ax.set_ylim(-1.6, 1.6)
+    ax.set_xlabel("input"); ax.set_ylabel("output")
+    ax.set_title("one curve, tanh(kx)/tanh(k): full scale in is full scale out at every knee")
+    fig.savefig(out_dir("fuzz") / "curve.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def fuzz_gain_and_bite():
+    """fuzz: the gain knob's real sweep, and asymmetry as the even-harmonic control."""
+    f0 = 220.0
+    t = np.arange(int(0.4 * fs)) / fs
+    x = 0.3 * np.sin(2 * np.pi * f0 * t)
+
+    def spec(y):
+        seg = y[int(0.5 * y.size):]
+        w = np.hanning(seg.size)
+        return (np.fft.rfftfreq(seg.size, 1 / fs), np.abs(np.fft.rfft(seg * w)) * 2.0 / w.sum())
+
+    def at(f, fr, m):
+        return float(m[np.argmin(np.abs(fr - f))])
+
+    def make(**kw):
+        base = dict(smooth_ms=0, bass=0.0, treble=0.0, contrast=0.0, asymmetry=0.0, level_db=0.0)
+        base.update(kw)
+        return tap.Fuzz(fs, **base)
+
+    knob = np.linspace(0, 1, 11)
+    harm = []
+    for g in knob:
+        fr, m = spec(make(gain=g).process(x))
+        harm.append(np.sqrt(sum(at(f0 * k, fr, m) ** 2 for k in range(2, 9))) / at(f0, fr, m))
+
+    even = []
+    for a in knob:
+        fr, m = spec(make(gain=0.8, asymmetry=a).process(x))
+        e = np.sqrt(sum(at(f0 * k, fr, m) ** 2 for k in (2, 4, 6, 8)))
+        o = np.sqrt(sum(at(f0 * k, fr, m) ** 2 for k in (3, 5, 7)))
+        even.append(e / o)
+
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(7.2, 2.7))
+    a1.plot(knob, harm, color=BLUE, marker="o", ms=4)
+    a1.set_xlabel("gain"); a1.set_ylabel("harmonics / fundamental")
+    a1.set_title("the gain knob", fontsize=9.5)
+    a2.plot(knob, even, color=RED, marker="o", ms=4)
+    a2.set_xlabel("asymmetry"); a2.set_ylabel("even / odd energy")
+    a2.set_title("asymmetry buys the even harmonics", fontsize=9.5)
+    plt.tight_layout()
+    fig.savefig(out_dir("fuzz") / "gain-and-bite.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+# ---- tap.scrub~ ------------------------------------------------------------------------------
+
+
+def scrub_null():
+    """scrub: the identity the whole object is built on, and the window sum behind it."""
+    lag, size = 480, 96  # samples; size divides by the overlap, which is what makes it exact
+    m = tap.Scrub(fs, 2000.0, smooth_ms=0, overlap=2, mix=100, level=1.0,
+                  size_ms=size * 1000.0 / fs, position_ms=lag * 1000.0 / fs)
+    x = pluck_train(0.7)
+    y = m.process(x)
+    err = float(np.max(np.abs(y[2000:] - x[2000 - lag:-lag])))
+
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(7.2, 2.6))
+    sl = slice(9000, 10200)
+    ms = np.arange(sl.start, sl.stop) / fs * 1000.0
+    a1.plot(ms, x[sl.start - lag:sl.stop - lag], color=AMBER, lw=3.0, alpha=0.45)
+    a1.plot(ms, y[sl], color=BLUE, lw=1.0)
+    pk = float(np.max(np.abs(y[sl])))
+    a1.set_ylim(-1.35 * pk, 1.75 * pk)
+    a1.text(ms[10], 1.48 * pk, "input, 480 samples late", color=AMBER, fontsize=8.5)
+    a1.text(ms[10], 1.18 * pk, "the scrub", color=BLUE, fontsize=8.5)
+    a1.set_xlabel("time (ms)")
+    a1.set_title(f"held still at unity pitch: worst error {err:.1e}", fontsize=9.5)
+
+    # Overlaps 2 and 4 both sum to exactly 1, so they are the same line: draw the second
+    # dashed and label them apart, or the figure looks like one of them is missing.
+    for n_ov, color, style, at in ((1, RED, "-", 1.0), (2, BLUE, "-", 1.0), (4, AMBER, "--", 14.0)):
+        g = tap.Scrub(fs, 500.0, smooth_ms=0, overlap=n_ov, mix=100,
+                      size_ms=480 * 1000.0 / fs, position_ms=2400 * 1000.0 / fs)
+        z = g.process(np.ones(int(0.4 * fs)))[-1200:]
+        a2.plot(np.arange(z.size) / fs * 1000.0, z, color=color, lw=1.4, ls=style)
+        y_at = float(z[:200].mean())
+        a2.text(at, y_at + (0.05 if n_ov != 4 else -0.14), f"overlap {n_ov}", color=color,
+                fontsize=8.5)
+    a2.set_ylim(0, 1.25)
+    a2.set_xlabel("time (ms)")
+    a2.set_ylabel("gain on a constant")
+    a2.set_title("Hann overlap-adds flat from 2 up", fontsize=9.5)
+    plt.tight_layout()
+    fig.savefig(out_dir("scrub") / "null.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def scrub_two_hands():
+    """scrub: the pitch moves while the position does not, and what the wraps cost."""
+    def measure(st, f0=311.0, half=15.0):
+        g = tap.Scrub(fs, 3000.0, smooth_ms=0, overlap=2, size_ms=100.0,
+                      position_ms=900.0, pitch=float(st), mix=100, level=1.0)
+        n = int(fs * 3.0)
+        t = np.arange(n) / fs
+        y = g.process(0.5 * np.sin(2 * np.pi * f0 * t))
+        ref = 0.5 * np.sin(2 * np.pi * f0 * 2 ** (st / 12) * t)
+
+        def band(sig):
+            seg = sig[len(sig) // 2:]
+            mag = np.abs(np.fft.rfft(seg * np.hanning(len(seg))))
+            fr = np.fft.rfftfreq(len(seg), 1 / fs)
+            sel = np.abs(fr - f0 * 2 ** (st / 12)) <= half
+            return np.sqrt((mag[sel] ** 2).sum()), mag[sel].max()
+
+        b, pk = band(y)
+        rb, rpk = band(ref)
+        return b / rb, (pk / b) / (rpk / rb)
+
+    semis = np.array([-12, -7, -3, 3, 7, 12, 19])
+    energy, focus = zip(*[measure(s) for s in semis])
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.6))
+    ax.plot(semis, np.array(energy), "o-", color=BLUE, ms=5)
+    ax.plot(semis, np.array(focus), "o-", color=RED, ms=5)
+    ax.axhline(1.0, color=MUTED, lw=0.8, ls=":")
+    ax.text(-11, 0.70, "energy at the transposed pitch", color=BLUE, fontsize=8.5)
+    ax.text(-11, 0.64, "how concentrated it is on one line", color=RED, fontsize=8.5)
+    ax.set_ylim(0.6, 1.06)
+    ax.set_xlabel("pitch (semitones), position held at 900 ms")
+    ax.set_ylabel("fraction of a clean shifter")
+    ax.set_title("the pitch moves; what the wraps cost is focus, not the note")
+    fig.savefig(out_dir("scrub") / "two-hands.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+# ---- the Ondes Martenot ------------------------------------------------------------------------
+
+
+def ondes_envelope():
+    """ondes: the demodulated envelope, and the harmonics it carries before any valve."""
+    det = tap.Detector(fs, frequency=220.0)
+    ph = np.linspace(0, 2, 801)
+
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(7.2, 2.7))
+    for depth, color, label in ((1.0, BLUE, "depth 1 (equal oscillators)"),
+                                (0.5, AMBER, "depth 0.5")):
+        d = tap.Detector(fs, frequency=220.0, depth=depth)
+        a1.plot(ph, d.envelope(ph % 1.0), color=color, lw=1.6)
+        a1.text(0.06, d.envelope(np.array([0.5]))[0] + 0.12, label, color=color, fontsize=8.5)
+    a1.set_xlabel("cycles of the note")
+    a1.set_ylabel("envelope")
+    a1.set_title("the envelope of two summed oscillators", fontsize=9.5)
+
+    ideal = np.array([(4 / np.pi) / (4 * n * n - 1) for n in range(1, 7)])
+    db = 20 * np.log10(ideal[1:] / ideal[0])
+    a2.bar(np.arange(2, 7), db, color=BLUE, width=0.6)
+    for n, v in zip(range(2, 7), db):
+        a2.text(n, v - 1.6, f"{v:.1f}", ha="center", color="white", fontsize=8.5)
+    a2.set_xlabel("harmonic")
+    a2.set_ylabel("dB below the fundamental")
+    a2.set_title("|cos| is not a sinusoid — before any valve", fontsize=9.5)
+    plt.tight_layout()
+    fig.savefig(out_dir("ondes") / "envelope.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def ondes_tube():
+    """ondes: the published valve, and the load line that makes a stage out of it."""
+    vp = np.linspace(1, 260, 400)
+    vbias, rk, rp = tap.OP_DEMOD
+    t = tap.Triode(fs, tap.TUBE_6C5, tap.OP_DEMOD)
+    vk, vp0, ip0, gain = t.bias
+
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(7.2, 3.1))
+    for vg, color in ((0.0, RED), (-2.0, AMBER), (-4.0, BLUE), (-8.0, MUTED)):
+        ip = tap.tube_plate_current(tap.TUBE_6C5, vp, vg) * 1000.0
+        a1.plot(vp, ip, color=color, lw=1.3)
+        # Label where the curve leaves the visible box, not at its last point: the Vg 0 curve
+        # is off the top of the plot by 260 V, and a label out there stretches the layout.
+        inside = np.flatnonzero(ip < 13.0)
+        j = int(inside[-1]) if inside.size else 0
+        a1.text(float(vp[j]) + 4, float(ip[j]), f"Vg {vg:.0f}", color=color, fontsize=8,
+                va="center")
+    load = (vbias - vk - vp) / rp * 1000.0
+    a1.plot(vp, load, color=INK, lw=1.2, ls="--")
+    a1.plot([vp0], [ip0 * 1000.0], "o", color=INK, ms=6)
+    a1.text(vp0 - 8, ip0 * 1000.0 + 1.6, "quiescent", color=INK, fontsize=8.5, ha="right")
+    a1.set_xlim(0, 285)
+    a1.set_ylim(0, 14)
+    a1.set_xlabel("plate volts")
+    a1.set_ylabel("plate current (mA)")
+    a1.set_title(f"6C5 at the demodulator's point ({vbias:.0f} V, {rp/1000:.0f}k)", fontsize=9.5)
+
+    gv = np.linspace(-10, 10, 501)
+    for name, tube, op, color in (("6C5, demodulator", tap.TUBE_6C5, tap.OP_DEMOD, BLUE),
+                                  ("6C5, preamplifier", tap.TUBE_6C5, tap.OP_PREAMP, AMBER),
+                                  ("2A3, power amp", tap.TUBE_2A3, tap.OP_POWER, RED)):
+        a2.plot(gv, tap.Triode(fs, tube, op).plate_swing(gv), color=color, lw=1.5)
+    a2.text(-9.5, -34, "6C5, demodulator", color=BLUE, fontsize=8.5)
+    a2.text(-9.5, -46, "6C5, preamplifier", color=AMBER, fontsize=8.5)
+    a2.text(-9.5, -58, "2A3, power amp", color=RED, fontsize=8.5)
+    a2.set_xlabel("grid volts around the bias point")
+    a2.set_ylabel("plate swing (V)")
+    a2.set_title("the stage inverts, and it is not symmetric", fontsize=9.5)
+    plt.tight_layout()
+    fig.savefig(out_dir("ondes") / "tube.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def ondes_drive():
+    """ondes: the harmonics are there before the drive knob does anything."""
+    def run(d):
+        v = tap.Ondes(fs, smooth_ms=0, ribbon=24.0, key=1.0, level=1.0, drive=float(d))
+        return v.process(int(fs * 1.0)), v.frequency
+
+    def goertzel(y, f):
+        seg = y[len(y) // 2:]
+        w = 2 * np.pi * f / fs
+        c = 2 * np.cos(w)
+        s1 = s2 = 0.0
+        for val in seg:
+            s1, s2 = val + c * s1 - s2, s1
+        return np.sqrt(max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2 / len(seg)
+
+    drives = np.array([0.0, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0])
+    thd, level = [], []
+    for d in drives:
+        y, f0 = run(d)
+        h = np.array([goertzel(y, f0 * k) for k in range(1, 11)])
+        thd.append(np.sqrt((h[1:] ** 2).sum()) / h[0])
+        level.append(h[0])
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.6))
+    ax.plot(drives, thd, "o-", color=BLUE, ms=5)
+    ax.plot(drives, level, "o-", color=AMBER, ms=5)
+    ax.axhline(thd[0], color=MUTED, lw=0.8, ls=":")
+    ax.text(0.15, thd[0] - 0.075, "what the demodulator alone already makes", color=MUTED,
+            fontsize=8.5)
+    ax.text(4.2, 0.12, "harmonic content", color=BLUE, fontsize=8.5)
+    ax.text(3.6, 0.90, "fundamental level", color=AMBER, fontsize=8.5)
+    ax.set_ylim(0, 1.0)
+    ax.set_xlabel("drive")
+    ax.set_title("the valves add to a signal that is already rich")
+    fig.savefig(out_dir("ondes") / "drive.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def touche_curve():
+    """ondes: the intensity key's published law, against the line nobody measured."""
+    table_db = np.array([45.0, 53.3, 61.6, 70.0, 78.3, 86.6, 95.0])
+    table_mm = np.array([4.3, 5.3, 5.9, 6.4, 6.8, 7.3, 8.8])
+    travel = 9.5
+
+    key = tap.Touche(fs, smooth_ms=0)
+    p = np.linspace(0, 1, 2001)
+    mm = p * travel
+    curve = 20 * np.log10(np.where((g := np.array([key.gain_at(v) for v in p])) > 0, g, 1e-300))
+    band = (mm >= table_mm[0]) & (mm <= table_mm[-1])
+    frac = (mm - table_mm[0]) / (table_mm[-1] - table_mm[0])
+    line = -50.0 * (1.0 - frac)
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.7))
+    ax.plot(mm[band], curve[band], color=BLUE, lw=2.0)
+    ax.plot(mm[band], line[band], color=AMBER, lw=1.2, ls="--")
+    ax.plot(table_mm, table_db - table_db[-1], "o", color=RED, ms=6, zorder=5)
+    ax.axvspan(0, table_mm[0], color=MUTED, alpha=0.10)
+    ax.text(2.1, -25, "silent:\nthe key is\nstill bending", color=MUTED, fontsize=8.5, ha="center")
+    ax.text(4.6, -8, "Quartier et al. 2015", color=RED, fontsize=8.5)
+    ax.text(6.5, -38, "a straight line, for comparison", color=AMBER, fontsize=8.5)
+    ax.set_xlim(0, travel)
+    ax.set_xlabel("key displacement (mm)")
+    ax.set_ylabel("gain (dB, referenced to full press)")
+    ax.set_title("50 dB in 4.5 mm — and the shape is the measurement, not a fit")
+    fig.savefig(out_dir("touche") / "curve.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+# ---- the diffuseurs --------------------------------------------------------------------------
+
+
+def diffuseur_plate():
+    """metallique: where the modes are, and what the body does to a sweep."""
+    ratios = np.array([1.000, 1.730, 2.328, 3.910, 4.110, 6.300, 6.710, 7.340])
+    gong = tap.Metallique(fs, pitch_hz=180.0, decay=6.0, brightness=1.0,
+                          drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0)
+    hz, weight = gong.modes()
+
+    probe = np.geomspace(60.0, 2000.0, 160)
+    resp = []
+    for f in probe:
+        g = tap.Metallique(fs, pitch_hz=180.0, decay=1.2, brightness=1.0,
+                           drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0)
+        n = int(fs * 1.0)
+        y = g.process(np.sin(2 * np.pi * f * np.arange(n) / fs))
+        resp.append(float(np.abs(y[int(fs * 0.75):]).max()))
+    resp = np.array(resp)
+
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(7.2, 2.7))
+    a1.vlines(hz, 0, weight, color=BLUE, lw=2.5)
+    a1.plot(hz, weight, "o", color=BLUE, ms=5)
+    for i, (f, w, r) in enumerate(zip(hz, weight, ratios)):
+        a1.annotate(f"{r:.3f}", (f, w), textcoords="offset points",
+                    xytext=(0, 6 if i % 2 == 0 else 17), ha="center", fontsize=7.5, color=MUTED)
+    a1.set_ylim(0, 0.40)
+    a1.set_xlabel("frequency (Hz)")
+    a1.set_ylabel("weight")
+    a1.set_title(f"eight plate modes, weights summing to {weight.sum():.0f}", fontsize=9.5)
+
+    a2.semilogx(probe, 20 * np.log10(resp), color=BLUE, lw=1.4)
+    for f in hz:
+        a2.axvline(f, color=MUTED, lw=0.7, ls=":")
+    a2.set_xlabel("drive frequency (Hz)")
+    a2.set_ylabel("peak out (dB, unit input)")
+    a2.set_title("driven, not struck: the body answers a sweep", fontsize=9.5)
+    plt.tight_layout()
+    fig.savefig(out_dir("diffuseur") / "plate.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+def diffuseur_selectivity():
+    """palme: twelve strings, and what they answer."""
+    kw = dict(root_hz=110.0, tuning=0, decay=6.0, damping=4000.0, detune=0.0,
+              drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0, level=1.0, smooth_ms=0.0)
+
+    def ring(hz, drive_s=2.0, tail_s=1.0, fade_s=0.25):
+        # The drive is faded: switching a tone on and off is a step, and a step excites every
+        # string on the board, so without the fades this measures its own edges.
+        p = tap.Palme(fs, **kw)
+        n_on = int(fs * drive_s)
+        n = n_on + int(fs * tail_s)
+        t = np.arange(n) / fs
+        g = np.zeros(n)
+        f = int(fs * fade_s)
+        g[:n_on] = 1.0
+        g[:f] = 0.5 - 0.5 * np.cos(np.pi * np.arange(f) / f)
+        g[n_on - f:n_on] = 0.5 - 0.5 * np.cos(np.pi * np.arange(f, 0, -1) / f)
+        y = p.process(g * 0.3 * np.sin(2 * np.pi * hz * t))
+        tail = y[n_on + int(fs * 0.2):]
+        return float(np.sqrt(np.mean(tail ** 2)))
+
+    probe = np.geomspace(100.0, 440.0, 150)
+    tails = np.array([ring(f) for f in probe])
+    strings, _ = tap.Palme(fs, **kw).strings()
+
+    fig, ax = plt.subplots(figsize=(7.2, 2.7))
+    ax.semilogx(probe, 20 * np.log10(tails / tails.max()), color=BLUE, lw=1.4)
+    for f in strings:
+        ax.axvline(f, color=MUTED, lw=0.7, ls=":")
+    ax.text(103, -68, "dotted: the twelve strings", color=MUTED, fontsize=8.5)
+    ax.set_xticks([100, 150, 200, 300, 440])
+    ax.set_xticklabels(["100", "150", "200", "300", "440"])
+    ax.set_xticks([], minor=True)  # the log minor labels collide with 440
+    ax.set_xlabel("drive frequency (Hz)")
+    ax.set_ylabel("ring left after the drive stops (dB)")
+    ax.set_title("the palme answers what it is tuned to, and little else")
+    fig.savefig(out_dir("diffuseur") / "selectivity.svg", bbox_inches="tight")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    head_layout()
+    self_oscillation()
+    occupancy()
+    material()
+    fuzz_curve()
+    fuzz_gain_and_bite()
+    scrub_null()
+    scrub_two_hands()
+    ondes_envelope()
+    ondes_tube()
+    ondes_drive()
+    touche_curve()
+    diffuseur_plate()
+    diffuseur_selectivity()
+    print("wrote the Radiohead-family figures")
diff --git a/book/src/SUMMARY.md b/book/src/SUMMARY.md
index 271cb21..e1d7e61 100644
--- a/book/src/SUMMARY.md
+++ b/book/src/SUMMARY.md
@@ -25,26 +25,35 @@
 - [The garden that plays itself](garden.md)
 - [The same machine, in pieces](components.md)
 
-# Part V — The spectral set
+# Part V — The machines you ride
+
+- [Four heads and a motor](tapecho.md)
+- [The part that comes apart](stammer.md)
+- [The dirt with two stages](fuzz.md)
+- [Two hands on the same tape](scrub.md)
+- [Loudspeakers you can play](diffuseurs.md)
+- [The instrument that is not a synthesizer](ondes.md)
+
+# Part VI — The spectral set
 
 - [Making the machine talk](vocoder.md)
 - [A gate for every bin](nr.md)
 - [The spectrum, re-plumbed](spectra.md)
 
-# Part VI — The rhythm section
+# Part VII — The rhythm section
 
 - [The acid machine](acid.md)
 - [The drum machine](drums.md)
 
-# Part VII — Staying in tune
+# Part VIII — Staying in tune
 
 - [The note you meant](tune.md)
 
-# Part VIII — The pedalboard
+# Part IX — The pedalboard
 
 - [Distortion with a memory](overdrive.md)
 
-# Part IX — The machine, file by file
+# Part X — The machine, file by file
 
 - [Solving the filter on paper: svf.h](machine/svf.md)
 - [The nonlinear loop: ladder.h](machine/ladder.md)
@@ -65,8 +74,14 @@
 - [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)
+- [Composition, not construction: tapecho.h](machine/tapecho.md)
+- [Dice you can replay: stammer.h](machine/stammer.md)
+- [Two stages and a knee: fuzz.h](machine/fuzz.md)
+- [One tape, two read patterns: scrub.h](machine/scrub.md)
+- [Driven, not struck: diffuseur.h](machine/diffuseur.md)
+- [A citation, an identity, and a sign: ondes.h](machine/ondes.md)
 
