Merge branch 'main' into feat/add-uv-support

Signed-off-by: Martijn Govers <[email protected]>

Author: mgovers

.github/workflows/sync-examples.yml

@@ -49,12 +49,3 @@ jobs:
           commit_user_name: GitHub Actions Bot
           commit_user_email: [email protected]
           commit_author: GitHub Actions Bot <[email protected]>
-  
-  keepalive-job:
-    name: Keepalive Workflow
-    runs-on: ubuntu-latest
-    permissions:
-      actions: write
-    steps:
-      - uses: actions/checkout@v4
-      - uses: gautamkrishnar/keepalive-workflow@v2

README.md

@@ -24,6 +24,7 @@ It is recommended to read the numpy structured array
 Please create the relevant environment before the workshop. If you encounter problems, please raise a question in the [discussion board](https://github.com/orgs/PowerGridModel/discussions)
 
 ### Preparation with uv (Windows/Mac/Linux)
+
 1. Open a terminal
    - Windows: Press `Windows-Key + R`, type `cmd`, press **OK**
    - Mac/Linux: Open a terminal
@@ -64,6 +65,7 @@ for your operating system (Windows/Mac/Linux)
    http://localhost:8888. If not, the console output will tell you where to find the jupyter notebook server.
     ```shell
     uv run jupyter notebook
+    ```
 
 ## License
 

examples/Asymmetric Calculation Example.ipynb

@@ -48,9 +48,16 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "from power_grid_model import LoadGenType, ComponentType, DatasetType\n",
-    "from power_grid_model import PowerGridModel, CalculationMethod, CalculationType, MeasuredTerminalType\n",
-    "from power_grid_model import initialize_array"
+    "from power_grid_model import (\n",
+    "    CalculationMethod,\n",
+    "    CalculationType,\n",
+    "    ComponentType,\n",
+    "    DatasetType,\n",
+    "    LoadGenType,\n",
+    "    MeasuredTerminalType,\n",
+    "    PowerGridModel,\n",
+    "    initialize_array,\n",
+    ")"
    ]
   },
   {
@@ -317,7 +324,7 @@
    "source": [
     "#### Accessing batch data\n",
     "\n",
-    "It may be a bit unintuitive to read the `output_data` or `update_data` of a component directly as they are a dictionary of 4 dimension data, ie. $ids \\times batches \\times  attributes \\times phases$. Remember that the `output_data` or `update_data` are a dictionary of numpy structured arrays. Hence the component should be indexed first. The index that follows can be [indexed with numpy structured arrays](https://numpy.org/doc/stable/user/basics.rec.html#indexing-structured-arrays).\n",
+    "It may be a bit unintuitive to read the `output_data` or `update_data` of a component directly as they are a dictionary of 4 dimension data, i.e., $ids \\times batches \\times  attributes \\times phases$. Remember that the `output_data` or `update_data` are a dictionary of numpy structured arrays. Hence the component should be indexed first. The index that follows can be [indexed with numpy structured arrays](https://numpy.org/doc/stable/user/basics.rec.html#indexing-structured-arrays).\n",
     "\n",
     "To read the result of a single batch, e.g. 1st batch,"
    ]
@@ -825,7 +832,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,