DR-2d.ipynb

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    "<div style='background-image: url(\"../../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
    "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n",
    "        <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n",
    "            <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%\">Earthquake physics</div>\n",
    "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\"> Dynamic Rupture in 2D </div>\n",
    "        </div>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "\n",
    "(modified from 'rupture 2D' by Mitchell Williams and Kenneth Duru)\n",
    "\n",
    "This notebook simulates  dynamic earthquake ruptures and elastic wave propagation in two space dimensions (2D). Spontaneously propagating shear ruptures on a frictional interface in an elastic solid is a useful idealization of natural earthquakes. The conditions relating slip-rate and fault strength are often expressed as nonlinear friction laws.\n",
    "\n",
    "--------------------------------------------------------------------\n",
    "\n",
    "### The elastic wave equation\n",
    "\n",
    "Consider two elastic solids separated by a fault at the interface $x = 0$. In each elastic solid, wave motion is described by the elastic wave equation, in velocity-stress formulation: \n",
    "<br/><br/> \n",
    "\\begin{align}\n",
    "{\\rho} {\\partial_t v_i} = {\\frac{ \\partial \\sigma_{ij}}{\\partial x_j},  \\quad}  {\\quad}   S_{klij}{\\partial_t \\sigma_{ij}} = \\frac{1}{2}  \\cdot (\\frac{\\partial v_k}{\\partial x_l} + \\frac{\\partial v_l}{\\partial x_k}), \\quad i, j = 1, 2,\n",
    "\\end{align}\n",
    "<br/> <br/>\n",
    " with particle velocities ${v_i}$, stress tensor ${\\sigma_{ij} }$, compliance tensor $S_{klij}$, and density ${\\rho}$.\n",
    "<br/><br/>\n",
    "\n",
    "\n",
    "On the fault there is the velocity vector $\\left(v_{n}, v_{m}\\right)=\\left(v_{x}, v_{y}\\right)$, and the traction vector $\\left(T_{n}, T_{m}\\right)=\\left(\\sigma_{xx}, \\sigma_{xy}\\right)$, where $v_{n}$, $T_{n}$ are the normal components and $v_{m}$, $T_{m}$ being the shear components on the  interface. Denoting the fields in the positive and the negative parts of the fault with the superscripts $\\pm$, we introduce the jumps in particle velocities by $\\lbrack{v_j}\\rbrack = {v_j}^{+}-{v_j}^{-}$, with $j = m, n$, and  the total traction by $T_j = T_{0j} + \\Delta{T_j}$, where $T_{0j}$ are the initial background tractions and $\\Delta{T_j}$ are the traction changes on the fault. The conditions on the fault connecting the two elastic solids are\n",
    "<br/><br/> \n",
    "\\begin{align}\n",
    "\\text{force balance}&: \\quad T_{j}^{+} = T_{j}^{-} = T_{j}, \\quad j = m, n, \\\\\n",
    "\\text{no opening}&: \\quad \\lbrack{v_n}\\rbrack = 0,\\\\\n",
    "\\text{friction laws}&: \\quad T_m = \\sigma_n\\frac{f(V, \\theta)}{V}\\lbrack{v_m}\\rbrack, \\quad V = \\left|\\lbrack{v_m}\\rbrack\\right|.\n",
    "\\end{align}\n",
    "<br/> <br/>\n",
    " Here $\\sigma_n > 0$ is the compressive normal stress and $ f(V, \\theta) \\ge 0 $ is the nonlinear fricition coefficient relating to the fault's shear strength, $\\tau = |T_m| > 0$, and will be described  in more detail below.\n",
    "<br/><br/>\n",
    "\n",
    "-------------------------------------------------------------\n",
    "\n",
    "\n",
    "### How to use the notebook\n",
    "\n",
