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This commit is contained in:
ndem0
2024-02-09 11:25:00 +00:00
committed by Nicola Demo
parent 591aeeb02b
commit cbb43a5392
64 changed files with 1323 additions and 955 deletions
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11
pina/geometry/__init__.py
View File

@@ -1,6 +1,13 @@
__all__ = [
'Location', 'CartesianDomain', 'EllipsoidDomain', 'Union', 'Intersection',
'Exclusion', 'Difference', 'OperationInterface', 'SimplexDomain'
"Location",
"CartesianDomain",
"EllipsoidDomain",
"Union",
"Intersection",
"Exclusion",
"Difference",
"OperationInterface",
"SimplexDomain",
]
from .location import Location

44
pina/geometry/cartesian.py
View File

@@ -72,17 +72,17 @@ class CartesianDomain(Location):
:rtype: torch.Tensor
"""
dim = bounds.shape[0]
if mode in ['chebyshev', 'grid'] and dim != 1:
raise RuntimeError('Something wrong in Span...')
if mode in ["chebyshev", "grid"] and dim != 1:
raise RuntimeError("Something wrong in Span...")
if mode == 'random':
if mode == "random":
pts = torch.rand(size=(n, dim))
elif mode == 'chebyshev':
pts = chebyshev_roots(n).mul(.5).add(.5).reshape(-1, 1)
elif mode == 'grid':
elif mode == "chebyshev":
pts = chebyshev_roots(n).mul(0.5).add(0.5).reshape(-1, 1)
elif mode == "grid":
pts = torch.linspace(0, 1, n).reshape(-1, 1)
# elif mode == 'lh' or mode == 'latin':
elif mode in ['lh', 'latin']:
elif mode in ["lh", "latin"]:
pts = torch_lhs(n, dim)
pts *= bounds[:, 1] - bounds[:, 0]
@@ -90,7 +90,7 @@ class CartesianDomain(Location):
return pts
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""Sample routine.
:param n: Number of points to sample, see Note below
@@ -145,7 +145,7 @@ class CartesianDomain(Location):
"""
def _1d_sampler(n, mode, variables):
""" Sample independentely the variables and cross the results"""
"""Sample independentely the variables and cross the results"""
tmp = []
for variable in variables:
if variable in self.range_.keys():
@@ -158,17 +158,18 @@ class CartesianDomain(Location):
result = tmp[0]
for i in tmp[1:]:
result = result.append(i, mode='cross')
result = result.append(i, mode="cross")
for variable in variables:
if variable in self.fixed_.keys():
value = self.fixed_[variable]
pts_variable = torch.tensor([[value]
]).repeat(result.shape[0], 1)
pts_variable = torch.tensor([[value]]).repeat(
result.shape[0], 1
)
pts_variable = pts_variable.as_subclass(LabelTensor)
pts_variable.labels = [variable]
result = result.append(pts_variable, mode='std')
result = result.append(pts_variable, mode="std")
return result
@@ -197,12 +198,13 @@ class CartesianDomain(Location):
for variable in variables:
if variable in self.fixed_.keys():
value = self.fixed_[variable]
pts_variable = torch.tensor([[value]
]).repeat(result.shape[0], 1)
pts_variable = torch.tensor([[value]]).repeat(
result.shape[0], 1
)
pts_variable = pts_variable.as_subclass(LabelTensor)
pts_variable.labels = [variable]
result = result.append(pts_variable, mode='std')
result = result.append(pts_variable, mode="std")
return result
def _single_points_sample(n, variables):
@@ -226,22 +228,22 @@ class CartesianDomain(Location):
result = tmp[0]
for i in tmp[1:]:
result = result.append(i, mode='std')
result = result.append(i, mode="std")
return result
if self.fixed_ and (not self.range_):
return _single_points_sample(n, variables)
if variables == 'all':
if variables == "all":
variables = list(self.range_.keys()) + list(self.fixed_.keys())
if mode in ['grid', 'chebyshev']:
if mode in ["grid", "chebyshev"]:
return _1d_sampler(n, mode, variables)
elif mode in ['random', 'lh', 'latin']:
elif mode in ["random", "lh", "latin"]:
return _Nd_sampler(n, mode, variables)
else:
raise ValueError(f'mode={mode} is not valid.')
raise ValueError(f"mode={mode} is not valid.")
def is_inside(self, point, check_border=False):
"""Check if a point is inside the ellipsoid.

11
pina/geometry/difference_domain.py
View File

@@ -20,7 +20,7 @@ class Difference(OperationInterface):
the dimension of the geometry space.
:param list geometries: A list of geometries from ``pina.geometry``
such as ``EllipsoidDomain`` or ``CartesianDomain``. The first
such as ``EllipsoidDomain`` or ``CartesianDomain``. The first
geometry in the list is the geometry from which points are
sampled. The rest of the geometries are the geometries that
are excluded from the first geometry to find the difference.
@@ -39,7 +39,7 @@ class Difference(OperationInterface):
Check if a point is inside the ``Difference`` domain.
:param point: Point to be checked.
:type point: torch.Tensor
:type point: torch.Tensor
:param bool check_border: If ``True``, the border is considered inside.
:return: ``True`` if the point is inside the Exclusion domain, ``False`` otherwise.
:rtype: bool
@@ -49,7 +49,7 @@ class Difference(OperationInterface):
return False
return self.geometries[0].is_inside(point, check_border)
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""
Sample routine for ``Difference`` domain.
@@ -77,9 +77,10 @@ class Difference(OperationInterface):
5
"""
if mode != 'random':
if mode != "random":
raise NotImplementedError(
f'{mode} is not a valid mode for sampling.')
f"{mode} is not a valid mode for sampling."
)
sampled = []

38
pina/geometry/ellipsoid.py
View File

@@ -85,7 +85,7 @@ class EllipsoidDomain(Location):
.. note::
When ``sample_surface`` in the ``__init()__``
is set to ``True``, then the method only checks
is set to ``True``, then the method only checks
points on the surface, and not inside the domain.
:param point: Point to be checked.
@@ -103,7 +103,7 @@ class EllipsoidDomain(Location):
# get axis ellipse as tensors
list_dict_vals = list(self._axis.values())
tmp = torch.tensor(list_dict_vals, dtype=torch.float)
ax_sq = LabelTensor(tmp.reshape(1, -1)**2, self.variables)
ax_sq = LabelTensor(tmp.reshape(1, -1) ** 2, self.variables)
# get centers ellipse as tensors
list_dict_vals = list(self._centers.values())
@@ -111,16 +111,18 @@ class EllipsoidDomain(Location):
centers = LabelTensor(tmp.reshape(1, -1), self.variables)
if not all([i in ax_sq.labels for i in point.labels]):
raise ValueError('point labels different from constructor'
f' dictionary labels. Got {point.labels},'
f' expected {ax_sq.labels}.')
raise ValueError(
"point labels different from constructor"
f" dictionary labels. Got {point.labels},"
f" expected {ax_sq.labels}."
)
# point square + shift center
point_sq = (point - centers).pow(2)
point_sq.labels = point.labels
# calculate ellispoid equation
eqn = torch.sum(point_sq.extract(ax_sq.labels) / ax_sq) - 1.
eqn = torch.sum(point_sq.extract(ax_sq.labels) / ax_sq) - 1.0
# if we have sampled only the surface, we check that the
# point is inside the surface border only
@@ -160,8 +162,9 @@ class EllipsoidDomain(Location):
dim = len(variables)
# get values center
pairs_center = [(k, v) for k, v in self._centers.items()
if k in variables]
pairs_center = [
(k, v) for k, v in self._centers.items() if k in variables
]
_, values_center = map(list, zip(*pairs_center))
values_center = torch.tensor(values_center)
@@ -171,7 +174,7 @@ class EllipsoidDomain(Location):
values_axis = torch.tensor(values_axis)
# Sample in the unit sphere
if mode == 'random':
if mode == "random":
# 1. Sample n points from the surface of a unit sphere
# 2. Scale each dimension using torch.rand()
# (a random number between 0-1) so that it lies within
@@ -192,7 +195,7 @@ class EllipsoidDomain(Location):
return pts
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""Sample routine.
:param int n: Number of points to sample in the shape.
@@ -238,12 +241,13 @@ class EllipsoidDomain(Location):
for variable in variables:
if variable in self.fixed_.keys():
value = self.fixed_[variable]
pts_variable = torch.tensor([[value]
]).repeat(result.shape[0], 1)
pts_variable = torch.tensor([[value]]).repeat(
result.shape[0], 1
)
pts_variable = pts_variable.as_subclass(LabelTensor)
pts_variable.labels = [variable]
result = result.append(pts_variable, mode='std')
result = result.append(pts_variable, mode="std")
return result
def _single_points_sample(n, variables):
@@ -267,17 +271,17 @@ class EllipsoidDomain(Location):
result = tmp[0]
for i in tmp[1:]:
result = result.append(i, mode='std')
result = result.append(i, mode="std")
return result
if self.fixed_ and (not self.range_):
return _single_points_sample(n, variables)
if variables == 'all':
if variables == "all":
variables = list(self.range_.keys()) + list(self.fixed_.keys())
if mode in ['random']:
if mode in ["random"]:
return _Nd_sampler(n, mode, variables)
else:
raise NotImplementedError(f'mode={mode} is not implemented.')
raise NotImplementedError(f"mode={mode} is not implemented.")

