examples/ex5.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Using 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 `!`.\n",
                "To run a command _from within_ IPython or Jupyter, place a `!` immediately before the command."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Conversely, all commands demonstrated in this notebook can be used in a terminal/shell by removing the preceeding `!` sign."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Set up on Windows\n",
                "\n",
                "Windows users will need to install [MSYS2](https://www.msys2.org/) to support most of the Linux shell commands.\n",
                "\n",
                "To install MSYS2-base, uncomment the following line and run it:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "execution": {
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                    "shell.execute_reply": "2021-09-26T22:41:51.247696Z"
                }
            },
            "outputs": [],
            "source": [
                "# !conda install -c msys2 -n base --yes m2-base"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Running Shell Commands"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "pycharm": {
                    "name": "#%% md\n"
                }
            },
            "source": [
                "For example, to list the directory , use `!ls`. This is equivalent to executing `ls` from the terminal."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:24.639596Z",
                    "start_time": "2021-03-18T00:45:24.077682Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:41:51.256015Z",
                    "iopub.status.busy": "2021-09-26T22:41:51.255664Z",
                    "iopub.status.idle": "2021-09-26T22:41:51.656973Z",
                    "shell.execute_reply": "2021-09-26T22:41:51.656673Z"
                },
                "pycharm": {
                    "name": "#%%\n"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "demonstration  ex1.ipynb  ex3.ipynb  ex5.ipynb\tex7.ipynb  ex9.ipynb\r\n",
                        "ex10.ipynb     ex2.ipynb  ex4.ipynb  ex6.ipynb\tex8.ipynb  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": 3,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:25.865997Z",
                    "start_time": "2021-03-18T00:45:24.640999Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:41:51.660699Z",
                    "iopub.status.busy": "2021-09-26T22:41:51.660454Z",
                    "iopub.status.idle": "2021-09-26T22:41:52.350068Z",
                    "shell.execute_reply": "2021-09-26T22:41:52.349497Z"
                },
                "pycharm": {
                    "name": "#%%\n"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\r\n",
                        "    _           _         | Version 1.6.4.post10.dev0+gd1a4589d\r\n",
                        "   /_\\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:30:40 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,prep,selftest,st,demo} ...\r\n",
                        "\r\n",
                        "positional arguments:\r\n",
                        "  {run,plot,doc,misc,prepare,prep,selftest,st,demo}\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": 4,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:30.187230Z",
                    "start_time": "2021-03-18T00:45:25.867982Z"
                },
                "execution": {
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                    "iopub.status.idle": "2021-09-26T22:41:56.765396Z",
                    "shell.execute_reply": "2021-09-26T22:41:56.764818Z"
                },
                "pycharm": {
                    "name": "#%%\n"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "    _           _         | Version 1.6.4.post10.dev0+gd1a4589d\n",
                        "   /_\\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:30:41 PM\n",
                        "  / _ \\| ' \\/ _` / -_|_-< | \n",
                        " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
                        "\n",
                        "Working directory: \"/home/hacui/repos/andes/examples\"\n",
                        "> Loaded config from file \"/home/hacui/.andes/andes.rc\"\n",
                        "> Loaded generated Python code in \"/home/hacui/.andes/pycode\".\n",
                        "Parsing input file \"../andes/cases/kundur/kundur_full.xlsx\"...\n",
                        "Input file parsed in 0.2502 seconds.\n",
                        "System internal structure set up in 0.0306 seconds.\n",
                        "-> System connectivity check results:\n",
                        "  No islanded bus detected.\n",
                        "  System is interconnected.\n",
