feat(sbr-b): allow multi building validation

notebooks/NBxxxxx_SBR.ipynb

@@ -622,25 +622,71 @@
     "    result = client.call_endpoint(\n",
     "        \"VibraCore\", f\"/{methode.lower()}/validation/single\", schema=single_payload\n",
     "    )\n",
-    "    cfc = CFC_FACTOR_FLOORS[installation_type][\n",
-    "        buildings.loc[buildings[\"name\"] == name][\"material\"].item()\n",
-    "    ][\"Cfc\"]\n",
+    "\n",
     "    fig = map_nuisance(\n",
     "        buildings,\n",
     "        source_location=location,\n",
     "        building_name=name,\n",
     "        response_dict=result,\n",
-    "        cfc=cfc,\n",
-    "        u_eff=0.54,\n",
+    "        vibration_type=vibration_type,\n",
+    "        installation_type=installation_type,\n",
     "        period=period,\n",
+    "        settings={\n",
+    "            \"source_location\": {\"label\": \"Trillingsbron\", \"color\": \"blue\"},\n",
+    "            \"levels\": [\n",
+    "                {\n",
+    "                    \"label\": \"<= 1 day\",\n",
+    "                    \"color\": \"darkred\",\n",
+    "                },\n",
+    "                {\n",
+    "                    \"label\": \">= 6 days; <26 days\",\n",
+    "                    \"color\": \"orange\",\n",
+    "                },\n",
+    "                {\n",
+    "                    \"label\": \">= 26 days; <78 days\",\n",
+    "                    \"color\": \"green\",\n",
+    "                },\n",
+    "                {\n",
+    "                    \"label\": \"Unlimited\",\n",
+    "                    \"color\": \"black\",\n",
+    "                },\n",
+    "            ],\n",
+    "        },\n",
     "    )\n",
     "\n",
     "    # add basemap\n",
     "    ctx.add_basemap(\n",
     "        fig.axes[0], crs=\"EPSG:28992\", source=ctx.providers.Esri.WorldTopoMap\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3db0419-ccb6-46bd-8959-67a5fd771790",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a dataframe for all buildings\n",
+    "_df_list = []\n",
+    "for name in buildings[\"name\"]:\n",
+    "    single_payload = create_single_payload(multi_vibration_payload, name=name)\n",
+    "    result = client.call_endpoint(\n",
+    "        \"VibraCore\", f\"/{methode.lower()}/validation/single\", schema=single_payload\n",
+    "    )\n",
+    "\n",
+    "    _df_list.append(\n",
+    "        df_nuisance(\n",
+    "            buildings,\n",
+    "            building_name=name,\n",
+    "            response_dict=result,\n",
+    "            vibration_type=vibration_type,\n",
+    "            installation_type=installation_type,\n",
+    "            period=period,\n",
+    "        )\n",
     "    )\n",
     "\n",
-    "    print(df_nuisance(response_dict=result, cfc=cfc, u_eff=0.54, period=period))"
+    "pd.concat(_df_list, axis=1)"
    ]
   },
   {

src/pyvibracore/results/nuisance.py

@@ -1,14 +1,15 @@
 from __future__ import annotations
 
-from typing import Any, List, Sequence, Tuple
+from copy import deepcopy
+from typing import Any, List, Literal, Sequence, Tuple
 
 import geopandas as gpd
 import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from numpy.typing import NDArray
-from scipy.interpolate import interpolate
+from scipy.interpolate import interp1d
 from shapely.geometry import LineString, Point, Polygon
 
 from pyvibracore.results.plot_utils import _north_arrow, _scalebar
@@ -34,9 +35,46 @@
     "5 days": [0.48, 6, 0.32],
     ">= 6 days; <26 days": [0.4, 6, 0.3],
     ">= 26 days; <78 days": [0.3, 6, 0.2],
+    "Unlimited": {
+        "short-term": {
+            "woonfunctie": [0.2, 0.8, 0.1],
+            "gezondheidsfunctie": [0.2, 0.8, 0.1],
+            "onderwijsfunctie": [0.3, 1.2, 0.15],
+        },
+        "repeated-short-term": {
+            "woonfunctie": [0.2, 0.8, 0.1],
+            "gezondheidsfunctie": [0.2, 0.8, 0.1],
+            "onderwijsfunctie": [0.3, 1.2, 0.15],
+        },
+        "continuous": {
+            "woonfunctie": [0.1, 0.4, 0.05],
+            "gezondheidsfunctie": [0.1, 0.4, 0.05],
+            "onderwijsfunctie": [0.15, 0.6, 0.07],
+        },
+    },
 }
 
 
+def _get_target_value(
+    vibration_type: Literal["short-term", "repeated-short-term", "continuous"],
+    building_function: str,
+) -> dict:
+    if "woonfunctie" in building_function:
+        _building_function = "woonfunctie"
+    elif "gezondheidsfunctie" in building_function:
+        _building_function = "gezondheidsfunctie"
+    elif "onderwijsfunctie" in building_function:
+        _building_function = "onderwijsfunctie"
+    else:
+        _building_function = "other"
+
+    _body = deepcopy(TARGET_VALUE)
+    _body["Unlimited"] = TARGET_VALUE["Unlimited"][vibration_type].get(  # type: ignore
+        _building_function, [np.nan, np.nan, np.nan]
+    )
+    return _body
+
+
 def _nuisance_prediction(
     target_value_one: List[float] | NDArray | Sequence,
     target_value_two: List[float] | NDArray | Sequence,
@@ -77,21 +115,21 @@ def _nuisance_prediction(
     ).drop_duplicates(subset=["vibrationVelocity_per", "vibrationVelocity_eff"])
 
