examples/seismic/abc_methods/04_habc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 - Hybdrid Absorbing Boundary Condition (HABC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 - Introduction\n",
    "\n",
    "In this notebook we describe absorbing boundary conditions and their use combined with the *Hybdrid Absorbing Boundary Condition* (*HABC*). The common points to the previous notebooks <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a>, <a href=\"02_damping.ipynb\">Damping</a> and  <a href=\"03_pml.ipynb\">PML</a> will be used here, with brief descriptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 - Absorbing Boundary Conditions\n",
    "\n",
    " We initially describe absorbing boundary conditions, the so called A1 and A2 Clayton's conditions and\n",
    " the scheme from Higdon. These methods can be used as pure boundary conditions, designed to reduce reflections,\n",
    " or as part of the Hybrid Absorbing Boundary Condition, in which they are combined with an absorption layer in a manner to be described ahead. \n",
    " \n",
    " In the presentation of these boundary conditions we initially consider the wave equation to be solved on\n",
    " the spatial domain $\\Omega=\\left[x_{I},x_{F}\\right] \\times\\left[z_{I},z_{F}\\right]$ as show in the figure bellow. More details about the equation and domain definition can be found in the <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a> notebook. \n",
    "\n",
    "<img src='domain1.png' width=500>\n",
    " \n",
    "## 4.2.1 - Clayton's A1 Boundary Condition\n",
    "\n",
    "Clayton's A1 boundary condition is based on a one way wave equation (OWWE). This simple condition\n",
    "  is such that outgoing waves normal to the border would leave without reflection. At the $\\partial \\Omega_1$ part of the boundary\n",
    "    we have, \n",
    "\n",
    "- $\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial t}-c(x,z)\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial x}=0.$\n",
    "\n",
    "while at $\\partial \\Omega_3$ the condition is\n",
    " \n",
    "- $\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial t}+c(x,z)\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial x}=0.$\n",
    "\n",
    "and at $\\partial \\Omega_2$\n",
    "\n",
    "- $\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial t}+c(x,z)\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial z}=0.$\n",
    "\n",
    "## 4.2.2 - Clayton's A2 Boundary Condition \n",
    "\n",
    "The A2 boundary condition aims to impose a boundary condition that would make outgoing waves leave the domain without being reflected. This condition is approximated (using a Padé approximation in the wave dispersion relation) by the following equation to be imposed on the boundary part $\\partial \\Omega_1$\n",
    "\n",
    "- $\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial t^{2}}+c(x,z)\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial x \\partial t}+\\frac{c^2(x,z)}{2}\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial z^{2}}=0.$\n",
    "\n",
    "At $\\partial \\Omega_3$ we have\n",
    "\n",
    "- $\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial t^{2}}-c(x,z)\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial z \\partial t}+\\frac{c^2(x,z)}{2}\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial x^{2}}=0.$\n",
    "\n",
    "while at $\\partial \\Omega_2$ the condition is\n",
    "\n",
    "- $\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial t^{2}}-c(x,z)\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial x \\partial t}+\\frac{c^2(x,z)}{2}\\displaystyle\\frac{\\partial^{2} u(x,z,t)}{\\partial z^{2}}=0.$\n",
    "\n",
    "At the corner points the condition is \n",
    "\n",
    "- $\\displaystyle\\frac{\\sqrt{2}\\partial u(x,z,t)}{\\partial t}+c(x,z)\\left(\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial x}+\\displaystyle\\frac{\\partial u(x,z,t)}{\\partial z}\\right)=0.$\n",
    "\n",
    "## 4.2.3 -  Higdon Boundary Condition\n",
    "\n",
    "The Higdon Boundary condition of order p is given at $\\partial \\Omega_1$ and $\\partial \\Omega_3$n by:\n",
    "\n",
    "- $\\Pi_{j=1}^p(\\cos(\\alpha_j)\\left(\\displaystyle\\frac{\\partial }{\\partial t}-c(x,z)\\displaystyle\\frac{\\partial }{\\partial x}\\right)u(x,z,t)=0.$\n",
    "\n",
    "and at $\\partial \\Omega_2$\n",
    "\n",
    "- $\\Pi_{j=1}^p(\\cos(\\alpha_j)\\left(\\displaystyle\\frac{\\partial}{\\partial t}-c(x,z)\\displaystyle\\frac{\\partial}{\\partial z}\\right)u(x,z,t)=0.$\n",
    "\n",
    " This method would make that outgoing waves with angle of incidence at the boundary equal to $\\alpha_j$ would\n",
    " present no reflection. The method we use in this notebook employs order 2 ($p=2$) and angles $0$ and $\\pi/4$.\n",
    " \n",
    " Observation: There are similarities between Clayton's A2 and the Higdon condition. If one chooses $p=2$ and\n",
    " both angles equal to zero in Higdon's method, this leads to the condition:\n",
    " $ u_{tt}-2cu_{xt}+c^2u_{xx}=0$. But, using the wave equation, we have that $c^2u_{xx}=u_{tt}-c^2u_{zz}$. Replacing this relation in the previous equation, we get: $2u_{tt}-2cu_{xt}-c^2u_{zz}=0$ which is Clayton's A2\n",
    " boundary condition. In this sence, Higdon's method would generalize Clayton's scheme. But the discretization of\n",
    " both methods are quite different, since in Higdon's scheme the boundary operators are unidirectional, while\n",
    " in Clayton's A2 not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 - Acoustic Problem with HABC\n",
    "\n",
    "In the hybrid absorption boundary condition (HABC) scheme we will also extend the spatial domain as $\\Omega=\\left[x_{I}-L,x_{F}+L\\right] \\times\\left[z_{I},z_{F}+L\\right]$.\n",
    "\n",
    "We added to the target domain $\\Omega_{0}=\\left[x_{I},x_{F}\\right]\\times\\left[z_{I},z_{F}\\right]$ an extension zone, of length $L$ in both ends of the direction $x$ and at the end of the domain in the direction $z$, as represented in the figure bellow.\n",
    "\n",
    "<img src='domain2.png' width=500>\n",
    "\n",
    " The difference with respect to previous schemes, is that this extended region will now be considered as the union of several gradual extensions. As represented in the next figure, we define a region $A_M=\\Omega_{0}$. The regions $A_k, k=M-1,\\cdots,1$ will be defined as the previous region $A_{k+1}$ to which we add one extra grid line to the left,\n",
    " right and bottom sides of it, such that the final region $A_1=\\Omega$ (we thus have $M=L+1$).\n",
    " \n",
    " <img src='region1.png' width=500>\n",
    " \n",
    " We now consider the temporal evolution\n",
    " of the solution of the HABC method. Suppose that $u(x,z,t-1)$ is the solution at a given instant $t-1$ in all the \n",
    " extended $\\Omega$ domain. We update it to instant $t$, using one of the absorbing boundary conditions described in the previous section (A1, A2 or Higdon) producing a preliminar new function $u(x,z,t)$. Now, call $u_{1}(x,z,t)$ the solution at instant $t$ constructed in the extended region, by applying the same absorbing boundary condition at the border of each of the domains  $A_k,k=1,..,M$. The HABC solution will be constructed as a convex combination of $u(x,z,t)$ and $u_{1}(x,z,t)$:\n",
    " \n",
    "- $u(x,z,t) = (1-\\omega)u(x,z,t)+\\omega u_{1}(x,z,t)$.\n",
    "\n",
    "The function $u_{1}(x,z,t)$ is defined (and used) only in the extension of the domain. The function $w$ is a \n",
    "weight function growing from zero at the boundary $\\partial\\Omega_{0}$ to one at $\\partial\\Omega$. The particular weight function to be used could vary linearly, as when the scheme was first proposed by Liu and Sen. But HABC produces better results with a non-linear weight function to be described ahead.\n",
    "\n",
