{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac9729b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\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.convection_diffusion_2D import DiffusionConvectionReaction2DEquation, Source\n",
    "from pheromone_dispersion.diffusion_tensor import DiffusionTensor\n",
    "from pheromone_dispersion.geom import MeshRect2D\n",
    "from pheromone_dispersion.velocity import Velocity\n",
    "\n",
    "from source_localization.cost import Cost\n",
    "from source_localization.control import Control\n",
    "from source_localization.adjoint_convection_diffusion_2D import AdjointDiffusionConvectionReaction2DEquation\n",
    "from source_localization.obs import Obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83c1983a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = 20#50 # the length along the x-axis\n",
    "Ly = 25#60 # the length along the y-axis\n",
    "Delta_x = 1. # 0.4 the space step along the x-axis\n",
    "Delta_y = 1. # 0.4 the space step along the y-axis\n",
    "T_final = 2. # the final time of the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b20e53f",
   "metadata": {},
   "source": [
    "# Test of the numerical scheme of the adjoint model\n",
    "\n",
    "This notebook aims to test the numerical scheme of the direct model and its implementation by comparing the results of the numerical solver with a reference solution used to derive an associated source term. \n",
    "\n",
    "## Reference solution\n",
    "\n",
    "In the present case, we consider a velocity field of shape $U(x,y) = (u(x,y),0)^T$ (with $u\\geq0$) with the horizontal velocity $u(x,y)=\\frac{4}{L_x^2}(x-L_x)^2\\frac{3}{2L_y}y$.\n",
    "Let us note that this velocity satisfies $u\\geq0$ and $up = 0$ at $x=L_x$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "012ba7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def velocity_horizontal(x,y):\n",
    "    xx, yy = np.meshgrid(x, y)\n",
    "    return 4 * (xx - Lx)**2 * 3 * yy / (Lx**2 * 2 * Ly)\n",
    "\n",
    "def velocity_field(msh): \n",
    "    \n",
    "    U_hi = np.zeros((msh.y_horizontal_interface.size, msh.x.size,2))\n",
    "    U_hi[:,:,0] = velocity_horizontal(msh.x, msh.y_horizontal_interface) \n",
    "    \n",
    "    U_vi = np.zeros((msh.y.size, msh.x_vertical_interface.size,2))\n",
    "    U_vi[:,:,0] = velocity_horizontal(msh.x_vertical_interface, msh.y)\n",
    "    \n",
    "    return Velocity(msh, U_vi, U_hi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f64022",
   "metadata": {},
   "source": [
    "The diffusion tensor is considered of shape $K=diag(K_x,K_y)$ with constant $K_x$ and $K_y$ and the reaction coefficient constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dd0d6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_x = 5./6  # diffusion coefficient in the crosswind direction (less weak)\n",
    "K_y = 0.01  # diffusion coefficient in the downwind direction (very weak)\n",
    "tau_loss = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af9a2b6",
   "metadata": {},
   "source": [
    "\n",
    "Hence, the 2D PDE reduces to $\\partial_tp+u\\partial_xp+K_x\\partial_{xx}p+K_y\\partial_{yy}p-\\tau_{loss}p = \\big(\\partial_cd(c(s))\\big)^*\\cdot\\nabla_dJ\\big(s,d(c(s))\\big)$\\\n",
    "with the final condition: $p(t=T)=0$,\\\n",
    "the boundary conditions:\\\n",
    "$K_x\\partial_xp = 0$ at $x = 0$ and $x = L_x$,\\\n",
    "$K_y\\partial_yp = 0$ at $y = 0$ and $y = L_y$,\\\n",
    "and $up = 0$ at $x= 0$.\n",
