diff --git a/heal/cli/extract.py b/heal/cli/extract.py index 0f1d07a..8dc5ecc 100644 --- a/heal/cli/extract.py +++ b/heal/cli/extract.py @@ -10,10 +10,17 @@ @click.option( "--input_file", "input_file", - required=True, help="name of file to extract HEAL-compliant VLMD file", + required=True, type=click.Path(writable=True), ) +@click.option( + "--title", + "title", + help="Root level title for the dictionary (required if extracting from csv to json)", + default=None, + type=str, +) @click.option( "--output_dir", "output_dir", @@ -22,12 +29,16 @@ type=click.Path(writable=True), show_default=True, ) -def extract(input_file, output_dir): +def extract(input_file, title, output_dir): """Extract HEAL-compliant VLMD file from input file""" logging.info(f"Extracting VLMD from {input_file}") try: - vlmd_extract(input_file, output_dir=output_dir) + vlmd_extract( + input_file, + title, + output_dir=output_dir, + ) except Exception as e: logging.error(f"Extraction error {str(e)}") diff --git a/heal/harvard_downloads.py b/heal/harvard_downloads.py index 770a8d0..ada9fad 100644 --- a/heal/harvard_downloads.py +++ b/heal/harvard_downloads.py @@ -11,7 +11,7 @@ from pathlib import Path from typing import Dict, List -from utils import unpackage_object, get_id, download_from_url +from heal.utils import unpackage_object, get_id, download_from_url from cdislogging import get_logger from gen3.tools.download.drs_download import DownloadStatus @@ -20,12 +20,14 @@ def get_harvard_dataverse_files( - file_metadata_list: List, download_path: str = "." + wts_hostname: str, auth, file_metadata_list: List, download_path: str = "." ) -> Dict: """ Retrieves external data from the Harvard Dataverse. Args: + wts_hostname (str): hostname for commons with wts (not being used at this moment) + auth (Gen3Auth): auth for commons with wts (not being used at this moment) file_metadata_list (List of Dict): list of studies or files download_path (str): path to download files and unpack @@ -102,6 +104,8 @@ def get_download_url_for_harvard_dataverse(file_metadata: Dict) -> str: base_url = "https://demo.dataverse.org/api/access" if "study_id" in file_metadata: url = f"{base_url}/dataset/:persistentId/?persistentId={file_metadata.get('study_id')}" + elif "file_id" in file_metadata: + url = f"{base_url}/datafile/{file_metadata.get('file_id')}" else: url = None @@ -122,7 +126,7 @@ def is_valid_harvard_file_metadata(file_metadata: Dict) -> bool: if not isinstance(file_metadata, dict): logger.critical(f"Invalid metadata - item is not a dict: {file_metadata}") return False - if "study_id" not in file_metadata: + if "study_id" not in file_metadata and "file_id" not in file_metadata: logger.critical( f"Invalid metadata - missing required Harvard Dataverse keys {file_metadata}" ) diff --git a/heal/qdr_downloads.py b/heal/qdr_downloads.py index 4b6d541..d8e5874 100644 --- a/heal/qdr_downloads.py +++ b/heal/qdr_downloads.py @@ -22,7 +22,7 @@ from pathlib import Path from typing import Dict, List -from utils import unpackage_object, get_id, download_from_url, get_idp_access_token +from heal.utils import unpackage_object, get_id, download_from_url, get_idp_access_token from cdislogging import get_logger from gen3.tools.download.drs_download import DownloadStatus, wts_get_token diff --git a/heal/vlmd/README.md b/heal/vlmd/README.md index 65c3474..0fa5112 100644 --- a/heal/vlmd/README.md +++ b/heal/vlmd/README.md @@ -57,7 +57,8 @@ except ExtractionError as e_err: The extract module implements extraction and conversion of dictionaries into different formats. -The current formats are csv, json, and tsv. +The current formats are csv, json, and tsv. A `title=` paramater should be supplied when +converting from non-json to json format. The `vlmd_extract()` method raises a `jsonschema.ValidationError` for an invalid input files and raises an `ExtractionError` for any other type of error. @@ -70,7 +71,7 @@ from jsonschema import ValidationError from heal.vlmd import vlmd_extract, ExtractionError try: - vlmd_extract("vlmd_for_extraction.csv", output_dir="./output") + vlmd_extract("vlmd_for_extraction.csv", title="the dictionary title", output_dir="./output") except ValidationError as v_err: # handle validation error @@ -115,4 +116,6 @@ For example, the following can validate a VLMD file in csv format: The following would extract a json format VLMD file from a csv format input file and write a json file in the directory `output`: -`heal vlmd extract --input_file "vlmd_for_extraction.csv" --output_dir "./output"` +`heal vlmd extract --input_file "vlmd_for_extraction.csv" --title "The dictionary title" --output_dir "./output"` + +The `--title` option is required when extracting from `csv` to `json`. diff --git a/heal/vlmd/config.py b/heal/vlmd/config.py index a86310c..cb96b2f 100644 --- a/heal/vlmd/config.py +++ b/heal/vlmd/config.py @@ -6,7 +6,7 @@ # file suffixes ALLOWED_INPUT_TYPES = ["csv", "tsv", "json"] -ALLOWED_FILE_TYPES = ["auto"] + ALLOWED_INPUT_TYPES +ALLOWED_FILE_TYPES = ["auto", "csv", "tsv", "json", "redcap"] ALLOWED_SCHEMA_TYPES = ["auto", "csv", "json", "tsv"] ALLOWED_OUTPUT_TYPES = ["csv", "json"] @@ -23,6 +23,8 @@ # schema JSON_SCHEMA_VERSION = JSON_SCHEMA.get("version", "0.3.2") +# The title is a default title used in the validation process. +# It will get overwritten by a user-specified title in the extraction process. TOP_LEVEL_PROPS = { "schemaVersion": JSON_SCHEMA_VERSION, "title": "HEAL Data Dictionary", diff --git a/heal/vlmd/extract/conversion.py b/heal/vlmd/extract/conversion.py index 335e514..101dc2d 100644 --- a/heal/vlmd/extract/conversion.py +++ b/heal/vlmd/extract/conversion.py @@ -7,6 +7,7 @@ from heal.vlmd.config import JSON_SCHEMA, TOP_LEVEL_PROPS from heal.vlmd.extract.csv_dict_conversion import convert_datadict_csv from heal.vlmd.extract.json_dict_conversion import convert_template_json +from heal.vlmd.extract.redcap_csv_dict_conversion import convert_redcap_csv from heal.vlmd.utils import clean_json_fields logger = get_logger("vlmd-conversion", log_level="debug") @@ -18,6 +19,7 @@ recode_map=mappings.recode_map, ), "json-template": convert_template_json, + "redcap-csv-dict": convert_redcap_csv, } ext_map = { @@ -45,7 +47,8 @@ def convert_to_vlmd( input_type (str): The input type. See keys of 'choice_fxn' dict for options, currently: csv-data-dict, json-template. data_dictionary_props (dict): - The other data-dictionary level properties. By default, will give the data_dictionary `title` property as the file name stem. + The other data-dictionary level properties. By default, + will give the data_dictionary `title` property as the file name stem. Returns Dictionary with: @@ -71,7 +74,6 @@ def convert_to_vlmd( data_dictionary_package = choice_fxn[input_type]( input_filepath, data_dictionary_props ) - logger.debug(f"Data Dictionary Package keys {data_dictionary_package.keys()}") # For now we return the csv and json in one package. # If any multiple data dictionaries are needed then implement the methods in diff --git a/heal/vlmd/extract/csv_dict_conversion.py b/heal/vlmd/extract/csv_dict_conversion.py index 61be068..bce00cb 100644 --- a/heal/vlmd/extract/csv_dict_conversion.py +++ b/heal/vlmd/extract/csv_dict_conversion.py @@ -4,10 +4,15 @@ import pandas as pd +from cdislogging import get_logger from heal.vlmd.config import JSON_SCHEMA from heal.vlmd.extract import utils +from heal.vlmd.extract.redcap_csv_dict_conversion import convert_redcap_csv +from heal.vlmd.utils import has_redcap_headers from heal.vlmd.validate.utils import read_delim +logger = get_logger("csv-conversion", log_level="debug") + def _parse_string_objects( tbl_csv: pd.DataFrame, field_properties: dict @@ -75,7 +80,8 @@ def convert_datadict_csv( The HEAL-specified data dictionary properties. rename_map: A mapping of source (current) column headers to target (desired -- conforming to CVS HEAL spec) column headers - recode_map: A mapping of values for each column in HEAL spec -- {..."column_name":{"old_value":"new_value"...}...} + recode_map: A mapping of values for each column in HEAL spec, eg, + {..."column_name":{"old_value":"new_value"...}...} drop_list: a list of variables to drop from headers before processing item_sep:str (default:"|") Used to split stringified items (in objects and arrays) key_val_sep:str (default:"=") Used to split stringified each key-value pair @@ -123,10 +129,22 @@ def infer_delim(series: pd.Series, char_list: list, firstmatch: bool): return inferred_delim if isinstance(csv_template, (str, PathLike)): + logger.debug("Getting data from path to CSV file") template_tbl = read_delim(str(Path(csv_template))) else: + logger.debug("Getting data from input dataframe") template_tbl = pd.DataFrame(csv_template) + # If REDCap then get dictionary and return. + column_names = template_tbl.columns + if has_redcap_headers(column_names): + logger.debug("File appears to have REDCap headers. Ready to convert.") + converted_dict = convert_redcap_csv(template_tbl) + return converted_dict + else: + logger.debug("File is CSV dictionary, not REDCap dictionary.") + + # Regular CSV, not REDCap. if not rename_map: rename_map = {} diff --git a/heal/vlmd/extract/extract.py b/heal/vlmd/extract/extract.py index a4f7b9a..e9d69c2 100644 --- a/heal/vlmd/extract/extract.py +++ b/heal/vlmd/extract/extract.py @@ -1,3 +1,4 @@ +import os from os.path import isfile from pathlib import Path @@ -9,13 +10,50 @@ ALLOWED_INPUT_TYPES, ALLOWED_OUTPUT_TYPES, ) +from heal.vlmd.extract.conversion import convert_to_vlmd from heal.vlmd.file_utils import get_output_filepath, write_vlmd_dict +from heal.vlmd.validate.validate import file_type_to_fxn_map + logger = get_logger("extract", log_level="debug") +def set_title_if_missing(file_type: str, title: str, converted_dict: dict) -> dict: + """ + JSON output should have a title. + If input file_type is not json then title should come from + parameter or standardsMappings[0].instrument.title + """ + if title is not None and title != converted_dict.get("title"): + logger.debug(f"JSON dictionary setting user-defined title '{title}'") + converted_dict["title"] = title + return converted_dict + + instrument_title = None + try: + standards_mappings = converted_dict.get("standardsMappings") + if standards_mappings and len(standards_mappings) >= 1: + instrument_title = standards_mappings[0].get("instrument").get("title") + except Exception as err: + logger.warning("standardsMapping does not have 'instrument.title'") + + if file_type != "json": + if title is None and instrument_title is None: + message = "Title must be supplied when extracting from non-json to json" + logger.error(message) + raise ExtractionError(message) + + elif instrument_title is not None: + logger.debug( + f"JSON dictionary setting title to instrument title '{instrument_title}'" + ) + converted_dict["title"] = instrument_title + + return converted_dict + + def vlmd_extract( - input_file, file_type="auto", output_dir=".", output_type="json" + input_file, title=None, file_type="auto", output_dir=".", output_type="json" ) -> bool: """ Extract a HEAL compliant csv and json format VLMD data dictionary @@ -24,9 +62,11 @@ def vlmd_extract( Args: input_file (str): the path of the input HEAL VLMD file to be extracted into HEAL-compliant VLMD file(s). + title (str): the root level title of the dictionary (required if extracting from csv to json) file_type (str): the type of the input file that will be extracted into a HEAL-compliant VLMD file. - Allowed values are "auto", “csv”, "json", "tsv". + Allowed values are "auto", “csv”, "json", "tsv", and "redcap" + where "redcap" is a csv file of a REDCap dictionary export. Defaults to “auto”. output_dir (str): the directory of where the extracted VLMD file will be written. Defaults to “.” @@ -38,7 +78,9 @@ def vlmd_extract( Raises ExtractionError in input VLMD is not valid or could not be converted. """ - logger.info(f"Extracting VLMD file '{input_file}' with file_type '{file_type}'") + logger.info( + f"Extracting VLMD file '{input_file}' with input file_type '{file_type}'" + ) file_suffix = Path(input_file).suffix.replace(".", "") if file_suffix not in