{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3a38be9-4747-425e-a7fc-73d2cf406e2e",
   "metadata": {},
   "source": [
    "# EMU Plotting and Analysis Tool Example: Forward Gradient Tool"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde5655a-562b-4d93-b711-5d6a7113117c",
   "metadata": {},
   "source": [
    "## Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27aef7d8-a995-45eb-82ce-19cba00bc12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load modules\n",
    "import runpy\n",
    "# The following are needed to load the plotting tool as a module\n",
    "import sys\n",
    "sys.path.append('/efs_ecco/ECCO/EMU/emu_userinterface_dir/')\n",
    "import emu_plot_arg_py as ept"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2e9892-f68e-4555-a72f-d3de9d2dbc70",
   "metadata": {},
   "source": [
    "## Invoking the Tool\n",
    "Invoking the tool is the same across all EMU tools. The tool determines what to plot based on the user-specified EMU run directory name. For example, 'emu_samp...' corresponds to the Sampling Tool, while 'emu_fgrd...' corresponds to the Forward Gradient Tool.\n",
    "\n",
    "There are two method to use the tool:\n",
    "- **Method 1: Menu-driven Input**\n",
    "  \n",
    "`runpy.run_path('/efs_ecco/ECCO/EMU/emu_userinterface_dir/python/emu_plot.py');`\n",
    "\n",
    "or `globals_dict = ept.emu_plot()` by using the ept module.\n",
    "\n",
    "This method is menu-driven and does not specify any arguments. This method is most suitable for users who are new to the EMU tool. The menu-driven method guides users through entering parameters and creating plots. \n",
    "\n",
    "- **Method 2: Argument-based Input**\n",
    "\n",
    "`globals_dict = ept.emu_plot(run_name=\"/PATH/emu_fgrd_7_15_743_5_-1.00E-01\")`\n",
    "\n",
    "This method uses the ept moudle and skips the step of creating plots in Method 1. Instead, it focuses on returning data for users to perform more advanced processing later. The arguments provide more flexible ways for users to extract data.\n",
    "\n",
    "For further details, including the available arguments, you can use \n",
    "\n",
    "`help(ept.emu_plot)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5723a99c-c618-4e25-8b74-5d986685b42c",
   "metadata": {},
   "source": [
    "### Method 1: Menu-driven Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa9adda8-cf5f-4741-940f-717073db73f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found file: /efs_ecco/ECCO/EMU/emu_userinterface_dir/emu_env.singularity\n",
      "EMU Input Files directory: /efs/owang/ECCO/EMU_test/emu_input_dir\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter directory of EMU run to examine; e.g., emu_samp_m_2_45_585_1 ... ?  /efs_ecco/owang/ECCO/EMU/tryout/emu_fgrd_7_15_743_5_-1.00E-01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading /efs_ecco/owang/ECCO/EMU/tryout/emu_fgrd_7_15_743_5_-1.00E-01\n",
      "\n",
      "Reading Forward Gradient Tool output ... \n",
      "\n",
      "Detected \n",
      "   395 files of state_2d_set1_day.*.data\n",
      "    12 files of state_2d_set1_mon.*.data\n",
      "    12 files of state_3d_set1_mon.*.data\n",
      "\n",
      "Choose variable to plot ...\n",
      "1) SSH\n",
      "2) OBP\n",
      "3) THETA\n",
      "4) SALT\n",
      "5) U\n",
      "6) V\n",
      "\n",
      "Select monthly or daily mean ... (m/d)?\n",
      "(NOTE: daily mean available for SSH and OBP only.)\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter 'm' for monthly or 'd' for daily:  m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Reading and plotting monthly means ...\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter variable # to plot ... (1-6)?  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting ... SSH\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter file # to read ... (1-12 or -1 to exit)? 1\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter file # to read ... (1-12 or -1 to exit)? -1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********************************************\n",
      "Returning variable \n",
      "   fgrd2d: last plotted gradient (2d)\n",
      "\n",
      "\n",
      "***********************\n",
      "EMU variables read as global variables in module global_emu_var (emu); e.g., emu.nx\n",
      "***********************\n",
      "cs                  drc                 drf                 dvol3d              \n",
      "dxc                 dxg                 dyc                 dyg                 \n",
      "fgrd2d              hfacc               hfacs               hfacw               \n",
      "nr                  nx                  ny                  rac                 \n",
      "ras                 raw                 raz                 rc                  \n",
      "rf                  sn                  xc                  xg                  \n",
      "yc                  yg                  \n"
     ]
    }
   ],
   "source": [
    "runpy.run_path('/efs_ecco/ECCO/EMU/emu_userinterface_dir/python/emu_plot.py');\n",
    "# The following that uses a module go through the same menu-driven process s using runpy above, but \n",
