02_fin_dif_modeling.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Finite difference modeling\n",
    "\n",
    "*Prof. dr.ir.T.N.Olsthoorn*\n",
    "\n",
    "*Heemstede, Sept. 2016, 24 May 2017*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach\n",
    "\n",
    "In this chapter we set-up a 3D steady-state finite difference model from scratch. We do this by computing a numerical groundwater problem step by step, by hand, using finite difference, building up the pieces of the model, which we will assemble in the next chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup of the model by specifying its dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3D steady state FDM will be based on a regular grid consisting of rows an columns and layers. The column widths and the row heigts are constant on a per column and per row basis, but the layer thickness can vary on a cell by cell basis. The grid of a full 3D model will thus be specified in general by a vector of x cell boundary coordinates, a vector y row boundary coordinates and a full 3D array of cell top and bottom coordinates.\n",
    "\n",
    "Notice that the arrays are interpreted as [z, y, x] or [layer row col]. This is a convenience in Python where when Phi is a 3D array of the shape of the grid [Nz, Ny, Nz] we have\n",
    "\n",
    "Phi[k].shape is [Ny, Nx], the entire layer number i.\n",
    "Phi[k][j] = Nx, the entire row j of layer i.\n",
    "Phi[k][j][i] = the head in cell [k, j, i] which is the same as Phi[k, j, i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "\n",
    "# specify a rectangular grid\n",
    "x = np.arange(-1000., 1000., 25.)\n",
    "y = np.arange(-1000., 1000., 25.) # backward, i.e. first row grid line has highest y\n",
    "z = np.arange(-100., 0., 20.)  # backward, i.e. from top to bottom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these coordinates we obtain the number of cells along each axis and the cell sizes and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# as well as the number of cells along the three axes\n",
    "Nx = len(x)-1\n",
    "Ny = len(y)-1\n",
    "Nz = len(z)-1\n",
    "\n",
    "sz = (Nz,Ny,Nx)  # the shape of the model\n",
    "Nod = np.prod(sz) # total number of cells in the model\n",
    "\n",
    "# from this we have the width of columns, rows and layers\n",
    "dx = np.diff(x).reshape(1, 1, Nx)\n",
    "dy = np.diff(y).reshape(1, Ny,1)\n",
    "dz = np.abs(np.diff(z)).reshape(Nz, 1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IBOUND array - telling which cells are active and which have a prescribed head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first specify which of the cells have their head prescrided and which cells are inactive. We have to tackle inactive cells early to make sure their conductance is made zero (in case there conductivities might be specified as non-zeros).\n",
    "\n",
    "We do that my means of a so-called boundary array IBOUND (MODFLOW terminology), which is an integer array of the shape of the model grid that tells which cells have a prescribed head, which cells are inactive (i.e. which cells does not take part of the computation, such as cells that represent impermeable rock) and for which cells the head should be computed.\n",
    "\n",
    "* IBOUND > 0,  means heads will be computed\n",
    "* IBOUND == 0, means  cells are inactive\n",
    "* IBOUND <0 ,  means heads prescribed\n",
    "\n",
    "In this particular example we specify that the vertical zx plane at the last row of the model will have prescribed heads equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "IBOUND = np.ones(sz)\n",
    "IBOUND[:,-1,:] = -1  # last row of model heads are prescribed\n",
    "IBOUND[:, 40:45, 20:70]=0 # these cells are inactive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This boundary array makes it easy telling which cells cells are active (head computed), inactive, and fixed-head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "source": [
    "active = (IBOUND>0).reshape(Nod)  # active is now a vector of booleans of length Nod\n",
    "inact  = (IBOUND==0).reshape(Nod) # dito for inact\n",
    "fxhd   = (IBOUND<0).reshape(Nod)  # dito for fxhd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cell conductancies: defining the ease of flow between adjacent cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to define based on the properties of the cells is the flow resistance of each cell in the 3 grid directions, `x`, `y` and `z`. For that we need the cell sizes from the coordinates and the hydraulic conductivities in the `x`, `y` and `z` direction. The latter are given as full 3D arrays `kx`, `ky`, `kz` whose shapes correspond to that of the model mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [],
