Merge pull request #184 from karllark/add_d22

Add D22 models

docs/dust_extinction/choose_model.rst

@@ -23,6 +23,7 @@ simplest models and include models for the MW
 :class:`~dust_extinction.averages.GCC09_MWAvg`,
 :class:`~dust_extinction.averages.F11_MWGC`,
 :class:`~dust_extinction.averages.G21_MWAvg`;
+:class:`~dust_extinction.averages.D22_MWAvg`;
 Note the different valid wavelength ranges), the LMC
 (:class:`~dust_extinction.averages.G03_LMCAvg`,
 :class:`~dust_extinction.averages.G03_LMC2`) and the SMC
@@ -44,6 +45,8 @@ Way, the usual average used is R(V) = 3.1.
 +--------------+-------------+------------------+--------------+
 | G21_MWAvg | 0.3125 - 1 | 1 - 32 | MW |
 +--------------+-------------+------------------+--------------+
+| D22_MWAvg | 0.2 - 1.25 | 0.8 - 4 | MW |
++--------------+-------------+------------------+--------------+
 | CT06_MWLoc | 0.037 - 0.8 | 1.24 - 27.0 | MW (Local) |
 +--------------+-------------+------------------+--------------+
 | RL85_MWGC | 0.08 - 0.8 | 1.25 - 13.0 | MW (GCenter) |
@@ -101,6 +104,8 @@ significantly more extinction curves than the
 +----------+-------------+-------------+------------------+--------------+
 | F19 | R(V) | 0.3 - 8.7 | 0.115 - 3.3 | MW |
 +----------+-------------+-------------+------------------+--------------+
+| D22 | R(V) | 0.2 - 1.25 | 0.8 - 5.0 | MW |
++----------+-------------+-------------+------------------+--------------+
 
 Notes
 -----

docs/dust_extinction/model_flavors.rst

@@ -68,15 +68,16 @@ Average models
  CT06_MWLoc,
  CT06_MWGC,
  F11_MWGC,
- G21_MWAvg)
+ G21_MWAvg,
+ D22_MWAvg)
 
  fig, ax = plt.subplots()
 
  # generate the curves and plot them
  x = 1.0 / (np.arange(1.0, 40.0 ,0.1) * u.micron)
 
  models = [RL85_MWGC, RRP89_MWGC, I05_MWAvg, CT06_MWLoc, CT06_MWGC,
- F11_MWGC, G21_MWAvg]
+ F11_MWGC, G21_MWAvg, D22_MWAvg]
 
  for cmodel in models:
  ext_model = cmodel()
@@ -123,7 +124,7 @@ R(V) (+ other variables) dependent prediction models
  import astropy.units as u
 
  from dust_extinction.parameter_averages import (CCM89, O94, F99, F04,
- VCG04, GCC09, M14, F19)
+ VCG04, GCC09, M14, F19, D22)
 
  fig, ax = plt.subplots()
 
@@ -132,7 +133,7 @@ R(V) (+ other variables) dependent prediction models
 
  Rv = 3.1
 
- models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19]
+ models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19, D22]
 
  for cmodel in models:
  ext_model = cmodel(Rv=Rv)
@@ -159,16 +160,16 @@ R(V) (+ other variables) dependent prediction models
  import astropy.units as u
 
  from dust_extinction.parameter_averages import (CCM89, O94, F99, F04,
- VCG04, GCC09, M14, F19)
+ VCG04, GCC09, M14, F19, D22)
 
  fig, ax = plt.subplots()
 
  # generate the curves and plot them
  x = np.arange(0.5,11.0,0.1)/u.micron
 
- Rv = 2.0
+ Rv = 2.5
 
- models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19]
+ models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19, D22]
 
  for cmodel in models:
  ext_model = cmodel(Rv=Rv)
@@ -181,7 +182,7 @@ R(V) (+ other variables) dependent prediction models
  ax.set_xlabel('$x$ [$\mu m^{-1}$]')
  ax.set_ylabel('$A(x)/A(V)$')
 
- ax.set_title('R(V) = 2.0')
+ ax.set_title('R(V) = 2.5')
 
  ax.legend(loc='best')
  plt.tight_layout()
@@ -195,7 +196,7 @@ R(V) (+ other variables) dependent prediction models
  import astropy.units as u
 
  from dust_extinction.parameter_averages import (CCM89, O94, F99, F04,
- VCG04, GCC09, M14, F19)
+ VCG04, GCC09, M14, F19, D22)
 
  fig, ax = plt.subplots()
 
@@ -204,7 +205,7 @@ R(V) (+ other variables) dependent prediction models
 
  Rv = 5.5
 
- models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19]
+ models = [CCM89, O94, F99, F04, VCG04, GCC09, M14, F19, D22]
 
  for cmodel in models:
  ext_model = cmodel(Rv=Rv)

docs/dust_extinction/references.rst

@@ -3,7 +3,7 @@ References
 ##########
 
 C11: `Compiegne et al. 2011, A&A, 525, 103
-<https://ui.adsabs.harvard.edu/abs/2011A%26A...525A.103C/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2011A%26A...525A.103C>`_
 
