# 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.
"""
import functools
import numbers
import types
import operator
import numpy as np
from .. import units as u
from ..units import Quantity, UnitsError
from ..utils import isiterable, OrderedDescriptor
from .utils import array_repr_oneline
from .utils import get_inputs_and_params
__all__ = ['Parameter', 'InputParameterError', 'ParameterError']
class ParameterError(Exception):
"""Generic exception class for all exceptions pertaining to Parameters."""
class InputParameterError(ValueError, ParameterError):
"""Used for incorrect input parameter values and definitions."""
class ParameterDefinitionError(ParameterError):
"""Exception in declaration of class-level Parameters."""
def _tofloat(value):
"""Convert a parameter to float or float array"""
if isiterable(value):
try:
value = np.asanyarray(value, dtype=float)
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, Quantity):
# Quantities are fine as is
pass
elif isinstance(value, np.ndarray):
# A scalar/dimensionless array
value = float(value.item())
elif isinstance(value, (numbers.Number, np.number)):
value = float(value)
elif isinstance(value, bool):
raise InputParameterError(
"Expected parameter to be of numerical type, not boolean")
else:
raise InputParameterError(
"Don't know how to convert parameter of {0} to "
"float".format(type(value)))
return value
# Helpers for implementing operator overloading on Parameter
def _binary_arithmetic_operation(op, reflected=False):
@functools.wraps(op)
def wrapper(self, val):
if self._model is None:
return NotImplemented
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
if reflected:
return op(val, self_value)
else:
return op(self_value, val)
return wrapper
def _binary_comparison_operation(op):
@functools.wraps(op)
def wrapper(self, val):
if self._model is None:
if op is operator.lt:
# Because OrderedDescriptor uses __lt__ to work, we need to
# call the super method, but only when not bound to an instance
# anyways
return super(self.__class__, self).__lt__(val)
else:
return NotImplemented
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
return op(self_value, val)
return wrapper
def _unary_arithmetic_operation(op):
@functools.wraps(op)
def wrapper(self):
if self._model is None:
return NotImplemented
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
return op(self_value)
return wrapper
class Parameter(OrderedDescriptor):
"""
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 parameter values
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. In other words, this is a high-level interface to a
model's adjustable parameter values.
*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.
See :ref:`modeling-parameters` for more details.
Parameters
----------
name : str
parameter name
.. warning::
The fact that `Parameter` accepts ``name`` as an argument is an
implementation detail, and should not be used directly. When
defining a new `Model` class, parameter names are always
automatically defined by the class attribute they're assigned to.
description : str
parameter description
default : float or array
default value to use for this parameter
unit : `~astropy.units.Unit`
if specified, the parameter will be in these units, and when the
parameter is updated in future, it should be set to a
:class:`~astropy.units.Quantity` that has equivalent units.
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
bounds : tuple
specify min and max as a single tuple--bounds may not be specified
simultaneously with min or max
model : `Model` instance
binds the the `Parameter` instance to a specific model upon
instantiation; this should only be used internally for creating bound
Parameters, and should not be used for `Parameter` descriptors defined
as class attributes
"""
constraints = ('fixed', 'tied', 'bounds')
"""
Types of constraints a parameter can have. Excludes 'min' and 'max'
which are just aliases for the first and second elements of the 'bounds'
constraint (which is represented as a 2-tuple).
