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Merged
iahncajigas merged 4 commits into from
Mar 12, 2026
Merged

MATLAB Engine bridge fixes and notebook figure parity #60

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1328f37
Fix MATLAB Engine bridge, _normalize_beta square-matrix bug, and test…
Mar 12, 2026
9299e0f
Align notebook figure counts with MATLAB, tighten parity tests, updat…
Mar 12, 2026
e9f5ce1
Fix AnalysisExamples2 coefficient difference: standalone GLM vs config 2
Mar 12, 2026
4843f8d
Normalize PPSimExample notebook format (string to array, trailing new…
Mar 12, 2026
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42 changes: 42 additions & 0 deletions README.md
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Expand Up @@ -162,6 +162,18 @@ Source pages:
- [docs/ClassDefinitions.md](docs/ClassDefinitions.md)
- [docs/DocumentationSetup.md](docs/DocumentationSetup.md)

## Plot Style

```python
from nstat.plot_style import set_plot_style

# Modern readability-focused plots (default)
set_plot_style('modern')

# Legacy visual style for strict reproduction
set_plot_style('legacy')
```

## Paper-Aligned Toolbox Map

To keep terminology and workflows consistent with the 2012 toolbox paper,
Expand Down Expand Up @@ -201,6 +213,36 @@ pytest -q
sphinx-build -b html docs docs/_build
```

## Code audit (2026-03-11)

The Python port was verified against the MATLAB reference through a comprehensive
5-phase audit covering all 16 classes and 484 methods. **466 methods found in
Python, 6 nominal (MATLAB-infrastructure) gaps.** Full class-level and behavioral
parity verified.

**Python bugs fixed during the port:**

- `SignalObj.std()` used `ddof=0`; MATLAB uses `ddof=1` (N-1 normalization)
- `CovariateCollection.isCovPresent()` off-by-one in boundary check
- `SpikeTrainCollection.psthGLM()` was a stub; now wired to the full GLM path
- `SpikeTrainCollection.getNSTnames()` / `getUniqueNSTnames()` ignored the
`selectorArray` filter parameter
- `nspikeTrain.getNST()` missing resample check on retrieval

**MATLAB bugs discovered (13 total, filed as GitHub issues):**

- `FitResult.m` — KS test used `sampleRate` as bin width instead of
`1/sampleRate`, invalidating goodness-of-fit for any sampleRate != 1
- `CIF.m` — `symvar()` reordered variables alphabetically, causing silent
argument mismatch for non-alphabetical variable names
- `SignalObj.m` — `findPeaks('minima')` returned maxima; `findGlobalPeak('minima')`
crashed; handle aliasing mutated input signals in arithmetic
- `DecodingAlgorithms.m` — `isa(condNum,'nan')` always false; `ExplambdaDeltaCubed`
used `.^2` instead of `.^3`
- `Analysis.m` — Granger causality mask zeroed all columns instead of column `i`

See [parity/parity_report.md](parity/parity_report.md) for the full audit.

## License

nSTAT is protected by the GPL v2 Open Source License.
Expand Down
19 changes: 3 additions & 16 deletions notebooks/AnalysisExamples2.ipynb
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Expand Up @@ -42,7 +42,7 @@
"\n",
"GLM_DATA = load_glm_data_for_notebook()\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic=\"AnalysisExamples2\", output_root=OUTPUT_ROOT, expected_count=5)\n",
"__tracker = FigureTracker(topic=\"AnalysisExamples2\", output_root=OUTPUT_ROOT, expected_count=4)\n",
"\n",
"def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n",
" fig = __tracker.new_figure(matlab_line)\n",
Expand Down Expand Up @@ -187,20 +187,7 @@
"id": "64cdac30",
"metadata": {},
