CONAN._classes.load_lightcurves.limb_darkening
==============================================

.. py:method:: CONAN._classes.load_lightcurves.limb_darkening(q1=0, q2=0, verbose=True)

   Setup Kipping quadratic limb darkening LD coefficient (q1, q2) for transit light curves.
   Different LD coefficients are required if observations of different filters are used.

   :param q1: Stellar quadratic limb darkening coefficients.
              if tuple, must be of - length 2 for normal prior (mean,std) or length 3 for uniform prior defined as (lo_lim, val, uplim).
              The values must obey: (0<q1<1) and (0<=q2<1)
   :type q1: float/tuple or list of float/tuple for each filter;
   :param q2: Stellar quadratic limb darkening coefficients.
              if tuple, must be of - length 2 for normal prior (mean,std) or length 3 for uniform prior defined as (lo_lim, val, uplim).
              The values must obey: (0<q1<1) and (0<=q2<1)
   :type q2: float/tuple or list of float/tuple for each filter;

   .. attribute:: _LD_dict

      dictionary of limb darkening parameters for each filter.

      :type: dict;

   .. rubric:: Examples

   # set the limb darkening coefficients for each filter
   >>> lc_obj.limb_darkening(q1=0.5, q2=0.2)  # fixed values for all filters
   >>> lc_obj.limb_darkening(q1=[0.5,0.6], q2=[0.2,0.3])  # different fixed values for each filter (2 filters)

   >>> lc_obj.limb_darkening(q1=(0.5,0.1), q2=(0.2,0.05))  # normal prior for all filters
   >>> lc_obj.limb_darkening(q1=[(0.5,0.1),(0.6,0.05)], q2=[(0.2,0.05),(0.3,0.1)])  # different normal prior for each filter (2 filters)

   >>> lc_obj.limb_darkening(q1=(0,0.1,1), q2=(0,0.05,1))  # uniform prior for all filters
   >>> lc_obj.limb_darkening(q1=[(0,0.1,1),(0.6,0.05,1)], q2=[(0,0.05,1),(0.3,0.1,1)])  # different uniform prior for each filter (2 filters