diff --git a/README.md b/README.md index 642582f..5c3df66 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,16 @@ The qdr_downloads module include a retriever function for downloading files from This is intended to be called by gen3sdk [external download functions](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/tools/download/external_file_download.py). It is also possible to write a wrapper script for the qdr download functions. +### Notebooks + +In the notebooks directory there are jupyter notebooks that may be used to download files from a corresponding platform. For instance the qdr_data_download.ipynb notebook may be used to download files from Syracuse QDR. + +These notebooks perform optimally within a HEAL Gen3 Workspace and the notebooks will be automatically installed to a user's workspace when the workspace is initiated. However, you may also use these notebooks on your local machine. + +### VLMD extraction and validation + +The [VLMD docs](heal/vlmd/README.md) describe how to use the SDK for extracting and validating VLMD dictionaries. + ### Run tests ``` diff --git a/heal/vlmd/README.md b/heal/vlmd/README.md new file mode 100644 index 0000000..01c0ebe --- /dev/null +++ b/heal/vlmd/README.md @@ -0,0 +1,72 @@ +# VLMD methods + +## VLMD validation + +This module validates VLMD data dictionaries against stored schemas. The `vlmd_validate()` method +will attempt an extraction as part of the validation process. + +The `vlmd_validate()` method raises a `jsonschema.ValidationError` for an invalid input file and +will raise an `ExtractionError` if the input_file cannot be converted + +Example validation code: + +``` +from jsonschema import ValidationError + +from heal.vlmd import vlmd_validate, ExtractionError + +input_file = "vlmd_dd.json" +try: + vlmd_validate(input_file) + +except ValidationError as v_err: + # handle validation error + +except ExtractionError as e_err: + # handle extraction error + +``` + +## VLMD extract + +The extract module implements extraction and conversion of dictionaries into different formats. + +The current formats are csv, json, and tsv. + +The `vlmd_extract()` method raises a `jsonschema.ValidationError` for an invalid input files +and raises an `ExtractionError` for any other type of error. + +Example extraction code: + +``` +from jsonschema import ValidationError + +from heal.vlmd import vlmd_extract, ExtractionError + +try: + vlmd_extract("vlmd_for_extraction.csv", output_dir="./output") + +except ValidationError as v_err: + # handle validation error + +except ExtractionError as e_err: + # handle extraction error +``` + +The above will write a HEAL-compliant VLMD json dictionary to + +`output/heal-dd_vlmd_for_extraction.json` + +## Adding new file types for extraction and validation + +The above moduels currently handle the following types of dictionaries: csv, json, tsv. + +To add code for a new dictionary file type: + +* Create a new schema for the data type or validate against the existing json schema +* If possible create a new validator module for the new file type +* Call the new validator module from the `validate.py` module +* Create a new extractor module for the new file type, possibly using `pandas` +* Call the new extractor module from the `conversion.py` module +* Add new file writing utilities if saving converted dictionaries in the new format +* Create unit tests as needed for new code diff --git a/heal/vlmd/__init__.py b/heal/vlmd/__init__.py new file mode 100644 index 0000000..df91732 --- /dev/null +++ b/heal/vlmd/__init__.py @@ -0,0 +1,4 @@ +from heal.vlmd.validate.validate import vlmd_validate, ExtractionError + +# place 'extract' import after 'validate' import +from heal.vlmd.extract.extract import vlmd_extract diff --git a/heal/vlmd/config.py b/heal/vlmd/config.py new file mode 100644 index 0000000..4269042 --- /dev/null +++ b/heal/vlmd/config.py @@ -0,0 +1,26 @@ +import json + +# file prefix +OUTPUT_FILE_PREFIX = "heal-dd" + +# file suffixes +ALLOWED_INPUT_TYPES = ["csv", "tsv", "json"] +ALLOWED_FILE_TYPES = ["auto"] + ALLOWED_INPUT_TYPES +ALLOWED_SCHEMA_TYPES = ["auto", "csv", "json", "tsv"] +ALLOWED_OUTPUT_TYPES = ["csv", "json"] + +# schemas +csv_schema_file = "heal/vlmd/schemas/heal_csv.json" +with open(csv_schema_file, "r") as f: + CSV_SCHEMA = json.load(f) + +json_schema_file = "heal/vlmd/schemas/heal_json.json" +with open(json_schema_file, "r") as f: + JSON_SCHEMA = json.load(f) + +# schema +JSON_SCHEMA_VERSION = JSON_SCHEMA.get("version", "0.3.2") +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 new file mode 100644 index 0000000..335e514 --- /dev/null +++ b/heal/vlmd/extract/conversion.py @@ -0,0 +1,91 @@ +from functools import partial +from pathlib import Path + +from cdislogging import get_logger + +from heal.vlmd import mappings +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.utils import clean_json_fields + +logger = get_logger("vlmd-conversion", log_level="debug") + +choice_fxn = { + "csv-data-dict": partial( + convert_datadict_csv, + rename_map=mappings.rename_map, + recode_map=mappings.recode_map, + ), + "json-template": convert_template_json, +} + +ext_map = { + ".csv": "csv-data-dict", + ".json": "json-template", +} + + +def _detect_input_type(filepath, ext_to_input_type=ext_map): + ext = filepath.suffix + input_type = ext_to_input_type.get(ext, None) + return input_type + + +def convert_to_vlmd( + input_filepath, + input_type=None, + data_dictionary_props=None, +) -> dict: + """ + Converts a data dictionary to HEAL compliant json or csv format. + + Args + input_filepath (str): Path to input file. Currently converts data dictionaries in csv, json, and tsv. + 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. + + Returns + Dictionary with: + 1. csvtemplated array of fields. + 2. jsontemplated data dictionary object as specified by an originally drafted design doc. + That is, a dictionary with title:,description:<description>,data_dictionary:<fields> + where data dictionary is an array of fields as specified by the JSON schema. + + """ + + input_filepath = Path(input_filepath) + + input_type = input_type or _detect_input_type(input_filepath) + logger.debug(f"Converting file '{input_filepath}' of input_type '{input_type}'") + if input_type not in choice_fxn.keys(): + logger.error(f"Unexpected input type {input_type}") + raise ValueError( + f"Unexpected input_type '{input_type}', not in {choice_fxn.keys()}" + ) + + # get data dictionary package based on the input type + data_dictionary_props = data_dictionary_props or {} + 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 + # https://github.com/HEAL/healdata-utils/blob/5080227454d8e731d46a51aa6933c93523eb3b9a/src/healdata_utils/conversion.py#L196 + package = data_dictionary_package + + # add schema version + for field in package["template_csv"]["fields"]: + field.update({"schemaVersion": JSON_SCHEMA["version"], **field}) + + # remove empty json fields, add schema version (in TOP_LEVEL_PROPS) + package["template_json"]["fields"] = clean_json_fields( + package["template_json"]["fields"] + ) + package["template_json"] = {**TOP_LEVEL_PROPS, **dict(package["template_json"])} + + return package diff --git a/heal/vlmd/extract/csv_dict_conversion.py b/heal/vlmd/extract/csv_dict_conversion.py new file mode 100644 index 0000000..61be068 --- /dev/null +++ b/heal/vlmd/extract/csv_dict_conversion.py @@ -0,0 +1,243 @@ +import re +from os import PathLike +from pathlib import Path + +import pandas as pd + +from heal.vlmd.config import JSON_SCHEMA +from heal.vlmd.extract import utils +from heal.vlmd.validate.utils import read_delim + + +def _parse_string_objects( + tbl_csv: pd.DataFrame, field_properties: dict +) -> pd.DataFrame: + """Parse string objects and array to create the dict (json) instance""" + tbl_json = tbl_csv.copy() + for column_name in tbl_json.columns.tolist(): + # NOTE: the below methodology uses the schema to instruct how to convert to json. + field_prop_name = utils.find_prop_name(column_name, field_properties) + field_prop = field_properties.get(field_prop_name) + if field_prop: + if field_prop["type"] == "object": + tbl_json[column_name] = tbl_csv[column_name].apply( + utils.parse_dictionary_str, item_sep="|", key_val_sep="=" + ) + + elif field_prop["type"] == "array": + tbl_json[column_name] = tbl_csv[column_name].apply( + utils.parse_list_str, item_sep="|" + ) + else: + # columns not included in schema ('custom' or 'other') + # CSV header should have format 'custom.<KEY>' + group = re.match(r"^custom\.(.+)$", column_name) + if group: + key = group[1] + if "custom" not in tbl_json: + tbl_json["custom"] = [{}] * len(tbl_json) + for i in range(len(tbl_csv)): + value = tbl_csv[column_name].iloc[i] + if value: + tbl_json.at[i, "custom"] = {key: value} + else: + # May throw an error unless schema has '"additionalProperties": true' + tbl_json[column_name] = tbl_csv[column_name] + + return tbl_json + + +def convert_datadict_csv( + csv_template: str, + data_dictionary_props: dict, + rename_map: dict = None, + recode_map: dict = None, + drop_list: dict = None, + item_sep: str = "|", + key_val_sep: str = "=", +) -> dict: + """ + Converts a CSV conforming to HEAL specifications (but see 2 additional notes below) + into a HEAL-specified data dictionary in both csv format and json format. + + Converts an in-memory data dictionary or a path to a data dictionary file into a HEAL-specified tabular template by: + 1. Adding missing fields, and + 2. Converting fields from a specified mapping. + NOTE: currently this mapping is only float/num to number or text/char to string (case insensitive) + In future versions, there will be a specified module for csv input mappings. + + Args + + csv_template : str or path-like or an object that can be inferred as data by frictionless's Resource class. + 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. + 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"...}...} + 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 + + Returns + A dictionary with two keys: + - 'template_json': the HEAL-specified JSON object. + - 'template_csv': the HEAL-specified tabular template. + + """ + + # get transforms + # cast numbers explicitly based on schema + # this is needed in case there is only one record in a string column that is a number (ie don't want to convert) + def infer_delim(series: pd.Series, char_list: list, firstmatch: bool): + """infer the delimiter by the highest frequency by row higest occuring character + can either take only the first match or all matches for each row""" + combined_pattern = "|".join(map(re.escape, char_list)) + + try: + # get most frequent delimiter per row + most_freq_chars = ( + series.rename_axis("row") + .str.extractall(f"({combined_pattern})") + .squeeze( + "rows" + ) # only 1 row, dont want to squeeze to scalar so spec rows + .pipe( + lambda s: s.unstack("match")[0] if firstmatch else s + ) # if first match specified + .groupby("row") + .value_counts() + .unstack() + .idxmax(axis=1) + ) + except KeyError: + return None + + try: + # infer delim by character with most per row max frequencies + inferred_delim = most_freq_chars.value_counts().sort_values().index[-1] + except IndexError: + return None + + return inferred_delim + + if isinstance(csv_template, (str, PathLike)): + template_tbl = read_delim(str(Path(csv_template))) + else: + template_tbl = pd.DataFrame(csv_template) + + if not rename_map: + rename_map = {} + + if not recode_map: + recode_map = {} + + if not drop_list: + drop_list = [] + + slugify = lambda s: s.strip().lower().replace("_", "-").replace(" ", "-") + # flattened properties + field_properties = utils.flatten_properties( + JSON_SCHEMA["properties"]["fields"]["items"]["properties"] + ) + + # init to-be formatted tables + tbl_csv = template_tbl.copy() + # transform each column with slugified mappings, harmonizing delims (if array, object) + for column_name in tbl_csv.columns.tolist(): + slugified_col = slugify(column_name) + new_column_name = column_name + + # rename based on slugified names or original col names + new_column_name = rename_map.get(slugified_col) or rename_map.get(column_name) + if new_column_name: + tbl_csv.rename(columns={column_name: new_column_name}, inplace=True) + else: + new_column_name = column_name + + # recode slugified old names to accepted names + if new_column_name in recode_map.keys(): + tbl_csv[new_column_name] = ( + tbl_csv[new_column_name] + .apply(slugify) + .replace(recode_map[new_column_name]) + ) + + if new_column_name in drop_list: + del tbl_csv[new_column_name] + + # NOTE: the below methodology uses the schema to instruct how to convert to json. + field_prop_name = utils.find_prop_name(new_column_name, field_properties) + field_prop = field_properties.get(field_prop_name) + # infer delimiters of stringified lists (note: stringified lists are identified from schema) + + if field_prop_name: + if field_prop["type"] == "integer": + tbl_csv[new_column_name] = tbl_csv[new_column_name].apply( + lambda s: int(float(s)) if s else s + ) + elif field_prop["type"] == "number": + tbl_csv[new_column_name] = tbl_csv[new_column_name].astype(float) + elif field_prop["type"] == "object": + possible_key_val = ["=", ":"] + possible_list = [";", "|"] + key_val_sep = ( + infer_delim( + tbl_csv[new_column_name], possible_key_val, firstmatch=True + ) + or "=" + ) + item_sep = ( + infer_delim( + tbl_csv[new_column_name], possible_list, firstmatch=False + ) + or "|" + ) + + tbl_csv[new_column_name] = ( + tbl_csv[new_column_name] + .str.replace(key_val_sep, "=") + .str.replace(item_sep, "|") + ) + + elif field_prop["type"] == "array": + possible_list = [";", "|"] + item_sep = ( + infer_delim( + tbl_csv[new_column_name], possible_list, firstmatch=False + ) + or "|" + ) + tbl_csv[new_column_name] = tbl_csv[new_column_name].replace( + item_sep, "|" + ) + + tbl_json = _parse_string_objects(tbl_csv, field_properties) + + # drop all custom columns (as I have nested already) + tbl_json.drop(columns=tbl_json.filter(regex="^custom\\.").columns, inplace=True) + + # refactor (i.e., cascade, move up to root) properties if present in all records + refactored_props, tbl_json = utils.refactor_field_props( + tbl_json, schema=JSON_SCHEMA + ) + + # data dictionary root level properties + data_dictionary_props_csv = dict(data_dictionary_props) + data_dictionary_props_json = { + **data_dictionary_props, + **utils.unflatten_from_json_path(refactored_props.to_dict()), + } + + # create the data dictionary objects + fields_json = [ + utils.unflatten_from_json_path(record) + for record in tbl_json.to_dict(orient="records") + ] + template_json = dict(**data_dictionary_props_json, fields=fields_json) + + fields_csv = tbl_csv.to_dict(orient="records") + template_csv = dict(**data_dictionary_props_csv, fields=fields_csv) + + return {"template_json": template_json, "template_csv": template_csv} diff --git a/heal/vlmd/extract/extract.py b/heal/vlmd/extract/extract.py new file mode 100644 index 0000000..846c79b --- /dev/null +++ b/heal/vlmd/extract/extract.py @@ -0,0 +1,81 @@ +from os.path import isfile +from pathlib import Path + +from cdislogging import get_logger + +from heal.vlmd import ExtractionError, vlmd_validate +from heal.vlmd.config import ( + ALLOWED_FILE_TYPES, + ALLOWED_INPUT_TYPES, + ALLOWED_OUTPUT_TYPES, +) +from heal.vlmd.file_utils import get_output_filepath, write_vlmd_dict + +logger = get_logger("extract", log_level="debug") + + +def vlmd_extract( + input_file, file_type="auto", output_dir=".", output_type="json" +) -> bool: + """ + Extract a HEAL compliant csv and json format VLMD data dictionary + from the input file. + + Args: + input_file (str): the path of the input HEAL VLMD file to be extracted + into HEAL-compliant VLMD file(s). + 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". + Defaults to “auto”. + output_dir (str): the directory of where the extracted VLMD file will + be written. Defaults to “.” + output_type (str): format of dictionary to write: "csv" or "json". + The default is "json". + + Returns: + True if the input is valid and is successfully converted and written. + 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}'") + + file_suffix = Path(input_file).suffix.replace(".", "") + if file_suffix not in ALLOWED_INPUT_TYPES: + raise ExtractionError(f"Input file must be one of {ALLOWED_INPUT_TYPES}") + if not isfile(input_file): + raise ExtractionError(f"Input file does not exist: {input_file}") + + if file_type not in ALLOWED_FILE_TYPES: + raise ExtractionError(f"File type must be one of {ALLOWED_FILE_TYPES}") + if file_type == "auto": + file_type = file_suffix + + if output_type not in ALLOWED_OUTPUT_TYPES: + raise ExtractionError( + f"Unrecognized output_type '{output_type}' - should be in {ALLOWED_OUTPUT_TYPES}" + ) + + # validate + try: + converted_dictionary = vlmd_validate( + input_file, output_type=output_type, return_converted_output=True + ) + except Exception as e: + logger.error(f"Error in validating and extracting dictionary from {input_file}") + logger.error(e) + raise ExtractionError(str(e)) + + # write to file + output_filepath = get_output_filepath( + output_dir, input_file, output_type=output_type + ) + 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: + logger.error("Error in writing converted dictionary") + logger.error(e) + 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 new file mode 100644 index 0000000..be671d8 --- /dev/null +++ b/heal/vlmd/extract/json_dict_conversion.py @@ -0,0 +1,94 @@ +import collections +import json +from os import PathLike +from pathlib import Path + +import pandas as pd + +from heal.vlmd.config import JSON_SCHEMA +from heal.vlmd.extract import utils + + +def convert_template_json( + json_template, + data_dictionary_props: dict = None, + fields_name: str = "fields", +) -> dict: + """ + Converts a JSON file or dictionary conforming to HEAL specifications + into a HEAL-specified data dictionary in both csv format and json format. + + Converts in-memory data or a path to a data dictionary file. + + If data_dictionary_props is specified, any properties passed in will be + overwritten. + + Args + json_template : str or path-like or an