06_Hyperbolic_PDEs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](figs/title.png)\n",
    "\n",
    "<h1><center>Module 6: Numerical Solutions to Hyperbolic PDEs</center></h1>\n",
    "\n",
    "This module focuses on generating numerical solutions of **hyperbolic** partial differential equations (PDEs).  Recall that these types of second-order PDEs\n",
    "\n",
    "$$Au_{xx}+Bu_{xy}+Cu_{yy}+Du_{x}+Eu_{y}+Fu+G=0 \\tag{1}$$\n",
    "\n",
    "are defined by the condition $B^2-4AC > 0$ (where $y$ is considered the $t$ variable and thus $A=1$, $C=-\\frac{1}{c}$ and all others are equal 0).\n",
    "\n",
    "Three interesting examples (with increasing complexity) are the following:\n",
    "\n",
    "   * **Acoustic wave equation (Homogeneous medium)**: The acoustic wave equation (AWE) is normally used to model the pressure disturbance $\\phi=\\phi(x,y,z,t)$ generated by an acoustic wave propagation through a fluid medium defined by constant velocity $v=\\sqrt{\\frac{K}{\\rho}}$ where $K$ is the bulk compressibility and $\\rho$ is the density.\n",
    "   \n",
    "$$\\frac{\\partial^2 \\phi}{\\partial t^2} - v^2\\nabla^2 \\phi = F(x,y,z,t) \\tag{2}$$ \n",
    "\n",
    "where $F(x,y,z,t)$ is an external force term.  Note that the acoustic wave equation is commonly used in exploration seismology to approximate compressional (i.e., P-wave) propagation through complex heterogeneous media.\n",
    "   \n",
    "   * **Acoustic wave equation (variable density)**: This is like the example above, except now that the density, $\\rho$, is assumed to spatially vary:\n",
    "\n",
    "$$\\frac{\\partial^2 \\phi}{\\partial t^2}  -  K \\nabla \\cdot \\left(\\frac{1}{\\rho} \\nabla \\phi  \\right) = F(x,y,z,t), \\tag{3}$$ \n",
    "   \n",
    "where $K$ is the bulk compressability.  This equation is commonly used in computational ocean acoustics because it allows one to introduce variations in density caused by temperature and salinity concentration among others.  This wave equation requires the same type of initial and boundary conditions as the homogenous equation above.\n",
    "\n",
    "\n",
    "   * **One-way wave equations**:  We have actually looked at this hyperbolic equation in the course notes on *Discretization and Derivatives*. One can recognize this to be the case if we **factorize** the 1D AWE given above in the following way\n",
    "   \n",
    "$$\\left[\\frac{\\partial^2 }{\\partial t^2} - v^2\\frac{\\partial}{\\partial x^2} \\right] \\phi = \n",
    "\\left(\\frac{\\partial }{\\partial t}-v \\frac{\\partial}{\\partial x} \\right)\\left( \\frac{\\partial }{\\partial t}+v \\frac{\\partial}{\\partial x}\\right) \\phi = F(x,t). \\tag{4}$$ \n",
    "Here we see that we have the produce of the left- and right-going advection equations. This was recognized in 1747 by French scientist [d'Alembert](https://en.wikipedia.org/wiki/D%27Alembert%27s_formula).  Note that one sometimes see the **d'Alembert operator** $\\Box^2$ in various problems in mathematical physics, which is given by the following in Cartesian coordinates:\n",
    "\n",
    "$$\\Box^2 \\phi = \\left[\\frac{\\partial^2 }{\\partial t^2} - v^2\\frac{\\partial}{\\partial x^2} \\right] \\phi.\\tag{5}$$\n",
    "\n",
    "### Initial and Boundary Conditions\n",
    "\n",
    "Because this is a **second-order** temporal partial derivative, uniquely defining a wavefield solution requires implementing **two initial conditions** that define the wavefield at, say, time zero: \n",
    "\n",
    "$$\\phi(x,y,z,t=0)=f(x,y,z), \\tag{6}$$ \n",
    "\n",
    "as well as its derivative, \n",
    "\n",
    "$$\\frac{\\partial \\phi}{\\partial t}(x,y,z,t=0) = g(x,y,z).\\tag{7}$$\n",
    "\n",
    "One also has to prescribe the behavior of the wavefield solution on the domain boundary $D$ (i.e., $\\left. \\phi\\right|_{\\partial D}$). In some circumstances, one may want to apply a Dirchelet BC\n",
    "\n",
    "$$ \\phi(\\mathbf {x}) = f(\\mathbf {x}), \\quad \\forall \\mathbf {x}\\in \\partial D, \\tag{8}$$\n",
    "\n",
    "where one sets $f(x)=0$ to obtain an inward moving reflected wave with the oppositing polarity. This is commonly used for waves approaching the free surface of a fluid acoustic medium.  However, in most other circumstances one wants to have perfectly absorbing boundary conditions such that no energy is reflected back into the finite-sized computational domain. These are the so-called (Sommerville) radiation boundary conditions which require the wave disturbance to vanish at infinity in an unbounded medium.  This topic, though, remains an active area of research and goes well beyond the scope of this course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D Acoustic wave equation\n",
    "\n",
    "Let's first look at the problem of an acoustic wave propagating on a 1D medium.  Because of the easy connection with everyday experience, let's assume examine the propagation of a wave on a homogeneous taut string of length $L$ where both of the string ends and fixed. In this case, we can assume that there is no external forcing function (i.e., $F(x,t)=0$) and the string starts from some combination of an initial displacement disturbance $\\phi(x,t=0) = f(x)$ and an initial velocity $\\frac{\\partial \\phi}{\\partial t}(x,t=0) = g(x)$. Thus, the PDE we are solving is\n",
    "\n",
    "$$\\frac{\\partial^2 \\phi}{\\partial t^2} = v^2\\frac{\\partial^2 \\phi}{\\partial x^2}. \\tag{9}$$ \n",
    "\n",
    "Let's now look at an **explicit** discretization method to solve equation 9 (as opposed to the **implicit** ones like Crank-Nicolson approach we studied in Module 9).  Let's start by using a standard **centered in time centered in space** (CTCS) $\\mathcal{O}(\\Delta x^2,\\Delta t^2)$ discretization:\n",
    "\n",
    "$$\\frac{\\phi^{n+1}_i - 2\\phi^{n}_i +\\phi^{n-1}_i}{\\Delta t^2} = \n",
    "v^2 \\left( \\frac{\\phi^{n}_{i+i} - 2\\phi^{n}_i +\\phi^{n}_{i-1}}{\\Delta x^2} \\right). \\tag{10}$$\n",
    "\n",
    "Let's now multiply through by $\\Delta t^2$ and use the square of the Courant number $C^2 = \\frac{v^2\\Delta t^2}{\\Delta x^2}$ from the previous modules to write:\n",
    "\n",
    "$$\\phi^{n+1}_i - 2\\phi^{n}_i +\\phi^{n-1}_i = C^2 \\left( \\phi^{n}_{i+i} - 2\\phi^{n}_i +\\phi^{n}_{i-1}\\right). \\tag{11}$$\n",
    "\n",
    "Rearranging terms to isolate the unknown quantity at time step $n+1$ on the left-hand side and keeping the known quantities on the right-hand side leads to:\n",
    "\n",
    "$$\\phi^{n+1}_i = 2\\phi^{n}_i -\\phi^{n-1}_i +C^2 \\left(\\phi^{n}_{i+i} -2 \\phi^{n}_i + \\phi^{n}_{i-1}\\right). \\tag{12}$$\n",
    "\n",
    "You may note that one could combine the $\\phi^n_i$ terms into a more compact stencil. However, I am avoiding doing this because we will look at higher-order  finite-difference expansions of the spatial second partial derivative in sections below \n",
    "\n",
    "### Initial Conditions\n",
    "\n",
    "Above we noted that two initial conditions are required for this PDE because of the second temporal derivative. However, based on the above discretization it may not be apparent how the derivative initial condition can be introduced.  One thing to note is that the discretization now contains term $\\phi^{n-1}_i$, which represents the wavefield at the previous time step. Thus, this can be used to set the second condition by:\n",
    "\n",
    "$$\\frac{\\partial \\phi}{\\partial t} \\approx \\frac{\\phi^n_i - \\phi^{n-1}_i}{\\Delta t} = g(x).  \\tag{13}$$\n",
    "\n",
    "The common assumption is that the wavefield is **quiescent** (i.e., no energy) at time steps $n=0$ and $n=-1$. This means that $g(x)=0$ in equation 13 and thus $\\phi^0_i=\\phi^{-1}_i=0$.  However, in cases where we have $g(x)\\neq 0$ then we can use a  finite-difference approximation to specify the wavefield at the previous time step as\n",
    "\n",
    "$$\\phi^{n-1}_i = \\phi^n_i-\\Delta t g(x).\\tag{14}$$\n",
    "\n",
