astropy:docs

Source code for astropy.modeling.parameters

# Licensed under a 3-clause BSD style license - see LICENSE.rst

"""
This module defines two classes that deal with parameters.

It is unlikely users will need to work with these classes directly, unless they
define their own models.
"""

from __future__ import division

import inspect
import functools
import numbers

import numpy as np

from ..utils import isiterable


__all__ = ['Parameter', 'InputParameterError']


[docs]class InputParameterError(ValueError): """Used for incorrect input parameter values and definitions."""
def _tofloat(value): """Convert a parameter to float or float array""" if isiterable(value): try: value = np.array(value, dtype=np.float) shape = value.shape except (TypeError, ValueError): # catch arrays with strings or user errors like different # types of parameters in a parameter set raise InputParameterError( "Parameter of {0} could not be converted to " "float".format(type(value))) elif isinstance(value, bool): raise InputParameterError( "Expected parameter to be of numerical type, not boolean") elif isinstance(value, (numbers.Number, np.number)): value = float(value) shape = () else: raise InputParameterError( "Don't know how to convert parameter of {0} to " "float".format(type(value))) return value, shape
[docs]class Parameter(object): """ Wraps individual parameters. This class represents a model's parameter (in a somewhat broad sense). It acts as both a descriptor that can be assigned to a class attribute to describe the parameters accepted by an individual model (this is called an "unbound parameter"), or it can act as a proxy for the parameter values on an individual model instance (called a "bound parameter"). Parameter instances never store the actual value of the parameter directly. Rather, each instance of a model stores its own parameters as either hidden attributes or (in the case of `~astropy.modeling.core.ParametricModel`) in an array. A *bound* Parameter simply wraps the value in a Parameter proxy which provides some additional information about the parameter such as its constraints. *Unbound* Parameters are not associated with any specific model instance, and are merely used by model classes to determine the names of their parameters and other information about each parameter such as their default values and default constraints. Parameters ---------- name : str parameter name default : float or array default value to use for this parameter getter : callable a function that wraps the raw (internal) value of the parameter when returning the value through the parameter proxy (eg. a parameter may be stored internally as radians but returned to the user as degrees) setter : callable a function that wraps any values assigned to this parameter; should be the inverse of getter fixed : bool if True the parameter is not varied during fitting tied : callable or False if callable is supplied it provides a way to link the value of this parameter to another parameter (or some other arbitrary function) min : float the lower bound of a parameter max : float the upper bound of a parameter model : object an instance of a Model class; this should only be used internally for creating bound Parameters """ # See the _nextid classmethod _nextid = 1 def __init__(self, name, description='', default=None, getter=None, setter=None, fixed=False, tied=False, min=None, max=None, model=None): super(Parameter, self).__init__() self._name = name self.__doc__ = description.strip() self._default = default self._attr = '_' + name self._default_fixed = fixed self._default_tied = tied self._default_min = min self._default_max = max self._order = None self._shape = None self._model = model # The getter/setter functions take one or two arguments: The first # argument is always the value itself (either the value returned or the # value being set). The second argument is optional, but if present # will contain a reference to the model object tied to a parameter (if # it exists) if getter is not None: self._getter = self._create_value_wrapper(getter, model) else: self._getter = None if setter is not None: self._setter = self._create_value_wrapper(setter, model) else: self._setter = None if model is not None: try: _, self._shape = self._validate_value(model, self.value) except AttributeError: # This can happen if the paramter's value has not been set yet pass else: # Only Parameters declared as class-level descriptors require # and ordering ID self._order = self._get_nextid() def __get__(self, obj, objtype): if obj is None: return self return self.