diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..0d20b648
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+*.pyc
diff --git a/code/BaseMesh.py b/SimPEG/BaseMesh.py
similarity index 99%
rename from code/BaseMesh.py
rename to SimPEG/BaseMesh.py
index 2b72ee80..a197b8a4 100644
--- a/code/BaseMesh.py
+++ b/SimPEG/BaseMesh.py
@@ -128,7 +128,7 @@ class BaseMesh(object):
 
     def nN():
         doc = "Total number of nodes"
-        fget = lambda self: self.n + 1
+        fget = lambda self: np.prod(self.n + 1)
         return locals()
     nN = property(**nN())
 
diff --git a/code/EldadsCode/GaussNewton.py b/SimPEG/EldadsCode/GaussNewton.py
similarity index 100%
rename from code/EldadsCode/GaussNewton.py
rename to SimPEG/EldadsCode/GaussNewton.py
diff --git a/code/EldadsCode/MFDdriver.py b/SimPEG/EldadsCode/MFDdriver.py
similarity index 100%
rename from code/EldadsCode/MFDdriver.py
rename to SimPEG/EldadsCode/MFDdriver.py
diff --git a/code/EldadsCode/getCellVolume.py b/SimPEG/EldadsCode/getCellVolume.py
similarity index 100%
rename from code/EldadsCode/getCellVolume.py
rename to SimPEG/EldadsCode/getCellVolume.py
diff --git a/code/EldadsCode/getDiffOps.py b/SimPEG/EldadsCode/getDiffOps.py
similarity index 100%
rename from code/EldadsCode/getDiffOps.py
rename to SimPEG/EldadsCode/getDiffOps.py
diff --git a/code/EldadsCode/getEdgeInnerProduct.py b/SimPEG/EldadsCode/getEdgeInnerProduct.py
similarity index 100%
rename from code/EldadsCode/getEdgeInnerProduct.py
rename to SimPEG/EldadsCode/getEdgeInnerProduct.py
diff --git a/code/EldadsCode/getEdgeTangent.py b/SimPEG/EldadsCode/getEdgeTangent.py
similarity index 100%
rename from code/EldadsCode/getEdgeTangent.py
rename to SimPEG/EldadsCode/getEdgeTangent.py
diff --git a/code/EldadsCode/getFaceInnerProduct.py b/SimPEG/EldadsCode/getFaceInnerProduct.py
similarity index 100%
rename from code/EldadsCode/getFaceInnerProduct.py
rename to SimPEG/EldadsCode/getFaceInnerProduct.py
diff --git a/code/EldadsCode/getFaceNormals.py b/SimPEG/EldadsCode/getFaceNormals.py
similarity index 100%
rename from code/EldadsCode/getFaceNormals.py
rename to SimPEG/EldadsCode/getFaceNormals.py
diff --git a/code/EldadsCode/getVolume.py b/SimPEG/EldadsCode/getVolume.py
similarity index 100%
rename from code/EldadsCode/getVolume.py
rename to SimPEG/EldadsCode/getVolume.py
diff --git a/code/EldadsCode/inv3X3BlockDiagonal.py b/SimPEG/EldadsCode/inv3X3BlockDiagonal.py
similarity index 100%
rename from code/EldadsCode/inv3X3BlockDiagonal.py
rename to SimPEG/EldadsCode/inv3X3BlockDiagonal.py
diff --git a/code/EldadsCode/meshUtils.py b/SimPEG/EldadsCode/meshUtils.py
similarity index 100%
rename from code/EldadsCode/meshUtils.py
rename to SimPEG/EldadsCode/meshUtils.py
diff --git a/code/EldadsCode/sputils.py b/SimPEG/EldadsCode/sputils.py
similarity index 100%
rename from code/EldadsCode/sputils.py
rename to SimPEG/EldadsCode/sputils.py
diff --git a/code/EldadsCode/tools.py b/SimPEG/EldadsCode/tools.py
similarity index 100%
rename from code/EldadsCode/tools.py
rename to SimPEG/EldadsCode/tools.py
diff --git a/code/EldadsCode/utils.py b/SimPEG/EldadsCode/utils.py
similarity index 100%
rename from code/EldadsCode/utils.py
rename to SimPEG/EldadsCode/utils.py
diff --git a/code/EldadsCode/zevel.py b/SimPEG/EldadsCode/zevel.py
similarity index 100%
rename from code/EldadsCode/zevel.py
rename to SimPEG/EldadsCode/zevel.py
diff --git a/code/TensorMesh.py b/SimPEG/TensorMesh.py
similarity index 95%
rename from code/TensorMesh.py
rename to SimPEG/TensorMesh.py
index 9e116f70..5f5e44b3 100644
--- a/code/TensorMesh.py
+++ b/SimPEG/TensorMesh.py
@@ -23,7 +23,7 @@ class TensorMesh(BaseMesh, TensorView):
     def __init__(self, h, x0=None):
         super(TensorMesh, self).__init__(np.array([len(x) for x in h]), x0)
 
-        assert len(h) == len(x0), "Dimension mismatch. x0 != len(h)"
+        assert len(h) == len(self.x0), "Dimension mismatch. x0 != len(h)"
 
         for i, h_i in enumerate(h):
             assert type(h_i) == np.ndarray, ("h[%i] is not a numpy array." % i)
diff --git a/SimPEG/TensorView.py b/SimPEG/TensorView.py
new file mode 100644
index 00000000..d78753ed
--- /dev/null
+++ b/SimPEG/TensorView.py
@@ -0,0 +1,166 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from mpl_toolkits.mplot3d import Axes3D
+
+
+class TensorView(object):
+    """
+    Provides viewing functions for TensorMesh
+
+    This class is inherited by TensorMesh
+    """
+    def __init__(self):
+        pass
+
+    def plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True):
+
+        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 direction in ["x", "y","z"], "direction must be either x,y, or z"
+
+
+        if imageType == 'CC':
+            assert I.size == self.nC, "Incorrect dimensions for CC."
