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): """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)