def run(args):
"""Run it"""
verbose(1, "Loading %d result files" % len(args.data))
filetype_in = guess_backend(args.data[0])
if filetype_in == 'nifti':
dss = [fmri_dataset(f) for f in args.data]
elif filetype_in == 'hdf5':
dss = [h5load(f) for f in args.data]
data = np.asarray([d.samples[args.isample] for d in dss])
if args.mask:
filetype_mask = guess_backend(args.mask)
if filetype_mask == 'nifti':
mask = nib.load(args.mask).get_data()
elif filetype_mask == 'hdf5':
mask = h5load(args.mask).samples
out_of_mask = mask == 0
else:
# just take where no voxel had a value
out_of_mask = np.sum(data != 0, axis=0) == 0
t, p = ttest_1samp(data,
popmean=args.chance_level,
axis=0,
alternative=args.alternative)
if args.stat == 'z':
if args.alternative == 'two-sided':
s = stats.norm.isf(p / 2)
else:
s = stats.norm.isf(p)
# take the sign of the original t
s = np.abs(s) * np.sign(t)
elif args.stat == 'p':
s = p
elif args.stat == 't':
s = t
else:
raise ValueError('WTF you gave me? have no clue about %r' %
(args.stat, ))
if s.shape != out_of_mask.shape:
try:
out_of_mask = out_of_mask.reshape(s.shape)
except ValueError:
raise ValueError('Cannot use mask of shape {0} with '
'data of shape {1}'.format(
out_of_mask.shape, s.shape))
s[out_of_mask] = 0
verbose(1, "Saving to %s" % args.output)
filetype_out = guess_backend(args.output)
if filetype_out == 'nifti':
map2nifti(dss[0], data=s).to_filename(args.output)
else: # filetype_out is hdf5
s = Dataset(np.atleast_2d(s), fa=dss[0].fa, a=dss[0].a)
h5save(args.output, s)
return s
def test_directaccess():
f = tempfile.NamedTemporaryFile()
h5save(f.name, 'test')
assert_equal(h5load(f.name), 'test')
f.close()
f = tempfile.NamedTemporaryFile()
h5save(f.name, datasets['uni4medium'])
assert_array_equal(h5load(f.name).samples, datasets['uni4medium'].samples)
def test_directaccess():
f = tempfile.NamedTemporaryFile()
h5save(f.name, "test")
assert_equal(h5load(f.name), "test")
f.close()
f = tempfile.NamedTemporaryFile()
h5save(f.name, datasets["uni4medium"])
assert_array_equal(h5load(f.name).samples, datasets["uni4medium"].samples)
def run(args):
"""Run it"""
verbose(1, "Loading %d result files" % len(args.data))
filetype_in = guess_backend(args.data[0])
if filetype_in == 'nifti':
dss = [fmri_dataset(f) for f in args.data]
elif filetype_in == 'hdf5':
dss = [h5load(f) for f in args.data]
data = np.asarray([d.samples[args.isample] for d in dss])
if args.mask:
filetype_mask = guess_backend(args.mask)
if filetype_mask == 'nifti':
mask = nib.load(args.mask).get_data()
elif filetype_mask == 'hdf5':
mask = h5load(args.mask).samples
out_of_mask = mask == 0
else:
# just take where no voxel had a value
out_of_mask = np.sum(data != 0, axis=0)==0
t, p = ttest_1samp(data, popmean=args.chance_level, axis=0,
alternative=args.alternative)
if args.stat == 'z':
if args.alternative == 'two-sided':
s = stats.norm.isf(p/2)
else:
s = stats.norm.isf(p)
# take the sign of the original t
s = np.abs(s) * np.sign(t)
elif args.stat == 'p':
s = p
elif args.stat == 't':
s = t
else:
raise ValueError('WTF you gave me? have no clue about %r' % (args.stat,))
if s.shape != out_of_mask.shape:
try:
out_of_mask = out_of_mask.reshape(s.shape)
except ValueError:
raise ValueError('Cannot use mask of shape {0} with '
'data of shape {1}'.format(out_of_mask.shape, s.shape))
s[out_of_mask] = 0
verbose(1, "Saving to %s" % args.output)
filetype_out = guess_backend(args.output)
if filetype_out == 'nifti':
map2nifti(dss[0], data=s).to_filename(args.output)
else: # filetype_out is hdf5
s = Dataset(np.atleast_2d(s), fa=dss[0].fa, a=dss[0].a)
h5save(args.output, s)
return s
def test_various_special_cases(fname):
# 0d object ndarray
a = np.array(0, dtype=object)
h5save(fname, a)
a_ = h5load(fname)
ok_(a == a_)
# slice
h5save(fname, slice(2, 5, 3))
sl = h5load(fname)
ok_(sl == slice(2, 5, 3))
def test_various_special_cases(fname):
