# epoching mismatch between python and matlab **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [November 24, 2019, 9:01pm UTC](https://mne.discourse.group/t/epoching-mismatch-between-python-and-matlab/2013 "2019-11-24T21:01:16Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [November 24, 2019, 9:01pm UTC](https://mne.discourse.group/t/epoching-mismatch-between-python-and-matlab/2013/1 "2019-11-24T21:01:16Z")
External Email - Use Caution Dear MNE people, I got a problem when epoching data in MNE Python using mne.Epochs. I am running some analysis with a Matlab code and I would like to transfer the same analysis to MNE Python. My problem is that if I compare one epoch from Matlab with the output of mne.Epochs in Python they do not correspond. Here below the code and a figure explaining the problem My question is: why   spike1[295, :] # epoch from matlab data   data[295, (np.arange(index-100,index+100))] # epoch from data imported in python and manually extracted with a sample reference 'index' do not correspond with   epochs9[0,295,:] # which is the output of mne.Epochs for the very same epoch? They both should refer to the sample 75650, with a pre- and post-trigger of 100 samples. I am not applying any SSP projection or baseline. What am I missing? Thanks in advance for your help, Best, Tommaso %%%%%%%%%%%%%%%%% code import numpy as np import mne import xlrd import [scipy.io](http://scipy.io) as sio import pandas as pd from mne.viz import plot\_evoked\_topo import matplotlib.pyplot as plt spike = sio.loadmat(         'E:/../spike75650.mat'         )# data imported from Matlab as dict spike.          #The field 'spike is an array float64 (404, 201) channels, samples raw = mne.io.read\_raw\_fif('E:/.../B1C2\_ii\_run1\_raw\_tsss\_mc\_art\_corr.fif') #data after tsss excel = xlrd.open\_workbook('E:/.../list raw detections B1C2.xlsx') #trigger info from excel file # create an array for mne.Epochs sheet = excel.sheet\_by\_index(2) sp=list() for i in range(1,61):     sp.append(sheet.cell\_value(i,2)) spevents = np.array(sp, ndmin=2, dtype=int) zeros = np.zeros((1,60), dtype=int) event\_id\_int = np.ones((1,60), dtype=int) event\_id\_int[:,:] = np.arange(0,60) event\_id\_str = list(map(str,np.arange(0,60))) events = np.hstack((spevents.T, zeros.T, event\_id\_int.T)) # array int32 (60,3) Nevents,3                # one row is 75650 0 9 # Create epochs including different events event\_id = dict(zip(event\_id\_str, np.arange(0,60))) epochs = mne.Epochs(raw, events, event\_id, tmin = -0.1, tmax = 0.1, proj = False) # Generate list of evoked objects from conditions names evokeds = [epochs[name].average() for name in np.arange(0,60)] ##################### Figure plt.close('all') plt.figure() ep\_id = 9 index = spevents[0,ep\_id] # trigger sample for continuous data, value 75650 # spike from matlab spike1 = spike['spike'] plt.plot(spike1[295, :],                        label = ['Matlab: spike from raw data - MEG2613']) # spike from continuous data plt.plot(data[295, (np.arange(index-100,index+100))]-5\*10\*\*-11,                             label = ['Python: spike from raw data - MEG2613']) # spike from MNE epochs epochs9 = epochs[ep\_id].get\_data() plt.plot(epochs9[0,295,:]-5\*2\*10\*\*-11,                         label = ['MNE Python: spike from EPOCHED data - MEG2613']) # spike from MNE average evoked9 = epochs[ep\_id].average() plt.plot(evoked9.data[295]-5\*3\*10\*\*-11,                         label = ['MNE Python: spike from averaged data - MEG2613']) plt.legend() # check that the channel labels is the same print(epochs.ch\_names[295]), print(raw.ch\_names[295]) %%%%%%%%%%%%%%%%%%%%%% FIGURE [image: image.png] -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20191124/144beac2/attachment-0001.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20191124/144beac2/attachment-0001.html) -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 202423 bytes Desc: not available Url : [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20191124/144beac2/attachment-0001.png](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20191124/144beac2/attachment-0001.png) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [November 27, 2019, 2:41pm UTC](https://mne.discourse.group/t/epoching-mismatch-between-python-and-matlab/2013/2 "2019-11-27T14:41:10Z")
