def run(args):
from mvpa2.base.hdf5 import h5save
ds = None
if not args.txt_data is None:
verbose(1, "Load data from TXT file '%s'" % args.txt_data)
samples = _load_from_txt(args.txt_data)
ds = Dataset(samples)
elif not args.npy_data is None:
verbose(1, "Load data from NPY file '%s'" % args.npy_data)
samples = _load_from_npy(args.npy_data)
ds = Dataset(samples)
elif not args.mri_data is None:
verbose(1, "Load data from MRI image(s) %s" % args.mri_data)
from mvpa2.datasets.mri import fmri_dataset
vol_attr = dict()
if not args.add_vol_attr is None:
# XXX add a way to use the mapper of an existing dataset to
# add a volume attribute without having to load the entire
# mri data again
vol_attr = dict(args.add_vol_attr)
if not len(args.add_vol_attr) == len(vol_attr):
warning("--vol-attr option with duplicate attribute name: "
"check arguments!")
verbose(2, "Add volumetric feature attributes: %s" % vol_attr)
ds = fmri_dataset(args.mri_data, mask=args.mask, add_fa=vol_attr)
if ds is None:
if args.data is None:
raise RuntimeError('no data source specific')
else:
ds = hdf2ds(args.data)[0]
else:
if args.data is not None:
verbose(
1,
'ignoring dataset input in favor of other data source -- remove either one to disambiguate'
)
# act on all attribute options
ds = process_common_dsattr_opts(ds, args)
if not args.add_fsl_mcpar is None:
from mvpa2.misc.fsl.base import McFlirtParams
mc_par = McFlirtParams(args.add_fsl_mcpar)
for param in mc_par:
verbose(
2, "Add motion regressor as sample attribute '%s'" %
('mc_' + param))
ds.sa['mc_' + param] = mc_par[param]
verbose(3, "Dataset summary %s" % (ds.summary()))
# and store
outfilename = args.output
if not outfilename.endswith('.hdf5'):
outfilename += '.hdf5'
verbose(1, "Save dataset to '%s'" % outfilename)
h5save(outfilename, ds, mkdir=True, compression=args.hdf5_compression)
def run(args):
from mvpa2.base.hdf5 import h5save
ds = None
if not args.txt_data is None:
verbose(1, "Load data from TXT file '%s'" % args.txt_data)
samples = _load_from_txt(args.txt_data)
ds = Dataset(samples)
elif not args.npy_data is None:
verbose(1, "Load data from NPY file '%s'" % args.npy_data)
samples = _load_from_npy(args.npy_data)
ds = Dataset(samples)
elif not args.mri_data is None:
verbose(1, "Load data from MRI image(s) %s" % args.mri_data)
from mvpa2.datasets.mri import fmri_dataset
vol_attr = dict()
if not args.add_vol_attr is None:
# XXX add a way to use the mapper of an existing dataset to
# add a volume attribute without having to load the entire
# mri data again
vol_attr = dict(args.add_vol_attr)
if not len(args.add_vol_attr) == len(vol_attr):
warning("--vol-attr option with duplicate attribute name: "
"check arguments!")
verbose(2, "Add volumetric feature attributes: %s" % vol_attr)
ds = fmri_dataset(args.mri_data, mask=args.mask, add_fa=vol_attr)
if ds is None:
if args.data is None:
raise RuntimeError('no data source specific')
else:
ds = hdf2ds(args.data)[0]
else:
if args.data is not None:
verbose(1, 'ignoring dataset input in favor of other data source -- remove either one to disambiguate')
# act on all attribute options
ds = process_common_dsattr_opts(ds, args)
if not args.add_fsl_mcpar is None:
from mvpa2.misc.fsl.base import McFlirtParams
mc_par = McFlirtParams(args.add_fsl_mcpar)
for param in mc_par:
verbose(2, "Add motion regressor as sample attribute '%s'"
% ('mc_' + param))
ds.sa['mc_' + param] = mc_par[param]
verbose(3, "Dataset summary %s" % (ds.summary()))
# and store
outfilename = args.output
if not outfilename.endswith('.hdf5'):
outfilename += '.hdf5'
verbose(1, "Save dataset to '%s'" % outfilename)
h5save(outfilename, ds, mkdir=True, compression=args.hdf5_compression)
def run(args):
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# slicing
sliceme = {'samples': slice(None), 'features': slice(None)}
# indices
for opt, col, which in ((args.samples_by_index, ds.sa, 'samples'),
(args.features_by_index, ds.fa, 'features')):
if opt is None:
continue
if len(opt) == 1 and opt[0].count(':'):
# slice spec
arg = opt[0].split(':')
spec = []
for a in arg:
if not len(a):
spec.append(None)
else:
spec.append(int(a))
sliceme[which] = slice(*spec)
else:
# actual indices
sliceme[which] = [int(o) for o in opt]
# attribute evaluation
for opt, col, which in ((args.samples_by_attr, ds.sa, 'samples'),
(args.features_by_attr, ds.fa, 'features')):
if opt is None:
continue
sliceme[which] = _eval_attr_expr(opt, col)
# apply selection
ds = ds.__getitem__((sliceme['samples'], sliceme['features']))
verbose(1, 'Selected %i samples with %i features' % ds.shape)
# strip attributes
for attrarg, col, descr in ((args.strip_sa, ds.sa, 'sample '),
(args.strip_fa, ds.fa, 'feature '),
(args.strip_da, ds.a, '')):
if not attrarg is None:
for attr in attrarg:
try:
del col[attr]
except KeyError:
warning("dataset has no %sattribute '%s' to remove"
% (descr, attr))
# and store
ds2hdf5(ds, args.output, compression=args.hdf5_compression)
return ds
def run(args):
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# get CV instance
cv = get_crossvalidation_instance(args.learner, args.partitioner,
args.errorfx, args.sampling_repetitions,
args.learner_space,
args.balance_training, args.permutations,
args.avg_datafold_results,
args.prob_tail)
res = cv(ds)
