{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.3.0.dev\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ColorLine"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"x = np.linspace(0, 2*np.pi, 300)\n",
"\n",
"lats = 20 * np.cos(x)\n",
"lons = 20 * np.sin(x)\n",
"colors = np.sin(5 * x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# FIXME: This example is broken!!!\n",
"\n",
"from folium import features\n",
"\n",
"m = folium.Map([0, 0], zoom_start=3)\n",
"\n",
"color_line = features.ColorLine(\n",
" list(zip(lats, lons)),\n",
" colors=colors,\n",
" colormap=['y', 'orange', 'r'],\n",
" weight=10)\n",
"\n",
"color_line.add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_0.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### WMS"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([40, -100], zoom_start=4)\n",
"\n",
"w = features.WmsTileLayer(\n",
" \"http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi\",\n",
" name='test',\n",
" format='image/png',\n",
" layers='nexrad-n0r-900913',\n",
" attr=u\"Weather data © 2012 IEM Nexrad\",\n",
" transparent=True\n",
")\n",
"\n",
"w.add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_1.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Marker, Icon, Popup"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import branca\n",
"\n",
"\n",
"f = branca.element.Figure(figsize=(8, 8))\n",
"m = folium.Map([0, 0], zoom_start=1)\n",
"mk = features.Marker([0, 0])\n",
"pp = features.Popup('hello')\n",
"ic = features.Icon(color='red')\n",
"\n",
"f.add_child(m)\n",
"mk.add_child(ic)\n",
"mk.add_child(pp)\n",
"m.add_child(mk)\n",
"\n",
"f.save(os.path.join('results', 'Features_2.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RegularPolygonMarker"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = branca.element.Figure()\n",
"m = folium.Map([0, 0], zoom_start=1)\n",
"mk = features.RegularPolygonMarker([0, 0])\n",
"mk2 = features.RegularPolygonMarker([0, 45])\n",
"\n",
"f.add_child(m)\n",
"m.add_child(mk)\n",
"m.add_child(mk2)\n",
"\n",
"f.save(os.path.join('results', 'Features_3.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega stuff"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# FIXME: This example is broken!!!\n",
"\n",
"import json\n",
"import vincent\n",
"\n",
"N = 100\n",
"\n",
"multi_iter2 = {\n",
" 'x': np.random.uniform(size=(N,)),\n",
" 'y': np.random.uniform(size=(N,)),\n",
"}\n",
"\n",
"scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=100, width=200)\n",
"data = json.loads(scatter.to_json())\n",
"\n",
"f = branca.element.Figure()\n",
"m = folium.Map([0, 0], zoom_start=1)\n",
"mk = features.Marker([0, 0])\n",
"p = features.Popup('Hello')\n",
"v = features.Vega(data, width='100%', height='100%')\n",
"\n",
"f.add_child(m)\n",
"mk.add_child(p)\n",
"p.add_child(v)\n",
"m.add_child(mk)\n",
"\n",
"f.save(os.path.join('results', 'Features_4.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega div"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 100\n",
"\n",
"multi_iter2 = {\n",
" 'x': np.random.uniform(size=(N,)),\n",
" 'y': np.random.uniform(size=(N,)),\n",
"}\n",
"\n",
"scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=400, width=600)\n",
"data = json.loads(scatter.to_json())\n",
"\n",
"f = branca.element.Figure()\n",
"v = features.Vega(data, height=40, width=600)\n",
"f.add_child(v)\n",
"\n",
"f.save(os.path.join('results', 'Features_5.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A div and a Map\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 100\n",
"\n",
"multi_iter2 = {\n",
" 'x': np.random.uniform(size=(N,)),\n",
" 'y': np.random.uniform(size=(N,)),\n",
"}\n",
"\n",
"scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=250, width=420)\n",
"data = json.loads(scatter.to_json())\n",
"\n",
"f = branca.element.Figure()\n",
"\n",
"# Create two maps.\n",
"m = folium.Map(location=[0, 0],\n",
" tiles='stamenwatercolor',\n",
" zoom_start=1,\n",
" position='absolute',\n",
" left='0%',\n",
" width='50%',\n",
" height='50%')\n",
"\n",
"m2 = folium.Map(location=[46, 3],\n",