"""
# Settings for OrderedDescriptor
_class_attribute_ = '_parameters_'
_name_attribute_ = '_name'
def __init__(self, name='', description='', default=None, unit=None,
getter=None, setter=None, fixed=False, tied=False, min=None,
max=None, bounds=None, model=None):
super().__init__()
self._name = name
self.__doc__ = self._description = description.strip()
# We only need to perform this check on unbound parameters
if model is None and isinstance(default, Quantity):
if unit is not None and not unit.is_equivalent(default.unit):
raise ParameterDefinitionError(
"parameter default {0} does not have units equivalent to "
"the required unit {1}".format(default, unit))
unit = default.unit
default = default.value
self._default = default
self._unit = unit
# NOTE: These are *default* constraints--on model instances constraints
# are taken from the model if set, otherwise the defaults set here are
# used
if bounds is not None:
if min is not None or max is not None:
raise ValueError(
'bounds may not be specified simultaneously with min or '
'or max when instantiating Parameter {0}'.format(name))
else:
bounds = (min, max)
self._fixed = fixed
self._tied = tied
self._bounds = bounds
self._order = None
self._model = None
# 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)
self._getter = self._create_value_wrapper(getter, None)
self._setter = self._create_value_wrapper(setter, None)
self._validator = None
# Only Parameters declared as class-level descriptors require
# and ordering ID
if model is not None:
self._bind(model)
def __get__(self, obj, objtype):
if obj is None:
return self
# All of the Parameter.__init__ work should already have been done for
# the class-level descriptor; we can skip that stuff and just copy the
# existing __dict__ and then bind to the model instance
parameter = self.__class__.__new__(self.__class__)
parameter.__dict__.update(self.__dict__)
parameter._bind(obj)
return parameter
def __set__(self, obj, value):
value = _tofloat(value)
# Check that units are compatible with default or units already set
param_unit = obj._param_metrics[self.name]['orig_unit']
if param_unit is None:
if isinstance(value, Quantity):
obj._param_metrics[self.name]['orig_unit'] = value.unit
else:
if not isinstance(value, Quantity):
raise UnitsError("The '{0}' parameter should be given as a "
"Quantity because it was originally initialized "
"as a Quantity".format(self._name))
else:
# We need to make sure we update the unit because the units are
# then dropped from the value below.
obj._param_metrics[self.name]['orig_unit'] = value.unit
# Call the validator before the setter
if self._validator is not None:
self._validator(obj, value)
if self._setter is not None:
setter = self._create_value_wrapper(self._setter, obj)
if self.unit is not None:
value = setter(value * self.unit).value
else:
value = setter(value)
self._set_model_value(obj, value)
def __len__(self):
if self._model is None:
raise TypeError('Parameter definitions do not have a length.')
return len(self._model)
def __getitem__(self, key):
value = self.value
if len(self._model) == 1:
# Wrap the value in a list so that getitem can work for sensible
# indices 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
n_models = len(self._model)
# if n_models == 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}'".format(self.name))
for idx, val in zip(range(*key.indices(len(self))), value):
self.__setitem__(idx, val)
else:
try:
oldvalue[key] = value
except IndexError:
raise InputParameterError(
"Input dimension {0} invalid for {1!r} parameter with "
"dimension {2}".format(key, self.name, n_models))
def __repr__(self):
args = "'{0}'".format(self._name)
if self._model is None:
if self._default is not None:
args += ', default={0}'.format(self._default)
else:
args += ', value={0}'.format(self.value)
if self.unit is not None:
args += ', unit={0}'.format(self.unit)
for cons in self.constraints:
val = getattr(self, cons)
if val not in (None, False, (None, None)):
# Maybe non-obvious, but False is the default for the fixed and
# tied constraints
args += ', {0}={1}'.format(cons, val)
return "{0}({1})".format(self.__class__.__name__, args)
@property
def name(self):
"""Parameter name"""
return self._name
@property
def default(self):
"""Parameter default value"""
if (self._model is None or self._default is None or
len(self._model) == 1):
return self._default
# Otherwise the model we are providing for has more than one parameter
# sets, so ensure that the default is repeated the correct number of
# times along the model_set_axis if necessary
n_models = len(self._model)
model_set_axis = self._model._model_set_axis
default = self._default
new_shape = (np.shape(default) +
(1,) * (model_set_axis + 1 - np.ndim(default)))
default = np.reshape(default, new_shape)
# Now roll the new axis into its correct position if necessary
default = np.rollaxis(default, -1, model_set_axis)
# Finally repeat the last newly-added axis to match n_models
default = np.repeat(default, n_models, axis=-1)
# NOTE: Regardless of what order the last two steps are performed in,
# the resulting array will *look* the same, but only if the repeat is
# performed last will it result in a *contiguous* array
return default
@property
def value(self):
"""The unadorned value proxied by this parameter."""
