#!/usr/bin/env python
""" This script defines the HST Advanced Products (HAP) generation portion of the
calibration pipeline. This portion of the pipeline produces mosaic and catalog
products. This script provides similar functionality as compared to the Hubble
Legacy Archive (HLA) pipeline in that it acts as the controller for the overall
sequence of processing.
Note regarding logging...
During instantiation of the log, the logging level is set to NOTSET which essentially
means all message levels (debug, info, etc.) will be passed from the logger to
the underlying handlers, and the handlers will dispatch all messages to the associated
streams. When the command line option of setting the logging level is invoked, the
logger basically filters which messages are passed on to the handlers according the
level chosen. The logger is acting as a gate on the messages which are allowed to be
passed to the handlers.
Creation of source catalogs can be controlled through the use of environment variables:
- SVM_CATALOG_HRC
- SVM_CATALOG_SBC
- SVM_CATALOG_WFC
- SVM_CATALOG_UVIS
- SVM_CATALOG_IR
These variables can be defined using values of:
- 'on', 'true', 'yes' : Create catalogs
- 'off', 'false', 'no' : Turn off generation of catalogs
The output products can be evaluated to determine the quality of the alignment and
output data through the use of the environment variable:
- SVM_QUALITY_TESTING : Turn on quality assessment processing. This environment
variable, if found with an affirmative value, will turn on processing to generate a JSON
file which contains the results of evaluating the quality of the generated products.
NOTE: Step 9 compares the output HAP products to the Hubble Legacy Archive (HLA)
products. In order for step 9 (run_sourcelist_comparison()) to run, the following
environment variables need to be set:
- HLA_CLASSIC_BASEPATH
- HLA_BUILD_VER
Alternatively, if the HLA classic path is unavailable, The comparison can be run using
locally stored HLA classic files. The relevant HLA classic imagery and sourcelist files
must be placed in a subdirectory of the current working directory called 'hla_classic'.
"""
import datetime
import fnmatch
import logging
import os
import pickle
import re
import shutil
import sys
import traceback
import glob
import numpy as np
from astropy.io import ascii, fits
from astropy.table import Table
import drizzlepac
from drizzlepac import util
from drizzlepac import updatehdr
from drizzlepac.haputils import config_utils
from drizzlepac.haputils import diagnostic_utils
from drizzlepac.haputils import hla_flag_filter
from drizzlepac.haputils import poller_utils
from drizzlepac.haputils import product
from drizzlepac.haputils import processing_utils as proc_utils
from drizzlepac.haputils import svm_quality_analysis as svm_qa
from drizzlepac.haputils.catalog_utils import HAPCatalogs
from . import __version__
from stsci.tools.fileutil import countExtn
from stsci.tools import logutil
from stwcs import updatewcs
from stwcs import wcsutil
__taskname__ = 'hapsequencer'
MSG_DATEFMT = '%Y%j%H%M%S'
SPLUNK_MSG_FORMAT = '%(asctime)s %(levelname)s src=%(name)s- %(message)s'
log = logutil.create_logger(__name__, level=logutil.logging.NOTSET, stream=sys.stdout,
format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)
# Environment variable which controls the quality assurance testing
# for the Single Visit Mosaic processing.
envvar_bool_dict = {'off': False, 'on': True, 'no': False, 'yes': True, 'false': False, 'true': True}
envvar_qa_svm = "SVM_QUALITY_TESTING"
envvar_cat_svm = {"SVM_CATALOG_SBC": 'on',
"SVM_CATALOG_HRC": 'on',
"SVM_CATALOG_WFC": 'on',
"SVM_CATALOG_UVIS": 'on',
"SVM_CATALOG_IR": 'on',
"SVM_CATALOG_PC": 'on'}
envvar_cat_str = "SVM_CATALOG_{}"
# --------------------------------------------------------------------------------------------------------------
[docs]
def create_catalog_products(total_obj_list, log_level, diagnostic_mode=False, phot_mode='both',
catalog_switches=None):
"""This subroutine utilizes haputils/catalog_utils module to produce photometric sourcelists for the specified
total drizzle product and it's associated child filter products.
PARAMETERS
-----------
total_obj_list : `~drizzlepac.haputils.product.TotalProduct`
total drizzle product that will be processed by catalog_utils.
catalog_utils will also create photometric
sourcelists for the child filter products of this total product.
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the .log file.
diagnostic_mode : bool, optional
generate ds9 region file counterparts to the photometric sourcelists? Default value is False.
phot_mode : str, optional
Which algorithm should be used to generate the sourcelists? 'aperture' for aperture photometry;
'segment' for segment map photometry; 'both' for both 'segment' and 'aperture'. Default value is 'both'.
catalog_switches : dict, optional
Specify which, if any, catalogs should be generated at all, based on detector. This dictionary
needs to contain values for all instruments; namely:
SVM_CATALOG_HRC, SVM_CATALOG_SBC, SVM_CATALOG_WFC, SVM_CATALOG_UVIS, SVM_CATALOG_IR, SVM_CATALOG_PC
These variables can be defined with values of 'on'/'off'/'yes'/'no'/'true'/'false'.
Returns
-------
product_list : list
list of all catalogs generated.