# some meaningful output
# XXX make condition on classification analysis only?
print cv.ca.stats
print 'Results\n-------'
if args.permutations > 0:
nprob = cv.ca.null_prob.samples
if res.shape[1] == 1:
# simple result structure
if args.permutations > 0:
p = ', p-value (%s tail)' % args.prob_tail
else:
p = ''
print 'Fold, Result%s' % p
for i in xrange(len(res)):
if args.permutations > 0:
p = ', %f' % nprob[i, 0]
else:
p = ''
print '%s, %f%s' % (res.sa.cvfolds[i], res.samples[i, 0], p)
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
if args.permutations > 0:
if args.output.endswith('.hdf5'):
args.output = args.output[:-5]
ds2hdf5(cv.ca.null_prob,
'%s_nullprob' % args.output,
compression=args.hdf5_compression)
return res
def run(args):
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# get CV instance
cv = get_crossvalidation_instance(
args.learner, args.partitioner, args.errorfx, args.sampling_repetitions,
args.learner_space, args.balance_training, args.permutations,
args.avg_datafold_results, args.prob_tail)
res = cv(ds)
# some meaningful output
# XXX make condition on classification analysis only?
print cv.ca.stats
print 'Results\n-------'
if args.permutations > 0:
nprob = cv.ca.null_prob.samples
if res.shape[1] == 1:
# simple result structure
if args.permutations > 0:
p=', p-value (%s tail)' % args.prob_tail
else:
p=''
print 'Fold, Result%s' % p
for i in xrange(len(res)):
if args.permutations > 0:
p = ', %f' % nprob[i, 0]
else:
p = ''
print '%s, %f%s' % (res.sa.cvfolds[i], res.samples[i, 0], p)
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
if args.permutations > 0:
if args.output.endswith('.hdf5'):
args.output = args.output[:-5]
ds2hdf5(cv.ca.null_prob, '%s_nullprob' % args.output,
compression=args.hdf5_compression)
return res
def run(args):
if os.path.isfile(args.payload) and args.payload.endswith('.py'):
measure = script2obj(args.payload)
elif args.payload == 'cv':
if args.cv_learner is None or args.cv_partitioner is None:
raise ValueError(
'cross-validation payload requires --learner and --partitioner'
)
# get CV instance
measure = get_crossvalidation_instance(
args.cv_learner, args.cv_partitioner, args.cv_errorfx,
args.cv_sampling_repetitions, args.cv_learner_space,
args.cv_balance_training, args.cv_permutations,
args.cv_avg_datafold_results, args.cv_prob_tail)
else:
raise RuntimeError("this should not happen")
dss = hdf2ds(args.data)
verbose(3, 'Loaded %i dataset(s)' % len(dss))
ds = vstack(dss)
verbose(3, 'Concatenation yielded %i samples with %i features' % ds.shape)
# setup neighborhood
# XXX add big switch to allow for setting up surface-based neighborhoods
from mvpa2.misc.neighborhood import IndexQueryEngine
qe = IndexQueryEngine(**dict(args.neighbors))
# determine ROIs
rids = None # all by default
aggregate_fx = args.aggregate_fx
if args.roi_attr is not None:
# first figure out which roi features should be processed
if len(args.roi_attr) == 1 and args.roi_attr[0] in ds.fa.keys():
# name of an attribute -> pull non-zeroes
rids = ds.fa[args.roi_attr[0]].value.nonzero()[0]
else:
# an expression?
from .cmd_select import _eval_attr_expr
rids = _eval_attr_expr(args.roi_attr, ds.fa).nonzero()[0]
seed_ids = None
if args.scatter_rois is not None:
# scatter_neighborhoods among available ids if was requested
from mvpa2.misc.neighborhood import scatter_neighborhoods
attr, nb = args.scatter_rois
coords = ds.fa[attr].value
if rids is not None:
# select only those which were chosen by ROI
coords = coords[rids]
_, seed_ids = scatter_neighborhoods(nb, coords)
if aggregate_fx is None:
# no custom one given -> use default "fill in" function
aggregate_fx = _fill_in_scattered_results
if args.enable_ca is None:
args.enable_ca = ['roi_feature_ids']
elif 'roi_feature_ids' not in args.enable_ca:
args.enable_ca += ['roi_feature_ids']
if seed_ids is None:
roi_ids = rids
else:
if rids is not None:
# we had to sub-select by scatterring among available rids
# so we would need to get original ids
roi_ids = rids[seed_ids]
else:
# scattering happened on entire feature-set
roi_ids = seed_ids
verbose(
3, 'Attempting %i ROI analyses' %
((roi_ids is None) and ds.nfeatures or len(roi_ids)))
from mvpa2.measures.searchlight import Searchlight
sl = Searchlight(measure,
queryengine=qe,
roi_ids=roi_ids,
nproc=args.nproc,
results_backend=args.multiproc_backend,
results_fx=aggregate_fx,
enable_ca=args.enable_ca,
disable_ca=args.disable_ca)
# XXX support me too!
# add_center_fa
# tmp_prefix
# nblocks
# null_dist
# run
res = sl(ds)
if (seed_ids is not None) and ('mapper' in res.a):
# strip the last mapper link in the chain, which would be the seed ID selection
res.a['mapper'] = res.a.mapper[:-1]
# XXX create more output
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
return res
def run(args):
if os.path.isfile(args.payload) and args.payload.endswith(".py"):
measure = script2obj(args.payload)
elif args.payload == "cv":
if args.cv_learner is None or args.cv_partitioner is None:
raise ValueError("cross-validation payload requires --learner and --partitioner")
# get CV instance
measure = get_crossvalidation_instance(
args.cv_learner,
args.cv_partitioner,
args.cv_errorfx,
args.cv_sampling_repetitions,
args.cv_learner_space,
args.cv_balance_training,
args.cv_permutations,
args.cv_avg_datafold_results,
args.cv_prob_tail,
)
else:
raise RuntimeError("this should not happen")
dss = hdf2ds(args.data)
verbose(3, "Loaded %i dataset(s)" % len(dss))
ds = vstack(dss)
verbose(3, "Concatenation yielded %i samples with %i features" % ds.shape)
# setup neighborhood
# XXX add big switch to allow for setting up surface-based neighborhoods
from mvpa2.misc.neighborhood import IndexQueryEngine
qe = IndexQueryEngine(**dict(args.neighbors))
# determine ROIs
rids = None # all by default
aggregate_fx = args.aggregate_fx
if args.roi_attr is not None:
# first figure out which roi features should be processed
if len(args.roi_attr) == 1 and args.roi_attr[0] in ds.fa.keys():
# name of an attribute -> pull non-zeroes
rids = ds.fa[args.roi_attr[0]].value.nonzero()[0]
else:
# an expression?
from .cmd_select import _eval_attr_expr
rids = _eval_attr_expr(args.roi_attr, ds.fa).nonzero()[0]
seed_ids = None
if args.scatter_rois is not None:
# scatter_neighborhoods among available ids if was requested
from mvpa2.misc.neighborhood import scatter_neighborhoods
attr, nb = args.scatter_rois
coords = ds.fa[attr].value
if rids is not None:
# select only those which were chosen by ROI
coords = coords[rids]
_, seed_ids = scatter_neighborhoods(nb, coords)
if aggregate_fx is None:
# no custom one given -> use default "fill in" function
aggregate_fx = _fill_in_scattered_results
if args.enable_ca is None:
args.enable_ca = ["roi_feature_ids"]
elif "roi_feature_ids" not in args.enable_ca:
args.enable_ca += ["roi_feature_ids"]
if seed_ids is None:
roi_ids = rids
else:
if rids is not None:
# we had to sub-select by scatterring among available rids
# so we would need to get original ids
roi_ids = rids[seed_ids]
else:
# scattering happened on entire feature-set
roi_ids = seed_ids
verbose(3, "Attempting %i ROI analyses" % ((roi_ids is None) and ds.nfeatures or len(roi_ids)))
from mvpa2.measures.searchlight import Searchlight
sl = Searchlight(
measure,
queryengine=qe,
roi_ids=roi_ids,
nproc=args.nproc,
results_backend=args.multiproc_backend,
results_fx=aggregate_fx,
enable_ca=args.enable_ca,
disable_ca=args.disable_ca,
)
# XXX support me too!
# add_center_fa
# tmp_prefix
# nblocks
# null_dist
# run
res = sl(ds)
if (seed_ids is not None) and ("mapper" in res.a):
# strip the last mapper link in the chain, which would be the seed ID selection
res.a["mapper"] = res.a.mapper[:-1]
# XXX create more output
# and store
ds2hdf5(res, args.output, compression=args.hdf5_compression)
return res
