diff --git a/experiments/ViterBrain/notebooks/fig1-fragments.ipynb b/experiments/ViterBrain/notebooks/fig1-fragments.ipynb new file mode 100644 index 000000000..f583d72e4 --- /dev/null +++ b/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 +} diff --git a/experiments/ViterBrain/notebooks/fig3-voxels.ipynb b/experiments/ViterBrain/notebooks/fig3-voxels.ipynb new file mode 100644 index 000000000..9c223bcd7 --- /dev/null +++ b/experiments/ViterBrain/notebooks/fig3-voxels.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/thomasathey/Documents/mimlab/mouselight/docs_env/lib/python3.8/site-packages/nilearn/datasets/__init__.py:86: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n", + " warn(\"Fetchers from the nilearn.datasets module will be \"\n" + ] + } + ], + "source": [ + "from cloudvolume import CloudVolume\n", + "from skimage.transform import downscale_local_mean\n", + "import napari\n", + "from skimage import io\n", + "import random\n", + "import h5py\n", + "from skimage import measure\n", + "from brainlit.preprocessing import removeSmallCCs\n", + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import ticker\n", + "import subprocess\n", + "import tables\n", + "from scipy.stats import gaussian_kde, kstest\n", + "from scipy import stats\n", + "from tqdm import tqdm\n", + "%gui qt5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KDEs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Predicted Foreground p-val norm: 0.0, p-val poisson: 0.0\n", + "Predicted Background p-val norm: 0.0, p-val poisson: 1.2114465892878338e-14\n", + "\n", + "\n", + "Predicted Foreground p-val norm: 0.0, p-val poisson: 0.0\n", + "Predicted Background p-val norm: 0.0, p-val poisson: 2.180975182979957e-21\n", + "\n", + "\n", + "Predicted Foreground p-val norm: 0.0, p-val poisson: 0.0\n", + "Predicted Background p-val norm: 0.0, p-val poisson: 1.2839004629221358e-82\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1,2)\n", + "ax_fg = axs[0]\n", + "ax_bg = axs[1]\n", + "\n", + "colors = ['r', 'b', 'g']\n", + "\n", + "for i in range(10,13):\n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus.tif\"\n", + " im = io.imread(fname)\n", + "\n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus_Labels.h5\"\n", + " with h5py.File(fname, 'r') as f:\n", + " print(f.keys())\n", + " labels = f['exported_data']\n", + " labels = labels[:,:,:,0]\n", + "\n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus_Simple Segmentation.h5\"\n", + " with h5py.File(fname, 'r') as f:\n", + " print(f.keys())\n", + " pred = f['exported_data']\n", + " pred = pred[:,:,:,0]\n", + "\n", + " fg_pred = im[pred == 2].flatten()\n", + " np.random.shuffle(fg_pred)\n", + " fg_pred = fg_pred[:5000]\n", + " _, p_norm = kstest(fg_pred, \"norm\")\n", + " _, p_poisson = kstest(fg_pred, \"poisson\", args = (np.mean(fg_pred),))\n", + " print(f\"Predicted Foreground p-val norm: {p_norm}, p-val poisson: {p_poisson}\")\n", + " bg_pred = im[pred == 1].flatten()\n", + " np.random.shuffle(bg_pred)\n", + " bg_pred = bg_pred[:5000]\n", + " _, p_norm = kstest(bg_pred, \"norm\")\n", + " _, p_poisson = kstest(bg_pred, \"poisson\", args = (np.mean(bg_pred),))\n", + " print(f\"Predicted Background p-val norm: {p_norm}, p-val poisson: {p_poisson}\")\n", + "\n", + " ts = np.arange(10000, 40000, 1)\n", + "\n", + " kde_fg = gaussian_kde(fg_pred, bw_method=\"scott\")\n", + " ax_fg.plot(ts, kde_fg.evaluate(ts), label=f\"Subvolume {i-10}\")\n", + "\n", + " kde_bg = gaussian_kde(bg_pred, bw_method=\"scott\")\n", + " ax_bg.plot(ts, kde_bg.evaluate(ts))\n", + "\n", + " kl_div_pred = np.sum(np.multiply(kde_fg.pdf(ts), kde_fg.logpdf(ts) - kde_bg.logpdf(ts)))\n", + " kl_div_lab = np.sum(np.multiply(kde_fg.pdf(ts), kde_fg.logpdf(ts) - kde_bg.logpdf(ts)))\n", + " \n", + "\n", + "\n", + "xmax = 20000\n", + "ax_fg.tick_params(axis='both', which='major', labelsize=14)\n", + "ax_fg.set_xlim([10000,xmax])\n", + "ax_fg.set_xticks([10000, (xmax+10000)/2, xmax])\n", + "ax_fg.set_xticklabels([10000, int((xmax+10000)/2), xmax], fontdict={'fontsize': 