def __init__(
self,
file_path: str,
study_id: ObjectId,
study_file_id: ObjectId,
name: str,
*,
domain_ranges: Dict = None,
**kwargs,
):
Annotations.__init__(self, file_path, self.ALLOWED_FILE_TYPES,
study_id, study_file_id)
# Lowercase coordinate headers, expected for df merge
for i, header in enumerate(self.headers):
if header in ["X", "Y", "Z"]:
self.headers[i] = self.headers[i].lower()
self.preprocess()
self.determine_coordinates_and_cell_names()
self.source_file_type = "cluster"
self.cluster_type = ("3d" if
("z" in self.coordinates_and_cell_headers or "Z"
in self.coordinates_and_cell_headers) else "2d")
self.name = name
# Check if domain_ranges is an empty dictionary
self.domain_ranges = domain_ranges if not (not domain_ranges) else None
def __init__(self, cluster_file, cell_metadata_file=None):
Annotations.__init__(self, cluster_file, self.ALLOWED_FILE_TYPES)
self.preprocess()
self.determine_coordinates_and_cell_names()
if cell_metadata_file is not None:
self.cell_metadata = Annotations(cell_metadata_file,
CellMetadata.ALLOWED_FILE_TYPES)
def __init__(
self,
file_path: str,
study_id: ObjectId,
study_file_id: ObjectId,
*args,
**kwargs,
):
self.study_accession = kwargs.pop("study_accession")
Annotations.__init__(self, file_path, self.ALLOWED_FILE_TYPES,
study_id, study_file_id)
self.cell_names = []
# lambda below initializes new key with nested dictionary as value and avoids KeyError
self.issues = defaultdict(
lambda: defaultdict(lambda: defaultdict(list)))
self.ontology = defaultdict(lambda: defaultdict(list))
self.ontology_label = dict()
self.cells = []
self.numeric_array_columns = {}