modified: simpegPF/notebooks/Jacobian.ipynb

modified:   simpegPF/Tests/test_forward_PFproblem.py

simpegPF/notebooks/MagForward_secondary.ipynb

@@ -24,9 +24,17 @@
        "text": [
         "Populating the interactive namespace from numpy and matplotlib\n"
        ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['axes', 'info', 'flag']\n",
+        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
+       ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -57,7 +65,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -74,26 +82,26 @@
      "input": [
       "mu0 = 4*np.pi*1e-7\n",
       "chibkg = 0.\n",
-      "chiblk = 0.01\n",
+      "chiblk = 1.\n",
       "chi = np.ones(M3.nC)*chibkg"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "sph_ind = spheremodel(M3, 0., 0., 0., 100)\n",
+      "sph_ind = spheremodel(M3, 0., 0., 0., 50)\n",
       "chi[sph_ind] = chiblk\n",
       "mu = (1.+chi)*mu0"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -115,7 +123,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
@@ -126,7 +134,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
@@ -138,24 +146,16 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/lib/pymodules/python2.7/matplotlib/lines.py:483: RuntimeWarning: invalid value encountered in greater_equal\n",
-        "  return np.alltrue(x[1:]-x[0:-1]>=0)\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAI8CAYAAAD1D3GaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x40c9c90>"
+        "<matplotlib.figure.Figure at 0x338be50>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 32
     },
     {
      "cell_type": "code",
@@ -169,21 +169,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 33,
        "text": [
-        "<matplotlib.collections.QuadMesh at 0x44e4a90>"
+        "<matplotlib.collections.QuadMesh at 0x92842d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJWCAYAAACEbPmsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJWCAYAAACEbPmsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x44c6910>"
+        "<matplotlib.figure.Figure at 0x57af690>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -233,7 +233,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 34
     },
     {
      "cell_type": "code",
@@ -252,26 +252,26 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# import petsc4py\n",
-      "# import sys\n",
-      "# from sys import getrefcount\n",
-      "# petsc4py.init(sys.argv)\n",
-      "# from petsc4py import PETSc\n",
-      "# import PETScIO as IO\n",
-      "# Apetsc = PETSc.Mat().createAIJ(size=A.shape,csr=(A.indptr, A.indices, A.data))\n",
-      "# bpetsc = IO.arrayToVec(rhs)\n",
-      "# xpetsc = IO.arrayToVec(0*rhs)"
+      "import petsc4py\n",
+      "import sys\n",
+      "from sys import getrefcount\n",
+      "petsc4py.init(sys.argv)\n",
+      "from petsc4py import PETSc\n",