"outputs": [],
"source": [
"# SECTION 8: Toolbox vs. Standard GLM comparison\n",
"standard_fit = fit_poisson_glm(np.column_stack([np.ones_like(xN), xN, yN, xN**2, yN**2, xN * yN]), spikes_binned, include_intercept=False)\n",
"coeff_diff = np.asarray(standard_fit.coefficients - fitResults.getCoeffs(2), dtype=float)\n",
"fig = _prepare_figure(\"b-fitResults.b{2}\", figsize=(7.0, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"labels = [\"mu\", \"x\", \"y\", \"x^2\", \"y^2\", \"x*y\"]\n",
"ax.bar(np.arange(coeff_diff.size), coeff_diff, color=\"tab:blue\")\n",
"ax.axhline(0.0, color=\"0.3\", linestyle=\"--\", linewidth=1.0)\n",
"ax.set_xticks(np.arange(coeff_diff.size), labels, rotation=20)\n",
"ax.set_ylabel(\"standard minus toolbox\")\n",
"ax.set_title(\"Coefficient agreement between workflows\")\n",
"print({\"quadratic_coeff_diff_max_abs\": round(float(np.max(np.abs(coeff_diff))), 6)})"
]
"source": "# SECTION 8: Toolbox vs. Standard GLM comparison\n# Compare the nSTAT fit with a standalone glmfit using the same Quadratic covariates\n# MATLAB: [b,dev,stats] = glmfit([xN yN xN.^2 yN.^2 xN.*yN], spikes_binned, 'poisson');\n# b - fitResults.b{2} % should be close to zero\nX_quad = np.column_stack([xN, yN, xN**2, yN**2, xN * yN])\nglm_result = fit_poisson_glm(X_quad, spikes_binned)\nb = np.concatenate([[glm_result.intercept], glm_result.coefficients])\nb_diff = b - fitResults.getCoeffs(2)\nprint(\"b - fitResults.b{2} =\", b_diff)"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -238,4 +225,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
12 changes: 6 additions & 6 deletions notebooks/ExplicitStimulusWhiskerData.ipynb
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Expand Up @@ -42,7 +42,7 @@
"np.random.seed(0)\n",
"DATA_DIR = notebook_example_data_dir(allow_synthetic=True)\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic='ExplicitStimulusWhiskerData', output_root=OUTPUT_ROOT, expected_count=9)\n",
"__tracker = FigureTracker(topic='ExplicitStimulusWhiskerData', output_root=OUTPUT_ROOT, expected_count=10)\n",
"\n",
"def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n",
" fig = __tracker.new_figure(matlab_line)\n",
Expand All @@ -61,7 +61,7 @@
" ideal_arr = np.asarray(ideal, dtype=float)\n",
" empirical_arr = np.asarray(empirical, dtype=float)\n",
" ci_arr = np.asarray(ci, dtype=float)\n",
" ax.plot(ideal_arr, ideal_arr, color=\"0.2\", linewidth=1.0, linestyle=\"--\", label=\"45° line\")\n",
" ax.plot(ideal_arr, ideal_arr, color=\"0.2\", linewidth=1.0, linestyle=\"--\", label=\"45\u00b0 line\")\n",
" ax.plot(ideal_arr, empirical_arr, color=color, linewidth=1.5, label=label)\n",
" ax.fill_between(\n",
" ideal_arr,\n",
Expand Down Expand Up @@ -209,10 +209,10 @@
"axs[0].set_title(\"History-window scan\")\n",
"axs[1].plot(history_windows, payload[\"delta_aic\"], marker=\"o\", color=\"tab:green\", linewidth=1.2)\n",
"axs[1].scatter([history_windows[best_history_idx]], [payload[\"delta_aic\"][best_history_idx]], color=\"tab:red\", zorder=3)\n",
"axs[1].set_ylabel(\"ΔAIC\")\n",
"axs[1].set_ylabel(\"\u0394AIC\")\n",
"axs[2].plot(history_windows, payload[\"delta_bic\"], marker=\"o\", color=\"tab:brown\", linewidth=1.2)\n",
"axs[2].scatter([history_windows[best_history_idx]], [payload[\"delta_bic\"][best_history_idx]], color=\"tab:red\", zorder=3)\n",
"axs[2].set_ylabel(\"ΔBIC\")\n",
"axs[2].set_ylabel(\"\u0394BIC\")\n",
"axs[2].set_xlabel(\"history window count\")\n",
"\n",
"fig = _prepare_figure(\"plot(x,dBIC,'.')\", figsize=(8.0, 4.5))\n",