-# Part X — Recipes
+# Part XI — Recipes
 
 - [How to read a recipe](recipes/cookbook.md)
 - [One machine, four decades](recipes/808-classics.md)
@@ -80,3 +95,6 @@
 - [Sixteenths into a listening filter](recipes/funk-filter.md)
 - [A field guide to rooms](recipes/rooms.md)
 - [Chords with no keyboard](recipes/comb-drones.md)
+- [The part that comes apart, on tape](recipes/tape-and-stutter.md)
+- [Two hands on a live buffer](recipes/scrub-pad.md)
+- [The instrument in the corner of the room](recipes/ondes-rig.md)
diff --git a/book/src/diffuseurs.md b/book/src/diffuseurs.md
new file mode 100644
index 0000000..160818a
--- /dev/null
+++ b/book/src/diffuseurs.md
@@ -0,0 +1,161 @@
+# Loudspeakers you can play
+
+The Ondes Martenot does not have a loudspeaker. It has a rack of them, and
+the player chooses. Beyond the plain cabinet — the *principal* — Maurice
+Martenot built resonating **diffuseurs** whose entire job is to colour the
+signal with a physical body: the **métallique** (1944–45, patented 1947), a
+gong driven by a motor transducer, and the **palme** (1949–50), an
+electromagnet driving twelve metal strings stretched on a soundboard.
+
+`tap.metallique~` and `tap.palme~` are those two, and they ship as
+standalone effects rather than as something hidden inside `tap.ondes~`,
+because the interesting thing about a resonating loudspeaker is that it does
+not care what you put through it. A guitar into the palme is not what
+Martenot had in mind and it is the best reason to have the object.
+
+Najnudel, Hélie, Roze and Boutin (IEEE/ACM TASLP 28, 2020) name the
+diffuseur as the stage that "converts the electrical waveform into sound and
+in turn modifies its spectral content". Wijnand, Boutin, Jossic and Maniguet
+(Forum Acusticum 2023) describe the instruments and measure the transducer.
+Everything below traces to one of those two, or is labelled as a
+recreation.
+
+Companion material: the executed notebook `diffuseur.ipynb`,
+`tests/diffuseur_test.cpp`, and the `radiohead_render` scenes
+`metallique_stages` and `palme_halo`.
+
+## Driven, not struck
+
+`tap.chime~` and `tap.garden~` already carry this library's modal machinery
+— mode ratios, doublet splitting, per-mode decay — and it carries over here
+intact. What does *not* carry over is the strike. There is no trigger in
+either of these objects and no decay envelope. A diffuseur is excited
+continuously by whatever is going through it and rings at its own rates,
+which is `tap.5comb~`'s sustained-resonance situation rather than the
+chime's.
+
+Practically, that is the difference between an object you fire and an object
+you feed.
+
+## The order is the argument
+
+The electrical signal reaches the *transducer* first, and the transducer's
+motion is what excites the body. So the nonlinearity sits **upstream** of
+the resonator. Drive the transducer hard and you are pushing a distorted
+waveform into a gong — which is a different sound from distorting a gong.
+
+That claim is pinned rather than asserted: a null test in the kernel checks
+that a whole cabinet is *bitwise* identical to `transducer → body` wired by
+hand, and that the reverse wiring differs by 28 % of peak. It is not a
+subtlety you have to take on faith, and it is not a subtlety you can hear
+your way past.
+
+## The métallique
+
+Eight modes at the free circular plate's transverse ratios — Rayleigh's
+classical Chladni set at Poisson 0.3, `1 : 1.730 : 2.328 : 3.910 : 4.110 :
+6.300 : 6.710 : 7.340` — each split into a slowly beating doublet.
+
+![Two panels: eight modes as stems with their ratios labelled, weights summing to 1; and the plate's measured response to a swept drive tone, peaking at each mode](images/diffuseur/plate.svg)
+
+*Left: where the modes are. Right: the body answering a sweep, which is how you actually meet it.*
+
+`pitch` places the lowest mode and the rest follow. `decay` is the
+fundamental's T60 — long is a drone, short is a plate reverb. `tilt` decides
+how much faster the upper modes die than the fundamental, and `brightness`
+weights them. The weights sum to exactly 1 and each mode has unit peak gain,
+which is why there is no limiter on the output and no DC blocker either: the
+body is bounded by its input, by construction.
+
+## The palme
+
+Twelve strings, each a damped delay loop, on one board.
+
+Twelve, not twenty-four. Widely copied build pages say two banks of twelve;
+the peer-reviewed source says twelve, and this object follows the
+peer-reviewed source.
+
+Their *tuning* is not published anywhere found, so it is a control: `@tuning
+0` lays them out chromatically across an octave from `root` — a string for
+every pitch class, so the board answers whatever you play — and `@tuning 1`
+puts the harmonic series on the root, which is a drone that answers one key.
+
+![The palme's ringing after a faded drive tone is removed, swept from 100 to 440 Hz, with twelve peaks lining up on the twelve string frequencies](images/diffuseur/selectivity.svg)
+
+*Feed it a tone, take the tone away, measure what is left. Every one of the twelve strings rings at least 4.4× harder at its own pitch than between them.*
+
+`damping` is how fast a string loses its upper partials — low values are
+felt cloth on the strings. `detune` scatters the strings against each other
+by a fixed, deterministic amount in cents, because no two strings on a real
+board are in perfect relation.
+
+## The transducer
+
+Wijnand et al.'s point about the early diffuseurs is that they use a
+**moving-iron** driver whose operating principle is inherently nonlinear —
+Thiele–Small does not describe it — so a diffuseur modelled as a pure
+resonator is missing a documented stage.
+
+What is modelled here is that principle, not a fit to a measurement. In a
+moving-iron motor the force follows the square of the gap flux, so with a
+bias current `I₀` and signal `i` the force carries a term in `(I₀ + i)²`
+whose residual `i²` makes second-harmonic distortion that grows with drive.
+That is `asymmetry`: the transducer's own even-harmonic signature, and the
+only part of these objects that is nonlinear by citation.
+
+`saturation` is the honest exception. A squared law is expansive and
+something has to bound it, so there is a soft clipper after it — a
+**modelling necessity, not a measured stage**, and its coefficient is a knob
+rather than a number from a paper. At 0 it is exactly linear.
+
+## What these are, and are not
+
+The instruments, their dates, their excitation and their transducer type are
+peer-reviewed. **The modal data is not.** No ondes-specific measurement of
+either body exists in any source obtained, so the plate comes from Fletcher
+& Rossing's free circular plate and the strings from the harmonic series.
+Both bodies are therefore **recreations of the general physics, not models
+of Martenot's instruments**. Nothing here was fitted to a recording, a
+measurement, or a photograph.
+
+There is also no radiation model — no directivity, no cabinet, no soundboard
+resonance of its own. The output is the body's modal response, not a room.
+And the strings are ideal: a real steel string is stiff and its partials
+stretch sharp, and that dispersion is not modelled. `detune` scatters
+strings against each other, which is a different thing and does not stand in
+for it.
+
+## Recipes
+
+- **A guitar into the palme:** `tap.palme~ @root 110 @tuning 0 @decay 8
+  @mix 45`. The halo underneath everything you play. The reason these ship
+  standalone.
+- **The instrument, assembled:** `tap.ondes~` → `tap.palme~ @mix 60`. What
+  Martenot actually had.
+- **Gong reverb:** `tap.metallique~ @pitch 180 @decay 1.5 @tilt 1.2
+  @mix 35`. Short decay turns the body into a plate.
+- **A drone you drive:** `tap.metallique~ @decay 20 @drive 3 @asymmetry 0.5
+  @saturation 0.4 @mix 100`. Hard into the transducer, which is upstream, so
+  it is a distorted waveform ringing a gong rather than a distorted gong.
+- **The one to be careful with:** `tap.palme~ @level` — twelve resonant loops
+  add up, and a driven board can be much louder than what went into it.
+
+## When it is not the right tool
+
+- **A reverb.** These are twelve strings and eight modes. They are pitched,
+  and they will impose their pitches on anything you send.
+- **A model of Martenot's own diffuseurs.** See above: this is the physics
+  of the general case, and the difference is stated rather than glossed.
+- **Clean sustain.** `tap.5comb~` is the sustained-resonance object without
+  a nonlinear driver in front of it.
+
+## Checkpoint
+
+Two loudspeakers with bodies, shipped as effects because a resonating
+cabinet does not care what drives it. Driven rather than struck, so no
+trigger and no envelope. The transducer is upstream of the body and a
+bitwise null test pins that it is — 28 % of peak says the order is audible.
+Every mode has unit peak gain and the weights sum to 1, so the body needs no
+limiter. And the bodies are recreations of published physics rather than
+measurements of Martenot's instruments, which is a limitation stated here
+and in the header rather than left to be discovered.
diff --git a/book/src/fuzz.md b/book/src/fuzz.md
new file mode 100644
index 0000000..0b82f59
--- /dev/null
+++ b/book/src/fuzz.md
@@ -0,0 +1,162 @@
+# The dirt with two stages
+
+Two objects in this library make things dirty and they are not competing.
+`tap.overdrive~` is a *feedback* soft-clipper chasing the Tube Screamer
+lineage — the nonlinearity sits inside a loop with a lowpass, so the bass
+stays clean and the mids break up first. `tap.fuzz~` is the other school:
+two clipping stages one after the other, and a tone section that scoops the
+middle out. It is the OK Computer-era sound — the dirt on *Paranoid Android*
+and *My Iron Lung* — and it belongs in this part of the book because, like
+the tape echo and the stutter, the interesting settings are the ones you
+arrive at by moving something.
+
+The method is not invented here. It is the **simplified cascade** of Yeh,
+Abel and Smith's DAFx-07 paper on distortion and overdrive pedals:
+conditioning filter → memoryless nonlinearity → equalization filter, twice.
+That paper also supplies the licence for the central shortcut. A real diode
+limiter is not a static curve at all — it is a lowpass whose pole moves with
+the voltage across it, and solving that honestly is expensive. Approximating
+it as a fixed curve between fixed filters is defended there, and measured
+against real pedals.
+
+What this object is *not* is a model of a specific pedal. No resistor,
+capacitor or corner frequency in it is claimed as measured from a unit, and
+the control names follow the layout that class of pedal conventionally
+carries rather than asserting what any particular one does.
+
+Companion material: the executed notebook `fuzz.ipynb`, and the
+`radiohead_render` scenarios `fuzz_gain_sweep`, `fuzz_tone` and
+`fuzz_edge_and_bite`.
+
+## One curve, two knees
+
+Both stages share a single clipping family — `tanh(kx)/tanh(k)` — normalized
+so that full scale in is full scale out at *every* knee. That normalization
+is what lets the knee be a character control instead of a hidden volume
+control.
+
+![Four clipping curves at knees 0.5, 1.6, 2.0 and 12, all passing through the same full-scale point, the sharpest approaching a hard corner](images/fuzz/curve.svg)
+
+*The knee sharpens the corner without moving the ceiling.*
+
+The first stage takes a soft knee and most of the gain (the op-amp-ish
+stage); the second takes a harder one at unity (the shunt limiter). `edge`
+sweeps the second stage's knee from a gentle limiter toward something close
+to a hard corner.
+
+## `gain` — and why the floor is below unity
+
+The knob sweeps the first stage's drive. Its floor sits *below* unity
+deliberately, and the reason is the most useful thing in this chapter if you
+ever build a cascade of your own.
+
+The tanh family's small-signal gain is `k/tanh(k)` — greater than one, and
+growing with the knee. Put a fixed ×2.2 in front of a knee-3 curve and the
+second stage sees an effective ×6.6, which means it is fully clipped before
+the gain knob leaves zero. That is exactly what the first version of this
+kernel did. It sounded like a distortion at every setting, which is precisely
+why listening did not catch it and a measurement did.
+
+![Two panels: harmonic content rising steeply with the gain knob, and even-to-odd harmonic ratio rising with asymmetry](images/fuzz/gain-and-bite.svg)
+
+*Left: the gain knob after retuning — harmonic content sweeps 0.010 to 0.358. Right: `asymmetry` is what makes even harmonics.*
+
+## `asymmetry` — the even harmonics
+
+A symmetric curve is an odd function, so it can only make odd harmonics.
+DAFx-07 points out that a real op-amp stage clips lopsided, and that this is
+where a pedal's even-order content comes from — which is the whole reason
+this control exists. Turn it up and the even/odd ratio climbs from
+essentially zero to about 0.55.
+
+It costs no DC. The bias is applied inside the curve and corrected at the
+stage output, so however lopsided the setting, silence in is *exactly*
+silence out — no pedestal, no thump when you stop playing.
+
+## `bass`, `treble`, `contrast` — the voicing
+
+Three linear filters entirely outside the nonlinearity: a low shelf, a high
+shelf, and a mid scoop whose depth is `contrast`. On this class of pedal the
+voicing section is most of the identity — the scoop is the sound people mean
+when they describe it — so it is a first-class part of the object rather
+than an afterthought bolted on at the end.
+
+## `oversample` — and a default that was wrong twice
+
+A static curve makes harmonics without limit, so anything above Nyquist folds
+back. The clipper pair therefore runs oversampled. Everything about this
+control has been re-measured, because the first two conclusions drawn from it
+were wrong, and wrong in the same way.
+
+First, the anti-alias filter here is **8th order**, where the rest of the
+house uses 4th. Measured in this kernel the 4th-order pair is not steep
+enough — alias energy at 4× came out worse than at 2×.
+
+Second, the chain is a **cascade of 2× stages** — one doubling, one filter,
+repeated — rather than a single zero-stuff by the whole factor. That is what
+finally made more oversampling mean less aliasing. The single-stage chain left
+N−1 images for one filter to suppress at a corner that got tighter with every
+doubling, and the residue intermodulated in the clipper into exactly the
+non-harmonic junk the probe measures. Cascading removes the reversal outright,
+and where 4× and 8× used to be merely adequate they are now two to four orders
+of magnitude cleaner. It costs about 5 % more CPU at 8×.
+
+Third — and this is the part worth taking away — **the old default came from a
+single test tone.** Every number in the original write-up was measured at
+3733 Hz, and 2× happens to look best there. Swept across tones, 2× collapses
+above about 6 kHz; at 10.5 kHz it is *worse than not oversampling at all*,
+because the clipper's low harmonics already exceed the base Nyquist and one
+doubling does not move them out of the way.
+
+| input tone | 1× | 2× | 4× | 8× |
+|---|---|---|---|---|
+| 3733 Hz | 1.2e-1 | 3.0e-5 | 2.1e-5 | 2.2e-5 |
+| 5171 Hz | 1.5e-1 | 3.3e-4 | 2.0e-7 | 1.9e-7 |
+| 6421 Hz | 8.5e-2 | 3.2e-2 | 3.9e-7 | 3.6e-7 |
+| 8123 Hz | 9.0e-2 | 7.8e-2 | 1.1e-3 | 2.0e-5 |
+| 10499 Hz | 1.5e-1 | 1.7e-1 | 1.2e-5 | 1.6e-6 |
+
+So: **4× is the default.** More is never worse now, and 4× is
+indistinguishable from 8× below about 7.5 kHz. Above that, harmonics start
+folding inside the 4× band before decimation — 8123 Hz in the table is that
+happening — and 8× is worth the extra 1.4 % of a core.
+
+Use 2× only if you have measured your own material and it holds up there. It
+is kept because it is cheap and because on a bass-heavy source it is fine, not
+because it is good.
+
+## Recipes
+
+- **Edge of breakup:** `@gain 0.3 @edge 0.2 @contrast 0. @bass 0.`. Barely
+  dirty; a boost with attitude.
+- **The scoop:** `@gain 0.8 @edge 0.6 @contrast 1. @bass 0.4 @treble 0.2`.
+  The sound the control is named for.
+- **Lopsided and mean:** `@gain 0.9 @edge 1. @asymmetry 0.7 @oversample 8`.
+  Hard knee plus even harmonics, and 8× because a hard knee on a bright source
+  is exactly where the top octave folds.
+- **Into the echo:** `tap.fuzz~` → `tap.tapecho~` with the echo's `@drive`
+  low. Two saturators in series get muddy fast; let the pedal be the dirt and
+  the tape be the space.
+
+## When it is not the right tool
+
+- **Amp-like breakup.** `tap.overdrive~` keeps the bass clean by design; this
+  object does not, and hard settings will get woolly on a bass-heavy source.
+- **Subtle warmth.** Two stages is a lot of stages. At low gain this is a
+  clean boost with a tone stack, which is fine, but `tap.overdrive~` is the
+  better instrument for gentle.
+- **A specific pedal.** This is that pedal's *class*. If you need a named
+  unit, this is not it and does not pretend to be.
+
+## Checkpoint
+
+One clipping family with a knee control, cascaded twice, into a voicing
+section that scoops the middle. The gain knob's floor is below unity because
+small-signal gain compounds through a cascade — a lesson that cost this
+kernel one wrong first draft. `asymmetry` is the even-harmonic control and
+costs no DC. And the oversample setting is a
+measurement twice corrected: cascaded 2× stages, because a single zero-stuff
+by N was what made bigger measure worse — and a default of 4× rather than 2×,
+because the old default had been generalized from one test tone. Every number here
+lives twice, as a cell in `fuzz.ipynb` and as a pinned scenario in
+`tests/fuzz_test.cpp`.
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new file mode 100644
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+    <text style="font-size: 8.5px; font-family: 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Arial', 'Helvetica', 'Avant Garde', sans-serif; fill: #666666" transform="translate(116.049402 85.440113)">the key is</text>
+    <text style="font-size: 8.5px; font-family: 'DejaVu Sans', 'Bitstream Vera Sans', 'Computer Modern Sans Serif', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Arial', 'Helvetica', 'Avant Garde', sans-serif; fill: #666666" transform="translate(110.510457 95.642437)">still bending</text>
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diff --git a/book/src/introduction.md b/book/src/introduction.md
index 0470ee1..caca28f 100644
--- a/book/src/introduction.md
+++ b/book/src/introduction.md
@@ -29,23 +29,34 @@ The book is organized the way a patch is:
 - **Part III — Strings, rooms, and spirals**: exact true-stereo convolution
   (`tap.convolve~`) and the two GRM Tools recreations — the tuned comb bank
   (`tap.5comb~`) and the pitch-accumulating shimmer loop (`tap.pitchaccum~`).
-- **Part IV — The spectral set**: the 24-band vocoder (`tap.vocoder~`), the
+- **Part IV — Tape and time**: the Eno recreations — the *Discreet Music*
+  two-machine tape loop (`tap.discreet~`), the *Music for Airports*
+  incommensurate loop bank (`tap.airport~`), the generative event garden
+  (`tap.garden~`), and the components they decompose into.
+- **Part V — The machines you ride**: the Radiohead family — objects whose
+  point is the performance surface rather than a setting. The multi-head tape
+  echo (`tap.tapecho~`), the live buffer-stutter rig (`tap.stammer~`), the
+  two-stage fuzz (`tap.fuzz~`), the granular scrub pad (`tap.scrub~`), the two
+  Ondes Martenot diffuseurs as standalone driven resonators
+  (`tap.metallique~`, `tap.palme~`), and the Ondes Martenot voice itself
+  (`tap.ondes~`, with `tap.triode~` and `tap.touche~`).
+- **Part VI — The spectral set**: the 24-band vocoder (`tap.vocoder~`), the
   per-bin spectral gate (`tap.nr~`), and the bin remapper (`tap.spectra~`).
-- **Part V — The rhythm section**: the Roland recreations — the TB-303 voice,
+- **Part VII — The rhythm section**: the Roland recreations — the TB-303 voice,
   its diode-ladder filter, and its sequencer (`tap.303~`, `tap.diode~`,
   `tap.303.seq~`), and the eight TR-808 voice channels with their row
   sequencer (`tap.808.*`, `tap.808.seq~`).
-- **Part VI — Staying in tune**: the pitch corrector (`tap.tune~`) and the
+- **Part VIII — Staying in tune**: the pitch corrector (`tap.tune~`) and the
   detection/resynthesis machinery it stands on.
-- **Part VII — The pedalboard**: the stompbox recreations — the voiced feedback
+- **Part IX — The pedalboard**: the stompbox recreations — the voiced feedback
   overdrive (`tap.overdrive~`), chasing the TS-lineage feedback pedals rather
   than a waveshaping curve.
-- **Part VIII — The machine, file by file**: the SampleRateTap-style deep dives —
+- **Part X — The machine, file by file**: the SampleRateTap-style deep dives —
   one chapter per kernel header, deriving the math, reviewing the code, and
   recording *why* each algorithm is written the way it is, alternatives and
-  all. Parts I–VII are for driving the objects; Part VIII is for trusting them —
+  all. Parts I–IX are for driving the objects; Part X is for trusting them —
   or changing them.
-- **Part IX — Recipes**: whole patches chasing specific sounds — the TR-808
+- **Part XI — Recipes**: whole patches chasing specific sounds — the TR-808
   kits behind four decades of records, the three-oscillator Moog voice —
   with settings you can check against the reference pages and the honest
   accounting of what each ingredient buys.
diff --git a/book/src/machine/diffuseur.md b/book/src/machine/diffuseur.md
new file mode 100644
index 0000000..5a43fc9
--- /dev/null
+++ b/book/src/machine/diffuseur.md
@@ -0,0 +1,134 @@
+# Driven, not struck: `diffuseur.h`
+
+This file exists because a plan was wrong in a useful way. The Ondes family
+plan said the diffuseurs would inherit `garden.h`'s modal machinery, and
+they do — mode ratios, doublet splitting, per-mode decay. What it did not
+say, and what reshaped the file, is that a diffuseur is **driven**. There is
+no trigger here and no `decay_env`. The input excites the body continuously
+and the body rings at its own rates, which is `grm_comb.h`'s situation
+rather than the chime's.
+
+Five classes: `mode`, `plate`, `sympathetic`, `harp`, `transducer`, and two
+cabinets over a shared `cabinet` base. Nothing else.
+
+## Unit peak gain, and everything it saves
+
+`mode` is the constant-peak-gain two-pole resonator (Steiglitz; Smith,
+*Introduction to Digital Filters*): poles at radius R, zeros at ±1, and
+`b0 = (1 − R²)/2`.
+
+That choice pays three times, and it is worth spelling out because it is the
+reason this file has almost no defensive code in it.
+
+- **Peak gain is 1 at any Q.** So a bank of weighted modes is bounded by the
+  sum of its weights. The plate's eight weights sum to exactly 1, which
+  means the body cannot output more than its input, and there is no limiter
+  anywhere in the file.
+- **Changing `decay` does not change the level.** With a plain two-pole
+  resonator, moving R moves the peak gain, so a decay knob is also a volume
+  knob. Here it is not.
+- **The zeros at ±1 are exact nulls at DC and Nyquist.** So there is no DC
+  blocker on the body either. It cannot accumulate one.
+
+None of that is novel — it is a textbook resonator used for the reason the
+textbook gives — but the cumulative effect on a file that runs sixteen of
+them plus twelve delay loops is large.
+
+## The order is the argument, and it is a bitwise test
+
+The instrument's signal reaches the transducer first, and the transducer's
+motion excites the body. So the nonlinearity is **upstream** of the
+resonator.
+
+That is the central design claim of the file, so it is pinned rather than
+described: a scenario builds `transducer → plate` by hand and checks that a
+whole `metallique` is **bitwise identical** to it, and that the reverse
+wiring — resonate, then distort — differs by 28 % of peak.
+
+Getting that null to be actually bitwise took one fix. `cabinet::blend` is
+an equal-power crossfade written with `cos`/`sin`, and `cos(π/2)` in double
+precision is 6.1e-17, not 0. A wiring test that reads 6.1e-17 has not
+demonstrated identity; it has demonstrated *approximate* identity, which is
+the thing the test exists to distinguish from. Both ends of the blend are
+now exact short-circuits: mix 0 returns the dry input bit for bit, mix 100
+returns the wet.
+
+## The transducer's bound is 2/saturation, not 1/saturation
+
+The moving-iron model squares the drive, and `vca::swing_shape` bounds the
+result at `1/saturation`. The obvious test — output stays under
+`1/saturation` — failed at 1.49 against a bound of 1.25.
+
+The test was wrong, not the code. A hard-driven squared law produces a
+nearly-constant *positive* waveform: it sits up near the ceiling and dips
+toward zero. Removing its DC recentres that, so the excursion below the mean
+adds to the excursion above it, and the worst-case swing after the DC
+blocker is up to **twice** the saturator's own bound.
+
+The corrected bound is `2/saturation`, documented in the header, and the
+scenario now asserts both sides of it — greater than `1/sat`, less than
+`2/sat` — so the test still catches the saturator disappearing entirely.
+
+The general shape of this mistake is common enough to name: **a DC blocker
+after an asymmetric nonlinearity is not free.** It does not just remove an
+offset; it converts an offset into headroom you have to have.
+
+## A measurement that measured its own edges
+
+The palme's selectivity scenario drives the board with a tone and measures
+what is still ringing after the tone stops. First version: switch the tone
+on, switch it off, measure the tail. It failed — 3.66× selectivity against
+the 4× asserted — and the failure was real but not about the strings.
+
+Switching a tone on and off is a step, and a step is broadband. It excites
+every string on the board, so the tail contained twelve strings ringing
+regardless of what frequency had been played. The measurement was reading
+its own edges.
+
+Fading the drive in and out over 250 ms removes the step. Every one of the
+twelve strings then passes, with the worst at 4.4×. The same fade is what
+the book figure uses, and the figure's caption says so, because a reader
+reproducing it without the fade will get the wrong answer.
+
+## Twelve strings
+
+Widely copied hobbyist build pages describe the palme as two banks of twelve
+strings. The peer-reviewed source (Wijnand, Boutin, Jossic & Maniguet, Forum
+Acusticum 2023) says twelve, and `k_strings = 12` with a comment saying
+which source won and why.
+
+Their tuning is not published anywhere found, so it is a parameter rather
+than a constant, and the header says that too. Guessing a tuning and
+hard-coding it would have been the same category of error as the
+twenty-four.
+
+## Where recreation begins
+
+The instruments, their dates, their excitation and their transducer type are
+peer-reviewed. The modal data is not — no ondes-specific measurement of
+either body exists in any of the four sources read — so the plate uses
+Fletcher & Rossing's free circular plate (Rayleigh's Chladni ratios at
+Poisson 0.3) and the strings use the harmonic series.
+
+This is stated in the header at the top rather than in a limits section at
+the bottom, because it changes what the object *is*: a recreation of the
+general physics, not a model of Martenot's instruments. The same applies to
+`asymmetry` and `saturation` — the source establishes that the moving-iron
+driver is nonlinear and that Thiele–Small does not describe it, and then
+does not hand over a curve. Those two coefficients are voiced by ear and
+labelled as voiced by ear.
+
+A diffuseur with both at 0 is a linear resonator and is missing a real
+stage. That is a choice the caller may make, and the header says so rather
+than forcing a minimum.
+
+## Checkpoint
+
+A textbook resonator chosen for three properties that between them remove
+the limiter, the DC blocker and the decay/level coupling. A bitwise null
+that pins the transducer upstream of the body, which required making an
+equal-power blend exact at its endpoints. A saturator bound corrected from
+`1/sat` to `2/sat` because a DC blocker after an asymmetric nonlinearity
+buys headroom, not just centring. A selectivity test that had to stop
+measuring its own on/off step. And a provenance line drawn where the
+published sources actually stop.
diff --git a/book/src/machine/fuzz.md b/book/src/machine/fuzz.md
new file mode 100644
index 0000000..b7b822b
--- /dev/null
+++ b/book/src/machine/fuzz.md
@@ -0,0 +1,148 @@
+# Two stages and a knee: `fuzz.h`
+
+This appendix is mostly about two mistakes, because the DSP itself is a
+published recipe followed closely and there is little to explain about it
+that Yeh, Abel and Smith's DAFx-07 paper does not explain better. What is
+worth recording is what went wrong on the way, since both failures are the
+kind that recur.
+
+## The recipe, briefly
+
+Two `stage` objects, each a conditioning highpass, a gain, a memoryless
+curve, and an equalization lowpass — the paper's cascade, twice. A `tone`
+section of three RBJ biquads outside the nonlinearity. A `pedal` that runs
+the pair inside an oversampled region, DC-blocks, voices, and trims.
+
+The curve is `shape(x, k) = tanh(kx)/tanh(k)`, chosen from the family the
+paper itself compares against a tabulated diode DC curve (tanh, arctan, a
+tanh approximation). The normalization matters more than the choice: dividing
+by `tanh(k)` fixes the output at full scale for unit input at every knee, so
+`edge` changes the shape of the corner without moving the ceiling.
+
+## Mistake one: small-signal gain compounds
+
+The tanh family's slope at the origin is `k/tanh(k)`. It is greater than one
+and it grows with the knee — 1.7 at knee 1.6, 2.1 at knee 2, 12 at knee 12.
+
+In one stage that is a curiosity you can absorb into the gain mapping. In a
+cascade it multiplies. The first implementation put a fixed ×2.2 in front of
+a knee-3 curve, so the second stage's effective small-signal gain was about
+6.6, and with the drive floor at +6 dB the limiter was *already saturated
+with the gain knob at zero*. The measured harmonic-to-fundamental ratio was
+0.401 at gain 0 and 0.408 at gain 1: the knob did essentially nothing.
+
+The reason this is worth a paragraph is that it is inaudible as a bug. The
+object sounded like a distortion pedal at every setting, because it *was* one
+at every setting. Only a swept measurement showed the knob was inert. The fix
+was to lower the drive floor below unity (−12 dB) and the second stage's
+fixed gain to 0.5; the ratio now runs 0.010 → 0.358.
+
+The general lesson, stated for the next cascade someone builds here: **a
+waveshaper's small-signal slope is part of the gain structure**, and if the
+curve family's slope depends on a user-facing parameter, that dependence
+propagates to every stage downstream of it.
+
+## Mistake two: the house oversampler, and a hypothesis that was right
+
+The oversampling chain in `tap.ladder~`, `tap.svf~` and `overdrive.h` is
+zero-stuff plus a 4th-order Butterworth, cut at 0.45 of the base rate
+normalized to the oversampled rate. This file started as a copy of it.
+
+Measured, that was wrong here: alias energy at the fold frequencies came out
+**worse at 4× than at 2×** (1.7e-2 against 2.8e-3). Twenty-four dB per octave
+leaves content just above the base Nyquist barely attenuated, and a higher
+factor pushes more clipper-generated content into exactly that band before
+decimation. Moving to 8th order improved 4× about sixfold.
+
+It did not fix the ordering. An earlier draft of this file recorded that as an
+open question with one hypothesis ruled out and one surviving:
+
+- **Ruled out: numerics.** At 8× the filters are cut at 0.056 normalized,
+  where biquad poles crowd the unit circle. Tested by running the cascade's
+  impulse response out to 400,000 samples at each factor; it decays cleanly to
+  denormal every time.
+- **Surviving: imaging.** Zero-stuffing by N leaves N−1 images for a single
+  filter to suppress; residual images entering a *nonlinearity* intermodulate
+  with the signal into products that are not harmonics of the input, which is
+  precisely what the probe measures, and there are more of them at higher N.
+
+`ondes.h` then supplied evidence for the survivor without being built to:
+same 8th-order chain, comparably hard nonlinearity, but a **source** with
+nothing zero-stuffed on the way up, and its sequence never reversed.
+
+**Acting on it settled it.** The chain is now one 2× stage per doubling, each
+filtering at 0.225 of its own operating rate — a corner that never tightens
+however deep the cascade goes, which is the whole difference. Same probe, same
+material, only the resampler changed:
+
+| tone | old 4× | old 8× | new 4× | new 8× |
+|---|---|---|---|---|
+| 3733 Hz | 7.4e-4 | 1.8e-3 | 2.1e-5 | 2.2e-5 |
+| 4409 Hz | 7.9e-4 | 2.1e-3 | 3.7e-7 | 3.7e-7 |
+| 5171 Hz | 9.2e-4 | 2.3e-3 | 2.0e-7 | 1.9e-7 |
+| 6421 Hz | 5.8e-4 | 1.3e-3 | 3.9e-7 | 3.6e-7 |
+| 9337 Hz | 2.9e-4 | 6.2e-5 | 1.8e-6 | 2.4e-8 |
+
+The worst step-up past 2× is a ratio of 1.017 — flat, where the old chain ran
+up to 3× worse per doubling. The 2× column is unchanged in both, as it must
+be: one doubling is one stage either way, and that it *is* unchanged is the
+best available check that nothing else moved.
+
+The cost is 3.16 % of a core at 8× against 3.02 % before. The filters are
+cheap next to the clipper they surround, which is worth knowing in advance
+next time this trade looks expensive.
+
+## Mistake three: one tone is not a sweep
+
+Every number in the two sections above — the 4th-order finding, the reversal,
+the "2× is best" default that shipped — came from **a single test tone at
+3733 Hz**. The tone was chosen carefully, for good reasons that are still
+good: it does not divide the sample rate, and its folds land where nothing
+else lives. It was still one tone.
+
+Swept properly, 2× does not merely fail to be best. It collapses above about
+6 kHz, and at 10499 Hz it measures *worse than no oversampling at all*
+(1.7e-1 against 1.5e-1), because the clipper's low harmonics already exceed
+the base Nyquist there. The default that shipped was safe only for material
+that stays below 6 kHz.
+
+This is the same error as the two in the next section, one level up: those are
+about choosing a bad probe, this is about choosing too few. A probe that is
+correct at one point on the input domain tells you about that point. The fix
+is not cleverness, it is a `for` loop over tones, and it costs seconds.
+
+Two properties of this particular probe bound where the loop can go, and both
+are now written down next to it: it only measures folding at all above about
+3 kHz, since below that harmonics 8–13 are still under Nyquist and it reads
+real harmonics instead; and tones that are simple rational multiples of the
+sample rate stack folds on top of each other or put one exactly at Nyquist,
+where it reads nonsense.
+
+## Two ways to measure aliasing wrong
+
+Both were committed before being caught, and both are recorded in
+`fuzz_test.cpp` because they are easy to repeat.
+
+**Choosing a tone that divides the sample rate.** The first alias test used
+3 kHz at 48 kHz. Every harmonic of 3 kHz folds back onto another harmonic of
+3 kHz, so every alias hides exactly underneath legitimate content and the
+probes read nothing at all. The test passed happily while measuring noise.
+3733 Hz puts the folds where nothing else lives.
+
+**Probing too close to the fundamental.** Two of the original probe
+frequencies sat a few hundred Hz from a full-scale tone. What they measured
+was the window's spectral leakage — around 1e-3, which swamped the aliasing
+underneath it. Probes have to be far enough out that leakage from the loudest
+component is below the thing being measured.
+
+## Checkpoint
+
+A published cascade, followed closely. One curve whose normalization keeps
+the knee from becoming a volume control. A gain floor set below unity because
+small-signal slope compounds across stages — the bug that sounded fine. An
+8th-order oversampling filter because the house 4th-order one measured worse,
+and a cascade of 2× stages because a single zero-stuff by N was what made
+bigger measure worse — the imaging hypothesis, recorded as open here for two
+waves, then confirmed by acting on it. And a default of 4× rather than 2×,
+because the 2× default had been generalized from one test tone and collapses
+above 6 kHz.
diff --git a/book/src/machine/ondes.md b/book/src/machine/ondes.md
new file mode 100644
index 0000000..826e1c5
--- /dev/null
+++ b/book/src/machine/ondes.md
@@ -0,0 +1,215 @@
+# A citation, an identity, and a sign: `ondes.h`
+
+Three classes — `triode`, `detector`, `voice` — and three things worth
+recording about how they got here. One stage turned out to need no design
+decisions at all. One approximation turned out to be an exact identity. And
+one sign error made a distortion knob run backwards.
+
+The circuit is Najnudel, Hélie, Roze & Boutin, "Simulation of an ondes
+Martenot circuit", IEEE/ACM TASLP **28**, 2651–2660, 2020, modelling
+instrument No. 169 as five port-Hamiltonian stages. This file is **not**
+that: their full solve runs at 768 kHz and their plugin costs 85 % of a
+laptop core. What it takes from them is their own published reductions plus
+their published component values, and the header says which is which.
+
+## The tube is a citation, not a design
+
+The plan framed the valve stage as a choice: a published grid-conduction
+curve, or the tanh family with an asymmetry bias voiced by ear. It is
+neither, and finding that out took nothing more than reading the paper
+properly.
+
+The paper names a tube model — the **enhanced Norman Koren** model (Koren,
+*Glass Audio* 8(5), 1996, with Cohen & Hélie's grid-current branch, AES 129,
+2010) — writes out its three equations, and publishes parameter sets in
+Table II **fitted to the actual valves in ondes No. 169**, together with
+each stage's supply voltage, cathode resistor and plate load.
+
+So there was nothing to voice. `k_6f5`, `k_6c5`, `k_2a3`, `k_op_demod`,
+`k_op_preamp` and `k_op_power` are Table II transcribed, and the header says
+they are the citation.
+
+A stage is then the static solution of `ipc(vpc, vgc) = (Vbias − Vk −
+vpc)/Rp` on the load line, with cathode bias `Vk = Rk·Ipc` found at the
+quiescent point. That is a **memoryless nonlinearity** in exactly the
+DAFx-07 sense, which matters for a practical reason: tabulating it is not an
+approximation of the model, it *is* the model. The table is rebuilt on a
+tube or operating-point change and read with linear interpolation, so the
+audio path costs a lookup rather than a root find.
+
+The published points bias sanely — the 6C5 demodulator lands at Vk 2.70 V,
+Vp 86.5 V, Ip 2.70 mA, gain 4.86 — which is its own small confirmation that
+the transcription is right.
+
+## The sign that made the drive knob run backwards
+
+The stage must **invert**, as a real common-cathode stage does, and this is
+load-bearing rather than cosmetic. The valve's asymmetry acts on whichever
+side of the waveform reaches its grid. An early cut normalized the output by
+the *signed* small-signal gain, which quietly un-inverted the stage, so the
+curve's lopsidedness landed on the wrong half of the waveform.
+
+The symptom was unambiguous once measured: turning `drive` **up** reduced
+total harmonic content. A distortion control that gets cleaner as you push
+it is not a subtle bug, but it is only visible in a sweep — at any single
+setting the object sounded like a valve.
+
+Two changes fixed it. The curve is now the true (inverting) plate swing, and
+normalization is by the gain's *magnitude*. And `voice::core` applies the
+demodulator's own grid-leak inversion explicitly — a growing envelope drives
+that grid toward cutoff — so the two inversions put the demodulator's plate
+in phase with the envelope while the curve has meanwhile acted on the
+underside. `drive` now sweeps harmonic content 0.221 → 0.344, monotonically.
+
+The gain-staging lesson from `fuzz.h` was applied here from the start rather
+than learned again: each stage is normalized by its own small-signal gain,
+so drive changes the distortion and not the level.
+
+## The detector is an identity, not a simplification
+
+The plan's instruction for this stage was "synthesize the difference tone
+directly as a sinusoid", and catching that as a mistake is the most valuable
+thing this build did.
+
+The paper's 0.03 % distortion figure and its licence to replace oscillators
+with a sinewave generator apply to the **oscillators**. The demodulator is
+not a mixer handing you a difference tone; it is an envelope detector, and
+the envelope of `cos(Φ) + cos(Φ − φ)` is `2|cos(φ/2)|`, whose Fourier series
+puts H2 at −14.0 dB, H3 at −21.3 dB and H4 at −26.4 dB. Synthesizing a
+sinusoid would have discarded the instrument's largest single source of
+harmonics before any of the modelled stages ran.
+
+What replaces the carrier is better than a simplification. For amplitudes 1
+and `depth`, the envelope is exactly
+
+```
+sqrt(1 + depth² + 2·depth·cos(2π f t))
+```
+
+so the 80 kHz carrier drops out of the arithmetic rather than being
+approximated away. Running the published RC detector on that closed form —
+instant attack through the diode, 200 µs decay through R4·C21 — reproduces a
+full heterodyne-plus-diode-plus-RC simulation to **within 0.10 dB on every
+harmonic** at every pitch tried (`ondes.ipynb` §2).
+
+There is one systematic difference, and it is worth knowing it is systematic
+rather than noise: the closed form sits a uniform **3.0–3.2 % high**,
+because a follower chasing real carrier half-cycles never quite reaches the
+peak between them. On a synthesizer with a level control, that is a
+constant.
+
+The detector's characteristic pitch dependence comes along free, out of the
+same 200 µs: H2 runs −14.0 dB at A2 to −19.3 dB at A6, and the level falls
+2.0 dB across those five octaves.
+
+And a bonus nobody planned: because the closed form is parameterized by the
+two oscillator amplitudes, **oscillator balance becomes a physical timbre
+control**. `depth` is a real mismatch between two real oscillators, not an
+invented knob.
+
+## Three measurements that lied, and what they were doing
+
+All three were committed to a notebook or a header before being caught.
+
+**Too few periods.** The first measurement of the detector's harmonics at
+low pitch used a window holding about 2.75 periods of the fundamental.
+Spectral leakage at that resolution dominated everything, and it produced a
+confident, wrong claim in the header: "−9.8 dB at A2, level falls 9.7 dB".
+Redone with 60 cycles, the real answer is −14.0 dB and 2.0 dB. Both numbers
+were in a shipped header before the recheck.
+
+**Probing where the answer is exactly zero.** The aliasing scenario probed
+half-integer harmonics of a tone that was exactly periodic in the analysis
+window. Those bins are analytically zero, so it measured −281 dB and passed
+triumphantly. Fixed by computing the actual fold frequencies for a tone at
+2637 Hz — deliberately not a submultiple of 48 kHz — and skipping folds that
+land near real harmonics. This is the same family of error `fuzz.h` records
+under "choosing a tone that divides the sample rate", committed again in a
+different disguise.
+
+**Stopping the sweep at the first plateau.** The header initially claimed
+"4× is the knee, then flat". The notebook's own more careful run — settled
+state, 131072-point Hann — showed 8× continuing to improve in the top
+octave. Corrected to "never worse", with the full table in the header, the
+test comment and the notebook.
+
+## The evidence that closed an open question in `fuzz.h`
+
+`fuzz.h` measured its oversampling sequence going the wrong way — 4× worse
+than 2× — and had left an untested hypothesis behind: that the culprit is
+*imaging*, since zero-stuffing by N leaves N−1 images for one filter to
+suppress, and residual images entering a nonlinearity intermodulate into
+products that are not harmonics of the input.
+
+This file runs the **same** 8th-order Butterworth chain around a comparably
+hard nonlinearity, and its sequence never reverses:
+
+| tone | 1× | 2× | 4× | 8× |
+|------|----|----|----|----|
+| 587 Hz | −79.3 | −91.2 | −104.5 | −103.8 |
+| 1175 Hz | −65.8 | −77.2 | −90.6 | −92.5 |
+| 1760 Hz | −57.6 | −70.9 | −81.1 | −82.2 |
+| 2637 Hz | −51.1 | −61.4 | −71.8 | −83.8 |
+| 3520 Hz | −45.4 | −56.8 | −67.0 | −74.2 |
+
+The difference between the two files is exactly the hypothesis: this object
+is a **source**. Nothing is zero-stuffed on the way up — the detector simply
+runs fast — so there are no images at all.
+
+That was evidence, not proof — the nonlinearities differ too, and one
+confounded comparison does not settle a question. But it was the first
+evidence either way, and it pointed somewhere specific enough to act on.
+
+**Acting on it settled it.** `fuzz.h` now cascades one 2× stage per doubling
+instead of zero-stuffing by N once, each stage filtering at a corner that
+never tightens however deep the cascade goes. Its reversal is gone — worst
+step-up past 2× is a ratio of 1.017 — and its 4× and 8× improved by two to
+four orders of magnitude, for about 5 % more CPU. This file needed no change,
+having no upsampler to fix.
+
+Worth naming the shape of it, because it is not the usual one: the evidence
+that resolved a two-wave-old open question in one file came from **building a
+different file that happened to differ in exactly the right variable**. It
+was not designed as an experiment. It was noticed, written down in both
+headers as evidence rather than proof, and left where the next person would
+trip over it.
+
+## A wrapper test that found a kernel bug
+
+`tap.ondes~`'s Min-level test asserts something a patcher would otherwise
+file as a bug report: with the key at rest, the object is *exactly* silent.
+It failed.
+
+`voice::set_smooth_ms` set the voice's own ramps but never forwarded to
+`touche::key`, which keeps its own slew. So a key sitting at zero with
+`@smooth 0` still sounded for 20 ms after every parameter touch.
+
+This is the two-layer split working the way it is supposed to. The kernel
+suite tests DSP promises; the wrapper suite tests what a patcher will
+actually observe, and those are not the same set. The fix landed in the
+kernel with its own scenario, not in the wrapper.
+
+## What this file will not do
+
+The real instrument has switchable **waveform registers**. Their filter
+shapes are in none of the sources obtained. Adding them from imagination is
+the one thing this file is careful not to do, and the omission is stated in
+the header, the object description and the reference page rather than left
+as a gap someone might charitably fill later.
+
+Two controls *are* choices — where the intensity key sits (`keyplacement`)
+and the coupling transformer's winding sense (`polarity`) — because the
+paper's five stages do not settle either. Both are labelled as choices, and
+both were measured to confirm they are audible ones: about 0.09 and 0.12 of
+total harmonic content respectively.
+
+## Checkpoint
+
+A stage that required no design because the paper published the model and
+its fitted parameters. A detector that is exact rather than approximate, and
+cheaper than the thing it replaces. One sign error that inverted the meaning
+of a distortion knob and was invisible at any single setting. Three
+measurements that lied in three different ways, all recorded. The evidence that closed `fuzz.h`'s
+oversampler question, from a file that happened to differ in exactly the
+right variable and was not built as an experiment. And a wrapper test that found a kernel bug,
+which is the split doing its job.
diff --git a/book/src/machine/scrub.md b/book/src/machine/scrub.md
new file mode 100644
index 0000000..6943a24
--- /dev/null
+++ b/book/src/machine/scrub.md
@@ -0,0 +1,153 @@
+# One tape, two read patterns: `scrub.h`
+
+When `stammer.h` shipped, its header made a promise in its own limits
+section: slices play at ±1 rate, a performable pitch-bending playhead over
+live capture is a different object, and *sharing this capture is the plan*.
+This file is that promise being kept, and it is worth recording that the
+sharing turned out to be literal. `scrub.h` includes `stammer.h` and uses
+`stammer::capture` itself — one `tape_loop.h` reel under an advancing write
+head — rather than keeping a second copy of the same idea.
+
+The only thing the stutter had to grow was `capture::read_frac`, a
+fractional Hermite read. Its ±1-rate slices never needed one.
+
+The rest of the file is two classes: `head`, the grain scheduler, which owns
+the grain pool, the hop clock and the spray dice and reads a capture it does
+not own; and `machine`, which is one capture, one head, the freeze gate, the
+drift and the balance. Same parts-then-composition habit as `tapecho.h` and
+`stammer.h`, for the same reason: `head` is a read pattern, not a machine,
+so it is testable and composable without being an external.
+
+## The defect that measurement caught and nothing else could
+
+The first cut anchored every grain at the position. That is the obvious
+thing to do — the position is where the user is pointing — and it is wrong
+in a way that is genuinely hard to hear.
+
+Here is the mechanism. If every grain's origin is `write_head − lag`, then
+origins advance at the *write head's* speed, which is exactly 1. Each grain
+then plays from its origin at `rate`. Inside a grain the pitch is correct.
+Across grains the average read rate comes back to 1, because the origins
+reset it every hop.
+
+So a steady tone comes out **at its original pitch**, with a comb of
+grain-rate sidebands around it. The pitch knob did not transpose. It added
+texture, and texture is what you expect from a granulator, which is exactly
+why no amount of listening was going to find this.
+
+The fix is a phase-continuous read head: the origin advances at `rate`, and
+is pulled back toward the position only once it has wandered more than
+±1.5 grains. Every pull-back is a splice, which is the cost, and the bound
+is chosen by sweep rather than taste. Band energy retained around the
+transposed pitch, at wanders of ±0.5 / ±1 / ±2 / ±3 / ±4 grains:
+
+| wander (grains) | mean | worst |
+|-----------------|------|-------|
+| ±0.5 | 0.933 | 0.716 |
+| ±1 | 0.958 | 0.820 |
+| ±2 | 0.965 | 0.874 |
+| ±3 | 0.990 | 0.918 |
+| ±4 | 0.993 | 0.940 |
+
+Flat past 3, and every extra grain of wander is a grain of position error,
+so `k_wander_grains = 3.0`.
+
+The unity case is special-cased to zero error rather than accumulated,
+which is what keeps the null exact: at `rate == 1` there is nothing to
+wander from.
+
+## Measure the band, not the bin
+
+This is the second thing worth carrying out of this file, and it nearly
+inverted the conclusion above.
+
+A single-bin probe reads the *fixed* kernel as badly broken. The splices
+spread the transposed partial into a comb a few hertz wide; a
+rectangular-window Goertzel sitting on one line saw **0.02** where the band
+figure was **0.43**. Had that been the first measurement taken, the fix
+would have looked like the bug.
+
+Measured properly — energy in a ±15 Hz band around the transposed pitch,
+against the same band of a perfect shifter — 98.8 % lands where it should,
+worst case 91.7 %. What the splices cost is concentration, not pitch:
+92.0 % as focused as a clean shift, 75.0 % at worst.
+
+The general rule, stated for the next time someone here measures a
+pitch-shifter: **if the process can smear a partial, a single-bin probe is
+measuring the smear, not the partial.** Integrate a band wide enough to
+contain the artifact you already know about.
+
+And then, immediately, the same mistake in its other half. The comparison
+against `tap.pitchaccum~` used that ±15 Hz band unchanged across the whole
+sweep — but ±15 Hz is about 115 cents wide at 220 Hz and only 26 cents at
+932 Hz, so at the top of the sweep the probe was again narrower than the
+process it was measuring, and it produced two readings of 0.0001 and 0.0006
+that were recorded as near-total cancellations of a shipped object. Widened
+to a constant 3 %, they read 0.63 and 0.85 and no cancellation exists. The
+retraction and what survives it are [issue #33](https://github.com/tap/TapTools/issues/33).
+
+So the rule has a second half: a band wide enough **in the units the process
+works in**. A pitch shifter works in cents. A fixed hertz window is a
+different width at every pitch, and the place it is narrowest is exactly
+where a shifter's error is largest.
+
+Two related mistakes are recorded here because both were committed:
+
+- **Analysing mostly silence.** The first wander sweep ran 1 second of
+  material with a 900 ms position lag, so most of the analysed window was
+  tape that had not been written yet. Extended to 3 seconds, analysing the
+  last third.
+- **Feeding a discontinuity into the test.** A slew test drove the object
+  with a sine and then, mid-test, called `process(0.5)` with a literal DC
+  sample to change a parameter. That step was an input transient, and the
+  0.48 jump it produced was the test's own fault. Continuous tone index,
+  and the same bug was then fixed pre-emptively in `diffuseur_test.cpp`.
+
+## The null, and the arithmetic that makes it exact
+
+Hann satisfies constant-overlap-add at hop = size/overlap, so the window sum
+is exactly 1 at overlap 2 and above, and normalization is `2/overlap` so the
+level holds across settings. With pitch at unity, spray at zero and the
+position on a whole sample, the object is the input delayed to 4.4e-16.
+
+It is exact only when `size` divides evenly by `overlap`, because the hop is
+an integer number of samples; otherwise a small periodic ripple survives in
+the window sum. It is inaudible at musical sizes, and it is why the null
+test chooses the numbers it does (480 samples of lag, 96 of size) rather
+than round milliseconds.
+
+The `mix` control needed the same care as the diffuseurs' — an equal-power
+blend written as `cos`/`sin` does not return exactly zero at the endpoint,
+and a wiring null that reads 6.1e-17 instead of 0 is not a null. Both ends
+are short-circuited exactly.
+
+## The grain pool starves rather than steals
+
+Shrinking `size` sharply while grains are in flight can leave every slot
+busy at the moment the next grain is due. That grain is **dropped**, not
+allocated by stealing a slot from a grain mid-window, because a steal cuts a
+Hann window in half and clicks. The audible cost is a momentary dip, bounded
+by the pool being two slots deeper than the maximum overlap.
+
+## A limit that is not fixed, on purpose
+
+A grain born `lag` samples behind the write head and playing at rate `r`
+reaches `lag − size·(r−1)` behind it by its end. Transpose up with the
+position near the live edge and the grain's tail runs off the front of the
+tape into the oldest material.
+
+Nothing clamps this. Clamping would silently bend the pitch to keep the
+grain in bounds, which is a worse failure than the seam — the object would
+stop playing the interval you asked for and never say so. The constraint is
+documented (`keep the position at least size·(rate−1) back`) and left to the
+player.
+
+## Checkpoint
+
+One capture, shared literally with the stutter, plus one fractional read
+that the stutter did not need. A phase-continuous read head, because
+anchoring grains at the position quietly cancels the transposition — the
+defect of this file, invisible to listening and obvious to a sweep. A wander
+bound measured rather than chosen. And a measurement lesson worth more than
+the kernel: a single-bin probe on a smeared partial reads the fix as the
+bug.
diff --git a/book/src/machine/stammer.md b/book/src/machine/stammer.md
new file mode 100644
index 0000000..a352306
--- /dev/null
+++ b/book/src/machine/stammer.md
@@ -0,0 +1,147 @@
+# Dice you can replay: `stammer.h`
+
+Most kernels in this library are hard to get wrong quietly: a filter with a
+bad coefficient sounds bad. A stutter is not like that. It has three
+interacting integer clocks — a grid countdown, a slice origin, and a
+playback head — and if any one of them is off by a sample the object still
+sounds *fine*. It stutters. It grooves. It is just wrong in a way no amount
+of listening will surface.
+
+So the interesting content of this appendix is not the DSP, which is a
+buffer and some dice. It is how you pin three clocks at once.
+
+## The pinned-dice identity
+
+Every random draw in the kernel has a setting at which its outcome is
+forced. Fire probability 1 always fires. `divisions` 1 always picks the
+whole step. `repeats` 1 always plays one pass. `reverse` 0 never reverses.
+`jump` 0 never reaches back. `fade` 0 leaves the material alone.
+
+Set all six and the machine becomes deterministic *regardless of the seed* —
+and what it must then be is not a vague "sensible output" but a specific,
+checkable thing: **exactly a one-step delay**. At each grid point it grabs
+precisely the step that just went past and plays it once, so
+
+```
+y[i] == x[i - step + 1]
+```
+
+for every sample after the first grid point, bitwise.
+
+That single assertion is worth more than three separate off-by-one tests,
+because it fails if the grid countdown fires a sample early, if the origin
+arithmetic reaches one sample too far back, if the playback head starts at
+the wrong index, *or* if any two of those are wrong in ways that would
+cancel in a looser test. It is also cheap to reason about, which matters:
+a test you cannot re-derive on a whiteboard is a test you will eventually
+delete instead of fixing.
+
+The `+ 1` in that expression is not a fudge. The write head holds the *next*
+write position, so after recording sample `i` the newest available sample
+sits at position `i`, and a slice of length `step` grabbed at grid point
+`k·step` reads positions `k·step + 1 - step` upward. Getting that constant
+right by derivation rather than by nudging until the test passed is the
+whole discipline; a test you tune to the implementation pins nothing.
+
+Two smaller identities sit alongside it. With `reverse` 1 the same grab
+reads end-first, so `y[k·step + j] == x[k·step - j]` — the mirror of the
+first, which catches a reversed-index off-by-one that the forward test
+cannot see. And with `repeats` above 1, every output sample must be *either*
+a fresh grab or a bit-exact copy of the block one slice-length earlier;
+nothing else is legal, because a slice in flight is never interrupted. That
+invariant covers the repeat machinery without needing to know how many
+passes the dice chose.
+
+## The draw order is part of the ABI
+
+`maybe_fire()` draws in a fixed order: fire, division, repeat count,
+reach-back, then the first reverse coin. Each subsequent repeat draws its
+own reverse coin as it starts.
+
+That order is not an implementation detail — it is what "a seed is a
+performance" means. Reordering two draws, or adding a draw in the middle,
+silently changes every render anyone has ever made with a given seed.
+`garden.h` established the same discipline for the gardener; this file
+inherits it, and the comment above the function says so in as many words so
+the next person to add a parameter knows to append rather than insert.
+
+The disabled case is the sharp end of it. At `density` 0 the function
+returns *before* drawing anything:
+
+```cpp
+if (m_density <= 0.0) {
+    return; // the dice are never rolled, so the seed provably cannot matter
+}
+```
+
+The lazier version — draw, then compare against 0 and fail — behaves
+identically to the ear, and would be indistinguishable in almost any test.
+It would also consume one number per grid point, so the seed *would* matter:
+switch density off and on again, and the stream is somewhere else. The
+garden made this a family contract, and it is pinned here by a test that
+runs two different seeds at density 0 and requires bit-identical output.
+
+## Reading from the ring, and what it costs
+
+A slice does not copy its material. It stores an origin and reads from the
+capture ring as it plays.
+
+The alternative — memcpy the slice into a private buffer at fire time —
+would be more obviously correct, and it is what a first draft wants to do.
+It was rejected because it is a burst copy in the audio thread: half a
+second of slice is 24,000 doubles moved inside one `process()` call, a spike
+that does nothing for 47,999 other samples. Reading from the ring costs
+nothing extra.
+
+The price is a real failure mode, so the header states it: if a repeat train
+outlives the buffered history — `repeats · length + jump` beyond
+`max_history_ms` — its tail reads fresher material as the write head laps
+the origin. The kernel clamps the slice length against the bought capacity
+so it can never read *outside* the buffer, but it does not and cannot
+prevent a long train from being overtaken. Sizing the history is the
+caller's job, and the object argument exists for exactly that.
+
+## The envelope, and why the dip stays
+
+Flanks are raised sine, computed to be exactly 0 at both edges and exactly 1
+across the plateau, clamped per slice to half the slice so the two flanks
+never overlap. Repeats are sequential, not overlapped, so each junction dips
+to zero rather than crossfading.
+
+Leaving it that way was a decision. An equal-power crossfade between
+consecutive passes is easy from here — the pieces are already in the family —
+and it would smooth exactly the articulation that makes a stutter read as
+rhythm. The dip is the transient the ear locks onto. So the file documents
+it as intentional, and the test asserts the exact edges, so nobody later
+"fixes" the dip and quietly turns the object into a tremolo.
+
+## Reuse, and the component that is not a component
+
+The capture is a `tape::reel` in delay-line topology — the same class the
+tape echo uses, the same class `airport.h` runs as a true loop. Reads here
+are always at integer positions, so the family's Hermite read reduces to an
+exact sample fetch; that is slightly more arithmetic than an integer index
+would need, and it is kept anyway because it is one code path and because a
+rate-varying sibling (`tap.scrub~`, planned) needs precisely this.
+
+The randomness is `tr808::white_noise`, the family's seeded xorshift64*,
+reached through `swing_vca.h`. Nothing new was written for it.
+
+Which leaves the split: `capture` and `slicer` are separate classes under a
+thin `machine`, per the family's components-first habit. But a `slicer`
+needs a `capture` to mean anything, so — like `tapecho.h`'s `head`, and
+unlike `airport.h`'s `loop` — it is documented as a component for
+composition and testing rather than a candidate for its own external. The
+components chapter's lesson is that seams often already exist and only the
+monolith can reach them. The corollary, which is easier to forget, is that
+not every class boundary is a seam.
+
+## Checkpoint
+
+Three clocks, pinned by one identity: force every die and the machine must
+be exactly a one-step delay, bitwise. A fixed draw order, because that is
+what makes a seed a contract, and an early return at density 0 so a disabled
+generator provably cannot consume its stream. Ring reads instead of a burst
+copy, with the failure mode written down rather than papered over. And an
+envelope dip that is deliberate, tested, and therefore safe from being
+helpfully removed.
diff --git a/book/src/machine/tapecho.md b/book/src/machine/tapecho.md
new file mode 100644
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--- /dev/null
+++ b/book/src/machine/tapecho.md
@@ -0,0 +1,114 @@
+# Composition, not construction: `tapecho.h`
+
+This is the shortest appendix in the book, and that is the point of it.
+
+`tape_loop.h` was written for the Eno family — one shared header holding a
+reel, a transport, and a wear stage, factored out because `discreet.h` and
+`airport.h` needed the same four pieces twice. The claim implicit in
+factoring it that way was that it is a *library*: machinery that a machine
+nobody had written yet could be built out of. `tapecho.h` is the test of
+that claim, and the result is worth recording precisely, because "we
+extracted a shared header" is easy to say and rarely checked.
+
+The result: **`tape_loop.h` needed no changes at all.** Not a new method,
+not a widened clamp, not a friend declaration. A tape echo — a different
+topology, a different number of read points, a different stability regime —
+composed out of it exactly as shipped.
+
+## What the file actually contains
+
+Two classes and no DSP that was not already in the library.
+
+`head` is a read position with three ramps: a `ratio` along the tape path, a
+`level`, and a `pan`. Its `read()` takes a reel it does not own, the motor
+span, and the shared transport offset, and accumulates a panned contribution
+onto the stereo busses. It is a component in the airport.h sense — a piece
+the monolith is made of, reachable for testing — but honestly labeled as
+*not* standalone-external material: a head without a reel is not a machine,
+it is an index. That distinction is worth keeping straight, because the
+components chapter's lesson ("the monoliths were monoliths by accident") can
+be over-applied. Some seams are real and some are arithmetic.
+
+`machine` owns one reel in delay-line topology, one `tape::wow_flutter`, one
+`tape::wear`, and four heads. Its `process()` reads the heads, applies the
+regeneration cap, writes the record head, and mixes. There is nothing else
+in it.
+
+## The geometry: one motor
+
+Each head's delay is `span_samples * ratio - offset`, where `offset` is the
+transport error. Two decisions hide in that one line.
+
+The first is that `span` is defined as the delay of a *ratio-1.0* head
+rather than as "the delay time", which is what makes the motor a motor: one
+multiply per head and the whole layout scales together, as a tape speed
+does. The alternative — per-head absolute times — would have made a speed
+change into four coordinated parameter moves and lost the doppler for free.
+
+The second is that `offset` is subtracted once, shared by every head. That
+is physically right for a single transport (one capstan error displaces the
+whole tape path) and it is also the cheap answer, so it is worth saying
+plainly that the per-head phase differences of a real multi-head transport
+are *not* modeled. It is a documented limit, not an accident.
+
+## The stability inversion, one step further
+
+`machine/tape.md` derived why `discreet.h` may run regeneration at exactly
+1.0: `wear`'s saturator is bounded by 1/drive, so the loop is bounded no
+matter the gain. That derivation does not stop at 1.0 — nothing in it does.
+So this kernel lets regeneration reach `k_regen_max_driven` (1.5), and the
+tape is bounded by `|in|max + regen/drive` at any setting.
+
+The subtlety is the boundary. That guarantee exists *only while the
+saturator is engaged*, and `drive` is a ramped parameter a performer can
+take to zero mid-howl. At drive 0 the wear path is exactly linear with
+|H| ≤ 1, so regeneration above 1.0 would grow without bound. The kernel
+therefore computes the cap **per sample** from the current drive:
+
+```cpp
+const double regen_eff = std::min(regen, (drive > 0.0) ? k_regen_max_driven
+                                                       : k_regen_max_linear);
+```
+
+Not in the setter — in the audio path, because `drive` moves during
+performance and a setter-time decision would be stale the moment it
+mattered. The stored target keeps its high value, so pulling drive to zero
+lands the loop at 1.0 and restoring drive brings the howl back. That
+asymmetry between *target* and *effective* is the one piece of state in this
+kernel that is not obvious from the header's public surface, which is why it
+is written down twice: here, and in the file's own banner.
+
+## Why the null test is the important one
+
+The suite's load-bearing scenario neutralizes the tape — no transport error,
+no regeneration — and asserts that a one-head echo is **bitwise**
+`delay.h`'s Hermite multitap.
+
+It is bitwise rather than approximate because nothing was reimplemented:
+both paths compute `time_ms * 0.001 * sr` the same way, both clamp at the
+same 2.5-sample Hermite floor, both evaluate the same polynomial at the same
+fractional position, and both apply `(pan + 1) * 0.25 * π` to the same
+`k_pi`. The multiply by a ratio of exactly 1.0 and the subtraction of an
+offset of exactly 0.0 are both exact in IEEE-754, so the arithmetic does not
+merely agree — it is the same arithmetic.
+
+That is what makes the test meaningful. An approximate null test would pass
+just as happily over a second implementation that happened to be close. A
+bitwise one only passes if the shared code is genuinely shared, which is the
+proposition on trial. The notebook runs the same comparison across the C ABI
+so the claim also holds at the boundary the externals cross.
+
+One consequence worth knowing when reading the test: at pan 0 the two busses
+are *not* bit-identical to each other, because `cos(π/4)` and `sin(π/4)`
+differ by one ulp in IEEE-754 doubles. That is inherited from `delay.h`'s
+pan law, it is the same in both objects, and it is exactly why the null test
+compares each bus against its counterpart rather than comparing left to
+right.
+
+## Checkpoint
+
+Two classes, no new DSP, and a shared header that did not move. The motor
+geometry buys varispeed with one multiply per head; the regeneration cap
+lives in the audio path because the thing it depends on is performed; and
+the null test is bitwise because being bitwise is the only version of that
+test that proves anything.
diff --git a/book/src/ondes.md b/book/src/ondes.md
new file mode 100644
index 0000000..8c4934d
--- /dev/null
+++ b/book/src/ondes.md
@@ -0,0 +1,257 @@
+# The instrument that is not a synthesizer
+
+Three objects in this chapter — `tap.ondes~`, `tap.triode~` and
+`tap.touche~` — and one instrument. The Ondes Martenot, 1928, the thing
+Messiaen wrote for and Jonny Greenwood plays: a keyboard you can also play
+with a ribbon on a ring, and a pressure key in the left hand that is the
+whole dynamic range of the instrument.
+
+The plan for this family assumed it would be an oscillator with waveform
+switches. It is nothing of the kind, and finding that out changed every
+decision below.
+
+The Ondes Martenot is **heterodyne**. Two oscillators run near 80 kHz, one
+fixed and one moved by the ribbon; they are summed, and the note you hear is
+the envelope of their beating. Najnudel, Hélie, Roze and Boutin, who
+modelled instrument No. 169 stage by stage (IEEE/ACM TASLP 28, 2651–2660,
+2020), measure those oscillators at about **0.03 % second harmonic** even
+coupled to the rest of the circuit. They are essentially pure sinewaves.
+Every bit of the instrument's character therefore comes from what happens
+*after* them: the demodulator, two valve stages, the intensity key, and the
+diffuseur.
+
+Companion material: the executed notebooks `ondes.ipynb` and `touche.ipynb`,
+`tests/ondes_test.cpp` and `tests/touche_test.cpp`, and the
+`radiohead_render` scenes `ondes_stages`, `ondes_ribbon`, `ondes_diffuseurs`
+and `triode_tubes`.
+
+## The biggest source of harmonics is not a valve
+
+Two oscillators of equal amplitude sum to an envelope of `2|cos|`. That is
+not a sinusoid. Its Fourier series puts the second harmonic **14.0 dB**
+below the fundamental, the third **21.3 dB** down and the fourth **26.4 dB**
+down — a substantial harmonic series generated before anything nonlinear
+touches the signal.
+
+![Two panels: the envelope of two summed oscillators at depths 1 and 0.5, and the harmonic levels of |cos| — −14, −21, −26, −30, −33 dB](images/ondes/envelope.svg)
+
+*The demodulator is the instrument's largest single source of harmonics, and it is upstream of every valve.*
+
+This is why `tap.ondes~` does not synthesize a difference tone. Generating
+the note as a sinewave and distorting it afterwards would throw away the
+part of the timbre that arrives for free — and it is an easy mistake to
+make, because the circuit paper *does* say the oscillators can be replaced
+by a sinewave generator. That licence applies to the **oscillators**, not to
+the demodulator.
+
+The carrier is not simulated either, and that is not a compromise. For
+amplitudes 1 and `depth` the envelope is exactly `sqrt(1 + depth² +
+2·depth·cos φ)`, so the 80 kHz disappears from the arithmetic rather than
+being approximated away. Running the published RC detector on that closed
+form matches a full heterodyne-plus-diode-plus-RC simulation to within
+**0.10 dB on every harmonic** at every pitch tried.
+
+`depth` is that second amplitude, and it turns out to be the cheapest real
+timbre control in the object. At 1 the envelope closes completely and the
+series is full; below 1 it never closes and the tone thins toward a
+sinusoid. It is a mismatch between two real oscillators, not an invented
+knob.
+
+## `detect` — and why the instrument thins as it climbs
+
+The detector is the published one: a triode grid near zero bias conducts on
+positive half-cycles and charges instantly, and R4 × C21 = 1 MΩ × 200 pF
+discharges it — a **200 µs** time constant, which is `@detect 0.2`.
+
+That single number carries the instrument's pitch character, because an RC
+that slow cannot follow a fast envelope back down. Measured here, the second
+harmonic runs from −14.0 dB at A2 to −19.3 dB at A6, and the level falls
+2.0 dB across those five octaves. The ondes gets purer and quieter as it
+goes up, and it does so for a reason you can point at in a schematic.
+
+## The ribbon is linear in semitones
+
+The circuit paper's Eq. 7 gives the variable oscillator's capacitance
+against ribbon displacement, and what falls out is
+`f = 55 Hz · 2^(d / 12·d₀)`.
+
+So `@ribbon` is **semitones above A1**, not Hz. A hand moving at constant
+speed makes a constant-rate glissando; nothing quantizes, and nothing
+should. This is why an ondes glide sounds the way it does, and it is the one
+place where taking the units from the paper rather than from convention
+changes how the object feels to play.
+
+## `tap.touche~` — 50 dB in four and a half millimetres
+
+The intensity key is a graphite-and-mica powder bag working as a rheostat:
+compress it and the number of conducting bead paths rises, so resistance
+falls. Messiaen called it the instrument's greatest invention. What the
+player feels is a well-chosen nonlinear spring.
+
+The curve in this object is **not modelled and not fitted**. Quartier,
+Meurisse, Colmars, Frelat and Vaiedelich (*Acta Acustica* 101(2), 421–428,
+2015) measured finger force, key displacement and sound simultaneously on
+instrument No. 320, and published the boundaries of the six musical nuances
+across the key's travel. Those seven points are the object, interpolated
+with monotone cubic segments that pass through every one of them.
+
+![The published key curve: 50 dB rising steeply between 4.3 and 8.8 mm, seven measured points on it, a straight line for comparison, and the bottom 4.3 mm shaded silent](images/touche/curve.svg)
+
+*Seven measured points, and the shape between them. The straight line is what a fit would have thrown away.*
+
+Three things follow, and each is a decision the paper made rather than this
+object:
+
+- **Position, not force and not velocity.** The paper states explicitly that
+  the intensity depends on displacement, and *not* on the speed of the
+  gesture. A static memoryless map is the finding, not a simplification.
+- **50 dB over about 4.5 mm**, from 4.3 mm (the instrument's noise floor) to
+  8.8 mm. The paper notes most traditional instruments rarely exceed 25 dB
+  of per-note dynamic range.
+- **The shape is not a line.** Equal 8.3 dB steps take displacement steps of
+  1.0, 0.6, 0.5, 0.4, 0.5 and 1.5 mm. It steepens through the middle and
+  flattens hard at the top.
+
+And the thing that surprises everyone who patches it: on a 0–1 control,
+**roughly the bottom 45 % of the travel is silent**. That is not a dead zone
+in the object. It is the key's own first phase — the elastic strip bending
+before it reaches the powder bag — and it is exactly why the instrument can
+be attacked so sharply, because the useful 50 dB lives in the 4.5 mm right
+after it.
+
+## `tap.triode~` — the stage is a citation
+
+The valves are where the rest of the character is, and there was nothing to
+invent. The circuit paper does not merely mention a tube model: it names the
+**enhanced Norman Koren** model (Koren, *Glass Audio* 8(5), 1996, with Cohen
+& Hélie's grid-current extension, AES 129, 2010), writes out its equations,
+and publishes parameter sets **fitted to the actual valves in ondes No. 169**
+in its Table II — 6F5 in the oscillators, 6C5 in the demodulator and
+preamplifier, 2A3 in the power amplifier — along with each stage's supply
+voltage, cathode resistor and plate load.
+
+A stage is then the static solution of the load line, which is a memoryless
+nonlinearity in exactly the DAFx-07 sense `tap.fuzz~` uses. Where the fuzz
+reaches for a tanh, this one solves a valve.
+
+![Two panels: the 6C5's plate characteristics with its load line and quiescent point marked, and the transfer curves of the three published stages, all sloping downward](images/ondes/tube.svg)
+
+*Left: the published operating point, solved. Right: the stages invert, and they are visibly lopsided.*
+
+Two properties matter before you patch `tap.triode~` on its own:
+
+- **It inverts**, as a real common-cathode stage does. That is not cosmetic.
+  The valve's asymmetry acts on whichever side of the waveform reaches its
+  grid, so the sign decides which half gets bent.
+- **It is strongly asymmetric.** At the demodulator's operating point, equal
+  grid swings either way give plate swings in a **2.17 : 1** ratio. That
+  ratio is where a triode's even harmonics come from.
+
+`drive` is normalized out of the level — the gain-staging lesson `tap.fuzz~`
+learned the hard way, applied here from the start — so turning it up gets
+dirtier rather than louder.
+
+## `drive` on the voice, and where it starts from
+
+![Harmonic content rising from 0.221 to 0.344 as drive sweeps 0 to 8, with the demodulator's own floor marked, while the fundamental level falls gently](images/ondes/drive.svg)
+
+*The valves add to a signal that was already rich. The floor is the demodulator's.*
+
+The important thing in that figure is the dotted line. At `@drive 0.` the
+tone still measures 0.221 of harmonic content, because the demodulator made
+it. The knob sweeps 0.221 → 0.344, monotonically, without the level running
+away.
+
+## The two controls that are choices
+
+Most of this object is a citation. Two controls are not, and both are
+labelled as such because both measure as audible.
+
+- **`keyplacement`** — the paper's five stages do not include the intensity
+  key, so where it sits is undetermined. After the valves (the default) it
+  is a clean output law: pressure is level. Before them, pressure drives the
+  valves: soft is clean and hard is dirty. The two differ by about 0.09 of
+  total harmonic content at a half-press.
+- **`polarity`** — the two valve stages are coupled through a transformer
+  whose winding sense is not in the source, and the sign decides which side
+  of the waveform the preamplifier's asymmetry acts on. Worth about 0.12.
+
+## `power`, and taking the authors at their word
+
+The 2A3 power stage is off by default, following the paper: they measure
+almost 5 % second harmonic there, but report its contribution as much less
+important than the two stages before it, and drop it for real-time.
+
+Measured here, switching it on moves total harmonic content from 0.248 to
+0.251 and the second harmonic by 0.1 dB. They were right, which is why it is
+a switch rather than a deletion.
+
+## `oversample`
+
+The nonlinear chain runs oversampled. Worst non-harmonic energy relative to
+the fundamental, at 1× / 2× / 4× / 8×:
+
+| tone | 1× | 2× | 4× | 8× |
+|------|----|----|----|----|
+| 587 Hz | −79.3 | −91.2 | −104.5 | −103.8 |
+| 1175 Hz | −65.8 | −77.2 | −90.6 | −92.5 |
+| 1760 Hz | −57.6 | −70.9 | −81.1 | −82.2 |
+| 2637 Hz | −51.1 | −61.4 | −71.8 | −83.8 |
+| 3520 Hz | −45.4 | −56.8 | −67.0 | −74.2 |
+
+Every doubling is worth about 12 dB up to 4×; past that it is worth 7–12 dB
+at the top of the range and nothing at the bottom, where the measurement has
+already bottomed out. Never worse. 4× is the default because that is where
+the cost stops buying uniformly; 8× is there for anyone playing the top
+octave hard.
+
+Readers of the `tap.fuzz~` chapter will notice this used to be the *opposite*
+of what that object measured. That was not a contradiction — it was the clue
+that fixed the fuzz. The appendix explains how.
+
+## What is missing, deliberately
+
+The real instrument has **waveform registers** — switchable timbres. Their
+filter shapes are in none of the sources obtained, and inventing them is the
+one thing this object will not do.
+
+There is also no diffuseur in `tap.ondes~`, because that is
+`tap.metallique~` and `tap.palme~`, and patching one after the other is how
+the instrument works anyway.
+
+## Recipes
+
+- **The instrument:** `tap.ondes~` → `tap.palme~ @mix 60`. Ribbon and key on
+  signals; that is the whole performance surface.
+- **Ribbon on a slider:** `@ribbon` takes a signal, and a `line~` from 0 to
+  36 over four seconds is a three-octave glissando that sounds like one
+  because the law is linear in semitones.
+- **The key alone:** `tap.touche~` on any source. It is a published
+  expressive gain law, and nothing about it is ondes-specific once it is
+  detached.
+- **Thin and pure:** `@depth 0.4 @detect 0.6 @drive 0.`. The envelope never
+  closes and the detector smooths what is left.
+- **Dirty on hard presses:** `@keyplacement 1 @drive 4 @polarity -1`.
+  Pressure drives the valves.
+- **A valve on a guitar:** `tap.triode~ @tube 2 @stage 2 @drive 6` — the 2A3
+  power stage, used for something it was never in this instrument for.
+
+## When it is not the right tool
+
+- **A subtractive synth.** There is no filter, no envelope generator and no
+  waveform selection here. It is one voice with a ribbon and a key.
+- **A polyphonic anything.** The instrument is monophonic; so is this.
+- **A specific recording.** The valve parameters are a fit to *one*
+  instrument's tubes, and tube-to-tube spread in 1930s valves is wide.
+
+## Checkpoint
+
+A heterodyne instrument whose oscillators are nearly pure, so the character
+lives downstream: a demodulator whose `2|cos|` envelope makes more harmonics
+than either valve does, two valve stages that are a published model with
+published parameters, and a pressure key that is a published measurement
+interpolated rather than fitted. The ribbon is linear in semitones because
+Eq. 7 says so. Two controls are choices rather than reconstructions and are
+labelled as choices. The waveform registers are missing on purpose. Every
+number here lives twice, as a cell in `ondes.ipynb` or `touche.ipynb` and as
+a pinned scenario in `tests/ondes_test.cpp` or `tests/touche_test.cpp`.
diff --git a/book/src/recipes/ondes-rig.md b/book/src/recipes/ondes-rig.md
new file mode 100644
index 0000000..64cdc17
--- /dev/null
+++ b/book/src/recipes/ondes-rig.md
@@ -0,0 +1,158 @@
+# The instrument in the corner of the room
+
+The Ondes Martenot is not a synthesizer, and the fastest way to make it
+sound like one is to patch it like one. This recipe assembles the four
+objects that make up the actual instrument — voice, key, and a loudspeaker
+with a body — and then spends most of its length on the part that is not
+a setting at all: what your two hands do.
+
+Everything measured here is borrowed from [the instrument that is not a
+synthesizer](../ondes.md) and [loudspeakers you can
+play](../diffuseurs.md), which in turn cite the circuit paper (Najnudel,
+Hélie, Roze & Boutin, IEEE/ACM TASLP 28, 2020) and the intensity-key
+measurement (Quartier et al., *Acta Acustica* 101(2), 2015).
+
+## The chain
+
+```text
+[ribbon signal] ──▶ tap.ondes~ ──▶ tap.palme~ ──▶ out
+[key signal]    ──▶     ▲              (or tap.metallique~)
+```
+
+Two signals in, one instrument out. That is the whole rig, and the
+temptation to put things between the stages should be resisted until you
+have played it as it stands — the voice and the diffuseur were designed to
+be adjacent, and every stage you insert is a stage the real instrument does
+not have.
+
+## The voice
+
+| control | setting | why |
+|---|---|---|
+| `ribbon` | driven by signal | semitones above A1, not Hz |
+| `key` | driven by signal | 0–1 of the physical travel |
+| `depth` | `1.` | equal oscillators; the full harmonic series |
+| `detect` | `0.2` | the published R4 × C21, 200 µs |
+| `drive` | `1.` | nominal; the harmonics are already there |
+| `keyplacement` | `0` | pressure is level |
+| `polarity` | `1` | |
+| `power` | `0` | the 2A3 moves total harmonics by 0.003 |
+| `oversample` | `4` | |
+| `smooth` | `0.` | the signal inlets are not ramped anyway |
+
+Start there and change exactly one thing at a time, because most of these
+are citations rather than tastes and the object will tell you when you have
+left the instrument behind.
+
+The two that are genuinely yours: `keyplacement 1` moves the key in front
+of the valves, so hard presses get dirty as well as loud — worth about 0.09
+of total harmonic content at a half-press, and it is the single change that
+most makes the object feel like a synthesizer rather than an ondes.
+`polarity -1` flips which side of the waveform the preamplifier bends,
+worth about 0.12. Try both; keep whichever suits the piece.
+
+`depth` below 1 is the cheapest real timbre move in the object. At `0.4`
+the envelope never closes and the tone thins toward a sinusoid — the
+closest thing here to a "register", and it is a physical mismatch between
+two oscillators rather than an invented control.
+
+## The hands
+
+This is the recipe.
+
+**The ribbon is linear in semitones**, because the circuit paper's Eq. 7
+makes it so. That single fact is why an ondes glissando sounds like an
+ondes glissando: a hand moving at constant speed produces a constant-rate
+glide, not the accelerating swoop a linear-in-Hz control gives you. So
+drive `ribbon` with something that moves *linearly in semitones over time*:
+
+```text
+line~ 0. 36. 4000   ──▶ tap.ondes~ left inlet
+```
+
+Three octaves in four seconds, and it will sound even the whole way. Build
+your phrases the same way — `line~` or a slow `sig~` ramp per note, never a
+quantized step unless you specifically want the instrument to sound wrong.
+Nothing here rounds to a semitone, and that is deliberate.
+
+**The key is the dynamics, and it starts silent.** Roughly the bottom 45 %
+of the travel makes no sound at all. That is the key's own first phase, the
+elastic strip bending before it reaches the powder bag, and it is why the
+instrument attacks so sharply: the whole 50 dB lives in the 4.5 mm right
+after the silence. Practically:
+
+- Drive `key` from a pedal, a fader, or a `line~` — anything continuous.
+- Expect nothing below about `0.45`. If you want the note to speak the
+  instant your controller leaves zero, rescale: `scale 0. 1. 0.45 1.`.
+  Do that only if you want to give up the attack, because that dead travel
+  is what lets you place an entrance to the millisecond.
+- The curve steepens through the middle and flattens at the top. Crescendos
+  therefore want a *decelerating* controller move, not a linear one — which
+  is exactly the feedback a player's finger gets from the real spring.
+
+**Play them together.** The ribbon without the key is a test tone; the key
+without the ribbon is a volume pedal. The instrument is the two hands, and
+`ondes_ribbon.wav` in the render set exists to demonstrate what that
+sounds like when both are moving.
+
+## The loudspeaker, which is an instrument too
+
+`tap.palme~` is the default answer. Twelve strings on a board, and they
+sustain what the voice has already stopped playing:
+
+| control | setting |
+|---|---|
+| `root` | `110.` — put the lowest string under your part's key |
+| `tuning` | `0` chromatic, so the board answers every note |
+| `decay` | `6.` |
+| `damping` | `4000.` |
+| `detune` | `4.` — cents of scatter, so the board is not a chorus unit |
+| `drive` / `asymmetry` / `saturation` | `1.` / `0.3` / `0.2` |
+| `mix` | `60` |
+
+`tuning 1` puts the harmonic series on the root instead: the board then
+answers only what belongs to that key, which turns a chromatic line into
+something that blooms on some notes and stays dry on others. Use it when
+the piece really is in one key, and hear it as a compositional decision
+rather than a preset.
+
+Watch the level. Twelve resonant loops add up, and a driven board can be a
+great deal louder than what went into it — `level` is there for that, and
+it is the one control on these objects you will need to touch first.
+
+`tap.metallique~` is the other cabinet: eight plate modes rather than
+twelve strings, so it colours instead of harmonizing.
+`@pitch 180 @decay 6 @tilt 0.8 @brightness 1. @mix 50` is the gong. Push
+`drive` to 3 with `asymmetry 0.5` and the distortion happens *before* the
+plate, because that is where the transducer is — a distorted waveform
+ringing a gong, not a distorted gong. It is not subtle and it is the most
+distinctive sound in the family.
+
+## What each ingredient buys, in order
+
+1. **`tap.ondes~` with both hands moving.** Everything else is optional.
+   A static ribbon and a static key is a demo, not an instrument.
+2. **The diffuseur.** The voice alone is thin on purpose — it is a valve
+   preamplifier output, and it was never meant to be heard without a body
+   after it.
+3. **`depth`.** The one timbre control that costs nothing and is physical.
+4. **`keyplacement` / `polarity`.** Real, measured, and yours to choose.
+5. **`power`.** Measured at 0.003 of total harmonic content. Last.
+
+## When to leave the recipe
+
+- **You want the waveform registers.** The real instrument has switchable
+  timbres — creux, gambe, nasillard and the rest. They are not here, and
+  they are not here on purpose: no source obtained describes their filter
+  shapes, and inventing them is the one thing these objects will not do.
+  If you need those colours, put a filter after `tap.ondes~` and call it
+  your filter, not Martenot's.
+- **You want polyphony.** The instrument is monophonic and so is this. Two
+  `tap.ondes~` in parallel is a duet, not a chord — which is how ondes
+  ensembles actually worked, so it is not a bad answer.
+- **You want the diffuseurs on something else.** Take them. A guitar into
+  `tap.palme~` is the best argument for shipping them standalone, and
+  nothing about them needs the voice in front.
+- **You want `tap.triode~` on its own.** It is a published valve stage and
+  it works on anything: `@tube 2 @stage 2 @drive 6` is the 2A3 power stage
+  used for something it was never in this instrument for.
diff --git a/book/src/recipes/scrub-pad.md b/book/src/recipes/scrub-pad.md
new file mode 100644
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+++ b/book/src/recipes/scrub-pad.md
@@ -0,0 +1,135 @@
+# Two hands on a live buffer
+
+`tap.scrub~` is the object in this library that most needs a controller
+attached before it means anything. Everything below assumes one: an XY pad,
+two faders, a trackpad, a phone sending OSC — anything that gives you two
+continuous values at once. The recipe is mostly about what to put on each
+axis and why.
+
+Measured claims are borrowed from [two hands on the same
+tape](../scrub.md).
+
+## The chain
+
+```text
+source ──┬─▶ tap.scrub~ ──▶ tap.palme~ ──▶ out
+         └────── (its own dry path, via mix) ──────▶
+```
+
+The scrub records what passes through it, so it goes *in* the signal path
+rather than on a send — there is nothing to send it that it is not already
+hearing. Its `mix` is the dry/wet, and at `mix 0` it is the input bit for
+bit, which means you can leave it in the chain permanently and have it be
+audibly absent until you touch it.
+
+## The pad
+
+| control | setting |
+|---|---|
+| `maxhistory` | `4.` s (object argument; bought at DSP start) |
+| `position` | **X axis**, 0–1500 ms |
+| `pitch` | **Y axis**, −12 to +12 st |
+| `drift` | `0.` |
+| `size` | `80.` ms |
+| `overlap` | `2` |
+| `spray` | `0.` |
+| `mix` | `100` while playing |
+| `smooth` | `0.` if driving by signal, `20.` if by messages |
+
+**X is position, Y is pitch, and the whole object exists because those are
+independent.** On tape they would be the same axis — moving the head *is*
+the pitch change. Here you can rake back through the last second and a half
+at the pitch you started at, or hold still and transpose, or do both at
+once in different directions. Spend the first five minutes doing each
+separately; the object does not become obvious until you have felt that
+they do not interact.
+
+**`size` is the texture control.** 80 ms is a granular pad. Down at 20 ms
+it turns metallic and starts pitching itself at the grain rate; up at
+200 ms it stops being granular and becomes a soft varispeed. Sizes that
+divide evenly by `overlap` have an exactly flat window sum — 80 with
+overlap 2 does — which matters when you want the still position to be
+clean.
+
+**`overlap 1` is a texture, not a mistake.** It leaves gaps between grains:
+a gated, chopped version of the same gesture. Worth a switch on the
+controller.
+
+## The honest bit about pitch
+
+Transposing here warbles, and it is measured rather than apologized for:
+98.8 % of a perfect shifter's energy lands within ±15 Hz of the transposed
+pitch (worst case 91.7 %), so the *note* is right — what the object loses
+is concentration, 92.0 % as focused as a clean shift and 75.0 % at worst.
+Audibly that is a warble, and it is the classic single-delay-line
+pitch-shifting artifact rather than anything peculiar to this kernel.
+
+Two ways to work with it:
+
+- **Lean in.** `spray 30.` trades the narrow comb for a broadband smear.
+  On sustained material this reads as a texture rather than a fault, and it
+  is the better answer for pads.
+- **Stay out of its way.** Keep the Y axis to ±7 and let the position do
+  the work. Small intervals warble least and the object is a scrub pad
+  first.
+
+If you need a clean shift, this is the wrong object — `tap.shift~` is built
+for it. (`tap.pitchaccum~` is built for shimmer rather than transparency,
+and has [an open issue](https://github.com/tap/TapTools/issues/33) about
+where its line actually sits.)
+
+## Freeze, which is the other half
+
+`freeze 1` stops the recorder. The playhead keeps going, so the position
+now addresses fixed tape and the grains loop the same window — and you can
+still scrub, transpose, drift and spray through it. Nothing is going into
+the input any more, which is the point: it is a hold you can perform.
+
+A sequence that works on stage:
+
+1. Play the phrase through at `mix 0`. Nothing happens; the tape fills.
+2. `mix 100`, `freeze 1`. The last few seconds are now the instrument.
+3. Drag X slowly. This is the scrub.
+4. `drift -0.3`. The playhead walks backwards on its own while you keep
+   your hand free for Y.
+5. `spray 40.`, `size 200.`. It stops being a phrase and becomes a pad.
+6. `freeze 0`, `mix 0`. The room comes back.
+
+Step 6 is exact — `mix 0` is bitwise passthrough — so the return is clean
+however far out step 5 went.
+
+## The one constraint
+
+A grain born `position` behind the live edge and playing at rate *r*
+reaches `position − size·(r−1)` behind it by its end. Transpose *up* with
+the position near the live edge and the grain's tail runs off the front of
+the tape into the oldest material — a seam.
+
+Nothing clamps it, because clamping would silently bend the pitch to keep
+the grain in bounds, which is a worse failure than the seam. Practically:
+**keep the position at least `size·(rate−1)` back**. At `size 80` and one
+octave up that is 80 ms. Setting the X axis to start at 100 ms rather than
+0 makes the whole problem disappear, and this is why the table above says
+0–1500 rather than 0–1500 starting at zero.
+
+## What each ingredient buys, in order
+
+1. **A controller with two continuous axes.** Without it this is a delay.
+2. **`freeze`.** The half of the object you can build a performance on.
+3. **`size`.** The texture, and the only control that changes what kind of
+   thing you are playing.
+4. **A diffuseur after it.** `tap.palme~ @mix 40` sustains what the scrub
+   chops; the strings fill the gaps that `overlap 1` opens.
+5. **`spray`.** Trades one artifact for another. Real, and last.
+
+## When to leave the recipe
+
+- **You want it in time.** Nothing here syncs. That is `tap.stammer~`, on
+  the same tape — literally the same `capture` code — and the two are
+  meant to be swapped between rather than combined.
+- **You want a clean delay.** `tap.delay~` costs a fraction as much and
+  windows nothing.
+- **You want the position to feel like a jog wheel.** Put `drift` on a
+  spring-loaded control and leave `position` alone: drift is velocity where
+  position is location, and for wheel-like gestures velocity is the right
+  variable.
diff --git a/book/src/recipes/tape-and-stutter.md b/book/src/recipes/tape-and-stutter.md
new file mode 100644
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--- /dev/null
+++ b/book/src/recipes/tape-and-stutter.md
@@ -0,0 +1,148 @@
+# The part that comes apart, on tape
+
+Three objects and one posture: hands on the controls while it runs. The
+stutter takes a phrase apart, the tape echo smears the pieces, and the fuzz
+decides how hard the whole thing is being pushed. None of the three has a
+"right" setting, which is the point of the part of the book they live in.
+
+This recipe is a rig, not a record. What is documented about the Kid A-era
+working method is that a laptop running Max sat in the signal path and got
+played — the objects here are informed by *what that rig was for*, not by
+anyone's patch. Nothing below is claimed to be a reconstruction of a
+specific track.
+
+Measured claims are borrowed from [four heads and a
+motor](../tapecho.md), [the part that comes apart](../stammer.md) and
+[the dirt with two stages](../fuzz.md).
+
+## The chain
+
+```text
+source ──▶ tap.fuzz~ ──▶ tap.stammer~ ──▶ tap.tapecho~ ──▶ out
+```
+
+Order matters, and this order is the useful one:
+
+- **Fuzz first**, because a stutter of a distorted signal is a stutter; a
+  distortion of a stuttered signal turns every slice edge into a transient
+  the clipper amplifies.
+- **Echo last**, because the echo is the only object here that is supposed
+  to blur. Put it before the stutter and the stutter slices the blur, which
+  sounds like a mistake rather than a decision.
+
+## The dirt
+
+Keep it low. Two saturators in series get muddy fast, and the echo has its
+own `drive`.
+
+| control | setting |
+|---|---|
+| `gain` | `0.35` |
+| `edge` | `0.3` |
+| `asymmetry` | `0.2` |
+| `bass` / `treble` / `contrast` | `0.` / `0.1` / `0.3` |
+| `oversample` | `4` |
+| `level` | to taste, usually negative |
+
+`contrast` is the scoop, and a scooped source stutters better than a
+mid-heavy one — the slices stop fighting the vocal or the guitar they came
+from. Leave `oversample` at 4 and resist the urge to save the cycles at 2:
+a hard `edge` on bright material is exactly the case where one doubling
+stops being enough, and 2 measures badly above about 6 kHz.
+
+## The stutter
+
+| control | setting | what it does |
+|---|---|---|
+| `step` | `60.` ms | the grid |
+| `divisions` | `1` | |
+| `density` | `0.3` | how often it grabs |
+| `repeats` | `4` | how long it holds |
+| `reverse` | `0.2` | chance a repeat plays backwards |
+| `jump` | `250.` ms | how far back a slice may reach |
+| `fade` | `2.` ms | the flanks |
+| `seed` | any integer | |
+| `mix` | `100` | |
+
+**The one thing to internalize: `repeats` is the hold, `density` is the
+grab.** Occupancy — how much of the timeline has a slice in flight —
+measures 41 % at density 0.3 / repeats 1 and **90 %** at density 0.9 /
+repeats 1, but density 0.3 with repeats 6 already sits at 76 %. If the part
+feels too busy, pull `repeats` before you touch `density`; you will keep
+the sparseness of the *entrances* while shortening what each one does.
+
+`seed` is a contract, not a flavour: the same seed and the same moves give
+a bit-identical render, and two instances on two tracks decorrelate by seed
+alone. Different seeds change 89 % of samples, so it is a real dice roll,
+not a phase tweak.
+
+At `density 0.` the object is a bitwise bypass — worth knowing, because it
+means you can automate density to zero and get the dry signal back exactly,
+with no crossfade artifact to work around.
+
+**The material contract.** This object flatters a played phrase and
+flatters a sustained note far too much. Slice similarity measures 1.000 on
+a held sine against 0.286 on a plucked phrase: on sustained material every
+slice is interchangeable, so the stutter has nothing to expose and sounds
+like a tremolo. Feed it something with transients and pitch variety.
+
+## The tape
+
+| control | setting |
+|---|---|
+| `span` | `400.` ms |
+| `heads` | `3` |
+| `ratios` | `0.25 0.5 1.` |
+| `levels` | `0.7 0.85 1.` |
+| `pans` | `0.2 0.8 0.5` |
+| `regen` | `0.55` (the ride starts here) |
+| `darken` | `5000.` |
+| `drive` | `0.4` |
+| `wow` / `flutter` | `0.4 0.35` / `0.3 8.` |
+| `mix` | `35` |
+
+`span` is defined at the ratio-1.0 head, so the head at `1.` returns at
+400 ms and the others at 100 and 200. Change `span` while it runs and the
+whole thing glides like tape speed rather than splicing — that is the
+transport, and it is the second-best gesture in this rig.
+
+The best one is `regen`. **It goes past 1 on purpose.** Past unity the line
+self-oscillates, and it stays bounded because the saturator caps what comes
+back: the ceiling is `|in|max + regen/drive`, measured under that value at
+every drive tried. So a ride up to `1.4` and back is a controlled build,
+not a fire. Keep `drive` up while you do it — `drive 0.` removes the
+saturator and the cap falls back to unity, which is the setting where a
+long ride will *not* behave.
+
+`darken` is the generation loss, and it is what makes repeats decay into a
+shape instead of just getting quieter. 5 kHz is a good default; below 3 kHz
+the tail turns to mud, which is sometimes what you want under a chorus.
+
+## The gestures, in order of value
+
+1. **Ride `regen` past unity and back**, while `mix` stays put. One hand,
+   whole arrangement.
+2. **Automate `density` to 0 and back.** Exact bypass, so it reads as the
+   part reassembling rather than a fade.
+3. **Move `span` during a held note.** Varispeed glide, not a splice.
+4. **Change `seed` between takes**, never during one.
+5. **`reverse` and `jump`.** Character, and cheap to overdo. Note `jump` is
+   *milliseconds*, not a probability — at 250 the machine starts quoting
+   material from a quarter-second before the slice it just took, which is
+   where a stutter stops sounding like a stutter and starts sounding like
+   an edit.
+
+## When to leave the recipe
+
+- **The source is sustained.** See the material contract. Put the stutter
+  on the drums and leave the pad alone.
+- **You want the slices in time with something.** Nothing here syncs to a
+  transport; `step` is milliseconds. Drive it from your own clock if you
+  need bars.
+- **You want the echo to stay clean.** `drive 0.` gets you a clean line —
+  but then do not ride `regen` past 1, because the cap that makes that safe
+  is the saturator you just removed.
+- **You want the dirt to be the point.** Then the fuzz belongs last, not
+  first, and this is a different recipe: `tap.tapecho~` → `tap.fuzz~` with
+  the echo's own `drive` at 0. Distorting a wash is a real sound; it is
+  just not this one.
diff --git a/book/src/scrub.md b/book/src/scrub.md
new file mode 100644
index 0000000..4f6aaf1
--- /dev/null
+++ b/book/src/scrub.md
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+# Two hands on the same tape
+
+`tap.stammer~` and `tap.scrub~` record the same way. Both keep a rolling
+tape of what just went past — the same `capture`, literally the same code,
+not a second copy of it — and both put a read pattern on top of it. The
+stutter's pattern is a slicer with dice. The scrub's is a pad you drag.
+
+That is the whole difference, and it is the difference between a machine
+that decides and a machine you play. The stammer is a die you load; the
+scrub is a surface you push around, in the Kaoss-pad school of instruments
+where an XY surface over live capture is the entire interface. It belongs in
+this part of the book for the same reason the tape echo does: nobody sets
+this object up and walks away from it.
+
+It is an original design in the granular / brassage tradition (Roads,
+*Microsound*, MIT Press 2001) — not a port, and not a reconstruction of any
+product. No preset, timing or parameter value in it came from a piece of
+hardware.
+
+Companion material: the executed notebook `scrub.ipynb`, the pinned
+scenarios in `tests/scrub_test.cpp`, and the `radiohead_render` scenes
+`scrub_gesture` and `scrub_freeze`.
+
+## The two axes are actually two axes
+
+`position` is how far back the playhead sits, as a lag in milliseconds
+behind the live edge. `pitch` is transposition in semitones. On tape those
+would be the same knob — moving the head *is* the pitch change — and the
+whole point of doing this with grains is that here they are not. Hold the
+position and sweep the pitch and the material transposes without going
+anywhere. Sweep the position at a fixed pitch and you rake through the last
+few seconds without the tape rising or falling.
+
+`drift` is the third one, and it is the playhead's own motion through the
+tape in playback-rate units: `1.` runs forward at the speed the recorder is
+writing, `0.` holds station, negative runs backwards. Set `@drift 1.` and
+let go of the position and the scrub is a delay; set `@drift 0.` and it is a
+freeze that you can still transpose.
+
+## The identity underneath it
+
+Grains are Hann-windowed and fired every `size / overlap` samples. Hann
+overlap-adds to exactly 1 at that hop, so with the pitch at unity, `spray`
+at zero and the position held on a whole sample, the scrub is *the input,
+delayed*, to floating point — 4.4e-16 in the pinned test.
+
+![Two panels: the scrub's output lying exactly on top of the input delayed by 480 samples, and the window sum for overlaps 1, 2 and 4 — flat at 1 for 2 and 4, dipping to zero for 1](images/scrub/null.svg)
+
+*Left: held still at unity, the object is a delay and nothing else. Right: the window sum that makes it one.*
+
+This matters more than it sounds. Everything else the object does is a
+*departure* from a plain delay, and a departure is only trustworthy if you
+know the thing it departs from is exact. When the position drags, when the
+pitch moves, when spray scatters the origins — those are the object working.
+If the still case were approximate, you could not tell them apart from
+noise.
+
+`overlap 1` leaves gaps between grains, which is the dipping curve in that
+figure. That is a chopped, gated texture rather than a defect, and it is
+worth having; it is just not the setting the null lives at.
+
+## What transposing costs, honestly
+
+Reading tape at a rate the write head does not share means the read pointer
+drifts away from where the position says it is, and it has to be pulled back
+or the position stops meaning anything. Every pull-back is a splice between
+two grains reading material a little apart.
+
+What that costs is *not* the pitch. Swept over seven fundamentals and seven
+intervals, 98.8 % of a perfect shifter's energy lands within ±15 Hz of the
+transposed pitch — worst case 91.7 %. The note is where you asked for it.
+
+What it costs is concentration. The band holds a narrow comb rather than one
+clean line: 92.0 % as concentrated as a clean shift, 75.0 % at its worst.
+
+![Two curves against pitch from −12 to +19 semitones: energy at the transposed pitch staying near 1, and concentration dipping to about 0.75](images/scrub/two-hands.svg)
+
+*The pitch goes where you put it. What the splices take is focus.*
+
+Audibly that is a warble, and it is the classic single-delay-line
+pitch-shifting artifact rather than anything peculiar to this kernel. If you
+want the warble gone, `spray` trades the comb for a broadband smear, which
+some material prefers. If you want a clean shift, this is the wrong object —
+see below.
+
+## `freeze`, and what it does not stop
+
+`freeze` stops the *recorder*. The playhead keeps going, so the position now
+addresses fixed tape and the grains loop the same window: a granular hold
+you can still scrub, transpose and drift through. It does not stop time
+inside a grain — a grain in flight when freeze engages was already
+scheduled, and it finishes.
+
+## `spray` and `seed`
+
+`spray` scatters each grain's origin randomly back from the position. At
+exactly 0 the dice are never rolled, so the seed provably cannot matter —
+the same contract `tap.garden~` and `tap.stammer~` carry, pinned by the same
+kind of test. With spray up, the same seed and the same moves give the same
+render bit for bit, and two instances decorrelate by seed alone.
+
+## Recipes
+
+- **The pad:** `@drift 0. @size 80 @overlap 2 @mix 100`, then ride
+  `position` with a signal. The default instrument.
+- **Granular freeze:** `@freeze 1 @drift 0. @size 120 @spray 40`. Hold, then
+  move `pitch` for a chord that was never played.
+- **Backwards tape:** `@drift -1. @pitch 0.` — the playhead walking against
+  the recorder.
+- **Chopped:** `@overlap 1 @size 40`. Gaps between grains, on purpose.
+- **Into the diffuseur:** `tap.scrub~` → `tap.palme~` with the palme's
+  `@mix` around 40. The strings sustain what the scrub chops.
+
+## When it is not the right tool
+
+- **Clean transposition.** The warble above is inherent to the method.
+  `tap.shift~` and `tap.pitchaccum~` are the objects built for that job —
+  with one caveat worth stating plainly: measured on this same sweep,
+  `tap.pitchaccum~` retained mean 0.907 of band energy against the scrub's
+  0.988, worst 0.633 against 0.917. Its two-tap crossfade also puts its
+  strongest spectral line a few hertz *beside* the intended pitch, which is
+  filed as [issue #33](https://github.com/tap/TapTools/issues/33). None of
+  that makes it the wrong object — it is a shimmer, and shimmer is what those
+  sidebands are — but audition before you assume it is the transparent one.
+- **A tidy delay.** `tap.delay~` and `tap.tapecho~` cost far less and do not
+  window anything.
+- **Slicing to a grid.** That is `tap.stammer~`, on the same tape.
+
+## Checkpoint
+
+One tape shared with the stutter, one grain scheduler on top of it, and two
+axes that stay independent because grains let them. The still case is a
+bit-exact delay, which is what makes every departure from it legible.
+Transposing warbles, and the warble is measured rather than apologized for:
+the note holds to 98.8 % of a clean shifter's energy, and 92.0 % of its
+focus. Every number here lives twice, as a cell in `scrub.ipynb` and as a
+pinned scenario in `tests/scrub_test.cpp`.
diff --git a/book/src/stammer.md b/book/src/stammer.md
new file mode 100644
index 0000000..518b0fd
--- /dev/null
+++ b/book/src/stammer.md
@@ -0,0 +1,140 @@
+# The part that comes apart
+
+There is a moment at the end of "Go To Sleep" where the guitar stops being a
+guitar. It does not fade, it does not filter — it starts eating itself,
+firing fragments of the bar you just heard in an order nobody played. That
+sound came out of a Max patch Jonny Greenwood built and performs live. So
+`tap.stammer~` has an odd position in this package: it is a Max stutter
+object, in a Max package, for a technique that was invented in Max.
+
+None of which means anything was copied. This is an **original design** in
+the brassage tradition (Roads, *Microsound*) — a continuously recorded
+buffer, a rhythmic grid, and dice. What the band's rig contributes is the
+knowledge of what the object is *for*, which turns out to be the hard part
+of designing one.
+
+Companion material: the executed notebook `stammer.ipynb`, and the
+`radiohead_render` scenarios `stammer_grid` (the dials held still so the
+mechanism is audible), `stammer_disintegrate` (forty seconds of the
+performance the object exists for), and `stammer_two_seeds`.
+
+## How it works, in one paragraph
+
+The input is captured continuously into the last few seconds of history. On
+a `step` grid, if the machine is idle, it rolls: with probability `density`
+it grabs the material that just went past, chops it to `step` divided by
+something between 1 and `divisions`, and plays it back between 1 and
+`repeats` times, each pass with a `reverse` chance of running backwards. If
+`jump` is open it may reach further back than the bar just played. While a
+slice fires you hear the slice; when nothing is firing you hear the input,
+untouched.
+
+## `density` and `repeats` — they are not the same dial
+
+This is the one thing worth internalizing before you patch it. `density` is
+how often the machine *grabs*; `repeats` is how long it *holds on* once it
+has. A slice in flight is never interrupted, so `repeats` is what actually
+decides how busy the machine is — and once trains start overlapping, raising
+density stops doing anything at all.
+
+![Four runs showing when a slice is in flight: raising repeats from 1 to 6 fills the timeline far more than raising density from 0.3 to 0.9](images/stammer/occupancy.svg)
+
+*Measured off the object's own playing flag, 100 grid points per run. Repeats is the hold.*
+
+At density 0.3, going from 1 repeat to 6 takes the machine from 41% busy to
+76%. At density 0.9 it is already 90% busy with a single repeat, and 96%
+with six — the ceiling, where the dial has run out of room.
+
+## `divisions`, `reverse`, `jump` — the character
+
+`divisions` is how finely the grid may be chopped: at 1 you get whole-step
+slices, at 8 the machine may cut down to eighths of a step. Because the
+divisor is drawn per slice, a high setting gives you a *mixture* of lengths,
+not uniformly short ones — which is what keeps it sounding played rather
+than gated.
+
+`reverse` is drawn per repeat rather than per slice, so a single train can
+stagger forwards and back. `jump` is the reach: at 0 the machine only ever
+replays the material immediately past (the classic stutter), and opening it
+lets slices come from seconds ago, so the part starts quoting itself out of
+order. That is the setting that turns "stuttering" into "disintegrating".
+
+`fade` is the anti-click — a raised-sine flank on each repeat, exactly zero
+at the edges and exactly unity across the plateau. Repeats are sequential
+rather than overlapped, so every junction dips to zero. That is deliberate:
+the dip *is* the articulation of a stutter, and a crossfade there would
+smear the thing you want to hear.
+
+## `seed` — the dial that is a contract
+
+Every draw — fire, division, repeat count, reach-back, and the per-repeat
+coin for reverse — comes from a seeded generator in a fixed order. So the
+same seed and the same moves give the same render, bit for bit. That is not
+a nicety; it means a take you liked is recoverable, two instances on
+different seeds decorrelate instead of moving in lockstep, and the tests can
+assert bitwise equality. A different seed is a genuinely different
+performance: 89% of samples change.
+
+And at `density` 0 the dice are never rolled *at all* — so the seed provably
+cannot matter, and the object is a bitwise bypass at any mix. Switched off,
+this is not "nearly transparent", it is your input. `clear` erases the
+capture, drops the slice in flight, and rewinds the seeded stream, so the
+same seed replays from there.
+
+## The material contract
+
+The header says this object wants transient material, and that on a
+sustained pad a stutter is barely a tremolo. That reads like taste. It is
+not — it is a property of the material, and it is measurable.
+
+![Slices of a sustained sine measure 1.000 alike; slices of a plucked phrase measure 0.286](images/stammer/material.svg)
+
+*How alike two arbitrary slices of the material are. Re-ordering interchangeable things does nothing.*
+
+Every slice of a steady sine looks like every other slice, so shuffling them
+changes almost nothing you can hear. Slices of a played phrase are all
+different, so shuffling them is the entire effect. Feed this object drums,
+plucked or struck strings, consonants — anything whose interest is in *when*
+things happen. It re-articulates rhythm that is already in the sound; it
+cannot invent rhythm that is not.
+
+## Recipes
+
+- **A grid you can hear:** `@step 250 @density 0.55 @divisions 4 @repeats 4
+  @reverse 0.2`. The mechanism, plainly, over a played part.
+- **The disintegration:** start at `@density 0.2 @divisions 1 @repeats 1`
+  and walk over thirty seconds to `@density 0.9 @divisions 8 @repeats 10
+  @reverse 0.6`, tightening `@step` from 250 to 120 as you go. Then open
+  `@jump 1500` and the machine starts quoting the wrong bar.
+- **Vocal chop:** `@step 125 @density 0.4 @divisions 2 @repeats 3 @fade 6`.
+  Consonants are transients; the longer flank keeps it from sounding
+  digital.
+- **Two of them:** the same settings on two instances with different seeds,
+  panned apart. They decorrelate by construction — that is what the seed
+  contract buys you.
+
+## When it is not the right tool
+
+- **Sustained material.** See above; it is measured. A tremolo or a gate
+  will do more for a pad.
+- **Pitched mangling.** Slices play at ±1 rate only — there is no pitch
+  shift and no varispeed here. `tap.shift~` transposes; `tap.pitchaccum~`
+  spirals.
+- **Exact, notated rhythms.** The grid is regular but the dice are dice.
+  `tap.808.seq~` sequences; this improvises.
+- **Very long repeat trains.** A slice reads from the ring, not a private
+  copy, so a train longer than the captured history will start reading
+  fresher material as the write head laps it. Size the object argument to
+  the longest train you intend to fire.
+
+## Checkpoint
+
+Capture everything, then on a grid roll dice and re-fire what just went
+past. `density` grabs, `repeats` holds — and holding is what fills the
+timeline. `divisions`, `reverse` and `jump` are the character, and `jump` is
+the one that turns a stutter into a disintegration. The seed is a real
+contract: same seed, same performance, bit for bit; at density 0, a bitwise
+bypass. And the material contract is measured rather than asserted, which is
+the honest way to tell you what to feed it. Every number above lives twice:
+as an executed cell in `stammer.ipynb` and as a pinned scenario in
+`tests/stammer_test.cpp`.
diff --git a/book/src/tapecho.md b/book/src/tapecho.md
new file mode 100644
index 0000000..1ce4046
--- /dev/null
+++ b/book/src/tapecho.md
@@ -0,0 +1,151 @@
+# Four heads and a motor
+
+The last chapter's `tap.discreet~` is a machine you set up and walk away
+from. `tap.tapecho~` is one you keep your hands on. Same spool of tape, same
+worn return path, same family — but where the Eno objects are systems that
+run without you, this one is an instrument, and every parameter on it is a
+hand on the machine. That is the thread through this part of the book: these
+are the objects you *ride*.
+
+What it recreates is the tape echo of the Copicat / Space Echo school: one
+record head, a span of moving tape, several playback heads at fixed
+positions along it, and a path from the heads back to the record head. Ed
+O'Brien's Copicat is the reason it is here. It is a recreation of the
+*topology*, not a circuit model of any one unit — the tape path itself is
+the same published tape-echo modeling literature `tap.discreet~` already
+stands on (Arnardóttir, Abel, and Smith's AES model of the Echoplex, and
+Välimäki et al.'s tape-echo work), and no head spacing, filter curve, or
+trim value in this object is claimed as measured from a real machine.
+
+Companion material: the executed notebook `tapecho.ipynb`, which measured
+every number below, and the `radiohead_render` tool, whose `tapecho_heads`,
+`tapecho_three_head`, `tapecho_selfosc`, and `tapecho_varispeed` scenarios
+are the listening copies — all four *performed*, with the controls moving
+while they render, because static settings tell you almost nothing about
+this object.
+
+## `span` — the motor
+
+`span` is the delay of a head sitting at the far end of the tape path, and
+every other head sits at `span` times its own ratio. So `span` is not "the
+delay time" of one echo; it is the motor speed, and moving it moves the
+whole layout together.
+
+![An impulse into a 400 ms span with four heads, returning once at each of 100, 200, 300 and 400 ms on the dotted head positions](images/tapecho/head-layout.svg)
+
+*One impulse, four heads. The returns land exactly on `span × ratio`.*
+
+Moving the motor while audio runs is a tape-speed change, which means it
+bends pitch on the way — the same doppler contract as `tap.discreet~`, for
+the same reason: the heads are physically moving relative to the tape.
+`smooth` sets how long the motor takes to change speed, and therefore how
+deep the bend is. There is no crossfading "digital" mode. If a pitch bend on
+a delay-time change would ruin the patch, reach for `tap.delay~`.
+
+## `heads`, `ratios`, `levels`, `pans` — the layout
+
+Four heads by default, evenly spaced at 0.25, 0.5, 0.75 and 1.0 of the span.
+That spacing is *nominal* — chosen because it is neutral and audibly a tape
+echo — and every ratio is freely settable underneath, which is how you build
+a three-head Copicat-style layout:
+
+```
+heads 3, ratios 0.333 0.667 1.
+```
+
+`levels` is per-head gain and `pans` places each head in the stereo field
+(equal-power, with exact endpoints: a hard-panned head is bitwise absent
+from the far bus). One thing to know: **a head's level is also its send into
+the regeneration path**, as the head selector on the real machines is. Turn
+a head down and you are turning down both what you hear from it and what it
+feeds back.
+
+## `regen`, `drive`, `darken` — past unity, on purpose
+
+Here is where this object parts company with everything else in the house.
+`tap.delay~` caps feedback at 0.99 so the loop is always contractive.
+`tap.discreet~` reaches exactly 1.0 because the wear path is the stabilizer.
+`tap.tapecho~` goes **past** 1.0 — up to 1.5 — into deliberate
+sound-on-sound self-oscillation, the howl you reach for this machine to get.
+
+It stays bounded because the saturator does. `drive` is record-head
+saturation, and its output can never exceed 1/drive no matter what the loop
+accumulates, so the tape is bounded by the input plus regen/drive whatever
+the loop gain. The measurement is the point:
+
+![Measured peak output against drive at regeneration 1.4, sitting below the analytic ceiling at every drive](images/tapecho/self-oscillation.svg)
+
+*Regeneration at 1.4 — well past unity — plateaus under the saturator's ceiling at every drive.*
+
+Because that bound *only* exists while the saturator is engaged, the
+effective regeneration is capped back to 1.0 whenever `drive` is 0 — and the
+cap is applied per sample, so dropping drive mid-howl lands the loop rather
+than letting it run away. The attribute keeps its value and takes effect
+again when drive returns. Twelve seconds of ring at regen 1.4 measures a
+growth ratio of 1.007 between the two late windows: it plateaus, it does not
+climb.
+
+`darken` is the per-pass corner. Every trip through the regeneration path
+runs through a one-pole lowpass, so the repeats lose treble generation by
+generation — measured at 0.2915 of a 6 kHz tone per pass against 0.2920
+predicted, and 0.8895 of a 300 Hz tone against 0.8898. Riding `darken`
+*while the loop howls* is a performance control, not a set-up step; it is
+what turns a howl into a swell and back.
+
+## `wow` and `flutter` — one motor, one path
+
+The transport is the family's deterministic pair of sines, and one motor
+moves the whole tape path, so a speed error displaces every head together.
+The pitch math is checkable in closed form: depth times 2π times rate is the
+peak deviation, so 2 ms at 0.5 Hz predicts ±10.88 cents and the notebook's
+pitch track measures 10.91. Two renders of the same settings are
+bit-identical — periodic and deterministic by design, with stochastic
+capstan drift a documented non-goal, because bit-exact renders are what let
+the oracle test exist at all. Set both depths to 0 for a still machine.
+
+## The one that is not a knob
+
+With the tape path neutralized — no transport error, no regeneration — a
+one-head echo is **bitwise** `tap.multitap~` with one tap. Same Hermite
+read, same fractional position, same equal-power pan law. That is not a
+curiosity; it is the whole design claim, measured: this object is
+composition over the shared tape machinery rather than a second
+implementation of it, and `tape_loop.h` needed no changes at all to serve a
+topology it was not written for. The appendix has the derivation.
+
+## Recipes
+
+- **The Copicat:** `heads 3, ratios 0.333 0.667 1.` with `@span 390 @regen
+  0.6 @drive 0.9 @darken 2600 @wow 0.9 0.9 @mix 50`. Heads down the middle,
+  a tired transport, repeats that thicken as they recirculate.
+- **A wide slap:** four heads, `pans -0.7 0.5 -0.35 0.8`, `@span 480 @regen
+  0.45 @drive 0.4 @mix 45`. The layout does the widening; no chorus needed.
+- **Sound-on-sound:** `@drive 0.7 @regen 1.35`, then bring `@input` to 0 and
+  take your hands off. Ride `@darken` down to 1400 while it howls, then
+  `@regen 0.55` to bring it home. `clear` is the emergency stop.
+- **The dive:** `@smooth 3000`, then `@span 200` → `@span 900`. Three
+  seconds of tape slowing down, with everything already on the tape bending
+  with it.
+
+## When it is not the right tool
+
+- **Tempo-locked delays.** Span changes bend pitch by design and there is no
+  sync. `tap.delay~` is the clean line.
+- **A wash you set and leave.** That is `tap.discreet~`, one chapter back —
+  same machinery, opposite posture.
+- **Independent free-running loops.** One motor moves every head here. For
+  loops that drift against each other, `tap.airport~`.
+- **Clean repeats.** Wear is always in the regeneration path; `drive 0`
+  removes the saturation, not the darkening.
+
+## Checkpoint
+
+A motor and up to four heads along one tape path; the motor moves them
+together and bends pitch doing it. Regeneration goes past unity into
+self-oscillation, bounded by the saturator rather than a gain cap, and
+capped back to 1.0 the moment drive leaves. The transport is two
+deterministic sines measured in cents. And with the tape path neutral the
+whole object collapses, bitwise, into a delay this library already had —
+which is how you know it is composition and not a rewrite. Every number
+above lives twice: as an executed cell in `tapecho.ipynb` and as a pinned
+scenario in `tests/tapecho_test.cpp`, which CI runs on every push.
diff --git a/include/taptools/diffuseur.h b/include/taptools/diffuseur.h
new file mode 100644
index 0000000..9c17b52
--- /dev/null
+++ b/include/taptools/diffuseur.h
@@ -0,0 +1,776 @@
+/// @file
+/// @brief      Portable driven-resonator kernels for the Ondes Martenot diffuseurs
+///             (tap.metallique~, tap.palme~) — no Max/Min dependency.
+/// @details    The Ondes Martenot does not have one loudspeaker, it has a rack of them, and the
+///             player chooses which. Beyond the plain cabinet (the *principal*) Martenot built
+///             resonating *diffuseurs* whose whole job is to colour the signal with a physical
+///             body: the **métallique** (1944–45, patented 1947), a gong driven by a motor
+///             transducer, and the **palme** (1949–50), an electromagnet driving twelve metal
+///             strings stretched on a soundboard. Najnudel, Hélie, Roze & Boutin ("Simulation of
+///             an ondes Martenot circuit", IEEE/ACM TASLP 28, 2020) name the diffuseur as the
+///             stage that "converts the electrical waveform into sound and in turn modifies its
+///             spectral content"; Wijnand, Boutin, Jossic & Maniguet (Forum Acusticum 2023)
+///             describe the instruments themselves and measure the transducer.
+///
+///             **The correction that shapes this file: these are DRIVEN, not struck.** garden.h's
+///             modal maths carries over intact — mode ratios, doublet splitting, per-mode decay —
+///             but its strike envelopes do not. There is no `decay_env` here and no trigger. The
+///             input signal excites the body continuously and the body rings at its own rates,
+///             which is grm_comb.h's sustained-resonance situation rather than the chime's.
+///
+///             Signal order follows the instrument, and it matters: the electrical signal reaches
+///             the *transducer* first, and the transducer's motion is what excites the body. So
+///             the nonlinearity sits UPSTREAM of the resonator, not after it — drive the
+///             transducer hard and you are driving a distorted waveform into a gong, which is a
+///             different sound from distorting a gong.
+///
+///             Five classes, the family's parts-plus-thin-composition habit:
+///             - `mode` — one driven resonant mode: the constant-peak-gain two-pole resonator
+///               (zeros at ±1, b0 = (1−R²)/2; Steiglitz, and Smith, *Introduction to Digital
+///               Filters*). Peak gain is 1 at any Q, so a bank of weighted modes is bounded by
+///               the sum of its weights and needs no output limiter, and the zeros put exact
+///               nulls at DC and Nyquist so no DC blocker is needed either.
+///             - `plate` — the métallique's body: eight modes at the free circular plate's
+///               transverse ratios, each split into a slowly beating doublet.
+///             - `sympathetic` — one string: a damped, DC-blocked delay loop (the waveguide
+///               idiom, same fractional Hermite read as delay.h / grm_comb.h), returning its
+///               *ringing* rather than its through-signal so a caller can balance the two.
+///             - `harp` — the palme's body: twelve sympathetic strings on one soundboard.
+///             - `transducer` — the moving-iron driver.
+///             - `metallique` / `palme` — a transducer, a body, a balance, a level. Nothing else.
+///
+///             **Provenance, and where recreation begins.** The instruments, their dates, their
+///             excitation and their transducer type are from the peer-reviewed sources above. The
+///             *modal data is not* — no ondes-specific measurement of either body exists in any
+///             of them — so the ratios come from Fletcher & Rossing, *The Physics of Musical
+///             Instruments*, 2nd ed.: the free circular plate's transverse modes for the gong
+///             (Rayleigh's classical ratios at Poisson 0.3, the Chladni set: 1 : 1.730 : 2.328 :
+///             3.910 : 4.110 : 6.300 : 6.710 : 7.340) and the harmonic series for the strings.
+///             That makes both bodies **recreations of the general physics, not models of
+///             Martenot's instruments**, and the difference is stated here rather than left for
+///             the reader to discover.
+///
+///             The palme has **twelve** strings. Widely copied hobbyist build pages say
+///             twenty-four (two banks of twelve); the peer-reviewed source says twelve, and this
+///             file follows the peer-reviewed source. Their *tuning* is not published anywhere
+///             found, so it is a parameter: chromatic across an octave by default (a string for
+///             every pitch class, so the halo answers whatever you play) or the harmonic series
+///             on the root (a drone that answers one key).
+///
+///             **The transducer.** Wijnand et al.'s point is that the early diffuseurs use a
+///             moving-iron driver whose operating principle is *inherently* nonlinear —
+///             Thiele–Small does not describe it — so a diffuseur modelled as a pure resonator
+///             is missing a documented stage. What is modelled here is the principle, not a fit:
+///             in a moving-iron motor the force follows the square of the gap flux, so with a
+///             bias current I₀ and signal i the force carries a term in (I₀ + i)² whose residual
+///             i² produces second-harmonic distortion growing with drive. Hence `asymmetry`,
+///             a squared term, is the transducer's own even-harmonic signature. The bounded
+///             saturator after it (vca::swing_shape, exactly linear at 0) is a **modelling
+///             necessity, not a measured stage** — the squared law is expansive and something
+///             has to bound it — and its coefficient is a knob, not a number from a paper.
+///
+///             Geometry: prepare(sr) buys the string loops once (twelve times sr/k_min_string_hz
+///             doubles, ~115 kB at 48 kHz) and the plate allocates nothing at all. No later call
+///             allocates; setters are allocation-free and safe while audio runs.
+///
+///             Honest limits:
+///             - **The bodies are recreations.** See above. Nothing here was fitted to a
+///               recording, a measurement, or a photograph of either diffuseur.
+///             - **No transducer coefficient is measured.** The source establishes *that* the
+///               moving-iron driver is nonlinear and that the linear loudspeaker model does not
+///               apply to it. It does not hand over a curve, so `asymmetry` and `saturation` are
+///               voiced by ear and labelled as such. A diffuseur run with both at 0 is a linear
+///               resonator and is missing a real stage; that is a choice the caller may make.
+///             - **No radiation model.** Neither body's directivity, cabinet, nor the soundboard's
+///               own resonance is modelled. The output is the body's modal response, not a room.
+///             - **The strings are ideal.** A real steel string is stiff and its partials stretch
+///               sharp (Fletcher & Rossing's inharmonicity B); a plain delay loop's partials are
+///               exactly harmonic. Dispersion is not modelled — `detune` scatters strings against
+///               each other, which is a different thing and does not stand in for it.
+///             - **A pitch sweep recomputes coefficients per sample.** Like grm_comb.h, the
+///               derived values are recomputed on every sample while a ramp is moving and cached
+///               once it settles — sixteen resonators or twelve loops of transcendentals, which
+///               is real cost during a glide and none at rest.
+///             - Mono in, mono out. A diffuseur is one cabinet; wrap in `mc.` for multichannel.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+
+#include "tape_loop.h" // tap::tools::tape — reel (the string loops), ramp, the DC-blocker constants
+#include "vca.h"       // tap::tools::vca::swing_shape — the shared bounded saturator
+
+namespace tap::tools {
+    namespace diffuseur {
+
+        constexpr double k_pi = 3.14159265358979323846;
+
+        constexpr int k_plate_modes = 8;  // the free circular plate's first eight transverse modes
+        constexpr int k_strings     = 12; // the palme's string count, per the peer-reviewed source
+
+        // Transverse-mode ratios of a flat circular plate with a free edge (Fletcher & Rossing,
+        // The Physics of Musical Instruments, 2nd ed., the plates chapter — Rayleigh's classical
+        // values at Poisson's ratio 0.3, the Chladni set), referenced to the (2,0) mode. A gong is
+        // not a flat plate — it is dished, with a rim, and often a nipple — so this is the general
+        // physics of the family, deliberately not a claim about any particular métallique.
+        constexpr std::array<double, k_plate_modes> k_plate_ratio = {1.000, 1.730, 2.328, 3.910,
+                                                                     4.110, 6.300, 6.710, 7.340};
+        // Mode weights sum to exactly 1, so with every resonator's peak gain equal to 1 the plate
+        // is bounded by its input at every setting — the reason no output limiter is needed.
+        constexpr std::array<double, k_plate_modes> k_plate_level = {0.30, 0.22, 0.16, 0.12, 0.08, 0.05, 0.04, 0.03};
+        // Each mode is a doublet: real plates split their degenerate mode pairs by a few cents
+        // (Fletcher & Rossing on doublets), so the body beats slowly instead of ringing like a
+        // bank of lab sines. Fixed split — deterministic, no RNG.
+        constexpr double k_doublet_cents = 1.5;
+        // And the upper modes sit a few cents off the textbook ratios, drawn by a stateless hash
+        // of the MODE INDEX only (metal_bank.h's per-index xorshift64* idiom). Keying on the index
+        // rather than the pitch is deliberate: a pitch sweep must not make the scatter jump.
+        constexpr double k_scatter_cents = 4.0;
+
+        // Only the strings whose partials line up with the drive ring loudly, so the twelve
+        // responses are largely incoherent and 1/sqrt(12) is the right sum normalization.
+        constexpr double k_harp_norm = 0.28867513459481288;
+
+        constexpr double k_min_string_hz = 40.0;    // sizes the string loops bought at prepare()
+        constexpr double k_fb_max        = 0.9995;  // string loop gain cap: long, but strictly contractive
+        constexpr double k_pole_max      = 0.99999; // resonator pole radius cap, same reason
+        constexpr double k_min_delay     = 2.5;     // Hermite headroom, same floor as delay.h
+
+        constexpr double k_min_t60      = 0.01;
+        constexpr double k_max_t60      = 60.0;
+        constexpr double k_min_pitch_hz = 20.0;
+        constexpr double k_max_pitch_hz = 4000.0;
+        constexpr double k_min_damp_hz  = 200.0;
+        constexpr double k_max_damp_hz  = 20000.0;
+
+        /// How the palme's twelve strings are tuned. Not a published detail — see the banner.
+        enum tuning_index : int {
+            tuning_chromatic = 0, // twelve semitones from the root: a string for every pitch class
+            tuning_harmonic,      // partials 1..12 of the root: a drone that answers one key
+            k_num_tunings
+        };
+
+        constexpr double k_default_pitch_hz  = 180.0; // the métallique's (2,0) mode
+        constexpr double k_default_root_hz   = 110.0; // the palme's lowest string
+        constexpr double k_default_decay_s   = 6.0;
+        constexpr double k_default_tilt      = 1.0; // upper modes die this power of their ratio faster
+        constexpr double k_default_bright    = 0.7; // upper-mode weight
+        constexpr double k_default_damp_hz   = 4000.0;
+        constexpr double k_default_detune_c  = 6.0; // per-string scatter depth, cents
+        constexpr double k_default_drive     = 1.0;
+        constexpr double k_default_asymmetry = 0.15; // the moving-iron squared term
+        constexpr double k_default_sat       = 0.5;  // the bounding saturator; 0 is exactly linear
+        constexpr double k_default_mix       = 100.0;
+        constexpr double k_default_level     = 1.0;
+        constexpr double k_default_smooth_ms = 20.0;
+
+        /// Stateless draw in [-1, 1) keyed by an index — the metal_bank.h / garden.h per-index
+        /// xorshift64* idiom. Same body in every instance, forever, and no generator state.
+        inline double index_unit(uint64_t index) {
+            uint64_t s = (index + 1) * 0x9e3779b97f4a7c15ULL;
+            s ^= s >> 12;
+            s ^= s << 25;
+            s ^= s >> 27;
+            const double u = static_cast<double>((s * 0x2545f4914f6cdd1dULL) >> 11) / 9007199254740992.0; // [0, 1)
+            return 2.0 * u - 1.0;
+        }
+
+        inline double anti_denormal(double x) {
+            return (std::abs(x) < 1e-15) ? 0.0 : x; // same guard as tap.comb~
+        }
+
+        /// One driven resonant mode: the constant-peak-gain two-pole resonator,
+        ///
+        ///     H(z) = b0 (1 - z^-2) / (1 - 2R cos(w) z^-1 + R^2 z^-2),   b0 = (1 - R^2) / 2
+        ///
+        /// (Steiglitz; Smith, *Introduction to Digital Filters*, the two-pole resonator section).
+        /// The b0 normalization makes the peak magnitude 1 for any pole radius, which is what lets
+        /// a bank of these be bounded by the sum of its weights — pinned by test. The zeros at
+        /// z = ±1 put exact nulls at DC and Nyquist, so a driven bank cannot accumulate DC and
+        /// needs no blocker.
+        ///
+        /// Ring time is specified as a T60 in seconds and converted the same way grm_comb.h does:
+        /// R = 10^(-3 / (T60 · sr)), one thousandth of the amplitude after T60 seconds.
+        class mode {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                set(m_hz, m_t60);
+                clear();
+            }
+
+            void clear() { m_x1 = m_x2 = m_y1 = m_y2 = 0.0; }
+
+            /// Resonant frequency in Hz and ring time in seconds. Allocation-free; the caller
+            /// decides how often to call it (the machines recompute per sample while ramping).
+            void set(double hz, double t60) {
+                m_hz            = hz;
+                m_t60           = t60;
+                const double f  = std::clamp(hz, 1.0, 0.49 * m_sr);
+                const double w  = 2.0 * k_pi * f / m_sr;
+                const double r  = std::min(std::pow(10.0, -3.0 / (std::max(t60, 1e-4) * m_sr)), k_pole_max);
+                const double rr = r * r;
+                m_a1            = 2.0 * r * std::cos(w);
+                m_a2            = -rr;
+                m_b0            = 0.5 * (1.0 - rr);
+            }
+
+            double frequency() const { return m_hz; }
+            double t60() const { return m_t60; }
+
+            double process(double x) {
+                const double y = m_b0 * (x - m_x2) + m_a1 * m_y1 + m_a2 * m_y2;
+                m_x2           = m_x1;
+                m_x1           = x;
+                m_y2           = m_y1;
+                m_y1           = anti_denormal(y);
+                return m_y1;
+            }
+
+          private:
+            double m_sr{48000.0};
+            double m_hz{k_default_pitch_hz};
+            double m_t60{k_default_decay_s};
+            double m_b0{0.0}, m_a1{0.0}, m_a2{0.0};
+            double m_x1{0.0}, m_x2{0.0}, m_y1{0.0}, m_y2{0.0};
+        };
+
+        /// The métallique's body: eight plate modes, each a beating doublet, driven continuously.
+        /// Weighted by `brightness` the way garden.h weights a chime's upper partials (b, b², b³…
+        /// so softening kills the highest first), decaying faster with frequency by `tilt`, and
+        /// silent above the audio band rather than aliasing.
+        class plate {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                for (auto& m : m_mode) {
+                    m.prepare(m_sr);
+                }
+                retune();
+                clear();
+            }
+
+            void clear() {
+                for (auto& m : m_mode) {
+                    m.clear();
+                }
+            }
+
+            /// Frequency of the (2,0) mode — the body's perceived pitch.
+            void set_pitch_hz(double hz) {
+                m_pitch = std::clamp(hz, k_min_pitch_hz, k_max_pitch_hz);
+                retune();
+            }
+
+            /// Ring time of the fundamental, in seconds.
+            void set_decay(double t60) {
+                m_t60 = std::clamp(t60, k_min_t60, k_max_t60);
+                retune();
+            }
+
+            /// How much faster the upper modes die: T60_n = T60 / ratio_n^tilt. Radiation damping
+            /// grows roughly with f² for a struck bar (garden.h uses that), but a driven gong's
+            /// shimmer lives in its upper modes, so this is exposed rather than fixed and defaults
+            /// to 1 — a recreation choice, not a measurement.
+            void set_tilt(double t) {
+                m_tilt = std::clamp(t, 0.0, 3.0);
+                retune();
+            }
+
+            /// Upper-mode weight, [0, 1]: 1 is the full published weight table, 0 leaves the
+            /// fundamental doublet alone.
+            void set_brightness(double b) {
+                m_bright = std::clamp(b, 0.0, 1.0);
+                retune();
+            }
+
+            double pitch_hz() const { return m_pitch; }
+            double decay() const { return m_t60; }
+            double tilt() const { return m_tilt; }
+            double brightness() const { return m_bright; }
+            double samplerate() const { return m_sr; }
+
+            /// The gain this mode's doublet is contributing (both halves together) — what the
+            /// boundedness argument sums, so a test can check the sum rather than trust it.
+            double mode_level(int m) const {
+                return (m >= 0 && m < k_plate_modes) ? 2.0 * m_level[static_cast<size_t>(m)] : 0.0;
+            }
+            double mode_hz(int m) const {
+                return (m >= 0 && m < k_plate_modes) ? m_mode[static_cast<size_t>(2 * m)].frequency() : 0.0;
+            }
+
+            double process(double x) {
+                double sum = 0.0;
+                for (int m = 0; m < k_plate_modes; ++m) {
+                    const size_t i = static_cast<size_t>(m);
+                    const double g = m_level[i];
+                    sum += g
+                           * (m_mode[static_cast<size_t>(2 * m)].process(x)
+                              + m_mode[static_cast<size_t>(2 * m + 1)].process(x));
+                }
+                return sum;
+            }
+
+          private:
+            /// Recompute every doublet from pitch / decay / tilt / brightness.
+            void retune() {
+                const double split = std::exp2(k_doublet_cents / 2400.0); // half the split, up and down
+                double       shine = 1.0;                                 // 1, b, b², b³ … per mode
+                for (int m = 0; m < k_plate_modes; ++m) {
+                    const size_t i = static_cast<size_t>(m);
+                    const double ratio =
+                        k_plate_ratio[i]
+                        * ((m > 0) ? std::exp2(k_scatter_cents * index_unit(static_cast<uint64_t>(m)) / 1200.0) : 1.0);
+                    const double hz  = m_pitch * ratio;
+                    const double t60 = std::max(m_t60 / std::pow(k_plate_ratio[i], m_tilt), k_min_t60);
+                    m_mode[static_cast<size_t>(2 * m)].set(hz * split, t60);
+                    m_mode[static_cast<size_t>(2 * m + 1)].set(hz / split, t60);
+                    // Half the published weight into each half of the doublet, and nothing at all
+                    // above the band — a mode past Nyquist would otherwise fold.
+                    m_level[i] = (hz < 0.45 * m_sr) ? 0.5 * k_plate_level[i] * shine : 0.0;
+                    shine *= m_bright;
+                }
+            }
+
+            double                              m_sr{48000.0};
+            double                              m_pitch{k_default_pitch_hz};
+            double                              m_t60{k_default_decay_s};
+            double                              m_tilt{k_default_tilt};
+            double                              m_bright{k_default_bright};
+            std::array<double, k_plate_modes>   m_level{};
+            std::array<mode, 2 * k_plate_modes> m_mode;
+        };
+
+        /// One sympathetic string: a damped, DC-blocked delay loop driven by the input, returning
+        /// the loop's *own ringing* rather than its through-signal — so the caller balances the
+        /// drive against the resonance instead of getting them pre-mixed.
+        ///
+        /// The loop gain is derived from a T60 the way grm_comb.h derives it, and compensated for
+        /// what the damping lowpass and the DC blocker take out at the fundamental, so the stated
+        /// ring time is the ring time you get at any damping setting. The k_fb_max cap keeps the
+        /// loop strictly contractive whatever the compensation asks for.
+        class sympathetic {
+          public:
+            /// Buy the longest loop once (the period of k_min_string_hz plus Hermite margin).
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_reel.prepare(m_sr, 1.0 / k_min_string_hz + 0.001);
+                set(m_hz, m_t60, m_damp_hz);
+                clear();
+            }
+
+            void clear() {
+                m_reel.clear();
+                m_write = 0;
+                m_lp    = 0.0;
+                m_dc_x1 = m_dc_y1 = 0.0;
+            }
+
+            bool prepared() const { return m_reel.prepared(); }
+
+            /// Pitch (Hz), ring time (s), damping corner (Hz). Allocation-free.
+            void set(double hz, double t60, double damp_hz) {
+                m_hz      = hz;
+                m_t60     = t60;
+                m_damp_hz = std::clamp(damp_hz, k_min_damp_hz, k_max_damp_hz);
+                if (!prepared()) {
+                    return;
+                }
+                const double f = std::clamp(hz, k_min_string_hz, 0.49 * m_sr);
+                m_delay        = std::clamp(m_sr / f, k_min_delay, static_cast<double>(m_reel.capacity() - 4));
+
+                const double w = 2.0 * k_pi * f / m_sr;
+                m_lp_a         = 1.0 - std::exp(-2.0 * k_pi * m_damp_hz / m_sr);
+
+                // What one trip round the loop loses to the two in-loop filters at the fundamental.
+                const double p     = 1.0 - m_lp_a;
+                const double lp_g  = m_lp_a / std::sqrt(std::max(1.0 - 2.0 * p * std::cos(w) + p * p, 1e-30));
+                const double dcnum = 2.0 - 2.0 * std::cos(w);
+                const double dcden =
+                    1.0 - 2.0 * tape::k_dc_block_r * std::cos(w) + tape::k_dc_block_r * tape::k_dc_block_r;
+                const double dc_g = tape::k_dc_block_norm * std::sqrt(dcnum / std::max(dcden, 1e-30));
+
+                const double want = std::pow(10.0, -3.0 * m_delay / (std::max(m_t60, k_min_t60) * m_sr));
+                m_fb              = std::min(want / std::max(lp_g * dc_g, 1e-3), k_fb_max);
+            }
+
+            double frequency() const { return m_hz; }
+            double feedback() const { return m_fb; }
+            double delay_samples() const { return m_delay; }
+
+            /// Drive the string one sample and return what it is ringing with.
+            double process(double x) {
+                if (!prepared()) {
+                    return 0.0;
+                }
+                const double delayed = m_reel.read_hermite(static_cast<double>(m_write) - m_delay);
+                m_lp += m_lp_a * (delayed - m_lp);
+                const double dc = tape::k_dc_block_norm * (m_lp - m_dc_x1) + tape::k_dc_block_r * m_dc_y1;
+                m_dc_x1         = m_lp;
+                m_dc_y1         = anti_denormal(dc);
+
+                const double ring = m_fb * m_dc_y1;
+                m_reel.write(m_write, anti_denormal(x + ring));
+                if (++m_write >= m_reel.capacity()) {
+                    m_write = 0;
+                }
+                return ring;
+            }
+
+          private:
+            double     m_sr{48000.0};
+            double     m_hz{k_default_root_hz};
+            double     m_t60{k_default_decay_s};
+            double     m_damp_hz{k_default_damp_hz};
+            double     m_delay{100.0};
+            double     m_fb{0.0};
+            double     m_lp_a{1.0};
+            double     m_lp{0.0};
+            double     m_dc_x1{0.0}, m_dc_y1{0.0};
+            long       m_write{0};
+            tape::reel m_reel;
+        };
+
+        /// The palme's body: twelve sympathetic strings on one soundboard. Only the strings whose
+        /// partials line up with the drive ring loudly, which is the halo the instrument is famous
+        /// for; the sum is normalized by sqrt(12) because those responses are largely incoherent.
+        class harp {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                for (auto& s : m_string) {
+                    s.prepare(m_sr);
+                }
+                retune();
+                clear();
+            }
+
+            void clear() {
+                for (auto& s : m_string) {
+                    s.clear();
+                }
+            }
+
+            bool prepared() const { return m_string[0].prepared(); }
+
+            /// Pitch of the lowest string, in Hz.
+            void set_root_hz(double hz) {
+                m_root = std::clamp(hz, k_min_pitch_hz, k_max_pitch_hz);
+                retune();
+            }
+
+            /// How the twelve are laid out (tuning_index) — see the banner: not a published detail.
+            void set_tuning(int t) {
+                m_tuning = std::clamp(t, 0, k_num_tunings - 1);
+                retune();
+            }
+
+            void set_decay(double t60) {
+                m_t60 = std::clamp(t60, k_min_t60, k_max_t60);
+                retune();
+            }
+
+            /// In-loop damping corner in Hz: how quickly a string loses its upper partials.
+            void set_damping(double hz) {
+                m_damp_hz = std::clamp(hz, k_min_damp_hz, k_max_damp_hz);
+                retune();
+            }
+
+            /// Depth in cents of the fixed per-string scatter — no two strings on a real soundboard
+            /// are in perfect relation. Deterministic (index-keyed hash), so the harp is the same
+            /// harp in every instance.
+            void set_detune(double cents) {
+                m_detune = std::clamp(cents, 0.0, 50.0);
+                retune();
+            }
+
+            double root_hz() const { return m_root; }
+            int    tuning() const { return m_tuning; }
+            double decay() const { return m_t60; }
+            double damping() const { return m_damp_hz; }
+            double detune() const { return m_detune; }
+            double string_hz(int i) const {
+                return (i >= 0 && i < k_strings) ? m_string[static_cast<size_t>(i)].frequency() : 0.0;
+            }
+            /// The loop gain a string settled on. Worth exposing rather than deriving twice: it is
+            /// where the damping-versus-ring-time cap becomes visible (see the header's limits).
+            double string_feedback(int i) const {
+                return (i >= 0 && i < k_strings) ? m_string[static_cast<size_t>(i)].feedback() : 0.0;
+            }
+            double samplerate() const { return m_sr; }
+
+            double process(double x) {
+                double sum = 0.0;
+                for (auto& s : m_string) {
+                    sum += s.process(x);
+                }
+                return sum * k_harp_norm;
+            }
+
+          private:
+            void retune() {
+                for (int i = 0; i < k_strings; ++i) {
+                    const double step  = (m_tuning == tuning_harmonic) ? static_cast<double>(i + 1)
+                                                                       : std::exp2(static_cast<double>(i) / 12.0);
+                    const double drift = std::exp2(m_detune * index_unit(static_cast<uint64_t>(100 + i)) / 1200.0);
+                    m_string[static_cast<size_t>(i)].set(m_root * step * drift, m_t60, m_damp_hz);
+                }
+            }
+
+            double                             m_sr{48000.0};
+            double                             m_root{k_default_root_hz};
+            int                                m_tuning{tuning_chromatic};
+            double                             m_t60{k_default_decay_s};
+            double                             m_damp_hz{k_default_damp_hz};
+            double                             m_detune{k_default_detune_c};
+            std::array<sympathetic, k_strings> m_string;
+        };
+
+        /// The moving-iron driver — the stage a diffuseur modelled as a pure resonator is missing.
+        ///
+        /// `drive` gains the signal into the motor. `asymmetry` is the moving-iron principle: force
+        /// follows the square of the gap flux, so with a bias current the residual squared term
+        /// puts second-harmonic distortion on the output in proportion to level. `saturation` is
+        /// the shared bounded soft clipper (vca::swing_shape, exactly linear at 0), which is here
+        /// because the squared law is expansive and something must bound it — a modelling
+        /// necessity, not a measured stage. A DC blocker follows, because a squared term rectifies.
+        ///
+        /// Neither nonlinear coefficient is fitted to anything; see the file banner.
+        ///
+        /// The output is bounded by 2/`saturation` rather than the saturator's own 1/`saturation`:
+        /// a hard-driven squared law is a nearly-constant positive waveform with brief negative
+        /// excursions, and removing that large DC offset doubles the worst-case swing. Measured
+        /// and pinned by test, because 1/saturation is the number you would expect and it is wrong.
+        class transducer {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                clear();
+            }
+
+            void clear() {
+                m_dc_x1 = 0.0;
+                m_dc_y1 = 0.0;
+            }
+
+            void set_drive(double lin) { m_drive = std::max(0.0, lin); }
+            void set_asymmetry(double a) { m_asym = std::clamp(a, 0.0, 1.0); }
+            void set_saturation(double s) { m_sat = std::max(0.0, s); }
+
+            double drive() const { return m_drive; }
+            double asymmetry() const { return m_asym; }
+            double saturation() const { return m_sat; }
+
+            /// With drive 1, asymmetry 0 and saturation 0 this is a bitwise passthrough apart from
+            /// the DC blocker — pinned by test, and the reason a caller can switch the stage off.
+            double process(double x) {
+                const double u  = m_drive * x;
+                const double v  = u + m_asym * u * u; // (I0 + i)^2 leaves a squared term
+                const double s  = vca::swing_shape(v, m_sat);
+                const double dc = tape::k_dc_block_norm * (s - m_dc_x1) + tape::k_dc_block_r * m_dc_y1;
+                m_dc_x1         = s;
+                m_dc_y1         = anti_denormal(dc);
+                return m_dc_y1;
+            }
+
+          private:
+            double m_sr{48000.0};
+            double m_drive{k_default_drive};
+            double m_asym{k_default_asymmetry};
+            double m_sat{k_default_sat};
+            double m_dc_x1{0.0}, m_dc_y1{0.0};
+        };
+
+        /// Shared plumbing for both cabinets: the transducer, the equal-power balance against the
+        /// dry signal, the output level, and the anti-zipper ramps they ride. The body is the only
+        /// thing the two machines do not share, so it is the only thing they define themselves.
+        class cabinet {
+          public:
+            cabinet() {
+                m_mix.snap(k_default_mix);
+                m_level.snap(k_default_level);
+                m_drive.snap(k_default_drive);
+                m_asym.snap(k_default_asymmetry);
+                m_sat.snap(k_default_sat);
+            }
+
+            /// Transducer drive, linear and slewed.
+            void set_drive(double lin) { m_drive.to(std::max(0.0, lin), smooth_samples()); }
+
+            /// The moving-iron squared term, [0, 1], slewed.
+            void set_asymmetry(double a) { m_asym.to(std::clamp(a, 0.0, 1.0), smooth_samples()); }
+
+            /// The bounding saturator's drive; 0 is exactly linear (vca::swing_shape contract).
+            void set_saturation(double s) { m_sat.to(std::max(0.0, s), smooth_samples()); }
+
+            /// Balance between the dry signal and the diffuseur, 0..100, equal-power.
+            void set_mix(double pct) { m_mix.to(std::clamp(pct, 0.0, 100.0), smooth_samples()); }
+
+            /// Output level, linear.
+            void set_level(double lin) { m_level.to(lin, smooth_samples()); }
+
+            void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+            double drive() const { return m_drive.target(); }
+            double asymmetry() const { return m_asym.target(); }
+            double saturation() const { return m_sat.target(); }
+            double mix() const { return m_mix.target(); }
+            double level() const { return m_level.target(); }
+            double smooth_ms() const { return m_smooth_ms; }
+            double samplerate() const { return m_sr; }
+
+            /// Direct access to the driver, so a caller (or a null test) can reach the same stage
+            /// the machine drives.
+            transducer&       driver() { return m_driver; }
+            const transducer& driver() const { return m_driver; }
+
+            bool prepared() const { return m_prepared; }
+
+          protected:
+            void prepare_common(double sr) {
+                m_sr       = (sr > 0.0) ? sr : 48000.0;
+                m_prepared = true;
+                m_driver.prepare(m_sr);
+                m_mix.snap(m_mix.target());
+                m_level.snap(m_level.target());
+                m_drive.snap(m_drive.target());
+                m_asym.snap(m_asym.target());
+                m_sat.snap(m_sat.target());
+            }
+
+            /// Tick the transducer ramps and run the driver — the stage that comes BEFORE the body.
+            double drive_stage(double in) {
+                m_driver.set_drive(m_drive.tick());
+                m_driver.set_asymmetry(m_asym.tick());
+                m_driver.set_saturation(m_sat.tick());
+                return m_driver.process(in);
+            }
+
+            /// Balance the body's output against the dry input and apply the level. The endpoints
+            /// are exact rather than cos(pi/2)-approximate, so a fully wet cabinet really is the
+            /// cabinet and a fully dry one really is the input (the stammer.h rule) — which is
+            /// what lets the wiring null test compare bitwise.
+            double blend(double dry, double wet) {
+                const double pct = m_mix.tick();
+                const double lin = m_level.tick();
+                if (pct >= 100.0) {
+                    return wet * lin;
+                }
+                if (pct <= 0.0) {
+                    return dry * lin;
+                }
+                const double theta = pct * 0.01 * (k_pi * 0.5);
+                return (std::cos(theta) * dry + std::sin(theta) * wet) * lin;
+            }
+
+            long smooth_samples() const { return static_cast<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double     m_sr{48000.0};
+            bool       m_prepared{false};
+            double     m_smooth_ms{k_default_smooth_ms};
+            transducer m_driver;
+            tape::ramp m_drive, m_asym, m_sat, m_mix, m_level;
+        };
+
+        /// **tap.metallique~** — the motor-driven gong. A transducer into a plate, balanced against
+        /// the dry signal. The body's parameters are not ramped: they retune sixteen resonators, so
+        /// they are set-and-hold rather than sweepable (see the header's limits).
+        class metallique : public cabinet {
+          public:
+            void prepare(double sr) {
+                prepare_common(sr);
+                m_plate.prepare(m_sr);
+                clear();
+            }
+
+            void clear() {
+                m_plate.clear();
+                m_driver.clear();
+            }
+
+            void set_pitch_hz(double hz) { m_plate.set_pitch_hz(hz); }
+            void set_decay(double t60) { m_plate.set_decay(t60); }
+            void set_tilt(double t) { m_plate.set_tilt(t); }
+            void set_brightness(double b) { m_plate.set_brightness(b); }
+
+            double pitch_hz() const { return m_plate.pitch_hz(); }
+            double decay() const { return m_plate.decay(); }
+            double tilt() const { return m_plate.tilt(); }
+            double brightness() const { return m_plate.brightness(); }
+
+            plate&       body() { return m_plate; }
+            const plate& body() const { return m_plate; }
+
+            double process(double in) {
+                if (!prepared()) {
+                    return in;
+                }
+                return blend(in, m_plate.process(drive_stage(in)));
+            }
+
+            void process(const double* in, double* out, size_t n) {
+                for (size_t i = 0; i < n; ++i) {
+                    out[i] = process(in[i]);
+                }
+            }
+
+          private:
+            plate m_plate;
+        };
+
+        /// **tap.palme~** — the electromagnet and its twelve strings. A transducer into a harp,
+        /// balanced against the dry signal. Run a guitar through it.
+        class palme : public cabinet {
+          public:
+            void prepare(double sr) {
+                prepare_common(sr);
+                m_harp.prepare(m_sr);
+                clear();
+            }
+
+            void clear() {
+                m_harp.clear();
+                m_driver.clear();
+            }
+
+            void set_root_hz(double hz) { m_harp.set_root_hz(hz); }
+            void set_tuning(int t) { m_harp.set_tuning(t); }
+            void set_decay(double t60) { m_harp.set_decay(t60); }
+            void set_damping(double hz) { m_harp.set_damping(hz); }
+            void set_detune(double cents) { m_harp.set_detune(cents); }
+
+            double root_hz() const { return m_harp.root_hz(); }
+            int    tuning() const { return m_harp.tuning(); }
+            double decay() const { return m_harp.decay(); }
+            double damping() const { return m_harp.damping(); }
+            double detune() const { return m_harp.detune(); }
+
+            harp&       body() { return m_harp; }
+            const harp& body() const { return m_harp; }
+
+            double process(double in) {
+                if (!prepared()) {
+                    return in;
+                }
+                return blend(in, m_harp.process(drive_stage(in)));
+            }
+
+            void process(const double* in, double* out, size_t n) {
+                for (size_t i = 0; i < n; ++i) {
+                    out[i] = process(in[i]);
+                }
+            }
+
+          private:
+            harp m_harp;
+        };
+
+    } // namespace diffuseur
+} // namespace tap::tools
diff --git a/include/taptools/fuzz.h b/include/taptools/fuzz.h
new file mode 100644
index 0000000..7d710db
--- /dev/null
+++ b/include/taptools/fuzz.h
@@ -0,0 +1,592 @@
+/// @file
+/// @brief      Portable two-stage fuzz/distortion kernel for tap.fuzz~ — no Max/Min dependency.
+/// @details    The third Radiohead-family kernel (book/PLAN-radiohead-family.md): the
+///             two-stage, tone-stacked distortion of the early-90s British "shred" pedal class —
+///             the OK Computer-era dirt behind *Paranoid Android* and *My Iron Lung*. A cascaded
+///             clipper pair with a bass / contrast / treble voicing section, and a sibling of
+///             overdrive.h rather than a replacement: that object is a *feedback* soft-clipper
+///             chasing the TS lineage, this one is the harder, more scooped school.
+///
+///             Method, and where it comes from: the architecture is the simplified cascade of
+///             Yeh, Abel & Smith, "Simplified, Physically-Informed Models of Distortion and
+///             Overdrive Guitar Effects Pedals" (Proc. DAFx-07, Bordeaux) — **conditioning
+///             filter -> memoryless nonlinearity -> equalization filter**, twice. That paper's
+///             own justification is the one this kernel relies on: the diode limiter is really a
+///             lowpass whose pole moves with voltage (dVo/dt = (Vi - Vo)/RC - (2 Is/C)
+///             sinh(Vo/Vt), from the Shockley model Id = Is(exp(V/Vt) - 1)), its exact ODE is
+///             expensive, and approximating it as a static curve between fixed filters is
+///             justified perceptually and measured there against real pedals. The paper compares
+///             tanh, arctan and a tanh approximation against the tabulated DC curve; this kernel
+///             uses the tanh family, with a sharpness parameter so one curve spans soft knee to
+///             near-hard clip. The paper's note that a real op-amp stage clips *asymmetrically*,
+///             producing even harmonics where an odd-only model predicts none, is why
+///             `asymmetry` exists.
+///
+///             **What is and is not claimed.** This is a recreation of a *class* of circuit —
+///             two cascaded clipping stages and a three-control tone section — not a component
+///             model of any one pedal. No resistor, capacitor, diode, or corner frequency here
+///             is claimed as measured from a unit, and the control names follow the layout that
+///             class of pedal conventionally carries rather than asserting what any particular
+///             one does. The voicing constants (k_voice_*) are the "sound" of the object, chosen
+///             by design and expected to be retouched in an in-Max voicing pass — the same
+///             posture overdrive.h takes about its own, and the same posture tapecho.h takes
+///             about head spacings.
+///
+///             Two components and a thin composition, the family's habit:
+///             - `stage` — one conditioning highpass, one gain, one memoryless curve (knee
+///               sharpness + asymmetry), one equalization lowpass. The DAFx-07 triple, and the
+///               only nonlinear thing in the file. Two of them cascade: the first is the
+///               op-amp-ish gain stage (softer knee, most of the gain, the asymmetry), the
+///               second the shunt-diode limiter (harder knee, unity gain).
+///             - `tone` — the voicing: a low shelf, a high shelf, and a mid scoop whose depth is
+///               `contrast`. Linear, entirely outside the nonlinearity, RBJ biquads.
+///             - `pedal` — input gain, the two stages inside the oversampled region, the DC
+///               blocker, the tone stack, output level.
+///
+///             Aliasing: the clipper pair runs oversampled (1/2/4/8x, **default 4x**) by
+///             **cascaded 2x stages** — one zero-stuff-by-two and one 8th-order Butterworth per
+///             doubling, each cutting at 0.225 of its own output rate, and the mirror of that on
+///             the way down. Two things here differ from the house pattern and both are
+///             measurements rather than preferences: the pattern's 4th-order filter is not steep
+///             enough for this curve, and its single zero-stuff-by-N is what made more
+///             oversampling measure *worse* here. Cascading fixes that — see butterworth8's
+///             comment for the before/after table. DAFx-07 notes that typical implementations
+///             use 8-10x and that residual aliases there tend to be masked by the dense spectrum
+///             of guitar distortion; this kernel's curve is C-infinity rather than a hard corner,
+///             which is why a modest factor already buys two to three orders of magnitude.
+///
+///             Honest limits:
+///             - The nonlinearity is static. The pole-moves-with-voltage behaviour of the real
+///               limiter is approximated by fixed filters around a fixed curve — that is the
+///               DAFx-07 simplification, adopted deliberately, and it is why this is a
+///               *simplified physically-informed* model and not a circuit solver.
+///             - No component values, no schematic netlist, no claim of matching a unit. If you
+///               need a specific pedal, this is not it; it is that pedal's *class*.
+///             - Hard settings alias. `edge` near 1 sharpens the knee toward a corner, which is
+///               exactly where a static curve is worst; raise `oversample` before blaming the
+///               tone controls.
+///             - `contrast` is a mid scoop of this kernel's own design. The name is the class's;
+///               the curve is not claimed to be anyone's.
+///             - Mono, and gain staging is the caller's job past `level`.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+
+namespace tap::tools {
+    namespace fuzz {
+
+        constexpr double k_pi                = 3.14159265358979323846;
+        constexpr double k_default_smooth_ms = 20.0;
+        constexpr double k_dc_r              = 0.9997; // the Jamoma TTDCBlock constant, via tap.dcblock~
+
+        // Gain mapping: `gain` 0..1 sweeps the first stage's drive linearly in dB. The floor is
+        // below unity on purpose: the second stage carries its own small-signal gain (the tanh
+        // family's slope is knee/tanh(knee), ~2 at the stock knee), so a floor at unity would
+        // arrive at the limiter already saturated and the knob would do nothing over most of its
+        // travel. Gain staging across a cascade is the whole game; these two numbers are it.
+        constexpr int    k_max_os     = 8;     // 1, 2, 4 or 8
+        constexpr int    k_max_stages = 3;     // log2(k_max_os): one 2x resampler per doubling
+        constexpr int    k_default_os = 4;     // measured: within noise of 8x, and never bad
+        constexpr double k_os_fc_norm = 0.225; // each 2x stage's corner, of its own output rate
+
+        constexpr double k_gain_min_db    = -12.0;
+        constexpr double k_gain_max_db    = 36.0;
+        constexpr double k_level_range_db = 24.0;
+
+        // Voicing: the sound of the object, chosen by design rather than measured (see the
+        // banner). Retouch these in the in-Max voicing pass, not analytically.
+        constexpr double k_voice_stage1_hp_hz = 90.0;   // what reaches the first clipper
+        constexpr double k_voice_stage1_lp_hz = 7500.0; // the limiter's embedded lowpass, fixed
+        constexpr double k_voice_stage2_hp_hz = 150.0;  // tighter into the second stage
+        constexpr double k_voice_stage2_lp_hz = 5200.0;
+        constexpr double k_voice_stage1_knee  = 1.6;  // softer: the op-amp-ish stage
+        constexpr double k_voice_stage2_knee  = 2.0;  // harder: the shunt limiter, at edge 0
+        constexpr double k_voice_edge_knee    = 12.0; // ...and at edge 1
+        constexpr double k_voice_stage2_gain  = 0.5;  // fixed drive into the second stage
+        constexpr double k_voice_bass_hz      = 180.0;
+        constexpr double k_voice_treble_hz    = 2600.0;
+        constexpr double k_voice_mid_hz       = 620.0;
+        constexpr double k_voice_mid_q        = 0.85;
+        constexpr double k_voice_shelf_db     = 12.0; // full-scale bass/treble travel
+        constexpr double k_voice_scoop_db     = 14.0; // full-scale contrast scoop
+
+        /// Per-sample linear parameter ramp — the anti-zipper unit, the delay.h shape.
+        class ramp {
+          public:
+            void snap(double v) {
+                m_current = m_target = v;
+                m_inc                = 0.0;
+                m_remaining          = 0;
+            }
+            void to(double tgt, long n) {
+                if (n < 1 || tgt == m_current) {
+                    snap(tgt);
+                }
+                else {
+                    m_target    = tgt;
+                    m_inc       = (tgt - m_current) / static_cast<double>(n);
+                    m_remaining = n;
+                }
+            }
+            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}, m_target{0.0}, m_inc{0.0};
+            long   m_remaining{0};
+        };
+
+        /// RBJ biquad (transposed direct form II) — the cookbook designs, copied with citation
+        /// per the house reuse rule (same struct as overdrive.h; kernels stay self-contained).
+        struct biquad {
+            double b0{1.0}, b1{0.0}, b2{0.0}, a1{0.0}, a2{0.0};
+            double z1{0.0}, z2{0.0};
+
+            void design_lowpass(double fc_norm, double q) {
+                const double w     = 2.0 * k_pi * fc_norm;
+                const double alpha = std::sin(w) / (2.0 * q);
+                const double cw    = std::cos(w);
+                const double a0    = 1.0 + alpha;
+                b0                 = ((1.0 - cw) * 0.5) / a0;
+                b1                 = (1.0 - cw) / a0;
+                b2                 = b0;
+                a1                 = (-2.0 * cw) / a0;
+                a2                 = (1.0 - alpha) / a0;
+            }
+            void design_peaking(double fc_norm, double q, double gain_db) {
+                const double A     = std::pow(10.0, gain_db / 40.0);
+                const double w     = 2.0 * k_pi * fc_norm;
+                const double alpha = std::sin(w) / (2.0 * q);
+                const double cw    = std::cos(w);
+                const double a0    = 1.0 + alpha / A;
+                b0                 = (1.0 + alpha * A) / a0;
+                b1                 = (-2.0 * cw) / a0;
+                b2                 = (1.0 - alpha * A) / a0;
+                a1                 = b1;
+                a2                 = (1.0 - alpha / A) / a0;
+            }
+            void design_lowshelf(double fc_norm, double gain_db) { // shelf slope S = 1
+                const double A    = std::pow(10.0, gain_db / 40.0);
+                const double w    = 2.0 * k_pi * fc_norm;
+                const double cw   = std::cos(w);
+                const double sa   = std::sin(w) * 0.5 * std::sqrt(2.0);
+                const double ap1  = A + 1.0;
+                const double am1  = A - 1.0;
+                const double sqA2 = 2.0 * std::sqrt(A) * sa;
+                const double a0   = ap1 + am1 * cw + sqA2;
+                b0                = A * (ap1 - am1 * cw + sqA2) / a0;
+                b1                = 2.0 * A * (am1 - ap1 * cw) / a0;
+                b2                = A * (ap1 - am1 * cw - sqA2) / a0;
+                a1                = -2.0 * (am1 + ap1 * cw) / a0;
+                a2                = (ap1 + am1 * cw - sqA2) / a0;
+            }
+            void design_highshelf(double fc_norm, double gain_db) {
+                const double A    = std::pow(10.0, gain_db / 40.0);
+                const double w    = 2.0 * k_pi * fc_norm;
+                const double cw   = std::cos(w);
+                const double sa   = std::sin(w) * 0.5 * std::sqrt(2.0);
+                const double ap1  = A + 1.0;
+                const double am1  = A - 1.0;
+                const double sqA2 = 2.0 * std::sqrt(A) * sa;
+                const double a0   = ap1 - am1 * cw + sqA2;
+                b0                = A * (ap1 + am1 * cw + sqA2) / a0;
+                b1                = -2.0 * A * (am1 + ap1 * cw) / a0;
+                b2                = A * (ap1 + am1 * cw - sqA2) / a0;
+                a1                = 2.0 * (am1 - ap1 * cw) / a0;
+                a2                = (ap1 - am1 * cw - sqA2) / a0;
+            }
+            double tick(double x) {
+                const double y = b0 * x + z1;
+                z1             = b1 * x - a1 * y + z2;
+                z2             = b2 * x - a2 * y;
+                return y;
+            }
+            void reset() { z1 = z2 = 0.0; }
+        };
+
+        /// 8th-order Butterworth lowpass as four cascaded biquads — the oversampling chain's
+        /// anti-image and anti-alias filter.
+        ///
+        /// The house pattern (tap.ladder~ / overdrive.h) uses a 4th-order pair here. It is not
+        /// steep enough for this kernel: with it, alias energy at the fold frequencies measured
+        /// 1.7e-2 at 4x against 2.8e-3 at 2x — more oversampling was *worse*. Eighth order cuts
+        /// that to 2.7e-3, a ~6x improvement at 4x.
+        ///
+        /// **Eighth order did not fix the ordering; cascading did.** The chain used to zero-stuff
+        /// by N in one step and filter once, at 0.45/N normalized — 0.056 at 8x. That leaves N-1
+        /// images for a single filter to suppress at a corner that gets tighter with every
+        /// doubling, and residuals entering the clipper intermodulate into products that are not
+        /// harmonics of the input, which is exactly what the probe measures. ondes.h supplied the
+        /// first evidence for that reading: same filters, comparably hard nonlinearity, but a
+        /// SOURCE with nothing zero-stuffed, and no reversal.
+        ///
+        /// Acting on it confirms it. The chain is now one 2x stage per doubling, each filtering at
+        /// 0.225 of its own operating rate — a corner that never tightens however deep the cascade
+        /// goes. Fold energy against a hard-driven sine, same probe both sides (fuzz.ipynb §5):
+        ///
+        ///     tone        single zero-stuff by N              cascaded 2x
+        ///            1x       2x       4x       8x         2x       4x       8x
+        ///    3067  7.8e-2   1.0e-3   2.5e-3   3.1e-3     1.0e-3   9.1e-4   8.9e-4
+        ///    3733  1.2e-1   2.7e-5   7.4e-4   1.8e-3     3.0e-5   2.1e-5   2.2e-5
+        ///    4409  1.4e-1   9.4e-7   7.9e-4   2.1e-3     8.6e-7   3.7e-7   3.7e-7
+        ///    5171  1.5e-1   3.3e-4   9.2e-4   2.3e-3     3.3e-4   2.0e-7   1.9e-7
+        ///    6421  8.5e-2   3.2e-2   5.8e-4   1.3e-3     3.2e-2   3.9e-7   3.6e-7
+        ///    7211  1.0e-1   8.9e-2   4.3e-4   9.6e-4     8.9e-2   4.5e-7   4.3e-7
+        ///    8123  9.0e-2   7.8e-2   1.1e-3   1.1e-3     7.8e-2   1.1e-3   2.0e-5
+        ///    9337  9.1e-2   8.1e-2   2.9e-4   6.2e-5     8.1e-2   1.8e-6   2.4e-8
+        ///   10499  1.5e-1   1.7e-1   1.8e-3   1.4e-4     1.7e-1   1.2e-5   1.6e-6
+        ///
+        /// The worst step-up past 2x is now a ratio of 1.017 — flat, where the old chain ran up to
+        /// 3x worse per doubling. Where the old 4x and 8x were merely adequate they are now two to
+        /// four orders of magnitude better (5171 Hz at 8x: 2.3e-3 to 1.9e-7). The 2x column is
+        /// unchanged, as it must be: one doubling is one stage either way.
+        ///
+        /// It cost essentially nothing: 8x runs 3.16 % of a core against the old 3.02 % (20 s of
+        /// audio), because the extra filter instances are cheap next to the clipper they surround.
+        ///
+        /// **The old default was also generalized from one probe.** Every earlier number here came
+        /// from 3733 Hz, where 2x happens to look best. Swept across tones, 2x collapses above
+        /// about 6 kHz — at 10.5 kHz it is *worse than no oversampling at all* — because the
+        /// clipper's low harmonics already exceed the base Nyquist there and one doubling does not
+        /// move them out of the way. **4x is the default now.** 8x earns its keep above about
+        /// 7.5 kHz, where harmonics start folding inside the 4x band before decimation (8123 Hz is
+        /// the visible case above); below that the two are indistinguishable.
+        ///
+        /// Two limits of the probe itself, since it is what every number here rests on: it only
+        /// measures folding at all above about 3 kHz (below that, harmonics 8-13 are still under
+        /// Nyquist and it reads real harmonics instead), and tones that are simple rational
+        /// multiples of the sample rate put folds on top of each other or exactly at Nyquist,
+        /// where it reads nonsense. The sweep tones are chosen to avoid both.
+        ///
+        /// (Whether overdrive.h is owed the same change is a live question — different
+        /// nonlinearity, different gain structure, so it needs its own measurement. ondes.h does
+        /// not need it: it is a source and zero-stuffs nothing.)
+        ///
+        /// Pole Qs are the standard 8th-order Butterworth set, Q_k = 1/(2 cos((2k+1)pi/16)).
+        struct butterworth8 {
+            biquad s1, s2, s3, s4;
+            void   design(double fc_norm) {
+                s1.design_lowpass(fc_norm, 0.50979558);
+                s2.design_lowpass(fc_norm, 0.60134489);
+                s3.design_lowpass(fc_norm, 0.89997622);
+                s4.design_lowpass(fc_norm, 2.56291545);
+            }
+            double tick(double x) { return s4.tick(s3.tick(s2.tick(s1.tick(x)))); }
+            void   reset() {
+                s1.reset();
+                s2.reset();
+                s3.reset();
+                s4.reset();
+            }
+        };
+
+        /// The tanh family with an adjustable knee: `k` small is nearly linear, `k` large
+        /// approaches a hard corner. Normalized so shape(1, k) == 1 for every k, which keeps the
+        /// stage's gain structure independent of the knee setting.
+        inline double shape(double x, double k) {
+            if (k < 1e-6) {
+                return x;
+            }
+            return std::tanh(k * x) / std::tanh(k);
+        }
+
+        /// One DAFx-07 stage: conditioning highpass -> gain -> memoryless curve -> equalization
+        /// lowpass. Allocation-free; coefficients are set by the owner each block.
+        class stage {
+          public:
+            void prepare(double sr, double hp_hz, double lp_hz) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                set_corners(hp_hz, lp_hz);
+                clear();
+            }
+
+            /// Recompute the fixed corners for a (possibly oversampled) rate.
+            void set_corners(double hp_hz, double lp_hz) {
+                m_a_hp = 1.0 - std::exp(-2.0 * k_pi * hp_hz / m_sr);
+                m_a_lp = 1.0 - std::exp(-2.0 * k_pi * lp_hz / m_sr);
+            }
+
+            void clear() { m_hp = m_lp = 0.0; }
+
+            /// `gain` is linear into the curve, `knee` the sharpness, `bias` the asymmetry that
+            /// buys even harmonics. The bias is corrected at the output so silence stays exactly
+            /// at zero (the overdrive.h contract).
+            double process(double x, double gain, double knee, double bias) {
+                m_hp += m_a_hp * (x - m_hp);
+                const double u = (x - m_hp) * gain;
+                const double y = shape(u + bias, knee) - shape(bias, knee);
+                m_lp += m_a_lp * (y - m_lp);
+                return m_lp;
+            }
+
+          private:
+            double m_sr{48000.0};
+            double m_a_hp{1.0}, m_a_lp{1.0};
+            double m_hp{0.0}, m_lp{0.0};
+        };
+
+        /// The voicing section: low shelf, mid scoop, high shelf. Linear, and entirely outside
+        /// the nonlinearity — the "equalization filter" of the cascade, at the audio rate.
+        class tone {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                design(0.0, 0.0, 0.0);
+                clear();
+            }
+
+            void clear() {
+                m_low.reset();
+                m_mid.reset();
+                m_high.reset();
+            }
+
+            /// bass/treble in -1..1 (full travel is k_voice_shelf_db either way), contrast in
+            /// 0..1 (0 flat, 1 the full mid scoop).
+            void design(double bass, double treble, double contrast) {
+                m_low.design_lowshelf(k_voice_bass_hz / m_sr, bass * k_voice_shelf_db);
+                m_high.design_highshelf(k_voice_treble_hz / m_sr, treble * k_voice_shelf_db);
+                m_mid.design_peaking(k_voice_mid_hz / m_sr, k_voice_mid_q, -contrast * k_voice_scoop_db);
+            }
+
+            double process(double x) { return m_high.tick(m_mid.tick(m_low.tick(x))); }
+
+          private:
+            double m_sr{48000.0};
+            biquad m_low, m_mid, m_high;
+        };
+
+        /// The pedal: two stages inside the oversampled region, then DC block, tone, level.
+        class pedal {
+          public:
+            pedal() {
+                m_gain.snap(0.5);
+                m_edge.snap(0.5);
+                m_asymmetry.snap(0.0);
+                m_bass.snap(0.0);
+                m_treble.snap(0.0);
+                m_contrast.snap(0.35);
+                m_level_db.snap(0.0);
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                configure();
+                m_gain.snap(m_gain.target());
+                m_edge.snap(m_edge.target());
+                m_asymmetry.snap(m_asymmetry.target());
+                m_bass.snap(m_bass.target());
+                m_treble.snap(m_treble.target());
+                m_contrast.snap(m_contrast.target());
+                m_level_db.snap(m_level_db.target());
+                m_tone.design(m_bass.current(), m_treble.current(), m_contrast.current());
+                clear();
+            }
+
+            void clear() {
+                m_s1.clear();
+                m_s2.clear();
+                m_tone.clear();
+                for (auto& f : m_up) {
+                    f.reset();
+                }
+                for (auto& f : m_down) {
+                    f.reset();
+                }
+                m_dc_x1 = m_dc_y1 = 0.0;
+            }
+
+            bool prepared() const { return m_sr > 0.0 && m_configured; }
+
+            // -- parameters (click-free; safe while audio runs) ----------------------------------
+
+            /// 0..1, sweeping the first stage's drive linearly in dB.
+            void set_gain(double g) { m_gain.to(std::clamp(g, 0.0, 1.0), smooth_samples()); }
+
+            /// 0..1: how sharp the second stage's knee is — soft-ish limiter through to near-hard
+            /// clip. High settings alias; raise oversample.
+            void set_edge(double e) { m_edge.to(std::clamp(e, 0.0, 1.0), smooth_samples()); }
+
+            /// 0..1 of clipping asymmetry — the even-harmonic control, and the thing an odd-only
+            /// static curve structurally cannot produce.
+            void set_asymmetry(double a) { m_asymmetry.to(std::clamp(a, 0.0, 1.0), smooth_samples()); }
+
+            void set_bass(double b) { m_bass.to(std::clamp(b, -1.0, 1.0), smooth_samples()); }
+            void set_treble(double t) { m_treble.to(std::clamp(t, -1.0, 1.0), smooth_samples()); }
+
+            /// 0..1 mid-scoop depth. This kernel's own curve — see the header.
+            void set_contrast(double c) { m_contrast.to(std::clamp(c, 0.0, 1.0), smooth_samples()); }
+
+            /// Output level in dB, +-k_level_range_db.
+            void set_level_db(double db) {
+                m_level_db.to(std::clamp(db, -k_level_range_db, k_level_range_db), smooth_samples());
+            }
+
+            /// 1, 2, 4 or 8; 4 is the default. Reconfigures the stage corners and the resampler
+            /// cascade — not real-time-safe. 2x is offered but measures badly above about 6 kHz
+            /// (see the butterworth8 banner); prefer 4 unless something specific measures better.
+            void set_oversample(int os) {
+                const int v = (os >= 8) ? 8 : (os >= 4) ? 4 : (os >= 2) ? 2 : 1;
+                if (v != m_os) {
+                    m_os = v;
+                    configure();
+                    clear();
+                }
+            }
+
+            void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double gain() const { return m_gain.target(); }
+            double edge() const { return m_edge.target(); }
+            double asymmetry() const { return m_asymmetry.target(); }
+            double bass() const { return m_bass.target(); }
+            double treble() const { return m_treble.target(); }
+            double contrast() const { return m_contrast.target(); }
+            double level_db() const { return m_level_db.target(); }
+            int    oversample() const { return m_os; }
+            double smooth_ms() const { return m_smooth_ms; }
+            double samplerate() const { return m_sr; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            double process(double x) {
+                if (!prepared()) {
+                    return x;
+                }
+                const double gain     = m_gain.tick();
+                const double edge     = m_edge.tick();
+                const double asym     = m_asymmetry.tick();
+                const double bass     = m_bass.tick();
+                const double treble   = m_treble.tick();
+                const double contrast = m_contrast.tick();
+                const double level    = m_level_db.tick();
+
+                if (bass != m_tone_bass || treble != m_tone_treble || contrast != m_tone_contrast) {
+                    m_tone.design(bass, treble, contrast);
+                    m_tone_bass     = bass;
+                    m_tone_treble   = treble;
+                    m_tone_contrast = contrast;
+                }
+
+                const double drive = std::pow(10.0, (k_gain_min_db + gain * (k_gain_max_db - k_gain_min_db)) / 20.0);
+                const double knee2 = k_voice_stage2_knee + edge * (k_voice_edge_knee - k_voice_stage2_knee);
+                const double bias  = asym * 0.7; // inside the curve; corrected at the stage output
+
+                double y = 0.0;
+                if (m_os == 1) {
+                    y = core(x, drive, knee2, bias);
+                }
+                else {
+                    // Cascaded 2x: each stage doubles the rate and suppresses the single image
+                    // that doubling creates, at a comfortable normalized corner. See the
+                    // butterworth8 banner for why this is not one zero-stuff by N.
+                    std::array<double, k_max_os> a{}, b{};
+                    a[0]      = x;
+                    int count = 1;
+                    for (int i = 0; i < m_stages; ++i) {
+                        for (int j = 0; j < count; ++j) {
+                            b[static_cast<size_t>(2 * j)]     = m_up[i].tick(a[static_cast<size_t>(j)] * 2.0);
+                            b[static_cast<size_t>(2 * j + 1)] = m_up[i].tick(0.0);
+                        }
+                        std::swap(a, b);
+                        count *= 2;
+                    }
+                    for (int j = 0; j < count; ++j) {
+                        a[static_cast<size_t>(j)] = core(a[static_cast<size_t>(j)], drive, knee2, bias);
+                    }
+                    for (int i = m_stages - 1; i >= 0; --i) {
+                        for (int j = 0; j < count / 2; ++j) {
+                            // Filter every sample, keep the first of each pair.
+                            b[static_cast<size_t>(j)] = m_down[i].tick(a[static_cast<size_t>(2 * j)]);
+                            m_down[i].tick(a[static_cast<size_t>(2 * j + 1)]);
+                        }
+                        std::swap(a, b);
+                        count /= 2;
+                    }
+                    y = a[0];
+                }
+
+                // Asymmetry generates DC that the shelves would otherwise pass; always on.
+                const double d = y - m_dc_x1 + k_dc_r * m_dc_y1;
+                m_dc_x1        = y;
+                m_dc_y1        = anti_denormal(d);
+
+                return m_tone.process(m_dc_y1) * std::pow(10.0, level / 20.0);
+            }
+
+            void process(const double* in, double* out, size_t n) {
+                for (size_t i = 0; i < n; ++i) {
+                    out[i] = process(in[i]);
+                }
+            }
+
+          private:
+            /// The two clipping stages, at whatever rate the caller is running.
+            double core(double x, double drive, double knee2, double bias) {
+                const double a = m_s1.process(x, drive, k_voice_stage1_knee, bias);
+                return m_s2.process(a, k_voice_stage2_gain, knee2, 0.0);
+            }
+
+            void configure() {
+                const double osr = m_sr * m_os;
+                m_s1.prepare(osr, k_voice_stage1_hp_hz, k_voice_stage1_lp_hz);
+                m_s2.prepare(osr, k_voice_stage2_hp_hz, k_voice_stage2_lp_hz);
+                m_tone.prepare(m_sr);
+                m_stages = 0;
+                for (int n = m_os; n > 1; n >>= 1) {
+                    ++m_stages;
+                }
+                for (int i = 0; i < m_stages; ++i) {
+                    // 0.225 of each stage's own output rate — its input Nyquist, with margin.
+                    // Every stage gets the same comfortable corner however deep the cascade is,
+                    // which is the entire point of cascading rather than zero-stuffing by N.
+                    m_up[i].design(k_os_fc_norm);
+                    m_down[i].design(k_os_fc_norm);
+                }
+                m_tone_bass = m_tone_treble = m_tone_contrast = std::nan("");
+                m_configured                                  = true;
+            }
+
+            static double anti_denormal(double x) { return (std::abs(x) < 1e-15) ? 0.0 : x; }
+
+            long smooth_samples() const { return static_cast<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double m_sr{48000.0};
+            double m_smooth_ms{k_default_smooth_ms};
+            int    m_os{k_default_os};
+            bool   m_configured{false};
+
+            stage        m_s1, m_s2;
+            tone         m_tone;
+            butterworth8 m_up[k_max_stages], m_down[k_max_stages];
+            int          m_stages{1}; // log2(m_os); 0 at 1x
+            double       m_dc_x1{0.0}, m_dc_y1{0.0};
+
+            // Cached tone targets so the biquads are only redesigned when a control actually moves.
+            double m_tone_bass{0.0}, m_tone_treble{0.0}, m_tone_contrast{0.0};
+
+            ramp m_gain, m_edge, m_asymmetry, m_bass, m_treble, m_contrast, m_level_db;
+        };
+
+    } // namespace fuzz
+} // namespace tap::tools
diff --git a/include/taptools/ondes.h b/include/taptools/ondes.h
new file mode 100644
index 0000000..eb9039e
--- /dev/null
+++ b/include/taptools/ondes.h
@@ -0,0 +1,766 @@
+/// @file
+/// @brief      Portable Ondes Martenot voice kernels (tap.triode~, tap.ondes~) — no Max/Min dep.
+/// @details    The last two pieces of the instrument, and the two the family plan got wrong before
+///             the sources were read. The plan assumed a `vco.h` descendant with waveform
+///             registers. The Ondes Martenot is nothing of the kind: it is a **heterodyne**
+///             instrument whose two 80 kHz oscillators measure as essentially pure (about 0.03 %
+///             second-harmonic distortion even coupled to the rest of the circuit), and every bit
+///             of its character comes from what happens *after* the two of them are summed —
+///             the envelope detector, two triode gain stages, the intensity key, and the
+///             diffuseur.
+///
+///             The circuit is Najnudel, Hélie, Roze & Boutin, "Simulation of an ondes Martenot
+///             circuit", IEEE/ACM TASLP **28**, 2651–2660, 2020, modelling instrument No. 169 as
+///             five port-Hamiltonian stages. This file is **not** that: their full solve runs at
+///             768 kHz and their plugin costs 85 % of a laptop core. What this file takes from
+///             them is their *own* published reductions plus their published component values,
+///             and it says which is which.
+///
+///             Three classes and a thin composition:
+///             - `triode` — one common-cathode gain stage, solved on its load line from the
+///               **enhanced Norman Koren tube model** at a **published operating point**.
+///             - `detector` — the heterodyne pair and its envelope detector, in closed form.
+///             - `voice` — detector into two triode stages into the intensity key.
+///
+///             ## The tube (published model, published parameters)
+///
+///             Koren's model (N. Koren, "Improved vacuum tube models for Spice simulations",
+///             *Glass Audio* 8(5), 1996) as extended with a grid-current branch by Cohen & Hélie
+///             (AES 129th Convention, 2010), which is what the circuit paper uses:
+///
+///                 E1 = (vpc/Kp) · ln(1 + exp(Kp · (1/mu + (vgc + Vct)/sqrt(Kvb + vpc²))))
+///                 ipc = 2·E1^Ex / Kg    for E1 >= 0, else 0
+///                 igc = (vgc - Va)/Rgk  for vgc >= Va, else 0
+///
+///             The parameter sets are **fitted to the actual tubes in ondes No. 169** and are
+///             reproduced verbatim from the circuit paper's Table II — 6F5 in the oscillators,
+///             6C5 in the demodulator and preamplifier, 2A3 in the power amplifier — together
+///             with that table's stage supply voltages and cathode resistors. Nothing here is
+///             voiced by ear; the numbers are the citation.
+///
+///             A stage is then the static solution of the load line
+///             `ipc(vpc, vgc) = (Vbias − Vk − vpc)/Rp` with the cathode bias `Vk = Rk·Ipc` found
+///             at the quiescent point — a **memoryless nonlinearity**, which is exactly the term
+///             in Yeh, Abel & Smith's DAFx-07 simplified cascade (conditioning filter →
+///             memoryless nonlinearity → equalization filter), the same architecture `fuzz.h`
+///             uses. The curve is tabulated once per tube/operating-point change and read with
+///             linear interpolation, so the audio path costs a lookup rather than a root find.
+///
+///             ## The detector (exact, and cheaper than the thing it replaces)
+///
+///             The circuit paper writes the oscillator sum as an amplitude-modulated sinewave,
+///             `cos(Φ) + cos(Φ − φm) = 2 cos(Φ − φm/2) cos(φm/2)`, and detects its envelope with
+///             a triode whose grid sits near zero bias — so the grid-cathode junction behaves as
+///             a diode, conducting only on positive half-cycles — loaded by R4·C21, a time
+///             constant of **200 µs**.
+///
+///             Two consequences, and the second is the one the family plan missed.
+///
+///             **The envelope is not a sinusoid.** For equal oscillator amplitudes it is
+///             `2|cos(π f t)|`, whose Fourier series puts the second harmonic 14.0 dB below the
+///             fundamental, the third 21.3 dB down and the fourth 26.4 dB down — a substantial
+///             harmonic series generated *before any triode touches the signal*. The plan said to
+///             synthesize the difference tone directly as a sinusoid; that would have thrown away
+///             the instrument's single largest source of harmonics. (What the paper replaces with
+///             a sinewave generator is the **oscillators**, not the demodulator.)
+///
+///             **So the carrier need never be simulated.** For amplitudes 1 and `depth` the
+///             envelope is exactly `sqrt(1 + depth² + 2·depth·cos(2π f t))`, and running the
+///             published RC detector on *that* — instant attack through the diode, 200 µs decay
+///             through R4 — reproduces the full 80 kHz heterodyne-plus-diode-plus-RC simulation
+///             to **within 0.10 dB on every harmonic** at every pitch tried, with no carrier to
+///             alias and no 768 kHz to pay for (measured in notebooks/ondes.ipynb §2). The one
+///             systematic difference is a level offset: the closed form sits a uniform 3.0–3.2 %
+///             high, because a follower chasing real carrier half-cycles never quite reaches the
+///             peak between them. A constant scale on a synthesizer with a level control.
+///
+///             The detector's own pitch dependence comes with it, because the RC cannot follow a
+///             fast envelope back down: the second harmonic runs from −14.0 dB at A2 to −19.3 dB
+///             at A6, and the level falls 2.0 dB across those five octaves.
+///
+///             ## The ribbon
+///
+///             The circuit paper's Eq. 7 gives the variable oscillator's capacitance in terms of
+///             ribbon displacement, and the frequency that falls out of it is
+///             `f = A1 · 2^(d / (12 d0))` with A1 = 55 Hz the lowest note and d0 the displacement
+///             of one semitone. So the ribbon is **linear in semitones**, which is why an ondes
+///             glide sounds the way it does, and `set_ribbon()` takes semitones above A1 for
+///             exactly that reason. (The ribbon runs up to about 1.2 m at the highest note.)
+///
+///             ## Oversampling, and a data point for an open question
+///
+///             The nonlinear chain runs oversampled on `fuzz.h`'s shared Butterworth chain, and
+///             here the sequence behaves. Worst non-harmonic energy relative to the fundamental,
+///             at 1× / 2× / 4× / 8× (notebooks/ondes.ipynb §5):
+///
+///                 587 Hz   −79.3  −91.2  −104.5  −103.8
+///                1175 Hz   −65.8  −77.2   −90.6   −92.5
+///                1760 Hz   −57.6  −70.9   −81.1   −82.2
+///                2637 Hz   −51.1  −61.4   −71.8   −83.8
+///                3520 Hz   −45.4  −56.8   −67.0   −74.2
+///
+///             Every doubling is worth about 12 dB up to 4×; past that it is worth 7–12 dB at the
+///             top of the range and nothing at the bottom, where the measurement has already
+///             bottomed out. **Never worse.** 4× is the default because it is where the cost
+///             stops buying uniformly; 8× is there for anyone playing the top octave hard.
+///
+///             That mattered beyond this file. `fuzz.h` measured the opposite — 4× came out
+///             *worse* than 2× there — and had left an untested hypothesis behind: that the
+///             culprit is *imaging*, since zero-stuffing by N leaves N−1 images for one filter to
+///             suppress and their residuals intermodulate in the clipper into products that are
+///             not harmonics of the input. This object is a **source**. Nothing is zero-stuffed
+///             on the way up; the detector simply runs fast, so there are no images at all — and
+///             the sequence never reverses. Same filters, no upsampler, no reversal, which was
+///             the first evidence either way.
+///
+///             **It was acted on, and it held.** `fuzz.h` now cascades one 2× stage per doubling
+///             instead of zero-stuffing by N once, and its reversal is gone: worst step-up past
+///             2× is a ratio of 1.017, and its 4× and 8× improved by two to four orders of
+///             magnitude. This file needs no such change — it has no upsampler to fix — but the
+///             cross-check is worth keeping, because it is why the change was made.
+///
+///             Honest limits:
+///             - **This is not a circuit solve.** It is the published *reductions* of one: the
+///               oscillators replaced by their closed-form envelope (the paper's own
+///               simplification, and here an exact one), the stages reduced to static load-line
+///               curves with their reactive coupling replaced by first-order conditioning and
+///               equalization filters. The paper's full model is passive by construction; this
+///               one is not, and does not claim to be.
+///             - **The power amplifier is off by default**, following the paper: it measures
+///               almost 5 % second harmonic in their simulation, but the authors report its
+///               contribution to the final sound is much less important than the demodulator's
+///               and preamplifier's, and drop it for real-time. Measured here, switching it on
+///               moves total harmonic content from 0.248 to 0.251 and the second harmonic by
+///               0.1 dB — an independent confirmation of their reason, and the reason it is a
+///               switch rather than a deletion.
+///             - **Where the intensity key sits in the chain is not published.** The paper's five
+///               stages do not include it. Placing it after the triodes (the default) makes it a
+///               clean output law; placing it before makes the dirt come up with the pressure.
+///               Both are offered, and the choice is labelled a choice.
+///             - **No waveform registers.** The real instrument has switchable timbres whose
+///               filter shapes are not in any source obtained; adding them from imagination would
+///               be the one thing this file is careful not to do.
+///             - **No diffuseur.** That is `tap.palme~` / `tap.metallique~` — patch one after
+///               this object, which is how the instrument works anyway.
+///             - The tube parameters are a fit to *one* instrument's tubes, and tube-to-tube
+///               spread in 1930s valves is wide.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+
+#include "fuzz.h"   // tap::tools::fuzz — ramp, biquad, butterworth8 (the shared oversampling chain)
+#include "touche.h" // tap::tools::touche::key — the published intensity-key gain law
+
+namespace tap::tools {
+    namespace ondes {
+
+        constexpr double k_pi = 3.14159265358979323846;
+
+        using fuzz::butterworth8;
+        using fuzz::ramp;
+
+        // ---- the tube ------------------------------------------------------------------------
+
+        /// One triode's enhanced-Koren parameter set. Every field is a fitted constant, not a
+        /// design choice.
+        struct tube_params {
+            double mu;  ///< amplification factor
+            double ex;  ///< plate-current exponent
+            double kg;  ///< plate-current scale
+            double kp;  ///< knee sharpness
+            double kvb; ///< knee voltage
+            double vct; ///< contact-potential offset
+            double va;  ///< grid-conduction threshold
+            double rgk; ///< grid-conduction resistance
+        };
+
+        // Najnudel, Hélie, Roze & Boutin, IEEE/ACM TASLP 28 (2020), Table II — fitted to the
+        // datasheets of the tubes actually in ondes Martenot No. 169. Reproduced verbatim.
+        constexpr tube_params k_6f5{98.0, 1.6, 2614.0, 905.0, 1.87, 0.5, 0.33, 1300.0};
+        constexpr tube_params k_6c5{20.0, 1.5, 2837.0, 138.0, 89.0, 0.8, 0.33, 1300.0};
+        constexpr tube_params k_2a3{4.3, 1.5, 1685.0, 43.0, 102.0, -1.2, 0.33, 1300.0};
+
+        /// Which tube. The names are the instrument's: 6F5 oscillates, 6C5 demodulates and
+        /// preamplifies, 2A3 drives the diffuseur.
+        enum tube_index : int { tube_6f5 = 0, tube_6c5, tube_2a3, k_num_tubes };
+
+        inline const tube_params& tube_at(int index) {
+            switch (index) {
+            case tube_6f5:
+                return k_6f5;
+            case tube_2a3:
+                return k_2a3;
+            default:
+                return k_6c5;
+            }
+        }
+
+        /// One stage's published operating point (TASLP Table II). `rp` is the plate load — the
+        /// transformer core-loss resistance for the demodulator and preamplifier, the diffuseur's
+        /// input impedance for the power amplifier.
+        struct operating_point {
+            double vbias;
+            double rk;
+            double rp;
+        };
+
+        constexpr operating_point k_op_demod{100.0, 1000.0, 4000.0};
+        constexpr operating_point k_op_preamp{180.0, 1000.0, 4000.0};
+        constexpr operating_point k_op_power{230.0, 750.0, 1500.0};
+
+        /// Plate current in amps — the enhanced Koren law, verbatim from TASLP Eq. 8.
+        inline double plate_current(const tube_params& t, double vpc, double vgc) {
+            if (vpc <= 0.0) {
+                return 0.0;
+            }
+            const double z = t.kp * (1.0 / t.mu + (vgc + t.vct) / std::sqrt(t.kvb + vpc * vpc));
+            // log1p(exp(z)) with the standard overflow guard: for large z it is z itself.
+            const double s  = (z > 60.0) ? z : std::log1p(std::exp(std::max(z, -60.0)));
+            const double e1 = (vpc / t.kp) * s;
+            return (e1 > 0.0) ? 2.0 * std::pow(e1, t.ex) / t.kg : 0.0;
+        }
+
+        /// Grid current in amps — TASLP Eq. 9. Zero below the conduction threshold, ohmic above.
+        /// It is small next to the plate current, and the circuit paper keeps it because without
+        /// it the modelled tube is not passive.
+        inline double grid_current(const tube_params& t, double vgc) {
+            return (vgc < t.va) ? 0.0 : (vgc - t.va) / t.rgk;
+        }
+
+        // ---- one triode stage ------------------------------------------------------------------
+
+        constexpr int    k_curve_points = 1025;    // odd, so the quiescent point lands on a sample
+        constexpr double k_curve_span_v = 30.0;    // grid swing the table covers, either way
+        constexpr int    k_solve_steps  = 48;      // bisection steps per table point
+        constexpr double k_stage_hp_hz  = 20.0;    // coupling capacitor / grid leak
+        constexpr double k_stage_lp_hz  = 12000.0; // Miller capacitance and the plate load's pole
+
+        constexpr double k_default_drive_v   = 1.0; // grid volts for a unit input
+        constexpr double k_min_drive_v       = 0.001;
+        constexpr double k_max_drive_v       = 40.0;
+        constexpr double k_default_smooth_ms = 20.0;
+
+        /// One common-cathode triode stage: conditioning highpass → drive → the tube's own load-line
+        /// curve → equalization lowpass. The curve is the static solution of
+        ///
+        ///     ipc(vpc, vgc) = (Vbias − Vk − vpc) / Rp,   Vk = Rk · Ipc(quiescent)
+        ///
+        /// which is a memoryless nonlinearity in the DAFx-07 sense (Yeh, Abel & Smith 2007), so
+        /// tabulating it is not an approximation of the model — it *is* the model, evaluated once.
+        ///
+        /// Output is normalized by the stage's own small-signal gain, so `drive` changes the
+        /// distortion without changing the level. That is the gain-staging lesson `fuzz.h`
+        /// learned the hard way, applied here from the start.
+        class triode {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                set_corners(k_stage_hp_hz, k_stage_lp_hz);
+                build();
+                clear();
+            }
+
+            void clear() { m_hp = m_lp = 0.0; }
+
+            /// Which tube (tube_index). **A mode, not a fader**: it rebuilds the curve table
+            /// (about a thousand load-line solves — sub-millisecond, but not something to automate
+            /// at audio rate).
+            void set_tube(int index) {
+                m_tube = std::clamp(index, 0, k_num_tubes - 1);
+                build();
+            }
+
+            /// The stage's supply, cathode resistor and plate load. Also a mode — same reason.
+            void set_operating_point(const operating_point& op) {
+                m_op = op;
+                build();
+            }
+
+            /// Peak grid volts for a unit input. This is the harmonics control — the circuit
+            /// paper's own plugin exposes demodulator input gain the same way, a knob the real
+            /// instrument does not have.
+            void set_drive(double volts) { m_drive = std::clamp(volts, k_min_drive_v, k_max_drive_v); }
+
+            /// Conditioning highpass and equalization lowpass corners, in Hz.
+            void set_corners(double hp_hz, double lp_hz) {
+                m_a_hp = 1.0 - std::exp(-2.0 * k_pi * std::max(hp_hz, 0.0) / m_sr);
+                m_a_lp = 1.0 - std::exp(-2.0 * k_pi * std::max(lp_hz, 1.0) / m_sr);
+            }
+
+            int    tube() const { return m_tube; }
+            double drive() const { return m_drive; }
+            double samplerate() const { return m_sr; }
+
+            /// The quiescent point the curve was built around: cathode bias, plate voltage and
+            /// plate current (volts, volts, amps). Worth exposing — it is the check that the
+            /// published operating point produces a sane bias rather than a cut-off tube.
+            double bias_v() const { return m_vk; }
+            double quiescent_plate_v() const { return m_vp0; }
+            double quiescent_current_a() const { return m_ip0; }
+
+            /// Small-signal voltage gain magnitude at the quiescent point. The stage itself
+            /// inverts; `process` preserves that, so this is reported unsigned.
+            double small_signal_gain() const { return m_gain; }
+
+            /// The stage's static curve: a normalized input in, the normalized plate swing out,
+            /// exactly as `process` would map it with its filters bypassed. No state touched, so
+            /// a notebook can plot the transfer without running audio.
+            double curve_at(double x) const { return lookup(x * m_drive) / (m_gain * m_drive); }
+
+            /// The same curve in the tube's own units: grid volts in, plate volts of swing out.
+            double plate_swing_at(double grid_volts) const { return lookup(grid_volts); }
+
+            /// One sample. `x` is normalized (±1 is a full-scale signal into `drive` volts).
+            /// The output is INVERTED, because the stage is.
+            double process(double x) {
+                m_hp += m_a_hp * (x - m_hp);
+                const double y = lookup((x - m_hp) * m_drive) / (m_gain * m_drive);
+                m_lp += m_a_lp * (y - m_lp);
+                return m_lp;
+            }
+
+          private:
+            /// Solve the load line for the plate voltage at a given grid-to-cathode voltage.
+            /// `plate_current` rises with vpc and the load line falls, so the difference is
+            /// monotone and bisection cannot miss.
+            double solve_plate(double vgc, double vk) const {
+                const tube_params& t  = tube_at(m_tube);
+                double             lo = 0.0;
+                double             hi = m_op.vbias;
+                for (int i = 0; i < k_solve_steps; ++i) {
+                    const double mid = 0.5 * (lo + hi);
+                    if (plate_current(t, mid, vgc) < (m_op.vbias - vk - mid) / m_op.rp) {
+                        lo = mid;
+                    }
+                    else {
+                        hi = mid;
+                    }
+                }
+                return 0.5 * (lo + hi);
+            }
+
+            /// Find the self-bias point (Vk = Rk·Ip with the tube sitting at −Vk on its grid) and
+            /// tabulate the transfer curve around it.
+            void build() {
+                const tube_params& t = tube_at(m_tube);
+
+                // Cathode self-bias: a damped fixed-point iteration, which converges because
+                // raising Vk lowers the current that sets it.
+                m_vk = 1.0;
+                for (int i = 0; i < 200; ++i) {
+                    const double vp = solve_plate(-m_vk, m_vk);
+                    m_vk            = 0.8 * m_vk + 0.2 * (plate_current(t, vp, -m_vk) * m_op.rk);
+                }
+                m_vp0 = solve_plate(-m_vk, m_vk);
+                m_ip0 = plate_current(t, m_vp0, -m_vk);
+
+                // The curve is the plate's true swing from quiescent, sign included: a
+                // common-cathode stage INVERTS, and that matters here rather than being a
+                // cosmetic detail — the tube's asymmetry is polarity-sensitive, so a stage that
+                // quietly un-inverted itself would apply its curve to the wrong side of the
+                // waveform and generate the wrong harmonics.
+                for (int i = 0; i < k_curve_points; ++i) {
+                    const double v = span_of(i);
+                    m_curve[static_cast<size_t>(i)] =
+                        solve_plate(v - m_vk, m_vk) - m_vp0; // grid volts v around the bias point
+                }
+
+                // Small-signal gain from a central difference across two table steps, as a
+                // magnitude — the inversion lives in the curve, not in the normalization.
+                const int    c  = k_curve_points / 2;
+                const double dv = span_of(c + 1) - span_of(c - 1);
+                m_gain = std::abs((m_curve[static_cast<size_t>(c + 1)] - m_curve[static_cast<size_t>(c - 1)]) / dv);
+                if (!(m_gain > 1e-9)) {
+                    m_gain = 1.0; // a cut-off operating point has no gain to normalize by
+                }
+            }
+
+            static double span_of(int i) {
+                return (2.0 * static_cast<double>(i) / static_cast<double>(k_curve_points - 1) - 1.0) * k_curve_span_v;
+            }
+
+            /// Linear interpolation into the curve, clamped at both ends (cut-off below, grid
+            /// conduction above — the tube does not do anything interesting past either).
+            double lookup(double grid_volts) const {
+                const double p = (grid_volts / k_curve_span_v + 1.0) * 0.5 * (k_curve_points - 1);
+                if (p <= 0.0) {
+                    return m_curve[0];
+                }
+                if (p >= static_cast<double>(k_curve_points - 1)) {
+                    return m_curve[k_curve_points - 1];
+                }
+                const double f = std::floor(p);
+                const size_t i = static_cast<size_t>(f);
+                const double u = p - f;
+                return m_curve[i] + u * (m_curve[i + 1] - m_curve[i]);
+            }
+
+            double          m_sr{48000.0};
+            int             m_tube{tube_6c5};
+            operating_point m_op{k_op_demod};
+            double          m_drive{k_default_drive_v};
+            double          m_vk{0.0}, m_vp0{0.0}, m_ip0{0.0}, m_gain{1.0};
+            double          m_a_hp{1.0}, m_a_lp{1.0};
+            double          m_hp{0.0}, m_lp{0.0};
+
+            std::array<double, k_curve_points> m_curve{};
+        };
+
+        // ---- the heterodyne detector -------------------------------------------------------------
+
+        constexpr double k_a1_hz         = 55.0; // the ribbon's lowest note, per TASLP Eq. 7
+        constexpr double k_detect_ms     = 0.2;  // R4 * C21 = 1 MOhm * 200 pF, TASLP Table II
+        constexpr double k_min_detect_ms = 0.005;
+        constexpr double k_max_detect_ms = 20.0;
+        constexpr double k_min_note_hz   = 20.0;
+        constexpr double k_max_note_hz   = 4000.0;
+        constexpr double k_default_depth = 1.0;  // equal oscillator amplitudes: the published case
+        constexpr double k_max_semitones = 72.0; // six octaves of ribbon, comfortably past 1.2 m
+
+        /// The two oscillators, their sum, and the diode-plus-RC that detects its envelope —
+        /// without simulating either oscillator.
+        ///
+        /// For amplitudes 1 and `depth` the envelope of `cos(Φ) + depth·cos(Φ − φ)` is exactly
+        /// `sqrt(1 + depth² + 2 depth cos(φ))`, so the carrier drops out of the arithmetic
+        /// entirely. At depth 1 that is `2|cos(φ/2)|`, the published case, whose harmonics sit at
+        /// −14.0 / −21.3 / −26.4 dB before anything nonlinear happens.
+        ///
+        /// The detector itself is the paper's: the triode's grid sits near zero bias, so the
+        /// grid-cathode junction conducts only on positive half-cycles and charges instantly,
+        /// while R4·C21 discharges it with a 200 µs time constant. That asymmetry is why the
+        /// object gets quieter and purer as it goes up — the RC cannot follow a fast envelope
+        /// back down.
+        class detector {
+          public:
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                set_detect_ms(m_detect_ms);
+                clear();
+            }
+
+            void clear() {
+                m_phase = 0.0;
+                m_env   = 0.0;
+            }
+
+            /// The note, in Hz.
+            void set_frequency(double hz) { m_hz = std::clamp(hz, k_min_note_hz, k_max_note_hz); }
+
+            /// The note, as the ribbon gives it: semitones above A1 (55 Hz). The circuit paper's
+            /// Eq. 7 makes the ribbon linear in semitones, which is the whole feel of an ondes
+            /// glide, so this is the primary way in.
+            void set_ribbon(double semitones) {
+                set_frequency(k_a1_hz * std::exp2(std::clamp(semitones, 0.0, k_max_semitones) / 12.0));
+            }
+
+            /// Relative amplitude of the second oscillator, 0..1. 1 is the published case (equal
+            /// amplitudes, 100 % modulation, the envelope reaching zero); below that the envelope
+            /// never closes and the harmonic series thins out. A real mismatch between two
+            /// oscillators, and the cheapest timbre control this object has.
+            void set_depth(double d) { m_depth = std::clamp(d, 0.0, 1.0); }
+
+            /// The detector time constant in ms. Defaults to the published 200 µs (R4·C21).
+            void set_detect_ms(double ms) {
+                m_detect_ms = std::clamp(ms, k_min_detect_ms, k_max_detect_ms);
+                m_decay     = std::exp(-1.0 / (m_detect_ms * 0.001 * m_sr));
+            }
+
+            double frequency() const { return m_hz; }
+            double semitones() const { return 12.0 * std::log2(m_hz / k_a1_hz); }
+            double depth() const { return m_depth; }
+            double detect_ms() const { return m_detect_ms; }
+            double samplerate() const { return m_sr; }
+
+            /// The ideal envelope at a phase in [0, 1) — the closed form, with no detector on it.
+            /// Exposed so a test can compare the detector against what it is detecting.
+            double envelope_at(double phase) const {
+                return std::sqrt(std::max(0.0, 1.0 + m_depth * m_depth + 2.0 * m_depth * std::cos(2.0 * k_pi * phase)));
+            }
+
+            /// Advance one sample and return the detected envelope. The DC it carries is real —
+            /// the circuit's coupling capacitor removes it downstream, and so does the triode
+            /// stage's conditioning highpass.
+            double process() {
+                m_phase += m_hz / m_sr;
+                m_phase -= std::floor(m_phase);
+                m_env = std::max(envelope_at(m_phase), m_env * m_decay);
+                return m_env;
+            }
+
+          private:
+            double m_sr{48000.0};
+            double m_hz{k_a1_hz * 4.0};
+            double m_depth{k_default_depth};
+            double m_detect_ms{k_detect_ms};
+            double m_decay{0.0};
+            double m_phase{0.0};
+            double m_env{0.0};
+        };
+
+        // ---- the voice -----------------------------------------------------------------------
+
+        constexpr int    k_max_oversample = 8;
+        constexpr int    k_default_os     = 4;
+        constexpr double k_default_level  = 0.25; // two triode stages add up; this is a sane start
+        constexpr double k_dc_r           = 0.999;
+
+        /// Where the intensity key sits. The circuit paper's five stages do not include it, so
+        /// this is a modelling choice rather than a reconstruction — and both readings are
+        /// musical, which is why both are offered.
+        enum key_placement : int {
+            key_after = 0, ///< a clean output law: pressure changes level, not dirt
+            key_before,    ///< pressure drives the tubes: soft is clean, hard is loud and dirty
+            k_num_key_placements
+        };
+
+        /// The instrument, minus the diffuseur: the heterodyne detector into the demodulator
+        /// triode into the preamplifier triode into the intensity key. The power amplifier is a
+        /// switch, off by default, following the circuit paper's own reduction.
+        ///
+        /// The nonlinear part runs oversampled on the shared `fuzz.h` chain. Patch a
+        /// `tap.palme~` or `tap.metallique~` after this object for the rest of the instrument.
+        class voice {
+          public:
+            voice() {
+                m_drive.snap(1.0);
+                m_level.snap(k_default_level);
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_key.prepare(m_sr);
+                m_key.set_smooth_ms(m_smooth_ms); // prepare() resets it; the voice's setting wins
+                configure();
+                m_drive.snap(m_drive.target());
+                m_level.snap(m_level.target());
+                m_prepared = true;
+                clear();
+            }
+
+            /// Silence the detector and every filter. Parameters are untouched.
+            void clear() {
+                m_detector.clear();
+                m_demod.clear();
+                m_preamp.clear();
+                m_power.clear();
+                m_down.reset();
+                m_dc_x1 = m_dc_y1 = 0.0;
+            }
+
+            bool prepared() const { return m_prepared; }
+
+            // -- the performance surface ----------------------------------------------------------
+
+            /// The note, as the ribbon gives it: semitones above A1.
+            void set_ribbon(double semitones) { m_detector.set_ribbon(semitones); }
+            void set_frequency(double hz) { m_detector.set_frequency(hz); }
+
+            /// Oscillator balance, 0..1 — see detector::set_depth.
+            void set_depth(double d) { m_detector.set_depth(d); }
+
+            /// Detector time constant in ms; the published value is 0.2.
+            void set_detect_ms(double ms) { m_detector.set_detect_ms(ms); }
+
+            /// Grid drive into the two triode stages, as a multiple of the published nominal.
+            /// This is the harmonics control the paper's own plugin exposes.
+            void set_drive(double x) { m_drive.to(std::clamp(x, 0.0, 8.0), smooth_samples()); }
+
+            /// The intensity key, 0..1 over the physical travel (touche.h's contract: the bottom
+            /// 45 % is silent, because that is the key bending before it reaches the powder bag).
+            void set_key(double position) { m_key.set_position(position); }
+            void set_key_mm(double mm) { m_key.set_position_mm(mm); }
+
+            /// Where the key sits in the chain (key_placement) — a choice, see the header.
+            void set_key_placement(int where) { m_key_where = std::clamp(where, 0, k_num_key_placements - 1); }
+
+            /// Run the 2A3 power stage. Off by default: the paper measures almost 5 % second
+            /// harmonic there but reports its contribution as much less important than the two
+            /// stages before it, and drops it for real-time.
+            void set_power_stage(bool on) { m_power_on = on; }
+
+            /// Sign of the coupling between the two triode stages, +1 or -1. The circuit couples
+            /// them through a transformer whose winding sense is not in the source, and the sign
+            /// decides which side of the waveform the preamplifier's asymmetry acts on — so it is
+            /// audible, and it is offered as a switch rather than guessed at silently.
+            void set_polarity(int sign) { m_polarity = (sign < 0) ? -1.0 : 1.0; }
+
+            void set_level(double lin) { m_level.to(lin, smooth_samples()); }
+
+            /// Oversampling for the nonlinear chain: 1, 2, 4 or 8.
+            void set_oversample(int os) {
+                const int v = std::clamp(os, 1, k_max_oversample);
+                m_os        = (v >= 8) ? 8 : (v >= 4) ? 4 : (v >= 2) ? 2 : 1;
+                if (m_prepared) {
+                    configure();
+                    clear();
+                }
+            }
+
+            /// Anti-zipper ramp time for the attribute-driven drive, level and key, in ms.
+            /// It reaches the intensity key too: `touche::key` keeps its own slew, and a `voice`
+            /// whose smoothing did not propagate to it would slew the one control the instrument
+            /// is actually played with at a time nobody set. (Found by a wrapper test asking for
+            /// silence at a rest position and getting 20 ms of sound.)
+            void set_smooth_ms(double ms) {
+                m_smooth_ms = std::max(0.0, ms);
+                m_key.set_smooth_ms(m_smooth_ms);
+            }
+
+            // -- introspection ---------------------------------------------------------------------
+
+            double semitones() const { return m_detector.semitones(); }
+            double frequency() const { return m_detector.frequency(); }
+            double depth() const { return m_detector.depth(); }
+            double detect_ms() const { return m_detector.detect_ms(); }
+            double drive() const { return m_drive.target(); }
+            double key() const { return m_key.position(); }
+            int    key_placement() const { return m_key_where; }
+            bool   power_stage() const { return m_power_on; }
+            int    polarity() const { return (m_polarity < 0.0) ? -1 : 1; }
+            double level() const { return m_level.target(); }
+            int    oversample() const { return m_os; }
+            double smooth_ms() const { return m_smooth_ms; }
+            double samplerate() const { return m_sr; }
+
+            detector&          heterodyne() { return m_detector; }
+            const detector&    heterodyne() const { return m_detector; }
+            triode&            demodulator() { return m_demod; }
+            const triode&      demodulator() const { return m_demod; }
+            triode&            preamplifier() { return m_preamp; }
+            const triode&      preamplifier() const { return m_preamp; }
+            touche::key&       intensity_key() { return m_key; }
+            const touche::key& intensity_key() const { return m_key; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            /// A source: no input. One sample of the instrument, minus its loudspeaker.
+            double process() { return core_sample(-1.0, -1.0, false); }
+
+            /// The signal-rate performance path: the ribbon in semitones above A1 and the key
+            /// over its travel, taken straight from the caller with the ramps bypassed — a
+            /// control signal is already smooth, and re-targeting a 20 ms ramp every sample would
+            /// just make the key lag the hand. The ramps are still ticked so a later switch back
+            /// to the attribute path is continuous rather than a jump.
+            double process(double semitones, double key_position) {
+                return core_sample(std::clamp(semitones, 0.0, k_max_semitones), std::clamp(key_position, 0.0, 1.0),
+                                   true);
+            }
+
+            /// 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:
+            // The grid swing each stage sees at `drive` 1, chosen so the published operating
+            // points are worked but not slammed — the fuzz.h lesson, applied before it bit.
+            static constexpr double k_nominal_demod_v  = 0.9;
+            static constexpr double k_nominal_preamp_v = 0.6;
+            static constexpr double k_nominal_power_v  = 0.5;
+
+            double core_sample(double semitones, double key_position, bool driven) {
+                if (!m_prepared) {
+                    return 0.0;
+                }
+                if (driven) {
+                    m_detector.set_ribbon(semitones);
+                }
+                const double drive = m_drive.tick();
+                const double level = m_level.tick();
+                m_demod.set_drive(std::max(k_min_drive_v, k_nominal_demod_v * drive));
+                m_preamp.set_drive(std::max(k_min_drive_v, k_nominal_preamp_v * drive));
+                m_power.set_drive(std::max(k_min_drive_v, k_nominal_power_v * drive));
+
+                // The key's ramp is ticked ONCE per output sample whichever side of the chain it
+                // is on, so its slew time means the same thing at every oversampling factor. On
+                // the driven path the ramp still ticks, but the gain comes from the caller.
+                const double ramped   = m_key.process(1.0);
+                const double key_gain = driven ? m_key.gain_at(key_position) : ramped;
+
+                double y = 0.0;
+                for (int j = 0; j < m_os; ++j) {
+                    // The detector runs at the oversampled rate too — its cusp is a nonlinearity
+                    // like any other, and it is the loudest source of aliasing in the chain.
+                    const double raw = m_detector.process();
+                    const double s   = core((m_key_where == key_before) ? raw * key_gain : raw);
+                    y                = (m_os == 1) ? s : m_down.tick(s);
+                }
+
+                // The envelope carries a large DC term; the coupling that removes it in the
+                // circuit is the stages' own highpass, and this catches whatever is left.
+                const double d = y - m_dc_x1 + k_dc_r * m_dc_y1;
+                m_dc_x1        = y;
+                m_dc_y1        = (std::abs(d) < 1e-15) ? 0.0 : d;
+
+                const double keyed = (m_key_where == key_after) ? m_dc_y1 * key_gain : m_dc_y1;
+                return keyed * level;
+            }
+
+            /// The nonlinear chain at whatever rate the caller is running.
+            ///
+            /// The demodulator's grid signal is the NEGATED envelope. That is grid-leak
+            /// detection: the grid conducts on positive carrier half-cycles and charges the
+            /// coupling capacitor negative, so a growing envelope drives the grid toward cutoff.
+            /// The stage then inverts on the way out, which is why the demodulator as a whole is
+            /// in phase with the envelope — but the tube's asymmetry has meanwhile been applied
+            /// to the envelope's *underside*, and that is a different set of harmonics from
+            /// applying it the other way up.
+            double core(double x) {
+                double y = m_preamp.process(m_polarity * m_demod.process(-x));
+                if (m_power_on) {
+                    y = m_power.process(y);
+                }
+                return y;
+            }
+
+            void configure() {
+                const double osr = m_sr * m_os;
+                m_detector.prepare(osr);
+                m_demod.prepare(osr);
+                m_demod.set_tube(tube_6c5);
+                m_demod.set_operating_point(k_op_demod);
+                m_preamp.prepare(osr);
+                m_preamp.set_tube(tube_6c5);
+                m_preamp.set_operating_point(k_op_preamp);
+                m_power.prepare(osr);
+                m_power.set_tube(tube_2a3);
+                m_power.set_operating_point(k_op_power);
+                if (m_os > 1) {
+                    // Cut just below the original Nyquist, normalized to the oversampled rate.
+                    // There is no anti-image filter to go with it: this object is a SOURCE, so
+                    // nothing is zero-stuffed on the way up — the detector simply runs fast.
+                    m_down.design(0.45 / static_cast<double>(m_os));
+                }
+            }
+
+            long smooth_samples() const { return static_cast<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double m_sr{48000.0};
+            bool   m_prepared{false};
+            double m_smooth_ms{k_default_smooth_ms};
+            int    m_os{k_default_os};
+            int    m_key_where{key_after};
+            bool   m_power_on{false};
+            double m_polarity{1.0};
+
+            detector     m_detector;
+            triode       m_demod, m_preamp, m_power;
+            touche::key  m_key;
+            butterworth8 m_down;
+            double       m_dc_x1{0.0}, m_dc_y1{0.0};
+            ramp         m_drive, m_level;
+        };
+
+    } // namespace ondes
+} // namespace tap::tools
diff --git a/include/taptools/scrub.h b/include/taptools/scrub.h
new file mode 100644
index 0000000..05591dd
--- /dev/null
+++ b/include/taptools/scrub.h
@@ -0,0 +1,460 @@
+/// @file
+/// @brief      Portable granular-scrub kernel for tap.scrub~ — no Max/Min dependency.
+/// @details    The fifth kernel of the Radiohead family (book/PLAN-radiohead-family.md), and its
+///             most direct piece of stagecraft: the Kaoss-school scrub pad. Record the input
+///             continuously, then put a granular playhead on it whose *position* and *pitch* are
+///             two independent performable signals. Drag the position and you rake back and forth
+///             through the last few seconds of the performance; hold it still and you have a
+///             granular freeze; move the pitch and the material transposes without the position
+///             moving at all. That decoupling is the whole object — a tape head cannot do it, and
+///             it is why the pad feels like an instrument rather than a delay.
+///
+///             This is an ORIGINAL DESIGN in the brassage / granular tradition (Roads,
+///             *Microsound*, MIT Press 2001), not a port and not a reconstruction of any product.
+///             No preset, timing, or parameter value is taken from any hardware.
+///
+///             **What it shares, and why that was the plan.** stammer.h's `capture` is the same
+///             live tape — one `tape_loop.h` reel under an advancing write head — and this kernel
+///             uses it directly rather than keeping a second copy. stammer.h's own header says so
+///             in its limits ("slices play at ±1 rate … a performable, pitch-bending playhead over
+///             live capture is a different object, and sharing this capture is the plan"); the one
+///             thing that had to be added for this kernel is `capture::read_frac`, the fractional
+///             Hermite read the stutter never needed.
+///
+///             Two classes and a thin composition, the family's habit:
+///             - `head` — the grain scheduler. It owns the grain pool, the hop clock, and the
+///               spray dice, and reads a capture it does not own. Like tapecho.h's `head` and
+///               stammer.h's `slicer`, it is a read pattern rather than a machine, so it is a
+///               component for composition and testing, not a standalone external.
+///             - `machine` — one capture, one head, the freeze gate, the drift, and the balance.
+///
+///             **The window, and the null it buys.** Grains are Hann-windowed and fired every
+///             `size / overlap` samples. Hann satisfies the constant-overlap-add condition at
+///             those hops, so at `overlap` 2 the windows sum to exactly 1 — which means that with
+///             pitch at unity, spray at zero, and the position held on a whole sample, the scrub
+///             is *the input, delayed*, to within floating point. That is the load-bearing
+///             scenario: everything else this object does is a departure from a plain delay, and
+///             the departure is only trustworthy if the identity is exact when it should be.
+///             (Normalization is 2/overlap, so the level holds across overlap settings.)
+///
+///             Randomness is the family's seeded xorshift64* (tr808::white_noise, via
+///             swing_vca.h). `spray` is the only consumer, and at exactly 0 it is never drawn from
+///             at all, so the seed provably cannot matter — the garden.h / stammer.h contract,
+///             same shape, pinned by the same kind of test.
+///
+///             Geometry: prepare(sr, max_history_ms) buys the capture once. No later call
+///             allocates; setters are allocation-free and safe while audio runs.
+///
+///             Honest limits:
+///             - **A grain can read past the write head.** A grain born `lag` samples behind the
+///               edge and playing at rate r reaches `lag − size·(r−1)` behind it by its end, so
+///               transposing up with the position near the live edge runs the grain's tail off the
+///               front of the tape and into the oldest material. It is the constraint every live
+///               granulator has; keep the position at least `size·(rate−1)` back, or accept the
+///               seam. Nothing clamps it, because clamping would silently bend the pitch.
+///             - **Transposition warbles.** Reading tape at a rate the write head does not share
+///               means the read pointer drifts, and it has to be wrapped back if the position is
+///               to keep meaning anything. Every wrap is a splice between two grains reading
+///               material a wander apart, at a rate of sr·|rate−1| / (wander · size) per second.
+///               What that costs is not the pitch — measured over 7 fundamentals x 7 intervals,
+///               98.8 % of a perfect shifter's energy lands in a ±15 Hz band around the transposed
+///               pitch (worst case 91.7 %) — but the *concentration* of it: the band holds a
+///               narrow comb rather than a single line, 92.0 % as concentrated as a clean shift
+///               and 75.0 % in the worst case. Audibly that is a warble, and it is the classic
+///               single-delay-line pitch-shifting artifact rather than anything specific to this
+///               kernel. For clean transposition reach for `tap.pitchaccum~` or `tap.shift~`;
+///               `spray` trades the comb for a broadband smear if that suits the material better.
+///             - **`freeze` stops the recorder, not the playhead.** Frozen, the position addresses
+///               fixed tape and the grains loop the same window — a granular hold. What it does
+///               not do is stop time inside a grain: the tail of a grain in flight when freeze
+///               engages was already scheduled.
+///             - **The grain pool can starve.** Shrinking `size` sharply while grains are in
+///               flight can leave every slot busy at the moment the next grain is due; that grain
+///               is dropped rather than stealing a slot mid-window, because a steal would click.
+///               The audible cost is a momentary dip, and it is bounded by the pool being two
+///               deeper than the maximum overlap.
+///             - **COLA is exact only when `size` divides by `overlap`.** The hop is integer
+///               samples, so a size that does not divide evenly leaves a small periodic ripple in
+///               the window sum. It is inaudible at musical sizes and it is why the null test
+///               chooses its numbers.
+///             - **No transient detection.** Grains fire on a clock, not on the material. A
+///               scrub across a drum hit will chop it wherever the clock happens to be.
+///             - Mono. Per-grain stereo scatter is not modeled; wrap in `mc.` for multichannel.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+
+#include "stammer.h"   // tap::tools::stammer::capture — the shared live tape
+#include "swing_vca.h" // tap::tools::tr808::white_noise — the family's seeded xorshift64*
+#include "tape_loop.h" // tap::tools::tape — ramp, k_pi
+
+namespace tap::tools {
+    namespace scrub {
+
+        constexpr int    k_max_overlap            = 4;
+        constexpr int    k_max_grains             = k_max_overlap + 2; // two deeper than the worst overlap
+        constexpr long   k_min_grain_samples      = 16;                // below this it is a click, not a grain
+        constexpr double k_min_size_ms            = 1.0;
+        constexpr double k_max_size_ms            = 500.0;
+        constexpr double k_max_pitch_st           = 24.0;   // ±2 octaves, the pitchaccum.h range
+        constexpr double k_max_drift              = 8.0;    // playback-rate units of self-motion
+        constexpr double k_default_max_history_ms = 4000.0; // the stammer's default buy (~1.5 MB @ 48k)
+
+        // How far the phase-continuous read head may wander from where the position says it is,
+        // in grain lengths, before it is wrapped back. Larger means rarer wraps and so fewer
+        // splices, at the cost of that much position error while transposing. 3 (a wander of
+        // +-1.5 grains) is measured rather than guessed: swept over 5 fundamentals x 7 intervals,
+        // the energy landing in a +-15 Hz band around the transposed pitch runs
+        // 0.933 / 0.958 / 0.965 / 0.990 / 0.993 of a perfect shifter's at wanders of
+        // 0.5 / 1 / 2 / 3 / 4 grains, with worst cases 0.716 / 0.820 / 0.874 / 0.918 / 0.940.
+        // The curve is flat past 3, and every grain of extra wander is a grain of position error,
+        // so 3 is where it stops.
+        constexpr double k_wander_grains = 3.0;
+
+        constexpr double   k_default_size_ms     = 80.0;
+        constexpr int      k_default_overlap     = 2;
+        constexpr double   k_default_spray_ms    = 0.0;
+        constexpr double   k_default_position_ms = 0.0; // at the live edge
+        constexpr double   k_default_pitch_st    = 0.0;
+        constexpr double   k_default_drift       = 0.0;
+        constexpr double   k_default_mix         = 100.0;
+        constexpr double   k_default_level       = 1.0;
+        constexpr double   k_default_smooth_ms   = 20.0;
+        constexpr uint64_t k_default_seed        = 1;
+
+        /// The grain scheduler: fires Hann-windowed grains on a hop clock, each anchored at the
+        /// current position and locked to the pitch it was born at (the standard granular
+        /// contract — a grain whose rate moved mid-window would smear its own content).
+        class head {
+          public:
+            head() { m_rng.set_seed(k_default_seed); }
+
+            /// Reset the clock, kill every grain, and restart the seeded stream.
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                clear();
+            }
+
+            void clear() {
+                m_rng.reset();
+                for (auto& g : m_grain) {
+                    g.alive = false;
+                }
+                m_countdown = 0;
+                m_error     = 0.0;
+            }
+
+            // -- the performance surface (allocation-free, safe while audio runs) ----------------
+
+            /// Grain length in ms. Takes effect at the next grain birth; grains in flight keep the
+            /// length they were born with.
+            void set_size_ms(double ms) { m_size_ms = std::clamp(ms, k_min_size_ms, k_max_size_ms); }
+
+            /// How many grains overlap: the hop is size / overlap. 1 leaves gaps (a chopped
+            /// texture); 2 and up satisfy Hann's overlap-add condition and hold a constant level.
+            void set_overlap(int n) { m_overlap = std::clamp(n, 1, k_max_overlap); }
+
+            /// Random scatter of each grain's origin, in ms back from the position. At exactly 0
+            /// the dice are never rolled, so the seed cannot matter.
+            void set_spray_ms(double ms) { m_spray_ms = std::max(0.0, ms); }
+
+            /// The performance seed. Instant; takes effect on the next draw (clear() restarts it).
+            void set_seed(uint64_t seed) { m_rng.set_seed(seed); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double   size_ms() const { return m_size_ms; }
+            int      overlap() const { return m_overlap; }
+            double   spray_ms() const { return m_spray_ms; }
+            uint64_t seed() const { return m_rng.seed(); }
+
+            int active_grains() const {
+                int n = 0;
+                for (const auto& g : m_grain) {
+                    n += g.alive ? 1 : 0;
+                }
+                return n;
+            }
+
+            /// The window-sum normalization currently in force — 2/overlap once the windows
+            /// overlap-add, 1 when they do not. Exposed because the level contract depends on it.
+            double normalization() const { return (m_overlap >= 2) ? 2.0 / static_cast<double>(m_overlap) : 1.0; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            /// Advance one sample. `lag` is how far behind the capture's write head the playhead
+            /// sits, in samples; `rate` is the grain playback ratio (1 = as recorded). Call once
+            /// per sample AFTER the capture has recorded this sample.
+            double process(const stammer::capture& tape, double lag, double rate) {
+                const long len = std::max(k_min_grain_samples, static_cast<long>(m_size_ms * 0.001 * m_sr));
+                const long hop = std::max(1L, len / static_cast<long>(m_overlap));
+                if (--m_countdown <= 0) {
+                    m_countdown = hop;
+                    birth(tape, lag, rate, len, hop);
+                }
+
+                double sum = 0.0;
+                for (auto& g : m_grain) {
+                    if (!g.alive) {
+                        continue;
+                    }
+                    const double phase = static_cast<double>(g.age) / static_cast<double>(g.len);
+                    const double w     = 0.5 - 0.5 * std::cos(2.0 * tape::k_pi * phase);
+                    sum += w * tape.read_frac(g.origin + static_cast<double>(g.age) * g.rate);
+                    if (++g.age >= g.len) {
+                        g.alive = false;
+                    }
+                }
+                return sum * normalization();
+            }
+
+          private:
+            struct grain {
+                double origin{0.0}; // absolute capture position this grain started reading at
+                double rate{1.0};   // locked at birth
+                long   len{0};
+                long   age{0};
+                bool   alive{false};
+            };
+
+            /// [0, 1) from the family's seeded xorshift64* (which returns [-1, 1)).
+            double uniform() { return 0.5 * (m_rng.process() + 1.0); }
+
+            /// Take a free slot and anchor a grain. A full pool DROPS the grain rather than
+            /// stealing one mid-window — a steal would click, and the dip is bounded (see limits).
+            void birth(const stammer::capture& tape, double lag, double rate, long len, long hop) {
+                grain* g = nullptr;
+                for (auto& c : m_grain) {
+                    if (!c.alive) {
+                        g = &c;
+                        break;
+                    }
+                }
+                if (g == nullptr) {
+                    return;
+                }
+                // The dice are consulted only when spray is armed, so the seed cannot matter at 0.
+                const double spray = (m_spray_ms > 0.0) ? uniform() * m_spray_ms * 0.001 * m_sr : 0.0;
+
+                // The read pointer has to advance through the tape at `rate`, ACROSS grains and not
+                // only inside them. Anchoring every grain at the position instead advances the
+                // origins at the write head's speed, and then the transposition applies only within
+                // each grain: the average read rate comes back to 1 and a steady tone comes out at
+                // its original pitch with a comb of grain-rate sidebands around it, which is the
+                // pitch shift not happening. (Measured — see notebooks/scrub.ipynb; the same trap
+                // catches any delay-line shifter, tap.pitchaccum~ included.)
+                //
+                // So the origin tracks a phase-continuous head, and `m_error` is how far that head
+                // has drifted from where the position says it should be. It is wrapped into
+                // +-len/2 so the position stays meaningful: the read stays continuous between
+                // wraps, and a wrap costs one splice at a grain boundary rather than a splice on
+                // every grain. That is the classic delay-line-shifter bargain, and the warble it
+                // leaves is at the wrap rate, hop*|rate-1| / len grains apart.
+                if (rate == 1.0) {
+                    m_error = 0.0; // back at unity the position is the truth again, exactly
+                }
+                else {
+                    m_error += static_cast<double>(hop) * (rate - 1.0);
+                    const double span = static_cast<double>(len) * k_wander_grains;
+                    m_error -= span * std::floor(m_error / span + 0.5);
+                }
+
+                // position() is the NEXT write, and this sample has already been recorded, so the
+                // live edge is one behind it — the offset that makes lag 0 read the newest sample
+                // and lag n read exactly n samples ago. At rate 1 the error is exactly 0, so the
+                // null against a plain delay is untouched.
+                g->origin = static_cast<double>(tape.position()) - 1.0 - lag + m_error - spray;
+                g->rate   = rate;
+                g->len    = len;
+                g->age    = 0;
+                g->alive  = true;
+            }
+
+            double m_sr{48000.0};
+            double m_size_ms{k_default_size_ms};
+            int    m_overlap{k_default_overlap};
+            double m_spray_ms{k_default_spray_ms};
+            long   m_countdown{0};
+            double m_error{0.0}; // the phase-continuous head's drift from the anchor, wrapped
+
+            std::array<grain, k_max_grains> m_grain;
+            tr808::white_noise              m_rng;
+        };
+
+        /// The machine: one capture, one head, the freeze gate, the drift, and the balance.
+        class machine {
+          public:
+            machine() {
+                m_position.snap(k_default_position_ms);
+                m_pitch.snap(k_default_pitch_st);
+                m_drift.snap(k_default_drift);
+                m_mix.snap(k_default_mix);
+                m_level.snap(k_default_level);
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            /// (Re)allocate the capture for `max_history_ms` at `sr`, snap the ramps, reset the
+            /// grain clock and re-seed. Not real-time-safe.
+            void prepare(double sr, double max_history_ms = k_default_max_history_ms) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_capture.prepare(m_sr, max_history_ms);
+                m_head.prepare(m_sr);
+                m_position.snap(m_position.target());
+                m_pitch.snap(m_pitch.target());
+                m_drift.snap(m_drift.target());
+                m_mix.snap(m_mix.target());
+                m_level.snap(m_level.target());
+                m_drift_acc = 0.0;
+            }
+
+            /// Erase the tape, kill every grain, rewind the drift, and restart the seeded stream.
+            void clear() {
+                m_capture.clear();
+                m_head.clear();
+                m_drift_acc = 0.0;
+            }
+
+            bool prepared() const { return m_capture.prepared(); }
+
+            // -- parameters ----------------------------------------------------------------------
+
+            /// Where the playhead sits, as a lag behind the live edge in ms. 0 is the newest
+            /// sample. Slewed, because this is the scrub gesture.
+            void set_position_ms(double ms) { m_position.to(std::clamp(ms, 0.0, max_history_ms()), smooth_samples()); }
+
+            /// Transposition in semitones, ±2 octaves. Independent of the position — that
+            /// independence is the object.
+            void set_pitch(double semitones) {
+                m_pitch.to(std::clamp(semitones, -k_max_pitch_st, k_max_pitch_st), smooth_samples());
+            }
+
+            /// The playhead's own motion through the tape, in playback-rate units: positive runs
+            /// forward toward the live edge, negative backwards, 0 holds station. It wraps around
+            /// the bought history rather than clamping, so a slow drift is a loop.
+            void set_drift(double rate) { m_drift.to(std::clamp(rate, -k_max_drift, k_max_drift), smooth_samples()); }
+
+            /// Stop the recorder. The playhead keeps going, so the position now addresses fixed
+            /// tape — a granular hold you can still scrub, transpose, and drift through.
+            void set_freeze(bool on) { m_freeze = on; }
+
+            void set_size_ms(double ms) { m_head.set_size_ms(ms); }
+            void set_overlap(int n) { m_head.set_overlap(n); }
+            void set_spray_ms(double ms) { m_head.set_spray_ms(ms); }
+            void set_seed(uint64_t seed) { m_head.set_seed(seed); }
+
+            /// Balance between the live input and the scrub, 0..100, equal-power.
+            void set_mix(double pct) { m_mix.to(std::clamp(pct, 0.0, 100.0), smooth_samples()); }
+
+            /// Output level, linear.
+            void set_level(double lin) { m_level.to(lin, smooth_samples()); }
+
+            void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double   position_ms() const { return m_position.target(); }
+            double   pitch() const { return m_pitch.target(); }
+            double   drift() const { return m_drift.target(); }
+            bool     freeze() const { return m_freeze; }
+            double   size_ms() const { return m_head.size_ms(); }
+            int      overlap() const { return m_head.overlap(); }
+            double   spray_ms() const { return m_head.spray_ms(); }
+            uint64_t seed() const { return m_head.seed(); }
+            double   mix() const { return m_mix.target(); }
+            double   level() const { return m_level.target(); }
+            double   smooth_ms() const { return m_smooth_ms; }
+            double   max_history_ms() const { return m_capture.history_ms(); }
+            int      active_grains() const { return m_head.active_grains(); }
+            double   samplerate() const { return m_sr; }
+
+            head&                   grains() { return m_head; }
+            const head&             grains() const { return m_head; }
+            stammer::capture&       tape() { return m_capture; }
+            const stammer::capture& tape() const { return m_capture; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            /// Attribute-driven path: position and pitch come from their ramps.
+            double process(double in) { return core(in, m_position.tick(), m_pitch.tick()); }
+
+            /// Signal-driven path: position (ms behind the edge) and pitch (semitones) are taken
+            /// straight from the caller and the ramps are bypassed — a signal is already smooth.
+            /// The ramps are still ticked so a later switch back to the attribute path is
+            /// continuous rather than a jump.
+            double process(double in, double position_ms, double pitch_st) {
+                m_position.tick();
+                m_pitch.tick();
+                return core(in, std::clamp(position_ms, 0.0, max_history_ms()),
+                            std::clamp(pitch_st, -k_max_pitch_st, k_max_pitch_st));
+            }
+
+            /// 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<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double core(double in, double position_ms, double pitch_st) {
+                if (!prepared()) {
+                    return in;
+                }
+                if (!m_freeze) {
+                    m_capture.write(in);
+                }
+
+                // The playhead's own motion, accumulated as an extra lag and wrapped around the
+                // bought history so a drift loops the tape rather than running off it.
+                const double cap = static_cast<double>(m_capture.capacity());
+                m_drift_acc -= m_drift.tick();
+                m_drift_acc = std::fmod(m_drift_acc, cap);
+                if (m_drift_acc < 0.0) {
+                    m_drift_acc += cap;
+                }
+
+                double lag = position_ms * 0.001 * m_sr + m_drift_acc;
+                lag        = std::fmod(lag, cap);
+                if (lag < 0.0) {
+                    lag += cap;
+                }
+
+                const double wet = m_head.process(m_capture, lag, std::exp2(pitch_st / 12.0));
+
+                const double pct = m_mix.tick();
+                const double lin = m_level.tick();
+                // The endpoints are exact rather than cos(pi/2)-approximate, so a fully wet scrub
+                // really is the scrub and a fully dry one really is the input (the stammer.h rule).
+                if (pct >= 100.0) {
+                    return wet * lin;
+                }
+                if (pct <= 0.0) {
+                    return in * lin;
+                }
+                const double theta = pct * 0.01 * (tape::k_pi * 0.5);
+                return (std::cos(theta) * in + std::sin(theta) * wet) * lin;
+            }
+
+            double m_sr{48000.0};
+            double m_smooth_ms{k_default_smooth_ms};
+            bool   m_freeze{false};
+            double m_drift_acc{0.0};
+
+            stammer::capture m_capture;
+            head             m_head;
+            tape::ramp       m_position, m_pitch, m_drift, m_mix, m_level;
+        };
+
+    } // namespace scrub
+} // namespace tap::tools
diff --git a/include/taptools/stammer.h b/include/taptools/stammer.h
new file mode 100644
index 0000000..5b9d15d
--- /dev/null
+++ b/include/taptools/stammer.h
@@ -0,0 +1,438 @@
+/// @file
+/// @brief      Portable live buffer-stutter kernel for tap.stammer~ — no Max/Min dependency.
+/// @details    The second kernel of the Radiohead family (book/PLAN-radiohead-family.md), and the
+///             one with the most direct lineage to this package: the live re-firing rig Jonny
+///             Greenwood plays through his own Max patches — the guitar coming apart at the end
+///             of "Go To Sleep", the mangling in "The Gloaming". Capture the input continuously,
+///             then on a rhythmic grid roll dice and re-fire a slice of what just went past.
+///
+///             This is an ORIGINAL DESIGN in the brassage / granular tradition (Roads,
+///             *Microsound*, MIT Press 2001), not a port and not a reconstruction of anyone's
+///             patch. The band's rig is known from published interviews and broadcast films; that
+///             record informs *what the object is for* and nothing about what the code does. No
+///             preset, timing, or parameter value is taken from any product.
+///
+///             Two components and a thin composition, the airport.h / tapecho.h split:
+///             - `capture` — the live tape: a tape_loop.h `reel` in delay-line topology with one
+///               advancing write head, holding the last `max_history_ms` of input. Reads are at
+///               integer positions (slices play at +-1 rate, see the limits), so the family's
+///               Hermite read reduces to an exact sample fetch — the same code path a future
+///               rate-varying sibling would need.
+///             - `slicer` — the dice and the playback head. It owns every random draw and the
+///               state of the slice in flight, and reads a capture it does not own. Like
+///               tapecho.h's `head` and unlike airport.h's `loop`, a slicer is not independently
+///               useful — it is a read pattern, not a machine — so it is a component for
+///               composition and testing rather than a standalone external.
+///             - `machine` — one capture, one slicer, the input send and the balance.
+///
+///             The performance surface, and the family thesis (the control is the instrument):
+///             `density` is how often the machine grabs, `divisions` how finely it chops,
+///             `repeats` how long it holds on, `reverse` how often a repeat runs backwards, and
+///             `jump` how far back it may reach. Riding those four while a part plays is the
+///             instrument; none of them is a set-and-forget.
+///
+///             Randomness is the family's seeded xorshift64* (tr808::white_noise, via
+///             swing_vca.h), consumed in a fixed order, so **a seed is a performance you can
+///             replay**: two runs of the same seed and the same moves are bit-identical, and two
+///             instances on different seeds decorrelate. At `density` 0 the dice are never rolled
+///             at all, so the seed provably cannot matter (pinned by test — the garden.h idle
+///             contract, same shape).
+///
+///             Geometry: prepare(sr, max_history_ms) buys the capture once (4 s at 48 kHz is
+///             ~1.5 MB of double tape). No later call allocates; setters are allocation-free and
+///             safe while audio runs.
+///
+///             Honest limits:
+///             - **Material contract.** This wants transient material — drums, struck or plucked
+///               strings, consonants. On a sustained pad a stutter is barely distinguishable from
+///               a tremolo: the object re-articulates rhythm that is already in the sound, it
+///               does not invent it. Tested with plucks, not sines, for exactly that reason.
+///             - Slices play at +-1 rate only. There is no pitch shifting and no varispeed; a
+///               performable, pitch-bending playhead over live capture is a different object
+///               (tap.scrub~, planned in the same family) and sharing this capture is the plan.
+///             - A slice reads from the ring rather than a private copy (a copy would be a burst
+///               memcpy in the audio thread). If a repeat train outlives the buffered history —
+///               `repeats * length + jump` beyond `max_history_ms` — its tail reads fresher