    "The wave equation employed here will be the same as in the previous notebooks, with same velocity model, source term and initial conditions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3.1 The weight  function $\\omega$\n",
    "\n",
    "One can choose a *linear* weight function as \n",
    "\n",
    "\\begin{equation}\n",
    "\\omega_{k} = \\displaystyle\\frac{M-k}{M};\n",
    "\\end{equation}\n",
    "\n",
    "or preferably a *non linear*\n",
    "\n",
    "\\begin{equation}\n",
    "\\omega_{k}=\\left\\{ \\begin{array}{ll}\n",
    "1, & \\textrm{if $1\\leq k \\leq P+1$,} \\\\ \\left(\\displaystyle\\frac{M-k}{M-P}\\right)^{\\alpha} , & \\textrm{if $P+2 \\leq k \\leq  M-1.$} \\\\ 0 , & \\textrm{if $k=M$.}\\end{array}\\right.\n",
    "\\label{eq:elo8}\n",
    "\\end{equation}  \n",
    "\n",
    "In general we take $P=2$ and we choose $\\alpha$ as follows:\n",
    "\n",
    "- $\\alpha = 1.5 + 0.07(npt-P)$, in the case of A1 and A2;\n",
    "- $\\alpha = 1.0 + 0.15(npt-P)$, in the case of Higdon.\n",
    "\n",
    "The value *npt* designates the number of discrete points that define the length of the blue band in the direction $x$ and/or $z$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.4 - Finite Difference Operators and Discretization of Spatial and Temporal Domains\n",
    "\n",
    " We employ the same methods as in the previous notebooks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.5 - Standard Problem\n",
    "\n",
    "Redeeming the Standard Problem definitions discussed on the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a> we have that:\n",
    "\n",
    "- $x_{I}$ =  0.0 Km;\n",
    "- $x_{F}$ =  1.0 Km =  1000 m;\n",
    "- $z_{I}$ =  0.0 Km;\n",
    "- $z_{F}$ =  1.0 Km =  1000 m;\n",
    "\n",
    "The spatial discretization parameters are given by:\n",
    "- $\\Delta x$ = 0.01 km = 10m;\n",
    "- $\\Delta z$ = 0.01 km = 10m;\n",
    "\n",
    "Let's consider a $I$ the time domain with the following limitations:\n",
    "\n",
    "- $t_{I}$ = 0 s = 0 ms;\n",
    "- $t_{F}$ = 1 s = 1000 ms;\n",
    "\n",
    "The temporal discretization parameters are given by:\n",
    "\n",
    "- $\\Delta t$ $\\approx$ 0.0016 s = 1.6 ms;\n",
    "- $NT$ = 626.\n",
    "\n",
    "The source term, velocity model and positioning of receivers will be as in the previous notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.6 - Numerical Simulations\n",
    "\n",
    "For the numerical simulations of this notebook we use several of the notebook codes presented in  <a href=\"02_damping.ipynb\">Damping</a> e <a href=\"03_pml.ipynb\">PML</a>. The new features will be described in more detail.\n",
    "\n",
    "So, we import the following Python and Devito packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "import numpy                   as np\n",
    "import matplotlib.pyplot       as plot\n",
    "import math                    as mt\n",
    "import matplotlib.ticker       as mticker    \n",
    "from   mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from   matplotlib              import cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Devito's library of examples we import the following structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "%matplotlib inline\n",
    "from   examples.seismic  import TimeAxis\n",
    "from   examples.seismic  import RickerSource\n",
    "from   examples.seismic  import Receiver\n",
    "from   examples.seismic  import plot_velocity\n",
    "from   devito            import SubDomain, Grid, NODE, TimeFunction, Function, Eq, solve, Operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mesh parameters that we choose define the domain $\\Omega_{0}$ plus the absorption region. For this, we use the following data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nptx   =  101\n",
    "nptz   =  101\n",
    "x0     =  0.\n",
    "x1     =  1000. \n",
    "compx  =  x1-x0\n",
    "z0     =  0.\n",
    "z1     =  1000.\n",
    "compz  =  z1-z0;\n",
    "hxv    =  (x1-x0)/(nptx-1)\n",
    "hzv    =  (z1-z0)/(nptz-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw previously, HABC has three approach possibilities (A1, A2 and Higdon) and two types of weights (linear and non-linear). So, we insert two control variables. The variable called *habctype* chooses the type of HABC approach and is such that:\n",
    "\n",
    "- *habctype=1* is equivalent to choosing A1;\n",
    "- *habctype=2* is equivalent to choosing A2;\n",
    "- *habctype=3* is equivalent to choosing Higdon;\n",
    "\n",
    "Regarding the weights, we will introduce the variable *habcw* that chooses the type of weight and is such that:\n",
    "\n",
    "- *habcw=1* is equivalent to linear weight;\n",
    "- *habcw=2* is equivalent to non-linear weights;\n",
    "\n",
    "In this way, we make the following choices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "habctype  = 3\n",
    "habcw     = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of points of the absorption layer in the directions $x$ and $z$ are given, respectively, by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "npmlx  = 20\n",
    "npmlz  = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lengths $L_{x}$ and $L_{z}$ are given, respectively, by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lx = npmlx*hxv\n",
    "lz = npmlz*hzv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the construction of the *grid* we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nptx    =  nptx + 2*npmlx\n",
    "nptz    =  nptz + 1*npmlz\n",
    "x0      =  x0 - hxv*npmlx\n",
    "x1      =  x1 + hxv*npmlx\n",
    "compx   =  x1-x0\n",
    "z0      =  z0\n",
    "z1      =  z1 + hzv*npmlz\n",
    "compz   =  z1-z0\n",
    "origin  = (x0,z0)\n",
    "extent  = (compx,compz)\n",
    "shape   = (nptx,nptz)\n",
    "spacing = (hxv,hzv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the case of the acoustic equation with Damping and in the acoustic equation with PML, we can define specific regions in our domain, since the solution $u_{1}(x,z,t)$ is only calculated in the blue region. We will soon follow a similar scheme for creating *subdomains* as was done on notebooks <a href=\"02_damping.ipynb\">Damping</a> and <a href=\"03_pml.ipynb\">PML</a>.\n",
    "\n",
    "First, we define a region corresponding to the entire domain, naming this region as *d0*. In the language of *subdomains* *d0* it is written as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class d0domain(SubDomain):\n",
    "    name = 'd0'\n",
    "    def define(self, dimensions):\n",
    "        x, z = dimensions\n",
    "        return {x: x, z: z}\n",
    "d0_domain = d0domain()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue region will be built with 3 divisions:\n",
    "\n",
    "- *d1* represents the left range in the direction *x*, where the pairs $(x,z)$ satisfy: $x\\in\\{0,npmlx\\}$ and $z\\in\\{0,nptz\\}$;\n",
    "- *d2* represents the rigth range in the direction *x*, where the pairs $(x,z)$ satisfy: $x\\in\\{nptx-npmlx,nptx\\}$ and $z\\in\\{0,nptz\\}$;\n",
    "- *d3* represents the left range in the direction *y*, where the pairs $(x,z)$ satisfy: $x\\in\\{npmlx,nptx-npmlx\\}$ and $z\\in\\{nptz-npmlz,nptz\\}$;\n",
    "\n",
    "Thus, the regions *d1*, *d2* and *d3* aare described as follows in the language of *subdomains*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class d1domain(SubDomain):\n",
    "    name = 'd1'\n",
    "    def define(self, dimensions):\n",
    "        x, z = dimensions\n",
    "        return {x: ('left',npmlx), z: z}\n",
    "d1_domain = d1domain()\n",
    "\n",
    "class d2domain(SubDomain):\n",
    "    name = 'd2'\n",
    "    def define(self, dimensions):\n",
    "        x, z = dimensions\n",
    "        return {x: ('right',npmlx), z: z}\n",
    "d2_domain = d2domain()\n",
    "\n",
    "class d3domain(SubDomain):\n",
    "    name = 'd3'\n",
    "    def define(self, dimensions):\n",
    "        x, z = dimensions\n",
    "        if((habctype==3)&(habcw==1)):\n",
