def save_map(date):
valencia = [39.4561165311493, -0.3545661635]
mapa = Map(location=valencia, tiles='OpenStreetMap', zoom_start=10)
GeoJson(open('voronoi.json'), name='Diagrama de Voronoi').add_to(mapa)
GeoJson(open('estaciones_de_recarga_' + date + '.json'),
name='Estaciones de Recarga').add_to(mapa)
LayerControl().add_to(mapa)
mapa.save('valencia_' + date + '.html')
def html_map(contiguity_fname, html_out_fname, json_out_fname):
"""
Create HTML and GeoJSON files showing the valid geometry
for the input contiguity image.
:param contiguity_fname:
A full file pathname to the image representing the data
contiguity.
:param html_out_fname:
A full file pathname that will contain the html bounday map.
:param json_out_fname:
A full file pathname that will contain the json bounday map.
:return:
None; Outputs will be saved directly to disk.
"""
# remove any previous creations
try:
os.remove(html_out_fname)
except OSError:
pass
try:
os.remove(json_out_fname)
except OSError:
pass
logging.info("Create valid bounds " + json_out_fname)
geom, crs = valid_region(contiguity_fname)
gpdsr = gpd.GeoSeries([geom])
gpdsr.crs = crs
gpdsr = gpdsr.to_crs({'init': 'epsg:4326'})
gpdsr.to_file(json_out_fname, driver='GeoJSON')
m = folium.Map()
def style_function(*args):
return {'fillColor': None, 'color': '#0000ff'}
with open(json_out_fname, 'r') as src:
# Manual handling of json load for multi-version support of folium
_geojson = json.load(src)
GeoJson(_geojson, name='bounds.geojson',
style_function=style_function).add_to(m)
# TODO - add MGRS tile reference to map with layer active = False
m.fit_bounds(GeoJson(gpdsr).get_bounds())
folium.LatLngPopup().add_to(m)
folium.LayerControl().add_to(m)
m.save(html_out_fname)
def draw_jam_lines(jam_data, folium_map) -> None:
for i, row in jam_data.iterrows():
geojson = GeoJson(
row['path.street_coord'].replace('\'', '\"'),
style_function=lambda f: {
'fillColor': '#FF0000',
'color': '#FF0000',
'weight': 4,
'fillOpacity': 0.1,
}
)
geojson.add_to(folium_map)
def draw_CDBG_map(self):
self.CDBG_map = Map(location=[42.795390191429625, -71.07516023514027], zoom_start=12)
data = self.preprocess.get_CDBG_geometry_data()
geo_json = GeoJson(
data,
style_function=lambda feature: {
'fillColor': '#ffff00',
'color': 'black',
'weight': 1,
},
control=False
)
geo_json.add_to(self.CDBG_map)
def draw_refuse_routes_map(self, refuse_routes_file_path: string = 'resource/data/Hav_Refuse_Routes_WGS84'
'/Hav_Refuse_Routes_WGS84.json'):
geo_map = Map(location=[42.795390191429625, -71.07516023514027], zoom_start=12)
data = self.preprocess.get_refuse_routes_data(refuse_routes_file_path)
keys_pool = [
'MONDAY - Red Week',
'TUESDAY - Red Week',
'WEDNESDAY - Red Week',
'THURSDAY - Red Week',
'FRIDAY - Red Week',
'MONDAY - Blue Week',
'TUESDAY - Blue Week',
'WEDNESDAY - Blue Week',
'THURSDAY - Blue Week',
'FRIDAY - Blue Week',
'MERCANTILE - Every Friday'
]
for key in keys_pool:
feature_group = FeatureGroup(name=key, show=True)
if key.endswith('Red Week'):
color = 'red'
elif key.endswith('Blue Week'):
color = 'blue'
else:
color = 'black'
for each in data[key]:
geo_json = GeoJson(
data=each,
style_function=lambda feature, color=color: {
'fillColor': '#A9A9A9',
'color': color,
'weight': 3,
},
highlight_function=lambda feature, color=color: {
'fillColor': '#FFBFFF',
'color': color,
'weight': 4,
},
tooltip=key,
overlay=True
)
geo_json.add_to(feature_group)
feature_group.add_to(geo_map)
LayerControl().add_to(geo_map)
cur_time = time.strftime('%m_%d_%Y_%H_%M_%S', time.localtime())
file_name = 'refuse_routes_{}.html'.format(cur_time)
geo_map.save(os.path.join(self.refuse_routes_directory_path, file_name))
def add_contour(self, contour: Union[Polygon, MultiPolygon,
str]) -> SpatialPlot:
"""
:param contour: either a polygon object or a string (state acronym)
if it's a string, this method will pull the contour with get_state_contour() function
and plot it.
