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Create test.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import napari\n",
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"from napari_matplotlib import FeaturesHistogramWidget\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"n_points = 1000\n",
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"random_points = np.random.random((n_points, 3)) * 10\n",
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"random_points2 = np.random.random((n_points, 3)) * 10\n",
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"random_vectors = random_points2 - random_points\n",
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"\n",
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"vectors = np.stack([random_points, random_vectors], axis=1)\n",
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"feature1 = np.random.random(n_points)\n",
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"feature2 = np.random.normal(size=n_points)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"viewer = napari.Viewer()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Vectors layer 'vectors1' at 0x1ec34e98130>"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"viewer.add_points(\n",
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" random_points,\n",
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" properties={\"feature1\": feature1, \"feature2\": feature2},\n",
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" name=\"points1\",\n",
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" face_color=\"feature1\",\n",
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" size=1,\n",
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")\n",
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"\n",
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"viewer.add_vectors(\n",
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" vectors,\n",
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" properties={\"feature1\": feature1, \"feature2\": feature2},\n",
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" name=\"vectors1\",\n",
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" edge_width=1,\n",
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" edge_color=\"feature1\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<napari._qt.widgets.qt_viewer_dock_widget.QtViewerDockWidget at 0x1ec387349d0>"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"widget = FeaturesHistogramWidget(viewer)\n",
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"viewer.window.add_dock_widget(widget)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'feature1'"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"widget.x_axis_key"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"widget.viewer.layers[widget.layers[0].name].refresh_colors(True)\n",
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"widget.viewer.layers[0].properties\n",
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"widget.viewer.layers[widget.layers[0].name].refresh_colors(True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"widget.viewer.layers[1]._update_properties"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"widget.viewer.layers[0].edge_color = \"feature2\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "stress",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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