    "Alternatively, it may also be possible that $\\phi^{n-1}_i$ is known analytically and can just be stated (e.g., it is a translation of the waveform at $\\phi^n_i$.\n",
    "\n",
    "### Handling Boundaries with Ghost Points\n",
    "\n",
    "We can also explore what happens at the left and right boundaries from equation 12.  At the left hand boundary (i.e., $i=0$) we can write\n",
    "\n",
    "$$\\phi^{n+1}_1 = 2\\phi^{n}_1 -\\phi^{n-1}_1 +C^2 \\left(\\phi^{n}_{2} -2 \\phi^{n}_1 + \\phi^{n}_{0}\\right). \\tag{14}$$\n",
    "\n",
    "Note that there is a $ \\phi^{n}_{0}$ term in this equation.  Currently, we are able to handle this because we can use the Dirchelet boundary condition to set this point equal to zero.  However, if we were to use higher-order finite-difference stencils for our second derivative, these would require using additional points such as $\\phi^n_{-1}$ which represent a point falling out of the grid! Let's assume that we can set $ \\phi^{n}_{-1}=0$ (as well those with an increasing negative index).  This is termed using **ghost points** which are not formally defined, but are conjured up for numerical expediency.  Similarly, on the right-hand side we will have to do the same at ghost point $ \\phi^{n}_{N+1}=0$ as well as those with greater indices.\n",
    "\n",
    "## 1D AWE Solver\n",
    "\n",
    "Given this machinary, we can now adapt the types of solvers we constructed in the previous modules to the current scenario. Let's first build a 1D AWE solver of $\\mathcal{O}(\\Delta x^2,\\Delta t^2)$ that is appropriate for homogeneous medium where $v$=const."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def awe_explicit_solver_homogeneous_2nd_order(Wo,Wm,dx,dt,v):\n",
    "    '''Set up second-order solver of the acoustic wave equation\n",
    "    usage: U = awe_explicit_solver_homogeneous(Wo,Wm,dx,dt,v)\n",
    "    input: \n",
    "        Wo: Acoustic pressure vector (nx) at time step n\n",
    "        Wm: Acoustic pressure vector (nx) at time step n-1\n",
    "        dx: Spatial sampling (float)\n",
    "        dt: Temporal sampling (float)\n",
    "        v : Homogeneous propagation velocity (float)\n",
    "    output:\n",
    "        Wm: Acoustic pressure vector (nx) at time n+1\n",
    "    dependencies:\n",
    "        None\n",
    "    written by Jeff Shragge, jshragge@mines.edu, 10/2019\n",
    "    '''        \n",
    "    ## . . Get dimensions of wavefield\n",
    "    nx = len(Wo)\n",
    "    \n",
    "    ## . . Define Courant number (squared)\n",
    "    C2 = (v*dt/dx)**2\n",
    "        \n",
    "    ## . . Update solution. Note that I am overwriting Wm because this wavefield\n",
    "    ## . . isn't needed after this calculation!\n",
    "    Wm[1:nx-1] = 2*Wo[1:nx-1]-Wm[1:nx-1]+C2*(Wo[2:nx]-2*Wo[1:nx-1]+Wo[0:nx-2])\n",
    "\n",
    "    return Wm ## . . Return updated wavefield at time step n+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now build up a scenario that we are trying to model.  I will assume that the length of the region to be modeled is $L=1000$ mm, that is discretized into $nx=201$ (and thus $\\Delta x = 5$ mm segments), and the velocity is $v=1000$ mm/s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Define spatial grid\n",
    "L  = 1000               # . . String length (mm)\n",
    "nx = 201                # . . Number of points in discretization \n",
    "dx = L/(nx-1)           # . . Discretization interval\n",
    "x = np.linspace(0,L,nx) # . . Xline\n",
    "v  = 1000               # . . Velocity (mm/s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now define the time stepping conditions.  Because we have define our $\\Delta x$ and $v$, we know that we need to obey the following Courant condition for stability (usually called the CFL condition):\n",
    "\n",
    "$$C \\equiv \\frac{v \\Delta t}{\\Delta x} \\le 1. \\tag{15}$$\n",
    "\n",
    "Thus, let's be a bit conservative and choose $C=0.5$.  Thus, we can rearrange this equation and set our $\\Delta t$ time step parameter to satisfy the CFL condition:\n",
    "\n",
    "$$ \\Delta t = \\frac{0.5\\Delta x}{v} = \\frac{0.5 \\times 5 {\\rm mm}}{1000 {\\rm mm/s}} = 0.0025 {\\rm s}. \\tag{16}$$\n",
    "\n",
    "We also want to define our two initial conditions, which we we assume to have the shape of a generalized stationary Ricker wavelet with no prior velocity:\n",
    "\n",
    "$$\\phi(x,t=0) = f(x) = \\left.\\frac{2}{\\sqrt{3\\sigma\\sqrt{\\pi}}} \\left(1 - ((x-3L/4-vt)/\\sigma)^2\\right)e^{-\\frac{(x-3L/4-vt)^2}{2\\sigma^2}} \\right|_{t=0}\\tag{17a}$$\n",
    "$$ \\quad \\quad \\quad=\\frac{2}{\\sqrt{3\\sigma\\sqrt{\\pi}}} \\left(1 - ((x-3L/4)/\\sigma)^2\\right)e^{-\\frac{(x-3L/4)^2}{2\\sigma^2}}\\tag{17b}$$\n",
    "\n",
    "and \n",
    "\n",
    "$$ \\phi^{-1}_i=0, \\tag{18}$$\n",
    "\n",
    "No prior velocity implies that the $-1$ time step is zero.  For this simulation we will use $\\sigma=40$, though this will be varied in the section below. Let's now compute the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_1.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## . . Time stepping parameters\n",
    "CC = 0.5     # . . Courant #\n",
    "nt = 800     # . . Number of time steps\n",
    "dt = CC*dx/v # . . Define dt based on Courant\n",
    "\n",
    "## . . Define Initial waveform\n",
    "ss=40        # . . sigma for Ricker wavelet\n",
    "\n",
    "## . . Init wavefields on spatial grid\n",
    "Uo = np.zeros((nx))\n",
    "Um = np.zeros((nx))\n",
    "\n",
    "## . . Define initial disturbance\n",
    "Uo = 2/np.sqrt(3*ss*np.sqrt(np.pi))*(1-((x-3*L/4)/ss)**2)*np.exp(-(x-3*L/4)**2/(2*ss**2))\n",
    "\n",
    "## . . Total Solution space\n",
    "c = np.zeros((nx,nt)) \n",
    "\n",
    "## . . Iterate over solution time steps\n",
    "for it in range(nt):\n",
    "    tmp = awe_explicit_solver_homogeneous_2nd_order(Uo,Um,dx,dt,v) #calc solution at n+1\n",
    "    c[:,it]=tmp  ## save solution vector\n",
    "    Um=Uo        ## move solution at n to n-1 to prepare for next iteration\n",
    "    Uo=tmp       ## move solution at n+1 to n to prepare for next iteration\n",
    "\n",
    "amax = np.max(np.abs(c))\n",
    "\n",
    "## . . 1D animation fucntion\n",
    "def AWE_1D_animate(i):\n",
    "    global k\n",
    "    ax1.clear()\n",
    "    ax1.plot(x,c[:,k],color='cyan',linewidth=3)\n",
    "    ax1.grid(True)\n",
    "    ax1.set_xlim([0,L])\n",
    "    ax1.set_ylim([-1.1*amax,1.1*amax])\n",
    "    ax1.set(xlabel='Distance (m)',ylabel='Amplitude')\n",
    "    k += kskip\n",
    "    \n",
    "k = 0\n",
    "kskip = 4\n",
    "\n",
    "## . . Set up movie\n",
    "fig1,ax1 = plt.subplots(1)\n",
    "fig1.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig1.set_dpi(100)\n",
    "\n",
    "## . . Call the animator.  \n",
    "anim1 = animation.FuncAnimation(fig1,AWE_1D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim1.save('./movies/Ex8_1.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_1.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 1. Illustration of wave equation solution when one only defines the wavefield $\\phi(x,t=0)$ and $\\phi(x,t=-\\Delta t)=0$. Note that there is a two-way solution because  we have not specified a constraint forcing the solution to propagate one way or the other (see below).**\n",
    "\n",
    "Let's now look at what happens when we specify the previous wavefield state at $\\phi(x,t=-\\Delta t)$, which requires establishing what the wave looks like at the previous time step. Given the initial Ricker wavelet above, it is reasonable to expect that the wavefield solution at $\\phi(x,t=-\\Delta t)$ will be given by some wavefield shift $t_0$ (see equation 17a):\n",
    "\n",
    "$$\\phi(x,t=-\\Delta t) = \\frac{2}{\\sqrt{3\\sigma\\sqrt{\\pi}}} \\left(1 - ((x-3L/4-v\\Delta t )/\\sigma)^2\\right)e^{-\\frac{(x-3L/4-v\\Delta t)^2}{2\\sigma^2}}, \\tag{19}$$\n",
    "\n",