    "            return {x: x, z: ('right',npmlz)}\n",
    "        else:\n",
    "            return {x: ('middle', npmlx, npmlx), z: ('right',npmlz)}\n",
    "d3_domain = d3domain()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure below represents the division of domains that we did previously:\n",
    "\n",
    "<img src='domain3.png' width=500>\n",
    "\n",
    "After we defining the spatial parameters and constructing the *subdomains*, we then generate the *spatial grid* and set the velocity field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = Grid(origin=origin, extent=extent, shape=shape, subdomains=(d0_domain,d1_domain,d2_domain,d3_domain), dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "v0 = np.zeros((nptx,nptz))                     \n",
    "X0 = np.linspace(x0,x1,nptx)\n",
    "Z0 = np.linspace(z0,z1,nptz)\n",
    "    \n",
    "x10 = x0+lx\n",
    "x11 = x1-lx\n",
    "        \n",
    "z10 = z0\n",
    "z11 = z1 - lz\n",
    "\n",
    "xm = 0.5*(x10+x11)\n",
    "zm = 0.5*(z10+z11)\n",
    "        \n",
    "pxm = 0\n",
    "pzm = 0\n",
    "        \n",
    "for i in range(0,nptx):\n",
    "    if(X0[i]==xm): pxm = i\n",
    "            \n",
    "for j in range(0,nptz):\n",
    "    if(Z0[j]==zm): pzm = j\n",
    "            \n",
    "p0 = 0    \n",
    "p1 = pzm\n",
    "p2 = nptz\n",
    "        \n",
    "v0[0:nptx,p0:p1] = 1.5\n",
    "v0[0:nptx,p1:p2] = 2.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we introduce the local variables *x10,x11,z10,z11,xm,zm,pxm* and *pzm* that help us to create a specific velocity field, where we consider the whole domain (including the absorpion region). Below we include a routine to plot the velocity field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph2dvel(vel):\n",
    "        plot.figure()\n",
    "        plot.figure(figsize=(16,8))\n",
    "        fscale =  1/10**(3)\n",
    "        scale  = np.amax(vel[npmlx:-npmlx,0:-npmlz])\n",
    "        extent = [fscale*(x0+lx),fscale*(x1-lx), fscale*(z1-lz), fscale*(z0)]\n",
    "        fig = plot.imshow(np.transpose(vel[npmlx:-npmlx,0:-npmlz]), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n",
    "        plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "        plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "        plot.title('Velocity Profile')\n",
    "        plot.grid()\n",
    "        ax = plot.gca()\n",
    "        divider = make_axes_locatable(ax)\n",
    "        cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "        cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
    "        cbar.set_label('Velocity [km/s]')\n",
    "        plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we include the plot of velocity field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "graph2dvel(v0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time parameters are defined and constructed by the following sequence of commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0    = 0.\n",
    "tn    = 1000.   \n",
    "CFL   = 0.4\n",
    "vmax  = np.amax(v0) \n",
    "dtmax = np.float64((min(hxv,hzv)*CFL)/(vmax))\n",
    "ntmax = int((tn-t0)/dtmax)+1\n",
    "dt0   = np.float64((tn-t0)/ntmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the temporal parameters, we generate the time properties with *TimeAxis* as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = TimeAxis(start=t0,stop=tn,num=ntmax+1)\n",
    "nt         = time_range.num - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The symbolic values associated with the spatial and temporal grids that are used in the composition of the equations are given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "(hx,hz) = grid.spacing_map  \n",
    "(x, z)  = grid.dimensions     \n",
    "t       = grid.stepping_dim\n",
    "dt      = grid.stepping_dim.spacing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the Ricker source: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f0      = 0.01\n",
    "nsource = 1\n",
    "xposf   = 0.5*(compx-2*npmlx*hxv)\n",
    "zposf   = hzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = RickerSource(name='src',grid=grid,f0=f0,npoint=nsource,time_range=time_range,staggered=NODE,dtype=np.float64)\n",
    "src.coordinates.data[:, 0] = xposf\n",
    "src.coordinates.data[:, 1] = zposf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we include the plot of Ricker source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UaRARyTQFDRlVKk8o0yAiIk1R0JBRxfEKPappEBGRJihoyChlGkREpFkKGjKqmUxDV97ImTINIiJZp6Aho0rlCXpjZhrMjN6uvNZpEBHJuNhBgwXON7NPmtmXzOzYcPtZZnZM65oorVBqItMAwaqQWhFSRCTbCnEOMrMB4IfAs4D9wCLgH4CHgLcCe4C/aFEbpQWayTQAyjSIiEjsTMMngDXAc4AjgOqnHP0E+L2E2yUtVhqfUKZBRESaEivTAFwAvMfdbzaz2o+nDxMEFJISlQlnrDIx+SCqOJRpEBGRuO8aC4FHptjXy6GZB+lwY2HGIHrkdRw9XXllGkREMi5u0HAP8KIp9p0F/CaZ5kg7RBmDZjINPYWcMg0iIhkXd3jis8BnzWwf8K/htqVm9ibgz4CLW9E4aY3SDDINvV159o2WW9UkERFJgVgfNd39C8BlwIeA+8PNPwauBD7l7l9vTfMOMrM1ZvZdM9tnZvvN7PtmtnYG13mfmbmZ/awV7UyDmWYaSso0iIhkWtxMA+7+V2b2OeCFwEpgN/Bjd3+wVY2LmFk/cD1QAt4IOHApcIOZPcXdh2NeZz3wfuCJVrU1DWaaaVBNg4hItsUOGgDc/SHgiy1qSyNvBdYDJ7n7/QBmdgdwH/A2gixIHJ8Dvg6cRJO/+3wSPUNCmQYREWnGlG+czab+3f3h2TdnSucDt0QBQ3i/LWb2c4LpoNMGDWb2WuBpwB8C329VQ9MgeoZEc5mGHEVlGkREMq3Rp+2tBMMAccV/B2reqcBVdbZvBl413cnhipaXA//T3feYZXuG6EwyDb2FvDINIiIZ1yhoeDMHg4Ye4K8JlpD+NvA4cBTwaoIlpT/cwjYCLAMG62zfAwzEOP8TwL3Al+PczMwuJpwRsnZt07WWHS/KNMR9NDZAjzINIiKZN2XQ4O5fjr43s08BvwJe4e5etf0S4P8CG1rWwqom1dk2bcrAzJ4LvAF4WnXbG97I/UqCmSFs3LixmWxLKkSZht4mlpHuLeSpTDjjlQkKeT0cVUQki+L+9f9D4IraN93w588Dr026YTUGCbINtQaon4GodgXwT8B2M1tqZksJgqV8+HNPoi1NgZlmGgBlG0REMqyZZaRXTLFvJbAgmeZMaTNBXUOtDcCd05x7CvB2guAiej0HOCP8/h3JNTMdZpRpCIsmtSqkiEh2xZ12eCPwUTO7y93/O9poZs8EPhLub6WrgU+a2fpoXQgzW0fw5v9X05z7/DrbPkVQuPnnHFysKjNKM8k0hEWTWqtBRCS74gYNf0bwCOxbzGwbQSHkkQRPt9wS7m+lL4T3uMrM/pqgvuHDwDaC4QcAzOxY4AHgEne/BMDdb6y9mJntBQr19mVBMZo9oUyDiIg0Ie4y0luAkwnS/NcRrAZ5HcHCSqe4+9ZWNTC8/zBwDsEMiK8SLNC0BTjH3YeqDjWCDIIq9Ro4mGloZnGn/CHniohI9jSzjHSZ4BP/F1rXnIb3fxi4cJpjthJjRoW7n51Mq9KpOF6hu5CjmfUqDhZCKtMgIpJV+kSeQaXyBL1NZBkgmHIZnSsiItkUK9NgZltovDqku/vxyTRJWq00XqGniSWkQZkGERGJPzxxE4cHDcuB3wWGCJ5AKSlRKk80Nd0SqjMNChpERLIqVtDg7hfV2x4ulPQjgpkVkhKl8YmmplvCwUyDplyKiGTXrGoa3H0vwXMdPpBIa6QtiuVK85kGTbkUEcm8JAohi8DqBK4jbTKTTEOvFncSEcm82FMua5lZAXgy8EGCZZ4lJWaSaehRpkFEJPPizp6YYOrZE/uBlybWImm50vgEi/u6mjpnMtOgKZciIpkVN9NwCYcHDUXgIeD/ufu+RFslLVUsV5paDRKgkM+Rz5mmXIqIZFjc2RMfbHE7pI1K4xOThY3N6C3kJh+rLSIi2RPr46aZXW9mJ0+x70lmpnUaUqQ03nymAYK6hpIyDSIimRX3neNsYPEU+xYBZyXSGmmLYlmZBhERaV4zHzenKoQ8nmBVSEmJmWYaervymnIpIpJhU9Y0mNmbgDeFPzpwpZkdqDmsj2Da5XWtaZ4kzd0plieafvYEQHchpymXIiIZ1ujj5gRQCV9W83P02g18Dvjj1jZTkjJWCTIFyjSIiEizpsw0uPtXgK8AmNkNwDvc/e52NUxaI6pJmFEhpDINIiKZFnfK5fNb3RBpj2j2w4wKIbvy7B0ZS7pJIiKSEo1qGt4A/Ie77w6/b8jd/yXRlklLlGaRaejtyml4QkQkwxplGr4MnEFQt/Dlaa7jgIKGFJhNpqGnkNfwhIhIhjUKGo4DdlR9L/PAbGoalGkQEcm2RoWQD9X7XtJNmQYREZmp5j9uSqqppkFERGaqUSHkFqZeBbKWu/vxyTRJWil6059NpsHdMbOkmyYiIh2uUU3DTcQPGiQlouGFnq6ZZRomHMoVp7ugoEFEJGsa1TRc1MZ2SJtEmYaewszWaQiuUaF7BsMbIiKSbvrLnzFRpqF3BpmGqA5CT7oUEcmm2O8cZnaimX3FzO41s+Hw65fN7IRWNlCSNZtMQ09VpkFERLIn1jLSZnY28ENgFPgP4HHgSODlwGvM7Dx3v6lFbZQEKdMgIiIzFfed4++BXwPHuvsb3P0v3f0NwDrgtnB/S5nZGjP7rpntM7P9ZvZ9M1sb47yNZnalmd1tZiNm9rCZfd3MMrlg1cHFnWZX0yAiItkTN2jYAHzc3YeqN7r7AeDjwKlJN6yamfUD1wMnA28E/gg4EbjBzBZMc/ofhO37NPBi4K+ApwGbzGxNyxrdoYrjFbryRj7X/OwHZRpERLIt1vAEsB3onmJfN/BIMs2Z0luB9cBJ7n4/gJndAdwHvA24rMG5H3f3ndUbzOznwJbwuh9oSYs7VLFcoXcGWQZQpkFEJOviZho+DnzIzFZVbwx//hvgo0k3rMb5wC1RwADg7luAnwMXNDqxNmAItz0E7ARWHX7G/FYsT0wWNDZrMmhQpkFEJJPiZhrOAhYBD5jZLRwshDwj/P7ssFgSgtUh35hwO08FrqqzfTPwqmYvZmanACuBu2bZrtQpjVdmVAQJ1cMTyjSIiGRR3KDhTKBC8NTLY8MXHHwK5nOrjm3FKpLLgME62/cAA81cyMwKwOcJMg3/NMUxFwMXA6xdO22tZaqUyhMzWkIaqocnlGkQEcmiWEGDu3fCTIN6wchM1jL+DPC7wEvdvV4ggrtfCVwJsHHjxnm1lHaxrEyDiIjMTNxMw1wbJMg21BqgfgaiLjP7W4IMwhvd/dqE2pYqxfEkCiGVaRARyaKmgoZwiuIaoLd2n7tfn1Sj6thM/WmdG4A741zAzN5PMN3yL9z9qwm2LVWK5Qn6Zjg8oUyDiEi2xV0Rcj3wdeCZ0abwq4ffOzCzd6J4rgY+aWbr3f3BsE3rgOcQBAINmdlfAJcC73f3f2hhOztesVxhoL9rRucq0yAikm1xMw1fBNYC7wLuBsZa1aApfAH4M+AqM/trgiDlw8