{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Command-Line Interface (CLI) from Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example notebook is a supplement to the ANDES tutorial. Make sure you have read the tutorial on using the CLI first.\n",
    "\n",
    "A brief version can be found at https://github.com/cuihantao/andes/blob/master/README.md#run-simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `!` magic in iPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to use the ANDES CLI from Jupyter Notebook.\n",
    "\n",
    "It is based on the iPython magic `!`. To run a shell command from within iPython or notebook, place a `!` before the command."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the commands demonstrated in this notebook can be used in a terminal/shell by removing the preceeding `!` sign."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "For example, to list the directory , use `!ls`. This is equivalent to running `ls` from the terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:24.639596Z",
     "start_time": "2021-03-18T00:45:24.077682Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10. load-frequency-control.ipynb  6. using-cli-from-notebook.ipynb\r\n",
      "1. simulate_and_plot.ipynb\t  7. parallel-simulation.ipynb\r\n",
      "2. inspect_data.ipynb\t\t  8. change-setpoints.ipynb\r\n",
      "3. eigenvalue.ipynb\t\t  9. batch-processing.ipynb\r\n",
      "4. inspect_models.ipynb\t\t  new_system.xlsx\r\n",
      "5. profiling.ipynb\t\t  verification\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Likewise, to run `andes`, use `!andes`. Addition arguments can be passed as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:25.865997Z",
     "start_time": "2021-03-18T00:45:24.640999Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "    _           _         | Version 1.3.4.post6.dev0+g1cc8c20\r\n",
      "   /_\\  _ _  __| |___ ___ | Python 3.8.6 on Linux, 03/17/2021 08:45:25 PM\r\n",
      "  / _ \\| ' \\/ _` / -_|_-< | \r\n",
      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n",
      "\r\n",
      "usage: andes [-h] [-v {1,10,20,30,40}]\r\n",
      "             {run,plot,doc,misc,prepare,selftest} ...\r\n",
      "\r\n",
      "positional arguments:\r\n",
      "  {run,plot,doc,misc,prepare,selftest}\r\n",
      "                        [run] run simulation routine; [plot] plot results;\r\n",
      "                        [doc] quick documentation; [misc] misc. functions;\r\n",
      "                        [prepare] prepare the numerical code; [selftest] run\r\n",
      "                        self test.\r\n",
      "\r\n",
      "optional arguments:\r\n",
      "  -h, --help            show this help message and exit\r\n",
      "  -v {1,10,20,30,40}, --verbose {1,10,20,30,40}\r\n",
      "                        Verbosity level in 10-DEBUG, 20-INFO, 30-WARNING, or\r\n",
      "                        40-ERROR.\r\n"
     ]
    }
   ],
   "source": [
    "!andes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Run a simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass the path to the case file and other arguments to andes from the command line as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:30.187230Z",
     "start_time": "2021-03-18T00:45:25.867982Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _           _         | Version 1.3.4.post6.dev0+g1cc8c20\n",
      "   /_\\  _ _  __| |___ ___ | Python 3.8.6 on Linux, 03/17/2021 08:45:26 PM\n",
      "  / _ \\| ' \\/ _` / -_|_-< | \n",
      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
      "\n",
      "Working directory: \"/home/hcui7/repos/andes/examples\"\n",
      "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\n",
      "Using generated Python code.\n",
      "Parsing input file \"../andes/cases/kundur/kundur_full.xlsx\"...\n",
      "Input file parsed in 0.4508 seconds.\n",
