def display_mapc(df):
px.set_mapbox_access_token(
"pk.eyJ1IjoibXV5ZGlwYWxtYSIsImEiOiJjazk4eTI2ZmIwMjAzM2Vua3N0cHJhNXR6In0.y-fZg2ncJlXhVTAPO_9mmw")
fig = px.scatter_mapbox(df, lat='longitud', lon='latitud', color='cc', hover_data=['punto_vacunacion'], hover_name='punto_vacunacion',
center={'lon': df.loc[-1,'latitud'], 'lat': df.loc[-1,'longitud']}, zoom=13,labels={},width=800)
return fig
def display_map(df):
st.subheader(" Displaying Point based map")
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(df, lat="Y", lon="X", color="METHOD", zoom=10)
return fig
def make_figure(size, color):
px.set_mapbox_access_token(
"pk.eyJ1IjoiYmFucXVldGt1bWEiLCJhIjoiY2p0YjZ4bGJ2MGlseTN5bzlxcnlsbW8xNCJ9.udbxOpc2gZQcUX4m1VIqBg"
)
return px.scatter_mapbox(GPS,
lat="緯度 (°)",
lon="経度 (°)",
color=color,
size=size,
size_max=15,
zoom=10,
color_continuous_scale=px.colors.cyclical.IceFire,
hover_name=size)
def display_map(df):
st.subheader(" Displaying Point based map")
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(df,
lat="lat",
lon="lon",
color="district",
size="confirmed",
color_continuous_scale=px.colors.cyclical.IceFire,
zoom=3,
width=900,
height=900)
return fig
def kumapx():
px.set_mapbox_access_token(
"pk.eyJ1IjoiYmFucXVldGt1bWEiLCJhIjoiY2p0YjZ4bGJ2MGlseTN5bzlxcnlsbW8xNCJ9.udbxOpc2gZQcUX4m1VIqBg"
)
#mapboxのtokenを読み込む
return px.scatter_mapbox(
df,
lat="Latitude",
lon="Longtitude",
color="Wait Time(min)",
size="Wait Time(min)",
size_max=60,
zoom=10,
#animation_frame="DATETIME",
#animation_group="attractions",
color_continuous_scale=px.colors.cyclical.IceFire,
hover_name="attractions",
title="Disney's Animal Kingdom Theme Park Wait Time from {} to {}".
format(df["DATETIME"].min(), df["DATETIME"].max()))
def map_box_plot(df, locations):
"""
Generate a map box chart
"""
if len(locations) > 0:
df = df[df['Capture Location'].isin(locations)]
args = {
'lat': 'lat',
'lon': 'long',
'size': 'Annuli',
'color': 'Gender',
'size_max': 20,
'zoom': 10,
}
px.set_mapbox_access_token(environ['MAP_BOX'])
fig = go.Figure(px.scatter_mapbox(df, **args))
fig['layout']['height'] = 600
graph = dcc.Graph(id='map-box-chart', figure=fig)
return graph
def carto_clean(new_df, counties, communes):
px.set_mapbox_access_token(open(".mapbox_token").read())
postal_code = pd.read_csv('population.csv')
postal_code['Code Postal'] = postal_code['Code Postal'].astype('str')
postal_code['Code Postal'] = postal_code['Code Postal'].str.replace(
'R', '0')
postal_code['Code Département'] = postal_code['Code Département'].astype(
'str')
postal_code['Code Département'] = postal_code[
'Code Département'].str.replace('R', '0')
form_presence = new_df[new_df['Code Postal'] != 'A DISTANCE']
form_presence['Code Postal'] = form_presence['Code Postal'].astype('str')
form_presence_by_city = form_presence.groupby(["Ville",
"Code Postal"]).count()
form_presence_by_city = form_presence_by_city['Nom']
form_presence_by_city.name = 'Nombre de formations'
form_presence_by_city = form_presence_by_city.reset_index()
form_by_city = pd.merge(postal_code,
form_presence_by_city,
left_on=['Commune', 'Code Postal'],
right_on=["Ville", "Code Postal"])
#st.write(form_presence_by_city[['Ville','Code Postal','Nombre de formations']][form_presence_by_city['Code Postal'].isin(form_by_city['Code Postal'])==False])
#st.write(form_presence_by_city[['Nombre de formations']][form_presence_by_city['Code Postal'].isin(form_by_city['Code Postal'])==False].sum())
form_by_city = form_by_city.dropna()
form_by_dep = form_by_city.groupby('Code Département').sum().reset_index()
form_by_dep = pd.merge(form_by_dep,
communes,
left_on='Code Département',
right_on='Code Dept')