"""
product_list = []
log.info("Generating total product source catalogs")
phot_mode = phot_mode.lower()
input_phot_mode = phot_mode
for total_product_obj in total_obj_list:
# Need to have direct exposures for catalog generation
if total_product_obj.edp_list:
cat_sw_name = envvar_cat_str.format(total_product_obj.detector.upper())
if catalog_switches[cat_sw_name] is False:
log.info("Catalog generation turned OFF for {}".format(total_product_obj.detector.upper()))
continue
# Make sure this is re-initialized for the new total product
phot_mode = input_phot_mode
# Instantiate filter catalog product object
total_product_catalogs = HAPCatalogs(total_product_obj.drizzle_filename,
total_product_obj.configobj_pars.get_pars('catalog generation'),
total_product_obj.configobj_pars.get_pars('quality control'),
total_product_obj.mask,
log_level,
types=input_phot_mode,
diagnostic_mode=diagnostic_mode)
# Identify sources in the input image and delay writing the total detection
# catalog until the photometric measurements have been done on the filter
# images and some of the measurements can be appended to the total catalog
total_product_catalogs.identify(mask=total_product_obj.mask)
# Build dictionary of total_product_catalogs.catalogs[*].sources to use for
# filter photometric catalog generation
sources_dict = {}
filter_catalogs = {}
source_mask = {}
for cat_type in total_product_catalogs.catalogs.keys():
sources_dict[cat_type] = {}
sources_dict[cat_type]['sources'] = total_product_catalogs.catalogs[cat_type].sources
# For the segmentation source finding, both the segmentation image AND the segmentation catalog
# computed for the total object need to be provided to the filter objects
if cat_type == "segment":
sources_dict['segment']['kernel'] = total_product_catalogs.catalogs['segment'].kernel
sources_dict['segment']['source_cat'] = total_product_catalogs.catalogs['segment'].source_cat
sources_dict['segment']['total_source_cat'] = total_product_catalogs.catalogs['segment'].total_source_cat
# Get parameter from config files for CR rejection of catalogs
cr_residual = total_product_obj.configobj_pars.get_pars('catalog generation')['cr_residual']
n1_exposure_time = 0
log.info("Generating filter product source catalogs")
for filter_product_obj in total_product_obj.fdp_list:
# Load a dictionary with the filter subset table for each catalog...
subset_columns_dict = {}
# Instantiate filter catalog product object
filter_product_catalogs = HAPCatalogs(filter_product_obj.drizzle_filename,
total_product_obj.configobj_pars.get_pars('catalog generation'),
total_product_obj.configobj_pars.get_pars('quality control'),
total_product_obj.mask,
log_level,
types=phot_mode,
diagnostic_mode=diagnostic_mode,
tp_sources=sources_dict)
flag_trim_value = filter_product_catalogs.param_dict['flag_trim_value']
# Perform photometry
# The measure method also copies a specified portion of the filter table into
# a filter "subset" table which will be combined with the total detection table.
filter_name = filter_product_obj.filters
filter_product_catalogs.measure(filter_name)
log.info("Flagging sources in filter product catalog")
filter_product_catalogs = run_sourcelist_flagging(filter_product_obj,
filter_product_catalogs,
log_level,
diagnostic_mode)
if total_product_obj.detector.upper() not in ['IR', 'SBC']:
# Apply cosmic-ray threshold criteria used by HLA to determine whether or not to reject
# the catalogs.
tot_exposure_time = 0
n1_factor = 0.0
n1_dict = {}
for edp in total_product_obj.edp_list:
if edp.filters not in n1_dict:
n1_dict[edp.filters] = {'n': 1, 'texptime': edp.exptime}
else:
n1_dict[edp.filters]['n'] += 1
n1_dict[edp.filters]['texptime'] += edp.exptime
for edp in total_product_obj.edp_list:
tot_exposure_time += edp.exptime
if n1_dict[edp.filters]['n'] == 1:
n1_exposure_time += edp.exptime
n1_factor += cr_residual
# Insure that n1_factor only improves the threshold, not make it worse.
n1_factor = min(n1_factor, 1.0)
# Account for the influence of the single-image cosmic-ray identification
# This fraction represents the residual number of cosmic-rays after single-image identification
if n1_exposure_time < tot_exposure_time:
n1_exposure_time *= n1_factor
# write out CI and FWHM values to file (if IRAFStarFinder was used instead of DAOStarFinder) for hla_flag_filter parameter optimization.
if diagnostic_mode and phot_mode in ['aperture', 'both']:
if "fwhm" in total_product_catalogs.catalogs['aperture'].sources.colnames:
diag_obj = diagnostic_utils.HapDiagnostic(log_level=log_level)
diag_obj.instantiate_from_hap_obj(filter_product_obj,
data_source=__taskname__,
description="CI vs. FWHM values")
output_table = Table([filter_product_catalogs.catalogs['aperture'].source_cat['CI'], total_product_catalogs.catalogs['aperture'].sources['fwhm']], names=("CI", "FWHM"))
diag_obj.add_data_item(output_table, "CI_FWHM")
diag_obj.write_json_file(filter_product_obj.point_cat_filename.replace(".ecsv", "_ci_fwhm.json"))
del output_table
del diag_obj
# Replace zero-value total-product catalog 'Flags' column values with meaningful filter-product catalog
# 'Flags' column values
for cat_type in filter_product_catalogs.catalogs.keys():
filter_product_catalogs.catalogs[cat_type].subset_filter_source_cat[
'Flags_{}'.format(filter_product_obj.filters)] = \
filter_product_catalogs.catalogs[cat_type].source_cat['Flags']
