#  Code for Monte Carlo class, Jonathan Goodman
#  http://www.math.nyu.edu/faculty/goodman/teaching/MonteCarlo20/
#  SamplerDemo.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.

# illustrate using the Python random number generator from numpy version 19

from numpy.random import Generator, PCG64       #  numpy randon number generator routines
import sys                                      #  for the maxsize function

print("\n   Illustrate the random number generator from numpy version 19\n")

bg1 = PCG64(12345)        # instantiate a bit generator with seed 12345
bg2 = PCG64(12345)        # another bit generator with the same seed
rg = Generator(bg1)       # instantiate a random number generator with ...
                          # ... the first bit generator
                          
mr = sys.maxsize          # supposedly the largest intetege, but not really

print(" first three numbers from seed 12345\n")

for i in range(3):
    r = bg2.random_raw()  # integer directly from the bit generator ...
    x =.5*(r/mr)          # ... made into a float between 0 and 1
    y = rg.random()       # uniform random numbers from the generator
    xyFormat = " x is {x:11.8f}, y is {y:11.8f}"
    output   = xyFormat.format( x = x, y = y)
    print(output + ", r is " + str(r) + ", of type " + str(type(r)))
    
s   = rg.__getstate__()   # the first random number generator "state"

print("\n next three numbers in this sequence\n")

for i in range(3):
    r = bg2.random_raw()  # integer directly from the bit generator ...
    x =.5*(r/mr)          # ... made into a float between 0 and 1
    y = rg.random()       # uniform random numbers from the generator
    xyFormat = " x is {x:11.8f}, y is {y:11.8f}"
    output   = xyFormat.format( x = x, y = y)
    print(output + ", r is " + str(r) + ", of type " + str(type(r)))
    
rg.__setstate__(s)     # reset the state to where it was after the first two numbers ...
                        # ... and generate again, should reproduce 3-rd and 4-th numbers
print("\n after resetting the state\n")

for i in range(3):
    r = bg2.random_raw()  # integer directly from the bit generator ...
    x =.5*(r/mr)          # ... made into a float between 0 and 1
    y = rg.random()       # uniform random numbers from the generator
    xyFormat = " x is {x:11.8f}, y is {y:11.8f}"
    output   = xyFormat.format( x = x, y = y)
    print(output + ", r is " + str(r) + ", of type " + str(type(r)))

print("\n Types of random number generator objets \n")

print("bg has type: " + str(type(bg1)))
print("rg has type: " + str(type(rg )))
print("s has type:  " + str(type(s  )))
print("the items in s are:")
for name in s:
   print("    " + "\"" + name + "\":         " + str(s[name]))