{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook : Création d'une modélisation 2D via script - version courte\n",
    "\n",
    "Afin de créer une simulation 2D, il est nécessaire de :\n",
    " \n",
    "  - créer une instance \"prev_sim2D\" en lui passant en argument le chemin d'accès complet et le nom générique de la simulation\n",
    "  - définir un contour vectoriel/polygone délimitant la zone de travail\n",
    "  - (optionnel) définir une \"grille magnétique\" servant d'accroche des vertices des polygones --> permet de s'aligner entre différentes modélisations par ex.\n",
    "  - définir la résolution spatiale du maillage sur lequel les données (topo, frottrement, inconnues...) seront fournies initialement\n",
    "  - définir des contours/polygones des blocs et leur résolution spatiale respective\n",
    "  - appeler le mailleur (appel au code Fortran)\n",
    "  - créer les matrices obligatoires et les remplir avec les valeurs souhaitées (via script(s) ou GUI)\n",
    "  - créer les bords potentiels pour les conditons aux limites (.sux, .suy)\n",
    "  - imposer les conditions aux limites nécessaires (info : rien == imperméable)\n",
    "  - paramétrer le problème\n",
    "  - exécuter le calcul\n",
    "  - traiter les résultats\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import des modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tempfile import TemporaryDirectory\n",
    "from pathlib import Path\n",
    "\n",
    "from datetime import datetime as dt\n",
    "from datetime import timezone as tz\n",
    "from datetime import timedelta as td\n",
    "from typing import Literal, Union\n",
    "\n",
    "from wolfhece.mesh2d.wolf2dprev import prev_sim2D\n",
    "from wolfhece.PyVertexvectors import zone, Zones, vector, wolfvertex\n",
    "from wolfhece.wolf_array import WolfArray, WolfArrayMB\n",
    "from wolfhece.wolfresults_2D import Wolfresults_2D, views_2D\n",
    "from wolfhece.mesh2d.cst_2D_boundary_conditions import BCType_2D, Direction, revert_bc_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Définition du répertoire de travail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n"
     ]
    }
   ],
   "source": [
    "outdir = Path(r'test')\n",
    "outdir.mkdir(exist_ok=True) # Création du dossier de sortie\n",
    "print(outdir.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vérification de la présence du code de calcul\n",
    "\n",
    "Retourne le chemin d'accès complet au code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Code found :  D:\\ProgrammationGitLab\\HECEPython\\wolfhece\\libs\\wolfcli.exe\n"
     ]
    }
   ],
   "source": [
    "wolfcli = prev_sim2D.check_wolfcli() # méthode de classe, il n'est donc pas nécessaire d'instancier la classe pour l'appeler\n",
    "\n",
    "if wolfcli:\n",
    "    print('Code found : ', wolfcli)\n",
    "else:\n",
    "    print('Code not found !')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Création d'un objet simulation 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No infiltration file found\n"
     ]
    }
   ],
   "source": [
    "# Passage en attribut du répertoire de travail auquel on a ajouté le nom générique de la simulation.\n",
    "# \"clear\" permet de supprimer les fichiers de la simulation précédente.\n",
    "newsim = prev_sim2D(fname=str(outdir / 'test'), clear=True)\n",
    "\n",
    "# Un message de type \"logging.info\" est émis car aucun fichier n'a été trouvé. Ceci est normal !\n",
    "# Si un fichier est trouvé, il est chargé."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Géométrie du problème"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Définition du polygone externe -- via une classe \"vector\" -- voir [wolfhece.PyVertexvectors](https://wolf.hece.uliege.be/autoapi/wolfhece/PyVertexvectors/index.html)\n",
    "\n",
    "Domaine carré de taille 1.0 m x 1.0 m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de vertices :  5\n",
      "5 vertices car la polygone est fermé et le dernier point est aux mêmes coordonnées que le premier.\n"
     ]
    }
   ],
   "source": [
    "# Define the extrenal contour\n",
    "extern = vector()\n",
    "extern.add_vertex(wolfvertex(0., 0.))\n",