source_mask[cat_type] = None
filter_catalogs[filter_product_obj.drizzle_filename] = filter_product_catalogs
# Determine which rows should be removed from each type of catalog based on Flag values
# Any source with Flag > 5 in any filter product catalog will be marked for removal from
# all catalogs.
# This requires collating results for each type of catalog from all filter products.
for cat_type in filter_product_catalogs.catalogs.keys():
subset_columns_dict[cat_type] = {}
subset_columns_dict[cat_type]['subset'] = \
filter_product_catalogs.catalogs[cat_type].subset_filter_source_cat
# ...and append the filter columns to the total detection product catalog.
log.info("create catalogs. combine() columns: {}".format(subset_columns_dict))
total_product_catalogs.combine(subset_columns_dict)
# At this point the total product catalog contains all columns contributed
# by each filter catalog. However, some of the columns originating in one or more of
# the filter catalogs contain no measurements for a particular source. Remove all
# rows which contain empty strings (masked values) for *all* measurements for *all*
# of the filter catalogs.
for cat_type in total_product_catalogs.catalogs.keys():
if cat_type == 'aperture':
all_columns = total_product_catalogs.catalogs[cat_type].sources.colnames
table_filled = total_product_catalogs.catalogs[cat_type].sources.filled(-9999.9)
else:
all_columns = total_product_catalogs.catalogs[cat_type].source_cat.colnames
table_filled = total_product_catalogs.catalogs[cat_type].source_cat.filled(-9999.9)
flag_columns = [colname for colname in all_columns if "Flags_" in colname]
filled_flag_columns = table_filled[flag_columns]
# work out what rows have flag values > flag_limit in ALL flag columns
flag_bitmasks = [np.logical_or(filled_flag_columns[col] > flag_trim_value,
np.isclose(filled_flag_columns[col], -9999.9))
for col in filled_flag_columns.colnames]
flag_mask = np.logical_and.reduce(flag_bitmasks)
# Get indices of all good rows
good_rows_index = np.where(flag_mask == False)[0]
if cat_type == 'aperture':
total_product_catalogs.catalogs[cat_type].sources = total_product_catalogs.catalogs[cat_type].sources[good_rows_index]
else:
total_product_catalogs.catalogs[cat_type].source_cat = total_product_catalogs.catalogs[cat_type].source_cat[good_rows_index]
# Determine whether any catalogs should be written out at all based on comparison to expected
# rate of cosmic-ray contamination for the total detection product
reject_catalogs = {}
reject_catalogs = total_product_catalogs.verify_crthresh(n1_exposure_time)
if diagnostic_mode:
# If diagnostic mode, we want to inspect the original full source catalogs
for cat_type in total_product_catalogs.catalogs.keys():
reject_catalogs[cat_type] = False
for filter_product_obj in total_product_obj.fdp_list:
filter_product_catalogs = filter_catalogs[filter_product_obj.drizzle_filename]
# Now write the catalogs out for this filter product
log.info("Writing out filter product catalog")
# Write out photometric (filter) catalog(s)
filter_product_catalogs.write(reject_catalogs)
# append filter product catalogs to list
if phot_mode in ['aperture', 'both']:
product_list.append(filter_product_obj.point_cat_filename)
if phot_mode in ['segment', 'both']:
product_list.append(filter_product_obj.segment_cat_filename)
log.info("Writing out total product catalog")
# write out list(s) of identified sources
total_product_catalogs.write(reject_catalogs)
# append total product catalogs to manifest list
if phot_mode in ['aperture', 'both']:
product_list.append(total_product_obj.point_cat_filename)
if phot_mode in ['segment', 'both']:
product_list.append(total_product_obj.segment_cat_filename)
return product_list
# ----------------------------------------------------------------------------------------------------------------------
[docs]
def create_drizzle_products(total_obj_list):
"""
Run astrodrizzle to produce products specified in the total_obj_list.
Parameters
----------
total_obj_list : list
List of TotalProduct objects, one object per instrument/detector combination is
a visit. The TotalProduct objects are comprised of FilterProduct and ExposureProduct
objects.
RETURNS
-------
product_list : list
A list of output products
"""
log.info("Processing with astrodrizzle version {}".format(drizzlepac.astrodrizzle.__version__))
# Get rules files
rules_files = {}
# Generate list of all input exposure filenames that are to be processed
edp_names = []
for t in total_obj_list:
edp_names += [e.full_filename for e in t.edp_list]
# Define dataset-specific rules filenames for each input exposure
for imgname in edp_names:
rules_files[imgname] = proc_utils.get_rules_file(imgname)
print('Generated RULES_FILE names of: \n{}\n'.format(rules_files))
# Keep track of all the products created for the output manifest
product_list = []
# For each detector (as the total detection product are instrument- and detector-specific),
# create the drizzle-combined filtered image, the drizzled exposure (aka single) images,
# and finally the drizzle-combined total detection image.
for total_obj in total_obj_list:
# Need to have direct exposures to drizzle
if total_obj.edp_list:
log.info("~" * 118)