# 0d object ndarray
a = np.array(0, dtype=object)
h5save(fname, a)
a_ = h5load(fname)
ok_(a == a_)
# slice
h5save(fname, slice(2,5,3))
sl = h5load(fname)
ok_(sl == slice(2,5,3))
def test_various_special_cases():
# 0d object ndarray
f = tempfile.NamedTemporaryFile()
a = np.array(0, dtype=object)
h5save(f.name, a)
a_ = h5load(f.name)
ok_(a == a_)
# slice
h5save(f.name, slice(2,5,3))
sl = h5load(f.name)
ok_(sl == slice(2,5,3))
def test_various_special_cases():
# 0d object ndarray
f = tempfile.NamedTemporaryFile()
a = np.array(0, dtype=object)
h5save(f.name, a)
a_ = h5load(f.name)
ok_(a == a_)
# slice
h5save(f.name, slice(2, 5, 3))
sl = h5load(f.name)
ok_(sl == slice(2, 5, 3))
def test_save_load_python_objs(fname, obj):
"""Test saving objects of various types
"""
# try:
# print type(obj), " ",
# print obj # , obj.shape
# except Exception as e:
# print e
# save/reload
try:
h5save(fname, obj)
except Exception as e:
raise AssertionError("Failed to h5save %s: %s" % (safe_str(obj), e))
try:
obj_ = h5load(fname)
except Exception as e:
raise AssertionError("Failed to h5load %s: %s" % (safe_str(obj), e))
assert_equal(type(obj), type(obj_))
if isinstance(obj, np.ndarray):
assert_equal(obj.dtype, obj_.dtype)
assert_array_equal(obj, obj_)
else:
assert_equal(obj, obj_)
def load_data():
data = h5load(os.path.join(pymvpa_datadbroot, 'mnist', "mnist.hdf5"))
traindata = data['train'].samples
trainlabels = data['train'].sa.labels
testdata = data['test'].samples
testlabels = data['test'].sa.labels
return traindata, trainlabels, testdata, testlabels
def hdf2ds(fnames):
"""Load dataset(s) from an HDF5 file
Parameters
----------
fname : list(str)
Names of the input HDF5 files
Returns
-------
list(Dataset)
All datasets-like elements in all given HDF5 files (in order of
appearance). If any given HDF5 file contains non-Dataset elements
they are silently ignored. If no given HDF5 file contains any
dataset, an empty list is returned.
"""
from mvpa2.base.hdf5 import h5load
dss = []
for fname in fnames:
content = h5load(fname)
if is_datasetlike(content):
dss.append(content)
else:
for c in content:
if is_datasetlike(c):
dss.append(c)
return dss
def test_save_load_object_dtype_ds(obj=None):
"""Test saving of custom object ndarray (GH #84)
"""
aobjf = np.asanyarray(obj).flatten()
if not aobjf.size and externals.versions['hdf5'] < '1.8.7':
raise SkipTest("Versions of hdf5 before 1.8.7 have problems with empty arrays")
# print obj, obj.shape
f = tempfile.NamedTemporaryFile()
# save/reload
h5save(f.name, obj)
obj_ = h5load(f.name)
# and compare
# neh -- not versatile enough
#assert_objectarray_equal(np.asanyarray(obj), np.asanyarray(obj_))
assert_array_equal(obj.shape, obj_.shape)
assert_equal(type(obj), type(obj_))
# so we could test both ds and arrays
aobjf_ = np.asanyarray(obj_).flatten()
# checks if having just array above
if aobjf.size:
assert_equal(type(aobjf[0]), type(aobjf_[0]))
assert_array_equal(aobjf[0]['d'], aobjf_[0]['d'])
def fetch(self,
n_subjects=10,
resume=True,
force=False,
check=True,
verbose=1):
"""data_types is a list, can contain: anat, diff, func, rest, psyc, bgnd
"""
if n_subjects > self.MAX_SUBJECTS:
raise ValueError('Max # subjects == %d' % self.MAX_SUBJECTS)
processed_files = [
'S%02d_func_mni.nii.gz' % subj_id
for subj_id in range(1, 1 + n_subjects)
]
processed_files.append('stims.csv')
processed_files = [
os.path.join(self.data_dir, f) for f in processed_files
]
raw_files = (
'http://data.pymvpa.org/datasets/hyperalignment_tutorial_data/hyperalignment_tutorial_data_2.4.hdf5.gz',
)
raw_files = self.fetcher.fetch(raw_files,
resume=resume,
force=force,
check=check,
verbose=verbose)
if force or np.any([not os.path.exists(f) for f in processed_files]):
# Import local version of pymvpa
cur_dir = os.path.dirname(os.path.abspath(__file__))
mvpa2_path = os.path.abspath(
os.path.join(cur_dir, '..', '..', 'core', '_external',
'pymvpa'))
