def _get_dataset_subtypes(self, dataset_name, dataset_type):
"""Get subtypes information for single dataset."""
if dataset_type == DATASET_TYPE_FC:
dataset = FeatureClass(os.path.join(self.gdb.path, dataset_name))
elif dataset_type == DATASET_TYPE_TABLE:
dataset = Table(os.path.join(self.gdb.path, dataset_name))
subtypes = dataset.get_subtypes()
if subtypes:
df_subtypes = self._map_boolean(pd.DataFrame.from_dict(subtypes))
return df_subtypes
def _get_dataset_indexes(self, dataset_name, dataset_type):
"""Get indexes information for single feature class."""
if dataset_type == DATASET_TYPE_FC:
dataset = FeatureClass(os.path.join(self.gdb.path, dataset_name))
elif dataset_type == DATASET_TYPE_TABLE:
dataset = Table(os.path.join(self.gdb.path, dataset_name))
indexes = dataset.get_indexes()
if indexes:
df_indexes = (pd.DataFrame.from_dict(indexes).sort_values(
by='Name'))
return df_indexes
def _get_fc_props(fc):
"""Get single geodatabase feature class props as ordered dict."""
fc_instance = FeatureClass(arcpy.Describe(fc).catalogPath)
od = OrderedDict()
passed_first_column = False
for k, v in GDB_FC_PROPS.items():
od[v] = getattr(fc_instance, k, '')
if not passed_first_column:
od['Feature dataset'] = ''
passed_first_column = True
# custom props
od['Row count'] = fc_instance.get_row_count()
num_attachments = fc_instance.get_attachments_count()
if num_attachments is not None:
od['Attachments enabled'] = True
od['Attachments count'] = num_attachments
else:
od['Attachments enabled'] = False
od['Attachments count'] = ''
return od
def _get_dataset_fields(self, dataset_name, dataset_type):
"""Get fields information for single dataset."""
if self.arcpy_found:
if dataset_type == DATASET_TYPE_FC:
dataset = FeatureClass(
os.path.join(self.gdb.path, dataset_name))
elif dataset_type == DATASET_TYPE_TABLE:
dataset = Table(os.path.join(self.gdb.path, dataset_name))
else:
if dataset_type == DATASET_TYPE_FC:
dataset = FeatureClassOgr(self.gdb, dataset_name)
elif dataset_type == DATASET_TYPE_TABLE:
dataset = TableOgr(self.gdb, dataset_name)
df_fields = self._map_boolean(
pd.DataFrame.from_dict(dataset.get_fields()))
# when there is a dataset with no fields
if not df_fields.empty:
df_fields['Default value'].fillna(value='', inplace=True)
else:
df_fields = None
return df_fields