External Email - Use Caution hi Tommaso, unfortunately it's really hard to help without more context and knowledge about the data. Best, Alex >         External Email - Use Caution > > Dear MNE people, > > I got a problem when epoching data in MNE Python using mne.Epochs. > > I am running some analysis with a Matlab code and I would like to transfer > the same analysis to MNE Python. > > My problem is that if I compare one epoch from Matlab with the output of > mne.Epochs in Python they do not correspond. > > Here below the code and a figure explaining the problem > > My question is: why > >   spike1[295, :] # epoch from matlab data >   data[295, (np.arange(index-100,index+100))] # epoch from data imported > in python and manually extracted with a sample reference 'index' > > do not correspond with > >   epochs9[0,295,:] # which is the output of mne.Epochs for the very same > epoch? > > They both should refer to the sample 75650, with a pre- and post-trigger > of 100 samples. > > I am not applying any SSP projection or baseline. What am I missing? > > Thanks in advance for your help, > > Best, > Tommaso > > %%%%%%%%%%%%%%%%% code > > import numpy as np > import mne > import xlrd > import scipy.io as sio > import pandas as pd > from mne.viz import plot\_evoked\_topo > import matplotlib.pyplot as plt > > spike = sio.loadmat( >         'E:/../spike75650.mat' >         )# data imported from Matlab as dict spike. >          #The field 'spike is an array float64 (404, 201) channels, samples > > raw = mne.io.read\_raw\_fif('E:/.../B1C2\_ii\_run1\_raw\_tsss\_mc\_art\_corr.fif') > #data after tsss > excel = xlrd.open\_workbook('E:/.../list raw detections B1C2.xlsx') > #trigger info from excel file > > # create an array for mne.Epochs > sheet = excel.sheet\_by\_index(2) > sp=list() > for i in range(1,61): >     sp.append(sheet.cell\_value(i,2)) > spevents = np.array(sp, ndmin=2, dtype=int) > zeros = np.zeros((1,60), dtype=int) > event\_id\_int = np.ones((1,60), dtype=int) > event\_id\_int[:,:] = np.arange(0,60) > event\_id\_str = list(map(str,np.arange(0,60))) > events = np.hstack((spevents.T, zeros.T, event\_id\_int.T)) # array int32 > (60,3) Nevents,3 > >                  # one row is 75650 0 9 > > # Create epochs including different events > event\_id = dict(zip(event\_id\_str, np.arange(0,60))) > epochs = mne.Epochs(raw, events, event\_id, tmin = -0.1, tmax = 0.1, proj > = False) > > # Generate list of evoked objects from conditions names > evokeds = [epochs[name].average() for name in np.arange(0,60)] > > ##################### Figure > plt.close('all') > plt.figure() > > ep\_id = 9 > index = spevents[0,ep\_id] # trigger sample for continuous data, value > 75650 > > # spike from matlab > spike1 = spike['spike'] > plt.plot(spike1[295, :], >                        label = ['Matlab: spike from raw data - MEG2613']) > > # spike from continuous data > plt.plot(data[295, (np.arange(index-100,index+100))]-5\*10\*\*-11, >                             label = ['Python: spike from raw data - > MEG2613']) > > # spike from MNE epochs > epochs9 = epochs[ep\_id].get\_data() > plt.plot(epochs9[0,295,:]-5\*2\*10\*\*-11, >                         label = ['MNE Python: spike from EPOCHED data - > MEG2613']) > > # spike from MNE average > evoked9 = epochs[ep\_id].average() > plt.plot(evoked9.data[295]-5\*3\*10\*\*-11, >                         label = ['MNE Python: spike from averaged data - > MEG2613']) > > plt.legend() > > # check that the channel labels is the same > print(epochs.ch\_names[295]), print(raw.ch\_names[295]) > > %%%%%%%%%%%%%%%%%%%%%% FIGURE > > [image: image.png] > > \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ > Mne\_analysis mailing list > Mne\_analysis at nmr.mgh.harvard.edu > [Mne\_analysis Info Page](https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis) -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20191127/8d63bb8c/attachment-0001.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20191127/8d63bb8c/attachment-0001.html) -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 202423 bytes Desc: not available Url : [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20191127/8d63bb8c/attachment-0001.png](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20191127/8d63bb8c/attachment-0001.png) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [November 27, 2019, 4:45pm UTC](https://mne.discourse.group/t/epoching-mismatch-between-python-and-matlab/2013/3 "2019-11-27T16:45:23Z")
External Email - Use Caution Dear Alex, this is actually the answer. I found out that MNE Python translates the sample info to the real time of the recording, which in my case is starting at t = 39 seconds. So my code works if I build the events array as follows \*events = np.vstack((spevents[0] + raw.first\_samp, np.zeros(60), np.arange(60)+1)).T.astype(np.int \<[http://np.int](http://np.int)\>) # array int32 (60,3) Nevents,3epochs = mne.Epochs(raw, events, tmin = -0.1, tmax = 0.1, proj = False, baseline=None, detrend=0, preload=False) \* where \*raw.first\_samp\* set the correct offset for MNE Python to understand where I want to point in my continuous data. Thanks, Best, Tommaso