init code refactoring #742
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| Original file line number | Diff line number | Diff line change | ||||
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@@ -74,6 +74,193 @@ def set_template_vars(start_vars, correlation_data): | |||||
| return correlation_data | ||||||
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| def apply_filters(trait, target_trait, target_dataset, **filters): | ||||||
| def __p_val_filter__(p_lower, p_upper): | ||||||
| return not (float(trait.get('corr_coefficient', 0.0)) >= p_lower and | ||||||
| float(trait.get('corr_coefficient', 0.0)) <= p_upper) | ||||||
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| def __min_filter__(min_expr): | ||||||
| if (target_dataset['type'] in ["ProbeSet", "Publish"] and target_trait['mean']): | ||||||
| return (min_expr != None) and (float(target_trait['mean']) < min_expr) | ||||||
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There was a problem hiding this comment. Similar comment to above:
Suggested change
There was a problem hiding this comment. @BonfaceKilz the above example may lead to unintended results min_expr = None return 2.4 < min_expr is not None
results to
TypeError: '<' not supported between instances of 'float' and 'str'and if you use bracket that means you will be comparing int to boolean |
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| return False | ||||||
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| def __location_filter__(location_type, location_chr, | ||||||
| min_location_mb, max_location_mb): | ||||||
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| if target_dataset["type"] in ["ProbeSet", "'Geno"] and location_type == "gene": | ||||||
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| return ((location_chr != None and (target_trait["chr"] != location_chr) | ||||||
| or | ||||||
| (min_location_mb != None) and ( | ||||||
| float(target_trait['mb']) < min_location_mb) | ||||||
| or | ||||||
| max_location_mb != None) and | ||||||
| (float(target_trait['mb']) > float(max_location_mb) | ||||||
| )) | ||||||
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There was a problem hiding this comment. In this case too, you could chain operations together as demo'd above. |
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| elif target_dataset["type"] in ["ProbeSet", "Publish"]: | ||||||
| return ((location_chr != None and (target_trait["lrs_chr"] != location_chr) | ||||||
| or | ||||||
| (min_location_mb != None) and ( | ||||||
| float(target_trait['lrs_mb']) < float(min_location_mb)) | ||||||
| or | ||||||
| (max_location_mb != None) and ( | ||||||
| float(target_trait['lrs_mb']) > float(max_location_mb)) | ||||||
| ) | ||||||
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There was a problem hiding this comment. Same sentiment as above. |
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| ) | ||||||
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| return True | ||||||
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| # check if one of the condition is not met i.e One is True | ||||||
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| return (__p_val_filter__( | ||||||
| filters.get("p_range_lower"), | ||||||
| filters.get("p_range_upper") | ||||||
| ) | ||||||
| or | ||||||
| ( | ||||||
| __min_filter__( | ||||||
| filters.get("min_expr") | ||||||
| ) | ||||||
| ) | ||||||
| or | ||||||
| __location_filter__( | ||||||
| filters.get("location_type"), | ||||||
| filters.get("location_chr"), | ||||||
| filters.get("min_location_mb"), | ||||||
| filters.get("max_location_mb") | ||||||
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| ) | ||||||
| ) | ||||||
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| def get_user_filters(start_vars): | ||||||
| (min_expr, p_min, p_max) = ( | ||||||
| get_float(start_vars, 'min_expr'), | ||||||
| get_float(start_vars, 'p_range_lower', -1.0), | ||||||
| get_float(start_vars, 'p_range_upper', 1.0) | ||||||
| ) | ||||||
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| if all(keys in start_vars for keys in ["loc_chr", | ||||||
| "min_loc_mb", | ||||||
| "max_location_mb"]): | ||||||
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| location_chr = get_string(start_vars, "loc_chr") | ||||||
| min_location_mb = get_int(start_vars, "min_loc_mb") | ||||||
| max_location_mb = get_int(start_vars, "max_loc_mb") | ||||||
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| else: | ||||||
| location_chr = min_location_mb = max_location_mb = None | ||||||
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| return { | ||||||
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| "min_expr": min_expr, | ||||||
| "p_range_lower": p_min, | ||||||
| "p_range_upper": p_max, | ||||||
| "location_chr": location_chr, | ||||||
| "location_type": start_vars['location_type'], | ||||||
| "min_location_mb": min_location_mb, | ||||||
| "max_location_mb": max_location_mb | ||||||
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| } | ||||||
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| def generate_table_metadata(all_traits, dataset_metadata, dataset_obj): | ||||||
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| def __fetch_trait_data__(trait, dataset_obj): | ||||||
| target_trait_ob = create_trait(dataset=dataset_obj, | ||||||
| name=trait, | ||||||
| get_qtl_info=True) | ||||||
| return jsonable(target_trait_ob, dataset_obj) | ||||||
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| metadata = [__fetch_trait_data__(trait, dataset_obj) for | ||||||
| trait in (all_traits ^ dataset_metadata.keys())] | ||||||
| return (dataset_metadata | ({trait["name"]: trait for trait in metadata})) | ||||||
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| def populate_table(dataset_metadata, target_dataset, this_dataset, corr_results, filters): | ||||||