sys.path = [mvpa2_path] + sys.path
from mvpa2.base.hdf5 import h5load
# Load the file and manipulate into expected form.
ds_all = h5load(raw_files[0])
for si, func_filename in enumerate(processed_files[:-1]):
if not os.path.exists(os.path.dirname(func_filename)):
os.makedirs(os.path.dirname(func_filename))
# Construct and save the image
func_data = np.transpose(ds_all[si].O, [1, 2, 3, 0])
func_affine = ds_all[si].a['imgaffine'].value
func_hdr = ds_all[si].a['imghdr'].value
img = nib.Nifti1Image(func_data,
affine=func_affine) #, header=func_hdr)
nib.save(img, func_filename)
# Construct and save the stimuli
value_arr = np.asarray([ds_all[0].T, ds_all[0].sa['chunks']])
csv_cols = np.vstack([['stim', 'chunk'], value_arr.T])
np.savetxt(processed_files[-1], csv_cols, delimiter=',', fmt='%s')
return Bunch(raw_data=raw_files[0],
func=processed_files[:-1],
stim=processed_files[-1])
def test_store_metaclass_types(fname):
from mvpa2.kernels.base import Kernel
allowedtype=Kernel
h5save(fname, allowedtype)
lkrn = h5load(fname)
assert_equal(lkrn, Kernel)
assert_equal(lkrn.__metaclass__, Kernel.__metaclass__)
def test_store_metaclass_types(fname):
from mvpa2.kernels.base import Kernel
allowedtype = Kernel
h5save(fname, allowedtype)
lkrn = h5load(fname)
assert_equal(lkrn, Kernel)
assert_equal(lkrn.__metaclass__, Kernel.__metaclass__)
def test_product_flatten():
nsamples = 17
product_name_values = [('chan', ['C1', 'C2']),
('freq', np.arange(4, 20, 6)),
('time', np.arange(-200, 800, 200))]
shape = (nsamples, ) + tuple(len(v) for _, v in product_name_values)
sample_names = ['samp%d' % i for i in xrange(nsamples)]
# generate random data in four dimensions
data = np.random.normal(size=shape)
ds = Dataset(data, sa=dict(sample_names=sample_names))
# apply flattening to ds
flattener = ProductFlattenMapper(product_name_values)
# test I/O (only if h5py is available)
if externals.exists('h5py'):
from mvpa2.base.hdf5 import h5save, h5load
import tempfile
import os
fd, testfn = tempfile.mkstemp('mapper.h5py', 'test_product')
os.close(fd)
h5save(testfn, flattener)
flattener = h5load(testfn)
os.unlink(testfn)
mds = flattener(ds)
prod = lambda x: reduce(operator.mul, x)
# ensure the size is ok
assert_equal(mds.shape, (nsamples, ) + (prod(shape[1:]), ))
ndim = len(product_name_values)
idxs = [range(len(v)) for _, v in product_name_values]
for si in xrange(nsamples):
for fi, p in enumerate(itertools.product(*idxs)):
data_tup = (si, ) + p
x = mds[si, fi]
# value should match
assert_equal(data[data_tup], x.samples[0, 0])
# indices should match as well
all_idxs = tuple(x.fa['chan_freq_time_indices'].value.ravel())
assert_equal(p, all_idxs)
# values and indices in each dimension should match
for i, (name, value) in enumerate(product_name_values):
assert_equal(x.fa[name].value, value[p[i]])
assert_equal(x.fa[name + '_indices'].value, p[i])
product_name_values += [('foo', [1, 2, 3])]
flattener = ProductFlattenMapper(product_name_values)
assert_raises(ValueError, flattener, ds)
def hdf2ds(fnames):
"""Load dataset(s) from an HDF5 file
Parameters
----------
fname : list(str)
Names of the input HDF5 files
Returns
-------
list(Dataset)
All datasets-like elements in all given HDF5 files (in order of
appearance). If any given HDF5 file contains non-Dataset elements
they are silently ignored. If no given HDF5 file contains any
dataset, an empty list is returned.
"""
from mvpa2.base.hdf5 import h5load
dss = []
for fname in fnames:
content = h5load(fname)
if is_datasetlike(content):
dss.append(content)
else:
for c in content:
if is_datasetlike(c):
dss.append(c)
return dss
def test_dataset_without_chunks(fname):
# ValueError: All chunk dimensions must be positive (Invalid arguments to routine: Out of range)
# MH: This is not about Dataset chunks, but about an empty samples array
ds = AttrDataset([8], a=dict(custom=1))
save(ds, fname, compression='gzip')
ds_loaded = h5load(fname)
ok_(ds_loaded.a.custom == ds.a.custom)
def load_data(data_path, runs=None):
"""
Parameters
----------
data_path: Path to data.
runs: If not None, which chunks/runs to keep.
Returns
-------
List of datasets.
