Using NBVAL as validation for jupyter notebooks

CHANGELOG.md

@@ -8,6 +8,7 @@ All notable changes to this project will be documented in this file.
 - Add project-level `CLAUDE.md` file [#329](https://github.com/ie3-institute/pypsdm/issues/329)
 - Adding congestion result handling [#198](https://github.com/ie3-institute/pypsdm/issues/198)
 - Add pre-commit hook for removing metadata from jupyter notebooks [#363](https://github.com/ie3-institute/pypsdm/issues/363)
+- Using `NBVAL` as validation for jupyter notebooks [#351](https://github.com/ie3-institute/pypsdm/issues/351) 
 
 ### Changed
 

docs/nbs/input_models.ipynb

@@ -2,39 +2,34 @@
  "cells": [
   {
    "cell_type": "code",
+   "id": "initial_id",
    "metadata": {
+    "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.341315Z",
-     "start_time": "2025-08-12T17:17:12.309989Z"
+     "end_time": "2025-08-19T16:40:01.862229Z",
+     "start_time": "2025-08-19T16:40:01.820265Z"
     }
    },
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# 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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "execution_count": 19
+   "outputs": [],
+   "execution_count": 1
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.485196Z",
-     "start_time": "2025-08-12T17:17:12.359095Z"
+     "end_time": "2025-08-19T16:40:03.565588Z",
+     "start_time": "2025-08-19T16:40:01.872411Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "from definitions import ROOT_DIR\n",
     "import os\n",
     "\n",
@@ -51,32 +46,35 @@
     "# You can also load a `GridWithResults` container which additionally contains the result\n",
     "# data. For more details see the `result_models.ipynb` notebook"
    ],
+   "id": "3d08b4fd2b690ca",
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001B[32m2025-08-12 19:17:12.481\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 /home/smdafeis/github/pypsdm/tests/resources/simple_grid/input\u001B[0m\n"
+      "\u001B[32m2025-08-19 18:40:03.551\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 /home/smdafeis/github/pypsdm/tests/resources/simple_grid/input\u001B[0m\n"
      ]
     }
    ],
-   "execution_count": 20
+   "execution_count": 2
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.538077Z",
-     "start_time": "2025-08-12T17:17:12.491016Z"
+     "end_time": "2025-08-19T16:40:05.421736Z",
+     "start_time": "2025-08-19T16:40:03.726177Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_SKIP\n",
     "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(grid)"
    ],
+   "id": "38b94c0e439ba698",
    "outputs": [
     {
      "data": {
@@ -1015,24 +1013,26 @@
      "output_type": "display_data"
     }
    ],
-   "execution_count": 21
+   "execution_count": 3
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.575596Z",
-     "start_time": "2025-08-12T17:17:12.542626Z"
+     "end_time": "2025-08-19T16:40:05.565587Z",
+     "start_time": "2025-08-19T16:40:05.534817Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# You can get a graph representation of the grid\n",
     "graph = grid.raw_grid.build_networkx_graph()\n",
     "# And a list of all the branches in the grid\n",
     "branches_list = grid.raw_grid.get_branches()\n",
     "branches_subgraphs = grid.raw_grid.get_branches(as_graphs=True)\n",
     "branches_list, branches_subgraphs"
    ],
+   "id": "3bef89c519ea7a2e",
    "outputs": [
     {
      "data": {
@@ -1041,25 +1041,26 @@
        "   'b7a5be0d-2662-41b2-99c6-3b8121a75e9e',\n",
        "   '1dcddd06-f41a-405b-9686-7f7942852196',\n",
        "   'e3c3c6a3-c383-4dbb-9b3f-a14125615386']],\n",
-       " [<networkx.classes.graph.Graph at 0x793323fdd790>])"
+       " [<networkx.classes.graph.Graph at 0x7e0971a5a960>])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 22
+   "execution_count": 4
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.618855Z",
-     "start_time": "2025-08-12T17:17:12.589209Z"
+     "end_time": "2025-08-19T16:40:05.612777Z",
+     "start_time": "2025-08-19T16:40:05.585043Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# A grid container consists of a raw grid container\n",
     "raw_grid = grid.raw_grid\n",
     "# consisting of lines, transformers and so on\n",
@@ -1075,6 +1076,7 @@
     "# The base data structure of all input model is a pandas DataFrame accessible via .data\n",
     "pvs.data"
    ],
+   "id": "57731ebb07b93950",
    "outputs": [
     {
      "data": {
@@ -1246,25 +1248,26 @@
        "</div>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 23
+   "execution_count": 5
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.661689Z",
-     "start_time": "2025-08-12T17:17:12.639427Z"
+     "end_time": "2025-08-19T16:40:05.663342Z",
+     "start_time": "2025-08-19T16:40:05.642882Z"
     }
    },
+   "cell_type": "code",
    "source": [
     "# You can access the columns via the data frame\n",
     "pvs.data[\"s_rated\"]"
    ],
+   "id": "f588fa60db607ebf",
    "outputs": [
     {
      "data": {
@@ -1276,25 +1279,26 @@
        "Name: s_rated, dtype: int64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 24
+   "execution_count": 6
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.715348Z",
-     "start_time": "2025-08-12T17:17:12.686303Z"
+     "end_time": "2025-08-19T16:40:05.723379Z",
+     "start_time": "2025-08-19T16:40:05.693807Z"
     }
    },
+   "cell_type": "code",
    "source": [
     "# or directly via the property attribute of the class\n",
     "pvs.s_rated"
    ],
+   "id": "ccdd8fd0a1d60968",
    "outputs": [
     {
      "data": {
@@ -1306,28 +1310,30 @@
        "Name: s_rated, dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 25
+   "execution_count": 7
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:12.787361Z",
-     "start_time": "2025-08-12T17:17:12.735393Z"
+     "end_time": "2025-08-19T16:40:05.792223Z",
+     "start_time": "2025-08-19T16:40:05.743618Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# The respective classes implement some useful methods for dealing with the data\n",
     "# e.g. retrieve all elements connected to one or multiple specified nodes\n",
     "# (Please check out the classes to see all the implemented methods)\n",
     "nodal_participants = participants.filter_by_nodes(grid.nodes.uuid.to_list()[::2])\n",
     "nodal_participants.pvs.data"
    ],
+   "id": "be125965fbe013b2",
    "outputs": [
     {
      "data": {
@@ -1472,22 +1478,23 @@
        "</div>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 26
+   "execution_count": 8
   },
   {
-   "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-08-12T17:17:13.058337Z",
-     "start_time": "2025-08-12T17:17:12.794784Z"
+     "end_time": "2025-08-19T16:40:06.074600Z",
+     "start_time": "2025-08-19T16:40:05.801528Z"
     }
    },
+   "cell_type": "code",
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "from tempfile import TemporaryDirectory\n",
     "\n",
     "# You can write data models with their respective to_csv method\n",
@@ -1501,37 +1508,38 @@
     "    # the == operator is overloaded to compare the underlying dataframes\n",
     "    assert grid == grid_from_csv"
    ],
+   "id": "f0d62146d59403f9",
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001B[32m2025-08-12 19:17:12.954\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 /tmp/tmplnxfgc6t\u001B[0m\n"
+      "\u001B[32m2025-08-19 18:40:05.968\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 /tmp/tmp97ev7h7m\u001B[0m\n"
      ]
     }
    ],
-   "execution_count": 27
+   "execution_count": 9
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pypsdm-sJkpnJQv-py3.11",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }