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🎨 Format Python code with psf/black

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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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34
pina/geometry/simplex.py
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@@ -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)