使用matplotlib绘制对数2D直方图

Question

这是我写的第一个Python程序，所以可能会有一些“奇怪”的地方。这个程序会从指定文件夹里读取3列数据，然后为每个文件计算直方图，最后把结果放到一个二维矩阵里，目的是为了创建一个类似于2D直方图的东西。

我遇到的困难是在我的第三个图表中，我希望y轴的数据使用对数刻度，并且数据能够按照这个刻度来展示。此外，我还想把输入数据中的“零”值去掉。我尝试使用numpy.where(matrix)来实现这个功能，但我不确定这样做是否真的能达到我的目的……

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()

数据处理数据可视化直方图 matplotlib 二维矩阵对数刻度输入数据

使用matplotlib绘制对数2D直方图

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