def test_surface_dset_niml_io_with_unicode(self, fn):
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)
# ensure sample attributes are of String type (not array)
niml_dict = afni_niml_dset.dset2rawniml(niml.to_niml(ds))
expected_dtypes = dict(PYMVPA_SA_unicode='String',
PYMVPA_SA_str='String',
PYMVPA_SA_targets='1*int32')
def assert_has_expected_datatype(name, expected_dtype, niml):
"""helper function"""
nodes = niml_dict['nodes']
for node in nodes:
if node['name'] == name:
assert_equal(node['ni_type'], expected_dtype)
return
raise ValueError('not found: %s', name)
for name, expected_dtype in expected_dtypes.iteritems():
assert_has_expected_datatype(name, expected_dtype, niml)
# test NIML I/O
niml.write(fn, ds)
# remove extra fields added when reading the file
ds2 = niml.from_any(fn)
ds2.a.pop('history')
ds2.a.pop('filename')
ds2.sa.pop('labels')
ds2.sa.pop('stats')
# NIML does not support int64, only int32;
# compare equality of values in samples by setting the
# datatype the same as in the input (int32 or int64 depending
# on the platform)
ds2.samples = np.asarray(ds2.samples, dtype=ds.samples.dtype)
assert_datasets_equal(ds, ds2)
def test_niml(self):
d = dict(
data=np.random.normal(size=(10, 2)),
node_indices=np.arange(10),
stats=["none", "Tstat(2)"],
labels=["foo", "bar"],
)
a = niml.from_niml(d)
b = niml.to_niml(a)
_, fn = tempfile.mkstemp(".niml.dset", "dset")
afni_niml_dset.write(fn, b)
bb = afni_niml_dset.read(fn)
cc = niml.from_niml(bb)
os.remove(fn)
for dset in (a, cc):
assert_equal(list(dset.sa["labels"]), d["labels"])
assert_equal(list(dset.sa["stats"]), d["stats"])
assert_array_equal(np.asarray(dset.fa["node_indices"]).ravel(), d["node_indices"])
eps_dec = 4
assert_array_almost_equal(dset.samples, d["data"].transpose(), eps_dec)
# some more tests to ensure that the order of elements is ok
# (row first or column first)
d = np.arange(10).reshape((5, -1)) + 0.5
ds = Dataset(d)
fn = _, fn = tempfile.mkstemp(".niml.dset", "dset")
writers = [niml.write, afni_niml_dset.write]
for i, writer in enumerate(writers):
for form in ("text", "binary", "base64"):
if i == 0:
writer(fn, ds, form=form)
else:
writer(fn, dict(data=d.transpose()), form=form)
x = afni_niml_dset.read(fn)
assert_array_equal(x["data"], d.transpose())
def test_niml(self, fn):
d = dict(data=np.random.normal(size=(10, 2)),
node_indices=np.arange(10),
stats=['none', 'Tstat(2)'],
labels=['foo', 'bar'])
a = niml.from_niml(d)
b = niml.to_niml(a)
afni_niml_dset.write(fn, b)
readers = (niml.from_any,
lambda x: niml.from_niml(afni_niml_dset.read(x)))
for reader in readers:
cc = reader(fn)
for dset in (a, cc):
assert_equal(list(dset.sa['labels']), d['labels'])
assert_equal(list(dset.sa['stats']), d['stats'])
assert_array_equal(
np.asarray(dset.fa['node_indices']).ravel(),
d['node_indices'])
eps_dec = 4
assert_array_almost_equal(dset.samples, d['data'].transpose(),
eps_dec)
# some more tests to ensure that the order of elements is ok
# (row first or column first)
d = np.arange(10).reshape((5, -1)) + .5
ds = Dataset(d)
writers = [niml.write, afni_niml_dset.write]
for i, writer in enumerate(writers):
for form in ('text', 'binary', 'base64'):
if i == 0:
writer(fn, ds, form=form)
else:
writer(fn, dict(data=d.transpose()), form=form)
x = afni_niml_dset.read(fn)
assert_array_equal(x['data'], d.transpose())
def test_surface_dset_niml_io_with_unicode(self, fn):
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)
# ensure sample attributes are of String type (not array)
niml_dict = afni_niml_dset.dset2rawniml(niml.to_niml(ds))
expected_dtypes = dict(PYMVPA_SA_unicode='String',
PYMVPA_SA_str='String',
PYMVPA_SA_targets='1*int32')
def assert_has_expected_datatype(name, expected_dtype, niml):
"""helper function"""
nodes = niml_dict['nodes']
for node in nodes:
if node['name'] == name:
assert_equal(node['ni_type'], expected_dtype)
return
raise ValueError('not found: %s', name)
for name, expected_dtype in expected_dtypes.items():
assert_has_expected_datatype(name, expected_dtype, niml)
# test NIML I/O
niml.write(fn, ds)
# remove extra fields added when reading the file
ds2 = niml.from_any(fn)
ds2.a.pop('history')
ds2.a.pop('filename')
ds2.sa.pop('labels')
ds2.sa.pop('stats')
# NIML does not support int64, only int32;
# compare equality of values in samples by setting the
# datatype the same as in the input (int32 or int64 depending
# on the platform)
ds2.samples = np.asarray(ds2.samples, dtype=ds.samples.dtype)
assert_datasets_equal(ds, ds2)
def test_niml(self, fn):
d = dict(data=np.random.normal(size=(10, 2)),
node_indices=np.arange(10),
stats=['none', 'Tstat(2)'],
labels=['foo', 'bar'])
a = niml.from_niml(d)
b = niml.to_niml(a)
afni_niml_dset.write(fn, b)
readers = (niml.from_any,
lambda x: niml.from_niml(afni_niml_dset.read(x)))
for reader in readers:
cc = reader(fn)
for dset in (a, cc):
assert_equal(list(dset.sa['labels']), d['labels'])
assert_equal(list(dset.sa['stats']), d['stats'])
assert_array_equal(np.asarray(dset.fa['node_indices']).ravel(),
d['node_indices'])
eps_dec = 4
assert_array_almost_equal(dset.samples, d['data'].transpose(),
eps_dec)
# some more tests to ensure that the order of elements is ok
# (row first or column first)
d = np.arange(10).reshape((5, -1)) + .5
ds = Dataset(d)
writers = [niml.write, afni_niml_dset.write]
for i, writer in enumerate(writers):
for form in ('text', 'binary', 'base64'):
if i == 0:
writer(fn, ds, form=form)
else:
writer(fn, dict(data=d.transpose()), form=form)
x = afni_niml_dset.read(fn)
assert_array_equal(x['data'], d.transpose())
