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
)Attributes Summary
Function (similar to the model's
evaluate
) to compute the derivatives of the model with respect to its parameters, for use by fitting algorithms.Names of the parameters that describe models of this type.
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¶
- n_outputs = 1¶
- 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