{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import folium\n",
"import pandas as pd\n",
"import json\n",
"\n",
"\n",
"url = (\"https://raw.githubusercontent.com/python-visualization/\"\n",
" \"folium/master/examples/data\")\n",
"\n",
"us_states = f\"{url}/us-states.json\"\n",
"US_Unemployment_Oct2012 = f\"{url}/US_Unemployment_Oct2012.csv\"\n",
"\n",
"state_data = pd.read_csv(US_Unemployment_Oct2012)\n",
"\n",
"m = folium.Map(location=[48, -102], zoom_start=3)\n",
"folium.Choropleth(\n",
" geo_data=us_states,\n",
" data=state_data,\n",
" columns=[\"State\", \"Unemployment\"],\n",
" key_on=\"feature.id\",\n",
" fill_color=\"YlGn\",\n",
" fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" legend_name=\"Unemployment Rate (%)\",\n",
" highlight=True\n",
").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from folium.plugins import Draw\n",
"\n",
"\n",
"m = folium.Map(\n",
" location=[-27.23, -48.36],\n",
" zoom_start=10,\n",
" tiles=\"https://server.arcgisonline.com/ArcGIS/rest/services/Ocean_Basemap/MapServer/tile/{z}/{y}/{x}\",\n",
"\tattr=\"Tiles © Esri — Sources: GEBCO, NOAA, CHS, OSU, UNH, CSUMB, National Geographic, DeLorme, NAVTEQ, and Esri\",\n",
")\n",
"\n",
"draw = Draw(export=True)\n",
"draw.add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from owslib.wms import WebMapService\n",
"\n",
"\n",
"url = \"http://oos.soest.hawaii.edu/thredds/wms/hioos/satellite/dhw_5km\"\n",
"\n",
"web_map_services = WebMapService(url)\n",
"\n",
"layer = \"CRW_SST\"\n",
"wms = web_map_services.contents[layer]\n",
"\n",
"name = wms.title\n",
"\n",
"lon = (wms.boundingBox[0] + wms.boundingBox[2]) / 2.\n",
"lat = (wms.boundingBox[1] + wms.boundingBox[3]) / 2.\n",
"center = lat, lon\n",
"\n",
"time_interval = \"{0}/{1}\".format(\n",
" wms.timepositions[0].strip(),\n",
" wms.timepositions[-1].strip()\n",
")\n",
"style = \"boxfill/sst_36\"\n",
"\n",
"if style not in wms.styles:\n",
" style = None"
]
},
{
"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(\n",
" location=[lat, lon],\n",
" zoom_start=5,\n",
" control_scale=True,\n",
" tiles=\"Stamen Toner\",\n",
")\n",
"\n",
"w = folium.raster_layers.WmsTileLayer(\n",
" url=url,\n",
" name=\"sea_surface_temperature\",\n",
" styles=style,\n",
" fmt=\"image/png\",\n",
" transparent=True,\n",
" layers=\"CRW_SST\",\n",
" overlay=True,\n",
").add_to(m)\n",
"\n",
"time = folium.plugins.TimestampedWmsTileLayers(\n",
" [w],\n",
" period=\"PT1H\",\n",
" time_interval=time_interval,\n",
")\n",
"\n",
"time.add_to(m)\n",
"\n",
"folium.LayerControl().add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df = pd.DataFrame(data=[[\"apple\", \"oranges\"], [\"other\", \"stuff\"]], columns=[\"cats\", \"dogs\"])\n",
"\n",
"m = folium.Map(\n",
" [43, -100],\n",
" zoom_start=4,\n",
" tiles=\"cartodbpositron\",\n",
")\n",
"\n",
"html = df.to_html(\n",
" classes=(\n",
" \"table table-striped \"\n",
" \"table-hover \"\n",
" \"table-condensed \"\n",
" \"table-responsive\"\n",
" )\n",
")\n",
"\n",
"popup = folium.Popup(html)\n",
"\n",
"folium.Marker([30, -100], popup=popup).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.plugins.DualMap(\n",
" location=(52.1, 5.1),\n",
" tiles=None,\n",
" zoom_start=8,\n",
")\n",
"\n",
"folium.TileLayer(\"Stamen Watercolor\").add_to(m.m1)\n",
"folium.TileLayer(\"cartodbpositron\").add_to(m.m2)\n",
"\n",
"folium.LayerControl(collapsed=False).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lat = +38.89399\n",
"lon = -77.03659\n",
"zoom_start = 17\n",
"\n",
"m = folium.Map(\n",
" tiles=\"Stamen Terrain\",\n",
" location=[lat, lon],\n",
