Source code for pymor.parameters.functionals

# This file is part of the pyMOR project (
# Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

from numbers import Number

import numpy as np

from pymor.parameters.interfaces import ParameterFunctionalInterface

[docs]class ProjectionParameterFunctional(ParameterFunctionalInterface): """|ParameterFunctional| returning a component of the given parameter. For given parameter `mu`, this functional evaluates to :: mu[component_name][coordinates] Parameters ---------- component_name The name of the parameter component to return. component_shape The shape of the parameter component. coordinates See above. name Name of the functional. """ def __init__(self, component_name, component_shape, coordinates=(), name=None): = name if isinstance(component_shape, Number): component_shape = () if component_shape == 0 else (component_shape,) self.build_parameter_type({component_name: component_shape}) self.component_name = component_name self.coordinates = coordinates assert len(coordinates) == len(component_shape) assert not component_shape or coordinates < component_shape
[docs] def evaluate(self, mu=None): mu = self.parse_parameter(mu) return mu[self.component_name].item(self.coordinates)
[docs]class GenericParameterFunctional(ParameterFunctionalInterface): """A wrapper making an arbitrary Python function a |ParameterFunctional| Note that a GenericParameterFunctional can only be :mod:`pickled <pymor.core.pickle>` if the function it is wrapping can be pickled. For this reason, it is usually preferable to use :class:`ExpressionParameterFunctional` instead of :class:`GenericParameterFunctional`. Parameters ---------- mapping The function to wrap. The function has signature `mapping(mu)`. parameter_type The |ParameterType| of the |Parameters| the functional expects. name The name of the functional. """ def __init__(self, mapping, parameter_type, name=None): = name self._mapping = mapping self.build_parameter_type(parameter_type)
[docs] def evaluate(self, mu=None): mu = self.parse_parameter(mu) value = self._mapping(mu) # ensure that we return a number not an array if isinstance(value, np.ndarray): return value.item() else: return value
[docs]class ExpressionParameterFunctional(GenericParameterFunctional): """Turns a Python expression given as a string into a |ParameterFunctional|. Some |NumPy| arithmetic functions like `sin`, `log`, `min` are supported. For a full list see the `functions` class attribute. .. warning:: :meth:`eval` is used to evaluate the given expression. Using this class with expression strings from untrusted sources will cause mayhem and destruction! Parameters ---------- expression A Python expression in the parameter components of the given `parameter_type`. parameter_type The |ParameterType| of the |Parameters| the functional expects. name The name of the functional. """ functions = {k: getattr(np, k) for k in {'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan', 'arctan2', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh', 'exp', 'exp2', 'log', 'log2', 'log10', 'sqrt', 'array', 'min', 'minimum', 'max', 'maximum', 'pi', 'e', 'sum', 'prod', 'abs', 'sign', 'zeros', 'ones'}} functions['norm'] = np.linalg.norm functions['polar'] = lambda x: (np.linalg.norm(x, axis=-1), np.arctan2(x[..., 1], x[..., 0]) % (2*np.pi)) functions['np'] = np def __init__(self, expression, parameter_type, name=None): self.expression = expression code = compile(expression, '<expression>', 'eval') functions = self.functions mapping = lambda mu: eval(code, functions, mu) super().__init__(mapping, parameter_type, name)
[docs] def __repr__(self): return 'ExpressionParameterFunctional({}, {})'.format(self.expression, repr(self.parameter_type))
[docs] def __reduce__(self): return (ExpressionParameterFunctional, (self.expression, self.parameter_type, getattr(self, '_name', None)))
[docs]class ProductParameterFunctional(ParameterFunctionalInterface): """Forms the product of a list of |ParameterFunctionals| or numbers. Parameters ---------- factors A list of |ParameterFunctionals| or numbers. name Name of the functional. """ def __init__(self, factors, name=None): assert len(factors) > 0 assert all(isinstance(f, (ParameterFunctionalInterface, Number)) for f in factors) = name self.factors = tuple(factors) self.build_parameter_type(*(f for f in factors if isinstance(f, ProductParameterFunctional)))
[docs] def evaluate(self, mu=None): mu = self.parse_parameter(mu) return np.array([f.evaluate(mu) if hasattr(f, 'evaluate') else f for f in self.factors]).prod()