                        "  Each island has a slack bus correctly defined and enabled.\n",
                        "\n",
                        "-> Power flow calculation\n",
                        "           Numba: Off\n",
                        "   Sparse solver: KLU\n",
                        " Solution method: NR method\n",
                        "Power flow initialized in 0.0029 seconds.\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)| = 3.745104027e-07\n",
                        "Converged in 5 iterations in 0.0032 seconds.\n",
                        "Initialization for dynamics completed in 0.0278 seconds.\n",
                        "Initialization was successful.\n",
                        "Report saved to \"kundur_full_out.txt\" in 0.0016 seconds.\n",
                        "\n",
                        "-> Time Domain Simulation Summary:\n",
                        "Sparse Solver: KLU\n",
                        "Simulation time: 0.0-20.0 s.\n",
                        "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
                        "<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      \n",
                        "100%|########################################| 100.0/100 [00:01<00:00, 86.61%/s]\n",
                        "Simulation completed in 1.1546 seconds.\n",
                        "Outputs to \"kundur_full_out.lst\" and \"kundur_full_out.npz\".\n",
                        "Outputs written in 0.0235 seconds.\n",
                        "-> Single process finished in 1.6411 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": 5,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T20:19:38.699061Z",
                    "start_time": "2021-03-18T20:19:37.327764Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:41:56.771768Z",
                    "iopub.status.busy": "2021-09-26T22:41:56.771261Z",
                    "iopub.status.idle": "2021-09-26T22:42:00.564323Z",
                    "shell.execute_reply": "2021-09-26T22:42:00.563938Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "    _           _         | Version 1.6.4.post10.dev0+gd1a4589d\n",
                        "   /_\\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:30:43 PM\n",
                        "  / _ \\| ' \\/ _` / -_|_-< | \n",
                        " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
                        "\n",
                        "Working directory: \"/home/hacui/repos/andes/examples\"\n",
                        "> Loaded config from file \"/home/hacui/.andes/andes.rc\"\n",
                        "> Loaded generated Python code in \"/home/hacui/.andes/pycode\".\n",
                        "Parsing input file \"../andes/cases/kundur/kundur_full.xlsx\"...\n",
                        "Input file parsed in 0.2501 seconds.\n",
                        "System internal structure set up in 0.0305 seconds.\n",
                        "-> System connectivity check results:\n",
                        "  No islanded bus detected.\n",
                        "  System is interconnected.\n",
                        "  Each island has a slack bus correctly defined and enabled.\n",
                        "\n",
                        "-> Power flow calculation\n",
                        "           Numba: Off\n",
                        "   Sparse solver: KLU\n",
                        " Solution method: NR method\n",
                        "Power flow initialized in 0.0029 seconds.\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)| = 3.745104027e-07\n",
                        "Converged in 5 iterations in 0.0032 seconds.\n",
                        "Initialization for dynamics completed in 0.0277 seconds.\n",
                        "Initialization was successful.\n",
                        "Report saved to \"kundur_full_out.txt\" in 0.0016 seconds.\n",
                        "\n",
                        "-> Time Domain Simulation Summary:\n",
                        "Sparse Solver: KLU\n",
                        "Simulation time: 0.0-20.0 s.\n",
                        "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
                        "<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      \n",
                        "100%|########################################| 100.0/100 [00:01<00:00, 86.32%/s]\n",
                        "Simulation completed in 1.1584 seconds.\n",
                        "Outputs to \"kundur_full_out.lst\" and \"kundur_full_out.npz\".\n",
                        "Outputs written in 0.0237 seconds.\n",
                        "-> Single process finished in 1.6446 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"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:00.567015Z",
                    "iopub.status.busy": "2021-09-26T22:42:00.566068Z",
