pvt.py

import math
 
# SUPPORTED CONFIDENCE LEVELS: 50%, 68%, 90%, 95%, and 99%
confidence_level_constant = [50,.67], [68,.99], [90,1.64], [95,1.96], [99,2.57]
 
# CALCULATE THE SAMPLE SIZE
def sample_size(population_size, confidence_level, confidence_interval):
  Z = 0.0
  p = 0.5
  e = confidence_interval/100.0
  N = population_size
  n_0 = 0.0
  n = 0.0
 
  # LOOP THROUGH SUPPORTED CONFIDENCE LEVELS AND FIND THE NUM STD
  # DEVIATIONS FOR THAT CONFIDENCE LEVEL
  for i in confidence_level_constant:
    if i[0] == confidence_level:
      Z = i[1]
 
  if Z == 0.0:
    return -1
 
  # CALC SAMPLE SIZE
  n_0 = ((Z**2) * p * (1-p)) / (e**2)
 
  # ADJUST SAMPLE SIZE FOR FINITE POPULATION
  n = n_0 / (1 + ((n_0 - 1) / float(N)) )
 
  return int(math.ceil(n)) # THE SAMPLE SIZE
 
def main():
  sample_sz = 0
  population_sz = 172881
  confidence_level = 99.0
  confidence_interval = .5
  total_polling_stations = 227
  avg_voters_per_station = population_sz / total_polling_stations
 
  sample_sz = sample_size(population_sz, confidence_level, confidence_interval)
  vote62_sample_size = sample_sz / avg_voters_per_station
 
  print("Number of polling stations required for .5% margin of error: {}".format(vote62_sample_size))
 
if __name__ == "__main__":
  main()