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SimPEG Play - DC Resistivity Inversion
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description.md

Working to incorporate DC resistivity inversions in the new SimPEG framework.

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<div class="highlight"><pre><span class="kn">from</span> <span class="nn">SimPEG</span> <span class="kn">import</span> <span class="o">*</span>
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Warning: mumps solver not available.
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<div class="highlight"><pre><span class="c"># Create the mesh</span>
<span class="n">h1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">h2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">21</span><span class="p">)</span>
<span class="n">M</span> <span class="o">=</span> <span class="n">Mesh</span><span class="o">.</span><span class="n">TensorMesh</span><span class="p">([</span><span class="n">h1</span><span class="p">,</span><span class="n">h2</span><span class="p">])</span>
<span class="c"># Create some parameters for the model</span>
<span class="n">sig1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">0.001</span><span class="p">)</span>
<span class="n">sig2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="c"># Create a synthetic model from a block in a half-space</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">LogModel</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
<span class="n">mSynth</span> <span class="o">=</span> <span class="n">Utils</span><span class="o">.</span><span class="n">ModelBuilder</span><span class="o">.</span><span class="n">defineBlockConductivity</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">gridCC</span><span class="p">,[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">10</span><span class="p">],[</span><span class="mi">80</span><span class="p">,</span> <span class="mi">16</span><span class="p">],[</span><span class="n">sig1</span><span class="p">,</span> <span class="n">sig2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">plotImage</span><span class="p">(</span><span class="n">mSynth</span><span class="p">))</span>
<span class="n">Tx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">10.5</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="n">Rx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">9.5</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="n">yLoc</span> <span class="o">=</span> <span class="mf">13.5</span>
<span class="n">plot</span><span class="p">(</span><span class="n">Rx</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">Rx</span><span class="p">)</span><span class="o">*</span><span class="n">yLoc</span><span class="p">,</span><span class="s">&#39;r.&#39;</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">Tx</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">Tx</span><span class="p">)</span><span class="o">*</span><span class="n">yLoc</span><span class="p">,</span><span class="s">&#39;c.&#39;</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
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[&lt;matplotlib.lines.Line2D at 0x10f9e1510&gt;]
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<div class="highlight"><pre><span class="n">inds</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Rx</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
<span class="k">for</span> <span class="n">jj</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Rx</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="n">ii</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">inds</span><span class="o">.</span><span class="n">append</span><span class="p">([</span> <span class="n">ii</span><span class="p">,</span> <span class="n">ii</span> <span class="o">+</span> <span class="n">jj</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">])</span>
<span class="n">posElectrodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">Rx</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span><span class="n">yLoc</span><span class="p">]</span> <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">inds</span><span class="p">])</span>
<span class="n">negElectrodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">Rx</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span><span class="n">yLoc</span><span class="p">]</span> <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">inds</span><span class="p">])</span>
<span class="n">Ppos</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">getInterpolationMat</span><span class="p">(</span><span class="n">posElectrodes</span><span class="p">,</span> <span class="s">&#39;CC&#39;</span><span class="p">)</span>
<span class="n">Pneg</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">getInterpolationMat</span><span class="p">(</span><span class="n">negElectrodes</span><span class="p">,</span> <span class="s">&#39;CC&#39;</span><span class="p">)</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">Ppos</span> <span class="o">-</span> <span class="n">Pneg</span>
<span class="n">inds</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Tx</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
<span class="k">for</span> <span class="n">jj</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Tx</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="n">ii</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">inds</span><span class="o">.</span><span class="n">append</span><span class="p">([</span> <span class="n">ii</span><span class="p">,</span> <span class="n">ii</span> <span class="o">+</span> <span class="n">jj</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">])</span>
<span class="n">posElectrodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">Tx</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span><span class="n">yLoc</span><span class="p">]</span> <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">inds</span><span class="p">])</span>