object that can be inferred as data by frictionless's Resource class. + 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 + A dictionary with two keys: + - 'template_json': the HEAL-specified JSON object. + - 'template_csv': the HEAL-specified tabular template. + + """ + + if isinstance(json_template, (str, PathLike)): + json_template_dict = json.loads(Path(json_template).read_text()) + elif isinstance(json_template, collections.abc.MutableMapping): + json_template_dict = json_template + else: + raise ValueError( + "json_template needs to be either dictionary-like or a path to a json" + ) + + 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 + if not json_template_dict.get(prop_name): + write_prop = True + elif prop and prop != json_template_dict.get(prop_name): + write_prop = True + else: + write_prop = False + + if write_prop: + json_template_dict[prop_name] = prop + + fields_json = json_template_dict.pop(fields_name) + data_dictionary_props = json_template_dict + + fields_schema = JSON_SCHEMA["properties"]["fields"]["items"] + flattened_fields = pd.DataFrame( + [utils.flatten_to_json_path(f, fields_schema) for f in fields_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 + ) + tbl_csv = ( + flattened_and_embedded.fillna("") + .map( + lambda v: utils.join_dict_items(v) + if isinstance(v, collections.abc.MutableMapping) + else v + ) + .map( + lambda v: utils.join_iter(v) + if isinstance(v, collections.abc.MutableSequence) + else v + ) + ) + fields_csv = tbl_csv.to_dict(orient="records") + + template_json = {**data_dictionary_props, "fields": fields_json} + template_csv = {**data_dictionary_props, "fields": fields_csv} + + return {"template_json": template_json, "template_csv": template_csv} diff --git a/heal/vlmd/extract/utils.py b/heal/vlmd/extract/utils.py new file mode 100644 index 0000000..12716ba --- /dev/null +++ b/heal/vlmd/extract/utils.py @@ -0,0 +1,291 @@ +"""Extract utilities/helper functions""" + +import re +from collections.abc import MutableMapping + +import jsonschema +import pandas as pd +from pandas.api.types import is_object_dtype + + +def _get_prop_names_to_rearrange(prop_names, schema): + """ + Get data dictionary props to search for refactoring or embedding + + Supports patternProperties + """ + + annotation_names = ["title", "description", "name", "additionalDescription"] + root_names = flatten_properties(schema["properties"]).keys() + field_names = flatten_properties( + schema["properties"]["fields"]["items"]["properties"] + ).keys() + props_to_rearrange = ( + set(root_names).intersection(field_names).difference(annotation_names) + ) + + prop_names_exp = "|".join("^" + name + "$" for name in props_to_rearrange) + names_to_rearrange = [name for name in prop_names if re.match(prop_names_exp, name)] + + return list(names_to_rearrange) + + +def embed_data_dictionary_props(flat_fields, flat_root, schema): + """ + Embed (flattened) root level props in each (flattened) field + if field level prop is missing but the field level prop exists + and is not an annotation property (title,description). + + Args + + flat_fields: array of dicts or pd.DataFrame (or something that can be converted into DataFrame) + flat_root: dict or pd.Series or something that can be converted into Series + schema: schema for determining what fields should be embedded (note, this is flattened in fxn) + + Returns + + pd.DataFrame with the flat fields with the embedded root properties + """ + flat_fields = pd.DataFrame(flat_fields) + prop_names = _get_prop_names_to_rearrange(list(flat_root.keys()), schema) + flat_root = pd.Series(flat_root).loc[prop_names] # take out annotation props + if len(flat_root) > 0: + for prop_name in prop_names: + if prop_name in flat_root: + if prop_name not in flat_fields: + flat_fields.insert(0, prop_name, flat_root[prop_name]) + else: + flat_fields[prop_name].fillna(flat_root[prop_name], inplace=True) + + return flat_fields + + +def refactor_field_props(flat_fields, schema): + """ + Given a flattened array of dicts corresponding to the unflattened schema, + move up (ie `refactor`) flattened properties that are both in the root + (ie table level; level up from field records) and in the fields. + """ + flat_fields_df = pd.DataFrame(flat_fields) + props = set(_get_prop_names_to_rearrange(flat_fields_df.columns.tolist(), schema)) + flat_record = pd.Series(dtype="object") + for name in props: + in_df = name in flat_fields_df + if in_df: + is_one_unique = None + # need to handle if some values are pandas series + if isinstance(flat_fields_df[name], pd.DataFrame): + is_one_unique = (flat_fields_df[name].nunique() == 1).all() + elif isinstance(flat_fields_df[name], pd.Series): + if is_object_dtype(flat_fields_df[name]): + is_one_unique = len(flat_fields_df[name].map(str).unique()) == 1 + else: + is_one_unique = flat_fields_df[name].nunique() == 1 + if is_one_unique: + flat_record[name] = flat_fields_df.pop(name).iloc[0] + if isinstance(flat_record[name], pd.Series): + flat_record[name] = flat_record[name].to_list() + + return flat_record, flat_fields_df + + +# individual cell utilities +def parse_dictionary_str(string, item_sep, key_val_sep): + """ + Parses a stringified dictionary into a dictionary + based on item separator + """ + if string != "" and string is not None: + str_items = string.strip().split(item_sep) + items = {} + + for str_item in str_items: + if str_item: + item = str_item.split(key_val_sep, 1) + items[item[0].strip()] = item[1].strip() + + return items + else: + return string + + +def parse_list_str(string, item_sep): + """Split a string into list using 'item_sep'""" + if string != "" and string is not None: + return string.strip().split(item_sep) + else: + return string + + +# dictionary utilities +def flatten_to_json_path(dictionary, schema, parent_key=False, sep="."): + """ + Turn a nested dictionary into a flattened dictionary (but see schema param) + + :param dictionary: The dictionary to flatten + :param schema: The schema to indicate which properties to flatten + This includes dictionaries (properties) with child dictionaries (properties) + and lists (items) with child dictionaries (properties) + :param parent_key: The string to prepend to dictionary's keys + :param sep: The string used to separate flattened keys + :return: A flattened dictionary + """ + # flatten if type array -> type object with properties + # flatten if type object with properties + items = [] + for key, value in dictionary.items(): + new_key = str(parent_key) + sep + key if parent_key else key + 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): + item = flatten_to_json_path( + dictionary=_value, + schema=prop, + parent_key=new_key, + sep=f"[{str(i)}]{sep}", + ) + items.extend(item.items()) + elif childprops: + item = flatten_to_json_path( + dictionary=value, schema=prop, parent_key=new_key, sep=sep + ) + items.extend(item.items()) + + else: + items.append((new_key, value)) + + return dict(items) + + +def unflatten_from_json_path(field): + """ + Converts a flattened dictionary with key names conforming to + JSONpath notation to the nested dictionary format. + """ + field_json = {} + + for prop_path, prop in field.items(): + prop_json = field_json + + nested_names = prop_path.split(".") + nested_names_length = len(nested_names) + for i, prop_name in enumerate(nested_names): + if "[" in prop_name: + g = re.match(r"(.+)\[(\d+)\]$", prop_name) + if not g: + raise jsonschema.ValidationError( + f"Incorrect array indexing in name {prop_name}" + ) + try: + array_name = g[1] + array_index = int(g[2]) + except Exception as e: + raise jsonschema.ValidationError(str(e)) + if array_name not in prop_json: + prop_json[array_name] = [{} for _ in range(array_index + 1)] + elif len(prop_json[array_name]) <= array_index: + prop_json[array_name].extend( + [ + {} + for _ in range(array_index - len(prop_json[array_name]) + 1) + ] + ) + if i == nested_names_length - 1: + if isinstance(prop_json[array_name][array_index], dict): + prop_json[array_name][array_index].update({array_name: prop}) + else: + prop_json[array_name][array_index] = {array_name: prop} + else: + prop_json = prop_json[array_name][array_index] + else: + if i == nested_names_length - 1: + prop_json[prop_name] = prop + else: + if prop_name not in prop_json: + prop_json[prop_name] = {} + prop_json = prop_json[prop_name] + + return field_json + + +# json to csv utils +def join_iter(iterable, sep_list="|"): + """Joins on iterable""" + return sep_list.join([str(p) for p in iterable]) + + +def join_dict_items(dictionary: dict, sep_key_val="=", sep_items="|"): + """Joins a mappable collection (ie dictionary) into a string + representation with specified separators for the key and value + in addition to items. + + All items are coerced to the string representation (eg if key or value + is None, this will be coerced to "None") + """ + dict_list = [] + for key, val in dictionary.items(): + key_str = str(key) + val_str = str(val) + dict_list.append(key_str + sep_key_val + val_str) + return sep_items.join(dict_list) + + +# Working with schemas +def flatten_properties(properties, parent_key="", sep=".", item_sep=r"\[\d+\]"): + """Flatten schema properties""" + properties_flattened = {} + for key, item in properties.items(): + # flattened keys + if parent_key: + flattened_key = parent_key + "." + key + else: + flattened_key = key + + if isinstance(item, MutableMapping): + props = item.get("properties") + items = item.get("items", {}).get("properties") + if props: + new_props = flatten_properties(props, parent_key=flattened_key) + properties_flattened.update(new_props) + + elif items: + new_props = flatten_properties( + items, parent_key=flattened_key + item_sep + ) + properties_flattened.update(new_props) + else: + properties_flattened[flattened_key] = item + + else: + properties_flattened[flattened_key] = item + + return properties_flattened + + +def find_prop_name(column_name, properties): + """ + Given a dictionary of json schema object properties OR a list of property names, return the + matching property name. + + This function is needed when a schema is flattened according to a json path + and converted into a regular expression for list (array) indices. + + """ + prop_match = [] + for name in list(properties): + if re.match("^" + name + "$", column_name): + prop_match.append(name) + + if len(prop_match) == 1: + return prop_match[0] + elif len(prop_match) > 1: + raise Exception( + f"Multiple matching properties found for {column_name}. Can only have one match" + ) + elif len(prop_match) == 0: + return None + else: + raise Exception(f"Unknown error when matching properties against {column_name}") diff --git a/heal/vlmd/file_utils.py b/heal/vlmd/file_utils.py new file mode 100644 index 0000000..258bf9e --- /dev/null +++ b/heal/vlmd/file_utils.py @@ -0,0 +1,89 @@ +import csv +import json +import os +from pathlib import Path + +import pandas as pd +from cdislogging import get_logger + +from heal.vlmd.config import OUTPUT_FILE_PREFIX + +logger = get_logger("vlmd-file-utils", log_level="debug") + + +# file writing +def get_output_filepath(output_dir, infile_name, output_type="auto"): + """ + Generate the output file path. + Replace path to infile_name with output_dir. + Replace suffix with 'json' if needed. + + Args: + output_dir: output directory + infile_name: the input file that dictionary is extracted from. + output_type (str): the type of file to write to, currently csv and json. + The default is "auto" which gets the type from the suffix of the input file name. + + Returns: + string with output filepath: + <output_dir>/<prefix>_<infile_name>.<suffix> + """ + + prefix = OUTPUT_FILE_PREFIX + file_name = os.path.basename(infile_name) + if output_type == "auto": + output_filepath = f"{output_dir}/{prefix}_{file_name}" + else: + file_name = os.path.splitext(file_name)[0] + output_filepath = f"{output_dir}/{prefix}_{file_name}.{output_type}" + return output_filepath + + +def write_vlmd_dict(dictionary, output_filepath, file_type="auto"): + """ + Write the json format dictionary to file. + + Args: + dictionary (dict or array): data to write to file + output_dir (path): path for output file + file_type (str): type of file to write - "auto", "csv", "json". + "auto" will get the type from the output_filepath suffix. + The default is "auto". + Returns: + True for successful write of csv or json. + None for unrecognized output type. + """ + if not output_filepath: + logger.error("Empty output_filepath in write_vlmd_dict") + raise ValueError("Empty output_filepath in write_vlmd_dict") + + output_filepath = Path(output_filepath) + + if file_type == "auto": + file_type = output_filepath.suffix.replace(".", "") + + logger.debug(f"Output dictionary type is {file_type}") + dirname = os.path.dirname(output_filepath) + if dirname != "": + os.makedirs(dirname, exist_ok=True) + + if file_type == "json": + if not isinstance(dictionary, dict): + msg = "Json output type should have dict data" + logger.error(msg) + raise ValueError(msg) + with open(output_filepath, "w") as f: + json.dump(dictionary, f, indent=4) + return True + + if file_type == "csv": + if not isinstance(dictionary, list): + msg = "CSV output type should have list data" + logger.error(msg) + raise ValueError(msg) + quoting = csv.QUOTE_MINIMAL + pd.DataFrame(dictionary).to_csv(output_filepath, quoting=quoting, index=False) + return True + + logger.warning(f"Unknown file type {file_type}") + return None diff --git a/heal/vlmd/mappings/__init__.py b/heal/vlmd/mappings/__init__.py new file mode 100644 index 0000000..8ad6be4 --- /dev/null +++ b/heal/vlmd/mappings/__init__.py @@ -0,0 +1,11 @@ +""" + +This module contains all property name and value mappings +for both the json and csv template. Note, the json property names +are represented as json paths. In v0.2 schema versions,csv field property +names must now conform to json path but in prior versions this was not the case. + +""" + +from .names import rename_map +from .values import recode_map diff --git a/heal/vlmd/mappings/names.py b/heal/vlmd/mappings/names.py new file mode 100644 index 0000000..33c3e0f --- /dev/null +++ b/heal/vlmd/mappings/names.py @@ -0,0 +1,53 @@ +condensed_rename_map = { + "type": [ + "data-type", + "data-types", + "field-type", + "field-types", + "types", + "variable-types", + "variable-type", + ], + "name": [ + "variable-name", + "variable-names", + "column-name", + "column-names", + "field-name", + "field-names", + ], + "title": [ + "variable-title", + "variable-titles", + "field-title", + "field-titles", + "field-label", + "field-labels", + "variable-label", + "variable-labels", + ], + "description": [ + "definition", + "definitions", + ], + "enumLabels": [ + "pv-description", + "permissible-value-description", + "variable-value-labels", + "value-labels", + ], + "constraints.enum": [ + "admissible-values", + "permissible-values", + "possible-values", + "enum", + "enumerated-values", + "valid-values", + ], +} + +rename_map = { + source: target + for target, source_list in condensed_rename_map.items() + for source in source_list +} diff --git a/heal/vlmd/mappings/values.py b/heal/vlmd/mappings/values.py new file mode 100644 index 0000000..5bed92d --- /dev/null +++ b/heal/vlmd/mappings/values.py @@ -0,0 +1,36 @@ +""" +defines potential source (input) values for the specified field property +that should map directly onto the target (the heal specification) without +any ambiguity +""" + + +def _flatten(map): + return {sourcename: item["target"] for item in map for sourcename in item["source"]} + + +type_map = [ + {"target": "integer", "source": ["int"]}, + { + "target": "string", + "source": [ + "str", + "character", + "char", + "text", + "varchar", + "alphanumeric", + "alphanum", + ], + }, + {"target": "number", "source": ["num", "float", "decimal", "numeric"]}, + {"target": "boolean", "source": ["bool"]}, +] +required_map = [ + {"target": True, "source": ["true", "1", "yes", "y", "required"]}, + {"target": False, "source": ["false", "0", "no", "not required", "n"]}, +] +recode_map = { + "type": _flatten(type_map), + "constraints.required": _flatten(required_map), +} diff --git a/heal/vlmd/mappings/versions.py b/heal/vlmd/mappings/versions.py new file mode 100644 index 0000000..07ff8c3 --- /dev/null +++ b/heal/vlmd/mappings/versions.py @@ -0,0 +1,101 @@ +""" +holds the mappings for previous schema version +field names (headers, properties) and the current name +in addition to these field name values or value mappings. + +""" +from heal.vlmd.config import JSON_SCHEMA + +VERSION = ["0", "3"] + +root_mappings = [ + {"target": "fields", "source": ["data_dictionary"]}, + {"target": "schemaVersion", "source": [None], "value": JSON_SCHEMA["version"]}, +] + + +field_mappings = [ + { + "target": "schemaVersion", + "source": [None], + # should be same as csv schema version + "values": JSON_SCHEMA["version"], + }, + {"target": "section", "source": ["module"]}, + {"target": "enumLabels", "source": ["encoding", "encodings"]}, + {"target": "enumOrdered", "source": ["ordered"]}, + { + "target": "standardsMappings[0].instrument.source", + "source": ["standardsMappings.type", "standardsMappings[0].source"], + }, + { + "target": "standardsMappings[0].instrument.title", + "source": ["standardsMappings.label", "standardsMappings[0].title"], + }, + { + "target": "standardsMappings[0].item.source", + "source": ["standardsMappings.source", "standardsMappings[0].source"], + }, + { + "target": "standardsMappings[0].item.id", + "source": ["standardsMappings.id", "standardsMappings[0].id"], + }, + { + "target": "standardsMappings[0].item.url", + "source": ["standardsMappings.url", "standardsMappings[0].url"], + }, + { + "target": None, + "source": ["repo_link"], + }, + { + "target": None, + "source": [ + "relatedConcepts.type", + "relatedConcepts.label", + "relatedConcepts.url", + "relatedConcepts.source", + "relatedConcepts.id", + "relatedConcepts", + ], + }, + { + "target": None, + "source": [ + "univarStats.median", + "univarStats.mean", + "univarStats.std", + "univarStats.min", + "univarStats.max", + "univarStats.mode", + "univarStats.count", + "univarStats.twentyFifthPercentile", + "univarStats.seventyFifthPercentile", + "univarStats.categoricalMarginals.name", + "univarStats.categoricalMarginals.count", + ], + }, +] + + +root_rename_map = { + source_name: item["target"] + for item in root_mappings + for source_name in item["source"] + if item["target"] and source_name +} +root_addmap = {"schemaVersion": JSON_SCHEMA["version"]} +fields_recode_map = {} +fields_rename_map = { + source_name: item["target"] + for item in field_mappings + for source_name in item["source"] + if item["target"] and source_name +} +fields_drop_list = [ + source_name + for item in field_mappings + for source_name in item["source"] + if not item["target"] and source_name +] +fields_addmap = {"schemaVersion": JSON_SCHEMA["version"]} diff --git a/heal/vlmd/schemas/heal_csv.json b/heal/vlmd/schemas/heal_csv.json new file mode 100644 index 0000000..100068d --- /dev/null +++ b/heal/vlmd/schemas/heal_csv.json @@ -0,0 +1,266 @@ +{ + "version": "0.3.2", + "title": "HEAL Variable Level Metadata Fields", + "description": "!!! note \"Highly encouraged\" - Only `name` and `description` properties are required. - For categorical variables, `constraints.enum` and `enumLabels` (where applicable) properties are highly encouraged. - For studies using HEAL or other common data elements (CDEs), `standardsMappings` information is highly encouraged. - `type` and `format` properties may be particularly useful for some variable types (e.g. date-like variables)", + "type": "object", + "required": [ + "name", + "description" + ], + "additionalProperties": false, + "properties": { + "schemaVersion": { + "type": "string", + "description": "The version of the schema used in agreed upon convention of major.minor.path (e.g., 1.0.2) NOTE: This is NOT for versioning of each indiviual data dictionary instance. Rather, it is the version of THIS schema document. See `version` property (below) if specifying the individual data dictionary instance version. If generating a vlmd document as a csv file, include this version in every row/record to indicate this is a schema level property (not applicable for the json version as this property is already at the schema/root level).", + "pattern": "\\d+\\.