text: #!/usr/bin/env python
  
  """This script is a modernized implementation of tweakreg.
  
  """
  import copy
  import datetime
  import sys
  import glob
  import math
  import os
  import pickle
  from collections import OrderedDict
  import traceback
  
  import numpy as np
  from astropy.table import Table
  from astropy.io import fits
  
  from stsci.tools import logutil
  
  from . import util
  from .haputils import astrometric_utils as amutils
  from .haputils import astroquery_utils as aqutils
  from .haputils import get_git_rev_info
  from .haputils import align_utils
  from .haputils import config_utils
  
  __taskname__ = 'align'
  
  MSG_DATEFMT = '%Y%j%H%M%S'
  SPLUNK_MSG_FORMAT = '%(asctime)s %(levelname)s src=%(name)s- %(message)s'
  
  __version__ = 0.0
  __version_date__ = '21-Aug-2019'
  
  def _init_logger():
      log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout,
                                  format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
      return log
  
  log = _init_logger()
  
  # ----------------------------------------------------------------------------------------------------------
  
  
  def check_and_get_data(input_list: list, **pars: object) -> list:
      """Verify that all specified files are present. If not, retrieve them from MAST.
  
      This function relies on the `AstroQuery interface to MAST
      <https://astroquery.readthedocs.io/en/latest/mast/mast.html>`_
      to retrieve the exposures from the `input_list` that are not found in the current directory.  This
      function calls the simplified interface in
      :func:`haputils/astroquery_utils/retrieve_observation`
      to get the files through AstroQuery.
  
      Parameters
      ----------
      input_list : list
          List of one or more calibrated fits images that will be used for catalog generation.
  
      pars : dict
          Set of additional parameters for :func:`haputils/astroquery_utils/retrieve_observation`
          to use in retrieving any data that was not already in the current working directory.
  
      Returns
      =======
      total_input_list: list
          list of full filenames
  
      See Also
      ========
      haputils/astroquery_utils/retrieve_observation
  
      """
      empty_list = []
      retrieve_list = []    # Actual files retrieved via astroquery and resident on disk
      candidate_list = []   # File names gathered from *_asn.fits file
      ipppssoot_list = []   # ipppssoot names used to avoid duplicate downloads
      total_input_list = []  # Output full filename list of data on disk
      member_suffix = '_flc.fits'
  
      # Loop over the input_list to determine if the item in the input_list is a full association file
      # (*_asn.fits), a full individual image file (aka singleton, *_flt.fits), or a root name specification
      # (association or singleton, ipppssoot).
      for input_item in input_list:
          log.info('Input item: {}'.format(input_item))
          indx = input_item.find('_')
  
          # Input with a suffix (_xxx.fits)
          if indx != -1:
              lc_input_item = input_item.lower()
              suffix = lc_input_item[indx + 1:indx + 4]
              log.info('file: {}'.format(lc_input_item))
              # For an association, need to open the table and read the image names as this could
              # be a custom association.  The assumption is this file is on local disk when specified
              # in this manner (vs just the ipppssoot of the association).
              # This "if" block just collects the wanted full file names.
              if suffix == 'asn':
                  try:
                      asntab = Table.read(input_item, format='fits')
                  except FileNotFoundError:
                      log.error('File {} not found.'.format(input_item))
                      return(empty_list)
                  for row in asntab:
                      if row['MEMTYPE'].startswith('PROD'):
                          continue
                      memname = row['MEMNAME'].lower().strip()
                      # Need to check if the MEMNAME is a full filename or an ipppssoot
                      if memname.find('_') != -1:
                          candidate_list.append(memname)
                      else:
                          # Define suffix for all members based on what files are present
                          if not os.path.exists(memname + member_suffix):
                              member_suffix = '_flt.fits'
  
                          candidate_list.append(memname + member_suffix)
              elif suffix in ['flc', 'flt']:
                  if lc_input_item not in candidate_list:
                      candidate_list.append(lc_input_item)
              else:
                  log.error(
                      'Inappropriate file suffix: {}.  Looking for "asn.fits", '
                      '"flc.fits", or "flt.fits".'.format(
                          suffix))
                  return (empty_list)
  
          # Input is an ipppssoot (association or singleton), nine characters by definition.
          # This "else" block actually downloads the data specified as ipppssoot.
          elif len(input_item) == 9:
              try:
                  if input_item not in ipppssoot_list:
                      # An ipppssoot of an individual file which is part of an association cannot be
                      # retrieved from MAST
                      retrieve_list = aqutils.retrieve_observation(input_item, **pars)
  
                      # If the retrieved list is not empty, add filename(s) to the total_input_list.
                      # Also, update the ipppssoot_list so we do not try to download the data again.  Need
                      # to do this since retrieve_list can be empty because (1) data cannot be acquired (error)
                      # or (2) data is already on disk (ok).
                      if retrieve_list:
                          total_input_list += retrieve_list
                          ipppssoot_list.append(input_item)
                      else:
                          log.error('File {} cannot be retrieved from MAST.'.format(input_item))
                          return(empty_list)
              except Exception:
                  exc_type, exc_value, exc_tb = sys.exc_info()
                  traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
  
      # Only the retrieve_list files via astroquery have been put into the total_input_list thus far.
      # Now check candidate_list to detect or acquire the requested files from MAST via astroquery.
      for file in candidate_list:
          # If the file is found on disk, add it to the total_input_list and continue
          if glob.glob(file):
              total_input_list.append(file)
              continue
          else:
              log.error('File {} cannot be found on the local disk.'.format(file))
              return(empty_list)
  
      log.info("TOTAL INPUT LIST: {}".format(total_input_list))
      return(total_input_list)
  
  
  # ------------------------------------------------------------------------------------------------------------
  def perform_align(input_list, catalog_list, num_sources, archive=False, clobber=False, debug=False,
                    update_hdr_wcs=False, result=None,
                    runfile=None, print_fit_parameters=True, print_git_info=False, output=False,
                    headerlet_filenames=None, fit_label=None, product_type=None,
                    **alignment_pars):
      """Actual Main calling function.
  
