def test_iirfilter():
# dataset with one feature from two waves
t = np.linspace(0, 1.0, 2001)
xlow = np.sin(2 * np.pi * 5 * t)
xhigh = np.sin(2 * np.pi * 250 * t)
x = xlow + xhigh
ds = Dataset(x, sa={'sid': np.arange(len(x))}, fa={'fid':['theone']})
# butterworth filter with a cutoff between the waves
from scipy import signal
b, a = signal.butter(8, 0.125)
mds = iir_filter(ds, b, a, padlen=150)
# check we get just the slow wave out (compensate for edge artifacts)
assert_false(np.sum(np.abs(mds.samples[100:-100,0] - xlow[100:-100]) > 0.001))
assert_equal(len(ds.sa), len(mds.sa))
assert_equal(len(ds.fa), len(mds.fa))
assert_array_equal(ds.fa.fid, mds.fa.fid)
assert_array_equal(ds.sa.sid, mds.sa.sid)
def test_iirfilter():
# dataset with one feature from two waves
t = np.linspace(0, 1.0, 2001)
xlow = np.sin(2 * np.pi * 5 * t)
xhigh = np.sin(2 * np.pi * 250 * t)
x = xlow + xhigh
ds = Dataset(x, sa={'sid': np.arange(len(x))}, fa={'fid':['theone']})
# butterworth filter with a cutoff between the waves
from scipy import signal
b, a = signal.butter(8, 0.125)
mds = iir_filter(ds, b, a, padlen=150)
# check we get just the slow wave out (compensate for edge artifacts)
assert_false(np.sum(np.abs(mds.samples[100:-100,0] - xlow[100:-100]) > 0.001))
assert_equal(len(ds.sa), len(mds.sa))
assert_equal(len(ds.fa), len(mds.fa))
assert_array_equal(ds.fa.fid, mds.fa.fid)
assert_array_equal(ds.sa.sid, mds.sa.sid)
def run(args):
if not args.chunks is None:
# apply global "chunks" setting
for cattr in ('detrend_chunks', 'zscore_chunks'):
if getattr(args, cattr) is None:
# only overwrite if individual option is not given
args.__setattr__(cattr, args.chunks)
ds = arg2ds(args.data)
if not args.poly_detrend is None:
if not args.detrend_chunks is None \
and not args.detrend_chunks in ds.sa:
raise ValueError(
"--detrend-chunks attribute '%s' not found in dataset" %
args.detrend_chunks)
from mvpa2.mappers.detrend import poly_detrend
verbose(1, "Detrend")
poly_detrend(ds,
polyord=args.poly_detrend,
chunks_attr=args.detrend_chunks,
opt_regs=args.detrend_regrs,
space=args.detrend_coords)
if args.filter_passband is not None:
from mvpa2.mappers.filters import iir_filter
from scipy.signal import butter, buttord
if args.sampling_rate is None or args.filter_stopband is None:
raise ValueError("spectral filtering requires specification of "
"--filter-stopband and --sampling-rate")
# determine filter type
nyquist = args.sampling_rate / 2.0
if len(args.filter_passband) > 1:
btype = 'bandpass'
if not len(args.filter_passband) == len(args.filter_stopband):
raise ValueError(
"passband and stopband specifications have to "
"match in size")
wp = [v / nyquist for v in args.filter_passband]
ws = [v / nyquist for v in args.filter_stopband]
elif args.filter_passband[0] < args.filter_stopband[0]:
btype = 'lowpass'
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
elif args.filter_passband[0] > args.filter_stopband[0]:
btype = 'highpass'
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
else:
raise ValueError("invalid specification of Butterworth filter")
# create filter
verbose(1, "Spectral filtering (%s)" % (btype, ))
try:
ord, wn = buttord(wp,
ws,
args.filter_passloss,
args.filter_stopattenuation,
analog=False)
b, a = butter(ord, wn, btype=btype)
except OverflowError:
raise ValueError(
"cannot contruct Butterworth filter for the given "
"specification")
ds = iir_filter(ds, b, a)
if args.zscore:
from mvpa2.mappers.zscore import zscore
verbose(1, "Z-score")
zscore(ds, chunks_attr=args.zscore_chunks, params=args.zscore_params)
verbose(3, "Dataset summary %s" % (ds.summary()))
# invariants?
if not args.strip_invariant_features is None:
from mvpa2.datasets.miscfx import remove_invariant_features
ds = remove_invariant_features(ds)
# and store
ds2hdf5(ds, args.output, compression=args.hdf5_compression)
return ds
def run(args):
if args.chunks is not None:
# apply global "chunks" setting
for cattr in ("detrend_chunks", "zscore_chunks"):
if getattr(args, cattr) is None:
# only overwrite if individual option is not given
args.__setattr__(cattr, args.chunks)
ds = arg2ds(args.data)
if args.poly_detrend is not None:
if args.detrend_chunks is not None and not args.detrend_chunks in ds.sa:
raise ValueError("--detrend-chunks attribute '%s' not found in dataset" % args.detrend_chunks)
from mvpa2.mappers.detrend import poly_detrend
verbose(1, "Detrend")
poly_detrend(
ds,
polyord=args.poly_detrend,
chunks_attr=args.detrend_chunks,
opt_regs=args.detrend_regrs,
space=args.detrend_coords,
)
if args.filter_passband is not None:
from mvpa2.mappers.filters import iir_filter
from scipy.signal import butter, buttord
if args.sampling_rate is None or args.filter_stopband is None:
raise ValueError("spectral filtering requires specification of " "--filter-stopband and --sampling-rate")
# determine filter type
nyquist = args.sampling_rate / 2.0
if len(args.filter_passband) > 1:
btype = "bandpass"
if not len(args.filter_passband) == len(args.filter_stopband):
raise ValueError("passband and stopband specifications have to " "match in size")
wp = [v / nyquist for v in args.filter_passband]
ws = [v / nyquist for v in args.filter_stopband]
elif args.filter_passband[0] < args.filter_stopband[0]:
btype = "lowpass"
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
elif args.filter_passband[0] > args.filter_stopband[0]:
btype = "highpass"
wp = args.filter_passband[0] / nyquist
ws = args.filter_stopband[0] / nyquist
else:
raise ValueError("invalid specification of Butterworth filter")
# create filter
verbose(1, "Spectral filtering (%s)" % (btype,))
try:
ord, wn = buttord(wp, ws, args.filter_passloss, args.filter_stopattenuation, analog=False)
b, a = butter(ord, wn, btype=btype)
except OverflowError:
raise ValueError("cannot contruct Butterworth filter for the given " "specification")
ds = iir_filter(ds, b, a)
if args.zscore:
from mvpa2.mappers.zscore import zscore
verbose(1, "Z-score")
zscore(ds, chunks_attr=args.zscore_chunks, params=args.zscore_params)
verbose(3, "Dataset summary %s" % (ds.summary()))
# invariants?
if args.strip_invariant_features is not None:
from mvpa2.datasets.miscfx import remove_invariant_features
ds = remove_invariant_features(ds)
# and store
ds2hdf5(ds, args.output, compression=args.hdf5_compression)
return ds