"""
if not os.path.exists(data_path):
raise ValueError("Data path doesn't exist: {0}".format(data_path))
dss = h5load(data_path)
if not isinstance(dss, (list, tuple, np.ndarray)):
raise TypeError("Input datasets should be a sequence "
"(of type list, tuple, or ndarray) of datasets.")
if runs is None:
return dss
else:
dss = [sd.select(sadict={'chunks': runs}) for sd in dss]
return dss
def test_save_load_object_dtype_ds(obj=None):
"""Test saving of custom object ndarray (GH #84)
"""
aobjf = np.asanyarray(obj).flatten()
if not aobjf.size and externals.versions['hdf5'] < '1.8.7':
raise SkipTest(
"Versions of hdf5 before 1.8.7 have problems with empty arrays")
# print obj, obj.shape
f = tempfile.NamedTemporaryFile()
# save/reload
h5save(f.name, obj)
obj_ = h5load(f.name)
# and compare
# neh -- not versatile enough
#assert_objectarray_equal(np.asanyarray(obj), np.asanyarray(obj_))
assert_array_equal(obj.shape, obj_.shape)
assert_equal(type(obj), type(obj_))
# so we could test both ds and arrays
aobjf_ = np.asanyarray(obj_).flatten()
# checks if having just array above
if aobjf.size:
assert_equal(type(aobjf[0]), type(aobjf_[0]))
assert_array_equal(aobjf[0]['d'], aobjf_[0]['d'])
def test_dataset_without_chunks(fname):
# ValueError: All chunk dimensions must be positive (Invalid arguments to routine: Out of range)
# MH: This is not about Dataset chunks, but about an empty samples array
ds = AttrDataset([8], a=dict(custom=1))
save(ds, fname, compression='gzip')
ds_loaded = h5load(fname)
ok_(ds_loaded.a.custom == ds.a.custom)
def load_data():
data = h5load(os.path.join(pymvpa_datadbroot, 'mnist', "mnist.hdf5"))
traindata = data['train'].samples
trainlabels = data['train'].sa.labels
testdata = data['test'].samples
testlabels = data['test'].sa.labels
return traindata, trainlabels, testdata, testlabels
def test_product_flatten():
nsamples = 17
product_name_values = [('chan', ['C1', 'C2']),
('freq', np.arange(4, 20, 6)),
('time', np.arange(-200, 800, 200))]
shape = (nsamples,) + tuple(len(v) for _, v in product_name_values)
sample_names = ['samp%d' % i for i in xrange(nsamples)]
# generate random data in four dimensions
data = np.random.normal(size=shape)
ds = Dataset(data, sa=dict(sample_names=sample_names))
# apply flattening to ds
flattener = ProductFlattenMapper(product_name_values)
# test I/O (only if h5py is available)
if externals.exists('h5py'):
from mvpa2.base.hdf5 import h5save, h5load
import tempfile
import os
_, testfn = tempfile.mkstemp('mapper.h5py', 'test_product')
h5save(testfn, flattener)
flattener = h5load(testfn)
os.unlink(testfn)
mds = flattener(ds)
prod = lambda x:reduce(operator.mul, x)
# ensure the size is ok
assert_equal(mds.shape, (nsamples,) + (prod(shape[1:]),))
ndim = len(product_name_values)
idxs = [range(len(v)) for _, v in product_name_values]
for si in xrange(nsamples):
for fi, p in enumerate(itertools.product(*idxs)):
data_tup = (si,) + p
x = mds[si, fi]
# value should match
assert_equal(data[data_tup], x.samples[0, 0])
# indices should match as well
all_idxs = tuple(x.fa['chan_freq_time_indices'].value.ravel())
assert_equal(p, all_idxs)
# values and indices in each dimension should match
for i, (name, value) in enumerate(product_name_values):
assert_equal(x.fa[name].value, value[p[i]])
assert_equal(x.fa[name + '_indices'].value, p[i])
product_name_values += [('foo', [1, 2, 3])]
flattener = ProductFlattenMapper(product_name_values)
assert_raises(ValueError, flattener, ds)
def test_state_cycle_with_custom_reduce(fname):
# BoxcarMapper has a custom __reduce__ implementation . The 'space'
# setting will only survive a svae/load cycle if the state is correctly
# handle for custom reduce iplementations.
bm = BoxcarMapper([0], 1, space='boxy')
h5save(fname, bm)
bm_rl = h5load(fname)
assert_equal(bm_rl.get_space(), 'boxy')
def test_state_cycle_with_custom_reduce(fname):