    "where $t_0=v\\Delta t$ has been introduced to account for a spatial shift.  Let's rerun the solution from above, but now where we set the value of $\\phi^{n-1}_i$ as non-zero: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_2.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Time stepping parameters\n",
    "CC = 0.5     # . . Courant #\n",
    "nt = 800     # . . Number of time steps\n",
    "dt = CC*dx/v # . . Define dt based on Courant\n",
    "\n",
    "## . . Define Initial waveform\n",
    "ss=40        # . . sigma for Ricker wavelet\n",
    "\n",
    "## . . reinit wavefields on spatial grid\n",
    "Uo = np.zeros((nx))\n",
    "Um = np.zeros((nx))\n",
    "\n",
    "## . . Define initial time solution at t=0\n",
    "Uo = 2/np.sqrt(3*ss*np.sqrt(np.pi))*(1-((x-3*L/4     )/ss)**2)*np.exp(-(x-3*L/4     )**2/(2*ss**2))\n",
    "\n",
    "## . . Define previous time step at t=-∆t\n",
    "Um = 2/np.sqrt(3*ss*np.sqrt(np.pi))*(1-((x-3*L/4-dt*v)/ss)**2)*np.exp(-(x-3*L/4-dt*v)**2/(2*ss**2))\n",
    "\n",
    "## . . Total Solution space\n",
    "c = np.zeros((nx,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_explicit_solver_homogeneous_2nd_order(Uo,Um,dx,dt,v) #calc solution at n+1\n",
    "    c[:,it]=tmp  ## save solution vector\n",
    "    Um=Uo        ## move solution at n to n-1 to prepare for next iteration\n",
    "    Uo=tmp       ## move solution at n+1 to n to prepare for next iteration\n",
    "    \n",
    "amax = np.max(np.abs(c))\n",
    "\n",
    "## . . 1D animation fucntion\n",
    "def AWE_1D_animate(i):\n",
    "    global k\n",
    "    ax1.clear()\n",
    "    ax1.plot(x,c[:,k],color='cyan',linewidth=3)\n",
    "    ax1.grid(True)\n",
    "    ax1.set_xlim([0,L])\n",
    "    ax1.set_ylim([-1.1*amax,1.1*amax])\n",
    "    ax1.set(xlabel='Distance (m)',ylabel='Amplitude')\n",
    "    k += kskip\n",
    "    \n",
    "k = 0\n",
    "kskip = 4\n",
    "\n",
    "## . . Set up movie\n",
    "fig2,ax1 = plt.subplots(1)\n",
    "fig2.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig2.set_dpi(100)\n",
    "\n",
    "## . . Call the animator.  \n",
    "anim2 = animation.FuncAnimation(fig2,AWE_1D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim2.save('./movies/Ex8_2.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_2.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 2. Illustration of wave-equation solution when defining the wavefield at $t=0$ and $t=-\\Delta t$.  Note that the wave initially travels to the left instead of both directions like in Figure 10.1 above.**\n",
    "\n",
    "Parameter $\\sigma$ is of course related to the **frequency** content of the Ricker wavelet. Let's now investigate what happens when we set $\\sigma=15$ instead of using $\\sigma=40$ as we did in the simulations above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_3.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Time stepping parameters\n",
    "CC = 0.5     # . . Courant #\n",
    "nt = 1000     # . . Number of time steps\n",
    "dt = CC*dx/v # . . Define dt based on Courant\n",
    "\n",
    "## . . Define Initial waveform\n",
    "ss = 15      # . . sigma for Ricker wavelet\n",
    "\n",
    "## . . Init wavefields on spatial grid\n",
    "Uo = np.zeros((nx))\n",
    "Um = np.zeros((nx))\n",
    "\n",
    "## . . Define wavefield at t=0\n",
    "Uo = 2/np.sqrt(3*ss*np.sqrt(np.pi))*(1-((x-3*L/4     )/ss)**2)*np.exp(-(x-3*L/4     )**2/(2*ss**2))\n",
    "\n",
    "## . . Define wavefield at t=-∆t\n",
    "Um = 2/np.sqrt(3*ss*np.sqrt(np.pi))*(1-((x-3*L/4-dt*v)/ss)**2)*np.exp(-(x-3*L/4-dt*v)**2/(2*ss**2))\n",
    "\n",
    "## . . Total Solution space\n",
    "c = np.zeros((nx,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_explicit_solver_homogeneous_2nd_order(Uo,Um,dx,dt,v) #calc solution at n+1\n",
    "    c[:,it]=tmp  ## save solution vector\n",
    "    Um=Uo        ## move solution at n to n-1 to prepare for next iteration\n",
    "    Uo=tmp       ## move solution at n+1 to n to prepare for next iteration\n",
    "    \n",
    "amax = np.max(np.abs(c))\n",
    "\n",
    "## . . 1D animation fucntion\n",
    "def AWE_1D_animate(i):\n",
    "    global k\n",
    "    ax1.clear()\n",
    "    ax1.plot(x,c[:,k],color='cyan',linewidth=3)\n",
    "    ax1.grid(True)\n",
    "    ax1.set_xlim([0,L])\n",
    "    ax1.set_ylim([-1.1*amax,1.1*amax])\n",
    "    ax1.set(xlabel='Distance (m)',ylabel='Amplitude')\n",
    "    k += kskip\n",
    "    \n",
    "k = 0\n",
    "kskip = 4\n",
    "\n",
    "## . . Set up movie\n",
    "fig3,ax1 = plt.subplots(1)\n",
    "fig3.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig3.set_dpi(100)\n",
    "\n",
    "## . . Call the animator.  \n",
    "anim3 = animation.FuncAnimation(fig3,AWE_1D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim3.save('./movies/Ex8_3.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_3.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 3. Illustration of how numerical dispersion can degrade the solution over time.  The initial wave packet starts to develop a long \"tail\" that represents non-physical dispersive behaviour.**\n",
    "\n",
    "## Higher-order derivatives\n",
    "\n",
    "It's clear that the numerical solution behavior noted in Figure 3 is less than idea.  Thus, we need to look at different methods for trying to eliminate these artifacts. One strategy would be to use a smaller $\\Delta x$, which is easy to do in a 1D solution because the computational cost scales according to $\\mathcal{O}(N)$ where $N$ is the number of points. However, in 3D this approach would scale according to $\\mathcal{O}(N^3)$ since we would have to apply it in each dimension!  This would significantly affect our computational efficiency. \n",
    "\n",
    "Another strategy would be to use higher-order spatial derivatives, which we we know lead to a more accurate representation of numerical derivatives. To do this, let's rewrite the 1D solver for homogeneous media from above to incorporate an $\\mathcal{O}(\\Delta x^8)$ centered approximation of the second-order spatial partial derivative.  This requires using a nine-point stencil that incorporates four points to the left and right of the  center point:\n",
    "\n",
    "$$\\phi^{n+1}_i = 2\\phi^{n}_i -\\phi^{n-1}_i +C^2 \\left(-\\frac{1}{560}\\phi^{n}_{i+4}+\\frac{8}{315}\\phi^{n}_{i+3}-\\frac{1}{5}\\phi^{n}_{i+2}+\\frac{8}{5}\\phi^{n}_{i+1} -\\frac{205}{72} \\phi^{n}_i +\\frac{8}{5} \\phi^{n}_{i-1}-\\frac{1}{5}\\phi^{n}_{i-2}+\\frac{8}{315}\\phi^{n}_{i-3}-\\frac{1}{560} \\phi^{n}_{i-4}\\right), \\tag{20}$$\n",
    "\n",
    "where these [finite-difference coefficients](https://en.wikipedia.org/wiki/Finite_difference_coefficient#Central_finite_difference) are for a **centered second derivative of $O(\\Delta x^8)$**. \n",
    "\n",
    "Note that we will again have issues at the boundaries where we will need **ghost points**. However, because we have longer stencil, we will now need to have four ghost points instead of just one.  Let's see how this work in our solver:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def awe_explicit_solver_homogeneous_8th_order(Wo,Wm,dx,dt,v):\n",
    "    '''Set up eight-order solver of the acoustic wave equation\n",
    "    usage: U = awe_explicit_solver_homogeneous(Wo,Wm,dx,dt,v)\n",
    "    input: \n",
    "        Wo: Acoustic pressure vector (nx) at time step n\n",
    "        Wm: Acoustic pressure vector (nx) at time step n-1\n",
    "        dx: Spatial sampling\n",
    "        dt: Temporal sampling\n",
    "        v : Homogeneous propagation velocity \n",
    "    output:\n",
    "        Wm: Acoustic pressure vector (nx) at time n+1\n",
    "    dependencies:\n",
    "        None\n",
    "    written by Jeff Shragge, jshragge@mines.edu, 10/2019\n",
    "    '''        \n",
    "    ## . . Get dimensions of wavefield\n",
    "    nx = len(Wo)\n",
    "    \n",
    "    ## . . Define Courant number (squared)\n",
    "    C2 = (v*dt/dx)**2\n",
    "        \n",
    "    ## . . Update solution. Note that I am overwriting Wm because this wavefield\n",
