From f8e6f61fe8047f6bf4ae1e24689a489ae2b0e2d2 Mon Sep 17 00:00:00 2001 From: rowanc1 Date: Fri, 24 Jan 2014 09:46:42 -0700 Subject: [PATCH] Deleted Notebooks. Use GISTS in github. --- .gitignore | 1 - notebooks/BFGS.ipynb | 158 - notebooks/Bound Constraint Problem.ipynb | 620 ---- notebooks/SliceThroughModel.ipynb | 3048 ------------------ notebooks/VisualizeWithvtkView-updated.ipynb | 142 - notebooks/exLomPlots.ipynb | 122 - notebooks/exPlotImage2D.ipynb | 235 -- 7 files changed, 4326 deletions(-) delete mode 100644 notebooks/BFGS.ipynb delete mode 100644 notebooks/Bound Constraint Problem.ipynb delete mode 100644 notebooks/SliceThroughModel.ipynb delete mode 100644 notebooks/VisualizeWithvtkView-updated.ipynb delete mode 100644 notebooks/exLomPlots.ipynb delete mode 100644 notebooks/exPlotImage2D.ipynb diff --git a/.gitignore b/.gitignore index 5c39733b..7678b15c 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,3 @@ *.sublime-project *.sublime-workspace docs/_build/ -myNotebooks/* diff --git a/notebooks/BFGS.ipynb b/notebooks/BFGS.ipynb deleted file mode 100644 index c2f68c99..00000000 --- a/notebooks/BFGS.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import SimPEG\n", - "from SimPEG import Solver\n", - "from SimPEG.mesh import TensorMesh\n", - "from SimPEG.regularization import Regularization\n", - "import SimPEG.inverse as inverse\n", - "from SimPEG.inverse import Minimize, Remember, IterationPrinters\n", - "import numpy as np\n", - "import scipy.sparse as sp" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "FUN = SimPEG.tests.Rosenbrock\n", - "FUN = SimPEG.tests.getQuadratic(sp.csr_matrix(([100,1],([0,1],[0,1])),shape=(2,2)),np.array([-5,-5]),100)\n", - "\n", - "x0 = np.array([1,0])\n", - "opt = inverse.BFGS()\n", - "xopt = opt.minimize(FUN,x0)\n", - "print xopt\n", - "opt = inverse.GaussNewton()\n", - "xopt = opt.minimize(FUN,x0)\n", - "print xopt\n", - "opt = inverse.SteepestDescent()\n", - "xopt = opt.minimize(FUN,x0)\n", - "print xopt" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "===================== BFGS =====================\n", - " # f |proj(x-g)-x| LS Comment \n", - "-----------------------------------------------\n", - " 0 1.45e+02 9.51e+01 0 \n", - " 1 1.14e+02 5.37e+01 6 \n", - " 2 1.04e+02 3.04e+01 6 \n", - " 3 8.83e+01 1.37e+01 0 \n", - " 4 8.76e+01 5.97e+00 0 Skip BFGS \n", - " 5 8.74e+01 2.61e+00 0 Skip BFGS \n", - " 6 8.74e+01 1.14e+00 0 Skip BFGS \n", - " 7 8.74e+01 5.01e-01 0 Skip BFGS \n", - " 8 8.74e+01 2.19e-01 0 Skip BFGS \n", - " 9 8.74e+01 9.60e-02 0 Skip BFGS \n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 1.9437e-04 <= tolF*(1+|f0|) = 1.4600e+01\n", - "1 : |xc-x_last| = 1.2663e-03 <= tolX*(1+|x0|) = 2.0000e-01\n", - "1 : |proj(x-g)-x| = 9.5952e-02 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 9.5952e-02 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 20 <= iter = 9\n", - "------------------------- DONE! -------------------------\n", - "[ 0.05095952 4.99977449]\n", - "=========== Gauss Newton ===========\n", - " # f |proj(x-g)-x| LS \n", - "-----------------------------------\n", - " 0 1.45e+02 9.51e+01 0 \n", - " 1 8.74e+01 4.44e-15 0 \n", - "------------------------- STOP! -------------------------\n", - "0 : |fc-fOld| = 5.7625e+01 <= tolF*(1+|f0|) = 1.4600e+01\n", - "0 : |xc-x_last| = 5.0894e+00 <= tolX*(1+|x0|) = 