{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First, ensure you've run \"`cargo build --release`\" from the repo root"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[0m\u001b[32m\u001b[1m Compiling\u001b[0m rdp v0.2.45 (file:///Users/sth/dev/rdp)\n",
"\u001b[0m\u001b[0m\u001b[32m\u001b[1m Finished\u001b[0m release [optimized] target(s) in 3.93 secs\n"
]
}
],
"source": [
"!CARGO_INCREMENTAL=\"0\" cargo build --release"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# You'll need the GeoJSON and Shapely packages. Install using pip\n",
"from ffi import simplify_coords, simplify_coords_vw\n",
"from shapely.geometry import LineString, shape, Point\n",
"import geojson\n",
"\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <style>\n",
" div.output_svg div {\n",
" width:50% !important;\n",
" height:50% !important;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"HTML(\n",
" \"\"\"\n",
" <style>\n",
" div.output_svg div {\n",
" width:50% !important;\n",
" height:50% !important;\n",
" }\n",
" </style>\n",
" \"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a long LineString from GeoJSON"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"832\n"
]
},
{
"data": {
"image/svg+xml": [
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-0.984019,51.995315 -0.984019,51.995319\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x1061704e0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quite a long, complex LineString\n",
"with open('examples/route1.geojson', 'r') as f:\n",
" orig_1 = shape(geojson.load(f))\n",
"print(len(orig_1.coords))\n",
"orig_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simplify it using Ramer–Douglas-Peucker"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"43\n"
]
},
{
"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=\"-1.0001938400000001 51.98287516 0.3359836800000001 0.25230868000000584\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,104.21805900000001)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.006719673600000003\" points=\"-0.701206,52.220489 -0.692289,52.21928 -0.686421,52.221763 -0.677259,52.22274 -0.676654,52.221637 -0.67791,52.214523 -0.68129,52.210605 -0.684259,52.188411 -0.687782,52.184238 -0.690582,52.170825 -0.69275,52.168346 -0.697526,52.167102 -0.700476,52.163345 -0.7025,52.159313 -0.701741,52.151065 -0.705776,52.145092 -0.702823,52.126766 -0.699003,52.119007 -0.690101,52.10831 -0.695929,52.084033 -0.698649,52.08023 -0.706741,52.076885 -0.72558,52.074058 -0.738997,52.066444 -0.768443,52.059032 -0.802709,52.048107 -0.804414,52.046199 -0.832137,52.05252 -0.844291,52.061008 -0.853143,52.064331 -0.86862,52.066532 -0.872032,52.063491 -0.870133,52.054862 -0.874653,52.049457 -0.908623,52.02898 -0.91462,52.023063 -0.919983,52.020591 -0.929226,52.020217 -0.95333,52.007808 -0.97125,52.001926 -0.982915,52.002502 -0.98775,51.999919 -0.984019,51.995319\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x1083ff748>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simplified_rdp = LineString(simplify_coords(orig_1.coords, 0.001))\n",
"print(len(simplified_rdp.coords))\n",
"simplified_rdp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simplify it interactively"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def interactive_simplify_dp(inp_geom, threshold=0.005):\n",
" ls = LineString(simplify_coords(inp_geom, threshold))\n",
" return ls"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b8055c25e7e64bfc8859794a6602ab3d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=0.005, description='threshold', max=0.05, min=9e-06, step=1e-06), Outp…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.interactive_simplify_dp(inp_geom, threshold=0.005)>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(interactive_simplify_dp, inp_geom=fixed(orig_1.coords), threshold=(0.000009, 0.050, 0.000001))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simplify it using Visvalingam-Whyatt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0c3e8eee6aff491ea059c0ff7a3f8377",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=0.000175, description='threshold', max=0.0009, min=1e-06, step=5e-08),…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.interactive_simplify_vw(inp_geom, threshold=0.000175)>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def interactive_simplify_vw(inp_geom, threshold=.000175):\n",
" ls = LineString(simplify_coords_vw(inp_geom, threshold))\n",
" return ls\n",
"\n",
"interact(interactive_simplify_vw, inp_geom=fixed(orig_1.coords), threshold=(.0000010, .00090, .00000005))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A more complex Polygon"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4e16639d205e4cd6b6ba9a3c2b32f414",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=100.0, description='threshold', max=1500.0, min=100.0, step=50.0), Out…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.interactive_simplify_vw(inp_geom, threshold=0.000175)>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('examples/polygon.json', 'r') as f:\n",
" poly = shape(geojson.load(f))\n",
"\n",
"interact(interactive_simplify_vw, inp_geom=fixed(poly.exterior.coords), threshold=(100., 1500., 50))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
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