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Sage Interactions - Fractal
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Contents
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Sage Interactions - Fractal
- Mandelbrot's Fractal Binomial Distribution
- Fractals Generated By Digit Sets and Dilation Matrices (Sage Days 9 - Avra Laarakker)
- Attempt at Generating all integer vectors with Digits D and Matrix A (How about vector([0,-1])?)
- Demonstrating that the Twin Dragon Matrix is likely to yield a Tiling of a Compact Interval of R^2 as k->infinity (It does!)
- Now in 3d
- Exploring Mandelbrot
Mandelbrot's Fractal Binomial Distribution
def muk_plot(m0,k): """ Return a plot of the binomial fractal measure mu_k associated to m0, 1-m0, and k. """ k = int(k) m0 = float(m0) m1 = float(1 - m0) assert m0 > 0 and m1 > 0, "both must be positive" v = [(0,0)] t = 0 two = int(2) delta = float(1/2^k) multiplier = float(2^k) for i in [0..2^k-1]: t = i * delta phi1 = i.str(two).count("1") phi0 = k - phi1 y = m0^(phi0)*m1^(phi1)*multiplier v.append((t,y)) v.append((t+delta,y)) return v html("<h1>Mandelbrot's Fractal Binomial Measure</h1>") @interact def _(mu0=(0.3,(0.0001,0.999)), k=(3,(1..14)), thickness=(1.0,(0.1,0.2,..,1.0))): v = muk_plot(mu0,k) line(v,thickness=thickness).show(xmin=0.5, xmax=0.5, ymin=0, figsize=[8,3])
Fractals Generated By Digit Sets and Dilation Matrices (Sage Days 9 - Avra Laarakker)
Attempt at Generating all integer vectors with Digits D and Matrix A (How about vector([0,-1])?)
A = matrix([[1,1],[-1,1]]) D = [vector([0,0]), vector([1,0])] @interact def f(A = matrix([[1,1],[-1,1]]), D = '[[0,0],[1,0]]', k=(3..17)): print "Det = ", A.det() D = matrix(eval(D)).rows() def Dn(k): ans = [] for d in Tuples(D, k): s = sum(A^n*d[n] for n in range(k)) ans.append(s) return ans G = points([v.list() for v in Dn(k)]) show(G, frame=True, axes=False)
Demonstrating that the Twin Dragon Matrix is likely to yield a Tiling of a Compact Interval of R^2 as k->infinity (It does!)
A = matrix([[1,1],[-1,1]]) D = [vector([0,0]), vector([1,0])] @interact def f(A = matrix([[1,1],[-1,1]]), D = '[[0,0],[1,0]]', k=(3..17)): print "Det = ", A.det() D = matrix(eval(D)).rows() def Dn(k): ans = [] for d in Tuples(D, k): s = sum(A^(-n)*d[n] for n in range(k)) ans.append(s) return ans G = points([v.list() for v in Dn(k)]) show(G, frame=True, axes=False)
Now in 3d
A = matrix([[0,0,2],[1,0,1],[0,1,-1]]) D = '[[0,0,0],[1,0,0]]' def Dn(D,A,k): ans = [] for d in Tuples(D, k): s = sum(A^n*d[n] for n in range(k)) ans.append(s) return ans @interact def f(A = matrix([[0,0,2],[1,0,1],[0,1,-1]]), D = '[[0,0,0],[1,0,0]]', k=(3..15), labels=True): print "Det = ", A.det() D = matrix(eval(D)).rows() print "D:" print D G = point3d([v.list() for v in Dn(D,A,k)], size=8)#, opacity=.85) if labels: G += sum([text3d(str(v),v) for v in Dn(D,A,k)]) show(G, axes=False, frame=False)
Exploring Mandelbrot
Pablo Angulo
%cython import numpy as np def mandelbrot_cython(float x0,float x1,float y0,float y1,int N=200, int L=50, float R=3): '''returns an array NxN to be plotted with matrix_plot ''' cdef complex c,z cdef int h, i, k m= np.zeros([N,N], dtype=np.int) for i in range(N): for k in range(N): c=complex(x0+i*(x1-x0)/N, y0+k*(y1-y0)/N) z=complex(0,0) h=0 while (h<L) and (abs(z)<R): z=z*z+c h+=1 m[i,k]=h return m
@interact def showme_mandelbrot(x0=-2, y0=-1.5, side=3.0,N=(100*i for i in range(1,11)), L=(20*i for i in range(1,11)) ): time m=mandelbrot_cython(x0 ,x0 + side ,y0 ,y0 + side , N, L ) time show(matrix_plot(m))