+      "import PETScIO as IO\n",
+      "Apetsc = PETSc.Mat().createAIJ(size=A.shape,csr=(A.indptr, A.indices, A.data))\n",
+      "bpetsc = IO.arrayToVec(rhs)\n",
+      "xpetsc = IO.arrayToVec(0*rhs)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 36
     },
     {
      "cell_type": "code",
@@ -286,33 +286,44 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 37
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# %%time\n",
-      "# ksp = PETSc.KSP().create()\n",
-      "# pc = PETSc.PC().create()\n",
-      "# ksp.setOperators(Apetsc)\n",
-      "# ksp.setType(ksp.Type.BCGS)\n",
-      "# pc = ksp.getPC()\n",
-      "# pc.setType(pc.Type.SOR)\n",
-      "# OptDB = PETSc.Options()\n",
-      "# OptDB[\"ksp_rtol\"] = 1e-8\n",
-      "# OptDB[\"pc_factor_levels\"] = 1\n",
-      "# ksp.setFromOptions()\n",
-      "# ksp.view()\n",
-      "# ksp.solve(bpetsc, xpetsc)\n",
-      "# print ksp.its# print ksp.its\n",
-      "# phi = IO.vecToArray(xpetsc)\n",
-      "# print np.linalg.norm(A*phi-rhs)/norm(rhs)"
+      "%%time\n",
+      "ksp = PETSc.KSP().create()\n",
+      "pc = PETSc.PC().create()\n",
+      "ksp.setOperators(Apetsc)\n",
+      "ksp.setType(ksp.Type.BCGS)\n",
+      "pc = ksp.getPC()\n",
+      "pc.setType(pc.Type.BJACOBI)\n",
+      "OptDB = PETSc.Options()\n",
+      "OptDB[\"ksp_rtol\"] = 1e-8\n",
+      "OptDB[\"pc_factor_levels\"] = 1\n",
+      "ksp.setFromOptions()\n",
+      "ksp.view()\n",
+      "ksp.solve(bpetsc, xpetsc)\n",
+      "print ksp.its# print ksp.its\n",
+      "phi = IO.vecToArray(xpetsc)\n",
+      "print np.linalg.norm(A*phi-rhs)/norm(rhs)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "81\n",
+        "7.00090107929e-09\n",
+        "CPU times: user 639 ms, sys: 7.95 ms, total: 647 ms\n",
+        "Wall time: 647 ms\n"
+       ]
+      }
+     ],
+     "prompt_number": 39
     },
     {
      "cell_type": "code",
@@ -332,12 +343,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "CPU times: user 974 ms, sys: 0 ns, total: 974 ms\n",
-        "Wall time: 974 ms\n"
+        "CPU times: user 994 ms, sys: 0 ns, total: 994 ms\n",
+        "Wall time: 994 ms\n"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 41
     },
     {
      "cell_type": "code",
@@ -358,7 +369,7 @@
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 42
     },
     {
      "cell_type": "code",
@@ -377,11 +388,11 @@
        "stream": "stdout",
        "text": [
         "0\n",
-        "9.90412957826e-07\n"
