{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0,'..')\n",
"import folium\n",
"#import pandas as pd\n",
"#folium.initialize_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Standard OSM\n",
"map_osm = folium.Map(location=[45.5236, -122.6750])\n",
"map_osm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stamen = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
" zoom_start=13)\n",
"stamen"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mt_hood = folium.Map(location=[45.372, -121.6972], zoom_start=12,\n",
" tiles='Stamen Terrain')\n",
"folium.Marker([45.3288, -121.6625], popup='Mt. Hood Meadows').add_to(mt_hood)\n",
"folium.Marker([45.3311, -121.7113], popup='Timberline Lodge', icon=folium.Icon()).add_to(mt_hood)\n",
"mt_hood"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"portland = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
" zoom_start=13)\n",
"folium.Marker([45.5244, -122.6699], popup='The Waterfront').add_to(portland)\n",
"folium.CircleMarker([45.5215, -122.6261], radius=500,\n",
" popup='Laurelhurst Park', color='#3186cc',\n",
" fill_color='#3186cc').add_to(portland)\n",
"portland"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latlng = folium.Map(location=[46.1991, -122.1889], tiles='Stamen Terrain',\n",
" zoom_start=13)\n",
"folium.LatLngPopup().add_to(latlng)\n",
"latlng"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waypoints = folium.Map(location=[46.8527, -121.7649], tiles='Stamen Terrain',\n",
" zoom_start=13)\n",
"folium.Marker([46.8354, -121.7325], popup='Camp Muir').add_to(waypoints)\n",
"folium.ClickForMarker().add_to(waypoints)\n",
"waypoints"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polygons = folium.Map(location=[45.5236, -122.6750], zoom_start=13)\n",
"\n",
"folium.RegularPolygonMarker([45.5012, -122.6655], popup='Ross Island Bridge',\n",
" fill_color='#132b5e', number_of_sides=3, radius=10).add_to(polygons)\n",
"folium.RegularPolygonMarker([45.5132, -122.6708], popup='Hawthorne Bridge',\n",
" fill_color='#45647d', number_of_sides=4, radius=10).add_to(polygons)\n",
"folium.RegularPolygonMarker([45.5275, -122.6692], popup='Steel Bridge',\n",
" fill_color='#769d96', number_of_sides=6, radius=10).add_to(polygons)\n",
"folium.RegularPolygonMarker([45.5318, -122.6745], popup='Broadway Bridge',\n",
" fill_color='#769d96', number_of_sides=8, radius=10).add_to(polygons)\n",
"polygons"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import vincent\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"NOAA_46041 = pd.read_csv(r'NOAA_46041.csv', index_col=3,\n",
" parse_dates=True)\n",
"NOAA_46050 = pd.read_csv(r'NOAA_46050_WS.csv', index_col=3,\n",
" parse_dates=True)\n",
"NOAA_46243 = pd.read_csv(r'NOAA_46243.csv', index_col=3,\n",
" parse_dates=True)\n",
"\n",
"NOAA_46041 = NOAA_46041.dropna()\n",
"\n",
"#Binned wind speeds for NOAA 46050\n",
"bins = range(0, 13, 1)\n",
"cuts = pd.cut(NOAA_46050['wind_speed_cwind (m/s)'], bins)\n",
"ws_binned = pd.Series(\n",
" np.histogram(NOAA_46050['wind_speed_cwind (m/s)'], bins=bins)[0],\n",
" index=bins[:-1])\n",
"\n",
"#NOAA 46401 Wave Period\n",
"vis1 = vincent.Line(NOAA_46041['dominant_wave_period (s)'],\n",
" width=400, height=200)\n",
"vis1.axis_titles(x='Time', y='Dominant Wave Period (s)')\n",
"vis1.to_json('vis1.json')\n",
"\n",
"#NOAA 46050 Binned Wind Speed\n",
"vis2 = vincent.Bar(ws_binned, width=400, height=200)\n",
"vis2.axis_titles(x='Wind Speed (m/s)', y='# of Obs')\n",
"vis2.to_json('vis2.json')\n",
"\n",
"#NOAA 46243 Wave Height\n",
"vis3 = vincent.Area(NOAA_46243['significant_wave_height (m)'],\n",
" width=400, height=200)\n",
"vis3.axis_titles(x='Time', y='Significant Wave Height (m)')\n",
"vis3.to_json('vis3.json')\n",
"\n",
"#Map all buoys\n",
"buoy_map = folium.Map(location=[46.3014, -123.7390], zoom_start=7,\n",
" tiles='Stamen Terrain')\n",
"\n",
"popup1 = folium.Popup(max_width=800,\n",
" ).add_child(folium.Vega(vis1, width=500, height=250))\n",
"folium.RegularPolygonMarker([47.3489, -124.708],\n",
" fill_color='#43d9de',\n",
" radius=12,\n",
" popup=popup1).add_to(buoy_map)\n",
"\n",
"popup2 = folium.Popup(max_width=800,\n",