     # interpolate and predict
-    f_eff = interpolate.interp1d(
+    f_eff = interp1d(
         df["vibrationVelocity_eff"],
         df["distance"],
-        kind="cubic",
+        kind="linear",
         assume_sorted=False,
         fill_value="extrapolate",
     )
     target_value_one_spaces = f_eff(target_value_one)
     target_value_two_spaces = f_eff(target_value_two)
 
     # interpolate and predict
-    f_per = interpolate.interp1d(
+    f_per = interp1d(
         df["vibrationVelocity_per"],
         df["distance"],
-        kind="cubic",
+        kind="linear",
         assume_sorted=False,
         fill_value="extrapolate",
     )
@@ -107,31 +145,53 @@ def _nuisance_prediction(
 
 
 def df_nuisance(
+    buildings: gpd.GeoDataFrame,
     response_dict: dict,
-    cfc: float,
-    u_eff: float,
-    period: float,
+    building_name: str,
+    vibration_type: Literal[
+        "short-term", "repeated-short-term", "continuous"
+    ] = "continuous",
+    installation_type: Literal["vibrate", "driving"] = "vibrate",
+    period: float = 10,
 ) -> pd.DataFrame:
     """
     Get a DataFrame that holds the distance of the different durations
 
     Parameters
     ----------
+    buildings:
+        GeoDataFrame of the input buildings
     response_dict:
         response of the single prepal or cur166 endpoint.
-    u_eff:
-        Vibration transfer to part of a building (u_eff) CUR 166-1997 page 514 [-]
-    cfc:
-        Vibration transfer to part of a building (Cfc) CUR 166-1997 table 5.20 or 5.21 [-]
+    building_name:
+        name of the building
+    vibration_type
+        Based on the SBR A table 10.4.
+    installation_type
+        Based on CUR 166 3rd edition table 5.20 or 5.21
+    period:
+        Operating period of the building code [hours]
     period:
         Operating period of the building code [hours]
 
     Returns
     -------
     dataframe
     """
+    building = buildings.get(buildings["name"] == building_name)
+    if building.empty:
+        raise ValueError(f"No buildings with name {building_name}.")
+
+    levels = _get_target_value(
+        vibration_type,
+        building_function=building["gebruiksdoel"].item(),
+    ).values()
     arr = np.array(response_dict["data"]["vibrationVelocity"])
-    a_one, a_two, a_three = [*zip(*TARGET_VALUE.values())]
+    a_one, a_two, a_three = [*zip(*levels)]
+
+    # safety factors
+    cfc = CFC_FACTOR_FLOORS[installation_type][building["material"].item()]["Cfc"]
+    u_eff = 0.64 if vibration_type == "continuous" else 0.42
 
     distances = _nuisance_prediction(
         target_value_one=a_one,
@@ -144,9 +204,9 @@ def df_nuisance(
 
     return pd.DataFrame(
         {
-            "labels": TARGET_VALUE.keys(),
-            "distance": distances,
-        }
+            building_name: distances,
+        },
+        index=list(TARGET_VALUE.keys()),
     )
 
 
@@ -155,9 +215,11 @@ def map_nuisance(
     source_location: Point | LineString | Polygon,
     building_name: str,
     response_dict: dict,
-    cfc: float,
-    u_eff: float,
-    period: float,
+    vibration_type: Literal[
+        "short-term", "repeated-short-term", "continuous"
+    ] = "continuous",
+    installation_type: Literal["vibrate", "driving"] = "vibrate",
+    period: float = 10,
     title: str = "Legend:",
     figsize: Tuple[float, float] = (10.0, 12.0),
     settings: dict | None = None,
@@ -176,10 +238,10 @@ def map_nuisance(
         location of the vibration source
     building_name:
         name of the building
-    u_eff:
-        Vibration transfer to part of a building (u_eff) CUR 166-1997 page 514 [-]
-    cfc:
-        Vibration transfer to part of a building (Cfc) CUR 166-1997 table 5.20 or 5.21 [-]
+    vibration_type
+        Based on the SBR A table 10.4.
+    installation_type
+        Based on CUR 166 3rd edition table 5.20 or 5.21
     period:
         Operating period of the building code [hours]
     title:
@@ -259,10 +321,20 @@ def map_nuisance(
     )
 
     # plot contour
-    levels = [TARGET_VALUE[values["label"]] for values in settings["levels"]]
+    levels = [
+        _get_target_value(
+            vibration_type,
+            building_function=building["gebruiksdoel"].item(),
+        )[values["label"]]
+        for values in settings["levels"]
+    ]
     arr = np.array(response_dict["data"]["vibrationVelocity"])
     a_one, a_two, a_three = [*zip(*levels)]
 
+    # safety factors
+    cfc = CFC_FACTOR_FLOORS[installation_type][building["material"].item()]["Cfc"]
+    u_eff = 0.64 if vibration_type == "continuous" else 0.42
+
     distances = _nuisance_prediction(
         target_value_one=a_one,
         target_value_two=a_two,