A24IroIDM7FngAuMTdLwm3/QHwKeBHwPVmdkbVdfe7e6xMxXxRLFdmPOUynzO68qZMg4hIRsUNGp4BXOTu32tlY6bi7sNmdg5wOfBVguzGdcC7alapNIKMR3Wl33nh9vPCV7WbgLNb1OyOVCxPTA4zzERPIa8VIUVEMqqZFSHbnV04hLs/DFw4zTFbqZlR4e4XARe1ql1pE6zTMPORpN6unFaEFBHJqLgfOT8KvDfGcx6kwxXLEzOePQHKNIiIZFncdRq+amYnA1vDFSFrpzm2YhVIaYHZrAgJ0KNMg4hIZsWdPXER8D6CVSGfxuFDFfNqAaT5qjLhlCs+q+EJZRpERLIrbk3Dh4B/A/7Y3fe2rjnSStGsh9lkGlTTICKSXXHfPZYD/6iAId0OBg2zKIQs5PWUSxGRjIobNPwMOKWVDZHWK4aLMs2qELIrR1GZBhGRTIo7PPFO4NtmNkiwSNJhz3twd3387HBRpqFnNsMTyjSIiGRW3KDhrvDrvzQ4ppXLSEsCkhieUKZBRCS74gYNl6AZEqkXzXqYzYqQyjSIiGRX3HUaPjjVPjM7G3hDMs2RViolUQipTIOISGbN6COnmZ1gZpeY2RaCZ0C8OtlmSStEb/azG55QpkFEJKtiBw1mtsTMLjaznwH3AO8nKIj8E+CYFrVPEhQNT8xqnYZCkGlw12iViEjWNHz3MLOcmb3EzL4J7AA+D6wDPhse8i53v8Ld97e2mZKEaFGm2U25zOMOYxVlG0REsmbKoMHMPgk8AvwAeDnBipDnAWuBD1DzNEnpfAczDbNZRjr4J1MaV9AgIpI1jQoh300wY+KHwEXuvjvaYWbKTadQEstI94QBR7FcYXFvVyLtEhGRdGj07vHPwAHgpcA9ZvYZM3tme5olrZBEpqE3yjSoGFJEJHOmDBrc/S3AUcDrgVuBtwM3m9ldwHvRug2pM7ki5GzWaQgDDj20SkQkexq+e7h70d3/1d3PBdYA/4vg8dh/RVDT8DEze72Z9ba+qTJbxfEKPYUcZjMvR4kCDj0eW0Qke2J/5HT3He7+cXd/MvAs4B+BEwmWlt7RovZJgkrliVllGUCZBhGRLJvRO4i7/7e7/xnB+gyvBG5KtFXSEsVyZVb1DFA1e0KZBhGRzIn77Im63L0MfD98SYdLImiIztdS0iIi2TO7XLWkSrE8MavplnDwsdqqaRARyR4FDRlSGk8g01BQTYOISFYpaMiQYnliVktIQ9XwhDINIiKZo6AhQ4rjlcnhhZk6WAipTIOISNYoaMiQoKYhqUJIZRpERLJGQUOGlDTlUkREZkFBQ4YUy5XJZ0fMVC5ndOdzmnIpIpJBqQkazGyNmX3XzPaZ2X4z+76ZrY15bq+ZfcLMdpjZqJndbGbPa3WbO01xfGLWNQ0QZBuKqmkQEcmcVAQNZtYPXA+cDLwR+COCJaxvMLMFMS7xT8BbgQ8ALyNY9voaM3tqSxrcoYJMw+yGJyB4PHZJNQ0iIpkzqxUh2+itwHrgJHe/H8DM7gDuA94GXDbViWZ2OvBa4M3u/qVw203AZuAS4PzWNr0zuHsiK0IC9HYp0yAikkWpyDQQvLHfEgUMAO6+Bfg5cEGMc8vAt6rOHQe+CZxrZj3JN7fzlCvOhDPrFSEhGJ5QpkFEJHvSkmk4FbiqzvbNwKtinLvF3UfqnNsNnBB+33I7D5QojVc4ekkf+dzMH089E9EKjslkGvJztk7DUGmcPUNjDI6MMVQaZ3zCqUxMMF5xKhPO+IQndq/kriQi0jqLewucfdLKttwrLUHDMmCwzvY9wMAszo32H8LMLgYuBli7NlatZSxfveUhPn3dfRRyxrojFvCMdct42VOO5tnrl5NrcRARreDYk0DQ0M5Mw1BpnB/c/ijX3fUEdz66j0f3FdtyXxGRtNhw9GIFDXXU++AX553Wmj3X3a8ErgTYuHFjYh84X3ra0Ry9pJeH94xw9479/OD2R/nGLx/mtFVLuOSCU/mdtdPFPzMX1SDMdsolBJmGVtc0uDtf+cVWPnXdfewdKbNmWR/PPG4ZJx65iCMX97K0r4uFvQW68jkKOSOfMwp5I2+GJRp/tTcjJCLSrJ4E/q7HlZagYZA6GQGCLEO9LEK1PUC9dMFA1f62OOmoRZx01KLJn4vlCj+4/VH+/tp7efUVN/PxC5/C//e01S25d5LDEz2FHAeK47O+zlRK4xX+4hu/5prNj/PcE4/gf7zwSfzOmqVYstGAiIg0KS1Bw2aC2oRaG4A7Y5z7CjPrr6lr2ACMAffXP631ervyvGrjGl506lG842u38u5v305/d4HznnxU4veKhicSq2lo0eJOExPOu799O9dsfpy/fukp/PGZxylYEBHpEGmZPXE1cIaZrY82mNk64DnhvunO7aKqYNLMCsBrgGvdvZR4a5u0pK+LL73pGZy+egnv+c7tbNk1nPg9JocnEpg9EQxPtKam4Uu/2Mp/3LGD9734ZN7y3PUKGEREOkhagoYvAFuBq8zsAjM7n2A2xTbgiuggMzvWzMbN7APRNne/jWC65afM7C1m9nsE0y2PA/6mfb9CYz2FPJ97/dMxg0t+kPxkjslCyCQWd2rRipDbB0f4+2vv4fknreDi562f/gQREWmrVAQN7j4MnAPcC3wV+DqwBTjH3YeqDjUgz+G/15uALwGXAv8BrAHOc/dftbjpTTlmaR9/fs4J3HDPTn52365Er518piH5oOHvr70Xd/jw7z9ZGQYRkQ6UlpoG3P1h4MJpjtlKnXJ3dx8F3h2+Otobnr2Or/ziIS7/yb2ceeIRiV23mGAhZH93ntGEg4ZH947yg9sf5Y2/u47VA/2JXltERJKRikxDlvR25bnod9dx60OD3PPYgcSuO1kImcDwRF9XnnLFKVeSq2v40s+34MCbnrMusWuKiEiyFDR0oAufvprufI5v/PLhxK6Z5PBEX3cQeCSVbRgbn+Dbm7Zz3pOPUpZBRKSDKWjoQMsWdPPi047ie7/antjUxtGx4DrRG/5sRNcojiXTtp8/sIt9o2Ve8dRViVxPRERaQ0FDhzr/9GM4UBznvx5MZu2pKCvQ3z37Mpb+MGgYSSho+OEdO1jUW+C5T0quhkNERJKnoKFDPeeEI+jtynH93U8kcr2RsQrdhVwiD8rq60pueGJsfIJrNj/GizYclch0UBERaR0FDR2qtyvPmSccwU/uehz32T/+YnRsfPLNfrb6wmxFEpmGWx8aZH9xnHNPPXLW1xIRkdZS0NDBzjn5SLYPjnLv40PTHzyNkbHK5LDCbEXBRxJrNdz84G5yBmccv3zW1xIRkdZS0NDBzjppBQC/eGD2Cz2NliuJFEFCsjUNNz+wi9NWLWFxb9esryUiIq2loKGDrVraxzFLetn00HQP8pze6FglseGJ3oRqGkbGxrlt216efbwKIEVE0kBBQ4d7+rpl3Lp1cNZ1DUkOT0TXGR2b3eOxN20dpFxxnq2hCRGRVFDQ0OE2HjvAY/uLPLJ3dFbXGSlXJgsYZ2ty9sQshyc2PTRIzoLfUUREOp+Chg63cV3whrpp6+yGKIpjFfoTmz0R1jTMcnjiN9v3cuLKRSzoSc0jUEREMk1BQ4c7+ajFLOwpcOss6xpGyuOJFUL2FHKYzW5FSHfnN4/s47TVSxJpk4iItJ6Chg6Xzxkbjl7MnTv2z+o6o2PJzZ4wM/q78rOaPfHY/iK7hsY4bZWCBhGRtFDQkAKnHL2Iu3bsZ2Ji5sWQowkOT0AwRDGb4Yk7tu8DUKZBRCRFFDSkwIZjFjMyVuHhPSMzOt/dw0LIZIOG2QxP/Gb7vsksioiIpIOChhQ4JXxjvWuGQxSl8Qnck3nCZaRvlsMTv310HyeuXDi55oOIiHQ+BQ0p8KQjF5EzZlzXEL25Jzs8UZjV4k73PnaAk49alFh7RESk9RQ0pEBvV571KxbOONMQvbknm2nIzXidhgPFMo/uK3LikQoaRETSREFDSpxy9GLu2nFgRudGKzcmtbgTQP8sMg0P7BwG4MSVCxNrj4iItJ6ChpR40sqFPLJ3lJEZLN3ckuGJrvyM2gJw7+NB8KNMg4hIuihoSIn1K4JP5Q+Gn9KbEQUNic+eKE/M6Nz7nxiiu5Bj7bL+xNojIiKtp6AhJY5fuQCAB3c1HzS0pqZh5pmG+x4/wPErFpLPWWLtERGR1lPQkBLrli/ADB7cOdT0uVHBYlJPuYyuNdOahvueGFI9g4hICiloSInerjyrB/omiwibcbCmIblCyGh4otlVKkfHKmwfHOUEBQ0iIqmjoCFF1h+xcIaZhmAYobc7uf/c0eOxi+PNZRse2RusannsctUziIikjYKGFDl+xUIe3Dnc/Kf7cjQ8keSUy/Dx2E2u1bBtzygAqwf6EmuLiIi0h4KGFFm/YgGj5QqP7S82dd7k7IkEp1xGyz83u8DT9sEg07BmQJkGEZG0SUXQYGY5M3ufmW01s6KZ3W5mF8Y4b7GZfcDMfmFmu81sb/j977eh2Ylbf0Qwg2JrkzMoRscqdBdyic5WiLIWzRZDbhscpbuQ44iFPYm1RURE2iMVQQPwYeCDwGeAFwO3AN8xs5dMc95a4E+Am4DXA68B7gX+zcz+tGWtbZE14boG2wabe9rlaLmS6MwJgL6wPqLZ4YntgyOsHugjp+mWIiKpk9wgd4uY2UrgPcDH3P2T4eYbzOwE4GPADxucvgVY7+7V77LXmNka4L3AZ1vR5lY5ekkv+ZxN1gXENTJWSXQ1SICFPV0ADJeaW6th++AoqzU0ISKSSmnINJwLdANfq9n+NeA0MztuqhPdfbgmYIhsAo5JrontUcjnOGZpLw/vaTLTMFahN+FMw4Ke4HoHis0FDdv2jKgIUkQkpdIQNJwKlID7a7ZvDr9umME1nwfcPZtGzZU1A/1ND08Mj42zIMGZEwCLZpBpGCqNMzhSVhGkiEhKpSFoWAbsdffaeYZ7qvbHZmYXA2cAf9voGDPbZGabdu7c2VRjW23tsv6mhyeGS+Ms7Ek2aIgyDUNNBA3RzAllGkRE0qntQYOZvcDMPMbrxugUoN7CBE1X0pnZ2cCnga+6+9enOs7dr3T3je6+ccWKFc3epqXWLOtn11Cpqec+HCiOs7A36aAhuF5TQYPWaBARSbW5KIT8BXBKjOOiHPweYMDMrCbbMFC1f1pm9gzgauB64I9jtrXjRG+42wdHeVLMR0sPlcZZlHCmoaeQoytvTQ1PTK7RoKdbioikUtuDhrAwsZl6gs1AD3A8h9Y1RLUMd053ATM7DbgGuA240N3LTdy/o0SPk962Z6SpoCHpTIOZsaCn0FSmYdvgKH1deZYv6E60LSIi0h5pqGn4ETAGvK5m++uB37r7lkYnm9mJwI+BB4GXuXtzBQEdJvqUHncGhbszVEy+pgFgYZNBQ7RGg5nWaBARSaOOX6fB3Z8ws8uB95nZAeBXBIs0nQNcUH2smV0HHOvuJ4Q/ryQIGLqBvwE21Lxh/drdS63/LZKzfEE3fV352MWQpfEJxic88UwDhEFDE1MugzUaVM8gIpJWHR80hN4PDAHvBI4C7gFe7e4/qDkuz6G/0wbg2PD7f69z3eOArYm2tMXMLJhBEXPaZZQJaFWmYbiJgsxte0Z42tqB6Q8UEZGOlIqgwd0rwKXhq9FxZ9f8fCMzmGXR6dYs62NbzOGJKBPQiqBhQU+BvSNjsY7dN1pmf3GcNcuUaRARSas01DRIjdUD/WzbM8LhS1ccrtWZhrg1DQfXaNDMCRGRtFLQkEJrl/UzPFZhz/D0n/KjZZ5bVdMwXIr3wKrtg1qjQUQk7RQ0pFD1Wg3TiTIB0bLPSWpmymXUVi0hLSKSXgoaUihK8T+yN07QECxJ0ZJMQ29QCDkxMf0wybY9IyzozrO0P/ngRURE2kNBQwqtCjMNj8TJNLSwEHJhTx53GClPP0SxfXCUNcv6tUaDiEiKKWhIoSV9XSzqKUwWFzYyFNYcLGpJTUP8J11GCzuJiEh6KWhIqVUDfbGHJwo5o6eQ/H/q6EmXB6ZZ4Mndw4WdVM8gIpJmChpSavVAX7xCyOI4C3oKLRkWiIY8pss07BstM1QaV6ZBRCTlFDSk1KqlfbFqGg6UWvPcCTgYNEw3g2Lb5COxlWkQEUkzBQ0ptXqgnwOlcfaNNn5g51BxvCX1DBBMuYTpg4aDj8RWpkFEJM0UNKRU3BkUQy3MNETByHTDE9u0GqSIyLygoCGlVi2NFnhqPINiuDTekjUaoJlMwyiLegss6dMaDSIiaaagIaWiosLpZlB0Qk3D9sFRrQQpIjIPKGhIqWULuuntyk0/PNHCmoaeQo6uvLF/dLpCSK3RICIyHyhoSCkzY9XS6addtrKmwcxY0tfdsBhTazSIiMwfChpSbPVAf8PhiXJlgpGxyuTKja2wpK/A/gZBw+7hMUbLFc2cEBGZBxQ0pNh0q0JGGYCBBa0MGrrYOzr1I7oPPhJbmQYRkbRT0JBiq5b2sWd4jJGx+jUFe0eCoKGVsxaW9jcentAaDSIi84eChhRbPc1aDfvCDMDS/u6WtWFJX9dkcFJPtBpkNEVURETSS0FDikVBw/YphiiiN/OlLcw0LOnrmjbTsLS/i0W9WqNBRCTtFDSk2KqlQZ3AVDMoJoOG/tYGDQeK41QmvO7+bVqjQURk3lDQkGIrF/XQlbcphyf2jkaZhtYOTwBTzqDYPqg1GkRE5gsFDSmWyxnHLJ16BsXekTFyRssWd4KDWYy9dYIGd+eRwVEFDSIi84SChpQLFniq//yJvSNllvR1kctZy+6/bEGQxdgzXDps384DJUrjE6xZpuEJEZH5QEFDyq0e6JtyeGJwZKylMycAli/oAWD30OFrNRx8uqUyDSIi84GChpRbtbSfJw6UKI1XDtu3a6jEEQtbHDSE1989fHjQ8NDuIGg4dvmClrZBRETaQ0FDyq0KP8Xv2Fs8bN+uoTGOWNjT0vtHwxO7hw4fnti6ewQzZRpEROYLBQ0pN7lWQ50hil1DpclMQKv0duVZ1FOom2l4ePcwxyzpo6eQb2kbRESkPVIRNJhZzszeZ2ZbzaxoZreb2YUzuM56MxsxMzezE1rR1naLgoZtNcWQ5coEe0fKLc80ACxb2F23pmHr7hHWHaEiSBGR+SIVQQPwYeCDwGeAFwO3AN8xs5c0eZ1/BPYl27S5dfSSPnoKOR7cOXTI9uhNvB1Bw/IF3eyuM3viod3DrF2megYRkfmi44MGM1sJvAf4mLt/0t1vcPe3ATcAH2viOq8Ffgf4eGtaOjfyOeO4Ixbw4M7hQ7bvCmsM2hE0HLGwh50HDg0a9o2WGRwps265Mg0iIvNFxwcNwLlAN/C1mu1fA04zs+Omu4CZDQCXEQQfe5Nu4Fw7fsVCHqjJNOwMg4YVi1pb0wBw9JLewwoxH9bMCRGReScNQcOpQAm4v2b75vDrhhjX+Dvgbnf/apIN6xTHr1jAtsHRQ6ZdPhquEnn0ktbPXDh6aR8HSuMcKB5cFXLr7iDzoZoGEZH5Iw1BwzJgr7vXPhFpT9X+KZnZmcAbgD+Je0Mzu9jMNpnZpp07dzbV2LmwfsVCKhM++ekegsdlF3LGkYt7W37/o5cE93hs38Fsw0Nh0LBWq0GKiMwbbQ8azOwF4eyF6V43RqcA9R6hOO3ayGbWDVwBXO7ud8Zto7tf6e4b3X3jihUr4p42Z45fsRDgkCGKR/aOctSSXvItXEI6cszSIJvxaFXQcO/jQ6xa2kd/d+ueeyEiIu01F3/RfwGcEuO46GPzHmDAzKwm2zBQtX8q7yLIRHzazJaG26KPvovMbJG7H4jV6g523IqgbuCBqmLIRwZHWbW0PYsqRZmGHVUPzrr38QOcdNSittxfRETao+1Bg7uPAHc3ccpmoAc4nkPrGqJahkYZhA3AUcAjdfb9CrgdeGoTbelIC3sKHLW4lweeODTT8Ozjl7fl/kct7qU7n2NrODxSrkzwwM4hzj5pZVvuLyIi7ZGG3PGPgDHgdcCHqra/Hvitu29pcO7HgC/XbDsPeG94/j3JNXNunXL0IjY/uh+AodI4O/YVOa5NMxcK+RzHHbGA+58Ikjb3PzFEueKcrEyDiMi80vFBg7s/YWaXA+8zswMEGYLXAOcAF1Qfa2bXAce6+wnhuXdTk9Uws3Xht//l7rUzMlLr9DVLufHe+xgqjXPPY8Gb98lHL27b/U84ciG/fSRYN+vXD+8F4Klrlrbt/iIi0nodHzSE3g8MAe8kGG64B3i1u/+g5rg86fmdEnX6mqW4wx3b97JlV1Db0M5P+ieuXMgPf7OD0bEKv3p4kGULujlWCzuJiMwrqXiDdfcKcGn4anTc2TGu9WUOH7JIvacfO0AhZ/z03l3sGS6xqLfQ1qdLnr46CFp+uXUPNz+wm6cfO4BZ62duiIhI+6QiaJDpLe7t4tnHL+ff73iU/aNlnn/Syra+aZ+xfjndhRyXXXsPj+wd5d0vfFLb7i0iIu2RhsWdJKbXPGMN2wdH2V8c5+WnH9PWe/d153nBKSu5ffs++rryvOjUI9t6fxERaT1lGuaRl552NFtfNMyS/m5ecEr7pzv+7SuewuLeLl79jDUs6u1q+/1FRKS17PDVmaXaxo0bfdOmTXPdDBERkbYws1vdfWO9fRqeEBERkVgUNIiIiEgsChpEREQkFgUNIiIiEouCBhEREYlFQYOIiIjEoqBBREREYlHQICIiIrEoaBAREZFYFDSIiIhILAoaREREJBYFDSIiIhKLggYRERGJRU+5nIaZ7QQeSvCSRwC7ErxeFqkPk6F+nD314eypD2cv6T481t1X1NuhoKHNzGzTVI8clXjUh8lQP86e+nD21Iez184+1PCEiIiIxKKgQURERGJR0NB+V851A+YB9WEy1I+zpz6cPfXh7LWtD1XTICIiIrEo0yAiIiKxKGhoAzNbY2bfNbN9ZrbfzL5vZmvnul1zzcxeaWbfM7OHzGzUzO4xs781s0U1xw2Y2RfNbJeZDZvZT8zstDrX6zWzT5jZjvB6N5vZ89r3G3UGM/uRmbmZXVqzXf04DTN7iZn91MyGwv9XN5nZOVX71YcNmNlzzOxaM3si7L9fmdmba45RH4bMbLWZ/UP4u42E/9+uq3Ncon1mZjkze5+ZbTWzopndbmYXxmq0u+vVwhfQD9wH/Bb4feAC4DfAA8CCuW7fHPfNLcC3gdcBZwHvAvaG23PhMQb8J7Ad+EPgPOAmgjnJq2uu9/Xw/LcCvwd8HxgFnjrXv2sb+/QPgR2AA5dWbVc/Tt93bwPKwOXAC4FzgfcCL1Mfxuq/p4S/4w3h37kXAleE/xbfoT6s22dnA48DPwSuCftqXc0xifcZ8BGgBLwHeH7432kCeMm0bZ7rTpvvL+CdQAU4oWrbccA48O65bt8c982KOtveEP6Pc0748wXhz8+vOmYJsAf4dNW208Pj3lS1rQDcA1w9179rm/pzKfBY+IelNmhQPzbuu3XhH9d3NThGfdi4Dz8KjAELa7bfAtysPqzbZ7mq799C/aAh0T4DVoYBw4dq7nMdcMd0bdbwROudD9zi7vdHG9x9C/Bzgn8MmeXuO+ts/u/w66rw6/nAo+5+Q9V5+4AfcGj/nU/wKfFbVceNA98EzjWzngSb3qn+Dtjs7t+os0/92NibCT5pfb7BMerDxroJfu/Rmu17OTgUrj6s4u4TMQ5Lus/OJfhv9bWa+3wNOM3MjmvUGAUNrXcqwdBErc3Ahja3JQ3OCr/eFX5t1H9rzWxh1XFb3H2kznHdwAlJN7STmNmZBFmaP5niEPVjY2cCdwN/YGYPmNm4md1vZn9adYz6sLEvh18/bWbHmNlSM4vS5JeH+9SHzUu6z04lyDTcX+c4mOZ9SUFD6y0DButs3wMMtLktHc3MVgGXAD9x903h5kb9Bwf7cLrjliXVzk5jZl0EY5KfdPd7pjhM/djYMcCJwCeAjwEvAn4MfMbM3hkeoz5swN1/SzBGfwHwCEEffBZ4u7t/MzxMfdi8pPtsGbDXwzGJBsfVVWjYVElKvcUwrO2t6GBhtHwVQa3Hm6p3Ea//4h43H70X6CMobpqK+rGxHLAIuMjdvx9uuz6sZH+fmX0a9WFDZnYi8D2CT6xvJximuAD4vJkV3f3rqA9nIuk+m1XfKmhovUHqR24D1I8KM8fMeoGrgfXAWe6+vWr3HqbuPzjYh3uAetNYB6r2zzsWTN19P0ERVU/NWG+PmS0FDqB+nM5ugkzDj2u2X0tQrX406sPpfJRgTP1l7l4Ot11nZsuB/2Nm30B9OBNJ99keYMDMrCbbEKtvNTzRepsJxpBqbQDubHNbOk6YWv8e8EyC6T6/qTmkUf897O5DVccdZ2b9dY4b4/Dxu/liPdBLUMQ0WPWCYDrVIHAa6sfpbJ5ie/TpawL14XROA26vChgivwSWE1Ttqw+bl3SfbQZ6gOPrHAfTvC8paGi9q4EzzGx9tCFMeT4n3JdZZpYjmFf8e8AF7n5LncOuBlaZ2VlV5y0GXs6h/Xc10AW8quq4AvAa4Fp3LyX/G3SE2wjmWde+IAgknk/wx0L92Ni/hV/Prdl+LrDd3R9DfTidx4Cnmll3zfZnAUWCT7Dqw+Yl3Wc/IggiXldzn9cDvw1n901truepzvcXsIDgj/ZvCMb3zgduBx6kZj5z1l7A5wjXEwDOqHmtDo/JAb8AtgF/QPBH/EaCP0Braq73TYJP1m8hCES+S/DH6mlz/bvOQd/WrtOgfmzcXwZcTzBM8XaCQsgrw368SH0Yqw9fGfbXNeHfuhcBnwm3XaY+bNhvr6z6e/iO8OezWtVnBMW+ReDdBMWrnyPIpr182vbOdYdl4UUwzvQ9YD/B+PL/pWYBjyy+gK3h/yT1Xh+sOm4Z8M/h/yQjBIuQnF7nen3AZQSfeIrAfwFnz/XvOUd9e0jQoH6M1WeLCar9Hyf4JHYH8Fr1YVN9+OLwDW1n+LfuNoJpwHn14ZR9NtXfwBtb1WdAHvhr4CGC6Zd3AK+M01495VJERERiUU2DiIiIxKKgQURERGJR0CAiIiKxKGgQERGRWBQ0iIiISCwKGkRERCQWBQ0iIiISi4IGkYwyM4/x2mpm68LvL5rrNkfMbJWZDZvZxjbdz8zs12b2l+24n0in0uJOIhllZmfUbPo3giXOP1i1rUTwAJvfAR5w953taV1jZvbPwEp3f1kb7/kKglX5jnf3LD1lUWSSggYRAcDMtgI/c/fXz3VbGjGzIwnW4X+Fu/9HG++bB7YDl7v737XrviKdRMMTItJQveEJM/uymW03s41m9gszGzWze8zspeH+d4dDG/vN7CozW1FzzYKZvc/M7jazkpk9amZ/b2a9MZp0EcFzDa6pueaNZvYzMzvPzG4L2/RrM3tWeL+PmtkOM9sTtn9BTXs+bGYPmFnRzHaF1zozOsbdK8B3CB4GJJJJhblugIik1mLgX4BPAo8C7we+Z2afBZ4E/ClwJPApggdBvbrq3K8RPNr34wRP8DsF+DCwDrhwmvueB9zs7uN19p0AfAL4CDAE/B3BI4OvJvh7d1F4r08ATwD/MzzvvcD/CH+H28LfbSPBg4Kq/RT4czNb7+4PTtNOkXlHQYOIzNQi4O3u/lMAM3uUoCbiZcCG8JM5ZvZkgjfavLtXzOy5wGuAN7r7v4TX+omZ7QG+ZmZPdffb6t3QzAx4FnD5FG1aDvxu9IZuZjngKuA4d39BeMw1ZvY84FUcDBqeDVzr7v+n6lo/qHP9X4dfzyB4vL1Ipmh4QkRmajgKGEJ3h19/EgUMVdsLwNHhz+cRPHr6e+GwQMHMCsC14f7nNbjnUoLH/05VkHlvTQYgatM1NcfdDawOgxCA/wZeYmYfMbMzzax7iutH9z2mQRtF5i0FDSIyU3urf3D3sfDbwZrjou1RvcJKoJtg+KBc9Xoi3L+8wT2ja5Sm2D/VvettLwD58OePAn8DnA/8J7DbzL5kZkfUnDcafu1r0EaReUvDEyLSbruBIvDcKfY/Os25AANJNsjdywT1FR83s6MIhlguA/oJhlIiUY3DriTvL5IWChpEpN1+RFB4uMTdr2vmRHcfM7MtwPqWtCy4x2PAF83sJcCTa3YfF369p1X3F+lkChpEpK3c/UYz+wbwXTO7DPglMEEwc+IlwHvd/d4Gl/gp8Mwk22RmVxEUcf6KYCjjdwhqL66oOfRZBEMptyR5f5G0UNAgInPh9cCfA28mmOZYArYSFCw+Ps253wLeYGbr3H1rQu35KcFsij8lGJJ4mGC65kdqjnsZcLW7jyR0X5FU0YqQIpIq4TTK+4AvufulbbzvMQTBxLnNDquIzBcKGkQkdczsdQSFise161O/mV0OnO7u57TjfiKdSMMTIpJG/wqsIqiDuLNN99wBXNmme4l0JGUaREREJBYt7iQiIiKxKGgQERGRWBQ0iIiISCwKGkRERCQWBQ0iIiISy/8P+pEnjYw0zhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "src.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the receivers: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrec   = nptx\n",
    "nxpos  = np.linspace(x0,x1,nrec)\n",
    "nzpos  = hzv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rec = Receiver(name='rec',grid=grid,npoint=nrec,time_range=time_range,staggered=NODE,dtype=np.float64)\n",
    "rec.coordinates.data[:, 0] = nxpos\n",
    "rec.coordinates.data[:, 1] = nzpos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The displacement field *u* and the velocity *vel* are allocated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = TimeFunction(name=\"u\",grid=grid,time_order=2,space_order=2,staggered=NODE,dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel = Function(name=\"vel\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
    "vel.data[:,:] = v0[:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We include the source term as *src_term* using the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "src_term = src.inject(field=u.forward,expr=src*dt**2*vel**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Receivers are again called *rec_term*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_term = rec.interpolate(expr=u)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to generate the $\\omega$ weights, which are selected using the *habcw* variable. Our construction approach will be in two steps: in a first step we build local vectors *weightsx* and *weightsz* that represent the weights in the directions $x$ and $z$, respectively. In a second step, with the *weightsx* and *weightsz* vectors, we distribute them in two global arrays called *Mweightsx* and *Mweightsz* that represent the distribution of these weights along the *grid* in the directions $x$ and $z$ respectively. The *generateweights* function below perform the operations listed previously:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateweights():\n",
    "    \n",
    "    weightsx   = np.zeros(npmlx)\n",
    "    weightsz   = np.zeros(npmlz)\n",
    "    Mweightsx  = np.zeros((nptx,nptz))\n",
    "    Mweightsz  = np.zeros((nptx,nptz))\n",
    "    \n",
    "    if(habcw==1):\n",
    "    \n",
    "        for i in range(0,npmlx):\n",
    "            weightsx[i] = (npmlx-i)/(npmlx)\n",
    "    \n",
    "        for i in range(0,npmlz):\n",
    "            weightsz[i] = (npmlz-i)/(npmlz)\n",
    "    \n",
    "    if(habcw==2):\n",
    "           \n",
    "        mx = 2\n",
    "        mz = 2\n",
    "                   \n",
    "        if(habctype==3):\n",
    "            \n",
    "            alphax  = 1.0 + 0.15*(npmlx-mx)    \n",
    "            alphaz  = 1.0 + 0.15*(npmlz-mz)\n",
    "            \n",
    "        else:\n",
    "    \n",
    "            alphax  = 1.5 + 0.07*(npmlx-mx)    \n",
    "            alphaz  = 1.5 + 0.07*(npmlz-mz)\n",
    "            \n",
    "        for i in range(0,npmlx):\n",
    "                \n",
    "            if(0<=i<=(mx)):\n",
    "                weightsx[i] = 1\n",
    "            elif((mx+1)<=i<=npmlx-1):\n",
    "                weightsx[i] = ((npmlx-i)/(npmlx-mx))**(alphax)\n",
    "            else:\n",
    "                weightsx[i] = 0\n",
    "    \n",
    "        for i in range(0,npmlz):\n",
    "                \n",
    "            if(0<=i<=(mz)):\n",
    "                weightsz[i] = 1\n",
    "            elif((mz+1)<=i<=npmlz-1):\n",
