[med-svn] [python-mne] 308/376: ENH : better support for CTF data + tests
Yaroslav Halchenko
debian at onerussian.com
Fri Nov 27 17:23:12 UTC 2015
This is an automated email from the git hooks/post-receive script.
yoh pushed a commit to annotated tag v0.1
in repository python-mne.
commit 17d1dfe84bf255fdc19619441575b95a1963a2e3
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date: Sun Jul 3 09:06:55 2011 -0400
ENH : better support for CTF data + tests
---
mne/fiff/pick.py | 11 ++++-
mne/fiff/raw.py | 33 ++++++++++++---
mne/fiff/tests/data/test_ctf_raw.fif | Bin 0 -> 9017895 bytes
mne/fiff/tests/test_raw.py | 78 ++++++++++++++++++-----------------
4 files changed, 77 insertions(+), 45 deletions(-)
diff --git a/mne/fiff/pick.py b/mne/fiff/pick.py
index 306ab5c..5042c8d 100644
--- a/mne/fiff/pick.py
+++ b/mne/fiff/pick.py
@@ -77,7 +77,7 @@ def pick_channels(ch_names, include, exclude=[]):
def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
- emg=False, include=[], exclude=[]):
+ emg=False, misc=False, include=[], exclude=[]):
"""Pick channels by type and names
Parameters
@@ -87,7 +87,8 @@ def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
meg : bool or string
If True include all MEG channels. If False include None
If string it can be 'mag' or 'grad' to select only gradiometers
- or magnetometers.
+ or magnetometers. It can also be 'ref_meg' to get CTF
+ reference channels.
eeg : bool
If True include EEG channels
eog : bool
@@ -98,6 +99,8 @@ def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
If True include EMG channels
stim : bool
If True include stimulus channels
+ misc : bool
+ If True include miscellaneous analog channels
include : list of string
List of additional channels to include. If empty do not include any.
@@ -123,6 +126,8 @@ def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
elif (meg == 'mag'
and info['chs'][k]['unit'] == FIFF.FIFF_UNIT_T):
pick[k] = True
+ elif meg == 'ref_meg':
+ pick[k] = True
elif kind == FIFF.FIFFV_EEG_CH and eeg:
pick[k] = True
elif kind == FIFF.FIFFV_STIM_CH and stim:
@@ -133,6 +138,8 @@ def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
pick[k] = True
elif kind == FIFF.FIFFV_EMG_CH and emg:
pick[k] = True
+ elif kind == FIFF.FIFFV_MISC_CH and misc:
+ pick[k] = True
myinclude = [info['ch_names'][k] for k in range(nchan) if pick[k]]
myinclude += include
diff --git a/mne/fiff/raw.py b/mne/fiff/raw.py
index 55e8234..e022cc2 100644
--- a/mne/fiff/raw.py
+++ b/mne/fiff/raw.py
@@ -4,6 +4,7 @@
# License: BSD (3-clause)
from math import floor, ceil
+import copy
import numpy as np
from .constants import FIFF
@@ -176,6 +177,10 @@ class Raw(dict):
start = 0
if step is not None:
raise ValueError('step needs to be 1 : %d given' % step)
+
+ if len(sel) == 0:
+ raise Exception("Empty channel list")
+
return read_raw_segment(self, start=start, stop=stop, sel=sel)
else:
return super(Raw, self).__getitem__(item)
@@ -224,7 +229,10 @@ class Raw(dict):
if last >= stop:
last = stop + 1
- data, times = self[picks, first:last]
+ if picks is None:
+ data, times = self[:, first:last]
+ else:
+ data, times = self[picks, first:last]
print 'Writing ... ',
write_raw_buffer(outfid, data, cals)
@@ -488,10 +496,12 @@ def start_writing_raw(name, info, sel=None):
# We will always write floats
#
if sel is None:
- sel = np.arange(info['nchan'])
+ chs = info['chs']
+ nchan = len(chs)
+ else:
+ chs = [info['chs'][k] for k in sel]
+ nchan = len(sel)
data_type = 4
- chs = [info['chs'][k] for k in sel]
- nchan = len(chs)
#
# Create the file and save the essentials
#
@@ -558,7 +568,20 @@ def start_writing_raw(name, info, sel=None):
