computations: correlations: Apply pep-8

1 files changed, 54 insertions, 88 deletions

diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index 0d15d9b..cd7d604 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -9,12 +9,17 @@ from typing import Callable
 import scipy.stats
 
 
-def map_shared_keys_to_values(target_sample_keys: List, target_sample_vals: dict)-> List:
-    """Function to construct target dataset data items given commoned shared\
-    keys and trait samplelist values for example given keys  >>>>>>>>>>\
-    ["BXD1", "BXD2", "BXD5", "BXD6", "BXD8", "BXD9"] and value object as\
-    "HCMA:_AT": [4.1, 5.6, 3.2, 1.1, 4.4, 2.2],TXD_AT": [6.2, 5.7, 3.6, 1.5, 4.2, 2.3]}\
-    return  results should be a list of dicts mapping the shared keys to the trait values"""
+def map_shared_keys_to_values(target_sample_keys: List,
+                              target_sample_vals: dict) -> List:
+    """Function to construct target dataset data items given common shared keys
+    and trait sample-list values for example given keys
+
+    >>>>>>>>>> ["BXD1", "BXD2", "BXD5", "BXD6", "BXD8", "BXD9"] and value
+    object as "HCMA:_AT": [4.1, 5.6, 3.2, 1.1, 4.4, 2.2],TXD_AT": [6.2, 5.7,
+    3.6, 1.5, 4.2, 2.3]} return results should be a list of dicts mapping the
+    shared keys to the trait values
+
+    """
     target_dataset_data = []
 
     for trait_id, sample_values in target_sample_vals.items():
@@ -32,9 +37,9 @@ def map_shared_keys_to_values(target_sample_keys: List, target_sample_vals: dict
 
 def normalize_values(a_values: List,
                      b_values: List) -> Tuple[List[float], List[float], int]:
-    """Trim two lists of values to contain only the values they both share
-    Given two lists of sample values, trim each list so that it contains only
-    the samples that contain a value in both lists. Also returns the number of
+    """Trim two lists of values to contain only the values they both share Given
+    two lists of sample values, trim each list so that it contains only the
+    samples that contain a value in both lists. Also returns the number of
     such samples.
 
     >>> normalize_values([2.3, None, None, 3.2, 4.1, 5],
@@ -62,16 +67,14 @@ pearson,spearman and biweight mid correlation return value is rho and p_value
         "pearson": scipy.stats.pearsonr,
         "spearman": scipy.stats.spearmanr
     }
-
     use_corr_method = corr_mapping.get(corr_method, "spearman")
-
     corr_coeffient, p_val = use_corr_method(primary_values, target_values)
-
     return (corr_coeffient, p_val)
 
 
 def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
-                                 target_samples_vals) -> Optional[Tuple[str, float, float, int]]:
+                                 target_samples_vals) -> Optional[
+                                     Tuple[str, float, float, int]]:
     """Given a primary trait values and target trait values calculate the
     correlation coeff and p value
 
@@ -90,7 +93,6 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
         # should use numpy.isNan scipy.isNan is deprecated
         if corr_coeffient is not None:
             return (trait_name, corr_coeffient, p_value, num_overlap)
-
     return None
 
 
@@ -99,15 +101,16 @@ def do_bicor(x_val, y_val) -> Tuple[float, float]:
 package :not packaged in guix
 
     """
-    _corr_input = (x_val, y_val)
-    return (0.0, 0.0)
+    x_val, y_val = 0, 0
+    return (x_val, y_val)
 
 
 def filter_shared_sample_keys(this_samplelist,
                               target_samplelist) -> Tuple[List, List]:
-    """Given primary and target samplelist\
-    for two base and target trait select\
-    filter the values using the shared keys"""
+    """Given primary and target sample-list for two base and target trait select
+    filter the values using the shared keys
+
+    """
     this_vals = []
     target_vals = []
     for key, value in target_samplelist.items():
@@ -120,21 +123,18 @@ def filter_shared_sample_keys(this_samplelist,
 def compute_all_sample_correlation(this_trait,
                                    target_dataset,
                                    corr_method="pearson") -> List:
-    """Given a trait data samplelist and\
-    target__datasets compute all sample correlation
+    """Given a trait data sample-list and target__datasets compute all sample
+    correlation
+
     """
     # xtodo fix trait_name currently returning single one
     # pylint: disable-msg=too-many-locals
-
     this_trait_samples = this_trait["trait_sample_data"]
     corr_results = []
     processed_values = []
     for target_trait in target_dataset:
         trait_name = target_trait.get("trait_id")
         target_trait_data = target_trait["trait_sample_data"]
-        # this_vals, target_vals = filter_shared_sample_keys(
-        #     this_trait_samples, target_trait_data)
-
         processed_values.append((trait_name, corr_method, *filter_shared_sample_keys(
             this_trait_samples, target_trait_data)))
     with multiprocessing.Pool(4) as pool:
@@ -144,7 +144,6 @@ def compute_all_sample_correlation(this_trait,
             if sample_correlation is not None:
                 (trait_name, corr_coeffient, p_value,
                  num_overlap) = sample_correlation
-
                 corr_result = {
                     "corr_coeffient": corr_coeffient,
                     "p_value": p_value,
@@ -152,7 +151,6 @@ def compute_all_sample_correlation(this_trait,
                 }
 
