class TestMapping(BaseTest):
"""
Test if the registration of Group as a
Mapping behaves as expected
"""
def setUp(self):
data = ('a', 'b')
self.f = File('foo.hdf5', 'w')
self.grp = self.f.create_group('bar')
self.attr = self.f.attrs.create('x', data)
def TearDown(self):
if self.f:
self.close()
def test_keys(self):
key_1 = self.f.keys()
self.assertIsInstance(repr(key_1), str)
key_2 = self.grp.keys()
self.assertIsInstance(repr(key_2), str)
def test_values(self):
value_1 = self.f.values()
self.assertIsInstance(repr(value_1), str)
value_2 = self.grp.values()
self.assertIsInstance(repr(value_2), str)
def test_items(self):
item_1 = self.f.items()
self.assertIsInstance(repr(item_1), str)
item_2 = self.grp.items()
self.assertIsInstance(repr(item_1), str)
def _scan_neurodata_types(grp: h5py.File) -> List[Tuple[Any, Any]]:
out = []
if "neurodata_type" in grp.attrs:
out.append(
(grp.attrs["neurodata_type"], grp.attrs.get("description", None)))
for v in list(grp.values()):
if isinstance(v, h5py._hl.group.Group):
out += _scan_neurodata_types(v)
return out
def get_table_df(hdf5_file: File, table_name: str,
columns: List[str]) -> pd.DataFrame:
columns_with_hour = columns[:] + ['hour']
df = pd.DataFrame(columns=columns_with_hour, index=None)
for hour in [x for x in hdf5_file.values()][0]:
try:
hour_int = int(hour.removeprefix('hour_'))
except ValueError:
logger.error('wrong hour format=%s != hour_{int}' % hour)
continue
hour_df = pd.DataFrame(columns=['node_from', 'node_to', 'flow'],
data=np.array(hdf5_file['results/' + hour +
'/' + table_name]))
hour_df['hour'] = [hour_int for _ in range(len(hour_df))]
df = df.append(hour_df, ignore_index=True)
return df