2.0000e-01\n", - "1 : |proj(x-g)-x| = 4.4409e-15 <= tolG = 1.0000e-01\n", - "1 : |proj(x-g)-x| = 4.4409e-15 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 20 <= iter = 1\n", - "------------------------- DONE! -------------------------\n", - "[ 0.05 5. ]\n", - "========= Steepest Descent =========\n", - " # f |proj(x-g)-x| LS \n", - "-----------------------------------\n", - " 0 1.45e+02 9.51e+01 0 \n", - " 1 1.14e+02 5.37e+01 6 \n", - " 2 1.04e+02 3.04e+01 6 \n", - " 3 1.00e+02 1.76e+01 6 \n", - " 4 9.88e+01 1.06e+01 6 \n", - " 5 9.82e+01 7.07e+00 6 \n", - " 6 9.80e+01 1.22e+01 5 \n", - " 7 9.73e+01 7.77e+00 6 \n", - " 8 9.68e+01 5.64e+00 6 \n", - " 9 9.65e+01 8.72e+00 5 \n", - " 10 9.60e+01 5.97e+00 6 \n", - " 11 9.58e+01 9.98e+00 5 \n", - " 12 9.53e+01 6.48e+00 6 \n", - " 13 9.53e+01 1.16e+01 5 \n", - " 14 9.46e+01 7.20e+00 6 \n", - " 15 9.43e+01 5.07e+00 6 \n", - " 16 9.41e+01 8.17e+00 5 \n", - " 17 9.37e+01 5.43e+00 6 \n", - " 18 9.36e+01 9.42e+00 5 \n", - " 19 9.32e+01 5.98e+00 6 \n", - " 20 9.29e+01 4.32e+00 6 \n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 2.5913e-01 <= tolF*(1+|f0|) = 1.4600e+01\n", - "1 : |xc-x_last| = 9.3379e-02 <= tolX*(1+|x0|) = 2.0000e-01\n", - "0 : |proj(x-g)-x| = 4.3246e+00 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 4.3246e+00 <= 1e3*eps = 1.0000e-02\n", - "1 : maxIter = 20 <= iter = 20\n", - "------------------------- DONE! -------------------------\n", - "[ 0.07777107 1.6849632 ]\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/Users/rowan/git/simpeg/SimPEG/inverse/Optimize.py:664: RuntimeWarning: divide by zero encountered in remainder\n", - " khat = np.mod(n-nn+k,nn)\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "A = sp.identity(2)\n", - "S = Solver(A)\n", - "\n", - "assert type(S) is Solver" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/Bound Constraint Problem.ipynb b/notebooks/Bound Constraint Problem.ipynb deleted file mode 100644 index efaa487c..00000000 --- a/notebooks/Bound Constraint Problem.ipynb +++ /dev/null @@ -1,620 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import SimPEG\n", - "from SimPEG.mesh import TensorMesh\n", - "from SimPEG.regularization import Regularization\n", - "import SimPEG.inverse as inverse\n", - "from SimPEG.inverse import Minimize\n", - "import numpy as np\n", - "import scipy.sparse as sp" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from SimPEG.forward.LinearProblem import example as LinExample\n", - "\n", - "prob, m_true = LinExample(1000)\n", - "M = prob.mesh\n", - "\n", - "reg = Regularization(M)\n", - "opt = inverse.InexactGaussNewton(maxIter=100,maxStep=0.2,debug=False,LSreduction=0.3,maxIterLS=50)\n", - "opt.remember('f', ('norm_g', lambda M:np.linalg.norm(M.g)))\n", - "inv = inverse.Inversion(prob,reg,opt,beta0=1e-4,maxIter=2,debug=False)\n", - "m0 = np.zeros_like(m_true)\n", - "\n", - "mrec = inv.run(m0)\n", - "\n", - "plt.plot(M.vectorCCx, m_true, 'b-')\n", - "plt.plot(M.vectorCCx, mrec, 'r-')\n", - "\n", - "figure()\n", - "plot(opt.recall('f'))\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "====================== Inexact Gauss Newton ======================\n", - " # beta phi_d phi_m f |proj(x-g)-x| LS \n", - "-----------------------------------------------------------------\n", - " 0 1.00e-04 9.62e+02 0.00e+00 9.62e+02 1.02e+04 0 \n", - " 1 1.00e-04 6.63e+02 