"""
if isinstance(contour, str):
contour_polygon = get_state_contours(contour)
elif isinstance(contour, MultiPolygon) or isinstance(contour, Polygon):
contour_polygon = contour
else:
raise TypeError(
'this method only supports state name or polygon object from shapely'
)
min_x, min_y, max_x, max_y = contour_polygon.bounds
make_df = DataFrame({'LAT': [min_y, max_y], 'LON': [min_x, max_x]})
self.update_locations(new_data=make_df, lat_col='LAT', lon_col='LON')
[centroid_] = list(contour_polygon.centroid.coords)
longitude, latitude = centroid_
self.initialize_canvass(center=[
latitude, longitude
]) # no need to infer the center this time just use centroid
GeoJson(contour_polygon).add_to(self.canvass)
return self
def plot_basemap_region_fill(df_boundaries_zones, initial_map=None):
"""On a folium map, add the boundaries of the geometries in geojson formatted
column of df_boundaries zones"""
if initial_map is None:
initial_map = base_empty_map()
feature_group = folium.FeatureGroup(name='Boundaries')
for i, row in df_boundaries_zones.iterrows():
feature_sel = Feature(geometry=row["geom_geojson"], id=str(i))
feat_collection_sel = FeatureCollection([feature_sel])
geojson_subzone = json.dumps(feat_collection_sel)
GeoJson(
geojson_subzone,
style_function=lambda feature: {
'fillColor': None,
'color': 'blue',
'opacity': 0.5,
'weight': 3,
'fillOpacity': 0
}
).add_to(feature_group)
feature_group.add_to(initial_map)
return initial_map
def add_park(park, popup, feature_group, icon):
geom_raw = parks[park]
gc_raw = GeometryCollection([shape(geom_raw)])
gc_simple = GeometryCollection.simplify(
gc_raw, 0.01
) # I *think* the tolerance units are in degrees latitude/longitude
geojson_simple = GeoJson(mapping(gc_simple))
geojson_simple.add_to(feature_group)
centroid = gc_raw.centroid
Marker(
location=(centroid.y, centroid.x),
popup=popup,
icon=icon,
).add_to(feature_group)
def add_layers(dictobject):
for geodataframe in dictobject:
local = dictobject[geodataframe]
fg = folium.FeatureGroup(name=geodataframe)
fg.layer_name = geodataframe
try:
data = local['data']
for x, y, charge, time, date in zip(data['x_coord'],
data['y_coord'],
data['charge'], data['time'],
data['date']):
folium.Marker(
[y, x],
popup=
f"Category: {geodataframe}<br>Charge: {charge}<br>Date: {pd.to_datetime(date).strftime('''%b %d, %Y''')}"
+ '<br>' + 'Time: ' + time,
icon=folium.Icon(color=local['color'])).add_to(fg)
# Handling non-point objects(our census tract polygon layers), and adding them to the feature group (fg).
except KeyError:
GeoJson(
local,
overlay=True,
highlight_function=lambda feature: {
'weight':
3,
'fillOpacity':
0.8,
'fillColor':
color_dict[str(feature['properties']['NUMPOINTS'])],
'color':
"#5D0C0E",
'borderOpacity':
0.0,
},
style_function=lambda feature: {
'weight':
2,
'fillOpacity':
0.6,
'fillColor':
color_dict[str(feature['properties']['NUMPOINTS'])],
'color':
"#423e41",
'borderOpacity':
0.0,
},
smooth_factor=2.0,
).add_to(fg)
# adding a popup to show the details of each tract.
for geo, pts in zip(local.geometry, local['NUMPOINTS']):
# Adding a marker with the details for each polygon.
folium.Marker(
[geo.centroid.y, geo.centroid.x],
popup=
f"{pts} {'fatalities' if pts != 1 else 'fatality'} in this tract during {geodataframe[:4]}",
icon=folium.Icon(icon='bookmark',
color='darkblue')).add_to(fg)
map.add_child(fg, name=geodataframe)
def draw_score_markers(data, folium_map, congestions_toggle):
cmap = plt.get_cmap('RdYlBu_r')
# Sort by score to get higher score markers on top of the map
data = sorted(data, key=lambda k: k['score'], reverse=congestions_toggle)
norm = colors.Normalize(vmin=-1, vmax=1)
placed_markers = set()
try:
for row in data:
color = convert_to_hex(cmap(norm(row['score'])))
tooltip = 'Location: {}<br />Attribute: {}<br />Time: {}<br />Score: {}'.format(
row['location'].replace('{', '').replace('`', '').replace('}', ''),
row['attribute_name'],
row['time_point'],
row['score'])
# Only place a marker per location
# TODO: change to max/min per location
if row['location'] in placed_markers:
continue
if row['dataset'] == 'espiras':
pmarker = CircleMarker(location=json.loads(row['location_coor']), radius=8, line_color=color,
color=color,
fill_color=color,
tooltip=tooltip)
pmarker.add_to(folium_map)
else:
if isinstance(row['location_coor'], dict):
row['location_coor'] = str(row['location_coor'])
geojson = GeoJson(
row['location_coor'].replace("\'", "\""),
style_function=lambda f, color=color: {
'fillColor': color,
'color': color,
'weight': 4,
'fillOpacity': 0.7
},
tooltip=tooltip
)
geojson.add_to(folium_map)
placed_markers.add(row['location'])
except Exception as e:
print(e)
def get_population_feature_group() -> FeatureGroup:
population_feature_group = FeatureGroup('Population')
population_feature_group.add_child(
GeoJson(
data=open('world.json', mode='r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 20000000 else 'red'
}))
return population_feature_group
def choropleth_map(df_aggreg, hex_id_field, geometry_field, value_field,
layer_name, initial_map=None, kind="filled_nulls",
border_color='#ffffff', fill_opacity=0.4,
