def getHeatmapOfMatches(self): #self.updateLocationDataFile() data = read_csv(self.location_data) print(data) geoplotlib.kde(data, bw=[5, 5]) geoplotlib.show()
def visualize_heatmap_for_data(self, df): """ Normally visualize accidents distributions across borough :param df: Dataset :return: None """ geo_data_for_plotting = {"lat": df["LATITUDE"], "lon": df["LONGITUDE"]} geoplotlib.kde(geo_data_for_plotting, 1) east = max(df["LONGITUDE"]) west = min(df["LONGITUDE"]) south = max(df["LATITUDE"]) north = min(df["LATITUDE"]) bbox = BoundingBox(north=north, west=west, south=south, east=east) geoplotlib.set_bbox(bbox) geoplotlib.tiles_provider('toner-lite') geoplotlib.show()
def _draw_hot(day, hour, minute): """ geoplotlib热力图 :param day: :param hour, minute: :param tp: :return: """ data = da.loc_state(day, hour, minute) bbox1 = BoundingBox(north=32, south=30.7, west=122.2, east=120.8) gp.set_bbox(bbox1) # gp.shapefiles('F:\\road_datas\\ShangHai\\boundary', shape_type='empty') gp.kde(data, bw=[0.5, 0.5], cmap='jet', scaling='wjk') gp.savefig('001') gp.show()
def saveGeoData(dataFrame, feature, label, tag): plot_inc_d = {} for inc in dataFrame[feature].unique(): accidents = dataFrame.loc[dataFrame[feature].isin([inc])] plot_inc = {} plot_inc = accidents[['LATITUDE', 'LONGITUDE']] plot_inc.columns = ['lat', 'lon'] for col in plot_inc.columns: plot_inc_d[col] = plot_inc[col].tolist() # Plotting the data w. geoplotlib print label + ":", inc print "Samples:", len(accidents) gpl.kde(plot_inc_d, bw=1, cut_below=2e-4) gpl.set_bbox( BoundingBox(north=40.93, west=-73.85, south=40.53, east=-73.83)) gpl.savefig('geo' + str(tag) + "_" + str(inc)) plot_inc_d = {}
def drawmap(self): """ Multiple examples of kernel density estimation visualization """ data = self.get_dao_object() # geoplotlib.kde(data, bw=5, cut_below=1e-4) # lowering clip_above changes the max value in the color scale # geoplotlib.kde(data, bw=5, cut_below=1e-4, clip_above=.1) # different bandwidths geoplotlib.kde(data, bw=20, cmap=self.cmap, cut_below=1e-4) # geoplotlib.kde(data, bw=2, cmap='PuBuGn', cut_below=1e-4) # linear colorscale # geoplotlib.kde(data, bw=5, cmap='jet', cut_below=1e-4, scaling='lin') geoplotlib.set_bbox(BoundingBox.from_nominatim('CHINA')) geoplotlib.savefig(self.ouput_filename)
import sys import numpy import geoplotlib from geoplotlib.utils import read_csv, BoundingBox from geoplotlib.colors import ColorMap data = read_csv('data/out_FL.csv') #TODO: apply filtering here rather than when making the csv bb = BoundingBox.from_points(lons=data['lon'], lats=data['lat']) #TODO: utilize layers #mark the station locations geoplotlib.dot(data, color=[0, 0, 0, 255]) #TODO: show based on zoom level #geoplotlib.labels(data,'Station Name (LEA)', color=[150,150,190,255], font_size=9, anchor_x='center') #geoplotlib.voronoi(data, cmap='Blues_r', max_area=8e3, alpha=200, f_tooltip=lambda d:d['Station Name (LEA)'] ) geoplotlib.kde(data, cmap='Blues_r', bw=10, cut_below=1e-4, scaling='lin') #geoplotlib.delaunay(data,cmap='hot_r') ##post geoplotlib.set_bbox(bb) geoplotlib.set_smoothing(True) geoplotlib.show()
""" Multiple examples of kernel density estimation visualization """ import geoplotlib from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject data = read_csv('data/opencellid_dk.csv') geoplotlib.kde(data, bw=[5, 5], cut_below=1e-6) # lowering clip_above changes the max value in the color scale #geoplotlib.kde(data, bw=[5,5], cut_below=1e-6, clip_above=1) # different bandwidths #geoplotlib.kde(data, bw=[20,20], cmap='coolwarm', cut_below=1e-6) #geoplotlib.kde(data, bw=[2,2], cmap='coolwarm', cut_below=1e-6) # linear colorscale #geoplotlib.kde(data, bw=[5,5], cmap='jet', cut_below=1e-6, scaling='lin') geoplotlib.set_bbox(BoundingBox.DK) geoplotlib.show()
