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Docs: rerun notebooks #131

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c4e7170
add support for 3.14
mgovers Jan 12, 2026
4034d8d
use more recent ubuntu version in build guide
mgovers Jan 12, 2026
8486a5a
pin <3.15
mgovers Jan 12, 2026
904e9aa
add poethepoet to dev dependencies
mgovers Jan 12, 2026
472852a
rerun all notebooks
mgovers Jan 12, 2026
90f56b0
Merge branch 'main' into feature/rerun-notebooks
mgovers Jan 12, 2026
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107 changes: 90 additions & 17 deletions docs/demos/connecting_new_consumer.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -62,7 +62,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -86,7 +86,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -127,7 +127,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -152,7 +152,7 @@
" x=new_consumer.x_coor[0],\n",
" y=new_consumer.y_coor[0],\n",
" )\n",
" closest_node = grid.node[closest_node_idx]\n",
" closest_node = grid.node.data[closest_node_idx]\n",
"\n",
" grid.append(new_consumer)\n",
" new_consumer_load.node = new_consumer.id\n",
Expand Down Expand Up @@ -188,9 +188,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.void' object has no attribute 'x_coor'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mconnect_new_consumer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_consumer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_consumer_load\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m update_grid(grid)\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 28\u001b[39m, in \u001b[36mconnect_new_consumer\u001b[39m\u001b[34m(grid, new_consumer, new_consumer_load)\u001b[39m\n\u001b[32m 25\u001b[39m new_consumer_load.node = new_consumer.id\n\u001b[32m 26\u001b[39m grid.append(new_consumer_load)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m dist = np.sqrt((\u001b[43mclosest_node\u001b[49m\u001b[43m.\u001b[49m\u001b[43mx_coor\u001b[49m - new_consumer.x_coor) ** \u001b[32m2\u001b[39m + (closest_node.y_coor - new_consumer.y_coor) ** \u001b[32m2\u001b[39m)\n\u001b[32m 30\u001b[39m new_line = ExtendedLineArray(\n\u001b[32m 31\u001b[39m from_node=[closest_node.id],\n\u001b[32m 32\u001b[39m to_node=[new_consumer.id],\n\u001b[32m (...)\u001b[39m\u001b[32m 39\u001b[39m i_n=[\u001b[32m200\u001b[39m],\n\u001b[32m 40\u001b[39m )\n\u001b[32m 41\u001b[39m grid.append(new_line)\n",
"\u001b[31mAttributeError\u001b[39m: 'numpy.void' object has no attribute 'x_coor'"
]
}
],
"source": [
"connect_new_consumer(grid, new_consumer, new_consumer_load)\n",
"update_grid(grid)"
Expand All @@ -209,27 +222,74 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id | u_rated | node_type | feeder_branch_id | feeder_node_id | u | x_coor | y_coor \n",
" 1 | 10500.0 | 0 | 64 | 61 | 0.0 |190.591..|391.821..\n",
" 2 | 10500.0 | 0 | 64 | 61 | 0.0 |107.276..|479.614..\n",
" 3 | 10500.0 | 0 | 63 | 61 | 0.0 |236.780..|418.712..\n",
" 4 | 10500.0 | 0 | 78 | 61 | 0.0 |399.420..|447.300..\n",
" 5 | 10500.0 | 0 | 63 | 61 | 0.0 |146.187..|177.586..\n",
" (..12 hidden rows..) \n",
" 18 | 10500.0 | 0 | 63 | 61 | 0.0 |444.001..|139.411..\n",
" 19 | 10500.0 | 0 | 78 | 61 | 0.0 |370.190..|268.868..\n",
" 20 | 10500.0 | 0 | 64 | 61 | 0.0 |306.867..|334.454..\n",
" 61 | 10500.0 | 1 | -2147483648 | -2147483648 | 0.0 |336.963..|414.615..\n",
" 93 | 10500.0 | 0 | -2147483648 | -2147483648 | 0.0 | 300.0 | 300.0 \n"
]
}
],
"source": [
"print(grid.node)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id | from_node | to_node | from_status | to_status | feeder_branch_id | feeder_node_id | is_feeder | r1 | x1 | c1 | tan1 | i_n | i_from \n",
" 63 | 61 | 17 | 1 | 1 | 63 | 61 | True |0.110..|0.013..| 0.0 | 0.0 |103.9613..| 0.0 \n",
