{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0007114d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import cg\n",
    "\n",
    "import pherosensor\n",
    "\n",
    "from pheromone_dispersion.convection_diffusion_2D import DiffusionConvectionReaction2DEquation\n",
    "from pheromone_dispersion.source_term import 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\n",
    "from source_localization.population_dynamique import PopulationDynamicModel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748fd4eb",
   "metadata": {},
   "source": [
    "# Test of the gradient \n",
    "\n",
    "This notebook aims to test the gradient of the cost function computed using the adjoint model.\\\n",
    "To do so, the derivative of the cost function in a given direction computed using this estimation of the gradient will be compared to a finite difference approximation of this derivative. \\\n",
    "Indeed, we have $dj(S)\\cdot\\delta S = <\\nabla j(S),\\delta S>_{L^2(\\Omega\\times[0;T])}$. In the following, $d_aj(S)\\cdot\\delta S$ will denote the estimation of the derivative computed from the estimation of the gradient computed by the adjoint model.\\\n",
    "On the other hand, we have the two following finite differnece approximation of the derivative: $d_+j(S)\\cdot\\delta S=\\frac{j(S+\\epsilon\\delta S)-j(S)}{\\epsilon}=dj(S)\\cdot\\delta S +\\mathcal{O}(\\epsilon||\\delta S||)$ and $d_-j(S)\\cdot\\delta S=\\frac{j(S)-j(S-\\epsilon\\delta S)}{\\epsilon}=dj(S)\\cdot\\delta S +\\mathcal{O}(\\epsilon||\\delta S||)$.\\\n",
    "Hence, we have the truncation error $|\\frac{d_+j(S)\\cdot\\delta S-dj(S)\\cdot\\delta S}{dj(S)\\cdot\\delta S}| = \\mathcal{O}(\\epsilon||\\delta S||)$ and $|\\frac{d_-j(S)\\cdot\\delta S-dj(S)\\cdot\\delta S}{dj(S)\\cdot\\delta S}| = \\mathcal{O}(\\epsilon||\\delta S||)$.\n",
    "\n",
    "## Instanciation of the parameters and of the direct model\n",
    "\n",
    "The parameters of the direct model and the direct model are instanciated.\\\n",
    "Two Source terms are instanciated. A circle source term $S^{circ}$ is going to be used as reference to generate the observations and the background value of the control (which is the source term, $S_b=S^{circ}$). A rectangular source term $S^{rect}$ is going to be used as point from which the cost function and its gradient are going to be computed.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96caace3",
   "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 = 0.1# 0.4 the space step along the x-axis\n",
    "Delta_y = 0.1# 0.4 the space step along the y-axis\n",
    "T_final = 2. # the final time of the simulation\n",
    "msh = MeshRect2D(Lx, Ly, Delta_x, Delta_y, T_final)\n",
    "msh.calc_dt_implicit_solver(0.1)\n",
    "\n",
    "ix = msh.x.size #125 # 62\n",
    "iy = msh.y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69bf9ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "#U_vi = np.load('./data_test_case/U_vi.npy')[:i,:i+1]\n",
    "#U_hi = np.load('./data_test_case/U_hi.npy')[:i+1,:i]\n",
    "\n",
    "U_hi = 2*np.ones((iy+1,ix,2))\n",
    "U_hi[:,:,1]=0\n",
    "U_vi = 2*np.ones((iy,ix+1,2))\n",
    "U_vi[:,:,1]=0\n",
    "\n",
    "U = Velocity(msh, U_vi, U_hi)\n",
    "print(np.min(U.div), np.max(U.div))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3976fcd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "K_u_t = 5./6  # diffusion coefficient in the crosswind direction (less weak)\n",
    "K_u = 0.01  # diffusion coefficient in the downwind direction (very weak)\n",
    "K = DiffusionTensor(U, K_u, K_u_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "855a6ea7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def circular(msh,xc,yc,r,amp): \n",
    "    res = np.ones((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "    xx, yy = np.meshgrid(msh.x, msh.y)\n",
    "    res[:, (xx-xc)**2 + (yy-yc)**2 < r**2] = 1 + amp\n",
    "    return res\n",
    "    \n",
    "\n",
    "S_value = circular(msh, 5., 5., 2.5, 1.) # np.load('../data_test_case/circular_Source_value.npy')[:,:iy,:ix]*1e4\n",
    "S_t = msh.t_array # np.load('../data_test_case/Source_time.npy')\n",
    "S_circ = Source(msh, S_value, t=S_t)\n",
    "#plot_colormap(msh,S_circ.value,'S^{circ}', 'g.m^{-2}', cmap=\"jet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "587079eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(msh, xc, yc, c, amp): \n",
    "    res = np.ones((msh.t_array.size, msh.y.size, msh.x.size))\n",
    "    xx, yy = np.meshgrid(msh.x, msh.y)\n",
