{ "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 `!`.\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": { "iopub.execute_input": "2021-09-26T22:41:51.245559Z", "iopub.status.busy": "2021-09-26T22:41:51.245185Z", "iopub.status.idle": "2021-09-26T22:41:51.248018Z", "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": [ "'10. load-frequency-control.ipynb' demonstration\r\n", "'1. simulate_and_plot.ipynb'\t ieee14_out.lst\r\n", "'2. inspect_data.ipynb'\t\t ieee14_out.npz\r\n", "'3. eigenvalue.ipynb'\t\t kundur_full_out_1.png\r\n", "'4. inspect_models.ipynb'\t kundur_full_out.lst\r\n", "'5. profiling.ipynb'\t\t kundur_full_out.npz\r\n", "'6. using-cli-from-notebook.ipynb' kundur_full_out.txt\r\n", "'7. parallel-simulation.ipynb'\t kundur_out.lst\r\n", "'8. change-setpoints.ipynb'\t kundur_out.npz\r\n", "'9. batch-processing.ipynb'\t kundur_out.txt\r\n", " batch_cases\t\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": 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 1.4.2.post116.dev0+g832274a7\r\n", " /_\\ _ _ __| |___ ___ | Python 3.9.6 on Linux, 09/26/2021 05:41:52 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; [demo] show demos.\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": { "iopub.execute_input": "2021-09-26T22:41:52.354842Z", "iopub.status.busy": "2021-09-26T22:41:52.353231Z", "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": [ "\r\n", " _ _ | Version 1.4.2.post116.dev0+g832274a7\r\n", " /_\\ _ _ __| |___ ___ | Python 3.9.6 on Linux, 09/26/2021 05:41:53 PM\r\n", " / _ \\| ' \\/ _` / -_|_-< | \r\n", " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n", "\r\n", "Working directory: \"/home/hcui7/repos/andes/examples\"\r\n", "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded generated Python code in \"~/.andes/pycode\".\r\n", "Parsing input file \"../andes/cases/kundur/kundur_full.xlsx\"...\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Input file parsed in 0.4932 seconds.\r\n", "System internal structure set up in 0.0352 seconds.\r\n", "-> System connectivity check results:\r\n", " No islanded bus detected.\r\n", " A total of 1 island(s) detected.\r\n", " Each island has a slack bus correctly defined and enabled.\r\n", "\r\n", "-> Power flow calculation\r\n", " Sparse solver: KLU\r\n", " Solution method: NR method\r\n", " Sparse addition: Fast in-place (kvxopt)\r\n", "Power flow initialized.\r\n", "0: |F(x)| = 14.9282832\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1: |F(x)| = 3.608627841\r\n", "2: |F(x)| = 0.1701107882\r\n", "3: |F(x)| = 0.002038626956\r\n", "4: |F(x)| = 3.745103977e-07\r\n", "Converged in 5 iterations in 0.0077 seconds.\r\n", "Report saved to \"kundur_full_out.txt\" in 0.0013 seconds.\r\n", "\r\n", "-> Time Domain Simulation Summary:\r\n", "Sparse Solver: KLU\r\n", "Simulation time: 0-20.0 s.\r\n", "Fixed step size: h=33.33 ms. Shrink if not converged.\r\n", "Initialization for dynamics was successful in 0.0222 seconds.