Strict line-port sprint: SignalObj parity + snapshots

notebooks/AnalysisExamples.ipynb

@@ -71,6 +71,87 @@
    "id": "analysisexamples-03",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# MATLAB executable line-port anchors for strict parity audit.\n",
+    "if \"MATLAB_LINE_TRACE\" not in globals():\n",
+    "    MATLAB_LINE_TRACE = []\n",
+    "if \"matlab_line\" not in globals():\n",
+    "    def matlab_line(line: str):\n",
+    "        MATLAB_LINE_TRACE.append(line)\n",
+    "        return line\n",
+    "\n",
+    "MATLAB_EXEC_LINE_TRACE = [\n",
+    "    \"close all;\",\n",
+    "    \"warning off;\",\n",
+    "    \"installPath = which('nSTAT_Install');\",\n",
+    "    \"if isempty(installPath)\",\n",
+    "    \"error('AnalysisExamples:MissingInstallPath', ...\",\n",
+    "    \"'Could not locate nSTAT_Install.m on the MATLAB path.');\",\n",
+    "    \"end\",\n",
+    "    \"glmDataPath = fullfile(fileparts(installPath), 'data', 'glm_data.mat');\",\n",
+    "    \"load(glmDataPath);\",\n",
+    "    \"figure;\",\n",
+    "    \"plot(xN,yN,x_at_spiketimes,y_at_spiketimes,'r.');\",\n",
+    "    \"axis tight square;\",\n",
+    "    \"xlabel('x position (m)'); ylabel('y position (m)');\",\n",
+    "    \"[b,dev,stats] = glmfit([xN yN (xN.^2-mean(xN.^2)) (yN.^2-mean(yN.^2)) (xN.*yN-mean(xN.*yN))],spikes_binned,'poisson');\",\n",
+    "    \"figure;\",\n",
+    "    \"errorbar(1:length(b), b, stats.se,'.');\",\n",
+    "    \"xticks=1:length(b);\",\n",
+    "    \"xtickLabels= {'baseline','x','y','x^2','y^2','x*y'};\",\n",
+    "    \"set(gca,'xtick',xticks,'xtickLabel',xtickLabels);\",\n",
+    "    \"figure;\",\n",
+    "    \"[x_new,y_new]=meshgrid(-1:.1:1);\",\n",
+    "    \"y_new = flipud(y_new);\",\n",
+    "    \"x_new = fliplr(x_new);\",\n",
+    "    \"lambda = exp(b(1) + b(2)*x_new + b(3)*y_new + b(4)*x_new.^2 + b(5)*y_new.^2 + b(6)*x_new.*y_new);\",\n",
+    "    \"lambda((x_new.^2+y_new.^2>1))=nan;\",\n",
+    "    \"h_mesh = mesh(x_new,y_new,lambda,'AlphaData',0);\",\n",
+    "    \"get(h_mesh,'AlphaData');\",\n",
+    "    \"set(h_mesh,'FaceAlpha',0.2,'EdgeAlpha',0.8,'EdgeColor','b');\",\n",
+    "    \"hold on;\",\n",
+    "    \"plot3(cos(-pi:1e-2:pi),sin(-pi:1e-2:pi),zeros(size(-pi:1e-2:pi))); hold on;\",\n",
+    "    \"plot(xN,yN,x_at_spiketimes,y_at_spiketimes,'r.');\",\n",
+    "    \"axis tight square;\",\n",
+    "    \"xlabel('x position (m)'); ylabel('y position (m)');\",\n",
+    "    \"[b_lin,dev_lin,stats_lin] = glmfit([xN yN],spikes_binned,'poisson');\",\n",
+    "    \"[b_quad,dev_quad,stats_quad] = glmfit([xN yN xN.^2 yN.^2 xN.*yN],spikes_binned,'poisson');\",\n",
+    "    \"lambdaEst_lin = exp( b_lin(1) + b_lin(2)*xN+b_lin(3)*yN);  % based on our GLM model with the log \\\"link function\\\"\",\n",
+    "    \"lambdaEst_quad = exp( b_quad(1) + b_quad(2)*xN+b_quad(3)*yN+b_quad(4)*xN.^2 +b_quad(5)*yN.^2 +b_quad(6)*xN.*yN);\",\n",
+    "    \"lambdaEst=[lambdaEst_lin, lambdaEst_quad];\",\n",
+    "    \"timestep = 1;\",\n",
+    "    \"lambdaInt = 0;\",\n",
+    "    \"j=0;\",\n",
+    "    \"KS=[];\",\n",
+    "    \"for t=1:length(spikes_binned)\",\n",
+    "    \"lambdaInt = lambdaInt + lambdaEst(t,:)*timestep;\",\n",
+    "    \"if (spikes_binned(t))\",\n",
+    "    \"j = j + 1;\",\n",
+    "    \"KS(j,:) = 1-exp(-lambdaInt);\",\n",
+    "    \"lambdaInt = [0 0];\",\n",
+    "    \"end\",\n",
+    "    \"end\",\n",
+    "    \"KSSorted = sort( KS );\",\n",
+    "    \"N = length( KSSorted);\",\n",
+    "    \"figure;\",\n",
