{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac9729b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker\n",
    "import numpy as np\n",
    "import numpy.linalg as npl\n",
    "from scipy.sparse.linalg import cg\n",
    "\n",
    "import pherosensor\n",
    "\n",
    "from pheromone_dispersion.advection_operator import Advection, AdvectionAdjoint\n",
    "from pheromone_dispersion.reaction_operator import Reaction\n",
    "from pheromone_dispersion.geom import MeshRect2D\n",
    "from pheromone_dispersion.velocity import Velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fe7068f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = 20\n",
    "Ly = 25\n",
    "Delta_x = 0.4\n",
    "Delta_y = 0.4\n",
    "T_final = 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66a1804",
   "metadata": {},
   "source": [
    "# Test of the numerical scheme of the adjoint of the advection operator\n",
    "This notebook aims to test the implementation of the numerical scheme of the adjoint operator of the advection operator.\n",
    "\n",
    "Recall that the advection operator is: $A:c(x,y)\\mapsto \\nabla\\cdot(U(x,y) c(x,y))~\\forall (x,y)\\in\\Omega$ such that $U(x,y)c(x,y)\\cdot \\vec{n} = 0~\\forall(x,y)\\in\\partial\\Omega_i$ with $\\partial\\Omega_i = \\{ (x,y)\\in\\partial\\Omega, U(x,y)\\cdot\\vec{n}<0\\}$.\\\n",
    "The adjoint of the advection operator is the operator $A^*:c(x,y)\\mapsto -U(x,y)\\cdot\\nabla c(x,y)~\\forall (x,y)\\in\\Omega$ such that $U(x,y)c(x,y)\\cdot \\vec{n} = 0~\\forall(x,y)\\in\\partial\\Omega_o$ with $\\partial\\Omega_o = \\{ (x,y)\\in\\partial\\Omega, U(x,y)\\cdot\\vec{n}>0\\}$.\n",
    "\n",
    "## Reference solution\n",
    "\n",
    "We consider the reference solution $c^{ref}(x,y) = sin\\left(\\frac{2\\pi n_x}{L_x}x\\right)sin\\left(\\frac{2\\pi n_y}{L_y}y\\right)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5501993",
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 5\n",
    "ny = 7\n",
    "lambda_x = 2 * np.pi * nx / Lx\n",
    "lambda_y = 2 * np.pi * ny / Ly\n",
    "\n",
    "def c_reference(x,y): \n",
    "    return np.sin(lambda_x * x) * np.sin(lambda_y * y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05eadad3",
   "metadata": {},
   "source": [
    "and the velocity $U(x,y)=(u,v)(x,y)=(\\frac{3}{L_x}x, \\frac{4}{L_y}y)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed274356",
   "metadata": {},
   "outputs": [],
   "source": [
    "def u(x):\n",
    "    return 3*x/Lx\n",
    "\n",
    "def v(y): \n",
    "    return 4*y/Ly    \n",
    "\n",
    "def velocity_field(msh): \n",
    "    x, yi = np.meshgrid(msh.x, msh.y_horizontal_interface)\n",
    "    U_hi = np.zeros((msh.y_horizontal_interface.size,msh.x.size,2))\n",
    "    U_hi[:,:,0] = u(x)\n",
    "    U_hi[:,:,1] = v(yi) \n",
    "    \n",
    "    xi, y = np.meshgrid(msh.x_vertical_interface, msh.y)\n",
    "    U_vi = np.zeros((msh.y.size,msh.x_vertical_interface.size,2))\n",
    "    U_vi[:,:,0] = u(xi)\n",
    "    U_vi[:,:,1] = v(y) \n",
    "    \n",
    "    return Velocity(msh, U_vi, U_hi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658c6d49",
   "metadata": {},
   "source": [
    "Therefore, we have:\\\n",
    "$\\partial_xc^{ref}(x,y)=\\frac{2\\pi n_x}{L_x}cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)sin\\left(\\frac{2\\pi n_yy}{L_y}\\right)$,\\\n",
    "$\\partial_yc^{ref}(x,y)=\\frac{2\\pi n_y}{L_y}sin\\left(\\frac{2\\pi n_xx}{L_x}\\right)cos\\left(\\frac{2\\pi n_yy}{L_y}\\right)$.\\\n",
    "We can note that the reference solution satisfies the boundary conditions.\n",
    "\n",
