python suggested "is not" instead of "!="

infocalc.py

@@ -105,18 +105,14 @@ elif args.mode == "SIM":
     samplesize = args.samplesize
 df = samplesize - 1 
     
-prob = 0
-
-if args.upperbound != None and args.lowerbound != None:
+prob_upper = 1
+prob_lower = 0
+if args.upperbound is not None:
     prob_upper = t.cdf(df=df,x=args.upperbound, loc=mean, scale=stddev)
+if args.lowerbound is not None:
     prob_lower = t.cdf(df=df,x=args.lowerbound, loc=mean, scale=stddev)
-    prob = prob_upper - prob_lower
-elif args.upperbound != None:
-    prob = t.cdf(df=df,x=args.upperbound, loc=mean, scale=stddev)
-elif args.lowerbound != None:
-    prob = 1 - t.cdf(df=df,x=args.lowerbound, loc=mean, scale=stddev)
-else:
-    prob = 1# no bounds set!
+prob = prob_upper - prob_lower
+
 #print("probability: %f", prob)
 info = -np.emath.log2(prob)
 #print("information content: %f bits", info)
@@ -148,9 +144,9 @@ y = norm.pdf(x, loc=mean, scale=stddev)
 if args.normalizey:
     y = y * stddev#rescale back to unity area
 plt.axvline(x=mean, color="green", linestyle="dashed", label="mean")
-if args.lowerbound != None:
+if args.lowerbound is not None:
     plt.axvline(args.lowerbound, color="red")
-if args.upperbound != None:
+if args.upperbound is not None:
     plt.axvline(args.upperbound, color="red")
 
 plt.plot(x, y, 'b-', label='Normal distribution')