    "\n",
    "We consider the reference solution $p^{ref}(x,y,t) =  (T-t)\\left(cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)+cos\\left(\\frac{2\\pi n_yy}{L_y}\\right)\\right)$ with $n_x$ and $n_y$ two integers.\\\n",
    "Let us note that the boundary conditions $K_x\\partial_xc = 0$ at $x = 0$ and $x = L_x$, $K_y\\partial_yc = 0$ at $y = 0$ and $y = L_y$ are then satisfied. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a59a547",
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 1\n",
    "ny = 3\n",
    "lambda_x = 2 * np.pi * nx / Lx\n",
    "lambda_y = 2 * np.pi * ny / Ly\n",
    "\n",
    "def p_reference(msh):\n",
    "    xx, yy = np.meshgrid(msh.x, msh.y)\n",
    "    p_ref = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "\n",
    "    for it,t in enumerate(msh.t_array): \n",
    "        p_ref[it,:,:] = (msh.T_final - t) * ( np.cos(lambda_x * xx) + np.cos(lambda_y * yy) )\n",
    "\n",
    "    return p_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8b680f",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "From this reference solution, we have : \\\n",
    "$\\partial_tp^{ref}(x,y,t) = - cos\\left(\\frac{2\\pi n_xx}{L_x}\\right) - cos\\left(\\frac{2\\pi n_yy}{L_y}\\right)$\\\n",
    "$\\partial_xp^{ref}(x,y,t)=-(T-t)\\frac{2\\pi n_x}{L_x}sin\\left(\\frac{2\\pi n_xx}{L_x}\\right)$, $\\partial_{xx}p^{ref}(x,y,t)=-(T-t)\\frac{4\\pi^2 n_x^2}{L_x^2}cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)$\\\n",
    "$\\partial_yp^{ref}(x,y,t)=-(T-t)\\frac{2\\pi n_y}{L_y}sin\\left(\\frac{2\\pi n_yy}{L_y}\\right)$, $\\partial_{yy}p^{ref}(x,y,t)=-(T-t)\\frac{4\\pi^2 n_y^2}{L_y^2}cos\\left(\\frac{2\\pi n_yy}{L_y}\\right)$ \\\n",
    "Hence, we have the associated source term: \n",
    "\\begin{align*}\n",
    "\\Sigma=\\big(\\partial_cd(c(s))\\big)^*\\cdot\\nabla_dJ\\big(s,d(c(s))\\big) (x,y,t) = &-\\left(1+\\tau_{loss}(x,y)(T-t)+K_x(T-t)\\frac{4\\pi^2 n_x^2}{L_x^2}\\right)cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)\\\\ \n",
    "          &- \\left(1+\\tau_{loss}(x,y)(T-t)+K_y(T-t)\\frac{4\\pi^2 n_y^2}{L_y^2}\\right)cos\\left(\\frac{2\\pi n_yy}{L_y}\\right) \\\\\n",
    "          &- u(x,y)(T-t)\\frac{2\\pi n_x}{L_x}sin\\left(\\frac{2\\pi n_xx}{L_x}\\right)\n",
    "\\end{align*}\n",
    "In the present context, we assume that $\\big(\\partial_cd(c(s))\\big)^*=\\Delta tId$ and $c(s)=0$.\\\n",
    "Moreover, $\\nabla_dJ\\big(s,d\\big) = 2(d-d^{obs})$. Hence, we have $\\Sigma (x,y,t) = -2d^{obs}(x,y,t)$ and  \n",
    "\\begin{align*}\n",
    "2\\Delta td^{obs}(x,y,t) &= \\left(1+\\tau_{loss}(x,y)(T-t)+K_x(T-t)\\frac{4\\pi^2 n_x^2}{L_x^2}\\right)cos\\left(\\frac{2\\pi n_xx}{L_x}\\right)\\\\ \n",
    "          &+ \\left(1+\\tau_{loss}(x,y)(T-t)+K_y(T-t)\\frac{4\\pi^2 n_y^2}{L_y^2}\\right)cos\\left(\\frac{2\\pi n_yy}{L_y}\\right) \\\\\n",
    "          &+ u(x,y)(T-t)\\frac{2\\pi n_x}{L_x}sin\\left(\\frac{2\\pi n_xx}{L_x}\\right)\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0cf44f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Obs_reference(msh): \n",
    "    \n",
    "    u = velocity_horizontal(msh.x,msh.y)\n",
    "    \n",
    "    N_obs = msh.y.size * msh.x.size * msh.t_array.size\n",
    "    N_xy = msh.y.size * msh.x.size\n",
    "    X_obs = np.zeros((N_obs,2))\n",
    "    t_obs = np.zeros((N_obs,))\n",
    "    C_obs = np.zeros((N_obs,))\n",
    "    i_obs = 0\n",
    "    \n",
    "    for it, tc in enumerate(msh.t_array): \n",
    "        dt = msh.T_final - tc\n",
    "        for ix, x in enumerate(msh.x): \n",