<span class="n">negElectrodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">Tx</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span><span class="n">yLoc</span><span class="p">]</span> <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">inds</span><span class="p">])</span>
<span class="n">Qpos</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">getInterpolationMat</span><span class="p">(</span><span class="n">posElectrodes</span><span class="p">,</span> <span class="s">&#39;CC&#39;</span><span class="p">)</span>
<span class="n">Qneg</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">getInterpolationMat</span><span class="p">(</span><span class="n">negElectrodes</span><span class="p">,</span> <span class="s">&#39;CC&#39;</span><span class="p">)</span>
<span class="n">Q</span> <span class="o">=</span> <span class="p">(</span><span class="n">Qpos</span> <span class="o">-</span> <span class="n">Qneg</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
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In&nbsp;[4]:
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<div class="highlight"><pre><span class="n">prob</span> <span class="o">=</span> <span class="n">Examples</span><span class="o">.</span><span class="n">DC</span><span class="o">.</span><span class="n">DCProblem</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="c"># Create some data</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">prob</span><span class="o">.</span><span class="n">createSyntheticData</span><span class="p">(</span><span class="n">mSynth</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">P</span><span class="o">=</span><span class="n">P</span><span class="p">,</span> <span class="n">RHS</span><span class="o">=</span><span class="n">Q</span><span class="p">)</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">prob</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="n">mSynth</span><span class="p">)</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">reshapeFields</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
<span class="n">M</span><span class="o">.</span><span class="n">plotImage</span><span class="p">(</span><span class="n">u</span><span class="p">[:,</span><span class="mi">2</span><span class="p">])</span>
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&lt;matplotlib.collections.QuadMesh at 0x10fc77310&gt;
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<div class="highlight"><pre><span class="c"># Now set up the prob to do some minimization</span>
<span class="c"># prob.dobs = dobs</span>
<span class="c"># prob.std = dobs*0 + 0.05</span>
<span class="n">m0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">nC</span><span class="p">)</span> <span class="o">+</span> <span class="n">sig2</span>
<span class="n">reg</span> <span class="o">=</span> <span class="n">Regularization</span><span class="o">.</span><span class="n">Tikhonov</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">beta</span> <span class="o">=</span> <span class="n">Parameters</span><span class="o">.</span><span class="n">BetaEstimate</span><span class="p">(</span><span class="n">beta0_ratio</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">obj</span> <span class="o">=</span> <span class="n">ObjFunction</span><span class="o">.</span><span class="n">BaseObjFunction</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">reg</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="n">beta</span><span class="p">)</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimization</span><span class="o">.</span><span class="n">InexactGaussNewton</span><span class="p">(</span><span class="n">maxIterLS</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">maxIterCG</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">inv</span> <span class="o">=</span> <span class="n">Inversion</span><span class="o">.</span><span class="n">BaseInversion</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">opt</span><span class="p">)</span>
<span class="c"># Check Derivative</span>
<span class="n">derChk</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">:</span> <span class="p">[</span><span class="n">obj</span><span class="o">.</span><span class="n">dataObj</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="n">obj</span><span class="o">.</span><span class="n">dataObjDeriv</span><span class="p">(</span><span class="n">m</span><span class="p">)]</span>
<span class="c"># Tests.checkDerivative(derChk, mSynth)</span>
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<div class="highlight"><pre><span class="n">m</span> <span class="o">=</span> <span class="n">inv</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">m0</span><span class="p">)</span>
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Regularization has not set mref. SimPEG will set it to m0.
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
0 1.80e+02 1.25e+06 0.00e+00 1.25e+06 5.24e+03 0
1 1.80e+02 1.21e+06 6.15e+00 1.21e+06 6.17e+03 0
2 1.80e+02 1.14e+06 9.45e+01 1.16e+06 6.83e+03 0
3 1.80e+02 9.87e+05 1.53e+02 1.01e+06 1.69e+04 0
4 1.80e+02 8.33e+05 1.73e+02 8.64e+05 6.31e+04 2
5 1.80e+02 6.92e+05 2.17e+02 7.31e+05 6.27e+04 1
6 1.80e+02 5.64e+05 2.30e+02 6.05e+05 3.48e+04 0
7 1.80e+02 4.35e+05 2.70e+02 4.83e+05 1.48e+05 0
8 1.80e+02 3.55e+05 2.79e+02 4.05e+05 3.84e+04 0
9 1.80e+02 2.84e+05 2.82e+02 3.34e+05 5.42e+04 0
10 1.80e+02 2.01e+05 3.24e+02 2.60e+05 6.50e+04 0
------------------------- STOP! -------------------------
1 : |fc-fOld| = 7.4684e+04 &lt;= tolF*(1+|f0|) = 1.2546e+05
0 : |xc-x_last| = 6.7408e+00 &lt;= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 6.5036e+04 &lt;= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 6.5036e+04 &lt;= 1e3*eps = 1.0000e-02
