绘制Mandelbrot集合

最后更新于:2022-04-01 11:16:17

# 绘制Mandelbrot集合 相关文档: [_Mandelbrot集合_](fractal_chaos.html#sec-mandelbrot) ![](https://docs.gechiui.com/gc-content/uploads/sites/kancloud/2016-03-19_56ed1bbaae9d4.png) ## 纯Python计算版本 ``` # -*- coding: utf-8 -*- import numpy as np import pylab as pl import time from matplotlib import cm def iter_point(c): z = c for i in xrange(1, 100): # 最多迭代100次 if abs(z)>2: break # 半径大于2则认为逃逸 z = z*z+c return i # 返回迭代次数 def draw_mandelbrot(cx, cy, d): """ 绘制点(cx, cy)附近正负d的范围的Mandelbrot """ x0, x1, y0, y1 = cx-d, cx+d, cy-d, cy+d y, x = np.ogrid[y0:y1:200j, x0:x1:200j] c = x + y*1j start = time.clock() mandelbrot = np.frompyfunc(iter_point,1,1)(c).astype(np.float) print "time=",time.clock() - start pl.imshow(mandelbrot, cmap=cm.Blues_r, extent=[x0,x1,y0,y1]) pl.gca().set_axis_off() x,y = 0.27322626, 0.595153338 pl.subplot(231) draw_mandelbrot(-0.5,0,1.5) for i in range(2,7): pl.subplot(230+i) draw_mandelbrot(x, y, 0.2**(i-1)) pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0) pl.show() ``` ## Weave版本 ``` # -*- coding: utf-8 -*- import numpy as np import pylab as pl import time import scipy.weave as weave from matplotlib import cm def weave_iter_point(c): code = """ std::complex<double> z; int i; z = c; for(i=1;i<100;i++) { if(std::abs(z) > 2) break; z = z*z+c; } return_val=i; """ f = weave.inline(code, ["c"], compiler="gcc") return f def draw_mandelbrot(cx, cy, d,N=200): """ 绘制点(cx, cy)附近正负d的范围的Mandelbrot """ x0, x1, y0, y1 = cx-d, cx+d, cy-d, cy+d y, x = np.ogrid[y0:y1:N*1j, x0:x1:N*1j] c = x + y*1j start = time.clock() mandelbrot = np.frompyfunc(weave_iter_point,1,1)(c).astype(np.float) print "time=",time.clock() - start pl.imshow(mandelbrot, cmap=cm.Blues_r, extent=[x0,x1,y0,y1]) pl.gca().set_axis_off() x,y = 0.27322626, 0.595153338 pl.subplot(231) draw_mandelbrot(-0.5,0,1.5) for i in range(2,7): pl.subplot(230+i) draw_mandelbrot(x, y, 0.2**(i-1)) pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0.02) pl.show() ``` ## NumPy加速版本 ``` # -*- coding: utf-8 -*- import numpy as np import pylab as pl import time from matplotlib import cm def draw_mandelbrot(cx, cy, d, N=200): """ 绘制点(cx, cy)附近正负d的范围的Mandelbrot """ global mandelbrot x0, x1, y0, y1 = cx-d, cx+d, cy-d, cy+d y, x = np.ogrid[y0:y1:N*1j, x0:x1:N*1j] c = x + y*1j # 创建X,Y轴的坐标数组 ix, iy = np.mgrid[0:N,0:N] # 创建保存mandelbrot图的二维数组,缺省值为最大迭代次数 mandelbrot = np.ones(c.shape, dtype=np.int)*100 # 将数组都变成一维的 ix.shape = -1 iy.shape = -1 c.shape = -1 z = c.copy() # 从c开始迭代,因此开始的迭代次数为1 start = time.clock() for i in xrange(1,100): # 进行一次迭代 z *= z z += c # 找到所有结果逃逸了的点 tmp = np.abs(z) > 2.0 # 将这些逃逸点的迭代次数赋值给mandelbrot图 mandelbrot[ix[tmp], iy[tmp]] = i # 找到所有没有逃逸的点 np.logical_not(tmp, tmp) # 更新ix, iy, c, z只包含没有逃逸的点 ix,iy,c,z = ix[tmp], iy[tmp], c[tmp],z[tmp] if len(z) == 0: break print "time=",time.clock() - start pl.imshow(mandelbrot, cmap=cm.Blues_r, extent=[x0,x1,y0,y1]) pl.gca().set_axis_off() x,y = 0.27322626, 0.595153338 pl.subplot(231) draw_mandelbrot(-0.5,0,1.5) for i in range(2,7): pl.subplot(230+i) draw_mandelbrot(x, y, 0.2**(i-1)) pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0) pl.show() ``` ## 平滑版本 ![](https://docs.gechiui.com/gc-content/uploads/sites/kancloud/2016-03-19_56ed1bbb05dd5.png) ``` # -*- coding: utf-8 -*- import numpy as np import pylab as pl from math import log from matplotlib import cm escape_radius = 10 iter_num = 20 def smooth_iter_point(c): z = c for i in xrange(1, iter_num): if abs(z)>escape_radius: break z = z*z+c absz = abs(z) if absz > 2.0: mu = i - log(log(abs(z),2),2) else: mu = i return mu # 返回正规化的迭代次数 def iter_point(c): z = c for i in xrange(1, iter_num): if abs(z)>escape_radius: break z = z*z+c return i def draw_mandelbrot(cx, cy, d, N=200): global mandelbrot """ 绘制点(cx, cy)附近正负d的范围的Mandelbrot """ x0, x1, y0, y1 = cx-d, cx+d, cy-d, cy+d y, x = np.ogrid[y0:y1:N*1j, x0:x1:N*1j] c = x + y*1j mand = np.frompyfunc(iter_point,1,1)(c).astype(np.float) smooth_mand = np.frompyfunc(smooth_iter_point,1,1)(c).astype(np.float) pl.subplot(121) pl.gca().set_axis_off() pl.imshow(mand, cmap=cm.Blues_r, extent=[x0,x1,y0,y1]) pl.subplot(122) pl.imshow(smooth_mand, cmap=cm.Blues_r, extent=[x0,x1,y0,y1]) pl.gca().set_axis_off() draw_mandelbrot(-0.5,0,1.5,300) pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0) pl.show() ```
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