Homec4science
Diffusion RationalROMPy (feature_greedy_coarsening) Browse

RROMPy/rrompy/utilities/parameter_sampling/manual_sampler.py
51cd9a0a331afeature_greedy_coarseni…

Last Change
Raw File

manual_sampler.py
View Options

# Copyright (C) 2018 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
import numpy as np
from rrompy.utilities.parameter_sampling.generic_sampler import GenericSampler
from rrompy.utilities.base.types import Np1D, Tuple, List
from rrompy.utilities.warning_manager import warn
__all__ = ['ManualSampler']
class ManualSampler(GenericSampler):
"""Manual generator of sample points."""
def __init__(self, lims:Tuple[Np1D, Np1D], points:Np1D,
scaling : List[callable] = None,
scalingInv : List[callable] = None):
super().__init__(lims = lims, scaling = scaling,
scalingInv = scalingInv)
self.points = points
def __str__(self) -> str:
return "{}[{}]".format(self.name(), "_".join(map(str, self.points)))
def __repr__(self) -> str:
return self.__str__() + " at " + hex(id(self))
def generatePoints(self, n:int) -> Tuple[List[Np1D], Np1D]:
"""Array of quadrature points and array of weights."""
super().generatePoints(None)
size = 1. / n
for j in range(len(self.lims[0])):
a, b = self.lims[0][j], self.lims[1][j]
if self.scaling is not None:
a, b = self.scaling[j](a), self.scaling[j](b)
size *= np.abs(a - b)
if n > len(self.points):
warn(("Requested more points than given. Looping over first "
"points."))
pts = np.tile(self.points,
[np.int(np.ceil(len(self.points) / n))])[: n]
else:
pts = self.points[: n]
return pts, np.ones(n) * size
def refine(self, n:int) -> Tuple[List[Np1D], Np1D]:
"""
Apply refinement. If points are not nested, equivalent to
generatePoints(2 * n - 1).
"""
super().generatePoints(None)
size = 1. / (n - 1)
for j in range(len(self.lims[0])):
a, b = self.lims[0][j], self.lims[1][j]
if self.scaling is not None:
a, b = self.scaling[j](a), self.scaling[j](b)
size *= np.abs(a - b)
if 2 * n - 1 > len(self.points):
warn(("Requested more points than given. Looping over first "
"points."))
pts = np.tile(self.points,
[np.int(np.ceil(len(self.points) / (2 * n - 1)))]
)[n : 2 * n - 1]
else:
pts = self.points[n : 2 * n - 1]
return pts, np.ones(n - 1) * size
c4science · Help