# BoxcarMapper has a custom __reduce__ implementation . The 'space'
# setting will only survive a svae/load cycle if the state is correctly
# handle for custom reduce iplementations.
bm = BoxcarMapper([0], 1, space='boxy')
h5save(fname, bm)
bm_rl = h5load(fname)
assert_equal(bm_rl.get_space(), 'boxy')
def test_store_metaclass_types():
f = tempfile.NamedTemporaryFile()
from mvpa2.kernels.base import Kernel
allowedtype = Kernel
h5save(f.name, allowedtype)
lkrn = h5load(f.name)
assert_equal(lkrn, Kernel)
assert_equal(lkrn.__metaclass__, Kernel.__metaclass__)
def test_store_metaclass_types():
f = tempfile.NamedTemporaryFile()
from mvpa2.kernels.base import Kernel
allowedtype=Kernel
h5save(f.name, allowedtype)
lkrn = h5load(f.name)
assert_equal(lkrn, Kernel)
assert_equal(lkrn.__metaclass__, Kernel.__metaclass__)
def load_results(path, name, task):
folder = '0_results'
print 'Opening ' + os.path.join(path, folder, name+'_'+task+'_120618_map.hdf5')
map = h5load(os.path.join(path, folder, name+'_'+task+'_120618_map.hdf5'))
mapper = pickle.load(open(os.path.join(path, folder, name+'_'+task+'_120618_mapper.pyobj'), 'r'))
rev_map = mapper.reverse(map.samples)
fileName = [elem for elem in os.listdir(os.path.join(path, name,'rest')) if elem.find('.nii.gz') != -1][0]
niftiimg = ni.load(os.path.join(path, name,'rest',fileName))
ni.save(ni.Nifti1Image(rev_map.squeeze(), niftiimg.get_affine()), os.path.join(path, name,name+'_120618_nifti_map.nii.gz'))
imgIn = os.path.join(path, name,name+'_120618_nifti_map.nii.gz')
refIn = '/usr/share/fsl/4.1/data/standard/MNI152_T1_2mm_brain.nii.gz'
mat = [elem for elem in os.listdir(os.path.join(path, name, 'rest')) if elem.find('.mat') != -1 and elem.find('mni') != -1][0]
command = 'flirt '+ \
' -in '+imgIn+ \
' -ref '+refIn+ \
' -init '+ os.path.join(path, name, 'rest',mat) +\
' -applyxfm' + \
' -out '+ os.path.join(path, name,name+'_nifti_map.nii.gz')[:-7] +'_120618_mni.nii.gz'
print command
os.system(command)
results = pickle.load(open(os.path.join(path, folder, name+'_'+task+'_120618_res.pyobj'), 'r'))
print '**************** '+name+' **********************'
print results.stats
mni_img = ni.load(os.path.join(path, name,name+'_nifti_map.nii.gz')[:-7] +'_120618_mni.nii.gz')
mni_mask = ni.load('/usr/share/data/fsl-mni152-templates/MNI152lin_T1_2mm_brain_mask.nii.gz')
brain = ni.load('/usr/share/fsl/4.1/data/standard/MNI152_T1_2mm.nii.gz')
res_masked = mni_img.get_data() * mni_mask.get_data()
res_masked = (res_masked - np.mean(res_masked))/np.std(res_masked)
ni.save(ni.Nifti1Image(res_masked, mni_mask.get_affine(), header = mni_mask.get_header()),
os.path.join(path, name,name+'_nifti_map.nii.gz')[:-7] +'_120618_res_masked_norm_mni.nii.gz')
ni.save(ni.Nifti1Image(brain.get_data(), mni_mask.get_affine(), header = mni_mask.get_header()),
os.path.join(path,'_MNI152_T1_2mm.nii.gz'))
return [results, res_masked]
def test_state_setter_getter(fname):
# make sure the presence of custom __setstate__, __getstate__ methods
# is honored -- numpy's RNGs have it
from numpy.random.mtrand import RandomState
r = RandomState()
h5save(fname, r)
rl = h5load(fname)
rl_state = rl.get_state()
for i, v in enumerate(r.get_state()):
assert_array_equal(v, rl_state[i])
def test_state_setter_getter(fname):
# make sure the presence of custom __setstate__, __getstate__ methods
# is honored -- numpy's RNGs have it
from numpy.random.mtrand import RandomState
r = RandomState()
h5save(fname, r)
rl = h5load(fname)
rl_state = rl.get_state()
for i, v in enumerate(r.get_state()):
assert_array_equal(v, rl_state[i])
def test_cosmo_io_h5py(fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
# Dataset from cosmo
ds = cosmo.from_any(_create_small_mat_dataset_dict())
h5save(fn, ds)
ds_loaded = h5load(fn)
_assert_ds_equal(ds, ds_loaded)
# Queryengine
qe = cosmo.from_any(_create_small_mat_nbrhood_dict())
h5save(fn, qe)
qe_loaded = h5load(fn)
assert_array_equal(qe.ids, qe_loaded.ids)
_assert_array_collectable_equal(qe.a, qe_loaded.a)
_assert_array_collectable_equal(qe.fa, qe_loaded.fa)
def run_hyperalignment(subjects_to_analyze, out_dir):
# Load subject data
ds_all = []
for subject_label in subjects_to_analyze:
ds_all.append(h5load('%s/sub-%s_data.hdf5' % (out_dir, subject_label)))
# Initialize searchlight hyperalignment
slhyper = SearchlightHyperalignment(radius=2, nblocks=10, sparse_radius=5,
dtype='float16')
hmappers = slhyper(ds_all)
return hmappers
def test_recursion(fname):
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
h5save(fname, obj)
lobj = h5load(fname)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_recursion(fname):
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
