mesh.ipynb

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    "---\n",
    "title: Finite Volume Mesh\n",
    "---\n",
    "\n",
    "```{figure} ./images/mesh.png\n",
    ":name: fig-mesh\n",
    "\n",
    "Anatomy of a finite volume cell.\n",
    "```"
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    "To bring our continuous equations into the computer, we need to discretize the earth and represent it using a finite(!) set of numbers. In this tutorial we will explain the discretization in 2D and generalize to 3D in the notebooks. A 2D (or 3D!) mesh is used to divide up space, and we can represent functions (fields, parameters, etc.) on this mesh at a few discrete places: the nodes, edges, faces, or cell centers. For consistency between 2D and 3D we refer to faces having area and cells having volume, regardless of their dimensionality. Nodes and cell centers naturally hold scalar quantities while edges and faces have implied directionality and therefore naturally describe vectors. The conductivity, $\\sigma$, changes as a function of space, and is likely to have discontinuities (e.g. if we cross a geologic boundary). As such, we will represent the conductivity as a constant over each cell, and discretize it at the center of the cell. The electrical current density, $\\vec{j}$, will be continuous across conductivity interfaces, and therefore, we will represent it on the faces of each cell. Remember that $\\vec{j}$ is a vector; the direction of it is implied by the mesh definition (i.e. in $x$, $y$ or $z$), so we can store the array $\\bf{j}$ as *scalars* that live on the face and inherit the face's normal. When $\\vec{j}$ is defined on the faces of a cell the potential, $\\vec{\\phi}$, will be put on the cell centers (since $\\vec{j}$ is related to $\\phi$ through spatial derivatives, it allows us to approximate centered derivatives leading to a staggered, second-order discretization). "
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   "source": [
    "## Implementation"
   ]
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import discretize\n",
    "import matplotlib.pyplot as plt"
   ]
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   "source": [
    "## Create a Mesh\n",
    "\n",
    "A mesh is used to divide up space, here we will use [SimPEG's mesh class](http://docs.simpeg.xyz/content/api_core/api_Mesh.html) to define a simple tensor mesh. By \"Tensor Mesh\" we mean that the mesh can be completely defined by the tensor products of vectors in each dimension; for a 2D mesh, we require one vector describing the cell widths in the x-direction and another describing the cell widths in the y-direction. \n",
    "\n",
    "Here, we define and plot a simple 2D mesh using SimPEG's mesh class. The cell centers boundaries are shown in blue, cell centers as red dots and cell faces as green arrows (pointing in the positive x, y - directions). Cell nodes are plotted as blue squares. "
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a simple tensor mesh\n",
    "hx = np.r_[2., 1., 1., 2.]  # cell widths in the x-direction\n",
    "hy = np.r_[2., 1., 1., 1., 2.]  # cell widths in the y-direction \n",
    "mesh2D = discretize.TensorMesh([hx,hy])  # construct a simple SimPEG mesh\n",
    "mesh2D.plot_grid(nodes=True, faces=True, centers=True);  # plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "hB09wkK3yYCYTa0FbrZV",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "2Wubu5Qc9nARiP9kOI37",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This can similarly be extended to 3D (this is a simple 2-cell mesh)\n",
    "hx = np.r_[2.,  2.]  # cell widths in the x-direction\n",
    "hy = np.r_[2.]  # cell widths in the y-direction \n",
    "hz = np.r_[1.]  # cell widths in the z-direction \n",
    "mesh3D = discretize.TensorMesh([hx,hy,hz])  # construct a simple SimPEG mesh\n",
    "mesh3D.plot_grid(nodes=True, faces=True, centers=True);  # plot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ftnDOw3xDDHI7AhwNt8w",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "## Counting things on the Mesh\n",
    "\n",
    "Once we have defined the vectors necessary for construsting the mesh, it is there are a number of properties that are often useful, including keeping track of the\n",
    "- number of cells: **`mesh.nC`**\n",
    "- number of cells in each dimension: **`mesh.vnC`**\n",
    "- number of faces: **`mesh.nF`**\n",
    "- number of x-faces: **`mesh.nFx`** (and in each dimension **`mesh.vnFx`** ...)\n",
    "and the list goes on. Check out [SimPEG's mesh documentation](http://docs.simpeg.xyz/content/api_core/api_FiniteVolume.html) for more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "G9qmjOQnYIxzwf2KsU1O",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "SJHqzfqQNfHBzziCHPFR",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct a simple 2D, uniform mesh on a unit square\n",
    "mesh = discretize.TensorMesh([10, 8])\n",
    "mesh.plot_grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "lvWVd47J43QrzRQ9sItP",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "dFlM1ZvjYYsjPX4NXYoO",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "user_expressions": [
     {
      "expression": "mesh.nC",
      "result": {
       "data": {
        "text/plain": "80"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.nF",
      "result": {
       "data": {
        "text/plain": "178"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnC[0]",
      "result": {
       "data": {
        "text/plain": "10"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnC[0]",
      "result": {
       "data": {
        "text/plain": "10"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnC[1]",
      "result": {
       "data": {
        "text/plain": "8"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.nFx",
