Bug Fixes in TensorView and test_utils

Updated ipnbs

SimPEG/TensorView.py

@@ -13,7 +13,7 @@ class TensorView(object):
     def __init__(self):
         pass
 
-    def plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True):
+    def plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True,annotationColor='w'):
         """
         Mesh.plotImage(I)
 
@@ -126,11 +126,11 @@ class TensorView(object):
                 nY = np.ceil(self.nCz/nX)
 
                 #  allocate space for montage
-                C = np.zeros((nX*self.nCx,nY*self.nCz))
-
-
                 nCx = self.nCx
                 nCy = self.nCy
+
+                C = np.zeros((nX*nCx,nY*nCy))
+
                 for iy in range(int(nY)):
                     for ix in range(int(nX)):
                         iz = ix + iy*nX
@@ -142,6 +142,7 @@ class TensorView(object):
                 C = np.ma.masked_where(np.isnan(C), C)
                 xx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), self.hx).ravel())]
                 yy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), self.hy).ravel())]
+                # Plot the mesh
                 ph = ax.pcolormesh(xx, yy, C.T)
                 # Plot the lines
                 gx =  np.arange(nX+1)*self.vectorNx[-1]
@@ -151,8 +152,8 @@ class TensorView(object):
                 gxY = np.kron(np.ones((nX+1, 1)), np.array([0, sum(self.hy)*nY, np.nan])).ravel()
                 gyX = np.kron(np.ones((nY+1, 1)), np.array([0, sum(self.hx)*nX, np.nan])).ravel()
                 gyY = np.c_[gy, gy, gy+np.nan].ravel()
-                ax.plot(gxX, gxY, 'w-', linewidth=2)
-                ax.plot(gyX, gyY, 'w-', linewidth=2)
+                ax.plot(gxX, gxY, annotationColor+'-', linewidth=2)
+                ax.plot(gyX, gyY, annotationColor+'-', linewidth=2)
 
                 if numbering:
                     pad = np.sum(self.hx)*0.04
@@ -161,7 +162,7 @@ class TensorView(object):
                             iz = ix + iy*nX
                             if iz < self.nCz:
                                 ax.text((ix+1)*self.vectorNx[-1]-pad,(iy)*self.vectorNy[-1]+pad,
-                                         '#%i'%iz,color='w',verticalalignment='bottom',horizontalalignment='right',size='x-large')
+                                         '#%i'%iz,color=annotationColor,verticalalignment='bottom',horizontalalignment='right',size='x-large')
 
         plt.show()
         return ph

SimPEG/parameters.py

@@ -5,64 +5,64 @@ import TensorView as tv
 
 def getIndecesBlock(p0,p1,ccMesh):
     """
-    Creates a vector containing the block indexes in the cell centerd mesh. 
-    Returns a tuple 
-    
+    Creates a vector containing the block indexes in the cell centerd mesh.
+    Returns a tuple
+
     The block is defined by the points
-    p0 : describe the position of the left  upper  front corner, and 
+    p0 : describe the position of the left  upper  front corner, and
     p1 : describe the position of the right bottom back  corner.
-    
+
     ccMesh represents the cell-centered mesh
-    
-    The points p0 and p1 must live in the the same dimensional space as the mesh.    
+
+    The points p0 and p1 must live in the the same dimensional space as the mesh.
     """
-    
+
     # Validation of the input
     assert type(p0) == np.ndarray, "Vector must be a numpy array"
     assert type(p1) == np.ndarray, "Vector must be a numpy array"
-    
+
     # Validation: p0 and p1 live in the same dimensional space
     assert len(p0) == len(p1), "Dimension mismatch. len(p0) != len(p1)"
 