24})\n", + "ax_fg.set_yticklabels([])\n", + "ax_fg.set_ylabel(\"Density\", fontsize=24)\n", + "ax_fg.set_xlabel(\"Image Intensity\", fontsize=24)\n", + "ax_fg.set_title(f\"Foreground Intensity KDEs\", fontsize=24)\n", + "\n", + "xmax = 13000\n", + "xmin = 11500\n", + "ax_bg.tick_params(axis='both', which='major', labelsize=14)\n", + "ax_bg.set_xlim([xmin,xmax])\n", + "ax_bg.set_xticks([xmin, (xmax+xmin)/2, xmax])\n", + "ax_bg.set_xticklabels([xmin, int((xmax+xmin)/2), xmax], fontdict={'fontsize': 24})\n", + "ax_bg.set_yticklabels([])\n", + "ax_bg.set_xlabel(\"Image Intensity\", fontsize=24)\n", + "ax_bg.set_title(f\"Background Intensity KDEs\", fontsize=24)\n", + "\n", + "ax_fg.legend(prop={'size': 24})\n", + "fig.set_figheight(7)\n", + "fig.set_figwidth(16)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autocorrelation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "\n", + "\n", + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 11961.3246 variance: 16291.15043484 of 5000 Background voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [03:11<00:00, 65234.23it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 36.70it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 12627.66 variance: 295463.6252 of 5000 Foreground voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [02:55<00:00, 71351.87it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 35.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "\n", + "\n", + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 11955.7504 variance: 13384.29249984 of 5000 Background voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [03:01<00:00, 68943.34it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 37.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 12245.6146 variance: 86162.93006684 of 5000 Foreground voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [02:54<00:00, 71812.90it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 36.86it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "\n", + "\n", + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 12307.1526 variance: 33232.109713239995 of 5000 Background voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [02:57<00:00, 70256.86it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 37.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finding coordinates...\n", + "Finding Intensities...\n", + "Mean intensity: 13313.1426 variance: 304416.60146524 of 5000 Foreground voxels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing Correlations: 100%|██████████| 12502500/12502500 [02:52<00:00, 72562.46it/s]\n", + "100%|██████████| 21/21 [00:00<00:00, 35.36it/s]\n" + ] + } + ], + "source": [ + "num_samples = 5000\n", + "\n", + "data = np.zeros((3,2,3,21)) #subvolume, voxel type, cor/error, data\n", + "\n", + "for i in range(10,13):\n", + " print(\"Loading data...\")\n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus.tif\"\n", + " im = io.imread(fname)\n", + "\n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus_Labels.h5\"\n", + " with h5py.File(fname, 'r') as f:\n", + " print(f.keys())\n", + " labels = f['exported_data']\n", + " labels = labels[:,:,:,0]\n", + "\n", + " \n", + " fname = \"/Users/thomasathey/Documents/mimlab/mouselight/octopus_experiment/ilastik_training/2018-08-01_\" + str(i) + \"_octopus_Simple Segmentation.h5\"\n", + " with h5py.File(fname, 'r') as f:\n", + " print(f.keys())\n", + " pred = f['exported_data']\n", + " pred = pred[:,:,:,0]\n", + "\n", + " labels = pred[:125,:125,:125]\n", + "\n", + " for val, type in zip([1, 2],[\"Background\", \"Foreground\"]):\n", + " \n", + " print(\"Finding coordinates...\")\n", + " coords = np.argwhere(labels == val)\n", + " coords = [coord for coord in coords]\n", + " random.shuffle(coords)\n", + " coords = coords[:num_samples]\n", + "\n", + " print(\"Finding Intensities...\")\n", + " ints = []\n", + " for coord in coords:\n", + " ints.append(im[coord[0],coord[1],coord[2]])\n", + " mean_int = np.mean(ints)\n", + " var_int = np.var(ints)\n", + " print(f\"Mean intensity: {mean_int} variance: {var_int} of {len(coords)} {type} voxels\")\n", + "\n", + " pairs_bg = [(c1, c2) for idx, c1 in enumerate(coords) for c2 in coords[idx:]]\n", + "\n", + "\n", + " dists = []\n", + " diffs = []\n", + " for pair in tqdm(pairs_bg, desc=\"Computing Correlations\"):\n", + " dist = np.linalg.norm(np.multiply(pair[0]-pair[1], [0.3,0.3,1]))\n", + " dists.append(dist)\n", + " a = float(im[pair[0][0],pair[0][1],pair[0][2]]) - mean_int\n", + " b = float(im[pair[1][0],pair[1][1],pair[1][2]]) - mean_int\n", + " diffs.append(a*b)\n", + "\n", + " diffs = np.array(diffs)\n", + " dists = np.array(dists)\n", + "\n", + " cor = []\n", + " errors = []\n", + " for dist in tqdm(np.arange(0,21,1)):\n", + " idxs = np.logical_and(dists <= dist, dists > dist-1)\n", + " dif_select = diffs[idxs]\n", + " mn = np.mean(dif_select)\n", + " correlation = mn/var_int\n", + " z_plus = 0.5*np.log((1+correlation)/(1-correlation))+1/np.sqrt(len(dif_select)-3)\n", + " z_minus = 0.5*np.log((1+correlation)/(1-correlation))-1/np.sqrt(len(dif_select)-3)\n", + " e_plus = (np.exp(2*z_plus)-1)/(np.exp(2*z_plus)+1) - correlation\n", + " e_minus = correlation - (np.exp(2*z_minus)-1)/(np.exp(2*z_minus)+1)\n", + " errors.append([e_minus, e_plus])\n", + " cor.append(correlation)\n", + " #print(f\"Distance {dist}, samples: {len(dif_select)} cor: {correlation}\")\n", + " \n", + " errors = np.array(errors).T\n", + "\n", + " data[i-10, val-1, 0, :] = cor\n", + " data[i-10, val-1, 1:, :] = errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1,2)\n", + "ax_fg = axs[0]\n", + "ax_bg = axs[1]\n", + "\n", + "min_cor = np.amin(data[:,:,0,:])\n", + "\n", + "for i in range(10,13):\n", + " for val, type in zip([1, 2],[\"Background\", \"Foreground\"]):\n", + " cor = data[i-10, val-1, 0, :]\n", + " errors = data[i-10, val-1, 1:, :]\n", + " if val == 1:\n", + " ax_bg.errorbar(np.arange(0,21,1), cor, yerr=errors, label=f\"Subvolume {i-10}\")\n", + " else:\n", + " ax_fg.errorbar(np.arange(0,21,1), cor, yerr=errors, label=f\"Subvolume {i-10}\")\n", + "\n", + "min_y = round(min_cor,1)\n", + "if min_y > min_cor:\n", + " min_y -= 0.1\n", + " \n", + "#y axis\n", + "ax_fg.set_ylim([min_y,1.1])\n", + "ax_fg.set_yticks(np.arange(0,1.2,0.2))\n", + "ax_fg.set_yticklabels([0, 0.2, 0.4, 0.6, 0.8, 1.0], fontdict={'fontsize': 24})\n", + "ax_fg.set_ylabel(\"Correlation\", fontsize=24)\n", + "\n", + "#x axis\n", + "ax_fg.set_xlabel(\"Distance (microns)\", fontsize=24)\n", + "ax_fg.set_xlim([-1,21])\n", + "ax_fg.set_xticks(np.arange(0,22,2), minor=False)\n", + "ax_fg.set_xticks(np.arange(1,21,2), minor=True)\n", + "ax_fg.set_xticklabels(np.arange(0,22,2), fontdict={'fontsize': 24})\n", + "\n", + "#other\n", + "ax_fg.set_title(f\"Foreground Intensity Autocorrelation\", fontsize=24)\n", + "ax_fg.legend(prop={'size': 24})\n", + "ax_fg.axhline(0, linestyle='--', color='k') # horizontal lines\n", + "\n", + "#y axis\n", + "ax_bg.set_ylim([min_y,1.1])\n", + "ax_bg.set_yticks(np.arange(0,1.2,0.2))\n", + "ax_bg.set_yticklabels([0, 0.2, 0.4, 0.6, 0.8, 1.0], fontdict={'fontsize': 24})\n", + "\n", + "#x axis\n", + "ax_bg.set_xlabel(\"Distance (microns)\", fontsize=24)\n", + "ax_bg.set_xlim([-1,21])\n", + "ax_bg.set_xticks(np.arange(0,22,2), minor=False)\n", + "ax_bg.set_xticks(np.arange(1,21,2), minor=True)\n", + "ax_bg.set_xticklabels(np.arange(0,22,2), fontdict={'fontsize': 24})\n", + "\n", + "#other\n", + "ax_bg.set_title(f\"Background Intensity Autocorrelation\", fontsize=24)\n", + "ax_bg.axhline(0, linestyle='--', color='k') # horizontal lines\n", + "\n", + "fig.set_figheight(7)\n", + "fig.set_figwidth(16)\n", + "fig.tight_layout()" + ] + }, + { + "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 +} diff --git a/experiments/ViterBrain/notebooks/results_plots.ipynb b/experiments/ViterBrain/notebooks/fig7-results.ipynb similarity index 98% rename from experiments/ViterBrain/notebooks/results_plots.ipynb rename to experiments/ViterBrain/notebooks/fig7-results.ipynb index 2e8b032aa..ebbf9c65b 100644 --- a/experiments/ViterBrain/notebooks/results_plots.ipynb +++ b/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]}\")" ] }, {