"""This module contains all the transforms that can be added to a model. These transforms are applied after the simulation, to incorporate nonlinear effects. """ import numpy as np from pandas import DataFrame from .decorators import set_parameter [docs]class ThresholdTransform: """ThresholdTransform lowers the simulation when it exceeds a certain value. Parameters ---------- value : float, optional The starting value above which the simulation is lowered vmin : float, optional The minimum value above which the simulation is lowered vmin : float, optional The maximum value above which the simulation is lowered name: str, optional Name of the transform nparam : int, optional The number of parameters. Default is nparam=2. The first parameter then is the threshold, and the second parameter is the factor with which the simulation is lowered. Notes ----- In geohydrology this transform can be used in a situation where the groundwater level reaches the surface level and forms a lake. Because of the larger storage of the lake, the (groundwater) level then rises slower when it rains. """ _name = "ThresholdTransform" [docs] def __init__(self, value=np.nan, vmin=np.nan, vmax=np.nan, name='ThresholdTransform', nparam=2): self.value = value self.vmin = vmin self.vmax = vmax self.name = name self.nparam = nparam self.parameters = DataFrame( columns=['initial', 'pmin', 'pmax', 'vary', 'name']) [docs] def set_model(self, ml): obs = ml.observations() if np.isnan(self.value): self.value = obs.min() + 0.75 * (obs.max() - obs.min()) if np.isnan(self.vmin): self.vmin = obs.min() + 0.5 * (obs.max() - obs.min()) if np.isnan(self.vmax): self.vmax = obs.max() self.set_init_parameters() [docs] def set_init_parameters(self): self.parameters.loc[self.name + '_1'] = ( self.value, self.vmin, self.vmax, True, self.name) if self.nparam == 2: self.parameters.loc[self.name + '_2'] = ( 0.5, 0., 1., True, self.name) @set_parameter def _set_initial(self, name, value): """Internal method to set the initial parameter value. Notes ----- The preferred method for parameter setting is through the model. """ self.parameters.loc[name, 'initial'] = value @set_parameter def _set_pmin(self, name, value): """Internal method to set the lower bound of the parameter value. Notes ----- The preferred method for parameter setting is through the model. """ self.parameters.loc[name, 'pmin'] = value @set_parameter def _set_pmax(self, name, value): """Internal method to set the upper bound of the parameter value. Notes ----- The preferred method for parameter setting is through the model. """ self.parameters.loc[name, 'pmax'] = value @set_parameter def _set_vary(self, name, value): """Internal method to set if the parameter is varied during optimization. Notes ----- The preferred method for parameter setting is through the model. """ self.parameters.loc[name, 'vary'] = bool(value) [docs] def simulate(self, h, p): if self.nparam == 1: # value above a threshold p[0] are equal to the threshold h[h > p[0]] = p[0] elif self.nparam == 2: # values above a threshold p[0] are scaled by p[1] mask = h > p[0] h[mask] = p[0] + p[1] * (h[mask] - p[0]) else: raise ValueError('Not yet implemented yet') return h [docs] def to_dict(self): data = { "transform": self._name, "value": self.value, "vmin": self.vmin, "vmax": self.vmax, "name": self.name, 'nparam': self.nparam } return data