"""
This file is part of CLIMADA.
Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
CLIMADA is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free
Software Foundation, version 3.
CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
---
Define ImpactFunc class.
"""
__all__ = ['ImpactFunc']
import logging
from typing import Optional, Union
import numpy as np
import matplotlib.pyplot as plt
import climada.util.checker as u_check
LOGGER = logging.getLogger(__name__)
[docs]class ImpactFunc():
"""Contains the definition of one impact function.
Attributes
----------
haz_type : str
hazard type acronym (e.g. 'TC')
id : int or str
id of the impact function. Exposures of the same type
will refer to the same impact function id
name : str
name of the ImpactFunc
intensity_unit : str
unit of the intensity
intensity : np.array
intensity values
mdd : np.array
mean damage (impact) degree for each intensity (numbers
in [0,1])
paa : np.array
percentage of affected assets (exposures) for each
intensity (numbers in [0,1])
"""
[docs] def __init__(
self,
haz_type: str = "",
id: Union[str, int] = "",
intensity: Optional[np.ndarray] = None,
mdd: Optional[np.ndarray] = None,
paa: Optional[np.ndarray] = None,
intensity_unit: str = "",
name: str = "",
):
"""Initialization.
Parameters
----------
haz_type : str, optional
Hazard type acronym (e.g. 'TC').
id : int or str, optional
id of the impact function. Exposures of the same type
will refer to the same impact function id.
intensity : np.array, optional
Intensity values. Defaults to empty array.
mdd : np.array, optional
Mean damage (impact) degree for each intensity (numbers
in [0,1]). Defaults to empty array.
paa : np.array, optional
Percentage of affected assets (exposures) for each
intensity (numbers in [0,1]). Defaults to empty array.
intensity_unit : str, optional
Unit of the intensity.
name : str, optional
Name of the ImpactFunc.
"""
self.id = id
self.name = name
self.intensity_unit = intensity_unit
self.haz_type = haz_type
# Followng values defined for each intensity value
self.intensity = intensity if intensity is not None else np.array([])
self.mdd = mdd if mdd is not None else np.array([])
self.paa = paa if paa is not None else np.array([])
[docs] def calc_mdr(self, inten: Union[float, np.ndarray]) -> np.ndarray:
"""Interpolate impact function to a given intensity.
Parameters
----------
inten : float or np.array
intensity, the x-coordinate of the
interpolated values.
Returns
-------
np.array
"""
# return np.interp(inten, self.intensity, self.mdd * self.paa)
return np.interp(inten, self.intensity, self.paa) * \
np.interp(inten, self.intensity, self.mdd)
[docs] def plot(self, axis=None, **kwargs):
"""Plot the impact functions MDD, MDR and PAA in one graph, where
MDR = PAA * MDD.
Parameters
----------
axis : matplotlib.axes._subplots.AxesSubplot, optional
axis to use
kwargs : optional
arguments for plot matplotlib function, e.g. marker='x'
Returns
-------
matplotlib.axes._subplots.AxesSubplot
"""
if axis is None:
_, axis = plt.subplots(1, 1)
title = '%s %s' % (self.haz_type, str(self.id))
if self.name != str(self.id):
title += ': %s' % self.name
axis.set_xlabel('Intensity (' + self.intensity_unit + ')')
axis.set_ylabel('Impact (%)')
axis.set_title(title)
axis.plot(self.intensity, self.mdd * 100, 'b', label='MDD', **kwargs)
axis.plot(self.intensity, self.paa * 100, 'r', label='PAA', **kwargs)
axis.plot(self.intensity, self.mdd * self.paa * 100, 'k--', label='MDR', **kwargs)
axis.set_xlim((self.intensity.min(), self.intensity.max()))
axis.legend()
return axis
[docs] def check(self):
"""Check consistent instance data.
