import sys
import os

from astropy.io import fits

from stsci.tools import logutil
from stwcs import updatewcs
from stwcs.wcsutil import headerlet
from ci_watson.hst_helpers import ref_from_image, download_crds

from .. import align

__taskname__ = 'testutils'

log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout)

def compare_wcs_alignment(dataset, force=False):
    """Return results from aligning dataset using all available WCS solutions.

        This code will ALWAYS make sure the ASTROMETRY_STEP_CONTROL variable
        is set to "ON" when running and will reset to the original state when
        completed.  This insures that the code ALWAYS queries the astrometry
        database to apply all avaialable a priori WCS solutions.

        Parameters
        -----------
        dataset : str
            Rootname of either a single (un-associated) exposure or an ASN

        force : bool
            Specify whether or not to overwrite dataset files found locally
            with fresh copies retrieved from MAST.

        Returns
        -------
        results : dict
            A dictionary whose keys are the WCS's found and fit to GAIA.
            Each WCS has entries for:

                * imageName - filenames of input exposures included in the fit
                * offset_x - offset in X (pixels)
                * offset_y - offset in X (pixels)
                * rotation - rotation in degrees
                * scale - scale from fit
                * rms_x - RMS in pixels
                * rms_y - RMS in pixels
                * fit_rms - RMS in arcseconds
                * total_rms - RMS of entire fit in arcseconds
                * status - flag indicating success/failure of fit
                * fit_qual - flag indicating quality of fit (1-5)
                * matched_sources - number of sources used in fit

        ASSUMPTIONS
        -----------
            - All images in dataset have the same set of a priori solutions
            - All images in dataset have the same setting for the IDCTAB file
    """
    # Setup
    # Remember what state the environment was in before this code runs
    control = os.environ.get('ASTROMETRY_STEP_CONTROL')

    #   Insure that database will be queried for new WCS solutions
    os.environ['ASTROMETRY_STEP_CONTROL'] = 'ON'

    try:
        # Step 1:
        #   Determine alignment for pipeline-defined WCS
        align_table = align.perform_align([dataset], clobber=force, debug=True)
        results = align_table.filtered_table
        if not results:
            msg = "No valid exposures found for {}.".format(dataset)
            msg += "\n            Please check that input was either a valid ASN"
            msg += "\n            or a single un-associated exposure."
            raise ValueError(msg)

        imglist = results['imageName'].astype(str).tolist()

        # Step 2:
        #   Create results output organized by WCSNAME
        default_wcsname = fits.getval(imglist[0], 'wcsname', ext=1)
        log.info("Default WCSNAME: {}".format(default_wcsname))
        alignment = {default_wcsname: extract_results(results)}

        # Download the calibration reference files to ensure availability
        ref_files = ref_from_image(imglist[0], ['IDCTAB', 'DGEOFILE', 'NPOLFILE', 'D2IMFILE'])
        for file in ref_files:
            download_crds(file, verbose=True)

        # Step 3:
        #   Update inputs with latest distortion model and pull in solutions from dB
        imglist = updatewcs.updatewcs(imglist)
        img0 = imglist[0]
        # Step 4:
        #   Loop over each WCS solution and perform alignment to GAIA
        wcsnames = headerlet.get_headerlet_kw_names(img0, kw='WCSNAME')
        if not wcsnames:
            msg = "No a priori solutions found for {}".format(img0)
            log.error(msg)
            raise ValueError(msg)

        for wcs in wcsnames:
            log.info("Starting with {}".format(wcs))
            if 'OPUS' in wcs or wcs == default_wcsname:
                continue  # skip default pipeline solutions, since we have already aligned it
            # apply WCS from headerlet
            for img in imglist:
                wnames = headerlet.get_headerlet_kw_names(img, kw='WCSNAME')
                hnames = headerlet.get_headerlet_kw_names(img)
                # print("[testutils]WCSNAMES[{}]: {}".format(img, wnames))

                if wcs in wnames:
                    hdrname = hnames[wnames.index(wcs)]
                    log.info("[testutils] Applying WCS {} to {}".format(hdrname, img))
                    headerlet.restore_from_headerlet(img, hdrname=hdrname,
                                                     archive=False, force=True)
        
            print("[testutils] Aligning: {} for WCSNAME: {}".format(dataset, wcs))
            align_table = align.perform_align([dataset], clobber=False, debug=True)
            results = align_table.filtered_table
            alignment[wcs] = extract_results(results)

    finally:
        # Regardless of what happens, always reset the environment variable
        # if it was modified in the first place.
        # Restore user environment to original state
        if control is None:  # Need to be explicit here since T/F are actually valid
            del os.environ['ASTROMETRY_STEP_CONTROL']
        else:
            os.environ['ASTROMETRY_STEP_CONTROL'] = control

    return alignment

def extract_results(results):
    """Return dict with select columns from alignment results Table."""
    results_dict = {'images': results['imageName'].astype(str).tolist(),
                                  'offset_x': results['offset_x'],
                                  'offset_y': results['offset_y'],
                                  'rotation': results['rotation'],
                                  'scale': results['scale'],
                                  'rms_x': results['rms_x'],  # RMS in pixels
                                  'rms_y': results['rms_y'],
                                  'fit_rms': results['fit_rms'],  # RMS in arcsec
                                  'total_rms': results['total_rms'],
                                  'status': results['status'],
                                  'fit_qual': results['fit_qual'],
                                  'matched_sources': results['matchSources']}
    return results_dict
