Python: 如何在 For 循环中嵌套 plt.imshow()？
Python: How to nest plt.imshow() in a For loop?
我是Pythonic新手。我有一个生成 256x256 矩阵并通过 pyplot 和 plt.imshow() 绘制它的代码。现在，我想通过使用一个较小的矩阵（这次是 101x101，用作移动 window）来生成相同 256x256 矩阵的 n 个子图。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，我想要一组 n+1 图像绘制在单独的数字中：一个用于 256x256 矩阵，n为动windows。
我现在拥有的代码将所有图像归结为一个。正如你在这里看到的，在尝试绘制 n+1=3 个矩阵时，只绘制了一个矩阵，最后一个，我所有的颜色条都堆叠起来，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 给出 n+1 个数字？我希望在这里把事情说清楚。 感谢各位指导！
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')
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"zoom"windows。
]
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()
我是Pythonic新手。我有一个生成 256x256 矩阵并通过 pyplot 和 plt.imshow() 绘制它的代码。现在，我想通过使用一个较小的矩阵（这次是 101x101，用作移动 window）来生成相同 256x256 矩阵的 n 个子图。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，我想要一组 n+1 图像绘制在单独的数字中：一个用于 256x256 矩阵，n为动windows。 我现在拥有的代码将所有图像归结为一个。正如你在这里看到的，在尝试绘制 n+1=3 个矩阵时，只绘制了一个矩阵，最后一个，我所有的颜色条都堆叠起来，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 给出 n+1 个数字？我希望在这里把事情说清楚。 感谢各位指导！
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')
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"zoom"windows。
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()