\\d+\\.\\d+", + "examples": [ + "1.0.0", + "0.2.0" + ] + }, + "section": { + "type": "string", + "title": "Section", + "description": "The section, form, survey instrument, set of measures or other broad category used to group variables. Previously called \"module.\"", + "examples": [ + "Demographics", + "PROMIS", + "Medical History" + ] + }, + "name": { + "type": "string", + "title": "Variable Name", + "description": "The name of a variable (i.e., field) as it appears in the data.", + "examples": [ + "gender_id" + ] + }, + "title": { + "type": "string", + "title": "Variable Label (i.e., Title)", + "description": "The human-readable title or label of the variable.", + "examples": [ + "Gender identity" + ] + }, + "description": { + "type": "string", + "title": "Variable Description", + "description": "An extended description of the variable. This could be the definition of a variable or the question text (e.g., if a survey).", + "examples": [ + "The participant's age at the time of study enrollment", + "What is the highest grade or level of school you have completed or the highest degree you have received?" + ] + }, + "type": { + "title": "Variable Type", + "type": "string", + "description": "A classification or category of a particular data element or property expected or allowed in the dataset.", + "additionalDescription": "enum definitions: - `number` (A numeric value with optional decimal places. (e.g., 3.14)) - `integer` (A whole number without decimal places. (e.g., 42)) - `string` (A sequence of characters. (e.g., \\\"test\\\")) - `any` (Any type of data is allowed. (e.g., true)) - `boolean` (A binary value representing true or false. (e.g., true)) - `date` (A specific calendar date. (e.g., \\\"2023-05-25\\\")) - `datetime` (A specific date and time, including timezone information. (e.g., \\\"2023-05-25T10:30:00Z\\\")) - `time` (A specific time of day. (e.g., \\\"10:30:00\\\")) - `year` (A specific year. (e.g., 2023) - `yearmonth` (A specific year and month. (e.g., \\\"2023-05\\\")) - `duration` (A length of time. (e.g., \\\"PT1H\\\") - `geopoint` (A pair of latitude and longitude coordinates. (e.g., [51.5074, -0.1278]))", + "enum": [ + "number", + "integer", + "string", + "any", + "boolean", + "date", + "datetime", + "time", + "year", + "yearmonth", + "duration", + "geopoint" + ] + }, + "format": { + "title": "Variable Format", + "type": "string", + "description": "Indicates the format of the type specified in the `type` property. Each format is dependent on the `type` specified. See [here](https://specs.frictionlessdata.io/table-schema/#types-and-formats) for more information about appropriate `format` values by variable `type`.", + "additionalDescription": "examples/definitions of patterns and possible values: Examples of date time pattern formats - `%Y-%m-%d` (for date, e.g., 2023-05-25) - `%Y%-%d` (for date, e.g., 20230525) for date without dashes - `%Y-%m-%dT%H:%M:%S` (for datetime, e.g., 2023-05-25T10:30:45) - `%Y-%m-%dT%H:%M:%SZ` (for datetime with UTC timezone, e.g., 2023-05-25T10:30:45Z) - `%Y-%m-%dT%H:%M:%S%z` (for datetime with timezone offset, e.g., 2023-05-25T10:30:45+0300) - `%Y-%m-%dT%H:%M` (for datetime without seconds, e.g., 2023-05-25T10:30) - `%Y-%m-%dT%H` (for datetime without minutes and seconds, e.g., 2023-05-25T10) - `%H:%M:%S` (for time, e.g., 10:30:45) - `%H:%M:%SZ` (for time with UTC timezone, e.g., 10:30:45Z) - `%H:%M:%S%z` (for time with timezone offset, e.g., 10:30:45+0300) Examples of string formats - `email` if valid emails (e.g., test@gmail.com) - `uri` if valid uri addresses (e.g., https://example.com/resource123) - `binary` if a base64 binary encoded string (e.g., authentication token like aGVsbG8gd29ybGQ=) - `uuid` if a universal unique identifier also known as a guid (eg., f47ac10b-58cc-4372-a567-0e02b2c3d479) Examples of geopoint formats The two types of formats for `geopoint` (describing a geographic point). - `array` (if 'lat,long' (e.g., 36.63,-90.20)) - `object` (if {'lat':36.63,'lon':-90.20})" + }, + "constraints.required": { + "type": "boolean", + "title": "Required variable", + "description": "If this variable is marked as true, then this variable's value must be present (i.e., not missing; see missingValues). If marked as false or not present, then the variable CAN be missing." + }, + "constraints.maxLength": { + "type": "integer", + "title": "Maximum Length", + "description": "Indicates the maximum length of an iterable (e.g., array, string, or object). For example, if 'Hello World' is the longest value of a categorical variable, this would be a maxLength of 11." + }, + "constraints.enum": { + "type": "string", + "title": "Variable Possible Values", + "description": "Constrains possible values to a set of values.", + "examples": [ + "1|2|3|4|5", + "Poor|Fair|Good|Very good|Excellent" + ], + "pattern": "^(?:[^|]+\\||[^|]*)(?:[^|]*\\|)*[^|]*$" + }, + "constraints.pattern": { + "type": "string", + "title": "Regular Expression Pattern", + "description": "A regular expression pattern the data MUST conform to." + }, + "constraints.maximum": { + "type": "integer", + "title": "Maximum Value", + "description": "Specifies the maximum value of a field (e.g., maximum -- or most recent -- date, maximum integer etc). Note, this is different then maxLength property." + }, + "constraints.minimum": { + "type": "integer", + "title": "Minimum Value", + "description": "Specifies the minimum value of a field." + }, + "enumLabels": { + "title": "Variable Value Encodings (i.e., mappings; value labels)", + "description": "Variable value encodings provide a way to further annotate any value within a any variable type, making values easier to understand. Many analytic software programs (e.g., SPSS,Stata, and SAS) use numerical encodings and some algorithms only support numerical values. Encodings (and mappings) allow categorical values to be stored as numerical values. Additionally, as another use case, this field provides a way to store categoricals that are stored as \"short\" labels (such as abbreviations). This field is intended to follow [this pattern](https://specs.frictionlessdata.io/patterns/#table-schema-enum-labels-and-ordering)", + "type": "string", + "examples": [ + "1=Poor|2=Fair|3=Good|4=Very good|5=Excellent", + "HW=Hello world|GBW=Good bye world|HM=Hi, Mike" + ], + "pattern": "^(?:.*?=.*?(?:\\||$))+$" + }, + "enumOrdered": { + "title": "An ordered variable", + "description": "Indicates whether a categorical variable is ordered. This variable is relevant for variables that have an ordered relationship but not necessarily a numerical relationship (e.g., Strongly disagree < Disagree < Neutral < Agree). This field is intended to follow the ordering aspect of this [this pattern][this pattern](https://specs.frictionlessdata.io/patterns/#table-schema-enum-labels-and-ordering)", + "type": "boolean" + }, + "missingValues": { + "title": "Missing Values", + "description": "A list of missing values specific to a variable.", + "examples": [ + "Missing|Skipped|No preference", + "Missing" + ], + "type": "string", + "pattern": "^(?:[^|]+\\||[^|]*)(?:[^|]*\\|)*[^|]*$" + }, + "trueValues": { + "title": "Boolean True Value Labels", + "description": "For boolean (true) variable (as defined in type field), this field allows a physical string representation to be cast as true (increasing readability of the field). It can include one or more values.", + "type": "string", + "examples": [ + "required|Yes|Checked", + "required" + ], + "pattern": "^(?:[^|]+\\||[^|]*)(?:[^|]*\\|)*[^|]*$" + }, + "falseValues": { + "title": "Boolean False Value Labels", + "description": "For boolean (false) variable (as defined in type field), this field allows a physical string representation to be cast as false (increasing readability of the field) that is not a standard false value. It can include one or more values.", + "type": "string", + "examples": [ + "Not required|NOT REQUIRED", + "No" + ], + "pattern": "^(?:[^|]+\\||[^|]*)(?:[^|]*\\|)*[^|]*$" + }, + "custom": { + "type": "string", + "description": "Additional properties not included a core property.", + "pattern": "^(?:.*?=.*?(?:\\||$))+$" + } + }, + "patternProperties": { + "^standardsMappings\\[\\d+\\].instrument.url$": { + "title": "Url", + "description": "A url (e.g., link, address) to a file or other resource containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "type": "string", + "format": "uri", + "examples": [ + "https://www.heal.nih.gov/files/CDEs/2023-05/adult-demographics-cdes.xlsx" + ] + }, + "^standardsMappings\\[\\d+\\].instrument.source$": { + "type": "string", + "title": "Source", + "description": "An abbreviated name/acronym from a controlled vocabulary referencing the resource (e.g., program or repository) containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "enum": [ + "heal-cde" + ] + }, + "^standardsMappings\\[\\d+\\].instrument.title$": { + "type": "string", + "title": "Title", + "examples": [ + "Adult demographics", + "adult-demographics" + ], + "description": "" + }, + "^standardsMappings\\[\\d+\\].instrument.id$": { + "type": "string", + "title": "Identifier", + "description": "A code or other string that identifies the instrument within the source. This should always be from the source's formal, standardized identification system", + "examples": [ + "5141" + ] + }, + "^standardsMappings\\[\\d+\\].item.url$": { + "title": "Standards mappings - Url", + "description": "The url that links out to the published, standardized mapping of a variable (e.g., common data element)", + "type": "string", + "format": "uri", + "examples": [ + "https://evs.nci.nih.gov/ftp1/CDISC/SDTM/SDTM%20Terminology.html#CL.C74457.RACE" + ] + }, + "^standardsMappings\\[\\d+\\].item.source$": { + "title": "Standards mappings - Source", + "description": "The source of the standardized variable. Note, this property is required if an id is specified.", + "examples": [ + "CDISC" + ], + "type": "string" + }, + "^standardsMappings\\[\\d+\\].item.id$": { + "title": "Standards Mappings - Id", + "type": "string", + "description": "The id locating the individual mapping within the given source. Note, the `standardsMappings[0].source` property is required if this property is specified.", + "examples": [ + "C74457" + ] + }, + "^relatedConcepts\\[\\d+\\].url$": { + "title": "Related Concepts - Url", + "description": "The url that links out to the published, related concept. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "format": "uri", + "examples": [ + "https://www.ebi.ac.uk/chebi/chebiOntology.do?chebiId=CHEBI:27808", + "http://purl.bioontology.org/ontology/RXNORM/3304" + ] + }, + "^relatedConcepts\\[\\d+\\].title$": { + "title": "Related concepts - Type", + "description": "A human-readable title (i.e., label) to a concept related to the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "examples": [ + "Heroin Molecular Structure", + "Heroin Ontology" + ] + }, + "^relatedConcepts\\[\\d+\\].source$": { + "title": "Related Concepts - Source", + "description": "The source (e.g., a dictionary or vocabulary set) to a concept related to the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "examples": [ + "CHEBI", + "RXNORM" + ] + }, + "^relatedConcepts\\[\\d+\\].id$": { + "title": "Related Concepts - Id", + "type": "string", + "description": "The id locating the individual concept within the source of the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "examples": [ + "27808", + "3304" + ] + } + } +} diff --git a/heal/vlmd/schemas/heal_json.json b/heal/vlmd/schemas/heal_json.json new file mode 100644 index 0000000..7b8fd33 --- /dev/null +++ b/heal/vlmd/schemas/heal_json.json @@ -0,0 +1,438 @@ +{ + "version": "0.3.2", + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "vlmd", + "title": "Variable Level Metadata (Data Dictionaries)", + "description": "This schema defines the variable level metadata for one data dictionary for a given study.Note a given study can have multiple data dictionaries.", + "type": "object", + "required": [ + "title", + "fields" + ], + "additionalProperties": false, + "properties": { + "title": { + "type": "string" + }, + "description": { + "type": "string" + }, + "schemaVersion": { + "type": "string", + "description": "The version of the schema used in agreed upon convention of major.minor.path (e.g., 1.0.2) NOTE: This is NOT for versioning of each indiviual data dictionary instance. Rather, it is the version of THIS schema document. See `version` property (below) if specifying the individual data dictionary instance version. If generating a vlmd document as a csv file, include this version in every row/record to indicate this is a schema level property (not applicable for the json version as this property is already at the schema/root level)", + "pattern": "\\d+\\.\\d+\\.\\d+", + "examples": [ + "1.0.0", + "0.2.0" + ] + }, + "version": { + "type": "string", + "description": "The specified individual data dictionary instance version." + }, + "standardsMappings": { + "type": "array", + "description": "A set of standardized instruments linked to all variables within the `fields` property (but see note). !!! note \"NOTE\" If `standardsMappings` is present at both the root (this property) and within `fields`, then the `fields` `standardsMappings` property takes precedence. Note, only instrument can be mapped to this property as opposed to the `fields` `standardsMappings` This property has the same specification as the `fields` `standardsMappings` to make the cascading logic easier to understand in the same way other standards implement cascading (e.g., `missingValues` in the [frictionless specification](https://specs.frictionlessdata.io/patterns/#missing-values-per-field))", + "items": { + "type": "object", + "properties": { + "instrument": { + "type": "object", + "title": "Standard mapping - instrument", + "description": "A standardized set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level). !!! note \"NOTE\" If information is present at both the root and the field level, then the information at the field level would take precedence (i.e., it would cascade).", + "properties": { + "url": { + "title": "Url", + "description": "A url (e.g., link, address) to a file or other resource containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "type": "string", + "format": "uri", + "examples": [ + "https://www.heal.nih.gov/files/CDEs/2023-05/adult-demographics-cdes.xlsx" + ] + }, + "source": { + "type": "string", + "title": "Source", + "description": "An abbreviated name/acronym from a controlled vocabulary referencing the resource (e.g., program or repository) containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "enum": [ + "heal-cde" + ] + }, + "title": { + "type": "string", + "title": "Title", + "examples": [ + "Adult demographics", + "adult-demographics" + ] + }, + "id": { + "type": "string", + "title": "Identifier", + "description": "A code or other string that identifies the instrument within the source. This should always be from the source's formal, standardized identification system", + "examples": [ + "5141" + ] + } + } + } + } + } + }, + "custom": { + "type": "object", + "description": "Additional properties not included as a core property." + }, + "fields": { + "type": "array", + "items": { + "title": "HEAL Variable Level Metadata Fields", + "description": " !!! note \"Highly encouraged\" - Only `name` and `description` properties are required. - For categorical variables, `constraints.enum` and `enumLabels` (where applicable) properties are highly encouraged. - For studies using HEAL or other common data elements (CDEs), `standardsMappings` information is highly encouraged. - `type` and `format` properties may be particularly useful for some variable types (e.g. date-like variables)", + "type": "object", + "required": [ + "name", + "description" + ], + "additionalProperties": false, + "properties": { + "schemaVersion": { + "type": "string", + "description": "The version of the schema used in agreed upon convention of major.minor.path (e.g., 1.0.2) NOTE: This is NOT for versioning of each indiviual data dictionary instance. Rather, it is the version of THIS schema document. See `version` property (below) if specifying the individual data dictionary instance version. If generating a vlmd document as a csv file, include this version in every row/record to indicate this is a schema level property (not applicable for the json version as this property is already at the schema/root level)", + "pattern": "\\d+\\.