      "System internal structure set up in 0.0289 seconds.\n",
      "-> System connectivity check results:\n",
      "  No islanded bus detected.\n",
      "  No islanded areas detected.\n",
      "  Each island has a slack bus correctly defined and enabled.\n",
      "\n",
      "-> Power flow calculation\n",
      "   Sparse solver: KLU\n",
      " Solution method: NR method\n",
      " Sparse addition: Fast in-place (kvxopt)\n",
      "Power flow initialized.\n",
      "0: |F(x)| = 14.9282832\n",
      "1: |F(x)| = 3.608627841\n",
      "2: |F(x)| = 0.1701107882\n",
      "3: |F(x)| = 0.002038626956\n",
      "4: |F(x)| = 3.745103977e-07\n",
      "Converged in 5 iterations in 0.0072 seconds.\n",
      "Report saved to \"kundur_full_out.txt\" in 0.0031 seconds.\n",
      "\n",
      "-> Time Domain Simulation Summary:\n",
      "Sparse Solver: KLU\n",
      "Simulation time: 0-20.0 s.\n",
      "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
      "Initialization for dynamics was successful in 0.0264 seconds.\n",
      "<Toggler 1>: Line.Line_8 status changed to 0 at t=2.0 sec.            \n",
      "100%|████████████████████████████████| 100/100 [00:02<00:00, 45.65%/s]\n",
      "Simulation completed in 2.1908 seconds.\n",
      "Outputs to \"kundur_full_out.lst\" and \"kundur_full_out.npz\".\n",
      "Outputs written in 0.0486 seconds.\n",
      "-> Single process finished in 2.9050 seconds.\n"
     ]
    }
   ],
   "source": [
    "!andes run ../andes/cases/kundur/kundur_full.xlsx -r tds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Case file names can be separated from the path, which can be passed to `-p`. The above command is equivanent to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T20:19:38.699061Z",
     "start_time": "2021-03-18T20:19:37.327764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _           _         | Version 1.3.4.post6.dev0+g1cc8c20\n",
      "   /_\\  _ _  __| |___ ___ | Python 3.8.6 on Linux, 03/18/2021 04:19:38 PM\n",
      "  / _ \\| ' \\/ _` / -_|_-< | \n",
      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
      "\n",
      "Working directory: \"/home/hcui7/repos/andes/examples\"\n",
      "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\n",
      "Using generated Python code.\n",
      "../andes/cases/kundur/kundur_full.xlsx\n",
      "\u001b[31mUnable to determine case format.\u001b[0m\n",
      "\u001b[31mInput format unknown for file \"../andes/cases/kundur/kundur_full.xlsx\".\u001b[0m\n",
      "-> Single process exit with an error in 0.2402 seconds.\n"
     ]
    }
   ],
   "source": [
    "!andes run kundur_full.xlsx -p \"../andes/cases/kundur/\" -r tds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:51:02.001910Z",
     "start_time": "2021-03-18T00:51:01.485412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/hcui7/repos/andes/examples\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:53:47.808152Z",
     "start_time": "2021-03-18T00:53:47.801977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.path.isfile('../andes/cases/kundur/kundur_full.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSS/E RAW and DYR Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run a simulation using PSS/E raw and dyr files, pass the dyr file to argument `--addfile`. \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:36.065075Z",
     "start_time": "2021-03-18T00:45:31.719026Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _           _         | Version 1.3.4.post6.dev0+g1cc8c20\n",
      "   /_\\  _ _  __| |___ ___ | Python 3.8.6 on Linux, 03/17/2021 08:45:32 PM\n",
      "  / _ \\| ' \\/ _` / -_|_-< | \n",
      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
      "\n",
      "Working directory: \"/home/hcui7/repos/andes/examples\"\n",
      "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\n",
      "Using generated Python code.\n",
      "Parsing input file \"../andes/cases/kundur/kundur.raw\"...\n",