ALLOWED_INPUT_TYPES: @@ -62,15 +104,58 @@ def vlmd_extract( logger.error(message) raise ExtractionError(message) + logger.debug(f"File type is set to '{file_type}'") # validate try: + # csv files are converted as part of validate converted_dictionary = vlmd_validate( - input_file, output_type=output_type, return_converted_output=True + input_file, + file_type=file_type, + output_type=output_type, + return_converted_output=True, ) - except Exception as e: + except Exception as err: logger.error(f"Error in validating and extracting dictionary from {input_file}") - logger.error(e) - raise ExtractionError(str(e)) + logger.error(err) + raise ExtractionError(str(err)) + + # input json file require explicit conversion and post validation steps + if file_type == "json": + file_convert_function = file_type_to_fxn_map.get(file_type) + data_dictionary_props = {} + try: + logger.debug("Ready to convert json input to VLMD") + data_dictionaries = convert_to_vlmd( + input_filepath=input_file, + input_type=file_convert_function, + data_dictionary_props=data_dictionary_props, + ) + if output_type == "json": + converted_dictionary = data_dictionaries["template_json"] + logger.debug( + f"Ready to validate converted dict with output type '{output_type}'" + ) + is_valid = vlmd_validate( + converted_dictionary, + file_type=file_type, + output_type=output_type, + return_converted_output=False, + ) + logger.debug(f"Converted dictionary is valid: {is_valid}") + else: + converted_dictionary = data_dictionaries["template_csv"]["fields"] + except Exception as err: + logger.error(f"Error in extracting JSON dictionary from {input_file}") + logger.error(err) + raise ExtractionError(str(err)) + + if output_type == "json": + converted_dictionary = set_title_if_missing( + file_type=file_type, title=title, converted_dict=converted_dictionary + ) + if converted_dictionary.get("title") is None: + logger.error("JSON dictionary is missing 'title'") + raise ExtractionError("JSON dictionary is missing 'title'") # write to file output_filepath = get_output_filepath( @@ -79,9 +164,9 @@ def vlmd_extract( logger.info(f"Writing converted dictionary to {output_filepath}") try: write_vlmd_dict(converted_dictionary, output_filepath, file_type=output_type) - except Exception as e: + except Exception as err: logger.error("Error in writing converted dictionary") - logger.error(e) + logger.error(err) raise ExtractionError("Error in writing converted dictionary") return True diff --git a/heal/vlmd/extract/json_dict_conversion.py b/heal/vlmd/extract/json_dict_conversion.py index be671d8..b334e2f 100644 --- a/heal/vlmd/extract/json_dict_conversion.py +++ b/heal/vlmd/extract/json_dict_conversion.py @@ -5,9 +5,12 @@ import pandas as pd +from cdislogging import get_logger from heal.vlmd.config import JSON_SCHEMA from heal.vlmd.extract import utils +logger = get_logger("json-conversion", log_level="debug") + def convert_template_json( json_template, @@ -38,14 +41,18 @@ def convert_template_json( """ if isinstance(json_template, (str, PathLike)): + logger.debug(f"Getting data from path to JSON file '{json_template}'") json_template_dict = json.loads(Path(json_template).read_text()) elif isinstance(json_template, collections.abc.MutableMapping): + logger.debug("Getting JOSN data from object") json_template_dict = json_template else: raise ValueError( "json_template needs to be either dictionary-like or a path to a json" ) + existing_title = json_template_dict.get("title") + if data_dictionary_props: for prop_name, prop in data_dictionary_props.items(): # determine if you should write or overwrite the root level data dictionary props @@ -69,7 +76,6 @@ def convert_template_json( flattened_data_dictionary_props = pd.Series( utils.flatten_to_json_path(data_dictionary_props, JSON_SCHEMA) ) - flattened_and_embedded = utils.embed_data_dictionary_props( flattened_fields, flattened_data_dictionary_props, JSON_SCHEMA ) @@ -89,6 +95,8 @@ def convert_template_json( fields_csv = tbl_csv.to_dict(orient="records") template_json = {**data_dictionary_props, "fields": fields_json} + if existing_title: + template_json["title"] = existing_title template_csv = {**data_dictionary_props, "fields": fields_csv} return {"template_json": template_json, "template_csv": template_csv} diff --git a/heal/vlmd/extract/redcap_csv_dict_conversion.py b/heal/vlmd/extract/redcap_csv_dict_conversion.py new file mode 100644 index 0000000..4723bfe --- /dev/null +++ b/heal/vlmd/extract/redcap_csv_dict_conversion.py @@ -0,0 +1,182 @@ +""" +Convert a REDCap data dictionary exported +in csv format to a heal-compliant json data dictionary. + +STEPS +#1 fill section headers +#2 sort rows to allow proper row indexing +#3 make conditionals for each map function +""" + +from os import PathLike + +import numpy as np +import pandas as pd + +import heal.vlmd.mappings.redcap_csv_headers as headers +from cdislogging import get_logger +from heal.vlmd.extract import utils +from heal.vlmd.extract.json_dict_conversion import convert_template_json +from heal.vlmd.mappings.redcap_field_mapping import type_mappings +from heal.vlmd.validate.utils import read_delim + +logger = get_logger("redcap-conversion", log_level="debug") + + +def read_from_file(file_path): + """ + Read csv from file. + + Returns dataframe. + """ + source_dataframe = read_delim(file_path) + return source_dataframe + + +def rename_and_fill(source_dataframe) -> list[dict]: + """ + Rename columns to heal compliant, ffill + + Returns dict + """ + source_dataframe = ( + source_dataframe.fillna("") + .rename(columns=headers.mapping) + .map(utils.strip_html) + ) + + # downfill section (if blank -- given we read in with petl, blanks are "" but ffill takes in np.nan) + source_dataframe["section"] = ( + source_dataframe.replace({"": np.nan}).groupby("form")["section"].ffill() + ) + # Recover any remaining np.nan (ie, not converted by 'ffill') back to "". + # Include the 'infer_objects' method as described here + # https://pandas.pydata.org/docs/whatsnew/v2.2.0.html#deprecated-automatic-downcasting + source_dataframe = source_dataframe.fillna("").infer_objects(copy=False) + return source_dataframe.to_dict(orient="records") + + +def _add_description(source_field: dict, target_field: dict) -> str: + if source_field.get("label"): + field_label = source_field["label"].strip() + else: + field_label = "" + + if source_field.get("section"): + field_section = source_field["section"].strip() + ": " + else: + field_section = "" + + if target_field.get("description"): + field_description = target_field["description"].strip() + else: + field_description = "" + + field_description = utils.strip_html( + (field_section + field_label + field_description).strip() + ) + if field_description: + return field_description + else: + return "No field label for this variable" + + +def _add_title(source_field: dict, target_field: dict) -> str: + target_title = target_field.get("title", "") + + if target_title: + return target_title + elif source_field.get("label"): + return target_title + utils.strip_html(source_field["label"].strip()) + else: + return "No field label for this variable" + + +def _add_section(source_field: dict): + if source_field.get("form"): + return source_field["form"] + + +def _add_metadata(source_field: dict, target_field: dict): + target_field["description"] = _add_description(source_field, target_field) + target_field["title"] = _add_title(source_field, target_field) + target_field["section"] = _add_section(source_field) + + +def gather(source_fields: list[dict]) -> list[dict]: + """Maps and translates fields from redcap to heal json""" + + source_data_fields = [ + field for field in source_fields if field["type"] in list(type_mappings) + ] + + target_fields = [] + for source_field in source_data_fields: + try: + source_fieldtype = source_field["type"] + target_field = type_mappings[source_fieldtype](source_field) + # NOTE if one source_field generates more than 1 target field (ie checkbox) need to iterate through + # if list (and hence not one to one mapping with source_field), assumes mandatory fields + if isinstance(target_field, list): + for _target_field in target_field: + assert "name" in _target_field and "type" in _target_field + _add_metadata(source_field, _target_field) + target_fields.append(_target_field) + else: + _add_metadata(source_field, target_field) + target_field_with_name = {"name": source_field["name"]} + target_field_with_name.update(target_field) + target_fields.append(target_field_with_name) + except Exception as err: + logger.error( + "REDCap conversion error for field '" f"{source_field['name']}" "'" + ) + raise ValueError(str(err)) + + return target_fields + + +def convert_redcap_csv(data_or_path, data_dictionary_props={}) -> dict[str, any]: + """ + Takes in an exported Redcap Data Dictionary csv, + and translates each field into a HEAL specified + data dictionary based on field type (e.g., checkbox, radio, text and + other conditional logic based on Redcap specifications.) + + + > While there are a variety of options for Redcap exports (eg directly through + the API or via an XML), the Redcap CSV provides an easy-to-edit format comfortable by + technical and non-technical users. + + Parameters + ---------- + data_or_path : str or path-like or a pandas DataFrame object. + Data or path to data with the data being a tabular HEAL-specified data dictionary. + This input can be any data object or path-like string excepted by a frictionless Resource object. + data_dictionary_props : dict + The HEAL-specified data dictionary properties. + + Returns + ------- + dict + A dictionary with two keys: + - 'template_json': the HEAL-specified JSON object. + - 'template_csv': the HEAL-specified tabular template. + """ + + if isinstance(data_or_path, (str, PathLike)): + source_fields = read_from_file(data_or_path) + elif not isinstance(data_or_path, pd.DataFrame): + raise ValueError( + "Input should be either dataframe or path to REDCap dictionary csv export" + ) + else: + source_fields = data_or_path + source_fields = rename_and_fill(source_fields) + target_fields = gather(source_fields) + + data_dictionary = data_dictionary_props.copy() + data_dictionary["fields"] = target_fields + + package = convert_template_json(data_dictionary) + return package diff --git a/heal/vlmd/extract/utils.py b/heal/vlmd/extract/utils.py index 12716ba..33e9b76 100644 --- a/heal/vlmd/extract/utils.py +++ b/heal/vlmd/extract/utils.py @@ -90,6 +90,14 @@ def refactor_field_props(flat_fields, schema): # individual cell utilities +def strip_html(html_string): + """Strip out html from string""" + if html_string: + return re.sub(r"<[^>]+>", "", html_string) + else: + return html_string + + def parse_dictionary_str(string, item_sep, key_val_sep): """ Parses a stringified dictionary into a dictionary @@ -135,12 +143,23 @@ def flatten_to_json_path(dictionary, schema, parent_key=False, sep="."): items = [] for key, value in dictionary.items(): new_key = str(parent_key) + sep + key if parent_key else key + try: + prop = schema["properties"] + except: + message = f"Missing key: 'properties' in prop {prop}" + raise KeyError(message) prop = schema["properties"].get(key, {}) childprops = prop.get("properties") child_item_props = prop.get("items", {}).get("properties") if child_item_props: for i, _value in enumerate(value): + # check that child prop has properties + try: + prop["properties"] + except: + message = f"Missing key 'properties' in child prop: {_value}" + raise KeyError(message) item = flatten_to_json_path( dictionary=_value, schema=prop, diff --git a/heal/vlmd/mappings/redcap_csv_headers.py b/heal/vlmd/mappings/redcap_csv_headers.py new file mode 100644 index 0000000..b0412f3 --- /dev/null +++ b/heal/vlmd/mappings/redcap_csv_headers.py @@ -0,0 +1,31 @@ +mapping = { + "Variable / Field Name": "name", + "Form Name": "form", + "Section Header": "section", + "Field Type": "type", + "Field Label": "label", + "Choices, Calculations, OR Slider Labels": "choice_calc_lbls", + "Field Note": "note", + "Text Validation Type OR Show Slider Number": "text_valid_slider_num", + "Text Validation