#!/usr/bin/env python
"""
Calculate output results dictionary.
"""

from collections import OrderedDict as odict

import numpy as np
import yaml
import numpy.lib.recfunctions as recfuncs

import astropy.coordinates
from astropy.coordinates import SkyCoord
import astropy.units as u

import ugali.analysis.source
import ugali.analysis.loglike

import ugali.utils.stats
from ugali.utils.stats import Samples
from ugali.utils.projector import dist2mod,mod2dist
from ugali.utils.projector import cel2gal,gal2cel,gal2cel_angle
from ugali.utils.projector import ang2const, ang2iau
from ugali.utils.config import Config
from ugali.utils.logger import logger

_alpha = 0.32

class Results(object):
    """
    Calculate results from a MCMC chain.
    """
    def __init__(self, config, loglike, samples=None):
        self.config = Config(config)
        self.alpha = self.config['results'].get('alpha',_alpha)
        self.nwalkers = self.config['mcmc'].get('nwalkers',100)
        self.nburn = self.config['results'].get('nburn',10)
        self.coordsys = self.config['coords']['coordsys'].lower()
        
        self.loglike = loglike
        self.source = self.loglike.source
        self.params = list(self.source.get_free_params().keys())
        self.samples = samples

    def load_samples(self,filename):
        samples = Samples(filename)
        self.samples = samples.supplement(coordsys=self.coordsys)


    def get_mle(self):
        mle = self.source.get_params()
        # FIXME: For composite isochrones
        if 'age' not in mle:
            mle['age'] = np.average(self.source.isochrone.age)
        if 'metallicity' not in mle:
            mle['metallicity'] = np.average(self.source.isochrone.metallicity)
            
        return mle

    def estimate(self,param,burn=None,clip=10.0,alpha=_alpha):
        """ Estimate parameter value and uncertainties 

        Parameters
        ----------
        param : parameter of interest
        burn  : number of burn in samples
        clip  : sigma clipping
        alpha : confidence interval

        Returns
        -------
        [mle, [lo,hi]] : value and interval
        """
        # FIXME: Need to add age and metallicity to composite isochrone params (currently properties)
        if alpha is None: alpha = self.alpha
        if param not in list(self.samples.names) + list(self.source.params) + ['age','metallicity']:
            msg = 'Unrecognized parameter: %s'%param
            raise KeyError(msg)

        # If the parameter is in the samples
        if param in self.samples.names:
            if param.startswith('position_angle'):
                return self.estimate_position_angle(param,burn=burn,
                                                    clip=clip,alpha=alpha)

            return self.samples.peak_interval(param,burn=burn,clip=clip,alpha=alpha)
 
        mle = self.get_mle()
        errors = [np.nan,np.nan] 

        # Set default value to the MLE value
        if param in self.source.params:
            err = self.source.params[param].errors
            if err is not None: errors = err

        # For age and metallicity from composite isochrone
        return [float(mle[param]),errors]

        ### if (param not in self.params) or (param not in : 
        ###     return [float(mle[param]),errors]
        ###  
        ### if param not in self.samples.names: 
        ###     return [float(mle[param]),errors]
        ###  
        ### msg = "Unrecognized parameter: %s"%param
        ### raise KeyError(msg)
 
    def estimate_params(self,burn=None,clip=10.0,alpha=None):
        """ Estimate all source parameters """
        if alpha is None: alpha = self.alpha
        mle = self.get_mle()
        out = odict()
        for param in mle.keys():
            out[param] = self.estimate(param,burn=burn,clip=clip,alpha=alpha)
        return out
 
    def estimate_position_angle(self,param='position_angle',burn=None,clip=10.0,alpha=None):
        """ Estimate the position angle from the posterior dealing
        with periodicity.
        """
        if alpha is None: alpha = self.alpha
        # Transform so peak in the middle of the distribution
        pa = self.samples.get(param,burn=burn,clip=clip)
        peak = ugali.utils.stats.kde_peak(pa)
        shift = 180.*((pa+90-peak)>180)
        pa -= shift
        # Get the kde interval
        ret = ugali.utils.stats.peak_interval(pa,alpha)
        if ret[0] < 0: 
            ret[0] += 180.; ret[1][0] += 180.; ret[1][1] += 180.;
        return ret
 
    def bayes_factor(self,param,burn=None,clip=10.0,bins=50):
        # CAREFUL: Assumes a flat prior...
        try: 
            data = self.samples.get(param,burn=burn,clip=clip)
        except ValueError as msg:
            logger.warning(msg)
            return ugali.utils.stats.interval(np.nan)
 
        bmin,bmax = self.source.params[param].bounds
        bins = np.linspace(bmin,bmax,bins)
        n,b = np.histogram(data,bins=bins,normed=True)
        prior = 1.0/(bmax-bmin)
        posterior = n[0]
        # Excluding the null hypothesis
        bf = prior/posterior
        return ugali.utils.stats.interval(bf)
 
    def get_results(self,**kwargs):
        kwargs.setdefault('alpha',self.alpha)
        kwargs.setdefault('burn',self.nburn*self.nwalkers)

