Matplotlib底图：自定义子图和颜色栏位置

Question

我有4个不同值的地图，我想用matplotlib Basemap一起绘制为1个图中的4个子图，每个子图都有自己的颜色条。但是我设置色条和子批次大小和方向的努力似乎失败了（见下面的代码）。有人知道我做错了什么吗？使用底图时，是否无法自定义子图和颜色栏设置？你知道吗

import math
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.image as mpimg
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy.ma as ma
import numpy as np
import pylab

# Create Variables:

B = np.full((4,60,360), np.nan)
B[0] = np.random.randint(16, size=(60, 360)) + 291
B[1] = np.random.randint(201, size=(60, 360)) - 100
B[2] = np.random.randint(56, size=(60, 360)) - 50
B[3] = np.random.randint(46, size=(60, 360))

# Colorbar Boundary Definitions: 
cmap_1 = cm.jet    
cmap_2 = cm.BrBG   
cmap_3 = cm.hot     
cmap_4 = cm.CMRmap_r
B_plot = np.ma.array ( B, mask=np.isnan(B))
bounds_B = []
norm_B = []
for b in np.arange(4):
    bounds_B.append(b)
    norm_B.append(b)
bounds_B[0] =  np.arange(291, 306, 1)      
bounds_B[1] =  np.arange(-100, 110, 10)    
bounds_B[2] =  np.arange(-50, -7.5, 2.5)   
bounds_B[3] =  np.arange(0, 47.5, 2.5)     
norm_B[0] = mpl.colors.BoundaryNorm(bounds_B[0], cmap_1.N)   
norm_B[1] = mpl.colors.BoundaryNorm(bounds_B[1], cmap_2.N)   
norm_B[2] = mpl.colors.BoundaryNorm(bounds_B[2], cmap_3.N)    
norm_B[3] = mpl.colors.BoundaryNorm(bounds_B[3], cmap_4.N)    

lat = (-1) * (np.arange(-29.5, 30.5, 1))
lon = np.arange(0.5, 360.5, 1)

llc_LON = 0.5
urc_LON = 359.5
llc_LAT = -29.5
urc_LAT = 29.5

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 5))

# Variable 1
m1 = Basemap(projection='cyl', llcrnrlon=llc_LON, urcrnrlon=urc_LON, llcrnrlat=llc_LAT, urcrnrlat=urc_LAT, resolution='c')
m1.drawcoastlines(color='k')
m1.fillcontinents(color='white')          # mask land mass
lons, lats = np.meshgrid(lon,lat)
x, y = m1(lons,lats)
m1.pcolor(x, y, B_plot[0], cmap=cmap_1, norm=norm_B[0])
img_1 = m1.pcolor(x, y, B_plot[0], cmap=cmap_1, norm=norm_B[0])
axes[0, 0].set_title('Variable 1')
axes[0, 0].set_xlim(llc_LON, urc_LON)
axes[0, 0].set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360], ['0', '45E', '90E', '135E', '180', '135W', '90W', '45W', '0'])    # 0E to 360E
axes[0, 0].set_ylim(llc_LAT, urc_LAT)
axes[0, 0].set_yticks([-30, -15, 0, 15, 30], ['30S', '15S', '0', '15N', '30N'])    # 30S to 30N
axes[0, 0].set_ylabel('Latitude')
plt.grid()
# Variable 2
m2 = Basemap(projection='cyl', llcrnrlon=llc_LON, urcrnrlon=urc_LON, llcrnrlat=llc_LAT, urcrnrlat=urc_LAT, resolution='c')
m2.drawcoastlines(color='k')
m2.fillcontinents(color='white')          # mask land mass
lons, lats = np.meshgrid(lon,lat)
x, y = m2(lons,lats)
m2.pcolor(x, y, B_plot[1], cmap=cmap_2, norm=norm_B[1])
img_2 = m2.pcolor(x, y, B_plot[1], cmap=cmap_2, norm=norm_B[1])
axes[0, 1].set_title('Variable 2')
axes[0, 1].set_xlim(llc_LON, urc_LON)
axes[0, 1].set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360], ['0', '45E', '90E', '135E', '180', '135W', '90W', '45W', '0'])    # 0E to 360E
axes[0, 1].set_ylim(llc_LAT, urc_LAT)
axes[0, 1].set_yticks([-30, -15, 0, 15, 30], ['30S', '15S', '0', '15N', '30N'])    # 30S to 30N
plt.grid()
# Variable 3
m3 = Basemap(projection='cyl', llcrnrlon=llc_LON, urcrnrlon=urc_LON, llcrnrlat=llc_LAT, urcrnrlat=urc_LAT, resolution='c')
m3.drawcoastlines(color='k')
m3.fillcontinents(color='white')          # mask land mass
lons, lats = np.meshgrid(lon,lat)
x, y = m3(lons,lats)
m3.pcolor(x, y, B_plot[2], cmap=cmap_3, norm=norm_B[2])
img_3 = m3.pcolor(x, y, B_plot[2], cmap=cmap_3, norm=norm_B[2])
axes[1, 0].set_title('Variable 3')
axes[1, 0].set_xlim(llc_LON, urc_LON)
axes[1, 0].set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360], ['0', '45E', '90E', '135E', '180', '135W', '90W', '45W', '0'])    # 0E to 360E
axes[1, 0].set_ylim(llc_LAT, urc_LAT)
axes[1, 0].set_yticks([-30, -15, 0, 15, 30], ['30S', '15S', '0', '15N', '30N'])    # 30S to 30N
axes[1, 0].set_ylabel('Latitude')
plt.grid()
# Variable 4
m4 = Basemap(projection='cyl', llcrnrlon=llc_LON, urcrnrlon=urc_LON, llcrnrlat=llc_LAT, urcrnrlat=urc_LAT, resolution='c')
m4.drawcoastlines(color='k')
m4.fillcontinents(color='white')          # mask land mass
lons, lats = np.meshgrid(lon,lat)
x, y = m4(lons,lats)
m4.pcolor(x, y, B_plot[3], cmap=cmap_4, norm=norm_B[3])
img_4 = m4.pcolor(x, y, B_plot[3], cmap=cmap_4, norm=norm_B[3])
axes[1, 1].set_title('Variable 4')
axes[1, 1].set_xlim(llc_LON, urc_LON)
axes[1, 1].set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360], ['0', '45E', '90E', '135E', '180', '135W', '90W', '45W', '0'])    # 0E to 360E
axes[1, 1].set_ylim(llc_LAT, urc_LAT)
axes[1, 1].set_yticks([-30, -15, 0, 15, 30], ['30S', '15S', '0', '15N', '30N'])    # 30S to 30N
plt.grid()

