# Handling missing data for mne\_connectivity

**URL:** <https://mne.discourse.group/t/handling-missing-data-for-mne-connectivity/8410>\
**Category:** Support & Discussions\
**Tags:** connectivity\
**Created:** [March 1, 2024, 8:43pm UTC](https://mne.discourse.group/t/handling-missing-data-for-mne-connectivity/8410 "2024-03-01T20:43:28Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![duodenum](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/duodenum/32/3730_2.png) [@duodenum](https://mne.discourse.group/u/duodenum)\
**Post date:** [March 1, 2024, 8:43pm UTC](https://mne.discourse.group/t/handling-missing-data-for-mne-connectivity/8410/1 "2024-03-01T20:43:28Z")

</div>

MNE version: 1.6.1  
Operating system: openSUSE Leap 15.3

Hello,

I would like to ask about how MNE-Connectivity (or MNE in general) handle bad data segments / missing data? For my research, I need to keep track of the timing and bad trials while calculating one functional connectivity metric per trial. I do this via a rather tedious and admittedly error prone procedure. Specifically, I initialize a new empty np array `con_epoch` below with the shape `n_chan, n_chan, ntrial, nfreq`. Then I loop across trials, if the trial is in `epochs.selection`, then calculate the FC and add it to the `con_epoch`, else, put nans to `con_epoch`. In code, looks like this:

```python
    ntrial = len(epochs.drop_log)
    nfreq = len(fmin)
    nchan = epochs.get_data().shape[1]
    con_epoch = np.zeros((nchan, nchan, ntrial, nfreq))
    c = 0
    for i in range(ntrial):
        if i in epochs.selection:
            con = spectral_connectivity_epochs(
                epochs[c],
                method=method,
                mode="multitaper",
                sfreq=epochs.info["sfreq"],
            )
            con_epoch[:, :, i, :] = con.get_data(output="dense")
            c += 1
        else:
            con_epoch[:, :, i] = np.nan

```

Note that I have to use two iteration variables, `i` and `c` since `epochs` do not contain the same amount of trials as `epochs.drop_log` which keeps track of bad data.

Is there a cleaner way to handle the procedure?

Thanks for the amazing work with the package,  
Yasir

edit: added more clarification