# Get the common WCS for all images which are part of a total detection product,
# where the total detection product is detector-dependent.
meta_wcs = total_obj.generate_metawcs()
# Create drizzle-combined filter image as well as the single exposure drizzled image
for filt_obj in total_obj.fdp_list:
log.info("~" * 118)
filt_obj.rules_file = proc_utils.get_rules_file(filt_obj.edp_list[0].full_filename,
rules_root=filt_obj.drizzle_filename)
# add filter rules files to dict of all rules files for deletion later
rules_files[filt_obj.drizzle_filename] = filt_obj.rules_file
print(f"Filter RULES_FILE: {filt_obj.rules_file}")
log.info("CREATE DRIZZLE-COMBINED FILTER IMAGE: {}\n".format(filt_obj.drizzle_filename))
filt_obj.wcs_drizzle_product(meta_wcs)
product_list.append(filt_obj.drizzle_filename)
product_list.append(filt_obj.trl_filename)
# Create individual single drizzled images
for exposure_obj in filt_obj.edp_list:
log.info("~" * 118)
exposure_obj.rules_file = rules_files[exposure_obj.full_filename]
log.info("CREATE SINGLE DRIZZLED IMAGE: {}".format(exposure_obj.drizzle_filename))
exposure_obj.wcs_drizzle_product(meta_wcs)
product_list.append(exposure_obj.drizzle_filename)
product_list.append(exposure_obj.full_filename)
# product_list.append(exposure_obj.headerlet_filename)
product_list.append(exposure_obj.trl_filename)
# Create drizzle-combined total detection image after the drizzle-combined filter image and
# drizzled exposure images in order to take advantage of the cosmic ray flagging.
log.info("CREATE DRIZZLE-COMBINED TOTAL IMAGE: {}\n".format(total_obj.drizzle_filename))
total_obj.rules_file = proc_utils.get_rules_file(total_obj.edp_list[0].full_filename,
rules_root=total_obj.drizzle_filename)
# add total rules files to dict of all rules files for deletion later
rules_files[total_obj.drizzle_filename] = total_obj.rules_file
print(f"Total product RULES_FILE: {total_obj.rules_file}")
total_obj.wcs_drizzle_product(meta_wcs)
product_list.append(total_obj.drizzle_filename)
product_list.append(total_obj.trl_filename)
# Ensure that all drizzled products have headers that are to specification
try:
log.info("Updating these drizzle products for CAOM compatibility:")
fits_files = fnmatch.filter(product_list, "*dr?.fits")
for filename in fits_files:
log.info(" {}".format(filename))
proc_utils.refine_product_headers(filename, total_obj_list)
except Exception:
log.critical("Trouble updating drizzle products for CAOM.")
exc_type, exc_value, exc_tb = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
logging.exception("message")
# Remove rules files copied to the current working directory
for rules_filename in list(rules_files.values()):
log.info("Removed rules file {}".format(rules_filename))
os.remove(rules_filename)
# Add primary header information to all objects
for total_obj in total_obj_list:
if total_obj.edp_list:
total_obj = poller_utils.add_primary_fits_header_as_attr(total_obj)
for filt_obj in total_obj.fdp_list:
filt_obj = poller_utils.add_primary_fits_header_as_attr(filt_obj)
for exposure_obj in filt_obj.edp_list:
exposure_obj = poller_utils.add_primary_fits_header_as_attr(exposure_obj)
# Return product list for creation of pipeline manifest file
return product_list
# ----------------------------------------------------------------------------------------------------------------------
[docs]
def run_hap_processing(input_filename, diagnostic_mode=False, input_custom_pars_file=None,
output_custom_pars_file=None, phot_mode="both", log_level=logutil.logging.INFO):
"""
Run the HST Advanced Products (HAP) generation code. This routine is the sequencer or
controller which invokes the high-level functionality to process the single visit data.
Parameters
----------
input_filename : str
The 'poller file' where each line contains information regarding an exposures taken
during a single visit.
diagnostic_mode : bool, optional
Allows printing of additional diagnostic information to the log. Also, can turn on
creation and use of pickled information.
input_custom_pars_file : str, optional
Represents a fully specified input filename of a configuration JSON file which has been
customized for specialized processing. This file should contain ALL the input parameters necessary
for processing. If not specified, default configuration parameter values will be used. The default is
None.
output_custom_pars_file : str, optional
Fully specified output filename which contains all the configuration parameters
available during the processing session. The default is None.
phot_mode : str, optional
Which algorithm should be used to generate the sourcelists? 'aperture' for aperture photometry;
'segment' for segment map photometry; 'both' for both 'segment' and 'aperture'. Default value is
'both'.
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 20, or 'info'.
RETURNS
-------
return_value: int
A return exit code used by the calling Condor/OWL workflow code: 0 (zero) for success, 1 for error
"""
# This routine needs to return an exit code, return_value, for use by the calling
# Condor/OWL workflow code: 0 (zero) for success, 1 for error condition
return_value = 0
log.setLevel(log_level)
# Define trailer file (log file) that will contain the log entries for all processing
logname = proc_utils.build_logname(input_filename)
# Initialize total trailer filename as temp logname
logging.basicConfig(filename=logname, format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT, force=True)
# start processing
starting_dt = datetime.datetime.now()
log.info("Run start time: {}".format(str(starting_dt)))
# Start by reading in any environment variable related to catalog generation that has been set
cat_switches = {sw: _get_envvar_switch(sw, default=envvar_cat_svm[sw]) for sw in envvar_cat_svm}
# Since these are used in the finally block, make sure they are initialized
total_obj_list = []
grism_product_list = []
ramp_product_list = []
manifest_name = ""
found_data = False
# The product_list is a list of all the output products which will be put into the manifest file
product_list = []
mfile_list = []
try:
