#! /usr/bin/env python
"""
Author: Valentino Gonzalez

MIT License

Copyright (c) 2019 GREATS team.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import sys

import numpy as np
from scipy.ndimage import rotate
from astropy.io import fits
from astropy.wcs import WCS


def _build_grid_wcs_for_map(header):
    """WCS structure to find best approximate positions in coarse grid.

    The psf reconstruction fits file provided with the GREATS data release has
    2 extensions: the first one is the master psf at position angle 0; the
    second extension is a map that contains information on the position angles
    and weights of the multiple images that are combined. This information is
    available on a coarse grid. When the psf is requested at a given RA and
    DEC, it is necessary to find the point in the coarse grid that best matches
    the RA and DEC requested. For this, the map contains, in the header, WCS
    information to find this point. This function simplifies the header and
    creates a astropy.wcs.WCS structure to handle the transformation from RA
    and DEC to grid position.
    """
    header['NAXIS'] = 2
    del header['NAXIS4']
    del header['NAXIS3']
    wcs = WCS(header)
    return wcs


def compose_psf_at(ra, dec, psf_fname):
    """Reconstructs the GREATS psf at a given RA and DEC.

    Parameters
    ----------
    ra, dec : float
        Right Ascension and Declination in degrees. This is the positions at
        which the psf will be reconstructed.
    psf_fname : str
        The name of the fits file that contains the information to reconstruct
        psf for the GREATS data release.

    Returns
    -------
    reconstructed_psf : np.ndarray
        2D numpy array with the reconstructed psf.  The pixel scale is the same
        as in the master psf in the fits file that contains the information to
        reconstruct the psf. The reconstructed psf is normalized.

    Example
    -------
    >>> psf_filename = 'GREATS_GOODS-S_CH1_v1.2_psf.fits'
    >>> reconstructed_psf = compose_psf_at(53.0575093, -27.7037789,
                                           psf_filename)
    >>> reconstructed_psf.shape
    (81, 81)

    """
    master_psf = fits.getdata(psf_fname, header=False, ext=1)
    psf_map, map_header = fits.getdata(psf_fname, header=True, ext=2)

    map_wcs = _build_grid_wcs_for_map(map_header)
    grid_positions = map_wcs.all_world2pix([[ra, dec]], 0)
    # We just round up to the closest coordinate in the grid but you may
    # consider keeping a finer grid and interpolating the weights and PAs.
    x, y = np.round(grid_positions).astype('int')[0]

    aor_weights = psf_map[:, 0, y, x]
    aor_pas = psf_map[:, 1, y, x]

    reconstructed_psf = np.zeros(master_psf.shape)
    for weight, pa in zip(aor_weights, aor_pas):
        if weight != 0:
            rotated_psf = rotate(master_psf, -pa, axes=(1, 0), reshape=False,
                                 output=None, order=3, mode='constant',
                                 cval=0.0, prefilter=True)
            reconstructed_psf += rotated_psf * weight
    reconstructed_psf = reconstructed_psf / np.sum(reconstructed_psf)

    return reconstructed_psf


if __name__ == "__main__":
    reconstructed_psf = compose_psf_at(float(sys.argv[1]), float(sys.argv[2]), sys.argv[3])
    hdu = fits.PrimaryHDU(reconstructed_psf)
    
    if len(sys.argv) == 5:
        fout=sys.argv[4]
    else:
        fout='psf.fits'
            
    hdu.writeto(fout, overwrite=True)