                 corr_results.append({trait_name: corr_result})
-
     return sorted(
         corr_results,
         key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coeffient"]))
@@ -160,42 +158,34 @@ def compute_all_sample_correlation(this_trait,
 
 def benchmark_compute_all_sample(this_trait,
                                  target_dataset,
-                                 corr_method="pearson") ->List:
-    """Temp function to benchmark with compute_all_sample_r\
-    alternative to compute_all_sample_r where we use \
-    multiprocessing
-    """
+                                 corr_method="pearson") -> List:
+    """Temp function to benchmark with compute_all_sample_r alternative to
+    compute_all_sample_r where we use multiprocessing
 
+    """
     this_trait_samples = this_trait["trait_sample_data"]
-
     corr_results = []
-
     for target_trait in target_dataset:
         trait_name = target_trait.get("trait_id")
         target_trait_data = target_trait["trait_sample_data"]
         this_vals, target_vals = filter_shared_sample_keys(
             this_trait_samples, target_trait_data)
-
         sample_correlation = compute_sample_r_correlation(
             trait_name=trait_name,
             corr_method=corr_method,
             trait_vals=this_vals,
             target_samples_vals=target_vals)
-
         if sample_correlation is not None:
-            (trait_name, corr_coeffient, p_value, num_overlap) = sample_correlation
-
+            (trait_name, corr_coeffient,
+             p_value, num_overlap) = sample_correlation
         else:
             continue
-
         corr_result = {
             "corr_coeffient": corr_coeffient,
             "p_value": p_value,
             "num_overlap": num_overlap
         }
-
         corr_results.append({trait_name: corr_result})
-
     return corr_results
 
 
@@ -205,11 +195,8 @@ list depending on whether both dataset and target_dataset are both set to
 probet
 
     """
-
     corr_results = {"lit": 1}
-
     if corr_type not in ("lit", "literature"):
-
         corr_results["top_corr_results"] = top_corr_results
         # run lit_correlation for  the given  top_corr_results
     if corr_type == "tissue":
@@ -255,8 +242,10 @@ def fetch_lit_correlation_data(
         input_mouse_gene_id: Optional[str],
         gene_id: str,
         mouse_gene_id: Optional[str] = None) -> Tuple[str, float]:
-    """Given input trait mouse gene id and mouse gene id fetch the lit\
-    corr_data"""
+    """Given input trait mouse gene id and mouse gene id fetch the lit
+    corr_data
+
+    """
     if mouse_gene_id is not None and ";" not in mouse_gene_id:
         query = """
         SELECT VALUE
@@ -283,7 +272,6 @@ def fetch_lit_correlation_data(
         lit_results = (gene_id, lit_corr_results.val)\
             if lit_corr_results else (gene_id, 0)
         return lit_results
-
     return (gene_id, 0)
 
 
@@ -295,11 +283,9 @@ def lit_correlation_for_trait_list(
     """given species,base trait gene id fetch the lit corr results from the db\
     output is float for lit corr results """
     fetched_lit_corr_results = []
-
     this_trait_mouse_gene_id = map_to_mouse_gene_id(conn=conn,
                                                     species=species,
                                                     gene_id=trait_gene_id)
-
     for (trait_name, target_trait_gene_id) in target_trait_lists:
         corr_results = {}
         if target_trait_gene_id:
@@ -307,29 +293,26 @@ def lit_correlation_for_trait_list(
                 conn=conn,
                 species=species,
                 gene_id=target_trait_gene_id)
-
             fetched_corr_data = fetch_lit_correlation_data(
                 conn=conn,
                 input_mouse_gene_id=this_trait_mouse_gene_id,
                 gene_id=target_trait_gene_id,
                 mouse_gene_id=target_mouse_gene_id)
-
             dict_results = dict(zip(("gene_id", "lit_corr"),
                                     fetched_corr_data))
             corr_results[trait_name] = dict_results
             fetched_lit_corr_results.append(corr_results)
-
     return fetched_lit_corr_results
 
 
 def query_formatter(query_string: str, *query_values):
-    """Formatter query string given the unformatted query string\
-    and the respectibe values.Assumes number of placeholders is
-    equal to the number of query values """
-    # xtodo escape sql queries
-    results = query_string % (query_values)
+    """Formatter query string given the unformatted query string and the
+    respectibe values.Assumes number of placeholders is equal to the number of
+    query values
 
