updated readme with infocalc info

.gitignore

@@ -1,2 +1,5 @@
 *~
 *.log
+flycheck_*.py
+\#*#
+test.pdf

README.md

@@ -1,3 +1,5 @@
 # ad-calc
 
-Tools to help calculating values for Axiomatic Design analysis
+Tools to help calculating values for Axiomatic Design analysis
+
+`infocalc.py` calculates information content based upon a csv file or statistical parameters and upper/lower limits

infocalc.py

@@ -11,7 +11,8 @@ from pathlib import PurePath##https://docs.python.org/3/library/pathlib.html#mod
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
-from scipy.stats import norm
+from scipy.stats import norm,t
+import scipy.stats
 import pandas as pd
 
 #Main program loop
@@ -96,25 +97,23 @@ if args.mode == "DATA":
     data = np.array(pd.read_csv(inpath)[args.column])
     mean = data.mean()
     stddev = data.std(ddof=1)
+    # Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
     samplesize = len(data)
 elif args.mode == "SIM":
     mean = args.mean
     stddev = args.stddev
     samplesize = args.samplesize
-
-# time to deal with the bounds
+df = samplesize - 1 
     
-# Delta Degrees of Freedom: ddof=0 for population, ddof=1 for sample std dev
 prob = 0
 if args.upperbound and args.lowerbound:
-    prob = norm.cdf(args.upperbound, mean, stddev) - norm.cdf(args.lowerbound, mean, stddev)
+    prob = t.cdf(df,args.upperbound, mean, stddev) - t.cdf(df,args.lowerbound, mean, stddev)
 elif args.upperbound:
-    prob = norm.cdf(args.upperbound, mean, stddev)
+    prob = t.cdf(df,args.upperbound, mean, stddev)
 elif args.lowerbound:
-    prob = 1 - norm.cdf(args.lowerbound, mean, stddev)
+    prob = 1 - t.cdf(df,args.lowerbound, mean, stddev)
 else:
     prob = 1# no bounds set!
-    ##TODO!!!!
 #print("probability: %f", prob)
 info = -np.emath.log2(prob)
 #print("information content: %f bits", info)