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jorisvandenbossche/geopandas-clip-explode-problem.ipynb

Created November 15, 2019 10:34
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geopandas-clip-explode-problem.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://gist.github.com/nkorinek/92a9fbdc6a8a83909bb6bc51d1d94d70"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from shapely.geometry import Polygon, Point, LineString\n",
"import shapely\n",
"import geopandas as gpd\n",
"import geopandas.clip as gc\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Making clipping polygon\n",
"poly_inters = Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)])\n",
"rectangle_gdf = gpd.GeoDataFrame([1], geometry=[poly_inters], crs={\"init\": \"epsg:4326\"})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Making points to buffer into polygons\n",
"pts = np.array([[2, 2], [3, 4], [9, 8], [-12, -15]])\n",
"points = gpd.GeoDataFrame([Point(xy) for xy in pts], columns=[\"geometry\"], crs={\"init\": \"epsg:4326\"},)\n",
"\n",
"# Buffering points\n",
"buffered_locs = points\n",
"buffered_locs[\"geometry\"] = buffered_locs.buffer(4)\n",
"buffered_locs[\"type\"] = \"plot\"\n",
"\n",
"# Creating a multipolygon\n",
"donut = gpd.overlay(buffered_locs, rectangle_gdf, how=\"symmetric_difference\")\n",
"multi_polygon = donut.unary_union\n",
"out_df = gpd.GeoDataFrame(geometry=gpd.GeoSeries(multi_polygon), crs={\"init\": \"epsg:4326\"})\n",
"multi_poly = out_df.rename(columns={0: \"geometry\"}).set_geometry(\"geometry\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MULTIPOLYGON (((-8 -15, -8.019261093311211 -15...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry\n",
"0 MULTIPOLYGON (((-8 -15, -8.019261093311211 -15..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi_poly"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f27b2050978>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = multi_poly.plot()\n",
"rectangle_gdf.plot(alpha=.5, ax=ax, edgecolor='k', linewidth=2, color='C1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clipping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Clipping two ways\n",
"clip_obj1 = gc._clip_line_poly(multi_poly, rectangle_gdf.unary_union)\n",
"clip_obj2 = gc.clip(multi_poly, rectangle_gdf.unary_union)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 MultiPolygon\n",
"dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multi_poly.geom_type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 GeometryCollection\n",
"dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clip_obj1.geom_type"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 MultiPolygon\n",
"0 MultiLineString\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clip_obj2.geom_type"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first way returns a GeometryCollection:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"metadata": {},
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"source": [
"clip_obj1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"however, plotting with geopandas does not work .."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f27a8edba20>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
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]
},
"metadata": {
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"output_type": "display_data"
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],
"source": [
"clip_obj1.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But it does actually contain objects:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coll = clip_obj1.iloc[0, 0]\n",
"coll"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second way returns two rows:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"clip_obj2.plot()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4 -0.4 10.8 10.8\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.21600000000000003\" opacity=\"0.6\" d=\"M 10.0,4.131875511680132 L 10.0,0.0 L 5.459108512695298,0.0 L 5.5276850573934215,0.11441305269601121 L 5.695518130045148,0.4692662705396424 L 5.827761342928836,0.8388612909821516 L 5.923141121612922,1.2196387119354877 L 5.933150651538313,1.2871174815280866 L 6.092041813450949,1.4624268633454203 L 6.325878449210182,1.777719067921593 L 6.5276850573934215,2.114413052696011 L 6.695518130045148,2.4692662705396424 L 6.827761342928836,2.838861290982152 L 6.923141121612922,3.2196387119354877 L 6.980738906688788,3.607931438681758 L 7.0,4.0 L 7.0,4.000000000000033 L 6.980738906688785,4.392068561318274 L 6.954629233863579,4.568085677990351 L 7.114413052696017,4.472314942606576 L 7.469266270539649,4.30448186995485 L 7.838861290982158,4.172238657071162 L 8.219638711935493,4.0768588783870765 L 