Add declarative DSL for authoring numba-compiled neuronumba models

examples/dsl_models/README.md

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+# Reference DSL specs
+
+This directory contains the canonical neuronumba models expressed in the DSL.
+Each spec produces a `Model` subclass that is **bit-equivalent** to its
+hand-written counterpart in `src/neuronumba/simulator/models/`.
+
+| Spec | Hand-written | LOC ratio |
+|---|---|---|
+| [hopf_dsl.py](hopf_dsl.py) | `hopf.py` | 38 vs 152 |
+| [deco2014_dsl.py](deco2014_dsl.py) | `deco2014.py` | 67 vs 416 |
+| [naskar2021_dsl.py](naskar2021_dsl.py) | `naskar2021.py` | 69 vs 104 |
+| [montbrio_dsl.py](montbrio_dsl.py) | `montbrio.py` | 88 vs 160 |
+| [zerlaut_dsl.py](zerlaut_dsl.py) (1st + 2nd order) | `zerlaut.py` | ~320 vs 800 |
+
+There is also [compare_simulations.py](compare_simulations.py): a runnable
+script that simulates pairs of DSL and hand-written models on the same inputs
+and plots their trajectories side-by-side.
+
+## Dual purpose
+
+These specs are both **examples** and **test references**:
+
+- **Examples** — read them top-to-bottom to learn the DSL. Copy any of them
+  into your own code and modify; you'll have a working model in minutes.
+- **Test references** — `tests/test_dsl_equivalence.py` imports each spec,
+  builds the DSL model, and compares it against the hand-written model on
+  random inputs. Disagreement fails the test.
+
+## Drift policy
+
+If you change a hand-written model (bug fix, numerical refinement), update the
+DSL spec here in the same commit. The equivalence test will fail loudly if you
+forget — read the failure message: it tells you both files involved.
+
+If you change a DSL spec (e.g. for a new feature you want to add), the
+equivalence test still has to pass against the hand-written reference. If they
+genuinely diverge, that's a model change and belongs in a separate PR.
+
+## What each spec illustrates
+
+Pick the spec that's closest to what you want to build:
+
+- **`hopf_dsl.py`** — minimal 2-variable oscillator. Shows the basic shape
+  of `state_vars` / `coupling_vars` / `parameters` / `equations` and the
+  `kind="diffusive"` coupling kernel.
+- **`deco2014_dsl.py`** — single-coupling-var rate model with declared
+  observables (`Ie`, `re`) and an EPS-fix using `np.where` in the equations.
+- **`naskar2021_dsl.py`** — extends Deco-style dynamics with a third state
+  variable that itself follows a slow plasticity rule.
+- **`montbrio_dsl.py`** — 6-variable Montbrio model with cross-coupling and
+  recurrent dependent parameters (`Parameter("J_N_ee", formula="...")`).
+- **`zerlaut_dsl.py`** — uses user-supplied `@nb.njit` helpers
+  (`get_fluct_regime_vars`, `threshold_func`, `TF`, `erfc_approx`) imported
+  from `neuronumba.simulator.models.zerlaut`. Demonstrates tuple-unpacking
+  from a multi-return helper, helper composition (`TF` calls the others
+  internally), and 1D-array parameters as defaults (length-10 polynomial
+  coefficient vectors).
+
+For matrix-valued parameters and conduction-delay coupling, see
+`tests/test_dsl_matrix_params.py` and `tests/test_dsl_delayed.py`.
+
+## Usage
+
+```python
+from examples.dsl_models import HopfDSL  # if examples/ is on sys.path
+# or
+from neuronumba.simulator.models.dsl import build_model
+from examples.dsl_models.hopf_dsl import hopf_spec
+HopfDSL = build_model(hopf_spec)
+
+m = HopfDSL(g=0.5).configure(weights=W)
+```
+
+For incremental construction (instead of writing a full `ModelSpec` literal),
+use `ModelBuilder`:
+
+```python
+from neuronumba.simulator.models.dsl import ModelBuilder
+
+HopfDSL = (ModelBuilder("Hopf")
+    .add_state("x").add_state("y")
+    .add_coupling("x", kind="diffusive")
+    .add_param("a", default=-0.5)
+    .add_param("omega", default=0.3)
+    .add_equation("d_x = (a - x*x - y*y)*x - omega*y + coupling.x")
+    .add_equation("d_y = (a - x*x - y*y)*y + omega*x")
+    .build())
+```

