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How to convert some points from WA state planar. A Gist for Georgia Andrews (https://www.codeforamerica.org/people/georgia-andrews).
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd487ad3128>" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fd48bcc0780>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"%matplotlib inline\n", | |
"\n", | |
"import geopandas as gpd\n", | |
"from shapely.geometry import Point\n", | |
"\n", | |
"xs = [1277385,1269590,1277140,1262047,1278215,1268625,1287189,1271206,1266568,1274438]\n", | |
"ys = [223125,240965,244827,205424,222698,229908,188607,250272,241099,261105]\n", | |
"\n", | |
"pts = []\n", | |
"for x, y in zip(xs, ys):\n", | |
" pts.append(Point(x, y))\n", | |
" \n", | |
"gdf = gpd.GeoDataFrame(geometry=pts)\n", | |
"gdf.plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd4801d46a0>" | |
] | |
}, | |
"execution_count": 21, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fd47e4855c0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Set original coordinate reference system to WA State Plane North Projection\n", | |
"# Lookup all potential candidates here: http://www.spatialreference.org/ref/?search=Washington\n", | |
"init_crs = {'init': 'epsg:32148'} # TODO: This is the only part you need to figure out.\n", | |
" # Change this projection ID to get the right reprojection\n", | |
" # results if mine are wrong.\n", | |
"gdf.crs = init_crs\n", | |
"\n", | |
"# Then reproject to web/degrees\n", | |
"want_crs = {'init':'epsg:4326'}\n", | |
"gdf_proj = gdf.to_crs(want_crs)\n", | |
"\n", | |
"# Visually inspect\n", | |
"gdf_proj.plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Wouldn't it be nice to see what that lies on? Let's\n", | |
"# pull in some OSM data\n", | |
"import osmnx as ox\n", | |
"from shapely.geometry import box\n", | |
"\n", | |
"b = box(*gdf_proj.total_bounds)\n", | |
"G = ox.graph_from_polygon(b)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd47e283ef0>" | |
] | |
}, | |
"execution_count": 20, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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RPwZjCq+EEUX4Az6fD7///ju8Xq8UJ1VOiSpZXxJCcPXqVSQmJsLpdMLtdoPj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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fd480178908>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, ax = ox.plot_graph(G, node_size=0, show=False, close=False)\n", | |
"gdf_proj.plot(ax=ax)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"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": "3.6.3" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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