updating docs

docs/dust_extinction/choose_model.rst

@@ -22,12 +22,13 @@ simplest models and include models for the MW
 :class:`~dust_extinction.averages.CT06_MWGC`,
 :class:`~dust_extinction.averages.GCC09_MWAvg`,
 :class:`~dust_extinction.averages.F11_MWGC`,
-:class:`~dust_extinction.averages.G21_MWAvg`;
+: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
-(:class:`~dust_extinction.averages.G03_SMCBar`).
+(:class:`~dust_extinction.averages.G03_SMCBar`,
+:class:`~dust_extinction.averages.G24_SMCAvg`).
 
 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
@@ -63,6 +64,8 @@ Way, the usual average used is R(V) = 3.1.  See the next section.
 +--------------+-------------+------------------+--------------+
 | G03_SMCBar   |  0.3 - 10.0 |        0.1 - 3.3 |          SMC |
 +--------------+-------------+------------------+--------------+
+| G24_SMCAvg   |  0.3 - 10.0 |        0.1 - 3.3 |          SMC |
++--------------+-------------+------------------+--------------+
 
 
 Parameter Dependent Average Curves

docs/dust_extinction/model_flavors.rst

@@ -13,8 +13,9 @@ Average models
    Models are provided for the Milky Way for the optical (Bastiaansen 1992),
    ultraviolet through near-infrared
    (Gordon, Cartlege, & Clayton 2009) and near- and mid-infrared
-   (Rieke & Lebofsky 1985; Indebetouw et al. 2005; Chiar & Tielens 2006; Fritz et al. 2011)
-   and the Magellanic Clouds (Gordon et al. 2003).
+   (Rieke & Lebofsky 1985; Indebetouw et al. 2005; Chiar & Tielens 2006; Fritz et al. 2011;
+   Gordon et al. 2021; Decleir et al. 2022)
+   and the Magellanic Clouds (Gordon et al. 2003; Gordon et al. 2024).
 
    For the Milky Way for the ultraviolet through near-infrared,
    one of the R(V) dependent models with R(V) = 3.1
@@ -31,14 +32,15 @@ Average models
                                          B92_MWAvg,
                                          G03_SMCBar,
                                          G03_LMCAvg,
-					                               G03_LMC2)
+					                          G03_LMC2,
+                                         G24_SMCAvg)
 
    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]
+   models = [GCC09_MWAvg, B92_MWAvg, G03_SMCBar, G03_LMCAvg, G03_LMC2, G24_SMCAvg]
 
    for cmodel in models:
       ext_model = cmodel()
@@ -111,8 +113,9 @@ R(V) (+ other variables) dependent prediction models
 
    The R(V) dependent models include CCM89 the original such model
    (Cardelli, Clayton, and Mathis 1989), the O94 model that updates the
-   optical portion of the CCM89 model (O'Donnell 1994), and the F99 model
-   (Fitzpatrick 1999) updated as F04 (Fitzpatrick 2004),
+   optical portion of the CCM89 model (O'Donnell 1994), the F99 model
+   (Fitzpatrick 1999) updated as F04 (Fitzpatrick 2004), and the
+   fully spectroscopic far-UV to mid-IR G23 (Gordon et al. 2023).
    These models are based on the average
    behavior of extinction in the Milky Way as a function of R(V).
    The M14 model refines the optical portion of the CCM89 model

dust_extinction/averages.py

@@ -24,7 +24,7 @@
     "G21_MWAvg",
     "D22_MWAvg",
     "G24_SMCAvg",
-    "G24_SMCBumps"
+    #    "G24_SMCBumps"
 ]
 
 
@@ -1581,7 +1581,7 @@ def __init__(self, **kwargs):
         self.obsdata_axav_unc = a["unc"].data
 
         # accuracy of the observed data based on published table
-        self.obsdata_tolerance = 0.2  # check
+        self.obsdata_tolerance = 0.1  # large value driven by short wavelength uncertainties
 
         super().__init__(**kwargs)
 
@@ -1614,9 +1614,7 @@ def evaluate(self, in_x):
         xo = 4.59
         gamma = 0.95
 
-        optnir_axav_x = 1.0 / np.array(
-            [2.198, 1.65, 1.25, 0.55, 0.44, 0.37]
-        )
+        optnir_axav_x = 1.0 / np.array([2.198, 1.65, 1.25, 0.55, 0.44, 0.37])
         optnir_axav_y = [0.075, 0.137, 0.333, 1.021, 1.345, 1.507]
 
         # return A(x)/A(V)

dust_extinction/tests/helpers.py

@@ -28,6 +28,7 @@
     F11_MWGC,
     G21_MWAvg,
     D22_MWAvg,
+    G24_SMCAvg,
 )
 from dust_extinction.grain_models import DBP90, WD01, D03, ZDA04, C11, J13, HD23
 
@@ -49,6 +50,7 @@
     F11_MWGC,
     G21_MWAvg,
     D22_MWAvg,
+    G24_SMCAvg,
 ]
 grain_models = [DBP90, WD01, D03, ZDA04, C11, J13, HD23]