      "  MODIFIED KUNDUR'S TWO-AREA TEST SYSTEM, DISTRIBUTED WITH ANDES\n",
      "  SEE THE BOOK \"POWER SYSTEM STABILITY AND CONTROL\" FOR ORIGINAL DATA\n",
      "Input file parsed in 0.0059 seconds.\n",
      "Parsing additional file \"../andes/cases/kundur/kundur_full.dyr\"...\n",
      "Addfile parsed in 0.3804 seconds.\n",
      "System internal structure set up in 0.0279 seconds.\n",
      "-> System connectivity check results:\n",
      "  No islanded bus detected.\n",
      "  No islanded areas detected.\n",
      "  Each island has a slack bus correctly defined and enabled.\n",
      "\n",
      "-> Power flow calculation\n",
      "   Sparse solver: KLU\n",
      " Solution method: NR method\n",
      " Sparse addition: Fast in-place (kvxopt)\n",
      "Power flow initialized.\n",
      "0: |F(x)| = 3.175850023\n",
      "1: |F(x)| = 3.176155228e-08\n",
      "Converged in 2 iterations in 0.0031 seconds.\n",
      "Report saved to \"kundur_out.txt\" in 0.0038 seconds.\n",
      "\n",
      "-> Time Domain Simulation Summary:\n",
      "Sparse Solver: KLU\n",
      "Simulation time: 0-20.0 s.\n",
      "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
      "Initialization for dynamics was successful in 0.0262 seconds.\n",
      "<Toggler Toggler_1>: Line.Line_8 status changed to 0 at t=2.0 sec.    \n",
      "100%|████████████████████████████████| 100/100 [00:02<00:00, 42.64%/s]\n",
      "Simulation completed in 2.3451 seconds.\n",
      "Outputs to \"kundur_out.lst\" and \"kundur_out.npz\".\n",
      "Outputs written in 0.0288 seconds.\n",
      "-> Single process finished in 2.9936 seconds.\n"
     ]
    }
   ],
   "source": [
    "!andes run ../andes/cases/kundur/kundur.raw --addfile ../andes/cases/kundur/kundur_full.dyr -r tds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Check the output `lst` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:36.629852Z",
     "start_time": "2021-03-18T00:45:36.066500Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0,                  Time [s],                            Time [s]\r\n",
      "     1,            delta GENROU 1,                   $\\delta$ GENROU 1\r\n",
      "     2,            delta GENROU 2,                   $\\delta$ GENROU 2\r\n",
      "     3,            delta GENROU 3,                   $\\delta$ GENROU 3\r\n",
      "     4,            delta GENROU 4,                   $\\delta$ GENROU 4\r\n",
      "     5,            omega GENROU 1,                   $\\omega$ GENROU 1\r\n",
      "     6,            omega GENROU 2,                   $\\omega$ GENROU 2\r\n",
      "     7,            omega GENROU 3,                   $\\omega$ GENROU 3\r\n",
      "     8,            omega GENROU 4,                   $\\omega$ GENROU 4\r\n",
      "     9,              e1q GENROU 1,                     $e'_q$ GENROU 1\r\n",
      "    10,              e1q GENROU 2,                     $e'_q$ GENROU 2\r\n",
      "    11,              e1q GENROU 3,                     $e'_q$ GENROU 3\r\n",
      "    12,              e1q GENROU 4,                     $e'_q$ GENROU 4\r\n",
      "    13,              e1d GENROU 1,                     $e'_d$ GENROU 1\r\n",
      "    14,              e1d GENROU 2,                     $e'_d$ GENROU 2\r\n",
      "    15,              e1d GENROU 3,                     $e'_d$ GENROU 3\r\n",
      "    16,              e1d GENROU 4,                     $e'_d$ GENROU 4\r\n",
      "    17,              e2d GENROU 1,                    $e''_d$ GENROU 1\r\n",
      "    18,              e2d GENROU 2,                    $e''_d$ GENROU 2\r\n",
      "    19,              e2d GENROU 3,                    $e''_d$ GENROU 3\r\n",
      "    20,              e2d GENROU 4,                    $e''_d$ GENROU 4\r\n",
      "    21,              e2q GENROU 1,                    $e''_q$ GENROU 1\r\n",
      "    22,              e2q GENROU 2,                    $e''_q$ GENROU 2\r\n",
