115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
from .utils import check_consistency, merge_tensors
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class Collector:
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def __init__(self, problem):
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# creating a hook between collector and problem
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self.problem = problem
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# this variable is used to store the data in the form:
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# {'[condition_name]' :
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# {'input_points' : Tensor,
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# '[equation/output_points/conditional_variables]': Tensor}
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# }
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# those variables are used for the dataloading
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self._data_collections = {name: {} for name in self.problem.conditions}
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self.conditions_name = {
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i: name
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for i, name in enumerate(self.problem.conditions)
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}
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# variables used to check that all conditions are sampled
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self._is_conditions_ready = {
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name: False
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for name in self.problem.conditions
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}
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self.full = False
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@property
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def full(self):
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return all(self._is_conditions_ready.values())
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@full.setter
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def full(self, value):
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check_consistency(value, bool)
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self._full = value
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@property
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def data_collections(self):
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return self._data_collections
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@property
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def problem(self):
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return self._problem
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@problem.setter
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def problem(self, value):
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self._problem = value
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def store_fixed_data(self):
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# loop over all conditions
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for condition_name, condition in self.problem.conditions.items():
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# if the condition is not ready and domain is not attribute
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# of condition, we get and store the data
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if (not self._is_conditions_ready[condition_name]) and (not hasattr(
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condition, "domain")):
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# get data
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keys = condition.__slots__
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values = [getattr(condition, name) for name in keys]
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self.data_collections[condition_name] = dict(zip(keys, values))
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# condition now is ready
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self._is_conditions_ready[condition_name] = True
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def store_sample_domains(self, n, mode, variables, sample_locations):
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# loop over all locations
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for loc in sample_locations:
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# get condition
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condition = self.problem.conditions[loc]
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keys = ["input_points", "equation"]
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# if the condition is not ready, we get and store the data
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if (not self._is_conditions_ready[loc]):
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# if it is the first time we sample
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if not self.data_collections[loc]:
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already_sampled = []
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# if we have sampled the condition but not all variables
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else:
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already_sampled = [
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self.data_collections[loc]['input_points']
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]
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# if the condition is ready but we want to sample again
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else:
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self._is_conditions_ready[loc] = False
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already_sampled = []
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# get the samples
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samples = [
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condition.domain.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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if (set(pts.labels).issubset(sorted(self.problem.input_variables))):
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pts = pts.sort_labels()
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if sorted(pts.labels) == sorted(self.problem.input_variables):
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self._is_conditions_ready[loc] = True
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values = [pts, condition.equation]
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self.data_collections[loc] = dict(zip(keys, values))
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else:
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raise RuntimeError(
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'Try to sample variables which are not in problem defined '
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'in the problem')
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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: Dictonary 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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if not self._is_conditions_ready[k]:
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raise RuntimeError(
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'Cannot add points on a non sampled condition')
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self.data_collections[k]['input_points'] = self.data_collections[k][
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'input_points'].vstack(v)
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