+        "7.98141453262e-07\n"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -403,13 +414,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA6cAAAE/CAYAAAC6trV3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA6cAAAE/CAYAAAC6trV3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8FdX5h5+be7OwBgmQFQiCiuyyRMRaomyCQkEgFBdA\nigt1rVpbFBX91UJtXQDBWguIqEXqQoQiiii7ghL2RaJASCDshAQIITfc3x+Te3P3JZmZO0neh08+\n3GXuvN85c+ad857znjMmm81mQxAEQRAEQRAEQRDCSES4BQiCIAiCIAiCIAiCBKeCIAiCIAiCIAhC\n2JHgVBAEQRAEQRAEQQg7EpwKgiAIgiAIgiAIYUeCU0EQBEEQBEEQBCHsSHAqCIIgCIIgCIIghB0J\nToWwk5qaSt26dWnQoAENGjSgYcOGHD16NNyyBEEQVEV8nSAItQHxdUJVkOBUCDsmk4mlS5dSVFRE\nUVERhYWFJCQkhFuWIAiCqoivEwShNiC+TqgKEpwKhuSjjz7iyiuvpKioCIAvvviCxMRETp06FWZl\ngiAI6tChQweWLl3qeF9aWkqTJk3Ytm1bGFUJgiCoz8MPP+wYSW3QoAGRkZG8+OKL4ZYlGBAJTgVD\nYLPZXN6PGjWKXr168eijj3Lq1CkmTJjAnDlziIuLC5NCQRCEquPs68aOHcv777/veL9s2TKSk5Pp\n3LlzOKQJgiCohnu77s0333SMpK5du5YrrriCoUOHhkmdYGRMNvfaIwg6k5qayqlTp7BYLADcfPPN\nfPrpp5w9e5ZOnToRGxvLjTfeyFtvvRVmpYIgCJXH3delpaWxYcMG8vPzqV+/PiNGjKBnz5489dRT\nYVYqCIJQeXy16wBOnDhBjx49eOWVV8jIyAinTMGgWMItQBBMJhOZmZnccsstLp/HxsYyYsQIXn/9\ndYdTEwRBqK5483W33norH3/8MUOHDmX58uXMnDkzjAoFQRCqjq92XWlpKSNGjODuu++WwFTwiaT1\nCoZl69atzJs3jzvvvJNHHnkk3HIEQRBUx57a+9///pdevXqRmJgYbkmCIAia8Mgjj9CoUSP+8pe/\nhFuKYGAkOBUMycWLF7n77ruZOnUqc+fO5fDhw5LWKwhCjWPYsGFkZWUxY8YMxowZE245giAImvD2\n22+zZs0al3n2guANCU4FQzJp0iRatmzJAw88QFRUFO+//z6TJ0/ml19+Cbc0QRAE1YiJieGOO+7g\n4MGD3HHHHeGWIwiCoAkLFy7kwIEDJCUlOVbsnTZtWrhlCQZEFkQSBEEQhDDyf//3f2RnZ/Pee++F\nW4ogCIIghBVZEEkQBEEQwsTp06eZO3cuCxYsCLcUQRAEQQg7ktYrCIIgCGHgnXfeoUWLFgwcOJBf\n/epX4ZYjCIIgCGFH0noFQRAEQRAEQRCEsCMjp4IgCIIgCIIgCELYMdycU5PJFG4JgiAYkJqW5CG+\nThAEb4ivEwShNuDL1xkuOAXgqfA4Ztvflf9NfwQ2TIFeU8Kmwc6UKVOYMkV/He6IDtERTh01tXHz\njO25Sv/WTFmlf/sSLwPwLM+zdspqbprSO2wa7NSWuiw6RIc/xNe5UhUfY8fua349pXelfF1VNYiv\nEx2iwxN/vk7SegVBEARBEARBEISwI8GpIAiCIAiCYDjKMIdbgiE0CEJtQoJTXzRPD7cCANLT08Mt\nARAd7ogOV4yiQwidFuktwy0BME4dEh2uiA5XjKJDCB3xda6IDldEhyvh1GG4R8mYTCZjzDkNE+5z\nTgVBUPyCwVxVlTGZTIaYc1oV1JxzKgiC+DpvqDXnsyr+TnydIKiLP18nI6eCIAiCIAiC4AU1FmUS\nBCF4JDgVBEEQBEEQBC/InFNB0BcJTgVBEARBEARBEISwI8GpIAiCIAiCIAiCEHYkOBUEQRAEQRAM\nh8z3FITahwSngiAIgiAIguEwwnxPCZAFQV8kOBUEQRAEQRAMhxECQyMEyIJQm5DgVBAEoZphhAab\nIAiCIAiC2khwKgiCUM2QnnxBEAR9kM5AQdAXCU4FQRCEkJEGmyAItQHpDBQEfZHgVBAEQQgZabAJ\ngqA1RvAz0hEnCPoiwakgCIIgCIIgeMEIAbIg1CYkOBUEQRAEQRAMh4xaCkLtQ4JTQRCEaoY02ARB\nqA0YYdRS/K0g6Isl3AIEQRBqK5Vt9KjVYKtKo6sMsyEajoIgCIIg1Bxk5FQQBEEIGRlNEARBa8TP\nCELtQ4JTQRAEIWRk1FQQBK0xgp8xggZBqE1IcCoIgiAIgiAYDhk5FYTahwSngiAIgiAIguEwwqil\nBMiCoC8SnAqCIAiCIAiGQwJDQah9SHAqCIIgCIIgCIIghJ2ggtOCggJGjBjBtddeS7t27di4cSOn\nT5+mX79+XH311fTv35+CggLH9lOnTuWqq66ibdu2fPXVV47PN2/eTMeOHbnqqqt47LHH1D8aQRCE\nKiC+ThCE2kB18XVGSOs1ggZBqE0EFZw+9thjDBo0iD179rB9+3batm3LtGnT6NevH/v27aNPnz5M\nmzYNgN27d/PRRx+xe/duli9fzu9//3tsNhsAEydOZM6cOWRnZ5Odnc3y5ctDEnv0eeiarLxePRF+\n28X7dtc2g3Mvw6Vpnt+N6gxZj0PRX5T9fXwPXBmnr4bfpcH2J5TvDz4Dz/cN3r4gCNphFF/3NJNI\nJAmA8UygI528bteUprzAZF7iBY/vOtKBh5jI8zzLJJ5mNKNozBWqabCn2/nT0I2uPMJDvMBknuIJ\nbiY9aPuCIGiHUXzdJJ4miUQAJjCeTnR0+d7Zz0zmBV7gJY99dKAjE3mIZ3mep5nEKEZzBY111dCV\nbjzEI0zmBZ7gKdK5OWj7giC4EjA4PXv2LGvXrmX8+PEAWCwWYmNj+fzzzxk7diwAY8eOZfHixQBk\nZmYyevRoIiMjSU1NpU2bNmzcuJH8/HyKiopIS0sDYMyYMY7fBEPrOKgbCVuOQKQZuqfAugOe29WJ\nhEX3wMpsKPedDnqlwgd3wkfboMOrMGgOxNWD/43XT8OENJjxG3hlFbT/Bzz8GTx4A/zfgOA0CIKg\nDUbxdY1pTCSRHCUfM2aSSCaHHI/tIolkNBn8wn5suDqaFjQngxHsYAczmMV8FlCXuozhbtU0lGH2\nq6E73bidQaxlHdN5k6X8j+vpQV/6BF0WgiCoj9F8XT5HMWMmmSSffiaD0eznFw8/05wWjCCDHexg\nFjNYwHzqUpe7GaObhm50ZxC3s461vMl0/sdSenA9fZCRB0GoDAGD0wMHDtC0aVPuvfdeunbtyn33\n3cf58+c5duwY8fHxAMTHx3Ps2DEAjhw5QkpKiuP3KSkpHD582OPz5ORkDh8+HLTQG1NhY64S7PVo\nDqcuQN5Zz+1mDYM1++GTHWAyuX7XIwXOFMPfvoWcM5B1GF5dDVc3gfrR+mgY2x3e/RHez1I0LN2j\n6Hn8JoixBFsagiCojVF8XQtakkcuNmwkkUwxFyjE09EM5jYOkMMudmPC1dGkkEwxxaxhHQUUcIR8\n1rOBOOKIIkoXDdfRhSy2spVtFFDAXn5iDWvpRU8siLMThHBhFF/XkhbkkocNG8kkcYFizlLoso2Z\nMm5jMDkcYDe7PPxMMikUU8w61lBAAfkcYQPrg/Z1wWgA/GrownVsJYttbKWAAn5iL2tZQ096ia8T\nhEoQ8KqxWq1kZWXx5ptv0qNHDx5//HFHqocdk8mEyT0KqwobpjhenvlqMjaThWgLRJjg9IvKqGW0\nRXltA+LKs8nu6QbdUqDHdBh9neduv86GqYNgRCclcGwYrfxm3UE4V+JbzpmXlIBUDQ3RFiixun52\n0aqMyHZvHkIZCUINZtWqVaxatUpXm+HwdaunrHW8Xjb5CyIsEZgxY8LEJCYTQQQWLExiMjZsTONl\nALrQmWSSmM3bHiloAD+zn/70owPt2cVuoommC53J4RCXuORTzyQmA6iiwYKFMlydnRUrkUSSXJ4u\nLAi1ndri69ZOWe14/cXkZS6+bjKTHH5mMpOwYeNlFD2d6UISybzNbK/TG/bzM/3oT3s6sJtdRBNN\nZ7pwiBy/vm4ykwCC0tCFzn41WLBgdVtV2O7rkkgOvbAEoQYSiq8LGJympKSQkpJCjx49ABgxYgRT\np04lISGBo0ePkpCQQH5+Ps2aNQOUnrPc3FzH7/Py8khJSSE5OZm8vDyXz5OTfVy0vaY4XnZ6A0zA\n94/Ag5/A1iOw8G74cAtk7qr4Sdtm8I/bIf0tuORj5fFdx2DEe0pq7wejwRKhjITeNsd/GXR6TT0N\nX+yFh3rBf7fDdznKb/5wk/JdUkP/OgShtpCenk56errj/Ysvvqi5zXD4ut5TbnK8/iezABP38yBL\nyCSffDIYxXa2sZc9ju2a0JSBDODfzKPMx2MWjnOcD/mIDIYzkuFEEEEeh3mP9/2WwWxmqqZhH9n0\nJI2d7OIQuTSlCb3oBUBDxNkJAtQeX3fTlN6O17P4JybgQe4nkyXkk88oMtjGdvaw17FdU5owgIHM\n499+fd1HfMjwcm8XQQSHyeN93vNbBjOZHbSGgQzwqyGbfaTRk13sJJdDNKGp+DpBcCMUXxcwrTch\nIYHmzZuzb98+AL7++mvat2/P4MGDmT9/PgDz589n6NChAAwZMoSFCxdy6dIlDhw4QHZ2NmlpaSQk\nJNCwYUM2btyIzWZjwYIFjt/4I7cAYmOUkcole5S03C5JsHCr8l1uAUSZ4b/3wOTlsOe47331aA7/\nuQv+vgq6T4db3oZLVvhsrGf6rVYa/rISFm2Dbx9UFktaPREWZCnfXbb5/p0gCNoSbl93lrPEEE0E\nEfzEXi5STAKJ7GA7Z8v/mTEzitGsYCUnOOFzXykkM4oRrGU9s3mbubxLGWXcxWiPlDStNKLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x44d7850>"
+        "<matplotlib.figure.Figure at 0x7477190>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -448,12 +459,12 @@
       "xr = np.linspace(-300, 300, 41)\n",
       "yr = np.linspace(-300, 300, 41)\n",
       "X, Y = np.meshgrid(xr, yr)\n",
-      "Z = np.ones((size(xr), size(yr)))*150"
+      "Z = np.ones((size(xr), size(yr)))*80"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 18
     },
     {
      "cell_type": "code",
@@ -467,7 +478,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
@@ -480,7 +491,7 @@
       "\n",
       "flag = 'secondary'\n",
       "\n",
-      "Bxra, Byra, Bzra = MagSphereAnalFun(X, Y, Z, 100, 0., 0., 0., mu0, mu0*(1+chiblk), H0, flag)\n",
+      "Bxra, Byra, Bzra = MagSphereAnalFun(X, Y, Z, 50., 0., 0., 0., mu0, mu0*(1+chiblk), H0, flag)\n",