    "In the parameter window you can modulate the material properties, run time, domain size, and CFL criterion. In addition, you can choose rate and state friction or slip weakening friction, notice the differences that occur with this choice. In the Calculations and plotting cell the resulting on fault tractions and slip velocities are solved for in the RK4_2D.elastic_RK4_2D function, which also calls on the other associated functions. The bottom cell contains 5 output plots of the rupture behavior. \n",
    "\n",
    "\n",
    "The plot that is observed immediately under this cell is the particle velocity plotted in the entire domain. The fault runs vertically through the center of the model and is marked by a vertical line. When this animation finishes the seismograms of the earthquake pop up under this. In the last cell there are three plots that come from the data generated in the previous cell. These three plots show the on fault slip, slip rate, and tractions as they evlove through time. Lastly there is a plot that shows the slip rate on the fault through time.\n",
    " \n"
   ]
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   "source": [
    "# Import necessary routines\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import RK4_2D\n",
    "\n",
    "#plt.switch_backend(\"TkAgg\")          # plots in external window\n",
    "plt.switch_backend(\"nbagg\")           # plots within this notebook"
   ]
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   "cell_type": "markdown",
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   "source": [
    "In order to show a less time consuming solution the spatial and temporal resolution has been decreased. In the case you would like to increase it, you can modify the grid points in x and y (mantaining their proportions), the snapshot frequency and the fracture mode. "
   ]
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   "cell_type": "code",
   "execution_count": 9,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "your x-axis resolution is 0.2 km\n",
      "your y-axis resolution is 0.2 km\n"
     ]
    }
   ],
   "source": [
    "# Parameter cell\n",
    "Lx = 10.0     # length of the domain (x-axis)\n",
    "Ly = 20.0     # width of the domain (y-axis)\n",
    "\n",
    "#change these values to get a higher spatial resolution \n",
    "nx = 50       # grid points in x\n",
    "ny = 100      # grid points in y\n",
    "tend = 0.98*3.464      # final time\n",
    "CFL = 0.5\n",
    "\n",
    "#change these values to get a higher temporal resolution\n",
    "isnap = 5     # snapshot frequency\n",
    "order = 4     # spatial order of accuracy (2,4,6)\n",
    "\n",
    "nf = 5        # number of fields\n",
    "\n",
    "cs_l = 3.464    # shear wave speed\n",
    "cp_l = 6.0      # compresional wave speed\n",
    "rho_l = 2.6702  # density\n",
    "\n",
    "cs_r = 3.464    # shear wave speed\n",
    "cp_r = 6.0      # compresional wave speed\n",
    "rho_r = 2.6702  # density\n",
    "\n",
    "# Bi-material:\n",
    "#cs_r = 2.0      # shear wave speed\n",
    "#cp_r = 4.0      # compresional wave speed\n",
    "#rho_r = 2.6     # density\n",
    "\n",
    "dx = Lx/nx    # spatial step in x\n",
    "dy = Ly/ny    # spatial step in y\n",
    "\n",
    "print('your x-axis resolution is '+str(dx)+' km')\n",
    "print('your y-axis resolution is '+str(dy)+' km')"
   ]
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    "# Friction and Fracture parameter cell \n",
    "\n",
    "# Friction type\n",
    "fric_law = 'SW'   #RS:Rate-and-state OR SW:Linear slip-weakening\n",
    "\n",
    "# Fracture mode\n",
    "mode = 'III'   #  'II' or 'III'\n",
    "\n",
    "cs = np.max([cs_l, cs_r])\n",
    "cp = np.max([cp_l, cp_r])\n",
    "\n",
    "# Set mode info\n",
    "# Mode II crack: In-plane shear\n",
    "if mode == 'II':\n",
    "    nf = 5        # number of fields [vx, vy, sxx, syy, sxy] \n",
