Tighten fidelity audits and regenerate help notebooks

.github/workflows/notebook-full-fidelity.yml

@@ -0,0 +1,31 @@
+name: notebook-full-fidelity
+
+on:
+  pull_request:
+    paths:
+      - "notebooks/**"
+      - "tools/notebooks/**"
+      - "nstat/notebook_*.py"
+      - "parity/notebook_fidelity.yml"
+      - "parity/report.md"
+  schedule:
+    - cron: "15 7 * * 6"
+  workflow_dispatch:
+
+jobs:
+  helpfile-full:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e .[dev]
+          python -m pip install ipykernel
+          python -m ipykernel install --user --name python3 --display-name "Python 3"
+      - name: Execute mapped MATLAB helpfile notebooks
+        run: python tools/notebooks/run_notebooks.py --group helpfile_full --timeout 1200

notebooks/AnalysisExamples.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "db89e36d",
+   "id": "0f7ae1f5",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `AnalysisExamples.mlx`\n",
-    "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: Advanced MATLAB algorithm-selection branches and some report plots are still lighter in Python."
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Advanced MATLAB algorithm-selection branches and report plots remain lighter in Python, and the notebook still contains tracker-only visualization sections rather than a fully executable MATLAB-equivalent workflow."
    ]
   },
   {

notebooks/DecodingExample.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "72ddd907",
+   "id": "e78ea1c1",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `DecodingExample.mlx`\n",
     "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: Workflow, model fitting, and decoded-stimulus figures now follow the MATLAB helpfile closely; exact traces still depend on stochastic simulation draws and Python plotting defaults.\n"
+    "- Remaining justified differences: Workflow, model fitting, and decoded-stimulus figures now follow the MATLAB helpfile closely; exact traces still depend on stochastic simulation draws and Python plotting defaults."
    ]
   },
   {
@@ -215,4 +215,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

notebooks/DecodingExampleWithHist.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f624a68c",
+   "id": "04553c3e",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `DecodingExampleWithHist.mlx`\n",
     "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: The notebook now mirrors the MATLAB history-aware decoding workflow closely; exact stochastic trajectories and figure styling still vary slightly under Python execution.\n"
+    "- Remaining justified differences: The notebook now mirrors the MATLAB history-aware decoding workflow closely; exact stochastic trajectories and figure styling still vary slightly under Python execution."
    ]
   },
   {
@@ -174,4 +174,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

notebooks/ExplicitStimulusWhiskerData.ipynb

@@ -2,20 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d90d2497",
+   "id": "1978cb81",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `ExplicitStimulusWhiskerData.mlx`\n",
     "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: The notebook now reproduces the dataset-backed lag search, stimulus-effect, and history-effect workflow with real figures; exact KS traces and coefficient values still vary modestly from MATLAB because the Python GLM backend and plotting defaults are different.\n"
+    "- Remaining justified differences: The notebook now reproduces the dataset-backed lag search, stimulus-effect, and history-effect workflow with real figures; exact KS traces and coefficient values still vary modestly from MATLAB because the Python GLM backend and plotting defaults are different."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "116b9deb",
+   "id": "9787cf60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30d8a376",
+   "id": "a60a7a6d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72f337ee",
+   "id": "862db342",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b14768a",
+   "id": "98d686d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3e25d63",
+   "id": "2b5d9da0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d1a12c7a",
+   "id": "4a033d5c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ebff91c2",
+   "id": "0a73d5e1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "368647ec",
+   "id": "5aa18805",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,4 +290,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

notebooks/HippocampalPlaceCellExample.ipynb

@@ -2,20 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0ec92178",
+   "id": "e0aeece6",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `HippocampalPlaceCellExample.mlx`\n",
     "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: The notebook now reproduces the dataset-backed place-cell model-comparison and field-visualization workflow with real figures; the Python port still uses an approximate Zernike-like basis rather than the original MATLAB toolbox implementation.\n"
+    "- Remaining justified differences: The notebook now reproduces the dataset-backed place-cell model-comparison and field-visualization workflow with real figures; the Python port still uses an approximate Zernike-like basis rather than the original MATLAB toolbox implementation."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad01cf3d",
+   "id": "cadf7961",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5354bf29",
+   "id": "8c9b854e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24af9e4c",
+   "id": "bca6b3c3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bff7c68f",
+   "id": "7ad76d69",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "03f1d8f7",
+   "id": "516eb14e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a190b5ab",
+   "id": "711a2d08",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,4 +270,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

notebooks/HybridFilterExample.ipynb

@@ -2,20 +2,20 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dcd3b4c5",
+   "id": "deae92f2",
    "metadata": {},
    "source": [
     "<!-- parity-note -->\n",
     "## MATLAB Parity Note\n",
     "- Source MATLAB helpfile: `HybridFilterExample.mlx`\n",
     "- Fidelity status: `high_fidelity`\n",
-    "- Remaining justified differences: The notebook now reproduces the hybrid-filter simulation, single-run decoding, and averaged summary figures with real outputs; the Python port still uses the current hybrid-filter implementation instead of every MATLAB-specific reporting branch.\n"
+    "- Remaining justified differences: The notebook now reproduces the hybrid-filter simulation, single-run decoding, and averaged summary figures with real outputs; the Python port still uses the current hybrid-filter implementation instead of every MATLAB-specific reporting branch."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e71ac4b2",
+   "id": "ad07c855",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3990bad9",
+   "id": "8ad6c4c4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,31 +87,29 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f7752054",
+   "id": "d3676bdb",
    "metadata": {},
    "outputs": [],
    "source": [
     "# SECTION 1: Problem Statement\n",
-    "# We infer both a discrete movement state and a continuous reach trajectory from point-process observations.\n",
-    "pass\n"
+    "# We infer both a discrete movement state and a continuous reach trajectory from point-process observations.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b271b968",
+   "id": "c0ac4ff8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# SECTION 2: Hybrid state-space setup\n",
-    "# The Python port keeps the same two-state problem structure as MATLAB: a low-motion state and a movement state.\n",
-    "pass\n"
+    "# The Python port keeps the same two-state problem structure as MATLAB: a low-motion state and a movement state.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4d6c294",
+   "id": "20954cd6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,19 +161,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ffd10a0a",
+   "id": "e59c36e8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# SECTION 4: Simulate Neural Firing\n",
-    "# The simulated spike population depends on the latent state and the movement dynamics.\n",
-    "pass\n"
+    "# The simulated spike population depends on the latent state and the movement dynamics.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "db1960aa",
+   "id": "1da0a680",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,4 +248,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}