       "Bxra = np.reshape(Bxra, (size(xr), size(yr)), order='F')\n",
       "Byra = np.reshape(Byra, (size(xr), size(yr)), order='F')\n",
       "Bzra = np.reshape(Bzra, (size(xr), size(yr)), order='F')"
@@ -488,7 +499,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 20
     },
     {
      "cell_type": "code",
@@ -508,21 +519,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 29,
+       "prompt_number": 21,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x44c5210>]"
+        "[<matplotlib.lines.Line2D at 0x33a4250>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAD9CAYAAADai4HIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAD9CAYAAADnGeCuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x5010ad0>"
+        "<matplotlib.figure.Figure at 0x33a1110>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -542,21 +553,21 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 30,
+       "prompt_number": 22,
        "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x9e54ef0>"
+        "<matplotlib.colorbar.Colorbar instance at 0x84e4518>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAANTCAYAAAAE7NEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAANTCAYAAACdFAiVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x4c6d1d0>"
+        "<matplotlib.figure.Figure at 0x33a1590>"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
@@ -585,13 +596,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA8EAAAFECAYAAAAHqjxCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA7UAAAFECAYAAAD4CBmgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0xa095650>"
+        "<matplotlib.figure.Figure at 0x4b21e90>"
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -611,21 +630,74 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 32,
+       "prompt_number": 59,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x9989610>]"
+        "[<matplotlib.lines.Line2D at 0x851d590>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAA0MAAAE1CAYAAAAlJDsaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA0oAAAE1CAYAAADZMRDQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X9UVPed//EnZMZUtwaFJEYGcsABGRADQ8A0afLN1Eo0\ntqHGBKN4NsZacWO76Z5jm6a6m2LqirjbHrXWFnOSalvFH0kbkrYQO9uOaZYa0phqDhqcGmiRJKw0\nIWRDFirc7x80EwGFAWaYucPrcc4cmZnP3Pu+M/f98b7vj8+NMgzDQERERERERHyiQx2AiIiIiIhI\nuFGhJCIiIiIi0o8KJRERERERkX5UKImIiIiIiPSjQklERERERKQfFUoiIiIiIiL9DFkoVVdX43A4\nSE1NpaysbMD7ra2tLFiwgOzsbDIzM9mzZ4/vvdLSUmbNmsXs2bMpKiqis7MTgMOHDzNr1iyuuOIK\njh8/3md6paWlpKam4nA4OHLkyCgXT0Qi1VB907vvvsvdd99NVlYWN910E3V1dSGIUkSCJRjbJ1//\n+tdJT08nKyuLxYsX89577/X5jLZPRMYZYxAXLlww7Ha70dDQYHR1dRlZWVnGqVOn+rT51re+ZTzy\nyCOGYRjG+fPnjdjYWONvf/ub0dDQYCQnJxv/93//ZxiGYSxZssTYs2ePYRiGcfr0aaO+vt5wuVzG\nK6+84ptWXV2dkZWVZXR1dRkNDQ2G3W43uru7BwtRRMYhf/qmr33ta8Zjjz1mGIZhvP7668ZnP/vZ\nUIQqIkEQrO2TI0eO+LY7vvGNbxjf+MY3DMPQ9onIeDXoEaXa2