    "extern.add_vertex(wolfvertex(1., 0.))\n",
    "extern.add_vertex(wolfvertex(1., 1.))\n",
    "extern.add_vertex(wolfvertex(0., 1.))\n",
    "extern.close_force()\n",
    "\n",
    "print('Nombre de vertices : ', extern.nbvertices)\n",
    "print('5 vertices car la polygone est fermé et le dernier point est aux mêmes coordonnées que le premier.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etapes de mise en place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Very Fast\" - One block\n",
    "# -----------------------\n",
    "\n",
    "# # with tuple\n",
    "# newsim.setup_oneblock((0.,0.,1,1,1.,1.), block_spatial_stepsize = 0.1, friction_coefficient =0.04)\n",
    "\n",
    "# # with dictionary\n",
    "# newsim.setup_oneblock({'ox': 0., 'oy': 0., 'nbx':1, 'nby':1, 'dx': 1., 'dy': 1.}, block_spatial_stepsize = 0.1, friction_coefficient = 0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meshing done !\n"
     ]
    }
   ],
   "source": [
    "# More verbose - One block\n",
    "# ------------------------\n",
    "\n",
    "# Grille magnétique - dx et dy sont les pas de la grille [m], origx et origy sont les coordonnées du premier point de la grille.\n",
    "newsim.set_magnetic_grid(dx=1., dy=1., origx=0., origy=0.)\n",
    "\n",
    "# Transfert du polygone comme contour externe de simulation.\n",
    "newsim.set_external_border_vector(extern)\n",
    "\n",
    "# Par défaut, les coordonnées du polygone seront translatées pour le que point (xmin, ymin) soit en (0, 0).\n",
    "newsim.translate_origin2zero = True\n",
    "\n",
    "# Choix du pas spatial de maillage fin [m].\n",
    "newsim.set_mesh_fine_size(dx=0.1, dy=0.1)\n",
    "\n",
    "# Ajout d'un bloc avec son pas spatial spécifique [m].\n",
    "newsim.add_block(extern, dx=0.2, dy=0.2)\n",
    "\n",
    "# Maillage du problème\n",
    "if newsim.mesh():\n",
    "    print('Meshing done !')\n",
    "else:\n",
    "    print('Meshing failed !')\n",
    "\n",
    "# Si \"with_tubulence\" est True, les fichiers \".kbin\" et \".epsbin\" seront créés en plus et contiendront l'énergie cinétique turbulente.\n",
    "newsim.create_fine_arrays(default_frot=0.04, with_tubulence=False)\n",
    "\n",
    "# Recherches des bords conditions aux limites potentiels sur base de la matrice \".napbin\" et écriture des fichiers \".sux\" et \".suy\"\n",
    "newsim.create_sux_suy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paramétrage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsim.parameters.set_params_time_iterations(nb_timesteps= 1000, optimize_timestep=True, writing_frequency=1, writing_mode='Iterations', initial_cond_reading_mode='Binary', writing_type='Binary compressed')\n",
    "newsim.parameters.set_params_temporal_scheme(RungeKutta='RK21', CourantNumber=0.35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vérifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Plusieurs niveaux de vebrosité sont disponibles:\n",
    "#   - 0 : Erreurs uniquement\n",
    "#   - 1 : Erreurs et Warnings\n",
    "#   - 2 : Erreurs, Warnings et Informations\n",
    "#   - 3 : 2 + en-têtes de sections\n",
    "print(newsim.check_all(verbosity= 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditions aux limites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsim.reset_all_boundary_conditions()\n",
    "\n",
    "# ou via des boucles, tout est possible !\n",
    "for j in range(5,15):\n",
    "    newsim.add_boundary_condition(i = 15, j = j, bc_type = BCType_2D.H, bc_value = 1.0, border=Direction.X)\n",
    "\n",
    "for j in range(5,15):\n",
    "    newsim.add_boundary_condition(i = 5, j = j, bc_type = BCType_2D.QX, bc_value = 1.0, border=Direction.X)\n",
    "    newsim.add_boundary_condition(i = 5, j = j, bc_type = BCType_2D.QY, bc_value = 0.0, border=Direction.X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listing des CL selon chaque axe :\n",
    "\n",
    "- Nombre de CL\n",
    "- Lister les indices\n",
    "- Obtenir une liste d'objets \"boundary_condition_2D\" - https://wolf.hece.uliege.be/autoapi/wolfgpu/simple_simulation/index.html#wolfgpu.simple_simulation.boundary_condition_2D\n",
    "- Vérifier l'existence ou non\n",
    "- Changer la valeur et/ou le type\n",
