Merge eec1bcb into cc1a3dd

docs/dust_extinction/choose_model.rst

@@ -2,9 +2,9 @@
 How to Choose a Model
 #####################
 
-The ``dust_extinction`` package provides a suite of dust extinction models.  
+The ``dust_extinction`` package provides a suite of dust extinction models.
 Which model to use can depend on the wavelength range of interest, the expected
-type of extinction, or some other property. 
+type of extinction, or some other property.
 
 Average Models
 ==============
@@ -14,7 +14,7 @@ Simple Average Curves
 
 These are straightforward averages of observed extinction curves.  They are the
 simpliest models and include models for the MW
-(:class:`~dust_extinction.dust_extinction.G09_MWAvg`), the LMC
+(:class:`~dust_extinction.dust_extinction.GCC09_MWAvg`), the LMC
 (:class:`~dust_extinction.dust_extinction.G03_LMCAvg`,
 :class:`~dust_extinction.dust_extinction.G03_LMC2`) and the SMC
 (:class:`~dust_extinction.dust_extinction.G03_SMCBar`).
@@ -23,17 +23,17 @@ 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
 Way, the usual average used is R(V) = 3.1.
 
-+------------+-------------+------------------+--------------+
-| Model      | x range     | wavelength range |       galaxy | 
-+============+=============+==================+==============+
-| G09_MWAvg  | 0.3 - 10.96 |     0.0912 - 3.3 |           MW |
-+------------+-------------+------------------+--------------+
-| G03_LMCAvg |  0.3 - 10.0 |        0.1 - 3.3 |          LMC |
-+------------+-------------+------------------+--------------+
-| G03_LMC2   |  0.3 - 10.0 |        0.1 - 3.3 | LMC (30 Dor) |
-+------------+-------------+------------------+--------------+
-| G03_SMCBar |  0.3 - 10.0 |        0.1 - 3.3 |          SMC |
-+------------+-------------+------------------+--------------+
++--------------+-------------+------------------+--------------+
+| Model        | x range     | wavelength range |       galaxy |
++==============+=============+==================+==============+
+| GCC09_MWAvg  | 0.3 - 10.96 |     0.0912 - 3.3 |           MW |
++--------------+-------------+------------------+--------------+
+| G03_LMCAvg   |  0.3 - 10.0 |        0.1 - 3.3 |          LMC |
++--------------+-------------+------------------+--------------+
+| G03_LMC2     |  0.3 - 10.0 |        0.1 - 3.3 | LMC (30 Dor) |
++--------------+-------------+------------------+--------------+
+| G03_SMCBar   |  0.3 - 10.0 |        0.1 - 3.3 |          SMC |
++--------------+-------------+------------------+--------------+
 
 
 Parameter Dependent Average Curves
@@ -55,14 +55,14 @@ is based on signifincatly more extinction curves than the
 :class:`~dust_extinction.dust_extinction.O94` models.
 
 +---------+-------------+-------------+------------------+--------------+
-| Model   | Parameters  | x range     | wavelength range |       galaxy | 
+| Model   | Parameters  | x range     | wavelength range |       galaxy |
 |         |             | [1/micron]  | [micron]         |              |
 +=========+=============+=============+==================+==============+
 | CCM89   |  R(V)       |  0.3 - 10.0 |        0.1 - 3.3 |           MW |
 +---------+-------------+-------------+------------------+--------------+
 | O94     |  R(V)       |  0.3 - 10.0 |        0.1 - 3.3 |           MW |
 +---------+-------------+-------------+------------------+--------------+
-| F99     |  R(V)       |  0.3 - 10.0 |        0.1 - 3.3 |           MW | 
+| F99     |  R(V)       |  0.3 - 10.0 |        0.1 - 3.3 |           MW |
 +---------+-------------+-------------+------------------+--------------+
 | G16     | R(V)_A, f_A |  0.3 - 10.0 |        0.1 - 3.3 | MW, LMC, SMC |
 +---------+-------------+-------------+------------------+--------------+
@@ -85,4 +85,3 @@ but only covers the UV wavelength range.
 +------------+--------------+------------------+-------------------+
 | P92        | 0.001 - 1000 |     0.001 - 1000 |  19 (24 possible) |
 +------------+--------------+------------------+-------------------+
-

docs/dust_extinction/fit_extinction.rst

@@ -6,14 +6,14 @@ The ``dust_extinction`` package is built on the `astropy.modeling
 <http://docs.astropy.org/en/stable/modeling/>`_ package.  Fitting is
 done in the standard way for this package where the model is initialized
 with a starting point (either the default or user input), the fitter
-is choosen, and the fit performed.  
+is choosen, and the fit performed.
 
 Example: FM90 Fit
 =================
 
 In this example, the FM90 model is used to fit the observed average
-extinction curve for the LMC outside of the LMC2 supershell region 
-(G03_LMCAvg ``dust_extinction`` model). 
+extinction curve for the LMC outside of the LMC2 supershell region
+(G03_LMCAvg ``dust_extinction`` model).
 