" zoom_start=zoom_start\n",
")\n",
"\n",
"kw = {\n",
" \"color\": \"red\",\n",
" \"fill\": True,\n",
" \"radius\": 20\n",
"}\n",
"\n",
"folium.CircleMarker([38.89415, -77.03738], **kw).add_to(m)\n",
"folium.CircleMarker([38.89415, -77.03578], **kw).add_to(m)\n",
"\n",
"\n",
"locations = [\n",
" [(38.893596444352134, -77.03814983367920), (38.893379333722040, -77.03792452812195)],\n",
" [(38.893379333722040, -77.03792452812195), (38.893162222428310, -77.03761339187622)],\n",
" [(38.893162222428310, -77.03761339187622), (38.893028615148424, -77.03731298446655)],\n",
" [(38.893028615148424, -77.03731298446655), (38.892920059048464, -77.03691601753235)],\n",
" [(38.892920059048464, -77.03691601753235), (38.892903358095296, -77.03637957572937)],\n",
" [(38.892903358095296, -77.03637957572937), (38.893011914220770, -77.03592896461487)],\n",
" [(38.893011914220770, -77.03592896461487), (38.893162222428310, -77.03549981117249)],\n",
" [(38.893162222428310, -77.03549981117249), (38.893404384982480, -77.03514575958252)],\n",
" [(38.893404384982480, -77.03514575958252), (38.893596444352134, -77.03496336936950)]\n",
"]\n",
"\n",
"folium.PolyLine(\n",
" locations=locations,\n",
" color=\"orange\",\n",
" weight=8,\n",
" opacity=1,\n",
" smooth_factor=0,\n",
").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from cartopy.io.shapereader import natural_earth\n",
"\n",
"import geopandas\n",
"\n",
"\n",
"fname = natural_earth(\n",
" name=\"admin_0_countries\",\n",
" resolution=\"10m\",\n",
" category=\"cultural\"\n",
")\n",
"gdf = geopandas.read_file(fname)\n",
"\n",
"south_america = gdf[gdf[\"CONTINENT\"] == \"South America\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from shapely.ops import unary_union\n",
"\n",
"\n",
"geometries = [\n",
" country[\"geometry\"] for k, country in south_america.iterrows()\n",
"]\n",
"sa = unary_union(geometries)\n",
"sa.boundary"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"fname = natural_earth(\n",
" name=\"rivers_lake_centerlines\",\n",
" resolution=\"10m\",\n",
" category=\"physical\"\n",
")\n",
"\n",
"rivers = geopandas.read_file(fname)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Ajajú',\n",
" 'Amapari',\n",
" 'Amazonas',\n",
" 'Apure',\n",
" 'Araguaia',\n",
" 'Aripuanã',\n",
" 'Atrato',\n",
" 'Beni',\n",
" 'Bermejo',\n",
" 'Bois',\n",
" 'Braco Menor',\n",
" 'Branco',\n",
" 'BÃ\\xado-BÃ\\xado',\n",
" 'Caquetá',\n",
" 'Carcarañá',\n",
" 'CaronÃ\\xad',\n",
" 'Casiquiare',\n",
" 'Cauca',\n",
" 'Chico',\n",
" 'Chubut',\n",
" 'Coari',\n",
" 'Coco',\n",
" 'Cojedes',\n",
" 'Colorado',\n",
" 'Contas',\n",
" 'Copiapó',\n",
" 'Corantijn',\n",
" 'Corumbá',\n",
" 'Courantyne',\n",
" 'Culuene',\n",
" 'Cuyuni',\n",
" 'Desaguadero',\n",
" 'Deseado',\n",
" 'Doce',\n",
" 'Dulce',\n",
" 'Essequibo',\n",
" 'Gallegos',\n",
" 'Grande',\n",
" 'GuainÃ\\xada',\n",
" 'Guaporé',\n",
" 'Gurupi',\n",
" 'Guárico',\n",
" 'Huallaga',\n",
" 'IbicuÃ\\xad',\n",
" 'Indaiá',\n",
" 'Iriri',\n",
" 'Itiquira',\n",
" 'Ituxi',\n",
" 'IvaÃ\\xad',\n",
" 'Ivinheima',\n",
" 'Jamanxim',\n",
" 'Japurá',\n",
" 'Jari',\n",
" 'Javari',\n",
" 'JejuÃ\\xad Guazú',\n",
" 'Jequitinhonha',\n",
" 'Jiparaná',\n",
" 'Juruena',\n",
" 'Juruá',\n",
" 'JutaÃ\\xad',\n",
" 'Kabalebo',\n",
" 'Lempa',\n",
" 'Limay',\n",
" 'Litani',\n",
" 'Madeira',\n",
" 'Madre de Dios',\n",
" 'Magdalena',\n",
" 'Maicuru',\n",
" 'Mamoré',\n",
" 'Marañón',\n",
" 'Maroni',\n",
" 'Mearim',\n",
" 'Mendoza',\n",
" 'Meta',\n",
" 'Mico',\n",
" 'Mira',\n",
" 'Miranda',\n",
" 'Napo',\n",
" 'Negro',\n",
" 'Neuquén',\n",
" 'Nhamundá',\n",
" None,\n",
