genPythonInv.py

#Generate an inverse solution via python

import mne
import os

fwdName = "fwd.fif"
rawName = "raw.fif"
covName = "noiseCov.fif"

fSaveInv = os.path.join(os.getcwd(), "invPython.fif")

### Read in raw data ###
raw = mne.fiff.Raw(rawName, preload=True)

### Read in forward solution ###
fwd = mne.read_forward_solution(fname=fwdName, surf_ori=True)

### Read in noise covariance #####
cov = mne.read_cov(covName)

###### Compute/save inverse solution ######
inv = mne.minimum_norm.make_inverse_operator(info=raw.info,
                                             forward=fwd,
                                             noise_cov=cov,
                                             fixed=True)

mne.minimum_norm.write_inverse_operator(fname=fSaveInv, inv=inv)

mneInvBug.sh

#!/bin/bash -f

#generate inverse via command line
mne_inverse_operator --fixed --senscov noiseCov.fif --fwd fwd.fif --meg --eeg --inv invCmdLine.fif

#generate inverse via python
python genPythonInv.py

#Now attempt to execute "mne_read_inverse_operator" on each inverse file
#Cmd line version should succeed while python version should fail