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