    "            for iy, y in enumerate(msh.y):\n",
    "                C_obs[i_obs] = 0.5 * (1 + tau_loss * dt + K_x * lambda_x**2 * dt) * np.cos(lambda_x * x)\n",
    "                C_obs[i_obs]+= 0.5 * (1 + tau_loss * dt + K_y * lambda_y**2 * dt) * np.cos(lambda_y * y)\n",
    "                C_obs[i_obs]+= 0.5 * u[iy,ix] * lambda_x * dt * np.sin(lambda_x * x)\n",
    "                X_obs[i_obs, 0] = x\n",
    "                X_obs[i_obs, 1] = y\n",
    "                t_obs[i_obs] = tc\n",
    "                i_obs +=1\n",
    "                \n",
    "    return Obs(t_obs, X_obs, C_obs/msh.dt, msh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692e0dfb",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Numerical solver\n",
    "\n",
    "The equation is solved using a semi-implicit finite volume scheme. Recall that the adjoint model is backward in time, a final condition is given.\\\n",
    "Let us denote $D$, $A^*$ and $R$ resp. the diffusion, adjoint of the advection and the reaction operator.\\\n",
    "Let us recall that \n",
    "\\begin{align*}\n",
    "A^*p(x,y)&= -U(x,y)\\cdot\\nabla p(x,y)=-\\nabla\\cdot(U(x,y) p(x,y))+p(x,y)\\nabla\\cdot U(x,y)\\\\ \n",
    "&=-\\nabla\\cdot(U(x,y) p(x,y))+p(x,y)\\nabla\\cdot U(x,y)1_{\\nabla\\cdot U>0}(x,y)+p(x,y)\\nabla\\cdot U(x,y)1_{\\nabla\\cdot U<0}(x,y)\n",
    "\\end{align*}\n",
    "This operator is then separated in an implicit and an explicit part, resp. $A^*_ip(x,y)=p(x,y)\\nabla\\cdot U(x,y)1_{\\nabla\\cdot U<0}(x,y)$ and $A^*_ep(x,y)=-\\nabla\\cdot(U(x,y) p(x,y))+p(x,y)\\nabla\\cdot U(x,y)1_{\\nabla\\cdot U>0}(x,y)$.\\\n",
    "The time discretization is the following: $\\frac{p^{n+1}-p^n}{\\Delta t}-Dp^{n}+A_i^*p^n+A_e^*p^{n+1}+Rp^{n}=\\Sigma^{n}$.\n",
    "\n",
    "The space discretization of the diffusion and advection terms are presented in the notebooks dedicated to the test of the associated schemes.\n",
    "\n",
    "In the following, $p^{solver}$ denotes the estimation of $p$ obtained by solving numerically the equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14151062",
   "metadata": {},
   "outputs": [],
   "source": [
    "def EDP(msh, dt):\n",
    "    \n",
    "    U = velocity_field(msh)\n",
    "    msh.calc_dt_implicit_solver(dt)\n",
    "    \n",
    "    K = DiffusionTensor(U, K_x, K_y)\n",
    "    \n",
    "    coeff_loss = tau_loss * np.ones((msh.y.size, msh.x.size))\n",
    "    \n",
    "    obs = Obs_reference(msh)\n",
    "    \n",
    "    S = Source(msh, np.zeros((msh.y.size, msh.x.size)))\n",
    "    ctrl = Control(S, msh)\n",
    "    cost = Cost(msh, obs, ctrl)\n",
    "    cost.obs.d_est = np.zeros(cost.obs.d_obs.shape)\n",
    "    return AdjointDiffusionConvectionReaction2DEquation(U, K, coeff_loss, msh, time_discretization='implicit'), cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2bc1cf2",
   "metadata": {},
   "source": [
    "## Analysis of the error estimate\n",
    "\n",
    "In this section, the estimate of the error $e(x,y,t)=|p^{solver}(x,y,t)-p^{ref}(x,y,t)|$ is analyzed.\n",
    "\n",
    "### Analysis of the error estimate for several space step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f33b1b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tmalou/anaconda3/envs/pherosensor-new/lib/python3.7/site-packages/pherosensor-0.1.1-py3.7.egg/pheromone_dispersion/diffusion_tensor.py:55: RuntimeWarning: invalid value encountered in true_divide\n",
      "  U_at_vertical_interface = self.U.at_vertical_interface / norm_U_at_vertical_interface[:, :, None]\n",