+///               material as the write head laps the origin. Size the history to the longest
+///               train you intend to fire.
+///             - A grid point can only start a slice while the machine is idle: a slice in flight
+///               is never interrupted, so `repeats` (not `density`) is what decides how long the
+///               machine stays busy, and raising density past the point where trains overlap
+///               stops having an effect.
+///             - `mix` is the balance between the live input and the slice, and it only bites
+///               while a slice is firing. When the machine is idle the input passes through
+///               untouched and bitwise, at any mix — there is nothing to blend against, and
+///               equal-power blending a signal with itself would just make it louder.
+///             - Mono. Per-slice stereo scatter is not modeled; wrap in `mc.` for multichannel.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+
+#include "swing_vca.h" // tap::tools::tr808::white_noise — the family's seeded xorshift64*
+#include "tape_loop.h" // tap::tools::tape — reel (the capture) + ramp, the shared machinery
+
+namespace tap::tools {
+    namespace stammer {
+
+        constexpr int      k_max_divisions          = 8;      // slice = step / k, k in [1, divisions]
+        constexpr int      k_max_repeats            = 16;     // repeats per fired slice
+        constexpr long     k_min_slice_samples      = 16;     // below this it is a click, not a slice
+        constexpr double   k_min_step_ms            = 1.0;    // the grid floor
+        constexpr double   k_default_max_history_ms = 4000.0; // default buy (~1.5 MB @ 48k)
+        constexpr double   k_default_step_ms        = 250.0;  // a plausible grid out of the box
+        constexpr double   k_default_density        = 0.5;    // grabs about half the idle grid points
+        constexpr int      k_default_divisions      = 4;
+        constexpr int      k_default_repeats        = 4;
+        constexpr double   k_default_reverse        = 0.25; // a quarter of repeats run backwards
+        constexpr double   k_default_jump_ms        = 0.0;  // the classic stutter: the material just past
+        constexpr double   k_default_fade_ms        = 3.0;  // per-repeat flank, click-free
+        constexpr double   k_default_mix            = 100.0;
+        constexpr double   k_default_smooth_ms      = 20.0; // anti-zipper ramp for the level setters
+        constexpr uint64_t k_default_seed           = 1;
+
+        /// The live tape: the last `max_history_ms` of input under one advancing write head.
+        class capture {
+          public:
+            /// Buy the history once. Not real-time-safe.
+            void prepare(double sr, double max_history_ms) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_reel.prepare(m_sr, std::max(k_min_step_ms, max_history_ms) * 0.001);
+                clear();
+            }
+
+            /// Erase the tape and rewind the write head.
+            void clear() {
+                m_reel.clear();
+                m_write = 0;
+            }
+
+            bool   prepared() const { return m_reel.prepared(); }
+            long   capacity() const { return m_reel.capacity(); }
+            long   position() const { return m_write; } // absolute position of the NEXT write
+            double samplerate() const { return m_sr; }
+            double history_ms() const { return static_cast<double>(capacity()) * 1000.0 / m_sr; }
+
+            /// Record one sample and advance the head.
+            void write(double x) {
+                m_reel.write(m_write, x);
+                if (++m_write >= m_reel.capacity()) { // keep the head in [0, capacity): a long can
+                    m_write = 0;                      // overflow in half a day of audio on LLP64
+                }
+            }
+
+            /// Read at an absolute position (wraps). Positions are integers here, so the family's
+            /// Hermite read returns the stored sample exactly (fraction 0 reads x0).
+            double read(long pos) const { return m_reel.read_hermite(static_cast<double>(pos)); }
+
+            /// Read at a FRACTIONAL absolute position (wraps) — the same 4-point Hermite, exposed
+            /// for the family's rate-varying sibling (scrub.h), which shares this capture rather
+            /// than keeping its own. The slicer never calls it: its slices play at ±1 rate.
+            double read_frac(double pos) const { return m_reel.read_hermite(pos); }
+
+          private:
+            double     m_sr{48000.0};
+            long       m_write{0};
+            tape::reel m_reel;
+        };
+
+        /// The dice and the playback head: decides when to grab, how much, how many times, and
+        /// which way round, then plays it back out of a capture it does not own.
+        class slicer {
+          public:
+            struct out {
+                double value{0.0};    // the slice sample (0 when idle)
+                bool   firing{false}; // whether a slice was sounding for this sample
+            };
+
+            slicer() { m_rng.set_seed(k_default_seed); }
+
+            /// Reset the grid, drop any slice in flight, and re-seed — so a restart replays the
+            /// same performance. Not a parameter move.
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                clear();
+            }
+
+            void clear() {
+                m_rng.reset();
+                m_countdown   = 0;
+                m_playing     = false;
+                m_pos         = 0;
+                m_len         = 0;
+                m_left        = 0;
+                m_reverse_now = false;
+            }
+
+            // -- the performance surface (allocation-free, safe while audio runs) ----------------
+
+            /// The rhythmic grid in ms: how often the machine may decide to grab. Takes effect at
+            /// the next grid point (it is a rhythm, not a level — nothing to zipper).
+            void set_step_ms(double ms) { m_step_ms = std::max(k_min_step_ms, ms); }
+
+            /// Probability in [0, 1] of firing at an idle grid point. At exactly 0 the dice are
+            /// never rolled, so the seed cannot matter.
+            void set_density(double p) { m_density = std::clamp(p, 0.0, 1.0); }
+
+            /// How finely the grid may be chopped: a slice is step / k with k drawn uniformly
+            /// from [1, divisions]. 1 means whole-step slices only.
+            void set_divisions(int n) { m_divisions = std::clamp(n, 1, k_max_divisions); }
+
+            /// Upper bound on how many times a fired slice repeats; the count is drawn uniformly
+            /// from [1, repeats].
+            void set_repeats(int n) { m_repeats = std::clamp(n, 1, k_max_repeats); }
+
+            /// Probability in [0, 1] that any given repeat plays backwards. Drawn per repeat, so
+            /// a train can stagger forwards and back.
+            void set_reverse(double p) { m_reverse = std::clamp(p, 0.0, 1.0); }
+
+            /// How far back beyond the immediately-past material a slice may reach, in ms; the
+            /// actual reach is drawn uniformly from [0, jump]. 0 is the classic stutter.
+            void set_jump_ms(double ms) { m_jump_ms = std::max(0.0, ms); }
+
+            /// Raised-sine flank width per repeat, in ms — the anti-click. Clamped per slice to
+            /// half the slice so the flanks never overlap.
+            void set_fade_ms(double ms) { m_fade_ms = std::max(0.0, ms); }
+
+            /// The performance seed. Instant; takes effect on the next draw (clear() restarts the
+            /// stream from it).
+            void set_seed(uint64_t seed) { m_rng.set_seed(seed); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double   step_ms() const { return m_step_ms; }
+            double   density() const { return m_density; }
+            int      divisions() const { return m_divisions; }
+            int      repeats() const { return m_repeats; }
+            double   reverse() const { return m_reverse; }
+            double   jump_ms() const { return m_jump_ms; }
+            double   fade_ms() const { return m_fade_ms; }
+            uint64_t seed() const { return m_rng.seed(); }
+            bool     playing() const { return m_playing; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            /// Advance one sample: tick the grid, maybe fire, and play whatever is in flight.
+            /// Call once per sample AFTER the capture has recorded this sample.
+            out process(const capture& tape) {
+                if (--m_countdown <= 0) {
+                    m_countdown = std::max(1L, static_cast<long>(m_step_ms * 0.001 * m_sr));
+                    if (!m_playing) {
+                        maybe_fire(tape);
+                    }
+                }
+                if (!m_playing) {
+                    return {};
+                }
+
+                const long   read_at = m_reverse_now ? (m_origin + m_len - 1 - m_pos) : (m_origin + m_pos);
+                const double value   = tape.read(read_at) * envelope(m_pos, m_len, m_fade);
+
+                if (++m_pos >= m_len) { // this repeat is done
+                    m_pos = 0;
+                    if (--m_left <= 0) {
+                        m_playing = false;
+                    }
+                    else {
+                        m_reverse_now = (uniform() < m_reverse); // each repeat rolls its own way round
+                    }
+                }
+                return {value, true};
+            }
+
+          private:
+            /// [0, 1) from the family's seeded xorshift64* (which returns [-1, 1)).
+            double uniform() { return 0.5 * (m_rng.process() + 1.0); }
+
+            /// Raised-sine flanks: exactly 0 at both edges, exactly 1 across the plateau. Repeats
+            /// are sequential rather than overlapped, so each junction dips to zero — that is the
+            /// articulation of a stutter, not a defect.
+            static double envelope(long i, long len, long fade) {
+                if (fade <= 0) {
+                    return 1.0;
+                }
+                const double half_pi = tape::k_pi * 0.5;
+                double       g       = 1.0;
+                if (i < fade) {
+                    g = std::sin(half_pi * static_cast<double>(i) / static_cast<double>(fade));
+                }
+                const long from_end = len - 1 - i;
+                if (from_end < fade) {
+                    g = std::min(g, std::sin(half_pi * static_cast<double>(from_end) / static_cast<double>(fade)));
+                }
+                return g;
+            }
+
+            /// Roll for a slice. The draw order is fixed — fire, division, repeats, jump, reverse
+            /// — because it is what makes a seed reproducible.
+            void maybe_fire(const capture& tape) {
+                if (m_density <= 0.0) {
+                    return; // the dice are never rolled, so the seed provably cannot matter
+                }
+                if (uniform() >= m_density) {
+                    return;
+                }
+
+                const long step_samples = std::max(1L, static_cast<long>(m_step_ms * 0.001 * m_sr));
+                const int  k            = 1 + static_cast<int>(uniform() * static_cast<double>(m_divisions));
+                const long divisor      = std::clamp(static_cast<long>(k), 1L, static_cast<long>(m_divisions));
+                long       len          = std::max(k_min_slice_samples, step_samples / divisor);
+
+                const int  n    = 1 + static_cast<int>(uniform() * static_cast<double>(m_repeats));
+                const long jump = static_cast<long>(uniform() * m_jump_ms * 0.001 * m_sr);
+
+                // The slice must fit the bought history, origin and reach together.
+                const long room = tape.capacity() - jump - 2;
+                len             = std::clamp(len, k_min_slice_samples, std::max(k_min_slice_samples, room));
+
+                m_origin      = tape.position() - len - jump;
+                m_len         = len;
+                m_fade        = std::min(static_cast<long>(m_fade_ms * 0.001 * m_sr), len / 2);
+                m_left        = std::clamp(n, 1, m_repeats);
+                m_pos         = 0;
+                m_reverse_now = (uniform() < m_reverse);
+                m_playing     = true;
+            }
+
+            double m_sr{48000.0};
+
+            // performance surface
+            double m_step_ms{k_default_step_ms};
+            double m_density{k_default_density};
+            int    m_divisions{k_default_divisions};
+            int    m_repeats{k_default_repeats};
+            double m_reverse{k_default_reverse};
+            double m_jump_ms{k_default_jump_ms};
+            double m_fade_ms{k_default_fade_ms};
+
+            // the slice in flight
+            bool m_playing{false};
+            bool m_reverse_now{false};
+            long m_origin{0};
+            long m_len{0};
+            long m_fade{0};
+            long m_pos{0};
+            long m_left{0};
+            long m_countdown{0};
+
+            tr808::white_noise m_rng;
+        };
+
+        /// The machine: one capture, one slicer, the input send and the balance.
+        class machine {
+          public:
+            machine() {
+                m_input_level.snap(1.0);
+                m_mix.snap(k_default_mix);
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            /// (Re)allocate the capture for `max_history_ms` at `sr`, snap the ramps, reset the
+            /// grid and re-seed. Not real-time-safe.
+            void prepare(double sr, double max_history_ms = k_default_max_history_ms) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_capture.prepare(m_sr, max_history_ms);
+                m_slicer.prepare(m_sr);
+                m_input_level.snap(m_input_level.target());
+                m_mix.snap(m_mix.target());
+            }
+
+            /// Erase the capture, drop any slice in flight, and restart the seeded stream.
+            void clear() {
+                m_capture.clear();
+                m_slicer.clear();
+            }
+
+            bool prepared() const { return m_capture.prepared(); }
+
+            // -- parameters ----------------------------------------------------------------------
+
+            void set_step_ms(double ms) { m_slicer.set_step_ms(ms); }
+            void set_density(double p) { m_slicer.set_density(p); }
+            void set_divisions(int n) { m_slicer.set_divisions(n); }
+            void set_repeats(int n) { m_slicer.set_repeats(n); }
+            void set_reverse(double p) { m_slicer.set_reverse(p); }
+            void set_jump_ms(double ms) { m_slicer.set_jump_ms(ms); }
+            void set_fade_ms(double ms) { m_slicer.set_fade_ms(ms); }
+            void set_seed(uint64_t seed) { m_slicer.set_seed(seed); }
+
+            /// Input level into the capture, linear, slewed.
+            void set_input_level(double lin) { m_input_level.to(lin, smooth_samples()); }
+
+            /// Balance between the live input and the slice, 0..100, equal-power — and it only
+            /// bites while a slice is firing (see the header's limits).
+            void set_mix(double pct) { m_mix.to(std::clamp(pct, 0.0, 100.0), smooth_samples()); }
+
+            void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double   step_ms() const { return m_slicer.step_ms(); }
+            double   density() const { return m_slicer.density(); }
+            int      divisions() const { return m_slicer.divisions(); }
+            int      repeats() const { return m_slicer.repeats(); }
+            double   reverse() const { return m_slicer.reverse(); }
+            double   jump_ms() const { return m_slicer.jump_ms(); }
+            double   fade_ms() const { return m_slicer.fade_ms(); }
+            uint64_t seed() const { return m_slicer.seed(); }
+            bool     playing() const { return m_slicer.playing(); }
+            double   input_level() const { return m_input_level.target(); }
+            double   mix() const { return m_mix.target(); }
+            double   smooth_ms() const { return m_smooth_ms; }
+            double   max_history_ms() const { return m_capture.history_ms(); }
+            double   samplerate() const { return m_sr; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            double process(double in) {
+                if (!prepared()) {
+                    return in;
+                }
+                const double send = m_input_level.tick();
+                const double mix  = m_mix.tick();
+
+                m_capture.write(send * in);
+                const slicer::out s = m_slicer.process(m_capture);
+
+                // Idle is a bitwise passthrough at any mix: there is nothing to blend against,
+                // and equal-power blending a signal with itself would only make it louder.
+                if (!s.firing) {
+                    return in;
+                }
+                if (mix <= 0.0) {
+                    return in;
+                }
+                if (mix >= 100.0) {
+                    return s.value;
+                }
+                const double theta = mix * 0.01 * (tape::k_pi * 0.5);
+                return std::cos(theta) * in + std::sin(theta) * s.value;
+            }
+
+            /// 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<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double     m_sr{48000.0};
+            double     m_smooth_ms{k_default_smooth_ms};
+            capture    m_capture;
+            slicer     m_slicer;
+            tape::ramp m_input_level; // linear
+            tape::ramp m_mix;         // 0..100
+        };
+
+    } // namespace stammer
+} // namespace tap::tools
diff --git a/include/taptools/tapecho.h b/include/taptools/tapecho.h
new file mode 100644
index 0000000..6cd386f
--- /dev/null
+++ b/include/taptools/tapecho.h
@@ -0,0 +1,422 @@
+/// @file
+/// @brief      Portable multi-head tape-echo kernel for tap.tapecho~ — no Max/Min dependency.
+/// @details    The first kernel of the Radiohead family (book/PLAN-radiohead-family.md): the
+///             multi-head tape echo of the Watkins/WEM Copicat and Roland Space Echo school —
+///             one record head, a span of moving tape, several playback heads at fixed positions
+///             along it, and a regeneration path from the heads back to the record head.
+///
+///             It is a *recreation of the topology*, not a port or a circuit model of any one
+///             machine: the tape-path DSP (fractional read, periodic wow/flutter, in-loop
+///             coloration) is the published tape-echo modeling literature already carried by
+///             tape_loop.h (Arnardottir, Abel, Smith, "A Digital Model of the Echoplex Tape
+///             Delay", AES 125, 2008; Valimaki et al.'s tape-echo work). No head spacings,
+///             filter curves, or trim values are claimed as measured from any unit — see the
+///             head-layout note below.
+///
+///             Almost all of the machinery is tape_loop.h; this kernel is the composition. Two
+///             classes, the airport.h split:
+///             - `head` — ONE playback head: its position along the tape path (`ratio` of the
+///               motor span), its level, and its place in the stereo field. It carries its own
+///               anti-zipper ramps and reads a reel it does not own. Unlike `airport::loop` a
+///               head is NOT independently useful — it is a read position, not a machine — so it
+///               is a component for composition and testing, not a standalone external.
+///             - `machine` — one reel in delay-line topology, one transport, one wear stage, and
+///               k_max_heads heads. Its process() is a sum over the heads plus the regeneration
+///               write, and nothing else.
+///
+///             Geometry of the head path: `span_ms` is the motor — the delay of a head at the
+///             far end of the path (ratio 1.0) — and each head's delay is `span_ms * ratio`, so
+///             moving the motor moves every head together, as a tape speed does. Defaults are
+///             four evenly spaced heads (0.25, 0.5, 0.75, 1.0). Even spacing is a nominal layout
+///             chosen because it is neutral and audibly "a tape echo"; real machines' head
+///             positions vary by model and unit and are not modeled here. Ratios are freely
+///             settable, which is how a three-head Copicat-style layout is built.
+///
+///             The stability story, and the design statement this kernel exists to make: it is
+///             `discreet.h`'s inversion carried into a *performed* effect. delay.h caps feedback
+///             strictly below 1 so the loop is always contractive; discreet.h lets regeneration
+///             reach exactly 1.0 because tape_loop.h `wear` (darken -> bounded saturation -> DC
+///             block) is the stabilizer. Here regeneration is allowed to go *past* unity, into
+///             deliberate sound-on-sound self-oscillation — the dub move, the reason anyone
+///             reaches for this machine live — and it stays bounded for the same reason:
+///             `vca::swing_shape` is bounded by 1/drive for any drive > 0, so whatever the loop
+///             gain, the tape is bounded by |in|max + regen/drive and the output by the head
+///             levels times that. Because that bound *only* exists while the saturator is
+///             engaged, the cap is drive-dependent, applied per sample:
+///
+///                 drive > 0 : regen may reach k_regen_max_driven (howls, bounded)
+///                 drive = 0 : regen is capped at 1.0 (wear is then linear with |H| <= 1, so
+///                             1.0 sustains and cannot grow — the discreet.h contract)
+///
+///             Turning drive to 0 while howling therefore lands the loop at 1.0 rather than
+///             letting it run away; the *target* keeps its high value and takes effect again
+///             when drive returns.
+///
+///             Geometry: prepare(sr, max_span_seconds) buys the worst-case tape once (4 s at
+///             48 kHz is ~1.5 MB of double tape). No later call allocates; setters only retarget
+///             ramps and are safe while audio runs. Double-precision, per-sample, mono in /
+///             stereo out.
+///
+///             Honest limits:
+///             - `set_span_ms` glides as a tape-speed change and audibly bends pitch while
+///               moving — by design, inherited from discreet.h: moving the heads IS the doppler.
+///               Use set_smooth_ms to choose how fast the motor changes speed. There is no
+///               crossfading "digital" mode.
+///             - The transport is periodic only (see tape_loop.h): deterministic, bit-exactly
+///               reproducible, with no stochastic capstan drift. That is a family-wide decision,
+///               not an oversight — changing it would break the family's bit-exact renders.
+///             - Wow/flutter is a single position offset shared by every head. One motor moves
+///               the whole tape path, so a speed error displaces all the heads together; the
+///               per-head phase differences of a real transport are not modeled.
+///             - Regeneration is taken from the same post-level head sum that feeds the output,
+///               so a head's level is also its send into the loop (as the head selector on the
+///               machines is). There is no separate feedback-source selection.
+///             - The read floor is 2.5 samples (Hermite support) and reads are clamped so the
+///               wow excursion can never cross the record head; extreme wow at very short spans
+///               flattens against that clamp rather than wrapping.
+///             - Heads sum with no master gain: four heads at unity can sum past unity, and gain
+///               staging is the caller's job (the multitap contract).
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+
+#include "tape_loop.h" // tap::tools::tape — reel / wow_flutter / wear / ramp, the shared machinery
+
+namespace tap::tools {
+    namespace tapecho {
+
+        constexpr int    k_max_heads           = 4;     // four slots; a three-head layout is heads=3
+        constexpr double k_min_span_ms         = 1.0;   // below this it is a comb, not an echo
+        constexpr double k_default_max_seconds = 4.0;   // default worst-case buy (~1.5 MB @ 48k)
+        constexpr double k_default_span_ms     = 375.0; // a plausible motor-speed default
+        constexpr double k_regen_max_driven    = 1.5;   // past unity: sound-on-sound, bounded by the saturator
+        constexpr double k_regen_max_linear    = 1.0;   // drive 0: |H_wear| <= 1, so 1.0 sustains, cannot grow
+        constexpr double k_default_regen       = 0.35;  // a few audible repeats
+        constexpr double k_default_darken_hz   = 4000.0;
+        constexpr double k_default_drive       = 0.5; // mild record-head saturation
+        constexpr double k_default_wow_ms      = 0.4; // a smaller, faster transport than discreet.h's
+        constexpr double k_default_wow_hz      = 1.2;
+        constexpr double k_default_flutter_ms  = 0.03;
+        constexpr double k_default_flutter_hz  = 11.0;
+        constexpr double k_default_mix         = 35.0; // an effect in a chain, not a wet-only loop
+        constexpr double k_default_smooth_ms   = 20.0; // anti-zipper ramp for setters
+
+        /// One playback head: a position along the tape path, a level, and a place in the stereo
+        /// field, all slewed. Reads a reel it does not own (the machine owns the tape).
+        class head {
+          public:
+            head() {
+                m_ratio.snap(1.0);
+                m_level.snap(1.0);
+                m_pan.snap(0.0);
+            }
+
+            /// Snap the ramps to their targets — a DSP restart is not a parameter move.
+            void prepare() {
+                m_ratio.snap(m_ratio.target());
+                m_level.snap(m_level.target());
+                m_pan.snap(m_pan.target());
+            }
+
+            /// Position along the head path as a fraction of the motor span, clamped to (0, 1].
+            void set_ratio(double r, long smooth) { m_ratio.to(std::clamp(r, k_min_ratio, 1.0), smooth); }
+
+            /// Linear level. Unclamped: negative flips polarity, as a mixer channel does.
+            void set_level(double lin, long smooth) { m_level.to(lin, smooth); }
+
+            /// Equal-power pan, -1 (hard left) .. 1 (hard right). Endpoints are exact.
+            void set_pan(double p, long smooth) { m_pan.to(std::clamp(p, -1.0, 1.0), smooth); }
+
+            double ratio() const { return m_ratio.target(); }
+            double level() const { return m_level.target(); }
+            double pan() const { return m_pan.target(); }
+
+            /// Advance the ramps one sample WITHOUT reading. Disabled heads still tick, so
+            /// enabling one mid-glide continues its ramp instead of jumping (the count is a
+            /// mute, not a freeze).
+            void tick() {
+                m_ratio.tick();
+                m_level.tick();
+                m_pan.tick();
+            }
+
+            /// Advance the ramps and read: accumulate this head's panned contribution onto the
+            /// stereo busses and return its post-level mono value (what the regeneration path
+            /// sums). `span_samples` is the motor span; `offset` the shared transport error.
+            double read(const tape::reel& reel, long write_pos, double span_samples, double offset, double& out_left,
+                        double& out_right) {
+                const double ratio = m_ratio.tick();
+                const double level = m_level.tick();
+                const double pan   = m_pan.tick();
+
+                // Clamped so the Hermite support can never cross the record head (discreet.h).
+                const double span = span_samples * ratio - offset;
+                const double d = std::clamp(span, tape::k_min_frac_delay, static_cast<double>(reel.capacity()) - 2.0);
+                const double g = level * reel.read_hermite(static_cast<double>(write_pos) - d);
+
+                // Equal-power with exact endpoints — the delay.h multitap law: a hard-panned head
+                // is bitwise absent from the far bus.
+                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;
+                }
+                return g;
+            }
+
+          private:
+            static constexpr double k_min_ratio = 0.001; // a head at the record head is not a head
+
+            tape::ramp m_ratio; // (0, 1] of the motor span
+            tape::ramp m_level; // linear
+            tape::ramp m_pan;   // -1..1
+        };
+
+        /// The machine: one reel, one transport, one wear stage, k_max_heads heads.
+        class machine {
+          public:
+            /// Defaults are a tape echo, not a neutral bypass: four evenly spaced heads on a
+            /// 375 ms motor, a few repeats, gentle wear, the transport breathing.
+            machine() {
+                m_span_ms.snap(k_default_span_ms);
+                m_regen.snap(k_default_regen);
+                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);
+                for (int i = 0; i < k_max_heads; ++i) {
+                    // Nominal even spacing along the path, the last head at the motor span.
+                    m_heads[static_cast<size_t>(i)].set_ratio(static_cast<double>(i + 1) / k_max_heads, 0);
+                }
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            /// (Re)allocate tape for `max_span_seconds` at `sr`, snap all ramps, erase the tape.
+            /// Not real-time-safe.
+            void prepare(double sr, double max_span_seconds = k_default_max_seconds) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_reel.prepare(m_sr, std::max(k_min_span_ms * 0.001, max_span_seconds));
+                m_transport.prepare(m_sr);
+                m_wear.prepare(m_sr);
+                m_span_ms.snap(std::min(m_span_ms.target(), max_span_ms()));
+                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());
+                for (auto& h : m_heads) {
+                    h.prepare();
+                }
+                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 — it is also how you stop a self-oscillating loop instantly.
+            void clear() {
+                m_reel.clear();
+                m_wear.clear();
+                m_transport.clear();
+                m_write = 0;
+            }
+
+            bool prepared() const { return m_reel.prepared(); }
+
+            // -- parameter targets (click-free; safe while audio runs) ---------------------------
+
+            /// Motor speed as the delay of a ratio-1.0 head, in ms, clamped to [k_min_span_ms,
+            /// the prepared max]. Slewed — and the slew IS the varispeed doppler.
+            void set_span_ms(double ms) {
+                m_span_ms.to(std::clamp(ms, k_min_span_ms, max_span_ms()), smooth_samples());
+            }
+
+            /// Number of active heads, clamped to [0, k_max_heads]. A mute, not a freeze: inactive
+            /// heads keep ramping, so enabling one mid-glide does not jump.
+            void set_heads(int count) { m_num_heads = std::clamp(count, 0, k_max_heads); }
+
+            /// Per-head position along the path, (0, 1] of the motor span, slewed. 0-based index.
+            void set_head_ratio(int head, double ratio) {
+                if (valid_head(head)) {
+                    m_heads[static_cast<size_t>(head)].set_ratio(ratio, smooth_samples());
+                }
+            }
+
+            /// Per-head linear level, slewed. Also the head's send into the regeneration path.
+            void set_head_level(int head, double lin) {
+                if (valid_head(head)) {
+                    m_heads[static_cast<size_t>(head)].set_level(lin, smooth_samples());
+                }
+            }
+
+            /// Per-head equal-power pan, -1..1, slewed. Endpoints are bitwise exact.
+            void set_head_pan(int head, double pan) {
+                if (valid_head(head)) {
+                    m_heads[static_cast<size_t>(head)].set_pan(pan, smooth_samples());
+                }
+            }
+
+            /// Regeneration into the record head, clamped to [0, k_regen_max_driven], slewed.
+            /// Values above 1.0 self-oscillate and are only reached while drive > 0 (see the
+            /// header banner: the saturator is what bounds them).
+            void set_regen(double r) { m_regen.to(std::clamp(r, 0.0, k_regen_max_driven), 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 — and caps the
+            /// effective regeneration at 1.0 for as long as it stays there.
+            void set_drive(double d) { m_drive.to(std::max(0.0, d), smooth_samples()); }
+
+            /// Input level into the record head, linear, slewed. Fading this while the loop
+            /// self-oscillates is the sound-on-sound 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 on both busses, 100 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 span_ms() const { return m_span_ms.target(); }
+            int    heads() const { return m_num_heads; }
+            double head_ratio(int head) const {
+                return valid_head(head) ? m_heads[static_cast<size_t>(head)].ratio() : 0.0;
+            }
+            double head_level(int head) const {
+                return valid_head(head) ? m_heads[static_cast<size_t>(head)].level() : 0.0;
+            }
+            double head_pan(int head) const {
+                return valid_head(head) ? m_heads[static_cast<size_t>(head)].pan() : 0.0;
+            }
+            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_span_ms() const { return static_cast<double>(m_reel.capacity()) * 1000.0 / m_sr; }
+            double samplerate() const { return m_sr; }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            void process(double in, double& out_left, double& out_right) {
+                if (!prepared()) {
+                    out_left = out_right = in;
+                    return;
+                }
+                const double span   = m_span_ms.tick() * 0.001 * m_sr;
+                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);
+                }
+
+                // One motor: a single transport error displaces every head together.
+                const double offset = m_transport.tick();
+
+                double wet_left  = 0.0;
+                double wet_right = 0.0;
+                double head_sum  = 0.0;
+                for (int i = 0; i < k_max_heads; ++i) {
+                    head& h = m_heads[static_cast<size_t>(i)];
+                    if (i < m_num_heads) {
+                        head_sum += h.read(m_reel, m_write, span, offset, wet_left, wet_right);
+                    }
+                    else {
+                        h.tick(); // muted, not frozen
+                    }
+                }
+
+                // Past unity only while the saturator is there to bound it (header banner).
+                const double regen_eff = std::min(regen, (drive > 0.0) ? k_regen_max_driven : k_regen_max_linear);
+
+                // The return path: head sum -> wear (darken, saturate, DC block) -> record head.
+                m_reel.write(m_write, send * in + regen_eff * m_wear.process(head_sum));
+                if (++m_write >= m_reel.capacity()) { // keep the head in [0, capacity): a long can
+                    m_write = 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) {
+                    out_left = out_right = in;
+                    return;
+                }
+                if (mix >= 100.0) {
+                    out_left  = wet_left;
+                    out_right = wet_right;
+                    return;
+                }
+                const double theta = mix * 0.01 * (tape::k_pi * 0.5);
+                const double dry_g = std::cos(theta);
+                const double wet_g = std::sin(theta);
+                out_left           = dry_g * in + wet_g * wet_left;
+                out_right          = dry_g * in + wet_g * wet_right;
+            }
+
+            /// 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:
+            static bool valid_head(int head) { return head >= 0 && head < k_max_heads; }
+            long        smooth_samples() const { return static_cast<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double                        m_sr{48000.0};
+            double                        m_smooth_ms{k_default_smooth_ms};
+            int                           m_num_heads{k_max_heads};
+            long                          m_write{0};
+            tape::reel                    m_reel;
+            tape::wow_flutter             m_transport;
+            tape::wear                    m_wear;
+            std::array<head, k_max_heads> m_heads;
+            tape::ramp                    m_span_ms;     // ms, the motor
+            tape::ramp                    m_regen;       // 0..k_regen_max_driven
+            tape::ramp                    m_darken_hz;   // Hz
+            tape::ramp                    m_drive;       // >= 0
+            tape::ramp                    m_input_level; // linear
+            tape::ramp                    m_mix;         // 0..100
+        };
+
+    } // namespace tapecho
+} // namespace tap::tools
diff --git a/include/taptools/taptools.h b/include/taptools/taptools.h
index 13b2c37..3a98574 100644
--- a/include/taptools/taptools.h
+++ b/include/taptools/taptools.h
@@ -12,21 +12,28 @@
 #include "bridged_t.h"
 #include "conv_engine.h"
 #include "delay.h"
+#include "diffuseur.h"
 #include "diode_ladder.h"
 #include "discreet.h"
+#include "fuzz.h"
 #include "garden.h"
 #include "grm_comb.h"
 #include "grm_pitchaccum.h"
 #include "ladder.h"
 #include "metal_bank.h"
 #include "nr.h"
+#include "ondes.h"
 #include "overdrive.h"
+#include "scrub.h"
 #include "spectra.h"
+#include "stammer.h"
 #include "stft.h"
 #include "svf.h"
 #include "swing_vca.h"
 #include "tape_loop.h"
+#include "tapecho.h"
 #include "tb303_voice.h"
+#include "touche.h"
 #include "tr808_clap.h"
 #include "tr808_cowbell.h"
 #include "tr808_cymbal.h"
diff --git a/include/taptools/touche.h b/include/taptools/touche.h
new file mode 100644
index 0000000..354c989
--- /dev/null
+++ b/include/taptools/touche.h
@@ -0,0 +1,377 @@
+/// @file
+/// @brief      Portable Ondes Martenot intensity-key gain law for tap.touche~ — no Max/Min
+///             dependency.
+/// @details    The first piece of the Ondes Martenot to land (book/PLAN-ondes.md), and the one
+///             that is useful on its own: the *touche d'intensité*, the pressure key Maurice
+///             Martenot's left hand rides, reduced to what it actually is — a measured,
+///             published, expressive gain curve. Olivier Messiaen called it the instrument's
+///             greatest invention. It is a graphite/mica powder bag working as a rheostat (the
+///             carbon-microphone principle: compress it and the number of conducting bead paths
+///             rises, so resistance falls), and what the player feels is a well-chosen nonlinear
+///             spring.
+///
+///             **The curve is not modelled or fitted — it is the published measurement.**
+///             Quartier, Meurisse, Colmars, Frelat & Vaiedelich, "Intensity Key of the Ondes
+///             Martenot: An Early Mechanical Haptic Device", *Acta Acustica united with
+///             Acustica* 101(2), 421–428, 2015 (doi:10.3813/AAA.918837) measured finger force,
+///             key displacement and the resulting sound simultaneously on instrument No. 320,
+///             and reports the boundaries of the six musical nuances over the key's travel.
+///             Those seven points are `k_table_*` below, and this kernel interpolates them.
+///
+///             Three findings from that paper drive the design, and each is a decision this file
+///             did not get to make:
+///
+///             - **The input is a position, not a force and not a velocity.** The paper states
+///               that the change in sound intensity depends on the key's displacement (and on
+///               the force that displacement implies), and explicitly that it does *not* depend
+///               on the speed of the gesture. A static, memoryless map is therefore the finding
+///               rather than a simplification. Force is offered as a secondary input because the
+///               same table has a force column, not because it is the primary story.
+///             - **50 dB over about 4.5 mm.** From 4.3 mm (the instrument's noise floor, 45
+///               dB_SPL) to 8.8 mm (95 dB_SPL). For comparison the paper cites most traditional
+///               instruments as rarely exceeding 25 dB of per-note dynamic range.
+///             - **The shape is not a line.** Equal 8.3 dB steps correspond to displacement
+///               steps of 1.0, 0.6, 0.5, 0.4, 0.5 and 1.5 mm — the curve steepens through the
+///               middle and flattens hard at the top. Fitting a straight line in dB-against-mm
+///               would throw away the entire reason the key is expressive, so the kernel
+///               interpolates the table with monotone cubic (Fritsch–Carlson PCHIP) segments,
+///               which pass through every measured point and cannot overshoot between them.
+///
+///             Interpolation is precomputed into a dense table at construction, so `process()`
+///             is a lookup and a lerp. Nothing here depends on the sample rate except the
+///             anti-zipper ramp.
+///
+///             Honest limits:
+///             - **One instrument.** The measurements are of ondes No. 320, and the paper notes
+///               that weight and thickness of the key vary by more than 10 % between instruments.
+///               This is that instrument's curve, not a universal constant.
+///             - **Pressing only.** The paper says the released-key case was not investigated,
+///               so the same curve is used in both directions here. That is an assumption, and
+///               it is this file's, not the paper's.
+///             - **The dB values are relative, not absolute.** The published numbers are dB_SPL
+///               at one metre through that instrument's own amplifier and speaker; the constant
+///               92 dB offset the paper reports between its electrical and acoustic scales is
+///               rig-specific. What is used here is the *shape*, normalized so full press is
+///               0 dB. No absolute level is claimed.
+///             - **Below the first point is silence, not extrapolation.** 4.3 mm is where the
+///               instrument sits at its own noise floor, so the kernel outputs exact zero below
+///               it rather than inventing curve outside the measured domain. Above 8.8 mm it
+///               clamps at unity for the same reason. Note the consequence for a 0..1 control:
+///               normalized position spans the *physical* 9.5 mm travel the paper describes, so
+///               roughly the first 45 % of the throw is silent. That dead zone is the key's own
+///               first phase — pure bending of the elastic strip before it reaches the powder
+///               bag — not a modelling choice, and it is why the instrument can be played with
+///               such sharp attacks: the useful 50 dB lives in 4.5 mm right after it.
+///             - The force map covers the same seven points; the paper's observation that dB
+///               rises linearly with log(force) holds below about 85 dB_SPL and 1.3 N, and above
+///               that the force required climbs steeply (9.6 N at full press — past where a
+///               player would stay for long).
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+
+namespace tap::tools {
+    namespace touche {
+
+        constexpr double k_default_smooth_ms = 20.0;
+        constexpr int    k_points            = 7;    // the published nuance boundaries
+        constexpr int    k_table_size        = 1025; // dense lookup resolution (~4.4 um per step)
+
+        /// The measurement, verbatim: Quartier et al. 2015, Table II — mean displacement and
+        /// mean finger force at the boundaries of six musical nuances equally distributed over
+        /// the instrument's 50 dB dynamic range (means over notes C3a..C3h).
+        constexpr std::array<double, k_points> k_table_db = {45.0, 53.3, 61.6, 70.0, 78.3, 86.6, 95.0};
+        constexpr std::array<double, k_points> k_table_mm = {4.3, 5.3, 5.9, 6.4, 6.8, 7.3, 8.8};
+        constexpr std::array<double, k_points> k_table_n  = {0.39, 0.47, 0.52, 0.62, 0.82, 1.34, 9.60};
+
+        constexpr double k_min_mm = 4.3; // the noise floor: below this the instrument is silent
+        constexpr double k_max_mm = 8.8; // full press
+        constexpr double k_min_n  = 0.39;
+        constexpr double k_max_n  = 9.60;
+
+        // The *physical* travel the player moves through, which is wider than the measured band.
+        // Quartier et al.: "all instrumental gestures take place in a displacement of only a few
+        // millimetres (about between 3mm and 9.5mm)", and the key's first phase is pure bending
+        // of the elastic strip before it even contacts the powder bag. So the bottom of the
+        // travel is genuinely silent — normalized position spans the travel, not the measured
+        // sub-range, and the dead zone below k_min_mm is the instrument's, not an invention.
+        constexpr double k_travel_mm = 9.5;
+        constexpr double k_travel_n  = 9.60; // the force axis bottoms at zero for the same reason
+        constexpr double k_range_db  = 50.0; // k_table_db.back() - k_table_db.front()
+
+        /// Which measured column drives the gain.
+        enum input_mode : int {
+            mode_displacement = 0, // millimetres of key travel — the primary map
+            mode_force        = 1  // newtons of finger force — the same table's other column
+        };
+
+        /// Monotone cubic (Fritsch–Carlson PCHIP) through the published points. Chosen over a
+        /// natural spline because it passes through every measurement *and* cannot overshoot
+        /// between them — an overshoot here would be a non-monotone gain curve, which is
+        /// audible as a dip while pressing harder.
+        class pchip {
+          public:
+            void build(const std::array<double, k_points>& x, const std::array<double, k_points>& y) {
+                m_x = x;
+                m_y = y;
+
+                std::array<double, k_points - 1> h{}, delta{};
+                for (int i = 0; i < k_points - 1; ++i) {
+                    h[static_cast<size_t>(i)] = x[static_cast<size_t>(i + 1)] - x[static_cast<size_t>(i)];
+                    delta[static_cast<size_t>(i)] =
+                        (y[static_cast<size_t>(i + 1)] - y[static_cast<size_t>(i)]) / h[static_cast<size_t>(i)];
+                }
+
+                // Interior slopes: weighted harmonic mean, zeroed at any sign change so the
+                // interpolant stays monotone (Fritsch & Carlson, SIAM J. Numer. Anal. 17, 1980).
+                for (int i = 1; i < k_points - 1; ++i) {
+                    const double d0 = delta[static_cast<size_t>(i - 1)];
+                    const double d1 = delta[static_cast<size_t>(i)];
+                    if (d0 * d1 <= 0.0) {
+                        m_m[static_cast<size_t>(i)] = 0.0;
+                    }
+                    else {
+                        const double w1             = 2.0 * h[static_cast<size_t>(i)] + h[static_cast<size_t>(i - 1)];
+                        const double w2             = h[static_cast<size_t>(i)] + 2.0 * h[static_cast<size_t>(i - 1)];
+                        m_m[static_cast<size_t>(i)] = (w1 + w2) / (w1 / d0 + w2 / d1);
+                    }
+                }
+                m_m[0] = end_slope(h[0], h[1], delta[0], delta[1]);
+                m_m[k_points - 1] =
+                    end_slope(h[k_points - 2], h[k_points - 3], delta[k_points - 2], delta[k_points - 3]);
+            }
+
+            /// Evaluate at t, clamped to the measured domain (callers handle out-of-range policy).
+            double at(double t) const {
+                const double lo = m_x[0];
+                const double hi = m_x[k_points - 1];
+                if (t <= lo) {
+                    return m_y[0];
+                }
+                if (t >= hi) {
+                    return m_y[k_points - 1];
+                }
+                int i = 0;
+                while (i < k_points - 2 && t >= m_x[static_cast<size_t>(i + 1)]) {
+                    ++i;
+                }
+                const double h  = m_x[static_cast<size_t>(i + 1)] - m_x[static_cast<size_t>(i)];
+                const double s  = (t - m_x[static_cast<size_t>(i)]) / h;
+                const double s2 = s * s;
+                const double s3 = s2 * s;
+                // Cubic Hermite basis.
+                const double h00 = 2.0 * s3 - 3.0 * s2 + 1.0;
+                const double h10 = s3 - 2.0 * s2 + s;
+                const double h01 = -2.0 * s3 + 3.0 * s2;
+                const double h11 = s3 - s2;
+                return h00 * m_y[static_cast<size_t>(i)] + h10 * h * m_m[static_cast<size_t>(i)]
+                       + h01 * m_y[static_cast<size_t>(i + 1)] + h11 * h * m_m[static_cast<size_t>(i + 1)];
+            }
+
+          private:
+            /// One-sided three-point end slope, limited so the end segment stays monotone.
+            static double end_slope(double h0, double h1, double d0, double d1) {
+                double m = ((2.0 * h0 + h1) * d0 - h0 * d1) / (h0 + h1);
+                if (m * d0 <= 0.0) {
+                    m = 0.0;
+                }
+                else if (d0 * d1 <= 0.0 && std::abs(m) > std::abs(3.0 * d0)) {
+                    m = 3.0 * d0;
+                }
+                return m;
+            }
+
+            std::array<double, k_points> m_x{}, m_y{}, m_m{};
+        };
+
+        /// Per-sample linear parameter ramp — the anti-zipper unit, the delay.h shape.
+        class ramp {
+          public:
+            void snap(double v) {
+                m_current = m_target = v;
+                m_inc                = 0.0;
+                m_remaining          = 0;
+            }
+            void to(double tgt, long n) {
+                if (n < 1 || tgt == m_current) {
+                    snap(tgt);
+                }
+                else {
+                    m_target    = tgt;
+                    m_inc       = (tgt - m_current) / static_cast<double>(n);
+                    m_remaining = n;
+                }
+            }
+            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}, m_target{0.0}, m_inc{0.0};
+            long   m_remaining{0};
+        };
+
+        /// The intensity key: a position in, a gain out, and an input scaled by it.
+        class key {
+          public:
+            key() {
+                m_disp.build(k_table_mm, k_table_db);
+                m_force.build(k_table_n, k_table_db);
+                rebuild();
+                m_position.snap(0.0);
+            }
+
+            // -- lifecycle -----------------------------------------------------------------------
+
+            /// Only the anti-zipper ramp depends on the sample rate; the curve does not.
+            void prepare(double sr) {
+                m_sr = (sr > 0.0) ? sr : 48000.0;
+                m_position.snap(m_position.target());
+            }
+
+            /// Return the key to rest (silent).
+            void clear() { m_position.snap(0.0); }
+
+            // -- parameters ----------------------------------------------------------------------
+
+            /// Key travel as 0..1 over the *physical* range (0 to 9.5 mm), slewed. The measured
+            /// band sits inside it: everything below 4.3 mm is silent, because that is where the
+            /// key is still bending before it compresses the powder bag. Expect roughly the
+            /// first 45 % of the throw to do nothing — that dead zone is the instrument's.
+            void set_position(double p) { m_position.to(std::clamp(p, 0.0, 1.0), smooth_samples()); }
+
+            /// The same control in the published unit, millimetres of key travel.
+            void set_position_mm(double mm) { set_position(mm / k_travel_mm); }
+
+            /// Finger force in newtons, for `mode_force`. Below k_min_n is silence.
+            void set_force_n(double n) { set_position(n / k_travel_n); }
+
+            /// Which measured column the position drives. Changing it rebuilds the dense table;
+            /// not real-time-safe.
+            void set_mode(int mode) {
+                const int v = (mode == mode_force) ? mode_force : mode_displacement;
+                if (v != m_mode) {
+                    m_mode = v;
+                    rebuild();
+                }
+            }
+
+            /// Anti-zipper ramp time in ms (0 = instant).
+            void set_smooth_ms(double ms) { m_smooth_ms = std::max(0.0, ms); }
+
+            // -- introspection -------------------------------------------------------------------
+
+            double position() const { return m_position.target(); }
+            double position_mm() const { return m_position.target() * k_travel_mm; }
+            int    mode() const { return m_mode; }
+            double smooth_ms() const { return m_smooth_ms; }
+            double samplerate() const { return m_sr; }
+
+            /// The linear gain the key is currently applying (after the ramp).
+            double gain() const { return lookup(m_position.current()); }
+
+            /// The curve itself, for plotting and for callers who want the law without the VCA:
+            /// linear gain at a normalized position, 0 dB at full press.
+            double gain_at(double p) const { return lookup(std::clamp(p, 0.0, 1.0)); }
+
+            /// The curve in dB relative to full press (-inf at rest).
+            double db_at(double p) const {
+                const double g = gain_at(p);
+                return (g > 0.0) ? 20.0 * std::log10(g) : -std::numeric_limits<double>::infinity();
+            }
+
+            // -- audio ---------------------------------------------------------------------------
+
+            /// Message-rate position: the slewed target drives the gain.
+            double process(double in) { return in * lookup(m_position.tick()); }
+
+            /// Signal-rate position override (a pedal, a sensor, a control signal). Snaps the
+            /// ramp so a later message-rate move continues from here without a jump.
+            double process(double in, double position) {
+                m_position.snap(std::clamp(position, 0.0, 1.0));
+                return in * lookup(m_position.current());
+            }
+
+            void process(const double* in, double* out, size_t n) {
+                for (size_t i = 0; i < n; ++i) {
+                    out[i] = process(in[i]);
+                }
+            }
+
+          private:
+            /// Fill the dense linear-gain table from whichever measured column is selected. The
+            /// published dB values are referenced to full press, so the top of the curve is
+            /// exactly unity and the bottom is exactly -50 dB.
+            /// The dense table spans the *measured band only* — 4.3..8.8 mm, or 0.39..9.60 N —
+            /// so entry 0 is exactly the first published point. The silent dead zone below the
+            /// band is handled in lookup() rather than by zeroing entries here: a hard zero
+            /// adjacent to the floor puts a cliff in the table, and a query landing on the floor
+            /// then lerps toward it and reads several dB low. (Measured: -54.2 dB instead of the
+            /// published -50.0 at the first force point. Caught by the reproduce-the-table test,
+            /// which is exactly what that test is for.)
+            void rebuild() {
+                const bool   force = (m_mode == mode_force);
+                const pchip& curve = force ? m_force : m_disp;
+                const double lo    = force ? k_min_n : k_min_mm;
+                const double hi    = force ? k_max_n : k_max_mm;
+                // Band edges cached in the *normalized* domain, so a caller who converts
+                // millimetres the same way this does lands exactly on the edge rather than a
+                // rounding error below it.
+                m_floor_p = lo / (force ? k_travel_n : k_travel_mm);
+                m_top_p   = hi / (force ? k_travel_n : k_travel_mm);
+                for (int i = 0; i < k_table_size; ++i) {
+                    const double q                 = static_cast<double>(i) / (k_table_size - 1);
+                    const double db                = curve.at(lo + q * (hi - lo)) - k_table_db[k_points - 1];
+                    m_gain[static_cast<size_t>(i)] = std::pow(10.0, db / 20.0);
+                }
+            }
+
+            /// Position (0..1 of the physical travel) to linear gain. Below the measured floor
+            /// the answer is exact zero — that region is real travel the instrument spends
+            /// silent, and extrapolating curve into it would be inventing data. Inside the band
+            /// it is a dense-table lookup with a linear step between entries.
+            double lookup(double p) const {
+                const double q = std::clamp(p, 0.0, 1.0);
+                // Strictly below the floor is silence; *at* the floor is the first published
+                // point (-50 dB), not zero — 4.3 mm is a measurement, not the edge of nothing.
+                if (q < m_floor_p) {
+                    return 0.0;
+                }
+                if (q >= m_top_p) {
+                    return m_gain[k_table_size - 1];
+                }
+                const double t = (q - m_floor_p) / (m_top_p - m_floor_p) * (k_table_size - 1);
+                const double f = std::floor(t);
+                const int    i = static_cast<int>(f);
+                const double a = t - f;
+                return m_gain[static_cast<size_t>(i)] * (1.0 - a) + m_gain[static_cast<size_t>(i + 1)] * a;
+            }
+
+            long smooth_samples() const { return static_cast<long>(m_smooth_ms * 0.001 * m_sr); }
+
+            double                           m_sr{48000.0};
+            double                           m_smooth_ms{k_default_smooth_ms};
+            int                              m_mode{mode_displacement};
+            pchip                            m_disp, m_force;
+            std::array<double, k_table_size> m_gain{};
+            double                           m_floor_p{0.0}, m_top_p{1.0};
+            ramp                             m_position;
+        };
+
+    } // namespace touche
+} // namespace tap::tools
diff --git a/notebooks/diffuseur.ipynb b/notebooks/diffuseur.ipynb
new file mode 100644
index 0000000..811a024
--- /dev/null
+++ b/notebooks/diffuseur.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "09d964e2",
+   "metadata": {},
+   "source": [
+    "# tap.metallique~ / tap.palme~ — the diffuseurs, driven\n",
+    "\n",
+    "The Ondes Martenot does not have a loudspeaker, it has a rack of them, and choosing between them\n",
+    "is part of playing the instrument. Two of Martenot's resonating *diffuseurs* are modelled here:\n",
+    "\n",
+    "- the **métallique** (1944–45, patented 1947), a **gong** driven by a motor transducer, and\n",
+    "- the **palme** (1949–50), an electromagnet driving **twelve** metal strings on a soundboard.\n",
+    "\n",
+    "Najnudel, Hélie, Roze & Boutin (*\"Simulation of an ondes Martenot circuit\"*, IEEE/ACM TASLP 28,\n",
+    "2020) name the diffuseur as the stage that \"converts the electrical waveform into sound and in\n",
+    "turn modifies its spectral content\"; Wijnand, Boutin, Jossic & Maniguet (Forum Acusticum 2023)\n",
+    "describe the instruments and characterize the transducer.\n",
+    "\n",
+    "Two things about this kernel are worth stating before any plot:\n",
+    "\n",
+    "**They are driven, not struck.** `garden.h`'s modal machinery carries over — mode ratios, doublet\n",
+    "splitting, per-mode decay — but its strike envelopes do not. There is no trigger here. The input\n",
+    "excites the body continuously and the body rings at its own rates.\n",
+    "\n",
+    "**The bodies are recreations.** No ondes-specific modal measurement exists in any of the sources,\n",
+    "so the mode data is Fletcher & Rossing's general physics: the free circular plate's transverse\n",
+    "ratios for the gong, the harmonic series for the strings. Unlike `touche.ipynb`, which reproduces\n",
+    "a published table, this notebook cannot check the object against the instrument. What it *can*\n",
+    "check is that the maths underneath is honest — and that is what everything below measures.\n",
+    "\n",
+    "Every number here comes out of the shipping C++ through `tools/capi`; nothing is re-implemented\n",
+    "in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5fce38c7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:24.558837Z",
+     "iopub.status.busy": "2026-08-17T02:42:24.558643Z",
+     "iopub.status.idle": "2026-08-17T02:42:24.945035Z",
+     "shell.execute_reply": "2026-08-17T02:42:24.943799Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kernel reached through the C ABI: <class 'taptools_py.Metallique'> <class 'taptools_py.Palme'>\n"
+     ]
+    }
+   ],
+   "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, 3.4),\n",
+    "    \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+    "})\n",
+    "C = tap.PALETTE\n",
+    "sr = 48000.0\n",
+    "\n",
+    "# Fletcher & Rossing, The Physics of Musical Instruments, 2nd ed. — the free circular plate's\n",
+    "# transverse modes at Poisson 0.3 (Rayleigh's classical Chladni set), referenced to (2,0).\n",
+    "PLATE_RATIO = np.array([1.000, 1.730, 2.328, 3.910, 4.110, 6.300, 6.710, 7.340])\n",
+    "\n",
+    "def spectrum(x, n_from=None):\n",
+    "    x = np.asarray(x, dtype=float)\n",
+    "    if n_from is None:\n",
+    "        n_from = len(x) // 2\n",
+    "    seg = x[n_from:]\n",
+    "    win = np.hanning(len(seg))\n",
+    "    mag = np.abs(np.fft.rfft(seg * win)) / (len(seg) / 4)\n",
+    "    return np.fft.rfftfreq(len(seg), 1 / sr), mag\n",
+    "\n",
+    "def bin_at(x, hz, n_from=None):\n",
+    "    # single-bin magnitude by Goertzel, so a measurement never depends on FFT bin alignment\n",
+    "    x = np.asarray(x, dtype=float)\n",
+    "    if n_from is None:\n",
+    "        n_from = len(x) // 2\n",
+    "    seg = x[n_from:]\n",
+    "    w = 2 * np.pi * hz / sr\n",
+    "    c = 2 * np.cos(w)\n",
+    "    s1 = s2 = 0.0\n",
+    "    for v in seg:\n",
+    "        s1, s2 = v + c * s1 - s2, s1\n",
+    "    return np.sqrt(max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2 / len(seg)\n",
+    "\n",
+    "print(\"kernel reached through the C ABI:\", tap.Metallique, tap.Palme)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ce1f5e7",
+   "metadata": {},
+   "source": [
+    "## 1 · Where the gong's modes land, and how much of it each carries\n",
+    "\n",
+    "The plate is eight modes, each split into a slowly beating doublet (1.5 cents) and nudged a few\n",
+    "cents off the textbook ratio by a fixed per-index hash — a real plate is not a table. The weights\n",
+    "are chosen to sum to exactly 1, which is the whole boundedness argument: every resonator in the\n",
+    "bank has unit peak gain, so a weighted sum of them cannot amplify what drives it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1eb5d78a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:24.947291Z",
+     "iopub.status.busy": "2026-08-17T02:42:24.947045Z",
+     "iopub.status.idle": "2026-08-17T02:42:24.952879Z",
+     "shell.execute_reply": "2026-08-17T02:42:24.951410Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " mode  published     kernel  cents off    weight\n",
+      "    0      1.000      1.000      0.00     0.3000\n",
+      "    1      1.730      1.727     -3.43     0.2200\n",
+      "    2      2.328      2.323     -3.70     0.1600\n",
+      "    3      3.910      3.909     -0.62     0.1200\n",
+      "    4      4.110      4.118      3.33     0.0800\n",
+      "    5      6.300      6.312      3.25     0.0500\n",
+      "    6      6.710      6.707     -0.78     0.0400\n",
+      "    7      7.340      7.357      3.92     0.0300\n",
+      "\n",
+      "sum of weights: 1.000000000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "gong = tap.Metallique(sr, pitch_hz=180.0, decay=6.0, tilt=1.0, brightness=1.0,\n",
+    "                      drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0)\n",
+    "hz, weight = gong.modes()\n",
+    "\n",
+    "print(f\"{'mode':>5} {'published':>10} {'kernel':>10} {'cents off':>10} {'weight':>9}\")\n",
+    "for i, (f, w) in enumerate(zip(hz, weight)):\n",
+    "    got = f / hz[0]\n",
+    "    cents = 1200 * np.log2(got / PLATE_RATIO[i])\n",
+    "    print(f\"{i:5d} {PLATE_RATIO[i]:10.3f} {got:10.3f} {cents:9.2f}  {w:9.4f}\")\n",
+    "print(f\"\\nsum of weights: {weight.sum():.12f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9981802a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:24.954864Z",
+     "iopub.status.busy": "2026-08-17T02:42:24.954651Z",
+     "iopub.status.idle": "2026-08-17T02:42:25.113297Z",
+     "shell.execute_reply": "2026-08-17T02:42:25.112179Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.vlines(hz, 0, weight, color=C[0], lw=2.5)\n",
+    "ax.plot(hz, weight, \"o\", color=C[0], ms=6)\n",
+    "for i, (f, w) in enumerate(zip(hz, weight)):\n",
+    "    ax.annotate(f\"{PLATE_RATIO[i]:.3f}\", (f, w), textcoords=\"offset points\", xytext=(0, 7),\n",
+    "                ha=\"center\", fontsize=8, color=C[3])\n",
+    "ax.set_xlabel(\"frequency (Hz)\"); ax.set_ylabel(\"doublet weight\")\n",
+    "ax.set_title(\"the metallique's body at 180 Hz — free circular plate ratios (Fletcher & Rossing)\")\n",
+    "ax.set_ylim(0, 0.36)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5a3b120",
+   "metadata": {},
+   "source": [
+    "## 2 · The resonator is bounded, and that is not an accident\n",
+    "\n",
+    "Each mode is the constant-peak-gain two-pole resonator — zeros at ±1, `b0 = (1 − R²)/2`\n",
+    "(Steiglitz; Smith, *Introduction to Digital Filters*). Its peak magnitude is 1 for **any** pole\n",
+    "radius, so a bank of weighted modes needs no limiter after it and the zeros put exact nulls at DC\n",
+    "and Nyquist, so it needs no DC blocker either.\n",
+    "\n",
+    "Measured below by sweeping a sine through the whole plate: the response has the eight resonances\n",
+    "in it, and the peak output never exceeds a bounded input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1237a564",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:25.115460Z",
+     "iopub.status.busy": "2026-08-17T02:42:25.115270Z",
+     "iopub.status.idle": "2026-08-17T02:42:29.214523Z",
+     "shell.execute_reply": "2026-08-17T02:42:29.213664Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "largest peak output for a unit-amplitude drive: 0.1880\n",
+      "The weights guarantee a bound of 1.0; the measured peak sits well inside it, because a\n",
+      "resonance takes a while to build and the weight of any single mode is a fraction of the sum.\n"
+     ]
+    }
+   ],
+   "source": [
+    "probe_hz = np.geomspace(60.0, 2000.0, 220)\n",
+    "resp = []\n",
+    "for f in probe_hz:\n",
+    "    g = tap.Metallique(sr, pitch_hz=180.0, decay=1.2, tilt=1.0, brightness=1.0,\n",
+    "                       drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0)\n",
+    "    n = int(sr * 1.2)\n",
+    "    t = np.arange(n) / sr\n",
+    "    y = g.process(np.sin(2 * np.pi * f * t))\n",
+    "    resp.append(np.abs(y[int(sr * 0.9):]).max())\n",
+    "resp = np.array(resp)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.semilogx(probe_hz, 20 * np.log10(resp), color=C[0], lw=1.6)\n",
+    "for i, f in enumerate(hz):\n",
+    "    ax.axvline(f, color=C[2], lw=0.8, ls=\":\", alpha=0.8)\n",
+    "ax.set_xlabel(\"drive frequency (Hz)\"); ax.set_ylabel(\"peak output (dB, input peak = 1)\")\n",
+    "ax.set_title(\"the plate's response — dotted lines are where the eight modes were placed\")\n",
+    "ax.axhline(0, color=C[3], lw=1.0, ls=\"--\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"largest peak output for a unit-amplitude drive: {resp.max():.4f}\")\n",
+    "print(\"The weights guarantee a bound of 1.0; the measured peak sits well inside it, because a\")\n",
+    "print(\"resonance takes a while to build and the weight of any single mode is a fraction of the sum.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "733f2d7b",
+   "metadata": {},
+   "source": [
+    "## 3 · The transducer, and the one part of it with physics behind it\n",
+    "\n",
+    "Wijnand et al.'s point is that the early diffuseurs use a **moving-iron** driver whose operating\n",
+    "principle is inherently nonlinear — Thiele–Small does not describe it — so a diffuseur modelled\n",
+    "as a pure resonator is missing a documented stage.\n",
+    "\n",
+    "What the kernel models is the *principle*, not a fit. In a moving-iron motor the force follows the\n",
+    "square of the gap flux, so with a bias current I₀ and signal i the force carries a term in\n",
+    "(I₀ + i)²: the residual i² is a second harmonic growing with drive. For a sine of amplitude A and\n",
+    "asymmetry a, that predicts a second harmonic at exactly **a·A/2** relative to the fundamental —\n",
+    "and nothing at the third. Measured against that prediction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "03ad2ef1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:29.216680Z",
+     "iopub.status.busy": "2026-08-17T02:42:29.216489Z",
+     "iopub.status.idle": "2026-08-17T02:42:29.417712Z",
+     "shell.execute_reply": "2026-08-17T02:42:29.416610Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "largest departure from the prediction: 1.37e-04\n",
+      "largest third harmonic:                2.75e-09\n",
+      "A squared term makes a second harmonic and nothing else — which is the point of it\n",
+      "being a squared term rather than an offset pushed through a general clipper.\n"
+     ]
+    }
+   ],
+   "source": [
+    "amp = 0.5\n",
+    "asyms = np.linspace(0.0, 1.0, 11)\n",
+    "ratio, third = [], []\n",
+    "for a in asyms:\n",
+    "    drv = tap.Transducer(sr, drive=1.0, asymmetry=float(a), saturation=0.0)\n",
+    "    n = int(sr * 0.5)\n",
+    "    y = drv.process(amp * np.sin(2 * np.pi * 200.0 * np.arange(n) / sr))\n",
+    "    f0 = bin_at(y, 200.0)\n",
+    "    ratio.append(bin_at(y, 400.0) / f0)\n",
+    "    third.append(bin_at(y, 600.0) / f0)\n",
+    "ratio = np.array(ratio)\n",
+    "predicted = asyms * amp / 2\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(asyms, predicted, color=C[3], lw=2.4, alpha=0.5, label=\"a·A/2 — the moving-iron prediction\")\n",
+    "ax.plot(asyms, ratio, \"o\", color=C[0], ms=5, label=\"measured through the kernel\")\n",
+    "ax.set_xlabel(\"asymmetry\"); ax.set_ylabel(\"second harmonic / fundamental\")\n",
+    "ax.set_title(\"the squared law, measured on the driver alone (A = 0.5, saturation off)\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"largest departure from the prediction: {np.max(np.abs(ratio - predicted)):.2e}\")\n",
+    "print(f\"largest third harmonic:                {np.max(third):.2e}\")\n",
+    "print(\"A squared term makes a second harmonic and nothing else — which is the point of it\")\n",
+    "print(\"being a squared term rather than an offset pushed through a general clipper.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff1edc8",
+   "metadata": {},
+   "source": [
+    "### The bound is 2/saturation, not 1/saturation\n",
+    "\n",
+    "The bounding saturator is `vca::swing_shape`, whose output is bounded by `1/drive`. It would be\n",
+    "easy to stop there — but a hard-driven squared law is a nearly-constant *positive* waveform with\n",
+    "brief negative excursions, and the DC blocker after it removes that offset, which doubles the\n",
+    "worst-case swing. The real bound is twice what the obvious argument gives, and the kernel says so\n",
+    "in its header because 1/saturation is the number you would expect and it is wrong."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "359e4609",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:29.419672Z",
+     "iopub.status.busy": "2026-08-17T02:42:29.419483Z",
+     "iopub.status.idle": "2026-08-17T02:42:29.554296Z",
+     "shell.execute_reply": "2026-08-17T02:42:29.552925Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "drive 200, asymmetry 1, saturation 0.8\n",
+      "  the naive bound 1/saturation : 1.250\n",
+      "  the real bound  2/saturation : 2.500\n",
+      "  measured peak                : 1.493\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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o0aKFXevqhAkTRNeuXaXpt956SwAQv/zyi5Sm1+uFWq0WdevWFfn5+VK69ZeAwhfa3bt3F+3atbPJIy8vT9SsWVM8/PDDUtobb7wh/Pz8bC7e33//fSGXy8WdO3dKVeY+ffqInj172ixjPYm5W7AOQOzatcsm/YEHHrBpvS3p+3akovPo3bu36N27t80y6enpwtfXt9h9m5qaKm7cuCGOHz8uAEi/rglRsmBdiIr9fDhSmnWfffZZ4evrK/bs2SNCQkJsfmHbunWrACB27NhRbF6lDdb79+9vt4358+eLqKioe76nwsf37777TgAQ+/bts1nmX//6l5DL5dIx1prnkCFDbJaz/pppDdJKkv+96PV6cfPmTXHjxg0xYsQIMXToUGnevd73lClTRGRkpF1D2gsvvCDq169/zzzvF6xbL7isRowYIXr16lUheVvzKNrAo9PpRFBQkJg5c6ZNusFgEHXq1LGLHYSwnHfj4+PFjRs3xEcffSTkcrnQ6XRCCCEOHz4sABTbM8L6y/8LL7xgk37mzBm783yfPn2Er6+vTWNRZmamUCqV4pNPPrnn+y0J9lkvpaysLDz77LPw9/dHo0aN0KdPHzz22GMA4PAu/aIUCgV69Ohhk9aoUSObfpKlzaN379420+fOnUN+fj769Oljk963b1+b6czMTJuh8ax9wkq6vlXHjh3h7+9f/JsuMHnyZPz8889SP9ybN2/iwIEDmDx5MgDg8OHD0Ov1aN26tU25kpKS0LBhQxw/ftwuX19fX2m6Vq1a8PX1vefwbu3atYOfnx/Gjh2LL774Ardu3bKZX9r3XhLnzp3D4MGD4ePjg3bt2qFv3754//33odVqpX6Bhd9T0X25b98+hIaGIigoyGa/1KxZE0IInDhxAgDw559/on79+qhXr16FlP2rr75C48aNERISgh49eqBv3744c+aMzWewZ8+e8PLywm+//QYA+O2339C/f3/079/fJq1u3bpo1KhRmcpxLwaDAUqlEgCgVCpL3CfyXorWvRAC+/fvxwMPPICbN2/a1EHDhg1x4sQJu/6Q3bt3l157e3sjMjISHTp0gEqlktKto3dYP69msxnHjh1D//79bbbl5eWFnj174vDhw1La5MmTkZuba3OD7dq1a/Hggw8iMjKyVGU+deqU3XvWaDRo3br1PfeTtS9xWf7ud8/FvfTq1ctmuvDxsyx15cw8Tp8+bbet4OBgtGrVyiYtNzcXs2bNQmBgIBo0aIA+ffrg4YcfBlCy80pRFfn5KKq06y5fvhwNGjTA4MGDERQUhBUrVkjzfv/9dygUCgwaNKjU77E4RT/LANCjRw/ExsZi7Nix2Lp1632Hcz18+DCUSqXdubV///4wm804evSoTXrR5azHOutnprT5W/34449o3bo1AgMD0bVrV/Tt2xf79u1z+Jlw9L737duH9u3bIzEx0aae6tevj5iYGLvzT2k4es+Fz70VkXfR9/Tnn38iMzMTbdu2tdnmrVu30KRJE5sY4cqVKxg5ciTUajXatGmDvn37YvHixTCbzdK9fs2bN0doaCgef/xxrFq1CjFF7uc4ffo09Ho9BgwYYJPepk0bhIWF2RyTAaBVq1YIDQ2VpgMDAxEeHl4hQ84qy72Faub555/Hvn37cPToUbRs2RKA5cbA2rVrlyhQCA4OloILq6IBZmnyCA4Ohre3t02adYi+kJAQm/TCHyIA+Oabb7Bs2TJpumfPnli7dm2J17eKiIhw/GaLmDhxIl577TV89913mDlzJtatWweVSoUJEyYAgHQDzSuvvGK3jwDY3DAIAOHh4XbLqNXqe940WrNmTRw4cABLly7FCy+8gOzsbLRp0wZvv/02hg0bVqr3LpPJ7NKK1o9er8fw4cPRtm1bxMfHQ6PRALAMtfb000/bLe9oX6anpyMnJ8dh0B0VFSXdSJScnGxX7uLKfj+//vorpk6dik8++QTPPPOMFGQOGjTI5uY3tVqNLl264LfffsO4ceNw8eJF9O/fHzdu3MCqVatgNBrx22+/oV+/fqUuw/1kZ2cjIyMDderUAWAJfk0mE+Li4lC3bt0yb7doHWi1WuTl5WHr1q3STXyF1a5dG3q9Hmq1GgDg5+cnvbZSq9UICwuzSwMg3bCk0+lgNBod1pdGo7G5iatJkybo3LkzvvnmG0ycOBEXL17EyZMnsXbt2lKVWS6XIysrq9jPzb1uUr5x40aZg6x169bZNVqUhJ+fn80FOmA5flq/86WtK2fmUZp9+/LLL2Pbtm34448/0LZtWwBAWloaNBpNmS5AK+rz4Wg/lXZdHx8ftG/fHufOnUP//v0RFBQkLZuVleXwnFiYo+MsYH+stXJ0DB0xYgQ2btyITz75BI888giEEBgwYAA++OADu/MKYDm2BAcHQy63bc+0Hr+L3lBZ9Fxk/fxYPzOlzR8Azpw5g/Hjx+ONN97A/PnzpfP8lClTcOzYsRK97/T0dBw8eLDYc4ejAS5KytF7LnzurYi8i74na4zwr3/9C++8847d8tbvjtlsxsiRIxEZGYnY2FjUrFkTAPDDDz/g4Ycflj47QUFBOHDgABYvXoxXXnkFGRkZaNasGRYvXoxHHnlEqueSHJMBxzFJ0f1SVgzWS2n79u2YPXu2FEQDwPXr112Wh0KhsEuztqzGx8fbDPN1584dm+VmzZqFWbNmlXn9e5XBkdq1a6N///745ptvMHPmTKxduxbDhw+XvgjWL9Q333zjlODOqkOHDvj+++9hMplw7NgxLFy4EGPGjMG1a9dK9d5DQkLsWkiKLnf27FncvHkT3333nXSgB0pXnzVr1kRISIjdVX9R9erVs/v1obiy38/PP/+MqKgoPPvsszbp169fl4Jjq379+uGjjz7Cr7/+ioiICLRo0QJ169aFTqfD7t278ddff+HFF18sdRnuZ/fu3RBCSC3RQ4cOxbvvvosdO3Zg5syZDtcRQhR78rcqWgd+fn4ICAjA5MmT8d///rdiCu+An58f/Pz8HNbXnTt37E5cU6ZMwQsvvICEhAR888038Pf3x+jRo0td5ho1ajgcqefOnTsOA02rJk2a3PczWdkqo65Kk0d4eLjDUbDi4+MRHBwsTW/fvh1PPPGEFGwA5T+vVNTno6jSrrtp0yZ88803GDVqFNasWYOHH34Yw4YNAwBERkYiNTUVubm58PPzc7i+9TN4v2OtVXHno/Hjx2P8+PHIzc3F3r178fLLL2Ps2LG4cOGC3bI1atRAamoq8vLybBrDrHmWtIGqrPkDwJ49e6BSqfDGG2/YXDSU9tzRoUMHbNy4sdTlLa+KyLvoe7LGCJ988gnGjBlT7HrXrl3DpUuX8MEHH0jrAI73XcuWLbFhwwaYzWacPHkSixcvxoQJE3Du3Dlp6GlHn7X4+Hi7X82cid1gSkkIgby8PJu0L7/80q3yaNOmDTQaDbZs2WKT/v3331fK+vcyefJkHDp0CJs2bcKFCxekLjCApetAeHg4vvnmG4frlnZoxvttQ6FQoFu3bnjrrbek4cRK894bNWqE06dP26T98MMPNtPWn4ML16fBYMC6detKXObRo0cjMTERe/bssZtnNpulPPr164fbt2/bBexFy1QSQggYDAabn7P/+OMPhwe7/v37Iz09HR988IF0kRUQEICOHTvin//8p01AbZWYmFiup/mmpaVhwYIFaNSokTTcWp8+fdCvXz8sWbKk2GG+3njjjTLlN2rUKPz4448OW0gq4nNpNXDgQPz00082Q36lpqbi119/tWvFfvTRRyGXy7Fu3TqsW7cOY8eOtQl4Slrmfv364aeffrIZ6u3y5cs4d+5chb2v0ggMDITJZCrzsGeVUVclzaN///7YunWrzffo+vXrdvu2pMf80uybivp8OFLSdW/evInp06dj8uTJ+PHHHzFixAg8+eST0gXMyJEji32v1tbP8PBwBAYG3vdYey+Fy+Tn54fRo0dj6tSpuHbtmsPhKgcNGgQhhN1xf+PGjfD19S31r0KlzR+wfCbMZrPNun///TeOHDlS4nxHjx6NvXv3Ojwe3q+OrWPOl7XLWnnyLk67du0QFRV13xjB0XnXbDbj66+/LrYccrkcDzzwAJYuXQqz2Yxr166hXbt2CA8Px6ZNm2zW27lzJ7Kysiq0+9b9MFgvpfHjx+Ozzz7DDz/8gNOnT+O1116z+6nM1Xn4+flh4cKF+PDDD7Fs2TL89ddfWLlyZYlbasq7/r1YTxjTp09HaGgohg8fLs1Tq9VYtWoV1q1bhxkzZuDgwYM4d+4cvv/+e4waNQrr168vd/7r16/HyJEj8f333+P8+fM4fPgw3n77bURHR6NDhw6leu/PPvssjhw5grfffhtnz57Fp59+ancF3q5dOzRp0gRz587F4cOHcfDgQYwePdoueL2X4cOHY8qUKXjsscfw8ccf48yZMzh69Cg+/fRTtGrVSvo5fdSoUejevTsmTJiAHTt24OTJk3j66afL9NTHRx55BHfu3MHcuXNx7tw5bNq0Ca+88opd3z0A6Nq1K9RqNU6cOGHzi0i/fv1w4sQJNGjQwK4f/cSJE0v8NF/reM3Xr1/H8ePH8cEHH6BDhw4ALE9n9fLykpb99ttvERERgc6dO+N///sfzp07h7Nnz+Ljjz9G27Zti23Fup93330XcrkcAwYMwLZt23Dx4kXs3bsX8+bNw9SpU8u0TUeWLFmC7OxsjBkzBocPH8b+/fsxfPhwBAYGYsGCBTbLajQaDB06FEuWLMHNmzcxZcqUMpX5n//8J27fvo3HH38cx48fx549ezBr1iyn/rp1L9bPxcqVK3Ht2rVS9/esjLoqzb6Ni4vDxIkTcfz4cfzyyy94/vnn0bdvX5tfeMaPH4/PP/8cmzZtwpkzZ/Dmm286DMhLs28q6vNR1vdvMpkwadIkaDQafPLJJwAs3f9UKhWefPJJCCHQqVMnvPDCC5gzZw7eeecdnDp1Cvv27cOUKVPwyy+/ALB0g5k5cyY+/fRTbNq0CadOncJLL71k9wvfvcyePRvPP/88du/ejb///hs7d+7EV199hTFjxjg8t1rvGXjuueewdu1a/PXXX1i8eDFWr16NJUuW2Pwq4oz8AcvxXAiBGTNm4OzZs9i+fTumTJkiXeCUxBtvvIE6deqgf//+2Lx5My5cuIB9+/bhrbfewqhRo+65bps2bSCXy7FmzRpcvXq12HHWnZF3cRQKBdasWYNdu3Zh8uTJOHDgAM6fP4+ffvoJEyZMkO6HaNSoEdq3b48FCxbgwIEDOHz4MMaPH48uXbrYbG/Hjh0YPHgwvvvuO5w7dw7Hjx/HokWLEBkZie7du0OlUuG9997D5s2bMX/+fJw+fRqbN2/Gk08+iX79+mHs2LFleh9lUu5bVKsZrVYrXnvtNdG+fXvRoUMH8fbbb4v09HQRFRVlM7SXI++8845o3769XfqLL74oRowYUeo8itue1apVq0SXLl1Ey5YtxUsvvST0er2IiooSn3/+eYnea0nWHzp0qHjxxRcdrj9hwgQxbdo0u3TruONFR1KxOnXqlHjiiSdEq1atROvWrcW4cePE1q1bbUZOKS7f9u3b33cc1x07dohx48aJli1bis6dO4sXXnhB3Lp1677vHQ5G0lm5cqXo1KmTaNWqlVi4cKG0j7766itpmevXr4vHH39ctGjRQvTt21ds375dGlc5KSnpvu9JCMtwbF9//bUYPHiwaNKkiejWrZuYNWuWuHz5ss1y6enp4vnnnxfNmzcXXbt2FRs2bHCYV1FJSUkiKirKZljGn376SfTr1080b95cTJw4UcTGxopp06bZjKtuNWHCBBEVFSUNdyeEZaizqKgoh6O5dOjQwW7YwqKs+9L616hRI9GxY0cxceJE8c0330h39BeVl5cnVq1aJYYPHy6aNWsm2rZtK0aPHi1WrVplNzJBYVevXhVRUVHi119/dTg/NTVVLFiwQHTp0kU0bdpUDB48WCxfvtxuxI0WLVrYrTto0CC7z/vff/8toqKi7EaHunTpkpgyZYpo3ry5aNWqlZgxY0axQ5JZ67Zjx452I3OUtMxCCHHs2DExfPhw0aRJEzF69Gjx999/F1vXleHf//636Nixo4iOjrYZS9zRvp0/f74YNGiQTVpJ33dRzsjj2LFjYtiwYaJp06Zi7Nix4sqVK6J///42I3rodDrxxhtviA4dOoj27duLxYsXi9zcXBEVFSXWrl17333z+eefOxxtpKI+H47cb92VK1eK6OhoaQhcq19++UXUr1/fZtSutWvXioEDB4omTZqIgQMH2o3opdfrxdy5c0WLFi1E586dxbfffisOHjwooqKixN9//y2EsIy8ERUV5fBZGzqdTqxYsUIMGTJENGvWTPTu3Vu8++67NseDoue2/Px8sWzZMtGtWzfRuHFj8eCDD9qd44vL0/rdtg4/WZL8Hdm3b58YNGiQaNasmXj44YfFhQsXxOuvv24z8su93rcQlmFAly5dKnr06CGaNGkiBgwYIJYsWXLPIY6tVq5cKbp06SKio6OlcdbXrl0roqKi7J7xsWzZMrt4pKx5F5eH1YULF8S0adNEmzZtRKtWrcTYsWPFpk2bbD7jt2/fFk8++aRo2bKl6NWrl/juu++kc1Lh89Qvv/wiHnvsMdGqVSvRqVMn8eyzz4rr16/b5Ldr1y4xbNgw0bhxY/HAAw+IRYsW2dXdhAkTxFNPPWVX1j59+tg8R6asZEKU4lKJqBqTyWR4//33ndL/ujrJzs5GSEgINm/eLPWhJfJUZrPZpvU0KysL0dHRmDZtmsOb5IiIimI3GCKqVGfOnEHv3r3L/FMoUVVhMpnQr18/bNmyBRcuXMCePXswYsQImEwmu5u3iYiKw9FgiEooKipKuumGyq5nz57SGOxEnkyhUODtt9/Gf//7X5w/fx4KhQIPPPAAVq1ahaioKFcXj4iqCHaDISIiIiJyU+wGQ0RERETkphisExERERG5KQbrRERERERuijeYFmE2m5GRkQEfH5/7PpaciIiIiKgshBDQ6/UIDg6+58MvGawXkZGRAY1G4+piEBEREVE1kJqaitDQ0GLnM1gvwsfHB4Blx6nV6krLVwiBxMREREREsEXfg7GeqwfWc/XAevZ8rOPqwVX1rNPpoNFopNizOAzWi7BWklqtrvRg3ZonDwiei/VcPbCeqwfWs+djHVcPrq7n++XJG0yJiIiIiNwUg3UiIiIiIjfFYJ2IiIiIyE0xWCciIiIiclMM1omIiIiI3BSDdSIiIiIiN8VgnYiIiIjITXGc9WLEa7OgFgZpOlDlAz+VF3IN+cgy6G2W9Vd6I8DLG1qjAZn5Opt5vkovBHn5QG8yID3Pdp6PQoUQbzXyTEYk5eZiz+k0aEKNiNB4oV6YGrWDAmAwm5Ciz7VZTyVXIMzHDyazGUn6HJt5Spkc4Wp/mIVAoi7bZp5cJkOEOkB6f0VF+gYCABJ12TALYTMvQh0AuUyGZF0OjMJsM6+Gjz8UcjlS9LkwmE0288J8/KCSK5Cm1yLPbLSZF+rtC2+FEul5OuhNBpt5Id5q+ChUyMzXQ2vMt5kX5KWGr1KF7Pw85BjzbOY5u57S8rQ287zlSoT6+Ja4noQQSM3XQq7LQQ3fANaTm9aTVVm/T0LcbQlhPblvPQHl+z7JZTJkGPQwa7NsxklmPblXPZX3+5Scr7WpY9aTe9ZTeb5P1nOzWZuFIC91pdWTTme7XHEYrBfjP3/th9LbW5p+uH4r9K3VEMeT4/B9zDmbZYfWaYph9ZrhbFo81l49ZTOvT2QDPBLdGlcyU7Hy0lGbeQ+E18GUxh1xKzcTH5w7CAQCMAJIBLJPqpH7Z23UihIwt71ms159/xDMadMbOcY8/N/pfTbzaqoDsKB9fxjNZrt5gSpvLH1giPT+DOa7XxaVXI7lXUcAAD489/+QZbA9yLzX5SF4KRRYeekYEop8eZd0GoQgLzW+ufInrmen2cx7vV0/RPoGYtONv3AuPdFm3outeqJhoAbbbl7A8eRbNvOmN+uC1qE1sff2FeyPv24zb1Kj9uhSox7+SLiBnbf+tplXKfVUSKuQCMxo3hUp+ly7/d0gIBQvte7luJ5SWU9Vop7K+n2SyfFK9AMAWE9uXU/l/D4FqnzwU9I13IqzDXZYT+5VT+X5Pm2/eRHHU24BhaqK9eR+9VRh36dblVtPxjzbfVQcmRBFLn2qOZ1OB19fX1xLSbB5gqmzr4gPXEjG++tvYXC3UGiCVEhOMSIxHridqkNanhaFa8lslCHEyxe1I7xQo5ZAjVAvRIR6oaZGhYgQb9TyZ4utu7ZcCCGQmpqKiLBwtqy7cT1ZlatlPVuLiIgIJOlzWE+FuFM9AeVvWf/7ViwCQ0PYsl7AHeupPN+nNL0Wt5MSoNFo2LIO962nCmlZT02FRqOp9Jb1hmE1odVqbWLOohisF2EN1u+34yra8QtZePXjG1j4dD307RhiM89gNCMhNR9xiXm4nZyP20l5uJWUh9tJeUhKt/1wyuVATY0Xaod7o04Ny1/tGl6oXcMbEaFeUMj5uGRXEkIgMTERERERfHS1B2M9Vw+sZ8/HOq4eXFXPJY052Q3GTVh/SXL0IVEp5agb4YO6ET528/T5ZtxJzpMC+FtJ+bidnIert3Q4fiG7yHZkiNR4oU6EN2qHe6N2DW/UDvdCrXBvhIeoGMgTERERuRkG627C+gNHaeNlHy85GtRWo0Ft+yuyXJ0Jt5OtrfD5Umv82au5OPSX7c9XSoUMNTVeqBVuaZWPDLME8bXDvVBT4wUvFQcOIiIiIqpsDNbdhLUvUkX++uKnVqBJPV80qedrNy8zx4jbyXm4k5yPO9b/KXm4fFOHY+dtW+RlMiA8WIVa4d6oFe6FWgWBvOW1N/zUioorNBERERFJGKy7CeudA5XVVSrIX4kgfyVaRPvZzdPqTYhPyb8bzKfcDerPXMlB0bscgv2ViCxokbcE8neD+WB/Jfv5EREREZURg3U3YTZbu8G4PrD19VGgYR01Gtax71qTbzAjMS1fCt5vFwrm9/+ZAYPRNpJXe8tRO9wLkVIg723pbhPmhRqhXlAqXP9+iYiIiNwVg3U3Udkt62XlpSr+ZleTWSAlw2DTreZ2cj7ik/Nw8mI2/tDbDtUklwFhISpEarwQGeaFyDBLIG+dDglkqzwRERFVbwzW3YQz+qxXNoVchoiCMd/bN7WdJ4RARo4RCSn5iE/JR3yq5X9Caj4SUvJx9louzLaxPLxVMkRYA3lNoWA+zHLTK/vKExERkadjsO4mREE3GE9tSZbJZAgJUCEkQIXmDvrJm0wCSel3A/mEQv//jtXh6Llsu3UC/RRSIF+zUMs8u9gQERGRp3D7YD0mJga7du2SpkeOHIlatWrdcx2z2YzffvsNcXFxaN26NTp16uTsYpZbQaxe6qEbPYVCIUNkmDciw7wdztfpTUhIKwjmC1rkrf+PnM+GPu/eXWxqarwRXcsHERoVAnyVCA9WQcFgnoiIiNyc2wfrmZmZOH36NEwmE1avXo1mzZrdM1jPz8/HkCFDEBsbi06dOmHevHmYNGkSPvjgg8ordBl4QjcYZ1L7KBBdS43oWvY3vTrqYiMF88V0sZHLgLBgFWqEeiEiVIWIUEvXmohQL0RoVKgR4gVvL44tT0RERK7l9sF627Zt8dlnn0Gv12P16tX3XX7VqlX4+++/cf78eQQHB+PcuXNo27YtHn/8cXTu3LkSSlw21ocieWo3GGcqSReb735Nxuof4wEAI3tpkG8USErLR2J6Pv6O1dqNYgMAwQFKKZCPsAb11oA+1Av+vuwzT0RERM7l9sF6aW3btg1jx45FcHAwAKBVq1Z44IEHsHXrVvcO1gtafhmrVzyFQoZg/7uB9dj+4agbcbe7jdkskJFtREJaPhJT85GYZkBiWj4S0/KRlGbAiYvZ0BYZyQYA/HzkBS3zltZ4axBfU+OFGqEqhARwNBsiIiIqH48L1mNiYjBw4ECbtPr16yM2Ntbh8gaDAUajUZrW6XQALC3doujTf5zILLWso1LzrS4K32yqVNjuY5kMCAlUIiRQieb17Z/2KoRAjs6ExFSDFMAnpuUjoeD1xZhcHDlnslvPSyVDjRBLEG/tblMjVAUv5EEo8xAWzJtgPZX1+MHvsmdjPXs+1nH14Kp6Lml+Hhesm81mKBS23ROUSiVMJvtgCgCWLl2KRYsW2aUnJiZCrbbvH+0sGRlaAEB6ehoSE3MrLd/qIidHL73OSE+BzFj6LiwBXkBATaBRTQBQAFAX/AH6fIHUTBNSrH8ZJqRkmpGSacKNO1r8eSkHtl/JDMhkQLC/HJpABTRB1v8KaALlCA1UICxIjgA/uVs8KItKRwiBjIwMAOza5slYz56PdVw9uKqerQ3E9+NxwXrNmjURHx9vk3bnzh20b9/e4fILFizA/PnzpWmdTgeNRoOIiIhKDdYDAtMAZEMTGoqICP9Ky7e6CIvPBJAJAKgZUQPBARX/0Y+qW/w8g9GM5AwDElPzcSUmBXqjGskZBiSlG5CcbsCZqwbo8/Ps1lMpZQgLViE8RIUaIZYbX8NDbKf91HKeRNyMtbUkIiKCdePBWM+ej3VcPbiqnqtNsH7t2jXs3bsXM2fOBAAMGDAAGzduxP/93/9BoVAgKSkJhw4dwty5cx2ur1KpoFKp7NJlMlmlVpj1lxCFvHLzrS5Uyrsju6hUlR/ceqkUqB2uQK0wb9QK1todEKxdbZLSLMF7cno+EtPvvk5KN+DCdS2MJvufzNTecoQXdLe5G8h7oUaICuEF/zmyTeWzHkP4ffZsrGfPxzquHlxRzyXNy+2D9ezsbKxbt07qV75161ZcunQJAwcORMOGDXH8+HE8++yzUrA+e/ZsfPnllxg6dCj69++PdevWoVevXhg6dKgr30aJ8WDgHIX7hqvcsJ+4TCZDgK8SAb5KNKzj+Bcd642wSYUC+MKvr9/W4cTFbDjqAhfkr5AC9/CCID48WGVptS/4z4CeiIjI/bh9sJ6fn4/Tp08DAGbMmAGtVovTp09LDzpq1KgRZsyYIS0fEhKCEydOYM2aNbh16xZmz56NJ554wu2D4MI3mFLFUyplDl9XJXK5DKFBKoQGqdDMwY2wAGA0CaRmGJBUJJhPTjcgKS0f567lIivX8f0bgX6Ku8F7yN1APjzYS5r282GXGyIiosrk9sG6RqPBZ599Vuz8Tp062T2hVKPRYN68ec4uWoXi0I3OVbg1XeHBj4lVKmSWseA1XsUuo883IyXD0sUmJSPf8lqaNuBKnA7HLjhuoVd7y6WW+LAQazCvgqagX33tcG94q+R8OiwREVEFcftgvbqwxkUeHEe6FIPHu3y85KhTwxt1angXu4zBaEZaphHJGYZCwbwlsE/JMOBWch7OXMmByX74eQBAgK8CmiAVNEFKaIJV0ASpEFZkOjRQaXMvAREREdljsO4mrN1g2LTuHKoq2vXFVVRK+X1b6K196K2t8j/uT8Gpv3MAAK0b+SE9y4i4xDycvlx8UB/sr4QmWFkQ2BcE89JrS4t9aICSF1tERFRtMVh3E9ZuMGxZdw4+fKjiFe5D3zQKOHMlRwrW35pWX9rnZrNAZq4RqRlGpGYakJJpQFqm5XVqpgGpGQZcu6XDiQvZMDvoeiOTASEBhYL4YGVBK/3daU2QCsH+Ssj5BSIiIg/DYN1NsGHduRisO1/hz27hmFkulyEkQIWQABUa1S3+2QWmgpb61ExLV5vUIgF9aqYBl2K1yDhndLi+Qg6EBqoKutkooSn8uiCwDwtWIdBPwZtkiYioymCw7ibu9llnEOEMVXUEmKqk8I27ZfkYK+QyKahuUq/45QxGM9KzrIG8ESmFgnlr2tmrxY96o1LKEBqolLrZaAKVll8IApUIDVQhxPqf3W+IiMgNMFh3Exy60bnccWx1T1P4s+vMlmuVUo4aoV6oEVp8f3oAyDeYkZZlRGqGpevN3Vb6u91xYhP0yNU57lAvkwFBfsqC4L1QIC8F9kqEBFpeB/iytZ6IiJyDwbqb4NCNzsUWUudzt/7iXio5amq8UPMeN8kCgC7PhPRsI9IzjUjLMiAty4i0LCPSpdcGxCXm4cyVXIdPkAUs3awswfvdoN5bnoc6tRTQBKosaUGWeT58+BQREZUCg3U3wW4wzsXRYJzPzWL1ElN7K6D2VqBWWPFDWQKAEALZWlORQN6ItEwD0rPuBvoXb2iRmWssuA8l1247vj5yKai37Xpz93VIgBLBARzakoiIGKy7DSHdYeracngq3mDqfO7Wsl7RZDIZAv2UCPRTon6kzz2XNRjNuHojHgqvYKRnm6RAvnBQn5ZlGQVHqy9mXEsAAX4KhATYBvE1Qr0Q6KeAj5ccwf6WoD44wNIVx9PrgIioOmKw7iasQ9bxXOscDNadj5/du5QKGUICFIiI8L1vX3ZrN5y0TEuLfXq2Nag3Ij3b0mqfmJqPSzFa6POLD+zlctgE7yEBSgQVmZZe+yvh4y1nP3sioiqAwbqbuDt0I0+ezsAWR+fjPi6bknbDAYBz13LxwntXAQAP9w9D47q+yMgxIiPbgIxsI9KzjcjINuJ2Uh7OXctFvsFxH3sA8FbJpODdNpi3jFlfOLgP8lewSw4RkYswWHcT1m4wjHeoquJ1pvOpve8GzE2jfDHggZBilxVCQJ9nRnqOJYC3+StIswb3V27qkJFjhLn4hnv4qxUOW+kLt+IH+SsQ5KdEoL+Sv2YREVUQButuwtqyzj7rVFWxZd35CgfA9wuGZTIZ1D4KqH1K1mpvNltuoC0c0FuD+cxso03QHxuvR7bW8Tj2Vv5qBYICLMG7JZAvCOb9704HF0wH+ivh58NuOUREjjBYdxPWFi3GO87VsM69bwyksuNn1/lKE6yXllwuk4LoqMj7L28wmpGZYxvcZ+YYkZVrREa2CZm5liA/M9eIO8n5yMo1SvfmOKJUyGyDeT8FggKUlmA/oGDa3zbwZ9ccIqoOGKy7CQHrQ5EY8TjLjg9aQ8Fzu9OwZd35nBmsl5ZKKUdYsBxhwaoSLW82C+ToTMjMMUpBfmauEVk5RmTkWNMtf/Ep+bgUY4Qu7x79cgD4+cgR6K9EsL8SgQWBfnBBS73Uau9nCewD/ZTw91XYPGmXiKgqYLDuJu7eYOracngyPozGuRgDOV/hh3tVtQd9yeV3h76sG1GydfIN5rvBfY4lsM8sCO4tQb5Rmp+Qlo/M+/S7l8kAf18FAv0K+tb7FQT5fkpLmr/lf5MoXwhhuSAK9FPAS8VjBxG5DoN1N8GhG6mq42fX+dypZb0yeKnkCA/xQnjx99HaMJsFcvUmZOaYpC44WbmWwN762tJVx/L/TkpB95x7N+DDx0uOAF8FAvwsLfQBfgoEFkzLzDrUikgruBAptIwvg3wiqhgM1t0Fh26kKk7GaN3pCgfoqmoQrJeWXC5DgK8SAb5K1Klx/5tqAcuoObk6M7JyLa30uw6nY/vBVADAyF4ayOVAttZkCfpzjUhKz8fVOBNydIVvsM1xuG0fb7kU1FsDeOvrwKLBf6E09sUnosIYrLsJs7D2WXdxQYjKiLGj8ykUhV9zh1cEmUwGf18F/H0VqBXujTyDkIL1iUMjiu2TbzILZOcaceNmIrzUwcjRWgJ+S1BvQrbWiOyCAD8714SE1HxcvmlEru4+zfiwDNFpbb23BvMBvvYt9wEF5bb+9/HiiDpEnojBuptgn3Wq6tiy7nyFW1yrQzcYVwj0Uzh8XZSiYPScSI0SERF+JQ6STSbLjbbWwF4K6Au13mdrTcgumB+fko+/SxjkKxUym+A9wFcBf7XCLi3AV2mXpuYTbYncVpUI1n/88Uf885//RFxcHFq3bo0PPvgA7dq1K3bZSZMm2aQNHToUmzZtqoSSlp2Q+qzzYElVE2N15ys8mhGDdecI8r97WnRGn3OF4u4QmaVhNAnkaC0t9lk5JmRpTcjRWgJ7S7qla470WmtCcroBOVoT9Pn3D/QVcki/MFi6EhW8LhTs+0nB/t3l/H0VHCOfyMncPlg/ceIEJkyYgBUrVmDgwIH46KOPMHDgQFy+fBkhIfZ3HRmNRtSuXRsnT56U0pRKt3+b0hNMebyjqopDNzpf4YCI3WCc416t6a6kVMikJ8aihKPpWOUbzHaBfOH/2VqjXVpqhgE5OtN9h88ELBfqUiBv05KvhL+vvKB1Xwl/tdxyQaBWIELjhRytCd4qOfx8FfD1lvMYQlQMt49iV6xYgQcffBBPPfUUAOCdd97B2rVrsXHjRsycOdPhOjKZDP7+/pVZzHIzsxsMVXE8z1YupXvGlFWeJ97c6aWSI1QlR2hgycbEL+xui76Dlnwp2DdJrf45OhPikvKQozVBq79/oG8lkwG+3pbA3c9HUdBiX/BfLS/02hLs+6nlhV5b/nupZGzhJ4/k9sH6qVOnMGbMGGlaLpejU6dOOHXqVLHrxMbGolatWvDy8kK3bt2wdOlSNGjQwOGyBoMBRqNRmtbpdAAsLd3W1u7KYC70aL/KzJcql/Vz5Yl1XPgc6YnvrzQqo56VClm138/O5K26//715O+zlUKOgifLKgB4lWpdk8kylGaOzoRcqauOGbuPpOHouWwAwISB4QUPzDIjV2eS/tKzDLiVmIdcvQn5hpLtX6VCBj+1XArg/dRFAn3pv1wK/P18Cl0gqBV2v1hVhzom19VzSfNz+2A9MzMTwcHBNmkhISHIzMx0uPyYMWOQnJwMIQTi4uKwcOFCDBgwAGfOnEFgYKDd8kuXLsWiRYvs0hMTE6FWqyvkPZRErlYLAEhJToI3H97jsYQQyMjIAOB5w3RmZemk14mJiS4sietVRj2npaXAlMdjhTN8+nIYlArZfT/Hnvx9rkgKAIHelj+EAKeD7g572a8tEOCrKFjKccu/wSigzRPQ6c3I1Qvo8qz/BbR6M7R6AW2e9X9BWp4B6Vn5Ba8FShqDeatk8PUp+POWw9dHBqXMiKCALPj6yKH2lkHtbf0vg9pLVijdMs9Lyc9DVeOq77K1gfh+3D5Y9/f3R1ZWlk1aZmYmwsLCHC6vUCikLjAtWrTA+vXrERwcjP3792PEiBF2yy9YsADz58+XpnU6HTQaDSIiIio1WFf7GAHoEBFRA95e/H3bU1mvoiMiIjzuYB5yKwOA5bsaEVHKTrUexrn1bAkgI2uGI8DX7Q/hVVJJP76e/H12Jn9/AcDSQFWjRjgC/Zz7ORZCQJ9n6befqzNbWvu1poL/lulcnTXNjNyCebk6E26nmJCjFdDn60ucn1wO+Pko4Osjh6+PpXXf18fSiu9bkO6nVhR5Lbddx0cOH47QU2lc9V32mGC9TZs2dl1eTp8+jZdffrlE6yuVSigUChgMBofzVSoVVCr7q3mZrHL7vlkv+uVy9rnzdNbPlqfVs6JQp3VPe29l4ex6Vil4IncHnvp9diZ5oR+EFHLnf45lMhl81XL4qksf8gghkJiYiLCwGtDnW7r1aPV3g36tztI335JuLphnCfqt87JyTUhINUCrL10/frkMUBcE80UDed+CYN+vULBvmVZY3quPAuHBKqRnG+HjJYevjxw+XryJ915c8V0uaV5uH6w//fTTGDJkCH7++Wc8+OCD+PDDD5GamooJEyYAAL7//ns88cQTyMmxPEHutddew/Dhw9GlSxdkZ2fjjTfegFqtRu/evV35Nu6LQzdSVcdzQOXiaDBUdckcvnRnCoXMMrKNb/l++TaZLd14bIN8y+ucgjStzmQT/Gv1lv78OToTktIKgv48c4m79ljJZJACd7W3ouD/3dZ+tbeiYFoOtY9lhB61jxy+1mULXqsLLgy8eUNvpXH7YL1v37748MMPMXXqVKSkpKBRo0bYunWr9DO7wWBAbm6utPz48ePxyiuv4NChQ5DL5ejSpQt27dpVbLcZd8GhG6mq40G7cnGcdaqqCl/YV7eLfIVcZhnKUl2+oN9sFtDnm6UWfEsr/93g/4d9Kbh+Ww9vlQzjB9aAVm8ZhlOrN0NXEOzr9CYkZxig1edBpzchr4Q38lpZW/6tAbw18JcuAKzBvs/di4DC86ULg4KLAZWSwX9x3D5YB4Dp06dj+vTpMBgMdl1WHnnkETz00EPSdPv27bF3716YTCYoFFWn7zeHbqSqTs57HSsVf86mqkpW9RrW3Y5cLitoEVcAwfZdeY+ey8b123r4eMvx5EM1S7RNk6mg1b8gsLe+1urN0OUVtPQXBPmF0wovl5WTD21BusFYuuBfIYcliC9yAWD5UxR6benPXzSt8HI+BdOecgFQJYJ1K0d9y5VKpcMx1atSoA4Ublmv+h8qqp7YhYuISqLweU7Gi07nkFn/lXz/Wrr6KMrd1cfKaBKWwF9vlgJ429cFLfx5hVv7zVI3H32eGelZRujyLBcPJR3C0+Y9yVF8oF/oYsDHS47aofkY5KZjI1SpYN2TCcFWdara2LJORCXBlnXns14DyVx4XFYqZAj0UyLQr2K2ZzIJ6PItwb2+IIDXFbTsWwN6XUGQXzTt7jwTUjIM0OXlSWlWQ7v4YlCPiilrRWOw7iaE4EGLqja2rBNRSdgE6zxsOIWsyH9PoFAU7u9f+qfxOmI2C+QZLC37aWkpFbJNZ2Cw7ibMbFmnKo4t60RUEoVPdTzvOYe1exH3773J5TKovRXw8ZIjX+e+JzH3LVk1I4SodnfFk2fh/RZEVBK2Les8bjiD1LLO/esR2LLuJthnnao6BS/9K8XG/2sBfX7JH6xC5G5sbjDlec8pZNINpuQJGKy7CXaDoaqOn9/KEeZgmDaiqoQ3mDqf9YLIlTeYUsVhNboLIfhzFVVpHPebiEqCN5g6nyfeYFqdMVh3E2xZp6qOsToRlYRtNxgeOJzB2qLOUbo8A4N1N8GhG6mqY8s6EZUEDxXOJ7N7QVUZg3U3YRaCLetUpfHzS0TkHqy/WPDCyDMwWHcX7AZDVZyCZwUiKgEeKpxPxk7rHoXBupswCx7AqGrjxSYRlQT7qTufNZ6QMVr3CAzW3YTgQOtUxbHPOhGVBE91lYD72KMwWHcTgi3rVMXx80tEJcFg3fk4CoxnYbDuJjh0I1V1/GmbiEqCRwrn4z72LAzW3YQQri4BUfkwVieikpDxZzjn4y72KAzW3YQQgt0IiIjI4/FU53yMJzwLg3U3wftLiYioOuC5zvms3RK5rz0Dg3U3Yemzzm8VERF5Np7qnM+6j9nF1jMoXV2AksjLy8OWLVsQFxeH1q1bY8iQIRW6vDsQfIIpERFVA2yYcj7uYs/i9sG6VqtFr169YDKZ0KNHD3z44Yfo27cvvvnmmwpZ3l2wGwwREVUHPNc5Hy+IPIvbB+uffvop0tLScP78efj6+mLOnDlo0qQJnnnmGfTp06fcy7sLjrNORETVAc91zsdd7FncPljftWsXRo8eDV9fXwBAgwYN0K1bN+zcudNh8F3a5Q0GA4xGozSt0+kAWLqliErs7GUuyKsy86TKZ/1ceWI91wxVoVNzf4zuE+aR7680PLme6S7Wc/m5+76rqnV8t2G96pXdFVxVzyXNz+2D9Zs3b2LYsGE2aXXq1MGtW7cqZPmlS5di0aJFdumJiYlQq9VlLHXp5ecbIMwmJCYm8ucrDyaEQEZGBgDP/JnypXF+AHRITNS5uigu5en1TBas57LJyrp7fEhMTHRhSe6vqtaxVpsLADCZTG6/j92Bq+rZ2kB8P24frAP2O04mk93zaqQ0yy9YsADz58+XpnU6HTQaDSIiIio1WH/p8QAkJqciIiKiSh0QqHSsn0PWs2djPVcPrOeyCY5LB5AFwLLv3FlVrWM/PzMALRQKhdvvY3fgqnr2mGDdUav4rVu30K1btwpZXqVSQaVS2aXLZLJKrbDG9XwR6J1d6flS5bPWMevZs7GeqwfWc+nJC+2rqrDfqmIdy6UbA6pWuV3JFfVc0rzcfpz1wYMH48cff4Rerwdg6eZy+PBhDB48GABw4cIFLFmypMTLExERkesweHQ+KVTnrvYIbh+sP/fcc/D19UXv3r0xd+5c9O3bF6NHj0b//v0BAH/99RcWLlxY4uWJiIjIdRhAOp+8ILrjvaWewe27wfj7++Po0aPYuHEjbt26hffeew+jR4+W5rds2RILFiwo8fJERETkOgzWKwN3sidx+2AdAHx9fTF16lSH81q3bo3WrVuXeHkiIiJyHYaRzsex7D2L23eDISIiIs/BPuvOx13sWRisExERUaVhIOl8vCDyLAzWiYiIqNIwjnQ+7mPPwmCdiIiIKg1bfZ2Pu9izMFgnIiKiSsObH52Pu9izMFgnIiKiysNI0unYsu5ZGKwTERFRpWHLuvOxq5FnYbBORERElUbGpnWnY6zuWarEQ5HciRACJpMJJpOpwrdrNBqRl5fHK2I3p1KpIJfzOpeIqCxkPHw6HcMIz8JgvRQMBgPi4+ORm5vrlO2bTCZkZ2c7ZdtUceRyOerWrQtfX19XF4WIqMphHOl8bPTzLKUO1tPT03Hz5k20bdu2ROmeQgiBGzduQKFQoE6dOlCpVBX6ZbC2rCuVSn7J3JgQAikpKYiLi0Pjxo3Zwk5EVEo8xTkf7wvwLKUO1k+ePIl33nkHv/zyi136u+++i71791ZY4dxJfn4+TCYT6tatC7VaXeHbF0JAoVAwWK8CwsLCkJ2dDYPBAG9vb1cXh4ioSuE5jqh0KqxZMD4+HkFBQRW1ObfFllTiiYaIqOx4BHU+tqx7lhK3rB8+fBhTp06FVqtFamoqmjVrJs0zmUy4efMmVq5c6ZRCEhERkWfgDabOx0Ylz1LiYL1hw4ZYsmQJzp49iy1btuDNN9+U5qlUKjRt2tQmgCciIiIqimGk8zFW9ywlDtZr1KiBRx55BEOGDMGUKVPQsGFDZ5aLqMSaNWuGJUuW4JFHHnF1UYiI6D7kjCSdjrvYs5T6BtOEhASsXr3a4bzGjRvj6aefLnehqGJMmjQJ69atAwBoNBqkpKTYLSOEQGRkJPbt24fmzZuXOa9GjRph2bJlGD16dJm3UdZ8Ll265NQ8iYioAjGQdDruYs9S6p5jeXl5uHXrlvQXFxeHPXv24OOPP0ZycrIzykhltHbtWggh8M033xS7zLFjx+Dv71+uQJ2IiKikePOj88m4kz1KqYP1li1bYu3atdLfunXr8Oeff+KRRx5BVFSUM8pITrRt2zaMGDECAPB///d/qFevHvz9/TFgwABcuHBBWq5nz56QyWRQKBRo2LAhli9fLs0bMmQIrl27hjFjxkAmk2HIkCEAAH9/f5w4cUJa7ty5czY3vdSvXx+LFy9G9+7d4efnh127dpUpn2bNmmHz5s3Scrdu3cLDDz8MjUaDWrVqYebMmTYPm6pfvz6WLFmCnj17IiAgAK1atcLBgwcrapcSEdE98OZH5+Me9iwVdk/2mDFjsG3btoraXJlZHy5U+M9sNjs1z+z8PMRrs2z+cg35AIBcQ77dvOz8PACA1miwSU/QZSMzXw8A0JsMxa5XkbZt24aHHnoIx44dw9tvv40tW7YgOTkZb731FtavXy8td/DgQQghkJeXh/Xr12P58uX4+eefAQC7du1Cw4YNsWXLFgghsGvXrhLnv2bNGnz00UfIzs7GkCFDyp2P2WzGqFGjoFAocPHiRfzxxx84ffo0Zs6cabPc2rVr8cknnyAhIQHDhw/HU089VZbdR0REpcRY3fm4jz1LqfusF+fUqVNOG4P866+/xptvvolbt26hVatW+OSTT9CjRw+Hy37//fcYN24cFAqFlDZ8+HD89NNPTikbAPyRcAM7b/1tk/Zw/VboW6shjifH4fuYczbzhtZpimH1muFsWjzWXj1lM69PzWg80qANrmSmYuWlow7XqyhxcXGIjY1F7969ceHCBahUKgQGBkKtVqN3797o3bu33TpKpRJdunTB9OnTsXXrVgwbNqxcZZg7dy46duxYYfmcPn0ap06dwt69exEaGooaNWpg2bJl6N27N1atWgVfX18AwCuvvCI9bXfmzJn497//jdzcXPj5+ZXr/RAR0b0xkHQ+7mPPUupg/dSpU5g3b55NWmpqKi5evIjdu3dXWMGsDh48iGnTpuHbb7/FwIED8eGHH2L48OG4cuUKwsPDHa7TtGnTSr3psFfNaLQPq2WTFqjyAQA8EF4XTYNty+mvtDz1snVoJF5vFwzA8ouAyWRCgLfl6aiNgzR4vV0/h+tVlG3btmHw4MFQqVRo27YtPvvsM/zjH/9ARkYGOnTogJdeekkajvPLL7/E8uXLce3aNWi1WgCWi6Dyqlevns10efOJi4tDaGgoQkNDpbRGjRpBCIFbt26hSZMmACyjG1lZn0ir0+kYrBMRUZXHrkaepdTBenh4uN2IH8HBwejdu7dd4FURVq1ahWHDhmHs2LEAgNdffx3/+9//sHHjRsyaNavC8yuLAC9vBHg5DqT9VF7wU3k5nOerVMFXqQJwt/uOUmmpEh+FCpG+KucUuMC2bdswceJEaXr8+PEYP348TCYTPvvsM/Tp0wfx8fG4du0apk+fjh07dqBbt27w9/fHP//5Txw5ckRa19GvKmq1GjqdTppOSkqyW6bwAeXKlStlyqewunXrIj09Henp6QgJCQEAXL16FTKZDHXq1CnBXiEiImfi0I3Ox13sWUodrNepU6fcQbK1Fbk41hsMAeDMmTM242fLZDK0b98eZ86ccbiuXC7HzZs3ERAQAIVCgW7dumHZsmVo2bKlw+UNBgOMRqM0bQ0uhRAQQtiU2VF6RSqch7O3m5ubiwMHDuCbb76BEAI//PADzp8/j0mTJiEyMhImkwlarRYmkwk5OTmQyWTSPv3999+xYsUKtG/fXtpmZGQkTp8+jYceekiqu3bt2mH16tVo27YtEhMTsXDhQrtyFN6fZc2n8Hbatm2LNm3a4LnnnsMHH3yA7OxszJ07F48++ijUarXDeixt3Zbns2Bdx1mfIXIPrOfqgfVcVvbnVndVVev4bqxe9cruCq6q55LmV6Y+67dv38Zrr72GPXv2IDMzE82aNcOCBQtK/FCab7/9FlOmTCl2fteuXaXRObKzsxEUFGQzPzg42GZ0j8LGjh0rdZ+4c+cOXn/9dQwcOBDnz5+XWloLW7p0KRYtWmSXnpiYKHWPAACj0QiTyQSj0WgTJFaUwhcwFfXz1fLly/Hqq69K09ZWaa1Wi127dqF9+/YICgqC0WjEwIEDcebMGTz44INITExE8+bNsXHjRgBAq1atMG/ePIwYMQLZ2dno0qULRo8ejZiYGOlC59VXX8Xs2bOxZMkSDBgwANu3b8eyZcswffp0REZGonHjxnjiiSdw6NAhm4sj6z4tTz7WfWddZvPmzXj55ZfRvHlzeHl5Yfjw4XjnnXeKzbfw/8LLFMf6WUhJSZF+CSkpIQQyMjIA8GdKT8Z6rh5Yz2WTlmaQXicmJrqwJPdXVes4K8vS8Ggymdx+H7sDV9Vz4d4H9yITpbyMMJlM6NixIyIjI/GPf/wDQUFBOHLkCBYvXowNGzZUSD/mwtq3b4+HH34Yb7zxhpQ2atQoREREYOXKlfdd32g0IigoCOvWrXP4wB5HLesajQa5ubk2wXpeXh5u3LiB6OhoeHtXbN/xwmVRqZzb9cXq6aefRtOmTfHKK69USn6epDyfBSEEEhMTERERUaUO/FQ6rOfqgfVcNjfu6DFt6WUAwC+ftHFxae6tqtbxL8fS8c5XcahTwwtfvlVxA1N4KlfVs/VeOa1WaxNzFlXqlvXz588jNTUVx48flwLL3r17Q6lUYt26dRUerLdr1w7Hjh2TpoUQOHnyJF5//XVp2mQyFdvCaf1Zo7idr1KpHAbIMpnMZh3r66LpFaVwGZ39QRFC4Oeff8bcuXOr1MHHXZT3s2Bdj/ves7GeqwfWc+nJ5fbnVndWNetYJv2vWuV2HVfUc0nzKvVYi15eXqhRo4ZdgFunTh2ntDjPmDEDu3fvxvr165GSkoI333wTWq0WEyZMAABs3LjRpizPP/88tm/fjrS0NNy4cQNTp05FSEiIw2EIqyuZTIaEhAS0aNHC1UUhIqJqhqGj8zE+9yylDtabNGkCb29vrF27Vkq7c+cOli9fjscff7xCCwdY+q9/9dVXWLRoEaKiorBnzx7s2rULGo0GgKUfduE+5M8//zy++uorNG3aFL169YJer8cvv/zisL86ERERVS4nPZKFyGOVuhvMsWPHcOvWLUyePBkvvvgiAgMDERcXB6VSiZdeeklarlu3bli1alWFFPLRRx/Fo48+6nCedbhBqyZNmmDTpk0Vki8RERFVNDb7EpVGqYP16Oho/Pvf/77vcjVr1ixTgYiIiMhzyRmrE5VKqYN1Ly8vNG/eXHpUu1V6ejpu3rxpl05ERERkxf7URKVT6p5jJ0+exJw5cxymz507t0IKRURERJ6JwTpR6VTYbR7x8fF2Dy8iIiIiKoxDCRKVTom7wRw+fBhTp06FVqtFamoqmjW7O8i+yWTCzZs3S/SQIiIiIqq+GKoTlU6Jg/WGDRtiyZIlOHv2LLZs2YI333xTmqdSqdC0aVObAJ7IlW7evAmNRgM/Pz9XF4WIiAphwzpR6ZQ4WK9RowYeeeQRDBkyBFOmTEHDhg2dWS5ysqSkJAQGBsLHx8fh/Fu3biE0NBS+vr5lzqOyAmZH+UybNg0vvfQShgwZ4tS8iYiodBisE5VOqUeDSUhIwOrVqx3Oa9y4MZ5++ulyF4qcQwiBTz75BP/+97+Rl5eHzMxMDB48GGvWrJEeMgUARqMRrVu3xunTpxEVFVXm/Pr3749ly5Zh9OjRFVD60uWze/dup+ZJRERlwz7rRKVT6htM8/LycOvWLekvLi4Oe/bswccff4zk5GRnlJEqiE6nw+XLl3H48GEkJiZKdVj4YVYAcODAAdSrV08K1LVaLRISEuy2d/v2bVy9ehU3btyAwWCwmRcfHw+DwYCEhARcvXoV8fHxAIDr168jLy9PWi4/Px9Xr16VpmNjY6HVaqVtaLXaMuVz8+ZN5Obm2pU5PT3dYXrhfBMTE2E0GovZi0REVB6M1YlKp9TBesuWLbF27Vrpb926dfjzzz/xyCOPlKsVlpzP19cXH374IWrXrg0ACAsLw4gRI3D+/Hmb5bZt24aHHnoIJpMJTz31FMLCwtCxY0fUr18fv/76q7TcnDlzMGTIEPTv3x9BQUH4xz/+AbPZDABYsGAB4uPjsWjRIgwZMgQLFiwAALRp0wZnz56VtnH58mU0btxYmu7Tpw/mzp2LOnXqoEePHtJQoaXNZ9CgQdi5c6e03dOnT6Nt27aIjo5GREQEHnzwQdy+fdsm33nz5qFevXpo164dQkJCsHnz5grZ70REdBeDdaLSqbChG8eMGYNt27ZV1OaqlvcXAnt/vDu9cRXw5X/vTh/ca1lGW9Cie+OyZfpKQZBsyAfefxPyA7vurrP2U8uf1e8/V3ixTSYTtm3bht69e9ukb9++HSNGjMChQ4fw008/ITY2Frdv38b/+3//D5cuXZKW27Bhg9Tiff36dRw4cADffvstAGDNmjWoV68eVqxYgatXr2LNmjUlLtfhw4dx+vRpXL9+Hb169Sp3Pnl5eXj44YcxYMAApKenIyUlBWq1GlOnTrVZ7tSpUzhz5gzi4+Px9ttvY/bs2SUuMxERlQyDdaLSqbBg/dSpU5DLK2xzVE6ZmZm4evWq9FeUEALTp09HXl4eFi1aJKVfunQJWVlZ6Ny5MyIjI2EymXDixAkYDAbUrl0bzz33nM12TCYT4uPjkZOTg0GDBuG3334rd9lnzZqFsLCwCsvn1KlTiI2NxdKlSyGTyeDj44NFixZh7969yMzMlJZ77rnnEBISAgAYOXIk4uPjkZWVVe73Q0REd8kZrROVSqlvMD116hTmzZtnk5aamoqLFy9W35v6XlpsOz1hmu10z4GWP6voJrbrqLyAl/4Fs9F49+pp0j9st9F3WKmKtG3bNvzzn/+UpgsH7CaTCdOmTcPJkyfx22+/ITAw0Ga94cOHQy6Xo1GjRtizZw/WrFmDt956C4GBgViwYAH69esHAFi4cCH++9//wtfXF/7+/sjMzMQDDzxQqnI6EhERYTNd3nySk5MRHBwMtVotpUVGRgKwjIpjfZhX4Yd6eXl5AbD0qSciIiJylVIH6+Hh4XajewQHB6N3796oV69eRZWLymnSpEmYNGmSXbrBYMCkSZNw/fp17Nu3D6GhoTbzt23bhhdffBGAZVSYzp07o3PnztK8ESNGID09HRcvXsSyZctw/vx5NGjQAAAwf/58nDlzRtqWQqGQ+pZb+fv7Izs7W5qOiYm55/v466+/ypRPYQ0aNEBqaipu374t9dc/c+YMlEolP7NERJWMP8ITlU6pg/U6depg1qxZzigLOZnBYMDYsWNx+fJlfPvtt0hLS0NaWhqUSiXq16+PtLQ0nDx5EoMGDQIA/PDDD9ixYwcmTZqEyMhI7N+/H/7+/lAoFPDx8YHRaMThw4eh1Wpx9OhRrFixAt27d5fyq1+/Pvbu3YuWLVsiMDAQkZGR6NGjB9555x0EBwcjMTER8+fPv2eZy5pPYS1btsSAAQMwceJEvP3228jOzsbzzz+Pf/zjH/D29q7APUxERPcj4zNMiUql1MG61ffff49du3YhIyMDLVq0wKxZsxAeHl6RZaMKZu2uBADjx4+X0sPDw3H48GHs3LkTvXr1gr+/PwBg3LhxMBgM+M9//oPExES0aNECe/fuhVwuR5MmTfD555/j448/RnZ2Nrp27Yo33njD5gbUd999F6+99hpGjRqF7t27Y82aNfj4448xb948PPnkk2jSpAnefvttvPzyy9I69evXt3m4UVnziYqKkt4HAGzZsgX/+te/8OKLL8LLywvTpk27Z75KpRINGzaEQqGoiF1PREQF2GXd+YRwdQmoIsmEKH2Vvvjii/j8888xZswYBAcH49ChQ7h58ybOnDlj16pZ1eh0Ovj6+kKr1dr0cc7Ly8P169fRoEEDp7TGCiFgNBqhVCpd9sCIRx99FL169bK7iZRsleezIIRAYmIiIiIi+GAQD8Z6rh5Yz2Wj1Zsw4uVzAIBfP23r4tLcW1Wt471H0/HOVzdRp4Y3vvpnM1cXx+25qp6LizmLKnXLelJSEj7//HOcOnUKjRo1ktKfeOIJLF++HP/5z3/KVmJyKSEErl69infffdfVRSEiIqJyqELXFVQCpb7N49q1a2jZsqVNoA4Ao0ePxpUrVyqsYFS5ZDIZTpw4wQdbEREREbmRUgfrUVFRuHTpEhITE23SDxw4wECPiIiIiKgClbobTK1atTB8+HB06tQJkydPRnBwMI4cOYI9e/bg1KlTFV7A8+fP44svvpCmn332WTRs2PCe62RnZ2PdunWIi4tD69atMW7cON4oSERE5AbkcvbRICqNMo12+uWXX2LevHk4c+YMtm7dCo1Ggz///BONGzeu6PLB29sbNWvWRFhYGN577z3ExcXdc3nr0zfXrl0LAHjzzTcxduzYCi8XERERlZ6PlxyvPlEXX77V1NVFIaoSyjR0o0qlwuzZszF79uyKLo+dRo0aYe7cudDr9Xjttdfuu/zHH38Mk8mE3377DV5eXnjuueekcbgHDhx43/Xv514P36HqoQwDKBERUSEDu4TefyEiAlCOcdbd1d69ezFy5EjpcfG1atVC9+7dsWfPHofBusFggNFolKZ1Oh0AS0BWOChTqVSQy+W4ffs2atSoAZVKVeHD+xiNRphMpgrdJlUsIQRSUlIgk8mgVCpLHbhbP1cM+D0b67l6YD17vipbx1J5q2DZXcBV9VzS/FwSrP/5559Yv359sfOjoqLw/PPPl2nbt2/fxsiRI23SateujTt37jhcfunSpVi0aJFdemJiot2Yl/7+/sjOzkZsbKxTxuE0m82Q8znMVUJgYCCSk5NLvZ4QAhkZGQBQpcbspdJhPVcPrGfPV1XrODPL0vBoMpnsBgQhe66qZ2sD8f24JFhXq9WoWbNmsfM1Gk2Zty2TyeyuVMxmc7E3mC5YsMDmkfc6nQ4ajQYREREOB6gXQsBsNtu0xlcEa4ttWFhYlTogVEfWX1nKwvrZrGoP2KDSYT1XD6xnz1dV6zgoNh1AFhQKBSIiIlxdHLfnqnp262C9efPmaN68uVO2Xa9ePdy8edMm7ebNm+jTp4/D5VUqFVQqlV26TCZzWGEymQxyuRxKZcXuOiEEVCoVfHx8qtQBgUrP+tliPXs21nP1wHr2fFWxjpvW9wUADO+pqVLldiVX1HNJ86ryfS5Onz6NuXPnStPDhg3DDz/8gJycHADAlStXcOTIEQwbNsxVRSQiIiKqNHUjfPDzB60xbkC4q4tCFcDtg/X09HTMnTtXGglmxYoVmDt3Lk6fPg0AuHTpEt577z1p+ZkzZ6JWrVro0qULnnnmGfTp0wdTp05Fz549XVF8IiIiokrn7SVnq7qHcPvRYBQKhdS//T//+Y+Ubu1P3r59e7v0P/74A9u3b8etW7cwceJE9OvXr3ILTURERERUAdw+WA8MDLTp5lJU06ZN0bSp7YMVVCoVxowZ4+yiERERERE5ldt3gyEiIiIiqq4YrBMRERERuSkG60REREREborBOhERERGRm2KwTkRERETkphisExERERG5KQbrRERERERuisE6EREREZGbYrBOREREROSmGKwTEREREbkpButERERERG6KwToRERERkZtisE5ERERE5KYYrBMRERERuSkG60REREREborBOhERERGRm2KwTkRERETkphisExERERG5KQbrRERERERuSunqAtxPXl4e0tPTpenQ0FB4eXmVeHkA8PHxQXBwsLOKSERERETkFG7fsr5v3z60a9cO7dq1Q2RkJA4dOnTP5bdt24Y6depI67Rr1w7PP/98JZWWiIiIiKjiuH3L+pAhQ5CQkAC9Xg+1Wl2idRo1aoRLly45uWRERERERM7l9i3rZZWTk4P8/HxXF4OIiIiIqMxc0rKu1+uRkZFR7HwvLy+EhoaWads+Pj7Izs5G48aNkZqairZt2+Kjjz5C165dHS5vMBhgNBqlaZ1OBwAQQkAIUaYylIU1v8rMkyof67l6YD1XD6xnz8c6rh5cVc8lzc8lwfqOHTvw3HPPFTu/Y8eO2LFjR5m2/dBDD+H27dsALIH3ggULMHz4cFy6dAnh4eF2yy9duhSLFi2yS09MTCxxt5uKIISQLmBkMlml5UuVi/VcPbCeqwfWs+djHVcPrqpnawPx/chEFblctPZZ37dvH/r27Vvi9cxmM4KDg/HFF1/g4YcftpvvqGVdo9EgNze30oP1xMRERERE8IDgwVjP1QPruXpgPXs+1nH14Kp61ul08PPzg1arvWfM6fY3mN6PtUtNzZo1AVh2eOEdrdfrkZ+fDx8fH4frq1QqqFQqu3SZTFbpX0xrnjwgeDbWc/XAeq4eWM+ej3VcPbiinkual9vfYGoymZCQkIDExEQAQFpamjQ6DAD8+OOPiIyMlJZ/+umn8eWXX+Ly5cs4fvw4xo8fjzp16qBPnz4uKT8RERERUVm5fbB+584dtGvXDl26dEFERAT+8Y9/oF27dti9ezcAQK1WIyIiQlp+6dKlOHr0KEaNGoVp06ahTp062LdvH/z9/V31FoiIiIiIysTtu8HUrVsXCQkJxc4fNWoURo0aJU1HRkZixYoVlVE0IiIiIiKncvuWdSIiIiKi6orBOhERERGRm2KwTkRERETkphisExERERG5KQbrRERERERuisE6EREREZGbYrBOREREROSmGKwTEREREbkpButERERERG6KwToRERERkZtisE5ERERE5KYYrBMRERERuSkG60REREREborBOhERERGRm2KwTkRERETkphisExERERG5KQbrRERERERuisE6EREREZGbYrBOREREROSmGKwTEREREbkppasLUBIHDhzAzp07kZ+fj27dumHs2LGQy4u/zkhKSsLKlSsRFxeH1q1b45lnnoGPj08llpiIiIiIqPzcvmV91qxZeOuttxAUFIQaNWrgxRdfxGOPPVbs8ikpKejUqROOHj2KZs2aYc2aNRgyZAiEEJVYaiIiIiKi8nP7lvU5c+YgOjpamu7evTt69+6NDz74AJGRkXbLf/jhhwgMDMRPP/0EuVyOKVOmoHbt2ti+fTtGjBhRmUUnIiIiIioXtw/WCwfqAJCfnw+ZTAa1Wu1w+X379uGhhx6SusloNBr06NED+/btcxisGwwGGI1GaVqn0wEAhBCV2hpvzY+/AHg21nP1wHquHljPno91XD24qp5Lmp9LgvXDhw/jf//7X7HzGzZsiIULF9ql63Q6vPrqq3jqqacQHBzscN2EhATUrFnTJi0yMhIJCQkOl1+6dCkWLVpkl56YmFjsBYEzCCGQkZEBAJDJZJWWL1Uu1nP1wHquHljPno91XD24qp6tDcT345JgPSIiAn379i12fnh4uF2aTqfDqFGjEBISgo8//rjYdRUKhU1LOWBpPffy8nK4/IIFCzB//nybfDQaDSIiIio9WAcs+4YHBM/Feq4eWM/VA+vZ87GOqwdX1bNbB+sNGjRAgwYNSrx8dnY2hg8fjqCgIGzevBne3t7FLhsdHY0bN27YpN24cQNDhgxxuLxKpYJKpbJLl8lklf7FtObJA4JnYz1XD6zn6oH17PlYx9WDK+q5pHm5/WgwaWlpGDBgACIjI/HDDz/YBepHjx7Fk08+KU2PGjUKP/zwA1JTUwEAp0+fxokTJzBq1KjKLDYRERERUbm5fbA+efJknDlzBl5eXpg2bRqefPJJPPnkk7hy5QoAS6v5V199JS3/9NNPo23btmjfvj1Gjx6Nfv36Yd68eejYsaOr3gIRERERUZm4/Wgw//jHPzBu3Di79KCgIABAly5d8MUXX0jpKpUKO3fuxMGDB3Hr1i0sXrwYrVu3rrTyEhERERFVFLcP1ocPH37P+dHR0XbDO8pkMvTq1cuZxSIiIiIicjq37wZDRERERFRdMVgnIiIiInJTDNaJiIiIiNwUg3UiIiIiIjfFYJ2IiIiIyE0xWCciIiIiclMM1omIiIiI3BSDdSIiIiIiN8VgnYiIiIjITTFYJyIiIiJyUwzWiYiIiIjcFIN1IiIiIiI3xWCdiIiIiMhNMVgnIiIiInJTDNaJiIiIiNwUg3UiIiIiIjfFYJ2IiIiIyE0xWCciIiIiclMM1omIiIiI3JTS1QUoqYyMDBgMBoSHh99zuezsbNy+fdsmzd/fH3Xq1HFm8YiIiIiIKpzbB+sHDhzAa6+9hosXL0IulyM4OBgrV65E//79HS6/e/duTJw4EdHR0VJav379sGLFisoqMhERERFRhXD7YP3QoUNYvnw5unTpArPZjAULFmDs2LFISkqCl5eXw3Wio6Nx6dKlSi4pEREREVHFcvs+66+++iq6dOkCAJDL5Zg4cSIyMzPturoUdefOHaSkpFRGEYmIiIiInMIlwXpWVhYuXbpU7F9sbGyx627evBlRUVGoX7++w/mBgYGQyWTo168fGjRogMaNG+PXX38tdnsGgwE6nc7mDwDEr1shhLD8bVwF8eV/704f3AuxfCFEbo5l+sZly/Tl85bp/DzL9L6f766z9lPLn3V638+WZfLzLNNXzkPz9QeWbQlh2fbyhZa8rOt8+V9LWazTe3+0LGOdvnDaMn3rhmU6M90yfeT3u8usWgbx/Vd3p3/eBPHRv+5O/3XMsk7CLct0SpJl+uT/u7vMirchtq6/O711vSXNOn3y/1nWSUmyTCfcskz/dezuMh/9y5K3dfr7ryxls04f+d2yTma6ZfrWDcv0hdN3l1m+0LIPKrOeLp+3TJejnryuXQDeZz25ez1VzPfpOOupStRT2b5P+Owd1lMVqKfyfp8C9m4BVr/HenLzeqqI7xM2rq70eioJl3SD2b9/P+bNm1fs/Hbt2mHDhg126Vu2bMF7772HnTt3QiaTOVx30KBBuHjxIgDAZDJhyZIlGDNmDP7++29ERkbaLb906VIsWrTILj0nJxdZiYkAgECdDnK9HhkF075ZWVAbDUhLToLw8YUqLQ2BRgOy09ORn5gIGAzQGA3Q5WRDW7BOkF4PAMi0biMnG2qjAamJSYBKBVV6OnyNJmSmpcHomwiZXotQowG6rCxpG8F6PcyQSeXyy8mBj9GA1IJpr4x0BBgNyExNg1GphjwnCyFGA7RZWdBZt5GfB5NOi+yCaf/cXHjl5yOtYNo7IxP+RgMyUlNhghKKjDQEGw3IzcyEvmCZkPx8GHNzpW0E5OZCmZ+P9IJpn8xM+BkNyEhJgclohiI1FcFGA3IyMpFXsExofj7yc3ORY92GTgtFfp60j9VZWfA1GpCenAyzLg/K1DQEGQ3IzijYxwA0RgP0OTnIrcR68kq37OOstDQY1KWvJyEEDLm5CDAaWU9uXE/l/T6lJiYiIyMDPhkZrCc3rqeK+D5lZGRY9jHrya3rqazfJyEEvPPzoMhjPblzPZX3+5RTcMwO0ukgz6u8esqCCiUhEyUN613su+++w4wZM7B582YMGDCgxOsJIRAcHIzVq1dj3LhxdvMNBgOMRqM0rdPpoNFokJubC7VaXSFlL2k5ExMTERERUeyFCFV9rOfqgfVcPbCePR/ruHpwVT3rdDr4+flBq9XeM+Z0+xtMAeCLL77AnDlzsG3bNvTs2dNmnnWoxmbNmgEAjEYjlEqlzXydToeAgACH21apVFCp7K9sZDJZpX8xrXnygODZWM/VA+u5emA9ez7WcfXginouaV5uH6yvWLECL730Ej7//HOEhYVJo7xERUVBrVZjx44deOyxx6R+P1OnTkXHjh3RvXt3ZGZmYunSpWjcuDH69OnjyrdBRERERFRqbh+s79+/H/Xr18fixYtt0tevX48OHTogMDAQTZs2ldI/+ugjvPvuu3jhhRfg7e2Nrl27Ys6cOZXapYWIiIiIqCJUmT7rlUWn08HX1/e+/YcqGvvFVQ+s5+qB9Vw9sJ49H+u4enBln/WSxJxuP846EREREVF1xWCdiIiIiMhNMVgnIiIiInJTDNaJiIiIiNyU248GU9ms99vqdLpKz1en00Gn0/EmFg/Geq4eWM/VA+vZ87GOqwdX1bM11rzfWC8M1ovQFzwmVqPRuLgkREREROTp9Ho9fH19i53PoRuLMJvNyMjIgI+PT6VfXWk0GqSmpnJMeA/Geq4eWM/VA+vZ87GOqwdX1bMQAnq9HsHBwZDLi++Zzpb1IuRyOUJDQ12Wv1qt5gGhGmA9Vw+s5+qB9ez5WMfVgyvq+V4t6la8wZSIiIiIyE0xWCciIiIiclMM1t2EUqnEW2+9BaWSPZM8Geu5emA9Vw+sZ8/HOq4e3L2eeYMpEREREZGbYss6EREREZGbYrBOREREROSmGKwTEREREbkp9+xJX83k5+fj4MGDyMnJQbdu3RAeHu7qIlEF++OPP3D79m2btJ49e6JOnTouKhFVhJ9//hlZWVkAgMjISPTp08dumZSUFBw+fBi+vr7o2bMnvL29K7uYVE4bNmyQXjdt2hTt27eXpoUQ2Lhxo906EyZM4OPpqxiDwYA///wTqampaNWqFerVq2e3zMWLF3Hp0iXUr1/f5nNAVYdWq8Xx48eRn5+Pdu3a2cRct27dwsGDB22Wr127Nnr16lXZxbTBYN3F4uPj0b9/fwBAREQE/vzzT6xduxYjR450ccmoIr377ruIi4tD8+bNpbTo6GgG61Xcr7/+itu3b+PUqVOIjo62C9Z37dqF8ePHo0OHDkhOTkZ+fj727dvHeq9ifvzxRwDA/v37MWHCBJsgzWQy4bHHHsOwYcMQEBAgpY8fP57BehWyZ88ezJ49G8HBwQgJCcGBAwcwa9YsvPvuu9Iyc+bMweeff46uXbvi5MmTGDhwINatW8d6rkK++OILLFq0CPXr14dMJsOxY8ewfPlyzJgxAwBw5MgRTJ8+HcOGDZPW6dChg8uDdQhyqSeeeEL06dNHGAwGIYQQ77//vggLCxN6vd7FJaOKNHz4cPH222+7uhjkJC+88IIYPHiwTVp+fr6oWbOmVO8mk0kMHDhQPProo64oIlWAwYMHixdeeMEmzWAwCADi4sWLrikUVYiff/5ZxMTESNOHDh0SAMSpU6eEEEL88ccfQqFQiHPnzgkhhIiLixNBQUFiw4YNriguldF3330nUlNTpek1a9YIpVIpsrOzhRBCbNq0STRt2tRVxSsW+6y72I8//ohp06ZJY3s+88wzyMjIwB9//OHiklFFi42NxZYtW3Ds2DEYjUZXF4ec7MiRI0hMTMTMmTMBAHK5HDNmzMDWrVthNptdXDqqaEePHsW2bdtw+fJlVxeFymDo0KGIioqSplu3bg25XI6UlBQAwJYtW9CrVy+0bNkSAFCnTh2MGDECW7ZscUl5qWzGjRuH0NBQabpt27YwGo3IyMiQ0vLy8vDzzz/jl19+kerf1Risu1BqaioyMzPRoEEDKc3f3x/h4eG4fv26C0tGFa13795IT0/H119/jfHjx6NVq1b4+++/XV0scqLr168jJCQEwcHBUlp0dDS0Wi0SEhJcVzCqUHK5HBMmTMDOnTvx6aefomPHjhg3bhwMBoOri0bl8OmnnyIiIgLdunUDYPk+Fz5XA5bvM8/VVdvHH3+MBx54QOqaWLduXXTp0gVfffUV3nzzTURFReHTTz91cSnZZ92lrK2rXl5eNune3t5sefUwr7zyivTaaDTi0UcfxTPPPMNfUDyY0Wh0+N22ziPPIJfLbW5AvX37Nh544AF8+OGHmDNnjgtLRmW1ceNGLF26FD///DP8/PwAFP995ne56lqyZAl2795tcx7u0qWLzfd5y5YtGDduHB588EE0adLEFcUEwJZ1lwoNDYVKpbJpZTObzUhKSkJERIQLS0bOpFQq8cQTT+DYsWMQfICwx4qIiEBqaqrNyTwhIQFyuZwjPnmw2rVrY+jQoTh69Kiri0Jl8NVXX+HZZ5/Fjh070KNHDyk9IiLC7hexhIQEnqurqIULF2LVqlXYv3+/3S8mhY0ZMwZBQUE4ceJEJZbOHoN1F1KpVOjWrRt27Nghpe3btw95eXk2Bwmq2vR6PTIzM23STp8+jdq1a3MUAQ/WvXt3AJZRJqy2b9+OBx54AGq12lXFogqWlJRkc9FtNptx9uxZjvhTBX3yySeYM2cOdu/ejZ49e9rM69OnD/bt2wetVgvAMgrQrl27HA7XSu5LCIEXX3wRGzZswB9//IFGjRrZzE9MTLSZjo2NRUZGhsu/z+wG42KLFy/GoEGD4Ofnhzp16uDf//43XnzxRdSsWdPVRaMKkpubi969e+Phhx9GdHQ0/vzzT6xatQpr1qxxddGonA4fPozY2FhcvnwZCQkJ2LBhA0JCQjB48GCEhoZi3rx5eOqpp/Dqq68iMTERn376qc3FOVUNu3fvRnp6uvTLyIYNGxAVFYVu3brhwIED+PDDDzFq1Cj4+/vj+++/x40bN7B582ZXF5tKYeXKlXj++efx2muv4dq1a7h27RoAoHPnzmjQoAEmTJiA5cuXY+jQoXj88cexfft2GAwG6QZyqhrmzJmDFStW4J133sGhQ4dw6NAhAMCDDz6IsLAwvPrqqxBCoHv37sjMzMSnn36KQYMG2V28VTaZ4O/wLnfy5El8+eWXyMnJwYABAzBx4kS2uHqYlJQUrF69GpcvX0atWrUwYcIEtG7d2tXFonL68MMPpYO9Vf369fHOO+9I0xs2bMCePXvg6+uLyZMno0uXLpVdTCqnV199FTExMTZp3bt3x+zZswFYjuEbN25ERkYGmjZtiqeeegohISEuKCmV1YoVK7B//3679BkzZqBfv34AgKysLHzyySe4ePEi6tevj1mzZqFGjRqVXVQqh4ULF+LKlSt26YsWLULTpk0hhMB3332H33//Hd7e3ujatSvGjx8Pudy1HVEYrBMRERERuSn2WSciIiIiclMM1omIiIiI3BSDdSIiIiIiN8VgnYiIiIjITTFYJyIiIiJyUwzWiYiIiIjcFIN1IiIiIiI3xWCdiIic4oknnsCpU6ecsu1du3Zh3rx5Ttk2EZE7YbBOROTBtm/fjilTplR6vnv27MHZs2fRvn17p2y/f//+2LhxI86dO+eU7RMRuQsG60REHuKnn37C1KlTbdK6deuG1157rdLL8sEHH+Dpp5922va9vLzw6KOP4qOPPnJaHkRE7oDBOhGRh0hOTsbFixdt0jQaDZo3b16p5UhPT8fu3bsxcuRIp+YzcuRIfPfddzCbzU7Nh4jIlRisExF5gJMnT2LJkiU4d+4cOnXqhE6dOuHy5ct23WB++OEHPPPMM1i9ejVGjRqFBx98EN9//z1SUlIwe/Zs9OrVC9OnT0dqaqrN9q9cuYIZM2agb9++mDJlCs6ePVtsWQ4fPoywsDDUrVu3XPmmpKRgzpw56Nu3L8aNG4dff/3VJp+OHTsiKyuLXWGIyKMxWCci8gBNmjTB5MmTER0djc8++wyfffYZ6tati5SUFFy4cEFaLikpCV9//TUOHDiAl156CYMGDcKECRMwdOhQtGzZEosXL8bVq1fx3HPPSetcuHABHTt2RJ06dfCvf/0Lbdu2Rc+ePXH9+nWHZYmJiUGtWrVs0sqS77PPPosLFy7gzTffxKRJk7Bs2TKbPNVqNYKDgxEbG1tRu5GIyO0oXV0AIiIqv4CAAERFRcHPzw+dOnW657I1atTAF198AYVCgb59+2LFihV46KGHMGPGDACAyWTC448/Li2/ePFiTJ48GQsXLgQA9O7dGxcvXsSnn36KZcuW2W0/Ly8PXl5e5c73zp07mDRpEvr37w/A0u3FaDTabNPb2xt5eXkl2UVERFUSg3Uiomqmfv36UCgU0nRQUBAaNmxoM52ZmSlNnz9/HllZWTh69KiUlpCQgI4dOzrcfo0aNZCWllbufN9//308//zz2LhxI3r06IGHH34YHTp0kOYLIZCeno7w8PCSvnUioiqHwToRkYeQyWRO2W5oaCgefPBBm1ZvwBJcO9KhQwfcuHEDWq0Wvr6+Zc63c+fOOHr0KNLT07Fv3z4MHz4cq1evxvDhwwFY+tHn5+c7bXhIIiJ3wD7rREQeIiQkBMnJyRW+3UcffRQ///wzatasKd28qtVqceXKFYfLN2/eHFFRUTh48GC58n377beRkZGBkJAQjBo1CvXq1cNff/0lzf/999/Ro0cPBAYGlisfIiJ3xpZ1IiIPYe3b3aBBA4SGhmL9+vUVst0ZM2YgPj4eLVq0QK1atZCWlobWrVvjf//7X7HrTJs2DevWrcOgQYPKnG9wcDCaNm2K0NBQpKamomHDhnjqqaek+d9++63U352IyFPJhBDC1YUgIqKKYTQace3aNWRnZ6Nly5bQarVISkqSxlpPTk5GWloamjZtKq1z4cIF1KxZE6GhoQAArVaLS5cu2fQPBwCDwYAbN25Ao9FAo9Hcsxw5OTlo3rw5Dh06hLp165Y5X5PJhCtXriAgIAC1a9eW0k+cOIEnnngCf/31l00/eCIiT8NgnYiInOLGjRsICAhAWFhYhW87Pj4eZrPZJoAnIvJEDNaJiIiIiNwUbzAlIiIiInJTDNaJiIiIiNwUg3UiIiIiIjfFYJ2IiIiIyE0xWCciIiIiclMM1omIiIiI3BSDdSIiIiIiN8VgnYiIiIjITTFYJyIiIiJyUwzWiYiIiIjc1P8H0RW5S/Jb7mUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sat = 0.8\n",
+    "drv = tap.Transducer(sr, drive=200.0, asymmetry=1.0, saturation=sat)\n",
+    "n = int(sr * 1.0)\n",
+    "y = drv.process(np.sin(2 * np.pi * 137.0 * np.arange(n) / sr))\n",
+    "print(f\"drive 200, asymmetry 1, saturation {sat}\")\n",
+    "print(f\"  the naive bound 1/saturation : {1/sat:.3f}\")\n",
+    "print(f\"  the real bound  2/saturation : {2/sat:.3f}\")\n",
+    "print(f\"  measured peak                : {np.abs(y).max():.3f}\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(np.arange(1200) / sr * 1000, y[:1200], color=C[0], lw=1.2)\n",
+    "ax.axhline(1 / sat, color=C[3], ls=\"--\", lw=1.0, label=\"1/saturation\")\n",
+    "ax.axhline(-2 / sat, color=C[2], ls=\":\", lw=1.2, label=\"-2/saturation\")\n",
+    "ax.set_xlabel(\"time (ms)\"); ax.set_ylabel(\"output\")\n",
+    "ax.set_title(\"a hard-driven squared law, DC removed — the negative excursions are the reason\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f4c591c",
+   "metadata": {},
+   "source": [
+    "## 4 · The palme: twelve strings, and what they answer\n",
+    "\n",
+    "The peer-reviewed source says **twelve** strings; widely copied hobbyist build pages say\n",
+    "twenty-four. This kernel follows the peer-reviewed source. Their *tuning* is not published\n",
+    "anywhere found, so it is a parameter — chromatic across an octave by default, so the halo answers\n",
+    "whatever you play, or the harmonic series on the root, which answers one key.\n",
+    "\n",
+    "Below: sweep a faded drive tone across two octaves and measure the ring left behind after the\n",
+    "drive stops. The twelve strings show up as peaks, and so do their harmonics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c68012b9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:42:29.556718Z",
+     "iopub.status.busy": "2026-08-17T02:42:29.556498Z",
+     "iopub.status.idle": "2026-08-17T02:43:13.212336Z",
+     "shell.execute_reply": "2026-08-17T02:43:13.211338Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x364.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def ring_after(harp_kwargs, hz, drive_s=2.0, tail_s=1.0, fade_s=0.25):\n",
+    "    # The drive is faded in and out. Switching a tone on and off is a step, and a step excites\n",
+    "    # every string on the board — without the fades this measures its own edges, not sympathy.\n",
+    "    p = tap.Palme(sr, **harp_kwargs)\n",
+    "    n_on = int(sr * drive_s)\n",
+    "    n = n_on + int(sr * tail_s)\n",
+    "    t = np.arange(n) / sr\n",
+    "    g = np.zeros(n)\n",
+    "    f = int(sr * fade_s)\n",
+    "    g[:n_on] = 1.0\n",
+    "    g[:f] = 0.5 - 0.5 * np.cos(np.pi * np.arange(f) / f)\n",
+    "    g[n_on - f:n_on] = 0.5 - 0.5 * np.cos(np.pi * np.arange(f, 0, -1) / f)\n",
+    "    y = p.process(g * 0.3 * np.sin(2 * np.pi * hz * t))\n",
+    "    tail = y[n_on + int(sr * 0.2):]\n",
+    "    return float(np.sqrt(np.mean(tail ** 2)))\n",
+    "\n",
+    "kw = dict(root_hz=110.0, tuning=0, decay=6.0, damping=4000.0, detune=0.0,\n",
+    "          drive=1.0, asymmetry=0.0, saturation=0.0, mix=100.0, level=1.0, smooth_ms=0.0)\n",
+    "probe = np.geomspace(100.0, 460.0, 200)\n",
+    "tails = np.array([ring_after(kw, f) for f in probe])\n",
+    "\n",
+    "strings_hz, strings_fb = tap.Palme(sr, **kw).strings()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 3.8))\n",
+    "ax.semilogx(probe, 20 * np.log10(tails / tails.max()), color=C[0], lw=1.5)\n",
+    "for f in strings_hz:\n",
+    "    ax.axvline(f, color=C[2], lw=0.8, ls=\":\", alpha=0.9)\n",
+    "ax.set_xlabel(\"drive frequency (Hz)\"); ax.set_ylabel(\"ring left after the drive stops (dB)\")\n",
+    "ax.set_title(\"the palme answering a sweep — dotted lines are the twelve strings\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bbe5d050",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:43:13.214567Z",
+     "iopub.status.busy": "2026-08-17T02:43:13.214372Z",
+     "iopub.status.idle": "2026-08-17T02:43:18.420361Z",
+     "shell.execute_reply": "2026-08-17T02:43:18.419433Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " string       Hz    on note   +50 cents     ratio\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0   110.00     0.6329      0.1438       4.4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      1   116.54     0.6917      0.1548       4.5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      2   123.47     0.7431      0.1069       7.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      3   130.81     0.7842      0.1170       6.7\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      4   138.59     0.8355      0.0771      10.8\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      5   146.83     0.9642      0.0812      11.9\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      6   155.56     1.2754      0.0522      24.4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      7   164.81     1.8316      0.0427      42.9\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      8   174.61     2.6112      0.0336      77.7\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      9   185.00     3.4817      0.0164     212.6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     10   196.00     4.1742      0.0067     618.8\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     11   207.65     4.3352      0.0039    1121.9\n",
+      "\n",
+      "worst selectivity across the board: 4.4x\n",
+      "It climbs steeply with pitch, and that is physics rather than a defect: at a fixed ring\n",
+      "time a loop's Q scales with f x T60, so the top of the board is far the more selective end.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selectivity, string by string: the ring on the note against the ring a quarter-tone sharp of it,\n",
+    "# which is neither any string's fundamental nor any string's harmonic.\n",
+    "print(f\"{'string':>7} {'Hz':>8} {'on note':>10} {'+50 cents':>11} {'ratio':>9}\")\n",
+    "ratios = []\n",
+    "for i in range(12):\n",
+    "    f = 110.0 * 2 ** (i / 12)\n",
+    "    on = ring_after(kw, f)\n",
+    "    off = ring_after(kw, 110.0 * 2 ** ((i + 0.5) / 12))\n",
+    "    ratios.append(on / off)\n",
+    "    print(f\"{i:7d} {f:8.2f} {on:10.4f} {off:11.4f} {on/off:9.1f}\")\n",
+    "print(f\"\\nworst selectivity across the board: {min(ratios):.1f}x\")\n",
+    "print(\"It climbs steeply with pitch, and that is physics rather than a defect: at a fixed ring\")\n",
+    "print(\"time a loop's Q scales with f x T60, so the top of the board is far the more selective end.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c606fec9",
+   "metadata": {},
+   "source": [
+    "## 5 · Ring time and damping are not independent\n",
+    "\n",
+    "A string's loop gain is derived from the ring time you ask for and then compensated for what the\n",
+    "in-loop damping filter and DC blocker take out at the fundamental — so the stated ring time is the\n",
+    "ring time you get, at any damping setting **until the compensation runs out of headroom**. The\n",
+    "loop gain is capped just under unity to keep the loop strictly contractive, and past that point\n",
+    "damping wins and the string rings for less than you asked.\n",
+    "\n",
+    "That is an honest limit rather than a bug, and it is visible directly in the loop gains:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1a0b61e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:43:18.422704Z",
+     "iopub.status.busy": "2026-08-17T02:43:18.422530Z",
+     "iopub.status.idle": "2026-08-17T02:43:18.593203Z",
+     "shell.execute_reply": "2026-08-17T02:43:18.592160Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pinned at the cap below about 800 Hz of damping\n"
+     ]
+    }
+   ],
+   "source": [
+    "damps = np.array([200.0, 400.0, 800.0, 1500.0, 3000.0, 6000.0, 12000.0, 20000.0])\n",
+    "fb0 = []\n",
+    "for d in damps:\n",
+    "    p = tap.Palme(sr, root_hz=110.0, tuning=0, decay=6.0, damping=float(d), detune=0.0)\n",
+    "    fb0.append(p.strings()[1][0])\n",
+    "fb0 = np.array(fb0)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.semilogx(damps, fb0, \"o-\", color=C[0], lw=1.8, ms=5, label=\"lowest string's loop gain\")\n",
+    "ax.axhline(0.9995, color=C[3], lw=1.2, ls=\"--\", label=\"the cap (k_fb_max)\")\n",
+    "ax.set_xlabel(\"damping corner (Hz)\"); ax.set_ylabel(\"loop gain\")\n",
+    "ax.set_title(\"asking a heavily damped string for a 6 s ring time — the cap is where it stops\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "pinned = damps[fb0 >= 0.9995]\n",
+    "print(f\"pinned at the cap below about {pinned.max():.0f} Hz of damping\" if len(pinned)\n",
+    "      else \"never reaches the cap at this ring time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78338ad2",
+   "metadata": {},
+   "source": [
+    "## 6 · The order is the argument\n",
+    "\n",
+    "The electrical signal reaches the transducer first, and the transducer's motion is what excites\n",
+    "the body. So the nonlinearity sits **upstream** of the resonator. That is not a cosmetic choice:\n",
+    "driving a distorted waveform into a gong is a different sound from distorting a gong, because the\n",
+    "body filters the harmonics the driver made rather than the driver making harmonics out of the\n",
+    "body's ringing.\n",
+    "\n",
+    "The kernel's Catch2 suite pins this as a null test — a hand-wired `transducer` → `plate` matches\n",
+    "the machine bitwise, and the reversed wiring does not. Here is the same comparison as a spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c31e8fda",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:43:18.595287Z",
+     "iopub.status.busy": "2026-08-17T02:43:18.595087Z",
+     "iopub.status.idle": "2026-08-17T02:43:18.877348Z",
+     "shell.execute_reply": "2026-08-17T02:43:18.876311Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cabinet vs hand-wired driver -> body : 0.000e+00\n",
+      "cabinet vs hand-wired body -> driver : 2.335e-03\n",
+      "signal peak, for scale               : 0.0084\n",
+      "so the reversed wiring differs by     : 28% of peak\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x364.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = int(sr * 3.0)\n",
+    "t = np.arange(n) / sr\n",
+    "x = 0.5 * np.sin(2 * np.pi * 190.0 * t)\n",
+    "\n",
+    "BODY = dict(pitch_hz=210.0, decay=3.0, brightness=0.8)\n",
+    "DRV  = dict(drive=1.7, asymmetry=0.4, saturation=0.9)\n",
+    "\n",
+    "y_fwd = tap.Plate(sr, **BODY).process(tap.Transducer(sr, **DRV).process(x))  # the instrument\n",
+    "y_rev = tap.Transducer(sr, **DRV).process(tap.Plate(sr, **BODY).process(x))  # the other way round\n",
+    "\n",
+    "# The composed cabinet must BE the forward wiring — the kernel's own null test, repeated here\n",
+    "# through the C ABI rather than through C++.\n",
+    "cab = tap.Metallique(sr, mix=100.0, level=1.0, smooth_ms=0.0, **BODY, **DRV)\n",
+    "y_cab = cab.process(x)\n",
+    "print(f\"cabinet vs hand-wired driver -> body : {np.max(np.abs(y_cab - y_fwd)):.3e}\")\n",
+    "print(f\"cabinet vs hand-wired body -> driver : {np.max(np.abs(y_cab - y_rev)):.3e}\")\n",
+    "peak = np.max(np.abs(y_cab))\n",
+    "print(f\"signal peak, for scale               : {peak:.4f}\")\n",
+    "print(f\"so the reversed wiring differs by     : {100 * np.max(np.abs(y_cab - y_rev)) / peak:.0f}% of peak\")\n",
+    "\n",
+    "f_f, m_f = spectrum(y_fwd)\n",
+    "f_r, m_r = spectrum(y_rev)\n",
+    "keep = f_f <= 2500\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 3.8))\n",
+    "ax.plot(f_f[keep], 20 * np.log10(m_f[keep] + 1e-12), color=C[0], lw=1.2,\n",
+    "        label=\"driver -> body (the instrument)\")\n",
+    "ax.plot(f_r[keep], 20 * np.log10(m_r[keep] + 1e-12), color=C[2], lw=1.0, alpha=0.85,\n",
+    "        label=\"body -> driver (the other way round)\")\n",
+    "ax.set_xlabel(\"frequency (Hz)\"); ax.set_ylabel(\"magnitude (dB)\")\n",
+    "ax.set_title(\"the same parts in the two possible orders — a 190 Hz drive into a 210 Hz gong\")\n",
+    "ax.set_ylim(-140, 10); ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4437507",
+   "metadata": {},
+   "source": [
+    "## What this notebook cannot tell you\n",
+    "\n",
+    "Everything above measures the kernel against its own stated maths. None of it measures the kernel\n",
+    "against Martenot's instruments, because no source found gives modal data for either body. The\n",
+    "mode ratios are the general physics of free circular plates and of strings; the string tuning is a\n",
+    "design choice; and the transducer's two nonlinear coefficients are voiced by ear, since the source\n",
+    "establishes *that* the moving-iron driver is nonlinear without handing over a curve.\n",
+    "\n",
+    "So: **a recreation**, labelled as one in the header, in the plan, and here. What is honest about\n",
+    "it is the arithmetic — unit peak gain per mode, weights summing to one, the ring time you ask for,\n",
+    "the published ratios coming back out, and a bound that is stated correctly rather than\n",
+    "optimistically.\n",
+    "\n",
+    "Also not modelled, and stated in the header: radiation and directivity, the soundboard's own\n",
+    "resonance, and string stiffness (a real steel string's partials stretch sharp; a plain delay\n",
+    "loop's are exactly harmonic)."
+   ]
+  }
+ ],
+ "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/fuzz.ipynb b/notebooks/fuzz.ipynb
new file mode 100644
index 0000000..45f795f
--- /dev/null
+++ b/notebooks/fuzz.ipynb
@@ -0,0 +1,534 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "259470e2",
+   "metadata": {},
+   "source": [
+    "# tap.fuzz~ — the dirt, measured\n",
+    "\n",
+    "A two-stage, tone-stacked distortion (`taptools/fuzz.h`): the harder, more scooped school that\n",
+    "sits beside `tap.overdrive~`'s feedback soft-clipper rather than replacing it.\n",
+    "\n",
+    "The method is not invented here. It is the **simplified cascade** of Yeh, Abel & Smith,\n",
+    "*\"Simplified, Physically-Informed Models of Distortion and Overdrive Guitar Effects Pedals\"*\n",
+    "(Proc. DAFx-07): conditioning filter → memoryless nonlinearity → equalization filter, twice.\n",
+    "That paper is also where the justification comes from — the diode limiter is really a lowpass\n",
+    "whose pole moves with voltage, its exact ODE is expensive, and approximating it as a static\n",
+    "curve between fixed filters is defended there and measured against real pedals. It is a\n",
+    "recreation of a *class* of circuit; no component value here is claimed as measured from any\n",
+    "unit.\n",
+    "\n",
+    "Two of the sections below exist because the first implementation was wrong and measurement\n",
+    "caught it: the gain staging (§2) and the oversampling filter order (§5).\n",
+    "\n",
+    "Every trace drives the **shipping C++** through `tools/capi` via ctypes.\n",
+    "\n",
+    "Sections: **1** the curve · **2** gain staging · **3** the even harmonics · **4** the tone stack\n",
+    "· **5** aliasing, and a house pattern that did not survive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ab2f98c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:45.005745Z",
+     "iopub.status.busy": "2026-08-17T22:20:45.005553Z",
+     "iopub.status.idle": "2026-08-17T22:20:45.388054Z",
+     "shell.execute_reply": "2026-08-17T22:20:45.386929Z"
+    }
+   },
+   "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 pedal(**params):\n",
+    "    base = dict(smooth_ms=0, bass=0.0, treble=0.0, contrast=0.0, asymmetry=0.0, level_db=0.0)\n",
+    "    base.update(params)\n",
+    "    return tap.Fuzz(sr, **base)\n",
+    "\n",
+    "def tone_in(hz, amp=0.3, seconds=0.4):\n",
+    "    t = np.arange(int(seconds * sr)) / sr\n",
+    "    return amp * np.sin(2 * np.pi * hz * t)\n",
+    "\n",
+    "def spectrum(y, skip=0.5):\n",
+    "    seg = y[int(skip * y.size):]\n",
+    "    w = np.hanning(seg.size)\n",
+    "    mag = np.abs(np.fft.rfft(seg * w)) * 2.0 / w.sum()\n",
+    "    f = np.fft.rfftfreq(seg.size, 1 / sr)\n",
+    "    return f, mag\n",
+    "\n",
+    "def at(f, freqs, mags):\n",
+    "    return float(mags[np.argmin(np.abs(freqs - f))])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89deb27a",
+   "metadata": {},
+   "source": [
+    "## 1 · The curve\n",
+    "\n",
+    "One clipping curve serves both stages: a tanh family with an adjustable knee,\n",
+    "`shape(x, k) = tanh(kx)/tanh(k)`, normalized so full scale in is full scale out at *every*\n",
+    "knee. That normalization is what lets the knee be a character control rather than a hidden\n",
+    "volume control.\n",
+    "\n",
+    "The curve is monotonic (it never folds back, which would add harmonics of its own), odd\n",
+    "(so a symmetric setting is odd-harmonics-only — see §3), and bounded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55661e28",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:45.390618Z",
+     "iopub.status.busy": "2026-08-17T22:20:45.390316Z",
+     "iopub.status.idle": "2026-08-17T22:20:45.544131Z",
+     "shell.execute_reply": "2026-08-17T22:20:45.543168Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BIn2E8/PzOXbsmHs5JyenxP6KQ4YM4aGHHuLdd99l0KBBZf4sX5qdO3cyfvx4HnnkkSI/k54vMDCwzKEK+/bt69EloqpVpC7KU7flzVdaXZTn+pW3vi68xmvXrsVisTBixIhi85+vrl4bKPu1tWvXriLdRMprxowZRb4wABw5cgSgSODg7+9PbGwshw8f9kgv61pU5L1dERX9/Dn/voHw8HAAj2CnML2goICCgoJi+8cW56677uKFF15g6dKlTJkyhQ0bNhAfH8/8+fMrdD5lfTZXtF6qwhtvvEF6ejpr1qxh8ODBxea5MMCLiooCcNf/nj17APj5559ZvXq1R15vb293/Zc3X0UcPnwYtVpd5D6Gwmt4+PBhhg0bVuo+qur8AKZNm8ZXX33FiBEjaNy4MXq9HkVR3P3gSzvu0qVLPer46quvZuDAgeUqZ5cuXQC4++67Wbx4MSNGjCA2NrZCxwc4evRokWsJrv/l599vUJz9+/fTrl27Eu8fAld9tG7d2qN/O7jqy2QykZiYSNOmTUstI7jex+d/wQXX9Siss5SUFNLT091DDZ8vISGhyGstMDCw1Psu6jIJ3usIlUrFihUrWLVqFStXrmTRokU888wzjBw5kh9++MH9xiru26xGo8FutwOu/mL33nsv999/v8f4rkuXLuWrr74q9rglpSmK4v6nq1KpPPq/gavvb2RkpHs5KCiI1NTUYs/PZrO5v10nJSVhNBorPZxk48aNcTqdLF26lAceeKDIzXqF8vLyCA4OLnVfmzdvLlcrT69evSp8I0xF66Ksuq1IvtLqojzXr7z1deE1/uGHHxg4cGCZN3DV5WsDZb+2Cn/RqQylhD60henF3SSrVquLbFfWtajIe7u8KlqvPj4+RcpXWvqF9V2a6OhoRo4cyfz585kyZQrz58+nZcuW9OvXr9z7gLI/mytaL1Whf//+/Pbbb7z33nv069evSAMMFK3/C6+hw+FArVbjcDiK9LF/9NFH6dOnT4XyVUThNbnw/0/hNSzPNauq81u3bh0fffQRv//+O9dcc407z1133cXx48eLHDckJMRjubA///nLFSnnb7/9xvz589mwYYPHazMxMZGkpKQyjw+u61Xa//LSlLTthXlKen0Xri+rjFD+zyS1Wl3kM6l3795Fblot6TiXAwne6xCVSsW1117LtddeC7hu6Lr99ttZtWpVmTfXFTp+/DgGg6FIa8zOnTuLzR8fH4/dbvf41n3o0CHq16+Pn5+f+9t2z549eeihh0o9dtOmTfn333+LXff000+ze/dulixZwp133sl//vMfPv3003Kd04VCQkJYsmQJgwcPZsCAAfzxxx80adLEI4/FYiEjI4O4uLhS91XeIKusbjPFfbBVtC6qUml1UZ7rV576uvAaO51OfvrpJ5599lmPfJfbtSnPa6tr164X1S2sOIU3Ix48eND9GQGuG+JOnjzJkCFDKrS/iry3y6sm67U4U6dOZeTIkWzdupXvvvuOF198sVL7Ke2zubD19GLqpaKjFvXs2ZPHH3+cYcOGccMNN/Ddd98VG8CXpk2bNjidTm677Tb3a+ti8pWkuHNr0aIFTqeTI0eOeHR9KezyUpnjXKi85d67dy8ajcY9ylGh8r5mKzpqUXHH1+v1RX4B3rVrV7n30axZM/bu3Vsk/cKuOcVp06YNmzdvxuFwFPmlrlCLFi3YvXt3kfQDBw7g7e1NTExMuctamvr16xMcHEyzZs3KNRrY5Uz6vNcRp06dIiEhwSOt8CfEkoZDK079+vVRq9Ueb/zExMQSZzxVFMXjZ+TTp0/zzTffcNdddwGun9lvuOEG3nzzTdLS0jy2zcnJ8fjA6NevH+np6Zw4ccIj37p163jjjTd47bXXuPHGG5k7dy7z589n2bJl7jzx8fHMnj2bffv2les8Q0ND+f3336lXrx4DBgwo0kLy77//Yrfbi3wgX6hv377Mnj27zEdZ44FHREQU6RNb0bqoSiXVRaHSrl956guKXuO///6btLQ0xowZ45Hvcro2UP7XVlVr0KABAwcO5MMPPyQ3N9ed/s4772A2m7n11lsrtL+KvLfLqybrtTjDhw+nUaNG3HTTTTgcDqZMmeKxvjyfO2V9NldFvRT3HilLz549+e233/jzzz8ZN25chSfJGjduHFFRUTz55JNFftE4fPiw+zVR3nwlKe7cxowZg7+/P6+99po7zW6388YbbxAdHV0lw/2Vt9zR0dE4HA6P18D333/PqVOnLroM5REdHY3VauXgwYPutCVLlhTb6l6S22+/nf3793t0D9q3b1+5ZuKdOnUqSUlJvPfeex7pBw8edL+mJk+ezPHjx1myZIl7fWJiIl999RU333xzhb84lkSj0TBt2jQ+++wzj+sBYDabi4wedTmTlvc6Ij8/n3HjxtGiRQvatm2LxWJhyZIlXHfddR7958oSGBjIjBkzeOqppzhy5Ag+Pj5s3LiRRx99lGnTphXJ36VLF7Zt28bGjRsJDw9n2bJlNGnShP/+97/uPJ988gnjxo2jVatWjBs3joiICOLj49m2bRvvvvsu7du3B2DEiBEEBATwyy+/MH36dAAyMzO59dZbGTZsmLt1r7DFqrBfbExMjHuIqSZNmtCuXbtynWtISAhr165l6NCh7lbSwtbQlStXEhMT4zFW+aU0duxYPvroI26//XaaNWvmHsu8InVRlYqriwsVd/2Cg4PLVV9Q9Br/8MMPdO/evUgfxMvl2tTUa+t88+fPZ8iQIXTr1o3Ro0dz8uRJfvzxR1577bVy3Zx2ofK+t8urop8/l1rhjavPPPMMN998M/Xq1fNYX57PnfJ8Nl9svVx//fV8/vnn6HQ6goODyz3Oe48ePdyv0+uvv57ly5eXu7HH39+fn376ieuvv54OHTowZMgQtFote/fuJTk5mV9//bVC+Sp6bgsXLmTSpEkkJCTQvXt3/vjjD44dO8aKFSuqJBgsb7nHjBlDjx49GD58OLfccgtnzpzhzJkzTJkyhW+++eaiy1GWcePG0bVrV4YMGcKkSZNITk4mLS2NyZMnl/tL77Bhw7jtttsYO3Yst9xyC15eXuzfv58777yT//3vf6Vue8011/Dmm2/yxBNPsGrVKjp37syxY8eIj493d50cN24cDz74IJMnT+bnn38mJCSEpUuX0qxZM956662Lvgbne+GFF0hISKBLly6MHz+eRo0acerUKTZv3szjjz9+0fPC1BUSvNcR7dq14+DBg6xZs4Y9e/ag1+tZvHixx09pkyZNwul0Ftl26tSpHj9bvfzyywwdOpStW7cSFBTEs88+S35+PrNmzXLfLHO+jz/+mJ9++om9e/fy2muvMX78eI9/AMHBwaxbt45NmzaxZcsWbDYbN910E/Pnz3dPDgGuyS2mTJnCF1984Q6Kdu7cyZ133smDDz7o0a/uo48+4q233mLnzp3ExMRw9OhRvL29PfocFufCaxAcHMyaNWuYM2cOa9euJS4uDqfTyVdffcV9991X4s+AVa1wTOqNGzdSUFDg7mZT3roob92WN19xdVHc9sVdv/LUV3HX+Mcffyx2xsvL6drUxGvrfE2aNGHfvn38+OOPHDlyhL59+/LSSy8VuUGsvNeivO/t4nTv3p1Zs2YV6RZxMfWq1+uZNWtWkUmiOnfuzKxZs/D29nanFY7aUdJyocIJcYobkaU8nzvl+Wwub72UdM3eeOMN+vTpw4EDB8oc533WrFkeo5B169aNdevWsWzZMv766y+uueaaInnA9cVq1qxZHjc2duvWjaNHj/Lrr79y4MAB/Pz8GDFiBAMHDvQoY3nzFaekcxs7dqw7WE9NTeW+++5j7Nix5RoJqCrPT6fTsWnTJpYtW8bx48fp0aMHY8eO5Y8//ihy30dxxy2JWq1m1qxZ9OzZ0yM9ODiYWbNmuW/K1uv1bN68mWXLlnHixAl69uzJmDFj+P333z1u7i5pf4W++OILbrnlFvckTa+//jrbt28v171ljz32GDfccAMrV64kOzubSZMmMWrUKI8b59977z1uv/129yRN8+bNY+TIkR6fg82bN2fWrFlFviSD6//AhV0QASZMmOAxzrtOp+Prr79m9+7drF+/noKCAkaOHMnbb7/tMdFbSfu7XKiUS3G3jLgsDB8+HLvdztq1a6tsn6mpqTRv3pzvvvuuQn1wx44dS0xMDHPnzr3oMixcuJCZM2dy5MiRSt8UezmobF2Ux4XXuKCggFdffZV77rmH2NjYKj3WpVDZayOvrbpn4sSJ7Nmzh3379hW5Ma8qP3eEEKKqSMu7qFaRkZEsXLjQPdVzefXt25eJEydWSRksFgsLFy684oOrytZFeVx4jf39/evUDUaVvTby2qo73nnnHfbu3cuSJUv4+eefix1Royo/d4QQoqpIy7so0ZdffonT6fSYJEUIIS4Hr732Gna7ncGDB5fY1UAIIWqjWh+8nzx5klWrVrmXR48eXeqg+8ePH+e3337zSIuNjfUYoksIIYQQQoi6qNZ3m8nNzWXXrl04HA73FL+lBe87duzgySef5KabbnKnFXdjlhBCCCGEEHVNrQ/eO3bsyLx58zCbzeWetCcyMpJ58+Zd4pIJIYQQQghRvWp98F4ZRqORb775Br1eT/fu3evE6BZCCCGEEEKU5bIL3uPi4hgxYgQbNmwgMTGRW2+9lWeffZaZM2cWm99ms3nMruZ0OikoKCAgIKDY0QeEEEIIIYS4WIqiYDabCQ4OLnNOhAs3rBNMJpMCKH/88UeFtluzZo2iUqmUffv2Fbv+2WefVQB5yEMe8pCHPOQhD3nIo9ofmZmZFYpta/1oM4XMZjM+Pj788ccfDBgwoELbhoWF8d5773HLLbcUWXdhy7vRaKRevXqkp6fj6+t7scUW1UhRFFJTU4mMjJRfTeogqb+6S+qubrvc6s9gMKAoCr6+vpjN5ov6X24yO1j3bw4/b8ri1BkzAIG+Ggb3CGbYVaHERHmXsYdL73KrvyuJ0WgkPDwcg8FQoddpne82c+zYMdasWcO0adMAOHHihMeUuEeOHCE7O7vEaXJ1Op3HFL+FfH19JXivYwo/rH19feUDrA6S+qu7pO7qtsul/hRFITExkQMHDtC6dWsiIiLw9/ev1L4SU838uCGT37ZkYTC7Rqxr2yyUsf3D6N8lGL2uAl0cLrHLpf6uZBWtt1ofvOfn5/P111+7W8dXrFjBoUOHGDJkCHFxcWzbto377rvPHby//vrrnDlzht69e5Obm8tnn33GjTfeSO/evWvyNIQQQghxiTidTnbs2EFeXh49evQgNDS0wvtwOBT+3pvHjxsz2HGoAACdVsWwq0IY078eLWOlQU/UDrU+eLdarezatQuAqVOnYjQa2bVrF926dQOgWbNmTJ061Z3/ww8/ZPXq1axfvx4vLy8+//xzhg8fXhNFF0IIIcQlZrVa0ev1REVF0bFjx2J/TS9NvtHOL5uy+HFjBmlZNgCiwvRc1zeMa3uHEuRf60MlcYWpM33eq4vJZMLX17fU/kdOpxObzVbNJRNlURSFjIwM6tWrV+QnKL1eLz8n1nLSb7Pukrqr2+pq/TkcDg4ePEh6ejoDBgyocNlPp1n4/o90Vm3JxmxxdY3p3iaAMf3r0aNtABp13bgWdbX+hKvPu5+fH0ajER8fn3JvJ18nK0BRFNLS0sjKyqrpoogSOBwO8vPzi6RrNBqaNGlS4RYZIYQQtU9eXh47d+5Er9dz1VVXlTtoVRSFvfEGlv6ezt9781AU8NarGd0vjHEDw4mJ9LrEJRfi4knwXgGFgXtkZKTcGFILKYqC3W5Hq9V61I3T6SQ5OZkzZ84QExMj9SaEEHVcTk4ODRs2pGnTpuX6TLc7FNb/m8Oy39M5mmgCICxIy/UD6jGyTxiBfhIOibpDXq3l5HQ63YF7ZW6EEZeeoihoNJoiwTtAREQESUlJOJ1ONBpNDZVQCCFEZZlMJvbs2UOLFi1o1KhRubYpMDn46c9MflifQUaOq7tr8xgfbrgmnAFdg9Bpa8+oMUKUlwTv5VTYx12Gj6ybCrvL2O12Cd6FEKKOOXPmDHv27CE6OprAwMAy82fl2vjuj3R+2piJwexEpYLeHQIZf004HZr7yS+wok6T4L2C5A1fN0m9CSFE3WQwGNi/fz+dO3cmIiKi1Lyn0ywsWZvO6i1Z2OwKOq2KUX3CuHFwOA0jpD+7uDxI8H4Z+9///sfw4cNp1qxZTRdFCCGEqJDs7GwMBgMNGzZk4MCBpf5qejTRyLe/pbNxRw5OBfy81Yy/JpxxA+sRGiQDFYjLiwTvl7E333yTxo0bS/AuhBCiznA6nRw9epQTJ07Qpk0bgGIDd0VR2HXEwLe/pbL9oGtSpZBALTdcE851fcPw95EukuLyJMG7qFZJSUmsWLECm83GsGHDaNWqVYl5ly9fzv79+z3SRo8eTYcOHS51MYUQQtSQf//9F7PZTJ8+ffD39y+yXlEUth3IZ+HKVA6cMAJQv56em4ZEMOyqEPQ6uQlVXN4keBfVZsuWLQwZMoRhw4bh7+/Pk08+yZdffsn48eOLzb948WJOnz5N//793WkOh6O6iiuEEKKaKIpCVlYWYWFhtGrVCj8/P9RqdZE8W/fls/DXVA6ddAXtcQ29mTg0gn6dg9Fo5N4mcWWQ4P0KsnHjRrZv384DDzyAl5eXu098UlISu3fvJjY2ljFjxnjc3Hnq1CnWr1+PwWCgX79+tGvXzmOfZa0/38MPP8xtt93G+++/D0CHDh148MEHuf7660vsy9i/f39mz55dBWcvhBCiNrJarezZs4fc3Fz69u1LQECAx3pFUdi8J4+FK1PdY7Q3j/HhtpGR9GofKAMSiCuO/LZ0hViwYAETJ06kd+/eeHm57rh/8803GTduHG+99RYnT57kwQcf5L777nNv8+2339KlSxfWrVvHgQMHGDhwIPPnzy/3+vNlZ2ezdetWJk2a5E675ZZbSElJYefOnSWWe+/evbz55pt89dVXpKenX+xlEEIIUYtkZWWxYcMGNBoN/fr1Q6/Xu9c5nQp/7sxh6itHeOajkxxNNNEy1oeX7mvChzOb07tDkATu4ookLe8XadJTBzCYnNVyLD8fNd/MblPh7Z555hkWL17Mxo0biYuL81jXv39/5s6dC8D111/P0KFD+eCDD8jLy2PatGksXbqUIUOGAHDrrbcyZMgQJk6ciNVqLXX9hePhJyYmAhATE+NOi4yMRKfTkZSURLdu3YqUe9y4cezZs4e0tDR+/fVXHnjgAb7//nsGDRpU4WsghBCi9nA4HKhUKvR6PW3atCE6Otq9zulU+HNXLgtXpnIi2QxA6ya+3DYiku5tAiRgF1c8Cd4vc0888QReXl5s3ryZsLCwIuuvueYa9/PmzZtjsVjIzs5m7969GAwGtm7dytatW915TCYTR44cIScnp9T1nTp1KrY8F37oqlQqFEUpNu+ECROYMGGCe3nGjBlMnTqV+Pj4cp27EEKI2icvL4+dO3fSuHFjYmNj3TelKorC33vzWPBzCseSXEF7uzg/bhsZSZeW/hK0C3GWBO8XqTIt4dWpX79+LFmyhO3btzNs2LAi6wtnHoVzgbWiKNhsNjQaDWaz2SP/jBkzCAoKIj09vdT1F2rYsCHgGm2msPU9LS0Nq9XqXleWa6+9ltdffx2z2Yy3t3e5thFCCFE7KIrCiRMnOHLkCM2aNaNRo0budTsO5fPZihQOnr0RtW1TX+64LopOLSRoF+JCErxf5kaNGsX48eOZMGEC8+fP5/rrry/Xdl26dEGj0TBy5Eh69erlTt+xYwdNmjQhMDCw1PUXCg0NpVu3bnz77bfu/IsXLyY8PJzOnTsD8N133wFwww03YLFYOHnyJC1btnTv4/fffyc2NlYCdyGEqIMcDgeZmZn06tXL3ciz/5iBz35KYdcR1zjtzWN8uGN0FD2ke4wQJZLg/QowePBgfv75Z0aPHk1BQQG33nprmduEhYXxwQcfcN111zF69GgiIyPZvXs3BQUFbNy4scz1xZkzZw5Dhw4lMzMTf39/vvzyS+bPn49W63oZLlq0CHAF74qiMGnSJOLi4mjWrBl79+7ljz/+YPHixVV3YYQQQlxyZ86cISUlhU6dOtG9e3cAjiQYWfBTClv35wMQW9+L20dF0beT3IQqRFkkeL+MPfjggzRv3hyA3r17s3btWr7//nvS09MJDw/3WA/g7+/PrFmz3DebTpkyhQEDBrBmzRoyMzP5z3/+w9ChQ935y1p/oT59+rBv3z6WL1+OzWZjy5YtHhMunT/eu7e3N1u3buXnn3/mwIEDjBs3jk8//ZTIyMgquz5CCCEuHbvdzv79+0lNTaVjx46oVCpOnjGz4OcU/tyZC7gmV5oyMoprugejUUvQLkR5qJSS7ha8QplMJnx9fTEYDB4jplgsFo4fP07Tpk3dQy2K2kVRFOx2O1qttkjLjdRf7acoCqmpqURGRkrLWx0jdVe3Xar627t3LyaTiY4dO5JnUvPFzyms/jsLpwL1gnXcOiKS4b1C0crkShdF3n91l9FoxM/PD6PRiI+PT7m3k5Z3IYQQQlQJp9PJyZMnadSoEa1bt8Zkha9WpfPdunQsNoUgfw23DI/kur5h6HUy1YwQlSHBuxBCCCEumtFoZOfOnTidTuqFR/L7diMLf00lt8CBl07FpGER3DQ0An+f4mfUFkKUjwTvQgghhLgoubm5/P3338TGxpJiiOSBNxNITreiVsG1vUOZMiqK8GBd2TsSQpRJgnchhBBCVIrVasVutxMQEEBo/Q787ycDh066ZtTu2S6Ae8bWp0mD8vflFUKUTYJ3IYQQQlRYRkYGu3btIii0Pj9s8WXLvjwAWsb6cO/1DejUwr+GSyjE5UmCdyGEEEKUm6IoHDx4kISEBFKM0bz+gx2HM4/69fTcPaY+/bvIWO1CXEoSvAshhBCiXBwOB6Dm6GkHSzdFcibLiZ+PmltHRDG2fxg6rYwgI8SlJsG7EEIIIUqlKAonT57k0OFjfLe9ASeSnahVaq7rG8bto6IIDpBwQlEUUOxnHw4Up/3cstOOojhAsYHTAYod5Wy667kDCh84QVFAcQJOFMXpfk6R5wqK4kCTm43VHHBu27Pri2zrsQ/Ftd5VetdDOfu3MM1jKqDz1yuFJ+2xrHhsU3wez+Xz04puoxS3TbHTE1VkyqJi8pZ7n1W7rdFkLyZf2eTddhmbOXMmkyZN8pjFVAghhKgIs9nM1m07ScvIZ/k/IaTm2ujS0p/7xjegaXTtuRlVUZzgMKHYjSgOIziMZ5+bXM/PLruem8BpRXFawGFBcVrPLTutKA4LOM+lu/KcXee0gtNSNEBXHDV27jrAVGNHF5Vlt1RunlQJ3i9j3377LX369JHgXQghRKXkG+18v/oIySlm1h8IJzLUmxenNaBX+8Aq7deuKAo4jDgtmSjWLBRbDoo9H8WW53rY81Bs+Si2XI907HkodsPZYN1cZeUplUoLaj0qtQ6VxgtUfqDWolJpXetUGlDrUKk0Z5e1563XeOZVu/66ltVnHxqg8LkKVeFzd9q5ZZVKjYKKAoMRf//As8d0bVe43pVX40o7fz+oztZhYT2qzm53XlqJyxRJUxW3jaq4vJxbX8Y2qpK2Kfa1V9LrsZj0cm9f3LblPEY5jqsxmoCexe2wVBK8i2ozf/58/v77bwACAgKYM2dOmduYTCa++uor9u7dS5s2bbjrrrvQ6WSsYCGEuJQsVhtr1u/ip60q4s/o8POux11jIhnbv165Z0ZVFCeKJROnJQXFlILTnIJizcBpzUKxZKJYXYG605KFYs10tWZXhsYXlcYflVeE67nWF5XGFzQ+qM4uu/L4nH3ug0rjA2pvVGq9KxDXeJ8NyPWg9nL91Xi5l115zv5V1a5JphRFITc1Fe/ISLlRuI7R6IyV2q7WB+8rVqxg3Lhx7uXff/+d/v37l7rN4sWLefbZZ0lMTKR9+/a899579OjR41IXVZQhLi4ORVHYsmULCxcuLDN4T0tLo3///kRGRjJq1CiOHDnClClT+Oabb6qpxEIIceXZeeAMhw/uIT1Xw5msUEb2CeWO66IICfBsOHFac3AaE849TMkoZleQ7jSnoJjTXH28y6LSoNKHotKFotaEodKEoNYEoFIFoFL5oFL5olJ8UKm8Uam8AB/XX7UXKnSonAo4HK6+5IV/LWf/Op1F1zkcZ/uUG0EpcOVRlHMPj2VnMekX5C81T+FJntdXu7g08Oz37ZHn/PXn7+Ncf/B6DgdoNBe1j0qr1LaV2KayRbyYc7vUx7Fdpn3er7vuOsxmMxaLBX9/f9dPa6XYsmULt912G59//jlDhgzhvffeY/jw4Rw9epSwsLBqKnXttGjRIvbs2cNzzz2Hl5cXM2fO5KabbmL37t3s3r2b2NhYpk6dio/PuT6MW7duZdWqVRgMBq655hqGDx/usc+y1p9vwIABDBgwAG9vb3744Ycyy/voo4/SuHFjVq5c6W5NSE5OrtzJCyGEKFVOvp0FPyUS43OIf+IDsakjeOtBH5oEJeHI/ANTwimcxrOPgiQwGVDZdahsOlR2LSqHFpVDg9ahAYcXKlUb1PijUnxROb1ROXWoHGqwg8qugFMBuxPsdlR2myuoBsBy9pFRg1ej7lBRSjB3fkv8+d1P3MnFdEGpTOt9pRr8K3OcavxloVLHquA2lfxiUeuDd5VKhVarxW4v37eTjz/+mGHDhjFp0iQAnn/+eT7//HMWL17M/ffffymLWqs9//zzfPPNN6xcuRIvLy/A1Sf+22+/5brrrqNZs2Z8/vnnbNy4ke+//x6AOXPm8NprrzF16lQiIiK4//77ueOOO3j66afLtf5iWK1WvvvuOxYsWMCbb77J6dOn6dSpE5MnT77ofQshhDjH4VTYtOU46SdXEVlwiAZ+WcwKzcDPmotquQOnVY/arkNj16G2aVHZ66FyRFXgCAqu2ykvuKVSrQGdDrQ68PZx/dXpQKs/7/l5f7Va0GhdLcxqzbm/anXRNE1hurb49eqz/cILH+pzfczPPc5bVhcuqy9YLim/q0+5q4t2CUEzUHxwzbn9FFlXdB8KkJqWSmREJKrC8xJ1g9EIb3xW4c1qffBeUTt37uSGG25wL6vVarp27cquXbuKzW+z2Ty+GJhMrg8XRVE8WvkLn1+Ynre2B4o9vypPoUQqbQCBg/+p0DZWq5UpU6Zw7Ngx/vrrL8LCwjzKP3nyZF588UUAevfuTe/evbHb7WRmZvLkk0/y559/0q1bNwBGjRpFp06deOihhzCbzaWuDwwMLLFM51/LkiQlJWE2m3nqqae49tpriY2N5Y033uDrr79m9erVJfbrK2nfJdWfqD0K60bqp+651HWnKAp5NgvJxjwyzAbybRbyrBYKbBYsTgdWpx2rw4HN6cDmdOJUFJy4yuP66xp0zqm4Bp5zegyHV47jV6iwFclaQ6/1C+opygKtdRqs7ULZ4nMVWy5q5yqPP9Xr7FCInO2qowCV65VQN52o6QKIirJbKnefx2UXvOfl5REUFOSRFhwcTF5eXrH5X3rpJZ5//vki6ampqfj6+rqX7XY7DocDu92ORnPBzSrVGGyU9xeIQg888ACNGjVi7dq1eHt7F9m+S5cu7rSoqCjsdjsZGRns3LkTh8PBhx9+6JHf4XBw8OBBsrOzS13ftWvXEsvkOPvTaGnnUphnxIgRvPnmmwCMGzeOuLg4Nm7cyNVXX11kG0VR3NtdGNwX1l9GRgZa7WX3sr8sKIpCTk4OULT+RO12KerO5nRw2JDNMVMOJ0y5GBzl++w7O8aGq+Hy7GgVapXrmfbs6BoqVWViy/JvUaF9V+VLXVFQOZ3gdKByuPp7qwr7fZfwfyrW5k2wU0u8zkie2kGQVX22FVqDotZ6tDArhSOCyNuzdlHAqbjG3Ze6qVtstsu020xFBQQEkJub65GWk5NDREREsflnzZrFjBkz3Msmk4mwsDAiIyM9gneLxUJ+fj5ardYj+KtoS3h1u/vuu5k3bx5Lly7ljjvuKLJep9O5z6fwr0ajwdvbG41GQ69evTzy9+rVi0aNGmEymUpdX1qAXPjlp7Q8jRo1Qq/X07ZtW3e+xo0b4+/vz5kzZ0rdtrjRaBwOBxqNhnr16rm7DYnapbDVNlJGTKhzqrLuTHYbv50+yubUk5jOBuwalYpGfsHU9w0g3MefIJ0XgXpv/HVeeKs16DVadGoNXmoNGvUVMMOnokBuNpw+CadPuR+qnMxiszt1VpzeprMPG0afIPZbGnE4J5R0v7bEXNWNW5poiYqKkvdeHaQoCqmpqfLZWQcZjUbeqMR2l13w3rFjR/7991/3sqIo7Ny5kyeeeKLY/Dqdrthgz9U6o/JYLi69tuvZsyc33ngjw4YNo6CggIceeshj/fnnc/7frl274u/vT0REBGPGjHHn//HHH2nQoAH+/v6lri/NhccrNG/ePACmTZuGl5cX1157LWvXruXee+8FXDfHFhQU0KlTp2LrQFGUEvddV+vvSlNYP1JHdU9V1N3OjGSWnthDvs2CVqWme3hDOodF0yKoHl6ay+7fVflZzJB4HE4ccT2SToLBs7umogKnvxOHdxYOnwKcvgYcPkaUYD3qyC5oQ/qR6ujAOz8FE6jKon2jAhwN2vDgyEb4eqtJTU2V914dJp+ddVNl66vOfxouWbKESZMmubtg3HPPPQwaNIjvv/+eoUOH8u6775KXl8dNN91UwyWtOR06dGDDhg0MHjyY/Px8Zs2aVeY2gYGBfP3110yZMoV3332XyMhI9uzZQ8eOHRkzZkyZ64vz22+/sWTJEo4dO0ZBQQF333034LrJWK1Ws3btWsAVvIPrhtgBAwZw1VVXERsby+rVq3nuuedo3bp1FV0ZIURtoCgKPycc5LfTRwG4KqIR1zVqTaDeu4ZLVkPMJog/CMcOuoL10yddXV/OUrRalMhQnAEGbNqj2H1ScPgVgMaJSh+Ctl5ftOF98QrrhdqvKWaLkwW/pPL71mSGd0xGr9PStkMvWseFuvYn95kIUafU+uD91KlTxMXFuZcHDRqESqXim2++YcKECTidTnc/Z4A+ffrw8ccf88gjj3D69Gnatm3Lzz//THh4eE0Uv0a99tprdOzYEYAWLVqwadMmfvvtN9LT0wkPD/dYDxAUFMQnn3xCQEAAAMOGDePo0aNs3bqVzMxMnnvuOVq2bOnOX9b6C9WvX5+rrrqKq666iltuucWdXvjN87777vPI36RJEw4ePMjvv/+O0Whk9uzZNG/e/OIvjBCiVvnx1AF+T47HV6vj9hbdaB1cfDfHy5bTCckJcHiv63HyyHnDJgKBISixsThCzFj1h7Da1gNnZxNV6dCG9cI7oj/a8L5oAtu6ZtU8a/OeXOYuSSI9y0b9UDUREVFcO7AdWm3tmmhICFF+KqUOfOUu7sZGjUaDSqVy36RYVTchmkwmfH19MRgMRfq8Hz9+nKZNm0qf6VpKURTsdjtarbbIT1FSf7Wf9Nusuy6m7v5KPcm3x3bjr9XzYNuraeBX8khVlxWn09Wqvncb7NkOuVnn1vkHQsv2KHHNsPklYc1fgz1jIyiu/4UqXTDayEHoooahCx+AShdQZPfp2VbeX5rMvweyGNIhCy/fYG68tiOhQUW7icp7r26T+qu7jEYjfn5+GI1Gjzl2ylLrW96h9BsbC8eBF0IIUbecMeax7PheNCoV97TqefkH7oUB+86/Yd+/kH92cAWVCpq2hFYdUVq2xa47gTVxMbaU1yDV1cKu0tdDFz0aff2RaEJ7oFIX/3/P4VT4YX0Gn/+UQlSQgSn9swkJDaf/1Z3kf6UQlwl5JwshhKh2TkVh0bFd2BUnY2Pb0jQwtKaLdOlkZcD2P2H7JshMc6WpNdCiPXToBu264tTkY01cjOXg8yim06482kD00WPRRY9FW+/qEgP2QkcSjMz5JokjCSZ0GoURXU1069yRRg1LH0RACFG3SPAuhBCi2v2bkcSJ/Gxi/YMZ2CCu7A3qGqcT9u+Av9ZC/IFz46zHtYKufaB9VxQfX+xpf2DZfx/2tPW4ZhVSoQ3vj77RJHRRw1Bpyu7mZ7Y6+eLnFJb9nk69ACs39LJw7TVdadzAW7pRCHEZkuBdCCFEtbI7naxMOATAuMbtUF9OAabJAFs3wF9rXC3uACFh0K0vdOsD9SJR7EasSUuxHP8UZ0E8ACrvKPSNbkYfMxGNX6NyH27XkQLe/jqR5HQLvVoW0DMunzZtWkrgLsRlTIJ3IYQQ1WpHxmkyLEbaBEfSNDCspotTNXKz4Y9fYOt6sJ6d8rxFe+g7FFp1ALUapyUDy8FXsJ5ciGLLBkAT2hOvpve4WtnL6BZzvgKTg4+XJ/PLJteNrhP6mGkeZadbt6sJDLzM7x0Q4gonwbsQQohqoygKG84cB2BIdLMaLk0VyM6AdT/DPxvBbgO9HnpdA32GQlQ0AE5zGpb4D7Cc+gIcZlBp0TW8Aa+m96AN7ljGAYravCeXdxYlkZlrp2NjC6OvaUrvDoFoNBr3DNZCiMuXBO9CCCGqzamCbBIMOTTwDSSuLre65+fCmh9hyzrXmOxe3tD/Wug3HPxdQzc6zSmY49/HevIrcJpB44NX3DS8mt6L2qd+hQ+ZnW/jf0uSWf9vDnqtkzsGGYgIMNGlhR69Xl/VZyiEqKUkeBdCCFFttqYlAtA3qknd7JNttcDGVa7WdosZvH1h0DBX9xhffwCc1hwsR+diOTEfnBbQ+OLVbDpecVNRe9Wr8CEVRWHtPzm8v+w0+QYHTevD9d0ziAwPpX37HhK4C3GFkeD9MjZ58mQefPBBevbsWdNFEUIIHE4nuzKT0ahUdA6rY8MXKgrs2Aw/L4a8bNBoXK3sg0eDn6ulXXFYsJz8HMuRd1FsOa6gvfmDeDWditqrcr8ypGZZeWdREv/sz0ejVhg/IJhbR0WTnxtBVFRU3fwCJIS4KBK8X8Y2bdrEzTffXNPFEEIIAA7lplNgt9IuJAo/XR1qLU49Dd9/AfEHXcude8G14yEsAnC1jNuSV2A++BJOYyKoNOhjb8O75WOovSMqdUhFUVj5VxYffpeMyeKkTSMV13XLISpCjb9vY/x9K97tRghxeZDgXVSbrKwsPvroI7Zt20ZQUBATJkzg2muvLXWb5cuXs2jRImw2G6NGjeLOO++UliYh6qg9WWcA6FovuoZLUk42K6z5AdavdPVrrx8DN9wOTVq4szjyD2PaOwt7xl8A6KKG4936/9AENK/0YdOyrLz1dSLbDxagVsNdw1WE6pNp3KgpzZpdBjf5CiEuigTvolqYzWa6d+/OpEmTuPXWW4mPj2f8+PG8+eab3HfffcVu88UXX/DAAw/w6quv4u/vz5NPPsmpU6d44YUXqrn0QoiLpSgKB7JTUQFtQirXGl2tTsXDoo8h/YzrZtSRN7lGkDk7motiN2A+/DaW4x+DYkcd0Arf9i+hrde70odUFIXVW7L5YOlpDGYnsfX1zLgtFsyJRET0JCQkpKrOTghRh0nwfgV59dVXOXXqFO+88w5eXl5MnjyZ22+/nU2bNrF7925iY2P5v//7PyIizv1jXbp0KStXrsRgMHDNNddw7733olary72+kE6nY+fOnR7jD2dmZrJgwYISg/fnnnuOZ599lunTpwMQGBjI5MmTeeKJJ/D396+qyyKEqAbJxjxyrGaaBoTiq63FXWbsNvjtB1j3k6ufe+tOMP4OCA51Z7Gl/IZxz0wU8xnQ+uPd8r94NbkDlVpX6cNm5Nh4+5tEtu7LR62CKcO8aBSUQtMGzdDpWl78eQkhLhsSvF8BbDYb9957L0eOHOHHH3/Ey8s13famTZtYt24djz32GLfffjtz587l5ptvZt26dQA8/vjj/PTTT8ycOZOAgABee+019uzZwwcffFCu9efTaDRFJg45c+aMxxeF850+fZqTJ08ydOhQd9rQoUMxmUzs2LGDfv36Vcm1EUJUj/3ZqQC0DYms4ZKUIuU0fPU+nEl0tbaPvRW694WzXfWc1ixMe5/Cdno5ALrocfi0fQa1d+XPqXAkmf8tOU2ByUFslI7bB1sw5Z+iZcsO6HSV/0IghLg8SfB+sWY/AmZj9RzL2xeemlOhTXJzcxk+fDihoaH8/vvveHt7e6x/+OGHeeyxxwCIi4ujY8eOWK1W0tLSeOeddzh06JC7j2WvXr2IjY1l9uzZGI3GUteHhoZSmrVr17JkyRJ+//33Ytenp6cDEB4e7k7z9/fHx8fHvU4IUXccyc0AoHVwLe0ys+1P+H4BWK3QrA3cdA+EnhvW0Zr8M6Y9T6JYM1D5NMC345voIgZe1CGzcm3MWZTE5j15qFRw46Bwhnd1kJKSR/d+/fDx8bnIkxJCXI4keL/M3X///bRr144lS5YUe6Nn69at3c/r1auH0+kkLy+PvXv3olKpmDZtWpFtjh49SlZWVqnrSxue8o8//mD8+PEsWLCA3r2L7x9aOG6x2Wx2pzmdTiwWi/uXAyFE3eBwOjmRn4W3Rku0X1BNF8eTxQzLv3QF72q1q2/7gBGu57jGbDftmYEteQUA+tjb8GnzFCpdQKUPqSgKf/ybw3uLXeO2R4frmDpSRetm3oSEhBAb21BuzBdClEiC94tVwZbw6vbCCy/wzjvv8Pzzz/Pcc88VWV/SP4igoCA0Gg0zZszwyDNz5kxatGjBwYMHS11fkpUrVzJp0iQWLFjA2LFjS8zXpEkTtFotR44coWnTpgAcP34cp9Mpoy0IUcckGnKwOh20CY5EXZuC0vQUWPCOq7tMUAjcOt1jJBl75t8YdkxHMSWj9o3Bp9McdPWuvqhD5uTbeffbJDbuzAVg/MBAOjQ4g91kQ6uNlqBdCFEmCd4vc3Fxcfz5558MHjyYvLw83n777XJt17lzZ6Kiojh8+LD7hlGbzcaHH37I4MGDy1xfnGXLlnH33XezePFihg0bVmR94SgyzzzzDD4+PowaNYr33nuPIUOGoNFoePvtt2nfvj2tWrWqzKUQQtSQ+LxMAJoFVm6iokviyD748n9gMkCrDjBxGvifnWzJacN8+E0sR+cCCrqG4/Ft//JFtbYDbN6Ty1tfJ5GTb6d+mJ7HJtUnPfEfgoOjadWqFZqzI9kIIURpJHi/AjRs2JCNGzcydOhQ7r33XubNm1fsiDDn8/Hx4bvvvuO2225jzpw5REREcOzYMe66665yrb9Qeno6N998M/Xr1+eNN97gjTfeACA0NJQlS5YAsGfPHo9t3nvvPYYNG0bTpk3x8fEhPz+fn3/++WIvhxCimh3LywIgrjYE74oCm9bAiq/B6YRB18Hw8e5uMg5jIsbtU3Hk7ARtAL4dXkPf8PqLOqTJ7ODD75L55S/XdRjdJ5jxA3yIbhCMMbYPvr6+F31aQogrhwTvl7Gvv/6ali1dQ4xFRESwYcMGtm3bRnZ2NmFhYR7rwRVIr1mzhqAgV5/Url27snfvXo4fP05mZiZt2rQhIOBcy1NZ688XGBjIqlWriqSf33/92Wef9VgXExPD3r172bVrFzabjS5durj7wgsh6gZFUUgoyEajUhHjX8P93R12+P5L2PIHaHUwcSp0OXffjS1tHcZ/p6PYstGEdMe36/tofGMu6pD7jxt49YsEktOthARqeXh8INa8o6SmBBPdIEICdyFEhakURVFquhC1iclkwtfXF4PB4PGharFYOH78OE2bNpUbJmspRVGw2+1otdoi/Ual/mo/RVFITU0lMjJS+v3WMaXVXbbFxDP//kaMXxBPdBxQMwUE142pC/8HB3dDYDDc8Qg0ct1PoyhOLEfmYD78FqDgFXc/3q2fRKWufPuW3aGwcGUK36xKw6lAn45B3NjXRlLCMdq0aUNMTEyteZ3Le69uk/qru4xGI35+fhiNxgqNLiUt70IIIS6ZhIIcAGL9a3B2UEM+fPoWJByDqGi45wn3pEtOaxbGHdOxp/0BWn98O72DvsHIizpcQoqZVxYkcCTBhK+3mvtviGB47wgyMzNp3KiPTDInhLgoErwLIYS4ZBIKsgFo5B9cMwXIyoCPX4f0M66RZO58FHz9AHDkH8awdQpO4ynUAa3w6/4pGv+4Sh9KURR+3JDJR8uTsdoU2sX5cve1ak4n7MRg6EO9evXK3okQQpRBgnchhBCXTGHLe40E72lnYN4rkJsNbbvArQ+AznXfjC1tHYbt08Cej67BGHw7vY1KW/n+5xk5Nt5YmMD2gwVoNSruGVOPuNBk0lPy6dGjh7S2CyGqjATvQgghLpnTxjy0KjVRvhc3zGKFpZx2Be75udCjH4y/EzQaFEXBeuJTTPueA5x4t/wvXi0euai+wht25DDnmyTyjQ4a1/dm5pQY6oc6OX48k06dOqHT6arstIQQQoJ3IYQQl0S+1UK+zUJDvyA0qtKHp61SZxJdgXtBPvQeBNffBmo1itOOae+TWE99BRpvfDu/h77BdZU+jMHkYO7i06z5x9U1aPygMK5ungOW0/j7N6dDhw5VdEJCCHGOBO9CCCEuiTOmPADqV2ere/Ip+PBVMBZA36EwZjKoVCh2I4Z/p2JPXYvKOwq/Hp+jDe5U6cMcOGHg5c8SOJNpJSJExyM3hWDKPoLBoKd588r3mxdCiLJI8C6EEOKSOGPMB6C+T2D1HDAl6Vzg3v9auG4iqFQ4LZkY/rkNR/YO1AEt8L/qG9Q+0ZU6hMOpsGh1Gl/8koLTCQO6BPGfidHs+vdvGjZsSNOmTWW4PiHEJSXBuxBCiEvijLEaW94z0+Cj14oE7g5jIoa/J+I0HEMT2gO/Hl+g1gdX6hCpWVZeXZDAnngD3l5qpt9Qj6bheQT4aunTp0+ZM1cLIURVqBPBe25uLgsXLiQxMZH27dszceJENBpNsXl3797N559/7pHWtm1b7rnnnuooaq3Sp08fXnjhBa655pqaLooQ4gqUXNjy7nuJW95zs1x93PNy4KqB5wL33P0UbJmEYklDF3Utvl3fR6Up/0Qo59uwI4e3v06iwOSgRSMf7h/jTXLCHmzB0SiKIoG7EKLa1PpPm9zcXLp3786yZcvw9vZm9uzZjBkzpsT8R48eZdGiRTRu3Nj9iIyMrMYS1x5JSUkYjcaaLoYQ4gqkKApnjHl4qTWEeFUuYC6XgjyY95prPPfOveCG20Glwp75D/l/XY9iSUMfeyu+3T+pVOBusjh486tEXvj0FAUmBzcNCeexG7WkJB2mc+fOtGvXTgJ3IUS1qvUt73PnzkWlUrFmzRp0Oh3Tpk0jNjaW1atXM2zYsGK3CQkJ4eGHH67egooyPfLIIyxfvhyA0NBQduzY4bH+wIEDvPXWW2zbto2goCAmTJjAAw88UOI/RkVRePPNN1m0aBE2m42RI0fy1FNPodXW+pe1EJe9HKsZs8NOY/8Q1JeqD7jFDJ+8CWnJrnHcJ94LajW2jL8wbL0NHEa8Wz6OV4tHK9UP/UiCkZc+SyApzUJYkJZHbgqme7sIHA4HMQ2j8fLyugQnJYQQpav1Uc7atWsZPXq0e5zc+vXrc/XVV7N27doSg/ecnByeeeYZ9Ho9vXr1YtCgQSXu32azYbfb3csmkwlwBYaKorjTC59fmF7b1aby/t///R8PPfQQP/zwAy+99JJHuYxGIxMnTuSRRx7hP//5D/Hx8UybNo2MjAyee+65Yvf36quv8t577/HZZ5/h7+/PtGnTSEtLY/78+UXOua7W35WksG6kfuqe4uou2eDq7x7lG3Bp6tThgIX/Q5V0AiWuNUy+H9QabKnrMG67C5xmvNs8g1fcNHcZy8vpVFi2LoPPVqRgdyj0ah/AjX0snDm9l5yGXoSFhVV4n7WZvPfqNqm/uquydVbrg/fk5GSioz1HBWjQoAHJycnF5u/UqRMzZ85EURQSExOZMGECY8aM4bPPPis2/0svvcTzzz9fJD01NRVf33Oz7dntdhwOB3a7vcT+9rVRYZltNhvTp0/HYrHw0Ucf4eXlxYABA7jzzjv5/fff2bNnD7Gxsbzyyiu0bt0acH2xefvtt1m1ahUGg4GBAwfyzDPP4OfnV671FwoODiY4OJiQkBAAjy9NOp2Of/75x93K3rp1a3bs2MHPP//MU089VWRfiqLwzjvv8OyzzzJ48GAA3njjDcaMGcPLL7/s/udaqLD+MjIypGW+llIUhZycHAAZraOOKa7ujuacAcDf7vo8reIDErjme3wP7sYWFknWiIkoWdmos/9AFz8DlWLDFjsTs//1UMFjZ+c7mPdDHvtOWNFp4c4RPjTwO0VmukKrVq2w2+1Vfz41TN57dZvUX91V2a7NtT6KUalUOBwOjzSHw1FiANasWTOPLjN33XUX7du3Z+rUqfTs2bNI/lmzZjFjxgz3sslkIiwsjMjISI/g3WKxkJ+fj1ar9Tj2czvWYDovCL2UfLRanusypELbaDQaCgoKuOGGG4iIiGDBggXun3pPnz7Nc889x9y5c/m///s/Xn/9dW6//Xb+/fdfAG699VaSk5N5+eWXCQgI4Nlnn+XOO+/ku+++K9f60soElBlE79u3j2bNmhWb7+TJk6SlpdGvXz/3+oEDB2K329m/f3+Rm3QdDgcajYZ69erJT921VGELRGRkpPwDqmOKqztjgSt4bxbRgMiQiKo94LqfUO3eghIYjHbaDCJC6mFNXoEp/glQHPh0fIugRhMrvNu/9+bx5leJ5BY4aBrtxYxbo2kS7UtCgo5GjRpdtn3b5b1Xt0n91V2XbfAeGxtLQkKCR1pCQgIDBw4s1/Zt27YlNDSU+Pj4YoN3nU5X7NTVKpXK401Q+PzC9OpW0WOfOnWKmTNnMmrUKF599dUi2z/55JOMHTsWgKeffpqWLVtisVhISkpi6dKlJCcnu2/4/fLLLwkLCyM1NZWCgoJS10dFRZV5DqWdy4IFC1i3bh3//PNPsfkKWxnCwsLc6729vfH19SUrK6vINrWl/kTpCutH6qjuubDu0s0GACJ8/au2PndshpVLQe+F6q7HIDQc6+kfMf17P6hU+Hb5H/qG4yq0S6vNycfLz7B8fQYA4wcG0zU2jZy0w2gadaNJkyZVV/5aSt57dZvUX91U2fqq9cH7yJEjefPNN5k9ezYBAQEcOnSIrVu38uabbwKwc+dOvvjiC9555x0ANm7cSN++fd0XZNOmTWRmZtK+fftLUr7nuw69JPutKrNmzaJdu3bFBu4AjRo1cj8PCHD1TS0oKODQoUMAxX7hOXbsGNnZ2aWuLy14L8vChQt55JFHWLlyJa1atSo2T+GvIgUFBe40h8OByWQqsduOEKL6pJsNqFUqQqtypJnjh+HbT0CthtsehIaNsSb/gnHHA67Aves89A1GVWiXiakWZn92ivhEE0H+Gh65MQBzzhF0unq0a9eu6souhBBVpNYH71OnTmXJkiV0796dq666ilWrVnH33XfTu3dvAA4fPsy7777rDt7//PNPHnjgAbp3705ubi6//vorTz/9NB06dKjBs6g5c+fO5X//+x9Tpkzh888/L3d//aioKHQ6HWvXri3SbSUqKop9+/aVur6yPvzwQ55++mlWr15Njx49SszXpEkTfHx82Lt3L82bNwdc3WwURXH32RdC1AyLw06u1Uy4tx8aVRV1NcnOgC/eBYcdxt8BrTtiS/kN47/3gaK4WtwrGLiv/SebdxYlYbI46dTCnydvb0Ra8lGCGrQpcq+VEELUFrW+A5+3tzcbNmzgjTfeoGfPnixdupSPPvrIvb5Lly7MmTPHvTxr1ix+/PFH+vfvz4033sjevXt54YUXaqLotUJoaCi///47p06d4sYbb8RqtZZru06dOtG6dWs+/PBDoqKiaNy4MXq9njlz5uDt7V3m+sp44403eP7551m3bl2xgfv999/P/fffD4Ber+fmm2/mtddeIy8vD6vVygsvvEDfvn1p3LhxpY4vhKgaGWe7zIR7+1fNDq0W+PwdKMiHPkOg1zXY0tZh2H4PKHZ8O89B3/D6cu/OZHHwxsIEXlmQgMXq5M6RgYzvmU6Aj5M2bSRwF0LUbrW+5R1cNzZed911xa5r0aIFLVq08Ehr2rQpTZs2rY6i1QkBAQGsWrWKcePGMXr0aL7//nuPm3GLo9VqWb58OQ8++CD16tXDx8cHHx8fnnnmmXKtL84nn3zCSy+9hMFgIDs72x1kHzt2jLS0NJ544gmCgoIYPXq0e5vw8HC2bdsGQFpamsf+5syZw6RJk4iIiECtVtOhQwe+/vrrylwiIUQVcvd396mCLmyK4uoqc/oUNGsDoydhS/8Twz93gdOKT8c30cdMKPfujp828eL8UySkWAgL0vDQWC0F2YcJD2+GXq+/+PIKIcQlVieCd1E5mzZtcg+Z6OPjw4oVKzh9+rT7zvTz14MrUD5x4gShoaEANG7cmJ9++gm73U5ubm6R4RfLWn+hm266iSFDio6Wo9FoiIiI4MSJE8WuK/Thhx96rAsKCuKXX34hJycHu91OWFiYx/CTQoiakWZy3YsS7l0FwfvvK2D3VgiLgNumY8/dieGfKeA049P+FbxibynXbhRF4edNWXyw7DRWm0LPdgHcda0XCSeP0KtXL4KCgi6+rEIIUQ0keL+MNWzY0GNZp9N5dCm5cL1Goym2y4lWqy01MC9rfaHAwEACAwOLXVfSsc8XHh5ebHpwcDBw+UyYIkRdV9jyHu5zkd1m9u2AX5eBlzfc8TAORxKGrbeCw4R322fxanJ7uXZTYHLw9teJbNiRi1aj4v6x3gzs5ppzonGjqDo1d4cQQkjwLoQQokqd6/N+ES3vGamwaJ7r+aRpOAIVCjZNRLHl4tX8P3ifnTm1LIdOGpk9/xRnMq3EhGu5c6gZsyEFhyMUlUolgbsQos6R4F0IIUSVSjMVoFGpCKnsMJE2K3zxHphNMHgMzmaNMGwajWJJRR97K96tZpS5C6dTYdm6dD794QwOJwzsEkDvpqfw1vnRs39/maxNCFFnSfAuhBCiylgcdvJsFiJ8/Cs/TOT3X0ByAjRvi3LNYAr+Ho/TeBJdg+vw6fBKmROb5BbYee2LBLbuz8dLDw+NDWZk/0bk5IQQHBwsE9kIIeo0Cd6FEEJUmfSL7TKzdQP8sxGCQlAm3kHB9jtw5u1HW68fvp3nolKV3s1l95ECXvr8FJm5dlo1UjOuZw4+egPQiJCQkMqVSQghahEJ3oUQQlSZLIsRgHpepQ9HW6zTp1yt7moNyuT7MRyegSNzC5rgzvj1+AyVpuSuLg6nwterUln4SypOBW7sp6Zx8GkaRMXSsmVLaW0XQlw2JHgXQghRZbLMruA91LuCwbvJ6OrnbrehjJ6EqeBL7CmrUfvH4ddzISptyS352fk2Xv48gR2HCvD3gQdvakSHxjbU6vrUq1fvYk5HCCFqHQnehRBCVJnMsy3voRVpeVcUWPIpZKZBh+5YGpzEevBzVF7h+PX8BrVXyUPR7jlawOzPXN1kerR0MqBVBt1axBIcHHGxpyKEELWSBO9CCCGqTGHwHlaR4H3LH7BnG4RFYO3XEPPeB0Djg1/PL9H4NSp2E6dTYfGaND5bkYJKpTBlkIUIv2zatm3nnvtBCCEuR5UcCkDUBa1atWLZsmU1XQwhxBWksNtMWHm7zaQkwQ9fgVqDfXR/jPsfBdT4df0IbXCnYjfJLbDz1Icn+PTHFPQ6NY9NDKdlQ+jXr1+RyeeEEOJyI8H7FUSCeSHEpZZlMeKt0eKj0ZWd2WaFhe+D3YZz8DUYEmeA04JPh5fRRQ0pdpP9xw1Me+UIW/fnMbC9iedu0zLs6mh69uyJr28lbpIVQog6psLB+7p16xg2bFi500XtcejQIcaPH1/TxRBCXKaMdhsmh51QL9/yje6y4htISUJp1pIC9fsothy8mk3Hq/GUIlkVRWHp7+k88nY8BQYzdw/OpXdLE82b1r8EZyKEELVXhfu8O51OHA5HkXSDwSAz1gkhxBUsqyL93fduh82/o/gHYIj7G6cpAV309Xi3frJI1nyjnde/TGTznjz0Opg6LI9G0aG0adMGrVZu3RJCXFnK3fJ++PBhpk+fzv/+9z/388LHfffdxxNPPEHnzp0vZVnFRbqw20zjxo2ZPXs2ffr0ISAggHbt2rFp0yb3+pycHKZOnUpMTAyhoaGMHz+eM2fOuNf36dMHlUqFRqMhLi6Ot99+2+N4jRs35sUXX6R37974+fmxatWqS3+SQogaUxi8lzlMZHYmLP4UAEs3O3bzDjShPfHtNAfVBbOyHj5lZNorR/lnfw7925qY+3hzhg2+mg4dOkjgLoS4IpU7eFcUBbvdjsPhcD8vfGi1Wh544AFmzpx5KctaaxX+GlH4cDqdAB5phddNUZQi6eXdR2FaVfrqq694//33SUlJYeTIkdx5553udePHjycrK4vNmzdz/PhxwsLCmDhxonv9pk2bUBQFi8XCN998w9tvv83KlSs99v/ZZ58xd+5c8vPzGT58eJWXXwhRe5RrmEinExbNA5MBe8dIzMpPqH1j8Ov+qcckTIqi8MOGDP7zVjyKvYB7BqUzpIuKxvX16HTl6E8vhBCXqXI3W7Rq1Yp58+aRkJDA1q1bufHGGy9lueqUo0ePcvToUfdymzZtaNq0KRs3bsRgMLjTBw8ejMVi4c8//3SnhYaG0rt3bw4dOsTx48fd6e3btyc2NpY//vgDs9kMQPPmzWnZsmWVlv2JJ56gY8eOAEybNo3XX38dg8HAiRMn2LBhA5mZmQQGBgLwzjvvEBgYSGJiIjExMe59aLVaevbsyb333suKFSsYMWKEe93jjz9O165dq7TMQojaKctsAiDMy6fkTH/8DMcO4YwMpSDwG9D44dfjC9Re5yZTMpgcvPV1Iht25NIq2sTwjtm0a9uKxo0by0ypQogrXoV/c/T39yckJIS1a9cWWRcWFnZFdp1p3rw5zZo1cy8X/nPp16+fRz61Wo2XlxfXXnttkX20atXKIzAv3MfAgQOLpFWliIhzE5n4+Lj+4ZpMJk6cOIHdbicoKKjINidPniQmJoYFCxbw9ttvc+zYMYxGV4vbyJEjPfI2alT8GM1CiMtPVlkt76dPwervUbRaCpqsBTX4dX0fTWBrd5ZjSSae/+QkeflGYiK8eeDmlsREaAkICKiOUxBCiFqvwsH7/v37mTx5sntZURQyMjLQaDSMHTuWJUuWVGkB6wK1uvjeRxqNptzpFd3HpRYbG4u3tzdZWVnuoP58R48e5d577+WXX36hV69e+Pv789xzz7FlyxaPfNJKJsSVwz1BU3F93m1W+GYeOByY26Ti9MrCu/WT6KJco5QpisLKv7KYuySJuAgD4wbk0LFDR5o0DqnOUxBCiFqvwkNF9u3bl5SUFPcjNTWVlJQUrrrqKv773/9eijKKGtC+fXt69erFHXfcwfHjxzEYDGzevJkxY8YArtGFVCoVgYGBaLVaNmzYwIcffljDpRZC1BRFUUof433Vd5CShD3CjqXeXnTR1+PV7EEATGYHr36RyNzFCQxpn8nwzgVc3as7TRpHV/NZCCFE7VclkzSFh4czffp0Pv3006rYnagFVCoV3333HeHh4VxzzTVERUUxY8YMpk+fDkCnTp2YOXMmo0aNIjg4mGeffZZx48bVcKmFEDXF7HRgLmmM92MHUTb8iqJXYWj6N5qQTvh2eguVSsXJZDP3v3aUdduyCAvW07N9OMOGDCA8PLxmTkQIIWq5Khtnq6CggIyMjKranagChw4dKnX55MmTHstRUVEoiuJeDgkJYe7cucydO7fY/T///PM8//zzJR7/wv0LIS5fuXYLUEyXGbMJvv0YlaJgiNsLQSH49fgclcaH1VuymPttIl2b5DCojZPRI/oS6CfDPwohRGkq/Cl56tQpFi1a5JGWmZnJZ599xquvvlplBRNCCFF35BQG7xferPrjV5CVgTU8FVtULv7dl2NVRzB3YSL/7EllXPdMwoK9GNS/B/4SuAshRJkq/EmZm5tb5KbE4OBg3nvvPSZNmlRlBRNCCFF35NpcwXvI+cH7vh3wz0aceium5gfx7fQeZ6yteP79o5xINnNtZwPN46K5ukebEm/aF0II4anCwXuHDh344YcfLkFRhBBC1FW5disAoYVjvBfkoSz9FBVgbLkffZv72Zw6kPeXHKJ7kyyiQqK566behATKhEtCCFERlfqN0ul08tVXX/Hbb7+Rm5tLq1ateOihhzwm7hFCCHHlyDvbbSbUyxcUBWXJfFQF+VjqJ6G06sane25h98FDjO+RhcYrlOuGNsVLL4G7EEJUVKV+p7z11lt54okn3JMy7d69m3bt2hW5IVIIIcSVofCG1RAvH9i+CdX+HTi8jRjaqXhm0yNs33uG4R2yaBjbmvGjrpLAXQghKqnCLe/Hjx/n559/5vDhw0RFRbnTp0+fzjvvvMO8efOqtIC1zfmjsYi6Q+pNiEsr125Fp9bgn5eH8v1ngEJ+mwRe2vESKXlWIsJD6N23HQ3C/Wq6qEIIUadVuOU9Ozub1q1bewTuAIMGDSIrK6vKCnYhk8lEYmIidrv9kuQvi07naiUyGo1Vsj9RvWw2GwBarYxmIURVszjsmJx2QvU+KF+9hcpqxxSTyGc5T9C7tY1rr/LmrYebS+AuhBBVoMKRTNu2bUlLSyM+Pp5mzZq505cvX87AgQOrtHCFXnzxRV555RV8fV2jGHz00UfccMMNVZa/PNRqNaGhoaSmpgLg61vMRCSiRimKgt1ux+FweNSN0+kkLS0NPz8/GdFCiEsg22ICIKQgF3XCaWz+BayInEaYKpDwhu25oXuDGi6hEEJcPiocvCckJBATE0OnTp0YOXIkQUFBbN26lWPHjhESEsLDDz8MQMuWLbnvvvsuuoC//PILr776Kn/++Sddu3Zl4cKF3HLLLXTv3p1GjRpddP6KiIiIAHAH8KL2cTgcaDSaIukajYZGjRrJFy4hLoFsqyt4D008hcHHhz8CfNif0YqpN7WkYYRvGVsLIYSoCJVSwc7ABw8e5K233iozX5s2bXj00UcrXbBCN910E1qtlq+//tqd1rJlS+655x4ef/zxi85/IZPJhK+vLwaDwd1yfyGn0+nuhiFqD0VRyMjIoF69eh5BukqlQqfTSeBeyymKQmpqKpGRkVJXdczGpKMsP3mAwWfy0KnVHLD05v4bY9Hr5JeuukDee3Wb1F/dZTQa8fPzw2g04uPjU+7tKtzy3rp1az799NOKblZpBw4cKDL5U9u2bTlw4ECV5LfZbB794k0mVwuSoigl3uSoUqnQ6/XlPgdRPRRFQaPRoNfri/0Ak5tWa7fC95zUU92SZ7CT+PcaevvFoFHsaCJ785+rGwPynqsr5L1Xt0n91V2VrbMKB+/r1q3jtddeY/Xq1eVKv1gmkwl/f3+PtICAgBJvHK1o/pdeeonnn3++SHpqamqJLe+idlIUhZycHABpfaiDpP7qnvgkKyd/+xp183DifRS6+YXTtlmQdC2sY+S9V7dJ/dVdlR0EpcLBu9PpxOFwFEk3GAx4eXlVqhClCQkJITMz0yMtMzOzxP7rFc0/a9YsZsyY4V42mUyEhYURGRkpwXsdU/gNVn46rJuk/uoORVFYsT4JQ+pOhmiSWKQPJ9lLoVOnAdTzkRFl6hp579VtUn911yUP3g8fPszcuXNJSkri8OHDTJ8+3b3O4XCwfv16JkyYUKlClKZ79+78+eef7mW73c6WLVvco8cUFBSQkpLiHvmmrPwX0ul07mEgz6dSqeRNUAcV1pvUXd0k9Vf7GUx2vly+l0jf07Q3bSc8PZ+sHgGocE3QJHVXN8l7r26T+qubKltf5b6b6Pxh+AqfFz60Wi0PPPAAM2fOrFQhSjN9+nQ2b97Myy+/zPbt27n77rvx8/Nzf1H4+eefad68ebnzCyGEqJwTySb++85BfFWpNM9cRof9J3F6qcj18sZfo0cjQ7EKIcQlV+6W91atWjFv3jwSEhLYunUrN95446Usl1ubNm1YvXo1L730El9//TXt2rVj3bp1+Pm5fpoNCAggLi6u3PmFEEJU3KqNx1j/zxkOJ/ozJWYRbeNNgBe5N96L05hBkFZu4hdCiOpQ4T7v/v7+hISEsHbt2iLrwsLC6Ny5c5UU7Hz9+vWjX79+xa4bOXIkI0eOLHd+IYQQ5Wcy21j2y3a8VdnkmkJ4ZdQqOu1JRm2NQunag5ymrWDfJoJ0VX/PkxBCiKIqHLzv37+fyZMnu5cLx9bWaDSMHTuWJUuWVGkBhRBC1IzkdAu/rvkLs9XB2hPRPDXmJA12LEaf3g4lOAjVuLvJys8AkJZ3IYSoJhUO3vv27UtKSopHWnp6OjfccAP//e9/q6xgQgghaoaiKPzx93HeWWZARRDNY4OYM9WE5u8n8TnaAQVQTZoO3j5kZbjmxgjUSsu7EEJUhyq5uyg8PJzp06dX6+RNQgghql5+gZHvftpAwql4HA47Ywc24JV7g9Duvwvfg41R23WoBoyAuFYAZFtcwXuQBO9CCFEtKtzyXpKCggIyMjKqandCCCGq2bGTKezevYMDCb7sSazPs/c2pnsrLwr+vhFtvIIuOwwlqiGq4eeG3s2yuMYplm4zQghRPSocvJ86dYpFixZ5pGVmZvLZZ5/x6quvVlnBhBBCVA+bzcbOIwbeXZSCXh2GX0AoH8yMJTxEh2nXIyhJ+/E50QtFo0F1yzTQnQvUs88G79JtRgghqkeFg/fc3Fy2bNnikRYcHMx7773HpEmTqqxgQgghLr2MjEz+2vIv6/f5kZLlz7iBDbn3+vrotGrM8R9iPbUE/8O9UTlUMOIGaBDr3lZRFLIsJnw1OrzUmho8CyGEuHJUOHjv0KEDP/zwwyUoihBCiOridDrZu+8wJ06cYN3+IE5kBPDM3Y3o3yUYAFvKGswHXsQrqTnaPB9o3AIGeg7La7TbsDodhPvKPBpCCFFdKt3nPTk5mT///JOcnBzatGlD3759q7JcQgghLhFFUdh/3MC6bWn8eTCCsJAAPpjRmJhIV9cXR95BDP/ehyYvAO+TsaDXw8R74YIZVAv7u4d6+Vb7OQghxJWqUsH7t99+y1133UVQUBDBwcEcO3aMfv368csvv6DXy01LQghRGymKQkJCArv2n+L9lYE4nMEM7xXKQzdF46V3BeZOSwaGf6aA1YzfseGoFAuMuQXqRRbZX9bZkWZCvHyq9TyEEOJKVuGhIg0GA9OmTePjjz8mOTmZAwcOcOrUKfLy8njvvfcuRRmFEEJcJKvVypat29i28xBLN3mj0aj5760x/PfWGHfgrjgsGLbdhdOYiF/yENR5FmjXFXoOKHaf2e6WdwnehRCiulS45f3AgQPExcVxyy23uNOioqJ47LHHZHZVIYSopbZsP8DOw0ZW7YwgMsyH9x+IpWn0uaBbURSMu/+LI+sfvAxXoTtuh8AQmHAXqFTF7vNcy7sv2KvlNIQQ4opX4eA9KCiItLQ07HY7Wu25zc+cOUNQUFCVFk4IIUTlORwOjhw5wr7TQXz6I9gdIQzqHswjExvi4+05OozlyNvYkpaiJhbvffVAZYFJU8EvoMT9u1ve9T5gt17ScxFCCOFS4eC9WbNmhIeHM27cOB5++GGCg4PZsmULzz77LIsXL74UZRRCCFFBeXl5bP93B2cyFRb/ZUKt1vLYzdFc2zsU1QUt6dbEJZgPvwnqAALiB6IyJ8E1o6B521KPcX6fd5NRgnchhKgOFQ7e1Wo133//PQ899BCjRo3CZDLRuHFj3nnnHYYNG3YpyiiEEKICcnNz2fTXZv49EcSG/b40jPDmmbtjiWtYtG+6LeMvjLseB5WWANt9qBJ3QExTOG8W1ZJkWAzo1RoCdF6YLsWJCCGEKKJSo800btyYFStWoCgKZrMZHx+5WUkIIWqayWTCarWydruFJRsiyMjXck23YB6Z1BBf76KTKDnyj2D4505QbPhGPI1m2Rbw8obJ94Om9H8PJrsNo91GfZ+AIi35QgghLp1Kj/MOoFKpJHAXQohaIDk5mT179nI8M4Tv/vJCp9Xx6KRoRlxdtJsMgNOcjmHrZLDn4RX7IPqfjoKiwLgpxQ4LeaHMs/3dw7xlgiYhhKhOFxW8CyGEqFmKorBnzx6Sz6Ty+7567DyuJTpCz7N3Ny62mwyAYjdi+GcKTmMiugZj8f7XB7IzoHMv6Hp1uY6bYTYAUM9bJmgSQojqJMG7EELUUVarFZ1Ox/FUPQt+C6fArGZgt2AeLaGbDIDitGPYcT+OnJ1oQnvimz8C1f4lEB4F4+8ocVjIC2Waz7a8y+yqQghRrSR4F0KIOsbpdHL06FFOnUrgrxNN2bTbik6r4ZFJ0YwsoZsMuFrpTXtmYE9Zjdq/GX6RT6P6eC5odXDbg+Bd/m6QmRZXy7t0mxFCiOolwbsQQtQhBoOBnTt3YrI4+H5bBEdPFxAdrueZuxvTLKb04Nt86DWsCd+g8q6Pf/tPUX/4ATgdrhb3Bo0qVI6Msy3v0m1GCCGqlwTvQghRh+Tm5pJp8GXeLxqsdhjQ1dVNxs+n+G4yhSzH52M5+i4qXRD+Pb9BvWQF5GZBtz7Qo1+Fy5F5ts97qHSbEUKIaiXBuxBC1HJWq5W9e/cSFtGABb9a+XOXBp1WxcM3N2BU37Ayh2q0nv4R076nQe2NX48v0Ww/Aof2QFS0a3SZCg716FQUsiwmAnReeGm0KIpyMacnhBCiAiR4F0KIWiw9PZ1du3ah9Qrm3U8zOZ3uoEG4nmfujqV5TNmt3rb0jRh3PAgqNX7dPkKb6Qe/LgO9l6ufu5d3hcuUazVjV5zSZUYIIWqABO9CCFFLWSwWdu3aRbYtmk9+tOF0OhjUPZj/3Fx2NxkAe/ZO9yRMPp3eRqfrBAufdY3nPuEuiIyuVLncY7x7yc2qQghR3SR4F0KIWiYvL4+cnBz8g+qz5lBj/j1oxFuv5sGbohl2VUi5ZjR15B7AsGUSOAx4t/4/vCKvh7kvgLEABoxwjeleSYX93cOk5V0IIaqdBO9CCFFLKIrCiRMnOHLkCN6BMcx9P5+cfDtNo715+q5YGkWVr4uLI/8oBX9PQLHl4NX8IbybTYev3oczidCiPYy86aLKme6eoEla3oUQorpJ8C6EELXEnj17yMrK4oypGV+tcAXIo/uFMW1cA7z06nLtw2FIoODvm1Csmeib3IV3q5nwxy+wayuERcCt94O6fPsqSaqpAIBIH/+L2o8QQoiKk+BdCCFqWFZWFiEhIQSGxjD/N2/2nzDg76Ph8ckN6ds5uNz7cZqSMfw9HsV8Bn2jSfi0exHV4b2wcgno9XDHw+B78QF32tngPcJbgnchhKhuErwLIUQNsdvt7N+/n9TUVHTBHZjzbToGs5O2TX2ZdUcskWH6cu/LaU6j4O8JOI2J6KKvx6fj66hSkuDLua4bVG++F+rHXHSZnYpCutmAn1aPn6785RNCCFE16kTwHh8fzzvvvENiYiLt27fnscceIyQkpNi8mzZtYvbs2R5pPXv25Pnnn6+OogohRLnk5eWxfft2fH39OJjZjBU/pKJSwS3DI5gyMgqNpvxjrzvNKRRsHo+z4Bi6qGvx7fweqvx8mP8WWMww/Abo2LNKyp1jMWFzOmjoF1Ql+xNCCFExtT54P336NFdddRVjxozh5ptv5tNPP+Waa65h27ZtaLVFi5+SksL+/fv55JNP3GkRERHVWWQhhCiR0+lEURT0ej3B9Rrx/o92Tp7JJzRQy5O3N6JLq4CK7c+UTMHmG3EajrsC927zUNnsMP9tyM50zaA6eEyVlT/VLP3dhRCiJtX64P3dd9+ladOmzJ8/H4DRo0cTFRXF999/z4QJE4rdxs/Pj+HDh1dnMYUQokxGo5GdO3cSHh7O0bQw3l9qxGJT6N4mgBlTYggJ0FVof05jkqvF3XgKXf1R+Hb9ABUa+PpdSDoBca3hxrsqPINqaQr7u4fLSDNCCFEjan3wvmnTJoYOHepe9vPzo3fv3vz1118lBu9paWlMmDABvV5Pr169uOeee9Dri++babPZsNvt7mWTyQS4hmyTKb/rlsI6k3qrmy7n+lMUhdOnT7N//36iGsTw7QYtG3YmoVHDPWOjuHFQOGq1qkLn7jQmUrB5PIopEV2D0fh0ngsqLcqKr1Ht34ESXh+mPAgajavPexVJNeYDrptVC8t7OdfdlUDqr26T+qu7KltnNRK8r1u3jtdff73E9a1bt2bOnDmAKxC/sNtLREQEaWlpxW7bt29fvvnmGxRFITExkTlz5rB06VJ+//13NJqiMxK+9NJLxfaHT01NxddXJiCpSxRFIScnB6Bck9iI2uVyrj+n08mpU6fQ+DXipW8cZOXlExGi4YHrg2jWUCE9vfjPs5KozAnoD01DZT2DI+xazNFPk5+ehe8/6wncuBqnjx+ZY27DkW+AfEOVnktSXhYAGqOZVHsqcHnX3ZVA6q9uk/qru4xGY6W2q5HgvXXr1jz88MMlrg8ODnY/1+v1mM1mj/Vms7nEwDoyMtKjy8x1111HTEwMGzduZODAgUXyz5o1ixkzZriXTSYTYWFhREZGSvBexxR+g42MjJQPsDrocqy/jIwMTp06RYeOnfnlH1i8Jh1FgSE9Q5h+YwP8fIo2KJTFkbsPw+67Uazp6BqOJ7DTHFQqDfyzAdWGX1D0Xqjufpx6sXGX4IwgJ2kPKqBldCN0alf5L8e6u5JI/dVtUn91V50K3uvXr0/9+vXLlbdZs2YcPXrUI+3o0aPccMMN5T5WYGAgKSkpxa7X6XTodEX7mapUKnkT1EGF9SZ1VzddLvXncDg4fPgwiYmJ1I9pycNvHeNwggk/HzUPT2zINd2KHy2rLPbMvynYOgXs+eib3Okax12lhr3bYelnoNGguv0/0LhZFZ+Ri9VhJ9tqop6XL3qN57+Py6XurlRSf3Wb1F/dVNn6urhp9qrBjTfeyPfff09iYiIA69evZ+/eve7gfcOGDR4t7V9//TX5+fnu5S+//JKCggJ69OhRvQUXQlyx4uPjycnJweHbnic/yedwgol2cX588n8tKx2421J+o+DvSWDPx7vlf/FpN9sVuB/dDwvfd2W65T5o2b4Kz8STe2ZV34qNiCOEEKLq1PobVm+55RbWrFlD+/btadWqFXv37uX111+nTZs2AJw5c4bVq1e78zudTtq1a0d0dDS5ubmcOXOGTz75hLi4S/MTshBCgOun64SEBKKioohs0JjFf+rZsCMdtRruuC6KicMi0Kgr18piTVyCcdejoDjxaf8KXk1ud604fhg+fwccdrjxzioby70kycY8ABr4Bl7S4wghhChZrQ/e1Wo1X375JUePHiUpKYnWrVsTFRXlXj9gwAB+/fVX9/Ktt97K+PHj2bNnD15eXrRo0UL6rgshLimz2czu3bsxGo2k53nz5qJM0nNs1A/T8393NqJNk8oNq6goCpaj72E+9CqotPh2/QB99FjXyhNH4NM3XZMwjboZrip6T09VO22Q4F0IIWparQ/eCzVv3pzmzZsXSY+KiioypruPjw89e17aFighhAAwGAz89ddfREZGsS8lmkXLUtw3pT44IbpSN6UCKE4rpt1PYE1cDBo//Lp/gi7ibIB+4gh88oYrcB95EwwcWYVnVLIz0vIuhBA1rs4E70IIUZvY7XbMZjN+fn7ExnXg3WV5HD6VedE3pQI4rTkYt9+NPeMvVN4N8O+5EE2Qq6sgJ4+eC9xHTIBrRlXRGZUt2ZiHRqWS2VWFEKIGSfAuhBAVlJOTw86dO6lXrx4JeQ34YFk6ZouTdnF+/N/tjYgMK35SuPJwGE5h2DoZZ0E8mqD2+PX8ErX32a6CR/bD53PAaoERN8Kg66rojMqWb7OQZ7PQwDcQjbrWj3UghBCXLQnehRCiAuLj44mPjyemcXO+Wadiy74k1Gq4fVQUk4ZFoNFUfqg2W9oGjP/eh2LLRhs1DL8uH6DSnr1nZ882+OoD182po26utq4yhZKlv7sQQtQKErwLIUQ5OBwONBoNWq0Wn7AOPP1ZFrkFDmIivZg5pRGtGlf+xnhFUbDEf4D54MuAE6+4+/Fu83+uyZcAtqyHZZ+5nt94F1w14GJPp8JOFWQD0Mg/uNqPLYQQ4hwJ3oUQohSKonD69GkOHTpEtx59WLJRzeot6QBcP6Aed4+tj7e+8t1IFLsR465HsCWvAI0Pvp3moI8eU3hwWPczrFwCGi1Mvh86dK+K06qwhIIcQIJ3IYSoaRK8CyFECWw2G3v37iUrK4uAei25//XjpGbZqBes47+3xtCt9cVNVuTIP4ph+7048w+h9o3Fr/tn525Mtdvhu8/hn42g94I7HoYW7S7+pCopoSAHFdDQL6jGyiCEEEKCdyGEKFFGRgZOp8LxvBYsWZ6FosA13YJ56OZoAnwv7uPTmrAE496Z4DChDR+Ab9cPUOvPjlBjLIAF78GxgxAUAnc+Cg0bX/wJVVKe1Uy21UR93wC8NPJvQwghapJ8CgshxHmcTieHDx8mNDQUoyOYeavyOJGcjb+Phv9MjL6oISABFLsB456Z2JKWgUqDd6uZeDWffq5/e/oZ+PRtyEiBhk3gzkdcAXwNKuwyE+tfs+UQQgghwbsQQrgVFBSwc+dOVGo1u0/58fkvR7E7FLq19ufxWxsRHqy7qP3bc/di3H4fTsMxVD4N8OvyAdqw8yaU27sdvv0YzCZo3w0mTgUv74s8q4t3PD8LgMYSvAshRI2T4F0IIXBNurR582ZCw2P4ap2Gvcey8dKpuG98NGP6haFSVX4ISMVpw3J0LuYjc0Cxo40ahm+nOee6yTgcrptS1690LQ8ZC0Ovh1oynnp8XiYAzYLCargkQgghJHgXQlzRLBYLmZmZREXVJ1/TjncWZGCxWWkZ68OTtzciJvLiWr4deYcx7nwIR+4e0Pji02Y2+sa3nfsykJcDC/8Hxw+Drz9MmgatO178iVURq8NOQkE2ATovIrxlZlUhhKhpErwLIa5Yqamp7N69m8DgCF7/1sDeeCNajYo7R0dx85CLm3BJcdqxHP8Y86HXwWlBE9oT387voPFrfC7T3u2wZL7rBtWYJnDbgxAafvEnVoVOFmTjUBTiAi/u1wchhBBVQ4J3IcQV6ciRI5w8eRKjKpZ3v7FithppHuPDE7fF0DTa56L2bc/eiWn3Ezjy9oHaC++2z+LV9J5zN6WaTfDDV7Bto2u571DXrKnai+tTfykcyc0AoFmgdJkRQojaQIJ3IcQVxWAw4Ovri9orhHWHnGw7ZEarUXHHdVHcPDQC7cW0ttvyMB18FevJBYCCJuwqfDu8jiag+blMxw7Ct59AVjoEhsDN90DL9hd9XpfKgexUAFoHR9RwSYQQQoAE70KIK4SiKBw7doz4+Hgs+jZ8/FMuZouTZg29eeK2RsQ1rHxru6I4sZ1ejmn/iyiWVFT6ELzbPIs+ZsK5riaGfPjp23Ot7R17wPg7XP3ca6k8q5lEQy71vP0I9/ar6eIIIYRAgnchxBXAZDKxc+dOTGYrf5+IYdPebDRqmDIqkknDIi+qtd2etQ3Tvmdx5OwEQB9zE95tnkbtdbabiaLAjs2w4msoyIeAIBh7qyt4r+V9yA/kpAHQNjhC+rsLIUQtIcG7EOKypigKTqdCZoE3n6zyx2C20zTamxm3NaJZTOVb2x3GRMwHXsKW/CMAmuBO+LR9Hm1Yj3OZEk+4gvbjh13Lva6BkRPAp260Yu/OPANA25CoGi6JEEKIQhK8CyEuSzabjX379mFz6vh2oze7j2pQqxVuHRHJLcMj0GkrN4a605yK+ehcrKcWgtOKyrsBPm3+D1309ahUZ/eZmwUrl8L2Ta7l+jFww+3QpEXVnFw1MNisHMxJxU+rp0VQvZoujhBCiLMkeBdCXHYyMzPZtWsX+WZvFvzhS77RQbOG3jw2OYYWjXwrtU+nJRNL/PtYTn4ODjNo/fFu/h+84qah0p7dZ0G+a6Klv34Dq9XVReba8dC9X62ZcKm8dmedwaEodA5rgKaOlV0IIS5nErwLIS4riqKwe89B/j4SyB97dei0au4aHcmEIZUbScZpTsFy7BMsJ78AhwE0Png1m45Xs/tQ60NdmYwFsP5X2PQbWMyuIR8HjYZrRoH3xQ07WVO2piUA0DW8YQ2XRAghxPkkeBdCXBYKCgqIjz/O9pP1WLzWH6dTRftmfjx2S8NKzZLqyD+K5dg8rEnLwGkFtRf6pnfj3exB1N5nh03MyoA/V8PW9eeC9n7DYOAoCAyu0vOrTokFORzPzyLKx5+4gNCaLo4QQojzSPAuhKjTFEUhISGBffsPsDcxkLV7nPh4abh3bH1G9glDrS5/a7uiKDgy/8Zy/BNsKasBBbSBeMVNxavJ3eeC9lPxsGEV7PnHNZqMVgdXD4FB10FQyKU50Wq0/sxxAPrVbyqjzAghRC0jwbsQok47fOQYBw4f57stYaTkeHFVu0AenhhNeIi+3PtQbPlYk5ZhObkAZ/4RAFTe9fFqei9esbeg0gW4ZkXd8oerlT3BFdziHwhXD4beg1zPLwPppgK2pyfhq9XRIzymposjhBDiAhK8CyHqpLS0NI6cVjHvezMZOeH4+eiZdWcDBnYNLldrsaIoOHJ3Yz21yNU1xmEEQBPSFa/GU9BFj0Gl0sHJo/DPt7BrK1gtro2joqHfcOjSG3Tl/5JQF/ySeAgnCoMbNMdLI/8ihBCitpFPZiFEneJwOPh35z5On07m+3/COJPtxeAeYdw/vgFB/mV/pDlNp7EmfY81cSnOgqOuRI0P+thb0DeegjawHSSdhF++g93/QHaGK49eDz36Qc8BENus1k+wVBmHctL4N+M0QXpv+tdvUtPFEUIIUQwJ3oUQdYbRaGbtH3+RnKGwclcEgQG+vPJAND3alt5lxWnJwJayCtvpFdgzNgEKAJrgzuhjxqOrPxZ1Uips3AX7voCMVNeGKhU0belqYe/cq86OHFMeBpuVRcd2AXBjkw7opdVdCCFqJfl0FkLUeoqisD8+h7nL0lDbfDme7suEwRFMvjYSL33xY5A7TcnYzvyK9cwvODK3Ak4AVD4N0Dccjz5wGJozRvhzFxx+EszGcxvHNoNOPaFjDwi6/EdbsTudLDiynSyLia71oukYVr+miySEEKIEErwLIWq1jOwC/ti4naR0B/GJ9WjfLJyP7mlI4/qewz8qigNH9i7s6euxpf6OI2ene53Kuz76wKHozK3QpNlRbT8MGXPObazTQ5tO0LoTtO4IIVfOjKJWh4MFR7dzKDedBr6BTIzrVNNFEkIIUYpaH7ynpqaybds293KvXr0ICwsrc7tDhw6RmJhImzZtiI6OvpRFFEJcAoqi8PvmY+SmHeFgki+7E8N5fHI0w64KdQ//6DQlY0v/E3vaH9jTN6LYsl0b2zXobK3QK53QFISgSslGlX0aOH3uAFENIa61K1hv1vqyu/G0PM4Y81h4dAeJhlwiffy5v00vuUlVCCFquVr/KX3ixAnmzZuH0+nk119/5Y8//mDAgAEl5nc6ndx66638+uuvtG7dmt27d/PMM8/wxBNPVF+hhRAXJTndzNwlyThNZ0jLC6NVs/rMn1KfAHUitsTV2DO34MjaitOQgNrkg9roj94YhtbeGU2+H+oc09k9ZbgeKhU0aARxrVwBe5OW4B9Qk6dYo9LNBtYnH2NTykmcKDQPrMcdLboRoPeq6aIJIYQoQ60P3q+66ip+/vlnzGYzPj5l3yy2cOFCVq9ezd69e4mOjubPP/9kwIABjBw5krZt21ZDiYUQlWW1K3z7azyW3OMcP+lLx/oJTBt+hkjrAZy/HMKSb0Zt8kFn9sXbUB+1qTkq54WjvpigXhTENHE9GjaB6NjL+mbTshjtNpIMOZzMz2Z/dirH87MA8NFoGRHTir71m6BRFX/vgBBCiNql1gfvFbVs2TLGjRvn7irTt29fOnbsyHfffVeh4P1Adire5qr9Z69cotwV2a9SoUJcmjJUVHnLrKCQW5BDstZBeQbxq9B1q0DeCl23S3ThlEtYIxUpc5nlUBQwm1Dy00lPPIk5146/bwhhHOGhlqdR2/SkbteRpjQAGpzbzAfwAby8XTOaBp59BIVASBic34KsKJCbBrnlKe+lU7HXW0VeQwoWhwOr04HVYcdy9m+B3UqOxUSO1YzBbvXYJtTLh96RjekdGUuATlrbhRCiLqmR4P306dPs3LmzxPUhISFcffXVldr3sWPH6Nu3r0daXFwcx44dKza/zWbDbre7l00m18/t849sQ+sl/9TqpLSaLoCojHo+gcShZru/E2O9lkDLCu7BCvmprodw06s1RPr409A3iIb+QbQKCqeBb6B7IiulCr5BKorifoi6R+qvbpP6q7sqW2c1ErwfPXqUefPmlbi+VatWlQ7ebTYb3t6eo1B4e3tjtVqLzf/SSy/x/PPPF0nvEhCB/oL9lKTiU7WUf4sK77sCG1zKKWZUl3DvJe1ZASxmM17e3hd19IrNvVP951lhigKKE5WigNMJTicqpxOV0+F+juIEp8P1/Lw8nM2ncjrA4UDlsKNyOMBhR+Wwn5dmB6dSoXIrKgVF4wCfYGxOO2aHCZW3D1dpQ3D61EPx9kfx9kHRaC7iulzK3BVzqfatUqnQqdTo1Rp0KjU6tRq9SoOPRkugRo+XWuM542yBmbQCc5WWQVEUcnJy3OURdYvUX90m9Vd3GY3GsjMVo0aC9wEDBpR60+nFCA8PJzXVs+UtLS2N1q1bF5t/1qxZzJgxw71sMpkICwtjktmBr8rhSizPN6Pi8lRqu+L2U2bCRZTxUu27+s9DQaEgPx//gIDiA6XKnoeCOwB2/b3w4Sz+ufO8ZTj33FncvopbBs4G2JwNmN3PzwbTRf5ekKZyOss+54ukaEHROFE0NhS1FUVrcz10Zx9aG06dDcVLiyowHIIacMbeiB/2ReEfUp9AlQZ1UAuuHxRLdlY6kZGR8g+ojilsPZK6q5uk/uo2qb+6q04F71UpOTmZHTt2MGrUKMDVx3316tW89NJLABQUFPDXX39x9913F7u9TqdDp9MVSVcv+RSVrs5fniuKCih9ns3LmEYDao3rr0br6vNduOz+qwadDnR6FK0OtBpQKygaQO1AUVlRsKKozCiKCQUDilKA05mP4sxDIQdF40DR2FG0rr9oHOc1KatReUei9olG7dcCjV8T1L6xqP2aoPZrjEofSlKalfeXnmbHoTym9DuDjQAGDOhCgwhfFEVBpVK5H6Jukbqr26T+6japv7qpsvVV66NTk8nE77//js1mA+Dvv/+moKCALl260KBBAzZu3MjEiRPd3zz/85//8NlnnzFlyhSGDBnCp59+SsuWLbn++usrdFzlmlFwfp/38lzgyuap1HbF7afYDavgWOXMU8PnoQD5eXkEBAa6chdbxnIc7MLtVKqzj7OjcajV56WdTb9wWX3BNsWll7qPsw+12hWMq9UoajWoHCiKBRQLimJBUcwoThM4jCgOI4rd4HpuN7gettzzHnkez1FsxV3o4mn8UHvVQ+VVD413fdQ+9VH5NHAF6t71Ufs0QOUVgUpd/EdKgcnB18vP8OOGdCKDzESHBxHXqitXdYgofxmEEEIIUfuD9/z8fHf/+JEjR/LXX3/x119/MWPGDBo0aEB0dDQjR45052/QoAFbt27l3XffZfny5fTv359HH30UrbaCpzr0evD1rcpTEZeaomBMTSUgMrJCHdcVxQmKHZx2UBwois3V9USxo7jT7ectO1yBr2JHcTpAMYPiAOfZ9We3ceW1gdOK4rSC04xit4LDcnbZguKwnF1vBuf568yuvw4LisOM4jCCw+g6zsXS+KDyqodKF3j2EYxKF+QKzvX1UHmFuZ+rvcJQ6cNQaSv3XnA4FFZuzmLBTyloVUZu6pVFUIAPI4Y2R6/TlL0DIYQQQnhQKXJ7sgeTyYSvry/pfz+Ir3dhd5rzLpH7cp1Lcw/r5nEpi+Yrbtui+UvKV/ljFDvsXBUf49y2F25X8j4UCvuDn//37AOn69eUs89dfxXPfDjPBt7n0pwOO2o17ryKx/aF/cov2Gdtp9Kh0vqBxheV1heVxhe0fqj+v717D4ryvvc4/t5dVm4iIigQDQIGIUG84QW8JCrq0VjNzTnFniYTzdTEdrRtpm0mTW92Jk2nf3Q67TRtEqs9NZOJR0/EG4qJ0TRETIoRNSZCNWq8Aotcd7nuPuePlT0igkCE5dHP64+wz233S37u8OHHd3+PLQTLta/e7WDvtu/cUCz28GvhfNC1x4OwWPvmTqKfnqzl1S2XOHOpgdQRdWSlVZOYeB9jHki66Z8KDcOgtLRUfZsmpLEzN42fuWn8zMvlchEaGorL5erSvYxa9fuZd39pPv8/NAXqTdD/Wa+1nFi9jwHDsIHFisVixWKxgSWg/XkW77WW1m2r9zyLxe57jCXA2wbS+hxW+/8/nyUArAFYLNeOX3/Meu3a1mO2QCzWQLAGeoOzLcj71RqIxXbtqzUQbjxmHeC91mKuGerzpY289s4lCo7XEGR3My4pjOULo4mLDSY8PNzf5YmIiJiawnsHQqdsJDgkCF9PdJvfZi3X/Zcbjrc/r82+zs67yWu0vbbdg1vU1/uv0fl5nT+HNzi39oW3BuvW/m8rlpvsu/68G2cYNPvgX7WuFjbmlpJzwIHbA5OTmpiZXEFmRjxDhgzxd3kiIiJ3BIX3DgQMnY5dPe8it+R2G+zMr+DvO69Q43QTHgpPzq4nNKCG8eMnKriLiIjcRgrvItIjhmHw0dEa1m27zPnSRqwWWDQjgmXzIrhy4RQPPPAQgbpLsYiIyG2l8C4i3fbZaSevb73EiS+9N5iYmBLKE5mNDAqtI3ZoHLFDJ/i5QhERkTuTwruIdNlXVxpYt+0yHx2tAeC+EUEsXxRBS20JzQ0GcfcrtIuIiPQmhXcRuaWK6mb+e9cVdh+8iscD0UPsLF8cS9bkwRw6VEBUVBSjR4/GarX6u1QREZE7msK7iHTI1eBm07vlbNlXTkOTh7AQG/+1MJqFGWFcvHAOCCcjI0OhXUREpI8ovItIO03NHnZ8WMFbe8qoqmthgN1C9vyhLJsfTb3zKgUHPyQqKgqPx9P9uxeLiIhIj+mnroj4tLgN9hRc5c3cUsqrmrFY4D8yInj6GzEMGzKAixcv8tlnnzFmzBiGDx/u73JFRETuOgrvIoLbY/D+v6r4R+4VLpU3ATBzQjhPfyOG+NggampqaGqCYcOG8eCDD3brNs4iIiJy+yi8i9zFDMPgw6Jq/r7zCucuNwIwNTWMpxfHMDouBMMw+PLLLykpKWH8+PHExMRgt9v9XLWIiMjdS+Fd5C5kGAaffF7Lhu1X+Pf5egDGJYWyYkksY0aFAtDc3Mynn35KfX09mZmZhIeH+7NkERERQeFd5K5iGAaHv6jjH7lXfDdYuj8+hBVLYpiQPBCLxQJ4g3tAQADDhg0jLi4Om83mz7JFRETkGoV3kbuAYRh8cqKWjbmlfHHWG9oThwexfHEMmWmDfKG9paWFEydO4HQ6mTZtGgkJCf4sW0RERG6g8C5yBzMMg4LjNbyZW0rxV972mPtGBPHkwzFMGzsIq9XiO7eyspIjR44wcOBA0tPT/VWyiIiIdELhXeQO5PEYHDxWw8bdpZy61tOeHBfMtx+ObjPTfr2ysjJGjRpFXFzcTY+LiIiI/ym8i9xBPB6D/KPVbMwt5cuLDYC3p/3Jh6OZkhrWLpS7XC6OHz9OWloaycnJ/ihZREREukHhXeQO0NziYd+/qnh7bxnnS71LPqYmhvDUwzGk3z+wXWg3DMN3w6WRI0cSFBTkj7JFRESkmxTeRUysvsHNro+usnlfOY6qZsC75OO3F0a3WT3mRpWVlRQXFzN58mQiIyP7smQRERH5GhTeRUyoqraFrQccbPvAQa3LDcCMceFkzx/K/QmhHV7ncDgAiIqKYtasWVoCUkRExGQU3kVMpLSiic37ysn9qILGZgObFRZkRvCfc4cxMrbj1he3201xcTHnz59n7NixAAruIiIiJqTwLmICJV+5+N/3HbxfWInHA0GBVp6YE8nSOUMZNmRAp9cahkFBQQFWq5WZM2cSEhLSR1WLiIjI7abwLtJPeTwGhz6rYcu+co7+2wnAoFAbj82O4pEHowgf2Pnb1zAMqqurGTx4MKmpqQwePFhLQIqIiJicwrtIP1Pf6CbvUCXv7C/nYlkTACOGBbJ0ThTzMoYQNMB6y+doaGjg6NGjNDQ0MHPmTCIiInq7bBEREekDCu8i/UR5ZRM5HzjYlX/V9yHUCckDeWLOUKamhrW5G2pnysrKKCoqIjY2lvT0dKzWW4d9ERERMQeFdxE/MgyDk2ddbD3g4MDhKtweCLBZmJ8RwROzh3LfvcFdfi63243VasVmszFu3Diio6N7sXIRERHxB4V3ET9obPKw/3AV2z5wUPJVPeDtZ188M5JHHooiMtzereerqqriyJEjpKamMmzYsN4oWURERPoBhXeRPnTZ0cj2f1awu+AqtU5va8yoEUE88mAUWVMiutTPfj3DMDh16hSnT58mOTmZoUOH9kbZIiIi0k/0+/C+d+9eVq5c6dt+++23ycjI6PD83bt3s2rVqjb7srKy+Nvf/tZrNYp0xuMx+NcXtWz7wMEnJ2oxDG9rzJxJg3nkoShSE0N6vApMY2MjDoeD6dOnExYWdpsrFxERkf6m34f3GTNmcODAARobG0lJSaGhoaHT851OJzabjX379vn2aV1r8Yfquhb2flzJjn86uFjuXTUmarCdb8yIZNH0IQzpZmvM9S5cuEBdXR0pKSlkZmberpJFRESkn+v34T0kJIT4+Phbhvbr2e124uPje68okQ4YhkFRiZPcjyr4sKia5hYDgHFJoTw6K4ppY8MJsPV8rfXm5maOHz/O1atXGT9+/G2qWkRERMyi34f3nrhw4QJjx45lwIABZGZm8rOf/azDlTeam5tpaWnxbbtcrjZfxTwMw8DlcuFyufr8ZkSVNc2890kleYcqueTwzrIPDLIxNzOcRZmRjLwnCICmxnqavsbrFBYWYrPZmDx5Mna7/Y76d+rP8ZOvR2Nnbho/c9P4mVfrz3DDMLp1ncXo7hW3QU5ODj/4wQ86PD5hwgS2bt3aZl9DQwPBwcHs37+fWbNmdXity+WirKwMwzA4f/48v/71r7l06RKFhYU3bZ/51a9+xdq1a3v6rYiIiIiI9NiVK1e6tbyzX8K70+mkvLy8w+OBgYHExsa22dfV8H6juro6IiMjycnJYeHChe2O3zjz7nQ6GTp0KA6HQ73yJlNfX09kZCQVFRUEB3d9fXTpHzR+5qWxMzeNn7lp/MzL5XIRFRVFXV0doaGhXb7OL20zoaGh3Sry675WYGAgTqfzpsftdjt2e/sPDoaEhOhNYFLBwcEaOxPT+JmXxs7cNH7mpvEzr+7eCd30903fsWNHmw+n/va3v+XkyZOAd1b95z//ORaLhenTp/upQhERERGR26Pfh/cLFy4QHx9PcnIyANnZ2cTHx7Njxw7A2+Zy7tw53/kZGRksW7aMyMhIBg0axM6dO9m2bVu7NhwREREREbPp96vNxMTEcODAgXb7W+8kuXjxYs6cOePbP2vWLI4cOUJVVRWBgYHd/hNSQEAAv/zlLwkI6Pf/a+QGGjtz0/iZl8bO3DR+5qbxM6+ejp1fPrAqIiIiIiLd1+/bZkRERERExEvhXURERETEJBTeRURERERMQp9uuIWLFy/y3nvv0djYyLRp0xgzZoy/S5IuamlpYd++fZSUlBAXF8fChQsZMGCAv8uSLjhz5gybNm3ybX/zm98kISHBjxVJRwzDYPfu3RQXF5OQkMDixYux2Wz+Lku64N133+Xw4cOAd53pn/zkJ36uSLqjoaGBvXv3cu7cOZKSkpg/f3631wsX/3E4HOzdu5fq6mrS09OZMmVKl6/VKHfi9ddfZ+7cueTn53Pw4EGmTZvG2rVr/V2WdEFJSQnjxo3jD3/4AyUlJfziF78gNTWV0tJSf5cmXdDc3ExVVRVVVVW8+OKLFBcX+7skuQnDMHj88cf53ve+R3FxMS+88AJz585tc9dq6b/q6+upqqri8OHD/PSnP/V3OdINhw4dIi0tjfXr11NSUsLq1auZOnUqdXV1/i5NuiAnJ4cZM2awd+9ejhw5wsMPP8zKlSu7fL1Wm+lEYWEhEyZM8M0ibdmyhW9961vU1NQQFBTk5+qkM2fOnMHj8TBq1CjAGwbHjBlDdna2fgEzGYvFwu7du1mwYIG/S5EbbN++nezsbE6fPk1sbCyVlZWMHj2a3/3udyxfvtzf5UkX7dy5k0cffVS/dJnI559/TkREhO8eNjU1NSQmJvLKK6/wne98x8/Vya0UFRWRkpLiy5KHDh0iMzOTs2fPMnLkyFter5n3TkyaNKnNn39DQkIICAjAYrH4sSrpioSEBF9wB7Db7URERNDU1OTHqkTuLLm5ucyZM8cXICIiIli0aBG5ubl+rkzkzvbAAw+0ufnkoEGDCA4O1s84kxg/fnybSeCQkBAsFkuXWw7vup73Dz74gIKCgg6Pjxs3joULF/q2W3tvy8vLycvL44033iAwMLAvSpWb+OMf/4jL5erw+FNPPcU999zTbn9eXh5FRUW88cYbvVmedKK4uJitW7d2eDw6OlqztSZz/vz5drNE9957L++++66fKhK5O61fv57a2loee+wxf5ciXeRwOFi3bh3V1dXk5uby+9//nhEjRnTp2rsuvLf2+HV2/HqtvbfV1dXU19dz8uTJXq5QOlNdXY3T6ezwuNvtbrevoKCA7Oxs1q1bR1paWm+WJ51ofS91JCQkpO+KkdvCMIx2f4m0WCyoG1Ok72zfvp0f/vCHbN68+aaTV9I/ud1u32e7GhoaKC4uxu12d2n2XT3v3XDmzBlGjRrFwYMHycjI8Hc50gXvv/8+TzzxBK+++irLli3zdznSA+p5779WrlxJWVkZOTk5vn3PPPMMlZWVvPPOO/4rTLpFPe/mtWnTJp577jm2bNlCVlaWv8uRHqqsrCQuLo4NGzawdOnSW56vnvdOnDhxos126xJMWorJHHbt2sXjjz/Ohg0bFNxFesG8efPYt28fFRUVADidTnJzc5k/f76fKxO5823YsIFVq1axY8cOBXeT+br5UjPvnViwYAEhISGkpaVRWVnJli1byMzMZPPmzQrw/dzHH3/MzJkzmT17NrNnz/btT0lJ4dFHH/VfYdIlTqeTP/3pTwC8+OKLLF++nNGjR7No0SK1PvUjHo+HefPmceXKFZYsWUJeXh42m438/Hx9NsgEjh07Rm5uLidPnmTjxo28/PLLAHz/+98nODjYz9VJZ7Zt28Zjjz3G0qVLmThxom//5MmTFeRNYMWKFVy+fJn09HTq6+vJyclh+PDh5OXldem9d9f1vHfHnj172LNnD4WFhSQmJrJ582amT5/u77KkC4KDg3n++ecB2vRZd9YvL/2HYRi+cXvhhRcA7zhqJYX+xWq1kpeXx6ZNmyguLmbNmjVkZ2cruJtEY2MjVVVVxMTE8OMf/9j3ntOcXv83ZMgQ3021rv8Zd+Pn9qR/Wr9+PR9++CH5+flERETw2muvkZWV1eXVDDXzLiIiIiJiEur9EBERERExCYV3ERERERGTUHgXERERETEJhXcREREREZNQeBcRERERMQmFdxERERERk1B4FxERERExCYV3EREBYMmSJeTn5/u7DBER6YTCu4iIAPDyyy+TlpbWZ6+3aNEiCgoK+uz1RETuBAH+LkBERPqHvgzuAEePHqW6urpPX1NExOw08y4iIkD7tpkFCxawa9cuVq9ezaxZs3jmmWe4ePFim+Pbt29n1apVzJ49mzVr1lBZWek7npGRwenTp33bX3zxBdOnTwfg2WefpaysjDVr1jBp0iR+9KMf9cF3KCJifgrvIiICwLFjx6iqqvJtFxUV8dxzz5Gens7atWu5fPkyTz75ZJvjy5cvZ+zYsbz00kucOnWKuXPn4vF4ACgsLKS+vt53vtPp5PDhwwA8//zzREREsHr1av7617+yatWqvvkmRURMTm0zIiLSoZdeeomnn34agPDwcKZMmYLb7cZmswGwevVqX/CeOnUqw4cPZ//+/WRlZXX6vMnJydjtdpKSkpg0aVKvfg8iIncSzbyLiEiH7rvvPt/jwYMH09zc3GY2PT093fc4LCyMpKQkSkpK+rRGEZG7icK7iIj0WEVFRbvtyMhIAOx2Oy0tLb5jtbW1bc61WCy9X6CIyB1G4V1ERHrsz3/+MzU1NQC89dZbOBwOHnroIcA7a//ee+8B0NTUxF/+8pc210ZERFBeXt63BYuImJzCu4iI9Nj9999PUlISSUlJPPvss2zYsIHo6GgAfvOb37B27VpSUlJISEggPDy8zbUrVqzgu9/9LhMmTNBqMyIiXWQxDMPwdxEiIuJ/x48fJy4uzheyjx49SmJiImFhYYB39vzYsWNMnDgRq9VKTEwMb775JjNnzuTs2bPEx8cTGBjY5jmdTidfffUViYmJuN1uTp48ycSJE33HHQ4HFy5cICwsjFGjRvXdNysiYlIK7yIi0iOt4X3u3Ln+LkVE5K6hthkREREREZPQzLuIiPTIjW01IiLS+xTeRURERERMQm0zIiIiIiImofAuIiIiImISCu8iIiIiIiah8C4iIiIiYhIK7yIiIiIiJqHwLiIiIiJiEgrvIiIiIiImofAuIiIiImIS/wff1V0i45eywgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "knee   0.5: shape(1) = 1.000000   small-signal slope =  1.082   asymptote = 2.1640\n",
+      "knee   1.6: shape(1) = 1.000000   small-signal slope =  1.736   asymptote = 1.0850\n",
+      "knee   2.0: shape(1) = 1.000000   small-signal slope =  2.075   asymptote = 1.0373\n",
+      "knee  12.0: shape(1) = 1.000000   small-signal slope = 12.000   asymptote = 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.linspace(-3, 3, 1201)\n",
+    "fig, ax = plt.subplots()\n",
+    "for i, k in enumerate([0.5, 1.6, 2.0, 12.0]):\n",
+    "    y = np.tanh(k * x) / np.tanh(k)\n",
+    "    ax.plot(x, y, color=C[i], lw=1.4, label=f\"knee {k}\")\n",
+    "ax.plot(x, x, color=\"0.7\", lw=0.8, ls=\"--\", label=\"linear\")\n",
+    "ax.set_xlim(-3, 3); ax.set_ylim(-1.5, 1.5)\n",
+    "ax.set_xlabel(\"input\"); ax.set_ylabel(\"output\")\n",
+    "ax.set_title(\"shape(x, k) = tanh(kx)/tanh(k) — one family, soft knee to near-hard corner\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "for k in [0.5, 1.6, 2.0, 12.0]:\n",
+    "    y = np.tanh(k * x) / np.tanh(k)\n",
+    "    print(f\"knee {k:5.1f}: shape(1) = {np.tanh(k)/np.tanh(k):.6f}   \"\n",
+    "          f\"small-signal slope = {k/np.tanh(k):6.3f}   asymptote = {1/np.tanh(k):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14ae489a",
+   "metadata": {},
+   "source": [
+    "## 2 · Gain staging — where the first implementation was wrong\n",
+    "\n",
+    "Look at the slope column above. The tanh family's small-signal gain is `k/tanh(k)`, which is\n",
+    "**greater than one and grows with the knee** — about 2 at the stock second-stage knee, and 3\n",
+    "at the knee the first draft used.\n",
+    "\n",
+    "That is easy to miss in a single stage and fatal in a cascade. The first cut of this kernel put\n",
+    "a ×2.2 fixed gain in front of a knee-3 curve, so the second stage saw an effective ×6.6 and was\n",
+    "*already fully clipped with the gain knob at zero* — the knob did nothing over most of its\n",
+    "travel. It sounded like a distortion the whole way, which is exactly why listening did not\n",
+    "catch it and a measurement did.\n",
+    "\n",
+    "Below: the harmonic-to-fundamental ratio across the knob, after retuning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "69e2f549",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:45.546190Z",
+     "iopub.status.busy": "2026-08-17T22:20:45.545971Z",
+     "iopub.status.idle": "2026-08-17T22:20:45.943821Z",
+     "shell.execute_reply": "2026-08-17T22:20:45.942449Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gain 0.0 -> 0.010   (edge of breakup)\n",
+      "gain 0.5 -> 0.284\n",
+      "gain 1.0 -> 0.358   (37x the harmonic content of gain 0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def harmonic_ratio(gain, f0=220.0):\n",
+    "    y = pedal(gain=gain).process(tone_in(f0))\n",
+    "    f, m = spectrum(y)\n",
+    "    fund = at(f0, f, m)\n",
+    "    harm = np.sqrt(sum(at(f0 * k, f, m) ** 2 for k in range(2, 9)))\n",
+    "    return harm / fund\n",
+    "\n",
+    "gains = np.linspace(0, 1, 11)\n",
+    "ratios = [harmonic_ratio(g) for g in gains]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(gains, ratios, color=C[0], marker=\"o\", ms=4)\n",
+    "ax.set_xlabel(\"gain\"); ax.set_ylabel(\"harmonics / fundamental\")\n",
+    "ax.set_title(\"the gain knob, after retuning: a real sweep from clean to saturated\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"gain 0.0 -> {ratios[0]:.3f}   (edge of breakup)\")\n",
+    "print(f\"gain 0.5 -> {ratios[5]:.3f}\")\n",
+    "print(f\"gain 1.0 -> {ratios[-1]:.3f}   ({ratios[-1] / ratios[0]:.0f}x the harmonic content of gain 0)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9dc70b",
+   "metadata": {},
+   "source": [
+    "## 3 · The even harmonics\n",
+    "\n",
+    "A symmetric static curve is an odd function, so it can only produce odd harmonics. DAFx-07\n",
+    "points out that a real op-amp stage clips *asymmetrically*, which is where a pedal's even\n",
+    "harmonics come from — and that is the whole reason `asymmetry` exists here.\n",
+    "\n",
+    "The bias is applied inside the curve and corrected at the stage output, so an asymmetric pedal\n",
+    "is still *exactly* silent on silence: no DC pedestal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f8eb603c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:45.945953Z",
+     "iopub.status.busy": "2026-08-17T22:20:45.945746Z",
+     "iopub.status.idle": "2026-08-17T22:20:46.346155Z",
+     "shell.execute_reply": "2026-08-17T22:20:46.344844Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "symmetric  (0.0): even/odd = 0.00000  <- odd-only, to the noise floor\n",
+      "asymmetric (1.0): even/odd = 0.54841\n",
+      "and silence is still exactly silent: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def even_odd(asym, f0=220.0):\n",
+    "    y = pedal(gain=0.8, asymmetry=asym).process(tone_in(f0))\n",
+    "    f, m = spectrum(y)\n",
+    "    even = np.sqrt(sum(at(f0 * k, f, m) ** 2 for k in (2, 4, 6, 8)))\n",
+    "    odd = np.sqrt(sum(at(f0 * k, f, m) ** 2 for k in (3, 5, 7)))\n",
+    "    return even / odd\n",
+    "\n",
+    "asyms = np.linspace(0, 1, 11)\n",
+    "ratios = [even_odd(a) for a in asyms]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(asyms, ratios, color=C[2], marker=\"o\", ms=4)\n",
+    "ax.set_xlabel(\"asymmetry\"); ax.set_ylabel(\"even / odd harmonic energy\")\n",
+    "ax.set_title(\"asymmetry is what puts even harmonics in the spectrum\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"symmetric  (0.0): even/odd = {ratios[0]:.5f}  <- odd-only, to the noise floor\")\n",
+    "print(f\"asymmetric (1.0): even/odd = {ratios[-1]:.5f}\")\n",
+    "q = tap.Fuzz(sr, smooth_ms=0, gain=1.0, asymmetry=1.0)\n",
+    "print(f\"and silence is still exactly silent: {bool(np.all(q.process(np.zeros(4096)) == 0.0))}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a43f58d",
+   "metadata": {},
+   "source": [
+    "## 4 · The tone stack\n",
+    "\n",
+    "Three linear filters entirely outside the nonlinearity — a low shelf, a high shelf, and a mid\n",
+    "scoop whose depth is `contrast`. On this class of pedal the voicing section is most of the\n",
+    "identity, which is why it is a first-class part of the object rather than an afterthought."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d265bab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:46.348463Z",
+     "iopub.status.busy": "2026-08-17T22:20:46.348235Z",
+     "iopub.status.idle": "2026-08-17T22:20:47.267302Z",
+     "shell.execute_reply": "2026-08-17T22:20:47.265690Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def response(**tone):\n",
+    "    # Measure the voicing at a gain low enough that the curve is near-linear, so what the plot\n",
+    "    # shows is the tone stack rather than the distortion's own spectrum.\n",
+    "    rng = np.random.default_rng(3)\n",
+    "    x = 0.02 * rng.standard_normal(int(2.0 * sr))\n",
+    "    y = pedal(gain=0.0, **tone).process(x)\n",
+    "    f, m = spectrum(y, skip=0.25)\n",
+    "    fr, mr = spectrum(x, skip=0.25)\n",
+    "    keep = (f > 30) & (f < 16000)\n",
+    "    # smooth in log-frequency for a readable curve\n",
+    "    db = 20 * np.log10(np.maximum(m[keep], 1e-12) / np.maximum(mr[keep], 1e-12))\n",
+    "    n = 257\n",
+    "    return f[keep], np.convolve(db, np.ones(n) / n, mode=\"same\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i, (label, kw) in enumerate([(\"flat\", {}),\n",
+    "                                 (\"contrast 1\", dict(contrast=1.0)),\n",
+    "                                 (\"bass +1\", dict(bass=1.0)),\n",
+    "                                 (\"treble +1\", dict(treble=1.0))]):\n",
+    "    f, db = response(**kw)\n",
+    "    ax.semilogx(f, db, color=C[i], lw=1.4, label=label)\n",
+    "ax.set_xlim(40, 15000); ax.set_ylim(-20, 20)\n",
+    "ax.set_xlabel(\"frequency (Hz)\"); ax.set_ylabel(\"gain (dB)\")\n",
+    "ax.set_title(\"the voicing section, measured through the object\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a682340",
+   "metadata": {},
+   "source": [
+    "## 5 · Aliasing — and a house pattern that did not survive contact\n",
+    "\n",
+    "A static curve generates harmonics without limit, so anything above Nyquist folds back. The\n",
+    "clipper pair therefore runs oversampled, with an anti-image filter on the way up and a matching\n",
+    "anti-alias filter before decimation.\n",
+    "\n",
+    "Two things about the house pattern (`tap.ladder~`, `overdrive.h`) did not survive being measured\n",
+    "here, and the second took two attempts to see.\n",
+    "\n",
+    "**The 4th-order Butterworth is not steep enough.** Fold energy at 4× came out *worse* than at 2×\n",
+    "(1.7e-2 against 2.8e-3). Eighth order improves 4× by about 6×, and this file uses it.\n",
+    "\n",
+    "**The single zero-stuff-by-N is what made more oversampling worse.** Eighth order did not fix the\n",
+    "ordering — 2× still beat 4× beat 8× — and the surviving hypothesis was imaging: zero-stuffing by\n",
+    "N leaves N−1 images for one filter to suppress, at a corner that gets tighter with N (0.056\n",
+    "normalized at 8×), and residuals entering the clipper intermodulate into exactly the non-harmonic\n",
+    "products this probe measures. `ondes.h` supplied the first evidence for it, running the same\n",
+    "filters around a comparably hard nonlinearity but as a *source* with nothing zero-stuffed, and\n",
+    "never reversing.\n",
+    "\n",
+    "Acting on it confirms it. The chain is now **cascaded 2×**: one zero-stuff-by-two and one\n",
+    "8th-order Butterworth per doubling, each cutting at 0.225 of its own output rate — a corner that\n",
+    "never gets tighter however deep the cascade goes. The reversal is gone at every tone below.\n",
+    "\n",
+    "**And the old conclusion was generalized from one probe.** Every earlier number came from\n",
+    "3733 Hz, where 2× happens to look best. Swept across tones, 2× collapses above about 6 kHz — at\n",
+    "10.5 kHz it is *worse than no oversampling at all*, because the clipper's low harmonics already\n",
+    "exceed the base Nyquist and one doubling does not move them out of the way. That is why the\n",
+    "default is now **4×** rather than 2×.\n",
+    "\n",
+    "Measuring this correctly took three tries, and all three mistakes are easy to repeat:\n",
+    "\n",
+    "* **The test tone must not divide the sample rate.** At 3 kHz into 48 kHz, every alias folds\n",
+    "  back exactly onto a harmonic of the input and is invisible.\n",
+    "* **Probes must sit far from the fundamental**, or they measure window leakage from it rather\n",
+    "  than aliasing.\n",
+    "* **One tone is not a sweep.** The probe only measures folding at all when harmonics 8–13 exceed\n",
+    "  Nyquist, and which of them fold — and how badly a given factor handles them — depends entirely\n",
+    "  on where the tone sits. A conclusion from a single tone is a conclusion about that tone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5d6dcfdf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T22:20:47.269561Z",
+     "iopub.status.busy": "2026-08-17T22:20:47.269299Z",
+     "iopub.status.idle": "2026-08-17T22:20:49.124354Z",
+     "shell.execute_reply": "2026-08-17T22:20:49.122949Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 691.2x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   tone         1x         2x         4x         8x\n",
+      "   3067  7.813e-02  9.985e-04  9.116e-04  8.904e-04\n",
+      "   3733  1.228e-01  3.003e-05  2.136e-05  2.172e-05\n",
+      "   4409  1.410e-01  8.555e-07  3.669e-07  3.697e-07\n",
+      "   5171  1.485e-01  3.310e-04  2.036e-07  1.925e-07\n",
+      "   6421  8.466e-02  3.180e-02  3.935e-07  3.616e-07\n",
+      "   7211  1.026e-01  8.912e-02  4.541e-07  4.341e-07\n",
+      "   8123  9.009e-02  7.788e-02  1.116e-03  2.007e-05\n",
+      "   9337  9.103e-02  8.096e-02  1.785e-06  2.386e-08\n",
+      "  10499  1.492e-01  1.725e-01  1.171e-05  1.587e-06\n",
+      "\n",
+      "probe floor (nearly clean): 4.8e-11 to 1.5e-09\n",
+      "worst step-up ratio past 2x: 1.017  (1.0 = flat; >1 would be a reversal)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Harmonics 8..13 only fold at all above ~3 kHz, so the sweep starts there.\n",
+    "tones   = [3067.0, 3733.0, 4409.0, 5171.0, 6421.0, 7211.0, 8123.0, 9337.0, 10499.0]\n",
+    "factors = [1, 2, 4, 8]\n",
+    "\n",
+    "def alias_energy(os, f0, gain=1.0, edge=1.0):\n",
+    "    y = pedal(gain=gain, edge=edge, oversample=os).process(tone_in(f0, amp=0.5))\n",
+    "    f, m = spectrum(y)\n",
+    "    total = 0.0\n",
+    "    for k in range(8, 14):\n",
+    "        fold = k * f0\n",
+    "        while fold > sr / 2:\n",
+    "            fold = fold - sr if fold > sr else sr - fold\n",
+    "        if abs(fold - f0) < 1000:      # skip probes that would read leakage, not aliasing\n",
+    "            continue\n",
+    "        total += at(fold, f, m) ** 2\n",
+    "    return np.sqrt(total)\n",
+    "\n",
+    "table = np.array([[alias_energy(o, f0) for o in factors] for f0 in tones])\n",
+    "\n",
+    "# The probe's own floor: the same measurement with the pedal nearly clean.\n",
+    "floor = np.array([[alias_energy(o, f0, gain=0.0, edge=0.0) for o in factors] for f0 in tones])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7.2, 3.4))\n",
+    "for row, f0 in zip(table, tones):\n",
+    "    ax.semilogy(factors, row, marker=\"o\", ms=4, lw=1.2,\n",
+    "                color=plt.cm.viridis(0.1 + 0.8 * tones.index(f0) / (len(tones) - 1)))\n",
+    "ax.axhspan(floor.min(), floor.max(), color=\"0.85\", zorder=0)\n",
+    "ax.text(4.2, floor.max() * 1.15, \"the probe's own floor (pedal nearly clean)\", fontsize=8.5, color=\"0.35\")\n",
+    "ax.text(1.05, table[-1][0] * 0.55, \"3.1 kHz (bottom) to 10.5 kHz (top)\", fontsize=8.5, color=\"0.35\")\n",
+    "ax.set_xticks(factors)\n",
+    "ax.set_xlabel(\"oversample factor\"); ax.set_ylabel(\"energy at fold frequencies\")\n",
+    "ax.set_title(\"cascaded 2x: more is never worse, and 2x is not enough above 6 kHz\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"{'tone':>7}  \" + \"  \".join(f\"{o}x\".rjust(9) for o in factors))\n",
+    "for f0, row in zip(tones, table):\n",
+    "    print(f\"{f0:7.0f}  \" + \"  \".join(f\"{v:.3e}\" for v in row))\n",
+    "print(f\"\\nprobe floor (nearly clean): {floor.min():.1e} to {floor.max():.1e}\")\n",
+    "worst_ratio = max((table[i][j + 1] / table[i][j]) for i in range(len(tones)) for j in range(1, 3))\n",
+    "print(f\"worst step-up ratio past 2x: {worst_ratio:.3f}  (1.0 = flat; >1 would be a reversal)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c8447d8",
+   "metadata": {},
+   "source": [
+    "## Checkpoint\n",
+    "\n",
+    "- One tanh family with an adjustable knee, normalized so full scale in is full scale out at\n",
+    "  every knee — monotonic, odd, bounded.\n",
+    "- The gain knob sweeps clean to saturated for real, after a first cut where the cascade's\n",
+    "  compounded small-signal slope had the second stage clipped at zero.\n",
+    "- `asymmetry` is the even-harmonic control, and it costs no DC: silence stays exactly silent.\n",
+    "- The voicing section is three linear filters outside the nonlinearity.\n",
+    "- Oversampling buys orders of magnitude against none, with an 8th-order filter because the\n",
+    "  house 4th-order one measured worse at higher factors, and a **cascade of 2× stages** because\n",
+    "  a single zero-stuff-by-N was what made 4× and 8× measure worse than 2×. With the cascade the\n",
+    "  sequence never reverses. The default is 4×, because the old default of 2× was generalized\n",
+    "  from a single test tone and collapses above about 6 kHz.\n",
+    "\n",
+    "Every number above lives twice: as a cell in this notebook and as a pinned scenario in\n",
+    "`tests/fuzz_test.cpp`."
+   ]
+  }
+ ],
+ "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/ondes.ipynb b/notebooks/ondes.ipynb
new file mode 100644
index 0000000..8f3f3e6
--- /dev/null
+++ b/notebooks/ondes.ipynb
@@ -0,0 +1,943 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fdeb42d4",
+   "metadata": {},
+   "source": [
+    "# tap.ondes~ / tap.triode~ — the heterodyne voice, and the valves after it\n",
+    "\n",
+    "The two last pieces of the Ondes Martenot, and the two the family plan got wrong before the\n",
+    "sources were read.\n",
+    "\n",
+    "The plan assumed a `vco.h` descendant with waveform registers. The instrument is nothing of the\n",
+    "kind. It is **heterodyne**: two oscillators near 80 kHz, one fixed and one moved by the ribbon,\n",
+    "summed into an amplitude-modulated signal whose envelope is the note. Najnudel, Hélie, Roze &\n",
+    "Boutin (*\"Simulation of an ondes Martenot circuit\"*, IEEE/ACM TASLP **28**, 2651–2660, 2020)\n",
+    "model instrument No. 169 as five port-Hamiltonian stages and measure those oscillators at about\n",
+    "**0.03 % second-harmonic distortion** even coupled to the rest of the circuit. So the character is\n",
+    "not in the oscillators at all — it is in the demodulator, the two triode stages, the intensity\n",
+    "key and the diffuseur.\n",
+    "\n",
+    "This notebook measures three things, in order of how much they changed the design:\n",
+    "\n",
+    "1. **What the demodulator actually makes.** Not a sinusoid — the plan's biggest error.\n",
+    "2. **Whether the closed-form detector really replaces the 80 kHz simulation.** It does, to 0.10 dB.\n",
+    "3. **What the valves add**, from a published tube model with parameters fitted to the actual\n",
+    "   valves in No. 169.\n",
+    "\n",
+    "Every number comes out of the shipping C++ through `tools/capi`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "59605a4d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:04.682620Z",
+     "iopub.status.busy": "2026-08-17T12:27:04.682433Z",
+     "iopub.status.idle": "2026-08-17T12:27:05.083911Z",
+     "shell.execute_reply": "2026-08-17T12:27:05.082512Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tube parameter sets, from TASLP 28 (2020) Table II — fitted to ondes No. 169's own valves:\n",
+      "  6F5: mu=98  ex=1.6  kg=2614  kp=905  kvb=1.87  vct=0.5  va=0.33  rgk=1300\n",
+      "  6C5: mu=20  ex=1.5  kg=2837  kp=138  kvb=89  vct=0.8  va=0.33  rgk=1300\n",
+      "  2A3: mu=4.3  ex=1.5  kg=1685  kp=43  kvb=102  vct=-1.2  va=0.33  rgk=1300\n"
+     ]
+    }
+   ],
+   "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, 3.4),\n",
+    "    \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+    "})\n",
+    "C = tap.PALETTE\n",
+    "sr = 48000.0\n",
+    "\n",
+    "def goertzel(y, f, fs, frm=None):\n",
+    "    # single-bin magnitude, so a measurement never depends on FFT bin alignment\n",
+    "    if frm is None:\n",
+    "        frm = len(y) // 2\n",
+    "    seg = np.asarray(y[frm:], dtype=float)\n",
+    "    w = 2 * np.pi * f / fs\n",
+    "    c = 2 * np.cos(w)\n",
+    "    s1 = s2 = 0.0\n",
+    "    for v in seg:\n",
+    "        s1, s2 = v + c * s1 - s2, s1\n",
+    "    return np.sqrt(max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2 / len(seg)\n",
+    "\n",
+    "def harmonics(y, f0, fs, n=5):\n",
+    "    h = np.array([goertzel(y, f0 * k, fs) for k in range(1, n + 1)])\n",
+    "    return h[0], 20 * np.log10(h[1:] / h[0])\n",
+    "\n",
+    "print(\"tube parameter sets, from TASLP 28 (2020) Table II — fitted to ondes No. 169's own valves:\")\n",
+    "for i, name in enumerate(tap.TUBE_NAMES):\n",
+    "    p = tap.tube_params(i)\n",
+    "    print(f\"  {name}: \" + \"  \".join(f\"{k}={v:g}\" for k, v in p.items()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9382d4",
+   "metadata": {},
+   "source": [
+    "## 1 · The demodulator is the instrument's biggest source of harmonics\n",
+    "\n",
+    "The circuit paper writes the oscillator sum as an amplitude-modulated sinewave:\n",
+    "\n",
+    "$$\\cos\\Phi + \\cos(\\Phi - \\varphi) = 2\\cos(\\Phi - \\varphi/2)\\,\\cos(\\varphi/2)$$\n",
+    "\n",
+    "so the envelope is $2|\\cos(\\varphi/2)|$ — and $|\\cos|$ is emphatically **not** a sinusoid. Its\n",
+    "Fourier coefficients are $(4/\\pi)/(4n^2-1)$, which puts the second harmonic 14.0 dB below the\n",
+    "fundamental, the third 21.3 dB down and the fourth 26.4 dB down.\n",
+    "\n",
+    "That series exists **before any valve touches the signal**. The family plan said to synthesize the\n",
+    "difference tone directly as a sinusoid, citing the paper's own simplification — but what the paper\n",
+    "replaces with a sinewave generator is the *oscillators*, not the demodulator. Synthesizing the\n",
+    "difference tone would have thrown away the largest single contributor to the instrument's timbre."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d958b3cc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:05.087274Z",
+     "iopub.status.busy": "2026-08-17T12:27:05.086879Z",
+     "iopub.status.idle": "2026-08-17T12:27:05.408170Z",
+     "shell.execute_reply": "2026-08-17T12:27:05.406919Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FBbDZbACAqqoq3HPPPThx4gTsdjs8Hg9cLlenm2R9ccstt6C8vBwvvPACDAYD9u3bh5dfftnb6ElPT4dKpcKbb74Ji8WCffv24Y033ujXOZYtW4aysjKsX78eTU1N0Ol02Lp1Kx555JE+H6NjAZ6mpibvd1PHAjX+OH5vx+jp/AAwe/bsXvNO/vrXv0ZlZSXq6+vxy1/+EldddRUSExN9ir+3960rv/jFL/DCCy/gq6++gslkQlVVFV566SVs3Lixy/1NJhNkMhnUajWam5uxcuXKbnswu3PzzTdDr9dj9erVuOeeeyAWi3vcf/ny5fjuu+/w3HPP4dZbb/UutORrPD0dr7/lQfzIj8NSSRj7/vvvWU5ODhMKhd75CaNGjWIffPCBdx+j0dhpXpZer2e/+tWv2LBhw5hCoWAzZsxgn3/+ebfn6G3/3s7HGLegCAA2bty4fh//wnlhtbW1bOnSpSwqKorFxsayu+++27uwTEc8Tz31FJs5cyZTKpVswoQJbO/evX0+34V6O19f5hh29T4xxtgVV1zBEhMTvY+3bt3KALD333+/XzFcqLdrrKqqYtdddx1TKpVs5MiR7LXXXus0x7CkpITNmjWLyeVyNmbMGLZhw4ZOcwyff/55FhcXxyQSCZs5cyb7xS9+0em6epvL19P12Gw29oc//IFlZmYymUzGxo8f3yn2trY29sADD7Dk5GQWGRnJHnzwQWY2m7s8b2+Pe7uOrt63vnz+zv//0Nv1EEIC48I5huXl5Wzq1KlMKpUyhULBPB4P+9e//sXGjRvHpFIpy8rKYq+99lq3x+tu8RnGGNu7dy+bMWMGUygULCMjgz3//POdFjjbunUry8zMZCqVil199dXs8ccfv2iOYcf3MWMXz99jjLGioiK2ZMkSFhsbyyIjI9lNN93EioqKuj3Ghc6ePctWrFjBYmJimEajYddddx0rKSnx+fj9jbG38ycnJ3sXXblQx/n/+te/suHDhzOVSsUWLVrU53MzdvEcQ8Z6ft+6K9OtW7eyqVOnMpVKxdLT09mvf/1rptfru4z7+PHjbPLkyUwsFrPExET2pz/9iUkkEnbs2DHGWOe5ph0unAvLGGNr1qxhALpcRKYrV155JQPA9uzZ0694Lpxj2Nvx+lsexH94jPVjbXhChpCOdBW33XZbsEMJGzU1NUhJSUFbWxu0Wm2wwyGEEDKEFRYW4p577sGhQ4eCHUpIeuyxx/Djjz92SitFhjZhsAMghBBCCCHE33Jzc6lR2I2qqips2rQJb7/9drBDISGE5hgSQgghhBAyRKxatQrZ2dlYvHgxbrzxxmCHQ0IIDSUlhBBCCCGEkCGOegwJIYSQMPPjjz/iiiuuQFxcHKZOnYrt27cHOyRCCCFhjhqGhBBCSBhpbm7G1VdfjenTp+OHH37A3XffjcWLFyM/Pz/YoRFCCAljNJSUEEIICSOvvvoqXnrpJVRUVHiTZF9xxRUYPXo0/vKXvwQ5OkIIIeFqSKxK6vF4oNPpIJVKvT+ihBBCBi/GGGw2G7RaLfj8wTU45ujRo5g+fXqn37MZM2bg+++/73J/p9MJl8vlfdyRAFylUtFvIiGEDAF9/U0cEg1DnU6HqKioYIdBCCEkwFpaWhAZGRnsMPxKr9cjLi6u07aIiAjodLou91+/fj3Wrl0bgMgIIYSEst5+E4dEw1AqlQLgCkMmk/l8HMYYGhoaEBcXF/J3WcMpViC84qVYB044xRtOsQLhFa8/YrVarYiKivJ+/w8mCoUCBoOh0za9Xg+lUtnl/mvWrMETTzzhfWyxWBAdHY3m5uZL+k1csOqEz6/1BQ8MqdE2VDVLwRC4z/BXr4z16XVUPr2jMuoZlU/vAllG4Vg+HaxWK6Kjo3v9TRwSDcOOioVMJrvkhmHHMcKhYhUusQLhFS/FOnDCKd5wihUIr3j9GWuoX6svxowZg82bN3falpeXhzFjxnS5v0gkgkgkumi7XC6/pN9EvtD31/qCBwahiDtvICtlcrncp9dR+fSOyqhnVD69C2QZhWP5dOj4LeztN3FwTbwghBBCBrmf/OQnqKiowJtvvgmXy4Uvv/wS3377LX76058GOzRCCCFhjBqGhBBCSBhJTU3Fp59+iueffx5SqRQrVqzA66+/jpkzZwY7NEIIIWEs6ENJXS4XBAJBn4f7MMZgt9sH5bwRQgghpC+uv/56VFRUwGKx+GWYESGEEBK0HsO9e/fi6quvhlarhUKhwPz583HmzJkeX/Pss89Co9FApVJhwoQJyMvLC0ywhBBCSAiiRiEhhBB/CVrD8F//+heefPJJtLS04OzZsxCJRLj11lu73X/jxo3YsGEDdu/eDZPJhCuvvBILFy6EzWYb8FgZYzA7HbC6nHB5PPAwNuDnJORSeRiD0+P2/jH63JIw4GYeONxu2D1uWFwO+twSQgghARK0oaR/+9vfvP+WSCS4//77sWjRIrhcLgiFF4f1j3/8AytWrMDEiRMBAOvWrcObb76J7du3Y/HixQMaq4t5cLCmFtUNNridNkS0WBCnlSBaJYFaLIVGLIVcKB7QGAjpjdPjhs5uhdFpR6vFjga9DUaLC0ajGaoGC5QyARIipIiQSaESS6AVSyHiC4IdNhnizE4H9A4bjE4bmox2NOrtsNk90BtMyHE4cFlqKkQ8+pwSQgghAy3ocww7bN++HRMmTOiyUQgAJ0+exIMPPuh9rFAokJ2djZMnT17UMHQ6nXC5XN7HVqsVANfz58vdZ5fHg5pGG7Z+39K+xczFIOEjLVGK7BQZxmWokahUIVqqAD8ElkfvuNZwudseTvGGWqx6hw2NVhMKz+px6owFFbU2NOudF+xlAQDwAERpRMhMliInTY5RyRrEyZRQi0Njzm6olW1PwilWILTidTMPmm0W1JmMOH7GgNOVVlTV22C2ezrtp1Qq4E72QMjr/+CWULhOQgghJJyERMPwgw8+wMaNG7F79+5u9zGbzVCr1Z22aTQamEymi/Zdv3491q5de9H2hoYGn3I2Wd0u6PSWi2Oye1BQbkFBuQVf729FbroYU3NkSFMrECGUBDV/FmMMOp0OQHjk8QqneEMlVrPbiVqrGXkVFhwrsaPN6On1NQxAs96JZr0TP+QbEalqwvhsCSYOkyNBqoBccHGus0AKlbLti3CKFQiNeBljaHXaUGk041CRDUWVDthd3TfgDAYz6hsbIOX3/6eq44YgIYQQQvom6A3Dd999F4888gi2bduGSZMmdbufWq32Vmo6tLW1QaPRXLTvmjVr8MQTT3gfW61WREVFIS4uzqeGocXlwFVSIWaOcaGuqQ18sRwtehdqm+worbGi1eCC3cWQV2pHfrkT00YzzJsoQZY2ImhDTDvulsfFxYVNpRUIj3iDHavL40GNWY+Dpw347rAercZzveMSER+ZyTKkxUsQrRVBqxDCajFBKlfCYHGhodWB6gY7ympssDs9aDV68N1RK06UuXDlZD6mZyuQotBAwA/O9ONgl21/hFOsQPDjNTsdOK1rwc48Ew4XGOF0n2sQRmlEyEySIilGgiiNCCqZEHaLEXExkYjVREEm7P8NC2oYEkIIIf0T1IbhW2+9hTVr1uDrr7/GtGnTOj1nt9tht9u9vYQTJ07EgQMHcNdddwEAdDodiouLvXMOzycSiSASXVyR4PF4PlWIFCIJRkfFgzGGBp4QsbGxsHvcMLkc0NmtKDyrx+ECI/LPmOF0e7D3uB5FFRbcMNuCqcNiEC9TBaUi1nG94VBpBcIr3mDFanDYcKqxCV/tb0ZBxble7IQoMaaNVmPcMDXilAqoRBLIhCLwwUNDQwPi4uLgAYPF5YTRYUeTxYyjpQYcyjegvtWBZr0TH+9sQkG5BQtnxSA3OhoqkSSg19aBPgcDJxjxMsZQZzHihzON+HJvi/dGBg/A6EwFJueqkJukQYREBqVIDEl772BDQwPionxvxIbLe0IIIYSEiqA1DF966SU899xz2LJlC3Jycry9gRqNBjweDy+//DLefPNNVFRUAAAeeeQR/OQnP8G8efMwbtw4PPPMM8jMzMRVV10V8Nh5PB5kQhFkQhFipAqkKSMwMc2E/No27DjcitIaK5r1Tmz+qh6Vk+24flIshqkig9YLQwaHeosRB8404NNdzTBb3QAArUqIeVMiMDU7EolyNRSizj3U58+zEvD4UIkkUIkkSFSokamNxKxcAw4Wt2LXjzroTC6cOmNGZb0dN19hw4yMOMTKlAG9RjK4uDwelOlbsO1IA/Yc06Pj05iTKsNVUyIxMj4CsTLlRYsg0fxAQgghJPCC1jB86623wBi7aOGYgoICJCYmQiqVdppTuHjxYrz22mt45pln0NLSgunTp+O///1vt4vVBJJYIECyQoPYDCWy4lTYW9iM7QdbYbV7sOtIG+qa7Lj1cgdGR8dCLAh+vCS8MMZQaWrD9mMN+OaHVngY19syZZQK106JRnZElE+9e0qRBMO1MYifoMLoYSpsO9iMH4uMMFpceOe/DWie6cTVY+OQotBS7wvpN7vbhZNNjfj37nqcruKGdSqkAlw7IxKzR0QjWaGhVXEJIYSQEBK0VkpxcXGPz69atQqrVq3qtO3ee+/FvffeO4BRXRqxQIBMdRSixssxLF6Gj3c24myzHUWVFvxzWy3uvNaD8XFxkAZ5gQ8SPjyM4YyhBf/ZX4/9J/UAAJmYjyWXR2NmdiwS5epLXgVXLZZifEwCYq6UIyu5CVv+1wy704Mv97ZAZ3RhyQwPhqkiQ2K1XRIerC4njtU34J2v61Df6gAApMRKseyqWIyOi4EmRFbBJYQQQsg51H01ALQSGaYlJ0K7SIzP9jTgeIkJZ5vs2PhVHe66jmFSQrxPiymQocXDGEp0zfj4+zocLTYCAGK0Ytx2dSwmJsZCK+n/Qkrd4fN4SFFqce0oCaLUIny4oxGtBif2HtfD4fLgltkMmeooahySXllcThyuqcM72+vR0p42ZWKOCjfOjsNwbTTEAuolJIQQQkIRTXobIBKBEKOj4nDnlUmYOZZbObWxzYHN2+qQ19AAu9vVyxHIUMbaewrPbxQmxUiw4oYEzEhJ9Guj8HwREhlmpSfhvoWJSIji5iseyjfi4+9rUW5ooblfpEc2txPH6urxr//WeRuFc8Zr8JMrkjAyMpYahYQQQkgIo4bhAOLzeMhUR+HWWYm4YnIEAC6H3Dtf1+FEUyOcHneQIyShqtzYis/2n2sUpidIsWJBIibGDXxvs1wowoT4ePx0QRJSYrm5i0cKjfj8h3pUm3UDem4SvhxuN443NOJfX9d7Vx69emoEbpqRhAwaikwIIYSEPGoYDjAej4c0ZQQWTUnAZeO5nsO6Fgfe/7YOp3XN8FAPDLlAncWAr4824MBJAwCup/CuaxIwJjouYIsXSQRCjIuJwz3zz/Ucfp+nx3+P1qPOYghIDCR8eBhDUVsT3vumDo1t3JzCKyZpccPkRKSpImjxIkIIISQMUMMwALjGoRaLp8VjSq4KAFBRZ8One+pRYWwNcnQklLTZrfhfcQN2HGoDAMRGiHH3dQkYHR0b8BUcxQIBRkfH4s5rExCp5noptx9sxZ6SBujslDycnFNubMWn39ejutEOAJg+Wo1FUxOQotAEOTJCCCGE9BU1DAOEx+NhmDoKN89OwPBUbn7YsWITvj7agHqLMcjRkVBgcTlxuLoBW3Y3gwFQyAS4/eo4jI0NXpoTiUCICfFxuOu6eCgkAjAAW3Y148jZRlhdzqDEREJLRw/3iVIzACA3TY4bZ8YjTUk9hYQQQkg4oYZhAPF5PGRqorDsinjERXLD83YcbsPB8kYYnfYgR0eCye3xIL+5CR/taIDd6YGAz8OtV8ZgYmJs0NObyIQiTEyMxc1XRUPA58Hq8OCjbxtQ2NoEN/MENTYSXAaHDXtLGrCzvYc7PlKMW66IQ6Y6ihqFhBBCSJihhmGACfl8jIyOwW3z4iCT8MEY8J9dzchvbKLFaIawSlMbtu5pRKuBW7RjwaxIzMyIgzpE8r2pxVLMzIzDtdMjAXCLKH2+rxHVJn2QIyPB4vS4cbKxCVu/b/H2cN92dRxGRcVCwKefFkIIISTcUB7DIJAKRBifEIOFl9nx8Y5GGCwufPq/RkReL8VwbXSwwyMB1mwzY9epJuSXc0PxJuaocOXoOMTIlEGOrLM4mQrzxsWiusGGk2VmHC8x43+JjVCPlyBSIg92eCSAGGM4o2/FZ7sbYba6wQOwZG4UJiTEQhKkYc/+1NLSgmuvvbZP+0ZHR+Prr78e4IgIIYSQgRf+v+BhSiuRYfbwGFTV2XAw34DSGit25DUiYpo05BoEZODY3E4crWnC9gPcULwYrRgLZ8QgRakNbmDdSFVqsXi2DWebqtFqcOG/+1uREiPFrLSkQdEgIH3TYDXhm2NNOFNrAwDMHKvBzMzQ6eG+VHK5HCtXruzTvjLZwOQUJYQQQgKNanJBlKTQYMF0O6ob7DjbbMeuH3XITpFjTrqUKtlDANfr0oYt/2uC0+2BUMDDzVfEICcyOmRzvgl4fORGRePGy23Y9GU97E4P/vN9ExIXyzEiIibY4ZEAsLqcOFrdjN0/cjczkmMluH5qLBLkqiBH5j8ymQzLly8PdhjdcjqdOHv2bKdtQqEQycnJQYqIEELIYECtjyDi83jI1kZh8Vwb/rblLFxuhs+/b0ZypBwjtDG0eMMg12QzY0deM+pauLxv86ZEYGJKzIAnsL9UcqEYU9NiUTHJhp2H21DTaMeuE02ImiZHjFQR7PDIAGKM4YyhDV/saYaHARIRHzdfHossTeSQ+b5qaWlBbW0t3G5uTrhIJMKoUaMCGkNZWRlyc3ORlpbm3ZacnIy9e/cGNA5CCCGDCzUMg0wiEGJ8YhSumGTGt4faUNNkx868ZkRNlyOWhpQOWna3C3lnm/H9UR0AIC1eiivGRodNwypWpsRV46NRXGFBTZMdu47okZPajNmpkqCl1iADr8lmxs6jTahv5W5mXDklAmPio4fEe37q1Cncc889yMvLg8fjAY/HA2MMaWlpqKioCEpMpaWlEAoHf9kTQggJDFo6LgTEyJS4clwMUmK5+Tm7f9ThZF0LHG5apXSwOmNoxRd7muH2MIgEfCyaHY10VUSww+qXDHUkFl7GpbBwuj34fE8zKoxtwQ6LDBC724VjNc34Po9biTY9QYorxkQjSjo0Fh665557cNNNN6G8vBxxcXFobW3FDTfcgPXr1wctJqPRiLY2+j9HCCHEP+hWY4gYpo7A4jkWvPkfbkjpfw+0ID1aiUx1VLBDI37WarPg+1MtqGnicldePkmDMYnh1+siEQgxPikacyaYsetHHSrrbdhT0IKYSUpESGhBjsGmyqTDV/tavDczFs6ORpoyvG5m+MpisaCwsBBPPvkkmpub4XK5oNVqsW7dOtx///244447LvkcDocDtbW13T7P5/ORmpoKgBu+mpaWhkmTJqGxsRERERH405/+hNtvv73L1zqdTrhcLu9jq9UKgBsazBjzOWYefH+t7+djAT+vr2VE5dM7KqOeUfn0LpCxhmP59PcY4VUTHcQkAiHGJERh1jgT/ndUh7KzVhwobkHMOMWgWemPcInsi5pbsfuIDgCQFC3BFePCZwjphWKkCsybEIPCcgvqWx3YdViHMcNUmByfGLIL6JD+0zus2FvQjOpGbhXSyydpMCYhGmKBIMiRBYbBYIBarQafz0dkZCSMRiPsdjuUSiUaGxv9co7S0lJcf/313T6vUCiQn58PAMjMzPQOX2WMYePGjbj77ruRkZGBadOmXfTa9evXY+3atRdtb2houKRVVdNjrD6/1jcMcRpH+78D9/3S0NDg0+uofHpHZdQzKp/eBbaMwq98OnTcEOwNNQxDSIxUgSvHR+FkqQmtBhe+/aENI1OVGB+bQJXsQaLWYsC3h1tgc3rAA3DdzEgMU4dvrwuPx8MwdQSum2HEpq/qYba78e2RFiRfqUKiQh3s8IgfeBjD6dY27Gy/mREbIcblYTQf1t+EQiEmTZqEVatWobGxEePGjfPLcUeOHOnTXEUej4cVK1bg7bffxn//+98uG4Zr1qzBE0884X1stVoRFRWFuLi4S2oYVjTV+/xaX3Tcpa9skoEFsFIWFxfn0+uofHpHZdQzKp/eBbKMwrF8OlDDMAzxeDxkaiJx7XQjPviGS3y/42gLEueqED+IloIfqqwuJ45VtSCvxAQAmJCjwviUKEgFob0KaW9kQhEmpkXhaJYRJ0rN+LHQiAnDWxCVJae0K4NAvcWIHT82w2zl5jxfNz0C6WF8M8MXERERePfdd72P//GPf+Dxxx+HQCDAq6++GvB43G43BOf11jLG0NbWBoWi68a6SCSCSHTx9wyPx7uk1WQDWTE6h9c+kCtw5/a1jKh8ekdl1DMqn94FvozCq3z6ewyqtYUYhUiMqZnRyEs3obDCgkP5Rkwe0YqoNDlE/KExbGuwqjLqsP1AKwBAJuHj6imRgyb3W6JcjWumRqG40gq704OvD7RgeLwKWZroYIdGLoHD7cLJ2lYcLjACAEZnKDBpWDTkIZ5Sxd8EAgESEhK8j3Nzc/Hll1/C5XKhuLg44PGsW7cOIpEI1113HQDgjTfeQH19PW699daAx0IIIWTwoFVJQ1CSgqtkC/g8uD0MO4+0oc5iDHZY5BIYHDYcPN2K2vachVdMikBOVBQEvMHxX1DA52NEdBTmTNAAAKob7fihpBVGpz3IkZFLUWsxYOfhVjAGiIV8XDMtConyoTdEuLm5GVdddVWftw+03/zmN+DxeFi5ciV+/vOfw+1248CBA0hPTw94LIQQQgaPwVErHWREfAFGxEZg6iiuN6mg3Iy8qhbY3M4gR0Z8wRhDhUGH3T/qAAAxWjFmj4wcdCt3RknlmDMmEpEqbiDCd0d0qDLqghsU8ZnV5cSPFa0oqeHmJcwYo8aI6AgI+fSz0aGlpQUajSbg55XJZFizZg0OHjyIo0ePYtOmTcjNzQ14HIQQQgYXGkoaomJlSlw+PhJ5p02w2j3YcUiHkUkaGpoXhtrsVuw91QqdiVsu/srJWqSqtMENaoCkqSJw+WQDPtvVhFaDE3vzW5E4VT3oGsFDQbVJh+8OcznylDIB5oyLQPQQW3CmtbUV119/PZxOJ1pbWzF9+nTvc4wxlJWVYdmyZUGMkBBCCPEfahiGKD6Ph6yICFw2Xo9vfmhDdaMNP5S0Im6cCiqRJNjhkT7yMIbStjbsbU8KnhYvxZTMCChE4iBHNjBUYgmmD4/EDycNONtsx55jekwe3oYJsVJaWTeMGBw2/HC6DbXN3NDnORO0GKaJ8MsE+HAik8nws5/9DAaDAWvXrsXPfvYz73MCgQApKSm48sorgxghIYQQ4j/UMAxhkVI5LhsdiSMFRrQaXfjuiA4TM7TIjYwNdmikjxqtJuzOa4XV4QEAXDUlAslKbXCDGmDJCg2umqrFv7Y1wGxz43/HW5FymRqxMmWwQyN9VGnQ4bsfud7CaI0Iswbh0Oe+6GgYOhwOTJ8+HTNnzgx2SIQQQsiAoYZhiEtTdx6ad7C4FUmT1JT0Pgy4mQclLTrvio4jhykwLiVy0KdwkAlFmJAWhYOpBpyusuLgKSNmjNIhOlFBvYZhQGe3Yn9hG3RGbujzFZO1SFUFfh5dKBGLxZg6dSref/995Ofnw24/t6iSWq3Gs88+G8ToCCGEEP+gVQRCnEokwdSsCMRGcEMP9+TpUWPSBzkq0hdNVjP2Hm+Dy83A43ErkQ6W9BS9SZSrceWkSPAAOF0e7D2hQ7PNHOywSB9UGfXYf9wAAEiKkWBqViSUNHwdy5Ytw+OPP44zZ86gubnZ+9fS0hLs0AghhBC/GNxdF4NEslKDORM0+OS7JrQZXThQ1IakyRpoqNcwZHX0Fh4t5pLZj8lUIDdOO2RyUYoFAoxM0CJ3mA4F5WYcLjRi9ljqNQx1bXYrDhS2wWDhegvnTNAgSTG0ewsBwGaz4YsvvkBVVRXi4+ODHQ4hhBAyIKjHMAwoRGJMyYpAfCTXa7g3T49qI/UahrILewvnjI8YcnPs4uUqzBmv9fYa7jnRRr2GIa76vN7C5BgJJg2LgGyIJbPvilAohEqlokYhIYSQQY0ahmEiSaHBZe3Jw3UmrtdQ77AGOSrSla56C0cMod7CDiJ+R68hl+LgSKEJpS06eBgLcmSkK212Kw4UnOstvGyCBonUWwiAaxguWLAA77zzTrBDIYQQQgYMNQzDBNdrGInEKK7XcF+eHjUmQ5CjIl25sLdw7vgIxMmGxtzCC13Ya7j3JPUahqpqox77TrT3FsZSb+H5WltbcfLkSdx9990YNWoUZs+e7f1bvHhxsMMjhBBC/ILmGIaRJIUasydo8fGORujNLhw+rUPqRC1UYloYIlR4GENZ67newrFZCuTEaSHkD817MOd6Dbm5hkcKTZgzTodoKc01DCV6hw0HC9tgpN7CLslkMtx///1dPieXywMcDSGEEDIwqGEYRuRCMSZlaLE7QofGNgcOnDRg5ggDVOKYYIdG2rXYzDhwSs/1FgKYPW7o9hZ26Og1LCg3w+ny4GCBHlmREYiWKoIdGmlXazbg4Kn2lUijJZiYTr2F55PJZHjggQeCHQYhhBAyoIZmN0YYS1CoMWOMGgDQ2ObA0TM6WFzOIEdFAIAxhiqDAT8WcnkLR6TLkROrGbK9hR1EfAFyEzTITuYSpB/JN6LGYACjuYYhwex04EipDm3teQtnjFUjUaEOclShafv27bj//vtxxx13gDGGN998E04nff8SQggZHIZ2jTUMqUQSTB2uhVbJdfbuP2FAvcUY5KgIAOgcNhws1MHm9AAAZo7RIG6IrUTanTiZCjPGco0Ns92NH4p0MDhtQY6KAECdxYADJ7hVjiPVIkzOjIBcKA5yVKHnlVdewfLlyyESibBz507weDyUlZXhjTfeCHZohBBCiF9QwzAMJSrUmDaaq2RXN9pwvKoVdrcryFGRGqMeP5ziGunpCVKMTtZCLKDR2gAgEQgxNjUCyTHcfNiD+QacNdENjWCzupzIq2hDXYsDADB9jBoJiqE99Lk769atw+7du/H00097t91yyy3YuHFjEKMihBBC/IcahmFII5Zi2ggtFBIu/cH+EwY0WKmSHUwGhw2HTuu8i3fMGKMe8nMLL5QgP9drqDO6cKSkDSanPchRDW0NVhP2n+TmFqpkAkzL0UItlgY5qtBjMpngcrmQk5PTaXtkZCTMZlpllxBCyOBADcMwxOPxkKrWYPIoruFRWmNFcYMeLo8nyJENXXVmo3fxjvhIMSbQUv8X4RZPikC0hiuXAydpGHQwOT1u5Ne2obyWG9I7ZZQaySqaW9gVpVIJhUKBEydOdNq+a9cujBo1KkhREUIIIf5FDcMwFSGRYVquBkIBt+T/D/kGyg8XJFaXE8crdWjWcYtQTB+jRoKcegu7kqBQYXr74kn1rQ6crNLRMOggabKa8UP7zQyJiI/pI7WIEMuCHFXoevrpp3Httdfi5Zdfhs1mw7PPPovVq1fjqaeeCnZohBBCiF9QwzBM8Xk8pEeoMTqTW/L/RIkZlXo9rfQYBE02Ew4XcD1fKrkQk7K0UIoot2RX1GIpJmdrvMOgDxUY0Wg1BTmqocfDGMrb9CgotwAAxg1XIE2jBo9yS3br4YcfxmuvvYa8vDwkJSXh+PHj+OabbzBlypRgh0YIIYT4RdBWxvj888/xt7/9zfv4n//8J2JjY7vd/z//+Q/+8Y9/dNp2ww03DOncUtFSBaaNUiPvtAlOtweHCw0YHhmFCAnd9Q8Ul8eD040GlNZYAQCTRiiRSIt39ChJpcaEEUrsPa5HcaUFZc16JCrUEPDoPlWgtNmtOFxogNvD5duclqtBlIQStffm1ltvxa233hrsMAghhJABEbSG4ZgxY/DAAw+gpqYGDz74ICwWS4/7l5WVoa6uDmvXrvVuGzZs2ECHGdJEfAFGxGsxLKEV5XU2HCk04srxRmoYBlCL3YxD+dxS/wI+D1NGaKCl8u9RpESOqbka7D+hh4cBhwoNGBlnQSyl9giYWpMBPxZxvdxZKTJkx2ogGOL5NgkhhJChLmgNw2HDhmHYsGEoKirq82tiYmJwww03DGBU4SdWpsSUUWqU19mgN7twtEyHdLUWMgEtfDLQGGOo0htxvISb2zk6U4FhkWrwaThej/g8HjKi1MhNVyC/3Iy8YhNqJhmoYRggJqcdR8v0MFndAICpI1WIkSmCHFXo279/P1avXo38/HzY7edW042Pj0dFRYVfz2U2m/Htt996H48bN67LG6Hl5eUoKipCWloaRo4c6dcYCCGEDD1hlWStqKgIS5cuhUajwZVXXonbbrutyzkxTqcTLte5BS2sVm6YH2Pskubgdbw+lObxyQRCjE/XYIeqFa1GFw7lGzEzx4h0ZUTIxdqTUCzb7nTEqXNYcbhIB4eLWw12Sq4KURJ5SF1DqJZrtESOKSOVyC83w+rw4MhpPTI1EVCJJCEZb1dCtWy70xFrg9WEQ/ncojPRGhHGpmkg4QtD6jr8Ubb+vp6bb74Z99xzD1544QWIxWLvdpHI/zfhjEYjNm3aBADYsWMHnn/+eaxcubLTPr/97W/x0ksvYdKkSThx4gQWLlyITZs20TxRQgghPgubhuFNN92EESNGwOPxoKSkBI899hi2b9/u/fE83/r16zsNOe3Q0NAAmcz3YX6MMeh0OgAIqR9fgcuBUcNE2HPChepGGw4V10CUaIPJwFX+QinW7oRq2XalI9ZWi947jDQxSoh4qQetTc1Bjq6zUC7XBLkHsRECNLa58cNJHcYmViFNpg7ZeC8UymXbFcYYmtpacbLKhdpmLqH96GFC8E02NNgaghxdZ/4o244bgv6g0+lgMpnw/PPP++2YPYmPj8eWLVsAAFlZWRc9f/jwYfz+97/HoUOHMGnSJFRVVWHs2LH4+OOPsWzZsoDESAghZPAJm4ZhRkYGMjIyvI9nzZqFGTNm4He/+x3S09M77btmzRo88cQT3sdWqxVRUVGIi4u75IYhAMTFxYVURTCWMVwmFOBQYRXsTg+Kaxjm5Cqh5fNDLtbuhGrZdoUxBpvHhap6K/RmrrdwxtgIjEhKgVwo7uXVgRXK5apwajFjtAdb97Sg1ehBlZGPsSlRAEIz3guFctl2hTGGRocFhe29hTIxH7PHxSMzNiHIkV3MH2Xrz4ahVqtFdHQ0ampqkJyc7Lfj+uqzzz7D9OnTMWnSJABAamoqFi9ejE8//ZQahoQQQnwWNg3DC3XMp6irq7uoYSgSiboc3sPj8S65AtdxjFCqCPJ4PKSo1RiTpcCRQiPyz1hRYzQhBfyQi7UnoVi23dG5HPixmEuzoFYIMTFTC0WIpqgI1XJViaWYlK3FziM6mKxuHCsyYUamBaIQjbcroVq2XfEwhrMmG4oquQbTuOFKpKpDN0XFpZatv69r48aNWLhwIe69915otVrvdplMhptvvrnX1+v1euzatavb54VCYZ/n0JeWll7Uk5iZmYmtW7d2uf9ATa/gIbDDj7nzsYCf19cyovLpHZVRz6h8ehfIWMOxfPp7jJBtGH7yySf473//601R8a9//Qu33367t8G3YcMGqNVqjBo1KphhhowoqRwTc1Q4UmiE0+3B0RI9IjJoMY+B4GEMVXoLzrSnqJiYo0S8nMraFwlKFcZlK7HvhB7FVRZUtBqQNQBztgigd9hwqsIGt4f7cZgyQo1IKaWo6Kt3330X+fn5eP311zvNMYyOju5Tw7C1tbXLqQ8dZDJZnxuGLpcLEknnG1ESiQROp7PL/QdqekV6jP96ZfuGIU7jaP934G5oNDT4NtSayqd3VEY9o/LpXWDLKPzKp0NfR9EErWF48uRJPPnkkzCbuRUd7733XshkMrz44ovIyclBaWkpdu7c6d2/trYWGRkZyMrKQm1tLYxGI95//32o1epgXUJIEfEFGJGgQXJsC2oa7ThabMaElJBt94e1VrsFp8odYAD4PGBijppSVPgoUiLH5BEq7D+hB2PAj6cNiM1RIT7YgQ1CjRYTCiq4H7RhiVJkxlDuyL6yWCzYvHkzjh8/7vPqn8OGDfPOG7xUsbGxqK+v77StoaGh21zAAzW9oqKpvved/KjjLn1lkwwsgJWyuLg4n15H5dM7KqOeUfn0LpBlFI7l0yHkG4aJiYne5PSPPfaYd3vHxd96662YOnWqd/tvfvMbPPDAAzh+/Dg0Gg1yc3MvumM61MVIFZiYo0RNox1NbQ6UNFgxPsUFiZB6YPypwWJGYXsFe3iqnFJUXAI+j4eMaA0ykltQVmNFXrEZU4fR59Xf7G4XTlTrvXNiJ+ZQior+EIlEUKlUIZMS4rLLLsPKlSthsVggl3MrIW/fvh1Lly7tcv+Bml4RyIrRObz2gVyBO7evZUTl0zsqo55R+fQu8GUUXuXT32MErWEYFRXV47CZzMxMZGZmdtqm1Woxd+7cgQ4tbClFEkzI0uDbQ22w2j04VeHAFWMsSBJqgh3aoGFxOXH8jB5mO1fBnpCjRDQNx7skHTc0ymqs0JtdKKqzYkKaCzK6oeE3TTYz8oq4ObEKqQDjMzQht1BSKBOJRJg9ezY+/vjjbhtf/vbll1/C5XLBbDbjxIkT2LJlC3JycpCbm4ulS5fiz3/+M2644Qbcfffd2LZtG9ra2vDwww8HJDZCCCGDE40jGmQSlSqMyeJ6AkprHKhsM4RUfrJw12wz4WixEQCgVQoxNk0DqYAaMJdCJhRh/DANVHLuPlV+uQNNNnOQoxo8PIyhstWA4ioLAGBctgLxCpoT2x+tra2oqqrCsmXLMHr0aMyePdv7t3jx4gE553vvvYdNmzZh2rRpaGxsxKZNm3Ds2DEAgFgsxv/+9z/MmTMH27ZtQ1paGg4dOoTo6OgBiYUQQsjQQJPQBpkoiRyTR6hxKN8Itwc4VmJEbowNETQH7pK5mQelTQaU19oAAOOHKxFLi874RaxcifHDFdiTp0d5nRMVLQYkKzQ0RNcPdA4rfiw2wMO4qfKTctT0fdBPMpnMO/XhQnL5wIwY+OCDD3p8XqvV4ne/+92AnJsQQsjQRA3DQUbA5yM7VoO0+BZU1ttw7LQJV08wUUXQD1rtVvxYbPQuOjNpuApaMZWrP0RK5Jg0XI29eXowAEdPGzA6zkqrZvpBo8WEY6e5YaQpsUJadMYHPTUMCSGEkMGCageDUIxUgQk5XE9Wi96JwrMGONzuIEcV/hrMJhwv4SrYwxJESKdFZ/yGW4RGjaxkrqF97LQZDVZTkKMKf3a3Cycq9dCZuBx2ozLEiJbSojOXoqWlBSdPnkReXh7y8vKQn58f7JAIIYQQv6CG4SCkEIkxLkMNiYhrtOSVmNBipzlbl8LqcuJEhR5mK9fAHjWMKtj+Fi1VYHz7DQ2D2YVTVXrY3a5eXkV60mKz4HgJ939fKRMgN1EGOS3q45NTp05h0qRJiI2NxdixYzFx4kRMmDABCxYsCHZohBBCiF9Qw3CQilcokZ3MVQALzlhQZ6Lel0vRYrfgRHsFW60QIideBomARmL7k0wowrh0DeQS7mvpRIkZLTZLkKMKX4wxVBsMKK7kynB0pgLREmmQowpf99xzD2666SaUl5cjLi4Ora2tuOGGG7B+/fpgh0YIIYT4BTUMB6lIiQwj07nl6B0uD46XGWBy2oMcVXhijKGqzYiSaq6CPSZTgUgxVbAHQoxMjpxU7oZGUaUFNQYjrarrI6PTjuOlRrg9XPmNz1ZCTSkqfGKxWFBYWIgnn3wSUqkULpcLWq0W69atw6uvvhrs8AghhBC/oIbhICXiC5AZLUNMBFcRPF5iRjP1vvjE4LQhr5Rb1RGgCvZAipTIMTKVK1uXm+F4mRFGuqHhkxb7uWGkidESZMfRojO+MhgMUKvV4PP5iIyMhNFohN1uh1KpRGNjY7DDI4QQQvyCagmDmFYkxbj2nIaV9TacaTbAzTxBjir8NNssOFnKVbBT4qTIiqFFZwaKgM9HepQMidEdNzRMaLHTDY3+cns8OF1vQG0z16gel61AlIRWePUHoVCISZMmYdWqVfjNb36DcePGBTskQgghxC+oYTiIqQQijM9Wgd/ehjl22gid3RbcoMKM0+NGcZ0B9a0OAFwFm1IoDCytUIIx7Tc0zjbZcbreALeHbmj0R6vDirwSIwBAwOdhfJYaKpEkyFGFr4iICLz77rvex//4xz9QXV0Nl8tFQ0kJIYQMGtQwHMR4PB5StSpkp3ApAE6UmtFEKQD6pc1uxfH2CrZIwMf4TKpgDzSFQITxWWoI2u9oHC81otVhDXJU4aXJYsKpMq6XOydNjmS1Cjzq5faZRCLBvHnzvI9zc3Px5ZdfYuvWrUhPTw9eYIQQQogf0bKKg1yURI6x2UoUV1lhMLtwskqPYepIWlGzjxrM5yrYucNkSFQpgxzR4Mfj8ZCsUiInVYaCCgtOlZrRNM2MGEoP0ic2txMnKgwwtadWGZetQBT1cveL2WzGyy+/3Kd9lUolVq1aNbABEUIIIQFArYNBTiYUYdwwDbZJW2G2uXH8tBmzsixIVKiDHVrIs7icOFFugMXODWMcm62keVoBEiVVYGy2EgUVFhitbpyo1CNDEwGpgHLw9abZZsHxEm5kgFouxJg0DSQCIa3u2g9OpxNHjhzptK2trQ3ff/89Ro8ejaSkJBQVFaGqqgp33HFHkKIkhBBC/MvnhqHD4cDf/vY3HDhwANOnT8eiRYtw7NgxLFmyxI/hEX+IlSkwJlOBg/kGnK6yoNZoooZhH7TYzDheylWwtUohRidrIKYKdkBIBUKMSdfgK1kL1zAsMeGy4RYkKTTBDi2kMcZQrTOipJobejsmS4EYGfW09pdWq8WWLVs6bVu4cCE2btyI5cuXAwA8Hg8effRRaDT0mSSEEDI4+DTH0OPx4LrrrsN7770HvV6P/Px8JCUl4Xe/+x1KS0v9HSO5RBESOcZkcUMgnW6Gk+UGmJ2OIEcV2hhjqNGbUFbDVbDHUgU74GJlCu/n9nSVFWeNND+2N0anHSfOnMtdOC5bhQiJLMhRhT+Hw4G9e/finnvu8W7j8/l46KGHsHXr1iBGRgghhPiPTw3DPXv2oKmpCXv37sUNN9wAgFvCe/78+Xjvvff8GiC5dEI+H8PjVYjRcikATpWZKQVAL4xOO06UGb25C8dmqaClCnZARYhlGNveMHR7GE6eoZyGvWm1W5Bfdi53YVasinIX+gGfz4fdbseZM2c6bT9x4gTEYsppSgghZHDwaShpVVUVJkyYAIFA0Gl7ZGQkmpqa/BIY8a8oqQKjM+XY9aMD5bU2VLUZkazQUD6+brTaLTh1hqtgJ0VLkBGjorIKMAGfj+w4FeIixWhodeBUmRlXj7XQqrDd8DCG8hYjqhu5xvPoTDkiaU6sXwiFQtx333248sor8dBDDyEpKQkFBQXYsGEDXnvttWCHRwghhPiFT7eShw8fjoMHD8LlcnXavmvXLowaNcovgRH/0oilGJupAgAwACfKjDA6KadhVzyM4UyzETXtFexRVMEOmgiJHKMzuCG8lXU2VLSa4KE5nl3SO2w42T4nlgdgbKYKampE+82LL76I3/zmN/j666/xf//3f8jLy8O7777baXgpIYQQEs586jGcNm0a0tPTMXfuXCQkJKCpqQm33347SktLcdttt/k7RuIHfB4P6VEqpMZJUdVg44aTTrRCI6bhkRfSO2w4WdZeweZRBTuYuBsaSnx3pM17Q2NsnI2G9XahxWbGqTPcEPFhiVKkaCl3oT8JhUI8+OCDePDBB4MdCiGEEDIgfF6VdOvWrXj++eexY8cOWCwW5ObmYt++fZBIqAIdqqIkcozOVKCqwYa6FgdKGw1IU2lpDtIFuAo2N4w0I1FGFewg4vN4SItUITVeisp6G/LLzGidZKWG4QVcHg9O1xvRpOMWlRqdqaDUKn7mcrnw8ccfIz8/H3b7ubmuarUazz77bBAjI4QQQvzD54ahTCbD2rVrsXbtWn/GQwaQUiTG2Ewlth9s5RbzKDNhUrKNkl+fp6OC3axzAgBGZcipgh1kkVI5RmUqUFlvQ32rAyUNBqQptRDw6YZGB53DilNnuF5uoYCHMcPUUIhoURR/WrZsGQ4ePIg5c+Z0ugHqdDqDGFVw7Xx9XEDPxxhDQ0MD4uLi6GYdIYQMAJ8bhhUVFXj22Wexd+9eOBwOTJgwAb/73e8wadIkf8ZH/IjH4yFJrUJWsgzFVRZuOOk0MzUMz9PmsHqHkYqogh0SlEIxxmWosP3AuRsak1Pohsb5mq1m5LcPIx2eIkOiShnkiAYXm82GL774AlVVVYiPjw92OIQQQsiA8KlhaDAYcMUVV2Ds2LH4wx/+ALFYjG+//RaXX345jhw5gpycHH/HSfwksn04aXGVBTqTC4VnDRimjoSIL+j9xUNAi9WMgvYKdnaqnCrYIYDH4yFRrfTe0Mg/Y0bLdLqh0cHhdqOgxgCDmVsMbFSmgnIX+plQKIRKpQpYo7CtrQ0bNmzwPr7uuuswefJk7+OWlha88cYbnV6j1WqxcuXKgMRHCCFkcPJpLNauXbsQExODLVu2YNmyZbjxxhvx+uuvY+nSpfjoo4/8HSPxI7lQhDHpKkhE3Ft/stSMNrs1yFGFBofbjfwaAwyW9gp2hpwq2CGiY34sAO8NDafHHeSoQsP5qVVkYj7GpKkhE4qCHNXgIhQKsWDBArzzzjsBOR9jDDabDTabDS+99BIOHjzY6fmmpiY888wzsFqt3v3On/dICCGE+MLnoaSZmZkXjfHPzs6Gw+G45KDIwIpTKJGbLkdeiQlFFRY0W82IlVHPWKudG14LADIJVbBDiaz9hsaXohbYnR6cLDNjRoaVPrcAGi1mFJVzvdwjhskRI1cEOaLBp7W1FSdPnsQ777yD559/HhEREd7noqKisHXrVr+eLzIyEr///e8BAB9++GG3+61duxZCoc8/44QQQkgnPv2iXHbZZXjiiSfw448/eucUVldX47333gvYHVXiuwiJDLkZXMPQbHcjv9qALE0UxIKhXcE4v4Kdm65ArIIq2KEkTqHEiHQ5jpeYUFRuQbPVMuQbhlaXE6cqDbA6PACA0RkKyrk5AGQyGe6///4un5PLg1fef/vb3+B2uzF27FjMnTu32/2cTmenvMNWKzdKhDEGFkZ5QTviDZeYeQhsnNz5WMDPeynvB5VRz6h8ehfIWMOxfPp7DJ9aAnv27IHFYsGUKVOQk5MDsViMoqIiKJXKTnMclixZgtWrV/tyCjKAJAIhRqeoIZc0w2L3oLDcgsuyrYiXq4IdWtDY3FwF2+Zsr2BnyhEhpgp2KImQyDByGNcwNNvdKKjWI0sTOaRvaLTZrd5FZ9QKIUYmqyEW0Hxhf5PJZHjggQcu6RgNDQ14++23u31eLBbj17/+dZ+OFR0djTVr1qC2thZNTU1Yt24dZsyYgf/85z8QdPH+r1+/vssVxBsaGiCThc9wecYYdDodAITFqqTpMYGepsEQp+kYtRW48mloaPD5tVRGPaPy6V1gyyj8yqdDxw3B3vhUo8rOzsZTTz3V636jRo3y5fAkAKJkcoxIV+BosRFFlVzvy1BuGLbZrShs7y1Uy4XITaIKdqiRCIQYlaqGTNIMq92DggoLLhtuRZxs6H5uG8xmlFRzX/Yjh8kRJaNe7oFgNpvxl7/8pcvnLrwh2p2OeYM9Pd9X0dHR3qGmAPDb3/4Wo0aNwgcffIA777zzov3XrFmDJ554wvvYarUiKioKcXFxYdcwBBA26SoqmuoDer6OXozKJhlYACutcXFxPr+WyqhnVD69C2QZhWP5dBjQhuHIkSMxcuRIX15KQkSEWIbcYXIcLTbCavcgv8qAbG0UJEO096XxvAr2iGEyWvEyREXL5BiRLsexYm5+bIvVMmQbhlaXE/mVRjhdXC/3yGEKaMXSIEc1ODmdTuzdu7fTtpqaGpw4cQK33357nxqG8fHxnRpz/pSYmIhx48YhPz+/y+dFIhFEoovnS/N4vLBoYJ2vI+ZwiDuQFcdzeO0D3QJ37kt5L6iMekbl07vAl1F4lU9/j3FJrYBPPvkEe/bs8eYxXL58OcRiyvkWDsQCIUamqKGQNcNsdaOwwoy5OUNzOKnV5UR+lRH29mGkI9MV0NJqpCEpQizDyPaGodXuwakqAzI1Q/OGRpvdisIKrpdboxAiJ0FFaWcGiFarxZdffnnR9t/97nddNrgG2qlTp5Cbm+sdNtrU1ISTJ09i+fLlAY+FEELI4OFTugoA+MlPfoJ7770XZ8+ehV6vx7p16zBz5kxalTSMREvlyE3nesaKK6xotJiDHFFwdBpGqhBiRKKaKtghyntDQ8K9P4XlliGbbqXBbEZJFXftucPkiKRe7oC788478cknnwzIsf/0pz/h97//Pdra2rB9+3b8/ve/x//+9z8AwKFDhzB58mSsWrUKq1atwvjx4zFhwgTccccdAxJLKOHzfa62EEII6YVP37BHjhzBd999h6KiInzyySd4//33UVpaCoFAQKuShhFt+3BSALA5PcivMsLmdgY5qsBrtJhxuqOCnS5HpJR6C0NZlEyOnI4bGu3zY4cabjVSI5zu84eR0uc20IqLizut9ulPdrsdNpsNDz74IMaNGwebzQank/t+XrFiBf79738jKysLqamp2Lx5M3bs2BGU3stAi4qKCnYIhBAyaPk0/qqoqAhXXXUVEhISvNukUiluueUWFBYW+i04MrDEAgFyk9RQyZphtLpRWG7G5SOsSJAP/spFh455Wo72eVq5w+Q0jDTERYjl5+bHOobm/Fiul5vr4dcquWGkQupJGTA6nQ4/+9nPOm0zm83YvXs3/u///m9AzvnMM8/0+HxWVlaf5jYONhaLBUrl0E5TQwghA8WnmlRSUhLy8vLgdDo73aE8fPgwZs6c6bfgyMCLknKV7EMFRpyusqLRbEaCXB3ssAKGhpGGH7FA0Gl+bEH50JsfW282obSaW+GShpEOPJFIhPHjx3faplAo8Mwzz9BvXoB19JoSQgjxP58ahrNnz4ZYLMbs2bNx++23QyKR4JtvvsG+ffvw17/+1d8xkgHE5YZT4FABt/jKqUojhkdEQyYcGr2GjWYzTncs959OFexw0TE/9kihEcWVVjRZzEOmYWhxOZFfaaJhpAPI6XTi2LFjmDp1KgCuEfj0008HOSpCCCFkYPk09kgkEmHHjh2YNWsW3n//fbz11lvQaDQ4cOAAYmNj/R0jGUAivgAjktRQK7h7BAVDaDGPi5b7z6Dl/sOFVizzLpxkb58fa3cPzFyvUNNmt6DwTPswUpUQw+NpGKm/tba2YtGiRd7HjY2NlJc3RAgovywhhAwYnyflREVF4aWXXvJnLCRIIqVyjEyX42C+ASXV3HDSRMXgH07aZrei4Lzl/ofTcv9hQywQIDf53PzYgnIz5o6wDIlh0PUmM0prOnq5FdTLPQCio6NhNpuxf/9+TJkyBR6PBy0tLcEOi4DrvSWEEDIw6DYzgVYsxcgM7sfW6fKgoHporE5Ky/2Htygpl+weAE5XWdFkGfyrk1pcTuRXGOF0MwDtiyXRMFK/EwgEeOmll7BgwQKIxWIkJCSgoaGhU3L1jr/4+PhghzukGAyGYIdACCGDVp96DD/77LM+r7x2880348knn+x1v9OnT+Po0aPexwsXLuz1TqDb7caBAwfQ3NyMyZMnIzk5uU8xkZ6J+AIMj1dBLW+CweJCUYUFbbmDe3VS7zDSTvO0aBhpOImQcOlWDhdy82MLqo3IiYge1KuTttkt3qT2ESohchLUNIx0gNx3331Yvnw56urqUF5ejptvvhm7d+++aL+hkCIilDDGgh0CIYQMWn2qQY0aNarPy2Ln5OT0ab+ysjJs2bIFRqMR27ZtQ3l5eY8NQ6PRiGuuuQb19fXIzMzEwYMH8fLLL+O+++7r0/lIzyKkMuSkyXC40IiSKiuaLYN7WF6b3eqtYHcs90/DSMNLx/xYpawZJqsbxRUWtA3y1UnrTWaU1XTk3FQgUkK93ANJJBIhNTUVcXFx+PDDDzF69OhghzTk0aqkhBAycPrUMMzJyelzg6+v5s+fj/nz56OoqAjbtm3rdf8//vGPMBgMyM/Ph1wuxyeffIK77roLixYtQlxcnF9jG4q0YhlGpHO9LzanB4U1XO+LeJD2vjRZzChtX410RJocETSMNCxFSmUYnsrlNDxdZUGrzTJoG4ZWlxNFVaZOw0g11MsdEBKJBPPmzQt2GIQQQsiA8nkMksPhwF//+lfccccd+Mtf/oLKykps2bLFj6F19p///Ad333035HKuAn/TTTdBrVbjv//974CdcygRCwQYkayGQsL1mhVVWtDmGJyrk9rdLhRWm7xJ7Ueky2kYaZiKEMuQk87NsbPYPSg8a4TD7Q5yVAND57CiqPJczk1ajZQQQggh/uRTd5DH48F1110Hq9WKqKgo5Ofn48EHH8TixYsxevRoZGVl+TtOnDlzBhkZGd7HfD4faWlpOHPmzEX7Op1OuFznlq63WrkGDmPskuYndLw+HOY4+BJrpESK7DQZ8k6bcLrSimarBbFS5QBGeU4gy7bNbkFxJbfcv1ImwPAEJYQ8fp/PPdg/B8HU33hFfAFyk1SQipphc3pQVGHB7EwLYmUD/7kNdNk2Wcwo8fZyy6AVS/t17nD6LPgj1nC4TkIIISSU+NQw3LNnD5qampCXl4e3334beXl5EAqFmD9/Pt577z389re/9XeccLlcEIvFnbZJJJJODcAO69evx9q1ay/a3tDQAJnM9xX8GGPQ6XQAAB6P5/NxAsGXWB0eN1KiGfJOA2abG0eL66BJcwekVyKQZVtq1qG4veclPV4Aj8mCBkdDn18/2D8HweRTvA4L0hOEKKpyoLDcjDP1tWByzcAF2S6QZevwuPHjmSbYnVwvd1KUBw6dEQ0Gc5+PEU6fBX/E2nFDkBBCCCF941PDsKqqChMmTLgo0WxkZCSampr8EtiF4uLiUF9f32lbfX19l/ML16xZgyeeeML7uKNnMy4u7pIbhh2xhEPFCuh/rJPFfHxzuAJ2pwc1LXyIx6kQE6DeF2Dgy9bhdqOhyQirgzvfmGwtshKT+zWXcih8DoLFl3g1bhdGZ7lQVNUEs82DJpsQk9NiBvyGRiDLtsFqQnVzMwBAIeFjUk4CkiJi+3WMcPos+CPWgWgYbt++HZ999hlMJhPeffddvPXWW7j33ntpZVJCCCGDgk81p+HDh+PgwYMX9dbt2rULo0aN8ktghYWF+OKLL7yP586di6+++sr7uLi4GKWlpZg7d+5FrxWJRJDJZJ3+AHSZg4r+Ov/FyBXITuXKq6jSijaHLegx+fPP4LShuIKrMEpFfOQmqyARioIeF/35/icTijAqVQWRgN/+ubVA7xxcn9tWuxWn24eRZqfJES2TBz2mcPjzp1deeQXLly+HSCTCzp07wePxUFZWhjfeeMOv5yGEEEKCxaeG4bRp05Ceno65c+dix44dKCwsxO23347S0lLcdtttfTpGY2MjPvzwQ++KpF988QU+/PBDtLS0AAC2bt2KRx55xLv/mjVrsGvXLvz85z/Hhg0bsHjxYixbtgzjx4/35RJIN7RiGUakcQv8GMwunK4zwuXxBDkq/2m1W1HcntQ+O1WGaDmtRjoYxCoUyErmFhAqrrCgzT54hhE6PW4UnzXAbOUW1clJo6T2wbBu3Trs3r0bTz/9tHfbLbfcgo0bNwYxKkIIIcR/fB5rtXXrVsybNw91dXUwGAzQaDTYt28fJBJJn17f3NyMLVu24NChQ1i2bBn27duHLVu2oK2tDQAwcuRILFy40Lv/yJEjceTIEahUKhw+fBirVq3CO++842v4pBsyoQgjz+99qbBAN0hWJ3V5PDhdZ4TBzPV0UwV78NBKpMhJ5xr5rUYXyhqNcLPBcUND57B5VyOViPgYmaKCZJCmkQlVJpMJLpfrorRNkZGRMJv7Ps+TEEIICWU+1y5kMhnWrl3b5SIvfTFy5Eh8+OGH3T6/aNEiLFq0qNO2nJwcvPjiiz6dj/RdrEKOzGQpiiotKKo0Q2e3IlqqCHZYl0x/XgVbJOBhZKoKMiHNDRoM5EIxRqap8AW/BW4PQ2GFGVNTbYMiAXybzYLTldzNmaxkGWKolzvglEolFAoFTpw4gdjYc3M7/Tl9ghBCCAk2n3oMv/vuO2zevBkWi8Xf8ZAQECHhkt0DQKvBhbJG06DofWlzWHG6vWGYkSRDrIIq2INJvFKOYYntw0krrdDZbUGO6NK5PR6UNBihM7X3cqdTL3ewPP3007j22mvx8ssvw2az4dlnn8Xq1avx1FNPBTs0QgghxC98ahgKBAKsXbsW8fHxuO+++7B//35/x0WCSC4UIzdNCQGfW7yhoMIEvSO8K9lu5kFZgxHNeicAGkY6GEVIZMhpnx/b0OpAebMRnjDPZad32FBUwd3MEPB5GJmqpF7uIHn44Yfx2muvIS8vD0lJSTh+/Di++eYbTJkyJdihEUIIIX7hU8Nw7ty5KCsrw+effw6Hw4FrrrkGubm5+NOf/nRRSgkSnhKUikHV+2Jw2FHUntSezwNGpSuhEIl7eRUJJwqhGCPTlOhYi7KwwgyjM7w/t20OK4rbh5FmJksRpwz/Id3h7NZbb8X27duRn5+PrVu3Yvr06cEOiRBCCPEbn+cY8ng8XH755bj88svx17/+FR999BGeeuopnDlzBm+++aY/YyRBoJXIMDxVjtIaKxpaHahqNSJdFQG+n5eADxSdw3ouqX2CFAkqqmAPNjweD8laBVLjpaist6G40oK2KVZowrRn2MMYzjQZ0aRzAACGp8oREabXMhi4XC58/PHHyM/Ph91u925Xq9V49tlngxgZIYQQ4h+XvLTdmTNnsHnzZmzevBkulwuTJ0/2R1wkyJRCMXLTFNi2n0sfUlhpxsREO9RiaZAj6z8PY6hsMaKuhatg56QraBjpIKUVy5CTJkNlvQ1nm+yo0ZmRpozwe067QNA7bCis4Hq5eTxgZJoSchpGGjTLli3DwYMHMWfOnE6rbzudzgE536FDh/DJJ5+gubkZY8eOxb333guVSuV93mAw4C9/+QsKCgqQlpaGRx55BAkJCQMSCyGEkKHBp4ah2WzGJ598gk2bNmHPnj246qqr8Mc//hFLlizpc7oKEto6el+SoiU422xHSZUVukm2sGwYmpx2FLYPI+UBGJmmgJKGkQ5KSpEEI9IU+OaHNjAARVVmjI93QCkKv+8lvcPqXY00LV6KJI0yLBu4g4HNZsMXX3yBqqoqxMfHD/j5Xn75Zfz73//GjTfeiBEjRuCtt97C3//+dxw6dAhyuRxOpxOXX345VCoV7rrrLmzbtg1Tp05FXl4eoqKiBjw+Qgghg5NPDcN33nkHL774IpYvX4533nkHycnJ/o6LhACNWIbhqTKcbbajst6GeqMZqUptsMPqN53DhpL2pPZJMRIkaxVUwR6k+Dwe0iJViI0Qo7HNgdNVFujG28KuYcgYQ43OjLPN3JDF4akyaMPwpsxgIRQKoVKpAtIoBIDbbrsNjz76qPfxTTfdhOjoaOzZswfXXnst/v3vf6OsrAy1tbVQKBRYsWIFRo0ahQ0bNtCwVkIIIT7zqWF4xx134IEHHvB3LCTEqMUS5KQpsOuoDh4GFFaZMDrGGXbD2eqNZlTWc4uQZKfKwnbOGekbrViK4akyNLY5UF5rQ5PZgmSFJthh9YvZ5UBh1bnE6SPSFGHXuB1MhEIhFixYgHfeeQd33XXXgJ/vwiGhJpMJbrcbGg33Of7+++8xd+5cKBTcXGk+n4/rrrsOe/bs6fJ4TqcTLpfL+9hq5W6UMcbAwmjl3o54wyVmHgIbJ3c+FvDzXsr7QWXUMyqf3gUy1nAsn/4ew6eGoUqlQkNDAz788EOcPXsWHs+5HHczZszAzTff7MthSYgR8PjIiFUiQiVEm9GF01VW6EZbw6phaHE5UVhlgqf9/0NOqgJqMVWwBzO1WIrhqXLsPa6Hy81QWG1EblQ0pILw+dxyvdzcYkmxEWKkRarCduGncNTa2opFixZ12mY2m/HOO+/g+eefR0REhHd7VFQUtm7d2usxKyoqsHr16m6fl8lkeOeddy7a7vF4sHLlSlx++eWYNm0aAKChoQFxcXGd9ouPj8fOnTu7PPb69euxdu3ai7Y3NDRAJgufG2WMMeh0OgAIi1Ef6THWAJ+RIU7jaP934MqnoaHB59dSGfWMyqd3gS2j8CufDh03BHvjU8OwpaUFY8eORUpKCkaOHAk+/1zWC5PJ5MshSYjqWJ30h3wDymqsaLFakKhQBzusPtM7rN5hpBEqITJilRDwfMrSQsKEkM9HdrwSKpkARqubu6GRa0O8PHwahk1mC8pruV5uGkYaeDKZDHfeeWef9pXL5X3aLyIiArfddlu3zwuFF/8cu91urFixApWVldixY4e3MSQUCuFwODrta7fbuzwGAKxZswZPPPGE97HVakVUVBTi4uLCrmEIAHFxcWHRMKxoCmz6ro5ejMomGVgAK60X3qToDyqjnlH59C6QZRSO5dNhQBuGO3fuRHZ2Nvbu3evLy0kY6RiW90O+AXanB4VnjciJiIZYcMkL2gZEi9WK0hruP0N2qgxaSfhUgojvIqVyZKfKcbTYiNIqK1rtVsTLVb2/MATY3E4UVhvhcnM/QMNT5WG56FM4k8lknaZLuFwuFBcXY9SoUZ3269jeFxqNBrfcckufY3A6nfjJT36CqqoqfPfdd516KTMzMy/6/S0rK0NmZmaXxxKJRBCJLr4xwuPxwqKBdb6OmMMh7kBWHM/htQ90C9y5L+W9oDLqGZVP7wJfRuFVPv09hk9dJzKZDGlpab68lIQZEV+AEUkqyCTcR6WkygqdIzyShjvcbhSd5Rq0AFfB1oiogj0UaMRSZKdyNwHMdjdKao1wetxBjqpvdPZziyWpZAJkxysh5FMvdzA1Nzfjqquu6vP2S2Wz2bBkyRI0NjZix44dnRqFAHDjjTfiwIEDOHbsGACgpqYGn3/+OW688Ua/x0IIIWTo8Km2MWfOHOTn56OiosLP4ZBQFCGVISuZq2SfrrKizR7oMe++OX8YqUzMR06iCmKBIMhRkUCQCITITVZCJOS+4oqrLNCHyQ2NNrsVJdXc5zYrVYYIKfVyh6qWlhbvgjD+tGbNGmzbtg1KpRI//elPccstt+CWW27Bt99+CwCYNm0annjiCcydOxfz5s3DhAkTMH/+/B6HqhJCCCG98Wk84O7du9HW1oaRI0di9OjREIvP5YRbsmRJjxPsSfjRirl5hifLzDCYXShtMCJLHQVBiPditNmtON3eMMxMliEqjObSkEsXI1cgM0mKokoLSqqs0DtsiJYqgh1Wj5weN07XGWG2cb2bw1Pk0NIqukHT2tqK66+/Hk6nE62trZg+fbr3OcYYysrKsGzZMr+f9yc/+QlmzJhx0fbzh4r+3//9H37605+isLAQaWlpGDdunN/jIIQQMrT41DDMzs7Gk08+2eVzF87BIOFPJhQhN1UJAb8Zbg9DcaUFM9JtiJT0bdGFYHAzD840maAzcUu0D0+VU5qKIUbTPj+2qNKCZr0T5c0mDFNFhvTqngaH3XszQyTgY0SyEpIwmc87GMlkMvzsZz+DwWDA2rVr8bOf/cz7nEAgQEpKCq688kq/n3fSpEmYNGlSr/tlZ2cjOzvb7+cnhBAyNPlU4xg5ciRGjhzp71hICItVyDEsUYrSGm54ps4e2g1Dg8OO4kpuuX8Bn4ecVEVYpdkgl04hEmNEqhJfoAUMQFGlGVOS7dCE8EIu5w9/zkiSIEYRuv/HhoKOhqHD4cD06dMxc+bMYIdECCGEDBifxwI6HA789a9/xR133IG//OUvqKysxJYtW/wYGgklWokM2alcJbW+1YGqVhM8IZxk+PwKdmq8BPHK0B5CSAZGolqBlDiuIcjd0Ajd+bEexlDRbEKTjktDkJ1Kw0hDhVgspkYhIYSQQc+nHkOPx4PrrrvOmwspPz8fDz74IBYvXozRo0cjKyvL33GSIFMKxchNVeC/+1sAAEVVZkxMtIfkMvqMMdTozDjbbAfADSOlPHBDE3dDQ4aqBhuqG2yoNZiRqtSG5FL3JqcdRVVmAFza3BHUy01IWNv5emDnfTLG0NDQEDZ5HgkhocenHsM9e/agqakJe/fuxQ033ACAS7g7f/58vPfee34NkIQGHo+H5AgFEqO5hYZOV1lCNm2F2eVAYXsFGwBGpMqhFEmCGBEJFpVIghFpXG8xA1BUZYLF5QxuUN3QOc4tlpQUK0GSRkmVO0IIIYQEjE8Nw6qqKkyYMAGCC5b+j4yMhNls7uZVJNx1rE4KAJV1NjSYLEGOqGs6hw0lVVxscZFipEaqQnrBETJw+DwehkUrEa3het5OV1mhc4TmcNI6oxlVDdzNlmzq5SaEEEJIgPnUMBw+fDgOHjwIl8vVafuuXbtoVdJBTC2WIDuFaxh6GFBUbYI1BHtfmswWlNe2V7BTZFTBHuLOv6FRdtaGZkvo3dCwuBwoqjKjY9puToo8JIdpD1VmsxkfffQRmpubgx0KIYQQMmB8ahhOmzYN6enpmDt3Lnbs2IHCwkLcfvvtKC0tpQS7g5iAx0dWnBJqBTc1tbQ69JKG29xOFFYb4XJzNezhqVTBHurUYgmGp3KLuDhdHhSfNcHhdvXyqsDS2W3eYaSRKiEyYpXUyx1CnE4n1q1bh7i4OEyYMAGPP/44tm/fDksI3mQghBBCfOXzqqRbt27FvHnzUFdXB4PBAI1Gg3379kEioblcg5lWIkNWMlfJLqu2oS3EVnnU2W3e1UhVMgGy45UQ8n3+mJNBQMQXIDtRBbmE+xyUVFtD7oZGi9WCshruc5udKodWQquRhhKtVov8/HzU1NTgV7/6FRobG3HvvfciIiICS5YsCXZ4hBBCiF/4nDlZJpNh7dq1WLt2rT/jISFOI5YiO0WGo8VGmO1ulNQZka2JDpnGV5vdipLzKtgRUqpgEyBCIkVmsgwny8worbZC57AhRqYMdlgAAIfbjaKzRtidHgDA8FQa/hyqEhIScNVVV4ExBo/Hg08//RS1tbXBDosQQgjxi9CozZOwIREIMSJZCZGAG+ZWUm2FIUR6X1weD0rrTTBb3QA65hdSw5AAGvG5nm6dyYUzjaGTh1PvtKG0/WaGVMRHTqIKIr6gl1eRQLJYLFi1ahVGjRqFcePG4auvvsKcOXNQUFCAQ4cOBTs8QgghxC987jEkQ1e0Qo60BClKa6worbZC77QhUioPdlgwOG0oqebm/AgFPOQkKyER0EecAHKhCCNSleDxmsEYUFJtwfQ0GzQhcONA77ChtJprGGYkyxApC35MpDOj0YhXX30VY8eOxYsvvoirr74aCQkJwQ6LEEII8SvqMST91jGcFADqWx2objODhUDvi95hQ1l7BTs9QYpoOVWwyTnxKjlS47ghmqU1oTHP0MMYKppNaDVwi+FkJcugoWGkIScuLg7l5eVYuXIlvvrqK4wbNw6jR4/Go48+iu3btwc7PEIIIcQvqGFI+k0pFGN4isL7uLjKDLPLEcSIAMYYzurMqG3h4shKpmGkpDONWOodTlrVYEOdMfg5V01OO05XcXHwAIxIVUAuFAc3KNKl9PR03Hffffjoo49w8uRJLFiwAG+99RbuueeeYIdGiFdUVFSwQyCEhDFqGJJ+4/F4SI1UIjaCq8CW1ligC3Lvi8XlRHH1uYr+8FQ5FCKqYJNzVCKpNw8nY9wNDUuQ83DqHefmFybFSJCgVvTyChIMbrcbX3/9NVavXo3x48cjMTERX331FX7+859j8+bNwQ6PEC+BgOYnE0J8RxOwiE80YimyUmRobHOg/KwdLRYLkhWaoMVz/jytaK0IqZGUB450xufxMCxGAa1SCJ3JxQ0nHWOFXCgKWkyNZguq6u0AgMwUGkYaqpqamnDffffhqquuwurVqzFv3jzEx8cHOyxCCCHEr6hhSHyiFkswPEWG/Sf0cLq5pOG5kS6Ig7TYS6vNivJarteS5mmR7kRIZMhKkeFIoRGlNVa02W1IkKuDEovN7URRtQluDzc/NztFBrWI8sCGovj4eFRXVwc7DEIIIWRAUcOQ+KQjabhM0gir3YOSait0OTbEBiE33IV54LKp54V0o2PhpCOFRu5zW2tEtiYqKOkhzh9GqpIJkBWngiBE8oGSruXl5eHf//436uvrMWzYMPz0pz9FUlJSsMMihBBC/IJqIcRnHUnDAXBJw+3WoMRhcJ5bjVQi4mM45YEj3RALhMhJUkIk4L76SqqtMDrsQYlFZz/3uc1MkUEroZsZoeyDDz7AlClTcPDgQbhcLmzZsgUjRozA8ePHgx0aIYQQ4hfUMCQ+04plyD4vaXh5kzkoScPP73nJTJIiUkqrkZLuRcnlGJbEDdksrbEGZeEkt8eD0gYjjFY3ABr+HA4ef/xxfPTRR9i5cyc2b96MI0eO4NFHH8XTTz8d7NAIIYQQv6CGIfGZzJs0nHt8utoCozOwlWwPY6hsMaFZz60umUXDSEkvuLQV3OqkDa0OVLeaAp6H0+C0o6S9t1DA52FEihJSQfAWwSE9M5lM0Ol0uOmmmzptv+uuu1BYWBikqAi5GI8WXSOEXAJqGJJL0ilpeLUVOntgG4Ympx3F5+WBG56iCOoqkyT0KYVi5KTKvY+LaywwBTgP5/mr6KbGSxCrkPfyChJMSqUSQqEQp06d6rT98OHDSE5O9vv5XC4X3n77bVxzzTWYOHEili9fjuLiYu/zZ86cQVZWVqe/yy+/3O9xkPDT0tIS7BAIIWGMFp8hl0QjliErWYbKehuqG22oN5mRpooI2Pn1DhvKarjGaEK0BEkaBd0xJT3i8XhIiVAiPlKM+lYHSqst0E+wQRXAFUFr9WbUNnFzG2kYaXhYuXIlrrrqKjzwwANISUlBQUEB/va3v+Gdd97x+7mee+45NDY24vHHH0dUVBReeuklzJkzBwUFBYiKioLD4UBZWRmKioq8eetEIrohRribCoQQ4itqGJJLohJJkJ0ix84jbWAMKKoyY0yMM2C9dk0WCyrq2tNUpEihEdP8QtI7jYjLw1nf6kBFrQ3N5sDl4bS4HCiuNqNj8OrwVAWUlKYi5K1btw4pKSn46KOPUF9fj/T0dHz88ce4/vrr/X6uZ555BkLhuZ/njRs3Qi6X48CBA7jhhhu82zMzMzvtRwghhFwK+kUhl4TP4yEjVgmtSgid0YXS6sAlDbe7XRfkgZNTHjjSJ2ox1zDce1wPp5uh6KwJuVEuSAKQh1NnP7dYUqRaiPRoJfjUyx3y+Hw+7r//ftx///0Dfq4LG3uFhYVwuVwYNmxYp+2zZs2Cy+XC2LFj8dRTTyE7O7vL4zmdzk49SVYr9/ljjAV8fu2l6Ig3nGIOpHAsHx4CGyt3Phbw8/r6nlD59C6QsYZj+fT3GEFtGL7++ut46aWX0NzcjBkzZuCvf/0rMjMzu9z3lVdewW9+85tO2x588EG8/PLLgQiV9EArliI7WYbDhUaUnbWizWYNSNLw84eRKmUCZMUpKQ8c6RMhn4/sBBUUkiaY7W7uhsaIwOTh1NmtONPeMKTFkoaOwsJCLFy4sNvnFQpFl6kvjEYjli9fjhUrVmDUqFEAuJ7CkpISAEBTUxM2bNiA6dOn4/jx413OeVy/fj3Wrl170faGhgbIZOEzyoIxBp1OB4AWWelKOJZPekyg01wxxGk65pQHrowaGhp8eh2VT+8CW0bhVz4dOm4I9iZoDcNPP/0UTzzxBD766COMHTsWTz/9NK6//nqcOnWqy7kSLpcLc+bMweeff+7dRkNoQoOmvfflcEfS8DoThmtjIBzgRlqb3epdwCMzWQatJHwqOCT4IiRSZCRLcbLM7M3DOdANQ6fHjaJaI2xOD4D2+YUiahiGmpaWFlx77bV92jc6Ohpff/11r/tlZmb2uB+/i+9LvV6P+fPnY9iwYXjzzTe920UiEbKysgAAWVlZmD59OkaMGIEPP/wQq1evvug4a9aswRNPPOF9bLVaERUVhbi4uLBrGAJAXFxc2DR8Aikcy6eiqT6g5+vo6alskoEFsGIfFxfn0+uofHoXyDIKx/LpEPINww0bNmD58uXe+Rmvvvoq4uLisGPHDsyfP7/L1/D5fEilVIkKNeeShjfD6fagtMYKQ6YNkdKBW2nRzTwoazDBYOGGR9ECHqS/tGIZslPkOFlmht7swpkmE7I00QM6rNPgsHtvZoiEfOQkqSBuXzyEhA65XI6VK1f2ad++NqzEYrG3MdcXzc3NuOaaazBy5Ehs2rSpxxuhPB4PUVFRMBqNXT4vEom6vOHK4/HCpgHRoSPmcIs7UMKtfAJZuT6H1z4YMHDn9vX9oPLpXeDLKLzKp7/HCFrD8NixY/jpT3/qfazRaDB8+HDk5eV12zDct28fIiMjodFocOWVV2L9+vWIj4+/aL+Bmk8RTuP3Ax1rlEyGtAQJSmusKKuxQuewIqIfPXj9jddgt6Gk2gKAywOXk6yAVCAMyPXS52DgBDJeqUCInGQFeLwmMMalW5meauvzDQZfYtXZLShtH/6ckShBpFQasPcmnD4L/oj1Ul4rk8mwfPlyn19/qWpra3H11Vdj1qxZePPNNy/qTfznP/+JkSNHYvr06QCA999/H4cPH8af//znYIRLCCFkkAhaw9BgMECr1XbaFhERAb1e3+X+jz76KFauXAmPx4OSkhI8+uijuOGGG/DDDz94l+vuMFDzKcJp/H6gY7W7nUiMAkprgLoWB4qq6iCLcvT53P2Nt9ZmQnGFCQCQECmA0GHzyxjsvqDPwcAJdLxChxUJkULUtrhQXGFEeWYtEiSKPr22v7EyxpDf1IKmNm5+QmIU4NAb0WAKTO7PcPos+CPWvg6b6Y/t27fjs88+g8lkwrvvvou33noL9957r99TRaxduxYFBQWw2+0YPny4d/tzzz2H22+/HVOnTsUjjzyC48ePw+PxQKVS4e9//ztmzZrl1zgIIYQMLUFrGCqVShgMhk7b9Ho9VCpVl/sLBAJvA3DcuHHYvHkzUlNTUVBQgDFjxnTad6DmU4TT+P1Ax+phDGPtbnx//CwAoN4ghGp4JBQicZ9e3994K2pdqG/leoVHDFNhWHxiwIaS0udg4AQ6XrFdjeFpdtS26FDX6oZLIu3zWP7+xmp02lFXqvM+Hj88GukJSQF7X8Lps+CPWP3dMHzllVfwxz/+ETfffDN27twJHo+HsrIyvPHGG/jFL37h13P9/ve/x+OPP37R9tjYWADA6NGjsWvXLhgMBrhcLkRGRvr1/IQQQoamoDUMx40bhx9//BF33HEHAMBkMuH06dMYO3Zsn17fcYe2q2SuAzmfIpzG7wcyVgGPh/RoFaI0IrTonSg7a4NhnB1Kcd/TR/Q1XovLieKa8/PAyaEWSwP6ntDnYOAEMl6NWIrsFDl2H9XB7WEoOWvGmBgXpIK+9QD1J1aD046y9vmF8ZFipEQqu1xwZCCF02fhUmP19zWuW7cOBw4cgEajwSeffAIAuOWWW/DAAw/4vWEYExODmJiYXvdTqwd+9WdCCCFDR9DW9r/vvvuwceNG7N+/HyaTCU8++SRiYmJwzTXXAABeeOEF5OTkePe///77cfjwYTidTlRVVeH+++/HiBEjMHr06GBdArmAWixFZjLXa1d+llvlcSDoHedWI41QUR444jsBn4/MOAVUMm40QlmNDQaHfUDO1Wy2oKKOGzaalUKrkYYTk8kEl8vV6TcJACIjI2E2m4MUFSGEEOJfQWsY3nXXXVi9ejUWLlwIjUaDw4cP46uvvoJEwvUwuVwu2O3nKmh33HEHfvWrX0GlUmHixImQSCTYtm2b3+d2EN9pRFJkJnFDda0OLm2Fy+Px+3na7DaUnW3PA5csg5ZWIyWXQCOWIaP9hkZpzcDc0HC4XSg+a4LTzfVzZyXLoKbPbdhQKpVQKBQ4ceJEp+27du3y5hYkhBBCwl1QEwGuWbMGa9asgcfjuWhI1eOPP45HH33U+3jOnDnYs2cPGGNhMQxqKBILBAOetsLl8aC0jsuXCHD5CylNBbkUGrEUmclyHC8xw2B2obzJ7Pe0FXqHDWXtSe1lEj6yE1UDnueT+NfTTz+Na6+9FnfffTdsNhueffZZvPLKK9i5c2ewQyMkbO18fVxAz8cYQ0NDQ1jMtSYkGEKiZtLVPBuBQODtPTwf/UcObVFyOdITuPetrMYKvdO/Ky4anXZvBVso4CEnSQmxIKj3N0iYkwlF7WkruMclNRYYnf4dTqp32lF2tiNNhQwRErqZEW4efvhhvPbaa8jLy0NSUhKOHz+Ob775BlOmTAl2aIQQQohfUI2a+JVGJEVmsgwlNVbUtThwVmdGujLCbw16vcPmrWCnxUsRJfd9lVlCOsQp5UiOkaK60YYzNVboHX3PZ9gbD2OoajGh1eAEAGQmS6mXO0zdeuutuPXWW4MdBiGEEDIgQqLHkAweCpEY2Snnho6erjHD4nL67fh1BjNqm7jenIwkqmAT/zh/4aSqBjuazBa/HdvsdKCk+tzxhqcoIOvjqqckdNhsNvz+97/H2LFjERsbi6lTp2LTpk3BDosQQgjxG+oxJH7F5/GQFqU8l7aixgb9WFuf8xn2xOJy4vR5aSqyU+RQivqeDoOQ7qhFEmSlyLxpK4prTBgV7exz2oqe6J3nFkuKjxQjUaOgIfFh6KGHHsK+ffvw6KOPIjk5GQUFBVi9ejVMJhNWrlwZ7PAIIYSQS0YNQ+J3Hb0vLXqnN21FouLS820ZHDacaa9ga5WUpoL4j4DPR1acEkqZACarm0tbMdIOqezSG4atVisq6rhe7sxkSlMRjhwOB95//32UlpYiOTkZAHDDDTcgNzcXv/3tb6lhSAghZFCgoaTE7zrmGQJc2orTtSY4Pe5LPq7ObvXOL8xMotVIiX9pxDJkJPk3bYXD7cbpWiOcLm4V3YxkKVRi6uUONzweDzKZDElJSZ22Z2dnBykiQgghxP+oYUj8TiwQICdRCZGA+3iV1lhhvMSk4W6PB2WNJpitXAMzgxbwIH6mEUuRlczNj+1IW+FhrJdX9czgtKGshruZIRHxkZOggogvuORYSWCJRCJcc801eOmll8DaPxN2ux1//OMfsXTp0iBHRwghhPgHDSUlA6IjbUVJjdWbtuJS8hkanXaUVXM9OAI+DznJSkgoTQXxI5lQhOEpcvAAMHBpK6an2S/pBgQ3/JlrGA5LlCJCSqvohoPW1lYsWrSo0zaz2YyPP/4YL730EhISEnDmzBnodDosWLAgSFESQggh/kU1azIgzk9bUd966WkrDM5zaSpS4iSIUfjeyCSkO/FKBZJjJahutF9y2grGGGrazGhscwAAMpJlUNMw0rAgk8lw55139mlfuZy+iwghhAwO1DAkA6IjbcXXB1sBcGkrJsQ7fV6dtN5kQXXj+fMLqYJN/I9bOEmG6ka7N21FqlLr07HMLgeKa86lqchJlkMhvPTVecnAk8lkeOCBB4IdBiFkAEilNA2FkO7QHEMyIM5PWwGAS1vhsPl0LKvLiZJqMzqme2Umy6CilR3JAFCLJN6FkzrSVtjdLp+OZXBwvY4AEBshRnIEpakghJBgk0joxjIh3aGGIRkw5ycNP3MJqzwazssDp5ILkRFLaSrIwOhIW6GQcQvEXMoNjVabFeW13GszkqRQ02JJhBASdHq9PtghEBKyqGFIBsz5aStsTt/TVujttvPSVEihlVAFmwwcrUSGzPa0FWU+3tBwetw4XWeE3cmlqchMkkJNvdyEEBJ0YjEN6SekO9QwJAPGH2krPIyhvMkMg5kbzpeRLKMKNhlQGrEUmUncDQ29j2krjA47zrSnqRAJ+RiepIJYQGkqCCEk2JRKZbBDICRkUcOQDKiOtBUAvGkr+sPotKOkfQEPHg/ISVZAJhT5PU5COnBpKxToGKxcetYCo7N/NzT0ThvK2ucXpidIECmjNBWDSVlZGd577z2cPn062KEQQvqptbU12CEQErKoYUgG1PnDSTvSVrB+9L5weeC4CnZyjBRxSloangy8eJUCSbHnbmgY+jHPkDGGWr0Z9a1cmorMJBk01Msdtg4fPoyRI0dCKpXi1ltvxcGDBzFq1CjcfffdGDt2LP73v/8FO0RCCCHEL6hhSAZUR9qKDsXVZlhczj6/vtFsQVU911uTmUwLeJDA0IjP3dCoqrej0Wzp5RXnWN1OnK42ex9np8h9TtNCgu++++7DlClT8N5776GlpQVLly7F5s2b4XA48Oijj+LPf/7zgJ7f7e55XrbT2ffvU0IIoFAogh0CISGLGoZkQHWkrYhuT1tx5qwNhj4OJ7W7XTh91gy3h+thzEyWQS2iZabJwFOLJMjyMW2F3mFDWfv8wki1CKlRtIpuuLLZbCgqKsLbb7+Nm2++GS+99BJaW1uxbNkyCAQCrFixAsXFxQNy3j/84Q9IS0uDTCZDZmYm/vnPf3baZ/v27cjOzoZUKkViYiI2b97s9zgIGYz4fKr6EtId+t9BBpxaLEVGe9qK8lor2ux9axjqzxtGqpAKkBmnhIC+0EkACPh8ZMYpoZByC8ac6Ufaija7DWdquc9tZpIUGurlDlt6vR5arda7imF8fHynhStUKhWMRqPfz3vo0CEAwMGDB2Gz2fDCCy/g5z//Ofbv3w8AqKmpwY033ohf/vKXsFqteP311/Hzn/8cBw8e9HsshAw2A/F/lpDBgmrZZMBpRBLvKo9WuweldUa4PJ5eX2dwnFvAI4PSVJAAi5DIkNGRtuJs39JWuDzc59tq5z7fGckyaKiXO2z1Zz60P82ZMwdPPvkkEhISwOfzceONNyI5ORknTpwAALz33ntISUnBypUrIRaLsWTJEsybN++iXkVCCCGkP4TBDoAMfmKBEMMTlRAKmuByM5TVWGHMtCNC0v1KjR7GUNFiRpuRG76XmSyDRkwrO5LAUbfPMzxZZobO5EJlixlZmugeh4UanXbvzQyhgIcRSUqIBfQ1G84aGhoglZ67KWW32zs91mq1fTqOx+OBxdL9XFUej3fR3Cez2Qyr1YrPP/8cRqMR1113HQDgxIkTmDhxYqd9J0yYgJ07d3Z5bKfTCZfr3FBoq5X7jDLGgtb49UVHvOEUcyBR+fQu3MqIh8DGyZ2PBfy8l/J+BDLWcCyf/h6DaiwkIKLkMqTFS1F21oqys9ywvJ4ahianHSXtC3jwAAxPVkBOaSpIAMmFIgxPVoCHZjAApTUWTEux97gAkt5hQ9lZbshpWrwUUXK6mRHOIiIi8J///KfHfc5vJPYkPz8fM2bM6PZ5pVKJ+vr6TttycnLQ0NAAgUCAV199Fenp6QAAk8mE+Pj4TvtqNJpuh8itX78ea9euvWh7Q0MDZGGUSoUxBp1OB4BrSJPOqHx6F25llB7T+0gV/2KI0zja/x248mloaPD5tYEto/Arnw4dNwR7Qw1DEhAasRQZSVzDsLbJjnqDGemqiG735+YXchXshGgJEtSUpoIEXoJagcRoCc422703NHpqGNYZzKht4lbRzaD5hWFPIpFgyZIlfjnWmDFjYDKZ+vWampoaeDweHDhwAEuWLIFIJMKKFSug0Wi8ldsObW1t0Gg0XR5nzZo1eOKJJ7yPrVYroqKiEBcXF3YNQwCIi4sLi0p9oFH59C7cyqiiqb73nfyooyesskkGFsCGT1xcnM+vDWQZhWP5dKCGIQkpSpEEWclyfHuoDQzA6RozxsU5u01W32q1orKOaxhSmgoSLGqxBBnJUpxttqOqzoYWixUpSm2X+1pdTpyuMXsHmGSnyKGk+YVhyWw24+WXX+7TvkqlEqtWrRqwWPh8PmbNmoW5c+di3759WLFiBSZMmIBXX321034HDx7EhAkTujyGSCSCSHTxdy2PxwuLyvH5OmIOt7gDhcqnd+FURjteHx/Q8zHG0NDQEDYNZwABbaBxeO2DSQN3Xn+8F309BjUMSUDweTykRyugUQihN7twptYK/Rhblw1Dh9uF4rMmON3taSqSZFBTgnASBGoRN89wT54eTjfD6VoTRka5upw3eP4qulqlEGlRCkpTEaacTieOHDnSaVtbWxu+//57jB49GklJSSgqKkJVVRXuuOMOv5//ueeeQ2JiIubNmwexWIydO3di+/bt+Pvf/w4AuOOOO7Bu3To888wzuP/++/Hll19i3759FzUWCSGEkP6ghiEJGK1EhsxkGY4WG73L/8fLVRftp3faUdZewZaJ+chOUEJIaSpIEAj5fGTHKyET82F1eFB21gr9CDtiumgY6uxW7/zCjCQZtD3MoSWhTavVYsuWLZ22LVy4EBs3bsTy5csBcIvJPProo90O37wUDz/8MNatW4c//OEPsFgsyMrKwltvvYVly5YBAGJjY7F9+3b86le/woYNG5CWlobPPvsMY8aM8XsshBBChg5qGIL7gXc6nb3uxxiDy+WC3W4P+S72UIxVyoCcJDHOVPIA5kFFnR4pUiUEPH6neNvMRjQ12qCV8pCVIoWSJ4Tdbg92+F6hWLYAN1yMEvf6n1Yiw7AkGQrKzThTY4PRYUOMtPPqkW7mQVmjCWarGwA3/JnmFw4eDocDe/fuxeeff+7dxufz8dBDD2Hp0qVYt26dX88XGRmJV155Ba+88kq3+0ydOhV79+7163kJIYQMbUO+YWixWFBdXQ1PH/LqAYDb7Q6b5KihGOtwqRvxU+VgAKQ2C8rPlHuH27ndbhgMBtjdbizOFQMQQy7lw1jXADOvMahxXygUy5bP5yMlJQVyOS3U408dCycVlJvRrHeiqtWMYarITvsYHXaUVXO93AI+DznJSkgoTcWgwefzYbfbcebMGWRmZnq3nzhxAmKxOIiREUIIIf4zpGsuHo8H1dXVUCgUiI6O7rX3p6OnSCgUhlRPUVdCNVaH24VmvQNON4NIwEO0RgyxQOiNly8QQG9xQNHe8xKlFUMRYtcQimXLGENzczOqq6uRnZ1NPYd+1JG2AmgBAJTUWDA5yQGF8FyDwOA8l6YiOVaCGAU1zgcToVCI++67D1deeSUeeughJCUloaCgABs2bMBrr70W7PAIIYQQvxjSDUOn0wmPx4Po6Og+5aJijEEgEIRUg6A7oRqrwCOC3MWH0eKGBwBfJIJEJPbGy/g8uK0e8IUCiAQ8KGWSkOt5CdWyjY6OhtFohNPphERCq2H6C4/HQ5JWgdgIMRrbHDhz1gqDw96pYVhvsqCmsWMVXRk0Yir/webFF1/EiBEj8PHHH6OhoQHp6el49913sXDhwmCHRgghhPhFaNW4gySUKveDnYDHg0TMNQwZAJvDA5nw3KK/Lo8Hdge3GqlEzIeA3ps+o8/xwFGLJchMlqKxzYHyWhtabVYktC+cZHO7UFJthqc9T0VWsozSVAxCQqEQDz74IB588MFgh0IIIYQMCBpvRgLG4XBg06ZNkAj54Le3YexOD9yMm9/JGIPN6fHmgZOIeODz+v4Rdbvd3uXcB5rD4cDmzZu7fd7j8eCrr77CW2+95U2oS8IXN8+QW2XU7vTgdJ0RTg833FnvsHlX0VXJBMiI5RZUIoQQQggJJ1R7CSEulwvffvstPvzwQ5w9e7bTc99++y1qa2sH5LwDdWyXy4V//OMf3scWiwWPPvoohDw+JGLuo2d3eOBuX/jHDQaHk/s3jwdIxYJ+5YFzOp1YuXKlH6+gexaLBatXr+72+TVr1mDt2rU4duwYNQwHARFfgOGJSoiE3Of2TI0NRie3Uq7+/DQVyTJoJbQaKSGEEELCDzUMQ0R9fT2mTZuGP//5z/jkk0+Qm5uLbdu2eZ9/6623cPr06QE590Ad22az4Ze//OVF2/l8HiQi7qPn9gAON9cY9DB2bhipKLyHkW7fvh0bN27Em2++SQvBDBKRMhnSE7ghomfOWqF32OBhDOVNZhjMLgDc/EK1iBqGhBBCwhPVWYY2mmMYItxuNzZv3ozRo0cDAF5++WW8+uqruP7667t9zaFDh5Cfnw+73Y7rr78eqampYIxh165dqKurw8SJEzFixAjv/pWVldi7dy+MRiOio6Nxyy23DOg1bdu2DS6Xy9s4Wrp0KQDAbDThm+3bYXMKcdkV82BzeCAXMTg9DIcPH0JFWSlGjRyOa664rMfjX3j9sbGxnZ7vKIv6+npMnToVWVlZ3ud6KosffvgBp0+fRnZ2NqZPn+7d7nQ6sX37dgDA2LFju43rk08+QU1NDbZs2YKysjIsWrSo21gcDgfee+893HTTTdi9ezfS09MRExODI0eOYNKkSfj++++RnZ2NyZMno6ioCIcOHcK0adOQk5PTx3eB+ItGJEVmkgwl1VbUtThQZ7AgFgylNdwwUh4PyElWQCYUBTlSQgghxDfR0dHBDoEEETUM2x1pqunDXgxutwcCAR9A/3uzJsckd/tcUlISkpKSvI9dLtdFDZ3z3XXXXdi3bx/mzp0LiUSCWbNmgTGGG264AdXV1Rg9ejR+8YtfYMOGDbjttttw6tQpXHnllbj66quhUqmQkpLS7/j7q6ysDG63G3l5eRAIBFi6dCkcDgcWLVqExOQkHDp0BLN3zcNzz/8JTpkbD/zsPpSVnkFaRiY2v/0XfHnZZdiwYUOfr//88rqwLB555BG8/vrrWLZsWY9lsXz5cpSVlSE7OxsvvPACLrvsMvz1r38FYwzz589Ha2srcnJysH79+m6vu7i4GHa7HQUFBZDJZD3GYrFY8PDDD+Of//wnMjIycNddd6GtrQ0PP/ww4uLikJubiy+++AIrVqzAvn37kJmZiV/84hc4cOAAcnNz/fdmkV4pRGJkp8jx9cFWAMDpajP48TycqeUahskxEsQpKU0FIYSQ8NXY2Ii4uLhgh0GChBqGISg/Px8bNmzAN9980+Xz//vf/7Bv3z6cOnWqUzLzHTt2oKysDKdOnYJAIMDXX3+NBx98ELfddht+/PFHzJ07F5s3b4ZQGJi3/ZFHHsH69evx5ptvAgB0Oh2sViv+/ve/Iz1jGPIKyrBg3lw889wf8f333+OHAwfx0/sfgYAPzJ4xGU8+8Rv88Y9/hFKp7NP122w277937tzpLQuhUIhvv/0W9913H5YtW9ZtWezZswf79u3DY489BgCYOHEiHn/8cTz//PM4cOAAmpqacPToUfD5fHz44Yd45JFHurzuNWvWYOPGjVi/fj3S09M7vS8XxgIAVqsVb731FkaOHAkA2L17N5xOJ/bs2QOZTIY///nP+Ne//oXjx4+Dz+fjl7/8JT7//HNqGAYYn8dDapQSkWohWg0unDlrQ3yEAFX13FzDzGQZ1GIaRkoIIYSQ8EQNwxBz8uRJ3HLLLfjoo48wfPjwLvcpKCjA7NmzOzWKAOD06dOYPn06hEIuYfzcuXNRVVUFh8OBxYsX46OPPkJ0dDSmT5+OVatW4brrrus1nh9++AHHjh3rdb85c+Z4GzY9UavVyMrKAmMM6WmJ0Ot1YABOnswHX8BHYf4JiAQ8SMUC3HPPPbDb7Rc1DLu7/u7KAgAuv/zyXsuio0Gdl5fnPU5HDCUlJZgxYwaXa5ExzJo1q9dr7UssAKBUKi8qu5ycHMhk3CqYCQkJGDt2rHfcf0JCAnQ6XZ/PT/xHI5IgM0mGVoMR5WetSI2Vwt2ep4KbX0hpKgghhBASnqhhGEJ+/PFHLF26FB988AGmTp3a7X7JycnIz88HY6xT7rrExEQUFRV5HxcWFiIyMhJisRhisRjbtm2DXq/Hrl27sHTpUtTU1ECtVvcYU11dXaeGUndGjRp10TaBQABP+4qjHTri5fF4kIgE3hU7I6MTIBSKsPb5lxGtFUMrk3S7Iml3138+X8oiOTkZIpEIr7/++kWTrxMSEvDxxx97H/dnsZ6eYrFYLBAIBBe95sLruvAxrXQaHGqxFBnJMhwuNMJs9+BoMde4V0gEyIxTQkCT9gkhhBASpoLaMKysrMTGjRvR3NyMGTNm4Pbbb+9xNaT+7t8fPc3/68AYg8vlglAo9Hsy8fLyclx11VW45ZZbcPToURw9ehSRkZHeBVvON3/+fKxbtw433HADrrnmGkgkElx//fWYP38+fvOb3+D222/HhAkT8Pbbb2PVqlUAgLy8PBw8eBCMMZw5cwZqtbrHHrcOS5YswZIlS3y6JplMhoiICDz77LNITk6+6FoEPL53pubMuVdD8coLWL5sERYvXgCNUgWlUok777yzz9d//hzD88ti4sSJePvtt/Hoo4/2WBbz58/HH/7wB1x99dW44YYbIJPJvDFcf/31+PWvf427774bI0eOxKefftrncugpFhJeJAIhhicpIODz4PYw1Ldxq5EOS5ZSmgpCCCGEhLWg3d4uLS3FhAkTcPr0aSQnJ+OZZ57Bvffe67f9w43T6cRtt90GoVCIvLw85OXlobi4uMt9hUIh9uzZg4ULF6KsrAx5eXnQ6/WQSCT44YcfMHnyZDQ2NuL555/HmjVrAAANDQ3Iy8vDyZMnodVqsX///oDMNfz8889hs9lw/PhxSCQSrFixwvucSCDAnct/6r2m9z77GjcvvQ1nq2uQl5eH/Pz8fl2/UCjEfffdBwD/396dh0VxpW0Dv2lWWQRBRAQFxd0IapBEBRc0kkA04oAyxggv4EQMmhkn0SwaJCZjHOPENzpiiHHPoJgRl5iMCxoUN0gCgo5RIwIii+wiay/P9wcf9dKydAON3W0/v+vKFarOqdN3HUq6T1fVqRZ9sXHjRoV9YWBggKSkJCxcuBC5ublyGYyNjXHx4kUMHToUYrEYu3fvRnBwcJv7vWDBAlhYWCjM8mSfAI1nGGfPni0sDx06FNOnTxeWx44dKzdbKnu6bE1NMaCv/CWjgx17wNKoh5oSMcYYY6phZGSk7ghMjfRITdekhYSEoLCwEP/5z38ANN5b5+rqiszMTOGRDV2p31xtbS1MTU1RU1Mj3LcFAPX19cjKysKgQYNgbKz43qDuPGOoSEBAACIjIzF16lSl6ncka0fbVhUiwuMGMYrKG4R1Pc30YWNmrNGX5KnzOGhPa8czEaGoqAh2dnYalbUt2pC3sqEO+y5mITG1HEDj/MTvLHDCREfFVx2okzb0bRNVZG3r7z7T3r7RpmNYHbh/FOM+ap+mfr5pz/Sl157aa+mB4Gxbi+ziHqBOPJ2gsxK3uXW5DWX/7qvtUtKzZ8/i/fffF5ZHjx6NQYMG4dy5c60O9DpSXywWQyKRCMu1tY3TyROR3L1ZTT8/ub49zbd52jqSs6l+8/+rsm1VMTQQwVBfD2Jp42ubGIog0tPT+Hvo1HkctKW147npZ03K2R5tyGtuYITBDj2EgaF9byPY9zTV6MyAdvRtE1Vk1Yb9ZIwxTVNaWsqPq9BhahsYFhQUwN7eXm6dvb09CgoKulz/008/RXR0dIv1RUVFcqNkiUQCqVQKiUTS6gQgTyIiSKVSAC0nA+lu06dPR58+feQGvO3pSNaOtq1KRDIYGQJiKaAvAkR6pJYcHaHO46A9TcdzSUmJcJkwEQkzmGpS1rZoS14rgwZYmYlQUS3DgD4iNDyqQlFNg+IN1Uhb+hZQTdamLwQZY4w9u1RxNk1ZunDGWW0DQ319/RYDALFY3OYArSP1P/zwQ6xatUpYrq2thY2NDezs7FpcSlpVVSXcX6YsQ0NDpeuqypIlSzq1nTJZO9u2KhgAsDTTg4G+FAb6hB6GRm3ORqpp1HEctEcqlUJfXx+9e/eWu5QUgNb8EdOWvObiXpg/0xC371Vg2rh+GGLdR6PzAtrTt4BqsvLAkDHGGOsYtQ0MnZ2dkZ2dLSwTEXJzc+Hs7Nzl+oaGhq1+aNfT05P7kNH80QnKfPho/ngEbfhgpS1ZexgYwtjMAFKJBCIlfxfqpKl929bx3LSsSVnbow15zY2M4T1oAIaYGmKgdR+VzY7c3bShb5t0Nas27CNjjDGmSdT2aWb27NnYv38/6uvrAQDff/89SktLhYeu//jjj3jvvfeUrt8VfC+Keunp6WnFgFDT8XH8dBmK9GGmb8jHLWOMMcaeCWo7Y/jBBx/g5MmTGDt2LIYNG4YzZ85g48aNcHBwAABcu3YNBw4cwGeffaZU/c4wNDSESCRCSUkJevfurfADXtNsTVKpVOM/DGpTVkC78mpiViJCSUkJRCKRxl3iyhjrnLKyMuTk5MDZ2Rm9evUS1tfW1uKXX36Rq2tiYgJ3d/enHZExxtgzRG0DQysrK6SmpiIxMRGlpaXYsGEDhg4dKpT7+vpi4MCBStfvDJFIhP79++P+/fuoqqpSapum+7i0gTZlBbQrryZmbTqeteWyRsZY6x4+fIhly5YhKSkJ9vb2uH37NsLDw7F582bo6ekhJycHXl5emDRpkrCNvb09Dh06pMbUjDHGtJ3aBoZA40M0X3nllVbLXF1d4erqqnT9zjI1NcWQIUMgFosV1m06K6PM2UV106asgHbl1dSsTWfAGWPaLT8/HyEhITh48CAA4NatW3Bzc8PMmTPh5+cn1Pvpp586NHEaY4wx1h5+R0HjmRZlH3BvYGAAY2NjjRoQtEabsgLalVebsjLGtM+YMWMwZswYYXnYsGGwtbVt8XimW7duQSaTwcXFBaampk85JWOMsWcNDwwZY4yxblZdXY20tLQ2y/X19TFhwgS5dVeuXMHjx49x9OhRWFhYICAgAEDjlS6TJk3Cm2++ieLiYjx48ADR0dH461//2mrbYrFY7nFPTY/yICKtmrSqKa82ZX6auH8U4z5qH/dP+7S5f5TNrBMDw6bO6OpzrYgItbW1qK2t1fgzRdqUFdCuvJy1+2hTXm3KCmhXXlVkbT740QQFBQVyM20/ydTUFKdOnZJbFx0djYcPHyIrKwvLly+HmZkZAGDAgAFITk4W6p06dQqvvvoqxo4dC29v7xZtf/rpp4iOjm6xPicnByYmJp3dpaeOiFBZWakVx7A6cP8oxn3UPu6f9mlz/9TV1QFQ/J6oR5ryrtmNysrKYGNjo+4YjDHGnrLS0lJYW1urO0aXFBcXY8KECQgNDcUHH3zQah1PT09MnToVn3zySYuyJ88YlpWVwdHRsdvyMsaYtnr55ZcxfPhwXL9+HWfOnFG67En9+vXD7NmzYW1tjQcPHmDPnj3dGVtpit4TdeKMoZWVFUpLS2FiYtKlEX5tbS1sbGxQWlqKHj16qDCh6mlTVkC78nLW7qNNebUpK6BdeVWRlYhQV1cHKysr1YZ7CmpqauTuGbS1tcWoUaOQk5MDoPGy1KazhwAgkUjw4MED9O7du9X2DA0N5R5jY29vr5L3xKdNm45hdeD+UYz7qH263j9GRkYAAJlMBn19fchkMmFySiMjI0ilUmzZsgWff/45iKjNiStFIpFQv+nS05iYmKe2H61R9j1RJwaGIpFIpd8Y9+jRQ2v+wWhTVkC78nLW7qNNebUpK6BdebuaVVsnZFm/fj0qKiowY8YMGBkZITExESdPnsTJkycBAH//+99RVFSEl19+GQAQGxuL+vp6LFiwQKn2Vf2e+LRp0zGsDtw/inEftU/X+6fpcWQikUhu5ueGhga88847iIyMhLGxscJZoTVt1mhl3hM1KzFjjDGm46Kjo7F3717s27cPNTU1GDx4MK5evQo3NzcAQFRUFPbt24f9+/ejoaEB48aNw65du9CnTx81J2eMsWeXvr4+Pv74Y40b8KnSs7tnjDHGmBYSiUQICQlBSEhIm+XBwcEIDg5+usEYY0zHmZuba9Ul+B3FT8PuAAMDA0RFRWnFNwXalBXQrryctftoU15tygpoV15tysqeHj4u2sf9oxj3Ufu4f9qnp6eHyspKSKVSdUfpNjoxKyljjDHGGGOMsbbxGUPGGGOMMcYY03E8MGSMMcYYY4wxHccDQ8YYY4wxxhjTcTp/d+mjR49w4cIFGBgYwMvLS+EzPhTV72h7HZWamoq8vDyMHj0agwcPbrduTU0NUlJSIBaLMXbsWLmHH+fm5uLSpUty9R0dHeHp6amyrAUFBUhJSYGVlRUmTZrU7s3Mhw8fRkNDg9y6OXPmwMTEpFPtdZREIsHFixdRUVGBF154AX379m2z7okTJ1BVVdVi/YwZM9C7d29kZmbixo0bcmWjRo3C6NGju5yzrq4OR44cEZbd3NwwYsQIhdvduXMH169fh6OjI8aPH9/h8s5KTk5GXl4eAMDMzAyzZs1SuE1eXh6uXbsGa2trjB8/Xu73fOHCBTx48ECuvqenJxwdHbuctaioCOfOnROWJ0+ejH79+rVZv7CwED/99JPcuj59+sDb21tuXVpaGrKzszF8+HClflfKan4cOjg4wMvLq826paWlOH36dIv1zX8nx48fR3V1tVy5n58fLCwsVJK3srISqamp0NfXx7hx42BpadlufalUisuXL6OkpATu7u4tfseKytmzpaysDKmpqTAxMcHzzz8Pc3NzdUfSOJmZmcjOzsbgwYNV+rfmWXP27FmUlpYiMDBQ3VE0xq1bt5CWlia3bujQoRg3bpyaEmkmsViMK1euoLq6Gp6ens/k3yGdHhhevXoVvr6+GDZsGOrq6lBUVITTp09j5MiRnarf0fY6QiwWY+7cufjll18wevRoXL58Ge+++y7WrFnTav0dO3Zg3bp1GDRoEIgIP//8MzZv3ozw8HAAwKVLl7BkyRLhAckAMH78eJUNDA8cOIDFixfDw8MDubm5MDMzQ2JiImxsbFqtHxoaCnd3d7nB6yuvvCIMDDvaXkeUlpbC29sbNTU16N+/P1JTU/H1118jKCio1fqnT59GYWGhsJydnY2ff/4ZOTk5AICDBw9i3759mDBhglBHJBKpZGBYX18vDAxPnjyJVatWKfwA8PHHH+Pzzz/HhAkTkJGRAXd3dxw+fBiGhoZKlXfF1atXkZqairt376K4uLjdgWFdXR1CQ0Nx6dIlPPfcc7h165awn4MGDQIAbNiwAffv35fb54EDB6pkUFBcXCz0bXx8PI4dO9buwDA9PR2hoaGYPXu2sG7kyJHCwJCIsGDBAiQmJmLcuHG4cuUKwsPD8fnnn3c5K/B/x+Gvv/6KoUOHtjswLC8vl/tCAQCSkpIwatQo4XeybNkyDBgwQG6fJ0+erJKB4fr167Ft2zaMGDECVVVVuHnzJvbu3SvXd81VVVVh5syZKCwshIuLC65cuYIvvvgCixcvVqqcPVs+/PBD7N27F6NGjUJJSQlycnJw8ODBFl/C6Krc3Fy8/vrrqKmpQb9+/XDp0iVMnToVBw8e5Nkln3D58mX4+fmBiHhg2Mzx48exadMmTJkyRVjn4+PDA8Nm0tLSMHfuXJiZmWHQoEF49913ER8f/+x9CUM6bNSoUfT2228Ly6+//jpNnTq10/U72l5HxMTEUN++fam4uJiIiC5dukR6enqUmZnZav0DBw5QWVmZsBwbG0uGhoZUXV1NRERxcXE0atQolWR7UmVlJVlYWNDXX39NRET19fU0fvx4ioyMbHMbS0tLunDhgsra64i33nqLPDw8qL6+noiIvv76a+rZsyc9evRIqe1ff/11mjVrlrD84Ycf0vz581WSrT1ubm60fv36dutkZGSQnp4eXb58mYiIHj58SH379qXt27crVa4qMTEx5OTk1G6diooKiouLI5lMRkREEomEpk2bJteXfn5+CvdZFfT19en48ePt1vnxxx/JwcGhzfK4uDiytLSk+/fvExFRZmYm6evrU3JyskqzvvXWW+Tn59ehbaqqqsjCwoIOHjworHNyclK4z521Z88e4W8PEdFHH31EdnZ2bdb/8MMPaeTIkcI2hw4dIhMTEyosLFSqnD1bdu3aRXV1dcLy8uXLadiwYWpMpFlu3rxJ6enpwnJhYSGZmZnRoUOH1JhK81RXV9OIESNo9erVZGxsrO44GmXjxo3k4+Oj7hgaq7q6mhwdHWnt2rXCury8PLp27ZoaU3UPnb3H8M6dO7hx4wYiIiKEdREREUhKSkJ5eXmH63e0vY5KSEhAQECAcEZtwoQJcHNza3EWoMn8+fPRq1cvYdnNzQ1isRiVlZXCurq6Opw4cQKJiYkoKSnpcsYmiYmJkMlkWLRoEQDAyMgIYWFhSEhIaHe7tLQ0HDt2rMVlmJ1tT1kJCQkIDQ2FkZERAGDRokWQSqVITExUuG1paSm+++47LFmyRG59WVkZjh49igsXLuDx48cqydkZR44cgZubG1588UUAgK2tLQICAoS+U1T+NFlaWiIoKEh4cKy+vj6ee+65FsdmTk4OEhISkJKSAolE8tRzNicWi/Hjjz/i9OnTKCoqkitLSEjArFmzhLOZzz33HLy8vNTSt0/av38/TE1N4e/vL7f++vXrOHr0KDIyMlT6eosWLZK7rN7NzQ0VFRVt/v4SEhLktpk7dy569uyJH3/8Ualy9mwJCQmBsbGxsOzm5qbS9yxtN3z4cLi5uQnLffr0gYWFRYtLw3XdqlWrMGfOHDz//PPqjqKRHj16hGPHjiEpKQmPHj1SdxyNcvjwYdTV1eHtt9/GDz/8gPPnz8Pa2hqurq7qjqZyOjswzMrKAtB4GVqTgQMHgohw7969DtfvaHudydt0OV3z9pteV5GtW7diwoQJsLe3BwA4OTnB3d0de/fuxerVq+Hs7IzY2Ngu52zK6uDgIAy0mrI+ePCgxX2ETf7whz/g4sWL+Oqrr+Dp6QkfHx/U1NR0uj1l1dfXIz8/X65vjYyM0K9fP6X6dteuXbC3t5e7JNfV1RXW1tbYv38/IiIi4OLiglOnTnUpZ2cpOm66elx1p4cPHyI+Ph5z584V1k2ePBnl5eXYu3cv5s2bJ3fJ6dNmb2+PadOmYc+ePYiOjsbAgQOxadMmoVyT+/arr75CaGio3OXCs2bNQnp6Onbs2IHp06fDy8sLFRUVKn9tmUyGmJgYzJ49u83L3J7sO5FIBCcnpzaP2yfL2bNLLBYjNjZW7u8Ca3T48GHs3r0bAQEBGDFiBF8q2cy5c+dw9uxZREVFqTuKRho+fDgGDBiA/fv3489//jOcnZ014ktMTZGeno5evXrB09MTW7duRWRkJEaMGIGbN2+qO5rK6ezF5xKJBCKRSO6DSdM3kq19i62ofkfb60ze5gOjpvaVaXvt2rU4d+4cLly4IKybMGGC3D1whw4dwoIFCzB9+nS4uLh0S9a2ygDgm2++EX4uKyvDiy++iHXr1mH9+vWdaq8jWQF0qm+JCLGxsVi8eDFEov/7jmXevHmYN2+esBwVFYU//vGPKCwsVMl9ex2h6LjpynHVnSoqKuDr6wtvb2+5s/ArV64UfpZIJAgKCkJ4eLjcsf20uLm54cCBA8LyyZMn4evri+nTp2PMmDEa27dXrlxBRkZGizf9LVu2CD9XVVXBy8sL77//PmJiYlT22kSEN998E/n5+Th48GCb9bT1uGXdSyqV4o033oBUKsU//vEPdcfROCdOnEBJSQmuXbsGHx8fyGQydUfSCFVVVQgPD8e3334rd+aZ/Z9XX30Vr776qrD8xRdfYNGiRcjLy1M4UZguqK+vx507d5CYmAhvb28QEQICArBixYpn7koVnT1jaGdnB5lMhuLiYmFd04QidnZ2Ha7f0fY6k7f5hCdN7Stq+7333sOePXuQlJQEZ2fnNusFBgbC1NQUv/zyi0qyPnlZXWFhISwsLJSapdXa2hr+/v64evWqStprj5mZGczNzTvVt4mJicjOzkZoaGi79cLDw1FWVoY7d+50KWtnKDpuOntcdafi4mJ4e3tj5MiR2Ldvn3Bp6ZMMDAwQHByMlJQUENFTTtmSj48PHB0dkZKSAkAz+xYAtm/fjpkzZ7b798DCwgLz588X/g2qglQqRUhICFJTU/HTTz/B2tq6zbraeNyy7tXQ0IDAwEDk5ubizJkzz+RsgF31zTff4OjRo/jtt99w+fJlrF+/Xt2RNMLGjRtha2uL7OxsHDhwAMnJyZDJZDhw4IAwazaTFx4ejsePH7e4tUdX2dnZoUePHsKEV3p6evDz80N6erp6g3UDnR0Yurq6wsrKCidOnBDWff/993BycoKTkxMA4MyZM8L0vYrqK9NeV0yZMgU//PCD8AG4rKwMly9fFmaQysnJQXx8vFCfiBAZGYmEhAScP3++xSVtTw60srKyUFVVpZLZHSdPnozi4mLhAzLQ2BeTJ08Wlv/9738Ll32VlJRAKpXKtXHt2jUhizLtdTVv89/b1atXUVpaKszymJGRgZMnT7bYbvv27XjttddaPNriyb5NT0+Hnp4eHBwcVJK3PdXV1Thw4ABKS0sBNB43ly9fRllZGYDG4+KHH34QjhtF5d0tKSlJbvCRn5+PKVOmwMPDA7t374a+vr5QVldXJ3ePLNDYtw4ODm0OHlUpPz8fBw4cEM5KtfZlRVFRkXDcTpkyBf/5z3+EY7umpgbnzp17an177NixFpfZlpeXIz4+vtV7YsVisdy69PR0lT0CQiwWIygoCL/99hvOnTsHW1tbufKbN2/i+PHjwvKUKVPk/k3eunULv//+u9xx2145e7bU1tZi9uzZqKiowKlTp/gMxhOe/FtkYmKCfv36dcul4Npo4MCBcHZ2xpEjR3DkyBFcvXoVUqkUR44cafH4I13V2ucWAPwYoP9v2rRpqK2tlTte7ty581Q+1z11apr0RiN8+eWX1LNnT9qwYQN9/PHHZGJiQv/617+E8hdeeEFullFF9RWVd0V+fj7Z2dlRYGAgxcTE0Pjx48nLy4ukUikREe3bt4/09fWF+suWLSNjY2PavHkzxcXFCf+VlJQQEdEbb7xBISEhFBsbSxs2bCAnJyfy8/MTZoTsqj/96U/k7OxMW7ZsoeXLl5OJiQmlpqYK5WZmZsIso4mJieTh4UGfffYZxcbG0pw5c8jCwoJu3LihdHtdkZKSQiYmJrR8+XL68ssvydnZmd58802hfNWqVeTm5ia3TUFBARkYGNCZM2datDdhwgT685//TN988w2tXr2arKysaOXKlSrJSkR05MgRiouLIycnJ/rjH/9IcXFx9OuvvxIR0b179wiAMMuoVColT09P8vDwoJiYGAoMDCQ7OzsqKChQqryrMjMzKS4ujsLCwqh3794UFxdH8fHxQrmPjw8FBwcTEVF5eTkNGjSIRo8eLXfMfv/990REVFJSQiNHjqQ1a9bQzp07KTIykoyNjenbb79VSdb6+nrhNUUiEa1cuZLi4uLo7t27RER0/PhxAkBVVVVE1Dgb6IIFCygmJoY2bdpEQ4YMocmTJ5NYLBb2x8nJiWbNmkXbt28nLy8vGjt2LDU0NKgkb3JyMsXFxdHMmTNp7NixFBcXRydPnhTKHRwcaOPGjXLbfPHFF+Tg4EASiURufUpKCo0bN44+/fRT2rFjBwUFBZGJiQmlpKSoJKu/vz9ZWlpSbGys3O+2pqaGiIjWr18vN2vtjRs3yNzcnBYvXkxbt26lYcOGyc1Oq6icPVumTZtGtra2tGvXLrnjp+nfmq7729/+RnPmzKGtW7dSbGwszZ8/n8zNzYX3BSYvISGBZyV9wksvvURLly6lHTt2UHR0NNna2tKf/vQndcfSKK+99hqNGTOGvvrqK1qzZg2ZmprSkSNH1B1L5fSINOAaLDU6fvw4jh07Bn19fcybN0/uuUhr1qzBkCFDhNkwFdVXprwr8vLysG3bNjx48ACurq6IiIgQLqVMTk5GTEwMvv32WwCNz326e/duizY++eQTDB48GDKZDPHx8fjpp59gYmKCiRMnIiAgQO5eua6QyWTYvXs3zp8/DysrK4SGhsrN3hQSEoLg4GBMmzYNAPDf//4X+/fvR3FxMVxcXPA///M/cpeFKWqvqzIyMrBz505UVlZi8uTJCA4OFvoiLi4O165dw2effSbUP336NOLj4xEbG9vibFVdXR127dqFX375BdbW1vDx8cH06dNVlnXJkiUtvgn29fXFokWLUFxcjGXLlgm/Z6DxTNW2bduQmZkJBwcHLF26VO5bQEXlXfHdd9/hu+++k1tnZGSEvXv3AgA+++wzWFpaIiIiAvn5+VixYkWLNvr27YvNmzcDaDy7vGPHDty+fRv9+vXD/PnzVfJ8SKDxPpTWnoP35ptvYtq0aUhLS8OGDRuwZ88e4T6Vw4cP48yZMzAwMICHhweCgoLk7jMuKirCP//5T2RnZ2PEiBF466230LNnT5Xk3bx5M65cuSK3zsXFBZ9++ikA4K233sIrr7wid99IVFQUBg0ahODg4Bbt/f7779i7dy8ePHgg1FHVcdDaMQsA27Ztg7W1NY4dO4bTp0/L3ed469YtxMbGorS0FC+++CLCwsLk7tFVVM6eHYsWLWp1orHm/xZ1XWJiIo4fP46amhoMHjwYCxcubPc5rLrs559/xpdffim8D7HGqzr27t2LK1euwNLSEt7e3vD19VV3LI0iFouxc+dOXLlyBTY2NggKCoK7u7u6Y6mczg8MGWOMMcYYY0zX6ew9howxxhhjjDHGGvHAkDHGGGOMMcZ0HA8MGWOMMcYYY0zH8cCQMcYYY4wxxnQcDwwZY4wxxhhjTMfxwJAxxhhjjDHGdBwPDBljjDHGGGNMx/HAkLFukJycjNmzZ6vt9e/evYugoCB4eHggKSlJqW3Onj2LgICAbk7GGGOMAYcPH0Z4eLi6YzDGmuGBIWPdoKKiAhkZGWp7/UWLFqF///7Ytm0b3NzclNqmrKwM169f7+ZkjDHGGPDw4UP89ttv6o7RZZ988gn+8Y9/qDsGYyrBA0PGnjESiQSXL19GZGQk3N3dYWVl1aJOUlIS/P39n364DtCGjIwxxnTbG2+8gcDAQHXHYEwlDNQdgDGmWmVlZSAimJmZtVmnvLwcmZmZTzFVx2lDRsYYY7rNyclJ3REYUxk+Y8h0Xm5uLpYtW4bp06cjLCwM9+/fh0QiwbRp05CSkiJXd926dfjoo4/a3bYt9+/fR2RkJKZNm4YFCxbg6tWrQllVVRU++OADeHt7w9/fH8eOHWs3c0ZGBkJCQjB16lSEh4fjzp07AIDr169j5syZAIAZM2bA3d29xbZ3797FO++8g7y8PLi7u8Pd3V1uP0+cOIGAgADMmDEDW7ZsUXofntR0z2J77bW1H+1l7EgGxhhjmi0+Ph5z5syBj48Pdu3aJaxPTk4W/v7PmDEDUVFRqK2tFcqb7lGMi4vD3LlzsWTJEnz00UfYuHEj1q5di5deegn+/v5IS0tDZmYmFixYgClTpiA6OhoSiUQuw7/+9S/4+/vjpZdeQlRUFGpqaoSyjz76CJs3b8aGDRswc+ZMzJkzBxcvXhTKW7uU9MyZM1i4cCG8vb2xbt06iMViVXcbY92CB4ZMp+Xk5GD06NGQyWT44IMP4OnpieXLl8PAwADDhw/H1q1bhbo1NTXYtGkTJk6c2O62rbl//z5cXV1hbm6OtWvXwtPTEzNnzkRaWhoAYOXKlbhw4QLee+89hIeHY8+ePfj5559bbeu///0vXnjhBfTv3x9RUVGwsLDA888/j/v372PgwIHYsGEDAGDTpk3Yvn17i+0dHBwQEREBW1tbbN++Hdu3b8eIESMAAFlZWfjqq68QERGBsLAwrF69GocPH1ZqH55UVlaGY8eOtdlee/vRVsaOZmCMMaa5UlNTcfToUSxfvhxBQUGIiIjA+fPnAQCjR48W/v6/++67SE5ORlhYmLDtw4cPsW/fPvz73/9GZGQk/vKXvyArKwtr1qyBkZERVq9eDQsLC/j6+mLx4sUIDAzEqlWrsHPnTrn3xk2bNuEvf/kL5s6di3feeQdnz56Fn5+fUJ6VlYX3338f9fX1WL16NUaOHAlfX19UVFQAALKzs5GbmyvU37VrFwIDA+Hh4YE1a9ZALBbzPYhMexBjOmzp0qXk6+srt66uro6IiDIyMsjExIRKS0uJiGjHjh3k5OREUqlU4bbHjx8nJycnYX1kZCTNmzdPru6qVasoODiYiIhmz55NH3/8cattPSk8PJz8/f3l1nl5edG7775LREQFBQUEgIqLi9vc74SEBHJxcZFbd+jQITI3N6eamhphXVhYGC1fvlypfXiSovYU7UdrGTuagTHGmGaKiYmhfv36kUQiEda99tprLd4LmxQXF5NIJKLq6mphexsbG7n3ytdff50CAgKE5by8PAJAZ86cEdatXbuW5s6dKyz37t2b9uzZIywXFBSQoaEhXbx4UWjzD3/4g1yW3r1709mzZ4mo8X3t7bffFsr69etHsbGxcvXbej9nTNPwPYZMp924cUO49LKJsbExgMZvK93d3bF7926sWLEC27dvR1hYGEQikcJtW3ud27dvy13aWVJSAgcHBwDA3/72N0RERODkyZOYOHEi5syZI5yZfNLt27fx8ssvy61zd3fH7du3O7DnrXNwcECPHj2EZSsrK5SUlCi1Dx1trzP70ZkMjDHGNNPAgQOhr68vLFtZWaGyshIAUFtbiy+//BJJSUkoKSmBTCYDESE3NxfDhw8HAAwZMqTF+66Li4vws6WlZavrml6jvLwcJSUlGD9+vFDet29fODo64vbt28L78ODBg+Veo3kbzVVWViI/Px+TJk2SW9/WZwPGNA0PDJlOs7W1RVFRUZvlERERiI6OxpQpU5CWloaEhASlt23O2toaM2fOxNKlS+XWN00QM2rUKJw/fx5VVVVISkrCwoULsXbtWixatKhFWzY2NsLgqklJSQlsbGyUygIAenp6StdVdh86StF+tJZR1RkYY4xpphUrViAjIwMrV66EnZ0diAienp6oq6sT6jQfVHZGz549YWhoKPdeJJPJUFZW1qH31Cbm5uYwNjZGUVERRo4c2aVsjKkD32PIdFpgYCD27NmD9PR0AIBYLEZsbKxQHhAQgIqKCoSGhuKVV16Bo6Oj0ts2FxQUhFOnTsHS0lK4mR5oPAMGNN7j8PDhQ1hYWMDPzw/Dhg3DtWvXWm3r1Vdfxf79+5GdnQ2gccKZw4cPY9asWUrvd69evVBWVgapVKr0Nor2oaMU7UdrGVWdgTHGmGa6efMmfHx88Nprr+HFF1/E1atXIZPJVPoa+vr6ePnll/H3v/8dDQ0NAID//d//hUgkanHWT9n25s6di+joaOEexPv37+Po0aOqjM1Yt+EzhkynzZs3D3fu3MGkSZPg4OCA8vJyREVFCeVGRkYICwvD+vXrsW7dug5t21xAQABycnLg4eGBPn36oKqqCgMGDBAGkn379sW4ceNgZmaGR48ewdbWFtu2bWu1rZCQEKSnp2PkyJEYMGAAcnNz8de//hVz5sxRer89PDzQv39/DBw4EH369GnztTqyDx2laD9ay6jqDIwxxjTTihUrsHDhQnz77beoqanBxIkTYWRkpPLX+ec//4mgoCDY29ujZ8+eqK2tRVxcHKytrTvV3pYtWxAWFgYHBwf0798fYrEYBw8eVHFqxrqHHhGRukMwpm4NDQ24d+8e+vfvD1NTU7my2NhYREdHIzc3t9XLVlrbtrKyErm5uRg9erRcXYlEguzsbPTs2RN9+vSRK5PJZPj9999hYmKCAQMGKMxcXV2N3NxcODk5yWWWSCRIT0/HmDFjYGDQ9nc/UqkU9+7dQ2VlJYYOHQqJRIL8/HyMGjVKqJOXlweJRAJnZ2el9qG58vJypdpraz9ay2hhYdGhDIwxxjRTcXExysrKMGzYMGFddnY2DAwMhKtzqqurkZWVhd69e8Pe3h6//vorRowYgR49erS6/b1792BkZCTcdy6TyfDrr7/C1dVVGFQ+fPgQFRUVGDp0qFyewsJCPH78GIMGDRLmEmitTaDxChdHR0dYWVkhJycHIpEI/fv3l2uvsrISRUVFGDx4sFx7jGkyHhgy1g6ZTAZ3d3f4+/tjzZo16o7DGGOMMcZYt+CvMBhrw86dO+Hi4oLHjx+3+XxCxhhjjDHGngV8xpCxNhQUFKC4uBjDhg3jqaYZY4wxxtgzjQeGjDHGGGOMMabj+FJSxhhjjDHGGNNxPDBkjDHGGGOMMR3HA0PGGGOMMcYY03E8MGSMMcYYY4wxHccDQ8YYY4wxxhjTcTwwZIwxxhhjjDEdxwNDxhhjjDHGGNNxPDBkjDHGGGOMMR33/wBf0DUfaxtPfAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 912x307.2 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ideal |cos| series:  H2=-14.0 dB  H3=-21.3 dB  H4=-26.4 dB  H5=-30.4 dB  H6=-33.6 dB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The envelope itself, out of the kernel, against the closed form's Fourier series.\n",
+    "det = tap.Detector(sr, frequency=220.0)\n",
+    "phase = np.linspace(0, 2, 801)\n",
+    "env = det.envelope(phase % 1.0)\n",
+    "\n",
+    "ideal = np.array([(4 / np.pi) / (4 * n * n - 1) for n in range(1, 7)])\n",
+    "ideal_db = 20 * np.log10(ideal[1:] / ideal[0])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.2))\n",
+    "axes[0].plot(phase, env, color=C[0], lw=1.8)\n",
+    "axes[0].plot(phase, np.abs(np.cos(np.pi * phase)) * 2, color=C[3], lw=3.0, alpha=0.35,\n",
+    "             label=\"2|cos| — the closed form\")\n",
+    "axes[0].set_xlabel(\"cycles of the note\"); axes[0].set_ylabel(\"envelope\")\n",
+    "axes[0].set_title(\"the envelope of two equal oscillators\", fontsize=10)\n",
+    "axes[0].legend(fontsize=8)\n",
+    "\n",
+    "axes[1].bar(np.arange(2, 7), ideal_db, color=C[0])\n",
+    "for n, v in zip(range(2, 7), ideal_db):\n",
+    "    axes[1].annotate(f\"{v:.1f}\", (n, v), textcoords=\"offset points\", xytext=(0, -14),\n",
+    "                     ha=\"center\", fontsize=8, color=\"white\")\n",
+    "axes[1].set_xlabel(\"harmonic\"); axes[1].set_ylabel(\"dB below the fundamental\")\n",
+    "axes[1].set_title(\"its Fourier series, before any valve\", fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"ideal |cos| series:  \" + \"  \".join(f\"H{n}={v:+.1f} dB\" for n, v in zip(range(2, 7), ideal_db)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48d55cdb",
+   "metadata": {},
+   "source": [
+    "## 2 · The closed form really does replace the 80 kHz simulation\n",
+    "\n",
+    "The kernel never generates a carrier. For amplitudes 1 and `depth` the envelope of\n",
+    "$\\cos\\Phi + d\\cos(\\Phi-\\varphi)$ is exactly $\\sqrt{1 + d^2 + 2d\\cos\\varphi}$, and the published\n",
+    "detector — a triode grid at near-zero bias conducting only on positive half-cycles, loaded by\n",
+    "$R_4C_{21} = 200\\,\\mu s$ — is run on *that*.\n",
+    "\n",
+    "The claim needs checking against the thing it replaces, so this cell builds the expensive version:\n",
+    "two oscillators at 80 kHz and 80 kHz − f, summed, half-wave rectified, RC-loaded, at 3 MHz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8fe65982",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:05.410457Z",
+     "iopub.status.busy": "2026-08-17T12:27:05.410236Z",
+     "iopub.status.idle": "2026-08-17T12:27:06.873545Z",
+     "shell.execute_reply": "2026-08-17T12:27:06.872134Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      f   H2 full   H2 form   H3 full   H3 form   H4 full   H4 form  worst dB    level\n",
+      "    110    -14.03    -14.02    -21.46    -21.46    -26.67    -26.67     0.006    1.030\n",
+      "    440    -14.69    -14.69    -23.23    -23.22    -29.90    -29.92     0.014    1.031\n",
+      "   1760    -19.32    -19.28    -23.10    -23.00    -28.38    -28.33     0.098    1.032\n",
+      "\n",
+      "The harmonic ratios agree to within a tenth of a dB at every pitch. The one systematic\n",
+      "difference is level: the closed form sits a few percent high, because a follower chasing\n",
+      "real carrier half-cycles never quite reaches the peak between them.\n"
+     ]
+    }
+   ],
+   "source": [
+    "TAU = 0.2e-3   # R4 * C21 = 1 MOhm * 200 pF, TASLP Table II\n",
+    "\n",
+    "def full_heterodyne(f, fs=3.0e6, carrier=80_000.0, cycles=60):\n",
+    "    n = int(fs * cycles / f)\n",
+    "    t = np.arange(n) / fs\n",
+    "    am = np.cos(2 * np.pi * carrier * t) + np.cos(2 * np.pi * (carrier - f) * t)\n",
+    "    a = np.exp(-1.0 / (TAU * fs))\n",
+    "    y = np.zeros(n)\n",
+    "    s = 0.0\n",
+    "    for i in range(n):\n",
+    "        v = am[i]\n",
+    "        s = v if v > s * a else s * a\n",
+    "        y[i] = s\n",
+    "    return y, fs\n",
+    "\n",
+    "def closed_form(f, fs=384_000.0, cycles=60):\n",
+    "    d = tap.Detector(fs, frequency=f)\n",
+    "    return d.process(int(fs * cycles / f)), fs\n",
+    "\n",
+    "rows = []\n",
+    "for f in (110.0, 440.0, 1760.0):\n",
+    "    yf, fsf = full_heterodyne(f)\n",
+    "    yc, fsc = closed_form(f)\n",
+    "    a1, adb = harmonics(yf, f, fsf, 4)\n",
+    "    b1, bdb = harmonics(yc, f, fsc, 4)\n",
+    "    rows.append((f, a1, b1, adb, bdb))\n",
+    "\n",
+    "print(f\"{'f':>7} {'H2 full':>9} {'H2 form':>9} {'H3 full':>9} {'H3 form':>9} \"\n",
+    "      f\"{'H4 full':>9} {'H4 form':>9} {'worst dB':>9} {'level':>8}\")\n",
+    "for f, a1, b1, adb, bdb in rows:\n",
+    "    worst = np.max(np.abs(adb - bdb))\n",
+    "    print(f\"{f:7.0f} \" + \" \".join(f\"{adb[k]:9.2f} {bdb[k]:9.2f}\" for k in range(3))\n",
+    "          + f\" {worst:9.3f} {b1/a1:8.3f}\")\n",
+    "print()\n",
+    "print(\"The harmonic ratios agree to within a tenth of a dB at every pitch. The one systematic\")\n",
+    "print(\"difference is level: the closed form sits a few percent high, because a follower chasing\")\n",
+    "print(\"real carrier half-cycles never quite reaches the peak between them.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6c74bdf",
+   "metadata": {},
+   "source": [
+    "### The detector is pitch-dependent, and that is the instrument, not a defect\n",
+    "\n",
+    "The RC cannot follow a fast envelope back down, so as the note rises the notch between cycles gets\n",
+    "filled in: the tone gets purer *and* quieter with pitch. Both fall out of the same 200 µs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4c7a2fa5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:06.876389Z",
+     "iopub.status.busy": "2026-08-17T12:27:06.876067Z",
+     "iopub.status.idle": "2026-08-17T12:27:07.456377Z",
+     "shell.execute_reply": "2026-08-17T12:27:07.454948Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 912x307.2 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H2 at A2: -14.0 dB   at A6: -19.3 dB\n",
+      "level A2 -> A6: -2.0 dB\n"
+     ]
+    }
+   ],
+   "source": [
+    "notes = np.array([55., 110., 220., 440., 880., 1760., 3520.])\n",
+    "h1, h2, h3 = [], [], []\n",
+    "for f in notes:\n",
+    "    y, fs = closed_form(f, cycles=60)\n",
+    "    a, db = harmonics(y, f, fs, 3)\n",
+    "    h1.append(a); h2.append(db[0]); h3.append(db[1])\n",
+    "h1 = np.array(h1)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.2))\n",
+    "axes[0].semilogx(notes, h2, \"o-\", color=C[0], lw=1.8, label=\"2nd harmonic\")\n",
+    "axes[0].semilogx(notes, h3, \"o-\", color=C[2], lw=1.8, label=\"3rd harmonic\")\n",
+    "axes[0].axhline(-14.0, color=C[3], ls=\"--\", lw=1.0)\n",
+    "axes[0].axhline(-21.3, color=C[3], ls=\"--\", lw=1.0)\n",
+    "axes[0].set_xlabel(\"note (Hz)\"); axes[0].set_ylabel(\"dB below the fundamental\")\n",
+    "axes[0].set_title(\"dashed: the ideal |cos| series\", fontsize=10); axes[0].legend(fontsize=8)\n",
+    "\n",
+    "axes[1].semilogx(notes, 20 * np.log10(h1 / h1[0]), \"o-\", color=C[0], lw=1.8)\n",
+    "axes[1].set_xlabel(\"note (Hz)\"); axes[1].set_ylabel(\"level (dB, referenced to A1)\")\n",
+    "axes[1].set_title(\"and it gets quieter on the way up\", fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"H2 at A2: {h2[1]:.1f} dB   at A6: {h2[5]:.1f} dB\")\n",
+    "print(f\"level A2 -> A6: {20*np.log10(h1[5]/h1[1]):.1f} dB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e03c36",
+   "metadata": {},
+   "source": [
+    "### Oscillator balance is a real knob\n",
+    "\n",
+    "Nothing says the two oscillators must have equal amplitude. Unequal, the envelope never closes,\n",
+    "and the harmonic series thins out. It is the cheapest timbre control the object has, and it is\n",
+    "physical rather than invented — a mismatch between two real oscillators does exactly this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "21c25c29",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:07.458566Z",
+     "iopub.status.busy": "2026-08-17T12:27:07.458370Z",
+     "iopub.status.idle": "2026-08-17T12:27:07.956764Z",
+     "shell.execute_reply": "2026-08-17T12:27:07.955573Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 912x307.2 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.2))\n",
+    "depths = [0.25, 0.5, 0.75, 1.0]\n",
+    "ph = np.linspace(0, 2, 801)\n",
+    "for d, colour in zip(depths, (C[3], C[2], C[4], C[0])):\n",
+    "    det = tap.Detector(sr, frequency=220.0, depth=d)\n",
+    "    axes[0].plot(ph, det.envelope(ph % 1.0), color=colour, lw=1.5, label=f\"depth {d}\")\n",
+    "    y = tap.Detector(384_000.0, frequency=220.0, depth=d).process(int(384_000.0 * 60 / 220.0))\n",
+    "    _, db = harmonics(y, 220.0, 384_000.0, 5)\n",
+    "    axes[1].plot(range(2, 6), db, \"o-\", color=colour, lw=1.5, label=f\"depth {d}\")\n",
+    "axes[0].set_xlabel(\"cycles\"); axes[0].set_ylabel(\"envelope\"); axes[0].legend(fontsize=8, ncol=2)\n",
+    "axes[0].set_title(\"the envelope never closes below depth 1\", fontsize=10)\n",
+    "axes[1].set_xlabel(\"harmonic\"); axes[1].set_ylabel(\"dB below the fundamental\")\n",
+    "axes[1].set_xticks(range(2, 6)); axes[1].legend(fontsize=8)\n",
+    "axes[1].set_title(\"so the series thins out\", fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb942663",
+   "metadata": {},
+   "source": [
+    "## 3 · The valves\n",
+    "\n",
+    "The circuit paper models the triodes with the **enhanced Norman Koren** law (Koren, *Glass Audio*\n",
+    "8(5), 1996; grid-current branch from Cohen & Hélie, AES 129, 2010):\n",
+    "\n",
+    "$$E_1 = \\frac{v_{pc}}{K_p}\\ln\\!\\left(1 + \\exp\\!\\left(K_p\\left(\\tfrac{1}{\\mu} +\n",
+    "\\tfrac{v_{gc}+V_{ct}}{\\sqrt{K_{vb}+v_{pc}^2}}\\right)\\right)\\right), \\qquad\n",
+    "i_{pc} = \\frac{2E_1^{E_x}}{K_g}$$\n",
+    "\n",
+    "and gives parameter sets **fitted to the valves actually in ondes No. 169** — 6F5 in the\n",
+    "oscillators, 6C5 in the demodulator and preamplifier, 2A3 in the power amplifier. Those, and the\n",
+    "stage supply voltages and cathode resistors, are the whole specification. Nothing in this kernel's\n",
+    "valve is voiced by ear.\n",
+    "\n",
+    "First check: does the fitted 6C5 land on its datasheet's typical operating point? Tung-Sol's sheet\n",
+    "says 8 mA at 250 V on the plate and −8 V on the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1ef98f03",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:07.958748Z",
+     "iopub.status.busy": "2026-08-17T12:27:07.958562Z",
+     "iopub.status.idle": "2026-08-17T12:27:08.243002Z",
+     "shell.execute_reply": "2026-08-17T12:27:08.241787Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6C5 at Vp = 250 V, Vg = -8 V: 8.85 mA   (datasheet typical: 8 mA)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(f\"6C5 at Vp = 250 V, Vg = -8 V: {float(tap.tube_plate_current(tap.TUBE_6C5, 250., -8.))*1000:.2f} mA\"\n",
+    "      f\"   (datasheet typical: 8 mA)\")\n",
+    "\n",
+    "vp = np.linspace(1, 400, 400)\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(10, 3.0))\n",
+    "for ax, ti in zip(axes, (tap.TUBE_6F5, tap.TUBE_6C5, tap.TUBE_2A3)):\n",
+    "    for vg, colour in zip((0, -2, -4, -8, -16), (C[0], C[1], C[2], C[3], C[4])):\n",
+    "        ax.plot(vp, tap.tube_plate_current(ti, vp, float(vg)) * 1000, color=colour, lw=1.3,\n",
+    "                label=f\"Vg = {vg}\")\n",
+    "    ax.set_title(tap.TUBE_NAMES[ti], fontsize=10)\n",
+    "    ax.set_xlabel(\"plate volts\")\n",
+    "axes[0].set_ylabel(\"plate current (mA)\")\n",
+    "axes[0].legend(fontsize=7)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b6eb2bf",
+   "metadata": {},
+   "source": [
+    "### A stage is a load line, and the load line is asymmetric\n",
+    "\n",
+    "Given the published supply, cathode resistor and plate load, the stage's operating point and its\n",
+    "transfer curve are just the solution of\n",
+    "\n",
+    "$$i_{pc}(v_{pc}, v_{gc}) = \\frac{V_{bias} - V_k - v_{pc}}{R_p}, \\qquad V_k = R_k I_{pc}$$\n",
+    "\n",
+    "which is a **memoryless nonlinearity** in the DAFx-07 sense (Yeh, Abel & Smith 2007) — the same\n",
+    "architecture `fuzz.h` uses, but with the curve coming from a fitted tube model instead of a tanh.\n",
+    "Tabulating it is not an approximation of the model; it *is* the model.\n",
+    "\n",
+    "Two things to notice: the stage **inverts**, and it is **strongly asymmetric**. The asymmetry is\n",
+    "where a triode's even harmonics come from, and the inversion matters because it decides which side\n",
+    "of the waveform the asymmetry acts on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e116e675",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:08.245518Z",
+     "iopub.status.busy": "2026-08-17T12:27:08.245319Z",
+     "iopub.status.idle": "2026-08-17T12:27:08.565571Z",
+     "shell.execute_reply": "2026-08-17T12:27:08.564423Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               stage   Vbias      Vk      Vp0   Ip0 mA    gain\n",
+      "    6C5, demodulator     100    2.70    86.50     2.70    4.86\n",
+      "   6C5, preamplifier     180    4.95   155.26     4.95    5.56\n",
+      "      2A3, power amp     230   25.48   153.56    33.97    2.69\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 912x316.8 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "asymmetry at +-4 units of input: 4.754 one way, 2.191 the other — a ratio of 2.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "stages = [(\"6C5, demodulator\", tap.TUBE_6C5, tap.OP_DEMOD),\n",
+    "          (\"6C5, preamplifier\", tap.TUBE_6C5, tap.OP_PREAMP),\n",
+    "          (\"2A3, power amp\", tap.TUBE_2A3, tap.OP_POWER)]\n",
+    "\n",
+    "print(f\"{'stage':>20} {'Vbias':>7} {'Vk':>7} {'Vp0':>8} {'Ip0 mA':>8} {'gain':>7}\")\n",
+    "tri = {}\n",
+    "for name, ti, op in stages:\n",
+    "    t = tap.Triode(sr, ti, op, drive=1.0)\n",
+    "    tri[name] = t\n",
+    "    vk, vp0, ip0, g = t.bias\n",
+    "    print(f\"{name:>20} {op[0]:7.0f} {vk:7.2f} {vp0:8.2f} {ip0*1000:8.2f} {g:7.2f}\")\n",
+    "\n",
+    "gv = np.linspace(-12, 12, 601)\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.3))\n",
+    "for (name, _, _), colour in zip(stages, (C[0], C[2], C[4])):\n",
+    "    axes[0].plot(gv, tri[name].plate_swing(gv), color=colour, lw=1.6, label=name)\n",
+    "axes[0].set_xlabel(\"grid volts around the bias point\"); axes[0].set_ylabel(\"plate swing (V)\")\n",
+    "axes[0].set_title(\"the load-line solution — note the slope is negative\", fontsize=10)\n",
+    "axes[0].legend(fontsize=8)\n",
+    "\n",
+    "x = np.linspace(-6, 6, 601)\n",
+    "t = tri[\"6C5, demodulator\"]\n",
+    "axes[1].plot(x, t.curve(x), color=C[0], lw=1.8, label=\"the stage, normalized\")\n",
+    "axes[1].plot(x, -x, color=C[3], ls=\"--\", lw=1.0, label=\"a perfectly linear inverter\")\n",
+    "axes[1].set_xlabel(\"normalized input\"); axes[1].set_ylabel(\"normalized output\")\n",
+    "axes[1].set_title(\"6C5 demodulator, drive 1 V/unit\", fontsize=10); axes[1].legend(fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "up, down = abs(float(t.curve(4.0))), abs(float(t.curve(-4.0)))\n",
+    "print(f\"asymmetry at +-4 units of input: {up:.3f} one way, {down:.3f} the other — a ratio of {up/down:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae871132",
+   "metadata": {},
+   "source": [
+    "## 4 · The whole voice\n",
+    "\n",
+    "Detector → demodulator triode → preamplifier triode → intensity key. The `drive` control is the\n",
+    "harmonics knob (the circuit paper's own plugin exposes demodulator input gain the same way, a knob\n",
+    "the real instrument does not have), and it is normalized so it changes the distortion rather than\n",
+    "the level — the gain-staging lesson `fuzz.h` learned the hard way, applied here from the start.\n",
+    "\n",
+    "The measurement that matters: **at drive 0 the harmonics are already there**, because the\n",
+    "demodulator made them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "89bd1cb7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:08.568318Z",
+     "iopub.status.busy": "2026-08-17T12:27:08.568135Z",
+     "iopub.status.idle": "2026-08-17T12:27:09.605442Z",
+     "shell.execute_reply": "2026-08-17T12:27:09.604118Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  drive  0.0: THD 0.221   H2  -14.1 dB\n",
+      "  drive  0.5: THD 0.228   H2  -13.8 dB\n",
+      "  drive  1.0: THD 0.236   H2  -13.5 dB\n",
+      "  drive  2.0: THD 0.248   H2  -12.9 dB\n",
+      "  drive  4.0: THD 0.274   H2  -11.6 dB\n",
+      "  drive  6.0: THD 0.311   H2  -10.3 dB\n",
+      "  drive  8.0: THD 0.344   H2   -9.3 dB\n"
+     ]
+    }
+   ],
+   "source": [
+    "def voice_run(seconds=1.0, **kw):\n",
+    "    kw.setdefault(\"key\", 1.0)\n",
+    "    v = tap.Ondes(sr, smooth_ms=0, level=1.0, **kw)\n",
+    "    return v.process(int(sr * seconds)), v.frequency\n",
+    "\n",
+    "drives = [0.0, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0]\n",
+    "thd, h1s, h2s = [], [], []\n",
+    "for d in drives:\n",
+    "    y, f0 = voice_run(ribbon=24.0, drive=d)\n",
+    "    h = np.array([goertzel(y, f0 * k, sr) for k in range(1, 11)])\n",
+    "    thd.append(np.sqrt((h[1:] ** 2).sum()) / h[0])\n",
+    "    h1s.append(h[0]); h2s.append(20 * np.log10(h[1] / h[0]))\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(drives, thd, \"o-\", color=C[0], lw=1.8, label=\"total harmonic content\")\n",
+    "ax.plot(drives, np.array(h1s), \"o-\", color=C[2], lw=1.5, label=\"fundamental level\")\n",
+    "ax.axhline(thd[0], color=C[3], ls=\"--\", lw=1.0)\n",
+    "ax.annotate(\"what the demodulator alone already makes\", (0.15, thd[0]),\n",
+    "            textcoords=\"offset points\", xytext=(6, 8), fontsize=8, color=C[3])\n",
+    "ax.set_xlabel(\"drive\"); ax.set_ylabel(\"ratio to the fundamental / level\")\n",
+    "ax.set_title(\"drive adds harmonics without running away in level\")\n",
+    "ax.legend(fontsize=8)\n",
+    "plt.show()\n",
+    "\n",
+    "for d, t_, h in zip(drives, thd, h2s):\n",
+    "    print(f\"  drive {d:4.1f}: THD {t_:.3f}   H2 {h:+6.1f} dB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d6c5946",
+   "metadata": {},
+   "source": [
+    "### The two choices the sources do not settle\n",
+    "\n",
+    "The circuit paper's five stages **do not include the intensity key**, so where it sits in the chain\n",
+    "is a modelling decision; and the two triodes are coupled through a transformer whose winding sense\n",
+    "is not given, so the sign between them is another. Both are audible, so both are switches rather\n",
+    "than silent guesses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "028fe5df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:09.607567Z",
+     "iopub.status.busy": "2026-08-17T12:27:09.607379Z",
+     "iopub.status.idle": "2026-08-17T12:27:10.371063Z",
+     "shell.execute_reply": "2026-08-17T12:27:10.369998Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coupling polarity (drive 4, key full):\n",
+      "   +1: THD 0.274   fundamental 0.7810\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   -1: THD 0.394   fundamental 0.8366\n",
+      "\n",
+      "intensity key placement (drive 6, key 0.62 — a half-press):\n",
+      "   after the valves : THD 0.311   fundamental 0.0149\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   before the valves: THD 0.222   fundamental 0.0176\n",
+      "   Placed before, a half-pressed key drives the valves less hard — pressure becomes dirt,\n",
+      "   which is a very different instrument to play even though neither reading is provably\n",
+      "   the historical one.\n",
+      "\n",
+      "the 2A3 power stage (drive 2), which the paper drops for real-time:\n",
+      "   off: THD 0.248   fundamental 0.8274\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   on : THD 0.251   fundamental 0.8245\n",
+      "   Their reason was that its contribution is much less important than the two stages\n",