    "Hence, we have $A^*c^{ref}(x,y) = U(x,y)\\cdot\\nabla c^{ref}(x,y) = u(x,y)\\frac{2\\pi n_x}{L_x}cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)sin\\left(\\frac{2\\pi n_yy}{L_y}\\right) + v(x,y)\\frac{2\\pi n_y}{L_y}sin\\left(\\frac{2\\pi n_xx}{L_x}\\right)cos\\left(\\frac{2\\pi n_yy}{L_y}\\right)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b85e931",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ATc_reference(x,y): \n",
    "    res = - u(x) * lambda_x * np.cos(lambda_x * x) * np.sin(lambda_y * y)\n",
    "    res+= - v(y) * lambda_y * np.sin(lambda_x * x) * np.cos(lambda_y * y)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e57a262",
   "metadata": {},
   "source": [
    "## Numerical scheme\n",
    "\n",
    "The numerical scheme used is a finite volume scheme. The average of the advection operator over a control volume $\\Omega_{i,j}$ is : \n",
    "\\begin{align*}\n",
    "-\\frac{1}{|\\Omega_{i,j}|} \\int_{\\Omega_{i,j}} U(x,y)\\cdot\\nabla c(x,y)dx dy &= -\\frac{1}{|\\Omega_{i,j}|} \\int_{\\Omega_{i,j}} \\nabla\\cdot(U(x,y) c(x,y))dx dy + \\frac1{|\\Omega_{i,j}|} \\int_{\\Omega_{i,j}} c(x,y)\\nabla\\cdot U(x,y)dx dy\\\\\n",
    "&= \\frac{1}{|\\Omega_{i,j}|} \\int_{\\partial \\Omega_{i,j}}-U(x,y)c(x,y)\\cdot \\vec{n} dx dy + \\frac1{|\\Omega_{i,j}|} \\int_{\\Omega_{i,j}} c(x,y)\\nabla\\cdot U(x,y)dx dy\n",
    "\\end{align*}\n",
    "In the present case, we use uniform cartesian meshes with the same space step along the two axis. We will denote by the indexes $i+\\frac{1}{2},j$ the vertical interface between the control volumes $i,j$ and $i+1,j$, and similarly for the horizontal interfaces. Moreover, let us note that on the vertical interfaces $\\vec{n}=(\\pm1,0)$ and on the horizontal interfaces $\\vec{n}=(0,\\pm1)$.\\\n",
    "Therefore, we have: $\\frac1{|\\Omega_{i,j}|} \\int_{\\partial \\Omega_{i,j}}U(x,y)c(x,y)\\cdot \\vec{n} dx dy \\approx \\frac1{|\\Omega_{i,j}|}\\left(-\\Delta y\\left(c_{i+\\frac{1}{2},j}u_{i+\\frac{1}{2},j}-c_{i-\\frac{1}{2},j}u_{i-\\frac{1}{2},j}\\right)-\\Delta x\\left(c_{i,j+\\frac{1}{2}}v_{i,j+\\frac{1}{2}}-c_{i,j-\\frac{1}{2}}v_{i,j-\\frac{1}{2}}\\right)\\right)$.\\\n",
    "In the present case, the scheme is a downwind scheme (upwind considering the opposite velocity field). Therefore, the concentration at the interfaces are given by: \n",
    "- if $U_{i\\pm1/2,j}\\cdot \\vec{n} = u_{i\\pm1/2,j} < 0$, then $c_{i\\pm1/2,j} = c_{i\\pm1/2-1/2,j}$, else $c_{i\\pm1/2,j} = c_{i\\pm1/2+1/2,j}$,\n",
    "- if $U_{i,j\\pm1/2}\\cdot \\vec{n} = v_{i,j\\pm1/2} < 0$, then $c_{i,j\\pm1/2} = c_{i,j\\pm1/2-1/2}$, else $c_{i,j\\pm1/2} = c_{i,j\\pm1/2+1/2}$.\n",
    "\n",
    "Moreover, the divergence of $U$ is computed using a centered scheme: $\\nabla\\cdot U_{i,j} = \\frac{u_{i+1/2,j}-u_{i-1/2,j}}{\\Delta x}+\\frac{v_{i,j+1/2}-v_{i,j-1/2}}{\\Delta y}$.\\\n",
    "And we denote $A^{*solver}c(x_i,y_j) = \\frac{1}{|\\Omega_{i,j}|}\\left(-\\Delta y\\left(c_{i+\\frac{1}{2},j}u_{i+\\frac{1}{2},j}-c_{i-\\frac{1}{2},j}u_{i-\\frac{1}{2},j}\\right)-\\Delta x\\left(c_{i,j+\\frac{1}{2}}v_{i,j+\\frac{1}{2}}-c_{i,j-\\frac{1}{2}}v_{i,j-\\frac{1}{2}}\\right)\\right)+\\left(\\frac{u_{i+1/2,j}-u_{i-1/2,j}}{\\Delta x}+\\frac{v_{i,j+1/2}-v_{i,j-1/2}}{\\Delta y}\\right)c_{i,j}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8eb39075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dx =  0.002\n",
      "\n",
      "dx =  0.004\n",
      "\n",
      "dx =  0.04000000000000001\n",
      "\n",
      "dx =  0.4\n"
     ]
    }
   ],
   "source": [
    "space_factor_a = [0.005, 0.01, 0.1, 1.]\n",
    "dx_a = np.zeros(len(space_factor_a))\n",
    "\n",
    "MAE = np.zeros(len(space_factor_a))\n",
    "RMSE = np.zeros(len(space_factor_a))\n",
    "\n",
    "for i, space_factor in enumerate(space_factor_a):\n",
    "    \n",
    "    msh = MeshRect2D(Lx, Ly, Delta_x*space_factor, Delta_y*space_factor, T_final)\n",
    "    x, y = np.meshgrid(msh.x, msh.y)\n",
    "    dx_a[i] = Delta_x*space_factor\n",