    "    ## . . isn't needed after this calculation! I am also starting at index 4 (and ending)\n",
    "    ## . . at index nx-4 because of the ghost points discussed above.\n",
    "    Wm[4:nx-4] = 2*Wo[4:nx-4]-Wm[4:nx-4]+C2*( \n",
    "                    -1/560 *Wo[0:nx-8] \n",
    "                    +8/315 *Wo[1:nx-7]  \n",
    "                    -1/5   *Wo[2:nx-6]  \n",
    "                    +8/5   *Wo[3:nx-5]  \n",
    "                    -205/72*Wo[4:nx-4]  \n",
    "                    +8/5   *Wo[5:nx-3]  \n",
    "                    -1/5   *Wo[6:nx-2]  \n",
    "                    +8/315 *Wo[7:nx-1]  \n",
    "                    -1/560 *Wo[8:nx  ])    \n",
    "    \n",
    "    return Wm ## . . Return updated wavefield at time step n+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now rerun our numerically dispersive example where we set $\\sigma=15$ from above but using our $\\mathcal{O}(\\Delta x^8)$ solver for homogeneous media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_4.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Time stepping parameters\n",
    "CC = 0.5     # . . Courant #\n",
    "nt = 1000     # . . Number of time steps\n",
    "dt = CC*dx/v # . . Define dt based on Courant\n",
    "\n",
    "## . . Define Initial waveform\n",
    "ss=15        # . . sigma for Ricker wavelet\n",
    "\n",
    "## . . Init wavefields on spatial grid\n",
    "Uo = np.zeros((nx))\n",
    "Um = np.zeros((nx))\n",
    "\n",
    "## . . Define wavefield at t=0\n",
    "Uo = 2./np.sqrt(3.*ss*np.sqrt(np.pi))*(1-((x-3.*L/4.     )/ss)**2)*np.exp(-(x-3.*L/4.     )**2/(2*ss**2))\n",
    "\n",
    "## . . Define wavefield at t=-∆t\n",
    "Um = 2./np.sqrt(3.*ss*np.sqrt(np.pi))*(1-((x-3.*L/4.-dt*v)/ss)**2)*np.exp(-(x-3.*L/4.-dt*v)**2/(2*ss**2))\n",
    "\n",
    "## . . Total Solution space\n",
    "d = np.zeros((nx,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_explicit_solver_homogeneous_8th_order(Uo,Um,dx,dt,v) #calc solution at n+1\n",
    "    d[:,it]=tmp  ## save solution vector\n",
    "    Um=Uo        ## move solution at n to n-1 to prepare for next iteration\n",
    "    Uo=tmp       ## move solution at n+1 to n to prepare for next iteration\n",
    "    \n",
    "amax = np.max(np.abs(d))\n",
    "\n",
    "## . . 1D animation fucntion\n",
    "def AWE_1D_animate(i):\n",
    "    global k\n",
    "    ax1.clear()\n",
    "    ax1.plot(x,d[:,k],color='cyan',linewidth=3)\n",
    "    ax1.grid(True)\n",
    "    ax1.set_xlim([0,L])\n",
    "    ax1.set_ylim([-1.1*amax,1.1*amax])\n",
    "    ax1.set(xlabel='Distance (m)',ylabel='Amplitude')\n",
    "    k += kskip\n",
    "    \n",
    "k = 0\n",
    "kskip = 4\n",
    "\n",
    "## . . Set up movie\n",
    "fig4,ax1 = plt.subplots(1)\n",
    "fig4.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig4.set_dpi(100)\n",
    "\n",
    "## . . Call the animator.  \n",
    "anim4 = animation.FuncAnimation(fig4,AWE_1D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim4.save('./movies/Ex8_4.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_4.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 4. As in Figure 3, but using the 8th-order solver for homogeneous media. Note that the previously noted \"tail\" has largely - but not completely - disappeared.  This indicates that the higher-order finite-difference approximation has helped to reduce numerical dispersion, but is not a panacea for this issue.**\n",
    "\n",
    "It is also illustrative to plot the two previous solutions over top of each other. Note that because of the different number of ghost points used, the location of the boundary reflection will be at slightly different locations; thus, I'll only plot it up to the point where the waves reach the boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_5.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 0\n",
    "kskip = 4\n",
    "amax = np.max(np.abs(d))\n",
    "def AWE_1D_animate2(i):\n",
    "    global k\n",
    "    ax1.clear()\n",
    "    ax1.plot(x,c[:,k],color='cyan',linewidth=3)\n",
    "    ax1.plot(x,d[:,k],color='magenta',linewidth=3)\n",
    "    ax1.legend(['2nd','8th'])\n",
    "    ax1.grid(True)\n",
    "    ax1.set_xlim([0,L])\n",
    "    ax1.set_ylim([-1.1*amax,1.1*amax])\n",
    "    ax1.set(xlabel='Distance (m)',ylabel='Amplitude')\n",
    "    k += kskip\n",
    "\n",
    "## . . Set up movie\n",
    "fig5,ax1 = plt.subplots(1)\n",
    "fig5.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig5.set_dpi(100)\n",
    "\n",
    "## . . Call the animator.  \n",
    "anim5 = animation.FuncAnimation(fig5,AWE_1D_animate2,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim5.save('./movies/Ex8_5.gif')\n",
    "plt.close()\n",
    "\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_5.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 5. Comparison of the solutions generated by the eighth- and second-order solvers. The higher-order method clearly produces a more accurate result - though it is itself not perfect.**\n",
    "\n",
    "\n",
    "# 2D Acoustic wave equation\n",
    "\n",
    "Let's now look at the problem of an acoustic wave propagating through a homogeneous 2D domain. In this case, we can assume that there is an external forcing function $F(x,y,t)\\neq0$ and the wave disturbance is initially zero in a **quiescent** state. Thus, the 2D PDE we are solving is\n",
    "\n",
    "$$\\frac{\\partial^2 \\phi}{\\partial t^2} = v^2\\left(\\frac{\\partial^2 \\phi}{\\partial x^2}+\\frac{\\partial^2 \\phi}{\\partial y^2} \\right) + F(x,y,t). \\tag{21}$$ \n",
    "\n",
    "One commmon thing that people use is to model the acoustic wavefield eminating from a spatially localized point source.  In these scenarios, one can write the following for the force source\n",
    "\n",
    "$$F(x,y,t) = W(t)\\delta(x-s_x)\\delta(y-s_y), \\tag{22}$$\n",
    "\n",
    "where point ${\\bf s}=[s_x,s_y]$ represents the coordinates of point source and $W(t)$ represents the wavelet to be **injected** into the domain. \n",
    "\n",
    "### Discretizing the 2D AWE\n",
    "\n",
    "Let's now look at an **explicit** discretization method.  Let's use a $\\mathcal{O}(\\Delta x^8,\\Delta t^2)$ discretization:\n",
    "\n",
    "$$\\frac{\\phi^{n+1}_{i,j} - 2\\phi^{n}_{i,j} +\\phi^{n-1}_{i,j}}{\\Delta t^2} = \n",
    "\\frac{v^2}{\\Delta x^2} \\sum_{k=-4}^4 c_k \\phi^{n}_{i+k,j}+ \\frac{v^2}{\\Delta y^2} \\sum_{k=-4}^4 c_k \\phi^{n}_{i,j+k} + F_{i,j}, \\tag{23}$$\n",
    "\n",
    "where $c_k$ are the finite-difference coefficients discussed above.  Let's now multiply through by $\\Delta t^2$ and use the square of the Courant number $C_x^2 = \\frac{v^2\\Delta t^2}{\\Delta x^2}$ and $C_y^2 = \\frac{v^2\\Delta t^2}{\\Delta y^2}$ from the previous modules to write\n",
    "\n",
    "$$\\phi^{n+1}_{i,j} = 2\\phi^{n}_{i,j} - \\phi^{n-1}_{i,j} +\n",
    "C_x^2 \\sum_{k=-4}^4 c_k \\phi^{n}_{i+k,j}+ C_y^2\\sum_{k=-4}^4 c_k \\phi^{n}_{i,j+k} +\\Delta t^2 F_{i,j}. \\tag{24}$$\n",
    "\n",
    "This is pretty much the same as in 1D, so let's just move right to the 2D solver!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def awe_2d_explicit_solver_homogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F,it,sx,sy):\n",
    "    '''Set up eight-order solver of the acoustic wave equation\n",
    "    usage: U= awe_2d_explicit_solver_homogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F)\n",
    "    input: \n",
    "        UUo: Acoustic pressure vector (nx,ny) at time step n\n",
    "        UUm: Acoustic pressure vector (nx,ny) at time step n-1\n",
    "        dx : Spatial sampling in x\n",
    "        dy : Spatial sampling in y        \n",
    "        dt : Temporal sampling\n",
    "        v  : Homogeneous propagation velocity \n",
    "        F  : Forcing function (nt)\n",
    "        it : Time index\n",
    "        sx : Location of source in x (meters)\n",
    "        sy : Location of source in y (meters)\n",
