def px_plot(data, colls, animate_or_not):
for cl in colls:
ind = np.where(data[cl].notna())[0]
data = data.iloc[ind, :]
print(cl)
if animate_or_not:
fig = px.density_mapbox(data,
lat='lat',
lon='lng',
z=cl,
radius=5,
center=dict(lat=0, lon=180),
zoom=0,
animation_frame=cl,
mapbox_style="stamen-terrain")
filen = (os.path.join(figures_path,
('html_files/htmls_animated/TEDS_nx_map_' +
str(cl) + '_animated.html')))
else:
fig = px.density_mapbox(data,
lat='lat',
lon='lng',
z=cl,
radius=10,
center=dict(lat=0, lon=180),
zoom=0,
mapbox_style="stamen-terrain")
filen = (os.path.join(
figures_path, ('html_files/TEDS_nx_map_' + str(cl) + '.html')))
fig.update_layout(
title_text=('2017 US state ' + str(cl) +
' <br>(Click legend to toggle traces)'),
showlegend=True,
paper_bgcolor='white',
# margin={"r":0,"t":0,"l":0,"b":0},
geo=dict(scope='usa', landcolor='rgb(217, 217, 217)'),
mapbox_style="carto-positron", #"carto-positron",
mapbox_zoom=3,
mapbox_center={
"lat": 37.0902,
"lon": -95.7129
},
)
# htmkl file
plotly.offline.plot(fig, filename=filen)
fig.show()
def update_graph(grade_bounds, safety_fil, radius_v):
[min_g, max_g] = grade_bounds
PG_bool = ('PG13' in safety_fil)
R_bool = ('R' in safety_fil)
X_bool = ('X' in safety_fil)
df_filter = filter_data(df, min_g, max_g, PG_bool, R_bool, X_bool)
fig = px.density_mapbox(
df_filter,
lat='latitude',
lon='longitude',
z='stars',
radius=radius_v,
hover_name='name',
hover_data={
'longitude': False,
'latitude': False,
'area': True,
'stars': True,
'rating': True
},
#colorscale = Jet,
center=dict(lat=34.012, lon=-116.168),
zoom=10,
opacity=0.7)
fig['layout']['uirevision'] = 'some-constant'
fig.update_layout(mapbox_style="open-street-map")
return fig
def cmap_three():
dalian_three = pd.read_csv(DATA_PATH.joinpath("clustering_three.csv"))
fig = px.density_mapbox(
dalian_three,
lat='lat',
lon='lon',
z='deal_totalPrice',
hover_data=["xiaoqu", "address", "deal_totalPrice", "deal_unitPrice"],
radius=10)
fig.update_layout(
autosize=True,
hovermode='closest',
mapbox_style="open-street-map",
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=38.9146, lon=121.619),
pitch=0,
zoom=9,
),
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 10})
return fig
def make_map():
import plotly.express as px
fig = px.density_mapbox(data, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
center=dict(lat=0, lon=180), zoom=0,
mapbox_style="stamen-terrain")
divs.append(html.Div([dcc.Graph(figure=fig)]))
def firms_heatmap(data):
trace = data
heat_fig = px.density_mapbox(trace,
lat="latitude",
lon="longitude",
radius=data["frp_scaled"],
zoom=3,
mapbox_style="open-street-map",
)
return(heat_fig)
def den_plot(par=den_group):
fig = px.density_mapbox(ALZ_map,
lat='start_lat',
lon='start_lng',
z=f'{par}',
radius=10,
zoom=3)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return st.plotly_chart(fig, use_container_width=True)
def mapplot(mongodb_collection, api_key):
query = {
'google_place_search_api_result.geometry.location': {
'$exists': 1
}
}
projection = {
'id': 1,
'name': 1,
'_id': 0,
'google_place_search_api_result.geometry.location.lat': 1,
'google_place_search_api_result.geometry.location.lng': 1,
'google_place_search_api_result.name': 1
}
results_raw = mongodb_collection.find(query, projection)
df = pd.DataFrame(
columns=['id', 'name', 'lat', 'lng', 'formatted_name', 'size'])
list_of_dicts = list()
for elem in results_raw:
size = 7
id = elem['id']