    "                weightsz[i] = ((npmlz-i)/(npmlz-mz))**(alphaz)\n",
    "            else:\n",
    "                weightsz[i] = 0\n",
    "        \n",
    "    for k in range(0,npmlx):\n",
    "            \n",
    "        ai = k\n",
    "        af = nptx - k - 1 \n",
    "        bi = 0\n",
    "        bf = nptz - k\n",
    "        Mweightsx[ai,bi:bf] = weightsx[k]\n",
    "        Mweightsx[af,bi:bf] = weightsx[k]\n",
    "                        \n",
    "    for k in range(0,npmlz):\n",
    "            \n",
    "        ai = k\n",
    "        af = nptx - k \n",
    "        bf = nptz - k - 1        \n",
    "        Mweightsz[ai:af,bf] = weightsz[k]\n",
    "        \n",
    "    return Mweightsx,Mweightsz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the *generateweights* function has been created, we execute it with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mweightsx,Mweightsz = generateweights();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we include a routine to plot the weight fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph2dweight(D):     \n",
    "    plot.figure()\n",
    "    plot.figure(figsize=(16,8))\n",
    "    fscale = 1/10**(-3)\n",
    "    fscale = 10**(-3)\n",
    "    scale  = np.amax(D)\n",
    "    extent = [fscale*x0,fscale*x1, fscale*z1, fscale*z0]\n",
    "    fig = plot.imshow(np.transpose(D), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n",
    "    plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "    plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "    plot.title('Weight Function')\n",
    "    plot.grid()\n",
    "    ax = plot.gca()\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
    "    cbar.set_label('Weights')\n",
    "    plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we include the plot of weights field in $x$ direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "graph2dweight(Mweightsx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we include the plot of weights field in $z$ direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "graph2dweight(Mweightsz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the fields for the weight arrays  *weightsx* and *weightsz*: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "weightsx = Function(name=\"weightsx\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
    "weightsx.data[:,:] = Mweightsx[:,:]\n",
    "\n",
    "weightsz = Function(name=\"weightsz\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
    "weightsz.data[:,:] = Mweightsz[:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the discretization of the A2 and Higdon's boundary conditions (to calculate $u_{1}(x,z,t)$) we need  information from three time levels, namely $u(x,z,t-1)$, $u (x,z,t)$ and $u(x,z,t+1)$. So it is convenient to create the three fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1  = Function(name=\"u1\"   ,grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
    "u2  = Function(name=\"u2\"   ,grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
    "u3  = Function(name=\"u3\"   ,grid=grid,space_order=2,staggered=NODE,dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will assign to each of them the three time solutions described previously, that is,\n",
    "\n",
    "- u1(x,z) = u(x,z,t-1);\n",
    "- u2(x,z) = u(x,z,t);\n",
    "- u3(x,z) = u(x,z,t+1);\n",
    "\n",
    "These three assignments can be represented by the *stencil01* given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "stencil01 = [Eq(u1,u.backward),Eq(u2,u),Eq(u3,u.forward)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An update of the term *u3(x,z)* will be necessary after updating *u(x,z,t+1)* in the direction $x$, so that we can continue to apply the HABC method. This update is given by *stencil02* defined as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stencil02 = [Eq(u3,u.forward)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the acoustic equation with HABC without the source term we need in $\\Omega$ \n",
    "\n",
    "- eq1 = u.dt2 - vel0 * vel0 * u.laplace;\n",
    "\n",
    "So the *pde* that represents this equation is given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde0 = Eq(u.dt2 - u.laplace*vel**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the *stencil* for *pde0* is given to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "stencil0  = Eq(u.forward, solve(pde0,u.forward))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the blue region we will divide it into $npmlx$ layers in the $x$ direction and $npmlz$ layers in the $z$ direction. In this case, the representation is a little more complex than shown in the figures that exemplify the regions $A_{k}$ because there are intersections between the layers.\n",
    "\n",
    "**Observation:** Note that the representation of the $A_{k}$ layers that we present in our text reflects the case where $npmlx=npmlz$. However, our code includes the case illustrated in the figure, as well as situations in which $npmlx\\neq npmlz$. The discretizations of the bounadry conditions A1, A2 and Higdon follow in the bibliographic references at the end. They will not be detailled here, but can be seen in the codes below. \n",
    "\n",
    "In the sequence of codes below we build the *pdes* that represent the *eqs* of the regions $B_{1}$, $B_{2}$ and $B_{3}$ and/or in the corners (red points in the case of *A2*) as represented in the following figure:\n",
    "\n",
    "<img src='region2.png' width=500>\n",
    "\n",
    "In the sequence, we present the *stencils* for each of these *pdes*.\n",
    "\n",
    "So, for the A1 case we have the following *pdes* and *stencils*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(habctype==1):\n",
    "\n",
    "    # Region B_{1}\n",
    "    aux1      = ((-vel[x,z]*dt+hx)*u2[x,z] + (vel[x,z]*dt+hx)*u2[x+1,z] + (vel[x,z]*dt-hx)*u3[x+1,z])/(vel[x,z]*dt+hx)\n",
    "    pde1      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux1\n",
    "    stencil1  = Eq(u.forward,pde1,subdomain = grid.subdomains['d1'])\n",
    "\n",
    "    # Region B_{3}\n",
    "    aux2      = ((-vel[x,z]*dt+hx)*u2[x,z] + (vel[x,z]*dt+hx)*u2[x-1,z] + (vel[x,z]*dt-hx)*u3[x-1,z])/(vel[x,z]*dt+hx)\n",
    "    pde2      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux2\n",
    "    stencil2  = Eq(u.forward,pde2,subdomain = grid.subdomains['d2'])\n",
    "\n",
    "    # Region B_{2}\n",
    "    aux3      = ((-vel[x,z]*dt+hz)*u2[x,z] + (vel[x,z]*dt+hz)*u2[x,z-1] + (vel[x,z]*dt-hz)*u3[x,z-1])/(vel[x,z]*dt+hz)\n",
    "    pde3      = (1-weightsz[x,z])*u3[x,z] + weightsz[x,z]*aux3\n",
    "    stencil3  = Eq(u.forward,pde3,subdomain = grid.subdomains['d3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the A2 case we have the following *pdes* and *stencils*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(habctype==2):\n",
    "   \n",
    "    # Region B_{1}\n",
    "    cte11 =  (1/(2*dt**2)) + (1/(2*dt*hx))*vel[x,z]\n",
    "    cte21 = -(1/(2*dt**2)) + (1/(2*dt*hx))*vel[x,z] - (1/(2*hz**2))*vel[x,z]*vel[x,z] \n",
    "    cte31 = -(1/(2*dt**2)) - (1/(2*dt*hx))*vel[x,z]\n",
    "    cte41 =  (1/(dt**2))\n",
    "    cte51 =  (1/(4*hz**2))*vel[x,z]**2\n",
    "\n",
    "    aux1      = (cte21*(u3[x+1,z] + u1[x,z]) + cte31*u1[x+1,z] + cte41*(u2[x,z]+u2[x+1,z]) + cte51*(u3[x+1,z+1] + u3[x+1,z-1] + u1[x,z+1] + u1[x,z-1]))/cte11\n",
    "    pde1      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux1\n",
    "    stencil1  = Eq(u.forward,pde1,subdomain = grid.subdomains['d1'])\n",
    "    \n",
    "    # Region B_{3}\n",
    "    cte12 =  (1/(2*dt**2)) + (1/(2*dt*hx))*vel[x,z]\n",
    "    cte22 = -(1/(2*dt**2)) + (1/(2*dt*hx))*vel[x,z] - (1/(2*hz**2))*vel[x,z]**2\n",
    "    cte32 = -(1/(2*dt**2)) - (1/(2*dt*hx))*vel[x,z]\n",
    "    cte42 =  (1/(dt**2))\n",
    "    cte52 =  (1/(4*hz**2))*vel[x,z]*vel[x,z]\n",
    "  \n",
    "    aux2      = (cte22*(u3[x-1,z] + u1[x,z]) + cte32*u1[x-1,z] + cte42*(u2[x,z]+u2[x-1,z]) + cte52*(u3[x-1,z+1] + u3[x-1,z-1] + u1[x,z+1] + u1[x,z-1]))/cte12\n",
    "    pde2      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux2\n",
    "    stencil2  = Eq(u.forward,pde2,subdomain = grid.subdomains['d2'])\n",
    "\n",
    "    # Region B_{2}\n",
    "    cte13 =  (1/(2*dt**2)) + (1/(2*dt*hz))*vel[x,z]\n",
    "    cte23 = -(1/(2*dt**2)) + (1/(2*dt*hz))*vel[x,z] - (1/(2*hx**2))*vel[x,z]**2\n",
    "    cte33 = -(1/(2*dt**2)) - (1/(2*dt*hz))*vel[x,z]\n",
    "    cte43 =  (1/(dt**2))\n",
    "    cte53 =  (1/(4*hx**2))*vel[x,z]*vel[x,z]\n",
    "\n",
    "    aux3      = (cte23*(u3[x,z-1] + u1[x,z]) + cte33*u1[x,z-1] + cte43*(u2[x,z]+u2[x,z-1]) + cte53*(u3[x+1,z-1] + u3[x-1,z-1] + u1[x+1,z] + u1[x-1,z]))/cte13\n",
    "    pde3      = (1-weightsz[x,z])*u3[x,z] + weightsz[x,z]*aux3\n",
    "    stencil3  = Eq(u.forward,pde3,subdomain = grid.subdomains['d3'])\n",
    "\n",
    "    # Red point rigth side\n",
    "    stencil4  = [Eq(u[t+1,nptx-1-k,nptz-1-k],(1-weightsz[nptx-1-k,nptz-1-k])*u3[nptx-1-k,nptz-1-k] + \n",
    "                 weightsz[nptx-1-k,nptz-1-k]*(((-(1/(4*hx))   + (1/(4*hz)) - (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u3[nptx-1-k,nptz-2-k] \n",
    "                 + ((1/(4*hx))  - (1/(4*hz)) - (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u3[nptx-2-k,nptz-1-k] \n",
    "                 + ((1/(4*hx))  + (1/(4*hz)) - (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u3[nptx-2-k,nptz-2-k] \n",
    "                 + (-(1/(4*hx)) - (1/(4*hz)) + (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u2[nptx-1-k,nptz-1-k] \n",
    "                 + (-(1/(4*hx)) + (1/(4*hz)) + (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u2[nptx-1-k,nptz-2-k] \n",
    "                 + ((1/(4*hx))  - (1/(4*hz)) + (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u2[nptx-2-k,nptz-1-k] \n",
    "                 + ((1/(4*hx))  + (1/(4*hz)) + (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))*u2[nptx-2-k,nptz-2-k])\n",
    "                 / (((1/(4*hx)) + (1/(4*hz)) + (np.sqrt(2))/(4*vel[nptx-1-k,nptz-1-k]*dt))))) for k in range(0,npmlz)] \n",
    "\n",
    "    # Red point left side\n",
    "    stencil5  = [Eq(u[t+1,k,nptz-1-k],(1-weightsx[k,nptz-1-k] )*u3[k,nptz-1-k] \n",
    "                                + weightsx[k,nptz-1-k]*(( (-(1/(4*hx)) + (1/(4*hz)) - (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u3[k,nptz-2-k] \n",
    "                                + ((1/(4*hx))  - (1/(4*hz)) - (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u3[k+1,nptz-1-k] \n",
    "                                + ((1/(4*hx))  + (1/(4*hz)) - (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u3[k+1,nptz-2-k] \n",
    "                                + (-(1/(4*hx)) - (1/(4*hz)) + (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u2[k,nptz-1-k] \n",
    "                                + (-(1/(4*hx)) + (1/(4*hz)) + (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u2[k,nptz-2-k] \n",
    "                                + ((1/(4*hx))  - (1/(4*hz)) + (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u2[k+1,nptz-1-k] \n",
    "                                + ((1/(4*hx))  + (1/(4*hz)) + (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))*u2[k+1,nptz-2-k])\n",
    "                                / (((1/(4*hx)) + (1/(4*hz)) + (np.sqrt(2))/(4*vel[k,nptz-1-k]*dt))))) for k in range(0,npmlx)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Higdon case we have the following *pdes* and *stencils*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(habctype==3):\n",
    "\n",
    "    alpha1 = 0.0\n",
    "    alpha2 = np.pi/4\n",
    "    a1 = 0.5\n",
    "    b1 = 0.5\n",
    "    a2 = 0.5\n",
    "    b2 = 0.5\n",
    "\n",
    "    # Region B_{1}\n",
    "    gama111 = np.cos(alpha1)*(1-a1)*(1/dt)\n",
    "    gama121 = np.cos(alpha1)*(a1)*(1/dt)\n",
    "    gama131 = np.cos(alpha1)*(1-b1)*(1/hx)*vel[x,z]\n",
    "    gama141 = np.cos(alpha1)*(b1)*(1/hx)*vel[x,z]\n",
    "            \n",
    "    gama211 = np.cos(alpha2)*(1-a2)*(1/dt)\n",
    "    gama221 = np.cos(alpha2)*(a2)*(1/dt)\n",
    "    gama231 = np.cos(alpha2)*(1-b2)*(1/hx)*vel[x,z]\n",
    "    gama241 = np.cos(alpha2)*(b2)*(1/hx)*vel[x,z]\n",
    "            \n",
    "    c111 =  gama111 + gama131\n",
    "    c121 = -gama111 + gama141\n",
    "    c131 =  gama121 - gama131\n",
    "    c141 = -gama121 - gama141\n",
    "            \n",
    "    c211 =  gama211 + gama231\n",
    "    c221 = -gama211 + gama241\n",
    "    c231 =  gama221 - gama231\n",
    "    c241 = -gama221 - gama241\n",
    "\n",
    "    aux1      = ( u2[x,z]*(-c111*c221-c121*c211) + u3[x+1,z]*(-c111*c231-c131*c211) + u2[x+1,z]*(-c111*c241-c121*c231-c141*c211-c131*c221) \n",
    "                + u1[x,z]*(-c121*c221) + u1[x+1,z]*(-c121*c241-c141*c221) + u3[x+2,z]*(-c131*c231) +u2[x+2,z]*(-c131*c241-c141*c231)\n",
    "                + u1[x+2,z]*(-c141*c241))/(c111*c211)\n",
    "    pde1      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux1\n",
    "    stencil1  = Eq(u.forward,pde1,subdomain = grid.subdomains['d1'])\n",
    "\n",
    "    # Region B_{3}\n",
    "    gama112 = np.cos(alpha1)*(1-a1)*(1/dt)\n",
    "    gama122 = np.cos(alpha1)*(a1)*(1/dt)\n",
    "    gama132 = np.cos(alpha1)*(1-b1)*(1/hx)*vel[x,z]\n",
    "    gama142 = np.cos(alpha1)*(b1)*(1/hx)*vel[x,z]\n",
    "            \n",
    "    gama212 = np.cos(alpha2)*(1-a2)*(1/dt)\n",
    "    gama222 = np.cos(alpha2)*(a2)*(1/dt)\n",
    "    gama232 = np.cos(alpha2)*(1-b2)*(1/hx)*vel[x,z]\n",
    "    gama242 = np.cos(alpha2)*(b2)*(1/hx)*vel[x,z]\n",
    "            \n",
    "    c112 =  gama112 + gama132\n",
    "    c122 = -gama112 + gama142\n",
    "    c132 =  gama122 - gama132\n",
    "    c142 = -gama122 - gama142\n",
    "            \n",
    "    c212 =  gama212 + gama232\n",
    "    c222 = -gama212 + gama242\n",
    "    c232 =  gama222 - gama232\n",
    "    c242 = -gama222 - gama242\n",
    "\n",
    "    aux2      = ( u2[x,z]*(-c112*c222-c122*c212) + u3[x-1,z]*(-c112*c232-c132*c212) + u2[x-1,z]*(-c112*c242-c122*c232-c142*c212-c132*c222) \n",
    "                + u1[x,z]*(-c122*c222) + u1[x-1,z]*(-c122*c242-c142*c222) + u3[x-2,z]*(-c132*c232) +u2[x-2,z]*(-c132*c242-c142*c232)\n",
    "                + u1[x-2,z]*(-c142*c242))/(c112*c212)\n",
    "    pde2      = (1-weightsx[x,z])*u3[x,z] + weightsx[x,z]*aux2\n",
    "    stencil2  = Eq(u.forward,pde2,subdomain = grid.subdomains['d2'])\n",
    "\n",
    "    # Region B_{2}\n",
    "    gama113 = np.cos(alpha1)*(1-a1)*(1/dt)\n",
    "    gama123 = np.cos(alpha1)*(a1)*(1/dt)\n",
    "    gama133 = np.cos(alpha1)*(1-b1)*(1/hz)*vel[x,z]\n",
    "    gama143 = np.cos(alpha1)*(b1)*(1/hz)*vel[x,z]\n",
    "                \n",
    "    gama213 = np.cos(alpha2)*(1-a2)*(1/dt)\n",
    "    gama223 = np.cos(alpha2)*(a2)*(1/dt)\n",
    "    gama233 = np.cos(alpha2)*(1-b2)*(1/hz)*vel[x,z]\n",
    "    gama243 = np.cos(alpha2)*(b2)*(1/hz)*vel[x,z]\n",
    "            \n",
    "    c113 =  gama113 + gama133\n",
    "    c123 = -gama113 + gama143\n",
    "    c133 =  gama123 - gama133\n",
    "    c143 = -gama123 - gama143\n",
    "            \n",
    "    c213 =  gama213 + gama233\n",
    "    c223 = -gama213 + gama243\n",
    "    c233 =  gama223 - gama233\n",
    "    c243 = -gama223 - gama243\n",
    "\n",
    "    aux3      = ( u2[x,z]*(-c113*c223-c123*c213) + u3[x,z-1]*(-c113*c233-c133*c213) + u2[x,z-1]*(-c113*c243-c123*c233-c143*c213-c133*c223) \n",
    "                + u1[x,z]*(-c123*c223) + u1[x,z-1]*(-c123*c243-c143*c223) + u3[x,z-2]*(-c133*c233) +u2[x,z-2]*(-c133*c243-c143*c233)\n",
    "                + u1[x,z-2]*(-c143*c243))/(c113*c213)\n",
    "    pde3      = (1-weightsz[x,z])*u3[x,z] + weightsz[x,z]*aux3\n",
