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add missing notebooks #127

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9 changes: 9 additions & 0 deletions notebooks/dommel/get_data.py
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# %%
from ribasim_nl import CloudStorage

cloud = CloudStorage()

dommel_url = cloud.joinurl("dommel", "verwerkt")

# %%
cloud.download_content(dommel_url)
298 changes: 298 additions & 0 deletions notebooks/vrij_afwaterend/hydamo_0_analyse_data_waterboard.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"from pandas_xlsx_tables import xlsx_tables_to_dfs\n",
"\n",
"sys.path.append(\"Ribasim-NL\\\\src\\\\hydamo\")\n",
"from hydamo.datamodel import HyDAMO\n",
"from ribasim_lumping_tools.LHM_data_bewerking_analyse_utils import (\n",
" check_ids_hydamo_data,\n",
" check_if_object_on_hydroobject,\n",
" read_original_data,\n",
" translate_data_to_hydamo_format,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"id": "2",
"metadata": {},
"source": [
"Vertaal originele data naar Hydamo data zoals gedefinieerd in de tabel hydamo_data_format.xlsx"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"base_dir = \"..\\\\\"\n",
"\n",
"waterboard = \"AAenMaas\"\n",
"waterboard_code = 1\n",
"path_hydamo_format = None # Daniel Tollenaar: toegevoegd opm 14/8 i.v.m. pre-commit\n",
"hydamo_format = None # Daniel Tollenaar: toegevoegd opm 14/8 i.v.m. pre-commit"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"waterboard_dir = Path(base_dir, waterboard, \"verwerkt\")\n",
"path_hydamo_format_table = Path(waterboard_dir, \"HyDAMO_format_AAenMaas.xlsx\")\n",
"hydamo_format_table = xlsx_tables_to_dfs(path_hydamo_format)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# eerst inlezen hydroobject, vertalen naar hydamo\n",
"hydamo_object = \"hydroobject\"\n",
"hydamo_translate_table, data_original = read_original_data(waterboard_dir, hydamo_format, hydamo_object, waterboard)\n",
"hydroobject = translate_data_to_hydamo_format(hydamo_translate_table, data_original)\n",
"\n",
"# maak een created_date aan indien nodig\n",
"if \"created_date\" not in data_original.columns:\n",
" hydroobject[\"created_date\"] = pd.NaT\n",
"# transformeer created_date waardes indien nodig\n",
"hydroobject[\"created_date\"] = hydroobject[\"created_date\"].replace(\"\", pd.NaT)\n",
"\n",
"# hydroobject.loc[hydroobject['code'].duplicated(keep=False), 'data_issue'] = 'duplicate_id'\n",
"data_hydamo_dict = dict(hydroobject=hydroobject.set_crs(28992)) # NOQA (FIXME: voor passen precommit)\n",
"\n",
"# geometry hydroobject bufferen met 10 cm voor de spatial join\n",
"hydroobject[\"buffer\"] = hydroobject.copy().buffer(5) # 5 meter buffer omdat anders relevante gemalen wegvallen\n",
"hydroobject_buffered = hydroobject.set_geometry(\"buffer\").set_crs(28992)"
]
},
{
"cell_type": "markdown",
"id": "6",
"metadata": {},
"source": [
"Specificeer welke HyDAMO data je wilt omzetten"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"hydamo_objects = [\n",
" \"stuw\",\n",
" \"gemaal\",\n",
" \"afvoergebiedaanvoergebied\",\n",
" \"pomp\",\n",
" ##'peilgebiedvigerend',\n",
" ##'peilgebiedpraktijk',\n",
" ##'streefpeil',\n",
" \"duikersifonhevel\",\n",
" ##'afsluiter',\n",
" ##'sluis',\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for hydamo_object in hydamo_objects:\n",
" # lees aangeleverde data en hydamo tabel voor gegeven kunstwerk en waterschap\n",
" table_hydamo, data_original = read_original_data(waterboard_dir, hydamo_format, hydamo_object, waterboard)\n",