__class__(self._name, default=self._default, getter=self._getter, setter=self._setter, model=obj) def __set__(self, obj, value): value, shape = self._validate_value(obj, value) # Compare the shape against the previous value's shape, if it exists if hasattr(obj, self._attr): current_shape = getattr(obj, self.name).shape if shape != current_shape: raise InputParameterError( "Input value for parameter {0!r} does not have the " "required shape {1}".format(self.name, current_shape)) if self._setter is not None: setter = self._create_value_wrapper(self._setter, obj) value = setter(value) setattr(obj, self._attr, value) def __len__(self): if self._model is None: raise TypeError('Parameter definitions do not have a length.') return self._model.param_dim def __getitem__(self, key): value = self.value if self._model.param_dim == 1: # Wrap the value in a list so that getitem can work for sensible # indcies like [0] and [-1] value = [value] return value[key] def __setitem__(self, key, value): # Get the existing value and check whether it even makes sense to # apply this index oldvalue = self.value param_dim = self._model.param_dim if param_dim == 1: # Convert the single-dimension value to a list to allow some slices # that would be compatible with a length-1 array like [:] and [0:] oldvalue = [oldvalue] if isinstance(key, slice): if len(oldvalue[key]) == 0: raise InputParameterError( "Slice assignment outside the parameter dimensions for " "{0!r}".format(self.name)) for idx, val in zip(range(*key.indices(len(self))), value): self.__setitem__(idx, val) else: try: oldvalue[key] = value if param_dim == 1: self.value = value except IndexError: raise InputParameterError( "Input dimension {0} invalid for {1!r} parameter with " "dimension {2}".format(key, self.name, param_dim)) def __repr__(self): if self._model is None: return 'Parameter({0!r})'.format(self._name) else: return 'Parameter({0!r}, value={1!r})'.format( self._name, self.value) @property
[docs] def name(self): """Parameter name""" return self._name
@property
[docs] def default(self): """Parameter default value""" if self._model is None: return self._default if self._model.param_dim == 1: return self._default else: return np.repeat(self._default, self._model.param_dim)
@property def value(self): """The unadorned value proxied by this parameter""" if self._model is not None: if not hasattr(self._model, self._attr): if self._default is not None: value = self.default else: raise AttributeError( 'Parameter value for {0!r} not set'.format(self._name)) else: value = getattr(self._model, self._attr) if self._getter is None: return value else: return self._getter(value) raise AttributeError('Parameter definition does not have a value') @value.setter
[docs] def value(self, val): if self._model is not None: if self._setter is not None: val = self._setter(val) setattr(self._model, self._attr, val) raise AttributeError('Cannot set a value on a parameter definition')
@property
[docs] def shape(self): """The shape of this parameter's value array.""" return self._shape
@property
[docs] def size(self): """The size of this parameter's value array.""" return np.size(self.value)
@property def fixed(self): """ Boolean indicating if the parameter is kept fixed during fitting. """ if self._model is not None: fixed = self._model._constraints.setdefault('fixed', {}) return fixed.setdefault(self._name, self._default_fixed) else: return self._default_fixed @fixed.setter
[docs] def fixed(self, value): """Fix a parameter""" if self._model is not None: assert isinstance(value, bool), "Fixed can be True or False" fixed = self._model._constraints.setdefault('fixed', {}) fixed[self._name] = value self._model._fit_parameters, self._model._fit_param_indices, \ = self._model._model_to_fit_params() else: raise AttributeError("can't set attribute 'fixed' on Parameter " "definition")
@property def tied(self): """ Indicates that this parameter is linked to another one. A callable which provides the relationship of the two parameters. """ if self._model is not None: tied = self._model._constraints.setdefault('tied', {}) return tied.setdefault(self._name, self._default_tied) else: return self._default_tied @tied.setter
[docs] def tied(self, value): """Tie a parameter""" if self._model is not None: assert callable(value) or value in (False, None), \ "Tied must be a callable" tied = self._model._constraints.setdefault('tied', {}) tied[self._name] = value self._model._fit_parameters, self._model._fit_param_indices, \ = self._model._model_to_fit_params() else: raise AttributeError("can't set attribute 'tied' on Parameter " "definition")