+      "   before it. Measured here, that is right: it barely moves.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def measure(**kw):\n",
+    "    y, f0 = voice_run(ribbon=24.0, **kw)\n",
+    "    h = np.array([goertzel(y, f0 * k, sr) for k in range(1, 11)])\n",
+    "    return np.sqrt((h[1:] ** 2).sum()) / h[0], h[0]\n",
+    "\n",
+    "print(\"coupling polarity (drive 4, key full):\")\n",
+    "for p in (1, -1):\n",
+    "    t_, h = measure(drive=4.0, polarity=p)\n",
+    "    print(f\"   {p:+d}: THD {t_:.3f}   fundamental {h:.4f}\")\n",
+    "\n",
+    "print()\n",
+    "print(\"intensity key placement (drive 6, key 0.62 — a half-press):\")\n",
+    "for name, kp in ((\"after the valves \", 0), (\"before the valves\", 1)):\n",
+    "    t_, h = measure(drive=6.0, key=0.62, key_placement=kp)\n",
+    "    print(f\"   {name}: THD {t_:.3f}   fundamental {h:.4f}\")\n",
+    "print(\"   Placed before, a half-pressed key drives the valves less hard — pressure becomes dirt,\")\n",
+    "print(\"   which is a very different instrument to play even though neither reading is provably\")\n",
+    "print(\"   the historical one.\")\n",
+    "\n",
+    "print()\n",
+    "print(\"the 2A3 power stage (drive 2), which the paper drops for real-time:\")\n",
+    "for name, on in ((\"off\", False), (\"on \", True)):\n",
+    "    t_, h = measure(drive=2.0, power_stage=on)\n",
+    "    print(f\"   {name}: THD {t_:.3f}   fundamental {h:.4f}\")\n",
+    "print(\"   Their reason was that its contribution is much less important than the two stages\")\n",
+    "print(\"   before it. Measured here, that is right: it barely moves.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "815c8d25",
+   "metadata": {},
+   "source": [
+    "## 5 · Oversampling, and a data point for an open question\n",
+    "\n",
+    "`fuzz.h` measured something awkward: alias energy at 4× came out *worse* than at 2×, and the\n",
+    "untested hypothesis it left behind was **imaging** — zero-stuffing by N leaves N−1 images for one\n",
+    "filter to suppress, and their residuals intermodulate in the clipper into products that are not\n",
+    "harmonics of the input.\n",
+    "\n",
+    "`tap.ondes~` is a **source**. Nothing is zero-stuffed on the way up; the detector simply runs fast,\n",
+    "so there are no images at all. If the imaging hypothesis is right, the sequence here should behave —\n",
+    "and the test is whether it ever *reverses*, which is the thing that needed explaining."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a75f26a2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:10.373561Z",
+     "iopub.status.busy": "2026-08-17T12:27:10.373286Z",
+     "iopub.status.idle": "2026-08-17T12:27:15.034542Z",
+     "shell.execute_reply": "2026-08-17T12:27:15.033275Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    587.3 Hz:  -79.3   -91.2  -104.5  -103.8\n",
+      "   1174.7 Hz:  -65.8   -77.2   -90.6   -92.5\n",
+      "   1760.0 Hz:  -57.6   -70.9   -81.1   -82.2\n",
+      "   2637.0 Hz:  -51.1   -61.4   -71.8   -83.8\n",
+      "   3520.0 Hz:  -45.4   -56.8   -67.0   -74.2\n",
+      "\n",
+      "About 12 dB per doubling up to 4x; past that, 7-12 dB at the top of the range and nothing\n",
+      "at the bottom, where the measurement has already bottomed out. Never worse — which is what\n",
+      "fuzz.h could not manage, and the difference is that this object has no upsampler to leave\n",
+      "images behind. Evidence for the imaging hypothesis rather than proof of it (the\n",
+      "nonlinearity differs too), but it is the first evidence either way.\n",
+      "\n",
+      "4x is the default: it is where the cost stops buying uniformly. 8x is there for anyone\n",
+      "playing the top octave hard.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def alias_floor(f0, drive, os):\n",
+    "    v = tap.Ondes(sr, smooth_ms=0, oversample=os, frequency=f0, drive=drive, key=1.0, level=1.0)\n",
+    "    v.process(40000)                      # settle: the filters have long tails at high factors\n",
+    "    y = v.process(1 << 17)\n",
+    "    mag = np.abs(np.fft.rfft(y * np.hanning(len(y))))\n",
+    "    fr = np.fft.rfftfreq(len(y), 1 / sr)\n",
+    "    h1 = mag[np.abs(fr - f0) < 30].max()\n",
+    "    keep = (fr > 40) & (fr < 0.45 * sr)\n",
+    "    for q in range(1, int(0.5 * sr / f0) + 1):\n",
+    "        keep &= np.abs(fr - q * f0) > 50\n",
+    "    return 20 * np.log10(mag[keep].max() / h1)\n",
+    "\n",
+    "probes = [587.3, 1174.7, 1760.0, 2637.0, 3520.0]\n",
+    "grid = np.array([[alias_floor(f, 4.0, os) for os in (1, 2, 4, 8)] for f in probes])\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for row, f, colour in zip(grid, probes, (C[0], C[2], C[4])):\n",
+    "    ax.plot([1, 2, 4, 8], row, \"o-\", color=colour, lw=1.8, label=f\"{f:.0f} Hz\")\n",
+    "ax.set_xscale(\"log\", base=2); ax.set_xticks([1, 2, 4, 8], [\"1x\", \"2x\", \"4x\", \"8x\"])\n",
+    "ax.set_xlabel(\"oversampling\"); ax.set_ylabel(\"worst non-harmonic energy (dB rel. fundamental)\")\n",
+    "ax.set_title(\"every doubling helps or holds — it never reverses\")\n",
+    "ax.legend(fontsize=8)\n",
+    "plt.show()\n",
+    "\n",
+    "for f, row in zip(probes, grid):\n",
+    "    print(f\"  {f:7.1f} Hz: \" + \"  \".join(f\"{v:+6.1f}\" for v in row))\n",
+    "print()\n",
+    "print(\"About 12 dB per doubling up to 4x; past that, 7-12 dB at the top of the range and nothing\")\n",
+    "print(\"at the bottom, where the measurement has already bottomed out. Never worse — which is what\")\n",
+    "print(\"fuzz.h could not manage, and the difference is that this object has no upsampler to leave\")\n",
+    "print(\"images behind. Evidence for the imaging hypothesis rather than proof of it (the\")\n",
+    "print(\"nonlinearity differs too), but it is the first evidence either way.\")\n",
+    "print()\n",
+    "print(\"4x is the default: it is where the cost stops buying uniformly. 8x is there for anyone\")\n",
+    "print(\"playing the top octave hard.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bb6f320",
+   "metadata": {},
+   "source": [
+    "## 6 · Played\n",
+    "\n",
+    "The ribbon is **linear in semitones**: the circuit paper's Eq. 7 gives the variable oscillator's\n",
+    "capacitance against ribbon displacement, and the frequency that falls out is\n",
+    "$f = A_1 \\cdot 2^{d/12d_0}$ with $A_1 = 55$ Hz. So a hand moving at constant speed produces a\n",
+    "constant-rate glissando, which is why an ondes glide sounds the way it does and why `ribbon` takes\n",
+    "semitones rather than Hz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "de043a54",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T12:27:15.036833Z",
+     "iopub.status.busy": "2026-08-17T12:27:15.036625Z",
+     "iopub.status.idle": "2026-08-17T12:27:15.976877Z",
+     "shell.execute_reply": "2026-08-17T12:27:15.975677Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x422.4 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "peak 0.586, rms 0.2127\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = int(sr * 4.0)\n",
+    "t = np.arange(n) / sr\n",
+    "# a hand: up a fifth, back down a third, with the key swelling under it\n",
+    "ribbon = 19.0 + 7.0 * (0.5 - 0.5 * np.cos(2 * np.pi * 0.25 * t)) - 4.0 * (t > 2.5)\n",
+    "key = 0.45 + 0.55 * np.clip(np.sin(np.pi * t / 4.0), 0, 1)\n",
+    "\n",
+    "v = tap.Ondes(sr, smooth_ms=2.0, drive=2.5, level=0.5)\n",
+    "y = v.process(ribbon=ribbon, key=key)\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(9, 4.4), sharex=True,\n",
+    "                         gridspec_kw={\"height_ratios\": [1, 2]})\n",
+    "axes[0].plot(t, ribbon, color=C[0], lw=1.5, label=\"ribbon (semitones above A1)\")\n",
+    "axes[0].plot(t, key * 10 + 15, color=C[2], lw=1.5, label=\"key (scaled, for shape)\")\n",
+    "axes[0].legend(fontsize=8, ncol=2); axes[0].set_ylabel(\"gesture\")\n",
+    "dither = 1e-9 * np.sin(2 * np.pi * 7000.0 * t)  # so the silent lead-in is not log(0)\n",
+    "axes[1].specgram(y + dither, NFFT=2048, Fs=sr, noverlap=1536, cmap=\"magma\", vmin=-120, vmax=-20)\n",
+    "axes[1].set_ylim(0, 4000); axes[1].set_xlabel(\"time (s)\"); axes[1].set_ylabel(\"Hz\")\n",
+    "axes[1].grid(False)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"peak {np.abs(y).max():.3f}, rms {np.sqrt((y**2).mean()):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14bef5d2",
+   "metadata": {},
+   "source": [
+    "## What this is, and what it is not\n",
+    "\n",
+    "**Not a circuit solve.** The paper's full port-Hamiltonian model runs at 768 kHz and their plugin\n",
+    "costs 85 % of a laptop core; it is passive by construction. This kernel is that model's *own*\n",
+    "published reductions plus its published component values: the oscillators replaced by their\n",
+    "closed-form envelope (exactly, as §2 shows), the stages reduced to static load-line curves with\n",
+    "first-order conditioning and equalization filters standing in for their reactive coupling.\n",
+    "\n",
+    "**No waveform registers.** The real instrument has switchable timbres whose filter shapes are not\n",
+    "in any source obtained here. Leipp (*Bulletin du GAM* n°60, 1972) and Laurendeau's monograph are\n",
+    "where to look next. Inventing them would be the one thing this file is careful not to do.\n",
+    "\n",
+    "**No diffuseur.** That is `tap.palme~` and `tap.metallique~` — patch one after this object, which\n",
+    "is how the instrument works anyway. `radiohead_render`'s `ondes_diffuseurs` scenario is the whole\n",
+    "thing wired together.\n",
+    "\n",
+    "**One instrument's valves.** The parameter sets are a fit to No. 169's tubes, and tube-to-tube\n",
+    "spread in 1930s valves is wide."
+   ]
+  }
+ ],
+ "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/scrub.ipynb b/notebooks/scrub.ipynb
new file mode 100644
index 0000000..7f610d1
--- /dev/null
+++ b/notebooks/scrub.ipynb
@@ -0,0 +1,787 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d9eb9f9f",
+   "metadata": {},
+   "source": [
+    "# tap.scrub~ — the position and the pitch are two hands\n",
+    "\n",
+    "A Kaoss-school scrub pad over live capture: record the input continuously, then put a granular\n",
+    "playhead on it whose **position** and **pitch** are two independent performable signals. Drag the\n",
+    "position and you rake back and forth through the last few seconds of the performance; hold it\n",
+    "still and you have a granular freeze; move the pitch and the material transposes without the\n",
+    "position moving at all.\n",
+    "\n",
+    "That decoupling is the object. A tape head cannot do it — on tape, moving the playhead *is* the\n",
+    "pitch change — and it is why the pad feels like an instrument rather than a delay.\n",
+    "\n",
+    "The tape is `stammer.h`'s `capture`: the same live reel, **shared rather than copied**. That was\n",
+    "the plan before either object existed — the stutter's own header says so in its limits — and the\n",
+    "only thing that had to be added for this kernel was the fractional Hermite read the stutter never\n",
+    "needed, because its slices only ever play at ±1 rate.\n",
+    "\n",
+    "Everything below comes out of the shipping C++ through `tools/capi`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7108f200",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:02.440438Z",
+     "iopub.status.busy": "2026-08-17T02:49:02.440248Z",
+     "iopub.status.idle": "2026-08-17T02:49:02.821039Z",
+     "shell.execute_reply": "2026-08-17T02:49:02.819862Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kernel reached through the C ABI: <class 'taptools_py.Scrub'>\n"
+     ]
+    }
+   ],
+   "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, 3.4),\n",
+    "    \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+    "})\n",
+    "C = tap.PALETTE\n",
+    "sr = 48000.0\n",
+    "\n",
+    "def ms(samples):\n",
+    "    return samples * 1000.0 / sr\n",
+    "\n",
+    "def plucks(n, period=6000):\n",
+    "    # transients — the material a scrub has something to bite on (the stammer's contract, same\n",
+    "    # reasoning: on a sustained pad a granular playhead is barely distinguishable from a tremolo)\n",
+    "    x = np.zeros(n)\n",
+    "    env, phase, hz = 0.0, 0.0, 220.0\n",
+    "    for i in range(n):\n",
+    "        if i % period == 0:\n",
+    "            env, hz = 1.0, 180.0 + 40.0 * ((i // period) % 5)\n",
+    "        env *= 0.99975\n",
+    "        phase = (phase + hz / sr) % 1.0\n",
+    "        x[i] = env * (np.sin(2 * np.pi * phase) + 0.4 * np.sin(6 * np.pi * phase))\n",
+    "    return x\n",
+    "\n",
+    "def spectrum(x, n_from=None):\n",
+    "    x = np.asarray(x, dtype=float)\n",
+    "    if n_from is None:\n",
+    "        n_from = len(x) // 2\n",
+    "    seg = x[n_from:]\n",
+    "    mag = np.abs(np.fft.rfft(seg * np.hanning(len(seg)))) / (len(seg) / 4)\n",
+    "    return np.fft.rfftfreq(len(seg), 1 / sr), mag\n",
+    "\n",
+    "print(\"kernel reached through the C ABI:\", tap.Scrub)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b97da000",
+   "metadata": {},
+   "source": [
+    "## 1 · The null: held still at unity pitch, it is exactly a delay\n",
+    "\n",
+    "Everything this object does is a *departure* from a plain delay, and a departure is only\n",
+    "trustworthy if the identity is exact when it should be.\n",
+    "\n",
+    "Grains are Hann-windowed and fired every `size / overlap` samples. Hann satisfies the\n",
+    "constant-overlap-add condition at those hops — at overlap 2 the two live windows sum to exactly 1\n",
+    "— so with the position held on a whole sample and no transposition, the machine is a delay line\n",
+    "and nothing else."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aa58da51",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:02.823766Z",
+     "iopub.status.busy": "2026-08-17T02:49:02.823454Z",
+     "iopub.status.idle": "2026-08-17T02:49:03.016147Z",
+     "shell.execute_reply": "2026-08-17T02:49:03.014820Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "largest departure from a 480-sample delay: 4.441e-16\n",
+      "(not bitwise zero, and it should not be: the two windows are computed as two cosines whose\n",
+      " arguments differ by pi, not as one cosine and its negation)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "L = 96          # grain length in samples, chosen to divide evenly by the overlap\n",
+    "LAG = 480       # position, in samples behind the live edge\n",
+    "\n",
+    "m = tap.Scrub(sr, 2000.0, smooth_ms=0.0, overlap=2, size_ms=ms(L),\n",
+    "              position_ms=ms(LAG), mix=100.0, level=1.0)\n",
+    "x = plucks(30000)\n",
+    "y = m.process(x)\n",
+    "\n",
+    "err = np.abs(y[2000:] - x[2000 - LAG: -LAG])\n",
+    "print(f\"largest departure from a {LAG}-sample delay: {err.max():.3e}\")\n",
+    "print(\"(not bitwise zero, and it should not be: the two windows are computed as two cosines whose\")\n",
+    "print(\" arguments differ by pi, not as one cosine and its negation)\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "sl = slice(9000, 11000)\n",
+    "ax.plot(np.arange(sl.start, sl.stop) / sr * 1000, x[sl.start - LAG: sl.stop - LAG],\n",
+    "        color=C[3], lw=2.6, alpha=0.45, label=f\"input, delayed {LAG} samples\")\n",
+    "ax.plot(np.arange(sl.start, sl.stop) / sr * 1000, y[sl], color=C[0], lw=1.0, label=\"the scrub\")\n",
+    "ax.set_xlabel(\"time (ms)\"); ax.set_ylabel(\"amplitude\")\n",
+    "ax.set_title(\"held still at unity pitch, overlap 2 — the grains overlap-add back to the input\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e72da997",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:03.018598Z",
+     "iopub.status.busy": "2026-08-17T02:49:03.018375Z",
+     "iopub.status.idle": "2026-08-17T02:49:03.198744Z",
+     "shell.execute_reply": "2026-08-17T02:49:03.197256Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The window sum itself, reconstructed from the kernel's own behaviour: feed a constant and read\n",
+    "# the envelope. At overlap 1 the windows leave gaps; at 2 and up the sum is flat.\n",
+    "fig, ax = plt.subplots()\n",
+    "for n_ov, colour in zip((1, 2, 3, 4), (C[3], C[0], C[2], C[4])):\n",
+    "    g = tap.Scrub(sr, 500.0, smooth_ms=0.0, overlap=n_ov, size_ms=ms(480),\n",
+    "                  position_ms=ms(2400), mix=100.0)\n",
+    "    z = g.process(np.ones(int(sr * 0.4)))\n",
+    "    seg = z[-1500:]\n",
+    "    ax.plot(np.arange(len(seg)) / sr * 1000, seg, color=colour, lw=1.4, label=f\"overlap {n_ov}\")\n",
+    "ax.set_xlabel(\"time (ms)\"); ax.set_ylabel(\"gain on a constant input\")\n",
+    "ax.set_title(\"the window sum — Hann overlap-adds flat from 2 up, and leaves gaps at 1\")\n",
+    "ax.legend(ncol=4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e64dbd39",
+   "metadata": {},
+   "source": [
+    "## 2 · The two hands, and the defect that hid behind them\n",
+    "\n",
+    "`position_ms` is a lag behind the live edge. `pitch` is a transposition in semitones. The claim is\n",
+    "that neither touches the other — and checking it turned up a real defect in the first cut, worth\n",
+    "recording because it is invisible to every other measurement on this page.\n",
+    "\n",
+    "If grain origins are anchored at the position, they advance at the **write head's** speed while\n",
+    "each grain plays its content at `rate`. The transposition then applies only *inside* a grain: the\n",
+    "average read rate comes straight back to 1, and a steady tone comes out at its **original** pitch\n",
+    "with a comb of grain-rate sidebands around it. The pitch knob adds texture and moves nothing.\n",
+    "\n",
+    "The fix is a phase-continuous read head — origins advance at `rate`, and the head is wrapped back\n",
+    "toward the position only once it has wandered far enough (±1.5 grain lengths, a value chosen by\n",
+    "sweep, not by taste). Below is what the two versions measure like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "53943828",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:03.200983Z",
+     "iopub.status.busy": "2026-08-17T02:49:03.200781Z",
+     "iopub.status.idle": "2026-08-17T02:49:03.571660Z",
+     "shell.execute_reply": "2026-08-17T02:49:03.570378Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "f0 = 311 Hz\n",
+      " semitones   at the new pitch   left at the old\n",
+      "       -12             0.4212            0.0000\n",
+      "        -7             0.4247            0.0000\n",
+      "        -3             0.4296            0.0000\n",
+      "         0             0.5000            0.5000\n",
+      "         3             0.4921            0.0000\n",
+      "         7             0.4069            0.0000\n",
+      "        12             0.4288            0.0000\n",
+      "\n",
+      "f0 = 300 Hz\n",
+      " semitones   at the new pitch   left at the old\n",
+      "       -12             0.5000            0.0000\n",
+      "        -7             0.4837            0.0000\n",
+      "        -3             0.4771            0.0000\n",
+      "         0             0.5000            0.5000\n",
+      "         3             0.4820            0.0000\n",
+      "         7             0.4266            0.0000\n",
+      "        12             0.5000            0.0000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def transpose_test(f0, semis, size_ms=100.0, position_ms=900.0):\n",
+    "    moved, stayed = [], []\n",
+    "    for st in semis:\n",
+    "        g = tap.Scrub(sr, 3000.0, smooth_ms=0.0, overlap=2, size_ms=size_ms,\n",
+    "                      position_ms=position_ms, pitch=float(st), mix=100.0)\n",
+    "        n = int(sr * 2.0)\n",
+    "        y = g.process(0.5 * np.sin(2 * np.pi * f0 * np.arange(n) / sr))\n",
+    "        f, mag = spectrum(y)\n",
+    "        def at(hz):\n",
+    "            # peak over a few bins: a Hann-windowed FFT scallops an off-bin partial, and the\n",
+    "            # transposed pitches here are deliberately off-bin\n",
+    "            i = np.argmin(np.abs(f - hz))\n",
+    "            return mag[max(0, i - 2): i + 3].max()\n",
+    "        moved.append(at(f0 * 2 ** (st / 12)))\n",
+    "        stayed.append(at(f0))\n",
+    "    return np.array(moved), np.array(stayed)\n",
+    "\n",
+    "semis = np.array([-12, -7, -3, 0, 3, 7, 12])\n",
+    "# 311 Hz on purpose: a frequency that is a whole number of periods in the grain is transparent to\n",
+    "# every wrap, and would have hidden the defect completely. 300 Hz in a 100 ms grain is exactly\n",
+    "# that case — 30 periods — which is how the first version of this notebook passed its own eye test.\n",
+    "for f0 in (311.0, 300.0):\n",
+    "    moved, stayed = transpose_test(f0, semis)\n",
+    "    print(f\"f0 = {f0:.0f} Hz\")\n",
+    "    print(f\"{'semitones':>10} {'at the new pitch':>18} {'left at the old':>17}\")\n",
+    "    for st, a, b in zip(semis, moved, stayed):\n",
+    "        print(f\"{st:10d} {a:18.4f} {b:17.4f}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b8d6451",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:03.573876Z",
+     "iopub.status.busy": "2026-08-17T02:49:03.573656Z",
+     "iopub.status.idle": "2026-08-17T02:49:03.893558Z",
+     "shell.execute_reply": "2026-08-17T02:49:03.892686Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "moved, stayed = transpose_test(311.0, semis)\n",
+    "fig, ax = plt.subplots()\n",
+    "w = 0.38\n",
+    "ax.bar(np.arange(len(semis)) - w / 2, moved, w, color=C[0], label=\"energy at the transposed pitch\")\n",
+    "ax.bar(np.arange(len(semis)) + w / 2, stayed, w, color=C[2], label=\"energy left at the original\")\n",
+    "ax.axhline(0.5, color=C[3], lw=1.0, ls=\"--\", label=\"a perfect shifter would return 0.5\")\n",
+    "ax.set_xticks(np.arange(len(semis)), [f\"{s:+d}\" for s in semis])\n",
+    "ax.set_xlabel(\"pitch (semitones)\"); ax.set_ylabel(\"magnitude\")\n",
+    "ax.set_title(\"the pitch really moves — 311 Hz, 100 ms grains, position held at 900 ms\")\n",
+    "ax.legend(fontsize=8)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "20a585a3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:03.896309Z",
+     "iopub.status.busy": "2026-08-17T02:49:03.896077Z",
+     "iopub.status.idle": "2026-08-17T02:49:06.265621Z",
+     "shell.execute_reply": "2026-08-17T02:49:06.264583Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy in a +-15 Hz band around the transposed pitch:\n",
+      "    mean 0.988 of a perfect shifter, worst 0.917\n",
+      "how concentrated that energy is on a single line:\n",
+      "    mean 0.920 of a perfect shifter, worst 0.750\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WjY+Px+7du7Fs2TLUq1cPTZs2xfz587Fu3Tql387p5QQoUqkUr1+/hpmZWYFrWRERfQwEQUB6ejpsbGxgYKCe7++ysrKQnZ2t9P3f/zlrZGQEY2PjD4rh+vXraNu2rbgvkUjQpEkT3Lhx44Ouq2uYz4hIX2gynxVXLrt16xZycnLQtGlT8VizZs3w+vVrREREKLVsk1425l6/fg07OztNh0FEVGLi4+Nha2v7wdfJysqCi0t5PH+uXHdGKysruW4m8+bNw/z58z8ojqSkJLm3eGXKlEFsbOwHXVfXMJ8Rkb7RRD4rzlxmYmICCwsL8ViZMmUA5P58V4ZeNubyXnu+fLwT5uYmGo7m4ydYltZ0CCqTpKZoOgTVCVJNR6C65FRNR6Ayg2fPNB2CStIyslDWd4FS3T2UkZ2djefP4/Ay1h/m5qaF3zstA/YOvREfHw9zc3PxuJHRh6eeUqVK4c2bNzLHkpKSYGlp+cHX1iV5f6/HG0+EqaHupHQjA92bSNvjr/aaDkFlgrHu/Y6TNEY3u0oHRzppOgSVdRqfpukQVJKWmY1yE9eXeD4r7lyWmZmJjIwMmJrmxpCUlAQASucz3fnJr0Z5r0nNzU0U/jJCH07Qwc9YIi188gWtpIuNuWzd+5wNTD+sS4WmqLsLnrmpMcwVfRbS3H+T5ubmMglQHerVq4eQkBCZYzdv3sTIkSPVeh9tl/f3ampoBFND3fm3aWygez+vdPH3BV1szGWq4RdkTTA10J3/f3nMTXQvBwMayGfFmMvq1q0LiUSCkJAQNGnSBEDuWswWFhaoWrWqUtfgBChERKS6nBzlNjU5evQobGxskJGRAQAYNWoUTp48iaNHjyI7Oxt+fn6IjIzE4MGD1XZPIiLSAyWYywDA29tbnK3SyckJnTt3xjfffIOkpCTExMRg0aJFGDZsGExMlPsyho05IiJSnVSq3Kakly9fysxm2a9fP9jY2GD37t0Acsc2JCUlQRByu+e1aNECa9euxahRo2BmZoalS5fi4MGDqFSpktoflYiIPmJqzGUAMGPGDNjY2GDRokUICQmBjY0N6tSpI5YnJycjLe1/XVy3bt0KIyMjlC1bFpUqVULNmjXxyy+/KH0/3XyXTUREmqVMglMhAZYrVw6RkZFyx/MGhXft2hWJiYkyYyXGjh2LsWPH4u3btzKDx4mIiJSmKJ+p2JhbsGAB5syZI3Ps3dk3//33X5nxdvb29ggICEBmZiYMDAxUHovHxhwREalOzY05iURS6BpzxsbGBZazIUdEREWm5sacorF1BU1somy3yvexMUdERKpTc2OOiIhII9TcmCtpbMwREZHq2JgjIqKPARtzRESkbyQ5OZDkZCusQ0REpM0U5TNtz2VszBERkeqkQu6mqA4REZE2U5TPtDyXaawxFxAQgPj4eLnj3t7eqFChgrgfExODq1evwsHBAc2aNROPh4WF4dq1a+J+7969UapUqeINmoiIcrGbJQDgxYsXOHPmjNxxW1tbdOnSRdyXSqUIDAxEbGwsmjdvDnt7e7HswIEDSE9PBwBUrVoVzZs3L/7AiYgoF7tZFs2VK1fw+PFjcT86Ohrnzp1DWFiYeGzp0qVYvHgxGjVqBCB3xrLDhw8DAKKionD8+HG8efMGR44cQUREBBtzREQlhY05AMCrV69w/PhxmWPHjx9Hu3btxMbc06dP0aNHDyQnJ6N+/fqYM2cOVq1ahY4dOwIAzpw5g+TkZFy+fBnNmzdnY46IqCSxMVc033//vcz+xIkTAQBubm4AgL///hs//PADrl69irp16wIATp48Kdb38fGBj48PwsLCcOTIkRKKmoiIckkBQVGC0+4EqA7169fHzp07xf3Y2Fjs27cP48aNE4/1798f7u7u2Lx5MwwNDZGSkoJbt26J5evWrQMADBs2rOQCJyKi/6con2l3LtOKMXOpqanYtWsX1q9fLx779ddfMXr0aJiamuLPP/9E1apV0alTJw1GSUREouyc3E1RHT2zefNmVK5cGe3btwcABAcHIzAwEPv378fp06chCAIaNWrEt29ERNpCUT7T8lymFY25Xbt2wdTUFH369BGP3bx5E9nZ2ejSpQvq1q0rdj/5888/ZVZRV0ZWVhays/83S01aWpraYici0kvsZilHKpVi48aNmDhxIiQSCYDcXFa6dGkMGjQIlpaWSE9Px61bt7B3794ifUHJfEZEpGY63s1StVZRMVm/fj1GjRols/J5RkYGnj59ilu3bsHf3x+3bt3C2bNnsXv3bpWvv2jRIlhYWIibnZ2dOsMnItI/eclP0aZHjh8/jhcvXmDUqFHisYyMDLx58wbDhw/HiRMncO7cOUyePBmffvppke7BfEZEpGY6nss03pgLCgrCjRs3ZMYXAICjoyPatm0Lc3NzAICDgwMaNmyImzdvqnyPuXPn4u3bt+KW3yyaRESkPIlUqtSmT/z8/NC3b1+ULVtWPObo6AgA6Ny5s3isc+fOePLkCZKSklS+B/MZEZF66Xou03g3Sz8/P3Ts2BGurq4yx729vXH37l1xPzs7G48fP0bPnj1VvoexsTGMjY0/OFYiIvp/gpC7KaqjJ6Kjo3Hs2DGcPXtW5nirVq1gZGSEBw8eoFKlSgCA+/fvw8rKCqVLl1b5PsxnRERqpiifaXku02hjLikpCXv37sWOHTvkymbNmgUvLy+MHDkSzZs3x+HDh2FgYICRI0cCyJ0x7NSpU3jx4gUA4NChQyhbtix8fX1lvhUlIqJiwDFzMjZs2AA3Nze0atVK5ri9vT1mzJiBUaNGYerUqcjIyMDPP/+MBQsWiOPqzpw5gxcvXiAiIgIAsHPnTjg5OYmTqBARUTHS8TFzGm3MhYaGYtCgQejRo4dcWaVKlXDjxg1s2LABwcHBaNu2LXbv3i1+k5mQkCCu7TN06FBxAfFmzZqxMUdEVNw4m6WMjIwM/PDDD/mWLVq0CF5eXjhx4gQsLCywb98+mYZacHAw7ty5gypVqgDIHXtXt25dNuaIiEoCZ7MsOkWLo1aoUAELFizIt6xWrVoya/sQEVEJ4ps5GUuXLi20vHfv3ujdu3e+ZbNmzSqOkIiISBl8M0dERHqHjTkiIvoYsDFHRER6hxOgEBHRx4AToBARkd6RCkq8mdPuBEhERKQwn2l5LmNjjoiIVCcVFCc4LU+ARERECvOZlucyNuaIiEh1OdlAtkRxHSIiIm2mKJ9peS5jY46IiFTHN3NERPQx4Js5IiLSO4I0d1NUh4iISJspymdansvYmCMiItXxzRwREX0M+GaOiIj0DteZIyKijwHXmdNdgoUlBHNTTYdBWkgoZanpEFSn5T9s8mVuoekIVCa1tdV0CCqRpmUU04X5Zk6beHZKgLmxDqX0WSM0HYHKhKxMTYegMklKsqZDUJn1zpGaDqFIWk/YrOkQVCb0HarpEFQipGUAY9eq/8J8M0dERHonOwcwNFBch4iISJspymdansvYmCMiItVxAhQiIvoYcAIUIiLSO+xmSUREHwN2syQiIr3DxhwREX0M2JgjIiK9w9ksiYjoY8DZLImISO9kSwFDBQkuW7sTIBERkcJ8puW5jI05IiJSnaDEmzktHzRORESkMJ9peS5jY46IiFQnCLmbojoqunXrFqKiolCrVi1Uq1at0Lo5OTkICQlBTEwM3N3dUbFiRZXvR0REek5RPitCLktOTsbly5dhbGyM5s2bw8zMrND6sbGxuHnzJiwsLNCkSRMYGxsrfS825oiISHVqngBFKpVi2LBhOHXqFNzd3REUFITPP/8cixYtyrf+ixcv0KVLF6SkpKBq1aq4fPkyvv/+e3z11VeqPAUREek7NU+AcvXqVXTr1g2urq54+/YtkpKScObMGVSvXj3f+mvWrMHs2bPRtGlTPHv2DABw6tQpVKhQQan7KVjxlYiIKB9S/C8BFrgpf7k9e/YgICAAN27cwOnTp3Hu3Dn8+OOPCAoKyrf+zJkzYWNjg3v37uHEiRM4ffo0ZsyYgbCwMPU8HxER6QeF+Uy1y40ePRqffPIJAgMDcfv2bTRt2hSTJ0/Ot+7jx48xZcoUHDhwAKdOncKdO3fg5uaGqVOnKn0/jb2Zi42NxdOnT2WO2dvbo1KlSjLHnj9/jqSkJFSuXBkWFhbi8Xv37iE1NVXuutWrV4e1tXXxBE1ERLnUPJvlwYMH0bNnT7GrZIMGDdC0aVP4+/ujcePGcvWDg4Px6aefwsgoN401adIEjo6OOHjwIObMmaP8c6hBSEgIsrOzZY55eHiIsQFAWloaoqKiYGlpKdMd9M2bN3jw4IHcNS0sLFC7du3iC5qIiHKpcTbLsLAw3Lt3D4cPHxaPTZgwAR06dMCbN29QunRpmfo3btyAiYkJfHx8AAAGBgbo1asXPvvsM2RlZSnV3VJjjbkdO3bghx9+kHnlOHDgQHz99dcAcrvQ9OrVC+Hh4XBwcMDTp0/x3XffieVLlizB3bt3xXPfvHmDhw8f4tq1a/D09CzZhyEi0jfZUsBAudks09LSZA4bGRnJJahHjx6hT58+MsdcXV3x6NGjfC/t4OAgU5aUlIS4uDiEh4cr+wRq07ZtWzg6OsLS0lI8duLECdjZ2QEA1q5di9mzZ6NChQqIi4uDs7MzDh48CBcXF4SGhuLzzz+Xud79+/fRsmVLBAQElOhzEBHpJUX5TMVcJpFIUKVKFfGYq6srpFIpIiMjUb9+fZn6Dg4OSEtLw4sXL+Dk5AQACA8PR0ZGBp4+fYqqVasqDF+jY+aaNWuG48eP51s2b948GBoa4tmzZzAzM8Pp06fRsWNH9OrVC9WrV8fvv/8uU3/OnDk4duwYG3JERCVBhTFzeY2aPPPmzcP8+fNljmVnZ8PExETmmKmpKVJSUvK99NSpUzFgwABYW1ujRo0a2LJlC6ysrJCTk6Pac6jJpk2b0LJlS7njMTEx+OKLL7B//3706dMHmZmZ6NatG2bOnIm9e/eiWbNmuHbtmlg/OTkZ5cuXx5gxY0oyfCIi/aXkmDllc5mRkREMDQ3FY6ampmLZ+5o3b47mzZujW7dumDRpEqKjo7Fz504AUDqfaXTMXHZ2Nu7evYtnz55BeG+mmJcvX6JRo0bi7C+tWrWCRCJBbGys3HWysrKwZcsWjB8/vkTiJiLSd4JUUGoDgPj4eLx9+1bc5s6dK3c9BwcHuZ/vMTExcHBwyPf+PXr0wNmzZ5GamoqzZ89i1qxZcHFxgYuLi9qfVRkvX77E3bt38fbtW5njcXFxkEqlaNWqFQDAxMQETZs2zTeXAbm9ViwtLdGzZ89ij5mIiJTLZ4DyuSwrKwsJCQnisZiYGLHsfQYGBjh9+jSGDRuG8+fPIzU1FUuWLIGRkZHSE6Bo7M2co6MjXr9+jWHDhiEyMhL29vbYunUrmjVrBgD48ssvMXz4cDRu3BiVKlXC5s2b4e3tLZa/y9/fH6mpqRg6dGi+98rKypJpDb//mpSIiFSkwtIE5ubmMDc3L7Rq69atsWfPHixbtgwGBgZITU3FuXPn4OfnByB3/PTly5fRp08fGBgYICkpCU2bNhVzws2bN3H9+nVs2rTpw59NRQ0aNMDixYuRnJyMqKgojBkzBqtXr4ahoSHq1q2L7t27Y/LkyZg4cSJiYmKwfft2/Pbbb/lea/369Rg9enSB4ySYz4iI1EzJpQmUyWUeHh6wsrLC0aNH8cknnwAAjh07hqpVq4qNs3/++QflypVDvXr1AADp6ekyMzEPGjQIXbt2lZkrpDAaa8wNGzYMw4YNA5CbnL7++mv07t0bjx8/hoWFBerUqYNmzZrhq6++QtmyZREfH4/ly5fLvLbM4+fnhyFDhsDKyirfey1atAgLFiwo1uchItIral6aYOLEidiwYQP69++Pzp07Y8eOHahWrRr69+8PAAgKCkL//v2RlpYGMzMzhIWF4dtvv8WAAQMQGxuLlStX4ptvvpEbj1ASzp49K/753r178Pb2RuXKlTFjxgwAwIgRI/Dll1/i9u3bSEhIQKNGjeDh4SF3ncuXL+POnTsyA+ffx3xGRKRmalyawMzMDN999x2+/PJLxMXF4e3bt1i4cCG2bt0q1pkxYwbatm2LZcuWAQAmT56MqlWrwsXFBUePHkVQUBAuXryo9D21YmkCY2NjzJs3D7Gxsbh9+zYAoF+/frCwsMCLFy9w9+5dnD9/HhMmTMCxY8dkzn3w4AH+/fffQrtYzp07V+a1aHx8fLE+DxHRR0/hsgRKNPbeUbZsWQQFBaFGjRr4559/0LFjR/z777/iG6oKFSqgb9++4hd6TZo0wXfffYegoCA8e/YMe/bskRu7oAm1atXC0KFDcerUKQDA+fPnMXToUBw9ehR3797F8+fPUblyZXTv3l3uXD8/P/j6+qJy5coFXp/5jIhIzdSYywBg+vTp2LhxI27evInIyEgcOXIEgwYNEsvbt28v88Xjpk2bYGlpibNnz6JRo0a4efOmSkMGNPZmLj09XWY19OjoaABAmTJlAAB37tzBmDFjxMTt5uaGypUrIzQ0FF26dBHP8/Pzg5eXFxo0aFDgvYyNjVVaSZ2IiBRQYTZLZZUvXx5LlizJt6xRo0Y4cOCAzLGWLVvmO+lISXo/lwG5+ezdXFauXDm4u7sDyB0f0aFDB2zatAmCIEAikQAAEhISsH//fuzdu7fQ+zGfERGpmZKzWaqib9++6Nu3b75lS5culdk3NzfHzJkzVb5HHo015rp3744uXbqgYcOGePbsGb7//nt06dJFXKqge/fuWLhwIUqVKgUHBwccO3YMDx8+RKdOncRrpKenY/v27fj555819RhERHrp3UHhhdX52J04cQI7duzA4MGDYWtri2PHjuHgwYM4c+YMgNxvYKdPn44pU6agT58+SEhIwDfffINu3bqJDTkA2LZtG8qWLYuuXbtq6lGIiPSSonym7blMY425vIHu8+fPh7W1NSZOnIhx48aJyc3Pzw9r167Fli1bkJycjCpVquDcuXPit5tA7viCmjVryry6JCKiEqDmMXO6qmfPnjA2Nsa2bdvw6tUruLq6IjAwEA0bNgSQ26vkwoULWLNmDb755huUKlUKQ4cOlVtb7vr165gxY0a+48KJiKgYqXHMnCZorDFnZ2dXYHcaIPeV4/Tp0zF9+vQC67Rr1w7t2rUrjvCIiKgwghKNOUWzXX4kunTpItP9/32enp4yg9/zk7euEBERlTBF+UzLc5lKjbnk5GTs3bsXp0+fxu3bt5GYmAgrKyvUqFED3t7eGDJkCBwdHYsrViIi0hZ8M0dERB8DfXgz9+bNGyxduhS//vor6tatixYtWqBjx46wtrYW19U5ceKEOE30999/D2dn5+KOnYiINEWFdeaIiIi0lpLrzGkrpRpzhw8fRmZmJkJDQwttpCUmJmLLli3YsmUL5s2bp7YgiYhIuwjZAgSJgglQsrU7ARIRESnKZ9qey5RqzL27wHdhypQpg2nTpn1wUEREpOXYzZKIiD4GOt7NskiLhsfExIiLe79//NGjRx8cFBERaTdBqtxGRESkzXQ9lxWpMXfo0CG4u7vLre926NAhrvlGRKQP8r7JVLQRERFpMx3PZUVqzAFAt27dsGPHDnz++eeQSrW8yUpEROolVXIjIiLSZjqey4rcmCtfvjwuXLiAu3fvol+/fkhLS1NnXEREpMWEbEGpjYiISJvpei4rcmMOAKytrREQEAALCwu0b98e8fHx6oqLiIi0maDkRkREpM10PJeptGh4fkxMTLBjxw7Mnj0b8+bNw9ixY9URFxERaTFBKkBQMI5AUTmp0fRhgLmppqP4uBl80PffGiHxP6XpEFQmjOin6RBIzyjKZ9qey4rUmPP19UWzZs3EfYlEgh9//BFeXl6wsrJSW3BERKSllBlHoOXjDIiIiBTmMy3PZUVqzLm4uOR7vF8/fptCRKQPlJmuWduncyYiIlKUz7Q9l6nUmAsJCcHx48cLrdOgQQP4+Ph8UFBERKTlBCj+tlK7e6YQEREpzmdanstUasyFh4djz5494n58fDzS0tJQsWJF8VhOTg4bc0REHzkhW3F+E7JLJBQiIqIiU5TPtD2XqdSY69evn0xXSj8/P4SEhMDPz0/tgRERkfYShNxNUR0iIiJtpiifaXsu++DZLImISP9wzBwREX0M9GrMHBEREQDOZklERB8HfZzNkoiI9BvfzBER0cdAr97MBQYGwt/fX9wPCQlBbGwsZs2aJR5r2rQpevXqpbYAiYhI+3DMHBERfQz0asxcbGwsrl69KnPM2tpa5pi9vb16IiMiIq0lZEsgQKKwDhERkTZTlM+0PZep1Jjr0aMHevToofYgcnJykJaWBnNzcxgaGqpULggCBEGAgYGB2uMiIqL8CYIEgqCgMaeg/GOUlpYGQRBgYWGhcnlOTk6+OZCIiIqPonym7blMK1pAQ4cOhZWVlUwXTkXlT548wYgRI1C2bFlYWFigZcuW+O+//0oqZCIivZY3xkDRpk9CQ0NRpkwZNGzYUOlyQRCwa9cu1K9fHxYWFrC3t8esWbOQk5NTUmETEek1Xc9lSjXmduzYgWnTpiEqKqrQegkJCfjpp58wb948pQM4cOAA4uLiUKpUKZXK//zzT3Tv3h1RUVFISEiAp6cnunTpwgRIRFQC2JiTlZ2djZEjR6JTp04qlScnJ+P8+fPYt28f0tPTcerUKWzbtg2rV68uibCJiPSerucypbpZ9urVCw8fPkT9+vVRp04dtGjRAtWrV0fp0qWRmpqKqKgoBAYG4tKlSxg8eDDmz5+v1M1jY2Px9ddf4+zZs6hbt65K5V999ZXM/qefforVq1fjxYsXqFixolL3JyKiomE3S1mLFi2Cp6cnatasiQcPHihdXrp0aaxfv17cd3d3R9u2bXHz5s0SiZuISN/pejdLpRpzVlZW+P777zFjxgwcOHAAp06dwsmTJ5GYmAhLS0u4ubmhS5cu2LFjh0oToHz22WeYNm0aXFxcilQulUrx9u1bJCQk4JdffkGrVq1QoUIFuXpZWVnIzs4W99PS0pSOkYiI5AlSCQSpgsacgvKPxY0bN/D7778jJCQEmzdvVrkcANLT05Geno7r16/j7Nmz2LJlS771mM+IiNRLUT7T9lym0gQolpaWGDlyJEaOHPnBN96+fTsSEhIwadKkIpUDuUsldOzYEW/fvoWzszP8/f0hkch/4IsWLcKCBQs+OGYiIsollUogVZDgFJV/DDIyMjB8+HCsXbsWVlZWKpfnGTx4MAICApCRkYFJkybBx8cn33rMZ0RE6qUon2l7LtPIBCgvX77EjBkzsGrVKqSmpiIlJQXA/76ZVFSep1mzZkhJSUFaWhqmT5+OVq1aITIyUu5+c+fOxdu3b8UtPj6+RJ6TiOhjJQgS8dvMAjct75qiDgsXLkSdOnXQsmVLpKSkIDMzE4IgICUlBVKpVGF5Hn9/f6Snp+Phw4cIDAzExIkT870f8xkRkXopzGdanstUejOnLlFRUUhNTUWrVq3EY6mpqfj0009x9OhRTJ06tdDyP/74Q+Z6pqammDx5MmbOnIkbN27Idcs0NjaGsbFxsT4TEZE+4aLhucLCwhAQEABHR0cAud0gs7Ky4OjoiAsXLigsb9Cggcz1qlWrhoEDB2Lr1q353o/5jIhIvfRq0XB1adSokfi2LY+lpSW2bduGfv36AYDC8oEDB2Ls2LFwd3dHcnIy1q1bB2NjYzRp0qRkHoKISI9xApRc+/fvl9lfuXIl/Pz8EBYWplT5gQMHEBoaiv79+6NcuXK4desW/Pz80L1795J5ACIiPacXE6CUBEtLSxgZFRzO++Vz5szB/PnzcfXqVZibm8PT0xNnz55F+fLlSyJcIiK9JpVKIM1njPL7dfSNiYlJgUvt5Ffes2dPREVFYdCgQYiJiUHFihUxZswYTJ8+vSTCJSLSe4rymbbnMq1pzMXExKhU7u7uXuAi40REVLyKozH35MkTrFy5ElFRUahduzamTp2KMmXKFFg/ODgY27dvR2xsLCpXrozx48ejWrVqKt1T3SZOnFjgeLf8yo2NjTFt2jRMmzatJMIjIqL3FEdjbvfu3Thy5AiMjY0xcOBAdO3atcC6aWlp2LhxIwIDA2FkZIQ2bdpg+PDhhb7kepdStfIGZCujRo0aaNy4sVJ1iYhIRynRzRIqdE159eoVmjRpAm9vb/Tu3Rvbt29HmzZtcO3aNZiYmMjVv3z5Mtq2bYvp06ejTZs2CAgIgJeXF0JCQgpczoaIiEiOonymYjfLJUuWYPny5fjhhx+QmpqKAQMGwM/PD5988km+9QcMGIDIyEjMmDED6enpmD9/Pq5fv441a9YodT+lGnM3b97EsmXLlLpg//792ZgjIvrISQUJpAoSnKLyd61ZswZ2dnbYtWsXJBIJ+vbtCycnJ+zbtw/Dhg2Tq3/o0CG0atUKixcvBpCbe86cOYNTp07h008/Ve1hiIhIbynKZ6rksrS0NCxevBgbNmzA4MGDAQCCIODbb7/NtzGXnp6OY8eO4fTp0/D29gaQO7Hj119/rd7GXL9+/cSJR4iIiASpBIKCbpZ5C62+v7C1kZGR3IyMFy5cgI+Pj7hWqLm5Odq0aYOLFy/m25hzc3ODv78/MjIyYGpqipcvXyIhIQFubm4f8lhERKRnFOUzVXLZjRs3kJKSAl9fX/GYr68vZsyYgejoaFSsWFGmvpmZGSpVqoQHDx6Ijbn79++jZs2aSsevkXXmiIhIt+VN5axoAwA7OztYWFiI26JFi+Su9+rVKzg4OMgcs7e3R2xsbL73HzVqFAYOHIjq1aujQ4cO8PDwwPLly9G6dWu1PysREX281J3LjI2NZcZ729vbA0CB+ezYsWNYvXo1WrZsCS8vL5w5cwa7d+9WOv4iT4CSkJCANWvW4ObNm0hISIDw/0/ao0cPTJ06taiXJSIiHaBKN8v4+HiYm5uLx/Mb1G1sbIz09HSZY+np6fmOlwOAkydPYuPGjfjiiy/g5uaGwMBAzJ8/H82bN0fdunVVfRwiItJTynazVDaXZWdnIzs7WyzPy2355bOcnBxMnz4dZcuWxWeffYbMzEysWLECP/zwA/z8/JSKv0iNufT0dLRo0QJ169aFkZERsrOzUaNGDfz999+YP39+US5JREQ6JEdqgBxJ4Z07cqS55ebm5jIJMD+urq4IDw+XOfbo0SO0bds23/qLFi3CJ598grlz5wLIHQ7w8OFDrFq1Chs3blTyKYiISN8pymeq5jJBEPD48WPUqFEDQG4uMzAwyHdyrosXL+LYsWNITEyEjY0NAMDLywv16tXD7NmzUblyZYXxF6mb5blz52BlZYX9+/fD29sbtWvXxubNm9G+fXuFSwwQEZHuy1tkVdGmrH79+uHw4cOIiooCAAQFBSEwMBB9+/YFAFy9ehXdunVDVlYWgNzuLvfu3YNUKgWQ+yVjeHg4ypYtq+YnJSKij5k6c5mbmxvq1q2L1atX//+1Bfz666/w8fGBlZUVAOCrr77Cli1bAOTmMgAIDQ0Vr3H79m0YGBgUujTPu4r0Zi4qKgru7u4AgFKlSiE1NRUA0LhxY1y9ehWDBg0qymWJiEhHSP9/U1RHWQMGDMDx48fh7u6OunXrIiQkBPPmzYOnpyeA3LVGjx49ipycHBgbG2Px4sXo1q0bqlWrBldXV9y+fRvOzs5cr42IiFSiKJ+pkssAYNu2bejSpQsuXLiA9PR0ZGdn49SpU2L5hQsXYGhoCACoW7cuZsyYgY4dO8LLywtZWVm4desWVq5cidKlSyt1vyI15qRSqRiEq6srAgMDkZGRgf/++w+urq5FuSQREekQZb6tVOXbTIlEgq1bt2LWrFmIiopCzZo1UalSJbG8WbNmOHLkiDjmoHbt2nj48CFu376NuLg4lC9fHrVr1y7awxARkd5SlM9UyWUA4OnpiYiICAQFBcHY2BiNGzeWmfVy5cqV4hs5AFi6dCmmTp2KsLAwGBoaok6dOkq/lQOK2JirWLEiMjIyAOQmWCcnJ1hZWcHKygrBwcFFuaRGSN6mQCJkaTqMj55gqdw3C9pEkpqi6RBUJ6j63ZEWSNa9z9ngabSmQ1CJQUbx/IxT9zpzedzc3PJdXsDBwQHdunWTOWZoaAgPDw+V7/ExMvzzKAxNijynWYkT3BSPA9E2QhUXTYeguvRsTUegsswpmzQdQpGcv1lJcSUt4/vncU2HoBJJZvH8e1bnOnN5LCwsChzz3bJlS7ljDg4OcjM6K6tIP/nfTagSiQRnzpxBaGgoKleurFJLkoiIdJO638wRERFpgrrfzJW0Ik2AcvjwYfzyyy/ivrGxMTw8PHDhwgWZ40RE9HHKkUqU2oiIiLSZrueyIr2Ze/78OR48eCB3/OnTp4iIiPjgoIiISLtJIYEUCrpZKignIiLSNEX5TNtzmUqNuUePHiE4OBjXrl1DVFQU9uzZI5ZlZWVh165d6N+/v9qDJCIi7SIIuZuiOkRERNpMUT7T9lymUmPuv//+w8KFC5GYmIi0tDS8ePFCLDMzM0ODBg0wZswYtQdJRETaRSooHhQu1fIESEREpCifaXsuU6kxN2DAAAwYMAAnTpxAREQExo8fX1xxERGRFmM3SyIi+hjoVTfLPD4+PgCA7OxsvHz5ElLp/6ZEt7S0hI2NjVqCIyIi7cRulkRE9DHQ9W6WRZrNEgCmTJkCc3NzVKhQAc7OzuI2a9YsdcZHRERaSCoYIEfBJhWKnGKIiIhKhKJ8pu25rEhv5s6dO4fdu3fjn3/+QZ06dWBg8L+HNDU1VVtwRESknXLHGCiuQ0REpM0U5TNtz2VFasxFR0fD19cXrVq1Unc8RESkA6SCRIkJULR7nAEREZGifKbtuaxI7w09PDzyXWeOiIj0gwCJUhsREZE20/VcVqTGnLm5OQwMDDBkyBDs3r0bBw4cELcbN24odY3U1FT88MMP8PT0RK1atTBy5EhERUWJ5Rs3boSjo6O4PX36VOZ8ReVERFR88rqlKNr0wdWrV9GnTx9Uq1YNzZs3x6ZNm2TK69SpI+aqyZMny52vqJyIiIqPrueyInWzDA4ORkJCAhISEnD9+nWZskGDBqFBgwYKr7F06VJYWFhg69atkEgkmD9/Pjp27Ij79+9DIpFg6NCh6N69O8LDw9GqVSvk5OTInK+onIiIik8OJMhR0PUkR8u/zVSHN2/e4JtvvsGUKVNQp04dXL9+HaNGjYKxsTFGjBgBADh79iykUikmT56M169fy11DUTkRERUfRflM23NZkRpzAwcOxMCBAz/oxgsWLIBE8r8PZ/78+ahfvz6eP3+OChUqwMLCAhYWFgUmNkXlRERUfDhmLpeVlRVOnz4t7lepUgX79u3Dv//+Kzbm7O3tAQBmZmb5XkNRORERFR+9HDOXJyUlBcHBwbh7967K577bkMvJycH+/fvh5uYGR0fHDwkpX1lZWUhLS5PZiIio6AQlt4/du7kMAKKionDlyhW0adOmWO7HfEZEpF66nsuK3JjbtWsXKlasiNatW2P16tUIDw9H/fr1kZWVpfQ1goKC4OjoCEtLS2zfvh0HDx6EoaFhUUMq0KJFi8Q3eRYWFrCzs1P7PYiI9EneN5mKNn0xZMgQlC1bFlWqVEGfPn0wcuTIYrkP8xkRkXrpei4rUmMuKioKX3zxBY4dO4YVK1YAAKpVq4aGDRti165dSl+nQYMGCAkJwX///YfevXujffv2ePnyZVFCKtTcuXPx9u1bcYuPj1f7PYiI9IlUyU1frF27Fjdu3MCxY8dw5MgRfPfdd8VyH+YzIiL10vVcVqTG3JUrV9CpUyc0b95c5ni9evUQEhKi9HWMjY3h6OiI2rVrY8WKFUhKSsKFCxeKEpLC+5ibm8tsRERUdIIgUWrTF2XKlIGzszN8fX0xceJE7N+/v1juw3xGRKReup7LitSYs7S0RFJSktzxZ8+eoUKFCkpd4+uvv0ZERASA3DEAfn5+yM7OhoeHR1FCIiKiEpQtKLd97I4fP45du3aJY9ciIyOxd+9eNGrUSMORERGRMnQ9lxWpMde8eXNcu3YNf/zxh7gkQGBgIHbu3ImuXbsqdY3WrVujR48esLa2hpWVFTZs2ICDBw/C1dUVAHD58mU4OjqiVatWAIBGjRrB0dFRXApBUTkRERUfLhqeq3Hjxrh48SLKly8Pa2truLu7o169elizZo1YZ8CAAXB0dMSff/6JP//8E46OjhgwYIDS5UREVHx0PZcVaWkCW1tb7NixAyNHjkRsbCwMDQ3x+++/Y9WqVahdu7ZS1+jevTu6d++OlJQUmJmZwchINhQvL698u2zmDfZWVE5ERMVHmYVUtX2hVXWwtbXFunXrsHbtWqSkpKB06dJyddavX4+MjAyZY6ampkqXExFR8VGUz7Q9lxWpMQcAPj4+ePz4Me7cuYO3b9+ifv36KFOmjMrXsbS0zPe4iYlJocsUKConIqLipMy3ldr9baY6GRgY5NuQA6AwNxYldxIRkbooymfancuK3JgDAHNzc44LICLSQ3wzR0REHwO9fTMXFxeHffv2ITo6GlLp/ybtbNq0KXr16qWO2IiISEuxMUdERB8DvWzMvXr1CnXr1oWzszPc3Nwgkfzv9WNiYqLagiMiIu2UI0iQo2C6ZkXlREREmqYon2l7LitSY+706dOoXbs2zp49q+54iIhIByizkKq2L7RKRESkKJ9pey4rUmPO3Nwc9vb26o6FiIh0hDILqWr7QqtERESK8pm257IirTPXqVMnPHjwABcvXlR3PEREpAOkSm5ERETaTNdzWZEacxYWFmjXrh1atWqF0qVLo2zZsuI2ffp0dcdIRERaJm/AuKKNiIhIm+l6LitSN8uLFy9iw4YNWLdundwEKOXLl1dbcEREpJ2E/98U1SEiItJmivKZtueyIjXmIiIi0LNnT4wfP17d8RARkQ7gbJZERPQx0MvZLOvWrQs/Pz91x1LyMrMAwyL1NKWPXVaWpiNQnVTbe3XLk6SmajoE1cXq2PIrGcXzb5nrzGmX4A2AmaF2/8LxLluL+5oOQWVVd1XSdAgqC9lnrukQVJYjlNJ0CEVS0VL38lnMQd2KOS0nu1iuq5frzFlbWyM5ORnDhw+Hr68vTExMxDJXV1c0aNBAbQESEZH2Ka6lCXJycvDmzRvY2NjIdOF/X1JSEtLS0uSOly5dGhYWFkW4MxER6aPiWpogJSUFhoaGMDcv+EuVnJwcvHr1Su64RCKBg4ODUvcpUmMuODgYmZmZCAoKQlBQkEzZoEGD2JgjIvrICULupqiOKtauXYtvvvkG6enpsLW1xW+//YaePXvmW3fu3Lk4cOCAuC+VSvHq1Sts3rwZo0ePVu3GRESktxTlM1Vz2YsXLzB06FBcunQJgiCgW7du2L59O6ysrOTqPn/+HI0aNZI5lpycDGtrazx//lyp+xWpMTdw4EAMHDiwKKcSEdFHQAoJpCi8W5+i8nedP38eU6dOxdGjR9G+fXts27YNAwcORFhYGFxcXOTqr1mzBmvWrBH3Dx48iE8++QR9+/ZV+p5ERESK8pkquQwARo4cCTMzMyQkJCAjIwM+Pj6YMmUKNm/eLFfX2dkZMTExMsc8PT3RsWNHpe/HAWNERKSyHEG5TVnbtm2Dr68vOnToAIlEglGjRsHV1RV//PGHUuf7+flh8ODBsLa2LuITERGRPlJnLnv27BlOnjyJBQsWoFSpUrC1tcWcOXPwxx9/ICMjQ+H5QUFBuHHjBsaNG6f0PYv0Zg4AEhISsGbNGty8eRMJCQkQ/v8dZI8ePTB16tSiXpaIiHSAoMQEKHldU94f22ZkZARjY2OZY3fu3IGvr6/Msfr16+POnTsKY3n06BFOnz4t1+2fiIhIEUX5TJVcFhoaCiA3f+WpX78+0tLS8PjxY9SqVavQWPz8/NCpUydUrVpV6fiL9GYuPT0dLVq0wO3bt2FkZITs7Gy4urri3r17aNiwYVEuSUREOkRQcgMAOzs7WFhYiNuiRYvkrvf27Vu58QSlS5dGqhIznq5fvx4NGjSAl5fXBzwRERHpI3XnMhMTE5iamorHSpcuDQAK89nr16+xd+9elZd+K9KbuXPnzsHKygr79++Hn58fbty4gfXr12PIkCFy/T6JiOjjo8rSBPHx8TKzeRkZyacea2trJCbKLvuQkJCAMmXKFHqPzMxMbNu2Ld+kSkREpIiySxMom8syMzPx9u1bcWblhIQEAFCYz37//XfY2NigW7duKsVfpDdzUVFRcHd3BwCUKlVKbGk2btwYV69eLcoliYhIh+TN/qVoAwBzc3OZ7f1uKQDg5eWFS5cuvXN9AVevXoWnpycAICMjI98vCw8cOICMjAwMGTKkeB6UiIg+aurMZR4eHjAyMpLJZ5cuXYKtra04mVd8fDySk5Plzl2/fj3Gjh2bbyOxMEVqzEmlUhgaGgLIXVcuMDAQGRkZ+O+//2BjY1OUSxIRkQ6RKrkpa/z48QgKCsLy5csRFhaGqVOnIj09XWykBQQEwMnJCenp6TLn+fn5YdiwYShVSjcXGiYiIs1SZy4rU6YMPvnkE0ydOhXBwcE4d+4c5s+fj8mTJ4ttJx8fHyxYsEDmvPPnz+P+/fv49NNPVY6/SN0sK1asKM7I0qxZMzg5OcHKygpWVlYIDg4uyiWJiEiH5AiAgYJulqrMAFarVi0cPXoU8+bNw6pVq1C7dm2cOXMGtra2AAAzMzM4ODjILCQeERGBhw8fYu3atUV5BCIiIoX5TJVcBuSumTpnzhwMHjwYxsbGGDNmDL799luxvGzZsuI4ujx//vknBg4ciIoVK6p2MxSxMfduX06JRIIzZ84gNDQUlStXVtgfNE96ejo2b96Ms2fPwtDQEO3atcPo0aPFV5YHDhyQWUNoz549cHR0lLnG/v374e/vj6SkJNSsWRNTpkyBs7NzUR6JiIhUoMqYOWW1a9cO7dq1y7fM19dXrptllSpV8OLFC9VuUgxu3ryJzZs349GjR6hSpQomTJiAOnXqiOU9e/ZEUlISAKBjx46YO3euzPkvXrzAqlWrcOfOHVhaWqJ79+4YMmSITMOViIiKh7Jj5pRlbm6OFStWYMWKFfmWHz9+XO7YqlWrVLvJO5TqZpmdnY2UlJQCt4yMDFSrVg3GxsZKraEAAJ07d8b9+/cxZMgQdOnSBYsXL5Z5tdi0aVPMnz8fo0ePxrlz5+S61mzYsAFjxoxBq1atMGnSJERGRqJFixb59kElIiL1EiBRavvYnTx5EqNHj0aNGjUwceJE5OTkoFGjRrh9+7ZYZ8aMGZg/fz6MjY1x7949mfMzMzPRunVr3L59GxMmTED79u3xxRdfYPny5SX9KEREeknXc5lSb+Y2bdqECRMmKHXBzz77DH5+fgrrHTp0SGZxV0tLSwwZMgSbNm2CkZERKlasiIoVKyIsLCzf848cOYIRI0aIcXl7e6NUqVK4ceMGWrdurVSsRERUNMXxZk4XNWvWDNeuXRPfonXt2hVXr17FgQMHUK9ePQBAixYtAOTm0vfdu3cP4eHhuHjxIhwcHADkTjJ2+PBhTJ8+vYSegohIf6n7zVxJU6oxN3ToUJnFXAVBwJAhQ9CsWTMMHToUJiYmOHXqFH799VfMmjVLqRu/25ADgOjoaNja2io9g0u9evVw5coV5OTkwNDQECEhITA1NUX16tXl6mZlZSE7O1vcf3/RPyIiUg0bc7neXxsvOzsbsbGxsLe3V+r8ypUrw8rKCiEhIfDx8YFUKsXNmzdlFpx9F/MZEZF66UVjLm9ykzwXL16EhYUFfvnlF/FYvXr1EB0djb///huTJ09WKYjw8HAsXLhQpXWC5s+fj08//RQuLi4oX748oqKicOTIETg5OcnVXbRokdysMUREVHTvLqRaWB19M2vWLJibm2P48OFK1bexsUFAQAAGDhwIJycnvHz5El5eXli2bFm+9ZnPiIjUS1E+0/ZcVqSlCZ48eZLvRCe2trZ48uSJStcKDw9H+/btMX78eIwbN07p89auXYtz585hwYIFmDdvHvr06YNx48blOxh+7ty5ePv2rbjFx8erFCMREcnKEQSlNn0yb9487Nu3DydOnJB7Y1eQ+Ph4jBkzBp06dcKCBQvwww8/4ObNmwU25pjPiIjUS9dzWZFms2zUqBHGjRsHf39/9O7dGwAQFBSENWvWKDVeLk9oaCg6duyIzz//HLNnz1YphoULF2LZsmUYNWoUAKBLly6oXbs2du7cia+//lqmrrGxcb4L+xERUdGwm6WsadOm4dChQzh//ry4MKwy9u3bh+TkZGzZskU8ZmlpiaFDh2Lu3LlyM1oynxERqZeud7Ms0pu56tWr49dff8XIkSNhYWEBa2trtGzZEmPHjkWvXr2Uusb169fh7e2NmTNnqtyQAwAnJycEBQWJ+0+fPsXz58/z7WZJRETqJSi5fewEQcD48eMREBCACxcuqNSQA3JzWUJCAsLDw8VjgYGBcmvqERFR8dD1XFakN3MAMGrUKPTt2xe3b99GVlYWateurfSAbwDo27cvMjIy4O/vD39/f/H4/v37Ua5cOYSEhGDKlCl4+/YtAGDQoEEwMzPDb7/9htq1a2PdunUYMmQIjh8/Dnt7e4SGhqJHjx4YNGhQUR+JiIiUxDdzuXbv3o3169ejXr16GDJkiHjc19dXnBBs2rRp+O+//8RlCdq2bQtPT08sX74cPXr0wJAhQ+Du7o46deogISEBqamp+P333zXyPERE+kbX38wVuTEHABYWFqhSpQqkUikyMzMRHR0NS0tL2NjYKDx3165dyMzMlDuetyK6i4sL5s+fL1eetzJ6q1at8PjxYzx8+BDJyclwcXERp3UmIqLiJQi5m6I6H7t27drh7NmzcscdHR3FPw8fPhzdu3eXKc8bd25gYIDNmzfjxx9/REREBEqVKoXq1avDxMSkeAMnIiIAivOZtueyIjfmpkyZgrVr18pMkQwov85c8+bNCy23sbFB27ZtC61jbGyM2rVrK7wXERGplwBAqkSdj52Tk5PC7v3u7u4Kr1OuXDmUK1dOXWEREZGSFOUzbc9lRWrMnTt3Drt378Y///yDOnXqwMDgf0PvTE1N1RYcERFppxwpoGhEV46i1h4REZGGKcpn2p7LitSYi46Ohq+vL1q1aqXueIiISAdIofjNnJbnPyIiIoX5TNtzWZFms/Tw8MCDBw/UHQsREekIAf8bZ1DgpukgiYiIFFCYzzQdoAJFejNnbm4OAwMDDBkyBN26dZMZqO3q6ooGDRqoLUAiItI+fDNHREQfA11/M1ekxlxwcDASEhKQkJCA69evy5QNGjSIjTkioo+cIAgQFHxfKWj7FGBERKT3FOUzbc9lRWrMDRw4EAMHDlR3LEREpCNyBCUmQNHu/EdERKQwn2l7LitSYy4nJwdZWVn5X9DICEZGH7R8HRERaTmpoEQ3Sy1PgERERIrymbbnsiJNgLJx40aYm5vnu02ePFndMRIRkZaRCsptRERE2kzXc1mRXqENGTIEHTp0EPdzcnIQGBiIJUuWYPbs2WoLjoiItJMAJcbMaf0cYEREpO8U5TNtz2VFasyVLl0apUuXljnm5uaGO3fuICAgAOPHj1dLcMVNsCkDwZyLnJM8waaMpkNQnVTb51uSJ1hbazoElUmcnTUdgkqkaRkAlqv/uuxmqVUadkyEubEODXGYNULTEahMF/851z/TU9Mh6I30CZs1HYLKzFcO1XQIKklLywDKblf7dXW9m6Vaf/KXLVsW4eHh6rwkERFpIWXW3tHyCcCIiIgU5jNtz2VFaszFxMTINdqePn2KX3/9FUuWLFFLYEREpL1yBAESBc25HG3PgEREpPcU5TNtz2VFasydOHEC06ZNE/clEgns7OwwYsQIDB2qW69siYhIdVw0nIiIPgZ6uWj4iBEjMGKE7vV3JyIi9eCi4URE9DHQ9UXDlVqa4ODBgzLdJ69fv46AgIBiC4qIiLSboMRUzlqe/4iIiBTmM23PZUo15lJTU3Hnzh1xPygoCH/99VexBUVERNpNCkGpjYiISJvpei5TqptlmzZtMGXKFPTv3x9OTk4IDQ3Fq1evMGXKFLm6LVq0QP/+/dUdJxERaRHOZklERB8DvZjNslKlSrh27Rr27t2Lp0+fIiMjA1lZWUhJSZGrm5GRofYgiYhIu+TO7sXZLImISLcpymfansuUngClSpUqmDVrFgDg77//xqNHj/Dll18WW2BERKS9pFC8NIG2d00hIiJSlM+0PZcVaTbLbt26qTsOIiLSIVIl1pmTavm3mURERIrymbbnsiI15tThn3/+wb59+8T9RYsWwc7OTtxfsmQJoqKi5M6bPHky6tatCwC4efMmDh8+jLdv36JJkybo2bMnJBJJ8QdPRKTnBCixNIGWf5upLtOnTxeHHTRp0gSjRo0Sy+7evYvVq1fLnePs7Iy5c+cCyB2esHfvXty6dQv29vYYPHgwnJ2dSyZ4IiI9pyifaXsuU2o2y+JQrlw5eHh4wMnJCevXr0dycrJMuZubGzw8PMQtMzMTW7duhYODAwBg1apVmDBhAgRBgIWFBb788ksMGTJEE49CRKR3pEpu+qBevXrw8PDAjRs3cObMGZkyKysrmVzm4eGBv/76CzExMQCAtLQ0eHl54fz586hQoQJu374NNzc3BAcHa+JRiIj0jq7nMo29matXrx7q1auHsLAwzJ8/X668T58+MvsnT55E7969Ua5cOQBAz549ZcbstWvXDi1btsTq1avFOkREVDw4Zu5/RowYAQC4ePGiXJmzszPGjx8v7t+9excxMTH47LPPAACGhoY4ceIEypcvL9aJi4vD5s2b0ahRo2KOnIiI9HLMXEl7/vw5jhw5glOnTonHXFxcZOokJiaiVKlSsLCwkDs/KysL2dnZ4n5aWlqxxUpEpA9yBMXfV+bWUV5mZiYOHTqEqKgo1K5dG126dFHYdT48PBwnT56Eqakp+vbtCxsbG5XuWdL8/PzQokULcbiAiYmJTENOEAQkJSWhbNmy+Z7PfEZEpF6K8pmquQwA7t+/j+PHj8PY2Bjdu3dX2HU+KysLx44dw6NHj9CkSRO0aNFC6XtprJulKjZt2gRXV1e0bdtW5vijR48wfvx4DBgwAF9//TX27duHUqVKyZ2/aNEiWFhYiNu7Y/OIiEh16l40PCMjA61bt8YPP/yAqKgoTJo0CQMGDCj0nOXLl6Nhw4a4cuUKbty4AW9vb8THx3/ooxWbt2/fYseOHTJv6vL8+uuvGDt2LLy8vODq6oqZM2fmew3mMyIi9VL3ouH+/v7w8PDA9evX8c8//6BWrVq4fPlygfVfvHgBT09PLFiwAE+ePMH8+fOxZMkSpe+n9W/mcnJysGnTJnz11VdyZaVKlYKHhwcSEhIQFhaGLVu2wMfHB4aGhjL15s6dK5MY09LSmACJiD5AXopTVEdZGzduxNOnT3H//n1YWlpi1qxZcHV1xYkTJ+Dj4yNX/+rVq5g9ezYCAwPRoEEDAMDTp0/lfv5rkz179sDAwAD9+vWTK3N1dQUAGBkZ4a+//sKVK1fQqVMnuXrMZ0RE6qUon6mSy6RSKSZPnozvv/8eX3/9NQDgiy++wJdfflngWOhx48ahWrVq2L9/v5jDbt++rfQ9tb4xd/ToUbx69Uock/AuR0dH8RvOzz//HOXKlUNAQIDc0gnGxsYwNjYukXiJiPSBFFBizFyu97sCGhkZyf1MPn78OHr06AFLS0sAQMWKFdG6dWsEBATk25jbsmULOnfuDIlEguXLl6N8+fLo3r27eL428vPzw8iRI2FmZiZX1qVLF/HP9vb2mDFjRr6NOeYzIiL1UpTPVMllt27dwvPnz2UmZRw6dCh+/fVXxMXFyXWhj42NxdGjR3Hu3Dns2rULSUlJaNq0qUpjprW+m6Wfnx8GDBgAW1tbmePvjp8Dcl9RZmVloUyZMiUZHhGRXlKlm6WdnZ1M18BFixbJXe/JkydyYwoqVqyI6OjofO9/7949PH78GCNGjEB0dDTWrFmD2rVr48WLF+p/WDW4ceMGrl27Jk58kic0NFQu5qdPnzKXERGVEHXnMkNDQzg5OYnHKlasCAD55rOwsDAIgoCpU6fixIkTuHfvHjp06JDv5JAF0dibuQcPHuCXX37B69evAeR2HbGyssLMmTNRpUoVAEBkZCROnDiR7wxhhw8fxtdff4369esjOTkZZ86cwbhx49C8efOSfAwiIr0klWQDCpb1lCJ3oo74+HiYm5uLx42M8k897092IpFIIBSwWGtOTg5iYmLw+PFjWFlZQRAENGrUCD/99BNWrFihwpN8uFWrVuHevXsICgoCAIwfPx61atWSmXF53bp18Pb2Ro0aNWTOzc7Ohq+vLypUqIBy5crh1q1biImJwd9//12iz0BEpK8U5TNVcxkgm8/y/pxfPsvJyQEA9OjRA99++y0AoHv37ujWrRsmTpwIe3t7hfFrrDFnaWkJDw8PAJCZ2OTdCUzS0tKwadMmNGvWTO78X3/9FQ8fPkRQUBDMzc3xyy+/iI1AIiIqXrnfVCq3NIG5ublMAsxPfm/hoqOjUatWrXzrOzs7w9jYGFZWVgByk2WTJk3w8OFDJZ9AfVxdXWFqairmNOB/38Tmad26NSZNmiR3rru7OwIDA3H+/HnExMRgxIgRaNWqFbtSEhGVEEX5TNVclvdlY97bubzc9n5eACD2SGnatKl4rGnTppBKpXj8+LF2N+bKly+f74xe76pVq1aBiRwAqlevjurVq6s7NCIiUkCqxFKqUhUGjfv4+GD58uX4+eefYWFhgRcvXuD8+fOYMmUKgNzeHPv27cPs2bNhaGgIHx8fzJo1C6mpqeKXgNeuXVNpOmd1eX+cdn6GDRtWYJmZmVm+4+OIiKj4KcpnquQyd3d3ODo6Ys+ePeLkjXv37kWDBg3EdbC3bNmCypUro3379qhevTqqVq2Ka9euoWPHjgCA4OBgGBoaihNjKaL1E6AQEZH2UfdsluPGjcP27dvRqlUreHt746+//kKnTp3EiUHu3r2Lb7/9FtOnT4ehoSGGDx+O7du3o0WLFujSpQsCAwMRExNT4JT+RERE+VHnbJaGhoZYuXIlRo4cibCwMLx9+xZ//vknAgICxDq//fYb2rZti/bt2wMA1q5di379+iE8PBzW1tbYvn075s2bJzb+FGFjjoiIVCaVSAGJ+t7MmZub4/Lly9i7dy+ioqKwZMkS9OnTRyx3c3PD3LlzxTEKRkZGOHPmDA4ePIgHDx5gxIgR6Nu3b75rjRIRERVEUT5TJZcBwMCBA1G7dm0cPXoUxsbGmD9/vsxbtjFjxsgMDfP19UVISAgOHz4MQRAQEBCAxo0bK30/NuaIiEhl6u5mCeR2N8xvGRogt9v9woULZY4ZGRkpXFiciIioMOrsZpmnXr16qFevXr5lEyZMkDtWrVo1TJ06VeX7AGzMERFREeQgW8H0J/+bAYyIiEhbKcpn2p7L2JgjIiKVSZEDRWsT5NYhIiLSXorymbbnMjbmiIhIZeqeAIWIiEgT1DkBiiawMUdERCpT9wQoREREmqDuCVBKGhtzRESkstzkVnjXE21PgERERIrymbbnMjbmiIhIZbndUgofM6ftXVOIiIgU5TNtz2VszBERkcpyhCyFCU4qaPeg8Y/K9GGAuammo1CaYcBpTYegspwOrTUdgsoMnj/XdAiqe52k6QhIzyjKZ9qey9iYIyIilQnIUbg0gaDlM4AREREpymfansvYmCMiIpXljiFQtDSBdndNISIiUpTPtD2XsTFHREQq45g5IiL6GHDMHBER6R9BcTdLaPk4AyIiIoX5TMtzGRtzRESkMimkkPDNHBER6ThF+Uzbcxkbc0REpDKpkAWJggQnaPm3mURERIrymbbnMjbmiIhIZXwzR0REHwO+mSMiIr0jCIqTmzJ1iIiINElRrtL2XMbGHBERqUyZbyq1/dtMIiIiRblK23MZG3NERKQyvpkjIqKPAd/MFaNXr17h7NmzSEpKQuvWreHm5iaW/fHHH4iNjZU7p2fPnqhSpUpJhklEpHdyB4wbFFpH2xNgSbp8+TJCQ0NRrlw5dO7cGaampgCAqKgo+Pv7y9UvV64chg4dWtJhEhHpHUX5TNtzWeGZWIP+/fdf1KhRA7///jsuXLgALy8vbNiwQSx/8eIFIiMjxe306dOYOnWqBiMmItIfgiBVatN3giCgf//+GDBgAAIDA7Fw4UJ4enoiISEBAJCWliaTyyIjI7F48WIcP35cw5ETEekHXc9lWvtmbvLkyRg3bhyWLl0KADh69Cj69++Pvn37ws7OTq7hNnr0aHTq1Ilv5YiISkDu7F+F0/ZxBiXhr7/+QkBAAMLDw+Ho6AipVIpWrVph0aJFWL58OWrWrImVK1eK9aOjo7FmzRp89tlnmguaiEiPKMpn2p7LtPLNXE5ODu7du4e2bduKx7y9vZGWloZTp07J1U9KSsLevXsxfvz4fK+XlZWFtLQ0mY2IiIqOb+aUc/v2bbi5ucHR0REAYGBggDZt2uCvv/7Kt/6GDRtQs2ZNtGzZMt9y5jMiIvXS9VymlY05Q0NDVKhQATdv3hSPhYSEAAAiIyPl6m/fvh02Njbo3r17vtdbtGgRLCwsxM3Ozq44wiYi0huCkKPUpu8qV66Mx48fIzk5WTwWEhKCqKgoubrZ2dnYvHlzgV9MAsxnRETqpuu5TGu7Wf7www+YMGECnj9/jjJlyuDAgQOwtbXNt+769esxduxYGBoa5ls+d+5czJw5U9xPS0tjAiQi+gBcmkA5AwYMwPLly9G2bVv06dMHISEhePjwYb51Dx8+jNevX+OTTz4p8HrMZ0RE6sWlCYrJiBEjULduXRw/fhxZWVnw9/dHw4YNUb16dZl658+fx/379/Hpp58WeC1jY2MYGxsXd8hERHpDKs2GRMLZLBUxMzNDYGAgdu/ejYcPH6Jbt25o1KgRtm7dKlfXz88PgwYNgrW1dYHXYz4jIlIvRflM23OZ1jbmnj59ioYNG8LT0xNA7ts3c3Nz+Pr6ytTz8/ND165dUbFiRU2ESUSkl/hmTnmxsbEYPXo0gNw3ae7u7hg5cqRMnUePHuH06dMIDAzUQIRERPqLb+aKyX///YdPPvkE7du3x/379/HXX3/hwIEDKFWqlFgnLi4OBw8exMGDBzUYKRGR/hEEAVCUAAWhZILRcqNHj0adOnVQunRpHDhwAC4uLpg+fbpMnfXr18PDwwONGjXSUJRERPpJUT7T9lymlROgAECvXr2wdOlSZGZmomHDhrh79y58fHxk6kRERODLL7+Ue1tHRETFi7NZKu/vv/9G7dq1IZVKsXTpUpw4cQImJiYydaytrfHjjz9qKEIiIv2l67lMa9/MAUCTJk3QpEmTAssbNWrEbzGJiDRCCiix0hwB5ubmhc5QCeRObEJERJqgKJ9pdy7T6sYcERFpp9xvKgtvzGl71xQiIiJF+Uzbcxkbc0REpDKpkA0JG3NERKTjFOUzbc9lbMwREVER5IDdLImISPcpymfancvYmCMiIpWxmyUREX0M2M2SiIj0ECdAISKij4FuT4CitUsTEBGRFhOkym0quHTpEjp16gQ3Nzf07t0bYWFhBdb9559/4OLiIrMNGzbsQ5+KiIj0jZpzWUZGBubMmYN69eqhYcOG+Omnnwp9u9e7d2+5fHb16lWl78c3c0REpDJBiW8qlamT5+HDh+jYsSPmzJmDbt26YcOGDfD29kZYWBisra3l6r99+xbZ2dm4ePGieMzc3Fzp+xEREQGKc5UquQwAvvzyS5w7dw4bNmxAamoqRo8ejczMTHzzzTf51n/x4gUmTJiAgQMHisccHR2Vvh8bc0REVAQ5gKC+bpa//fYbPD09xWS3Zs0aHDp0CLt27cLEiRPzPcfIyAguLi5K34OIiEieonymfC57/fo1tmzZgiNHjqBVq1YAgAULFmDOnDmYM2cODAzy7xRpZ2dX5Hyml425vFedaWkZGo5ETxTwD5fUTKpaNwCtoGLXBW0gyc7WdAgqyfs5p/4B3ILS31ampaXJ7BsZGcHY2FjmWHBwsJj4AMDAwADNmzfHtWvXCrzuy5cv4e7uDiMjIzRp0gTffPMNypcvr8Iz6D5dzWeGGVmaDkFlOTr2GQOAQXqmpkNQnS7GDCBNx3IDAEDH/k1rOp8pk8tCQkKQlZWFli1bisdatWqFuLg4REZGomrVqvle+8cff8SyZcvg7OyM0aNHY/DgwUpHr5eNufT0dACAg2NvDUdCRFQy0tPTYWFh8cHXMTIyQvny5fH8+XOl6ltZWcHOzk7m2Lx58zB//nyZY4mJibC1tZU5Zmdnh9jY2Hyv2759e9y9excAEB0djUWLFqFNmza4fv06rKyslHwa3SfmM/ueGo5EH/yk6QCI1GvrHk1HUCSayGeq5DITExOUKlVKPJZ3XkJCQr6NOX9/f2RkZCAtLQ0XLlzAZ599hri4OHz++efKPYdStT4yNjY2iI+Ph5mZGSQSRd2EVJOWlgY7OzvEx8frzPgNxlwydDFmQDfjZsz/IwgC0tPTYWNjo5brGRsbIzIyEtlKfgstCILcz1kjI/nUY2FhgZSUFJljycnJMgnxXebm5mKXFBcXFxw8eBDlypXD6dOn0bu3/nxRx3wmizGXDMZcMhizLE3mM1VyWWZmJjIzM2FiYgIgN5cBKDCfOTk5iX+uVasW4uPj4efnx8ZcYQwMDOS+AVY3c3NznfmPl4cxlwxdjBnQzbgZcy51fIP5LmNjY7muJR+qTp06uH37tsyx27dvY9CgQUqdn/e5paamqjUubcd8lj/GXDIYc8lgzP+j7fmsdu3aAIA7d+6gYcOGAHJzmZmZWYFdLN9na2urUi7jYCYiItK44cOH49ixY7h06RIAYM+ePbh//77YmDt58iRcXFyQkZE7ZuLnn3/GnTt3AADZ2dn4/vvvkZaWhtatW2vmAYiISO85OzvD29sbCxYsQGZmJpKTk/Hjjz9iwIABMDU1BQD06NEDixcvBgDcv38fK1euxNu3bwEA4eHh+OWXX9ClSxel76mXb+aIiEi7tGvXDosWLYKPj484qHznzp1wdXUFkLsUQVRUlDjwvXnz5hg1ahQePXqE9PR0uLq6wt/fH5UqVdLkYxARkZ7bvn07BgwYABsbG0ilUnh7e2PVqlVi+fPnz5GQkAAAqFy5MmJjY1GhQgUYGxsjJSUFw4YNw88//6z0/diYUzMjIyPMmzcv33602ooxlwxdjBnQzbgZs26aNm0aPv/8c8TFxcHBwQGGhoZiWadOnRARESF+s9miRQsEBwcjKSkJxsbGau96Q7r5b5IxlwzGXDIYs25ydnbGlStXEBcXByMjI7kxfkeOHIGZmRkAwMzMDEuWLMGiRYsQHx+PsmXLqjz+WSKof35PIiIiIiIiKmYcM0dERERERKSD2JgjIiIiIiLSQWzMERERERER6SD9HZ2oJllZWVi+fLm436JFC7Rq1UqmTmpqKk6ePIlnz56hZs2aaN++vdoXd/0QERER2Lt3r9xxBwcHjBo1SgMRKe/atWsICgqCtbU1fH19YWdnp+mQRDExMfD398eECRMKrBMaGoqLFy/CzMwMHTp0QIUKFUowQnlpaWlYtWoVZs2alW/56tWrxelz3zV27FiULVu2uMMT/fLLLxgzZgysra3lyq5du4bTp0+L+xMmTJCr9/DhQ5w7dw6GhoZo3bq1OGNicdq8eTM6duyY72yLDx8+xJ9//inuDxkyRK5eYmIiTpw4gdjYWDRq1AjNmzcv9phJv5w7dw5XrlwR97/++muZSWiA3PWSLl26BAsLC7Rv317jP7Pet2vXLjx9+lTueO/eveHm5qaBiJTz5s0bnDhxAi9fvkS9evW0bomNgwcPolq1aqhfv36+5enp6Thx4gSePHmCGjVqoGPHjjAw0Oz7gjNnzsDExETudzIACAkJwfHjx+WO16hRA3369CmJ8ADk5quYmBh069Yt3/J3c27dunXl6mVlZeHUqVN49OgRqlSpAl9f32KfeOThw4cICgrC0KFD8y3fvn07Xrx4ASB3IpD86gUGBuLatWuwtLREjx49UKZMmWKN+WPHN3MfSBAEvH79Gq9fv8batWtx4sQJmfIzZ87A3d0dO3bsQFhYGMaMGQNvb29xrSRtkJWVJT5D3rZixQqcO3dO06EVqk+fPvjyyy9x79497Ny5E1WqVBHXqNIG0dHRWLp0aYHln3/+OT755BPcunULhw8fRo0aNeDv71+CEcpLTU3F7NmzCyxPSkqS+Xdy8uRJLFmypMQXM/3uu++QmJiYb1lGRgZev36Nx48fY/bs2XL1Jk+ejF69eiEoKAgnT55E3bp1sX79+mKPecWKFXj8+HG+ZXn/B+Pj4zF79my5erdu3YKbmxu2bduG+/fvo1evXpgxY0axx0z6JS0tDa9fv8atW7cwe/ZsZGVlyZQPGDAAn3zyCW7evAl/f39Ur14dBw8e1FC0+UtOTpb5GfXff/9hzpw5WvUF6vvOnTuHxo0bw9/fH6GhoRg8eDB69+4NqVSq6dBEv//+O4KCgvItu3PnDjw9PbFjxw48ePAAkydPRuvWrZGWllbCUco6cuSI3O9kefLyxLvb/PnzcevWrRKN8eLFi9izZ0+B5Xk5d+fOnXL1bt26hXr16uG3337DgwcPMHPmTLi7u4tT3heX0NBQrFu3rsDyvP+DR44cybfe5MmT0b17d4SGhmLPnj2oVasWHjx4UJwhf/wEUpsWLVoIc+fOlTkWEhIivHr1Stx/+fKlYGFhIezbt6+kw1NaZGSkYGBgIFy+fFnToRTqypUrMvtDhw4VunXrpqFo5AUHBwuVK1cusPz9+GfOnCl4eHgUc1SFe/XqlaDKj4XWrVsLEydOLMaI8leqVCkhIiKi0Dr37t0TAMjVO336tCCVSsX9lStXCtbW1jLHikOdOnWEs2fPFlonLS1NACBXz9fXVxgyZIi4f+/ePcHAwEC4efNmMURK+u7s2bMCACEtLU3m+KlTp2T2Z82aJdSoUaMkQ1PZuHHjhHbt2mk6jEKFhoYKb968EfcjIiIEAMLVq1c1GJWsnj17Chs3bsy37NGjR8LLly/F/devXwsWFhbCgQMHSiq8fH355Zdyv5MV5Pz584KhoaHw9OnTYo5K1ooVK4ShQ4cqrDd06FC5eg8ePBAiIyPF/bdv3wrOzs7CTz/9pPY43+Xv7y+0aNFCYb25c+fK1bt27ZoAQLh79654bNiwYUKPHj3UHqc+4Zu5Yubu7i7T/czW1hbGxsbIzMzUYFSF27BhA+rWrYtmzZppOpRCNW3aVGbfwsICxsbGGopGdboe/71793D+/HmMHz9e06Go5P1uzg4ODsjOztaqb8Hfd/fuXbRs2VLcr1mzJuzs7HD06FENRkX6pkOHDjL7Dg4OWp3LkpOTsXv3bq3/GVW7dm1YWVmJ+3k9HXQlH1StWhXlypUT901NTWFkZKQz8QOAn58funbtiooVK2o6FKVVr14dlStXFvfNzc1RunRprf4/effuXdja2qJWrVrisVatWuH48ePIzs7WYGS6jWPmStiqVatgamqKzp07azqUfGVlZWHLli347rvvNB2KUk6cOIH//vsPYWFhYndLTdqzZw8iIyMBAM+ePUNSUhJ+/PFHsdzX1xceHh7i/uXLl3Hu3DlEREQgMDAQGzZsKOGIgYCAANy8eRMAxL7578bcrFkztGnTRu48Pz8/NG/eHPXq1Sv2GC9fvozz58+L+1lZWVi3bp3Yz7527dro0aOHytdNS0vDsmXL8Mknn8iNDfpQoaGhOHLkiLgfFxeH3bt34+rVqwCAChUq4JNPPlHqWlWqVMH169fF/aioKMTHxyMqKkqtMRMpKzExEWvWrMHIkSM1HUqBdu7ciVKlSqFXr16aDkWhvPHKKSkpCAgIwNy5c9GwYUONxfP8+XP8/vvv4v7Dhw8REBCAuLg4ALlfPn7xxRcy5yxbtgzJyck4ffo0Bg8eXOA4sOKSmpqKX3/9Vdy/du0aTExMZPLZtGnT5BqZcXFx+PPPP0tsmMOyZcvEhsulS5cQHR0tE2NRx6AfPHgQERERGDBggNpizbNx40bEx8cDyG2QvR9z//79lRp7XqVKFSQmJiIiIgJVqlQBAPz333/IzMxETEyMTjWmtQkbcyrYvHkzXr16BQBo0KABfHx8VDp/165d+P7773Hs2DHY2toWR4gKHThwAOHh4QByv017/z/9oUOHkJycjGHDhmkivHydOnUK//33HwDAzs4On376qViWlpaGxMREJCcnIykpCVFRURod5J6amorXr18DyP1WWCqVivsA5MZKZmRkIDExEW/evEFycjIePXqEJk2alGDE/xsnk/dnADIx5zfuIS0tDb///jtWr15dEiGK4xvyCIKAN2/eiG/YUlNTi3TN/v37w9LSEr/88ou6QhXljYPLk5OTg5SUFPFY6dKllb7WggUL0KVLF6SmpqJKlSo4dOgQKlSooNXjgEh7vT/hzqRJk2TeDCny5s0bdO3aFfXr18c333xTHCEq9PLlS2zZskXcHzZsmNwvguvXr8eYMWO05g1RRkYGVqxYIe53794dderUAfC/8fcpKSlIT09HREQE0tLSSnw8cp6cnByZn19ZWVkyueL98ZRA7viupKQkpKen4+nTp0hMTCzRScnyPsM8GRkZcscEQZA7b+vWrXByclL5d7qiSkpKEj+/tLS0fHOFqv755x+MGjUKO3bsQPXq1dUVqihvHByQm2/z+/ehjJYtW6J79+7w9vbG0KFDERERIX6ZzHxWdGzMqeDdf8yqDuzdtGkTZs6ciYCAAI12X3y3sZHfL8B+fn4YMmSISom9uL2bQExMTGTKevXqJX7runLlSgwfPhwxMTElHOH/jBkzRvzztWvX8O+//8p8e/U+b29veHt7A8htaA8dOhQ9e/ZEqVKlij3WPH369BFn74qLi8Pq1asLjRnIfQNpYGCA/v37l0SIMp8TAKxZswYzZ86Ei4tLka739u1b9OzZExKJBMeOHSuWX5g8PDxk3sL+/fffGDduHNq2bavytdq0aYM7d+7gyJEjePPmDfbv349evXqhRo0a6guY9Mb7vzzm9wtuQRISEuDj4wNXV1fs3LlT7W+0lfX+L5Pv/wJ85coV3L59G4cOHSrZwArxfsPi3V+ALSwsxJ+7aWlpqFGjBjZv3ozJkyeXdJgAcmchfDcPhIWFoVu3bhg7dmyB5/zwww8Acv8uGjdujJ9//llhLlEnS0tLmftNmTIFlpaWWLhwYYHnCIKADRs24NNPPy2x2TfzPicg9/eWa9eufdDndOzYMQwZMgTbt28vtrfQU6dOFf986NAhxMbGFjnmQ4cO4a+//sLt27fRoUMHdO3aFRMmTICjo6O6wtU7bMypYMqUKUU6b+XKlVi8eDFOnTql0W4TADBixIgCyx4+fIizZ8/i559/LsGIFOvRo4dcF7rY2FgYGRnJfOtnYGCg8amQlZX3xYCzs7N4zMDAAIIgaOyXI1X4+flhxIgRMDMz03QoKktKSkLXrl1hZ2eHffv2wdTUVNMhKfTs2TM4OTmJ3ZqOHDmCp0+fFkt3Gvr41a5du0i/iMXExKBjx45o1KgRNm3apNGft05OToU+g5+fH3x8fIr8hU9xMDMzyzfm0NBQ8Q0dkJsLJBKJzuSzu3fvonbt2uJ+Xuy6EP/p06cRFRUl80WsLjlw4ADGjh2LvXv3ltibxQ/16NEj8Yv4rKwseHt7Y9iwYTrxu4+2YmNODdavX4/ExERER0fj8uXL+PHHH+Hu7o7OnTvj999/x1dffYXhw4fj5MmTOHnyJIDcV83vTmigDfz8/ODl5aXxBqcyUlJS0Lt3bzRu3BjOzs548OAB/P39ZfrLa7OsrCz06NED9evXR9WqVREVFYX9+/fj22+/1foGUkhICIKCgmTGU2iTJ0+eYPfu3WKX6LyxdcOHD0f58uXRvXt33LlzB9OnT5fp8qRqVzN1SkpKwrp168RxFHlj63r27IlatWohJiYGPXv2RMeOHfH8+XP8+eef2LRpk9at8UW67d69e/jrr7/EpTGWLVsGIyMjca3G1q1bIzk5GdWrV8dPP/0knjdz5kyt6iKVmJiIffv25bt+qjbasWMHAgMD0bRpU0ilUvz999+wsbHB4MGDNR2aUk6fPo0xY8agVatWMDIywunTp/Hy5Uutn3gGyP29p1evXnBwcNB0KPnKG4cfGhoKIHc8u4uLCwYNGoSzZ89i0KBB6NKlC27cuIEbN24AAOrVq4euXbtqLOa8cfiXL18Wx9aVKVMGn332GYDcN5M5OTmoWLEiAgICYGZmhiVLlmgs3o8BG3NqkPeWZdCgQQByxxvldcN0dHTEzJkzxeN50tPTSzxORRwdHQtdF02buLq64tKlS/D390dERARat26NJUuW5Lsgs6Y4OTkVuGC4ra0trl69ikOHDiEsLAyenp6YOXOmxhe1tbCwEP+9FiQuLg7Lly/XaKzTpk3Ld8FwAMjOzsbr169hbGws838vrxuWj48PmjdvjpSUFJnzVOlqVhRjx44t8N/nu2Mr3405rxuWp6cn9uzZgz///BP29vaYP3++OHicSF3yul/a2tpi5syZ4v+RvJle87pjJyUlyZwnCIJWNeaePHmCadOmafQXWlX8+OOPuHbtGv755x8AwNKlS9G5c2etelPRt29f1KxZM9+yL774Ah06dMDx48eRkZGBGTNmoHv37hrv9dChQwe5oRnvkkqlqF27Nvr27VuCUclq1KgRnJycCizPGxqT99bt9evX4hAZS0tLTJ8+XTyeJ28is+JSo0aNQudVyBsa07hxYzRu3BivX7+WWch869atOHDgAO7du4d58+ahZ8+eWvVvXRdJhOL+DYaIiIiIiIjUTvs7NBMREREREZEcNuaIiIiIiIh0EBtzREREREREOoiNOSIiIiIiIh3ExhwREREREZEOYmOOiIiIiIhIB7ExR0REREREpIPYmCMqJn/99RdGjRql6TCIiIg+SI8ePXDx4kVNh0FE+WBjjqiYtGjRAjNnzhT3Dx48iLFjx2owIiIiItUtWrQI9erVE/e7du2KK1euaDAiIspjpOkAiD5WZcuWRdmyZcX9ly9fIiwsTIMRERERqe7dhhwA3Lx5E0lJSRqKhojexTdzpDdevXqFr776Cm3btsWAAQNw9uxZmfL79+9j3LhxaNu2LUaMGIHQ0FCxbN++ffjss8+wYcMG9OjRAx07dsShQ4fw6tUrTJ48Ga1atcJnn32GhIQE8Zx3u1kGBQVhyZIluHnzJry8vODl5YVHjx4BAC5fvoyhQ4eibdu2mDRpEp49eyZ33z/++AO9evWCj48Pfv/9d6XjVvTMRESkew4fPoxevXrB29sbc+bMQUpKilgmlUrx22+/oWfPnvDx8cGyZcuQnZ0tlvv6+mLfvn2YOHEi2rRpg3HjxiEhIQH+/v7o2rUrOnXqhH379snc791ulp999hlevnyJL774Al5eXpg+fToAID09HQsXLoSPjw969uyJbdu2yVzD19cXR48exeeff462bdtizJgxMvlOUdyFPTORXhOI9ESvXr2Erl27CmfOnBH8/f2FTp06CVFRUYIgCMKtW7cES0tLYeHChcK5c+eEn376SShdurQQGRkpCIIg/Prrr4KxsbEwcuRI4ezZs8LixYsFQ0NDoVGjRsKGDRuEs2fPCm3atBGGDh0q3m/jxo1CkyZNBEEQhDdv3gizZ88W3N3dheDgYCE4OFhIS0sTzp07J5iZmQlLly4Vzpw5I4wcOVJwcHAQEhMTxfuamJgIw4YNE/755x9h06ZNgomJiXDp0iWl4i7smYmISPfcunVLMDc3FzZt2iScP39e+Omnn4TJkyeL5cOHDxe8vLyEQ4cOCSdOnBCaN28ujBs3Tix3cHAQKlasKPz+++/C6dOnBQ8PD6FmzZpCjx49hNOnTwsbN24UjI2NhdDQUPGcypUrC0eOHBEEQRDCwsIEe3t7YfXq1UJwcLAQHh4uCIIg+Pr6Ck2aNBECAgKE3bt3C05OTsIPP/wgd9+tW7cK//77r9C5c2fB29tbqbgVPTORPmNjjvRG48aNhQ0bNoj7UqlUyMzMFARBEPr16yd88cUXMvVHjBghzJw5UxCE3EZVpUqVhJycHLG8YsWKwsKFC8X948ePC+XLlxf3323MCYIgrFu3TmjRooXMPbp16yZMmDBB3M/JyRHc3NyE5cuXi/d1dnaWuW/Xrl2FxYsXKxV3Yc9MRES65+TJk4Kzs7OQkZEhHktPTxcEIbehZWhoKLx48UIse/r0qWBgYCAkJSUJgpDbqPLz8xPLt23bJpibmwtv374Vj7Vs2VL47bffxP13G3OCIAgVKlQQAgICxP2QkBDBwMBAePr0qXhs7969QqlSpcT85eDgIKxbt04sv3HjhmBsbCxkZ2crjLuwZybSdxwzR3pj5cqV+PLLL7F79260aNEC/fr1g4eHBwAgNDQUwcHBuHTpklj/xYsXaNq0qbhfpUoVGBj8r2eytbU1XF1dZfZVHUPw4MED9OnTR9w3MDBAw4YN8eDBA/FY1apVZe5rY2Mj3kdR3IU9MxER6Z727dujb9++8PDwgJeXF9q0aYMhQ4YAyM0JBgYG6Natm8w5giDg0aNHaNCgAQDI5a7y5cvD3Nxc5pgq+ezBgwdwdHRExYoVxWNeXl5ITU3F8+fPxePVqlUTy21sbJCVlYW0tDSFcRf2zET6jo050hvNmjVDUFAQEhMTcebMGXH8mY+PD2xtbdG1a1cMHDhQ5hwbGxu13V8ikcgds7OzQ1xcnMyxuLg4VKlSRalrKoq7sGcmIiLdY2BggBUrVkAqleLu3btYsWIF1q9fj6CgINja2sLU1BR+fn5y59WoUUNtMbyfz+zs7PD69Wvk5OTA0NAQQG4uk0gksLW1VXg9RXEX9sxE+o4ToJDeWLx4MZKSklCmTBn06tULzs7OuHXrFgBg0KBBOHr0KJycnMQJSlJSUhAeHq62+5cpUwavXr2SOdatWzds2LABL1++BABcvHgRZ8+elft2siCK4i7smYmISPecOXMGp06dgoGBAerWrYvevXvjzp07kEqlaNy4McqVK4dz587B09MTXl5ecHNzw/Hjx1GqVCm1xfB+PmvUqBEsLS3xyy+/AACysrKwZMkStGvXDhYWFgqvpyjuwp6ZSN+xMUd6o3Tp0qhevTpq1aoFJycnmJqaYuTIkQCASZMmoU+fPqhVqxZq1aoFe3t7LFy4ENWrV1fb/Tt06IDMzExUrVpVnM1y6tSpaNq0KapWrQo3Nzf4+vpi2bJlaNasmVLXVBR3Yc9MRES6p1q1avjll19gb2+POnXqYNiwYfjpp59gYGAACwsLHDp0CHv27IGDgwNq1aqFqlWrqrUhBwCjR4/GxIkT0aBBA0yfPh1WVlbYs2cPVq1aBRcXF5QvXx7Pnj3Dxo0blbqeorgLe2YifScRBEHQdBBEJSU7Oxvh4eEoXbo0ypcvL1eemZmJiIgIlCtXTqZryKtXr5CQkAA3NzfxWGhoKMqXL48yZcoAAFJTU/HgwQNxTEJcXBzi4uJQs2ZN8ZysrCxERETgzZs3qFu3LszMzAAAb968wfPnz1GlShWYmpoWet+IiAiYmJigQoUKCuNW5pmJiEj3xMXF4dWrV3BxcZEZ75bn1atXSExMhKurq9j1EchdI65q1aqwsrICALx+/RrR0dGoW7euWCc8PByWlpZwdHQEANy+fRuVKlWCtbW1zP2jo6NhZWUljsGTSqWIjIyEmZmZXL55/76ZmZm4desWGjZsKNMoKyhuZZ6ZSB+xMUdERERERKSD+H6aiIiIiIhIB7ExR0REREREpIPYmCMiIiIiItJBbMwRERERERHpIDbmiIiIiIiIdBAbc0RERERERDqIjTkiIiIiIiIdxMYcERERERGRDmJjjoiIiIiISAexMUdERERERKSD2JgjIiIiIiLSQf8HQTwW10oyr1sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 912x307.2 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The claim behind the header's numbers, measured here rather than remembered. Two figures, and\n",
+    "# the difference between them is the whole story: how much energy lands in a narrow band around\n",
+    "# the transposed pitch (did the pitch move?), and how CONCENTRATED that energy is (is it one line\n",
+    "# or a comb?).\n",
+    "def retained(f0, st, half=15.0):\n",
+    "    g = tap.Scrub(sr, 3000.0, smooth_ms=0.0, overlap=2, size_ms=100.0,\n",
+    "                  position_ms=900.0, pitch=float(st), mix=100.0)\n",
+    "    n = int(sr * 3.0)\n",
+    "    t = np.arange(n) / sr\n",
+    "    y = g.process(0.5 * np.sin(2 * np.pi * f0 * t))\n",
+    "    ref = 0.5 * np.sin(2 * np.pi * f0 * 2 ** (st / 12) * t)   # what a perfect shifter would give\n",
+    "\n",
+    "    def band(sig):\n",
+    "        seg = sig[len(sig) // 2:]\n",
+    "        mag = np.abs(np.fft.rfft(seg * np.hanning(len(seg))))\n",
+    "        f = np.fft.rfftfreq(len(seg), 1 / sr)\n",
+    "        sel = np.abs(f - f0 * 2 ** (st / 12)) <= half\n",
+    "        return np.sqrt((mag[sel] ** 2).sum()), mag[sel].max()\n",
+    "\n",
+    "    b, p = band(y)\n",
+    "    rb, rp = band(ref)\n",
+    "    return b / rb, (p / b) / (rp / rb)\n",
+    "\n",
+    "sweep_f0 = [97.0, 173.0, 218.0, 311.0, 443.0, 587.0, 761.0]\n",
+    "sweep_st = [-12, -7, -3, 3, 7, 12, 19]\n",
+    "energy = np.zeros((len(sweep_f0), len(sweep_st)))\n",
+    "focus = np.zeros_like(energy)\n",
+    "for r, f0 in enumerate(sweep_f0):\n",
+    "    for c, st in enumerate(sweep_st):\n",
+    "        energy[r, c], focus[r, c] = retained(f0, st)\n",
+    "\n",
+    "print(f\"energy in a +-15 Hz band around the transposed pitch:\")\n",
+    "print(f\"    mean {energy.mean():.3f} of a perfect shifter, worst {energy.min():.3f}\")\n",
+    "print(f\"how concentrated that energy is on a single line:\")\n",
+    "print(f\"    mean {focus.mean():.3f} of a perfect shifter, worst {focus.min():.3f}\")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.2))\n",
+    "for ax, grid, title in zip(axes, (energy, focus),\n",
+    "                           (\"energy at the transposed pitch\", \"concentration on one line\")):\n",
+    "    im = ax.imshow(grid, aspect=\"auto\", origin=\"lower\", vmin=0.5, vmax=1.0, cmap=\"magma\")\n",
+    "    ax.set_xticks(range(len(sweep_st)), [f\"{s:+d}\" for s in sweep_st])\n",
+    "    ax.set_yticks(range(len(sweep_f0)), [f\"{f:.0f}\" for f in sweep_f0])\n",
+    "    ax.set_xlabel(\"semitones\"); ax.set_title(title, fontsize=10)\n",
+    "    ax.grid(False)\n",
+    "    fig.colorbar(im, ax=ax)\n",
+    "axes[0].set_ylabel(\"fundamental (Hz)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e7cf8f5",
+   "metadata": {},
+   "source": [
+    "### What is left over, stated plainly\n",
+    "\n",
+    "The pitch moves — 98.8 % of a perfect shifter's energy lands in a ±15 Hz band around the\n",
+    "transposed pitch, 91.7 % in the worst corner of the sweep. What the wraps cost is *concentration*:\n",
+    "the band holds a narrow comb instead of a single line, 92.0 % as focused as a clean shift and\n",
+    "75.0 % at worst. Audibly that is a warble, at a rate of `sr·|rate−1| / (wander · size)` per second\n",
+    "— slow and flutter-like at small intervals, faster and rougher at large ones.\n",
+    "\n",
+    "That is the classic single-delay-line pitch-shifting artifact, not something specific to this\n",
+    "kernel. So: **`tap.scrub~`'s pitch is a granular texture, not a hi-fi shift.** For clean\n",
+    "transposition reach for `tap.pitchaccum~` or `tap.shift~`; `spray` will trade the comb for a\n",
+    "broadband smear if that suits the material better.\n",
+    "\n",
+    "**A measurement warning worth carrying**, because it nearly sent a working kernel back for repair:\n",
+    "a single-bin probe at the transposed frequency reads this object as badly broken. The comb is a\n",
+    "few Hz wide, a rectangular-window Goertzel on one line sees whichever comb tooth happens to sit\n",
+    "there, and a first pass measured 0.02 where the truth was 0.43. Measure the band, not the bin.\n",
+    "\n",
+    "The wander bound was chosen the same way. Swept over 5 fundamentals × 7 intervals, band energy\n",
+    "retained runs 0.933 / 0.958 / 0.965 / **0.990** / 0.993 at wanders of 0.5 / 1 / 2 / 3 / 4 grains.\n",
+    "The curve is flat past 3, and every extra grain of wander is a grain of position error, so 3 is\n",
+    "where it stops."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "995b6d45",
+   "metadata": {},
+   "source": [
+    "## 3 · Freeze stops the recorder, not the playhead\n",
+    "\n",
+    "Frozen, the position addresses fixed tape and the grains loop the same window — a granular hold\n",
+    "you can still scrub, transpose and drift through. Below: two and a half seconds of a phrase go in,\n",
+    "the recorder stops, and **nothing at all** goes into the input afterwards. Everything you see after\n",
+    "the freeze mark is made out of tape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "464431d9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:06.267725Z",
+     "iopub.status.busy": "2026-08-17T02:49:06.267544Z",
+     "iopub.status.idle": "2026-08-17T02:49:06.783423Z",
+     "shell.execute_reply": "2026-08-17T02:49:06.782161Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms while live                       : 0.3468\n",
+      "rms frozen, with a silent input      : 0.4942\n",
+      "rms unfrozen, last second of silence : 0.000e+00\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = tap.Scrub(sr, 3000.0, smooth_ms=20.0, overlap=3, size_ms=110.0,\n",
+    "              position_ms=900.0, mix=100.0, seed=7)\n",
+    "live = int(sr * 2.5)\n",
+    "held = int(sr * 5.0)\n",
+    "y_live = m.process(plucks(live))\n",
+    "m.set(freeze=True)\n",
+    "y_held = m.process(np.zeros(held))\n",
+    "y = np.concatenate([y_live, y_held])\n",
+    "\n",
+    "# ... and once the recorder is running again on a silent input, the tape fills with silence.\n",
+    "m.set(freeze=False)\n",
+    "y_after = m.process(np.zeros(int(sr * 3.0)))\n",
+    "\n",
+    "def rms(a):\n",
+    "    return float(np.sqrt(np.mean(np.asarray(a) ** 2)))\n",
+    "\n",
+    "print(f\"rms while live                       : {rms(y_live):.4f}\")\n",
+    "print(f\"rms frozen, with a silent input      : {rms(y_held):.4f}\")\n",
+    "print(f\"rms unfrozen, last second of silence : {rms(y_after[-int(sr):]):.3e}\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 3.2))\n",
+    "t = np.arange(len(y)) / sr\n",
+    "ax.plot(t, y, color=C[0], lw=0.5)\n",
+    "ax.axvline(live / sr, color=C[2], lw=1.4, ls=\"--\")\n",
+    "ax.annotate(\"freeze; input goes silent here\", (live / sr, 0.9 * np.abs(y).max()),\n",
+    "            xytext=(8, 0), textcoords=\"offset points\", color=C[2], fontsize=9)\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"amplitude\")\n",
+    "ax.set_title(\"the recorder stops and the playhead does not\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca3dce21",
+   "metadata": {},
+   "source": [
+    "## 4 · Drift walks the playhead on its own\n",
+    "\n",
+    "`drift` is the playhead's own motion through the tape, in playback-rate units: positive runs\n",
+    "forward toward the live edge, negative backwards, 0 holds station. It wraps around the bought\n",
+    "history rather than clamping, so a slow drift is a loop rather than a dead end.\n",
+    "\n",
+    "Measured here on a frozen tape holding a chirp, so the drift's motion shows up directly as a\n",
+    "change in the frequency coming out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c3761fa3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:06.785690Z",
+     "iopub.status.busy": "2026-08-17T02:49:06.785471Z",
+     "iopub.status.idle": "2026-08-17T02:49:07.062250Z",
+     "shell.execute_reply": "2026-08-17T02:49:07.061057Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Record a slow chirp, freeze, then read it back at three drift rates. Because the tape holds a\n",
+    "# chirp, WHERE the head sits is audible as WHAT frequency comes out.\n",
+    "def frozen_chirp(drift):\n",
+    "    g = tap.Scrub(sr, 3000.0, smooth_ms=5.0, overlap=2, size_ms=60.0,\n",
+    "                  position_ms=1200.0, mix=100.0)\n",
+    "    n = int(sr * 2.0)\n",
+    "    t = np.arange(n) / sr\n",
+    "    g.process(0.5 * np.sin(2 * np.pi * (200.0 + 500.0 * t / 2.0) * t))  # 200 -> 700 Hz\n",
+    "    g.set(freeze=True, drift=float(drift))\n",
+    "    out = g.process(np.zeros(int(sr * 1.5)))\n",
+    "    # track the strongest partial in short windows\n",
+    "    win = 4096\n",
+    "    track = []\n",
+    "    for a in range(0, len(out) - win, win // 2):\n",
+    "        f, mag = spectrum(out[a:a + win], 0)\n",
+    "        track.append(f[np.argmax(mag)])\n",
+    "    return np.array(track)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for d, colour in zip((-0.5, 0.0, 0.5), (C[2], C[3], C[0])):\n",
+    "    tr = frozen_chirp(d)\n",
+    "    ax.plot(np.arange(len(tr)) * 2048 / sr, tr, \"o-\", color=colour, ms=3, lw=1.3,\n",
+    "            label=f\"drift {d:+.1f}\")\n",
+    "ax.set_xlabel(\"time since the freeze (s)\"); ax.set_ylabel(\"strongest partial (Hz)\")\n",
+    "ax.set_title(\"a frozen chirp read at three drift rates — the head goes where it is told\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9185473",
+   "metadata": {},
+   "source": [
+    "## 5 · Spray, and the family's seed contract\n",
+    "\n",
+    "`spray` scatters each grain's origin back from the position. It is the only consumer of the seeded\n",
+    "xorshift64* the family shares, and at exactly 0 the dice are **never rolled** — so with spray off\n",
+    "the seed provably cannot matter. That is the same contract `garden.h` and `stammer.h` keep, and it\n",
+    "is what makes a seed a performance you can replay rather than a decoration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "386f4f6d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:07.064352Z",
+     "iopub.status.busy": "2026-08-17T02:49:07.064162Z",
+     "iopub.status.idle": "2026-08-17T02:49:07.397964Z",
+     "shell.execute_reply": "2026-08-17T02:49:07.396786Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "two seeds, spray off : 0.000e+00   <- the dice are never rolled, so the seed cannot matter\n",
+      "two seeds, spray on  : 1.662e+00   <- a seed is a different performance\n",
+      "the same seed twice  : 0.000e+00   <- and a replayable one\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = plucks(int(sr * 1.0))\n",
+    "\n",
+    "def run(seed, spray):\n",
+    "    g = tap.Scrub(sr, 2000.0, smooth_ms=0.0, overlap=2, size_ms=50.0,\n",
+    "                  position_ms=250.0, spray_ms=spray, seed=seed, mix=100.0)\n",
+    "    return g.process(x)\n",
+    "\n",
+    "quiet = np.max(np.abs(run(1, 0.0) - run(999999, 0.0)))\n",
+    "loud  = np.max(np.abs(run(1, 120.0) - run(999999, 120.0)))\n",
+    "same  = np.max(np.abs(run(4242, 120.0) - run(4242, 120.0)))\n",
+    "\n",
+    "print(f\"two seeds, spray off : {quiet:.3e}   <- the dice are never rolled, so the seed cannot matter\")\n",
+    "print(f\"two seeds, spray on  : {loud:.3e}   <- a seed is a different performance\")\n",
+    "print(f\"the same seed twice  : {same:.3e}   <- and a replayable one\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "sl = slice(20000, 24000)\n",
+    "ax.plot(np.arange(sl.start, sl.stop) / sr, run(1, 120.0)[sl], color=C[0], lw=1.0, label=\"seed 1\")\n",
+    "ax.plot(np.arange(sl.start, sl.stop) / sr, run(999999, 120.0)[sl], color=C[2], lw=1.0,\n",
+    "        alpha=0.8, label=\"seed 999999\")\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"amplitude\")\n",
+    "ax.set_title(\"the same settings, two seeds, 120 ms of spray\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f02acecf",
+   "metadata": {},
+   "source": [
+    "## 6 · The limit worth knowing: a grain can read past the write head\n",
+    "\n",
+    "A grain born `lag` samples behind the live edge and playing at rate *r* reaches\n",
+    "`lag − size·(r−1)` behind it by the time it ends. Transpose up with the position near the edge and\n",
+    "the grain's tail runs off the front of the tape and into the oldest material on it.\n",
+    "\n",
+    "Nothing in the kernel clamps this, and that is deliberate: clamping would silently bend the pitch,\n",
+    "which is worse than a seam you can hear and move away from. The rule is to keep the position at\n",
+    "least `size·(rate−1)` back — measured below as the error against a correctly-positioned reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "66455ac0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-17T02:49:07.400243Z",
+     "iopub.status.busy": "2026-08-17T02:49:07.400050Z",
+     "iopub.status.idle": "2026-08-17T02:49:07.706460Z",
+     "shell.execute_reply": "2026-08-17T02:49:07.705021Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "grain size 100 ms, pitch +12 st -> a grain needs 100 ms of room\n",
+      " position (ms)    rms out\n",
+      "            10     0.3072\n",
+      "            25     0.3217\n",
+      "            50     0.3367\n",
+      "            80     0.3513\n",
+      "           110     0.3700\n",
+      "           160     0.4135\n",
+      "           250     0.4172\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           400     0.4234\n",
+      "\n",
+      "The level falls away as the position approaches the live edge: the last part of every\n",
+      "grain is reading tape that has not been written yet, which on a circular buffer is the\n",
+      "OLDEST material rather than silence, so what you lose is coherence rather than signal.\n"
+     ]
+    }
+   ],
+   "source": [
+    "SIZE_MS = 100.0\n",
+    "rate = 2 ** (12 / 12)  # up an octave: rate 2, so the grain needs size x (rate - 1) = 100 ms of room\n",
+    "x = plucks(int(sr * 2.0))\n",
+    "\n",
+    "def at(position_ms):\n",
+    "    g = tap.Scrub(sr, 2000.0, smooth_ms=0.0, overlap=2, size_ms=SIZE_MS,\n",
+    "                  position_ms=position_ms, pitch=12.0, mix=100.0)\n",
+    "    return g.process(x)\n",
+    "\n",
+    "safe = at(400.0)\n",
+    "positions = np.array([10.0, 25.0, 50.0, 80.0, 110.0, 160.0, 250.0, 400.0])\n",
+    "print(f\"grain size {SIZE_MS:.0f} ms, pitch +12 st -> a grain needs {SIZE_MS*(rate-1):.0f} ms of room\")\n",
+    "print(f\"{'position (ms)':>14} {'rms out':>10}\")\n",
+    "for p in positions:\n",
+    "    print(f\"{p:14.0f} {np.sqrt(np.mean(at(p)[int(sr*1.0):] ** 2)):10.4f}\")\n",
+    "print()\n",
+    "print(\"The level falls away as the position approaches the live edge: the last part of every\")\n",
+    "print(\"grain is reading tape that has not been written yet, which on a circular buffer is the\")\n",
+    "print(\"OLDEST material rather than silence, so what you lose is coherence rather than signal.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eda059ca",
+   "metadata": {},
+   "source": [
+    "## The rest of the limits, stated\n",
+    "\n",
+    "- **`freeze` does not stop time inside a grain.** The tail of a grain in flight when freeze\n",
+    "  engages was already scheduled and plays out.\n",
+    "- **The grain pool can starve.** Shrinking `size` sharply while grains are in flight can leave\n",
+    "  every slot busy when the next grain is due; that grain is dropped rather than stealing a slot\n",
+    "  mid-window, because a steal would click. The cost is a momentary dip, bounded by the pool being\n",
+    "  two deeper than the maximum overlap.\n",
+    "- **Overlap-add is exact only when `size` divides by `overlap`.** The hop is integer samples, so a\n",
+    "  size that does not divide evenly leaves a small periodic ripple in the window sum. Inaudible at\n",
+    "  musical sizes, and the reason section 1 chose its numbers.\n",
+    "- **No transient detection.** Grains fire on a clock, not on the material. A scrub across a drum\n",
+    "  hit chops it wherever the clock happens to be.\n",
+    "- **Mono.** Per-grain stereo scatter is not modelled; wrap in `mc.` for multichannel."
+   ]
+  }
+ ],
+ "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/stammer.ipynb b/notebooks/stammer.ipynb
new file mode 100644
index 0000000..3862c03
--- /dev/null
+++ b/notebooks/stammer.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "76a3e29d",
+   "metadata": {},
+   "source": [
+    "# tap.stammer~ — the stutter, measured\n",
+    "\n",
+    "The live buffer-stutter rig (`taptools/stammer.h`): the input is captured continuously, and on a\n",
+    "rhythmic grid the machine rolls dice and re-fires a slice of what just went past. It is an\n",
+    "original design in the brassage tradition (Roads, *Microsound*, MIT Press 2001) — the lineage to\n",
+    "Jonny Greenwood's own Max patches is about *what the object is for*, not about anything in the\n",
+    "code.\n",
+    "\n",
+    "Two contracts carry it. The first is an identity: pin the dice to a single outcome and the whole\n",
+    "machine must reduce to **exactly a one-step delay**, bitwise — which is how the grid timing, the\n",
+    "slice origin arithmetic and the playback head get pinned together rather than one at a time. The\n",
+    "second is the family's seeded-randomness convention: **a seed is a performance you can replay**,\n",
+    "and at `density` 0 the dice are never rolled at all, so the seed provably cannot matter.\n",
+    "\n",
+    "Every trace drives the **shipping C++** through `tools/capi` via ctypes.\n",
+    "\n",
+    "Sections: **1** the pinned-dice identity · **2** what the dials do · **3** a seed is a performance\n",
+    "· **4** the disabled-generator contract · **5** the per-repeat flanks · **6** the material contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cbf3fc13",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:21.310820Z",
+     "iopub.status.busy": "2026-08-15T23:45:21.310609Z",
+     "iopub.status.idle": "2026-08-15T23:45:21.804915Z",
+     "shell.execute_reply": "2026-08-15T23:45:21.803919Z"
+    }
+   },
+   "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 stammer(**params):\n",
+    "    # Instant setters and a full-wet balance; sections opt into the dice.\n",
+    "    base = dict(smooth_ms=0, mix=100, input_level=1.0)\n",
+    "    base.update(params)\n",
+    "    return tap.Stammer(sr, 4000.0, **base)\n",
+    "\n",
+    "def pluck_train(seconds, period=0.31):\n",
+    "    # The documented material: transients, and a phrase rather than one note repeating — real\n",
+    "    # playing is what this object is for, and a single repeated note would flatter it.\n",
+    "    pitches = np.array([196.0, 233.1, 261.6, 349.2, 293.7])\n",
+    "    t = np.arange(int(seconds * sr)) / sr\n",
+    "    phi = np.mod(t, period)\n",
+    "    hz = pitches[(t // period).astype(int) % pitches.size]\n",
+    "    out = np.zeros_like(t)\n",
+    "    for k in range(1, 6):\n",
+    "        out += (1.0 / k) * np.exp(-phi * (4.0 + 3.0 * k)) * np.sin(2 * np.pi * hz * k * phi)\n",
+    "    return 0.5 * out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "259288df",
+   "metadata": {},
+   "source": [
+    "## 1 · The pinned-dice identity\n",
+    "\n",
+    "Set the dice so only one outcome is possible — always fire (`density` 1), whole-step slices\n",
+    "(`divisions` 1), a single pass (`repeats` 1), forwards (`reverse` 0), no reach-back (`jump` 0), no\n",
+    "flank (`fade` 0) — and every grid point grabs exactly the step that just went past and plays it\n",
+    "once. That is a pure delay of one step less one sample, and it holds sample for sample.\n",
+    "\n",
+    "The value of this as a test is that three separate mechanisms fail it: an off-by-one in the grid\n",
+    "countdown, in the slice origin, or in the playback head. (Pinned by the kernel scenario *\"with the\n",
+    "dice pinned, the machine is exactly a one-step delay\"*.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bdd2343",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:21.808200Z",
+     "iopub.status.busy": "2026-08-15T23:45:21.807853Z",
+     "iopub.status.idle": "2026-08-15T23:45:22.102204Z",
+     "shell.execute_reply": "2026-08-15T23:45:22.101106Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bitwise a one-step delay: True\n",
+      "max |difference|:         0.0\n",
+      "and not a bypass:         True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tWgDYaZawnvcpsHDhQqRSKezYsSNgr/c51PteovoUZZ+u74B4n2pVo3r7dsABB+Coo46q+5Cl9evX48gjjwz168ADD9Re/8QTT+Dcc88NBM6vvPKK9tqPfOQj+Pa3v43bb78dN910E4477jgcccQRgWvOOeccnHPOOQCA++67D2eeeSauvPJKfP/739e2OZmxo8Nkn4+KWv2N6tt0fyY6LFiwQLvLs379+lDA3ch72pn+T2YMTXXfYkwv4jr0MWYdzjnnHJTLZdx4443SVqlUcPPNN9d83fnnnw9CCC677LKAvVKpYGhoCADwgQ98AK7r4kc/+lHo9T09PXWxmLfddluArbn11lsxNDSE9773vRO+dipQ73uYzHN89NFHceutt07Jfdvb2zE0NBSo53377beHgsJ6Pq+dwdlnn41yuYybbrpJ2izLmvB91kKjY9OPicbPOeecA9u28Ytf/EJe47ourr32WhxwwAGhLfXJoq2tDaZpYmRkZMJrU6kU3vCGN+Dpp58O2Ot9DvW+l6g+Rdmn4zsAMHnGfvvtF5II1YsPf/jDuOuuu7By5crQ3zZt2hSy/eY3vwn8ft111+Gwww7DUUcdpW2/vb091M4111yjDa4WLVqEM888E5dffjnuvfdefOITn5B/c10XW7duDVx/6qmnYt68eaHdGD/ErkQ9Y0eHyT6fyfQ3qm/T/ZnocMwxx6BSqeCJJ56QtieeeAKrV6+ekvdUCxP1v54xNF19izG9iBn6GLMORxxxBC666CJ88pOfxMqVK7Hvvvvitttuw1lnnYXnn38+8nWHHXYYrrnmGlx00UVYvXo1Tj75ZPT19eGWW27B7373O7S3t+Owww7Dddddh0996lNYtmwZTjrpJNi2jeeeew4PPvggli1bNiFzf+655+Jd73oXzj33XHR3d+Oqq67CJz7xCakLnm7U+x4m8xzFtnytRUm99/3Qhz6Eyy+/HO9617vwoQ99CGvXrsVTTz2Fs846SzKzor2JPq+dwZFHHomLLroIH//4x7FixQosXrwYd955J971rnfhmWeeaYhhanRs+jHR+Hn961+Pr33ta/j85z+P9evX47DDDsPtt9+OZcuW4c9//rOsdd0oTNPEW9/6Vlx33XXI5/NoamqqWYf+7LPPxsUXXxxg2et9DvW+l1p90tmn4zsAAH/9619x9tlnN/xsv/rVr+LZZ5/Fsccei0996lM49NBDsX37djzwwAPYb7/9cP311weu7+jowEc+8hEce+yxuO+++3D33XfjT3/6U+Rn/B//8R/4yEc+gs9+9rM46qijcPfdd+PEE08MLFr9+MQnPoF3vvOdaG1tDdQIdxwHJ510Eo499li84Q1vQHNzM+6++24UCoWALEfF8ccfj3Q6jS9+8Yt45zvfiWQyOana45N9PpPpb1Tfpvsz0eHtb387Xv/61+O8887Dl770JQwODuLZZ5/Fu971LmzZsmWn31MtTNT/esbQdPUtxvQiZuhjzEpcffXV+O1vf4ve3l6sWLECV111Fd797ndjyZIlUp6hOynz05/+NF544QUccsgheOyxx2DbNu64445AIuRHPvIRrFixAkceeSSefPJJrF+/HieeeCJeeeUV7eEaKo488kj85Cc/wdq1a7Ft2zb84he/wLXXXiv/rvYrl8thyZIloW38efPmYcmSJTJps97rJvMe6nmOAPCWt7ylLmdcz3332msvvPDCCzjxxBPx2GOPYeHChbj77rvxlre8Be94xzsC7U30eU3mmegg3n9PTw9WrlyJK6+8UrbV2tpa8x6AfozV+0yjMNH4AYBLL70U999/P1zXxT/+8Q8cd9xxWL58Od761rdG9m0yz+qmm27CkiVL8OCDD+LOO+/Etm3bIvv7kY98BI7jhDTI9T6Het5LrT5F2af6O/DEE09g7dq1+PSnPx35LGo9Z4B9JnfccQduvvlmVKtVPPjgg6hUKvjWt76lDVa/9rWv4bTTTsNTTz2FffbZB08//XRAuqeeQPqv//qvuO+++0ApxbJly/DJT34SX/ziF7FkyRIcdthhofbf/va3I51O44ILLghInpLJJFasWIEzzjgDGzZswOOPP45jjz0WK1euDMg1VCxevBgPPPAAOjo68Kc//Ql33XUXALY4X7JkSej61772tTjzzDMbfj6T6W9U36b6M9FBaM/FgXiEENx///346Ec/iqeeegq5XA433ngj3vKWt+CUU07Z6fdUCxP1v54xNF19izG9ILRWFkqMGDEktm7disWLF+Omm27C+9///l3dnRh1oK+vD/Pnzw/YzjzzTKxatSqw/T0TmMvj55vf/Cb+/Oc/N7yzMRdwzjnnoLm5GTfccMO03+vKK6/El7/85ZpJoFOBO++8E2effTZefvnlkH4+RhA785lcdtlluOSSSwIyw5nGTI2pGLMXseQmRowYuy2uuuoqrF+/HieddBJc18Vdd92Fhx9+GLfffvuu7tqcwte+9jVs2rQJq1evlse/704YGBhAJpPBpZdeuqu7MiV44okn8NJLL+E73/kOPvCBD8TB/DRhaGgIt99+O372s5/hrLPO2tXdibGHIw7oY8SoE7W22mPMTlx66aW488478fDDD2NwcBDHH388fvKTn+ySoHQuj5+mpqZQst3uhM7OzkDi7HQjSqIyVXj22Wfx+OOP46KLLsK///u/T9t9dic08pkMDQ3h0UcfxWc+8xl85jOfmaae1YfpHlMxZj9iyU2MGDFixIgRI0aMGHMYcVJsjBgxYsSIESNGjBhzGHFAHyNGjBgxYsSIESPGHEYc0MeIESNGjBgxYsSIMYcRJ8VOEq7rYnh4GJlMZrct3xYjRowYMWLEiBFj14NSinK5jLa2NnnWgQ5xQD9JDA8PyyOPY8SIESNGjBgxYsSYbgwMDKCjoyPy73FAP0lkMhkA7MH6T96bCVQdG9979gF843UnI2XGH10MD/HYiBGFeGzEqIV4fMSIQjw2ZgdKpRI6Oztl/BmFuGzlJFEqlZDL5VAsFmc8oKeUoqenB11dXbHcJ0YA8diIEYV4bMSohXh8xIhCPDZmB+qNO+Ok2DkEh7p4ebwfDnV3dVdizDLEYyNGFOKxEaMW4vERIwrx2JhbiAP6OQSXUrw01g833lSJoSAeGzGiEI+NGLUQj48YUYjHxtxCHNDPISQNE+ctOgxJw9zVXYkxyxCPjRhRiMdGjFqIx0eMKMRjY24hDujnEBzXxd8HN8Nx4+2vGEHEYyNGFOKxEaMW4vERIwrx2JhbiAP6OQY33vmKEYF4bMSIQjw2YtRCPD5iRCEeG3MHcUA/h2AaBk7u3BdmjYMFYuyZiMdGjCjEYyNGLcTjI0YU4rExtxB/SnMIluvg5u5VsFxnUq9zHAonXmbv1mh0bIBSwLGnp1MxZgUaHhsx9gjM5PiglKJqxfKNuYIZ9x1WdWbus5siDujnEAxCcHhTB4xJ1oM9/5srcMmvNk1Tr2LMBjQ6NnDv74GvXDg9nYoxK9Dw2IixR2Amx8dtf+vHOz/30rTfJ8bUYEZ9R2EM+NpHgViv3zDm1NFfO3bswMDAAA455BCkUqnI61asWIFCoRCyH3zwwWhtbUVPTw+2bNkS+NuCBQuw7777TnmfpxImMXB083yYZHLrsP5hC8tWjk9Tr2LMBjQ6NrB1w/R0KMasQcNjI8YegZkcH5t2lKf9HjGmDjPqO2yL/axWgMzMHtq5u2BOeHjLsnD++efjoIMOwhlnnIG99toL99xzT+T1l156KT71qU/Jf+effz7e8IY3YO3atQCA3/72tzj55JMD19x8880z9XYahuU6+Mnm5xra/nJjyc1ujYbHRhzk7fbYGb8RY/dHPD5iRGFGx4aIUeJx2DDmBEP/4x//GI8++ig2bNiArq4u/PjHP8b555+PjRs3orW1NXT9b37zm8Dv//mf/4k///nPeN3rXidtb37zm/GXv/xl2vs+lTCJgXO7DmlotezE8fxujYbHRpzstNtjZ/xGjN0fMzk+4vOJ5hZm1HeI02hj8rFhzAkPf+ONN+LCCy9EV1cXAODTn/40ANRk6QUsy8KvfvUrfOpTnwrYbdvGK6+8gm3btoHOES9DAHQkM2hEzUbjL8lujYbHRhzQ7/bYGb8RY/fHTI4PnbspVRz8/M7tM3D3GJPFLvEdVNHQ/+V2oBhLhuvBnJjNV65cicMPP1z+nkwmceCBB2LVqlUTvvaOO+5AoVDABz7wAWlbuHAhhoeHccEFF+Coo47C4Ycfjscff1z7esuyUCqVAv8Alq0/0/+qroMrNj6DqutM6nUAW/Sq9pM/8wIefm54l7yX+N/sGBuUu+qQ/SffBf3Vf+/y9xX/24VjI/63R/ybyfEhAkO/beXGIpbe17fLn0P8b9eODUE6UtcN2HH/naAvPbPLn8Wu/lcP5oTkplKpoKmpKWBrampCuTxxgs21116L888/H83NzdJ2wQUX4IILLgDAAvYvf/nLOPvss7F+/XrkcrnA6y+55BJcfPHFoXZ7enqQzc5s4obruvhk28EY7O2DMUlm1XEoenp6QvZnl/fhkEVxotJcR6Njo7VaRRYIjY2FG1YDALo1YybG3MLO+I0Yuz9mcnyUy4wQ8/ub4aFqyBZjdmAmx4Y5PID5APp6e+CWKtK+EMDoyAhKe/D4EETyRJgTAX1nZyf6+/sDtv7+fnR2dtZ83erVq/Hggw/iqquuirwmmUziW9/6Fq6++mq89NJLeNOb3hT4+9e//nV89atflb+XSiV0dnaiq6trxgN6x3WxvVLAgq6uSR700AOXQkqW/PZ8vkljjzHX0PDY4AvYqDEQj425j8b9Row9ATM5PnI5G0Ap4Fd6xwoAhmJfMwsxo77DZD/md3YC7fMCf2ppbkbLHjw+dquA/thjj8Xf/vY3XHghq5e9Y8cOrFq1CsceeywAoLu7G319fTjqqKMCr7v22mvx+te/Hq95zWsC9nK5jEwmI3/funUrAKC9vT1072QyiWQyGbITQkBmuK6zC4o/9q/H0fsegEQD99b119gF7yPG1KPhscGddNQYiMfG3MfO+o0YuzdmcnyImNDvV0xDyP4Aw/Ds3QNVLOyMLk8dY/oxo76Dt08olf+Xf6Ju0DY6DGRzQHLPGB/1zsNzgq75yle+gltvvRWXX3457r33XixZsgQnnHACjj/+eADAr3/9a5x55pmB15TLZdxwww2hZFgAOPPMM/HDH/4QDz30EG688Ua8973vxemnn46DDz54Rt5Po0gaJj69+BgkDXPK2tSNk56B+LS2uYaGx8ZknfTIYHyy7BzDdPiNGLsPZnJ86LyNCPLV08w/8M0VWL25OO19ihGNGfUdQieu04urRT0u/ixw66+mv09zDHMioD/++OPxpz/9Cf/4xz/w3e9+F2984xtx1113yVXLokWLcPTRRwde89hjj+Gwww7D+9///lB7S5cuRW9vL7797W/jlltuwWc+8xncfvvts56NdKiL50Z74ahZ4DsB9bvjuhTnf3MF+oatKbtHjOlHw2NjsmP+O58D7rl9cq+JsUsxHX4jxu6DmRwfujlW2HQHhI4W4prkuxIz6ztEQK+5ly7I799zNfVRmBOSGwA4+eSTcfLJJ2v/9uEPfxgf/vCHA7a3ve1teNvb3qa9vrOzE5deeumU93G64VKK9aVhuHVmPNcDtSnx+3jRxvy2sNQoxuxEw2OjkfrCA7EjnUuYDr8RY/fBTI4PHX8gGHrd4Ycqax9jZjGjvkPcQveZ64J83QpwD8ecYOhjMCQNE+/pOmRKt7/UL6r8TsXflTmFhsdGI7tS8eCYU5gOvxFj98FMjg9tQM+NjsatOMqJiH1D1ZAtxvRhRn0HrcHQ6+Yc9UTZUoH924MRB/RzCLbr4r7+jbCnMKAKM/RUa48xu9Hw2GgkoI8Hx5zCdPiNGLsPZnJ86CU37KeOjVeD9/d/fQWW3t87LX2LEcbM+o4aGnqdzVEC+qu+Dlz21fB1exDigH4OgQDIGIkpPbVN9aHie6My94OjFq6+aesU3jnGVKLhsdFQQK9x7rf+Mj7Nb5ZiOvxGjN0HMzk+DM1NouYcwNsx9qN3MM7vminMqO+oJQ/QLiiU0TE0AIyNTHWv5hTigH4OwTQMvLVjnymtB6ueQCadq/L9eXbFGP7wyMCU3TfG1KLhsTFVkpsnHgS2bpx8WzGmHdPhN2LsPtjV44PywEznVnTErG5REGN6MLNjowZDr1vaxTvFIcQefg7Bch383/YVsFTt2E5AdaJeXkrwy2LGXnRWo+GxMZWSG3ULNMaswHT4jRi7D2ZyfOjiQo9Eqi8pVm2jZ6CK4bG4lO50YJf4jnpXdvXIgMoloGf7zvdpjiAO6OcQTGLgtS0LYDZSmSQCqsOUGnrlu5JIxAH9bEbDY6ORsRTlSOP69LMS0+E3Yuw+mMnxUas0dL1xnIrzv7kC/37Vmp3oVYwozKjvqJUUW6+uXsWdvwMu33N09bGHn0MwCMHh+U5ZFWBKoH4n+O9OnQz98JiNVZviwz92NRoeG9PJ0LsusPLFybcfY0oxLX4jxm6DmRwftTT06pwDRAT5mnb7hmJd/XRgl/iOejX09QT0ldLO92cOIQ7o5xCqjoMfbnwW1SmUNoSSYsVP5ftjyOO5gy+46v+24DOXxezIrkbDY2OqkmKBsNPdsh74+RWTbz/GlGI6/EaM3QczOT507qY2MatJlNXEcbYd66mnAzPqO2qdFKu11SG5MefMUUtTgjign0NIGAY+tNerkJiJpFjFLg//UL5XxXJcCm82oOGxMZV16FWna8cSnNmA6fAbMXYfzOT4qCm50TH0dcZ26nVrt5Tw8HPDk+xdDBUz6zumQXJjaurnl0vA3/80ua7NEcQefg6BAMiY01y2kv9UD/kQW6VqXeD4JL/ZgYbHhtBGTqZiQGRAr9hFIlVc/3yXYjr8RozdBzM5PsQ9dMy77mAp3XX1nFp6xe+24OKfb5ps92IomFHfIeUBDa7idNBp/1e/DNy9dFJdmyuIA/o5BMt18eNNz8Ga0oOlojQ3QbOQ3KgBvK4yAQBs6amge6A6FV2MUQcaHhtipTYVJcDUe4vfVXupCGxau/P3i1EXpsNvxNh9MJPjQ8RX/luJOUg3l9TL0Kuw7HisTwVm1HdE1cwG6mftVexheUNxQD+HkDJN/L8D34iUbhupQYTjee5cVclNxPHcUVuo/3rxSnz0u6umppMxJkTDY0N8fpNx2PVq6MXsrVa/+cttwI8vrv9+MXYK0+E3Yuw+mMnxQTjX659fap0n1Gi1woQZnpcopSiV4zySyWCX+I6p1NDrpEJGxHuplCdub5YjDujnEFxKsaE0UteWY/1tBn+PWiSTCMmNWWMElasxSzJTaHxsCIZ+Ep9V1FZnOJOaX6/YK5X67xVjpzEdfiPG7oOZHB+Ghj/Q5W155ZPrPGRIQUJTTuf+J4dwxhderruvMWbYd9Rk6BvcqtEF9LrFiesC//lxYHx04jZnMeKAfg7BoS4eGNgMZzLB10SICOjD9en1diM+cGpWoOGxMRUMfeQqkP9UDyWZyvEbY0JMi9+IsdtgRseHcDf+YEzjPrwgP9xEPdJp3by0rS+WgE4WM+s7piEpVqeh1xFNVU4yled2mcs4oJ9DSBomPrbPUUhGbRk1AHXlHX1qH/s9Klm2Xtz96AC298UM7VSj4bERFdDX0h6GdFoRAb3MsK5zm3vdCmDF8/VdG6NuTIffiLH7YCbHh5ah5z91MpypJGb3MDn1lGBGfYc3EDR/a1BDrwtQdIkcu0l+URzQzyE41MVTI92TXi1PKjaTGnr9dWqgb2q0irXwwxu34oY/9UzqNTEmRqNjQw4O9XWTGTSRdrGtozL0Ea//6feBX1wVfd8YDaHhsRFjj8CMjg8izjPxTF5SrO+6iB1h9bVR0DH0uiC/UnXxye+v0lbTiTHTvmOSDH09JFRNht4/L0Xc+9dXAzu2aHs7GxEH9HMIlALdlcKkC5LUis2iAvdQNZsoyU0DtEdMlEw9Gh0bEyZN1HqN/N2NsEc4yXjynFE0PDZi7BGYyfEhvIp/HvGSYutl6CfuqO5kc91cNTBiYe3WMqz4YCotdonvaDQpVjdnaZNitaWWwjYAeOkZ4MWno/s6yxAH9HMICcPAWQsOmvQhD7WC7qiylaEdqAiytaHzJuKIfsrR6NiQiKpQo0MoQI+yN7BYiDHl2OmxEWO3xkyOD22gLskin0mw9nXWq1ehjeM0bsfmRR7UYg8xGGbUd9RKiq0rUVbH0New+Rn6WqWW5lB1sNjDzyHYros/9a2HPUm916QYev4zlBQrNfQ7z9DrcPJnXsCO/lhb3ygaHRuRLPpkBk3UKpBO0Srw2xcBf7t7cq+JIdHw2IixR2BGx0fNmvNUvUzPDDfI0BONTXTDVvpzz2MDqFrx92WX+I5Gk2LrZei181KNxcQcIqDigH4OgQDoSGYmTXDXVE9EkqoRVW4UJqMWkTtZbO2NA/pG0ejYkJiOpFjZthLQT3bQjI2wrc8YDWGnx0aM3RozOT501WuiOAX1ulo2FTr3VSvdS+UcrvzdVjz24twuYTgVmFHfsbNlK7WZ0DXmGq3kRlPAYQ4RIXFAP4dgGgbe3LYXzCmU3ISq3Ah7VMESNSl2Cr/pc+h7M+vQ6NhoTEMf8UFFrQ5VeyNbmHFCZ8NoeGzE2CMwk+Ojll7e0TH0jSbFavyXjqEXN7c1kps4UXaGfUetbZlGNfQ622SD9zkUmMQefg7Bch1cv+1lWLqBWAP6MU35z9AfAGi2RCPivobq0Ef4SXWxULVc/PHh/sm3vwei0bHhZffXsX0pXzJJhj58vHD93RPQtf3437z6wTEi0fjYiLEnYEbHh8bd6NxHraTYeg45qldDHyUlZW0EX/Dky6PoG7YmvPfuhF3iO3TMeT0ynHoD+poHH+gC+rnjN+OAfg7BJAbe0r4PzElKFnSOTPivqDr0KmERJblpJKDXOU8g/F16duU4frR026Tb3xPR6NiYEoY+sg59BBMSxfbUYoF0jva264Hly6JfEwPAToyNGHsEZnJ86Nh4YQ2w9jIpVtNGHUoLfZWb6LYczb1V/OdPN+Dqm7Zq/7a7YkZ9h46hnwxrX+8hUnKbyJ8UO8mE3FmK2MPPIRiE4J9ybZNORCVail75qfyqBu5RkptGduKiAvqpaHtPRaNjIzqgrxVcRzBkUXXo6227Xr2j5hYxotHw2IixR2Amx4eOcNXmbck4TpM8qyr46qw5LyQ3wYUD++mf72om5O5h2CW+YzqSYvWHHIRt2kSOuRPQJ3Z1B+rF4OAgbrnlFvT39+PNb34zTj755Mhr//GPf+Cee+4J2I499licccYZDbU3W1B1HFy+4WlcOu+dSCfq/+h0cZJcpEbFYCGGXmxNBu3ii04p1S8cNIjMnQzp8wm/Jw047XLVxeCIhb3mp+u6356ARsdGZMnJRiQ3UeUsoxh61w3OvIYBRO1u1ptwC7CDQBYtjmhoz0PDYyPGHoGZHB/ewYVhDb0mno9g6JXCDNo4LmwU+V4u9ZhM0ZRfQx9FXrF2g78/t2ochACvPiQf7sRugBn1HZNl6FXUOlhKu4LUlK3UnWo+hwL6OcGBbtu2DUcffTRuu+029Pf347zzzsNXvvKVyOsff/xx3HLLLchkMvJfMplsuL3ZgoRh4FOLj26gDn3Y5mnog18U4Wgjq9yoQbeIzSbBZkRKbhSzyJ1Udwuu+/12fPBbK+u/4R6ARsdGNIs+BZIbLROCiJP6UHtLJiopVr1nXzdw5X/qHfMeisbHRow9ATM6PmrIlx0Nc65l6EOSm/rkNYKhD7DxUu6j62P43mp3vnT1OnzxR+vCN9tNMLO+QzePTJCf5Yf2Q+c2Z4Ka85OtsDNLMSfomu9973s44IADcP/994MQgiVLluD444/HJz7xCfzTP/2T9jUHHnggvvGNb0xZe7MFkwmcBXTMua58GKCvQuC/PiyL8ZykbutTh1CN+4hFhGD/bYci5a3HMDRq13WfPQ2NjI0pOSl2ojr0UYG74wAJ3wdr1Kh+Uy9D7/CxYVtz6kCQ6UZDYyPGHoOZGh/evKOT14RM2kOkwju54WtqBflayY2u6o7m3lFk1O6MGXvL2kBb/KyHoa9TQw/dvBRLbmYM9957Lz7/+c/LwPTNb34zFi9ejPvvvz8yAN++fTu+//3vo7W1FSeddBKOOOKIhtqzLAu27QWQpVIJAAtCZ7qsleU6+MXWl/C9RXtNStMmHJm/v5KhdxW7K6Q1VHu94wTtohu2Q5FM6BiNsE1tQzjYqLYt2wWl3pdVfEfVtgsl9gVtyu55gVyjYwOUggCgrhN0msRgdnV7W9j8dle04Sp2l9kdtW2itxs17qm2LeyOq2/btoCUT5LlOEBhDGhpq/08dkM0PDZi7BGYyfHhMeKer5eVZnz+X8xDrhueZ10KZZ7wZJ8CujnP8M1V6g61bVPfHOdG39vVz2l+29+fGcZDy4bx7U/sH/0g5ghm1HeIuYj6fLrOBu9MWKrz/YpkJzS/CZt//nE1NkA/N+4C1BtrzomAfuvWrdhnn30Ctr333htbt+ozzk844QSUy2XYto0nnngCX/rSl/Dd734XX/rSlybd3iWXXIKLL744ZO/p6UE2m230LTUESik+2nYQhvr669arA4DLV5g9PT3SVrXYAClXKgF73yBbvIyMjsNnxvBwGQAwMDiEnp6StJdLRQBAd3cPcpnwCtnftnxNuRqwi4B+aHgEPT1eWbDBwSpvuxelvBek21ZF2/bXfzaA8ZKLqz83P3TP3R2Njo3mQgFNAAb7+2GlPR3ofOrCRPgZLwRzer0+OykV0QWgWBjHmM+eHh5GO4DhwQFUfPZ8oYg8gN7ubtBck++eNPKerm2hT2MfGxlG0Wc3B/oxH0Dfjh1w8y3Snn3+cbTe/3t0f/mK+h7MboRGx0aMPQMzOT7GxwsAgL6+ATTx3bmhQebPmf9nPr9QcuX1qj+oKHMWEJ7fKpVyyDYyyuat7p5eNOfYXDUwwOabvv4BdOTGAABVm81Hw8p8BAClsnpvhO5z59+HsHxjVXvdXMNMjo3U0CA6AIyNjHg+3baxEEClVMaw73ku5D/9z3i+G54/cmNjaAHQ190NN89ildQgu89gfz+sLJsjzJFBzAcwPDgYmKsWAigVChjdxZ+lIJInwpwI6IHwCqVWEuaxxx6LY489Vv5+zjnn4L3vfS8+9KEPYcGCBZNq7+tf/zq++tWvyt9LpRI6OzvR1dU14wG947pYUxzCwQsWTOqgh0RiEICLrq4uaStXXQC9SCZTAbtFKgAGkMs1Bewt20cAjKC5uRVdXW3S3tRkAyiho3M+WvPecDJID1yKQBsMPUgkkwE7S0jqRT7fgq6uTu/K0XEAQ2jrmIcF7SnvnrkqgHKo7Y3d7EsXvufuj0bHBnI5AEBHexvgf24m+yx1z5IQxV5gE2EunUbOb+/dAgBoa2kJtt3E7rmgsyPImPM8F909DUK09uamHJr9dsom4Pkd7UD7PM9eLUW2vbuj4bERY4/ATI6PpiYKoID2jg50dTE/0No/BmAYzc0t6OrqAACMFmwAfchmc8p3tgcJZc5KJAYA2AFbU84CUML8+Quk/KalZRDAKDo65qGjlS8mykUAg2hta0dXFyM0KnxubFLmI6AHyUQq1B8g6FcSyXEA1YCNUorBURudrT6J4RzAjPqO0QEAQHM+7/l0iy3w0mn1uTMEbAkzbMuzz3R+ZyfQxsYWhthn1tHa6s1LCfbe2pqbg3MVgGwmg+wunjd2q4B+3333xZYtWwI2HcsehVNOOQWO42DNmjVYsGDBpNpLJpOBhFoBQsiMs10uKB4f3oFjcQgSDUhudP11qWoXpb0irg/Z+Xan0r5hELiOfpHkhNqg2r5QEO31iQSJfD86e89AFe0tCaSSu28w0+jYECCUBnXzRvQzDl3LP6ewPeJ6GnVPI/qebOA1bicR76dUAGwbaG4NtbG7YGfHRozdG7tifFDqmz991We87yfR2MRrlblGM7+JfC6K8Dzt+m2+9B8p3Ym4D+uPfk6byPbUK2P4z//ZgAd+ekzoutmMXTE2AvMCmWBuCfj4GvMHDc8TARupdZ3+3jOJemPNORHhnHbaabj55puldOThhx/G9u3bceqppwIAHnnkEVx11VXy+qeffjrw+r/85S8wTRMHH3xwXe3NViQNEx/e+wgkayUPaiB9lzYRSZ/gGHngVERCq1pUpNZiPlTjXjrViHsq19ebfCtw/jdX4Ke3bZ/Ua+YaGh0b0VVuZKKC5iVqJjV/bdSJeiF7VLLsJJJio5J5ZR26Ok90/NkVwLcvqu/aOYrGx0aMPQEzOT5qJcXqqsroK80oxRNq1KH3zx3yMMWJSlRGzHXMFjKFoJuf5mohh5n1HZrE1J0tW6mdJzTzT80qN3MnKXZOBPTf+MY3sH37dpx44on49Kc/jbPPPhv/7//9Pxx44IEAWN35a665Rl7/k5/8BCeeeCI+//nP433vex8+9KEP4YorrpBym4nam61wXBf/GNoGZ5JZ1yKZRXtYmhqbRcZJIllWscu+TexkBaLuGWpbJCwpAX1CV9ZgAvQN7d5Hdjc6NiascqNzpNEHCdTXtrdSU+5Z62Ap5dqo2veIaDtqhdm7ey/0gJ0YGzH2CMzk+NC5BF3FtVrxlWrTTTWy+toEgbrOFUXNaezeEweWpmZ+ilqYXP6bzaH5bTZhRn1HrcHRaEAvoAt+HN0gnNt16OeE5GbhwoV48cUXcfvtt2NgYAB33XUXTjjhBPn3t771rcjnvYS+G264AU8++SSeeOIJHHPMMfjBD34QCNYnam+2ggIYsauTPhxTxEmOS6WzifqeRDLx/GcUux5Vn16HEBMv2o6wqw5vqk6nffi5YRx7ZMtuIcVpdGxEO0xZ4y38wCMHTQQTr9rlanIn6tBH3VN8zo7CiJkRri7KvnENk+F0Loju0xxBw2Mjxh6BmRwfHpEUZskD5SRF5RttRZng73qGPhzQ18vG11xM1PGQ6p2fShUX9z4xhAvPXIj5vhyx2YRd4jvq2RXWQffgtYF6LVtctnJG0NLSggsvvFD7t+OOOw7HHXdcwPamN70Jb3rTmxpqb7YiYRg4ff4Bkz7kQSxcdQxIKDmY/4w6IyjENEQSotGr5VDg7itRFrTr245i6A0S7XAtO/yHi3++CV+/cF+87Q3tkX2dK2h0bER+4JKhn8QWZIiJF/YoyU3EgVM6hLdvgj9V2GpAb3r39N8nKqC/5jtA117AVy6L7tMcQcNjI8YegRkdH1oSlmps4esE1HlCr7QI7+7WYuN119UjASIk7IJ0khtBpPlPPS9VeHlM5fXbeitYNC9Vcw6dKczs2KjF0NcRVOt2eGu1qd2W0TD0seQmxnTAdl3c2bMW9iRXjEQmuoYZkAgJfTRDX6/kps7dr9pts79YdvAPMuFJ6bxuq1PAjhA/WrN4u3MyaHRsRAfXRG8Hohn6KFlMvZKbmhr6OnX4chWoBPSibfX6RI17VqvRf5tDaHxsxNgTMJPjw5t3JgiWawXVIYY++n4BFyNlo/7gXWMTuwN1HCylDd41NkFC2T5iSTSlkk0f+vZK3PfkUPjmuwC7xHdoZZ6NSm50Ab1os04N/Rzym3FAP4dACLB3Jj/phGtdbBaZiKo7Ctt3verQxNapKsWpydA7ahsRfeG/2koMJgL3yWjr1TbUewiMjNs4+TMv1H2Qw2xBo2MjkgEhkwjoJ3tSrEAUQ19XIu4Ech7VLhh6lbk3am1Savrxvc8DWzbUeM3sQ8NjI8YegZkcH1H5UgDgd+eRO8XQEDk1TkKfSHKjcxdRc516HaCXlupsYjoMEEhUT1gBomznrseM+o5abHxdDH2tpNiJ5DU15qo4oI8xHTCJgTe0LoRZK3mwBobHPScR5VjF2I2scqMy2hHOr1YlmpAuMqIvE1W5sRVmo3ZAH7w29D44eocYK6uT6Mxm7OzYQI+aHFpLcqP//OqW1sgPVg2uaywiovoQajuCoZdHGivJ0bUYet0QGOoH1q2YuH+zCDs9NmLs1pjJ8SG+tuu3lkI2V6dj1zH5iqmW0mIibbx0F9pEWU1Ar85zmjlHZxNlB+th6AGETmX92PdW4Xf3zPzhRjPrO/hzWL3cZ6LBn7VQbyEHXZBfcwUZB/QxpgGW6+DnW1+EFcV4RkCM1W9dtzH0N53DBHTJr+GtSX/bk5FDq9e6UW1Lhl5l/9lPVS4j6tProAb/VUv/JU3yAybESboC5aqLlRuLke3vajQ6NuRDvudW9Q/sR13BtZwV9W2HWPQomU+ELEZ7zwnY/6h7Toqhj4DaBjCrg/yGx0aMPQK7Ynxcd8cO+X9dzOYx52F5TCgptgZ9rJdJT8DaR8w7QHgXWXdvnU30PcDG1wjo1fts2F7GP14YCV033ZjRsSHe8pb1YWNdAb1mh7dmWSUdQx9LbmLMEExi4NTO/Se9Whbj17+NJ2ybdlRQKjshe9069yiGXpbKnJjliGJERKAfcqwyNlMC+hq7AmobNu9DFFNfUQL+m+7twb9dviay/V2NRsdGJMRj6d5a/7VRTj+qtGRUlRtdYlKozYiBKn5Xg245SysMvTkZ3T70bRTGgJ9+X56YO9sw5WMjxm6F2TI+rv9jt/eLIHl0sZkqueHd9s81WpKoFmsfCPL1BBO7R/B3nfvQ2cR9LCfcR3UeA/SLCTXw/8vjg/jNn7pD100ldvnYEG953YqJzxapqS3W+HKdzkqrq587u/Wxh59DMAjBftmWmoyEDsJB+Rls6tMT/OXxwZC9XslNVEAvYzOtDrE+Jl5AtdMIey3JTWixwL/zauAumBR2/LeHcnV2f6kbHRvRzAe3/++l9TTCftSdFAu9XdZXnYR+NLImfp0MfVSVG9210q5MLGJiqFai29qFaHhsxNgjMJPjQ58eEzYKk/9AJnGVyg+IfC1dFTdHE0AH5yRh81lkdbWJyahaFW1cjVbfH7yL/+kKM9iaebOqaO2vvX07bvjT9MpwZtZ36OYin61YqP3yWpXZtMH7BKz9ZCrszBLEAf0cQtVxcMn6J1Gth8H0g4/LtrwXvPh9aCZthOwPPD0ccVKeahNMd9AqNIPBsxvEYkFtQc/ES7K1Tua+dpUb/SKiYunvWVXYkHRS3/aPb96KL1+9LvK+M4WGx0ZUheGJ1i87tviurbFdCdRfn74RmU/kSbERDH2oPn0Nhj6KEQrJecSgUQL6tSuAL34wuv0ZQuNjI8aegJkcHxOEbCHby+sKXsAfQZh6+fv1sfETJcBG7Uar1wH64g9id9o/5wg35WfZqcYm76P5KEI70hqJ6ZaeMm66rzf84gYxo75DJ5Xx2/z+VXzoLy8L2yY8mEpHQNVg8mPJTYzpQNIw8Pn9XovkJGvCUgCH7JvFYfvlgkaOdMTBSmMFvxSHvaBcVRlt9jOcLMTtddT3rZWEBGiC8QhHWDMpNqKaTzXi/aga+6jDp/72zDCWrRqPvO9ModGxAZcC+/1T+AAl8SDyLfrXPf9k2KYGtBOVrYxi0euS3CCijYik2EYkN5EBvdK2GFtqmct65EozgIbHRow9AjM5PiilmNeWxGH753w23YXef4WfF6atvUGZqDxESqON18Vi+io34XlKt2OsBvnaKjempj8aEspj7cOd1M2Fan+SmoB+6X19+MWdO0L2RjGjvoMCmL+Q/V98cP637Pe7Ingv+/LaxA7v1g3htidKgPUGgs8WM/QxphEUQMGxJn1qG6VAV2cq5MiSCYJD98sG2OjAgtgX1Ar77X/rD/UJCAf0ovb9RCXC/G2HWXTB/qvBuP56UbBEt4UbtShQFyiC/besiZ0nAGRT+q/Q0vt68fhLo9q/TQcaHRsABVraNKUiKHDgocBhR+tf5g/0xfPevjm4LTpR4F5v9ZuofgP1l630Ei+C5poa+qgdh4jFgqUE9Km0/vVrlgP3/j76vlOMxsdGjD0BMz0+OlvCMrf5bUnks9530d8XMT/5/fqmbo880JWE1JFEWjZeV+UGYRsibLpdYV3RBi94Dwf5WslNHRr6pJbACr+uWHZwxW83a66dGDM7NiiQ4Qs96WMpC96bW4P+OMlP1tX52D/e5GuS93yieWmyibKzFHFAP4dguy5u3LFy8oc8UMZeq9uPhAAL2lMh+4L2JICg7CTqCy2+B8vXBfVt2sSiCDY/soTmBIF+lORGt1WqbldKUjWkoed2hTURAb26uIhi7n9+5w788q6pY0omws6MDZimJugG0NoRdmapFJDLA8mkvr2CZhGjsvmR1W/4z3reg/gAQ8H1JBl6wwz+vR6E5Dxiy0gJ6MUzUt/PXf8H3HdH/ffbSTQ8NmLsEZjJ8UEpk4r4WWlKgWSSBAJ2SimaMgYSJpG7qBN9Rf1kk27+0ROz4jqfrUbBhHqq3Hiqv/BiQkee1ZsUGyKwNCQT0fRn1aYS/vL4kJbomggz6jso9XKaxNwg+pxIBueLXBPQ1Kwnf7r29jfKftx0Xdg20amw4nE18Nx2FeKAfg4hZZr43H6vRaoWq6gBBUveCe4mURCwLUNX4yjmtSVDDH1nqyaBkL/0//4S1O15rHvoUo3kRh+gC0Tp36Pq0FuakpRRibWqht6T3KgMvcGvV6U4NRJxNQzPXx4fxMj41B8a0ujYACgLanX13KMC/UX7AJYVvDaTZf8P2Pn7Hwru6khE1a2vS685EUOvymIiAn2pDZvEZxKlz7fUGvc8oFcD/ajPaM1yYOvG+vtRJxofGzH2BMzk+KCU+VJbmYsSBgnLUQgjUlRmeu/5KZQq4d1jfy6Y8AN/fGQgcB9g4prz3iafLqAP/l6roo2tldx4DdSqQ98oQ69L0pU7GMrrP//fayc8wGrGfYdBGNOoymvUuYgCSCSCvphSYF4X0D4veJ1oQ7X5SRVtgkVEntYsRhzQzyE41MUr4wNwJqnpopQiYZJA5RrG0BOYJgklrhKwQNUf1DLmPhV0mmASlWSC4ODFWeWm7Mcdf+8LtA3Uf4CUCPzDSbHsp7pVKZyZmtCqa1uwMCpDL56RahdOUZXoRDH07CZh0xW/3YJ7nxgM/2En0ejYkKyIzmnp7JSy7U5/kEr5tZ0Lwg62rVN/TwC48Vq93a0juI6U80xUtjKiyk1URRsdIiU3Sg6B3HtXAnpELAKv/QFw3WX196NONDw2YuwRmOnxkTD10pXAvECZbNMwvIMIxdeMLQhc/6XIpA3kM0bABgAbd5ShGp/z5TzJ4F2zk6w7XbyeKjc6gkobvGvmMa9e/cRBviCZ/NARTEQzd9kOxYtrC9jWW7sy14yODSEbCM07hJFOqr7dVIkoyoL8gH+mTJZz9BuDtvDN2Y9lj2n+NHf8ZhzQzyG4lOL5sd7Iw6CiQCmX3GgYEMMg4W1Jwq4POBAeuOvY7yMObEIuExxKHhsf7AfAnImu9n295Skx2euhqazDf68o5Sh1W6P+16vMfarGYVY6hh4I6/Ndl+KPjwxoa/bXi0bHBoAIJp47TB1bbibC+kPC7aoznbfAu0ZFvjl8TwDYviVor3WkdxSbH1lDXj1Yio/biWoc+xEZ0CttiLesJsvWSjArl8K2F54CxhvPx9ipsRFjt8dMjg9KKQ/eg3NOlCTUNEgoPzKhiePSSRKSs6STBMccnA/cBwiy9sLoD2yjDjoEwsSQSMj1y1m0UlGNTVeHXioJNXOY+vHoJDdCGurvj5iz/LvR4oArla857xuv4Mnlnq+Zed9BmH/0S24ImE390A0zLJExlMWAywkoW9k5TqaARYuDrwWC/le859Ap6rMXcUA/h5A0TJy/6HAkjUlKbqhgRfxGHoMZJJQ4ZBAS2uqkYKt/x1Xq61LmRHSlJY88KBesfe+7ZOUmLztdmPuGgoFPJHOvcYTs+mh2Q4W4IlyHnv0Ma+u5Uwwx9NEBvSrnEVAniqExGz+6aStGC/VITfRodGzomQ4AiLBTyqQkthK4g/AtUCt8LRBmVw44BOjsCt8TCFcpSAs5j39XICKgj2LiJXOvBN1RB1HVeo4qdReloZd2hQUTCV066BYiv7kGePCe6NdMgIbHRow9AjM5PmTwrgS7CZOAUm9uCRBO/Frhg+tdECSUeYlSIJMy8PrD8wEbADy4zDuFNWpuAKKr3Kj3AYLzkAzyNRp6XTKvrg69Cl2hBsHa++/taOauqFyx3kELTy/3DshLGibO7jgU1JmBOvQyeFcJJh1DDw0RpZuzNDaxK+0or02lgueSyA+oqidaZiHigH4OwXFdPDi4Bc4kNV1s7IedoJCmhZgRCIZeSVzizkLVOiYTJJzYQ1k5zIAOn/88ZN9sQAMpAvS1W8taxzheCgY5UYy+V+t34ucTJa3x7Ooigv0Ma+ijv0JRiwI1oBe/FivB9zk0ZuHP/xhAPWh0bACI1sqHHCv/g7qtKQeT6iThS3JSFgDJtJ7RXnxAuOJOlgf0G9coFyNYtgzwBfp11qGPSpZNJIJ/96PeKjdy1lQD+oiE4lrQvc8H/1yXVGinxkaM3R4zOT7kbrFCrHoFDYLfN9PwHXLIfyQTKglFw5IdUC7NUeaqJFECbbbzvKAjicCFAF5YPXE5YpG3pWXedVIabeDvmwsjZKY66DT0IsgPyGV58/6AXs5nmgMT/UG+47r4r/vX4Fs/2zhhf3YenBjys/Fyq0YzFxmmcpoY9LvHuteG5jAKJFLMFtoKSc7aU8BVxAH9HIPVgJ6LUsqYeDfoYCRDr2joheRGZeiFs7AUliGZIJo670A6ZSg6fPb/TMoIatEpkM+aMAhQKIWlOJv8OkifPbJCTQQzHrhWODk1KZbbVcmNp7kP2nVbnALqteK5hc8lYvZiKfh+HnxmGFf9X/21zBsZG2xLZjIMPcIVB8QMqGPovQMJPLvL2ZCQtpwyvWOonj1YZZ3ieNAGAH07wo4Z0DDxEfaocpZiZ0Enxak7oBeDSa1+kwq+rh5Ugt8BUMrKs+3Yor9eQUNjI8YegxkbHzSCTVcqlEkVn6+Yg3iJOmfJNhXNetIMEk3UpUhpdpMzKUM7pzlucI4RbPxY0fv+C8lNIDDWEE461l4eqKhh7aPOZPFDW+WG//QTT2KBUQkUueDzmYb8qipBvkNcvLIxWMWue6CK5esnOLl1svCzjDIA533RSW5CibKufs5S5zcdQ+/fTXZ9ch+AzUmTybHahYgD+jkE0zBwSud+MBs45CFhkuCYBgCikdyAfaeitkUJUbWBNBT8czPSKSPIaPNLsmkjxBYQg9mLAW09RVs+gW19ihSH/4yyT0pyE6pDz36GmXt+vcrQa7Y4BdQ2xPMM175nP0sKQ5+KqHH/2Isj+IVSErPhsREludGxHeIPphkOoiVDrzDxxODbpSpDnwJsNQAGkMroA+NMFiiXg7ZUmr1mdERpBEC/eiR6ROAexdwLhl4r84kqWxmxiFDfj06GNBHUNuSRxkqgPzrMkmt92Bm/EWP3x0yPD9MMS2ESct0v7NTT0IukWP6X0IIAQEIjHU0mg0QTBfPXagCdSpJAXpNLvYo5fn+f5v64byi8yN/e79fgs5/qIgGIkNwoOwaqLQo6hl43T3n5Yv6dAGEL36cSWMQY6Hm2I8RJfPeXm/DvV66dsI8NIcDQI6LKDQ0H+RT663SsvZahVwokiAeXTE4ux2oXIvbwcwiW6+DW7tWwohL+IiC3OZUAXVYRcMP2ZIKEsu9FoB+W3BjaxNWUIsURtwkz94xZyGbMYDkyAM1NZoC1F33JpA1sVTL0BdOtBun6Z8IZighZjMqu65wi4LEkapAOhBNxxe9Rh1n53zvAFjhAmK254U89uOneYJnQRseGx9BHJcVGVLlRg1Gp31J0jgR6e0iHDx6ka5h7ChbQV5WKFYQA2aagHMWlLNDv6w73G4g+zVZ12PJ6TUCv9jsqcI+6pwjoVXstRLH/KnO/ZT0rf+l/aaNjI8YegZkcHxScXAr4NCqrxdhSL++bn8S1/EeowANlflidx0JSUG5TZS/plKEw1RQGL5lZVQL9bNoISEDFV7x30PLZmDG428wDdf8ugkjfqfOUWhWJRHh+8E5AD7dZ1th0eQL+92y5DvY5sSdUh16n31+xoYALL145Yb8jITrl18tLmxGec9QEWEk2qYkOmuIOoV1mhGvgS41XKg7oY0w9DEJwSFO79jCLWhBOdKIqAn57wjRCwTghJBzQC8epqTiTShoh2Q6gu57qE3Glsw0H1y1NJsaLwS+ky/vSPRgMfkRlMVvjTFXJTTRDrw/0BepbRLCfoYCe/6oG9Gmuz1ftugMCDUKw/uUUrr1tsodZUY+hp8HPJFRFQLyJUP1fF2y7R1NaDBHMfVIX0EOvw6dueAFAXTZQk0mFGafswBE1iUksUNSa+HJm00haAIWh588i6uTbyKRYVVrEoQb0tRhS9VrJ0Ctti/wD3wRkEIIjhoZhfP+L0e3H2GPR6LzSCCjlbLpa/YwE5yjpOgJVbtjf6kqKBZfcaDT0ar5XKsHmKRF0Uz7XpZIktMOcSqq70RSmARQ0VdvGfPOTzJMqhRdNKrkFaA5N1LgGIVPyL0Y8KWlYclPVyXA085a/PYMQjG3JhdSBaU0xiKdXjGFzj0KyURo6dDIaVC+50SXKUsoTLJSBpL1Op6vXMPQGCe4+i/eciBn6GNMAkxg4pnk+TDVpcAKwWEZfnlJrB9fQK05FJstqtPXhk1hp+FCQCIbF5fFgeLFAkUmxkwKDpwgCTRkzJFGhlGJRZwo9SkCf5SU1/Qc6idZUhxZVKScqGBfXlzVblyokE1/Wt6EG7mJ+9cuQAIBoah+bxMCLj2Vx7+NDob9t7a2EFijezeExExNtX8qb1UqKVYJ0LXNPNfWCeUNamQ/49ZpdAXU7lFIgnWEBeuD9uEDHAmAwIqAfV5OedAG9mG13MilWausVu+4Yc4FQAnFE2zJZ2FvQmMTAcStfQWJ4MNzu2MhOlcSMMffR6LzSCCj0ZSsJCEzDp6EH+3r7yyqLIR+uWU/53OE3aZJiKdtNDpJGFKkkq7AjrnV5XJlOGgrTTUOSHdflGnyFyQeAUf98w21DY7bvOmYs6ko4q4dIaWrOi/QkdRcB0EtuypoqN7qd5UDeADEwsr4ZoME5p+b5Kz70D1v496vW1kV4eaWN/Aw9t+mq1+h2luupciMLO6jzDJ/D5HzCH1KINJq9iAP6OQTLdfDTzc83IKvQVBagvqQjDXOfCFUSgFwAqPV1dQy9x2b4JTTsmlA5MbDFsbpYcClzqi5FKHE3lzFCATAFkMuaKCt28b0cHvM7WBGIBxcLUXr2CbX1k2Do1bbFe1P7LXxQMSLQ98NyHfzTv2yFi3A/Pvztlbj9b33hF7FeeTMD1TjCEJWGsDOUg0kTuEcF+lrJTYSjFQx9KPmVsMoEatCdzrD/q4dfZbPhEpIyoFeCWvnBlsPXRgXoUQF9vYG+COh11UbUfrsRiwLB2PsCest18IPDDoKl2wH4xVXA1d8K22PsMWh4XmkENEzoCJh+KY7fpagBvRFOgNW1GZ5naEhyI3Z1AR8rzu+dSBBYygFWKkNPwXKd1FPVTQMY9TH04hWDo1bgOiBYxU0E+QWFxEnotmWFG1EWKEBQcuNqpKi15i2/rl7MK8RQdo81+V0in8xf1losGNQTad/9pZdDxS48Nl5h3kmtspUTMfSIkHxqGHp5looi94kimWYh4oB+DsEkBt694J8mzaS4nMFQS30ZhPCx7neETCuf1FS50WnoXQ0TwtrnCUgamUvCVJKV/Pp8RQcpHEdwuzAs5xHtqwm3ou8tTSaGfYyJ6wLtLQlYNg1qIvlLR8YV9l84xYiqOOo9ddVvxMRUiGTo1UUEtyvOXXvkNzGw9dH5kTuDfo2nuOcPb9zKPntRf1p7Ep8qoUE4uAbgSWt0Ab2OoU+ytlWHrA3oEbGI0EhuRNuA4rB5Eq0qUaGUVdBRA3pKgebWoF28/2IhfC1QI3F1kgG9TlsfaiMioBd98C1ETGLg/K09MHULha0bwrsWACuJuX1T2B5jt0Oj80ojoOAaeuqrOS/chJ+N9yXFOkpsx5QWQbJJJylVJTdR5JNaelLcW915Fgy/2mY6aQTmBUopWvMJjBX88wplc9BYmLX3S0d1ch3Ay6fSBeVVXaCuKRcd6KPrEVoqKgpDv+3R+aBucM7RSW4yqXAfi3yuKygV3MaKDl7ZEPSjW3vKbLFlKJIbQJMAK66rg40PVblx2VxFqc/OCSJVciPmr7jKTYypBgEwP5WLOjg+EmyRSkJOEBqH6ZGtYfkLgUaXGOEkBZuh6t91FXSEZjGhOlCwaw2iaNej7kmZ41PlLy4FOlqSAWcK/l5yGSPgbEQQ7ZfnyOeF4Daq//pyqPqNvsQnEA7QozT0UQy9qMDgXywQAJXhZCgRNwquC9z96ADGi7bH0PMbjozzWryhpCMOnf5Q6BxVrTygr4qjC7qF89UdFhVaRPAoIKRvpMz5q06Y0ogKOhRoadXITijQlAdKxeC1os+qzAcACkrdahnoR+wKqMG42HqpaA4xiVosqNcKu4+hJwC6XF7Srt5a43+8CfjTLWF7SJoUY66j0XmlEQj5JxAsxhAK6GUcF7SJuSmc96jR0GsOlkpq5h7TYHIf4avFfJQ09eUsA3XjKUU6RUIMfUtTIlDekoL5bVXuAwQDejGfjBWcgH8XBxgOj4d3mQtKZThg4io3ni08YfjlQwQALYTPzdBJbsQGoJ+wEu9X10d1rvrtn3uwflsloI13HAoqGHqtvKYO1j6qDj3gK1EJDflEo3eYZynigH4OwXJdXL7xaViTPQBEBsABU8iJCjt4gqpaIYDoJDcIs/YAPA2j4hCFzEd1tETTDnU9piToRMPsP7PTcI173k5zk6kkKbGbplNhRt80wgG9eET+LVO/XU3QFZrHsoY9UQN0T0upBvTMrmruBRPil+hYrovDz9sMYuojeocG7eIzZwy9wT5cx8HL6wo45yvL4bEdOoZeSYoV7EYUi65j7pOa0o2Ut62tuKO04YpBo9S+l8WqNffU1rh3GUNf0rDuqbReQ2+YwYNGJK3mL58Jj0UvqYdCRbDrUtiqbkUjWrYTlcxb8UtuXHzj4MVMcjOZEw+LmgXKtz4DbFhdfxsxZj0anlcagAiKAX/NeTZmTVOpckNIIO+RckmGTibKSKLgfUKyUQgZjRJUEwTmKjEf6Sq9hZJiKdfaK1LRTNoIBMtiztIRPLodYtuhAf8uXuWflzytfpjh988Zco1fDS5EgAjJjRWcVw48NzyviIINwUOx+L0r4QWKv+KPJOdCUxVlY8IXvF/3+23sOahVbiIZejUBFvod4qiKNv7gXbKeOr397MScCejvuecenHbaaXj961+Pz372sxgc1CR4cQwNDeHiiy/GSSedhJNPPhmXXHIJCgVvwv71r3+NI488MvDv+9///ky8jZ1C0jDwlf3fgOQk6wULFiJcnpIzG0pwLfXsofr0RMuuJxOsEoGrLLnDekPqS34N9o8Y0fcMJTKBaqsVCImOelIsRbiEJnufBJlksFY+k+Ikw5IbSpHPmhgYUZh7/p6HxvQr+EDpMnmAVH0MvZjw1KRYwXD5mZCkYWDFTftGHtGtMjFeQO9CJiK5DvqHWXBMJdvB7r16cxG/vYeXghRyGfnG4NM5ap6DytAD7FpCwux6VO17LUNvaLT43K7bRUil9dUKovT8KqMvZtvmFiWgd4FsjtWAV9sANIG+CNw1lXgAPUMfJblRg39p99pIGga+u3EHkq4bXrjUgtq2eHbqbkalDPzf/9bfboxZhUbnlcZAPYY+ELyLuci7ksmpSfA66M5OoaHgHZRG7CYb4XNXEJx7RH8SiWB9egrGTKtzVCpJYPtzxShFWs0fc8NFIihlZZkDAT3YfJpJG0ENPm8qELwLWyEs4wnaBGEUDvwnktwkDQM9f9o/NK+IRZn/9VJSWgoH+f55ULSvK7pBgUAA/tiLo7BdBOaip5aPskVDiI0Hn4N0Qb5GmgME9fLECAb/kmmMGfopxUMPPYSzzz4bp59+Oq688kqsWLECZ5xxRmBLyo/3vOc9IITgO9/5Dr7whS/gd7/7HT784Q/Lv/f396OtrQ1Lly6V/y688MKZejsNgwLoqxbDC9sJX0eR8CUXCZvQI2qlNZrkISaXCdfNDSUVwUs2CiQV8SA6fKof1TL04p5JRbpDKQvQVYfgUsZe62rIp5JGqP4w4Qx9MPufoi3PnKyaeNWcC1fWEe9zaFQvxdFpJisWDSccI6yhl45YTf4VC4NycCJonhcOVsUiKyrh1nHkLAm4rtzapS4NOLcb7u7B7/7MD2syPYZ+YMTCyHg1+vCPKA29aEetlqMtW0n17L/siyJ/0TlhSlk9Yd3hT7qcAEpZcq2OoW9qBsbHg/ZsE+uHjgnS6fMBjT0iSAfCi5woyY0moKcAuhMm8xvqbkGtyjpl5Vrx3tSFSO92YNlj4bZtC+jZFt1+jFmBRueVhu5FvRKMnl5eVLlR5DWibKX4vlDO2ptQcrDC+VcuZXXa1bktJNX0B++CofflkenmOjUo1zHvKYW1l4G/Zk6pWlRKdlzKCLWmjBGS4mSUevniuYwWnECbADA8HrbprlO1+oBSRhNAtl2QPOEREthF0Mxj3mIifJ1KbBFQVtLBp5cn7NMIsPF/fGTA21nmtlLF8eSjruL3o6qyGUaQjdcVdlB19bMciV3dgXpw5ZVX4v3vfz/+/d//HQBw+OGHY++998YjjzyCt771raHr77nnHqTT3kTlOA7e8573wLZtJLh2Kp/P48gjj5yZNzBFcKiLO3vX4sjF+8OczFqMhpN5ZDwUURPYNFXJDQ+6E0pgDOod2+1QIOm1E6pDLxcFYYmOYP/V7UxxwEeA7aAIyXmYPaxnBFhQG6q4IwN6EqwIQD2NetVykeUreXGYlcp0M72k7vArlnTrZ+4dyqrzFMsuShUX+RxrW0xiqnONql0sHp3fSTrUxcI392HsD3vBcTwWLGqxICaDAEPv2HBd7/36mQ3DYM4VQCC4/uo167FwfAu+1xoVuOv0j2IgaGrLa5NiedDtZ5flto5Oz6/RPVLKDq2yLe8acc9kMnziqk6iI1eYyim34jArgC0AzKzXhqrDF3Y14Va0k8nqGXr/ewN8++hKvzXVeRzqYumCDnzdMGCoDH06E33AVWgHQWwlFfTXFcbYToXAY38D7vodcNVv9e3HmBVoeF5pACyWEiUq/YF6OCkWIuaSyaq+6xR3kjBJUCMOfa5WMsFKVDouO8xK9CcZYuhJxLwTzi3TEU7ppBHw5958GNyRlImklotU0uAyU8J3mtVFAtHWrA/eh+0OjGkY+hGN/l6tPgME874c6iL/6l6QjYtQsVgZaf+9/bvKriSa/Kw9++mfH8VnPB4K6OFJbvgHLPcFfGQRO4RMzE+CtR/DgaMW8gobPzxmoTVlgqi6elVKE5g3NDY7vPCZjZgTAf2TTz6Jyy67TP7e1dWFQw89FE8++aQ2oPcH8wCwcuVK7LXXXjKYB4DnnnsOb3zjG9Ha2oq3ve1t+I//+A9ks9lQW5ZlwfYFHaUSm7wo9Q6imCkkiIFPLz4GCWJM6t4UjBVxHP/hGdRjOxw3aAdj4ktlzy6lyQZg2/7rPQmI5bjIUD4hCDbEd2CH+MKr93Rd/ym0buh6dmJf0J5NG4FrRR9TCcaMBOrWw2NW1HumkwbKVSdgF/r3iuXK4N51GZtfLDvKPXmgb6l9oWjJJTBW9LXNA+1MimC8ZKMpGyzzNTJmB9pwuDf0twF4DP140bs+QQwMP7QvqOtivGSjpYmNdZu3oZbnFIsh23ZZmM6dqMeQUVCDJ5ZSKqssAAAVwTKl2LC9jKxhg7bxD7ZSASjFxy5Zjf+3XwkHpeCx6JJpk6vGsF2wIf7x7WdZhN3lWZ4JXuVG17blu566QDLFtnVty0vKFXIeW3PPVIpJXQL35HKewD35QsQwWHAsyma6jqfD97ftct1+cVy5p8sC+nIpYBeTGi0V2E4Ab4MIm9K2ak8QA1/ZuB1J1w1fn0qz6103UA+VAEC1EvQzjsOT5JR+++2dCzz7UD+zq77qTzczedJ5n0SMXY9G55VGwJqnkjCilEpygc0tzEZdGiirTCn15g+D+UY5B4HNHQVlfhPBd8Am5irLhZEyZJsJHrz775NIkNBckvRdJ94Pq03vnys5m24F59WUMgc5LoXBpabVqguapXJeSiUIKr55SSwSAvMgpbzwRHBuTCv3dl0WiBdKTuDeQDj5VsB2XJgGQYIYsJ/cD9S1MVawkea5T+L1pUq4Tf99xLMslMM2dV4D19BTw2C+k1KI8vuUcA09pZxcEte5AKVw+HixKEGCX1cqO3jkuRG87XUtyHEbwIpSZAGvcAKlnj8zfHOSIDDU+WsXoN7v5U4F9Js3b8azzz6LBQsW4DWveQ0qlQra29t3pkktBgcHMW/evICts7Ozpo5e4IUXXsAll1yC6667TtouvPBCnHbaaXBdF2vWrMF//dd/4eGHH8af//xnEKXI9yWXXIKLL7441G5PT492ATCdcFwXT/ZuxpsohTkJvaPrUoyNDsOyXfT0MNlEf78F13VQGB9DsViW9qGhCizbRqVUxNi4I+3jYwWUyg5cx8bA4DB6ehgDWKlUMDY6AtMAdnT3odhkyL4Wx0dQqXptDI46AChKxXGMj9vSPjBgwXUc2FYZg0MuenrYAmp0rIhy2QKBi97eQXTmmMyhVCohaTAGQ7QBALbtwKoWUCxVA3bqAq5dwchI1XfPKlzHAXWBvn7v/QwPl2FbVSRMYEd3L8rNjLEeGx+HCRulsh1ou1QqI2G4GBktBuyOQ2EaDvoHhtHTw9jcvgEboEzWsm17H2Cxr1//QJU/n2C/R0YYs9vdN4aeHu/zLpYYo7plxyD26SjK553aawQYzmPTlh4saGdti1JlY4Vg22JLtlq1MTo2ijyAwb5eDA21sjHjuBgdG0ObbaO3pweEViRDPzQ2hpZKBf2yPQrHcVAqlWEUxzHW04ON28tYbo1irwPKMB0HpcFBlHt6cMNfRvGZYgnu2CjyhoGhnh7YlD3jebaDQqGIvFVFn6+vC6iLYqWCRKGAYW5PDAyg3XVRrlZBR4Yxzu2Z4WFkrCoSAEb6emGlWQDcUizByVTQDKB3+zbQNPvutlcqcCwbyXIJA757dlEXRcsBKY1glNuN8VHMB1B1XZQGBlDm9tTgIPK2g4SZxMCO7XCKbCzlRkeRNUwYpULg/bSVSzAMA87YGEZ89vm2AzebRbG/DyV/X0BAQNG/aQOc9vkAAHOwD/MBOKNDvs8ByI2OoAVAqb9P9ttxXazKpXEiIRjv6UZpgXd9JzGQBNCzbZuXqAxgIf/pHzNGYRwLABT7ezHms6cGB9EBYGj7VlT58waAfKmEPICeHdu90qgA5j/5EMziOLrf9m74kX3uMdjzF8Ha5wDEmDk0Oq80gnKphPExCwYBunv64FRMjI4WUalUQamD/oFB9DSPsznBdWBbFQwOjaCnpyrnj3K5CNf1xqZt27CqJRSL3lxTLldgpW1ULd8cVijIDcHt3T3IpQ0Mj5RQrVZA4KC3bwBtmTF5b9euYnCQ+W8ZtNoVDI/YEMPfsm24DjBW9uaiUqmCpGGgVLakbXRsHNS1ULW8OXhosALHtpEwge3dfbDKJnqH2HskcNDTN4ieFrYbZjsOMinC594qv08ZCRMYGRmXbRaKRSRNirGCN6ePjBSRSgDFktef/j72IEbGrcB3nBAWt27Y1I3mnAHHdUEXDAPbm7Bhcw+cCvMRY+OsX9t3DKA1zfKJRkeZrbvP6+PgIJur+gbG0NPDYquBUTb3DAwVAvdm75OiYtkoDw2h3NODdJI9jWKlDEqBcd9cVChXYFgVjPb0YHSkDIBiuFBGW6WMgZ4ejJdcEFCMFqvIOhZ6+b1uvWsHztq3iMWEYKC3B47twhwYRKtlA4kECoMDqPT0wBgbxnwApWoVzsgICkpfZxKCSJ4IDQf0V111Fb71rW+hpaUFZ511Fg488EC87W1vw7PPPotcLjdxA5NAJpMJJLUCQKFQQCaTqfm6F154Aaeeeiq+853v4P3vf7+0d3Z2orOzEwBw9NFH4/DDD8cRRxyB1atX49BDDw208fWvfx1f/epX5e+lUgmdnZ3o6uqa8YDecmxs612D+QvmI2lO5qPrw/z5HaB0DF1dXQCAsWoJpjmGzvZWmAkq7a19o0ilqmhra8ZouSztTfkeFC0LuVwV+XwrurraAACpVBFtba1ImGNob+/E/PYUAICQfsyf1w7HHZVtGKkqTGMAba0t2DZYlPbBYhGJ5Djy+RyyuQy6uljQks/3IZcrI5cpI9/Shq6uZgBAOlNFe2sSjlvE/PkLYPCTUw1jEPM6WkCpJdsGAIoetDTnQAikvW+8gGSiiHxTGtlcM7q6OgAAzZuHkMm4SCVttLbNQ9c89n5yORctzWVsH3ADbSdTRTQ3OTDMZMAO9KIln0Yqk5fvp0LLMM0RZFIGmprb0dXFvic7RsbRlh/FWDHYdlN+AMAYqk6w7VSqCKACYjahq4stdC3HRnrRcpAVeaRzXtusRGYvHNcItGGmLQD9oCBoaW0DkknMa2tDvtAMYAQEQPu8+QBlfWprcUDAJCLt8xcA/FkapIefx5FAvqUVcCzkuroA9MBxCbK5HGCVkcrn0Tx/Ae57qgcfOcTAvNZWIJVGZ2srqh3z8cDTw3inaaClnX0OgWdJgabWNmBkwLPbZcBMINfcApgJNAn75hbGkKcz6GhtAYQ9kwba2kBNEwva25nkBWDsfEsL0Lc9dM9cWxswMoSsbCMFGAZSuSakmnJoFfbBbsbmp9OY579nUx7INQHjo8G2kymgqRlJkyDjtxsERlMeLZk0WoSdUqYjbWrGvEzaaxtsQjbLpWDbTXkAQJY6st+WY+MPrXm8hRC0JBJe2wDAfWhXa7P3THwItD3Cvwtw+WfMMbAdANCeSfn6B6CNkTtdLc0s70CguRUojivfF4D89Q7QxQcCn/t2sBPLHgMOf3VQzhNjytD4vDJ5pDMVNDdnkTBL6OjoRNf8NFqaB5DJjCOTrqKF+/nhchGmOY5cLoN8vgVdXR0wUlUYZACtLXkUy56vNMwhtDQ3YWjcm6/S6RJamtOgtCJtuZzLVRRFdHTMR2s+gZaWIWQyFJm0heaWdnR15TFUKiKZGEe+KYNsjvlYxj73oiWfQzpj+u49gpbmLAZHbd+9i2hrScGlnq0pR5FvqsBxvblp88AYkskK0imK1rYOdHVl4BgVmMYQctkUmvKt6Opi30lCBpDLJJDJNkufn0qVkUk5SKWzss1sxkY248AwUtKWbx5ANlOGS4m0FewSMqkhlKtuYA4l6GElNps60LUgDcuxkVjwCghpQjLThq4u7l+yDoAisk2t6OpqYc+3qQfAOIxEzosnekcBDIOSjPd9T1YB9MN2g/OakNyks1mk83m0dnUhk1oDWgVy+Wbp5zva2I5gIpNHmlBku7qQ3zzE5qFMMxIGm+vSXO5KUjkQ1xsvBEDZMkASScxrbwO6uvDf//MszgfFwkVZtDU3Mz+WMgHDQLa5GchkkFf81UxiWgP6VatW4bLLLsNzzz2HBx54AM8//zwWLVqEE088Eb/+9a/xmc98ppFmI3H44YfjlVdekb9Xq1WsW7cOhx9+eORrnnzySZx++um49NJL8YlPfKJm+4sWLQIAjIyMhP6WTCaR9DFXAoSQEJs/3UiaCbx34aFImolJ3VuUkHS4Pg8AQEQyEMv699ojvgRVBOwiodX2XS8OqBIn0Uo7gHTKhGW7Phu7qXpPyu8qSmv6r/fKVvr6SIFkUuwEIKAXz6RNVO1g/wCmXyyW3cBzIwakVtG7nkjdfvB98ratYBuUAlmN3ZX2cNuiVr6/7aasieFxB47rHXRCKUuOGhl3Qm3nsyZGC549aSYw9NRCEOqiUPL64nLBRqmi9M+XFEtE4iqlAeUKEfp0QniVSPZHkuQSFULQlDVByr42HEdKN2zx2XNdY6nqlzsRWaJyS08VV/3fVpy2F4WRTMo2ShUHO/qrOBBg7DHvi/cBEu9gKdUutJByzNCgbl9eTzU2bk+lg3YC8aWJ6Esq3HYqw/T2atsi4dZvd10gnWOyHX+/AZBMNmgHgEwOpFxk28HJFL+eVdwhhXF5bdJM4F+39iBBKbve34Zov1IGWto8e5KdwEvEe/Nf62tb9hsAKUW0XSwA+RZfv7P8kYV9GCmMKc8KwI3XAu/5V+C4k4Ntb14H7PdPoTZiTA6NziuNgch8KZaSwgNJoY2XNhI4s8SzESRMA47r84lU76/V+UTcR52rCAivT89slBl5Ui2f68WzUtukXq6Y9LlUM6+AIO3LKRN2wyA8qdb3vg3wa4PzqSjs4H8/oesoLwxhq3OXEZgXAIJ0iqBcZfLwtOnre9Kbb5NmApUXFjHJTdEJtAkgNI8BwFjRDfQbYJIb9brxYnBeI/z+xEwwP0aYPJVWASLysAiRsq0qNZDhNqbApLCoASJsfM6quAaI0MUTtttpOfw+XGrYP1RFIeMyG583fn7XDlxQpciaGn8/w6j3e9nQ/tqzzz6LU089FQcffHDAfsghh2DNmjWNNFkTH/jAB/CrX/0K27axiglXX301TNPEO9/5TgDAL3/5S7zjHe+Q1z/44IM47bTTcPXVV2uD+e9///sYGBgAwBYH3/jGN7Bw4UIcddRRU973qYTjurh/YBOcSdYLFhrD0GFO0JSKpP5T8pQkUl1pSS5ZTqh17l3WhuMvZ8nvqVbWEcmvCVMtLUml3Z+ow7SDbOiqVQNY4pAbsAHMwdqaijupJAmd8ifqEgfvyRxl6KTYGnb11FqXUhgGQTZtBJOJqJeIq17flDVD5SwpZQm3/kNGHNdF/lUDaM4bgQQkWcteLYkptpAdHuhyh+k9Ih7oUgq4LgvCxZ98CadNWZOXGxOBu3dvy+K6bK6LL/DkLfaZEalNFNV6XLY6k0lJd/y9Hx+/ZDXXqHuVaCpVl+08EK8N7+G4nl2XLJtUDqKilAWvuio3yVS4xr1/URC4pyGD4EAb6XRQby/aiTq1NpsN1pYXOs50Jlihx3VZ24SwvAV/G/kWoOAl3Dquiz8u6IDT1ByuXKM5iAqADLoDCbOiL1EHaKnJsmIs+Et8AkC6xs6m6ttE22obo8PAjy/WlwpVy4TGqIlG55VGIOaXQHVAwFdgxPueeHlVVF7H6tArldYg5pqgD04mjFA1HHbviBKVgfKYJFBJTbSSShrBeRE0XFaZCr17cF4RFcS8+vvsfSd95Z3F8RoprpeXr+c5XP4ymi6lSKXCZ7SopZspBTJKey6lspa8qJwjyK+0r1Kc47pIHNyHVApKXX32/2DZSvbTn5Drla0MV7kJJ8WKijZezXn2zAgc4jtsis9FZdu7Tnz2ZceziTmr4gYPTiSgXilMX3a1K3KvuN96ac04ix/mUB36hhj6zs5ObN++PWRfu3YtDjnkkJ3ulIqLLroIL7zwAg466CC0t7eDUoqbb74Zzc1sG7evrw+rVq2S119wwQWwLAs/+MEP8IMf/EDaH3jgAXR1dWHx4sU45phjkEwm0d/fj4MPPhh33XXXjEtoGkGygeO5KdVXlvEz7n54jtTfhldaMqqOr1obWDgw26FIyYoC4WO6xSFPjP1Qv/gkVC0HABL8LCT15D21bKVwHqmEuigQdtXRsecSLk/G2rYdKiskACLhyJD122U7rkiWDfbFICx4Dxy+4bJKQabBgtWmrMntQDYTrJMPMOfVmjdDh1m5NkFLUyJY11jEWhUWlBuy37wt8bx5Uqx3HDv1Dt9wHcmAAODBNbtHNm1IR0wSwdNZ5aTCHaJ4H7YtElqZfbzqOWC2KGBtiGoJbLcgKSsNfPPaDXA3r8eVHcHgulh2kHHZjhGUvngrUqVCDaU8yI8I6NUAXTD06qLAIF6CrrS7LHCnNMyip9LBfgi7WnVGfFCpTPDEWddlE1+ojy6Ttwz2B5pOOQ4/QCvikCu1so7wM8WCL8mXX6seOCXtSkAvJkH1eiGVdGxvjAmoFXfE4kY9P0C0OdgX3Fl4/gngdz8NV9ZxHNZGurZMc09FI/NKo2CkjnIqLGFlK+UhUny+MU0CGZvSiLnGFVXcvHtQGj5Yit2bIKHUsZfznbiR+Dr7qtz4CzQUlfW27oyTlCaoFiSUZblImKZ0J/45SOx4q6esswCcBMpWyoRchZBTK+yIwg3qvGgafJ6zvMUEAJnQK6+1DRbk6w6RKgfnsaas/uT1YJUb9lNXMpMCoAZn2QGkE2zWsVwCk9vEQqjk+AN6zsZH2QAWqJsmCOELwrRXjpKAl5L2lahMmMSriz+ZA/l2IRoK6I8//nisWbMGV1xxBSzLQrVaxe23345bb70VzzzzzFT3EYlEAtdffz2uvPJKDA0NYf/99w9UrPnYxz6Gc845R/7+wAMPwFLrTQNSN//BD34QF1xwATZv3ozW1la0tbVNeZ+nA6Zh4KSOxQ0lLoltRlHdxnUBcchTuCY60daQl6UlQ3V8SfgEWe5sABZ0p5K+RYHqAOEF0arTMYxwqTCXAsQIl7mknJ2oKod8APoSmoaBkOMEIHcoVAcmSoxVLRfZtCnbEVVu/Ihi6AkJ18pnfSGc6Q+WAcumTYwVqpq2zQBjYhoGhpe3Y9+FiYBD9S+cShVvsSA+c8nQc2ZCXE7YjMgbceBQH0PvKxUpylk6LmAYQYbedX1tO7bsi23z4Jqz66KWsStrwjv8Nj7pSjIJOOw7vXFHGfNKTii4fv/XX8Fn9x3EKW1Bhr5YdpDzS24sJRhX69CLVZDK0AsmXne9ZP/95SxdXzlLf0BPGbuuY+jTmWDQLfoimH61LyndrkCwfr5pGDitpx9mNqc/zIqQGiUqx4H2TsWmMvHCHsH+q4y+eCbFQli3r/ZDBPhqG+JeKhsvFgBW1XveACuf+eRDwGW/Cl+fTHnn1u+B2Jl5ZbIQ80iwRKXHnPttYn7yM/TyOn/wDv3Osa6OvGTjBSPuC9TFvOYRTOHSyGzh4H0PKXSMOEUm6VW0IVwmIs5rqdoUWXjzQTLhzUEud40hhh5skeCfN1jlNaKpd2+g6vNbYl4UldjE/E8M5d78WfjnJ9MwUF7bgUwyvDsAQDnDhc9LykGNuYyBQuBQK/a+x3k1HCk/Inx+oAaSgmWnjCyqugQZ15snCChj4/l84zictXeI9DvyDBZxKJbjAEmFoXe98pgODc4bqQTbHZhLB0s1FNDn83nccccd+Nd//VcsX74cAHD33Xfjl7/8Jfbbb78p7aAf/mRWP+bNmxeogqMmtupACJnWvk4HLNfBjTtW4KL585CaRPISpZ7O3KUA/6+3/Ri1Lak4CiDqNFe93TSZZtw7sIM999BBHK4IooNykcAhV5ayuABkyS4Bl4qtxaAjBTSn1vK/hyQ6nOFVGX2xZUoIO3gjm/bdMxUO6IV9rBh0rAYhocOsHJdJi1KKdMfVLApEHzM5M3Coh+U6mH/CDuS7D1IkN+z5mQZBsex47D9/P45D2a6EYQCOC8dhTBBAA6fpMf/K++YLaB3H0z6qB3DIBYAoicm75QgqjbcjJvFAQO+63vsWds6itzUnYIxSSGfL7YWSi807ykB7MHA/8wsvY+lhVcxn+9sySB8t2GiRDL0S5AO85KR6aFUEQy809GpwbYgyl1UATZ5dlLP0g1LGxI8MRfRFs1uQTIUZfSWgt1wHP9t/b3xqazdSOslNRhPoOw57hn7W3XHZ/Spl9ncxPsQiLkpyU4vRVwN69QAtMWhCp9OW9HbhF0eHgyU0t23SnxL8iyuBjvnhEprFApMd7QGBfqPzSiMQgbrhP6CQ+lh7RfaSCNj8rH3QN+t2iBNKzXmX+3zdIiFhesG7sCV9hJFc42sOlvIHxQBX1KUMuBQ8JwreYVXK69kcBIWh19ScdxHQ4ItrU2qg7QKZVPC1QoZDKXvmrBa/KI/p3x1g12fSHilmuQ5yr9sO8/l9lHmS/fRLOR2Xhth9l1I0ZcxAbXrXZQdqjRYclCsushnmRwwCUBAW0LtBGVDVH7wLlaBDApIbAoqS7R0WJWxlWwT03txkOwhIbqTcx2drzhmgI4BDEjDnSEDfsLd6/etfj5dffhnbt2/H2rVr0d3djbPOOmsq+xZDgUEIXt28gEkKJgHm8Nj/VeeonvwKcF1fFLuhZfSBhKG5HkHmwwvcFafIfyY1jLtgSqqKIzOEzl3VKfKDL/yaREAw9MHgn0CndRTMhcpIiASm8AIgkzYCh1MJJ5RJGahWg45MJDxVFGbDMIisM+xvO5sO6/N1gb5BCMbWNyOXTgQWCy6fzBhLEnaqBBTlKpUaetelrIY9BessP01PnOYLwHN6lMpg3NFoEgNO0nUUhp7IOsBycnWpVzrRF+gTydCztluaEiAEoASAGQzGyxUHLNBnjL6sg1y0PXbdsjAwYuHsLy9n2+yJlFwUUErRO8BZ3pTK0It+R2noNex/lCxGF9BrgnE5c+rshgEkNYF+Ks36LQ4FIwRvHByGoTtEynVZJR4dQ9/UHAzoecItCAmy8VGSm0iJTkSgr77Of63KxMsTaxRtvbhe1daLpFwa/C5h/SrgmUfDffjmp4DHHgjbhwbCbcxxNDqvNAIxL5hqoE6YLRDkq3p3IMTaC3sidOATZM15nbzGf6iVlFgGFhhEOT2WQT3tVdSXV3en0z55DcC/rpIo8myCyPJ2DATppcpr+KGJyhzGJDfqdeHAPyP08pZ3HyHtUedKv4beIASVLS1IJ83QHJxOkuBcI+YlZYGRyxrKdWwhYhpA2U+E8e+V5RL5PaYuBQVQdb3gnc1FnKHngbZMBQoE+WysVSyqHFZFQwG9IYa+z5ZOEVAQlGwSmNdmM+oK6IeHh7Fy5Urtv5GREViWhdWrV2PlypWhuqIxpg4mMfCqfCfMSeodmcPjVWEmco7cuarHZgPU52ChuT7sZEWyrHBgwqY7gc8wgluh4nq9nh2A1BkG2/ESfbzJAmCZ+/7AXegkk6FkoejTaUktFl2TiCuq2Xh2xv5nUsFTDV3XY2UqGgdpOzQoORKBvu9akxgY25xHPhNMohX9zmVMmXwKMPYimTCQMNgBWuCHebgUaMmbkLSZDPSBTJp7PXEoEw/Sc2k+Qfq2K0WSr+NSyaLLgN5xfQmttozNXCF/AQDb5k6av29f8is7YZfCpeHtUKbPJzK4FvkEruuz25bMP6harpcU67p4aNkIPvitlayxZEoG6MNjNnb0VYS4Vp8sG6W5TyY1QXdGz9CnM0EpThRDLwP6ZNgudOLcbhIDrx4cgZnOBhNoRfvZprCG3nVZCUx/oO+6bDxkm5RTe/k9VYbecdgCQJXLSHpPuV6w4eo9Ac2puhHMvROxAMixcnuBhOOJsH5V8PdyCfjefwA7toTvuWlt/e3OMjQ6rzQEX/Auk0NFP0wEZThQ5icRaCukEnjw7pcXMsUg81f+BFggKNX0z1O2j42XhSH8ZAPCxRIoEGbJKXstIf55CJKc8r9ex8YzmxFm2TUMfToVzg1LKzvG4lmYwtdDkfsIhj4guWE2kxio7MizNpUE2JxSsEE3L1FKkUubqFT9B115uWRil5kd1MnijqrryWYof3DVgJSGa+iVpFgCoGj7kmcdFvhXLMpJJWYnANvhMbxk12yaBe+u4ZtPXOoL6Hcjhn7p0qU4/PDD6/r3rW99a7r7vMei6ji4etMyVCe7WqTwGHrhRLlzVPWIopRYMlRxhv1UE1cBjeMF5El/fh2in7VXKwAQBB2oen1I/65j+inrR8IkoW3EdMoIJRkRybirC5rwIsLluwJhdj06EZclIvmu5Y5MldwI9p+1rQbu7IPTBfr+hUXVcbDfWZuRzZLgNihlJ+s1ZQwU/clKPB40DaBqQzITjkuRz5ogYqYzvWTZ1hx3F2JbnrPrLTkTDkWA3TC5tr5qw6ug44iAnvebs+iOf9I0+K6AbUldJIBgJRoKGFy3r1acIYLa44sI8YxkxR3O0Is+2LbDmHgAcGxULNdL/vUF0b/8ww785/+s81h+fs+X1xXQN1Tx2tZp7n0LA2anEZIbwdArEprIxQJn6KsK+59M8Yxxds+q4+C7RxyCaiYXDmgF667aXZdJTvx2ORNnwvamvF5y09xWfxKtgJ91j2LohZ5/LCKgVxl6QcGNDOrvqcO4ck+x0OjbEbRvWM0q7qif59AAsPLF+u+3i9DwvNIA/IG6PynW09Xz67jz9wfvcoPMQGiXWCWUAMjCBaGKNmqbCM5fcn1uKmQU9HNUKhnMK/PkLJ4Uh5FTwTlL6ND95JHQ76tSUIrwbjKr6qbuXvPAX91dJsGFh5B/+nPLJEOf9OaWquOg7W0bWVEu5T65jBGQfVIalOuINrMZJvcRbYpqb5mUN9+xt02RMA1Y1JPXUJe994qPeWdljykLtP0aekJRrBKIogri+VZsGpCDirKVfg19iifABnYC+O5A0TbCRRNmKeoK6D/5yU/Csiz5r1gs4rjjjsNVV12F7u5uDA4O4uabb8ahhx4aB/TTiIRh4PxFhyExSV0ncyZMz65uaUZVuTFN1UmxNkydtIaIhUGQuVB1iILlDyWowltEhFhxQ8+Wg+v/1EQdwp2hmrkfZg6EDj/sgIScR3VgQnMfKmeZ9hKOAASSiyqKnpAY3K4w9CQi0E8m2AJFvT7LWQ+BhGFgy9+6kM8kAk6WBe4E2YwZSExyeK4AC+hpgIkXDDgFZIUah0txZGkvgOviKZpzBmw3qD8U251V6UxZSczWvMmOSQd40C3aNvkk7gXGQgPJ3qBPt88Xiw4F/Br6PC+h6cJrw3VEQO+T4tj+gN71kietKhvn4p6+pNhtvRVfXzyG/ktXr8PN9/X4gm5mv/PBfoyO29HBeDrDPhw1uTbE0PNFgVrmUujXUinNAkAsIipybHxi9Tokslm95EaXLOu6jImvKiU9iMF2FyoKi97UHE6KdRyguaU+yQ2lXjt+u+sydr1SDj7DiSQ3Y8PhvgDAiGIXizldycbQYoF/VkoFIXmvfmV3+uG/AD+/ItzuyheBFc+H7bsIjc4rjUDsrqrzhQzyNQmwobKVGslNMiQFpdr5jd3HPw/qtO3errOqoddWtNHIOdnOsiElN7IcdDLYpmGQQFAuA+2EQhK54RKVMngPsfaMSBLzEXUZScUCdbFw8HYhqlbwPaZT3mIiYRgYfnwRskkzJAdtUnaDpYZeIaUyKYPl3cviBwjNd0ICayY4G++IhRTrO0t2dQLXFgMMvbD5A3Lmy6XkxvWquHkSUVvehwX0vrKXDg/ord2MoSeEIJFIyH+PP/44Ojo68IUvfAFdXV1ob2/H+973PrznPe/B0qVLp7vPeywIgCYz6bGWdUIyG0oVAS2zTj1WXHVcnoP0tQ3PrpPFqA5MsN8BqYxUQ4SdEwFCSbSCLQ/rDL2kWxmMSyelVr/hTiVJAqw4lcF1MBFXkJM6/XuGJxypzl8NuoWzzqSCUhxKGZsUku64jF1Pp0hAa0hpWOZDAFhFE7lMsCQmpRQmYRr6olJizDQZ21URLLrjegw9uL5RMvRAaxMP5Anh1zO5THPWYA7S8BgPx6EsOctGYFHQ3sxq21s2lTIaxwU6W5OMoZcBPZPoZIXMx1da0uZbqbZg6HnViUzagAHKJh4lcJcMfTJod2zK9V6MubdsGmToeeDO6u3znABfoG/ZlNVdlvp8FnRec8s2LF83HgjoLdvFv12+BrbleJVeeJBetVw9cy/3/zWBu9DnqxIdI7i4IACaqxZIOhMM0EU72QgNvcrQOw77jNOZoHRHBOLlkqJ/d4F8q15yo+rzxeuaW4NBuuOwHYF0JsjSu/wZRkluRkci7ENBe4YnKqvtANGLgiEloBcLGdUuFgtqMu6v/hv4xVXh+936S2D5srBdl8w7hWh0Xmn4foJlV9ZQ/so3fnmNXYNNZ3aq1dCHFgkaOamradObpwyoGvqA1p63qZWEggXv1Rpzn24H2lMGhs9TCTH0Mn9LnY+ITMgVNlXaQ/nObSoQ5HMiykd+EQBO2Qzo6sW9m7Kmkq+ll5+ahpjzvMWNQRCwiUA7YQrJjaehJ4TPI4HqNTRUh94gQKGKUH6XlNzwOUiMc9enq2dyIxIoe+lym3/hMNvR0JK8p6cHupOrDMOINfTTCMt18cNNy2BN4gAQsUqXAb3PuenLVnr2QCUBiC+cnglRNYyAd1iUt50YTgISbUctCnTyF/Z+iGYBAMnQVxUnlU4qzIHPoYUZel6JRpXiGOIgKv9ihEq9eNW3pQggVIdeSMdVjaO0J42Qg5TOUNEqZpQdh6rr4OBztyCbUSQ3nFRVKw04Lt9xITyYND2GPp1iteXLFcFs2B6LLhpI+CU6RkArL/qYFSXVRNlKh/XbNAhKVUj9u+1QtLckvK0XzoA7DjCvhS8ifDIXx6FIJfiE5bunmJDLFgKLAoAH9L7AXSxWHcf1An2rGpb58KDYNAgMgJU28wXRbc0Jxuj7dPuyao/tBNoeHLGxcmORLaxMk/2zqli3tYR3fu5FH0PPguXRgo11Wwq8vqrK0FOfXTn8SZSz5HbLdfHt1xwJK5NjE5Nan19l6AVbrkpxxD3TCkNP+aKAUkX/7gD5COY+rzD3gllvVhYALl9E5PLhBUBLu0Zbz3dcIhl6RXIjpDt+iY6IyEpF7/+Ax9KpgbtoW2Xuxc6Per1hQosnHgQe+GPQ5rrAVz8CrFketq9+Wd/OJNHIvNIoxNNUK9pIdZ8vCGVzgl9eE9bfC0RVXzN9bDwF+Nym2oK6ehHkBzT0Uh5jhO7DDpvyz08+yY1P+mkoO9b6SjPU09Arc16YjdftAgfLK8v3EwrewVl7bz4LkFZCqui6mH/aJqTT4QOsGHkU3IHOptXAn30Ofr282DX26/KZy2Qkk5oUSwgJyGsY8w6U/AmwDoVBKMarhnw4bP4UAb0glZjkxgWBA1/wTinMBN8JEKw9l98WdjeGXsXxxx+Pv//977jmmmswMDCAsbEx/P73v8c111yDU045Zar7GIMjZZr4+oFvQsqMmBA0EPMRAQmcsOc5PI0jRA2GPoKNCLXDnWKgAoDPpspWROAeqjcvtgWVmvCewwze09BsawqZj5ooBJBweTDu/DIKE+/XIao6RqHbr/icNxB2tpRrB9UEI3GYiN/psba9uvUTVRNIEBMr/m9/NGeToSo3BmGVBgoqQy819NSrZsOr4oCwY739DH1LzoAMd3kCrOswJ+5SyEoClDJ9ezZlcP2i50xNA0iZQLHiymDc5Tp8gJXQ9CfRdrbwJFmf5MZ2KNqaTI+h5wym41I0pfkigi8KbIctRFzXZX0RDL3YenVcQLDuVhWWQ4OSG9cBHEfuCjgUARZ9XluS2SVzX5Xfm3LF8VW5seTJvpYlAn3WzobtZe+evoD+lr/24Rs/Xe9Ja7j9iZdGsXztGF8FqgG9+KJ6Mp+UaeLKJ5chpTv9VTD0FSWgB1g5S5WJlwG9Yk8kGKNfUoLuqITb5pb6GXrD5LsFymKhhV/rD0Qdmx00Na6R4uSbw3bHYbsF/kDf356/zKdk6AfCbQPskCs/xMJpQLGLg7B0rJ+6myEWJts2Be3bNwPXXRa+50Av8MAfwu3WQCPzSsPgc0Bot5gEq9zIwD/E0IeTYillc0TgcfI5xb+jLF6vq6bDrnMDtqRGcpNMKvOiywgklwKq1j9QvYZPiKGcMuU+Yi7UVVPzS2HEtYzYCRJPiYQBw0CQjSfBuY66rHx1MKj2pJ/CljJN7LjrQGSTavU0VmhBlXeq8lMx5wXYeM28Jk51NU0Dlo8lZ/MxYaUnfcE3ABQtQ+rlBUM/LjgPTiClEiygp76DE5lWn5XHlN9dyvT7Qa2+y+ZBm+xeGnoV++67L26++WZceeWVmDdvHlpaWvDRj34Ul156KU4++eSp7mMMDpdSbCqNygFdD+SVBGjNJ2RQIbY0BZsttrz85cJUDb1Ok0gDjjPoWMC3N73EIH0tXp0DFX/QLiLgq08fYA2oJ7nxBdeStdck1oZPimVtZ9JG6NAMIdFRrze4RMfPfgC6g6UQ2mqUdi6tqShO0zDCgT6T3JiB6je24yK3oIyswpoIOU8uY4aSYk2DwDCAigUW1IyPyp0LAopS1Q2wzs05E5SKgN4rOZlLc8mNyQ6WEu9f1kP2MfSmQZAQAT1fFDguRXNTgpUYs6ksRem4FB2SoecMPt8VaMoYLO4yEwHmPpMmLOHJp8PPpA0kTaBYplIWYzusDr88/Cog0eFvwCeZsHmgb1MSYMWTCcbcVywqn5X43pQrjk/PXsVoQQT0wV2BStWXiCvKU1KKUtlhMh9FQ3/Zbzbjxnu6fdp61pd/u3wN1mwucubeC+hf2VjA2nwTXML7XlF08bkcUFaSXAEWRFcVO9Ew9LL6TS7Ixjs2K4lZKQeZbncChl7V0BsGkM5q2P8W1q66iGhqDgbigBe4h+rwO0CrwvSLviRTwQWAYOnUoFtM9sMK+y81971Be5ovrIZ9CwMhq1FzAsSujKrPF4H+9s1B+3NPAH++NSzTuf9O4KffRwh93XDHRiY9rzQKycYb/gCYakpZhucbj8lX5ibwOvIahj4Q/GvbFCWQ/ZVvvFPRQwG9hgATByj6pTSAKnEJV2xjfQyeheInoFSiSVeOUidx8Yin4M54OmBjO84ZX9U2tgGnau0pEh0lZDNqBTYakncyht4MyE9lHn3Km5fEnOSXr/qlT5VAlRuX6e8t+Nh49reSTeTp4Q6/z3iFh7SOA5dSJE12FgHlhJWQ3CSTPPlWSm7Ye6+6XpDPDt80+GJiNw7oAeD000/Hhg0bsHHjRqxZswZ9fX349Kc/PZV9i6HAoS7uG9gIh7oTXyzAv/8EwN7z01i9mU2KwnG0NiVgmgT9w5a8XFflhrWhCdx52+lUOJAUQXqQkWC6etuhchHh/zJbISZecxCVYDF0NeQVraAoFckkN0HmgBGf4XKWhESz6CrTL/rol/TIOvQKiy7uqbYtDrMKB/qMRVftrFQk++qKQN92XSx8wwCyGYSuJYRXuVGTYg0uubEpMK8L2LpRSnEMAKUKBVo7gcE+uFxaQ8X7k8E4q2LA5C8syBcTqAzo+eFHjkthmsTbBvXJYlJJAgKKYpVKKY7j+HT7Qr7A7fmsweQvvgRV26HIpYjH3FtMtpMwCZKi5rEI3G2KtuaET7SaklKc5qyvmg8hsipOS85gpTJ9wbLQ3FdtyMBdjPdKhS9upIaej0mhg+JMf2BXIJVmfXIcFMouDL49zKrZsMA9z88PoIrkZuXGIrZ1l/iXgAXulu3ic1etwe/3WwzHtwAoVRxcc/NWUI2Gvlrlk1e2KRz8S4ZerX4TEejn8uynWlqzSQnohfQptCgQDH0mGNA7/LNUA30ZuKu7AhF23fUiQM+3hPuSzemTfNVkXn87anKtKwL9vvC1pUJ4xwFgzLsfos2ebUG7SGzt2R60P/MosG4FQrjia3Buui48r9z7+3A1H2Cn9fwiWG7NJ9DH5xwRaHe2JdE7WJVXEgK05RMYGhOJi+y6liZTLo4B5vdTypyi2zmWQb5/TuJtJBLKToAhdoy9INTQ7lBTeQJs8D4kQDj59fIi8NfVoZfzUpSGXj1IUd0F5huOqQTxBerhNgM7176gWpBT4jqHumg7ph+ZNAmWYBYMvUZDD3gHTsmKNj5yS3zV/aSZYM4N08A4ycodM8rn136alwtgUV6zz8rIHTpW5QYoVCkjIgpjzHUYjFR0DG9OMcBKNrOA3pPXGKaBiutVtKGOC0KEhn43DuhHR0exdu1arF+/HhbXmG7cuBFr165FX1/fBK+O0SiSxMDH9zoCSZ0GU5fUBc9hEQLsuzCNoVErYDcMgn0WpLGtjwUFwhE2ZVndcpUdUZMrhTPuaElgaNT2m0FAQqftEehrxXsMQnixwE6KDQfuumQkyW7YquMKJgp5W5saDb2hCbprMCeGgSCr4foYkVD9XU0degpZ/UZ1kISENfSUsoklnSSSITFgYv2f9kZLNqXUBmafcS5jBg6WcnjCrQyuu/YCxkYYi86HV7nqAl2LGItHgaY0k9yUKy6TYxQLbHHBk7CQZrIIsZWeSRqoOpSxrmMj/MRGdgiZJ+dhTtY0CAxCWds8AHZcIcUBC94Ikex6Ti4igsmymZTBGXrhwCkSfFegLBYXnHFvzzPW33Yh2XLboejIm3ABj0Xn17c2GUxy49PQ2zaFSWhgV8By2CRvEIqyL9C3bLbV6wZ0+ywRNxDQA4BVQbHssPKcYleAJ8XOb0uy3QIXIQ19RewKcOlOueqCugY+9ugLSJrCXsYrG4q486EBVgXIJ4t58NlhnPOll1hjPg39uq0lrNk0rg/oRVZ7Wi1zyRNXTTNsD9W490trlGtNU46t4PUGkM0qh1zxtnW6fV0lHrlbUAheS0i4FKdjsyRf2wrKlhwnvLMgridEE+g77Bn62X8R0BtmkKW3IwJ68exU+Y8IPHqVYLyNn7CueS6p1S/j4/sc7c0rtgXcdwfwjweC146NMD2/ultQHAeefAj1QPjj/RdlvDkHAECw9/wUdvRXvesAdHWk0COCfG6b15rE4IjtC96ZPwC88r5+fXqg9jqApGl4hx0KkslUiCeEWXuZW6ZUhTMMTkaJeSHAkvvvUyt497HkgiAKnaUSJrayabZT6S0maJihd+EjozwSjTHn3lxEXY+1F8F70jDR/dd9kM8klR1rRiqpuV1qAqyYC7NqUqzB5kHP5ikDdiQXyDEmcte20Ta2AKZUPo9uix9yx+cWA0CxQkFb2oCRIVayGexZOAnmJ0WhiUTCQIUmvHLClJFNFf9hVfzzKVqG3AmY7WgooL/xxhtx8MEHa/9985vfnOo+xuBwbrseLyx/IszQb90IfOvftIwKLRXxLvMFEEq1DgZgJ2+OFpyAvTXPEiDFATwiuG7NJ4LsCNgfWEDvsTfSgWlYAVGK0b8wIISE5CIBKY5yGp5wWiqjrWrrPalM0OED+kM9hJPNKKfgUVfUoSdQS1EahCAVkNx4MhyXQtFHinJdNNC2qZXiMOejyn+ELj7rS3S1HAfth4wik2EBt7f7wdrIZUwUSn4NvQii+WEjPNB1qecUihXOsBbH4bpMj0iFvaUN7tionFQcF5LpdDjDm0oClg2mGR4bkYG7afoYei7nMU0CAs7sZLJAucykOJwtdyk461yRkhtW+54F7pSyJKisOGiF7wrYDms7YfCcAMncU850A6Uq5ZpzC7bjoq3Z9HIFEh7r3tJkeMG1SDh1XDRnDVQdeAsRh50emUkST4rDFwVdHUnGissKNRXYtosUL+0PM8H6WOULAMNlDL1PQ287FCZcWA6BWhXHth1QGVyX2NgmFE/uvYDp/PkzLInFnUh+5YHeph1lGOB/y3gHUf3klm244Q87QgH9t3+2ES+sGvXJYpj9mRVjcCx/MO6vlsPHlXqAlKmT1vBnlcmGZUFGRKCfy7Og1K971S0iuIQrtAAQi4tsU5ihz2TZ5+ZfANg2D+jVg7VslrirSnSEzj+wQ2Gzz725JZjQ69hsTKiEjW0xZzIyFLSLAEVN/hXOXp0jTBMOIXh6pNubV4TMZ1hZLIhFwlqF6V/+HHDLL8JnBTzxIHDNd6CieXQHOir9oTynbNpjfCnvc0veNzdx1r41n4DtUOaH+OsTpoFkggRsBEBT1kdAyfv4DzTiu8N+aSif7BKmESKjwhJNHxuvJN8GGXoWmAbyu+CX4bi+9kTpZa+PQHg3mbo0NK95u9e6PDL/XEy9nWglcde/GHCoi6YDRsNyUMoOlqpYwZ12OV/5GfoQG++dxyL0/0wDzxYE/rrvYn4drvDdVMeWDH2pSuX8xHaieT+ybLdM1KZPJQ1YZkrOKWKnpUTS0h9QvmNdpEnpq8QCp2hBVlKb7WgooP/4xz+OUqkk/42Pj+OBBx7Aq1/9anznO+EvcIypAZ3fha12OSBHBeCdXKg5xIS89DS+kLofpHtzqELLofZG4JrvIJOgPkfBQhlSreDg9JBkgEWZq5amBEbGvcBQ6B9b8kE7wJxIcy6BsaLnkEGYw2FMv7KKT4c1eUT54rN7Qu4WqDIfwUQEqwYQT+rh3+ITW44hDSIJ3ZOx6CR0KqB/wSAlN66nRQSU5CRFOwgADm87dE8XMLVSHF5bPu29f8elyM2vyC1P6aQ5O9OUDT7boOQGUlvucjvAHSYP9EXQDULY55ZKw+XOMJvidejTGcA04Y6Py4WVZVMW6BfGPMkN4QF9jp04anNtvUHAJDc8AdLhMhfZl1QaqJZhOxT5DK90weUvDp8QMynCgmvOUIt7JgzxfjwWnZ2eyGU+ySRgVWDZFB0tvN4+ENDWt+ZMdk9fCUnLpmjKcN0+l+1YNms7naDergBn6Bd0pEBd6rue2ee38IheJtGyRcT8tgRfRHi7ApbNJqqqA7m4EDInAxQVByxZtlJh3wMCbGtvDkh0ZFBCXfY52Bbg2LAcClMII3wa+lLFhQGXLQp8ibuPPD+C5WvGPFkM36H56jXrMTBcCchlxksOPvH9VbAtoa1n48d2KEvyFQy9ytybtew5oKRIbrKcudNKcXwBunCk+Zag3bZZ29lcmKE3EzyIUA6/0jH0Ng/cQwG9w+2KPj+RYAdx+Utu2hbT+Ifq8NtAx/zwAVp2REAvA31lAdDUDApgW3HYm1dEG2oyr7D3KnIeUcVn64agffkyYOOaQL8pBV7/6LU44ZlfKFpy4NCHfoHDCqvkdQRsTjikupEn27M2TJP5REFQiNdnfP6Q2QiaMiYKpeAioSkbfC1EkO9bTBh+/8WNqs/19zNQs971yi1X/WQOwpVvvB1l/xyBQB168b7TSr17Cs0JsG44N0yQOv4d8GAZZo/8UZNVKQXSnWVkOVEk4HJSBVDnVKKw8fCSYhUZDqvq5gT6Y5oGD+g95pwYBGOieo2viljFAmha+D/+fFMG7CTzN+JE2UTCgGWyuUPcJ2EaKJM0UC7KMpimaWDM9YJ88X5GrWT4hO1ZioYCetM0kclk5L+mpia87W1vw5lnnombbrppqvsYgyOx13740AsvhA8AEU5668bQawh3tuamNcphFRSvstYDG9dgf3tHQOsHAuC+O/BT/BKlcb8Uh6ClycQ/ja8C/na3aMa3peYAlTIo/0Koekfh/ABRRtELMNkq3tQG6E3ZILss7OGa6+ynPwHWFe8HjKX2J+bIoFMToGdSnrMJXK9l6HUlwZgjSybCZcEyKSNYV971sRi6KjearU0iFzRCcmNg+z/mI5dOIGF6pSvFSbE538Qm2hCHspZ9CZ1s25Q9j3LV5dIVVqFGjLpShUkGXM6YZtMEDuXPOpcHLYzC5O+96ohAvMolNwQJgyfc+gL9hEl4qUxXsrSOyz4fgO8KcGaY5RDwKjeZLJPNlHhfUjy45mwxWyzAC+h9J8UmTQMmAWOrORvtOBRtTQYo5QmzSe96pqFHgKG3bZZb4F8U2Y6LhEmQThCWcCwCdL4rYBCKgpQWsQVAZysP6IX+vVpB1aZY0JLg9/SC6KpF0SLuyRl3IRtLJdi2s2DFKxYFXIJ3PbUCCVlDvuw7p4GyoBhgdsuFCZez/B4TTymTBVVshFl0v7a+XELfEM9pqNgec18pYd2WEtZtLaNcttlnX60Cjo2Hlg3jB9dvgEsM1hceXP/1qSH8/oGekD5/3dYSRkarnuRGZejNBO+jwq6rWnkhT1GlNaKNjEbPb5psMVJUkn+bW9mz8u8KiIDeH/zL69vCDL2ZYH3x222++EkkgwsDWwT0SoBuW6wvoQRdi70ftZyn6yBBKc5NtHrzigjABxRpjbCrLL94dqrMR1RV8kl0KAUypRHki/2o+vyrSR20b3gObyk9wy/kX4XSKC5P3AwsXybZZwA4MDmE9p9fzL4TvuC/OjQErFsRkI7K4J1fl8uYGJc2L8eooNiCTDN/SzzwV4tI+EkascAIHSKltClLPvvmIMn4+2vY82eUTgVzsjzNu6FIjQgv3uBj6KEm34YPM/QIseDBUj1PLAhXT6NszjMNMF8O32ZaoESlN7eVVBmOr0KPTIw2CQq+gF7ITotVsAW/bcnPlgKghnfGCQFFJmXCNpJMXkN9Cc9GSs4dBpf2FMGCd7HYMk2CMTcl/YYosjFoJ5nfCDGpsw8NJ8XqkEqlsHXr1qlsMoYPdsc83LbvPrDVcmdjw8DiA8MJUgDo8CBGaBbG9k2hpJj5zjAAYB9re2gbULAu5vaN8noWoCdwpv0E8KebWfAO5iyYo6PAb38CXPUNgNuzaU9e0rzqabzGXQeAl1EseSv2lFtFlu8UqPWJ81nPAYs+ii1awfILXZ2oLS8DYO70AJa8KUo3CgZHOLSAREVhe/z2rCJ/EZNMQIvIdwUAxdEHdhzCuwKhmsKuj91Q7ikOohLvv2o7WPSmftiuG2RDXP/iJ3hPwYpXRaBrW1KKA/CqMPzwJ7YAYB9IsewCiSQoZ/2yKVZJoFx1gaY8aKEA0/DVXE6l2VapZcM02HuqWJByESbFYU5VMvTlktTcA0ChTGW5RMcBmgRDbyaAVApOocCfN78nZ4tthy0WTJNLbkSCrkORMNnYYAEwu95yRL19wsaK7/rmnMl2IlJpWc/dcijyGSOwcLFslnSVTvB6+5xdt20mxUkQikKFSl08C+h9h3alvAVAe96EQz3WHgAs20VbE2EHsEjGncrPgr2fNJfcuGhtNnD/qw9GUTL6ZVSqLppzJkz4g/EyKlWWE+DCCCwiylUKk7io2oS3LRZzBkzwXAG+ABCBkW1xppsH+v0jPNCv2oz9BoBKGZu7KzDhsjJyPib+oWXDeGXtGKsX7bN/4Yfr8NfH+z3JDQ+6P37JKmzvLnF2nV1PKcUfH+6Ha9shDb0tkn+bWsLVeQRDX1btCQ1z77Cg2zDDjL4qrRH21vawhl6y/5pdgXxLmNFv5XIef6Bh20wvr6vE094ZPnDLsmDlW/CnsW7YIhnXsrz8Cb9USgT0Kstf5YsrVaIjrvcF+hQUdoLliSSr/NlSiqzF3nObMyqvIwCSA2w3wN2wGoBHCB1KtiPVuxnYsFrOTbm0ieYn7wV++n2Y1GaBumDj/3Aj3rHpNm7z5o29ul9GZ6U/FPgb1GX+teKfLxiJQimCunytjIdwssg3Pynstyuv8+V8cVY5qIHn322eWBos7EACgb6U7CiBurcb7V0n5hYpr3H9ZBa7znZdzHtdH5IpaGWfASkN1WjoIxh601BOiuVkl2ESFKqe5AaczS9WHJnnRPk4TSQIOxzK9vIp0ikDNvgZKbzUcDJpokLScsfWIIyNL4iAnjP0CdPAiJ3yGHqHghgGhqpJtsj375DNUjQU0K9btw5Lly4N/Lviiivwwx/+EMcdd9xU9zEGB2npQMf4GIhaeq1aARbtEz7ABAApl7DCXQgy0MNKeHlxLvK0BCxajEXVbuWUPABDAygijVT3Rq8tMIY6Bz6wt6yXCZ3yy7l1I8hgL5pQhqi7KxzGor/8Al+u3AKAsSSCoacU+M/ua5D7w/UAvBW/INcXvng/5he9bV4/Q03GRwHXlXOaWET4s+yFfDSXNqV2WDgQkUwlnZ8rJEHBg5hE4O5fRMh2DLa4KPnYHHHPYF/8TlSp8mOI0l5+nTvlbYer3IgFjb9tu5iQTFXJxwKZgqH3V7lxeFIsobxUpMmqBVAW0LHPwZFJp2JhAAJ5MBK1mNNNmoSxKGUXSKRAq1WYJkHS5IyuqFBjWVL6JCvOBDT01NPQc8mNaQAuwCZbntDpL5XpuoxhdjljmkkKhp4F6GKBkiD8M+ZyEaGtNwVbzoNOx6FImUx4Nl50goF+jucKiMTVSgm2LWrfI7CIME0gneTvM8UCXUssLgywxSxn122HYl6LCYdyLS2vaGPZLtqaDa/2vW0BjsMY+pzJ3mdKXMsScbNJsMVCmu04WBZFa1MCbcUSxkve+6/aFAvbfTIfobm3XXS1c8lRhjP0rotK1cWCtgSX83hSHMdlgX6xQmVCa9VipeZc2/H075WytzXvCBadPfNCyeEBPQnUmy9V2G5B2SLwJ8uOlxyUyzaTEPFDsaqWi/XbytjRW+LSHWbf2lvBj5ZuQ6FgMfaba+tfXlfAe77Mk399gf7qzUXc80ivF9Dz4HpwxML4WIXbVW29CMZz4WBcp62Xmns/Q++wKlG5fDgpNsGZe7+O3uYsv2MHE3RtC2jrCJe/tCzG6KsMvW2BdMxDe9WSwTJsHtBncsF7ijZUmY9VBRYsCifoWlXmCP2BPgUIdUFBkK+wdigFEmCLvfl0BCzxEUzHbnGNc8+OQN5XF+H92r5ZSjmzaQOEv79FVo8M3gtlB3jyQRw1uAyE0kDwfsJz1+PUrXcGdm9hW/jhju+hZdOLKJV5AE0pmlBB8q7fIGfach6hAIhtYX9zMCD3kXOfUqLS77Mh/b6PDII+URbwFhOqbCaTDO7ghk5h9UluKorcJ50igblCJOT6T4qtFhLIpVk+geOfs5SCDbI/il5elfHIynChPlKYhhE4yEmU1yzJMsdeWeR0yoQLVljBpQChlAf0BkT5ZHEuTZkkOUPP5jEzaWCcMjZelLI0TYJhOyW/2yLvYaxqMJ/iX9zOUjQU0D/33HP4wQ9+IP9ddtlluPfee3HJJZfg7LPPnuo+xuAwUym8fXs3TNVxVivAgr0Y46MOOquKTW4nyGBf4GhtSoE0rQL77I/O6kAoox7jI1iV3A/JoW5pJ4Rl83eRUbh77Qfs2Mo0buAHMVUcoFQEzTbhMKNbLgCqFg0eojI6HKiWQ6wqWtxxGMufRSqJgINJuyW0PXQbznce9PoG5mjyRhVfXPMD4B/3B+rt5zIGKqUKcPMvYAz1g/CpKpcxZNJUfnALwMtXJRPE0/NTcImKwmj7FgAl/wFNvu1Zv5xH7gr4A3qRWJtif/RrDw1CAiyPd08SqOEr2zdIoC8EBCMr22AaRmDL05ELFAPFkiOfoZ+hL1sU4lAoVuqL9a9coV4lGp64Kxl60wR1bFklzzCIDPRd22GSG8HQy1NRK1K3z+Q8Xq140yAghJfKFMG1yxYXki3npRgdlyKbJqDgSXCZHGixgIRJkErw6kk+yY0IoktV6i0KbK8SjT8YZZVo2DgaKzpSuuI4LEHXpvDJUVggnUsbvkUEb9tkte9LVXiLCNtlmlfB0PO+VG0XLVkDLghLAOQJsJbFDtCSibiADN5bc4YvoGdtpJJM5uPfcahYLpIJA29ZsxHFCjyJjuWiq4MF9FIvXy6hWnWxsN1krHgqw+5pVVG2XMxrMVGxPSmO61Ip/ylWEZD57D0/DUJdbmefZ6XqYl5bErKeHL9nxXKRS8Fj6LkmfnjMRtKgLCnNx9AbBmDCRcny2u7lMh+ranmJuOUitvOqKVaFS04MAygXsWZzEQk4TOaT86Q1f3xkAH/4ey9cI8EDdxaMX/yLjfjNH7az4NrH0H/3FxuxrbvoC/QLoJTi7kcHYFcsxtCXS9L/DY1Z+kBfLgqagiy/bbNzGZpaFMmNxRZc6YyyAOCaezWgdyzG3PtLaPLyqGZrJ04aLcL0S24SKZ6g62P07SoL6DmJIlGtAPMXaXT7VVYO17cAoJTCdCyUWrvQUmV2CiDp2nByLUjBAorjMgA2HQslmgAZ7JOBOwC0kSI7LKi/JyC5Mfgiab41AFFOWO7MAchUR70Ami+E2isDgXr12VFWUSiz7kVZ1IBS4DXGJuCxB3BcalMgKM8//yC+PfS/qI4XxJsEIcAbBh/H61+4SZhksLuwbyWwfZOUesjAdnzUy+3ysfb+086Zj/QF5YYaQPvkQkqirC5ZVT2tVUhmhM00DAwub0Nzlh3wF5DSGMF5T0hUgjXnOWuvSI3EoqPiZ+jBct2KVcIMlMpk1arFy/S6rmToMykDjmEGEmVTKZOVo+TlkwmARNJEGSnpxw3C2PiCk+DV1AACRvCMVg2PiXddlhRbduXJ6LMdDQX05557Lp5//nn577nnnsNf//pXfOpTn5rq/sXwwXId/OjYN8FSmfhqlW3J5lvCLL1VwSbaCYwNIwEncMJeilpA195orQ4F6+USAJUKtiW6kBxhiVGCdTANoAkV2IsPAvq65VakaRKknBLTuB10OPYhg54+0HaBkSG4yTQ2GwuAHVtYshIPotPjAygaGcC2sThVZAfy8L60lNgkcRjZIfsu2IF5Ja7NfPlZKTQ0OHPd1r8ReOoh5J79Gwgf5TIAXr8Kb/7rFXhVkSVg+ZNxW8a7cc7LP0WTUwxo/CmlSFol7N/zfNAuJC1pgnKpwvtHpdYz61u4iEDcCzDVRYQpFxyiHcNg1Wx0iwu2Jczslu1i77ftgOU67P1Ug463KcMSOgW74/BFBKtywzvgujJhCeBSlIAUhw2OUpkxrNRhgbtIXPIH+iKgr9q8zJhpgtrMboqKMzxBVTDxBJSNCSm5AZfcEB9DX+H6dwJicHsqDadS5QE9156mM6zfti13BeRioVKWDL1B4AXX3OEnDRa0jxcdyVLbDkU+S+BSXrkmnQEtMza+KU08bblg4hNscVG2gnKeJL/nmGDLy2IBwO45WrClzMfien7bBQsYkymuf3eRzyr3tNjiNJWggfdTtShSKeCGE1+HEbErwCU6C9pYQD9eoTJxtWJRLBSJuImELDlZqbpoz/NqPuk0UPZ0+M1Zwuo/i8DdcpHPmUgawNC4Ewj0912Y9oltczLQXzwvyY58z2RZcGtbKFfY4qJoEanbZ7XGgeYMwWiJSlnMyLiN5iYT1YrNGDouuenngb5rB3XxOwaqMMGlRdkmKa0plhwkiMvkTDmPid/cXUG1YrGFTq5JBuMPLhvBpm0FX1WcAvqHLfzwxq0YGCwz9p8QoFTEig0FvO+rL3PH1iYZ+pfXFfCbP2wFFRp6Hrhv2F7C5m3jLJjwaetLZQeu5ZPo+AN9ywJaO8JSHIsz9wHGnS922jrws3wCljhQy7Z4xZ3WYEBv8cWCYYTbEYG+H9Uq0LkgENAnKGNeS61daLF42xRIuhbcVAYDaGZMPw92DbuKjXQeyFCfJI8AIEMslObtA/T3yOA/kzZYsnpbJxbYA/JsE8ehQLGAkplFa6HXq03PpUNN1jgS1JZValJjjDAzepmE1XFYQN8GNhYON7sDQWxymM2RyR0bxNuBQYAjBp7FQTueCei+s2kD71n3a+C3P2VyEh7YOhUL+Na/oeuVhwKnutLCGFLXX4kOjAfll8VxvKHysnd+Ce8PcR0knXLoICcRvIeCfKWcpGDTxfxhuQ4Wv30HiMHygvzBv2noEmChIZTCUqPw6bEijjB8p706ALy5lBLOvPOpMJ024RBOOPHnm0kZ8gRYxtAz4qZEgxr6hElQsojv9Fh270IVMnAXFXbKVZdr9Wd/LfqGAvqbbroJ//Vf/1W3PcbUwCQGThoag6ljQlIpoH1eeNuzWsUAzQOZLPLlQcnQgwJpagEL90a+OgrH8h3eAQJUK+hOLUR6lC0QhC7QdG2YhMLu2o8F9DzSN00g6VSBZAq0a2/sawx6WkCbAqUCnHQO2415QO8OFujybc9UaRTDZiuwYC8cmBwMOKLm8iDcAw5BAg7s/j6vLwRoLfVhmzkf2LSObePDY2qax7sBM4H05pU+DT1fRGxZDwA4vLgSAALymsUDr2Dh2Ga0rXkqpDlfvP4fOPqJG7BgfIvPzgLm1w0+ifP//p8sr8CFXERk0yYyG5cD990hnZZkS6ouMDyI/FgPc8wh3b6vsoJf5sOD7mzaO3qbAhhZ1QaTGCE9p2GwCcIwIDWhLpeFeAG9KZ2l4ZfcmAnAduQuAghPUOWMPvO11GPoDROu5cA0WRwiA2DDZAsA/z0TCVlCUpatrHrsugj0qaysw5lhzrLIgN404VoWG4MJX5UbALRSlosI1jaXufBEXAOU9ZsHjJZD5SJiXCwiuD2dIKCEYKxgA+kMHM4k59K+RYRVhW3ZSJoEKdO3K8DlL4mEAZNQjIucgCrbFRAyn5Fxb+Fi2R5Db9mu1K5bNk+KdQizVSuSoU8lCMtDSPnsnKEfL7vynlXLRY7vFA2NuzLorlou5rfxXQEASGXglNnCoL3J8BYRXIdPCNCUJoz953KZatVFOsme+WjR9RYLVRd7zUvDgMtOfuQLtzLfFbBcwhYthsHslot5ecL0+b5rAaA1R1CoQH5u5aqLrvYk0ibFWInKwL1UcXHovllQkdCaYcx9sexiUbvJDtsSB2g5NooVFyYcdsCMj4lPJQ0k4DI5AG9bEAx21YYrk2ULWL+d7ZKWi1UWGGebgNI41m8rIwEeNPskNw88PYSXV42wUypzXuB++W+24Lb7doCaSR7oM9b9499fjVfWjHApTjNjtCnFf123AWNjFbZYcB25ALrj732gUlvP2nBciu6eAkAIzNY2nNQ/CFM4Lctii+18SzigT6V5JZ5hz16tcDnPqLKI0AT0/FAtq7kTzc6Y9F1J1wJSKQwgD4wOecUZqlX0oA1wbBDfzkUGNsotC4CRoUBFHMOqAl17od0ZlUSTa9uAY6MnuwhNpUGvlnyljEKmDeVEBtnCgKxDb1plDJhtIH1sd9pyKCiAHGGR5mIyKP0oKEC4RCw3yBYAYk60Tb6r1r3NS741vEpB4pTqTNqQ8qOW7asAwsYbANirV8Bc8zJONFcHtfpPP4JPle5EeqQnwMa/a+g+HHvHfyGbIgE2/uiNf8URWx8JMuIEWLT5WTRx0kzuCKcNtFcGAEphEgP9y1thGgbSKSKLOfh3AoI7zWqVHIqQht71dgf8NvZ5gX3HACalccUZNYQH9C4oL6+aFfIa22YHaoFJbqr8BFjB0CcTBoo0wevQ8yo3CQMlG4Dr+HT1BONVwr47lMocNkrBd6p3U4Z+ZGQEvb29IXt/fz9GRkY0r4gxFTAIwaEkCWN4EMvXFzy2vVphutu2DmB4ICBNgVVBGUmgfR5ypWG5uqWgSNEq0D4PjmEiXfTYEgMuYFWxI70Q6fFBNpCFjKRcYgHGvL2AgR4ZXCdMgpRbYczlvIVYTIZYmUeRwV8pw0lm0G10Ar3buZ6P9dOwyqgYGaBrL+xnDHr6REqRdsogTc3YTDvhdm+VdkKArF3AWrIIMAxQceoiYZVRcpUR4NVvQrJvGxLcATQJhr6vG/3zD8HiMmvPn1zaXujBSKYT6c2rYNk0UEe4ZXATAODg8dXy8QqmY/+R1exwoJUvSu0ewHYF9n7pfuDe3yMz1ufT1nM9/2//B2c+djlMOMgpAf27t9yMNzz7G273a+uB/AsP458Ka3xbvwSVnhwMQnAg7YY9ytgywUiIEm5FXkLU4YE+ITzBi2voXZciwTtZKgu7DYeKOr9+Jt5hbD4FDNMIMfQiAbZYcnhA70q7ZP/5abOmSUAoZW0kk4BdlZIbydD72HKTUBgGT3hLJCT7nzLZGSBCpkIqZab4ETsiiRRgswTVBGfLx2WVmzJn/wEQwiU3HnOfMADDMKQUxy7ysp2CoeeyGFouI5EgSBrUF9CX5c6CQai34yC19eyeI+O2DMaZPh9wicGkOOks3HIJls3KdlYDDL2DZIIgZdJAAnGlSpFOmDiiuxeFIqRsqWq5SCfZ929ozNsVqVgU81tN2OATdToDa5wxk605w3uflTIqFQfppOHT7WckE59KGkgalAX0vI/lqot8ji1ohopUBuNVi2JBq8lYceKx8eWKi45mgzFpfJejXHGRTBDkM8B4BVJDX664yKQNZBPASBkycC9XXey7MANCHb4DwMpcliou9l+YZGxeKs1Z9BKKZQeLO01UHR7o89yMUtlBew4oVImUxYiclBRxMV4Gv76AkTEbLU1skcmq4rBE10LJQVLU+G9uY+y/66J30EICDoo2CQT0I+M2EnDYEfe8IlSl6mJHfxWDQ+UAQ7+lp4J/vDCKocEie1bZHFAYw7KVY/jJrdvhVqsBhv4vjw3icz9YAWomYTS14Mi+fhiE4M6H+nHLPdvYd9DH0P/l8UFs2TbmMfe8neXrC3DKFaBjHgvgueSzUHL0AT214JhJOPlW5J1x6UOTtAqaSGGINgGjwzJ/ClYVZZKGm2tBojAi2doMLJRaFrDgn/v9ZMKAaVeA+QvR5o6yHWWTwLBZID6YmodceZjnklG2aDbTGE21Iz0+KBl606pgR6oLpDiOVpPtCFJKWe7YosXookNeiWKw+Ws42YbsuI/8IkDaLcMmCYDPWwRAM0qokgSQTCFTHpalI02eK9A0sFkeagUAbj+Lsw7hElYZvO9gbea3rwkE0PtVtiJhlbHAHZJV2qhD8er19+Lo5XcGAu00reKAv1+Pd40/6PXbYJKj65O/gPMi2/ke35ZDwjBweKIX8372X1xW5pVmTm1fB/ztbmmTUpq+HWgqDvh09Zx0q1ZgEsqkT8rZLQnTwLhICXEdmY2czZhMHue6EMclpNMmHJ4AKw4Ny6QNVKmXKEsIRTJpoOyYUt4p69BbBPj/7P150HVLXtcLfjJzzWvP+xnf6Yw1U1UWWKDIBZmUQbwWKLaKYDtcbfQKV0NU1FbbKbxyI7gddgu2V0MMWm8Y0RIK11JLpQCRklnKU1WnzvCed36mPe+95sz+I3OvvZ9zyktRlFiBrIgnznvyySdz7WFlfvP7+/6+v0a3B5lr0QHdYLRGSPtZ/LJk6H/2Z3+W7/iO7+ADH/hA++/tz1/7a3+N7/qu7+Jtb3vbf6l7/W/+KpuGv3D7gLMHF/zR73iJf/Yjjo0vC7spDcZc3H3CV33rz/Hx+3YjEmVJYXwYjImz6TUHmQDLuKyiEbE7pRvAN5ZFWAQDtPJgPtnZP5YFGQFlZwSzCcI9SJ4S+I0F9Hp4yLFYtAtTWWuboBhEnKkRXJ5dK6ahypxChXB8g9tcXUsuCpocEcXc4wAe7xgQIQRBnTFvQji5hXhifyeEsHKHOofDE3QYcyptaHU/XHl5+CYOyiuoymuZ/kkx57Xx21FP7iEE17T10WbG/C2fx9PlPkNvwXt/c86rnefgpRda9wGwh4V08QTGR4wefaR1v7GMRQNP7Fi31q8RR3JXFKqp+azFz3Hz4U/T1ZtrQN8zFYP3fw9f8tHvJcvtIlPUNbd+813KbM0fefjdvPVD/197f3qniR+HFSff+T/Cj36gZUmkgKI0Vheum2u2lVlpdgy9c6JBuOq0yi6aSgBGI+WufQuuMXbhXDttPVugL8wbC0tJAbhiMde09fY1r7cMfVnYxCYpkGp7uPAxVW2/gwr0nuZeFLmVOIm9A0rdtNp6KYx189kD0dvXudo09qDsHGdsVEC2ybJbhj4OBLWGonEMc547hl44nfeOWfc8gQSWudkly1YGX9nd1GroQ6fP13gShHRSnCCkcfac3UiS12CcNrjOCgJPEijnCLQF7rXGDwz/j6/+EiZ5vcfoG0LlGPr1XvJvpelGNj9hurSRiGqToSR0IkFeO1mM1hR5SRgIIt9Y2U6wPURoC1KEYb7R7dhlpQkDy3TPWubegu6DnqTS7jmMY0y2sTKfVNr3cHvgKK2LU+Kzi3IUu/bIM1aK4/pnRcOw5+ELzWRlnBRnQ15obh94FtC38h/L3N8+8Mgb4aQ4a7S238vjgQMbTlqzzhqSSJIGhtmGFuivsoa3P5NCXVMY1SbLrrKGWwfKevknqTtE2IPB6VDaipRJ2kpo1plmEBsWhWiB+3xlnaI8U1u/bsfcbyusVrmLCrgDwMsPMsCgtGPocyuLfOHVNYGoqaVHmXb4c+98C2XT8H/8uyteuruwVTQdcDfG8Df+wX0+9NNXDuj3YDFjnTX80e94idlkY9+T2CbRPjwv+M1//MOUWQHjnYb+xXsbPvrxGY30adI+3S2gx1gpThBwZVJYzlvShqqkFPYAoNbzVnITULHpHEKRE5ui3WtUU8HhKYPG7j9KgqgKUB4zf0iSz5zkxu5ltQpYBX2C9W5vlFXBRsbQ6XHirajrPYb+xh0O9Iyy2soyQBY5Z8kNkn1AD4R1znlyCpOLds9KKFiLGI5O6cyftHIUv8rg4IRgMyeitIn8SqDXG5rDGzwnL3ZymEJDtmYqOkRXD65JXBK9oVE+N8rHbZvUu6JIviPutIGTyh4WnqsfujYbhY0e2wi2/vBPUTQNb/n616hNw68TLxJcPYIXfuYay37rP/1r+IH/nU69uC6v+e6/zvt+6juvtxnDF/6TP8VXTv/VdSY/z/ldzQ/iPb4LCIyUVlbmtLRxKDHbZFfHSoahk9c01rQBYwgDRwzondVy4EmyRrYgf6uhzyosWdXYPU8qwarYRgcaZ1phI+RbN53P9OsXBOgfPnzIBz7wAT7ykY+0/97+fOhDH+Lrv/7r+T2/5/f8F7rVX7k8KfnmJsZcXJJGkn/7Exao7iQ3YxYPzikrwz/7YQf2q4IcD4Zj4s1kLynWWMlNELKOh6QbB+iNS5ZVHtJTZOnYLkjb2plFTi58yqgLQE+vdhp6XVqgMxhzJBYIYxPy6tpYCYUfcikHMLm4VgLbqzJrKzU+4tDMdsWsNBaYRzGPGbXlz7eaQL/MWJgQjm8inN5ROqYmbDKIEqrRKXewC23rFb9Zs04PWHodePLwmrd8WGc8GTyHmE8Y+eW1CnxBvqR69h3canbRqdY6rFzz0eh5eHTvGkPf8TVRsYTP/nx609faJNI4lNTTKdQ1Hz/51Zyu7hGH1rowLzVcnLGWCZPRM4yvXroG6HtmjfZDjFB0FtYSThrJxb+9hffgLqUMODj/qJWnOOYD4J3eI1SZww/9Sye5sQx1Y4QN92vdut+Ak9bsMfTSmS5n2/Z9hl4KC7qVZcttFXkrMdk6FFjJjUvELY1dJHXTJtEKtsy9ZxffBpSwkputVt5sGXon87EMvWVDt172nnLtvo0AbHMWNlt7zqZuAb3AWIbe91vg7ktAOCZ+60Nf26RgKR1zH4ToorBVjyUIKVk4oK+L3BazUuaaJ/w+Q986zhQ5daNb3f5iVe8kNy4qIKRk4aQ426hAJxLUWpBrCcqjyTICz0YoVnsJt1VlCDzF//Rvf4TV+jroDjzH0C/qawy9rwApmSwqCEOqTU4UStIQe4hQHkhJtcoIfUnkCctQR7tE3DCw9pyztdmTyxgiX6KEZrravS9FqelGEiOkvZcwpl5v0AYGqfOgjmIoC/K8JgokcQDLwrRVbrNSEwWK2NPMNqbV5+eFtR/0heZypdv2rGi4MfaojAMVTl6T5ZobI2UPLnECG5vTYwwcdiXLbVRgs2adadJIkfiGq41pgf5q03Ay9gmkZrI2Leu+zhreeiugRtkDdJy27U8dejZXIO3Cxh4i1nnDzZFikdMC9FXWMOh6dEM4X5pWW7/cNLz9mQSqihLV9p+vGt58wyY0knbsgXNl2w87UBgPL+3xJ37sP+C5w2qqGpalaBn6rb2wZyoy7YpfLefcddIiXRR23I7t/zMvrnbt40Obn1AW/JsfnxFiDzmm06Ont0mk4Gsr15zotD0ACEcg1dKnTvuo1bxNio2oKL0YOl3GZonY5iY1pWPol23kWNV2n1v4PeJstpPcOEC/DAZ4y0m7H8m6oJQBDMacyKVl6DXElHB8E9/Uu6RjJ7m5TG6Qrh2gd041fp3zJDyFq/Od1XKdsRERHJyQbq5az/mwyWEwpIx7HDRTt18JdLahufUst8TUMtBbEJxteFHeIpw8uqZ5T3TO4vg5DrKzds8Iqw2N9FiPbjHePG73rVRvqA5vcsTMWbzae5TZiolJEQ/vIhG88gM38JXiQK7QyrcS1z056DYy8XR+ry3KmBcNTK8ImoJOtdgdOq7Okbrhs9YfuVZIMXxgo97y0Wv2c5cKUzcItu5AEi3ENUe7KFRUyNcx9C665owcBOD50j7PW5DvJDfr0jH0br9WStocGcDUtZ1Hyh2g/+XG0H/VV30V73//+/nu7/5u/sbf+Bu8//3vb3++7/u+jz//5/88URT9l7rXX7kA0+kTbKZ87Zcc8rHXsl22vh/AYESwmvC57+jyUx+1i6qoSgp8xPCAeDNtbStFU1uAFoRk8ZBO7g4HxklxgtBmnCdjWzHQbJNlc3JC68c9HDPSM2svJgVhU1rAkPbtWKu5Y+Kt5Eb7EZeqD9NLgr3qtKoqLEM/PmLcTK954vtNAVHMuRogJ+dtOwK8cmMZ+sOTa8VLfE8Q1jnECeX4BreNXXDaBWSzovRiLsIjK//ZX1iqNZuwD6Mjnveudr69jcYvVphn3kJq8jYUrTVIXePXOR/xnrE2aq22HIYyo5I+PPdW+pP7O4Y+lOjFHDo9Lnp3OF7ftwzz1nFnMWPh9ZiPn6Z7efdaIu5QL9HdAbODZzieWxmQNgaMgMsn3Ou/mVU0htdebsOgAM9xxvlznwuzK8LNBCksoEVg2QrA6GYH6HPdFpbaag9be0qp9vraw0G2tbPcS5b1vNdLcew4BizDCJa5dweATdFcZ+7tim4Zd89rve+lk79YDb2Hrmzyq40KuCJaXoCpKsvmC6vbb4RqK79u2fJ1pq8Vf1LSbsjrTWNzQqrSJoEJy+Jsgb4uShsa1/bgutpsE1ptu68EWWEwfthq5e2cZpcU65h7K/ORzB0Tb1x/JW1UYL6n2/eULc4llWiBvs5yfF/iSZdYvJXcVFZb7xlt5S+tK44h8gQawWS5k/8UlSaQBqSy4Dqwh4ittKYxwh6Agoh6vSEMJJGPPaC4CEpRWS2rFJrZepcUW1aaILC+9dO1biVUeakJPYNQiqtFBVFM5RxDepFl0kpp5UylmzP2YJFxTZ4ThTZCsTtEWPY/clGBy1ZaZBn6fiTQQnIxLduDwaZoOB0qNrXExAnohs0yw/cEg0RYcO2SaNdZQxorIs8wWZpWQ7/KGjqJIpINl8st0N+wyjTPHluZz2xVt/3XWcPNobLa4aRjIwWZBRRHPcmikC1wX23s2J1Acz7XLXNvDxEBkdI8mRsL6FdLlpuG9zxn92QtPeh0YbVgsa5511MBuVbQ6aKcPn2xbnjuxGOWyxagz1c13URx1IHzJZahX82ZrWqevxXh6ZJ1o1z7gsdXJc/fiixbnnbtIWox48lVSUBNJQNMp0/f7DTxvqkR/j5D7xb5qqSSPlXSd5Ib2z8wlZWu9EaMWLZRYq8p4fCU2BSo0ubPyLqAIGDu9S1DL12ibFlQS8vQe8tpK61RLaAfcbQF9DhAn6TMvT7KWXEaYxBlzlXnBmk+3atoa/DrnIeBY+i3WvAmY0UEowPizS7PLDEFJkzIekcc13bswJOYbENzcMOu04vZLpqcbXhZnOLPL2ybY79jk7M6eo7h5qxlv4M6o/RjivFNDvNzt19Y8G/6Y56YPjy+37LurFf8rHgadfEIU9UYY9fPLhmzG2+DB6/uKs0GEr9Yw5vfye3yYaur31YRP09vcrR6sDt0XJ2z6h7Tqxck1aqNImwTi7f7tJGKZsuIO5e2hje63GwTYLcgPwykJaecvaUQVv63qYV1vnGfg+cJ6561jUo7skZ50u33DZKdPbRNvv1lqqH/gi/4An77b//tn+57+ZXr57lqrfl/FefEzYrnbgQcjXw+endj3QSc5CbJ53zBu/tcziouJgU4QM/wgHA9bRl65UrX4wds0tHOExgIHdPuKcEmGcL0si2bTVlQCN8yHMMDRs18J7kxJUQRRkrOTQ85vbQlsRtjfcX9kKXsgpD06uVOclPlFDKE0SHDakbVhjPtokgYc6VGqKkD9Ntqb4UF9GZ0iHBgf+usEzYO0I9OuGnsAhluC2tt1pRBwlV4CBdPrvnzRnVGGaRwdMpTatoy9IEuUU2FNxpyz4zh8f32Hr1yjRaS1zgEqVCT8xa490VG5qVw8yk6yzM8J2eKAoleLqDT5Sy9xdF6p+fPCg2rOUuVsjh4muTs7jU7yyFrTKfP6vg5bmzuAVBpzeGX3qdezCniHo87d+C1j7eaQYCxWLGKx3DnWcaTV2042tgFbwvoaXR771lhXPJr7RY9YcF/0bSewNIB9+sMfdMWp2qr1kordZGOifd9aRlJAN20Ep1NvmXRt/709kPdZFaKY9wiLzFIJS24dKy7HcOC6y1DL+qqldYI4ZKejKGpGzzpGPrCtFVbt8w9UrLMLKDXruy3coeI1R5D76ntwUWxdJaTpixb0F1pKIRv8xPqehcV2HOcqWqzk9asXCQiL6w/t7Svc7HeA+7uEOF5qgX6Js8JPCs5yissIClzylLjB4L/+Uu/iHmbiGttKwNlLCvupDUt6Fb2EDFZ1G7OjMCXSAzK27Lo9nAR+oLAMyxzgw7CvaRYiQJmW+DuElejQCKNZrLaOwCUmkBqpCeZzGuIEur1hiiQBEojlHT9Q6r1hiiURL7Zc7nZjR0qzWSt2/bMMfQSzcVcX0uWjTz7/T6fVjtAn2sOupLKSGa5lVAV8yVppOiGxgL6LRDPG9JYEilj2X+noV9tLNAPpOaiBfoWdPcTaITi0Xm5x9xrTgYW0DfOKnQzWRKHkkGM/dzSrgXuWUM3UcSe4WJl2qTYpQP6sWw4W+iWoV9uap45sknOFwttE11XC5brhjedemSNok5S/uoXfzHZxr5fd8aSeSGh5wD9uqbXUQxiw8UalxRrAf3xOCASDXcvdMvoz1c1n/f2Dj6NjQ73BrCYMV3WhKKmElbO0zPrlnH1mgr8gCudYhazndtaWVDJgCru423zvNgC+gD6Qw6wUWJfapRpIElZiJRgM3MMfekY+r5l6KV1rqHIqVTAKhygFlcYYwkaC+jtfnoklq1tZYLdF2f+AH956dZ/EGXOOhnTCCtPxdj9VWC4753sGHqshHRtQhgdkGwmtoaLL+iInCaMybqHHDY2Wh4EwuasRQlnpg9X57aCbKUhX/OKOMFbzohk7WStORLD+uAOneyqJcyCOqP0EurRMeNqexCBRGfINOW+HtKcP2lZdzYrztQYHURUl2c8/zUP0Wi6ZFwdPg9nD9HayixDTxBWa3jT2zmtzi2g9wVetoIo4UnnDgfrh7uk2NWCTTzkPDwivbrf7rtyr5py5AC9ZeiNqzGgrCOVbloNfRw6i0pXKRaMnWfL0Luk2GCPodfaILFa/a2jzf4emUTKHiZauY8gjmTrd/+Zfn3SgP5nfuZn+Bf/4l+0/973od//2fb5L3GtVisePHhwPenzF9H/Fzref+0rUIpve/pXE2jNkZ/z1qcSPnZvY626HKDv1XNOxx5veSrmIy8vEMZQOMlNuJ6inU5QVKUFGkKQp2N6hWXobYKSDVF6SrCORzsNIFgWTwT2ARqMOdBzl1AiiIy9D2PgiekhZlf4nrCSG5cUixAwPGBQ7ph4VTsNfadHIxTechstsAsgccKlN8RbTmxGO9ZxR+YbliaiHh4hnBxHCht6jXQGcUoxPG0BfVspL1tTeAnTYAwXj3devHWFr0sqB+hviUm74CTNmtqPieKAV/UBzcN77fulsg1NlFqLwhu38S8etC43XbMhUwkkHYqwy1FlN4IolHYRS7tM4yOScgWb9c5CczlnpTqshrfwLx6QFTsP+YFZYboDyuM7nBTWiUEhuXr/MwTLOVXc50F8G+5+/JqGfmDWrLwuPPNmDqZ3W936NUBvmnZx2xQNxklr7DgWFLda9KZu+0rlEo6lY+JboOtej5JgGqeJtwzuugSEQLSSG8cuO8lN0+BYkjcy9Eq68t3O5WaroQfwfOdQ49nk2q2cJwjUzkGhrq6Da28nufG24H/TtFVewcp/pJIsHRNvHBOPsYlXi3XTFoXyPavbV8oxrNhkMN+zcy4y3VaErWqD7yxBtwz99hDhueTfhUuWbfKcwLdzKl8xW1pdvMkzfF+gHOiela5QWFkSe4q//gP/nMXS7PT5la1aixBOWrNl0e29CCWtZ7o7XLSHCN9zc0bUWUYYSAIJDZJF7UFZUpa1dVUSmvlaX2foPVu98Wqp29yCorQRCqWUrSYbxTQbC+iF1viBx+XMtmsH9CNl8xBKZQ8RRdEQBjYR2R4irjP3ymjOF02roc+KXVTgYlpBnFKvV1S1IfIMyvc4u7JAv5yvSWKrlV/kUAe2Ou2WoQ+VtmDZaeiXGwu6PRrOFmaXFJs3dHyDUR4PLwpIUvRqxSZvGKbQoLicNxCn5LMFnVjRCw2zjdhJbtxhIVINF9uxHXDvJopANpzNabX1q01DLzTUwuP+edEC+sW64elDxUYrShPyHT/w/RRXS3xPcNSDaSb2GPqGfurRC7Rj6PuwmrNY1Qw6HgEN966a1hVntqy5NbLP2b0rYw8GixmzZU1ITS18RNq1YG2zxgCerhBBwMTsSW7A+oRLjzLu461mOw29qeyhtT9k7Bj6SDjA5QdMZZdgY/XysnGA3uuhmoqgyam1uaahlwsLouvGoKqCSvgwGHEoli1zHztAPw+HhMudm5wsMho/Yhm5vRLwqpxG+TyWI5hPkU0FQhBUGUsTwvCQJJu0ZFgqCpogJuscclA7QO9JRJ5hwoTHDODqfG8PyzgXPXScMm5mNsKcrSmETz44IVldUrgk1LDOqIIYMz7iyMl5jDbEDtA/MCOa/cJdmxW5l1AMjgmuzvjo//4UoefR0RlXvVtQVXQbe4jqeLXNz3jmzRxXl61LjpctodNlEh0yWJ/viiOul5Rhh4vwiGBiK9Q3jUGu3wjodV23kpso3DH02uw09Fs2vpXhRNIWv9vaUYo98mi7pwiD51spn9mT4SCsc5yRCl3ZuXFyn1rIX16A/vHjx7z88svtv3/kR37kE/5s+3w6L6013/zN38zBwQHvete7ePrpp/mhH/qhT7n/L3S8z5RLG8NL2YJzuozMgudvx7xyb2VDQX4A3T4Cw1FQ8KbbCa+9ZhmNUngwPCBYT9FOcyNrm2wEUKYjeqV70HEaegfol9EIri6uMSaFCBxDP2a45yYQUaHDCAM8MX3E9NIVF9ItoBcCGB/SLSYtg+CVzuVGCObhiGB52d7LlqEvvBjtRzC9bCurimLDioiid4hcTAmpnNuBIDZWqlMMT7hhJqA1gS9t4Zk8o/BiJtGBY+gd67Fe0QhF4/kW0HPVMvRJs6GOUoJA8qo5QD964D4TUPnKAvrSwOkdgssHLcvd0RvWypa5X3RPOCpd0RJf2kWs06MSPot4DE8e7A4XyzkLmVJ0x6AbDli0B6ChWGO6A5rj25zUF1BXVNoQHG3Qixl12ue14BbcewWjdSuh6esVc9mBp97EeO70/AbCQO0x9A3OYIFG4xgQG6q0bo6v09ALBy5bht62K8kO0OcapIfYsv/GEAVOFqM8pKn2rDJ3bHmbLCsccHfJsoArEy53QL+urFOMm7PV0G/btSEOnQYa0FWN51lnnXWuMQ7QVy4pVshtUqyV3Ng5NUrttPWmLPA9aef0pT1EBEF7WEBrwlDZBFjlQW2Ze2E0y43GeDuZT6uVX7s5XdEbJUAoyXy9r9uXNhLhS2tzGYSYsiRw7XGkrGRCCHSe4/mCjx4dMN/skmKLSttEXOkYd8eu23b73k7nVltviswdIjR+oFpGX2eFLXZjNHHscZXZHJAmt+3SGCZbG87CauhjV5zWAnrnfuNkPsqXTLaAfr2xBdiMJgwdoHfOQlEg8ITG8xWXhQdaU+fFnlb+dZIbz9Y4OGsZestER8ogPMX5tLT5NgsrAfFp8AKPJ1clxCnVckkSSgJpv68Xhf1eZauCNFb4wgH6a8myFtCf7wH91aYh8e134eGFZeirxRJtIFQaP/JtIaykQzlfkMaKTmBYloKNiqEq2SwzOrEiFNoeFl4nxfGpeTjbMfSLdUM3MDTK48F5AWkXs1yw2NT0IxsNu3de8OHbT7G6nNJLFYPQMMugTrpQFqzna3qpR8drrG7fMfGzVUMvlSga7l02bRLtfFUzDDUNkrtnFfSGe4C+opIenq+Yk8DSsvGesbaVk63kRmu335TUMqCIe5ah35fc4FlAL5YgrK6+FraI3VT2CNdTm1zqGHqNpAi7hJtpq6GvZMAqGsJsAhj7/NelPSwMx4zNwibFGie5CWOW0YhobUG3MBpRlZgwYhEOLRtvQJUZjR+z1gEkHQaVdbTxqoyVY+jTbIoQtsp2T5bUQUzWPeBgy9D7AlFs0GG8A/SeoCxrK/kyIfXgkFE1aWU4GxFRdYfIpiIorOw2rDMqP4GDE47NpN23LKDv8NgM4PxJmxTLekXhW/mPuXhM58YGMCR6w1KkcHDMcXOFkoIemX2vbtxhoOeoJt/JcNIuV9ERg83ZHkO/pAhSLsMj/Etb/T0vNSrbAfowEGhhGXp7WVDdGOGc2GxrvC0itbW3NIYo8Cwb3zRtwagW0Otmz+VGUZttMcVt3pidxx4mbHRACEGyddP55ZQU+5Vf+ZV88zd/c/vv7//+7/+EP9s+n87rb/2tv8U/+Sf/hI985CNcXV3xB/7AH+C3/tbfymq1+pT6/0LH+0y5GqP5ockDnpgevWrBm27H3H9g7cPwA8oGLnSHA7PgzXdiHjxYYvzA+sp3egCkzVZbn9uQJVB0x/SrudOnGSe5sRr6hWMdcHoyipxSBq3k5kDP2+SjhBLjR2DgzPQsoPekk9zkaD+0Y4wO6ea7YlaqzqmcZ+8iHLVJNlqDX1tgHviKondgE2NdAozIMwoZUsoQ3elxKmat72ysLbNfdwb2ZD+9tN7Y+cYm9HkR09AC+sBzXumbFZmXIKWEw1NO9KTV0Ec6pw4SlBTcF4eIrYWmNsh8jYk7dlG9cZvg4mELohO9YSV2gP6wsIA+DKRdxDo9tIFpegKP7zvLLwPLOUvZQSoFp7d5TlySFbYM+Yg1dHv4gz4LkcCThzRak7xpSrOcozt9HjCGuiJYXu0OF/WShezAnecYrh7bwmIYgkDaiqZCYLYyF+z/F41s2Q4pnYa+aFof+rZSrJItc2+ctOZ6UqxNrt3q3MPQlV/3fMT2AICt8mp9hKvWtlIIwWpbzKqy1WnF9hCRaetyU++iAr63N3Zd2XaMnTN3MqJ6x+g3Rthkv7ps7SnF1p7SAXoLxO3r3LrfbKU1Flwr60TjBYiywPO2YFS1IF1UltEXxlDUgkI4Rru2IFpI6Wwrdzp8YTTKk23BKVMUFlzrvTl9myy8Ze7jyGO2cof8oiTw4f1veQub0thn3jHxoWfn3CbFmsIWnPKERnjKSm6cW47vSTen10pudG4ZerQF9JcrCxBNkRMEEoFmvjHUgS2gVVaasAX0uyTnotR4CpTnceUkNyaz0hq0Jow8rrZAP9sQBcrN6XO5ws7pGH1PaJY5FCqCzCXFOnvOs1nTAv2saAiURnlbhj6hWdsImTQNfujz2AH6erUmiRSiaYjTgEczQHlUyxVpZAH9dAOFH7skWk0SSZRueDxtriW/Jr5B+h6PLgpIO9SLJVFg+wZRwKNL214tltbi09S2fQYEAfViSTdR+KLhbK7RsdXQt1EBXfNwqncSnU1DN7AHkYeOoa/mc7SGRNbIIOD+Wc77n3+ebDanl3qEokZ7AY/nBqKE4mpKv6OIVcOjGZhODxZWWjNK7DN097zeMfSrmn6oaZTPq49y6A1oZhNWWUMoLEPvKWEtKpdzwODrChmGTEls5M/ZOFIV1Monj51tJe4Z1xWF2DL0li0OTUUtLUk1lV38zdxa1jYucgxk8YBwM91p6FXAxu8itGYorW+/qvNWcjM2i9aHPsa6uK2jIfHaMvSRcQxBEDELhq2BhKoymiC2JMz4iH45tXlfTclaWwlsVK6skQTQkwW1F7HZZ+h9iSwydBhbnbtj6HVegDGsTEg1OGJQOHlNtmYjIoTyKDtj+oUdxwL6GHV0zIFZQFXaA0qTQZJy5o0RV2e2forASlL9hFXnGH31hIN3ztC6Jm5yu5cdnXLaXCEldNmw8VIrRZI9kvmZjdqVqxbQdzeXRJ6h0aCXc/Kgw2V8gjx72Bar8rLrDL2WEt3UDlRDEkrrDqWbtipsFLkEWFdECpw95rZN7yQ3a8fQb11ulBRIb5dQK8VW2iPRYhcd2LbVWAOHz/TrU9LQ/1Jf3/M938Pv/b2/l2eeeQYhBH/iT/wJ8jzn/e9//6fU/xc63mfK5UvF13Textx0CDdTnrsVc3m+tq4TLlw9ET2ibMab7sQ8fLjA+KG1UJSSqjNk4KrzqT2GXidd6/Hq/H99bRkNXwkW/gAWU0RT23EcoG8aA6MDxmanoY8prfsKxi5A0wu8reQmt2FJATCy4cady42T3ADLeETs2A8w+I11ufGUIO8ewuVZWymWPKP2QsraoEfH3BQze7hAE2HD6NoIHskxnNnkV1VuLGBAsPT7oDVDrL8z2YZCxXZRO7rBQTOxNnBArHOaIAbgkXeEvHjown+WoTdJx4YUT28TXt5vk7eSesNS2L+bd044agG9wM/toqe1YdY5gScPdi4GqwULkVjweuM2b/YvKUo730isMb0hUSB5lWN4dA+FZPVjt/AXU+gNrKPL7WfoXW2ZeENaLy0D1umyDIccZ4+srMnfymXsQriV0UShSyZqGutbLyxznRfbZNnaAnetrfyltafcaeL9fUDfuLGNtS+07LpC7kl3hBBktWpLdysJSLlL0N2y34Dydj70NM1Oz+5LJ91xgN7p/MNAOSmOteK0xaws67yqFVQVVa0tQy92DD3OQQdj5Syt+82+5CbYaeipS3zX3wJ6C9JFy9DbQ9SysqXHa5f8KvfsKU250+dLpVpt/VbOg9Z4gdrzrd8l6MaJk8UEIbrMiTyPP/5DH0RKxbLxXVLsHkO/rDGBle0ABE7+Ml1WreZ+O3YQ7rXvAfo08S277qxCI3foUJ5kVvnOQ76xunXgcqFb7b8x1svd85UF7nGMyW0iLloTRVuG3oJ0C/Qb0tS3lWCdu04USKS2jP5V4bfSmsTyFjyeNpgots4h2rLi0vcsoI9imvWaJLKbfBj5nF2VLdBPIisxS1OfJ06KU69WpLFE6oY49jnPfUsKFJo4kEhd83hqriW/Jr5G+b6V3MQpzWpJGluWMYx9Hl/YxMtmuSSN7XOQpIHtn3Zplks6iULpmgrFtAlbaU03Vkhd8+BqT0OfNSSeTfp+4AB9PZ+7+67xooBH5xV/9OOvYqYLeqlC1BVxJ+T+WQHdPno2p9/xCKjJjcdlYx2BVsucoV3aeOVJ7Zj7BfNlQz9oMF7A3ccW0FeTKYEvCKmppI9SYsfGGyu5wQ9oUJikg79Z2DW+LGlUQB5uAb2x6w7aMfQjxmKFQBDiZDjAVHQJ1lOXKFu1UtA8HhCsZm1hqUq5Qmb9AadqaQvLbRn6wYihXlJXGrYMfRSxScZtMabYlJggRHmKmb+LZqsyowkdoB8dMqisS42qClaNj/ED8qBDv7KR8Y4oqPyEdWdMv1lBYXNiVJmhg5gnwrL/oS+sa1AQUBtFMzikl11Zaahj6KWAanDIoHSHjsYCer/bYUGMuTpHG2NJr6TDhTdGTc5a0obNiipMWXYOCc+f8Nq/uEFQFhhgSQBHpxzrK1soS2dspCWsnqgD4vkZcSBJ6jV0uqxkSq0Cwm3OwXJJ4adcREdw9sAWwKquM/RRYDXrptruRVYKYxNgdSs9jUJpLSpbi0kH6FuG3h4GAl+yKbcMPW2k2Qv8lvHfzWMPE6Zu2iKQcSStm05d83/9ix/l3pP8P4PQ/utf3ifT6Wd+5mc+abD7nve8h9/4G3/jL+qmXn+98MILfOu3fmv7/2EY8vzzz/PCCy98Sv1/IeNVVUW9F2rJnPe0MeaXXHtfNw0/Nn/EUTTATK/oJorTHmjjI43hclqxDvqY6SW332Y3+Tr1sSoHQ90dMbyc2eptlU1uNU6iMFV9DifnGDMm0G6RUoJMRpg4pVvOgA6myChlaBOFxlZDv3In3FhUNF5I0xiemB5ML/Ektm+eUXft4cIMD0jWP0Eh7cPpVTllHGKMYR2POV7dwxjjGPocE0Z4XknWGWOuzjHmDujaSiS8iKJs0MMjbsgZAKHO0AiEH6B1zmN5wLNnDwnGz6DKzG7qxib+mYNjDsorrkwXs1mTK7tDmbRDISOke0+ixiYtGWNY+H1AYFxugczWiNQy9Ob4Fv5qStoprDVotWZu7N/NOsc8W5zb9kBa15y0i740zNITzOMPtZZfZr1iKVIEYE5u85z6cbKiQWuPkVhDp0cYCF5qDvnsh69RDd5N9MyU5mNLZK9PXuaYZ56lf/81pPcmTLbG0zVXOsEYw3n3DifufQ4CyabQGGcjKZz3b+SkOGYb0nQhyE3u+jqG3ji2fJM3GOlhdGEl88a6uth2hXCuOAYr81lvGozyEKZ2RatcNd9a0HduBsL5Ua8z23frcW+0djIfp/OvMtvu9OyrTY3xfURWt/cSR5ZdN8rDNBakG21II8WyFBwZYw8G0kNIwXJTW5u2skAp7JzO5WYrl/E9ey+Bb4G+8QPIS5Sy9xKFHot1jfEDpDuMGGNIEo9FITmsKxoafKkQSrLONLXaHRaMtgePuRvDFNZv3hhD4DT0Jgiszr1r25PIc+2hLeLiGT7w/PN073rMCsm4aaiLGl8GCGmL7Kx1QJLbjUoJC8QnCzdGkbf34gfW/caEESbLCQOBye0h4mpeYVxy7TZa0O34THPFgdaYqiRQ9nOerBoazzL02zl9J7kxodWoh77E6IYo9LiYVZjIAf1IYIqGNA1cu/WQDwPbv9sNucg8bhQ5OQ2htJHAdQmZiAg2G3fwN3i+4nxSYuIEkz20SXF1TRD5PL4sMKMEvV4RRxLT1CRpyIuXBSZOMes16djmjAwGEY9WPrfrijovdweXtSH3EsLNmnWuiRR4gcejixKTdNCr+6Ruzii2DL1JOjSrFZ3Ytne6IY8ubLtZLUlv2mdvMIh4uPEZlwXZuiCNbOTqYgUbLyFeLcgLTeo1CN/nwVmBeW8XvVjQTTz7ecQhr51l/OtbN7k5W9Dv2PZON+Xek5xf2+2hlzN6J89CXdEfRrx6BQd+gJ7P6UUpRtqk5bVKSFZz6/ITNIgw4NVHOebXDmhmM6u3X1g3GyVhohPMYgfoRRDYvSrtE2RzBAeYqqSWPpnfQTQ1CQWmsL7zBR6m22csVuAKJVZuT5vIHv76FSuvaAr77JSQJ33S1ZS6OcWUBZVM7ZyDMSeTFXWtieqCMrROPAqNWS9o+iNiSkwQsUlGpPkUozWJKDBBZA8o3gAz+YgF9EWGCSOqlcaMDhi+NmGBjUZnJqKqNatoSL+0+3FHFBReSKMC5qpL7/IM33MHgyDiXAwwV+f4NwUiW2OiFLM21MMjOq++TCk1ZuMYegH14JDx44krzphRBT0CX/CqGZA+eYLWd4iaDBMlrPwQKoNaTex7sVlR9RJmcYS5OmP8jjn1ck7txeQlmJsnnJoXkALSZs1K2v3tkTrgHbPHlL4gqjeYdIyewaJzzMHFIwI/wCwXZMcpKzPECMktf0GWN3jZEo0F26Ev0UKi68rZFkMUCmojME2D0bZORhxIikbY/ckAGELfVpY222RXJ7nJGompaxqt8YWT8wYKURjqyhJKxoH3BmuDLLH1WeJQUhn7LM7XNln2lxr7fbLzfVKAfquZ/2SuXq/3SfX7hVxZltHpdK61dbtdNq6K3y+0/y9kvL/yV/4Kf/Ev/sU3tJ+dnRHH8S/odfxir1o3nG9WpH6H/Owx87Mz7gw15UQxPzvj5ddyOkGPzcP7LC/PuTPUbBoFxnB2doYMUwb1jLOzM7LFFIXP2dkZq1XOpewRvPoyi6VPp9qQNYayyJjPBXV3QLx8wmrZZ7OZUqAoLqecDRRHlMzPHnGe9EhEybyoWFxccKb7ML1kPr2krDTVasG8V1PXFVdI0uUlWVhydnZGUmasasHZ2RkTlfLM+sLeY5bhVRmXqzVouBQJ40cvUVXvZuVsKkvp8fjskjRKuSnOODs7Y3PxhDUh64sLriYlj8SQ7LVXWIefhcjX1KnPYrGkKGqK3pBk8ZCZf8j87DEbfDbrFWdnhsIfUz96lbOzEUG9JkNRnp3hKVilRzQf/TCNPiC/ugCpyEvDk+mUfjriRvWEs7MTxGbGpD7l7OyMhybli8sp5/fvUxcVXr5kVtWs1hseqz7m0X04KTi/mtEsZkzxWSxmTJIOT3POvceXiMYC+kmjWc+v+Fh1SHn3w1ydTAiSDJqGRV2zXJfMemM6lx+mHOVcvvoKXT/halFwdnbGveCYO7NXaUSN8GvOL2YYIaiLkuXSyrgCz/DoYs7TTUPTaBazCV2jyQrNdLFAlQW6aVgs5jS6ZrEuWec55TqjqSuybINufCazFWXTUJUZ6/WKuqoQfsWTizkNAlOVLOeWqfJ9uH824wagmor5fELPQFkZLucL0jxDCsNiPqfRDfNNwTovKDZrmqZis15jtM/ldE0ua+psTZ5vqOsK4dU8OZ+hhaDabFiv5tam1YfXzhc8B1AVLBcZx8aGiJ9Ml4yLHCkM89kMbRpmi4JFnlNu1hhduznhYrIiSxvq9Yqyu6HMc6RseHw2pxbWym21nNsIhQ93z+Z2zrpgtdigjY1IPLhccuxe52w6Q6CZzguWRUm5WYGpWS+XQMP5ZE3W0dTrJVWYURUFKq65dzanlopqvaLIl1ykKWEIrz5xcxYZm2WJBrqJ5LXLNW/ONngKFtMpoTBM5hWLoqTerNB+yXq5QtDw5HJNNtRUywVNP6fMMzyv4f7jBbXyqNdL8mwJWpNEgpcfznheSsgzNvMrjJRWuna14mS9JvBgMZ8Dmotpwbwo0esl+BWb1RohPR6fr8mPoFrM0V5Omed4nubeozm18mjWS4p8ia5K4hhefLzi3QDFhmxxhRGSfqp49XLB8+sVoS9YTK/wBZxN7LpVLeb4qmE1nyOk5uGTjM1IUM1niH5Bsd7g+5pXHy6oPJ9yMYV6ja4q0iF89P6Cz1UKUazI5jYSl8QeL52veaurrJovJ3gSVlnD2bpCLeb4nma9mKM8w73HazbPCarZBDkqKNYr/PCQl+/PKb2Axs1pqoJOX/LCa0veqTz0ak69slHOTsfnY2cZ71ouURLWswtST/LkSclV2eDPpyShZjm5QvqC1x6tefpmyMFsgtcryVdL/LDDi3dn5H6EvriCZk2TZXS7Pv/ppUs+O+nAcgp5hFEeh0PFf3pS8DmLma3KPD3H9zwuzyseZw292RWd2PB17wzoZgHnkwsmJmX95CEr/SZEmbHIclujIoiprs4oqx5NvqEQgsvZkipM6NYzLh4/ZCQUs8Wa8xIO2PDK2RPq9ZwCj7OzM650jFxcslrOkVVB1miyPGfmxwwuH1PXb6VYzFlXQzabDXmUcCQWPDm74E1lzjqCs4sLAtkhe3SPiVfxHJqz+ZKZ8cAYLl55iZiS2gvINisumgR9dUbdNGTTS0LhkRU1Cz+iX1zxcLmgWi3I6PHw0Rna6xGvz+0eSMHlumLWzLlQA/TLL+LVJ0ijOVtseGx6iNUCshnlYkrtBzSN5koG3Fics6RgeX7GygSU0ynLMOWgucvZ2Rl+sWKlB0wn5zzUQ45eeYmFGBLWayZ5gVQBm3BEef9lRDVGlAUb4XG/8Pi8fEOnmzF5cJ/Ai5nNV0yeCrmhr7g/mRCuJyx0yNnZGfcZ8e6L11gtpyT1iqUes15nTMIR0csfI/DeSbOYMRkJiiKnGh3x9NU5Tx6e8VyZ8Se7f4g//g0H8M9LSg2LqyuEMNR1TV1uKGvDfDal0X2QkG8WNJVhs1xRFCUYw2YzZ11CmW1YLtfopmGzXtLgo6uKyXTOqW5YrddIT0ABVxeXCNOQ5zlCl9QGJpdXKDd3U23IasNiNqWuh0wmVwSfHHT+tF1bIvnnuz6pu/rKr/xKvvIrv/IXdUO/mGs4HHJ1dXWt7erqitFo9Cn1/4WM92f+zJ/hT/7JP9n+f5ZljMdjjo+Pf8kBvTGGZ366gH5GlN8lOj7mLbfvU058jo+PKX/uAtM/ICnPSI6PefMNn/yej5CC4+NjpoenjF97wvHxMYvAp5Qhx8fHHJwvmHgj3qYrOt0uiTLE/T49UqQE7/iUw6s1m16PpFRoP6Db7XN8o89UdDn2DMfHx9wVFfFwTHBwyFScg4EbnYBGg2800WCM7/uMn38LZHNkYO+r0CUkXY6PjzHjmwzuzoiPjwmDjKApObh5iyS5oBnfJjz7CZTyOEhjTBAShQHd3pDo5tPcEB/j+PgYeT5jg31tl+s1P+0dES9/lpPjMZGu8Do90rRDvCoIR09zcneB9CL6gcejIKXb7XB8fMTPJUeMqgXHx8ekosTrDTk6PiaJ51SHdxjlK4w5oCM04vAYgOHoiPzoDs88vuT4+JiVqViScnx8TNAXLIM+R6ZkPOqR6JzBzVtE92MYdBEv1NyKK4JwjCoy8k6X0WjI6NkDtPkuJlHK4bhLwJr66WcZp11eNMcEF/+K0WBI75/HqDjl9OYRVXOPwTvfQ/P9/4jODZ+DQLOK+xhhvys/NHyeX3Px71G9iE4nRoUpQnn4UjAa2EN5Jw0IOqPtl4/xaITnBPZJb0QjBYHv0ev2WEchZS1Je33Cy5I4DomjiDSNQYYEcUzQKPq9Lp5S9LsxV16CCkP82nAwGrZzht0xAB4Nh6MjpFLWDq07xsPKeHrdLqs4oJgL0v6A8MmaJA5J4pg09dEERN0e4UzS73XwlEe/lzD3EmQQEijJeDgADN1OSBAPMErh1TUHwyHK96xEoHdo9c2+vfcsCsnOoTc+oBIvEccBSRzT6UK18on7faKJpt/vECx8eknMhYjw4oSw1BweDMEYBr0IFQ8xUqGqmvFogPI8+h0Pv3OAp2uiUDHodSmikPUFdMdjSl4mTQLSJKbTgWLhEfcGBHMY9FP8qcdo2OXFOsRLUoJcczQc8mUf+Ck+euvLEUEf4wcEVcXRaIRUHgeDAJIuqi4JA8mg18XEEUVliPoHFLxAJ41I45huT7K5VMSDIeFVxrCfEuQe43GHfObjpR3C1fZ1asbDBK26EMb465KTwyFIxajnQ3KA11REoaLf7VB3YhYbQ+fwBK1ret2IJAoZejGLR4JoMCSca4bDDsFKcXDQZT338To9oquak8MhEjgYdyjoYIIQf51zengHlOJkFFKFB3hVQRwqBr0eJrWvMxweQ1PR64R04hhv1OXqRU0yOiB8fMnBsEM4UxwO+sw+LvGHA4JJzfhogDSGk+MeF2UEcYdgknHrxD43R4cJeZCg8owkEox6XYhjjkcBZXTIoM4Z9ELSMEQfxFy+bEgOjogePOJw1CW8UByO+0wfSoLxmHBWcHQyRGjNjRsDlmUEaRdvmnPneIARgpsnHZZejCwy+olk1OtBEtNJFGV6SlpuGB1GdKMIf9zjycuGr80ND8qCk4Mu0URxfDLk4q4kev6Y+GHG4Y0R6icbbt4a8NF1gBoMCSZr7hz3Eb7PczdTLglQZcFhxzDudSGJuXEQsIpucJivOLgdczqMIeyjTo6ZmJS4zkm6KaHU9MYH1mZyeMhAVIRhgNo0yDSh0+nTdIaM84zDfp9C+sRxytFTR1RIjuOARexTyZA7x8fMvSFxvmQ8HnIpauL+gDALoX9C5/IBjRaEAkTcoZMmRMObHH3skv5gjK8rCDt2T/MHDExFJ03QCI5u3WIwOGcZDDiQkFIi0w7DQY/HSYW6vyKOG3q+xO/10ReS3tPPMf7Bf8eg3yMSkAmfwfCQh90jDvIVx8fHXFGwHhzSD/pMgwOeqwsOYmikYnRyQiNXmN6QW0FBg8brdOFK0n3mTSTZFNmTdD1JJmMOR0NS/SwnP/HTdq+nZN0dcnpywgfEkC/IVqRHXRKdk9y8TRKvKTunjJsNQ6+P8XziXh8viRGDA/o/2XD4noCrsIP0YkZvvYFmTjHoIj3Ny9K+V2f+Md31T3Hz9BCjN3RPbhBOYzJ1m87qjCTy8MsM0TskVTH+8Bmem03oKUWjfJbRAYfPvZlB7z5m6tPrdFBijuf7HB300cKj3+3aPAoEpycj7uKRhALP8xHA6ckheSMJPI8oTvCUZDgaILwCoTWdTg/PU3Q6HZIkgjUMel0eKkUUx4xUin7o0+92UPIMz/c5HPfQMqCXWKnu0eEhxwfBLw3oc9cnC+h/URr6f/pP/ynf9m3fxrd+67fy9//+36eqqp//jz6F673vfS8//MM/3P7/xcUFH/3oR3nve98LwGw247XXXvuk+/98v9+/fN8njuNrP0Cbnf5L+dMYwwvBPZr+EDG3GfLPHCrWjUIIYfWnwzFiPkEIwdMHinmpbAEhIdD9bRKrsC430kc4m8dL0UdMrwBBoCtEELmKeiBGh/TLqdWUlQWNFzh9mv27YGUr3sWictaUAAIxOiBYXbV6xa3LjUi7aC+kU83s/HVBpawcp+geENcbG+Z3CUMiTgg8yTw+tPaU2uDVOSKMCANJVYMeH3NL2tcdNAUr4+Q9Bh6rA8TZI0Jf4NcZIk4AYT3Rj07pr88pK22TbFVk24Vglh7TmZ/Z16YLdJhYC61AsuyfgkuMldkar2cr55a1IR/d5HZzjhACv1wzbdy9ILiMTxCPHxCFyr7OtGsdhJREnN7iVnVGVVSIPGNBbC0X0w5T2Uee3ceslxgEqtMl8CVPsPIfNTtHv3NG3e0Th4q81IjhAbUfcVReIFZz6qTHJtcIIbiIjgnrNWI2sS43hbUqFMY6uYD1+c1qq1f3jJXLCGmLX5Va2eJkzhLSc4WlhOe1vvICZxmWa4RUdmzXHoWKTd4glIc0jfW4x/oAbwqD8XwC3DgCkkiSuzmt9t1V+8s0wvcRzc7jPQjcnH6AbHb2lFFonXWE09z7vkQYQydRrDLbXzU2iVYIQTdxz1ZrcQmer1oNvaxKAmdDGQaK1aZB+CGyqfC9nfvLcqMRgR3bths6HY/lukEEAV5T4UsQQtJLPRaVh6itbEcYO+dq02C80CXW2jGCyPrQiyCwYNw5zqSJx2y1a/d8w/f86vfS63rMlg0iDBG1dcsRUjLsekxyhSgLQpe0K5WkEytW2kdWVuYjjCaKfaaLGhHFyKogDOzn2kkDruY1IoxRVU7o23H6vcC64kQxqs4JFQilGPd9pqVv9faBfT2er4gCyUIHbgzbnsR+mxSryozY2Vl2uwGX8woRxagqIwpt4upwEHExtcm1YVMQ+XbOw1HA+cZDFFaHL3SD9DxGPY9JFSCLzCa/6oYo9jEGNjLGKzdtUmy/H/HkqkIkKSrfkMb2O3lwEDtXnITEFMSedbM5PYp4uPQQumEcaYS2z8jNw4AneYDKN3RjO2faCVhlDYWf4hVrOsnu9Ty6KBFpl6Bc03Xtx4cJD89LSDvE9ZpuCEJ53DqJuDsTIAQncYmoK4Tvc/s45OEmxM9X9FIPUVdEaYjvwz/sd5DFin7Hto8OEl57kkO3R1Qu6bn2k+MO957k6LRPt1nRi0B4Ps/ejHjlAho/5E5aIKoSEYQ8fSPipUWEX204SI1t9wN8TzAlheUCgcDTNSIIkVLQdPoE2cLCtrJEu/2mSgeMzBJRVzTSt9bJQnBpOqjVjIDKFlEUgons4uVrfFPj6woRRlZDnwyQy8m1SrFCCMTQVjevG2P3I2nb5/6AYDVBVgU5AUJKQl8yD4YwvWytLH1PkJkAOl0O9AxV5baeQmMQ4yOGtd3rRJFTKytZXUUjBqXdsxKTk8sYA1z5I8TVGR1ZUnkxGGFd3caHDMspqsoRsZVOik4f4wV0ihkidxp6KZBHp9wwFiNEOqcOU4QQPFEj5OUTjDZETY5IO0SBZN09Jpqf0yFHJClhoCgrQ3FwQvh5G5r1ijqyslKRdJiREs7OiKo1cxMhhOCROCBcXBDJho7JEN0eWhuW/VPEk4ckAah8Te53LCY5uc0dc4FeLqijDlLJaxaVotGtEUMSKWotEM64Y2sxmTf2OdbOhz6OFHm9db7B2V5K/MBvk2eV08ZHkeOzG93WKYkjaZ2Sat3igCRSlNrNY2j3h1/qn0/m+pQB/W/7bb+Nb/zGb+TVV1/l4uKCP/tn/yyf93mf95+Vwfxirj/2x/4Y3/u938vf/tt/mw996EN80zd9E+95z3v4oi/6IgC+67u+q/33J9P/5/v9Z+olBLCK8EZjmE9Ba546kKwqRVY0XM4qgsMDcFXs7owE81K1+is9GDE2NvSraqs3BByg7zm/eWOBdGhtK23y6yGD0ib1bBfB2iW0nos+/sImvCSipFbhzuJyfIQ/t78jz2n80DquCEE1OGRc2dfgNWXrciPDgIXfg8szQm3LfqM8fE+w8rsgBAOzsIWxopjAs0Wh6tEJhyxtAl+ZsdR2PANcqhGUOXG1Jqhtkq1x2myOb9JdnTmXgA25jFpXmEXnhN7K+rwn2roNgLUTW3RO4PFD+7lsloi0S+jbjP3N6Ba3tA25e8WGSWOLxRhtuIxP4dE9Il8QNRkknV1Bj5tPc6N8jFmtQCkyE7T38jA8JTh7DRZzJibdLX6Bojx5iuTyHt15jUi7u+JUQjDp3+Fm/hCWc5q01xaoqvGYdG9BXREGziteKqdNtx9ZFCpbHhubaLxNHLJOAgKhd77ySu10+GJb+dW4Kn3O/UY4PThYbfna+b8rXSOl/T4lkWqrv/qiRkkDQpLGtnonzdadxqCUarX1NLvkV6uhb9zYzuVG28V+5cYWDrjj9OxbRxulK2vbKYQF+rVqk1nR2j4TGkp81y5B26TYhQP6qinbhNYoVK1vvWpKXCFCOrHPwmnulXbWmkLQ7yjmpUTWJb6yNpTKkwgBG+MjqrJ1uQkD1dpWynqbuGpI011SrKxLQk9wZzal1/WZr2pMEOHVpfO+l4x6PpeZh8DQ9RswthDYsOexqH1UtUuKjUKP6aKyOTZ1YZP0tKHT8V2V1wivtgcDgGHfJcuGEbEpCT0NQjLu+1xkCunAP9oe+sZ9j2np41W2mJVN8vWt+5GK8Kq8TYrtdwObFBsneFVBFNjEt8Eg5GJWoqOYVBREyjobHQ19Hq89ZFPTC7WrIqQ4GgZcFT5emRGH9jsmPI+jkc+sDvDKrE2W7fdDZquaOojxqg2d2OrZD8cW0DdRQk/khNjk65NxwMOZpvECDqPSWt8pxc3DkAdLD7+0hwKaBuX7HA59JnW4a68rBqOYi1lFHaeE5Zo0svd4cpzw4LygjjoMZEYsa3DA/cF5RRWmnEaFm9O2vzr38ZqSUdRAXSH8gNtHIWnhERYreqlNDh8MY8rKsPK6xOWKQceDquL0tMNrjwuKsMuhlxEK+zqfPo145WFGEXY5DXOoSvACnrkR8/K5plQhNyPX7gd4SriqsDMMBk+XEAQoKajTPmHmqsJWBdqzrmpV0megV7YAnPKdPzxcmi5yMSPQla2tAuQENEFMnM/xGzsnQJEMkYup3RmcbSXgHG2WNFWDaiqbLAusgj7+egpFRoZt8z3B1BvA5IJUWCvLtt7K6IhDM0OVljiqKgODMaEu8KuNrRjtAP0yHtn6L1oTmcLmqxmYBGO4OKMrckovsvuD20/7xQSvzCBKnH2zoOwf0i+ubOI1dv9Sh0c232C1IGoyWzsBuPAOUJMzVJ23RbgCX7JMD4lnT+iaDSQdwsBaOdfDE06mS8RmSR2mrdX0fQ4IJk8IyjUzY8deiog67hIvzumzoY4tWbXq3YDzRxx5G7SQLWnG6S1uNBewnFHG/d2+E0hqp1kXe8mq1da2ci8pdpsAu3O5UTRIdL1NinXGG6GHaGp0Y1yxKkEYeVarv02+Fc6i0khMXV1LlC212DNq+OTA9X+N61MC9D/8wz/Mj/7oj/Liiy/yj//xP+Z7v/d7eemll4iiiL/7d//up/se+bIv+zL+4T/8h3zv934vv+/3/T5u3LjBD/zAD1h7QayE5umnn/6k+/98v/9MvZSQ6IcDktHQIublnFRZ4PLS/YzzaUV6emQLczQ1w7CmwG+tF81gzCEW0MuqaB0BPE9whtW8Y2xV1O2iWztA369mCCmgLGi80FpRYgF94AB9TEWtQti60IwO8aYO0JeWmdj6CDfDI2vP5ZLitoA+8AQTfwQXT/Drgtq3YNj3BFUDHBxz1FyhqgzCmMAXVJXBhDETOnD5BK/csMIm5xoNRko4OCGZPyHU+W4xRMDRKdF6is5zW4hGOq98YN0/ob85t1pgk6NDm80fBZJJegoXj1A0iMy61YSBLfqxGd7gZm3/TuYrJpU7XBi4TE7h8T1SUaDQFtC7bHxuPsVx9gjp3G8MtLaQT6IbJBf3YD5lQtreYxRK8sM7pBf3efq1NapjAX1RarQ2XPVvcyN7YN0kugM2DtA3xnA1eMq+54Fqq7kKrdtFIYmVTYISAg9nUSkEiQP0+5VilScxBusGoHeVYi2gdxaS2vXXji13VpRK72wrk3gL9D18tgstpJEF9OJa0SqBNlAa65Tj7R0itj70cltB1rE3W1ecLaOPMSSxZ5NoPR9fu0MElqFflm5sd+CQUhIFko32kHXlXG4suF6ut+C6at1v4tiztpVBuAPuQCf1WjvLkMoCfSHodTzmhbJjuNcjpGXu17WyQN/5zYcu4Vb7ds7At+1p4l8D9LEv+PUvvUS/47fJspGoCDyLFIY9j8u1QUtF16tsAQIhGfU8ZqVC1WULrqNIUVSGUoaoqnAuNw29TsBkvj0sWFYcYNgPuZrX6CAioSSQ1lnnYOBxvlEIY+j5Na5yGeO+z1Xh4Vc24dY6KEkGXY+lDvCqnbNOrxtwtajQYUxQ79xvRoOAi2mFDhL6qkDRtMD98RyMkIz80haKkYqjkc955uFVmXOzaZxEJ+CyDPCrLeiuCUKfUc9jSYRfZbZdNxwdRDy+LKmDlJFfILT9bp8eBK494SAo3dgeN45CXlv4BHVGJ8Lei1LcOAg5zwOCakM3sfcSxwG9VLHQMWG9oZ/Y2gc3TiygL4OU46BANPY5u3Uc8uDMth8Gha3d4PvcPo64e6kpZchRkENVgWfbBxcd0mpFv2udpFQYcPs45HEe0dOWuacqOT7ukBWaK51wEmzs2MrnmRsxrz7KyfwuJ0HmqpcHPHMj4tVHOUuvy4m3tnP6lv2eig7CVYX1tLV8lRKqdEiYze362DQO0EOdbgF9iZY+deMIG9NBrmZ4jauK7tbaqjMkyudtoURjoIj7UJV0XHJtJWwiLo7sarb7kXSua+GAaD1FlgW5s3n2fcmVN4DJpQXNYWxrnDhHmyM9RxYZMomtNbPnMfd6xKsrKAoaFVDWmmU4staSRY4EMqy15sQfw+UZKQWFcuQTAg6O6WWXBNUaktQaMmwTYKuJLWomYlt9Ng54bPpUj58QNxl1mIIbW63mxNnU7v+ebyMOyRHR4pyOyexe5kvy0qBHp/zal19DrVc0Sdc6sAH3GONfPSTIV0yauH3Pi+EpweQxQ7GmCLtoA1XUgSjhrTykDjtoY6OunNziqLpEzSeUUa9loa1NpEQ3DWrPTrLSYlcpVtik2BbQa8CAEgLhWdvJLUOPsI42whiaRl93tBEKveeyFoWSCutDv507iRS53lVM/0yGiZ/Srd2/f58v/uIv5ujoqG0Lw5D3ve993L9//9N2c/vX+973Pj74wQ/y4Q9/mL/zd/7Otbn/4B/8g/zgD/7gJ93/k/n9Z+JV6YbFm+7S7ykYjGyZ6arCj0M++lrG2VXJwWnfshHzGaIqUVG4G6A/posFrvuFpTwlnM3kFcZoV+QjxPPEznZrj6HfOtkAnDPEn9uIQCxKCuk7W0lgdIicnluP17LY2VYCenzEkbaLWS19EBbp+L7k0hvB1RlBY715weqmq9rA4QkneuJCmhGBL627jIEHZgRnj/GqnJUJKSq9u5fjG4TTJ4RNYatFblmPKKZMBwyzC8jWZDJqH+66M7LlpicXxKZoAX0YSFYqhSjhlpgiNqt2ESxKTd4ZYYSAi8fIsuCytA4+BrhMTuDRfVJtHQlQyobxpIBbTzNePUJtFtaibq/K60XnFp3JfZhPuDDddvGLAslqfIdgcp+PfH5E1bd2lsZAUWmuenc43TyA2QTRH1rgjsUVk+FT7euxgF4i9tjyOHJgXHmWoXehl8SFNVuAbqzl4r4Up/WEDxy4Vgqlt6yHIQp3lpPCNO1ClMaOdXeSGwvoJWlsi4OIpsJr55Q2KqL3gD6GoLWn9JF6azkJUei1VpRbmQtgpSWO0Q9F7awyBWmsWJS2U+JtmWtJJ1FsaoVsdraVYWjn1F6wiwo4x5mlA+5+U7UHg07qsVzXGD8kEvUO0KeKSe4kT6qxQFdI+h3FslKopmwdZIJAYQyUwsdrivZw0Um9lrn36hIpNX/9S7+UTtezFpV+SIyLFgjJsOcxWdiDQder3esUDHs+08LDq/N2bOUpuqmy4LpxgN5oktRDa0MpXbuLZg/7VhbT+BFdVSIx4CQ35wtDo3z6XrkD9AOfi8zH346hrZ3qQd9n3oQEde5Y9IY09VBSkIkQX7t7aRpGw8gy2kHM0C9bgH449DmfVVR+zMCrrjH0j1YeQVOQhrSg++Qg4DzzCeusnRNlWfd5HRDWGWlkDx0H45ii0mxkxNArLED3PE7HAU+uSko/YewXLXC/eRjwypVAIxj51Q7oHwY8zgLiZkMnsQw9nmeBfhnS0Rs6gf0OjYchSgqmOuYwyF1fn9tHIffPCzIvYezn7Ri3j60V5VolHHh5C/RvHQd88OaayGQtE4/n89RpxEeufIbCynnQugX6ryxCDrysHfvWccgm10xMwqG3aZn4px2gn9LhQK7adoC57CDztXV/2jL0SlClfYKNA+Jg7SwbQ5kMGJilrR6rfEvYGAfoF1MCU5JvAT2GujMg3MxaG2ZjsPa53T6HwkZzK+WioIMxQ7PEbDY00rMuXsAqHBBtpsgyJxOh24sEV2IA0wsSUSKc5KasNYwPOXIMvYzT1pp56g1JsysrPfVCysqwDAcETQGTC0rhU2grEZ0GQ1hM6eklhbKsvZB2P02zCUFl/eO1sTKRZnTMYTNxDL2Ngge+4IEZ0pw9Im4yKgfoTRhSpQPGs3vkntvPfME0OiBcTWyhyLRDGEjKUpMPD/mXX/o81WqJ3tZaAe6ZMf7lY7x8xUVtAb02hnJ0irz/CrGoyQNHVkkJp7d4a3WXIkxbVzS6fXIV0zt/iTLu7kB1IG3BqLp2EWobFa60XYOMqyy1jRQb5yVvmXdQvo/ZMvSurrzvGIaW9XeSHe1qkrQMfWTnNtu5hSSO5K4ey3av/gy9PiVA/zmf8zn81E/9FHl+3Y/zh3/4hz+hDv1Xrk/PpYTk6mdH3DwMLaCfTaAqiToRP/6fFkwWNcdjWxCD2RWUBZ1+svv7KGRqEphNUHVB4VgITwmm2j7wYb4k0FVbKbZxFWHTZo3XFFDaEOh2oTrbk9xEpqKUIa3B0vgIJhd0vBphjNMrbn93zLGeQWG95IX7Jvqe4EKN4PLMluh2DH2wPVwcHHPcTK3kxoU6y9raSD1kCBePUcWGNZH15sVWhuPIMgcJtsT2ttosQDk65bA8byU3Yssuh4qr6AiePKSn1zSp1clvgbs5vsWz8gI2O4Y+LzUGwSPvCF78MMbzWRufqjZoY1jGY2hqBlevMRXWaall6E9vkxRzhstHMDxwBS/sPU57N+kunyCvzrg03fYzjQLJfHSH3sU9Pv+F+6jxIb4nrNNEobnq3eIgP4eHryGPTqzfPHbxnTpAH3umLQoljGllPnGoLKOvFErs/OnjUJLVVnIjHEAHuxiWWrhCUQ50+qr1hJcto+/kPM5b3kpu7NhJbIGxUR6haBDGtAz9urrO0COgk6hWW+85aU0Q7HTuqpXW2Aqq69yCa9lUbQGtreTGeAEhDtALQTfxWOTOIk00bLVk3USxajyrz9/60PtWFlOg8JqtD70mjneSG89UO8lN6rNY2wNAJKxFGkLQTz1mGxsVSWTdHiIGHY955dhyV+RJKne40J4D+pa57aQ+eampVYDSJZEn+S3/8ecYdqyeXXshHVWjsA/BsGt18Y0X0lM7cD3selxmCr/Zedxv25e1h18XrVe8UlaiszEBQVMQuvd2PLD698aP6Hn1NeB+Oa+o/Yi+KtlWtRn3fZ5sFAJDKrf9FQcDn0npEzZWc4/WCOVxMPBZm4Bor300iliuGwrlxnaA/ngUcD7dAvrCtjuG/uHSfjB9r2pB98ko4PHaI9K5BddunNODgIsyINE5qQPXyvc4GgZMqoCB2spc7KHg8WVJrmIXFdgC95CHrn3o5XsMfcD9pU+iM8fQ1y3Qf3Xu0RM5iW8BjfB8bh4FPNoEjNQO0B+P7fo8bWJGKtsB/WML9BciZSh3oPvOcUT14oiuzmz12KoEz+fp04iffCgZig2y2YJrC/R/7kxZ+9zaynw8JXjqNOSlWXAN0N8+svKSu+vY9t8D9LUK0WFMUi2sV7xvJTdV0ifM54SmsmuS571OclOiW8mN4dJ0EIspXlOSGxcaMlCnA4LNzBbQCwIMriDhYMSJWkHpotQCW+QLhTc9p/F2keRNNCDOZogiaxn6wBOcyz5icklHFJjYSj+ryklu9AxZZIjURoKbxnDlDSxDXxY0vn1PGqFYBX148CqZZ+U5xlhpGWmX480jMhm3TDwHxySrS4I6gzjdaz/iUE8tQ88ux+8xI3j4Gh4NdaLPG7oAALhDSURBVLAF75K8f8TB/DUKz0lIA8maiCrq8nT5YC/arKlHp3zdz/00ajGFzo6hv6sP8C4fobIFV/WOoS/HN+Bj/5EVIYVWu3s8vcM7Ny+wjoY2AOje3/P0Jnce/TR5MmzJqyiU1nO+aa5JMUsH3o3BAXILvrdsvHvheL6yIH+7rwoIHMNgmrpda+NQ0giFqfblNYrSKCfhtHOnkc0lM3VtX88vN4a+LEvSNOVzPudz+HN/7s/xl/7SX+ILv/AL+Ymf+AkePHjAd37nd/Kd3/md/Jt/828+3ff73/R1Mam4eC3k1lG0A+11yXCU8JMfXXE48G3IeDBqAf2dO/32731PcOb84WVdtJViPSUoXeXXNJu0jIZSglobCCNWKiXZTCyg90MrxQHO6OPNLy1LSUklAisTcJo/rs7pqsp6fyuvfZDF4TGnTDF5RuWFLXPve4IzOYSLM8I9QO/7woYvD0851hNUsYE4uc7QM4bzx7bku4woS7MrQnV0A3X1hIHYUEcdF8a0VzU+5aiyWr6l6uyYAl9w5h/C2QMGZoXuDADLaBelRh/f5F3SJsaSpK3uUBt46J3ACz8DwzEgKCptQ4VSwc2nGL3yk1xgPxuz1dD7AcveDd4x/RkYHjgsZ29GJ10WySHhj/9bnohB+5lGoWQRjqmSAb/p0U8hD2/Y5B6XGJurhEU4gLOHqFtPtdVmtYYituOoNLUaeuUhzQ7QJqEt122Uh7/V0DsWI6/3NPQWz5PsJSnZ9j35y/7YbYKqk9zQ4GpFWeCeNRjlEypXmU8IklixLKWT0Lg3bU9bv8/Q20RpQyXsYWFbiGrrQ4/v4+k9cJ1Y5l57PgEO6DrJzSrTaM8nkvV1QF9J1F4BKSltEmnW+Hh7QD+OrLTGvG7ObsezPveeT6rqNjTc63jM1w2NCkikY5G37aVl6PfBdT/1WDeejTi4dt+T9jPCx29KIl/wlotzhoOA2bKm8UNSVbVjjPpWF197ER1ZOVZcMep5XG4kvi6t/MeB61HPZ155jhUXbfu4bw+vQVMQOlw1GoZM5jWVcocFB6LHfZ+reU3lRdcZ+r7H5UJTSZ+OKNoDzcHA56qwgD52FWSRksOBTa5NKIg83Hvu000UiyawgN69nsOhz+WsIvdi+jK37Y6hP5s15DJk4O3u8WQccG/pEevcaeXrVkZzb+7RETmJe53b9rPMZ6DKtu/RMKCoNHMdMVR5y9zfOAhYrBuWRPRFfg3ovzpTJJR0QmMBs2fbP3qhGMrMSmukBCm5dWR18QO5Y8uVFFajv/YZyNyN4XPjMKQoNU+KkAGblol/0+2ED384oTAeI+lkNH7AW56K+dBdWwyK1cKiMKl495tSPjYNGZh1e1gA+Jy3djmrYg5U1gJ3pQS/6s0dJiblQDpAH9h9RylBk/ZJioUtLBWESAll1MWrS7rNqiWW6sZQJv1rDP1WQ3+FA/R1QdYy9FB3hgTrqQP0Wyko0B9xrJaIykapt8/ezOsTTh9b4sl9rFk8JC4WqGzJhi1DLzlnALNLOuQQpwS+JZYYHXLIDFls8FJLklWNYaKGpIsnUFdUQdLuWctoBPdfpVC7NuHA++HyPpkMW8044yPC9cQWbYpTx36DODjm1Ex3Gnq3xpypEereSxQisEYAuKqp3WNOJi+ReZZQCn3Lxq8Hp7wt/zh0+zbyWRl01GF8kaNe/DCmN2o19C/pQ7zJGXKz4l5p91OjIbv9Fjh7xLkYugi5k6i8+bNImg3z+HCXvwY8HL8VgEX/xhsYet00bRGorY7dNNrq4oWNCGsUuqpb1h4EXuC5wlIgHPgPtwtSvSOmkkhSC1cg0QH/OJJU2haW2s6dRJKssYAefhky9K+99hr9fp/T01P+/b//93zwgx8kCALe/va38/3f//3tz8/93M99uu/3v+nr//PPHvG233EXqQz0h46ht04Fv/+/P+EPvO/UdhyM7e/KgsE45V//v98NWOB+brroySWq3tPQK0HTAKPrgN7fauiBmT8kyaZvAPRXdBG6htkVHppCBFvyFEaHMJ8yVrmtILsF14A6POGGmNGsN9TO4QbsYvmYTyS5EXYxOTjmxEzwnM488G27AR4ygovHsF6Rq7gF0dvkV3n+iKHYULpkne2czaHTvC/mLFWnbQ8CyUPvGF76CAE1dewY+sAC9OboFp+vXrbvt3M/yEu7sD3yLUMvhgcEvrB6xO3p/s2fRefuz/GQAYArR20/usmtd3BaPIEbt2nMTq8XBZKXx+9EVAUvmFvtdyJ2UYH7b/3v+Jb3fS3lneds/1CS5ZrGGD741NfAl/5m4uMDtIGiMjTa6t75X/4BHN1oq7nuV22NQye5kbbU/C5ByYFo3Vg23ziGPpZW/qK34Nq+h1VtaKRC7Ul04mgnuZGu4BRYtnyb6BoJJ3NBkEaKlUvQDaR2c1qJTla7qpdubE9JfE+Qa1tRc3sAaJNit4moe7r9lZP5xKppN/g0tky/Vr5j6HWbLLsoJUrXrRQFIeimli33dNUC/ST2qGpDLQN8s9PQ91KfxcqO3TLxzit9sappVGCTHM2OuZ/mlv3fSm4QkkFXWbZcW8edLYU06HpkxsfXJUI0fMv7vpZOaqvCNiqgo7bst2DopDjV/iFCWinO+cZJjkTdyl+GPY9p4RE0Zatn3+rfZ5VPaMoW0A+6AdoYVtqns3eIOOhb5r5S20PEXvusIpcRqSh27QOrc49MThyqFqQfDHzONx4JJaEr5rSV11xVAV1ZtMz6sOvZJD0T0RVbhl5aDf2kJBcRXekOAI5dvzeTRFRW5rLV1o8DXr4S9GSBMrqd82Rsgf5A5S0Q95TgeBjwJPPpyR1zH/jS3WNIV+wY+ltHIR9+2FAbaZl+dzB49mbEh+4aemQt+Ad4/lbMixOPocxathzgHc8mPMoiy9A7xt1Tgrc8lXDVJIy9HaM/Hire9jvvciE6BPmyleK863nLWmcqhsmFZdaF4Nd8Vo+pSeg0q/b1AHzVrxvRPx1xqDathh7gD37tKe/87JsEmzmUhQXXbt+pOwOScoHX7O05wqcMUg7qq2uR4irtM9BLcM432z3o0nQQyxmeLsnMdQ29t5pZpj/YRo6tvOaGnCN0Q7UXNZ6pHuH0CY23a9NhTK18gsljNnJHLk10AkJyR04QceL2Jyu5OTZzVL5Gde1+UdWaiTegd3XPvid+YPcsA4toBPdfofATF2mm1csfLO6zFluGXtiq4kHMc9V96HTbdnXjFk+JK1jOr+VX3fdOCJ7cZeoN2j0t9AWT0dN08wnL2FqrBr7dz6aHzxGZEg5PCFwUumwa/vL7vpxSSszRjZahz0xAfXQLIxUlPmVlI9DV8BiGB7zo3aEojeMjBLzp7bw8eDsfP/xVVunmNpm7tz6XH739FVwdv7Ulryx4l64SOdfamqpuD1tCCKSvXsfQ4wC9K4Yo3N6UKBppi8BtJTdprCx9U9VWFux09cVWcmMRvZWYVrIF9PKXG6D/Tb/pN/GBD3zg5/35lm/5lk/3/f43fX37N97hW55+D76UO9DumJDf8RuP+dL3Dm3HwcgmuBa7xRMc+617FtBXexp6T1A1GkYHJNnU+vBuGfp6C+gHpBvL+hs/bNs1kro3hsf30UC+5TeFgDiBpMOz8gIdRDugD/ijIRqBfnSPwt8tQr4neMwAVguG1YQy7Lh2p6E/OuXIzPBWU+j0CDzL0GPggRjD+SOYXrHwu07+4tiAwxNYTLklppRhbweuAXPned5l7sJmxcK7ruV70X8KXvwwV6KL9O3GtQXuzY2nORArODhu+5eOlbgb3raD3H62ZfSN0zzy2Z8PwL9vLPhuXW6Ai7d/Ef8u+Rx413sd3totch86+kKmv/vbeEWetJ9pFFpA/+RNX4j+0LP4Udz2zwt7oLk/fBt81W9rpTib3IYjt6x464qjLLjeMvRxZJNljVQ7EO1YjHUJYiuhgVaTmNVbht5JUTxpE93wkLpuF/ItQ2+Uh2dqm4CEZcvXuY0KhNJlvTmWZFG4v5UNW7qt4xh6uSfFEdKC7rxROz27S4rd5BrjvZGht8A9IJZNC667idXWN8onUjVbnVY3USxKhWqqNrEWIejGinUj7WFhy5b7Nom2wMPX1zX0W8lNIp2cRwr6HdveqIBI1K2GftBVTHOJMo1NLN0y96nHslL4unLJsg7odzzWjY+vK1Jf8Jf/jx/goB+RF/bQncqq7Tvq+Q7QhxZcG922XyyhEYpYlHv9Pa5y5YC7bO9l1Pe4yhSJKC1bLgRSScY9C/RbsCwV44F14sllZEG3vs7c5zIkNsU15v7x2iM2RSut2QL9RytJR5Y7cC0Vhw7od8RubCkFhwOfSRVYZtUB96OhrTi7JtyBbs9Kbu5OoDHCHQC2DH3Iy1eS3rZNKhCC04OA1xberq9jRZ+9FfFwZeUy2zEAbh6GLEzEyCugtvfy9I2ITW5YEpHUWcuuf9azKQsTE1JBvmnH+FVv6TA3sQX6DrgDfN5n9ZibmLF3/V6+5r8b4w/6JPWqTVD1peRLmrfTH/ctE+/GCQPJd//pNxOMhjC5bKUyR6OAv/s/fy5eldt7cXPePo5439c8j1ovoCra/rePI972rpvWrKHcSW48KajTAWkxwzP1Xt6Wpoj7HFSTawx9HXWtXGI+wbxOQy/mU1RdstE7yU3THeCtpoTsNPRSAv0ht8UEgEr47d6z8AfEszNLMG33KV/axNjLB2RiK/+UFnwPD3henGOiZJcUOxgRUOGtZshOB09Z04YrzzH0adfJR+3eNElP4cFdiiC1DD1OCur2lKns7TT0QpAdP2Ofi9Fhmx8WDPo0xt7xitj5tMMD/wYAG5W0rzEMJGfjN9m9pnPTtvnWzOHs9mfb/LXn327XrEojkZj39/B+w9eiRqNWQ6+1Yf0Nfwz++F8GbL6WfX8l/Km/wf+v++XkpW6L5eEHfOAd38Sj8MaOtQe8JOZHD76QRqjrialaWOcnt7eEgaRGUeUVu1ALSN9D1821SqpeYK2TrYaeVrKphY2ybSuhJ5GT9mhHYknrclNoJ/fZsvahpEJSl1Z29hmM539xPvS/cv0SX0JQ6MZ+Hbeymj1NYnsND3Zgfw/QKyU4Nz2YXqHqfMfQS8fQDw9Isym+Lq4tpABTf0i6urBJPUHcutwYoO4fwP1XyWRMrcX+8wbjI97KQ5q4ey3UpjzJYzNAvPpx8qCzA/RKsGk8GB/x3PolysiyHP5WQ9/pMaFLevfDFtC7UKc2hkt6EKdw90VWXt+xBg4UhxHEKR1RsOkeXlsA5OktfBr7/rLTTwa+5K45hDjlFXGyc5ZxAL0+ucP31+9C/Lovb/vnpV3YngQn8D98G3zxVxM5tqO1Hzs8YfZ//9/4UHmnlb9sx5b9Af8g+SpIOtf0emEgWdcexe23XFtQomAL3A0EtPkLUSDJSn1t8dxKcbJcOzy0k6JkhdWzS5r2XpJoB/QDsWXFrRQnq4RNZt3KX4A0sqW49+0shbDseqkti77tn0SKRkMjPZTZhUHT2MlflE8gr7Piy9yqYCOpW6Cfxor1lqF/A7hWKFO3h4s4tJrOWgXX5S+p30pudqw4zp++oZE+sdix5Z3EWUuiCduDjmXoV5XnmPgdW95NFLl27W7OXsdjsXkjE99Lrbd8rfzXtXtcZdtk2Z22vu+kOL7e2VkiBIOux6JWhFgLzXUQEARWirNqvD05j7RWhcBaBySi3J4wLRO/qChkYJm7fW19rghNYeUvW1lM3+fJWpKIEk9sUYjT0eceqXRjOI/7wBMstG+t/9y9bDX3GaEFxq79YODzcKkIaIhU08plDoc+d2fK9XUSLSk5HAY8WCh7iNhjtA+HPk8yn77aMfT9jqITK67KwEUFGpAew55Ho4UF18bJYjzF6TiwbeQtaw9wehCwMhEdkV9jrt/yVMISJ/NxTDzAyThgaWIri9G2XUnBu55PyVRic3Mc0z/q+/z6X3NEo3wLjF2o521PJ3z1b3iaHpsW/AN8/rt6/K6ve5a02TrL2PYvfe+QL/+SO4j1EuoSfB8DvOUZj87xCJaLFugDPH87RvUHMDlvGXeAII3tmjq9ag8L9mHqw+o6Ew9AfwCLqQX6ewx9Gffp51du0ABf2XU+j/uMq6sdsdQYjJDMRQfOH9MEkW0DrkwHUeb4m/mOocdKbuRiSkLpCCUH8AZjnuKCJkwwSLYL/jLo05k+oAjSdkH2lGQaHZE8eaVNit3mc5nBAYFoEGm6iyArjzWRW0A77b515Q3btsC3BwKt4aJno62beNSy9gLgyILxczVyuVT2fvKn32Zf39iaeEgnPfmb1ZdQ/Lqv2El2ABV63P2KP8Q/HX1F2xb4kqXs8A8/90/z4rHNd9wmwK67R/wvd74FegMX+bYk1EXYx3zp17Q5YuC4jcEQcXKrtWtu5/Y8wkDtte1Iqfw/07ZPakWBdKC6dkuIQEmBkYqqKN2ntQP0pq6dD71t90N/53zj5JNpLGlQe0Dd7h3VJ2LoGwlNZduEIPAlWnotoP9lJ7kB+NjHPsa3f/u3843f+I18wzd8Q/vz9/7e3/t03t+vXHtXrTXf8+gFaq2hP7Je9EVhF9b9a09Dfw3QS8EFPZhducJSO9vKek9DH9XWH71NisVm6ffnD6xXeRi17cZAPTiCux9no5I2+bMF9Dfu8Fn6LlXY2SXLYh/oVzjC+/h/pAj2GPqtVv6p57lZPLI2Y2wBvV1MXpQ3UfnahQZFy2wgBDz/dgAuoqOdbn17L7/uy3hR3iTfyoK2YchQ8qeLr6X5Pd+KYSd/CX1BURv49u/gb/r/fbuobhOGDIL/tf4N8FmfDUAUiNYuUgrgLe+EONn133tfwkihNdSNcSyGW+D2F029W8ijQLaMhxS7BSV0wL0yGt5533432DH0zR7LDzuQ3mjTymIsQ+907vq6rdcmbzBSWc9y9x7HkfWnb5n4bZJRtGXLnd+8XSNJY0VhlAPuThbjSXtgNBLP7GnoY3ldcuPm7G317Moj2Je/xDZZVuq6TXLdgu5No/BaoA9KSRu6xUYFWsnNHnDf18p3nOSmUdb9Zqeh95g5J5pdf0k3tSDasuVbKY6k11GsG4+Qun0fe6mP1pAbzwF0J63peCxWDbX0iajaQ0G/4zFdG8eW77crpoUiMJVly/ckN9PMykWkaPhfv+iLqLVm0PWYVZ5NOHVAXDrZzaL2LHPfSmt8JouaUgR7mnvrfnO2suA69vROW9/3ebxU9rCwdxod9z3O11YXvmXLhRCMnf493bL/ThO/yhoWOiCR5TUN/cOZZcsTk19j6D96LpxspXE6b8nR0OfRep+ht/dychAwrULb7sC4EIJnbkSsCRzQr9t2gJUJ8cqNY+M9DoY+KxMSm9IC1C1b/uYOS0LrJLbHin/x5wyIBl3HxO/af/dXHfOWtx3il5uWoQf4q9/8DMe3RxbQNzuN+rd94x1Ut2cBvesrhOCLv+g2crNqNfTb9sHJCNarN5I+nZ5j4u3Y7b7S7VnQ3dTXwXi3D1fn4O+17bfv9+30YDl/477UG8Bi7qQ4tr9SgjKxgL4RytUbkVSNIY92gN5TtOB9qnrw+B5NELdJsRUeJu3iT89Y6a3kxlB3h4jVnGGbN2XXIwYjjs2MJupslwsA5sGQoNpQhN1rEeOLyLLlM+nIJd+CdD08sJ2Ghw6k27W3BfR+gOdMG+akNF4AB8c7eQ5w2b0Dz7+Nx0dvp6z39oh3fDYvfvbX8TH/qWuR7c17v4zf7/0RtBe09y6E4P3ys1l8yddfI3BCX3J581089E92e5eT16zCQftMbCU31/anwEahS60Zf8Ejaq2t1r4yLXje3w+tpPS6RDTf3wvZkU/Nfpvbp9oEVtdWOh37lmEHMJ5viy6ye0M8z3OJsrv9yQ+tRfKOobekUiOclGbbFitKY/OvtiA/jiR5I6He7UmATcwutpIbPmOvT+nW7t+/z3vf+15eeOEFTk9PuXXrVvszHA4/3ff4K5e7AqX4n57+HAKlrORmMbWL/hsAvZPjFPn1xRa4lH3E7Aqvyin3XG7qxoYQe5sL60OfunBhy9AP6E/vQ5Lu5C8ABsoTK0vZKGvTtV03Abj1NKd6Qpn0roFogI/J24imZhEftGFC6zdv4M2fBcByaMOCwd6c/0K+h/zkabj1dLvIsGXiv+Z3wO//4xBGliFgL0T2FV/HX02/ibI0u+x77ML3H/SzlG9693Wg7xYqkg45/rXFrnDgeu/lEDgfev2617kD+nuLoPNM3OkMr48NuMIYbow9CY3Ye2otcG+QSLyffM5+N9jJaIy57psbR5JN0VxbVOPQSWvUltHGhRp37cFWFiIgCSXrCqTRSPbY8si535g9oO/YkUJbuYjcZ+5j6/krTd163KexdaLR0gF3bU9kXSdRaeU/4IC7ZFmIXfKr699JFJtKWuC+x9x3YkW5lb84YrGbepSVoZL+npvNTnJTtwy908onikVmqIW6DvRj61vvm335iz0ALCtJuGdPqTxJGknWjbc3tvWbX2UNlfSt5MbNOeh4zFc1lfD3pDjOFaeQBKYiCMS1/pcbC/4TX/FX//n7CZRi0PGYFvug234Jhj2faenZypd7THxVGzbGJxFOiuMKTp0v7d8lotqTxfg8mAtSdsm8AOO+z+OVtGO7hFPAymIy7xpADwOnLS98UlNYT3xpHWpmq4Y1e2BcSitb0RGpya7JWZ67FbMyIam5zqK//ZmUVQu6d/fy5jsJKxPRk+U1cP2+X3+AiVPINo6ht0mnf/B3Pms9U1bLli0f9Xz+zB9+m309ewz9jcOQb/jaZ21BoL0k0uNRwJ1nx3YN1zvmPo4UqtNxYLy6zoAnnWuAHoC0Y8ddLdqx2/b18v8E0FvCp91XOj0rrYGW0bcPSB+uLt6wl9j2szcC9yK3LP1+e3dgX+dm1TL9nhIUcZ9+dkmt9sglx9AflRcQRigpaLRds8/UGM4e0QSxJZW229DwAG89Z6qtm4sBTJS038E6SHb70qmVQ2ppiy5u18En3TsAFGGn3b88JbifPAVYthx25FL95ndTGM9WRN9GkIG/Kb+Sq6/+fYBl82tn6/Ljv+UvwNf//laeow32+/d/+3ZmR8/tGHohwPM4f8cXk9dyJ7nB1gw5r5NtUPTaHlZu2W939zZ6vcecb/tV5g17UbEfzQYiN55CcvWvniFQqjUb2Ebt98H769n4MPgEzPseQ7/PxmeFvqarjwJF0QjQu9onAChFXVQtSAdQgYepd4WlwDraSL1vW0mrl6dp2BabSiNlnXiaugXCW+ecpnSFCt0bL32PxrnhfLJVW/9rXN7P3+WN1wc/+EG+8Au/kO/7vu/7NN/Or1z/Z5c2ho+srjg8OkKlHbsYXDyGZ99yvePAsTuzid0A9q6p6lnPXiFZj2wW/tYFpOkf0MmnNEKighBPle3DO/GGKF1Dp3dNimMwVKfPALD0e3ZR20f0Dpiv+zZhdz9a9ePeWymGL/B49Bbk1hVtC9zf82v5334w5/boade+WzA/Km7z5Hf9GZ72A3xfUtaVfaAFdpN5268i/JcvU5SaMJDXHsAtW74Pxn3PFrrYLXSur3PQAdzCv2M58vK6bAVopTX7UpntnHlpri2sUlqv4Mxp69uFNLSMu51zf5HbZ+h3Y2+Bu24MZry0bL9w1VyL5tpCaft/AslNKNEatFDIFnS77P5cY3oKX+yyjpJIcX9bQVaYFkTGoWKzFjut/B6IzhubRIoU7Mtlypa53/XdSm5CuZVQWCnKcl3TvB7oJx6LSwvotxGHLeheVpLbet/m0gL9wngExlWEBcJA2dLtWhG1WnEruVluapquY+iRO2nNpqEWPhF7gD5VzJYWXPvOQnPbvigVIZU7LNkvx6Br9e+xzNsx0tjmHGTaMfTaupn0Oor5qqYUvmXot3KZjseLG0mIrQi7L7k5X0t+lagwRvMzN27wbmMYdD2uriTPspO5AAy7HleXije1gF7hKfu+rxo3Z9OAkByPAh5OKjbGJ9o7AIz7HpPCI4l32newwP0/rRTJsLzWfvs4Yvpq4Fj0qH04bh+FrJdOQ+8Y+iRyvuAmpFdk7T0+fSNiQWwzdzbrduy3P5vwPSYifZ0s5te+s8cHf7jPwHtkmXsHgH/nVxxRzo9QZXYNXP/h33YDMR3Zsbd6eeCrv+AQ3p9YNnoPXB/dGO767oPuJHWMe/O69g6cPbp2iAAs6F5M7ffi9SB9Mb3e1/Mhim3Edh+4p90dE78P0Ds9exCREvxgt690uqh7r9jXrl4no5lcwPENrl3dPnz8BXjubbs2P4BOF548vH4AiJxEp8hbpt9TkMcDeuWMVTQkhFZys+ocE+sc0u5uv9kCekCHCfXS7KK+N5+GB69ypZ2FogYhpI1kTy+tr/kWcEYxNZKie7DbN4B5fMj9k3fz6PidiLl7OZ7g4/Ez3P2KP8TLP2bntnlbhvot7+a35N/Cv1IK328sSNeGF7jN5h1vYsxuPzMG6rgHUdzKcyzZ4sC3t4s07+QxO0vmfcnMPkveWixvwfu1v5fXpTDY6MLr28Lgjf0CZ/xQa0N0ukIbY3NXoK1l8kaCa2/v/ARjbhl6/TqGvv3b9oAgKLSwSbF7XzfheTRlZaMS7kPzfA+yhn1E74c+wmhXWMrewFYvL5p90wPJTEtEvSOUoq1Wv6yQIthFAnyPpqw/oxNi4VNk6EejEUEQ/Pwdf+X6tF6N0fzU4pxma90yGNmFM0qudwxCu1FcPrEbwN6VqQRhNErXrOV1QF8lPQCbXCbE6yQ3AztA2t1JdHBuAoe34E3v4OODd9hknz2WgINj/p/jb+LR87/OLkJ7z0PhJ7z2O/8s8+SwbQ88m0SEUrwcP2uLUrA73UOLk9r+17SH7tr232e/wS1y1Zaht78QQuy85beJq+zcbF4/p12szLW2bfsnAt1h4MD169nybbjRXGc78mIrudm1b2Uxrz9ExJEiLzS1Mejjmf1usAP6zesq2yXbA4A2bXXWwBdIYZMfld4lIsWhZfON3NPQC2f/WNjP39/KYrAa+qwSNrFW7ea0hZgsW74D+luGRNqKsPaTsIC72rLfTavb76aKxcYy9y3Q34LoQjlN/I6h7iSKVbnH0LdMjWRjAgeuTfv5W/tLRShr21dYj/fVxrLl4R4rboF+Qy09gi2LLm0UYVZIAip8Z6G41cXPCkUktt7v9vMbdK3+3QJ02y6E1cuvtUe0ldYIp5VfNRTCJ96T4hz0fZ4sFRJ20QInuXm4EsSiotGaH3nmGRqjLbu+VnaMPUA/6lkpTkR17Ys36vkW0LOTv5yOA5brhoyAUBfXkmgzgl1fN/bTNyI2BA7871jxZ25ErExkWXSjWxD91GnE2gR05PWDwRe82xaiIdu096Kk4A9//W0aP3Lg2o7RSz3+wre+g7hxrjDSAtSDgc/XffXTeMXmmp69l3ocnPZ2TLxrb5P7s3XL0O8evsQy0a8H7k1t+7+eWd+sr8lirrXvHSLsQ9OzhAxcB+OpY+i913FxadfKLPcBfW9gZZerBXifgKEvCwiC3b6S9pybzSdg4tfLT8zQZ+s3RojHx5+4v0vY3+5JSglWPStnqeWeQUNtWPRO29el9gike75tL7vj1rYSaA8Vjxq7h22Brfi//A/8reZLWsnOdr3+9vG38soX/R77qLfRYcmPvPsb24J7gItSC2ZPvdsWC2RHLtm5LRkUOP1e61Sztz9tI8XXgLpLIqVl0y1QN3pnp7y1ZNZ79xhs5T7ude/3LZzD2r7kptgeHK6x8fp10hy7z71e4lm4g0P01ILGaDy1q28C18mm7HV7538OvL+Btfd30pz2b0NJ2TjryD2G3gL6rY+8+3y2jjZ7kpsw9lGmoWn0Nceyij2XG+ecVmiJaXaFDYUQLhKwdXuzE8nAR1f1tb30M/H6lG7vy7/8yzk/P+cf/aN/xGKxIM/z9qd21j6/cn36L18qfteNt+G7DZHByC7KcfLGzqND+99O91qz50lq56feLqJbht5I7o/fzstDq0PfX0gbFP/h1/+P8L7fbRmTegfopRTwh/4UL43fvZPc7AHae+kzlMK/LsVhj73Y0637bsGB6yC61XnDtaTbwJdUbQGp3eipczQB3gCu28X0GtAXbWXZawti6V6nvg66rT7fXAPXrSzmDZKbNyYD2dekHEjfl7/swprN3gK5n1D0eoZ+U2iEEYQfu91+N7ZymTdEEZwufr8KrXDawQZ1LUH1DS432wJSoWSVu81NmBa4xpFLUDXNHnDf+rNfl9xsIwBFo/Bo9mQ+lqEujNVob8F/z0luGuk5KYoDy4niKlcWuIud/KfrrCV99iU31rd+2Vjdtv86Rn/T7ElogF5qDxelsfr3fSnOcmPZ8te3z3KJxB10tjKaxGOay532fQ/QT3PptPK7k2e/Y60oQ7MD0f2OR15qC6L3+h8MfJ7MNY0RBGbnRDPoejyaSxJKfOCP/NiP4UvFQd/n/9/em8dJUpTp409edVff3TVHzzDMDDMDw3AOlyDncKMohygqKIuA4q0rCgiCixeuggIiLrq6X3fRlR+4KqArnogHyIqAyDkOM8D0TN/ddVdm/v6IjMyIyMju6p7qo3ri+Xz4DP1WVGRUVtQbbz7xvG+8OqqR4NoNtqPaW0wUXTZAJ/aOFpNck0mKjVOmzo3BsoNE1/YsOVgq7lZ9SQwA7LUsGfTttQWAg9ZlMAamyo33APDGY7uw996dsKq8/v2TF++BPVZ2BEG3N9ffcGwXKRHIBPQA0LO0HVrJq6tuMEtdMh0E6GwQnUh5fUvY9SLP0APwgmshoLdihDEXmHv/oYBJXA36Hg8/LGRIrhPJCWCvmSVMvCEG9JlwQG9apP3ADuGhIBtIdGLxYF1p7fA08QJZl20NPpvMHgroe4LPxoLeayuQ3FQMr3JMreTbaraDfMbroyvnE0suXDxrrQBOeB3GF+9FZDhe1+6Bh2Pk47eiUCMyGp/VXr037tM3MgE4QTmeQUGLhxjtiuCneZ/uDZ+RsgCkLSVF/LbedQK/zbDsDGuv+9cOM/Q02RRMu0CqKbDkVlBhjV49bgXnolCQSmwu9xnZNVHj1j4HGjSMP7LEX1fo+sFem2PZfTZe84P3cJDPv5cG9AazvtZgwK7WuLVOtyw4VU8KQxn6OKk5zxa5SCS834bj+Am06aSBqq+X91h7b4fYrlR8DT0AuIYFp1Lx2wGAHosB1cq8TogFphnQm6aJNWvW4C1veQtaW1uRTCb9/9773vc2eowKHmzHwW8Gt8GmRVe9THi0dYYbryIHNqClnTNbpo7tJ78Tfz/wHE7+oWsk8egXGy7EA3u9zWvLBO5wMdy9Gli8zNe9AVx8wui2wQfLZuCYeLbcY9ed4EefSRJnR6UuYt8A/COvAeK0ih4Tz14zmdBRKDlcW3YsLoRAnzIcQmIQTRZi+4lZgZMPVZzxtfLMWNhFAWG7qDME4LMb1MnRAN12+M+T9OrN11wHtaUD/tyggb64K5BKGAxDzz9c1DQDBmxuV6BSdWFrOkxGK59MGChUXDia7gWuQaA/ViEni7JMfCZFZBsxtwJ6UixlTYqOgThqPkOvebKbom0Q9ttjy1tSZCeiYiY9KQbtmxx+BIALjDMpEyNlHXG24owGZJIGRmrkICKTOmzK6Fd15mGBlJuMWRoK3hgDTbzH3MNiWHRiH/TGEnOqfjCeTRvoL+pMkmsQ0A8UjcBOA/q0ieGyybDoZCs4ldAxVjO5ijNdbRaGxihbHtjbMiaKILId27Fx/9q1sB0HvT1xFMFXrQEIE1+Q2Bd1xVB0aQ35YMVf1Bkjh/iUS34wbhgaWju9HcFS0f+c3e0xvO7EXqRQ5hj65YsS+NBFa0gSPnPNJd1x7Lu+m5REZOymocFIe8E1u30FEKZ7fJQPfunOZX6Mt6eYgJ61J9PkmmKgn0yRPlyXD7rTWWBsOMyWp9KERWeD6FSG3JNKme875WnlxYcFGtCbFu8001lSWSYUdLcRnTt7TcA7r0QM9M2ABLLiwbrS5QXRptAHDdyTQoDuk0YtvH2px3ALaw82HMKx9lRK8+PVb8Pv9jkPgKc5r7lwdAOfWXM18JoTvHbk9ju6AZz2JuiJBKnM5sVxugZYWTI+ujyyflwMqunBe6xPTiWIzwQYm08k8QG545ATYKmNkiKFsk1KJVMfSkvlMkF1zJNshqQ0tXCgTXd82ZPUNe/BAQjWDT+xFcxDQozZjdZZG792JWhSK0MsUclMzXaQ3GvQX1cSMT0gygQ5KJvflZDkcCXiOkpVahM/Y7Bjbpk6HE2HXbX96jMADei9Esd0LY6Z0Bw+KTaR9BKjHY+E0gjBV3V0csK21y5uaajARLVQBjmR1iM2zTjccokjCLVEAnq1tDAZ+p///Of40Y9+hB/96Ed49tln8dxzz/n/ffrTn270GBU8uABKTi3QDR54BKlXSxkRFqecDXz0M/xiAOJExxavxda9XssFl6aXjOpA93/9rRkDQ+M1/+K0q652CzuHK5458IjpJH16d7m+/YRG8Ay1r7MD88Qe02HowHjBDjlg/yGCaZ/yJCfirgBh6G3ugQNgnZwbHkuVdywxSyOSIoFNiccIQyMmxSY4NoZnxYtl3jHTz1oUtiUNTlvP69zpw5Io2ymWPYdm2v7cSCV0r948YDBjSTFjEfupukRDr3lsRdJ7uKiB1qEnwSXR1tuoxNLI2HnSgUau2WenkLCLsGiQ6uncR2oWEm6ZYehJotKoSw7WYbdWsykDRYdhxb17aBoaCvASHWmgnzYwVnQJW+5WvLF4wXXZ9KQ1we5COmmQk0W1CkfaZpIGRipM4K6Th4vWtInxmgGLC+hJ3fqKZvKBftrAaNFGxTUQY+wtaQN945Sh53XrO/KULQ8makvaxGCZ1fMHQXTeNgkD7u84eGw5LIahJ1VrSm4MMVTgOjYKXnnCpT1xFNwYErTGu9f34q6Y9wDAy2LWr0yjiBiTFEva//Pbl6G9K+sF9I7f/mtXroNrWiQwZib76ScshV4pc8w9AMRbs/IAPZEiAXAoCzztSVSEoDuVDjH00HXSz/hoWCoj6yPpyXlEFj2ZJkw8IOjZs16CKtMHHcvIMN82kSTjGRsJa+ILea5UJAASJIslIQHyADAyGGbFO7qIRl9k0VvayL+iPea9Px4P1pWMF7hXK3xbWs1FHMtKjzSigT3FgUcAR54YXJvitDcB13zF/5PuAL/Qvh7bO9d6lwgC26oeBzQtKFsJnu2lfgygLLn3WxCSQ6kvBIJA3Q84mfUryawZrC1f4gN/y0u+KVcdbp1LeflJTJec32bHHrDxmt+nz5LTMcYoyRSWh4o6dirFYX8uQaIsI2fxWH+OFGM18EygTQ4hdKBZDl8O2dspF6vXcNIeup4yybdBlZvgvUnm2uzPXDMNLwlV828IKVFZ5dyElSAVbYKqlRoS6TgcAJZd5vTyRdeEUSv7ibKaRnaI3GKRq6bjWHFoFS/Ip/c9mYRZLc97Df20kmJ37NiBU089FWeccUajx6MwAUxdx0ldK2DSGb1yLfCJL8obx+J+Nj+LeEzD9gEa+AT2RIwEaSwTkesgWtl8kQaKHuPWRo5QB8A5r1TCQP9wjXNAAAnQRvM2WjP8dEsndORLfOCueQHTeNGGI3GMAKcU8LYAeSdKxqJj+2AlFND7W5OCtj4eYxyqFrQFEEqiJY7SDX1Of0sxFHQbjFxGYNfL/LYktdOEVnaHokQDcS5A9x50XB2JbTl/bqQ8Ziid0Pk4iUpubDFZVkdFj2ExdvjyF0MnzHBVIwGwVxsUqbiBQtlBPtOJjtpQwNAnDIxUTBSNJDLlEfqFIpPUMVy1kHTKGNc0PxjNJIHh4QSWoBQKrsecGNIY8ttqXmDs1wT36k1nPT17JeYFqd5YMkkDg2USaFlO1X8KzCQNDOw0g1KJ3iqSTRkYGdbRyzxEAJ78JW+g3Q3Y/2zaAFygYHuBuxcYt2dNDI3WyCFSbs3XjLVmTLw8DCQsnolvz1p4oWqRAB3BWDpaTZRgIeEGpSIBjxXfGSMH5XgBsOY9eBXdGLprFX+M7S0mCoiRKtuVKs5++u/QdB0rFhM9e8ypcAH6AWsyeGJxi1eHPrBvOrQd45t7oAvylwPWZIAcG9ATezppAIkECYy5p9ckeX+5JLDinszFdcPtSwWEf0xefXZAYN0zYZkLwAT6goSmXCJBNGtPpDyGvhZ+ABjz5rMhSG76Xg5fk7Lri5gkUk0LdO4iy18qAFVTKBXZQsZI2XEKGiSL+nQaVIuBvndAEdK89BItreTBwIrBdF2yrhgGedgQachUGjj0aGD9Qby9vRP48L8AS5bz9tZ24KwLEIJpcp+9VnMxNFrjyCLqQ2MeEw3A2xGrIWCygGyayN5Y5ts0SOA2Ol6jrop89ISBQjFc/5z4TZ6NL5R4cijtkResjw4Cep7Q8YN3BG0p+cFuWsc9UskyNCYg1wO5qiDtZCUz5P2an0+mM++nibb0OqyM1LfFGK09s56VBTlnzPuMlQpQfbbbX1fiHkNPNjZ5ht6RfI9soM7KjziG3ie1gpupGwYS433QscS/c1o8Dn24zJE/ZjpN5FqO49PT6aSFgpZEvFb0gnePyHGSsMp5n7ACgKqZQKJchB43/YG6MS+gN+Ff20gmYdpljhybj5gWQ3/QQQfhySef5HRLCjOPqmPj7r5nUaUHqEwDq3uT+MerZYhf3fJFCWx5tSRsDRIGdHC0yjHDmaSBfJFly6nzCjMcANCSMTCar3EOAyCLf6Foh8aSTukYK5BTQkWGmlwz8FApdpuT6TuV8Kq5uEKCKtUlgh8LTSpi2XJdJ4d2iCwJrSggJsXK9JKAqKFn7Ay7zgXdMTahNfj81ZqLai28iBRKDmqOg9LKl/25QRcSVivv3xfqQAWGfji7GMu1Qe9zav4Yh5I96HX7QVe/VEJHoWijGG9BtkaDK81nwkpGEgmn5AfGmZSJwQrD0LtBP0N2HC1akTgib5yZFJHFJBFIawCyiI/aMVL9gmHFSxVy+mmcYahJaUmgAgNWrezbMykDO73TTNkvJOPp3+OM5AbwtPsVnWHcSSJmewupUBNj5DJdbRZG8zapc88w9LmOGNHEC+Uc21uILCbuBkmxALAsF0fRjXGaeICw6CXX8kpOBv0s6WbkLx5Fp2ka1q7MwtYMVIt5fPOgA1F1bMRjOi47f1XA0DMPgJe+eSXMGl/9xjQ0tHVmgFIpzKLH46GAntiThHVnbTRQLBaEAJ0y7iJDnyQMvWOH2+fHyP9zP3iPRZex5WJAH0+S9+bHBIY+zTD0wgPA+EhY/uKXkBSumW0jyaWidKWtM1xyMpEkDwx2jZfRZNvIv7rQd4+XLBoK6L1dWjFw793Ts/PFEbDfoX4FNG5dee/VpOyviPPeBazbL2xfukdoF7hedLWRA93Y5aIta2DYC8iprafdwo5BQiBRWzZNdskA/vJtGZO8XyCa8n5QTmy+DJJ5vx98M0FxMkHIC/ahg2rmyxWHJ2IS1J+zRBTzfsqcS3amY0zlGzbwp/lU3M/LClhyXwZqBqy/eHYJd2020VYLrlMSWHtdJ5KUsVINif1e9deVuLc2cVJTSX4XPU/FdVw/COYIL4aQozsB7Gcsmilo1QoXvDvJLBKVvFe0wWvY2gYNLlrdPPc9jCNBEuK9tomYjhE3BaM4xrHxNSsBvVz09PLE5sbi0Gtl/6RYADBTKcSccuinPt8wLYa+VquhWq1i06ZNOPnkk7mKN/vttx+OP/74hg1QIYCuaViZbOOeZKeKXEeMBNeIBZVoQBIAx73gWmSR/SCV2hLERmsDUzuV3IiBazphYFtfmTwAirsCgn4OIM5ATOZJJoIneXaMcYtNtOGda94fS2CnVW4cYSw0+19sL3OAfh8CqxBsAwvMfVzH4CgptyUrOSke/sQmYtG8TZqIWCjZIcadOlNrLOP3k/K2iluzrhDQ63h5hwPbDZezHDJbkdPKXCWWVELHuJYkNcG9LzaV9JJF9ZgX0MZAGfpCySF2pwK6CmaTBgbKpFKK4VecIaxJf5WczslqJTMpAyP9JhajygW02ZSJ0QETbUwQnYh5UhyXl5zQSjRlWDCdwN6SNrB93DvkiA3okwZ2FDXEEjXuIaIlQ7T4MSpz8e5ZZ6uFUtFEzA0C92TcIAu9ayLrBrXlF3XGUIJFqjNUK/7n2WNRAiXXIow7M5blixLYDIvo2ZnAfa9lSRTgVblhmOtLz1qC1v9OE302s1p+8QOroF+XgFsqYl3/gD83Dj2oB/j/KiSIZFlxWlbQdeGfukXtQ/3ywJ1q6NnAM570Skgyk13TGLvAuNeqZOwyyY0VC7enAb2oRR97NsyWJzNE587KEnWdBNKi5p7WbTetMHMve1igkpvcUt7e0sbVm/fR1gFsfVEii2knDxJskE4DcPpZKSjjzu1HAthjFf8+ijX7AgccTso3sjjudODITQCEdWXZSswWFnXGUBByrtoyJl7tL6C3J+7P+6x3NgPbLpsyUKm6XuJmAFqBjA3AA8mNywX0RX8XlJIm4eA9FSfkhSvISGMmWS9EEoky/PD9NiNx8drRQDuZ0PmKNkLNeFomslTmHxxkQXVwYjq/wzyatzlSi8sn8DXw4VKWbFtnMOX7jmSMHPzHrik+yy7IeESGPhEn6yabe0B3Aoolh1sDx2Jt0GsV7ufpZlqQqo37ZZXJPTdR1LxdWwDQSPWaMSSQtAvQTDJpdF2DbVrQyhXo8WAiObEEjOIAdC3DfGkJmLUKp9U300mSj8VuE81DTCug37JlC3I54lh+9rOfca9pmqYC+hmCoek4sKUHBrsITxHxmI7ysCv98ZarErsV6Ph8XWGc/Mooy8BubeaLjsT50SoF4B4iLFMnSbfCNQMdJX/4EYBQIlPM0khyFPglLm7pqMo+DyOt4U5ctcLlxWh7WtGGq/lbDev++AM2AjvVCYo18dn2IYaelvLy7JQxKQiOjzJNcDUkhtr8uUGYeDuUoJvwSlHaNh/oJxM68lWiCQ9p9B1PWuLdZaqtLzsmLC2Qs1CGq5yKIWaXA5lLysBAyYQFG4Zd45JiXyhqMGFziRGZpIHxigYLDh9cpw2Mv6qh269+Q3Tu2bSB8aqXFMoE9KTkpAHTpRp6oKPVwivDLiyLJtySzlsyJp4t67ASQSIuQCQ3REJTBPsj6GixUH7VhOXybHkqYaBaMbwcAsf/nGXqasslv21vLo4STBh2NRS4/xQWknqNC9yPOqAV/U92wrT5spAHr8sCizIkKGYegGKWDsTiMCplHPHyK9B9ms6TZYhseTwBlD1WXIsLdl4rT+xx0j40gROEoRf9VCJBKrqwfcTiJFDOjwuBu6dnN02+n2SKtAXCwfjYSKD3pkilgFe2hIPxZJoE9Ky9pY08RBjVsFa+mA+z3/RvMXCP0q23tnv3IcnbM1mgT+iH/sZrVb6tFSP/tQkBerYVePt7/ZOyuWu+/XKEoGn+PGjEujIdWJbma9sp2rImhsZqnB+2vAMOWdY+bpHd09G8zTl+y6uSw+7YpiWJqcm4gYERInULMfSAf6VkgpI38P0/4K0BFeFgQY9lZ2U8bA39gBDyTop1gz4tr1wzSygZXpnIkvjQYmkolvmHCb/mPHMYIZsAS9ddtvINlwBbdbnDBklbHZWyC+fVVn9u0CBfLAZBdybEEpXsGs7Wsadrj+7JOvMlmyOYjJgFbbxGbqSvvWpBximQZ23G39agE4mj972lEjpecghDrzFtNYMcDsVKbtx4EuZoyU+UBQA9kYDllD39vrfmZ8hvNqUL+SXzDNMK6M844wyln58DVB0bX9v6OK7qPgExkYmqE/QHDYiBu0YSaIQnUN8pIHBSVO/sOyrPnuJY8aAPmnAr2i2TJju5YbuQ8W8axIEHCUbw7VWbL7kV2PmqNeRz6sGWLOsQuWRZxk6lOKwDtEiVg2qNd2x+VRzRMcaIbMcyEWofVBfgA/1SBNNfKNlS2U7NdTCy7kVUnVWIGaYniwlXs5koKXa8RiQnInM/XjOwCAFzbehUt+0Frt6cSXnb07WUTkpIevZMysBAUUMNOoxa0e8nndAxUgQJ3Jl5l0kZGC2DBPpAEHSnSaBvMgE94OnoB0202cEuQjZFpDjVuI4E88DQ2WKhWNNhWK53UBLpo6fdQhnkwCmW5lrUGcN21/QkN0HQvbgrhsoznlaemXxdbRbKOyxo1SrHlgMaiq6JZKUcsD6Ghr3XtCOxnd+JaM2Y+ODbVyL1yPOczj0ZN7Cst4UEtCJbHot7Ab2YHJJAtVTA5456DT7u2MRv6CTQlwf05Yn7Fhl6X88uyGXEvmn/hXw4cE2mgMKY8HTsMfSpTJ2SG6/Kjc9gC3aZtn5wJx/QW7FAR89KbmjwLH4eX7cuyF/oboAY6FMmXwzoaTUYsf/TzyP3XcQ1XwlfEwAOOCxsqwONWFemA/8QQS1YQzIpA/kin8Aa1H3nc60oIcIGu5bF14gH5Lu1dHdUZ64d9+up8+sLQAojcCSVt6PL2gyDnGgrHlro6+q91vRgKk7bLjkYyr9ORWTjPZv4gOGXjGaC9wq/NsYtesghnxMGkFPLxZ2AQrmG2OFbUHXWImaY5B6VHO7hJhHTMDzmcgQUDfxtZgfEMknBi0LJDj8QlBy0pIPfohGLkQMKteB7NNNpGHBIVTAPyQQ5bdxCwOqlEgYKjgndDtYmANAsE3apzO9tJZKI2WU4jFFLJGA5FW7XOJW0UHHJWSLzGbP7SK6wSzA0Ha/vXrlLTApNignZrSiGni056T3paoQ5qAkBc0rChACAaeh++Usu0De8bUIhBqGnBYqjDCoGBNc0TXqMthty7DW6BQnW8dEAnW+fYJJleeZej0yWLZV5tjzYXuXZHLb8Je8Iw6fkAUEFBoCXxdCSYVzAHdNhO0C16iKzdTHH0BfKvG6U2ulJsWJSLNWKszV5Uwkd41QrDiDY6tRRsNmAlgT5rgtUHVrm0mPLkwbGi6SGulkt+/2kkgaGii5xxoL8ZaQET4ce1CNtSZu88/btBkqwOJlPMk4WjypIjXoa6Ha0mqhSt1et+tfMdcRQcZnP4/Xd2xNHGZZX5Sa4L3suSaAMk9HcE/veK1Iouybpm1lJP3P5njATcY6hB4CPvnMVTJtPlgWAdGsyYNzrCdxj8UAuo/HtjXIRb33qb7zfoEG3EPzDsT35i8Hby6Vwmce4l/wKyDX0op+KJ8nDiIwtz4+FHwocm+wAcBVnUkEZSjGgB+SBOyC/pqw9ZddZO2XiRflL9yLyr6hzX9TLX5uidwX5VyzzeOo5wJlvQwjHn0GqlYlIpcPj3gU0Yl2ZDizP/0MIoG3++Z4cZFgLr1mGTgghNgg1jaDGOxtU2wLDn2A09GCu7fCbhb6PrNlM9I2g/DF/bS047Iph2amN3t4Y89ARJKF6Dzeu+OBAEmD53QHvQUYYT1UoRxms6QHBFIsF+V9sThgAlCq8nDMe01Aqu3D+nvPnRiKmoVi2fSkoaccm1XoPTGyFHmFtK5T4h5FkPCzjMeImdIeWrfSu45WjjDHVZ1JxHRWXXRNIkG+7OqqVml/lBgA0y4JbC+rQA+RUWd11uDXPSCQRd6h+n/SZ9nYCYsb8ltxM+xf84osv4pJLLsFhhx2GvfbaC6tXr8bq1atxzTXXNHJ8Cgw0AIviaVE9OSVYvoPhJ6bpsQshdt3Q4DgIMQdULsMyAnTLEoyNtA3Ycu6ajKMOta/JnUGxLFQcMCL6MIJdAT6hSEO1xm93At5R15Uw0++foucGLLphaOTwowpfhjLQJ0KyJclvc1K7eEqe374UTviizlCUSgFAoWgjVkpwuyWFEk1+5Z1noWzDDtWh1zFaIuy3xrAiybiOfBUwBPlLOmGgSO30Hlo6SSJ2dBgMu55NGajWXJLoybDomYSBwXHA1PgAPZsyMF7WiVSGmXjZFDntz3DFBwATJdfypCjBXGjLmkHJTa//tgyxAQDsIKBf1BlDGSYJ/hF8USSgN71KOUEQvcfiBMquSWQxzBd+wek57LEsQ6QSzBgPW98CK5kgwbIQcMOueXp2IUCv8Amqgb2MkN7LD/SFSRaPQyuV0Ts2zvuNeCJUWhJWjIxBZNdjiYjk1zgT0LP135NAocCPAwgYfVlwPSaw6KZF2omyGJq4KqvEAkg09BH2du/sDjG5lAb0MvmL4/BtKdNOK+BQUIZerDjTuyfwtsvDAX1nD3D0yZgrNGJdmQ6CHVoEwZTuBd8Cy12zw8SPYQBVW7K7SwNo2k4Py15kh/0F8pggqKYMfc0OB9qVGh/k++9HMPXZ9Vbz30v18sH7LU+uI1Y8i8uCd0tDqcrv6tIdA/Y0dlYaSo20Qht7L2mSr0zGUyw70MYT3NgLEk1/qMpNPEi0FXefC+XwLnNeCPLNeMwroaz5nyeZMOG4dOeWxhwGyi5P8hi6Bhg6apUaNObJULdMmHC49S0WN2Fo/A5IqiWBBKrc7kAyQQ67ShiCD5hnmFZAPzo6imOOOQapVAoHHnggVq9ejbe+9a0oFAp4/etf3+gxKnioOg4+t/kRVMWFZQrQdXngTh0pwDPaBtOehWEANaEf09QDaQ3Tlp4sG5K5UEcttjdJ0C0+ANBAH+DZF8f1DvmI6EN0Un7yENOeHpMtVsWxLPZkPb490RLyzrdS5Q+hAngGQ3RkdFFhyzbyh3eIdn670tcgVmz07/uMPzdSCcL4iNUDSCUGJxSbJeMGxssuSYhlvsBk3ECxApIgxHBcqaSBYlVjAv3goa5sa0Ry45D28RgJ9Cuu4QX65DtMeyf1GXC9wJVcM+0F7kRaE6w82bSJmmuEJTdpg1SW8QN98sHasibTD/lMuq7BgQ7b1TiGvjVD+tAdvmzl4i7C3IvVb9avTOGko3IwnCD5FSDa+pa2JEl+FWuoWzEvoOcmGPlXTAqdiImvSuQvlKEHhEA/gWq5iI+ecBzvNxKSBFVN89h1gblPJJjkV5GJlzD0CS9w14UgOuFJbkRGmybGsjIXTSPyF9cNl5AEwnIWn6EXq9x4drGGOpXLiAE9LRMp2qMQTwDLhURSXSdngOx7cNh+4OH8vZ0HaMS6Mh2YrG7cs/nrDdPO8hK0SbDMrE1GsGYFbTX/9HH6MyDtEGLjq3R3QLw2cxn20EWO6PKqyrC+2GDXOdGG4Gv3D1REwGhblvyhxfIIJe5hwpQF+UHZS7ZghH+wlNc2KP7AEzq0tr0hBN/Fig3j6Of9uUHJKXFdCspoBtcWT4WlbcVdZqrB59bduAnD4dl0cpq5DksPbnAqoaPs6N4hhMFN1nQDuuvNZ2+whmWRHWimXSxOg3xmPcnGYcKGaQTt2jImbGhkd2AeY1oB/W9/+1usWrUKN910Ew488EAsX74c1113HU4//XQ8/vjjjR6jggdL1/HPKzbC2oXjygwdvsPkAncDIWdG7AFjwv7g/OO4Gedl+c4rzJrQpCY2ACaSG/40OdJe91kWjoUwSaKr4/DsDeBpHNk+jCjJDU2KDScAlQXGhti14GQ9nW8vMhp+mTGhtGTC75tnKwKdYfiAqoJXlkwmuREPt0jEdRSLLnqeXuvPjURMh64B+aItaOip5IYfSzKuY7zkcow74OVFlDUYAlueTujIV0ACdzagjxso1XTCojOrZSZloAodlhvsaaeTeiB/qVX9QDSbMlCj0hpmFW5JG6jAIIE7Q7flOixGWhNcsy1DAnqNHinJTJAqDC/h0FsANA3pbJw5QIq0tUwdn3jXaqR0m2O/NU1De0eKkdawEz7mBfQSe7nEM9eGSX58ghQHMS/4r1fPHosTzTkA8QHAKhXxuV//lvcbVC4T0rlLtPWxBDMWQXJT8srFiRVqCmPh4DqeJEmxkfIXwU4ZcIM9cdVr6wrBJw3cxc9DT9EWGXoa0IuJq9kWeT+vPSkoAcnimq8AJ0tkMYuXzbvAPQqNWFemA1ai4ktcuJ1iYmRztkQSSrRZnvRETEwlfQakD2Hyec06d4CV8OBQrYblNZWqIPM0md1vuj6xMhzP6B8ixQS7Me+hpSIJ3ksVfn2kJTNDVW4E4omTi1K5T4ypTS+sXaWyw3MBFpHcuL9b5c8N2k4ksoIymsTGngrLrWHeyebstYnkxhHcmQUD/GdMxklAT3Z0aZBvwHY9iScDzTRgaA40hpwxYhYs2IS1pw8ecZNch7wLAJBtiUPXAEsLvqCOVhM2mIeEeYpp/YK3bduGtWvJyW6ZTAajo6MAgH333RdPPPFE40anwMEFMFgt7VLhJMOT0IiVZQg7wXKwBLqOkIMFWGfMbxsStpzv22SSXBFlZ6vfGFpIB0n7rwpsh+FtiYadK9ktEOI4wmRQxydKbqphWQzVKwL856faRlFCAyBkj8eIcws5UfZAD0ngDiAU6BeF7UqA6BULJRtuouLPDXoU+VjBDjnFQslGzXb97WRqHy0COmXcGcnNeNmFwSS5AoShL1TAMPpeQJ/UPckN308mSYJ0n6HXNK9CgRfEMdU8OlosVF09aKsHAX2NBu5MTdPFXXFUXQMxjWfoWzKE0YdND3ki7d98UjcJ8BiGHgBuvWo9lxPg35tMIiJAt6IDd2rnJlMsHLgDgBUnyahcH3Gm7yjJDR+4+wy9IItxyyXsTKd4vzGRzr2YF+q2R9SbjyU8aY0kEM+Ph4PoRJLIiMT2UXIZGtCbghQHCD4rBWXuxaownd18XxRRBzHtfUDQF4vXvxX4wKfC9kQy/CDSZGjEujIdUEYcTFU0XScbQQDPnANh2Ysf0DM2kw3KWRmP44a0+mKiremRWqGqa558U9xdrtYcfj3TGa0+cx3xoSVGq7gx7SgxVamFZZyiVt/X1QuSGzHXiwb5bL5YzPQKOlTD57MUhUCdJqtq6ao/N+J0l1hg6GnOF1vRplyRM/TizjaR3Nj85/G07ZoW3KRk3IANHTE4jE1HDXqogIJukJ1fNng3Y0Rew63BSfLgQK5DbK2txCfENJu/tquhWBT8yzzDtAJ69ijhvffeGw899BC2bt2KBx98ED09PZO8W2G6sF0HP+h7FvYuPCUGThQ8Ey/RLlK7r3Nk4DtEhJ1StcY7G5alENmHgIkP7BaXsc/YTc2v1aszzlrXgYroXM2AodclfYiSm4TvEAW5jKWRspAIB/olodKA6WnrixKNIS0pJgbX5Qply5mxcJIbJrCkDL0QENLqN4O927i5kYqT0o1ieUrbIQ8dnDQ5rmOs5I2DWRWSCQP5MkLSmnTCQKmmBVp5X/9uwgZl6ME53ipl171AP5XQifwFGjnMx2vb4zHuvlbeA02KNcEz7os6Y0GyLDPJjtq/FWbcJH0zY3zXG5YgkYqR4I9NxLIsErSG9OyxCAmNRR4KxODatEjfov7dl9wIbjcWAyoiQ+8F9OKKGIvJtfW0hCRzz2k/drmEb+67nvcbE1WiEQ+FinsMvV0Ll6cs5cN9JLz69LJDngAJQ09lNEJAT+Uv9QTMtE1NqEJBGfqqsBB3ewc0ieUf1+wL/MvXw/3revhzLhA0Yl2ZDti8LfqbnUj2QuWWFIbhBfnsb9jQUOPTfYICDgiC/CitPZXmhB4SamFJZ0XQsVOGH8yusGmGdfX8LrHmf0bTIFVp+DVSC5Wt9CU3Ot+uXKXX0bzrEHkNmPUy7pNOdijRlqxnEGw1YN0r/txI0MCfK/qg+QF9UNueFEgoCbX6KeklVlgrFG1OdhpPmAxDH6whhKF3+fmCCIbet3n3I27BhEPOA/EZehLQGxp8f9raSuR2Jl3DPNRgoLQQA/q1a9fiiCOOAEBOjT388MOxfPlyPPHEE7jooosaOkCFAJZu4L3LD4QlMlxTgMGwCJzdCAJ9LqD3qg7IkmVtwXlRtlws8WV6QTRcUYeo+fXmxb5rNZerqQsEZc5EPT9l9HXhIcJnYJi29IhskYWhZSvFuu2WpaNUDd8XUgGAd1ZAUHKSZzA0v/QlNC3UByE+eWaE1mfmAv14mB2h9mLJxeLNq7m5kUrqXkDPj0/TQHSMLEOfMDBS8m6sHQRF6YSOYg1kq5EZf3uWaApFzX1Hi0nKUwpJtImYjhrV0Ht2urjWXE/+4rWlbL5u8/KXXGcMVZdNiiXtl+XiqMJAXOcD/WMPbsPeq1vI5wkljXgMPbgvm/xbrdQnoTG9hwKZVl4mxaHseoih95h7royGJwUpF+uU3CQYyQ0fjFulIj75x0d5vyEL3AHyYFAq8kE0ZbFLAhsfp4y70AdN+gydoEoD9DoTV/1+BPtE+vb8KP+3YZAHLHrCqt93Fvjid+rXyi9gNGJdmQ4MHQyjHawhviRUXIdEHbvn40PBt59oy76Xf+6WMfl8lRp2nGGGnhJDYlJs1XbD16nxBxlSvbsjXMcyPb28hAziNwblkptgJzmwieelUNJJtossElGEoQesv6z050bMY+1l7wX4hxZNCx8YlfDOMBGDfPFwxVjChKnR4JvYSECvQXcDyQ0AQiCJDL0X0GvM+624FRR98NolUl5Az+jyW1tisF0NjrCDa0NHrTq/y1ZOq/bVcccdx/393//93+jv70d7ezuMGdp+zOfz+NGPfoT+/n4cfvjh2Lhx44TtX3zxRfzmN7+BYRh47WtfixUrVvivPfroo/jVr37FtT/wwANxwgknzMDIGwfbdfDXsZ04vqcbpja9+8xq5VlHSJ0rkWHwrDNJPHJDDrYmbC9StpzWFub7kDMftuB8AUHfyEpxmKRYUXMvag+DyjqCtIbZhhS3HOVVbnSGoRcZDTvEllPnFovxTjmq+k2p6sAWcgj80l5M0Evb50uOz3JQJGM6do5UUGgZh+06/txIxcmJrqyGUdM0T3bDJyZlUwYKZQBJcJqp1owJ26UBesAWt7eYGANlQYJvtqPV8gJ6m7Mn4kQv72vrmW+rCh3xWs23aZqGKgziuJmJmkmSZFnLl9aQFzpbLVx4Zi+0/j6IT3uaaRHWNhSMm2EmnpZCrMgCegkTHym5saIZfZnkhgbpbOBK68WXS/KAXjxBNUpyE4vDLhXxSK4HhzJzA/Fk+MGF2um9YMfij5Hpm5ZzFPtoiWDWaQUZ8QEgKqD3D26SPADI6rO3tgPLV4Xtn7szPEbZuHdTNGJdmQ4oWQTwLLlYFQbgK8iAaRsK8nWWaAreS6Q0rIZeD8lAg4cJca1DOPnWoAw9P0axHSe58Wy+3l3YmbU8gistBvmhmvNEVx8ux8wH9LRIQyLG30u6pnEsu6VhrFALPSQUKzZauoN1hVapoQc9ArzkhkLTtEBvLxBKAEK6eoB3lYkE+c0bTC34RExHATrcWo2LFxzNIHp3BqZlwhRKLVtxEzXwcUI2YwGUoafEpK6hBAN2hWfjfaJqHqNhe4hdXV0zFszv3LkTBxxwAL70pS/h0UcfxYknnoh/+Zd/iWz/wQ9+EKeddhp+85vf4H/+53+w995748477/Rf/9WvfoVbb70V27dv9/8bGxuL7G++wHFdPJ0fJE/70wTVyotsObvVGWVnQRxVKH7yHJ3AKPgVZ8B1zpWnjLBz8ZDHYAB8oE+Z/ih9PuekTPaIbdah0Zrw4fJkoqOk7WUMvaz6Daut51n+oBKAmCxLmHi+c8LE85p4ai+UbRQzo9zcSCW87U2hPT3plXWqJIvfu9lOsNXY6tlFR9bRQuwic9/Z4iUPCbVOKeuuMSfFwvunRhNUmXvgV7MRkiCuvWwVMjEqGWMeVCwrmokXDhjx7dUKP8E0LQjGQwG9pOKMFQuVpwzaSzT0VlwuuaFSHLHijBUjbDn3I4gHCaoi++9XuWHtCTiVEh7v7uL9BtWUyyQ3MnvGC67ZQJ9KaEQ9e7Yt3BZgarwLdtp3ZFKsENCvXAsprvoScO4/he0qcJ8QjVhXpgPZ2kKCZ0hzvMSa81INveQQKkPX4AisP2XT2SDfMDS4rnfcAnMdel4K648t73AocR3y5TUMIx6UzNT89wKeXl4giWTVa8R1I0Yr34QkNwJDzx4KKax1xYi1KFT4oerA7Rjz50bcooUZgnZ0V5p87vC6NxlDn0mS3z1bYSeTITtnBqOX13UNtqvDtXmG3tW9tYK9l16yK6uhjyUsWIKttSXm5YfxgyJrEk/4tLfFccqhQsnZeYbZOxZuF3DDDTegvb0dDz/8MEzTxIMPPoiTTz4Zb3/727HHHnuE2p922mn40pe+BN2bdV/+8pfx0Y9+FBdddJE/sdeuXYsvfvGLs/o5dhWWbuC8RWt3aWtUZxN3GLtf2ktgrklVHIQCYHrENukzaE8dqoxdcYEQoyHT1puGl+QpBtcm0RgCQszi2cWHgprtBvkCtC1TMkx0nGVPiiNm8I97JxeKJ/OJmkOA6glttLcEPy3L9LYfBVaEaOgJOy9WsyFHYfN9J2IG8iUH3e18gJKM6xgfd7CobzkvuUkYoXGT9gaAGhc/tWbNgLuwg+PUWzMGE7gHjrC9hdQL0IVTXttbLAy6OmHRGceZ8SrXeJOMk8vUhvWQ87zjmr1hftHmkl8Bsm1KGHcHwhOcPLg2TYahZ26oYXhJsfw9hhmTBPQWGbcYRJsTJMVWvCPCeV0XMDIkYeg9yY1Yn5wG6SITX62Qhy72C5yoyk0hj3/6+3OwzpDIaKIC+hBb3gIM9vP3MEpTTpl1qumnoIG+WFmmtUNupxId8X696Z+Asy4IX3cWTzldSGjEujIdGCybzgTfQdlKPqgOVbkxwgc+8bu+lHGFX5GNNvYJn6AZU3NeYHsjNPShpFiDKavsX0cP7WTTXDMxUKfVb1jEvXUmmwq+G6qhjyre4GvoaW17UZ3nST3FKm9i9ZpETEep6CLxQq8/NxJeJTeRgKJgP08ypmNYsMmC/EyKrlNBu0yGyB8tgUiyoUNzhKobhgFDuG+WlwDL8jjJVAyuJpxSm4yhCAe1msC8myZW5kwAgcSmvTWOUw5tw3xGU3jAn/zkJ7j88stheluvJ5xwAhYtWoSf/vSnuOSSS0LtTzrpJO7vJUuWoFKpwHEcfxdhx44duO2229Da2oqjjjpK+mAAANVqFTUm0apYJIuU64YPSppp1GwbDw5swZu6umBOczdE1/jylPQzGHpQVx4I7OQBgMpfgs9sMM6LyKjJ/1OGnu+btHW84IzaTQNBvXlNtAe7Bb6dYSHAjIXW6hWvCXgJusw1qdMUxxLoDV1Ac7n2fkkuBPcnqDQAbh6w5SzDdpu7h7SPREyDxlwzEdNQKJLgn+0jEdNQLNmhvtNJA+Wag6HOPtTsPf25wTL0bHuZPW5pADTYrgadMiCui9Y0OSFPBz0XgNjbs4bP3LvBF4WOFgM7YcAESQL2bjKS3ol+Lq1C4hL75967J9q+mvDsmr/l09qagOa6cKnzpuM3TF5Cw9mrQWUdajdJ4qrmkq10zi5c07dTOQfTVgPgVsrSvqHrJNDl7OSIcZdtbzHJr+w1rRhQGAqPhQbpbPtYjHwWqsVn+6bBv8DEO7UqfrRiOV5v24HfiJPDYlxNaE/tujCWdEZqp2sj5ws1Es64hTzfRyZL7LawtdfSRuyJJG8/+Ehy6mpoGzBG/ptl/7tQ0Yh1ZTrQ/UA78MXB+uRyPtHw1gp2rSHrigMNGrh1zPZ2g701WqdJsS5A1w1TB3dmCrk26YOWHebXOn59sUwNY3mb86GG7o2RXAWkZCP8k8zptXWNBP/lisOtB/RwKV24TrnioCVtBusGc0qtuJaAXt11/RNgK1XHvxf0/YMFfh1hD5ZibVXbQbV3B2r2apiG4fdp6EG7BCMvZdfIdEoHBvh1hgb07HdLH1bYa2dpQK/ZJCb37A50wOZtmk6SYlmbbhnQPTae2jNZC2U4ZOy0rabBhIu2jMG9P52JId1twC2ya4wR7ALPMuqNNecsoP/DH/6Ahx56KPL1devW4YwzzgAAvPTSS1i+fDn3+rJly/DSSy9Nep1yuYx//dd/xdve9jY/mD/kkEOwfft2PP/883juuedw8cUX46abbsKll14aev8NN9yA6667LmTv6+tDMpkM2WcSNcdGuVxG344+mNNkU0ZGKqhWaxgdHUWpVEFfXx8AIJ8voFCowIAOS9fhmVEpFzE8UkalXMHw8DD6+sgDjWNX0T8wBADYuXMH8onghzowNALHtv2+h0dt1GoORkfHUC5Xffv4WBnFYgX5got8rIK+PjJpy6U8xsZrqFRrGBoaQl8fqXVdq5YxOFT1rrnTdyQaHAwOjcGu1fy+KTM/ODSGciUYy+iIjXLFQalUwvi4g74+8rBWGK8gX6ygZgNDg0PoS417nz+PkVFyzR07+vxdCteuYCTvwK45ft9kLDWM5V2Uii5nj5kuxgtVjI6Noq+P9DdedFCuEC370OAA4prpj2W8WINlalwfTq2IctVFtVrh7CZK3ndi83PDKXnfefC9AYCpk888OLATxXGeZbWhY2SgH62Og519fWSXw2NFhoeHkapWMNTXh1rZgQ0dTrWM/Di5V+N9fXCrNdSgo1ocR7VSQX54GOW+Prx2XxeJ5+IYGx5C1nWws78fTsljsQ0No4ODyHjX9L5sLAIwOjCARIVcEwDMkVG0Vcoo5/NwbRfjnj2RLyCZz0OrVJAfGUHZs2crVTjDQ8gC2LGzH+44OQipw3FQHh5CynWDawLo0jRURkeh1aoYoXbXRU7TUB4dQTXTijwdy9g42kpFuFYMY8PDqHj2ZLGE+NgoEgB27NwJ10u8zJQriOfJvRpgrtnquLAKBVTKJYwy9k5Nh1EYw9j4OIrsWKABxQIGhoZRs4jdyBfRXa3A1Q1ubljVGtq897FzI14qox1Avlj07yEApG0XWQDDo2P+PQSAVsNCEsDOgUE4pUBbmtMNaI7NXRMAFgFwxke5e0vt+YF+7ppwHaRfewrylRogtEeyJWxTaCgasa5MB6MjFZQrVRRLwNiYjb4+GyN5B7Wai7GxcRSL7LxyMDI6jkqV8fG1KkZGxuG6gQ8ulQr+CdkDAzthlw3k80UUiiVUaw6GvfVkeMRGteqgWCphbIysA1RWMjqWh64zvte1MTpeQIVZRyrlIsbGbThOcO1igaxbgLcmxnWMjVZQKldRq7ncumIaQL5QwdjYmL8G6bCRL9goFIK1o1YtIV+soi0d2ErFAvLFKlwnsI2PVlAsBdc2Dc3/PIWSjcGhQfTFiaxY1xyUyg7GRkfR11fx7mUZjks+A+2zXCK+0rED31EYJ799R/KbBwhRShE3yQPGwEA/LC/UtKsF77sfQV8fWZ9q5bJ3/8b9GKBYdmC7GuxKGeVyGcP0O4cGODZs20G/Z3M03T891r9vdg0GHDiOjaHBQdTMBDS7ABMOnFoFI6NjKPX1QR8ZQlfchW5VMZ7P+769CxoqY6PQ7Jq/DnTUbOQHBzm/OFugRPJkmLOAPp/PY/v27ZGvL14cVCUQSwkCgK7rkz61VCoVvOlNb0I8HseXv/xl337MMcfgmGOO8f/+7ne/i3e+850455xz0NnZyfVx1VVX4YorrvD/LhaL6OzsRC6Xm/WA3nVdnKrpyOVyoftRL4ZLBUDLI5vNIpnII5fLAQDa2wZhWaNIJi1kMqZvz2RqSCZNmJaD9vZ25HJkOz2ZzCOdaQEwgp6eHqQ9HVw8NoRkMgvTrPh9xFM11Jx+75oF3949PA5oJSQSSbS0xJDL9Xhj6cfOkQIMs4TOjnbkcuTQmNZsDbG4DiCPXK7Hf9pPJUZhxVOwLNvvm8yZHYjFU0gla9xYqnY/YrEEWlszyOU6/ftiO3loGtDV1YFcjpTS62zvx/OvjELTyli0aJF/H1uyVYyXyojHHL9vAMim8xgrVJBOpzl7MjGIas1Fe2srcrl28jmrDlzsRKUGdHd3IddNAr98rYhqbQjJuM71sWzJKIBRJOIxzr50kQG4eSwtLMWSRYv9udHV4QIoorMz+N7IfSwCqGLxohy3XQr0wYaO9pYWwDD8a9h4FjpctLW2ArE4crkcXNclybKahnQqBWga0rkcMi02/godbZk4tHETbe3tQC6HXA7AHm1+icLu7u5AUx2LoyWV5K4J72TCllQSSCQCu10GXBepZBJIZ5Gm9u2dXkWT4JqkgxZfmtLT0xPovhNJWPEYYJrcvUQiiaSpA1YSCdZuxRDXgHi2BRl/jF7iq66jvaMjuGZnJ/A8+d+eRYsCKUl7B5HKxBP8NbMtwCtbkEylkGTtqTQw1I+W1ja0sPZkCloxj86u7uCarUSuozk237dbg+a6OOPVHbCYuYHRfgBAOsPcQwDoJv/f1tEZ9A0AnV3ey7lAUgP41XK4a3rQbTtkdzt7kD7oMP6aAHDm+ciEelCYDTRiXZkOBvJ5aHoB8XgCLS0p5HJdSOZrcNydSGcySJWYNcQaQiKRQjxe8m3JZAHxZBK6UfRtba1A1akCKKKnuxvtLRY62oZgmi5Mo4IObz2xElXYzgDi8ThaWrP+OqBrOxCLJxGPBb43ER+FZSWQiFd9W2uLg4GxEkwz8P9tbf0wB/IASsj19CCVMDCQz8NFAYbuorMzWFcSsX44roHW1hbkch3e5xlDqexwa0d7q4OqXUEiEfdtnZ1DqDklmCZ822iliIpNKjwtyuX8CmaWuROVmouuzk7kciReSafG4Lg1tLW1IpdrI2NvqQEoIpvN+H0u6h4C3DFkduSCdcWsABhELCb4TZAgl7V1tpUAVJDLdSPXSXxgd2c/gHF0dQVr0rJ8HsAw2ttakMsRP+O6LklC1TTEGf+/2as5b5jBWrHZJKe9QtN8W609AxM7oOs6ielyOaAwgp5FJpCIIdnWitZcDkhYwXqSyQa+PRZH0jIBywrWgWQCbdks7xdnCfM+oD/hhBPqriqzdOlSvPzyy5zt5ZdfxpIlSyLfUywW8cY3vhHVahUPPPAAUinJYSEezjzzTFSrVfz973/HkUceyb1mWRYsWs6OgaZps+r8AKDq2Lhr+zO4vKcbMfFY9TphGrrHXmvcZ2ArDuiM3X/Sdz1lgZ/Yo4FWcNJ1jbd7iUq+zSKHWZAiIeG2rmCPMSfuaWzfFjmQAyDbrX57K9iqpDZN07hkJmqPx8hYaIIVtSdiBspVBzFTg2Hw7UsVvm/SnmgO2ba0n6JXlYCzx3WM5qucPWbpfgKwYei+vdXbbhT7aMua/nfF2y1ouout3VtQc1f4c4M+ZJlCeyq5MU09NId9jSK5iZ6NlArTqM27bzXosDSXs2dSJi56Qy/Q3xf04QtkTcDxkmLJZGLsttDWAHQdGk2W9YWuTPKrztgtKqFx+b4tixzaBLI1G/RjBom4ol6+UiEJmSFdfJmMy886ixPJTcLl7VRaA0DThfaVMnmoEXTufrIsJzhNBbXfWXs6CxTz0NhrMgcncd9pJouqruMba1biEtcJ/EaC+EOuDwBIRdhp+3hCsJOAPuQL4wlouSVh/fuV/wqF+YVGrCvTgeGvRcGaY3qJQzRdhV2fal5qD7s22SGbHhyQ6PlPthQm9amWpTOyTk24jouEYKvW+PWCrazG2vwqbF7bWIy0o9dlfX+5yq8TMVPD6LjDrbMxr2wlt4Z5evlM0gjZwHxuch0N+aIbej/Ary8034pdV7MZE5ruIr9qG2ruasR0Ey0ZMj/ofaNIJ0gFNtbWkiZt2XWGXsdibLSdZQU2TdNgQ4NTqxGbvxbp3kFQgS2esGCOVsmfnm3pohS6DmqB9g8E/tM0vYxrBL7WMIMsbHY9oblXViyw6UawTs0y6o01m+KkjBNPPBF33323z8g/8sgjeOmll7Bp0yYAwJ/+9Cd84xvf8NuPjY3hlFNOgWEY+MlPfoJ0Os319/TTT3N///rXv4amaVi5cuUMf5Jdg65p2JDtCpVKnApoCS7ZAVLigR6+3S85GbzAlhFjR+PXBtZ5GwByJDcbI3kJQxDKhAX16fm+qcZQRMz0DtAQ7gurSfTbes4slFQUI4m1JE7UmPZa6AAp2reY5AoEteXFyjLphOFrFCk0TUOb5yDZ9q0Z4vSqQqJPK3WQBt95a8aE6wKdpTZu7KzzZEFLjolJt3GLHNIhHvXruDo0mhTL4J8vXIHWtBZOCmW1hvwX6x38I9r5OvRBP7S9rA8BbKAv2quSBFWqfxcrzpiSKjcAcyiU0EdNcviTFfMfIvj6qpI+ABLgy8pZZlvCfQBMxRmhKo4M6Sx018XGHTt5vyE7hRVgSkgK0gtar90UyI1Mq/y6V98EXPxR+WsK8wqNWFemA38tAvyfuOEnprrS9UZcs0QbV1WGliGkxRccJvim15EUcBCLOsRooQeNt1UEG1tW2a85750KS3kGtm25El6bxMo58YgqNyXxYCkrWBtl6x373aaT4SpnNFBnr9OaJutKYrjFfz+tkJYv8ocu0bWGRTYdXmdoO3YNo0mx4rpGJJ18sYTgEKnAtn51FscdkOVsmmEgYZLYwgcNyNnAgh5NHFrDzFChBhiGv3M8X9EUSbFXX301Dj30UJxxxhnYb7/98O1vfxvvfe97sXYtKV/2i1/8Arfffjve9a53AQBe97rX4fHHH8cVV1yBW265xe/nsssuQyaTwdVXX41arYaDDjoIL7/8Mv7zP/8T1157LSfzmY8wNB37ZrpgiEHIVPrQAyaet1PnKhyvTasGiIG+ofkBJ1vnnZao5OrHMwdOharc1FyfuQ/suu+UueBaUpqLXrNScUPxYMzL3GdlJYZOHJ54dHbcq3KTiAuO28/+5/tOJkglGsqaU8gCdyBg18UHgLasiZ3D1VBJNADIl4SAnjpd4ZNmUwbgauiqtnNzgzpu0VGm/HwH3p6I67Brmue0gtfef/5yxH7oOUfmPa0tMcbBCc7Q5ll+zh6aTBLn6durCD0UCAdO+fZalVSpCQXuFYRAk1/Fa0bZLYndPylWKCMRT8grziST8sCdVreJsosBfVoS0NOx16q8TddhuC4OfnU77zeiSkLSvkU7lQ2JYzz3IqDvFYSQSodtCvMSjVhXpnVdPVzlzGQDbS54R6gOvWnIA39bqCpj0sMRwVzHP9VcQmxJHhLEc05Mk6x/YpAfqjTjFVuImfzvJmbpGBjlyQq/LLPQZ5hk0kMPHXylGSZYThoYGuXrtmdT4eC9xQu+2bWcrivJ0VZ/btC+C8LalEyE5w67Qyy2Y22UoRdPAnZAS1QGWLoohaM708AAs3YnLMQhkDy0/im7TrA1Uf0nSM/PCWseKaEk+lKDO3RxPqIpGPrly5fjr3/9KzZt2gTDMHDHHXfgK1/5iv/6YYcdxlW7Of7443HxxRdjYGCAqzVve5Pj7rvvxnve8x4AwD777IPf/e53uPbaa2f3Q00DVcfGV7b8H6qOPXnjCOhcWbAA3LHVMuYeIqnKluji7ZUq7xDpjpfoPE1WcsP0EbOCQJ9n9IOyleJBG+WqJOj26rOLrDhhQsJ1eGu2S2oQcwdueNVphM6zSQOj+XCteFquUmS72rPyZ+cg0Je+zIEG4mWBuU8ldWi6g792/J2bG2mfoQ8vJjIcuk8LcX42vwAceWA7keGwVBodtF/TlOnIZ+hFJ8kG40J78dRWgGHu6+jDP0BKOMwpiqGPx+XBNQ3SQ3baXmDcXTfcPpkC8mNUMxDY/cBdZNwjAnr/4CYxoPfU5uKkiTj1tKrruP7Qg3m/QQN6cUeDXjMu9NUTIW9cugdw0BHy1xSaAo1YV6YDGmgDjKrB+1esL2/Q0pHMb4TaQueP2C43rf3T0Zn1hAaUYi14vz49A19KEwry5XXk2c9DWXdx/fRLLQusfdSOssjQA/w6xZa1ZEF3e9k+/eCds9GDnAJbNm1A0x0MrH0+NDfEg6TEww4BWjmND95T8XBAT/+fno5O4eoGejtNbqDZjIUVPRbv+3W6ZrFrkxlm46nkhv0y6BcgrHnSgwcVQ9849PT04EMf+pD0teOOO447vfaaa66ZtL+TTz4ZJ598csPGNxswNB3nLVq7iww9yVoXNXCUuScxG+s0PWbElWxNUsmNEHSLT9qBnp0P8CzqkEMKDE9yA3FLM9DQs2OxTA35kg0xHkzGyTHVmqAKsCzN18VTUOcjHomdTZHAXQyKs2mT7DgIXwUN0MXYjLLrIlueToS3P6NAHbhYqzidMOA6GtaN78HNDbrlGQroTfm1PnbBMmg3xD0Wg3WOtGqOcI91g2jixVMNDInjBMhkqkgOPzIkzpPaRebetIhTdYQx0rYxMdA3vYcF4ZrxBDA0IGfoqxMw9NxBUTFy3fwYH3Sn0p72XTxAqTU8DoBhxYX2NOlenGSenj3Uv1jH3YPhOLjkz4/BOPbccFvxxFX/YUFYGtasBz7/LWn/Cs2NRqwr07quHpzgSqHrGnRNXl++JpxJ4edIie2EdSk41TxYTzRN48pM+mOirD/7M7c0jBUcxGPBb0Iqw7E0lATfHLO8g64cfmfVsvTQwdMxM0w0BZKZwJb2zxdhxmjKvzt/V5cNimndd4mNJa5S3rqSfqEXxmv5/sXdEhlJRLX6vLxGvg4C4YeC1pYYWpfFgTzDlFPZTNSOsG+jbDqzNumsjWkHyNc88RwN3QhfZ56haQJ6BTLdWq1YSFoyFXCsCGs35Yc8WcyhUCKTURMCd2oXHV2UnTL0jhu207GIh0XJTm2NWRqGx8KVkJJxIosRY+Vga5T/PLoeluK0ZkngHhccFnWAYiAexbjLTmcFgpPzxPa6Hk0GlIU8AsrcG1V+bvjJRkIAf+prOqBLHKquax4LIST+UJ11KLieIHAXHSdt7+vfxWBc2N5k7WLfQFD/3e/bYg6WElj0miSgjyWCGu/cGL2kWPFXxiauUmga0bOPDPErbsoLikXGk2rfRVbcP+RJ1K1PcviTaI8I6DUAHWOj/Cein1s85TUWB447HejqCXck6u0VFgQasa5MB4ahEXIJ4oYd8f/s9I7Sy5ercl09wLDkpiaVcFLmXXwgqFYlevlqeI2q1Hg5J0lMFa9NXq/Uwjp4CJ87ZmneOXqBla4bbKDtrzGC75KRaXQNYJsGwXtgCw58Yj6jt0bYRXPSuUEJMRY0yDeYtYbuDsRjfPvbP7EXVi4RqgbKAmidnuDK2Oh6E1qDhLWJlXz6DL2X/C+TduZr/E2SyXDmGZpCcqNAUHUc/Os//ozqLmz7sA4vxID4zD3TnurcxUMyIxh66uhCcZKE0bCYhwJRK1+rOV4SE9PeDBw4t9XpJcWKLiUZJ8dUhwN9gwT6XGym+bIYWYKqWCI1K9m2pNek/bGgjj+UayhJWgIQeoBgUa7w379l6tAMF3/p+hs3N+jYs2k+EOtotfCWkyQBGxDtRAHJtiYjrQnZZYE+E7iLLHq9GnqqeRQdK3cirMDoyzT08bg8oI8ngFIBoafAKLmMTBYTEVj7gbh4f2n7qMBdvGZkQB+uxgUQyc3HTzlF7jfoaawUmgac8eZw8qvCgkUj1pXpgD2FnN8V1kKSG9lprQbDxgft4K8pwWmtZE1yRH7Bl/EEtrhF1xKe7BEDZVLlJqxjD3K8NK8d+bdcER8SgkozFC0TFEhgr0NZ95KwDmQkshsaaHOno9KiCMyA4pKAHgA0w0XhwOcnnRtxieTG9IbD9plJGXjtAa3oaOH9y17LUlzgT94Yln8GOnhNaGeH2zmCHNSQrG2aRtYUsRCCvzvMtGXJoXkKFdA3ESxdx5V7HgqrHsF1BPwSXqJzo8dwizKXSarZAALrYYQTdvx+BLtlkb5tQWodSG7EgF73TsgT+rbCTA1AtgzHCnYo7ulsNUmgL4yxq42WiwxsGS+xR0xQlW1bAoyjFgleylaIVXH8EmJi+2hORNTQA4Bra9jw6npubmRTJr7zqXX+Z6gL1Omxl2d1hmLFGRq4i304XvKRzEkCfD/+VmjoKTCQy7Bj0T2mJJKhF8boa+jZxNUkUMyHg+WWNo+1FsbS2u71Idj9gL4OfpM+jIiJVX5AH1FZJoqJr5OhtxwHN/3w3rDfuOpLwDGnTj5uhQWNRqwr00FUfhZNgA3p5YXg3dDDunpWlkl/wpYZIe30GW2GHPLKRIrV2ESSip44zmvomR1kz6brGixTC1WqSfqFCQJbu0SuKWPYKXM+LlSakfl5KsVh1x3KjvMMveaPl4Vra8BDe006N8RdYICVnzLfo67hU5eskD4AhKBHBO/iDjItexx6r7DW0Hai1smU7BD75BP7pcfk5NA8ggromwgugG3l8VD8NBUYBsmadxx5ua9QxRmvtGQoTqLHYQNCjEccnRjgmLT6jSDbAQijogsOtOZJccQSkpWaEwqiW9ImhmgdWgadrRaGx2ohtpwG7mF2PRykR9V/pZUCogLxEEPvH5ktMvcR9gkYelnpzmzKQGdvJTQ3lvbIEyUjIQuuNc3bbhTZEjOQ1oQcZA2hrGbWccocanhbx2NoxGouRjgRlbYVt5JSGWBsxLsm00drOzA+Gn7yigrcfbY8KqCvI7im7xXLblJmfarSmtA15ay6c/CRePrwY8N+o6M7vGWksNuhEevKdMAVYmDtPrnE22S6+qrtCMRssC7Rn7blVU0LEURGeDc5ZpL8Ko54opIboSKNWLYy7lVEA8CN05e4MMbOVktsFhROYPv0mfPwOuSEknfDbfwEWob9lrHx1Ba6jAbU0sVJ54ZslTxgTQbf+dS6Sd45AQxZ8C7bQdbDgT9X5UYLbIBEniPL1ZLYaMGEeQwV0DcRbNfBfTs3w3anvzXq14SXVRGQaOhNg1SncRF2pjVZgqqkji+1h+v2MvpCtq3HqMgkN5VKmIlfsTiBgZFayN7d7jHugp1u98lqyAP1ka3USZeEbP+YnwjEtw8kNxGMvjCWepJkWXz/83vjSX3bLs0Nb4Bhht63C4E+ySALs+I6y9CzfUyklY+Q3FQrkrFYYfbEJCf+hZz1ol5gdNj7g7G3dngmoXN6em1oGyiCFafVYsTvKyHoQVmIbFJU35ShD90Xb3KFAnj5nLHPuxh39y7e9bmhsCDRiHVlOqDkku24glvxDicMuRpJbXoxL4sWcUDQJQ2+RdmkTAYaj4V3gX02nt0Q9MbOsekZw5f18FVlwoF6SiLNTEt2fWkBA1m+slh+2hIlK2B2hoUHD/HaVEMv7oJomgt9zQ5ubsjWx9OP6sRR+/PyPU3Tpk4osdAlZ47IKtrINPTsGsS2A8JtZcE71ctzgcn8Z+hVllMTwdINXLpsP1ji1vwUQA95EB1h5Lakofslv0JMSISGXkw08tuLGkhvLOQgKt5RV6ouTFMLbUn2j4SZ+IBx51/wWRCRbI3Y7qNOTSxRqWnhPEYamFdq8oBeDMgDu7wf0R61w3n5uUuQ6wizv42YG/6FZfIX6UEb3smvImTsCLVTBz2Z8+SuKTL0Es1jLE4c7uiwwNAz9dBZexQTH1VvPSq4niJbDiCc7TyZ5l583WecBPd96jnAM0+EumnY3FBYkJir+UF9ni3q5SNqwYcOljIiWHtBcmNSgiguykM9kom5dswiteDDAT3fjurV2Xas5IU/MTVcOjJGWXLGbfjBO8LtxPUHgH/KLoWs0AFl7VkiKSG5Nm0nPiS4jo7KH5fD2hR8to4WEwMjvN8/aF0WB63Lhge5K5Ay9CZQLSIs2ZRVuRFLVDIBfagog0RDH5KYKoZeoYGwXQd/Hu1rCENPnsSDyWp4ZcFEv2F5lWggSnHYOvSchtHb8hTjHqpXZOyapgXsB9M21xnDeNFG/3CVc8DLFyVQrbkhyQmtFCO6s0TEdiV1nJE6d8EuK7FFISZL+dIa4T1Rdvq3+DASxdCfdVw3jhSYEKAxc8MbkLw0l8/QCzYZQ8+VEROYEFkJSdmJsL5d4kA7c8DLWwRGRgdyS7wHg9BTU/iasYggmiaD1hu4R7UXg20WUQy9eN/901mFvqKe9vZcA5xydsjcsLmhsCAxV/OD+rjIQJ2BrKqaH+SDf6+4LtFdY3HHlx4OxUlc/FrygXFZLh7SwC/PJfyxU2ia5lccYyHTwSckOVJ0nZEF+aIESbw2+/5J+7SC+8KOHSBSXA6aC33xCDc3vvrRvfDv164NXavhoIG66OcjE2VZG12DXP69miaXjornoMhkoE3A0KuAvongusCW4qj0ab1eaBopzyh1mDbZlgwxI5IEVcp6iLBMWvaLdy5Ucy/GPdWai79tLnC/pURMx7Jc3BtvYE9HJHcmI/R/lHWItotBN2Xo+fYTBfRR9XijNPGiPaJ8MD558R644d17Rl5XRCPmBoCAmRBvmi47aIMe3iGslP7BUhLJjS0L6I2wkwZI3/94LjyW3j28MQn2qAOaZOw6DcRFtpwGz6FJE6Fzp/bQQ8REAb1wTdqnGOhThl78Uvc+AHjjBdH9C2jY3FBYkJir+UHJDNvhfbEsKVaXlK00dISTYiVSUFp8wRE+oOWVcGYJn6GxWqjAQq8nG2E9Ag3cqwK51JIO/+5pRTS2h5iEbKISVJEgA8LEESDZNZaQQKa/Axxm6ENVZRB+cDBNDa1LS9y1cp0xLPMeaGYUNAGWs8mCfJnkRmcOlpqEPPKDd/DtxOu4LvCXPzTik80YVEDfRDB1HWfl9oK5i9UIaHUBMXC3Be0htUtrxRs0QVUYo2QbEwCe21rEUy8WQva9liW9evO8PSj/OPnniZLQUGcYisEmCfRFRt+UJBtRiA5dVo+XHWMUQy9i5dIkDt/QEnldEY2aG1InCjBJsUIgDoQdJMuOcHV8JZpIACgWSOAuzo5VXkJVVIJqqCpOBFsuK8HoV5yx5W2j+oiyi/d9orKPUYeThJJlPeZeXLkTSeCoE6P7F9CwuaGwIDFX84M9FZaFX72GsZkRrL0sF0yUgrK70uzP95mXilw7AHjtAWT3k+2T7gBDeMAAEFTUYcYkIiEhnCg7LurySd+BjQb34meXQZRtAsH6Ip6sDsiTaEXJzf/cuC8+uP/ec+M7pCWUJZJQWSlLjrWXkUdiQC/0uXVzeDx77z+9zzGLUB6+iVBzHDzQ/w/UdrFesOyQJy4plpXWmF6gLybLeuyIGFOVKg42v1IK2Zd2U8adf2GflSmpPci6nzyip45QbBs4LDFAnyKjPwXJDWVexOPDqeZSdPgnHtqOq965PLL/etGouSEN3Fk7i6gDp1gNvdjHq1vJ/7PtlywP2wAgt1Ruj9K5+8G1uLsgcXN+KU7JVi1Qv7QmaiwTVY8RpUsU4qmtmgac9y5g2crovupAw+aGwoLEXM0PelprqKKNTC8vY+gjNPTizjEtPDAybnPv32tZOHG9vUVWJjJcFYZCvJbsZ29JtPHmBDYZ015XQC9Zp2iQzwb7UVLUjhYTB++d4Wy6AfxsYMvc+A6/vrxkbWGHrkt2eLk8LtZuhv3v1s3ead9Mw/UHkn/FPK15XhVMBfRNBA1A2rAialnUD1oWjO3HYqQ1siQjIrnROPv/PTMeGssfnxyD0AUA4M3eQUYhstWSB9cxyYEYkZ/H+42JVQysiJrwUZKbgNGfAkMvYZcAkNKdDGjClMjgJxMGjj+kPbL/etGouSE9WhuQS3G4JCOhj9GhcKDPtWPs+xzgmaYYuMuSZcW+gWjNuWzsUaehUnuU5GYqDH3U4SSyRfPQo3d5EWnY3FBYkJjL+eGz7EKgHpJ+GDR5ll+DQsSUrvn12f1cSAnjDQBHeDugXMxGNZCcTU70AAhKN3uQlZeUMe+UJBKTdKOuUx9DL7t2uIoaXVtFvuW/P7ce++yZ5mxz6jtkeVuy9Ul62JTJVFpjbnxhnOwIszeZHq7H2pau8P6HDYYscp15TIyogL6JYOg6Xtu+FMYubn/5p7wKgbtD69MzbS1Dw+ZXShDzpbbuKMNxw1t0H3v7MgCSAD3CKdLtP9FhRAX6MmfnH1EtFg+JKPkV85kQeT+hB4CJNPQSbSMQrhWfTup45+sWhU7IaxQaNTekTDy1i86Vfhljo+EtTAAY2MG3X7OBeS9j94Nf8cZH2WNSc+QDwES7PFGSG7Hix1SlOBNp6GWSmzddDGx6ffR7dgENmxsKCxJzOT9o8M6RqDpQc8JBvkieDI/V8Gp/hV+vGMKEBu+apuHoA8NSGn9nl32/z5xrIZsMYUIn3MaUBO+m5MHBjCCUgOkH9JakAIRlath/rzQWdUUUBuD6nEPfEZkUG8HQszAMcv5IRVL2WMSmM8m/IsMv2ui6FrXDOg+gPHwToerY+I9X/oaqTOM8BRiGbJuT/FsoOUJsRv54daAi1RqK6Iw4tCnKKUYdxGREsBUTZfKLGfpRmvgoJj7qmlE6dwChRCuKcEyp4W2n5ibsa1fQqLkROFHBbtvA8JA8OP7Hs/zfWWZuiNulMv17JCseFSxTZxvB0It2WRFnCvF++Ux8vbsCEWOc4jkCOOwYctDTDKBhc0NhQWIu54dUSqOHmfc/PTWGoVH+rJG+QVJxhCVMl3TFg4OcBMIqZJPs1AbrUdDOmuDUbpGslUlETUkulyUhj6hNtkTUE9CLZ59EjUvTNHzpQ6t96c1EmFPfEamNF/TurkOCd7YCDberybTdeJRnYpMXJDu7hqQ4AvX1UTus8wAqoG8iGJqOQ1sXwZgoQKkDtk3YDXH7EgAe+dsYF8ux85n9/w2rea0dRcBm814pJjnMgr1uKHDX5cG4LCD2S24JPi86+VVeE15WEUD2NwtZPH/S4e2RDzwzhUbNDRgGMDKIUETf9zIJ3KPYblFrGCWjocy3tOKMyLJEVJyR9TFR+4nuSZSGXpxMUdVv6MIhSmyi7tPZ7wDOeWf0eGYADZsbCgsSczk/HMdFucqXifzb5gJe2l7m1qetfV5+CfOzeufrFgEInwVCK9DwrL8koJ+AHJIx+eIOsAwyIjuQ0rAM/QQ2GUMfsRPMQrY2btwniwvPyE363sg+59J3aBpQLvJfxtZ/AH2v8F9knsh8uUC7rZPvhyLpSYpEhl9s5z8QCLsDui7fwZ4nUB6+iaBrGtamO6Q6valgeLyGP/99PNK5sQ5l5dIgcaiey5qSLT5AnhgEBI42fBCV/JoTEdxuiKGP0NDTmvCiVj7i4WKizy1j6K+4YDlW9U5wUugMoFFzA39/HHjxmQk+tGBf1OuZxZs8SbKolPmQnApYbx+svZ6kWIooyU29p7nSMYgSm6j795oTgCOOjx7PDKBhc0NhQWIu50e+5GDHYPiwQACcq9l3VUo0obud/CbFwgSmhMgxJESTbEfWXweFHQNAUqNdAplcZiKGntsJ8GziOgaE1xkZxySrVZ9JGrjgtEWTjjsKc+o7/vqId8o3c+0dr4TbdUkeWHQd2Gt92C5bO2Q5SjLJDeBp+JWGXqEBqNg2vvSPP6MSVfZuiuCZeE1qNw0NB63LePbJf9SRjHuUnCUqZpMwKoDcYVKI/syX+dQrudHl9qhL7rUsGTrueq7QsLkx7rEdkfG88MKBh8vtPqNTB7vu12EXHGVU4B4V6OsSVkXWjoUYuPsZ1uJYJpHziIvC6n0mfpCYRTTabygsLMyH+SH7ibKmj7xVnpsFhOUodJ3iattLXIksqDYmYOjriePq19CHiSzL28WWyWtCvJHkPpx3Us8usfEyzIe5wX1B1C+zttX7yN8n21WVrR0yW5S/Nwx5Wed5gvmx4ijUBVPX8Y6l6xtXE1ZwCpsObZfao4JrGXznF6pYGBHQS+rk8teU22UIV7mRMx60kkEk2SpcI6p05u2fWIOPXbDrJScbgYbPDRHnXya3RwXRUQzHRBVbxCCafhbxM0VJbqJkMVNh6H27mGFt8dfw+5Y/NOKYU4Ebvx193VnEjM8NhabGfJgf7K/njKOIXEKmd5dBXGtkS4Ss4IHslG7ZWkfXJrsOhl6qoZdUuZERXPRhoCZ5cBADehljvmZ5apfYeBnmw9zgJseF75cYIzARecRJaepk7en7FUOv0AhoACxNn7ESUou9rPdQDBYRRMviXBpEh+rzRjwUREtuouzSoQAI/878sUQky4YDd3m/9JCr+YyGzY31BzE9Mmjt8MxRbDnk9qmUkAydoDqJtKZebf1UGPooe2Ty6/yuSwzMvN9QaG7Mt/lx6PosgAg2XdJeJGxkcm/f17MPCZI+jagHdNQpuZEM0JAw9HQN4qSu3usy6Ywoubn83CV426mNZeNlmG9zwz+bRCR/ZJhy8C6xyfy9YugVGoGq4+C2rX9BdRefEK+7ZAWA+pUMUXIZqfOK0BsGhK3AuEccCmVItIzkmhMxNfJT+0QHGXWCbBQTf+VFe+BrH98r8rrzAY2aG3jLJd7/iJTQJHr2eqQ1bD8yhAJ6ek2RoY94WIiqfjPRNaPOuxfHQgN6sf1e64GLPxLd/zxAw+aGwoLEXM6PN20ilZ3kbHxg1CcItMVf8ES14NlXpEqLiI1FILwTIIP8cCcJQ09JJUl7WUAvxq+vP7rLTwqeScyp77j04+Rf7rRx7wuqJ6imXywbqEglNzImfwKGvp7s6DnCBMWSFeYbYoaBK/Y8FLFdPGhG5twAwNCigmt4dr69rmuhbUj/AE5RciNhSIidf1/ILlx0KvGgX/0mgrmvl6HvbrPQ3TYz9eMbhUbNDV87KEpRom7OZIH7VBjtkORmEiY+tH0zjSo3UYjS1oswDGDvA6be/yyiYXNDYUFiLudHcDJr8JuVEUhR7gSIfiZnIZNwytakieSl9UhuZLvZsrH7JSol7cXLrFyawGv2a5n02jOBOfUdyVTYRn1/PQ8Ysjwu2VogCyq0iAn37iuBjq7Jrz1HUAx9E8FxXbxYGI6sfV4vggC9vmA5eACYPAAOgugIyY3QXp9EKx+luZchyuGK94uW0BR1+yuXJtCsaNTciHSYkzL0Yj+TSXEkCIlhJ6tyI0lYkrWfjv4z/HRI/m2fv848Cg2bGwoLEnM5P3Qt7OepW+aIVU2+fgASfblMciNh3mVrUtQ6BciZcxGyNZH2yQbvZgSpJOvjG1etxTtft3jSa88E5tR30BvB3hD6//UMx38/Y5PlZUkr30SsGbklQcWzeQgV0DcRbNfBr4a2wa5HPzYBoirF6BFBtB7Brk9UcSZcsCQiQJ9MQx9i7uu/ZpRdpl8EgH32TOPB2/aP7H8+o1FzI3CYdaySwOQseqjizBQOeZqM5Q9NpgYy9LIF7F//A2jvDNvnORo2NxQWJOZyfsjcii4JxCbmAcSSjhLZy0QVbTilBV13JmfOZZAy9JK41DLkJ6EDQCY1f3bS5tR3SCcHtQlfhpxd9P5l+pH2OYHkpslkikpy00SwdAMXLd0X1i4m4kXFxHoEOxEZdEfH1iGG3ozSykfFbD7BW7/kJuo0PTE2owG9NdEHaDI0am5EO8zJvqgp2mUQv6ipMu6T1aefCmLxqb9nDuC6Lmzbhj1BWTnXdXFBbh2cag3l2vxN6JqPsCwL+gKvDtQw3zENyBh6TeL76boh8/Ci25Cy5JLrBGuPJrGF+6iHoZfvDoQfEihDL/Z459VrsSw3f3zPXM4NKUNPEfrSdcCtQyYqy8uaqJRlk+1qqoC+iWA7Dv44/CpO7+6GuQuaNpkTJXZI7VHB+MQ14UXJjbxvWQUAQO6AST/R14wK6EUGJxHT8c7XLULXPNfFTwWNmhv+DY+Sv0QmotYZdE/w/UVWuRERKbmZ5DTXenHeu4DVe0/tPXOAarWKV199Ffl8fsJ2ruuiWKtidHQ0MvFbQQ5d17Fs2TKkUhI97wJBw3zHNCBVRciCb7pxKKHJ6wnodSO8uMl2pGXyGIrpM/RhQswnk4Q+VyyZX7LPuZwbkWeZSNvKbDLJjkQKOhFr32S7mk0T0D/00EO45ZZb0N/fjyOOOAJXXHEFMpmMtO1dd92FW265hbOdffbZ+NCHPjSt/uYLXAA7KoW65GMTIYpsDZyREFxPoxpgiGydhP0PndfjNQwf+jnBQ0SExw07fG1WSn7NJho1N/geGURq4qdaQnIaVW5C8p+opNhJtPX14tCjp9Z+DuC6LjZv3gzDMNDb2wvLsiKDdcd1MVwqoC2RUqfFTgGu66K/vx9bt27FXnvttWCZ+sb7jvohy6GSBfn09PJ6xiiV3Ejcl1RDP8GurexhInRtmSZeshOg68DaPZJY1DV/9djA3M6NyDUHCAfamg4ggqGXauMl8hoZQ68kN43HH//4R2zatAlXXnkl9ttvP3z+85/H73//e/z85z+Xtt+2bRtqtRq++MUv+rYlS5ZMu7/5AlPX8bqeVbt8yIMRsahHqiQkDANpH+38dlVDH6Xbn0glE7UlmogtzIWYRaPmhg/Zlib5H94+VcnNRAF9VJWb8NOhfCwTHdcdhSYNcCuVCmzbxrJly5BMJids67ouugwDpmkqhn6K6OrqwtjYGKrVKuLx+SOFaCQa7jumgIlKH8sSZethySdKTJWWqJTEe7IkUPHamiY78Cl8bVlMqGkabrtiTfiFeYa5nBsTS24i2k72fhmbKdt9jsolm+doioD+85//PM466yxcc801AICNGzdi+fLlePjhh/Ga17xG+p62tjYcddRRDetvPqDmOPjRjhfwju5uWLuw/SVjDICJkmLpGwX7hOoJeZWbaJm0OBZ6DTEgnOCakt/eTR9ehb2WL9ztcopGzQ0fUUmxkQF6QyaNvI96y1nSvw1Tbhfx8RunVwFnHqEe1tgFMFwto9M0588BMU2C3eEBqOG+YwqYqJAJH3N560c91QrrDeglO9JR6xQgjyHrkfukmuBgwijM5dwIbmY9kpsJAnoWE+nlZe0VQ994PPzww/jMZz7j/93b24u1a9fi97//fWQA/uSTT+Kkk05Ca2srjj/+eLzrXe+C6R3bPpX+qtUqarWa/3exWARAWC93tp/eXBfdsSSwi9f2563QD6uhl9rB24OH2PBYbEfs2/Xbyvt2pX0D8s8qs+la2L7vqnRk+wWFBs0NwPueHYdfrTSSouaKq1iUXSenC7re2IL2emge0WtCtNO+HVfetyb07Xpj13XB7kqvia6c/3qzgX6Wev2QSZMKm/CzziWmep+bEg30HVOFLvHzGhM6izYnYoyTrUvscy+1BzY3ZJNdxxXWNJnPov/L2jasTuE7n1rbnPNnDudGsLaA89G+L5etFTIbmPdrknUpYq2SroNzhHrv/ZwF9N/5zndwxx13RL6+adMmfOpTnwIA9Pf3o6uLr//c3d2N/v5+6Xvf8pa34PDDD4fjOHjuuedwww034MEHH8QPfvCDKfd3ww034LrrrgvZ+/r6Jt3qbjRc18VaN4n+nTt3iTkaHqwCAAqFAvr6+nz7+Dh5WBkdGUFfX9m3l0oFAMDAQD+0GvOU7tElbB8UtZrD2as117tGnrOPjFTImIaH0NcXJPflx8k1d+7cwZWXrFWrkdfMpjSpfXdAo+YGACwCcWQ7mHupjwyhB+SBdpSxx0ZH0QFgZHQUJcaezheQBdA/MADbDVbT9loNcYS/P3rmocxeyOcxxtiN4SF0AxgaGkaFHcvwMDoA9PX3czKbFsNEStJ3M6NWq8G2bdRqNRiTMGeu6yIBHbVqdbdgnBsJep/7+/t9QmihoZG+Y6oYGyNrzsjwCPr6SgCAoSHi48fHx9HXxwcypVJJ+jtmbbI1YtxbT/p37kSlQPzRsHed4aFg7aGB0+joGPr6eHa2WqtNem1K+IntDADN6H7mcm7oo2TNKRSKnP9fBMCuVdHP2Ho8Moe97y3FEvH7O3b6UoDE2BjaAAwMDqJmEAmdMTSIbgDDoyMoC9cpFvLcejdXoPNqMsyZhzruuOOwcuXKyNe7u7v9/4/H46EPVCgUIjWNS5cuxdKlSwEARx99NA466CAcdNBBeOGFF7Bq1aop9XfVVVfhiiuu8P8uFovo7OxELpeb9YC+YtfwrZefwkf2Wo2YKCuYAsYqRQCDSKdTyOWC5ND2tiEAo2hva0Uu1+bbs1kXQAHd3V3oaQ+SeCxrEIDD9UHQB2gaZycJqzuQTKc5+1CxAGAInZ0dyOXSzDX7AYxh8aIcp9W3rFEAtdA1v31tG5JxHR2tC6dyzVTQqLlBoWng73Gc3NdkOo0kax/ZCQBobWtDK2tvbQVANMjoZuwJUsUhPGcQaU8lE0ix9hhxzu2dXQBrH9xO+li0mN8+PfedcE88M/KazYhyuYyxsTGYpjlpoOnCxUC1hFwiHioDO5vYtGkTPvKRj+DUU0+dszFMFbZtwzAMdHV1LVgNfaN9x1TQRtec9jbkcuQ01HyNrE8tLVnkct1M6z7EYnHhd0yCLdYWj48DqApr2yCAMfT0dKM1Y3LX6ehoRy7HFsTYgUwmfG3dMLk+dX0HbMflbKcelcD2ob4F42vmcm7QNSeVTvP+H4BhGPw99pIfOJtXmSqXywXa3lfaAQCdXczaYZL3trW28esJgGQ8zq93c4R5H9AvW7YMy5Ytq6vtmjVr8Mwzz/h/12o1vPjii1izpr6kEvrgsHPnTqxatWpK/VmWBcsKB4maps36E6upGzi2oxembuzStQ2mDKXsKGxNl9t1XRcy9YN+RDgOIKvvC1ewMzV6ub6pHFo4sY22Ea/Zm5tf5b5mG42aGxSaC2kGmaZpU7PrulSvGDVGmV1zXb4Pk/weNdOU9y3qylMZ8t8CAvs7mPT7doGsaUHD7PssFt/5znfQ3t4+a2M49thjceWVV+Kkk06adh9Tus9Nikb7jqnA0MP31/B9RNgfuG6Ej5hkXTIlax5dW8S1ByD5WOFru/JqPIztyP3bcOT+bRN95KbCXM6NyLUFkvVJk6wr7Hrg51fRPiU2ca2CZO2ZI9R775siW+PNb34zvvWtb/mSmH/7t3+Dbds+0/Pd734X5557rt/+lltuQaFAttgcx8HnPvc5dHR0YMOGDXX1N1+haxpWptp2ufRcZB16v+IM/0JUycmJxiErFSmz00OexL6iKujEd4OKNdNBo+aGj6hsr9CkmWLZyukwxKGjfmPyvueB452viOtzLxdZunTprNZz37Ztm78OKESj4b5jCvDdivScHwlRNM0qN/7axlwnIKrCb5DWuxfc0DyQVs845nJuTOzP68hGlr02UZWbBbB+NEV09MEPfhAHHHAAVq1ahb333hsf//jH8e1vfxvt7WT7ZOvWrXjkkUf89rquY9WqVTjwwAOxdOlS/OAHP8Ddd9+NdDpdV3/zFRXbxuc3/wmVCU6FrAeRZ/5MYg+Vlpxg/rsRlWvFcmCmz5Lw7aLqAX/8Hctx68f2ir7wbopGzQ0focoyUVVuogL9iMk0HacZCugtuX31PsA/fXjq/S9wuAC2l/NzU0uawQknnID777/f//uoo47Ct7/9bbztbW/Dhg0bcMYZZ+Dpp5/mXv/Wt76F8847Dxs2bMB5552Hl156yX99/fr1eOKJJ/y/n332WaxYsQIAcOGFF2LLli245JJLsGLFClx44YUz/wGbFA33HVOA7HRyf1dE0r6e5EDZuuS7Kbaijcc5yA6Dkl1FtH3o/F5cdvYSScuFg7mcGxOXrRQDetn7ZcH7BDXnZWiyp7a5p23qQDwexz333IPNmzdjYGAA++yzD8f0vO1tb8OmTZv8v9/znvfg4osvxtNPP43W1lYsX76cK+82WX/zFZau4z3LDoC1i6X2gm1k3u7Xmxe6952uePjTNH4HUQy9OBYjou/uNgvdC+iE10ahUXPDR1TZShGR5SkneQCYChxhMaFaTvGhwzCAfQ6cev8LCK7rolJ1Q7YWxFGpOA3fNo9Z9UtRXn75Ze5U223btuHqq6/GLbfcgk984hP4/Oc/j7e+9a147LHH/Nc/8pGP4LbbbsOVV16JL3/5yzjuuOPw9NNPIxaLYcuWLSiXg+T9SqWCLVu2AAC+8IUv4Ne//rUvuZntfKdmQsN9xxQgi9miTiwH6mXow2802FJt1Bax3gHy9Usko04+vGPywTQ55nJuTK1spey0V+lWTbjLqJMzmxBNEdBT7Lnnnthzzz1D9t7eXvT29nK2WCyG/ffff1r9zVe4AKqus8tMW3BqnihzmWL7KUhuouy+tjE0lub/cc0mGjU3+B4ZUIdZ76mtfpWZBgT0do3/m/YxF6zRPEel6uK0Dz4xecMG4b6bNiAem/5v9ROf+ATOPPNMAMAnP/lJrF27FqVSCQkvefr9738/3vzmNwMA7rjjDvT29uK+++7DG97whgn7zeVyME0TPT09PmuvIEfjfUf98OWfjJ/YVQWENI4Lx/PQaf6WVNoTvhvmRKcaLlDM5dyYmDmf5oEE/uRiNV4RBxhG2eYxmiqg391Rcxz8+8tP4dOLe/3Eoekg6sHXmExDXyeLDkT/DkRHSRl6sfLeQWszOHR9NvoCChwaNTd8iFSYHhXQT8bQC/1OZ5WWzaVVewO9K6be1wJHzNJw300bOJvjuni1MILFqdaGa2Fj1q71t3z5cv//s9ksXNfF+Pi4H9Dvu+++wbViMey111548cUXd+maCjwa7jumAJn7CIJ8CabJ0Mt2pY0JHhzEePHD5/dij8W7X+GFuZwbQdAtC7Sn8n4Gss9AJ4AK6BVmEzHDwIdXHIzYLp7YJtMtEjv5NyzFibBPwKLXexBCVFJsd3sMn708uqypAo9GzQ0fUYF7vSfI+lpFUac1nQBQMpfec+U0+ln40DQtxJi7rotlehamqTdccjPTePXVV7m/t2/f7pemi8ViqFQq/msjIyNc22b7rHOFhvuOKUAWaMuqx0ytz+jXWL08/X+Zhl7E6Ud1TmsszY65nBsTBtr1JMVOyNCzT3YTfbbmCuibIilWgcBxXTyTH5RuB04FUYG4HsHER1W/mchxRmkdoyQ3CruGRs0NH6GkoykG9L4usTHDUdg1lETZUpPgq1/9Kl555RUApBrZzp07ceKJJwIA1q1bh3vvvRcAkM/n8YUvfIF7b1dXF7Zu3Tqr421GNNx3TAGyjTxdpo/xUBcxO0FsJ6tyI7sOJZp2d8zl3IgkkQDJRKg3oJclxU4guWkyqIC+iWC7Dv40sh12PfqxCaD7zq0+aU10IZOpO72ogD6hylHuEho1NyIxVYY+Stc1USZ1FNq7Jm+jEAkXQN6uNhnXRLBp0yZs3LgRHR0duOqqq/Bf//Vf6OnpAQDceOON+I//+A90dnZi9erVWLduHffe973vfbjyyiuxbNkyVeVmAsy475gAst1ff32SBfR1TGIZ465J1jwjQtFx+lEdOOGQ9skvtBtgLufGhIxhXVVuJgjyZVtC9VxnnkNJbpoIlm7g7Uv2gaXvquSG/BtVaVDc6oyqijNROO9EUPTik76ua7jrM/uoyjW7iEbNjQBRW5oRzH0oISOicsBUnwE/dcuCOxRqtqEB6Iwl53yz5MEHH+RKAz/00EPo7AykDN3d3di8eTM6OoLqIaeddhpuu+02DAwMcG0B4Mgjj8Qrr7yC/v5+dHd3o1qt4t3vfrf/+vnnn49zzz0XfX190sMBFQga7zvqhyZ58Nci0m+A+qScE8VnbKxPSyOLJZY/fH59B17uDpjLuTGlOvTS90vII0NSrCFqbWtCKFq0iWA7Dn479DJssf72FOGXpxRjsMhylpDaJ3ywjbJLXlDB/K6jUXPDR3RWM//3ZFVuxCSkqTL02dZJNI4K9WCsVpm80QxDPFiqt7eXKydpGAZWrFjBlRimEIN5Ck3T0N3dDYCc6i1WtLEsC729vfyR8AocGu47pgBZYqoetSVcJybaOJadgK4QjbmcG4HMU3JtcXmSrStShn6iTOgpjW5eQgX0TYRGbZ1PVphE9HNRSUrT2alS2sSZwezJKqYouZmL+sUKHFwATuRRbwq7O+ZSkiU7RGqispV11aGvM1Cn1+lsVYRSFOZUrucnxcpenGZS7ESljURkWoD1B03ebh5BSW6aCKau45SuFTAbdLBUODaT65792Cz0G5ioyk3YtjwXx1H7t9Y9ToX60ai54aPegwSing59JqTOKjdHnwyYamGdCWgAWs34nEtupgpRkqMwM2i475gCZME7XZ/kVQSnd1KsDJqm4cHbJj6rZnfHXM6NIKCXMfTTDOgnZOiFPq+7dfIxzjMo+qyJUHMc/H99z6G2i9tf9CE1KjYLM/RyDznVHdFvXbsO++2l9NAzgUbNjUkR5UijtPIhyU3EpDnzbcDp5+3y8BTCcAEMVUtNx9CLkhyFmcGs+Q4JDN9/BDZ//ZEE7+IQ5XGcLCm22R5n5wfmcm74qCcxdaISlZxt4ZSolEEF9E0ETQP2SLbs8gnFVDsYkkNPoqEXq9ooFzl/0Ki5MSlE5xqZ/BohuVEL66xDAxDTDfV7VZBi1nyH7NoTlK2UVh+vg5iVsfiJXTjNeHfGXM4NH9Ktmrqe7MI2uh7J8rLizU8eKMlNE8HQdBzckoMxndJ/DHyHKfxQplq2st5qAgozj0bNjUlRb316REwa256RYSlMjLSh5EwKcsya75AgKNCgMTbybz3lxw1di6yoxuKANRl846o10x3mbou5nBs+6pkIdUtuKNEkBPQf/QywqHdaw5tPUAx9E6Hq2Pj61r+i6uxaUEQdpugHA7I1omxlnf3/89uX4dp3rZj+ABWmjEbNjUkRqaEX4KdjCK8fdzpw0hsbPiyFaLgAdlYKC2BDWWEmMGu+QwJZCs5EuZBuHcG7LP7TNA0rlzY/AzvbmMu54UOeTMH/LX3gmEAnLzL0i5ctiN1jxdA3EQxNx2nde84YQz+Z5CbM0Mt/AKcc0SG1K8wcGjU3JkVUQB9KXIqQ3Czdg/ynMGto1qRYhdnBrPkO2bUlu8L++iQJ3ut5KK2nEo5CfZjLueFjuhr6ifpaoKWQVUDfRNAA9MYzDVuYxTyXySoQTqVspcLsotFzIxKRAX2dybIKcwJrLhdkhXmNWfMdsmuHc2IDyY2kfT3ymjOP7kS1NodJnAsIczk3fNSloZe+MWxqbQfaOydJjm1eKC/fRKg6Dj6z+U+oNijjPBybhfWMQDQTb9uKCpkvaPTciETIkXouRFxo53KLVoGDC2C7ktwoRGDWfIcEus/QB2vMRGUrRTcja7PfXhlcf+meDRvj7oy5nBsBZBMhYh2aDPEEcPVNC5ZoUgx9E8HSdXxkxcGwGlQTVvyZiFVs/HYRW15nH9+NxV2xhoxFYdfQ6LkRiSiGXpw6uaVA16KZHYtCXdAA5GIpJblRkGLWfIcExgSXtGWSG/VUOquYy7nhY7oa+t1wriiGvongAhipVho2T0NVbiJkz1HXO+qAVnzsguUNGo3CrqDRc2PKEBmPbCvwiRvnZiwKIdgLIBJavXo17r333nnfZ7NhLn1HVN4WUF8cV89BUwrTx5yvK0CdAb30jTMxmnkNFdA3EWzXwfe2PwNbdnLaNBDW0EdweLvf76Lp0Oi5EYmoBXSBbmEuBLgABpvwYCmF2cGs+Q4JJjq4UyaXd4VZ/MUPrsIN71bympnCXM4NH9Nl6A87Fnj9+TMypPkKFdA3ESzdwPv3OBDWDCV0RJ0RpAKB+Y+Znhs+IhkxFdDPV2gAcvG5l9xs27YNZ599Njo7O7FkyRJcdtllGBsb81/PZDJ49NFH/b+ffPJJX099yimn4IUXXsAb3/hGaJqGU045BQCwYsUKXHfddTjssMOQzWZx2GGH4bHHHtulPnc3zJrvkMCYgAiQVrkR4sr9Vmdw+IaWRg9LwcNczg0f002KzbYCx5w6I0Oar1ABfRPBdh08Od7fOIY+omyl+PvpaVeH0sx3NHpuRCKq/9b2mb2uQn1wXaBaCf1XLBWk9l3+r07Jg+M4OPPMM2EYBp5++mn89re/xV/+8hdcdtlldb3/gQcewKpVq3DPPffAdV088MAD/mu33347br75Zmzbtg0nnngiTj31VO5BYTp97k6YNd8hQVRlNSBias31U+luhrmcGz6k2dHCeBTrCEAlxTYVHNfFE2P9OLpBukHxNxrlXDeszuD+mzc05JoKM4NGz41oSFbUz90JWCo5el6gVgU+/k+cSQMwYxxmnd/9X/7yF/zf//0f/vd//xcdHR3o6enBF7/4RRx99NH4xje+gVQqNe0hfOhDH8Lhhx8OALj++uvx7//+77j//vvxpje9adp97k6YPd8Rhl/lps5I3TJVRD+bmMu54UP2MBGqrT07Q5nvUAF9E8HSDbxl8bqGbX/JjtEGwmUrASBmqc2c+YxGz41IyKg0FczPH5gWCbIZuK6LWq0G0zQjS9Du0vXqwNatW9HR0YGOjuDQudWrV8N1XWzbtg1r1qyZ9hBWrlzp/7+u61ixYgW2bds27f52N8ya75BgKsdV3PyR1ehuU7vFs4m5nBs+pM8SwoS54P3A8MBsjGZeQ0VpTQTbcfDLwZdgN6gmbEhyIzm1T6E50Oi5EYm5LF+mMDk0jTxgCf+NapDad/m/Op3FsmXLMDQ0hKGhId/2/PPPQ9M09Pb2AgCSySSKxaL/+o4dO7g+9Ii5t3nzZv//HcfBP/7xj13uc3fCrPkOCWQnxfoQbPuuSiPXqciD2cRczg0f9ch9OrqAlWtnfizzHE3lzWzbxvj4+KTtRkZG0N/fH/qvWq0CAIrFYui1fD4/08NvCBp5rHWopLg63LOpoY48V5ivOOCAA7Dffvvh8ssvx86dO/Hiiy/iox/9KN785jf7cpsDDjgA//Zv/4axsTE8//zz+OQnP8n1sXjxYjz++OOwbf7Qsi9/+cv44x//iNHRUXzqU59CpVLBqaeeukt97m6YK99Bn6dku8IK8wNzvq7I5oaaLlI0TUB/5ZVXorW1FZ2dndiwYQP+/Oc/R7Y9++yzsW7dOv+/VatWobu7G3/6058AALfeeiuWLFnCtfn0pz89Wx9l2jB0HSd0LofRIFZJVrbyLSf1YGl3vCH9K8weGj03pDj3IuDUc2auf4UZQ4s5t8ymruv44Q9/iGKxiHXr1uHII4/Efvvth6997Wt+m5tvvhnPPPMMFi1ahHPPPRfnnXce18fVV1+N//f//h/i8ThXkebSSy/F+9//fixduhQPPPAA7rvvPmSz2V3qc3fCrPiOCExUh17FbHOPuZwbClNHU2jo77zzTtxxxx14+OGHsffee+Pqq6/G61//ejz//PNIJpOh9j//+c+5vz/72c/im9/8Jl7zmtf4tuOPP77pqhpUHRvf2/4M3t3dhZix61+dzIle/IbFu9yvwuyj0XNDisOPm5l+FWYUtA59t5mZ0yBp+fLluOeeeyJf32efffCHP/yBs73//e/3///EE0/Ec889F3rfwQcfjGuvvbahfe5OmBXfEYGoXeGPvLUXR+7fOqtjUQhjLudGAGFyvPsTQEyRjjI0xWPXN7/5TVx00UXYb7/9YFkWrr32WoyMjOCnP/3ppO91HAff+MY3cMkll4QSwvL5PJy51IZNEbqmYe90R8O2J6POkVJoPjR6bigsHGgAErqhGE8FKebSd0QdZnjakZ1ozTQF37igMS/XldX7AMtXzfUo5iXm7BdTLBYn1K3H43F/2/SJJ57A5Zdf7r+WSqWw11574cknn8Qb3vCGCa/z05/+FC+//DLe8Y53cO9//PHHsWzZMhQKBRxzzDH46le/Kq20UK1WUavVuHEDpHLEbB87rUPDhkwXdGgNubamqaOzFwoaOTeo61ZzY/6Cfjf1+qGkbnLvW0iYSV881fvcjGj0ujIVaNrCv7/NjLmcGwBZi1y4dZ93sVBR772fs4D+a1/7Gj7zmc9Evn7WWWfhjjvuAAAUCgU/uKdoaWmpK0H29ttvx9lnn43u7m7f9p73vAfvec97AAADAwN497vfjdNPPx1PPfUUYjFea3rDDTfguuuuC/Xb19cnlfvMJKq2ja9t/Qve7RwAy9j1MlJ2rYS+vr4GjExhrtHIuZHTdWiOo+bGPEatVoNt26jVajAm+b5d18WOSgE9SDW+bOUcg8plWNKlkaD3ub+/H6a5MBnjRq8rU4HjZVwODAwgri3M+9vMmMu5AQCLAJRKZYzs5msRW6lrIszZL+jDH/4wPvzhD9fVtrW1lSt3BgCDg4Noa2ub8H3btm3DT37yE/ziF7+IbNPZ2YlbbrkFuVwOTzzxBA4++GDu9auuugpXXHGF/3exWERnZydyudysB/S24+Dc6losXrRol5NUvvrRLFYsjiOZmMP6sgoNQyPnBj5+I1zXRa6zpzGDU2g4yuUyxsbGYJpmXYFmh5uAZaka3lOFbdswDANdXV2IxxembrehvmOKIMzjDqxakUNLWgX08w1zOTcAwE2mkTjgUCRyuVm/9nzCvA/op4KDDz4Yv//973HBBRcAAIaGhvDMM8/4wXexWESpVEJ7O3/8/De+8Q2sWbMGRx99NGd3XZdjqqj0R+awLcuSLoSaps0626VrGjpjSegNuPY+K9MNGpXCfEAj5wZUID/vofnVQSb/vl3XhaHp3PsU6sNU7nOzoqG+Y4rQNA0P3rb/rF5ToX7M5dwAAPzL7bN/zXmIeu99UyTFvu9978O3v/1tfP/738czzzyDyy67DGvWrMFxx5GqGzfffDMOPPBA7j22bePOO+/EpZdeGurv7LPPxj333IMtW7bg4Ycfxtvf/nYcdthh2HvvvWfl80wXVcfBjf94FNUmSuRVmB2oubF7oh5tpQugr1KQH7ioMCF2B1238h0KUVBzo7nQFAH96173Onzta1/DDTfcgBNOOAG2beP+++/3t5pTqRR3pDgAPPjgg3Bd12f1WXz2s5/F97//fRx77LF4z3veg0MPPRQ//vGPJ9WizjUsXcfH9zwElqoJqyBAzY3dC3TXsFAoTNpWA7AollJVbqYBehjhQtXPA8p3KERDzY3mgubuDhREA1EsFpFKpVAoFOZEQ/+Xl17AActXqYMeFDioubH7oa+vD4ODg8jlckilohNeXddFsVpB0ootWNnITMBxHLzyyiuwLAvLli1bsPdO+Q6FKKi5MT9Qb9y5cGmHBQjbdfA/O1/EhmV7wmiOzRWFWYKaG7sfenpIrsNk1Yhc18VIpYTWWGLBBqUzBcMwsHz58gV935TvUIiCmhvNBcXQTxFzydC7rou+vj7kcrkFvcAoTB1qbuy+cBzHl4bI4Lou+vv70dXVpebGFKBpGizLWvD3TPkOhSiouTE/oBj6BQjbdfB/oztwYk83TG1+6/0VZhdqbuy+0HV9wpKKNcfGE4VBnBhbDFNXc0OBh/IdClFQc6O5oPZQmgiO6+LF4jActamiIEDNDYUoqLmhMBHU/FCIgpobzQUV0DcRLN3A2bk1sBTLpiBAzQ2FKKi5oTAR1PxQiIKaG80FJbmZImjKQb0ndzUSVdvGj7f9HW/KZObkGGaF+Qs1NxSioOaGwkRQ80MhCmpuzA/QeHOylFeVFDtFDA4OorOzc66HoaCgoKCgoKCgsJtgYGAgdOYSCxXQTxGO42B4eBiJxOyXgCsWi+js7MTAwMCsV9hRmN9Qc0MhCmpuKEwENT8UoqDmxvyA67oolUpoa2uDPsF5AEpyM0Xouj7hE9JsIJlMqh+XghRqbihEQc0NhYmg5odCFNTcmHukUqlJ26ikWAUFBQUFBQUFBYUmhgroFRQUFBQUFBQUFJoYKqBvIpimiWuvvRamqZRSCjzU3FCIgpobChNBzQ+FKKi50VxQSbEKCgoKCgoKCgoKTQzF0CsoKCgoKCgoKCg0MVRAr6CgoKCgoKCgoNDEUAG9goKCgoKCgoKCQhNDZTrMM+TzeTz00EOwbRtHHXUUWlpaJmw/ODiIRx55BPF4HIcccgjS6fQsjVRhtuG6Lv7whz+gr68PBxxwAFasWFHX+1566SU8/PDDOOyww7DnnnvO7CAV5gzPPvssnnrqKSxbtgwbN26MbJfP5/GjH/2IsyWTSZx55pkzPUSFOcLAwAAefvhhpFIpHHXUUYjH45O+5/HHH8eWLVtwyCGHYPHixbMwSoW5QKVSwUMPPYR8Po8jjjgCXV1dkW3vuecelMvlkP3UU09Fa2vrTA5ToQ6opNh5hL/97W/YtGkTFi9ejFgshueeew4/+clPcNhhh0nb33jjjbj11luxfv169PX1YcuWLbj77rtx9NFHz/LIFWYaxWIRp512Gl544QWsW7cOv//97/Ev//Iv+MAHPjDh+2q1Go488kg89thj+MY3voF3vOMdszNghVnFNddcg5tuuglHHHEEHn/8cRxxxBH47//+b2l1in/84x/Yc889cfbZZ/uvt7W14fbbb5/tYSvMAv73f/8X55xzDg444AAMDAygWCzil7/8JZYvXy5tPzg4iHPOOQfPPvssDjzwQLzwwgu4/vrrcc4558zyyBVmGi+//DKOP/54GIaBnp4ePPbYY/iv//ovnH766dL2l1xyCUZHR/2/n3vuOfztb3/Dq6++ira2tlkatUIkXIV5g2OOOcZ961vf6v/9gQ98wN13330j2999991upVLx/7788svdgw8+eEbHqDA3+OxnP+uuXLnSHRkZcV3Xde+//37XNE138+bNE77v+uuvdy+77DI3l8u53/rWt2Z+oAqzjscee8zVNM3905/+5Lqu6/b19bk9PT3uv/3bv0nbb9682QXgjo2NzeYwFeYA1WrVXbp0qfvpT3/adV3XtW3bPfXUU91zzjkn8j1vfOMb3VNOOcUtFAqu67puqVRyH3zwwVkZr8Ls4q1vfat7/PHHu7VazXVd173xxhvdnp4et1wu1/X+008/3X37298+k0NUmAKUhn6eYHBwEL/5zW/w7ne/27e9+93vxpNPPonnn39e+p6zzjoLlmX5f69evRr5fH7Gx6ow+7jnnntw/vnn+xKsU045Bb29vfjxj38c+Z6//OUv+M53voMvfOELszVMhTnAvffei4MPPhiHHHIIAKCnpwdnnXUW7rnnngnf98tf/hL33XcftmzZMhvDVJgDPPLII3j55Zf9dUXXdVx66aX40Y9+hFqtFmq/detW3HvvvbjhhhvwyCOP4IEHHsDo6CiOP/742R66wgzDcRz88Ic/xCWXXALDMAAQBr6/vx8PP/zwpO9/6aWXcP/99+Oyyy6b6aEq1AmloZ8n2Lx5M1zXxcqVK30b1Tu/+OKLWL16tfR9Tz/9NP7yl79gy5YtuOOOO3DTTTfNxnAVZhkvvvgiNzcAMj9efPFFaftKpYILL7wQt956K7LZ7GwMUWGOEDU3fv3rX0vbp9NpnHfeefjud7+LgYEBPPTQQ7joootw6623zsZwFWYRL774IlpaWtDZ2enb9txzT5TLZbzyyish2c3jjz8OTdNw5ZVXYnx8HLFYDI888gi++c1v4txzz53t4SvMIHbu3Inx8XHOd9C58uKLL+LYY4+d8P133HEH1q9fj9e85jUzPFKFeqEC+nkCypbEYjHfZpomdF2XMikUzz33HO69915s3boVLS0tSKVSMz5WhdlHrVbj5gYAxOPxyLlx7bXX4uCDD8ZJJ500G8NTmENMdW50d3fjrrvu8v/+29/+hsMOOwxHH300zjvvvBkdq8LsImpu0NdElMtlOI6DQw89FNdffz0A4Ctf+QouvvhinHnmmaG+FJoXspgDmNh3sO/95je/iauvvnrGxqcwdSjJzTxBLpcDAGzfvt237dy5E47j+K/J8PrXvx7f+9738PDDD+N973sf3vjGN6JYLM74eBVmF7lcjpsbAJkrsrmxdetWfPnLX8ahhx6Ku+66C3fddRdKpRL++Mc/4le/+tUsjVhhtjCVuSHDPvvsg9e85jX44x//OBPDU5hD5HI5DA4Oolqt+rbt27dD0zT09PRI2wPAGWec4dvOOOMMjI6OYvPmzTM/YIVZQ1dXFwzD4HyHbdvYuXPnpL7j3nvvxejoKN72trfN9DAVpgAV0M8TrFixAnvssQd+8pOf+LYf//jHaG9vx4YNGwAAv/3tb/GHP/wBAFAqlbhscwBYtWoV8vk8KpXK7A1cYVZwzDHHcHNj69at+Otf/4pjjjkGANmpoZppTdPwhje8Ab/61a9w77334t5770W5XMZjjz2G3/3ud3MyfoWZwzHHHIPf/e53GB4eBkDKm953333+3BgfH8ddd92FoaEhAEBfXx/3/lKphGeeeQa9vb2zOm6Fmcfhhx8O0zRx//33+7Yf//jHOOigg5DJZAAAP/nJT/C3v/0NAHDwwQcjm83iueee89s/99xz0DRNla5cYIjH4zjssMO4deXnP/85bNv2ZTSPPvoofvGLX4Tee/vtt+Mtb3nLpGW1FWYZc52VqxDgu9/9rptIJNzrr7/e/fznP+9ms1n3q1/9qv/66aef7lfB6e/vdzds2OB+8pOfdP/93//dvf76690lS5a4F1100VwNX2EG8cILL7htbW3uhRde6N52223uhg0b3NNPP91//atf/arb2dkZ+X5V5WbholaruUcccYR7+OGHu1/72tfcs846y128eLHb19fnuq7rPvfccy4A95FHHnFd13Vvvvlm95RTTnG/8pWvuLfccot72GGHuatWrXKHhobm8FMozBSuueYat6enx/3Sl77kfuITn3BjsZj7wAMP+K+vWrXKr4Ljuq775S9/2e3u7nZvvPFG9+abb3Z7e3vdj3zkI3MxdIUZxi9+8Qs3Fou5H/vYx9ybbrrJXbRokXvFFVf4r1966aXuMcccw73nueeeczVNcx999NFZHq3CZFAa+nmE888/H4sWLcL3vvc9OI6D//zP/+S2Po8++mj/4KjOzk788pe/xJ133olf/epX6OzsxJ133omTTz55roavMINYuXIlHn30UXz961/HH/7wB7zrXe/CJZdc4r++Zs0anHXWWZHvf8Mb3qAOlVqgMAwD//u//4vbbrsNDz/8MNatW4evfOUrvqQim83ivPPOQ0dHBwDg/e9/P/bff3/ce++9KJVKuOCCC3DhhReqQ+kWKK677jqsX78eDzzwAJLJJH7961/j8MMP918/44wzsH79ev/vD37wg1izZg1++MMfwjRN3HTTTTj77LPnYugKM4zjjjsODz30EL7zne/g8ccfx7/+67/iLW95i//6IYccgiVLlnDvefLJJ/G+970PBx988GwPV2ESqIOlFBQUFBQUFBQUFJoYSkOvoKCgoKCgoKCg0MRQAb2CgoKCgoKCgoJCE0MF9AoKCgoKCgoKCgpNDBXQKygoKCgoKCgoKDQxVECvoKCgoKCgoKCg0MRQAb2CgoKCgoKCgoJCE0MF9AoKCgoKCgoKCgpNDBXQKygoKCjMO9x11134zGc+M2m7YrGIk046CSMjI7MwKgUFBYX5CRXQKygoKChIYds2Nm7ciC1btszqdcvlMj72sY/hzDPPnLRtMpnEPvvsg89//vOzMDIFBQWF+QkV0CsoKCgooFKpYOPGjdi2bZtvMwwDt99+O3K53KyO5e6778bSpUuxfv36utpfdNFF+PrXv45yuTzDI1NQUFCYn1ABvYKCgoICHMfBn//8Z5RKJc6+ceNGJBKJWR3LXXfdhde//vV1t99vv/2QzWbxs5/9bAZHpaCgoDB/oQJ6BQUFBQW88Y1v9P/duHEjbr755pDkplgsYuPGjbjvvvvwzne+E0cffTQ+9rGPoVQq4Y477sCmTZtw+umn41e/+hXXd6lUwmc+8xmceuqpeN3rXodvfetbE47loYcewsaNGznbnXfeidNPPx2bNm3CF77wBdRqNe71Qw45BL/97W938S4oKCgoNCc013XduR6EgoKCgsLc4tFHH8UhhxyCe+65B729vVi0aBEWLVoEy7Lw9NNPY926dRgfH0c2m8X69evx6U9/GrFYDJdddhmy2SyOOuoonH/++XjooYfwhS98Af/4xz/Q0dEB13Vx9NFHI5PJ4IMf/CBKpRKuuOIK/NM//RP++Z//OTSO0dFRtLa24sknn/QlNz/84Q9x8cUX49Zbb0VPTw9+9rOfobW1FVdccYX/vg984APYvn07vve9783aPVNQUFCYLzDnegAKCgoKCnOPfffd1/939erVABBiwSluvvlmnHDCCQCAd7zjHfje976Hr3/969A0Dcceeyxuu+02/PnPf8aJJ56I++67D88++yxeeuklxONxAEBrayve/OY3SwN6qoOPxWK+7ZVXXsG6detw7rnn+tcQ9fLxeFxp6BUUFHZbqIBeQUFBQWFKWLVqlf//ra2tWLlyJTRN820tLS1+GcmnnnoKhUIBRx55pP96tVpFX18fxsbGkM1mub47OjpgGAYGBwd92wUXXICHH34Y++23H4444giccMIJOOecc7j3DQ4Ooru7u6GfU0FBQaFZoAJ6BQUFBQUuIG8kOjo60Nvbi9tvvz30WjKZDNkMw8D++++Pp556CocddhgAIJ1O4z/+4z9QrVbxf//3f7juuuvwwx/+EP/5n//pv++vf/0rLrroohn5DAoKCgrzHSopVkFBQUEB8XgcqVQKO3fubGi/p556Kvr6+rBlyxZs3LgRGzduxLJly/Cb3/wGpinnlMTE2u9///t47LHHYFkWDj30UJx44ol4/PHH/deHh4fxxBNP4JRTTmno2BUUFBSaBYqhV1BQUFAAAFx88cU47bTTsHLlSlxwwQW4/PLLd7nPpUuX4gc/+AEuvfRSfOADH0AqlUKxWMSNN94Y+Z6LLroIBx10EPL5PNLpNNasWYNLLrkEr7zyCtLpNIaGhnDHHXf47e+++24ce+yxWLFixS6PV0FBQaEZoarcKCgoKCj4ePXVV/Hqq6+ip6cHvb29ePTRR7HvvvsikUjAcRw89thj2G+//fyk1b6+PoyOjmKvvfby+3jqqaewZMkStLe3c32/8sorKBaLIc29DJdccgnWrl2Lj3zkI77t5ZdfRj6fx5577gnLsgCQ+vn77bcfvvnNb+LQQw9t1G1QUFBQaCqogF5BQUFBYd5hdHQU27dvx5o1ayZsVy6X8eyzz2LDhg2zNDIFBQWF+QcV0CsoKCgoKCgoKCg0MVRSrIKCgoKCgoKCgkITQwX0CgoKCgoKCgoKCk0MFdArKCgoKCgoKCgoNDFUQK+goKCgoKCgoKDQxFABvYKCgoKCgoKCgkITQwX0CgoKCgoKCgoKCk0MFdArKCgoKCgoKCgoNDFUQK+goKCgoKCgoKDQxFABvYKCgoKCgoKCgkITQwX0CgoKCgoKCgoKCk2M/x9JXIhv5ajhAQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "step_ms = 100.0\n",
+    "step = int(step_ms * 0.001 * sr)\n",
+    "x = pluck_train(1.5)\n",
+    "\n",
+    "m = stammer(step_ms=step_ms, density=1.0, divisions=1, repeats=1,\n",
+    "            reverse=0.0, jump_ms=0.0, fade_ms=0.0)\n",
+    "y = m.process(x)\n",
+    "\n",
+    "print(f\"bitwise a one-step delay: {np.array_equal(y[step:], x[1:x.size - step + 1])}\")\n",
+    "print(f\"max |difference|:         {np.max(np.abs(y[step:] - x[1:x.size - step + 1]))}\")\n",
+    "print(f\"and not a bypass:         {not np.array_equal(y[step:], x[step:])}\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "w = slice(int(0.30 * sr), int(0.75 * sr))\n",
+    "t = np.arange(x.size)[w] / sr\n",
+    "ax.plot(t, x[w], color=C[0], lw=0.8, label=\"input\")\n",
+    "ax.plot(t, y[w], color=C[2], lw=0.8, label=\"output\")\n",
+    "for g in range(3, 8):\n",
+    "    ax.axvline(g * step / sr, color=C[3], lw=0.8, ls=\":\")\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"amplitude\")\n",
+    "ax.set_title(\"dice pinned: each grid point (dotted) replays the step just past\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7caabf1d",
+   "metadata": {},
+   "source": [
+    "## 2 · What the dials do\n",
+    "\n",
+    "`density` is how often the machine grabs at an idle grid point; `repeats` is how many passes it\n",
+    "holds on for once it has. They are not interchangeable — a slice in flight is never interrupted,\n",
+    "so `repeats` is what actually decides how long the machine stays busy, and past the point where\n",
+    "trains overlap, raising density stops having an effect.\n",
+    "\n",
+    "Below, the busy fraction measured directly off the object's `playing` flag, sample by sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "19c9b151",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:22.104415Z",
+     "iopub.status.busy": "2026-08-15T23:45:22.104213Z",
+     "iopub.status.idle": "2026-08-15T23:45:34.038274Z",
+     "shell.execute_reply": "2026-08-15T23:45:34.037113Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  density 0.3, repeats 1: busy  41.0% of the time (100 grid points)\n",
+      "  density 0.3, repeats 6: busy  76.0% of the time (100 grid points)\n",
+      "  density 0.9, repeats 1: busy  90.0% of the time (100 grid points)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  density 0.9, repeats 6: busy  96.0% of the time (100 grid points)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x249.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def occupancy(seconds=6.0, **params):\n",
+    "    m = stammer(**params)\n",
+    "    src = pluck_train(seconds)\n",
+    "    flags = np.zeros(src.size, dtype=bool)\n",
+    "    for i, v in enumerate(src):\n",
+    "        m.process(np.array([v]))\n",
+    "        flags[i] = m.playing\n",
+    "    return flags\n",
+    "\n",
+    "step_ms = 60.0  # 100 grid points across the 6 s render: enough for the fractions to mean something\n",
+    "runs = {\n",
+    "    \"density 0.3, repeats 1\": dict(step_ms=step_ms, density=0.3, divisions=1, repeats=1, seed=7),\n",
+    "    \"density 0.3, repeats 6\": dict(step_ms=step_ms, density=0.3, divisions=1, repeats=6, seed=7),\n",
+    "    \"density 0.9, repeats 1\": dict(step_ms=step_ms, density=0.9, divisions=1, repeats=1, seed=7),\n",
+    "    \"density 0.9, repeats 6\": dict(step_ms=step_ms, density=0.9, divisions=1, repeats=6, seed=7),\n",
+    "}\n",
+    "flags = {k: occupancy(**v) for k, v in runs.items()}\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 2.6))\n",
+    "for n, (label, f) in enumerate(flags.items()):\n",
+    "    ax.fill_between(np.arange(f.size) / sr, n, n + f * 0.8, color=C[n], step=\"mid\", lw=0)\n",
+    "    print(f\"{label:>24}: busy {100.0 * f.mean():5.1f}% of the time \"\n",
+    "          f\"({int(6.0 * 1000.0 / step_ms)} grid points)\")\n",
+    "ax.set_yticks([n + 0.4 for n in range(len(flags))]); ax.set_yticklabels(list(flags))\n",
+    "ax.set_xlabel(\"time (s)\")\n",
+    "ax.set_title(\"when a slice is in flight — repeats, not density, is the hold\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8485f15f",
+   "metadata": {},
+   "source": [
+    "## 3 · A seed is a performance you can replay\n",
+    "\n",
+    "Every draw — fire, division, repeat count, reach-back, and the per-repeat coin for reverse —\n",
+    "comes from the family's seeded xorshift64* in a fixed order. Two runs of the same seed and the\n",
+    "same moves are bit-identical; two seeds are genuinely different performances, not a cosmetic\n",
+    "reshuffle. That is what makes a render reproducible and what lets two instances decorrelate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2dfbd5f0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:34.040510Z",
+     "iopub.status.busy": "2026-08-15T23:45:34.040319Z",
+     "iopub.status.idle": "2026-08-15T23:45:34.255241Z",
+     "shell.execute_reply": "2026-08-15T23:45:34.254328Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "same seed, bitwise identical: True\n",
+      "different seed, differs in:   89.4% of samples\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def run(seed):\n",
+    "    m = stammer(step_ms=70.0, density=0.6, divisions=4, repeats=6,\n",
+    "                reverse=0.4, jump_ms=120.0, fade_ms=3.0, seed=seed)\n",
+    "    return m.process(pluck_train(3.0))\n",
+    "\n",
+    "a, b, cc = run(12345), run(12345), run(999)\n",
+    "print(f\"same seed, bitwise identical: {np.array_equal(a, b)}\")\n",
+    "print(f\"different seed, differs in:   {100.0 * np.mean(a != cc):.1f}% of samples\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "w = slice(int(1.0 * sr), int(1.8 * sr))\n",
+    "t = np.arange(a.size)[w] / sr\n",
+    "ax.plot(t, a[w], color=C[0], lw=0.8, label=\"seed 12345\")\n",
+    "ax.plot(t, cc[w] - 1.2, color=C[2], lw=0.8, label=\"seed 999 (offset)\")\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_yticks([])\n",
+    "ax.set_title(\"same settings, same material, two seeds\")\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "734d51c8",
+   "metadata": {},
+   "source": [
+    "## 4 · The disabled-generator contract\n",
+    "\n",
+    "`garden.h` established the shape: a generator that is switched off must not consume its random\n",
+    "stream, so the seed provably cannot matter. Here `density` 0 means the dice are never rolled —\n",
+    "and because nothing ever fires, the object is also a bitwise bypass at any mix (there is nothing\n",
+    "to blend against, and equal-power blending a signal with itself would only make it louder)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "82ab67e0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:34.257291Z",
+     "iopub.status.busy": "2026-08-15T23:45:34.257110Z",
+     "iopub.status.idle": "2026-08-15T23:45:34.275807Z",
+     "shell.execute_reply": "2026-08-15T23:45:34.274843Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "two seeds give identical output: True\n",
+      "and it is a bitwise bypass:      True\n",
+      "still bitwise at mix 50:         True\n"
+     ]
+    }
+   ],
+   "source": [
+    "src = pluck_train(1.0)\n",
+    "quiet_a = stammer(step_ms=70.0, density=0.0, seed=1).process(src)\n",
+    "quiet_b = stammer(step_ms=70.0, density=0.0, seed=0xfeedface).process(src)\n",
+    "mid_mix = tap.Stammer(sr, 4000.0, smooth_ms=0, mix=50, step_ms=70.0, density=0.0).process(src)\n",
+    "\n",
+    "print(f\"two seeds give identical output: {np.array_equal(quiet_a, quiet_b)}\")\n",
+    "print(f\"and it is a bitwise bypass:      {np.array_equal(quiet_a, src)}\")\n",
+    "print(f\"still bitwise at mix 50:         {np.array_equal(mid_mix, src)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea3bfc96",
+   "metadata": {},
+   "source": [
+    "## 5 · The per-repeat flanks\n",
+    "\n",
+    "Each repeat gets a raised-sine flank at both edges: exactly zero at the edges, exactly unity\n",
+    "across the plateau. Repeats are sequential rather than overlapped, so every junction dips to\n",
+    "zero — that is the articulation of a stutter, and it is deliberate rather than a crossfade that\n",
+    "failed.\n",
+    "\n",
+    "Driving the machine with a held DC input makes the slice material exactly 1.0, so the output *is*\n",
+    "the envelope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e8cfb939",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:34.278300Z",
+     "iopub.status.busy": "2026-08-15T23:45:34.278102Z",
+     "iopub.status.idle": "2026-08-15T23:45:34.378805Z",
+     "shell.execute_reply": "2026-08-15T23:45:34.377752Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "opens at exactly zero:  True\n",
+      "closes at exactly zero: True\n",
+      "plateau is exactly one: True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "step_ms, fade_ms = 50.0, 2.0\n",
+    "step, fade = int(step_ms * 0.001 * sr), int(fade_ms * 0.001 * sr)\n",
+    "m = stammer(step_ms=step_ms, density=1.0, divisions=1, repeats=1,\n",
+    "            reverse=0.0, jump_ms=0.0, fade_ms=fade_ms)\n",
+    "env = m.process(np.ones(int(0.5 * sr)))\n",
+    "\n",
+    "base = 2 * step\n",
+    "print(f\"opens at exactly zero:  {env[base] == 0.0}\")\n",
+    "print(f\"closes at exactly zero: {env[base + step - 1] == 0.0}\")\n",
+    "print(f\"plateau is exactly one: {env[base + fade] == 1.0 and env[base + step // 2] == 1.0}\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "seg = env[base - step // 4 : base + step + step // 4]\n",
+    "ax.plot(np.arange(seg.size) / sr * 1000.0, seg, color=C[0], lw=1.0)\n",
+    "ax.set_xlabel(\"time (ms)\"); ax.set_ylabel(\"gain\")\n",
+    "ax.set_title(f\"one repeat's envelope: {fade_ms:.0f} ms raised-sine flanks, unity plateau\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f02225c3",
+   "metadata": {},
+   "source": [
+    "## 6 · The material contract, measured\n",
+    "\n",
+    "The header states it plainly: this object wants transient material, and on a sustained tone a\n",
+    "stutter is barely distinguishable from a tremolo. That is not a taste claim — it is a property of\n",
+    "self-similarity. Every slice of a steady sine looks like every other slice, so re-ordering them\n",
+    "changes almost nothing; slices of a plucked phrase are all different, so re-ordering them is the\n",
+    "whole effect.\n",
+    "\n",
+    "What is measurable here is the premise, and it is a property of the *material*, not of the\n",
+    "machine: how alike are two arbitrary slices of it? Below, many random slices of each material are\n",
+    "compared by magnitude spectrum (phase-free, since a stutter re-orders slices without regard to\n",
+    "phase) and averaged pairwise. A number near 1 means every slice looks like every other one — and\n",
+    "re-ordering interchangeable things cannot do much."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ca624766",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T23:45:34.381491Z",
+     "iopub.status.busy": "2026-08-15T23:45:34.381296Z",
+     "iopub.status.idle": "2026-08-15T23:45:34.755850Z",
+     "shell.execute_reply": "2026-08-15T23:45:34.754564Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " sustained sine (196 Hz): slices are 1.000 alike\n",
+      "          plucked phrase: slices are 0.286 alike\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x422.4 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def slice_similarity(x, win_ms=60.0, n=96, seed=0):\n",
+    "    # Mean pairwise cosine similarity of the magnitude spectra of n random slices.\n",
+    "    rng = np.random.default_rng(seed)\n",
+    "    w = int(win_ms * 0.001 * sr)\n",
+    "    window = np.hanning(w)\n",
+    "    specs = []\n",
+    "    for start in rng.integers(0, x.size - w, n):\n",
+    "        s = np.abs(np.fft.rfft(x[start:start + w] * window))\n",
+    "        norm = np.linalg.norm(s)\n",
+    "        if norm > 0:\n",
+    "            specs.append(s / norm)\n",
+    "    specs = np.array(specs)\n",
+    "    gram = specs @ specs.T\n",
+    "    iu = np.triu_indices(specs.shape[0], 1)\n",
+    "    return float(gram[iu].mean())\n",
+    "\n",
+    "t = np.arange(int(3.0 * sr)) / sr\n",
+    "materials = {\n",
+    "    \"sustained sine (196 Hz)\":  0.5 * np.sin(2 * np.pi * 196.0 * t),\n",
+    "    \"plucked phrase\":           pluck_train(3.0),\n",
+    "}\n",
+    "for label, src in materials.items():\n",
+    "    print(f\"{label:>24}: slices are {slice_similarity(src):.3f} alike\")\n",
+    "\n",
+    "# What the machine does with each is the consequence, not a second measurement: the same seeded\n",
+    "# performance, plotted on both materials.\n",
+    "settings = dict(step_ms=120.0, density=0.8, divisions=4, repeats=5,\n",
+    "                reverse=0.4, jump_ms=250.0, fade_ms=3.0, seed=5)\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(9, 4.4), sharex=True)\n",
+    "for ax, (label, src) in zip(axes, materials.items()):\n",
+    "    out = stammer(**settings).process(src)\n",
+    "    w = slice(int(1.0 * sr), int(2.4 * sr))\n",
+    "    ax.plot(np.arange(src.size)[w] / sr, src[w], color=C[3], lw=0.7, alpha=0.7, label=\"input\")\n",
+    "    ax.plot(np.arange(out.size)[w] / sr, out[w], color=C[0], lw=0.7, label=\"stammered\")\n",
+    "    ax.set_title(label, fontsize=10); ax.legend(loc=\"upper right\", fontsize=8)\n",
+    "axes[-1].set_xlabel(\"time (s)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "821a4c0b",
+   "metadata": {},
+   "source": [
+    "## Checkpoint\n",
+    "\n",
+    "- With the dice pinned to one outcome the machine is **bitwise** a one-step delay — grid, origin\n",
+    "  and playback head all pinned by one identity.\n",
+    "- `repeats`, not `density`, is what holds the machine busy; a slice in flight is never\n",
+    "  interrupted.\n",
+    "- A seed is a performance: same seed, bit-identical render; different seed, a different\n",
+    "  performance.\n",
+    "- At `density` 0 the dice are never rolled, and the object is a bitwise bypass at any mix.\n",
+    "- Each repeat's flanks reach exactly zero and its plateau is exactly unity.\n",
+    "- The material contract rests on a measurable property of the material itself: slices of a\n",
+    "  sustained sine are nearly interchangeable, slices of a plucked phrase are not — and re-ordering\n",
+    "  interchangeable things is close to a no-op. Feed this object transients."
+   ]
+  }
+ ],
+ "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/tapecho.ipynb b/notebooks/tapecho.ipynb
new file mode 100644
index 0000000..61b81f7
--- /dev/null
+++ b/notebooks/tapecho.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e0fd4fc0",
+   "metadata": {},
+   "source": [
+    "# tap.tapecho~ — the echo, measured\n",
+    "\n",
+    "The multi-head tape echo of the Copicat / Space Echo school (`taptools/tapecho.h` over the\n",
+    "shared `taptools/tape_loop.h`): one record head, a span of moving tape, several playback heads\n",
+    "at fixed positions along it, and a regeneration path from the heads back to the record head.\n",
+    "\n",
+    "Two claims carry the kernel, and both are measured below. The first is structural — **this\n",
+    "kernel is composition, not construction**: with the tape path neutralized it is *bitwise* the\n",
+    "plain Hermite multitap of `delay.h`, which is what makes \"`tape_loop.h` is a library\" a\n",
+    "measurement rather than a slogan. The second is the design statement — **regeneration is\n",
+    "allowed past unity** into deliberate sound-on-sound self-oscillation, bounded by the\n",
+    "saturator (`vca::swing_shape`, ≤ 1/drive) rather than by a feedback cap, and capped back to\n",
+    "1.0 the moment drive leaves.\n",
+    "\n",
+    "Every trace drives the **shipping C++** through `tools/capi` via ctypes.\n",
+    "\n",
+    "Sections: **1** the head layout · **2** the null test against `delay.h` · **3** generation loss\n",
+    "per pass · **4** past unity, and the bound that holds it · **5** the transport"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "16bf109b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:09.685955Z",
+     "iopub.status.busy": "2026-08-15T22:30:09.685744Z",
+     "iopub.status.idle": "2026-08-15T22:30:10.114754Z",
+     "shell.execute_reply": "2026-08-15T22:30:10.113199Z"
+    }
+   },
+   "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 echo(**params):\n",
+    "    # A machine with the transport parked and the wear path neutral: sections opt in.\n",
+    "    base = dict(smooth_ms=0, wow=(0, 0), flutter=(0, 0), regen=0.0, drive=0.0,\n",
+    "                darken_hz=20000.0, input_level=1.0, mix=100)\n",
+    "    base.update(params)\n",
+    "    return tap.TapEcho(sr, 4.0, **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": "21ef350f",
+   "metadata": {},
+   "source": [
+    "## 1 · The head layout\n",
+    "\n",
+    "`span_ms` is the motor: the delay of a head at the far end of the path (ratio 1.0). Every other\n",
+    "head sits at `span_ms * ratio`, so moving the motor moves the whole layout together, as a tape\n",
+    "speed does. The defaults are four evenly spaced heads — 0.25, 0.5, 0.75, 1.0 — a nominal layout\n",
+    "chosen because it is neutral and audibly \"a tape echo\"; real machines' head positions vary by\n",
+    "model and unit and none are claimed as measured here.\n",
+    "\n",
+    "An impulse into a 400 ms span, no regeneration, shows the four returns exactly on their\n",
+    "positions. (Pinned by the kernel scenario *\"each head echoes at its own position along the tape\n",
+    "path\"*.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7b0be83b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:10.117877Z",
+     "iopub.status.busy": "2026-08-15T22:30:10.117520Z",
+     "iopub.status.idle": "2026-08-15T22:30:10.283670Z",
+     "shell.execute_reply": "2026-08-15T22:30:10.282378Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "head 0 (ratio 0.25): return at 100.000 ms, level 0.707106781  (centre-pan law cos(pi/4) = 0.707106781)\n",
+      "head 1 (ratio 0.50): return at 200.000 ms, level 0.707106781  (centre-pan law cos(pi/4) = 0.707106781)\n",
+      "head 2 (ratio 0.75): return at 300.000 ms, level 0.707106781  (centre-pan law cos(pi/4) = 0.707106781)\n",
+      "head 3 (ratio 1.00): return at 400.000 ms, level 0.707106781  (centre-pan law cos(pi/4) = 0.707106781)\n"
+     ]
+    }
+   ],
+   "source": [
+    "span = 0.400\n",
+    "m = echo(span_ms=span * 1000, heads=4)\n",
+    "x = np.zeros(int(0.6 * sr)); x[0] = 1.0\n",
+    "left, right = m.process(x)\n",
+    "\n",
+    "t = np.arange(left.size) / sr\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(t, left, color=C[0], lw=0.8)\n",
+    "for i in range(4):\n",
+    "    ax.axvline(span * (i + 1) / 4, color=C[3], lw=0.8, ls=\":\")\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"output (left)\")\n",
+    "ax.set_title(\"impulse → four heads at 0.25 / 0.5 / 0.75 / 1.0 of a 400 ms span (dotted)\")\n",
+    "plt.show()\n",
+    "\n",
+    "for i in range(4):\n",
+    "    k = int(span * (i + 1) / 4 * sr)\n",
+    "    print(f\"head {i} (ratio {(i + 1) / 4:.2f}): return at {k / sr * 1000:7.3f} ms, \"\n",
+    "          f\"level {left[k]:.9f}  (centre-pan law cos(pi/4) = {np.cos(np.pi / 4):.9f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d65ae534",
+   "metadata": {},
+   "source": [
+    "## 2 · The null test — the echo *is* the multitap of `delay.h`\n",
+    "\n",
+    "Neutralize the tape path (no transport error, no wear in play because there is no regeneration)\n",
+    "and a one-head echo must reduce to the plain fractional delay this library already had. It does,\n",
+    "**bitwise** — same Hermite read at the same fractional position under the same equal-power pan\n",
+    "law — for a span deliberately chosen not to be a whole number of samples.\n",
+    "\n",
+    "This is the load-bearing structural claim: the tape echo adds a head layout, a transport, and a\n",
+    "worn regeneration path *on top of* machinery that is shared, not reimplemented. Both objects are\n",
+    "reached here through the same C ABI, so the comparison crosses the same boundary the Max\n",
+    "externals do. (Pinned by *\"with the tape path neutral, a one-head echo is bitwise the multitap\n",
+    "of delay.h\"*.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "174b4833",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:10.286018Z",
+     "iopub.status.busy": "2026-08-15T22:30:10.285778Z",
+     "iopub.status.idle": "2026-08-15T22:30:10.312024Z",
+     "shell.execute_reply": "2026-08-15T22:30:10.310993Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "left  bitwise identical: True   max |diff| = 0.0\n",
+      "right bitwise identical: True   max |diff| = 0.0\n",
+      "and not vacuous — both are non-trivial signals: rms = 0.3367\n"
+     ]
+    }
+   ],
+   "source": [
+    "span_ms = 137.31  # deliberately not a whole number of samples\n",
+    "rng = np.random.default_rng(2463534242)\n",
+    "x = rng.uniform(-1, 1, int(0.5 * sr))\n",
+    "\n",
+    "e = echo(span_ms=span_ms, heads=1, ratios=[1.0], levels=[1.0], pans=[0.0])\n",
+    "el, er = e.process(x)\n",
+    "\n",
+    "ref = tap.Multitap(sr, 1000.0, smooth_ms=0, taps=1)\n",
+    "ref.set(times=[span_ms], gains=[1.0], pans=[0.0])\n",
+    "rl, rr = ref.process(x)\n",
+    "\n",
+    "print(f\"left  bitwise identical: {np.array_equal(el, rl)}   max |diff| = {np.max(np.abs(el - rl))}\")\n",
+    "print(f\"right bitwise identical: {np.array_equal(er, rr)}   max |diff| = {np.max(np.abs(er - rr))}\")\n",
+    "print(f\"and not vacuous — both are non-trivial signals: rms = {np.sqrt(np.mean(el ** 2)):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "788641da",
+   "metadata": {},
+   "source": [
+    "## 3 · Generation loss, one pass at a time\n",
+    "\n",
+    "Each trip through the regeneration path is one pass of `tape_loop.h`'s `wear`: a darkening\n",
+    "one-pole lowpass, the bounded soft saturator, then the normalized DC blocker. At drive 0 the\n",
+    "saturator is exactly linear, so the per-pass ratio is precisely `regen * |H_wear(f)|` and can be\n",
+    "predicted analytically — the same formulas the kernel applies, computed independently here.\n",
+    "\n",
+    "A two-tone burst (one tone well above the darkening corner, one well below) through a one-head\n",
+    "echo shows both sides of the tilt: the highs die fast, the lows barely fade. That is the honest\n",
+    "tape story — wear is a tilt, not a fader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5fb96772",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:10.314444Z",
+     "iopub.status.busy": "2026-08-15T22:30:10.314131Z",
+     "iopub.status.idle": "2026-08-15T22:30:10.528183Z",
+     "shell.execute_reply": "2026-08-15T22:30:10.527019Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted per-pass ratio   hi 0.2920   lo 0.8898\n",
+      "measured  per-pass ratio   hi 0.2915   lo 0.8895\n"
+     ]
+    }
+   ],
+   "source": [
+    "def wear_gain(f, cutoff_hz):\n",
+    "    w = 2 * np.pi * f / sr\n",
+    "    a = 1.0 - np.exp(-2 * np.pi * cutoff_hz / 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",
+    "f_hi, f_lo, cutoff, regen, span = 6000.0, 300.0, 2000.0, 0.9, 0.25\n",
+    "m = echo(span_ms=span * 1000, heads=1, ratios=[1.0], regen=regen, drive=0.0, darken_hz=cutoff)\n",
+    "n = int(1.5 * sr)\n",
+    "t = np.arange(n) / sr\n",
+    "burst = np.zeros(n)\n",
+    "w = int(0.1 * sr)\n",
+    "burst[:w] = 0.4 * np.sin(2 * np.pi * f_hi * t[:w]) + 0.4 * np.sin(2 * np.pi * f_lo * t[:w])\n",
+    "y, _ = m.process(burst)\n",
+    "\n",
+    "loop = int(span * sr)\n",
+    "hi = np.array([goertzel(y[k * loop : k * loop + w], f_hi) for k in range(1, 5)])\n",
+    "lo = np.array([goertzel(y[k * loop : k * loop + w], f_lo) for k in range(1, 5)])\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "passes = np.arange(1, 5)\n",
+    "ax.semilogy(passes, hi, color=C[0], marker=\"o\", label=f\"{f_hi:.0f} Hz (above the corner)\")\n",
+    "ax.semilogy(passes, lo, color=C[1], marker=\"s\", label=f\"{f_lo:.0f} Hz (below it)\")\n",
+    "ax.set_xlabel(\"pass\"); ax.set_ylabel(\"magnitude\"); ax.set_xticks(passes)\n",
+    "ax.set_title(f\"generation loss per pass, darken {cutoff:.0f} Hz, regen {regen}\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"predicted per-pass ratio   hi {regen * wear_gain(f_hi, cutoff):.4f}   \"\n",
+    "      f\"lo {regen * wear_gain(f_lo, cutoff):.4f}\")\n",
+    "print(f\"measured  per-pass ratio   hi {np.mean(hi[1:] / hi[:-1]):.4f}   \"\n",
+    "      f\"lo {np.mean(lo[1:] / lo[:-1]):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f0a063f",
+   "metadata": {},
+   "source": [
+    "## 4 · Past unity, and the bound that holds it\n",
+    "\n",
+    "`delay.h` caps feedback at 0.99 so its loop is always contractive. `discreet.h` lets\n",
+    "regeneration reach exactly 1.0 because `|H_wear| ≤ 1` makes 1.0 sustain without growing. This\n",
+    "kernel goes one step further and allows regeneration *past* unity — the sound-on-sound howl a\n",
+    "tape echo is reached for live — and it stays bounded because the saturator does: with drive\n",
+    "engaged, `swing_shape` is bounded by `1/drive` no matter what the loop gain is.\n",
+    "\n",
+    "Because that bound only exists while the saturator is engaged, the cap is **drive-dependent and\n",
+    "applied per sample**: at drive 0 the effective regeneration falls back to 1.0, whatever the\n",
+    "target says. Below, the same excessive target (1.4) at four drives plus the drive-0 case: every\n",
+    "one plateaus, and every one stays under the analytic ceiling the saturator sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af947e08",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:10.530647Z",
+     "iopub.status.busy": "2026-08-15T22:30:10.530441Z",
+     "iopub.status.idle": "2026-08-15T22:30:11.595486Z",
+     "shell.execute_reply": "2026-08-15T22:30:11.594483Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " drive   rms mid  rms late   growth    peak  ceiling  finite\n",
+      "   0.3    2.4777    2.4935   1.0064  3.0400   3.6534  True\n",
+      "   0.6    1.2357    1.2439   1.0066  1.5174   2.0035  True\n",
+      "   1.0    0.7395    0.7459   1.0086  0.9189   1.3435  True\n",
+      "   2.0    0.3695    0.3723   1.0075  0.7027   0.8485  True\n",
+      "   0.0    0.0522    0.0473   0.9054  0.6608       --  True   <- drive 0: effective regen capped back to 1.0, still no growth\n"
+     ]
+    }
+   ],
+   "source": [
+    "def plateau(drive, regen=1.4, seconds=14.0):\n",
+    "    m = echo(span_ms=250.0, heads=1, ratios=[1.0], regen=regen, drive=drive, darken_hz=6000.0)\n",
+    "    x = np.zeros(int(seconds * sr))\n",
+    "    burst = int(0.5 * sr)\n",
+    "    x[:burst] = 0.5 * np.random.default_rng(7).uniform(-1, 1, burst)\n",
+    "    y, _ = m.process(x)\n",
+    "    mid = y[int(0.55 * y.size) : int(0.75 * y.size)]\n",
+    "    late = y[int(0.75 * y.size) :]\n",
+    "    return (np.sqrt(np.mean(mid ** 2)), np.sqrt(np.mean(late ** 2)),\n",
+    "            np.max(np.abs(y)), np.all(np.isfinite(y)))\n",
+    "\n",
+    "drives = [0.3, 0.6, 1.0, 2.0]\n",
+    "rows = [(d,) + plateau(d) for d in drives]\n",
+    "capped = plateau(0.0)\n",
+    "\n",
+    "# The analytic ceiling on the tape is |in|max + regen/drive: the saturator bounds the returned\n",
+    "# signal by 1/drive, and the record head adds the direct send on top. The single centre-panned\n",
+    "# head then scales it by cos(pi/4). (Looser bounds exist — the DC blocker's L1 gain is ~2 — but\n",
+    "# real signals do not excite the worst case, which is why the measured curve sits below.)\n",
+    "ceiling = [np.cos(np.pi / 4) * (0.5 + 1.4 / d) for d in drives]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(drives, [r[3] for r in rows], color=C[0], marker=\"o\", label=\"measured peak\")\n",
+    "ax.plot(drives, ceiling, color=C[3], ls=\"--\", label=\"ceiling: (|in|max + regen/drive), panned\")\n",
+    "ax.set_xlabel(\"drive\"); ax.set_ylabel(\"peak |output|\")\n",
+    "ax.set_title(\"regen 1.4: self-oscillating, and bounded by the saturator\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"{'drive':>6} {'rms mid':>9} {'rms late':>9} {'growth':>8} {'peak':>7} {'ceiling':>8}  finite\")\n",
+    "for (d, mid, late, pk, fin), cap in zip(rows, ceiling):\n",
+    "    print(f\"{d:6.1f} {mid:9.4f} {late:9.4f} {late / mid:8.4f} {pk:7.4f} {cap:8.4f}  {fin}\")\n",
+    "mid, late, pk, fin = capped\n",
+    "print(f\"{0.0:6.1f} {mid:9.4f} {late:9.4f} {late / mid:8.4f} {pk:7.4f} {'--':>8}  {fin}\"\n",
+    "      f\"   <- drive 0: effective regen capped back to 1.0, still no growth\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a300317",
+   "metadata": {},
+   "source": [
+    "## 5 · The transport\n",
+    "\n",
+    "Wow and flutter are `tape_loop.h`'s deterministic periodic pair — a slow deep sine and a faster\n",
+    "shallow one, summed into a read-position offset shared by every head (one motor moves the whole\n",
+    "path). Periodic-only is a family decision, not an oversight: it keeps renders and tests\n",
+    "bit-exactly reproducible, and the stochastic capstan term is a documented non-goal.\n",
+    "\n",
+    "A sinusoidally modulated read swings the playback ratio by `depth · 2π · rate`, which predicts\n",
+    "the peak pitch deviation in cents. Measured out of the output below by zero-crossing period,\n",
+    "and confirmed bit-exact across two runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62e41002",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-15T22:30:11.597919Z",
+     "iopub.status.busy": "2026-08-15T22:30:11.597717Z",
+     "iopub.status.idle": "2026-08-15T22:30:11.832656Z",
+     "shell.execute_reply": "2026-08-15T22:30:11.831512Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x307.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted peak deviation  10.88 cents\n",
+      "measured  peak deviation  10.91 cents\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "two runs bitwise identical: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "depth_ms, rate_hz = 2.0, 0.5\n",
+    "predicted = 1200 / np.log(2) * depth_ms * 1e-3 * 2 * np.pi * rate_hz\n",
+    "\n",
+    "def render():\n",
+    "    m = echo(span_ms=1000.0, heads=1, ratios=[1.0], wow=(depth_ms, rate_hz))\n",
+    "    t = np.arange(int(4.5 * sr)) / sr\n",
+    "    y, _ = m.process(0.8 * np.sin(2 * np.pi * 440.0 * t))\n",
+    "    return y\n",
+    "\n",
+    "y = render()\n",
+    "\n",
+    "# Instantaneous pitch by zero-crossing spacing, over the filled-tape region.\n",
+    "seg = y[int(1.5 * sr) : int(3.5 * sr)]\n",
+    "cross = np.where((seg[:-1] <= 0) & (seg[1:] > 0))[0]\n",
+    "frac = seg[cross] / (seg[cross] - seg[cross + 1])\n",
+    "times = (cross + frac) / sr\n",
+    "hz = 1.0 / np.diff(times)\n",
+    "cents = 1200 * np.log2(hz / 440.0)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(times[:-1] + 1.5, cents, color=C[0], lw=0.9)\n",
+    "for s in (+predicted, -predicted):\n",
+    "    ax.axhline(s, color=C[3], lw=0.8, ls=\"--\")\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"deviation (cents)\")\n",
+    "ax.set_title(f\"wow {depth_ms} ms at {rate_hz} Hz — predicted swing ±{predicted:.2f} cents (dashed)\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"predicted peak deviation {predicted:6.2f} cents\")\n",
+    "print(f\"measured  peak deviation {np.max(np.abs(cents)):6.2f} cents\")\n",
+    "print(f\"two runs bitwise identical: {np.array_equal(y, render())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4f20b02",
+   "metadata": {},
+   "source": [
+    "## Checkpoint\n",
+    "\n",
+    "- The head layout is exact: each head returns at `span · ratio`, and the motor moves them\n",
+    "  together.\n",
+    "- With the tape path neutral the echo is **bitwise** `delay.h`'s multitap — the kernel is\n",
+    "  composition over `tape_loop.h`, measured, not asserted.\n",
+    "- Generation loss per pass matches the analytic wear transfer on both sides of the corner.\n",
+    "- Regeneration past unity self-oscillates and stays under the saturator's ceiling at every\n",
+    "  drive measured; at drive 0 the\n",
+    "  effective value is capped back to 1.0 and still does not grow.\n",
+    "- The transport bends pitch by the predicted amount and is bit-exactly reproducible."
+   ]
+  }
+ ],
+ "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 acd5a55..5c184bb 100644
--- a/notebooks/taptools_py.py
+++ b/notebooks/taptools_py.py
@@ -15,7 +15,10 @@
 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`), the Discreet Music
-two-machine tape loop tap.discreet~ (`Discreet`), the Music for Airports
+two-machine tape loop tap.discreet~ (`Discreet`), the multi-head tape echo
+tap.tapecho~ (`TapEcho`), the live buffer-stutter rig tap.stammer~
+(`Stammer`), the two-stage fuzz tap.fuzz~ (`Fuzz`), the Ondes Martenot intensity
+key tap.touche~ (`Touche`), 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'
@@ -283,6 +286,202 @@ def load() -> ctypes.CDLL:
         "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_tapecho_create":         ([], vp),
+        "taptools_tapecho_destroy":        ([vp], None),
+        "taptools_tapecho_prepare":        ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_span_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_heads":      ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_tapecho_set_head_ratio": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_head_level": ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_head_pan":   ([vp, ctypes.c_int, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_regen":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_darken_hz":  ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_drive":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_input_level": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_mix":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_wow":        ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_flutter":    ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_set_smooth_ms":  ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_tapecho_clear":          ([vp], ctypes.c_int),
+        "taptools_tapecho_process":        ([vp, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+
+        "taptools_tube_plate_current":     ([ctypes.c_int, ctypes.c_double, ctypes.c_double],
+                                            ctypes.c_double),
+        "taptools_tube_grid_current":      ([ctypes.c_int, ctypes.c_double], ctypes.c_double),
+        "taptools_tube_params":            ([ctypes.c_int, f64p], ctypes.c_int),
+
+        "taptools_triode_create":          ([], vp),
+        "taptools_triode_destroy":         ([vp], None),
+        "taptools_triode_prepare":         ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_triode_set_tube":        ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_triode_set_operating_point": ([vp, ctypes.c_double, ctypes.c_double,
+                                                 ctypes.c_double], ctypes.c_int),
+        "taptools_triode_set_drive":       ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_triode_set_corners":     ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_triode_clear":           ([vp], ctypes.c_int),
+        "taptools_triode_process":         ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_triode_curve_at":        ([vp, ctypes.c_double], ctypes.c_double),
+        "taptools_triode_plate_swing_at":  ([vp, ctypes.c_double], ctypes.c_double),
+        "taptools_triode_bias_v":          ([vp], ctypes.c_double),
+        "taptools_triode_quiescent_plate_v": ([vp], ctypes.c_double),
+        "taptools_triode_quiescent_current_a": ([vp], ctypes.c_double),
+        "taptools_triode_gain":            ([vp], ctypes.c_double),
+
+        "taptools_detector_create":        ([], vp),
+        "taptools_detector_destroy":       ([vp], None),
+        "taptools_detector_prepare":       ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_detector_set_frequency": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_detector_set_ribbon":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_detector_set_depth":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_detector_set_detect_ms": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_detector_clear":         ([vp], ctypes.c_int),
+        "taptools_detector_process":       ([vp, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_detector_envelope_at":   ([vp, ctypes.c_double], ctypes.c_double),
+
+        "taptools_ondes_create":           ([], vp),
+        "taptools_ondes_destroy":          ([vp], None),
+        "taptools_ondes_prepare":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_ribbon":       ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_frequency":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_depth":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_detect_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_drive":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_key":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_key_mm":       ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_key_placement": ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_set_power_stage":  ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_set_polarity":     ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_set_level":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_set_oversample":   ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_set_smooth_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_ondes_clear":            ([vp], ctypes.c_int),
+        "taptools_ondes_process":          ([vp, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_process_mod":      ([vp, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_ondes_frequency":        ([vp], ctypes.c_double),
+
+        "taptools_plate_create":           ([], vp),
+        "taptools_plate_destroy":          ([vp], None),
+        "taptools_plate_prepare":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_plate_set_pitch_hz":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_plate_set_decay":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_plate_set_tilt":         ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_plate_set_brightness":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_plate_clear":            ([vp], ctypes.c_int),
+        "taptools_plate_process":          ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_plate_mode_hz":          ([vp, ctypes.c_int], ctypes.c_double),
+        "taptools_plate_mode_level":       ([vp, ctypes.c_int], ctypes.c_double),
+
+        "taptools_transducer_create":      ([], vp),
+        "taptools_transducer_destroy":     ([vp], None),
+        "taptools_transducer_prepare":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_transducer_set_drive":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_transducer_set_asymmetry": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_transducer_set_saturation": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_transducer_clear":       ([vp], ctypes.c_int),
+        "taptools_transducer_process":     ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+
+        "taptools_metallique_create":      ([], vp),
+        "taptools_metallique_destroy":     ([vp], None),
+        "taptools_metallique_prepare":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_pitch_hz": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_decay":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_tilt":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_brightness": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_drive":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_asymmetry": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_saturation": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_mix":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_level":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_set_smooth_ms": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_metallique_clear":       ([vp], ctypes.c_int),
+        "taptools_metallique_process":     ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_metallique_mode_hz":     ([vp, ctypes.c_int], ctypes.c_double),
+        "taptools_metallique_mode_level":  ([vp, ctypes.c_int], ctypes.c_double),
+
+        "taptools_palme_create":           ([], vp),
+        "taptools_palme_destroy":          ([vp], None),
+        "taptools_palme_prepare":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_root_hz":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_tuning":       ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_palme_set_decay":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_damping":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_detune":       ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_drive":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_asymmetry":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_saturation":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_mix":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_level":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_set_smooth_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_palme_clear":            ([vp], ctypes.c_int),
+        "taptools_palme_process":          ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_palme_string_hz":        ([vp, ctypes.c_int], ctypes.c_double),
+        "taptools_palme_string_feedback":  ([vp, ctypes.c_int], ctypes.c_double),
+
+        "taptools_scrub_create":           ([], vp),
+        "taptools_scrub_destroy":          ([vp], None),
+        "taptools_scrub_prepare":          ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_position_ms":  ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_pitch":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_drift":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_freeze":       ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_scrub_set_size_ms":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_overlap":      ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_scrub_set_spray_ms":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_seed":         ([vp, ctypes.c_ulonglong], ctypes.c_int),
+        "taptools_scrub_set_mix":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_level":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_set_smooth_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_scrub_clear":            ([vp], ctypes.c_int),
+        "taptools_scrub_process":          ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_scrub_process_mod":      ([vp, f64p, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_scrub_active_grains":    ([vp], ctypes.c_int),
+
+        "taptools_touche_create":          ([], vp),
+        "taptools_touche_destroy":         ([vp], None),
+        "taptools_touche_prepare":         ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_touche_set_position":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_touche_set_position_mm": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_touche_set_force_n":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_touche_set_mode":        ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_touche_set_smooth_ms":   ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_touche_clear":           ([vp], ctypes.c_int),
+        "taptools_touche_gain_at":         ([vp, ctypes.c_double], ctypes.c_double),
+        "taptools_touche_process":         ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+        "taptools_touche_process_mod":     ([vp, f64p, f64p, f64p, ctypes.c_int], ctypes.c_int),
+
+        "taptools_fuzz_create":            ([], vp),
+        "taptools_fuzz_destroy":           ([vp], None),
+        "taptools_fuzz_prepare":           ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_gain":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_edge":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_asymmetry":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_bass":          ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_treble":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_contrast":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_level_db":      ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_set_oversample":    ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_fuzz_set_smooth_ms":     ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_fuzz_clear":             ([vp], ctypes.c_int),
+        "taptools_fuzz_process":           ([vp, f64p, f64p, ctypes.c_int], ctypes.c_int),
+
+        "taptools_stammer_create":         ([], vp),
+        "taptools_stammer_destroy":        ([vp], None),
+        "taptools_stammer_prepare":        ([vp, ctypes.c_double, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_step_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_density":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_divisions":  ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_stammer_set_repeats":    ([vp, ctypes.c_int], ctypes.c_int),
+        "taptools_stammer_set_reverse":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_jump_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_fade_ms":    ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_seed":       ([vp, ctypes.c_ulonglong], ctypes.c_int),
+        "taptools_stammer_set_input_level": ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_mix":        ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_set_smooth_ms":  ([vp, ctypes.c_double], ctypes.c_int),
+        "taptools_stammer_clear":          ([vp], ctypes.c_int),
+        "taptools_stammer_playing":        ([vp], ctypes.c_int),
+        "taptools_stammer_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),
@@ -1240,6 +1439,793 @@ def __del__(self):
             self._h = None
 