+        elif imageType == 'N':
+            assert I.size == self.nN, "Incorrect dimensions for N."
+
+        if ax is None:
+            fig = plt.figure(figNum)
+            fig.clf()
+            ax = plt.subplot(111)
+        else:
+            assert isinstance(ax,matplotlib.axes.Axes), "ax must be an Axes!"
+            fig = ax.figure
+
+        if self.dim == 1:
+            if imageType == 'CC':
+                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:
+            if imageType == 'CC':
+                C = I[:].reshape(self.n, order='F')
+            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:])
+
+            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':
+                nX = np.ceil(np.sqrt(self.nCz))
+                nY = np.ceil(self.nCz/nX)
+                C = np.zeros((nX*self.nCx, nY*self.nCy))
+
+                Ic = I[:].reshape(self.n, order='F')
+
+                nCx = self.nCx
+                nCy = self.nCy
+                for iy in range(int(nY)):
+                    for ix in range(int(nX)):
+                        iz = ix + iy*nX
+                        if iz < self.nCz:
+                            C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = Ic[:, :, iz]
+                        else:
+                            C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = np.nan
+
+                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())]
+                ph = ax.pcolormesh(xx, yy, C.T)
+                # Plot the lines
+                gx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), np.sum(self.hy)).ravel())]
+                gy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), np.sum(self.hx)).ravel())]
+                # Repeat and seperate with NaN
+                gxX = np.c_[gx, gx, gx+np.nan].ravel()
+                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)
+
+                if numbering:
+                    pad = np.sum(self.hx)*0.04
+                    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')
+
+        fig.show()
+        return ph
+
+    def plotGrid(self):
+        """Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions."""
+        if self.dim == 1:
+            fig = plt.figure(1)
+            fig.clf()
+            ax = plt.subplot(111)
+            xn = self.gridN
+            xc = self.gridCC
+            ax.hold(True)
+            ax.plot(xn, np.ones(np.shape(xn)), 'bs')
+            ax.plot(xc, np.ones(np.shape(xc)), 'ro')
+            ax.plot(xn, np.ones(np.shape(xn)), 'k--')
+            ax.grid(True)
+            ax.hold(False)
+            ax.set_xlabel('x1')
+            fig.show()
+        elif self.dim == 2:
+            fig = plt.figure(2)
+            fig.clf()
+            ax = plt.subplot(111)
+            xn = self.gridN
+            xc = self.gridCC
+            xs1 = self.gridFx
+            xs2 = self.gridFy
+
+            ax.hold(True)
+            ax.plot(xn[:, 0], xn[:, 1], 'bs')
+            ax.plot(xc[:, 0], xc[:, 1], 'ro')
+            ax.plot(xs1[:, 0], xs1[:, 1], 'g>')
+            ax.plot(xs2[:, 0], xs2[:, 1], 'g^')
+            ax.grid(True)
+            ax.hold(False)
+            ax.set_xlabel('x1')
+            ax.set_ylabel('x2')
+            fig.show()
+        elif self.dim == 3:
+            fig = plt.figure(3)
+            fig.clf()
+            ax = fig.add_subplot(111, projection='3d')
