241 lines
8.4 KiB
Python
241 lines
8.4 KiB
Python
""" Module for AbstractProblem class """
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from abc import ABCMeta, abstractmethod
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from ..utils import check_consistency
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from ..domain import DomainInterface, CartesianDomain
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from ..condition.domain_equation_condition import DomainEquationCondition
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from ..condition import InputPointsEquationCondition
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from copy import deepcopy
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from .. import LabelTensor
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from ..utils import merge_tensors
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class AbstractProblem(metaclass=ABCMeta):
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"""
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The abstract `AbstractProblem` class. All the class defining a PINA Problem
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should be inherited from this class.
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In the definition of a PINA problem, the fundamental elements are:
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the output variables, the condition(s), and the domain(s) where the
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conditions are applied.
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"""
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def __init__(self):
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self._discretised_domains = {}
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# create collector to manage problem data
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# create hook conditions <-> problems
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for condition_name in self.conditions:
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self.conditions[condition_name].problem = self
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self._batching_dimension = 0
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# Store in domains dict all the domains object directly passed to
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# ConditionInterface. Done for back compatibility with PINA <0.2
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if not hasattr(self, "domains"):
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self.domains = {}
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for cond_name, cond in self.conditions.items():
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if isinstance(cond, (DomainEquationCondition,
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InputPointsEquationCondition)):
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if isinstance(cond.domain, DomainInterface):
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self.domains[cond_name] = cond.domain
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cond.domain = cond_name
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# @property
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# def collector(self):
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# return self._collector
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@property
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def batching_dimension(self):
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return self._batching_dimension
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@batching_dimension.setter
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def batching_dimension(self, value):
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self._batching_dimension = value
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@property
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def discretised_domains(self):
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return self._discretised_domains
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# TODO this should be erase when dataloading will interface collector,
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# kept only for back compatibility
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@property
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def input_pts(self):
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to_return = {}
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for cond_name, cond in self.conditions.items():
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if hasattr(cond, "input_points"):
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to_return[cond_name] = cond.input_points
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elif hasattr(cond, "domain"):
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to_return[cond_name] = self._discretised_domains[cond.domain]
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return to_return
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def __deepcopy__(self, memo):
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"""
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Implements deepcopy for the
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:class:`~pina.problem.abstract_problem.AbstractProblem` class.
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:param dict memo: Memory dictionary, to avoid excess copy
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:return: The deep copy of the
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:class:`~pina.problem.abstract_problem.AbstractProblem` class
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:rtype: AbstractProblem
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"""
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cls = self.__class__
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result = cls.__new__(cls)
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memo[id(self)] = result
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for k, v in self.__dict__.items():
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setattr(result, k, deepcopy(v, memo))
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return result
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@property
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def are_all_domains_discretised(self):
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"""
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Check if all the domains are discretised.
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:return: True if all the domains are discretised, False otherwise
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:rtype: bool
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"""
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return all(
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[
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domain in self.discretised_domains
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for domain in self.domains.keys()
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]
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)
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@property
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def input_variables(self):
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"""
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The input variables of the AbstractProblem, whose type depends on the
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type of domain (spatial, temporal, and parameter).
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:return: the input variables of self
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:rtype: list
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"""
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variables = []
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if hasattr(self, "spatial_variables"):
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variables += self.spatial_variables
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if hasattr(self, "temporal_variable"):
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variables += self.temporal_variable
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if hasattr(self, "parameters"):
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variables += self.parameters
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return variables
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@input_variables.setter
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def input_variables(self, variables):
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raise RuntimeError
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@property
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@abstractmethod
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def output_variables(self):
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"""
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The output variables of the problem.
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"""
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pass
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@property
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@abstractmethod
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def conditions(self):
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"""
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The conditions of the problem.
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"""
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return self.conditions
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def discretise_domain(self,
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n=None,
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mode="random",
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domains="all",
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sample_rules=None):
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"""
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Generate a set of points to span the `Location` of all the conditions of
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the problem.
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:param n: Number of points to sample, see Note below
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for reference.
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:type n: int
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:param mode: Mode for sampling, defaults to ``random``.
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Available modes include: random sampling, ``random``;
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latin hypercube sampling, ``latin`` or ``lh``;
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chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
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:param variables: variable(s) to sample, defaults to 'all'.
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:type variables: str | list[str]
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:param domains: problem's domain from where to sample, defaults to 'all'.
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:type domains: str | list[str]
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:Example:
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>>> pinn.discretise_domain(n=10, mode='grid')
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>>> pinn.discretise_domain(n=10, mode='grid', domain=['bound1'])
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>>> pinn.discretise_domain(n=10, mode='grid', variables=['x'])
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.. warning::
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``random`` is currently the only implemented ``mode`` for all geometries, i.e.
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``EllipsoidDomain``, ``CartesianDomain``, ``SimplexDomain`` and the geometries
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compositions ``Union``, ``Difference``, ``Exclusion``, ``Intersection``. The
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modes ``latin`` or ``lh``, ``chebyshev``, ``grid`` are only implemented for
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``CartesianDomain``.
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"""
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# check consistecy n, mode, variables, locations
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if sample_rules is not None:
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check_consistency(sample_rules, dict)
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if mode is not None:
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check_consistency(mode, str)
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check_consistency(domains, (list, str))
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# check correct location
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if domains == "all":
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domains = self.domains.keys()
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elif not isinstance(domains, (list)):
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domains = [domains]
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if n is not None and sample_rules is None:
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self._apply_default_discretization(n, mode, domains)
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if n is None and sample_rules is not None:
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self._apply_custom_discretization(sample_rules, domains)
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elif n is not None and sample_rules is not None:
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raise RuntimeError(
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"You can't specify both n and sample_rules at the same time."
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)
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elif n is None and sample_rules is None:
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raise RuntimeError(
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"You have to specify either n or sample_rules."
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)
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def _apply_default_discretization(self, n, mode, domains):
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for domain in domains:
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self.discretised_domains[domain] = (
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self.domains[domain].sample(n, mode).sort_labels()
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)
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def _apply_custom_discretization(self, sample_rules, domains):
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if sorted(list(sample_rules.keys())) != sorted(self.input_variables):
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raise RuntimeError(
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"The keys of the sample_rules dictionary must be the same as "
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"the input variables."
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)
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for domain in domains:
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if not isinstance(self.domains[domain], CartesianDomain):
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raise RuntimeError(
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"Custom discretisation can be applied only on Cartesian "
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"domains")
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discretised_tensor = []
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for var, rules in sample_rules.items():
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n, mode = rules['n'], rules['mode']
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points = self.domains[domain].sample(n, mode, var)
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discretised_tensor.append(points)
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self.discretised_domains[domain] = merge_tensors(
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discretised_tensor).sort_labels()
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def add_points(self, new_points_dict):
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"""
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Add input points to a sampled condition
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:param new_points_dict: Dictionary of input points (condition_name:
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LabelTensor)
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:raises RuntimeError: if at least one condition is not already sampled
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"""
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for k, v in new_points_dict.items():
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self.discretised_domains[k] = LabelTensor.vstack(
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[self.discretised_domains[k], v])
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