examples/dsl_models/__init__.py

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+"""Reference DSL specs for neuronumba's canonical models.
+
+These specs reproduce the existing hand-written models exactly. They serve two
+purposes simultaneously:
+
+1. **User-facing examples.** Read them to learn how to express a model in the
+   DSL. Each file is self-contained and small (~30-300 LOC vs 100-800 LOC for
+   the hand-written equivalents).
+
+2. **Regression test references.** `tests/test_dsl_equivalence.py` imports
+   these specs and compares the resulting DSL-built models against the
+   hand-written ones bit-by-bit. If you change a hand-written model (e.g. a
+   numerical bug fix), the corresponding spec here must follow, or the
+   equivalence test will fail.
+"""
+from .deco2014_dsl import deco_spec, Deco2014DSL
+from .hopf_dsl import hopf_spec, HopfDSL
+from .montbrio_dsl import montbrio_spec, MontbrioDSL
+from .naskar2021_dsl import naskar_spec, Naskar2021DSL
+from .zerlaut_dsl import (
+    zerlaut_first_order_spec,
+    zerlaut_second_order_spec,
+    ZerlautAdaptationFirstOrderDSL,
+    ZerlautAdaptationSecondOrderDSL,
+)
+
+__all__ = [
+    "Deco2014DSL", "deco_spec",
+    "HopfDSL", "hopf_spec",
+    "MontbrioDSL", "montbrio_spec",
+    "Naskar2021DSL", "naskar_spec",
+    "ZerlautAdaptationFirstOrderDSL", "zerlaut_first_order_spec",
+    "ZerlautAdaptationSecondOrderDSL", "zerlaut_second_order_spec",
+]

examples/dsl_models/compare_simulations.py

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+"""Run paired (hand-written, DSL) simulations and show that they agree.
+
+For each pair we:
+  1. Run the hand-written model and the DSL spec on the same connectivity,
+     params, and deterministic integrator.
+  2. Plot the two trajectories overlaid (visually identical).
+  3. Plot the difference, which should sit at machine-precision noise.
+
+Run:  PYTHONPATH=src python examples/dsl_models/compare_simulations.py
+"""
+from __future__ import annotations
+
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from neuronumba.simulator.simulator import simulate_nodelay
+from neuronumba.simulator.integrators.euler import EulerDeterministic
+from neuronumba.simulator.models import Hopf, Deco2014
+
+# Make the dsl_models package importable when run as a script. The script
+# lives inside the package, so we add its parent directory to sys.path.
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from dsl_models import HopfDSL, Deco2014DSL  # noqa: E402
+
+
+def make_W(n: int = 8, seed: int = 0) -> np.ndarray:
+    """Symmetric, zero-diagonal connectivity normalized to max 1.0."""
+    rng = np.random.default_rng(seed)
+    W = rng.random((n, n))
+    W = (W + W.T) / 2
+    np.fill_diagonal(W, 0)
+    W /= W.max()
+    return W
+
+
+def run(model_cls, weights, obs_var: str, *, t_max: float = 1000.0,
+        dt: float = 0.1, sampling_period: float = 1.0, **model_kwargs):
+    """Run a deterministic simulation and return the observed signal."""
+    np.random.seed(0)  # simulate_nodelay draws random tract lengths internally
+    model = model_cls(**model_kwargs)
+    integrator = EulerDeterministic(dt=dt)
+    return simulate_nodelay(
+        model, integrator, weights, obs_var,
+        sampling_period=sampling_period,
+        t_max_neuronal=t_max,
+        t_warmup=0.0,
+    )
+
+
+def plot_pair(ax_overlay, ax_diff, ref, dsl, *, title: str, n_show: int = 4):
+    """Overlay (left axis) + difference (right axis) for one model pair."""
+    t = np.arange(ref.shape[0])
+    for i in range(min(n_show, ref.shape[1])):
+        ax_overlay.plot(t, ref[:, i], color="black", lw=1.0, alpha=0.6,
+                        label="hand-written" if i == 0 else None)
+        ax_overlay.plot(t, dsl[:, i], color="tab:red", lw=0.8, ls="--",
+                        label="DSL" if i == 0 else None)
+    ax_overlay.set_title(f"{title} — trajectories (first {n_show} ROIs)")
+    ax_overlay.set_xlabel("sample index")
+    ax_overlay.legend(loc="upper right", fontsize=9)
+
+    diff = ref - dsl
+    max_abs = np.abs(diff).max()
+    ax_diff.plot(t, diff, lw=0.6)
+    ax_diff.set_title(f"{title} — difference (max |Δ| = {max_abs:.2e})")
+    ax_diff.set_xlabel("sample index")
+    ax_diff.axhline(0.0, color="black", lw=0.5, alpha=0.3)
+
+
+def main():
+    n_rois = 8
+    W = make_W(n=n_rois)
+
+    # Hopf: diffusive coupling, observe state variable x.
+    hopf_ref = run(Hopf, W, obs_var="x", g=0.5)
+    hopf_dsl = run(HopfDSL, W, obs_var="x", g=0.5)
+
+    # Deco2014: linear coupling, observe state variable S_e.
+    deco_ref = run(Deco2014, W, obs_var="S_e", g=0.5, auto_fic=False)
+    deco_dsl = run(Deco2014DSL, W, obs_var="S_e", g=0.5)
+
+    fig, axes = plt.subplots(2, 2, figsize=(12, 7))
+    plot_pair(axes[0, 0], axes[0, 1], hopf_ref, hopf_dsl, title="Hopf")
+    plot_pair(axes[1, 0], axes[1, 1], deco_ref, deco_dsl, title="Deco2014")
+    fig.suptitle("DSL vs hand-written: deterministic Euler, n=8 ROIs", y=1.0)
+    fig.tight_layout()
+
+    print(f"Hopf      max |Δ|: {np.abs(hopf_ref - hopf_dsl).max():.3e}")
+    print(f"Deco2014  max |Δ|: {np.abs(deco_ref - deco_dsl).max():.3e}")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()

