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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PCSWMM AI Engineering Dashboard v0.2.1\n",
"\n",
"Read-only dashboard. Open a PCSWMM project first."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load SDK"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import importlib\n",
"from pathlib import Path\n",
"\n",
"REQUIRED_SDK_VERSION = \"0.2.1\"\n",
"NOTEBOOK_ROOT = Path.cwd()\n",
"\n",
"# PCSWMM JupyterLab starts in the main Notebooks directory, not in the\n",
"# notebook's own folder. Select v0.2.1 explicitly instead of taking the\n",
"# first pcswmm_ai directory found by rglob().\n",
"preferred_roots = [\n",
" NOTEBOOK_ROOT / \"PCSWMM_AI_SDK_v0_2_1\",\n",
" NOTEBOOK_ROOT / \"PCSWMM_AI_SDK_v0_2_1\" / \"PCSWMM_AI_SDK_v0_2_1\",\n",
" NOTEBOOK_ROOT / \"PCSWMM_AI_SDK_v0_2\",\n",
" NOTEBOOK_ROOT / \"PCSWMM_AI_SDK_v0_2\" / \"PCSWMM_AI_SDK_v0_2\",\n",
"]\n",
"\n",
"SDK_ROOT = next(\n",
" (path for path in preferred_roots if (path / \"pcswmm_ai\").is_dir()),\n",
" None,\n",
")\n",
"\n",
"if SDK_ROOT is None:\n",
" candidates = sorted(\n",
" {\n",
" package_dir.parent\n",
" for package_dir in NOTEBOOK_ROOT.rglob(\"pcswmm_ai\")\n",
" if package_dir.is_dir()\n",
" and \"v0_2\" in str(package_dir.parent).lower()\n",
" },\n",
" reverse=True,\n",
" )\n",
" SDK_ROOT = candidates[0] if candidates else None\n",
"\n",
"if SDK_ROOT is None:\n",
" raise FileNotFoundError(\n",
" \"Could not locate PCSWMM AI SDK v0.2.x below:\\n\"\n",
" f\"{NOTEBOOK_ROOT}\"\n",
" )\n",
"\n",
"# Remove an earlier SDK version already cached by this kernel.\n",
"for module_name in list(sys.modules):\n",
" if module_name == \"pcswmm_ai\" or module_name.startswith(\"pcswmm_ai.\"):\n",
" del sys.modules[module_name]\n",
"\n",
"# Put the selected SDK ahead of v0.1 and all other paths.\n",
"sys.path = [\n",
" path for path in sys.path\n",
" if \"PCSWMM_AI_SDK_v0_1\" not in str(path)\n",
" and \"PCSWMM_AI_SDK_v0_2\" not in str(path)\n",
"]\n",
"sys.path.insert(0, str(SDK_ROOT))\n",
"importlib.invalidate_caches()\n",
"\n",
"from pcswmm_ai import PCSWMMClient, __version__ as SDK_VERSION\n",
"import pcswmm_ai\n",
"\n",
"print(\"SDK root:\", SDK_ROOT)\n",
"print(\"SDK package:\", Path(pcswmm_ai.__file__).resolve())\n",
"print(\"SDK version:\", SDK_VERSION)\n",
"\n",
"if SDK_VERSION not in {\"0.2.0\", REQUIRED_SDK_VERSION}:\n",
" raise RuntimeError(\n",
" f\"Wrong SDK version loaded: {SDK_VERSION}. \"\n",
" f\"Expected {REQUIRED_SDK_VERSION}.\"\n",
" )\n",
"\n",
"client = PCSWMMClient(pcpy)\n",
"\n",
"# Verify that the v0.2 services are present before proceeding.\n",
"required_services = [\n",
" \"objects\",\n",
" \"topology\",\n",
" \"engineering\",\n",
" \"result_queries\",\n",
"]\n",
"missing_services = [\n",
" name for name in required_services if not hasattr(client, name)\n",
"]\n",
"\n",
"if missing_services:\n",
" raise RuntimeError(\n",
" \"The selected SDK does not contain the v0.2 services: \"\n",
" + \", \".join(missing_services)\n",
" )\n",
"\n",
"client.health()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Project and collection inventory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from IPython.display import display, Markdown\n",
"display(pd.DataFrame(list(client.project.summary().items()),columns=[\"Property\",\"Value\"]))\n",
"display(pd.DataFrame(client.collections.summary()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Object tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for name in [\"Nodes\",\"Links\",\"Subcatchments\",\"Storages\",\"Outfalls\",\"Conduits\"]:\n",
" display(Markdown(\"### \"+name))\n",