    "    stencil3  = Eq(u.forward,pde3,subdomain = grid.subdomains['d3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The surface boundary conditions of the problem are the same as in the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a>. They are placed in the term *bc* and have the following form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc  = [Eq(u[t+1,x,0],u[t+1,x,1])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then define the operator (*op*) that will join the acoustic equation, source term, boundary conditions and receivers.\n",
    "\n",
    "- 1. The acoustic wave equation in the *d0* region: *[stencil01];*\n",
    "- 2. Source term: *src_term;*\n",
    "- 3. Updating solutions over time: *[stencil01,stencil02];*\n",
    "- 4. The acoustic wave equation in the *d1*, *d2* e *d3* regions: *[stencil1,stencil2,stencil3];*\n",
    "- 5. The equation for red points for A2 method: *[stencil5,stencil4];*\n",
    "- 6. Boundry Conditions: *bc;*\n",
    "- 7. Receivers: *rec_term;*\n",
    "\n",
    "We then define two types of *op*:\n",
    "\n",
    "- The first *op* is for the cases A1 and Higdon;\n",
    "- The second *op* is for the case A2;\n",
    "\n",
    "The *ops* are constructed by the following commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "if(habctype!=2):\n",
    "    op  = Operator([stencil0] + src_term + [stencil01,stencil3,stencil02,stencil2,stencil1] + bc + rec_term,subs=grid.spacing_map)\n",
    "else:\n",
    "    op  = Operator([stencil0] + src_term + [stencil01,stencil3,stencil02,stencil2,stencil1,stencil02,stencil4,stencil5] + bc + rec_term,subs=grid.spacing_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "u.data[:]  = 0.\n",
    "u1.data[:] = 0.\n",
    "u2.data[:] = 0.\n",
    "u3.data[:] = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign to *op* the number of time steps it must execute and the size of the time step in the local variables *time* and *dt*, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` run in 0.04 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
       "                     PerfEntry(time=0.009703000000000015, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
       "                    (PerfKey(name='section1', rank=None),\n",
       "                     PerfEntry(time=3.8999999999999986e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
       "                    (PerfKey(name='section2', rank=None),\n",
       "                     PerfEntry(time=0.028597999999999967, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
       "                    (PerfKey(name='section3', rank=None),\n",
       "                     PerfEntry(time=0.0011050000000000083, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "op(time=nt,dt=dt0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We view the result of the displacement field at the end time using the *graph2d* routine given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph2d(U,i):    \n",
    "    plot.figure()\n",
    "    plot.figure(figsize=(16,8))\n",
    "    fscale =  1/10**(3)\n",
    "    x0pml  = x0 + npmlx*hxv\n",
    "    x1pml  = x1 - npmlx*hxv\n",
    "    z0pml  = z0            \n",
    "    z1pml  = z1 - npmlz*hzv\n",
    "    scale  = np.amax(U[npmlx:-npmlx,0:-npmlz])/10.\n",
    "    extent = [fscale*x0pml,fscale*x1pml,fscale*z1pml,fscale*z0pml]\n",
    "    fig = plot.imshow(np.transpose(U[npmlx:-npmlx,0:-npmlz]),vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n",
    "    plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "    plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "    plot.axis('equal')\n",
    "    if(i==1): plot.title('Map - Acoustic Problem with Devito - HABC A1')\n",
    "    if(i==2): plot.title('Map - Acoustic Problem with Devito - HABC A2')\n",
    "    if(i==3): plot.title('Map - Acoustic Problem with Devito - HABC Higdon')\n",
    "    plot.grid()\n",
    "    ax = plot.gca()\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
    "    cbar.set_label('Displacement [km]')\n",
    "    plot.draw()\n",
    "    plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "graph2d(u.data[0,:,:],habctype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the Receivers shot records using the *graph2drec* routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph2drec(rec,i):    \n",
    "        plot.figure()\n",
    "        plot.figure(figsize=(16,8))\n",
    "        fscaled = 1/10**(3)\n",
    "        fscalet = 1/10**(3)\n",
    "        x0pml  = x0 + npmlx*hxv\n",
    "        x1pml  = x1 - npmlx*hxv\n",
    "        scale   = np.amax(rec[:,npmlx:-npmlx])/10.\n",
    "        extent  = [fscaled*x0pml,fscaled*x1pml, fscalet*tn, fscalet*t0]\n",
    "        fig = plot.imshow(rec[:,npmlx:-npmlx], vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n",
    "        plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
    "        plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f s'))\n",
    "        plot.axis('equal')\n",
    "        if(i==1): plot.title('Receivers Signal Profile - Devito with HABC A1')\n",
    "        if(i==2): plot.title('Receivers Signal Profile - Devito with HABC A2')\n",
    "        if(i==3): plot.title('Receivers Signal Profile - Devito with HABC Higdon')\n",
    "        ax = plot.gca()\n",
    "        divider = make_axes_locatable(ax)\n",
    "        cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "        cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
    "        plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "graph2drec(rec.data,habctype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.isclose(np.linalg.norm(rec.data), 990, rtol=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.7 - Conclusions\n",
    "\n",
    "We have presented the HABC method for the acoustic wave equation, which can be used with any of the \n",
    "absorbing boundary conditions A1, A2 or Higdon. The notebook also include the possibility of using these boundary conditions alone, without being combined with the HABC. The user has the possibilty of testing several combinations of parameters and observe the effects in the absorption of spurious reflections on computational boundaries.\n",
    "\n",
    " The relevant references for the boundary conditions are furnished next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.8 - References\n",
    "\n",
    "- Clayton, R., & Engquist, B. (1977). \"Absorbing boundary conditions for acoustic and elastic wave equations\", Bulletin of the seismological society of America, 67(6), 1529-1540. <a href=\"https://pubs.geoscienceworld.org/ssa/bssa/article/67/6/1529/117727?casa_token=4TvjJGJDLQwAAAAA:Wm-3fVLn91tdsdHv9H6Ek7tTQf0jwXVSF10zPQL61lXtYZhaifz7jsHxqTvrHPufARzZC2-lDw\">Reference Link.</a>\n",
    "\n",
    "- Engquist, B., & Majda, A. (1979). \"Radiation boundary conditions for acoustic and elastic wave calculations,\" Communications on pure and applied mathematics, 32(3), 313-357. DOI: 10.1137/0727049.  <a href=\"https://epubs.siam.org/doi/abs/10.1137/0727049\">Reference Link.</a>\n",
    "\n",
    "- Higdon, R. L. (1987). \"Absorbing boundary conditions for difference approximations to the multidimensional wave equation,\" Mathematics of computation, 47(176), 437-459. DOI: 10.1090/S0025-5718-1986-0856696-4. <a href=\"https://www.ams.org/journals/mcom/1986-47-176/S0025-5718-1986-0856696-4/\">Reference Link.</a>\n",
    "\n",
    "- Higdon, Robert L. \"Numerical absorbing boundary conditions for the wave equation,\" Mathematics of computation, v. 49, n. 179, p. 65-90, 1987. DOI: 10.1090/S0025-5718-1987-0890254-1. <a href=\"https://www.ams.org/journals/mcom/1987-49-179/S0025-5718-1987-0890254-1/\">Reference Link.</a>\n",
    "\n",
    "- Liu, Y., & Sen, M. K. (2018). \"An improved hybrid absorbing boundary condition for wave equation modeling,\" Journal of Geophysics and Engineering, 15(6), 2602-2613. DOI: 10.1088/1742-2140/aadd31. <a href=\"https://academic.oup.com/jge/article/15/6/2602/5209803\">Reference Link.</a>"
   ]
  }
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