{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial - Simple\n",
    "A simple optimization problem related to production optimization under resource constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "IMPORT PACKAGE, DEFINE MODEL NAME AND ROOT PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cvxlab\n",
    "\n",
    "model_dir_name = ''\n",
    "main_dir_path = r''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- - - \n",
    "MODEL DIRECTORY GENERATION\n",
    "\n",
    "Generation of a model directory in a defined path. Notes:\n",
    "- Blank .yml or .xlsx files can be generated\n",
    "- API_reference.ipynb can be generated as guidance for main APIs\n",
    "- user_operators.py and user_constants.py templates can be generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvxlab.create_model_dir(\n",
    "    model_dir_name=model_dir_name,\n",
    "    main_dir_path=main_dir_path,\n",
    "    template_file_type='xlsx', \n",
    "    include_tutorial=True,\n",
    "    include_user_operators_template=True,\n",
    "    include_user_constants_template=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "UTILITY FILE GENERATION\n",
    "\n",
    "Useful in case the model directory already exists and the utility files are missing. Notes:\n",
    "- API_reference.ipynb can be generated as guidance for main APIs\n",
    "- user_operators.py and user_constants.py templates can be generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvxlab.copy_utility_files(\n",
    "    path_destination=main_dir_path + '\\\\' + model_dir_name,\n",
    "    include_custom_constants_template=True,\n",
    "    include_custom_operators_template=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "MODEL GENERATION FROM SCRATCH\n",
    "\n",
    "Step-by-step model creation with sets, data and problems generation.\n",
    "\n",
    "- Generation of a Model instance defined by settings files filled by user\n",
    "- model_settings_from: select among yml and xlsx\n",
    "- use_existing_data=True : model coordinates are loaded and numerical problem initialized (model ready to be run). \n",
    "- use_existing_data=False : sets excel file generated only, to be filled by user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cvxlab.Model(\n",
    "    model_dir_name=model_dir_name,\n",
    "    main_dir_path=main_dir_path,\n",
    "    log_level='info',\n",
    "    model_settings_from='xlsx',\n",
    "    multiple_input_files=False,\n",
    "    use_existing_data=True,\n",
    "    detailed_validation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once sets.xlsx file has filled with sets data:\n",
    "\n",
    "- load_model_coordinates(): load user defined sets, fill coordinates\n",
    "- initialize_blank_data_structure():\n",
    "    - generate blank sqlite database with sets and variables (empty numerical values)\n",
    "    - generate blank xlsx files for exogenous data to be filled by the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_model_coordinates()\n",
    "model.initialize_blank_data_structure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once exogenous xlsx files has filled:\n",
    "\n",
    "- load_exogenous_data_to_sqlite_database(): load input data into sqlite database\n",
    "- initialize_problems(): load and validate symbolic problem, generate numerical problem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_exogenous_data_to_sqlite_database(force_overwrite=True)\n",
    "model.initialize_problems()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "MODEL GENERATION FROM EXISTING DATA\n",
    "\n",
    "If use_existing_data=True, generation of Model instance working with existing database and data input files.\n",
    "\n",
    "The model is generated and ready to be solved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cvxlab.Model(\n",
    "    model_dir_name=model_dir_name,\n",
    "    main_dir_path=main_dir_path,\n",
    "    log_level='debug',\n",
    "    model_settings_from='xlsx',\n",
    "    use_existing_data=True,\n",
    "    detailed_validation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.run_model(\n",
    "    verbose=False,\n",
    "    solver='GUROBI',\n",
    "    integrated_problems=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - \n",
    "SAVING/LOADING MODEL INSTANCE\n",
    "\n",
    "In case model instance generation is taking huge time, it is possible to save/load model instance to avoid regenerating it several times.\n",
    "\n",
    "Notice that - for unknown reasons - model instance can be loaded only if it has not already solve (i.e. do not save model instance if you run the model!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvxlab.handle_model_instance(\n",
    "    action='save',\n",
    "    instance=model,\n",
    "    file_name='instance_name'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cvxlab.handle_model_instance(\n",
    "    action='load',\n",
    "    file_name='file_name',\n",
    "    source_dir_path=r\"D:\\git_repos\\pyesm\\tests\\models\\integrated\\1_coupled_model\\instances\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "DATA and SYMBOLIC PROBLEM UPDATE without regenerating Model instance\n",
    "\n",
    "- initialize_problems(): if symbolic problem has modified, upload it and generates a new numerical model\n",
    "\n",
    "- update_database_and_problem(): in case of modifications in input data files (but not in sets, nor in variables structures) and symbolic problem, update database and problem\n",
    "\n",
    "- reinitialize_sqlite_database(): reinitialize sqlite database endogenous variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case of need for generating only one or more data input files/tabs\n",
    "model.generate_input_data_files(table_key_list=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case of modifications in symbolic problem only,\n",
    "# update problems dataframe and symbolic problem\n",
    "model.initialize_problems()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case of modifications in input data files (but not in sets, nor in \n",
    "# variables structures) and symbolic problem, update database and problem\n",
    "model.update_database_and_problem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case of modifications in input data files (but not in sets, nor in \n",
    "# variables structures) and symbolic problem, reinitialize sqlite database for\n",
    "# endogenous variables\n",
    "model.reinitialize_sqlite_database(force_overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "SOLVE NUMERICAL PROBLEM and ENDOGENOUS DATA EXPORT\n",
    "\n",
    "- run_model(): run model with various settings\n",
    "- load_results_to_database(): once model has successfully solved, load endogenous parameters data to sqlite database.\n",
    "- check_model_results(): check model results compared to an existing database (path and name of the database with the expected results in model settings attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.run_model(\n",
    "    verbose=False,\n",
    "    solver='GUROBI',\n",
    "    integrated_problems=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_results_to_database(force_overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.check_model_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -\n",
    "UTILITIES\n",
    "\n",
    "- sets(): list of model sets\n",
    "- variables(): dictionary with variable name (key) and shape of teh variable (value)\n",
    "- variable(name, ...): visual check of variables content in the database\n",
    "- set(name): visual check of sets content in the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.variables\n",
    "model.sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set('products')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.variable(name='L',)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cvxlab",
   "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": "3.10.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}