    "    \n",
    "    c_ref = c_reference(x, y)\n",
    "    ATc_ref = ATc_reference(x,y)\n",
    "\n",
    "    U = velocity_field(msh)\n",
    "    \n",
    "    AT = AdvectionAdjoint(U, msh) \n",
    "    ATc_solver = AT.matvec(c_ref.reshape((msh.y.size * msh.x.size,)))\n",
    "    ATc_solver = ATc_solver.reshape((msh.y.size, msh.x.size))\n",
    "    \n",
    "    RMSE[i] = npl.norm(ATc_solver - ATc_ref) / np.sqrt(ATc_ref.size)\n",
    "    MAE[i] = np.mean(np.abs(ATc_solver-ATc_ref))\n",
    "    \n",
    "    print(\"\")\n",
    "    print(\"dx = \", msh.dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cab73f",
   "metadata": {},
   "source": [
    "## Analysis of the truncation error\n",
    "In the present case, since the components of the velocity field are positive ($u,v\\geq0$) and due to the uniform cartesian mesh (with $\\Delta x = \\Delta y$), the upwind finite volume scheme is equivalent to a standard downwind finite difference scheme.\\\n",
    "Hence, we have the truncation error $Rc(x,y) = |A^{*solver}c(x,y) - A^*c(x,y)|=\\mathcal{O}(\\Delta x)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0782208a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x71bed6d46290>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope_MAE = (np.log(MAE[0]) - np.log(MAE[-1])) / (np.log(dx_a[0]) - np.log(dx_a[-1])) \n",
    "slope_RMSE = (np.log(RMSE[0]) - np.log(RMSE[-1]) ) / ( np.log(dx_a[0]) - np.log(dx_a[-1])) \n",
    "\n",
    "fontsize = 25\n",
    "fig, ax1 = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax1.plot(dx_a,RMSE,'-ob',label=r'$\\frac{1}{\\sqrt{|\\Omega|}}||Rc^{ref}||_{L^2(\\Omega)}$'+f', slope = {\"{:.5f}\".format(slope_RMSE)}')\n",
    "ax1.plot(dx_a,MAE,'-or',label=r'$\\frac{1}{|\\Omega|}||Rc^{ref}||_{L^1(\\Omega)}$'+f', slope = {\"{:.5f}\".format(slope_MAE)}')\n",
    "\n",
    "ax1.tick_params(axis='both',labelsize=fontsize-5)\n",
    "ax1.set_ylabel(r'error', fontsize=fontsize)\n",
    "ax1.set_xlabel('$\\Delta x$ ($m$)', fontsize=fontsize)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "ax1.legend(loc='upper left',prop={'size': fontsize})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc698d44",
   "metadata": {},
   "source": [
    "# Scalar product test for the adjoint of the advection operator\n",
    "\n",
    "\n",
    "This section aims at performing the scalar product test between the advection operator and its adjoint. It also enables to check that the scheme of the adjoint of the advection operator corresponds the adjoint of the scheme of the advection operator. \n",
    "\n",
    "The test of the scalar product consists in checking that $<Ac_1,c_2>\\approx<c_1,A^*c_2>$ for any $c_1$ and $c_2$ (here picked randomly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94dd8781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Ac_1,c_2> =  -170.63794325367223\n",
      "<c_1,A*c_2> =  -170.63794325367212\n",
      "|<Ac_1,c_2>-<c_1,A*c_2>| =  1.1368683772161603e-13\n"
     ]
    }
   ],
   "source": [
    "msh = MeshRect2D(Lx, Ly, Delta_x, Delta_y, T_final)\n",
    "U = velocity_field(msh)\n",
    "\n",
    "AT = AdvectionAdjoint(U, msh) \n",
    "A = Advection(U, msh)\n",
    "\n",
    "np.random.seed(0)\n",
    "c1 = np.random.normal(0, 1, size=msh.x.size*msh.y.size)\n",
    "c2 = np.random.normal(0, 1, size=msh.x.size*msh.y.size)\n",
    "\n",
    "prod_scal_A = np.dot(c2,A.matvec(c1))\n",
    "prod_scal_AT = np.dot(c1,AT.matvec(c2))\n",
    "\n",
    "print(\"<Ac_1,c_2> = \",prod_scal_A)\n",
    "print(\"<c_1,A*c_2> = \",prod_scal_AT)\n",
    "print(\"|<Ac_1,c_2>-<c_1,A*c_2>| = \", np.abs(prod_scal_A-prod_scal_AT))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pherosensor-new",
   "language": "python",
   "name": "pherosensor-new"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}