    "    output:\n",
    "        UUm: Acoustic pressure vector (nx,ny) at time n+1\n",
    "    dependencies:\n",
    "        None\n",
    "    written by Jeff Shragge, jshragge@mines.edu, 11/2019\n",
    "    '''        \n",
    "    ## . . Get dimensions of wavefield\n",
    "    nx,ny = np.size(UUo,0),np.size(UUo,1)\n",
    "    \n",
    "    ## . . Define Courant numbers (squared)\n",
    "    Cx2,Cy2 = (v*dt/dx)**2,(v*dt/dy)**2\n",
    "        \n",
    "    ## Source location\n",
    "    isx,isy = int(sx/dx),int(sy/dy) ## . . Force to be integer (note assumes model origin at [0,0])\n",
    " \n",
    "    ## . . Inject wavelet\n",
    "    UUo[isx,isy] += dt*dt*F[it]\n",
    "    \n",
    "    ## . . Update solution. Again, I am starting the array at index 4 and ending \n",
    "    ## . . at index nx-4 and ny-4 because of the ghost points\n",
    "    UUm[4:nx-4,4:ny-4] =  2*UUo[4:nx-4,4:ny-4]-UUm[4:nx-4,4:ny-4]+Cx2*( \n",
    "                    -1/560 *UUo[0:nx-8,4:ny-4] \n",
    "                    +8/315 *UUo[1:nx-7,4:ny-4]  \n",
    "                    -1/5   *UUo[2:nx-6,4:ny-4]  \n",
    "                    +8/5   *UUo[3:nx-5,4:ny-4]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[5:nx-3,4:ny-4]  \n",
    "                    -1/5   *UUo[6:nx-2,4:ny-4]  \n",
    "                    +8/315 *UUo[7:nx-1,4:ny-4]  \n",
    "                    -1/560 *UUo[8:nx  ,4:ny-4])+Cy2*( \n",
    "                    -1/560 *UUo[4:nx-4,0:ny-8] \n",
    "                    +8/315 *UUo[4:nx-4,1:ny-7]  \n",
    "                    -1/5   *UUo[4:nx-4,2:ny-6]  \n",
    "                    +8/5   *UUo[4:nx-4,3:ny-5]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[4:nx-4,5:ny-3]  \n",
    "                    -1/5   *UUo[4:nx-4,6:ny-2]  \n",
    "                    +8/315 *UUo[4:nx-4,7:ny-1]  \n",
    "                    -1/560 *UUo[4:nx-4,8:ny  ])\n",
    "\n",
    "    return UUm ## . . Return updated wavefield at time step n+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll note that I've changed things a bit here since I am now **injecting the wavelet** into the 2D domain at the current time step $it$.  Here, I've calculated the location of the incidies $[isx,isy]$ at which to inject the wavelet.  I could have also created a force source term $F^n_{i,j}$ as a 3D array; however, this would be wasteful of memory since I'm only injecting at one location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Define spatial grid\n",
    "Lx,Ly = 2000,2000           # . . length (m)\n",
    "nx,ny = 401,401             # . . Number of points in discretization \n",
    "dx,dy = Lx/(nx-1),Ly/(ny-1) # . . Discretization interval\n",
    "x ,y  = np.linspace(0,Lx,nx),np.linspace(0,Ly,ny) # . . Xline\n",
    "v  = 1000        # . . Velocity (m/s)\n",
    "\n",
    "## . . Meshgrid\n",
    "yyy,xxx = np.meshgrid(x,y)\n",
    "\n",
    "## . . Init wavefields on spatial grid\n",
    "UUo,UUm = np.zeros((nx,ny)),np.zeros((nx,ny))\n",
    "\n",
    "## Time stepping parameters\n",
    "CC = 0.5     # . . Courant #\n",
    "nt,dt,t0 = 800,CC*dx/v,0.1       # . . Number of time steps, dt and wavelet shift\n",
    "t  = np.linspace(0,nt*dt,nt)\n",
    "\n",
    "## . . Define Initial waveform\n",
    "ss=0.02 ## sigma for Ricker wavelet\n",
    "F = (1-((t-t0)/ss)**2)*np.exp(-(t-t0)**2/(2*ss**2))\n",
    "sx,sy=Lx/2,Ly/2\n",
    "\n",
    "## . . Total Solution space\n",
    "eee = np.zeros((nx,ny,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_2d_explicit_solver_homogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F,it,sx,sy) #calc solution at n+1\n",
    "    eee[:,:,it]=tmp  ## save solution vector\n",
    "    UUm=UUo          ## move solution at n to n-1 to prepare for next iteration\n",
    "    UUo=tmp          ## move solution at n+1 to n to prepare for next iteration\n",
    "    \n",
    "amax = np.max(np.abs(eee))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_6.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## . . Animate Solution and compute error\n",
    "k,kskip = 0,20\n",
    "\n",
    "## . . Set up movie\n",
    "fig6,ax1 = plt.subplots(1)\n",
    "fig6.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig6.set_dpi(100)\n",
    "\n",
    "def AWE_2D_animate(i):\n",
    "    global k\n",
    "    x = eee[:,:,k]\n",
    "    ax1.imshow(x,cmap='gray',extent=[0,Lx,0,Ly],vmin=-0.1*amax,vmax=0.1*amax)\n",
    "    ax1.set(xlabel='X(m)', ylabel='Y(m)')\n",
    "    ax1.set_title('NUMERICAL Step %s'%k,fontsize=14)\n",
    "    k += kskip\n",
    "\n",
    "## . . Call the animator\n",
    "anim6 = animation.FuncAnimation(fig6,AWE_2D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim6.save('./movies/Ex8_6.gif')\n",
    "plt.close()\n",
    "\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_6.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 6. Propagating 2D acoustic wavefield generated by the 8th-order finite-difference AWE solver.  Note that the reflections from the boundary are with the opposite sign.**\n",
    "\n",
    "## Wavefields, surface seismic and VSP data\n",
    "\n",
    "Since we have the full 3D volume of $[nx,ny,nt]$ it is possible to slice it in different ways. Figure 7 shows the 2D slice at a constant depth (a) representing a surface seismic data set  as well as a 2D slice taken a constant x-location (b) which would represent a vertical seismic profile (VSP) data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.imshow(eee[5,:,:].T,cmap='gray',extent=[0,Lx,nt*dt*1000,0])\n",
    "plt.xlabel('Distance (m)',fontsize=12)\n",
    "plt.ylabel('Time (ms)',fontsize=12)\n",
    "plt.title('(a) Surface Seismic data from buried source',fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(eee[:,int(nx/4),:],cmap='gray',extent=[0,Lx,nt*dt*1000,0])\n",
    "plt.ylabel('Depth (m)',fontsize=12)\n",
    "plt.xlabel('Time (ms)',fontsize=12)\n",
    "plt.title('(b) VSP data from buried source',fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8. (a) Constant depth slice extracted 5m down from the first ghost point representing surface seismic data. (b) Constant X-location slice extracted at 500m distance representing a vertical seismic profile (VSP).**\n",
    "\n",
    "## 2D Acoustic Wave Equation for Heterogeneous Media\n",
    "\n",
    "While solutions of the 2D acoustic wave equation for homogeneous media are important for evaluating our finite-difference scheme, they have quite limited use in practice. This is because velocity models usually are heterogeneous: $v=v(x,y)$. Thus, we need to account for this spatial heterogeneity in our numerical solvers.  To do so, let's adapt our eight-order solver above to include heterogeneity. The most common practice in this situation would be to introduce a spatially varying velocity function into equation 24 above:\n",
    "\n",
    "$$\\phi^{n+1}_{i,j} = 2\\phi^{n}_{i,j} - \\phi^{n-1}_{i,j} +\n",
    "\\left(\\frac{v_{i,j}\\Delta t}{\\Delta x}\\right)^2 \\sum_{k=-4}^4 c_k \\phi^{n}_{i+k,j}+ \\left(\\frac{v_{i,j}\\Delta t}{\\Delta y}\\right)^2\\sum_{k=-4}^4 c_k \\phi^{n}_{i,j+k} +\\Delta t^2 F_{i,j}, \\tag{25}$$\n",
    "\n",
    "which leads to only a modest change in the *awe_2d_explicit_solver_heterogeneous_8th_order* solver below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def awe_2d_explicit_solver_heterogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F,it,sx,sy):\n",
    "    '''Set up eight-order solver of the acoustic wave equation\n",
    "    usage: U=awe_2d_explicit_solver_heterogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F,it,sx,sy):\n",
    "    input: \n",
    "        UUo: Acoustic pressure vector (nx,ny) at time step n\n",
    "        UUm: Acoustic pressure vector (nx,ny) at time step n-1\n",
    "        dx : Spatial sampling in x\n",
    "        dy : Spatial sampling in y        \n",
    "        dt : Temporal sampling\n",
    "        v  : Heterogeneous propagation velocity (nx,ny)\n",
    "        F  : Forcing function (nt)\n",