    "# globals_dict is a dictionary, containing 'return_vars' and 'emu'.\n",
    "# return_vars' contains the data used to make the plot.\n",
    "# emu contains more variables that were used in ept.\n",
    "# globals_dict = ept.emu_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc7ca28-daf2-4b59-a46e-a129fbf238ca",
   "metadata": {},
   "source": [
    "### Method 2: Argument-based Input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4497b852-f6ec-42e1-a401-dbb3c930a75e",
   "metadata": {},
   "source": [
    "***To get help information, including the available arguments, uncomment `help(ept.emu_plot)`.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd317c61-36f3-4f03-995c-dcf8b15410d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(ept.emu_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28edc063-d7b2-40a3-84cf-2d3f0a403a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found file: /efs_ecco/ECCO/EMU/emu_userinterface_dir/emu_env.singularity\n",
      "EMU Input Files directory: /efs/owang/ECCO/EMU_test/emu_input_dir\n",
      "\n",
      "Specified directory of EMU run to examine: /efs_ecco/owang/ECCO/EMU/tryout/emu_fgrd_7_15_743_5_-1.00E-01\n",
      "\n",
      "Reading /efs_ecco/owang/ECCO/EMU/tryout/emu_fgrd_7_15_743_5_-1.00E-01\n",
      "\n",
      "Reading Forward Gradient Tool output ... \n",
      "\n",
      "Detected \n",
      "   395 files of state_2d_set1_day.*.data\n",
      "    12 files of state_2d_set1_mon.*.data\n",
      "    12 files of state_3d_set1_mon.*.data\n",
      "\n",
      "Choose variable to plot ...\n",
      "1) SSH\n",
      "2) OBP\n",
      "3) THETA\n",
      "4) SALT\n",
      "5) U\n",
      "6) V\n",
      "\n",
      "Select monthly or daily mean ... (m/d)?\n",
      "(NOTE: daily mean available for SSH and OBP only.)\n",
      "Specified frequency 'm' for monthly or 'd' for daily: m\n",
      "Specified start and end files #: 0 and 1000000\n",
      "==> Reading and plotting monthly means ...\n",
      "\n",
      "Specified variable # to plot: 1\n",
      "\n",
      "Plotting ... SSH\n",
      "\n",
      "***********************\n",
      "EMU variables read as global variables in module global_emu_var (emu); e.g., emu.nx\n",
      "***********************\n",
      "cs                  drc                 drf                 dvol3d              \n",
      "dxc                 dxg                 dyc                 dyg                 \n",
      "fgrd2d              hfacc               hfacs               hfacw               \n",
      "nr                  nx                  ny                  rac                 \n",
      "ras                 raw                 raz                 rc                  \n",
      "rf                  sn                  xc                  xg                  \n",
      "yc                  yg                  \n"
     ]
    }
   ],
   "source": [
    "# globals_dict is a dictionary, containing 'return_vars' and 'emu'.\n",
    "# return_vars' contains the data used to make the plot.\n",
    "# emu contains more variables that were used in ept.\n",
    "\n",
    "# Forward Gradient Tool\n",
    "globals_dict = ept.emu_plot(run_name=\"/efs_ecco/owang/ECCO/EMU/tryout/emu_fgrd_7_15_743_5_-1.00E-01\",\n",
    "                            frequency='m',\n",
    "                            pfile_beg=0, pfile_end=1000000,\n",
    "                            pvar=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15be927-ee10-4785-9cbb-9c5dae23cd96",
   "metadata": {},
   "source": [
    "#### Example of Extracting Return Variables and Generating Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c32b55-e4c5-4824-b9f9-f6e8b9173f47",
   "metadata": {},
   "source": [
    "***Extract return variables***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f50b6f60-a327-4424-b716-42f8bd447e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return variables used to make the plot\n",
    "return_vars_dict = globals_dict.get('return_vars')\n",
    "#  More variables used by ept\n",
    "# emu = globals_dict.get('return_vars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b7a6a86-531f-46f8-8bfc-6dc06990a3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['variable', 'pinfo'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the keys in the dictionary\n",
    "return_vars_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad6e3df0-1ffc-48fd-a909-f19fa4f337b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Return Variables and Reproudce plot by Method 1\n",
    "import lib_python\n",
    "\n",
    "fgrd2d_all = return_vars_dict['variable']\n",
    "#pinfo = return_vars_dict['pinfo']\n",
    "pinfo = 'SSH state_2d_set1_mon.0000000732.data'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902e6a38-2ff3-44e1-98bc-2a0ac893d336",
   "metadata": {},
   "source": [
    "***Reproduce plots geneated by Method 1***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc7a3847-e177-43ab-b607-323f3a427d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib_python.plt_state2d(fgrd2d_all[0], pinfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e097a0e-8645-4c54-9878-3cc71c7248de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}