def test_append_data(self):
"""Try appending data to existing NaN-padded nonuniform dataset"""
# start over for appending data
writer = nsdf.NSDFWriter(self.filepath, mode='a',
dialect=nsdf.dialect.NANPADDED)
source_ds = writer.mapping[nsdf.NONUNIFORM][self.popname]
self.assertTrue(nsdf.match_datasets(self.sources, source_ds))
rate = 100.0
new_dlen = np.random.poisson(lam=rate, size=len(self.sources))
for ii, uid in enumerate(self.sources):
data = np.cumsum(np.random.exponential(scale=1.0/rate,
size=new_dlen[ii]))
time = np.random.uniform(0, 1, size=new_dlen[ii])
self.data_object.put_data(uid, (data, time))
writer.add_nonuniform_nan(source_ds, self.data_object)
del writer
with h5.File(self.filepath, 'r') as fd:
data_path = '/data/{}/{}/{}'.format(nsdf.NONUNIFORM,
self.popname,
self.data_object.name)
dataset = fd[data_path]
time_ds = dataset.dims[1]['time']
for ii, uid in enumerate(self.sources):
orig_data, orig_time = self.data_object.get_data(uid)
file_data = dataset[ii,
self.dlen[ii]:self.dlen[ii]+new_dlen[ii]]
nptest.assert_allclose(orig_data, file_data)
nptest.assert_allclose(dataset[self.dlen[ii] +
new_dlen[ii]:], np.nan)
file_time = time_ds[ii,
self.dlen[ii]:self.dlen[ii]+new_dlen[ii]]
nptest.assert_allclose(orig_time, file_time)
nptest.assert_allclose(time_ds[ii, self.dlen[ii] +
new_dlen[ii]:], np.nan)
os.remove(self.filepath)
def test_append_data(self):
"""Try appending data to existing NaN-padded nonuniform dataset"""
# start over for appending data
writer = nsdf.NSDFWriter(self.filepath,
mode='a',
dialect=nsdf.dialect.NANPADDED)
source_ds = writer.mapping[nsdf.NONUNIFORM][self.popname]
self.assertTrue(nsdf.match_datasets(self.sources, source_ds))
rate = 100.0
new_dlen = np.random.poisson(lam=rate, size=len(self.sources))
for ii, uid in enumerate(self.sources):
data = np.cumsum(
np.random.exponential(scale=1.0 / rate, size=new_dlen[ii]))
time = np.random.uniform(0, 1, size=new_dlen[ii])
self.data_object.put_data(uid, (data, time))
writer.add_nonuniform_nan(source_ds, self.data_object)
del writer
with h5.File(self.filepath, 'r') as fd:
data_path = '/data/{}/{}/{}'.format(nsdf.NONUNIFORM, self.popname,
self.data_object.name)
dataset = fd[data_path]
time_ds = dataset.dims[1]['time']
for ii, uid in enumerate(self.sources):
orig_data, orig_time = self.data_object.get_data(uid)
file_data = dataset[ii,
self.dlen[ii]:self.dlen[ii] + new_dlen[ii]]
nptest.assert_allclose(orig_data, file_data)
nptest.assert_allclose(dataset[self.dlen[ii] + new_dlen[ii]:],
np.nan)
file_time = time_ds[ii,
self.dlen[ii]:self.dlen[ii] + new_dlen[ii]]
nptest.assert_allclose(orig_time, file_time)
nptest.assert_allclose(
time_ds[ii, self.dlen[ii] + new_dlen[ii]:], np.nan)
os.remove(self.filepath)
def test_source_ds(self):
with h5.File(self.filepath, 'r') as fd:
source_ds_name = '/map/{}/{}'.format(nsdf.NONUNIFORM, self.popname)
source_ds = fd[source_ds_name]
self.assertTrue(
nsdf.match_datasets(source_ds, self.data_object.get_sources()))
os.remove(self.filepath)
def test_source_ds(self):
with h5.File(self.filepath, 'r') as fd:
source_ds_name = '/map/{}/{}'.format(nsdf.NONUNIFORM,
self.popname)
source_ds = fd[source_ds_name]
self.assertTrue(nsdf.match_datasets(source_ds,
self.data_object.get_sources()))
os.remove(self.filepath)
def test_source_ds(self):
with h5.File(self.filepath, 'r') as fd:
try:
source_ds_path = 'map/{}/{}'.format(nsdf.EVENT, self.popname)
source_ds = fd[source_ds_path]
except KeyError:
self.fail('{} does not exist after'
' adding event data sources'.source_ds_path)
self.assertTrue(nsdf.match_datasets(source_ds, self.sources))
os.remove(self.filepath)
def test_source_ds(self):
with h5.File(self.filepath, 'r') as fd:
try:
source_ds_path = 'map/{}/{}'.format(nsdf.EVENT,
self.popname)
source_ds = fd[source_ds_path]
except KeyError:
self.fail('{} does not exist after'
' adding event data sources'.source_ds_path)
self.assertTrue(nsdf.match_datasets(source_ds,
self.sources))
os.remove(self.filepath)
def test_source_ds(self):
"""Add the soma (gc_0) all the granule cells in olfactory bulb model
as data sources for nonuniformly sampled data.
"""
with h5.File(self.filepath, 'r') as fd:
try:
source_ds_path = '/map/{}/{}/{}'.format(
nsdf.NONUNIFORM,
self.popname, self.data_object.name)
source_ds = fd[source_ds_path]
except KeyError:
self.fail('{} not created.'.format(source_ds_path))
self.assertTrue(nsdf.match_datasets(source_ds['source'],
self.data_object.get_sources()))
os.remove(self.filepath)
def test_source_ds(self):
"""Add the soma (gc_0) all the granule cells in olfactory bulb model
as data sources for nonuniformly sampled data.
"""
with h5.File(self.filepath, 'r') as fd:
try:
source_ds_path = '/map/{}/{}/{}'.format(
nsdf.NONUNIFORM, self.popname, self.data_object.name)
source_ds = fd[source_ds_path]
except KeyError:
self.fail('{} not created.'.format(source_ds_path))
self.assertTrue(
nsdf.match_datasets(source_ds['source'],
self.data_object.get_sources()))
os.remove(self.filepath)
def test_add_uniform_ds(self):
"""Add the soma (gc_0) all the granule cells in olfactory bulb model
as data sources for uniformly sampled data.
"""
with h5.File(self.filepath, 'r') as fd:
try:
uniform_group = fd['map']['uniform']
except KeyError:
self.fail('/map/uniform group does not exist after'
' adding uniform data sources')
try:
uniform_ds = fd['/map/uniform/pop0']
except KeyError:
self.fail('pop0 not created.')
self.assertTrue(nsdf.match_datasets(uniform_ds, self.granule_somata))
os.remove(self.filepath)
def test_add_uniform_ds(self):
"""Add the soma (gc_0) all the granule cells in olfactory bulb model
as data sources for uniformly sampled data.
"""
with h5.File(self.filepath, 'r') as fd:
try:
uniform_group = fd['map']['uniform']
except KeyError:
self.fail('/map/uniform group does not exist after'
' adding uniform data sources')
try:
uniform_ds = fd['/map/uniform/pop0']
except KeyError:
self.fail('pop0 not created.')
self.assertTrue(
nsdf.match_datasets(uniform_ds, self.granule_somata))
os.remove(self.filepath)
