Add materials for Numpy Tutorial article.

Author: rpalo

numpy-tutorial/README.md

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+# Code Materials for the Numpy Tutorial
+
+These are code resources for the Numpy Tutorial article by @rpalo.

numpy-tutorial/bad-gray.jpg

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+"""Introduction example: curving a series of test grades using Numpy."""
+
+import numpy as np
+
+
+CURVE_CENTER = 80
+
+
+def curve(grades):
+    """Adjusts an array of grades so that the average is roughly shifted
+    to the specified curve center.
+
+    This will never cause a student's grade to decrease, and it will
+    never cause the final grade to go over 100%.
+
+    Parameters:
+        grades (np.ndarray): The individual student grades, between 0
+                             and 100.
+
+    Returns:
+        (np.ndarray): A new array of grades, adjusted upwards, but in
+                      the same order.
+    """
+    average = grades.mean()
+    change = CURVE_CENTER - average
+    new_grades = grades + change
+    return np.clip(new_grades, grades, 100)
+
+
+if __name__ == "__main__":
+    grades = np.array([72, 35, 64, 88, 51, 90, 74, 12])
+    print(curve(grades))
+    # => array([91.25, 54.25, 83.25, 100.0, 70.25, 100.0, 93.25, 31.25])

numpy-tutorial/blue.jpg

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+"""Validating Dürer's magic square."""
+
+import numpy as np
+
+square = np.array(
+    [
+        [16, 3, 2, 13],
+        [5, 10, 11, 8],
+        [9, 6, 7, 12],
+        [4, 15, 14, 1],
+    ]
+)
+
+for i in range(4):
+    assert square[i, :].sum() == 34
+    assert square[:, i].sum() == 34
+
+assert square[:2, :2].sum() == 34
+assert square[2:, :2].sum() == 34
+assert square[:2, 2:].sum() == 34
+assert square[2:, 2:].sum() == 34
+
+print("All assertions passed!")

numpy-tutorial/curve_grades.py

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+import numpy as np
+import matplotlib.image as mpimg
+
+img = mpimg.imread("kitten.png")
+print(type(img))
+print(img.shape)
+
+# => numpy.ndarray
+# => (866, 1280, 3)
+
+output = img.copy()  # The original image is read-only!
+output[:, :, :2] = 0  # Drop out Red and Green channels
+mpimg.imsave("blue.jpg", output)
+
+averages = img.mean(axis=2)  # Take the average of each R, G, and B
+mpimg.imsave("bad-gray.jpg", averages, cmap="gray")
+
+weights = np.array([0.3, 0.59, 0.11])
+greyscale = np.dot(img, weights)
+mpimg.imsave("good-gray.jpg", greyscale, cmap="gray")

numpy-tutorial/durer.py

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+"""Estimates e^x^ by using an approximated Maclauren series."""
+
+from math import e, factorial
+
+import numpy as np
+
+fac = np.vectorize(factorial)
+
+
+def e_x(x, terms=10):
+    """Approximates e^x^ using 'terms' terms of the Maclauren Series"""
+    n = np.arange(terms)
+    return np.sum((x ** n) / fac(n))
+
+
+if __name__ == "__main__":
+    print("Actual:", e ** 3)
+
+    print("N (terms)\tMaclauren\tError")
+
+    for n in range(1, 14):
+        maclauren = e_x(3, terms=n)
+        print(f"{n}\t\t{maclauren:.03f}\t\t{e**3 - maclauren:.03f}")
+
+
+# Actual: 20.085536923187664
+# N (terms) Maclauren Error
+# 1         1.000     19.086
+# 2         4.000     16.086
+# 3         8.500     11.586
+# 4        13.000      7.086
+# 5        16.375      3.711
+# 6        18.400      1.686
+# 7        19.412      0.673
+# 8        19.846      0.239
+# 9        20.009      0.076
+# 10       20.063      0.022
+# 11       20.080      0.006
+# 12       20.084      0.001
+# 13       20.085      0.000

numpy-tutorial/good-gray.jpg

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+"""Validating the Normal Distribution."""
+
+from numpy.random import default_rng
+
+
+rng = default_rng()
+values = rng.standard_normal(10000)
+
+print(values[:5])
+# => array([.9779210858, 1.8361585253, -.3641365235, -.1311344527,
+#          1.286542056 ])
+
+std = values.std()
+filtered = values[(values > -2 * std) & (values < 2 * std)]
+
+print(filtered.size)
+# => 9565
+print(values.size)
+# => 10000
+print(filtered.size / values.size)
+# => 0.9565 (actual is .9545)