formatted code using black

Author: Syed Nasim

clusters.py

@@ -1,46 +1,50 @@
 import points as pointsclass
 import math
 
+
 def sdown(x):
-    y = x*10000
+    y = x * 10000
     z = int(y)
-    return (z/10000)
+    return z / 10000
+
+
+def dist(epi_x, epi_y, pt_x, pt_y):
+    # return (((epi_x-pt_x)**2) + ((epi_y-pt_y)**2))**0.5
+    return math.sqrt(math.pow(epi_x - pt_x, 2.0) + math.pow(epi_y - pt_y, 2.0))
 
-def dist(epi_x,epi_y, pt_x,pt_y):
-    #return (((epi_x-pt_x)**2) + ((epi_y-pt_y)**2))**0.5
-    return math.sqrt(math.pow(epi_x - pt_x,2.0) + math.pow(epi_y - pt_y,2.0))
 
 def finalize(clusters, points):
-	nos = len(clusters)
-	for i in range(1, nos+1):
-		pts = pointsclass.getpoints_cl(points, i-1)
-		clusters[i-1] ['points'].append(pts)
-	return clusters
+    nos = len(clusters)
+    for i in range(1, nos + 1):
+        pts = pointsclass.getpoints_cl(points, i - 1)
+        clusters[i - 1]["points"].append(pts)
+    return clusters
+
 
 def update_epi(clusters, points):
 
-	for i in range(1, len(clusters)+1):
-		xsum = 0
-		ysum = 0
+    for i in range(1, len(clusters) + 1):
+        xsum = 0
+        ysum = 0
 
-		pts = pointsclass.getpoints_cl(points, i-1)
-		net = len(pts)
+        pts = pointsclass.getpoints_cl(points, i - 1)
+        net = len(pts)
 
-		#Ignore clusters with no points
-		if net == 0:
-			continue
+        # Ignore clusters with no points
+        if net == 0:
+            continue
 
-		for x in pts:
-			xsum += int(x['x'])
-		for y in pts:
-			ysum += int(y['y'])
+        for x in pts:
+            xsum += int(x["x"])
+        for y in pts:
+            ysum += int(y["y"])
 
-		xav = (xsum/net)
-		yav = (ysum/net)
+        xav = xsum / net
+        yav = ysum / net
 
-		clusters[i-1]['epi_x'] = xav
-		clusters[i-1]['epi_y'] = yav
+        clusters[i - 1]["epi_x"] = xav
+        clusters[i - 1]["epi_y"] = yav
 
-		#print(i, clusters[i-1]['epi_x'], clusters[i-1]['epi_y'])
+        # print(i, clusters[i-1]['epi_x'], clusters[i-1]['epi_y'])
 
-	return clusters
+    return clusters

ident.py

@@ -1,38 +1,40 @@
 def sdown(x):
-    y = x*100
+    y = x * 100
     z = int(y)
-    return (z/100)
+    return z / 100
+
 
 def nameclusters(clusters, setup):
 
     if setup == True:
         rep = input("Names have already been set. Do you want to repeat? (y/n)")
-        if rep == 'n':
+        if rep == "n":
             return [clusters, setup]
         else:
-            pass            
+            pass
 
     i = 1
     print()
 
     print(len(clusters), "clusters have been identified.")
     for c0 in clusters:
-        pts = len(c0['points'][0])
+        pts = len(c0["points"][0])
         print(" - Cluster", i, "has ", pts, "elements.")
         i += 1
     print()
 
     i = 1
     for c1 in clusters:
-        print(" - Cluster", i, ": ", "(", sdown(c1['epi_x']), "," , sdown(c1['epi_y']), ")")
+        print(
+            " - Cluster", i, ": ", "(", sdown(c1["epi_x"]), ",", sdown(c1["epi_y"]), ")"
+        )
         i += 1
     print()
-    
+
     i = 1
     for c2 in clusters:
         print(" - Enter title for cluster ", i, end="")
-        c2['title'] = input(": ")
+        c2["title"] = input(": ")
         i += 1
 
-    
-    return [clusters, True]
+    return [clusters, True]

main.py

@@ -26,6 +26,7 @@
 # STEP 5: Update epicenters
 # STEP 6: Repeat 3 until epicenters don't change
 
+
 def train(csvfile):
 