8.607931438681764,4.019261093311211 L 9.000000000000007,4.0 L 9.392068561318249,4.019261093311213 L 9.780361288064519,4.076858878387079 L 10.0,4.131875511680132 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.21600000000000003\" opacity=\"0.6\" d=\"M 0.0,6.639125628455881 L 0.0,10.0 L 5.540891487304703,10.0 L 5.4723149426065785,9.885586947303988 L 5.304481869954852,9.530733729460357 L 5.172238657071164,9.161138709017846 L 5.076858878387077,8.78036128806451 L 5.019261093311212,8.392068561318238 L 5.0,7.999999999999996 L 5.019261093311213,7.607931438681753 L 5.045370766136422,7.431914322009649 L 4.885586947303975,7.527685057393429 L 4.530733729460343,7.695518130045153 L 4.161138709017835,7.82776134292884 L 3.7803612880644994,7.923141121612924 L 3.3920685613182298,7.980738906688789 L 2.9999999999999885,8.0 L 2.6079314386817476,7.980738906688787 L 2.2196387119354783,7.92314112161292 L 1.8388612909821436,7.8277613429288335 L 1.4692662705396353,7.695518130045144 L 1.1144130526960052,7.527685057393418 L 0.7777190679215882,7.32587844921018 L 0.4624268633454163,7.092041813450947 L 0.17157287525380926,6.82842712474619 L 0.0,6.639125628455881 z\" /></g></g></svg>"
],
"text/plain": [
"<shapely.geometry.multipolygon.MultiPolygon at 0x7f27b05def28>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multipoly = clip_obj2.iloc[0,0]\n",
"multipoly"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4 -0.4 10.8 10.8\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><g><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,0.0 5.459108512695298,0.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,1.3608743715441185 0.0,0.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"10.0,4.131875511680132 10.0,10.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"10.0,10.0 5.540891487304703,10.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,5.459108512695298 0.0,1.3608743715441185\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,6.639125628455881 0.0,5.459108512695298\" opacity=\"0.8\" /></g></g></svg>"
],
"text/plain": [
"<shapely.geometry.multilinestring.MultiLineString at 0x7f27b05de400>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multiline = clip_obj2.iloc[1,0]\n",
"multiline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But, we can see that the GeometryCollection actually contains the same multipolygon/linestring:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'GEOMETRYCOLLECTION (LINESTRING (0 6.639125628455881, 0 5.459108512695298), LINESTRING (0 5.459108512695298, 0 1.360874371544118), LINESTRING (0 1.360874371544118, 0 0), LINESTRING (0 0, 5.459108512695298 0), LINESTRING (10 4.131875511680132, 10 10), LINESTRING (10 10, 5.540891487304703 10), POLYGON ((0 6.639125628455881, 0 10, 5.540891487304703 10, 5.472314942606578 9.885586947303988, 5.304481869954852 9.530733729460357, 5.172238657071164 9.161138709017846, 5.076858878387077 8.78036128806451, 5.019261093311212 8.392068561318238, 5 7.999999999999996, 5.019261093311213 7.607931438681753, 5.045370766136422 7.431914322009649, 4.885586947303975 7.527685057393429, 4.530733729460343 7.695518130045153, 4.161138709017835 7.82776134292884, 3.780361288064499 7.923141121612924, 3.39206856131823 7.980738906688789, 2.999999999999988 8, 2.607931438681748 7.980738906688787, 2.219638711935478 7.92314112161292, 1.838861290982144 7.827761342928834, 1.469266270539635 7.695518130045144, 1.114413052696005 7.527685057393418, 0.7777190679215882 7.32587844921018, 0.4624268633454163 7.092041813450947, 0.1715728752538093 6.82842712474619, 0 6.639125628455881)), POLYGON ((10 4.131875511680132, 10 0, 5.459108512695298 0, 5.527685057393422 0.1144130526960112, 5.695518130045148 0.4692662705396424, 5.827761342928836 0.8388612909821516, 5.923141121612922 1.219638711935488, 5.933150651538313 1.287117481528087, 6.092041813450949 1.46242686334542, 6.325878449210182 1.777719067921593, 6.527685057393422 2.114413052696011, 6.695518130045148 2.469266270539642, 6.827761342928836 2.838861290982152, 6.923141121612922 3.219638711935488, 6.980738906688788 3.607931438681758, 7 4, 7 4.000000000000033, 6.980738906688785 4.392068561318274, 6.954629233863579 4.568085677990351, 7.114413052696017 4.472314942606576, 7.469266270539649 4.30448186995485, 7.838861290982158 4.172238657071162, 8.219638711935493 4.076858878387077, 8.607931438681764 4.019261093311211, 9.000000000000007 4, 9.392068561318249 4.019261093311213, 9.780361288064519 4.076858878387079, 10 4.131875511680132)))'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coll.wkt"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<shapely.geometry.linestring.LineString at 0x7f27a9253080>,\n",
" <shapely.geometry.linestring.LineString at 0x7f27a9253978>,\n",