" 'Ocoña',\n",
" 'Orinoco',\n",
" 'Panama Canal',\n",
" 'Paraiba',\n",
" 'Paranapanema',\n",
" 'ParanaÃ\\xadba',\n",
" 'Paraná',\n",
" 'Pardo',\n",
" 'ParnaÃ\\xadba',\n",
" 'PiauÃ\\xad',\n",
" 'Pilcomayo',\n",
" 'Pisco',\n",
" 'Purús',\n",
" 'Putumayo',\n",
" 'Ribeira',\n",
" 'Rio Negro',\n",
" 'Rio Rapel',\n",
" 'Rio das Mortes',\n",
" 'RÃ\\xado Chirripó del Atlántico',\n",
" 'RÃ\\xado Grande de Matagalpa',\n",
" 'RÃ\\xado Negro',\n",
" 'Salado',\n",
" 'San Juan',\n",
" 'San Martin',\n",
" 'San Miguel',\n",
" 'Santa',\n",
" 'Santa Cruz',\n",
" 'Santa Maria',\n",
" 'Santiago',\n",
" 'Suriname',\n",
" 'São Francisco',\n",
" 'Tambo',\n",
" 'Tapajós',\n",
" 'Taquari',\n",
" 'Teles Pires',\n",
" 'Tercero',\n",
" 'Tietê',\n",
" 'Tocantins',\n",
" 'Trombetas',\n",
" 'Tumbes',\n",
" 'Ucayali',\n",
" 'Una',\n",
" 'Unini',\n",
" 'Uruguay',\n",
" 'Vaupés',\n",
" 'Velhas',\n",
" 'Ventuari',\n",
" 'Verde',\n",
" 'Xingu',\n",
" 'Yapacani'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from shapely.geometry import box\n",
"\n",
"sasq = box(*sa.bounds)\n",
"\n",
"mask = []\n",
"for k, river in rivers.iterrows():\n",
" geom = river[\"geometry\"]\n",
" if geom:\n",
" ans = sasq.contains(geom)\n",
" else:\n",
" ans = False\n",
" mask.append(ans)\n",
" \n",
"\n",
"sa_rivers = rivers[mask]\n",
"\n",
"# I ? unicode!\n",
"set(sa_rivers[\"name\"])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"include = {\n",
" 2: \"Amazonas\",\n",
" 78: \"Negro\",\n",
" 91: \"Madeira\",\n",
" 99: \"Paraná\",\n",
" 100: \"Araguaia\",\n",
" 104: \"São Francisco\",\n",
" 108: \"ParanaÃba\",\n",
" 115: \"Tocantins\",\n",
" 197: \"Xingu\",\n",
" 200: \"Tapajós\",\n",
" 284: \"Jequitinhonha\",\n",
" 293: \"Uruguay\",\n",
" 335: \"Tocantins\",\n",
" 400: \"Paranapanema\",\n",
" 436: \"ParnaÃba\",\n",
" 515: \"Doce\",\n",
" 688: \"Tietê\",\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for rivernum, name in include.items():\n",
" rivers.loc[rivers[\"rivernum\"] == rivernum, \"name\"] = name"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def invert(coords):\n",
" if hasattr(coords, '__iter__'):\n",
" if hasattr(coords[0], '__iter__'):\n",
" return list(map(invert, coords))\n",
" else:\n",
" return list(coords[::-1])\n",
" else:\n",
" return coords\n",
"\n",
"\n",
"def polyline(geom):\n",
" line = unary_union(geom)\n",
" if isinstance(geom, MultiLineString):\n",
" line = linemerge(line)\n",
" coords = line.__geo_interface__['coordinates']\n",
" return invert(coords)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from shapely.ops import unary_union, linemerge\n",
"from shapely.geometry import MultiLineString\n",
"from shapely.geometry import LineString\n",
"\n",
"import folium\n",
"from folium.plugins import Fullscreen\n",
"from folium.plugins import AntPath\n",
"\n",
"\n",
"url = \"https://{s}.tile.opentopomap.org/{z}/{x}/{y}.png\"\n",
"attr = \"Map data: © OpenStreetMap, SRTM | Map style: © OpenTopoMap (CC-BY-SA)\"\n",
"\n",
"m = folium.Map(tiles=url, attr=attr)\n",
"Fullscreen(position=\"topright\", force_separate_button=True).add_to(m)\n",
"\n",
"for name in set(include.values()):\n",
" geom = rivers.loc[rivers[\"name\"] == name, \"geometry\"].squeeze()\n",
" if name in [\"Amazonas\", \"Tocantins\"]:\n",
" reverse = True\n",
" else:\n",
" reverse = False\n",
" AntPath(\n",
" locations=polyline(geom),\n",
" tooltip=f\"{name}\",\n",
" reverse=reverse,\n",
" ).add_to(m)\n",
" \n",
"\n",
"m.fit_bounds(m.get_bounds())\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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}