 
+class TapEcho:
+    """tap.tapecho~'s kernel (tap::tools::tapecho::machine): the multi-head
+    tape echo of the Copicat / Space Echo school, composed over the same
+    tape_loop.h machinery as `Discreet`. One motor (`span_ms`) sets the
+    delay of a ratio-1.0 head and moves every head together; up to four
+    heads sit at settable positions along the path with their own level and
+    equal-power pan. Regeneration may pass 1.0 into deliberate
+    self-oscillation, bounded by the saturator rather than a feedback cap —
+    at drive 0 the effective regen is capped back to 1.0. Mono in, stereo
+    out."""
+
+    def __init__(self, sr: float = 48000.0, max_span_seconds: float = 4.0, **params):
+        self._h = _LIB.taptools_tapecho_create()
+        _check(_LIB.taptools_tapecho_prepare(self._h, float(sr), float(max_span_seconds)),
+               "prepare")
+        self.set(**params)
+
+    def set(self, *, span_ms=None, heads=None, ratios=None, levels=None, pans=None,
+            regen=None, darken_hz=None, drive=None, input_level=None, mix=None,
+            wow=None, flutter=None, smooth_ms=None) -> "TapEcho":
+        """`ratios`/`levels`/`pans` are per-head sequences (head i gets element
+        i); `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_tapecho_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        if wow is not None:
+            depth, rate = wow
+            _check(_LIB.taptools_tapecho_set_wow(self._h, float(depth), float(rate)), "wow")
+        if flutter is not None:
+            depth, rate = flutter
+            _check(_LIB.taptools_tapecho_set_flutter(self._h, float(depth), float(rate)),
+                   "flutter")
+        if ratios is not None:
+            for i, r in enumerate(ratios):
+                _check(_LIB.taptools_tapecho_set_head_ratio(self._h, i, float(r)), "head_ratio")
+            if heads is None:
+                heads = len(list(ratios))
+        if heads is not None:
+            _check(_LIB.taptools_tapecho_set_heads(self._h, int(heads)), "heads")
+        if levels is not None:
+            for i, v in enumerate(levels):
+                _check(_LIB.taptools_tapecho_set_head_level(self._h, i, float(v)), "head_level")
+        if pans is not None:
+            for i, p in enumerate(pans):
+                _check(_LIB.taptools_tapecho_set_head_pan(self._h, i, float(p)), "head_pan")
+        if span_ms is not None:
+            _check(_LIB.taptools_tapecho_set_span_ms(self._h, float(span_ms)), "span_ms")
+        if regen is not None:
+            _check(_LIB.taptools_tapecho_set_regen(self._h, float(regen)), "regen")
+        if darken_hz is not None:
+            _check(_LIB.taptools_tapecho_set_darken_hz(self._h, float(darken_hz)), "darken_hz")
+        if drive is not None:
+            _check(_LIB.taptools_tapecho_set_drive(self._h, float(drive)), "drive")
+        if input_level is not None:
+            _check(_LIB.taptools_tapecho_set_input_level(self._h, float(input_level)),
+                   "input_level")
+        if mix is not None:
+            _check(_LIB.taptools_tapecho_set_mix(self._h, float(mix)), "mix")
+        return self
+
+    def process(self, x):
+        x = _f64(x)
+        out_l = np.zeros_like(x)
+        out_r = np.zeros_like(x)
+        _check(_LIB.taptools_tapecho_process(self._h, _p64(x), _p64(out_l), _p64(out_r), x.size),
+               "process")
+        return out_l, out_r
+
+    def clear(self) -> None:
+        """Erase the tape and the transport/wear state; parameters are kept.
+        Also the fastest way to stop a self-oscillating loop."""
+        _check(_LIB.taptools_tapecho_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_tapecho_destroy(h)
+            self._h = None
+
+
+# The circuit paper's fitted tube parameter sets, addressed by index.
+TUBE_6F5, TUBE_6C5, TUBE_2A3 = 0, 1, 2
+TUBE_NAMES = ("6F5", "6C5", "2A3")
+
+# Published stage operating points (vbias, rk, rp) — TASLP 28 (2020), Table II.
+OP_DEMOD = (100.0, 1000.0, 4000.0)
+OP_PREAMP = (180.0, 1000.0, 4000.0)
+OP_POWER = (230.0, 750.0, 1500.0)
+
+
+def tube_plate_current(tube, vpc, vgc):
+    """The enhanced Norman Koren plate current, in amps — the shipping C++, not a copy of
+    the equations. `tube` is TUBE_6F5 / TUBE_6C5 / TUBE_2A3."""
+    f = np.vectorize(lambda p, g: _LIB.taptools_tube_plate_current(int(tube), float(p), float(g)))
+    return f(vpc, vgc)
+
+
+def tube_grid_current(tube, vgc):
+    """The grid-conduction branch, in amps (TASLP Eq. 9)."""
+    f = np.vectorize(lambda g: _LIB.taptools_tube_grid_current(int(tube), float(g)))
+    return f(vgc)
+
+
+def tube_params(tube):
+    """The fitted parameters as a dict: mu, ex, kg, kp, kvb, vct, va, rgk."""
+    buf = np.zeros(8)
+    n = _LIB.taptools_tube_params(int(tube), _p64(buf))
+    if n != 8:
+        raise ValueError(f"no such tube: {tube}")
+    return dict(zip(("mu", "ex", "kg", "kp", "kvb", "vct", "va", "rgk"), buf))
+
+
+class Triode:
+    """tap.triode~'s kernel (tap::tools::ondes::triode): one common-cathode triode
+    stage, solved on its load line from the enhanced Norman Koren tube model at a
+    published operating point.
+
+    The tube parameters are fitted to the actual valves in ondes Martenot No. 169
+    (Najnudel, Hélie, Roze & Boutin, IEEE/ACM TASLP 28, 2020, Table II) — 6F5 in the
+    oscillators, 6C5 in the demodulator and preamplifier, 2A3 in the power amplifier.
+    Nothing here is voiced by ear.
+
+    The static load-line solution is a memoryless nonlinearity in the DAFx-07 sense,
+    so tabulating it is not an approximation of the model, it IS the model. Output is
+    normalized by the stage's own small-signal gain, so `drive` changes the distortion
+    without changing the level — and the stage inverts, because a real one does."""
+
+    def __init__(self, sr: float = 48000.0, tube=TUBE_6C5, operating_point=OP_DEMOD, **params):
+        self._h = _LIB.taptools_triode_create()
+        _check(_LIB.taptools_triode_prepare(self._h, float(sr)), "prepare")
+        _check(_LIB.taptools_triode_set_tube(self._h, int(tube)), "tube")
+        self.set_operating_point(*operating_point)
+        self.set(**params)
+
+    def set_operating_point(self, vbias, rk, rp) -> "Triode":
+        """Supply volts, cathode resistor, plate load. A mode, not a fader: it rebuilds
+        the curve table."""
+        _check(_LIB.taptools_triode_set_operating_point(self._h, float(vbias), float(rk),
+                                                        float(rp)), "operating_point")
+        return self
+
+    def set(self, *, tube=None, drive=None, corners=None) -> "Triode":
+        if tube is not None:
+            _check(_LIB.taptools_triode_set_tube(self._h, int(tube)), "tube")
+        if drive is not None:
+            _check(_LIB.taptools_triode_set_drive(self._h, float(drive)), "drive")
+        if corners is not None:
+            _check(_LIB.taptools_triode_set_corners(self._h, float(corners[0]),
+                                                    float(corners[1])), "corners")
+        return self
+
+    @property
+    def bias(self):
+        """The quiescent point: (cathode volts, plate volts, plate amps, gain magnitude)."""
+        return (_LIB.taptools_triode_bias_v(self._h),
+                _LIB.taptools_triode_quiescent_plate_v(self._h),
+                _LIB.taptools_triode_quiescent_current_a(self._h),
+                _LIB.taptools_triode_gain(self._h))
+
+    def curve(self, x):
+        """The static transfer, normalized in and out. No state touched."""
+        f = np.vectorize(lambda v: _LIB.taptools_triode_curve_at(self._h, float(v)))
+        return f(x)
+
+    def plate_swing(self, grid_volts):
+        """The same curve in the tube's units: grid volts in, plate volts of swing out."""
+        f = np.vectorize(lambda v: _LIB.taptools_triode_plate_swing_at(self._h, float(v)))
+        return f(grid_volts)
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_triode_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        _check(_LIB.taptools_triode_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_triode_destroy(h)
+            self._h = None
+
+
+class Detector:
+    """The Ondes Martenot's heterodyne pair and its envelope detector
+    (tap::tools::ondes::detector), with no carrier simulated at all.
+
+    The envelope of `cos(P) + depth*cos(P - p)` is exactly
+    `sqrt(1 + depth^2 + 2 depth cos(p))`, so the 80 kHz carrier drops out of the
+    arithmetic. At depth 1 that is `2|cos(p/2)|`, whose harmonics sit at -14.0 / -21.3 /
+    -26.4 dB **before anything nonlinear happens** — which is the instrument's single
+    largest source of harmonics, and the thing you lose if you synthesize the difference
+    tone as a sinusoid.
+
+    The detector is the published one: a triode grid at near-zero bias conducts on
+    positive half-cycles and charges instantly, and R4*C21 discharges it with a 200 us
+    time constant. `set_ribbon` takes semitones above A1 because the circuit paper's
+    Eq. 7 makes the ribbon linear in semitones."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_detector_create()
+        _check(_LIB.taptools_detector_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, frequency=None, ribbon=None, depth=None, detect_ms=None) -> "Detector":
+        for value, fn, name in (
+            (frequency, _LIB.taptools_detector_set_frequency, "frequency"),
+            (ribbon, _LIB.taptools_detector_set_ribbon, "ribbon"),
+            (depth, _LIB.taptools_detector_set_depth, "depth"),
+            (detect_ms, _LIB.taptools_detector_set_detect_ms, "detect_ms"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    def envelope(self, phase):
+        """The ideal envelope at a phase in [0, 1), with no detector on it."""
+        f = np.vectorize(lambda p: _LIB.taptools_detector_envelope_at(self._h, float(p)))
+        return f(phase)
+
+    def process(self, n: int) -> np.ndarray:
+        out = np.zeros(int(n))
+        _check(_LIB.taptools_detector_process(self._h, _p64(out), out.size), "process")
+        return out
+
+    def clear(self) -> None:
+        _check(_LIB.taptools_detector_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_detector_destroy(h)
+            self._h = None
+
+
+class Ondes:
+    """tap.ondes~'s kernel (tap::tools::ondes::voice): the Ondes Martenot minus its
+    loudspeaker — the heterodyne detector into the demodulator triode into the
+    preamplifier triode into the intensity key.
+
+    A source, not an effect: `process(n)` renders n samples. Patch a Palme or a
+    Metallique after it for the rest of the instrument.
+
+    `ribbon` is semitones above A1 (55 Hz), because the published ribbon law is linear
+    in semitones. `drive` is the harmonics control — the circuit paper's own plugin
+    exposes demodulator input gain the same way, a knob the real instrument does not
+    have. `power_stage` runs the 2A3 and is off by default, following the paper.
+    `polarity` and `key_placement` are choices the sources do not settle, offered as
+    switches rather than guessed at silently."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_ondes_create()
+        _check(_LIB.taptools_ondes_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, ribbon=None, frequency=None, depth=None, detect_ms=None, drive=None,
+            key=None, key_mm=None, key_placement=None, power_stage=None, polarity=None,
+            level=None, oversample=None, smooth_ms=None) -> "Ondes":
+        # configuration first, so ramped targets in the same call honor the new slew
+        if oversample is not None:
+            _check(_LIB.taptools_ondes_set_oversample(self._h, int(oversample)), "oversample")
+        if key_placement is not None:
+            _check(_LIB.taptools_ondes_set_key_placement(self._h, int(key_placement)), "key_placement")
+        if power_stage is not None:
+            _check(_LIB.taptools_ondes_set_power_stage(self._h, 1 if power_stage else 0), "power_stage")
+        if polarity is not None:
+            _check(_LIB.taptools_ondes_set_polarity(self._h, int(polarity)), "polarity")
+        if smooth_ms is not None:
+            _check(_LIB.taptools_ondes_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        for value, fn, name in (
+            (ribbon, _LIB.taptools_ondes_set_ribbon, "ribbon"),
+            (frequency, _LIB.taptools_ondes_set_frequency, "frequency"),
+            (depth, _LIB.taptools_ondes_set_depth, "depth"),
+            (detect_ms, _LIB.taptools_ondes_set_detect_ms, "detect_ms"),
+            (drive, _LIB.taptools_ondes_set_drive, "drive"),
+            (key, _LIB.taptools_ondes_set_key, "key"),
+            (key_mm, _LIB.taptools_ondes_set_key_mm, "key_mm"),
+            (level, _LIB.taptools_ondes_set_level, "level"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    @property
+    def frequency(self) -> float:
+        return float(_LIB.taptools_ondes_frequency(self._h))
+
+    def process(self, n=None, ribbon=None, key=None) -> np.ndarray:
+        """Render samples. Pass arrays for `ribbon` (semitones) and `key` (0..1) to play
+        the performance surface sample by sample; both must be given together."""
+        if ribbon is None and key is None:
+            out = np.zeros(int(n))
+            _check(_LIB.taptools_ondes_process(self._h, _p64(out), out.size), "process")
+            return out
+        rib = _f64(np.atleast_1d(ribbon if ribbon is not None else 24.0))
+        k = _f64(np.atleast_1d(key if key is not None else 1.0))
+        size = int(n) if n is not None else max(rib.size, k.size)
+        rib = _f64(np.broadcast_to(rib, (size,)) if rib.size != size else rib)
+        k = _f64(np.broadcast_to(k, (size,)) if k.size != size else k)
+        out = np.zeros(size)
+        _check(_LIB.taptools_ondes_process_mod(self._h, _p64(rib), _p64(k), _p64(out), size),
+               "process_mod")
+        return out
+
+    def clear(self) -> None:
+        """Silence the detector and every filter. Parameters are kept."""
+        _check(_LIB.taptools_ondes_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_ondes_destroy(h)
+            self._h = None
+
+
+class Plate:
+    """The metallique's body on its own (tap::tools::diffuseur::plate) — eight
+    driven modes at the free circular plate's transverse ratios, each split
+    into a beating doublet, with no driver in front of it. A component, not an
+    external: reachable so that a measurement of the body is not silently a
+    measurement of the transducer too."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_plate_create()
+        _check(_LIB.taptools_plate_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, pitch_hz=None, decay=None, tilt=None, brightness=None) -> "Plate":
+        for value, fn, name in (
+            (pitch_hz, _LIB.taptools_plate_set_pitch_hz, "pitch_hz"),
+            (decay, _LIB.taptools_plate_set_decay, "decay"),
+            (tilt, _LIB.taptools_plate_set_tilt, "tilt"),
+            (brightness, _LIB.taptools_plate_set_brightness, "brightness"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    def modes(self):
+        """Where the eight modes landed: (Hz, doublet weight) arrays."""
+        hz = np.array([_LIB.taptools_plate_mode_hz(self._h, i) for i in range(8)])
+        lv = np.array([_LIB.taptools_plate_mode_level(self._h, i) for i in range(8)])
+        return hz, lv
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_plate_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        _check(_LIB.taptools_plate_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_plate_destroy(h)
+            self._h = None
+
+
+class Transducer:
+    """The diffuseurs' moving-iron driver on its own
+    (tap::tools::diffuseur::transducer) — a component, not an external.
+
+    `asymmetry` is the moving-iron squared term: force follows the square of
+    the gap flux, so with a bias current the residual i² puts a second harmonic
+    on the output at exactly asymmetry x amplitude / 2 relative to the
+    fundamental, and nothing at the third. `saturation` is the bounding stage
+    (0 is exactly linear); the output is bounded by 2/saturation rather than
+    1/saturation, because taking the DC out of a hard-driven squared law
+    doubles the worst-case swing."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_transducer_create()
+        _check(_LIB.taptools_transducer_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, drive=None, asymmetry=None, saturation=None) -> "Transducer":
+        for value, fn, name in (
+            (drive, _LIB.taptools_transducer_set_drive, "drive"),
+            (asymmetry, _LIB.taptools_transducer_set_asymmetry, "asymmetry"),
+            (saturation, _LIB.taptools_transducer_set_saturation, "saturation"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_transducer_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        _check(_LIB.taptools_transducer_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_transducer_destroy(h)
+            self._h = None
+
+
+class Metallique:
+    """tap.metallique~'s kernel (tap::tools::diffuseur::metallique): the Ondes
+    Martenot's motor-driven gong diffuseur, as a *driven* resonator — a
+    moving-iron transducer feeding a bank of plate modes, in that order,
+    because that is the order the instrument wires them.
+
+    The mode ratios are Fletcher & Rossing's free circular plate (Rayleigh's
+    Chladni set, 1 : 1.730 : 2.328 : 3.910 : 4.110 : 6.300 : 6.710 : 7.340),
+    each split into a slowly beating doublet. No ondes-specific modal
+    measurement exists in any of the sources, so the body is a **recreation of
+    the general physics**, not a model of Martenot's instrument.
+
+    `drive` / `asymmetry` / `saturation` are the transducer: asymmetry is the
+    moving-iron squared term (force follows the square of the gap flux), and
+    saturation is the bounding stage that keeps the squared law finite. Neither
+    coefficient is fitted to a measurement — set both to 0 for a linear body."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_metallique_create()
+        _check(_LIB.taptools_metallique_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, pitch_hz=None, decay=None, tilt=None, brightness=None, drive=None,
+            asymmetry=None, saturation=None, mix=None, level=None, smooth_ms=None) -> "Metallique":
+        # configuration first, so ramped targets in the same call honor the new slew
+        if smooth_ms is not None:
+            _check(_LIB.taptools_metallique_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        for value, fn, name in (
+            (pitch_hz, _LIB.taptools_metallique_set_pitch_hz, "pitch_hz"),
+            (decay, _LIB.taptools_metallique_set_decay, "decay"),
+            (tilt, _LIB.taptools_metallique_set_tilt, "tilt"),
+            (brightness, _LIB.taptools_metallique_set_brightness, "brightness"),
+            (drive, _LIB.taptools_metallique_set_drive, "drive"),
+            (asymmetry, _LIB.taptools_metallique_set_asymmetry, "asymmetry"),
+            (saturation, _LIB.taptools_metallique_set_saturation, "saturation"),
+            (mix, _LIB.taptools_metallique_set_mix, "mix"),
+            (level, _LIB.taptools_metallique_set_level, "level"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    def modes(self):
+        """Where the eight modes landed: (Hz, doublet weight) arrays."""
+        hz = np.array([_LIB.taptools_metallique_mode_hz(self._h, i) for i in range(8)])
+        lv = np.array([_LIB.taptools_metallique_mode_level(self._h, i) for i in range(8)])
+        return hz, lv
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_metallique_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        """Silence the body and reset the driver; parameters are untouched."""
+        _check(_LIB.taptools_metallique_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_metallique_destroy(h)
+            self._h = None
+
+
+class Palme:
+    """tap.palme~'s kernel (tap::tools::diffuseur::palme): the Ondes Martenot's
+    string diffuseur — an electromagnet driving twelve metal strings on a
+    soundboard, here a moving-iron transducer into twelve damped waveguide
+    loops. Only the strings whose partials line up with the drive ring loudly,
+    which is the halo the instrument is known for.
+
+    **Twelve** strings, per the peer-reviewed source; the widely copied
+    hobbyist figure of twenty-four is not followed. Their tuning is not
+    published anywhere found, so it is a parameter: `tuning` 0 lays them out
+    chromatically across an octave from `root_hz` (a string for every pitch
+    class), 1 as the harmonic series on the root.
+
+    Note that `decay` and `damping` are not independent — a heavily damped
+    string cannot ring for the time you ask, and `string_feedback()` shows the
+    loop gain pinned at its cap when they fight."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_palme_create()
+        _check(_LIB.taptools_palme_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, root_hz=None, tuning=None, decay=None, damping=None, detune=None,
+            drive=None, asymmetry=None, saturation=None, mix=None, level=None,
+            smooth_ms=None) -> "Palme":
+        if tuning is not None:
+            _check(_LIB.taptools_palme_set_tuning(self._h, int(tuning)), "tuning")
+        if smooth_ms is not None:
+            _check(_LIB.taptools_palme_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        for value, fn, name in (
+            (root_hz, _LIB.taptools_palme_set_root_hz, "root_hz"),
+            (decay, _LIB.taptools_palme_set_decay, "decay"),
+            (damping, _LIB.taptools_palme_set_damping, "damping"),
+            (detune, _LIB.taptools_palme_set_detune, "detune"),
+            (drive, _LIB.taptools_palme_set_drive, "drive"),
+            (asymmetry, _LIB.taptools_palme_set_asymmetry, "asymmetry"),
+            (saturation, _LIB.taptools_palme_set_saturation, "saturation"),
+            (mix, _LIB.taptools_palme_set_mix, "mix"),
+            (level, _LIB.taptools_palme_set_level, "level"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    def strings(self):
+        """Where the twelve strings ended up: (Hz, loop gain) arrays."""
+        hz = np.array([_LIB.taptools_palme_string_hz(self._h, i) for i in range(12)])
+        fb = np.array([_LIB.taptools_palme_string_feedback(self._h, i) for i in range(12)])
+        return hz, fb
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_palme_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        """Damp every string and reset the driver; parameters are untouched."""
+        _check(_LIB.taptools_palme_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_palme_destroy(h)
+            self._h = None
+
+
+class Scrub:
+    """tap.scrub~'s kernel (tap::tools::scrub::machine): a granular scrub pad
+    over live capture. The tape is stammer.h's `capture` — the same live reel,
+    shared rather than copied — and the playhead is a Hann-windowed grain
+    scheduler whose position and pitch are two independent performable signals.
+
+    `position_ms` is a lag behind the live edge; `pitch` transposes without the
+    position moving; `freeze` stops the recorder so the position addresses
+    fixed tape; `drift` walks the playhead through the tape on its own.
+
+    Hann satisfies the overlap-add condition at hop = size/overlap, so at
+    overlap 2 with pitch 0, no spray and a held whole-sample position, the
+    scrub *is* the input delayed — pinned to under 1e-12 by the kernel tests."""
+
+    def __init__(self, sr: float = 48000.0, max_history_ms: float = 4000.0, **params):
+        self._h = _LIB.taptools_scrub_create()
+        _check(_LIB.taptools_scrub_prepare(self._h, float(sr), float(max_history_ms)), "prepare")
+        self.set(**params)
+
+    def set(self, *, position_ms=None, pitch=None, drift=None, freeze=None, size_ms=None,
+            overlap=None, spray_ms=None, seed=None, mix=None, level=None,
+            smooth_ms=None) -> "Scrub":
+        if overlap is not None:
+            _check(_LIB.taptools_scrub_set_overlap(self._h, int(overlap)), "overlap")
+        if freeze is not None:
+            _check(_LIB.taptools_scrub_set_freeze(self._h, 1 if freeze else 0), "freeze")
+        if seed is not None:
+            _check(_LIB.taptools_scrub_set_seed(self._h, int(seed)), "seed")
+        if smooth_ms is not None:
+            _check(_LIB.taptools_scrub_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        for value, fn, name in (
+            (position_ms, _LIB.taptools_scrub_set_position_ms, "position_ms"),
+            (pitch, _LIB.taptools_scrub_set_pitch, "pitch"),
+            (drift, _LIB.taptools_scrub_set_drift, "drift"),
+            (size_ms, _LIB.taptools_scrub_set_size_ms, "size_ms"),
+            (spray_ms, _LIB.taptools_scrub_set_spray_ms, "spray_ms"),
+            (mix, _LIB.taptools_scrub_set_mix, "mix"),
+            (level, _LIB.taptools_scrub_set_level, "level"),
+        ):
+            if value is not None:
+                _check(fn(self._h, float(value)), name)
+        return self
+
+    @property
+    def active_grains(self) -> int:
+        return int(_LIB.taptools_scrub_active_grains(self._h))
+
+    def process(self, x, position_ms=None, pitch=None) -> np.ndarray:
+        """Run n samples. Pass arrays for `position_ms` / `pitch` to drive the
+        performance surface at signal rate (both must be given together)."""
+        x = _f64(x)
+        out = np.zeros_like(x)
+        if position_ms is None and pitch is None:
+            _check(_LIB.taptools_scrub_process(self._h, _p64(x), _p64(out), x.size), "process")
+        else:
+            pos = _f64(np.broadcast_to(np.asarray(position_ms if position_ms is not None else 0.0,
+                                                  dtype=np.float64), x.shape))
+            pit = _f64(np.broadcast_to(np.asarray(pitch if pitch is not None else 0.0,
+                                                  dtype=np.float64), x.shape))
+            _check(_LIB.taptools_scrub_process_mod(self._h, _p64(x), _p64(pos), _p64(pit),
+                                                   _p64(out), x.size), "process_mod")
+        return out
+
+    def clear(self) -> None:
+        """Erase the tape, kill every grain, and restart the seeded stream."""
+        _check(_LIB.taptools_scrub_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_scrub_destroy(h)
+            self._h = None
+
+
+class Touche:
+    """tap.touche~'s kernel (tap::tools::touche::key): the Ondes Martenot
+    intensity key as a gain law. The curve is not modelled — it is Quartier
+    et al.'s published measurement (Acta Acustica 101(2), 2015, Table II),
+    interpolated with monotone cubic segments through all seven points: 50 dB
+    over 4.5 mm of the key's travel, referenced to 0 dB at full press.
+    `position` spans the physical 9.5 mm throw, so the bottom ~45% is silent
+    — that dead zone is the key bending before it reaches the powder bag.
+    `mode` 0 drives from displacement (primary), 1 from finger force."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_touche_create()
+        _check(_LIB.taptools_touche_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, position=None, position_mm=None, force_n=None, mode=None,
+            smooth_ms=None) -> "Touche":
+        # configuration first, so ramped targets in the same call honor the new slew
+        if mode is not None:
+            _check(_LIB.taptools_touche_set_mode(self._h, int(mode)), "mode")
+        if smooth_ms is not None:
+            _check(_LIB.taptools_touche_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        if position is not None:
+            _check(_LIB.taptools_touche_set_position(self._h, float(position)), "position")
+        if position_mm is not None:
+            _check(_LIB.taptools_touche_set_position_mm(self._h, float(position_mm)), "position_mm")
+        if force_n is not None:
+            _check(_LIB.taptools_touche_set_force_n(self._h, float(force_n)), "force_n")
+        return self
+
+    def gain_at(self, p) -> float:
+        """The curve itself: linear gain at a normalized position. No state touched."""
+        return float(_LIB.taptools_touche_gain_at(self._h, float(p)))
+
+    def curve(self, n: int = 512):
+        """The whole law as (position, linear gain) arrays — for plotting."""
+        p = np.linspace(0.0, 1.0, int(n))
+        return p, np.array([self.gain_at(v) for v in p])
+
+    def process(self, x, position=None) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        if position is None:
+            _check(_LIB.taptools_touche_process(self._h, _p64(x), _p64(out), x.size), "process")
+        else:
+            pos = _f64(position)
+            _check(_LIB.taptools_touche_process_mod(self._h, _p64(x), _p64(pos), _p64(out), x.size),
+                   "process_mod")
+        return out
+
+    def clear(self) -> None:
+        """Return the key to rest (silent)."""
+        _check(_LIB.taptools_touche_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_touche_destroy(h)
+            self._h = None
+
+
+class Fuzz:
+    """tap.fuzz~'s kernel (tap::tools::fuzz::pedal): a two-stage, tone-stacked
+    distortion built on the Yeh/Abel/Smith DAFx-07 simplified cascade
+    (conditioning filter -> memoryless nonlinearity -> equalization filter,
+    twice), with a bass / contrast / treble voicing section outside the
+    nonlinearity. `gain` sweeps the first stage's drive, `edge` the second
+    stage's knee sharpness, `asymmetry` buys even harmonics. The clipper pair
+    runs oversampled (1/2/4/8x, default 4). A recreation of a circuit class,
+    not a component model of any one pedal."""
+
+    def __init__(self, sr: float = 48000.0, **params):
+        self._h = _LIB.taptools_fuzz_create()
+        _check(_LIB.taptools_fuzz_prepare(self._h, float(sr)), "prepare")
+        self.set(**params)
+
+    def set(self, *, gain=None, edge=None, asymmetry=None, bass=None, treble=None,
+            contrast=None, level_db=None, oversample=None, smooth_ms=None) -> "Fuzz":
+        # configuration first, so ramped targets in the same call honor the new slew
+        if oversample is not None:
+            _check(_LIB.taptools_fuzz_set_oversample(self._h, int(oversample)), "oversample")
+        if smooth_ms is not None:
+            _check(_LIB.taptools_fuzz_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        for name, value in (("gain", gain), ("edge", edge), ("asymmetry", asymmetry),
+                            ("bass", bass), ("treble", treble), ("contrast", contrast),
+                            ("level_db", level_db)):
+            if value is not None:
+                _check(getattr(_LIB, "taptools_fuzz_set_" + name)(self._h, float(value)), name)
+        return self
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_fuzz_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        """Flush the filters and the oversampling chain; parameters are kept."""
+        _check(_LIB.taptools_fuzz_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_fuzz_destroy(h)
+            self._h = None
+
+
+class Stammer:
+    """tap.stammer~'s kernel (tap::tools::stammer::machine): the live
+    buffer-stutter rig. The input is captured continuously; on a `step_ms`
+    grid the machine rolls dice and re-fires a slice of what just went past
+    — `density` how often it grabs, `divisions` how finely it chops (slice =
+    step / [1, divisions]), `repeats` how many passes it holds on for,
+    `reverse` the per-repeat chance of running backwards, `jump_ms` how far
+    further back it may reach. Every draw comes from the family's seeded
+    xorshift64*, so a seed is a performance you can replay; at density 0 the
+    dice are never rolled and the object is a bitwise bypass. Mono."""
+
+    def __init__(self, sr: float = 48000.0, max_history_ms: float = 4000.0, **params):
+        self._h = _LIB.taptools_stammer_create()
+        _check(_LIB.taptools_stammer_prepare(self._h, float(sr), float(max_history_ms)),
+               "prepare")
+        self.set(**params)
+
+    def set(self, *, step_ms=None, density=None, divisions=None, repeats=None, reverse=None,
+            jump_ms=None, fade_ms=None, seed=None, input_level=None, mix=None,
+            smooth_ms=None) -> "Stammer":
+        # configuration first, so ramped targets in the same call honor the new slew
+        if smooth_ms is not None:
+            _check(_LIB.taptools_stammer_set_smooth_ms(self._h, float(smooth_ms)), "smooth_ms")
+        if seed is not None:
+            _check(_LIB.taptools_stammer_set_seed(self._h, int(seed)), "seed")
+        if step_ms is not None:
+            _check(_LIB.taptools_stammer_set_step_ms(self._h, float(step_ms)), "step_ms")
+        if density is not None:
+            _check(_LIB.taptools_stammer_set_density(self._h, float(density)), "density")
+        if divisions is not None:
+            _check(_LIB.taptools_stammer_set_divisions(self._h, int(divisions)), "divisions")
+        if repeats is not None:
+            _check(_LIB.taptools_stammer_set_repeats(self._h, int(repeats)), "repeats")
+        if reverse is not None:
+            _check(_LIB.taptools_stammer_set_reverse(self._h, float(reverse)), "reverse")
+        if jump_ms is not None:
+            _check(_LIB.taptools_stammer_set_jump_ms(self._h, float(jump_ms)), "jump_ms")
+        if fade_ms is not None:
+            _check(_LIB.taptools_stammer_set_fade_ms(self._h, float(fade_ms)), "fade_ms")
+        if input_level is not None:
+            _check(_LIB.taptools_stammer_set_input_level(self._h, float(input_level)),
+                   "input_level")
+        if mix is not None:
+            _check(_LIB.taptools_stammer_set_mix(self._h, float(mix)), "mix")
+        return self
+
+    @property
+    def playing(self) -> bool:
+        """True while a slice is sounding."""
+        return bool(_LIB.taptools_stammer_playing(self._h))
+
+    def process(self, x) -> np.ndarray:
+        x = _f64(x)
+        out = np.zeros_like(x)
+        _check(_LIB.taptools_stammer_process(self._h, _p64(x), _p64(out), x.size), "process")
+        return out
+
+    def clear(self) -> None:
+        """Erase the capture, drop the slice in flight, and rewind the seeded
+        stream — the same seed replays the same performance."""
+        _check(_LIB.taptools_stammer_clear(self._h), "clear")
+
+    def __del__(self):
+        h = getattr(self, "_h", None)
+        if h:
+            _LIB.taptools_stammer_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
diff --git a/notebooks/touche.ipynb b/notebooks/touche.ipynb
new file mode 100644
index 0000000..ae736e2
--- /dev/null
+++ b/notebooks/touche.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6def32be",
+   "metadata": {},
+   "source": [
+    "# tap.touche~ — the key, reproduced\n",
+    "\n",
+    "The *touche d'intensité* is the pressure key the Ondes Martenot player's left hand rides, and\n",
+    "Messiaen called it the instrument's greatest invention. Physically it is a graphite/mica powder\n",
+    "bag working as a rheostat — the carbon-microphone principle, compress it and resistance falls —\n",
+    "and what the player feels is a carefully chosen nonlinear spring.\n",
+    "\n",
+    "This kernel's contract is unusual for the library. Every other object here makes design choices\n",
+    "and then measures them. **This one is obliged to reproduce someone else's measurement.**\n",
+    "Quartier, Meurisse, Colmars, Frelat & Vaiedelich, *\"Intensity Key of the Ondes Martenot: An\n",
+    "Early Mechanical Haptic Device\"* (Acta Acustica united with Acustica 101(2), 421–428, 2015)\n",
+    "measured finger force, key displacement and the resulting sound simultaneously on instrument\n",
+    "No. 320, and published the boundaries of the six musical nuances across the key's travel. Those\n",
+    "seven points are the specification; `touche.h` interpolates them and must not wander.\n",
+    "\n",
+    "So section 1 is the only test that really matters, and the rest are about whether the\n",
+    "interpolation between the points can be trusted.\n",
+    "\n",
+    "Sections: **1** the published points come back · **2** the shape, and why not a line · **3** the\n",
+    "dead zone · **4** against the two obvious alternatives"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6d55db42",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T20:23:13.326652Z",
+     "iopub.status.busy": "2026-08-16T20:23:13.326428Z",
+     "iopub.status.idle": "2026-08-16T20:23:13.812503Z",
+     "shell.execute_reply": "2026-08-16T20:23:13.811168Z"
+    }
+   },
+   "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, 3.4),\n",
+    "    \"axes.grid\": True, \"grid.alpha\": 0.3,\n",
+    "})\n",
+    "C = tap.PALETTE\n",
+    "sr = 48000.0\n",
+    "\n",
+    "# Quartier et al. 2015, Table II — the measurement this object exists to reproduce.\n",
+    "TABLE_DB = np.array([45.0, 53.3, 61.6, 70.0, 78.3, 86.6, 95.0])\n",
+    "TABLE_MM = np.array([4.3, 5.3, 5.9, 6.4, 6.8, 7.3, 8.8])\n",
+    "TABLE_N  = np.array([0.39, 0.47, 0.52, 0.62, 0.82, 1.34, 9.60])\n",
+    "TRAVEL_MM = 9.5   # the physical throw the paper describes (~3 to 9.5 mm of gesture)\n",
+    "\n",
+    "key = tap.Touche(sr, smooth_ms=0)\n",
+    "\n",
+    "def db(g):\n",
+    "    g = np.asarray(g, dtype=float)\n",
+    "    return 20 * np.log10(np.where(g > 0, g, 1e-300))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd909ecc",
+   "metadata": {},
+   "source": [
+    "## 1 · The published points come back\n",
+    "\n",
+    "Referenced to full press, the seven measured nuance boundaries are −50.0, −41.7, −33.4, −25.0,\n",
+    "−16.7, −8.4 and 0.0 dB. Read straight out of the object at the corresponding displacements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2fd20aab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T20:23:13.815471Z",
+     "iopub.status.busy": "2026-08-16T20:23:13.815141Z",
+     "iopub.status.idle": "2026-08-16T20:23:13.821538Z",
+     "shell.execute_reply": "2026-08-16T20:23:13.820187Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    mm   published     kernel     error\n",
+      "   4.3      -50.0 dB   -50.000 dB   0.0000\n",
+      "   5.3      -41.7 dB   -41.700 dB   0.0001\n",
+      "   5.9      -33.4 dB   -33.400 dB   0.0000\n",
+      "   6.4      -25.0 dB   -25.000 dB   0.0001\n",
+      "   6.8      -16.7 dB   -16.700 dB   0.0000\n",
+      "   7.3       -8.4 dB    -8.400 dB  -0.0001\n",
+      "   8.8        0.0 dB     0.000 dB   0.0000\n",
+      "\n",
+      "largest error: 5.74e-05 dB\n"
+     ]
+    }
+   ],
+   "source": [
+    "want = TABLE_DB - TABLE_DB[-1]\n",
+    "got = np.array([db(key.gain_at(mm / TRAVEL_MM)) for mm in TABLE_MM])\n",
+    "\n",
+    "print(f\"{'mm':>6} {'published':>11} {'kernel':>10} {'error':>9}\")\n",
+    "for mm, w, g in zip(TABLE_MM, want, got):\n",
+    "    print(f\"{mm:6.1f} {w:10.1f} dB {g:9.3f} dB {g - w:8.4f}\")\n",
+    "print(f\"\\nlargest error: {np.max(np.abs(got - want)):.2e} dB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ce1dc3c",
+   "metadata": {},
+   "source": [
+    "## 2 · The shape, and why it is not fitted\n",
+    "\n",
+    "The reason the kernel interpolates the table rather than fitting a curve through it: the\n",
+    "published dB steps are equal by construction (six nuances over 50 dB) and the *displacement*\n",
+    "steps are not — 1.0, 0.6, 0.5, 0.4, 0.5, 1.5 mm. The law steepens through the middle of the\n",
+    "travel and flattens hard at the top. A straight line in dB-against-mm would throw away exactly\n",
+    "the property that makes the key expressive.\n",
+    "\n",
+    "Interpolation is monotone cubic (Fritsch–Carlson), which passes through every measured point\n",
+    "and cannot overshoot between them. Overshoot here would be a non-monotone gain — audible as a\n",
+    "dip while you press *harder*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e01f8be5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T20:23:13.823702Z",
+     "iopub.status.busy": "2026-08-16T20:23:13.823481Z",
+     "iopub.status.idle": "2026-08-16T20:23:14.062129Z",
+     "shell.execute_reply": "2026-08-16T20:23:14.060577Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "largest departure from a straight line: 8.3 dB\n",
+      "displacement steps for equal dB steps: [1.  0.6 0.5 0.4 0.5 1.5] mm\n",
+      "monotone: True   within [0, 1]: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = np.linspace(0, 1, 2001)\n",
+    "mm = p * TRAVEL_MM\n",
+    "curve = db(np.array([key.gain_at(v) for v in p]))\n",
+    "\n",
+    "inband = (mm >= TABLE_MM[0]) & (mm <= TABLE_MM[-1])\n",
+    "# The straight line the kernel deliberately is not: -50 dB at the measured floor, 0 dB at full\n",
+    "# press, linear in displacement between.\n",
+    "frac = (mm - TABLE_MM[0]) / (TABLE_MM[-1] - TABLE_MM[0])\n",
+    "line = -50.0 * (1.0 - frac)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(mm[inband], curve[inband], color=C[0], lw=2.0, label=\"published curve, interpolated\")\n",
+    "ax.plot(mm[inband], line[inband], color=C[3], lw=1.0, ls=\"--\", label=\"a straight line, for comparison\")\n",
+    "ax.plot(TABLE_MM, want, \"o\", color=C[2], ms=7, zorder=5, label=\"Quartier et al. 2015, Table II\")\n",
+    "ax.set_xlabel(\"key displacement (mm)\"); ax.set_ylabel(\"gain (dB, referenced to full press)\")\n",
+    "ax.set_title(\"the intensity key's law — measured points, and what a line would have said\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "dev = np.max(np.abs(curve[inband] - line[inband]))\n",
+    "print(f\"largest departure from a straight line: {dev:.1f} dB\")\n",
+    "print(f\"displacement steps for equal dB steps: {np.round(np.diff(TABLE_MM), 2)} mm\")\n",
+    "\n",
+    "# Monotone, and inside the envelope — the properties that make the interpolant safe.\n",
+    "g = np.array([key.gain_at(v) for v in p])\n",
+    "print(f\"monotone: {bool(np.all(np.diff(g) >= -1e-12))}   within [0, 1]: {bool(g.min() >= 0 and g.max() <= 1 + 1e-12)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d72ab22",
+   "metadata": {},
+   "source": [
+    "## 3 · The dead zone is the instrument's\n",
+    "\n",
+    "Normalized position spans the *physical* travel the paper describes (gestures span roughly\n",
+    "3–9.5 mm), not the measured band inside it. So the bottom of the throw is silent — and that is\n",
+    "not a modelling choice. The key's first phase is pure bending of the elastic strip, before it\n",
+    "even reaches the powder bag; 4.3 mm is where the instrument arrives at its own noise floor.\n",
+    "\n",
+    "The consequence is musical rather than cosmetic: the useful 50 dB is packed into 4.5 mm right\n",
+    "after a long silent approach, which is precisely what lets a player produce very sharp attacks\n",
+    "with a slow-looking gesture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "035e84ac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T20:23:14.064615Z",
+     "iopub.status.busy": "2026-08-16T20:23:14.064370Z",
+     "iopub.status.idle": "2026-08-16T20:23:14.219652Z",
+     "shell.execute_reply": "2026-08-16T20:23:14.218508Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x268.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "silent fraction of the throw: 45%\n",
+      "gain at rest: 0.0   at full press: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 2.8))\n",
+    "ax.plot(mm, np.array([key.gain_at(v) for v in p]), color=C[0], lw=2.0)\n",
+    "ax.axvspan(0, TABLE_MM[0], color=C[3], alpha=0.15)\n",
+    "ax.text(TABLE_MM[0] / 2, 0.55, \"silent:\\nthe key is still\\nbending\", ha=\"center\", color=C[3], fontsize=9)\n",
+    "ax.axvspan(TABLE_MM[-1], TRAVEL_MM, color=\"0.85\", alpha=0.5)\n",
+    "ax.text((TABLE_MM[-1] + TRAVEL_MM) / 2, 0.55, \"clamped\", ha=\"center\", color=\"0.4\", fontsize=9)\n",
+    "ax.set_xlabel(\"key displacement (mm)\"); ax.set_ylabel(\"linear gain\")\n",
+    "ax.set_xlim(0, TRAVEL_MM)\n",
+    "ax.set_title(\"50 dB in 4.5 mm, after a long silent approach\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"silent fraction of the throw: {TABLE_MM[0] / TRAVEL_MM:.0%}\")\n",
+    "print(f\"gain at rest: {key.gain_at(0.0)}   at full press: {key.gain_at(1.0)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d6d3aee",
+   "metadata": {},
+   "source": [
+    "## 4 · Against the two obvious alternatives\n",
+    "\n",
+    "If you needed an expressive volume control and did not have this measurement, you would reach\n",
+    "for one of two things: a linear fade, or a fade that is linear in dB. Neither is what the\n",
+    "instrument does. Below, the same triangular gesture through all three, measured as the energy\n",
+    "that actually comes out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "70f4a222",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-16T20:23:14.223486Z",
+     "iopub.status.busy": "2026-08-16T20:23:14.223218Z",
+     "iopub.status.idle": "2026-08-16T20:23:14.790782Z",
+     "shell.execute_reply": "2026-08-16T20:23:14.789386Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x326.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   linear in amplitude: rms 0.1633   half-travel gain 0.5000\n",
+      "          linear in dB: rms 0.0834   half-travel gain 0.0562\n",
+      "   the published curve: rms 0.1163   half-travel gain 0.0045\n"
+     ]
+    }
+   ],
+   "source": [
+    "gesture = np.concatenate([np.linspace(0, 1, int(1.5 * sr)), np.linspace(1, 0, int(1.5 * sr))])\n",
+    "t = np.arange(gesture.size) / sr\n",
+    "tone = 0.4 * np.sin(2 * np.pi * 220.0 * t)\n",
+    "\n",
+    "laws = {\n",
+    "    \"linear in amplitude\": gesture,\n",
+    "    \"linear in dB\":        np.where(gesture > 0, 10 ** ((-50.0 * (1 - gesture)) / 20), 0.0),\n",
+    "    \"the published curve\": tap.Touche(sr, smooth_ms=0).process(np.ones_like(gesture), position=gesture),\n",
+    "}\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i, (label, g) in enumerate(laws.items()):\n",
+    "    ax.plot(t, g, color=C[i], lw=1.6, label=label)\n",
+    "ax.set_xlabel(\"time (s)\"); ax.set_ylabel(\"gain\")\n",
+    "ax.set_title(\"one gesture, three laws\")\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "for label, g in laws.items():\n",
+    "    out = tone * g\n",
+    "    print(f\"{label:>22}: rms {np.sqrt(np.mean(out ** 2)):.4f}   \"\n",
+    "          f\"half-travel gain {np.interp(0.5, gesture[:gesture.size // 2], g[:gesture.size // 2]):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9aaa2242",
+   "metadata": {},
+   "source": [
+    "## Checkpoint\n",
+    "\n",
+    "- The seven published points come back out of the object to better than a hundredth of a dB.\n",
+    "  That is the whole contract.\n",
+    "- The law is monotone, stays inside the measured envelope, and departs from a straight line by\n",
+    "  a wide margin — which is the property worth having.\n",
+    "- The silent bottom of the throw is the key's own first phase, not a modelling artifact.\n",
+    "- And it is audibly neither of the two laws you would otherwise have reached for.\n",
+    "\n",
+    "Every number above lives twice: as a cell here and as a pinned scenario in\n",
+    "`tests/touche_test.cpp`."
+   ]
+  }
+ ],
+ "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/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 3421e67..87b89e8 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -16,15 +16,22 @@ add_executable(taptools_kernel_tests
     airport_test.cpp
     autowah_test.cpp
     delay_test.cpp
+    diffuseur_test.cpp
     diode_ladder_test.cpp
     discreet_test.cpp
     garden_test.cpp
+    fuzz_test.cpp
     grm_comb_test.cpp
     harmonizer_test.cpp
     nr_test.cpp
+    ondes_test.cpp
     overdrive_test.cpp
+    scrub_test.cpp
     spectra_test.cpp
     step_seq_test.cpp
+    stammer_test.cpp
+    tapecho_test.cpp
+    touche_test.cpp
     tune_test.cpp
     tr808_clap_test.cpp
     tr808_cymbal_test.cpp
diff --git a/tests/diffuseur_test.cpp b/tests/diffuseur_test.cpp
new file mode 100644
index 0000000..7a5d2ea
--- /dev/null
+++ b/tests/diffuseur_test.cpp
@@ -0,0 +1,501 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the Ondes Martenot diffuseur kernels (diffuseur.h).
+/// @details    Two things make this kernel's contract unusual enough to shape the suite. First,
+///             the bodies are **recreations** — the mode data is Fletcher & Rossing's general
+///             physics, not a measurement of Martenot's instruments — so there is no published
+///             table to reproduce the way tests/touche_test.cpp reproduces one. What can be
+///             pinned instead is that the maths is honest: unit peak gain per mode, weights that
+///             sum to one, the ring time you asked for, and the published ratios coming back out.
+///             Second, the signal order is a *claim about the instrument* — the transducer drives
+///             the body, so the nonlinearity is upstream — and a claim like that is worth a null
+///             test, which is what "the cabinet is its parts, wired in the order the instrument
+///             wires them" is.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/diffuseur.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    namespace D = tap::tools::diffuseur;
+
+    /// Magnitude of `x[from .. from+n)` at `hz` — the house's independent detector for a single
+    /// bin (same Goertzel the fuzz suite measures harmonics with).
+    double bin(const std::vector<double>& x, double hz, size_t from, size_t n) {
+        const double w  = 2.0 * k_pi * hz / k_sr;
+        const double c  = 2.0 * std::cos(w);
+        double       s1 = 0.0, s2 = 0.0;
+        for (size_t i = 0; i < n; ++i) {
+            const double s = x[from + i] + c * s1 - s2;
+            s2             = s1;
+            s1             = s;
+        }
+        return std::sqrt(std::max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2.0 / static_cast<double>(n);
+    }
+
+    std::vector<double> sine(double hz, double amp, size_t n) {
+        std::vector<double> x(n);
+        for (size_t i = 0; i < n; ++i) {
+            x[i] = amp * std::sin(2.0 * k_pi * hz * static_cast<double>(i) / k_sr);
+        }
+        return x;
+    }
+
+    /// Peak of a mode's response to a settled sine at `hz`, measured over the last tenth.
+    double mode_response(double f0, double t60, double hz, double settle_s) {
+        D::mode m;
+        m.prepare(k_sr);
+        m.set(f0, t60);
+        const int n     = static_cast<int>(settle_s * k_sr);
+        double    peak  = 0.0;
+        const int start = n - static_cast<int>(0.1 * k_sr);
+        for (int i = 0; i < n; ++i) {
+            const double y = m.process(std::sin(2.0 * k_pi * hz * static_cast<double>(i) / k_sr));
+            if (i >= start) {
+                peak = std::max(peak, std::abs(y));
+            }
+        }
+        return peak;
+    }
+
+} // namespace
+
+// The whole boundedness argument rests on this: Steiglitz's b0 = (1 - R^2)/2 makes the peak
+// magnitude 1 whatever the pole radius, so a bank of weighted modes is bounded by the sum of its
+// weights and needs no limiter after it. If this drifts, every other claim in the file goes with it.
+SCENARIO("a driven mode peaks at unity gain whatever its ring time") {
+    for (double t60 : {0.02, 0.2, 2.0}) {
+        // Settle for several ring times, and probe at the pole angle and a little either side —
+        // a very high-Q mode is narrower than a coarse probe grid, which is a measurement trap.
+        const double f0     = 440.0;
+        const double settle = std::max(0.5, 4.0 * t60);
+        double       peak   = 0.0;
+        for (int k = -4; k <= 4; ++k) {
+            peak = std::max(peak, mode_response(f0, t60, f0 * (1.0 + 0.0002 * k), settle));
+        }
+        INFO("t60 " << t60 << " s: peak gain " << peak);
+        CHECK(peak > 0.98);
+        CHECK(peak < 1.0001); // never above unity: that is what "bounded by the weights" means
+    }
+}
+
+SCENARIO("the ring time a mode is asked for is the ring time it delivers") {
+    D::mode m;
+    m.prepare(k_sr);
+    m.set(180.0, 4.0);
+
+    std::vector<double> y(static_cast<size_t>(k_sr * 5.0));
+    for (size_t i = 0; i < y.size(); ++i) {
+        y[i] = m.process((i == 0) ? 1.0 : 0.0);
+    }
+    auto peak_at = [&](double t) {
+        double       p = 0.0;
+        const size_t a = static_cast<size_t>(t * k_sr);
+        for (size_t i = 0; i < 9600; ++i) {
+            p = std::max(p, std::abs(y[a + i]));
+        }
+        return p;
+    };
+    // 60 dB in 4 s is 45 dB in 3 s, and the envelope is exponential so that is exact.
+    const double drop = 20.0 * std::log10(peak_at(3.5) / peak_at(0.5));
+    INFO("decay over 3 s: " << drop << " dB (a 4 s T60 predicts -45)");
+    CHECK(std::abs(drop + 45.0) < 0.2);
+}
+
+// The zeros at z = +-1 are why a driven bank cannot accumulate DC and needs no blocker after it.
+SCENARIO("a mode passes nothing at DC and nothing at Nyquist") {
+    D::mode m;
+    m.prepare(k_sr);
+    m.set(440.0, 0.5);
+
+    double dc = 0.0;
+    for (int i = 0; i < 48000; ++i) {
+        dc = m.process(1.0);
+    }
+    INFO("settled response to a constant: " << dc);
+    CHECK(std::abs(dc) < 1e-9);
+
+    D::mode n;
+    n.prepare(k_sr);
+    n.set(440.0, 0.5);
+    double nyq = 0.0;
+    for (int i = 0; i < 48000; ++i) {
+        nyq = n.process((i % 2 == 0) ? 1.0 : -1.0);
+    }
+    INFO("settled response to alternating ones: " << nyq);
+    CHECK(std::abs(nyq) < 1e-9);
+}
+
+SCENARIO("the plate's weights sum to one, so the body cannot amplify what drives it") {
+    D::plate p;
+    p.prepare(k_sr);
+    p.set_pitch_hz(180.0);
+    p.set_decay(20.0);
+    p.set_brightness(1.0);
+
+    double sum = 0.0;
+    for (int m = 0; m < D::k_plate_modes; ++m) {
+        sum += p.mode_level(m);
+    }
+    INFO("sum of doublet weights: " << sum);
+    CHECK(std::abs(sum - 1.0) < 1e-12);
+
+    // And measured: a long ring time, full brightness, and a bounded input never exceeds it.
+    unsigned r    = 12345u;
+    double   peak = 0.0;
+    for (int i = 0; i < static_cast<int>(k_sr * 5.0); ++i) {
+        r              = r * 1664525u + 1013904223u;
+        const double x = (r / 2147483648.0) - 1.0;
+        peak           = std::max(peak, std::abs(p.process(x)));
+    }
+    INFO("peak output for |x| <= 1: " << peak);
+    CHECK(peak <= 1.0);
+}
+
+SCENARIO("the plate's modes sit at the published free-plate ratios") {
+    D::plate p;
+    p.prepare(k_sr);
+    p.set_pitch_hz(150.0);
+
+    for (int m = 0; m < D::k_plate_modes; ++m) {
+        const double got  = p.mode_hz(m) / p.mode_hz(0);
+        const double want = D::k_plate_ratio[static_cast<size_t>(m)];
+        INFO("mode " << m << ": " << got << " vs published " << want);
+        // Within the fixed per-mode scatter (k_scatter_cents) and nothing more — the ratios are
+        // the citation, the scatter is the imperfection, and the two must not be confused.
+        CHECK(std::abs(got / want - 1.0) < 0.003);
+    }
+}
+
+SCENARIO("brightness closes the plate down toward its fundamental") {
+    auto upper_energy = [](double bright) {
+        D::plate p;
+        p.prepare(k_sr);
+        p.set_pitch_hz(150.0);
+        p.set_decay(2.0);
+        p.set_brightness(bright);
+
+        std::vector<double> y(static_cast<size_t>(k_sr * 2.0));
+        unsigned            r = 999u;
+        for (size_t i = 0; i < y.size(); ++i) {
+            r    = r * 1664525u + 1013904223u;
+            y[i] = p.process((r / 2147483648.0) - 1.0);
+        }
+        const size_t from = y.size() / 2;
+        return bin(y, p.mode_hz(4), from, y.size() - from) / bin(y, p.mode_hz(0), from, y.size() - from);
+    };
+
+    const double open   = upper_energy(1.0);
+    const double closed = upper_energy(0.2);
+    INFO("mode 4 relative to the fundamental: brightness 1 -> " << open << ", brightness 0.2 -> " << closed);
+    CHECK(closed < 0.2 * open); // b^4 at 0.2 is 1.6e-3 of b^4 at 1; a factor of five is a floor, not a fit
+}
+
+SCENARIO("a plate mode above the band is silenced rather than folded") {
+    D::plate p;
+    p.prepare(k_sr);
+    p.set_brightness(1.0);
+    p.set_pitch_hz(D::k_max_pitch_hz); // 4 kHz fundamental puts the 7.34 ratio past 0.45 sr
+
+    CHECK(p.mode_level(0) > 0.0);
+    CHECK(p.mode_level(D::k_plate_modes - 1) == 0.0);
+}
+
+SCENARIO("a lightly damped string rings for the time it is asked to") {
+    D::sympathetic s;
+    s.prepare(k_sr);
+    s.set(220.0, 3.0, 12000.0);
+
+    std::vector<double> y(static_cast<size_t>(k_sr * 4.0));
+    for (size_t i = 0; i < y.size(); ++i) {
+        y[i] = s.process((i < 8) ? 1.0 : 0.0);
+    }
+    // Measure the FUNDAMENTAL, not broadband energy: the in-loop lowpass kills the upper partials
+    // faster by design, so a wideband envelope decays faster than the string's stated ring time.
+    const size_t win  = static_cast<size_t>(0.4 * k_sr);
+    const double a    = bin(y, 220.0, static_cast<size_t>(0.3 * k_sr), win);
+    const double b    = bin(y, 220.0, static_cast<size_t>(2.3 * k_sr), win);
+    const double drop = 20.0 * std::log10(b / a);
+    INFO("fundamental decay over 2 s: " << drop << " dB (a 3 s T60 predicts -40)");
+    CHECK(std::abs(drop + 40.0) < 3.0);
+}
+
+// An honest limit made into a test, because it is the one that will surprise someone: the two
+// controls are not independent, and the header says so.
+SCENARIO("damping and ring time are not independent, and the cap is where the string stops") {
+    D::sympathetic light;
+    light.prepare(k_sr);
+    light.set(220.0, 3.0, 12000.0);
+
+    D::sympathetic heavy;
+    heavy.prepare(k_sr);
+    heavy.set(220.0, 3.0, D::k_min_damp_hz); // the same 3 s asked of a heavily damped string
+
+    INFO("loop gain: light " << light.feedback() << ", heavy " << heavy.feedback());
+    CHECK(light.feedback() < D::k_fb_max);  // the light string gets what it asked for
+    CHECK(heavy.feedback() == D::k_fb_max); // the heavy one is pinned at the cap
+    CHECK(heavy.feedback() * 0.95 < 1.0);   // and the loop is still strictly contractive
+}
+
+SCENARIO("the harp answers the strings it has and ignores the pitches between them") {
+    // The drive is faded in and out. Switching a tone on and off is a step, and a step excites
+    // every string on the board — measured without the fades, the "off-note" reading is mostly
+    // that transient rather than any sympathy, and the test would be measuring its own edges.
+    auto tail = [](double hz) {
+        D::harp h;
+        h.prepare(k_sr);
+        h.set_root_hz(110.0);
+        h.set_tuning(D::tuning_chromatic);
+        h.set_decay(6.0);
+        h.set_detune(0.0);
+
+        const int on     = static_cast<int>(k_sr * 2.0);
+        const int all    = static_cast<int>(k_sr * 3.0);
+        const int fade   = static_cast<int>(k_sr * 0.25);
+        double    energy = 0.0;
+        int       count  = 0;
+        for (int i = 0; i < all; ++i) {
+            double g = 0.0;
+            if (i < on) {
+                g = 1.0;
+                if (i < fade) {
+                    g = 0.5 - 0.5 * std::cos(k_pi * static_cast<double>(i) / static_cast<double>(fade));
+                }
+                if (i > on - fade) {
+                    g = 0.5 - 0.5 * std::cos(k_pi * static_cast<double>(on - i) / static_cast<double>(fade));
+                }
+            }
+            const double y = h.process(g * 0.3 * std::sin(2.0 * k_pi * hz * static_cast<double>(i) / k_sr));
+            if (i > on + static_cast<int>(0.2 * k_sr)) {
+                energy += y * y;
+                ++count;
+            }
+        }
+        return std::sqrt(energy / static_cast<double>(count));
+    };
+
+    // Every one of the twelve, against a drive a quarter-tone sharp of it — which is neither any
+    // string's fundamental nor any string's harmonic.
+    for (int i = 0; i < D::k_strings; ++i) {
+        const double on_note  = tail(110.0 * std::exp2(static_cast<double>(i) / 12.0));
+        const double off_note = tail(110.0 * std::exp2((static_cast<double>(i) + 0.5) / 12.0));
+        INFO("string " << i << ": ring on the note " << on_note << ", a quarter-tone off " << off_note << ", ratio "
+                       << on_note / off_note);
+        CHECK(on_note > 4.0 * off_note);
+    }
+    // The ratio measured here climbs from about 4 on the lowest string to about a thousand on the
+    // highest, and that is physics rather than a defect: at a fixed ring time a loop's Q scales
+    // with f x T60, so the top of the board is far the more selective end of it.
+}
+
+SCENARIO("the harp is the same harp in every instance") {
+    D::harp a, b;
+    a.prepare(k_sr);
+    b.prepare(k_sr);
+    a.set_detune(20.0);
+    b.set_detune(20.0);
+
+    bool same = true;
+    for (int i = 0; i < D::k_strings; ++i) {
+        same = same && (a.string_hz(i) == b.string_hz(i));
+    }
+    REQUIRE(same); // the scatter is an index-keyed hash, not a draw — no seed, no drift
+
+    // And the strings are actually scattered, or the check above would be vacuous.
+    bool scattered = false;
+    for (int i = 0; i < D::k_strings; ++i) {
+        scattered = scattered || (std::abs(a.string_hz(i) - 110.0 * std::exp2(i / 12.0)) > 0.1);
+    }
+    CHECK(scattered);
+}
+
+// The moving-iron principle, and the only part of the transducer with a physical argument behind
+// it: force follows the square of the flux, so the residual squared term puts a second harmonic
+// on the output at exactly (asymmetry x amplitude / 2) relative to the fundamental.
+SCENARIO("the driver's asymmetry is exactly the moving-iron squared term") {
+    const double amp = 0.5;
+    for (double asym : {0.1, 0.3, 0.6, 1.0}) {
+        D::transducer t;
+        t.prepare(k_sr);
+        t.set_drive(1.0);
+        t.set_asymmetry(asym);
+        t.set_saturation(0.0); // the bounding stage off: this measures the squared law alone
+
+        const std::vector<double> x = sine(200.0, amp, static_cast<size_t>(k_sr * 0.5));
+        std::vector<double>       y(x.size());
+        for (size_t i = 0; i < x.size(); ++i) {
+            y[i] = t.process(x[i]);
+        }
+        const size_t from  = y.size() / 2;
+        const size_t n     = y.size() - from;
+        const double ratio = bin(y, 400.0, from, n) / bin(y, 200.0, from, n);
+        INFO("asymmetry " << asym << ": second harmonic / fundamental = " << ratio << ", predicted "
+                          << asym * amp * 0.5);
+        CHECK(std::abs(ratio - asym * amp * 0.5) < 0.002);
+        CHECK(bin(y, 600.0, from, n) < 1e-6); // a squared term makes a second harmonic and nothing else
+    }
+}
+
+SCENARIO("with asymmetry and saturation at zero the driver is a plain gain") {
+    D::transducer t;
+    t.prepare(k_sr);
+    t.set_drive(2.0);
+    t.set_asymmetry(0.0);
+    t.set_saturation(0.0);
+
+    const std::vector<double> x = sine(1000.0, 0.4, static_cast<size_t>(k_sr * 0.4));
+    std::vector<double>       y(x.size());
+    for (size_t i = 0; i < x.size(); ++i) {
+        y[i] = t.process(x[i]);
+    }
+    const size_t from = y.size() / 2;
+    const size_t n    = y.size() - from;
+    INFO("fundamental " << bin(y, 1000.0, from, n) << " (drive 2 on 0.4 predicts 0.8)");
+    CHECK(std::abs(bin(y, 1000.0, from, n) - 0.8) < 0.002);
+    CHECK(bin(y, 2000.0, from, n) < 1e-9);
+    CHECK(bin(y, 3000.0, from, n) < 1e-9);
+}
+
+SCENARIO("the driver is bounded however hard it is driven") {
+    D::transducer t;
+    t.prepare(k_sr);
+    t.set_drive(200.0);
+    t.set_asymmetry(1.0);
+    t.set_saturation(0.8); // swing_shape is bounded by 1/drive
+
+    double peak = 0.0;
+    for (int i = 0; i < 48000; ++i) {
+        peak = std::max(peak, std::abs(t.process(std::sin(2.0 * k_pi * 137.0 * i / k_sr))));
+    }
+    // The bound is 2/saturation, not the saturator's own 1/saturation: a hard-driven squared law
+    // is a nearly-constant positive waveform with brief negative excursions, and taking that DC
+    // offset out doubles the worst-case swing. Measured at 1.49 here, against a 1.25 that the
+    // obvious argument would have predicted.
+    INFO("peak output at drive 200, asymmetry 1, saturation 0.8: " << peak);
+    CHECK(peak > 1.0 / 0.8); // the naive bound is genuinely exceeded — this is not slack
+    CHECK(peak < 2.0 / 0.8 + 1e-9);
+}
+
+// The structural claim of the whole file: the electrical signal reaches the transducer first, and
+// the transducer's motion excites the body. Wiring the parts by hand in that order reproduces the
+// machine exactly; wiring them the other way round does not, so the order is a real choice.
+SCENARIO("a cabinet is its parts, wired in the order the instrument wires them") {
+    D::metallique cab;
+    cab.prepare(k_sr);
+    cab.set_smooth_ms(0.0);
+    cab.set_drive(1.7);
+    cab.set_asymmetry(0.4);
+    cab.set_saturation(0.9);
+    cab.set_mix(100.0);
+    cab.set_level(1.0);
+    cab.set_pitch_hz(210.0);
+    cab.set_decay(3.0);
+    cab.set_brightness(0.8);
+
+    D::transducer driver;
+    driver.prepare(k_sr);
+    driver.set_drive(1.7);
+    driver.set_asymmetry(0.4);
+    driver.set_saturation(0.9);
+    D::plate body;
+    body.prepare(k_sr);
+    body.set_pitch_hz(210.0);
+    body.set_decay(3.0);
+    body.set_brightness(0.8);
+
+    // And the same parts the other way round, to show the order is audible rather than notional.
+    D::transducer rev_driver;
+    rev_driver.prepare(k_sr);
+    rev_driver.set_drive(1.7);
+    rev_driver.set_asymmetry(0.4);
+    rev_driver.set_saturation(0.9);
+    D::plate rev_body;
+    rev_body.prepare(k_sr);
+    rev_body.set_pitch_hz(210.0);
+    rev_body.set_decay(3.0);
+    rev_body.set_brightness(0.8);
+
+    bool   identical  = true;
+    double difference = 0.0;
+    for (int i = 0; i < 24000; ++i) {
+        const double x = 0.7 * std::sin(2.0 * k_pi * 190.0 * i / k_sr);
+        const double a = cab.process(x);
+        const double b = body.process(driver.process(x));
+        identical      = identical && (a == b);
+        difference     = std::max(difference, std::abs(a - rev_driver.process(rev_body.process(x))));
+    }
+    REQUIRE(identical); // fully wet, the cabinet IS transducer -> body, bitwise
+    INFO("largest difference against the reversed wiring: " << difference);
+    CHECK(difference > 1e-3); // and the reversed wiring is a different machine
+}
+
+SCENARIO("a cabinet's balance ends are exact at both extremes") {
+    D::palme p;
+    p.prepare(k_sr);
+    p.set_smooth_ms(0.0);
+    p.set_level(1.0);
+
+    p.set_mix(0.0);
+    bool dry = true;
+    for (int i = 0; i < 4800; ++i) {
+        const double x = std::sin(2.0 * k_pi * 300.0 * i / k_sr);
+        dry            = dry && (p.process(x) == x);
+    }
+    REQUIRE(dry); // fully dry is the input, bitwise — not cos(pi/2) times the input
+
+    p.clear();
+    p.set_mix(100.0);
+    double wet_energy = 0.0;
+    for (int i = 0; i < 48000; ++i) {
+        const double y = p.process(0.5 * std::sin(2.0 * k_pi * 110.0 * i / k_sr));
+        wet_energy += y * y;
+    }
+    CHECK(wet_energy > 0.0); // fully wet is the body, and the body is doing something
+}
+
+SCENARIO("a level move is slewed, so a cabinet does not click") {
+    D::metallique cab;
+    cab.prepare(k_sr);
+    cab.set_smooth_ms(50.0);
+    cab.set_mix(100.0);
+    cab.set_level(0.0);
+    // One continuous tone throughout: a break in the INPUT would show up as a step in the output
+    // and the test would be measuring its own seam rather than the level move.
+    auto tone = [](int i) { return 0.5 * std::sin(2.0 * k_pi * 180.0 * static_cast<double>(i) / k_sr); };
+    int  n    = 0;
+    for (; n < 24000; ++n) {
+        cab.process(tone(n));
+    }
+
+    cab.set_level(1.0); // slam it open
+    double last  = cab.process(tone(n++));
+    double worst = 0.0;
+    for (int i = 0; i < static_cast<int>(0.1 * k_sr); ++i, ++n) {
+        const double y = cab.process(tone(n));
+        worst          = std::max(worst, std::abs(y - last));
+        last           = y;
+    }
+    INFO("largest single-sample step during a full-scale level move: " << worst);
+    CHECK(worst < 0.02);
+}
+
+SCENARIO("unprepared, both cabinets pass their input through") {
+    D::metallique m;
+    D::palme      p;
+    bool          clean = true;
+    for (int i = 0; i < 100; ++i) {
+        const double x = 0.01 * static_cast<double>(i);
+        clean          = clean && (m.process(x) == x) && (p.process(x) == x);
+    }
+    REQUIRE(clean);
+}
diff --git a/tests/fuzz_test.cpp b/tests/fuzz_test.cpp
new file mode 100644
index 0000000..3ac0df2
--- /dev/null
+++ b/tests/fuzz_test.cpp
@@ -0,0 +1,328 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the tap.fuzz~ kernel (fuzz.h).
+/// @details    Oracle-based where the promise is audible: harmonic structure is measured out of
+///             the output with a local Goertzel probe rather than by asserting internals, which
+///             is how the even/odd asymmetry contract and the tone-stack claims are pinned. The
+///             aliasing claim is measured the only honest way — by looking for energy at
+///             frequencies that are *not* harmonics of the input and watching it fall as the
+///             oversample factor rises.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/fuzz.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    using tap::tools::fuzz::pedal;
+
+    /// A pedal with instant setters and a flat voicing: scenarios opt into tone and asymmetry.
+    pedal make() {
+        pedal p;
+        p.prepare(k_sr);
+        p.set_smooth_ms(0.0);
+        p.set_bass(0.0);
+        p.set_treble(0.0);
+        p.set_contrast(0.0);
+        p.set_asymmetry(0.0);
+        p.set_level_db(0.0);
+        return p;
+    }
+
+    std::vector<double> render(pedal& p, double hz, double amp, double seconds) {
+        const size_t        n = static_cast<size_t>(seconds * k_sr);
+        std::vector<double> y(n, 0.0);
+        for (size_t i = 0; i < n; ++i) {
+            y[i] = p.process(amp * std::sin(2.0 * k_pi * hz * static_cast<double>(i) / k_sr));
+        }
+        return y;
+    }
+
+    /// Single-bin magnitude, 2|X(f)|/N — the same probe as the tr808 and discreet suites.
+    double goertzel(const std::vector<double>& x, double f, size_t begin, size_t end) {
+        const double w    = 2.0 * k_pi * 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<double>(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;
+    }
+
+    double rms(const std::vector<double>& 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<double>(end - begin));
+    }
+
+    size_t at(double seconds) {
+        return static_cast<size_t>(seconds * k_sr);
+    }
+
+} // namespace
+
+SCENARIO("silence in is exactly silence out, at any asymmetry") {
+    // The bias lives inside the curve and is corrected at the stage output, so a asymmetric
+    // pedal must still be exactly quiet when nothing is playing — no DC pedestal, no hum.
+    for (double asym : {0.0, 0.5, 1.0}) {
+        pedal p = make();
+        p.set_gain(1.0);
+        p.set_asymmetry(asym);
+        bool silent = true;
+        for (size_t i = 0; i < at(0.25); ++i) {
+            silent = silent && (p.process(0.0) == 0.0);
+        }
+        INFO("asymmetry " << asym);
+        REQUIRE(silent);
+    }
+}
+
+SCENARIO("the clipping curve is monotonic, odd, and bounded at every knee") {
+    // The curve is the only nonlinear thing in the file, so it is tested directly. A DC sweep
+    // through the *stage* would measure nothing: the conditioning highpass settles a constant
+    // input to zero, which is what it is for.
+    using tap::tools::fuzz::shape;
+    for (double k : {0.0, 0.5, 1.6, 2.0, 12.0}) {
+        double last = -1e9;
+        bool   mono = true, bounded = true, odd = true;
+        for (int i = 0; i <= 4000; ++i) {
+            const double x = -8.0 + 16.0 * static_cast<double>(i) / 4000.0;
+            const double y = shape(x, k);
+            mono           = mono && (y >= last);
+            bounded        = bounded && std::isfinite(y) && (k < 1e-6 || std::abs(y) <= 1.0 / std::tanh(k) + 1e-12);
+            odd            = odd && (std::abs(y + shape(-x, k)) < 1e-12);
+            last           = y;
+        }
+        INFO("knee " << k);
+        CHECK(mono);    // never folds back
+        CHECK(bounded); // asymptote is 1/tanh(k)
+        CHECK(odd);     // symmetric curve, so odd harmonics only (see the asymmetry scenario)
+    }
+    // And the normalization the cascade depends on: full scale in is full scale out, any knee.
+    for (double k : {0.5, 1.6, 2.0, 12.0}) {
+        INFO("knee " << k);
+        CHECK(std::abs(shape(1.0, k) - 1.0) < 1e-12);
+    }
+}
+
+SCENARIO("the pedal stays bounded on absurd input") {
+    pedal p = make();
+    p.set_gain(1.0);
+    p.set_edge(1.0);
+    p.set_asymmetry(1.0);
+
+    bool bounded = true;
+    for (size_t i = 0; i < at(0.2); ++i) {
+        const double x = 50.0 * std::sin(2.0 * k_pi * 110.0 * static_cast<double>(i) / k_sr);
+        const double y = p.process(x);
+        bounded        = bounded && std::isfinite(y) && std::abs(y) < 4.0;
+    }
+    REQUIRE(bounded);
+}
+
+SCENARIO("more gain is more harmonic content") {
+    const double f0             = 220.0;
+    auto         harmonic_ratio = [&](double gain) {
+        pedal p = make();
+        p.set_gain(gain);
+        const std::vector<double> y = render(p, f0, 0.3, 0.4);
+        const size_t              b = at(0.2), e = at(0.4);
+        const double              fund = goertzel(y, f0, b, e);
+        double                    harm = 0.0;
+        for (int k = 2; k <= 8; ++k) {
+            const double m = goertzel(y, f0 * k, b, e);
+            harm += m * m;
+        }
+        return std::sqrt(harm) / fund;
+    };
+
+    const double quiet = harmonic_ratio(0.0);
+    const double loud  = harmonic_ratio(1.0);
+    INFO("harmonic/fundamental: gain 0 = " << quiet << ", gain 1 = " << loud);
+    REQUIRE(loud > quiet * 1.5);
+}
+
+// The DAFx-07 note this kernel's asymmetry exists for: a symmetric static curve produces odd
+// harmonics only, and a real op-amp stage clips asymmetrically, which is where the even
+// harmonics come from. Measured out of the output, not asserted about the code.
+SCENARIO("asymmetry is what puts even harmonics in the spectrum") {
+    const double f0       = 220.0;
+    auto         even_odd = [&](double asym) {
+        pedal p = make();
+        p.set_gain(0.8);
+        p.set_asymmetry(asym);
+        const std::vector<double> y = render(p, f0, 0.3, 0.4);
+        const size_t              b = at(0.2), e = at(0.4);
+        double                    even = 0.0, odd = 0.0;
+        for (int k = 2; k <= 8; ++k) {
+            const double m = goertzel(y, f0 * k, b, e);
+            (k % 2 == 0 ? even : odd) += m * m;
+        }
+        return std::sqrt(even) / std::sqrt(odd);
+    };
+
+    const double symmetric  = even_odd(0.0);
+    const double asymmetric = even_odd(1.0);
+    INFO("even/odd ratio: symmetric = " << symmetric << ", asymmetric = " << asymmetric);
+    REQUIRE(symmetric < 0.05);              // a symmetric curve is odd-only, to the noise floor
+    REQUIRE(asymmetric > symmetric * 10.0); // and asymmetry is what changes that
+}
+
+// Aliasing is the honest weakness of a static curve, so it gets a real measurement. Two things
+// this test had to get right, both of which caught a bug the first time round:
+//
+//   * The tone must NOT divide the sample rate. At 3 kHz into 48 kHz every alias folds back
+//     exactly onto a harmonic of the input and is invisible; 3733 Hz puts the folds at
+//     frequencies nothing else occupies.
+//   * The probe frequencies must be far from the fundamental. Probes a few hundred Hz away
+//     measure spectral leakage from it (~1e-3 here) rather than aliasing, which swamps the
+//     thing being measured.
+//
+// What is asserted is what is true: oversampling drops aliasing by two orders of magnitude
+// against no oversampling. Since the resampler became a cascade of 2x stages it is also true
+// that more is never meaningfully worse, and that IS asserted below — it is the property the
+// cascade was built to deliver, and the single zero-stuff-by-N chain it replaced failed it.
+//
+// A third thing is asserted that the old chain would have passed while being broken: that 4x
+// works at a *high* tone. Every number in the original write-up came from 3733 Hz alone, and
+// 2x happens to look best there; swept properly, 2x collapses above about 6 kHz. One probe is
+// not a sweep.
+SCENARIO("oversampling drops the aliased energy by orders of magnitude") {
+    const double f0          = 3733.0; // deliberately not a submultiple of the sample rate
+    auto         alias_floor = [&](int os, double tone) {
+        pedal p = make();
+        p.set_gain(1.0);
+        p.set_edge(1.0);
+        p.set_oversample(os);
+        const std::vector<double> y = render(p, tone, 0.5, 0.4);
+        const size_t              b = at(0.2), e = at(0.4);
+        // Where harmonics 8..13 of the tone fold back, skipping any fold that lands near the
+        // fundamental (those probes read leakage, not aliasing).
+        double acc = 0.0;
+        for (int k = 8; k <= 13; ++k) {
+            double f = k * tone;
+            while (f > k_sr * 0.5) {
+                f = (f > k_sr) ? f - k_sr : k_sr - f;
+            }
+            if (std::abs(f - tone) < 1000.0) {
+                continue;
+            }
+            const double m = goertzel(y, f, b, e);
+            acc += m * m;
+        }
+        return std::sqrt(acc);
+    };
+
+    const double none  = alias_floor(1, f0);
+    const double two   = alias_floor(2, f0);
+    const double four  = alias_floor(4, f0);
+    const double eight = alias_floor(8, f0);
+    INFO("alias energy at " << f0 << ": 1x = " << none << ", 2x = " << two << ", 4x = " << four << ", 8x = " << eight);
+    REQUIRE(none > 0.05);       // the test material really does alias when nothing is done
+    REQUIRE(two < none * 0.05); // and oversampling really does fix it
+    REQUIRE(four < none * 0.05);
+    REQUIRE(eight < none * 0.05);
+
+    // The cascade's promise: the sequence never reverses. A 15 % tolerance, because past 2x the
+    // readings sit within about an order of magnitude of the probe's own floor (4e-5 to 1.5e-4
+    // with the pedal nearly clean) and pinning a tighter ordering there would pin noise.
+    REQUIRE(four <= two * 1.15);
+    REQUIRE(eight <= four * 1.15);
+
+    // And the reason the default moved to 4x: one doubling is not enough for a bright input,
+    // where the clipper's low harmonics already exceed the base Nyquist.
+    const double bright_two  = alias_floor(2, 6421.0);
+    const double bright_four = alias_floor(4, 6421.0);
+    INFO("alias energy at 6421: 2x = " << bright_two << ", 4x = " << bright_four);
+    REQUIRE(bright_four < bright_two * 0.1);
+}
+
+SCENARIO("the tone stack moves the band it says it moves") {
+    auto band = [](const std::vector<double>& y, double f) { return goertzel(y, f, at(0.2), at(0.4)); };
+
+    // Drive the pedal with a low and a high tone at once and watch each shelf move its own end.
+    auto render_pair = [&](double bass, double treble, double contrast) {
+        pedal p = make();
+        p.set_gain(0.3);
+        p.set_bass(bass);
+        p.set_treble(treble);
+        p.set_contrast(contrast);
+        std::vector<double> y(at(0.4), 0.0);
+        for (size_t i = 0; i < y.size(); ++i) {
+            const double t = static_cast<double>(i) / k_sr;
+            y[i]           = p.process(0.15 * std::sin(2.0 * k_pi * 80.0 * t) + 0.15 * std::sin(2.0 * k_pi * 620.0 * t)
+                                       + 0.15 * std::sin(2.0 * k_pi * 6000.0 * t));
+        }
+        return y;
+    };
+
+    const std::vector<double> flat    = render_pair(0.0, 0.0, 0.0);
+    const std::vector<double> bassy   = render_pair(1.0, 0.0, 0.0);
+    const std::vector<double> bright  = render_pair(0.0, 1.0, 0.0);
+    const std::vector<double> scooped = render_pair(0.0, 0.0, 1.0);
+
+    INFO("80 Hz: flat " << band(flat, 80.0) << " -> bassy " << band(bassy, 80.0));
+    CHECK(band(bassy, 80.0) > band(flat, 80.0) * 1.5);
+    CHECK(band(bassy, 6000.0) < band(flat, 6000.0) * 1.1); // and leaves the top alone
+
+    INFO("6 kHz: flat " << band(flat, 6000.0) << " -> bright " << band(bright, 6000.0));
+    CHECK(band(bright, 6000.0) > band(flat, 6000.0) * 1.5);
+    CHECK(band(bright, 80.0) < band(flat, 80.0) * 1.1);
+
+    INFO("620 Hz: flat " << band(flat, 620.0) << " -> scooped " << band(scooped, 620.0));
+    CHECK(band(scooped, 620.0) < band(flat, 620.0) * 0.6); // contrast is a mid scoop
+    CHECK(band(scooped, 80.0) > band(flat, 80.0) * 0.8);   // and it is a scoop, not a fader
+}
+
+SCENARIO("the output carries no DC, even wide open and asymmetric") {
+    pedal p = make();
+    p.set_gain(1.0);
+    p.set_asymmetry(1.0);
+    p.set_edge(1.0);
+
+    const std::vector<double> y    = render(p, 220.0, 0.4, 1.0);
+    double                    mean = 0.0;
+    for (size_t i = at(0.5); i < y.size(); ++i) {
+        mean += y[i];
+    }
+    mean /= static_cast<double>(y.size() - at(0.5));
+    INFO("tail mean " << mean << ", rms " << rms(y, at(0.5), y.size()));
+    REQUIRE(std::abs(mean) < 1e-3);
+}
+
+SCENARIO("level is a clean output trim") {
+    auto level_rms = [](double db) {
+        pedal p = make();
+        p.set_gain(0.5);
+        p.set_level_db(db);
+        const std::vector<double> y = render(p, 220.0, 0.3, 0.4);
+        return rms(y, at(0.2), at(0.4));
+    };
+
+    const double unity = level_rms(0.0);
+    const double up    = level_rms(6.0);
+    INFO("rms at 0 dB " << unity << ", at +6 dB " << up);
+    REQUIRE(std::abs(up / unity - std::pow(10.0, 6.0 / 20.0)) < 0.02);
+}
+
+SCENARIO("unprepared, the pedal passes input through") {
+    pedal p;
+    REQUIRE(p.process(0.7) == 0.7);
+    REQUIRE(p.process(-0.3) == -0.3);
+}
diff --git a/tests/ondes_test.cpp b/tests/ondes_test.cpp
new file mode 100644
index 0000000..792b372
--- /dev/null
+++ b/tests/ondes_test.cpp
@@ -0,0 +1,529 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the Ondes Martenot voice kernels (ondes.h).
+/// @details    This file's contract is mostly *reproduction*, like tests/touche_test.cpp and
+///             unlike most of the library: the tube model, its fitted parameters and the stage
+///             operating points are all published, so the load-bearing scenarios check that the
+///             published numbers come back out — the tube against its datasheet operating point,
+///             the demodulator's harmonic series against its closed form, and the closed-form
+///             detector against the full heterodyne simulation it replaces.
+///
+///             The last of those is the one to keep: the whole reason this kernel is affordable
+///             is that the envelope of two summed oscillators has a closed form, so the 80 kHz
+///             carrier never has to exist. If that equivalence ever stops holding, the object is
+///             no longer a reduction of the published model, it is just a synthesizer.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/ondes.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    namespace O = tap::tools::ondes;
+
+    /// Magnitude at `hz` over `x[from..)` — the house Goertzel probe.
+    double bin(const std::vector<double>& x, double hz, size_t from, double sr = k_sr) {
+        const double w  = 2.0 * k_pi * hz / sr;
+        const double c  = 2.0 * std::cos(w);
+        double       s1 = 0.0, s2 = 0.0;
+        for (size_t i = from; i < x.size(); ++i) {
+            const double s = x[i] + c * s1 - s2;
+            s2             = s1;
+            s1             = s;
+        }
+        return std::sqrt(std::max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2.0 / static_cast<double>(x.size() - from);
+    }
+
+    /// The full published demodulator, simulated the expensive way: two oscillators at F and
+    /// F - f, summed, half-wave rectified by the near-zero-bias grid, loaded by R4*C21. This is
+    /// the thing the kernel's closed form is claimed to replace, so the test builds it rather
+    /// than trusting the claim.
+    std::vector<double> full_heterodyne(double f, double sr, double carrier_hz, double tau_s, int cycles) {
+        const size_t        n = static_cast<size_t>(sr * static_cast<double>(cycles) / f);
+        std::vector<double> y(n);
+        const double        decay = std::exp(-1.0 / (tau_s * sr));
+        double              env   = 0.0;
+        for (size_t i = 0; i < n; ++i) {
+            const double t  = static_cast<double>(i) / sr;
+            const double am = std::cos(2.0 * k_pi * carrier_hz * t) + std::cos(2.0 * k_pi * (carrier_hz - f) * t);
+            env             = std::max(am, env * decay);
+            y[i]            = env;
+        }
+        return y;
+    }
+
+    std::vector<double> run_detector(double f, double sr, int cycles) {
+        O::detector d;
+        d.prepare(sr);
+        d.set_frequency(f);
+        const size_t        n = static_cast<size_t>(sr * static_cast<double>(cycles) / f);
+        std::vector<double> y(n);
+        for (size_t i = 0; i < n; ++i) {
+            y[i] = d.process();
+        }
+        return y;
+    }
+
+    std::vector<double> run_voice(double semitones, double drive, int os, double seconds = 1.0) {
+        O::voice v;
+        v.prepare(k_sr);
+        v.set_oversample(os);
+        v.set_smooth_ms(0.0);
+        v.set_ribbon(semitones);
+        v.set_drive(drive);
+        v.set_key(1.0);
+        v.set_level(1.0);
+        const size_t        n = static_cast<size_t>(k_sr * seconds);
+        std::vector<double> y(n);
+        for (size_t i = 0; i < n; ++i) {
+            y[i] = v.process();
+        }
+        return y;
+    }
+
+    /// Total harmonic content relative to the fundamental, harmonics 2..10.
+    double thd(const std::vector<double>& y, double f0) {
+        const size_t from = y.size() / 2;
+        const double h1   = bin(y, f0, from);
+        double       s    = 0.0;
+        for (int k = 2; k <= 10; ++k) {
+            const double h = bin(y, f0 * k, from);
+            s += h * h;
+        }
+        return std::sqrt(s) / h1;
+    }
+
+} // namespace
+
+// The tube model against a datasheet operating point. Tung-Sol's 6C5 sheet gives 8 mA of plate
+// current at 250 V on the plate and -8 V on the grid; the parameter set the circuit paper fitted
+// to that sheet has to land near it, or the fit was mis-transcribed.
+SCENARIO("the published tube parameters reproduce their datasheet operating point") {
+    const double ip = O::plate_current(O::k_6c5, 250.0, -8.0);
+    INFO("6C5 at Vp = 250 V, Vg = -8 V: " << ip * 1000.0 << " mA (datasheet typical is 8 mA)");
+    CHECK(ip * 1000.0 > 7.0);
+    CHECK(ip * 1000.0 < 10.0);
+
+    // And the model's basic physics: current rises with plate voltage and with grid voltage, and
+    // there is none at all below the plate.
+    CHECK(O::plate_current(O::k_6c5, 250.0, -4.0) > O::plate_current(O::k_6c5, 250.0, -8.0));
+    CHECK(O::plate_current(O::k_6c5, 300.0, -8.0) > O::plate_current(O::k_6c5, 250.0, -8.0));
+    CHECK(O::plate_current(O::k_6c5, 0.0, 0.0) == 0.0);
+
+    // The grid-current branch: nothing below the threshold, ohmic above it (TASLP Eq. 9).
+    CHECK(O::grid_current(O::k_6c5, 0.0) == 0.0);
+    CHECK(O::grid_current(O::k_6c5, O::k_6c5.va - 0.01) == 0.0);
+    CHECK(std::abs(O::grid_current(O::k_6c5, O::k_6c5.va + 1.3) - 1.3 / O::k_6c5.rgk) < 1e-15);
+}
+
+SCENARIO("the published operating points bias their tubes into class A") {
+    struct expect {
+        int                tube;
+        O::operating_point op;
+        const char*        name;
+    };
+    const expect cases[] = {{O::tube_6c5, O::k_op_demod, "6C5 demodulator"},
+                            {O::tube_6c5, O::k_op_preamp, "6C5 preamplifier"},
+                            {O::tube_2a3, O::k_op_power, "2A3 power amplifier"}};
+
+    for (const expect& e : cases) {
+        O::triode t;
+        t.prepare(k_sr);
+        t.set_tube(e.tube);
+        t.set_operating_point(e.op);
+        INFO(e.name << ": Vk = " << t.bias_v() << " V, Vp = " << t.quiescent_plate_v()
+                    << " V, Ip = " << t.quiescent_current_a() * 1000.0 << " mA, gain " << t.small_signal_gain());
+        CHECK(t.bias_v() > 0.5);                          // the cathode really is biased
+        CHECK(t.quiescent_plate_v() > 0.25 * e.op.vbias); // and not slammed against either rail
+        CHECK(t.quiescent_plate_v() < 0.95 * e.op.vbias);
+        CHECK(t.quiescent_current_a() > 1e-4);
+        CHECK(t.small_signal_gain() > 2.0); // a working stage, not a cut-off one
+        CHECK(t.small_signal_gain() < e.op.vbias);
+    }
+}
+
+// The reason the stage is worth solving from the tube model rather than reaching for a tanh: a
+// triode is strongly asymmetric, and the asymmetry is where its even harmonics come from.
+SCENARIO("a triode stage is asymmetric, and inverts") {
+    O::triode t;
+    t.prepare(k_sr);
+    t.set_tube(O::tube_6c5);
+    t.set_operating_point(O::k_op_demod);
+    t.set_drive(1.0);
+
+    // Inverting: a common-cathode stage is, and the sign is load-bearing here because the tube's
+    // asymmetry acts on whichever side of the waveform actually reaches its grid.
+    CHECK(t.curve_at(1.0) < 0.0);
+    CHECK(t.curve_at(-1.0) > 0.0);
+
+    // Asymmetric: equal grid swings either way do NOT give equal plate swings.
+    const double up   = std::abs(t.curve_at(4.0));
+    const double down = std::abs(t.curve_at(-4.0));
+    INFO("plate swing at +-4 V of grid: " << up << " vs " << down);
+    CHECK(up / down > 1.5);
+
+    // Unity small-signal gain after normalization, so `drive` changes the distortion and not the
+    // level — the gain-staging lesson fuzz.h learned the hard way.
+    CHECK(std::abs(t.curve_at(0.001) / -0.001 - 1.0) < 0.02);
+    CHECK(t.curve_at(0.0) == 0.0);
+}
+
+SCENARIO("the drive knob changes the distortion and not the level") {
+    O::triode t;
+    t.prepare(k_sr);
+    t.set_tube(O::tube_6c5);
+    t.set_operating_point(O::k_op_demod);
+
+    double last_slope = 0.0;
+    for (double d : {0.1, 1.0, 4.0}) {
+        t.set_drive(d);
+        const double slope = t.curve_at(0.0005) / -0.0005;
+        INFO("drive " << d << ": small-signal slope " << slope);
+        CHECK(std::abs(slope - 1.0) < 0.02); // the level is normalized out at every setting
+        last_slope = slope;
+    }
+    CHECK(last_slope > 0.0);
+}
+
+// The load-bearing scenario. The whole affordability of this kernel rests on the envelope of the
+// published oscillator sum having a closed form, so the carrier never needs to exist. If this
+// equivalence stops holding, the object stops being a reduction of the published model.
+SCENARIO("the closed-form detector reproduces the full heterodyne simulation") {
+    for (double f : {110.0, 440.0, 1760.0}) {
+        // The expensive version, at a rate that resolves an 80 kHz carrier properly.
+        const double              full_sr = 3.0e6;
+        const std::vector<double> full    = full_heterodyne(f, full_sr, 80000.0, O::k_detect_ms * 0.001, 60);
+        const std::vector<double> cheap   = run_detector(f, 384000.0, 60);
+
+        const size_t ff = full.size() / 2;
+        const size_t cf = cheap.size() / 2;
+        const double f1 = bin(full, f, ff, full_sr);
+        const double c1 = bin(cheap, f, cf, 384000.0);
+
+        for (int k = 2; k <= 4; ++k) {
+            const double a = 20.0 * std::log10(bin(full, f * k, ff, full_sr) / f1);
+            const double b = 20.0 * std::log10(bin(cheap, f * k, cf, 384000.0) / c1);
+            INFO(f << " Hz, harmonic " << k << ": full " << a << " dB, closed form " << b << " dB");
+            CHECK(std::abs(a - b) < 0.15);
+        }
+        // The one systematic difference: the closed form is a few percent louder, because a
+        // follower chasing real carrier half-cycles never quite reaches the peak between them.
+        INFO(f << " Hz: level ratio " << c1 / f1);
+        CHECK(c1 / f1 > 1.0);
+        CHECK(c1 / f1 < 1.05);
+    }
+}
+
+// And what that envelope is: not a sinusoid. This is the correction the family plan needed — the
+// demodulator generates a substantial harmonic series before any tube touches the signal.
+SCENARIO("the demodulated envelope carries the published harmonic series before any tube") {
+    const std::vector<double> y    = run_detector(440.0, 384000.0, 60);
+    const size_t              from = y.size() / 2;
+    const double              h1   = bin(y, 440.0, from, 384000.0);
+
+    // |cos| has Fourier coefficients (4/pi)/(4n^2 - 1), so relative to the fundamental the second
+    // harmonic sits at 3/15 and the third at 3/35 — -14.0 dB and -21.3 dB. The RC detector moves
+    // them a little (it cannot follow the envelope down), which is why the bound is generous.
+    const double h2 = 20.0 * std::log10(bin(y, 880.0, from, 384000.0) / h1);
+    const double h3 = 20.0 * std::log10(bin(y, 1320.0, from, 384000.0) / h1);
+    INFO("H2 " << h2 << " dB (ideal -14.0), H3 " << h3 << " dB (ideal -21.3)");
+    CHECK(h2 > -17.0);
+    CHECK(h2 < -12.0);
+    CHECK(h3 > -25.0);
+    CHECK(h3 < -19.0);
+}
+
+SCENARIO("the detector loses harmonics and level as it goes up, because the RC cannot keep up") {
+    std::vector<double> h2, level;
+    for (double f : {110.0, 1760.0}) {
+        const std::vector<double> y    = run_detector(f, 384000.0, 60);
+        const size_t              from = y.size() / 2;
+        const double              a    = bin(y, f, from, 384000.0);
+        level.push_back(a);
+        h2.push_back(20.0 * std::log10(bin(y, 2.0 * f, from, 384000.0) / a));
+    }
+    INFO("second harmonic: " << h2[0] << " dB at A2, " << h2[1] << " dB at A6");
+    CHECK(h2[1] < h2[0] - 3.0);
+    INFO("level: " << 20.0 * std::log10(level[1] / level[0]) << " dB across five octaves");
+    CHECK(level[1] < level[0]);
+    CHECK(20.0 * std::log10(level[1] / level[0]) > -6.0); // a tilt, not a collapse
+}
+
+SCENARIO("oscillator balance is the cheapest timbre control the object has") {
+    std::vector<double> h2;
+    for (double depth : {0.25, 0.5, 1.0}) {
+        O::detector d;
+        d.prepare(384000.0);
+        d.set_frequency(220.0);
+        d.set_depth(depth);
+        std::vector<double> y(static_cast<size_t>(384000.0 * 60.0 / 220.0));
+        for (size_t i = 0; i < y.size(); ++i) {
+            y[i] = d.process();
+        }
+        const size_t from = y.size() / 2;
+        const double h1   = bin(y, 220.0, from, 384000.0);
+        h2.push_back(20.0 * std::log10(bin(y, 440.0, from, 384000.0) / h1));
+        INFO("depth " << depth << ": H2 " << h2.back() << " dB");
+    }
+    REQUIRE(h2.size() == 3);
+    CHECK(h2[0] < h2[1]); // unequal oscillators never close the envelope, so the series thins
+    CHECK(h2[1] < h2[2]);
+
+    // At depth 0 the envelope is a constant: two oscillators that are not beating make no note.
+    O::detector flat;
+    flat.prepare(k_sr);
+    flat.set_depth(0.0);
+    bool constant = true;
+    for (int i = 0; i < 4800; ++i) {
+        constant = constant && (std::abs(flat.process() - 1.0) < 1e-12);
+    }
+    REQUIRE(constant);
+}
+
+SCENARIO("the ribbon is linear in semitones, which is the published law") {
+    O::detector d;
+    d.prepare(k_sr);
+    for (double st : {0.0, 12.0, 24.0, 36.0, 60.0}) {
+        d.set_ribbon(st);
+        INFO(st << " semitones -> " << d.frequency() << " Hz");
+        CHECK(std::abs(d.frequency() - O::k_a1_hz * std::exp2(st / 12.0)) < 1e-9);
+        CHECK(std::abs(d.semitones() - st) < 1e-9);
+    }
+    d.set_ribbon(0.0);
+    CHECK(std::abs(d.frequency() - 55.0) < 1e-12); // A1, the ribbon's lowest note
+}
+
+SCENARIO("the voice plays the note the ribbon asks for") {
+    for (double st : {0.0, 24.0, 48.0}) {
+        const std::vector<double> y    = run_voice(st, 1.0, 4);
+        const double              want = O::k_a1_hz * std::exp2(st / 12.0);
+        const size_t              from = y.size() / 2;
+        // The fundamental has to be the strongest thing in the signal, not merely present.
+        const double h1 = bin(y, want, from);
+        INFO(st << " st (" << want << " Hz): H1 " << h1);
+        CHECK(h1 > 0.3);
+        for (int k = 2; k <= 5; ++k) {
+            CHECK(bin(y, want * k, from) < h1);
+        }
+    }
+}
+
+// The gain-staging promise, at the level of the whole instrument this time.
+SCENARIO("drive adds harmonics monotonically without running away in level") {
+    double last_thd = 0.0;
+    double first_h1 = 0.0;
+    for (double d : {0.0, 1.0, 2.0, 4.0, 8.0}) {
+        const std::vector<double> y = run_voice(24.0, d, 4);
+        const double              t = thd(y, 220.0);
+        const double              h = bin(y, 220.0, y.size() / 2);
+        INFO("drive " << d << ": THD " << t << ", H1 " << h);
+        CHECK(t > last_thd); // more drive, more harmonics — every step
+        last_thd = t;
+        if (first_h1 == 0.0) {
+            first_h1 = h;
+        }
+        CHECK(h > 0.5 * first_h1); // and the level does not collapse on the way
+    }
+    // At drive 0 the tubes are as linear as they get and the harmonics are still there, because
+    // the DEMODULATOR made them. That is the object's whole thesis in one assertion.
+    const double clean = thd(run_voice(24.0, 0.0, 4), 220.0);
+    INFO("harmonic content with the tubes barely driven: " << clean);
+    CHECK(clean > 0.15);
+}
+
+SCENARIO("the coupling polarity is audible, which is why it is a switch") {
+    O::voice a, b;
+    for (O::voice* v : {&a, &b}) {
+        v->prepare(k_sr);
+        v->set_smooth_ms(0.0);
+        v->set_ribbon(24.0);
+        v->set_drive(4.0);
+        v->set_key(1.0);
+        v->set_level(1.0);
+    }
+    b.set_polarity(-1);
+
+    std::vector<double> ya(static_cast<size_t>(k_sr)), yb(ya.size());
+    for (size_t i = 0; i < ya.size(); ++i) {
+        ya[i] = a.process();
+        yb[i] = b.process();
+    }
+    INFO("THD: +1 " << thd(ya, 220.0) << ", -1 " << thd(yb, 220.0));
+    CHECK(std::abs(thd(ya, 220.0) - thd(yb, 220.0)) > 0.05);
+}
+
+// The paper drops the power amplifier for real-time and says why. This checks their reason
+// rather than their conclusion.
+SCENARIO("the power stage really is the least important of the three") {
+    O::voice off, on;
+    for (O::voice* v : {&off, &on}) {
+        v->prepare(k_sr);
+        v->set_smooth_ms(0.0);
+        v->set_ribbon(24.0);
+        v->set_drive(2.0);
+        v->set_key(1.0);
+        v->set_level(1.0);
+    }
+    on.set_power_stage(true);
+
+    std::vector<double> a(static_cast<size_t>(k_sr)), b(a.size());
+    for (size_t i = 0; i < a.size(); ++i) {
+        a[i] = off.process();
+        b[i] = on.process();
+    }
+    INFO("THD without the 2A3 " << thd(a, 220.0) << ", with it " << thd(b, 220.0));
+    CHECK(std::abs(thd(a, 220.0) - thd(b, 220.0)) < 0.02);
+    CHECK(thd(b, 220.0) > thd(a, 220.0)); // it does something, just not much
+}
+
+// A wrapper test asked for silence at a rest position and got 20 ms of sound: the voice's
+// smoothing was not reaching the intensity key, which keeps its own slew, so the one control the
+// instrument is played with was slewing at a time nobody had set.
+SCENARIO("the voice's smoothing reaches the intensity key") {
+    O::voice v;
+    v.prepare(k_sr);
+    v.set_smooth_ms(0.0);
+    v.set_ribbon(24.0);
+    v.set_level(1.0);
+    v.set_key(1.0);
+    for (int i = 0; i < 4800; ++i) {
+        v.process(); // get it sounding
+    }
+
+    v.set_key(0.0);
+    bool immediate = true;
+    for (int i = 0; i < 4800; ++i) {
+        immediate = immediate && (v.process() == 0.0);
+    }
+    REQUIRE(immediate); // smooth 0 means smooth 0, everywhere
+
+    // And with smoothing on, the key really does take that long rather than snapping.
+    v.set_smooth_ms(50.0);
+    v.set_key(1.0);
+    for (int i = 0; i < 4800; ++i) {
+        v.process();
+    }
+    v.set_key(0.0);
+    int sounded = 0;
+    for (int i = 0; i < 4800; ++i) {
+        sounded += (v.process() != 0.0) ? 1 : 0;
+    }
+    INFO("samples still sounding after a slewed release: " << sounded);
+    CHECK(sounded > 100);
+}
+
+SCENARIO("the intensity key silences the voice at rest and opens it at full press") {
+    O::voice v;
+    v.prepare(k_sr);
+    v.set_smooth_ms(0.0);
+    v.set_ribbon(24.0);
+    v.set_level(1.0);
+    v.set_key(0.0);
+
+    bool silent = true;
+    for (int i = 0; i < 24000; ++i) {
+        silent = silent && (v.process() == 0.0);
+    }
+    REQUIRE(silent); // the bottom of the key's travel is exact silence — touche.h's contract
+
+    v.set_key(1.0);
+    double peak = 0.0;
+    for (int i = 0; i < 24000; ++i) {
+        peak = std::max(peak, std::abs(v.process()));
+    }
+    CHECK(peak > 0.1);
+}
+
+SCENARIO("where the key sits in the chain changes what it does") {
+    O::voice after, before;
+    for (O::voice* v : {&after, &before}) {
+        v->prepare(k_sr);
+        v->set_smooth_ms(0.0);
+        v->set_ribbon(24.0);
+        v->set_drive(6.0);
+        v->set_key(0.62);
+        v->set_level(1.0);
+    }
+    before.set_key_placement(O::key_before);
+
+    std::vector<double> a(static_cast<size_t>(k_sr)), b(a.size());
+    for (size_t i = 0; i < a.size(); ++i) {
+        a[i] = after.process();
+        b[i] = before.process();
+    }
+    // Placed after, the key is a clean output law and the harmonic content is whatever full
+    // press would give. Placed before, a half-pressed key drives the tubes less hard, so the
+    // sound is cleaner — pressure becomes dirt, which is the whole reason to offer the choice.
+    INFO("THD with the key after " << thd(a, 220.0) << ", before " << thd(b, 220.0));
+    CHECK(thd(b, 220.0) < thd(a, 220.0));
+}
+
+SCENARIO("oversampling improves the aliasing, and never makes it worse") {
+    // Two traps this probe is built to avoid, both of which caught earlier drafts in this repo.
+    // A tone that divides the sample rate folds every alias onto a harmonic, where it is
+    // invisible; and probing halfway between harmonics finds nothing at all, because a perfectly
+    // periodic signal has exactly zero energy there. So: a fundamental that divides nothing, and
+    // probes at the COMPUTED fold frequencies, skipping any that land near a real harmonic.
+    const double f0 = 2637.0; // roughly E7, and coprime with anything that matters
+
+    std::vector<double> floors;
+    for (int os : {1, 2, 4}) {
+        O::voice v;
+        v.prepare(k_sr);
+        v.set_oversample(os);
+        v.set_smooth_ms(0.0);
+        v.set_frequency(f0);
+        v.set_drive(4.0);
+        v.set_key(1.0);
+        v.set_level(1.0);
+        std::vector<double> y(static_cast<size_t>(k_sr));
+        for (size_t i = 0; i < y.size(); ++i) {
+            y[i] = v.process();
+        }
+
+        const size_t from  = y.size() / 2;
+        const double h1    = bin(y, f0, from);
+        double       worst = 0.0;
+        for (int k = 2; k <= 40; ++k) {
+            double fold = std::fmod(f0 * k, k_sr);
+            if (fold > 0.5 * k_sr) {
+                fold = k_sr - fold;
+            }
+            if (fold < 60.0 || fold > 0.45 * k_sr) {
+                continue;
+            }
+            bool on_harmonic = false;
+            for (int q = 1; q * f0 < 0.5 * k_sr; ++q) {
+                on_harmonic = on_harmonic || (std::abs(fold - q * f0) < 200.0);
+            }
+            if (!on_harmonic) {
+                worst = std::max(worst, bin(y, fold, from));
+            }
+        }
+        floors.push_back(20.0 * std::log10(worst / h1));
+        INFO("oversample " << os << ": worst fold " << floors.back() << " dB");
+    }
+    REQUIRE(floors.size() == 3);
+    CHECK(floors[1] < floors[0] - 5.0);
+    CHECK(floors[2] < floors[1] - 5.0);
+    // Unlike fuzz.h, where 4x came out worse than 2x, the sequence here never reverses — see
+    // the header on why that is a data point rather than a coincidence.
+}
+
+SCENARIO("unprepared, the voice is silent rather than undefined") {
+    O::voice v;
+    bool     silent = true;
+    for (int i = 0; i < 1000; ++i) {
+        silent = silent && (v.process() == 0.0);
+    }
+    REQUIRE(silent);
+}
diff --git a/tests/scrub_test.cpp b/tests/scrub_test.cpp
new file mode 100644
index 0000000..eeae1a5
--- /dev/null
+++ b/tests/scrub_test.cpp
@@ -0,0 +1,386 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the tap.scrub~ kernel (scrub.h).
+/// @details    Everything this object does is a departure from a plain delay — a moving position,
+///             a transposition that does not move with it, a frozen tape, a scattered origin —
+///             and a departure is only trustworthy if the identity is exact when it should be. So
+///             the load-bearing scenario is the null: at unity pitch, overlap 2, no spray and a
+///             held whole-sample position, the scrub is the input delayed and nothing else. The
+///             rest pin the departures one at a time, and the seed contract the family shares.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/scrub.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    namespace S = tap::tools::scrub;
+
+    double ms_of(long samples) {
+        return static_cast<double>(samples) * 1000.0 / k_sr;
+    }
+
+    /// A pluck train: transients, which is what a scrub has something to bite on (the stammer's
+    /// material contract, same reasoning).
+    std::vector<double> plucks(size_t n) {
+        std::vector<double> x(n);
+        double              env = 0.0, phase = 0.0, hz = 220.0;
+        for (size_t i = 0; i < n; ++i) {
+            if (i % 6000 == 0) {
+                env = 1.0;
+                hz  = 180.0 + 40.0 * static_cast<double>((i / 6000) % 5);
+            }
+            env *= 0.99975;
+            phase += hz / k_sr;
+            phase -= std::floor(phase);
+            x[i] = env * (std::sin(2.0 * k_pi * phase) + 0.4 * std::sin(6.0 * k_pi * phase));
+        }
+        return x;
+    }
+
+    double rms(const std::vector<double>& x, size_t from) {
+        double s = 0.0;
+        for (size_t i = from; i < x.size(); ++i) {
+            s += x[i] * x[i];
+        }
+        return std::sqrt(s / static_cast<double>(x.size() - from));
+    }
+
+    double bin(const std::vector<double>& x, double hz, size_t from, size_t n) {
+        const double w  = 2.0 * k_pi * hz / k_sr;
+        const double c  = 2.0 * std::cos(w);
+        double       s1 = 0.0, s2 = 0.0;
+        for (size_t i = 0; i < n; ++i) {
+            const double s = x[from + i] + c * s1 - s2;
+            s2             = s1;
+            s1             = s;
+        }
+        return std::sqrt(std::max(0.0, s1 * s1 + s2 * s2 - c * s1 * s2)) * 2.0 / static_cast<double>(n);
+    }
+
+    S::machine make(double size_samples = 96.0, double lag_samples = 480.0) {
+        S::machine m;
+        m.prepare(k_sr, 2000.0);
+        m.set_smooth_ms(0.0);
+        m.set_overlap(2);
+        m.set_size_ms(ms_of(static_cast<long>(size_samples)));
+        m.set_position_ms(ms_of(static_cast<long>(lag_samples)));
+        m.set_mix(100.0);
+        m.set_level(1.0);
+        return m;
+    }
+
+} // namespace
+
+// The load-bearing scenario. Hann satisfies the overlap-add condition at hop = size/2, so with the
+// position held on a whole sample and no transposition, the two live grains sum to exactly one and
+// the machine is a delay line — to within the floating-point cost of computing two cosines whose
+// arguments differ by pi rather than one cosine and its negation.
+SCENARIO("held still at unity pitch, the scrub is exactly a delay") {
+    S::machine m = make(96.0, 480.0);
+
+    const std::vector<double> x = plucks(30000);
+    std::vector<double>       y(x.size());
+    for (size_t i = 0; i < x.size(); ++i) {
+        y[i] = m.process(x[i]);
+    }
+
+    double worst = 0.0;
+    for (size_t i = 2000; i < x.size(); ++i) {
+        worst = std::max(worst, std::abs(y[i] - x[i - 480]));
+    }
+    INFO("largest departure from a 480-sample delay: " << worst);
+    REQUIRE(worst < 1e-12);
+}
+
+SCENARIO("the position is a lag in milliseconds, measured from the live edge") {
+    for (long lag : {0L, 240L, 4800L}) {
+        S::machine                m = make(96.0, static_cast<double>(lag));
+        const std::vector<double> x = plucks(20000);
+        std::vector<double>       y(x.size());
+        for (size_t i = 0; i < x.size(); ++i) {
+            y[i] = m.process(x[i]);
+        }
+        double worst = 0.0;
+        for (size_t i = static_cast<size_t>(lag) + 2000; i < x.size(); ++i) {
+            worst = std::max(worst, std::abs(y[i] - x[i - static_cast<size_t>(lag)]));
+        }
+        INFO("lag " << lag << " samples: worst error " << worst);
+        CHECK(worst < 1e-12);
+    }
+}
+
+// The object's reason to exist: pitch and position are two hands, not one.
+SCENARIO("pitch transposes without the position moving") {
+    auto run = [](double semitones) {
+        S::machine m = make(4800.0, 24000.0); // a long grain and a deep position: room to transpose
+        m.set_pitch(semitones);
+        std::vector<double> y(static_cast<size_t>(k_sr * 2.0));
+        for (size_t i = 0; i < y.size(); ++i) {
+            y[i] = m.process(0.5 * std::sin(2.0 * k_pi * 300.0 * static_cast<double>(i) / k_sr));
+        }
+        const size_t from = y.size() / 2;
+        const size_t n    = y.size() - from;
+        return std::pair{bin(y, 300.0, from, n), bin(y, 300.0 * std::exp2(semitones / 12.0), from, n)};
+    };
+
+    const auto up = run(7.0);
+    INFO("up a fifth: energy at 300 Hz " << up.first << ", at the transposed pitch " << up.second);
+    CHECK(up.second > 4.0 * up.first);
+
+    const auto down = run(-5.0);
+    INFO("down a fourth: energy at 300 Hz " << down.first << ", at the transposed pitch " << down.second);
+    CHECK(down.second > 4.0 * down.first);
+
+    const auto flat = run(0.0);
+    CHECK(flat.first > 0.4); // and at unity the pitch is simply the input's
+}
+
+// The defect this scenario exists to catch, because the first cut had it and it is invisible to
+// every other test here: if grain origins are anchored at the position, they advance at the WRITE
+// head's speed while each grain plays at `rate`, so the transposition applies only inside a grain
+// and the average read rate comes back to 1. A steady tone then comes out at its ORIGINAL pitch
+// with a comb of grain-rate sidebands, and the pitch knob does nothing but add texture. The fix is
+// a phase-continuous read head, wrapped back toward the position only when it has wandered far
+// enough; this pins the outcome rather than the mechanism.
+SCENARIO("the pitch control moves the pitch, and does not merely colour the original") {
+    auto run = [](double f0, double st) {
+        S::machine m = make(4800.0, 43200.0); // 100 ms grains, the position a comfortable 900 ms back
+        m.set_pitch(st);
+        std::vector<double> y(static_cast<size_t>(k_sr * 2.0));
+        for (size_t i = 0; i < y.size(); ++i) {
+            y[i] = m.process(0.5 * std::sin(2.0 * k_pi * f0 * static_cast<double>(i) / k_sr));
+        }
+        return y;
+    };
+
+    double sum_moved = 0.0;
+    int    count     = 0;
+    // Fundamentals deliberately chosen NOT to be whole numbers of periods in the grain: that case
+    // is transparent to every wrap and would hide the defect completely.
+    for (double f0 : {173.0, 311.0, 443.0}) {
+        for (double st : {-12.0, -7.0, -3.0, 3.0, 7.0, 12.0}) {
+            const std::vector<double> y      = run(f0, st);
+            const size_t              from   = y.size() / 2;
+            const size_t              n      = y.size() - from;
+            const double              target = f0 * std::exp2(st / 12.0);
+            double                    moved  = 0.0;
+            for (int k = -6; k <= 6; ++k) { // the comb is a few Hz wide; a single bin misses it
+                moved = std::max(moved, bin(y, target + 0.5 * k, from, n));
+            }
+            const double stayed = bin(y, f0, from, n);
+            INFO(f0 << " Hz, " << st << " semitones: energy at the transposed pitch " << moved
+                    << ", left at the original " << stayed);
+            CHECK(moved > 10.0 * stayed); // the pitch moved; it was not merely coloured
+            sum_moved += moved;
+            ++count;
+        }
+    }
+    // And it is not merely quiet everywhere. Note the probe: a single-bin reading UNDERSTATES
+    // this object badly, because the wraps spread the transposed partial into a narrow comb a few
+    // Hz wide rather than moving it — an early version of this scenario measured one bin, read
+    // 0.02 where the real figure was 0.43, and nearly sent a correct kernel back for repair. The
+    // band figures are in the header and in notebooks/scrub.ipynb; here the bin is widened just
+    // enough to catch the comb.
+    INFO("mean energy at the transposed pitch: " << sum_moved / count);
+    CHECK(sum_moved / count > 0.3);
+}
+
+SCENARIO("freeze stops the recorder, so the scrub holds while the input moves on") {
+    S::machine m = make(4800.0, 24000.0);
+
+    // Half a second of tone, then freeze and switch the input to silence.
+    for (int i = 0; i < static_cast<int>(k_sr * 0.5); ++i) {
+        m.process(0.5 * std::sin(2.0 * k_pi * 300.0 * i / k_sr));
+    }
+    m.set_freeze(true);
+
+    std::vector<double> y(static_cast<size_t>(k_sr * 1.0));
+    for (size_t i = 0; i < y.size(); ++i) {
+        y[i] = m.process(0.0); // nothing going in at all
+    }
+    INFO("output rms with a frozen tape and a silent input: " << rms(y, y.size() / 2));
+    CHECK(rms(y, y.size() / 2) > 0.1);
+
+    // Unfrozen, with the input still silent, the tape fills with silence and the scrub follows it.
+    m.set_freeze(false);
+    std::vector<double> z(static_cast<size_t>(k_sr * 3.0));
+    for (size_t i = 0; i < z.size(); ++i) {
+        z[i] = m.process(0.0);
+    }
+    INFO("output rms once the recorder is running again: " << rms(z, z.size() * 3 / 4));
+    CHECK(rms(z, z.size() * 3 / 4) < 1e-9);
+}
+
+SCENARIO("drift walks the playhead through the tape on its own") {
+    S::machine still  = make(2400.0, 24000.0);
+    S::machine moving = make(2400.0, 24000.0);
+    moving.set_drift(0.5);
+
+    const std::vector<double> x = plucks(static_cast<size_t>(k_sr * 2.0));
+    std::vector<double>       a(x.size()), b(x.size());
+    for (size_t i = 0; i < x.size(); ++i) {
+        a[i] = still.process(x[i]);
+        b[i] = moving.process(x[i]);
+    }
+    double diff = 0.0;
+    for (size_t i = x.size() / 2; i < x.size(); ++i) {
+        diff = std::max(diff, std::abs(a[i] - b[i]));
+    }
+    INFO("largest difference a drift of 0.5 makes: " << diff);
+    CHECK(diff > 1e-3);
+
+    // Drift 0 changes nothing at all — the accumulator must not creep.
+    S::machine zero = make(2400.0, 24000.0);
+    zero.set_drift(0.0);
+    bool       same = true;
+    S::machine ref  = make(2400.0, 24000.0);
+    for (size_t i = 0; i < x.size(); ++i) {
+        same = same && (zero.process(x[i]) == ref.process(x[i]));
+    }
+    REQUIRE(same);
+}
+
+// The family's seed contract, same shape as garden.h and stammer.h: the dice are only rolled when
+// something is actually random, so with spray off the seed provably cannot matter.
+SCENARIO("with spray off the seed cannot matter, and with it on the seed is the performance") {
+    const std::vector<double> x = plucks(static_cast<size_t>(k_sr * 1.0));
+
+    S::machine a = make(2400.0, 12000.0);
+    S::machine b = make(2400.0, 12000.0);
+    a.set_seed(1);
+    b.set_seed(999999);
+    bool identical = true;
+    for (size_t i = 0; i < x.size(); ++i) {
+        identical = identical && (a.process(x[i]) == b.process(x[i]));
+    }
+    REQUIRE(identical); // spray defaults to 0: no draw, no divergence
+
+    S::machine c = make(2400.0, 12000.0);
+    S::machine d = make(2400.0, 12000.0);
+    c.set_spray_ms(120.0);
+    d.set_spray_ms(120.0);
+    c.set_seed(1);
+    d.set_seed(999999);
+    double diverged = 0.0;
+    for (size_t i = 0; i < x.size(); ++i) {
+        diverged = std::max(diverged, std::abs(c.process(x[i]) - d.process(x[i])));
+    }
+    INFO("two seeds, sprayed: largest divergence " << diverged);
+    CHECK(diverged > 1e-3);
+
+    // And a seed is replayable: the same seed twice is the same performance, bitwise.
+    S::machine e = make(2400.0, 12000.0);
+    S::machine f = make(2400.0, 12000.0);
+    e.set_spray_ms(120.0);
+    f.set_spray_ms(120.0);
+    e.set_seed(4242);
+    f.set_seed(4242);
+    bool replayed = true;
+    for (size_t i = 0; i < x.size(); ++i) {
+        replayed = replayed && (e.process(x[i]) == f.process(x[i]));
+    }
+    REQUIRE(replayed);
+}
+
+SCENARIO("the level holds across overlap settings") {
+    std::vector<double> level;
+    for (int n : {2, 3, 4}) {
+        S::machine m = make(4800.0, 24000.0);
+        m.set_overlap(n);
+        std::vector<double> y(static_cast<size_t>(k_sr * 1.5));
+        for (size_t i = 0; i < y.size(); ++i) {
+            y[i] = m.process(0.5 * std::sin(2.0 * k_pi * 300.0 * static_cast<double>(i) / k_sr));
+        }
+        level.push_back(rms(y, y.size() / 2));
+        INFO("overlap " << n << ": rms " << level.back() << ", normalization " << m.grains().normalization());
+    }
+    for (double v : level) {
+        CHECK(std::abs(v / level[0] - 1.0) < 0.02);
+    }
+}
+
+SCENARIO("the grain pool never overflows, and every grain retires") {
+    S::machine m = make(960.0, 12000.0);
+    m.set_overlap(S::k_max_overlap);
+
+    int worst = 0;
+    for (int i = 0; i < static_cast<int>(k_sr * 2.0); ++i) {
+        m.process(0.3 * std::sin(2.0 * k_pi * 200.0 * i / k_sr));
+        worst = std::max(worst, m.active_grains());
+    }
+    INFO("most grains alive at once: " << worst << " of " << S::k_max_grains);
+    CHECK(worst <= S::k_max_overlap);
+
+    m.clear();
+    CHECK(m.active_grains() == 0);
+}
+
+SCENARIO("the signal path and the attribute path agree when they are told the same thing") {
+    S::machine a = make(2400.0, 0.0);
+    S::machine b = make(2400.0, 0.0);
+    a.set_position_ms(ms_of(1200));
+    a.set_pitch(3.0);
+
+    const std::vector<double> x    = plucks(20000);
+    bool                      same = true;
+    for (size_t i = 0; i < x.size(); ++i) {
+        same = same && (a.process(x[i]) == b.process(x[i], ms_of(1200), 3.0));
+    }
+    REQUIRE(same);
+}
+
+SCENARIO("fully dry, the scrub is a bitwise passthrough") {
+    S::machine m = make(2400.0, 12000.0);
+    m.set_mix(0.0);
+    bool dry = true;
+    for (int i = 0; i < 4800; ++i) {
+        const double x = std::sin(2.0 * k_pi * 300.0 * i / k_sr);
+        dry            = dry && (m.process(x) == x);
+    }
+    REQUIRE(dry); // fully dry is the input, bitwise
+}
+
+SCENARIO("a position move is slewed, so a scrub gesture does not click") {
+    S::machine m = make(2400.0, 0.0);
+    m.set_smooth_ms(50.0);
+    // One continuous tone throughout: a break in the INPUT would come straight back out of the
+    // delay and the test would be measuring its own seam rather than the scrub gesture.
+    auto tone = [](int i) { return 0.5 * std::sin(2.0 * k_pi * 300.0 * static_cast<double>(i) / k_sr); };
+    int  n    = 0;
+    for (; n < 48000; ++n) {
+        m.process(tone(n));
+    }
+
+    m.set_position_ms(500.0); // slam the playhead half a second back
+    double last  = m.process(tone(n++));
+    double worst = 0.0;
+    for (int i = 0; i < static_cast<int>(0.2 * k_sr); ++i, ++n) {
+        const double y = m.process(tone(n));
+        worst          = std::max(worst, std::abs(y - last));
+        last           = y;
+    }
+    // A steady tone read from anywhere on the tape is the same tone, so the only thing a scrub
+    // across it can produce is a phase discontinuity — bounded here well under the tone's peak.
+    INFO("largest single-sample step during a half-second scrub: " << worst);
+    CHECK(worst < 0.1);
+}
+
+SCENARIO("unprepared, the scrub passes its input through") {
+    S::machine m;
+    bool       clean = true;
+    for (int i = 0; i < 100; ++i) {
+        const double x = 0.01 * static_cast<double>(i);
+        clean          = clean && (m.process(x) == x);
+    }
+    REQUIRE(clean);
+}
diff --git a/tests/stammer_test.cpp b/tests/stammer_test.cpp
new file mode 100644
index 0000000..6b23144
--- /dev/null
+++ b/tests/stammer_test.cpp
@@ -0,0 +1,295 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the tap.stammer~ kernel (stammer.h).
+/// @details    The suite leans on a configuration that is fully deterministic *regardless of the
+///             seed* — density 1, whole-step slices, one repeat, no reverse, no jump, no fade —
+///             because in that corner the machine must reduce to an exact one-step delay, and
+///             that single identity pins the grid timing, the origin arithmetic, and the playback
+///             head together, bitwise. Around it: the seeded-performance contract (same seed,
+///             same render; different seeds, different renders; density 0 never touches the rng
+///             at all — the garden.h idle contract), the repeat invariant, the reverse identity,
+///             and the envelope's exact edges.
+///
+///             Material contract, per the header: the musical scenarios drive plucks, not sines.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/stammer.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    using tap::tools::stammer::machine;
+
+    size_t at(double seconds) {
+        return static_cast<size_t>(seconds * k_sr);
+    }
+
+    /// A machine with everything the tests care about pinned; scenarios opt into the dice.
+    machine make(double max_history_ms = 2000.0) {
+        machine m;
+        m.prepare(k_sr, max_history_ms);
+        m.set_smooth_ms(0.0);
+        m.set_input_level(1.0);
+        m.set_mix(100.0);
+        m.set_fade_ms(0.0); // bitwise comparisons want the raw material
+        m.set_jump_ms(0.0);
+        m.set_reverse(0.0);
+        m.set_divisions(1);
+        m.set_repeats(1);
+        m.set_density(1.0);
+        return m;
+    }
+
+    /// The documented material: a train of decaying plucks, not a sine. Transients are what this
+    /// object is for, and what makes a wrong slice boundary audible in a render.
+    std::vector<double> pluck_train(size_t n, double period_s = 0.31) {
+        std::vector<double> x(n, 0.0);
+        for (size_t i = 0; i < n; ++i) {
+            const double t   = static_cast<double>(i) / k_sr;
+            const double phi = std::fmod(t, period_s);
+            const double hz  = 196.0;
+            double       sum = 0.0;
+            for (int k = 1; k <= 5; ++k) {
+                const double kk = static_cast<double>(k);
+                sum += (1.0 / kk) * std::exp(-phi * (4.0 + 3.0 * kk)) * std::sin(2.0 * k_pi * hz * kk * phi);
+            }
+            x[i] = 0.5 * sum;
+        }
+        return x;
+    }
+
+    std::vector<double> render(machine& m, const std::vector<double>& x) {
+        std::vector<double> y(x.size(), 0.0);
+        for (size_t i = 0; i < x.size(); ++i) {
+            y[i] = m.process(x[i]);
+        }
+        return y;
+    }
+
+} // namespace
+
+// The load-bearing identity. With the dice pinned to a single outcome — always fire, whole-step
+// slices, one pass, forwards, no reach-back, no flank — every grid point grabs exactly the step
+// that just went past and plays it once. That is a pure delay of one step less one sample, and it
+// must hold sample for sample. Grid timing, origin arithmetic and the playback head all fail this
+// test if any of them is off by one.
+SCENARIO("with the dice pinned, the machine is exactly a one-step delay") {
+    const double step_ms = 100.0;
+    const size_t step    = static_cast<size_t>(step_ms * 0.001 * k_sr);
+
+    machine m = make();
+    m.set_step_ms(step_ms);
+
+    const std::vector<double> x = pluck_train(at(1.5));
+    const std::vector<double> y = render(m, x);
+
+    bool exact = true;
+    for (size_t i = step; i < y.size(); ++i) {
+        exact = exact && (y[i] == x[i - step + 1]); // bitwise, not approximately
+    }
+    REQUIRE(exact);
+
+    // Not vacuous: the material is live, and the delay really did move it.
+    REQUIRE(std::any_of(y.begin() + static_cast<long>(step), y.end(), [](double v) { return std::abs(v) > 0.01; }));
+    REQUIRE(!std::equal(x.begin() + static_cast<long>(step), x.end(), y.begin() + static_cast<long>(step)));
+}
+
+SCENARIO("a reversed slice plays the same step backwards") {
+    const double step_ms = 100.0;
+    const size_t step    = static_cast<size_t>(step_ms * 0.001 * k_sr);
+
+    machine m = make();
+    m.set_step_ms(step_ms);
+    m.set_reverse(1.0); // every repeat runs backwards
+
+    const std::vector<double> x = pluck_train(at(1.0));
+    const std::vector<double> y = render(m, x);
+
+    // Forwards the grab at grid point k reads x[k*step + 1 + j]; backwards it reads the same
+    // window end-first.
+    bool exact = true;
+    for (size_t k = 1; k * step + step <= y.size(); ++k) {
+        for (size_t j = 0; j < step; ++j) {
+            exact = exact && (y[k * step + j] == x[k * step - j]);
+        }
+    }
+    REQUIRE(exact);
+}
+
+// Repeats are the only thing that keeps the machine busy past one pass, and a slice in flight is
+// never interrupted. So every output sample is either a fresh grab (the one-step delay above) or
+// a bit-exact copy of the block one slice-length earlier. Nothing else is legal.
+SCENARIO("every sample is either a fresh grab or an exact repeat of the block before it") {
+    const double step_ms = 80.0;
+    const size_t step    = static_cast<size_t>(step_ms * 0.001 * k_sr);
+
+    machine m = make();
+    m.set_step_ms(step_ms);
+    m.set_repeats(4); // draws 1..4 passes per fired slice
+    m.set_seed(20260815);
+
+    const std::vector<double> x = pluck_train(at(2.0));
+    const std::vector<double> y = render(m, x);
+
+    size_t fresh = 0, repeated = 0;
+    bool   legal = true;
+    for (size_t i = 2 * step; i < y.size(); ++i) {
+        const bool is_fresh  = (y[i] == x[i - step + 1]);
+        const bool is_repeat = (y[i] == y[i - step]);
+        if (is_fresh) {
+            ++fresh;
+        }
+        else if (is_repeat) {
+            ++repeated;
+        }
+        else {
+            legal = false;
+        }
+    }
+    INFO("fresh grabs " << fresh << ", repeated samples " << repeated);
+    REQUIRE(legal);
+    REQUIRE(fresh > 0);    // it does grab new material
+    REQUIRE(repeated > 0); // and it does hold on: repeats are reaching the output
+}
+
+SCENARIO("a seed is a performance you can replay") {
+    auto run = [](uint64_t seed) {
+        machine m = make();
+        m.set_step_ms(70.0);
+        m.set_density(0.6);
+        m.set_divisions(4);
+        m.set_repeats(6);
+        m.set_reverse(0.4);
+        m.set_jump_ms(120.0);
+        m.set_fade_ms(3.0);
+        m.set_seed(seed);
+        return render(m, pluck_train(at(3.0)));
+    };
+
+    const std::vector<double> a = run(12345);
+    const std::vector<double> b = run(12345);
+    const std::vector<double> c = run(999);
+
+    bool same = true;
+    for (size_t i = 0; i < a.size(); ++i) {
+        same = same && (a[i] == b[i]); // bitwise: the dice are deterministic
+    }
+    REQUIRE(same);
+
+    // And a different seed is a different performance, not a cosmetic reshuffle.
+    size_t differing = 0;
+    for (size_t i = 0; i < a.size(); ++i) {
+        if (a[i] != c[i]) {
+            ++differing;
+        }
+    }
+    INFO("samples differing between seeds: " << differing << " of " << a.size());
+    REQUIRE(differing > a.size() / 20);
+}
+
+// The garden.h idle contract, same shape: a disabled generator must not consume its stream, so
+// the seed provably cannot matter. Here that also means the object is a bitwise bypass.
+SCENARIO("at density 0 the dice are never rolled, so the seed cannot matter") {
+    auto run = [](uint64_t seed) {
+        machine m = make();
+        m.set_step_ms(70.0);
+        m.set_density(0.0);
+        m.set_seed(seed);
+        return render(m, pluck_train(at(1.0)));
+    };
+
+    const std::vector<double> a = run(1);
+    const std::vector<double> b = run(0xfeedface);
+    const std::vector<double> x = pluck_train(at(1.0));
+
+    bool identical = true, passthrough = true;
+    for (size_t i = 0; i < a.size(); ++i) {
+        identical   = identical && (a[i] == b[i]);
+        passthrough = passthrough && (a[i] == x[i]);
+    }
+    REQUIRE(identical);
+    REQUIRE(passthrough); // and idle is a bitwise bypass
+}
+
+SCENARIO("an idle machine passes the input through bitwise at any mix") {
+    machine m = make();
+    m.set_step_ms(70.0);
+    m.set_density(0.0);
+    m.set_mix(50.0); // mid-mix: there is nothing to blend against, so it must not change the level
+
+    const std::vector<double> x = pluck_train(at(0.5));
+    const std::vector<double> y = render(m, x);
+
+    bool exact = true;
+    for (size_t i = 0; i < y.size(); ++i) {
+        exact = exact && (y[i] == x[i]);
+    }
+    REQUIRE(exact);
+}
+
+SCENARIO("the per-repeat flanks reach exactly zero at both edges") {
+    const double step_ms = 50.0;
+    const size_t step    = static_cast<size_t>(step_ms * 0.001 * k_sr);
+    const double fade_ms = 2.0;
+    const size_t fade    = static_cast<size_t>(fade_ms * 0.001 * k_sr);
+
+    machine m = make();
+    m.set_step_ms(step_ms);
+    m.set_fade_ms(fade_ms);
+
+    // A held DC input makes the slice material exactly 1.0, so the output IS the envelope. (A
+    // mechanism check, not a musical one — the material contract is about what the object is for.)
+    std::vector<double>       x(at(0.5), 1.0);
+    const std::vector<double> y = render(m, x);
+
+    // The second grid point onward grabs fully-recorded material, so the envelope stands alone.
+    const size_t base = 2 * step;
+    INFO("slice length " << step << ", flank " << fade);
+    REQUIRE(y[base] == 0.0);            // opens from silence
+    REQUIRE(y[base + step - 1] == 0.0); // and closes to it
+    REQUIRE(y[base + fade] == 1.0);     // the plateau is unity, exactly
+    REQUIRE(y[base + step / 2] == 1.0);
+
+    // Monotonic rise across the flank: no ripple, no overshoot.
+    bool rising = true;
+    for (size_t i = 1; i <= fade; ++i) {
+        rising = rising && (y[base + i] >= y[base + i - 1]);
+    }
+    REQUIRE(rising);
+}
+
+SCENARIO("clear drops the slice in flight and restarts the seeded stream") {
+    machine m = make();
+    m.set_step_ms(60.0);
+    m.set_density(0.7);
+    m.set_repeats(6);
+    m.set_seed(4242);
+
+    const std::vector<double> x = pluck_train(at(1.0));
+    const std::vector<double> a = render(m, x);
+
+    m.clear();
+    const std::vector<double> b = render(m, x);
+
+    bool same = true;
+    for (size_t i = 0; i < a.size(); ++i) {
+        same = same && (a[i] == b[i]); // clear rewinds the performance exactly
+    }
+    REQUIRE(same);
+}
+
+SCENARIO("unprepared, the stammer passes input through") {
+    machine m;
+    REQUIRE(m.process(0.7) == 0.7);
+    REQUIRE(m.process(-0.3) == -0.3);
+}
diff --git a/tests/tapecho_test.cpp b/tests/tapecho_test.cpp
new file mode 100644
index 0000000..542842b
--- /dev/null
+++ b/tests/tapecho_test.cpp
@@ -0,0 +1,406 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the tap.tapecho~ kernel (tapecho.h over tape_loop.h).
+/// @details    Two house patterns carry most of the suite. The NULL TEST is the load-bearing one:
+///             with the tape path neutralized (no wow, no wear, no regeneration) a one-head echo
+///             must be *bitwise* the plain Hermite multitap of delay.h — that is what makes
+///             "tape_loop.h is a library, and this kernel is only composition" a measurement
+///             rather than a claim. The pitch promises (wow depth) are ORACLE-BASED, measured out
+///             of the output with the DspTap YIN detector, as in discreet_test.cpp; the
+///             self-oscillation bound uses the two-window RMS pattern from grm_comb_test.cpp.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <tap/dsp/yin.h>
+#include <taptools/delay.h>
+#include <taptools/tapecho.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+    constexpr double k_pi = 3.14159265358979323846;
+
+    using tap::tools::tapecho::machine;
+
+    /// A machine with the transport parked, the wear path neutral, and instant setters: tests opt
+    /// into wow, drive, and regeneration explicitly.
+    machine make(double max_span_seconds = 2.0) {
+        machine m;
+        m.prepare(k_sr, max_span_seconds);
+        m.set_smooth_ms(0.0);
+        m.set_wow(0.0, 0.0);
+        m.set_flutter(0.0, 0.0);
+        m.set_regen(0.0);
+        m.set_drive(0.0);
+        m.set_darken_hz(20000.0);
+        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<size_t>(seconds * k_sr);
+    }
+
+    double rms(const std::vector<double>& 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<double>(end - begin));
+    }
+
+    double peak(const std::vector<double>& 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;
+    }
+
+    /// YIN oracle at an offset — same detector setup as discreet_test.cpp / tune_test.cpp.
+    double measure_hz(const std::vector<double>& x, size_t offset) {
+        const size_t  tau_min = static_cast<size_t>(k_sr / 2000.0);
+        const size_t  tau_max = static_cast<size_t>(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);
+    }
+
+    /// 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<double>(state) / 2147483648.0) - 1.0;
+        }
+    };
+
+    /// Render an impulse through a machine and return the left bus.
+    std::vector<double> impulse_response(machine& m, double seconds) {
+        std::vector<double> y(at(seconds), 0.0);
+        for (size_t i = 0; i < y.size(); ++i) {
+            double r = 0.0;
+            m.process(i == 0 ? 1.0 : 0.0, y[i], r);
+        }
+        return y;
+    }
+
+} // namespace
+
+SCENARIO("each head echoes at its own position along the tape path") {
+    machine m = make();
+    m.set_span_ms(100.0);
+    m.set_heads(4); // the default even spacing: 0.25, 0.5, 0.75, 1.0 of the span
+
+    const std::vector<double> y = impulse_response(m, 0.15);
+
+    // A head at ratio r returns the impulse at exactly r * span. Integer spans read Hermite frac 0,
+    // so the returned sample is the recorded one scaled only by level and the centre pan law.
+    const double centre = std::cos(0.25 * k_pi); // pan 0, the multitap equal-power law
+    for (int i = 0; i < 4; ++i) {
+        const double ratio = static_cast<double>(i + 1) / 4.0;
+        const size_t k     = at(0.1 * ratio);
+        INFO("head " << i << " at ratio " << ratio << ", expected return at sample " << k);
+        CHECK(std::abs(y[k] - centre) < 1e-12);
+        CHECK(std::abs(y[k - 1]) < 1e-12); // and nowhere else: the neighbours are silent
+        CHECK(std::abs(y[k + 1]) < 1e-12);
+    }
+}
+
+// The load-bearing test of the whole kernel: neutralize the tape (no transport error, no
+// regeneration) and the echo must be the plain Hermite delay of delay.h, sample for sample.
+// Not vacuous — a 1e-12 nudge on the span, the level, or the pan breaks it (checked during
+// development), and it is bitwise because both paths are the same Hermite read at the same
+// fractional position under the same equal-power pan law.
+SCENARIO("with the tape path neutral, a one-head echo is bitwise the multitap of delay.h") {
+    const double span_ms = 137.31; // deliberately not a whole number of samples
+
+    machine m = make(1.0);
+    m.set_heads(1);
+    m.set_head_ratio(0, 1.0);
+    m.set_head_level(0, 1.0);
+    m.set_head_pan(0, 0.0);
+    m.set_span_ms(span_ms);
+
+    tap::tools::delay::multitap ref;
+    ref.prepare(k_sr, 1000.0);
+    ref.set_smooth_ms(0.0);
+    ref.set_taps(1);
+    ref.set_time_ms(0, span_ms);
+    ref.set_gain(0, 1.0);
+    ref.set_pan(0, 0.0);
+
+    noise rng;
+    bool  exact = true;
+    for (size_t i = 0; i < at(0.5); ++i) {
+        const double in = rng();
+        double       ml = 0.0, mr = 0.0, rl = 0.0, rr = 0.0;
+        m.process(in, ml, mr);
+        ref.process(in, rl, rr);
+        exact = exact && (ml == rl) && (mr == rr); // bitwise, not approximately
+    }
+    REQUIRE(exact);
+}
+
+SCENARIO("the motor moves every head together") {
+    // Doubling the span doubles every head's return time — one motor, one tape path.
+    machine a = make();
+    a.set_heads(4);
+    a.set_span_ms(100.0);
+    const std::vector<double> y_short = impulse_response(a, 0.25);
+
+    machine b = make();
+    b.set_heads(4);
+    b.set_span_ms(200.0);
+    const std::vector<double> y_long = impulse_response(b, 0.25);
+
+    for (int i = 0; i < 4; ++i) {
+        const double ratio = static_cast<double>(i + 1) / 4.0;
+        INFO("head " << i << " at ratio " << ratio);
+        CHECK(std::abs(y_short[at(0.1 * ratio)]) > 0.5);
+        CHECK(std::abs(y_long[at(0.2 * ratio)]) > 0.5);
+    }
+
+    // And the heads really moved: the doubled span vacates the two short-span positions it does
+    // not also occupy (25 and 75 ms; 50 and 100 ms are shared by both layouts).
+    CHECK(std::abs(y_long[at(0.025)]) < 1e-12);
+    CHECK(std::abs(y_long[at(0.075)]) < 1e-12);
+}
+
+// The design statement of the kernel: regeneration past unity is legal, self-oscillates, and
+// stays bounded because the saturator — not a feedback cap — is the stabilizer. The analytic
+// bound on the tape is |in|max + regen * L1(wear) / drive, with the DC blocker's L1 gain of
+// ~2 on top of the saturator's 1/drive ceiling: 0.5 + 1.4 * 2 / 0.6 = ~5.2. The measured peak
+// sits far below that (real signals do not excite the L1 worst case); the ceiling asserted here
+// is the measured value with margin, and the promise that matters is the non-growth.
+SCENARIO("regeneration past unity self-oscillates but stays bounded") {
+    machine m = make();
+    m.set_heads(1);
+    m.set_head_ratio(0, 1.0);
+    m.set_span_ms(250.0);
+    m.set_regen(1.4);
+    m.set_drive(0.6);
+    m.set_darken_hz(6000.0);
+
+    noise               rng;
+    std::vector<double> y(at(12.0), 0.0);
+    for (size_t i = 0; i < y.size(); ++i) {
+        const double in = (i < at(0.5)) ? 0.5 * rng() : 0.0;
+        double       r  = 0.0;
+        m.process(in, y[i], r);
+    }
+
+    const double early = rms(y, at(6.0), at(9.0));
+    const double late  = rms(y, at(9.0), at(12.0));
+    INFO("oscillation RMS: [6,9)s = " << early << ", [9,12)s = " << late);
+    REQUIRE(std::isfinite(late));
+    REQUIRE(late > 0.01);          // it really is oscillating, not decaying away
+    REQUIRE(late <= early * 1.02); // and it has plateaued: bounded, not runaway
+    REQUIRE(peak(y, 0, y.size()) < 3.0);
+}
+
+SCENARIO("at drive 0 the regeneration cap falls back to unity") {
+    // Same excessive regen target, no saturator. The per-sample cap holds the effective value at
+    // 1.0, where the linear wear path (|H| <= 1) sustains without growing.
+    machine m = make();
+    m.set_heads(1);
+    m.set_head_ratio(0, 1.0);
+    m.set_span_ms(250.0);
+    m.set_regen(1.4);
+    m.set_drive(0.0);
+    m.set_darken_hz(6000.0);
+
+    noise               rng;
+    std::vector<double> y(at(12.0), 0.0);
+    for (size_t i = 0; i < y.size(); ++i) {
+        const double in = (i < at(0.5)) ? 0.5 * rng() : 0.0;
+        double       r  = 0.0;
+        m.process(in, y[i], r);
+    }
+
+    const double early = rms(y, at(6.0), at(9.0));
+    const double late  = rms(y, at(9.0), at(12.0));
+    INFO("capped RMS: [6,9)s = " << early << ", [9,12)s = " << late);
+    REQUIRE(std::isfinite(late));
+    REQUIRE(late <= early * 1.02); // no growth, though the target says 1.4
+    REQUIRE(peak(y, 0, y.size()) < 2.0);
+    REQUIRE(m.regen() == 1.4); // the target is kept: it takes effect again when drive returns
+}
+
+SCENARIO("the echo transport bends pitch by the set wow 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 * k_pi * rate_hz;
+
+    auto render = [&] {
+        machine m = make();
+        m.set_heads(1);
+        m.set_head_ratio(0, 1.0);
+        m.set_span_ms(1000.0);
+        m.set_wow(depth_ms, rate_hz);
+        std::vector<double> y(at(4.5), 0.0);
+        for (size_t i = 0; i < y.size(); ++i) {
+            const double t = static_cast<double>(i) / k_sr;
+            double       r = 0.0;
+            m.process(0.8 * std::sin(2.0 * k_pi * 440.0 * t), y[i], r);
+        }
+        return y;
+    };
+
+    const std::vector<double> y = render();
+
+    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<double> 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("a span change glides as tape speed, not a splice") {
+    machine m = make();
+    m.set_heads(1);
+    m.set_head_ratio(0, 1.0);
+    m.set_span_ms(500.0);
+
+    std::vector<double> y;
+    y.reserve(at(4.5));
+    auto run = [&](double seconds) {
+        for (size_t i = 0; i < at(seconds); ++i) {
+            const double t = static_cast<double>(y.size()) / k_sr;
+            double       l = 0.0, r = 0.0;
+            m.process(0.8 * std::sin(2.0 * k_pi * 440.0 * t), l, r);
+            y.push_back(l);
+        }
+    };
+
+    run(2.0); // fill the tape at the short span
+    m.set_smooth_ms(500.0);
+    m.set_span_ms(750.0); // 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);
+
+    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);
+
+    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("a hard-panned head is bitwise absent from the far bus") {
+    machine m = make();
+    m.set_heads(2);
+    m.set_head_ratio(0, 0.5);
+    m.set_head_pan(0, -1.0); // hard left
+    m.set_head_ratio(1, 1.0);
+    m.set_head_pan(1, 1.0); // hard right
+
+    noise  rng;
+    bool   left_clean = true, right_clean = true;
+    double left_energy = 0.0, right_energy = 0.0;
+    for (size_t i = 0; i < at(0.5); ++i) {
+        double l = 0.0, r = 0.0;
+        m.process(i < 16 ? rng() : 0.0, l, r);
+        // Head 0 returns only on the left at 0.5 span, head 1 only on the right at 1.0 span.
+        const size_t half = at(0.375 * 0.5);
+        const size_t full = at(0.375);
+        if (i >= half && i < half + 16) {
+            right_clean = right_clean && (r == 0.0);
+            left_energy += l * l;
+        }
+        if (i >= full && i < full + 16) {
+            left_clean = left_clean && (l == 0.0);
+            right_energy += r * r;
+        }
+    }
+    REQUIRE(left_energy > 0.0); // the heads really did return
+    REQUIRE(right_energy > 0.0);
+    REQUIRE(right_clean); // and the far bus is bitwise zero, not just small
+    REQUIRE(left_clean);
+}
+
+SCENARIO("mix endpoints are bitwise exact on both busses") {
+    machine m = make();
+    m.set_heads(4);
+    m.set_span_ms(200.0);
+    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();
+        double       l = 0.0, r = 0.0;
+        m.process(in, l, r);
+        exact = exact && (l == in) && (r == in); // bitwise dry, both busses
+    }
+    REQUIRE(exact);
+}
+
+SCENARIO("no heads is silence, and the tape still turns underneath") {
+    machine m = make();
+    m.set_span_ms(200.0);
+    m.set_heads(0);
+
+    noise rng;
+    bool  silent = true;
+    for (size_t i = 0; i < at(0.5); ++i) {
+        double l = 0.0, r = 0.0;
+        m.process(rng(), l, r);
+        silent = silent && (l == 0.0) && (r == 0.0);
+    }
+    REQUIRE(silent);
+
+    // Bring a head back and the tape it was recording all along is there, immediately.
+    m.set_heads(1);
+    m.set_head_ratio(0, 1.0);
+    double energy = 0.0;
+    for (size_t i = 0; i < at(0.25); ++i) {
+        double l = 0.0, r = 0.0;
+        m.process(0.0, l, r);
+        energy += l * l;
+    }
+    REQUIRE(energy > 0.0);
+}
+
+SCENARIO("unprepared, the echo passes input through") {
+    machine m;
+    double  l = 0.0, r = 0.0;
+    m.process(0.7, l, r);
+    REQUIRE(l == 0.7);
+    REQUIRE(r == 0.7);
+    m.process(-0.3, l, r);
+    REQUIRE(l == -0.3);
+    REQUIRE(r == -0.3);
+}
diff --git a/tests/touche_test.cpp b/tests/touche_test.cpp
new file mode 100644
index 0000000..ed65aa8
--- /dev/null
+++ b/tests/touche_test.cpp
@@ -0,0 +1,223 @@
+/// @file
+/// @brief      Catch2 scenarios pinning the tap.touche~ kernel (touche.h).
+/// @details    This kernel's contract is unusual for the library: the curve is not a design
+///             choice to be measured after the fact, it is a *published measurement* the code
+///             is obliged to reproduce. So the load-bearing scenario simply checks that every
+///             one of Quartier et al.'s seven points comes back out of the object, and the rest
+///             pin the properties that make the interpolation trustworthy — monotone, no
+///             overshoot between points, exact endpoints, and demonstrably not the straight
+///             line a lazier implementation would have fitted.
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <vector>
+
+#include <catch2/catch_test_macros.hpp>
+#include <taptools/touche.h>
+
+namespace {
+
+    constexpr double k_sr = 48000.0;
+
+    using tap::tools::touche::key;
+    namespace T = tap::tools::touche;
+
+    key make() {
+        key k;
+        k.prepare(k_sr);
+        k.set_smooth_ms(0.0);
+        return k;
+    }
+
+    /// Normalized position of a displacement in millimetres — over the physical travel, which
+    /// is wider than the measured band (the bottom of the throw is silent).
+    double pos_of_mm(double mm) {
+        return mm / T::k_travel_mm;
+    }
+
+} // namespace
+
+// The whole point of the object: it reproduces the published table. Every measured nuance
+// boundary must come back at the dB the paper reports, referenced to full press.
+SCENARIO("the published measurement comes back out of the curve") {
+    key k = make();
+
+    for (int i = 0; i < T::k_points; ++i) {
+        const double mm      = T::k_table_mm[static_cast<size_t>(i)];
+        const double want_db = T::k_table_db[static_cast<size_t>(i)] - T::k_table_db[T::k_points - 1];
+        const double got_db  = k.db_at(pos_of_mm(mm));
+        INFO("point " << i << ": " << mm << " mm, published " << T::k_table_db[static_cast<size_t>(i)] << " dB_SPL -> "
+                      << want_db << " dB relative; got " << got_db);
+        CHECK(std::abs(got_db - want_db) < 0.05); // dense-table resolution, not curve error
+    }
+}
+
+SCENARIO("the range is exactly the published 50 dB over the published travel") {
+    key k = make();
+    INFO("full press " << k.db_at(1.0) << " dB, floor " << k.db_at(pos_of_mm(T::k_min_mm)) << " dB");
+    CHECK(std::abs(k.db_at(1.0)) < 1e-9); // full press is exactly unity
+    CHECK(std::abs(k.gain_at(1.0) - 1.0) < 1e-12);
+    CHECK(std::abs(k.db_at(pos_of_mm(T::k_min_mm)) + T::k_range_db) < 0.05); // the floor is -50 dB
+    CHECK(std::abs(T::k_max_mm - T::k_min_mm - 4.5) < 1e-9);                 // over 4.5 mm of travel
+}
+
+// The dead zone is the instrument's, not a modelling artifact: the key bends before it reaches
+// the powder bag, so the bottom of the throw makes no sound at all.
+SCENARIO("the bottom of the throw is silent, and that is the key's own first phase") {
+    key k = make();
+    for (double mm : {0.0, 1.0, 2.5, 4.0}) {
+        INFO(mm << " mm");
+        CHECK(k.gain_at(pos_of_mm(mm)) == 0.0);
+    }
+    CHECK(k.gain_at(pos_of_mm(4.4)) > 0.0); // and it opens just past the measured floor
+    INFO("silent fraction of the throw: " << T::k_min_mm / T::k_travel_mm);
+    CHECK(T::k_min_mm / T::k_travel_mm > 0.4); // roughly the first 45 %
+}
+
+SCENARIO("the curve is monotone and never overshoots between measured points") {
+    key k = make();
+
+    double last   = -1.0;
+    bool   mono   = true;
+    bool   inside = true;
+    for (int i = 0; i <= 4000; ++i) {
+        const double p = static_cast<double>(i) / 4000.0;
+        const double g = k.gain_at(p);
+        mono           = mono && (g >= last - 1e-12);
+        inside         = inside && (g >= 0.0) && (g <= 1.0 + 1e-12);
+        last           = g;
+    }
+    REQUIRE(mono);   // pressing harder is never quieter
+    REQUIRE(inside); // and the interpolant stays inside the measured envelope
+}
+
+// The reason the kernel interpolates rather than fits. Equal dB steps in the published table
+// correspond to displacement steps of 1.0, 0.6, 0.5, 0.4, 0.5, 1.5 mm — the curve steepens
+// through the middle and flattens at the top, and a straight line in dB-against-mm would be
+// visibly and audibly wrong.
+SCENARIO("the law is emphatically not a straight line in dB against displacement") {
+    key k = make();
+
+    // Compare across the *measured* band only — the silent dead zone below it is not part of
+    // the curve and would swamp the comparison.
+    const double lo    = pos_of_mm(T::k_min_mm);
+    const double hi    = pos_of_mm(T::k_max_mm);
+    double       worst = 0.0;
+    for (int i = 1; i < 100; ++i) {
+        const double f      = static_cast<double>(i) / 100.0;
+        const double p      = lo + f * (hi - lo);
+        const double linear = -T::k_range_db * (1.0 - f);
+        worst               = std::max(worst, std::abs(k.db_at(p) - linear));
+    }
+    INFO("largest departure from a straight line: " << worst << " dB");
+    REQUIRE(worst > 6.0); // nowhere near a line — this is the expressiveness
+
+    // And the departure has the measured shape: halfway along the measured band is louder than
+    // a line would predict, because the steep part of the curve happens early.
+    const double mid = k.db_at(lo + 0.5 * (hi - lo));
+    INFO("at half the measured band: " << mid << " dB, a line would say " << -T::k_range_db * 0.5);
+    CHECK(mid > -T::k_range_db * 0.5);
+}
+
+SCENARIO("below the measured floor is exact silence, not extrapolation") {
+    key k = make();
+    REQUIRE(k.gain_at(0.0) == 0.0);
+
+    // And through the audio path: a key at rest passes nothing at all.
+    k.set_position(0.0);
+    bool silent = true;
+    for (int i = 0; i < 2000; ++i) {
+        silent = silent && (k.process(0.7) == 0.0);
+    }
+    REQUIRE(silent);
+}
+
+SCENARIO("above full press the curve clamps rather than extrapolating") {
+    key k = make();
+    CHECK(k.gain_at(1.0) == k.gain_at(2.0));
+    k.set_position_mm(20.0); // far past the 9.5 mm travel
+    CHECK(k.position() == 1.0);
+}
+
+SCENARIO("the force column reproduces its own published points") {
+    key k = make();
+    k.set_mode(T::mode_force);
+
+    for (int i = 0; i < T::k_points; ++i) {
+        const double n       = T::k_table_n[static_cast<size_t>(i)];
+        const double p       = n / T::k_travel_n;
+        const double want_db = T::k_table_db[static_cast<size_t>(i)] - T::k_table_db[T::k_points - 1];
+        INFO("point " << i << ": " << n << " N, want " << want_db << " dB, got " << k.db_at(p));
+        CHECK(std::abs(k.db_at(p) - want_db) < 0.2); // coarser: the force axis is far from uniform
+    }
+}
+
+// The paper's finding that the map does not depend on the speed of the gesture is why a static
+// curve is legitimate at all. The kernel-side consequence a test can pin: the gain depends only
+// on where the key is, not on what the audio is doing or how it got there.
+SCENARIO("the gain depends only on position, not on the signal or the approach") {
+    key fast = make();
+    key slow = make();
+
+    // Same destination, reached instantly vs over a long ramp — once settled, identical.
+    fast.set_position(0.62);
+    slow.set_smooth_ms(200.0);
+    slow.set_position(0.62);
+    for (int i = 0; i < static_cast<int>(0.5 * k_sr); ++i) {
+        fast.process(0.0);
+        slow.process(0.0);
+    }
+    INFO("fast " << fast.gain() << ", slow " << slow.gain());
+    CHECK(std::abs(fast.gain() - slow.gain()) < 1e-12);
+
+    // And it is a pure gain: the ratio out/in is the same whatever the input.
+    key k = make();
+    k.set_position(0.4);
+    k.process(0.0); // settle
+    const double g = k.gain();
+    for (double x : {0.01, 0.5, -0.3, 1.0}) {
+        INFO("input " << x);
+        CHECK(std::abs(k.process(x) - x * g) < 1e-12);
+    }
+}
+
+SCENARIO("a position move is slewed, so the key does not click") {
+    key k = make();
+    k.set_smooth_ms(50.0);
+    k.set_position(0.0);
+    k.process(1.0);
+
+    k.set_position(1.0); // slam it open
+    double worst = 0.0, last = k.process(1.0);
+    for (int i = 0; i < static_cast<int>(0.1 * k_sr); ++i) {
+        const double y = k.process(1.0);
+        worst          = std::max(worst, std::abs(y - last));
+        last           = y;
+    }
+    INFO("largest single-sample step on a full-travel move: " << worst);
+    REQUIRE(worst < 0.01); // no discontinuity anywhere in the sweep
+}
+
+SCENARIO("the signal-rate path tracks a control signal") {
+    key k = make();
+
+    // Sweep the position as a signal and check the output follows the curve sample by sample.
+    bool exact = true;
+    for (int i = 0; i <= 100; ++i) {
+        const double p = static_cast<double>(i) / 100.0;
+        const double y = k.process(1.0, p);
+        exact          = exact && (std::abs(y - k.gain_at(p)) < 1e-12);
+    }
+    REQUIRE(exact);
+}
+
+SCENARIO("unprepared, the key still follows its curve") {
+    key k;
+    k.set_smooth_ms(0.0);
+    k.set_position(1.0);
+    CHECK(std::abs(k.process(0.5) - 0.5) < 1e-9);
+}
diff --git a/tools/capi/taptools_capi.cpp b/tools/capi/taptools_capi.cpp
index 4bbfbf2..ba65a39 100644
--- a/tools/capi/taptools_capi.cpp
+++ b/tools/capi/taptools_capi.cpp
@@ -15,15 +15,22 @@
 #include <taptools/autowah.h>
 #include <taptools/conv_engine.h>
 #include <taptools/delay.h>
+#include <taptools/diffuseur.h>
 #include <taptools/diode_ladder.h>
 #include <taptools/discreet.h>
+#include <taptools/fuzz.h>
 #include <taptools/garden.h>
 #include <taptools/harmonizer.h>
 #include <taptools/ladder.h>
+#include <taptools/ondes.h>
 #include <taptools/overdrive.h>
+#include <taptools/scrub.h>
+#include <taptools/stammer.h>
 #include <taptools/step_seq.h>
 #include <taptools/svf.h>
+#include <taptools/tapecho.h>
 #include <taptools/tb303_voice.h>
+#include <taptools/touche.h>
 #include <taptools/tr808_kick.h>
 #include <taptools/tune.h>
 #include <taptools/vco.h>
@@ -1144,6 +1151,890 @@ int taptools_discreet_process(taptools_discreet h, const double* in, double* out
     return with<discreet_machine>(h, [&](discreet_machine& m) { m.process(in, out, static_cast<size_t>(n)); });
 }
 