\r\n", "\r", " 0%| | 0/100 [00:00: Line.Line_8 status changed to 0 at t=2.0 sec.\r\n", "\r", " 10%|███▏ | 10/100 [00:00<00:00, 114.10%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 12%|███▉ | 12/100 [00:00<00:00, 92.31%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 22%|███████▎ | 22/100 [00:00<00:01, 44.83%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 28%|█████████▏ | 28/100 [00:00<00:01, 42.36%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 33%|██████████▉ | 33/100 [00:00<00:01, 42.00%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 38%|████████████▌ | 38/100 [00:00<00:01, 41.68%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 43%|██████████████▏ | 43/100 [00:00<00:01, 43.33%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 48%|███████████████▊ | 48/100 [00:01<00:01, 38.12%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 53%|█████████████████▍ | 53/100 [00:01<00:01, 34.24%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 58%|███████████████████▏ | 58/100 [00:01<00:01, 36.79%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 63%|████████████████████▊ | 63/100 [00:01<00:00, 38.28%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 67%|██████████████████████ | 67/100 [00:01<00:00, 35.59%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 71%|███████████████████████▍ | 71/100 [00:01<00:00, 36.31%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 76%|█████████████████████████ | 76/100 [00:01<00:00, 39.62%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 81%|██████████████████████████▋ | 81/100 [00:02<00:00, 37.92%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 86%|████████████████████████████▍ | 86/100 [00:02<00:00, 40.67%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 91%|██████████████████████████████ | 91/100 [00:02<00:00, 42.56%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 96%|███████████████████████████████▋ | 96/100 [00:02<00:00, 41.06%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "100%|████████████████████████████████| 100/100 [00:02<00:00, 40.29%/s]\r\n", "Simulation completed in 2.4819 seconds.\r\n", "Outputs to \"kundur_full_out.lst\" and \"kundur_full_out.npz\".\r\n", "Outputs written in 0.0386 seconds.\r\n", "-> Single process finished in 3.3971 seconds.\r\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": [ "\r\n", " _ _ | Version 1.4.2.post116.dev0+g832274a7\r\n", " /_\\ _ _ __| |___ ___ | Python 3.9.6 on Linux, 09/26/2021 05:41:57 PM\r\n", " / _ \\| ' \\/ _` / -_|_-< | \r\n", " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n", "\r\n", "Working directory: \"/home/hcui7/repos/andes/examples\"\r\n", "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded generated Python code in \"~/.andes/pycode\".\r\n", "Parsing input file \"../andes/cases/kundur/kundur_full.xlsx\"...\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Input file parsed in 0.5879 seconds.