diff --git a/CHANGELOG.md b/CHANGELOG.md index a5819491..3218fc5c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ All notable changes to this project will be documented in this file. ### Added - Add project-level `CLAUDE.md` file [#329](https://github.com/ie3-institute/pypsdm/issues/329) +- Add example for Line Results to documentation [#341](https://github.com/ie3-institute/pypsdm/issues/341) ### Changed diff --git a/docs/nbs/result_models.ipynb b/docs/nbs/result_models.ipynb index 79cbc8fc..77b93af7 100644 --- a/docs/nbs/result_models.ipynb +++ b/docs/nbs/result_models.ipynb @@ -2,31 +2,29 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:46:35.584992Z", + "start_time": "2025-07-27T18:46:34.856263Z" + } + }, "source": [ "# Some jupyter notebook magic to reload modules automatically when they change\n", "# not necessary for this specific notebook but useful in general\n", "%load_ext autoreload\n", "%autoreload 2" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[32m2024-04-30 11:20:03.169\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mpypsdm.models.gwr\u001B[0m:\u001B[36mfrom_csv\u001B[0m:\u001B[36m275\u001B[0m - \u001B[1mReading grid from /Users/thomas/coding/python/pypsdm/pypsdm-feature/tests/resources/simbench/input\u001B[0m\n", - "\u001B[32m2024-04-30 11:20:03.344\u001B[0m | \u001B[34m\u001B[1mDEBUG \u001B[0m | \u001B[36mpypsdm.models.primary_data\u001B[0m:\u001B[36mfrom_csv\u001B[0m:\u001B[36m266\u001B[0m - \u001B[34m\u001B[1mNo primary data in path /Users/thomas/coding/python/pypsdm/pypsdm-feature/tests/resources/simbench/input\u001B[0m\n", - "\u001B[32m2024-04-30 11:20:03.344\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mpypsdm.models.gwr\u001B[0m:\u001B[36mfrom_csv\u001B[0m:\u001B[36m287\u001B[0m - \u001B[1mReading results from /Users/thomas/coding/python/pypsdm/pypsdm-feature/tests/resources/simbench/results\u001B[0m\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:47:46.813106Z", + "start_time": "2025-07-27T18:46:35.631586Z" } - ], + }, "source": [ "from definitions import ROOT_DIR\n", "import os\n", @@ -40,44 +38,64 @@ "result_path = os.path.join(ROOT_DIR, \"tests\", \"resources\", \"simbench\", \"results\")\n", "# IO data models in general have a from_csv method to parse psdm files\n", "gwr = GridWithResults.from_csv(grid_path, result_path)" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-07-27 20:46:44.961\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mpypsdm.models.gwr\u001b[0m:\u001b[36mfrom_csv\u001b[0m:\u001b[36m289\u001b[0m - \u001b[1mReading grid from C:\\Users\\smdafeis\\PycharmProjects\\pypsdm\\tests\\resources\\simbench\\input\u001b[0m\n", + "\u001b[32m2025-07-27 20:46:46.044\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mpypsdm.models.primary_data\u001b[0m:\u001b[36mfrom_csv\u001b[0m:\u001b[36m273\u001b[0m - \u001b[34m\u001b[1mNo primary data in path C:\\Users\\smdafeis\\PycharmProjects\\pypsdm\\tests\\resources\\simbench\\input\u001b[0m\n", + "\u001b[32m2025-07-27 20:46:46.047\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mpypsdm.models.gwr\u001b[0m:\u001b[36mfrom_csv\u001b[0m:\u001b[36m301\u001b[0m - \u001b[1mReading results from C:\\Users\\smdafeis\\PycharmProjects\\pypsdm\\tests\\resources\\simbench\\results\u001b[0m\n" + ] + } + ], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:48:04.365963Z", + "start_time": "2025-07-27T18:47:57.271352Z" + } + }, + "source": [ + "from pypsdm.plots.grid import grid_plot\n", + "\n", + "# Use the grid_plot method to visualize the grid model\n", + "# only works if the underlying node input files have associated coordinates\n", + "grid_plot(gwr.grid)" + ], "outputs": [ { "data": { "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - 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"ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" }, "title": { "x": 0.05 }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 + "mapbox": { + "style": "light" } } - } + }, + "mapbox": { + "center": { + "lat": 53.64525238095238, + "lon": 11.368850340136055 + }, + "style": "open-street-map", + "zoom": 11.961600000000004 + }, + "margin": { + "r": 0, + "t": 0, + "l": 0, + "b": 0 + }, + "showlegend": false + }, + "config": { + "plotlyServerURL": "https://plot.ly" } } }, @@ -6350,32 +6041,34 @@ "output_type": "display_data" } ], - "source": [ - "from pypsdm.plots.grid import grid_plot\n", - "\n", - "# Use the grid_plot method to visualize the grid model\n", - "# only works if the underlying node input files have associated coordinates\n", - "grid_plot(gwr.grid)" - ] + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:48:19.218273Z", + "start_time": "2025-07-27T18:48:16.796140Z" + } + }, "source": [ "results = gwr.results\n", "# The grid results are symmetrical to the input grid, so there is a result container\n", "# for participants and for the raw grid.