def PlotSCD( files='*.pk' ): import pylab if isinstance( files, basestring ): if os.path.isdir( files ): files = os.path.join( files, '*.pk' ) files = glob.glob( files ) d = DataFiles.load( files ) r = SigTools.correlate( d['x'], d['y'], axis=0 ) SigTools.imagesc( r*numpy.abs(r), y=d['channels'], x=SigTools.samples2msec( range( r.shape[1] ), d['fs'] ), aspect='auto', balance=0.0, colorbar=True ) pylab.title( ', '.join( [ '%d: %d' % ( yi, ( d['y'] == yi ).sum() ) for yi in numpy.unique( d['y'] ) ] ) ) pylab.draw()
def PlotTrials( files='*.pk', channel='Cz' ): import pylab if isinstance( files, basestring ): if os.path.isdir( files ): files = os.path.join( files, '*.pk' ) files = glob.glob( files ) d = DataFiles.load( files ) chind = d[ 'channels' ].index( channel ) v = [ ( d[ 'y' ][ i ], i, vi ) for i, vi in enumerate( d[ 'x' ][ :, chind, : ] ) ] v = numpy.array( [ vi for yi, i, vi in sorted( v ) ] ) SigTools.imagesc( v, x=SigTools.samples2msec( range( v.shape[1] ), d['fs'] ), aspect='auto', balance=0.0, colorbar=True ) pylab.title( ', '.join( [ '%d: %d' % ( yi, ( d['y'] == yi ).sum() ) for yi in numpy.unique( d['y'] ) ] ) + ( ' (channel %s)' % channel ) ) pylab.draw()
def PlotSCD(files='*.pk'):
import pylab
if isinstance(files, basestring):
if os.path.isdir(files): files = os.path.join(files, '*.pk')
files = glob.glob(files)
d = DataFiles.load(files)
r = SigTools.correlate(d['x'], d['y'], axis=0)
SigTools.imagesc(r * numpy.abs(r),
y=d['channels'],
x=SigTools.samples2msec(range(r.shape[1]), d['fs']),
aspect='auto',
balance=0.0,
colorbar=True)
pylab.title(', '.join([
'%d: %d' % (yi, (d['y'] == yi).sum()) for yi in numpy.unique(d['y'])
]))
pylab.draw()
def PlotTrials(files='*.pk', channel='Cz'):
import pylab
if isinstance(files, basestring):
if os.path.isdir(files): files = os.path.join(files, '*.pk')
files = glob.glob(files)
d = DataFiles.load(files)
chind = d['channels'].index(channel)
v = [(d['y'][i], i, vi) for i, vi in enumerate(d['x'][:, chind, :])]
v = numpy.array([vi for yi, i, vi in sorted(v)])
SigTools.imagesc(v,
x=SigTools.samples2msec(range(v.shape[1]), d['fs']),
aspect='auto',
balance=0.0,
colorbar=True)
pylab.title(', '.join(
['%d: %d' % (yi, (d['y'] == yi).sum())
for yi in numpy.unique(d['y'])]) + (' (channel %s)' % channel))
pylab.draw()
def StimulusTiming(filename='.',
ind=None,
channels=0,
trigger='StimulusCode > 0',
msec=200,
rectify=False,
threshold=0.5,
use_eo=True,
save=None,
**kwargs):
"""
In <filename> and <ind>, give it
- a directory and ind=None: for all .dat files in the directory, in session/run order
- a directory and ind=an index or list of indices: for selected .dat files in the directory
- a dat-file name and ind=anything: for that particular file
- a list of filenames and ind=anything: for certain explicitly-specified files
<channels> may be a 0-based index, list of indices, list of channel names, or space- or comma-
delimited string of channel names
<rectify> subtracts the median and takes the abs before doing anything else
<threshold> is on the normalized scale of min=0, max=1 within the resulting image
<use_eo> uses the EventOffset state to correct timings
"""###
if hasattr(filename, 'filename'): filename = filename.filename
if ind == None:
ind = -1
if os.path.isdir(filename): filename = ListDatFiles(filename)
if not isinstance(filename, (tuple, list)): filename = [filename]
if not isinstance(ind, (tuple, list)): ind = [ind]
n = max(len(filename), len(ind))
if len(filename) == 1: filename = list(filename) * n
if len(ind) == 1: ind = list(ind) * n
if isinstance(channels, basestring):
channels = channels.replace(',', ' ').split()
if not isinstance(channels, (tuple, list)): channels = [channels]
out = [
SigTools.sstruct(
files=[],
events=[],
t=None,
channel=ch,
img=[],
edges=[],
threshold=None,
EventOffsets=[],
UseEventOffsets=False,
) for ch in channels
]
if len(filename) == 0: raise ValueError("no data files specified")
for f, i in zip(filename, ind):
b = bcistream(filename=f, ind=i)
nsamp = b.msec2samples(msec)
sig, st = b.decode('all')
statenames = zip(*sorted([(-len(x), x) for x in st]))[1]
criterion = trigger
for x in statenames:
criterion = criterion.replace(x, "st['%s']" % x)
criterion = numpy.asarray(eval(criterion)).flatten()
startind = RisingEdge(criterion).nonzero()[0] + 1
print "%d events found in %s" % (len(startind), b.filename)
for s in out:
s.files.append(b.filename)
s.events.append(len(startind))
ch = s.channel
if isinstance(ch, basestring):
chn = [x.lower() for x in b.params['ChannelNames']]
if ch.lower() in chn: ch = chn.index(ch.lower())
else:
raise ValueError("could not find channel %s in %s" %
(ch, b.filename))
if len(b.params['ChannelNames']) == len(sig):
s.channel = b.params['ChannelNames'][ch]
xx = numpy.asarray(sig)[ch]
if rectify: xx = numpy.abs(xx - numpy.median(xx))
xx -= xx.min()
if xx.max(): xx /= xx.max()
s.threshold = threshold
for ind in startind:
if 'EventOffset' in st:
eo = st['EventOffset'].flat[ind]
if use_eo:
ind += eo - 2**(b.statedefs['EventOffset']['length'] -
1)
s.UseEventOffsets = True
else:
eo = 0
s.EventOffsets.append(eo)
x = xx[ind:ind + nsamp].tolist()
x += [0.0] * (nsamp - len(x))
s.img.append(x)
for s in out:
s.img = numpy.asarray(s.img)
s.edges = [
min(list(x.nonzero()[0]) + [numpy.nan])
for x in (s.img > s.threshold)
]
s.edges = b.samples2msec(numpy.asarray(s.edges))
s.t = b.samples2msec(numpy.arange(nsamp))
import pylab
pylab.clf()
for i, s in enumerate(out):
pylab.subplot(1, len(out), i + 1)
y = y = range(1, len(s.img) + 1)
SigTools.imagesc(s.img, x=s.t, y=y, aspect='auto', **kwargs)
xl, yl = pylab.xlim(), pylab.ylim()
pylab.plot(s.edges, y, 'w*', markersize=10)
pylab.xlim(xl)
pylab.ylim(yl)
pylab.grid('on')
#pylab.ylim([len(s.img)+0.5,0.5]) # this corrupts the image!!
pylab.draw()
if save:
pylab.gcf().savefig(save, orientation='portrait')
return out
def StimulusTiming(filename='.', ind=None, channels=0, trigger='StimulusCode > 0', msec=200, rectify=False, threshold=0.5, use_eo=True, save=None, **kwargs):
"""
In <filename> and <ind>, give it
- a directory and ind=None: for all .dat files in the directory, in session/run order
- a directory and ind=an index or list of indices: for selected .dat files in the directory
- a dat-file name and ind=anything: for that particular file
- a list of filenames and ind=anything: for certain explicitly-specified files
<channels> may be a 0-based index, list of indices, list of channel names, or space- or comma-
delimited string of channel names
<rectify> subtracts the median and takes the abs before doing anything else
<threshold> is on the normalized scale of min=0, max=1 within the resulting image
<use_eo> uses the EventOffset state to correct timings
"""###
if hasattr(filename, 'filename'): filename = filename.filename
if ind==None:
ind = -1
if os.path.isdir(filename): filename = ListDatFiles(filename)
if not isinstance(filename, (tuple,list)): filename = [filename]
if not isinstance(ind, (tuple,list)): ind = [ind]
n = max(len(filename), len(ind))
if len(filename) == 1: filename = list(filename) * n
if len(ind) == 1: ind = list(ind) * n
if isinstance(channels, basestring): channels = channels.replace(',', ' ').split()
if not isinstance(channels, (tuple,list)): channels = [channels]
out = [SigTools.sstruct(
files=[],
events=[],
t=None,
channel=ch,
img=[],
edges=[],
threshold=None,
EventOffsets=[],
UseEventOffsets=False,
) for ch in channels]
if len(filename) == 0: raise ValueError("no data files specified")
for f,i in zip(filename, ind):
b = bcistream(filename=f, ind=i)
nsamp = b.msec2samples(msec)
sig,st = b.decode('all')
statenames = zip(*sorted([(-len(x),x) for x in st]))[1]
criterion = trigger
for x in statenames: criterion = criterion.replace(x, "st['%s']"%x)
criterion = numpy.asarray(eval(criterion)).flatten()
startind = RisingEdge(criterion).nonzero()[0] + 1
print "%d events found in %s" % (len(startind), b.filename)
for s in out:
s.files.append(b.filename)
s.events.append(len(startind))
ch = s.channel
if isinstance(ch, basestring):
chn = [x.lower() for x in b.params['ChannelNames']]
if ch.lower() in chn: ch = chn.index(ch.lower())
else: raise ValueError("could not find channel %s in %s" % (ch,b.filename))
if len(b.params['ChannelNames']) == len(sig):
s.channel = b.params['ChannelNames'][ch]
xx = numpy.asarray(sig)[ch]
if rectify: xx = numpy.abs(xx - numpy.median(xx))
xx -= xx.min()
if xx.max(): xx /= xx.max()
s.threshold = threshold
for ind in startind:
if 'EventOffset' in st:
eo = st['EventOffset'].flat[ind]
if use_eo:
ind += eo - 2**(b.statedefs['EventOffset']['length']-1)
s.UseEventOffsets = True
else:
eo = 0
s.EventOffsets.append(eo)
x = xx[ind:ind+nsamp].tolist()
x += [0.0] * (nsamp - len(x))
s.img.append(x)
for s in out:
s.img = numpy.asarray(s.img)
s.edges = [min(list(x.nonzero()[0])+[numpy.nan]) for x in (s.img > s.threshold)]
s.edges = b.samples2msec(numpy.asarray(s.edges))
s.t = b.samples2msec(numpy.arange(nsamp))
import pylab
pylab.clf()
for i,s in enumerate(out):
pylab.subplot(1, len(out), i+1)
y = y=range(1,len(s.img)+1)
SigTools.imagesc(s.img, x=s.t, y=y, aspect='auto', **kwargs)
xl,yl = pylab.xlim(),pylab.ylim()
pylab.plot(s.edges, y, 'w*', markersize=10)
pylab.xlim(xl); pylab.ylim(yl)
pylab.grid('on')
#pylab.ylim([len(s.img)+0.5,0.5]) # this corrupts the image!!
pylab.draw()
if save:
pylab.gcf().savefig(save, orientation='portrait')
return out
