diff --git a/notebooks/IPinverse.ipynb b/notebooks/IPinverse.ipynb index e7dca246..65944715 100644 --- a/notebooks/IPinverse.ipynb +++ b/notebooks/IPinverse.ipynb @@ -1,586 +1,590 @@ { - "metadata": { - "name": "", - "signature": "sha256:7a25294a07f33a379f31ea5a89ae7d8acb31779a85ce8f5d01cd1d33d466c89b" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from SimPEG import *\n", - "import simpegDCIP as DC\n", - "%pylab inline\n", - "from pymatsolver import MumpsSolver" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "Vendor: Continuum Analytics, Inc.\n", - "Package: mkl\n", - "Message: trial mode expires in 30 days\n" - ] - } - ], - "prompt_number": 1 + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "cs = 12.5\n", - "nc = 500/cs+1\n", - "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n", - "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n", - "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n", - "print mesh" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " ---- 3-D TensorMesh ---- \n", - " x0: -541.97\n", - " y0: -416.97\n", - " z0: -548.22\n", - " nCx: 55\n", - " nCy: 35\n", - " nCz: 28\n", - " hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", - " hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", - " hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sighalf = 1e-2\n", - "sigma = np.ones(mesh.nC)*sighalf\n", - "p0 = np.r_[-50., 50., -50.]\n", - "p1 = np.r_[ 50.,-50., -150.]\n", - "blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)\n", - "sigma[blk_ind] = 1e-3" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "eta = np.zeros_like(sigma)\n", - "eta[blk_ind] = 0.1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sigmaInf = sigma.copy()\n", - "sigma0 = sigma*(1-eta)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dat = mesh.plotSlice(eta, normal='Y')\n", - "plt.colorbar(dat[0])\n", - "axis('equal')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "(-600.0, 600.0, -600.0, 0.0)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nElecs = 21\n", - "x_temp = np.linspace(-250, 250, nElecs)\n", - "aSpacing = x_temp[1]-x_temp[0]\n", - "y_temp = 0.\n", - "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 48 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n", - "survey = DC.SurveyDC(srcList)\n", - "print len(survey.srcList)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "63\n" - ] - } - ], - "prompt_number": 49 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n", - "indz = 22\n", - "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n", - "ax.set_xlim(-300, 300)\n", - "ax.set_ylim(-300, 300)\n", - "cb = plt.colorbar(dat[0])\n", - "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n", - "\n", - "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n", - "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 50, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 50 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "expmap = Maps.ExpMap(mesh)\n", - "m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n", - "imap = Maps.IdentityMap(mesh)\n", - "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n", - "problem.Solver = MumpsSolver\n", - "try:\n", - " problem.pair(survey)\n", - "except Exception, e:\n", - " survey.unpair()\n", - " problem.pair(survey)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 51 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "phi0 = survey.dpred(sigma0)\n", - "phiInf = survey.dpred(sigmaInf)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 52 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "phiIP_true = phi0-phiInf" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 53 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# F = problem.fields(mesh, survey)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "surveyIP = DC.SurveyIP(srcList)\n", - "problemIP = DC.ProblemIP(mesh, sigma=sigma)\n", - "problemIP.pair(surveyIP)\n", - "problemIP.Solver = MumpsSolver" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "datasyn = surveyIP.dpred(eta)\n", - "surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n", - "plot(np.c_[surveyIP.dobs,datasyn])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 56, - "text": [ - "[,\n", - " ]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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cDByj211a/AFmt9q0zDg5G1raT5Tj/iD2dHEBaoDWVkgPt3EmWlwcYsLSz5aO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\n3FPm4vRUaxvEuOEDqYzaRVVNYB+pY7VF9O2uxyB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- "text": [ - "" - ] - } - ], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "problemIP.mapping = imap" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dmis = DataMisfit.l2_DataMisfit(surveyIP)\n", - "dmis.Wd = 1./abs(phi0)*1e3\n", - "reg = Regularization.Tikhonov(mesh,mapping=imap)\n", - "# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n", - "opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n", - "opt.remember('xc')\n", - "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n", - "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n", - "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n", - "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 64 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "reg.alpha_s = 1e-4\n", - "reg.alpha_x = 1.\n", - "reg.alpha_z = 1.\n", - "reg.alpha_y = 1." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 65 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "opt.upper = Inf\n", - "opt.lower = 0." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 66 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "m0 = np.ones(problemIP.mapping.nP)*1e-10\n", - "mopt = inv.run(m0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "SimPEG.InvProblem will set Regularization.mref to m0.\n", - "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", - " ***Done using same solver as the problem***\n", - "=============================== Projected GNCG ===============================" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", - "-----------------------------------------------------------------------------\n", - " 0 5.74e+00 9.43e+01 4.96e-16 9.43e+01 1.69e+03 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 1 5.74e+00 7.46e+00 6.14e-03 7.49e+00 3.03e+02 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 2 5.74e+00 3.18e+00 1.34e-02 3.25e+00 2.68e+02 0 Skip BFGS " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 3 5.74e+00 2.15e+00 1.83e-02 2.26e+00 2.91e+02 0 Skip BFGS " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 9.9700e-01 <= tolF*(1+|f0|) = 9.5290e+00\n", - "0 : |xc-x_last| = 4.8192e-02 <= tolX*(1+|x0|) = 1.0000e-15\n", - "0 : |proj(x-g)-x| = 2.9060e+02 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 2.9060e+02 <= 1e3*eps = 1.0000e-02\n", - "1 : maxIter = 3 <= iter = 3\n", - "------------------------- DONE! -------------------------\n" - ] - } - ], - "prompt_number": 67 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print mesh" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " ---- 3-D TensorMesh ---- \n", - " x0: -541.97\n", - " y0: -416.97\n", - " z0: -548.22\n", - " nCx: 55\n", - " nCy: 35\n", - " nCz: 28\n", - " hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", - " hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", - " hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n" - ] - } - ], - "prompt_number": 74 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n", - "axis('equal')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 75, - "text": [ - "(-600.0, 600.0, -600.0, 0.0)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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0RZymjDTVKF/RMhnNLLVRJAE1Wq11rNJRWbpbYlpteXts5HOCHt7leuFl9ysk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\nuaOlx+hyku0LZpFMCHk/cGY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qQWQzgynKmQ1n8u5xfQXCZrQRnKaaEK5+F7WlmntJiMzOzT/sp3HOUNarqdva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\no++EAV5/Xc8KPvdc7306VSd5u/P42qyvAfpia0eBOC/9POIj49lWss1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- "text": [ - "" - ] - } - ], - "prompt_number": 71 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 24 + "name": "stderr", + "output_type": "stream", + "text": [ + "Vendor: Continuum Analytics, Inc.\n", + "Package: mkl\n", + "Message: trial mode expires in 11 days\n" + ] } ], - "metadata": {} + "source": [ + "from SimPEG import *\n", + "import simpegDCIP as DC\n", + "%pylab inline\n", + "from pymatsolver import MumpsSolver" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = 12.5\n", + "nc = 500/cs+1\n", + "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n", + "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n", + "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ---- 3-D TensorMesh ---- \n", + " x0: -541.97\n", + " y0: -416.97\n", + " z0: -548.22\n", + " nCx: 55\n", + " nCy: 35\n", + " nCz: 28\n", + " hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", + " hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", + " hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n" + ] + } + ], + "source": [ + "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n", + "print mesh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sighalf = 1e-2\n", + "sigma = np.ones(mesh.nC)*sighalf\n", + "p0 = np.r_[-50., 50., -50.]