                    "iopub.status.idle": "2021-09-26T22:42:00.736941Z",
                    "shell.execute_reply": "2021-09-26T22:42:00.736668Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "/home/hacui/repos/andes/examples\r\n"
                    ]
                }
            ],
            "source": [
                "!pwd"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:53:47.808152Z",
                    "start_time": "2021-03-18T00:53:47.801977Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:00.742580Z",
                    "iopub.status.busy": "2021-09-26T22:42:00.742249Z",
                    "iopub.status.idle": "2021-09-26T22:42:00.744781Z",
                    "shell.execute_reply": "2021-09-26T22:42:00.744429Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "True"
                        ]
                    },
                    "execution_count": 7,
                    "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": 8,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:36.065075Z",
                    "start_time": "2021-03-18T00:45:31.719026Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:00.748212Z",
                    "iopub.status.busy": "2021-09-26T22:42:00.747184Z",
                    "iopub.status.idle": "2021-09-26T22:42:03.214671Z",
                    "shell.execute_reply": "2021-09-26T22:42:03.214342Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "    _           _         | Version 1.6.4.post10.dev0+gd1a4589d\n",
                        "   /_\\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:30:46 PM\n",
                        "  / _ \\| ' \\/ _` / -_|_-< | \n",
                        " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
                        "\n",
                        "Working directory: \"/home/hacui/repos/andes/examples\"\n",
                        "> Loaded config from file \"/home/hacui/.andes/andes.rc\"\n",
                        "> Loaded generated Python code in \"/home/hacui/.andes/pycode\".\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 0.0024 seconds.\n",
                        "Parsing additional file \"../andes/cases/kundur/kundur_full.dyr\"...\n",
                        "Addfile parsed in 0.2239 seconds.\n",
                        "System internal structure set up in 0.0297 seconds.\n",
                        "-> System connectivity check results:\n",
                        "  No islanded bus detected.\n",
                        "  System is interconnected.\n",
                        "  Each island has a slack bus correctly defined and enabled.\n",
                        "\n",
                        "-> Power flow calculation\n",
                        "           Numba: Off\n",
                        "   Sparse solver: KLU\n",
                        " Solution method: NR method\n",
                        "Power flow initialized in 0.0028 seconds.\n",
                        "0: |F(x)| = 3.175850023\n",
                        "1: |F(x)| = 3.176155228e-08\n",
                        "Converged in 2 iterations in 0.0014 seconds.\n",
                        "Initialization for dynamics completed in 0.0276 seconds.\n",
                        "Initialization was successful.\n",
                        "Report saved to \"kundur_out.txt\" in 0.0014 seconds.\n",
                        "\n",
                        "-> Time Domain Simulation Summary:\n",
                        "Sparse Solver: KLU\n",
                        "Simulation time: 0.0-20.0 s.\n",
                        "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
                        "<Toggle Toggle_1>: Line.Line_8 status changed to 0 at t=2.0 sec.              \n",
                        "100%|########################################| 100.0/100 [00:01<00:00, 86.44%/s]\n",
                        "Simulation completed in 1.1569 seconds.\n",
                        "Outputs to \"kundur_out.lst\" and \"kundur_out.npz\".\n",
                        "Outputs written in 0.0235 seconds.\n",
                        "-> Single process finished in 1.6165 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": 9,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:36.629852Z",
                    "start_time": "2021-03-18T00:45:36.066500Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:03.218155Z",
                    "iopub.status.busy": "2021-09-26T22:42:03.217034Z",
                    "iopub.status.idle": "2021-09-26T22:42:03.382361Z",
                    "shell.execute_reply": "2021-09-26T22:42:03.381945Z"
                },
                "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,                    q PV 2,                            $q$ PV 2\r\n",
                        "    74,                    q PV 3,                            $q$ PV 3\r\n",