plt.subplots_adjust(bottom=0.1, hspace=0.2)
colorbar_1 = fig.add_axes([0.1, 0.48, 0.30, 0.015])          
fig.colorbar(img_1, cax=colorbar_1, orientation="horizontal", label='Unit 1', ticks=[291, 293, 295, 297, 299, 301, 303, 305])
colorbar_2 = fig.add_axes([0.55, 0.48, 0.30, 0.015])             
fig.colorbar(img_2, cax=colorbar_2, orientation="horizontal", label='Unit 2', ticks=[-100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100])
plt.subplots_adjust(bottom=0.05, right=0.80, left=0.21, hspace=0.2)
colorbar_3 = fig.add_axes([0.1, 0.08, 0.30, 0.015])            
fig.colorbar(img_3, cax=colorbar_3, orientation="horizontal", label='Unit 3', ticks=[-50, -45, -40, -35, -30, -25, 20, -15, -10])
colorbar_4 = fig.add_axes([0.55, 0.08, 0.30, 0.015])            
fig.colorbar(img_4, cax=colorbar_4, orientation="horizontal", label='Unit 4', ticks=[0, 5, 10, 15, 20, 25, 30, 35, 40, 45])

plt.show()

Answer 1

主要问题是如何定义plotbar。在这里，我用一个for循环中的当前轴来做。另外，不显示xthick，我使用set_xticklabels来显示它们。你知道吗

然后，我清理了你的代码，因为你几乎做了4次同样的事情。你知道吗

import math
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.image as mpimg
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy.ma as ma
import numpy as np
import pylab

# Create Variables:
B = np.array([np.random.randint(16, size=(60, 360)) + 291,
              np.random.randint(201, size=(60, 360)) - 100,
              np.random.randint(56, size=(60, 360)) - 50,
              np.random.randint(46, size=(60, 360))])

# Colorbar Boundary Definitions:
cmap = [cm.jet, cm.BrBG, cm.hot, cm.CMRmap_r]

B_plot = np.ma.array(B, mask=np.isnan(B))
norm_B = []
bounds_B = [np.arange(291, 306, 1),
          np.arange(-100, 110, 10),
          np.arange(-50, -7.5, 2.5),
          np.arange(0, 47.5, 2.5)]
for b in np.arange(4):
    norm_B.append(mpl.colors.BoundaryNorm(bounds_B[b], cmap[b].N))

lat = (-1) * (np.arange(-29.5, 30.5, 1))
lon = np.arange(0.5, 360.5, 1)

llc_LON = 0.5
urc_LON = 359.5
llc_LAT = -29.5
urc_LAT = 29.5

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 5))

thicks = [[291, 293, 295, 297, 299, 301, 303, 305],
          [-100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100],
          [-50, -45, -40, -35, -30, -25, 20, -15, -10],
          [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]]

lons, lats = np.meshgrid(lon, lat)

for row_ind in range(2):
    for col_ind in range(2):
        index = row_ind*2+col_ind
        current_ax = axes[row_ind, col_ind]
        print("row_ind: {0}    col_ind: {1}   index: {2}".format(row_ind, col_ind, index))

        map = Basemap(projection='cyl', llcrnrlon=llc_LON, urcrnrlon=urc_LON,
                      llcrnrlat=llc_LAT, urcrnrlat=urc_LAT, resolution='c',
                      ax=current_ax)
        map.drawcoastlines(color='k')
        map.fillcontinents(color='white')          # mask land mass

        x, y = map(lons, lats)

        img_colors = map.pcolor(x, y, B_plot[index], cmap=cmap[index], norm=norm_B[index])

        fig.colorbar(img_colors, ax=current_ax, orientation="horizontal",
                    label='Unit ' + str(index+1), ticks=thicks[index])

        current_ax.set_title('Variable ' + str(index+1))
        current_ax.set_xlim(llc_LON, urc_LON)
        current_ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360]) 
        current_ax.set_xticklabels(['0', '45E', '90E', '135E', '180', '135W', '90W', '45W', '0'])  # 0E to 360E
        current_ax.set_ylim(llc_LAT, urc_LAT)
        current_ax.set_yticks([-30, -15, 0, 15, 30])  # 30S to 30N
        current_ax.set_yticklabels(['30S', '15S', '0', '15N', '30N'])
        current_ax.set_ylabel('Latitude')


fig.suptitle('My maps')
plt.show()