def __init__(self, order, dims):
"""
:arg order: The total degree to which the quadrature rule is exact
for *interpolation*.
:arg dims: The number of dimensions for the quadrature rule.
2 for quadrature on triangles and 3 for tetrahedra.
"""
if dims == 2:
from modepy.quadrature.vr_quad_data_tri import triangle_data as table
ref_volume = 2
elif dims == 3:
from modepy.quadrature.vr_quad_data_tet import tetrahedron_data as table
ref_volume = 4 / 3
else:
raise ValueError("invalid dimensionality")
from modepy.tools import EQUILATERAL_TO_UNIT_MAP
e2u = EQUILATERAL_TO_UNIT_MAP[dims]
try:
order_table = table[order]
except KeyError:
raise QuadratureRuleUnavailable
nodes = e2u(order_table["points"])
wts = order_table["weights"]
wts = wts * (ref_volume / np.sum(wts))
Quadrature.__init__(self, nodes, wts)
self.exact_to = order_table["quad_degree"]
def __init__(self, order, dimension):
"""
:arg order: A parameter correlated with the total degree of polynomials
that are integrated exactly. (See also :attr:`exact_to`.)
:arg dimension: The number of dimensions for the quadrature rule.
Any positive integer.
"""
s = order
n = dimension
d = 2*s + 1
self.exact_to = d
if dimension == 0:
nodes = np.zeros((dimension, 1))
weights = np.ones(1)
Quadrature.__init__(self, nodes, weights)
return
from pytools import \
generate_decreasing_nonnegative_tuples_summing_to, \
generate_unique_permutations, \
factorial, \
wandering_element
points_to_weights = {}
for i in range(s + 1):
weight = (-1)**i * 2**(-2*s) \
* (d + n - 2*i)**d \
/ factorial(i) \
/ factorial(d + n - i)
for t in generate_decreasing_nonnegative_tuples_summing_to(s - i, n + 1):
for beta in generate_unique_permutations(t):
denominator = d + n - 2*i
point = tuple(
_simplify_fraction((2*beta_i + 1, denominator))
for beta_i in beta)
points_to_weights[point] = \
points_to_weights.get(point, 0) + weight
from operator import add
vertices = ([-1 * np.ones((n,))]
+ [np.array(x)
for x in wandering_element(n, landscape=-1, wanderer=1)])
nodes = []
weights = []
dim_factor = 2**n
for p, w in points_to_weights.items():
real_p = reduce(add, (a/b * v for (a, b), v in zip(p, vertices)))
nodes.append(real_p)
weights.append(dim_factor * w)
Quadrature.__init__(self, np.array(nodes).T, np.array(weights))
def __init__(self, order, dims):
"""
:arg order: The total degree to which the quadrature rule is exact.
:arg dims: The number of dimensions for the quadrature rule.
2 for quadrature on triangles and 3 for tetrahedra.
"""
if dims == 2:
from modepy.quadrature.xg_quad_data import triangle_table as table
elif dims == 3:
from modepy.quadrature.xg_quad_data import tetrahedron_table as table
else:
raise ValueError("invalid dimensionality")
try:
order_table = table[order]
except KeyError:
raise QuadratureRuleUnavailable
from modepy.tools import EQUILATERAL_TO_UNIT_MAP
e2u = EQUILATERAL_TO_UNIT_MAP[dims]
nodes = e2u(order_table["points"].T)
wts = order_table["weights"] * e2u.jacobian
Quadrature.__init__(self, nodes, wts)
self.exact_to = order
def __init__(self, order, dims):
"""
:arg order: The total degree to which the quadrature rule is exact.
:arg dims: The number of dimensions for the quadrature rule.
2 for quadrature on triangles and 3 for tetrahedra.
"""
if dims == 2:
from modepy.quadrature.xg_quad_data import triangle_table as table
elif dims == 3:
from modepy.quadrature.xg_quad_data import tetrahedron_table as table
else:
raise ValueError("invalid dimensionality")
try:
order_table = table[order]
except KeyError:
raise QuadratureRuleUnavailable
from modepy.tools import EQUILATERAL_TO_UNIT_MAP
e2u = EQUILATERAL_TO_UNIT_MAP[dims]
nodes = e2u(order_table["points"].T)
wts = order_table["weights"]*e2u.jacobian
Quadrature.__init__(self, nodes, wts)
self.exact_to = order
def __init__(self, order, dims):
"""
:arg order: The total degree to which the quadrature rule is exact
for *interpolation*.