 .. plot::
    :include-source:
@@ -50,7 +50,7 @@ extinction curve for the LMC outside of the LMC2 supershell region
    ax.plot(x, y, 'ko', label='Observed Curve')
    ax.plot(x[gindxs], fm90_init(x[gindxs]), label='Initial guess')
    ax.plot(x[gindxs], g03_fit(x[gindxs]), label='Fitted model')
-   
+
    ax.set_xlabel('$x$ [$\mu m^{-1}$]')
    ax.set_ylabel('$E(x-V)/E(B-V)$')
 
@@ -64,7 +64,7 @@ Example: P92 Fit
 ================
 
 In this example, the P92 model is used to fit the observed average
-extinction curve for the MW (G09_MWAvg ``dust_extinction`` model).
+extinction curve for the MW (GCC09_MWAvg ``dust_extinction`` model).
 The fit is done using the observed uncertainties that are passed
 as weights.  The weights assume the noise is Gaussian and not correlated
 between data points.
@@ -80,10 +80,10 @@ between data points.
 
    from astropy.modeling.fitting import LevMarLSQFitter
 
-   from dust_extinction.dust_extinction import (P92, G09_MWAvg)
+   from dust_extinction.dust_extinction import (P92, GCC09_MWAvg)
 
    # get an observed extinction curve to fit
-   g09_model = G09_MWAvg()
+   g09_model = GCC09_MWAvg()
 
    # get an observed extinction curve to fit
    x = g09_model.obsdata_x
@@ -106,14 +106,14 @@ between data points.
    p92_init.FIR_amp.fixed = True
    p92_init.FIR_lambda.fixed = True
    p92_init.FIR_b.fixed = True
-   
+
    # pick the fitter
    fit = LevMarLSQFitter()
 
    # set to avoid the "fit may have been unsuccessful" warning
    #   fit is fine, but this means the build of the docs fails
    warnings.simplefilter('ignore', category=AstropyWarning)
-   
+
    # fit the data to the P92 model using the fitter
    #   use the initialized model as the starting point
    #   accuracy set to avoid warning the fit may have failed
@@ -129,7 +129,7 @@ between data points.
    ax.set_xlabel('$x$ [$\mu m^{-1}$]')
    ax.set_ylabel('$A(x)/A(V)$')
 
-   ax.set_title('Example P92 Fit to MW average curve')
+   ax.set_title('Example P92 Fit to GCC09_MWAvg average curve')
 
    ax.legend(loc='best')
    plt.tight_layout()

docs/dust_extinction/model_flavors.rst

@@ -2,7 +2,7 @@
 Model Flavors
 #############
 
-There are three differnet types of models: average, R(V)+ dependent prediction,
+There are three different types of models: average, R(V)+ dependent prediction,
 and shape fitting.
 
 Average models
@@ -21,51 +21,50 @@ Average models
    import numpy as np
    import matplotlib.pyplot as plt
    import astropy.units as u
-   
-   from dust_extinction.dust_extinction import (G09_MWAvg,
+
+   from dust_extinction.dust_extinction import (GCC09_MWAvg,
                                                 G03_SMCBar,
                                                 G03_LMCAvg,
-					        G03_LMC2)
-      
+					                                      G03_LMC2)
+
    fig, ax = plt.subplots()
-      
+
    # generate the curves and plot them
    x = np.arange(0.3,10.0,0.1)/u.micron
+   ext_model = GCC09_MWAvg()
+   ax.plot(x,ext_model(x),label='GCC09 MWAvg')
 
-   ext_model = G09_MWAvg()
-   ax.plot(x,ext_model(x),label='G09 MWAvg')
-
    ext_model = G03_SMCBar()
    ax.plot(x,ext_model(x),label='G03 SMCBar')
 
    ext_model = G03_LMCAvg()
    ax.plot(x,ext_model(x),label='G03 LMCAvg')
-   
+
    ext_model = G03_LMC2()
    ax.plot(x,ext_model(x),label='G03 LMC2')
-   
+
    ax.set_xlabel('$x$ [$\mu m^{-1}$]')
    ax.set_ylabel('$A(x)/A(V)$')
-      
+
    ax.legend(loc='best')
    plt.tight_layout()
    plt.show()
-     
+
 R(V) (+ other variables) dependent prediction models
 ====================================================
 
    These models provide predictions of the shape of the dust extinction
-   given input variable(s).  
+   given input variable(s).
 
    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 F99 model
    (Fitzpatrick 1999).  These models are based on the average
    behavior of extinction in the Milky Way as a function of R(V).
 
-   In addition, the (R(V), f_A) two parameter relationship from 
+   In addition, the (R(V), f_A) two parameter relationship from
    Gordon et al. (2016) is included.  This model is based on the average
-   behavior of extinction in the Milky Way, Large Magellanic Cloud, and 
+   behavior of extinction in the Milky Way, Large Magellanic Cloud, and
    Small Magellanic Cloud.
 
 .. plot::
@@ -121,7 +120,7 @@ R(V) (+ other variables) dependent prediction models
    ax.legend(loc='best')
    plt.tight_layout()
    plt.show()
-   
+
 .. plot::
 
    import numpy as np
@@ -219,7 +218,7 @@ Shape fitting models
    These models are used to fit the detailed shape of dust extinction curves.
    The FM90 (Fitzpatrick & Mass 1990) model uses 6 parameters to fit the
    shape of the ultraviolet extinction.
-   The P92 (Pei 1992) uses 19 parameters to fit the shape of the X-ray to 
+   The P92 (Pei 1992) uses 19 parameters to fit the shape of the X-ray to
    far-infrared extinction.
 
 .. plot::
@@ -273,27 +272,27 @@ Shape fitting models
    ext_model = P92()
    ax.plot(1/x,ext_model(x),label='total')
 
-   ext_model = P92(FUV_amp=0., NUV_amp=0.0, 
+   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, 
+   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, 
+   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, 
+   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, 
+   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')
 
@@ -310,6 +309,3 @@ Shape fitting models
    ax.legend(loc='best')
    plt.tight_layout()
    plt.show()
-
-
-