:py:mod:`exo_k.fit`
===================

.. py:module:: exo_k.fit

.. autoapi-nested-parse::

   @author: jeremy leconte

   This module contain classes to fit a spectrum



Module Contents
---------------

.. py:class:: Fit(spectrum_to_fit=None, uncertainties=None, fitting_parameters=None, **kwargs)


   Bases: :py:obj:`object`

   Class to fit spectra


   Initialization method.


   .. py:method:: init_atmosphere(composition=None, Mp=None, **kwargs)

      initializes atmospheric model

      :param Mp: Mass of the planet in Kg.
      :type Mp: :class:`float`


   .. py:method:: set_fitting_parameters(fitting_parameters=None)

      :param fitting_paramaters: The parameters to fit.
                                 Must be in T, Rp, logx_MOL
      :type fitting_paramaters: :class:`list` of :class:`str`


   .. py:method:: set_spectrum_to_fit(spectrum_to_fit=None, uncertainties=None)

      :param spectrum_to_fit: The spectrum to fit
      :type spectrum_to_fit: :class:`Spectrum` object
      :param uncertainties: The uncertainty on each spectral point.
      :type uncertainties: :class:`float` or :class:`array`


   .. py:method:: plot_spectrum_to_fit(ax, capsize=0, fmt='k.', ecolor='.7', ylabel='Depth', xlabel='Wavelength', **kwargs)

      Plots the spectrum that is fitted with uncertainties



   .. py:method:: transmission_spectrum(parameters, rebin_spectrum=False, **kwargs)

      Method to compute the spectrum with a new set of parameters



   .. py:method:: set_cost_function()

      Defines the cost (or merit) function to minimize.


   .. py:method:: set_progress_report(Nprint=None)


   .. py:method:: minimize(initial_guess=None, bounds=None, method='Nelder-Mead', verbose=False, tol=0.1, Nprint=None, **kwargs)

      Minimizes the cost function over the fitting parameters.

      :param initial_guess: Initial value for the fitting parameters.
                            Must be in the order declared in fitting_parameters.
      :type initial_guess: :class:`list`
      :param bounds: Lower and upper values for the parameters.
                     Must be in the order declared in fitting_parameters.
      :type bounds: :class:`list` or :class:`2-value arrays`
      :param method: Method used for minimization.
                     For now, only 'Nelder-Mead' seems to handle bounds and yield relatively
                     good results.
      :type method: :class:`str`
      :param tol: tolerance transmitted to sciopt.minimize
      :type tol: :class:`float`


   .. py:method:: best_fit()

      Output last fit results in a dictionary



   .. py:method:: best_fit_spectrum(rebin_spectrum=False)

      Returns the spectrum computed with the best fit parameters



   .. py:method:: contributions(rebin_spectrum=False)

      Computes a spectrum with each molecule contribution



   .. py:method:: set_log_likelihood()

      Defines the log likelihood.


   .. py:method:: set_bounds(bounds=None)


   .. py:method:: set_prior_transform(bounds=None)

      Defines the piors.


   .. py:method:: sample(bounds=None, method='single', npoints=10, maxcall=None, verbose=False, **kwargs)

      Minimizes the cost function over the fitting parameters.

      :param initial_guess: Initial value for the fitting parameters.
                            Must be in the order declared in fitting_parameters.
      :type initial_guess: :class:`list`
      :param bounds: Lower and upper values for the parameters.
                     Must be in the order declared in fitting_parameters.
      :type bounds: :class:`list` or :class:`2-value arrays`
      :param method: Method used for minimization.
                     For now, only 'Nelder-Mead' seems to handle bounds and yield relatively
                     good results.
      :type method: :class:`str`
      :param tol: tolerance transmitted to sciopt.minimize
      :type tol: :class:`float`