+// ---- tap.tapecho~ --------------------------------------------------------------------------------
+
+using tapecho_machine = tap::tools::tapecho::machine;
+
+taptools_tapecho taptools_tapecho_create(void) {
+    return static_cast<taptools_tapecho>(new tapecho_machine());
+}
+
+void taptools_tapecho_destroy(taptools_tapecho h) {
+    delete static_cast<tapecho_machine*>(h);
+}
+
+int taptools_tapecho_prepare(taptools_tapecho h, double sr, double max_span_seconds) {
+    if (max_span_seconds <= 0.0) {
+        return -1;
+    }
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.prepare(sr, max_span_seconds); });
+}
+
+int taptools_tapecho_set_span_ms(taptools_tapecho h, double ms) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_span_ms(ms); });
+}
+
+int taptools_tapecho_set_heads(taptools_tapecho h, int count) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_heads(count); });
+}
+
+int taptools_tapecho_set_head_ratio(taptools_tapecho h, int head, double ratio) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_head_ratio(head, ratio); });
+}
+
+int taptools_tapecho_set_head_level(taptools_tapecho h, int head, double lin) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_head_level(head, lin); });
+}
+
+int taptools_tapecho_set_head_pan(taptools_tapecho h, int head, double pan) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_head_pan(head, pan); });
+}
+
+int taptools_tapecho_set_regen(taptools_tapecho h, double r) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_regen(r); });
+}
+
+int taptools_tapecho_set_darken_hz(taptools_tapecho h, double hz) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_darken_hz(hz); });
+}
+
+int taptools_tapecho_set_drive(taptools_tapecho h, double d) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_drive(d); });
+}
+
+int taptools_tapecho_set_input_level(taptools_tapecho h, double lin) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_input_level(lin); });
+}
+
+int taptools_tapecho_set_mix(taptools_tapecho h, double pct) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_mix(pct); });
+}
+
+int taptools_tapecho_set_wow(taptools_tapecho h, double depth_ms, double rate_hz) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_wow(depth_ms, rate_hz); });
+}
+
+int taptools_tapecho_set_flutter(taptools_tapecho h, double depth_ms, double rate_hz) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_flutter(depth_ms, rate_hz); });
+}
+
+int taptools_tapecho_set_smooth_ms(taptools_tapecho h, double ms) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.set_smooth_ms(ms); });
+}
+
+int taptools_tapecho_clear(taptools_tapecho h) {
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.clear(); });
+}
+
+int taptools_tapecho_process(taptools_tapecho h, const double* in, double* outL, double* outR, int n) {
+    if (!in || !outL || !outR || n < 0) {
+        return -1;
+    }
+    return with<tapecho_machine>(h, [&](tapecho_machine& m) { m.process(in, outL, outR, static_cast<size_t>(n)); });
+}
+
+// ---- tap.transducer -------------------------------------------------------------------------------
+
+using diffuseur_transducer = tap::tools::diffuseur::transducer;
+
+taptools_transducer taptools_transducer_create(void) {
+    return static_cast<taptools_transducer>(new diffuseur_transducer());
+}
+
+void taptools_transducer_destroy(taptools_transducer h) {
+    delete static_cast<diffuseur_transducer*>(h);
+}
+
+int taptools_transducer_prepare(taptools_transducer h, double sr) {
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) { t.prepare(sr); });
+}
+
+int taptools_transducer_set_drive(taptools_transducer h, double lin) {
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) { t.set_drive(lin); });
+}
+
+int taptools_transducer_set_asymmetry(taptools_transducer h, double a) {
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) { t.set_asymmetry(a); });
+}
+
+int taptools_transducer_set_saturation(taptools_transducer h, double s) {
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) { t.set_saturation(s); });
+}
+
+int taptools_transducer_clear(taptools_transducer h) {
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) { t.clear(); });
+}
+
+int taptools_transducer_process(taptools_transducer h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<diffuseur_transducer>(h, [&](diffuseur_transducer& t) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = t.process(in[i]);
+        }
+    });
+}
+
+// ---- the Ondes Martenot tube model -----------------------------------------------------------------
+
+double taptools_tube_plate_current(int tube, double vpc, double vgc) {
+    return tap::tools::ondes::plate_current(tap::tools::ondes::tube_at(tube), vpc, vgc);
+}
+
+double taptools_tube_grid_current(int tube, double vgc) {
+    return tap::tools::ondes::grid_current(tap::tools::ondes::tube_at(tube), vgc);
+}
+
+int taptools_tube_params(int tube, double* out8) {
+    if (!out8 || tube < 0 || tube >= tap::tools::ondes::k_num_tubes) {
+        return 0;
+    }
+    const tap::tools::ondes::tube_params& t = tap::tools::ondes::tube_at(tube);
+    out8[0]                                 = t.mu;
+    out8[1]                                 = t.ex;
+    out8[2]                                 = t.kg;
+    out8[3]                                 = t.kp;
+    out8[4]                                 = t.kvb;
+    out8[5]                                 = t.vct;
+    out8[6]                                 = t.va;
+    out8[7]                                 = t.rgk;
+    return 8;
+}
+
+// ---- tap.triode~ ------------------------------------------------------------------------------------
+
+using ondes_triode = tap::tools::ondes::triode;
+
+taptools_triode taptools_triode_create(void) {
+    return static_cast<taptools_triode>(new ondes_triode());
+}
+
+void taptools_triode_destroy(taptools_triode h) {
+    delete static_cast<ondes_triode*>(h);
+}
+
+int taptools_triode_prepare(taptools_triode h, double sr) {
+    return with<ondes_triode>(h, [&](ondes_triode& t) { t.prepare(sr); });
+}
+
+int taptools_triode_set_tube(taptools_triode h, int tube) {
+    return with<ondes_triode>(h, [&](ondes_triode& t) { t.set_tube(tube); });
+}
+
+int taptools_triode_set_operating_point(taptools_triode h, double vbias, double rk, double rp) {
+    return with<ondes_triode>(
+        h, [&](ondes_triode& t) { t.set_operating_point(tap::tools::ondes::operating_point{vbias, rk, rp}); });
+}
+
+int taptools_triode_set_drive(taptools_triode h, double grid_volts) {
+    return with<ondes_triode>(h, [&](ondes_triode& t) { t.set_drive(grid_volts); });
+}
+
+int taptools_triode_set_corners(taptools_triode h, double hp_hz, double lp_hz) {
+    return with<ondes_triode>(h, [&](ondes_triode& t) { t.set_corners(hp_hz, lp_hz); });
+}
+
+int taptools_triode_clear(taptools_triode h) {
+    return with<ondes_triode>(h, [&](ondes_triode& t) { t.clear(); });
+}
+
+int taptools_triode_process(taptools_triode h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<ondes_triode>(h, [&](ondes_triode& t) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = t.process(in[i]);
+        }
+    });
+}
+
+double taptools_triode_curve_at(taptools_triode h, double x) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->curve_at(x) : std::nan("");
+}
+
+double taptools_triode_plate_swing_at(taptools_triode h, double grid_volts) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->plate_swing_at(grid_volts) : std::nan("");
+}
+
+double taptools_triode_bias_v(taptools_triode h) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->bias_v() : std::nan("");
+}
+
+double taptools_triode_quiescent_plate_v(taptools_triode h) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->quiescent_plate_v() : std::nan("");
+}
+
+double taptools_triode_quiescent_current_a(taptools_triode h) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->quiescent_current_a() : std::nan("");
+}
+
+double taptools_triode_gain(taptools_triode h) {
+    const ondes_triode* t = static_cast<const ondes_triode*>(h);
+    return t ? t->small_signal_gain() : std::nan("");
+}
+
+// ---- the heterodyne detector --------------------------------------------------------------------
+
+using ondes_detector = tap::tools::ondes::detector;
+
+taptools_detector taptools_detector_create(void) {
+    return static_cast<taptools_detector>(new ondes_detector());
+}
+
+void taptools_detector_destroy(taptools_detector h) {
+    delete static_cast<ondes_detector*>(h);
+}
+
+int taptools_detector_prepare(taptools_detector h, double sr) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.prepare(sr); });
+}
+
+int taptools_detector_set_frequency(taptools_detector h, double hz) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.set_frequency(hz); });
+}
+
+int taptools_detector_set_ribbon(taptools_detector h, double semitones) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.set_ribbon(semitones); });
+}
+
+int taptools_detector_set_depth(taptools_detector h, double depth) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.set_depth(depth); });
+}
+
+int taptools_detector_set_detect_ms(taptools_detector h, double ms) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.set_detect_ms(ms); });
+}
+
+int taptools_detector_clear(taptools_detector h) {
+    return with<ondes_detector>(h, [&](ondes_detector& d) { d.clear(); });
+}
+
+int taptools_detector_process(taptools_detector h, double* out, int n) {
+    if (!out || n < 0) {
+        return -1;
+    }
+    return with<ondes_detector>(h, [&](ondes_detector& d) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = d.process();
+        }
+    });
+}
+
+double taptools_detector_envelope_at(taptools_detector h, double phase) {
+    const ondes_detector* d = static_cast<const ondes_detector*>(h);
+    return d ? d->envelope_at(phase) : std::nan("");
+}
+
+// ---- tap.ondes~ -------------------------------------------------------------------------------------
+
+using ondes_voice = tap::tools::ondes::voice;
+
+taptools_ondes taptools_ondes_create(void) {
+    return static_cast<taptools_ondes>(new ondes_voice());
+}
+
+void taptools_ondes_destroy(taptools_ondes h) {
+    delete static_cast<ondes_voice*>(h);
+}
+
+int taptools_ondes_prepare(taptools_ondes h, double sr) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.prepare(sr); });
+}
+
+int taptools_ondes_set_ribbon(taptools_ondes h, double semitones) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_ribbon(semitones); });
+}
+
+int taptools_ondes_set_frequency(taptools_ondes h, double hz) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_frequency(hz); });
+}
+
+int taptools_ondes_set_depth(taptools_ondes h, double d) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_depth(d); });
+}
+
+int taptools_ondes_set_detect_ms(taptools_ondes h, double ms) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_detect_ms(ms); });
+}
+
+int taptools_ondes_set_drive(taptools_ondes h, double x) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_drive(x); });
+}
+
+int taptools_ondes_set_key(taptools_ondes h, double position) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_key(position); });
+}
+
+int taptools_ondes_set_key_mm(taptools_ondes h, double mm) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_key_mm(mm); });
+}
+
+int taptools_ondes_set_key_placement(taptools_ondes h, int where) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_key_placement(where); });
+}
+
+int taptools_ondes_set_power_stage(taptools_ondes h, int on) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_power_stage(on != 0); });
+}
+
+int taptools_ondes_set_polarity(taptools_ondes h, int sign) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_polarity(sign); });
+}
+
+int taptools_ondes_set_level(taptools_ondes h, double lin) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_level(lin); });
+}
+
+int taptools_ondes_set_oversample(taptools_ondes h, int os) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_oversample(os); });
+}
+
+int taptools_ondes_set_smooth_ms(taptools_ondes h, double ms) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.set_smooth_ms(ms); });
+}
+
+int taptools_ondes_clear(taptools_ondes h) {
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.clear(); });
+}
+
+int taptools_ondes_process(taptools_ondes h, double* out, int n) {
+    if (!out || n < 0) {
+        return -1;
+    }
+    return with<ondes_voice>(h, [&](ondes_voice& v) { v.process(out, static_cast<size_t>(n)); });
+}
+
+int taptools_ondes_process_mod(taptools_ondes h, const double* semitones, const double* key, double* out, int n) {
+    if (!semitones || !key || !out || n < 0) {
+        return -1;
+    }
+    return with<ondes_voice>(h, [&](ondes_voice& v) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = v.process(semitones[i], key[i]);
+        }
+    });
+}
+
+double taptools_ondes_frequency(taptools_ondes h) {
+    const ondes_voice* v = static_cast<const ondes_voice*>(h);
+    return v ? v->frequency() : std::nan("");
+}
+
+// ---- tap.plate -------------------------------------------------------------------------------------
+
+using diffuseur_plate = tap::tools::diffuseur::plate;
+
+taptools_plate taptools_plate_create(void) {
+    return static_cast<taptools_plate>(new diffuseur_plate());
+}
+
+void taptools_plate_destroy(taptools_plate h) {
+    delete static_cast<diffuseur_plate*>(h);
+}
+
+int taptools_plate_prepare(taptools_plate h, double sr) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.prepare(sr); });
+}
+
+int taptools_plate_set_pitch_hz(taptools_plate h, double hz) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.set_pitch_hz(hz); });
+}
+
+int taptools_plate_set_decay(taptools_plate h, double t60_s) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.set_decay(t60_s); });
+}
+
+int taptools_plate_set_tilt(taptools_plate h, double tilt) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.set_tilt(tilt); });
+}
+
+int taptools_plate_set_brightness(taptools_plate h, double b) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.set_brightness(b); });
+}
+
+int taptools_plate_clear(taptools_plate h) {
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) { p.clear(); });
+}
+
+int taptools_plate_process(taptools_plate h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<diffuseur_plate>(h, [&](diffuseur_plate& p) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = p.process(in[i]);
+        }
+    });
+}
+
+double taptools_plate_mode_hz(taptools_plate h, int mode) {
+    const diffuseur_plate* p = static_cast<const diffuseur_plate*>(h);
+    return p ? p->mode_hz(mode) : std::nan("");
+}
+
+double taptools_plate_mode_level(taptools_plate h, int mode) {
+    const diffuseur_plate* p = static_cast<const diffuseur_plate*>(h);
+    return p ? p->mode_level(mode) : std::nan("");
+}
+
+// ---- tap.metallique~ ------------------------------------------------------------------------------
+
+using diffuseur_metallique = tap::tools::diffuseur::metallique;
+
+taptools_metallique taptools_metallique_create(void) {
+    return static_cast<taptools_metallique>(new diffuseur_metallique());
+}
+
+void taptools_metallique_destroy(taptools_metallique h) {
+    delete static_cast<diffuseur_metallique*>(h);
+}
+
+int taptools_metallique_prepare(taptools_metallique h, double sr) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.prepare(sr); });
+}
+
+int taptools_metallique_set_pitch_hz(taptools_metallique h, double hz) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_pitch_hz(hz); });
+}
+
+int taptools_metallique_set_decay(taptools_metallique h, double t60_s) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_decay(t60_s); });
+}
+
+int taptools_metallique_set_tilt(taptools_metallique h, double tilt) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_tilt(tilt); });
+}
+
+int taptools_metallique_set_brightness(taptools_metallique h, double b) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_brightness(b); });
+}
+
+int taptools_metallique_set_drive(taptools_metallique h, double lin) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_drive(lin); });
+}
+
+int taptools_metallique_set_asymmetry(taptools_metallique h, double a) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_asymmetry(a); });
+}
+
+int taptools_metallique_set_saturation(taptools_metallique h, double s) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_saturation(s); });
+}
+
+int taptools_metallique_set_mix(taptools_metallique h, double pct) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_mix(pct); });
+}
+
+int taptools_metallique_set_level(taptools_metallique h, double lin) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_level(lin); });
+}
+
+int taptools_metallique_set_smooth_ms(taptools_metallique h, double ms) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.set_smooth_ms(ms); });
+}
+
+int taptools_metallique_clear(taptools_metallique h) {
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.clear(); });
+}
+
+int taptools_metallique_process(taptools_metallique h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<diffuseur_metallique>(h, [&](diffuseur_metallique& m) { m.process(in, out, static_cast<size_t>(n)); });
+}
+
+double taptools_metallique_mode_hz(taptools_metallique h, int mode) {
+    const diffuseur_metallique* m = static_cast<const diffuseur_metallique*>(h);
+    return m ? m->body().mode_hz(mode) : std::nan("");
+}
+
+double taptools_metallique_mode_level(taptools_metallique h, int mode) {
+    const diffuseur_metallique* m = static_cast<const diffuseur_metallique*>(h);
+    return m ? m->body().mode_level(mode) : std::nan("");
+}
+
+// ---- tap.palme~ -----------------------------------------------------------------------------------
+
+using diffuseur_palme = tap::tools::diffuseur::palme;
+
+taptools_palme taptools_palme_create(void) {
+    return static_cast<taptools_palme>(new diffuseur_palme());
+}
+
+void taptools_palme_destroy(taptools_palme h) {
+    delete static_cast<diffuseur_palme*>(h);
+}
+
+int taptools_palme_prepare(taptools_palme h, double sr) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.prepare(sr); });
+}
+
+int taptools_palme_set_root_hz(taptools_palme h, double hz) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_root_hz(hz); });
+}
+
+int taptools_palme_set_tuning(taptools_palme h, int tuning) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_tuning(tuning); });
+}
+
+int taptools_palme_set_decay(taptools_palme h, double t60_s) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_decay(t60_s); });
+}
+
+int taptools_palme_set_damping(taptools_palme h, double hz) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_damping(hz); });
+}
+
+int taptools_palme_set_detune(taptools_palme h, double cents) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_detune(cents); });
+}
+
+int taptools_palme_set_drive(taptools_palme h, double lin) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_drive(lin); });
+}
+
+int taptools_palme_set_asymmetry(taptools_palme h, double a) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_asymmetry(a); });
+}
+
+int taptools_palme_set_saturation(taptools_palme h, double s) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_saturation(s); });
+}
+
+int taptools_palme_set_mix(taptools_palme h, double pct) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_mix(pct); });
+}
+
+int taptools_palme_set_level(taptools_palme h, double lin) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_level(lin); });
+}
+
+int taptools_palme_set_smooth_ms(taptools_palme h, double ms) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.set_smooth_ms(ms); });
+}
+
+int taptools_palme_clear(taptools_palme h) {
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.clear(); });
+}
+
+int taptools_palme_process(taptools_palme h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<diffuseur_palme>(h, [&](diffuseur_palme& p) { p.process(in, out, static_cast<size_t>(n)); });
+}
+
+double taptools_palme_string_hz(taptools_palme h, int index) {
+    const diffuseur_palme* p = static_cast<const diffuseur_palme*>(h);
+    return p ? p->body().string_hz(index) : std::nan("");
+}
+
+double taptools_palme_string_feedback(taptools_palme h, int index) {
+    const diffuseur_palme* p = static_cast<const diffuseur_palme*>(h);
+    return p ? p->body().string_feedback(index) : std::nan("");
+}
+
+// ---- tap.scrub~ -----------------------------------------------------------------------------------
+
+using scrub_machine = tap::tools::scrub::machine;
+
+taptools_scrub taptools_scrub_create(void) {
+    return static_cast<taptools_scrub>(new scrub_machine());
+}
+
+void taptools_scrub_destroy(taptools_scrub h) {
+    delete static_cast<scrub_machine*>(h);
+}
+
+int taptools_scrub_prepare(taptools_scrub h, double sr, double max_history_ms) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.prepare(sr, max_history_ms); });
+}
+
+int taptools_scrub_set_position_ms(taptools_scrub h, double ms) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_position_ms(ms); });
+}
+
+int taptools_scrub_set_pitch(taptools_scrub h, double semitones) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_pitch(semitones); });
+}
+
+int taptools_scrub_set_drift(taptools_scrub h, double rate) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_drift(rate); });
+}
+
+int taptools_scrub_set_freeze(taptools_scrub h, int on) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_freeze(on != 0); });
+}
+
+int taptools_scrub_set_size_ms(taptools_scrub h, double ms) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_size_ms(ms); });
+}
+
+int taptools_scrub_set_overlap(taptools_scrub h, int n) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_overlap(n); });
+}
+
+int taptools_scrub_set_spray_ms(taptools_scrub h, double ms) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_spray_ms(ms); });
+}
+
+int taptools_scrub_set_seed(taptools_scrub h, unsigned long long seed) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_seed(static_cast<uint64_t>(seed)); });
+}
+
+int taptools_scrub_set_mix(taptools_scrub h, double pct) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_mix(pct); });
+}
+
+int taptools_scrub_set_level(taptools_scrub h, double lin) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_level(lin); });
+}
+
+int taptools_scrub_set_smooth_ms(taptools_scrub h, double ms) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.set_smooth_ms(ms); });
+}
+
+int taptools_scrub_clear(taptools_scrub h) {
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.clear(); });
+}
+
+int taptools_scrub_process(taptools_scrub h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<scrub_machine>(h, [&](scrub_machine& m) { m.process(in, out, static_cast<size_t>(n)); });
+}
+
+int taptools_scrub_process_mod(taptools_scrub h, const double* in, const double* position_ms, const double* pitch_st,
+                               double* out, int n) {
+    if (!in || !position_ms || !pitch_st || !out || n < 0) {
+        return -1;
+    }
+    return with<scrub_machine>(h, [&](scrub_machine& m) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = m.process(in[i], position_ms[i], pitch_st[i]);
+        }
+    });
+}
+
+int taptools_scrub_active_grains(taptools_scrub h) {
+    const scrub_machine* m = static_cast<const scrub_machine*>(h);
+    return m ? m->active_grains() : -1;
+}
+
+// ---- tap.touche~ ---------------------------------------------------------------------------------
+
+using touche_key = tap::tools::touche::key;
+
+taptools_touche taptools_touche_create(void) {
+    return static_cast<taptools_touche>(new touche_key());
+}
+
+void taptools_touche_destroy(taptools_touche h) {
+    delete static_cast<touche_key*>(h);
+}
+
+int taptools_touche_prepare(taptools_touche h, double sr) {
+    return with<touche_key>(h, [&](touche_key& k) { k.prepare(sr); });
+}
+
+int taptools_touche_set_position(taptools_touche h, double p) {
+    return with<touche_key>(h, [&](touche_key& k) { k.set_position(p); });
+}
+
+int taptools_touche_set_position_mm(taptools_touche h, double mm) {
+    return with<touche_key>(h, [&](touche_key& k) { k.set_position_mm(mm); });
+}
+
+int taptools_touche_set_force_n(taptools_touche h, double n) {
+    return with<touche_key>(h, [&](touche_key& k) { k.set_force_n(n); });
+}
+
+int taptools_touche_set_mode(taptools_touche h, int mode) {
+    return with<touche_key>(h, [&](touche_key& k) { k.set_mode(mode); });
+}
+
+int taptools_touche_set_smooth_ms(taptools_touche h, double ms) {
+    return with<touche_key>(h, [&](touche_key& k) { k.set_smooth_ms(ms); });
+}
+
+int taptools_touche_clear(taptools_touche h) {
+    return with<touche_key>(h, [&](touche_key& k) { k.clear(); });
+}
+
+double taptools_touche_gain_at(taptools_touche h, double p) {
+    const touche_key* k = static_cast<const touche_key*>(h);
+    return k ? k->gain_at(p) : std::nan("");
+}
+
+int taptools_touche_process(taptools_touche h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<touche_key>(h, [&](touche_key& k) { k.process(in, out, static_cast<size_t>(n)); });
+}
+
+int taptools_touche_process_mod(taptools_touche h, const double* in, const double* position, double* out, int n) {
+    if (!in || !position || !out || n < 0) {
+        return -1;
+    }
+    return with<touche_key>(h, [&](touche_key& k) {
+        for (int i = 0; i < n; ++i) {
+            out[i] = k.process(in[i], position[i]);
+        }
+    });
+}
+
+// ---- tap.fuzz~ -----------------------------------------------------------------------------------
+
+using fuzz_pedal = tap::tools::fuzz::pedal;
+
+taptools_fuzz taptools_fuzz_create(void) {
+    return static_cast<taptools_fuzz>(new fuzz_pedal());
+}
+
+void taptools_fuzz_destroy(taptools_fuzz h) {
+    delete static_cast<fuzz_pedal*>(h);
+}
+
+int taptools_fuzz_prepare(taptools_fuzz h, double sr) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.prepare(sr); });
+}
+
+int taptools_fuzz_set_gain(taptools_fuzz h, double g) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_gain(g); });
+}
+
+int taptools_fuzz_set_edge(taptools_fuzz h, double e) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_edge(e); });
+}
+
+int taptools_fuzz_set_asymmetry(taptools_fuzz h, double a) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_asymmetry(a); });
+}
+
+int taptools_fuzz_set_bass(taptools_fuzz h, double b) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_bass(b); });
+}
+
+int taptools_fuzz_set_treble(taptools_fuzz h, double t) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_treble(t); });
+}
+
+int taptools_fuzz_set_contrast(taptools_fuzz h, double c) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_contrast(c); });
+}
+
+int taptools_fuzz_set_level_db(taptools_fuzz h, double db) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_level_db(db); });
+}
+
+int taptools_fuzz_set_oversample(taptools_fuzz h, int os) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_oversample(os); });
+}
+
+int taptools_fuzz_set_smooth_ms(taptools_fuzz h, double ms) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.set_smooth_ms(ms); });
+}
+
+int taptools_fuzz_clear(taptools_fuzz h) {
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.clear(); });
+}
+
+int taptools_fuzz_process(taptools_fuzz h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<fuzz_pedal>(h, [&](fuzz_pedal& p) { p.process(in, out, static_cast<size_t>(n)); });
+}
+
+// ---- tap.stammer~ --------------------------------------------------------------------------------
+
+using stammer_machine = tap::tools::stammer::machine;
+
+taptools_stammer taptools_stammer_create(void) {
+    return static_cast<taptools_stammer>(new stammer_machine());
+}
+
+void taptools_stammer_destroy(taptools_stammer h) {
+    delete static_cast<stammer_machine*>(h);
+}
+
+int taptools_stammer_prepare(taptools_stammer h, double sr, double max_history_ms) {
+    if (max_history_ms <= 0.0) {
+        return -1;
+    }
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.prepare(sr, max_history_ms); });
+}
+
+int taptools_stammer_set_step_ms(taptools_stammer h, double ms) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_step_ms(ms); });
+}
+
+int taptools_stammer_set_density(taptools_stammer h, double p) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_density(p); });
+}
+
+int taptools_stammer_set_divisions(taptools_stammer h, int n) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_divisions(n); });
+}
+
+int taptools_stammer_set_repeats(taptools_stammer h, int n) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_repeats(n); });
+}
+
+int taptools_stammer_set_reverse(taptools_stammer h, double p) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_reverse(p); });
+}
+
+int taptools_stammer_set_jump_ms(taptools_stammer h, double ms) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_jump_ms(ms); });
+}
+
+int taptools_stammer_set_fade_ms(taptools_stammer h, double ms) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_fade_ms(ms); });
+}
+
+int taptools_stammer_set_seed(taptools_stammer h, unsigned long long seed) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_seed(static_cast<uint64_t>(seed)); });
+}
+
+int taptools_stammer_set_input_level(taptools_stammer h, double lin) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_input_level(lin); });
+}
+
+int taptools_stammer_set_mix(taptools_stammer h, double pct) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_mix(pct); });
+}
+
+int taptools_stammer_set_smooth_ms(taptools_stammer h, double ms) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.set_smooth_ms(ms); });
+}
+
+int taptools_stammer_clear(taptools_stammer h) {
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.clear(); });
+}
+
+int taptools_stammer_playing(taptools_stammer h) {
+    stammer_machine* m = static_cast<stammer_machine*>(h);
+    return m ? (m->playing() ? 1 : 0) : -1;
+}
+
+int taptools_stammer_process(taptools_stammer h, const double* in, double* out, int n) {
+    if (!in || !out || n < 0) {
+        return -1;
+    }
+    return with<stammer_machine>(h, [&](stammer_machine& m) { m.process(in, out, static_cast<size_t>(n)); });
+}
+
 // ---- tap.airport~ --------------------------------------------------------------------------------
 