    "- Retirer une condition imposée précédemment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb CL selon X :  30\n",
      "Nb CL selon Y :  0\n"
     ]
    }
   ],
   "source": [
    "print('Nb CL selon X : ', newsim.nb_weak_bc_x)\n",
    "print('Nb CL selon Y : ', newsim.nb_weak_bc_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sauvegarde sur disque\n",
    "\n",
    "Les paramètres et les conditions aux limites peuvent être sauvegardés sur disque dans le fichier \".par\" via la méthode \"save\".\n",
    "\n",
    "L'écriture sur disque ne peut donc être valablement réalisée **qu'après** avoir défini à la fois les paramètres utiles et les CL du problème."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No infiltration data to write\n"
     ]
    }
   ],
   "source": [
    "newsim.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditions initiales / Données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topographie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mise à zéro de la topographie\n",
    "newsim.top.array[:,:] = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hauteur d'eau initiale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comme la topo est plate, on peut facilement éditer la matrice de hauteur d'eau\n",
    "newsim.hbin.array[:,:] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Débits selon X et Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsim.qxbin.array[:,:] = 0.\n",
    "newsim.qybin.array[:,:] = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frottement\n",
    "\n",
    "On garde la valeur par défaut de 0.04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarde\n",
    "\n",
    "Les matrices sont pointées dans l'objet.\n",
    "\n",
    "On force la mise à jour des fichiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsim.save_arrays_modifications()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcul\n",
    "\n",
    "Une fois que tous les fichiers sont écrits sur disque, il est temps de lancer le calcul.\n",
    "\n",
    "Pour cela, soit utiliser \"run_wolfcli\" ou alors exécuter la commande \"wolfcli run_wolf2d_prev genfile=pathtosim' dans une fenêtre de commande, pour autant que wolfcli y soit accessible (via le PATH par ex.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsim.run_wolfcli()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soyez patient !! \n",
    "\n",
    "Le calcul est en cours... du moins si une fenêtre de commande apparaît et que des informations de calcul s'affichent.\n",
    "\n",
    "Le temps de calcul total est impossible à prédire.\n",
    "\n",
    "**ATTENTION** Si une erreur d'accès fichier est mentionnée, cela est vraisemblablement lié à une autre fenêtre encore ouverte ou à une liaison de ficier non refermée. Commencer par fermer toutes les fenêtres de commandes précédentes. et relancer le script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse de résultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instanciation d'un objet résultat\n",
    "res = Wolfresults_2D(newsim.filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Récupération du nombre de résultats\n",
    "res.get_nbresults()\n",
    "\n",
    "# 1001 car 1000 itérations + 1 état initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Récupération des résultats d'un pas spécifique (0-based) -- \"-1\" pour le dernier pas\n",
    "res.read_oneresult(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Affichage Matplotlib simple du dernier pas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.read_oneresult(-1)\n",
    "res.set_current(views_2D.WATERDEPTH) # Calcul de la hauteur d'eau\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum  1.0003558\n",
      "Maximum  1.0017345\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('Minimum ', res[0].array.min())\n",
    "print('Maximum ', res[0].array.max())\n",
    "\n",
    "fig,ax = res[0].plot_matplotlib()\n",
    "fig.suptitle('Hauteur d\\'eau')\n",
    "fig.colorbar(ax.images[0], ax=ax)\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Il est peut-être aussi temps d'ouvrir l'interface WOLF pour plus d'interactivité...**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.10",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}