" <shapely.geometry.linestring.LineString at 0x7f27a9253588>,\n",
" <shapely.geometry.linestring.LineString at 0x7f27a9253f98>,\n",
" <shapely.geometry.linestring.LineString at 0x7f27b1ed62b0>,\n",
" <shapely.geometry.linestring.LineString at 0x7f27a9372be0>,\n",
" <shapely.geometry.polygon.Polygon at 0x7f27b029ea20>,\n",
" <shapely.geometry.polygon.Polygon at 0x7f27b029eda0>]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(coll)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from shapely.geometry import MultiPolygon, MultiLineString"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4 -0.4 10.8 10.8\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><g><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,6.639125628455881 0.0,5.459108512695298\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,5.459108512695298 0.0,1.3608743715441185\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,1.3608743715441185 0.0,0.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"0.0,0.0 5.459108512695298,0.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"10.0,4.131875511680132 10.0,10.0\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.21600000000000003\" points=\"10.0,10.0 5.540891487304703,10.0\" opacity=\"0.8\" /></g></g></svg>"
],
"text/plain": [
"<shapely.geometry.multilinestring.MultiLineString at 0x7f27b033fc18>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MultiLineString(list(coll)[:6])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MultiLineString(list(coll)[:6]).equals(multiline)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.4 -0.4 10.8 10.8\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10.0)\"><g><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.21600000000000003\" opacity=\"0.6\" d=\"M 0.0,6.639125628455881 L 0.0,10.0 L 5.540891487304703,10.0 L 5.4723149426065785,9.885586947303988 L 5.304481869954852,9.530733729460357 L 5.172238657071164,9.161138709017846 L 5.076858878387077,8.78036128806451 L 5.019261093311212,8.392068561318238 L 5.0,7.999999999999996 L 5.019261093311213,7.607931438681753 L 5.045370766136422,7.431914322009649 L 4.885586947303975,7.527685057393429 L 4.530733729460343,7.695518130045153 L 4.161138709017835,7.82776134292884 L 3.7803612880644994,7.923141121612924 L 3.3920685613182298,7.980738906688789 L 2.9999999999999885,8.0 L 2.6079314386817476,7.980738906688787 L 2.2196387119354783,7.92314112161292 L 1.8388612909821436,7.8277613429288335 L 1.4692662705396353,7.695518130045144 L 1.1144130526960052,7.527685057393418 L 0.7777190679215882,7.32587844921018 L 0.4624268633454163,7.092041813450947 L 0.17157287525380926,6.82842712474619 L 0.0,6.639125628455881 z\" /><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.21600000000000003\" opacity=\"0.6\" d=\"M 10.0,4.131875511680132 L 10.0,0.0 L 5.459108512695298,0.0 L 5.5276850573934215,0.11441305269601121 L 5.695518130045148,0.4692662705396424 L 5.827761342928836,0.8388612909821516 L 5.923141121612922,1.2196387119354877 L 5.933150651538313,1.2871174815280866 L 6.092041813450949,1.4624268633454203 L 6.325878449210182,1.777719067921593 L 6.5276850573934215,2.114413052696011 L 6.695518130045148,2.4692662705396424 L 6.827761342928836,2.838861290982152 L 6.923141121612922,3.2196387119354877 L 6.980738906688788,3.607931438681758 L 7.0,4.0 L 7.0,4.000000000000033 L 6.980738906688785,4.392068561318274 L 6.954629233863579,4.568085677990351 L 7.114413052696017,4.472314942606576 L 7.469266270539649,4.30448186995485 L 7.838861290982158,4.172238657071162 L 8.219638711935493,4.0768588783870765 L 8.607931438681764,4.019261093311211 L 9.000000000000007,4.0 L 9.392068561318249,4.019261093311213 L 9.780361288064519,4.076858878387079 L 10.0,4.131875511680132 z\" /></g></g></svg>"
],
"text/plain": [
"<shapely.geometry.multipolygon.MultiPolygon at 0x7f27a9144240>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MultiPolygon(list(coll)[6:])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MultiPolygon(list(coll)[6:]).equals(multipoly)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# The types show why the MultiPolygon has to be exploded. If the polygon is unexploded \n",
"# and has slivers, the slivers throw the GeoPandas intersection function off and it \n",
"# returns an empty GeometryCollection instead of a properly clipped MultiPolygon. The \n",
"# MultiPolygon and MultiLine includes the sliver geometries alongside everything else \n",
"# that should be included in the clip, where as the traditional clip method fails\n",
"# when presented with this problem. Hope this clears some confusion up!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (dev)",
"language": "python",
"name": "dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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