" 64 | 61 | 13 | 1 | 1 | 64 | 61 | True |0.325..|0.015..| 0.0 | 0.0 |100.4538..| 0.0 \n",
" 65 | 17 | 5 | 1 | 1 | 63 | 61 | False |0.657..|2.575..| 0.0 | 0.0 |1311.550..| 0.0 \n",
" 66 | 13 | 11 | 1 | 1 | 64 | 61 | False |0.213..|0.016..| 0.0 | 0.0 |114.4995..| 0.0 \n",
" 67 | 5 | 18 | 1 | 1 | 63 | 61 | False |0.061..|0.029..| 0.0 | 0.0 |170.8020..| 0.0 \n",
" (..20 hidden rows..) \n",
" 88 | 19 | 15 | 1 | 0 | -2147483648 | -2147483648 | False |0.003..|0.003..| 0.0 | 0.0 |203.2692..| 0.0 \n",
" 89 | 1 | 18 | 1 | 0 | -2147483648 | -2147483648 | False |0.341..|0.012..| 0.0 | 0.0 |221.8579..| 0.0 \n",
" 90 | 18 | 2 | 1 | 0 | -2147483648 | -2147483648 | False |0.122..|0.006..| 0.0 | 0.0 |387.5294..| 0.0 \n",
" 91 | 61 | 17 | 1 | 0 | -2147483648 | -2147483648 | True |0.210..|0.001..| 0.0 | 0.0 |267.6662..| 0.0 \n",
" 92 | 3 | 11 | 1 | 0 | -2147483648 | -2147483648 | False |0.030..|0.050..| 0.0 | 0.0 |272.8941..| 0.0 \n"
]
}
],
"source": [
"print(grid.line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overloaded nodes: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 61 93]\n",
"Overloaded lines: []\n"
]
}
],
"source": [
"print(f\"Overloaded nodes: {grid.node[grid.node.is_overloaded].id}\")\n",
"print(f\"Overloaded lines: {grid.line[grid.line.is_overloaded].id}\")"
Expand All @@ -244,9 +304,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.void' object has no attribute 'x_coor'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m10\u001b[39m):\n\u001b[32m 2\u001b[39m new_consumer, new_consumer_load = create_new_consumer_arrays(\n\u001b[32m 3\u001b[39m \u001b[32m10_500\u001b[39m, np.random.uniform(\u001b[32m0\u001b[39m, \u001b[32m500\u001b[39m), np.random.uniform(\u001b[32m0\u001b[39m, \u001b[32m500\u001b[39m), \u001b[32m1_000_000\u001b[39m, \u001b[32m200_000\u001b[39m\n\u001b[32m 4\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43mconnect_new_consumer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_consumer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_consumer_load\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6\u001b[39m update_grid(grid)\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mOverloaded nodes: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgrid.node[grid.node.is_overloaded].id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 28\u001b[39m, in \u001b[36mconnect_new_consumer\u001b[39m\u001b[34m(grid, new_consumer, new_consumer_load)\u001b[39m\n\u001b[32m 25\u001b[39m new_consumer_load.node = new_consumer.id\n\u001b[32m 26\u001b[39m grid.append(new_consumer_load)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m dist = np.sqrt((\u001b[43mclosest_node\u001b[49m\u001b[43m.\u001b[49m\u001b[43mx_coor\u001b[49m - new_consumer.x_coor) ** \u001b[32m2\u001b[39m + (closest_node.y_coor - new_consumer.y_coor) ** \u001b[32m2\u001b[39m)\n\u001b[32m 30\u001b[39m new_line = ExtendedLineArray(\n\u001b[32m 31\u001b[39m from_node=[closest_node.id],\n\u001b[32m 32\u001b[39m to_node=[new_consumer.id],\n\u001b[32m (...)\u001b[39m\u001b[32m 39\u001b[39m i_n=[\u001b[32m200\u001b[39m],\n\u001b[32m 40\u001b[39m )\n\u001b[32m 41\u001b[39m grid.append(new_line)\n",
"\u001b[31mAttributeError\u001b[39m: 'numpy.void' object has no attribute 'x_coor'"
]
}
],
"source": [
"for _ in range(10):\n",
" new_consumer, new_consumer_load = create_new_consumer_arrays(\n",
Expand All @@ -262,7 +335,7 @@
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"display_name": "power-grid-model-ds",
"language": "python",
"name": "python3"
},
Expand All @@ -276,7 +349,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.6"
"version": "3.14.2"
}
},
"nbformat": 4,
Expand Down
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