def rawniml2annot(p):
'''Converts raw NIML to annotation format'''
if type(p) is list:
return map(rawniml2annot, p)
r = dset.rawniml2dset(p)
for nd in p['nodes']:
if nd.get('dset_type', None) == 'LabelTableObject':
r[r'AFNI_labeltable'] = dset.rawniml2dset(nd)
return r
def rawniml2annot(p):
'''Converts raw NIML to annotation format'''
if type(p) is list:
return map(rawniml2annot, p)
r = dset.rawniml2dset(p)
for nd in p['nodes']:
if nd.get('dset_type', None) == 'LabelTableObject':
r[r'AFNI_labeltable'] = dset.rawniml2dset(nd)
return r
def rawniml2annot(p):
if type(p) is list:
return map(rawniml2annot, p)
r = dset.rawniml2dset(p)
for node in p['nodes']:
name = node.get('name', None)
if name == 'AFNI_labeltable':
t = _rawniml_labeltable2annot(node)
r.update(t)
elif name == 'AFNI_atr' and node['atr_name'].startswith(
'UNIQUE_VALS_'):
data = node['data']
unique_keys = set([data[i, 0] for i in xrange(len(data))])
keys = r['keys']
r['key2row'] = dict((keys[r], r) for r in xrange(len(keys)))
if len(unique_keys - set(r['keys'])) > 0:
raise ValueError('key mismatch')
# clean up
r.pop('stats')
return r
def _rawniml_labeltable2annot(p):
t = dset.rawniml2dset(p)
r = dict()
table = t['data']
nrows = len(table[0])
r['rgba'] = [(table[0][i], table[1][i], table[2][i], table[3][i])
for i in xrange(nrows)]
r['keys'] = table[4]
r['names'] = table[5]
return r
def _rawniml_labeltable2annot(p):
t = dset.rawniml2dset(p)
r = dict()
table = t['data']
nrows = len(table[0])
r['rgba'] = [(table[0][i],
table[1][i],
table[2][i],
table[3][i]) for i in xrange(nrows)]
r['keys'] = table[4]
r['names'] = table[5]
return r
def rawniml2annot(p):
if type(p) is list:
return map(rawniml2annot, p)
r = dset.rawniml2dset(p)
for node in p['nodes']:
name = node.get('name', None)
if name == 'AFNI_labeltable':
t = _rawniml_labeltable2annot(node)
r.update(t)
elif name == 'AFNI_atr' and node['atr_name'].startswith('UNIQUE_VALS_'):
data = node['data']
unique_keys = set([data[i, 0] for i in xrange(len(data))])
keys = r['keys']
r['key2row'] = dict((keys[r], r) for r in xrange(len(keys)))
if len(unique_keys - set(r['keys'])) > 0:
raise ValueError('key mismatch')
# clean up
r.pop('stats')
return r
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)
