Merge branch 'master' of https://bitbucket.org/rcockett/simpeg into diffOperators

SimPEG/TensorView.py

@@ -14,10 +14,26 @@ class TensorView(object):
         pass
 
     def plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True):
+        """
+        Mesh.plotImage(I)
 
+        Plots scalar fields on the given mesh.
+
+        Input:
+
+            I           - scalar field (np.array)
+
+        Optional Input:
+
+            imageType   - type of image ('CC','N','Fx','Fy','Fz','Ex','Ey','Ez')
+            figNum      - number of figure to plot to
+            ax          - axis to plot to
+            direction   - 3D only. slice dimensions
+            numbering   - 3D only. show numbering of slices
+        """
         assert type(I) == np.ndarray, "I must be a numpy array"
         assert type(numbering) == bool, "numbering must be a bool"
-        assert imageType in ["CC", "N"], "imageType must be 'CC' or 'N'"
+        assert imageType in ["CC", "N","Fx","Fy","Fz","Ex","Ey","Ez"], "imageType must be 'CC', 'N','Fx','Fy','Fz','Ex','Ey','Ez'"
         assert direction in ["x", "y","z"], "direction must be either x,y, or z"
 
 
@@ -25,6 +41,18 @@ class TensorView(object):
             assert I.size == self.nC, "Incorrect dimensions for CC."
         elif imageType == 'N':
             assert I.size == self.nN, "Incorrect dimensions for N."
+        elif imageType == 'Fx':
+            assert I.size == np.prod(self.nFx), "Incorrect dimensions for Fx."
+        elif imageType == 'Fy':
+            assert I.size == np.prod(self.nFy), "Incorrect dimensions for Fy."
+        elif imageType == 'Fz':
+            assert I.size == np.prod(self.nFz), "Incorrect dimensions for Fz."
+        elif imageType == 'Ex':
+            assert I.size == np.prod(self.nEx), "Incorrect dimensions for Ex."
+        elif imageType == 'Ey':
+            assert I.size == np.prod(self.nEy), "Incorrect dimensions for Ey."
+        elif imageType == 'Ez':
+            assert I.size == np.prod(self.nEz), "Incorrect dimensions for Ez."
 
         if ax is None:
             fig = plt.figure(figNum)
@@ -39,7 +67,6 @@ class TensorView(object):
                 ph = ax.plot(self.vectorCCx, I, '-ro')
             elif imageType == 'N':
                 ph = ax.plot(self.vectorNx, I, '-bs')
-            ax.set_xticks(self.vectorNx)
             ax.set_xlabel("x")
             ax.axis('tight')
         elif self.dim == 2:
@@ -48,21 +75,58 @@ class TensorView(object):
             elif imageType == 'N':
                 C = I[:].reshape(self.n+1, order='F')
                 C = 0.25*(C[:-1, :-1] + C[1:, :-1] + C[:-1, 1:] + C[1:, 1:])
+            elif imageType == 'Fx':
+                C = I[:].reshape(self.nFx, order='F')
+                C = 0.5*(C[:-1, :] + C[1:, :] )
+            elif imageType == 'Fy':
+                C = I[:].reshape(self.nFy, order='F')
+                C = 0.5*(C[:, :-1] + C[:, 1:] )
+            elif imageType == 'Ex':
+                C = I[:].reshape(self.nEx, order='F')
+                C = 0.5*(C[:,:-1] + C[:,1:] )
+            elif imageType == 'Ey':
+                C = I[:].reshape(self.nEy, order='F')
+                C = 0.5*(C[:-1,:] + C[1:,:] )
 
             ph = ax.pcolormesh(self.vectorNx, self.vectorNy, C.T)
             ax.axis('tight')
             ax.set_xlabel("x")
             ax.set_ylabel("y")
-            ax.set_xticks(self.vectorNx)
-            ax.set_yticks(self.vectorNy)
 