   "source": [
    "k = 10.0 # m/d uniform conductivity\n",
    "kx = k * np.ones(sz) # [L/T] 3D kx array\n",
    "ky = k * np.ones(sz) # [L/T] 3D ky array with same values as kx \n",
    "kz = k * np.ones(sz) # [L/T] 3D kz array with same values as kx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flow resistances for each cell is the head loss across opposite cell faces due to a unit flux through the cell along the axis perperndicular to them. These resistances are cell properties that can immediately be computed for the entire grid of the model. Because we always need the resistance between the cell center and its outer faces, we use the factor 0.5 (flow over half the lenght of the cell in each direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# half cell flow resistances\n",
    "Rx = 0.5 * dx / (dy * dz) / kx  # [T/L2], flow resistance half cell in x-direction\n",
    "Ry = 0.5 * dy / (dz * dx) / ky  # same in y-direction\n",
    "Rz = 0.5 * dz / (dx * dy) / kz  # same in z-direction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make inactive cells inactive by setting their resistance to np.Inf (infinite):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "source": [
    "Rx = Rx.reshape(Nod,); Rx[inact] = np.Inf; Rx=Rx.reshape(sz)\n",
    "Ry = Ry.reshape(Nod,); Ry[inact] = np.Inf; Ry=Ry.reshape(sz)\n",
    "Rz = Rz.reshape(Nod,); Rz[inact] = np.Inf; Rz=Rz.reshape(sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we compute the conductance between each pair of adjacent cells across their connecting cell face. The conductance is just the reverse of the resistance of the two connected half cells. This resistance is the sum of the resistances of the two connected half cells because these resistances are placed in series with respect to the flow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "outputs": [],
   "source": [
    "# conductances between adjacent cells\n",
    "Cx = 1 / (Rx[:, :, :-1] + Rx[:, :,1:]) # [L2/T] in x-direction\n",
    "Cy = 1 / (Ry[:, :-1,:] + Ry[:, 1:,:]) # idem in y-direction\n",
    "Cz = 1 / (Rz[:-1,:,:] + Rz[1:,:,:]) # idem in z-direction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the system matrix - set of water balance equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The system matrix has size of (`Nod`, `Nod`) allowing a connection between each pair of cells. Of course only cells that share their cell face are connected in reality. In a 3D model this means that each cell is connected to its 6 neighbors instead of to all other cells in the model. This means that most of the matrix entries will be zero.\n",
    "\n",
    "To be able to indentify adjacent cells we generate cell numbers in an array that has the size of the model grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "NOD = np.arange(Nod).reshape(sz) # this is a full 3D array of node numbers (cell numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this array it's easy to identify adjacent cells by their cell number. Thus we generate arrays with the cel numbers of right hand neigbor of the cells (east neighbor), the left hand neighbor (the west neigbor), the north neighbor, south neighbor, the top neighbor and the bottom neighbor as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IE = NOD[:, :, 1:] # numbers of the eastern neighbors of each cell\n",
    "IW = NOD[:, :, :-1] # same western neighbors\n",
    "IN = NOD[:, :-1,:] # same northern neighbors\n",
    "IS = NOD[:, 1:,:] # southern neighbors\n",
    "IT = NOD[:-1,:,:] # top neighbors\n",
    "IB = NOD[1:,:,:] # bottom neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the shape of the `IE` and `IW` is the same as that of `Cx`, the size of `IN` and `IS` is the same as that of `Cy` and the size of `IT` and `IB` is the same as that of `Cx`.\n",
    "\n",
    "To put the conductances into the system matrix we need their row and column indices together with their value, so that we can say `a`[`j`,`i`] = `value`. Because we have the numbers of adjacent cells in the arrays `IE`, `IW` etc, we can immediately place all the system matrix coefficiencts at the place into a sparse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import scipy.sparse as sp\n",
    "\n",
    "R = lambda x : x.ravel() # define short hand for x.ravel()\n",
    "\n",
    "# notice the call signature:\n",
    "#      csc_matrix( (data, (row_index, col_index) ), (M,N)); This is a tuple within tuple.\n",
    "A = sp.csc_matrix(( np.concatenate(( R(Cx), R(Cx), R(Cy), R(Cy), R(Cz), R(Cz)) ),\\\n",
    "                   (np.concatenate(( R(IE), R(IW), R(IN), R(IS), R(IB), R(IT)) ),\\\n",
    "                    np.concatenate(( R(IW), R(IE), R(IS), R(IN), R(IT), R(IB)) ),\\\n",