                        "    75,                    q PV 4,                            $q$ PV 4\r\n",
                        "    76,                 q Slack 1,                         $q$ Slack 1\r\n",
                        "    77,                 p Slack 1,                         $p$ Slack 1\r\n",
                        "    78,               Id GENROU 1,                      $I_d$ GENROU 1\r\n",
                        "    79,               Id GENROU 2,                      $I_d$ GENROU 2\r\n",
                        "    80,               Id GENROU 3,                      $I_d$ GENROU 3\r\n",
                        "    81,               Id GENROU 4,                      $I_d$ GENROU 4\r\n",
                        "    82,               Iq GENROU 1,                      $I_q$ GENROU 1\r\n",
                        "    83,               Iq GENROU 2,                      $I_q$ GENROU 2\r\n",
                        "    84,               Iq GENROU 3,                      $I_q$ GENROU 3\r\n",
                        "    85,               Iq GENROU 4,                      $I_q$ GENROU 4\r\n",
                        "    86,               vd GENROU 1,                      $V_d$ GENROU 1\r\n",
                        "    87,               vd GENROU 2,                      $V_d$ GENROU 2\r\n",
                        "    88,               vd GENROU 3,                      $V_d$ GENROU 3\r\n",
                        "    89,               vd GENROU 4,                      $V_d$ GENROU 4\r\n",
                        "    90,               vq GENROU 1,                      $V_q$ GENROU 1\r\n",
                        "    91,               vq GENROU 2,                      $V_q$ GENROU 2\r\n",
                        "    92,               vq GENROU 3,                      $V_q$ GENROU 3\r\n",
                        "    93,               vq GENROU 4,                      $V_q$ GENROU 4\r\n",
                        "    94,               tm GENROU 1,                   $\\tau_m$ GENROU 1\r\n",
                        "    95,               tm GENROU 2,                   $\\tau_m$ GENROU 2\r\n",
                        "    96,               tm GENROU 3,                   $\\tau_m$ GENROU 3\r\n",
                        "    97,               tm GENROU 4,                   $\\tau_m$ GENROU 4\r\n",
                        "    98,               te GENROU 1,                   $\\tau_e$ GENROU 1\r\n",
                        "    99,               te GENROU 2,                   $\\tau_e$ GENROU 2\r\n",
                        "   100,               te GENROU 3,                   $\\tau_e$ GENROU 3\r\n",
                        "   101,               te GENROU 4,                   $\\tau_e$ GENROU 4\r\n",
                        "   102,               vf GENROU 1,                      $v_f$ GENROU 1\r\n",
                        "   103,               vf GENROU 2,                      $v_f$ GENROU 2\r\n",
                        "   104,               vf GENROU 3,                      $v_f$ GENROU 3\r\n",
                        "   105,               vf GENROU 4,                      $v_f$ GENROU 4\r\n",
                        "   106,           XadIfd GENROU 1,             $X_{ad}I_{fd}$ GENROU 1\r\n",
                        "   107,           XadIfd GENROU 2,             $X_{ad}I_{fd}$ GENROU 2\r\n",
                        "   108,           XadIfd GENROU 3,             $X_{ad}I_{fd}$ GENROU 3\r\n",
                        "   109,           XadIfd GENROU 4,             $X_{ad}I_{fd}$ GENROU 4\r\n",
                        "   110,               Pe GENROU 1,                      $P_e$ GENROU 1\r\n",
                        "   111,               Pe GENROU 2,                      $P_e$ GENROU 2\r\n",
                        "   112,               Pe GENROU 3,                      $P_e$ GENROU 3\r\n",
                        "   113,               Pe GENROU 4,                      $P_e$ GENROU 4\r\n",
                        "   114,               Qe GENROU 1,                      $Q_e$ GENROU 1\r\n",
                        "   115,               Qe GENROU 2,                      $Q_e$ GENROU 2\r\n",
                        "   116,               Qe GENROU 3,                      $Q_e$ GENROU 3\r\n",
                        "   117,               Qe GENROU 4,                      $Q_e$ GENROU 4\r\n",
                        "   118,             psid GENROU 1,                   $\\psi_d$ GENROU 1\r\n",
                        "   119,             psid GENROU 2,                   $\\psi_d$ GENROU 2\r\n",
                        "   120,             psid GENROU 3,                   $\\psi_d$ GENROU 3\r\n",
                        "   121,             psid GENROU 4,                   $\\psi_d$ GENROU 4\r\n",
