diff --git a/notebooks/DCinverse.ipynb b/notebooks/DCinverse.ipynb
index 2cf8b20b..f0be58c0 100644
--- a/notebooks/DCinverse.ipynb
+++ b/notebooks/DCinverse.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:789aaa72085626eec282d4a2b06df533d7a48163114ea5f5bee10382f0f44bf2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,29 +14,22 @@
      "input": [
       "from SimPEG import *\n",
       "import simpegDC as DC\n",
-      "%pylab inline"
+      "from pymatsolver import MumpsSolver"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 1
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cs = 25.\n",
-      "hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
-      "hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
-      "hz = [(cs,7, -1.3),(cs,20)]"
+      "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": {},
@@ -46,11 +40,29 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
+      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
+      "print mesh"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "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
     },
     {
@@ -73,7 +85,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "nElecs = 31 \n",
+      "nElecs = 21\n",
       "x_temp = np.linspace(-250, 250, nElecs)\n",
       "aSpacing = x_temp[1]-x_temp[0]\n",
       "y_temp = 0.\n",
@@ -82,13 +94,136 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "txList = DC.Examples.WennerArray.getTxList(nElecs,aSpacing)\n",
+      "survey = DC.SurveyDC(txList)\n",
+      "print len(survey.txList)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "63\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "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.txList]).flatten()\n",
+      "ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x109dd95d0>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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p06fj5MmTPtulyiMcS6dPi88sz3x/eb7KGQ/NNjhLre/FldimQONSy73lScmX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\nAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1095a2d90>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "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(mesh, mapping= imap )\n",
+      "problem.Solver = MumpsSolver\n",
+      "problem.pair(survey)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "datasyn = survey.dpred(sighalf*np.ones(mesh.nC))\n",
+      "survey.makeSyntheticData(sigma,std=0.01,force=True)\n",
+      "plot(np.log(np.c_[survey.dobs,datasyn]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x102073390>,\n",
+        " <matplotlib.lines.Line2D at 0x102073610>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Z/v6cTigqgvx8OH4cdu2C7du16zfdBDfeCElJMHcuPPss9O3bqW9XtIPssimE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\noAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x109dc9d90>"
+       ]
+      }
+     ],
      "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "txList = DC.Examples.WennerArray.getTxList(nElecs,aSpacing)"
+      "problem.mapping = expmap * m2to3"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
+      "reg = Regularization.Tikhonov(mesh,mapping=m2to3)\n",
+      "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n",
+      "opt.remember('xc')\n",
+      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+      "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n",
+      "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
+      "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
      ],
      "language": "python",
      "metadata": {},
@@ -99,14 +234,55 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
-      "indz = 23\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",
-      "ax.plot(xyz[:,0], xyz[:,1], 'w.', ms = 3)"
+      "m0 = np.log(np.ones(problem.mapping.nP)*sighalf)\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",
+        "SimPEG.l2_DataMisfit is creating default weightings for Wd."
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "============================ Inexact Gauss Newton ============================"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+        "-----------------------------------------------------------------------------\n",
+        "   0  6.56e+00  1.91e+03  0.00e+00  1.91e+03    2.21e+03      0              "
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "   1  6.56e+00  2.10e+02  1.61e+01  3.15e+02    1.80e+02      0              "
+       ]
+      }
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "colorbar(mesh.plotSlice(np.log10(expmap * m2to3 * mopt), normal='Y', ind=indz, grid=True)[0])"
      ],
      "language": "python",
      "metadata": {},
@@ -114,61 +290,61 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 31,
        "text": [
-        "[<matplotlib.lines.Line2D at 0xab90c50>]"
+        "<matplotlib.colorbar.Colorbar instance at 0x10bbbbd40>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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t3DkcwGMrOYH0Vo8J5/nnn0dxcTFuvPFGrFixAs888wy0Wi3a29thMpkkTTgd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\nWloKi8XiEWOxWFBcXAwAqKi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7Gs4gvYlTtOew+glx+ivO08YUli9cDuNiiztpeel7Lp4PM8VdwF7+rsJe7hoA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\nBoIgCALwfwEwulPiyuCzdQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa5a5550>"
+        "<matplotlib.figure.Figure at 0x10cd55b90>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "survey = DC.SurveyDC(txList)\n",
-      "problem = DC.ProblemDC(mesh)\n",
-      "problem.pair(survey)"
+      "dpred = survey.dpred(mopt)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "datasyn = survey.dpred(sigma)"
+      "plot(np.c_[dpred,survey.dobs])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "ename": "KeyboardInterrupt",
-       "evalue": "",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-14-b30b5a6cd927>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdatasyn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32mC:\\Users\\SEOGI\\simpeg\\SimPEG\\Utils\\CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcounter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32mC:\\Users\\SEOGI\\simpeg\\SimPEG\\Utils\\codeutils.pyc\u001b[0m in \u001b[0;36mrequiresVarWrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    222\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mextra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequiresVarWrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32mC:\\Users\\SEOGI\\simpeg\\SimPEG\\Survey.pyc\u001b[0m in \u001b[0;36mdpred\u001b[1;34m(self, m, u)\u001b[0m\n\u001b[0;32m    305\u001b[0m             \u001b[0mWhere\u001b[0m \u001b[0mP\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0mprojection\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfields\u001b[0m \u001b[0monto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         \"\"\"\n\u001b[1;32m--> 307\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkvc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojectFields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32mC:\\Users\\SEOGI\\Documents\\simpegdc\\simpegDC\\BaseDC.pyc\u001b[0m in \u001b[0;36mfields\u001b[1;34m(self, m)\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mAinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSolver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[0mQ\u001b[0m    \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetRhs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m         \u001b[0mPhi\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mAinv\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    148\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mPhi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32mC:\\Users\\SEOGI\\simpeg\\SimPEG\\Utils\\SolverUtils.pyc\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m     48\u001b[0m                     \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m                     \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcheckAccuracy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x10b6164d0>,\n",
+        " <matplotlib.lines.Line2D at 0x10b616750>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10c3c3fd0>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 24
     },
     {
      "cell_type": "code",