Dice simulation to show Spyder debugging tools.

dice_simulation_after.py

"""Simulating the sum of two dice.

Created on Fri Jan 22 19:02:16 2021

@author: spyderlieber
"""

from random import randrange
import numpy as np
import matplotlib.pyplot as plt


def sim_dice(faces=6):
    """
    Roll and sum two dice of a given number of faces.

    Parameters
    ----------
    faces : int, optional
        Dice number of faces. The default is 6.

    Returns
    -------
    int
        Sum of two simulated dice.

    """
    d1 = randrange(1, faces + 1)
    d2 = randrange(1, faces + 1)
    return d1 + d2


def init_count(faces=6):
    """
    Initialize expected probabilities and the event counter.

    Parameters
    ----------
    faces : TYPE, optional
        DESCRIPTION. The default is 6.

    Returns
    -------
    expected : dict
        Expected probabilities for each possible outcome.
    counts : dict
        Counter initialized at zero for each possible outcome.

    """
    expected = {
        i: (faces - abs(faces - (i - 1))) / faces ** 2
        for i in range(2, (faces * 2) + 1)
    }
    counts = {i: 0 for i in range(2, (faces * 2) + 1)}

    return expected, counts


runs = 10000
faces = 6

expected, counts = init_count(faces)
for i in range(runs):
    t = sim_dice(faces)
    counts[t] = counts[t] + 1
print("Total    Simulated     Expected")
print("           Percent      Percent")
for i in sorted(counts.keys()):
    print("%5d %12.2f %12.2f" % (i, counts[i] / runs * 100, expected[i] * 100))

x = np.array(list(expected.keys()))
width = 0.35
simulated = np.array(list(counts.values())) / runs * 100
expected = np.array(list(expected.values())) * 100

with plt.xkcd():
    fig, ax = plt.subplots()
    bar1 = ax.bar(x - width / 2, simulated, width, color="blue", alpha=0.5)
    bar2 = ax.bar(x + width / 2, expected, width, color="orange", alpha=0.5)
    ax.legend([bar1, bar2], ["simulated", "expected"])

dice_simulation_before.py

"""Simulating the sum of two dice.

Created on Fri Jan 22 19:02:16 2021

@author: spyderlieber
"""

from random import randrange
import numpy as np
import matplotlib.pyplot as plt


def sim_dice(faces=6):
    """
    Roll and sum two dice of a given number of faces.

    Parameters
    ----------
    faces : int, optional
        Dice number of faces. The default is 6.

    Returns
    -------
    int
        Sum of two simulated dice.

    """
    d1 = randrange(1, faces)
    d2 = randrange(1, faces)
    return d1 + d2


def init_count(faces=6):
    """
    Initialize expected probabilities and the event counter.

    Parameters
    ----------
    faces : TYPE, optional
        DESCRIPTION. The default is 6.

    Returns
    -------
    expected : dict
        Expected probabilities for each possible outcome.
    counts : dict
        Counter initialized at zero for each possible outcome.

    """
    expected = {
        i: (faces - abs(faces - (i - 1))) / faces ** 2
        for i in range(2, (faces * 2) + 1)
    }
    counts = {i: 0 for i in range(2, (faces * 2) + 1)}

    return expected, counts


runs = 10000
faces = 6

expected, counts = init_count(faces)
for i in range(runs):
    t = sim_dice(faces)
    counts[t] = counts[t] + 1
print("Total    Simulated     Expected")
print("           Percent      Percent")
for i in sorted(counts.keys()):
    print("%5d %12.2f %12.2f" % (i, counts[i] / runs * 100, expected[i] * 100))

x = np.array(list(expected.keys()))
width = 0.35
simulated = np.array(list(counts.values())) / runs * 100
expected = np.array(list(expected.values())) * 100

with plt.xkcd():
    fig, ax = plt.subplots()
    bar1 = ax.bar(x - width / 2, simulated, width, color="blue", alpha=0.5)
    bar2 = ax.bar(x + width / 2, expected, width, color="orange", alpha=0.5)
    ax.legend([bar1, bar2], ["simulated", "expected"])

Dice simulation to show Spyder debugging tools.