if self._model is None:
raise AttributeError('Parameter definition does not have a value')
value = self._get_model_value(self._model)
if self._getter is None:
return value
else:
raw_unit = self._model._param_metrics[self.name]['raw_unit']
orig_unit = self._model._param_metrics[self.name]['orig_unit']
if raw_unit is not None:
return np.float64(self._getter(value, raw_unit, orig_unit).value)
else:
return self._getter(value)
@value.setter
def value(self, value):
if self._model is None:
raise AttributeError('Cannot set a value on a parameter '
'definition')
if self._setter is not None:
val = self._setter(value)
if isinstance(value, Quantity):
raise TypeError("The .value property on parameters should be set to "
"unitless values, not Quantity objects. To set a "
"parameter to a quantity simply set the parameter "
"directly without using .value")
self._set_model_value(self._model, value)
@property
def unit(self):
"""
The unit attached to this parameter, if any.
On unbound parameters (i.e. parameters accessed through the
model class, rather than a model instance) this is the required/
default unit for the parameter.
"""
if self._model is None:
return self._unit
else:
# orig_unit may be undefined early on in model instantiation
return self._model._param_metrics[self.name].get('orig_unit',
self._unit)
@unit.setter
def unit(self, unit):
self._set_unit(unit)
def _set_unit(self, unit, force=False):
if self._model is None:
raise AttributeError('Cannot set unit on a parameter definition')
orig_unit = self._model._param_metrics[self.name]['orig_unit']
if force:
self._model._param_metrics[self.name]['orig_unit'] = unit
else:
if orig_unit is None:
raise ValueError('Cannot attach units to parameters that were '
'not initially specified with units')
else:
raise ValueError('Cannot change the unit attribute directly, '
'instead change the parameter to a new quantity')
@property
def quantity(self):
"""
This parameter, as a :class:`~astropy.units.Quantity` instance.
"""
if self.unit is not None:
return self.value * self.unit
else:
return None
@quantity.setter
def quantity(self, quantity):
if not isinstance(quantity, Quantity):
raise TypeError("The .quantity attribute should be set to a Quantity object")
self.value = quantity.value
self._set_unit(quantity.unit, force=True)
@property
def shape(self):
"""The shape of this parameter's value array."""
if self._model is None:
raise AttributeError('Parameter definition does not have a '
'shape.')
shape = self._model._param_metrics[self._name]['shape']
if len(self._model) > 1:
# If we are dealing with a model *set* the shape is the shape of
# the parameter within a single model in the set
model_axis = self._model._model_set_axis
if model_axis < 0:
model_axis = len(shape) + model_axis
shape = shape[:model_axis] + shape[model_axis + 1:]
return shape
@property
def size(self):
"""The size of this parameter's value array."""
# TODO: Rather than using self.value this could be determined from the
# size of the parameter in _param_metrics
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['fixed']
return fixed.get(self._name, self._fixed)
else:
return self._fixed
@fixed.setter
def fixed(self, value):
"""Fix a parameter"""
if self._model is not None:
if not isinstance(value, bool):
raise TypeError("Fixed can be True or False")
self._model._constraints['fixed'][self._name] = value
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['tied']
return tied.get(self._name, self._tied)
else:
return self._tied
@tied.setter
def tied(self, value):
"""Tie a parameter"""
if self._model is not None:
if not callable(value) and value not in (False, None):
raise TypeError("Tied must be a callable")
self._model._constraints['tied'][self._name] = value
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['bounds']
return bounds.get(self._name, self._bounds)
else:
return self._bounds
@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:
if not isinstance(_min, numbers.Number):
raise TypeError("Min value must be a number")
_min = float(_min)
if _max is not None:
if not isinstance(_max, numbers.Number):
raise TypeError("Max value must be a number")
_max = float(_max)
bounds = self._model._constraints.setdefault('bounds', {})
self._model._constraints['bounds'][self._name] = (_min, _max)
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")
@property
def validator(self):
"""
Used as a decorator to set the validator method for a `Parameter`.
