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116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
"""
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Description
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The Koch snowflake is a fractal curve and one of the earliest fractals to
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have been described. The Koch snowflake can be built up iteratively, in a
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sequence of stages. The first stage is an equilateral triangle, and each
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successive stage is formed by adding outward bends to each side of the
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previous stage, making smaller equilateral triangles.
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This can be achieved through the following steps for each line:
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1. divide the line segment into three segments of equal length.
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2. draw an equilateral triangle that has the middle segment from step 1
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as its base and points outward.
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3. remove the line segment that is the base of the triangle from step 2.
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(description adapted from https://en.wikipedia.org/wiki/Koch_snowflake )
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(for a more detailed explanation and an implementation in the
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Processing language, see https://natureofcode.com/book/chapter-8-fractals/
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#84-the-koch-curve-and-the-arraylist-technique )
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Requirements (pip):
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- matplotlib
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- numpy
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"""
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from __future__ import annotations
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import matplotlib.pyplot as plt
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import numpy as np
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# initial triangle of Koch snowflake
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VECTOR_1 = np.array([0, 0])
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VECTOR_2 = np.array([0.5, 0.8660254])
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VECTOR_3 = np.array([1, 0])
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INITIAL_VECTORS = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
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# uncomment for simple Koch curve instead of Koch snowflake
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# INITIAL_VECTORS = [VECTOR_1, VECTOR_3]
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def iterate(initial_vectors: list[np.ndarray], steps: int) -> list[np.ndarray]:
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"""
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Go through the number of iterations determined by the argument "steps".
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Be careful with high values (above 5) since the time to calculate increases
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exponentially.
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>>> iterate([np.array([0, 0]), np.array([1, 0])], 1)
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[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
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0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
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"""
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vectors = initial_vectors
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for _ in range(steps):
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vectors = iteration_step(vectors)
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return vectors
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def iteration_step(vectors: list[np.ndarray]) -> list[np.ndarray]:
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"""
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Loops through each pair of adjacent vectors. Each line between two adjacent
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vectors is divided into 4 segments by adding 3 additional vectors in-between
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the original two vectors. The vector in the middle is constructed through a
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60 degree rotation so it is bent outwards.
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>>> iteration_step([np.array([0, 0]), np.array([1, 0])])
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[array([0, 0]), array([0.33333333, 0. ]), array([0.5 , \
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0.28867513]), array([0.66666667, 0. ]), array([1, 0])]
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"""
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new_vectors = []
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for i, start_vector in enumerate(vectors[:-1]):
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end_vector = vectors[i + 1]
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new_vectors.append(start_vector)
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difference_vector = end_vector - start_vector
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new_vectors.append(start_vector + difference_vector / 3)
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new_vectors.append(
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start_vector + difference_vector / 3 + rotate(difference_vector / 3, 60)
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)
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new_vectors.append(start_vector + difference_vector * 2 / 3)
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new_vectors.append(vectors[-1])
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return new_vectors
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def rotate(vector: np.ndarray, angle_in_degrees: float) -> np.ndarray:
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"""
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Standard rotation of a 2D vector with a rotation matrix
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(see https://en.wikipedia.org/wiki/Rotation_matrix )
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>>> rotate(np.array([1, 0]), 60)
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array([0.5 , 0.8660254])
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>>> rotate(np.array([1, 0]), 90)
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array([6.123234e-17, 1.000000e+00])
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"""
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theta = np.radians(angle_in_degrees)
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c, s = np.cos(theta), np.sin(theta)
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rotation_matrix = np.array(((c, -s), (s, c)))
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return np.dot(rotation_matrix, vector)
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def plot(vectors: list[np.ndarray]) -> None:
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"""
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Utility function to plot the vectors using matplotlib.pyplot
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No doctest was implemented since this function does not have a return value
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"""
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# avoid stretched display of graph
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axes = plt.gca()
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axes.set_aspect("equal")
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# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
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# y-coordinates as inputs, which are constructed from the vector-list using
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# zip()
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x_coordinates, y_coordinates = zip(*vectors)
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plt.plot(x_coordinates, y_coordinates)
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plt.show()
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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processed_vectors = iterate(INITIAL_VECTORS, 5)
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plot(processed_vectors)
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