def test_niml_dset(self):
d = dict(data=np.random.normal(size=(10, 2)),
node_indices=np.arange(10),
stats=['none', 'Tstat(2)'],
labels=['foo', 'bar'])
a = niml_dset.from_niml_dset(d)
b = niml_dset.to_niml_dset(a)
_, fn = tempfile.mkstemp('.niml.dset', 'dset')
afni_niml_dset.write(fn, b)
bb = afni_niml_dset.read(fn)
cc = niml_dset.from_niml_dset(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)) + .5
ds = Dataset(d)
fn = _, fn = tempfile.mkstemp('.niml.dset', 'dset')
writers = [niml_dset.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_dset(self):
d = dict(data=np.random.normal(size=(10, 2)),
node_indices=np.arange(10),
stats=['none', 'Tstat(2)'],
labels=['foo', 'bar'])
a = niml_dset.from_niml_dset(d)
b = niml_dset.to_niml_dset(a)
_, fn = tempfile.mkstemp('.niml.dset', 'dset')
afni_niml_dset.write(fn, b)
bb = afni_niml_dset.read(fn)
cc = niml_dset.from_niml_dset(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)) + .5
ds = Dataset(d)
fn = _, fn = tempfile.mkstemp('.niml.dset', 'dset')
writers = [niml_dset.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):
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 afni_niml_zscore_makefull(fnin, fnout, pad_to_ico_ld=None, pad_to_node=None):
dset = afni_niml_dset.read(fnin)
dset_z = afni_niml_zscore_data(dset)
if pad_to_ico_ld or pad_to_node:
dset_z_full = afni_niml_dset.sparse2full(dset_z, pad_to_ico_ld=pad_to_ico_ld, pad_to_node=pad_to_node)
else:
dset_z_full = dset_z
afni_niml_dset.write(fnout, dset_z_full)
return fnout
def afni_niml_zscore_makefull(fnin, fnout, pad_to_ico_ld=None, pad_to_node=None):
dset = afni_niml_dset.read(fnin)
dset_z = afni_niml_zscore_data(dset)
if pad_to_ico_ld or pad_to_node:
dset_z_full = afni_niml_dset.sparse2full(dset_z, pad_to_ico_ld=pad_to_ico_ld, pad_to_node=pad_to_node)
else:
dset_z_full = dset_z
afni_niml_dset.write(fnout, dset_z_full)
return fnout
def test_afni_niml_dset(self):
sz = (100, 45) # dataset size
rng = self._get_rng() # generate random data
expected_vals = {
(0, 0): -2.13856,
(sz[0] - 1, sz[1] - 1): -1.92434,
(sz[0], sz[1] - 1): None,
(sz[0] - 1, sz[1]): None,
sz: None
}
# test for different formats in which the data is stored
fmts = ['text', 'binary', 'base64']
# also test for different datatypes
tps = [np.int32, np.int64, np.float32, np.float64]
# generated random data
data = rng.normal(size=sz)
# set labels for samples, and set node indices
labels = [
'lab_%d' % round(rng.uniform() * 1000) for _ in xrange(sz[1])
]
node_indices = np.argsort(rng.uniform(size=(sz[0], )))
node_indices = np.reshape(node_indices, (sz[0], 1))
eps = .00001
# test I/O
_, fn = tempfile.mkstemp('data.niml.dset', 'test')
# depending on the mode we do different tests (but on the same data)
modes = ['normal', 'skipio', 'sparse2full']
for fmt in fmts:
for tp in tps:
for mode in modes:
# make a dataset
dset = dict(data=np.asarray(data, tp),
labels=labels,
node_indices=node_indices)
dset_keys = dset.keys()
if mode == 'skipio':
# try conversion to/from raw NIML
# do not write to disk
r = afni_niml_dset.dset2rawniml(dset)
s = afni_niml.rawniml2string(r)
r2 = afni_niml.string2rawniml(s)
dset2 = afni_niml_dset.rawniml2dset(r2)[0]
else:
# write and read from disk
afni_niml_dset.write(fn, dset, fmt)
dset2 = afni_niml_dset.read(fn)
os.remove(fn)
