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docs: use more descriptive attribute names
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@RDWimmers
RDWimmers committed May 30, 2024
1 parent d12953e commit cec5b6e
Showing 1 changed file with 66 additions and 48 deletions.
114 changes: 66 additions & 48 deletions src/pyvibracore/results/nuisance.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,19 @@
from __future__ import annotations

from typing import Any, List, Tuple
from typing import Any, List, 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 import interpolate
from scipy.interpolate import interpolate
from shapely.geometry import LineString, Point, Polygon

from pyvibracore.results.plot_utils import _north_arrow, _scalebar

# CUR 166-1997 Tabel 5.20 Factor Cfc voor flooren
# CUR 166-1997 Tabel 5.20 Factor Cfc
CFC_FACTOR_FLOORS = {
"driving": {
"concrete": {"Cfc": 1.4, "Vfc": 0.17},
Expand All @@ -26,6 +25,7 @@
},
}

# SBR-B Trillingen meet en beoordelingsrichtlijnen hinder voor personen in gebouwen [2002] art. 10.5.4 Table 4
TARGET_VALUE = {
"<= 1 day": [0.8, 6, 0.4],
"2 days": [0.72, 6, 0.38],
Expand All @@ -38,31 +38,30 @@


def _nuisance_prediction(
A1: List[float],
A2: List[float],
A3: List[float],
vibrationVelocity_eff: List[float],
vibrationVelocity_per: List[float],
distance: List[float],
target_value_one: List[float] | NDArray | Sequence,
target_value_two: List[float] | NDArray | Sequence,
target_value_three: List[float] | NDArray | Sequence,
vibration_velocity_eff: List[float] | NDArray,
vibration_velocity_per: List[float] | NDArray,
distance: List[float] | NDArray,
) -> NDArray:
"""
Based on the 'Handleiding meten en rekenen industrielawaai' 2004 methode l.
More information: https://open.overheid.nl/Details/ronl-15eb5528-d835-4f6a-b3a1-fc0851b334f9/1
Based on the 'SBR-B Trillingen meet en beoordelingsrichtlijnen hinder voor personen in gebouwen [2002]'.
Parameters
----------
A1:
target value [-]
A2:
target value [-]
A3:
target value [-]
vibrationVelocity_eff:
target_value_one:
target value SBR-B [2002] art. 10.5.1 [-]
target_value_two:
target value SBR-B [2002] art. 10.5.1 [-]
target_value_three:
target value SBR-B [2002] art. 10.5.1 [-]
vibration_velocity_eff:
vibration velocity [mm/s]
vibrationVelocity_per: list
vibration_velocity_per: list
vibration velocity [mm/s]
distance:
distance with respect ot building [m]
distance with respect to building [m]
Returns
-------
Expand All @@ -71,8 +70,8 @@ def _nuisance_prediction(

df = pd.DataFrame(
{
"vibrationVelocity_per": vibrationVelocity_per,
"vibrationVelocity_eff": vibrationVelocity_eff,
"vibrationVelocity_per": vibration_velocity_per,
"vibrationVelocity_eff": vibration_velocity_eff,
"distance": distance,
}
).drop_duplicates(subset=["vibrationVelocity_per", "vibrationVelocity_eff"])
Expand All @@ -85,8 +84,8 @@ def _nuisance_prediction(
assume_sorted=False,
fill_value="extrapolate",
)
A1_d = f_eff(A1)
A2_d = f_eff(A2)
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(
Expand All @@ -96,9 +95,15 @@ def _nuisance_prediction(
assume_sorted=False,
fill_value="extrapolate",
)
A3_d = f_per(A3)
target_value_three_spaces = f_per(target_value_three)

return np.min([np.max([A2_d, A3_d], axis=0), A1_d], axis=0)
return np.min(
[
np.max([target_value_two_spaces, target_value_three_spaces], axis=0),
target_value_one_spaces,
],
axis=0,
)


def df_nuisance(
Expand All @@ -107,11 +112,33 @@ def df_nuisance(
u_eff: float,
period: float,
) -> pd.DataFrame:
"""
Get a DataFrame that holds the distance of the different durations
Parameters
----------
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 [-]
period:
Operating period of the building code [hours]
Returns
-------
dataframe
"""
arr = np.array(response_dict["data"]["vibrationVelocity"])
a_one, a_two, a_three = [*zip(*TARGET_VALUE.values())]

distances = _nuisance_prediction(
*zip(*TARGET_VALUE.values()),
vibrationVelocity_per=arr * cfc * u_eff * np.sqrt(period / 12),
vibrationVelocity_eff=arr * cfc * u_eff,
target_value_one=a_one,
target_value_two=a_two,
target_value_three=a_three,
vibration_velocity_per=arr * cfc * u_eff * np.sqrt(period / 12),
vibration_velocity_eff=arr * cfc * u_eff,
distance=response_dict["data"]["distance"],
)

Expand Down Expand Up @@ -168,28 +195,15 @@ def map_nuisance(
"source_location": {"label": "Trillingsbron", "color": "blue"},
"levels": [
{
"label": ">80 db [0 dagen]",
"level": 80,
"label": "<= 1 day",
"color": "darkred",
},
{
"label": ">75 db [5 dagen]",
"level": 75,
"color": "red",
},
{
"label": ">70 db [15 dagen]",
"level": 70,
"label": ">= 6 days; <26 days",
"color": "orange",
},
{
"label": ">65 db [30 dagen]",
"level": 65,
"color": "darkgreen",
},
{
"label": ">60 db [50 dagen]",
"level": 60,
"label": ">= 26 days; <78 days",
"color": "green",
},
],
Expand Down Expand Up @@ -247,10 +261,14 @@ def map_nuisance(
# plot contour
levels = [TARGET_VALUE[values["label"]] for values in settings["levels"]]
arr = np.array(response_dict["data"]["vibrationVelocity"])
a_one, a_two, a_three = [*zip(*levels)]

distances = _nuisance_prediction(
*zip(*levels),
vibrationVelocity_per=arr * cfc * u_eff * np.sqrt(period / 12),
vibrationVelocity_eff=arr * cfc * u_eff,
target_value_one=a_one,
target_value_two=a_two,
target_value_three=a_three,
vibration_velocity_per=arr * cfc * u_eff * np.sqrt(period / 12),
vibration_velocity_eff=arr * cfc * u_eff,
distance=response_dict["data"]["distance"],
)
colors = [values["color"] for values in settings["levels"]]
Expand Down

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