#!/usr/bin/env python2 import geoplotlib from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject data = read_csv('filtered_lonlat.csv') # http://andreacuttone.com/geoplotlib/api.html#module-geoplotlib geoplotlib.dot(data, color=[0,0,0], point_size=1.5) geoplotlib.kde(data, bw=10, cmap='PuBuGn', cut_below=1e-4, clip_above=1e-2, alpha=180) geoplotlib.graph(read_csv('group0.csvgraph.csv'), src_lat='flat', src_lon='flon', dest_lat='tlat', dest_lon='tlon', color=[0,0,0], linewidth=2) geoplotlib.graph(read_csv('group1.csvgraph.csv'), src_lat='flat', src_lon='flon', dest_lat='tlat', dest_lon='tlon', color=[0,255,0], linewidth=2) geoplotlib.graph(read_csv('group2.csvgraph.csv'), src_lat='flat', src_lon='flon', dest_lat='tlat', dest_lon='tlon', color=[128,0,128], linewidth=2) geoplotlib.kde(read_csv('chokepoints.csv'), bw=10, cmap='hot', cut_below=1e-4, clip_above=1e-2, alpha=180) bbox = BoundingBox(north=25.7188,west=-80.280,south=25.711,east=-80.280) geoplotlib.set_bbox(bbox) geoplotlib.set_window_size(1400, 1600) #geoplotlib.set_window_size(700, 800) geoplotlib.tiles_provider('toner') geoplotlib.set_smoothing(True) geoplotlib.savefig('output') #geoplotlib.show()
# https://www.researchgate.net/publication/305983877_Geoplotlib_a_Python_Toolbox_for_Visualizing_Geographical_Data # git repo: https://github.com/andrea-cuttone/geoplotlib # wiki: https://github.com/andrea-cuttone/geoplotlib/wiki/User-Guide#adding-interactivity-to-layers import geoplotlib #import os #for filename in os.listdir(): # print(filename) print("trying to read plot.csv...") thedata = geoplotlib.utils.read_csv('../data/ourplot.csv') print("success") # basic dot map #geoplotlib.dot(thedata) # heat map geoplotlib.kde(thedata, bw=[5, 5]) # lowering clip_above changes the max value in the color scale #geoplotlib.kde(thedata, bw=5, cut_below=1e-4, clip_above=.1) # different bandwidths #geoplotlib.kde(thedata, bw=20, cmap='PuBuGn', cut_below=1e-4) #geoplotlib.kde(thedata, bw=2, cmap='PuBuGn', cut_below=1e-4) print("showing map") geoplotlib.show() print("success")
""" Multiple examples of kernel density estimation visualization """ import geoplotlib from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject data = read_csv('data/opencellid_dk.csv') geoplotlib.kde(data, bw=5, cut_below=1e-4) # lowering clip_above changes the max value in the color scale #geoplotlib.kde(data, bw=5, cut_below=1e-4, clip_above=.1) # different bandwidths #geoplotlib.kde(data, bw=20, cmap='PuBuGn', cut_below=1e-4) #geoplotlib.kde(data, bw=2, cmap='PuBuGn', cut_below=1e-4) # linear colorscale #geoplotlib.kde(data, bw=5, cmap='jet', cut_below=1e-4, scaling='lin') geoplotlib.set_bbox(BoundingBox.KBH) geoplotlib.show()
#!/usr/bin/env python # -*- coding: utf-8 -*- import geoplotlib from geoplotlib.utils import read_csv, BoundingBox import sys args = sys.argv if len(args) != 2: sys.exit(f"usage: ./{args[0]} /path/to/.csv") data = read_csv(args[1]) geoplotlib.kde(data, 5, cut_below=1e-4, show_colorbar=False) geoplotlib.set_bbox(BoundingBox.USA) geoplotlib.show()
""" Multiple examples of kernel density estimation visualization """ import geoplotlib from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject data = read_csv('data/opencellid_dk.csv') geoplotlib.kde(data, bw=[5,5], cut_below=1e-6) # lowering clip_above changes the max value in the color scale #geoplotlib.kde(data, bw=[5,5], cut_below=1e-6, clip_above=1) # different bandwidths #geoplotlib.kde(data, bw=[20,20], cmap='coolwarm', cut_below=1e-6) #geoplotlib.kde(data, bw=[2,2], cmap='coolwarm', cut_below=1e-6) # linear colorscale #geoplotlib.kde(data, bw=[5,5], cmap='jet', cut_below=1e-6, scaling='lin') geoplotlib.set_bbox(BoundingBox.DK) geoplotlib.show()
""" Multiple examples of kernel density estimation visualization """ import geoplotlib from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject data = read_csv('data/opencellid_dk.csv') #geoplotlib.kde(data, bw=5, cut_below=1e-4) # lowering clip_above changes the max value in the color scale #geoplotlib.kde(data, bw=5, cut_below=1e-4, clip_above=.1) # different bandwidths geoplotlib.kde(data, bw=20, cmap='OrRd', cut_below=1e-4) #geoplotlib.kde(data, bw=2, cmap='PuBuGn', cut_below=1e-4) # linear colorscale #geoplotlib.kde(data, bw=5, cmap='jet', cut_below=1e-4, scaling='lin') geoplotlib.set_bbox(BoundingBox.from_nominatim('CHINA')) geoplotlib.savefig('full.png') geoplotlib.show()
def show_geoplot(results, mbc): data=DataAccessObject.from_dataframe(results[["lat","lon"]]) gpl.hist(data, colorscale='sqrt', binsize=4) gpl.kde(data, bw=5, cut_below=1e-3) gpl.set_bbox(BoundingBox(mbc[0],mbc[1],mbc[2],mbc[3])) gpl.show()