.. _modeling-units:
********************************
Support for units and quantities
********************************
.. note:: The functionality presented here was recently added. If you run into
any issues, please don't hesitate to open an issue in the `issue
tracker `_.
The `astropy.modeling` package includes partial support for the use of units and
quantities in model parameters, models, and during fitting. At this time, only
some of the built-in models (such as
:class:`~astropy.modeling.functional_models.Gaussian1D`) support units, but this
will be extended in future to all models where this is appropriate.
Setting parameters to quantities
================================
Models can take :class:`~astropy.units.Quantity` objects as parameters::
>>> from astropy import units as u
>>> from astropy.modeling.models import Gaussian1D
>>> g1 = Gaussian1D(mean=3 * u.m, stddev=2 * u.cm, amplitude=3 * u.Jy)
Accessing the parameter then returns a Parameter object that contains the value
and the unit::
>>> g1.mean
Parameter('mean', value=3.0, unit=m)
It is then possible to access the individual properties of the parameter::
>>> g1.mean.name
'mean'
>>> g1.mean.value
3.0
>>> g1.mean.unit
Unit("m")
If a parameter has been initialized as a Quantity, it should always be set to a
quantity, but the units don't have to be compatible with the initial ones::
>>> g1.mean = 3 * u.s
>>> g1 # doctest: +FLOAT_CMP
To change the value of a parameter and not the unit, simply set the value
property::
>>> g1.mean.value = 2
>>> g1 # doctest: +FLOAT_CMP
Setting a parameter which was originally set to a quantity to a scalar doesn't
work because it's ambiguous whether the user means to change just the value and
preserve the unit, or get rid of the unit::
>>> g1.mean = 2 # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnitsError : The 'mean' parameter should be given as a Quantity because it
was originally initialized as a Quantity
On the other hand, if a parameter previously defined without units is given a
Quantity with a unit, this works because it is unambiguous::
>>> g2 = Gaussian1D(mean=3)
>>> g2.mean = 3 * u.m
In other words, once units are attached to a parameter, they can't be removed
due to ambiguous meaning.
Evaluating models with quantities
=================================
Quantities can be passed to model during evaluation::
>>> g3 = Gaussian1D(mean=3 * u.m, stddev=5 * u.cm)
>>> g3(2.9 * u.m) # doctest: +FLOAT_CMP
>>> g3(2.9 * u.s) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnitsError : Units of input 'x', s (time), could not be converted to
required input units of m (length)
In this case, since the mean and standard deviation have units, the value passed
during evaluation also needs units::
>>> g3(3) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnitsError : Units of input 'x', (dimensionless), could not be converted to
required input units of m (length)
Equivalencies
-------------
Equivalencies require special care - a Gaussian defined in frequency space is
not a Gaussian in wavelength space for example. For this reason, we don't allow
equivalencies to be attached to the parameters themselves. Instead, we take the
approach of converting the input data to the parameter space, and any
equivalencies should be applied at evaluation time to the data (not the
parameters).
Let's consider a model that is Gaussian in wavelength space::
>>> g4 = Gaussian1D(mean=3 * u.micron, stddev=1 * u.micron, amplitude=3 * u.Jy)
By default, passing a frequency will not work:
>>> g4(1e2 * u.THz) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnitsError : Units of input 'x', THz (frequency), could not be converted to
required input units of micron (length)
But you can pass a dictionary of equivalencies to the equivalencies argument
(this needs to be a dictionary since some models can contain multiple inputs)::
>>> g4(110 * u.THz, equivalencies={'x': u.spectral()}) # doctest: +FLOAT_CMP
The key of the dictionary should be the name of the inputs according to::
>>> g4.inputs
('x',)
It is also possible to set default equivalencies for the input parameters using
the input_units_equivalencies property::
>>> g4.input_units_equivalencies = {'x': u.spectral()}
>>> g4(110 * u.THz) # doctest: +FLOAT_CMP
Fitting models with units to data
=================================
Fitting models with units to data with units should be seamless provided that
the model supports fitting with units. To demonstrate this, we start off by
generating synthetic data:
.. plot::
:context: reset
:include-source:
import numpy as np
from astropy import units as u
import matplotlib.pyplot as plt
x = np.linspace(1, 5, 30) * u.micron
y = np.exp(-0.5 * (x - 2.5 * u.micron)**2 / (200 * u.nm)**2) * u.mJy
plt.plot(x, y, 'ko')
plt.xlabel('Wavelength (microns)')
plt.ylabel('Flux density (mJy)')
and we then define the initial guess for the fitting and we carry out the fit as
we would without any units:
.. plot::
:context:
:include-source:
from astropy.modeling import models, fitting
g5 = models.Gaussian1D(mean=3 * u.micron, stddev=1 * u.micron, amplitude=1 * u.Jy)
fitter = fitting.LevMarLSQFitter()
g5_fit = fitter(g5, x, y)
plt.plot(x, y, 'ko')
plt.plot(x, g5_fit(x), 'r-')
plt.xlabel('Wavelength (microns)')
plt.ylabel('Flux density (mJy)')
Fitting with equivalencies
--------------------------
Let's now consider the case where the data is not equivalent to those of the
parameters, but they are convertible via equivalencies. In this case, the
equivalencies can either be passed via a dictionary as shown higher up for the
evaluation examples:
.. plot::
:context:
:include-source:
g6 = models.Gaussian1D(mean=110 * u.THz, stddev=10 * u.THz, amplitude=1 * u.Jy)
g6_fit = fitter(g6, x, y, equivalencies={'x': u.spectral()})
plt.plot(x, g6_fit(x, equivalencies={'x': u.spectral()}), 'b-')
plt.xlabel('Wavelength (microns)')
plt.ylabel('Flux density (mJy)')
In this case, the fit (in blue) is slightly worse, because a Gaussian in
frequency space (blue) is not a Gaussian in wavelength space (red). As mentioned
previously, you can also set input_units_equivalencies on the model itself to
avoid having to pass extra arguments to the fitter::
g6.input_units_equivalencies = {'x': u.spectral()}
g6_fit = fitter(g6, x, y)