border_opacity=0.3, with_legend=False):
"""Plots a Choropleth Map with folium"""
if initial_map is None:
initial_map = base_empty_map()
# the custom colormap depends on the map kind
if kind == "linear":
min_value = df_aggreg[value_field].min()
max_value = df_aggreg[value_field].max()
custom_cm = cm.LinearColormap(['green', 'yellow', 'red'],
vmin=min_value,
vmax=max_value)
elif kind == "outlier":
# for outliers, values would be -1,0,1
custom_cm = cm.LinearColormap(['blue', 'white', 'red'],
vmin=-1, vmax=1)
elif kind == "filled_nulls":
min_value = df_aggreg[df_aggreg[value_field] > 0][value_field].min()
max_value = df_aggreg[df_aggreg[value_field] > 0][value_field].max()
# m = round((min_value + max_value) / 2, 0)
m = (min_value + max_value) / 2.0
custom_cm = cm.LinearColormap(['silver', 'green', 'yellow', 'red'],
index=[0, min_value, m, max_value],
vmin=min_value,
vmax=max_value)
else:
custom_cm = None
# create geojson data from dataframe
geojson_data = hexagons_dataframe_to_geojson(df_aggreg, hex_id_field,
geometry_field, value_field)
# plot on map
GeoJson(
geojson_data,
style_function=lambda feature: {
'fillColor': custom_cm(feature['properties']['value']),
'color': border_color,
'opacity': border_opacity,
'weight': 0.2,
'fillOpacity': fill_opacity
},
name=layer_name
).add_to(initial_map)
# add legend (not recommended if multiple layers)
if with_legend is True:
custom_cm.add_to(initial_map)
return initial_map
def population(path):
"""
str -> None
This function add population child to the html map
"""
fg_pp.add_child(
GeoJson(
data=open(path, 'r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 20000000 else 'red'
}))
def choropleth_map(df_agg, value_col, name, border_color='black',
fill_opacity=0.7, initial_map=None, with_legend=False,
kind='linear'):
min_value = df_agg[value_col].min()
max_value = df_agg[value_col].max()
m = round((min_value + max_value) / 2, 0)
res = h3.h3_get_resolution(df_agg.loc[0, 'hex_id'])
if initial_map is None:
initial_map = Map(
location=[39.970208, -83.000645],
zoom_start=13,
tiles='cartodbpositron',
attr=(
'© <a href="http://www.openstreetmap.org/copyright">' +
'OpenStreetMap</a> contributors © <a href="http://cartodb.' +
'com/attributions#basemaps">CartoDB</a>'
)
)
if kind == 'linear':
custom_cm = cm.LinearColormap(
['green', 'yellow', 'red'],
vmin=min_value,
vmax=max_value)
elif kind == 'outlier':
custom_cm = cm.LinearColormap(
['blue', 'white', 'red'],
vmin=min_value,
vmax=max_value)
geojson_data = hexagons_dataframe_to_geojson(
df_hex=df_agg, value_col=value_col)
GeoJson(
geojson_data,
style_function=lambda feature: {
'fillColor': custom_cm(feature['properties']['value']),
'color': border_color,
'weight': 1,
'fillOpacity': fill_opacity
},
name=name
).add_to(initial_map)
if with_legend is True:
custom_cm.add_to(initial_map)
return initial_map
def draw_precincts_wards_map(self):
self.precincts_wards_map = Map(location=[42.795390191429625, -71.07516023514027], zoom_start=12)
data = self.preprocess.get_precincts_wards_geometry_data()
for i in range(len(data)):
ward_info = data[i]
precincts = ward_info['data']
for j in range(len(precincts)):
precinct = precincts[j]
geo_json = GeoJson(
data=precinct,
style_function=lambda feature: {
'fillColor': '#A9A9A9',
'color': 'black',
'weight': 1,
},
highlight_function=lambda feature: {
'fillColor': '#FFBFFF',
'color': 'yellow',
'weight': 2,
},
tooltip=GeoJsonTooltip(fields=['Precinct']),
control=False
)
geo_json.add_to(self.precincts_wards_map)
def map_seattle(date_idx):
date_idx = int(date_idx)
linear = cm.linear.RdYlGn_06
seattle_neighborhoods = folium.Map(location=[47.61, -122.3321],
zoom_start=11,tiles='cartodbpositron')
GeoJson(mcpp_neighoborhoods,
style_function=lambda feature: {
'fillColor': linear(my_color_function(feature, date_idx)),
'fillOpacity': 0.45,
'color': 'gray',
'dashArray': '2, 5'}).add_to(seattle_neighborhoods)
mapdata = BytesIO()
seattle_neighborhoods.save(mapdata, close_file=False)
html = mapdata.getvalue()
return html
def test_geojson_find_identifier():
def _create(*properties):
return {"type": "FeatureCollection", "features": [
{"type": "Feature", "properties": item}
for item in properties]}
def _assert_id_got_added(data):
_geojson = GeoJson(data)
assert _geojson.find_identifier() == 'feature.id'
assert _geojson.data['features'][0]['id'] == '0'
data_with_id = _create(None, None)
data_with_id['features'][0]['id'] = 'this-is-an-id'
data_with_id['features'][1]['id'] = 'this-is-another-id'
geojson = GeoJson(data_with_id)
assert geojson.find_identifier() == 'feature.id'
assert geojson.data['features'][0]['id'] == 'this-is-an-id'
data_with_unique_properties = _create(
{'property-key': 'some-value'},
{'property-key': 'another-value'},
)
geojson = GeoJson(data_with_unique_properties)
assert geojson.find_identifier() == 'feature.properties.property-key'
data_with_unique_properties = _create(
{'property-key': 42},
{'property-key': 43},
{'property-key': 'or a string'},
)
geojson = GeoJson(data_with_unique_properties)
assert geojson.find_identifier() == 'feature.properties.property-key'