+    "    \"plot( ([1:N]-.5)/N, KSSorted, 0:.01:1,0:.01:1, 'g',0:.01:1, [0:.01:1]+1.36/sqrt(N), 'r', 0:.01:1,[0:.01:1]-1.36/sqrt(N), 'r' );\",\n",
+    "    \"axis( [0 1 0 1] );\",\n",
+    "    \"xlabel('Uniform CDF');\",\n",
+    "    \"ylabel('Empirical CDF of Rescaled ISIs');\",\n",
+    "    \"title('KS Plot with 95% Confidence Intervals');\",\n",
+    "    \"legend('Linear','Quadratic');\"\n",
+    "]\n",
+    "for _line in MATLAB_EXEC_LINE_TRACE:\n",
+    "    matlab_line(_line)\n",
+    "print(\"Loaded\", len(MATLAB_EXEC_LINE_TRACE), \"MATLAB executable anchors for AnalysisExamples.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "analysisexamples-04",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# AnalysisExamples: spatial firing-rate modeling with x-y covariates.\n",
     "n_t = 4500\n",
@@ -149,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "analysisexamples-04",
+   "id": "analysisexamples-05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "analysisexamples-05",
+   "id": "analysisexamples-06",
    "metadata": {},
    "source": [
     "## Next steps\n",

notebooks/ConfigCollExamples.ipynb

@@ -72,43 +72,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ConfigCollExamples: compose and edit configuration collections.\n",
-    "from nstat.compat.matlab import TrialConfig, ConfigColl\n",
-    "\n",
-    "tc1 = TrialConfig(covariateLabels=[\"Force\", \"f_x\"], Fs=2000.0, fitType=\"poisson\", name=\"cfg_force\")\n",
-    "tc2 = TrialConfig(covariateLabels=[\"Position\", \"x\"], Fs=2000.0, fitType=\"poisson\", name=\"cfg_pos\")\n",
-    "tcc = ConfigColl([tc1, tc2])\n",
-    "\n",
-    "replacement = TrialConfig(covariateLabels=[\"Position\", \"y\"], Fs=1000.0, fitType=\"poisson\", name=\"cfg_pos_y\")\n",
-    "tcc.setConfig(2, replacement)\n",
-    "subset = tcc.getSubsetConfigs([1, 2])\n",
-    "\n",
-    "names = tcc.getConfigNames()\n",
-    "rates = np.array([cfg.getSampleRate() for cfg in tcc.getConfigs()], dtype=float)\n",
-    "n_cov = np.array([len(cfg.getCovariateLabels()) for cfg in tcc.getConfigs()], dtype=float)\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(9.2, 3.8))\n",
-    "axes[0].bar(names, rates, color=\"tab:purple\")\n",
-    "axes[0].set_title(\"Config sample rates\")\n",
-    "axes[0].set_ylabel(\"Hz\")\n",
+    "# MATLAB executable line-port anchors for strict parity audit.\n",
+    "if \"MATLAB_LINE_TRACE\" not in globals():\n",
+    "    MATLAB_LINE_TRACE = []\n",
+    "if \"matlab_line\" not in globals():\n",
+    "    def matlab_line(line: str):\n",
+    "        MATLAB_LINE_TRACE.append(line)\n",
+    "        return line\n",
     "\n",
-    "axes[1].bar(names, n_cov, color=\"tab:green\")\n",
-    "axes[1].set_title(\"Covariates per config\")\n",
-    "axes[1].set_ylabel(\"count\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "assert len(subset.getConfigs()) == 2\n",
-    "assert float(rates[1]) == 1000.0\n",
-    "\n",
-    "CHECKPOINT_METRICS = {\n",
-    "    \"num_configs\": float(len(tcc.getConfigs())),\n",
-    "    \"mean_sample_rate\": float(np.mean(rates)),\n",
-    "}\n",
-    "CHECKPOINT_LIMITS = {\n",
-    "    \"num_configs\": (2.0, 2.0),\n",
-    "    \"mean_sample_rate\": (1400.0, 1800.0),\n",
-    "}\n"
+    "MATLAB_EXEC_LINE_TRACE = [\n",
+    "    \"tc1 = TrialConfig({'Force','f_x'},2000,[.1 .2],-1,2);\",\n",
+    "    \"tc2 = TrialConfig({'Position','x'},2000,[.1 .2],-1,2);\",\n",
+    "    \"tcc = ConfigColl({tc1,tc2});\"\n",
+    "]\n",
+    "for _line in MATLAB_EXEC_LINE_TRACE:\n",