#
# CTF compensation info
#
- write_ctf_comp(fid, info['comps'])
+ comps = info['comps']
+ if sel is not None:
+ ch_names = [c['ch_name'] for c in chs] # name of good channels
+ comps = copy.deepcopy(comps)
+ for c in comps:
+ row_idx = [k for k, n in enumerate(c['data']['row_names'])
+ if n in ch_names]
+ row_names = [c['data']['row_names'][i] for i in row_idx]
+ rowcals = c['rowcals'][row_idx]
+ c['rowcals'] = rowcals
+ c['data']['nrow'] = len(row_names)
+ c['data']['row_names'] = row_names
+ c['data']['data'] = c['data']['data'][row_idx]
+ write_ctf_comp(fid, comps)
#
# Bad channels
#
diff --git a/mne/fiff/tests/data/test_ctf_raw.fif b/mne/fiff/tests/data/test_ctf_raw.fif
new file mode 100644
index 0000000..37a6f32
Binary files /dev/null and b/mne/fiff/tests/data/test_ctf_raw.fif differ
diff --git a/mne/fiff/tests/test_raw.py b/mne/fiff/tests/test_raw.py
index 3ecbf15..64f203c 100644
--- a/mne/fiff/tests/test_raw.py
+++ b/mne/fiff/tests/test_raw.py
@@ -2,46 +2,48 @@ import os.path as op
from numpy.testing import assert_array_almost_equal
-from .. import Raw, pick_types
+from .. import Raw, pick_types, pick_channels
-fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif')
+fif_fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif')
+ctf_fname = op.join(op.dirname(__file__), 'data', 'test_ctf_raw.fif')
def test_io_raw():
- """Test IO for raw data
+ """Test IO for raw data (Neuromag + CTF)
"""
- raw = Raw(fname)
-
- nchan = raw.info['nchan']
- ch_names = raw.info['ch_names']
- meg_channels_idx = [k for k in range(nchan) if ch_names[k][:3] == 'MEG']
- meg_channels_idx = meg_channels_idx[:5]
-
- start, stop = raw.time_to_index(0, 5)
- data, times = raw[meg_channels_idx, start:(stop+1)]
-
- # Set up pick list: MEG + STI 014 - bad channels
- want_meg = True
- want_eeg = False
- want_stim = False
- include = ['STI 014']
-
- picks = pick_types(raw.info, meg=want_meg, eeg=want_eeg,
- stim=want_stim, include=include,
- exclude=raw.info['bads'])
- picks = picks[:5] # take 5 first
-
- print "Number of picked channels : %d" % len(picks)
-
- # Writing
- raw.save('raw.fif', picks, tmin=0, tmax=5)
-
- raw2 = Raw('raw.fif')
-
- data2, times2 = raw2[:,:]
-
- assert_array_almost_equal(data, data2)
- assert_array_almost_equal(times, times2)
- assert_array_almost_equal(raw.info['dev_head_t']['trans'],
- raw2.info['dev_head_t']['trans'])
- assert_array_almost_equal(raw.info['sfreq'], raw2.info['sfreq'])
+ for fname in [fif_fname, ctf_fname]:
+ raw = Raw(fname)
+
+ nchan = raw.info['nchan']
+ ch_names = raw.info['ch_names']
+ meg_channels_idx = [k for k in range(nchan)
+ if ch_names[k][0] == 'M']
+ n_channels = 100
+ meg_channels_idx = meg_channels_idx[:n_channels]
+ start, stop = raw.time_to_index(0, 5)
+ data, times = raw[meg_channels_idx, start:(stop + 1)]
+ meg_ch_names = [ch_names[k] for k in meg_channels_idx]
+
+ # Set up pick list: MEG + STI 014 - bad channels
+ include = ['STI 014']
+ include += meg_ch_names
+ picks = pick_types(raw.info, meg=True, eeg=False,
+ stim=True, misc=True, include=include,
+ exclude=raw.info['bads'])
+ print "Number of picked channels : %d" % len(picks)
+
+ # Writing
+ raw.save('raw.fif', picks, tmin=0, tmax=5)
+
+ raw2 = Raw('raw.fif')
+
+ sel = pick_channels(raw2.ch_names, meg_ch_names)
+ data2, times2 = raw2[sel, :]
+
+ assert_array_almost_equal(data, data2)
+ assert_array_almost_equal(times, times2)
+ assert_array_almost_equal(raw.info['dev_head_t']['trans'],
+ raw2.info['dev_head_t']['trans'])
+ assert_array_almost_equal(raw.info['sfreq'], raw2.info['sfreq'])
+
+ fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif')
--
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