LinearPhaseFunc

class sbpy.photometry.LinearPhaseFunc(H, S, **kwargs)

Bases: sbpy.photometry.core.DiskIntegratedPhaseFunc

Linear phase function model

Examples

>>> # Define a linear phase function model with absolute magnitude
>>> # H = 5 and slope = 0.04 mag/deg = 2.29 mag/rad
>>> import astropy.units as u
>>> from sbpy.calib import solar_fluxd
>>> from sbpy.photometry import LinearPhaseFunc
>>>
>>> linear_phasefunc = LinearPhaseFunc(5 * u.mag, 0.04 * u.mag/u.deg,
...     radius = 300 * u.km, wfb = 'V')
>>> with solar_fluxd.set({'V': -26.77 * u.mag}):
...     pha = np.linspace(0, 180, 200) * u.deg
...     mag = linear_phasefunc.to_mag(pha)
...     ref = linear_phasefunc.to_ref(pha)
...     geomalb = linear_phasefunc.geomalb
...     phaseint = linear_phasefunc.phaseint
...     bondalb = linear_phasefunc.bondalb
>>> print('Geometric albedo is {0:.3}'.format(geomalb))
Geometric albedo is 0.0487
>>> print('Bond albedo is {0:.3}'.format(bondalb))
Bond albedo is 0.0179
>>> print('Phase integral is {0:.3}'.format(phaseint))
Phase integral is 0.367

Initialize DiskIntegratedPhaseFunc

Parameters

radiusastropy.units.Quantity, optional

Radius of object. Required if conversion between magnitude and reflectance is involved.

wfbQuantity, SpectralElement, string

Wavelengths, frequencies, or bandpasses. Bandpasses may be a filter name (string). Required if conversion between magnitude and reflectance is involved.

**kwargsoptional parameters accepted by

astropy.modeling.Model.__init__()

Attributes Summary

H
S
input_units
param_names	Names of the parameters that describe models of this type.

Methods Summary

evaluate(a, H, S)	Evaluate the model on some input variables.
fit_deriv(a, H, S)

Attributes Documentation

H = Parameter('H', value=nan)

S = Parameter('S', value=nan)

input_units = {'x': Unit("deg")}

param_names = ('H', 'S')

Names of the parameters that describe models of this type.

The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.

When defining a custom model class the value of this attribute is automatically set by the Parameter attributes defined in the class body.

Methods Documentation

static evaluate(a, H, S)

Evaluate the model on some input variables.

static fit_deriv(a, H, S)