color对照 python_python3 matplotlib多个子图对应同一个colorbar

# -*- coding: utf-8 -*-

"""

Created on Tue Nov 12 19:31:07 2019

@author: BAI Depei

"""

from osgeo import gdal

import numpy as np

import sympy as sp

import matplotlib.pyplot as plt

from matplotlib.colors import Normalize

#-------------------------读取2007、2008、2009年TRMM降水遥感图像-----------------------

path = 'E:\\课程PPT\\Python空间数据处理\\python空间数据处理-期中大作业02\\卫星降雨数据TRMM3B43_分辨率0.25度\\'

#有时候路径必须双反斜杠

dataset = gdal.Open(path + '3B43_2007.TIF')

dataset2 = gdal.Open(path + '3B43_2008.TIF')

dataset3 = gdal.Open(path + '3B43_2009.TIF')

samples = dataset.RasterXSize

lines = dataset.RasterYSize

bands = dataset.RasterCount

img_geotrans = dataset.GetGeoTransform()

#Out[10]: (-180.0, 0.25, 0.0, 50.0, 0.0, -0.25)

img_proj = dataset.GetProjection()

#Out[12]: 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84"...]]]

#可以看到此遥感图像未进行投影定义，因此还不是投影坐标，只是地理坐标

#在求解这些实测站点位于图像中哪个行列位置时不用将其转为投影坐标

im_data1 = dataset.ReadAsArray(0,0,samples,lines)

im_data2 = dataset2.ReadAsArray(0,0,samples,lines)

im_data3 = dataset3.ReadAsArray(0,0,samples,lines)

del dataset

del dataset2

del dataset3

#-------------------------------找到研究区范围的行列号-------------------------------------

#-----------------------转换左上、右下经纬度坐标到对应遥感图像上的行列号----------

im_loc = [[],[]]

#第一个列表放行号，存纬度换出来的值，后者为列号，存经度换出来的值

# variable += [value] 等同于 variable.append(value)

#左上角 115 25.5

#右下角 117 27.5

pnt_coordinates = [[115,117],[27.5,25.5]]

for i in range(len(pnt_coordinates[0])):

sample = sp.Symbol('sample')

line = sp.Symbol('line')

#pnt_coordinates[0][i] = img_geotrans[0] + sample*img_geotrans[1] + line*img_geotrans[2]

#pnt_coordinates[1][i] = img_geotrans[3] + sample*img_geotrans[4] + line*img_geotrans[5]

a = -pnt_coordinates[0][i] + img_geotrans[0] + sample*img_geotrans[1] + line*img_geotrans[2]

b = -pnt_coordinates[1][i] + img_geotrans[3] + sample*img_geotrans[4] + line*img_geotrans[5]

answer = sp.solve([a,b],[sample,line])#解二元一次方程组

#Out[15]: {x: 0, y: 1}

line = int(np.floor(answer[line]))

sample = int(np.floor(answer[sample]))

im_loc[0] += [line]

im_loc[1] += [sample]

#--------------------------------校正数据---------------------------------------

cor_data1 = 0.797 * im_data1 + 352.279

cor_data2 = 0.797 * im_data2 + 352.279

cor_data3 = 0.797 * im_data3 + 352.279

#------------------------------起始终止行列号------------------------------------

start_line = im_loc[0][0]

end_line = im_loc[0][1]

start_sample = im_loc[1][0]

end_sample = im_loc[1][1]

#------------------------------裁剪图像-----------------------------------------

data1 = im_data1[start_line:end_line,start_sample:end_sample]

data2 = im_data2[start_line:end_line,start_sample:end_sample]

data3 = im_data3[start_line:end_line,start_sample:end_sample]

cdata1 = cor_data1[start_line:end_line,start_sample:end_sample]

cdata2 = cor_data2[start_line:end_line,start_sample:end_sample]

cdata3 = cor_data3[start_line:end_line,start_sample:end_sample]

#----------------------------用于生产colorbar的vmin和vmax-----------------------

#求最小值

mins = []

mins.append(np.min(data1))

mins.append(np.min(data2))

mins.append(np.min(data3))

mins.append(np.min(cdata1))

mins.append(np.min(cdata2))

mins.append(np.min(cdata3))

vmin = np.min(mins)

#求最大值

maxes = []

maxes.append(np.max(data1))

maxes.append(np.max(data2))

maxes.append(np.max(data3))

maxes.append(np.max(cdata1))

maxes.append(np.max(cdata2))

maxes.append(np.max(cdata3))

vmax = np.max(maxes)

#fig,((ax0,ax1),(ax2,ax3),(ax4,ax5)) = plt.subplots(nrows = 3, ncols = 2,figsize = (18,12))

#------------------------------------绘图---------------------------------------

fig,axs = plt.subplots(nrows = 2, ncols = 3,figsize = (12,8))

#figsize = (width，hight)

extent = (0,1,0,1)

#将x周和y轴都归一化。方便指定label的位置(0,1)相对比例，否则指定label位置时不好指定

norm = Normalize(vmin = vmin,vmax = vmax)

#Normalize()跟归一化没有任何关系，函数的作用是将颜色映射到vmin-vmax上，即让接下来的颜色表/颜色柱的起始和终止分别取值vmin和vmax

#axs[0,0].imshow(data1,extent = extent,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[0,0].imshow(data1,extent = extent,norm = norm,cmap = 'jet_r')

axs[0,0].set_xlabel('2007 Original',fontdict = {'family' : 'Times New Roman','fontweight':'bold'})

axs[0,0].set_xticks(np.linspace(0,1,5))

font = {'fontweight':'bold'}

axs[0,0].set_xticklabels(('115.0E','115.5E','116.0E','116.5E','117.0E'),fontdict = font)

axs[0,0].set_yticks(np.linspace(0,1,5))

axs[0,0].set_yticklabels(('25.5 N','26.0 N','26.5 N','27.0 N','27.5 N'))

axs[0,1].imshow(data2,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[0,1].set_xlabel('2008 Original',fontdict = {'family' : 'Times New Roman'})

axs[0,2].imshow(data3,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[0,2].set_xlabel('2009 Original',fontdict = {'family' : 'Times New Roman'})

axs[1,0].imshow(cdata1,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[1,0].set_xlabel('2007 Corrected',fontdict = {'family' : 'Times New Roman'})

axs[1,1].imshow(cdata2,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[1,1].set_xlabel('2008 Corrected',fontdict = {'family' : 'Times New Roman'})

sc = axs[1,2].imshow(cdata3,vmin = vmin,vmax = vmax,cmap = 'jet_r')

axs[1,2].set_xlabel('2009 Corrected',fontdict = {'family' : 'Times New Roman'})

#norm = plt.colors.Normalize(vmin=vmin, vmax=vmax)

#前面三个子图的总宽度 为 全部宽度的 0.9；剩下的0.1用来放置colorbar

fig.subplots_adjust(right=0.9)

#colorbar 左 下 宽 高

l = 0.92

b = 0.53

w = 0.015

h = 0.35

#对应 l,b,w,h；设置colorbar位置；

rect = [l,b,w,h]

cbar_ax = fig.add_axes(rect)

plt.colorbar(sc, cax=cbar_ax)

plt.savefig('correct_pictures.png',dpi = 300,bbox_inches='tight')

plt.show()