     # Validation: mesh and points live in the same dimensional space
     dimMesh = np.size(ccMesh[0,:])
     assert len(p0) == dimMesh, "Dimension mismatch. len(p0) != dimMesh"
-    
+
     if dimMesh == 1:
        # Define the reference points
-        x1 = p0[0]   
-        x2 = p1[0]   
-  
+        x1 = p0[0]
+        x2 = p1[0]
+
         indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
         ind  = np.where(indX)
-          
+
     elif dimMesh == 2:
        # Define the reference points
-        x1 = p0[0]   
-        y1 = p0[1]  
-    
-        x2 = p1[0]   
-        y2 = p1[1]  
-    
+        x1 = p0[0]
+        y1 = p0[1]
+
+        x2 = p1[0]
+        y2 = p1[1]
+
         indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
         indY = (y1 <= ccMesh[:,1]) & (ccMesh[:,1] <= y2)
-    
+
         ind  = np.where(indX & indY)
-   
+
     else:
         # Define the points
-        x1 = p0[0]   
-        y1 = p0[1]  
+        x1 = p0[0]
+        y1 = p0[1]
         z1 = p0[2]
-    
-        x2 = p1[0]   
-        y2 = p1[1]  
+
+        x2 = p1[0]
+        y2 = p1[1]
         z2 = p1[2]
-    
+
         indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
         indY = (y1 <= ccMesh[:,1]) & (ccMesh[:,1] <= y2)
         indZ = (z1 <= ccMesh[:,2]) & (ccMesh[:,2] <= z2)
-    
+
         ind  = np.where(indX & indY & indZ)
 
     # Return a tuple
@@ -76,28 +76,28 @@ def defineBlockConductivity(p0,p1,ccMesh,condVals):
     """
     sigma = np.zeros(ccMesh.shape[0]) + condVals[1]
     ind   = getIndecesBlock(p0,p1,ccMesh)
-    
+
     sigma[ind] = condVals[0]
-    
+
     return sigma
-    
+
 def defineTwoLayeredConductivity(depth,ccMesh,condVals):
     """
     Define a two layered model.  Depth of the first layer must be specified.
     CondVals vector with the conductivity values of the layers.  Eg:
-    
-    Convention to number the layers:  
+
+    Convention to number the layers:
         <----------------------------|------------------------------------>
         0                          depth                                 zf
              1st layer                       2nd layer
     """
     sigma = np.zeros(ccMesh.shape[0]) + condVals[1]
-   
+
     dim = np.size(ccMesh[0,:])
-   
+
     p0 = np.zeros(dim)
     p1 = np.zeros(dim)
-    
+
     # Identify 1st cell centered reference point
     p0[0] = ccMesh[0,0]
     p0[1] = ccMesh[0,1]
@@ -107,30 +107,30 @@ def defineTwoLayeredConductivity(depth,ccMesh,condVals):
     p1[0] = ccMesh[-1,0]
     p1[1] = ccMesh[-1,1]
     p1[2] = ccMesh[-1,2] - depth;
-    
+
     ind   = getIndecesBlock(p0,p1,ccMesh)
-    
+
     sigma[ind] = condVals[0];
-        
+
     return sigma
- 
+
 def scalarConductivity(ccMesh,pFunction):
     """
-    Define the distribution conductivity in the mesh according to the 
-    analytical expression given in pFunction   
+    Define the distribution conductivity in the mesh according to the
+    analytical expression given in pFunction
     """
     xCC = ccMesh[:,0]
     yCC = ccMesh[:,1]
     zCC = ccMesh[:,2]
-     
+
     sigma = pFunction(xCC,yCC,zCC)
-     
+
     return sigma
 
 if __name__ == '__main__':
-    
+
     # Define the mesh
- 
+
     testDim = 3
     h1 = 0.3*np.ones(7)
     h1[0] = 0.5
@@ -149,44 +149,44 @@ if __name__ == '__main__':
         h = [h1, h2, h3]
 
     M = tm.TensorMesh(h, x0)
-    
+
     ccMesh = M.gridCC
-    
+
     # ------------------- Test conductivities! --------------------------
     print('Testing 1 block conductivity')
-    
+
     p0 = np.array([0.5,0.5,0.5])
     p1 = np.array([1.0,1.0,1.0])
     condVals = np.array([100,1e-6])
-    
-    sigma = defineBlockConductivity(p0,p1,ccMesh,condVals)    
+
+    sigma = defineBlockConductivity(p0,p1,ccMesh,condVals)
 