" tiles='OpenStreetMap',\n",
" zoom_start=4,\n",
" position='absolute',\n",
" left='50%',\n",
" width='50%',\n",
" height='50%',\n",
" top='50%')\n",
"\n",
"# Create two Vega.\n",
"v = features.Vega(data, position='absolute', left='50%', width='50%', height='50%')\n",
"\n",
"v2 = features.Vega(data, position='absolute', left='0%', width='50%', height='50%', top='50%')\n",
"\n",
"f.add_child(m)\n",
"f.add_child(m2)\n",
"f.add_child(v)\n",
"f.add_child(v2)\n",
"\n",
"f.save(os.path.join('results', 'Features_6.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GeoJson"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 1000\n",
"\n",
"lons = +5 - np.random.normal(size=N)\n",
"lats = 48 - np.random.normal(size=N)\n",
"\n",
"data = {\n",
" \"type\": \"FeatureCollection\",\n",
" \"features\": [\n",
" {\n",
" \"type\": \"Feature\",\n",
" \"geometry\": {\n",
" \"type\": \"MultiPoint\",\n",
" \"coordinates\": [[lon, lat] for (lat, lon) in zip(lats, lons)],\n",
" },\n",
" \"properties\": {\"prop0\": \"value0\"}\n",
" },\n",
" ],\n",
"}\n",
"\n",
"m = folium.Map([48, 5], zoom_start=6)\n",
"m.add_child(features.GeoJson(data))\n",
"\n",
"m.save(os.path.join('results', 'Features_7.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Marker Cluster"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 100\n",
"\n",
"data = np.array(\n",
" [\n",
" np.random.uniform(low=35, high=60, size=N), # Random latitudes in Europe.\n",
" np.random.uniform(low=-12, high=30, size=N), # Random longitudes in Europe.\n",
" range(N), # Popups text will be simple numbers .\n",
" ]\n",
").T\n",
"\n",
"m = folium.Map([45, 3], zoom_start=4)\n",
"mc = features.MarkerCluster()\n",
"\n",
"for k in range(N):\n",
" mk = features.Marker([data[k][0], data[k][1]])\n",
" p = features.Popup(str(data[k][2]))\n",
" mk.add_child(p)\n",
" mc.add_child(mk)\n",
"\n",
"m.add_child(mc)\n",
"\n",
"m.save(os.path.join('results', 'Features_8.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Div"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N = 100\n",
"\n",
"multi_iter2 = {\n",
" 'x': np.random.uniform(size=(N,)),\n",
" 'y': np.random.uniform(size=(N,)),\n",
"}\n",
"\n",
"scatter = vincent.Scatter(multi_iter2, iter_idx='x', height=250, width=420)\n",
"data = json.loads(scatter.to_json())\n",
"\n",
"f = branca.element.Figure()\n",
"\n",
"d1 = f.add_subplot(1, 2, 1)\n",
"d2 = f.add_subplot(1, 2, 2)\n",
"\n",
"d1.add_child(folium.Map([0, 0], tiles='stamenwatercolor', zoom_start=1))\n",
"d2.add_child(folium.Map([46, 3], tiles='OpenStreetMap', zoom_start=5))\n",
"\n",
"f.save(os.path.join('results', 'Features_9.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LayerControl"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(tiles=None)\n",
"\n",
"folium.TileLayer('OpenStreetMap').add_to(m)\n",
"folium.TileLayer('stamentoner').add_to(m)\n",
"\n",
"folium.LayerControl().add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_10.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line example"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Coordinates are 15 points on the great circle from Boston to\n",
"# San Francisco.\n",
"# Reference: http://williams.best.vwh.net/avform.htm#Intermediate\n",
"coordinates = [\n",
" [42.3581, -71.0636],\n",
" [42.82995815, -74.78991444],\n",
" [43.17929819, -78.56603306],\n",
" [43.40320216, -82.37774519],\n",
" [43.49975489, -86.20965845],\n",
" [43.46811941, -90.04569087],\n",
" [43.30857071, -93.86961818],\n",
" [43.02248456, -97.66563267],\n",
" [42.61228259, -101.41886832],\n",
" [42.08133868, -105.11585198],\n",
" [41.4338549, -108.74485069],\n",
" [40.67471747, -112.29609954],\n",
" [39.8093434, -115.76190821],\n",
" [38.84352776, -119.13665678],\n",
" [37.7833, -122.4167]]\n",
"\n",
"\n",
"# Create the map and add the line\n",
"m = folium.Map(location=[41.9, -97.3], zoom_start=4)\n",
"folium.PolyLine(coordinates, color='#FF0000', weight=5).add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_11.html'))\n",
"\n",
"m"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}