return form_by_dep, form_by_city
def display_map(df):
px.set_mapbox_access_token("pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w")
fig = px.scatter_mapbox(df, lat='latitude', lon='longitude', zoom=10)
return fig
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import plotly_express as px
import pandas as pd
import flask
########### Set up the chart
px.set_mapbox_access_token(
'pk.eyJ1Ijoibmlja21jY2FydHkiLCJhIjoiY2pxemppMDZoMGJxeDQ0dDJ5OWxhcXA3dCJ9.k6cGbYHEbY2UDT-D6chFbw'
)
fatalities = pd.read_csv('Data/fatalities_geocoded.csv')
fatalities = fatalities.drop('Unnamed: 0', axis=1)
test = fatalities.groupby(
['state', 'race', 'location', 'latitude', 'longitude'])['name'].count()
test = test.reset_index()
test = test.rename({'name': 'count'}, axis=1)
test = test.sort_values('count', ascending=False)
fig = px.scatter_mapbox(test,
lat="latitude",
lon="longitude",
color="race",
size="count",
color_continuous_scale=px.colors.cyclical.IceFire,
size_max=20,
zoom=3,
group_id=item,
value_deserializer=lambda x: loads(x.decode('utf-8')))
consumer.subscribe(item)
consumers.append(consumer)
print('created consumers')
# create app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
print('initiated app')
# setmapbox style
# current script path
scripty_path = os.path.dirname(os.path.realpath(__file__))
mapbox_access_token = 'pk.eyJ1IjoibXo4NiIsImEiOiJja2I5bWozeGgwZTJ0MnJwZjdtbHBjZjNxIn0.pSTk6D13HreCcxth26axeg'
px.set_mapbox_access_token(mapbox_access_token)
print('set map box')
# sql connection
engine = create_engine(sql_key.key, echo=False)
print('set sql engine')
app.layout = html.Div(
html.Div([
html.H4('Traffic Live Update'),
dcc.Graph(id='live-update-graph'),
dcc.Interval(
id='interval-component',
interval=5 * 1000, # in milliseconds
n_intervals=0)
]))
geometry=gpd.points_from_xy(df.X_from, df.Y_from))
gdf["geometry_to"] = [Point(xy) for xy in zip(gdf.X_to, gdf.Y_to)]
gdf['line'] = gdf.apply(
lambda row: LineString([row['geometry_to'], row['geometry']]), axis=1)
line_gdf = gdf[["node_start", "node_end", "line"]].set_geometry('line')
return line_gdf
line_gdf = create_line_gdf(df)
line_gdf.plot()
start = df[df["node_start"] == start_node]
end = df[df["node_end"] == end_node]
px.set_mapbox_access_token(
"pk.eyJ1IjoiamV4ZnJlZG8iLCJhIjoiY2twZW1nbm82MXluYjJubmx6endsN25tdyJ9.AR5KtIhw-_kDoe3NUahpHg"
)
px.scatter_mapbox(df, lon="X_from", lat="Y_from", zoom=12)
fig = px.scatter_mapbox(df,
lon="X_from",
lat="Y_from",
width=800,
height=400,
zoom=12)
fig.add_trace(px.line_mapbox(df, lon="X_from", lat="Y_from").data[0])
fig.data[1].marker = dict(size=15, color="red")
fig.add_trace(px.scatter_mapbox(end, lon="X_from", lat="Y_from").data[0])
fig.data[2].marker = dict(size=15, color="green")
fig.add_trace(px.line_mapbox(df, lon="X_from", lat="Y_from").data[0])
fig.show()
import pandas as pd
import numpy as np
import time
import ast
import webbrowser
import googlemaps
import gmplot
import polyline
import plotly_express as px
from haversine import haversine
from hdbscan import HDBSCAN
with open(".secret.key", "r") as f:
api_keys = ast.literal_eval(f.read().strip())
px.set_mapbox_access_token(api_keys["mapbox"])
gmaps = googlemaps.Client(key=api_keys["samesame_gmaps"])
def get_loc(query, current=True):
"""Initialize a location using one of two methods:
----------------
current = True:
Use data from cell towers, WiFi, and GPS
current = False:
Ask the user for a starting location
This can be a string (search google maps, be specific)
or a latitude-longitude coordinate
----------------
returns a dictionary of the form:
{'lat': 47.606269, 'lng': -122.334747}
def display_map(df):
px.set_mapbox_access_token(
"pk.eyJ1IjoibXV5ZGlwYWxtYSIsImEiOiJjazk4eTI2ZmIwMjAzM2Vua3N0cHJhNXR6In0.y-fZg2ncJlXhVTAPO_9mmw")
fig = px.scatter_mapbox(df, lat='longitud', lon='latitud',hover_name='punto_vacunacion',hover_data=None,zoom=10)
return fig
import pandas as pd
import geopandas as gpd
import plotly_express as px
import matplotlib.pyplot as plt
df = pd.read_csv('data/go_track_trackspoints_edited.csv')
gdf = gpd.GeoDataFrame( df, geometry=gpd.points_from_xy(df.longitude, df.latitude))
px.set_mapbox_access_token('pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w')
polygon = gpd.read_file('data/CENTERAREA.geojson')
mask = (polygon.loc[0, 'geometry'])
pip_mask_geofence = gdf.within(mask)
gdf.loc[:,'geofence'] = pip_mask_geofence
gdf['geofence'] = gdf['geofence'].replace({True: 'In', False: 'Out'})
print(gdf)
fig = px.scatter_mapbox(gdf,
lat='latitude_edit', lon='longitude_edit',
color='type',
size='track_id',
animation_frame='time',
animation_group='track_id',
size_max=10,
zoom=12,
text='altitude',
width=1200,
height=800)
fig.show()
title='''Weight vs height relation of the offenders
({} years old)'''.format(AGE),
color='race',
size='age',
hover_data=['birth date', 'block'],
hover_name="name")
FIG_HISTO_AVG = px.histogram(CSV,
x="race",
y='age',
color="race",
title="Age average per race",
histfunc='avg')
px.set_mapbox_access_token(
'pk.eyJ1IjoiYmFwdGlzdGVhZGFtIiwiYSI6ImNrMmdwdmNncDByZWIzbG9lbTExMmY5YmkifQ.D7vXobtSiwy2Z-_OAtUoeQ'
)
FIG_MAP = px.scatter_mapbox(CSV,
lat="lat",
lon="lon",
title='Map of the offences',
color="race",
size="age",
hover_name='name',
hover_data=['birth date', 'block'],
zoom=9)
FIG_HISTO_1.update_layout(legend_orientation="h",
legend=dict(x=-0.01, y=1.115))
FIG_HISTO_2.update_layout(legend_orientation="h",
legend=dict(x=-0.01, y=1.1),
dinosaur, flight_of_passage, Expedition_Everest, kilimanjaro_safaris,
Navi_River
])
#DateFrameを縦につなげる
df = df.sort_values("DATETIME")
#日付時刻順にソートしておく
df = df.dropna(subset=["wait_time(actual)"])
#何故か"wait_time(actual)"列にNaNが残っているので、もう一回弾く
df["Wait Time(min)"] = df["wait_time(actual)"]
#"wait_time(actual)"列を"Wait Time(min)"列にコピーする
px.set_mapbox_access_token(
"pk.eyJ1IjoiYmFucXVldGt1bWEiLCJhIjoiY2p0YjZ4bGJ2MGlseTN5bzlxcnlsbW8xNCJ9.udbxOpc2gZQcUX4m1VIqBg"
)
#mapboxのtokenを読み込む
px.scatter_mapbox(
df,
lat="Latitude",
lon="Longtitude",
color="Wait Time(min)",
size="Wait Time(min)",
size_max=60,
zoom=10,
animation_frame="DATETIME",
animation_group="attractions",
color_continuous_scale=px.colors.cyclical.IceFire,
hover_name="attractions",
def main():
st.sidebar.markdown("### Recommendation type")
section = st.sidebar.selectbox('choose recommendation type', [
'Pick a Value', 'Location based', 'Content based',
'Collaborative Filtering'
],
key=1)
#fig.update_layout(mapbox_style="dark")
#fig.show()
#
if section == "Pick a Value":
st.markdown("## How to get the most out of this platform")
st.markdown(
'This platform contains 3 recommendation system models to recommend to you restaurants based on Yelp reviews in Toronto city'
)
st.markdown(
"- If you're a new user of this platform or in this city and you have never tried any restaurant around toronto, please select the **location based** recommender on the sidebar to get recommended top restaurants around where you are."
)
st.markdown(
"- If you want recommendations of restaurants similar to one you have previously visited and liked, please select **content-based** on the sidebar."
)
st.markdown(
"- If this isn't your first time using this platform and would like to get recommendations based on previous restaurants you have visited and rated please select the **collaborative filtering** option on the sidebar."