     # Plot sigma model
-    #M.plotImage(sigma)
-    print sigma
+    print sigma.shape
+    M.plotImage(sigma)
     print 'Done with block! :)'
-    
+
     # -----------------------------------------
-    print('Testing the two layered model') 
+    print('Testing the two layered model')
     condVals = np.array([100,1e-5]);
     depth    = 1.0;
-    
+
     sigma = defineTwoLayeredConductivity(depth,ccMesh,condVals)
-    
-    #M.plotImage(sigma)
+
+    M.plotImage(sigma)
     print sigma
     print 'layer model!'
-    
+
     # -----------------------------------------
     print('Testing scalar conductivity')
-    
+
     pFunction = lambda x,y,z: np.exp(x+y+z)
-    
+
     sigma = scalarConductivity(ccMesh,pFunction)
-    
+
     # Plot sigma model
     M.plotImage(sigma)
     print sigma
     print 'Scalar conductivity defined!'
-    
+
     # -----------------------------------------

SimPEG/tests/test_utils.py

@@ -2,8 +2,7 @@ import numpy as np
 import unittest
 import sys
 sys.path.append('../')
-from utils import mkvc, ndgrid, indexCube
-from sputils import sdiag, inv3X3BlockDiagonal, inv2X2BlockDiagonal, sp
+from utils import mkvc, ndgrid, indexCube, sdiag, inv3X3BlockDiagonal, inv2X2BlockDiagonal
 
 
 class TestSequenceFunctions(unittest.TestCase):
@@ -64,6 +63,7 @@ class TestSequenceFunctions(unittest.TestCase):
         self.assertTrue(np.all(indexCube('H', nN) == np.array([10, 11, 13, 14, 19, 20, 22, 23])))
 
     def test_invXXXBlockDiagonal(self):
+        import scipy.sparse as sp
 
         a = [np.random.rand(5, 1) for i in range(4)]
 

