#!/bin/python3
import numpy as np

# import pandas as pd
import time

# Print the current time
print(time.strftime("%Y-%m-%d %H:%M:%S"))

a = {1, 2, 3}
assert 1 in a, "1 is not in a"
assert 4 not in a, "4 is in a"

b = {"a": 1, "b": 2, "c": 3}
assert "a" in b, "a is not in b"
assert "d" not in b, "d is in b"

c = a.union(b.values())
assert 1 in c, "1 is not in c"

# Xor
assert 1 ^ 1 == 0
assert 1 ^ 0 == 1
assert 0 ^ 1 == 1
assert 0 ^ 0 == 0

a = 0b1010  # 10
a = a ^ 0b1010  # 10
assert a == 0, "a should be 0"

# Poisson distribution
# print(np.random.poisson(5, 1))
# print(np.random.binomial(10, 0.5, 10))


# Probability mass function of poisson distribution
def pmf_poisson(k, l):
    return (l**k) * np.exp(-l) / np.math.factorial(k)


def mean(l):
    return sum(l) / len(l)


def median(l):
    l.sort()
    n = len(l)
    if n % 2 == 0:
        return (l[n // 2 - 1] + l[n // 2]) / 2
    else:
        return l[n // 2]

def mode(l):
    return max(set(l), key=l.count)


assert median([1, 2, 3, 4]) == 2.5
assert median([1, 2, 3, 4, 5]) == 3

quantile = np.quantile
# def quantile(l, q):

assert quantile([1, 2, 3, 4, 5], 0.5) == 3
assert quantile([1, 2, 3, 4, 5], 0.25) == 2
assert quantile([1, 2, 3, 4, 5], 0.75) == 4


def whisker_plot(l):
    q1 = quantile(l, 0.25)  # Lower quartile
    q3 = quantile(l, 0.75)  # Upper quartile
    iqr = q3 - q1  # Interquartile range
    lw = q1 - 1.5 * iqr  # Lower whisker
    uw = q3 + 1.5 * iqr  # Upper whisker
    return lw, q1, q3, uw


def outliers(l):
    lw, q1, q3, uw = whisker_plot(l)
    return [x for x in l if x < lw or x > uw]


values = [16, 18, 28, 13, 50, 31, 25, 22, 18, 23, 29, 38]

assert median(values) == 24
# assert quantile(values, 0.25) == 18
# assert quantile(values, 0.75) == 30

print(quantile(values, 0.25))
print(quantile(values, 0.75))


print(whisker_plot(values))
print(outliers(values))

# Plot the box diagram
import matplotlib.pyplot as plt

# plt.boxplot(values, vert=False)
# plt.show()


# Plot the poisson distribution when lambda = 5
# import matplotlib.pyplot as plt

# s = np.random.poisson(3, 10000)
# count, bins, ignored = plt.hist(s, 10, density=True)
# plt.show()