# Parse the poller file and generate the obs_info_dict, as well as the total detection
# product lists which contain the ExposureProduct, FilterProduct, and TotalProduct objects
# A poller file contains visit data for a single instrument. The TotalProduct discriminant
# is the detector. A TotalProduct object is composed of FilterProducts and ExposureProducts
# where its FilterProduct is distinguished by the filter in use, and the ExposureProduct
# is the atomic exposure data. Note: the TotalProduct was enhanced to also be comprised
# of an GrismExposureProduct list which is exclusive to the TotalProduct.
log.info("Parse the poller and determine what exposures need to be combined into separate products.\n")
obs_info_dict, total_obj_list = poller_utils.interpret_obset_input(input_filename, log_level)
# Generate the name for the manifest file which is for the entire visit. It is fine
# to use only one of the Total Products to generate the manifest name as the name is not
# dependent on the detector.
# Example: instrument_programID_obsetID_manifest.txt (e.g.,wfc3_b46_06_manifest.txt)
manifest_name = total_obj_list[0].manifest_name
log.info("Generate the manifest name for this visit.")
log.info("The manifest will contain the names of all the output products.")
# It is possible the total_obj_list output from the poller_utils contains only Grism/Prism
# data and no direct images, so no further processing should be done. If this is the case,
# there is actually nothing to be done for the visit, except write out an empty manifest file.
# Check every item in the total data product list.
for total_item in total_obj_list:
# Indicator of viable (e.g., not spectroscopic, etc.) direct images for processing
if total_item.edp_list and not found_data:
found_data = True
# Indicator of only Grism/Prism data found with no direct images, so no processing done.
# Leaving the no_data_trl variable and just not adding it to the product_list in case
# the requirement changes again to write out a trailer file in this instance.
elif total_item.grism_edp_list:
no_data_trl = total_item.trl_filename
if not found_data:
log.warning("")
log.warning("There are no viable direct images in any Total Data Product for this visit. No processing can be done.")
log.warning("No SVM processing is done for the Grism/Prism data - no SVM output products are generated.")
sys.exit(0)
# Update all of the product objects with their associated configuration information.
for total_item in total_obj_list:
for edp_file in total_item.edp_list:
# Pull in any manifest files from previous processing and include in product_list
mfiles = glob.glob(f"{edp_file.exposure_name}*_manifest.txt")
# If nothing is found, no nothing gets added to the product_list
for mfile in mfiles:
with open(mfile, 'r') as finput:
_ = [product_list.append(fname.strip("\n")) for fname in finput.readlines()]
# Once it has been merged in with product_list for output to the manifest file,
# record it's name so it can be deleted upon successful completion of this processing.
mfile_list.append(mfile)
log.info("Preparing configuration parameter values for total product {}".format(total_item.drizzle_filename))
total_item.configobj_pars = config_utils.HapConfig(total_item,
log_level=log_level,
input_custom_pars_file=input_custom_pars_file,
output_custom_pars_file=output_custom_pars_file)
for filter_item in total_item.fdp_list:
log.info("Preparing configuration parameter values for filter product {}".format(filter_item.drizzle_filename))
filter_item.configobj_pars = config_utils.HapConfig(filter_item,
log_level=log_level,
input_custom_pars_file=input_custom_pars_file,
output_custom_pars_file=output_custom_pars_file)
for expo_item in total_item.edp_list:
log.info("Preparing configuration parameter values for exposure product {}".format(expo_item.drizzle_filename))
expo_item.configobj_pars = config_utils.HapConfig(expo_item,
log_level=log_level,
input_custom_pars_file=input_custom_pars_file,
output_custom_pars_file=output_custom_pars_file)
expo_item = poller_utils.add_primary_fits_header_as_attr(expo_item, log_level)
log.info("The configuration parameters have been read and applied to the drizzle objects.")
# Check to ensure the object contains a list of direct images (protects against processing Grism/Prism data)
if total_item.edp_list:
reference_catalog = run_align_to_gaia(total_item, log_level=log_level, diagnostic_mode=diagnostic_mode)
if reference_catalog:
product_list += reference_catalog
# Need to delete the Ramp filter Exposure objects from the *Product lists as
# these images should not be processed beyond the alignment to Gaia (run_align_to_gaia).
_ = delete_ramp_exposures(total_item.fdp_list, 'FILTER')
ramp_product_list = delete_ramp_exposures(total_item.edp_list, 'EXPOSURE')
product_list += ramp_product_list
# If there are Grism/Prism images present in this visit, as well as corresponding direct images
# for the same detector, update the primary WCS in the direct and/or Grism/Prism images as
# appropriate to be an 'a priori' or the pipeline default (fallback) solution. Note: there
# is no need to delete the Grism/Prism lists after the WCS update as the Grism/Prism
# exposures are stored in an list ignored by a majority of the processing.
if total_item.grism_edp_list and total_item.edp_list:
grism_product_list = update_wcs_in_visit(total_item)
product_list += grism_product_list
# Need a trailer file to log the situation in this special case of a total data product in the visit with
# only Grism/Prism and no direct images. No processing is actually done in this case.
if total_item.grism_edp_list and not total_item.edp_list:
log.warning("This Total Data Product only has Grism/Prism data and no direct images: {}".format(total_item.drizzle_filename))
log.warning("No SVM processing is done for the Grism/Prism data - no SVM output products are generated.")