    "        it : Time index\n",
    "        sx : Location of source in x (meters)\n",
    "        sy : Location of source in y (meters)\n",
    "    output:\n",
    "        UUm: Acoustic pressure vector (nx,ny) at time n+1\n",
    "    dependencies:\n",
    "        None\n",
    "    written by Jeff Shragge, jshragge@mines.edu, 10/2019\n",
    "    '''        \n",
    "    ## . . Get dimensions of wavefield\n",
    "    nx,ny = np.size(UUo,0),np.size(UUo,1)\n",
    "    \n",
    "    ## . . Define Courant numbers (squared)\n",
    "    dtdx2,dtdy2 = (dt/dx)**2,(dt/dy)**2\n",
    "        \n",
    "    ## Source location\n",
    "    isx,isy = int(sx/dx),int(sy/dy) ## . . Force to be integer\n",
    " \n",
    "    ## . . Inject wavelet\n",
    "    UUo[isx,isy] += dt*dt*F[it]\n",
    "\n",
    "    ## . . Update solution \n",
    "    UUm[4:nx-4,4:ny-4] =  2*UUo[4:nx-4,4:ny-4]-UUm[4:nx-4,4:ny-4]+dtdx2*v[4:nx-4,4:ny-4]**2*( \n",
    "                    -1/560 *UUo[0:nx-8,4:ny-4] \n",
    "                    +8/315 *UUo[1:nx-7,4:ny-4]  \n",
    "                    -1/5   *UUo[2:nx-6,4:ny-4]  \n",
    "                    +8/5   *UUo[3:nx-5,4:ny-4]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[5:nx-3,4:ny-4]  \n",
    "                    -1/5   *UUo[6:nx-2,4:ny-4]  \n",
    "                    +8/315 *UUo[7:nx-1,4:ny-4]  \n",
    "                    -1/560 *UUo[8:nx  ,4:ny-4])+dtdy2*v[4:nx-4,4:ny-4]**2*( \n",
    "                    -1/560 *UUo[4:nx-4,0:ny-8] \n",
    "                    +8/315 *UUo[4:nx-4,1:ny-7]  \n",
    "                    -1/5   *UUo[4:nx-4,2:ny-6]  \n",
    "                    +8/5   *UUo[4:nx-4,3:ny-5]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[4:nx-4,5:ny-3]  \n",
    "                    -1/5   *UUo[4:nx-4,6:ny-2]  \n",
    "                    +8/315 *UUo[4:nx-4,7:ny-1]  \n",
    "                    -1/560 *UUo[4:nx-4,8:ny  ])\n",
    "\n",
    "    return UUm ## . . Return updated wavefield at time step n+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now generate a simple 1D velocity model that has a single reflector located at 250m depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## . . Define spatial grid\n",
    "Lx,Ly = 2000,1200           # . . lengths (m)\n",
    "nx,ny = 401,241             # . . Number of points in discretization \n",
    "dx,dy = Lx/(nx-1),Ly/(ny-1) # . . Discretization intervals\n",
    "\n",
    "## . . Create velocity function\n",
    "v = np.zeros((nx,ny))+1500\n",
    "v[:,50:]=2000\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(v.T,cmap='jet',extent=[0,Lx,Ly,0])\n",
    "cbar=plt.colorbar()\n",
    "cbar.set_label('Velocity (m/s)',fontsize=12)\n",
    "plt.xlabel('Distance (m)',fontsize=12)\n",
    "plt.ylabel('Depth (m)',fontsize=12)\n",
    "plt.title('Heterogeneous Velocity Model',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 9. Heterogeneous velocity model with a single discontinuity located at 250 m depth.**\n",
    "\n",
    "Let's now call our solver for heterogeneous media:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Init wavefields on spatial grid\n",
    "UUo = np.zeros((nx,ny))\n",
    "UUm = np.zeros((nx,ny))\n",
    "\n",
    "## Time stepping parameters\n",
    "CC = 0.5                     # . . Courant #\n",
    "nt = 800                     # . . Number of time steps\n",
    "dt = CC*dx/np.max(v)         # . . Define dt based on Courant\n",
    "t  = np.linspace(0,nt*dt,nt) # . . Time lin\n",
    "t0 = 0.05                    # . . Wavelet shift \n",
    "\n",
    "## . . Define forcing function\n",
    "ss=0.01                      # . . sigma for Ricker wavelet\n",
    "F = (1-((t-t0)/ss)**2)*np.exp(-(t-t0)**2/(2*ss**2))\n",
    "\n",
    "## . . Define source location\n",
    "sx,sy=Lx/2,25                # . . source x,y location (in physical m units)\n",
    "\n",
    "## . . Total Solution space\n",
    "fff = np.zeros((nx,ny,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_2d_explicit_solver_heterogeneous_8th_order(UUo,UUm,dx,dy,dt,v,F,it,sx,sy) #calc solution at n+1\n",
    "    fff[:,:,it]=tmp  ## save solution vector\n",
    "    UUm=UUo          ## move solution at n to n-1 to prepare for next iteration\n",
    "    UUo=tmp          ## move solution at n+1 to n to prepare for next iteration\n",
    "\n",
    "## . . compute maximum for scaling\n",
    "amax = np.max(np.abs(fff))\n",
    "\n",
    "## . . Add (demeaned) velocity model to wavefield to illustrate interactions\n",
    "vscale = (v.T-np.mean(v))\n",
    "vscale = -vscale/np.max(vscale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_7.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def AWE_2D_animate(i):\n",
    "    global k\n",
    "    wfld = fff[:,:,k].T/np.max(np.abs(fff))\n",
    "    data = wfld+0.0025*vscale[:,::-1]\n",
    "    ax1.clear()\n",
    "    ax1.imshow(data,cmap='gray',extent=[0,Lx,0,Ly],vmin=-0.08,vmax=0.08)\n",
    "    ax1.set(xlabel='X(m)', ylabel='Y(m)')\n",
    "    ax1.set_title('2D AWE Step %s'%k,fontsize=14)\n",
    "    k += kskip\n",
    "\n",
    "## . . Animate Solution and compute error\n",
    "k,kskip = 0,5\n",
    "\n",
    "## . . Set up movie\n",
    "fig7,ax1 = plt.subplots(1)\n",
    "fig7.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig7.set_dpi(100)\n",
    "\n",
    "## . . Call the animator\n",
    "anim7 = animation.FuncAnimation(fig7,AWE_2D_animate,frames=int((nt-2*kskip)/kskip),interval=100)\n",
    "anim7.save('./movies/Ex8_7.gif')\n",
    "plt.close()\n",
    "\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_7.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 10. Animation of wavefield propagation in layered half space. For illustration purposes, I have added in a scaled version of the velocity model to the (scaled) wavefield solution so that it is easy to see where all of the wavefield scattering occurs.**\n",
    "\n",
    "We can also look at what the surface seismic data look like by taking a slice through the second coordinate of the 3D wavefield solution volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amax = np.max(np.abs(fff[:,5,:].T))\n",
    "plt.figure()\n",
    "plt.imshow(fff[:,5,:].T,cmap='gray',extent=[0,Lx,dt*nt*1000,0],vmin=-0.1*amax,vmax=0.1*amax)\n",
    "plt.xlabel('Distance (m)',fontsize=12)\n",
    "plt.ylabel('Time (ms)',fontsize=12)\n",
    "plt.title('Seismic data',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Figure 11.  Surface seismic data from the wavefield animation shown in Figure 10.  We observe the direct and reflected arrivals.**\n",
    "\n",
    "Let's now create a velocity model with a lot more spatial variations. For this example, I'm going to import an image from the **skimage** package, convert it to float, scale it by $1000\\times$, and add it to a background velocity of $1500$ m/s. **(Note: you may need to install the scikit-image package to run this example, the command for which is given below.)** The result is shown in Figure 12 below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imageio import imread\n",
    "testin = imread('https://raw.githubusercontent.com/scikit-image/scikit-image/v0.10.1/skimage/data/coins.png')\n",
    "test = testin.astype(float)\n",
    "\n",
    "## . . Define model dimensions\n",
    "nx,ny = np.size(test,0),np.size(test,1)\n",
    "dx,dy=5,5\n",
    "Lx = (nx-1)*dx\n",
    "Ly = (ny-1)*dy\n",
    "\n",
    "## . . Define velocity model\n",
    "newvel = np.zeros((nx,ny))\n",
    "newvel = 1500+10*test[::-1,:]\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Velocity Model',fontsize=14)\n",
    "plt.imshow(newvel, cmap=plt.cm.jet,extent=[0,nx*dx,ny*dy,0])\n",
    "plt.xlabel('Distance (m)',fontsize=12)\n",
    "plt.ylabel('Depth (m)',fontsize=12)\n",
    "cbar=plt.colorbar()\n",
    "cbar.set_label('Velocity (m/s)',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 12. A whimsical velocity model made from the *coins* image in the skimage tool kit.**\n",