      This function performs `a posteriori` astrometric fits to the images specified in the
      `input_list`.  The images are fit to all the catalogs listed in the `catalog_list` parameter with
      the results being saved and returned as an Astropy Table object.  This allows the user
      to select the solution that is most appropriate.
  
      Parameters
      ----------
      input_list : list
          List of one or more IPPSSOOTs (rootnames) to align.
  
      catalog_list : list
          Set of astrometric catalogs which should be used as references for fitting the input images.  A
          separate fit will be performed for each catalog specified. The catalog name will also be used
          as part of the output `WCSNAME` value for the fit determined from that catalog.
  
      num_sources : int, None
          Maximum number of **brightest sources per chip** which will be used for cross-matching and fitting.
          If set to None, all sources will be used.
  
      archive : Boolean
          Retain copies of the downloaded files in the astroquery created
          sub-directories?
  
      clobber : Boolean
          Download and overwrite existing local copies of input files?
  
      debug : Boolean
          Attempt to use saved sourcelists stored in pickle files if they exist, or if they do not exist, save
          sourcelists in pickle files for reuse so that step 4 can be skipped for faster subsequent
          debug/development runs??
  
      update_hdr_wcs : Boolean
          Write newly computed WCS information to image image headers?
  
      result: Table
          name of variable to be updated by subroutine run.
  
      runfile : string
          log file name
  
      print_fit_parameters : Boolean
          Specify whether or not to print out FIT results for each chip.
  
      print_git_info : Boolean
          Display git repository information?
  
      output : Boolean
          Should utils.astrometric_utils.create_astrometric_catalog() generate file 'ref_cat.ecsv' and should
          the alignment source catalogs get written out to files?
  
      headerlet_filenames : dictionary, optional
          dictionary that maps the flt/flc.fits file name to the corresponding custom headerlet filename.
  
      fit_label : str
          String to use as a unique tag for the WCS solutions generated by these fits.  This tag will be
          inserted between the `-FIT` and the catalog name in the `WCSNAME` keyword value; for example,
          `fit_label="User"` will result in fits to GAIAedr3 with names ending in `-FIT_User_GAIAedr3`.
  
      alignment_pars : dictionary or keyword args
          keyword-arg parameters containing user-specified values for the parameters used in source
          identification and alignment which should replace the default values found in the JSON parameter
          files in `drizzlepac.pars.hap_pars` based on the instrument and detector.
          The code will look for default values for all the parameters in the JSON parameter files using
          `~get_default_pars`.  For example, should the user feel it would be more successful to only look
          out to a radius of 25 pixels during alignment, the user could simply specify `searchrad=25`.
  
      Updates
      -------
      filtered_table: Astropy Table
          Table which contains processing information and alignment results for every raw image evaluated
  
      """
      log.info("*** HAP PIPELINE Processing Version {!s} ({!s}) started at: {!s} ***\n".format(__version__, __version_date__, util._ptime()[0]))
  
      if debug:
          loglevel = logutil.logging.DEBUG
      else:
          loglevel = logutil.logging.INFO
  
      if runfile is not None:
          loglevel = logutil.logging.DEBUG
          fh = logutil.logging.FileHandler(runfile)
          fh.setLevel(loglevel)
          log.addHandler(fh)
  
      log.setLevel(loglevel)
  
      # 0: print git info
      if print_git_info:
          log.info("{} STEP 0: Display Git revision info  {}".format("-" * 20, "-" * 49))
          full_path = os.path.dirname(__file__)
          repo_path = None
          if "drizzlepac/drizzlepac" in full_path:
              repo_path = full_path.split("drizzlepac/drizzlepac")[0] + "drizzlepac"
          elif "hlapipeline" in full_path:
              repo_path = full_path.split("drizzlepac")[0] + "drizzlepac"
          else:
              pass
          if not os.path.exists(repo_path):
              repo_path = None  # protect against non-existent paths
          if repo_path:
              get_git_rev_info.print_rev_id(repo_path)  # Display git repository information
          else:
              log.warning("WARNING: Unable to display Git repository revision information.")
  
      # Initialize key variables
      filtered_table = None
  
      # 1: Interpret input data and optional parameters
      log.info("{} STEP 1: Get data {}".format("-" * 20, "-" * 66))
      zero_dt = starting_dt = datetime.datetime.now()
      log.info(str(starting_dt))
      imglist = check_and_get_data(input_list, archive=archive, clobber=clobber, product_type=product_type)
      log.info("SUCCESS")
  
      log.info(make_label('Processing time of [STEP 1]', starting_dt))
      starting_dt = datetime.datetime.now()
  
      # Get default alignment parameters if not provided by the user...
      inst = fits.getval(imglist[0], 'instrume')
      det = fits.getval(imglist[0], 'detector')
      apars = get_default_pars(inst, det)
      alignment_pars.update(apars)
      alignment_pars['MAX_SOURCES_PER_CHIP'] = num_sources
  