\\d+\\.\\d+", + "examples": [ + "1.0.0", + "0.2.0" + ] + }, + "section": { + "type": "string", + "title": "Section", + "description": "The section, form, survey instrument, set of measures or other broad category used to group variables. Previously called \"module.\"", + "examples": [ + "Demographics", + "PROMIS", + "Medical History" + ] + }, + "name": { + "type": "string", + "title": "Variable Name", + "description": "The name of a variable (i.e., field) as it appears in the data.", + "examples": [ + "gender_id" + ] + }, + "title": { + "type": "string", + "title": "Variable Label (i.e., Title)", + "description": "The human-readable title or label of the variable.", + "examples": [ + "Gender identity" + ] + }, + "description": { + "type": "string", + "title": "Variable Description", + "description": "An extended description of the variable. This could be the definition of a variable or the question text (e.g., if a survey).", + "examples": [ + "The participant's age at the time of study enrollment", + "What is the highest grade or level of school you have completed or the highest degree you have received?" + ] + }, + "type": { + "title": "Variable Type", + "type": "string", + "description": "A classification or category of a particular data element or property expected or allowed in the dataset.", + "additionalDescription": "enum definitions: - `number` (A numeric value with optional decimal places. (e.g., 3.14)) - `integer` (A whole number without decimal places. (e.g., 42)) - `string` (A sequence of characters. (e.g., \\\"test\\\")) - `any` (Any type of data is allowed. (e.g., true)) - `boolean` (A binary value representing true or false. (e.g., true)) - `date` (A specific calendar date. (e.g., \\\"2023-05-25\\\")) - `datetime` (A specific date and time, including timezone information. (e.g., \\\"2023-05-25T10:30:00Z\\\")) - `time` (A specific time of day. (e.g., \\\"10:30:00\\\")) - `year` (A specific year. (e.g., 2023) - `yearmonth` (A specific year and month. (e.g., \\\"2023-05\\\")) - `duration` (A length of time. (e.g., \\\"PT1H\\\") - `geopoint` (A pair of latitude and longitude coordinates. (e.g., [51.5074, -0.1278]))", + "enum": [ + "number", + "integer", + "string", + "any", + "boolean", + "date", + "datetime", + "time", + "year", + "yearmonth", + "duration", + "geopoint" + ] + }, + "format": { + "title": "Variable Format", + "type": "string", + "description": "Indicates the format of the type specified in the `type` property. Each format is dependent on the `type` specified. See [here](https://specs.frictionlessdata.io/table-schema/#types-and-formats) for more information about appropriate `format` values by variable `type`.", + "additionalDescription": "examples/definitions of patterns and possible values: Examples of date time pattern formats - `%Y-%m-%d` (for date, e.g., 2023-05-25) - `%Y%-%d` (for date, e.g., 20230525) for date without dashes - `%Y-%m-%dT%H:%M:%S` (for datetime, e.g., 2023-05-25T10:30:45) - `%Y-%m-%dT%H:%M:%SZ` (for datetime with UTC timezone, e.g., 2023-05-25T10:30:45Z) - `%Y-%m-%dT%H:%M:%S%z` (for datetime with timezone offset, e.g., 2023-05-25T10:30:45+0300) - `%Y-%m-%dT%H:%M` (for datetime without seconds, e.g., 2023-05-25T10:30) - `%Y-%m-%dT%H` (for datetime without minutes and seconds, e.g., 2023-05-25T10) - `%H:%M:%S` (for time, e.g., 10:30:45) - `%H:%M:%SZ` (for time with UTC timezone, e.g., 10:30:45Z) - `%H:%M:%S%z` (for time with timezone offset, e.g., 10:30:45+0300) Examples of string formats - `email` if valid emails (e.g., test@gmail.com) - `uri` if valid uri addresses (e.g., https://example.com/resource123) - `binary` if a base64 binary encoded string (e.g., authentication token like aGVsbG8gd29ybGQ=) - `uuid` if a universal unique identifier also known as a guid (eg., f47ac10b-58cc-4372-a567-0e02b2c3d479) Examples of geopoint formats The two types of formats for `geopoint` (describing a geographic point). - `array` (if 'lat,long' (e.g., 36.63,-90.20)) - `object` (if {'lat':36.63,'lon':-90.20})" + }, + "constraints": { + "type": "object", + "properties": { + "required": { + "type": "boolean", + "title": "Required variable", + "description": "If this variable is marked as true, then this variable's value must be present (i.e., not missing; see missingValues). If marked as false or not present, then the variable CAN be missing." + }, + "maxLength": { + "type": "integer", + "title": "Maximum Length", + "description": "Indicates the maximum length of an iterable (e.g., array, string, or object). For example, if 'Hello World' is the longest value of a categorical variable, this would be a maxLength of 11." + }, + "enum": { + "type": "array", + "title": "Variable Possible Values", + "description": "Constrains possible values to a set of values.", + "examples": [ + [ + 1, + 2, + 3, + 4, + 5 + ], + [ + "Poor", + "Fair", + "Good", + "Very good", + "Excellent" + ] + ] + }, + "pattern": { + "type": "string", + "title": "Regular Expression Pattern", + "description": "A regular expression pattern the data MUST conform to." + }, + "maximum": { + "type": "integer", + "title": "Maximum Value", + "description": "Specifies the maximum value of a field (e.g., maximum -- or most recent -- date, maximum integer etc). Note, this is different then maxLength property." + }, + "minimum": { + "type": "integer", + "title": "Minimum Value", + "description": "Specifies the minimum value of a field." + } + } + }, + "enumLabels": { + "title": "Variable Value Encodings (i.e., mappings; value labels)", + "description": "Variable value encodings provide a way to further annotate any value within a any variable type, making values easier to understand. Many analytic software programs (e.g., SPSS,Stata, and SAS) use numerical encodings and some algorithms only support numerical values. Encodings (and mappings) allow categorical values to be stored as numerical values. Additionally, as another use case, this field provides a way to store categoricals that are stored as \"short\" labels (such as abbreviations). This field is intended to follow [this pattern](https://specs.frictionlessdata.io/patterns/#table-schema-enum-labels-and-ordering)", + "type": "object", + "examples": [ + { + "1": "Poor", + "2": "Fair", + "3": "Good", + "4": "Very good", + "5": "Excellent" + }, + { + "HW": "Hello world", + "GBW": "Good bye world", + "HM": "Hi, Mike" + } + ] + }, + "enumOrdered": { + "title": "An ordered variable", + "description": "Indicates whether a categorical variable is ordered. This variable is relevant for variables that have an ordered relationship but not necessarily a numerical relationship (e.g., Strongly disagree < Disagree < Neutral < Agree). This field is intended to follow the ordering aspect of this [this pattern][this pattern](https://specs.frictionlessdata.io/patterns/#table-schema-enum-labels-and-ordering)", + "type": "boolean" + }, + "missingValues": { + "title": "Missing Values", + "description": "A list of missing values specific to a variable.", + "examples": [ + [ + "Missing", + "Skipped", + "No preference" + ], + [ + "Missing" + ] + ], + "type": "array" + }, + "trueValues": { + "title": "Boolean True Value Labels", + "description": "For boolean (true) variable (as defined in type field), this field allows a physical string representation to be cast as true (increasing readability of the field). It can include one or more values.", + "type": "array", + "examples": [ + [ + "required", + "Yes", + "Checked" + ], + [ + "required" + ] + ] + }, + "falseValues": { + "title": "Boolean False Value Labels", + "description": "For boolean (false) variable (as defined in type field), this field allows a physical string representation to be cast as false (increasing readability of the field) that is not a standard false value. It can include one or more values.", + "type": "array", + "examples": [ + [ + "Not required", + "NOT REQUIRED" + ], + [ + "No" + ] + ] + }, + "custom": { + "type": "object", + "description": "Additional properties not included a core property." + }, + "standardsMappings": { + "type": "array", + "description": "A set of instrument and item references to standardized data elements designed to document the [HEAL common data elements program](https://heal.nih.gov/data/common-data-elements) and other standardized/common element sources to facilitate cross-study comparison and interoperability of data. One can either map an individual data element or an instrument in which the field is a part of. __**All Fields Mapped (Both Instrument and Item)**__ ```json \"standardsMappings\": [ { \"instrument\": { \"url\": \"https://www.heal.nih.gov/files/CDEs/2023-05/adult-demographics-cdes.xlsx\", \"source\": \"heal-cde\", \"title\": \"adult-demographics\", \"id\": \"5141\" }, \"item\": { \"url\": \"https://evs.nci.nih.gov/ftp1/CDISC/SDTM/SDTM%20Terminology.html#CL.C74457.RACE\", \"source\": \"CDISC\", \"id\": \"C74457\" } } ] ``` __**Only Instrument Title of Form CDE File Mapped**__ In this scenario, especially as CDE variables do not have associated CDISC ids listed, only instrument information is given. ```json \"standardsMappings\": [ { \"instrument\": { \"source\": \"heal-cde\", \"title\": \"Adult demographics\" } } ] ``` __**Only Instrument ID of HEAL CDE Mapped**__ ```json \"standardsMappings\": [ { \"instrument\": { \"source\": \"heal-cde\", \"id\": \"5141\" } } ] ``` __**Other Non-HEAL CDE Use Cases**__ Only item matched (for example if found in the NIH (not HEAL) CDE repository). Folks would enter the information in the \"Identifier\" section. Similar to the above, they could also just enter the \"url\". ```json \"standardsMappings\": [ { \"item\": { \"source\": \"NLM\", \"id\": \"Fakc6Jy2x\" } } ] ``` __**Multiple CDE Mappings**__ Two separate records. If desired, multiple standard mappings can be entered, say from the NIH HEAL CDE repo and the NIH CDE lookup (NLM) by way of two separate records in the list. ```json \"standardsMappings\": [ { \"instrument\": { \"source\": \"heal-cde\", \"title\": \"Adult demographics\" }, \"item\": { \"source\": \"CDISC\", \"id\": \"C74457\" }, }, { \"item\": { \"source\": \"NLM\", \"id\": \"Fakc6Jy2x\" } } ] ```", + "items": { + "type": "object", + "properties": { + "instrument": { + "type": "object", + "title": "Standard mapping - instrument", + "description": "A standardized set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level). !!! note \"NOTE\" If information is present at both the root and the field level, then the information at the field level would take precedence (i.e., it would cascade).", + "properties": { + "url": { + "title": "Url", + "description": "A url (e.g., link, address) to a file or other resource containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "type": "string", + "format": "uri", + "examples": [ + "https://www.heal.nih.gov/files/CDEs/2023-05/adult-demographics-cdes.xlsx" + ] + }, + "source": { + "type": "string", + "title": "Source", + "description": "An abbreviated name/acronym from a controlled vocabulary referencing the resource (e.g., program or repository) containing the instrument, or a set of items which encompass a variable in this variable level metadata document (if at the root level or the document level) or the individual variable (if at the field level).", + "enum": [ + "heal-cde" + ] + }, + "title": { + "type": "string", + "title": "Title", + "examples": [ + "Adult demographics", + "adult-demographics" + ] + }, + "id": { + "type": "string", + "title": "Identifier", + "description": "A code or other string that identifies the instrument within the source. This should always be from the source's formal, standardized identification system", + "examples": [ + "5141" + ] + } + } + }, + "item": { + "type": "object", + "title": "Standards mappings - Item", + "description": "A standardized item (i.e., field, variable etc) mapped to this individual variable.", + "properties": { + "url": { + "title": "Standards mappings - Url", + "description": "The url that links out to the published, standardized mapping of a variable (e.g., common data element)", + "type": "string", + "format": "uri", + "examples": [ + "https://evs.nci.nih.gov/ftp1/CDISC/SDTM/SDTM%20Terminology.html#CL.C74457.RACE" + ] + }, + "source": { + "title": "Standards mappings - Source", + "description": "The source of the standardized variable. Note, this property is required if an id is specified.", + "examples": [ + "CDISC" + ], + "type": "string" + }, + "id": { + "title": "Standards Mappings - Id", + "type": "string", + "description": "The id locating the individual mapping within the given source. Note, the `standardsMappings[0].source` property is required if this property is specified.", + "examples": [ + "C74457" + ] + } + } + } + } + } + }, + "relatedConcepts": { + "title": "Related Concepts", + "description": "__**[Under development]**__ Mappings to a published set of concepts related to the given field such as ontological information (eg., NCI thesaurus, bioportal etc)", + "type": "array", + "items": { + "type": "object", + "properties": { + "url": { + "title": "Related Concepts - Url", + "description": "The url that links out to the published, related concept. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "format": "uri", + "examples": [ + "https://www.ebi.ac.uk/chebi/chebiOntology.do?chebiId=CHEBI:27808", + "http://purl.bioontology.org/ontology/RXNORM/3304" + ] + }, + "title": { + "title": "Related concepts - Type", + "description": "A human-readable title (i.e., label) to a concept related to the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "examples": [ + "Heroin Molecular Structure", + "Heroin Ontology" + ] + }, + "source": { + "title": "Related Concepts - Source", + "description": "The source (e.g., a dictionary or vocabulary set) to a concept related to the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "type": "string", + "examples": [ + "CHEBI", + "RXNORM" + ] + }, + "id": { + "title": "Related Concepts - Id", + "type": "string", + "description": "The id locating the individual concept within the source of the given field. The listed examples could both be attached to any variable related to, for example, heroin use.", + "examples": [ + "27808", + "3304" + ] + } + } + } + } + } + } + } + }, + "propertyNames": { + "description": "To allow additional properties for compatibility with other standards at the \"table\", or root, but not included in the core `properties` set: [Frictionless Data package table schema standard](https://specs.frictionlessdata.io/table-schema): `missingValues`|`primaryKey`|`foreignKeys`", + "enum": [ + "title", + "description", + "schemaVersion", + "version", + "standardsMappings", + "fields", + "custom", + "missingValues", + "primaryKey", + "foreignKeys" + ] + } +} diff --git a/heal/vlmd/utils.py b/heal/vlmd/utils.py new file mode 100644 index 0000000..e0d2b1e --- /dev/null +++ b/heal/vlmd/utils.py @@ -0,0 +1,78 @@ +def add_missing_type(prop_name: str, prop, schema: dict) -> dict: + """ + Add types to properties. + + Args: + prop_name (str): property name + prop (str or dict): property + schema (dict): schema + + Returns: + schema (dict) + + """ + missing_values = ["", None] # NOTE: include physical rep and logical for now + if prop_name in schema.get("required", []): + # if required value: MUST be NOT missing value and the property + newprop = {"allOf": [prop, {"not": {"enum": missing_values}}]} + else: + # if not required value: MUST be property OR the specified missing value + newprop = {"anyOf": [prop, {"enum": missing_values}]} + return newprop + + +def add_types_to_props(schema: dict) -> dict: + """ + Add missing types to the schema for validating csv style data. + + Args: + schema (dict) + + Returns: + schema (dict) + """ + + props_with_missing = {} + for prop_name, prop in schema.get("items", {}).get("properties", {}).items(): + props_with_missing[prop_name] = add_missing_type(prop_name, prop, schema) + + patterns_with_missing = {} + for pattern_name, prop in ( + schema.get("items", {}).get("patternProperties", {}).items() + ): + patterns_with_missing[pattern_name] = add_missing_type( + pattern_name, prop, schema + ) + + schema = {"type": "array", "items": {}} + schema["items"]["properties"] = props_with_missing + schema["items"]["patternProperties"] = patterns_with_missing + + return schema + + +def remove_empty_props(props: dict) -> dict: + """ + Remove items with empty values from a dict, ie, + from a 'field' dict in a json dictionary. + """ + if isinstance(props, dict): + new_dict = {} + for k, v in props.items(): + cleaned_value = remove_empty_props(v) + if cleaned_value or cleaned_value == 0: + new_dict[k] = cleaned_value + return new_dict + return props + + +def clean_json_fields(fields: list) -> list: + """ + Remove any 'fields' with emtpy values. + Can be used for json dictionaries that have been extracted from csv dictionaries. + """ + cleaned_fields = [] + for field in fields: + new_field = remove_empty_props(field) + cleaned_fields.append(new_field) + return cleaned_fields diff --git a/heal/vlmd/validate/json_validator.py b/heal/vlmd/validate/json_validator.py new file mode 100644 index 0000000..92ab983 --- /dev/null +++ b/heal/vlmd/validate/json_validator.py @@ -0,0 +1,55 @@ +import json +from os import PathLike +from pathlib import Path +from typing import Dict + +import jsonschema +from cdislogging import get_logger + +from heal.vlmd.config import ALLOWED_SCHEMA_TYPES +from heal.vlmd.validate.utils import get_schema + +logger = get_logger("json-validator", log_level="debug") + + +def read_data_from_json_file(input_file: str) -> Dict: + """Loads the data from a json input file""" + data = json.loads(Path(input_file).read_text()) + return data + + +def vlmd_validate_json(data_or_path, schema_type: str) -> bool: + """ + Validate json file against specified schema type. + + Args: + data_or_path: the path of the input HEAL VLMD file that to be validated + schema_type (str): the type of the schema that can be validated against. + Allowed values for now are “csv”, “tsv”, “json” and “auto”. Defaults to “auto” + + Returns: + True for valid data and schema. + Raises jsonschema.ValidationError if data is not valid + or jsonschema.SchemaError if schema is not valid + """ + + if schema_type not in ALLOWED_SCHEMA_TYPES: + raise ValueError(f"Schema type must be in {ALLOWED_SCHEMA_TYPES}") + + if isinstance(data_or_path, (str, PathLike)): + logger.debug("Getting json data from file") + data = read_data_from_json_file(data_or_path) + else: + logger.debug("Getting json data from object") + data = data_or_path + + schema = get_schema(data_or_path, schema_type) + if schema is None: + raise ValueError(f"Could not get schema for type = {schema_type}") + logger.debug("Checking schema") + jsonschema.validators.Draft7Validator.check_schema(schema) + + logger.debug("Validating data") + jsonschema.validate(instance=data, schema=schema) + + return True diff --git a/heal/vlmd/validate/utils.py b/heal/vlmd/validate/utils.py new file mode 100644 index 0000000..67f3b69 --- /dev/null +++ b/heal/vlmd/validate/utils.py @@ -0,0 +1,101 @@ +import json +import os +from pathlib import Path +from typing import Dict + +import charset_normalizer +import pandas as pd +from cdislogging import get_logger + +from heal.vlmd.config import CSV_SCHEMA, JSON_SCHEMA + +logger = get_logger("validate-utils", log_level="debug") + + +def detect_file_encoding(file_path): + """ + detects file encoding using charset_normalizer package + """ + + with open(file_path, "rb") as f: + data = f.read() + encoding_for_input = charset_normalizer.detect(data) + + is_confident = encoding_for_input["confidence"] == 1 + if not is_confident: + logger.warning("Be careful, the detected file encoding for:") + logger.warning(f"{file_path}") + logger.warning(r"has less than 100% confidence") + + return encoding_for_input["encoding"] + + +def read_delim(file_path, cast_d_type="string"): + """ + reads in a tabular file (ie spreadsheet) after detecting + encoding and file extension without any type casting. + + currently supports csv and tsv + + defaults to not casting values (ie all columns are string dtypes) + and not parsing strings into NA values (eg "" is kept as "") + """ + ext = Path(file_path).suffix + if ext == ".csv": + sep = "," + elif ext == ".tsv": + sep = "\t" + else: + raise ValueError("Delimited file must be csv or tsv") + + encoding = detect_file_encoding(file_path) + file_encoding = pd.read_csv( + file_path, sep=sep, encoding=encoding, dtype=cast_d_type, keep_default_na=False + ) + + return file_encoding + + +def read_data_from_json_file(input_file: str) -> Dict: + """Loads the data from a json input file""" + data = json.loads(Path(input_file).read_text()) + return data + + +def get_schema(data_or_path, schema_type: str): + """ + Get the schema for the specified schema_type. + Use the input_file suffix for "auto". + + Args: + data_or_path: json dictionary or path to input file + schema_type (str): the type of the schema that can be validated against. + Allowed values for now are “csv”, “tsv”, “json” and “auto”. Defaults to “auto” + Returns: + schema (dict) + """ + + if isinstance(data_or_path, (str, os.PathLike)): + dictionary_type = Path(data_or_path).suffix.replace(".", "") + elif isinstance(data_or_path, dict): + dictionary_type = "json" + elif isinstance(data_or_path, list): + dictionary_type = "csv" + else: + logger.error("Cannot get schema. Input is not path or dict or list") + raise ValueError("Input should be path or dict or list") + + if schema_type == "csv" or (schema_type == "auto" and dictionary_type == "csv"): + schema = {"type": "array", "items": CSV_SCHEMA} + logger.debug("Validator will use CSV schema") + return schema + if schema_type == "tsv" or (schema_type == "auto" and dictionary_type == "tsv"): + schema = {"type": "array", "items": CSV_SCHEMA} + logger.debug("Validator will use CSV schema for TSV file") + return schema + if schema_type == "json" or (schema_type == "auto" and dictionary_type == "json"): + schema = JSON_SCHEMA + logger.debug("Validator will use json schema") + return schema + + return None diff --git a/heal/vlmd/validate/validate.py b/heal/vlmd/validate/validate.py new file mode 100644 index 0000000..e4b23be --- /dev/null +++ b/heal/vlmd/validate/validate.py @@ -0,0 +1,153 @@ +from os.path import isfile +from pathlib import Path + +import jsonschema +from cdislogging import get_logger +from jsonschema import ValidationError + +from heal.vlmd.config import ( + ALLOWED_INPUT_TYPES, + ALLOWED_OUTPUT_TYPES, + ALLOWED_SCHEMA_TYPES, +) +from heal.vlmd.extract.conversion import convert_to_vlmd +from heal.vlmd.utils import add_types_to_props +from heal.vlmd.validate.utils import get_schema, read_data_from_json_file, read_delim + +logger = get_logger("vlmd-validate-extract", log_level="debug") + + +class ExtractionError(Exception): + pass + + +file_type_to_fxn_map = { + "csv": "csv-data-dict", + "json": "json-template", + "tsv": "csv-data-dict", +} + + +def vlmd_validate( + input_file: str, + schema_type="auto", + output_type="json", + return_converted_output=False, +): + """ + Validates the input file against a VLMD schema. + + Args: + + input_file (str): the path of the input HEAL VLMD file. + 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. + output_type (str): format of the converted dictionary: "csv" or "json. + The default is "json". + return_converted_output (bool): set to True to get converted output, else + get a boolean for valid/invalid input. + + Returns: + True if input is valid and return_converted_output=False. + Returns a converted dictionary if input is valid and return_converted_output=True. + 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.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: + raise ValueError(f"Input file must be one of {ALLOWED_INPUT_TYPES}") + if not isfile(input_file): + raise IOError(f"Input file does not exist: {input_file}") + + if schema_type not in ALLOWED_SCHEMA_TYPES: + raise ValueError(f"Schema type must be in {ALLOWED_SCHEMA_TYPES}") + schema = get_schema(input_file, schema_type) + if schema is None: + raise ValueError(f"Could not get schema for type = {schema_type}") + + output_type = output_type if output_type else "json" + if output_type not in ALLOWED_OUTPUT_TYPES: + raise ValueError( + f"Unrecognized output_type '{output_type}' - should be in {ALLOWED_OUTPUT_TYPES}" + ) + + # 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") + jsonschema.validators.Draft7Validator.check_schema(schema) + + # read the input file + if file_suffix in ["csv", "tsv"]: + logger.debug("Getting csv data from file") + data = read_delim(input_file).to_dict(orient="records") + if len(data) == 0: + raise ValidationError("Could not read csv data from input") + elif file_suffix == "json": + logger.debug("Getting json data from file") + data = read_data_from_json_file(input_file) + + # if json then try a validation + if file_suffix == "json": + logger.debug("Validating json data") + try: + jsonschema.validate(instance=data, schema=schema) + except jsonschema.ValidationError as e: + 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 ExtractionError( + f"Could not get conversion function from file_suffix '{file_suffix}'" + ) + data_dictionaries = {} + logger.debug(f"Verifying vlmd can be converted using input_type '{input_type}'") + data_dictionary_props = {} + try: + data_dictionaries = convert_to_vlmd( + input_filepath=input_file, + input_type=input_type, + data_dictionary_props=data_dictionary_props, + ) + except Exception as e: + logger.error(f"Error in converting dictionary from {input_file}") + logger.error(e) + raise ExtractionError(str(e)) + if output_type == "json": + converted_dictionary = data_dictionaries["template_json"] + elif output_type == "csv": + converted_dictionary = data_dictionaries["template_csv"]["fields"] + + # get a new schema if the output_type is different than input file_type. + if output_type != file_suffix and not ( + file_suffix == "tsv" and output_type == "csv" + ): + logger.debug( + f"Getting schema for '{output_type}' to validate converted dictionary" + ) + 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: + raise ValueError(f"Could not get schema for type = {schema_type}") + + try: + jsonschema.validate(instance=converted_dictionary, schema=schema) + except jsonschema.ValidationError as e: + logger.error("Error in validating converted dictionary") + raise e + + if return_converted_output: + return converted_dictionary + else: + return True diff --git a/notebooks/NDA_data_package_download.ipynb b/notebooks/NDA_data_package_download.ipynb new file mode 100644 index 0000000..9671671 --- /dev/null +++ b/notebooks/NDA_data_package_download.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "95392afb-c92b-442a-b769-1eebd9b6b928", + "metadata": {}, + "source": [ + "## NIMH Data Archive Data Access\n", + "\n", + "In this notebook a HEAL user can access a NIMH Data Archive (NDA) data package using a miNDAR data package ID, a NDA username and password for the data package, and the NDA API.\n", + "\n", + "Before using this notebook, the user must have been granted access to the dataset they wish to download, and have created a miNDAR for the data package. (A miNDAR is a cloud-based Oracle database that contains a copy of a data package.) \n", + "\n", + "* You can request access to a NDA dataset from the [Data Permissions tab on the NDA site](https://nda.nih.gov/user/dashboard/data_permissions.html). If you do not have an NDA account, you can create one by first logging in using your NIH login credentials.\n", + "* Once you have access, you can create a miNDAR from the data package by [following the instructions here](https://docs.google.com/document/d/1-DuyUke3I7CK_riRvVDIesgtnjhJBQVlDu9tQpe2JYo/edit?usp=sharing). \n", + "\n", + "Once you have a miNDAR for your dataset, the only inputs required from the user of this notebook is to create a nimh_credentials.json file in the second cell of this notebok using your NDA NIMH username and password and to define the miNDAR data package ID in the third cell of this notebook. \n", + "\n", + "* The **miNDAR ID** can be found in the `ID` column on the [Data Packages tab of the NDA portal](https://nda.nih.gov/user/dashboard/packages.html) \n", + "* Data packages and their corresponding **miNDAR IDs** expire after 60 days and must be generated again in order to download the data.\n", + "\n", + "Please note: Users are responsible for complying with all aspects of their Data Use Agreement, including deleting any accessed data in accordance with the parameters specified in their DUA." + ] + }, + { + "cell_type": "markdown", + "id": "fadcd3c8-fbb4-4cc6-b5fb-5951775cf4d4", + "metadata": {}, + "source": [ + "### Import Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "724c0b85-e666-40bc-89a3-b01353165195", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import base64\n", + "import requests\n", + "import json\n", + "import urllib.request\n", + "import shutil\n", + "from pathlib import Path\n", + "import os\n", + "import getpass" + ] + }, + { + "cell_type": "markdown", + "id": "be23668d-578c-43c2-a18a-0825503b0f61", + "metadata": { + "tags": [] + }, + "source": [ + "### Create Your NDA Credentials JSON File\n", + "\n", + "Input your NDA username and password in the 'nda_username' and 'nda_password' fields in the nda_credentials dictionary object. Note these are NOT your RAS credentials; this username and password are set when you create an account at NDA. You can view your NDA username and reset your NDA password if needed by visiting your Profile at the NIMH Data Archive. \n", + "\n", + "If you have already created your nda_credentials.json file, then there is no need to run the cell below.\n", + "\n", + "After you create the nda_credentials.json file we suggest that you delete the input of your NDA username and password in the cell below to minimize the risk of potentially sharing these credentials. Your newly created nda_credentials.json file will persist between workspace sessions under the /pd directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "603297cf-a6b0-40d2-ae4e-67c3fedc722f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "nda_username = getpass.getpass()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c76cb3d1-ca38-4308-807a-6bd4e8b7b96c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "nda_password = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "f80ad469-dc43-498b-b517-3bef2f01fd7f", + "metadata": {}, + "source": [ + "### Define NIMH Data Package ID\n", + "\n", + "Enter your miNDAR ID (replace '123')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02222358-2b9f-4320-aeea-90cf94d58fcf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "packageId = 123 # miNDAR package ID as integer " + ] + }, + { + "cell_type": "markdown", + "id": "4cacf612-b6cd-4fb2-a6c8-a7578a04597a", + "metadata": {}, + "source": [ + "### Encode A Credentials Object Using The Credentials JSON File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73aafcdb-7526-43fc-93ab-939dc01555d8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "credentials = nda_username + ':' + nda_password\n", + "credentials = base64.b64encode(credentials.encode('ascii')).decode('utf-8')" + ] + }, + { + "cell_type": "markdown", + "id": "7b6ddcce-bda0-4840-bed3-4d95c9591527", + "metadata": {}, + "source": [ + "### Create Header And Test Request\n", + "Create the headers to be used for all requests and send initial HTTP request to test connection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85c730d3-190a-4ac4-bf4c-9e46f554b699", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "headers = {\n", + " 'Authorization': 'Basic ' + credentials,\n", + " 'User-Agent': 'Example Client',\n", + " 'Accept': 'application/json'\n", + "}\n", + "\n", + "response = requests.get('https://nda.nih.gov/api/package/auth', headers=headers)\n", + "\n", + "if response.status_code != requests.codes.ok:\n", + " print('failed to authenticate')\n", + " response.raise_for_status()" + ] + }, + { + "cell_type": "markdown", + "id": "293c664c-c1d9-4dc9-8941-2c07cbddca38", + "metadata": {}, + "source": [ + "### Request File IDs & Names\n", + "Return all file names and associated file IDs as a nested json/dictionary object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "989f934c-3ef6-4e0b-a4ad-edad0dd2f06e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = requests.get('https://nda.nih.gov/api/package/' + str(packageId) + '/files', headers=headers)\n", + "results = response.json()['results']\n", + "\n", + "api_files = {}\n", + "for f in results:\n", + " api_files[f['package_file_id']] = {'name': f['download_alias']}\n", + "api_files" + ] + }, + { + "cell_type": "markdown", + "id": "15cce5ee-80d1-4b7a-adc0-aa76b7b2f0db", + "metadata": {}, + "source": [ + "### Request Files' Presigned URLs\n", + "Use the data package file IDs to return the presigned URLs for each file in the data package. Manually selecting the presigned download URL can be used to download the associated file to your local machine.\n", + "\n", + "Define the file ID (file_id) by either copying and pasting one of the file IDs listed above or index a file ID from the list of file IDs as can be seen below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cb9d661-1495-450f-9c25-f00d958bdbaa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = requests.post('https://nda.nih.gov/api/package/' + str(packageId) + '/files/batchGeneratePresignedUrls', json=list(api_files.keys()), headers=headers)\n", + "results = response.json()['presignedUrls']\n", + "\n", + "for url in results:\n", + " api_files[url['package_file_id']]['download'] = url['downloadURL']\n", + "\n", + "file_ids = list(api_files.keys())\n", + "api_files[file_ids[0]]['download'] " + ] + }, + { + "cell_type": "markdown", + "id": "b4a35ad7-ad2d-4a36-94e9-a573d736aa79", + "metadata": {}, + "source": [ + "### Download All Files In The Data Package\n", + "Use the complete list of data package file IDs to download all of the files in the data package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "181053d6-eb37-403f-aff1-c48f9d1aac02", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for key in list(api_files.keys()):\n", + "\n", + " name = api_files[key]['name']\n", + " downloadUrl = api_files[key]['download']\n", + " \n", + " # Create a downloads directory - you may change the download directory path here\n", + " file = 'downloads/' + name\n", + " # Strip out the file's name for creating non-existent directories\n", + " directory = file[:file.rfind('/')]\n", + "\n", + " # Create non-existent directories, package files have their\n", + " # own directory structure, and this will ensure that it is\n", + " # kept in tact when downloading.\n", + " Path(directory).mkdir(parents=True, exist_ok=True)\n", + "\n", + " # Initiate the download.\n", + " with urllib.request.urlopen(downloadUrl) as dl, open(file, 'wb') as out_file:\n", + " try:\n", + " shutil.copytree(dl, out_file)\n", + " except:\n", + " pass\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "ec724c2c-e2a2-4fc6-9869-e647fd287640", + "metadata": {}, + "source": [ + "### Download Select Files By File ID\n", + "Download files associated with select data package file IDs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1976154a-fd08-4675-a0d7-f45c20cff02d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "select_file_ids = [file_ids[0], file_ids[1], file_ids[2]]\n", + "for key in select_file_ids:\n", + "\n", + " name = api_files[key]['name']\n", + " downloadUrl = api_files[key]['download']\n", + " \n", + " # Create a downloads directory - you may change the download directory path here\n", + " file = 'downloads/' + name\n", + " # Strip out the file's name for creating non-existent directories\n", + " directory = file[:file.rfind('/')]\n", + " \n", + " # Create non-existent directories; package files have their\n", + " # own directory structure, and this will ensure that the structure is\n", + " # kept intact when downloading.