def get_county_choropleth(start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)['countyId']
county_names = d.get_county()
data = data.value_counts()
data.index = data.index.map(lambda p: county_names.loc[p]['name'])
zeroes = pd.Series(data=0, index=county_names.name)
data = data + zeroes
data = data.fillna(0)
data = data.sort_values()
df = pd.DataFrame(dict(county=data.index, count=data.values))
geojson_file = os.path.join(os.path.dirname(__file__), 'regions_epsg_4326.geojson.txt')
with open(geojson_file, encoding='utf-8') as file:
geo_counties = json.loads(file.read())
fig = px.choropleth(data_frame=df, geojson=geo_counties, featureidkey='properties.NM4', locations='county', color='count',
color_continuous_scale='tealrose',
range_color=(0, df['count'].mean()*2),
projection='sinusoidal',
labels={'count':'Počet nehôd'},
hover_data={'county':False},
hover_name=df['county'])
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(
margin={"r":0,"t":0,"l":0,"b":0},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
geo=dict(bgcolor= 'rgba(0,0,0,0)'),
coloraxis_showscale=False,
)
return plots.encode_plot(fig, output)
def get_accident_scatter_map(data, output, zoom, center, size_max = None):
if size_max is None:
size_max = 50
fig = px.scatter_mapbox(data, lat='latitude', lon='longitude',
mapbox_style="open-street-map", size='marker_size', size_max=size_max, opacity=0.8,
hover_data={'latitude':False, 'longitude':False, 'marker_size':False}, zoom=zoom, center = center)
fig.update_layout(
margin={"r":0,"t":0,"l":0,"b":0},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
)
return plots.encode_plot(fig, output)
def get_map_with_most_frequent_accidents_for_road(road_number, max_number_accidents_returned, start_datetime, end_datetime, start_km=0, end_km=999999999, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.roadNumber == road_number]
data = data.loc[(data.roadPosition >= start_km) & (data.roadPosition <= end_km)]
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 8)
if data.size > 0:
shape = d.get_road_shape(road_number)
if shape is not None:
shape_list = parse_shape_string(shape)
shape_list = filter_shape(shape_list, start_km, end_km)
for s in shape_list:
fig.add_trace(go.Scattermapbox(
mode = 'lines',
line=dict(width=4, color="#006699"),
showlegend=False,
lon = s[1],
lat = s[0],
hoverinfo='skip'))
return plots.encode_plot(fig, output)
def get_map_with_most_frequent_accidents_for_country(max_number_accidents_returned, start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 6, center = {'lat':48.663863, 'lon':19.502998})
return plots.encode_plot(fig, output)
def get_map_with_most_frequent_accidents_for_district(district_id, max_number_accidents_returned, start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.districtId == district_id]
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 9.5)
return plots.encode_plot(fig, output)