product_list += [total_item.trl_filename]
# Add skycell header keyword to SVM flt(c) files
# iterates over all exposure (names) in the total product
for image in total_item.edp_list:
proc_utils.add_skycell_to_header(image.full_filename)
# Run AstroDrizzle to produce drizzle-combined products
log.info("\n{}: Create drizzled imagery products.".format(str(datetime.datetime.now())))
driz_list = create_drizzle_products(total_obj_list)
product_list += driz_list
# Create source catalogs from newly defined products (HLA-204)
log.info("{}: Create source catalog from newly defined product.\n".format(str(datetime.datetime.now())))
if "total detection product 00" in obs_info_dict.keys():
catalog_list = create_catalog_products(total_obj_list, log_level,
diagnostic_mode=diagnostic_mode,
phot_mode=phot_mode,
catalog_switches=cat_switches)
product_list += catalog_list
else:
log.warning("No total detection product has been produced. The sourcelist generation step has been skipped")
# Store total_obj_list to a pickle file to speed up development
if log_level <= logutil.logging.DEBUG:
pickle_filename = "total_obj_list_full.pickle"
if os.path.exists(pickle_filename):
os.remove(pickle_filename)
pickle_out = open(pickle_filename, "wb")
pickle.dump(total_obj_list, pickle_out)
pickle_out.close()
log.info("Successfully wrote total_obj_list to pickle file {}!".format(pickle_filename))
# Quality assurance portion of the processing - done only if the environment
# variable, SVM_QUALITY_TESTING, is set to 'on', 'yes', or 'true'.
qa_switch = _get_envvar_switch(envvar_qa_svm)
# If requested, generate quality assessment statistics for the SVM products
if qa_switch:
log.info("SVM Quality Assurance statistics have been requested for this dataset, {}.".format(input_filename))
svm_qa.run_quality_analysis(total_obj_list, log_level=log_level)
# 10: Return exit code for use by calling Condor/OWL workflow code: 0 (zero) for success, 1 for error condition
return_value = 0
except Exception:
return_value = 1
print("\a\a\a")
exc_type, exc_value, exc_tb = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
logging.exception("message")
# This except handles sys.exit() which raises the SystemExit exception which inherits from BaseException.
except BaseException:
exc_type, exc_value, exc_tb = sys.exc_info()
formatted_lines = traceback.format_exc().splitlines()
log.info(formatted_lines[-1])
return_value = exc_value
finally:
# If poller_utils.py did not exit with an exception, the manifest filename exists.
# If the manifest filename does not exist, need to construct a filename by using the
# input_filename so the shutdown is tidy.
if manifest_name == "":
# If the input filename is a string, it could be a poller file or it could
# be a file containing filenames. Either way, if read as a table only the
# first column of the first row is needed. It is desired to use the contents of the
# FITS header keywords INSTRUME and ROOTNAME to use/parse for necessary information.
# co = close out
h0 = None
if type(input_filename) == str:
co_filename= ascii.read(input_filename, format='no_header')["col1"][0]
h0 = fits.getheader(co_filename)
# Maybe the input filename was actually a Python list
elif type(input_filename) == list:
h0 = fits.getheader(input_filename[0])
if h0:
co_inst = h0["INSTRUME"].lower()
co_root = h0["ROOTNAME"].lower()
tokens_tuple = (co_inst, co_root[1:4], co_root[4:6], "manifest.txt")
manifest_name = "_".join(tokens_tuple)
# Problem case - just give it the base name
if type(input_filename) != str and type(input_filename) != list:
manifest_name = "manifest.txt"
if manifest_name and manifest_name != "manifest.txt":
# Write out manifest file listing all products generated during processing
log.info("Creating manifest file {}.".format(manifest_name))
log.info(" The manifest contains the names of products generated during processing.")
with open(manifest_name, mode='w') as catfile:
if total_obj_list:
[catfile.write("{}\n".format(name)) for name in product_list]
# Remove additional manifest files from previous processing now as well,
# Keep those manifest files, though, if running in diagnotic mode
if not diagnostic_mode:
for mfile in mfile_list:
if os.path.exists(mfile):
os.remove(mfile)
end_dt = datetime.datetime.now()
log.info('Processing completed at {}'.format(str(end_dt)))
log.info('Total processing time: {} sec'.format((end_dt - starting_dt).total_seconds()))
log.info("Return exit code for use by calling Condor/OWL workflow code: 0 (zero) for success, 1 for error ")
log.info("Return condition {}".format(return_value))
logging.shutdown()
# The Grism/Prism SVM FLT/FLC images which have had their WCS reconciled with the
# corresponding direct images need trailer files. This is also true of the Ramp images
# which have only been processed through the "align to Gaia" stage. At this time, just
# copy the total trailer file, and rename it appropriately.
if total_obj_list:
for tot_obj in total_obj_list:
for gitem in grism_product_list:
if gitem.endswith('trl.txt'):
shutil.copy(logname, gitem)
for ritem in ramp_product_list:
if ritem.endswith('trl.txt'):
shutil.copy(logname, ritem)
# Append total trailer file (from astrodrizzle) to all total log files
if total_obj_list:
for tot_obj in total_obj_list:
if found_data:
proc_utils.append_trl_file(tot_obj.trl_filename, logname, clean=False)
if tot_obj.edp_list:
# Update DRIZPARS keyword value with new logfile name in ALL drizzle products
tot_obj.update_drizpars()
# Now remove single temp log file
if os.path.exists(logname):
os.remove(logname)
else:
print("Master log file not found. Please check logs to locate processing messages.")
