adding in SMC Bumps average

docs/dust_extinction/choose_model.rst

@@ -28,7 +28,8 @@ Note the different valid wavelength ranges), the LMC
 (:class:`~dust_extinction.averages.G03_LMCAvg`,
 :class:`~dust_extinction.averages.G03_LMC2`) and the SMC
 (:class:`~dust_extinction.averages.G03_SMCBar`,
-:class:`~dust_extinction.averages.G24_SMCAvg`).
+:class:`~dust_extinction.averages.G24_SMCAvg`,
+:class:`~dust_extinction.averages.G24_SMCBumps`).
 
 One often used alternative to these straight average models is to use one of
 the parameter dependent models with the average R(V) value. For the Milky
@@ -66,7 +67,8 @@ Way, the usual average used is R(V) = 3.1. See the next section.
 +--------------+-------------+------------------+--------------+
 | G24_SMCAvg | 0.3 - 10.0 | 0.1 - 3.3 | SMC |
 +--------------+-------------+------------------+--------------+
-
+| G24_SMCBumps | 0.3 - 10.0 | 0.1 - 3.3 | SMC |
++--------------+-------------+------------------+--------------+
 
 Parameter Dependent Average Curves
 ----------------------------------

docs/dust_extinction/model_flavors.rst

@@ -33,14 +33,16 @@ Average models
  G03_SMCBar,
  G03_LMCAvg,
  G03_LMC2,
- G24_SMCAvg)
+ G24_SMCAvg,
+ G24_SMCBumps)
 
  fig, ax = plt.subplots()
 
  # generate the curves and plot them
  x = np.arange(0.3,11.0,0.1)/u.micron
 
- models = [GCC09_MWAvg, B92_MWAvg, G03_SMCBar, G03_LMCAvg, G03_LMC2, G24_SMCAvg]
+ models = [GCC09_MWAvg, B92_MWAvg, G03_SMCBar, G03_LMCAvg, G03_LMC2, 
+ G24_SMCAvg, G24_SMCBumps]
 
  for cmodel in models:
  ext_model = cmodel()

docs/dust_extinction/references.rst

@@ -52,9 +52,11 @@ G16: `Gordon et al. 2016, ApJ 826, 104
 G21: `Gordon et al. 2021, ApJ, 916, 33
 <https://ui.adsabs.harvard.edu/abs/2021ApJ...916...33G>`_
 
-G23: `Gordon et al. 2022, ApJ, 950, 86
+G23: `Gordon et al. 2023, ApJ, 950, 86
 <https://ui.adsabs.harvard.edu/abs/2023ApJ...950...86G/abstract>`_
 
+G24: Gordon et al. 2024, ApJ, in press
+
 HD23: `Hensley & Draine 2023, ApJ, 948, 55
 <https://ui.adsabs.harvard.edu/abs/2023ApJ...948...55H/abstract>`_
 

dust_extinction/averages.py

@@ -24,7 +24,7 @@
  "G21_MWAvg",
  "D22_MWAvg",
  "G24_SMCAvg",
- # "G24_SMCBumps"
+ "G24_SMCBumps",
 ]
 
 
@@ -1581,7 +1581,9 @@ def __init__(self, **kwargs):
  self.obsdata_axav_unc = a["unc"].data
 
  # accuracy of the observed data based on published table
- self.obsdata_tolerance = 0.1 # large value driven by short wavelength uncertainties
+ self.obsdata_tolerance = (
+ 0.1 # large value driven by short wavelength uncertainties
+ )
 
  super().__init__(**kwargs)
 