-    return results
+    """
+    # xtodo escape sql queries
+    return query_string % (query_values)
 
 
 def map_to_mouse_gene_id(conn, species: Optional[str],
@@ -342,26 +325,23 @@ def map_to_mouse_gene_id(conn, species: Optional[str],
         return None
     if species == "mouse":
         return gene_id
-
     cursor = conn.cursor()
     query = """SELECT mouse
                 FROM GeneIDXRef
                 WHERE '%s' = '%s'"""
-
     query_values = (species, gene_id)
     cursor.execute(query_formatter(query,
                                    *query_values))
     results = cursor.fetchone()
-
     mouse_gene_id = results.mouse if results is not None else None
-
     return mouse_gene_id
 
 
 def compute_all_lit_correlation(conn, trait_lists: List,
                                 species: str, gene_id):
-    """Function that acts as an abstraction for
-    lit_correlation_for_trait_list"""
+    """Function that acts as an abstraction for lit_correlation_for_trait_list
+
+    """
 
     lit_results = lit_correlation_for_trait_list(
         conn=conn,
@@ -378,47 +358,37 @@ 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\
-    required input are target tissue object and primary tissue trait\
-    target tissues data contains the trait_symbol_dict and symbol_tissue_vals
+    """Function acts as an abstraction for tissue_correlation_for_trait_list
+    required input are target tissue object and primary tissue trait target
+    tissues data contains the trait_symbol_dict and symbol_tissue_vals
 
     """
-
     tissues_results = []
-
     primary_tissue_vals = primary_tissue_dict["tissue_values"]
     traits_symbol_dict = target_tissues_data["trait_symbol_dict"]
     symbol_tissue_vals_dict = target_tissues_data["symbol_tissue_vals_dict"]
-
     target_tissues_list = process_trait_symbol_dict(
         traits_symbol_dict, symbol_tissue_vals_dict)
-
     for target_tissue_obj in target_tissues_list:
         trait_id = target_tissue_obj.get("trait_id")
-
         target_tissue_vals = target_tissue_obj.get("tissue_values")
-
         tissue_result = tissue_correlation_for_trait_list(
             primary_tissue_vals=primary_tissue_vals,
             target_tissues_values=target_tissue_vals,
             trait_id=trait_id,
             corr_method=corr_method)
-
         tissue_result_dict = {trait_id: tissue_result}
         tissues_results.append(tissue_result_dict)
-
-    sorted_tissues_results = sorted(
+    return sorted(
         tissues_results,
         key=lambda trait_name: -abs(list(trait_name.values())[0]["tissue_corr"]))
 
-    return sorted_tissues_results
-
 
 def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> List:
-    """Method for processing trait symbol\
-    dict given the symbol tissue values """
-    traits_tissue_vals = []
+    """Method for processing trait symbol dict given the symbol tissue values
 
+    """
+    traits_tissue_vals = []
     for (trait, symbol) in trait_symbol_dict.items():
         if symbol is not None:
             target_symbol = symbol.lower()
@@ -427,25 +397,21 @@ def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> Lis
                 target_tissue_dict = {"trait_id": trait,
                                       "symbol": target_symbol,
                                       "tissue_values": trait_tissue_val}
-
                 traits_tissue_vals.append(target_tissue_dict)
-
     return traits_tissue_vals
 
 
 def compute_tissue_correlation(primary_tissue_dict: dict,
                                target_tissues_data: dict,
                                corr_method: str):
-    """Experimental function that uses multiprocessing\
-    for computing tissue correlation
-    """
+    """Experimental function that uses multiprocessing for computing tissue
+    correlation
 
+    """
     tissues_results = []
-
     primary_tissue_vals = primary_tissue_dict["tissue_values"]
     traits_symbol_dict = target_tissues_data["trait_symbol_dict"]
     symbol_tissue_vals_dict = target_tissues_data["symbol_tissue_vals_dict"]
-
     target_tissues_list = process_trait_symbol_dict(
         traits_symbol_dict, symbol_tissue_vals_dict)
     processed_values = []