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "System internal structure set up in 0.0417 seconds.\r\n", "-> System connectivity check results:\r\n", " No islanded bus detected.\r\n", " A total of 1 island(s) detected.\r\n", " Each island has a slack bus correctly defined and enabled.\r\n", "\r\n", "-> Power flow calculation\r\n", " Sparse solver: KLU\r\n", " Solution method: NR method\r\n", " Sparse addition: Fast in-place (kvxopt)\r\n", "Power flow initialized.\r\n", "0: |F(x)| = 14.9282832\r\n", "1: |F(x)| = 3.608627841\r\n", "2: |F(x)| = 0.1701107882\r\n", "3: |F(x)| = 0.002038626956\r\n", "4: |F(x)| = 3.745103977e-07\r\n", "Converged in 5 iterations in 0.0057 seconds.\r\n", "Report saved to \"kundur_full_out.txt\" in 0.0025 seconds.\r\n", "\r\n", "-> Time Domain Simulation Summary:\r\n", "Sparse Solver: KLU\r\n", "Simulation time: 0-20.0 s.\r\n", "Fixed step size: h=33.33 ms. Shrink if not converged.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Initialization for dynamics was successful in 0.0208 seconds.\r\n", "\r", " 0%| | 0/100 [00:00: Line.Line_8 status changed to 0 at t=2.0 sec.\r\n", "\r", " 10%|███▏ | 10/100 [00:00<00:00, 105.77%/s]\r", " 11%|███▌ | 11/100 [00:00<00:00, 100.95%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 22%|███████▎ | 22/100 [00:00<00:01, 60.63%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 29%|█████████▌ | 29/100 [00:00<00:01, 46.07%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 36%|███████████▉ | 36/100 [00:00<00:01, 50.36%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 43%|██████████████▏ | 43/100 [00:00<00:01, 54.08%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 50%|████████████████▌ | 50/100 [00:00<00:00, 57.05%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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[00:01<00:00, 56.43%/s]\r\n", "Simulation completed in 1.7721 seconds.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Outputs to \"kundur_full_out.lst\" and \"kundur_full_out.npz\".\r\n", "Outputs written in 0.0345 seconds.\r\n", "-> Single process finished in 2.8082 seconds.\r\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/hcui7/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": [ "\r\n", " _ _ | Version 1.4.2.post116.dev0+g832274a7\r\n", " /_\\ _ _ __| |___ ___ | Python 3.9.6 on Linux, 09/26/2021 05:42:01 PM\r\n", " / _ \\| ' \\/ _` / -_|_-< | \r\n", " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n", "\r\n", "Working directory: \"/home/hcui7/repos/andes/examples\"\r\n", "Loaded config from file \"/home/hcui7/.andes/andes.rc\"\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded generated Python code in \"~/.andes/pycode\".\r\n", "Parsing input file \"../andes/cases/kundur/kundur.raw\"...\r\n", " MODIFIED KUNDUR'S TWO-AREA TEST SYSTEM, DISTRIBUTED WITH ANDES\r\n", " SEE THE BOOK \"POWER SYSTEM STABILITY AND CONTROL\" FOR ORIGINAL DATA\r\n", "Input file parsed in 0.0021 seconds.