Source code for astropy.modeling.mappings
"""
Special models useful for complex compound models where control is needed over
which outputs from a source model are mapped to which inputs of a target model.
"""
from .core import FittableModel
__all__ = ['Mapping', 'Identity']
[docs]class Mapping(FittableModel):
"""
Allows inputs to be reordered, duplicated or dropped.
Parameters
----------
mapping : tuple
A tuple of integers representing indices of the inputs to this model
to return and in what order to return them. See
:ref:`compound-model-mappings` for more details.
n_inputs : int
Number of inputs; if `None` (default) then ``max(mapping) + 1`` is
used (i.e. the highest input index used in the mapping).
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict-like
Free-form metadata to associate with this model.
Raises
------
TypeError
Raised when number of inputs is less that ``max(mapping)``.
Examples
--------
>>> from astropy.modeling.models import Polynomial2D, Shift, Mapping
>>> poly1 = Polynomial2D(1, c0_0=1, c1_0=2, c0_1=3)
>>> poly2 = Polynomial2D(1, c0_0=1, c1_0=2.4, c0_1=2.1)
>>> model = (Shift(1) & Shift(2)) | Mapping((0, 1, 0, 1)) | (poly1 & poly2)
>>> model(1, 2) # doctest: +FLOAT_CMP
(17.0, 14.2)
"""
linear = True # FittableModel is non-linear by default
def __init__(self, mapping, n_inputs=None, name=None, meta=None):
if n_inputs is None:
self._inputs = tuple('x' + str(idx)
for idx in range(max(mapping) + 1))
else:
self._inputs = tuple('x' + str(idx)
for idx in range(n_inputs))
self._outputs = tuple('x' + str(idx) for idx in range(len(mapping)))
self._mapping = mapping
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super().__init__(name=name, meta=meta)
@property
def inputs(self):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
return self._inputs
@property
def outputs(self):
"""The name(s) of the output(s) of the model."""
return self._outputs
@property
def mapping(self):
"""Integers representing indices of the inputs."""
return self._mapping
def __repr__(self):
if self.name is None:
return '<Mapping({0})>'.format(self.mapping)
else:
return '<Mapping({0}, name={1})>'.format(self.mapping, self.name)
[docs] def evaluate(self, *args):
if len(args) != self.n_inputs:
name = self.name if self.name is not None else "Mapping"
raise TypeError('{0} expects {1} inputs; got {2}'.format(
name, self.n_inputs, len(args)))
result = tuple(args[idx] for idx in self._mapping)
if self.n_outputs == 1:
return result[0]
return result
@property
def inverse(self):
"""
A `Mapping` representing the inverse of the current mapping.
Raises
------
`NotImplementedError`
An inverse does no exist on mappings that drop some of its inputs
(there is then no way to reconstruct the inputs that were dropped).
"""
try:
mapping = tuple(self.mapping.index(idx)
for idx in range(self.n_inputs))
except ValueError:
raise NotImplementedError(
"Mappings such as {0} that drop one or more of their inputs "
"are not invertible at this time.".format(self.mapping))
inv = self.__class__(mapping)
inv._inputs = self._outputs
inv._outputs = self._inputs
return inv
[docs]class Identity(Mapping):
"""
Returns inputs unchanged.
This class is useful in compound models when some of the inputs must be
passed unchanged to the next model.
Parameters
----------
n_inputs : int
Specifies the number of inputs this identity model accepts.
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict-like
Free-form metadata to associate with this model.
Examples
--------
Transform ``(x, y)`` by a shift in x, followed by scaling the two inputs::
>>> from astropy.modeling.models import (Polynomial1D, Shift, Scale,
... Identity)
>>> model = (Shift(1) & Identity(1)) | Scale(1.2) & Scale(2)
>>> model(1,1) # doctest: +FLOAT_CMP
(2.4, 2.0)
>>> model.inverse(2.4, 2) # doctest: +FLOAT_CMP
(1.0, 1.0)
"""
linear = True # FittableModel is non-linear by default
def __init__(self, n_inputs, name=None, meta=None):
mapping = tuple(range(n_inputs))
super().__init__(mapping, name=name, meta=meta)
def __repr__(self):
if self.name is None:
return '<Identity({0})>'.format(self.n_inputs)
else:
return '<Identity({0}, name={1})>'.format(self.n_inputs, self.name)
@property
def inverse(self):
"""
The inverse transformation.
In this case of `Identity`, ``self.inverse is self``.
"""
return self