Fix linting errors

Issue:
https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/partial-correlations.gmi

1 files changed, 70 insertions, 61 deletions

diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index 869bee4..231b0a7 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -14,12 +14,20 @@ import pingouin
 from scipy.stats import pearsonr, spearmanr
 
 from gn3.settings import TEXTDIR
+from gn3.random import random_string
 from gn3.function_helpers import  compose
 from gn3.data_helpers import parse_csv_line
 from gn3.db.traits import export_informative
 from gn3.db.traits import retrieve_trait_info, retrieve_trait_data
 from gn3.db.species import species_name, translate_to_mouse_gene_id
-from gn3.db.correlations import get_filename, fetch_all_database_data
+from gn3.db.correlations import (
+    get_filename,
+    fetch_all_database_data,
+    check_for_literature_info,
+    fetch_tissue_correlations,
+    fetch_literature_correlations,
+    check_symbol_for_tissue_correlation,
+    fetch_gene_symbol_tissue_value_dict_for_trait)
 
 def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]):
     """
@@ -311,7 +319,7 @@ def compute_partial(
 
         zero_order_corr = pingouin.corr(
             datafrm["x"], datafrm["y"], method=(
-            "pearson" if "pearson" in method.lower() else "spearman"))
+                "pearson" if "pearson" in method.lower() else "spearman"))
 
         if math.isnan(pc_coeff):
             return (
@@ -371,9 +379,10 @@ def partial_correlations_normal(# pylint: disable=R0913
 
     return len(trait_database), all_correlations
 
-def partial_corrs(
-        conn, samples , primary_vals, control_vals, return_number, species, input_trait_geneid,
-        input_trait_symbol, tissue_probeset_freeze_id, method, dataset, database_filename):
+def partial_corrs(# pylint: disable=[R0913]
+        conn, samples, primary_vals, control_vals, return_number, species,
+        input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
+        method, dataset, database_filename):
     """
     Compute the partial correlations, selecting the fast or normal method
     depending on the existence of the database text file.
@@ -404,8 +413,7 @@ def partial_corrs(
         data_start_pos, dataset, method)
 
 def literature_correlation_by_list(
-        conn: Any, input_trait_mouse_geneid: int, species: str,
-        trait_list: Tuple[dict]) -> Tuple[dict]:
+        conn: Any, species: str, trait_list: Tuple[dict]) -> Tuple[dict]:
     """
     This is a migration of the
     `web.webqtl.correlation.CorrelationPage.getLiteratureCorrelationByList`
@@ -415,16 +423,16 @@ def literature_correlation_by_list(
             bool(t.get("tissue_corr")) and
             bool(t.get("tissue_p_value"))))(trait)
            for trait in trait_list):
-        temp_table_name = f"LITERATURE{random_string(8)}"
-        q1 = (
+        temporary_table_name = f"LITERATURE{random_string(8)}"
+        query1 = (
             f"CREATE TEMPORARY TABLE {temporary_table_name} "
             "(GeneId1 INT(12) UNSIGNED, GeneId2 INT(12) UNSIGNED PRIMARY KEY, "
             "value DOUBLE)")
-        q2 = (
+        query2 = (
             f"INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
             "SELECT GeneId1, GeneId2, value FROM LCorrRamin3 "
             "WHERE GeneId1=%(geneid)s")
-        q3 = (
+        query3 = (
             "INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
             "SELECT GeneId2, GeneId1, value FROM LCorrRamin3 "
             "WHERE GeneId2=%s AND GeneId1 != %(geneid)s")
@@ -433,7 +441,8 @@ def literature_correlation_by_list(
             if trait.get("geneid"):
                 return {
                     **trait,
-                    "mouse_geneid": translate_to_mouse_gene_id(trait.get("geneid"))
+                    "mouse_geneid": translate_to_mouse_gene_id(
+                        species, trait.get("geneid"), conn)
                 }
             return {**trait, "mouse_geneid": 0}
 
@@ -441,13 +450,13 @@ def literature_correlation_by_list(
             cursor.execute(
                 f"SELECT GeneId2, value FROM {temporary_table_name} "
                 "WHERE GeneId2 IN %(geneids)s",
-                geneids = geneids)
-            return {geneid: value for geneid, value in cursor.fetchall()}
+                geneids=geneids)
+            return dict(cursor.fetchall())
 
         with conn.cursor() as cursor:
-            cursor.execute(q1)
-            cursor.execute(q2)
-            cursor.execute(q3)
+            cursor.execute(query1)
+            cursor.execute(query2)
+            cursor.execute(query3)
 
             traits = tuple(__set_mouse_geneid__(trait) for trait in trait_list)
             lcorrs = __retrieve_lcorr__(
@@ -470,9 +479,9 @@ def tissue_correlation_by_list(
     `web.webqtl.correlation.CorrelationPage.getTissueCorrelationByList`
     function in GeneNetwork1.
     """
-    def __add_tissue_corr__(trait, primary_trait_value, trait_value):
+    def __add_tissue_corr__(trait, primary_trait_values, trait_values):
         result = pingouin.corr(
-            primary_trait_values, target_trait_values,
+            primary_trait_values, trait_values,
             method=("spearman" if "spearman" in method.lower() else "pearson"))
         return {
             **trait,
@@ -484,7 +493,8 @@ def tissue_correlation_by_list(
         prim_trait_symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait(
             (primary_trait_symbol,), tissue_probeset_freeze_id, conn)
         if primary_trait_symbol.lower() in prim_trait_symbol_value_dict:
-            primary_trait_value = prim_trait_symbol_value_dict[prim_trait_symbol.lower()]
+            primary_trait_value = prim_trait_symbol_value_dict[
+                primary_trait_symbol.lower()]
             gene_symbol_list = tuple(
                 trait for trait in trait_list if "symbol" in trait.keys())
             symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait(
@@ -504,7 +514,7 @@ def tissue_correlation_by_list(
         } for trait in trait_list)
     return trait_list
 