      "    23,              e2q GENROU 3,                    $e''_q$ GENROU 3\r\n",
      "    24,              e2q GENROU 4,                    $e''_q$ GENROU 4\r\n",
      "    25,             LAG_y TGOV1 1,                   $y_{LAG}$ TGOV1 1\r\n",
      "    26,             LAG_y TGOV1 2,                   $y_{LAG}$ TGOV1 2\r\n",
      "    27,             LAG_y TGOV1 3,                   $y_{LAG}$ TGOV1 3\r\n",
      "    28,             LAG_y TGOV1 4,                   $y_{LAG}$ TGOV1 4\r\n",
      "    29,              LL_x TGOV1 1,                   $x'_{LL}$ TGOV1 1\r\n",
      "    30,              LL_x TGOV1 2,                   $x'_{LL}$ TGOV1 2\r\n",
      "    31,              LL_x TGOV1 3,                   $x'_{LL}$ TGOV1 3\r\n",
      "    32,              LL_x TGOV1 4,                   $x'_{LL}$ TGOV1 4\r\n",
      "    33,                vp EXDC2 1,                       $V_p$ EXDC2 1\r\n",
      "    34,                vp EXDC2 2,                       $V_p$ EXDC2 2\r\n",
      "    35,                vp EXDC2 3,                       $V_p$ EXDC2 3\r\n",
      "    36,                vp EXDC2 4,                       $V_p$ EXDC2 4\r\n",
      "    37,              LS_y EXDC2 1,                    $y_{LS}$ EXDC2 1\r\n",
      "    38,              LS_y EXDC2 2,                    $y_{LS}$ EXDC2 2\r\n",
      "    39,              LS_y EXDC2 3,                    $y_{LS}$ EXDC2 3\r\n",
      "    40,              LS_y EXDC2 4,                    $y_{LS}$ EXDC2 4\r\n",
      "    41,              LL_x EXDC2 1,                   $x'_{LL}$ EXDC2 1\r\n",
      "    42,              LL_x EXDC2 2,                   $x'_{LL}$ EXDC2 2\r\n",
      "    43,              LL_x EXDC2 3,                   $x'_{LL}$ EXDC2 3\r\n",
      "    44,              LL_x EXDC2 4,                   $x'_{LL}$ EXDC2 4\r\n",
      "    45,              LA_y EXDC2 1,                    $y_{LA}$ EXDC2 1\r\n",
      "    46,              LA_y EXDC2 2,                    $y_{LA}$ EXDC2 2\r\n",
      "    47,              LA_y EXDC2 3,                    $y_{LA}$ EXDC2 3\r\n",
      "    48,              LA_y EXDC2 4,                    $y_{LA}$ EXDC2 4\r\n",
      "    49,               W_x EXDC2 1,                    $x'_{W}$ EXDC2 1\r\n",
      "    50,               W_x EXDC2 2,                    $x'_{W}$ EXDC2 2\r\n",
      "    51,               W_x EXDC2 3,                    $x'_{W}$ EXDC2 3\r\n",
      "    52,               W_x EXDC2 4,                    $x'_{W}$ EXDC2 4\r\n",
      "    53,                   a Bus 1,                      $\\theta$ Bus 1\r\n",
      "    54,                   a Bus 2,                      $\\theta$ Bus 2\r\n",
      "    55,                   a Bus 3,                      $\\theta$ Bus 3\r\n",
      "    56,                   a Bus 4,                      $\\theta$ Bus 4\r\n",
      "    57,                   a Bus 5,                      $\\theta$ Bus 5\r\n",
      "    58,                   a Bus 6,                      $\\theta$ Bus 6\r\n",
      "    59,                   a Bus 7,                      $\\theta$ Bus 7\r\n",
      "    60,                   a Bus 8,                      $\\theta$ Bus 8\r\n",
      "    61,                   a Bus 9,                      $\\theta$ Bus 9\r\n",
      "    62,                  a Bus 10,                     $\\theta$ Bus 10\r\n",
      "    63,                   v Bus 1,                           $V$ Bus 1\r\n",
      "    64,                   v Bus 2,                           $V$ Bus 2\r\n",
      "    65,                   v Bus 3,                           $V$ Bus 3\r\n",
      "    66,                   v Bus 4,                           $V$ Bus 4\r\n",
      "    67,                   v Bus 5,                           $V$ Bus 5\r\n",
      "    68,                   v Bus 6,                           $V$ Bus 6\r\n",