formatted_name = elem['google_place_search_api_result']['name']
name = elem['name']
lat = elem['google_place_search_api_result']['geometry']['location'][
'lat']
lng = elem['google_place_search_api_result']['geometry']['location'][
'lng']
dict_ = {
"id": id,
"name": name,
"latitude": lat,
"longitude": lng,
"formatted_name": formatted_name,
"size": size
}
list_of_dicts.append(dict_)
df = pd.DataFrame.from_dict(list_of_dicts, orient='columns')
BBox = ((df.longitude.min(), df.longitude.max(), df.latitude.min(),
df.latitude.max()))
# set mapbox acess token
px.set_mapbox_access_token(api_key)
# fig = px.scatter_mapbox(df, lat="latitude", lon="longitude", text="formatted_name", zoom=4)
# fig = px.scatter_mapbox(df, lat="latitude", lon="longitude", zoom=4, size="size")
# plot figure (density mapbox)
fig = px.density_mapbox(df,
lat="latitude",
lon="longitude",
radius=25,
zoom=2,
mapbox_style="light")
fig.show()
def update_output(start_date, end_date):
# print("Start date: " + start_date)
# print("End date: " + end_date)
dff = df.loc[start_date:end_date]
# print(dff[:5])
fig = px.density_mapbox(dff, lat='LATITUDE', lon='LONGITUDE', z='APP_SQ_FT', radius=13, zoom=10, height=650,
center=dict(lat=40.751418, lon=-73.963878), mapbox_style="carto-positron",
hover_data={'BUSINESS_NAME': True, 'LATITUDE': False, 'LONGITUDE': False,
'APP_SQ_FT': True})
return fig
def plot_heatmap(df):
center = dict(lat=df['Latitude'].mean(), lon=df['Longitude'].mean())
fig = px.density_mapbox(
df,
lat='Latitude',
lon='Longitude', # z='Magnitude',
radius=10,
center=center,
zoom=10,
mapbox_style="open-street-map")
# fig.update_layout(height=800)
return fig
def update_heatmap(weight, allday, borough, year, week, day):
# if only one value chosen its just a string so we cast to array
if type(borough) == str:
borough = [borough]
if type(year) == str:
year= [year]
if type(week) == str:
week= [week]
if type(day) == str:
day= [day]
# if allday is chosen use whole data set else only corresponding hour
if allday == '0':
currDisplayData = heat_data_all_hours
else:
currDisplayData = hour_options[weight]
lat_lon_data=currDisplayData[(currDisplayData['BOROUGH'].isin(borough)) & (currDisplayData['Year'].isin(year)) & (currDisplayData['Week'].isin(week)) & (currDisplayData['WeekDay'].isin(day))]
# plot all lot, lon data from the current filters
if lat_lon_data.shape[0] < 1:
fig = px.density_mapbox(lat=[0], lon=[0], radius=10,
center=dict(lat=40.7812, lon=-73.9665), zoom=10, opacity=0.0,
mapbox_style="stamen-toner", height=500)
else:
fig = px.density_mapbox(lat_lon_data, lat='LATITUDE', lon='LONGITUDE', radius=5,
center=dict(lat=40.7812, lon=-73.9665), zoom=10, opacity=0.5,
mapbox_style="stamen-toner", height=500)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.update_traces(hovertemplate=None, hoverinfo='skip')
fig.update_layout(uirevision=True)
# if allday is chosen show slider and label else hide it
if allday == '0':
return fig, {'display': 'none'}, {'display': 'none'}
else:
return fig, {'display': 'block'}, {'display': 'block'}
def update_map(selected_charities):
# x data is the charities selected in the dropdown, y data is the corresponding number of interactions
# for these charities
# df = df[selected_charities]
# print(df.head())
df_map = df[df['charity'].isin(selected_charities)]
fig = px.density_mapbox(df_map, lat='latitude', lon='longitude', radius=10,
center=dict(lat=51.453, lon=-2.585), zoom=13,
mapbox_style="stamen-terrain")