    "\n",
    "Let's now rerun the wave propagation simulation for heterogeneous media from above, but using this new velocity model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Init wavefields on spatial grid\n",
    "UUo,UUm  = np.zeros((nx,ny)),np.zeros((nx,ny))\n",
    "\n",
    "## Time stepping parameters\n",
    "CC = 0.5                     # . . Courant #\n",
    "nt = 500                     # . . Number of time steps\n",
    "dt = CC*dx/np.max(newvel)    # . . Define dt based on Courant\n",
    "t  = np.linspace(0,nt*dt,nt) # . . Time lin\n",
    "t0 = 0.05                    # . . Wavelet shift \n",
    "\n",
    "## . . Define forcing function\n",
    "ss=0.01                      # . . sigma for Ricker wavelet\n",
    "F = (1-((t-t0)/ss)**2)*np.exp(-(t-t0)**2/(2*ss**2))\n",
    "\n",
    "## . . Define source location\n",
    "sx,sy=Lx/2,Ly/2                # . . source x,y location (in physical m units)\n",
    "\n",
    "## . . Total Solution space\n",
    "ggg = np.zeros((nx,ny,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_2d_explicit_solver_heterogeneous_8th_order(UUo,UUm,dx,dy,dt,newvel,F,it,sx,sy) #calc solution at n+1\n",
    "    ggg[:,:,it]=tmp  ## save solution vector\n",
    "    UUm=UUo          ## move solution at n to n-1 to prepare for next iteration\n",
    "    UUo=tmp          ## move solution at n+1 to n to prepare for next iteration\n",
    "\n",
    "## . . compute maximum for scaling\n",
    "amax = np.max(np.abs(ggg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_8.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vscale = (newvel.T-np.mean(newvel))\n",
    "vscale = -vscale/np.max(vscale)\n",
    "\n",
    "def AWE_2D_animate(i):\n",
    "    global k\n",
    "    wfld = ggg[:,:,k].T/np.max(np.abs(ggg))\n",
    "    data = wfld+0.02*vscale[:,::-1]\n",
    "    ax1.clear()\n",
    "    ax1.imshow(data,cmap='gray',extent=[0,Lx,0,Ly],vmin=-0.08,vmax=0.08)\n",
    "    ax1.set(xlabel='X(m)', ylabel='Y(m)')\n",
    "    ax1.set_title('2D AWE Step %s'%k,fontsize=14)\n",
    "    k += kskip\n",
    "\n",
    "## . . Animate Solution and compute error\n",
    "k,kskip = 0,5\n",
    "\n",
    "## . . Set up movie\n",
    "fig8,ax1 = plt.subplots(1)\n",
    "fig8.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig8.set_dpi(100)\n",
    "\n",
    "## . . Call the animator\n",
    "anim8 = animation.FuncAnimation(fig8,AWE_2D_animate,frames=int((nt-2*kskip)/kskip),interval=75)\n",
    "anim8.save('./movies/Ex8_8.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_8.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 13. 2D Acoustic wave propagation through velocity model shown in Figure 12.**\n",
    "\n",
    "# Time-reversal acoustics\n",
    "\n",
    "One of the most important features of the acoustic wave equation (and hyperbolic PDEs in general) is that one can time reverse the wave propagation (but it gets complicated at the boundary) from maximum time back to zero time.  This is due to the analytical PDE operator being [self-adjoint](https://en.wikipedia.org/wiki/Self-adjoint_operator). (In principle, this does not mean that the numerical implementation is necessarily self-adjoint; one needs to ensure that this is so by applying a [dot-product test](http://sepwww.stanford.edu/sep/prof/bei/conj/paper_html/node11.html)).\n",
    "\n",
    "To illustrate this principle, we can use the last two time-steps of the wavefield generated from the previous example which we'll call $\\psi^n_i$. Let's initialize the present time-reverse wavefield simulation using:\n",
    "\n",
    "$$\\phi^0_i = \\psi^{nt-2}_i \\tag{26}$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\\phi^{-1}_i = \\psi^{nt-1}_i \\tag{27}$$\n",
    "\n",
    "where you should note reverse ordering of $[-1,0]$ for $\\phi$ and $[nt-1,nt-2]$ for $\\psi$. Let's rerun the simulation from above **but this time not inject any source term $F(x) = F_{i,j}=0$**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Init wavefields on spatial grid\n",
    "UUo,UUm  = np.zeros((nx,ny)),np.zeros((nx,ny))\n",
    "\n",
    "## Time stepping parameters\n",
    "CC = 0.5                     # . . Courant #\n",
    "nt = 450                     # . . Number of time steps\n",
    "dt = CC*dx/np.max(newvel)    # . . Define dt based on Courant\n",
    "t  = np.linspace(0,nt*dt,nt) # . . Time line\n",
    "t0 = 0.05                    # . . Wavelet shift \n",
    "\n",
    "## . . Define forcing function\n",
    "ss=0.01                      # . . sigma for Ricker wavelet\n",
    "F = np.zeros((nt))\n",
    "\n",
    "## . . Define source location\n",
    "sx,sy=Lx/2,Ly/2                # . . source x,y location (in physical m units)\n",
    "\n",
    "## . . Define initial wavefields for time reversal\n",
    "UUo = ggg[:,:,nt-2]\n",
    "UUm = ggg[:,:,nt-1]\n",
    "\n",
    "## . . Total Solution space\n",
    "hhh = np.zeros((nx,ny,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "for it in range(nt):\n",
    "    tmp = awe_2d_explicit_solver_heterogeneous_8th_order(UUo,UUm,dx,dy,dt,newvel,F,it,sx,sy) #calc solution at n+1\n",
    "    hhh[:,:,it]=tmp  ## save solution vector\n",
    "    UUm=UUo          ## move solution at n to n-1 to prepare for next iteration\n",
    "    UUo=tmp          ## move solution at n+1 to n to prepare for next iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute the animation of the time-reversing acoustic wavefield through the same velocity model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 450 is out of bounds for axis 2 with size 450",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_387/1052520610.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m## . . Call the animator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0manim9\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manimation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mAWE_2D_animate2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkskip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkskip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0manim9\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./movies/Ex8_9.gif'\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     27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0manim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_anim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\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   1172\u001b[0m                     \u001b[0;31m# TODO: See if turning off blit is really necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m                     \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_next_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   1174\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mprogress_callback\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[1;32m   1175\u001b[0m                         \u001b[0mprogress_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_number\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_frames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_next_frame\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1206\u001b[0m         \u001b[0;31m# post- draw, as well as the drawing of the frame itself.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pre_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1208\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\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   1209\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1774\u001b[0m         \u001b[0;31m# Call the func with framedata and args. If blitting is desired,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m         \u001b[0;31m# func needs to return a sequence of any artists that were modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1776\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframedata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\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   1777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_387/1052520610.py\u001b[0m in \u001b[0;36mAWE_2D_animate2\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mAWE_2D_animate2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\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      7\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mwfld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhhh\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mamax\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwfld\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.02\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mvscale\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\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;31mIndexError\u001b[0m: index 450 is out of bounds for axis 2 with size 450"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## . . compute maximum for scaling\n",