-def partial_correlations_entry(
+def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
         conn: Any, primary_trait_name: str,
         control_trait_names: Tuple[str, ...], method: str,
         criteria: int, group: str, target_db_name: str) -> dict:
@@ -524,7 +534,7 @@ def partial_correlations_entry(
 
     primary_trait = retrieve_trait_info(threshold, primary_trait_name, conn)
     primary_trait_data = retrieve_trait_data(primary_trait, conn)
-    primary_samples, primary_values, primary_variances = export_informative(
+    primary_samples, primary_values, _primary_variances = export_informative(
         primary_trait_data)
 
     cntrl_traits = tuple(
@@ -537,8 +547,8 @@ def partial_correlations_entry(
 
     (cntrl_samples,
      cntrl_values,
-     cntrl_variances,
-     cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
+     _cntrl_variances,
+     _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
 
     common_primary_control_samples = primary_samples
     fixed_primary_vals = primary_values
@@ -547,8 +557,8 @@ def partial_correlations_entry(
         (common_primary_control_samples,
          fixed_primary_vals,
          fixed_control_vals,
-         primary_variances,
-         cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
+         _primary_variances,
+         _cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
 
     if len(common_primary_control_samples) < corr_min_informative:
         return {
@@ -580,7 +590,6 @@ def partial_correlations_entry(
 
     tissue_probeset_freeze_id = 1
     db_type = primary_trait["db"]["dataset_type"]
-    db_name = primary_trait["db"]["dataset_name"]
 
     if db_type == "ProbeSet" and method.lower() in (
             "sgo literature correlation",
@@ -605,10 +614,11 @@ def partial_correlations_entry(
                 "associated Literature Information."),
             "error_type": "Literature Correlation"}
 
-    if (method.lower() in (
-            "tissue correlation, pearson's r",
-            "tissue correlation, spearman's rho")
-        and input_trait_symbol is None):
+    if (
+            method.lower() in (
+                "tissue correlation, pearson's r",
+                "tissue correlation, spearman's rho")
+            and input_trait_symbol is None):
         return {
             "status": "error",
             "message": (
@@ -616,11 +626,12 @@ def partial_correlations_entry(
                 "any associated Tissue Correlation Information."),
             "error_type": "Tissue Correlation"}
 
-    if (method.lower() in (
-            "tissue correlation, pearson's r",
-            "tissue correlation, spearman's rho")
-        and check_symbol_for_tissue_correlation(
-            conn, tissue_probeset_freeze_id, input_trait_symbol)):
+    if (
+            method.lower() in (
+                "tissue correlation, pearson's r",
+                "tissue correlation, spearman's rho")
+            and check_symbol_for_tissue_correlation(
+                conn, tissue_probeset_freeze_id, input_trait_symbol)):
         return {
             "status": "error",
             "message": (
@@ -629,7 +640,7 @@ def partial_correlations_entry(
             "error_type": "Tissue Correlation"}
 
     database_filename = get_filename(conn, target_db_name, TEXTDIR)
-    total_traits, all_correlations = partial_corrs(
+    _total_traits, all_correlations = partial_corrs(
         conn, common_primary_control_samples, fixed_primary_vals,
         fixed_control_vals, len(fixed_primary_vals), species,
         input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
@@ -637,11 +648,11 @@ def partial_correlations_entry(
 
 
     def __make_sorter__(method):
-        def __sort_6__(x):
-            return x[6]
+        def __sort_6__(row):
+            return row[6]
 
-        def __sort_3__(x):
-            return x[3]
+        def __sort_3__(row):
+            return row[3]
 
         if "literature" in method.lower():
             return __sort_6__
@@ -655,33 +666,31 @@ def partial_correlations_entry(
         all_correlations, key=__make_sorter__(method))
 
     add_lit_corr_and_tiss_corr = compose(
-        partial(
-            literature_correlation_by_list, conn, input_trait_mouse_geneid,
-            species),
+        partial(literature_correlation_by_list, conn, species),
         partial(
             tissue_correlation_by_list, conn, input_trait_symbol,
             tissue_probeset_freeze_id, method))
 
     trait_list = add_lit_corr_and_tiss_corr(tuple(
-            {
-                **retrieve_trait_info(
-                    threshold,
-                    f"{primary_trait['db']['dataset_name']}::{item[0]}",
-                    conn),
-                "noverlap": item[1],
-                "partial_corr": item[2],
-                "partial_corr_p_value": item[3],
-                "corr": item[4],
-                "corr_p_value": item[5],
-                "rank_order": (1 if "spearman" in method.lower() else 0),
-                **({
-                    "tissue_corr": item[6],
-                    "tissue_p_value": item[7]}
+        {
+            **retrieve_trait_info(
+                threshold,
+                f"{primary_trait['db']['dataset_name']}::{item[0]}",
+                conn),
+            "noverlap": item[1],
+            "partial_corr": item[2],
+            "partial_corr_p_value": item[3],
+            "corr": item[4],
+            "corr_p_value": item[5],
+            "rank_order": (1 if "spearman" in method.lower() else 0),
+            **({
+                "tissue_corr": item[6],
+                "tissue_p_value": item[7]}
                if len(item) == 8 else {}),
-                **({"l_corr": item[6]}
+            **({"l_corr": item[6]}
                if len(item) == 7 else {})
-            }
+        }
         for item in
-            sorted_correlations[:min(criteria, len(all_correlations))]))
+        sorted_correlations[:min(criteria, len(all_correlations))]))
 
     return trait_list