def __call__(self, start, stop, chans=None):
"""Called when Stream object is called using (). start and stop indicate start and end
timepoints in us wrt t=0. Returns the corresponding WaveForm object with just the
specified chans"""
if chans is None:
chans = self.chans
if not set(chans).issubset(self.chans):
raise ValueError("requested chans %r are not a subset of available enabled "
"chans %r in %s stream" % (chans, self.chans, self.kind))
nchans = len(chans)
chanis = self.ADchans.searchsorted(chans)
rawtres = self.rawtres # float us
resample = self.sampfreq != self.rawsampfreq or self.shcorrect == True
if resample:
# excess data in us at either end, to eliminate interpolation distortion at
# key.start and key.stop
xs = KERNELSIZE * rawtres # float us
else:
xs = 0.0
# stream limits, in sample indices:
t0i = intround(self.t0 / rawtres)
t1i = intround(self.t1 / rawtres)
# get a slightly greater range of raw data (with xs) than might be needed:
t0xsi = intfloor((start - xs) / rawtres) # round down to nearest mult of rawtres
t1xsi = intceil((stop + xs) / rawtres) # round up to nearest mult of rawtres
# stay within stream limits, thereby avoiding interpolation edge effects:
t0xsi = max(t0xsi, t0i)
t1xsi = min(t1xsi, t1i)
# convert back to nearest float us:
t0xs = t0xsi * rawtres
t1xs = t1xsi * rawtres
# these are slice indices, so don't add 1:
ntxs = t1xsi - t0xsi # int
tsxs = np.linspace(t0xs, t0xs+(ntxs-1)*rawtres, ntxs)
#print('ntxs: %d' % ntxs)
# slice out excess data on requested channels, init as int32 so we have bitwidth
# to rescale and zero, convert to int16 later:
dataxs = np.int32(self.wavedata[chanis, t0xsi:t1xsi])
# bitshift left by 4 to scale 12 bit values to use full 16 bit dynamic range, same as
# * 2**(16-12) == 16. This provides more fidelity for interpolation, reduces uV per
# AD to about 0.02
if self.bitshift:
dataxs <<= self.bitshift # data is still int32 at this point
# do any resampling if necessary:
if resample:
#tresample = time.time()
dataxs, tsxs = self.resample(dataxs, tsxs, chans)
#print('resample took %.3f sec' % (time.time()-tresample))
# now trim down to just the requested time range:
lo, hi = tsxs.searchsorted([start, stop])
data = dataxs[:, lo:hi]
ts = tsxs[lo:hi]
# should be safe to convert back down to int16 now:
data = np.int16(data)
return WaveForm(data=data, ts=ts, chans=chans)
def __call__(self, start, stop, chans=None):
"""Called when Stream object is called using (). start and stop indicate start and end
timepoints in us wrt t=0. Returns the corresponding WaveForm object with just the
specified chans"""
if chans is None:
chans = self.chans
if not set(chans).issubset(self.chans):
raise ValueError("requested chans %r are not a subset of available enabled "
"chans %r in %s stream" % (chans, self.chans, self.kind))
nchans = len(chans)
rawtres = self.rawtres # float us
resample = self.sampfreq != self.rawsampfreq or self.shcorrect == True
if resample:
# excess data in us at either end, to eliminate interpolation distortion at
# key.start and key.stop
xs = KERNELSIZE * rawtres # float us
else:
xs = 0.0
# stream limits, in sample indices:
t0i = intround(self.t0 / rawtres)
t1i = intround(self.t1 / rawtres)
# get a slightly greater range of raw data (with xs) than might be needed:
t0xsi = intfloor((start - xs) / rawtres) # round down to nearest mult of rawtres
t1xsi = intceil((stop + xs) / rawtres) # round up to nearest mult of rawtres
# stay within stream limits, thereby avoiding interpolation edge effects:
t0xsi = max(t0xsi, t0i)
t1xsi = min(t1xsi, t1i)
# convert back to nearest float us:
t0xs = t0xsi * rawtres
t1xs = t1xsi * rawtres
# these are slice indices, so don't add 1:
ntxs = t1xsi - t0xsi # int
tsxs = np.linspace(t0xs, t0xs+(ntxs-1)*rawtres, ntxs)
#print('ntxs: %d' % ntxs)
