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()