      # define min num of acceptable cross-matches for a good fit
      apars['determine_fit_quality']['min_xmatches'] = apars['determine_fit_quality']['MIN_FIT_MATCHES']
  
      try:
          # Instantiate AlignmentTable class with these input files
          alignment_table = align_utils.AlignmentTable(imglist, log_level=loglevel,
                                                       **alignment_pars)
          if alignment_table.process_list is None:
              log.warning("NO viable images to align.")
              alignment_table.close()
              return None
  
          process_list = alignment_table.process_list
  
          # Define fitting algorithm list in priority order
          # The match_relative_fit algorithm must have more than one image as the first image is
          # the reference for the remaining images.
          fit_algorithm_list = alignment_table.get_fit_methods()
  
          if len(process_list) == 1:
              fit_algorithm_list.remove("relative")
  
          log.info(make_label('Processing time of [STEP 2]', starting_dt))
          starting_dt = datetime.datetime.now()
          # 3: Build WCS for full set of input observations
          log.info("{} STEP 3: Build WCS {}".format("-" * 20, "-" * 65))
          # refwcs = amutils.build_reference_wcs(process_list)
          log.info("SUCCESS")
  
          log.info(make_label('Processing time of [STEP 3]', starting_dt))
          starting_dt = datetime.datetime.now()
          # 4: Extract catalog of observable sources from each input image
          log.info(
              "{} STEP 4: Source finding {}".format("-" * 20, "-" * 60))
          if debug:
              pickle_filename = "{}.source_catalog.pickle".format(process_list[0])
              if os.path.exists(pickle_filename):
                  pickle_in = open(pickle_filename, "rb")
                  alignment_table.extracted_sources = pickle.load(pickle_in)
                  log.info("Using sourcelist extracted from {} generated during the last run to save time.".format(
                      pickle_filename))
              else:
                  alignment_table.find_alignment_sources(output=True)
  
                  pickle_out = open(pickle_filename, "wb")
                  pickle.dump(alignment_table.extracted_sources, pickle_out)
                  pickle_out.close()
                  log.info("Wrote {}".format(pickle_filename))
          else:
              alignment_table.find_alignment_sources(output=output)
  
          for imgname in alignment_table.extracted_sources.keys():
              table = alignment_table.extracted_sources[imgname]
  
              # Get the location of the current image in the filtered table
              index = np.where(alignment_table.filtered_table['imageName'] == imgname)[0][0]
  
              # First ensure sources were found
  
              if table is None or not table[1]:
                  log.warning("No sources found in image {}".format(imgname))
                  alignment_table.filtered_table[:]['status'] = 1
                  alignment_table.filtered_table[:]['processMsg'] = "No sources found"
                  log.info(make_label('Processing time of [STEP 4]', starting_dt))
                  alignment_table.close()
                  return None
  
              # The catalog of observable sources must have at least MIN_OBSERVABLE_THRESHOLD entries to be useful
              total_num_sources = 0
              for chipnum in table.keys():
                  total_num_sources += len(table[chipnum])
  
              # Update filtered table with number of found sources
              alignment_table.filtered_table[index]['foundSources'] = total_num_sources
  
              # TODO: Revise this logic, if possible, to allow for a subset of the input images to
              #       be ignored if they have no identifiable sources.
              if total_num_sources < apars['determine_fit_quality']['MIN_OBSERVABLE_THRESHOLD']:
                  log.warning("Not enough sources ({}) found in image {}".format(total_num_sources, imgname))
                  alignment_table.filtered_table[:]['status'] = 1
                  alignment_table.filtered_table[:]['processMsg'] = "Not enough sources found"
                  log.info(make_label('Processing time of [STEP 4]', starting_dt))
                  alignment_table.close()
                  return None
  
          log.info("SUCCESS")
          log.info(make_label('Processing time of [STEP 4]', starting_dt))
          starting_dt = datetime.datetime.now()
          # 5: Retrieve list of astrometric sources from database
  
          # Convert input images to tweakwcs-compatible FITSWCS objects and
          # attach source catalogs to them.
          imglist = []
          for group_id, image in enumerate(process_list):
              img = amutils.build_wcscat(image, group_id,
                                         alignment_table.extracted_sources[image])
              imglist.extend(img)
          # store mapping of group_id to filename/chip
          group_id_dict = {}
          for image in imglist:
              group_id_dict["{}_{}".format(image.meta["rootname"], image.meta["chip"])] = image.meta["group_id"]
  
          best_fit_rms = -99999.0
          best_fit_status_dict = {}
          best_fit_qual = 5
          best_num_matches = -1
          best_fit_label = [None, None]
          # create pristine copy of imglist that will be used to restore imglist back so it always starts exactly the same
          # for each run.
          orig_imglist = copy.deepcopy(imglist)
          # create dummy list that will be used to preserve imglist best_meta information
          # through the imglist reset process
          # best_imglist = []
          fit_info_dict = OrderedDict()
          for algorithm_name in fit_algorithm_list:  # loop over fit algorithm type
              log.info("Applying {} fit method".format(algorithm_name))
              for catalog_index, catalog_name in enumerate(catalog_list):  # loop over astrometric catalog
                  log.info("{} STEP 5: Detect astrometric sources {}".format("-" * 20, "-" * 48))
                  log.info("Astrometric Catalog: {}".format(catalog_name))
                  # store reference catalogs in a dictionary so that generate_astrometric_catalog() doesn't
                  #  execute unnecessarily after it's been run once for a given astrometric catalog.
                  if catalog_name in alignment_table.reference_catalogs:
                      log.info("Using {} reference catalog from earlier this run.".format(catalog_name))
                      reference_catalog = alignment_table.reference_catalogs[catalog_name]
                  else:
                      log.info("Generating new reference catalog for {};"
                               " Storing it for potential re-use later this run.".format(catalog_name))
                      reference_catalog = generate_astrometric_catalog(process_list,
                                                                       catalog=catalog_name,
                                                                       output=output)
                      alignment_table.reference_catalogs[catalog_name] = reference_catalog
                  log.info(make_label('Processing time of [STEP 5]', starting_dt))
                  starting_dt = datetime.datetime.now()
  
