printerror(msg)
printusage()
sys.exit(1)
return optlist, args
optlist, args = parseargs("")
for opt, arg in optlist:
pass
if len(args) != 1:
printusage()
sys.exit(1)
dsname = args[0]
ds = pyctf.dsopen(dsname)
T = ds.getNumberOfTrials()
if T > 1:
printerror("This dataset has more than one trial.")
sys.exit(1)
ch = ds.getChannelIndex("SCLK01")
d = ds.getDsRawData(0, ch)
if d[-1] != 0.:
printerror("This dataset does not appear to end with zero.")
sys.exit(1)
l = d.tolist()
samp = l.index(0.)
import sys, os
from pyctf import dsopen
from pyctf import ctf
if len(sys.argv) < 2:
print "usage: %s [-f] dataset" % sys.argv[0]
exit(1)
n = 1
fixit = False
if sys.argv[1][0:2] == '-f':
fixit = True
n = 2
dsname = sys.argv[n]
ds = dsopen(dsname)
srate = ds.getSampleRate()
ntrial = ds.getNumberOfTrials()
nsamp = ds.getNumberOfSamples()
nch = ds.getNumberOfChannels()
res4name = ds.getDsFileNameExt(".res4")
r = ctf.read_res4_structs(res4name)
for ch in range(nch):
sr = list(r.sensRes[ch][0])
if sr[ctf.sr_type] == ctf.TYPE_UADC:
print ds.getChannelName(ch), sr
if not fixit:
import numpy
import pyctf
ds_fname = '/mnt/neuro/MEG_data/raw/20110429/CXIFPSQC_rest_20110429_01.ds/'
ds = pyctf.dsopen(ds_fname)
# Continuous head localization channels.
fids = ['Na', 'Le', 'Re']
chans = {
fids[0]: ['HLC0011', 'HLC0012', 'HLC0013'],
fids[1]: ['HLC0021', 'HLC0022', 'HLC0023'],
fids[2]: ['HLC0031', 'HLC0032', 'HLC0033']
}
all_ds = []
for chan in fids:
i = fids.index(chan)
o = ds.head[i]
c = chans[chan]
ch_map = ds.channel
# make an nx3; one channel per column
trial = 0
l = []
for ch in c:
x = ds.getDsData(trial, ch_map[ch])
x.shape = (x.shape[0], 1)
l.append(x)
d = numpy.hstack(l) * 100. # m -> cm
#! /usr/bin/env python
import os
import numpy as np
import pyctf
import mne
from mne.io import read_raw_ctf
tmin, tmax = -0.1, 0.5
raw_path = os.getenv('ds')
raw = read_raw_ctf(raw_path, clean_names=True, preload=True)
ds = pyctf.dsopen(raw_path)
# pick MEG channels
picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=True)
# Compute epochs
events = mne.find_events(raw, stim_channel='UPPT001')
m = 'pic1'
srate = raw.info['sfreq']
tlen = ds.getNumberOfSamples()
ev = []
for tr, t in ds.marks[m]:
s = tr * tlen + int(t * srate + .5)
ev.append([s, 0, 1])
epochs = mne.Epochs(raw, ev, {'pic1': 1}, tmin, tmax, picks=picks,
baseline=(None, 0), reject=None, preload=False)
import numpy
import pyctf
ds_fname = '/mnt/neuro/MEG_data/raw/20110429/CXIFPSQC_rest_20110429_01.ds/'
ds = pyctf.dsopen(ds_fname)
# Continuous head localization channels.
fids = ['Na', 'Le', 'Re']
chans = {fids[0]: ['HLC0011', 'HLC0012', 'HLC0013'],
fids[1]: ['HLC0021', 'HLC0022', 'HLC0023'],
fids[2]: ['HLC0031', 'HLC0032', 'HLC0033']}
all_ds = []
for chan in fids:
i = fids.index(chan)
o = ds.head[i]
c = chans[chan]
ch_map = ds.channel
# make an nx3; one channel per column
trial=0
l = []
for ch in c:
x = ds.getDsData(trial, ch_map[ch])
x.shape = (x.shape[0], 1)
l.append(x)
d = numpy.hstack(l) * 100. # m -> cm
# Transform to relative head coordinates.