h5save(fname, obj)
lobj = h5load(fname)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_cosmo_io_h5py(fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
# Dataset from cosmo
ds = cosmo.from_any(_create_small_mat_dataset_dict())
h5save(fn, ds)
ds_loaded = h5load(fn)
_assert_ds_equal(ds, ds_loaded)
# Queryengine
qe = cosmo.from_any(_create_small_mat_nbrhood_dict())
h5save(fn, qe)
qe_loaded = h5load(fn)
assert_array_equal(qe.ids, qe_loaded.ids)
_assert_array_collectable_equal(qe.a, qe_loaded.a)
_assert_array_collectable_equal(qe.fa, qe_loaded.fa)
def test_recursion():
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
f = tempfile.NamedTemporaryFile()
h5save(f.name, obj)
lobj = h5load(f.name)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_recursion():
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
f = tempfile.NamedTemporaryFile()
h5save(f.name, obj)
lobj = h5load(f.name)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_function_ptrs(fname):
skip_if_no_external('nibabel')
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
h5save(fname, ds)
ds_loaded = h5load(fname)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh), ds.samples)
def test_function_ptrs(fname):
skip_if_no_external('nibabel')
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
h5save(fname, ds)
ds_loaded = h5load(fname)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh),
ds.samples)
def fetch(self, n_subjects=10, resume=True, force=False, check=True,
verbose=1):
"""data_types is a list, can contain: anat, diff, func, rest, psyc, bgnd
"""
if n_subjects > self.MAX_SUBJECTS:
raise ValueError('Max # subjects == %d' % self.MAX_SUBJECTS)
processed_files = ['S%02d_func_mni.nii.gz' % subj_id
for subj_id in range(1, 1 + n_subjects)]
processed_files.append('stims.csv')
processed_files = [op.join(self.data_dir, f)
for f in processed_files]
raw_files = ('http://data.pymvpa.org/datasets/'
'hyperalignment_tutorial_data/'
'hyperalignment_tutorial_data_2.4.hdf5.gz',)
raw_files = self.fetcher.fetch(raw_files, resume=resume, force=force,
check=check, verbose=verbose)
if force or np.any([not op.exists(f) for f in processed_files]):
# Import local version of pymvpa
if sys.version_info[0] > 2:
raise NotImplementedError("pymvpa only works in Python 2,"
"to convert this file.")
cur_dir = op.dirname(op.abspath(__file__))
mvpa2_path = op.abspath(op.join(cur_dir, '..', '..', 'core',
'_external', 'pymvpa'))
sys.path = [mvpa2_path] + sys.path
from mvpa2.base.hdf5 import h5load
# Load the file and manipulate into expected form.
ds_all = h5load(raw_files[0])
for si, func_filename in enumerate(processed_files[:-1]):
if not op.exists(op.dirname(func_filename)):
os.makedirs(op.dirname(func_filename))
# Construct and save the image
func_data = np.transpose(ds_all[si].O, [1, 2, 3, 0])
func_affine = ds_all[si].a['imgaffine'].value
func_hdr = ds_all[si].a['imghdr'].value
img = nib.Nifti1Image(func_data, affine=func_affine,
extra=func_hdr)
nib.save(img, func_filename)
# Construct and save the stimuli
value_arr = np.asarray([ds_all[0].T, ds_all[0].sa['chunks']])
csv_cols = np.vstack([['stim', 'chunk'], value_arr.T])
np.savetxt(processed_files[-1], csv_cols, delimiter=',', fmt='%s')
return dict(
raw_data=raw_files[0],
func=processed_files[:-1],
stim=processed_files[-1])
def load_ds(subnr, mask=None, zscore_ds=True):
ds = h5load(fns.betafn(subnr))
if mask is not None:
ds = ds[:, mask]
ds = ds[ds.sa.condition != 'self']
#if zscore_ds:
# zscore(ds, chunks_attr='chunks')
# add familiarity
ds.sa['familiarity'] = [
'familiar' if l.startswith('f') else 'control' for l in ds.sa.condition
]
return ds
def test_gifti_dataset_h5py(fn, include_nodes):
if not externals.exists('h5py'):
raise SkipTest
from mvpa2.base.hdf5 import h5save, h5load
ds = _get_test_dataset(include_nodes)
h5save(fn, ds)
ds2 = h5load(fn)
assert_datasets_equal(ds, ds2)
def run_hyperalignment(subjects_to_analyze, out_dir):
# Load subject data
ds_all = []
for subject_label in subjects_to_analyze:
ds_all.append(h5load('%s/sub-%s_data.hdf5' % (out_dir, subject_label)))
# Initialize searchlight hyperalignment
slhyper = SearchlightHyperalignment(radius=2,
nblocks=10,
sparse_radius=5,
dtype='float16')
hmappers = slhyper(ds_all)
return hmappers
def test_generate_testing_fmri_dataset(tempfile):
skip_if_no_external('nibabel')
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5load
from mvpa2.testing.regress import generate_testing_fmri_dataset
ds, filename = generate_testing_fmri_dataset(tempfile)
assert_equal(tempfile, filename)
assert_true(exists(tempfile))
ds_reloaded = h5load(tempfile)
assert_datasets_equal(ds, ds_reloaded)
def test_generate_testing_fmri_dataset(tempfile):