@@ -1632,3 +1634,126 @@ def evaluate(self, in_x):
  self.x_range,
  self.__class__.__name__,
  )
+
+
+class G24_SMCBumps(BaseExtModel):
+ r"""
+ Gordon et al (2024) SMC Bumps Extinction Curve
+
+ Parameters
+ ----------
+ None
+
+ Raises
+ ------
+ None
+
+ Notes
+ -----
+ From Gordon et al. (2024, ApJ, in press)
+
+ Two data points in the FUV from the data file giving the observed average were removed
+ as they are *very* deviate from the FM90 parametrization. This cause the automated tests
+ to fail.
+
+ Example showing the average curve
+
+ .. plot::
+ :include-source:
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ import astropy.units as u
+
+ from dust_extinction.averages import G24_SMCBumps
+
+ fig, ax = plt.subplots()
+
+ # define the extinction model
+ ext_model = G24_SMCBumps()
+
+ # generate the curves and plot them
+ x = np.arange(ext_model.x_range[0], ext_model.x_range[1],0.1)/u.micron
+
+ ax.plot(x,ext_model(x),label='G24 SMC Bumps')
+ ax.plot(ext_model.obsdata_x, ext_model.obsdata_axav, 'ko',
+ label='obsdata')
+
+ ax.set_xlabel(r'$x$ [$\mu m^{-1}$]')
+ ax.set_ylabel(r'$A(x)/A(V)$')
+
+ ax.legend(loc='best')
+ plt.show()
+ """
+
+ x_range = [0.3, 10.0]
+
+ Rv = 2.59
+
+ def __init__(self, **kwargs):
+
+ # get the tabulated information
+ data_path = pkg_resources.resource_filename("dust_extinction", "data/")
+
+ # D22 sigma clipped average of 13 diffuse sightlines
+ a = Table.read(data_path + "G24_SMCBumps.dat", format="ascii.commented_header")
+
+ # data
+ self.obsdata_x = 1.0 / a["wave"].data
+ self.obsdata_axav = a["A(l)/A(V)"].data
+ self.obsdata_axav_unc = a["unc"].data
+
+ # accuracy of the observed data based on published table
+ self.obsdata_tolerance = (
+ 0.1 # large value driven by short wavelength uncertainties
+ )
+
+ super().__init__(**kwargs)
+
+ def evaluate(self, in_x):
+ """
+ G24 SMCBumps function
+
+ Parameters
+ ----------
+ in_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
+ """
+ C1 = -2.87
+ C2 = 1.51
+ C3 = 2.64
+ C4 = 0.25
+ xo = 4.73
+ gamma = 1.16
+
+ optnir_axav_x = 1.0 / np.array([2.198, 1.65, 1.25, 0.55, 0.44, 0.37])
+ optnir_axav_y = [0.068, 0.174, 0.302, 1.017, 1.394, 1.675]
+
+ # return A(x)/A(V)
+ return _curve_F99_method(
+ in_x,
+ self.Rv,
+ C1,
+ C2,
+ C3,
+ C4,
+ xo,
+ gamma,
+ optnir_axav_x,
+ optnir_axav_y,
+ self.x_range,
+ self.__class__.__name__,
+ )

dust_extinction/data/G24_SMCBumps.dat

@@ -1,7 +1,5 @@
 # wave A(l)/A(V) unc type
-0.112305922546031 4.27938281479861 0.41979972549018957 spec
 0.11701444543424377 5.4176459980372185 0.1032219276339054 spec
-0.12192037721493712 4.272988148773956 0.8216371204158461 spec
 0.12703199442658303 4.752853473898855 0.08375995156522892 spec
 0.13235792060868376 4.671941852629287 0.08390085400631052 spec
 0.13790714085008998 4.395775332012629 0.0583456651927308 spec
@@ -21,19 +19,12 @@
 0.24507681076473067 2.636005637080303 0.014538738385996147 spec
 0.25535186791839903 2.514649982179777 0.007126586158241167 spec
 0.26605771572574743 2.299400811815393 0.011083699616338564 spec
-0.27094539999961853 2.334321136586501 0.03195466433109193 WFC3_F275W
 0.27721241545733843 2.1865630620178007 0.02234139631596784 spec
 0.28883478561811626 2.1714747651498394 0.007120837703763066 spec
 0.30094443369511337 2.013597970861842 0.0120703720266099 spec
-0.33499833941459656 1.8257437897333624 0.008129228237896528 WFC3_F336W
 0.3628658354282379 1.6746842300512743 0.00040831423990903573 JohnU
 0.43783867359161377 1.3942325108128082 0.006685972865933271 JohnB
-0.46907517313957214 1.2932579177380588 0.00267344543998287 ACS_F475W
 0.5442439317703247 1.01686775136653 0.0002159286715026245 JohnV
-0.5572436451911926 0.9611119466969889 0.04265954068073023 ACS_F550M
-0.7932101488113403 0.5757920680725783 0.013510368208852347 ACS_F814W
-1.1072901487350464 0.30360021912429525 0.04379160995122052 WFC3_F110W
 1.2309569120407104 0.3024797256658269 0.04839927738226376 JohnJ
-1.5236843824386597 0.09086306686909129 0.07626826420000873 WFC3_F160W
 1.6215280294418335 0.1738600487817531 0.04846869511215765 JohnH
 2.1742196083068848 0.06818160198036438 0.0798256207489614 JohnK

dust_extinction/tests/helpers.py

@@ -29,6 +29,7 @@
  G21_MWAvg,
  D22_MWAvg,
  G24_SMCAvg,
+ G24_SMCBumps,
 )
 from dust_extinction.grain_models import DBP90, WD01, D03, ZDA04, C11, J13, HD23
 
@@ -51,6 +52,7 @@
  G21_MWAvg,
  D22_MWAvg,
  G24_SMCAvg,
+ G24_SMCBumps,
 ]
 grain_models = [DBP90, WD01, D03, ZDA04, C11, J13, HD23]