      "result": {
       "data": {
        "text/plain": "88"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnFx[0]",
      "result": {
       "data": {
        "text/plain": "11"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnFx[1]",
      "result": {
       "data": {
        "text/plain": "8"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.nFy",
      "result": {
       "data": {
        "text/plain": "90"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnFy[0]",
      "result": {
       "data": {
        "text/plain": "10"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.vnFy[1]",
      "result": {
       "data": {
        "text/plain": "9"
       },
       "metadata": {},
       "status": "ok"
      }
     }
    ]
   },
   "source": [
    "The mesh has {eval}`mesh.nC` cells and {eval}`mesh.nF` faces.\n",
    "\n",
    "In the $x$ dimension we have {eval}`mesh.vnC[0]` cells. This is because our mesh is {eval}`mesh.vnC[0]` x {eval}`mesh.vnC[1]`.\n",
    "\n",
    "Faces are vectors so the number of faces pointing in the each direction is:\n",
    "\n",
    "- In the $x$ direciton we have {eval}`mesh.nFx` faces. ({eval}`mesh.vnFx[0]` x {eval}`mesh.vnFx[1]`)\n",
    "- In the $y$ direciton we have {eval}`mesh.nFy` faces. ({eval}`mesh.vnFy[0]` x {eval}`mesh.vnFy[1]`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "YqKilx0rAN3jNZJcSHeL",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "## Simple properties of the mesh\n",
    "\n",
    "There are a few things that we will need to know about the mesh and each of it's cells, including the  \n",
    "- cell volume: **`mesh.vol`**,\n",
    "- face area: **`mesh.area`**.\n",
    "\n",
    "For consistency between 2D and 3D we refer to faces having area and cells having volume, regardless of their dimensionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "KBXPgaqIeIFC8qwzST5N",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "WYdC9NmPz5GcfQ6PTIyZ",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On a uniform mesh, not suprisingly, the cell volumes are all the same\n",
    "plt.colorbar(mesh.plot_image(mesh.cell_volumes, grid=True)[0])\n",
    "plt.title('Cell Volumes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "VlfHhMsbuBQ6RYGtTVKe",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "KxHb4NhsoonX0EiddenS",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# All cell volumes are defined by the product of the cell widths \n",
    "# All cells have the same volume on a uniform, unit cell mesh\n",
    "assert (np.all(mesh.cell_volumes == 1./mesh.vnC[0] * 1./mesh.vnC[1]))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": [
     {
      "expression": "1./mesh.vnC[0]",
      "result": {
       "data": {
        "text/plain": "0.1"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "1./mesh.vnC[1]",
      "result": {
       "data": {
        "text/plain": "0.125"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.cell_volumes[0]",
      "result": {
       "data": {
        "text/plain": "0.0125"
       },
       "metadata": {},
       "status": "ok"
      }
     }
    ]
   },
   "source": [
    "The cell volume is the product of the cell widths in the $x$ and $y$ dimensions:\\\n",
    "{eval}`1./mesh.vnC[0]` x {eval}`1./mesh.vnC[1]` = {eval}`mesh.cell_volumes[0]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "oA7f5Uusvs1wKuWwJKVg",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "xx02RHnr99AUiNYfu73e",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Similarly, all x-faces should have the same area, equal to that of the length in the y-direction\n",
    "assert np.all(mesh.face_areas[:mesh.nFx] == 1.0/mesh.shape_cells[1])  # because our domain is a unit square\n",
    "\n",
    "# and all y-faces have an \"area\" equal to the length in the x-dimension\n",
    "assert np.all(mesh.face_areas[mesh.nFx:] == 1.0/mesh.shape_cells[0])\n",
    "\n",
    "mesh.plot_grid(faces=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": [
     {
      "expression": "mesh.face_areas[0]",
      "result": {
       "data": {
        "text/plain": "0.125"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.face_areas[mesh.nFx]",
      "result": {
       "data": {
        "text/plain": "0.1"
       },
       "metadata": {},
       "status": "ok"
      }
     }
    ]
   },
   "source": [
    "The area of the x-faces is {eval}`mesh.face_areas[0]` and the area of the y-faces is {eval}`mesh.face_areas[mesh.nFx]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "QTSmVl7BRDmfL2so2o5S",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "pSWs4leGY5sKmTTsd90q",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On a non-uniform tensor mesh, the first mesh we defined, the cell volumes vary\n",
    "\n",
    "# hx = np.r_[2., 1., 1., 2.]  # cell widths in the x-direction\n",
    "# hy = np.r_[2., 1., 1., 1., 2.]  # cell widths in the y-direction \n",
    "# mesh2D = Mesh.TensorMesh([hx,hy])  # construct a simple SimPEG mesh\n",
    "\n",
    "plt.colorbar(mesh2D.plot_image(mesh2D.cell_volumes, grid=True)[0])\n",
    "plt.title('Cell Volumes');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "iYuxGz2XNeayDw5XhYBl",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "## Grids and Putting things on a mesh\n",
    "\n",
    "When storing and working with features of the mesh such as cell volumes, face areas, in a linear algebra sense, it is useful to think of them as vectors... so the way we unwrap is super important. \n",
    "\n",
