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October 26, 2022 09:10
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A light-weight implementation of a Django `views.py` approach to parsing the JQuery QueryBuilder object.
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| from django.db.models import Q | |
| def operator_check(operator: str, filter_base: str, search_value: str) -> Q: | |
| """Implements Djangos double-underscore filters such as less-than-or-equal. | |
| Args: | |
| rule (dict): A dictionary entry representing an individual JQuery QueryBuilder rule. | |
| filter_base (str): A string representing the base Django model filter string to which we will add the appropriate logic filter. | |
| Returns: | |
| Q: A Q() object representation of the rule, complete with its appropriate logic. | |
| """ | |
| base_dictionary = dict() | |
| if operator == "less": | |
| base_dictionary[filter_base + "__lt"] = search_value | |
| return Q(**base_dictionary) | |
| elif operator == "greater": | |
| base_dictionary[filter_base + "__gt"] = search_value | |
| return Q(**base_dictionary) | |
| elif operator == "less or equal": | |
| base_dictionary[filter_base + "__lte"] = search_value | |
| return Q(**base_dictionary) | |
| elif operator == "greater or equal": | |
| base_dictionary[filter_base + "__gte"] = search_value | |
| return Q(**base_dictionary) | |
| elif operator == "equal": | |
| base_dictionary[filter_base + "__contains"] = search_value | |
| return Q(**base_dictionary) | |
| elif operator == "not_equal": | |
| base_dictionary[filter_base + "__contains"] = search_value | |
| return ~Q(**base_dictionary) | |
| def filter_type_to_q(rule: str) -> Q: | |
| """Check what type of filter it is and process to Q() object. (Calls another function for -> Q() conversion) | |
| Args: | |
| rule (dict): The individual JQuery QueryBuilder rule as a dictionary | |
| Returns: | |
| Q: A Django Q() object instance representing the JQuery QueryBuilder rule. | |
| """ | |
| if rule["type"] == "integer": | |
| if rule["field"] == "RVIS": # <Select> element | |
| filter_string_base = "gene__rvis_score" | |
| return operator_check(rule["operator"], filter_string_base, 0) | |
| elif rule["field"] == "P-value": # <Select> element | |
| print("ITS A P-VALUE") | |
| filter_string_base = "study_variants__p_value" | |
| return operator_check(rule["operator"], filter_string_base, rule["value"]) | |
| elif rule["type"] == "string": | |
| if rule["field"] == "Condition": # <Radio> element | |
| filter_string_base = "study_variants__p_value" | |
| return operator_check(rule["operator"], filter_string_base, rule["value"]) | |
| elif rule["field"] == "Gene HPO": # <Text> element | |
| filter_string_base = "gene__gene_hpo" | |
| return operator_check(rule["operator"], filter_string_base, rule["value"]) | |
| elif rule["field"] == "Variant consequences": # <Select> element | |
| filter_string_base = "ensembl_vep__consequence_terms" | |
| return operator_check(rule["operator"], filter_string_base, rule["value"]) | |
| elif rule["field"] == "Predicted variant effect": # <Radio> element | |
| if rule["value"] == "Pathogenic": | |
| full_query = Q() | |
| for key, value in { | |
| "ensembl_vep__polyphen2_hvar_pred": "P", | |
| "ensembl_vep__sift_prediction": "pathogenic", | |
| "ensembl_vep__sift4g_pred": "P", | |
| "ensembl_vep__fathmm_pred": "P", | |
| }.items(): | |
| full_query.add(operator_check(rule["operator"], key, value), Q.OR) | |
| return full_query | |
| elif rule["value"] == "Deleterious": | |
| full_query = Q() | |
| for key, value in { | |
| "mt_vep__query_prediction": "disease causing", | |
| "ensembl_vep__polyphen2_hvar_pred": "D", | |
| "ensembl_vep__sift_prediction": "deleterious", | |
| "ensembl_vep__sift4g_pred": "D", | |
| "ensembl_vep__fathmm_pred": "D", | |
| }.items(): | |
| full_query.add(operator_check(rule["operator"], key, value), Q.OR) | |
| return full_query | |
| def tree_explorer(data: dict) -> Q: | |
| """Generates a full Django Q() object with complex nested AND/OR logic. | |
| Args: | |
| data (dict): A python dictionary of the JQuery QueryBuilder JSON output. | |
| Returns: | |
| Q: A complex Django-compatible Q() object representing the complex query. | |
| Yields: | |
| Iterator[Q]: Q() instance generator for recursive parsing of nested complex queries. | |
| """ | |
| if "type" in data: | |
| # Assume its a valid rule and yield into Q() representation: | |
| yield filter_type_to_q(data) | |
| elif "condition" in data: | |
| # Assume it is a nested group and call this generator recursively, | |
| # constructing this groups complex Q() object in the process: | |
| query = Q() | |
| for rule in data["rules"]: | |
| # Get next() result from generator above | |
| # (bc recursive yields) and process logic: | |
| for rule_instance in tree_explorer(rule): | |
| if data["condition"] == "AND": | |
| query.add(rule_instance, Q.AND) | |
| if data["condition"] == "OR": | |
| query.add(rule_instance, Q.OR) | |
| # Yield the completed, compiled logic for this branch | |
| # of query back up the pipe: | |
| yield query |
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