notebooks/exLomPlots.ipynb

@@ -1,6 +1,6 @@
 {
  "metadata": {
-  "name": "exLomPlots"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,13 +16,13 @@
       "\n",
       "import numpy as np\n",
       "import matplotlib.pyplot as plt\n",
-      "from SimPEG import LogicallyOrthogonalMesh, utils, exampleLomGird\n",
+      "from SimPEG import LogicallyOrthogonalMesh, utils\n",
       "mkvc = utils.mkvc"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 38
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -37,13 +37,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "X, Y = exampleLomGird([3,3],'rotate')\n",
+      "X, Y = utils.exampleLomGird([3,3],'rotate')\n",
       "M = LogicallyOrthogonalMesh([X, Y])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 39
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -55,14 +55,15 @@
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEKCAYAAADw2zkCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVHX++PHXAOZdUPFaIIKKmCmaiGYiKYnFUm2XXW27\nmG1hbanr99ttwxWz3Wp3K5Vcl761mVu2F+1XFq0K6kClKFZ4RUyNwGuigjdELuf3x2G4KKDinPOZ\nmfN+Ph7zcM7MgfM+w8fznvP+fM7n2DRN0xBCCGFpXqoDEEIIoZ4kAyGEEJIMhBBCSDIQQgiBJAMh\nhBBIMhBCCIFByWDKlCl069aNG264ocH3P/zwQwYPHszgwYO5//772b17txFhCCGEuEyGJINHHnmE\nlStXNvp+cHAwmZmZbNmyhdjYWObOnWtEGEIIIS6TzaiLzvLz84mPj2fbtm1NrldUVMTQoUMpKCgw\nIgwhhBCXwUd1AG+//Tbx8fEXvW6z2RREI4QQ7q853/GVJoP09HQ++OAD1q9f3+D7njxTRlJSEklJ\nSarDsBT5zM0nn7n5mvtFWlky2Lp1K1OnTmXlypX4+fmpCkOZ/Px81SFYjnzm5pPP3H0oGVpaUFDA\nPffcw4cffkifPn1UhCCEEKIOQ84MJk2aREZGBkVFRQQEBDBnzhzKy8sBSEhI4KWXXuL48eNMnToV\ngBYtWrBp0yYjQnFZkydPVh2C5chnbj75zN2HYaOJrpbNZvPoPgMhhDBCc4+dcgWyIna7XXUIliOf\nufnkM3cfkgyEEEJImUgIITyJlImEEEI0myQDRaSWaj75zM0nn7n7kGQghBBC+gyEEMKTSJ+BEEKI\nZpNkoIjUUs0nn7n55DN3H5IMhBBCSJ+BEEJ4EukzEEII0WySDBSRWqr55DM3n3zm7kOSgRBCCOkz\nEEIITyJ9BkIIIZpNkoEiUks1n3zm5pPP3H1IMhBCCCF9BkII4Umkz0AIIUSzSTJQRGqp5pPP3Hzy\nmbsPSQZCCCEkGagSHR2tbtuLo7Hn25VtX5Wk/CRL7rfKv7fKdi6ujCQDIYQQkgxUkVqq+Yp3FasO\nwXKknbsPSQZCCCEkGagitVTz+fX3Ux2C5Ug7dx+SDCzIqhfzyX4L0TinJ4MpU6bQrVs3brjhhkbX\neeGFFwgODubGG29k165dzg7BpaWmZhIbm0h4+GRiYxNJTc00dfuapnH4vd2mHyAc+x0dnaRsv/e9\nud2S+63y762qnYtm0JwsMzNT+/bbb7WBAwc2+P7GjRu1UaNGaceOHdOWLl2qxcXFNbieAaEp9/nn\nGVpIyO800DRYp4GmhYT8Tvv88wzTYvjvf/6jPdbKW/vLojmmbbP+fmvK9jvuGi9L7rfav7eadm5l\nzT12GnLE/eGHHxpNBgsWLNDefPPNmuXg4OCGA/PAZDB+/Iv1DgyOR2xsoinbr6qq0maMGKFVgTYl\nPEyrqqoyZbuy37LfZu631TX32Olj9pnIpk2bePDBB2uWu3Tpwt69ewkJCblo3cmTJxMUFASAn58f\n4eHhNR1SjiFr7rR85Mj+Ontnr/43ms2bvbnpJn25c2d9/WPHnL985lgGc/ZsxQbcnrOLhe37M7xt\nPwC+OX8MgBuv6VyzvLPTzeQP+NNVb/+bb3zq7a9j/zdsKCQ+HsP217F88tBybv32OzKAl3PyWN8x\ngq22ay7aX8dyVve7+GfbkKve/o4d8veu+/c+fLiw9tNwgf+PnrJst9tZvHgxQM3xsjkMmbU0Pz+f\n+Ph4tm3bdtF7DzzwAA8++CCxsbEAjBgxgqVLlxIcHFw/MA+ctTQ2NpHVq1+uXrLj+I8yZMgs5syZ\na+i2NU3j42dv4r28LGyABsy/NpSRk/+EzWZr8GfOdA/mdOD1V73t2bMT+e67ly963ez9zgDGYL39\nVvv3tuNo57Gxs1i50tj9Fldx7HTWqUldlyoTvfHGGzXLVioTNVxLfcGUWup///MfbWWbNvXO2//b\npo22ctkyw7fdcO3c/P1eZ9H9Vvv3NredCzcqE0VGRjJz5kweeughVq1aRVhYmNkhKBMXFwVAcvIs\nsrK86ddvDbNnT6h53Uj2L76gZUQEG+q8pgFlqanE3nOPodt27N8bb8wiI8ObmJhKnn5azX7bMX+/\nX3llFt9+601UlLr9BvP3W0U7F83n9DLRpEmTyMjIoKioiG7dujFnzhzKy8sBSEhIAOD555/nX//6\nF506deKDDz5oMCF4YpmoruhoSErS/7WKnBx46CHYulV1JOb6+GN4/3349FPVkZjPiu1cteYeO51+\nZvDRRx9dcp1XX32VV1991dmbdivFxXZqO9esoaAAAgPVbd9utyu5IjYvD/r1M32zLsGK7dxdyRXI\nwjSFhWqTgSp5eRAaqjoKIZomyUARP79o1SGYTvWZgap5cqycDKzYzt2VJANhGtXJQAVNs3YyEO5D\nkoEiei3VWgoKICBA3fZVzK1fVKQnhC5dTN+0S7BiO3dXkgyEaax4ZuA4K2jkOi8hXIYkA0WsVkst\nL4cjR6BnT3UxqBpJZOUSkdXauTuTZCBMcfAgdOsGLVqojsRcVk8Gwn1IMlDEarVUVygRqegzsHoy\nsFo7d2eSDIQp5BoDIVybJANFrFZLdYUzA7P7DMrLIT8f+vQxdbMuxWrt3J1JMhCmcIVkYLYfftA7\nzFu1Uh2JEJcmyUARq9VSVV9jAOb3GezcVUG/UM+dbPFyWK2duzNJBsIUVjwzeGf7mxwLf0F1GEJc\nFkkGilitluoKycDsPoM9xXn07RJk6jZdjdXauTuTZCAMV1ICFRXQsaPqSMx1qDyPYUEylEi4B0kG\nilipluoYVqp6Sgaz+wxOXZPHmIHWTgZWaufuTpKBInv6zCDHIv9RrHiNQf7hE2g+pQzp00N1KEpZ\nqZ27O0kGivhc56c6BNO4Qn8BmNtnsGZLHq3P9sPLy9oz1Fmpnbs7p9/2UogLuUoyMFrS5Mn6VWZA\nZuvDdOl0gqToaAgKImnxYoWRCXFpcmagSMX+YtUhmMZVkoHhfQb5+SRlZJCUkcGI8jymHPiJpIyM\nmgRhRVZq5+5OkoEw3Kr2kzjX8TvVYZgqrzOEHlMdhRCXT5KBIlaqpZ5ok0XfAF/VYZjaZ5DnD6FF\n+vPww4dhwwY4fty07bsKK7Vzdyd9BiaqW1P26ZPDV2/NoHien0fXlM+XV1LR+iA39r1WdSim+mgZ\n9Ks+Mwg+cQKmTdOnMG3ZUp/G1PG4+26Pm8nOiu3cE0gyMFN1TRngk9bw1N4tROdDktKgjLX1h8N4\nlXWiQ9uWqkPBbrebdnZww0+1zz8OC2OQ3a7fDPnwYT0pOB4nTpgSj6ks2M49gSQDYahv9xbQutwF\neo/NEBREEno1qLAQBg+ufR3Qr7rr0UN/KLgFpxBNkWSgiF93YJf+fNK2bRAf3/CKd90Fjz5qWlzO\ntvNAAX4210gGRp8VOEog8+dD5W5IWmjo5tyCVdq5J5BkoMgf19TWlL/p2ZPQxx9veMXgYPOCcpK6\nNeP17Qvp0PK8pcbb5+VB//6qo3ANntzOPY0kA0XOF4J/9fPdnTs3/o3JHdWpGR+7DUIOwIys/cpr\nxmb1GeTlwZ13Gr4Zt+DR7dzDGJIMMjMzSUhIoKKigmnTpvH000/Xe7+0tJSpU6eydetWOnTowMyZ\nM7nTCv97qmvKAOmbixnWxw8/P2pryh6owBduyVcdhbksf99jC7Zzj6AZIDw8XMvIyNDy8/O10NBQ\n7ejRo/XeX7RokfbEE09omqZp+fn5WnBwsFZVVVVvHYNCcxljxmjaunWqozDG7DFjNE0fO6OFJ6Bl\n99SfL7nhBk3buVPTTp1SHaJhTp3StNatNa2yUnUkrsGT27mrau6x0+kXnZWUlAAQFRVFr169GD9+\nPBs3bqy3jq+vL6dOnaK8vJzjx4/Tpk0bbKrnNxaGePbr2prxTYWFekdhly7QuTMMGQJ33AFPPQXZ\n2WoDdZLdu/XLBrzkck7hZpxeJsrOzqZ/nd6zAQMGkJWVRVxcXM1rkyZN4rPPPsPf35+Kigo2bNjQ\n4O+aPHkyQdWnln5+foSHh9fUfB3zzLjr8v7988jJ8Zz9uWgZ3aTt1Cwv7tWLxTk5oGnYP/0UfvqJ\n6C5doKAA+5YtcOaMofHl5OQwY8YMQ/f/0KFoQkNd4PN3keXiYoBol4nHE5ftdjuLqwdmBF1FKc5W\nfVrhNOnp6bz77rt89NFHAPztb3/jwIEDzJ07t2adt956i02bNvG3v/2Nbdu2ce+99/Ljjz/i5VX7\ndcpms+Hk0FxKeLidefOi8cTh5o7RRGfPwvbtMHx49RuKRxPZTehATkrS7+r28suGbsZteHI7d1XN\nPXY6/cwgIiKCZ555pmZ5x44dTJgwod46mZmZPProo7Rp04bIyEh69uzJ7t27651ReDpPvjes44Cf\nlgbfvgZJ6WrjcTA6EYDeeXz77YZvxm14cjv3NE6vbPr66hOSZWZmkp+fT1paGpGRkfXWGTduHJ99\n9hlVVVXs27eP48ePWyoRWIWrTF1tJsuPJBJuy5Burnnz5pGQkEBMTAxPPvkk/v7+pKSkkJKSAsDE\niRPx9vZm2LBhPPHEE8yfP9+IMFyaFe4N62rJwFFnNYqm6R3IkgxqWaGdewpDrjMYM2YMubm59V5L\nSEioee7r62vJBGA1BQUwerTqKMxz4AC0awe+6mfrFuKKyQA4RaxQSy0ogIAA1VHUMrrPQEpEF7NC\nO/cUkgyEYVytTGQ0SQbCnUkyUMTTa6lVVfo0zq50ZmB0n4Ekg4t5ejv3JJIMhCGOHoX27aFNG9WR\nmEeSgXBnkgwU8fRaamGh65WIpM/AfJ7ezj2JJANhCKv1F5SWwqFD0Lu36kh0peWlHn0Fv3Line truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10f20bdd0>"
+        "<matplotlib.figure.Figure at 0x10b6cc110>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -77,13 +78,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "X, Y, Z = exampleLomGird([3,3,3],'rotate')\n",
+      "X, Y, Z = utils.exampleLomGird([3,3,3],'rotate')\n",
       "M = LogicallyOrthogonalMesh([X, Y, Z])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 41
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -95,14 +96,15 @@
      "metadata": {},
      "outputs": [
       {
+       "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10c9d91d0>"
+        "<matplotlib.figure.Figure at 0x10d796110>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 9
     },
     {
      "cell_type": "code",