# make the data list for a bar plot using the x and y data and set the layout parameters.
# These are returned to update the bar plot
return fig
def plot_heatmap(input_range=[0,168]):
df_crashes_merged_plot = df_crash[(df_crash['Hour_of_the_week']>input_range[0]) & (df_crash['Hour_of_the_week']<input_range[1])]
global observations_in_timeframe
observations_in_timeframe= len(df_crashes_merged_plot)
fig_map = px.density_mapbox(df_crashes_merged_plot, lat='LATITUDE', lon='LONGITUDE', z='Involved', radius=3,
hover_data={'LATITUDE':False,'LONGITUDE':False,'Killed':True,'Injured':True, 'Time':True},
center = {"lat": 40.730610, "lon": -73.935242}, zoom=9, mapbox_style="carto-positron")
fig_map.update_layout(
margin=dict(l=0, r=0, t=0, b=0),
hovermode="closest",
font=dict(color='#737a8d'),
coloraxis_showscale=False)
return fig_map
def grafica_2(id):
df2 = load_grafica2()
return px.density_mapbox(
df2,
lat="lat",
lon="long",
z="size",
radius=40,
center=dict(lat=5, lon=-75),
zoom=4,
mapbox_style="stamen-terrain",
title="Distribución geografica de ganadores",
)
def plot_heatmap(df, parameter, state):
latitude = df[df['state_name'] == state][['latitude', 'longitude'
]].mean(axis=0)['latitude']
longitude = df[df['state_name'] == state][['latitude', 'longitude'
]].mean(axis=0)['longitude']
df = df[df['parameter_name'] == parameter]
fig = px.density_mapbox(df,\
lat='latitude',\
lon='longitude',\
z='arithmetic_mean',\
radius=10,
animation_frame= 'year',
center=dict(lat=latitude, lon=longitude), zoom=5.5,
mapbox_style= 'carto-positron')
fig
def heat_map():
""" Build heatmap with color gradient for iptcc """
# mark_size = [100 for i in df.index]
fig = px.density_mapbox(df,
lat='latitude',
lon='longitude',
z='iptcc',
radius=30,
center=dict(lat=0, lon=180),
zoom=4,
height=450)
# fig = px.scatter_mapbox(df, lat="latitude", lon="longitude", hover_data=["iptcc"],
# color="iptcc", color_continuous_scale=['#7BD150', '#F6E626', '#F6E626', '#FC9129', '#FF1B00', '#6E1E80'], size=mark_size, size_max=10, zoom=4, )
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def plotavg(hour = -2):
path = '/Users/andrew/non_icloud_ncf/distcomp_project/avgDay/'
file = path + 'Avg.csv'
avgdf = pd.read_csv(file)
if hour == -2:
hours = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]
if hour != -2:
hours = [hour]
for h in hours:
havgdf=avgdf[(avgdf.Hour == h)]
fig = px.density_mapbox(havgdf, lat='Latitudea', lon='Longitudea', z='Average', radius=10, center=dict(lat=40.730, lon=-73.935), zoom=8.3, mapbox_style="stamen-terrain")
writepath = path + 'maps/hour'+str(h)+'.html'
fig.write_html(writepath)
def compute(n_clicks, input1, input2, input3, input4, input5, input6):
print(1)
res = lats(input1, input2, input3, input4, input5, input6)
fig = px.scatter_mapbox(res,
lat="latitude",
lon="longitude",
hover_name="city",
hover_data=[
'provstate', 'country_txt', 'region_txt',
'attacktype1_txt', 'iday', 'imonth', 'iyear'
],
labels={
'provstate': 'State',
'country_txt': 'Country',
'region_txt': 'Region',
'iday': 'Day',
'imonth': 'Month',
'iyear': 'Year',
'attacktype1_txt': 'Attack Type'
},
color='attacktype1',
zoom=4,
height=900)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig1 = px.density_mapbox(res,
lat="latitude",
lon="longitude",
hover_name="city",
hover_data=[
'provstate', 'country_txt', 'region_txt',
'attacktype1_txt', 'iday', 'imonth', 'iyear'
],
labels={
'provstate': 'State',
'country_txt': 'Country',
'region_txt': 'Region',
'iday': 'Day',
'imonth': 'Month',
'iyear': 'Year',
'attacktype1_txt': 'Attack Type'
},
zoom=3,
mapbox_style='open-street-map',
height=400)
return (fig, fig1)
def heatmap(stream_file=""):
twitter_df = pd.read_json(stream_file, lines=True)
json_struct = json.loads(twitter_df.to_json(orient="records"))
