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Merged
iahncajigas merged 2 commits into from
Mar 8, 2026
Merged

Tighten MATLAB fidelity for signals and spike trains #37

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9594eea
Tighten MATLAB fidelity for signals and spike trains
Mar 8, 2026
39fd764
Keep notebook parity CI data-free
Mar 8, 2026
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24 changes: 11 additions & 13 deletions notebooks/AnalysisExamples.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "667810f9",
"id": "fc7be361",
"metadata": {},
"source": [
"<!-- parity-note -->\n",
Expand All @@ -15,7 +15,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1f210963",
"id": "45d93add",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -34,15 +34,13 @@
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"\n",
"from nstat import Analysis, Covariate, nspikeTrain\n",
"from nstat.data_manager import ensure_example_data\n",
"from nstat.glm import fit_poisson_glm\n",
"from nstat.notebook_data import load_glm_data_for_notebook\n",
"from nstat.notebook_figures import FigureTracker\n",
"\n",
"DATA_DIR = ensure_example_data(download=True)\n",
"GLM_DATA = loadmat(DATA_DIR / \"glm_data.mat\", squeeze_me=True, struct_as_record=False)\n",
"GLM_DATA = load_glm_data_for_notebook()\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic=\"AnalysisExamples\", output_root=OUTPUT_ROOT, expected_count=4)\n",
"\n",
Expand Down Expand Up @@ -79,7 +77,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "401a35e2",
"id": "3c621348",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -91,7 +89,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "501a6470",
"id": "c1d9b5e4",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -115,7 +113,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "ee81820c",
"id": "b5f3a818",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -133,7 +131,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1056f293",
"id": "396cb183",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -152,7 +150,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "98e73438",
"id": "49d54a88",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -177,7 +175,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "46235a71",
"id": "8b700118",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -197,7 +195,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "a5a09608",
"id": "9bd202c8",
"metadata": {},
"outputs": [],
"source": [
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28 changes: 13 additions & 15 deletions notebooks/AnalysisExamples2.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "4acc696f",
"id": "2468a9e7",
"metadata": {},
"source": [
"<!-- parity-note -->\n",
Expand All @@ -15,7 +15,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "06139fcd",
"id": "5e1d1998",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -34,15 +34,13 @@
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"\n",
"from nstat import Analysis, ConfigColl, CovColl, Covariate, FitResSummary, Trial, TrialConfig, nspikeTrain, nstColl\n",
"from nstat.data_manager import ensure_example_data\n",
"from nstat.glm import fit_poisson_glm\n",
"from nstat.notebook_data import load_glm_data_for_notebook\n",
"from nstat.notebook_figures import FigureTracker\n",
"\n",
"DATA_DIR = ensure_example_data(download=True)\n",
"GLM_DATA = loadmat(DATA_DIR / \"glm_data.mat\", squeeze_me=True, struct_as_record=False)\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",
"\n",
Expand Down Expand Up @@ -75,7 +73,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "dec86050",
"id": "45dc365a",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -87,7 +85,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d1533de9",
"id": "2a9182fe",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -98,7 +96,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "e35b302e",
"id": "126391f1",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -114,7 +112,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "8ec23b9f",
"id": "8aaea418",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -132,7 +130,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d2d28f3d",
"id": "d17e023e",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -151,7 +149,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0a79790c",
"id": "4ab39635",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -165,7 +163,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "cf575ee5",
"id": "db6c7107",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -188,7 +186,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "fd4cd7e9",
"id": "5a1dbe4c",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -209,7 +207,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d9198965",
"id": "85a9d741",
"metadata": {},
"outputs": [],
