refactoring

analysis/workflow1.py

@@ -1,44 +1,51 @@
 from math import *
+import csv
 
 # read sample files
+def read_csv_file(file_name):
+    """This function reads csv files into a list
+    :param file_name:    name of the file to be read into a list
+    :param return:       returns the data in a list format"""
+    with open(file_name) as file:
+        reader = csv.read(file)
+        data = []
+        for row in reader:
+            data.append(row)
+    return data
 
-with open('data1.csv') as file1:
-    lines1 = file1.readlines()
-    data1 = []
-    for line in lines1:
-        row = []
-        for n in line.split(','):
-            row.append(float(n.strip()))
-        data1.append(row)
+#read data 
+data1 = read_csv_file('data1.csv')
+data2 = read_csv_file('data2.csv')
+weights = read_csv_file('weights.csv')
+sample1 = read_csv_file('samples1.csv')
+sample2 = read_csv_file('samples2.csv')
 
-with open('data2.csv') as file2:
-    lines2 = file2.readlines()
-    data2 = []
-    for line in lines2:
-        row = []
-        for n in line.split(','):
-            row.append(float(n.strip()))
-        data2.append(row)
-
-with open('weights.csv') as filew:
-    linew = filew.read()
-    w = []
-    for n in linew.split(','):
-        w.append(float(n.strip()))
-
-results = []
-for i in range(len(data1)):
-    s = 0
-    for j in range(len(w)):
-        d = data1[i][j] - data2[i][j]
-        s += w[j] * abs(d)
-    results.append(s)
+def results_func(data_set_1, data_set_2, weights):
+    """Computes the results based on their weights
+    :param data_set_1:     the data set to be substracted to 
+    :param data_set2:      the data set that will be subtracted
+    :param weights:        the specific weight of the difference
+    :param return:         returns the weighted results 
+    """
+    results = []
+    for i in range(len(data_set_1)):
+        s = 0
+        for j in range(len(weights)):
+            d = data_set_1[i][j] - data_set_2[i][j]
+            s += weights[j] * abs(d)
+        results.append(s)
+    return results 
 
+results1 = results_func(data1, data2, weights)
 critical = 0
-for i in range(len(results)):  # for all i
-    if results[i] > 5:
+for i in range(len(results1)):  # for all i
+    if results1[i] > 5:
         critical = critical + 1  # increase by 1
-if critical == 1:
-    print("criticality: 1 result above 5")
-else:
-    print("criticality:", critical, "results above 5")
+
+print("criticality:", critical, "results above 5")
+
+results2 = results_func(sample1, sample2, weights)
+dsum =  0
+for i in range(len(results2)):
+    dsum = dsum + results2[i]
+print("d-index:", dsum/len(results2))