# 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']
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
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
def name(self):
"""Parameter name"""
return self._name
@property
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
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
def shape(self):
"""The shape of this parameter's value array."""
return self._shape
@property
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
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
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
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
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
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 __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 * (-1)
def __abs__(self):
return np.abs(self.value)