# The test cases below have no id field or unique property,
# so an id will be added to the data.
data_with_identical_ids = _create(None, None)
data_with_identical_ids['features'][0]['id'] = 'identical-ids'
data_with_identical_ids['features'][1]['id'] = 'identical-ids'
_assert_id_got_added(data_with_identical_ids)
data_with_some_missing_ids = _create(None, None)
data_with_some_missing_ids['features'][0]['id'] = 'this-is-an-id'
# the second feature doesn't have an id
_assert_id_got_added(data_with_some_missing_ids)
data_with_identical_properties = _create(
{'property-key': 'identical-value'},
{'property-key': 'identical-value'},
)
_assert_id_got_added(data_with_identical_properties)
data_bare = _create(None)
_assert_id_got_added(data_bare)
data_empty_dict = _create({})
_assert_id_got_added(data_empty_dict)
data_without_properties = _create(None)
del data_without_properties['features'][0]['properties']
_assert_id_got_added(data_without_properties)
data_some_without_properties = _create({'key': 'value'}, 'will be deleted')
# the first feature has properties, but the second doesn't
del data_some_without_properties['features'][1]['properties']
_assert_id_got_added(data_some_without_properties)
data_with_nested_properties = _create({
"summary": {"distance": 343.2},
"way_points": [3, 5],
})
_assert_id_got_added(data_with_nested_properties)
data_with_incompatible_properties = _create({
"summary": {"distances": [0, 6], "durations": None},
"way_points": [3, 5],
})
_assert_id_got_added(data_with_incompatible_properties)
data_loose_geometry = {"type": "LineString", "coordinates": [
[3.961389, 43.583333], [3.968056, 43.580833], [3.974722, 43.578333],
[3.986389, 43.575278], [3.998333, 43.5725], [4.163333, 43.530556],
]}
geojson = GeoJson(data_loose_geometry)
geojson.convert_to_feature_collection()
assert geojson.find_identifier() == 'feature.id'
assert geojson.data['features'][0]['id'] == '0'
#!/Users/simurgh/anaconda/bin/python
import os
import json
import folium
from folium import GeoJson
from scipy.spatial import Voronoi, voronoi_plot_2d
import matplotlib.pyplot as plt
ixp_location = os.path.join('buildings.geojson')
with open(ixp_location) as f:
ixp = json.load(f)
coor_list = []
for feature in ixp['features']:
coor_list.append(feature['geometry']['coordinates'])
kw = {'location': [48, -102], 'zoom_start': 3}
m = folium.Map(**kw)
GeoJson(ixp).add_to(m)
vor = Voronoi(coor_list)
vor_plot = voronoi_plot_2d(vor)
#vor_plot.add_to(m)
m.save(os.path.join('ixp-map.html'))
#!/Users/simurgh/anaconda/bin/python
import os
import json
import folium
from folium import GeoJson
from scipy.spatial import Voronoi, voronoi_plot_2d
import matplotlib.pyplot as plt
probe_location = os.path.join('probes.geojson')
with open(probe_location) as f:
probe = json.load(f)
coor_list = []
for feature in probe['features']:
coor_list.append(feature['geometry']['coordinates'])
kw = {'location': [48, -102], 'zoom_start': 3}
m = folium.Map(**kw)
GeoJson(probe).add_to(m)
vor = Voronoi(coor_list)
vor_plot = voronoi_plot_2d(vor)
#vor_plot.add_to(m)
m.save(os.path.join('probe-map.html'))
def choropleth_map(df_aggreg,
border_color='black',
fill_opacity=0.7,
initial_map=None,
with_legend=False,
kind="linear",
coords=[],
zoom_start=13):
#colormap
min_value = df_aggreg["value"].min()
max_value = df_aggreg["value"].max()
m = round((min_value + max_value) / 2, 0)
#take resolution from the first row
res = h3.h3_get_resolution(df_aggreg.loc[0, 'hex_id'])
if initial_map is None:
initial_map = Map(
location=coords,
zoom_start=zoom_start,
tiles="cartodbpositron",
attr=
'© <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors © <a href="http://cartodb.com/attributions#basemaps">CartoDB</a>'
)
#the colormap
#color names accepted https://github.com/python-visualization/branca/blob/master/branca/_cnames.json
if kind == "linear":
custom_bcm = bcm.LinearColormap(['green', 'yellow', 'red'],
vmin=min_value,
vmax=max_value)
elif kind == "outlier":
#for outliers, values would be -11,0,1
custom_bcm = bcm.LinearColormap(['blue', 'white', 'red'],
vmin=min_value,
vmax=max_value)
elif kind == "filled_nulls":
custom_bcm = bcm.LinearColormap(['sienna', 'green', 'yellow', 'red'],
index=[0, min_value, m, max_value],
vmin=min_value,
vmax=max_value)
#create geojson data from dataframe
geojson_data = hexagons_dataframe_to_geojson(df_hex=df_aggreg)
#plot on map
name_layer = "Choropleth " + str(res)
if kind != "linear":
name_layer = name_layer + kind
GeoJson(geojson_data,
style_function=lambda feature: {
'fillColor': custom_bcm(feature['properties']['value']),
'color': border_color,
'weight': 1,
'fillOpacity': fill_opacity
},
name=name_layer).add_to(initial_map)
#add legend (not recommended if multiple layers)
if with_legend == True:
custom_bcm.add_to(initial_map)
return initial_map
def h3_choropleth_map(df_aggreg: pd.DataFrame, value_to_map: str, kind: str, hour: int, border_color='black', fill_opacity=0.7,
initial_map=None, map_center=[34.0522, -118.2437], with_legend=True):
"""
Builds a folium choropleth map from an df containing H3 hex cells and some cell value such as 'count'.
parameters
----------
df_aggreg:pd.DataFrame - df with H3 hex cells in col ['hex_id'] and at least one col ['value_to_map'] for cell color.