      "    69,                   v Bus 7,                           $V$ Bus 7\r\n",
      "    70,                   v Bus 8,                           $V$ Bus 8\r\n",
      "    71,                   v Bus 9,                           $V$ Bus 9\r\n",
      "    72,                  v Bus 10,                          $V$ Bus 10\r\n",
      "    73,                    p PV 2,                            $p$ PV 2\r\n",
      "    74,                    p PV 3,                            $p$ PV 3\r\n",
      "    75,                    p PV 4,                            $p$ PV 4\r\n",
      "    76,                    q PV 2,                            $q$ PV 2\r\n",
      "    77,                    q PV 3,                            $q$ PV 3\r\n",
      "    78,                    q PV 4,                            $q$ PV 4\r\n",
      "    79,                 p Slack 1,                         $p$ Slack 1\r\n",
      "    80,                 q Slack 1,                         $q$ Slack 1\r\n",
      "    81,               Id GENROU 1,                      $I_d$ GENROU 1\r\n",
      "    82,               Id GENROU 2,                      $I_d$ GENROU 2\r\n",
      "    83,               Id GENROU 3,                      $I_d$ GENROU 3\r\n",
      "    84,               Id GENROU 4,                      $I_d$ GENROU 4\r\n",
      "    85,               Iq GENROU 1,                      $I_q$ GENROU 1\r\n",
      "    86,               Iq GENROU 2,                      $I_q$ GENROU 2\r\n",
      "    87,               Iq GENROU 3,                      $I_q$ GENROU 3\r\n",
      "    88,               Iq GENROU 4,                      $I_q$ GENROU 4\r\n",
      "    89,               vd GENROU 1,                      $V_d$ GENROU 1\r\n",
      "    90,               vd GENROU 2,                      $V_d$ GENROU 2\r\n",
      "    91,               vd GENROU 3,                      $V_d$ GENROU 3\r\n",
      "    92,               vd GENROU 4,                      $V_d$ GENROU 4\r\n",
      "    93,               vq GENROU 1,                      $V_q$ GENROU 1\r\n",
      "    94,               vq GENROU 2,                      $V_q$ GENROU 2\r\n",
      "    95,               vq GENROU 3,                      $V_q$ GENROU 3\r\n",
      "    96,               vq GENROU 4,                      $V_q$ GENROU 4\r\n",
      "    97,               tm GENROU 1,                   $\\tau_m$ GENROU 1\r\n",
      "    98,               tm GENROU 2,                   $\\tau_m$ GENROU 2\r\n",
      "    99,               tm GENROU 3,                   $\\tau_m$ GENROU 3\r\n",
      "   100,               tm GENROU 4,                   $\\tau_m$ GENROU 4\r\n",
      "   101,               te GENROU 1,                   $\\tau_e$ GENROU 1\r\n",
      "   102,               te GENROU 2,                   $\\tau_e$ GENROU 2\r\n",
      "   103,               te GENROU 3,                   $\\tau_e$ GENROU 3\r\n",
      "   104,               te GENROU 4,                   $\\tau_e$ GENROU 4\r\n",
      "   105,               vf GENROU 1,                      $v_f$ GENROU 1\r\n",
      "   106,               vf GENROU 2,                      $v_f$ GENROU 2\r\n",
      "   107,               vf GENROU 3,                      $v_f$ GENROU 3\r\n",
      "   108,               vf GENROU 4,                      $v_f$ GENROU 4\r\n",
      "   109,           XadIfd GENROU 1,             $X_{ad}I_{fd}$ GENROU 1\r\n",
      "   110,           XadIfd GENROU 2,             $X_{ad}I_{fd}$ GENROU 2\r\n",
      "   111,           XadIfd GENROU 3,             $X_{ad}I_{fd}$ GENROU 3\r\n",
      "   112,           XadIfd GENROU 4,             $X_{ad}I_{fd}$ GENROU 4\r\n",
      "   113,             psid GENROU 1,                   $\\psi_d$ GENROU 1\r\n",
      "   114,             psid GENROU 2,                   $\\psi_d$ GENROU 2\r\n",
      "   115,             psid GENROU 3,                   $\\psi_d$ GENROU 3\r\n",