 CCM89: `Cardelli, Clayton, & Mathis 1989, ApJ, 345, 245
 <https://ui.adsabs.harvard.edu/abs/1989ApJ...345..245C>`_
@@ -12,12 +12,14 @@ CT06: `Chiar & Tielens 2006, ApJ, 637 774
 <https://ui.adsabs.harvard.edu/abs/2006ApJ...637..774C>`_
 
 D03: `Draine 2003, ARA&A, 41, 241
-<https://ui.adsabs.harvard.edu/abs/2003ARA%26A..41..241D/abstract>`_;
+<https://ui.adsabs.harvard.edu/abs/2003ARA%26A..41..241D>`_;
 `Draine 2003, ApJ, 598, 1017
-<https://ui.adsabs.harvard.edu/abs/2003ApJ...598.1017D/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2003ApJ...598.1017D>`_
+
+D22: Decleir et al. 2022, ApJ, in press
 
 DBP90: `Desert, Boulanger, & Puget 1990, A&A, 237, 215
-<https://ui.adsabs.harvard.edu/abs/1990A%26A...237..215D/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/1990A%26A...237..215D>`_
 
 F99: `Fitzpatrick 1999, PASP, 111, 63
 <https://ui.adsabs.harvard.edu/abs/1999PASP..111...63F>`_
@@ -46,17 +48,17 @@ GCC09: `Gordon, Cartledge, & Clayton 2009, ApJ, 705, 1320
 G16: `Gordon et al. 2016, ApJ 826, 104
 <https://ui.adsabs.harvard.edu/abs/2016ApJ...826..104G>`_
 
-G21: `Gordon et al. 2021, ApJ, in press
-<https://arxiv.org/abs/2105.05087>`_
+G21: `Gordon et al. 2021, ApJ, 916, 33
+<https://ui.adsabs.harvard.edu/abs/2021ApJ...916...33G>`_
 
 I05: `Indebetouw et al. 2005, ApJ, 619, 931
 <https://ui.adsabs.harvard.edu/abs/2005ApJ...619..931I>`_
 
 J13: `Jones et al. 2013, A&A, 558, 62
-<https://ui.adsabs.harvard.edu/abs/2013A%26A...558A..62J/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2013A%26A...558A..62J>`_
 
 KJY14: `Kohler, Jones, & Ysard 2014, A&A, 565, 9
-<https://ui.adsabs.harvard.edu/abs/2014A%26A...565L...9K/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2014A%26A...565L...9K>`_
 
 M14: `Maiz Apell\’aniz et al. 2014, A&A, 564, 63
 <https://ui.adsabs.harvard.edu/abs/2014A%26A...564A..63M>`_
@@ -74,10 +76,10 @@ VCG04: `Valencic, Clayton, & Gordon 2014, 616, 912
 <https://ui.adsabs.harvard.edu/abs/2004ApJ...616..912V>`_
 
 WG01: `Weingartner & Draine 2001, ApJ, 548, 296
-<https://ui.adsabs.harvard.edu/abs/2001ApJ...548..296W/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2001ApJ...548..296W>`_
 
 ZDA04: `Zubko, Dwek, & Arendt 2004, ApJS, 152, 211
-<https://ui.adsabs.harvard.edu/abs/2004ApJS..152..211Z/abstract>`_
+<https://ui.adsabs.harvard.edu/abs/2004ApJS..152..211Z>`_
 
 Naming Convention
 =================

dust_extinction/averages.py

@@ -5,6 +5,7 @@
 import numpy as np
 from scipy.interpolate import interp1d
 from astropy.table import Table
+from astropy.modeling.models import PowerLaw1D
 
 from .helpers import _get_x_in_wavenumbers, _test_valid_x_range
 from .baseclasses import BaseExtModel
@@ -23,6 +24,7 @@
  "GCC09_MWAvg",
  "F11_MWGC",
  "G21_MWAvg",
+ "D22_MWAvg",
 ]
 
 
@@ -1276,7 +1278,7 @@ class G21_MWAvg(BaseExtModel):
 
  Notes
  -----
- From Gordon et al. (2021, ApJ, submitted)
+ From Gordon et al. (2021, ApJ, 916, 33)
 
  Example showing the average curve
 
@@ -1398,4 +1400,118 @@ def evaluate(self, in_x):
  )
 