7.73e+02 6.63e+02 8.47e+03 0 \n", - " 2 1.00e-04 4.15e+02 4.39e+03 4.15e+02 6.63e+03 0 \n", - " 3 1.00e-04 2.26e+02 6.85e+03 2.27e+02 4.80e+03 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 4 1.00e-04 9.74e+01 1.07e+04 9.84e+01 3.02e+03 0 \n", - " 5 1.00e-04 2.42e+01 1.64e+04 2.58e+01 1.35e+03 0 \n", - " 6 1.00e-04 1.06e+00 2.34e+04 3.39e+00 9.21e+00 0 \n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 2.2421e+01 <= tolF*(1+|f0|) = 9.6325e+01\n", - "0 : |xc-x_last| = 2.1351e+00 <= tolX*(1+|x0|) = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 9.2073e+00 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 9.2073e+00 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 100 <= iter = 6\n", - "1 : phi_d = 1.0595e+00 <= phi_d_target = 2.0000e+01 \n", - "------------------------- DONE! -------------------------\n", - "-------------------------STOP!-------------------------\n", - "0 : maxIter = 2 <= iter = 1\n", - "1 : phi_d = 1.0595e+00 <= phi_d_target = 2.0000e+01 \n", - "-------------------------DONE!-------------------------\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "FUN = SimPEG.tests.getQuadratic(sp.identity(2),np.array([-5,-5]))\n", - "# FUN = SimPEG.tests.Rosenbrock\n", - "\n", - "f = FUN(np.array([1,2]))\n", - "print f\n", - "n,l,u = 50,-10,10\n", - "I = np.zeros((n,n))\n", - "X = np.linspace(l,u,n)\n", - "for i, x in enumerate(X):\n", - " for j, y in enumerate(X):\n", - " f, g, H = FUN(np.array([x,y]))\n", - " I[i,j] = f\n", - "\n", - "colorbar(contourf(X,X, I.T))\n", - "\n", - "GN = inverse.GaussNewton()\n", - "xopt = GN.minimize(FUN,np.array([0,0]))\n", - "print xopt" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(-12.5, array([-4., -3.]), <2x2 sparse matrix of type ''\n", - "\twith 2 stored elements (1 diagonals) in DIAgonal format>)\n", - "=========== Gauss Newton ===========" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " # f |proj(x-g)-x| LS \n", - "-----------------------------------\n", - " 0 0.00e+00 7.07e+00 0 \n", - " 1 -2.50e+01 0.00e+00 0 \n", - "------------------------- STOP! -------------------------\n", - "0 : |fc-fOld| = 2.5000e+01 <= tolF*(1+|f0|) = 1.0000e-01\n", - "0 : |xc-x_last| = 7.0711e+00 <= tolX*(1+|x0|) = 1.0000e-01\n", - "1 : |proj(x-g)-x| = 0.0000e+00 <= tolG = 1.0000e-01\n", - "1 : |proj(x-g)-x| = 0.0000e+00 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 20 <= iter = 1\n", - "------------------------- DONE! -------------------------\n", - "[ 5. 5.]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "opt = inverse.ProjectedGradient(maxIter=50,maxStep=np.inf, maxIterLS=20, debug=False)\n", - "opt.remember('f')\n", - "opt.lower = -2\n", - "opt.upper = 2\n", - "opt.minimize(FUN,np.array([0,0]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "======================= Projected Gradient =======================\n", - " # f |proj(x-g)-x| LS itType aSet bSet Comment\n", - "------------------------------------------------------------------\n", - " 0 0.00e+00 2.83e+00 0 SD 0 0 \n", - " 1 -1.60e+01 0.00e+00 0 SD 2 2 \n", - "------------------------- STOP! -------------------------\n", - "0 : |fc-fOld| = 1.6000e+01 <= tolF*(1+|f0|) = 1.0000e-01\n", - "0 : |xc-x_last| = 2.8284e+00 <= tolX*(1+|x0|) = 1.0000e-01\n", - "1 : |proj(x-g)-x| = 0.0000e+00 <= tolG = 1.0000e-01\n", - "1 : |proj(x-g)-x| = 0.0000e+00 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 50 <= iter = 1\n", - "1 : probSize = 2 <= bindingSet = 2\n", - "------------------------- DONE! -------------------------\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "array([ 2., 2.])" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "opt = inverse.ProjectedGradient(maxIter=50, maxStep=0.3,debug=False, maxIterLS=20, tolCG=1e-5, maxIterCG=1000)\n", - "opt.lower, opt.upper = -0.4, 0.9\n", - "opt.remember('f', 'xc', ('norm_g', lambda M: np.linalg.norm(M.g)), 'phi_d', 'phi_m')\n", - "inv = inverse.Inversion(prob,reg,opt,beta0=1e-3,debug=False)\n", - "inv.remember(('phi_d',lambda I:I.opt.recall('phi_d')),('phi_m',lambda I:I.opt.recall('phi_m')))\n", - "m0 = np.zeros_like(m_true)\n", - "mrecB = inv.run(m0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "====================================== Projected Gradient ======================================\n", - " # beta phi_d phi_m f |proj(x-g)-x| LS itType aSet bSet Comment\n", - "------------------------------------------------------------------------------------------------\n", - " 0 1.00e-03 9.62e+02 0.00e+00 9.62e+02 2.14e+01 0 SD 0 0 \n", - " 1 1.00e-03 9.53e+02 8.49e-03 9.53e+02 2.18e+01 9 SD 0 0 \n", - " 2 1.00e-03 5.32e+02 1.42e+03 5.33e+02 2.20e+01 0 .CG. 0 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 3 1.00e-03 2.32e+02 5.66e+03 2.38e+02 2.26e+01 0 .CG. 0 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 4 1.00e-03 1.29e+02 8.85e+03 1.38e+02 2.30e+01 1 .CG. 0 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 5 1.00e-03 1.10e+02 9.74e+03 1.20e+02 2.15e+01 3 .CG. 22 22 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 6 1.00e-03 1.09e+02 9.74e+03 1.19e+02 2.36e+01 10 SD 22 0 Stop SD\n", - " 7 1.00e-03 7.65e+01 1.33e+04 8.98e+01 2.21e+01 1 .CG. 85 75 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 8 1.00e-03 5.11e+01 1.33e+04 6.44e+01 2.38e+01 9 SD 75 3 \n", - " 9 1.00e-03 5.07e+01 1.33e+04 6.41e+01 2.11e+01 12 SD 4 4 \n", - " 10 1.00e-03 5.06e+01 1.33e+04 6.40e+01 2.20e+01 12 SD 17 9 Stop SD\n", - " 11 1.00e-03 5.04e+01 1.34e+04 6.37e+01 2.05e+01 8 .CG. 85 81 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 12 1.00e-03 5.03e+01 1.34e+04 6.37e+01 1.65e+01 13 SD 81 26 \n", - " 13 1.00e-03 5.03e+01 1.34e+04 6.37e+01 1.67e+01 15 SD 30 30 \n", - " 14 1.00e-03 5.03e+01 1.34e+04 6.36e+01 1.42e+01 15 SD 84 84 Stop SD\n", - " 15 1.00e-03 2.65e+01 2.26e+04 4.91e+01 2.52e+01 0 .CG. 190 50 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 16 1.00e-03 1.51e+01 2.26e+04 3.77e+01 2.24e+01 10 SD 50 1 \n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 1.1392e+01 <= tolF*(1+|f0|) = 9.6325e+01\n", - "1 : |xc-x_last| = 3.4809e-03 <= tolX*(1+|x0|) = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 2.2369e+01 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 2.2369e+01 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 50 <= iter = 16\n", - "0 : probSize = 1000 <= bindingSet = 1\n", - "1 : phi_d = 1.5077e+01 <= phi_d_target = 2.0000e+01 \n", - "------------------------- DONE! -------------------------\n", - "-------------------------STOP!-------------------------\n", - "0 : maxIter = 10 <= iter = 1\n", - "1 : phi_d = 1.5077e+01 <= phi_d_target = 2.0000e+01 \n", - "-------------------------DONE!