前往小程序,Get更优阅读体验!
立即前往
首页
学习
活动
专区
工具
TVP
发布
社区首页 >专栏 >如何用Python画各种著名数学图案 | 附图+代码

如何用Python画各种著名数学图案 | 附图+代码

作者头像
大数据文摘
发布2018-05-21 18:02:17
2.2K0
发布2018-05-21 18:02:17
举报
文章被收录于专栏:大数据文摘大数据文摘

大数据文摘作品,转载具体要求见文末

编译团队:Aileen,徐凌霄

用Python绘制著名的数学图片或动画,展示数学中的算法魅力。

本项⽬目将持续更更新,数学中有着太多有趣的事物可以⽤用代码去展示。 欢迎提出建议和参与建设!

后台回复“数学”查看完整代码集哦

Mandelbrot 集

代码:46 lines (34 sloc) 1.01 KB

'''

A fast Mandelbrot set wallpaper renderer

reddit discussion: https://www.reddit.com/r/math/comments/2abwyt/smooth_colour_mandelbrot/

'''

import numpy as np

from PIL import Image

from numba import jit

MAXITERS = 200

RADIUS = 100

@jit

def color(z, i):

v = np.log2(i + 1 - np.log2(np.log2(abs(z)))) / 5

if v < 1.0:

return v**4, v**2.5, v

else:

v = max(0, 2-v)

return v, v**1.5, v**3

@jit

def iterate(c):

z = 0j

for i in range(MAXITERS):

if z.real*z.real + z.imag*z.imag > RADIUS:

return color(z, i)

z = z*z + c

return 0, 0 ,0

def main(xmin, xmax, ymin, ymax, width, height):

x = np.linspace(xmin, xmax, width)

y = np.linspace(ymax, ymin, height)

z = x[None, :] + y[:, None]*1j

red, green, blue = np.asarray(np.frompyfunc(iterate, 1, 3)(z)).astype(np.float)

img = np.dstack((red, green, blue))

Image.fromarray(np.uint8(img*255)).save('mandelbrot.png')

if __name__ == '__main__':

main(-2.1, 0.8, -1.16, 1.16, 1200, 960)

多米诺洗牌算法

代码链接:https://github.com/neozhaoliang/pywonderland/tree/master/src/domino

正二十面体万花筒

代码:53 lines (40 sloc) 1.24 KB

'''

A kaleidoscope pattern with icosahedral symmetry.

'''

import numpy as np

from PIL import Image

from matplotlib.colors import hsv_to_rgb

def Klein(z):

'''Klein's j-function'''

return 1728 * (z * (z**10 + 11 * z**5 - 1))**5 / \

(-(z**20 + 1) + 228 * (z**15 - z**5) - 494 * z**10)**3

def RiemannSphere(z):

'''

map the complex plane to Riemann's sphere via stereographic projection

'''

t = 1 + z.real*z.real + z.imag*z.imag

return 2*z.real/t, 2*z.imag/t, 2/t-1

def Mobius(z):

'''

distort the result image by a mobius transformation

'''

return (z - 20)/(3*z + 1j)

def main(imgsize):

x = np.linspace(-6, 6, imgsize)

y = np.linspace(6, -6, imgsize)

z = x[None, :] + y[:, None]*1j

z = RiemannSphere(Klein(Mobius(Klein(z))))

# define colors in hsv space

H = np.sin(z[0]*np.pi)**2

S = np.cos(z[1]*np.pi)**2

V = abs(np.sin(z[2]*np.pi) * np.cos(z[2]*np.pi))**0.2

HSV = np.dstack((H, S, V))

# transform to rgb space

img = hsv_to_rgb(HSV)

Image.fromarray(np.uint8(img*255)).save('kaleidoscope.png')

if __name__ == '__main__':

import time

start = time.time()

main(imgsize=800)

end = time.time()

print('runtime: {:3f} seconds'.format(end - start))

Newton 迭代分形

代码:46 lines (35 sloc) 1.05 KB

import numpy as np

import matplotlib.pyplot as plt

from numba import jit

# define functions manually, do not use numpy's poly1d funciton!

@jit('complex64(complex64)', nopython=True)

def f(z):

