{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7.0+12.gfdf158a.dirty\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": {},
"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": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from folium import features\n",
"\n",
"m = folium.Map([0, 0], zoom_start=3)\n",
"\n",
"\n",
"color_line = features.ColorLine(\n",
" positions=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": [
"### Marker, Icon, Popup"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([0, 0], zoom_start=1)\n",
"mk = features.Marker([0, 0])\n",
"pp = folium.Popup('hello')\n",
"ic = features.Icon(color='red')\n",
"\n",
"mk.add_child(ic)\n",
"mk.add_child(pp)\n",
"m.add_child(mk)\n",
"\n",
"m.save(os.path.join('results', 'Features_1.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega popup"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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",
"m = folium.Map([0, 0], zoom_start=1)\n",
"mk = features.Marker([0, 0])\n",
"p = folium.Popup('Hello')\n",
"v = features.Vega(data, width='100%', height='100%')\n",
"\n",
"mk.add_child(p)\n",
"p.add_child(v)\n",
"m.add_child(mk)\n",
"\n",
"m.save(os.path.join('results', 'Features_2.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega-Lite popup"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from altair import Chart, load_dataset\n",
"\n",
"# load built-in dataset as a pandas DataFrame\n",
"cars = load_dataset('cars')\n",
"\n",
"scatter = Chart(cars).mark_circle().encode(\n",
" x='Horsepower',\n",
" y='Miles_per_Gallon',\n",
" color='Origin',\n",
")\n",
"\n",
"vega = folium.features.VegaLite(\n",
" scatter,\n",
" width='100%',\n",
" height='100%',\n",
")\n",
"\n",
"m = folium.Map(location=[-27.5717, -48.6256])\n",
"\n",
"marker = folium.features.Marker([-27.57, -48.62])\n",
"\n",
"popup = folium.Popup()\n",
"\n",
"vega.add_to(popup)\n",
"popup.add_to(marker)\n",
"\n",
"marker.add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_3.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega div and a Map\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import branca\n",
"\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(\n",
" multi_iter2,\n",
" iter_idx='x',\n",
" height=250,\n",
" width=420\n",
")\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(\n",
" data,\n",
" position='absolute',\n",
" left='50%',\n",
" width='50%',\n",
" height='50%'\n",
")\n",
"\n",
"v2 = features.Vega(\n",
" data,\n",
" position='absolute',\n",
" left='0%',\n",
" width='50%',\n",
" height='50%',\n",
" top='50%'\n",
")\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_4.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vega-Lite div and a Map"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"N = 100\n",
"\n",
"multi_iter2 = pd.DataFrame({\n",
" 'x': np.random.uniform(size=(N,)),\n",
" 'y': np.random.uniform(size=(N,)),\n",
"})\n",
"\n",
"scatter = Chart(multi_iter2).mark_circle().encode(x='x', y='y')\n",
"scatter.width = 420\n",
"scatter.height = 250\n",
"data = json.loads(scatter.to_json())\n",
"\n",
"f = branca.element.Figure()\n",
"\n",
"# Create two maps.\n",
"m = folium.Map(\n",
" location=[0, 0],\n",
" tiles='stamenwatercolor',\n",
" zoom_start=1,\n",
" position='absolute',\n",
" left='0%',\n",
" width='50%',\n",
" height='50%'\n",
")\n",
"\n",
"m2 = folium.Map(\n",
" 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",
"\n",
"# Create two Vega.\n",
"v = features.VegaLite(\n",
" data,\n",
" position='absolute',\n",
" left='50%',\n",
" width='50%',\n",
" height='50%'\n",
")\n",
"\n",
"v2 = features.VegaLite(\n",
" data,\n",
" position='absolute',\n",
" left='0%',\n",
" width='50%',\n",
" height='50%',\n",
" top='50%'\n",
")\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_5.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GeoJson"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"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_6.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Div"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"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(\n",
" multi_iter2,\n",
" iter_idx='x',\n",
" height=250,\n",
" width=420\n",
")\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_7.html'))\n",
"\n",
"f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LayerControl"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(tiles=None)\n",
"\n",
"folium.raster_layers.TileLayer('OpenStreetMap').add_to(m)\n",
"folium.raster_layers.TileLayer('stamentoner').add_to(m)\n",
"\n",
"folium.LayerControl().add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Features_8.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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}