df_flat = pd.json_normalize(json_struct)
df_flat['latlon'] = df_flat['user.location'].apply(
lambda x: get_location(x))
df_flat[['lat', 'lon']] = pd.DataFrame(df_flat['latlon'].tolist(),
index=df_flat.index)
fig = px.density_mapbox(df_flat,
lat="lat",
lon="lon",
radius=5,
center=dict(lat=0, lon=180),
zoom=0,
mapbox_style="open-street-map")
py.offline.plot(fig, filename="test.html")
def compute1(n_clicks, inp1, inp2, inp3, inp4):
print(1)
res1 = lats1(inp1, inp2, inp3, inp4)
data1 = data[data['country_txt'] == 'India']
state_cnt_list = []
for i in data1['provstate'].unique():
state_cnt_list.append(data['provstate'].value_counts()[i])
m = interp1d([1, max(state_cnt_list)], [5, 18])
circle_radius = m(state_cnt_list)
fig1 = px.density_mapbox(res1,
lat="latitude",
lon="longitude",
hover_name="city",
zoom=3,
radius=circle_radius,
mapbox_style='open-street-map',
height=400)
return (fig1)
def new_map(self):
df = pd.read_csv(
'../novel-corona-virus-2019-dataset/covid_19_data.csv',
parse_dates=['Last Update'])
df.rename(columns={
'ObservationDate': 'Date',
'Country/Region': 'Country'
},
inplace=True)
df_confirmed = pd.read_csv(
"../novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv"
)
df_confirmed.rename(columns={'Country/Region': 'Country'},
inplace=True)
df_confirmed = df_confirmed[[
"Province/State", "Lat", "Long", "Country"
]]
df_temp = df.copy()
df_temp['Country'].replace({'Mainland China': 'China'}, inplace=True)
df_latlong = pd.merge(df_temp,
df_confirmed,
on=["Country", "Province/State"])
fig = px.density_mapbox(
df_latlong,
lat="Lat",
lon="Long",
hover_name="Province/State",
hover_data=["Confirmed", "Deaths", "Recovered"],
animation_frame="Date",
color_continuous_scale="Portland",
radius=7,
zoom=0,
height=700)
fig.update_layout(
title=
'Worldwide Corona Virus Cases Time Lapse - Confirmed, Deaths, Recovered',
font=dict(family="Courier New, monospace",
size=18,
color="#7f7f7f"))
fig.update_layout(mapbox_style="open-street-map", mapbox_center_lon=0)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
#self.MplWidget.canvas.figure=fig
fig.show()
def create_US_GeoMap_Death(self):
df_confirmed_US = pd.read_csv(
"../novel-corona-virus-2019-dataset/time_series_covid_19_deaths_US.csv"
)
df_confirmed_US = df_confirmed_US.drop([
'UID', 'iso2', 'iso3', 'code3', 'FIPS', 'Admin2', 'Combined_Key',
'Population'
],
axis=1)
df_confirmed_US.rename(columns={
'Country_Region': 'Country',
'Province_State': 'Province/State',
'Long_': 'Long'
},
inplace=True)
df_confirmed_US = pd.melt(
df_confirmed_US,
id_vars=['Province/State', 'Country', 'Lat', 'Long'],
var_name='Date',
value_name='Deaths')
df_confirmed_US = df_confirmed_US[df_confirmed_US['Deaths'] > 0]
fig = px.density_mapbox(df_confirmed_US,
lat="Lat",
lon="Long",
hover_name="Province/State",
hover_data=["Deaths"],
animation_frame="Date",
color_continuous_scale="Portland",
radius=7,
zoom=0,
height=700)
fig.update_layout(title='Worldwide Deaths Cases',
font=dict(family="Courier New, monospace",
size=18,
color="#7f7f7f"))
fig.update_layout(mapbox_style="open-street-map", mapbox_center_lon=0)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
app = dash.Dash()
app.layout = html.Div([dcc.Graph(figure=fig)])
app.run_server(debug=True, use_reloader=False)
def mapping():
message = 'Done'
if request.method == 'GET':
region = request.args.get('region', type=int)
country = request.args.get('country', type=int)
attacktype = request.args.get('attacktype', type=str)
df = pd.read_csv("map.csv")
if region == 0 and country == 0 and attacktype == 'All': #000
data = df
elif region == 0 and country == 0: #001
f = (df['attacktype1_txt'] == attacktype)
data = df[f]
elif region == 0 and attacktype == 'All': #010
f = (df['country'] == country)
data = df[f]
elif region == 0: #0 11
