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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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78
pina/problem/abstract_problem.py
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@@ -1,4 +1,5 @@
""" Module for AbstractProblem class """
from abc import ABCMeta, abstractmethod
from ..utils import merge_tensors, check_consistency
import torch
@@ -40,13 +41,13 @@ class AbstractProblem(metaclass=ABCMeta):
"""
variables = []
if hasattr(self, 'spatial_variables'):
if hasattr(self, "spatial_variables"):
variables += self.spatial_variables
if hasattr(self, 'temporal_variable'):
if hasattr(self, "temporal_variable"):
variables += self.temporal_variable
if hasattr(self, 'parameters'):
if hasattr(self, "parameters"):
variables += self.parameters
if hasattr(self, 'custom_variables'):
if hasattr(self, "custom_variables"):
variables += self.custom_variables
return variables
@@ -62,9 +63,9 @@ class AbstractProblem(metaclass=ABCMeta):
:rtype: list[Location]
"""
domains = [
getattr(self, f'{t}_domain')
for t in ['spatial', 'temporal', 'parameter']
if hasattr(self, f'{t}_domain')
getattr(self, f"{t}_domain")
for t in ["spatial", "temporal", "parameter"]
if hasattr(self, f"{t}_domain")
]
if len(domains) == 1:
@@ -77,7 +78,7 @@ class AbstractProblem(metaclass=ABCMeta):
[domain.update(d) for d in domains]
return domain
else:
raise RuntimeError('different domains')
raise RuntimeError("different domains")
@input_variables.setter
def input_variables(self, variables):
@@ -105,24 +106,27 @@ class AbstractProblem(metaclass=ABCMeta):
"""
for condition_name in self.conditions:
condition = self.conditions[condition_name]
if hasattr(condition, 'input_points'):
if hasattr(condition, "input_points"):
samples = condition.input_points
self.input_pts[condition_name] = samples
self._have_sampled_points[condition_name] = True
if hasattr(self, 'unknown_parameter_domain'):
if hasattr(self, "unknown_parameter_domain"):
# initialize the unknown parameters of the inverse problem given
# the domain the user gives
self.unknown_parameters = {}
for i, var in enumerate(self.unknown_variables):
range_var = self.unknown_parameter_domain.range_[var]
tensor_var = torch.rand(1, requires_grad=True) * range_var[1] + range_var[0]
self.unknown_parameters[var] = torch.nn.Parameter(tensor_var)
tensor_var = (
torch.rand(1, requires_grad=True) * range_var[1]
+ range_var[0]
)
self.unknown_parameters[var] = torch.nn.Parameter(
tensor_var
)
def discretise_domain(self,
n,
mode='random',
variables='all',
locations='all'):
def discretise_domain(
self, n, mode="random", variables="all", locations="all"
):
"""
Generate a set of points to span the `Location` of all the conditions of
the problem.
@@ -157,28 +161,32 @@ class AbstractProblem(metaclass=ABCMeta):
# check consistency mode
check_consistency(mode, str)
if mode not in ['random', 'grid', 'lh', 'chebyshev', 'latin']:
raise TypeError(f'mode {mode} not valid.')
if mode not in ["random", "grid", "lh", "chebyshev", "latin"]:
raise TypeError(f"mode {mode} not valid.")
# check consistency variables
if variables == 'all':
if variables == "all":
variables = self.input_variables
else:
check_consistency(variables, str)
if sorted(variables) != sorted(self.input_variables):
TypeError(f'Wrong variables for sampling. Variables ',
f'should be in {self.input_variables}.')
TypeError(
f"Wrong variables for sampling. Variables ",
f"should be in {self.input_variables}.",
)
# check consistency location
if locations == 'all':
if locations == "all":
locations = [condition for condition in self.conditions]
else:
check_consistency(locations, str)
if sorted(locations) != sorted(self.conditions):
TypeError(f'Wrong locations for sampling. Location ',
f'should be in {self.conditions}.')
TypeError(
f"Wrong locations for sampling. Location ",
f"should be in {self.conditions}.",
)
# sampling
for location in locations:
@@ -208,10 +216,10 @@ class AbstractProblem(metaclass=ABCMeta):
# the condition is sampled if input_pts contains all labels
if sorted(self.input_pts[location].labels) == sorted(
self.input_variables):
self.input_variables
):
self._have_sampled_points[location] = True
def add_points(self, new_points):
"""
Adding points to the already sampled points.
@@ -221,8 +229,10 @@ class AbstractProblem(metaclass=ABCMeta):
"""
if sorted(new_points.keys()) != sorted(self.conditions):
TypeError(f'Wrong locations for new points. Location ',
f'should be in {self.conditions}.')
TypeError(
f"Wrong locations for new points. Location ",
f"should be in {self.conditions}.",
)
for location in new_points.keys():
# extract old and new points
@@ -231,11 +241,14 @@ class AbstractProblem(metaclass=ABCMeta):
# if they don't have the same variables error
if sorted(old_pts.labels) != sorted(new_pts.labels):
TypeError(f'Not matching variables for old and new points '
f'in condition {location}.')
TypeError(
f"Not matching variables for old and new points "
f"in condition {location}."
)
if old_pts.labels != new_pts.labels:
new_pts = torch.hstack(
[new_pts.extract([i]) for i in old_pts.labels])
[new_pts.extract([i]) for i in old_pts.labels]
)
new_pts.labels = old_pts.labels
# merging
@@ -266,4 +279,3 @@ class AbstractProblem(metaclass=ABCMeta):
if not is_sample:
not_sampled.append(condition_name)
return not_sampled