      "/home/tmalou/anaconda3/envs/pherosensor-new/lib/python3.7/site-packages/pherosensor-0.1.1-py3.7.egg/pheromone_dispersion/diffusion_tensor.py:74: RuntimeWarning: invalid value encountered in true_divide\n",
      "  U_at_horizontal_interface = self.U.at_horizontal_interface / norm_U_at_horizontal_interface[:, :, None]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dx =  0.5\n",
      "dt =  0.025\n",
      "t = 0.000 / 2.000 s\n",
      "dx =  1.0\n",
      "dt =  0.025\n",
      "t = 0.000 / 2.000 s\n",
      "dx =  1.5\n",
      "dt =  0.025\n",
      "t = 0.000 / 2.000 s\n",
      "dx =  2.0\n",
      "dt =  0.025\n",
      "t = 0.000 / 2.000 s"
     ]
    }
   ],
   "source": [
    "coeff_dx_a = [0.5, 1., 1.5, 2.]\n",
    "abs_a = np.zeros((len(coeff_dx_a)))\n",
    "\n",
    "MAE_vs_space_step = np.zeros((len(coeff_dx_a)))\n",
    "RMSE_vs_space_step = np.zeros((len(coeff_dx_a)))\n",
    "\n",
    "\n",
    "for i, coeff_dx in enumerate(coeff_dx_a):\n",
    "    \n",
    "    msh = MeshRect2D(Lx, Ly, Delta_x * coeff_dx, Delta_y * coeff_dx, T_final)\n",
    "    \n",
    "    model_adjoint, cost = EDP(msh, 0.025)\n",
    "    \n",
    "    p_ref = p_reference(msh)\n",
    "    \n",
    "    abs_a[i] = msh.dx + msh.dt\n",
    "    print(\"\")\n",
    "    print(\"dx = \", msh.dx)\n",
    "    print(\"dt = \", msh.dt)\n",
    "    \n",
    "    p_solver = model_adjoint.solver(cost.obs.adjoint_derivative_obs_operator, cost) \n",
    "    p_solver = p_solver.reshape((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "    \n",
    "    RMSE_vs_space_step[i] = npl.norm(p_solver - p_ref) / np.sqrt(p_solver.size)\n",
    "    MAE_vs_space_step[i] = np.mean(np.abs(p_solver - p_ref))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9afafde",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope_MAE_vs_space_step = ( np.log(MAE_vs_space_step[0]) - np.log(MAE_vs_space_step[-1]) ) / ( np.log(abs_a[0]) - np.log(abs_a[-1]) ) \n",
    "slope_RMSE_vs_space_step = ( np.log(RMSE_vs_space_step[0]) - np.log(RMSE_vs_space_step[-1]) ) / ( np.log(abs_a[0]) - np.log(abs_a[-1]) ) \n",
    "\n",
    "fontsize = 25\n",
    "fig, ax1 = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax1.plot(abs_a,RMSE_vs_space_step,'-ob',label=r'$\\frac{1}{\\sqrt{T|\\Omega|}}||e||_{L_2([0;T]\\times\\Omega)}$'+f', slope = {\"{:.5f}\".format(slope_RMSE_vs_space_step)}')\n",
    "ax1.tick_params(axis='both',labelsize=fontsize-5)\n",
    "ax1.set_ylabel(r'$\\frac{1}{\\sqrt{T|\\Omega|}}||e||_{L_2([0;T]\\times\\Omega)}$', fontsize=fontsize)\n",
    "ax1.set_xlabel('$\\Delta t + \\Delta x$', fontsize=fontsize)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_title(r'for a fixed $\\Delta t$ and varying $\\Delta x$', fontsize=fontsize)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(abs_a,MAE_vs_space_step,'-or',label=r'$\\frac{1}{T|\\Omega|}||e||_{L_1([0;T]\\times\\Omega)}$'+f', slope = {\"{:.5f}\".format(slope_MAE_vs_space_step)}')\n",
    "ax2.tick_params(axis='both',labelsize=fontsize-5)\n",
    "ax2.set_ylabel(r'$\\frac{1}{T|\\Omega|}||e||_{L_1([0;T]\\times\\Omega)}$', fontsize=fontsize)\n",
    "ax2.set_yscale('log')\n",
    "\n",
    "line1, label1 = ax1.get_legend_handles_labels()\n",
    "line2, label2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(line1+line2,label1+label2,loc='upper left',prop={'size': fontsize})#, bbox_to_anchor=(1.17,1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "582e9eec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}