        # Calculate best-fit parameters from MCMC chain
        logger.debug('Estimating parameters...')
        estimate = self.estimate_params(**kwargs)
        params = {k:v[0] for k,v in estimate.items()}
        results = dict(estimate)

        # Extra parameters from the MCMC chain
        logger.debug('Estimating auxiliary parameters...')
        logger.debug("alpha = %.2f"%kwargs['alpha'])
        results['alpha'] = kwargs['alpha']
        try: 
            results['ra']  = self.estimate('ra',**kwargs)
            results['dec'] = self.estimate('dec',**kwargs)
            results['glon'] = self.estimate('glon',**kwargs)
            results['glat'] = self.estimate('glat',**kwargs)
        except KeyError:
            logger.warn("Didn't find 'ra' or 'dec' in Samples...")
            if self.coordsys == 'gal':
                results['glon'] = results['lon']
                results['glat'] = results['lat']
                ra,dec = gal2cel(results['lon'][0],results['lat'][0])
                results['ra'] = ugali.utils.stats.interval(ra)
                results['dec'] = ugali.utils.stats.interval(dec)
            else:
                results['ra'] = results['lon']
                results['dec'] = results['lat']
                glon,glat = cel2gal(results['lon'][0],results['lat'][0])
                results['glon'] = ugali.utils.stats.interval(glon)
                results['glat'] = ugali.utils.stats.interval(glat)

        lon,lat   = results['lon'][0],results['lat'][0]
        ra,dec    = results['ra'][0],results['dec'][0]
        glon,glat = results['glon'][0],results['glat'][0]
        results.update(gal=[float(glon),float(glat)])
        results.update(cel=[float(ra),float(dec)])

        try:
            results['position_angle_cel']  = self.estimate('position_angle_cel',**kwargs)
        except KeyError:
            results['position_angle_cel'] = ugali.utils.stats.interval(np.nan)

        # Update the loglike to the best-fit parameters from the chain
        logger.debug('Calculating TS...')
        ts = 2*self.loglike.value(**params)
        results['ts'] = ugali.utils.stats.interval(ts,np.nan,np.nan)
 
        # Celestial position angle
        # Break ambiguity in direction with '% 180.'
        pa,pa_err = results['position_angle']
        pa_cel = gal2cel_angle(lon,lat,pa) % 180.
        pa_cel_err = np.array(pa_err) - pa + pa_cel
        results['position_angle_cel'] = ugali.utils.stats.interval(pa_cel,pa_cel_err[0],pa_cel_err[1])
        
        mod,mod_err = estimate['distance_modulus']
        dist = mod2dist(mod)
        dist_lo,dist_hi = [mod2dist(mod_err[0]),mod2dist(mod_err[1])]
        results['distance'] = ugali.utils.stats.interval(dist,dist_lo,dist_hi)
        dist,dist_err = results['distance']
 
        ext,ext_err = estimate['extension']
        ext_sigma = np.nan_to_num(np.array(ext_err) - ext)
        results['extension_arcmin'] = ugali.utils.stats.interval(60*ext,60*ext_err[0],60*ext_err[1])
 
        # Radially symmetric extension (correct for ellipticity).
        ell,ell_err = estimate['ellipticity']
        rext,rext_err = ext*np.sqrt(1-ell),np.array(ext_err)*np.sqrt(1-ell)
        rext_sigma = np.nan_to_num(np.array(rext_err) - rext)
        results['extension_radial'] = ugali.utils.stats.interval(rext,rext_err[0],rext_err[1])
        results['extension_radial_arcmin'] = ugali.utils.stats.interval(60*rext,60*rext_err[0],60*rext_err[1])
 
        # Bayes factor for ellipticity
        results['ellipticity_bayes_factor'] = self.bayes_factor('ellipticity',burn=kwargs['burn'])
 