-------------------------\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# First set up the figure, the axis, and the plot element we want to animate\n", - "fig = plt.figure()\n", - "ax = plt.axes()\n", - "ax.plot(M.vectorCCx, m_true, 'b-')\n", - "txt = plt.text(0.8,0.9,'')\n", - "line, = ax.plot([], [], 'r-', lw=1)\n", - "\n", - "def animate(i):\n", - " txt.set_text('iteration %d'%i)\n", - " line.set_data(M.vectorCCx, np.array(opt.recall('xc')[i]))\n", - "\n", - "SimPEG.utils.animate(fig, animate, frames=len(opt.recall('xc')))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot(opt.recall('phi_d'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/SliceThroughModel.ipynb b/notebooks/SliceThroughModel.ipynb deleted file mode 100644 index 1259ebf6..00000000 --- a/notebooks/SliceThroughModel.ipynb +++ /dev/null @@ -1,3048 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from SimPEG import mesh, utils\n", - "sz = [10,20,30]\n", - "M = mesh.TensorMesh(sz)\n", - "mtrue = utils.ModelBuilder.randomModel(sz,seed=786,its=20)\n", - "M.videoSlicer(utils.mkvc(mtrue), normal='x')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "models = [utils.ModelBuilder.randomModel(sz,seed=786,its=i) for i in range(40)]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def function(var, ax, clim, tlt, i):\n", - " p = M.slicer(var, normal='y', index=5, imageType='CC', ax=ax, clim=clim)\n", - " tlt.set_text('%d smoothing iterations'%(i))\n", - " return p\n", - " \n", - "M.video(models,function)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/VisualizeWithvtkView-updated.ipynb b/notebooks/VisualizeWithvtkView-updated.ipynb deleted file mode 100644 index f4e8a3a7..00000000 --- a/notebooks/VisualizeWithvtkView-updated.ipynb +++ /dev/null @@ -1,142 +0,0 @@ -{ - "metadata": { - "name": "VisualizeWithvtkView-updated" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import SimPEG as simpeg, matplotlib as mpl" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "The history saving thread hit an unexpected error (OperationalError('disk I/O error',)).History will not be written to the database.\n", - "Warning: mumps solver not available." - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simple notebook of how to use vtkView to visualize SimPEG models. It will pop-up external vtk windows." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Make a mesh and model\n", - "x0 = np.zeros(3)\n", - "h1 = np.ones(60)*50\n", - "h2 = np.ones(60)*100\n", - "h3 = np.ones(50)*200\n", - "\n", - "mesh = simpeg.mesh.TensorMesh([h1,h2,h3],x0)\n", - "\n", - "# Make a models that correspond to the cells, faces and edges.\n", - "t = np.ones(mesh.nC)\n", - "t[10000:50000] = 100\n", - "t[100000:120000] = 100\n", - "t[100000:120000] = 50\n", - "# Make models called 'Test' for all with a range. \n", - "models = {'C':{'Test':np.arange(0,mesh.nC),'Model':t},'F':{'Test':np.arange(0,mesh.nF)},'E':{'Test':np.arange(0,mesh.nE)}}\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Make the vtk viewer object.\n", - "vtkViewer = simpeg.visualize.vtk.vtkView(mesh,models)\n", - "# Set the .viewprop for which model to view\n", - "vtkViewer.viewprop = {'F':'Test'}\n", - "# Show the image\n", - "vtkViewer.Show()\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Set subset of the mesh to view (remove padding)\n", - "vtkViewer.extent = [4,14,0,7,0,3]\n", - "vtkViewer.Show()\n", - "\n", - "# Change viewing property \n", - "vtkViewer.viewprop = {'C':'Model'}\n", - "# Set the color range\n", - "# Reset extent. Error check will reset the limits correctly.