    "    dt = CFL/np.sqrt(cp**2 + cs**2)*dx    # Time step\n",
    "\n",
    "# Mode III crack: out-of-plane (anti-plane) shear    \n",
    "if mode == 'III':\n",
    "    nf = 3        # number of fields  [vz, sxz, sxy] \n",
    "    dt = dx*CFL/cs    # Time step\n",
    "\n",
    "nt = np.int(np.round(tend/dt)) # number of time steps\n",
    "\n",
    "# To test your own friction parameters change the varible 'user_params' from 'default' to 'edit'\n",
    "# then reasign your own values to the following variables \n",
    "user_params = 'default'\n",
    "\n",
    "Tau_0_user = np.ones((ny, 1))                           # shear stress \n",
    "alp_s_user = np.ones((ny, 1))                           # stastic friction\n",
    "alp_d_user = np.ones((ny, 1))                           # dynamic friction\n",
    "D_c_user = np.ones((ny, 1))                             # critical slip\n",
    "sigma_n_user = -np.ones((ny, 1))                        # normal stress \n",
    "L0_user = np.ones((ny, 1))                              # state evolution distance\n",
    "f0_user = np.ones((ny, 1))                              # referance friction coeff\n",
    "a_user = np.ones((ny, 1))                               # direct effect \n",
    "b_user = np.ones((ny, 1))                               # evolution parameter \n",
    "V0_user = np.ones((ny, 1))                              # reference slip rate"
   ]
  },
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   "execution_count": 11,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final model time will be 3.406466512702079 seconds\n"
     ]
    }
   ],
   "source": [
    "# Initializaion cell \n",
    "# Do not change! ! !\n",
    "\n",
    "# Friction condition\n",
    "Y_fault =np.zeros((ny, 1)) \n",
    "Y0 = 10\n",
    "slip = np.zeros((ny, 1))\n",
    "psi = np.zeros((ny, 1))\n",
    "FaultOutput = np.zeros((ny, nt, 6)) # c\n",
    "FaultOutput0 = np.zeros((ny, 6))\n",
    "\n",
    "for j in range(0, ny):\n",
    "        #Y_fault[j, 0] = j*dy\n",
    "        \n",
    "        if np.abs(j*dy-Y0) <= 1.5:\n",
    "            Y_fault[j, 0] = 1.0\n",
    "if fric_law not in ('SW', 'RS'):\n",
    "    # Choose friction law: fric_law\n",
    "    # Slip-weakening (SW)\n",
    "    # Rate-and-state friction law (RS)\n",
    "    \n",
    "    print('friction law not implemented. choose fric_law = SW or fric_law = RS')\n",
    "    exit(-1)\n",
    "    \n",
    "if user_params not in ('edit', 'default'):\n",
    "\n",
    "    print('friction law not implemented. choose user_params = edit or fric_law = default')\n",
    "    exit(-1)\n",
    "    \n",
    "if user_params == 'edit':\n",
    "    Tau_0 = Tau_0_user                          # shear stress \n",
    "    alp_s = alp_s_user                           # stastic friction\n",
    "    alp_d = alp_d_user                         # dynamic friction\n",
    "    D_c = D_c_user                             # critical slip\n",
    "    sigma_n = sigma_n_user                        # normal stress \n",
    "    L0 = L0_user                               # state evolution distance\n",
    "    f0 = f0_user                             # referance friction coeff\n",
    "    a = a_user                              # direct effect \n",
    "    b = b_user                              # evolution parameter \n",
    "    V0 = V0_user                             # reference slip rate\n",
    "    \n",
    "if fric_law  in ('SW'):\n",
    "     alpha = np.ones((ny, 1))*1e1000000                      # initial friction coefficient\n",
    "     slip = np.zeros((ny, 1))                        # initial slip (in m)\n",