examples/dsl_models/deco2014_dsl.py

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+"""Reduced Wong-Wang / Deco 2014 dynamic mean-field, expressed in the DSL.
+
+DSL equivalent of `neuronumba.simulator.models.deco2014.Deco2014` (416 LOC).
+Note this spec covers only the dfun + linear coupling — the hand-written class
+also has `auto_fic` (FIC steady-state computation) which is not part of the
+core differential equations. To use FIC with this DSL model you would override
+`_init_dependant` in a subclass. See `tests/test_dsl_subclass.py` for the
+subclassing pattern.
+
+`tests/test_dsl_equivalence.py::test_equivalence_deco2014` asserts dfun
+equivalence against the hand-written class to 1e-12 (with `auto_fic=False` so
+both share the same `J=1.0`).
+"""
+from neuronumba.simulator.models.dsl import (
+    ModelSpec, StateVar, CouplingVar, Parameter, build_model,
+)
+
+
+# The hand-written Deco2014 has an EPS-fix to avoid div-by-0 in the firing-rate
+# sigmoid. We faithfully reproduce that with `np.where`.
+deco_spec = ModelSpec(
+    name="Deco2014DSL",
+    state_vars=[
+        StateVar("S_e", initial=0.001, bounds=(0.0, 1.0)),
+        StateVar("S_i", initial=0.001, bounds=(0.0, 1.0)),
+    ],
+    coupling_vars=[CouplingVar("S_e", kind="linear")],
+    observables=["Ie", "re"],
+    parameters=[
+        Parameter("tau_e",      default=100.0),
+        Parameter("tau_i",      default=10.0),
+        Parameter("gamma_e",    default=0.000641),
+        Parameter("gamma_i",    default=0.001),
+        Parameter("I0",         default=0.382),
+        Parameter("Jext_e",     default=1.0),
+        Parameter("Jext_i",     default=0.7),
+        Parameter("w",          default=1.4),
+        Parameter("J_NMDA",     default=0.15),
+        Parameter("J",          default=1.0),
+        Parameter("ae",         default=310.0),
+        Parameter("be",         default=125.0),
+        Parameter("de",         default=0.16),
+        Parameter("ai",         default=615.0),
+        Parameter("bi",         default=177.0),
+        Parameter("di",         default=0.087),
+        Parameter("I_external", default=0.0),
+    ],
+    equations="""
+        Ie = Jext_e * I0 + w * J_NMDA * S_e + J_NMDA * coupling.S_e - J * S_i + I_external
+        Ii = Jext_i * I0 + J_NMDA * S_e - S_i
+
+        y_e = ae * Ie - be
+        denom_e = 1.0 - np.exp(-de * y_e)
+        denom_e = np.where(np.abs(denom_e) < 1e-12, 1e-12, denom_e)
+        re = y_e / denom_e
+
+        y_i = ai * Ii - bi
+        denom_i = 1.0 - np.exp(-di * y_i)
+        denom_i = np.where(np.abs(denom_i) < 1e-12, 1e-12, denom_i)
+        ri = y_i / denom_i
+
+        d_S_e = -S_e / tau_e + gamma_e * (1.0 - S_e) * re
+        d_S_i = -S_i / tau_i + gamma_i * ri
+    """,
+)
+
+Deco2014DSL = build_model(deco_spec)

examples/dsl_models/hopf_dsl.py

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+"""Hopf supercritical bifurcation model, expressed in the neuronumba DSL.
+
+This is the DSL equivalent of `neuronumba.simulator.models.hopf.Hopf` (152 LOC
+hand-written). The math is identical — `tests/test_dsl_equivalence.py::
+test_equivalence_hopf` compares the dfun and coupling-kernel outputs against
+the hand-written class on random inputs and asserts agreement to 1e-12.
+
+If this file changes, that test must still pass; if `hopf.py` changes (e.g. a
+numerical bug fix), this file must follow.
+"""
+from neuronumba.simulator.models.dsl import (
+    ModelSpec, StateVar, CouplingVar, Parameter, build_model,
+)
+
+
+hopf_spec = ModelSpec(
+    name="HopfDSL",
+    state_vars=[
+        StateVar("x", initial=0.1),
+        StateVar("y", initial=0.1),
+    ],
+    coupling_vars=[
+        CouplingVar("x", kind="diffusive"),
+        CouplingVar("y", kind="diffusive"),
+    ],
+    observables=[],
+    parameters=[
+        Parameter("a",          default=-0.5),
+        Parameter("omega",      default=0.3),
+        Parameter("I_external", default=0.0),
+    ],
+    equations="""
+        d_x = (a - x*x - y*y) * x - omega * y + coupling.x + I_external
+        d_y = (a - x*x - y*y) * y + omega * x + coupling.y
+    """,
+)
+
+HopfDSL = build_model(hopf_spec)