11
pina/geometry/exclusion_domain.py
View File

@@ -37,7 +37,7 @@ class Exclusion(OperationInterface):
Check if a point is inside the ``Exclusion`` domain.
:param point: Point to be checked.
:type point: torch.Tensor
:type point: torch.Tensor
:param bool check_border: If ``True``, the border is considered inside.
:return: ``True`` if the point is inside the Exclusion domain, ``False`` otherwise.
:rtype: bool
@@ -48,7 +48,7 @@ class Exclusion(OperationInterface):
flag += 1
return flag == 1
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""
Sample routine for ``Exclusion`` domain.
@@ -76,9 +76,10 @@ class Exclusion(OperationInterface):
5
"""
if mode != 'random':
if mode != "random":
raise NotImplementedError(
f'{mode} is not a valid mode for sampling.')
f"{mode} is not a valid mode for sampling."
)
sampled = []
@@ -104,4 +105,4 @@ class Exclusion(OperationInterface):
sampled_points.append(sample)
sampled += sampled_points
return LabelTensor(torch.cat(sampled), labels=self.variables)
return LabelTensor(torch.cat(sampled), labels=self.variables)

11
pina/geometry/intersection_domain.py
View File

@@ -20,7 +20,7 @@ class Intersection(OperationInterface):
with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N`
the dimension of the geometry space.
:param list geometries: A list of geometries from ``pina.geometry``
:param list geometries: A list of geometries from ``pina.geometry``
such as ``EllipsoidDomain`` or ``CartesianDomain``. The intersection
will be taken between all the geometries in the list. The resulting
geometry will be the intersection of all the geometries in the list.
@@ -39,7 +39,7 @@ class Intersection(OperationInterface):
Check if a point is inside the ``Intersection`` domain.
:param point: Point to be checked.
:type point: torch.Tensor
:type point: torch.Tensor
:param bool check_border: If ``True``, the border is considered inside.
:return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise.
:rtype: bool
@@ -50,7 +50,7 @@ class Intersection(OperationInterface):
flag += 1
return flag == len(self.geometries)
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""
Sample routine for ``Intersection`` domain.
@@ -78,9 +78,10 @@ class Intersection(OperationInterface):
5
"""
if mode != 'random':
if mode != "random":
raise NotImplementedError(
f'{mode} is not a valid mode for sampling.')
f"{mode} is not a valid mode for sampling."
)
sampled = []

11
pina/geometry/operation_interface.py
View File

@@ -26,7 +26,7 @@ class OperationInterface(Location, metaclass=ABCMeta):
@property
def geometries(self):
"""
"""
The geometries to perform set operation.
"""
return self._geometries
@@ -40,15 +40,15 @@ class OperationInterface(Location, metaclass=ABCMeta):
:rtype: list[str]
"""
return self.geometries[0].variables
@ abstractmethod
@abstractmethod
def is_inside(self, point, check_border=False):
"""
Check if a point is inside the resulting domain after
a set operation is applied.
:param point: Point to be checked.
:type point: torch.Tensor
:type point: torch.Tensor
:param bool check_border: If ``True``, the border is considered inside.
:return: ``True`` if the point is inside the Intersection domain, ``False`` otherwise.
:rtype: bool
@@ -64,4 +64,5 @@ class OperationInterface(Location, metaclass=ABCMeta):
for geometry in geometries:
if geometry.variables != geometries[0].variables:
raise NotImplementedError(
f'The geometries need to have same dimensions and labels.')
f"The geometries need to have same dimensions and labels."
)