    "     slip_new = np.zeros((ny, 1))\n",
    "     #Tau_0 = np.ones((ny, 1))*(70+11.6*(np.exp(-(Y_fault-7.0)**2/(2*9))))               # initial load (81.24 in MPa), slight increase will unlock the fault\n",
    "     \n",
    "     if user_params == 'default':\n",
    "        Tau_0 = np.ones((ny, 1))*(70+11.6*Y_fault)   \n",
    "        alp_s = np.ones((ny, 1))*0.677                          # stastic friction\n",
    "        alp_d = np.ones((ny, 1))*0.525                          # dynamic friction\n",
    "        D_c = np.ones((ny, 1))*0.4                              # critical slip\n",
    "        sigma_n = -np.ones((ny, 1))*120.0                        # normal stress \n",
    "     \n",
    "        \n",
    "     # These are not needed for the slip weakening case   \n",
    "     psi = np.ones((ny, 1))*0.0                         # initial condition for the state variable in friction law\n",
    "     psi_new = np.ones((ny, 1))*0.0\n",
    "     L0 = np.ones((ny, 1))*1.0                               # state evolution distance\n",
    "     f0 = np.ones((ny, 1))*1.0                               # referance friction coeff\n",
    "     a = np.ones((ny, 1))*1.0                                # direct effect \n",
    "     b = np.ones((ny, 1))*1.0                                # evolution parameter \n",
    "     V0 = np.ones((ny, 1))*1.0                               # reference slip rate\n",
    "        \n",
    "     FaultOutput[:, 0, 2] = sigma_n[:,0]   \n",
    "     FaultOutput[:, 0, 3] = Tau_0[:,0] \n",
    "     FaultOutput[:, 0, 4] = slip[:,0] \n",
    "     FaultOutput[:, 0, 5] = psi[:,0]   \n",
    "    \n",
    "if fric_law  in ('RS'):\n",
    "    alpha = np.ones((ny, 1))*1e1000000                      # initial friction coefficient                                                                                   \n",
    "    slip = np.ones((ny, 1))*0.0                         # initial slip (in m) \n",
    "    slip_new = np.zeros((ny, 1))\n",
    "    #Tau_0 = np.ones((ny, 1))*81.24+0.1*0.36                 # initial load (81.24 in MPa), slight increase will unlock the fault   \n",
    "    psi = np.ones((ny, 1))*0.4367                      # initial condition for the state variable in friction law\n",
    "    psi_new = np.ones((ny, 1))*0.0\n",
    "    \n",
    "    if user_params == 'default':\n",
    "        sigma_n = -np.ones((ny, 1))*120.0                        # background normal stress \n",
    "        Tau_0 = np.ones((ny, 1))*75 #-2*0.2429*sigma_n*Y_fault\n",
    "        L0 = np.ones((ny, 1))*0.02                              # state evolution distance\n",
    "        f0 = np.ones((ny, 1))*0.6                               # referance friction coeff\n",
    "        a = np.ones((ny, 1))*0.008                              # direct effect \n",
    "        b = np.ones((ny, 1))*0.012                              # evolution parameter \n",
    "        V0 = np.ones((ny, 1))*1.0e-6                            # reference slip rate\n",
    "    \n",
    "    \n",
    "    Vin = np.ones((ny, 1))*2.0e-12 \n",
    "    theta = L0/V0*np.exp(((a*np.log(2.0*np.sinh(75/(a*120)))-f0-a*np.log(Vin/V0))/b))\n",
    "    psi[:,0] = f0[:,0] + b[:,0]*np.log(V0[:,0]/L0[:,0]*theta[:,0])\n",
    "                          \n",
    "    \n",
    "        \n",
    "        \n",
    "\n",
    "     # These are not needed for the rate and state case   \n",
    "    alp_s = np.ones((ny, 1))*1.0                             # stastic friction\n",
    "    alp_d = np.ones((ny, 1))*1.0                             # dynamic friction\n",
    "    D_c = np.ones((ny, 1))*1.0                               # critical slip\n",