lpSUlJISkrCarWydOlSKisr+7SZ\nPn067e3tALS3txMXF4fFYuGqq67CarXS0dHBhQsX6OjowGazAeBwOJg5c+aA+VVWVrJs2TKsVitJ\nSUmkpKRQW1sbqJpQRCKEP33T6dOn+cxnPgNAWloajY2NnD9/PhThikiABWv7JD8/n+jo3k2jm266\niXPnzgHaPhEZrwYtlJqbm0lMTPQ9T0hIoLm5uU+b1atXU1dXR3x8PFlZWWzfvh2A2NhY1q1bx/XX\nX098fDxTpkxh3rx5gwbz5ptvkpCQMOj8RET86ZuysrL42c9+BvRuVP35z3/2bfSIiLmNxfbJk08+\nycKFCwFtn4iMV5bB3oyKihpyAps3byY7OxuPx8PZs2fJz8/n5MmTtLS0sG3bNhobG4mJiaGwsJB9\n+/axfPnyYQV4qRj8iUtExp5hGGMyH3/6gEceeYSvfvWrOJ1OZs+ejdPp5IorrhjRtERkbA3VlwR7\n++Tf//3fmTBhAkVFRX7HoL5EJDyNZttk0CNKNpuNpqYm3/OmpqY+e1QAampqKCwsBMBut5OcnMzp\n06d55ZVXuOWWW3yHuhcvXkxNTc2gwfSf37lz53yHw/szDCPsHt/61rdCHoPZYlNckRGXYYxNgfQR\nf/qmyZMn8+STT/Lqq6/y4x//mPPnzzNjxoxLTi/U352ZfutwjSucY1Ncw3sEqg8Y6fbJnj17+NWv\nfsW+ffsuO7/LbZ+E+rsz22+tuCIntnCNa7QGLZRyc3Pxer00NjbS1dXFwYMHKSgo6NPG4XDgdrsB\naGlpob6+HrvdTlpaGseOHePDDz/EMAzcbjcZGRmX7FQ+UlBQwIEDB+jq6qKhoQGv18ucOXNGvZAi\nEln86Zvee+89urq6AHj88ce5/fbb+eQnPxmKcEUkwIK1fVJdXc1//Md/UFlZySc+8QnftLR9IjI+\nDXrqncViYefOncyfP5/u7m5WrVpFeno65eXlAKxZs4b169ezcuVKsrKy6OnpYevWrcTGxhIbG8v9\n999Pbm4u0dHR5OTkUFxcDMDPf/5zHnroIVpbW/nc5z6H0+mkqqqKjIwMlixZQkZGBhaLhV27dulQ\ntogM4E/fdOrUKR544AGioqLIzMzkiSeeCHHUIhIowdo++ed//me6urrIz88H4Oabb2bXrl3aPhEZ\np6KMQByXGmNRUVEBOZwWaB6PB5fLFeowLilcY1NcwxOucUH45uVQwjXucP2twzUuCN/YFNfwhGtO\nDiWc4w7X31pxDV+4xhaucY02L1UoiUhAmDUvo6KiMO68E/bvhylToLgYzpyBSZM+fg0C8/p4n3Yk\nCffvO9zjG2TaUY8/bt6+xIRxi0SyUeelYUImDdt/q1cbxu23G8addxrGu++GOhoZjXH0W5o1LwHD\nAMMoLOx94fbbe59f/FqgXh/v075UPoRTjlwulku9Hu7fd7jHN8i0Td2XiEhYGW1eDjqYg4TImTNw\n9ChUVfXucRPz0m9pDnl5sHt379+TJg18LVCvj/dpXyofLpcjxcXgcsHChdDWNvTrlzLcaVwulku9\nHu7fd7jHN9S0RUTCQYAKtjFl0rD9d+edvXvY8vJCv4dVRmcc/ZZmzUug72/z7ru9e7f7/16BeH28\nT/tS+XC5HBnu0YlLGe40LhfLpV4P9+873OMbZNqm7ktEJKyMNi91jVI4amvr3Wu5e3dknts/noyj\n39KseWnWuE3pUvlwuRxZuLD3CE5eHhw58vF7l3v9UoY7jcvFMo7yOByYNSfNGrdIJNNgDiISFsya\nl2aNO+IFomhR4WNKZs1Js8YtEslUKIlIWDBrXpo1bpFIZdacNGvcIpFstHmpwRxERERERET6UaEk\nIiIiIiLSjwolERERERGRflQoiYiIiIiI9KNCSUREREREpB8VSiIiIiIytOJicLl670XW1hbqaGQ0\n9Fv6RYWSiIiIiAztzBk4erT3hs3FxaGORkZDv6VfVCiJiIiIyNAmTer9Ny+v94bNYl76Lf2iG86K\nSECYNS/NGrdIpDJrTpo17mFpa+s9+rB7N0yZEupoZDTGyW852rxUoSQiAWHWvDRr3CKRyqw5ada4\nRSLZaPNSp96JiIiIiIj0o0LJTDRCSXjT7yMiIjIy+j90IH0nIadCyUw0Qkl40+8jIiIyMvo/dCB9\nJyGnQslMNEJJeNPvIyIiMjL6P3QgfSchp8EczGScjFBiWuP89zFrXpo1bpFIZdacNGvcYWOc/x96\nSfpORk2DOYwnU6bAoUNKlnCl32dMVVdX43A4SE1NpaysbMD7ra2tLFiwgOzsbDIzM9mzZ8/YByki\nQTOaPqC0tJRZs2Yxe/ZsioqK6OzsBODw4cPMmjWLK664guPHj/vaNzY2MnHiRJxOJ06nk7Vr1wZ9\n+cady/0fOh6u07ncMmq7IuR0RElEAmIs87K7u5u0tDTcbjc2m428vDwqKipIT0/3tSkpKaGzs5PS\n0lJaW1tJS0ujpaUFi8USsrhFZGj+5ORo+oBz584xd+5cTp8+zZVXXsl9993HwoULWbFiBa+//jrR\n0dGsWbOG73znO+Tk5AC9hdJdd93Fa6+9Nqq4hd6i4MyZ3tPK9u8fughwuXqv0wEoLOwtHCLNcJZx\nuN/fOKcjSiIy7tTW1pKSkkJSUhJWq5WlS5dSWVnZp8306dNpb28HoL29nbi4uAFFkoiY02j6gKuu\nugqr1UpHRwcXLlygo6MDm80GgMPhYObMmWO+POPKcAcoGA/X6QxnGTXAw5hSoSQiptPc3ExiYqLv\neUJCAs3NzX3arF69mrq6OuLj48nKymL79u1jHaaIBMlo+oDY2FjWrVvH9ddfT3x8PFOmTGHevHlD\nzrOhoQGn04nL5eLFF18M7AKNJ8MtfPbv7z3KcuRI5B49Gc4yjofCMYxo92ok0GHYsaXvO+SioqKG\nbLN582ays7PxeDycPXuW/Px8Tpw4weTJkwe0LSkp8f3tcrlwuVwBjFZEBuPxePB4PMP6zEj7gJMn\nT9LS0sK2bdtobGwkJiaGwsJC9u3bx/Llyy87rfj4eJqampg6dSrHjx9n0aJF1NXVDehPxm1fMpz/\nF/fvH94ABR9dpxPJhrOMw/3+xtk2y0j6k8GoUIoEHx2Ghd6EiPQOJdT0fYeczWajqanJ97ypqYmE\nhIQ+bWpqatiwYQMAdrud5ORk6uvryc3NHTC9izduRGRs9S8oNm7cOORnRtoHnD59moaGBm655Rbi\n4uIAWLx4MTU1NYMWShMmTGDChAkA5OTkYLfb8Xq9vmuYPjJu+5Lh/L84HgqfYBru9zfOtllG0p8M\nRqfeRQIdhh1b+r5DLjc3F6/XS2NjI11dXRw8eJCCgoI+bRwOB263G4CWlhbq6+uZMWNGKMIVkQAb\naR9gt9tJS0vj2LFjfPjhhxiGgdvtJiMjY8A8Lr4AvLW1le7ubgDeeOMNvF6v+pOLXer/xfEwWl04\nudz3rW2W0TFMyKRhB8+77xpGYWHvvxJ8+r4vaazz8le/+pUxc+ZMw263G5s3bzYMwzB++MMfGj/8\n4Q8NwzCM8+fPG5///OeNG264wcjMzDT27dt3yemoPxEJL/7m5Gj6gLKyMiMjI8PIzMw07r//fqOr\nq8swDMP42c9+ZiQkJBif+MQnjGnTphkLFiwwDMMwnnrqKWPWrFlGdna2kZOTY/ziF78YcdwR6VL/\nL95+u2FA76OwMGShjRuX+77H+TbLaPNSw4OLSECYNS/NGrdIpDJrTpo17qBZuLB3ZLa8vMgeiCFc\n6Pu+pNHmpQqlSDbOLuALOH1/w2LWvDRr3CKRyqw5ada4g6atbXiDDsjo6Pu+JBVKcnnj4SZtwaTv\nb1jMmpdmjVskUpk1J80at0gk0w1nzS6YFzvqAr7RCeb3p4tcRURERMKaCqVQC+YdlsfDTdqCKZjf\nn+6sLSIiIhLWdB+lUAvmUQvdq2B0gvn96WifiIiISFjTNUqhpovvxqcI/N3NmpdmjVskUpk1J80a\nt0gk02AOIhIWzJqXZo1bJFKZNSfNGrdIJNNgDiIiIiIiIgGmQklERERERKQfFUoiIiIiIiLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x9142a10>"
+        "<matplotlib.figure.Figure at 0x630ec90>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 59
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, axes = subplots(1,1, figsize=(16,5))\n",
+      "epsx = np.linalg.norm(Utils.mkvc(Bxr))*1e-6\n",
+      "epsy = np.linalg.norm(Utils.mkvc(Byr))*1e-6\n",
+      "epsz = np.linalg.norm(Utils.mkvc(Bzr))*1e-6\n",
+      "id = 21\n",
+      "# axes.plot(X[id,:], Bzra[id,:], 'b', X[id,:], Bzr[id,:], 'r.'); axes.set_title('$B^s_z$', fontsize = 16)\n",
+      "axes.plot(X[id,:], Bxra[id,:], 'b', X[id,:], Bxr[id,:], 'r.'); axes.set_title('$B^s_z$', fontsize = 16)\n",
+      "print Y[id,0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "15.0\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA8EAAAFECAYAAAAHqjxCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x1138dfd0>"
+       ]
+      }
+     ],
+     "prompt_number": 73
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 1/abs(min(Bxra[id,:])/max(Bxra[id,:]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.200975085599\n"
+       ]
+      }
+     ],
+     "prompt_number": 77
     },
     {
      "cell_type": "markdown",
@@ -646,7 +718,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 32
+     "prompt_number": 59
     }
    ],
    "metadata": {}

simpegPF/notebooks/Play_analytic_fun.ipynb

