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| author | Alexander Kabui | 2021-05-12 19:49:55 +0300 |
|---|---|---|
| committer | Alexander Kabui | 2021-05-12 19:49:55 +0300 |
| commit | f88a2c3161c71d58c91c3030bd303a86846c5a73 (patch) | |
| tree | feeb4baeb516877a75358368c53fe8ad5624b286 /gn3/computations | |
| parent | beccacde5c9c7317bfe795e5c8c4ebe033f39f89 (diff) | |
| download | genenetwork3-f88a2c3161c71d58c91c3030bd303a86846c5a73.tar.gz | |
rename tissue_correlation_for_trait_list with tissue_correlation_for_trait
Diffstat (limited to 'gn3/computations')
| -rw-r--r-- | gn3/computations/correlations.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index 3cea69d..21bc82e 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -222,7 +222,7 @@ probet return corr_results -def tissue_correlation_for_trait_list( +def tissue_correlation_for_trait( primary_tissue_vals: List, target_tissues_values: List, corr_method: str, @@ -378,7 +378,7 @@ def compute_all_lit_correlation(conn, trait_lists: List, def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissues_data: dict, corr_method: str): - """Function acts as an abstraction for tissue_correlation_for_trait_list\ + """Function acts as an abstraction for tissue_correlation_for_trait\ required input are target tissue object and primary tissue trait\ target tissues data contains the trait_symbol_dict and symbol_tissue_vals @@ -398,7 +398,7 @@ def compute_all_tissue_correlation(primary_tissue_dict: dict, target_tissue_vals = target_tissue_obj.get("tissue_values") - tissue_result = tissue_correlation_for_trait_list( + tissue_result = tissue_correlation_for_trait( primary_tissue_vals=primary_tissue_vals, target_tissues_values=target_tissue_vals, trait_id=trait_id, @@ -459,7 +459,7 @@ def compute_tissue_correlation(primary_tissue_dict: dict, with multiprocessing.Pool(4) as pool: results = pool.starmap( - tissue_correlation_for_trait_list, processed_values) + tissue_correlation_for_trait, processed_values) for result in results: tissues_results.append(result) |