Min": "text_valid_min", + "Text Validation Max": "text_valid_max", + "Identifier?": "identifier", + "Branching Logic (Show field only if...)": "skip_logic", + "Required Field?": "required", + "Custom Alignment": "align", + "Question Number (surveys only)": "question_num", + "Matrix Group Name": "matrix_group", +} + +CHOICES_LABEL_INPUT = "Choices, Calculations, OR Slider Labels" +CHOICES_FIELD_NAME = SLIDER_FIELD_NAME = CALC_FIELD_NAME = mapping[CHOICES_LABEL_INPUT] +TEXT_VALID_FIELD_NAME = mapping["Text Validation Type OR Show Slider Number"] + +# This document lists 'Field Label' as required but Google AI excludes it +# https://ws.engr.illinois.edu/sitemanager/getfile.asp?id=1365 +redcap_required_fields = [ + "Variable / Field Name", + "Form Name", + "Field Type", + "Field Label", +] diff --git a/heal/vlmd/mappings/redcap_field_mapping.py b/heal/vlmd/mappings/redcap_field_mapping.py new file mode 100644 index 0000000..3b218a4 --- /dev/null +++ b/heal/vlmd/mappings/redcap_field_mapping.py @@ -0,0 +1,373 @@ +""" +Functions for each REDCap field type that uses REDCap field info to determine +various pieces of field metadata. + +Input assumes a dictionary with {"REDCap field_name": "REDCap value"} + +REDCap dictionary fields: + +NOTES - large text box for lots of text +DROPDOWN - dropdown menu with options +RADIO - radio buttons with options +CHECKBOX - checkboxes to allow selection of more than one option +FILE - upload a document +CALC - perform real-time calculations +SQL - select query statement to populate dropdown choices +DESCRIPTIVE - text displayed with no data entry and optional image/file attachment +SLIDER - visual analogue scale; coded as 0-100 +YESNO - radio buttons with yes and no options; coded as 1, Yes | 0, No +TRUEFALSE - radio buttons with true and false options; coded as 1, True | 0, False +""" + +import re + +from cdislogging import get_logger + +from heal.vlmd.extract import utils +from heal.vlmd.mappings.redcap_csv_headers import ( + CALC_FIELD_NAME, + CHOICES_FIELD_NAME, + CHOICES_LABEL_INPUT, + SLIDER_FIELD_NAME, + TEXT_VALID_FIELD_NAME, +) + +logger = get_logger("redcap-mapping", log_level="warning") + + +def _parse_field_properties_from_encodings(encodings_string: str) -> dict: + """ + Many of the field types have the same logic + for conversion to data types and just differ + in presentation (dropbox, radio box) so making + fxn to support this + + Currently supports strings, ints, and nums for types + """ + # parse encodings + field_encodings = utils.parse_dictionary_str( + encodings_string, item_sep="|", key_val_sep="," + ) + # get enums + field_enums = list(field_encodings.keys()) + # interpret type from enums + for value in field_enums: + value = value.strip() + if value.isnumeric(): + try: + int(value) + field_type = "integer" + except ValueError: + field_type = "number" + else: + field_type = "string" + + return { + "type": field_type, + "enumLabels": { + key.strip(): value.strip() for key, value in field_encodings.items() + }, + "constraints": {"enum": [value.strip() for value in field_enums]}, + } + + +def map_text(field: dict) -> dict: + """ + TEXT - single-line text box (for text and numbers) + + Looks at the text validation field, defined by + 'TEXT_VALID_FIELD_NAME' in 'redcap_csv_headers.py' + """ + integer_pattern = r"^-?\s*\d+\s*$" + number_pattern = r"^-?\s*\d+(\.\d+)?([eE][-+]?\d+)?\s*$" + if field.get(TEXT_VALID_FIELD_NAME): + text_validation = field[TEXT_VALID_FIELD_NAME].lower() + else: + text_validation = "" + field_format = None + field_pattern = None + constraints_min = None + constraints_max = None + if "datetime" in text_validation: + field_type = "datetime" + field_format = "any" + elif "date" in text_validation: + field_type = "date" + field_format = "any" + elif text_validation == "email": + field_type = "string" + field_format = "email" + elif text_validation == "integer": + field_type = "integer" + min_value = field.get("text_valid_min") + if min_value: + if re.match(integer_pattern, min_value): + constraints_min = int(min_value) + else: + warning_message = ( + f"Skipping conversion of min value '{min_value}' for field '" + f"{field.get('name')}" + "'" + ) + logger.warning(warning_message) + max_value = field.get("text_valid_max") + if max_value: + if re.match(integer_pattern, max_value): + constraints_max = int(max_value) + else: + warning_message = ( + f"Skipping conversion of max value '{max_value}' for field '" + f"{field.get('name')}" + "'" + ) + logger.warning(warning_message) + elif text_validation == "alpha_only": + field_type = "string" + field_pattern = "^[a-zA-Z]+$" + elif "number" in text_validation: + field_type = "number" + min_value = field.get("text_valid_min") + if min_value: + if "comma_decimal" in text_validation: + min_value.replace(",", ".") + if re.match(number_pattern, min_value): + constraints_min = float(min_value) + else: + warning_message = ( + f"Skipping conversion of min value '{min_value}' for field '" + f"{field.get('name')}" + "'" + ) + logger.warning(warning_message) + max_value = field.get("text_valid_max") + if max_value: + if "comma_decimal" in text_validation: + max_value = max_value.replace(",", ".") + if re.match(number_pattern, max_value): + constraints_max = float(max_value) + else: + warning_message = ( + f"Skipping conversion of max value '{max_value}' for field '" + f"{field.get('name')}" + "'" + ) + logger.warning(warning_message) + elif text_validation == "phone": + field_type = "string" + field_pattern = "^[0-9]{3}-[0-9]{3}-[0-9]{4}$" + elif text_validation == "postalcode_australia": + field_type = "string" + field_pattern = "^[0-9]{4}$" + elif text_validation == "postalcode_canada": + field_type = "string" + field_pattern = "^[A-Z][0-9][A-Z] [0-9][A-Z][0-9]$" + elif text_validation == "ssn": + field_type = "string" + field_pattern = "^[0-9]{3}-[0-9]{2}-[0-9]{4}$" + elif "time" in text_validation: + field_type = "time" + field_format = "any" + elif text_validation == "vmrn": + field_type = "string" + field_pattern = "^[0-9]{10}$" + elif text_validation == "zipcode": + field_type = "string" + field_pattern = "^[0-9]{5}$" + else: + field_type = "string" + + props = dict( + zip( + ["type", "format", "constraints"], + [ + field_type, + field_format, + { + "pattern": field_pattern, + "minimum": constraints_min, + "maximum": constraints_max, + }, + ], + ) + ) + + # delete props without values (ie None) + for prop_name in ["type", "format"]: + if props[prop_name] is None: + del props[prop_name] + # constraints will be cleaned later + + return props + + +def map_notes(field: dict) -> dict: + """ + NOTES - large text box for lots of text + """ + return {"type": "string"} + + +def map_dropdown(field: dict) -> dict: + """ + DROPDOWN - dropdown menu with options + + Determined by "options" (ie Choices, Calculations, OR Slider Labels) + """ + encodings_string = field[CHOICES_FIELD_NAME] + if not encodings_string: + error_message = ( + "Missing value in dropdown field '" + f"{field.get('name')}" + f"' in column '{CHOICES_LABEL_INPUT}'." + ) + raise ValueError(error_message) + return _parse_field_properties_from_encodings(encodings_string) + + +def map_radio(field: dict) -> dict: + """ + RADIO - radio buttons with options + + Determined by "options" (ie Choices, Calculations, OR Slider Labels) + """ + encodings_string = field[CHOICES_FIELD_NAME] + if not encodings_string: + error_message = ( + "Missing value in radio field '" + f"{field.get('name')}" + f"' in column '{CHOICES_LABEL_INPUT}'." + ) + raise ValueError(error_message) + return _parse_field_properties_from_encodings(encodings_string) + + +def map_checkbox(field: dict) -> dict: + """ + CHECKBOX - checkboxes to allow selection of more than one option + + ## Are data from checkbox (choose all that apply) field types handled + differently from other field types when imported or exported? + Yes. When your data are exported, each option from a checkbox field + becomes a separate variable coded 1 or 0 to reflect whether it is checked + or unchecked. By default, each option is pre-coded 0, so even if you have + not yet collected any data, you will see 0's for each checkbox option. + The variable names will be the name of the field followed by the option number. + So, for example, if you have a field coded as follows: + + Race + + 1, Caucasian + 2, African American + 3, Asian + 4, Other + + In your exported dataset, you will have four variables representing the field + Race that will be set as 0 by default, coded 1 if the option was checked for + a record. The variable names will consist of the field name, three underscores, + and the choice value: + + race___1 + race___2 + race___3 + race___4 + + Notes: + + when you import data into a checkbox field, you must code it based on the same model + negative values can be used as the raw coded values for checkbox fields. Due to certain + limitations, negative values will not work when importing values using the + Data Import Tool, API and cause problems when exporting data into a statistical + analysis package. The workaround is that negative signs are replaced by an underscore + in the export/import-specific version of the variable name (e.g., for a checkbox + named "race", its choices "2" and "-2" would export as the fields + + race___2 + race____2 + + A checkbox field can be thought of as a series of yes/no questions in one field. + Therefore, a yes (check) is coded as 1 and a no (uncheck) is coded a 0. + An unchecked response on a checkbox field is still regarded as an answer and + is not considered missing. + """ + checkbox_name = field["name"] + choices = utils.parse_dictionary_str( + field[CHOICES_FIELD_NAME], item_sep="|", key_val_sep="," + ) + field_type = "boolean" + field_enums = ["0", "1"] + field_encodings = {"0": "Unchecked", "1": "Checked"} + + fields_new = [ + { + "description": f"[choice={choice}]", + "title": checkbox_name.title() + ": " + choice, + "name": checkbox_name + + "___" + + re.sub( + "^\-", "_", val + ).strip(), # NOTE: REDCAP changes negative sign to underscore + "type": field_type, + "constraints": {"enum": field_enums}, + "enumLabels": field_encodings, + } + for val, choice in choices.items() + ] + return fields_new + + +def map_file(field: dict) -> dict: + return {"type": "string"} + + +def map_calc(field: dict) -> dict: + return {"description": f"[calculation: {field[CALC_FIELD_NAME]}]", "type": "number"} + + +def map_sql(field: dict): + return None + + +def map_yes_no(field: dict) -> dict: + return { + "type": "boolean", + "constraints": {"enum": ["0", "1"]}, + "enumLabels": {"0": "No", "1": "Yes"}, + } + + +def map_true_false(field: dict) -> dict: + return { + "type": "boolean", + "constraints": {"enum": ["0", "1"]}, + "enumLabels": {"0": "False", "1": "True"}, + } + + +def map_slider(field: dict) -> dict: + vallist = ["0", "50", "100"] + lbllist = utils.parse_list_str(field[SLIDER_FIELD_NAME], "|") + field_encodings = {vallist[i]: lbl for i, lbl in enumerate(lbllist)} + return { + "type": "integer", + "constraints": {"minimum": 0, "maximum": 100}, + "enumLabels": field_encodings, + } + + +def map_descriptive(field: dict): + return None + + +# not mapping descriptives or sql (TODO: mapping sql?) +type_mappings = { + "text": map_text, + "notes": map_notes, + "dropdown": map_dropdown, + "radio": map_radio, + "checkbox": map_checkbox, + "slider": map_slider, + "yesno": map_yes_no, + "truefalse": map_true_false, + "calc": map_calc, + "file": map_file, +} diff --git a/heal/vlmd/utils.py b/heal/vlmd/utils.py index e0d2b1e..c4f25cc 100644 --- a/heal/vlmd/utils.py +++ b/heal/vlmd/utils.py @@ -1,3 +1,6 @@ +from heal.vlmd.mappings.redcap_csv_headers import redcap_required_fields + + def add_missing_type(prop_name: str, prop, schema: dict) -> dict: """ Add types to properties. @@ -76,3 +79,10 @@ def clean_json_fields(fields: list) -> list: new_field = remove_empty_props(field) cleaned_fields.append(new_field) return cleaned_fields + + +def has_redcap_headers(column_names: list) -> bool: + """ + Check if all required REDCap headers are present in the in column_names + """ + return