Update python version

phykit/helpers/boolean_argument_parsing.py

@@ -1,11 +1,12 @@
 import argparse
 
+
 def str2bool(v):
     if isinstance(v, bool):
-       return v
-    if v.lower() in ('true', 't', '1'):
+        return v
+    if v.lower() in ("true", "t", "1"):
         return True
-    elif v.lower() in ('false', 'f', '0'):
+    elif v.lower() in ("false", "f", "0"):
         return False
     else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
+        raise argparse.ArgumentTypeError("Boolean value expected.")

phykit/helpers/files.py

@@ -32,12 +32,12 @@ def get_alignment_and_format(alignment_file_path: str):
             print("Please check file name and pathing")
             sys.exit()
 
+
 def read_single_column_file_to_list(single_col_file_path: str) -> list:
     try:
         with open(single_col_file_path) as f:
-            return [line.rstrip('\n').strip() for line in f]
+            return [line.rstrip("\n").strip() for line in f]
     except FileNotFoundError:
         print(f"{single_col_file_path} corresponds to no such file or directory.")
         print("Please check file name and pathing")
         sys.exit()
-

phykit/helpers/stats_summary.py

@@ -1,31 +1,27 @@
 import statistics as stat
-import sys
 
 import numpy as np
 
 
-def calculate_summary_statistics_from_arr(
-    arr    
-):
+def calculate_summary_statistics_from_arr(arr):
     """
     calcuate summary statistics for an input list
     """
     stats = dict(
-        mean               = stat.mean(arr),
-        median             = stat.median(arr),
-        twenty_fifth       = np.percentile(arr, 25),
-        seventy_fifth      = np.percentile(arr, 75),
-        minimum            = np.min(arr),
-        maximum            = np.max(arr),
-        standard_deviation = stat.stdev(arr),
-        variance           = stat.variance(arr)
+        mean=stat.mean(arr),
+        median=stat.median(arr),
+        twenty_fifth=np.percentile(arr, 25),
+        seventy_fifth=np.percentile(arr, 75),
+        minimum=np.min(arr),
+        maximum=np.max(arr),
+        standard_deviation=stat.stdev(arr),
+        variance=stat.variance(arr),
     )
 
     return stats
 
-def calculate_summary_statistics_from_dict(
-    dat: dict    
-):
+
+def calculate_summary_statistics_from_dict(dat: dict):
     """
     calcuate summary statistics for a dictionary
     """
@@ -37,16 +33,14 @@ def calculate_summary_statistics_from_dict(
         minimum=np.min([*dat.values()]),
         maximum=np.max([*dat.values()]),
         standard_deviation=stat.stdev([*dat.values()]),
-        variance=stat.variance([*dat.values()])
+        variance=stat.variance([*dat.values()]),
     )
 
     return stats
 
-def print_summary_statistics(
-    stats: list
-):
-    """
-    """
+
+def print_summary_statistics(stats: list):
+    """ """
     try:
         print(f"mean: {round(stats['mean'], 4)}")
         print(f"median: {round(stats['median'], 4)}")