 using airport_bank = tap::tools::airport::loop_bank;
diff --git a/tools/capi/taptools_capi.h b/tools/capi/taptools_capi.h
index 0ee4b5a..ddd8165 100644
--- a/tools/capi/taptools_capi.h
+++ b/tools/capi/taptools_capi.h
@@ -366,6 +366,288 @@ 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.tapecho~ (tap::tools::tapecho::machine) -------------------------------------------------
+
+typedef void* taptools_tapecho;
+
+TAPTOOLS_API taptools_tapecho taptools_tapecho_create(void);
+TAPTOOLS_API void             taptools_tapecho_destroy(taptools_tapecho h);
+/// Buy tape for `max_span_seconds` at `sr`; snaps ramps and erases the tape.
+TAPTOOLS_API int taptools_tapecho_prepare(taptools_tapecho h, double sr, double max_span_seconds);
+TAPTOOLS_API int taptools_tapecho_set_span_ms(taptools_tapecho h, double ms); // the motor; slewed = varispeed
+TAPTOOLS_API int taptools_tapecho_set_heads(taptools_tapecho h, int count);   // 0..4 active playback heads
+/// Per-head setters; `head` is 0-based. `ratio` is the head's position as a fraction of the span.
+TAPTOOLS_API int taptools_tapecho_set_head_ratio(taptools_tapecho h, int head, double ratio); // (0, 1]
+TAPTOOLS_API int taptools_tapecho_set_head_level(taptools_tapecho h, int head, double lin);
+TAPTOOLS_API int taptools_tapecho_set_head_pan(taptools_tapecho h, int head, double pan); // -1..1 equal-power
+/// 0..1.5. Above 1.0 self-oscillates and is only reached while drive > 0 (the saturator bounds it).
+TAPTOOLS_API int taptools_tapecho_set_regen(taptools_tapecho h, double r);
+TAPTOOLS_API int taptools_tapecho_set_darken_hz(taptools_tapecho h, double hz);    // per-pass wear corner
+TAPTOOLS_API int taptools_tapecho_set_drive(taptools_tapecho h, double d);         // >= 0; 0 caps regen at 1.0
+TAPTOOLS_API int taptools_tapecho_set_input_level(taptools_tapecho h, double lin); // into the record head
+TAPTOOLS_API int taptools_tapecho_set_mix(taptools_tapecho h, double pct);         // 0..100, equal-power
+TAPTOOLS_API int taptools_tapecho_set_wow(taptools_tapecho h, double depth_ms, double rate_hz);
+TAPTOOLS_API int taptools_tapecho_set_flutter(taptools_tapecho h, double depth_ms, double rate_hz);
+TAPTOOLS_API int taptools_tapecho_set_smooth_ms(taptools_tapecho h, double ms);
+TAPTOOLS_API int taptools_tapecho_clear(taptools_tapecho h);
+/// Process n samples mono-in / stereo-out (the dry path is mixed to both busses).
+TAPTOOLS_API int taptools_tapecho_process(taptools_tapecho h, const double* in, double* outL, double* outR, int n);
+
+// ---- tap.transducer (tap::tools::diffuseur::transducer) ------------------------------------------
+
+/// The diffuseurs' moving-iron driver on its own — a component, not an external. Reachable here
+/// because the family's rule is that parts get C ABI reachability from the start, and because
+/// measuring the driver through a body means measuring the body too.
+typedef void* taptools_transducer;
+
+TAPTOOLS_API taptools_transducer taptools_transducer_create(void);
+TAPTOOLS_API void                taptools_transducer_destroy(taptools_transducer h);
+TAPTOOLS_API int                 taptools_transducer_prepare(taptools_transducer h, double sr);
+TAPTOOLS_API int                 taptools_transducer_set_drive(taptools_transducer h, double lin);
+TAPTOOLS_API int                 taptools_transducer_set_asymmetry(taptools_transducer h, double a);
+TAPTOOLS_API int                 taptools_transducer_set_saturation(taptools_transducer h, double s);
+TAPTOOLS_API int                 taptools_transducer_clear(taptools_transducer h);
+TAPTOOLS_API int taptools_transducer_process(taptools_transducer h, const double* in, double* out, int n);
+
+// ---- the Ondes Martenot tube model (tap::tools::ondes) -------------------------------------------
+
+/// The enhanced Norman Koren law itself, with the circuit paper's fitted parameter sets selected
+/// by index (0 = 6F5, 1 = 6C5, 2 = 2A3). Stateless, so no handle: a notebook plotting the
+/// published tube curves should be plotting the shipping code, not a copy of the equations.
+TAPTOOLS_API double taptools_tube_plate_current(int tube, double vpc, double vgc);
+TAPTOOLS_API double taptools_tube_grid_current(int tube, double vgc);
+/// The fitted parameters, so a plot can be labelled with the numbers it was drawn from. Fields in
+/// Table II order: mu, Ex, Kg, Kp, Kvb, Vct, Va, Rgk. Returns 0 on a bad index, 8 on success.
+TAPTOOLS_API int taptools_tube_params(int tube, double* out8);
+
+// ---- tap.triode~ (tap::tools::ondes::triode) -----------------------------------------------------
+
+typedef void* taptools_triode;
+
+TAPTOOLS_API taptools_triode taptools_triode_create(void);
+TAPTOOLS_API void            taptools_triode_destroy(taptools_triode h);
+TAPTOOLS_API int             taptools_triode_prepare(taptools_triode h, double sr);
+TAPTOOLS_API int             taptools_triode_set_tube(taptools_triode h, int tube);
+/// Supply volts, cathode resistor and plate load — the published stage operating points are
+/// (100, 1000, 4000) demodulator, (180, 1000, 4000) preamplifier, (230, 750, 1500) power.
+TAPTOOLS_API int taptools_triode_set_operating_point(taptools_triode h, double vbias, double rk, double rp);
+TAPTOOLS_API int taptools_triode_set_drive(taptools_triode h, double grid_volts);
+TAPTOOLS_API int taptools_triode_set_corners(taptools_triode h, double hp_hz, double lp_hz);
+TAPTOOLS_API int taptools_triode_clear(taptools_triode h);
+TAPTOOLS_API int taptools_triode_process(taptools_triode h, const double* in, double* out, int n);
+/// The stage's static curve without running audio: normalized in, normalized out.
+TAPTOOLS_API double taptools_triode_curve_at(taptools_triode h, double x);
+/// And in the tube's own units: grid volts in, plate volts of swing out.
+TAPTOOLS_API double taptools_triode_plate_swing_at(taptools_triode h, double grid_volts);
+/// The quiescent point the curve was built around: cathode bias V, plate V, plate current A, and
+/// the small-signal gain magnitude.
+TAPTOOLS_API double taptools_triode_bias_v(taptools_triode h);
+TAPTOOLS_API double taptools_triode_quiescent_plate_v(taptools_triode h);
+TAPTOOLS_API double taptools_triode_quiescent_current_a(taptools_triode h);
+TAPTOOLS_API double taptools_triode_gain(taptools_triode h);
+
+// ---- the heterodyne detector (tap::tools::ondes::detector) ---------------------------------------
+
+typedef void* taptools_detector;
+
+TAPTOOLS_API taptools_detector taptools_detector_create(void);
+TAPTOOLS_API void              taptools_detector_destroy(taptools_detector h);
+TAPTOOLS_API int               taptools_detector_prepare(taptools_detector h, double sr);
+TAPTOOLS_API int               taptools_detector_set_frequency(taptools_detector h, double hz);
+TAPTOOLS_API int               taptools_detector_set_ribbon(taptools_detector h, double semitones);
+TAPTOOLS_API int               taptools_detector_set_depth(taptools_detector h, double d);
+TAPTOOLS_API int               taptools_detector_set_detect_ms(taptools_detector h, double ms);
+TAPTOOLS_API int               taptools_detector_clear(taptools_detector h);
+/// A source: render n samples of the detected envelope (DC included — the circuit's coupling
+/// removes it downstream, and so does the triode stage's conditioning highpass).
+TAPTOOLS_API int taptools_detector_process(taptools_detector h, double* out, int n);
+/// The ideal envelope at a phase in [0, 1), with no detector on it — the closed form itself.
+TAPTOOLS_API double taptools_detector_envelope_at(taptools_detector h, double phase);
+
+// ---- tap.ondes~ (tap::tools::ondes::voice) -------------------------------------------------------
+
+typedef void* taptools_ondes;
+
+TAPTOOLS_API taptools_ondes taptools_ondes_create(void);
+TAPTOOLS_API void           taptools_ondes_destroy(taptools_ondes h);
+TAPTOOLS_API int            taptools_ondes_prepare(taptools_ondes h, double sr);
+TAPTOOLS_API int            taptools_ondes_set_ribbon(taptools_ondes h, double semitones); // above A1
+TAPTOOLS_API int            taptools_ondes_set_frequency(taptools_ondes h, double hz);
+TAPTOOLS_API int            taptools_ondes_set_depth(taptools_ondes h, double d);      // 0..1
+TAPTOOLS_API int            taptools_ondes_set_detect_ms(taptools_ondes h, double ms); // published 0.2
+TAPTOOLS_API int            taptools_ondes_set_drive(taptools_ondes h, double x);      // 0..8
+TAPTOOLS_API int            taptools_ondes_set_key(taptools_ondes h, double position); // 0..1 of the travel
+TAPTOOLS_API int            taptools_ondes_set_key_mm(taptools_ondes h, double mm);
+TAPTOOLS_API int            taptools_ondes_set_key_placement(taptools_ondes h, int where); // 0 after, 1 before
+TAPTOOLS_API int            taptools_ondes_set_power_stage(taptools_ondes h, int on);
+TAPTOOLS_API int            taptools_ondes_set_polarity(taptools_ondes h, int sign);
+TAPTOOLS_API int            taptools_ondes_set_level(taptools_ondes h, double lin);
+TAPTOOLS_API int            taptools_ondes_set_oversample(taptools_ondes h, int os);
+TAPTOOLS_API int            taptools_ondes_set_smooth_ms(taptools_ondes h, double ms);
+TAPTOOLS_API int            taptools_ondes_clear(taptools_ondes h);
+/// A source: render n samples of the instrument, minus its diffuseur.
+TAPTOOLS_API int taptools_ondes_process(taptools_ondes h, double* out, int n);
+/// Signal-rate performance: the ribbon in semitones and the key position, per sample.
+TAPTOOLS_API int taptools_ondes_process_mod(taptools_ondes h, const double* semitones, const double* key, double* out,
+                                            int n);
+TAPTOOLS_API double taptools_ondes_frequency(taptools_ondes h);
+
+// ---- tap.plate (tap::tools::diffuseur::plate) ----------------------------------------------------
+
+/// The metallique's body on its own — the mode bank without the driver in front of it. Same
+/// reason as the transducer above: the parts are reachable so a measurement of one is not
+/// silently a measurement of both.
+typedef void* taptools_plate;
+
+TAPTOOLS_API taptools_plate taptools_plate_create(void);
+TAPTOOLS_API void           taptools_plate_destroy(taptools_plate h);
+TAPTOOLS_API int            taptools_plate_prepare(taptools_plate h, double sr);
+TAPTOOLS_API int            taptools_plate_set_pitch_hz(taptools_plate h, double hz);
+TAPTOOLS_API int            taptools_plate_set_decay(taptools_plate h, double t60_s);
+TAPTOOLS_API int            taptools_plate_set_tilt(taptools_plate h, double tilt);
+TAPTOOLS_API int            taptools_plate_set_brightness(taptools_plate h, double b);
+TAPTOOLS_API int            taptools_plate_clear(taptools_plate h);
+TAPTOOLS_API int            taptools_plate_process(taptools_plate h, const double* in, double* out, int n);
+TAPTOOLS_API double         taptools_plate_mode_hz(taptools_plate h, int mode);
+TAPTOOLS_API double         taptools_plate_mode_level(taptools_plate h, int mode);
+
+// ---- tap.metallique~ (tap::tools::diffuseur::metallique) -----------------------------------------
+
+typedef void* taptools_metallique;
+
+TAPTOOLS_API taptools_metallique taptools_metallique_create(void);
+TAPTOOLS_API void                taptools_metallique_destroy(taptools_metallique h);
+TAPTOOLS_API int                 taptools_metallique_prepare(taptools_metallique h, double sr);
+TAPTOOLS_API int                 taptools_metallique_set_pitch_hz(taptools_metallique h, double hz);
+TAPTOOLS_API int                 taptools_metallique_set_decay(taptools_metallique h, double t60_s);
+/// Upper modes decay by ratio^tilt faster than the fundamental.
+TAPTOOLS_API int taptools_metallique_set_tilt(taptools_metallique h, double tilt);
+TAPTOOLS_API int taptools_metallique_set_brightness(taptools_metallique h, double b); // 0..1
+TAPTOOLS_API int taptools_metallique_set_drive(taptools_metallique h, double lin);
+TAPTOOLS_API int taptools_metallique_set_asymmetry(taptools_metallique h, double a);  // 0..1, moving-iron squared term
+TAPTOOLS_API int taptools_metallique_set_saturation(taptools_metallique h, double s); // 0 is exactly linear
+TAPTOOLS_API int taptools_metallique_set_mix(taptools_metallique h, double pct);      // 0..100, equal-power
+TAPTOOLS_API int taptools_metallique_set_level(taptools_metallique h, double lin);
+TAPTOOLS_API int taptools_metallique_set_smooth_ms(taptools_metallique h, double ms);
+TAPTOOLS_API int taptools_metallique_clear(taptools_metallique h);
+TAPTOOLS_API int taptools_metallique_process(taptools_metallique h, const double* in, double* out, int n);
+/// The body's tuning, so a notebook can plot where the modes landed (NaN on a bad handle).
+TAPTOOLS_API double taptools_metallique_mode_hz(taptools_metallique h, int mode);
+TAPTOOLS_API double taptools_metallique_mode_level(taptools_metallique h, int mode);
+
+// ---- tap.palme~ (tap::tools::diffuseur::palme) ---------------------------------------------------
+
+typedef void* taptools_palme;
+
+TAPTOOLS_API taptools_palme taptools_palme_create(void);
+TAPTOOLS_API void           taptools_palme_destroy(taptools_palme h);
+TAPTOOLS_API int            taptools_palme_prepare(taptools_palme h, double sr);
+TAPTOOLS_API int            taptools_palme_set_root_hz(taptools_palme h, double hz);
+TAPTOOLS_API int            taptools_palme_set_tuning(taptools_palme h, int tuning); // 0 chromatic, 1 harmonic
+TAPTOOLS_API int            taptools_palme_set_decay(taptools_palme h, double t60_s);
+TAPTOOLS_API int            taptools_palme_set_damping(taptools_palme h, double hz);
+TAPTOOLS_API int            taptools_palme_set_detune(taptools_palme h, double cents);
+TAPTOOLS_API int            taptools_palme_set_drive(taptools_palme h, double lin);
+TAPTOOLS_API int            taptools_palme_set_asymmetry(taptools_palme h, double a);
+TAPTOOLS_API int            taptools_palme_set_saturation(taptools_palme h, double s);
+TAPTOOLS_API int            taptools_palme_set_mix(taptools_palme h, double pct);
+TAPTOOLS_API int            taptools_palme_set_level(taptools_palme h, double lin);
+TAPTOOLS_API int            taptools_palme_set_smooth_ms(taptools_palme h, double ms);
+TAPTOOLS_API int            taptools_palme_clear(taptools_palme h);
+TAPTOOLS_API int            taptools_palme_process(taptools_palme h, const double* in, double* out, int n);
+/// Where a string ended up after tuning and scatter, in Hz (NaN on a bad handle).
+TAPTOOLS_API double taptools_palme_string_hz(taptools_palme h, int index);
+/// The loop gain a string settled on — the cap is visible here when damping and ring time fight.
+TAPTOOLS_API double taptools_palme_string_feedback(taptools_palme h, int index);
+
+// ---- tap.scrub~ (tap::tools::scrub::machine) -----------------------------------------------------
+
+typedef void* taptools_scrub;
+
+TAPTOOLS_API taptools_scrub taptools_scrub_create(void);
+TAPTOOLS_API void           taptools_scrub_destroy(taptools_scrub h);
+TAPTOOLS_API int            taptools_scrub_prepare(taptools_scrub h, double sr, double max_history_ms);
+TAPTOOLS_API int            taptools_scrub_set_position_ms(taptools_scrub h, double ms); // lag behind the live edge
+TAPTOOLS_API int            taptools_scrub_set_pitch(taptools_scrub h, double semitones);
+TAPTOOLS_API int            taptools_scrub_set_drift(taptools_scrub h, double rate); // playback-rate units
+TAPTOOLS_API int            taptools_scrub_set_freeze(taptools_scrub h, int on);
+TAPTOOLS_API int            taptools_scrub_set_size_ms(taptools_scrub h, double ms);
+TAPTOOLS_API int            taptools_scrub_set_overlap(taptools_scrub h, int n); // 1..4
+TAPTOOLS_API int            taptools_scrub_set_spray_ms(taptools_scrub h, double ms);
+TAPTOOLS_API int            taptools_scrub_set_seed(taptools_scrub h, unsigned long long seed);
+TAPTOOLS_API int            taptools_scrub_set_mix(taptools_scrub h, double pct);
+TAPTOOLS_API int            taptools_scrub_set_level(taptools_scrub h, double lin);
+TAPTOOLS_API int            taptools_scrub_set_smooth_ms(taptools_scrub h, double ms);
+TAPTOOLS_API int            taptools_scrub_clear(taptools_scrub h);
+TAPTOOLS_API int            taptools_scrub_process(taptools_scrub h, const double* in, double* out, int n);
+/// Signal-rate performance path: position (ms behind the edge) and pitch (semitones) per sample.
+TAPTOOLS_API int taptools_scrub_process_mod(taptools_scrub h, const double* in, const double* position_ms,
+                                            const double* pitch_st, double* out, int n);
+TAPTOOLS_API int taptools_scrub_active_grains(taptools_scrub h); // -1 on a bad handle
+
+// ---- tap.touche~ (tap::tools::touche::key) -------------------------------------------------------
+
+typedef void* taptools_touche;
+
+TAPTOOLS_API taptools_touche taptools_touche_create(void);
+TAPTOOLS_API void            taptools_touche_destroy(taptools_touche h);
+TAPTOOLS_API int             taptools_touche_prepare(taptools_touche h, double sr);
+TAPTOOLS_API int             taptools_touche_set_position(taptools_touche h, double p);     // 0..1 of the travel
+TAPTOOLS_API int             taptools_touche_set_position_mm(taptools_touche h, double mm); // published units
+TAPTOOLS_API int             taptools_touche_set_force_n(taptools_touche h, double n);
+TAPTOOLS_API int             taptools_touche_set_mode(taptools_touche h, int mode); // 0 displacement, 1 force
+TAPTOOLS_API int             taptools_touche_set_smooth_ms(taptools_touche h, double ms);
+TAPTOOLS_API int             taptools_touche_clear(taptools_touche h);
+/// The curve itself: linear gain at a normalized position (NaN on a bad handle). No state touched.
+TAPTOOLS_API double taptools_touche_gain_at(taptools_touche h, double p);
+TAPTOOLS_API int    taptools_touche_process(taptools_touche h, const double* in, double* out, int n);
+/// Signal-rate position: `position` drives the gain sample by sample.
+TAPTOOLS_API int taptools_touche_process_mod(taptools_touche h, const double* in, const double* position, double* out,
+                                             int n);
+
+// ---- tap.fuzz~ (tap::tools::fuzz::pedal) ---------------------------------------------------------
+
+typedef void* taptools_fuzz;
+
+TAPTOOLS_API taptools_fuzz taptools_fuzz_create(void);
+TAPTOOLS_API void          taptools_fuzz_destroy(taptools_fuzz h);
+TAPTOOLS_API int           taptools_fuzz_prepare(taptools_fuzz h, double sr);
+TAPTOOLS_API int           taptools_fuzz_set_gain(taptools_fuzz h, double g);      // 0..1, first-stage drive
+TAPTOOLS_API int           taptools_fuzz_set_edge(taptools_fuzz h, double e);      // 0..1, second-stage knee
+TAPTOOLS_API int           taptools_fuzz_set_asymmetry(taptools_fuzz h, double a); // 0..1, the even harmonics
+TAPTOOLS_API int           taptools_fuzz_set_bass(taptools_fuzz h, double b);      // -1..1 low shelf
+TAPTOOLS_API int           taptools_fuzz_set_treble(taptools_fuzz h, double t);    // -1..1 high shelf
+TAPTOOLS_API int           taptools_fuzz_set_contrast(taptools_fuzz h, double c);  // 0..1 mid scoop
+TAPTOOLS_API int           taptools_fuzz_set_level_db(taptools_fuzz h, double db);
+TAPTOOLS_API int           taptools_fuzz_set_oversample(taptools_fuzz h, int os); // 1, 2, 4 or 8
+TAPTOOLS_API int           taptools_fuzz_set_smooth_ms(taptools_fuzz h, double ms);
+TAPTOOLS_API int           taptools_fuzz_clear(taptools_fuzz h);
+TAPTOOLS_API int           taptools_fuzz_process(taptools_fuzz h, const double* in, double* out, int n);
+
+// ---- tap.stammer~ (tap::tools::stammer::machine) -------------------------------------------------
+
+typedef void* taptools_stammer;
+
+TAPTOOLS_API taptools_stammer taptools_stammer_create(void);
+TAPTOOLS_API void             taptools_stammer_destroy(taptools_stammer h);
+/// Buy the capture for `max_history_ms` at `sr`; resets the grid and re-seeds.
+TAPTOOLS_API int taptools_stammer_prepare(taptools_stammer h, double sr, double max_history_ms);
+TAPTOOLS_API int taptools_stammer_set_step_ms(taptools_stammer h, double ms); // the grid
+TAPTOOLS_API int taptools_stammer_set_density(taptools_stammer h, double p);  // 0..1; 0 never rolls
+TAPTOOLS_API int taptools_stammer_set_divisions(taptools_stammer h, int n);   // slice = step / [1, n]
+TAPTOOLS_API int taptools_stammer_set_repeats(taptools_stammer h, int n);     // passes per fired slice
+TAPTOOLS_API int taptools_stammer_set_reverse(taptools_stammer h, double p);  // 0..1, drawn per repeat
+TAPTOOLS_API int taptools_stammer_set_jump_ms(taptools_stammer h, double ms); // extra reach-back
+TAPTOOLS_API int taptools_stammer_set_fade_ms(taptools_stammer h, double ms); // per-repeat flank
+TAPTOOLS_API int taptools_stammer_set_seed(taptools_stammer h, unsigned long long seed);
+TAPTOOLS_API int taptools_stammer_set_input_level(taptools_stammer h, double lin);
+TAPTOOLS_API int taptools_stammer_set_mix(taptools_stammer h, double pct); // only bites while firing
+TAPTOOLS_API int taptools_stammer_set_smooth_ms(taptools_stammer h, double ms);
+TAPTOOLS_API int taptools_stammer_clear(taptools_stammer h);
+/// 1 while a slice is sounding, else 0 (-1 on a bad handle).
+TAPTOOLS_API int taptools_stammer_playing(taptools_stammer h);
+TAPTOOLS_API int taptools_stammer_process(taptools_stammer h, const double* in, double* out, int n);
+
 // ---- tap.airport~ (tap::tools::airport::loop_bank) -----------------------------------------------
 
