phenopacket_mapper.pipeline.input module
- phenopacket_mapper.pipeline.input.read_file(path: str | Path, data_model: DataModel = DataModel(data_model_name='ERDRI_CDS', fields=[], resources=[]), file_type: Literal['csv', 'excel', 'unknown'] = 'unknown') List[DataModelInstance][source]
Reads a csv file in using a DataModel definition and returns a list of DataModelInstances
- Parameters:
path – Path to formatted csv or excel file
file_type – Type of file to read, either ‘csv’ or ‘excel’
data_model – DataModel to use for reading the file
- Returns:
List of DataModelInstances
- phenopacket_mapper.pipeline.input.read_data_model(data_model_name: str, resources: List[CodeSystem], path: str | Path, file_type: Literal['csv', 'excel', 'unknown'] = 'unknown', column_names: Dict[str, str] = mappingproxy({'name': 'data_field_name', 'section': 'data_model_section', 'description': 'description', 'data_type': 'data_type', 'required': 'required', 'specification': 'specification', 'ordinal': 'ordinal'}), parse_data_types: bool = False, compliance: Literal['soft', 'hard'] = 'soft', remove_line_breaks: bool = False, parse_ordinals: bool = True) DataModel[source]
Reads a Data Model from a file
- Parameters:
data_model_name – Name to be given to the DataModel object
resources – List of CodeSystem objects to be used as resources in the DataModel
path – Path to Data Model file
file_type – Type of file to read, either ‘csv’ or ‘excel’
column_names – A dictionary mapping from each field of the DataField (key) class to a column of the file (value). Leaving a value empty (‘’) will leave the field in the DataModel definition empty.
parse_data_types – If True, parses the string to a list of CodeSystems and types, can later be used to check validity of the data. Optional, but highly recommended.
compliance – Only applicable if parse_data_types=True, otherwise does nothing. ‘soft’ raises warnings upon encountering invalid data types, ‘hard’ raises ValueError.
remove_line_breaks – Whether to remove line breaks from string values
parse_ordinals – Whether to extract the ordinal number from the field name. Warning: this can overwrite values Ordinals could look like: “1.1.”, “1.”, “I.a.”, or “ii.”, etc.
- phenopacket_mapper.pipeline.input.read_redcap_api(data_model: DataModel) List[DataModelInstance][source]
Reads data from REDCap API and returns a list of DataModelInstances
- Parameters:
data_model – DataModel to use for reading the file
- Returns:
List of DataModelInstances