" if data_original is None:\n",
" data_hydamo_dict[hydamo_object] = None\n",
" else:\n",
" # vertaal data naar hydamo-ribasim format\n",
" data_hydamo = translate_data_to_hydamo_format(table_hydamo, data_original)\n",
"\n",
" # maak een created_date aan indien nodig\n",
" if \"created_date\" not in data_original.columns and hydamo_object != \"sluis\":\n",
" hydroobject[\"created_date\"] = pd.NaT\n",
" if \"last_edited_date\" not in data_original.columns and hydamo_object == \"afsluiter\":\n",
" hydroobject[\"last_edited_date\"] = pd.NaT\n",
" if \"lvpublicatiedatum\" not in data_original.columns and hydamo_object == \"afsluiter\":\n",
" hydroobject[\"lvpublicatiedatum\"] = pd.NaT\n",
"\n",
" # transformeer created_date waardes indien nodig\n",
" if hydamo_object != \"sluis\":\n",
" data_hydamo[\"created_date\"] = data_hydamo[\"created_date\"].replace(\"\", pd.NaT)\n",
" if hydamo_object == \"afsluiter\":\n",
" data_hydamo[\"last_edited_date\"] = data_hydamo[\"last_edited_date\"].replace(\"\", pd.NaT)\n",
" data_hydamo[\"lvpublicatiedatum\"] = data_hydamo[\"lvpublicatiedatum\"].replace(\"\", pd.NaT)\n",
"\n",
" # check dubbele id's\n",
" if hydamo_object not in [\"streefpeil\"]: # streefpeil heeft geen code, alleen globalid etc\n",
" data_hydamo.loc[data_hydamo[\"code\"].duplicated(keep=False), \"data_issue\"] = \"duplicate_id\"\n",
" # TODO check op 'code' lijkt met logischer want die kolom wordt vaker gebruikt. Maar bij WDOD bijv. is die niet ingevuld. Toch op globalid?\n",
" # check of kuntstwerk op hydroobject ligt\n",
" if hydamo_object in [\"stuw\", \"gemaal\", \"duikersifonhevel\", \"sluis\"]:\n",
" data_hydamo = check_if_object_on_hydroobject(\n",
" data_hydamo=data_hydamo, hydroobject_buffered=hydroobject_buffered\n",
" )\n",
" # verwijder kunstwerken die niet op hydroobject liggen\n",
" data_hydamo = data_hydamo[data_hydamo[\"code_hydroobject\"] != \"niet op hydroobject\"]\n",
" data_hydamo = data_hydamo.reset_index()\n",
" # voeg toe aan de hydamo dataset\n",
" data_hydamo_dict[hydamo_object] = data_hydamo"
]
},
{
"cell_type": "markdown",
"id": "9",
"metadata": {},
"source": [
"Waterschap specifieke acties"
]
},
{
"cell_type": "markdown",
"id": "10",
"metadata": {},
"source": [
"Export normal"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# for hydamo_object in ['hydroobject'] + hydamo_objects:\n",
"# # export to geopackage\n",
"# export_to_geopackage(\n",
"# data_hydamo=data_hydamo_dict[hydamo_object],\n",
"# hydamo_format=hydamo_format,\n",
"# waterboard=waterboard,\n",
"# hydamo_object=hydamo_object\n",
"# )"
]
},
{
"cell_type": "markdown",
"id": "12",
"metadata": {},
"source": [
"### ribasim-nl hydamo"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"hydamo = HyDAMO(version=\"2.2.1_sweco\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14",
"metadata": {},
"outputs": [],
"source": [
"for hydamo_object in [\"hydroobject\"] + hydamo_objects:\n",
" data_hydamo = data_hydamo_dict[hydamo_object]\n",
" if hydamo_object == \"stuw\":\n",
" data_hydamo = data_hydamo.drop(columns=[\"code_hydroobject\", \"data_issue\"]) # ,'index_right'\n",
" data_hydamo = check_ids_hydamo_data(data_hydamo, waterboard_code, hydamo_object)\n",
" setattr(hydamo, hydamo_object, data_hydamo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"hydamo.to_geopackage(\"..\\\\hydamo.gpkg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ribasim_lumping_venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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