def maps_view(request, pk): device_data = Data.objects.get(pk=pk) latfloat = float(device_data.latitude) longfloat = float(device_data.longitude) gmaps.configure(api_key='AIzaSyAH6Tx3JJQvAkt4Tbw3tBWiSO8bLFrN41w') new_york_coordinates = (latfloat, longfloat) gmaps.figure(center=new_york_coordinates, zoom_level=12) return render(request, 'mapsview.html')
def fun(request): gmaps.configure(api_key = 'AIzaSyBcT_KzlYcyYf-171L7pR6ngBgZHYq24C4') dataset = gmaps.datasets.load_dataset_as_df('earthquakes') dataset.head() location = dataset[['latitude','longitude']] weight = dataset['magnitude'] fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(location,weights = weight)) fig = gmaps.figure(map_type='ROADMAP') # 'ROADMAP', 'HYBRID', 'TERRAIN', 'SATELLITE' return render(request,'index.html',{'fig':fig})
def route_explorer_from_file(): global reff_fig global reff_drawing global reff_dropdown global reff_routes reff_routes = open('routes.ipts').read().splitlines() reff_dropdown = widgets.Dropdown( options=[''] + reff_routes, description='Route', disabled=False, ) def route_changed(r): global reff_drawing if r.new != '': map_plot(list(map(int, r.new.split(','))), reff_drawing) else: reff_drawing.features = [] reff_dropdown.observe(route_changed, 'value') reff_fig = gmaps.figure(center=(55.785822, 12.521520), zoom_level=16, layout={ 'height': '800px', 'width': '800px' }) reff_drawing = gmaps.drawing_layer(show_controls=False) reff_fig.add_layer(reff_drawing) display(reff_dropdown) display(reff_fig)
def plot_base_map(self, place='switzerland'): ''' Create base map of switzerland arguments: ---------- none return: ------- fig - gmaps figure ''' figure_layout = { 'width': '1600px', 'height': '1100px', 'border': '1px solid black', 'padding': '1px' } if place == 'switzerland': center_coords = self._config['switzerland_ctr'] else: center_coords = self._config['winterthur_ctr'] gmaps.configure(api_key=self._config['api_key']) fig = gmaps.figure(layout=figure_layout, center=center_coords, zoom_level=9) return fig
def print_city_districts(city, opacity=None): """ It prints all the districts of a city by looking at its geojson file. Parameters ---------- city : string Name of the city whose districts we want to display. opacity : float It defines the opacity of the district layers. It supports a value in the range of [0,1] Returns ------- my_map : gmaps object This object will be used to plot the map and all activities locations in a Jupyter Notebook. """ my_map = gmaps.figure() with open('geojson/{}.geojson'.format(city), 'r') as f: districts_geometry = json.load(f) my_map.add_layer( gmaps.geojson_layer(districts_geometry, stroke_color='black', fill_opacity=(opacity or co.LAYER_TRANSPARENCY))) return my_map
def googleMaps(dataset): ownAPIKey= 'go_get_it_from_google_maps_api' gmaps.configure(api_key=ownAPIKey) # create the info box template info_box_template = """ <dl> <dt>Name</dt><dd>{name}</dd> <dt>ID</dt><dd>{id}</dd> <dt>Score</dt><dd>{score}</dd> <dt>Location</dt><dd>{location}</dd> <dt>Availability (%)</dt><dd>{available}</dd> <dt>URL</dt><dd>{listing_url}</dd> </dl> """ dataset.drop(columns=['description'], inplace=True) # drop description as it is too long gmap_dict= dataset.to_dict('records') # convert each row into a dictionary of the list gmap_locations =dataset['location'].to_list() # to show the markers on the map gmap_info = [info_box_template.format(**id) for id in gmap_dict] #map the gmap_dict with the info box template marker_layer = gmaps.marker_layer(gmap_locations, info_box_content=gmap_info) # create the markers to be shown on google map fig = gmaps.figure() fig.add_layer(marker_layer) # combine with the current map embed_minimal_html('map.html', views=[fig])