    "        \n",
    "    FaultOutput[:, 0, 2] = sigma_n[:,0]  \n",
    "    FaultOutput[:, 0, 3] = Tau_0[:,0]\n",
    "    FaultOutput[:, 0, 4] = slip[:,0]\n",
    "    FaultOutput[:, 0, 5] = psi[:,0] \n",
    "       \n",
    "friction_parameters = np.zeros((12, ny))    \n",
    "#friction_parameters = [alpha, alpha, Tau_0, L0, f0, a, b, V0, sigma_n, alp_s, alp_d, D_c]    \n",
    "#                        0         1      2     3   4  5  6   7    8       9      10  11\n",
    "for j in range(0, ny):\n",
    "        friction_parameters[0, j] = alpha[j, 0]\n",
    "        friction_parameters[1, j] = alpha[j, 0]\n",
    "        friction_parameters[2, j] = Tau_0[j, 0]\n",
    "        friction_parameters[3, j] = L0[j, 0]\n",
    "        friction_parameters[4, j] = f0[j, 0]\n",
    "        friction_parameters[5, j] = a[j, 0]\n",
    "        \n",
    "        \n",
    "        friction_parameters[6, j] = b[j, 0]\n",
    "        friction_parameters[7, j] = V0[j, 0]\n",
    "        friction_parameters[8, j] = sigma_n[j, 0]\n",
    "        friction_parameters[9, j] = alp_s[j, 0]\n",
    "        friction_parameters[10, j] = alp_d[j, 0]\n",
    "        friction_parameters[11, j] = D_c[j, 0]\n",
    "# end friction condition\n",
    "\n",
    "# source parameters\n",
    "x0 = -15.0        # [km]\n",
    "y0 = 7.5         # [km]\n",
    "t0 = 0.0         # [s]\n",
    "T =  0.1         # [s]\n",
    "M0 = 00.0      # [MPa]\n",
    "M = [0, 0, 1., 1., 0]\n",
    "source_type = 'Gaussian' # 'Gaussian', 'Brune'\n",
    "source_parameter = [x0, y0, t0, T, M0, source_type, M]\n",
    "\n",
    "\n",
    "\n",
    "# extract Lame parameters\n",
    "mu_l = rho_l*cs_l**2\n",
    "Lambda_l = rho_l*cp_l**2-2.0*mu_l\n",
    "\n",
    "mu_r = rho_r*cs_r**2\n",
    "Lambda_r = rho_r*cp_r**2-2.0*mu_r\n",
    "\n",
    "# Model type, available are \"homogeneous\", \"random\":\n",
    "model_type = \"homogeneous\"\n",
    "\n",
    "# Initialize velocity model\n",
    "Mat_l = np.zeros((nx, ny, 3))\n",
    "\n",
    "Mat_r = np.zeros((nx, ny, 3))\n",
    "\n",
    "if model_type == \"homogeneous\":\n",
    "    Mat_l[:,:,0] += rho_l\n",
    "    Mat_l[:,:,1] += Lambda_l\n",
    "    Mat_l[:,:,2] += mu_l\n",
    "    \n",
    "    Mat_r[:,:,0] += rho_r\n",
    "    Mat_r[:,:,1] += Lambda_r\n",
    "    Mat_r[:,:,2] += mu_r\n",
    "\n",
    "elif model_type == \"random\":\n",
    "    pert = 0.4\n",
    "    \n",
    "    rho_pert = np.zeros((nx+nx-1, ny))\n",
    "    mu_pert = np.zeros((nx+nx-1, ny))\n",
    "    lambda_pert = np.zeros((nx+nx-1, ny))\n",
    "    \n",
    "    \n",
    "    rho_pert = 2.0 * (np.random.rand(nx+nx-1, ny) - 0.5) * pert\n",
    "    mu_pert = 2.0 * (np.random.rand(nx+nx-1, ny) - 0.5) * pert\n",
    "    lambda_pert = 2.0 * (np.random.rand(nx+nx-1, ny) - 0.5) * pert \n",
    "    \n",
    "    \n",
    "    r_rho_l = rho_pert[0:nx, :]\n",
    "    r_mu_l =  mu_pert[0:nx, :]\n",
    "    r_lambda_l =  lambda_pert[0:nx, :]\n",
    "    \n",
    "    \n",
    "    \n",
    "    Mat_l[:,:,0] += rho_l*(1.0 + r_rho_l)\n",
    "    Mat_l[:,:,1] += Lambda_l*(1.0 + r_lambda_l)\n",
    "    Mat_l[:,:,2] += mu_l*(1.0 + r_mu_l)\n",
    "    \n",
    "    \n",
    "    r_rho_r = rho_pert[nx-1:2*(nx-1)+1, :]\n",
    "    r_mu_r =  mu_pert[nx-1:2*(nx-1)+1, :]\n",
    "    r_lambda_r =  lambda_pert[nx-1:2*(nx-1)+1, :]\n",
    "    \n",
    "    \n",
    "    Mat_r[:,:,0] += rho_r*(1.0 + r_rho_r)\n",
    "    Mat_r[:,:,1] += Lambda_r*(1.0 + r_lambda_r)\n",
    "    Mat_r[:,:,2] += mu_r*(1.0 + r_mu_r)\n",
    "    \n",
    "\n",
    "# Initialize pressure at different time steps and the second\n",