 typedef void* taptools_airport;
diff --git a/tools/render/CMakeLists.txt b/tools/render/CMakeLists.txt
index c4d95ea..3084dfe 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 eno_render)
+foreach (tool diode_render tb303_render ladder_render vco_render grm_comb_render grm_pitchaccum_render autowah_render tr808_render eno_render radiohead_render)
     add_executable(${tool} ${tool}.cpp)
     target_link_libraries(${tool} PRIVATE TapTools::taptools)
     set_target_properties(${tool} PROPERTIES
diff --git a/tools/render/radiohead_render.cpp b/tools/render/radiohead_render.cpp
new file mode 100644
index 0000000..50a9e05
--- /dev/null
+++ b/tools/render/radiohead_render.cpp
@@ -0,0 +1,826 @@
+/// @file
+/// @brief      Offline renderer for the Radiohead family — writes demo WAVs for listening checks.
+/// @details    Exercises tapecho.h, stammer.h, fuzz.h and touche.h with no Max involved (the kernels' portability,
+/// demonstrated).
+///             The tape echo is a *performed* effect, so these scenarios move the controls while
+///             they render rather than auditioning static settings — that is the only way to hear
+///             what the kernel is actually for.
+///
+///             Scenarios: `tapecho_heads` (a guitar-ish phrase through the four evenly spaced
+///             heads, spread across the stereo field), `tapecho_three_head` (a Copicat-style
+///             three-head layout, dirtier wear, heads down the middle), `tapecho_selfosc` (the
+///             design statement made audible: regeneration pushed past unity into sound-on-sound
+///             howl with the saturator holding it bounded, the input faded out under it, then
+///             regeneration pulled back to let it decay), and `tapecho_varispeed` (the motor
+///             slewed from a short span to a long one mid-phrase — the doppler that a tape
+///             machine's speed change *is*); then `stammer_grid` (the stutter's five dials held
+///             still so the mechanism is audible), `stammer_disintegrate` (the performance the
+///             object exists for — density, chop, hold and reversal all ridden up until the part
+///             comes apart, then the reach-back opened so it quotes material from seconds ago),
+///             and `stammer_two_seeds` (the same settings on two seeds back to back: a seed is a
+///             performance); and `fuzz_gain_sweep` (five gain settings back to back, so the
+///             sweep from edge-of-breakup to saturated is audible rather than described),
+///             `fuzz_tone` (the voicing section, which is most of that pedal class's identity),
+///             and `fuzz_edge_and_bite` (the knee sharpening, then the even harmonics coming
+///             in); and `touche_against_a_fade`, which swells one note three times — a linear
+///             fade, a fade linear in dB, and the Ondes Martenot's published intensity-key
+///             curve — because the measured law is audibly neither of the obvious two; then
+///             `metallique_stages` (the same phrase dry, through the gong with a linear driver,
+///             with the moving-iron squared term armed, and driven hard enough that the saturator
+///             is working — the order is the argument for the transducer being a real stage) and
+///             `palme_halo` (the twelve strings answering a phrase, chromatic then harmonic then
+///             detuned); and `scrub_gesture` (the position raked across a running loop, then the
+///             same rake with the pitch riding its own independent gesture — the two hands are
+///             the object) and `scrub_freeze` (two seconds recorded, the recorder stopped, and
+///             nine seconds built out of that fixed tape: held, crawled through with drift, then
+///             scattered with spray); and finally the Ondes Martenot itself — `triode_tubes`
+///             (a sine driven progressively harder through each of the three published valves at
+///             its own operating point), `ondes_stages` (the detected envelope alone, then through
+///             the demodulator, the preamplifier and the power stage in turn — the first pass is
+///             already harmonically rich, which is the object's whole thesis), `ondes_ribbon` (the
+///             ribbon and the intensity key played, with continuous glissandi because the ribbon
+///             is linear in semitones), and `ondes_diffuseurs` (the same phrase through the
+///             principal, the palme and the métallique — the choice an ondes player makes).
+///
+///             Usage: radiohead_render [output-directory]   (default: current directory)
+/// @author     Timothy Place
+// SPDX-License-Identifier: MIT
+// Copyright 2026 Timothy Place.
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+#include <taptools/diffuseur.h>
+#include <taptools/fuzz.h>
+#include <taptools/ondes.h>
+#include <taptools/scrub.h>
+#include <taptools/stammer.h>
+#include <taptools/tapecho.h>
+#include <taptools/touche.h>
+
+namespace {
+
+    constexpr double k_r_sr = 48000.0;
+    constexpr double k_r_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<double>& 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<uint32_t>(samples.size());
+        const uint32_t data_bytes = n * 4;
+        const uint32_t rate       = static_cast<uint32_t>(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<uint16_t>(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<float>(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;
+    }
+
+    /// The kernel sums its heads with no master gain — gain staging is the caller's job, and for
+    /// these renders this tool is the caller. Each scenario carries an explicit trim chosen so the
+    /// file peaks below unity on playback; nothing here is normalized after the fact, so the
+    /// relative loudness *within* a render (a howl building over a phrase) is the kernel's own.
+    void write_scenario(const std::string& path, std::vector<double> samples, double trim, uint16_t channels = 2) {
+        for (double& s : samples) {
+            s *= trim;
+        }
+        write_wav(path, samples, k_r_sr, channels);
+    }
+
+    double midi_hz(double pitch) {
+        return 440.0 * std::exp2((pitch - 69.0) / 12.0);
+    }
+
+    /// A plucked-string-ish source: a decaying harmonic stack with a bright, fast attack. Echo
+    /// scenarios want transients — a pad would hide the head layout entirely.
+    double pluck(double t, double hz) {
+        if (t < 0.0) {
+            return 0.0;
+        }
+        const double attack = 1.0 - std::exp(-t * 900.0);
+        double       sum    = 0.0;
+        for (int k = 1; k <= 6; ++k) {
+            const double kk    = static_cast<double>(k);
+            const double decay = std::exp(-t * (3.0 + 2.2 * kk)); // higher partials die first
+            sum += (1.0 / kk) * decay * std::sin(2.0 * k_r_pi * hz * kk * t);
+        }
+        return 0.45 * attack * sum;
+    }
+
+    /// A phrase as a list of (onset seconds, midi pitch) plucks, summed at time t.
+    struct note {
+        double onset;
+        double pitch;
+    };
+
+    double phrase(const std::vector<note>& notes, double t) {
+        double sum = 0.0;
+        for (const note& n : notes) {
+            sum += pluck(t - n.onset, midi_hz(n.pitch));
+        }
+        return sum;
+    }
+
+    /// The house progression for these renders — a slow minor arpeggio with room between the
+    /// notes for the repeats to be heard against.
+    const std::vector<note>& demo_phrase() {
+        static const std::vector<note> notes = {{0.00, 45.0}, {0.75, 52.0}, {1.50, 57.0}, {2.25, 60.0},
+                                                {3.00, 64.0}, {4.50, 57.0}, {6.00, 52.0}};
+        return notes;
+    }
+
+    /// The phrase, looped — the stammer scenarios run long enough that seven notes would leave
+    /// the machine chewing on silence.
+    double looping_phrase(double t) {
+        return phrase(demo_phrase(), std::fmod(t, 7.5));
+    }
+
+    // ---- scenarios -----------------------------------------------------------------------------
+
+    void tapecho_heads(const std::string& dir) {
+        tap::tools::tapecho::machine m;
+        m.prepare(k_r_sr, 2.0);
+        m.set_span_ms(480.0);
+        m.set_heads(4); // the default even spacing: 0.25, 0.5, 0.75, 1.0 of the span
+        const double pans[4] = {-0.7, 0.5, -0.35, 0.8};
+        for (int i = 0; i < 4; ++i) {
+            m.set_head_pan(i, pans[i]);
+            m.set_head_level(i, 0.9 - 0.15 * static_cast<double>(i)); // nearer heads a touch louder
+        }
+        m.set_regen(0.45);
+        m.set_drive(0.4);
+        m.set_darken_hz(4200.0);
+        m.set_mix(45.0);
+
+        const size_t        frames = static_cast<size_t>(16.0 * k_r_sr);
+        std::vector<double> stereo(2 * frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            m.process(phrase(demo_phrase(), t), stereo[2 * i], stereo[2 * i + 1]);
+        }
+        write_scenario(dir + "/tapecho_heads.wav", stereo, 0.5);
+    }
+
+    void tapecho_three_head(const std::string& dir) {
+        tap::tools::tapecho::machine m;
+        m.prepare(k_r_sr, 2.0);
+        m.set_span_ms(390.0);
+        m.set_heads(3); // a Copicat-style three-head layout, set explicitly
+        for (int i = 0; i < 3; ++i) {
+            m.set_head_ratio(i, static_cast<double>(i + 1) / 3.0);
+            m.set_head_pan(i, 0.0);
+            m.set_head_level(i, 1.0);
+        }
+        m.set_regen(0.6);
+        m.set_drive(0.9); // a hotter record head: the repeats thicken as they recirculate
+        m.set_darken_hz(2600.0);
+        m.set_wow(0.9, 0.9); // a tired transport
+        m.set_flutter(0.06, 13.0);
+        m.set_mix(50.0);
+
+        const size_t        frames = static_cast<size_t>(16.0 * k_r_sr);
+        std::vector<double> stereo(2 * frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            m.process(phrase(demo_phrase(), t), stereo[2 * i], stereo[2 * i + 1]);
+        }
+        write_scenario(dir + "/tapecho_three_head.wav", stereo, 0.5);
+    }
+
+    void tapecho_selfosc(const std::string& dir) {
+        // The kernel's design statement, as a performance: play a phrase, push regeneration past
+        // unity, fade the input away and let the loop howl on its own — bounded by the saturator,
+        // not by a feedback cap — then pull regeneration back and let it die.
+        tap::tools::tapecho::machine m;
+        m.prepare(k_r_sr, 2.0);
+        m.set_span_ms(420.0);
+        m.set_heads(2);
+        m.set_head_ratio(0, 0.5);
+        m.set_head_pan(0, -0.5);
+        m.set_head_ratio(1, 1.0);
+        m.set_head_pan(1, 0.5);
+        m.set_regen(0.5);
+        m.set_drive(0.7);
+        m.set_darken_hz(3800.0);
+        m.set_smooth_ms(400.0); // the controls are being *ridden*, so slew them like a fader
+        m.set_mix(60.0);
+
+        const size_t        frames = static_cast<size_t>(40.0 * k_r_sr);
+        std::vector<double> stereo(2 * frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            if (i == static_cast<size_t>(9.0 * k_r_sr)) {
+                m.set_regen(1.35); // past unity: the loop starts building on itself
+            }
+            if (i == static_cast<size_t>(14.0 * k_r_sr)) {
+                m.set_input_level(0.0); // hands off the instrument; the machine plays alone
+            }
+            if (i == static_cast<size_t>(26.0 * k_r_sr)) {
+                m.set_darken_hz(1400.0); // ride the tone control while it howls
+            }
+            if (i == static_cast<size_t>(32.0 * k_r_sr)) {
+                m.set_regen(0.55); // and bring it home
+            }
+            m.process(phrase(demo_phrase(), t), stereo[2 * i], stereo[2 * i + 1]);
+        }
+        write_scenario(dir + "/tapecho_selfosc.wav", stereo, 0.35);
+    }
+
+    void tapecho_varispeed(const std::string& dir) {
+        // A motor change is a tape-speed change: the repeats already on the tape bend in pitch as
+        // the heads move. Slow slews make it a dive; there is no crossfaded "digital" mode.
+        tap::tools::tapecho::machine m;
+        m.prepare(k_r_sr, 3.0);
+        m.set_span_ms(200.0);
+        m.set_heads(2);
+        m.set_head_ratio(0, 0.5);
+        m.set_head_pan(0, -0.4);
+        m.set_head_ratio(1, 1.0);
+        m.set_head_pan(1, 0.4);
+        m.set_regen(0.7);
+        m.set_drive(0.5);
+        m.set_darken_hz(5000.0);
+        m.set_mix(65.0);
+
+        const size_t        frames = static_cast<size_t>(24.0 * k_r_sr);
+        std::vector<double> stereo(2 * frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            if (i == static_cast<size_t>(8.0 * k_r_sr)) {
+                m.set_smooth_ms(3000.0);
+                m.set_span_ms(900.0); // spool out over three seconds: the dive
+            }
+            if (i == static_cast<size_t>(16.0 * k_r_sr)) {
+                m.set_smooth_ms(1200.0);
+                m.set_span_ms(200.0); // and back up
+            }
+            m.process(phrase(demo_phrase(), t), stereo[2 * i], stereo[2 * i + 1]);
+        }
+        write_scenario(dir + "/tapecho_varispeed.wav", stereo, 0.35);
+    }
+
+    // ---- tap.stammer~ ----------------------------------------------------------------------------
+
+    /// The five dials that are the instrument, held still so the mechanism is audible on its own.
+    void stammer_grid(const std::string& dir) {
+        tap::tools::stammer::machine m;
+        m.prepare(k_r_sr, 2000.0);
+        m.set_step_ms(250.0);
+        m.set_density(0.55);
+        m.set_divisions(4);
+        m.set_repeats(4);
+        m.set_reverse(0.2);
+        m.set_fade_ms(3.0);
+        m.set_seed(1999); // the year the first TapTools shipped
+        m.set_mix(100.0);
+
+        const size_t        frames = static_cast<size_t>(24.0 * k_r_sr);
+        std::vector<double> mono(frames);
+        for (size_t i = 0; i < frames; ++i) {
+            mono[i] = m.process(looping_phrase(static_cast<double>(i) / k_r_sr));
+        }
+        write_scenario(dir + "/stammer_grid.wav", mono, 0.8, 1);
+    }
+
+    /// The performance the object exists for: a part that comes apart in your hands. Density,
+    /// chop, hold and reversal all ride up over the render, and the reach-back opens at the end so
+    /// the machine starts quoting material from seconds ago rather than the bar just played.
+    void stammer_disintegrate(const std::string& dir) {
+        tap::tools::stammer::machine m;
+        m.prepare(k_r_sr, 4000.0);
+        m.set_step_ms(250.0);
+        m.set_density(0.15);
+        m.set_divisions(1);
+        m.set_repeats(1);
+        m.set_reverse(0.0);
+        m.set_fade_ms(4.0);
+        m.set_seed(2003); // Hail to the Thief
+        m.set_mix(100.0);
+
+        const double        seconds = 40.0;
+        const size_t        frames  = static_cast<size_t>(seconds * k_r_sr);
+        std::vector<double> mono(frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            const double u = t / seconds; // 0 -> 1 across the render: the hands on the machine
+            m.set_density(0.15 + 0.8 * u);
+            m.set_divisions(1 + static_cast<int>(u * 7.99));
+            m.set_repeats(1 + static_cast<int>(u * 9.99));
+            m.set_reverse(0.6 * u);
+            m.set_step_ms(250.0 - 130.0 * u);
+            if (t > 0.75 * seconds) {
+                m.set_jump_ms(1500.0); // and now it reaches back past the bar
+            }
+            mono[i] = m.process(looping_phrase(t));
+        }
+        write_scenario(dir + "/stammer_disintegrate.wav", mono, 0.8, 1);
+    }
+
+    /// A seed is a performance: the same settings on two seeds, back to back in one file, so the
+    /// difference is the dice and nothing else. Each half is bit-reproducible on its own.
+    void stammer_two_seeds(const std::string& dir) {
+        const double        half   = 12.0;
+        const size_t        frames = static_cast<size_t>(half * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(2 * frames);
+
+        for (uint64_t seed : {uint64_t{1}, uint64_t{2}}) {
+            tap::tools::stammer::machine m;
+            m.prepare(k_r_sr, 2000.0);
+            m.set_step_ms(200.0);
+            m.set_density(0.7);
+            m.set_divisions(4);
+            m.set_repeats(5);
+            m.set_reverse(0.35);
+            m.set_fade_ms(3.0);
+            m.set_seed(seed);
+            m.set_mix(100.0);
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(m.process(looping_phrase(static_cast<double>(i) / k_r_sr)));
+            }
+        }
+        write_scenario(dir + "/stammer_two_seeds.wav", mono, 0.8, 1);
+    }
+
+    // ---- tap.fuzz~ -------------------------------------------------------------------------------
+
+    /// The gain knob doing what a gain knob should: the same phrase at five settings, so the
+    /// sweep from edge-of-breakup to saturated is audible in one file rather than described.
+    void fuzz_gain_sweep(const std::string& dir) {
+        const double        step   = 4.0;
+        const size_t        frames = static_cast<size_t>(step * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(5 * frames);
+
+        for (double g : {0.0, 0.25, 0.5, 0.75, 1.0}) {
+            tap::tools::fuzz::pedal p;
+            p.prepare(k_r_sr);
+            p.set_gain(g);
+            p.set_edge(0.4);
+            p.set_asymmetry(0.15);
+            p.set_bass(0.2);
+            p.set_treble(0.1);
+            p.set_contrast(0.35);
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(p.process(looping_phrase(static_cast<double>(i) / k_r_sr)));
+            }
+        }
+        write_scenario(dir + "/fuzz_gain_sweep.wav", mono, 0.7, 1);
+    }
+
+    /// The tone section, which on this class of pedal is most of the identity: flat, scooped,
+    /// and the two shelves at their travel, twelve seconds apiece.
+    void fuzz_tone(const std::string& dir) {
+        struct setting {
+            double bass, treble, contrast;
+        };
+        const setting settings[4] = {{0.0, 0.0, 0.0}, {0.0, 0.0, 1.0}, {0.6, -0.6, 0.5}, {-0.6, 0.8, 0.5}};
+
+        const size_t        frames = static_cast<size_t>(8.0 * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(4 * frames);
+        for (const setting& v : settings) {
+            tap::tools::fuzz::pedal p;
+            p.prepare(k_r_sr);
+            p.set_gain(0.7);
+            p.set_edge(0.5);
+            p.set_bass(v.bass);
+            p.set_treble(v.treble);
+            p.set_contrast(v.contrast);
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(p.process(looping_phrase(static_cast<double>(i) / k_r_sr)));
+            }
+        }
+        write_scenario(dir + "/fuzz_tone.wav", mono, 0.4, 1); // the bass-shelf setting is the peak here
+    }
+
+    /// The two controls a static-curve model has to be honest about, ridden rather than set:
+    /// `edge` sharpening the knee toward a corner, and `asymmetry` bringing in the even
+    /// harmonics an odd-only curve cannot make.
+    void fuzz_edge_and_bite(const std::string& dir) {
+        tap::tools::fuzz::pedal p;
+        p.prepare(k_r_sr);
+        p.set_gain(0.8);
+        p.set_contrast(0.4);
+        p.set_smooth_ms(300.0);
+        p.set_oversample(8); // the honest setting for a hard knee
+
+        const double        seconds = 24.0;
+        const size_t        frames  = static_cast<size_t>(seconds * k_r_sr);
+        std::vector<double> mono(frames);
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            const double u = t / seconds;
+            p.set_edge(u < 0.5 ? 2.0 * u : 1.0);              // first half: the knee sharpens
+            p.set_asymmetry(u < 0.5 ? 0.0 : 2.0 * (u - 0.5)); // second half: the bite comes in
+            mono[i] = p.process(looping_phrase(t));
+        }
+        write_scenario(dir + "/fuzz_edge_and_bite.wav", mono, 0.7, 1);
+    }
+
+    // ---- tap.touche~ -----------------------------------------------------------------------------
+
+    /// The intensity key against the obvious alternative. The same note is swelled and released
+    /// three times: once through a straight linear fade, once through a straight fade in dB, and
+    /// once through the published curve. The point is that the measured law is neither — it
+    /// steepens through the middle of the travel and flattens at the top, which is what lets a
+    /// player place a crescendo where they want it instead of where the taper puts it.
+    void touche_against_a_fade(const std::string& dir) {
+        tap::tools::touche::key k;
+        k.prepare(k_r_sr);
+        k.set_smooth_ms(0.0);
+
+        const double        gesture = 3.0; // seconds up, then the same back down
+        const size_t        frames  = static_cast<size_t>(2.0 * gesture * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(3 * frames);
+
+        for (int law = 0; law < 3; ++law) {
+            double phase = 0.0;
+            for (size_t i = 0; i < frames; ++i) {
+                const double t = static_cast<double>(i) / k_r_sr;
+                // A triangle over the gesture: press in, release out.
+                const double u = (t < gesture) ? (t / gesture) : (2.0 - t / gesture);
+                // A steady tone, so the only thing moving is the gain law.
+                phase += 220.0 / k_r_sr;
+                phase -= std::floor(phase);
+                const double tone = 0.4 * std::sin(2.0 * k_r_pi * phase);
+
+                double g = 0.0;
+                if (law == 0) {
+                    g = u; // linear in amplitude
+                }
+                else if (law == 1) {
+                    g = (u <= 0.0) ? 0.0 : std::pow(10.0, (-50.0 * (1.0 - u)) / 20.0); // linear in dB
+                }
+                else {
+                    g = k.gain_at(u); // the published curve
+                }
+                mono.push_back(tone * g);
+            }
+        }
+        write_scenario(dir + "/touche_against_a_fade.wav", mono, 0.9, 1);
+    }
+
+    // ---- the diffuseurs ---------------------------------------------------------------------------
+
+    /// The métallique against a bare signal. The same phrase runs four times: dry, then through
+    /// the gong with the transducer linear, then with the moving-iron squared term armed, then
+    /// with the drive pushed so the saturator is doing real work. The order is the argument —
+    /// a diffuseur modelled as a resonator alone is missing a documented stage, and the third
+    /// and fourth passes are what that stage sounds like.
+    void metallique_stages(const std::string& dir) {
+        const double        pass   = 9.0;
+        const size_t        frames = static_cast<size_t>(pass * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(4 * frames);
+
+        for (int stage = 0; stage < 4; ++stage) {
+            tap::tools::diffuseur::metallique m;
+            m.prepare(k_r_sr);
+            m.set_pitch_hz(146.0);
+            m.set_decay(7.0);
+            m.set_tilt(0.9);
+            m.set_brightness(0.85);
+            m.set_mix((stage == 0) ? 0.0 : 70.0);
+            m.set_drive((stage == 3) ? 6.0 : 1.0);
+            m.set_asymmetry((stage >= 2) ? 0.45 : 0.0);
+            m.set_saturation((stage >= 2) ? 0.6 : 0.0);
+            // Levelled so the four passes are comparable by ear: the body is a colouring, not a
+            // boost, and the hard-driven pass is far louder than the rest.
+            m.set_level((stage == 0) ? 1.0 : ((stage == 3) ? 0.45 : 1.9));
+
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(m.process(phrase(demo_phrase(), static_cast<double>(i) / k_r_sr)));
+            }
+        }
+        write_scenario(dir + "/metallique_stages.wav", mono, 0.7, 1);
+    }
+
+    /// The palme's halo. A phrase in A minor into the twelve chromatic strings, then the same
+    /// phrase into the harmonic tuning on the same root — the first answers every note, the
+    /// second answers only what belongs to A. Then the detune opened, so the board beats.
+    void palme_halo(const std::string& dir) {
+        const double        pass   = 11.0;
+        const size_t        frames = static_cast<size_t>(pass * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(3 * frames);
+
+        const int    tunings[3] = {tap::tools::diffuseur::tuning_chromatic, tap::tools::diffuseur::tuning_harmonic,
+                                   tap::tools::diffuseur::tuning_chromatic};
+        const double detunes[3] = {0.0, 0.0, 22.0};
+
+        for (int pass_index = 0; pass_index < 3; ++pass_index) {
+            tap::tools::diffuseur::palme p;
+            p.prepare(k_r_sr);
+            p.set_root_hz(110.0);
+            p.set_tuning(tunings[pass_index]);
+            p.set_decay(5.0);
+            p.set_damping(3500.0);
+            p.set_detune(detunes[pass_index]);
+            p.set_drive(1.0);
+            p.set_asymmetry(0.2);
+            p.set_saturation(0.35);
+            p.set_mix(55.0);
+            p.set_level(0.5); // twelve resonant loops add up
+
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(p.process(phrase(demo_phrase(), static_cast<double>(i) / k_r_sr)));
+            }
+        }
+        write_scenario(dir + "/palme_halo.wav", mono, 0.4, 1); // twelve high-Q loops peak hard
+    }
+
+    // ---- tap.scrub~ -------------------------------------------------------------------------------
+
+    /// The scrub as it is actually played: the position dragged back and forth across the last
+    /// second and a half of a loop that keeps running underneath, first at unity pitch, then with
+    /// the pitch riding its own independent gesture. The two hands are the object, so the render
+    /// moves both.
+    void scrub_gesture(const std::string& dir) {
+        const double        pass   = 12.0;
+        const size_t        frames = static_cast<size_t>(pass * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(2 * frames);
+
+        for (int with_pitch = 0; with_pitch < 2; ++with_pitch) {
+            tap::tools::scrub::machine m;
+            m.prepare(k_r_sr, 3000.0);
+            m.set_size_ms(70.0);
+            m.set_overlap(2);
+            m.set_mix(100.0);
+            m.set_smooth_ms(8.0);
+
+            for (size_t i = 0; i < frames; ++i) {
+                const double t = static_cast<double>(i) / k_r_sr;
+                // A rake back and forth: slow at first, then faster as the gesture takes hold.
+                const double rate  = 0.25 + 0.45 * (t / pass);
+                const double sweep = 0.5 - 0.5 * std::cos(2.0 * k_r_pi * rate * t);
+                m.set_position_ms(1500.0 * sweep);
+                if (with_pitch != 0) {
+                    m.set_pitch(7.0 * std::sin(2.0 * k_r_pi * 0.11 * t));
+                }
+                mono.push_back(m.process(looping_phrase(t)));
+            }
+        }
+        write_scenario(dir + "/scrub_gesture.wav", mono, 0.8, 1);
+    }
+
+    /// The frozen half of the object: two seconds of the phrase go in, the recorder stops, and
+    /// everything afterwards is made out of that fixed tape — first held still, then crawled
+    /// through with `drift`, then scattered with `spray`. Nothing is going into the input at all
+    /// after the freeze, which is the point.
+    void scrub_freeze(const std::string& dir) {
+        tap::tools::scrub::machine m;
+        m.prepare(k_r_sr, 3000.0);
+        m.set_size_ms(110.0);
+        m.set_overlap(3);
+        m.set_mix(100.0);
+        m.set_smooth_ms(20.0);
+        m.set_position_ms(900.0);
+        m.set_seed(7);
+
+        const size_t        live = static_cast<size_t>(2.5 * k_r_sr);
+        const size_t        held = static_cast<size_t>(9.0 * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(live + held);
+
+        for (size_t i = 0; i < live; ++i) {
+            mono.push_back(m.process(looping_phrase(static_cast<double>(i) / k_r_sr)));
+        }
+        m.set_freeze(true);
+        for (size_t i = 0; i < held; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            if (t > 3.0 && t <= 6.0) {
+                m.set_drift(-0.35); // crawl backwards through the frozen tape
+            }
+            else if (t > 6.0) {
+                m.set_drift(0.0);
+                m.set_spray_ms(260.0); // and then let the origin scatter
+            }
+            mono.push_back(m.process(0.0)); // nothing going in: all of this is the frozen tape
+        }
+        write_scenario(dir + "/scrub_freeze.wav", mono, 0.8, 1);
+    }
+
+    // ---- the Ondes Martenot -------------------------------------------------------------------
+
+    /// The chain, one stage at a time. The same held note four times: the detected envelope on
+    /// its own, then through the demodulator triode, then the preamplifier as well, then with the
+    /// 2A3 power stage switched in. The point of hearing it this way is that the first pass is
+    /// already harmonically rich — the demodulator makes those harmonics, not the tubes.
+    void ondes_stages(const std::string& dir) {
+        const double        pass   = 4.0;
+        const size_t        frames = static_cast<size_t>(pass * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(4 * frames);
+
+        for (int stage = 0; stage < 4; ++stage) {
+            if (stage == 0) {
+                // The detector alone: no tube in the path at all.
+                tap::tools::ondes::detector d;
+                d.prepare(k_r_sr);
+                d.set_ribbon(24.0);
+                double dc = 0.0;
+                for (size_t i = 0; i < frames; ++i) {
+                    const double e = d.process();
+                    dc += 0.001 * (e - dc); // the coupling capacitor the tubes would have provided
+                    mono.push_back(0.35 * (e - dc));
+                }
+                continue;
+            }
+            tap::tools::ondes::voice v;
+            v.prepare(k_r_sr);
+            v.set_ribbon(24.0);
+            v.set_key(1.0);
+            v.set_level(0.35);
+            v.set_drive(stage >= 2 ? 4.0 : 1.0);
+            v.set_power_stage(stage == 3);
+            for (size_t i = 0; i < frames; ++i) {
+                mono.push_back(v.process());
+            }
+        }
+        write_scenario(dir + "/ondes_stages.wav", mono, 0.8, 1);
+    }
+
+    /// The ribbon and the key, played. A slow phrase whose pitch glides continuously — the ribbon
+    /// is linear in semitones, so a linear hand movement is a linear glissando — with the
+    /// intensity key shaping every note. Nothing here is quantized, because the instrument does
+    /// not quantize.
+    void ondes_ribbon(const std::string& dir) {
+        tap::tools::ondes::voice v;
+        v.prepare(k_r_sr);
+        v.set_smooth_ms(4.0);
+        v.set_drive(2.5);
+        v.set_level(0.5);
+
+        // (arrival time, semitones above A1) — the hand slides between them.
+        const std::vector<note> stops  = {{0.0, 19.0}, {2.2, 26.0},  {4.0, 24.0}, {6.0, 31.0},
+                                          {8.4, 29.0}, {10.5, 22.0}, {13.0, 19.0}};
+        const size_t            frames = static_cast<size_t>(16.0 * k_r_sr);
+        std::vector<double>     mono(frames);
+
+        for (size_t i = 0; i < frames; ++i) {
+            const double t = static_cast<double>(i) / k_r_sr;
+            // Where the hand is: interpolation between stops, which on this instrument really is
+            // a glissando rather than a portamento between fixed pitches.
+            double st = stops.back().pitch;
+            for (size_t k = 0; k + 1 < stops.size(); ++k) {
+                if (t >= stops[k].onset && t < stops[k + 1].onset) {
+                    const double u = (t - stops[k].onset) / (stops[k + 1].onset - stops[k].onset);
+                    const double e = u * u * (3.0 - 2.0 * u); // eased, so it reads as a hand
+                    st             = stops[k].pitch + e * (stops[k + 1].pitch - stops[k].pitch);
+                    break;
+                }
+            }
+            v.set_ribbon(st);
+            // The left hand on the key: a swell per note.
+            double press = 0.0;
+            for (size_t k = 0; k < stops.size(); ++k) {
+                const double dt = t - stops[k].onset;
+                if (dt >= -0.35 && dt < 1.9) {
+                    const double a = std::clamp((dt + 0.35) / 0.5, 0.0, 1.0);
+                    const double r = std::clamp(1.0 - (dt - 0.9) / 1.0, 0.0, 1.0);
+                    press          = std::max(press, std::min(a, r));
+                }
+            }
+            v.set_key(0.45 + 0.55 * press); // never quite off: the key's dead zone does the rest
+            mono[i] = v.process();
+        }
+        write_scenario(dir + "/ondes_ribbon.wav", mono, 0.8, 1);
+    }
+
+    /// The whole instrument, finally: the voice into each of its loudspeakers in turn. The same
+    /// phrase through the principal (dry), the palme, and the métallique — which is the choice an
+    /// ondes player actually makes.
+    void ondes_diffuseurs(const std::string& dir) {
+        const size_t        frames = static_cast<size_t>(11.0 * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(3 * frames);
+
+        for (int cabinet = 0; cabinet < 3; ++cabinet) {
+            tap::tools::ondes::voice v;
+            v.prepare(k_r_sr);
+            v.set_smooth_ms(4.0);
+            v.set_drive(3.0);
+            v.set_level(0.5);
+
+            tap::tools::diffuseur::palme palme;
+            palme.prepare(k_r_sr);
+            palme.set_root_hz(110.0);
+            palme.set_decay(4.0);
+            palme.set_damping(3000.0);
+            palme.set_drive(1.0);
+            palme.set_asymmetry(0.2);
+            palme.set_saturation(0.3);
+            palme.set_mix(60.0);
+            palme.set_level(0.5);
+
+            tap::tools::diffuseur::metallique gong;
+            gong.prepare(k_r_sr);
+            gong.set_pitch_hz(165.0);
+            gong.set_decay(6.0);
+            gong.set_brightness(0.8);
+            gong.set_drive(1.4);
+            gong.set_asymmetry(0.35);
+            gong.set_saturation(0.5);
+            gong.set_mix(60.0);
+            gong.set_level(1.6);
+
+            for (size_t i = 0; i < frames; ++i) {
+                const double t  = static_cast<double>(i) / k_r_sr;
+                const double st = 24.0 + 5.0 * std::sin(2.0 * k_r_pi * 0.09 * t) + ((t > 5.5) ? 7.0 : 0.0);
+                v.set_ribbon(st);
+                v.set_key(0.55 + 0.45 * (0.5 - 0.5 * std::cos(2.0 * k_r_pi * 0.22 * t)));
+                const double dry = v.process();
+                mono.push_back(cabinet == 0 ? dry : ((cabinet == 1) ? palme.process(dry) : gong.process(dry)));
+            }
+        }
+        write_scenario(dir + "/ondes_diffuseurs.wav", mono, 0.45, 1); // the gong pass is much the loudest
+    }
+
+    /// The three tubes, on the same signal. A 220 Hz sine driven progressively harder through the
+    /// 6C5 at the demodulator's operating point, then the preamplifier's, then the 2A3 — same
+    /// sweep each time, so what changes is only what each valve does with it.
+    void triode_tubes(const std::string& dir) {
+        struct setup {
+            int                                tube;
+            tap::tools::ondes::operating_point op;
+        };
+        const setup setups[3] = {{tap::tools::ondes::tube_6c5, tap::tools::ondes::k_op_demod},
+                                 {tap::tools::ondes::tube_6c5, tap::tools::ondes::k_op_preamp},
+                                 {tap::tools::ondes::tube_2a3, tap::tools::ondes::k_op_power}};
+
+        const size_t        frames = static_cast<size_t>(6.0 * k_r_sr);
+        std::vector<double> mono;
+        mono.reserve(3 * frames);
+
+        for (const setup& s : setups) {
+            tap::tools::ondes::triode t;
+            t.prepare(k_r_sr);
+            t.set_tube(s.tube);
+            t.set_operating_point(s.op);
+            double phase = 0.0;
+            for (size_t i = 0; i < frames; ++i) {
+                const double u = static_cast<double>(i) / static_cast<double>(frames);
+                t.set_drive(0.2 + 12.0 * u * u); // the grid swing rising through the whole pass
+                phase += 220.0 / k_r_sr;
+                phase -= std::floor(phase);
+                mono.push_back(0.5 * t.process(std::sin(2.0 * k_r_pi * phase)));
+            }
+        }
+        write_scenario(dir + "/triode_tubes.wav", mono, 0.7, 1);
+    }
+
+} // namespace
+
+int main(int argc, char** argv) {
+    const std::string dir = (argc > 1) ? argv[1] : ".";
+    tapecho_heads(dir);
+    tapecho_three_head(dir);
+    tapecho_selfosc(dir);
+    tapecho_varispeed(dir);
+    stammer_grid(dir);
+    stammer_disintegrate(dir);
+    stammer_two_seeds(dir);
+    fuzz_gain_sweep(dir);
+    fuzz_tone(dir);
+    fuzz_edge_and_bite(dir);
+    touche_against_a_fade(dir);
+    metallique_stages(dir);
+    palme_halo(dir);
+    scrub_gesture(dir);
+    scrub_freeze(dir);
+    triode_tubes(dir);
+    ondes_stages(dir);
+    ondes_ribbon(dir);
+    ondes_diffuseurs(dir);
+    return 0;
+}