:orphan:

Analyzing MCMC Calculations
===========================
If you don't yet have a dataset to work with, you can make one by following :doc:`example_mcmc_gs` or :doc:`example_mcmc_lf`. Also, the analysis machinery for MCMC calculations is identical to that used to analyze model grids, so it may also be useful to look at :doc:`example_grid_analysis`.

.. Specialized Analysis: Global 21-cm Signal
.. -----------------------------------------
.. 
.. ::
.. 
..     anl = ares.analysis.ModelSetLF('lffit')
..     
..     mp2 = anl.TrianglePlot(anl.parameters, color_by_like=True, 
..         colors=['g', 'b'], fig=2)
.. 

    

.. Specialized Analysis: Galaxy Luminosity Functions
.. -------------------------------------------------
.. There is yet another specialized class for analyzing fits of LF data: ``ModelSetGalaxyPopulation``, which just inherits the standard ``ModelSet`` class and has a few convenience routines for plotting.
.. 
.. First, create an instance of said class:
.. 
.. ::
.. 
..     anl = ares.analysis.ModelSetGalaxyPopulation('lffit')
..     
.. Let's have a look at a triangle plot showing constraints on all parameters:    
..     
.. ::    
..     
..     mp = anl.TrianglePlot(anl.parameters, color_by_like=True, 
..         colors=['g', 'b'], fig=1)
.. 
.. Now, let's have a look at how our best fit does compared to some data. First, show a few observational datasets (by default, will include whatever exists in ``$ARES/input/litdata`` -- see :doc:`example_litdata` -- at the right redshifts :math:`\pm \texttt{round_z}`):
.. 
.. ::
.. 
..     redshifts = [6,7,8]
..     obslf = ares.analysis.GalaxyPopulation()
.. 
..     mp2 = obslf.MultiPlot(redshifts, round_z=0.4, ncols=3, fig=2)
..     
.. Next, loop over the redshifts of interest and plot the best fit:    
.. 
.. ::
.. 
..     # Plot a semi-opaque band representing confidence level `like`
..     mp2 = anl.RecoveredModel(redshifts, quantity='lf',
..         compare_to='bouwens2015', color='darkgray', percentile=0.95)   
..     # Plot the best-fit (taken here to be the median)
..     mp2 = anl.RecoveredModel(redshifts, color='k', ls='-', quantity='lf',
..         compare_to='bouwens2015', mp=mp2, best_fit='median')
.. 
..     mp.fix_ticks() # eliminates redundant ticks, scales all axes to span common range
..     
.. If you'd like to visualize individual samples of the posterior, you can do so via, e.g.,
.. 
.. ::
.. 
..     for i, z in enumerate(redshifts):
..         j = obslf.redshifts_in_mp[i]
..         
..         # Notice `samples` keyword argument!
..         anl.LuminosityFunction(z, shade_by_like=False, samples=100,
..             ax=mp2.grid[j], color='b', alpha=0.1)    
..     
.. 
.. To access the best fit parameters directly, 
.. 
.. ::
.. 
..     kw = anl.max_likelihood_parameteris(method='median')
.. 
.. or 
.. 
.. ::
.. 
..     kw = anl.max_likelihood_parameteris(method='maxL')
..     
.. which you can then use to re-run a model if you'd like, e.g.,
.. 
.. ::
.. 
..     pars = anl.base_kwargs.copy()
..     pars.update(kw)
..     sim = ares.simulations.Global21cm(**pars)
.. 
.. or, if you setup the fit in such a way that only parameters for a single population were supplied (i.e., not all the parameters needed for a ``Global21cm`` calculation were given), you'd instead do 
.. 
.. ::
.. 
..     pop = ares.populations.GalaxyPopulation(**pars)
..     
.. and go on from there.    
.. 
.. 
..