    "                  )),(Nod,Nod))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have to define the diagonal elements of the system matrix `A`, i.e. the values `a`[`i`,`i`] for `i`=[0:`Nod`].\n",
    "\n",
    "These are just the negative sum of the row coefficients. Hence we sum `A` over the second axis (`axis`=1) to get them in a [`Nod`,1] sized vector. (Notice stat sparace matrix derived vectors keep their orientation, contrary to vectors obained from numpy arrays, which produce dimensionless vectors).\n",
    "\n",
    "Generate the diagonal values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# to use the vector of diagonal values int a call of sp.diags() we need to have it aa a \n",
    "# standard nondimensional numpy vector.\n",
    "# To get this:\n",
    "# - first turn the matrix obtained by A.sum(axis=1) into a np.array by np.array( .. )\n",
    "# - then take the whole column to loose the array orientation (to get a dimensionless numpy vector)\n",
    "adiag = np.array(-A.sum(axis=1))[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then generate a diagonal array from these values, that we can add it to `A`, to complete `A`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Adiag = sp.diags(adiag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex alternative: Generate diagonal array by calling the csr_matrix constructor:\n",
    "\n",
    "Adiag = sp.csr_matrix((adiag,(np.arange(Nod),np.arange(Nod))),(Nod,Nod))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boundary conditions\n",
    "\n",
    "For this chapter we only use fixed flow and fixed head boundary conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed flows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixed flow boundary conditions are specified by an 3D array of the size of the grid. Each values specifies the inflow for the corresponding cell (injections are positive). Cells without a specified flow are, in fact, cells where the specified flow is zero. Hence the fixed-flows array is a full 3D array with flow values that are zero where no flow enters or leaves the cells and have non-zero values elsewhere.\n",
    "\n",
    "For this example, we specify a single extraction of Q=-1200 m3/d in cell [30,25,2]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FQ = np.zeros(sz)    # all flows zero. Note sz is the shape of the model grid\n",
    "FQ[2, 30, 25] = -1200  # [m3/d] extraction in this cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The righ-hand size of the matrix equation to be solved, the vector RHS, contains the flows. So we can generate it by assignment of `FQ` and converting it to a numpy vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RHS = FQ.reshape(Nod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See further down how we use `RHS` for only the active and non-fixed head rows.\n",
    "\n",
    "The next step is to add fixed head boundary conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed heads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixed heads are known heads. This implies that in the set of equations that represent the model, i.e\n",
    "\n",
    "$A \\times Phi = RHS$\n",
    "\n",
    "Some of the Phis are prescribed and should not be computed as defined by `IBOUND` and contained in the boolean vectors `active`, `fxhd` and `inact` specified and computed above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know which cell have fixed heads, we can multiply out these heads with the corresponding columns of the system matrix, which yields a vector of constant values with dimension flow [m3/d] that can be added to the fixed flow vector in the `RHS` vector. The `RHS` vector is now the sum of the `FQ` and the contribution from the fixed heads.\n",
    "\n",
    "Notice that the fixed heads will be obtained from the given array `HI` of the initial heads, where the head in the cells where `IBOUND`>0 correspond with the fixed heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "HI = np.zeros(sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reshape `FQ` and `HI` to a column vector to allow matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RHS = FQ.reshape(Nod,1) - A[:,fxhd].dot(HI.reshape(Nod,1)[fxhd])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now the complete `RHS` of the matrix equation to solve:\n",
    "\n",
    "$A \\times Phi = RHS$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the matrix equation for the unknown heads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the sparse matrix solver in module `scipy.sparse.linalg` to compute the unknown heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import spsolve # import with from to use its short name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course we only need the active rows and columns of `A` and the active rows from `RHS`.\n",
