Merge pull request #336 from neurodata/tathey1-voxelclass

Tathey1 voxelclass

experiments/ViterBrain/notebooks/fig1-fragments.ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import napari\n",
+    "from skimage import io, measure\n",
+    "import h5py\n",
+    "from napari_animation import AnimationWidget\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = io.imread(\"/Users/thomasathey/Documents/mimlab/mouselight/input/images/first10_quantitative/images/2018-08-01_1_first10_quantitative.tif\")\n",
+    "\n",
+    "f = h5py.File(\"/Users/thomasathey/Documents/mimlab/mouselight/input/images/first10_quantitative/2018-08-01_1_first10_quantitative_Probabilities.h5\", 'r')\n",
+    "pred = f.get('exported_data')\n",
+    "pred = pred[:,:,:,1]\n",
+    "probs = pred\n",
+    "\n",
+    "thresh = 0.5\n",
+    "mask = probs > thresh\n",
+    "labs = measure.label(mask)\n",
+    "\n",
+    "res = [0.3,0.3,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/thomasathey/Documents/mimlab/mouselight/docs_env/lib/python3.8/site-packages/napari_animation/_qt/keyframeslist_widget.py:156: FutureWarning: Themes were changed to use evented model with Pydantic's color type rather than the `rgb(x, y, z)`. You can get the old color by calling `color.as_rgb()`. The `as_dict=True` option will be removed in 0.X.X\n",
+      "  self.setStyleSheet(template(qss_template, **get_theme(theme_name)))\n"
+     ]
+    }
+   ],
+   "source": [
+    "viewer = napari.Viewer(ndisplay=3)\n",
+    "viewer.add_image(im, scale=res)\n",
+    "viewer.add_labels(mask, scale=res)\n",
+    "viewer.add_labels(labs, scale=res)\n",
+    "animation_widget = AnimationWidget(viewer)\n",
+    "viewer.window.add_dock_widget(animation_widget, area=\"right\")\n",
+    "viewer.camera.angles = [90, 90, 0]\n",
+    "viewer.scale_bar.visible = True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "5dc00d68ff54f8375e99934614da4863299fb9e10af4294c095b7f517546ff26"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit ('docs_env': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

experiments/ViterBrain/notebooks/results_plots.ipynb → experiments/ViterBrain/notebooks/fig7-results.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -89,24 +89,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Viterbrain vs App2: 5.491012510881023e-09\n",
-      "APP2 vs Advantra: 0.039425832629443\n"
+      "P values for 2 Proportion Z Tests\n",
+      "Viterbrain vs App2: 0.041448687457663176\n",
+      "Viterbrain vs Snake: 0.041397618661288514\n",
+      "APP2 vs Advantra: 0.039425832629443\n",
+      "APP2 vs GTree: 0.393153437674003\n",
+      "APP2 vs Snake: 0.26272224684071965\n"
      ]
     }
    ],
    "source": [
     "from statsmodels.stats.proportion import proportions_ztest\n",
     "\n",
-    "print(f\"Viterbrain vs App2: {proportions_ztest(count=[data[0][0], data[1][0]], nobs=[10, 35])[1]}\")\n",
-    "\n",
-    "print(f\"APP2 vs Advantra: {proportions_ztest(count=[data[1][0], data[3][0]], nobs=[35, 35])[1]}\")"
+    "print(\"P values for 2 Proportion Z Tests\")\n",
+    "print(f\"Viterbrain vs App2: {proportions_ztest(count=[data[0][0], data[1][0]], nobs=[35, 35])[1]}\")\n",
+    "print(f\"Viterbrain vs Snake: {proportions_ztest(count=[data[0][0], data[4][0]], nobs=[35, 10])[1]}\")\n",
+    "print(f\"APP2 vs Advantra: {proportions_ztest(count=[data[1][0], data[3][0]], nobs=[35, 35])[1]}\")\n",
+    "print(f\"APP2 vs GTree: {proportions_ztest(count=[data[1][0], data[2][0]], nobs=[35, 35])[1]}\")\n",
+    "print(f\"APP2 vs Snake: {proportions_ztest(count=[data[1][0], data[4][0]], nobs=[35, 10])[1]}\")"
    ]
   },
   {