#  Code for Monte Carlo class, Jonathan Goodman
#  http://www.math.nyu.edu/faculty/goodman/teaching/MonteCarlo20/
#  MakeFakeData.py
#  The author gives permission for anyone to use this publicly posted 
#  code for any purpose.  The code was written for teaching, not research
#  or commercial use.  It has not been tested thoroughly and probably has
#  serious bugs.  Results may be inaccurate, incorrect, or just wrong.

# Make a csv file with fake data to be read by PDFapproxDemo.py
# See week 2 notes for an explanation in mathematical notation.
# The notation in the code is the same as the notation in the notes.

#  In this experiment, the fake data are Gaussian

from numpy.random import Generator, PCG64    #  numpy randon number generator
import numpy as np
import csv               #  package for reading and writing .csv files

#         Define fake data parameters

n   = 20                 #  size of the data file
mu  = 2.                 #  gaussian mean
sig = 3.                 #  gaussian standard deviation

FakeDataFileName = "GaussianFakeData"       #  name the output .csv file ...

FakeDataFileName = FakeDataFileName + ".csv"     #  ... filename.csv
csvfile = open( FakeDataFileName, 'w', newline = '')   #  open the file for writing
cfr     = csv.writer( csvfile)                         #  instantiate the writer object

seed = 12344             # change this to get different answers
bg   = PCG64(seed)       # instantiate a bit generator
rg   = Generator(bg)     # instantiate a random number generator with ...
                         # ... this bit generator
                         
form = " {xi:10.5f} "
for i in range(n):
   xi = rg.normal(mu,sig)
   xis = form.format(xi = xi)
   print(xis)
   cfr.writerow([xis])
csvfile.close()