{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick start guide to model building and running with SPyice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demonstrates a simple example for a freezing sea ice model for a simple heat diffusion setup with default settings of initial conditions of 265K at the top boundary and Salinity of 34ppt choosing constant fixed Dirichlet boundary conditions. For the one phase distinct interface system where sea water is at melt temperature of 271.25K. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T13:43:47.963303300Z",
     "start_time": "2026-03-09T13:43:46.675432700Z"
    }
   },
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "from omegaconf import OmegaConf\n",
    "\n",
    "from spyice.utils import create_output_directory\n",
    "from spyice.postprocess import Analysis, VisualiseModel\n",
    "from spyice.utils import ConfigSort\n",
    "from spyice.models import SeaIceModel\n",
    "from spyice.preprocess import PreProcess"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Inputs and Project Output paths"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-09T13:44:11.699164500Z",
     "start_time": "2026-03-09T13:44:11.658403900Z"
    }
   },
   "source": [
    "# creates a OmegaConf object from a dictionary for fast testing only for parameters: constants, dt, S_IC, iter_max, dz\n",
    "constants_dict = {\n",
    "    \"constants\": {\"constants\": \"real\"},\n",
    "    \"dt\": {\"dt\": 47},\n",
    "    \"S_IC\": {\"S_IC\": \"S34\"},\n",
    "    \"iter_max\": {\"iter_max\": 1500},\n",
    "    \"dz\": {\"dz\": 0.01},\n",
    "}\n",
    "config_raw = OmegaConf.create(constants_dict)\n",
    "config = ConfigSort.getconfig_dataclass(config_raw, config_type=\"jupyter\")\n",
    "\n",
    "# choose your output directory\n",
    "base_dir = Path.cwd()\n",
    "output_base_dir = Path(base_dir, \"output\")\n",
    "wo_hydra_dir = Path(output_base_dir, \"without_hydra\")\n",
    "out_dir_final = create_output_directory(wo_hydra_dir, config.initial_salinity.S_IC, \"2\", \"0.01\", \"47\", \"1500\", \"example\")"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing...\n",
      "User Configuration Data Setup Complete...\n",
      "Geometry Data Setup Complete...\n",
      "Results Data Setup Complete...\n",
      "Time step set to: 47s\n",
      "Applied Initial & Boundary Conditions...\n",
      "Preprocessing done.\n"
     ]
    }
   ],
   "source": [
    "# preprocess the data\n",
    "preprocess_data, userinput_data = PreProcess.get_variables(config_raw, out_dir_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model...\n",
      "Model run complete and Ready for Analysis.\n",
      "Running error analysis...\n",
      "Calculating errors...\n",
      "Residuals exported successfully.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 420x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run the model\n",
    "results_data = SeaIceModel.get_results(preprocess_data, userinput_data)\n",
    "analysis_data = Analysis.get_error_results(\n",
    "            t_k_diff=results_data.t_k_diff,\n",
    "            t_stefan_diff=results_data.t_stefan_diff,\n",
    "            residual=results_data.residual_voller_all,\n",
    "            temperature_mushy=results_data.t_k_iter_all,\n",
    "            phi_mushy=results_data.all_phi_iter_all,\n",
    "            salinity_mushy=results_data.s_k_iter_all,\n",
    "            output_dir=out_dir_final,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of Model:\n",
    "for more info on other visualization options look at:  [visualize model](http://[::1]:8000/api/spyice.postprocess.visualise_model.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualisation object created...\n"
     ]
    }
   ],
   "source": [
    "model_visualization_object = VisualiseModel(\n",
    "    user_input_dataclass=userinput_data,\n",
    "    results_dataclass=results_data,\n",
    "    error_analysis_dataclass=analysis_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Temperature heatmap...\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "model_visualization_object.plot_temperature_heatmap(savefig=True, export_csv=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spyice (3.12.10)",
   "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": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}