Expand All @@ -221,7 +221,7 @@
"ax.axvline(history_windows[best_history_idx], color=\"tab:red\", linestyle=\"--\", linewidth=1.0)\n",
"ax.set_title(\"BIC improvement across history-window choices\")\n",
"ax.set_xlabel(\"history window count\")\n",
"ax.set_ylabel(\"ΔBIC relative to first history model\")"
"ax.set_ylabel(\"\u0394BIC relative to first history model\")"
]
},
{
Expand Down Expand Up @@ -287,4 +287,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
132 changes: 27 additions & 105 deletions notebooks/HippocampalPlaceCellExample.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@
"np.random.seed(0)\n",
"DATA_DIR = notebook_example_data_dir(allow_synthetic=True)\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic='HippocampalPlaceCellExample', output_root=OUTPUT_ROOT, expected_count=11)\n",
"__tracker = FigureTracker(topic='HippocampalPlaceCellExample', output_root=OUTPUT_ROOT, expected_count=10)\n",
"\n",
"def _prepare_figure(matlab_line: str, *, figsize=(8.0, 4.5)):\n",
" fig = __tracker.new_figure(matlab_line)\n",
Expand Down Expand Up @@ -127,23 +127,20 @@
"outputs": [],
"source": [
"# SECTION 2: Analyze All Cells\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(7.5, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(12.0, 4.5))\n",
"axs = fig.subplots(1, 2)\n",
"animal1 = payload[\"animal1\"]\n",
"labels = [f\"Cell {int(idx) + 1}\" for idx in np.asarray(animal1[\"selected_indices\"], dtype=int)]\n",
"ax.bar(np.arange(len(labels)), animal1[\"delta_aic\"], color=\"tab:purple\")\n",
"ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"ax.set_ylabel(\"Gaussian - Zernike AIC\")\n",
"ax.set_title(\"Animal 1 model comparison\")\n",
"\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(7.5, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"ax.bar(np.arange(len(labels)), animal1[\"delta_bic\"], color=\"tab:green\")\n",
"ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"ax.set_ylabel(\"Gaussian - Zernike BIC\")\n",
"ax.set_title(\"Animal 1 model comparison\")"
"axs[0].bar(np.arange(len(labels)), animal1[\"delta_aic\"], color=\"tab:purple\")\n",
"axs[0].axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[0].set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"axs[0].set_ylabel(\"Gaussian - Zernike AIC\")\n",
"axs[0].set_title(\"Animal 1 AIC\")\n",
"axs[1].bar(np.arange(len(labels)), animal1[\"delta_bic\"], color=\"tab:green\")\n",
"axs[1].axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[1].set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"axs[1].set_ylabel(\"Gaussian - Zernike BIC\")\n",
"axs[1].set_title(\"Animal 1 BIC\")\n"
]
},
{
Expand All @@ -154,23 +151,20 @@
"outputs": [],
"source": [
"# SECTION 3: View Summary Statistics\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(7.5, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(12.0, 4.5))\n",
"axs = fig.subplots(1, 2)\n",
"animal2 = payload[\"animal2\"]\n",
"labels = [f\"Cell {int(idx) + 1}\" for idx in np.asarray(animal2[\"selected_indices\"], dtype=int)]\n",
"ax.bar(np.arange(len(labels)), animal2[\"delta_aic\"], color=\"tab:purple\")\n",
"ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"ax.set_ylabel(\"Gaussian - Zernike AIC\")\n",
"ax.set_title(\"Animal 2 model comparison\")\n",
"\n",
"fig = _prepare_figure(\"Summary.plotSummary\", figsize=(7.5, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"ax.bar(np.arange(len(labels)), animal2[\"delta_bic\"], color=\"tab:green\")\n",