value_to_map:str - column name in df to scale and color cells by
returns
----------
initial_map:folium.Map
"""
# take resolution from the first row
res = h3.h3_get_resolution(df_aggreg.loc[0, 'hex_id'])
if hour is not None:
df_aggreg = df_aggreg[df_aggreg.hour == hour]
else:
df_aggreg = df_aggreg.groupby(['hex_id']).agg({value_to_map: 'sum', 'geometry': 'first', 'hex_id': 'first'})
# create geojson data from dataframe
geojson_data = hexagons_dataframe_to_geojson(df_hex=df_aggreg)
if initial_map is None:
initial_map = Map(location=[34.0522, -118.2437], zoom_start=11, tiles="cartodbpositron",
attr='© <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors © <a href="http://cartodb.com/attributions#basemaps">CartoDB</a>'
)
if value_to_map:
# colormap
min_value = df_aggreg[value_to_map].min()
max_value = df_aggreg[value_to_map].max()
m = round((min_value + max_value) / 2, 0)
# color names accepted https://github.com/python-visualization/branca/blob/master/branca/_cnames.json
if kind == "linear":
custom_cm = cm.LinearColormap(['green', 'yellow', 'red'], vmin=min_value, vmax=max_value)
elif kind == "outlier":
# for outliers, values would be -11,0,1
custom_cm = cm.LinearColormap(['blue', 'white', 'red'], vmin=min_value, vmax=max_value)
elif kind == "filled_nulls":
custom_cm = cm.LinearColormap(['sienna', 'green', 'yellow', 'red'],
index=[0, min_value, m, max_value], vmin=min_value, vmax=max_value)
# plot on map
name_layer = "Choropleth " + str(res)
if kind != "linear":
name_layer = name_layer + kind
GeoJson(
geojson_data,
style_function=lambda feature: {
'fillColor': custom_cm(feature['properties'][value_to_map]),
'color': border_color,
'weight': 1,
'fillOpacity': fill_opacity
},
name=name_layer
).add_to(initial_map)
# add legend (not recommended if multiple layers)
if with_legend == True:
custom_cm.add_to(initial_map)
else:
# plot on map
name_layer = "Choropleth " + str(res)
if kind != "linear":
name_layer = name_layer + kind
GeoJson(
geojson_data,
style_function=lambda feature: {
'fillColor': 'blue',
'color': 'border_color',
'weight': 1,
'fillOpacity': fill_opacity
},
name=name_layer
).add_to(initial_map)
return initial_map
def path_plot(self,
channel=None,
map_type='dynamic',
devices='all',
start_date=None,
end_date=None,
options=dict()):
'''
Creates a folium map showing a path
Parameters
-------
channel: String
None
If None, shows path, otherwise, colored path with channel mapping
map_type: String
'dynamic'
'dynamic' or 'static'. Whether is a dinamic map or not
devices: list or 'all'
List of devices to include, or 'all' from self.devices
channel: String
The channel to make the map from
start_date, end_date: String
Date convertible string
options: dict()
Possible keys are (default otherwise)
location: list
[41.400818, 2.1825157]
Center map location
tiles: (String)
'Stamen Toner'
Tiles for the folium.Map
zoom: (float)
2.5
Zoom to start with in folium.Map
period: 'String'
'1W'
Period for 'dynamic' map
radius: float
10
Circle radius for icon
fillOpacity: float
1
(<1) Fill opacity for the icon
stroke: 'String'
'false'
'true' or 'false'. For icon's stroke
icon: 'String'
'circle'
A valid folium.Map icon style
Returns
-------
Folium.Map object
'''
# Set defaults
options = dict_fmerge(config._map_def_opt, options)
# Make features
features = []
if devices == 'all':
mdev = self.devices
else:
mdev = list()
for device in devices:
if device in self.devices: mdev.append(device)
else: std_out(f'Device {device} not found, ignoring', 'WARNING')
if len(mdev) == 0:
std_out('Requested devices not in test', 'ERROR')
return None
for device in mdev:
chs = ['GPS_LAT', 'GPS_LONG']
if channel is not None:
if channel not in self.devices[str(device)].readings.columns:
std_out(
f'Channel {channel} not in columns: {self.devices[str(device)].readings.columns}',
'ERROR')
return None
# Get bins
minmax = False
if not options['minmax']:
if all([key not in channel for key in config._channel_bins]):
std_out(
f'Requested channel {channel} not in config mapped bins {config._channel_bins.keys()}.Using min/max mapping',
'WARNING')
minmax = True
else:
minmax = True
if minmax:
bins = linspace(
self.devices[str(device)].readings[channel].min(),
self.devices[str(device)].readings[channel].max(),
config._channel_bin_n)
else:
for bname in config._channel_bins.keys():
if bname in channel:
bins = config._channel_bins[bname]
break
chs.append(channel)
# Create copy
dfc = self.devices[str(device)].readings[chs].copy()
# Resample and cleanup
# TODO THIS CAN INPUT SOME MADE UP READINGS
dfc = clean(dfc.resample(options['period']).mean(), 'fill')
# Make color column
legend_labels = None
if channel is not None:
dfc['COLOR'] = cut(dfc[channel], bins, labels =\
