1b9d2228bc40f817ef1765686bc2ec6e81079d13,mne/preprocessing/tests/test_ssp.py,,test_compute_proj_ctf,#,136

Before Change

    // Test with and without gradient compensation
    for c in [0, 1]:
        raw.apply_gradient_compensation(c)
        for average in [False, True]:
            n_projs_init = len(raw.info["projs"])
            projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
                                             average=average,
                                             ch_name="EEG059",
                                             avg_ref=True, no_proj=False,
                                             l_freq=None, h_freq=None,
                                             reject=None, tmax=dur_use,
                                             filter_length=6000)
            _check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
            assert len(projs) == (5 + n_projs_init)

            projs, events = compute_proj_ecg(raw, n_mag=1, n_grad=1, n_eeg=2,
                                             average=average,
                                             ch_name="EEG059",
                                             avg_ref=True, no_proj=False,
                                             l_freq=None, h_freq=None,
                                             reject=None, tmax=dur_use,
                                             filter_length=6000)
            _check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
            assert len(projs) == (4 + n_projs_init)


run_tests_if_main()

After Change

    eeg_picks = pick_types(raw.info, meg=False, eeg=True, ref_meg=False,
                           exclude="bads")[2::3]
    n_eegs = len(eeg_picks)
    ref_picks = pick_types(raw.info, meg=False, ref_meg=True)
    raw.pick(np.sort(np.concatenate(
        [mag_picks, grad_picks, eeg_picks, ref_picks])))
    del mag_picks, grad_picks, eeg_picks, ref_picks

Instances