def PlotMap(): # Fucntion to create basemap global count global community gmaps.configure('AIzaSyBwEyjaABv6E1VJK3P_GKmMrvCIs8QEBJI') # ============================================================================= # m = Basemap(projection='mill',llcrnrlon=min(count['lon']),llcrnrlat=min(count['lat']),urcrnrlat=max(count['lat']),urcrnrlon=max(count['lon'])) # m.drawstates() # m.drawcoastlines() # m.drawcounties() # ============================================================================= #Plotting the data # ============================================================================= # lon=np.array(count['lon']) # lat=np.array(count['lat']) # data=np.array(count['english']) # x,y = m(lon,lat) # m.scatter(x,y,data) # ============================================================================= # ============================================================================= # data = [(float(count.iloc[i]['lat']), float(count.iloc[i]['lon'])) for i in range(len(count))] # print(data) # gmaps.heatmap(data) # ============================================================================= locations = count[['lat', 'lon']] weight = count['english'] fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(locations, weights=weight)) embed_minimal_html('export.html', views=[fig]) return fig
def hl_street(): ''' highlight streets with the most parking citations on Google map ''' df = pd.read_csv('parking-citations-processed.csv', usecols=['Latitude_WGS', 'Longitude_WGS', 'Street Name'], low_memory=True) freq = df.groupby([ 'Street Name' ]).size().reset_index(name='freq').sort_values(by=['freq'], ascending=False) most_streetslist = list(freq['Street Name'])[0:20] freq = df.groupby([ 'Street Name', 'Latitude_WGS', 'Longitude_WGS' ]).size().reset_index(name='freq').sort_values(by=['freq'], ascending=False) freq = freq.set_index('Street Name') most_streetpoint = freq.loc[most_streetslist] most_points = list( zip(list(most_streetpoint['Latitude_WGS']), list(most_streetpoint['Longitude_WGS']))) most_points = list(set(most_points)) fig = gmaps.figure(center=los_angeles_coordinates, zoom_level=12) most = gmaps.symbol_layer(most_points[::20], fill_color='blue', stroke_color='blue', scale=3, stroke_opacity=0) fig.add_layer(most) # fig.add_layer(gmaps.traffic_layer()) return (fig)
def show_google_map(paths, API_key, region): lines = [] for f in pbar()(paths.fragments): flines = [] for l in f: line_coords = np.r_[list(l.coords.xy)].T for i in range(len(line_coords) - 1): flines.append( gmaps.Line(start=tuple(line_coords[i][::-1]), end=tuple(line_coords[i + 1][::-1]))) lines.append(flines) lines = flatten(lines) print "found", len(lines), "line segments" markers = [] for o, f in pbar()(zip(flatten(paths.resampled_orientations), flatten(paths.resampled_fragments))): coords = np.r_[list(f.xy)].T markers.append([ gmaps.Marker((coords[i][1], coords[i][0]), info_box_content=str(o[i])) for i in range(len(coords)) ]) markers = flatten(markers) print "found", len(markers), "sampling locations" gmaps.configure(api_key=API_key) gmap_b = gmaps.Polygon([(i[1], i[0]) for i in region]) fig = gmaps.figure(center=tuple(region.mean(axis=0)[::-1]), zoom_level=16) fig.add_layer(gmaps.drawing_layer(features=[gmap_b] + lines + markers)) return fig
def get(self, request, format=None): """Get route for crime map.""" gmaps.configure(api_key=os.environ.get('MAPS_API')) locations = [] for each in Crimes.objects.all(): temp = [] temp.append(each.latitude) temp.append(each.longitude) locations.append(temp) try: heatmap_layer = gmaps.heatmap_layer(locations) except TraitError: heatmap_layer = gmaps.heatmap_layer( [[47.465568160532435, -122.50131030799446]]) heatmap_layer.gradient = [(0, 0, 0, 0.7), (255, 105, 180, 0.4), (255, 0, 0, 0.8)] fig = gmaps.figure() fig.add_layer(heatmap_layer) embed_minimal_html('export.html', views=[fig]) export = open('export.html').read() return Response(export)
def make_heatmap(locations, weights=None): fig = gmaps.figure() heatmap_layer = gmaps.heatmap_layer(locations) heatmap_layer.max_intensity = 100 heatmap_layer.point_radius = 8 fig.add_layer(heatmap_layer) return fig
def heatmap(tweets_location): print("Info: Building heatmap...", end='') #Seattle 47.60° N, 122.33° W #Miami 25.76° N, 80.19° W Lon = np.arange(-71.21, -71, 0.0021) Lat = np.arange(42.189, 42.427, 0.00238) Crime_counts = np.zeros((100,100)) longitude_values = [Lon,]*100 latitude_values = np.repeat(Lat,100) Crime_counts.resize((10000,)) heatmap_data = {'Counts': Crime_counts, 'latitude': latitude_values, 'longitude' : np.concatenate(longitude_values)} df = pd.DataFrame(data=heatmap_data) locations = df[['latitude', 'longitude']] weights = df['Counts'] fig = gmaps.figure() heatmap_layer = gmaps.heatmap_layer(locations, weights=weights) fig.add_layer(gmaps.heatmap_layer(locations, weights=weights)) print(fig) print("DONE!") return 0