\n", + " Path(directory).mkdir(parents=True, exist_ok=True)\n", + " \n", + " # Initiate the download.\n", + " with urllib.request.urlopen(downloadUrl) as dl, open(file, 'wb') as out_file:\n", + " try:\n", + " shutil.copytree(dl, out_file)\n", + " except:\n", + " pass\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/qdr_data_download.ipynb b/notebooks/qdr_data_download.ipynb new file mode 100644 index 0000000..9181ea4 --- /dev/null +++ b/notebooks/qdr_data_download.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fe6889dc-40fb-4277-99af-dabf450aecac", + "metadata": {}, + "source": [ + "# Qualitative Data Repository (QDR) Data Download\n", + "\n", + "This notebook allows HEAL platform users to download and access data from QDR using the Gen3 SDK and the HEAL SDK." + ] + }, + { + "cell_type": "markdown", + "id": "c2d8de4f-3751-4562-abbc-e7156c0c84c5", + "metadata": {}, + "source": [ + "#### Import Required Packages\n", + "Install and import the Gen3 SDK, the HEAL Platform SDK, and some additional packages for logging the results of the data download." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d2f840a-5c67-4903-a903-bb6cc91d90fe", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install gen3 -U -q \n", + "\n", + "import os\n", + "import json\n", + "import sys\n", + "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", + "\n", + "from gen3.auth import Gen3Auth\n", + "from gen3.tools.download.external_file_download import download_files_from_metadata, extract_external_file_metadata\n", + "\n", + "logging.basicConfig(filename=\"output.log\", level=logging.DEBUG)\n", + "logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))" + ] + }, + { + "cell_type": "markdown", + "id": "be28921b-304d-41a0-aec2-1ef2acbf6698", + "metadata": {}, + "source": [ + "### Import Data Manifest\n", + "\n", + "Access the data manifest for the study you wish to download and extract the relevant file metadata using the Gen3 SDK. Please fill in the missing string value below with the folder name of your exported metadata file. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6c7435b-1cce-4030-b05e-b0cc2bd7fd30", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#The folder may be found under the /data/{hostname}/exported-metadata\n", + "exported_metadata_folder = \"placeholder\" # Example - exported-metadata-2024-06-24T17-45-13.702855\n", + "# The hostname of this environment\n", + "hostname = \"healdata.org\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61d298e2-0a26-4bd3-9e14-40fda0a3bbc1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "f = open(f'/data/{hostname}/exported-metadata/{exported_metadata_folder}/metadata.json') \n", + "data = json.load(f) \n", + "external_file_metadata = data[0]['external_file_metadata']\n", + "external_file_metadata" + ] + }, + { + "cell_type": "markdown", + "id": "2765f6ea-d0f1-460b-90b2-bed389b82aea", + "metadata": {}, + "source": [ + "#### Configure Download \n", + "\n", + "Authorize with the Gen3 SDK using their HEAL user credentials and configure your download request for the HEAL Platform SDK. \n", + "\n", + "You can access your HEAL user credentials in your HEAL Profile page. Make sure your QDR keycloack login is authorized prior to generating your HEAL user credentials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ebeb5ac-4a2f-49c2-9437-f06f2d10d4bd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# User credentials\n", + "credentials_file = \"credentials.json\"\n", + "\n", + "# Referenced retriever function\n", + "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", + "\n", + "# Use Gen3Auth to request QDR token\n", + "auth = Gen3Auth(refresh_file=credentials_file)" + ] + }, + { + "cell_type": "markdown", + "id": "b671af40-d101-48da-aec4-c6d9d65ee144", + "metadata": {}, + "source": [ + "### Download Files & Create Status Logs\n", + "\n", + "The following command will start to download your requested study from QDR and log the status of your download. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e719427-d152-4ee7-867d-36600c92fed5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "download_status = download_files_from_metadata(\n", + " hostname=hostname,\n", + " auth=auth,\n", + " external_file_metadata=external_file_metadata,\n", + " retrievers=retrievers,\n", + " download_path=download_path)\n", + "logging.info(f\"Download status: {download_status}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "655958b6-1f15-455a-b0a8-cfa83a89abb0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/poetry.lock b/poetry.lock index 2b8a662..63ff17c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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description = "HEAL Platform SDK" -authors = ["Center for Translational Data Science at the University of Chicago <support@datacommons.io>"] +authors = ["Center for Translational Data Science at the University of Chicago <support@gen3.org>"] license = "Apache-2.0" packages = [ { include = "heal" }, diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..31b75a4 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,150 @@ +import json +import pytest + +from heal.vlmd.config import ( + ALLOWED_INPUT_TYPES, + ALLOWED_SCHEMA_TYPES, + CSV_SCHEMA, + JSON_SCHEMA, + JSON_SCHEMA_VERSION, +) +from heal.vlmd.utils import clean_json_fields + + +@pytest.fixture() +def VALID_CSV_SCHEMA(): + return CSV_SCHEMA + + +@pytest.fixture() +def VALID_JSON_SCHEMA(): + return JSON_SCHEMA + + +@pytest.fixture() +def allowed_input_types(): + return ALLOWED_INPUT_TYPES + + +@pytest.fixture() +def allowed_schema_types(): + return ALLOWED_SCHEMA_TYPES + + +@pytest.fixture() +def invalid_csv_schema(): + # type=5 is not of type 'object', 'boolean' + return { + "type": "array", + "items": { + "version": JSON_SCHEMA_VERSION, + "properties": { + "title": 5, + "items": { + "foo": "bar", + }, + }, + }, + } + + +@pytest.fixture() +def 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)", + "type": "object", + "required": ["title", "fields"], + "properties": { + "title": 5, + "version": { + "type": "string", + "description": "The version", + }, + }, + } + + +@pytest.fixture() +def valid_array_data(): + return [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "format": "", + "constraints.maxLength": "", + "constraints.enum": "", + "constraints.pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + "constraints.maximum": "", + "constraints.minimum": "", + "enumLabels": "", + "enumOrdered": "", + "missingValues": "", + "trueValues": "", + "falseValues": "", + "custom.notes": "This is a note", + }, + { + "section": "Demographics", + "name": "race", + "title": "Race", + "description": "Self-reported race", + "type": "integer", + "format": "", + "constraints.maxLength": "", + "constraints.enum": "1|2|3|4|5|6|7|8", + "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", + "enumOrdered": "", + "missingValues": "99", + "trueValues": "", + "falseValues": "", + "custom.notes": "This is a custom note", + }, + ] + + +@pytest.fixture() +def valid_json_data(): + with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r") as f: + data = json.load(f) + return data + + +@pytest.fixture() +def valid_json_output(): + header = {"schemaVersion": JSON_SCHEMA_VERSION} + with open("tests/test_data/vlmd/valid/vlmd_valid.json", "r") as f: + data = json.load(f) + header.update(data) + return header + + +@pytest.fixture() +def valid_converted_csv_to_json(): + with open("tests/test_data/vlmd/valid/vlmd_csv_to_json.json", "r") 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: + 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: + 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 diff --git a/tests/test_data/vlmd/invalid/vlmd_additional_properties.json b/tests/test_data/vlmd/invalid/vlmd_additional_properties.json new file mode 100644 index 0000000..9c4a84c --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_additional_properties.json @@ -0,0 +1,17 @@ +{ + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "encoding": "this is not in the schema", + "constraints": { + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]" + } + } + ] +} diff --git a/tests/test_data/vlmd/invalid/vlmd_invalid.txt b/tests/test_data/vlmd/invalid/vlmd_invalid.txt new file mode 100644 index 0000000..85fb00d --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_invalid.txt @@ -0,0 +1 @@ +Text files are not allowed diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_description.csv b/tests/test_data/vlmd/invalid/vlmd_missing_description.csv new file mode 100644 index 0000000..8b3103d --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_description.csv @@ -0,0 +1,3 @@ +title,name,type,format,constraints.maxLength,constraints.enum,constraints.pattern,constraints.maximum,constraints.minimum,enumLabels,enumOrdered,missingValues,trueValues,falseValues,custom.notes +Participant Id,particpant_id,string,,5,,[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9],,,,,,,,This is a note +Race,race,integer,,,1|2|3|4|5|6|7|8,,,,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,,99,,,This is a custom note diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_description.json b/tests/test_data/vlmd/invalid/vlmd_missing_description.json new file mode 100644 index 0000000..fc03260 --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_description.json @@ -0,0 +1,15 @@ +{ + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "type": "string", + "constraints": { + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]" + } + } + ] +} diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_description.tsv b/tests/test_data/vlmd/invalid/vlmd_missing_description.tsv new file mode 100644 index 0000000..a34f9d8 --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_description.tsv @@ -0,0 +1,3 @@ +section name title type format constraints.maxLength constraints.enum constraints.pattern constraints.maximum constraints.minimum enumLabels enumOrdered missingValues trueValues falseValues custom.notes +Enrollment participant_id Participant Id string [A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9] This is a note +Demographics race Race integer 1|2|3|4|5|6|7|8 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 99 This is a custom note diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_name.csv b/tests/test_data/vlmd/invalid/vlmd_missing_name.csv new file mode 100644 index 0000000..de3515e --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_name.csv @@ -0,0 +1,3 @@ +section,title,description,type,format,constraints.maxLength,constraints.enum,constraints.pattern,constraints.maximum,constraints.minimum,enumLabels,enumOrdered,missingValues,trueValues,falseValues,custom.notes +Enrollment,Participant Id,Unique identifier for participant,string,,,,[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9],,,,,,,,This is a note +Demographics,Race,Self-reported race,integer,,,1|2|3|4|5|6|7|8,,,,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,,99,,,This is a custom note diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_name.json b/tests/test_data/vlmd/invalid/vlmd_missing_name.json new file mode 100644 index 0000000..cdc3759 --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_name.json @@ -0,0 +1,15 @@ +{ + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "constraints": { + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]" + } + } + ] +} diff --git a/tests/test_data/vlmd/invalid/vlmd_missing_name.tsv b/tests/test_data/vlmd/invalid/vlmd_missing_name.tsv new file mode 100644 index 0000000..cf4c3c4 --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_missing_name.tsv @@ -0,0 +1,3 @@ +section title description type format constraints.maxLength constraints.enum constraints.pattern constraints.maximum constraints.minimum enumLabels enumOrdered missingValues trueValues falseValues custom.notes +Enrollment Participant Id Unique identifier for participant string [A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9] This is a note +Demographics Race Self-reported race integer 1|2|3|4|5|6|7|8 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 99 This is a custom note diff --git a/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.csv b/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.csv new file mode 100644 index 0000000..7991730 --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.csv @@ -0,0 +1,3 @@ +section,name,title,description,type,format,constraints.maxLength,constraints.enum,constraints.pattern,constraints.maximum,constraints.minimum,enumLabels,enumOrdered,missingValues,trueValues,falseValues,custom.notes +Enrollment,participant_id,Participant Id,Unique identifier for participant,string,,foo,,[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9],,,,,,,,This is a note +Demographics,race,Race,Self-reported race,integer,,,1|2|3|4|5|6|7|8,,,,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,,99,,,This is a custom note diff --git a/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.json b/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.json new file mode 100644 index 0000000..5a8fa5f --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.json @@ -0,0 +1,16 @@ +{ + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "description": "Unique identifier for participant", + "title": "Participant Id", + "type": "string", + "constraints": { + "maxLength": "foo" + } + } + ] +} diff --git a/tests/test_data/vlmd/invalid/vlmd_text_data.csv b/tests/test_data/vlmd/invalid/vlmd_text_data.csv new file mode 100644 index 0000000..85fb00d --- /dev/null +++ b/tests/test_data/vlmd/invalid/vlmd_text_data.csv @@ -0,0 +1 @@ +Text files are not allowed diff --git a/tests/test_data/vlmd/valid/vlmd_csv_to_csv.json b/tests/test_data/vlmd/valid/vlmd_csv_to_csv.json new file mode 100644 index 0000000..607ac7a --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_csv_to_csv.json @@ -0,0 +1,41 @@ +{ + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "format": "", + "constraints.maxLength": "", + "constraints.enum": "","constraints.pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + "constraints.maximum": "", + "constraints.minimum": "", + "enumLabels": "", + "enumOrdered": "", + "missingValues": "", + "trueValues": "", + "falseValues": "", + "custom.notes": "This is a note" + }, + { + "section": "Demographics", + "name": "race", + "title": "Race", + "description": "Self-reported race", + "type": "integer", + "format": "", + "constraints.maxLength": "", + "constraints.enum": "1|2|3|4|5|6|7|8", + "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", + "enumOrdered": "", + "missingValues": "99", + "trueValues": "", + "falseValues": "", + "custom.notes": "This is a custom note" + } + ] +} diff --git a/tests/test_data/vlmd/valid/vlmd_csv_to_json.json b/tests/test_data/vlmd/valid/vlmd_csv_to_json.json new file mode 100644 index 0000000..679a0cb --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_csv_to_json.json @@ -0,0 +1,52 @@ +{ + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "format": "", + "constraints": { + "maxLength": "", + "enum": "", + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + "maximum": "", + "minimum": "" + }, + "enumLabels": "", + "enumOrdered": "", + "missingValues": "", + "trueValues": "", + "falseValues": "", + "custom": { + "notes": "This is a note" + } + }, + { + "section": "Demographics", + "name": "race", + "title": "Race", + "description": "Self-reported race", + "type": "integer", + "format": "", + "constraints": { + "maxLength": "", + "enum": ["1", "2", "3", "4", "5", "6", "7", "8"], + "pattern": "", + "maximum": "", + "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" + }, + "enumOrdered": "", + "missingValues": ["99"], + "trueValues": "", + "falseValues": "", + "custom": { + "notes": "This is a custom note" + } + } + ] +} diff --git a/tests/test_data/vlmd/valid/vlmd_test_custom_root_level.csv b/tests/test_data/vlmd/valid/vlmd_test_custom_root_level.csv new file mode 100644 index 0000000..2ece4db --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_test_custom_root_level.csv @@ -0,0 +1,3 @@ +section,name,title,description,type,format,constraints.maxLength,constraints.enum,constraints.pattern,constraints.maximum,constraints.minimum,enumLabels,enumOrdered,missingValues,trueValues,falseValues,custom.root_notes +Enrollment,participant_id,Participant Id,Unique identifier for participant,string,,,,[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9],,,,,,,,This is a root level note +Demographics,race,Race,Self-reported race,integer,,,1|2|3|4|5|6|7|8,,,,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,,99,,,This is a root level note diff --git a/tests/test_data/vlmd/valid/vlmd_valid.csv b/tests/test_data/vlmd/valid/vlmd_valid.csv new file mode 100644 index 0000000..47ccdf5 --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_valid.csv @@ -0,0 +1,3 @@ +section,name,title,description,type,format,constraints.maxLength,constraints.enum,constraints.pattern,constraints.maximum,constraints.minimum,enumLabels,enumOrdered,missingValues,trueValues,falseValues,custom.notes +Enrollment,participant_id,Participant Id,Unique identifier for participant,string,,,,[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9],,,,,,,,This is a note +Demographics,race,Race,Self-reported race,integer,,,1|2|3|4|5|6|7|8,,,,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,,99,,,This is a custom note diff --git a/tests/test_data/vlmd/valid/vlmd_valid.json b/tests/test_data/vlmd/valid/vlmd_valid.json new file mode 100644 index 0000000..0918dde --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_valid.json @@ -0,0 +1,111 @@ +{ + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "constraints": { + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]" + } + }, + { + "section": "Demographics", + "name": "race", + "title": "Race", + "description": "Self-reported race", + "type": "integer", + "constraints": { + "enum": [ + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8" + ] + }, + "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" + }, + "missingValues": [ + "99" + ] + }, + { + "section": "Demographics", + "name": "age", + "title": "Age", + "description": "What is your age? (age at enrollment)", + "type": "integer", + "constraints": { + "maximum": 90, + "minimum": 0 + } + }, + { + "section": "Demographics", + "name": "hispanic", + "title": "Hispanic, Latino, or Spanish Origin", + "description": "Are you of Hispanic, Latino, or Spanish origin?", + "type": "boolean", + "missingValues": [ + "Not reported" + ], + "trueValues": [ + "No" + ], + "falseValues": [ + "Yes" + ] + }, + { + "section": "Demographics", + "name": "sex_at_birth", + "title": "Sex at Birth", + "description": "The self-reported sex of the participant/subject at birth", + "type": "string", + "constraints": { + "enum": [ + "Male", + "Female", + "Intersex", + "None of these describe me", + "Prefer not to answer", + "Unknown" + ] + }, + "missingValues": [ + "Prefer not to answer", + "Unknown" + ] + }, + { + "section": "Substance Use", + "name": "SU4", + "title": "Heroin Days Used", + "description": "During the past 30 days how many days did you use heroin (alone or mixed with other drugs)? ] [Write 0 days if no use]", + "type": "integer" + }, + { + "section": "Biomeasures", + "name": "pulse_rate", + "title": "Pulse Rate", + "description": "Heart rate measured at systemic artery", + "type": "number" + } + ] +} diff --git a/tests/test_data/vlmd/valid/vlmd_valid.tsv b/tests/test_data/vlmd/valid/vlmd_valid.tsv new file mode 100644 index 0000000..79f6482 --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_valid.tsv @@ -0,0 +1,3 @@ +section name title description type format constraints.maxLength constraints.enum constraints.pattern constraints.maximum constraints.minimum enumLabels enumOrdered missingValues trueValues falseValues custom.notes +Enrollment participant_id Participant Id Unique identifier for participant string [A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9] This is a note +Demographics race Race Self-reported race integer 1|2|3|4|5|6|7|8 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 99 This is a custom note diff --git a/tests/test_data/vlmd/valid/vlmd_valid_output.json b/tests/test_data/vlmd/valid/vlmd_valid_output.json new file mode 100644 index 0000000..83462f1 --- /dev/null +++ b/tests/test_data/vlmd/valid/vlmd_valid_output.json @@ -0,0 +1,83 @@ +{ + "schemaVersion": "0.3.2", + "title": "Example VLMD", + "description": "This is an example description", + "fields": [ + { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "constraints": + { + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]" + } + }, + { + "section": "Demographics", + "name": "race", + "title": "Race", + "description": "Self-reported race", + "type": "integer", + "constraints":{ + "enum": ["1", "2", "3", "4", "5", "6", "7", "8"] + }, + "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" + }, + "missingValues": ["99"] + }, + { + "section": "Demographics", + "name": "age", + "title": "Age", + "description": "What is your age? (age at enrollment)", + "type": "integer", + "constraints": {"maximum": 90, "minimum": 0} + }, + { + "section": "Demographics", + "name": "hispanic", + "title": "Hispanic, Latino, or Spanish Origin", + "description": "Are you of Hispanic, Latino, or Spanish origin?", + "type": "boolean", + "missingValues": ["Not reported"], + "trueValues": ["No"], + "falseValues": ["Yes"] + }, + { + "section": "Demographics", + "name": "sex_at_birth", + "title": "Sex at Birth", + "description": "The self-reported sex of the participant/subject at birth", + "type": "string", + "constraints": { + "enum": ["Male", "Female", "Intersex", "None of these describe me", "Prefer not to answer", "Unknown"] + }, + "missingValues": ["Prefer not to answer", "Unknown"] + }, + { + "section": "Substance Use", + "name": "SU4", + "title": "Heroin Days Used", + "description": "During the past 30 days how many days did you use heroin (alone or mixed with other drugs)?] [Write 0 days if no use]", + "type": "integer" + }, + { + "section": "Biomeasures", + "name": "pulse_rate", + "title": "Pulse Rate", + "description": "Heart rate measured at systemic artery", + "type": "number" + } + ] +} diff --git a/tests/test_vlmd_conversion.py b/tests/test_vlmd_conversion.py new file mode 100644 index 0000000..809d07e --- /dev/null +++ b/tests/test_vlmd_conversion.py @@ -0,0 +1,65 @@ +import pytest + +from heal.vlmd.extract.conversion import convert_to_vlmd + + +def test_convert_valid_json_input(valid_json_output): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + input_type = "json-template" + result = convert_to_vlmd(input_file, input_type=input_type) + + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == [ + "schemaVersion", + "title", + "description", + "fields", + ] + assert list(result.get("template_csv").keys()) == ["title", "description", "fields"] + assert result.get("template_json") == valid_json_output + + +@pytest.mark.parametrize( + "input_file, expected_json_keys", + [ + ( + "tests/test_data/vlmd/valid/vlmd_valid.csv", + [ + "schemaVersion", + "title", + "fields", + ], + ), + ( + "tests/test_data/vlmd/valid/vlmd_test_custom_root_level.csv", + [ + "schemaVersion", + "title", + "custom", + "fields", + ], + ), + ], +) +def test_convert_valid_csv_input(input_file, expected_json_keys): + input_type = "csv-data-dict" + result = convert_to_vlmd(input_file, input_type=input_type) + + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == expected_json_keys + assert list(result.get("template_csv").keys()) == ["fields"] + + +def test_convert_unknown_input_type(): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + input_type = "txt" + data_dictionary_props = {} + with pytest.raises(ValueError) as e: + convert_to_vlmd( + input_file, + input_type=input_type, + data_dictionary_props=data_dictionary_props, + ) + + expected_message = f"Unexpected input_type '{input_type}'" + assert expected_message in str(e.value) diff --git a/tests/test_vlmd_csv_conversion.py b/tests/test_vlmd_csv_conversion.py new file mode 100644 index 0000000..d6e8c20 --- /dev/null +++ b/tests/test_vlmd_csv_conversion.py @@ -0,0 +1,78 @@ +from io import StringIO + +import pandas as pd +from pandas.testing import assert_frame_equal + +from heal.vlmd.extract import utils +from heal.vlmd.extract.csv_dict_conversion import ( + _parse_string_objects, + convert_datadict_csv, +) + + +def test_csv_conversion_valid_file(valid_csv_output, valid_converted_csv_to_json): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + data_dictionary_props = {} + + result = convert_datadict_csv( + input_file, data_dictionary_props=data_dictionary_props + ) + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == ["fields"] + assert list(result.get("template_csv").keys()) == ["fields"] + assert result.get("template_json") == valid_converted_csv_to_json + assert result.get("template_csv") == valid_csv_output + + +def test_csv_conversion_valid_data( + valid_array_data, valid_csv_output, valid_converted_csv_to_json +): + data_dictionary_props = {} + result = convert_datadict_csv( + valid_array_data, data_dictionary_props=data_dictionary_props + ) + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == ["fields"] + assert list(result.get("template_csv").keys()) == ["fields"] + assert result.get("template_json") == valid_converted_csv_to_json + assert result.get("template_csv") == valid_csv_output + + +def test_parse_string_objects(VALID_JSON_SCHEMA): + field_properties = utils.flatten_properties( + VALID_JSON_SCHEMA["properties"]["fields"]["items"]["properties"] + ) + + csv_data = """ + { + "section": ["Enrollment", "Demographics"], + "name": ["participant_id", "race"], + "title": ["Participant Id", "Race"], + "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"], + "custom.notes": ["This is a note", "This is a custom note"] + } + """ + csv_data_io = StringIO(csv_data) + tbl_csv = pd.read_json(csv_data_io) + + expected_json_data = """ + { + "section": ["Enrollment", "Demographics"], + "name": ["participant_id", "race"], + "title": ["Participant Id", "Race"], + "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"}], + "custom.notes": ["This is a note", "This is a custom note"], + "custom": [{"notes": "This is a note"}, {"notes": "This is a custom note"}] + } + """ + expected_json_data_io = StringIO(expected_json_data) + expected_tbl_json = pd.read_json(expected_json_data_io) + + result = _parse_string_objects(tbl_csv, field_properties) + assert_frame_equal(result, expected_tbl_json) diff --git a/tests/test_vlmd_extract.py b/tests/test_vlmd_extract.py new file mode 100644 index 0000000..e00ffed --- /dev/null +++ b/tests/test_vlmd_extract.py @@ -0,0 +1,122 @@ +import json +import os +from unittest.mock import patch + +import pytest + +from heal.vlmd.config import ALLOWED_OUTPUT_TYPES, OUTPUT_FILE_PREFIX +from heal.vlmd.extract.extract import ExtractionError, vlmd_extract + + +@pytest.mark.parametrize( + "file_type, output_type", + [ + ("csv", "csv"), + ("csv", "json"), + ("json", "csv"), + ("json", "json"), + ("tsv", "csv"), + ("tsv", "json"), + ], +) +def test_extract_valid_input(file_type, output_type, tmp_path): + 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) + assert result + assert os.path.isfile(expected_file_name) + if output_type == "json": + with open(expected_file_name, "r") as f: + data = json.load(f) + assert "fields" in data.keys() + assert "schemaVersion" in data.keys() + + +@pytest.mark.parametrize( + "input_file, expected_message, error_type", + [ + ( + "tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.csv", + "could not convert string to float: 'foo'", + ExtractionError, + ), + ( + "tests/test_data/vlmd/invalid/vlmd_missing_description.csv", + "'description' is a required property", + ExtractionError, + ), + ( + "tests/test_data/vlmd/invalid/vlmd_missing_name.csv", + "'name' is a required property", + ExtractionError, + ), + ], +) +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) + + +def test_extract_unallowed_input_type(): + input_file = "tests/test_data/vlmd/invalid/vlmd_invalid.txt" + with pytest.raises(ExtractionError) as e: + vlmd_extract(input_file) + expected_message = "Input file must be one of" + assert expected_message in str(e.value) + + +def test_extract_unallowed_file_type(): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + with pytest.raises(ExtractionError) as e: + vlmd_extract(input_file, file_type="txt") + expected_message = "File type must be one of" + assert expected_message in str(e.value) + + +def test_extract_input_does_not_exist(tmp_path): + input_file = f"{tmp_path}/foo.csv" + with pytest.raises(ExtractionError) as e: + vlmd_extract(input_file) + expected_message = f"Input file does not exist: {input_file}" + assert expected_message in str(e.value) + + +def test_extract_unallowed_output(): + 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) + expected_message = f"Unrecognized output_type '{unallowed_output}' - should be in {ALLOWED_OUTPUT_TYPES}" + assert expected_message in str(e.value) + + +def test_extract_failed_dict_write(): + 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) + + +def test_extract_invalid_converted_data(): + # 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) + + # 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) diff --git a/tests/test_vlmd_extract_utils.py b/tests/test_vlmd_extract_utils.py new file mode 100644 index 0000000..27dfd31 --- /dev/null +++ b/tests/test_vlmd_extract_utils.py @@ -0,0 +1,288 @@ +import pytest +from jsonschema import ValidationError + +from heal.vlmd.extract.utils import ( + _get_prop_names_to_rearrange, + embed_data_dictionary_props, + find_prop_name, + flatten_properties, + flatten_to_json_path, + join_dict_items, + parse_dictionary_str, + parse_list_str, + refactor_field_props, + unflatten_from_json_path, +) + +schema_version = {"schemaVersion": {"type": "string"}} +another_field_to_rearrange = { + "anotherFieldToEmbed": { + "items": {"type": "object", "properties": {"thisone": {"type": "string"}}} + } +} +schema_to_rearrange = { + "version": "0.2.0", + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "test", + "type": "object", + "properties": { + "title": {"type": "string"}, + "description": {"type": "string"}, + **schema_version, + **another_field_to_rearrange, + "anotherFieldToEmbed": { + "items": { + "type": "object", + "properties": {"thisone": {"type": "string"}}, + } + }, + "fields": { + "type": "array", + "items": { + "properties": { + "type": "object", + "some_field": {"type": "string"}, + **schema_version, + **another_field_to_rearrange, + } + }, + }, + }, +} + + +def test_unflatten_from_json_path(): + input = { + "module": "Testing", + "constraints.enum": "1|2|3|4", + "standardsMappings[1].item.url": "http//:helloitem1", + "standardsMappings[0].item.url": "http//:helloitem0", + "standardsMappings[0].instrument.url": "http//:helloworld0", + "standardsMappings[1].instrument.url": "http//:helloworld1", + "standardsMappings[2].item.url": "http//:helloitem2", + "test1.test2.test3[1]": "test3_1", + "test1.test2.test3[0].test4": "test4_1", + } + + output = { + "module": "Testing", + "constraints": {"enum": "1|2|3|4"}, + "standardsMappings": [ + { + "item": {"url": "http//:helloitem0"}, + "instrument": {"url": "http//:helloworld0"}, + }, + { + "item": {"url": "http//:helloitem1"}, + "instrument": {"url": "http//:helloworld1"}, + }, + {"item": {"url": "http//:helloitem2"}}, + ], + "test1": {"test2": {"test3": [{"test4": "test4_1"}, {"test3": "test3_1"}]}}, + } + field_json = unflatten_from_json_path(input) + assert ( + field_json == output + ), "Problem with converting input dictionary to output dictionary" + + +@pytest.mark.parametrize( + "invalid_input, invalid_name", + [ + ( + { + "module": "Testing", + "constraints.enum": "1|2|3|4", + "standardsMappings[1].item.url": "http//:helloitem1", + "standardsMappings['some_string'].item.url": "http//:helloitem0", + "standardsMappings[2].item.url": "http//:helloitem2", + }, + "standardsMappings['some_string']", + ), + ( + { + "module": "Testing", + "constraints.enum": "1|2|3|4", + "[1].item.url": "http//:helloitem1", + "standardsMappings[0].item.url": "http//:helloitem0", + "standardsMappings[2].item.url": "http//:helloitem2", + }, + "[1]", + ), + ( + { + "module": "Testing", + "constraints.enum": "1|2|3|4", + "standardsMappings[1).item.url": "http//:helloitem1", + "standardsMappings[0].item.url": "http//:helloitem0", + "standardsMappings[2].item.url": "http//:helloitem2", + }, + "standardsMappings[1)", + ), + ], +) +def test_unflatten_from_json_path_invalid_input(invalid_input, invalid_name): + with pytest.raises(ValidationError) as e: + unflatten_from_json_path(invalid_input) + expected_error_message = f"Incorrect array indexing in name {invalid_name}" + assert expected_error_message in str(e.value) + + +def test_get_prop_names_to_rearrange(): + flat_root = { + "schemaVersion": "0.2.0", + "title": "Example VLMD", + "description": "This is an example description", + "anotherFieldToEmbed[0].thisone": "helloworld", + } + prop_names = list(flat_root.keys()) + expected_prop_names = ["schemaVersion", "anotherFieldToEmbed[0].thisone"] + names_to_rearrange = _get_prop_names_to_rearrange(prop_names, schema_to_rearrange) + assert names_to_rearrange == expected_prop_names + + +def test_flatten_to_json_path(valid_json_data, VALID_JSON_SCHEMA): + # example field item + dict_field = { + "section": "Enrollment", + "name": "participant_id", + "constraints": {"pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]"}, + } + expected_flattened_dict = { + "section": "Enrollment", + "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"] + flattened_dict = flatten_to_json_path(dict_field, fields_schema) + assert flattened_dict == expected_flattened_dict + + +def test_flatten_properties(): + schema_props = schema_to_rearrange["properties"] + expected_props = { + "title": {"type": "string"}, + "description": {"type": "string"}, + "schemaVersion": {"type": "string"}, + "anotherFieldToEmbed\\[\\d+\\].thisone": {"type": "string"}, + "fields\\[\\d+\\].type": "object", + "fields\\[\\d+\\].some_field": {"type": "string"}, + "fields\\[\\d+\\].schemaVersion": {"type": "string"}, + "fields\\[\\d+\\].anotherFieldToEmbed\\[\\d+\\].thisone": {"type": "string"}, + } + assert flatten_properties(schema_props) == expected_props + + +@pytest.mark.parametrize( + "name, field_props, expected_name", + [ + ( + "foo", + { + "title": "HEAL Variable Level Metadata Fields", + "description": "Some description", + "foo": "bar", + }, + "foo", + ), + ( + "foo", + { + "title": "HEAL Variable Level Metadata Fields", + "description": "Some description", + "bar": "foo", + }, + None, + ), + ( + "foo", + { + "title": "HEAL Variable Level Metadata Fields", + "description": "Some description", + "barfoo": "bar", + }, + None, + ), + ], +) +def test_find_prop_names(name, field_props, expected_name): + assert find_prop_name(name, field_props) == expected_name + + +def test_embed_data_dictionary_props(): + flat_root = { + "schemaVersion": "0.2.0", + "anotherFieldToEmbed[0].thisone": "helloworld", + } + flat_fields_array = [{"some_field": "cool"}, {"some_field": "sad"}] + flat_fields = embed_data_dictionary_props( + flat_fields_array, flat_root, schema_to_rearrange + ) + + assert flat_fields.to_dict(orient="records") == [ + { + "some_field": "cool", + "schemaVersion": "0.2.0", + "anotherFieldToEmbed[0].thisone": "helloworld", + }, + { + "some_field": "sad", + "schemaVersion": "0.2.0", + "anotherFieldToEmbed[0].thisone": "helloworld", + }, + ] + + +def test_refactor_field_props(): + flat_fields_array = [ + {"some_field": "cool", "anotherFieldToEmbed[0].thisone": "helloworld"}, + {"some_field": "sad", "anotherFieldToEmbed[0].thisone": "helloworld"}, + ] + flat_root, flat_fields = refactor_field_props( + flat_fields_array, schema_to_rearrange + ) + + assert flat_root.to_dict() == {"anotherFieldToEmbed[0].thisone": "helloworld"} + assert flat_fields.to_dict(orient="records") == [ + {"some_field": "cool"}, + {"some_field": "sad"}, + ] + + +def