      "   116,             psid GENROU 4,                   $\\psi_d$ GENROU 4\r\n",
      "   117,             psiq GENROU 1,                   $\\psi_q$ GENROU 1\r\n",
      "   118,             psiq GENROU 2,                   $\\psi_q$ GENROU 2\r\n",
      "   119,             psiq GENROU 3,                   $\\psi_q$ GENROU 3\r\n",
      "   120,             psiq GENROU 4,                   $\\psi_q$ GENROU 4\r\n",
      "   121,            psi2q GENROU 1,                $\\psi_{aq}$ GENROU 1\r\n",
      "   122,            psi2q GENROU 2,                $\\psi_{aq}$ GENROU 2\r\n",
      "   123,            psi2q GENROU 3,                $\\psi_{aq}$ GENROU 3\r\n",
      "   124,            psi2q GENROU 4,                $\\psi_{aq}$ GENROU 4\r\n",
      "   125,            psi2d GENROU 1,                $\\psi_{ad}$ GENROU 1\r\n",
      "   126,            psi2d GENROU 2,                $\\psi_{ad}$ GENROU 2\r\n",
      "   127,            psi2d GENROU 3,                $\\psi_{ad}$ GENROU 3\r\n",
      "   128,            psi2d GENROU 4,                $\\psi_{ad}$ GENROU 4\r\n",
      "   129,             psi2 GENROU 1,                   $\\psi_a$ GENROU 1\r\n",
      "   130,             psi2 GENROU 2,                   $\\psi_a$ GENROU 2\r\n",
      "   131,             psi2 GENROU 3,                   $\\psi_a$ GENROU 3\r\n",
      "   132,             psi2 GENROU 4,                   $\\psi_a$ GENROU 4\r\n",
      "   133,               Se GENROU 1,          $S_e(|\\psi_{a}|)$ GENROU 1\r\n",
      "   134,               Se GENROU 2,          $S_e(|\\psi_{a}|)$ GENROU 2\r\n",
      "   135,               Se GENROU 3,          $S_e(|\\psi_{a}|)$ GENROU 3\r\n",
      "   136,               Se GENROU 4,          $S_e(|\\psi_{a}|)$ GENROU 4\r\n",
      "   137,           XaqI1q GENROU 1,             $X_{aq}I_{1q}$ GENROU 1\r\n",
      "   138,           XaqI1q GENROU 2,             $X_{aq}I_{1q}$ GENROU 2\r\n",
      "   139,           XaqI1q GENROU 3,             $X_{aq}I_{1q}$ GENROU 3\r\n",
      "   140,           XaqI1q GENROU 4,             $X_{aq}I_{1q}$ GENROU 4\r\n",
      "   141,              paux TGOV1 1,                   $P_{aux}$ TGOV1 1\r\n",
      "   142,              paux TGOV1 2,                   $P_{aux}$ TGOV1 2\r\n",
      "   143,              paux TGOV1 3,                   $P_{aux}$ TGOV1 3\r\n",
      "   144,              paux TGOV1 4,                   $P_{aux}$ TGOV1 4\r\n",
      "   145,              pout TGOV1 1,                   $P_{out}$ TGOV1 1\r\n",
      "   146,              pout TGOV1 2,                   $P_{out}$ TGOV1 2\r\n",
      "   147,              pout TGOV1 3,                   $P_{out}$ TGOV1 3\r\n",
      "   148,              pout TGOV1 4,                   $P_{out}$ TGOV1 4\r\n",
      "   149,              wref TGOV1 1,              $\\omega_{ref}$ TGOV1 1\r\n",
      "   150,              wref TGOV1 2,              $\\omega_{ref}$ TGOV1 2\r\n",
      "   151,              wref TGOV1 3,              $\\omega_{ref}$ TGOV1 3\r\n",
      "   152,              wref TGOV1 4,              $\\omega_{ref}$ TGOV1 4\r\n",
      "   153,              pref TGOV1 1,                   $P_{ref}$ TGOV1 1\r\n",
      "   154,              pref TGOV1 2,                   $P_{ref}$ TGOV1 2\r\n",
      "   155,              pref TGOV1 3,                   $P_{ref}$ TGOV1 3\r\n",
      "   156,              pref TGOV1 4,                   $P_{ref}$ TGOV1 4\r\n",
      "   157,                wd TGOV1 1,              $\\omega_{dev}$ TGOV1 1\r\n",
      "   158,                wd TGOV1 2,              $\\omega_{dev}$ TGOV1 2\r\n",
      "   159,                wd TGOV1 3,              $\\omega_{dev}$ TGOV1 3\r\n",
      "   160,                wd TGOV1 4,              $\\omega_{dev}$ TGOV1 4\r\n",
      "   161,                pd TGOV1 1,                       $P_d$ TGOV1 1\r\n",
      "   162,                pd TGOV1 2,                       $P_d$ TGOV1 2\r\n",