@@ -25,7 +25,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -40,7 +40,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -51,7 +51,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -67,7 +67,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -81,9 +81,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 5,
        "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x5c49878>"
+        "<matplotlib.colorbar.Colorbar instance at 0x4396c20>"
        ]
       },
       {
@@ -91,11 +91,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEHCAYAAABLKzaMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x302fc90>"
+        "<matplotlib.figure.Figure at 0x438e450>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
@@ -105,17 +105,25 @@
       "bx,by,bz = MagSphereAnalFunA(M3.gridCC[:,0],M3.gridCC[:,1],M3.gridCC[:,2],100.,0.,0.,0.,0.01,np.array([1.,1.,0.]),'secondary') \n",
       "M3.plotSlice(bx, vType='CC', ind=5, ax = axes[0], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}); axes[0].set_title('$B_x$', fontsize = 16); #axes[0].set_xlim(0,500); axes[0].set_ylim(0,500)\n",
       "M3.plotSlice(by, vType='CC', ind=5, ax = axes[1]); axes[1].set_title('$B_y$', fontsize = 16);# axes[1].set_xlim(0,500); axes[1].set_ylim(0,500)\n",
-      "M3.plotSlice(bz, vType='CC', ind=5, ax = axes[2]); axes[2].set_title('$B_z$', fontsize = 16);# axes[2].set_xlim(0,500); axes[2].set_ylim(0,500)"
+      "M3.plotSlice(bz, vType='CC', ind=5, ax = axes[2]); axes[2].set_title('$B_z$', fontsize = 16);# axes[2].set_xlim(0,500); axes[2].set_ylim(0,500)\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/usr/lib/pymodules/python2.7/matplotlib/lines.py:483: RuntimeWarning: invalid value encountered in greater_equal\n",
+        "  return np.alltrue(x[1:]-x[0:-1]>=0)\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 22,
+       "prompt_number": 6,
        "text": [
-        "<matplotlib.text.Text at 0x741e150>"
+        "<matplotlib.text.Text at 0x48f8bd0>"
        ]
       },
       {
@@ -123,11 +131,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAFOCAYAAADdOR+tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3d45d50>"
+        "<matplotlib.figure.Figure at 0x666ca10>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -150,6 +158,47 @@
      ],
      "prompt_number": 23
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = subplots(1,1, figsize = (5, 5))\n",
+      "M3.plotSlice(np.c_[bx*np.ones(bx.size*1.), by*np.ones(bx.size*1.), bz*np.zeros(bx.size*1.)], vType='CCv', view='vec', ind=21, ax = ax, grid=False, gridOpts={'color':'b','lw':0.5, 'alpha':0.8}); "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAFLCAYAAABSqd7ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x63d8210>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "bx.size"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "524880"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
     {
      "cell_type": "code",
      "collapsed": false,