108 lines
2.1 KiB
Python
108 lines
2.1 KiB
Python
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#!/bin/python3
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import numpy as np
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# import pandas as pd
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import time
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# Print the current time
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print(time.strftime("%Y-%m-%d %H:%M:%S"))
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a = {1, 2, 3}
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assert 1 in a, "1 is not in a"
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assert 4 not in a, "4 is in a"
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b = {"a": 1, "b": 2, "c": 3}
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assert "a" in b, "a is not in b"
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assert "d" not in b, "d is in b"
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c = a.union(b.values())
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assert 1 in c, "1 is not in c"
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# Xor
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assert 1 ^ 1 == 0
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assert 1 ^ 0 == 1
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assert 0 ^ 1 == 1
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assert 0 ^ 0 == 0
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a = 0b1010 # 10
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a = a ^ 0b1010 # 10
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assert a == 0, "a should be 0"
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# Poisson distribution
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# print(np.random.poisson(5, 1))
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# print(np.random.binomial(10, 0.5, 10))
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# Probability mass function of poisson distribution
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def pmf_poisson(k, l):
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return (l**k) * np.exp(-l) / np.math.factorial(k)
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def mean(l):
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return sum(l) / len(l)
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def median(l):
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l.sort()
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n = len(l)
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if n % 2 == 0:
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return (l[n // 2 - 1] + l[n // 2]) / 2
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else:
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return l[n // 2]
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def mode(l):
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return max(set(l), key=l.count)
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assert median([1, 2, 3, 4]) == 2.5
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assert median([1, 2, 3, 4, 5]) == 3
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quantile = np.quantile
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# def quantile(l, q):
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assert quantile([1, 2, 3, 4, 5], 0.5) == 3
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assert quantile([1, 2, 3, 4, 5], 0.25) == 2
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assert quantile([1, 2, 3, 4, 5], 0.75) == 4
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def whisker_plot(l):
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q1 = quantile(l, 0.25) # Lower quartile
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q3 = quantile(l, 0.75) # Upper quartile
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iqr = q3 - q1 # Interquartile range
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lw = q1 - 1.5 * iqr # Lower whisker
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uw = q3 + 1.5 * iqr # Upper whisker
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return lw, q1, q3, uw
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def outliers(l):
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lw, q1, q3, uw = whisker_plot(l)
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return [x for x in l if x < lw or x > uw]
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values = [16, 18, 28, 13, 50, 31, 25, 22, 18, 23, 29, 38]
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assert median(values) == 24
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# assert quantile(values, 0.25) == 18
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# assert quantile(values, 0.75) == 30
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print(quantile(values, 0.25))
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print(quantile(values, 0.75))
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print(whisker_plot(values))
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print(outliers(values))
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# Plot the box diagram
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import matplotlib.pyplot as plt
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# plt.boxplot(values, vert=False)
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# plt.show()
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# Plot the poisson distribution when lambda = 5
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# import matplotlib.pyplot as plt
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# s = np.random.poisson(3, 10000)
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# count, bins, ignored = plt.hist(s, 10, density=True)
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# plt.show()
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