# init data as int32 so we have bitwidth to rescale and zero, convert to int16 later
dataxs = np.zeros((nchans, ntxs), dtype=np.int32) # any gaps will have zeros
# Find all contiguous tranges that t0xs and t1xs span, if any. Note that this
# can now deal with case where len(trangeis) > 1. Test by asking for a slice
# longer than any one trange or gap between tranges, like by calling:
# >>> self.hpstream(201900000, 336700000)
# on file ptc15.74.
trangeis, = np.where((self.tranges[:, 0] <= t1xs) & (t0xs < self.tranges[:, 1]))
tranges = []
if len(trangeis) > 0:
tranges = self.tranges[trangeis]
#print('tranges:'); print(tranges)
# collect relevant records from spanned tranges, if any:
records = []
for trange in tranges:
trrec0i, trrec1i = self.records['TimeStamp'].searchsorted(trange)
trrecis = np.arange(trrec0i, trrec1i)
trrts = self.records['TimeStamp'][trrecis]
trrecs = self.records[trrecis]
rec0i, rec1i = trrts.searchsorted([t0xs, t1xs])
rec0i = max(rec0i-1, 0)
recis = np.arange(rec0i, rec1i)
records.append(trrecs[recis])
if len(records) > 0:
records = np.concatenate(records)
# load up data+excess, from all relevant records
# TODO: fix code duplication
#tload = time.time()
if self.kind == 'highpass': # straightforward
chanis = self.layout.ADchanlist.searchsorted(chans)
for record in records: # iterating over highpass records
d = self.f.loadContinuousRecord(record)[chanis] # record's data on chans
nt = d.shape[1]
t0i = intround(record['TimeStamp'] / rawtres)
t1i = t0i + nt
# source indices
st0i = max(t0xsi - t0i, 0)
st1i = min(t1xsi - t0i, nt)
# destination indices
dt0i = max(t0i - t0xsi, 0)
dt1i = min(t1i - t0xsi, ntxs)
dataxs[:, dt0i:dt1i] = d[:, st0i:st1i]
else: # kind == 'lowpass', need to load chans from subsequent records
chanis = [ int(np.where(chan == self.layout.chans)[0]) for chan in chans ]
"""NOTE: if the above raises an error it may be because this particular
combination of LFP chans was incorrectly parsed due to a bug in the .srf file,
and a manual remapping needs to be added to Surf.File.fixLFPlabels()"""
# assume all lpmc records are same length:
nt = intround(records[0]['NumSamples'] / self.nADchans)
d = np.zeros((nchans, nt), dtype=np.int32)
for record in records: # iterating over lowpassmultichan records
for i, chani in enumerate(chanis):
lprec = self.f.lowpassrecords[record['lpreci']+chani]
d[i] = self.f.loadContinuousRecord(lprec)
t0i = intround(record['TimeStamp'] / rawtres)
t1i = t0i + nt
# source indices
st0i = max(t0xsi - t0i, 0)
st1i = min(t1xsi - t0i, nt)
# destination indices
dt0i = max(t0i - t0xsi, 0)
dt1i = min(t1i - t0xsi, ntxs)
dataxs[:, dt0i:dt1i] = d[:, st0i:st1i]
#print('record.load() took %.3f sec' % (time.time()-tload))
# bitshift left to scale 12 bit values to use full 16 bit dynamic range, same as
# * 2**(16-12) == 16. This provides more fidelity for interpolation, reduces uV per
# AD to about 0.02
dataxs <<= 4 # data is still int32 at this point
# do any resampling if necessary:
if resample:
#tresample = time.time()
dataxs, tsxs = self.resample(dataxs, tsxs, chans)
#print('resample took %.3f sec' % (time.time()-tresample))
# now trim down to just the requested time range:
lo, hi = tsxs.searchsorted([start, stop])
data = dataxs[:, lo:hi]
ts = tsxs[lo:hi]
# should be safe to convert back down to int16 now:
data = np.int16(data)
return WaveForm(data=data, ts=ts, chans=chans)
def __call__(self, start, stop, chans=None):
"""Called when Stream object is called using (). start and stop indicate start and end