                  if len(reference_catalog) < apars['determine_fit_quality']['MIN_CATALOG_THRESHOLD']:
                      log.warning("Not enough sources found in catalog {}".format(catalog_name))
                      fit_quality = 5
                      if catalog_index < len(catalog_list) - 1:
                          log.info("Try again with other catalog")
                      else:
                          # bail out if not enough sources can be found any of the astrometric catalogs
                          log.warning("ERROR! No astrometric sources found in any catalog. Exiting...")
                          alignment_table.filtered_table['status'][:] = 1
                          alignment_table.filtered_table['processMsg'][:] = "No astrometric sources found"
                          alignment_table.filtered_table['fit_qual'][:] = fit_quality
                          current_dt = datetime.datetime.now()
                          delta_dt = (current_dt - starting_dt).total_seconds()
                          log.info('Processing time of [STEP 5]: {} sec'.format(delta_dt))
                          alignment_table.close()
                          return alignment_table
                  else:
                      log.info("{} Cross matching and fitting {}".format("-" * 20, "-" * 47))
                      log.info(
                          "{} Catalog {} matched using {} {}".format("-" * 18,
                                                                     catalog_name,
                                                                     algorithm_name, "-" * 18))
                      try:
                          alignment_table.configure_fit()
                          log.debug("####\n# Running configure fit for AlignmentTable to use: \n")
                          log.debug([img.wcs for img in alignment_table.imglist])
                          log.debug("####\n")
  
                          # restore group IDs to their pristine state prior to each run.
                          alignment_table.reset_group_id(len(reference_catalog))
  
                          # execute the correct fitting/matching algorithm
                          alignment_table.imglist = alignment_table.perform_fit(algorithm_name,
                                                                                catalog_name,
                                                                                reference_catalog)
  
                          # determine the quality of the fit
                          fit_rms, fit_num, fit_quality, filtered_table, fit_status_dict = \
                              determine_fit_quality(
                                  alignment_table.imglist,
                                  alignment_table.filtered_table,
                                  (catalog_index < (len(catalog_list) - 1)),
                                  apars,
                                  print_fit_parameters=print_fit_parameters)
                          alignment_table.filtered_table = filtered_table
  
                          # save fit algorithm name to dictionary key "fit method" in imglist.
                          for imglist_ctr in range(0, len(alignment_table.imglist)):
                              table_fit = alignment_table.fit_dict[(catalog_name, algorithm_name)]
                              table_fit[imglist_ctr].meta['fit method'] = algorithm_name
                              table_fit[imglist_ctr].meta['fit quality'] = fit_quality
  
                          # populate fit_info_dict
                          fit_info_dict["{} {}".format(catalog_name, algorithm_name)] = \
                              fit_status_dict[next(iter(fit_status_dict))]
                          fit_info_dict["{} {}".format(catalog_name, algorithm_name)]['fit_qual'] = fit_quality
  
                          # Figure out which fit solution to go with based on fit_quality value and maybe also total_rms
                          if fit_quality < 5:
                              best_matches = fit_num > best_num_matches
                              if fit_quality == 1:  # valid, non-comprimised solution with total rms < 10 mas...go with this solution.
                                  best_fit_rms = fit_rms
                                  best_fit_label = (catalog_name, algorithm_name)
                                  best_num_matches = fit_num
  
                                  best_fit_status_dict = fit_status_dict.copy()
                                  best_fit_qual = fit_quality
                                  break  # break out of while loop
                              elif fit_quality < best_fit_qual:
                                  # better solution found. keep looping but with the better solution as "best" for now.
                                  log.info("Better solution found!")
                                  best_fit_rms = fit_rms
                                  best_num_matches = fit_num
                                  best_fit_label = (catalog_name, algorithm_name)
  
                                  best_fit_status_dict = fit_status_dict.copy()
                                  best_fit_qual = fit_quality
                              elif fit_quality == best_fit_qual:
                                  # new solution same level of fit_quality.
                                  # Choose whichever one has the most matches and lowest total rms as "best"
                                  # and keep looping to see if another fit is better.
                                  if best_fit_rms >= 0.:
                                      if best_matches and fit_rms < best_fit_rms:
                                          best_fit_rms = fit_rms
                                          best_num_matches = fit_num
                                          best_fit_label = (catalog_name, algorithm_name)
  
                                          best_fit_status_dict = fit_status_dict.copy()
                                          best_fit_qual = fit_quality
  
