forked from TheAlgorithms/Python
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mandelbrot.py
151 lines (123 loc) · 5.17 KB
/
mandelbrot.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
"""
The Mandelbrot set is the set of complex numbers "c" for which the series
"z_(n+1) = z_n * z_n + c" does not diverge, i.e. remains bounded. Thus, a
complex number "c" is a member of the Mandelbrot set if, when starting with
"z_0 = 0" and applying the iteration repeatedly, the absolute value of
"z_n" remains bounded for all "n > 0". Complex numbers can be written as
"a + b*i": "a" is the real component, usually drawn on the x-axis, and "b*i"
is the imaginary component, usually drawn on the y-axis. Most visualizations
of the Mandelbrot set use a color-coding to indicate after how many steps in
the series the numbers outside the set diverge. Images of the Mandelbrot set
exhibit an elaborate and infinitely complicated boundary that reveals
progressively ever-finer recursive detail at increasing magnifications, making
the boundary of the Mandelbrot set a fractal curve.
(description adapted from https://en.wikipedia.org/wiki/Mandelbrot_set )
(see also https://en.wikipedia.org/wiki/Plotting_algorithms_for_the_Mandelbrot_set )
"""
import colorsys
from PIL import Image # type: ignore
def get_distance(x: float, y: float, max_step: int) -> float:
"""
Return the relative distance (= step/max_step) after which the complex number
constituted by this x-y-pair diverges. Members of the Mandelbrot set do not
diverge so their distance is 1.
>>> get_distance(0, 0, 50)
1.0
>>> get_distance(0.5, 0.5, 50)
0.061224489795918366
>>> get_distance(2, 0, 50)
0.0
"""
a = x
b = y
for step in range(max_step): # noqa: B007
a_new = a * a - b * b + x
b = 2 * a * b + y
a = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def get_black_and_white_rgb(distance: float) -> tuple:
"""
Black&white color-coding that ignores the relative distance. The Mandelbrot
set is black, everything else is white.
>>> get_black_and_white_rgb(0)
(255, 255, 255)
>>> get_black_and_white_rgb(0.5)
(255, 255, 255)
>>> get_black_and_white_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def get_color_coded_rgb(distance: float) -> tuple:
"""
Color-coding taking the relative distance into account. The Mandelbrot set
is black.
>>> get_color_coded_rgb(0)
(255, 0, 0)
>>> get_color_coded_rgb(0.5)
(0, 255, 255)
>>> get_color_coded_rgb(1)
(0, 0, 0)
"""
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(distance, 1, 1))
def get_image(
image_width: int = 800,
image_height: int = 600,
figure_center_x: float = -0.6,
figure_center_y: float = 0,
figure_width: float = 3.2,
max_step: int = 50,
use_distance_color_coding: bool = True,
) -> Image.Image:
"""
Function to generate the image of the Mandelbrot set. Two types of coordinates
are used: image-coordinates that refer to the pixels and figure-coordinates
that refer to the complex numbers inside and outside the Mandelbrot set. The
figure-coordinates in the arguments of this function determine which section
of the Mandelbrot set is viewed. The main area of the Mandelbrot set is
roughly between "-1.5 < x < 0.5" and "-1 < y < 1" in the figure-coordinates.
Commenting out tests that slow down pytest...
# 13.35s call fractals/mandelbrot.py::mandelbrot.get_image
# >>> get_image().load()[0,0]
(255, 0, 0)
# >>> get_image(use_distance_color_coding = False).load()[0,0]
(255, 255, 255)
"""
img = Image.new("RGB", (image_width, image_height))
pixels = img.load()
# loop through the image-coordinates
for image_x in range(image_width):
for image_y in range(image_height):
# determine the figure-coordinates based on the image-coordinates
figure_height = figure_width / image_width * image_height
figure_x = figure_center_x + (image_x / image_width - 0.5) * figure_width
figure_y = figure_center_y + (image_y / image_height - 0.5) * figure_height
distance = get_distance(figure_x, figure_y, max_step)
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
pixels[image_x, image_y] = get_color_coded_rgb(distance)
else:
pixels[image_x, image_y] = get_black_and_white_rgb(distance)
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
img = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()