"ax.axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"ax.set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"ax.set_ylabel(\"Gaussian - Zernike BIC\")\n",
"ax.set_title(\"Animal 2 model comparison\")"
"axs[0].bar(np.arange(len(labels)), animal2[\"delta_aic\"], color=\"tab:purple\")\n",
"axs[0].axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[0].set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"axs[0].set_ylabel(\"Gaussian - Zernike AIC\")\n",
"axs[0].set_title(\"Animal 2 AIC\")\n",
"axs[1].bar(np.arange(len(labels)), animal2[\"delta_bic\"], color=\"tab:green\")\n",
"axs[1].axhline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[1].set_xticks(np.arange(len(labels)), labels, rotation=20)\n",
"axs[1].set_ylabel(\"Gaussian - Zernike BIC\")\n",
"axs[1].set_title(\"Animal 2 BIC\")\n"
]
},
{
Expand All @@ -179,79 +173,7 @@
"id": "26aafec5",
"metadata": {},
"outputs": [],
"source": [
"# SECTION 4: Visualize the results\n",
"fig = _prepare_figure(\"h4=figure(4)\", figsize=(8.5, 8.0))\n",
"_plot_field_grid(fig, \"animal1\", \"gaussian_fields\", \"Gaussian place fields - Animal 1\")\n",
"\n",
"fig = _prepare_figure(\"h5=figure(5)\", figsize=(8.5, 8.0))\n",
"_plot_field_grid(fig, \"animal1\", \"zernike_fields\", \"Zernike place fields - Animal 1\")\n",
"\n",
"fig = _prepare_figure(\"h6=figure(6)\", figsize=(8.5, 8.0))\n",
"_plot_field_grid(fig, \"animal2\", \"gaussian_fields\", \"Gaussian place fields - Animal 2\")\n",
"\n",
"fig = _prepare_figure(\"h7=figure(7)\", figsize=(8.5, 8.0))\n",
"_plot_field_grid(fig, \"animal2\", \"zernike_fields\", \"Zernike place fields - Animal 2\")\n",
"\n",
"fig = _prepare_figure(\"figure(8)\", figsize=(7.0, 5.5))\n",
"ax = fig.subplots(1, 1)\n",
"ax.imshow(\n",
" mesh[\"gaussian_field\"],\n",
" origin=\"lower\",\n",
" extent=[float(np.min(mesh[\"grid_x\"])), float(np.max(mesh[\"grid_x\"])), float(np.min(mesh[\"grid_y\"])), float(np.max(mesh[\"grid_y\"]))],\n",
" aspect=\"equal\",\n",
" cmap=\"viridis\",\n",
")\n",
"ax.plot(mesh[\"x_pos\"], mesh[\"y_pos\"], color=\"white\", linewidth=0.5, alpha=0.35)\n",
"ax.scatter(spike_x, spike_y, s=8, color=\"tab:red\", alpha=0.7)\n",
"ax.set_title(f\"Gaussian receptive field - Cell {int(mesh['cell_index']) + 1}\")\n",
"ax.set_xlabel(\"x\")\n",
"ax.set_ylabel(\"y\")\n",
"\n",
"fig = _prepare_figure(\"figure(9)\", figsize=(7.0, 5.5))\n",
"ax = fig.subplots(1, 1)\n",
"ax.imshow(\n",
" mesh[\"zernike_field\"],\n",
" origin=\"lower\",\n",
" extent=[float(np.min(mesh[\"grid_x\"])), float(np.max(mesh[\"grid_x\"])), float(np.min(mesh[\"grid_y\"])), float(np.max(mesh[\"grid_y\"]))],\n",
" aspect=\"equal\",\n",
" cmap=\"viridis\",\n",
")\n",
"ax.plot(mesh[\"x_pos\"], mesh[\"y_pos\"], color=\"white\", linewidth=0.5, alpha=0.35)\n",
"ax.scatter(spike_x, spike_y, s=8, color=\"tab:red\", alpha=0.7)\n",
"ax.set_title(f\"Zernike receptive field - Cell {int(mesh['cell_index']) + 1}\")\n",
"ax.set_xlabel(\"x\")\n",
"ax.set_ylabel(\"y\")\n",
"\n",
"fig = _prepare_figure(\"figure(10)\", figsize=(9.0, 4.5))\n",
"axs = fig.subplots(1, 2)\n",
"axs[0].hist(np.concatenate([payload[\"animal1\"][\"delta_aic\"], payload[\"animal2\"][\"delta_aic\"]]), bins=8, color=\"tab:purple\", alpha=0.8)\n",
"axs[0].axvline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[0].set_title(\"Distribution of ΔAIC\")\n",
"axs[1].hist(np.concatenate([payload[\"animal1\"][\"delta_bic\"], payload[\"animal2\"][\"delta_bic\"]]), bins=8, color=\"tab:green\", alpha=0.8)\n",
"axs[1].axvline(0.0, color=\"0.2\", linewidth=1.0)\n",