\n", "raw_grid_res = results.raw_grid\n", "participants_res = results.participants" - ] + ], + "outputs": [], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:48:40.920576Z", + "start_time": "2025-07-27T18:48:39.206636Z" + } + }, "source": [ "# Results for each grid element and participant is a discrete event time series\n", "# which basically means each recorded state of e.g. a node (consisting of current magnitude, and angle)\n", @@ -6384,15 +6077,44 @@ "nodes_res = raw_grid_res.nodes\n", "# Reminder: you can also access the nodes result directly from the gwr e.g. gwr.nodes_res\n", "nodes_res = gwr.nodes_res" - ] + ], + "outputs": [], + "execution_count": 5 }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:49:08.441773Z", + "start_time": "2025-07-27T18:49:06.895256Z" + } + }, + "source": [ + "# The underlying structure of the result data is a dictionary subclass\n", + "# the values of the dictionary are dependent on the result model\n", + "# e.g. the result dictionary NodesResult contains ComplexVoltage objects\n", + "\n", + "\n", + "# You can access items as you would with a standard dictionary\n", + "uuid = list(nodes_res.keys())[0]\n", + "node_res = nodes_res[uuid]\n", + "\n", + "# Similar to the input models the underlying data structure of each singular result object\n", + "# is a pandas DataFrame with a date time index\n", + "node_res.data.head()" + ], "outputs": [ { "data": { + "text/plain": [ + " v_ang v_mag\n", + "time \n", + "2016-01-02 00:00:00 -1.870384 1.028498\n", + "2016-01-02 01:00:00 -1.762598 1.029275\n", + "2016-01-02 02:00:00 -1.484783 1.031397\n", + "2016-01-02 03:00:00 -1.761274 1.029855\n", + "2016-01-02 04:00:00 -1.776910 1.029577" + ], "text/html": [ "
\n", "\n", + " load_profile \\\n", + "uuid \n", + "a0330517-9705-4d0a-bcaf-71f203cd6187 No load profile assigned \n", + "\n", + " node \\\n", + "uuid \n", + "a0330517-9705-4d0a-bcaf-71f203cd6187 090d13e8-3cce-4793-816f-4c50f23f3f7f \n", + "\n", + " operates_from operates_until operator \\\n", + "uuid \n", + "a0330517-9705-4d0a-bcaf-71f203cd6187 NaN NaN NaN \n", + "\n", + " q_characteristics s_rated \n", + "uuid \n", + "a0330517-9705-4d0a-bcaf-71f203cd6187 cosPhiFixed:{(0.0,0.93)} 668.0 " + ], + "text/html": [ + "
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cos_phi_rateddsme_cons_annualidload_profile
a0330517-9705-4d0a-bcaf-71f203cd61870.93False0.0MV3.101 Load 61No load profile assigned
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" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 16 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Line Results\n", + "\n", + "Here is some example for line results to analyse for line ratings etc." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-27T18:51:50.147737Z", + "start_time": "2025-07-27T18:51:49.741903Z" + } + }, + "cell_type": "code", + "source": [ + "# If you care about the individual line element, you can filter down the\n", + "# result container to the systems connected to a specified line\n", + "\n", + "line_uuid = gwr.lines.data.iloc[0].name\n", + "line_gwr = gwr.lines_res[line_uuid]\n", + "line_gwr.data.head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " i_a_ang i_a_mag i_b_ang i_b_mag\n", + "time \n", + "2016-01-02 00:00:00 -16.907664 92.385576 162.980422 92.433464\n", + "2016-01-02 01:00:00 -16.605600 85.653368 163.273446 85.700759\n", + "2016-01-02 02:00:00 -16.931381 73.715509 162.928117 73.765059\n", + "2016-01-02 03:00:00 -14.665853 93.995163 165.222982 94.036282\n", + "2016-01-02 04:00:00 -14.757302 92.907599 165.130276 92.949001" ], + "text/html": [ + "
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