skip_if_no_external('nibabel')
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5load
from mvpa2.testing.regress import generate_testing_fmri_dataset
ds, filename = generate_testing_fmri_dataset(tempfile)
assert_equal(tempfile, filename)
assert_true(exists(tempfile))
ds_reloaded = h5load(tempfile)
assert_datasets_equal(ds, ds_reloaded, ignore_a={'wtf'})
def test_gifti_dataset_h5py(fn, include_nodes):
if not externals.exists('h5py'):
raise SkipTest
from mvpa2.base.hdf5 import h5save, h5load
ds = _get_test_dataset(include_nodes)
h5save(fn, ds)
ds2 = h5load(fn)
assert_datasets_equal(ds, ds2)
def test_save_load_python_objs(obj):
"""Test saving objects of various types
"""
# print obj, obj.shape
f = tempfile.NamedTemporaryFile()
# save/reload
h5save(f.name, obj)
obj_ = h5load(f.name)
assert_equal(type(obj), type(obj_))
assert_equal(obj, obj_)
def test_save_load_python_objs(obj):
"""Test saving objects of various types
"""
# print obj, obj.shape
f = tempfile.NamedTemporaryFile()
# save/reload
h5save(f.name, obj)
obj_ = h5load(f.name)
assert_equal(type(obj), type(obj_))
assert_equal(obj, obj_)
def test_surface_dset_h5py_io_with_unicode(self, fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
ds = dataset_wizard(np.arange(20).reshape((4, 5)), targets=1, chunks=1)
ds.sa['unicode'] = ['u1', 'uu2', 'uuu3', 'uuuu4']
ds.sa['str'] = ['s1', 'ss2', 'sss3', 'ssss4']
ds.fa['node_indices'] = np.arange(5)
# test h5py I/O
h5save(fn, ds)
ds2 = h5load(fn)
assert_datasets_equal(ds, ds2)
def _get_data(f):
"""Adapter to load data from various formats
"""
if f.endswith('.hdf5'):
from mvpa2.base.hdf5 import h5load
data = h5load(f).samples
else: #if f.endswith('.nii.gz') or f.endswith('.img') or f.endswith('.hdr'):
n = nb.load(f)
data = n.get_data()
# strip rudimentary 4th dimension
if len(data.shape) == 4 and data.shape[-1] == 1:
data = data[:, :, :, 0]
return data
def test_function_ptrs():
if not externals.exists('nibabel'):
raise SkipTest
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
f = tempfile.NamedTemporaryFile()
h5save(f.name, ds)
ds_loaded = h5load(f.name)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh), ds.samples)
def _get_data(f):
"""Adapter to load data from various formats
"""
if f.endswith('.hdf5'):
from mvpa2.base.hdf5 import h5load
data = h5load(f).samples
else: #if f.endswith('.nii.gz') or f.endswith('.img') or f.endswith('.hdr'):
n = nb.load(f)
data = n.get_data()
# strip rudimentary 4th dimension
if len(data.shape) == 4 and data.shape[-1] == 1:
data = data[:, :, :, 0]
return data
def test_surface_dset_h5py_io_with_unicode(self, fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
ds = dataset_wizard(np.arange(20).reshape((4, 5)), targets=1, chunks=1)
ds.sa['unicode'] = [u'u1', u'uu2', u'uuu3', u'uuuu4']
ds.sa['str'] = ['s1', 'ss2', 'sss3', 'ssss4']
ds.fa['node_indices'] = np.arange(5)
# test h5py I/O
h5save(fn, ds)
ds2 = h5load(fn)
assert_datasets_equal(ds, ds2)
def test_surface_outside_volume_voxel_selection(self, fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
vol_shape = (10, 10, 10, 1)
vol_affine = np.identity(4)
vg = volgeom.VolGeom(vol_shape, vol_affine)
# make surfaces that are far away from all voxels
# in the volume
sphere_density = 4
far = 10000.
outer = surf.generate_sphere(sphere_density) * 10 + far
inner = surf.generate_sphere(sphere_density) * 5 + far
vs = volsurf.VolSurfMaximalMapping(vg, inner, outer)
radii = [10., 10] # fixed and variable radii
outside_node_margins = [0, far, True]
for outside_node_margin in outside_node_margins:
for radius in radii:
selector = lambda: surf_voxel_selection.voxel_selection(vs,
radius,
outside_node_margin=outside_node_margin)
if type(radius) is int and outside_node_margin is True:
assert_raises(ValueError, selector)
else:
sel = selector()
if outside_node_margin is True:
# it should have all the keys, but they should
# all be empty
assert_array_equal(sel.keys(), range(inner.nvertices))
for k, v in sel.iteritems():
assert_equal(v, [])
else:
assert_array_equal(sel.keys(), [])
if outside_node_margin is True and \
externals.versions['hdf5'] < '1.8.7':
raise SkipTest("Versions of hdf5 before 1.8.7 have "
"problems with empty arrays")
h5save(fn, sel)
sel_copy = h5load(fn)
assert_array_equal(sel.keys(), sel_copy.keys())
for k in sel.keys():
assert_equal(sel[k], sel_copy[k])
assert_equal(sel, sel_copy)
def test_surface_outside_volume_voxel_selection(self, fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
vol_shape = (10, 10, 10, 1)
vol_affine = np.identity(4)
vg = volgeom.VolGeom(vol_shape, vol_affine)
# make surfaces that are far away from all voxels
# in the volume
sphere_density = 4
far = 10000.