     # STEP 1
@@ -42,24 +43,26 @@ def train(csvfile):
     glob_cluster_list = []
     glob_nos_clusters = 0
 
-    glob_nos_clusters = int(input("\nEnter the number of clusters clearly visible in the plot: "))
-    for i in range (1, glob_nos_clusters+1):
+    glob_nos_clusters = int(
+        input("\nEnter the number of clusters clearly visible in the plot: ")
+    )
+    for i in range(1, glob_nos_clusters + 1):
         temp_dict = {}
-        temp_dict["epi_x"] = random.randint(1,10)
-        temp_dict["epi_y"] = random.randint(1,10)
-        temp_dict['points'] = []
-        temp_dict['title'] = i
+        temp_dict["epi_x"] = random.randint(1, 10)
+        temp_dict["epi_y"] = random.randint(1, 10)
+        temp_dict["points"] = []
+        temp_dict["title"] = i
 
         glob_cluster_list.append(temp_dict)
-    #print("- Initialized ", glob_nos_clusters, " clusters")
+    # print("- Initialized ", glob_nos_clusters, " clusters")
 
-    #print ("INIT DEBUG \n")
-    #for k in glob_cluster_list:
+    # print ("INIT DEBUG \n")
+    # for k in glob_cluster_list:
     #    print (k['epi_x'], k['epi_y'], end=" ")
     #    print()
 
-    for l in range (glob_nos_points*5):
-        #Run assignment
+    for l in range(glob_nos_points * 5):
+        # Run assignment
         glob_points_list = points.nextassign(glob_cluster_list, glob_points_list)
         glob_cluster_list = clusters.update_epi(glob_cluster_list, glob_points_list)
 
@@ -69,33 +72,33 @@ def train(csvfile):
         new_x = new_y = 0
         change = 0
 
-        #record previous epicenters:
+        # record previous epicenters:
         for i in glob_cluster_list:
-            prev_x += i['epi_x']
-            prev_y += i['epi_y']
+            prev_x += i["epi_x"]
+            prev_y += i["epi_y"]
 
-        #Run assignment
+        # Run assignment
         glob_points_list = points.nextassign(glob_cluster_list, glob_points_list)
         glob_cluster_list = clusters.update_epi(glob_cluster_list, glob_points_list)
 
-        #record new epicenters:
+        # record new epicenters:
         for j in glob_cluster_list:
-            new_x += j['epi_x']
-            new_y += j['epi_y']
+            new_x += j["epi_x"]
+            new_y += j["epi_y"]
 
         change = abs(prev_x - new_x) + abs(prev_y - new_y)
         if change == 0:
             break
 
-    print ("FIN DEBUG \n")
+    print("FIN DEBUG \n")
     for k in glob_cluster_list:
-            print (k['epi_x'], k['epi_y'], end=" ")
-            print()
-    #print ("\n\nPOINTS:: ")
-    #for p in glob_points_list:
+        print(k["epi_x"], k["epi_y"], end=" ")
+        print()
+    # print ("\n\nPOINTS:: ")
+    # for p in glob_points_list:
     #    print(p)
 
-    glob_cluster_list = clusters.finalize(glob_cluster_list,glob_points_list)
+    glob_cluster_list = clusters.finalize(glob_cluster_list, glob_points_list)
 
     print("Hopefully, now the epicenters are correctly arranged")
     global glob_trained
@@ -104,36 +107,36 @@ def train(csvfile):
 
 print(" ** \n Welcome to KMeansExample.\n **\n")
 csvfile = input("Please enter the csv file containing the student records: ")
-#csvfile = 'data/simple.csv'
+# csvfile = 'data/simple.csv'
 print("Working on student records at ", csvfile, " ..")
 
 while True:
-    print ("\n\n")
-    print ("  * (1) for previewing the records")
-    print ("  * (2) for proceeding with training")
-    print ("  * (3) for getting a prediction")
-    print ("  * (4) for Exiting the predictor")
+    print("\n\n")
+    print("  * (1) for previewing the records")
+    print("  * (2) for proceeding with training")
+    print("  * (3) for getting a prediction")
+    print("  * (4) for Exiting the predictor")
 
     if glob_trained == True:
-        print ("  * (5) for Rich preview")
-        print ("  * (6) for Naming clusters")
-        print ("  * (7) for Viewing points")
+        print("  * (5) for Rich preview")
+        print("  * (6) for Naming clusters")
+        print("  * (7) for Viewing points")
 
     i = int(input("Enter action: "))
 
     if i == 1:
         print("Previewing the records: ")
         preview.preview(csvfile)
-    
+
     elif i == 2:
         train(csvfile)
 
     elif i == 3:
         predict.predict(glob_cluster_list)
-    
+
     elif i == 4:
         break
-    
+
     elif i == 5:
         pass
         preview.richprev(glob_cluster_list)
@@ -143,7 +146,7 @@ def train(csvfile):
         glob_cluster_list = ret[0]
         glob_named = ret[1]
         print("Names set.")
-    
+
     elif i == 7:
         points.view(glob_cluster_list)