1 : maxIter = 10 &lt;= iter = 10
------------------------- DONE! -------------------------
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<div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">plotImage</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
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&lt;matplotlib.colorbar.Colorbar instance at 0x111e14368&gt;
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In&nbsp;[7]:
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notebook.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from SimPEG import *"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Warning: mumps solver not available.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create the mesh\n",
"h1 = np.ones(100)\n",
"h2 = np.ones(21)\n",
"M = Mesh.TensorMesh([h1,h2])\n",
"\n",
"# Create some parameters for the model\n",
"sig1 = np.log(0.001)\n",
"sig2 = np.log(1)\n",
"\n",
"# Create a synthetic model from a block in a half-space\n",
"model = Model.LogModel(M)\n",
"mSynth = Utils.ModelBuilder.defineBlockConductivity(M.gridCC,[50, 10],[80, 16],[sig1, sig2])\n",
"plt.colorbar(M.plotImage(mSynth))\n",
"Tx = np.arange(10.5,90,5)\n",
"Rx = np.arange(9.5,90,3)\n",
"yLoc = 13.5\n",
"plot(Rx,np.ones_like(Rx)*yLoc,'r.',ms=20)\n",
"plot(Tx,np.ones_like(Tx)*yLoc,'c.',ms=20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"[<matplotlib.lines.Line2D at 0x10f9e1510>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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X4GW/gheOFuOrbiK+nlwl0LuC/LAhlPTv1+R5avc7GOF9jjvtPzSIz8h7Ue3txZ32H6jC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\nW0TEhJS8RURMSMlbRMSE/j/sCNWYpPWVAwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f9e1350>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inds = []\n",
"for ii in range(Rx.size):\n",
" for jj in range(Rx.size - ii - 1):\n",
" inds.append([ ii, ii + jj + 1 ])\n",
"posElectrodes = np.array([[Rx[ind[0]],yLoc] for ind in inds])\n",
"negElectrodes = np.array([[Rx[ind[1]],yLoc] for ind in inds])\n",
"\n",
"Ppos = M.getInterpolationMat(posElectrodes, 'CC')\n",
"Pneg = M.getInterpolationMat(negElectrodes, 'CC')\n",
"P = Ppos - Pneg\n",
"\n",
"inds = []\n",
"for ii in range(Tx.size):\n",
" for jj in range(Tx.size - ii - 1):\n",
" inds.append([ ii, ii + jj + 1 ])\n",
"posElectrodes = np.array([[Tx[ind[0]],yLoc] for ind in inds])\n",
"negElectrodes = np.array([[Tx[ind[1]],yLoc] for ind in inds])\n",
"\n",
"Qpos = M.getInterpolationMat(posElectrodes, 'CC')\n",
"Qneg = M.getInterpolationMat(negElectrodes, 'CC')\n",
"Q = (Qpos - Qneg).T.toarray()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"prob = Examples.DC.DCProblem(M, model)\n",
"# Create some data\n",
"data = prob.createSyntheticData(mSynth, std=0.05, P=P, RHS=Q)\n",
"u = prob.field(mSynth)\n",
"u = data.reshapeFields(u)\n",
"M.plotImage(u[:,2])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.collections.QuadMesh at 0x10fc77310>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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X8IXZRlsLXA5tLMaEdwgAAEkEAgDARiAAACQRCAAAG4EAAJBEIAAAbAQCAEASgQAAsBEI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\n",
"text": [
"<matplotlib.figure.Figure at 0x10fc48550>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# Now set up the prob to do some minimization\n",
"# prob.dobs = dobs\n",
"# prob.std = dobs*0 + 0.05\n",
"m0 = np.zeros(M.nC) + sig2\n",
"\n",
"reg = Regularization.Tikhonov(model)\n",
"beta = Parameters.BetaEstimate(beta0_ratio=0.5)\n",
"obj = ObjFunction.BaseObjFunction(data, reg, beta=beta)\n",
"opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, maxIterCG=3)\n",
"inv = Inversion.BaseInversion(obj, opt)\n",
"\n",
"# Check Derivative\n",
"derChk = lambda m: [obj.dataObj(m), obj.dataObjDeriv(m)]\n",
"# Tests.checkDerivative(derChk, mSynth)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = inv.run(m0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Regularization has not set mref. SimPEG will set it to m0.\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
" 0 1.80e+02 1.25e+06 0.00e+00 1.25e+06 5.24e+03 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 1 1.80e+02 1.21e+06 6.15e+00 1.21e+06 6.17e+03 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 2 1.80e+02 1.14e+06 9.45e+01 1.16e+06 6.83e+03 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 3 1.80e+02 9.87e+05 1.53e+02 1.01e+06 1.69e+04 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 4 1.80e+02 8.33e+05 1.73e+02 8.64e+05 6.31e+04 2 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 5 1.80e+02 6.92e+05 2.17e+02 7.31e+05 6.27e+04 1 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 6 1.80e+02 5.64e+05 2.30e+02 6.05e+05 3.48e+04 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 7 1.80e+02 4.35e+05 2.70e+02 4.83e+05 1.48e+05 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 8 1.80e+02 3.55e+05 2.79e+02 4.05e+05 3.84e+04 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 9 1.80e+02 2.84e+05 2.82e+02 3.34e+05 5.42e+04 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 10 1.80e+02 2.01e+05 3.24e+02 2.60e+05 6.50e+04 0 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 7.4684e+04 <= tolF*(1+|f0|) = 1.2546e+05\n",
"0 : |xc-x_last| = 6.7408e+00 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 6.5036e+04 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 6.5036e+04 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 10 <= iter = 10\n",
"------------------------- DONE! -------------------------\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.colorbar(M.plotImage(m))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.colorbar.Colorbar instance at 0x111e14368>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1107f9190>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
}
],
"metadata": {}
}
]
}
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