Converting matlab code to python

Question

Okay so I have a datafile from an EEG scan (a binary file, data.eeg), and in matlab the code to read the file and plot a section of the data goes like this:

sr=400;                                                     % Sample Rate
Nyq_freq=sr/2;                                              % Nyquist Frequency
fneeg=input('Filename  (with path and extension) :', 's');  
t=input('How many seconds in total of EEG ? : ');
ch=input('How many channels of EEG ? : ');
le=t*sr;                                                    % Length of the Recording
fid=fopen(fneeg, 'r', 'l');                                 % Open the file to read 
EEG=fread(fid,[ch,le],'int16');                             % Read Data -> EEG Matrix
fclose ('all');     
plot(EEG(:,3))

Here is my attempt to "translate"

from numpy import *
from matplotlib.pylab import *

sample_rate = 400
Nyquist = sample_rate/2.
fneeg = raw_input("Filename (full path & extension): ")
t = int(raw_input("How many secs in total of EEG?: "))
ch = int(raw_input("How many channels of EEG?: "))
le = t*sample_rate
fid = open(fneeg, 'r')
EEG = fromfile(fneeg, int16)

Here's where things get confusing to me. According to the documentation, matlab's fread is a method of reading binary files via fread(loaded_file, size, data_type). The alternative in python is using numpy's fromfile and reshaping (according to this thread here: MATLAB to Python fread) using the built in reshape function. I'm not sure how this works, or even relates to the matlab method? I'm sorry if my question is confusing, matlab is still very new to me

Edit: If you wanna look have a look at the file here it is: https://www.dropbox.com/s/zzm6uvjfm9gpamk/data.eeg

Edit2: The answers to the raw inputs are t=10, ch=32. In fact I'm not sure why I'm even asking for user input now that I think about it..

Answer 1

As discussed in the comments by yourself and @JoeKington, this should work (I removed the input stuff for testing)

import numpy as np

sample_rate = 400
Nyquist = sample_rate/2.0
fneeg = 'data.eeg'
t = 10 
ch = 32
le = t*sample_rate
EEG = np.fromfile(fneeg, 'int16').reshape(ch, le, order='F')

Without the reshape, you get:

In [45]: EEG
Out[45]: array([ -39,  -25,  -22, ..., -168, -586,  -46], dtype=int16)

In [46]: EEG.shape
Out[46]: (128000,)

With reshaping:

In [47]: EEG.reshape(ch, le, order='F')
Out[47]: 
array([[ -39,  -37,  -12, ...,    5,   19,   21],
       [ -25,  -20,    7, ...,   20,   36,   36],
       [ -22,  -20,    0, ...,   18,   34,   36],
       ..., 
       [ 104,  164,   44, ...,   60,  -67, -168],
       [ 531,  582,   88, ...,   29, -420, -586],
       [ -60,  -63,  -92, ...,  -17,  -44,  -46]], dtype=int16)

In [48]: EEG.reshape(ch, le, order='F').shape
Out[48]: (32, 4000)