#############
Model Flavors
#############

There are three differnet types of models: average, R(V)+ dependent prediction,
and shape fitting.

Average models
==============

   These models provide averages from the literature with the ability to
   interpolate between the observed data points.
   For the Milky Way, one of the R(V) dependent models  with R(V) = 3.1
   (see next section) are often used for the Milky Way 'average'.
   Models are provided for the Magellanic Clouds from Gordon et al. (2003).
   Models for the Milky Way still to be added (both UV/optical/NIR and IR).

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u
   
   from dust_extinction.dust_extinction import (F99,
                                                G03_SMCBar,
                                                G03_LMCAvg,
					        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 = F99()
   ax.plot(x,ext_model(x),label='MW Average (F99 w/ $R(V)=3.1$)')
   
   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).  

   These include CCM89 the original R(V) dependent model from 
   Cardelli, Clayton, and Mathis (1989) and updated versions F99 
   (Fitzpatrick 1999).  These models are based on the average
   behavior of extinction in the Milky Way.

   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 
   Small Magellanic Cloud.

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import CCM89

   fig, ax = plt.subplots()

   # generate the curves and plot them
   x = np.arange(0.5,10.0,0.1)/u.micron

   Rvs = ['2.0','3.0','4.0','5.0','6.0']
   for cur_Rv in Rvs:
      ext_model = CCM89(Rv=cur_Rv)
      ax.plot(x,ext_model(x),label='R(V) = ' + str(cur_Rv))

   ax.set_xlabel('$x$ [$\mu m^{-1}$]')
   ax.set_ylabel('$A(x)/A(V)$')

   ax.set_title('CCM89')

   ax.legend(loc='best')
   plt.tight_layout()
   plt.show()

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import F99

   fig, ax = plt.subplots()

   # temp model to get the correct x range
   text_model = F99()

   # generate the curves and plot them
   x = np.arange(text_model.x_range[0], text_model.x_range[1],0.1)/u.micron

   Rvs = ['2.0','3.0','4.0','5.0','6.0']
   for cur_Rv in Rvs:
      ext_model = F99(Rv=cur_Rv)
      ax.plot(x,ext_model(x),label='R(V) = ' + str(cur_Rv))

   ax.set_xlabel('$x$ [$\mu m^{-1}$]')
   ax.set_ylabel('$A(x)/A(V)$')

   ax.set_title('F99')

   ax.legend(loc='best')
   plt.tight_layout()
   plt.show()

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import G16

   fig, ax = plt.subplots()

   # temp model to get the correct x range
   text_model = G16()

   # generate the curves and plot them
   x = np.arange(text_model.x_range[0], text_model.x_range[1],0.1)/u.micron

   Rvs = ['2.0','3.0','4.0','5.0','6.0']
   for cur_Rv in Rvs:
      ext_model = G16(RvA=cur_Rv, fA=1.0)
      ax.plot(x,ext_model(x),label=r'$R_A(V) = ' + str(cur_Rv) + '$')

   ax.set_xlabel('$x$ [$\mu m^{-1}$]')
   ax.set_ylabel('$A(x)/A(V)$')

   ax.set_title('G16; $f_A = 1.0$; $R(V)_A$ variable')

   ax.legend(loc='best', title=r'$f_A = 1.0$')
   plt.tight_layout()
   plt.show()

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import G16

   fig, ax = plt.subplots()

   # temp model to get the correct x range
   text_model = G16()

   # generate the curves and plot them
   x = np.arange(text_model.x_range[0], text_model.x_range[1],0.1)/u.micron

   fAs = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
   for cur_fA in fAs:
      ext_model = G16(RvA=3.1, fA=cur_fA)
      ax.plot(x,ext_model(x),label=r'$f_A = ' + str(cur_fA) + '$')

   ax.set_xlabel('$x$ [$\mu m^{-1}$]')
   ax.set_ylabel('$A(x)/A(V)$')

   ax.set_title('G16; $f_A$ variable; $R(V)_A = 3.1$')

   ax.legend(loc='best', title=r'$R_A(V) = 3.1$')
   plt.tight_layout()
   plt.show()


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 
   far-infrared extinction.

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import FM90

   fig, ax = plt.subplots()

   # generate the curves and plot them
   x = np.arange(3.8,8.6,0.1)/u.micron

   ext_model = FM90()
   ax.plot(x,ext_model(x),label='total')

   ext_model = FM90(C3=0.0, C4=0.0)
   ax.plot(x,ext_model(x),label='linear term')

   ext_model = FM90(C1=0.0, C2=0.0, C4=0.0)
   ax.plot(x,ext_model(x),label='bump term')

   ext_model = FM90(C1=0.0, C2=0.0, C3=0.0)
   ax.plot(x,ext_model(x),label='FUV rise term')

   ax.set_xlabel('$x$ [$\mu m^{-1}$]')
   ax.set_ylabel('$E(\lambda - V)/E(B - V)$')

   ax.set_title('FM90')

   ax.legend(loc='best')
   plt.tight_layout()
   plt.show()

.. plot::

   import numpy as np
   import matplotlib.pyplot as plt
   import astropy.units as u

   from dust_extinction.dust_extinction import P92

   fig, ax = plt.subplots()

   # generate the curves and plot them
   lam = np.logspace(-3.0, 3.0, num=1000)
   x = (1.0/lam)/u.micron

   ext_model = P92()
   ax.plot(1/x,ext_model(x),label='total')

   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, 
                   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, 
                   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, 
                   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, 
                   SIL1_amp=0.0, SIL2_amp=0.0)
   ax.plot(1./x,ext_model(x),label='BKG+FIR only')

   ax.set_xscale('log')
   ax.set_yscale('log')

   ax.set_ylim(1e-3,10.)

   ax.set_xlabel('$\lambda$ [$\mu$m]')
   ax.set_ylabel('$A(x)/A(V)$')

   ax.set_title('P92')

   ax.legend(loc='best')
   plt.tight_layout()
   plt.show()