timepoints in us wrt t=0. Returns the corresponding WaveForm object with just the
specified chans"""
if chans is None:
chans = self.chans
if not set(chans).issubset(self.chans):
raise ValueError("requested chans %r are not a subset of available enabled "
"chans %r in %s stream" % (chans, self.chans, self.kind))
nchans = len(chans)
chanis = self.f.fileheader.chans.searchsorted(chans)
rawtres = self.rawtres
resample = self.sampfreq != self.rawsampfreq or self.shcorrect == True
# excess data in us at either end, to eliminate filtering and interpolation
# edge effects:
#print('NSXXSPOINTS: %d' % NSXXSPOINTS)
xs = intround(NSXXSPOINTS * rawtres)
#print('xs: %d, rawtres: %g' % (xs, rawtres))
# stream limits, in us and in sample indices, wrt t=0 and sample=0:
t0, t1, nt = self.t0, self.t1, self.f.nt
t0i, t1i = self.f.t0i, self.f.t1i
# get a slightly greater range of raw data (with xs) than might be needed:
t0xsi = intfloor((start - xs) / rawtres) # round down to nearest mult of rawtres
t1xsi = intceil((stop + xs) / rawtres) # round up to nearest mult of rawtres
# stay within stream limits, thereby avoiding interpolation edge effects:
t0xsi = max(t0xsi, t0i)
t1xsi = min(t1xsi, t1i)
# convert back to nearest float us:
t0xs = t0xsi * rawtres
t1xs = t1xsi * rawtres
# these are slice indices, so don't add 1:
ntxs = t1xsi - t0xsi # int
tsxs = np.linspace(t0xs, t0xs+(ntxs-1)*rawtres, ntxs)
#print('ntxs: %d' % ntxs)
# init data as int32 so we have bitwidth to rescale and zero, then convert to int16
dataxs = np.zeros((nchans, ntxs), dtype=np.int32) # any gaps will have zeros
#tload = time.time()
# load up data+excess, same data for high and low pass, difference will only be in the
# filtering. It would be convenient to immediately subsample to get lowpass, but that's
# not a valid thing to do: you can only subsample after filtering.
# source indices:
st0i = max(t0xsi - t0i, 0)
st1i = min(t1xsi - t0i, nt)
assert st1i-st0i == ntxs
# destination indices:
dt0i = max(t0i - t0xsi, 0)
dt1i = min(t1i - t0xsi, ntxs)
dataxs[:, dt0i:dt1i] = self.f.data[chanis, st0i:st1i]
#print('data load took %.3f sec' % (time.time()-tload))
#print('filtmeth: %s' % self.filtmeth)
if self.filtmeth == None:
pass
elif self.filtmeth == 'BW':
# high-pass filter using butterworth filter:
dataxs, b, a = filterord(dataxs, sampfreq=self.rawsampfreq, f0=BWF0, f1=None,
order=BWORDER, rp=None, rs=None,
btype='highpass', ftype='butter')
elif self.filtmeth == 'WMLDR':
# high-pass filter using wavelet multi-level decomposition and reconstruction:
## TODO: fix weird slow wobbling of amplitude as a function of exactly what
## the WMLDR filtering time range happens to be. Setting a much bigger xs
## helps, but only until you move xs amount of time away from the start of
## the recording
dataxs = WMLDR(dataxs)
else:
raise ValueError('unknown filter method %s' % self.filtmeth)
# do any resampling if necessary:
if resample:
#tresample = time.time()
dataxs, tsxs = self.resample(dataxs, tsxs, chans)
#print('resample took %.3f sec' % (time.time()-tresample))
#nresampletxs = len(tsxs)
#print('ntxs, nresampletxs: %d, %d' % (ntxs, nresampletxs))
#assert ntxs == len(tsxs)
# now trim down to just the requested time range:
lo, hi = tsxs.searchsorted([start, stop])
data = dataxs[:, lo:hi]
ts = tsxs[lo:hi]
#print(0, lo, hi, nresampletxs)
# should be safe to convert back down to int16 now:
data = np.int16(data)
return WaveForm(data=data, ts=ts, chans=chans)