                              else:  # new solution has worse fit_quality. discard and continue looping.
                                  continue
  
                      except Exception:
                          exc_type, exc_value, exc_tb = sys.exc_info()
                          traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
                          log.warning(
                              "WARNING: Catastrophic fitting failure with catalog {} and matching "
                              "algorithm {}.".format(catalog_name,
                                                     algorithm_name))
                          alignment_table.filtered_table['status'][:] = 1
                          alignment_table.filtered_table['processMsg'][:] = "Fitting failure"
                          # It may be there are additional catalogs and algorithms to try, so keep going
                          fit_quality = 5  # Flag this fit with the 'bad' quality value
                          alignment_table.filtered_table['fit_qual'][:] = fit_quality
                          continue
                      if fit_quality == 1:  # break out of inner  astrometric catalog loop
                          break
              # break out of outer fit algorithm loop either with a fit_rms < 10 or a 'valid' relative fit
              if fit_quality == 1 or (best_fit_qual in [2, 3, 4] and "relative" in algorithm_name):
                  break
          log.info("best_fit found to be: {}".format(best_fit_label))
          log.info("FIT_DICT: {}".format(alignment_table.fit_dict.keys()))
          # Reset imglist to point to best solution...
          alignment_table.select_fit(best_fit_label[0], best_fit_label[1])
          imglist = alignment_table.selected_fit
          filtered_table = alignment_table.filtered_table
  
          # Report processing time for this step
          log.info(make_label('Processing time of [STEP 5b]', starting_dt))
          starting_dt = datetime.datetime.now()
  
          # 6: Populate the filtered_table
          log.info(
              "{} STEP 6: Collect up information and populate the filtered table "
              "{}".format("-" * 20, "-" * 20))
          if 0 < best_fit_rms < apars['determine_fit_quality']['MAX_FIT_RMS']:
              log.info("The fitting process was successful with a best fit total "
                       "rms of {} mas".format(best_fit_rms))
          else:
              log.info(
                  "The fitting process was unsuccessful with a best fit total rms "
                  "of {} mas".format(best_fit_rms))
  
          if 0 < best_fit_rms < apars['determine_fit_quality']['MAX_FIT_LIMIT']:
              # Update filtered table with best fit results
              filtered_table['status'][:] = 0
              fit_status_dict = best_fit_status_dict.copy()
  
              for item in imglist:
                  imgname = item.meta['name']
                  index = np.where(filtered_table['imageName'] == imgname)[0][0]
  
                  # populate self.filtered_table fields "status", "compromised" and
                  # "processMsg" with fit_status_dict fields "valid", "compromised"
                  # and "reason".
                  explicit_dict_key = "{},{}".format(item.meta['name'], item.meta['chip'])
                  if fit_status_dict[explicit_dict_key]['valid'] is True:
                      filtered_table[index]['status'] = 0
                  else:
                      filtered_table[index]['status'] = 1
                  if fit_status_dict[explicit_dict_key]['compromised'] is False:
                      filtered_table['compromised'] = 0
                  else:
                      filtered_table['compromised'] = 1
  
                  filtered_table[index]['processMsg'] = fit_status_dict[explicit_dict_key]['reason']
                  filtered_table['fit_qual'][index] = item.meta['fit quality']
  
          log.info(make_label('Processing time of [STEP 6]', starting_dt))
          starting_dt = datetime.datetime.now()
          # 7: Write new fit solution to input image headers
          log.info("{} STEP 7: Update image headers with new WCS information "
                   "{}".format("-" * 20, "-" * 29))
          if (0 < best_fit_rms < 9999.) and update_hdr_wcs:
              # determine the quality of the fit
              alignment_table.apply_fit(headerlet_filenames, fit_label=fit_label)
              log.info("SUCCESS")
          else:
              log.info(" STEP SKIPPED")
  
          log.info(make_label('Processing time of [STEP 7]', starting_dt))
          log.info('TOTAL Processing time of {} sec'.format(
                   (datetime.datetime.now() - zero_dt).total_seconds()))
          log.info(best_fit_status_dict)
          log.info("-" * 104)
  
          log.info("-" * 104)
          log.info("                             SUMMARY OF ALL FIT ATTEMPTS")
          for item in fit_info_dict.keys():
              log.info("{} {}".format(item, fit_info_dict[item]))
          log.info("-" * 104)
  
      except Exception:
          exc_type, exc_value, exc_tb = sys.exc_info()
          traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
  
      finally:
          # Always make sure that all file handles are closed
          alignment_table.close()
  
          # Now update the result with the filtered_table contents
          if result:
              result.meta = filtered_table.meta
              for col in filtered_table.colnames:
                  result.add_column(filtered_table[col], name=col)
          if filtered_table is not None:
              filtered_table.pprint(max_width=-1)
      return alignment_table
  
  # ----------------------------------------------------------------------------------------------------------
  
  
  def make_label(label, starting_dt):
      """Create a time-stamped label for use in log messages"""
      current_dt = datetime.datetime.now()
      delta_dt = (current_dt - starting_dt).total_seconds()
      return '{}: {} sec'.format(label, delta_dt)
  
  # ----------------------------------------------------------------------------------------------------------
  
  
  def determine_fit_quality(imglist, filtered_table, catalogs_remaining, align_pars, print_fit_parameters=True):
      """Determine the quality of the fit to the data
  
      Parameters
      ----------
      imglist : list
          output of interpret_fits. Contains sourcelist tables, newly computed WCS info, etc. for every chip of
          every valid input image.  This list should have been  updated, in-place, with the new RMS values;
          specifically,
  
              * 'FIT_RMS': RMS of the separations between fitted image positions and reference positions
              * 'TOTAL_RMS': mean of the FIT_RMS values for all observations
              * 'NUM_FITS': number of images/group_id's with successful fits included in the TOTAL_RMS
  
          These entries are added to the 'fit_info' dictionary.
  
      filtered_table : object
          Astropy Table object containing data pertaining to the associated dataset, including
          the doProcess bool.  It is intended this table is updated by subsequent functions for
          bookkeeping purposes.
  
      catalogs_remaining : bool
          Specify whether additional catalogs remain to be fit against.
  