    "Most importantly we want some compatibility with [Kronecker products](https://en.wikipedia.org/wiki/Vectorization_(mathematics)#Compatibility_with_Kronecker_products) as we will see later! This actually leads to us thinking about unwrapping our vectors column first. This column major ordering is inspired by linear algebra conventions which are the standard in Matlab, Fortran, Julia, but sadly not Python. To make your life a bit easier, you can use our MakeVector `mkvc` function from `SimPEG.utils`\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "zUbGxQp2QwBBlE8xkLUm",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "kv2nZ3gpFcflCBvotkbY",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from SimPEG.utils import mkvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "zxMDGVcacqEW9Munuecg",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "PJiiIJebzHM3nfSaXgUY",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row major ordering (standard python)\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "\n",
      "Column major ordering (what we want!)\n",
      "[[ 0  3  6  9]\n",
      " [ 1  4  7 10]\n",
      " [ 2  5  8 11]]\n",
      "\n",
      "We get back the expected vector using mkvc: [ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "mesh = discretize.TensorMesh([3,4])\n",
    "\n",
    "vec = np.arange(mesh.nC)\n",
    "\n",
    "row_major = vec.reshape(mesh.vnC, order='C')\n",
    "print('Row major ordering (standard python)')\n",
    "print(row_major)\n",
    "\n",
    "col_major = vec.reshape(mesh.vnC, order='F')\n",
    "print('\\nColumn major ordering (what we want!)')\n",
    "print(col_major)\n",
    "\n",
    "# mkvc unwraps using column major ordering, so we expect \n",
    "assert np.all(mkvc(col_major) == vec)\n",
    "\n",
    "print('\\nWe get back the expected vector using mkvc: {vec}'.format(vec=mkvc(col_major)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "tw93rPtVHI9QuDdriDNz",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "### Grids on the Mesh\n",
    "\n",
    "When defining where things are located, we need the spatial locations of where we are discretizing different aspects of the mesh. A SimPEG Mesh has several grids. In particular, here it is handy to look at the \n",
    "- Cell centered grid: **`mesh.gridCC`**\n",
    "- x-Face grid: **`mesh.gridFx`**\n",
    "- y-Face grid: **`mesh.gridFy`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": [
     {
      "expression": "mesh.gridCC.shape[0]",
      "result": {
       "data": {
        "text/plain": "12"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.gridCC.shape[1]",
      "result": {
       "data": {
        "text/plain": "2"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.nC",
      "result": {
       "data": {
        "text/plain": "12"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.dim",
      "result": {
       "data": {
        "text/plain": "2"
       },
       "metadata": {},
       "status": "ok"
      }
     }
    ]
   },
   "source": [
    "The cell centered grid is {eval}`mesh.gridCC.shape[0]` x {eval}`mesh.gridCC.shape[1]` since we have {eval}`mesh.nC` cells in the mesh and it is {eval}`mesh.dim` dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "HXACXgIWqDWvjIqmqOqN",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "O2AH2dpGVmS0XyPlS7F9",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The first column is the x-locations, and the second the y-locations\n",
    "\n",
    "mesh.plot_grid()\n",
    "plt.plot(mesh.gridCC[:,0], mesh.gridCC[:,1],'ro');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": [
     {
      "expression": "mesh.gridFx.shape[0]",
      "result": {
       "data": {
        "text/plain": "16"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.gridFx.shape[1]",
      "result": {
       "data": {
        "text/plain": "2"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.nFx",
      "result": {
       "data": {
        "text/plain": "16"
       },
       "metadata": {},
       "status": "ok"
      }
     },
     {
      "expression": "mesh.dim",
      "result": {
       "data": {
        "text/plain": "2"
       },
       "metadata": {},
       "status": "ok"
      }
     }
    ]
   },
   "source": [
    "Similarly, the x-Face grid is {eval}`mesh.gridFx.shape[0]` x {eval}`mesh.gridFx.shape[1]` since we have {eval}`mesh.nFx` x-faces in the mesh and it is {eval}`mesh.dim` dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "GTqLVInFdl54om9fDgJJ",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "0a9nswPq2UKzuHVPq3qw",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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06W3/Hzy4bTskkauYkKuIkK0YkK2TSsVcUW664WDTQX319Vfavn+7rl9/tep8JTqatl2OY/4bm5I2bpSmTpX27Qvd/uWXbdv5ZSGJXEWNXEWMbEWJbEUkFXNFuYmDVrVKkppdn+mA736Nf+5ylf9fOb8wOgsGpXnzpHBr0r5t/vy2/SCpa64u//VYcnUschUTshUBshW1VDoWUm7iydX2jf3vAzv0vXXf0+hnRuv13a8bHipFVFV1/ddPZ44j7d3bth9CfZOrj+qqydWxyFX3kK3jI1uxS4FjIeUmAYJOW5P/YP8HmvvqXMPTpIja2vju1wuRqzDIVVyQrTDIVreZzBXlJgHcrrZH0Y8cOFIrJq8wPE2KyM2N7369ELkKg1zFBdkKg2x1m8lc8VTweHLSJFerLu5/iUon/LuKzi2Sy+UyPVVqKCyU8vLaHogX7vqry9X28cLC5M+W6r7J1fABl+qh8UvJVWfkqnvI1vGRrdilwLGQchMHaUpTq1rldc7Vac03a/PN9+i0UzJNj5Va3G5p+fK2Zxi4XKG/LNpDX1bG347o5NhcbZ25SFk+j+mxUgu5ignZigDZiloqHQu5LNUN/bP6a8ApA1QwsECvTPujBviXqU9rAf/yOZ4pU6QNG6SzzgrdnpfXtn3KFDNzpRhyFSVyFTGyFSWyFZFUzJXxMzcrV67Uo48+qtraWl100UUqKytT4QlO8/n9fj344IN67rnnVFdXp7y8PC1evFi33357Eqduk5edpz3z9sjr9upoICiXypM+Q48zZYp03XX8tc8TIFcxIFcRIVsxIFsnlYq5Mlpu1q9fr/nz52vlypW67LLL9Mtf/lKTJ0/Wzp07dfbZZ4e9zY033qivvvpKq1ev1j/+4z/qwIEDamlpSfLk3/Kl+4x97h7L7ZauuML0FCmNXMWAXEWEbMWAbJ1UquXKaLlZtmyZZs2apdmzZ0uSysrKVF5erlWrVqm0tLTL/q+99pq2bNmi3bt36/TTT5ckDR48+ISfw+/3y+/3d7zf0NAgSQoEAgoEAnH6SqRAoCXk7XjeN0K1r21vWOPQXAUUcCXvj2H1pnU2ydQ6m8yWKWQ68RJ5LIzmvoyVm+bmZm3fvl0LFy4M2V5UVKR33nkn7G1+//vfa8SIEXrkkUf029/+VllZWbr22mv1//7f/1OfPn3C3qa0tFRLlizpsr2iokKZmfF7oJM/KLUv5+bNm+XjjGXCVVZWmh4h4Trnqry8wkiuesM6p4Jkr3MqZMsUMp04iTwWNjU1RbyvsXJTX1+vYDConJyckO05OTmqq6sLe5vdu3frrbfeUkZGhl5++WXV19eruLhYf/3rX7VmzZqwt1m0aJFKSko63m9oaFB+fr6KioqUnZ0dt6+nqblF972