)
#st.markdown("- If you just want to compare the ratings of different restaurants you have in mind, please select **Restaurant Analytics** on the sidebar.")
st.subheader("Graphical Overview of Restaurants in Toronto City")
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(business_data,
lat="latitude",
lon="longitude",
color="stars",
size='review_count',
size_max=15,
zoom=10,
width=1000,
height=700)
st.plotly_chart(fig)
if section == "Location based":
st.subheader('Location Based Recommendation System')
st.markdown("please enter your location")
location = st.text_area('Input your location here')
if location:
URL = "https://geocode.search.hereapi.com/v1/geocode"
api_key = 'ODfYgIX45wrL41qboC3F_z2hg8e5_ABJYi71Pu6o948' # Acquire from developer.here.com
PARAMS = {'apikey': api_key, 'q': location}
lat_long = LOC.get_location(URL, PARAMS)
latitude = lat_long[0]
longitude = lat_long[1]
df = KM.location_based_recommendation(business_data, latitude,
longitude)
if st.sidebar.checkbox("Show data", False):
st.write(df)
st.markdown(
"## Geographical Plot of Nearby Recommended Restaurants from "
+ location)
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(df,
lat="latitude",
lon="longitude",
zoom=10,
width=1000,
height=700,
hover_data=[
'name', 'latitude', 'longitude',
'categories', 'stars', 'review_count'
])
fig.add_scattermapbox(lat=[latitude],
lon=[longitude]).update_traces(
dict(mode='markers',
marker=dict(size=15)))
fig.update_layout(mapbox_style="dark")
st.plotly_chart(fig)
if section == 'Content based':
st.subheader('Content based recommendation system')
st.markdown(
"please select a restaurant similar to the one you'd like to visit"
)
restaurant = st.selectbox('select restaurant',
toronto_data['name'].unique())
if restaurant:
restaurant_recommendations = CT.content_based_recommendations(
restaurant)
restaurant1 = toronto_data[toronto_data['name'] ==
restaurant_recommendations[0]][[
'name', 'categories', 'super_score'
]].groupby(['name', 'categories'],
as_index=False).mean()
restaurant2 = toronto_data[toronto_data['name'] ==
restaurant_recommendations[1]][[
'name', 'categories', 'super_score'
]].groupby(['name', 'categories'],
as_index=False).mean()
restaurant3 = toronto_data[toronto_data['name'] ==
restaurant_recommendations[2]][[
'name', 'categories', 'super_score'
]].groupby(['name', 'categories'],
as_index=False).mean()
restaurant4 = toronto_data[toronto_data['name'] ==
restaurant_recommendations[3]][[
'name', 'categories', 'super_score'
]].groupby(['name', 'categories'],
as_index=False).mean()
restaurant5 = toronto_data[toronto_data['name'] ==
restaurant_recommendations[4]][[
'name', 'categories', 'super_score'
]].groupby(['name', 'categories'],
as_index=False).mean()
rest_merged = pd.concat([
restaurant1.head(1),
restaurant2.head(1),
restaurant3.head(1),
restaurant4.head(1),
restaurant5.head(1)
])
st.write(rest_merged)
# st.subheader('Collaborative Filtering recommendation system')
# if restaurant:
# collab_recommendations = content_based_recommendations(restaurant)
# collab_recommendations = pd.DataFrame(data = restaurant_recommendations)
# st.write(restaurant_recommendations)
if section != 'Pick a Value':
if st.sidebar.checkbox("Compare restaurants by sentiments", False):
choice = st.sidebar.multiselect('Pick restaurants',
toronto_data['name'].unique())
if len(choice) > 0:
st.subheader("Breakdown restaurant by sentiment")
fig_3 = make_subplots(rows=1,
cols=len(choice),
subplot_titles=choice)
for i in range(1):
for j in range(len(choice)):
fig_3.add_trace(go.Bar(
x=plot_sentiment(choice[j]).Sentiment,
y=plot_sentiment(choice[j]).text,
showlegend=False),
row=i + 1,
col=j + 1)
fig_3.update_layout(height=600, width=800)
st.plotly_chart(fig_3)
import pandas as pd
import numpy as np
import time
import ast
import webbrowser
import googlemaps
import gmplot
import polyline
import plotly_express as px
from haversine import haversine
from hdbscan import HDBSCAN
with open('.secret.key', 'r') as f:
api_keys = ast.literal_eval(f.read().strip())
px.set_mapbox_access_token(api_keys['mapbox'])
gmaps = googlemaps.Client(key=api_keys['samesame_gmaps'])
def get_loc(query, current=True):
"""Initialize a location using one of two methods:
----------------
current = True:
Use data from cell towers, WiFi, and GPS
current = False:
Ask the user for a starting location
This can be a string (search google maps, be specific)
or a latitude-longitude coordinate
----------------
returns a dictionary of the form:
{'lat': 47.606269, 'lng': -122.334747}