"source": [
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19 changes: 1 addition & 18 deletions notebooks/ConfigCollExamples.ipynb
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Expand Up @@ -22,34 +22,17 @@
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"\n",
"from nstat.data_manager import ensure_example_data\n",
"from nstat.notebook_figures import FigureTracker\n",
"\n",
"np.random.seed(0)\n",
"DATA_DIR = ensure_example_data(download=True)\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic='ConfigCollExamples', output_root=OUTPUT_ROOT, expected_count=0)\n",
"\n",
"def _load_example_globals(name: str) -> dict[str, object]:\n",
" candidates = [\n",
" Path(name),\n",
" DATA_DIR / name,\n",
" DATA_DIR / \"mEPSCs\" / name,\n",
" DATA_DIR / \"Place Cells\" / name,\n",
" DATA_DIR / \"Explicit Stimulus\" / name,\n",
" ]\n",
" for path in candidates:\n",
" if path.exists():\n",
" data = loadmat(path)\n",
" return {k: v for k, v in data.items() if not k.startswith(\"__\")}\n",
" return {}\n",
"\n",
"# SECTION 0: Section 0\n",
"# ConfigColl Examples\n",
"# tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)\n",
"__tracker.finalize()"
"__tracker.finalize()\n"
]
}
],
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19 changes: 1 addition & 18 deletions notebooks/CovariateExamples.ipynb
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Expand Up @@ -22,33 +22,16 @@
"matplotlib.use(\"Agg\")\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import loadmat\n",
"\n",
"from nstat.data_manager import ensure_example_data\n",
"from nstat.notebook_figures import FigureTracker\n",
"\n",
"np.random.seed(0)\n",
"DATA_DIR = ensure_example_data(download=True)\n",
"OUTPUT_ROOT = REPO_ROOT / \"output\" / \"notebook_images\"\n",
"__tracker = FigureTracker(topic='CovariateExamples', output_root=OUTPUT_ROOT, expected_count=2)\n",
"\n",
"def _load_example_globals(name: str) -> dict[str, object]:\n",
" candidates = [\n",
" Path(name),\n",
" DATA_DIR / name,\n",
" DATA_DIR / \"mEPSCs\" / name,\n",
" DATA_DIR / \"Place Cells\" / name,\n",
" DATA_DIR / \"Explicit Stimulus\" / name,\n",
" ]\n",
" for path in candidates:\n",
" if path.exists():\n",
" data = loadmat(path)\n",
" return {k: v for k, v in data.items() if not k.startswith(\"__\")}\n",
" return {}\n",
"\n",
"# SECTION 0: Section 0\n",
"# Test the Cov class\n",
"# Covariates are just like signals with a mean and a standard deviation They have two representations, the default (original representation) and a zero-mean representation"
"# Covariates are just like signals with a mean and a standard deviation They have two representations, the default (original representation) and a zero-mean representation\n"
]
},
{
Expand Down
26 changes: 13 additions & 13 deletions notebooks/ExplicitStimulusWhiskerData.ipynb
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Expand Up @@ -2,20 +2,20 @@
"cells": [
{
"cell_type": "markdown",
"id": "1978cb81",
"id": "8b8aa493",
"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."
"- 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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9787cf60",
"id": "dbf8e486",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -35,12 +35,12 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from nstat.data_manager import ensure_example_data\n",
"from nstat.notebook_data import notebook_example_data_dir\n",
"from nstat.notebook_figures import FigureTracker\n",
"from nstat.paper_examples_full import run_experiment2\n",
"\n",
"np.random.seed(0)\n",
"DATA_DIR = ensure_example_data(download=True)\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",
"\n",
Expand Down Expand Up @@ -83,7 +83,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "a60a7a6d",
"id": "e296bd4d",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -106,7 +106,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "862db342",
"id": "6a18af75",
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -134,7 +134,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "98d686d5",
"id": "fd207a34",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -149,7 +149,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "2b5d9da0",
"id": "2afb535d",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -170,7 +170,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "4a033d5c",
"id": "7a48f375",
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -198,7 +198,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0a73d5e1",
"id": "043ef33a",
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -230,7 +230,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "5aa18805",
"id": "d66ac872",
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -290,4 +290,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
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