alpha=0.75)
# plot formating options
plt.xlabel('Dice faces')
plt.ylabel('Probability', fontweight="bold", fontsize="17")
plt.title('Categorical Distribution', fontweight="bold", fontsize="17")
plt.grid(True, color='r')
# save and show figure
plt.savefig("Dice.png")
#plt.show()
# an array of rayleigh numbers using our Random class
myx = []
for i in range(0, 10000):
myx.append(random_number.Rayleigh())
# create histogram of our data
plt.figure()
plt.hist(myx, 70, density=True, facecolor='y', alpha=0.75)
# plot formating options
plt.xlabel('x')
plt.ylabel('Probability')
plt.title("Rayleigh Distribution", fontweight="bold")
plt.grid(True)
# show figure (program only ends once closed
plt.show()
plt.savefig("Rayleigh.png")
# main function for this Python code
if __name__ == "__main__":
# Set up parser to handle command line arguments
# Run as 'python PHSX815_HW2_KurtH.py -h' to see all available commands
parser = argparse.ArgumentParser()
parser.add_argument("--seed", "-s", help="Seed number to use")
args = parser.parse_args()
# default seed
seed = 5555
if args.seed:
print("Set seed to %s" % args.seed)
seed = args.seed
# set random seed for numpy
np.random.seed(seed)
# class instance of our Random class using seed
random = Random(seed)
# create some random data
N = 10000
# an array of random numbers using our Random class
myx = np.array([])
for i in range(0, N):
myx = np.append(myx, random.Rayleigh())
make_plot(myx)
plt.show()
if args.Nsides:
print("Number sides per die: %s" % args.Nsides)
Nsides = np.uint64(args.Nsides)
Nrolls = 100
if args.Nrolls:
print("Number of rolls per experiment: %s" % args.Nrolls)
Nrolls = np.uint64(args.Nrolls)
Nexp = 1000
if args.Nexp:
print("Number of experiments: %s" % args.Nexp)
Nexp = np.uint64(args.Nexp)
# Create weights by pulling from a rayleigh distribution
weights = np.array([random.Rayleigh() for i in range(Nsides)])
# The weigths need to sum to one, so normalize them
weights /= weights.sum()
print(f'Weights: {weights}')
rolls_test = np.zeros((Nexp, Nrolls))
rolls_fair = np.zeros((Nexp, Nrolls))
for i in range(Nexp):
for j in range(Nrolls):
rolls_test[i, j] = random.roll_die(Nsides=Nsides, weights=weights)
rolls_fair[i, j] = random.roll_die(Nsides=Nsides)
# Save the normalized results
np.savetxt('data/dice_rolls.txt', rolls_test, delimiter=',', fmt="%d")
np.savetxt('data/fair_dice_rolls.txt', rolls_fair, delimiter=',', fmt="%d")