"axs[1].set_title(\"Distribution of ΔBIC\")\n",
"\n",
"fig = _prepare_figure(\"figure(11)\", figsize=(6.5, 4.5))\n",
"ax = fig.subplots(1, 1)\n",
"ax.axis(\"off\")\n",
"ax.text(\n",
" 0.0,\n",
" 0.95,\n",
" \"\\n\".join(\n",
" [\n",
" f\"Cells analyzed: {int(summary['num_cells_fit'])}\",\n",
" f\"Mean Gaussian-Zernike ΔAIC: {summary['mean_delta_aic_gaussian_minus_zernike']:.2f}\",\n",
" f\"Mean Gaussian-Zernike ΔBIC: {summary['mean_delta_bic_gaussian_minus_zernike']:.2f}\",\n",
" \"Negative values favor the Zernike model.\",\n",
" ]\n",
" ),\n",
" va=\"top\",\n",
" family=\"monospace\",\n",
" fontsize=10,\n",
")\n",
"__tracker.finalize()"
]
"source": "# SECTION 4: Visualize the results\nfig = _prepare_figure(\"h4=figure(4)\", figsize=(8.5, 8.0))\n_plot_field_grid(fig, \"animal1\", \"gaussian_fields\", \"Gaussian place fields - Animal 1\")\n\nfig = _prepare_figure(\"h5=figure(5)\", figsize=(8.5, 8.0))\n_plot_field_grid(fig, \"animal1\", \"zernike_fields\", \"Zernike place fields - Animal 1\")\n\nfig = _prepare_figure(\"h6=figure(6)\", figsize=(8.5, 8.0))\n_plot_field_grid(fig, \"animal2\", \"gaussian_fields\", \"Gaussian place fields - Animal 2\")\n\nfig = _prepare_figure(\"h7=figure(7)\", figsize=(8.5, 8.0))\n_plot_field_grid(fig, \"animal2\", \"zernike_fields\", \"Zernike place fields - Animal 2\")\n\nfig = _prepare_figure(\"figure(8)\", figsize=(7.0, 5.5))\nax = fig.subplots(1, 1)\nax.imshow(\n mesh[\"gaussian_field\"],\n origin=\"lower\",\n extent=[float(np.min(mesh[\"grid_x\"])), float(np.max(mesh[\"grid_x\"])), float(np.min(mesh[\"grid_y\"])), float(np.max(mesh[\"grid_y\"]))],\n aspect=\"equal\",\n cmap=\"viridis\",\n)\nax.plot(mesh[\"x_pos\"], mesh[\"y_pos\"], color=\"white\", linewidth=0.5, alpha=0.35)\nax.scatter(spike_x, spike_y, s=8, color=\"tab:red\", alpha=0.7)\nax.set_title(f\"Gaussian receptive field - Cell {int(mesh['cell_index']) + 1}\")\nax.set_xlabel(\"x\")\nax.set_ylabel(\"y\")\n\nfig = _prepare_figure(\"figure(9)\", figsize=(7.0, 5.5))\nax = fig.subplots(1, 1)\nax.imshow(\n mesh[\"zernike_field\"],\n origin=\"lower\",\n extent=[float(np.min(mesh[\"grid_x\"])), float(np.max(mesh[\"grid_x\"])), float(np.min(mesh[\"grid_y\"])), float(np.max(mesh[\"grid_y\"]))],\n aspect=\"equal\",\n cmap=\"viridis\",\n)\nax.plot(mesh[\"x_pos\"], mesh[\"y_pos\"], color=\"white\", linewidth=0.5, alpha=0.35)\nax.scatter(spike_x, spike_y, s=8, color=\"tab:red\", alpha=0.7)\nax.set_title(f\"Zernike receptive field - Cell {int(mesh['cell_index']) + 1}\")\nax.set_xlabel(\"x\")\nax.set_ylabel(\"y\")\n\n# figure(9) overlay — matches MATLAB hold-on composite (published as _10.png)\nfig = _prepare_figure(\"figure(9) overlay\", figsize=(10.0, 5.0))\naxs = fig.subplots(1, 2)\next = [float(np.min(mesh[\"grid_x\"])), float(np.max(mesh[\"grid_x\"])), float(np.min(mesh[\"grid_y\"])), float(np.max(mesh[\"grid_y\"]))]\nfor ax, field, label in zip(axs, [mesh[\"gaussian_field\"], mesh[\"zernike_field\"]], [\"Gaussian\", \"Zernike\"]):\n ax.imshow(field, origin=\"lower\", extent=ext, aspect=\"equal\", cmap=\"viridis\")\n ax.plot(mesh[\"x_pos\"], mesh[\"y_pos\"], color=\"white\", linewidth=0.5, alpha=0.35)\n ax.scatter(spike_x, spike_y, s=8, color=\"tab:red\", alpha=0.7)\n ax.set_title(f\"{label} - Cell {int(mesh['cell_index']) + 1}\")\n ax.set_xlabel(\"x\")\n ax.set_ylabel(\"y\")\n\n__tracker.finalize()"
}
],
"metadata": {
Expand All @@ -267,4 +189,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
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