        # Physical Size (should do this with the posteriors)
        # Radially symmetric
        dist_sigma = np.nan_to_num(np.array(dist_err) - dist)

        size = np.arctan(np.radians(ext)) * dist
        size_sigma = size * np.sqrt((ext_sigma/ext)**2 + (dist_sigma/dist)**2)
        size_err = [size-size_sigma[0],size+size_sigma[1]]
        results['physical_size'] = ugali.utils.stats.interval(size,size_err[0],size_err[1])

        rsize = np.arctan(np.radians(rext)) * dist
        rsize_sigma = rsize * np.sqrt((rext_sigma/rext)**2 + (dist_sigma/dist)**2)
        rsize_err = [rsize-rsize_sigma[0],rsize+rsize_sigma[1]]
        results['physical_size_radial'] = ugali.utils.stats.interval(rsize,rsize_err[0],rsize_err[1])
 
        # Richness
        rich,rich_err = estimate['richness']
 
        # Number of observed stars (sum of p-values)
        nobs = self.loglike.p.sum()
        nobs_lo,nobs_hi = nobs + np.sqrt(nobs)*np.array([-1,1])
        results['nobs'] = ugali.utils.stats.interval(nobs,nobs_lo,nobs_hi)
 
        # Number of predicted stars (pixelization effects?)
        npred = self.loglike.f*rich
        npred_lo,npred_hi = rich_err[0]*self.loglike.f,rich_err[1]*self.loglike.f
        results['npred'] = ugali.utils.stats.interval(npred,npred_lo,npred_hi)
        
        # Careful, depends on the isochrone...
        stellar_mass = self.source.stellar_mass()
        mass = rich*stellar_mass
        mass_lo,mass_hi = rich_err[0]*stellar_mass,rich_err[1]*stellar_mass
        results['mass'] = ugali.utils.stats.interval(mass,mass_lo,mass_hi)
 
        stellar_luminosity = self.source.stellar_luminosity()
        lum = rich*stellar_luminosity
        lum_lo,lum_hi = rich_err[0]*stellar_luminosity,rich_err[1]*stellar_luminosity
        results['luminosity'] = ugali.utils.stats.interval(lum,lum_lo,lum_hi)

        # Absolute magnitude only calculated for DES isochrones with g,r 
        try:
            Mv = self.source.absolute_magnitude(rich)
            Mv_lo = self.source.absolute_magnitude(rich_err[0])
            Mv_hi = self.source.absolute_magnitude(rich_err[1])
            results['Mv'] = ugali.utils.stats.interval(Mv,Mv_lo,Mv_hi)
        except ValueError as e:
            logger.warning("Skipping absolute magnitude")
            logger.warn(str(e))
            Mv = np.nan
            results['Mv'] = Mv

        mu = surfaceBrightness(Mv, rsize, dist) ##updated from size-->rsize
        results['surface_brightness'] = ugali.utils.stats.interval(mu,np.nan,np.nan)

        # ADW: WARNING this is very fragile.
        # Also, this is not quite right, should cut on the CMD available space
        kwargs = dict(richness=rich,mag_bright=16., mag_faint=23.,
                      n_trials=5000,alpha=kwargs['alpha'], seed=0)
        martin = self.config['results'].get('martin')
        if martin:
            logger.info("Calculating Martin magnitude...")
            if martin > 1: kwargs['n_trials'] = martin
            Mv_martin = self.source.isochrone.absolute_magnitude_martin(**kwargs)
            results['Mv_martin'] = Mv_martin

            mu_martin = surfaceBrightness(Mv_martin, rsize, dist) ##updated from size-->rsize
            results['surface_brightness_martin'] = ugali.utils.stats.interval(mu_martin,np.nan,np.nan)
        else:
            logger.warning("Skipping Martin magnitude")
            results['Mv_martin'] = np.nan
            results['surface_brightness_martin'] = np.nan

        try: 
            results['constellation'] = ang2const(lon,lat,self.coordsys)[1]
        except:
            pass
        results['iau'] = ugali.utils.projector.ang2iau(lon,lat,self.coordsys)
 
        coord = SkyCoord(ra*u.deg,dec*u.deg,distance=dist*u.kpc)
        results['ra_sex'] = str(coord.ra.to_string())
        results['dec_sex'] = str(coord.dec.to_string())
 
        # Calculate some separations from GC, LMC, SMC
        #NED coordinates with de Grisj distance
        LMC = SkyCoord(80.8939*u.deg,-69.7561*u.deg,distance=49.89*u.kpc)
        #NED coordinates with de Grisj distance
        SMC = SkyCoord(13.1866*u.deg,-72.8286*u.deg,distance=61.94*u.kpc)
        # GC from astropy?
        GC = SkyCoord(266.4168262*u.deg,-29.0077969*u.deg,distance=8.0*u.kpc)
         
        results['d_gc'] = coord.separation_3d(GC).value
        results['d_lmc'] = coord.separation_3d(LMC).value