outer = surf.generate_sphere(sphere_density) * 10 + far
inner = surf.generate_sphere(sphere_density) * 5 + far
vs = volsurf.VolSurfMaximalMapping(vg, inner, outer)
radii = [10., 10] # fixed and variable radii
outside_node_margins = [0, far, True]
for outside_node_margin in outside_node_margins:
for radius in radii:
selector = lambda: surf_voxel_selection.voxel_selection(vs,
radius,
outside_node_margin=outside_node_margin)
if type(radius) is int and outside_node_margin is True:
assert_raises(ValueError, selector)
else:
sel = selector()
if outside_node_margin is True:
# it should have all the keys, but they should
# all be empty
assert_array_equal(sel.keys(), range(inner.nvertices))
for k, v in sel.iteritems():
assert_equal(v, [])
else:
assert_array_equal(sel.keys(), [])
if outside_node_margin is True and \
externals.versions['hdf5'] < '1.8.7':
raise SkipTest("Versions of hdf5 before 1.8.7 have "
"problems with empty arrays")
h5save(fn, sel)
sel_copy = h5load(fn)
assert_array_equal(sel.keys(), sel_copy.keys())
for k in sel.keys():
assert_equal(sel[k], sel_copy[k])
assert_equal(sel, sel_copy)
def __handle_results(self, results):
if self.results_backend == 'hdf5':
# 'results' must be just a filename
assert(isinstance(results, str))
if __debug__:
debug('SLC', "Loading results from %s" % results)
results_data = h5load(results)
os.unlink(results)
if __debug__:
debug('SLC_', "Loaded results of len=%d from"
% len(results_data))
return results_data
else:
return results
def __handle_results(self, results):
if self.params.results_backend == 'hdf5':
# 'results' must be just a filename
assert (isinstance(results, str))
if __debug__:
debug('SLC', "Loading results from %s" % results)
results_data = h5load(results)
os.unlink(results)
if __debug__:
debug('SLC_',
"Loaded results of len=%d from" % len(results_data))
for isub, res in enumerate(results_data):
self.projections[isub] = self.projections[isub] + res
return
def test_function_ptrs():
if not externals.exists('nibabel'):
raise SkipTest
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
f = tempfile.NamedTemporaryFile()
h5save(f.name, ds)
ds_loaded = h5load(f.name)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh),
ds.samples)
def load_results_map (path, namelist, datetime, task, mask='none'):
"""
@param datetime: Indicates the moment of the analysis in a string formatted as AAAAMMGG_HHMM, could be used only a portion of that
@param mask: Optional param, is only setted when we want to store the result of a particular brain mask used during analysis
"""
resFolder = '0_results'
fileList = os.listdir(os.path.join(path, resFolder))
if (mask == 'none'):
mask = ''
fileList = [f for f in fileList if f.find(task) != -1
and f.find(datetime) != -1
and f.find('map') != -1
and f.find(mask) != -1
]
fileList.sort()
if (len(fileList) == 0):
print 'Results not found!'
return;
fileName = [elem for elem in os.listdir(os.path.join(path, 'andant','rest')) if elem.find('.nii.gz') != -1][0]
niftiimg = ni.load(os.path.join(path, 'andant', 'rest', fileName))
affine_tr = niftiimg.get_affine()
for i in range(0, len(fileList), 2):
mapFile = fileList[i]
mapperFile = fileList[i+1]
parts = mapFile.split('_')
name = parts[2]
print 'Opening: ' + os.path.join(path, resFolder, mapFile)
map = h5load(os.path.join(path, resFolder, mapFile))
print 'Opening: ' + os.path.join(path, resFolder, mapperFile)
mapper = pickle.load(open(os.path.join(path, resFolder, mapperFile), 'r'))
rev_map = mapper.reverse(map.samples)
fileName = [elem for elem in os.listdir(os.path.join(path, name,'rest')) if elem.find('.nii.gz') != -1][0]
niftiimg = ni.load(os.path.join(path, name,'rest',fileName))
print 'Saving results at: ' + os.path.join(path, name, mapFile.split('.')[0] + '.nii.gz')
ni.save(ni.Nifti1Image(rev_map.squeeze(), affine_tr), os.path.join(path, name, mapFile.split('.')[0] + '.nii.gz'))