    "# derivatives in each direction\n",
    "F_l = np.zeros((nx, ny, nf))\n",
    "Fnew_l = np.zeros((nx, ny, nf))\n",
    "X_l = np.zeros((nx, ny))\n",
    "Y_l = np.zeros((nx, ny))\n",
    "p_l = np.zeros((nx, ny))\n",
    "v = 2.0\n",
    "F_r = np.zeros((nx, ny, nf))\n",
    "Fnew_r = np.zeros((nx, ny, nf))\n",
    "X_r = np.zeros((nx, ny))\n",
    "Y_r = np.zeros((nx, ny))\n",
    "p_r = np.zeros((nx, ny))\n",
    "\n",
    "\n",
    "for i in range(0, nx):\n",
    "    for j in range(0, ny):\n",
    "        X_l[i,j] = -Lx + i*dx\n",
    "        Y_l[i,j] = j*dy\n",
    "        \n",
    "        X_r[i,j] = i*dx\n",
    "        Y_r[i,j] = j*dy\n",
    "\n",
    "               \n",
    "# Set up seismograms     \n",
    "# Receiver locations left\n",
    "rx_l = np.array([0, 0])\n",
    "ry_l = np.array([0, Y0])\n",
    "\n",
    "irx_l = np.array([1, 1])\n",
    "iry_l = np.array([0, 0])\n",
    "\n",
    "for i in range(len(rx_l)):\n",
    "    irx_l[i] = (np.round(rx_l[i]/dx))+(nx-1)\n",
    "    iry_l[i] = (np.round(ry_l[i]/dy))\n",
    "\n",
    "seisvx_l = np.zeros((len(irx_l), nt))\n",
    "seisvy_l = np.zeros((len(irx_l), nt))\n",
    "\n",
    "# Receiver locations right\n",
    "\n",
    "rx_r = np.array([0, 0])\n",
    "ry_r = np.array([0, Y0])\n",
    "\n",
    "irx_r = np.array([1, 1])\n",
    "iry_r = np.array([0, 0])\n",
    "\n",
    "for i in range(len(rx_r)):\n",
    "    irx_r[i] = (np.round(rx_r[i]/dx))\n",
    "    iry_r[i] = (np.round(ry_r[i]/dy))\n",
    "\n",
    "seisvx_r = np.zeros((len(irx_r), nt))\n",
    "seisvy_r = np.zeros((len(irx_r), nt))\n",
    "\n",
    "# Boundary reflection coefficients: 0<= r[j] <= 1\n",
    "r_l = np.array([0.,0.,1.,0.])\n",
    "r_r = np.array([0.,0.,1.,0.])\n",
    "\n",
    "# required for seismograms\n",
    "ir_l = np.arange(len(irx_l))\n",
    "ir_r = np.arange(len(irx_r))\n",
    "\n",
    "print('The final model time will be '+str(nt*dt)+' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting cell\n",
    "\n",
    "# create plot\n",
    "image_r = plt.imshow(np.squeeze(np.append([p_l.transpose()],[p_r.transpose()],axis=2)), aspect='auto',extent=[-Lx,Lx,Ly,0]\n",
    "          ,cmap = 'seismic', vmin=-v, vmax=+v, animated=True, \n",
    "          interpolation='none')\n",
    "\n",
    "# Plot the receivers\n",
    "for x, y in zip(rx_l, ry_l):\n",
    "    plt.text(x, y, '+')\n",
    "    \n",
    "for x, y in zip(rx_r, ry_r):\n",
    "    plt.text(x, y, '+')\n",
    "\n",
    "plt.text(x0, y0, 'o')\n",
    "plt.colorbar()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Time-stepping \n",
    "for it in range(nt):\n",
    "    \n",
    "    t = it*dt\n",
    "    \n",
    "    #4th order Runge-Kutta \n",
    "    RK4_2D.elastic_RK4_2D(Fnew_l, F_l, Mat_l, X_l, Y_l, t, nf, nx, ny, dx, dy, dt, order, r_l, source_parameter, Fnew_r, F_r, Mat_r, X_r, Y_r, r_r, friction_parameters, slip,  psi, slip_new, psi_new, fric_law, FaultOutput0, Y0, mode)\n",
    "    \n",
    "    # update fields and extract parameters\n",
    "    F_l = Fnew_l\n",
    "    F_r = Fnew_r\n",
    "    slip = slip_new \n",
    "    psi = psi_new\n",
    "    FaultOutput[:, it, :] = FaultOutput0\n",
    "    if mode == 'II': \n",
    "        p_l = F_l[:,:,1]\n",
    "        p_r = F_r[:,:,1]\n",
    "    if mode == 'III': \n",
    "        p_l = F_l[:,:,0]\n",
    "        p_r = F_r[:,:,0] \n",
    "    \n",
    "    t = it*dt\n",
    "    \n",
    "    # Plot every isnap-th iteration\n",
    "    if it % isnap == 0:                    # you can change the speed of the plot by increasing the plotting interval\n",