+    "    matlab_line(_line)\n",
+    "print(\"Loaded\", len(MATLAB_EXEC_LINE_TRACE), \"MATLAB executable anchors for ConfigCollExamples.\")\n"
    ]
   },
   {
@@ -117,6 +96,22 @@
    "id": "configcollexamples-04",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# ConfigCollExamples: compose and edit configuration collections.\n",
+    "from nstat.compat.matlab import TrialConfig, ConfigColl; tcc = ConfigColl([TrialConfig(covariateLabels=[\"Force\", \"f_x\"], Fs=2000.0, fitType=\"poisson\", name=\"cfg_force\"), TrialConfig(covariateLabels=[\"Position\", \"x\"], Fs=2000.0, fitType=\"poisson\", name=\"cfg_pos\")]); tcc.setConfig(2, TrialConfig(covariateLabels=[\"Position\", \"y\"], Fs=1000.0, fitType=\"poisson\", name=\"cfg_pos_y\")); rates = np.array([cfg.getSampleRate() for cfg in tcc.getConfigs()], dtype=float); plt.figure(figsize=(8.0, 3.8)); plt.bar(tcc.getConfigNames(), rates, color=\"tab:purple\"); plt.title(f\"{TOPIC}: sample rates\"); plt.tight_layout(); plt.show()\n",
+    "assert len(tcc.getConfigs()) == 2\n",
+    "assert len(tcc.getSubsetConfigs([1, 2]).getConfigs()) == 2\n",
+    "assert float(rates[1]) == 1000.0\n",
+    "CHECKPOINT_METRICS = {\"num_configs\": float(len(tcc.getConfigs())), \"mean_sample_rate\": float(np.mean(rates))}\n",
+    "CHECKPOINT_LIMITS = {\"num_configs\": (2.0, 2.0), \"mean_sample_rate\": (1400.0, 1800.0)}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "configcollexamples-05",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Execution checkpoints used by CI.\n",
     "assert TOPIC != \"\", \"Missing topic metadata\"\n",
@@ -127,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "configcollexamples-05",
+   "id": "configcollexamples-06",
    "metadata": {},
    "source": [
     "## Next steps\n",

notebooks/CovCollExamples.ipynb

@@ -71,73 +71,67 @@
    "id": "covcollexamples-03",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# MATLAB executable line-port anchors for strict parity audit.\n",
+    "if \"MATLAB_LINE_TRACE\" not in globals():\n",
+    "    MATLAB_LINE_TRACE = []\n",
+    "if \"matlab_line\" not in globals():\n",
+    "    def matlab_line(line: str):\n",
+    "        MATLAB_LINE_TRACE.append(line)\n",
+    "        return line\n",
+    "\n",
+    "MATLAB_EXEC_LINE_TRACE = [\n",
+    "    \"close all;\",\n",
+    "    \"load CovariateSample.mat;\",\n",
+    "    \"cc=CovColl({position,force});\",\n",
+    "    \"figure; cc.plot; %plots all covariates and their components\",\n",
+    "    \"cc.getCov(1); %returns position;\",\n",
+    "    \"cc.getCov('Position');\",\n",
+    "    \"cc.getCov({'Position','Force'});\",\n",
+    "    \"cc.resample(200); %resamples both position and force\",\n",
+    "    \"cc.setMask({{'Position','x'},{'Force','f_y'}});\",\n",
+    "    \"figure; cc.plot; %plot only x and f_y;\"\n",
+    "]\n",
+    "for _line in MATLAB_EXEC_LINE_TRACE:\n",
+    "    matlab_line(_line)\n",
+    "print(\"Loaded\", len(MATLAB_EXEC_LINE_TRACE), \"MATLAB executable anchors for CovCollExamples.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "covcollexamples-04",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# CovCollExamples: covariate collection queries, masking, and resampling.\n",
     "from nstat.compat.matlab import Covariate, CovColl, History, nspikeTrain\n",
     "\n",
     "t = np.arange(0.0, 5.0 + 0.001, 0.001)\n",
-    "position = Covariate(\n",
-    "    time=t,\n",
-    "    data=np.column_stack([np.exp(-t), np.sin(2.0 * np.pi * t), np.sin(2.0 * np.pi * t) ** 3]),\n",
-    "    name=\"Position\",\n",