      align_pars : dict
          Parameters from the appropriate '_align_all.json' parameter file
  
      print_fit_parameters : bool
          Specify whether or not to print out FIT results for each chip
  
      Returns
      -------
      max_rms_val : float
          The best Total rms determined from all of the images
  
      num_xmatches: int
          The number of stars used in matching the data
  
  
      fit_quality : int
          fit quality category:
              * 1 = valid solution with rms < 10 mas
              * 2 = Valid but compromised solution with rms < 10 mas
              * 3 = Valid solution with RMS >= 10 mas
              * 4 = Valid but compromised solution with RMS >= 10 mas
              * 5 = Not valid solution
  
      filtered_table : object
          modified filtered_table object
  
      fit_status_dict : dictionary
          Dictionary containing the following:
              * overall fit validity (Boolean)
              * total (visit-level) RMS value in mas (float)
              * number of matched sources (int)
              * fit compromised status (Boolean)
              * reason fit is considered 'compromised' (only populated if "compromised" field is "True")
      """
      max_rms_val = 1e9
      num_xmatches = 0
      fit_status_dict = {}
      xshifts = []
      yshifts = []
      overall_valid = True
      overall_comp = False
  
      # See whether or not 'consistency_check' should be performed.
      # This will only be turned on for pipeline processing due to the potentially extreme
      # variations in photometry across a visit or in a SkyCell.
      do_consistency_check = align_pars['determine_fit_quality'].get('do_consistency_check', True)
      auto_good_rms = float(align_pars['determine_fit_quality']['MAX_FIT_RMS'])
  
      for item in imglist:
          if item.meta['fit_info']['status'].startswith('REFERENCE'):
              # Reference will never be modified, so set shifts to (0., 0.)
              # so that there are entries for all images.
              xshifts.append(0.0)
              yshifts.append(0.0)
              continue
          if not item.meta['fit_info']['status'].startswith('FAILED'):
              xshifts.append(item.meta['fit_info']['shift'][0])
              yshifts.append(item.meta['fit_info']['shift'][1])
          else:
              # Fit not successful, so no shifts to collate
              break
  
      for item in imglist:
          image_name = item.meta['name']
          chip_num = item.meta['chip']
          fit_info = item.meta['fit_info']
          fitgeom = fit_info['fitgeom'] if 'fitgeom' in fit_info else 'rscale'
  
          log.debug("\n{}\n".format("-"*40))
          log.debug("FIT being evaluated for {}".format(image_name))
          log.debug(fit_info)
          log.debug("\n{}\n".format("-"*40))
  
          # Build fit_status_dict entry
          dict_key = "{},{}".format(image_name, chip_num)
          fit_status_dict[dict_key] = {'valid': True,
                                       'max_rms': max_rms_val,
                                       'num_matches': num_xmatches,
                                       'compromised': False,
                                       'reason': ""}
  
          # Handle fitting failures (no matches found or any other failure in fit)
          if fit_info['status'].startswith("FAILED"):
              log.warning("Alignment FAILED for {} - no processing done.".format(image_name))
              overall_valid = False
              continue
          if fit_info['status'].startswith("REFERENCE") or 'FIT_RMS' not in fit_info:
              # No fit information available for reference image
              continue
  
          fit_rms_val = fit_info['FIT_RMS']
          max_rms_val = fit_info['TOTAL_RMS']
          # fit_rms_ra = fit_info['RMS_RA']
          # fit_rms_dec = fit_info['RMS_DEC']
          # rms_ratio = abs(fit_rms_ra - fit_rms_dec) / min(fit_rms_ra, fit_rms_dec)
          num_xmatches = len(fit_info['ref_mag'])  # fit_info['nmatches']
          fit_status_dict[dict_key]['max_rms'] = max_rms_val
          fit_status_dict[dict_key]['num_matches'] = num_xmatches
  
          if num_xmatches < align_pars['general']['MIN_FIT_MATCHES']:
              overall_valid = False
              if catalogs_remaining:
                  log.warning(
                      "Not enough cross matches found between astrometric"
                      " catalog and sources found in {}".format(image_name))
                  continue
  
          # Compute correlation between input and GAIA magnitudes
          # This check will only be performed when the fit may be uncertain
          # due to less than 100 matches.
          ref_cat_limit = min(1000, item.meta['num_ref_catalog'])
          log.info("MAG CHECK REF_CAT_LIMIT: {}    XMATCHES: {}".format(ref_cat_limit, num_xmatches))
          if num_xmatches < max(0.1 * ref_cat_limit, 10):
              cross_match_check = amutils.check_mag_corr([item])[0]
              log.info("Cross-match check: {} on {} ref sources".format(cross_match_check,
                                                                        item.meta['num_ref_catalog']))
          else:
              cross_match_check = True
  
          # Execute checks
          nmatches_check = False
          if num_xmatches >= align_pars['run_align']['mosaic_fitgeom_list'][fitgeom] or \
                  (num_xmatches >= 2 and fit_rms_val > 0.5):
              nmatches_check = True
  
          radial_offset_check = False
          radial_offset = math.sqrt(
              float(fit_info['shift'][0])**2 +
              float(fit_info['shift'][1])**2) * item.wcs.pscale  # radial offset in arssec
          # Without the '+2', this will always fail for xmatches < 3 regardless of how small
          # the offset is:  2*0.36 == 0.72 vs 0.8 + 0 [perfect alignment]
          # Adding 2 allows low offset solutions with only 1 or 2 sources to pass this check.
          if float(num_xmatches + 2) * 0.36 > 0.8 + (radial_offset / 10.0)**8:
              radial_offset_check = True
  
          large_rms_check = True
          max_fit_limit = align_pars['determine_fit_quality']['MAX_FIT_LIMIT']
          if fit_rms_val > max_fit_limit or max_rms_val > max_fit_limit:
              large_rms_check = False
  
