linter fixes

1 files changed, 30 insertions, 33 deletions

diff --git a/wqflask/wqflask/correlation/rust_correlation.py b/wqflask/wqflask/correlation/rust_correlation.py
index 95354994..7d796e70 100644
--- a/wqflask/wqflask/correlation/rust_correlation.py
+++ b/wqflask/wqflask/correlation/rust_correlation.py
@@ -3,7 +3,8 @@ import json
 from functools import reduce
 from utility.db_tools import mescape
 from utility.db_tools import create_in_clause
-from wqflask.correlation.correlation_functions import get_trait_symbol_and_tissue_values
+from wqflask.correlation.correlation_functions\
+    import get_trait_symbol_and_tissue_values
 from wqflask.correlation.correlation_gn3_api import create_target_this_trait
 from wqflask.correlation.correlation_gn3_api import lit_for_trait_list
 from wqflask.correlation.correlation_gn3_api import do_lit_correlation
@@ -14,9 +15,7 @@ from gn3.computations.rust_correlation import parse_tissue_corr_data
 from gn3.db_utils import database_connector
 
 
-
-
-def chunk_dataset(dataset,steps,name):
+def chunk_dataset(dataset, steps, name):
 
     results = []
 
@@ -39,7 +38,8 @@ def chunk_dataset(dataset,steps,name):
         matrix = list(dataset[i:i + steps])
         trait_name = traits_name_dict[matrix[0][0]]
 
-        strains = [trait_name] + [str(value) for (trait_name, strain, value) in matrix]
+        strains = [trait_name] + [str(value)
+                                  for (trait_name, strain, value) in matrix]
         results.append(",".join(strains))
 
     return results
@@ -48,18 +48,16 @@ def chunk_dataset(dataset,steps,name):
 def compute_top_n_sample(start_vars, dataset, trait_list):
     """check if dataset is of type probeset"""
 
-    if dataset.type.lower()!= "probeset":
-        return  {}
+    if dataset.type.lower() != "probeset":
+        return {}
 
     def __fetch_sample_ids__(samples_vals, samples_group):
 
-
         all_samples = json.loads(samples_vals)
         sample_data = get_sample_corr_data(
             sample_type=samples_group, all_samples=all_samples,
             dataset_samples=dataset.group.all_samples_ordered())
 
-
         with database_connector() as conn:
 
             curr = conn.cursor()
@@ -75,21 +73,20 @@ def compute_top_n_sample(start_vars, dataset, trait_list):
 
             )
 
-            return (sample_data,dict(curr.fetchall()))
-
-    (sample_data,sample_ids) = __fetch_sample_ids__(start_vars["sample_vals"], start_vars["corr_samples_group"])
-
+            return (sample_data, dict(curr.fetchall()))
 
+    (sample_data, sample_ids) = __fetch_sample_ids__(
+        start_vars["sample_vals"], start_vars["corr_samples_group"])
 
     with database_connector() as conn:
 
         curr = conn.cursor()
 
-        #fetching strain data in bulk
+        # fetching strain data in bulk
 
         curr.execute(
 
-        """
+            """
             SELECT * from ProbeSetData
             where StrainID in {}
             and id in (SELECT ProbeSetXRef.DataId
@@ -98,21 +95,25 @@ def compute_top_n_sample(start_vars, dataset, trait_list):
             and ProbeSetFreeze.Name = '{}'
             and ProbeSet.Name in {}
             and ProbeSet.Id = ProbeSetXRef.ProbeSetId)
-           """.format(create_in_clause(list(sample_ids.values())),dataset.name,create_in_clause(trait_list))
+           """.format(create_in_clause(list(sample_ids.values())), dataset.name, create_in_clause(trait_list))
 
 
         )
 
-        corr_data = chunk_dataset(list(curr.fetchall()),len(sample_ids.values()),dataset.name)
+        corr_data = chunk_dataset(list(curr.fetchall()), len(
+            sample_ids.values()), dataset.name)
 
-        return run_correlation(corr_data,list(sample_data.values()),"pearson",",")
+        return run_correlation(corr_data,
+                               list(sample_data.values()),
+                               "pearson", ",")
 
 
 def compute_top_n_lit(corr_results, this_dataset, this_trait) -> dict:
     (this_trait_geneid, geneid_dict, species) = do_lit_correlation(
         this_trait, this_dataset)
 
-    geneid_dict = {trait_name: geneid for (trait_name, geneid) in geneid_dict.items() if
+    geneid_dict = {trait_name: geneid for (trait_name, geneid)
+                   in geneid_dict.items() if
                    corr_results.get(trait_name)}
     with database_connector() as conn:
         return reduce(
@@ -166,8 +167,6 @@ def merge_results(dict_a: dict, dict_b: dict, dict_c: dict) -> list[dict]:
     return [__merge__(tname, tcorrs) for tname, tcorrs in dict_a.items()]
 
 
-
-
 def __compute_sample_corr__(
         start_vars: dict, corr_type: str, method: str, n_top: int,
         target_trait_info: tuple):
@@ -247,7 +246,6 @@ def compute_correlation_rust(
 
     # END: Replace this with `match ...` once we hit Python 3.10
 
-
     top_a = top_b = {}
 
     if compute_all:
@@ -255,28 +253,27 @@ def compute_correlation_rust(
         if corr_type == "sample":
 
             top_a = compute_top_n_tissue(
-            this_dataset, this_trait, results, method)
-        
-            top_b = compute_top_n_lit(results, this_dataset, this_trait)
+                this_dataset, this_trait, results, method)
 
+            top_b = compute_top_n_lit(results, this_dataset, this_trait)
 
         elif corr_type == "lit":
 
-            #currently fails for lit
+            # currently fails for lit
 
-            top_a = compute_top_n_sample(start_vars,target_dataset,list(results.keys()))
-            top_b =  compute_top_n_tissue(
-            this_dataset, this_trait, results, method)
+            top_a = compute_top_n_sample(
+                start_vars, target_dataset, list(results.keys()))
+            top_b = compute_top_n_tissue(
+                this_dataset, this_trait, results, method)
 
         else:
 
-            top_a = compute_top_n_sample(start_vars,target_dataset,list(results.keys()))
+            top_a = compute_top_n_sample(
+                start_vars, target_dataset, list(results.keys()))
 
             top_b = compute_top_n_lit(results, this_dataset, this_trait)
 
-
-
-    return  {
+    return {
         "correlation_results": merge_results(
             results, top_a, top_b),
         "this_trait": this_trait.name,