这是我用python编写的第一个程序,所以在我的程序中可能有一些“有趣”的东西。程序从给定目录中找到的文件中读取3列。然后计算每个文件的直方图,并将结果添加到二维矩阵中,以便创建类似2D Hist的内容。在
我的困难是在我的第三个绘图中,我希望y轴的数据是在一个对数刻度上,并且数据是根据刻度来显示的。此外,我想从我的输入项中删除“零”项。我试着用numpy.where(matrix)
来做这个,但我不知道这是否真的符合我的要求。。。在
这是我的代码:
#!/usr/bin/python
# Filename: untitled.py
# encoding: utf-8
from __future__ import division
from matplotlib.colors import LogNorm
import matplotlib
import numpy as np
import matplotlib.pylab as plt
import os
import matplotlib.cm as cm
def main():
dataFiles = [filename for filename in os.listdir(".") if (filename[-4:]==".log" and filename[0]!='.')]
dataFiles.sort()
p = []
matrix1 = []
matrix2 = []
matrix3 = []
for dataFile in dataFiles:
p += [ eval(dataFile[11:16]) ]
data = np.loadtxt(dataFile, skiprows=7)[:,1:4]
matrix1 += [ data[:,0] ]
matrix2 += [ data[:,1] ]
matrix3 += [ data[:,2] ]
matrixList = [matrix1, matrix2, matrix3]
#make histograms out of the matrices
matrix1Hist = [ np.histogram( matrixColumn, bins=30, range=(np.min(np.where(matrix1 != 0)), np.max(matrix1)))[0] for matrixColumn in matrix1 ]
matrix2Hist = [ np.histogram( matrixColumn, bins=200, range=(np.min(np.where(matrix2 != 0)), np.max(matrix2)))[0] for matrixColumn in matrix2 ]
matrix3Hist = [ np.histogram( matrixColumn, bins=50, range=(np.min(np.where(matrix3 != 0)), np.max(matrix3)))[0] for matrixColumn in matrix3 ]
# convert the matrixHistogramsto numpy arrays and swap axes
matrix1Hist = np.array(matrix1Hist).transpose()
matrix2Hist = np.array(matrix2Hist).transpose()
matrix3Hist = np.array(matrix3Hist).transpose()
matrixHistList = [matrix1Hist, matrix2Hist, matrix3Hist]
fig = plt.figure(0)
fig.clf()
for i,matrixHist in enumerate( [matrix1Hist, matrix2Hist, matrix3Hist] ):
ax = fig.add_subplot(2, 2, i+1)
ax.grid(True)
ax.set_title('matrix'+str(i+1))
if i < 2:
result = ax.imshow(matrixHist,
cmap=cm.gist_yarg,
origin='lower',
aspect='auto', #automatically span matrix to available space
interpolation='hanning',
extent= [ p[0], p[-1], np.floor( np.min( matrixList[i])), np.ceil( np.max( matrixList[i])) ] ,
)
elif i == 2:
result = ax.imshow(matrixHist,
cmap=cm.gist_yarg,
origin='lower',
aspect='auto', #automatically span matrix to available space
interpolation='hanning',
extent= [ p[0], p[-1], 1, np.log10(np.max( matrixList[i])) ] ,
)
ticks_at = [ 0 , abs(matrixHist).max()]
fig.colorbar(result, ticks=ticks_at,format='%1.2g')
plt.show()
if __name__ == '__main__':
main()
对于问题的第一部分,你有以下几种选择:
np.log(my_array)
。在hist
图缩放轴:Plot logarithmic axes with matplotlib in python。在norm
关键字传递一个matplotlib.colors.LogNorm实例来缩放类似pcolor
和imshow
的二维彩色图。i、 e.imshow(my_array, cmap=mpl.cm.jet, norm=mpl.colors.LogNorm)
关于从数组中过滤零值的问题的第二部分,请尝试:
my_array = my_array[my_array != 0]
my_array != 0
创建一个True
和{NaN
)。。。在my_array[my_array != 0] = np.NaN
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