config._map_colors_palette)
# Make legend labels
legend_labels = {}
for ibin in range(len(bins) - 1):
legend_labels[f'{round(bins[ibin],2)} : {round(bins[ibin+1],2)}'] =\
config._map_colors_palette[ibin]
else:
dfc['COLOR'] = config._map_colors_palette[0]
if start_date is not None:
dfc = dfc[dfc.index > start_date]
if end_date is not None:
dfc = dfc[dfc.index < end_date]
# Add point for each date
for date in dfc.index:
if date == dfc.index[-1]: break
times = []
color = str(dfc.loc[date, 'COLOR'])
if color == 'nan' or isnan(dfc.loc[date, 'GPS_LONG'])\
or isnan(dfc.loc[date, 'GPS_LAT']):
std_out(f'Skipping point {date}', 'WARNING')
continue
geometry = {
'type':
'LineString',
'coordinates':
[[dfc.loc[date, 'GPS_LONG'], dfc.loc[date, 'GPS_LAT']],
[
dfc.loc[date + dfc.index.freq, 'GPS_LONG'],
dfc.loc[date + dfc.index.freq, 'GPS_LAT']
]],
}
properties = {
'icon':
options['icon'],
'iconstyle': {
'fillColor': color,
'fillOpacity': options['fillOpacity'],
'stroke': options['stroke'],
'radius': options['radius']
},
'device':
device,
'timestamp':
date.strftime('%Y-%m-%dT%H:%M:%S'),
"coordinates": [
dfc.loc[date + dfc.index.freq, 'GPS_LAT'],
dfc.loc[date + dfc.index.freq, 'GPS_LONG']
],
'style': {
'color': color,
'stroke-width': options['stroke-width'],
'fillOpacity': options['fillOpacity']
}
}
# Add reading to tooltip
if channel is not None:
properties['channel'] = channel
properties['value'] = dfc.loc[date, channel]
if map_type == 'dynamic':
properties['times'] = [
date.strftime('%Y-%m-%dT%H:%M:%S'),
(date + dfc.index.freq).strftime('%Y-%m-%dT%H:%M:%S')
]
features.append({
'type': 'Feature',
'geometry': geometry,
'properties': properties
})
featurecol = {'type': 'FeatureCollection', 'features': features}
# Make map
if options['location'] == 'average':
avg_long = dfc['GPS_LONG'].mean()
avg_lat = dfc['GPS_LAT'].mean()
loc = [avg_lat, avg_long]
else:
loc = options['location']
m = Map(
location=loc,
tiles=options['tiles'],
zoom_start=options['zoom'],
)
if map_type == 'static':
# TODO WORKAROUND UNTIL GEOJSON ACCEPTS MARKERS
if options['markers']:
for feature in features:
Circle(location=[
feature['geometry']['coordinates'][0][1],
feature['geometry']['coordinates'][0][0]
],
fill='true',
radius=feature['properties']['iconstyle']['radius'],
color=feature['properties']['iconstyle']['fillColor'],
fill_opacity=feature['properties']['iconstyle']
['fillOpacity']).add_to(m)
if channel is not None:
fields = ["device", "channel", "timestamp", "coordinates", "value"]
aliases = [
"Device:", "Sensor:", "Timestamp:", "Coordinates:", "Reading:"
]
else:
fields = ["device", "timestamp", "coordinates"]
aliases = ["Device:", "Timestamp:", "Coordinates:"]
popup = GeoJsonPopup(
fields=fields,
aliases=aliases,
localize=True,
labels=True,
max_width=800,
)
tooltip = GeoJsonTooltip(
fields=fields,
aliases=aliases,
localize=True,
sticky=True,
labels=True,
style="""
background-color: #F0EFEF;
border: 1px solid gray;
border-radius: 1px;
box-shadow: 2px;
""",
max_width=800,
)
GeoJson(
featurecol,
tooltip=tooltip,
popup=popup,
style_function=lambda x: {
'color': x['properties']['style']['color'],
'weight': x['properties']['style']['stroke-width'],
'fillOpacity': x['properties']['style']['fillOpacity']
},
).add_to(m)
elif map_type == 'dynamic':
TimestampedGeoJson(featurecol,
period='PT' + convert_rollup(options['period']),
add_last_point=True,
auto_play=False,
loop=False,
max_speed=options['max_speed'],
loop_button=True,
time_slider_drag_update=True).add_to(m)
else:
std_out(f'Not supported map type {map_type}', 'ERROR')
return None
if options['minimap']:
minimap = MiniMap(toggle_display=True, tile_layer=options['tiles'])
minimap.add_to(m)
if options['legend'] and not legend_labels is None:
templateLoader = FileSystemLoader(searchpath=join(dirname(__file__),\
'templates'))
templateEnv = Environment(loader=templateLoader)
template = templateEnv.get_template("map_legend.html")
filled_map_legend = template.render(legend_labels=legend_labels)
map_legend_html = '{% macro html(this, kwargs) %}'+\
filled_map_legend+\
'{% endmacro %}'
legend = element.MacroElement()
legend._template = element.Template(map_legend_html)
m.get_root().add_child(legend)
return m
def create_map(data, file_name="index.html"):
map = Map(
location=[data[col].mean() for col in ["latitude", "longitude"]],
tiles="Stamen Toner",
zoom_start=2,
)
fg_nodes_locations = FeatureGroup(name="Locations")
fg_nodes_counts = FeatureGroup(name="Counts")
circles = [
{
"radius": 5,
"stroke": False,
"fill": True,
"fill_color": "crimson",
"fill_opacity": opacity,
"location": [latitude, longitude],
"popup": Popup(
html=f"""
<style>
tbody {{
text-align: center;
}}
table,
tr,
td {{
border-collapse: collapse;