set(redcap_required_fields).issubset(column_names) diff --git a/heal/vlmd/validate/utils.py b/heal/vlmd/validate/utils.py index 67f3b69..45bc46f 100644 --- a/heal/vlmd/validate/utils.py +++ b/heal/vlmd/validate/utils.py @@ -39,6 +39,8 @@ def read_delim(file_path, cast_d_type="string"): defaults to not casting values (ie all columns are string dtypes) and not parsing strings into NA values (eg "" is kept as "") + + Returns a pandas dataframe """ ext = Path(file_path).suffix if ext == ".csv": diff --git a/heal/vlmd/validate/validate.py b/heal/vlmd/validate/validate.py index 01d6857..890df72 100644 --- a/heal/vlmd/validate/validate.py +++ b/heal/vlmd/validate/validate.py @@ -1,3 +1,4 @@ +import os from os.path import isfile from pathlib import Path @@ -6,6 +7,7 @@ from jsonschema import ValidationError from heal.vlmd.config import ( + ALLOWED_FILE_TYPES, ALLOWED_INPUT_TYPES, ALLOWED_OUTPUT_TYPES, ALLOWED_SCHEMA_TYPES, @@ -25,11 +27,13 @@ class ExtractionError(Exception): "csv": "csv-data-dict", "json": "json-template", "tsv": "csv-data-dict", + "redcap": "redcap-csv-dict", } def vlmd_validate( input_file: str, + file_type="auto", schema_type="auto", output_type="json", return_converted_output=False, @@ -40,6 +44,10 @@ def vlmd_validate( Args: input_file (str): the path of the input HEAL VLMD file. + file_type (str): the type of input file. + Allowed values for now are “csv”, “tsv”, “json”, "redcap", and “auto”. + Defaults to “auto” which will use the suffix of the input file. + Using "auto" or "csv" will auto-detect if the file is a REDCap dictionary export. schema_type (str): the type of the schema to be validated against. Allowed values for now are “csv”, “tsv”, “json” and “auto”. Defaults to “auto” which will use the suffix of the input file. @@ -50,30 +58,47 @@ def vlmd_validate( Returns: True if input is valid and return_converted_output=False. - Returns a converted dictionary if input is valid and return_converted_output=True. + Returns a dictionary if input is valid and return_converted_output=True, + where dictionary is converted if csv and dictionary is raw input if json. Raises ValidationError if the input VLMD is not valid. Raises ValueError for unallowed input file types or unallowed schema types. Raises SchemaError if the schema is invalid. Raises ExctractionError if the input cannot be converted to VLMD dictionary. """ + logger.debug("In vlmd validate") + + if isinstance(input_file, (str, os.PathLike)): + logger.info( + f"Validating VLMD file '{input_file}' against schema type '{schema_type}'" + ) + if not isfile(input_file): + message = f"Input file does not exist: {input_file}" + logger.error(message) + raise IOError(message) + logger.debug(f"File_type = {file_type}") + file_suffix = Path(input_file).suffix.replace(".", "") + data = None + else: + logger.info(f"Validating VLMD json data against schema type '{schema_type}'") + logger.debug(f"File_type = {file_type}") + file_suffix = "json" + data = input_file + + if file_type not in ALLOWED_FILE_TYPES: + message = f"File type must be one of {ALLOWED_FILE_TYPES}" + logger.error(message) + raise ValueError(message) - logger.info( - f"Validating VLMD file '{input_file}' against schema type '{schema_type}'" - ) - file_suffix = Path(input_file).suffix.replace(".", "") if file_suffix not in ALLOWED_INPUT_TYPES: message = f"Input file must be one of {ALLOWED_INPUT_TYPES}" logger.error(message) raise ValueError(message) - if not isfile(input_file): - message = f"Input file does not exist: {input_file}" - logger.error(message) - raise IOError(message) if schema_type not in ALLOWED_SCHEMA_TYPES: message = f"Schema type must be in {ALLOWED_SCHEMA_TYPES}" logger.error(message) raise ValueError(message) + schema = get_schema(input_file, schema_type) if schema is None: message = f"Could not get schema for type = {schema_type}" @@ -86,7 +111,6 @@ def vlmd_validate( logger.error(message) raise ValueError(message) - # TODO: We need this for csv - see if we can add this to get_schema if file_suffix in ["csv", "tsv"]: schema = add_types_to_props(schema) logger.debug("Checking schema") @@ -100,32 +124,43 @@ def vlmd_validate( message = "Could not read csv data from input" logger.error(message) raise ValidationError(message) - elif file_suffix == "json": + elif file_suffix == "json" and data is None: logger.debug("Getting json data from file") data = read_data_from_json_file(input_file) - # if json then try a validation + # if input is json then try a validation and return input if file_suffix == "json": logger.debug("Validating json data") try: jsonschema.validate(instance=data, schema=schema) - except jsonschema.ValidationError as e: + except jsonschema.ValidationError as err: logger.error("Error in validating json input") - raise e - - # convert - input_type = file_type_to_fxn_map.get(file_suffix) - if not input_type: + raise err + logger.debug("JSON input is valid") + if return_converted_output: + return data + else: + return True + + # else non-json data should first be converted and then validated + if file_type == "auto": + logger.debug(f"Using file_suffix for file_type {file_suffix}") + file_convert_function = file_type_to_fxn_map.get(file_suffix) + else: + file_convert_function = file_type_to_fxn_map.get(file_type) + if not file_convert_function: message = f"Could not get conversion function from file_suffix '{file_suffix}'" logger.error(message) raise ExtractionError(message) data_dictionaries = {} - logger.debug(f"Verifying vlmd can be converted using input_type '{input_type}'") + logger.debug( + f"Verifying vlmd can be converted using file conversion function '{file_convert_function}'" + ) data_dictionary_props = {} try: data_dictionaries = convert_to_vlmd( input_filepath=input_file, - input_type=input_type, + input_type=file_convert_function, data_dictionary_props=data_dictionary_props, ) except Exception as e: @@ -146,7 +181,6 @@ def vlmd_validate( ) schema = get_schema(converted_dictionary, schema_type=output_type) if output_type == "csv": - # TODO: see if we can add this to get_schema schema = add_types_to_props(schema) if schema is None: message = f"Could not get schema for type = {schema_type}" @@ -155,9 +189,10 @@ def vlmd_validate( try: jsonschema.validate(instance=converted_dictionary, schema=schema) - except jsonschema.ValidationError as e: + except jsonschema.ValidationError as err: logger.error("Error in validating converted dictionary") - raise e + raise err + logger.debug("Converted dict is valid") if return_converted_output: return converted_dictionary diff --git a/notebooks/qdr_data_download.ipynb b/notebooks/qdr_data_download.ipynb index 9181ea4..711140f 100644 --- a/notebooks/qdr_data_download.ipynb +++ b/notebooks/qdr_data_download.ipynb @@ -36,8 +36,8 @@ "import logging\n", "\n", "# install heal-platform-sdk from github\n", - "os.system(\"pip install -e git+https://git@github.com/uc-cdis/heal-platform-sdk.git#egg=heal -q\")\n", - "from src.heal.heal.qdr_downloads import get_syracuse_qdr_files \n", + "os.system(\"pip install git+https://git@github.com/uc-cdis/heal-platform-sdk.git#egg=heal -q\")\n", + "from heal.qdr_downloads import get_syracuse_qdr_files \n", "\n", "from gen3.auth import Gen3Auth\n", "from gen3.tools.download.external_file_download import download_files_from_metadata, extract_external_file_metadata\n", @@ -114,7 +114,7 @@ "retrievers = {\"QDR\": get_syracuse_qdr_files}\n", "\n", "# Download data path, this is the directory where downloaded QDR file(s) will be stored at\n", - "download_path = \"./qdr_downloads\"\n", + "download_path = \"./\"\n", "\n", "# Use Gen3Auth to request QDR token\n", "auth = Gen3Auth(refresh_file=credentials_file)" diff --git a/poetry.lock b/poetry.lock index 63ff17c..0cb080e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. [[package]] name = "aiofiles" @@ -6,6 +6,8 @@ version = "0.8.0" description = "File support for asyncio." optional = false python-versions = ">=3.6,<4.0" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "aiofiles-0.8.0-py3-none-any.whl", hash = "sha256:7a973fc22b29e9962d0897805ace5856e6a566ab1f0c8e5c91ff6c866519c937"}, {file = "aiofiles-0.8.0.tar.gz", hash = "sha256:8334f23235248a3b2e83b2c3a78a22674f39969b96397126cc93664d9a901e59"}, @@ -13,136 +15,133 @@ files = [ [[package]] name = "aiohappyeyeballs" -version = "2.4.3" +version = "2.4.6" description = "Happy Eyeballs for asyncio" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" +groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, - {file = "aiohappyeyeballs-2.4.3.tar.gz", hash 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description = "Backport of pathlib-compatible object wrapper for zip files" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" +groups = ["main"] +markers = "python_full_version <= \"3.9.0\"" files = [ - {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"}, - {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"}, + {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, + {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] [package.extras] @@ -1967,6 +2096,6 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = "^3.9" content-hash = "097be482bf39894cee33e1007daed486cf5323fb2983c03d6d2085cb2f723273" diff --git a/tests/conftest.py b/tests/conftest.py index d28d12c..24dd43e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,28 +13,32 @@ @pytest.fixture() -def VALID_CSV_SCHEMA(): +def valid_csv_schema(): + """a valid csv schema""" return CSV_SCHEMA @pytest.fixture() -def VALID_JSON_SCHEMA(): +def valid_json_schema(): + """a valid json schema""" return JSON_SCHEMA @pytest.fixture() def allowed_input_types(): + """the allowed input types""" return ALLOWED_INPUT_TYPES @pytest.fixture() def allowed_schema_types(): + """the allowed schema types""" return ALLOWED_SCHEMA_TYPES @pytest.fixture() def invalid_csv_schema(): - # type=5 is not of type 'object', 'boolean' + """an invalid csv schema: type=5 is not of type 'object', 'boolean'""" return { "type": "array", "items": { @@ -51,7 +55,7 @@ def invalid_csv_schema(): @pytest.fixture() def invalid_json_schema(): - # type=5 is not of type 'object', 'boolean' + """an invalid json schema: type=5 is not of type 'object', 'boolean'""" return { "$schema": "http://json-schema.org/draft-07/schema#", "title": "Variable Level Metadata (Data Dictionaries)", @@ -69,6 +73,7 @@ def invalid_json_schema(): @pytest.fixture() def valid_array_data(): + """valid array data""" return [ { "section": "Enrollment", @@ -101,7 +106,11 @@ def valid_array_data(): "constraints.pattern": "", "constraints.maximum": "", "constraints.minimum": "", - "enumLabels": "1=White|2=Black or African American|3=American Indian or Alaska Native|4=Native| 5=Hawaiian or Other Pacific Islander|6=Asian|7=Some other race|8=Multiracial|99=Not reported", + "enumLabels": ( + "1=White|2=Black or African American|3=American Indian or Alaska Native" + "|4=Native| 5=Hawaiian or Other Pacific Islander|6=Asian|7=Some other race" + "|8=Multiracial|99=Not reported" + ), "enumOrdered": "", "missingValues": "99", "trueValues": "", @@ -113,15 +122,17 @@ def valid_array_data(): @pytest.fixture() def valid_json_data(): - with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r") as f: + """valid json data""" + with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r", encoding="utf-8") as f: data = json.load(f) return data @pytest.fixture() def valid_json_output(): + """valid extracted json data""" header = {"schemaVersion": JSON_SCHEMA_VERSION} - with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r") as f: + with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r", encoding="utf-8") as f: data = json.load(f) header.update(data) return header @@ -129,23 +140,42 @@ def valid_json_output(): @pytest.fixture() def valid_converted_csv_to_json(): - with open("tests/test_data/vlmd/valid/vlmd_csv_to_json.json", "r") as f: + """valid csv to json extracted data""" + with open( + "tests/test_data/vlmd/valid/vlmd_csv_to_json.json", "r", encoding="utf-8" + ) as f: data = json.load(f) return data @pytest.fixture() def valid_csv_output(): - with open("tests/test_data/vlmd/valid/vlmd_csv_to_csv.json", "r") as f: + """valid extracted csv data""" + with open( + "tests/test_data/vlmd/valid/vlmd_csv_to_csv.json", "r", encoding="utf-8" + ) as f: data = json.load(f) return data @pytest.fixture() def valid_converted_csv_to_json_output(): - with open("tests/test_data/vlmd/valid/vlmd_csv_to_json.json", "r") as f: + """valid extracted csv to json output""" + with open( + "tests/test_data/vlmd/valid/vlmd_csv_to_json.json", "r", encoding="utf-8" + ) as f: data = json.load(f) data["fields"] = clean_json_fields(data["fields"]) header = {"schemaVersion": JSON_SCHEMA_VERSION, "title": "HEAL Data Dictionary"} header.update(data) return header + + +@pytest.fixture() +def valid_redcap_csv_dict(): + """valid redcap csv dict""" + with open( + "tests/test_data/vlmd/valid/vlmd_redcap_dict_small", "r", encoding="utf-8" + ) as f: + data = json.load(f) + return data diff --git a/tests/test_cli.py b/tests/test_cli.py index f0cceef..4d89522 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -37,8 +37,10 @@ def test_extract_help(): """Test the extract submenu""" runner = CliRunner() expected_text = "Extract HEAL-compliant VLMD file from input file" + # truncated to avoid wrapped lines expected_commands = [ "--input_file PATH name of file to extract HEAL-compliant VLMD file", + "--title TEXT Root level title for the dictionary (required if", "--output_dir PATH directory to write converted dictionary' [default: .]", ] result = runner.invoke(cli_module.main, ["vlmd", "extract", "--help"]) @@ -52,12 +54,22 @@ def test_extract(tmp_path): """Test the cli extract""" runner = CliRunner() input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + title = "Test dictionary" expected_output_file = file_utils.get_output_filepath( tmp_path, input_file, output_type="json" ) result = runner.invoke( cli_module.main, - ["vlmd", "extract", "--input_file", input_file, "--output_dir", tmp_path], + [ + "vlmd", + "extract", + "--input_file", + input_file, + "--title", + title, + "--output_dir", + tmp_path, + ], ) assert result.exit_code == 0 assert os.path.isfile(expected_output_file) diff --git a/tests/test_data/vlmd/valid/vlmd_redcap_dict.csv b/tests/test_data/vlmd/valid/vlmd_redcap_dict.csv new file mode 100644 index 0000000..5c87562 --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_redcap_dict.csv @@ -0,0 +1,124 @@ +Variable / Field Name,Form Name,Section Header,Field Type,Field Label,"Choices, Calculations, OR Slider Labels",Field Note,Text Validation Type OR Show Slider Number,Text Validation Min,Text Validation Max,Identifier?,Branching Logic (Show field only if...),Required Field?,Custom Alignment,Question Number (surveys only),Matrix Group Name +study_id,demographics,,text,Study ID,,,,,,,,,,, +date_enrolled,demographics,Demographic Characteristics,text,Date subject signed consent,,YYYY-MM-DD,date,,,,,,,, +first_name,demographics,,text,First Name,,,,,,y,,,,, +last_name,demographics,,text,Last Name,,,,,,y,,,,, +address,demographics,Contact Information,notes,"Street, City, State, ZIP",,,,,,y,,,,, +telephone_1,demographics,,text,Phone number,,Include Area Code,phone,,,y,,,,, +telephone_2,demographics,,text,Second phone number,,Include Area Code,phone,,,y,,,,, +email,demographics,,text,E-mail,,,email,,,y,,,,, +sex,demographics,,dropdown,Gender,"0, Female | 1, Male",,,,,,,y,,, +given_birth,demographics,,dropdown,Has the subject given birth before?,"0, No | 1, Yes",,,,,,"[sex] = ""0""",,,, +num_children,demographics,,text,How many times has the subject given birth?,,,integer,0,,,"[sex] = ""0"" and [given_birth] = ""1""",,,, +ethnicity,demographics,,radio,Ethnicity,"0, Hispanic or Latino | 1, NOT Hispanic or Latino | 2, Unknown / Not Reported",,,,,,,,,, +race,demographics,,checkbox,Race,"0, American Indian/Alaska Native | 1, Asian | 2, Native Hawaiian or Other Pacific Islander | 3, Black or African American | 4, White | 5, More Than One Race | 6, Unknown / Not Reported",,,,,,,,,, +dob,demographics,,text,Date of birth,,,date,,,y,,,,, +age,demographics,,calc,Age (years),"round(datediff([dob],'today','y'),0)",,,,,,,,,, +height,demographics,,text,Height (cm),,,number,130,215,,,,,, +weight,demographics,,text,Weight (kilograms),,,integer,35,200,,,,,, +bmi,demographics,,calc,BMI,"round(([weight]*10000)/(([height])^(2)),1)",,,,,,,,,, +patient_document,demographics,,file,Patient document,,,,,,,,,,, +comorbidities,demographics,,text,Any comorbid condition,,,,,,,,,,, +diabetes,demographics,,dropdown,Patient has a diagnosis of diabetes mellitus?,"0, No | 1, Yes",,,,,,,,,, +diabetes_type,demographics,,dropdown,Type of Diabetes Mellitus,"0, Type 1 insulin-dependent | 1, Type 2 insulin-dependent | 2, Type 2 non insulin-dependent",,,,,,,,,, +gym,demographics,Please provide the patient's weekly schedule for the activities below.,checkbox,Gym (Weight Training),"0, Monday | 1, Tuesday | 2, Wednesday | 3, Thursday | 4, Friday",,,,,,,,,,weekly_schedule +aerobics,demographics,,checkbox,Aerobics,"0, Monday | 1, Tuesday | 2, Wednesday | 3, Thursday | 4, Friday",,,,,,,,,,weekly_schedule +eat,demographics,,checkbox,Eat Out (Dinner/Lunch),"0, Monday | 1, Tuesday | 2, Wednesday | 3, Thursday | 4, Friday",,,,,,,,,,weekly_schedule +drink,demographics,,checkbox,Drink (Alcoholic Beverages),"0, Monday | 1, Tuesday | 2, Wednesday | 3, Thursday | 4, Friday",,,,,,,,,,weekly_schedule +dialysis_initiation,demographics,Dialysis Information,text,Date of first outpatient dialysis treatment,,,date,,,,,,,, +access_type,demographics,,dropdown,Type of vascular access,"0, Graft | 1, Fistula | 2, Catheter with maturing graft | 3, Catheter with maturing fistula",,,,,,,,,, +access_location,demographics,,dropdown,Location of currently used vascular access,"0, Forearm | 1, Upper arm | 2, Internal jugular vein | 3, Subclavian vein | 4, Other",,,,,,,,,, +dialysis_unit_name,demographics,,text,Name of dialysis unit,,,,,,,,,,, +dialysis_unit_phone,demographics,,text,Phone number,,Include Area Code,phone,,,,,,,, +dialysis_schedule_days,demographics,,radio,Days of the week patient is dialyzed,"0, Monday-Wednesday-Friday | 1, Tuesday-Thursday-Saturday | 2, Other",,,,,,,,,, +dialysis_schedule_time,demographics,,dropdown,Shift patient is dialyzed,"0, First shift | 1, Second shift | 2, Third shift | 3, Fourth shift",,,,,,,,,, +etiology_esrd,demographics,,dropdown,Etiology of ESRD,"0, Diabetes | 1, Hypertension | 2, Glomerulonephritis | 3, Polycystic Kidney Disease | 4, Interstitial Nephritis | 5, Hereditary Nephritis | 6, Other",,,,,,,,,, +subject_comments,demographics,General Comments,notes,Comments,,,,,,,,,,, +date_visit_b,baseline_data,Baseline Measurements,text,Date of baseline visit,,,date,,,,,,,, +date_blood_b,baseline_data,,text,Date blood was drawn,,,date,,,,,,,, +alb_b,baseline_data,,text,Serum Albumin (g/dL),,,integer,3,5,,,,,, +prealb_b,baseline_data,,text,Serum Prealbumin (mg/dL),,,number,10,40,,,,,, 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1-7),,,number,0.9,7.1,,,,,, +date_supplement_dispensed,baseline_data,,text,Date patient begins supplement,,,date,,,,,,,, +date_visit_1,month_1_data,Month 1,text,Date of Month 1 visit,,,date,,,,,,,, +alb_1,month_1_data,,text,Serum Albumin (g/dL),,,number,3,5,,,,,, +prealb_1,month_1_data,,text,Serum Prealbumin (mg/dL),,,number,10,40,,,,,, +creat_1,month_1_data,,text,Creatinine (mg/dL),,,number,0.5,20,,,,,, +npcr_1,month_1_data,,text,Normalized Protein Catabolic Rate (g/kg/d),,,number,0.5,2,,,,,, +chol_1,month_1_data,,text,Cholesterol (mg/dL),,,number,100,300,,,,,, +transferrin_1,month_1_data,,text,Transferrin (mg/dL),,,number,100,300,,,,,, +kt_v_1,month_1_data,,text,Kt/V,,,number,0.9,3,,,,,, +drywt_1,month_1_data,,text,Dry weight (kilograms),,,number,35,200,,,,,, +no_show_1,month_1_data,,text,Number of treatments missed,,,number,0,7,,,,,, +compliance_1,month_1_data,,dropdown,How compliant was the patient in drinking the supplement?,"0, 100 percent | 1, 99-75 percent | 2, 74-50 percent | 3, 49-25 percent | 4, 0-24 percent",,,,,,,,,, +hospit_1,month_1_data,Hospitalization Data,dropdown,Was patient hospitalized since last visit?,"0, No | 1, Yes",,,,,,,,,, +cause_hosp_1,month_1_data,,dropdown,What was the cause of hospitalization?,"1, Vascular access related events | 2, CVD events | 3, Other",,,,,,,,,, +admission_date_1,month_1_data,,text,Date of hospital admission,,,date,,,,,,,, +discharge_date_1,month_1_data,,text,Date of hospital discharge,,,date,,,,,,,, +discharge_summary_1,month_1_data,,dropdown,Discharge summary in patients binder?,"0, No | 1, Yes",,,,,,,,,, +death_1,month_1_data,Mortality Data,dropdown,Has patient died since last visit?,"0, No | 1, Yes",,,,,,,,,, +date_death_1,month_1_data,,text,Date of death,,,date,,,,,,,, +cause_death_1,month_1_data,,dropdown,What was the cause of death?,"1, All-cause | 2, Cardiovascular",,,,,,,,,, +date_visit_2,month_2_data,Month 2,text,Date of Month 2 visit,,,date,,,,,,,, +alb_2,month_2_data,,text,Serum Albumin (g/dL),,,number,3,5,,,,,, +prealb_2,month_2_data,,text,Serum Prealbumin (mg/dL),,,number,10,40,,,,,, +creat_2,month_2_data,,text,Creatinine (mg/dL),,,number,0.5,20,,,,,, +npcr_2,month_2_data,,text,Normalized Protein Catabolic Rate (g/kg/d),,,number,0.5,2,,,,,, +chol_2,month_2_data,,text,Cholesterol (mg/dL),,,number,100,300,,,,,, +transferrin_2,month_2_data,,text,Transferrin (mg/dL),,,number,100,300,,,,,, +kt_v_2,month_2_data,,text,Kt/V,,,number,0.9,3,,,,,, +drywt_2,month_2_data,,text,Dry weight (kilograms),,,number,35,200,,,,,, +no_show_2,month_2_data,,text,Number of treatments missed,,,number,0,7,,,,,, +compliance_2,month_2_data,,dropdown,How compliant was the patient in drinking the supplement?,"0, 100 percent | 1, 99-75 percent | 2, 74-50 percent | 3, 49-25 percent | 4, 0-24 percent",,,,,,,,,, +hospit_2,month_2_data,Hospitalization Data,dropdown,Was patient hospitalized since last visit?,"0, No | 1, Yes",,,,,,,,,, +cause_hosp_2,month_2_data,,dropdown,What was the cause of hospitalization?,"1, Vascular access related events | 2, CVD events | 3, Other",,,,,,,,,, +admission_date_2,month_2_data,,text,Date of hospital admission,,,date,,,,,,,, +discharge_date_2,month_2_data,,text,Date of hospital discharge,,,date,,,,,,,, +discharge_summary_2,month_2_data,,dropdown,Discharge summary in patients binder?,"0, No | 1, Yes",,,,,,,,,, +death_2,month_2_data,Mortality Data,dropdown,Has patient died since last visit?,"0, No | 1, Yes",,,,,,,,,, +date_death_2,month_2_data,,text,Date of death,,,date,,,,,,,, +cause_death_2,month_2_data,,dropdown,What was the cause of death?,"1, All-cause | 2, Cardiovascular",,,,,,,,,, +date_visit_3,month_3_data,Month 3,text,Date of Month 3 visit,,,date,,,,,,,, +date_blood_3,month_3_data,,text,Date blood was drawn,,,date,,,,,,,, +alb_3,month_3_data,,text,Serum Albumin (g/dL),,,number,3,5,,,,,, +prealb_3,month_3_data,,text,Serum Prealbumin (mg/dL),,,number,10,40,,,,,, +creat_3,month_3_data,,text,Creatinine (mg/dL),,,number,0.5,20,,,,,, +npcr_3,month_3_data,,text,Normalized Protein Catabolic Rate (g/kg/d),,,number,0.5,2,,,,,, +chol_3,month_3_data,,text,Cholesterol (mg/dL),,,number,100,300,,,,,, +transferrin_3,month_3_data,,text,Transferrin (mg/dL),,,number,100,300,,,,,, +kt_v_3,month_3_data,,text,Kt/V,,,number,0.9,3,,,,,, +drywt_3,month_3_data,,text,Dry weight (kilograms),,,number,35,200,,,,,, +plasma1_3,month_3_data,,dropdown,Collected Plasma 1?,"0, No | 1, Yes",,,,,,,,,, +plasma2_3,month_3_data,,dropdown,Collected Plasma 2?,"0, No | 1, Yes",,,,,,,,,, +plasma3_3,month_3_data,,dropdown,Collected Plasma 3?,"0, No | 1, Yes",,,,,,,,,, +serum1_3,month_3_data,,dropdown,Collected Serum 1?,"0, No | 1, Yes",,,,,,,,,, +serum2_3,month_3_data,,dropdown,Collected Serum 2?,"0, No | 1, Yes",,,,,,,,,, +serum3_3,month_3_data,,dropdown,Collected Serum 3?,"0, No | 1, Yes",,,,,,,,,, +sga_3,month_3_data,,text,Subject Global Assessment (score = 1-7),,,number,0.9,7.1,,,,,, +no_show_3,month_3_data,,text,Number of treatments missed,,,number,0,7,,,,,, +compliance_3,month_3_data,,dropdown,How compliant was the patient in drinking the supplement?,"0, 100 percent | 1, 99-75 percent | 2, 74-50 percent | 3, 49-25 percent | 4, 0-24 percent",,,,,,,,,, +hospit_3,month_3_data,Hospitalization Data,dropdown,Was patient hospitalized since