我是一个Pythonic新手。我有一个生成256x256矩阵的代码，并通过pyplot和plt.imshow()进行绘制。现在，我想通过使用更小的矩阵来生成相同256x256矩阵的 n 子图，这次是101x101，它用作移动窗口。在视觉上，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，我希望在单独的图中绘制一组 n + 1个图像：一个用于256x256矩阵， n 用于移动窗口。 我现在的代码现在将所有图像归结为一个。正如你在这里看到的那样，为了绘制n + 1 = 3个矩阵，只绘制了一个矩阵，最后一个，我的所有颜色条都堆叠起来，我的文本字符串不可读。
如何在for循环中嵌套plt.imshow()调用，以便Python为我提供 n + 1 数字？我希望在这里说清楚。 感谢任何提供指导的人！
这是我目前的代码：
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')
答案 0 :(得分：2)
以下代码显示主图，然后根据nx和ny的值显示不同的“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()