    "    res[:, np.maximum(np.abs(xx-xc), np.abs(yy-yc) ) < c] = 1 + amp\n",
    "    return res\n",
    "\n",
    "S_b_value = square(msh, 5., 5., 2.5, 1.) # np.load('../data_test_case/rectangular_Source_value.npy')[:,:iy,:ix]*1e4/5\n",
    "S_b_t = msh.t_array # np.load('../data_test_case/Source_time_background.npy')\n",
    "\n",
    "S_rect = Source(msh, S_b_value, t=S_b_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f943cfa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff_loss = np.ones((msh.y.size, msh.x.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd16534",
   "metadata": {},
   "source": [
    "## Instanciation and solving the direct model\n",
    "The direct model is solved using the reference source term. The results of the direct model are then used to instanciate the Obs class.\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfc96f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "EDP = DiffusionConvectionReaction2DEquation(U, K, coeff_loss, S_circ, msh, time_discretization='implicit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab5cb1a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 2.000 / 2.000 s"
     ]
    }
   ],
   "source": [
    "t_o, C_o = EDP.solver()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74f0bcc",
   "metadata": {},
   "source": [
    "## Instanciation of the Obs class \n",
    "\n",
    "The observations are generated almost everywhere (1/5 of the points and not in a square $10<x,y<15$) and at several times ($t\\in\\{0.5,1,1.5,2\\}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff2a9898",
   "metadata": {},
   "outputs": [],
   "source": [
    "def observed(msh,t, C):\n",
    "    X_obs = [] # np.zeros((N_obs,2))\n",
    "    t_obs = [] # np.zeros((N_obs,))\n",
    "    D_obs = [] # np.zeros((N_obs,))\n",
    "    \n",
    "    for it, tc in enumerate(t): \n",
    "        if np.abs(tc - .5) < 1e-12 or np.abs(tc - 1.) < 1e-12 or np.abs(tc - 1.5) < 1e-12 or np.abs(tc - 2.) < 1e-12:\n",
    "            for ix, x in enumerate(msh.x[::5]): \n",
    "                for iy, y in enumerate(msh.y[::5]): \n",
    "                    if not(10<x and x<15 and 10<y and y<15):\n",
    "                        D_obs.append((C[it-1, iy, ix]+C[it, iy, ix])*msh.dt)\n",
    "                        X_obs.append([x, y])\n",
    "                        t_obs.append(tc)\n",
    "    return np.array(t_obs), np.array(X_obs), np.array(D_obs)\n",
    "\n",
    "t_obs, X_obs, D_obs = observed(msh,t_o,C_o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "954faafe-e497-45c6-b5c2-79ad0b8206c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = Obs(t_obs, X_obs, D_obs, msh, dt_obs_operator=2*msh.dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4603e070",
   "metadata": {},
   "source": [
    "## Instanciation of the class Control and Cost\n",
    "\n",
    "Two object of the class Control are instanciated, one with the circular source and one with the rectangular source.\\ \n",
    "Then, the class Cost is instanciated using the the circular source term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5419f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdm = PopulationDynamicModel(msh)\n",
    "ctrl_rect = Control(S_rect, msh, population_dynamique_model=pdm)\n",
    "ctrl_value_save = np.copy(ctrl_rect.background_value)\n",
    "\n",
    "ctrl_circ = Control(S_circ, msh, population_dynamique_model=pdm)\n",
    "cost = Cost(msh, obs, ctrl_circ, regularization_types='Population dynamic', alpha=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4839bd",
   "metadata": {},
   "source": [
    "## Instanciation of the adjoint model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b19c0e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Adj_model = AdjointDiffusionConvectionReaction2DEquation(U, K, coeff_loss, msh, time_discretization='implicit')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8273ee2",
   "metadata": {},
   "source": [
    "## Test of the gradient\n",
    "\n",
    "In a first time, the cost $j(S^{rect})$ and the estimation of the gradient of the cost $\\nabla j(S^{rect})$, and the resulting estimation of $d_aj(S^{rect})\\cdot\\delta S=<\\nabla j(S^{rect}),\\delta S>$, are conputed from the rectangular source term using the adjoint model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9190602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running the direct model\n",
      "running the observation operator\n",
      "running the adjoint model\n",
      "\n",
      "jc= 0.20051549023931095\n",
      "jc_obs= 0.08551549023929995\n",
