def plot_linestring(geometry, speed_color=None, config=None):
if (type(geometry) == LineString):
geometry = gpd.GeoSeries(geometry)
# set initial coords as first point of first linestring
geometry_ = geometry.geometry[0]
initial_coords = [geometry_.xy[1][0], geometry_.xy[0][0]]
map_ = folium.Map(initial_coords, zoom_start=14)
traces_json = json.loads(geometry.buffer(.00001).to_json())
if (speed_color is not None):
if (np.mean(speed_color) > 1): # if absolute speeds
red_speed, yellow_speed, blue_speed = 0, 25, 40
cmap_caption = 'Speed Km/h'
else: # if relative speed
red_speed, yellow_speed, blue_speed = 0, .4, .8
cmap_caption = 'Relative Speed to Maximum %'
speed_cm = LinearColormap(
['red', 'yellow', 'blue'],
vmin=red_speed,
vmax=blue_speed + (blue_speed * .2), # 20% maxspeed
index=[red_speed, yellow_speed, blue_speed])
speed_cm.caption = cmap_caption
for elem, speed in zip(traces_json['features'], speed_color):
elem['properties']['style'] = {}
elem['properties']['style']['color'] = speed_cm(speed)
elem['properties']['style']['fillOpacity'] = 1
map_.add_child(speed_cm)
traces = folium.features.GeoJson(traces_json)
map_.add_child(traces)
return map_
'style': {
'color': row['Color']
}
}
}
features.append(feature)
if row["id"] in ["DNK", "FRA", "ITA", "GBR", "GRC"]:
for geometry in compl.get(row["id"]).get("geometries"):
feature_new = dict(feature)
feature_new["geometry"] = geometry
features.append(feature_new)
new_map = folium.Map([50.736455, 17.666], zoom_start=4.5)
TimestampedGeoJson({
'type': 'FeatureCollection',
'features': features
},
period='P1M',
duration='P1M',
add_last_point=True,
auto_play=False,
loop=False,
max_speed=1,
loop_button=True,
date_options='YYYY/MM',
time_slider_drag_update=True).add_to(new_map)
colormap = LinearColormap(color_scale, vmin=0, vmax=bin_edges[-1])
colormap.caption = "Monthly average " + pollutant + " (in µg/m3)"
colormap.add_to(new_map)
new_map.save("../maps/avg_" + pollutant + ".html")
def create_state_count_choropleth(df, designation):
'''
This function counts the number of parks per state and stores the
result in a dataframe. Then it creates a linear color map using the
range of parks per state. The function then reads in a GeoJSON file
of the United States and merges this with the parks per state
dataframe. These objects are then used to create a choropleth map
of the United States with state color based on the number of parks
in that state. If there are no parks in a state, it will be gray.
Parameters
----------
df : Pandas DataFrame
DataFrame of all parks to add to the map.
designation : str
Designation of parks in the dataframe.
Returns
-------
map : Folium map object
Folium map with choropleth color added.
'''
# Create a two-column dataframe of state and a count of the number
# of parks in that state.
state_list = df['states'].apply(lambda x: x.split(','))
state_list = reduce(operator.add, state_list)
parks_per_state = (pd.DataFrame.from_dict(Counter(state_list),
orient='index').reset_index())
parks_per_state = (parks_per_state.rename(columns={
'index': 'state',
0: 'park_count'
}))
# Create the color map.
color_scale = LinearColormap(['yellow', 'green', 'blue'],
vmin=parks_per_state['park_count'].min(),
vmax=parks_per_state['park_count'].max())
color_scale.caption = (
"Number of parks per state ({})".format(designation))
# Create a dataframe from the json file using GeoPandas.
df_geo = gpd.read_file('_reference_data/us-states.json')
df_geo = df_geo.merge(parks_per_state,
left_on='id',
right_on='state',
how='left').fillna(0)
# Find the center of the data and create an empty map.
centroid = df_geo.geometry.centroid
map = folium.Map(location=[centroid.y.mean(),
centroid.x.mean()],
zoom_start=3)
# Color each state based on the number of parks in it.
folium.GeoJson(
data=df_geo[['geometry', 'name', 'state', 'park_count']],
name='United States of America',
style_function=lambda x: {
'fillColor': get_state_color(x, parks_per_state, color_scale),
'fillOpacity': 0.7,
'color': 'black',
'line_opacity': 0.5,
'weight': 0.5
},
tooltip=folium.features.GeoJsonTooltip(fields=[
'name',
'park_count',
],
aliases=["State", "# of parks"
])).add_to(map)
# Add color scale legend.
map.add_child(color_scale)
# Save choropleth to file.
map.save(set_filename('choropleth_map', 'html', designation))
return map