    "\n",
    "But first allocate a full-fledged vector of heads to store the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Phi = HI.flatten() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then compute the unknown heads (i.e. the active cels only).\n",
    "\n",
    "Remark: If we want to select a submatrix from `A` defiend by a given vectors of row and column indices, we can do so in sequence: Rows (I) first, columns (J) next, like so:\n",
    "\n",
    "$A[I][:,J]$\n",
    "\n",
    "which we apply in the next line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " Phi[active] = spsolve( (A+Adiag)[active][:,active] ,RHS[active] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we solved the problem and now have the heads for all cells in the vector Phi.\n",
    "\n",
    "We didn't touch the rows and columns that are inactive. So the heads of these inactive cells whatever they are in `HI` are know still in `Phi`. Just to make sure we detect them and won't use them, set them to `NaN` (Not a Number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Phi[inact] = np.NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we reshape the head vector to that of the model grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Phi=Phi.reshape(sz) # reshape vector Phi to 3D shape of the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "        [ 0.02763571,  0.02761016,  0.02755909, ...,  0.01708962,\n",
       "          0.01712512,  0.01714299],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[ 1.77107246,  1.77132861,  1.77183761, ...,  1.56399507,\n",
       "          1.56320139,  1.56280385],\n",
       "        [ 1.77081631,  1.77107575,  1.77159133, ...,  1.56360172,\n",
       "          1.56280525,  1.56240631],\n",
       "        [ 1.77030071,  1.77056677,  1.77109553, ...,  1.56281367,\n",
       "          1.56201158,  1.56160982],\n",
       "        ..., \n",
       "        [ 0.05529699,  0.05524583,  0.0551436 , ...,  0.03419641,\n",
       "          0.03426788,  0.03430386],\n",
       "        [ 0.02763571,  0.02761016,  0.02755909, ...,  0.01708962,\n",
       "          0.01712512,  0.01714299],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Phi # show Phi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the heads as contours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the required plotting module and setup the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt # combines namespace of numpy and pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For coordinates of the cells use their centers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xm = 0.5 * (x[:-1] + x[1:]) # [L] coordinates of column centers\n",
    "ym = 0.5 * (y[:-1] + y[1:]) # [L] coordinates of row centers\n",
    "layer = 2 # contours for this layer\n",
    "nc = 50   # number of contours in total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results using plt functions like in Matlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x10e3410f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.xlabel('x [m]')\n",
    "plt.ylabel('y [m]')\n",
    "plt.title(\"Contours (%d in total) of the head in layer %d with inactive section\" % (nc, layer))\n",
    "plt.contour(xm, ym, Phi[layer], nc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The white area is the aquifer part that was defined as inactive (impervious).\n",
    "* The red trough is the well location.\n",
    "* The two flanks and the front sides are closed (no FQ and no fixed head).\n",
    "* The head at the back side is prescribed and maintained at zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter we have developed, from scratch, a full 3D finite difference model for which fixed heads, fixed flows and inactive subareas are be prescribed. While developing we also computed a concrete example of which the head contours were finally shown.\n",
    "\n",
    "To turn this model into a general Python function that can compute arbitrary 3D steady-state models of this kind, we only have to gather the developed lines and put them under a function definition, add some help strings (a doc string) and add error checking for convenience, while the data for this specific case, like the grid dimensions (`x`,`y`,`z`) the conductivities (`kx`,`ky`,`kz`), the prescribed flows `FQ` and initial heads `HI` plus the boundary array `IBOUND` that tells which cells have fixed heads and which are inactive, are to be passed as in user-given arguments at the function call like so:\n",
    "\n",
    "Phi = fdm3(x,y,z,kx,ky,kz,FQ,IH,IBOUND) # function that solves arbitrary 3D steady steate finite difference model\n",
    "\n",
    "We do this in the next section."
   ]
  }
 ],
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