Update Directives in Tutotial MAG

simpegPF/Dev/MAG/Intgrl_MAG_Inv_Driver.py

@@ -217,8 +217,11 @@ update_beta = Directives.Scale_Beta(tol = 0.05)
 #betaest = Directives.BetaEstimate_ByEig()
 target = Directives.TargetMisfit()
 IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid )
+update_Jacobi = Directives.Update_lin_PreCond()
+save_log = Directives.SaveOutputEveryIteration()
+save_log.fileName = 'LogName_blabla'
 
-inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS,update_beta])
+inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS,update_beta,update_Jacobi,save_log])
 
 m0 = mrec
 

simpegPF/Dev/MAG/LogName_blabla.log.txt

@@ -0,0 +1,21 @@
+  #     beta     phi_d     phi_m       f
+   1 1.5360e+05 5.0131e+01 7.3332e-03 4.4174e+02
+   2 3.7391e+05 1.2652e+02 3.6764e-03 1.0807e+03
+   3 2.5341e+05 4.5447e+02 2.4502e-03 2.6354e+03
+   4 1.4461e+05 5.3971e+02 4.6665e-03 2.1548e+03
+   5 1.0937e+05 4.0726e+02 6.5473e-03 1.5100e+03
+   6 1.1622e+05 2.8985e+02 9.3653e-03 1.6067e+03
+   7 1.1622e+05 3.1487e+02 9.3157e-03 1.5644e+03
+   8 1.0482e+05 3.4151e+02 9.7362e-03 1.5894e+03
+   9 9.6785e+04 3.3356e+02 9.5567e-03 1.4910e+03
+  10 7.6095e+04 3.9174e+02 8.8846e-03 1.6031e+03
+  11 5.3847e+04 4.3526e+02 1.2266e-02 1.3899e+03
+  12 4.4794e+04 3.7025e+02 1.3672e-02 1.1416e+03
+  13 4.1906e+04 3.2923e+02 1.6171e-02 1.0732e+03
+  14 4.1906e+04 3.0335e+02 1.8262e-02 1.0765e+03
+  15 4.1906e+04 2.9834e+02 1.7878e-02 1.0506e+03
+  16 4.1906e+04 3.0216e+02 1.8782e-02 1.0953e+03
+  17 4.1906e+04 3.0924e+02 1.8673e-02 1.0995e+03
+  18 4.1906e+04 3.1922e+02 1.8988e-02 1.1262e+03
+  19 3.9225e+04 3.2905e+02 1.9461e-02 1.1463e+03
+  20 3.5542e+04 3.3992e+02 2.0384e-02 1.1604e+03

simpegPF/Dev/MAG/LogName_blabla.txt

@@ -0,0 +1,21 @@
+  #     beta     phi_d     phi_m       f
+   1 1.5360e+05 5.0131e+01 7.3332e-03 4.4174e+02
+   2 3.7391e+05 1.2652e+02 3.6764e-03 1.0807e+03
+   3 2.5341e+05 4.5447e+02 2.4502e-03 2.6354e+03
+   4 1.4461e+05 5.3971e+02 4.6665e-03 2.1548e+03
+   5 1.0937e+05 4.0726e+02 6.5473e-03 1.5100e+03
+   6 1.1622e+05 2.8985e+02 9.3653e-03 1.6067e+03
+   7 1.1622e+05 3.1487e+02 9.3157e-03 1.5644e+03
+   8 1.0482e+05 3.4151e+02 9.7362e-03 1.5894e+03
+   9 9.6785e+04 3.3356e+02 9.5567e-03 1.4910e+03
+  10 7.6095e+04 3.9174e+02 8.8846e-03 1.6031e+03
+  11 5.3847e+04 4.3526e+02 1.2266e-02 1.3899e+03
+  12 4.4794e+04 3.7025e+02 1.3672e-02 1.1416e+03
+  13 4.1906e+04 3.2923e+02 1.6171e-02 1.0732e+03
+  14 4.1906e+04 3.0335e+02 1.8262e-02 1.0765e+03
+  15 4.1906e+04 2.9834e+02 1.7878e-02 1.0506e+03
+  16 4.1906e+04 3.0216e+02 1.8782e-02 1.0953e+03
+  17 4.1906e+04 3.0924e+02 1.8673e-02 1.0995e+03
+  18 4.1906e+04 3.1922e+02 1.8988e-02 1.1262e+03
+  19 3.9225e+04 3.2905e+02 1.9461e-02 1.1463e+03
+  20 3.5542e+04 3.3992e+02 2.0384e-02 1.1604e+03

simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -53,15 +53,19 @@
      "output_type": "stream",
      "text": [
       "Using matplotlib backend: nbAgg\n",
-      "Populating the interactive namespace from numpy and matplotlib\n"
+      "Populating the interactive namespace from numpy and matplotlib\n",
+      "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
+      "\n",
+      "            python setup.py build_ext --inplace\n",
+      "    \n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
-      "`%matplotlib` prevents importing * from pylab and numpy\n"
+      "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
+      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
      ]
     }
    ],
@@ -75,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -176,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -227,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -971,7 +975,7 @@
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+       "<img 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       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -983,10 +987,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x0000000016712A48>"
+       "<matplotlib.colorbar.Colorbar instance at 0x0000000016394448>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1022,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -1800,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -1820,7 +1824,7 @@
     "beta_in = 1e+4\n",
     "\n",
     "# Create a jacobi pre-conditioner\n",
-    "diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*wr\n",
+    "diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()\n",
     "PC     = Utils.sdiag(diagA**-1.)\n",
     "\n",
     "# Define the misfit function\n",
@@ -1843,7 +1847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -1858,16 +1862,15 @@
       "=============================== Projected GNCG ===============================\n",
       "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
       "-----------------------------------------------------------------------------\n",
-      "   0  1.00e+04  6.70e+04  0.00e+00  6.70e+04    8.30e+01      0              \n",
-      "   1  5.00e+03  2.94e+03  4.20e-02  3.15e+03    6.89e+01      0              \n",
-      "   2  2.50e+03  3.61e+02  6.08e-02  5.13e+02    7.04e+01      0              \n",
-      "   3  1.25e+03  2.58e+02  5.84e-02  3.31e+02    8.36e+01      0   Skip BFGS  \n",
+      "   0  1.00e+04  6.73e+04  0.00e+00  6.73e+04    8.30e+01      0              \n",
+      "   1  5.00e+03  1.33e+03  3.52e-02  1.50e+03    6.45e+01      0              \n",
+      "   2  2.50e+03  3.14e+02  5.17e-02  4.44e+02    7.06e+01      0   Skip BFGS  \n",
       "------------------------- STOP! -------------------------\n",
-      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.6998e+03\n",
-      "1 : |xc-x_last| = 8.5475e-03 <= tolX*(1+|x0|) = 1.0111e-01\n",
-      "0 : |proj(x-g)-x|    = 8.3598e+01 <= tolG          = 1.0000e-01\n",
-      "0 : |proj(x-g)-x|    = 8.3598e+01 <= 1e3*eps       = 1.0000e-02\n",
-      "0 : maxIter   =      10    <= iter          =      4\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7288e+03\n",
+      "1 : |xc-x_last| = 1.6646e-02 <= tolX*(1+|x0|) = 1.0111e-01\n",
+      "0 : |proj(x-g)-x|    = 7.0593e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 7.0593e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      10    <= iter          =      3\n",
       "------------------------- DONE! -------------------------\n"
      ]
     }
@@ -1885,7 +1888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -2629,7 +2632,7 @@
     {
      "data": {
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@@ -2669,7 +2672,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -3413,7 +3416,7 @@
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3425,10 +3428,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x0000000020177B48>"
+       "<matplotlib.colorbar.Colorbar instance at 0x000000001BC6F288>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }