import os.path as op
from mne.io import read_raw_fif

base_dir = op.join('/Users/hoechenberger/Development/mne-python/mne', 'io',
                   'tests', 'data')
raw_ctf_fname = op.join(base_dir, 'test_ctf_raw.fif')

raw = read_raw_fif(raw_ctf_fname, preload=False)
raw.apply_gradient_compensation(3)
raw.copy().pick_types(meg=True).get_data()

Opening raw data file /Users/hoechenberger/Development/mne-python/mne/io/tests/data/test_ctf_raw.fif...
    Read 5 compensation matrices
    Range : 24000 ... 31200 =     10.000 ...    13.000 secs
Ready.
Current compensation grade : 0
Compensator constructed to change 0 -> 3
Traceback (most recent call last):
  File "/private/tmp/mwe.py", line 10, in <module>
    raw.copy().pick_types(meg=True).get_data()
  File "<decorator-gen-166>", line 21, in get_data
  File "/Users/hoechenberger/Development/mne-python/mne/io/base.py", line 823, in get_data
    data, times = self[picks, start:stop]
  File "/Users/hoechenberger/Development/mne-python/mne/io/base.py", line 772, in __getitem__
    data = self._read_segment(start=start, stop=stop, sel=sel,
  File "<decorator-gen-163>", line 21, in _read_segment
  File "/Users/hoechenberger/Development/mne-python/mne/io/base.py", line 375, in _read_segment
    mult = self._comp[idx] * cals
ValueError: operands could not be broadcast together with shapes (304,309) (1,304) 

raw = read_raw_fif(raw_ctf_fname, preload=True)