\n", - "vtkViewer.extent = [-1,1000,-1,1000,-1,1000]\n", - "# Set the range\n", - "vtkViewer.range = [0.,100.]\n", - "# Show\n", - "vtkViewer.Show()\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/home/Gudni/Codes/python/simpeg/SimPEG/visualize/vtk/vtkView.py:116: UserWarning: Lower bounds smaller then 0\n", - " warnings.warn('Lower bounds smaller then 0')\n", - "/home/Gudni/Codes/python/simpeg/SimPEG/visualize/vtk/vtkView.py:128: UserWarning: Upper bounds greater then number of cells\n", - " warnings.warn('Upper bounds greater then number of cells')\n", - "/home/Gudni/Codes/python/simpeg/SimPEG/visualize/vtk/vtkView.py:137: UserWarning: Changed given extent from [-1, 1000, -1, 1000, -1, 1000] to [0, 59, 0, 59, 0, 49]\n", - " warnings.warn('Changed given extent from {:s} to {:s}'.format(value,valnp.tolist()))\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Change color scale, has to be set to bytes=True.\n", - "vtkViewer.cmap = mpl.cm.copper(np.arange(0.,1.,0.01),bytes=True)\n", - "vtkViewer.Show()\n", - "# Set limits of values to view \n", - "vtkViewer.limits = [5.0,100.0]\n", - "vtkViewer.Show()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/exLomPlots.ipynb b/notebooks/exLomPlots.ipynb deleted file mode 100644 index 3170f831..00000000 --- a/notebooks/exLomPlots.ipynb +++ /dev/null @@ -1,122 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import sys\n", - "sys.path.append('../')\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from SimPEG import LogicallyOrthogonalMesh, utils\n", - "mkvc = utils.mkvc" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 2D Plots\n", - "\n", - "For 2D nodal or cell-centered plots are supported.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X, Y = utils.exampleLomGird([3,3],'rotate')\n", - "M = LogicallyOrthogonalMesh([X, Y])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.plotGrid()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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FqOhTAEhZAeOK67ZP17Oki7fy9nruLSQpCFPIKCRdt3Pg4523GxdeQpKCBfQd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\n9Wp4n7Nn9YRxeb9GRkbdz4c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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 3D Plots\n", - "\n", - "Plot x, y, and z coordinates of cell-centred points" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X, Y, Z = utils.exampleLomGird([3,3,3],'rotate')\n", - "M = LogicallyOrthogonalMesh([X, Y, Z])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.plotGrid()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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YKPH22zKbN6scPUpw5ll+vk6fPjZOO424l/0AP/0E+fn2oPvXokV+Bg48Qmpq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\nOgTAgwfXBuCkJJNVq1TefVdl7lwvhw5JHDggLotoj7w1uUdLfRIIBCI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2bfI2aElh926Z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vfcoyLLIRDu0cOge/dIT1OrwtULNc+vvgrX/yYnu8jLk7n8cigqknjxRYu3FX4TDocD0zRZuVLi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\nQWHoUAVq1hRw5IgBQUGPd8jBlXWOp9rDo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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 42 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/exPlotImage2D.ipynb b/notebooks/exPlotImage2D.ipynb deleted file mode 100644 index 6dce54c1..00000000 --- a/notebooks/exPlotImage2D.ipynb +++ /dev/null @@ -1,235 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import sys\n", - "sys.path.append('../')\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from SimPEG import TensorMesh\n", - "%pylab inline" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 1D Plots\n", - "\n", - "For 1D nodal or cell-centered plots are supported.