def __call__(self, input_file: InputFile) -> List[Indexable]:
"""
Load tagged variables.
Presumes a harmonized variable list as a CSV file as input.
A naming convention such that <prefix>_variables_<version>.csv will be the form of the filename.
An adjacent file called <prefix>_tags_<version.json will exist.
:param input_file: A list of study variables linked to tags, studies, and other relevant data.
:returns: Returns variables, a list of parsed variable dictionaries and tags, a list of parsed
tag definitions linked to the variabels.
"""
logger.debug(input_file)
if not input_file.endswith(".csv"):
return []
# Now loop through associated variables and associate each with its parent concept/tag
elements: List[Indexable] = []
with open(input_file, newline='') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
row = {k.strip(): v for k, v in row.items()}
elem = DugElement(elem_id=row['variable_full_accession'],
name=row['variable_name'],
desc=row['variable_desc'],
elem_type="DbGaP",
collection_id=row['study_full_accession'],
collection_name=row['study_name'])
# Create DBGaP links as study/variable actions
elem.collection_action = utils.get_dbgap_study_link(
study_id=elem.collection_id)
elem.action = utils.get_dbgap_var_link(
study_id=elem.collection_id,
variable_id=elem.id.split(".")[0].split("phv")[1])
# Add element to list of elements
elements.append(elem)
logger.debug(elem)
return elements
def __call__(self, input_file: InputFile) -> List[Indexable]:
logger.debug(input_file)
tree = ET.parse(input_file)
root = tree.getroot()
study_id = root.attrib['study_id']
participant_set = root.get('participant_set', '0')
# Parse study name from file handle
study_name = self.parse_study_name_from_filename(str(input_file))
if study_name is None:
err_msg = f"Unable to parse DbGaP study name from data dictionary: {input_file}!"
logger.error(err_msg)
raise IOError(err_msg)
elements = []
for variable in root.iter('variable'):
elem = DugElement(
elem_id=f"{variable.attrib['id']}.p{participant_set}",
name=variable.find('name').text,
desc=variable.find('description').text.lower(),
elem_type="DbGaP",
collection_id=f"{study_id}.p{participant_set}",
collection_name=study_name)
# Create DBGaP links as study/variable actions
elem.collection_action = utils.get_dbgap_study_link(
study_id=elem.collection_id)
elem.action = utils.get_dbgap_var_link(
study_id=elem.collection_id,
variable_id=elem.id.split(".")[0].split("phv")[1])
# Add to set of variables
logger.debug(elem)
elements.append(elem)
# You don't actually create any concepts
return elements
def __call__(self, input_file: InputFile) -> List[Indexable]:
"""
Load tagged variables.
Presumes a harmonized variable list as a CSV file as input.
A naming convention such that <prefix>_variables_<version>.csv will be the form of the filename.
An adjacent file called <prefix>_tags_<version.json will exist.
:param input_file: A list of study variables linked to tags, studies, and other relevant data.
:returns: Returns variables, a list of parsed variable dictionaries and tags, a list of parsed
tag definitions linked to the variabels.
"""
logger.debug(input_file)
if not input_file.endswith(".csv"):
return []
tags_input_file = input_file.replace(".csv", ".json").replace(
"_variables_", "_tags_")
if not os.path.exists(tags_input_file):
raise ValueError(
f"Accompanying tags file: {tags_input_file} must exist.")
# Read in huamn-created tags/concepts from json file before reading in elements
with open(tags_input_file, "r") as stream:
tags = json.load(stream)
# Loop through tags and create concepts for each one
concepts = {}
for tag in tags:
concept_id = f"TOPMED.TAG:{tag['pk']}"
concept = DugConcept(
concept_id,
name=tag['fields']['title'],
desc=
f'{tag["fields"]["description"]}. {tag["fields"]["instructions"]}',
concept_type="TOPMed Phenotype Concept")
# Only use the description for annotation
concept.ml_ready_desc = tag["fields"]["description"]
concepts[str(tag['pk'])] = concept
logger.debug(concept)
# Now loop through associated variables and associate each with its parent concept/tag
elements: List[Indexable] = []
with open(input_file, newline='') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
row = {k.strip(): v for k, v in row.items()}
elem = DugElement(elem_id=row['variable_full_accession'],
name=row['variable_name'] if 'variable_name'
in row else row['variable_full_accession'],
desc=row['variable_desc'] if 'variable_name'
in row else row['variable_full_accession'],
elem_type="DbGaP",
collection_id=row['study_full_accession'],
collection_name=row['study_name'])
# Create DBGaP links as study/variable actions
elem.collection_action = utils.get_dbgap_study_link(
study_id=elem.collection_id)
elem.action = utils.get_dbgap_var_link(
study_id=elem.collection_id,
variable_id=elem.id.split(".")[0].split("phv")[1])
# Add concept parsed from tag file to element
concept_group = row['tag_pk']
if concept_group not in concepts:
# Raise error if the tag_id parsed from the variable file didn't exist in the tag file
err_msg = f"DbGaP variable '{elem.id}' maps to a tag group that doesn't exist in tag file: '{concept_group}'"
logger.error(err_msg)
raise IOError(err_msg)
elem.add_concept(concepts[row['tag_pk']])
# Add element to list of elements
elements.append(elem)
logger.debug(elem)
return list(concepts.values()) + elements