The validator method validates any value set for that parameter.
It takes two arguments--``self``, which refers to the `Model`
instance (remember, this is a method defined on a `Model`), and
the value being set for this parameter. The validator method's
return value is ignored, but it may raise an exception if the value
set on the parameter is invalid (typically an `InputParameterError`
should be raised, though this is not currently a requirement).
The decorator *returns* the `Parameter` instance that the validator
is set on, so the underlying validator method should have the same
name as the `Parameter` itself (think of this as analogous to
``property.setter``). For example::
>>> from astropy.modeling import Fittable1DModel
>>> class TestModel(Fittable1DModel):
... a = Parameter()
... b = Parameter()
...
... @a.validator
... def a(self, value):
... # Remember, the value can be an array
... if np.any(value < self.b):
... raise InputParameterError(
... "parameter 'a' must be greater than or equal "
... "to parameter 'b'")
...
... @staticmethod
... def evaluate(x, a, b):
... return a * x + b
...
>>> m = TestModel(a=1, b=2) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
InputParameterError: parameter 'a' must be greater than or equal
to parameter 'b'
>>> m = TestModel(a=2, b=2)
>>> m.a = 0 # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
InputParameterError: parameter 'a' must be greater than or equal
to parameter 'b'
On bound parameters this property returns the validator method itself,
as a bound method on the `Parameter`. This is not often as useful, but
it allows validating a parameter value without setting that parameter::
>>> m.a.validator(42) # Passes
>>> m.a.validator(-42) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
InputParameterError: parameter 'a' must be greater than or equal
to parameter 'b'
"""
if self._model is None:
# For unbound parameters return the validator setter
def validator(func, self=self):
self._validator = func
return self
return validator
else:
# Return the validator method, bound to the Parameter instance with
# the name "validator"
def validator(self, value):
if self._validator is not None:
return self._validator(self._model, value)
return types.MethodType(validator, self)
def copy(self, name=None, description=None, default=None, unit=None,
getter=None, setter=None, fixed=False, tied=False, min=None,
max=None, bounds=None):
"""
Make a copy of this `Parameter`, overriding any of its core attributes
in the process (or an exact copy).
The arguments to this method are the same as those for the `Parameter`
initializer. This simply returns a new `Parameter` instance with any
or all of the attributes overridden, and so returns the equivalent of:
.. code:: python
Parameter(self.name, self.description, ...)
"""
kwargs = locals().copy()
del kwargs['self']
for key, value in kwargs.items():
if value is None:
# Annoying special cases for min/max where are just aliases for
# the components of bounds
if key in ('min', 'max'):
continue
else:
if hasattr(self, key):
value = getattr(self, key)
elif hasattr(self, '_' + key):
value = getattr(self, '_' + key)
kwargs[key] = value
return self.__class__(**kwargs)
@property
def _raw_value(self):
"""
Currently for internal use only.
Like Parameter.value but does not pass the result through
Parameter.getter. By design this should only be used from bound
parameters.
This will probably be removed are retweaked at some point in the
process of rethinking how parameter values are stored/updated.
"""
return self._get_model_value(self._model)
def _bind(self, model):
"""
Bind the `Parameter` to a specific `Model` instance; don't use this
directly on *unbound* parameters, i.e. `Parameter` descriptors that
are defined in class bodies.
"""
self._model = model
self._getter = self._create_value_wrapper(self._getter, model)
self._setter = self._create_value_wrapper(self._setter, model)
# TODO: These methods should probably be moved to the Model class, since it
# has entirely to do with details of how the model stores parameters.
# Parameter should just act as a user front-end to this.
def _get_model_value(self, model):
"""
This method implements how to retrieve the value of this parameter from
the model instance. See also `Parameter._set_model_value`.