# z*z*z is faster than z**3

return z*z*z - 1

@jit('complex64(complex64)', nopython=True)

def df(z):

return 3*z*z

@jit('float64(complex64)', nopython=True)

def iterate(z):

num = 0

while abs(f(z)) > 1e-4:

w = z - f(z)/df(z)

num += np.exp(-1/abs(w-z))

z = w

return num

def render(imgsize):

x = np.linspace(-1, 1, imgsize)

y = np.linspace(1, -1, imgsize)

z = x[None, :] + y[:, None] * 1j

img = np.frompyfunc(iterate, 1, 1)(z).astype(np.float)

fig = plt.figure(figsize=(imgsize/100.0, imgsize/100.0), dpi=100)

ax = fig.add_axes([0, 0, 1, 1], aspect=1)

ax.axis('off')

ax.imshow(img, cmap='hot')

fig.savefig('newton.png')

if __name__ == '__main__':

import time

start = time.time()

render(imgsize=400)

end = time.time()

print('runtime: {:03f} seconds'.format(end - start))

李代数E8 的根系

代码链接:https://github.com/neozhaoliang/pywonderland/blob/master/src/misc/e8.py

模群的基本域

代码链接:

https://github.com/neozhaoliang/pywonderland/blob/master/src/misc/modulargroup.py

彭罗斯铺砌

代码链接:

https://github.com/neozhaoliang/pywonderland/blob/master/src/misc/penrose.py

Wilson 算法

代码链接:https://github.com/neozhaoliang/pywonderland/tree/master/src/wilson

反应扩散方程模拟

代码链接:https://github.com/neozhaoliang/pywonderland/tree/master/src/grayscott

120 胞腔

代码:69 lines (48 sloc) 2.18 KB

# pylint: disable=unused-import

# pylint: disable=undefined-variable

from itertools import combinations, product

import numpy as np

from vapory import *

class Penrose(object):

GRIDS = [np.exp(2j * np.pi * i / 5) for i in range(5)]

def __init__(self, num_lines, shift, thin_color, fat_color, **config):

self.num_lines = num_lines

self.shift = shift

self.thin_color = thin_color

self.fat_color = fat_color

self.objs = self.compute_pov_objs(**config)

def compute_pov_objs(self, **config):

objects_pool = []

for rhombi, color in self.tile():

p1, p2, p3, p4 = rhombi

polygon = Polygon(5, p1, p2, p3, p4, p1,

Texture(Pigment('color', color), config['default']))

objects_pool.append(polygon)

for p, q in zip(rhombi, [p2, p3, p4, p1]):

cylinder = Cylinder(p, q, config['edge_thickness'], config['edge_texture'])

objects_pool.append(cylinder)

for point in rhombi:

x, y = point

sphere = Sphere((x, y, 0), config['vertex_size'], config['vertex_texture'])

objects_pool.append(sphere)

return Object(Union(*objects_pool))

def rhombus(self, r, s, kr, ks):

if (s - r)**2 % 5 == 1:

color = self.thin_color

else:

color = self.fat_color

point = (Penrose.GRIDS[r] * (ks - self.shift[s])

- Penrose.GRIDS[s] * (kr - self.shift[r])) *1j / Penrose.GRIDS[s-r].imag

index = [np.ceil((point/grid).real + shift)

for grid, shift in zip(Penrose.GRIDS, self.shift)]

vertices = []

for index[r], index[s] in [(kr, ks), (kr+1, ks), (kr+1, ks+1), (kr, ks+1)]:

vertices.append(np.dot(index, Penrose.GRIDS))

vertices_real = [(z.real, z.imag) for z in vertices]

return vertices_real, color

def tile(self):

for r, s in combinations(range(5), 2):

for kr, ks in product(range(-self.num_lines, self.num_lines+1), repeat=2):

yield self.rhombus(r, s, kr, ks)

def put_objs(self, *args):

return Object(self.objs, *args)

后台回复“数学”查看完整代码集哦

一人一笔 | 数据团队建设“全景报告”

清华数据科学研究院联合大数据文摘,发起一次数据团队全行业调研。本次调研将对国内外数据团队发展现状进行盘点和趋势预测,同时探索数据团队应如何建设。我们将结合一系列专访与调查问卷内容,在7月初发布《数据团队建设全景报告》。

如果你是数据团队的一员、和数据团队一起工作,或者希望了解其他数据团队的发展现状和未来。

那么恳请你花费5分钟时间,点击“阅读原文”填写问卷,帮助我们完成这次调研。

原文链接:https://github.com/neozhaoliang/pywonderland/blob/master/README.md

本文参与 腾讯云自媒体分享计划,分享自微信公众号。
原始发表:2017-04-15,如有侵权请联系 cloudcommunity@tencent.com 删除

本文分享自 大数据文摘 微信公众号,前往查看

如有侵权,请联系 cloudcommunity@tencent.com 删除。

本文参与 腾讯云自媒体分享计划  ,欢迎热爱写作的你一起参与!

评论
登录后参与评论
0 条评论
热度
最新
推荐阅读
领券
问题归档专栏文章快讯文章归档关键词归档开发者手册归档开发者手册 Section 归档