def TimingWindow(filename='.', ind=-1, save=None):
"""
Recreate BCI2000's timing window offline, from a saved .dat file specified by <filename>.
It is also possible to supply a directory name as <filename>, and an index <ind> (default
value -1 meaning "the last run") to choose a file automatically from that directory.
Based on BCI2000's src/shared/modules/signalsource/DataIOFilter.cpp where the timing window
content is computed in DataIOFilter::Process(), this is what appears to happen:
Begin SampleBlock #t:
Enter SignalSource module's first Process() method (DataIOFilter::Process)
Save previous SampleBlock to file
Wait to acquire new SampleBlock from hardware
+--------- Measure SourceTime in SignalSource module
| | | Make a local copy of all states (NB: all except SourceTime were set during #t-1) ---+
B| R| S| Pipe the signal through the rest of BCI2000 |
| | +- Measure StimulusTime in Application module, on leaving last Process() method |
| | |
| | |
| | Begin SampleBlock #t+1: |
| +----- Enter SignalSource module's first Process() method (DataIOFilter::Process) |
| Save data from #t, SourceTime state from #t, and other states from #t-1, to file <--+
| Wait to acquire new SampleBlock from hardware
+--------- Measure SourceTime in SignalSource module
Make a local copy of all states (NB: all except SourceTime were set during #t)
Leave DataIOFilter::Process() and pipe the signal through the rest of BCI2000
Measure StimulusTime in Application module, on leaving last Process() method
B stands for Block duration.
R stands for Roundtrip time (visible in VisualizeTiming, not reconstructable from the .dat file)
S is the filter cascade time (marked "Stimulus" in the VisualizeTiming window).
Note that, on any given SampleBlock as saved in the file, SourceTime will be *greater* than
any other timestamp states (including StimulusTime), because it is the only state that is
updated in time to be saved with the data packet it belongs to. All the others lag by one
packet. This is corrected for at the point commented with ??? in the Python code.
"""
if hasattr(filename, 'filename'): filename = filename.filename
b = bcistream(filename=filename, ind=ind)
out = SigTools.sstruct()
out.filename = b.filename
#print "decoding..."
sig, states = b.decode('all')
#print "done"
b.close()
dT, T, rT = {}, {}, {}
statenames = ['SourceTime', 'StimulusTime'
] + ['PythonTiming%02d' % (x + 1) for x in range(2)]
statenames = [s for s in statenames if s in states]
for key in statenames:
dT[key], T[key] = SigTools.unwrapdiff(states[key].flatten(),
base=65536,
dtype=numpy.float64)
sel, = numpy.where(dT['SourceTime'])
for key in statenames:
dT[key] = dT[key][sel[1:]]
if key == 'SourceTime': tsel = sel[:-1] # ??? why the shift
else: tsel = sel[1:] # ??? relative to here?
T[key] = T[key][tsel + 1]
t0 = T['SourceTime'][0]
for key in statenames:
T[key] -= t0
t = T['SourceTime'] / 1000
expected = b.samples2msec(b.params['SampleBlockSize'])
datestamp = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(b.datestamp))
paramstr = ', '.join([
'%s=%s' % (x, b.params[x]) for x in [
'SampleBlockSize', 'SamplingRate', 'VisualizeTiming',
'VisualizeSource'
]
])
chainstr = '-'.join([
x for x, y in b.params['SignalSourceFilterChain'] +
b.params['SignalProcessingFilterChain'] +
b.params['ApplicationFilterChain']
])
titlestr = '\n'.join([b.filename, datestamp, paramstr, chainstr])
SigTools.plot(t[[0, -1]], [expected] * 2, drawnow=False)
SigTools.plot(t, dT['SourceTime'], hold=True, drawnow=False)
for key in statenames:
if key == 'SourceTime': continue
rT[key] = T[key] - T['SourceTime']
SigTools.plot(t, rT[key], hold=True, drawnow=False)
import pylab
pylab.title(titlestr)
pylab.grid(True)
pylab.xlabel('seconds')
pylab.ylabel('milliseconds')
ymin, ymax = pylab.ylim()
pylab.ylim(ymax=max(ymax, expected * 2))
pylab.xlim(xmax=t[-1])
pylab.draw()
out.params = SigTools.sstruct(b.params)
out.summarystr = titlestr
out.t = t
out.SourceTime = T['SourceTime']
out.StimulusTime = T['StimulusTime']
out.BlockDuration = dT['SourceTime']
out.BlockDuration2 = dT['StimulusTime']
out.ProcessingTime = out.StimulusTime - out.SourceTime
out.ExpectedBlockDuration = expected
out.rT = rT
out.dT = dT
out.T = T
if save:
pylab.gcf().savefig(save, orientation='landscape')
return out
def TimingWindow(filename='.', ind=-1, save=None):
"""
Recreate BCI2000's timing window offline, from a saved .dat file specified by <filename>.