                        "   122,             psiq GENROU 1,                   $\\psi_q$ GENROU 1\r\n",
                        "   123,             psiq GENROU 2,                   $\\psi_q$ GENROU 2\r\n",
                        "   124,             psiq GENROU 3,                   $\\psi_q$ GENROU 3\r\n",
                        "   125,             psiq GENROU 4,                   $\\psi_q$ GENROU 4\r\n",
                        "   126,            psi2q GENROU 1,                $\\psi_{aq}$ GENROU 1\r\n",
                        "   127,            psi2q GENROU 2,                $\\psi_{aq}$ GENROU 2\r\n",
                        "   128,            psi2q GENROU 3,                $\\psi_{aq}$ GENROU 3\r\n",
                        "   129,            psi2q GENROU 4,                $\\psi_{aq}$ GENROU 4\r\n",
                        "   130,            psi2d GENROU 1,                $\\psi_{ad}$ GENROU 1\r\n",
                        "   131,            psi2d GENROU 2,                $\\psi_{ad}$ GENROU 2\r\n",
                        "   132,            psi2d GENROU 3,                $\\psi_{ad}$ GENROU 3\r\n",
                        "   133,            psi2d GENROU 4,                $\\psi_{ad}$ GENROU 4\r\n",
                        "   134,             psi2 GENROU 1,                   $\\psi_a$ GENROU 1\r\n",
                        "   135,             psi2 GENROU 2,                   $\\psi_a$ GENROU 2\r\n",
                        "   136,             psi2 GENROU 3,                   $\\psi_a$ GENROU 3\r\n",
                        "   137,             psi2 GENROU 4,                   $\\psi_a$ GENROU 4\r\n",
                        "   138,               Se GENROU 1,          $S_e(|\\psi_{a}|)$ GENROU 1\r\n",
                        "   139,               Se GENROU 2,          $S_e(|\\psi_{a}|)$ GENROU 2\r\n",
                        "   140,               Se GENROU 3,          $S_e(|\\psi_{a}|)$ GENROU 3\r\n",
                        "   141,               Se GENROU 4,          $S_e(|\\psi_{a}|)$ GENROU 4\r\n",
                        "   142,           XaqI1q GENROU 1,             $X_{aq}I_{1q}$ GENROU 1\r\n",
                        "   143,           XaqI1q GENROU 2,             $X_{aq}I_{1q}$ GENROU 2\r\n",
                        "   144,           XaqI1q GENROU 3,             $X_{aq}I_{1q}$ GENROU 3\r\n",
                        "   145,           XaqI1q GENROU 4,             $X_{aq}I_{1q}$ GENROU 4\r\n",
                        "   146,              paux TGOV1 1,                   $P_{aux}$ TGOV1 1\r\n",
                        "   147,              paux TGOV1 2,                   $P_{aux}$ TGOV1 2\r\n",
                        "   148,              paux TGOV1 3,                   $P_{aux}$ TGOV1 3\r\n",
                        "   149,              paux TGOV1 4,                   $P_{aux}$ TGOV1 4\r\n",
                        "   150,              pout TGOV1 1,                   $P_{out}$ TGOV1 1\r\n",
                        "   151,              pout TGOV1 2,                   $P_{out}$ TGOV1 2\r\n",
                        "   152,              pout TGOV1 3,                   $P_{out}$ TGOV1 3\r\n",
                        "   153,              pout TGOV1 4,                   $P_{out}$ TGOV1 4\r\n",
                        "   154,              wref TGOV1 1,              $\\omega_{ref}$ TGOV1 1\r\n",
                        "   155,              wref TGOV1 2,              $\\omega_{ref}$ TGOV1 2\r\n",
                        "   156,              wref TGOV1 3,              $\\omega_{ref}$ TGOV1 3\r\n",
                        "   157,              wref TGOV1 4,              $\\omega_{ref}$ TGOV1 4\r\n",
                        "   158,              pref TGOV1 1,                   $P_{ref}$ TGOV1 1\r\n",
                        "   159,              pref TGOV1 2,                   $P_{ref}$ TGOV1 2\r\n",
                        "   160,              pref TGOV1 3,                   $P_{ref}$ TGOV1 3\r\n",
                        "   161,              pref TGOV1 4,                   $P_{ref}$ TGOV1 4\r\n",
                        "   162,                wd TGOV1 1,              $\\omega_{dev}$ TGOV1 1\r\n",
                        "   163,                wd TGOV1 2,              $\\omega_{dev}$ TGOV1 2\r\n",
                        "   164,                wd TGOV1 3,              $\\omega_{dev}$ TGOV1 3\r\n",
                        "   165,                wd TGOV1 4,              $\\omega_{dev}$ TGOV1 4\r\n",
                        "   166,                pd TGOV1 1,                       $P_d$ TGOV1 1\r\n",
                        "   167,                pd TGOV1 2,                       $P_d$ TGOV1 2\r\n",
                        "   168,                pd TGOV1 3,                       $P_d$ TGOV1 3\r\n",