" display(pd.DataFrame(client.objects.collection_records(name)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. GIS layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"layer_names=client.layers.names()\n",
"display(pd.DataFrame({\"Layer\":layer_names}))\n",
"if \"Subcatchments\" in layer_names:\n",
" display(pd.DataFrame(client.layers.records(\"Subcatchments\")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Topology screening"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(\"### Link endpoints\"))\n",
"display(pd.DataFrame(client.topology.link_endpoints()))\n",
"display(Markdown(\"### Orphan links\"))\n",
"display(pd.DataFrame(client.topology.orphan_links()))\n",
"display(Markdown(\"### Isolated nodes\"))\n",
"display(pd.DataFrame(client.topology.isolated_nodes()))\n",
"display(Markdown(\"### Duplicate directed link pairs\"))\n",
"display(pd.DataFrame(client.topology.duplicate_link_pairs()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Engineering screening"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(pd.DataFrame(list(client.engineering.model_screening_summary().items()),columns=[\"Check\",\"Finding count\"]))\n",
"display(Markdown(\"### Conduit findings\"))\n",
"display(pd.DataFrame(client.engineering.conduit_screening()))\n",
"display(Markdown(\"### Subcatchment findings\"))\n",
"display(pd.DataFrame(client.engineering.subcatchment_screening()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Run simulation (explicit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment to run:\n",
"# display(pd.DataFrame([client.simulation.run()]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Peak flow query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment after confirming units and result availability:\n",
"# peaks=pd.DataFrame(client.result_queries.peak_for_objects(\"Links\",\"Flow\",\"CMS\",client.collections.keys(\"Links\")))\n",
"# display(peaks.sort_values(\"maximum\",ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Controlled commands"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def execute_command(command):\n",
" c=command.strip().lower()\n",
" if c==\"project summary\": return pd.DataFrame(list(client.project.summary().items()),columns=[\"Property\",\"Value\"])\n",
" if c==\"model inventory\": return pd.DataFrame(client.collections.summary())\n",
" if c==\"list nodes\": return pd.DataFrame({\"Node\":client.collections.keys(\"Nodes\")})\n",
" if c==\"list links\": return pd.DataFrame({\"Link\":client.collections.keys(\"Links\")})\n",
" if c==\"topology review\": return {\"orphan_links\":client.topology.orphan_links(),\"isolated_nodes\":client.topology.isolated_nodes(),\"duplicate_link_pairs\":client.topology.duplicate_link_pairs()}\n",
" if c==\"engineering review\": return client.engineering.model_screening_summary()\n",
" mapping={\"show nodes\":\"Nodes\",\"show links\":\"Links\",\"show subcatchments\":\"Subcatchments\",\"show storages\":\"Storages\",\"show outfalls\":\"Outfalls\"}\n",
" if c in mapping: return pd.DataFrame(client.objects.collection_records(mapping[c]))\n",
" return {\"error\":\"Unsupported command\",\"supported\":[\"project summary\",\"model inventory\",\"list nodes\",\"list links\",\"show nodes\",\"show links\",\"show subcatchments\",\"show storages\",\"show outfalls\",\"topology review\",\"engineering review\"]}\n",
"# execute_command(\"engineering review\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Screening results identify conditions for engineering review; they do not independently establish municipal non-compliance."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
} |