notebooks/exPlotImage2D.ipynb

@@ -1,6 +1,6 @@
 {
  "metadata": {
-  "name": "exPlotImage2D"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -16,12 +16,21 @@
       "\n",
       "import numpy as np\n",
       "import matplotlib.pyplot as plt\n",
-      "from SimPEG import TensorMesh"
+      "from SimPEG import TensorMesh\n",
+      "%pylab inline"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -43,52 +52,40 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 18
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "sin = lambda x: np.sin(x)\n",
-      "xc   = mesh.gridCC\n",
-      "xn   = mesh.gridN\n",
-      "\n",
-      "fig = plt.figure(1)\n",
-      "fig.clf()\n",
-      "ax1 = subplot(121)\n",
-      "ax2 = subplot(122)\n",
-      "ph1 = mesh.plotImage(sin(xc),ax=ax1)\n",
-      "ph2 = mesh.plotImage(sin(xn),ax=ax2,imageType='N')\n",
+      "ax1 = plt.subplot()\n",
       "ax1.set_title('sin(x) on CC grid')\n",
-      "ax2.set_title('sin(x) on N grid')"
+      "ph1 = mesh.plotImage(np.sin(mesh.gridCC),ax=ax1)\n",
+      "ax2 = plt.subplot()\n",
+      "ax2.set_title('sin(x) on N grid')\n",
+      "ph2 = mesh.plotImage(np.sin(mesh.gridN), ax=ax2,imageType='N')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Users/larsruthotto/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.py:362: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
-        "  \"matplotlib is currently using a non-GUI backend, \"\n"
-       ]
-      },
-      {
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "<matplotlib.text.Text at 0x10c3ba050>"
-       ]
-      },
-      {
+       "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x10c3803d0>"
+        "<matplotlib.figure.Figure at 0x10f27cf10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x10f51c910>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -111,41 +108,49 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 20
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "fig = plt.figure(1)\n",
-      "fig.clf()\n",
-      "ax1 = subplot(121)\n",
-      "ax2 = subplot(122)\n",
+      "ax1 = plt.subplot()\n",
+      "ax1.set_title('mesh.gridCC[:,0]')\n",
       "mesh.plotImage(mesh.gridCC[:,0],ax = ax1)\n",
-      "ax1.set_title('mesh.gridCC[:,0]') \n",
-      "mesh.plotImage(mesh.gridFx[:,1],ax = ax2,imageType='Fx')\n",
-      "ax2.set_title('mesh.gridFx[:,1]')  \n",
-      "\n"
+      "ax2 = plt.subplot()\n",
+      "ax2.set_title('mesh.gridFx[:,1]')\n",
+      "mesh.plotImage(mesh.gridFx[:,1],ax = ax2,imageType='Fx')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "pyout",
-       "prompt_number": 5,
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
        "text": [
-        "<matplotlib.text.Text at 0x10c81eb90>"
+        "<matplotlib.figure.Figure at 0x10cbb52d0>"
        ]
       },
       {
+       "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10bf3a1d0>"
+        "<matplotlib.figure.Figure at 0x10f8ca8d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "<matplotlib.collections.QuadMesh at 0x10e09c750>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -162,49 +167,75 @@
       "h1 = np.linspace(.1,.5,3)\n",
       "h1 = np.r_[1,2,1]\n",
       "h2 = np.r_[1,3]\n",
-      "h3 = np.linspace(.1,.5,5)\n",
+      "h3 = np.linspace(.1,.5,10)\n",
       "\n",
       "mesh = TensorMesh([h1,h2,h3],x0)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "ax1 = ax=subplot(131)\n",
-      "ax2 = ax=subplot(132)\n",
-      "ax3 = ax=subplot(133)\n",
-      "a1 = mesh.plotImage(mesh.gridCC[:,0],ax=ax1)\n",
-      "a2 = mesh.plotImage(mesh.gridFx[:,1],ax=ax2,imageType='Fx')\n",
-      "a3 = mesh.plotImage(mesh.gridEz[:,2],ax=ax3,imageType='Ez')\n",
-      "\n",
+      "ax1 = plt.subplot()\n",
       "ax1.set_title('mesh.gridCC[:,0]')\n",
+      "a1 = mesh.plotImage(mesh.gridCC[:,0],ax=ax1,annotationColor='w')\n",
+      "ax2 = plt.subplot()\n",
       "ax2.set_title('mesh.gridFx[:,1]')\n",
-      "ax3.set_title('mesh.gridEz[:,2]')\n"
+      "a2 = mesh.plotImage(mesh.gridFx[:,1],ax=ax2,imageType='Fx')\n",
+      "ax3 = plt.subplot()\n",
+      "ax3.set_title('mesh.gridEz[:,2]')\n",
+      "mesh.plotImage(mesh.gridEz[:,2],ax=ax3,imageType='Ez')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "pyout",
-       "prompt_number": 7,
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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        "text": [
-        "<matplotlib.text.Text at 0x10cb23490>"
+        "<matplotlib.figure.Figure at 0x10e0916d0>"
        ]
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       {
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10c846d90>"
+        "<matplotlib.figure.Figure at 0x10fe8fb90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x10cae5410>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": [
+        "<matplotlib.collections.QuadMesh at 0x10e7ca410>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
     }
    ],
    "metadata": {}