It is also possible to supply a directory name as <filename>, and an index <ind> (default
value -1 meaning "the last run") to choose a file automatically from that directory.
Based on BCI2000's src/shared/modules/signalsource/DataIOFilter.cpp where the timing window
content is computed in DataIOFilter::Process(), this is what appears to happen:
Begin SampleBlock #t:
Enter SignalSource module's first Process() method (DataIOFilter::Process)
Save previous SampleBlock to file
Wait to acquire new SampleBlock from hardware
+--------- Measure SourceTime in SignalSource module
| | | Make a local copy of all states (NB: all except SourceTime were set during #t-1) ---+
B| R| S| Pipe the signal through the rest of BCI2000 |
| | +- Measure StimulusTime in Application module, on leaving last Process() method |
| | |
| | |
| | Begin SampleBlock #t+1: |
| +----- Enter SignalSource module's first Process() method (DataIOFilter::Process) |
| Save data from #t, SourceTime state from #t, and other states from #t-1, to file <--+
| Wait to acquire new SampleBlock from hardware
+--------- Measure SourceTime in SignalSource module
Make a local copy of all states (NB: all except SourceTime were set during #t)
Leave DataIOFilter::Process() and pipe the signal through the rest of BCI2000
Measure StimulusTime in Application module, on leaving last Process() method
B stands for Block duration.
R stands for Roundtrip time (visible in VisualizeTiming, not reconstructable from the .dat file)
S is the filter cascade time (marked "Stimulus" in the VisualizeTiming window).
Note that, on any given SampleBlock as saved in the file, SourceTime will be *greater* than
any other timestamp states (including StimulusTime), because it is the only state that is
updated in time to be saved with the data packet it belongs to. All the others lag by one
packet. This is corrected for at the point commented with ??? in the Python code.
"""
if hasattr(filename, 'filename'): filename = filename.filename
b = bcistream(filename=filename, ind=ind)
out = SigTools.sstruct()
out.filename = b.filename
#print "decoding..."
sig,states = b.decode('all')
#print "done"
b.close()
dT,T,rT = {},{},{}
statenames = ['SourceTime', 'StimulusTime'] + ['PythonTiming%02d' % (x+1) for x in range(2)]
statenames = [s for s in statenames if s in states]
for key in statenames:
dT[key],T[key] = SigTools.unwrapdiff(states[key].flatten(), base=65536, dtype=numpy.float64)
sel, = numpy.where(dT['SourceTime'])
for key in statenames:
dT[key] = dT[key][sel[1:]]
if key == 'SourceTime': tsel = sel[:-1] # ??? why the shift
else: tsel = sel[1:] # ??? relative to here?
T[key] = T[key][tsel+1]
t0 = T['SourceTime'][0]
for key in statenames: T[key] -= t0
t = T['SourceTime'] / 1000
expected = b.samples2msec(b.params['SampleBlockSize'])
datestamp = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(b.datestamp))
paramstr = ', '.join(['%s=%s' % (x,b.params[x]) for x in ['SampleBlockSize', 'SamplingRate', 'VisualizeTiming', 'VisualizeSource']])
chainstr = '-'.join([x for x,y in b.params['SignalSourceFilterChain']+b.params['SignalProcessingFilterChain']+b.params['ApplicationFilterChain']])
titlestr = '\n'.join([b.filename, datestamp, paramstr, chainstr])
SigTools.plot(t[[0,-1]], [expected]*2, drawnow=False)
SigTools.plot(t, dT['SourceTime'], hold=True, drawnow=False)
for key in statenames:
if key == 'SourceTime': continue
rT[key] = T[key] - T['SourceTime']
SigTools.plot(t, rT[key], hold=True, drawnow=False)
import pylab
pylab.title(titlestr)
pylab.grid(True)
pylab.xlabel('seconds')
pylab.ylabel('milliseconds')
ymin,ymax = pylab.ylim(); pylab.ylim(ymax=max(ymax,expected*2))
pylab.xlim(xmax=t[-1])
pylab.draw()
out.params = SigTools.sstruct(b.params)
out.summarystr = titlestr
out.t = t
out.SourceTime = T['SourceTime']
out.StimulusTime = T['StimulusTime']
out.BlockDuration = dT['SourceTime']
out.BlockDuration2 = dT['StimulusTime']
out.ProcessingTime = out.StimulusTime - out.SourceTime
out.ExpectedBlockDuration = expected
out.rT = rT
out.dT = dT
out.T = T
if save:
pylab.gcf().savefig(save, orientation='landscape')
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
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