f = (df['country'] == country) & (df['attacktype1_txt']
== attacktype)
data = df[f]
elif country == 0 and attacktype == 'All': #1 00
f = (df['region'] == country)
data = df[f]
elif country == 0: #1 01
f = (df['region'] == country) & (df['attacktype1_txt']
== attacktype)
data = df[f]
elif attacktype == 'All': #1 10
f = (df['country'] == country) & (df['region'] == region)
data = df[f]
else: #1 11
f = (df['country'] == country) & (df['region'] == region) & (
df['attacktype1_txt'] == attacktype)
data = df[f]
fig = px.density_mapbox(data,
lat='latitude',
lon='longitude',
radius=5,
zoom=4,
mapbox_style='open-street-map')
plotly.offline.plot(fig, filename='templates/names.html')
return render_template('names.html')
def plotday(day = 'all', hour = -2, method='jnorm'):
#change this path to your downloaded folder
path = '/Users/andrew/non_icloud_ncf/distcomp_project/events/'
#if a day is not specified, it will default to outputting every day
if day == 'all':
csvs = ['04_07','05_09','05_26','07_04','07_24','08_25','08_30','09_21','09_25']
if day != 'all':
csvs = [day]
#if hour is -2, this will output htmls for every hour of every specified day
#the hour -1 may be in the data, this would correspond with the daily data
if hour == -2:
hours = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]
if hour != -2:
hours = [hour]
for file in csvs:
filename = path + file + '.csv'
currentdf = pd.read_csv(filename)
currentdf.drop(columns=['Unnamed: 0','Yearb','Monthb','Dayb','TempC','Type'], inplace=True)
for h in hours:
dfhour = currentdf[(currentdf.Hourb == h)]
dfhour['Count'].fillna(0,inplace=True)
dfhour['diff'] = dfhour.apply (lambda row: (row['Count']-row['Average']), axis=1)
if method == 'anorm':
absmax=max(abs(dfhour["diff"]))
dfhour['diffnorm'] = dfhour.apply (lambda row: ((row['diff']+absmax)/absmax), axis=1)
if method == 'jnorm':
mi = min(dfhour["diff"])
mx = max(dfhour["diff"])
mn = dfhour["diff"].mean()
dfhour['diffnorm'] = dfhour.apply (lambda row: (reshape(row['diff'], mi, mx, mn)), axis=1)
# dfhour = dfhour[(round(dfhour.diffnorm,2) != .5)]
# dfplotmore = dfhour[(dfhour.diffnorm > .5)]
# dfplotless = dfhour[(dfhour.diffnorm < .5)]
if method == 'count':
# mi = min(dfhour["diff"])
dfhour['diffnorm'] = dfhour['diff']#dfhour.apply (lambda row: (dfhour['diff']), axis=1)
#plotting both conditions
fig = px.density_mapbox(dfhour, lat='Latitudeb', lon='Longitudeb', z='diffnorm', radius=10, center=dict(lat=40.730, lon=-73.935), zoom=8.3, mapbox_style="stamen-terrain")
writepath = path + file + '/hour'+str(h)+'.html'
fig.write_html(writepath)
def generate_density_map(current_df: pd.DataFrame):
df = current_df.copy()
df['lat-lng'] = current_df['customer.address.co_ordinate.coordinates']
fig = px.density_mapbox(df,
lat=df['lat-lng'].str[1],
lon=df['lat-lng'].str[0],
z=df['payment_value'],
radius=20,
mapbox_style='basic',
color_continuous_scale='RdYlGn',
range_color=[0, df['payment_value'].max()],
hover_data=[
df['payment_value'], df['product.category'],
df['customer.address.customer_city'],
df['customer.address.zip_code']
])
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def create_density_map(data, data2):
fig3 = px.density_mapbox(data,
lat='latitude',
lon='longitude',
radius=10,
center=dict(lat=0, lon=180),
zoom=0,
mapbox_style="stamen-terrain",
height=350,
title='Densidade de Avistamentos de OVNIs')
fig3.add_trace(
go.Scattermapbox(lat=data2['Latitude'],
lon=data2['Longitude'],
mode='markers',
marker=go.scattermapbox.Marker(size=5,
color='rgb(255, 0, 0)',
opacity=0.7),
text=data2['Name'],
hoverinfo='text'))
fig3.update_layout(margin={
"r": 0,
"t": 50,
"l": 0,
"b": 0
},
paper_bgcolor=colors['background'],
font_color=colors['text'],
title={
'xanchor': 'center',
'yanchor': 'top',
'y': 0.9,
'x': 0.5
},
mapbox={