def drawMarkerOnMap(df): df_ = __getDfWithLoaction(df) popUpList = __makePopUpList(df_) post_locations = [post['location'] for post in popUpList] info_box_template = """ <dl> <dt>User</dt><dd>{owner_name}</dd> <dt>Is_Video</dt><dd>{is_video}</dd> <dt>Likes</dt><dd>{likes}</dd> <dt>Time</dt><dd>{time}</dd> <dt>Location Name</dt><dd>{loc_name}</dd> <dt>Comment Count</dt><dd>{comment_cnt}</dd> <dt>View</dt><dd>{video_view_count}</dd> <dt>Tags</dt><dd>{tags}</dd> </dl> """ post_info = [info_box_template.format(**post) for post in popUpList] marker_layer = gmaps.marker_layer(post_locations, info_box_content=post_info) fig = gmaps.figure(center=(37.532600, 127.024612), zoom_level=10) fig.add_layer(marker_layer) print(len(post_info)) return fig
def get(self, request, format=None): """Get route for entertainment map.""" gmaps.configure(api_key=os.environ.get('MAPS_API')) locations = [] for each in Entertainment.objects.all(): temp = [] p = re.compile('[()°,]') # I know this is bad regex split_location = p.sub('', str(each.location)).split() try: if split_location[0] != 'None' or split_location[1] != 'None': temp.append(float(split_location[0])) temp.append(float(split_location[1])) locations.append(temp) except IndexError: pass heatmap_layer = gmaps.heatmap_layer(locations) heatmap_layer.gradient = [(0, 0, 0, 0.7), (255, 178, 102, 0.4), (255, 128, 0, 0.8)] fig = gmaps.figure() fig.add_layer(heatmap_layer) embed_minimal_html('export.html', views=[fig]) export = open('export.html').read() return Response(export)
def markers_direction(): figure_layout = { 'width': '1600px', 'height': '1100px', 'border': '1px solid black', 'padding': '1px' } switzerland_ctr_coord = config['switzerland_ctr'] gmaps.configure(api_key=config['api_key']) fig = gmaps.figure(layout=figure_layout, center=switzerland_ctr_coord, zoom_level=9) winterthur_lat, winterthur_lon = map.city_lat_lon('Winterthur') zurich_lat, zurich_lon = map.city_lat_lon('Zurich') winterthur_point = map.city_point('Winterthur') zurich_point = map.city_point('Zurich') locations = [(winterthur_lat, winterthur_lon), (zurich_lat, zurich_lon)] fig = map.add_marker(fig, locations) fig = map.add_direction(fig, winterthur_point, zurich_point, mode='DRIVING')
def get_lat_lng(apiKey, address): #2 farklı fonksiyon belirliyoruz. global output_address url = ( 'https://maps.googleapis.com/maps/api/geocode/json?address={}&key={}'. format(address.replace(' ', '+'), apiKey) ) #Sonucu bir yere getirmek için json formatında bir url kullanıyoruz. try: response = requests.get( url ) #url deki değerleri en baştaki requests kütüphanesi sayesinde sorguluyoruz./#We query values in url with the requests library at the very beginning. resp_json_payload = response.json( ) #Hatırlarsanız URL miz json formatındaydı.önceki koddaki sorgudan çekdiğimiz değeri json olarak ayarlıyoruz. #If you remember, our URL was in json format. We set the value we got from the query in the previous code to json. output_address = resp_json_payload["results"][0]["formatted_address"] lat = resp_json_payload['results'][0]['geometry']['location'][ 'lat'] #Burda lokasyonumuzun geometrik değerlerini lat türünden alıyoruz. lng = resp_json_payload['results'][0]['geometry']['location'][ 'lng'] #Burda lokasyonumuzun geometrik değerlerini lng türünden alıyoruz. gmaps.configure(api_key=apiKey) location = (lat, lng ) #lat ve lng değerlerini lokasyon değerine aktarıyoruz. fig = gmaps.figure( center=location, zoom_level=15 ) #orta merkezi lokasyon olarak seçiyoruz ve yakınlaştırmayı 15 yapıyoruz ki daha dinamik olsun. except: #Bir hatamız olursa diye bir dönüt yazıyoruz. print('HATA: Lokasyon bulunamadı.'.format(address)) lat = 0 lng = 0 return lat, lng, output_address, fig
def get(self, request, format=None): """Get route for Dirtiness map.""" gmaps.configure(api_key=os.environ.get('MAPS_API')) locations = [] for each in Dirtiness.objects.all(): temp = [] if each.latitude and each.longitude: temp.append(each.latitude) temp.append(each.longitude) locations.append(temp) heatmap_layer = gmaps.heatmap_layer(locations) heatmap_layer.gradient = [(0, 0, 0, 0.7), (255, 178, 102, 0.4), (102, 51, 0, 0.8)] fig = gmaps.figure() fig.add_layer(heatmap_layer) embed_minimal_html('export.html', views=[fig]) export = open('export.html').read() return Response(export)
