command-line app to calculate probabilities from a normal distribution. uses scipy.stats for calculations

normal-probability.py

#!/usr/bin/python
import sys
import os
import math
import scipy.stats

normal_distribution_calculations =[ 
    (1, "Probability that score is less than x (area below)", ["Enter score: "], lambda dist,score: dist.cdf(score)),
    (2, "Maximum score to satisfy Pr(x) (area below)", ["Enter probability: "], lambda dist,prob: dist.ppf(prob)),
    (3, "Probability that score is more than x (area above)", ["Enter score: "], lambda dist,score: dist.sf(score)), 
    (4, "Minimum score to satisfy Pr(x) (area above)", ["Enter probability: "], lambda dist,prob: dist.sf(score)),
    (5, "Probability score lies within range (area between)", ["Enter low score: ", "Enter high score :"], lambda dist,scores: dist.cdf(scores[1]) - dist.cdf(scores[0])),
    (6, "Confidence interval of Pr(x)", ["not available"], lambda dist,prob: "not implemented")
  ]

def prob_types():
  ret = ["  {0}. {1}".format(c[0],c[1]) for c in normal_distribution_calculations]
  return "\n".join(ret) 
  
def get_prob(prob_type):
  for c in normal_distribution_calculations:
    if c[0] == prob_type: return c

def get_args(prob):
  args = [float(raw_input(a)) for a in prob[2]]
  return args[0] if len(args) == 1 else args 

# #####
# start program
# #####

print "Define a normal distribution"
dist_mean = float(raw_input("mean = "))
dist_stdv = float(raw_input("standard deviation = "))

# set up our normal distribution
dist = scipy.stats.norm(loc=dist_mean,scale=dist_stdv)
calculate = True

while calculate:

  result = 'invalid selection'
  prob_type = int(raw_input("\nChoose probability calculation type: \n" + prob_types() + "\nEnter choice: "))

  prob = get_prob(prob_type)
  args = get_args(prob)
  result = prob[3](dist,args)
  
  print result

  calculate = raw_input("\nMake another calculation? Y/n ").upper() == "Y"