      "   163,                pd TGOV1 3,                       $P_d$ TGOV1 3\r\n",
      "   164,                pd TGOV1 4,                       $P_d$ TGOV1 4\r\n",
      "   165,              LL_y TGOV1 1,                    $y_{LL}$ TGOV1 1\r\n",
      "   166,              LL_y TGOV1 2,                    $y_{LL}$ TGOV1 2\r\n",
      "   167,              LL_y TGOV1 3,                    $y_{LL}$ TGOV1 3\r\n",
      "   168,              LL_y TGOV1 4,                    $y_{LL}$ TGOV1 4\r\n",
      "   169,              vout EXDC2 1,                   $v_{out}$ EXDC2 1\r\n",
      "   170,              vout EXDC2 2,                   $v_{out}$ EXDC2 2\r\n",
      "   171,              vout EXDC2 3,                   $v_{out}$ EXDC2 3\r\n",
      "   172,              vout EXDC2 4,                   $v_{out}$ EXDC2 4\r\n",
      "   173,              vref EXDC2 1,                   $V_{ref}$ EXDC2 1\r\n",
      "   174,              vref EXDC2 2,                   $V_{ref}$ EXDC2 2\r\n",
      "   175,              vref EXDC2 3,                   $V_{ref}$ EXDC2 3\r\n",
      "   176,              vref EXDC2 4,                   $V_{ref}$ EXDC2 4\r\n",
      "   177,                Se EXDC2 1,            $S_e(|V_{out}|)$ EXDC2 1\r\n",
      "   178,                Se EXDC2 2,            $S_e(|V_{out}|)$ EXDC2 2\r\n",
      "   179,                Se EXDC2 3,            $S_e(|V_{out}|)$ EXDC2 3\r\n",
      "   180,                Se EXDC2 4,            $S_e(|V_{out}|)$ EXDC2 4\r\n",
      "   181,                vi EXDC2 1,                       $V_i$ EXDC2 1\r\n",
      "   182,                vi EXDC2 2,                       $V_i$ EXDC2 2\r\n",
      "   183,                vi EXDC2 3,                       $V_i$ EXDC2 3\r\n",
      "   184,                vi EXDC2 4,                       $V_i$ EXDC2 4\r\n",
      "   185,              LL_y EXDC2 1,                    $y_{LL}$ EXDC2 1\r\n",
      "   186,              LL_y EXDC2 2,                    $y_{LL}$ EXDC2 2\r\n",
      "   187,              LL_y EXDC2 3,                    $y_{LL}$ EXDC2 3\r\n",
      "   188,              LL_y EXDC2 4,                    $y_{LL}$ EXDC2 4\r\n",
      "   189,               W_y EXDC2 1,                     $y_{W}$ EXDC2 1\r\n",
      "   190,               W_y EXDC2 2,                     $y_{W}$ EXDC2 2\r\n",
      "   191,               W_y EXDC2 3,                     $y_{W}$ EXDC2 3\r\n",
      "   192,               W_y EXDC2 4,                     $y_{W}$ EXDC2 4\r\n"
     ]
    }
   ],
   "source": [
    "!cat kundur_full_out.lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Windows users may not have `cat` by default in the search path. Do instead\n",
    "\n",
    "```\n",
    "with open('kundur_full_out.lst', 'r') as f:\n",
    "    print(f.read())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plot and save to file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found a limitation of using `andes plot` from within Notebook/iPython. The figure won't be displayed correctly. The workaround is to save the image as a file and display it from the notebook.\n",
    "\n",
    "Please let us know if you have better solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:39.374231Z",
     "start_time": "2021-03-18T00:45:36.631417Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved to \"kundur_full_out_1.png\".\r\n",
      "Figure(640x480)\r\n"
     ]
    }
   ],
   "source": [
    "!andes plot kundur_full_out.lst 0 5 6 --save"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:39.391336Z",
     "start_time": "2021-03-18T00:45:39.376617Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"kundur_full_out_1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `xargs` for index loop up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convenient tool in Linux/macOS is `xargs`, which turns the standard output of one program into arguments for another.\n",