+            xn = self.gridN
+            xc = self.gridCC
+            xfs1 = self.gridFx
+            xfs2 = self.gridFy
+            xfs3 = self.gridFz
+
+            xes1 = self.gridEx
+            xes2 = self.gridEy
+            xes3 = self.gridEz
+
+            ax.hold(True)
+            ax.plot(xn[:, 0], xn[:, 1], 'bs', zs=xn[:, 2])
+            ax.plot(xc[:, 0], xc[:, 1], 'ro', zs=xc[:, 2])
+            ax.plot(xfs1[:, 0], xfs1[:, 1], 'g>', zs=xfs1[:, 2])
+            ax.plot(xfs2[:, 0], xfs2[:, 1], 'g<', zs=xfs2[:, 2])
+            ax.plot(xfs3[:, 0], xfs3[:, 1], 'g^', zs=xfs3[:, 2])
+            ax.plot(xes1[:, 0], xes1[:, 1], 'k>', zs=xes1[:, 2])
+            ax.plot(xes2[:, 0], xes2[:, 1], 'k<', zs=xes2[:, 2])
+            ax.plot(xes3[:, 0], xes3[:, 1], 'k^', zs=xes3[:, 2])
+            ax.grid(True)
+            ax.hold(False)
+            ax.set_xlabel('x1')
+            ax.set_ylabel('x2')
+            ax.set_zlabel('x3')
+            fig.show()
diff --git a/SimPEG/__init__.py b/SimPEG/__init__.py
new file mode 100644
index 00000000..2d1b1e99
--- /dev/null
+++ b/SimPEG/__init__.py
@@ -0,0 +1 @@
+from TensorMesh import TensorMesh
diff --git a/code/getDIV.py b/SimPEG/getDIV.py
similarity index 100%
rename from code/getDIV.py
rename to SimPEG/getDIV.py
diff --git a/code/sputils.py b/SimPEG/sputils.py
similarity index 100%
rename from code/sputils.py
rename to SimPEG/sputils.py
diff --git a/code/tests/runTests.py b/SimPEG/tests/runTests.py
similarity index 100%
rename from code/tests/runTests.py
rename to SimPEG/tests/runTests.py
diff --git a/code/tests/runTests.sh b/SimPEG/tests/runTests.sh
similarity index 100%
rename from code/tests/runTests.sh
rename to SimPEG/tests/runTests.sh
diff --git a/code/tests/test_basemesh.py b/SimPEG/tests/test_basemesh.py
similarity index 100%
rename from code/tests/test_basemesh.py
rename to SimPEG/tests/test_basemesh.py
diff --git a/code/tests/test_div.py b/SimPEG/tests/test_div.py
similarity index 100%
rename from code/tests/test_div.py
rename to SimPEG/tests/test_div.py
diff --git a/code/tests/test_tensorMesh.py b/SimPEG/tests/test_tensorMesh.py
similarity index 100%
rename from code/tests/test_tensorMesh.py
rename to SimPEG/tests/test_tensorMesh.py
diff --git a/code/tests/test_utils.py b/SimPEG/tests/test_utils.py
similarity index 100%
rename from code/tests/test_utils.py
rename to SimPEG/tests/test_utils.py
diff --git a/code/utils.py b/SimPEG/utils.py
similarity index 100%
rename from code/utils.py
rename to SimPEG/utils.py
diff --git a/code/TensorView.py b/code/TensorView.py
deleted file mode 100644
index 18a7bd00..00000000
--- a/code/TensorView.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-
-class TensorView(object):
-    """
-    Provides viewing functions for TensorMesh
-
-    This class is inherited by TensorMesh
-    """
-    def __init__(self):
-        pass
-
-    def plotImage(self, I):
-
-        if self.dim == 1:
-            fig = plt.figure(1)
-            fig.clf()
-            ax = plt.subplot(111)
-            if np.size(I) == self.n[0]:
-                print 'cell-centered image'
-                xx = self.gridCC
-                ax.plot(xx[0], I, 'ro')
-            elif np.size(I) == self.n[0]+1:
-                print 'nodal image'
-                xx = self.gridN
-                ax.plot(xx[0], I, 'bs')
-
-            fig.show()
-
-    def plotGrid(self):
-        """Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions."""
