From: Sam Moore Date: Tue, 21 Jan 2014 13:21:27 +0000 (+0800) Subject: De'Casteljau and Splines X-Git-Url: https://git.ucc.asn.au/?a=commitdiff_plain;ds=sidebyside;p=matches%2FFYP2014.git De'Casteljau and Splines Should have committed ages ago, but got distracted by other things happening. Performance test shows Goldman's algorithm is really slow. Because he is making a bigger matrix and then inverting it multiple times. Other than that it is the same as De'Casteljau anyway. Splines are a thing. Tried to work them out myself, ended up just copying the algorithm from wikipedia. Quad trees are a thing that I have seen before (Barnes Hut; efficient N-body simulations). In a graphics context they are a way to work out what parts of a scene to render? Maybe look at them more. TODO: Decide exactly on scope for project. All I can think of is implementing the "document viewer" on a mobile device. Not sure I want to do that... Design of document format / viewer should come before putting it on a mobile device anyway. Will waste some time by looking at PostScript some more for now. --- diff --git a/.gitignore b/.gitignore index 877fa0d..c9121d4 100644 --- a/.gitignore +++ b/.gitignore @@ -9,5 +9,6 @@ *.o *.test nogit/* +*.mp4 references/*.pdf ipython_notebooks/.ipynb_checkpoints diff --git a/ipython_notebooks/de_Casteljau.ipynb b/ipython_notebooks/de_Casteljau.ipynb index c7a2805..1ebf62e 100644 --- a/ipython_notebooks/de_Casteljau.ipynb +++ b/ipython_notebooks/de_Casteljau.ipynb @@ -500,6 +500,93 @@ } ], "prompt_number": 18 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Approximating a Circle using Cubic Beziers" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "k = 4. * (2.**0.5 - 1.) / 3.\n", + "points = [(0,1), (k, 1), (1, k), (1,0)]\n", + "c = BezierCurve(points, 50) # This appears to be more accurate than using the de Casteljau algorithm :S\n", + "c += list(array([-1,1]) * c)\n", + "c += list(array([1,-1]) * c)\n", + "scatter([p[0] for p in c], [p[1] for p in c])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 125, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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D5s1bjrzZdBn5dk+hRImSNG3aVNtgilLAFC5cmK5dOwL9gBrI2fF9Wbt2o7bB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\nKo/cVq47crhnFP7+v3L4cJT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+ "text": [ + "" + ] + } + ], + "prompt_number": 125 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "scatter([arctan2(p[0], p[1]) for p in c], [p[0]**2. + p[1]**2. for p in c])\n", + "title(\"$r$ vs Angle\")\n", + "xlabel(\"Angle (rad)\")\n", + "ylabel(\"$r$\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 126, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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4mhBw5NKl71S6j3q3bwjVaYtuWvlLGRPTUVeZ58+f124+bKz9DSKQT0VxqNG0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\nAORP/uRP/uSvmn962aV7SlGUKreZMWM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+ "text": [ + "" + ] + } + ], + "prompt_number": 126 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect circle $\\implies r = 1 \\forall \\theta$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] } ], "metadata": {} diff --git a/ipython_notebooks/splines.ipynb b/ipython_notebooks/splines.ipynb new file mode 100644 index 0000000..41178de --- /dev/null +++ b/ipython_notebooks/splines.ipynb @@ -0,0 +1,268 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Splines\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Piecewise continuous curves. $S:[a,b]\\to \\Re$\n", + "\n", + "$S = P_i (t) \\quad t_{i-1} \\leq t \\lt t_i$ for $i=0,...k$\n", + "\n", + "$P_i$ are polynomials. Chosen to gaurantee smoothness of $S$. $P_i^{(j)} (t_i) = P_{i+1}^{(j)} (t_i)$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The spline is defined by $\\{q_0(x) ... q_n(x)\\}$ for $n$ points $x_i$.