                        "   169,                pd TGOV1 4,                       $P_d$ TGOV1 4\r\n",
                        "   170,              LL_y TGOV1 1,                    $y_{LL}$ TGOV1 1\r\n",
                        "   171,              LL_y TGOV1 2,                    $y_{LL}$ TGOV1 2\r\n",
                        "   172,              LL_y TGOV1 3,                    $y_{LL}$ TGOV1 3\r\n",
                        "   173,              LL_y TGOV1 4,                    $y_{LL}$ TGOV1 4\r\n",
                        "   174,                 v EXDC2 1,                  $E_{term}$ EXDC2 1\r\n",
                        "   175,                 v EXDC2 2,                  $E_{term}$ EXDC2 2\r\n",
                        "   176,                 v EXDC2 3,                  $E_{term}$ EXDC2 3\r\n",
                        "   177,                 v EXDC2 4,                  $E_{term}$ EXDC2 4\r\n",
                        "   178,              vout EXDC2 1,                   $v_{out}$ EXDC2 1\r\n",
                        "   179,              vout EXDC2 2,                   $v_{out}$ EXDC2 2\r\n",
                        "   180,              vout EXDC2 3,                   $v_{out}$ EXDC2 3\r\n",
                        "   181,              vout EXDC2 4,                   $v_{out}$ EXDC2 4\r\n",
                        "   182,              vref EXDC2 1,                   $V_{ref}$ EXDC2 1\r\n",
                        "   183,              vref EXDC2 2,                   $V_{ref}$ EXDC2 2\r\n",
                        "   184,              vref EXDC2 3,                   $V_{ref}$ EXDC2 3\r\n",
                        "   185,              vref EXDC2 4,                   $V_{ref}$ EXDC2 4\r\n",
                        "   186,                Se EXDC2 1,            $S_e(|V_{out}|)$ EXDC2 1\r\n",
                        "   187,                Se EXDC2 2,            $S_e(|V_{out}|)$ EXDC2 2\r\n",
                        "   188,                Se EXDC2 3,            $S_e(|V_{out}|)$ EXDC2 3\r\n",
                        "   189,                Se EXDC2 4,            $S_e(|V_{out}|)$ EXDC2 4\r\n",
                        "   190,                vi EXDC2 1,                       $V_i$ EXDC2 1\r\n",
                        "   191,                vi EXDC2 2,                       $V_i$ EXDC2 2\r\n",
                        "   192,                vi EXDC2 3,                       $V_i$ EXDC2 3\r\n",
                        "   193,                vi EXDC2 4,                       $V_i$ EXDC2 4\r\n",
                        "   194,              LL_y EXDC2 1,                    $y_{LL}$ EXDC2 1\r\n",
                        "   195,              LL_y EXDC2 2,                    $y_{LL}$ EXDC2 2\r\n",
                        "   196,              LL_y EXDC2 3,                    $y_{LL}$ EXDC2 3\r\n",
                        "   197,              LL_y EXDC2 4,                    $y_{LL}$ EXDC2 4\r\n",
                        "   198,               W_y EXDC2 1,                     $y_{W}$ EXDC2 1\r\n",
                        "   199,               W_y EXDC2 2,                     $y_{W}$ EXDC2 2\r\n",
                        "   200,               W_y EXDC2 3,                     $y_{W}$ EXDC2 3\r\n",
                        "   201,               W_y EXDC2 4,                     $y_{W}$ EXDC2 4\r\n"
                    ]
                }
            ],
            "source": [
                "!cat kundur_full_out.lst"
            ]
        },
        {
            "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": 10,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:39.374231Z",
                    "start_time": "2021-03-18T00:45:36.631417Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:03.386065Z",
                    "iopub.status.busy": "2021-09-26T22:42:03.384821Z",
                    "iopub.status.idle": "2021-09-26T22:42:04.785443Z",
                    "shell.execute_reply": "2021-09-26T22:42:04.785161Z"
                },
                "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": 11,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:39.391336Z",
                    "start_time": "2021-03-18T00:45:39.376617Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:04.788767Z",
                    "iopub.status.busy": "2021-09-26T22:42:04.788494Z",
                    "iopub.status.idle": "2021-09-26T22:42:04.792063Z",
                    "shell.execute_reply": "2021-09-26T22:42:04.792268Z"
                },
                "pycharm": {
                    "name": "#%%\n"
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "execution_count": 11,
                    "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 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": 12,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:40.703534Z",