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x0 = np.zeros(1)\n", - "h = np.random.rand(32)\n", - "mesh = TensorMesh([h],x0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ax1 = plt.subplot()\n", - "ax1.set_title('sin(x) on CC grid')\n", - "ph1 = mesh.plotImage(np.sin(mesh.gridCC),ax=ax1)\n", - "ax2 = plt.subplot()\n", - "ax2.set_title('sin(x) on N grid')\n", - "ph2 = mesh.plotImage(np.sin(mesh.gridN), ax=ax2,imageType='N')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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YdJo6AyXR1tLvXd7mfvky8NNPwMMPm++cdnZsdduiRewxnpAOr77KnqxMWWzm4cFmg1qt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\niY1F0s1do5CaKt8dpHjg4gJ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YWBjeeustu/usWLECw4YNw7hx45Cfn+/uKXWLN2bMGB2jGIGzGKXReWtERLB6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\nrcCxYxIA7cv/0uIlzzN7NrB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RAAAAAElFTkSuQmCC\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 2D Plots\n", - "\n", - "Plot x and y coordinates of cell-centred points" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x0 = np.zeros(2)\n", - "h1 = np.linspace(.1,.5,3)\n", - "h2 = np.linspace(.1,.5,5)\n", - "mesh = TensorMesh([h1,h2],x0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure(1)\n", - "ax1 = plt.subplot()\n", - "ax1.set_title('mesh.gridCC[:,0]')\n", - "mesh.plotImage(mesh.gridCC[:,0],ax = ax1)\n", - "ax2 = plt.subplot()\n", - "ax2.set_title('mesh.gridFx[:,1]')\n", - "mesh.plotImage(mesh.gridFx[:,1],ax = ax2,imageType='Fx')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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GGjZsmEaPHq09e/bowIEDtf4QO3AluqJ+wQ1oLJYsWaJmzZpp1KhRqq6uVt++\nfVVYWKjU1FSnowE/GnsMAAADxxgAAAaKAQBgoBgAAAaKAQBgoBgAAAaKAQBgoBgAAAaKAQBg+F94\niFFs+w+jIAAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test 3D Plots\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x0 = np.zeros(3)\n", - "h1 = np.linspace(.1,.5,3)\n", - "h1 = np.r_[1,2,1]\n", - "h2 = np.r_[1,3]\n", - "h3 = np.linspace(.1,.5,10)\n", - "\n", - "mesh = TensorMesh([h1,h2,h3],x0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ax1 = plt.subplot()\n", - "ax1.set_title('mesh.gridCC[:,0]')\n", - "a1 = mesh.plotImage(mesh.gridCC[:,0],ax=ax1,annotationColor='w')\n", - "ax2 = plt.subplot()\n", - "ax2.set_title('mesh.gridFx[:,1]')\n", - "a2 = mesh.plotImage(mesh.gridFx[:,1],ax=ax2,imageType='Fx')\n", - "ax3 = plt.subplot()\n", - "ax3.set_title('mesh.gridEz[:,2]')\n", - "mesh.plotImage(mesh.gridEz[:,2],ax=ax3,imageType='Ez')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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BaGoCJOmO126K9OnnrRoqKwEAvlOnIm/XLm2AE92NeIqhjKpQiYrCQjgOHIjc7dtRUVAA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\nTbr2k3RjaD8HP/0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