from tqdm import tqdm
import h5py as h5
import numpy as np
import configparser
import sys
from contextlib import contextmanager
import os
import Pk_library as PKL
import glob
import pylab as pl
pl.ion()

@contextmanager
def suppress_stdout():
    with open(os.devnull, 'w') as devnull:
        old_stdout = sys.stdout
        sys.stdout = devnull
        try:
            yield
        finally:
            sys.stdout = old_stdout


def get_mcmc_steps(dirname, nframe, iter_max=-1, iter_min=-1):
    """
    Obtain evenly-spaced sample of MCMC steps

    Args:
        dirname (str): Directory containing MCMC files
        nframe (int): Number of frames to sample
        iter_max (int): Maximum MCMC step to sample
        iter_min (int): Minimum MCMC step to sample

    Returns:
        all_mcmc (list): List of MCMC steps to sample

    """
    
    # Get all MCMC steps
    all_mcmc = glob.glob(dirname + '/mcmc_*.h5')
    all_mcmc = np.sort([int(os.path.basename(m)[len('mcmc_'):-3]) for m in all_mcmc])

    # Get in range
    if iter_max >= 0:
        all_mcmc = all_mcmc[all_mcmc <= iter_max]
    all_mcmc = all_mcmc[all_mcmc >= iter_min]
    
    # Subsample
    if nframe > 0:
        max_out = max(all_mcmc)
        min_out = min(all_mcmc)
        step = max(int((max_out - min_out+1) / nframe), 1)
        all_mcmc = all_mcmc[::step]
        if max_out not in all_mcmc:
            all_mcmc = np.concatenate([all_mcmc, [max_out]])

    return all_mcmc


def compute_ensemble_mean_field(ini_name, dirname, mcmc_steps, which_field='BORG_final_density'):
    """
    Compute the mean and std deviation of the inferred density field
    """

    print('Computing ensemble mean field')

    #COMPUTE THE MEAN-DENSITY FIELD
    for i in tqdm(range(len(mcmc_steps))):
        idx = mcmc_steps[i]
        with h5.File(dirname + "/mcmc_%d.h5" % idx,'r') as mcmc_file:
            temp_field = np.array(mcmc_file[f'scalars/{which_field}'][...],dtype=np.float64)
            if i == 0:
                mean_field = np.array(np.full(temp_field.shape,0),dtype=np.float64)
                std_field = np.array(np.full(temp_field.shape,0),dtype=np.float64)
            mean_field += temp_field
            std_field += temp_field*temp_field
    mean_field = mean_field/np.float64(len(mcmc_steps))
    std_field = std_field/np.float64(len(mcmc_steps)) # < delta^2 >
    std_field = np.sqrt(std_field - mean_field **2) # < delta^2 > - < delta >^2
        
    return mean_field, std_field


def get_mock_field(ini_name, dirname, which_field='BORG_final_density'):
    
    with h5.File(f'{dirname}/mock_data.h5', 'r') as f:
        dens = f[f'scalars/{which_field}'][:]

    config = configparser.ConfigParser()
    config.read(ini_name)
    L = float(config['system']['L0'])

    return dens, L


def get_spectra(ini_name, dirname, mcmc_steps, which_field='BORG_final_density', MAS=''):
    
    if MAS == '':
        if which_field == 'BORG_final_density':
            MAS = "CIC"
        elif which_field == 'ics':
            MAS = None
        elif which_field.startwith('BORG_final_velocity'):
            MAS = "CIC"
        else:
            raise NotImplementedError
    
    # Compute original power spectrum
    delta1, boxsize = get_mock_field(ini_name, dirname, which_field=which_field)
        
    # Turn to overdensity
    if which_field == 'BORG_final_density':
        delta1 = (1. + delta1) / (1. + delta1).mean() - 1.
        
    print("BOXSIZE", boxsize)
    Pk = PKL.Pk(delta1.astype(np.float32), boxsize, axis=0, MAS=MAS, threads=1, verbose=True)
    k      = Pk.k3D
    Pk_true     = Pk.Pk[:,0]
    
    # Get other spectra
    all_pk = np.zeros((len(mcmc_steps), len(k)))
    all_r = np.zeros((len(mcmc_steps), len(k)))
    for i in tqdm(range(len(mcmc_steps))):
        idx = mcmc_steps[i]

        with h5.File(dirname + "/mcmc_%d.h5" % idx,'r') as mcmc_file:
            delta2 = np.array(mcmc_file[f'scalars/{which_field}'][...],dtype=np.float64)

        with suppress_stdout():
            Pk = PKL.XPk([delta1.astype(np.float32),delta2.astype(np.float32)], boxsize, axis=0, MAS=[MAS, MAS], threads=1)
        all_pk[i,:] = Pk.Pk[:,0,1]  #monopole of field 2
        all_r[i,:] = Pk.XPk[:,0,0] / np.sqrt(Pk.Pk[:,0,1] * Pk.Pk[:,0,0])
        
    return k, Pk_true, all_pk, all_r

def plot_dens_fields(mean_field, std_field, true_field, slice):
    """
    """
    sides = mean_field.shape[0]
    x = np.arange(sides)
    y = x
    X, Y = np.meshgrid(x, y)
    fig, axes = pl.subplots(figsize=(16,7), ncols=3) 
    _, _, _, im0 = axes[0].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=true_field[slice].ravel())
    axes[0].set_xlabel('x')
    axes[0].set_ylabel('y')
    axes[0].set_title('Generated mock density')
    pl.colorbar(im0)

    _, _, _, im1 = axes[1].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=mean_field[slice].ravel())
    axes[1].set_xlabel('x')
    axes[1].set_ylabel('y') 
    axes[1].set_title('Mean reconstructed density')	
    pl.colorbar(im1)

    _, _, _, im2 = axes[2].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=std_field[slice].ravel())
    axes[2].set_xlabel('x')
    axes[2].set_ylabel('y') 
    axes[2].set_title('Std of reconstructed density')	
    pl.colorbar(im2)