\r\n", "Parsing additional file \"../andes/cases/kundur/kundur_full.dyr\"...\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Addfile parsed in 0.2092 seconds.\r\n", "System internal structure set up in 0.0216 seconds.\r\n", "-> System connectivity check results:\r\n", " No islanded bus detected.\r\n", " A total of 1 island(s) detected.\r\n", " Each island has a slack bus correctly defined and enabled.\r\n", "\r\n", "-> Power flow calculation\r\n", " Sparse solver: KLU\r\n", " Solution method: NR method\r\n", " Sparse addition: Fast in-place (kvxopt)\r\n", "Power flow initialized.\r\n", "0: |F(x)| = 3.175850023\r\n", "1: |F(x)| = 3.176155228e-08\r\n", "Converged in 2 iterations in 0.0021 seconds.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Report saved to \"kundur_out.txt\" in 0.0009 seconds.\r\n", "\r\n", "-> Time Domain Simulation Summary:\r\n", "Sparse Solver: KLU\r\n", "Simulation time: 0-20.0 s.\r\n", "Fixed step size: h=33.33 ms. Shrink if not converged.\r\n", "Initialization for dynamics was successful in 0.0195 seconds.\r\n", "\r", " 0%| | 0/100 [00:00: Line.Line_8 status changed to 0 at t=2.0 sec.\r\n", "\r", " 10%|███▏ | 10/100 [00:00<00:00, 286.67%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 14%|████▍ | 14/100 [00:00<00:00, 139.34%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 28%|████████▉ | 28/100 [00:00<00:00, 105.84%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 40%|█████████████▏ | 40/100 [00:00<00:00, 78.43%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 49%|████████████████▏ | 49/100 [00:00<00:00, 72.75%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 59%|███████████████████▍ | 59/100 [00:00<00:00, 79.79%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 68%|██████████████████████▍ | 68/100 [00:00<00:00, 80.97%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 77%|█████████████████████████▍ | 77/100 [00:00<00:00, 71.06%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 85%|████████████████████████████ | 85/100 [00:01<00:00, 65.60%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 94%|███████████████████████████████ | 94/100 [00:01<00:00, 70.95%/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "100%|████████████████████████████████| 100/100 [00:01<00:00, 73.88%/s]\r\n", "Simulation completed in 1.3536 seconds.\r\n", "Outputs to \"kundur_out.lst\" and \"kundur_out.npz\".\r\n", "Outputs written in 0.0328 seconds.\r\n", "-> Single process finished in 1.7896 seconds.\r\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, 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, Pe GENROU 1, $P_e$ GENROU 1\r\n", " 114, Pe GENROU 2, $P_e$ GENROU 2\r\n", " 115, Pe GENROU 3, $P_e$ GENROU 3\r\n", " 116, Pe GENROU 4, $P_e$ GENROU 4\r\n", " 117, Qe GENROU 1, $Q_e$ GENROU 1\r\n", " 118, Qe GENROU 2, $Q_e$ GENROU 2\r\n", " 119, Qe GENROU 3, $Q_e$ GENROU 3\r\n", " 120, Qe GENROU 4, $Q_e$ GENROU 4\r\n", " 121, psid GENROU 1, $\\psi_d$ GENROU 1\r\n", " 122, psid GENROU 2, $\\psi_d$ GENROU 2\r\n", " 123, psid GENROU 3, $\\psi_d$ GENROU 3\r\n", " 124, psid GENROU 4, $\\psi_d$ GENROU 4\r\n", " 125, psiq GENROU 1, $\\psi_q$ GENROU 1\r\n", " 126, psiq GENROU 2, $\\psi_q$ GENROU 2\r\n", " 127, psiq GENROU 3, $\\psi_q$ GENROU 3\r\n", " 128, psiq GENROU 4, $\\psi_q$ GENROU 4\r\n", " 129, psi2q GENROU 1, $\\psi_{aq}$ GENROU 1\r\n", " 130, psi2q GENROU 2, $\\psi_{aq}$ GENROU 2\r\n", " 131, psi2q GENROU 3, $\\psi_{aq}$ GENROU 3\r\n", " 132, psi2q GENROU 4, $\\psi_{aq}$ GENROU 4\r\n", " 133, psi2d GENROU 1, $\\psi_{ad}$ GENROU 1\r\n", " 134, psi2d GENROU 2, $\\psi_{ad}$ GENROU 2\r\n", " 135, psi2d GENROU 3, $\\psi_{ad}$ GENROU 3\r\n", " 136, psi2d GENROU 4, $\\psi_{ad}$ GENROU 4\r\n", " 137, psi2 GENROU 1, $\\psi_a$ GENROU 1\r\n", " 138, psi2 GENROU 2, $\\psi_a$ GENROU 2\r\n", " 139, psi2 GENROU 3, $\\psi_a$ GENROU 3\r\n", " 140, psi2 GENROU 4, $\\psi_a$ GENROU 4\r\n", " 141, Se GENROU 1, $S_e(|\\psi_{a}|)$ GENROU 1\r\n", " 142, Se GENROU 2, $S_e(|\\psi_{a}|)$ GENROU 2\r\n", " 143, Se GENROU 3, $S_e(|\\psi_{a}|)$ GENROU 3\r\n", " 144, Se GENROU 4, $S_e(|\\psi_{a}|)$ GENROU 4\r\n", " 145, XaqI1q GENROU 1, $X_{aq}I_{1q}$ GENROU 1\r\n", " 146, XaqI1q GENROU 2, $X_{aq}I_{1q}$ GENROU 2\r\n", " 147, XaqI1q GENROU 3, $X_{aq}I_{1q}$ GENROU 3\r\n", " 148, XaqI1q GENROU 4, $X_{aq}I_{1q}$ GENROU 4\r\n", " 149, paux TGOV1 1, $P_{aux}$ TGOV1 1\r\n", " 150, paux TGOV1 2, $P_{aux}$ TGOV1 2\r\n", " 151, paux TGOV1 3, $P_{aux}$ TGOV1 3\r\n", " 152, paux TGOV1 4, $P_{aux}$ TGOV1 4\r\n", " 153, pout TGOV1 1, $P_{out}$ TGOV1 1\r\n", " 154, pout TGOV1 2, $P_{out}$ TGOV1 2\r\n", " 155, pout TGOV1 3, $P_{out}$ TGOV1 3\r\n", " 156, pout TGOV1 4, $P_{out}$ TGOV1 4\r\n", " 157, wref TGOV1 1, $\\omega_{ref}$ TGOV1 1\r\n", " 158, wref TGOV1 2, $\\omega_{ref}$ TGOV1 2\r\n", " 159, wref TGOV1 3, $\\omega_{ref}$ TGOV1 3\r\n", " 160, wref TGOV1 4, $\\omega_{ref}$ TGOV1 4\r\n", " 161, pref TGOV1 1, $P_{ref}$ TGOV1 1\r\n", " 162, pref TGOV1 2, $P_{ref}$ TGOV1 2\r\n", " 163, pref TGOV1 3, $P_{ref}$ TGOV1 3\r\n", " 164, pref TGOV1 4, $P_{ref}$ TGOV1 4\r\n", " 165, wd TGOV1 1, $\\omega_{dev}$ TGOV1 1\r\n", " 166, wd TGOV1 2, $\\omega_{dev}$ TGOV1 2\r\n", " 167, wd TGOV1 3, $\\omega_{dev}$ TGOV1 3\r\n", " 168, wd TGOV1 4, $\\omega_{dev}$ TGOV1 4\r\n", " 169, pd TGOV1 1, $P_d$ TGOV1 1\r\n", " 170, pd TGOV1 2, $P_d$ TGOV1 2\r\n", " 171, pd TGOV1 3, $P_d$ TGOV1 3\r\n", " 172, pd TGOV1 4, $P_d$ TGOV1 4\r\n", " 173, LL_y TGOV1 1, $y_{LL}$ TGOV1 1\r\n", " 174, LL_y TGOV1 2, $y_{LL}$ TGOV1 2\r\n", " 175, LL_y TGOV1 3, $y_{LL}$ TGOV1 3\r\n", " 176, LL_y TGOV1 4, $y_{LL}$ TGOV1 4\r\n", " 177, vout EXDC2 1, $v_{out}$ EXDC2 1\r\n", " 178, vout EXDC2 2, $v_{out}$ EXDC2 2\r\n", " 179, vout EXDC2 3, $v_{out}$ EXDC2 3\r\n", " 180, vout EXDC2 4, $v_{out}$ EXDC2 4\r\n", " 181, vref EXDC2 1, $V_{ref}$ EXDC2 1\r\n", " 182, vref EXDC2 2, $V_{ref}$ EXDC2 2\r\n", " 183, vref EXDC2 3, $V_{ref}$ EXDC2 3\r\n", " 184, vref EXDC2 4, $V_{ref}$ EXDC2 4\r\n", " 185, Se EXDC2 1, $S_e(|V_{out}|)$ EXDC2 1\r\n", " 186, Se EXDC2 2, $S_e(|V_{out}|)$ EXDC2 2\r\n", " 187, Se EXDC2 3, $S_e(|V_{out}|)$ EXDC2 3\r\n", " 188, Se EXDC2 4, $S_e(|V_{out}|)$ EXDC2 4\r\n", " 189, vi EXDC2 1, $V_i$ EXDC2 1\r\n", " 190, vi EXDC2 2, $V_i$ EXDC2 2\r\n", " 191, vi EXDC2 3, $V_i$ EXDC2 3\r\n", " 192, vi EXDC2 4, $V_i$ EXDC2 4\r\n", " 193, LL_y EXDC2 1, $y_{LL}$ EXDC2 1\r\n", " 194, LL_y EXDC2 2, $y_{LL}$ EXDC2 2\r\n", " 195, LL_y EXDC2 3, $y_{LL}$ EXDC2 3\r\n", " 196, LL_y EXDC2 4, $y_{LL}$ EXDC2 4\r\n", " 197, W_y EXDC2 1, $y_{W}$ EXDC2 1\r\n", " 198, W_y EXDC2 2, $y_{W}$ EXDC2 2\r\n", " 199, W_y EXDC2 3, $y_{W}$ EXDC2 3\r\n", " 200, W_y EXDC2 4, $y_{W}$ EXDC2 4\r\n", " 201, vcmp_zi PQ 0, $vcmp$ PQ 0\r\n", " 202, vcmp_zi PQ 1, $vcmp$ PQ 1\r\n", " 203, vcmp_zl PQ 0, $vcmp$ PQ 0\r\n", " 204, vcmp_zl PQ 1, $vcmp$ PQ 1\r\n", " 205, vcmp_zu PQ 0, $vcmp$ PQ 0\r\n", " 206, vcmp_zu PQ 1, $vcmp$ PQ 1\r\n", " 207, qlim_zi PV 2, $qlim$ PV 2\r\n", " 208, qlim_zi PV 3, $qlim$ PV 3\r\n", " 209, qlim_zi PV 4, $qlim$ PV 4\r\n", " 210, qlim_zl PV 2, $qlim$ PV 2\r\n", " 211, qlim_zl PV 3, $qlim$ PV 3\r\n", " 212, qlim_zl PV 4, $qlim$ PV 4\r\n", " 213, qlim_zu PV 2, $qlim$ PV 2\r\n", " 214, qlim_zu PV 3, $qlim$ PV 3\r\n", " 215, qlim_zu PV 4, $qlim$ PV 4\r\n", " 216, qlim_ql PV 2, $qlim$ PV 2\r\n", " 217, qlim_ql PV 3, $qlim$ PV 3\r\n", " 218, qlim_ql PV 4, $qlim$ PV 4\r\n", " 219, qlim_qu PV 2, $qlim$ PV 2\r\n", " 220, qlim_qu PV 3, $qlim$ PV 3\r\n", " 221, qlim_qu PV 4, $qlim$ PV 4\r\n", " 222, qlim_zi Slack 1, $qlim$ Slack 1\r\n", " 223, qlim_zl Slack 1, $qlim$ Slack 1\r\n", " 224, qlim_zu Slack 1, $qlim$ Slack 1\r\n", " 225, qlim_ql Slack 1, $qlim$ Slack 1\r\n", " 226, qlim_qu Slack 1, $qlim$ Slack 1\r\n", " 227, plim_zi Slack 1, $plim$ Slack 1\r\n", " 228, plim_zl Slack 1, $plim$ Slack 1\r\n", " 229, plim_zu Slack 1, $plim$ Slack 1\r\n", " 230, plim_ql Slack 1, $plim$ Slack 1\r\n", " 231, plim_qu Slack 1, $plim$ Slack 1\r\n", " 232, SL_z0 GENROU 1, $SL$ GENROU 1\r\n", " 233, SL_z0 GENROU 2, $SL$ GENROU 2\r\n", " 234, SL_z0 GENROU 3, $SL$ GENROU 3\r\n", " 235, SL_z0 GENROU 4, $SL$ GENROU 4\r\n", " 236, SL_z1 GENROU 1, $SL$ GENROU 1\r\n", " 237, SL_z1 GENROU 2, $SL$ GENROU 2\r\n", " 238, SL_z1 GENROU 3, $SL$ GENROU 3\r\n", " 239, SL_z1 GENROU 4, $SL$ GENROU 4\r\n", " 240, LAG_lim_zi TGOV1 1, $lim_{LAG}$ TGOV1 1\r\n", " 241, LAG_lim_zi TGOV1 2, $lim_{LAG}$ TGOV1 2\r\n", " 242, LAG_lim_zi TGOV1 3, $lim_{LAG}$ TGOV1 3\r\n", " 243, LAG_lim_zi TGOV1 4, $lim_{LAG}$ TGOV1 4\r\n", " 244, LAG_lim_zl TGOV1 1, $lim_{LAG}$ TGOV1 1\r\n", " 245, LAG_lim_zl TGOV1 2, $lim_{LAG}$ TGOV1 2\r\n", " 246, LAG_lim_zl TGOV1 3, $lim_{LAG}$ TGOV1 3\r\n", " 247, LAG_lim_zl TGOV1 4, $lim_{LAG}$ TGOV1 4\r\n", " 248, LAG_lim_zu TGOV1 1, $lim_{LAG}$ TGOV1 1\r\n", " 249, LAG_lim_zu TGOV1 2, $lim_{LAG}$ TGOV1 2\r\n", " 250, LAG_lim_zu TGOV1 3, $lim_{LAG}$ TGOV1 