\n", + "p1 = np.r_[ 50.,-50., -150.]\n", + "blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)\n", + "sigma[blk_ind] = 1e-3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "eta = np.zeros_like(sigma)\n", + "eta[blk_ind] = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sigmaInf = sigma.copy()\n", + "sigma0 = sigma*(1-eta)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-600.0, 600.0, -600.0, 0.0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SKuAU2y1n9PRqguj/AmYCd5eTbf7e9lW2t0u6DdgOHACuasjcVwEfB06huPp8VKKPiJhu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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dat = mesh.plotSlice(eta, normal='Y')\n", + "plt.colorbar(dat[0])\n", + "axis('equal')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "nElecs = 21\n", + "x_temp = np.linspace(-250, 250, nElecs)\n", + "aSpacing = x_temp[1]-x_temp[0]\n", + "y_temp = 0.\n", + "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63\n" + ] + } + ], + "source": [ + "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n", + "survey = DC.SurveyDC(srcList)\n", + "print len(survey.srcList)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Ev0t6C3BKRLy+qF2tnOF0cu+dwqzx+f3yisodr3/bjjcz3sQ2DRovW94rr0x+aSV66bWb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\n4ov2Grd9Sa8E3h8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n", + "indz = 22\n", + "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n", + "ax.set_xlim(-300, 300)\n", + "ax.set_ylim(-300, 300)\n", + "cb = plt.colorbar(dat[0])\n", + "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n", + "\n", + "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n", + "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "expmap = Maps.ExpMap(mesh)\n", + "m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n", + "imap = Maps.IdentityMap(mesh)\n", + "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n", + "problem.Solver = MumpsSolver\n", + "try:\n", + " problem.pair(survey)\n", + "except Exception, e:\n", + " survey.unpair()\n", + " problem.pair(survey)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi0 = survey.dpred(sigma0)\n", + "phiInf = survey.dpred(sigmaInf)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phiIP_true = phi0-phiInf" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# F = problem.fields(mesh, survey)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "surveyIP = DC.SurveyIP(srcList)\n", + "problemIP = DC.ProblemIP(mesh, sigma=sigma)\n", + "problemIP.pair(surveyIP)\n", + "problemIP.Solver = MumpsSolver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1t9o87g9Rnyou/gBgMVowJZwcOjecv/IFRJNDbHSXYEE43ShbiImJoptqXuBo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\noJT6HogXkeQWyh4tY7xONcqvV0p5gwA2A9Ei0o4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datasyn = surveyIP.dpred(eta)\n", + "surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n", + "plot(np.c_[surveyIP.dobs,datasyn])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "problemIP.mapping = imap" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dmis = DataMisfit.l2_DataMisfit(surveyIP)\n", + "dmis.Wd = 1./abs(phi0)*1e3\n", + "reg = Regularization.Tikhonov(mesh,mapping=imap)\n", + "# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n", + "opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n", + "opt.remember('xc')\n", + "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n", + "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n", + "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n", + "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reg.alpha_s = 1e-4\n", + "reg.alpha_x = 1.\n", + "reg.alpha_z = 1.\n", + "reg.alpha_y = 1." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "opt.upper = Inf\n", + "opt.lower = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimPEG.InvProblem will set Regularization.mref to m0.