    "amax = np.max(np.abs(hhh))\n",
    "vscale = (newvel.T-np.mean(newvel))\n",
    "vscale = -vscale/np.max(vscale)\n",
    "\n",
    "def AWE_2D_animate2(i):\n",
    "    global k\n",
    "    wfld = hhh[:,:,k].T/amax\n",
    "    data = wfld+0.02*vscale[:,::-1]\n",
    "    ax1.clear()\n",
    "    ax1.imshow(data,cmap='gray',extent=[0,Lx,0,Ly],vmin=-0.08,vmax=0.08)\n",
    "    ax1.set(xlabel='X(m)', ylabel='Y(m)')\n",
    "    ax1.set_title('2D AWE Step %s'%k,fontsize=14)\n",
    "    k += kskip\n",
    "\n",
    "## . . Animate Solution and compute error\n",
    "k,kskip = 0,5\n",
    "\n",
    "## . . Set up movie\n",
    "fig9,ax1 = plt.subplots(1)\n",
    "fig9.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig9.set_dpi(100)\n",
    "\n",
    "## . . Call the animator\n",
    "anim9 = animation.FuncAnimation(fig9,AWE_2D_animate2,frames=int((nt-2*kskip)/kskip),interval=75)\n",
    "anim9.save('./movies/Ex8_9.gif')\n",
    "plt.close()\n",
    "\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_9.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Figure 14. Movie showing time-reverse propagation of that shown in Figure 12 above.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def awe_2d_heterogeneous_8th_order_data_time_reverse(UUo,UUm,dx,dy,dt,v,D,it,ry):\n",
    "    '''Set up eight-order solver of the acoustic wave equation\n",
    "    usage: U=awe_2d_heterogeneous_8th_order_data_time_reverse(UUo,UUm,dx,dy,dt,v,D,it,ry):\n",
    "    input: \n",
    "        Uo: Acoustic pressure vector (nx,ny) at time step n\n",
    "        Um: Acoustic pressure vector (nx,ny) at time step n-1\n",
    "        dx: Spatial sampling in x\n",
    "        dy: Spatial sampling in y        \n",
    "        dt: Temporal sampling\n",
    "        v : Heterogeneous propagation velocity (nx,ny)\n",
    "        D : Data to be time reversed\n",
    "        it: Time index\n",
    "        ry: Injection depth (meters)\n",
    "    output:\n",
    "        Um: Acoustic pressure vector (nx,ny) at time n+1\n",
    "    dependencies:\n",
    "        None\n",
    "    written by Jeff Shragge, jshragge@mines.edu, 10/2019\n",
    "    '''        \n",
    "    ## . . Get dimensions of wavefield\n",
    "    nx,ny = np.size(UUo,0),np.size(UUo,1)\n",
    "    \n",
    "    ## . . Define Courant numbers (squared)\n",
    "    dtdx2 = (dt/dx)**2\n",
    "    dtdy2 = (dt/dy)**2\n",
    "        \n",
    "    ## Source location\n",
    "    iry = int(ry/dy) ## . . Force to be integer\n",
    " \n",
    "    ## . . Inject wavelet\n",
    "    UUo[:,iry] += dt*dt*D[:,it]\n",
    "\n",
    "    ## . . Update solution \n",
    "    UUm[4:nx-4,4:ny-4] =  2*UUo[4:nx-4,4:ny-4]-UUm[4:nx-4,4:ny-4]+dtdx2*v[4:nx-4,4:ny-4]**2*( \n",
    "                    -1/560 *UUo[0:nx-8,4:ny-4] \n",
    "                    +8/315 *UUo[1:nx-7,4:ny-4]  \n",
    "                    -1/5   *UUo[2:nx-6,4:ny-4]  \n",
    "                    +8/5   *UUo[3:nx-5,4:ny-4]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[5:nx-3,4:ny-4]  \n",
    "                    -1/5   *UUo[6:nx-2,4:ny-4]  \n",
    "                    +8/315 *UUo[7:nx-1,4:ny-4]  \n",
    "                    -1/560 *UUo[8:nx  ,4:ny-4])+dtdy2*v[4:nx-4,4:ny-4]**2*( \n",
    "                    -1/560 *UUo[4:nx-4,0:ny-8] \n",
    "                    +8/315 *UUo[4:nx-4,1:ny-7]  \n",
    "                    -1/5   *UUo[4:nx-4,2:ny-6]  \n",
    "                    +8/5   *UUo[4:nx-4,3:ny-5]  \n",
    "                    -205/72*UUo[4:nx-4,4:ny-4]  \n",
    "                    +8/5   *UUo[4:nx-4,5:ny-3]  \n",
    "                    -1/5   *UUo[4:nx-4,6:ny-2]  \n",
    "                    +8/315 *UUo[4:nx-4,7:ny-1]  \n",
    "                    -1/560 *UUo[4:nx-4,8:ny  ])\n",
    "\n",
    "    return UUm ## . . Return updated wavefield at time step n+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## . . Define spatial grid\n",
    "Lx,Ly = 2000,1200        # . . length (m)\n",
    "nx,ny = 401,241      # . . Number of points in discretization \n",
    "dx = Lx/(nx-1) # . . Discretization interval\n",
    "dy = Ly/(ny-1) # . . Discretization interval\n",
    "\n",
    "## . . Create velocity function\n",
    "v = np.zeros((nx,ny))+1500\n",
    "v[:,50:]=2000\n",
    "\n",
    "## . . Init wavefields on spatial grid\n",
    "UUo = np.zeros((nx,ny))\n",
    "UUm = np.zeros((nx,ny))\n",
    "\n",
    "## Time stepping parameters\n",
    "CC = 0.5                     # . . Courant #\n",
    "nt = 800                     # . . Number of time steps\n",
    "dt = CC*dx/np.max(v)         # . . Define dt based on Courant\n",
    "t  = np.linspace(0,nt*dt,nt) # . . Time lin\n",
    "t0 = 0.05                    # . . Wavelet shift \n",
    "\n",
    "## . . Define forcing function\n",
    "ss=0.01                      # . . sigma for Ricker wavelet\n",
    "\n",
    "## . . Get the data from fff above\n",
    "data = fff[:,5,:]\n",
    "\n",
    "## . . Define source location\n",
    "ry=25                        # . . receiver injection location in y (in physical m units)\n",
    "\n",
    "## . . Total Solution space\n",
    "mmm = np.zeros((nx,ny,nt)) \n",
    "\n",
    "## . . Iterate over solution\n",
    "## . . Note time reversal\n",
    "for it in range(nt-1,0,-1):\n",
    "    tmp = awe_2d_heterogeneous_8th_order_data_time_reverse(UUo,UUm,dx,dy,dt,v,data,it,ry) #calc solution at n+1\n",
    "    mmm[:,:,nt-1-it]=tmp  ## save solution vector\n",
    "    UUm=UUo          ## move solution at n to n-1 to prepare for next iteration\n",
    "    UUo=tmp          ## move solution at n+1 to n to prepare for next iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<img src=\"./movies/Ex8_10.gif\" width=\"500\" />\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## . . compute maximum for scaling\n",
    "amax = np.max(np.abs(mmm))\n",
    "vscale = (v.T-np.mean(v))\n",
    "vscale = -vscale/np.max(vscale)\n",
    "\n",
    "def AWE_2D_animate3(i):\n",
    "    global k\n",
    "    wfld = mmm[:,:,k].T/amax\n",
    "    data = wfld+0.0025*vscale[:,::-1]\n",
    "    ax1.clear()\n",
    "    ax1.imshow(data,cmap='gray',extent=[0,Lx,0,Ly],vmin=-0.08,vmax=0.08)\n",
    "    ax1.set(xlabel='X(m)', ylabel='Y(m)')\n",
    "    ax1.set_title('2D AWE Step %s'%k,fontsize=14)\n",
    "    k += kskip\n",
    "\n",
    "## . . Animate Solution and compute error\n",
    "k,kskip = 0,5\n",
    "\n",
    "## . . Set up movie\n",
    "fig10,ax1 = plt.subplots(1)\n",
    "fig10.subplots_adjust(0.1,0.1,0.9,0.9)\n",
    "fig10.set_dpi(100)\n",
    "\n",
    "## . . Call the animator\n",
    "anim10 = animation.FuncAnimation(fig10,AWE_2D_animate3,frames=int((nt-2*kskip)/kskip),interval=75)\n",
    "anim10.save('./movies/Ex8_10.gif')\n",
    "plt.close()\n",
    "\n",
    "HTML(\"\"\"\n",
    "<img src=\"./movies/Ex8_10.gif\" width=\"500\" />\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Figure 15.  Movie showing the time-reverse behavior in the layered half-space model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}