/WZI0fvx49c3KiNt9I1QgEFBlZaUmTpwoj8fuZ3h0ztWkSUXK9CbvR7Y3rbNJptbZZLZMIdOJl8hjYfuVl0gYT/Oxj6Z2HOe4j7BubW2Vy+XSunXr1LdvX0ltl7amTp2qJ598MuzZG5/PJ5+v67VAj8cT13B7nG9n9njS+cFJgnh/D1NRaK488niS/yPbG9Y5FSR7nVMhW6aQ6cRJ5LEwmvsy9lTwfv36ye12dzlLc+DAgS5nc9rl5ubqrLPO6ig2kjR06FA5jqN9J3oNEAAA0GsYKzder1cFBQVdrn1WVlZq7NixYW9z2WWXaf/+/Tpy5EjHtk8//VRpaWnKy8tL6LwAAKBnMPpH/EpKSvTMM89ozZo12rVrlxYsWKCamhrNmTNHUtvjZWbMmNGx//Tp03XGGWfotttu086dO7V161bde++9uv3224/7gGIAANC7GL3IOm3aNB06dEgPPvigamtrNWzYMG3atEmDBg2SJNXW1qqmpqZj/1NOOUWVlZW66667NGLECJ1xxhm68cYbtXTpUlNfAgAASDHGH0FWXFys4uLisB9bu3Ztl20XXHABT+MDAADHxWtLAQAAq1BuAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKtQbgAAgFUoNwAAwCqUGwAAYBXKDQAAsEpU5eajjz7S0qVLtXLlStXX14d8rKGhQbfffntchwMAAIhWxOWmoqJCo0aN0u9+9zs9/PDDGjp0qN54442Ojx89elS//vWvEzIkAABApCIuNw888IDuuece/fnPf9aePXt033336dprr9Vrr72WyPkAAACikh7pjp988ol++9vfSpJcLpfuvfde5eXlaerUqXrhhRc0atSohA3ZEzjOt28fbQ7K42kxN4zlAoEW+YNSU3OLPI7L9DgJ1dQcDPt2MvSmdTbJ1DqbzJYpZDrxOmep83Ex2VyOE9mn79+/v1599VUVFBSEbF+/fr1uv/12Pf7447rzzjsVDKb2D0lDQ4P69u2rw4cPKzs7O273W3/ErxFLX4/b/QEA0JO99+NxGvAPp8Tt/qI5fkd85uaSSy7RG2+80aXcTJs2Ta2trZo5c2Zs0wIAAMRRxOXmhz/8obZu3Rr2Yz/4wQ8kSU8//XR8puqB+njcHW+/9+Nxys7KMDiN3QKBgMrLKzRpUpE8Ho/pcRKqqTnYcUZw208nKNPrPskt4qc3rbNJptbZZLZMIdOJ1zlXGR5zmYq43Nxwww264YYb9Prrr2vChAldPv6DH/xADQ0NcR2uJ3F1unzbx+tWpjfipUWUAi5HPreU6U2Xx9N71jkzybnqreucbKmwzsnOlimpsNa9icvgw5qi/iN+V199te6++241Nzd3bDt48KCuueYaLVq0KK7DAQAARCvqcrN161b94Q9/0MiRI/XJJ5/oj3/8o4YNG6YjR47oo48+SsSMAAAAEYu63IwePVrV1dW6+OKLVVBQoBtuuEF33323Nm/erPz8/ETMCAAAELGYXlvqf//3f/XBBx8oLy9P6enp+p//+R81NTXFezYAAICoRV1ufv7zn2vMmDGaOHGi/vznP+uDDz7oOJPz7rvvJmJGAACAiEVdbpYvX65XXnlFTzzxhDIyMnTRRRfp/fff15QpU3TFFVckYEQAAIDIRf1cuI8//lj9+vUL2ebxePToo4/qX//1X+M2GAAAQCyiPnNzbLHpbNy4cd0aBgAAoLtiekAxAABAqqLcAAAAq1BuAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKtQbgAAgFWMl5uVK1dqyJAhysjIUEFBgaqqqiK63dtvv6309HRdcskliR0QAAD0KEbLzfr16zV//nwtXrxY1dXVKiws1OTJk1VTU3PC2x0+fFgzZszQv/zLvyRpUgAA0FMYLTfLli3TrFmzNHv2bA0dOlRlZWXKz8/XqlWrTni7O+64Q9OnT9eYMWOSNCkAAOgp0k194ubmZm3fvl0LFy4M2V5UVKR33nnnuLd79tln9dlnn+m5557T0qVLT/p5/H6//H5/x/sNDQ2SpEAgoEAgEOP0XQUCLSFvx/O+Eap9bXvDGofmKqCAy0ni5+4962ySqXU2mS1TyHTiJfJYGM19GSs39fX1CgaDysnJCdmek5Ojurq6sLf5y1/+ooULF6qqqkrp6ZGNXlpaqiVLlnTZXlFRoczMzOgHPw5/UGpfzs2bN8vnjttd4zgqKytNj5BwnXNVXl5hJFe9YZ1TQbLXORWyZQqZTpxEHgubmpoi3tdYuWnncrlC3nccp8s2SQoGg5o+fbqWLFmi888/P+L7X7RokUpKSjreb2hoUH5+voqKipSdnR374Mdoam7Rfe9vliSNHz9efbMy4nbfCBUIBFRZWamJEyfK4/GYHiehOudq0qQiZXqT9yPbm9bZJFPrbDJbppDpxEvksbD9ykskjKW5X79+crvdXc7SHDhwoMvZHElqbGzUtm3bVF1drR/96EeSpNbWVjmOo/T0dFVUVGj8+PFdbufz+eTz+bps93g8cQ23x/m2kHk86fzgJEG8v4epKDRXHnk8yf+R7Q3rnAqSvc6pkC1TyHTiJPJYGM19GUuz1+tVQUGBKisrdcMNN3Rsr6ys1HXXXddl/+zsbH388cch21auXKnNmzdrw4YNGjJkSMJnDsff4pfX7TXyuXusYFCqqpJqa6XcXKmwUHL3onPiESBXMSBXESFbMSBbJ5VquTJa1UtKSnTLLbdoxIgRGjNmjJ5++mnV1NRozpw5ktouKX355Zf6zW9+o7S0NA0bNizk9v3791dGRkaX7cmy9/BejfzVSJ3d92wtLnxAjhy51PWSGjrZuFGaN0/at+/bbXl50vLl0pQp5uZKIeQqBuQqImQrBmTrpFIxV0bLzbRp03To0CE9+OCDqq2t1bBhw7Rp0yYNGjRIklRbW3vSv3lj0sGmg/rq66908OuDun791fL6ztNpgZvlOFeaHi01bdwoTZ0qOcc8K+PLL9u2b9jALwudKFdFpkdLTeQqYmQrSmQrIql4LDT+F4qLi4u1Z88e+f1+bd++XZdffnnHx9auXas333zzuLd94IEHtGPHjsQPeRKtapUkNbs+0wHf/Rr/3OUq/79yOcf+QPRmwWDbv37CrUn7tvnz2/aDpK65uvzXY8nVschVTMhWBMhW1FLpWGi83FjF1faN/e8DO/S9dd/T6GdG6/XdrxseKkVUVYWe1j2W40h797bth1Df5OqjumpydSxy1T1k6/jIVuxS4FhIuUmAoNPW5D/Y/4HmvjrX8DQporY2vvv1QuQqDHIVF2QrDLLVbSZzRblJALer7VH0IweO1IrJKwxPkyJyc+O7Xy9ErsIgV3FBtsIgW91mMle95w8bJIOTJrladXH/S1Q64d9VdG5R2D9I2CsVFrY9w+DLL8Nfw3a52j5eWJj82VLdN7kaPuBSPTR+KbnqjFx1D9k6PrIVuxQ4FlJu4iBNaWpVq7zOuTqt+WZtvvkenXZK/F7awQpud9tTJ6dObful0PmXRXvoy8r42xGdHJurrTMXKcvHHx4LQa5iQrYiQLailkrHQi5LdUP/rP4acMoAFQws0CvT/qgB/mXq01rAv3yOZ8qUtqdOnnVW6Pa8PJ5S2Qm5ihK5ihjZihLZikgq5oozN92Ql52nPfP2yOv26mggKJfKTY+U+qZMka67jr/2eQLkKgbkKiJkKwZk66RSMVeUm27ypXd93SqchNstXXGF6SlSGrmKAbmKCNmKAdk6qVTLFZelAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKtQbgAAgFUoNwAAwCqUGwAAYBXKDQAAsArlBgAAWIVyAwAArEK5AQAAVqHcAAAAq1BuAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBV0k0PYAvH+fbto81BeTwt5oaxXCDQIn9QampukcdxmR4noZqag2HfTobetM4mmVpnk9kyhUwnXucsdT4uJpvLcUx++uRraGhQ3759dfjwYWVnZ8ftfuuP+DVi6etxuz8AAHqy9348TgP+4ZS43V80x28uSwEAAKtwWSpO+njcHW+/9+Nxys7KMDiN3QKBgMrLKzRpUpE8Ho/pcRKqqTnYcUZw208nKNPrPskt4qc3rbNJptbZZLZMIdOJ1zlXGR5zmaLcxImr0+XbPl63Mr0sbaIEXI58binTmy6Pp/esc2aSc9Vb1znZUmGdk50tU1JhrXsTl8GHNXFZCgAAWIVyAwAArEK5AQAAVqHcAAAAq1BuAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsYrzcrFy5UkOGDFFGRoYKCgpUVVV13H03btyoiRMn6swzz1R2drbGjBmj8vLyJE4LAABSndFys379es2fP1+LFy9WdXW1CgsLNXnyZNXU1ITdf+vWrZo4caI2bdqk7du368orr9Q111yj6urqJE8OAABSldFys2zZMs2aNUuzZ8/W0KFDVVZWpvz8fK1atSrs/mVlZbrvvvs0cuRInXfeeXrooYd03nnn6Q9/+EOSJwcAAKnK2MuiNjc3a/v27Vq4cGHI9qKiIr3zzjsR3Udra6saGxt1+umnH3cfv98vv9/f8X5DQ4MkKRAIKBAIxDB5eIFAS8jb8bxvhGpf296wxqG5CijgcpL4uXvPOptkap1NZssUMp14iTwWRnNfxspNfX29gsGgcnJyQrbn5OSorq4uovt4/PHH9fXXX+vGG2887j6lpaVasmRJl+0VFRXKzMyMbugT8Ael9uXcvHmzfO643TWOo7Ky0vQICdc5V+XlFUZy1RvWORUke51TIVumkOnESeSxsKmpKeJ9jZWbdi6XK+R9x3G6bAvnhRde0AMPPKD//M//VP/+/Y+736JFi1RSUtLxfkNDg/Lz81VUVKTs7OzYBz9GU3OL7nt/syRp/Pjx6puVEbf7RqhAIKDKykpNnDhRHo/H9DgJ1TlXkyYVKdObvB/Z3rTOJplaZ5PZMoVMJ14ij4XtV14iYSzN/fr1k9vt7nKW5sCBA13O5hxr/fr1mjVrll588UVNmDDhhPv6fD75fL4u2z0eT1zD7XG+LWQeTzo/OEkQ7+9hKgrNlUceT/J/ZHvDOqeCZK9zKmTLFDKdOIk8FkZzX8YeUOz1elVQUNDl9GBlZaXGjh173Nu98MILuvXWW/X888/r6quvTvSYAACghzFa1UtKSnTLLbdoxIgRGjNmjJ5++mnV1NRozpw5ktouKX355Zf6zW9+I6mt2MyYMUPLly/Xd7/73Y6zPn369FHfvn2NfR0AACB1GC0306ZN06FDh/Tggw+qtrZWw4YN06ZNmzRo0CBJUm1tbcjfvPnlL3+plpYW3Xnnnbrzzjs7ts+cOVNr165N9vgAACAFGb/IWlxcrOLi4rAfO7awvPnmm4kfCAAA9GjGX34BAAAgnig3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFjF+KuC93T+Fr+8bq/pMXqWYFCqqpJqa6XcXKmwUHK7TU+VUshVDMhVRMhWDMjWSaVarjhz0w17D+/VoLJBGv3MaFXurpAjx/RIqW/jRmnwYOnKK6Xp09v+P3hw23ZIIlcxIVcRIVsxIFsnlYq5otx0w8Gmg/rq66+0ff92Xb/+atX5SnQ0bbscx/w3NiVt3ChNnSrt2xe6/csv27bzy0ISuYoauYoY2YoS2YpIKuaKchMHrWqVJDW7PtMB3/0a/9zlKv+/cn5hdBYMSvPmSeHWpH3b/Plt+0FS11xd/uux5OpY5ComZCsCZCtqqXQspNzEk6vtG/vfB3boe+u+p9HPjNbru183PFSKqKrq+q+fzhxH2ru3bT+E+iZXH9VVk6tjkavuIVvHR7ZilwLHQspNAgSdtib/wf4PNPfVuYanSRG1tfHdrxciV2GQq7ggW2GQrW4zmSvKTQK4XW2Poh85cKRWTF5heJoUkZsb3/16IXIVBrmKC7IVBtnqNpO54qng8eSkSa5WXdz/EpVO+HcVnVskl8tleqrUUFgo5eW1PRAv3PVXl6vt44WFyZ8t1X2Tq+EDLtVD45eSq87IVfeQreMjW7FLgWMh5SYO0pSmVrXK65yr05pv1uab79Fpp2SaHiu1uN3S8uVtzzBwuUJ/WbSHvqyMvx3RybG52jpzkbJ8HtNjpRZyFROyFQGyFbVUOhZyWaob+mf114BTBqhgYIFemfZHDfAvU5/WAv7lczxTpkgbNkhnnRW6PS+vbfuUKWbmSjHkKkrkKmJkK0pkKyKpmCvO3HRDXnae9szbI6/bq6OBoFwqNz1S6psyRbruOv7a5wmQqxiQq4iQrRiQrZNKxVxRbrrJl+4zPULP43ZLV1xheoqURq5iQK4iQrZiQLZOKtVyxWUpAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKtQbgAAgFUoNwAAwCqUGwAAYBXKDQAAsArlBgAAWIVyAwAArEK5AQAAVqHcAAAAq1BuAACAVSg3AADAKpQbAABgFcoNAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVjJeblStXasiQIcrIyFBBQYGqqqpOuP+WLVtUUFCgjIwMnXPOOXrqqaeSNCkAAOgJjJab9evXa/78+Vq8eLGqq6tVWFioyZMnq6amJuz+n3/+ua666ioVFhaqurpaP/nJTzR37ly99NJLSZ4cAACkqnSTn3zZsmWaNWuWZs+eLUkqKytTeXm5Vq1apdLS0i77P/XUUzr77LNVVlYmSRo6dKi2bdumxx57TN///veTOXoXjvPt20ebg/J4WswNY7lAoEX+oNTU3CKP4zI9TkI1NQfDvp0MvWmdTTK1ziazZQqZTrzOWep8XEw2l+OY+fTNzc3KzMzUiy++qBtuuKFj+7x587Rjxw5t2bKly20uv/xyXXrppVq+fHnHtpdfflk33nijmpqa5PF4utzG7/fL7/d3vN/Q0KD8/HzV19crOzs7bl/PoSN+fffhrjMDANAbVd19mQaclhW3+2toaFC/fv10+PDhkx6/jZ25qa+vVzAYVE5OTsj2nJwc1dXVhb1NXV1d2P1bWlpUX1+v3NzcLrcpLS3VkiVLumyvqKhQZmZmN76CUI0ByfCJMAAAUsaWLVt0atdzDjFramqKeF/jR2OXK/TUoOM4XbadbP9w29stWrRIJSUlHe+3n7kpKiqK65kbx3E0blyTtmzZokkTrpQ3zFkkxEcg0KLNmzdr/Pjx8niMRzihHEf6e6DtNG+Gx60T/GjEXW9aZ5NMrbPJbJlCphPPcaTGo35t2bJFN0yeIK/XG7f7bmhoiHhfY9/dfv36ye12dzlLc+DAgS5nZ9oNGDAg7P7p6ek644wzwt7G5/PJ5/N12e7xeMJexuqOAae5dKpHOi2rT9zvG98KBALyuaW+WRmscwKxzsnBOicPa50cWb50neqRvF5vXNc5mvsy9mwpr9ergoICVVZWhmyvrKzU2LFjw95mzJgxXfavqKjQiBEjCCoAAJBk+KngJSUleuaZZ7RmzRrt2rVLCxYsUE1NjebMmSOp7ZLSjBkzOvafM2eOvvjiC5WUlGjXrl1as2aNVq9erXvuucfUlwAAAFKM0YuO06ZN06FDh/Tggw+qtrZWw4YN06ZNmzRo0CBJUm1tbcjfvBkyZIg2bdqkBQsW6Mknn9TAgQO1YsUK408DBwAAqcP4I6qKi4tVXFwc9mNr167tsm3cuHH68MMPEzwVAADoqYy//AIAAEA8UW4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKsY/wvFyeY4jqToXjo9UoFAQE1NTWpoaOCFPBOIdU4O1jk5WOfkYa2TI1Hr3H7cbj+On0ivKzeNjY2SpPz8fMOTAACAaDU2Nqpv374n3MflRFKBLNLa2qr9+/fr1FNPlcvliut9NzQ0KD8/X3v37lV2dnZc7xvfYp2Tg3VODtY5eVjr5EjUOjuOo8bGRg0cOFBpaSd+VE2vO3OTlpamvLy8hH6O7OxsfnCSgHVODtY5OVjn5GGtkyMR63yyMzbteEAxAACwCuUGAABYhXITRz6fT/fff798Pp/pUazGOicH65wcrHPysNbJkQrr3OseUAwAAOzGmRsAAGAVyg0AALAK5QYAAFiFcgMAAKxCuYnSypUrNWTIEGVkZKigoEBVVVUn3H/Lli0qKChQRkaGzjnnHD311FNJmrRni2adN27cqIkTJ+rMM89Udna2xowZo/Ly8iRO23NFm+d2b7/9ttLT03XJJZckdkBLRLvOfr9fixcv1qBBg+Tz+XTuuedqzZo1SZq254p2ndetW6fhw4crMzNTubm5uu2223To0KEkTdszbd26Vddcc40GDhwol8ulV1555aS3MXIcdBCx3/3ud47H43F+9atfOTt37nTmzZvnZGVlOV988UXY/Xfv3u1kZmY68+bNc3bu3On86le/cjwej7Nhw4YkT96zRLvO8+bNcx5++GHn/fffdz799FNn0aJFjsfjcT788MMkT96zRLvO7f72t78555xzjlNUVOQMHz48OcP2YLGs87XXXuuMHj3aqaysdD7//HPnT3/6k/P2228nceqeJ9p1rqqqctLS0pzly5c7u3fvdqqqqpyLLrrIuf7665M8ec+yadMmZ/Hixc5LL73kSHJefvnlE+5v6jhIuYnCqFGjnDlz5oRsu+CCC5yFCxeG3f++++5zLrjggpBtd9xxh/Pd7343YTPaINp1DufCCy90lixZEu/RrBLrOk+bNs356U9/6tx///2UmwhEu86vvvqq07dvX+fQoUPJGM8a0a7zo48+6pxzzjkh21asWOHk5eUlbEbbRFJuTB0HuSwVoebmZm3fvl1FRUUh24uKivTOO++Evc27777bZf9JkyZp27ZtCgQCCZu1J4tlnY/V2tqqxsZGnX766YkY0QqxrvOzzz6rzz77TPfff3+iR7RCLOv8+9//XiNGjNAjjzyis846S+eff77uueceHT16NBkj90ixrPPYsWO1b98+bdq0SY7j6KuvvtKGDRt09dVXJ2PkXsPUcbDXvXBmrOrr6xUMBpWTkxOyPScnR3V1dWFvU1dXF3b/lpYW1dfXKzc3N2Hz9lSxrPOxHn/8cX399de68cYbEzGiFWJZ57/85S9auHChqqqqlJ7Or45IxLLOu3fv1ltvvaWMjAy9/PLLqq+vV3Fxsf7617/yuJvjiGWdx44dq3Xr1mnatGn6+9//rpaWFl177bV64oknkjFyr2HqOMiZmyi5XK6Q9x3H6bLtZPuH245Q0a5zuxdeeEEPPPCA1q9fr/79+ydqPGtEus7BYFDTp0/XkiVLdP755ydrPGtEk+fW1la5XC6tW7dOo0aN0lVXXaVly5Zp7dq1nL05iWjWeefOnZo7d65+9rOfafv27Xrttdf0+eefa86cOckYtVcxcRzkn18R6tevn9xud5d/BRw4cKBLK203YMCAsPunp6frjDPOSNisPVks69xu/fr1mjVrll588UVNmDAhkWP2eNGuc2Njo7Zt26bq6mr96Ec/ktR2EHYcR+np6aqoqND48eOTMntPEkuec3NzddZZZ6lv374d24YOHSrHcbRv3z6dd955CZ25J4plnUtLS3XZZZfp3nvvlSRdfPHFysrKUmFhoZYuXcqZ9TgxdRzkzE2EvF6vCgoKVFlZGbK9srJSY8eODXubMWPGdNm/oqJCI0aMkMfjSdisPVks6yy1nbG59dZb9fzzz3PNPALRrnN2drY+/vhj7dixo+O/OXPm6Dvf+Y527Nih0aNHJ2v0HiWWPF922WXav3+/jhw50rHt008/VVpamvLy8hI6b08Vyzo3NTUpLS30EOh2uyV9e2YB3WfsOJjQhytbpv2phqtXr3Z27tzpzJ8/38nKynL27NnjOI7jLFy40Lnllls69m9/CtyCBQucnTt3OqtXr+ap4BGIdp2ff/55Jz093XnyySed2trajv/+9re/mfoSeoRo1/lYPFsqMtGuc2Njo5OXl+dMnTrV+eSTT5wtW7Y45513njN79mxTX0KPEO06P/vss056erqzcuVK57PPPnPeeustZ8SIEc6oUaNMfQk9QmNjo1NdXe1UV1c7kpxly5Y51dXVHU+5T5XjIOUmSk8++aQzaNAgx+v1Ov/0T//kbNmypeNjM2fOdMaNGxey/5tvvulceumljtfrdQYPHuysWrUqyRP3TNGs87hx4xxJXf6bOXNm8gfvYaLNc2eUm8hFu867du1yJkyY4PTp08fJy8tzSkpKnKampiRP3fNEu84rVqxwLrzwQqdPnz5Obm6uc9NNNzn79u1L8tQ9yxtvvHHC37epchx0OQ7n3wAAgD14zA0AALAK5QYAAFiFcgMAAKxCuQEAAFah3AAAAKtQbgAAgFUoNwAAwCqUGwAAYBXKDQAAsArlBoBVamtrNX36dH3nO99RWlqa5s+fb3okAElGuQFgFb/frzPPPFOLFy/W8OHDTY8DwADKDYAe5eDBgxowYIAeeuihjm1/+tOf5PV6VVFRocGDB2v58uWaMWOG+vbta3BSAKakmx4AAKJx5plnas2aNbr++utVVFSkCy64QDfffLOKi4tVVFRkejwAKYByA6DHueqqq/Rv//ZvuummmzRy5EhlZGTo5z//uemxAKQILksB6JEee+wxtbS06D/+4z+0bt06ZWRkmB4JQIqg3ADokXbv3q39+/ertbVVX3zxhelxAKQQLksB6HGam5t10003adq0abrgggs0a9Ysffzxx8rJyTE9GoAUQLkB0OMsXrxYhw8f1ooVK3TKKafo1Vdf1axZs/Rf//VfkqQdO3ZIko4cOaKDBw9qx44d8nq9uvDCCw1ODSBZXI7jOKaHAIBIvfnmm5o4caLeeOMN/fM//7MkqaamRhdffLFKS0v1wx/+UC6Xq8vtBg0apD179iR5WgAmUG4AAIBVeEAxAACwCuUGAABYhXIDAACsQrkBAABWodwAAACrUG4AAIBVKDcAAMAqlBsAAGAVyg0AALAK5QYAAFiFcgMAAKzy/wEGc7fiLrd3LAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh.plot_grid()\n",
    "plt.plot(mesh.gridCC[:,0], mesh.gridCC[:,1],'ro')\n",
    "plt.plot(mesh.gridFx[:,0], mesh.gridFx[:,1],'g>');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "6c0LSWMZUTwCuNv5WE5x",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "## Putting a Model on a Mesh\n",
    "\n",
