chore: removed second copy of module and optimized the function

src/mean-variance-function.py

-import glob, io
-import matplotlib.pyplot as plt
-import csv
-import os
-
-
-
-
-def mean_variance(filepath):
-    """Given the counts observed for a given gene across all cells, calculate the mean and variance.
-
-    Input: 
-        directory with files of gene expression counts in individual cells
-    
-    Output:
-        1. Path to Csv-formatted table with GeneID, Mean, Variance of the counts
-        2. Scatterplot of mean vs variance for all genes
-    """
-    # Open each file in the input directory, raises error if no file is fund
-    files = [file for file in glob.glob(filepath)]
-
-    if len(files) == 0:
-        raise ValueError('No files in directory:', filepath)
-
-    # Creates all required dictionaries to cinstruct the mean, variance
-    gene_counts = {}
-    occurence = {}
-    individual_values = {}
-    mean = {}
-    variance = {}
-
-    # Added together all gene counts in gene_counts, and occurence in occurence
-    for file_name in files:
-        with io.open(file_name, 'r') as fh:
-            for line in fh:
-                geneid, copies = str(line.split()[0]), int(line.split()[1])
-                if geneid not in gene_counts:
-                    gene_counts[geneid] = copies
-                    occurence[geneid] = 1
-                    individual_values[geneid] = [copies]
-                else:
-                    gene_counts[geneid] += copies
-                    occurence[geneid] += 1
-                    individual_values[geneid] += [copies]
-    
-    # Calculate mean of each gene
-    for i in gene_counts:
-        mean[i] = gene_counts[i]/occurence[i] 
-
-    # Calculate the variance
-    for i in individual_values:
-        for j in range(0, len(individual_values[i])):
-            variance[i] = (individual_values[i][j]-mean[i])**2/occurence[i] 
-
-    # Plot mean against variance
-    plt.scatter(mean.values(), variance.values())
-    for value in list(mean.keys()):
-        plt.text(mean[value], variance[value], value)
-    plt.xlabel('mean')
-    plt.ylabel('variance')
-    plt.title('Mean gene expression vs. variance')
-    plt.show()
-    
-    # Constructs csv file and saves it in the users directory
-    with open(os.path.expanduser("~")+'/results_mean_var_function.csv', 'w') as csv_file:
-        filewriter = csv.writer(csv_file, delimiter = ',', quotechar = '|', quoting = csv.QUOTE_MINIMAL)
-        filewriter.writerow(['geneid', 'mean', 'variance'])
-        for id in gene_counts.keys():
-            filewriter.writerow([id, mean[id], variance[id]])
-    
-    return os.path.expanduser("~")+'/results_mean_var_function.csv'
-
-
-

src/meanvariancefunction.py

-import glob
-import io
-import matplotlib.pyplot as plt
 import csv
+from glob import glob
+import io
 import os
 
+import matplotlib.pyplot as plt
+
 
-def mean_variance(filepath):
+def mean_variance(filepath: str) -> str:
     """Given the counts observed for a given gene across all cells,
     calculate the mean and variance.
 
-    Input:
-        directory with files of gene expression counts in individual cells
-    Output:
-        1. Path to Csv-formatted table with GeneID, Mean, Variance of the count
-        2. Scatterplot of mean vs variance for all genes
+    Args:
+        directory with files of gene expression counts in individual cells.
+    Returns:
+        1. Path to Csv-formatted table with GeneID, Mean, Variance of the count.
+        2. Scatterplot of mean vs variance for all genes.
 
     Raises:
-        ValueError: If there are no files in diretory
+        ValueError: If there are no files in directory
     """
-    # Open each file in the input directory, raises error if no file is fund
-    files = [file for file in glob.glob(filepath)]
+    # Open each file in the input directory, raises error if no file is found
+    files = [file for file in glob(filepath)]
 
     if len(files) == 0:
         raise ValueError('No files in directory:', filepath)
@@ -27,7 +28,6 @@ def mean_variance(filepath):
     # Creates all required dictionaries to construct the mean and variance
     gene_counts = {}
     occurence = {}
-    individual_values = {}
     mean = {}
     variance = {}
 
@@ -37,21 +37,19 @@ def mean_variance(filepath):
             for line in fh:
                 geneid, copies = str(line.split()[0]), int(line.split()[1])
                 if geneid not in gene_counts:
-                    gene_counts[geneid] = copies
+                    gene_counts[geneid] = [copies]
                     occurence[geneid] = 1
-                    individual_values[geneid] = [copies]
                 else:
-                    gene_counts[geneid] += copies
+                    gene_counts[geneid] += [copies]
                     occurence[geneid] += 1
-                    individual_values[geneid] += [copies]
     # Calculates mean of each gene
     for i in gene_counts:
-        mean[i] = gene_counts[i]/occurence[i]
+        mean[i] = sum(gene_counts[i])/occurence[i]
 
     # Calculates the variance
-    for i in individual_values:
-        for j in range(0, len(individual_values[i])):
-            variance[i] = (individual_values[i][j]-mean[i])**2/occurence[i]
+    for i in gene_counts:
+        for j in range(0, len(gene_counts[i])):
+            variance[i] = (gene_counts[i][j]-mean[i])**2/occurence[i]
 
     # Plots mean against variance
     plt.scatter(mean.values(), variance.values())