'accesstoken': token,
'zoom': 9,
'center': dict(lat=40.7, lon=-73.9)
})
return fig3
def update_map(list_from_click, title):
tw=select_tw(title)
if list_from_click:
list_of_stocks=[clk['x'].lower() for clk in list_from_click['points']]
else:
list_of_stocks=[tw.default_category]
selected_df=tw.df_geo[tw.df_geo.Term.isin(list_of_stocks)]
rng_min=selected_df.Tweets.min()
rng_max=selected_df.Tweets.max()
figure=px.density_mapbox(data_frame=selected_df,lat='lat', lon='lon', z='Tweets', radius=20,
center = {"lat": 37.0902, "lon": -0.7129},zoom=0,
hover_name="original_location", hover_data=["Tweets", "Term"],
color_continuous_scale="Viridis",
range_color=(rng_min, rng_max),
mapbox_style="carto-positron")
#figure.update_layout(margin={"r":1,"t":1.5,"l":1,"b":0.5})
figure.update_layout(margin={"t":3,"b":6})
return figure
def plotweather(type = 'all'):
path = '/Users/andrew/non_icloud_ncf/distcomp_project/weather/'
if type == 'all':
types = ['broken_clouds','scattered_clouds','mist','clear','light_rain','few_clouds','overcast','moderate_rain','haze','fog','heavy_rain']
if type != 'all':
types = [type]
for t in types:
file = path + t + '.csv'
wtdf = pd.read_csv(file)
wtdf['diff']= wtdf.apply(lambda row: (row['Count']-row['Average']), axis=1)
wtdf.drop(['Count','Average'], inplace=True, axis = 1)
wtdf2=wtdf.groupby(['Lat', 'Lng'], as_index=False).sum()
mi = min(wtdf2["diff"])
mx = max(wtdf2["diff"])
mn = wtdf2["diff"].mean()
wtdf2['diffnorm'] = wtdf.apply (lambda row: (reshape(row['diff'], mi, mx, mn)), axis=1)
fig = px.density_mapbox(wtdf2, lat='Lat', lon='Lng', z='diffnorm', radius=10, center=dict(lat=40.730, lon=-73.935), zoom=8.3, mapbox_style="stamen-terrain")
writepath = path + t + '.html'
fig.write_html(writepath)
def generate_pred_plot(df_with_geometry):
"""Plots predicted Fuel Load."""
# assumes 'lat', 'lon' and 'predicted_load' as column headers for Latitude, Longitude, and Predicted Fuel Load
fig_pred = px.density_mapbox(
df_with_geometry,
lat="lat",
lon="lon",
z="predicted_load",
radius=3,
center=dict(lat=0, lon=180),
zoom=0,
range_color=[0, 1.592246e11],
mapbox_style="open-street-map",
title="Predicted Fuel Load Estimate for " + month + " 2016",
)
fig_pred.write_html(output_file_path_pred)
print(
"Predicted FL plot successfully generated! File saved to ",
output_file_path_pred,
)
def get_map_figure(self, dataframe, frame):
figure_map = px.density_mapbox(
dataframe[dataframe['Variable'] == self.variable],
lat='latitude',
lon='longitude',
z=self.measure,
radius=50,
animation_group='Estación',
animation_frame=frame,
hover_name='Estación',
color_continuous_scale='Jet',
opacity=0.9)
figure_map.update_layout(mapbox_style="open-street-map",
mapbox_zoom=9.5,
mapbox_center={
"lat": 4.60971,
"lon": -74.08175
})
figure_map.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return figure_map
def need_map(df_latest):
title = "Location and received donations per project"
value = 'target_amount_in_euro'
px.set_mapbox_access_token(
"pk.eyJ1Ijoia2V2aW5oZWxkIiwiYSI6ImNrNnRreGxmYjAwYXAzZnBoMmFoYnNzNXQifQ.JtnVD89sYgpWVcIA0ZW3fQ"
)
#df = df.round({'latitude': 0, 'longitude': 0})
#df_group = df.groupby(["latitude", "longitude"], as_index=False).agg({value: 'sum', "country":"first"})
#df_latest = df.query('country != "Deutschland"')
fig = px.density_mapbox(
df_latest,
lat="latitude",
lon="longitude",
#color="country",
z=value,
#color_continuous_scale=px.colors.cyclical.IceFire,
radius=15,
zoom=1,
hover_name="title",
hover_data=["carrier_name"],
labels={value: "Donations (€)"})
fig.update_layout(showlegend=False,
margin={
"l": 2,
"r": 2,
"t": 2,
"b": 2
},
updatemenus=[{
"type": "dropdown"
}],
hovermode=False)
return dbc.Card(
[dbc.CardHeader(html.B(title)),
dbc.CardBody([dcc.Graph(figure=fig)])],
className="mt-2")