test_parse_dictionary_str(): + 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", + "fields": "[{'section': 'Enrollment', 'name': 'participant_id'}]", + } + output_dict = parse_dictionary_str(input_string, item_sep="|", key_val_sep="=") + assert output_dict == expected_dict + + +@pytest.mark.parametrize( + "input_string, separator, expected_output_list", + [ + ("a,b,c", ",", ["a", "b", "c"]), + ("a|b|c", "|", ["a", "b", "c"]), + ("a|b|c", "#", ["a|b|c"]), + ], +) +def test_parse_list_str(input_string, separator, expected_output_list): + result = parse_list_str(input_string, separator) + assert result == expected_output_list + + +def test_join_dict_items(): + dict = { + "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 new file mode 100644 index 0000000..cf5d516 --- /dev/null +++ b/tests/test_vlmd_file_utils.py @@ -0,0 +1,62 @@ +import json +import os + +import pandas as pd + +from heal.vlmd.config import OUTPUT_FILE_PREFIX +from heal.vlmd.file_utils import get_output_filepath, write_vlmd_dict + + +def test_write_json(tmp_path, valid_json_data): + output_filepath = f"{tmp_path}/some/path/valid_dict.json" + if os.path.isfile(output_filepath): + os.unlink(output_filepath) + result = write_vlmd_dict( + valid_json_data, output_filepath=output_filepath, file_type="json" + ) + assert result + + with open(output_filepath, "r") as f: + data = json.load(f) + assert data == valid_json_data + + +def test_write_csv(tmp_path, valid_array_data): + output_filepath = f"{tmp_path}/some/other/path/valid_dict.csv" + if os.path.isfile(output_filepath): + os.unlink(output_filepath) + result = write_vlmd_dict( + valid_array_data, output_filepath=output_filepath, file_type="csv" + ) + assert result + + # reconstruct the array_data from the written csv + df = pd.read_csv(output_filepath) + df = df.fillna("") + df["missingValues"] = df["missingValues"].astype(str).replace("99.0", "99") + read_as_array = df.to_dict(orient="records") + + assert read_as_array == valid_array_data + + +def test_get_output_filepath(): + output_dir = "tmp/foo" + input_filename = "data/valid/valid_dict.csv" + output_type = "auto" + expected_output_filepath = f"tmp/foo/{OUTPUT_FILE_PREFIX}_valid_dict.csv" + result = get_output_filepath(output_dir, input_filename, output_type=output_type) + assert result == expected_output_filepath + + output_type = "json" + expected_output_filepath = f"tmp/foo/{OUTPUT_FILE_PREFIX}_valid_dict.json" + result = get_output_filepath(output_dir, input_filename, output_type=output_type) + assert result == expected_output_filepath + + +def test_write_vlmd_unallowed(): + dictionary = "some text" + output_filepath = "tmp/foo/unallowed_suffix.txt" + result = write_vlmd_dict( + dictionary, output_filepath=output_filepath, file_type="auto" + ) + assert result is None diff --git a/tests/test_vlmd_json_conversion.py b/tests/test_vlmd_json_conversion.py new file mode 100644 index 0000000..5718c4b --- /dev/null +++ b/tests/test_vlmd_json_conversion.py @@ -0,0 +1,43 @@ +import pytest + +from heal.vlmd.extract.json_dict_conversion import convert_template_json + + +def test_json_conversion_valid_file(valid_json_output): + 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) + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == [ + "title", + "description", + "fields", + ] + assert list(result.get("template_csv").keys()) == ["title", "description", "fields"] + assert result.get("template_json") == valid_json_output + + +def test_json_conversion_valid_data(valid_json_data, valid_json_output): + valid_json_output.pop("schemaVersion", None) + result = convert_template_json(valid_json_data) + assert list(result.keys()) == ["template_json", "template_csv"] + assert list(result.get("template_json").keys()) == [ + "title", + "description", + "fields", + ] + assert list(result.get("template_csv").keys()) == ["title", "description", "fields"] + assert result.get("template_json") == valid_json_output + + +@pytest.mark.parametrize("unallowed_input", [(["some", "list"], ("some", "set"), 5)]) +def test_json_conversion_unallowed_input(unallowed_input): + with pytest.raises(ValueError) as e: + convert_template_json(unallowed_input) + + expected_message = ( + "json_template needs to be either dictionary-like or a path to a json" + ) + assert expected_message in str(e.value) diff --git a/tests/test_vlmd_utils.py b/tests/test_vlmd_utils.py new file mode 100644 index 0000000..9724fc0 --- /dev/null +++ b/tests/test_vlmd_utils.py @@ -0,0 +1,153 @@ +import pytest + +from heal.vlmd.utils import ( + add_missing_type, + add_types_to_props, + clean_json_fields, + remove_empty_props, +) + +input_prop_with_empties = { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "format": "", + "constraints": { + "maxLength": 5, + "enum": "", + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + "maximum": 10.0, + "minimum": "", + }, + "empty_constraints": {"maxLength": ""}, + "empty_enum": [], + "missingValues": "", + "trueValues": "", + "falseValues": "", +} + +expected_prop = { + "section": "Enrollment", + "name": "participant_id", + "title": "Participant Id", + "description": "Unique identifier for participant", + "type": "string", + "constraints": { + "maxLength": 5, + "pattern": "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + "maximum": 10.0, + }, +} + + +def test_add_missing_type(): + # Required prop_name gets "allOf" in prop value + prop_name = "description" + prop_value = "Some string value" + schema = {"required": ["description"]} + expected_new_prop = {"allOf": [prop_value, {"not": {"enum": ["", None]}}]} + + new_prop = add_missing_type(prop_name, prop_value, schema) + assert new_prop == expected_new_prop + + # Non-required prop_name gets "anyOf" in prop value + prop_name = "name of non-required prop" + expected_new_prop = {"anyOf": [prop_value, {"enum": ["", None]}]} + + new_prop = add_missing_type(prop_name, prop_value, schema) + assert new_prop == expected_new_prop + + +@pytest.mark.parametrize( + "input_schema,expected_schema", + [ + ( + {"type": "array", "comment": "empty items", "items": {}}, + {"type": "array", "items": {"properties": {}, "patternProperties": {}}}, + ), + ( + { + "type": "array", + "comment": "required field: 'description'", + "items": { + "version": "0.3.2", + "properties": { + "title": {"type": "string"}, + "description": {"type": "string"}, + }, + }, + "required": ["description"], + }, + { + "type": "array", + "items": { + "properties": { + "title": {"anyOf": [{"type": "string"}, {"enum": ["", None]}]}, + "description": { + "allOf": [{"type": "string"}, {"not": {"enum": ["", None]}}] + }, + }, + "patternProperties": {}, + }, + }, + ), + ( + { + "type": "array", + "comment": "includes 'patternProperties'", + "items": { + "version": "0.3.2", + "properties": { + "title": {"type": "string"}, + "description": {"type": "string"}, + "items": { + "foo": "bar", + }, + }, + "patternProperties": { + "^standardsMappings\\[\\d+\\].instrument.url$": { + "title": "Url", + "description": "A url", + }, + }, + }, + "required": ["description"], + }, + { + "type": "array", + "items": { + "properties": { + "title": {"anyOf": [{"type": "string"}, {"enum": ["", None]}]}, + "description": { + "allOf": [{"type": "string"}, {"not": {"enum": ["", None]}}] + }, + "items": {"anyOf": [{"foo": "bar"}, {"enum": ["", None]}]}, + }, + "patternProperties": { + "^standardsMappings\\[\\d+\\].instrument.url$": { + "anyOf": [ + {"title": "Url", "description": "A url"}, + {"enum": ["", None]}, + ] + } + }, + }, + }, + ), + ], +) +def test_add_types_to_props(input_schema, expected_schema): + # Propeties get wrapped in 'anyOf' or 'allOf' + assert add_types_to_props(input_schema) == expected_schema + + +def test_remove_empty_props(): + assert remove_empty_props(input_prop_with_empties) == expected_prop + + +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 diff --git a/tests/test_vlmd_validate.py b/tests/test_vlmd_validate.py new file mode 100644 index 0000000..7718b77 --- /dev/null +++ b/tests/test_vlmd_validate.py @@ -0,0 +1,172 @@ +from unittest.mock import patch + +import pytest +from jsonschema import SchemaError, ValidationError + +from heal.vlmd import ExtractionError, vlmd_validate +from heal.vlmd.config import ALLOWED_OUTPUT_TYPES + + +@pytest.mark.parametrize( + "file_type, output_type", + [ + ("csv", "csv"), + ("csv", "json"), + ("json", "csv"), + ("json", "json"), + ("tsv", "csv"), + ("tsv", "json"), + ], +) +def test_validate_valid_input(file_type, output_type): + input_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" + result = vlmd_validate( + input_file, output_type=output_type, return_converted_output=False + ) + assert result + assert isinstance(result, bool) + + +@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 +): + input_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" + result = vlmd_validate(input_file, output_type="json", return_converted_output=True) + assert isinstance(result, dict) + if file_type in ["csv", "tsv"]: + assert result == valid_converted_csv_to_json_output + if file_type == "json": + assert result == valid_json_output + + +@pytest.mark.parametrize("file_type", ["csv", "json", "tsv"]) +def test_invalid_missing_required_fields(file_type): + test_file = f"tests/test_data/vlmd/invalid/vlmd_missing_description.{file_type}" + with pytest.raises(ValidationError) as e: + vlmd_validate(input_file=test_file, schema_type="auto") + expected_message = "'description' is a required property" + assert expected_message in str(e.value) + + +def test_invalid_has_additional_properties(): + test_file = "tests/test_data/vlmd/invalid/vlmd_additional_properties.json" + with pytest.raises(ValidationError) as e: + vlmd_validate(input_file=test_file, schema_type="auto") + expected_message = "Additional properties are not allowed" + assert expected_message in str(e.value) + + +@pytest.mark.parametrize( + "file_type, expected_message, error_type", + [ + ("json", "'foo' is not of type 'integer'", ValidationError), + ("csv", "could not convert string to float: 'foo'", ExtractionError), + ], +) +def test_invalid_incorrect_type(file_type, expected_message, error_type): + test_file = f"tests/test_data/vlmd/invalid/vlmd_string_in_maxLength.{file_type}" + with pytest.raises(error_type) as e: + vlmd_validate(input_file=test_file, schema_type="auto") + assert expected_message in str(e.value) + + +def test_input_does_not_exist(): + file_dne = "does_not_exist.json" + with pytest.raises(IOError) as e: + vlmd_validate(input_file=file_dne, schema_type="auto") + + expected_message = f"Input file does not exist: {file_dne}" + assert str(e.value) == expected_message + + +def test_unallowed_input_type(allowed_input_types): + unallowed_file = "vlmd_unallowed_type.txt" + with pytest.raises(ValueError) as e: + vlmd_validate(input_file=unallowed_file, schema_type="auto") + + expected_message = f"Input file must be one of {allowed_input_types}" + assert str(e.value) == expected_message + + +def test_extract_unallowed_output(): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + unallowed_output = "txt" + with pytest.raises(ValueError) as e: + vlmd_validate(input_file, schema_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) + + +@pytest.mark.parametrize("test_schema_type", ["auto", "csv"]) +def test_read_non_delim_data_in_csv(test_schema_type): + test_file = "tests/test_data/vlmd/invalid/vlmd_text_data.csv" + + with pytest.raises(ValidationError) as e: + vlmd_validate(test_file, test_schema_type) + expected_message = "Could not read csv data from input" + assert expected_message in str(e.value) + + +def test_incorrent_schema_type(allowed_schema_types): + test_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + with pytest.raises(ValueError) as e: + vlmd_validate(input_file=test_file, schema_type="txt") + + expected_message = f"Schema type must be in {allowed_schema_types}" + assert str(e.value) == expected_message + + +def test_invalid_converted_data(): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + fail_message = "Failed validation" + + with patch("jsonschema.validate") as mock_validate: + mock_validate.side_effect = ValidationError(fail_message) + with pytest.raises(ValidationError) as e: + vlmd_validate(input_file, output_type="json") + assert fail_message in str(e.value) + + +def test_extract_conversion_error(): + input_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + fail_message = "Some conversion error" + + with patch("heal.vlmd.validate.validate.convert_to_vlmd") as mock_conversion: + mock_conversion.side_effect = Exception(fail_message) + with pytest.raises(ExtractionError) as e: + vlmd_validate(input_file, output_type="json") + assert fail_message in str(e.value) + + +def test_invalid_csv_schema(invalid_csv_schema): + test_file = "tests/test_data/vlmd/valid/vlmd_valid.csv" + + with patch("heal.vlmd.validate.validate.get_schema") as mock_get_schema: + mock_get_schema.return_value = invalid_csv_schema + with pytest.raises(SchemaError) as e: + vlmd_validate(input_file=test_file, schema_type="csv") + expected_message = "is not valid under any of the given schemas" + assert expected_message in str(e.value) + + +def test_invalid_json_schema(invalid_json_schema): + test_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + with patch("heal.vlmd.validate.validate.get_schema") as mock_get_schema: + mock_get_schema.return_value = invalid_json_schema + with pytest.raises(SchemaError) as e: + vlmd_validate(input_file=test_file, schema_type="json") + expected_message = "5 is not of type 'object'" + assert expected_message in str(e.value) + + +def test_could_not_get_schema(): + schema_type = "json" + test_file = f"tests/test_data/vlmd/valid/vlmd_valid.{schema_type}" + + with patch("heal.vlmd.validate.validate.get_schema") as mock_get_schema: + mock_get_schema.return_value = None + with pytest.raises(ValueError) as e: + vlmd_validate(input_file=test_file, schema_type=schema_type) + expected_message = f"Could not get schema for type = {schema_type}" + assert expected_message in str(e.value) diff --git a/tests/test_vlmd_validate_utils.py b/tests/test_vlmd_validate_utils.py new file mode 100644 index 0000000..a865d9b --- /dev/null +++ b/tests/test_vlmd_validate_utils.py @@ -0,0 +1,149 @@ +import json +from pathlib import Path + +import pandas as pd +import pytest + +from heal.vlmd.validate.utils import ( + detect_file_encoding, + get_schema, + read_data_from_json_file, + read_delim, +) + + +def test_read_input_file(): + test_file = "tests/test_data/vlmd/valid/vlmd_valid.json" + result = read_data_from_json_file(test_file) + assert isinstance(result, dict) + assert "title" in result + assert "description" in result + assert "fields" in result + + +@pytest.mark.parametrize("file_type", ["csv", "json", "tsv"]) +def test_detect_file_encoding(file_type): + test_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" + result = detect_file_encoding(test_file) + assert result == "ascii" + + +@pytest.mark.parametrize("file_type", ["csv", "tsv"]) +def test_read_delim(file_type): + test_file = f"tests/test_data/vlmd/valid/vlmd_valid.{file_type}" + + with open(test_file) as f: + header_line = f.readline().strip("\n") + file_suffix = Path(test_file).suffix.replace(".", "") + if file_suffix == "csv": + expected_data_columns = header_line.split(",") + elif file_suffix == "tsv": + expected_data_columns = header_line.split("\t") + + # get a pandas.dataFrame of the tabular data + result = read_delim(test_file) + assert result.columns.values.tolist() == expected_data_columns + + # dataFrame structure is column and values; this data matches the test input files + expected_df = pd.DataFrame( + { + "section": {0: "Enrollment", 1: "Demographics"}, + "name": {0: "participant_id", 1: "race"}, + "title": {0: "Participant Id", 1: "Race"}, + "description": { + 0: "Unique identifier for participant", + 1: "Self-reported race", + }, + "type": {0: "string", 1: "integer"}, + "format": {0: "", 1: ""}, + "constraints.maxLength": {0: "", 1: ""}, + "constraints.enum": {0: "", 1: "1|2|3|4|5|6|7|8"}, + "constraints.pattern": { + 0: "[A-Z][0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]", + 1: "", + }, + "constraints.maximum": {0: "", 1: ""}, + "constraints.minimum": {0: "", 1: ""}, + "enumLabels": { + 0: "", + 1: "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": {0: "", 1: ""}, + "missingValues": {0: "", 1: "99"}, + "trueValues": {0: "", 1: ""}, + "falseValues": {0: "", 1: ""}, + "custom.notes": {0: "This is a note", 1: "This is a custom note"}, + } + ) + assert result.to_dict() == expected_df.to_dict() + + +def test_read_delim_unallowed_file_type(): + test_file = "test_data/vlmd/invalid/vlmd_invalid.txt" + with pytest.raises(ValueError) as e: + read_delim(test_file) + + expected_message = "Delimited file must be csv or tsv" + assert expected_message in str(e.value) + + +@pytest.mark.parametrize("file_type", ["csv", "tsv"]) +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} + + result = get_schema(test_file, "auto") + assert result == expected_schema + + result = get_schema(test_file, file_type) + assert result == expected_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} + + result = get_schema(valid_array_data, "auto") + assert result == expected_schema + + result = get_schema(valid_array_data, file_type) + assert result == expected_schema + + +@pytest.mark.parametrize("schema_type", ["auto", "json"]) +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 + + +@pytest.mark.parametrize("schema_type", ["auto", "json"]) +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 + + +@pytest.mark.parametrize("invalid_data", [("set", "not", "a", "dict"), 5]) +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" + assert expected_message in str(e.value) + + +def test_get_schema_for_string_without_suffix(): + data = "Some string" + result = get_schema(data_or_path=data, schema_type="auto") + assert result is None + + +def test_get_schema_unknown_type(allowed_schema_types): + test_file = "vlmd.json" + unallowed_schema_type = "foo" + assert unallowed_schema_type not in allowed_schema_types + result = get_schema(test_file, unallowed_schema_type) + assert result is None