Raises
------
ValueError
"""
num_exp = len(self.intensity)
u_check.size(num_exp, self.mdd, 'ImpactFunc.mdd')
u_check.size(num_exp, self.paa, 'ImpactFunc.paa')
if num_exp == 0:
LOGGER.warning("%s impact function with name '%s' (id=%s) has empty"
" intensity.", self.haz_type, self.name, self.id)
return
[docs] @classmethod
def from_step_impf(
cls,
intensity: tuple[float, float, float],
haz_type: str,
mdd: tuple[float, float] = (0, 1),
paa: tuple[float, float] = (1, 1),
impf_id: int = 1,
**kwargs):
""" Step function type impact function.
By default, everything is destroyed above the step.
Useful for high resolution modelling.
This method modifies self (climada.entity.impact_funcs instance)
by assigning an id, intensity, mdd and paa to the impact function.
Parameters
----------
intensity: tuple(float, float, float)
tuple of 3-intensity numbers: (minimum, threshold, maximum)
haz_type: str
the reference string for the hazard (e.g., 'TC', 'RF', 'WS', ...)
mdd: tuple(float, float)
(min, max) mdd values. The default is (0, 1)
paa: tuple(float, float)
(min, max) paa values. The default is (1, 1)
impf_id : int, optional, default=1
impact function id
kwargs :
keyword arguments passed to ImpactFunc()
Return
------
impf : climada.entity.impact_funcs.ImpactFunc
Step impact function
"""
inten_min, threshold, inten_max = intensity
intensity = np.array([inten_min, threshold, threshold, inten_max])
paa_min, paa_max = paa
paa = np.array([paa_min, paa_min, paa_max, paa_max])
mdd_min, mdd_max = mdd
mdd = np.array([mdd_min, mdd_min, mdd_max, mdd_max])
return cls(haz_type=haz_type, id=impf_id,
intensity=intensity, mdd=mdd, paa=paa, **kwargs)
[docs] def set_step_impf(self, *args, **kwargs):
"""This function is deprecated, use ImpactFunc.from_step_impf instead."""
LOGGER.warning("The use of ImpactFunc.set_step_impf is deprecated." +
"Use ImpactFunc.from_step_impf instead.")
self.__dict__ = ImpactFunc.from_step_impf(*args, **kwargs).__dict__
[docs] @classmethod
def from_sigmoid_impf(
cls,
intensity: tuple[float, float, float],
L: float,
k: float,
x0: float,
haz_type: str,
impf_id: int = 1,
**kwargs):
"""Sigmoid type impact function hinging on three parameter.
This type of impact function is very flexible for any sort of study,
hazard and resolution. The sigmoid is defined as:
.. math:: f(x) = \\frac{L}{1+exp^{-k(x-x0)}}
For more information: https://en.wikipedia.org/wiki/Logistic_function
This method modifies self (climada.entity.impact_funcs instance)
by assining an id, intensity, mdd and paa to the impact function.
Parameters
----------
intensity: tuple(float, float, float)
tuple of 3 intensity numbers along np.arange(min, max, step)
L : float
"top" of sigmoid
k : float
"slope" of sigmoid
x0 : float
intensity value where f(x)==L/2
haz_type: str
the reference string for the hazard (e.g., 'TC', 'RF', 'WS', ...)
impf_id : int, optional, default=1
impact function id
kwargs :
keyword arguments passed to ImpactFunc()
Return
------
impf : climada.entity.impact_funcs.ImpactFunc
Sigmoid impact function
"""
inten_min, inten_max, inten_step = intensity
intensity = np.arange(inten_min, inten_max, inten_step)
paa = np.ones(len(intensity))
mdd = L / (1 + np.exp(-k * (intensity - x0)))
return cls(haz_type=haz_type, id=impf_id, intensity=intensity,
paa=paa, mdd=mdd, **kwargs)
[docs] def set_sigmoid_impf(self, *args, **kwargs):
"""This function is deprecated, use LitPop.from_countries instead."""
LOGGER.warning("The use of ImpactFunc.set_sigmoid_impf is deprecated."
"Use ImpactFunc.from_sigmoid_impf instead.")
self.__dict__ = ImpactFunc.from_sigmoid_impf(*args, **kwargs).__dict__