34
pina/geometry/simplex.py
View File

@@ -94,7 +94,7 @@ class SimplexDomain(Location):
# respective coord bounded by the lowest and highest values
span_dict[coord] = [
float(sorted_vertices[0][i]),
float(sorted_vertices[-1][i])
float(sorted_vertices[-1][i]),
]
return CartesianDomain(span_dict)
@@ -120,16 +120,19 @@ class SimplexDomain(Location):
"""
if not all(label in self.variables for label in point.labels):
raise ValueError("Point labels different from constructor"
f" dictionary labels. Got {point.labels},"
f" expected {self.variables}.")
raise ValueError(
"Point labels different from constructor"
f" dictionary labels. Got {point.labels},"
f" expected {self.variables}."
)
point_shift = point - self._vertices_matrix[-1]
point_shift = point_shift.tensor.reshape(-1, 1)
# compute barycentric coordinates
lambda_ = torch.linalg.solve(self._vectors_shifted * 1.0,
point_shift * 1.0)
lambda_ = torch.linalg.solve(
self._vectors_shifted * 1.0, point_shift * 1.0
)
lambda_1 = 1.0 - torch.sum(lambda_)
lambdas = torch.vstack([lambda_, lambda_1])
@@ -137,8 +140,9 @@ class SimplexDomain(Location):
if not check_border:
return all(torch.gt(lambdas, 0.0)) and all(torch.lt(lambdas, 1.0))
return all(torch.ge(lambdas, 0)) and (any(torch.eq(lambdas, 0))
or any(torch.eq(lambdas, 1)))
return all(torch.ge(lambdas, 0)) and (
any(torch.eq(lambdas, 0)) or any(torch.eq(lambdas, 1))
)
def _sample_interior_randomly(self, n, variables):
"""
@@ -163,9 +167,9 @@ class SimplexDomain(Location):
sampled_points = []
while len(sampled_points) < n:
sampled_point = self._cartesian_bound.sample(n=1,
mode="random",
variables=variables)
sampled_point = self._cartesian_bound.sample(
n=1, mode="random", variables=variables
)
if self.is_inside(sampled_point, self._sample_surface):
sampled_points.append(sampled_point)
@@ -196,9 +200,9 @@ class SimplexDomain(Location):
# extract number of vertices
number_of_vertices = self._vertices_matrix.shape[0]
# extract idx lambda to set to zero randomly
idx_lambda = torch.randint(low=0,
high=number_of_vertices,
size=(1, ))
idx_lambda = torch.randint(
low=0, high=number_of_vertices, size=(1,)
)
# build lambda vector
# 1. sampling [1, 2)
lambdas = torch.rand((number_of_vertices, 1))
@@ -236,4 +240,4 @@ class SimplexDomain(Location):
else:
raise NotImplementedError(f"mode={mode} is not implemented.")
return LabelTensor(sample_pts, labels=self.variables)
return LabelTensor(sample_pts, labels=self.variables)

10
pina/geometry/union_domain.py
View File

@@ -20,7 +20,7 @@ class Union(OperationInterface):
with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N`
the dimension of the geometry space.
:param list geometries: A list of geometries from ``pina.geometry``
:param list geometries: A list of geometries from ``pina.geometry``
such as ``EllipsoidDomain`` or ``CartesianDomain``.
:Example:
@@ -50,7 +50,7 @@ class Union(OperationInterface):
return True
return False
def sample(self, n, mode='random', variables='all'):
def sample(self, n, mode="random", variables="all"):
"""
Sample routine for ``Union`` domain.
@@ -93,8 +93,10 @@ class Union(OperationInterface):
# different than zero. Notice that len(geometries) is
# always smaller than remaider.
sampled_points.append(
geometry.sample(num_points + int(i < remainder), mode,
variables))
geometry.sample(
num_points + int(i < remainder), mode, variables
)
)
# in case number of sampled points is smaller than the number of geometries
if len(sampled_points) >= n:
break