def heatmap_plot(hotels): """ Generates a heatmap of the ideal cities Parameters ---------- ideal_cities : TYPE: DataFrame DESCRIPTION. : ideal cities subset of entire cities sample based on the humidity Returns ------- None. """ print("generating heatmap plots hotels") hotel_locations = [(hotel["lat"], hotel["lng"]) for hotel in hotels] motel_info = [hotel["name"] for hotel in hotels] heat_layer = gmaps.heatmap_layer(hotel_locations, dissipating=False, max_intensity=100, point_radius=5) # marker_layer = gmaps.marker_layer(motel_locations, # info_box_content=motel_info) fig = gmaps.figure() fig.add_layer(heat_layer) # fig.add_layer(marker_layer) print("generation complete") show(block=False)
def _render_map(self, initial_include_starbucks, initial_include_kfc): """ Render the initial map """ fig = gmaps.figure(layout={'height': '500px'}) symbols = self._generate_symbols(True, True) self._symbol_layer = gmaps.Markers(markers=symbols) fig.add_layer(self._symbol_layer) return fig
def showfig(datasets, weights, weekday): ''' ''' assert weekday in range(7) fig = gmaps.figure(center=(34.0522, -118.2437), zoom_level=11) fig.add_layer( gmaps.heatmap_layer(datasets[weekday], weights=weights[weekday])) return fig
def gen_heat_map(x, name): locations = np.stack((x[:, 0], x[:, 1]), axis=-1) weights = x[:, 2] fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(locations, weights=weights)) embed_minimal_html('export_{}.html'.format(name), views=[fig])
def _render_map(self, initial_year): fig = gmaps.figure(map_type='HYBRID') self._heatmap = gmaps.heatmap_layer( self._locations_for_year(initial_year), max_intensity=100, point_radius=8) fig.add_layer(self._heatmap) return fig
def drawHeatMap(location, val, zoom, intensity, radius): heatmap_layer = gmaps.heatmap_layer(locations, val, dissipating = True) heatmap_layer.max_intensity = intensity heatmap_layer.point_radius = radius fig = gmaps.figure(map_type='HYBRID') fig.add_layer(heatmap_layer) return fig
def plotData(distanceData, radius=0.005): valid = [] for trip in distanceData: if trip[-1] <= radius and trip[-2] <= radius: valid.append([trip[2], trip[3]]) valid = np.array(valid) fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(valid)) return fig
def __init__(self, datasets, weights): self._datasets = datasets self._weights = weights self._figure = gmaps.figure(center=(34.0522, -118.2437), zoom_level=11) self._current_index = 0 self._heatmap = gmaps.heatmap_layer( datasets[self._current_index], weights=weights[self._current_index]) self._figure.add_layer(self._heatmap)
def create_map(pairs, key): new_york_coordinates = (40.75, -74.00) gmaps.configure(api_key=key) fig = gmaps.figure(center=new_york_coordinates, zoom_level=11) heatmap_layer = gmaps.heatmap_layer(pairs) heatmap_layer.max_intensity = 1 heatmap_layer.point_radius = 15 fig.add_layer(heatmap_layer) return fig
def myfunpro(po): #argument:dataframe(df) lat_list = list(po["latitude"]) long_list = list(po["longitude"]) gmaps.configure(api_key="AIzaSyDmXhcX8z4d4GxPxIiklwNvtqxcjZoWsWU") fig = gmaps.figure() var1 = json.dumps( [{'lat': country, 'lng': wins} for country, wins in zip(lat_list, long_list)] ) return var1
def draw(dataframe): locations = dataframe[["latitude", "longitude"]] weights = dataframe["i alt"] fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(locations, weights=weights)) return fig
def geneva2zurich(): geneva = (46.2, 6.1) montreux = (46.4, 6.9) zurich = (47.4, 8.5) fig = gmaps.figure() geneva2zurich = gmaps.directions_layer(geneva, zurich) return geneva, zurich, geneva2zurich
def get_traffic_html(): gmaps.configure(api_key=config.google_config['API_key']) # Map centered on London fig = gmaps.figure(center=(51.5, -0.2), zoom_level=11) # fig.add_layer(gmaps.bicycling_layer()) fig.add_layer(gmaps.traffic_layer()) print(help(fig)) # embed_minimal_html('export.html', views=[fig]) return None
import gmaps import gmaps.datasets gmaps.configure(api_key="AI...") # Your Google API key locations = gmaps.datasets.load_dataset("taxi_rides") fig = gmaps.figure() # locations could be an array, a dataframe or just a Python iterable fig.add_layer(gmaps.heatmap_layer(locations)) fig