P92¶

class dust_extinction.shapes.P92(BKG_amp=218.57142857142858, BKG_lambda=0.047, BKG_b=90.0, BKG_n=2.0, FUV_amp=18.545454545454547, FUV_lambda=0.07, FUV_b=4.0, FUV_n=6.5, NUV_amp=0.05961038961038961, NUV_lambda=0.22, NUV_b=-1.95, NUV_n=2.0, SIL1_amp=0.0026493506493506496, SIL1_lambda=9.7, SIL1_b=-1.95, SIL1_n=2.0, SIL2_amp=0.0026493506493506496, SIL2_lambda=18.0, SIL2_b=-1.8, SIL2_n=2.0, FIR_amp=0.015896103896103898, FIR_lambda=25.0, FIR_b=0.0, FIR_n=2.0, **kwargs)[source]¶

Bases: BaseExtModel, Fittable1DModel

Pei (1992) 24 parameter shape model

Parameters:

BKG_ampfloat: background term amplitude
BKG_lambdafloat: background term central wavelength
BKG_bfloat: background term b coefficient
BKG_nfloat: background term n coefficient [FIXED at n = 2]
FUV_ampfloat: far-ultraviolet term amplitude
FUV_lambdafloat: far-ultraviolet term central wavelength
FUV_bfloat: far-ultraviolet term b coefficent
FUV_nfloat: far-ultraviolet term n coefficient
NUV_ampfloat: near-ultraviolet (2175 A) term amplitude
NUV_lambdafloat: near-ultraviolet (2175 A) term central wavelength
NUV_bfloat: near-ultraviolet (2175 A) term b coefficent
NUV_nfloat: near-ultraviolet (2175 A) term n coefficient [FIXED at n = 2]
SIL1_ampfloat: 1st silicate feature (~10 micron) term amplitude
SIL1_lambdafloat: 1st silicate feature (~10 micron) term central wavelength
SIL1_bfloat: 1st silicate feature (~10 micron) term b coefficent
SIL1_nfloat: 1st silicate feature (~10 micron) term n coefficient [FIXED at n = 2]
SIL2_ampfloat: 2nd silicate feature (~18 micron) term amplitude
SIL2_lambdafloat: 2nd silicate feature (~18 micron) term central wavelength
SIL2_bfloat: 2nd silicate feature (~18 micron) term b coefficient
SIL2_nfloat: 2nd silicate feature (~18 micron) term n coefficient [FIXED at n = 2]
FIR_ampfloat: far-infrared term amplitude
FIR_lambdafloat: far-infrared term central wavelength
FIR_bfloat: far-infrared term b coefficent
FIR_nfloat: far-infrared term n coefficient [FIXED at n = 2]

Notes

From Pei (1992, ApJ, 395, 130)

Applicable from the extreme UV to far-IR

Example showing a P92 curve with components identified.

import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u

from dust_extinction.shapes import P92

fig, ax = plt.subplots()

# generate the curves and plot them
lam = np.logspace(-3.0, 3.0, num=1000)
x = (1.0/lam)/u.micron

ext_model = P92()
ax.plot(1/x,ext_model(x),label='total')

ext_model = P92(FUV_amp=0., NUV_amp=0.0,
                SIL1_amp=0.0, SIL2_amp=0.0, FIR_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG only')

ext_model = P92(NUV_amp=0.0,
                SIL1_amp=0.0, SIL2_amp=0.0, FIR_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG+FUV only')

ext_model = P92(FUV_amp=0.,
                SIL1_amp=0.0, SIL2_amp=0.0, FIR_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG+NUV only')

ext_model = P92(FUV_amp=0., NUV_amp=0.0,
                SIL2_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG+FIR+SIL1 only')

ext_model = P92(FUV_amp=0., NUV_amp=0.0,
                SIL1_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG+FIR+SIL2 only')

ext_model = P92(FUV_amp=0., NUV_amp=0.0,
                SIL1_amp=0.0, SIL2_amp=0.0)
ax.plot(1./x,ext_model(x),label='BKG+FIR only')

# Milky Way observed extinction as tabulated by Pei (1992)
MW_x = [0.21, 0.29, 0.45, 0.61, 0.80, 1.11, 1.43, 1.82,
        2.27, 2.50, 2.91, 3.65, 4.00, 4.17, 4.35, 4.57, 4.76,
        5.00, 5.26, 5.56, 5.88, 6.25, 6.71, 7.18, 7.60,
        8.00, 8.50, 9.00, 9.50, 10.00]
MW_x = np.array(MW_x)
MW_exvebv = [-3.02, -2.91, -2.76, -2.58, -2.23, -1.60, -0.78, 0.00,
             1.00, 1.30, 1.80, 3.10, 4.19, 4.90, 5.77, 6.57, 6.23,
             5.52, 4.90, 4.65, 4.60, 4.73, 4.99, 5.36, 5.91,
             6.55, 7.45, 8.45, 9.80, 11.30]
MW_exvebv = np.array(MW_exvebv)
Rv = 3.08
MW_axav = MW_exvebv/Rv + 1.0
ax.plot(1./MW_x, MW_axav, 'o', label='MW Observed')

ax.set_xscale('log')
ax.set_yscale('log')

ax.set_ylim(1e-3,10.)