         elif self.dim == 3:
             if direction == 'z':
+
+                # get copy of image and average to cell-centres is necessary
+                if imageType == 'CC':
+                    Ic = I[:].reshape(self.n, order='F')
+                elif imageType == 'N':
+                    Ic = I[:].reshape(self.n+1, order='F')
+                    Ic = .125*(Ic[:-1,:-1,:-1]+Ic[1:,:-1,:-1] + Ic[:-1,1:,:-1]+ Ic[1:,1:,:-1]+ Ic[:-1,:-1,1:]+Ic[1:,:-1,1:] + Ic[:-1,1:,1:]+ Ic[1:,1:,1:] )
+                elif imageType == 'Fx':
+                    Ic = I[:].reshape(self.nFx, order='F')
+                    Ic = .5*(Ic[:-1,:,:]+Ic[1:,:,:])
+                elif imageType == 'Fy':
+                    Ic = I[:].reshape(self.nFy, order='F')
+                    Ic = .5*(Ic[:,:-1,:]+Ic[:,1:,:])
+                elif imageType == 'Fz':
+                    Ic = I[:].reshape(self.nFz, order='F')
+                    Ic = .5*(Ic[:,:,:-1]+Ic[:,:,1:])
+                elif imageType == 'Ex':
+                    Ic = I[:].reshape(self.nEx, order='F')
+                    Ic = .25*(Ic[:,:-1,:-1]+Ic[:,1:,:-1]+Ic[:,:-1,1:]+Ic[:,1:,:1])
+                elif imageType == 'Ey':
+                    Ic = I[:].reshape(self.nEy, order='F')
+                    Ic = .25*(Ic[:-1,:,:-1]+Ic[1:,:,:-1]+Ic[:-1,:,1:]+Ic[1:,:,:1])
+                elif imageType == 'Ez':
+                    Ic = I[:].reshape(self.nEz, order='F')
+                    Ic = .25*(Ic[:-1,:-1,:]+Ic[1:,:-1,:]+Ic[:-1,1:,:]+Ic[1:,:1,:])
+
+                # determine number oE slices in x and y dimension
                 nX = np.ceil(np.sqrt(self.nCz))
                 nY = np.ceil(self.nCz/nX)
-                C = np.zeros((nX*self.nCx, nY*self.nCy))
+                #  allocate space for montage
+                C = np.zeros((nX*self.nCx,nY*self.nCz))
 
-                Ic = I[:].reshape(self.n, order='F')
 
                 nCx = self.nCx
                 nCy = self.nCy
@@ -94,8 +158,9 @@ class TensorView(object):
                     for iy in range(int(nY)):
                         for ix in range(int(nX)):
                             iz = ix + iy*nX
-                            ax.text((ix+1)*self.vectorNx[-1]-pad,(iy)*self.vectorNy[-1]+pad,
-                                     '#%i'%iz,color='w',verticalalignment='bottom',horizontalalignment='right',size='x-large')
+                            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')
 
         fig.show()
         return ph

notebooks/exPlotImage2D.ipynb

@@ -21,7 +21,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -37,13 +37,13 @@
      "collapsed": false,
      "input": [
       "x0 = np.zeros(1)\n",
-      "h = np.random.rand(51)\n",
+      "h = np.random.rand(32)\n",
       "mesh = TensorMesh([h],x0)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -55,21 +55,40 @@
       "\n",
       "fig = plt.figure(1)\n",
       "fig.clf()\n",
-      "ph1 = mesh.plotImage(sin(xc),ax=subplot(111))\n",
-      "ph2 = mesh.plotImage(sin(xn),ax=subplot(111),imageType='N')\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.set_title('sin(x) on CC grid')\n",
+      "ax2.set_title('sin(x) on N grid')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEECAYAAADHzyg1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "output_type": "stream",
+       "stream": "stderr",
        "text": [
-        "<matplotlib.figure.Figure at 0x10b3f14d0>"
+        "/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>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAETCAYAAADNpUayAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c3803d0>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -92,7 +111,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -103,29 +122,30 @@
       "ax1 = subplot(121)\n",
       "ax2 = subplot(122)\n",
       "mesh.plotImage(mesh.gridCC[:,0],ax = ax1)\n",
-      "ax1.set_title('x coordinates') \n",
-      "mesh.plotImage(mesh.gridCC[:,1],ax = ax2)\n",
-      "ax2.set_title('y coordinates')            "
+      "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"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 5,
        "text": [
-        "<matplotlib.text.Text at 0x10b3d0f90>"
+        "<matplotlib.text.Text at 0x10c81eb90>"
        ]
       },
       {
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAETCAYAAAA23nEoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAETCAYAAAA23nEoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10ac769d0>"
+        "<matplotlib.figure.Figure at 0x10bf3a1d0>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -149,7 +169,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -159,40 +179,32 @@
       "ax2 = ax=subplot(132)\n",
       "ax3 = ax=subplot(133)\n",
       "a1 = mesh.plotImage(mesh.gridCC[:,0],ax=ax1)\n",
-      "a2 = mesh.plotImage(mesh.gridCC[:,1],ax=ax2)\n",
-      "a3 = mesh.plotImage(mesh.gridCC[:,2],ax=ax3)\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.set_title('x coordinate')\n",
-      "ax2.set_title('y coordinate')\n",
-      "ax3.set_title('z coordinate')\n"
+      "ax1.set_title('mesh.gridCC[:,0]')\n",
+      "ax2.set_title('mesh.gridFx[:,1]')\n",
+      "ax3.set_title('mesh.gridEz[:,2]')\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": 2,
+       "prompt_number": 7,
        "text": [
-        "<matplotlib.text.Text at 0x10cc8ab10>"
+        "<matplotlib.text.Text at 0x10cb23490>"
        ]
       },
       {
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEICAYAAACZJtWMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEICAYAAACZJtWMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x10c7671d0>"
+        "<matplotlib.figure.Figure at 0x10c846d90>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 7
     }
    ],
    "metadata": {}