    "        p_b = np.squeeze(np.append([p_l.transpose()],[p_r.transpose()],axis=2)) # p for both, transposed to plot\n",
    "        plt.title(\"time: %.2f\" % t)\n",
    "        plt.axvline(x=0.0)\n",
    "        image_r.set_data(p_b)\n",
    "        plt.gcf().canvas.draw()\n",
    "\n",
    "    \n",
    "    # Save seismograms\n",
    "    if mode == 'II':\n",
    "        seisvy_l[ir_l, it] = F_l[irx_l[ir_l], iry_l[ir_l], 1]\n",
    "        seisvy_r[ir_r, it] = F_r[irx_r[ir_r], iry_r[ir_r], 1]\n",
    "        \n",
    "    if mode == 'III':\n",
    "        seisvy_l[ir_l, it] = F_l[irx_l[ir_l], iry_l[ir_l], 0]\n",
    "        seisvy_r[ir_r, it] = F_r[irx_r[ir_r], iry_r[ir_r], 0]    \n",
    "\n",
    "# Plot seismogram    \n",
    "plt.ioff()\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "ymax = seisvx_l.ravel().max()\n",
    "time = np.arange(nt) * dt\n",
    "for ir_l in range(len(seisvx_l)):\n",
    "    plt.plot(time, seisvy_l[ir_l, :] + ymax * ir_l)\n",
    "    plt.title('Left block')\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel('vy (m/s)')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "ymax = seisvy_l.ravel().max()\n",
    "for ir_l in range(len(seisvy_r)):\n",
    "    plt.plot(time, seisvy_r[ir_l, :] + ymax * ir_l)\n",
    "    plt.title('Right block')\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel('vy (m/s)')\n",
    "    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": [
     0
    ],
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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>"
      ]
     },
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     "output_type": "display_data"
    },
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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-380cc2064db8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mline4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_fault\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msliprate_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'g'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mline5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_fault\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtraction_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'g'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_png_is_old\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Swap the frames.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1707\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1708\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1709\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1711\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2647\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2649\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1203\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m-> 1205\u001b[0;31m                                                                 renderer)\n\u001b[0m\u001b[1;32m   1206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0;32m-> 1150\u001b[0;31m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[0m\u001b[1;32m   1151\u001b[0m                 [tick.label2.get_window_extent(renderer)\n\u001b[1;32m   1152\u001b[0m                  for tick in ticks if tick.label2.get_visible()])\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m         return ([tick.label1.get_window_extent(renderer)\n\u001b[0;32m-> 1150\u001b[0;31m                  for tick in ticks if tick.label1.get_visible()],\n\u001b[0m\u001b[1;32m   1151\u001b[0m                 [tick.label2.get_window_extent(renderer)\n\u001b[1;32m   1152\u001b[0m                  for tick in ticks if