    "In [index.ipynb](index.ipynb), we constructed a model of a block in a whole-space, here we revisit it having defined the elements of the mesh we are using. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "iooxa": {
     "id": {
      "block": "f3i0WEJ4xANDPhosKmtn",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     },
     "outputId": {
      "block": "RA8M46spaOZUYPnvnqPX",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e99NyfVW3bl399NNPHn2TJ0/WlClTfN5X+XuLKvre3b9/f5X3c0ElGpL3S67KZ34qq05MmjRJ6enp7s8FBQVq0aKFeqqfIuVdAQEAoFypTmmTVvv8agJflJSU6HBemfbvaKWYev5XTQqLXGqZ/G/l5OQoJibG3e9PNeNMFX3v+jIjcEElGk2bNvV6M2ReXp4iIyO9XmtdrrKSUaRqKtJBogEAOIdfVjFWx1R73XoO1a3n/3FcOj02JibGI9HwV9OmTSWdrmycOWOQl5fnVeU4lwvq7a3dunVTVlaWR9/atWuVkpJS4foMAAAuFGXGFXALpqSkJDVt2tTje7ekpETr169X9+7dq7yfkFY0fvrpJ3377bfuz/v27dPOnTvVoEEDtWjRQpMmTdKhQ4f02muvSTp9h8msWbOUnp6u3/72t9qyZYvmz5+vZcuWheoUAAAICpeMXPL/RlB/xp7ve3j8+PGaPn26WrdurdatW2v69OmqXbu2hg4dWuVjhDTR2L59u2688Ub35/K1FMOHD9eiRYuUm5urAwcOuH+elJSk1atXa8KECXr55ZeVkJCgF198UXfccUe1xw4AwIXufN/Df/jDH3TixAmlpaXp6NGj6tq1q9auXevTmhXbPEejuhQWFio2NlY3aCBrNAAA51RqTmmd/q6CgoKgrHuoSPn30vdfNQ94MWhCm4OWxuqPC2oxKAAA4arMGJUF8G//QMZa6YJaDAoAAC4sVDQAALCBUCwGrQ4kGgAA2IBLRmVhmGgwdQIAACxDRQMAABtg6gQAAFiGu04AAAB8REUDAAAbcP3SAhlvRyQaAADYQFmAd50EMtZKJBoAANhAmTndAhlvR6zRAAAAlqGiAQCADbBGAwAAWMYlh8rkCGi8HTF1AgAALENFAwAAG3CZ0y2Q8XZEogEAgA2UBTh1EshYKzF1AgAALENFAwAAGwjXigaJBgAANuAyDrlMAHedBDDWSkydAAAAy1DRAADABpg6AQAAlilTDZUFMNFQFsRYgolEAwAAGzABrtEwrNEAAAAXGyoaAADYAGs0AACAZcpMDZWZANZo2PQR5EydAAAAy1DRAADABlxyyBXAv/9dsmdJg0QDAAAbCNc1GkydAABwkSoqKtL48ePVsmVL1apVS927d9e2bduCegwqGgAA2EDgi0F9nzoZPXq0vvjiC73++utKSEjQ4sWLdcstt2j37t1q1qyZ37GciYoGAAA2cHqNRmDNFydOnNDbb7+t5557Ttdff70uv/xyTZkyRUlJScrMzAzaeVHRAAAgjBQWFnp8djqdcjqdXtuVlpaqrKxM0dHRHv21atXSpk2bghYPFQ0AAGzA9cu7Tvxt5XesJCYmKjY21t0yMjIqPF69evXUrVs3Pfnkk/r+++9VVlamxYsXa+vWrcrNzQ3aeVHRAADABoK1RiMnJ0cxMTHu/oqqGeVef/113XfffWrWrJkiIiJ0zTXXaOjQofr000/9juNsJBoAANiA64yqhH/jTycaMTExHonGuVx22WVav369jh8/rsLCQsXHx2vw4MFKSkryO46zMXUCAMBFrk6dOoqPj9fRo0e1Zs0aDRw4MGj7pqIBAIANlBmHygJ41bs/Y9esWSNjjNq0aaNvv/1WEydOVJs2bTRy5Ei/4zgbiQYAADZQvqjT//G+P0ejoKBAkyZN0sGDB9WgQQPdcccdevrpp1WzZk2/4zgbiQYAABepu+++W3fffbelxyDRAADABlymhlwB3HXi8uPJoNWBRAMAABsIxdRJdeCuEwAAYBkqGgAA2IBL/t05cuZ4OyLRAADABgJ/YJc9JynsGRUAAAgLVDQAALCBwN91Ys/aAYkGAAA24JJDLgWyRsP/sVYi0QAAwAbCtaJhz6gAAEBYoKIBAIANBP7ALnvWDkg0AACwAZdxyBXIczQCGGsle6Y/AAAgLFDRAADABlwBTp3Y9YFdJBoAANhA4G9vtWeiYc+oAABAWKCiAQCADZTJobIAHroVyFgrkWgAAGADTJ0AAAD4iIoGAAA2UKbApj/KghdKUJFoAABgA+E6dUKiAQCADfBSNYvMnj1bSUlJio6OVnJysjZu3HjO7ZcsWaLOnTurdu3aio+P18iRI3XkyJFqihYAAPgipInG8uXLNX78eD3++OPKzs5Wr1691LdvXx04cKDC7Tdt2qRhw4Zp1KhR+vLLL/Xmm29q27ZtGj16dDVHDgBAcBk55AqgGZve3hrSRGPGjBkaNWqURo8erXbt2mnmzJlKTExUZmZmhdt//PHHatWqlcaNG6ekpCT17NlT999/v7Zv317NkQMAEFzlUyeBNDsKWVQlJSXasWOHUlNTPfpTU1O1efPmCsd0795dBw8e1OrVq2WM0Q8//KC33npLt912W6XHKS4uVmFhoUcDAADVI2SJRn5+vsrKyhQXF+fRHxcXp8OHD1c4pnv37lqyZIkGDx6sqKgoNW3aVPXr19dLL71U6XEyMjIUGxvrbomJiUE9DwAAgqH8NfGBNDsKeZ3F4fC8MMYYr75yu3fv1rhx4/TEE09ox44dev/997Vv3z6NGTOm0v1PmjRJBQUF7paTkxPU+AEACIayX97eGkizo5Dd3tqoUSNFRER4VS/y8vK8qhzlMjIy1KNHD02cOFGS1KlTJ9WpU0e9evXSU089pfj4eK8xTqdTTqcz+CcAAADOK2TpT1RUlJKTk5WVleXRn5WVpe7du1c45ueff1aNGp4hR0RESDpdCQEA4ELF1IkF0tPT9be//U0LFizQnj17NGHCBB04cMA9FTJp0iQNGzbMvf2AAQO0YsUKZWZmau/evfroo480btw4denSRQkJCaE6DQAAAuZSjYCbL0pLS/XHP/5RSUlJqlWrli699FJNmzZNLpcrqOcV0ieDDh48WEeOHNG0adOUm5urjh07avXq1WrZsqUkKTc31+OZGiNGjFBRUZFmzZql3//+96pfv75uuukmPfvss6E6BQAALkjPPvusXnnlFb366qvq0KGDtm/frpEjRyo2NlYPPfRQ0I7jMBfZnENhYaFiY2N1gwYq0lEz1OEAAGys1JzSOv1dBQUFiomJseQY5d9LD2z8tZx1/f9eKv7plDJ7rahyrP3791dcXJzmz5/v7rvjjjtUu3Ztvf76637HcTZ7LlEFAOAiE6w1Gmc/O6q4uLjC4/Xs2VMffvihvv76a0nSrl27tGnTJvXr1y+o58VL1QAAsAET4NtbzS9jz35e1OTJkzVlyhSv7R955BEVFBSobdu2ioiIUFlZmZ5++mkNGTLE7xgqQqIBAEAYycnJ8Zg6qewRD8uXL9fixYu1dOlSdejQQTt37tT48eOVkJCg4cOHBy0eEg0AAGygTA6VBfBitPKxMTExVVqjMXHiRD366KO65557JElXXnml9u/fr4yMDBINAADCjcsooGdhuHy8taOyZ1OF1e2tAAAgNAYMGKCnn35aLVq0UIcOHZSdna0ZM2bovvvuC+pxSDQAALABV4CLQX0d+9JLL+lPf/qT0tLSlJeXp4SEBN1///164okn/I6hIiQaAADYgEsOuQJYo+Hr2Hr16mnmzJmaOXOm38esCp6jAQAALENFAwAAGygzDpUFsBg0kLFWItEAAMAGqnuNRnWxZ1QAACAsUNEAAMAGXHIE9hyNABaSWolEAwAAGzAB3nViSDQAAEBlznwDq7/j7Yg1GgAAwDJUNAAAsIFwveuERAMAABtg6gQAAMBHVDQAALCB6n7XSXUh0QAAwAaYOgEAAPARFQ0AAGwgXCsaJBoAANhAuCYaTJ0AAADLUNEAAMAGwrWiQaIBAIANGAV2i6oJXihBRaIBAIANhGtFgzUaAADAMlQ0AACwgXCtaJBoAABgA+GaaDB1AgAALENFAwAAGwjXigaJBgAANmCMQyaAZCGQsVZi6gQAAFiGigYAADbgkiOgB3YFMtZKJBoAANhAuK7RYOoEAABYhkQDAAAbKF8MGkjzRatWreRwOLza2LFjg3peTJ0AAGAD1T11sm3bNpWVlbk/f/HFF+rTp4/uuusuv2OoCIkGAAA2UN23tzZu3Njj8zPPPKPLLrtMvXv39juGipBoAAAQRgoLCz0+O51OOZ3Oc44pKSnR4sWLlZ6eLocjuItKWaMBAIANmF+mTvxt5RWNxMRExcbGultGRsZ5j/3OO+/o2LFjGjFiRNDPi4oGAAA2YCQZE9h4ScrJyVFMTIy7/3zVDEmaP3+++vbtq4SEBP8DqASJBgAAYSQmJsYj0Tif/fv364MPPtCKFSssiYdEAwAAG3DJIUcIngy6cOFCNWnSRLfddpvfxz4XEg0AAGwgFC9Vc7lcWrhwoYYPH67ISGtSAhaDAgBwkfrggw904MAB3XfffZYdg4oGAAA24DIOOar5XSepqakygaxArQISDQAAbMCYAO86sTZf8BtTJwAAwDJUNAAAsIFQLAatDiQaAADYAIkGAACwTCgWg1YH1mgAAADLUNEAAMAGwvWuExINAABs4HSiEcgajSAGE0RMnQAAAMtQ0QAAwAa46wQAAFjG/NICGW9HTJ0AAADLUNEAAMAGmDoBAADWCdO5k5BPncyePVtJSUmKjo5WcnKyNm7ceM7ti4uL9fjjj6tly5ZyOp267LLLtGDBgmqKFgAAi/xS0fC3iYqGt+XLl2v8+PGaPXu2evTooTlz5qhv377avXu3WrRoUeGYu+++Wz/88IPmz5+vyy+/XHl5eSotLa3myAEAQFWENNGYMWOGRo0apdGjR0uSZs6cqTVr1igzM1MZGRle27///vtav3699u7dqwYNGkiSWrVqVZ0hAwBgiXB9MmjIpk5KSkq0Y8cOpaamevSnpqZq8+bNFY5ZtWqVUlJS9Nxzz6lZs2a64oor9PDDD+vEiROVHqe4uFiFhYUeDQAAuwlk2iTQhaRWCllFIz8/X2VlZYqLi/Poj4uL0+HDhyscs3fvXm3atEnR0dFauXKl8vPzlZaWph9//LHSdRoZGRmaOnVq0OMH/LHm+12hDuGCdWtC51CHAMAPIV8M6nB4ZmDGGK++ci6XSw6HQ0uWLFGXLl3Ur18/zZgxQ4sWLaq0qjFp0iQVFBS4W05OTtDPAQCAgJUv6Ayk2VDIKhqNGjVSRESEV/UiLy/Pq8pRLj4+Xs2aNVNsbKy7r127djLG6ODBg2rdurXXGKfTKafTGdzgAQAIMtZoBFlUVJSSk5OVlZXl0Z+VlaXu3btXOKZHjx76/vvv9dNPP7n7vv76a9WoUUPNmze3NF4AAOC7kE6dpKen629/+5sWLFigPXv2aMKECTpw4IDGjBkj6fS0x7Bhw9zbDx06VA0bNtTIkSO1e/dubdiwQRMnTtR9992nWrVqheo0AAAInAlCs6GQ3t46ePBgHTlyRNOmTVNubq46duyo1atXq2XLlpKk3NxcHThwwL193bp1lZWVpd/97ndKSUlRw4YNdffdd+upp54K1SkAABAUPILcImlpaUpLS6vwZ4sWLfLqa9u2rdd0CwAAsKeQJxoAAOAXNp3+CASJBgAANsDUCQAAsA5vbwUAAPANFQ0AAGzB8UsLZLz9UNEAAMAOQvAcjUOHDum///u/1bBhQ9WuXVtXXXWVduzYEfi5nIGKBgAAF6GjR4+qR48euvHGG/Xee++pSZMm+u6771S/fv2gHodEAwAAO6jmxaDPPvusEhMTtXDhQndfq1atAgigYkydAABgB0F6e2thYaFHKy4urvBwq1atUkpKiu666y41adJEV199tebNmxf00yLRAAAgjCQmJio2NtbdMjIyKtxu7969yszMVOvWrbVmzRqNGTNG48aN02uvvRbUeJg6AQDABoL1mvicnBzFxMS4+51OZ4Xbu1wupaSkaPr06ZKkq6++Wl9++aUyMzM9XmgaKCoaAADYQZDuOomJifFolSUa8fHxat++vUdfu3btPF5mGgwkGgAAXIR69Oihr776yqPv66+/dr9BPVhINAAAsIMgLQatqgkTJujjjz/W9OnT9e2332rp0qWaO3euxo4dG9TTItEAAMAGHCbw5otrr71WK1eu1LJly9SxY0c9+eSTmjlzpn7zm98E9bxYDAoAgB2E4KVq/fv3V//+/QM46PlR0QAAAJahogEAgB34sc7Ca7wNkWgAAGAHIZg6qQ5MnQAAAMtQ0QAAwA7CtKJBogEAgB2EaaLB1AkAALAMFQ0AAOyAu04AAIBV/Hm659nj7YipEwAAYBkqGgAA2AGLQU8bMWKENmzYYEUsAAAgzPicaBQVFSk1NVWtW7fW9OnTdejQISviAgDgouJQgG9vDfUJVMLnROPtt9/WoUOH9OCDD+rNN99Uq1at1LdvX7311ls6deqUFTECAIALlF+LQRs2bKiHHnpI2dnZ+uSTT3T55Zfr3nvvVUJCgiZMmKBvvvkm2HECABDeym9vDaTZUEB3neTm5mrt2rVau3atIiIi1K9fP3355Zdq3769/vrXvwYrRgAAwp8JQrMhnxONU6dO6e2331b//v3VsmVLvfnmm5owYYJyc3P16quvau3atXr99dc1bdo0K+IFAAAXEJ9vb42Pj5fL5dKQIUP0ySef6KqrrvLa5tZbb1X9+vWDEB4AABeJML291edE469//avuuusuRUdHV7rNJZdcon379gUUGAAAF5NwfTKoz4nGvffea0UcAAAgDPFkUAAA7ICpEwAAYJkwTTR4qRoAALAMFQ0AAGyAxaAAAMA6gT7d06ZPBiXRAADADlijAQAA4BsqGgAA2ABrNAAAgHWYOgEAAPANiQYAAHZg/m/6xJ/ma0VjypQpcjgcHq1p06ZBPy2mTgAAsIMQTJ106NBBH3zwgftzREREAAFUjEQDAICLVGRkpCVVjDMxdQIAgB2YIDRJhYWFHq24uLjSQ37zzTdKSEhQUlKS7rnnHu3duzfop0WiAQCADQSyPuPMW2MTExMVGxvrbhkZGRUer2vXrnrttde0Zs0azZs3T4cPH1b37t115MiRoJ4XUycAAISRnJwcxcTEuD87nc4Kt+vbt6/7/7/yyivVrVs3XXbZZXr11VeVnp4etHhINAAACCMxMTEeiUZV1alTR1deeaW++eaboMbD1AkAAHYQpDUa/iouLtaePXsUHx8f2I7OQqIBAIANBGuNRlU9/PDDWr9+vfbt26etW7fqzjvvVGFhoYYPHx7U82LqBACAi9DBgwc1ZMgQ5efnq3Hjxrruuuv08ccfq2XLlkE9DokGAAB2UY3vK3njjTeq5TgkGgAA2AEvVQMAAPANFQ0AAGzAnwWdZ4+3IxINAADsgKkTAAAA31DRAADABsJ16iTkFY3Zs2crKSlJ0dHRSk5O1saNG6s07qOPPlJkZKSuuuoqawMEAKA6hPjJoFYJaaKxfPlyjR8/Xo8//riys7PVq1cv9e3bVwcOHDjnuIKCAg0bNkw333xzNUUKAAD8EdJEY8aMGRo1apRGjx6tdu3aaebMmUpMTFRmZuY5x91///0aOnSounXrVk2RAgBgMSoawVVSUqIdO3YoNTXVoz81NVWbN2+udNzChQv13XffafLkyVU6TnFxsQoLCz0aAAB2U93vOqkuIVsMmp+fr7KyMsXFxXn0x8XF6fDhwxWO+eabb/Too49q48aNioysWugZGRmaOnVqwPECwXBrQudQhwDArri91RoOh8PjszHGq0+SysrKNHToUE2dOlVXXHFFlfc/adIkFRQUuFtOTk7AMQMAgKoJWUWjUaNGioiI8Kpe5OXleVU5JKmoqEjbt29Xdna2HnzwQUmSy+WSMUaRkZFau3atbrrpJq9xTqdTTqfTmpMAACBYwrSiEbJEIyoqSsnJycrKytLtt9/u7s/KytLAgQO9to+JidHnn3/u0Td79mz985//1FtvvaWkpCTLYwYAwCrh+hyNkD6wKz09Xffee69SUlLUrVs3zZ07VwcOHNCYMWMknZ72OHTokF577TXVqFFDHTt29BjfpEkTRUdHe/UDAAB7CGmiMXjwYB05ckTTpk1Tbm6uOnbsqNWrV6tly5aSpNzc3PM+UwMAgLAQplMnDmOMTUOzRmFhoWJjY3WDBirSUTPU4QAAbKzUnNI6/V0FBQWKiYmx5Bjl30vtHpyuCGe03/spKz6pPbMeszRWf4T8rhMAABC+eKkaAAB2EKZTJyQaAADYQZgmGkydAAAAy1DRAADABhy/tEDG2xGJBgAAdhCmUyckGgAA2EC4PhmUNRoAAMAyVDQAALADpk4AAIClbJosBIKpEwAAYBkqGgAA2EC4LgYl0QAAwA7CdI0GUycAAEAZGRlyOBwaP358UPdLRQMAABsI5dTJtm3bNHfuXHXq1Mn/nVSCigYAAHZggtD88NNPP+k3v/mN5s2bp0suuSSwc6gAiQYAABexsWPH6rbbbtMtt9xiyf6ZOgEAwAaCNXVSWFjo0e90OuV0Oisc88Ybb+jTTz/Vtm3b/D/weVDRAADADoI0dZKYmKjY2Fh3y8jIqPBwOTk5euihh7R48WJFR0dbdlpUNAAAsIMg3d6ak5OjmJgYd3dl1YwdO3YoLy9PycnJ7r6ysjJt2LBBs2bNUnFxsSIiIgII6DQSDQAAwkhMTIxHolGZm2++WZ9//rlH38iRI9W2bVs98sgjQUkyJBINAABsobpvb61Xr546duzo0VenTh01bNjQqz8QJBoAANhBmD4ZlEQDAABIktatWxf0fZJoAABgAw5j5DD+lyUCGWslEg0AAOwgTKdOeI4GAACwDBUNAABsIJQvVbMSiQYAAHbA1AkAAIBvqGgAAGADTJ0AAADrhOnUCYkGAAA2EK4VDdZoAAAAy1DRAADADpg6AQAAVrLr9EcgmDoBAACWoaIBAIAdGHO6BTLehkg0AACwAe46AQAA8BEVDQAA7IC7TgAAgFUcrtMtkPF2xNQJAACwDBUNAADsgKkTAABglXC964REAwAAOwjT52iwRgMAAFiGigYAADbA1AkAALBOmC4GZeoEAABYhooGAAA2wNQJAACwDnedAAAA+IaKBgAANhCuUydUNAAAsAMThOaDzMxMderUSTExMYqJiVG3bt303nvvBedczkCiAQDARah58+Z65plntH37dm3fvl033XSTBg4cqC+//DKox2HqBAAAG6juqZMBAwZ4fH766aeVmZmpjz/+WB06dPA/kLOQaAAAYAcuc7oFMt5PZWVlevPNN3X8+HF169bN/xgqQKIBAIAdBOnJoIWFhR7dTqdTTqezwiGff/65unXrppMnT6pu3bpauXKl2rdvH0AQ3lijAQBAGElMTFRsbKy7ZWRkVLptmzZttHPnTn388cd64IEHNHz4cO3evTuo8VDRAADABhwKcI3GL/83JydHMTEx7v7KqhmSFBUVpcsvv1ySlJKSom3btumFF17QnDlz/A/kLCQaAADYQZCeDFp+u6p/uzAqLi72P4YKkGgAAHAReuyxx9S3b18lJiaqqKhIb7zxhtatW6f3338/qMcJ+RqN2bNnKykpSdHR0UpOTtbGjRsr3XbFihXq06ePGjdu7H64yJo1a6oxWgAArFF+e2sgzRc//PCD7r33XrVp00Y333yztm7dqvfff199+vQJ6nmFtKKxfPlyjR8/XrNnz1aPHj00Z84c9e3bV7t371aLFi28tt+wYYP69Omj6dOnq379+lq4cKEGDBigrVu36uqrrw7BGQAAECRBuuukqubPnx/AwarOYUzoXvfWtWtXXXPNNcrMzHT3tWvXToMGDTrnKtkzdejQQYMHD9YTTzxRpe0LCwsVGxurGzRQkY6afsUNALg4lJpTWqe/q6CgwO91D+dT/r3U88YpioyM9ns/paUntelfUyyN1R8hmzopKSnRjh07lJqa6tGfmpqqzZs3V2kfLpdLRUVFatCgQaXbFBcXq7Cw0KMBAGA3DmMCbnYUskQjPz9fZWVliouL8+iPi4vT4cOHq7SPv/zlLzp+/LjuvvvuSrfJyMjwuJ84MTExoLgBALCEKwjNhkK+GNThcHh8NsZ49VVk2bJlmjJlipYvX64mTZpUut2kSZNUUFDgbjk5OQHHDAAAqiZki0EbNWqkiIgIr+pFXl6eV5XjbMuXL9eoUaP05ptv6pZbbjnntud69CoAAHYR6PQHUydniYqKUnJysrKysjz6s7Ky1L1790rHLVu2TCNGjNDSpUt12223WR0mAADVwwSh2VBIb29NT0/Xvffeq5SUFHXr1k1z587VgQMHNGbMGEmnpz0OHTqk1157TdLpJGPYsGF64YUXdN1117mrIbVq1VJsbGzIzgMAgIAF6cmgdhPSRGPw4ME6cuSIpk2bptzcXHXs2FGrV69Wy5YtJUm5ubk6cOCAe/s5c+aotLRUY8eO1dixY939w4cP16JFi6o7fAAAcB4hfwR5Wlqa0tLSKvzZ2cnDunXrrA8IAIAQ8OfpnmePt6OQJxoAAEBhO3US8ttbAQBA+KKiAQCADThcp1sg4+2IRAMAADtg6gQAAMA3VDQAALCDan5NfHUh0QAAwAZ4BDkAAICPqGgAAGAHYboYlEQDAAA7MJICuUXVnnkGiQYAAHbAGg0AAAAfUdEAAMAOjAJcoxG0SIKKRAMAADsI08WgTJ0AAADLUNEAAMAOXJIcAY63IRINAABsgLtOAAAAfERFAwAAOwjTxaAkGgAA2EGYJhpMnQAAAMuQaAAAYAflFY1Amg8yMjJ07bXXql69emrSpIkGDRqkr776KuinRaIBAIAduILQfLB+/XqNHTtWH3/8sbKyslRaWqrU1FQdP348OOfzC9ZoAABgA9V9e+v777/v8XnhwoVq0qSJduzYoeuvv97vOM5GRQMAAKigoECS1KBBg6Dul4oGAAB2EKS7TgoLCz26nU6nnE7neYYapaenq2fPnurYsaP/MVSAigYAAHbgMoE3SYmJiYqNjXW3jIyM8x76wQcf1GeffaZly5YF/bSoaAAAEEZycnIUExPj/ny+asbvfvc7rVq1Shs2bFDz5s2DHg+JBgAAdhCkqZOYmBiPRKPyzY1+97vfaeXKlVq3bp2SkpL8P/Y5kGgAAGALASYa8m3s2LFjtXTpUv39739XvXr1dPjwYUlSbGysatWqFUAcnlijAQDARSgzM1MFBQW64YYbFB8f727Lly8P6nGoaAAAYAfV/K4TU03vRiHRAADADlxGvk5/eI+3H6ZOAACAZahoAABgB8Z1ugUy3oZINAAAsINqXqNRXUg0AACwA9ZoAAAA+IaKBgAAdsDUCQAAsIxRgIlG0CIJKqZOAACAZahoAABgB0ydAAAAy7hckgJ4FobLns/RYOoEAABYhooGAAB2wNQJAACwTJgmGkydAAAAy1DRAADADsL0EeQkGgAA2IAxLpkA3sAayFgrkWgAAGAHxgRWlWCNBgAAuNhQ0QAAwA5MgGs0bFrRINEAAMAOXC7JEcA6C5uu0WDqBAAAWIaKBgAAdsDUCQAAsIpxuWQCmDqx6+2tTJ0AAADLUNEAAMAOmDoBAACWcRnJEX6JBlMnAADAMlQ0AACwA2MkBfIcDXtWNEg0AACwAeMyMgFMnRibJhohnzqZPXu2kpKSFB0dreTkZG3cuPGc269fv17JycmKjo7WpZdeqldeeaWaIgUAwELGFXjz0YYNGzRgwAAlJCTI4XDonXfeCfpphTTRWL58ucaPH6/HH39c2dnZ6tWrl/r27asDBw5UuP2+ffvUr18/9erVS9nZ2Xrsscc0btw4vf3229UcOQAAF77jx4+rc+fOmjVrlmXHcJgQ1lq6du2qa665RpmZme6+du3aadCgQcrIyPDa/pFHHtGqVau0Z88ed9+YMWO0a9cubdmypUrHLCwsVGxsrG7QQEU6agZ+EgCAsFVqTmmd/q6CggLFxMRYcgz395Lj9oC+l0rNKa0zK/2O1eFwaOXKlRo0aJDfMVQkZBWNkpIS7dixQ6mpqR79qamp2rx5c4VjtmzZ4rX9rbfequ3bt+vUqVOWxQoAgOVCMHVSHUK2GDQ/P19lZWWKi4vz6I+Li9Phw4crHHP48OEKty8tLVV+fr7i4+O9xhQXF6u4uNj9uaCgQJJUqlMBPRcFABD+SnX6H7HVUfwP9HupPNbCwkKPfqfTKafTGUhoAQn5XScOh8PjszHGq+9821fUXy4jI0NTp0716t+k1b6GCgC4SB05ckSxsbGW7DsqKkpNmzbVpsOBfy/VrVtXiYmJHn2TJ0/WlClTAt63v0KWaDRq1EgRERFe1Yu8vDyvqkW5pk2bVrh9ZGSkGjZsWOGYSZMmKT093f352LFjatmypQ4cOGDZH004KywsVGJionJyciybrwxXXDv/ce0Cw/XzX0FBgVq0aKEGDRpYdozo6Gjt27dPJSUlAe+ron+sh7KaIYUw0YiKilJycrKysrJ0++23u/uzsrI0cODACsd069ZN7777rkff2rVrlZKSopo1K15AU1nJKDY2lv/BBSAmJobr5yeunf+4doHh+vmvRg1rlzRGR0crOjra0mOESkhvb01PT9ff/vY3LViwQHv27NGECRN04MABjRkzRtLpasSwYcPc248ZM0b79+9Xenq69uzZowULFmj+/Pl6+OGHQ3UKAABcsH766Sft3LlTO3fulHT6MRI7d+6s9DET/gjpGo3BgwfryJEjmjZtmnJzc9WxY0etXr1aLVu2lCTl5uZ6nGxSUpJWr16tCRMm6OWXX1ZCQoJefPFF3XHHHaE6BQAALljbt2/XjTfe6P5cvtRg+PDhWrRoUVCOEfLFoGlpaUpLS6vwZxWdZO/evfXpp5/6fTyn06nJkyeHfM7qQsX18x/Xzn9cu8Bw/fwX7tfuhhtusPyOmpA+sAsAAIS3kL/rBAAAhC8SDQAAYBkSDQAAYBkSDQAAYJmwTDRmz56tpKQkRUdHKzk5WRs3bjzn9uvXr1dycrKio6N16aWX6pVXXqmmSO3Jl+u3YsUK9enTR40bN1ZMTIy6deumNWvWVGO09uLr3165jz76SJGRkbrqqqusDdDGfL12xcXFevzxx9WyZUs5nU5ddtllWrBgQTVFaz++Xr8lS5aoc+fOql27tuLj4zVy5EgdOXKkmqK1jw0bNmjAgAFKSEiQw+HQO++8c94xfGf4yISZN954w9SsWdPMmzfP7N692zz00EOmTp06Zv/+/RVuv3fvXlO7dm3z0EMPmd27d5t58+aZmjVrmrfeequaI7cHX6/fQw89ZJ599lnzySefmK+//tpMmjTJ1KxZ03z66afVHHno+Xrtyh07dsxceumlJjU11XTu3Ll6grUZf67dr371K9O1a1eTlZVl9u3bZ7Zu3Wo++uijaozaPny9fhs3bjQ1atQwL7zwgtm7d6/ZuHGj6dChgxk0aFA1Rx56q1evNo8//rh5++23jSSzcuXKc27Pd4bvwi7R6NKlixkzZoxHX9u2bc2jjz5a4fZ/+MMfTNu2bT367r//fnPddddZFqOd+Xr9KtK+fXszderUYIdme/5eu8GDB5s//vGPZvLkyRdtouHrtXvvvfdMbGysOXLkSHWEZ3u+Xr/nn3/eXHrppR59L774omnevLllMV4IqpJo8J3hu7CaOikpKdGOHTuUmprq0Z+amqrNmzdXOGbLli1e2996663avn27Tp06ZVmsduTP9Tuby+VSUVGRpS8gsiN/r93ChQv13XffafLkyVaHaFv+XLtVq1YpJSVFzz33nJo1a6YrrrhCDz/8sE6cOFEdIduKP9eve/fuOnjwoFavXi1jjH744Qe99dZbuu2226oj5Asa3xm+C/mTQYMpPz9fZWVlXm9/jYuL83rra7nDhw9XuH1paany8/MVHx9vWbx248/1O9tf/vIXHT9+XHfffbcVIdqWP9fum2++0aOPPqqNGzcqMjKs/qfoE3+u3d69e7Vp0yZFR0dr5cqVys/PV1pamn788ceLbp2GP9eve/fuWrJkiQYPHqyTJ0+qtLRUv/rVr/TSSy9VR8gXNL4zfBdWFY1yZ78i11Tw2tzzbV9R/8XC1+tXbtmyZZoyZYqWL1+uJk2aWBWerVX12pWVlWno0KGaOnWqrrjiiuoKz9Z8+btzuVxyOBxasmSJunTpon79+mnGjBlatGjRRVnVkHy7frt379a4ceP0xBNPaMeOHXr//fe1b98+9wstcW58Z/gmrP4Z1ahRI0VERHhl8Xl5eV4ZaLmmTZtWuH1kZKQaNmxoWax25M/1K7d8+XKNGjVKb775pm655RYrw7QlX69dUVGRtm/fruzsbD344IOSTn95GmMUGRmptWvX6qabbqqW2EPNn7+7+Ph4NWvWTLGxse6+du3ayRijgwcPqnXr1pbGbCf+XL+MjAz16NFDEydOlCR16tRJderUUa9evfTUU0/xr/Jz4DvDd2FV0YiKilJycrKysrI8+rOystS9e/cKx3Tr1s1r+7Vr1yolJUU1a9a0LFY78uf6SacrGSNGjNDSpUsv2jleX69dTEyMPv/8c/frmXfu3KkxY8aoTZs22rlzp7p27VpdoYecP393PXr00Pfff6+ffvrJ3ff111+rRo0aat68uaXx2o0/1+/nn39WjRqe//mPiIiQJMtfsHWh4zvDDyFahGqZ8tu85s+fb3bv3m3Gjx9v6tSpY/79738bY4x59NFHzb333uvevvxWpQkTJpjdu3eb+fPnX9S3Kvl6/ZYuXWoiIyPNyy+/bHJzc93t2LFjoTqFkPH12p3tYr7rxNdrV1RUZJo3b27uvPNO8+WXX5r169eb1q1bm9GjR4fqFELK1+u3cOFCExkZaWbPnm2+++47s2nTJpOSkmK6dOkSqlMImaKiIpOdnW2ys7ONJDNjxgyTnZ3tvjWY74zAhV2iYYwxL7/8smnZsqWJiooy11xzjVm/fr37Z8OHDze9e/f22H7dunXm6quvNlFRUaZVq1YmMzOzmiO2F1+uX+/evY0krzZ8+PDqD9wGfP3bO9PFnGgY4/u127Nnj7nllltMrVq1TPPmzU16err5+eefqzlq+/D1+r344oumffv2platWiY+Pt785je/MQcPHqzmqEPvX//61zn/G8Z3RuB4TTwAALBMWK3RAAAA9kKiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAQAALEOiAVzE/vOf/6hp06aaPn26u2/r1q2KiorS2rVrQxgZgHDBu06Ai9zq1as1aNAgbd68WW3bttXVV1+t2267TTNnzgx1aADCAIkGAI0dO1YffPCBrr32Wu3atUvbtm1TdHR0qMMCEAZINADoxIkT6tixo3JycrR9+3Z16tQp1CEBCBOs0QCgvXv36vvvv5fL5dL+/ftDHQ6AMEJFA7jIlZSUqEuXLrrqqqvUtm1bzZgxQ59//rni4uJCHRqAMECiAVzkJk6cqLfeeku7du1S3bp1deONN6pevXr63//931CHBiAMMHUCXMTWrVunmTNn6vXXX1dMTIxq1Kih119/XZs2bVJmZmaowwMQBqhoAAAAy1DRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAliHRAAAAlvn/wPfG1npkYzwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mesh = discretize.TensorMesh([100, 80])  # setup a mesh on which to solve\n",
    "\n",
    "# model parameters\n",
    "sigma_background = 1.  # Conductivity of the background, S/m\n",
    "sigma_block = 10.  # Conductivity of the block, S/m\n",
    "\n",
    "# add a block to our model\n",
    "x_block = np.r_[0.4, 0.6]\n",
    "y_block = np.r_[0.4, 0.6]\n",
    "\n",
    "# assign them on the mesh\n",
    "sigma = sigma_background * np.ones(mesh.nC)  # create a physical property model \n",
    "\n",
    "block_indices = ((mesh.gridCC[:,0] >= x_block[0]) & # left boundary \n",
    "                 (mesh.gridCC[:,0] <= x_block[1]) & # right boudary\n",
    "                 (mesh.gridCC[:,1] >= y_block[0]) & # bottom boundary\n",
    "                 (mesh.gridCC[:,1] <= y_block[1]))  # top boundary\n",
    "\n",
    "# add the block to the physical property model\n",
    "sigma[block_indices] = sigma_block\n",
    "\n",
    "# plot it!\n",
    "plt.colorbar(mesh.plot_image(sigma)[0])\n",
    "plt.title('Electrical Conductivity, $\\sigma$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "FE3aXye2AXg0v2XloGeS",
      "project": "5dxSs15hZtvQDKSzFgjP",
      "version": 1
     }
    }
   },
   "source": [
    "## Next up ...\n",
    "In the [next notebook](divergence.ipynb), we will work through defining the discrete divergence. "
   ]
  }
 ],
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  "interpreter": {
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