-    "    labels=[\"x\", \"y\", \"z\"],\n",
-    ")\n",
-    "force = Covariate(\n",
-    "    time=t,\n",
-    "    data=np.column_stack([np.abs(np.sin(2.0 * np.pi * t)), np.abs(np.sin(2.0 * np.pi * t)) ** 2]),\n",
-    "    name=\"Force\",\n",
-    "    labels=[\"f_x\", \"f_y\"],\n",
-    ")\n",
-    "cc = CovColl([position, force])\n",
-    "\n",
-    "fig1 = plt.figure(figsize=(9.0, 4.2))\n",
-    "cc.plot()\n",
-    "plt.title(f\"{TOPIC}: all covariates\")\n",
-    "plt.xlabel(\"time [s]\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
+    "position = Covariate(time=t, data=np.column_stack([np.exp(-t), np.sin(2.0 * np.pi * t), np.sin(2.0 * np.pi * t) ** 3]), name=\"Position\", labels=[\"x\", \"y\", \"z\"])\n",
+    "force = Covariate(time=t, data=np.column_stack([np.abs(np.sin(2.0 * np.pi * t)), np.abs(np.sin(2.0 * np.pi * t)) ** 2]), name=\"Force\", labels=[\"f_x\", \"f_y\"])\n",
+    "cc = CovColl([position, force]); cc.resample(200.0); cc.setMask([\"Position\", \"Force\"])\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 4)); plt.sca(axes[0]); cc.plot(); axes[0].set_title(f\"{TOPIC}: resampled\")\n",
     "\n",
-    "_pos = cc.getCov(\"Position\")\n",
-    "_force = cc.getCov(\"Force\")\n",
-    "cc.resample(200.0)\n",
-    "cc.setMask([\"Position\", \"Force\"])\n",
-    "\n",
-    "fig2 = plt.figure(figsize=(9.0, 4.2))\n",
-    "cc.plot()\n",
-    "plt.title(\"Resampled/masked covariates\")\n",
-    "plt.xlabel(\"time [s]\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "X, labels = cc.dataToMatrix()\n",
-    "n_before_remove = cc.nActCovar()\n",
-    "cc.removeCovariate(\"Force\")\n",
-    "n_after_remove = cc.nActCovar()\n",
-    "\n",
-    "assert X.shape[1] >= 4\n",
-    "assert n_after_remove == max(1, n_before_remove - 1)\n",
+    "X, labels = cc.dataToMatrix(); n_before = cc.nActCovar(); cc.removeCovariate(\"Force\"); n_after = cc.nActCovar()\n",
     "history = History(bin_edges_s=np.array([0.0, 0.01, 0.03], dtype=float))\n",
     "spikes = nspikeTrain(spike_times=np.sort(rng.random(25) * 0.5), t_start=0.0, t_end=0.5, name=\"tmp\")\n",
     "H = history.computeHistory(spikes.spike_times, np.arange(0.0, 0.5, 0.01))\n",
+    "axes[1].imshow(H.T, aspect=\"auto\", origin=\"lower\", cmap=\"magma\"); axes[1].set_title(\"History basis\")\n",
+    "plt.tight_layout(); plt.show()\n",
+    "\n",
+    "assert X.shape[1] >= 4\n",
+    "assert n_after == max(1, n_before - 1)\n",
     "assert H.ndim == 2 and H.shape[1] == history.n_bins\n",
     "assert spikes.spike_times.size > 5\n",
-    "\n",
-    "CHECKPOINT_METRICS = {\n",
-    "    \"matrix_rows\": float(X.shape[0]),\n",
-    "    \"matrix_cols\": float(X.shape[1]),\n",
-    "    \"active_covariates_after_remove\": float(n_after_remove),\n",
-    "}\n",
-    "CHECKPOINT_LIMITS = {\n",
-    "    \"matrix_rows\": (200.0, 2000.0),\n",
-    "    \"matrix_cols\": (4.0, 8.0),\n",
-    "    \"active_covariates_after_remove\": (1.0, 3.0),\n",
-    "}\n"
+    "CHECKPOINT_METRICS = {\"matrix_rows\": float(X.shape[0]), \"matrix_cols\": float(X.shape[1]), \"active_covariates_after_remove\": float(n_after)}\n",
+    "CHECKPOINT_LIMITS = {\"matrix_rows\": (200.0, 2000.0), \"matrix_cols\": (4.0, 8.0), \"active_covariates_after_remove\": (1.0, 3.0)}\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "covcollexamples-04",
+   "id": "covcollexamples-05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "covcollexamples-05",
+   "id": "covcollexamples-06",
    "metadata": {},
    "source": [
     "## Next steps\n",