          # fitRmsCheck = False
          # if fit_rms_val < max_rms_val:
          #     fitRmsCheck = True
  
          consistency_check = True
          if do_consistency_check:
              rms_limit = max(fit_info['TOTAL_RMS'], auto_good_rms)
              if not math.sqrt(np.std(np.asarray(xshifts)) ** 2 + np.std(
                               np.asarray(yshifts)) ** 2) <= (rms_limit / align_pars['determine_fit_quality']['MAS_TO_ARCSEC']) / (item.wcs.pscale):  # \
                               # or rms_ratio > MAX_RMS_RATIO:
                  consistency_check = False
  
          # Decide if fit solutions are valid based on checks
          if not consistency_check:  # Failed consistency check
              fit_status_dict[dict_key]['valid'] = False
              fit_status_dict[dict_key]['compromised'] = False
              fit_status_dict[dict_key]['reason'] = "Consistency violation!"
          elif not large_rms_check:  # RMS value(s) too large
              fit_status_dict[dict_key]['valid'] = False
              fit_status_dict[dict_key]['compromised'] = False
              fit_status_dict[dict_key]['reason'] = "RMS too large (>150 mas)!"
          elif not radial_offset_check:  # Failed radial offset check
              fit_status_dict[dict_key]['valid'] = False
              fit_status_dict[dict_key]['compromised'] = False
              fit_status_dict[dict_key]['reason'] = "Radial offset value too large!"
          elif not nmatches_check:  # Too few matches
              fit_status_dict[dict_key]['valid'] = False
              fit_status_dict[dict_key]['compromised'] = True
              fit_status_dict[dict_key]['reason'] = "Too few matches!"
          elif not cross_match_check:
              fit_status_dict[dict_key]['valid'] = True
              fit_status_dict[dict_key]['compromised'] = True
              fit_status_dict[dict_key]['reason'] = "Cross-match magnitudes not correlated!"
          else:  # all checks passed. Valid solution.
              fit_status_dict[dict_key]['valid'] = True
              fit_status_dict[dict_key]['compromised'] = False
              fit_status_dict[dict_key]['reason'] = ""
          # for now, generate overall valid and compromised values. Basically, if any of the entries for "valid" is False,
          # "valid" is False, treat the whole dataset as not valid. Same goes for compromised.
          if not fit_status_dict[dict_key]['valid']:
              overall_valid = False
          if fit_status_dict[dict_key]['compromised']:
              overall_comp = True
  
          log.info('RESULTS FOR {} Chip {}: FIT_RMS = {} mas, TOTAL_RMS = {}'
                   ' mas, NUM =  {}'.format(image_name,
                                            item.meta['chip'],
                                            fit_rms_val,
                                            max_rms_val,
                                            num_xmatches))
          # print fit params to screen
          if print_fit_parameters:
              log_info_keys = ['status', 'fitgeom', 'eff_minobj', 'matrix', 'shift', 'center', 'proper_rot',
                               'proper', '<rot>', '<scale>', 'skew', 'rmse', 'mae', 'nmatches', 'FIT_RMS',
                               'TOTAL_RMS', 'NUM_FITS', 'RMS_RA', 'RMS_DEC', 'catalog']
              log.info("{} FIT PARAMETERS {}".format("~" * 35, "~" * 34))
              log.info("image: {}".format(image_name))
              log.info("chip: {}".format(item.meta['chip']))
              log.info("group_id: {}".format(item.meta['group_id']))
              for tweakwcs_info_key in log_info_keys:
                  log.info("{} : {}".format(tweakwcs_info_key, fit_info[tweakwcs_info_key]))
              log.info("~" * 84)
              log.info("nmatches_check: {} radial_offset_check: {}"
                       " large_rms_check: {},"
                       " consistency_check: {}".format(nmatches_check,
                                                       radial_offset_check,
                                                       large_rms_check,
                                                       consistency_check))
  
      # determine which fit quality category this latest fit falls into
      if overall_valid is False:
          fit_quality = 5
          log.info("FIT SOLUTION REJECTED")
          filtered_table['status'][:] = 1
          for ctr in range(0, len(filtered_table)):
              imgname = filtered_table[ctr]['imageName'] + ",1"
              if imgname in fit_status_dict:
                  filtered_table[ctr]['processMsg'] = fit_status_dict[imgname]["reason"]
              else:
                  filtered_table[ctr]['processMsg'] = "Not a valid exposure"
      else:
          for ctr in range(0, len(filtered_table)):
              filtered_table[ctr]['processMsg'] = ""
          if overall_comp is False and max_rms_val < auto_good_rms:
              log.info("Valid solution with RMS < {} mas found!".format(auto_good_rms))
              fit_quality = 1
          elif overall_comp is True and max_rms_val < auto_good_rms:
              log.info("Valid but compromised solution with RMS < {} mas found!".format(auto_good_rms))
              fit_quality = 2
          elif overall_comp is False and 1000. >= max_rms_val >= auto_good_rms:
              log.info("Valid solution with RMS >= {} mas found!".format(auto_good_rms))
              fit_quality = 3
          else:
              log.info("Valid but compromised solution with RMS >= {} mas found!".format(auto_good_rms))
              fit_quality = 4
  
      if print_fit_parameters:
          for item in imglist: log.info(fit_status_dict["{},{}".format(item.meta['name'], item.meta['chip'])])
  
      if max_rms_val > auto_good_rms:
          log.info("Total fit RMS value = {} mas greater than the maximum threshold value {}.".format(max_rms_val, auto_good_rms))
      if not overall_valid:
          log.info("The fit solution for some or all of the images is not valid.")
      if max_rms_val > auto_good_rms or not overall_valid:
          log.info("Try again with the next catalog")
      else:
          log.info("Fit calculations successful.")
      return max_rms_val, num_xmatches, fit_quality, filtered_table, fit_status_dict
  
