import seaborn as sns
%matplotlib inline
sns.set(font_scale=1.5)

import gmplot       # plot the locations on google map
import numpy as np    # linear algebra
import pandas as pd    # data processing, CSV file I/O (e.g. pd.read_csv())
import matplotlib.pyplot as plt # data visualization
import seaborn as sns    # data visualization
df = pd.read_csv("data.csv")
df = pd.DataFrame(df)
df_td = pd.read_csv("datacopy.csv")
df_td = pd.DataFrame(df_td)
# print df.dtypes
print (df.shape)
print (df_td.shape)
def plot_heat_map(data, number):
  latitude_array = data['INTPTLAT'].values
  latitude_list = latitude_array.tolist()
  print(latitude_list[0])
  Longitude_array = data['INTPTLONG'].values
  longitude_list = Longitude_array.tolist()
  print(longitude_list[0])
  # Initialize the map to the first location in the list
  gmap = gmplot.GoogleMapPlotter(latitude_list[0], longitude_list[0], 10)
  # gmap.scatter(latitude_list, longitude_list, edge_width=10)
  gmap.heatmap(latitude_list, longitude_list)
  # Write the map in an HTML file
  # gmap.draw('Paths_map.html')
  gmap.draw('{}_Paths_map.html'.format(number))
plot_heat_map(df,'4')

# -*- coding: utf-8 -*-
from pyheatmap.heatmap import HeatMap
import numpy as np
N = 10000
X = np.random.rand(N) * 255 # [0, 255]
Y = np.random.rand(N) * 255
data = []
for i in range(N):
 tmp = [int(X[i]), int(Y[i]), 1]
 data.append(tmp)
heat = HeatMap(data)
heat.clickmap(save_as="1.png") #点击图
heat.heatmap(save_as="2.png") #热图

import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import LogNorm
import numpy as np
x, y = np.random.rand(10), np.random.rand(10)
z = (np.random.rand(9000000)+np.linspace(0,1, 9000000)).reshape(3000, 3000)
plt.imshow(z+10, extent=(np.amin(x), np.amax(x), np.amin(y), np.amax(y)),
  cmap=cm.hot, norm=LogNorm())
plt.colorbar()
plt.show()