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$A(x)/A(V)$')

ax.legend(loc='best')
plt.show()

(Source code, png, hires.png, pdf)

../_images/dust_extinction-shapes-P92-1.png

Attributes Summary

`AbAv`
`BKG_amp`
`BKG_b`
`BKG_lambda`
`BKG_n`
`FIR_amp`
`FIR_b`
`FIR_lambda`
`FIR_n`
`FUV_amp`
`FUV_b`
`FUV_lambda`
`FUV_n`
`NUV_amp`
`NUV_b`
`NUV_lambda`
`NUV_n`
`SIL1_amp`
`SIL1_b`
`SIL1_lambda`
`SIL1_n`
`SIL2_amp`
`SIL2_b`
`SIL2_lambda`
`SIL2_n`
`fit_deriv`	Function (similar to the model's `evaluate`) to compute the derivatives of the model with respect to its parameters, for use by fitting algorithms.
`n_inputs`	The number of inputs.
`n_outputs`	The number of outputs.
`param_names`	Names of the parameters that describe models of this type.
`x_range`

Methods Summary

`__call__`(*inputs[, model_set_axis, ...])	Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.
`evaluate`(x, BKG_amp, BKG_lambda, BKG_b, ...)	P92 function

Attributes Documentation

AbAv = 1.3246753246753247¶

BKG_amp = Parameter('BKG_amp', value=218.57142857142858, bounds=(0.0, None))¶

BKG_b = Parameter('BKG_b', value=90.0)¶

BKG_lambda = Parameter('BKG_lambda', value=0.047)¶

BKG_n = Parameter('BKG_n', value=2.0, fixed=True)¶

FIR_amp = Parameter('FIR_amp', value=0.015896103896103898, bounds=(0.0, None))¶

FIR_b = Parameter('FIR_b', value=0.0)¶

FIR_lambda = Parameter('FIR_lambda', value=25.0, bounds=(20.0, 30.0))¶

FIR_n = Parameter('FIR_n', value=2.0, fixed=True)¶

FUV_amp = Parameter('FUV_amp', value=18.545454545454547, bounds=(0.0, None))¶

FUV_b = Parameter('FUV_b', value=4.0)¶

FUV_lambda = Parameter('FUV_lambda', value=0.07, bounds=(0.06, 0.08))¶

FUV_n = Parameter('FUV_n', value=6.5)¶

NUV_amp = Parameter('NUV_amp', value=0.05961038961038961, bounds=(0.0, None))¶

NUV_b = Parameter('NUV_b', value=-1.95)¶

NUV_lambda = Parameter('NUV_lambda', value=0.22, bounds=(0.2, 0.24))¶

NUV_n = Parameter('NUV_n', value=2.0, fixed=True)¶

SIL1_amp = Parameter('SIL1_amp', value=0.0026493506493506496, bounds=(0.0, None))¶

SIL1_b = Parameter('SIL1_b', value=-1.95)¶

SIL1_lambda = Parameter('SIL1_lambda', value=9.7, bounds=(7.0, 13.0))¶

SIL1_n = Parameter('SIL1_n', value=2.0, fixed=True)¶

SIL2_amp = Parameter('SIL2_amp', value=0.0026493506493506496, bounds=(0.0, None))¶

SIL2_b = Parameter('SIL2_b', value=-1.8)¶

SIL2_lambda = Parameter('SIL2_lambda', value=18.0, bounds=(15.0, 21.0))¶

SIL2_n = Parameter('SIL2_n', value=2.0, fixed=True)¶

fit_deriv = None¶: Function (similar to the model’s evaluate) to compute the derivatives of the model with respect to its parameters, for use by fitting algorithms. In other words, this computes the Jacobian matrix with respect to the model’s parameters.

n_inputs = 1¶: The number of inputs.

n_outputs = 1¶: The number of outputs.

param_names = ('BKG_amp', 'BKG_lambda', 'BKG_b', 'BKG_n', 'FUV_amp', 'FUV_lambda', 'FUV_b', 'FUV_n', 'NUV_amp', 'NUV_lambda', 'NUV_b', 'NUV_n', 'SIL1_amp', 'SIL1_lambda', 'SIL1_b', 'SIL1_n', 'SIL2_amp', 'SIL2_lambda', 'SIL2_b', 'SIL2_n', 'FIR_amp', 'FIR_lambda', 'FIR_b', 'FIR_n')¶

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.

x_range = [0.001, 1000.0]¶

Methods Documentation

__call__(*inputs, model_set_axis=None, with_bounding_box=False, fill_value=nan, equivalencies=None, inputs_map=None, **new_inputs)¶: Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.

evaluate(x, BKG_amp, BKG_lambda, BKG_b, BKG_n, FUV_amp, FUV_lambda, FUV_b, FUV_n, NUV_amp, NUV_lambda, NUV_b, NUV_n, SIL1_amp, SIL1_lambda, SIL1_b, SIL1_n, SIL2_amp, SIL2_lambda, SIL2_b, SIL2_n, FIR_amp, FIR_lambda, FIR_b, FIR_n)[source]¶

P92 function

Parameters:

x: float

expects either x in units of wavelengths or frequency or assumes wavelengths in wavenumbers [1/micron]

internally wavenumbers are used

Returns:

axav: np array (float): A(x)/A(V) extinction curve [mag]

Raises:

ValueError: Input x values outside of defined range

Navigation

P92¶