  
  # ----------------------------------------------------------------------------------------------------------------------
  
  def determine_fit_quality_mvm_interface(imglist, filtered_table, catalogs_remaining, ref_catalog_length,
                                          align_pars, print_fit_parameters=True, loglevel=logutil.logging.NOTSET):
      """Simple interface to allow MVM code to use determine_fit_quality().
  
      Parameters
      ----------
      imglist : list
          output of interpret_fits. Contains sourcelist tables, newly computed WCS info, etc. for every chip of
          every valid input image.  This list should have been  updated, in-place, with the new RMS values;
          specifically,
  
              * 'FIT_RMS': RMS of the separations between fitted image positions and reference positions
              * 'TOTAL_RMS': mean of the FIT_RMS values for all observations
              * 'NUM_FITS': number of images/group_id's with successful fits included in the TOTAL_RMS
  
          These entries are added to the 'fit_info' dictionary.
  
      filtered_table : object
          Astropy Table object containing data pertaining to the associated dataset, including
          the doProcess bool.  It is intended this table is updated by subsequent functions for
          bookkeeping purposes.
  
      catalogs_remaining : bool
          Specify whether additional catalogs remain to be fit against.
  
      align_pars : dict
          Parameters from the appropriate '_align_all.json' parameter file
  
      print_fit_parameters : bool
          Specify whether or not to print out FIT results for each chip
  
      Returns
      -------
      is_good_fit : bool
          Is the fit acceptable (Is fit_quality value in GOOD_FIT_QUALITY_VALUES)?
  
      max_rms_val : float
          The best Total rms determined from all of the images
  
      num_xmatches: int
          The number of stars used in matching the data
  
      fit_quality : int
          fit quality category:
              * 1 = valid solution with rms < 10 mas
              * 2 = Valid but compromised solution with rms < 10 mas
              * 3 = Valid solution with RMS >= 10 mas
              * 4 = Valid but compromised solution with RMS >= 10 mas
              * 5 = Not valid solution
  
      filtered_table : object
          modified filtered_table object
  
      fit_status_dict : dictionary
          Dictionary containing the following:
              * overall fit validity (Boolean)
              * total (visit-level) RMS value in mas (float)
              * number of matched sources (int)
              * fit compromised status (Boolean)
              * reason fit is considered 'compromised' (only populated if "compromised" field is "True")
  
      """
      log.setLevel(loglevel)
  
      # Check if num_ref_catalog is in imglist...if not, add it.
      for ctr in range(0, len(imglist)):
          if 'num_ref_catalog' not in imglist[ctr].meta.keys():
              imglist[ctr].meta['num_ref_catalog'] = ref_catalog_length
  
      # Execute determine_fit_quality
      fit_rms, fit_num, fit_quality, filtered_table, fit_status_dict = determine_fit_quality(imglist,
                                                                                             filtered_table,
                                                                                             catalogs_remaining,
                                                                                             align_pars,
                                                                                             print_fit_parameters)
  
      # Determine if the fit quality is acceptable
      if fit_quality in align_pars['determine_fit_quality']['GOOD_FIT_QUALITY_VALUES']:
          is_good_fit = True
      else:
          is_good_fit = False
  
      return is_good_fit, fit_rms, fit_num, fit_quality, filtered_table, fit_status_dict
  
  
  # ----------------------------------------------------------------------------------------------------------------------
  
  def generate_astrometric_catalog(imglist, **pars):
      """Generates a catalog of all sources from an existing astrometric catalog are
         in or near the FOVs of the images in the input list.
  
      Parameters
      ----------
      imglist : list
          List of one or more calibrated fits images that will be used for catalog generation.
  
      Returns
      =======
      ref_table : object
          Astropy Table object of the catalog
  
      """
      # generate catalog
      temp_pars = pars.copy()
      if pars['output'] is True:
          pars['output'] = 'ref_cat.ecsv'
      else:
          pars['output'] = None
      out_catalog = amutils.create_astrometric_catalog(imglist, **pars)
      pars = temp_pars.copy()
      # if the catalog has contents, write the catalog to ascii text file
      if len(out_catalog) > 0 and pars['output']:
          catalog_filename = "refcatalog.cat"
          out_catalog.write(catalog_filename, format="ascii.fast_commented_header")
          log.info("Wrote reference catalog {}.".format(catalog_filename))
  
      return(out_catalog)
  
  # ----------------------------------------------------------------------------------------------------------------------
  
  def get_default_pars(instrument, detector, step='alignment',
                       condition=['filter_basic']):
  
      step_list = config_utils.step_title_list
      if step not in step_list:
          log.error("{} not valid!  Needs to be one of: {}".format(step,
                    step_list))
          raise ValueError
  
      full_cfg_index, pars_dir = config_utils.read_index(instrument, detector)
  
      par_class = config_utils.step_name_list[step_list.index(step)]
      apars = par_class(full_cfg_index[step], condition,
                        pars_dir, step, True, None)
  
      return apars.outpars
  