      "jc_reg= {'Population dynamic': 0.115000000000011}\n",
      "dj/N= 3.01015941995588e-09\n",
      "dj= 0.003160667390953674\n"
     ]
    }
   ],
   "source": [
    "jc, grad = cost.cost_and_gradient(ctrl_value_save, EDP, Adj_model, display=True) \n",
    "\n",
    "ds = np.random.normal(0, 1, size=grad.size)\n",
    "\n",
    "dj = np.dot(grad, ds)*msh.mass_cell*msh.dt\n",
    "N = ctrl_value_save.size\n",
    "print(\"\")\n",
    "print(\"jc=\",jc)\n",
    "print(\"jc_obs=\",cost.j_obs)\n",
    "print(\"jc_reg=\",cost.j_reg)\n",
    "print(\"dj/N=\",dj/N)\n",
    "print(\"dj=\",dj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0d6fc2",
   "metadata": {},
   "source": [
    "Then, the costs $j(S^{rect}\\pm\\epsilon\\delta S)$ are computed for different value of $\\epsilon$, and the finite difference approximations of the derivative $d_\\pm j(S^{rect})\\cdot\\delta S$ as well.\n",
    "\n",
    "In the present case, we use $\\delta S=1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87cfac05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-10\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n",
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-09\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n",
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-08\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n",
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-07\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n",
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-06\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n",
      "\n",
      "###############\n",
      "\n",
      "epsilon= 1e-05\n",
      "t = 2.000 / 2.000 s\n",
      "###############\n"
     ]
    }
   ],
   "source": [
    "eps_a = np.array([1e-10,1e-9,1e-8,1e-7,1e-6,1e-5])\n",
    "jm = np.zeros(eps_a.shape)\n",
    "jp = np.zeros(eps_a.shape)\n",
    "\n",
    "dj_m = np.zeros(eps_a.shape)\n",
    "dj_p = np.zeros(eps_a.shape)\n",
    "\n",
    "for i,eps in zip(range(eps_a.size),eps_a): \n",
    "    \n",
    "    print('')\n",
    "    print('###############')\n",
    "    print('epsilon=',eps)\n",
    "    \n",
    "    cost.ctrl.value = ctrl_value_save - eps * ds\n",
    "    cost.ctrl.apply_control(EDP)\n",
    "    EDP.solver_est_at_obs_times(cost.obs)\n",
    "    cost.obs.obs_operator()\n",
    "    cost.objectif()\n",
    "    jm[i] = np.copy(cost.j)\n",
    "    \n",
    "    cost.ctrl.value = ctrl_value_save + eps * ds\n",
    "    cost.ctrl.apply_control(EDP)\n",
    "    EDP.solver_est_at_obs_times(cost.obs)\n",
    "    cost.obs.obs_operator()\n",
    "    cost.objectif()\n",
    "    jp[i] = np.copy(cost.j)\n",
    "    \n",
    "    dj_p[i] = (jp[i]-jc)/eps\n",
    "    dj_m[i] = (jc-jm[i])/eps\n",
    "    \n",
    "    print('')\n",
    "    print('###############')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee16542b",
   "metadata": {},
   "source": [
    "Finally, the resulting difference $e_\\pm = |\\frac{d_\\pm j(S)\\cdot\\delta S-dj(S)\\cdot\\delta S}{dj(S)\\cdot\\delta S}|$ are plotted for different $\\epsilon$.\\\n",
    "Recall we have the truncation error $|\\frac{d_+j(S)\\cdot\\delta S-dj(S)\\cdot\\delta S}{dj(S)\\cdot\\delta S}| = \\mathcal{O}(\\epsilon||\\delta S||)$ and $|\\frac{d_-j(S)\\cdot\\delta S-dj(S)\\cdot\\delta S}{dj(S)\\cdot\\delta S}| = \\mathcal{O}(\\epsilon||\\delta S||)$, Hence, we expecte to have slopes equal to 1 in log scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "138a6eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_p = np.abs(1-(dj_p/dj))\n",
    "error_m = np.abs(1-(dj_m/dj))\n",
    "\n",
    "slope_p = (np.log(error_p[-1]) - np.log(error_p[-2]))/(np.log(eps_a[-1]) - np.log(eps_a[-2]))\n",
    "slope_m = (np.log(error_m[-1]) - np.log(error_m[-2]))/(np.log(eps_a[-1]) - np.log(eps_a[-2]))\n",
    "\n",
    "fontsize = 25\n",
    "\n",
    "plt.figure(0, figsize=(20, 10))\n",
    "plt.plot(eps_a, error_p,'b', label=r'$e_+$'+f', slope = {\"{:.5f}\".format(slope_p)}')\n",
    "plt.plot(eps_a, error_m,'g', label=r'$e_-$'+f', slope = {\"{:.5f}\".format(slope_m)}')\n",
    "plt.xlabel(r'$\\epsilon$', fontsize=fontsize)\n",
    "plt.ylabel(r'$e_\\pm$', fontsize=fontsize)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.legend(loc='upper left',prop={'size': fontsize})\n",
    "plt.tick_params(axis='both',labelsize=fontsize-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6143a5-be73-49f0-ab17-4d597c597d96",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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