" ).add_child(folium.Vega(vis2, width=500, height=250))\n",
"folium.RegularPolygonMarker([44.639, -124.5339],\n",
" fill_color='#43d9de',\n",
" radius=12,\n",
" popup=popup2).add_to(buoy_map)\n",
"\n",
"popup3 = folium.Popup(max_width=800,\n",
" ).add_child(folium.Vega(vis3, width=500, height=250))\n",
"folium.RegularPolygonMarker([46.216, -124.1280],\n",
" fill_color='#43d9de',\n",
" radius=12,\n",
" popup=popup3).add_to(buoy_map)\n",
"\n",
"buoy_map"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/journois/anaconda3/envs/test/lib/python3.5/site-packages/ipykernel/__main__.py:12: FutureWarning: 'threshold_scale' default behavior has changed. Now you get a linear scale between the 'min' and the 'max' of your data. To get former behavior, use folium.utilities.split_six.\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_geo = r'us-states.json'\n",
"state_unemployment = r'US_Unemployment_Oct2012.csv'\n",
"\n",
"state_data = pd.read_csv(state_unemployment)\n",
"\n",
"#Let Folium determine the scale\n",
"states = folium.Map(location=[48, -102], zoom_start=3)\n",
"states.choropleth(geo_path=state_geo, data=state_data,\n",
" columns=['State', 'Unemployment'],\n",
" key_on='feature.id',\n",
" fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,\n",
" legend_name='Unemployment Rate (%)')\n",
"\n",
"states"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"states2 = folium.Map(location=[48, -102], zoom_start=3)\n",
"states2.choropleth(geo_path=state_geo, data=state_data,\n",
" columns=['State', 'Unemployment'],\n",
" threshold_scale=[5, 6, 7, 8, 9, 10],\n",
" key_on='feature.id',\n",
" fill_color='BuPu', fill_opacity=0.7, line_opacity=0.5,\n",
" legend_name='Unemployment Rate (%)',\n",
" reset=True)\n",
"states2"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"county_data = r'us_county_data.csv'\n",
"county_geo = r'us_counties_20m_topo.json'\n",
"\n",
"df = pd.read_csv(county_data, na_values=[' '])\n",
"\n",
"\n",
"colorscale = folium.colormap.linear.YlOrRd.scale(0,50e3)\n",
"employed_series = df.set_index('FIPS_Code')['Employed_2011']\n",
"\n",
"def style_function(feature):\n",
" employed = employed_series.get(int(feature['id'][-5:]),None)\n",
" return {\n",
" 'fillOpacity' : 0.5,\n",
" 'weight' : 0,\n",
" 'fillColor' : '#black' if employed is None else colorscale(employed)\n",
" }\n",
"\n",
"#Number of employed with auto scale\n",
"map_1 = folium.Map(location=[48, -102], tiles='cartodbpositron',\n",
" zoom_start=3)\n",
"\n",
"folium.TopoJson(open(county_geo),\n",
" 'objects.us_counties_20m',\n",
" style_function=style_function).add_to(map_1)\n",
"\n",
"map_1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colorscale = folium.colormap.linear.YlGnBu.scale(0,30)\n",
"employed_series = df.set_index('FIPS_Code')['Unemployment_rate_2011']\n",
"\n",
"def style_function(feature):\n",
" employed = employed_series.get(int(feature['id'][-5:]),None)\n",
" return {\n",
" 'fillOpacity' : 0.5,\n",
" 'weight' : 0,\n",
" 'fillColor' : '#black' if employed is None else colorscale(employed)\n",
" }\n",
"\n",
"#Number of employed with auto scale\n",
"map_2 = folium.Map(location=[48, -102], tiles='cartodbpositron',\n",
" zoom_start=3)\n",
"\n",
"folium.TopoJson(open(county_geo),\n",
" 'objects.us_counties_20m',\n",
" style_function=style_function).add_to(map_2)\n",
"\n",
"map_2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colorscale = folium.colormap.linear.PuRd.scale(0,100000)\n",
"employed_series = df.set_index('FIPS_Code')['Median_Household_Income_2011'].dropna()\n",
"\n",
"def style_function(feature):\n",
" employed = employed_series.get(int(feature['id'][-5:]),None)\n",
" return {\n",
" 'fillOpacity' : 0.5,\n",
" 'weight' : 0,\n",
" 'fillColor' : '#black' if employed is None else colorscale(employed)\n",
" }\n",
"\n",
"#Number of employed with auto scale\n",
"map_3 = folium.Map(location=[48, -102], tiles='cartodbpositron',\n",
" zoom_start=3)\n",
"\n",
"folium.TopoJson(open(county_geo),\n",
" 'objects.us_counties_20m',\n",
" style_function=style_function).add_to(map_3)\n",
"\n",
"map_3"
]
}
],
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"language_info": {
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