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.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 02e2d48c69ef120d6574fdb583c98e46
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tags: 645f666f9bcd5a90fca523b33c5a78b7

.gitignore

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.napari/config.yml

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.pre-commit-config.yaml

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.readthedocs.yaml

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LICENSE

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MANIFEST.in

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README.md

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Histograms\n"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import napari\n\nviewer = napari.Viewer()\nviewer.open_sample(\"napari\", \"kidney\")\n\nviewer.window.add_plugin_dock_widget(\n plugin_name=\"napari-matplotlib\", widget_name=\"Histogram\"\n)\n\nif __name__ == \"__main__\":\n napari.run()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Scattering features\n"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import napari\nimport numpy as np\nfrom skimage.measure import regionprops_table\n\n# make a test label image\nlabel_image = np.zeros((100, 100), dtype=np.uint16)\n\nlabel_image[10:20, 10:20] = 1\nlabel_image[50:70, 50:70] = 2\n\nfeature_table_1 = regionprops_table(\n label_image, properties=(\"label\", \"area\", \"perimeter\")\n)\nfeature_table_1[\"index\"] = feature_table_1[\"label\"]\n\n# make the points data\nn_points = 100\npoints_data = 100 * np.random.random((100, 2))\npoints_features = {\n \"feature_0\": np.random.random((n_points,)),\n \"feature_1\": np.random.random((n_points,)),\n \"feature_2\": np.random.random((n_points,)),\n}\n\n# create the viewer\nviewer = napari.Viewer()\nviewer.add_labels(label_image, features=feature_table_1)\nviewer.add_points(points_data, features=points_features)\n\n# make the widget\nviewer.window.add_plugin_dock_widget(\n plugin_name=\"napari-matplotlib\", widget_name=\"FeaturesScatter\"\n)\n\nif __name__ == \"__main__\":\n napari.run()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Scatter plots\n"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import napari\n\nviewer = napari.Viewer()\nviewer.open_sample(\"napari\", \"kidney\")\n\nviewer.window.add_plugin_dock_widget(\n plugin_name=\"napari-matplotlib\", widget_name=\"Scatter\"\n)\n\nif __name__ == \"__main__\":\n napari.run()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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