Source code for stride.optimisation.pipelines.steps.clip

import numpy as np

from ....core import Operator

[docs]class Clip(Operator): """ Clip data between two extreme values. Parameters ---------- min : float, optional Lower value for the clipping, defaults to None (no lower clipping). max : float, optional Upper value for the clipping, defaults to None (no upper clipping). """ def __init__(self, **kwargs): super().__init__(**kwargs) self.min = kwargs.pop('min', None) self.max = kwargs.pop('max', None)
[docs] def forward(self, field, **kwargs): out_field = field.alike(name='clipped_%s' % out_field.extended_data[:] = field.extended_data if self.min is not None or self.max is not None: out_field.extended_data[:] = np.clip(field.extended_data, self.min, self.max) return out_field
[docs] def adjoint(self, d_field, field, **kwargs): raise NotImplementedError('No adjoint implemented for step %s' % self.__class__.__name__)