last visit?,"0, No | 1, Yes",,,,,,,,,, +cause_hosp_3,month_3_data,,dropdown,What was the cause of hospitalization?,"1, Vascular access related events | 2, CVD events | 3, Other",,,,,,,,,, +admission_date_3,month_3_data,,text,Date of hospital admission,,,date,,,,,,,, +discharge_date_3,month_3_data,,text,Date of hospital discharge,,,date,,,,,,,, +discharge_summary_3,month_3_data,,dropdown,Discharge summary in patients binder?,"0, No | 1, Yes",,,,,,,,,, +death_3,month_3_data,Mortality Data,dropdown,Has patient died since last visit?,"0, No | 1, Yes",,,,,,,,,, +date_death_3,month_3_data,,text,Date of death,,,date,,,,,,,, +cause_death_3,month_3_data,,dropdown,What was the cause of death?,"1, All-cause | 2, Cardiovascular",,,,,,,,,, +complete_study,completion_data,Study Completion Information,dropdown,Has patient completed study?,"0, No | 1, Yes",,,,,,,,,, +withdraw_date,completion_data,,text,Put a date if patient withdrew study,,,date,,,,,,,, +withdraw_reason,completion_data,,dropdown,Reason patient withdrew from study,"0, Non-compliance | 1, Did not wish to continue in study | 2, Could not tolerate the supplement | 3, Hospitalization | 4, Other",,,,,,,,,, +complete_study_date,completion_data,,text,Date of study completion,,,date,,,,,,,, +study_comments,completion_data,General Comments,notes,Comments,,,,,,,,,,, diff --git a/tests/test_data/vlmd/valid/vlmd_redcap_dict_small.csv b/tests/test_data/vlmd/valid/vlmd_redcap_dict_small.csv new file mode 100644 index 0000000..6de0155 --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_redcap_dict_small.csv @@ -0,0 +1,7 @@ +Variable / Field Name,Form Name,Section Header,Field Type,Field Label,"Choices, Calculations, OR Slider Labels",Field Note,Text Validation Type OR Show Slider Number,Text Validation Min,Text Validation Max,Identifier?,Branching Logic (Show field only if...),Required Field?,Custom Alignment,Question Number (surveys only),Matrix Group Name +study_id,demographics,,text,Study ID,,,,,,,,,,, +date_enrolled,demographics,Demographic Characteristics,text,Date subject signed consent,,YYYY-MM-DD,date,,,,,,,, +telephone_1,demographics,,text,Phone number,,Include Area Code,phone,,,y,,,,, +sex,demographics,,dropdown,Gender,"0, Female | 1, Male",,,,,,,y,,, +height,demographics,,text,Height (cm),,,number,130,215,,,,,, +weight,demographics,,text,Weight (kilograms),,,integer,35,200,,,,,, diff --git a/tests/test_harvard_download.py b/tests/test_harvard_download.py index 526376c..5354c8f 100644 --- a/tests/test_harvard_download.py +++ b/tests/test_harvard_download.py @@ -30,10 +30,16 @@ def download_dir(tmpdir_factory): ( { "external_oidc_idp": "test-external-idp", - "file_retriever": "harvard", + "file_retriever": "Dataverse", "study_id": "harvard_study_01", } ), + ( + { + "file_retriever": "Dataverse", + "file_id": "harvard_file_01", + } + ), ], ) def test_is_valid_harvard_file_metadata(file_metadata: Dict): @@ -45,7 +51,7 @@ def test_is_valid_harvard_file_metadata_failed(): file_metadata = ( { "external_oidc_idp": "test-external-idp", - "file_retriever": "harvard", + "file_retriever": "Dataverse", "description": "missing study_id or file_id", }, ) @@ -62,7 +68,7 @@ def test_is_valid_harvard_file_metadata_failed(): ( { "external_oidc_idp": "test-external-idp", - "file_retriever": "harvard", + "file_retriever": "Dataverse", "study_id": "harvard_study_01", }, "harvard_study_01", @@ -77,7 +83,7 @@ def test_get_id_bad_input(): # missing study_id file_metadata = { "external_oidc_idp": "test-external-idp", - "file_retriever": "harvard", + "file_retriever": "Dataverse", } assert get_id(file_metadata) == None @@ -91,6 +97,12 @@ def test_get_id_bad_input(): }, "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=harvard_study_01", ), + ( + { + "file_id": "harvard_file_01", + }, + "https://dataverse.harvard.edu/api/access/datafile/harvard_file_01", + ), ], ) def test_get_download_url_for_qdr(file_metadata: Dict, expected: str): @@ -103,7 +115,7 @@ def test_get_download_url_for_qdr(file_metadata: Dict, expected: str): ( { "external_oidc_idp": "test-external-idp", - "file_retriever": "harvard", + "file_retriever": "Dataverse", "study_id": "harvard_study_01", "use_harvard_staging": True, }, @@ -283,9 +295,13 @@ def test_get_harvard_dataverse_files(download_dir): headers=valid_response_headers, content=bytes(test_data, "utf-8"), ) + mock_auth = MagicMock() + mock_auth.get_access_token.return_value = "some_token" # Call the function to test result = get_harvard_dataverse_files( + wts_hostname="", + auth=mock_auth, file_metadata_list=file_metadata_list, download_path=download_dir, ) diff --git a/tests/test_vlmd_csv_conversion.py b/tests/test_vlmd_csv_conversion.py index d6e8c20..ce3dc70 100644 --- a/tests/test_vlmd_csv_conversion.py +++ b/tests/test_vlmd_csv_conversion.py @@ -38,9 +38,9 @@ def test_csv_conversion_valid_data( assert result.get("template_csv") == valid_csv_output -def test_parse_string_objects(VALID_JSON_SCHEMA): +def test_parse_string_objects(valid_json_schema): field_properties = utils.flatten_properties( - VALID_JSON_SCHEMA["properties"]["fields"]["items"]["properties"] + valid_json_schema["properties"]["fields"]["items"]["properties"] ) csv_data = """ @@ -51,7 +51,10 @@ def test_parse_string_objects(VALID_JSON_SCHEMA): "description": ["Unique identifier for participant", "Self-reported race"], "constraints.enum": ["", "1|2|3|4|5|6|7|8"], "constraints.pattern": ["[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", ""], - "enumLabels": ["", "1=White|2=Black or African American|3=American Indian or Alaska Native|4=Native| 5=Hawaiian or Other Pacific Islander|6=Asian|7=Some other race|8=Multiracial|99=Not reported"], + "enumLabels": [ + "", + "1=White|2=Black or African American|3=American Indian or Alaska Native|4=Native| 5=Hawaiian or Other Pacific Islander|6=Asian|7=Some other race|8=Multiracial|99=Not reported" + ], "custom.notes": ["This is a note", "This is a custom note"] } """ @@ -66,7 +69,20 @@ def test_parse_string_objects(VALID_JSON_SCHEMA): "description": ["Unique identifier for participant", "Self-reported race"], "constraints.enum": ["", ["1", "2", "3", "4", "5", "6", "7", "8"]], "constraints.pattern": ["[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", ""], - "enumLabels": ["", {"1": "White", "2": "Black or African American", "3": "American Indian or Alaska Native", "4": "Native", "5": "Hawaiian or Other Pacific Islander", "6": "Asian", "7": "Some other race", "8": "Multiracial", "99": "Not reported"}], + "enumLabels": [ + "", + { + "1": "White", + "2": "Black or African American", + "3": "American Indian or Alaska Native", + "4": "Native", + "5": "Hawaiian or Other Pacific Islander", + "6": "Asian", + "7": "Some other race", + "8": "Multiracial", + "99": "Not reported" + } + ], "custom.notes": ["This is a note", "This is a custom note"], "custom": [{"notes": "This is a note"}, {"notes": "This is a custom note"}] } diff --git a/tests/test_vlmd_extract.py b/tests/test_vlmd_extract.py index e00ffed..8da34d1 100644 --- a/tests/test_vlmd_extract.py +++ b/tests/test_vlmd_extract.py @@ -5,7 +5,13 @@ import pytest from heal.vlmd.config import ALLOWED_OUTPUT_TYPES, OUTPUT_FILE_PREFIX -from heal.vlmd.extract.extract import ExtractionError, vlmd_extract +from heal.vlmd.extract.extract import ( + ExtractionError, + set_title_if_missing, + vlmd_extract, +) + +test_title = "Test title for unit tests" @pytest.mark.parametrize( @@ -20,10 +26,13 @@ ], ) def test_extract_valid_input(file_type, output_type, tmp_path): + """Extract various valid input types""" input_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" expected_file_name = f"{tmp_path}/{OUTPUT_FILE_PREFIX}_vlmd_valid.{output_type}" - result = vlmd_extract(input_file, output_dir=tmp_path, output_type=output_type) + result = vlmd_extract( + input_file, title=test_title, output_dir=tmp_path, output_type=output_type + ) assert result assert os.path.isfile(expected_file_name) if output_type == "json": @@ -54,69 +63,214 @@ def test_extract_valid_input(file_type, output_type, tmp_path): ], ) def test_extract_invalid_input(input_file, expected_message, error_type): - with pytest.raises(error_type) as e: - vlmd_extract(input_file) - assert expected_message in str(e.value) + """Invalid input triggers error""" + with pytest.raises(error_type) as err: + vlmd_extract(input_file, test_title) + assert expected_message in str(err.value) def test_extract_unallowed_input_type(): + """Unallowed input type triggers error""" input_file = "tests/test_data/vlmd/invalid/vlmd_invalid.txt" - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file) + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, test_title) expected_message = "Input file must be one of" - assert expected_message in str(e.value) + assert expected_message in str(err.value) def test_extract_unallowed_file_type(): + """Unallowed file type triggers error""" input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file, file_type="txt") + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, title=test_title, file_type="txt") expected_message = "File type must be one of" - assert expected_message in str(e.value) + assert expected_message in str(err.value) def test_extract_input_does_not_exist(tmp_path): + """Input file does-not-exist triggers error""" input_file = f"{tmp_path}/foo.csv" - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file) + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, test_title) expected_message = f"Input file does not exist: {input_file}" - assert expected_message in str(e.value) + assert expected_message in str(err.value) def test_extract_unallowed_output(): + """Unallowed output type triggers error""" input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" unallowed_output = "txt" - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file, file_type="json", output_type=unallowed_output) + with pytest.raises(ExtractionError) as err: + vlmd_extract( + input_file, title=test_title, file_type="json", output_type=unallowed_output + ) expected_message = f"Unrecognized output_type '{unallowed_output}' - should be in {ALLOWED_OUTPUT_TYPES}" - assert expected_message in str(e.value) + assert expected_message in str(err.value) + + +def test_extract_missing_title(): + """ + Title should be supplied when converting from non-json to json, + unless the data contains standardsMappings.instrument.title + """ + input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + # missing standardsMapping + with patch("heal.vlmd.extract.extract.vlmd_validate") as mock_validate: + mock_validate.return_value = { + "schemaVersion": "0.3.2", + "title": "default title from config - not a user supplied title", + "fields": [], + } + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, file_type="csv", output_type="json") + expected_message = "Title must be supplied when extracting from non-json to json" + assert expected_message in str(err.value) + + # have standardsMapping, but no instrument + with patch("heal.vlmd.extract.extract.vlmd_validate") as mock_validate: + mock_validate.return_value = { + "schemaVersion": "0.3.2", + "title": "default title from config - not a user supplied title", + "standardsMappings": [ + { + "some other field, not instrument": { + "title": test_title, + "id": "1234", + "url": "https://theurl", + } + } + ], + "fields": [], + } + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, file_type="csv", output_type="json") + expected_message = "Title must be supplied when extracting from non-json to json" + assert expected_message in str(err.value) + + +def test_get_title_from_standards_mapping(tmp_path): + """ + If a CSV input has standardsMapping.instrument.title then use that + for title and don't raise an error + """ + input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + output_file_name = f"{tmp_path}/output_with_title" + test_title = "Instrument Test Title" + with patch("heal.vlmd.extract.extract.vlmd_validate") as mock_validate, patch( + "heal.vlmd.extract.extract.get_output_filepath" + ) as mock_output_filepath: + # converted CSV has a standardsMappings title + # and a default title from config + mock_validate.return_value = { + "schemaVersion": "0.3.2", + "title": "default title from config - not a user supplied title", + "standardsMappings": [ + { + "instrument": { + "title": test_title, + "id": "1234", + "url": "https://theurl", + } + } + ], + "fields": [], + } + mock_output_filepath.return_value = output_file_name + # call extract without title - get title from standardsMappings + result = vlmd_extract(input_file, file_type="csv", output_type="json") + assert result + assert os.path.isfile(output_file_name) + with open(output_file_name, "r") as json_file: + data = json.load(json_file) + assert "standardsMappings" in data.keys() + assert data["title"] == test_title + + # call extract with a title so we don't use the standardsMappings title + result = vlmd_extract( + input_file, title="Some other title", file_type="csv", output_type="json" + ) + assert result + assert os.path.isfile(output_file_name) + with open(output_file_name, "r") as json_file: + data = json.load(json_file) + assert "standardsMappings" in data.keys() + assert data["title"] == "Some other title" def test_extract_failed_dict_write(): + """Failed conversion should trigger error""" input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" fail_message = "Error in writing converted dictionary" with patch("heal.vlmd.extract.extract.write_vlmd_dict") as mock_write: mock_write.side_effect = Exception("some exception") - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file, output_type="csv") - assert fail_message in str(e.value) + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, title=test_title, output_type="csv") + assert fail_message in str(err.value) def test_extract_invalid_converted_data(): + """Invalid converted data should trigger error""" # convert csv to invalid json input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" fail_message = "Failed extraction" with patch("heal.vlmd.extract.extract.vlmd_validate") as mock_validate: mock_validate.side_effect = ExtractionError(fail_message) - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file, output_type="json") - assert fail_message in str(e.value) + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, title=test_title, output_type="json") + assert fail_message in str(err.value) # convert json to invalid csv input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" with patch("heal.vlmd.extract.extract.vlmd_validate") as mock_validate: mock_validate.side_effect = ExtractionError(fail_message) - with pytest.raises(ExtractionError) as e: - vlmd_extract(input_file, output_type="csv") - assert fail_message in str(e.value) + with pytest.raises(ExtractionError) as err: + vlmd_extract(input_file, title=test_title, output_type="csv") + assert fail_message in str(err.value) + + +@pytest.mark.parametrize( + "converted_dict, title, expected_title", + [ + ( + { + "schemaVersion": "0.3.2", + "fields": [], + }, + "New title", + "New title", + ), + ( + { + "schemaVersion": "0.3.2", + "title": "old title", + "fields": [], + }, + "New title", + "New title", + ), + ( + { + "schemaVersion": "0.3.2", + "standardsMappings": [ + { + "instrument": { + "title": "standardsMappings title", + "id": "1234", + "url": "https://theurl", + } + } + ], + "fields": [], + }, + None, + "standardsMappings title", + ), + ], +) +def test_set_title_if_missing(converted_dict, title, expected_title): + """Test that set_title uses title or standardsMapping""" + new_dict = set_title_if_missing( + file_type="csv", title=title, converted_dict=converted_dict + ) + assert new_dict.get("title") == expected_title diff --git a/tests/test_vlmd_extract_utils.py b/tests/test_vlmd_extract_utils.py index 27dfd31..811fd18 100644 --- a/tests/test_vlmd_extract_utils.py +++ b/tests/test_vlmd_extract_utils.py @@ -11,6 +11,7 @@ parse_dictionary_str, parse_list_str, refactor_field_props, + strip_html, unflatten_from_json_path, ) @@ -141,7 +142,7 @@ def test_get_prop_names_to_rearrange(): assert names_to_rearrange == expected_prop_names -def test_flatten_to_json_path(valid_json_data, VALID_JSON_SCHEMA): +def test_flatten_to_json_path(valid_json_data, valid_json_schema): # example field item dict_field = { "section": "Enrollment", @@ -153,7 +154,7 @@ def test_flatten_to_json_path(valid_json_data, VALID_JSON_SCHEMA): "name": "participant_id", "constraints.pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", } - fields_schema = VALID_JSON_SCHEMA["properties"]["fields"]["items"] + fields_schema = valid_json_schema["properties"]["fields"]["items"] flattened_dict = flatten_to_json_path(dict_field, fields_schema) assert flattened_dict == expected_flattened_dict @@ -249,8 +250,17 @@ def test_refactor_field_props(): ] +def test_strip_html(): + input_string = "<b><head>Here is a header</head></b>" + expected = "Here is a header" + assert strip_html(input_string) == expected + + def test_parse_dictionary_str(): - input_string = "title=Example VLMD|description=This is an example description|fields=[{'section': 'Enrollment', 'name': 'participant_id'}]" + input_string = ( + "title=Example VLMD|description=This is an example description" + "|fields=[{'section': 'Enrollment', 'name': 'participant_id'}]" + ) expected_dict = { "title": "Example VLMD", "description": "This is an example description", @@ -284,5 +294,8 @@ def test_join_dict_items(): }, ], } - expected = "title=Example VLMD|description=This is an example description|fields=[{'section': 'Enrollment', 'name': 'participant_id'}]" + expected = ( + "title=Example VLMD|description=This is an example description" + "|fields=[{'section': 'Enrollment', 'name': 'participant_id'}]" + ) assert join_dict_items(dict) == expected diff --git a/tests/test_vlmd_file_utils.py b/tests/test_vlmd_file_utils.py index cf5d516..9d4b952 100644 --- a/tests/test_vlmd_file_utils.py +++ b/tests/test_vlmd_file_utils.py @@ -8,6 +8,7 @@ def test_write_json(tmp_path, valid_json_data): + """Write a valid json output file""" output_filepath = f"{tmp_path}/some/path/valid_dict.json" if os.path.isfile(output_filepath): os.unlink(output_filepath) @@ -22,6 +23,7 @@ def test_write_json(tmp_path, valid_json_data): def test_write_csv(tmp_path, valid_array_data): + """Write a valid csv file""" output_filepath = f"{tmp_path}/some/other/path/valid_dict.csv" if os.path.isfile(output_filepath): os.unlink(output_filepath) @@ -40,6 +42,7 @@ def test_write_csv(tmp_path, valid_array_data): def test_get_output_filepath(): + """Test the get_output_filepath method""" output_dir = "tmp/foo" input_filename = "data/valid/valid_dict.csv" output_type = "auto" @@ -54,6 +57,7 @@ def test_get_output_filepath(): def test_write_vlmd_unallowed(): + """Test that unallowed suffixes do not get written""" dictionary = "some text" output_filepath = "tmp/foo/unallowed_suffix.txt" result = write_vlmd_dict( diff --git a/tests/test_vlmd_json_conversion.py b/tests/test_vlmd_json_conversion.py index 5718c4b..d576347 100644 --- a/tests/test_vlmd_json_conversion.py +++ b/tests/test_vlmd_json_conversion.py @@ -4,8 +4,8 @@ def test_json_conversion_valid_file(valid_json_output): + """Test conversion of a valid json input file""" input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" - input_type = "json-template" valid_json_output.pop("schemaVersion", None) result = convert_template_json(input_file) @@ -20,6 +20,7 @@ def test_json_conversion_valid_file(valid_json_output): def test_json_conversion_valid_data(valid_json_data, valid_json_output): + """Test the conversion of array data (like csv data)""" valid_json_output.pop("schemaVersion", None) result = convert_template_json(valid_json_data) assert list(result.keys()) == ["template_json", "template_csv"] @@ -34,6 +35,7 @@ def test_json_conversion_valid_data(valid_json_data, valid_json_output): @pytest.mark.parametrize("unallowed_input", [(["some", "list"], ("some", "set"), 5)]) def test_json_conversion_unallowed_input(unallowed_input): + """Test that unallowed input triggers error""" with pytest.raises(ValueError) as e: convert_template_json(unallowed_input) diff --git a/tests/test_vlmd_redcap_conversion.py b/tests/test_vlmd_redcap_conversion.py new file mode 100644 index 0000000..5fc5cdc --- /dev/null +++ b/tests/test_vlmd_redcap_conversion.py @@ -0,0 +1,254 @@ +from unittest.mock import patch + +import pytest + +from heal.vlmd.extract.redcap_csv_dict_conversion import ( + _add_description, + _add_title, + convert_redcap_csv, + gather, + read_from_file, + rename_and_fill, +) + +VALID_REDCAP_SOURCE_FIELDS = [ + { + "name": "study_id", + "form": "demographics", + "section": "", + "type": "text", + "label": "Study ID", + "choice_calc_lbls": "", + "note": "", + "text_valid_slider_num": "", + "text_valid_min": "", + "text_valid_max": "", + "identifier": "", + "skip_logic": "", + "required": "", + "align": "", + "question_num": "", + "matrix_group": "", + }, + { + "name": "date_enrolled", + "form": "demographics", + "section": "Demographic Characteristics", + "type": "text", + "label": "Date subject signed consent", + "choice_calc_lbls": "", + "note": "YYYY-MM-DD", + "text_valid_slider_num": "date", + "text_valid_min": "", + "text_valid_max": "", + "identifier": "", + "skip_logic": "", + "required": "", + "align": "", + "question_num": "", + "matrix_group": "", + }, + { + "name": "email", + "form": "demographics", + "section": "Demographic Characteristics", + "type": "text", + "label": "E-mail", + "choice_calc_lbls": "", + "note": "", + "text_valid_slider_num": "email", + "text_valid_min": "", + "text_valid_max": "", + "identifier": "", + "skip_logic": "", + "required": "", + "align": "", + "question_num": "", + "matrix_group": "", + }, + { + "name": "sex", + "form": "demographics", + "section": "Demographic Characteristics", + "type": "dropdown", + "label": "Gender", + "choice_calc_lbls": "0, Female | 1, Male", + "note": "", + "text_valid_slider_num": "", + "text_valid_min": "", + "text_valid_max": "", + "identifier": "", + "skip_logic": "", + "required": "", + "align": "", + "question_num": "", + "matrix_group": "", + }, + { + "name": "height", + "form": "demographics", + "section": "Demographic Characteristics", + "type": "number", + "label": "Gender", + "choice_calc_lbls": "0, Female | 1, Male", + "note": "", + "text_valid_slider_num": "", + "text_valid_min": "130", + "text_valid_max": "215", + "identifier": "", + "skip_logic": "", + "required": "", + "align": "", + "question_num": "", + "matrix_group": "", + }, +] + + +def test_rename_and_fill(): + """Test rename_and_fill""" + + input_path = "tests/test_data/vlmd/valid/vlmd_redcap_dict_small.csv" + expected_renamed_columns = VALID_REDCAP_SOURCE_FIELDS[0].keys() + + # read the test file + source_dataframe = read_from_file(input_path) + # pass the dataframe to 'rename_and_fill' + source_data_table = rename_and_fill(source_dataframe) + + assert set(source_data_table[0].keys()) == set(expected_renamed_columns) + + for record in source_data_table: + if record["name"] == "study_id": + assert record["type"] == "text" + assert record["text_valid_slider_num"] == "" + if record["name"] == "date_enrolled": + assert record["type"] == "text" + assert record["text_valid_slider_num"] == "date" + if record["name"] == "email": + assert record["type"] == "text" + assert record["text_valid_slider_num"] == "email" + if record["name"] == "sex": + assert record["type"] == "dropdown" + assert record["text_valid_slider_num"] == "" + assert record["choice_calc_lbls"] == "0, Female | 1, Male" + + +def test_gather(): + """Test gather""" + + expected_target_fields = [ + "name", + "type", + "description", + "title", + "section", + "constraints", + ] + + target_fields = gather(VALID_REDCAP_SOURCE_FIELDS) + # check keys + assert set(target_fields[0].keys()) == set(expected_target_fields) + + # check some values + for record in target_fields: + if record["name"] == "study_id": + assert record["type"] == "string" + if record["name"] == "date_enrolled": + assert record["type"] == "date" + assert record["format"] == "any" + if record["name"] == "email": + assert record["type"] == "string" + assert record["format"] == "email" + if record["name"] == "sex": + assert