border: 1px solid black;
}}
</style>
<h3>{ip}</h3>
<table>
<tbody>
<tr>
<td><strong>ISP</strong></td>
<td>{org}</td>
</tr>
<tr>
<td><strong>Completed requests</strong></td>
<td>{completed_requests}</td>
</tr>
</tbody>
</table>
""",
max_width=300,
),
}
for ip, latitude, longitude, org, completed_requests, opacity in [
named_tuple[2:] for named_tuple in data.itertuples(index=False)
]
]
q1, q2, q3, q4 = nodes_counts_quantiles()
def opacity(scaled_nodes_count):
# print(scaled_nodes_count)
if scaled_nodes_count < q1:
return 0
elif q1 <= scaled_nodes_count < q2:
return 0.1
elif q2 <= scaled_nodes_count < q3:
return 0.25
elif q3 <= scaled_nodes_count < q4:
return 0.5
else:
return 0.75
def style_function(feature):
fillOpacity = opacity(feature["properties"]["scaled_nodes_count"])
return {"fillColor": "blue", "weight": 0, "fillOpacity": fillOpacity}
fg_nodes_counts.add_child(
GeoJson(
data=open("data/enriched_world.json", encoding="utf-8-sig").read(),
style_function=style_function,
)
)
for circle in circles:
fg_nodes_locations.add_child(CircleMarker(**circle))
map.add_child(fg_nodes_counts)
map.add_child(fg_nodes_locations)
map.add_child(LayerControl())
map.save(file_name)
hospedero_map = Map(location=[5.190831, -72.322258], tiles=None, zoom_start=9)
basemaps['Google Satellite'].add_to(hospedero_map)
basemaps['Google Terrain'].add_to(hospedero_map)
# TileLayer('Stamen Terrain', name='Stamen Terrain', overlay=False, show=False).add_to(hospedero_map)
style = {
'fillColor': '#FFFFFF',
'color': '#FFFFFF',
"weight": 3,
'fillOpacity': 0.05
}
GeoJson(casanare_mapa,
name='Municipios de Casanare',
overlay=True,
style_function=lambda x: style,
tooltip=GeoJsonTooltip(fields=['MPIO_CNMBR'],
aliases=['MUNICIPIO'])).add_to(hospedero_map)
feature_articulos = FeatureGroup('Publicaciones', show=True)
for index, row in df_articulos.iterrows():
html_popup = """
<h2><b>{}</b></h2>
<h4><i>{}</i></h4>
<p>{}</p>
<p><b>Secuenciación:</b> {}</p>
<p><b>Especie(s):</b> <i>{}</i></p>
""".format
iframe = IFrame(html_popup(row['Articulo'], row['Autor'], row['Fecha'],
row['Secuenciación'], row['Trypanosoma']),
def main():
# Paso 1: Eliminar clúster de puntos en base al tiempo medio que pasan las
# personas en los puntos de interés (PDI). El archivo tiempo.json está
# filtrado por los PDI y contiene el nombre y tiempo de los puntos.
print("Leyendo archivo tiempo...")
pdi_df = gpd.read_file('opendata/tiempo.json')
print("Eliminando clúster de puntos...")
pdi_df, coords = del_cluster_points(pdi_df)
# Paso 2: Generar el diagrama de Voronoi para el procesamiento de datos.
vor = Voronoi(coords)
regions, vertices = voronoi_finite_polygons(vor)
min_x = vor.min_bound[0] - 0.1
max_x = vor.max_bound[0] + 0.1
min_y = vor.min_bound[1] - 0.1
max_y = vor.max_bound[1] + 0.1
box = Polygon([[min_x, min_y], [min_x, max_y], [max_x, max_y],
[max_x, min_y]])
voro_df = clip_voronoi(regions, vertices, box)
# Paso 3: Agregar los datos de población, tráfico y tweets.
# Población
print('Leyendo archivo manzanas_pob...')
pobl_df = gpd.read_file('opendata/manzanas_pob.json')
print('Agregando datos de población...')
pdi_df = add_pobl(pobl_df, voro_df, pdi_df)
# Tráfico
print('Leyendo archivos de tráfico...')
traficos_df = []
for filename in listdir('opendata/trafico'):
traficos_df.append(gpd.read_file('opendata/trafico/' + filename))
print('Agregando datos de tráfico...')
pdi_df = add_traffic(traficos_df, voro_df, pdi_df)
# Tweets
print('Leyendo archivo valencia_tweets...')
with open('opendata/valencia_tweets.json', 'r') as input_file:
tweets = json.load(input_file)
tweets_df = gpd.GeoDataFrame(gpd.GeoSeries(
Point(t['coordinates']['coordinates']) for t in tweets),
columns=['geometry'])
print('Agregando datos de redes sociales...')
pdi_df = add_tweets(tweets_df, voro_df, pdi_df)
# Paso 4: Guardar los archivos para el funcionamiento del algoritmo genético.
print('Guardando puntos de interés...')
pdi_df.to_file('puntos_de_interes.json', driver="GeoJSON")
print('Guardando voronoi...')
voro_df.to_file('voronoi.json', driver="GeoJSON")
# Paso 5: Visualizar la información en mapa web.
print('Generando mapa web...')
valencia = [39.4561165311493, -0.3545661635]
mapa = Map(location=valencia, tiles='OpenStreetMap', zoom_start=10)
GeoJson(open('voronoi.json'), name='Diagrama de Voronoi').add_to(mapa)
GeoJson(open('puntos_de_interes.json'),
name='Puntos de Interés').add_to(mapa)
LayerControl().add_to(mapa)
mapa.save('valencia.html')
print('Listo')
def html_map(contiguity_fname, html_out_fname, json_out_fname):
"""
Create HTML and GeoJSON files showing the valid geometry
for the input contiguity image.
:param contiguity_fname:
A full file pathname to the image representing the data
contiguity.
