46f3fa12d06827d975ba925f183637ad6916eaa8,mne/io/ctf/tests/test_ctf.py,,test_saving_picked,#,274

Before Change

    // If comp is applied, picking should error
    raw.apply_gradient_compensation(1)
    assert raw.compensation_grade == get_current_comp(raw.info) == 1
    with pytest.raises(RuntimeError, match="Compensation grade 1 has been"):
        raw.copy().pick_types(**pick_kwargs)


run_tests_if_main()

After Change

    assert raw.compensation_grade == get_current_comp(raw.info) == 0
    assert len(raw.info["comps"]) == 5
    pick_kwargs = dict(meg=True, ref_meg=False, verbose=True)
    for comp_grade in [0, 1]:
        raw.apply_gradient_compensation(comp_grade)
        with catch_logging() as log:
            raw_pick = raw.copy().pick_types(**pick_kwargs)
        assert len(raw.info["comps"]) == 5
        assert len(raw_pick.info["comps"]) == 0
        log = log.getvalue()
        assert "Removing 5 compensators" in log
        raw_pick.save(out_fname, overwrite=True)  // should work
        raw2 = read_raw_fif(out_fname)
        assert (raw_pick.ch_names == raw2.ch_names)
        assert_array_equal(raw_pick.times, raw2.times)
        assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
                        atol=1e-20)  // atol is very small but > 0

        raw2 = read_raw_fif(out_fname, preload=True)
        assert (raw_pick.ch_names == raw2.ch_names)
        assert_array_equal(raw_pick.times, raw2.times)
        assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
                        atol=1e-20)  // atol is very small but > 0


run_tests_if_main()

Instances