"""The samples template."""
from equadratures.sampling_methods.montecarlo import Montecarlo
from equadratures.sampling_methods.tensorgrid import Tensorgrid
from equadratures.sampling_methods.sparsegrid import Sparsegrid
from equadratures.sampling_methods.userdefined import Userdefined
from equadratures.sampling_methods.induced import Induced
from equadratures.sampling_methods.sampling_template import Sampling
import numpy as np
[docs]class Quadrature(object):
""" The class defines a Sampling object. It serves as a template for all sampling methodologies.
Parameters
----------
parameters : list
A list of parameters, where each element of the list is an instance of the Parameter class.
basis : Basis
An instance of the Basis class corresponding to the multi-index set used.
points : numpy.ndarray
Array containing the quadrature points.
mesh : numpy.ndarray
Description needed.
corr : numpy.ndarray, optional
Description needed.
oversampling : float, optional
Description needed.
"""
def __init__(self, parameters, basis, points, mesh, corr=None, oversampling=7.0):
self.parameters = parameters
self.basis = basis
self.points = points
self.mesh = mesh
if self.mesh.lower() == 'tensor-grid' or self.mesh.lower() == 'univariate':
self.samples = Tensorgrid(self.parameters, self.basis)
self.list = None
elif self.mesh.lower() == 'sparse-grid':
self.samples = Sparsegrid(self.parameters, self.basis)
self.list = self.samples.tensor_product_list
self.sparse_weights = self.samples.sparse_weights
elif self.mesh.lower() == 'monte-carlo':
self.samples = Montecarlo(self.parameters, self.basis, corr, oversampling)
self.list = None
elif self.mesh.lower() == 'induced':
self.samples = Induced(self.parameters, self.basis)
elif self.mesh.lower() == 'user-defined':
self.samples = Userdefined(self.parameters, self.basis, self.points)
else:
error_message()
[docs] def get_points(self):
""" Returns the quadrature points.
Returns
-------
numpy.ndarray
Array of quadrature points.
"""
return self.samples.points
[docs] def get_weights(self):
""" Returns the quadrature weights.
Returns
-------
numpy.ndarray
Array of quadrature weights
"""
return self.samples.weights
[docs] def get_points_and_weights(self):
""" Returns the quadrature points and weights.
Returns
-------
tuple
Tuple (points,weights) containing two numpy.ndarray's; the quadrature points and weights.
"""
return self.samples.points, self.samples.weights
def error_message():
raise ValueError( 'Oh no. Something went wrong in quadrature.py!')