return return_value
# ------------------------------------------------------------------------------------------------------------
def run_align_to_gaia(tot_obj, log_level=logutil.logging.INFO, diagnostic_mode=False):
# Run align.py on all input images sorted by overlap with GAIA bandpass
log.info("\n{}: Align the all filters to GAIA with the same fit".format(str(datetime.datetime.now())))
gaia_obj = None
headerlet_filenames = []
# Start by creating a FilterProduct instance which includes ALL input exposures
for exp_obj in tot_obj.edp_list:
if gaia_obj is None:
prod_list = exp_obj.info.split("_")
prod_list[5] = "metawcs"
gaia_obj = product.FilterProduct(prod_list[0], prod_list[1], prod_list[2],
prod_list[3], prod_list[4], prod_list[5], "all",
prod_list[6][0:3], log_level)
gaia_obj.configobj_pars = tot_obj.configobj_pars
gaia_obj.add_member(exp_obj)
log.info("\n{}: Combined all filter objects in gaia_obj".format(str(datetime.datetime.now())))
# Now, perform alignment to GAIA with 'match_relative_fit' across all inputs
# Need to start with one filt_obj.align_table instance as gaia_obj.align_table
# - append imglist from each filt_obj.align_table to the gaia_obj.align_table.imglist
# - reset group_id for all members of gaia_obj.align_table.imglist to the unique incremental values
# - run gaia_obj.align_table.perform_fit() with 'match_relative_fit' only
# - migrate updated WCS solutions to exp_obj instances, if necessary (probably not?)
# - re-run tot_obj.generate_metawcs() method to recompute total object meta_wcs based on updated
# input exposure's WCSs
catalog_list = gaia_obj.configobj_pars.pars['alignment'].pars_multidict['all']['run_align']['mosaic_catalog_list']
align_table, filt_exposures = gaia_obj.align_to_gaia(catalog_list=catalog_list,
output=diagnostic_mode,
fit_label='SVM')
tot_obj.generate_metawcs()
log.info("\n{}: Finished aligning gaia_obj to GAIA".format(str(datetime.datetime.now())))
log.info("ALIGNED WCS: \n{}".format(tot_obj.meta_wcs))
# Clean up if the align_table does not exist - the metawcs file
# may already be deleted, but make sure here.
if align_table is None:
headerlet_filenames = []
try:
gaia_obj.refname = None
except (OSError, TypeError):
pass
else:
# Get names of all headerlet files written out to file
headerlet_filenames = [f for f in align_table.filtered_table['headerletFile'] if f != "None"]
# Remove all reference catalogs written out to disk, if not in diagnostic mode
if not diagnostic_mode:
log.info(f"Removing reference catalogs:\n {gaia_obj.refcat_filenames}")
for fname in gaia_obj.refcat_filenames:
try:
os.remove(fname)
except (OSError, TypeError):
pass
return headerlet_filenames
# ----------------------------------------------------------------------------------------------------------------------
def run_sourcelist_flagging(filter_product_obj, filter_product_catalogs, log_level, diagnostic_mode=False):
"""
Super-basic and profoundly inelegant interface to hla_flag_filter.py.
Execute haputils.hla_flag_filter.run_source_list_flaging() to populate the "Flags" column in the catalog tables
generated by HAPcatalogs.measure().
Parameters
----------
filter_product_obj : drizzlepac.haputils.product.FilterProduct object
object containing all the relevant info for the drizzled filter product
filter_product_catalogs : drizzlepac.haputils.catalog_utils.HAPCatalogs object
drizzled filter product catalog object
log_level : int
The desired level of verboseness in the log statements displayed on the screen and written to the .log file.
diagnostic_mode : Boolean, optional.
create intermediate diagnostic files? Default value is False.
Returns
-------
filter_product_catalogs : drizzlepac.haputils.catalog_utils.HAPCatalogs object
updated version of filter_product_catalogs object with fully populated source flags
"""
drizzled_image = filter_product_obj.drizzle_filename
flt_list = []
for edp_obj in filter_product_obj.edp_list:
flt_list.append(edp_obj.full_filename)
param_dict = filter_product_obj.configobj_pars.as_single_giant_dict()