# data in dset and dset2 should be identical
for k in dset_keys:
# general idea is to test whether v is equal to v2
v = dset[k]
v2 = dset2[k]
if k == 'data':
if mode == 'sparse2full':
# test the sparse2full feature
# this changes the order of the data over columns
# so we skip testing whether dset2 is equal to dset
nfull = 2 * sz[0]
dset3 = afni_niml_dset.sparse2full(
dset2, pad_to_node=nfull)
assert_equal(dset3['data'].shape[0], nfull)
idxs = dset['node_indices'][:, 0]
idxs3 = dset3['node_indices'][:, 0]
vbig = np.zeros((nfull, sz[1]))
vbig[idxs, :] = v[np.arange(sz[0]), :]
v = vbig
v2 = dset3['data'][idxs3, :]
else:
# check that data is as expected
for pos, val in expected_vals.iteritems():
if val is None:
assert_raises(IndexError,
lambda x: x[pos], v2)
else:
val2 = np.asarray(val, tp)
assert_true(abs(v2[pos] - val2) < eps)
if type(v) is list:
assert_equal(v, v2)
else:
eps_dec = 4
if mode != 'sparse2full' or k == 'data':
assert_array_almost_equal(v, v2, eps_dec)
def test_afni_niml_dset(self, fn):
sz = (100, 45) # dataset size
rng = self._get_rng() # generate random data
expected_vals = {(0, 0):-2.13856 , (sz[0] - 1, sz[1] - 1):-1.92434,
(sz[0], sz[1] - 1):None, (sz[0] - 1, sz[1]):None,
sz:None}
# test for different formats in which the data is stored
fmts = ['text', 'binary', 'base64']
# also test for different datatypes
tps = [np.int32, np.int64, np.float32, np.float64]
# generated random data
data = rng.normal(size=sz)
# set labels for samples, and set node indices
labels = ['lab_%d' % round(rng.uniform() * 1000)
for _ in xrange(sz[1])]
node_indices = np.argsort(rng.uniform(size=(sz[0],)))
node_indices = np.reshape(node_indices, (sz[0], 1))
eps = .00001
# test I/O
# depending on the mode we do different tests (but on the same data)
modes = ['normal', 'skipio', 'sparse2full']
for fmt in fmts:
for tp in tps:
for mode in modes:
# make a dataset
dset = dict(data=np.asarray(data, tp),
labels=labels,
node_indices=node_indices)
dset_keys = dset.keys()
if mode == 'skipio':
# try conversion to/from raw NIML
# do not write to disk
r = afni_niml_dset.dset2rawniml(dset)
s = afni_niml.rawniml2string(r)
r2 = afni_niml.string2rawniml(s)
dset2 = afni_niml_dset.rawniml2dset(r2)[0]
else:
# write and read from disk
afni_niml_dset.write(fn, dset, fmt)
dset2 = afni_niml_dset.read(fn)
os.remove(fn)
# data in dset and dset2 should be identical
for k in dset_keys:
# general idea is to test whether v is equal to v2
v = dset[k]
v2 = dset2[k]
if k == 'data':
if mode == 'sparse2full':
# test the sparse2full feature
# this changes the order of the data over columns
# so we skip testing whether dset2 is equal to dset
nfull = 2 * sz[0]
dset3 = afni_niml_dset.sparse2full(dset2,
pad_to_node=nfull)
assert_equal(dset3['data'].shape[0], nfull)
idxs = dset['node_indices'][:, 0]
idxs3 = dset3['node_indices'][:, 0]
vbig = np.zeros((nfull, sz[1]))
vbig[idxs, :] = v[np.arange(sz[0]), :]
v = vbig
v2 = dset3['data'][idxs3, :]
else:
# check that data is as expected
for pos, val in expected_vals.iteritems():
if val is None:
assert_raises(IndexError, lambda x:x[pos], v2)
else:
val2 = np.asarray(val, tp)
assert_true(abs(v2[pos] - val2) < eps)
if type(v) is list:
assert_equal(v, v2)
else:
eps_dec = 4
if mode != 'sparse2full' or k == 'data':
assert_array_almost_equal(v, v2, eps_dec)