  # return A(x)/A(V)
+ # G21 a full dust_extinction model, hence send in x with units
  return g21_fit(in_x)
+
+
+class D22_MWAvg(BaseExtModel):
+ r"""
+ Decleir et al. (2022) Milky Way Average Extinction Curve
+
+ Parameters
+ ----------
+ None
+
+ Raises
+ ------
+ None
+
+ Notes
+ -----
+ From Decleir et al. (2022, ApJ, submitted)
+
+ 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 D22_MWAvg
+
+ fig, ax = plt.subplots()
+
+ # generate the curves and plot them
+ lam = np.logspace(np.log10(0.8), np.log10(4.9), num=1000)
+ x = (1.0 / lam) / u.micron
+
+ # define the extinction model
+ ext_model = D22_MWAvg()
+
+ ax.plot(1.0 / x, ext_model(x), label="D22_MWAvg")
+ ax.errorbar(
+ 1.0 / ext_model.obsdata_x,
+ ext_model.obsdata_axav,
+ yerr=ext_model.obsdata_axav_unc,
+ fmt="ko",
+ label="obsdata",
+ )
+
+ ax.set_xlabel(r"$\lambda$ [$\mu m$]")
+ ax.set_ylabel(r"$A(x)/A(V)$")
+
+ ax.set_xscale("log")
+ ax.set_yscale("log")
+
+ ax.legend(loc="best")
+ plt.show()
+
+ """
+
+ x_range = [1.0 / 5.0, 1.0 / 0.8]
+
+ Rv = 3.12
+
+ 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 + "D22.dat", format="ascii.commented_header")
+
+ # Spex data
+ self.obsdata_x = 1.0 / a["wavelength[micron]"].data
+ self.obsdata_axav = a["ave"].data
+ self.obsdata_axav_unc = a["ave_unc"].data
+
+ # accuracy of the observed data based on published table
+ self.obsdata_tolerance = 0.2 # check
+
+ super().__init__(**kwargs)
+
+ def evaluate(self, in_x):
+ """
+ D22_MWAvg 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
+ """
+ x = _get_x_in_wavenumbers(in_x)
+
+ # check that the wavenumbers are within the defined range
+ _test_valid_x_range(x, self.x_range, self.__class__.__name__)
+
+ # setup the model
+ d22_fit = PowerLaw1D(alpha=1.71, amplitude=0.386, x_0=1.0)
+
+ # return A(x)/A(V)
+ # Note that model in D22 was done versus wavelength in microns
+ return d22_fit(1.0 / x)

dust_extinction/data/D22.dat

@@ -0,0 +1,63 @@
+# wavelength[micron] ave ave_unc ave_fit intercept slope std
+0.8500000000000001 0.5229116082191467 0.022688888013362885 0.5090679346458734 0.5043843287104457 -0.6293039288140487 0.05562848830036259
+0.9000000000000001 0.45997095108032227 0.02032790146768093 0.46166625669933375 0.45549648735093856 -0.6174529410812017 0.050836719542353026
+0.9500000000000002 0.42000287771224976 0.02005036734044552 0.42089729924118685 0.4136214060035163 -0.6385516873757641 0.0478619405952933
+1.0000000000000002 0.3810850977897644 0.019589412957429886 0.38555305537535717 0.3774589648330171 -0.6469538603772321 0.04522852235644631
+1.0500000000000003 0.34920358657836914 0.019196437671780586 0.35469172892455036 0.34599950638712346 -0.6512808886202338 0.043619971939746316
+1.1000000000000003 0.32195335626602173 0.018980903550982475 0.3275698003772987 0.3184488876969109 -0.6583070646901519 0.0417168231073246
+1.1500000000000004 0.30355149507522583 0.018601318821310997 0.30359359134808894 0.29417524356493724 -0.6680955040414542 0.03936325326515428
+1.2000000000000004 0.28181931376457214 0.01777954027056694 0.28228416140059226 0.27267054082171815 -0.6345926051206287 0.03833801592606844
+1.2500000000000004 0.2640969157218933 0.01732826977968216 0.2632514869766455 0.25352239430475715 -0.6211808201417144 0.03714196876173308
+1.3000000000000005 0.24801427125930786 0.016910871490836143 0.24617520939060414 0.23639312104983637 -0.6322691076743284 0.03624935439391689
+1.3500000000000005 0.23989495635032654 0.01684890314936638 0.23079010189928667 0.22100397773182737 -0.638483589495678 0.03559657117065511
+1.4000000000000006 0.2204539030790329 0.020606864243745804 0.21687497364660085 0.20712316154029303 -0.6171752583533128 0.034496160362062656
+1.4500000000000006 0.20975157618522644 0.018769754096865654 0.2042441084260786 0.1945565786103972 -0.5759689452024471 0.03344508246073979
+1.5000000000000007 0.19530680775642395 0.014611581340432167 0.1927405948483919 0.18314067171798756 -0.5268239963976614 0.03310755967105022
+1.5500000000000007 0.184793159365654 0.013940279372036457 0.18223108310987315 0.17273679698212466 -0.4834620236847841 0.033611979598619154
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