-        if self.dim == 1:
-            fig = plt.figure(1)
-            fig.clf()
-            ax = plt.subplot(111)
-            xn = self.gridN
-            xc = self.gridCC
-            print xn
-            ax.hold(True)
-            ax.plot(xn, np.ones(np.shape(xn)), 'bs')
-            ax.plot(xc, np.ones(np.shape(xc)), 'ro')
-            ax.plot(xn, np.ones(np.shape(xn)), 'k--')
-            ax.grid(True)
-            ax.hold(False)
-            ax.set_xlabel('x1')
-            fig.show()
-        elif self.dim == 2:
-            fig = plt.figure(2)
-            fig.clf()
-            ax = plt.subplot(111)
-            xn = self.gridN
-            xc = self.gridCC
-            xs1 = self.gridFx
-            xs2 = self.gridFy
-
-            ax.hold(True)
-            ax.plot(xn[:, 0], xn[:, 1], 'bs')
-            ax.plot(xc[:, 0], xc[:, 1], 'ro')
-            ax.plot(xs1[:, 0], xs1[:, 1], 'g>')
-            ax.plot(xs2[:, 0], xs2[:, 1], 'g^')
-            ax.grid(True)
-            ax.hold(False)
-            ax.set_xlabel('x1')
-            ax.set_ylabel('x2')
-            fig.show()
-        elif self.dim == 3:
-            fig = plt.figure(3)
-            fig.clf()
-            ax = fig.add_subplot(111, projection='3d')
-            xn = self.gridN
-            xc = self.gridCC
-            xfs1 = self.gridFx
-            xfs2 = self.gridFy
-            xfs3 = self.gridFz
-
-            xes1 = self.gridEx
-            xes2 = self.gridEy
-            xes3 = self.gridEz
-
-            ax.hold(True)
-            ax.plot(xn[:, 0], xn[:, 1], 'bs', zs=xn[:, 2])
-            ax.plot(xc[:, 0], xc[:, 1], 'ro', zs=xc[:, 2])
-            ax.plot(xfs1[:, 0], xfs1[:, 1], 'g>', zs=xfs1[:, 2])
-            ax.plot(xfs2[:, 0], xfs2[:, 1], 'g<', zs=xfs2[:, 2])
-            ax.plot(xfs3[:, 0], xfs3[:, 1], 'g^', zs=xfs3[:, 2])
-            ax.plot(xes1[:, 0], xes1[:, 1], 'k>', zs=xes1[:, 2])
-            ax.plot(xes2[:, 0], xes2[:, 1], 'k<', zs=xes2[:, 2])
-            ax.plot(xes3[:, 0], xes3[:, 1], 'k^', zs=xes3[:, 2])
-            ax.grid(True)
-            ax.hold(False)
-            ax.set_xlabel('x1')
-            ax.set_ylabel('x2')
-            ax.set_zlabel('x3')
-            fig.show()
diff --git a/notebooks/exPlotImage2D.ipynb b/notebooks/exPlotImage2D.ipynb
new file mode 100644
index 00000000..f75e96d5
--- /dev/null
+++ b/notebooks/exPlotImage2D.ipynb
@@ -0,0 +1,201 @@
+{
+ "metadata": {
+  "name": "exPlotImage2D"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "sys.path.append('../')\n",
+      "\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "from SimPEG import TensorMesh"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Test 1D Plots\n",
+      "\n",
+      "For 1D nodal or cell-centered plots are supported.\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x0 = np.zeros(1)\n",
+      "h = np.random.rand(51)\n",
+      "mesh = TensorMesh([h],x0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "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",
+      "ph1 = mesh.plotImage(sin(xc),ax=subplot(111))\n",
+      "ph2 = mesh.plotImage(sin(xn),ax=subplot(111),imageType='N')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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nWZ/e0hKDaXVgQ7SKy8vZ/SszmqhVPP7EE0tCITAxgaUtDtPywEcyhMV08PML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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10b3f14d0>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Test 2D Plots\n",
+      "\n",
+      "Plot x and y coordinates of cell-centred points"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x0 = np.zeros(2)\n",
+      "h1 = np.linspace(.1,.5,3)\n",
+      "h2 = np.linspace(.1,.5,5)\n",
+      "mesh = TensorMesh([h1,h2],x0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig = plt.figure(1)\n",
+      "fig.clf()\n",
+      "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')            "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "<matplotlib.text.Text at 0x10b3d0f90>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10ac769d0>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Test 3D Plots\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x0 = np.zeros(3)\n",
+      "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",
+      "\n",
+      "mesh = TensorMesh([h1,h2,h3],x0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "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.gridCC[:,1],ax=ax2)\n",
+      "a3 = mesh.plotImage(mesh.gridCC[:,2],ax=ax3)\n",
+      "\n",
+      "ax1.set_title('x coordinate')\n",
+      "ax2.set_title('y coordinate')\n",
+      "ax3.set_title('z coordinate')\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,
+       "text": [
+        "<matplotlib.text.Text at 0x10cc8ab10>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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OFuyiG89pTZ2Eqk01OHIiqjpes+5mO3lcQ3+gWZVeGar7uWb2D6A6VvMJxp07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\nSzVUp+joUK5dK+bYsUwaNPC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+       "text": [
+        "<matplotlib.figure.Figure at 0x10c7671d0>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file