\n", + "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using same solver as the problem***\n", + "=============================== Projected GNCG ===============================\n", + " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", + "-----------------------------------------------------------------------------\n", + " 0 5.84e+00 9.43e+01 0.00e+00 9.43e+01 1.69e+03 0 \n", + " 1 5.84e+00 7.43e+00 6.13e-03 7.46e+00 3.04e+02 0 \n", + " 2 5.84e+00 3.10e+00 1.33e-02 3.18e+00 2.65e+02 0 Skip BFGS \n", + " 3 5.84e+00 2.13e+00 1.82e-02 2.24e+00 2.49e+02 0 Skip BFGS \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 9.4407e-01 <= tolF*(1+|f0|) = 9.5287e+00\n", + "0 : |xc-x_last| = 4.4997e-02 <= tolX*(1+|x0|) = 1.0000e-15\n", + "0 : |proj(x-g)-x| = 2.4936e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.4936e+02 <= 1e3*eps = 1.0000e-02\n", + "1 : maxIter = 3 <= iter = 3\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "m0 = np.ones(problemIP.mapping.nP)*1e-10\n", + "mopt = inv.run(m0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ---- 3-D TensorMesh ---- \n", + " x0: -541.97\n", + " y0: -416.97\n", + " z0: -548.22\n", + " nCx: 55\n", + " nCy: 35\n", + " nCz: 28\n", + " hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", + " hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", + " hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n" + ] + } + ], + "source": [ + "print mesh" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-600.0, 600.0, -600.0, 0.0)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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QZID/UIbDk/Bhf/cTppaO5x9ZzsPl7cwv7U9peVIRlGbBOmBBKNVWQ+lAWHHO+awt72BB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\nDZXWxBQqndg9VN6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ys5ejWSpy8QrCcRynCYYG8x+jg7/5PUO//X2t7NWu/XMZOSg4K03q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\n4yavLtDJAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n", + "axis('equal')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# sigma_est = expmap * m2to3 * mopt" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dpred = surveyIP.dpred(mopt)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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QwpFj7sSc+N8r468vBvZJKV+PH88npRwv+tOVwPh4vNAo/lucDQuiXF1Mfj8Y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\nysEzAAfagkiPS++6D+sB0mLTOFB+gIbGhlbH3APV5eV6HYz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.c_[dpred,surveyIP.dobs], '.-')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/simpegDCIP/BaseDC.py b/simpegDCIP/BaseDC.py index 3a853203..f03908da 100644 --- a/simpegDCIP/BaseDC.py +++ b/simpegDCIP/BaseDC.py @@ -245,15 +245,15 @@ class ProblemDC_CC(Problem.BaseProblem): dCdm_x_v[:, i] = dAdsig * dsigdm_x_v # Take derivative of $C(m,u)$ w.r.t. $u$ - dCdu = self.A + dA_du = self.A # Solve for $\deriv{u}{m}$ # dCdu_inv = self.Solver(dCdu, **self.solverOpts) if self.Ainv is None: - self.Ainv = self.Solver(dCdu, **self.solverOpts) + self.Ainv = self.Solver(dA_du, **self.solverOpts) P = self.survey.getP(self.mesh) - J_x_v = - P * mkvc( self.Ainv * dCdm_x_v ) - return J_x_v + Jv = - P * mkvc( self.Ainv * dCdm_x_v ) + return Jv def Jtvec(self, m, v, u=None): @@ -271,12 +271,11 @@ class ProblemDC_CC(Problem.BaseProblem): D = self.mesh.faceDiv G = self.mesh.cellGrad - A = self.A + dA_du = self.A mT_dm = self.mapping.deriv(m) # We probably always need this due to the linesearch .. (?) - dCdu = A.T - self.Ainv = self.Solver(dCdu, **self.solverOpts) + self.Ainv = self.Solver(dA_du.T, **self.solverOpts) # if self.Ainv is None: # self.Ainv = self.Solver(dCdu, **self.solverOpts) diff --git a/simpegDCIP/Tests/test_forward_DCproblem.py b/simpegDCIP/Tests/test_forward_DCproblem.py index de50be9b..97e92383 100644 --- a/simpegDCIP/Tests/test_forward_DCproblem.py +++ b/simpegDCIP/Tests/test_forward_DCproblem.py @@ -1,6 +1,6 @@ import unittest from SimPEG import * -import simpegDC as DC +import simpegDCIP as DC class DCProblemTests(unittest.TestCase): diff --git a/simpegDCIP/Tests/test_forward_DCproblem_analytic.py b/simpegDCIP/Tests/test_forward_DCproblem_analytic.py index 3e104e5b..15493115 100644 --- a/simpegDCIP/Tests/test_forward_DCproblem_analytic.py +++ b/simpegDCIP/Tests/test_forward_DCproblem_analytic.py @@ -1,5 +1,5 @@ import unittest -import simpegDC as DC +import simpegDCIP as DC class DCAnalyticTests(unittest.TestCase): diff --git a/simpegDCIP/Tests/test_forward_IPproblem.py b/simpegDCIP/Tests/test_forward_IPproblem.py index f108be6b..5a4e9fd5 100644 --- a/simpegDCIP/Tests/test_forward_IPproblem.py +++ b/simpegDCIP/Tests/test_forward_IPproblem.py @@ -1,5 +1,5 @@ import unittest -import simpegDC as DC +import simpegDCIP as DC from SimPEG import * from pymatsolver import MumpsSolver diff --git a/simpegDCIP/Tests/test_sens_IPproblem.py b/simpegDCIP/Tests/test_sens_IPproblem.py index d095ab09..6b8160a3 100644 --- a/simpegDCIP/Tests/test_sens_IPproblem.py +++ b/simpegDCIP/Tests/test_sens_IPproblem.py @@ -1,6 +1,6 @@ import unittest from SimPEG import * -import simpegDC as DC +import simpegDCIP as DC from pymatsolver import MumpsSolver