These methods take an explicit model argument rather than using
self._model so that they can be used from unbound `Parameter`
instances.
"""
if not hasattr(model, '_parameters'):
# The _parameters array hasn't been initialized yet; just translate
# this to an AttributeError
raise AttributeError(self._name)
# Use the _param_metrics to extract the parameter value from the
# _parameters array
param_metrics = model._param_metrics[self._name]
param_slice = param_metrics['slice']
param_shape = param_metrics['shape']
value = model._parameters[param_slice]
if param_shape:
value = value.reshape(param_shape)
else:
value = value[0]
return value
def _set_model_value(self, model, value):
"""
This method implements how to store the value of a parameter on the
model instance.
Currently there is only one storage mechanism (via the ._parameters
array) but other mechanisms may be desireable, in which case really the
model class itself should dictate this and *not* `Parameter` itself.
"""
def _update_parameter_value(model, name, value):
# TODO: Maybe handle exception on invalid input shape
param_metrics = model._param_metrics[name]
param_slice = param_metrics['slice']
param_shape = param_metrics['shape']
param_size = np.prod(param_shape)
if np.size(value) != param_size:
raise InputParameterError(
"Input value for parameter {0!r} does not have {1} elements "
"as the current value does".format(name, param_size))
model._parameters[param_slice] = np.array(value).ravel()
_update_parameter_value(model, self._name, value)
if hasattr(model, "_param_map"):
submodel_ind, param_name = model._param_map[self._name]
if hasattr(model._submodels[submodel_ind], "_param_metrics"):
_update_parameter_value(model._submodels[submodel_ind], param_name, value)
@staticmethod
def _create_value_wrapper(wrapper, model):
"""Wraps 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")
elif wrapper is None:
# Just allow non-wrappers to fall through silently, for convenience
return None
else:
inputs, params = get_inputs_and_params(wrapper)
nargs = len(inputs)
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 = inputs[1].name
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 __array__(self, dtype=None):
# Make np.asarray(self) work a little more straightforwardly
arr = np.asarray(self.value, dtype=dtype)
if self.unit is not None:
arr = Quantity(arr, self.unit, copy=False)
return arr
def __bool__(self):
if self._model is None:
return True
else:
return bool(self.value)
__add__ = _binary_arithmetic_operation(operator.add)
__radd__ = _binary_arithmetic_operation(operator.add, reflected=True)
__sub__ = _binary_arithmetic_operation(operator.sub)
__rsub__ = _binary_arithmetic_operation(operator.sub, reflected=True)
__mul__ = _binary_arithmetic_operation(operator.mul)
__rmul__ = _binary_arithmetic_operation(operator.mul, reflected=True)
__pow__ = _binary_arithmetic_operation(operator.pow)
__rpow__ = _binary_arithmetic_operation(operator.pow, reflected=True)
__div__ = _binary_arithmetic_operation(operator.truediv)
__rdiv__ = _binary_arithmetic_operation(operator.truediv, reflected=True)
__truediv__ = _binary_arithmetic_operation(operator.truediv)
__rtruediv__ = _binary_arithmetic_operation(operator.truediv, reflected=True)
__eq__ = _binary_comparison_operation(operator.eq)
__ne__ = _binary_comparison_operation(operator.ne)
__lt__ = _binary_comparison_operation(operator.lt)
__gt__ = _binary_comparison_operation(operator.gt)
__le__ = _binary_comparison_operation(operator.le)
__ge__ = _binary_comparison_operation(operator.ge)
__neg__ = _unary_arithmetic_operation(operator.neg)
__abs__ = _unary_arithmetic_operation(operator.abs)
def param_repr_oneline(param):
"""
Like array_repr_oneline but works on `Parameter` objects and supports
rendering parameters with units like quantities.
"""
out = array_repr_oneline(param.value)
if param.unit is not None:
out = '{0} {1!s}'.format(out, param.unit)
return out