Python：如何在 For 循环中嵌套 plt.imshow()？

Question

我是 Pythonic 新手。我有一个代码，它产生一个 256x256 矩阵并通过pyplot 和plt.imshow() 绘制它。现在，我想通过使用较小的矩阵（这次是 101x101）生成相同 256x256 矩阵的 n 个子图，该矩阵用作移动窗口。从视觉上看，我想得到这样的东西（在确定了子图的数量和它们的起源位置之后。

最后，我想要一组 n+1 幅图像分别绘制在不同的图中：一幅用于 256x256 矩阵，n 用于移动窗口. 我现在拥有的代码将所有图像归结为一张。正如您在此处看到的，在尝试绘制 n+1=3 个矩阵时，只绘制了一个矩阵，最后一个矩阵，我所有的颜色条都堆叠起来了，我的文本字符串不可读。

如何将 plt.imshow() 调用嵌套在 for 循环中，以便 Python 为我提供 n+1 个数字？我希望在这里把事情说清楚。 感谢任何提供指导的人！

这是我当前的代码：

from __future__ import division #Avoids the floor of the mathematical result of division if the args are ints or longs
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mc
import SSFM2D 
import random


#Parameter assignments
max_level=8               #This is the exponent controlling the grid size. In this case N=2^8=256. Use only integers.
N=2**max_level
sigma=1                   #Variation for random Gauss generation (standardised normal distribution)
H=0.8                     #Hurst exponent (0.8 is a recommended value for natural phenomena)
seed = random.random()    #Setting the seed for random Gauss generation
print ('The lattice size is '+str(N)+'x'+str(N))


#Lattice initialization
Lattice=np.zeros((256,256))


#Calling Spectral fBm function
Lattice=SSFM2D.SpectralSynthesisFM2D(max_level, sigma, H, seed, normalise=True, bounds=[0,1])


#Plotting the original 256x256 lattice
M=np.zeros((257,257))      
for i in range(0,256):
    for j in range(0,256):
        M[i][j]=Lattice[i][j]


#Normalizing the output matrix
print ('Original sum: '+str(round(M[-257:,-257:].sum(),3)))
M = M/M[-257:, -257:].max()       #Matrix normalization with respect to max


#Lattice statistics
print ('Normalized sum: '+str(round(M[-257:,-257:].sum(),3)))
print ('Normalized max: '+str(round(M[-257:,-257:].max(),3)))
print ('Normalized min: '+str(round(M[-257:,-257:].min(),3)))
print ('Normalized avg: '+str(round(M[-257:,-257:].mean(),3)))      


#Determining the footprint 
footprint=0 #Initializing the footprint count variable
for i in range(M.shape[0]):
    for j in range(M.shape[1]):
        if M[i][j]>0.15:     # Change here to set the failure threshold
            footprint=footprint+1
print ('Event footprint: '+str(round(footprint*100/(256*256),2))+'%')


#Plotting the 256x256 "mother" matrix
plt.imshow(M[-257:,-257:].T, origin='lower',interpolation='nearest',cmap='Reds', norm=mc.Normalize(vmin=0,vmax=M.max()))
title_string=('Spatial Loading of a Generic Natural Hazard')
subtitle_string=('Inverse FFT on Spectral Synthesis')
plt.suptitle(title_string, y=0.99, fontsize=17)
plt.title(subtitle_string, fontsize=8)
plt.show()
#Making a custom list of tick mark intervals for color bar (assumes minimum is always zero)
numberOfTicks = 5
ticksListIncrement = M.max()/(numberOfTicks)
ticksList = []
for i in range((numberOfTicks+1)):
    ticksList.append(ticksListIncrement * i) 
plt.tick_params(axis='x', labelsize=8)
plt.tick_params(axis='y', labelsize=8)
cb=plt.colorbar(orientation='horizontal', format='%0.2f', ticks=ticksList)
cb.ax.tick_params(labelsize=8)  
cb.set_label('Loading',fontsize=12) 
plt.show()
plt.xlim(0, 255)
plt.xlabel('Easting (Cells)',fontsize=12) 
plt.ylim(255, 0)
plt.ylabel('Northing (Cells)',fontsize=12)
plt.annotate('fractional Brownian motion on a 256x256 lattice | H=0.8 | Dim(f)= '+str(3-H), xy=(0.5,0), xycoords=('axes fraction', 'figure fraction'), xytext=(0, 0.5), textcoords='offset points', size=10, ha='center', va='bottom')
plt.annotate('Max: '+str(round(M[-257:,-257:].max(),3))+' | Min: '+str(round(M[-257:,-257:].min(),3))+' | Avg: '+str(round(M[-257:,-257:].mean(),3))+' | Footprint: '+str(round(footprint*100/(256*256),2))+'%', xy=(0.5,0), xycoords=('axes fraction', 'figure fraction'), xytext=(0, 15), textcoords='offset points', size=10, ha='center', va='bottom')


