updating docs to use G23

docs/dust_extinction/choose_model.rst

@@ -112,7 +112,7 @@ to the :class:`~dust_extinction.parameter_averages.F99` model with f\ :sub:`A`\
 +----------+-------------+--------------+------------------+--------------+
 | D22      |  R(V)       |   0.2 - 1.25 |        0.8 - 5.0 |           MW |
 +----------+-------------+--------------+------------------+--------------+
-| G23      |  R(V)       | 0.033 - 11.0 |    0.0912 - 30.0 |           MW |
+| G23      |  R(V)       | 0.032 - 11.0 |    0.0912 - 32.0 |           MW |
 +----------+-------------+--------------+------------------+--------------+
 
 Notes

docs/dust_extinction/extinguish.rst

@@ -18,16 +18,17 @@ Extinguish a Blackbody
    :include-source:
 
    import matplotlib.pyplot as plt
+   from matplotlib.ticker import ScalarFormatter
    import numpy as np
 
    import astropy.units as u
    from astropy.modeling.models import BlackBody
 
-   from dust_extinction.parameter_averages import F19
+   from dust_extinction.parameter_averages import G23
 
-   # generate wavelengths between 0.12 and 3 microns
-   #    within the valid range for the F19 R(V) dependent relationship
-   lam = np.logspace(np.log10(0.12), np.log10(3.0), num=1000)
+   # generate wavelengths between 0.0912 and 32 microns
+   #    within the valid range for the G23 R(V) dependent relationship
+   lam = np.logspace(np.log10(0.0912), np.log10(32.0), num=1000)
 
    # setup the inputs for the blackbody function
    wavelengths = lam*1e4*u.AA
@@ -38,7 +39,7 @@ Extinguish a Blackbody
    flux = bb_lam(wavelengths)
 
    # initialize the model
-   ext = F19(Rv=3.1)
+   ext = G23(Rv=3.1)
 
    # get the extinguished blackbody flux for different amounts of dust
    flux_ext_av05 = flux*ext.extinguish(wavelengths, Av=0.5)
@@ -57,6 +58,7 @@ Extinguish a Blackbody
    ax.set_ylabel('$Flux$')
 
    ax.set_xscale('log')
+   ax.xaxis.set_major_formatter(ScalarFormatter())
    ax.set_yscale('log')
 
    ax.set_title('Example extinguishing a blackbody')

docs/dust_extinction/model_flavors.rst

@@ -186,7 +186,7 @@ R(V) (+ other variables) dependent prediction models
    fig, ax = plt.subplots(ncols=2, figsize=(10, 4))
 
    # generate the curves and plot them
-   x = np.arange(1./30., 1./0.0912, 0.001)/u.micron
+   x = np.arange(1./32., 1./0.0912, 0.001)/u.micron
 
    Rv = 2.5
 
@@ -234,7 +234,7 @@ R(V) (+ other variables) dependent prediction models
    fig, ax = plt.subplots(ncols=2, figsize=(10, 4))
 
    # generate the curves and plot them
-   x = np.arange(1./30., 1./0.0912, 0.001)/u.micron
+   x = np.arange(1./32., 1./0.0912, 0.001)/u.micron
 
    Rv = 5.5
 

docs/index.rst

@@ -74,10 +74,10 @@ Define a model, specifically the F99 model with an R(V) = 3.1.
 
 .. code-block:: python
 
-    from dust_extinction.parameter_averages import F99
+    from dust_extinction.parameter_averages import G23
 
     # define the model
-    ext = F99(Rv=3.1)
+    ext = G23(Rv=3.1)
 
 Extinguish (redden) a spectrum with a screen of F99 dust with an E(B-V) of 0.5.
 Can also specify the dust column with Av (this case equivalent to Av = 0.5*Rv =
@@ -102,10 +102,10 @@ equivalent A(V) column.
     import matplotlib.pyplot as plt
     import astropy.units as u
     from astropy.modeling.models import BlackBody
-    from dust_extinction.parameter_averages import F99
+    from dust_extinction.parameter_averages import G23
 
     # define the model
-    ext = F99(Rv=3.1)
+    ext = G23(Rv=3.1)
 
     # wavelengths and spectrum are 1D arrays
     # wavelengths between 1000 and 30000 A