:param html_out_fname:
A full file pathname that will contain the html bounday map.
:param json_out_fname:
A full file pathname that will contain the json bounday map.
:return:
None; Outputs will be saved directly to disk.
"""
# remove any previous creations
try:
os.remove(html_out_fname)
except OSError:
pass
try:
os.remove(json_out_fname)
except OSError:
pass
# Find metadata yaml and add to geopandas attributes
#metadata = os.path.abspath(os.path.join(out_dir, "..", "ARD-METADATA.yaml"))
logging.info("Create valid bounds " + json_out_fname)
geom, crs = valid_region(contiguity_fname)
gpdsr = gpd.GeoSeries([geom])
gpdsr.crs = crs
gpdsr = gpdsr.to_crs({'init': 'epsg:4326'})
# TODO - Add metadata to PopUp
#with open(metadata, 'r') as stream:
# try:
# yaml_metadata = yaml.load(stream)
# except yaml.YAMLError as exc:
# print(exc)
#gpdyaml = gpd.GeoSeries(yaml_metadata)
gpdsr.to_file(json_out_fname, driver='GeoJSON')
m = folium.Map()
#GeoJson(gpdsr, name='geojson').add_to(m)
style_function = lambda x: {'fillColor': None, 'color': '#0000ff'}
GeoJson(json_out_fname,
name='bounds.geojson',
style_function=style_function).add_to(m)
# TODO - add MGRS tile reference to map with layer active = False
#style_function = lambda x: {'fillColor': None, 'fillOpacity': 0.1, 'weight': 0.1,
# 'color' : '#ff0000'}
#GeoJson('/home/simonaoliver/reference/agdcv2-reference/MGRS_Australia.geojson', name='MGRS_tiles.geojson', style_function=style_function).add_to(m)
m.fit_bounds(GeoJson(gpdsr).get_bounds())
folium.LatLngPopup().add_to(m)
#m.add_child(folium.Popup("Insert Date Here"))
folium.LayerControl().add_to(m)
m.save(html_out_fname)
def test_geojson_find_identifier():
def _create(*properties):
return {
"type":
"FeatureCollection",
"features": [{
"type": "Feature",
"properties": item
} for item in properties]
}
def _assert_id_got_added(data):
_geojson = GeoJson(data)
assert _geojson.find_identifier() == 'feature.id'
assert _geojson.data['features'][0]['id'] == '0'
data_with_id = _create(None, None)
data_with_id['features'][0]['id'] = 'this-is-an-id'
data_with_id['features'][1]['id'] = 'this-is-another-id'
geojson = GeoJson(data_with_id)
assert geojson.find_identifier() == 'feature.id'
assert geojson.data['features'][0]['id'] == 'this-is-an-id'
data_with_unique_properties = _create(
{'property-key': 'some-value'},
{'property-key': 'another-value'},
)
geojson = GeoJson(data_with_unique_properties)
assert geojson.find_identifier() == 'feature.properties.property-key'
data_with_unique_properties = _create(
{'property-key': 42},
{'property-key': 43},
{'property-key': 'or a string'},
)
geojson = GeoJson(data_with_unique_properties)
assert geojson.find_identifier() == 'feature.properties.property-key'
# The test cases below have no id field or unique property,
# so an id will be added to the data.
data_with_identical_ids = _create(None, None)
data_with_identical_ids['features'][0]['id'] = 'identical-ids'
data_with_identical_ids['features'][1]['id'] = 'identical-ids'
_assert_id_got_added(data_with_identical_ids)
data_with_some_missing_ids = _create(None, None)
data_with_some_missing_ids['features'][0]['id'] = 'this-is-an-id'
# the second feature doesn't have an id
_assert_id_got_added(data_with_some_missing_ids)
data_with_identical_properties = _create(
{'property-key': 'identical-value'},
{'property-key': 'identical-value'},
)
_assert_id_got_added(data_with_identical_properties)
data_bare = _create(None)
_assert_id_got_added(data_bare)
data_empty_dict = _create({})
_assert_id_got_added(data_empty_dict)
data_without_properties = _create(None)
del data_without_properties['features'][0]['properties']
_assert_id_got_added(data_without_properties)
data_some_without_properties = _create({'key': 'value'}, 'will be deleted')
# the first feature has properties, but the second doesn't
del data_some_without_properties['features'][1]['properties']
_assert_id_got_added(data_some_without_properties)
data_with_nested_properties = _create({
"summary": {
"distance": 343.2
},
"way_points": [3, 5],
})
_assert_id_got_added(data_with_nested_properties)
data_with_incompatible_properties = _create({
"summary": {
"distances": [0, 6],
"durations": None
},
"way_points": [3, 5],
})
_assert_id_got_added(data_with_incompatible_properties)
data_loose_geometry = {
"type":
"LineString",
"coordinates": [
[3.961389, 43.583333],
[3.968056, 43.580833],
[3.974722, 43.578333],
[3.986389, 43.575278],
[3.998333, 43.5725],
[4.163333, 43.530556],
]
}
geojson = GeoJson(data_loose_geometry)
geojson.convert_to_feature_collection()
assert geojson.find_identifier() == 'feature.id'
assert geojson.data['features'][0]['id'] == '0'
def _assert_id_got_added(data):
_geojson = GeoJson(data)
assert _geojson.find_identifier() == 'feature.id'
assert _geojson.data['features'][0]['id'] == '0'
def _assert_id_got_added(data):
_geojson = GeoJson(data)
assert _geojson.find_identifier() == 'feature.id'
assert _geojson.data['features'][0]['id'] == '0'