加载matlab和python二进制文件的不同结果

Question

我试图读取一个二进制文件从读出板，将转换成图像。在Matlab中，所有的字节都被正确读取，图像被完全填充。但是在python（使用anaconda的ver2.7）中，每127列有一行零。 Matlab代码是：

fid = fopen(filename);
Rawdata = fread(fid,'uint8');
Data1d = Rawdata(2:2:end).* 256+ Rawdata(1:2:end)  ;
% converts Data1 to a 2D vector, adding a row of zeros to make the reshape
% possible to 3D
Data2d = [reshape(Data1d,4127,1792); zeros(1,1792)];
% reshapes again, but adding a new dimension
Data3d = reshape(Data2d(:),129,32,1792);
% selects the first 128 values in the first dimension 
Data3d = Data3d(1:128,:,:);
Data2d = reshape(Data3d(:),4096,1792);
Data2d = Data2d';
CMVimage = Data2d;   
fclose(fid); %VGM 2017-01-14 the file should be closed.

我试过用pythonnp.fromfile文件（）并使用f.read（）直接从python读取 同样的结果。你知道吗

import numpy as np
import matplotlib.pyplot as plt
"""
reads the input .dat file and converts it to an image
Problem: line of zeros every 127 columns in columns: 127,257,368...
curiosly, the columns are in the position of the new byte. 
In matlab it works very well. 
"""


def readDatFile(filename):
""" reads the binary file in python not in numpy
the data is byte type and it is converted to integer. 

    """
    import binascii
    f = open(filename, 'rb')
    data = f.read()
    #dataByte = bytearray(data)

    f.close()
    data_out = []
    for num in data:
        aux = int(binascii.hexlify(num), 16)
        data_out.append(aux) 
        #print aux

    myarray = np.asarray(data_out) 
    return myarray 




def rawConversionNew(filename):
    # reads data from a binary file with tupe uint
#    f = open(filename, 'rb')
#    Rawdata = np.fromfile(f, dtype=np.uint8)
#    f.close()

    Rawdata = readDatFile(filename)

    ## gets the image
    Data1d = 256*Rawdata[1::2] + Rawdata[0::2]               
    Data2d = Data1d.reshape(1792,4127)
    Data2d = Data2d.T 
    Data2d = np.vstack([Data2d,np.zeros((1,1792),dtype=np.uint16)] )
    Data3d = Data2d.reshape(129,32,1792)
    Data3d = Data3d[0:128,:,:]
    #plt.figure()
    #plt.plot(np.arange(Data3d.shape[0]),Data3d[:,1,1])
    #print (Data3d[:,0,0])
    CMVimage = Data3d.reshape(4096,1792).T

  return CMVimage

Answer 1

实际上有两个错误，没有将文件标记为二进制（“rb”）和重塑，这是在Matlab和numpy中以不同的方式完成的。 如果使用重塑（dim1，dim2，order='F'）完成重塑，则结果相同。支票：Matlab vs Python: Reshape