303 lines
10 KiB
Python
303 lines
10 KiB
Python
""" Module for AbstractProblem class """
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from abc import ABCMeta, abstractmethod
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from ..utils import merge_tensors, check_consistency
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from copy import deepcopy
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import torch
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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 inheritied 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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# variable storing all points
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self.input_pts = {}
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# varible to check if sampling is done. If no location
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# element is presented in Condition this variable is set to true
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self._have_sampled_points = {}
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for condition_name in self.conditions:
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self._have_sampled_points[condition_name] = False
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# put in self.input_pts all the points that we don't need to sample
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self._span_condition_points()
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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 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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if hasattr(self, "custom_variables"):
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variables += self.custom_variables
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return variables
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@property
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def domain(self):
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"""
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The domain(s) where the conditions of the AbstractProblem are valid.
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If more than one domain type is passed, a list of Location is
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retured.
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:return: the domain(s) of ``self``
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:rtype: list[Location]
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"""
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domains = [
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getattr(self, f"{t}_domain")
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for t in ["spatial", "temporal", "parameter"]
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if hasattr(self, f"{t}_domain")
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]
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if len(domains) == 1:
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return domains[0]
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elif len(domains) == 0:
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raise RuntimeError
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if len(set(map(type, domains))) == 1:
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domain = domains[0].__class__({})
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[domain.update(d) for d in domains]
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return domain
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else:
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raise RuntimeError("different domains")
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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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pass
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def _span_condition_points(self):
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"""
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Simple function to get the condition points
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"""
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for condition_name in self.conditions:
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condition = self.conditions[condition_name]
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if hasattr(condition, "input_points"):
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samples = condition.input_points
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self.input_pts[condition_name] = samples
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self._have_sampled_points[condition_name] = True
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if hasattr(self, "unknown_parameter_domain"):
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# initialize the unknown parameters of the inverse problem given
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# the domain the user gives
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self.unknown_parameters = {}
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for i, var in enumerate(self.unknown_variables):
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range_var = self.unknown_parameter_domain.range_[var]
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tensor_var = (
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torch.rand(1, requires_grad=True) * range_var[1]
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+ range_var[0]
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)
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self.unknown_parameters[var] = torch.nn.Parameter(
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tensor_var
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)
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def discretise_domain(
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self, n, mode="random", variables="all", locations="all"
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):
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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: problem's variables to be sampled, defaults to 'all'.
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:type variables: str | list[str]
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:param locations: problem's locations from where to sample, defaults to 'all'.
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:type locations: 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', location=['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
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check_consistency(n, int)
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# check consistency mode
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check_consistency(mode, str)
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if mode not in ["random", "grid", "lh", "chebyshev", "latin"]:
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raise TypeError(f"mode {mode} not valid.")
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# check consistency variables
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if variables == "all":
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variables = self.input_variables
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else:
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check_consistency(variables, str)
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if sorted(variables) != sorted(self.input_variables):
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TypeError(
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f"Wrong variables for sampling. Variables ",
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f"should be in {self.input_variables}.",
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)
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# check consistency location
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if locations == "all":
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locations = [condition for condition in self.conditions]
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else:
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check_consistency(locations, str)
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if sorted(locations) != sorted(self.conditions):
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TypeError(
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f"Wrong locations for sampling. Location ",
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f"should be in {self.conditions}.",
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)
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# sampling
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for location in locations:
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condition = self.conditions[location]
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# we try to check if we have already sampled
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try:
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already_sampled = [self.input_pts[location]]
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# if we have not sampled, a key error is thrown
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except KeyError:
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already_sampled = []
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# if we have already sampled fully the condition
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# but we want to sample again we set already_sampled
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# to an empty list since we need to sample again, and
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# self._have_sampled_points to False.
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if self._have_sampled_points[location]:
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already_sampled = []
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self._have_sampled_points[location] = False
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# build samples
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samples = [
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condition.location.sample(n=n, mode=mode, variables=variables)
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] + already_sampled
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pts = merge_tensors(samples)
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self.input_pts[location] = pts
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# the condition is sampled if input_pts contains all labels
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if sorted(self.input_pts[location].labels) == sorted(
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self.input_variables
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):
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self._have_sampled_points[location] = True
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self.input_pts[location] = self.input_pts[location].extract(
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sorted(self.input_variables)
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)
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def add_points(self, new_points):
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"""
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Adding points to the already sampled points.
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:param dict new_points: a dictionary with key the location to add the points
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and values the torch.Tensor points.
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"""
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if sorted(new_points.keys()) != sorted(self.conditions):
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TypeError(
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f"Wrong locations for new points. Location ",
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f"should be in {self.conditions}.",
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)
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for location in new_points.keys():
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# extract old and new points
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old_pts = self.input_pts[location]
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new_pts = new_points[location]
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# if they don't have the same variables error
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if sorted(old_pts.labels) != sorted(new_pts.labels):
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TypeError(
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f"Not matching variables for old and new points "
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f"in condition {location}."
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)
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if old_pts.labels != new_pts.labels:
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new_pts = torch.hstack(
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[new_pts.extract([i]) for i in old_pts.labels]
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)
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new_pts.labels = old_pts.labels
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# merging
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merged_pts = torch.vstack([old_pts, new_points[location]])
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merged_pts.labels = old_pts.labels
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self.input_pts[location] = merged_pts
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@property
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def have_sampled_points(self):
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"""
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Check if all points for
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``Location`` are sampled.
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"""
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return all(self._have_sampled_points.values())
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@property
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def not_sampled_points(self):
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"""
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Check which points are
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not sampled.
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"""
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# variables which are not sampled
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not_sampled = None
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if self.have_sampled_points is False:
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# check which one are not sampled:
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not_sampled = []
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for condition_name, is_sample in self._have_sampled_points.items():
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if not is_sample:
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not_sampled.append(condition_name)
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return not_sampled
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