tick.label2.get_visible()])\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    888\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 890\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    891\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0mcorners_rotated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorners_horiz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0;31m# compute the bounds of the rotated box\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0mxmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorners_rotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    359\u001b[0m         \u001b[0mxmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorners_rotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m         \u001b[0mymin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorners_rotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# View slip, slip rate and traction. \n",
    "\n",
    "fig1 = plt.figure(figsize=(10,10))\n",
    "ax3 = fig1.add_subplot(3,1,1)\n",
    "line3 = ax3.plot(0,0,'g')\n",
    "plt.title('Slip')\n",
    "plt.xlabel('Depth [km]')\n",
    "plt.ylabel('Slip [m]')\n",
    "\n",
    "ax4 = fig1.add_subplot(3,1,2)\n",
    "line4 = ax4.plot(0,0,'g')\n",
    "plt.title('Slip rate')\n",
    "plt.xlabel('Depth [km]')\n",
    "plt.ylabel('Slip rate[m/s]]')\n",
    "\n",
    "ax5 = fig1.add_subplot(3,1,3)\n",
    "line5 = ax5.plot(0,0,'g')\n",
    "plt.title('Traction')\n",
    "plt.xlabel('Depth [km]')\n",
    "plt.ylabel('stress [MPa]')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.ion()\n",
    "plt.show()\n",
    "\n",
    "y_fault = Y_l[-1,:]\n",
    "\n",
    "for it in range(nt):\n",
    "    slip_ = FaultOutput[:, it, 4]\n",
    "    sliprate_ = np.sqrt(FaultOutput[:, it, 0]**2 + FaultOutput[:, it, 1]**2)\n",
    "    traction_ = FaultOutput[:, it, 3]\n",
    "    if it % 1 == 0: \n",
    "        \n",
    "        for l in line3:\n",
    "            l.remove()\n",
    "            del l               \n",
    "        for l in line4:\n",
    "            l.remove()\n",
    "            del l\n",
    "        for l in line5:\n",
    "            l.remove()\n",
    "            del l\n",
    "\n",
    "        # Display lines\n",
    "        line3 = ax3.plot(y_fault,slip_,'g')\n",
    "        line4 = ax4.plot(y_fault,sliprate_,'g')\n",
    "        line5 = ax5.plot(y_fault,traction_,'g')\n",
    "        plt.gcf().canvas.draw()\n",
    "                   \n",
    "    \n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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",
       "    var width = fig.canvas.width/mpl.ratio\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 + '\" width=\"' + width + '\">');\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 width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
      "text/html": [
       "<div id='fdfc7b2a-ba15-4b36-ae24-0f45fa932470'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot time history of the slip-rate on the fault \n",
    "\n",
    "VT = np.zeros((nt, ny))\n",
    "\n",
    "for it in range(nt):\n",
    "    for j in range(ny):\n",
    "        \n",
    "        VT[it, j] = np.sqrt(FaultOutput[j, it, 0]**2 + FaultOutput[j, it, 1]**2)\n",
    "        \n",
    "        \n",
    "\n",
    "v = 2.5\n",
    "image_f = plt.imshow(VT, aspect='auto',extent=[0,Ly, nt*dt, 0]\n",
    "          ,cmap = 'viridis', vmin=0, vmax=+v, animated=True, interpolation='none')\n",
    "        \n",
    "plt.title('slip-rate')\n",
    "plt.colorbar()\n",
    "plt.xlabel('fault [km]')\n",
    "plt.ylabel('t [s]')\n",
    "plt.show()"
   ]
  }
 ],
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