                    "start_time": "2021-03-18T00:45:39.394998Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:04.795384Z",
                    "iopub.status.busy": "2021-09-26T22:42:04.794246Z",
                    "iopub.status.idle": "2021-09-26T22:42:05.381130Z",
                    "shell.execute_reply": "2021-09-26T22:42:05.380872Z"
                }
            },
            "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": 13,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:43.751254Z",
                    "start_time": "2021-03-18T00:45:40.704926Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:05.383935Z",
                    "iopub.status.busy": "2021-09-26T22:42:05.383640Z",
                    "iopub.status.idle": "2021-09-26T22:42:06.464911Z",
                    "shell.execute_reply": "2021-09-26T22:42:06.464568Z"
                }
            },
            "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": 14,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:46.697152Z",
                    "start_time": "2021-03-18T00:45:43.752578Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:06.469374Z",
                    "iopub.status.busy": "2021-09-26T22:42:06.468941Z",
                    "iopub.status.idle": "2021-09-26T22:42:07.904385Z",
                    "shell.execute_reply": "2021-09-26T22:42:07.904584Z"
                }
            },
            "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": 15,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:47.287001Z",
                    "start_time": "2021-03-18T00:45:46.698453Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:07.908464Z",
                    "iopub.status.busy": "2021-09-26T22:42:07.908222Z",
                    "iopub.status.idle": "2021-09-26T22:42:08.064952Z",
                    "shell.execute_reply": "2021-09-26T22:42:08.065218Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "removed 'kundur_full_out_1.png'\r\n"
                    ]
                }
            ],
            "source": [
                "!rm -v *.png"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "ExecuteTime": {
                    "end_time": "2021-03-18T00:45:48.521518Z",
                    "start_time": "2021-03-18T00:45:47.288522Z"
                },
                "execution": {
                    "iopub.execute_input": "2021-09-26T22:42:08.067965Z",
                    "iopub.status.busy": "2021-09-26T22:42:08.067679Z",
                    "iopub.status.idle": "2021-09-26T22:42:08.594268Z",
                    "shell.execute_reply": "2021-09-26T22:42:08.593930Z"
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\"/home/hacui/repos/andes/examples/kundur_full_out.npz\" removed.\r\n",
                        "\"/home/hacui/repos/andes/examples/kundur_full_out.txt\" removed.\r\n",
                        "\"/home/hacui/repos/andes/examples/kundur_out.lst\" removed.\r\n",
                        "\"/home/hacui/repos/andes/examples/kundur_out.npz\" removed.\r\n",
                        "\"/home/hacui/repos/andes/examples/kundur_full_out.lst\" removed.\r\n",
                        "\"/home/hacui/repos/andes/examples/kundur_out.txt\" removed.\r\n"
                    ]
                }
            ],
            "source": [
                "!andes misc -C"
            ]
        }
    ],
    "metadata": {
        "interpreter": {
            "hash": "4c42303ec617988e96980582546035234a0dbb343f6614254a1d5bddbb9babb9"
        },
        "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": "3.9.10"
        },
        "pycharm": {
            "stem_cell": {
                "cell_type": "raw",
                "metadata": {
                    "collapsed": false
                },
                "source": []
            }
        },
        "varInspector": {
            "cols": {
                "lenName": 16,
                "lenType": 16,
                "lenVar": 40
            },
            "kernels_config": {
                "python": {
                    "delete_cmd_postfix": "",
                    "delete_cmd_prefix": "del ",
                    "library": "var_list.py",
                    "varRefreshCmd": "print(var_dic_list())"
                },
                "r": {
                    "delete_cmd_postfix": ") ",
                    "delete_cmd_prefix": "rm(",
                    "library": "var_list.r",
                    "varRefreshCmd": "cat(var_dic_list()) "
                }
            },
            "types_to_exclude": [
                "module",
                "function",
                "builtin_function_or_method",
                "instance",
                "_Feature"
            ],
            "window_display": false
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}