    "\n",
    "`andes plot --xargs` accepts an input of search pattern for variable names and returns a list of arguments, including the matched indices, that can be understood by `andes plot`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convenient tool in Linux/macOS is `xargs`, which turns the standard output of one program into arguments for another.\n",
    "\n",
    "`andes plot --xargs` accepts an input of search pattern for variable names and returns a list of arguments, including the matched indices, that can be understood by `andes plot`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `xargs` for index lookup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convenient tool in Linux/macOS is `xargs`, which turns the standard output of one program into arguments for another.\n",
    "\n",
    "`andes plot --xargs` accepts an input of search pattern for variable names and returns a list of arguments, including the matched indices, that can be understood by `andes plot`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate, let's look at an example output of `andes plot --xargs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:40.703534Z",
     "start_time": "2021-03-18T00:45:39.394998Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kundur_full_out.lst 0 5 6 7 8\r\n"
     ]
    }
   ],
   "source": [
    "!andes plot kundur_full_out.lst --xargs \"omega GENROU\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output consists of the lst file name, the default x-axis index `0`, and the indices for the found variables. The full output can be passed to `andes plot` without modification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following command to pass the arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:43.751254Z",
     "start_time": "2021-03-18T00:45:40.704926Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(640x480)\r\n"
     ]
    }
   ],
   "source": [
    "!andes plot kundur_full_out.lst --xargs \"omega GENROU\" | xargs andes plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `|` is the pipe operator in shell for piping the standard output of the left-hand side to the right-hand side, `xargs` captures the pipe-in and appends it to `andes plot`. \n",
    "\n",
    "The command is equivalent to manually running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:46.697152Z",
     "start_time": "2021-03-18T00:45:43.752578Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(640x480)\r\n"
     ]
    }
   ],
   "source": [
    "!andes plot kundur_full_out.lst 5 6 7 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the saved `png` image files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:47.287001Z",
     "start_time": "2021-03-18T00:45:46.698453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "removed 'kundur_full_out_1.png'\r\n"
     ]
    }
   ],
   "source": [
    "!rm -v *.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T00:45:48.521518Z",
     "start_time": "2021-03-18T00:45:47.288522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "    _           _         | Version 1.3.4.post6.dev0+g1cc8c20\r\n",
      "   /_\\  _ _  __| |___ ___ | Python 3.8.6 on Linux, 03/17/2021 08:45:48 PM\r\n",
      "  / _ \\| ' \\/ _` / -_|_-< | \r\n",
      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n",
      "\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_full_out.npz\" removed.\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_full_out.lst\" removed.\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_full_out.txt\" removed.\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_out.lst\" removed.\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_out.npz\" removed.\r\n",
      "\"/home/hcui7/repos/andes/examples/kundur_out.txt\" removed.\r\n"
     ]
    }
   ],
   "source": [
    "!andes misc -C"
   ]
  }
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