Upgrade class fidelity to high-fidelity MATLAB parity

notebooks/AnalysisExamples.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db89e36d",
+   "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."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/DecodingExample.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c488b5fa",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `DecodingExample.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Core decoding workflow is present, but MATLAB decoding options and reference outputs are not yet fully matched."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/DecodingExampleWithHist.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "739c56fe",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `DecodingExampleWithHist.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: History-aware decoding is available, but the MATLAB workflow still has richer option handling and reference outputs."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/ExplicitStimulusWhiskerData.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8bf801b2",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `ExplicitStimulusWhiskerData.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Dataset-backed workflow is present, but figure-level and narrative parity with MATLAB are still incomplete."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/HippocampalPlaceCellExample.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cd1a2218",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `HippocampalPlaceCellExample.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Core place-cell workflow is ported, but MATLAB figure sequencing and summary outputs are not yet exact."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/HybridFilterExample.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0e36ffa9",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `HybridFilterExample.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Hybrid filtering workflow executes, but MATLAB-specific output details and downstream validation remain incomplete."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -22,30 +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.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='HybridFilterExample', 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",
     "# Hybrid Point Process Filter Example\n",
     "# This example is based on an implementation of the Hybrid Point Process filter described in General-purpose filter design for neural prosthetic devices by Srinivasan L, Eden UT, Mitter SK, Brown EN in J Neurophysiol. 2007 Oct, 98(4):2456-75."

notebooks/PPSimExample.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8968c9f0",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `PPSimExample.mlx`\n",
+    "- Fidelity status: `high_fidelity`\n",
+    "- Remaining justified differences: MATLAB plotting/report formatting remains lighter, but the core point-process simulation workflow is closely aligned."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/StimulusDecode2D.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "59333bc7",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `StimulusDecode2D.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: The 2D stimulus decoding workflow runs, but MATLAB-equivalent outputs and tolerance-backed parity checks still need expansion."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/TrialExamples.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "93f72e2f",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `TrialExamples.mlx`\n",
+    "- Fidelity status: `high_fidelity`\n",
+    "- Remaining justified differences: Some MATLAB plotting/display details remain simplified, but the core Trial object workflow now follows the MATLAB semantics closely."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/ValidationDataSet.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "575b2a91",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `ValidationDataSet.mlx`\n",
+    "- Fidelity status: `partial`\n",
+    "- Remaining justified differences: Validation dataset coverage exists, but MATLAB reference summaries and figure parity are not yet complete."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/nSTATPaperExamples.ipynb

@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "21e0c2ce",
+   "metadata": {},
+   "source": [
+    "<!-- parity-note -->\n",
+    "## MATLAB Parity Note\n",
+    "- Source MATLAB helpfile: `nSTATPaperExamples.mlx`\n",
+    "- Fidelity status: `high_fidelity`\n",
+    "- Remaining justified differences: Python uses standalone figshare-backed data access and generated gallery assets rather than MATLAB path-based setup."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

nstat/ConfidenceInterval.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .confidence_interval import ConfidenceInterval as _ConfidenceInterval
-
-
-class ConfidenceInterval(_ConfidenceInterval):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.ConfidenceInterval.ConfidenceInterval", "nstat.confidence_interval.ConfidenceInterval")
-        super().__init__(*args, **kwargs)
-
+from .confidence_interval import ConfidenceInterval
 
 __all__ = ["ConfidenceInterval"]

nstat/ConfigColl.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .trial import ConfigCollection
-
-
-class ConfigColl(ConfigCollection):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.ConfigColl.ConfigColl", "nstat.trial.ConfigCollection")
-        super().__init__(*args, **kwargs)
-
+from .trial import ConfigCollection as ConfigColl
 
 __all__ = ["ConfigColl"]

nstat/CovColl.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .trial import CovariateCollection
-
-
-class CovColl(CovariateCollection):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.CovColl.CovColl", "nstat.trial.CovariateCollection")
-        super().__init__(*args, **kwargs)
-
+from .trial import CovariateCollection as CovColl
 
 __all__ = ["CovColl"]

nstat/DecodingAlgorithms.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .decoding_algorithms import DecodingAlgorithms as _DecodingAlgorithms
-
-
-class DecodingAlgorithms(_DecodingAlgorithms):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.DecodingAlgorithms.DecodingAlgorithms", "nstat.decoding.DecoderSuite")
-        super().__init__(*args, **kwargs)
-
+from .decoding_algorithms import DecodingAlgorithms
 
 __all__ = ["DecodingAlgorithms"]

nstat/FitResSummary.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .fit import FitResSummary as _FitResSummary
-
-
-class FitResSummary(_FitResSummary):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.FitResSummary.FitResSummary", "nstat.fit.FitSummary")
-        super().__init__(*args, **kwargs)
-
+from .fit import FitResSummary
 
 __all__ = ["FitResSummary"]

nstat/FitResult.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .fit import FitResult as _FitResult
-
-
-class FitResult(_FitResult):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.FitResult.FitResult", "nstat.fit.FitResult")
-        super().__init__(*args, **kwargs)
-
+from .fit import FitResult
 
 __all__ = ["FitResult"]

nstat/TrialConfig.py

@@ -1,13 +1,5 @@
 from __future__ import annotations
 
-from ._compat import warn_deprecated_adapter
-from .trial import TrialConfig as _TrialConfig
-
-
-class TrialConfig(_TrialConfig):
-    def __init__(self, *args, **kwargs) -> None:
-        warn_deprecated_adapter("nstat.TrialConfig.TrialConfig", "nstat.trial.TrialConfig")
-        super().__init__(*args, **kwargs)
-
+from .trial import TrialConfig
 
 __all__ = ["TrialConfig"]

nstat/__init__.py

@@ -1,3 +1,6 @@
+import sys as _sys
+from types import ModuleType as _ModuleType
+
 from .ConfidenceInterval import ConfidenceInterval
 from .ConfigColl import ConfigColl
 from .CovColl import CovColl
@@ -27,6 +30,29 @@
 from .nspikeTrain import nspikeTrain
 from .nstColl import nstColl
 
+from . import analysis as _analysis_module
+from . import cif as _cif_module
+from . import events as _events_module
+from . import history as _history_module
+from . import trial as _trial_module
+
+_sys.modules.setdefault(f"{__name__}.Analysis", _analysis_module)
+_sys.modules.setdefault(f"{__name__}.CIF", _cif_module)
+_sys.modules.setdefault(f"{__name__}.Events", _events_module)
+_sys.modules.setdefault(f"{__name__}.History", _history_module)
+_sys.modules.setdefault(f"{__name__}.Trial", _trial_module)
+
+
+class _NstatModule(_ModuleType):
+    def __getattribute__(self, name: str):
+        value = super().__getattribute__(name)
+        if isinstance(value, _ModuleType) and hasattr(value, name):
+            return getattr(value, name)
+        return value
+
+
+_sys.modules[__name__].__class__ = _NstatModule
+
 
 def __getattr__(name: str):
     if name == "nstat_install":