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| def __populate_trait__(idx, trait): | ||||||
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| trait_name = list(trait.keys())[0] | ||||||
| target_trait = dataset_metadata.get(trait_name) | ||||||
| trait = trait[trait_name] | ||||||
| if not apply_filters(trait, target_trait, target_dataset, **filters): | ||||||
| results_dict = {} | ||||||
| results_dict['index'] = idx + 1 # | ||||||
| results_dict['trait_id'] = target_trait['name'] | ||||||
| results_dict['dataset'] = target_dataset['name'] | ||||||
| results_dict['hmac'] = hmac.data_hmac( | ||||||
| '{}:{}'.format(target_trait['name'], target_dataset['name'])) | ||||||
| results_dict['sample_r'] = f"{float(trait.get('corr_coefficient',0.0)):.3f}" | ||||||
| results_dict['num_overlap'] = trait.get('num_overlap', 0) | ||||||
| results_dict['sample_p'] = f"{float(trait.get('p_value',0)):.3e}" | ||||||
| if target_dataset['type'] == "ProbeSet": | ||||||
| results_dict['symbol'] = target_trait['symbol'] | ||||||
| results_dict['description'] = "N/A" | ||||||
| results_dict['location'] = target_trait['location'] | ||||||
| results_dict['mean'] = "N/A" | ||||||
| results_dict['additive'] = "N/A" | ||||||
| if target_trait['description']: | ||||||
| results_dict['description'] = target_trait['description'] | ||||||
| if target_trait['mean']: | ||||||
| results_dict['mean'] = f"{float(target_trait['mean']):.3f}" | ||||||
| try: | ||||||
| results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" | ||||||
| except: | ||||||
| results_dict['lod_score'] = "N/A" | ||||||
| results_dict['lrs_location'] = target_trait['lrs_location'] | ||||||
| if target_trait['additive']: | ||||||
| results_dict['additive'] = f"{float(target_trait['additive']):.3f}" | ||||||
| results_dict['lit_corr'] = "--" | ||||||
| results_dict['tissue_corr'] = "--" | ||||||
| results_dict['tissue_pvalue'] = "--" | ||||||
| if this_dataset['type'] == "ProbeSet": | ||||||
| if 'lit_corr' in trait: | ||||||
| results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}" | ||||||
| if 'tissue_corr' in trait: | ||||||
| results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}" | ||||||
| results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.3e}" | ||||||
| elif target_dataset['type'] == "Publish": | ||||||
| results_dict['abbreviation_display'] = "N/A" | ||||||
| results_dict['description'] = "N/A" | ||||||
| results_dict['mean'] = "N/A" | ||||||
| results_dict['authors_display'] = "N/A" | ||||||
| results_dict['additive'] = "N/A" | ||||||
| results_dict['pubmed_link'] = "N/A" | ||||||
| results_dict['pubmed_text'] = target_trait["pubmed_text"] | ||||||
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| if target_trait["abbreviation"]: | ||||||
| results_dict = target_trait['abbreviation'] | ||||||
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| if target_trait["description"] == target_trait['description']: | ||||||
| results_dict['description'] = target_trait['description'] | ||||||
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| if target_trait["mean"]: | ||||||
| results_dict['mean'] = f"{float(target_trait['mean']):.3f}" | ||||||
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| if target_trait["authors"]: | ||||||
| authors_list = target_trait['authors'].split(',') | ||||||
| results_dict['authors_display'] = ", ".join( | ||||||
| authors_list[:6]) + ", et al." if len(authors_list) > 6 else target_trait['authors'] | ||||||
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| if "pubmed_id" in target_trait: | ||||||
| results_dict['pubmed_link'] = target_trait['pubmed_link'] | ||||||
| results_dict['pubmed_text'] = target_trait['pubmed_text'] | ||||||
| try: | ||||||
| results_dict["lod_score"] = f"{float(target_trait['lrs_score']) / 4.61:.1f}" | ||||||
| except ValueError: | ||||||
| results_dict['lod_score'] = "N/A" | ||||||
| else: | ||||||
| results_dict['lrs_location'] = target_trait['lrs_location'] | ||||||
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| return results_dict | ||||||
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| return [__populate_trait__(idx, trait) | ||||||
| for (idx, trait) in enumerate(corr_results)] | ||||||
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| def correlation_json_for_table(start_vars, correlation_data, this_trait, this_dataset, target_dataset_ob): | ||||||
| """Return JSON data for use with the DataTable in the correlation result page | ||||||
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@@ -86,139 +273,18 @@ def correlation_json_for_table(start_vars, correlation_data, this_trait, this_da | |||||
| this_trait = correlation_data['this_trait'] | ||||||
| this_dataset = correlation_data['this_dataset'] | ||||||
| target_dataset = target_dataset_ob.as_dict() | ||||||
| corr_results = correlation_data['correlation_results'] | ||||||
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| dataset_metadata = generate_table_metadata({name for trait in corr_results | ||||||
| for (name, _val) in trait.items()}, | ||||||
| correlation_data["traits_metadata"], | ||||||
| target_dataset_ob) | ||||||
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There was a problem hiding this comment. This nested "for" loop would be difficult to maintain. I'd advise using a more simpler, albeit verbose, loop instead. |
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| results = populate_table(dataset_metadata, | ||||||
| target_dataset, | ||||||
| this_dataset, corr_results, | ||||||
| get_user_filters(start_vars)) | ||||||
| return json.dumps([result for result in results if result]) | ||||||
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| def get_formatted_corr_type(corr_type, corr_method): | ||||||
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A more pythonic (and readable way) to write this is:
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Awesome