#Producing the 101x101 image(s)
numfig=int(raw_input('Insert the number of 101x101 windows to produce: '))
N=np.zeros((101,101))
x=0 #Counts the iterations towards the chosen number of images
for x in range (1,numfig+1):
    print('Image no. '+str(x)+' of '+str(numfig))
    north=int(raw_input('Northing coordinate (0 thru 155, integer): '))
    east=int(raw_input('Easting coordinate (0 thru 155, integer): '))
    for i in range(101):
        for j in range(101):
            N[i][j]=M[north+i][east+j]
    #Writing X, Y and values to a .csv file from scratch
    import numpy
    import csv 
    with open('C:\\Users\\Francesco\\Desktop\\Python_files\\csv\\fBm_101x101_'+str(x)+'of'+str(numfig)+'.csv', 'w') as f: #Change directory if necessary
        writer = csv.writer(f)
        writer.writerow(['X', 'Y', 'Value'])
        for (x, y), val in numpy.ndenumerate(M):
            writer.writerow([x, y, val])  

    #Plotting the 101x101 "offspring" matrices
    plt.imshow(N[-101:,-101:].T, origin='lower',interpolation='nearest',cmap='Reds', norm=mc.Normalize(vmin=0,vmax=M.max()))
    title_string=('Spatial Loading of a Generic Natural Hazard')
    subtitle_string=('Inverse FFT on Spectral Synthesis | Origin in the 256x256 matrix: '+str(east)+' East; '+str(north)+' North')
    plt.suptitle(title_string, y=0.99, fontsize=17)
    plt.title(subtitle_string, fontsize=8)
    plt.show()
    #Making a custom list of tick mark intervals for color bar (assumes minimum is always zero)
    numberOfTicks = 5
    ticksListIncrement = M.max()/(numberOfTicks)
    ticksList = []
    for i in range((numberOfTicks+1)):
        ticksList.append(ticksListIncrement * i) 
    plt.tick_params(axis='x', labelsize=8)
    plt.tick_params(axis='y', labelsize=8)
    cb=plt.colorbar(orientation='horizontal', format='%0.2f', ticks=ticksList)
    cb.ax.tick_params(labelsize=8)  
    cb.set_label('Loading',fontsize=12) 
    plt.show()
    plt.xlim(0, 100)
    plt.xlabel('Easting (Cells)',fontsize=12) 
    plt.ylim(100, 0)
    plt.ylabel('Northing (Cells)',fontsize=12)
    plt.annotate('fractional Brownian motion on a 101x101 lattice | H=0.8 | Dim(f)= '+str(3-H), xy=(0.5,0), xycoords=('axes fraction', 'figure fraction'), xytext=(0, 0.5), textcoords='offset points', size=10, ha='center', va='bottom')
    plt.annotate('Max: '+str(round(N[-101:,-101:].max(),3))+' | Min: '+str(round(N[-101:,-101:].min(),3))+' | Avg: '+str(round(N[-101:,-101:].mean(),3))+' | Footprint: '+str(round(footprint*100/(101*101),2))+'%', xy=(0.5,0), xycoords=('axes fraction', 'figure fraction'), xytext=(0, 15), textcoords='offset points', size=10, ha='center', va='bottom')

Answer 1

下面的代码显示了主图，然后根据nx和ny的值显示了不同的“缩放”窗口。

import numpy as np
import matplotlib.pyplot as plt
import random

Lattice = np.random.normal(size=(256,256))

f = plt.imshow( Lattice )
plt.show()

nx = 4
ny = 2

for i in range(nx):
    for j in range(ny):
        f = plt.imshow( Lattice )
        f.axes.set_xlim( [ i*256/nx, (i+1)*256/nx ] )
        f.axes.set_ylim( [ j*256/ny, (j+1)*256/ny ] )
        plt.show()