record["type"] == "integer" + assert record["enumLabels"] == {"0": "Female", "1": "Male"} + assert record["constraints"] == {"enum": ["0", "1"]} + if record["name"] == "height": + assert record["type"] == "number" + assert record["constraints"] == {"min": 130.0, "max": 215.0} + pass + + +def test_convert_redcap_csv_bad_input(): + """Test the converter with input that is not path or dataframe""" + unallowed_input = ["some", "random", "list"] + with pytest.raises(ValueError) as err: + convert_redcap_csv(unallowed_input) + + expected_message = ( + "Input should be either dataframe or path to REDCap dictionary csv export" + ) + assert expected_message in str(err.value) + + +def test_convert_failed_mapping(): + """Test that converted raises a value error on failed mapping""" + expected_message = "some mapping exception" + with patch( + "heal.vlmd.extract.redcap_csv_dict_conversion._add_metadata" + ) as mock_mappings: + mock_mappings.side_effect = Exception("some mapping exception") + with pytest.raises(ValueError) as err: + result = gather(VALID_REDCAP_SOURCE_FIELDS) + print(f"Result {result}") + + mock_mappings.assert_called() + assert expected_message in str(err.value) + + +@pytest.mark.parametrize( + "source_field, target_field, expected_description", + [ + ( + { + "name": "height", + "section": "Demographics", + "label": "Source Height (cm)", + }, + {"type": "number", "description": "Subject height"}, + "Demographics: Source Height (cm)Subject height", + ), + ( + { + "name": "height", + "section": "Demographics", + "label": "Source Height (cm)", + }, + { + "type": "number", + }, + "Demographics: Source Height (cm)", + ), + ( + {"name": "height", "label": "Source Height (cm)"}, + {"type": "number", "description": "Subject height"}, + "Source Height (cm)Subject height", + ), + ({"name": "height"}, {"type": "number"}, "No field label for this variable"), + ], +) +def test_add_description(source_field, target_field, expected_description): + """Test the add_title method""" + assert _add_description(source_field, target_field) == expected_description + + +@pytest.mark.parametrize( + "source_field, target_field, expected_title", + [ + ( + {"name": "height", "label": "Source Height (cm)"}, + {"type": "number"}, + "Source Height (cm)", + ), + ( + {"name": "height", "label": "Height (cm)"}, + {"type": "number", "title": "Target Height (cm)"}, + "Target Height (cm)", + ), + ({"name": "height"}, {"type": "number"}, "No field label for this variable"), + ], +) +def test_add_title(source_field, target_field, expected_title): + """Test the add_title method""" + assert _add_title(source_field, target_field) == expected_title diff --git a/tests/test_vlmd_redcap_mappings.py b/tests/test_vlmd_redcap_mappings.py new file mode 100644 index 0000000..3c84056 --- /dev/null +++ b/tests/test_vlmd_redcap_mappings.py @@ -0,0 +1,219 @@ +import pytest + +from heal.vlmd.mappings.redcap_csv_headers import ( + CHOICES_FIELD_NAME, + CHOICES_LABEL_INPUT, +) +from heal.vlmd.mappings.redcap_field_mapping import ( + _parse_field_properties_from_encodings, + map_checkbox, + map_dropdown, + map_radio, + map_text, +) + + +def test_parse_field_properties(): + """Test mapping function parse_field_properties_from_encodings""" + encodings_string = "0, No | 1, Yes" + expected_dict = { + "type": "integer", + "enumLabels": {"0": "No", "1": "Yes"}, + "constraints": {"enum": ["0", "1"]}, + } + assert _parse_field_properties_from_encodings(encodings_string) == expected_dict + + +@pytest.mark.parametrize( + "input_dict, expected_output_dict", + [ + ( + { + "name": "some_field_name", + "text_valid_slider_num": "date", + "text_valid_min": "", + }, + { + "type": "date", + "format": "any", + "constraints": {"pattern": None, "minimum": None, "maximum": None}, + }, + ), + ( + {"name": "some_field_name", "text_valid_slider_num": ""}, + { + "type": "string", + "constraints": {"pattern": None, "minimum": None, "maximum": None}, + }, + ), + ( + {"text_valid_slider_num": "email"}, + { + "type": "string", + "format": "email", + "constraints": {"pattern": None, "minimum": None, "maximum": None}, + }, + ), + ( + { + "text_valid_slider_num": "integer", + "text_valid_min": "0", + "text_valid_max": "9", + }, + { + "type": "integer", + "constraints": {"pattern": None, "minimum": 0, "maximum": 9}, + }, + ), + ( + {"text_valid_slider_num": "alpha_only"}, + { + "type": "string", + "constraints": { + "pattern": "^[a-zA-Z]+$", + "minimum": None, + "maximum": None, + }, + }, + ), + ( + { + "text_valid_slider_num": "number", + "text_valid_min": "1.1", + "text_valid_max": "2.2", + }, + { + "type": "number", + "constraints": {"pattern": None, "minimum": 1.1, "maximum": 2.2}, + }, + ), + ( + {"text_valid_slider_num": "phone"}, + { + "type": "string", + "constraints": { + "pattern": "^[0-9]{3}-[0-9]{3}-[0-9]{4}$", + "minimum": None, + "maximum": None, + }, + }, + ), + ( + {"text_valid_slider_num": "zipcode"}, + { + "type": "string", + "constraints": { + "pattern": "^[0-9]{5}$", + "minimum": None, + "maximum": None, + }, + }, + ), + ], +) +def test_map_text(input_dict, expected_output_dict): + """Test mapping function map_text""" + assert map_text(input_dict) == expected_output_dict + + +@pytest.mark.parametrize( + "input_dict, expected_output_dict", + [ + ( + { + "text_valid_slider_num": "integer", + "text_valid_min": "0", + "text_valid_max": "[calculated_value]", + }, + { + "type": "integer", + "constraints": {"pattern": None, "minimum": 0, "maximum": None}, + }, + ), + ( + { + "text_valid_slider_num": "number", + "text_valid_min": "[calculated_value]", + "text_valid_max": "2.2", + }, + { + "type": "number", + "constraints": {"pattern": None, "minimum": None, "maximum": 2.2}, + }, + ), + ( + { + "text_valid_slider_num": "number, comma_decimal", + "text_valid_min": "[calculated_value]", + "text_valid_max": "2,2", + }, + { + "type": "number", + "constraints": {"pattern": None, "minimum": None, "maximum": 2.2}, + }, + ), + ], +) +def test_map_integer_skip_calc(input_dict, expected_output_dict): + """Test that non-numeric (eg, calculated) min/max get skipped""" + assert map_text(input_dict) == expected_output_dict + + +def test_map_dropdown_with_error(): + """Test that map_dropdown raises error with empty Choices field""" + input_row = { + "name": "some_field_name", + CHOICES_FIELD_NAME: "", + } + expected_message = ( + "Missing value in dropdown field '" + f"{input_row.get('name')}" + f"' in column '{CHOICES_LABEL_INPUT}'." + ) + with pytest.raises(ValueError) as err: + map_dropdown(input_row) + assert expected_message in str(err.value) + + +def test_map_radio_with_error(): + """Test that map_radio raises error with empty Choices field""" + input_row = { + "name": "some_field_name", + CHOICES_FIELD_NAME: "", + } + expected_message = ( + "Missing value in radio field '" + f"{input_row.get('name')}" + f"' in column '{CHOICES_LABEL_INPUT}'." + ) + with pytest.raises(ValueError) as err: + map_radio(input_row) + assert expected_message in str(err.value) + + +def test_map_checkbox(): + """Test the mapping of checkbox data into expanded list of bools""" + input_dict = { + "name": "gym", + "type": "checkbox", + "choice_calc_lbls": "0, Monday | 1, Tuesday", + } + expected_output_list = [ + { + "description": "[choice=Monday]", + "title": "Gym: Monday", + "name": "gym___0", + "type": "boolean", + "constraints": {"enum": ["0", "1"]}, + "enumLabels": {"0": "Unchecked", "1": "Checked"}, + }, + { + "description": "[choice=Tuesday]", + "title": "Gym: Tuesday", + "name": "gym___1", + "type": "boolean", + "constraints": {"enum": ["0", "1"]}, + "enumLabels": {"0": "Unchecked", "1": "Checked"}, + }, + ] + assert map_checkbox(input_dict) == expected_output_list diff --git a/tests/test_vlmd_utils.py b/tests/test_vlmd_utils.py index 9724fc0..089583e 100644 --- a/tests/test_vlmd_utils.py +++ b/tests/test_vlmd_utils.py @@ -4,6 +4,7 @@ add_missing_type, add_types_to_props, clean_json_fields, + has_redcap_headers, remove_empty_props, ) @@ -151,3 +152,19 @@ def test_clean_json_fields(): fields = [input_prop_with_empties, input_prop_with_empties] expected_fields = [expected_prop, expected_prop] assert clean_json_fields(fields) == expected_fields + + +def test_has_redcap_headers(): + """Test has_redcap_headers""" + test_fields = [ + "Variable / Field Name", + "Form Name", + "Field Type", + "Field Label", + "Another Field", + "And another", + ] + assert has_redcap_headers(test_fields) + + test_fields = ["Some Field", "And another"] + assert has_redcap_headers(test_fields) is False diff --git a/tests/test_vlmd_validate.py b/tests/test_vlmd_validate.py index 7718b77..484ed6d 100644 --- a/tests/test_vlmd_validate.py +++ b/tests/test_vlmd_validate.py @@ -29,7 +29,7 @@ def test_validate_valid_input(file_type, output_type): @pytest.mark.parametrize("file_type", ["csv", "tsv", "json"]) def test_validate_with_json_output( - file_type, valid_json_output, valid_converted_csv_to_json_output + file_type, valid_json_data, valid_converted_csv_to_json_output ): input_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" result = vlmd_validate(input_file, output_type="json", return_converted_output=True) @@ -37,7 +37,7 @@ def test_validate_with_json_output( if file_type in ["csv", "tsv"]: assert result == valid_converted_csv_to_json_output if file_type == "json": - assert result == valid_json_output + assert result == valid_json_data @pytest.mark.parametrize("file_type", ["csv", "json", "tsv"]) @@ -81,7 +81,7 @@ def test_input_does_not_exist(): def test_unallowed_input_type(allowed_input_types): - unallowed_file = "vlmd_unallowed_type.txt" + unallowed_file = "tests/test_data/vlmd/invalid/vlmd_invalid.txt" with pytest.raises(ValueError) as e: vlmd_validate(input_file=unallowed_file, schema_type="auto") diff --git a/tests/test_vlmd_validate_utils.py b/tests/test_vlmd_validate_utils.py index a865d9b..96ab522 100644 --- a/tests/test_vlmd_validate_utils.py +++ b/tests/test_vlmd_validate_utils.py @@ -88,9 +88,9 @@ def test_read_delim_unallowed_file_type(): @pytest.mark.parametrize("file_type", ["csv", "tsv"]) -def test_get_schema_csv_file(file_type, VALID_CSV_SCHEMA): +def test_get_schema_csv_file(file_type, valid_csv_schema): test_file = f"vlmd.{file_type}" - expected_schema = {"type": "array", "items": VALID_CSV_SCHEMA} + expected_schema = {"type": "array", "items": valid_csv_schema} result = get_schema(test_file, "auto") assert result == expected_schema @@ -100,8 +100,8 @@ def test_get_schema_csv_file(file_type, VALID_CSV_SCHEMA): @pytest.mark.parametrize("file_type", ["csv", "tsv"]) -def test_get_schema_csv_list(file_type, VALID_CSV_SCHEMA, valid_array_data): - expected_schema = {"type": "array", "items": VALID_CSV_SCHEMA} +def test_get_schema_csv_list(file_type, valid_csv_schema, valid_array_data): + expected_schema = {"type": "array", "items": valid_csv_schema} result = get_schema(valid_array_data, "auto") assert result == expected_schema @@ -111,24 +111,24 @@ def test_get_schema_csv_list(file_type, VALID_CSV_SCHEMA, valid_array_data): @pytest.mark.parametrize("schema_type", ["auto", "json"]) -def test_get_schema_json_file(schema_type, VALID_JSON_SCHEMA): +def test_get_schema_json_file(schema_type, valid_json_schema): test_file = "vlmd.json" result = get_schema(test_file, schema_type) - assert result == VALID_JSON_SCHEMA + assert result == valid_json_schema @pytest.mark.parametrize("schema_type", ["auto", "json"]) -def test_get_schema_json_object(schema_type, VALID_JSON_SCHEMA): +def test_get_schema_json_object(schema_type, valid_json_schema): test_file = "tests/test_data/vlmd/valid/vlmd_valid.json" data = json.loads(Path(test_file).read_text()) result = get_schema(data_or_path=data, schema_type=schema_type) - assert result == VALID_JSON_SCHEMA + assert result == valid_json_schema @pytest.mark.parametrize("invalid_data", [("set", "not", "a", "dict"), 5]) -def test_get_schema_json_invalid_object(invalid_data, VALID_JSON_SCHEMA): +def test_get_schema_json_invalid_object(invalid_data, valid_json_schema): with pytest.raises(ValueError) as e: get_schema(data_or_path=invalid_data, schema_type="json") expected_message = "Input should be path or dict"