\n", + "\n", + "For cubic splines have: \n", + "\\begin{align}\n", + " q_i(x) &= a_i x^3 + b_i x^2 + c_i x + d_i \\\\\n", + " q_i(x_i) = y_i &= q_{i+1}(x_i) \\\\\n", + " &\\implies x_i^3 (a_{i} - a_{i+1}) + x_i^2 (b_{i} - b_{i+1}) + x_i (c_{i} - c_{i+1}) + (d_{i} - d_{i+1}) = 0 \\quad \\forall i\\\\\n", + " q'_i(x_i) &= q'_{i+1}(x_i) \\\\\n", + " &\\implies 3 x_i^2 (a_{i} - a_{i+1}) + 2 x_i (b_{i} - b_{i+1}) + (c_{i} - c_{i+1}) = 0 \\quad \\forall i\\\\\n", + " q''_i(x_i) &= q''_{i+1}(x_i) \\\\\n", + " &\\implies 6 x_i (a_{i} - a_{i+1}) + 2 (b_{i} - b_{i+1}) = 0 \\quad \\forall i\n", + "\\end{align}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Not useful:** Summing:\n", + "\n", + "\\begin{align}\n", + " 0 &= a_0 (x_0^3 + 3 x_0^2 + 6 x_0) + b_0 (x_0^2 + 2x_0+2) + c_0 (x_0 + 1) + d_0 \\\\\n", + " &+ \\sum_{i=1}^{n} a_i \\left[ (x_i^3 - x_{i-1}^3) + 3(x_i^2 - x_{i-1}^2) + 6(x_i - x_{i-1})\\right]\n", + " + b_i\\left[(x_i^2 - x_{i-1}^2) + 2(x_i - x_{i-1})\\right] + c_i\\left[x_i - x_{i-1}\\right] \\\\\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$q_i(x) = a_i x^3 + b_i x^2 + c_i x + d_i$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$q_i'(x) = 3 a_i x^2 + 2 b_i x + c_i$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$q_i''(x) = 6 a_i x + 2 b_i$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$x_i^3 a_i + x_i^2 b_i + x_i c_i + d_i = y_i = x_i^3 a_{i+1} + x_i^2 b_{i+1} + x_i c_{i+1} + d_{i+1}$$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def Spline(P):\n", + " \"\"\" Make a cubic spline that goes through the knots P \"\"\"\n", + " x = [1.*p[0] for p in P]\n", + " y = [1.*p[1] for p in P]\n", + " \n", + " n = len(P)-1\n", + " \n", + " A = zeros((n+1,)*2)\n", + " B = 3. * ones(n+1)\n", + " for i in xrange(1,n):\n", + " A[i, i-1:i+2] = [1./(x[i] - x[i-1]), 2.*(1./(x[i]-x[i-1]) + 1./(x[i+1]-x[i])), 1./(x[i+1]-x[i])]\n", + " \n", + " A[0,0:2] = [2./(x[1]-x[0]), 1./(x[1]-x[0])]\n", + " B[0] = 3. * ((y[1] - y[0]) / (x[1] - x[0])**2.)\n", + " A[n,n-1:] = [1./(x[n] - x[n-1]), 2./(x[n]-x[n-1])]\n", + " B[n] = 3. * ((y[n] - y[n-1]) / (x[n] - x[n-1])**2.)\n", + " \n", + " k = solve(A, B)[-1::-1]\n", + "\n", + " \n", + " def p(xx):\n", + " for i in xrange(n):\n", + " if xx >= x[i] and xx <= x[i+1]:\n", + " break\n", + " if (i >= n):\n", + " i = n-1\n", + " \n", + " t = (xx - x[i]) / (x[i+1] - x[i])\n", + " a = k[i]*(x[i+1] - x[i]) - (y[i+1] - y[i])\n", + " b = -k[i+1]*(x[i+1]-x[i]) - (y[i+1] - y[i])\n", + " return (1. - t)*y[i] + t*y[i+1] + t*(1.-t)*(a*(1.-t)+b*t)\n", + " \n", + " return p\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 216 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "points = [(-1,0),(0,-1),(1,0)]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 245 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "p1 = Spline(points)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 246 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x = linspace(-1,1)\n", + "plot(x, map(p1, x))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 247, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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