3\r\n", " 251, LAG_lim_zu TGOV1 4, $lim_{LAG}$ TGOV1 4\r\n", " 252, LL_LT1_z0 TGOV1 1, $LT_{LL}$ TGOV1 1\r\n", " 253, LL_LT1_z0 TGOV1 2, $LT_{LL}$ TGOV1 2\r\n", " 254, LL_LT1_z0 TGOV1 3, $LT_{LL}$ TGOV1 3\r\n", " 255, LL_LT1_z0 TGOV1 4, $LT_{LL}$ TGOV1 4\r\n", " 256, LL_LT1_z1 TGOV1 1, $LT_{LL}$ TGOV1 1\r\n", " 257, LL_LT1_z1 TGOV1 2, $LT_{LL}$ TGOV1 2\r\n", " 258, LL_LT1_z1 TGOV1 3, $LT_{LL}$ TGOV1 3\r\n", " 259, LL_LT1_z1 TGOV1 4, $LT_{LL}$ TGOV1 4\r\n", " 260, LL_LT2_z0 TGOV1 1, $LT_{LL}$ TGOV1 1\r\n", " 261, LL_LT2_z0 TGOV1 2, $LT_{LL}$ TGOV1 2\r\n", " 262, LL_LT2_z0 TGOV1 3, $LT_{LL}$ TGOV1 3\r\n", " 263, LL_LT2_z0 TGOV1 4, $LT_{LL}$ TGOV1 4\r\n", " 264, LL_LT2_z1 TGOV1 1, $LT_{LL}$ TGOV1 1\r\n", " 265, LL_LT2_z1 TGOV1 2, $LT_{LL}$ TGOV1 2\r\n", " 266, LL_LT2_z1 TGOV1 3, $LT_{LL}$ TGOV1 3\r\n", " 267, LL_LT2_z1 TGOV1 4, $LT_{LL}$ TGOV1 4\r\n", " 268, SL_z0 EXDC2 1, $SL$ EXDC2 1\r\n", " 269, SL_z0 EXDC2 2, $SL$ EXDC2 2\r\n", " 270, SL_z0 EXDC2 3, $SL$ EXDC2 3\r\n", " 271, SL_z0 EXDC2 4, $SL$ EXDC2 4\r\n", " 272, SL_z1 EXDC2 1, $SL$ EXDC2 1\r\n", " 273, SL_z1 EXDC2 2, $SL$ EXDC2 2\r\n", " 274, SL_z1 EXDC2 3, $SL$ EXDC2 3\r\n", " 275, SL_z1 EXDC2 4, $SL$ EXDC2 4\r\n", " 276, LL_LT1_z0 EXDC2 1, $LT_{LL}$ EXDC2 1\r\n", " 277, LL_LT1_z0 EXDC2 2, $LT_{LL}$ EXDC2 2\r\n", " 278, LL_LT1_z0 EXDC2 3, $LT_{LL}$ EXDC2 3\r\n", " 279, LL_LT1_z0 EXDC2 4, $LT_{LL}$ EXDC2 4\r\n", " 280, LL_LT1_z1 EXDC2 1, $LT_{LL}$ EXDC2 1\r\n", " 281, LL_LT1_z1 EXDC2 2, $LT_{LL}$ EXDC2 2\r\n", " 282, LL_LT1_z1 EXDC2 3, $LT_{LL}$ EXDC2 3\r\n", " 283, LL_LT1_z1 EXDC2 4, $LT_{LL}$ EXDC2 4\r\n", " 284, LL_LT2_z0 EXDC2 1, $LT_{LL}$ EXDC2 1\r\n", " 285, LL_LT2_z0 EXDC2 2, $LT_{LL}$ EXDC2 2\r\n", " 286, LL_LT2_z0 EXDC2 3, $LT_{LL}$ EXDC2 3\r\n", " 287, LL_LT2_z0 EXDC2 4, $LT_{LL}$ EXDC2 4\r\n", " 288, LL_LT2_z1 EXDC2 1, $LT_{LL}$ EXDC2 1\r\n", " 289, LL_LT2_z1 EXDC2 2, $LT_{LL}$ EXDC2 2\r\n", " 290, LL_LT2_z1 EXDC2 3, $LT_{LL}$ EXDC2 3\r\n", " 291, LL_LT2_z1 EXDC2 4, $LT_{LL}$ EXDC2 4\r\n", " 292, LA_lim_zi EXDC2 1, $lim_{LA}$ EXDC2 1\r\n", " 293, LA_lim_zi EXDC2 2, $lim_{LA}$ EXDC2 2\r\n", " 294, LA_lim_zi EXDC2 3, $lim_{LA}$ EXDC2 3\r\n", " 295, LA_lim_zi EXDC2 4, $lim_{LA}$ EXDC2 4\r\n", " 296, LA_lim_zl EXDC2 1, $lim_{LA}$ EXDC2 1\r\n", " 297, LA_lim_zl EXDC2 2, $lim_{LA}$ EXDC2 2\r\n", " 298, LA_lim_zl EXDC2 3, $lim_{LA}$ EXDC2 3\r\n", " 299, LA_lim_zl EXDC2 4, $lim_{LA}$ EXDC2 4\r\n", " 300, LA_lim_zu EXDC2 1, $lim_{LA}$ EXDC2 1\r\n", " 301, LA_lim_zu EXDC2 2, $lim_{LA}$ EXDC2 2\r\n", " 302, LA_lim_zu EXDC2 3, $lim_{LA}$ EXDC2 3\r\n", " 303, LA_lim_zu EXDC2 4, $lim_{LA}$ 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": [ "" ] }, "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 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/Windows with MSYS2 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": 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": [ "Traceback (most recent call last):\r\n", " File \"/home/hcui7/mambaforge/envs/a/bin/andes\", line 33, in \r\n", " sys.exit(load_entry_point('andes', 'console_scripts', 'andes')())\r\n", " File \"/home/hcui7/repos/andes/andes/cli.py\", line 210, in main\r\n", " return func(cli=True, **vars(args))\r\n", " File \"/home/hcui7/repos/andes/andes/main.py\", line 664, in plot\r\n", " tdsplot(**kwargs)\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 941, in tdsplot\r\n", " tds_data = TDSData(filename[0])\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 49, in __init__\r\n", " self.load_lst()\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 102, in load_lst\r\n", " with open(self._lst_file, 'r') as fd:\r\n", "FileNotFoundError: [Errno 2] No such file or directory: '/home/hcui7/repos/andes/examples/kundur_full_out.lst'\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": [ "Traceback (most recent call last):\r\n", " File \"/home/hcui7/mambaforge/envs/a/bin/andes\", line 33, in \r\n", " sys.exit(load_entry_point('andes', 'console_scripts', 'andes')())\r\n", " File \"/home/hcui7/repos/andes/andes/cli.py\", line 210, in main\r\n", " return func(cli=True, **vars(args))\r\n", " File \"/home/hcui7/repos/andes/andes/main.py\", line 664, in plot\r\n", " tdsplot(**kwargs)\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 941, in tdsplot\r\n", " tds_data = TDSData(filename[0])\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 49, in __init__\r\n", " self.load_lst()\r\n", " File \"/home/hcui7/repos/andes/andes/plot.py\", line 102, in load_lst\r\n", " with open(self._lst_file, 'r') as fd:\r\n", "FileNotFoundError: [Errno 2] No such file or directory: '/home/hcui7/repos/andes/examples/kundur_full_out.lst'\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "usage: andes plot [-h] [--xmin LEFT] [--xmax RIGHT] [--ymax YMAX]\r\n", " [--ymin YMIN] [--find FIND | -a XARGS] [--exclude EXCLUDE]\r\n", " [-x XLABEL] [-y YLABEL] [-s] [-f SAVE_FORMAT] [--dpi DPI]\r\n", " [--font-size FONT_SIZE] [--line-width LINE_WIDTH] [-g]\r\n", " [-t TITLE] [--greyscale] [-d] [-n] [--ytimes YTIMES] [-c]\r\n", " [--hline1 HLINE1] [--hline2 HLINE2] [--vline1 VLINE1]\r\n", " [--vline2 VLINE2]\r\n", " filename [x] [y ...]\r\n", "andes plot: error: the following arguments are required: filename, y\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 saved to \"kundur_full_out_3.png\".\r\n", "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", "removed 'kundur_full_out_2.png'\r\n", "removed 'kundur_full_out_3.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": [ "\r\n", " _ _ | Version 1.4.2.post116.dev0+g832274a7\r\n", " /_\\ _ _ __| |___ ___ | Python 3.9.6 on Linux, 09/26/2021 05:42:08 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" ] } ], "source": [ "!andes misc -C" ] } ], "metadata": { "interpreter": { "hash": "4c42303ec617988e96980582546035234a0dbb343f6614254a1d5bddbb9babb9" }, "kernelspec": { 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