{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbf305d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Préparation commune des TP Python\n",
    "# Le notebook utilise uniquement les ressources fournies dans le dossier codes/.\n",
    "from pathlib import Path\n",
    "import importlib.util\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "REQUIRED_PACKAGES = {'numpy': 'numpy', 'pandas': 'pandas', 'matplotlib': 'matplotlib'}\n",
    "missing = [package for module, package in REQUIRED_PACKAGES.items()\n",
    "           if importlib.util.find_spec(module) is None]\n",
    "if missing:\n",
    "    print(\"Installation des paquets manquants :\", \", \".join(missing))\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", *missing])\n",
    "\n",
    "WORKDIR = Path.cwd().resolve()\n",
    "CODES_DIR = WORKDIR.parent if WORKDIR.name == \"correction\" else WORKDIR\n",
    "TOOLBOX_DIR = CODES_DIR / \"toolbox\"\n",
    "DATA_DIR = CODES_DIR / \"data\"\n",
    "if not CODES_DIR.is_dir():\n",
    "    raise FileNotFoundError(\"Exécutez le notebook depuis le dossier codes/.\" )\n",
    "if TOOLBOX_DIR.is_dir():\n",
    "    sys.path.insert(0, str(TOOLBOX_DIR))\n",
    "\n",
    "DICE_FILE = TOOLBOX_DIR / \"DICE.py\"\n",
    "if not DICE_FILE.is_file():\n",
    "    raise FileNotFoundError(\n",
    "        f\"Module du cours introuvable : {DICE_FILE}. \"\n",
    "        \"Téléchargez le dossier codes complet, avec toolbox/.\"\n",
    "    )\n",
    "\n",
    "DATA_FILE = DATA_DIR / \"ngfs_scenarios.csv\"\n",
    "if not DATA_FILE.is_file():\n",
    "    raise FileNotFoundError(\n",
    "        f\"Donnée du cours introuvable : {DATA_FILE}. \"\n",
    "        \"Téléchargez le dossier codes complet, avec data/.\"\n",
    "    )\n",
    "\n",
    "print(\"Environnement prêt :\", CODES_DIR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e10c6e5",
   "metadata": {},
   "source": [
    "# TP4 — Scénarios de transition et prix du carbone\n",
    "\n",
    "**Date de la séance :** mercredi 9 septembre 2026, 10:45–12:45\n",
    "\n",
    "**Objectifs**\n",
    "- Simuler des politiques climatiques contrastées avec DICE (variable de contrôle `mu`)\n",
    "- Comparer transition précoce et transition tardive\n",
    "- Manipuler les scénarios NGFS (prix du carbone, émissions, température)\n",
    "\n",
    "**Prérequis** : TP2, TP3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac20718",
   "metadata": {},
   "source": [
    "## Partie 1 — Scénarios de transition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced9d52a",
   "metadata": {},
   "source": [
    "**Question 1.** Simulez trois scénarios de politique climatique :\n",
    "- Aucune action : mu=0.03 (baseline)\n",
    "- Transition modérée : mu=0.3 dès 2025\n",
    "- Transition forte : mu=0.7 dès 2025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3ff14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import DICE, numpy as np, pandas as pd, matplotlib.pyplot as plt\n",
    "\n",
    "p_base = DICE.Params()\n",
    "nT = p_base.nT\n",
    "\n",
    "scenarios_mu = {\n",
    "    'Aucune action (mu=0.03)': 0.03,\n",
    "    'Transition modérée (mu=0.3)': 0.3,\n",
    "    'Transition forte (mu=0.7)': 0.7\n",
    "}\n",
    "paths_transition = {}\n",
    "for name, mu_val in scenarios_mu.items():\n",
    "    p = DICE.Params()\n",
    "    path = DICE.init_states(p)\n",
    "    path[1:, p.i_mu] = mu_val\n",
    "    timevec = range(1, p.nT)\n",
    "    path = DICE.update_path(path, timevec, p)\n",
    "    paths_transition[name] = (p, path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7aa3aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE: tracez les émissions et températures pour les 3 scénarios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95029407",
   "metadata": {},
   "source": [
    "**Question 2.** Calculez la réduction d'émissions cumulées entre 2025 et 2100 pour chaque scénario par rapport au scénario de référence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be977815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e37d05",
   "metadata": {},
   "source": [
    "## Partie 2 — Transition ordonnée vs désordonnée"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30b9a0d",
   "metadata": {},
   "source": [
    "**Question 3.** Comparez deux politiques :\n",
    "- Action précoce : mu=0.5 dès 2025\n",
    "- Action tardive : mu=0.5 seulement à partir de 2040"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19fdd6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8dad473",
   "metadata": {},
   "source": [
    "## Partie 3 — Scénarios NGFS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "895f0e55",
   "metadata": {},
   "source": [
    "**Question 4.** Chargez les données NGFS et tracez les trajectoires de prix du carbone et d'émissions pour les trois scénarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e01a016",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ngfs = pd.read_csv(DATA_DIR / \"ngfs_scenarios.csv\")\n",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfa0085",
   "metadata": {},
   "source": [
    "## Interprétation"
   ]
  }
 ],
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