@property def bounds(self): """The minimum and maximum values of a parameter as a tuple""" if self._model is not None: bounds = self._model._constraints.setdefault('bounds', {}) return bounds.setdefault(self._name, (self._default_min, self._default_max)) else: return (self._default_min, self._default_max) @bounds.setter
[docs] def bounds(self, value): """Set the minimum and maximum values of a parameter from a tuple""" if self._model is not None: _min, _max = value if _min is not None: assert isinstance(_min, numbers.Number), \ "Min value must be a number" _min = float(_min) if _max is not None: assert isinstance(_max, numbers.Number), \ "Max value must be a number" _max = float(_max) bounds = self._model._constraints.setdefault('bounds', {}) bounds[self._name] = (_min, _max) self._model._model_to_fit_params() else: raise AttributeError("can't set attribute 'bounds' on Parameter " "definition")
@property def min(self): """A value used as a lower bound when fitting a parameter""" return self.bounds[0] @min.setter
[docs] def min(self, value): """Set a minimum value of a parameter""" if self._model is not None: self.bounds = (value, self.max) else: raise AttributeError("can't set attribute 'min' on Parameter " "definition")
@property def max(self): """A value used as an upper bound when fitting a parameter""" return self.bounds[1] @max.setter
[docs] def max(self, value): """Set a maximum value of a parameter.""" if self._model is not None: self.bounds = (self.min, value) else: raise AttributeError("can't set attribute 'max' on Parameter " "definition")
@classmethod def _get_nextid(cls): """Returns a monotonically increasing ID used to order Parameter descriptors declared at the class-level of Model subclasses. This allows the desired parameter order to be determined without having to list it manually in the param_names class attribute. """ nextid = cls._nextid cls._nextid += 1 return nextid def _validate_value(self, model, value): if model is None: return param_dim = model.param_dim if param_dim == 1: # Just validate the value with _tofloat return _tofloat(value) else: try: # Validate each value value, shape = _tofloat(value) except (TypeError, IndexError): raise InputParameterError( "Expected a multivalued input of dimension {0} " "for parameter {1!r}".format(param_dim, self.name)) return value, shape def _create_value_wrapper(self, wrapper, model): """Wrappers a getter/setter function to support optionally passing in a reference to the model object as the second argument. If a model is tied to this parameter and its getter/setter supports a second argument then this creates a partial function using the model instance as the second argument. """ if isinstance(wrapper, np.ufunc): if wrapper.nin != 1: raise TypeError("A numpy.ufunc used for Parameter " "getter/setter may only take one input " "argument") else: wrapper_args = inspect.getargspec(wrapper) nargs = len(wrapper_args.args) if nargs == 1: pass elif nargs == 2: if model is not None: # Don't make a partial function unless we're tied to a # specific model instance model_arg = wrapper_args.args[1] wrapper = functools.partial(wrapper, **{model_arg: model}) else: raise TypeError("Parameter getter/setter must be a function " "of either one or two arguments") return wrapper def __add__(self, val): return self.value + val def __radd__(self, val): return self.value + val def __sub__(self, val): return self.value - val def __rsub__(self, val): return val - self.value def __mul__(self, val): return self.value * val def __rmul__(self, val): return self.value * val def __pow__(self, val): return self.value ** val def __rpow__(self, val): return val ** self.value def __div__(self, val): return self.value / val def __rdiv__(self, val): return val / self.value def __truediv__(self, val): return self.value / val def __rtruediv__(self, val): return val / self.value def __eq__(self, val): return (np.asarray(self) == np.asarray(val)).all() def __ne__(self, val): return not (np.asarray(self) == np.asarray(val)).all() def __lt__(self, val): return (np.asarray(self) < np.asarray(val)).all() def __gt__(self, val): return (np.asarray(self) > np.asarray(val)).all() def __le__(self, val): return (np.asarray(self) <= np.asarray(val)).all() def __ge__(self, val): return (np.asarray(self) >= np.asarray(val)).all() def __neg__(self): return -self.value def __abs__(self): return np.abs(self.value)

Page Contents