:arg dims: The number of dimensions for the quadrature rule.
2 for quadrature on triangles and 3 for tetrahedra.
"""
if dims == 2:
from modepy.quadrature.vr_quad_data_tri import triangle_data as table
ref_volume = 2
elif dims == 3:
from modepy.quadrature.vr_quad_data_tet import tetrahedron_data as table
ref_volume = 4/3
else:
raise ValueError("invalid dimensionality")
from modepy.tools import EQUILATERAL_TO_UNIT_MAP
e2u = EQUILATERAL_TO_UNIT_MAP[dims]
try:
order_table = table[order]
except KeyError:
raise QuadratureRuleUnavailable
nodes = e2u(order_table["points"])
wts = order_table["weights"]
wts = wts * (ref_volume/np.sum(wts))
Quadrature.__init__(self, nodes, wts)
self.exact_to = order_table["quad_degree"]
def __init__(self, order, dimension):
"""
:arg order: A parameter correlated with the total degree of polynomials
that are integrated exactly. (See also :attr:`exact_to`.)
:arg dimension: The number of dimensions for the quadrature rule.
Any positive integer.
"""
s = order
n = dimension
d = 2*s+1
self.exact_to = d
if dimension == 0:
nodes = np.zeros((dimension, 1))
weights = np.ones(1)
Quadrature.__init__(self, nodes, weights)
return
from pytools import \
generate_decreasing_nonnegative_tuples_summing_to, \
generate_unique_permutations, \
factorial, \
wandering_element
points_to_weights = {}
for i in range(s+1):
weight = (-1)**i * 2**(-2*s) \
* (d + n-2*i)**d \
/ factorial(i) \
/ factorial(d+n-i)
for t in generate_decreasing_nonnegative_tuples_summing_to(s-i, n+1):
for beta in generate_unique_permutations(t):
denominator = d+n-2*i
point = tuple(
_simplify_fraction((2*beta_i+1, denominator))
for beta_i in beta)
points_to_weights[point] = \
points_to_weights.get(point, 0) + weight
from operator import add
vertices = [-1 * np.ones((n,))] \
+ [np.array(x)
for x in wandering_element(n, landscape=-1, wanderer=1)]
nodes = []
weights = []
dim_factor = 2**n
for p, w in six.iteritems(points_to_weights):
real_p = reduce(add, (a/b*v for (a, b), v in zip(p, vertices)))
nodes.append(real_p)
weights.append(dim_factor*w)
Quadrature.__init__(self, np.array(nodes).T, np.array(weights))
def __init__(self, alpha, beta, N, backend=None): # noqa
# default backend
if backend is None:
backend = 'builtin'
if backend == 'builtin':
x, w = self.compute_weights_and_nodes(N, alpha, beta)
elif backend == 'scipy':
from scipy.special.orthogonal import roots_jacobi
x, w = roots_jacobi(N + 1, alpha, beta)
else:
raise NotImplementedError("Unsupported backend: %s" % backend)
self.exact_to = 2*N + 1
Quadrature.__init__(self, x, w)
def __init__(self, alpha, beta, N, backend=None): # noqa: N803
r"""
:arg backend: Either ``"builtin"`` or ``"scipy"``. When the
``"builtin"`` backend is in use, there is an additional
requirement that :math:`\alpha + \beta \ne -1`, with the exception
of the Chebyshev quadrature :math:`\alpha = \beta = -1/2`. The
``"scipy"`` backend has no such restriction.
"""
if backend is None:
backend = "builtin"
if backend == "builtin":
x, w = self.compute_weights_and_nodes(N, alpha, beta)
elif backend == "scipy":
from scipy.special.orthogonal import roots_jacobi
x, w = roots_jacobi(N + 1, alpha, beta)
else:
raise NotImplementedError("Unsupported backend: %s" % backend)
self.exact_to = 2 * N + 1
Quadrature.__init__(self, x, w)
def __init__(self, alpha, beta, N): # noqa
x, w = self.compute_weights_and_nodes(N, alpha, beta)
Quadrature.__init__(self, x, w)
def __init__(self, N): # noqa
x, w = _fejer(N, 'cc')
self.exact_to = N
Quadrature.__init__(self, x, w)