plate_scale = wcsutil.HSTWCS(drizzled_image, ext=('sci', 1)).pscale
median_sky = filter_product_catalogs.image.bkg_median
# Create mask array that will be used by hla_flag_filter.hla_nexp_flags() for both point and segment catalogs.
if not hasattr(filter_product_obj, 'hla_flag_msk'):
filter_product_obj.hla_flag_msk = hla_flag_filter.make_mask_array(drizzled_image)
ci_lookup_file_path = "svm_parameters/any"
output_custom_pars_file = filter_product_obj.configobj_pars.output_custom_pars_file
for cat_type in filter_product_catalogs.catalogs.keys():
exptime = filter_product_catalogs.catalogs[cat_type].image.imghdu[0].header['exptime'] # TODO: This works for ACS. Make sure that it also works for WFC3. Look at "TEXPTIME"
catalog_name = filter_product_catalogs.catalogs[cat_type].sourcelist_filename
catalog_data = filter_product_catalogs.catalogs[cat_type].source_cat
drz_root_dir = os.getcwd()
log.info("Run source list flagging on catalog file {}.".format(catalog_name))
# Code for flagging parameters
write_flag_filter_pickle_file = False
if write_flag_filter_pickle_file:
pickle_dict = {"drizzled_image": drizzled_image,
"flt_list": flt_list,
"param_dict": param_dict,
"exptime": exptime,
"plate_scale": plate_scale,
"median_sky": median_sky,
"catalog_name": catalog_name,
"catalog_data": catalog_data,
"cat_type": cat_type,
"drz_root_dir": drz_root_dir,
"hla_flag_msk": filter_product_obj.hla_flag_msk,
"ci_lookup_file_path": ci_lookup_file_path,
"output_custom_pars_file": output_custom_pars_file,
"log_level": log_level,
"diagnostic_mode": diagnostic_mode}
out_pickle_filename = catalog_name.replace("-cat.ecsv", "_flag_filter_inputs.pickle")
pickle_out = open(out_pickle_filename, "wb")
pickle.dump(pickle_dict, pickle_out)
pickle_out.close()
log.info("Wrote hla_flag_filter param pickle file {} ".format(out_pickle_filename))
if catalog_data is not None and len(catalog_data) > 0:
source_cat = hla_flag_filter.run_source_list_flagging(drizzled_image,
flt_list,
param_dict,
exptime,
plate_scale,
median_sky,
catalog_name,
catalog_data,
cat_type,
drz_root_dir,
filter_product_obj.hla_flag_msk,
log_level,
diagnostic_mode)
else:
source_cat = catalog_data
filter_product_catalogs.catalogs[cat_type].source_cat = source_cat
return filter_product_catalogs
def _get_envvar_switch(envvar_name, default=None):
"""
This private routine interprets any environment variable, such as SVM_QUALITY_TESTING.
PARAMETERS
-----------
envvar_name : str
name of environment variable to be interpreted
default : str or None
Value to be used in case environment variable was not defined or set.
.. note :
This is a copy of the routine in runastrodriz.py. This code should be put in a common place.
"""
if envvar_name in os.environ:
val = os.environ[envvar_name].lower()
if val not in envvar_bool_dict:
msg = "ERROR: invalid value for {}.".format(envvar_name)
msg += " \n Valid Values: on, off, yes, no, true, false"
raise ValueError(msg)
switch_val = envvar_bool_dict[val]
else:
switch_val = envvar_bool_dict[default] if default else None
return switch_val
# ------------------------------------------------------------------------------
def update_wcs_in_visit(tdp):
"""Examine entries in the total data product for Grism/Prism data
This routine is only invoked if the total data product for a particular detector
for the visit is comprised of both an ExposureProduct list AND a
GrismExposureProduct list which contain entries.
*** If the visit contains Grism/Prism data, then either the 'a priori' WCS for
the Grism/Prism data or the pipeline default WCS for the direct images will be
set as the primary/active WCS for all the Grism/Prism and direct image data for
that detector - whichever WCS is common to all the images.
Note: The direct exposure image active WCS solutions will be modified in-place.
Parameters
----------
tdp : total data product (TotalProduct) object
Object comprised of FilterProduct, ExposureProduct, and GrismExposureProduct
Returns
-------
grism_product_list : list
List of all the SVM Grism/Prism FLT/FLC files updated with the common WCS
and their corresponding trailer filenames
"""
log.info("\n***** Grism/Prism Image Processing *****")
# The TotalProduct (tdp) for this instrument/dectector has both a Grism/Prism
# exposure list and a direct exposure list - both with contents.
# Every image should (!) have an IDC_?????????-GSC240 solution.
wcs_preference = ['IDC_?????????-GSC240', 'IDC_?????????']
# Grism output product list for the manifest
grism_product_list = []
grism_wcs_set, skip_grism_list, g_keyword_wcs_names_dict, grism_dict = collect_wcs_names(tdp.grism_edp_list, 'GRISM')
log.info("WCS solutions common to all viable Grism/Prism images: {}".format(grism_wcs_set))
# There is a preference list for the active WCS for the viable images in the visit
# Check the grism_wcs_set for the preferential solutions
match_list = []
grism_wcsname = ''
for wcs_item in wcs_preference:
match_list = fnmatch.filter(grism_wcs_set, wcs_item)
if match_list:
grism_wcsname = match_list[0]
log.info("Proposed WCS for use after Grism/Prism examination: {}".format(grism_wcsname))
break
if not grism_wcsname:
log.error("")
log.error("None of the preferred WCS names are present in the common set of WCS names for the Grism/Prism images.")
log.error("There is a problem with this visit. Deleting all SVM Grism/Prism FLT/FLC files.")
log.error("")
try:
for image_file in tdp.grism_edp_list:
os.remove(image_file.full_filename)
log.warning("Deleted Grism/Prism image {}.".format(image_file.full_filename))
tdp.grism_edp_list = []
return grism_product_list
except OSError:
pass
sys.exit(1)
log.info("\nProposed WCS for use after Grism/Prism examination: {}".format(grism_wcsname))
# If it is now known there is at least one viable Grism/Prism observation, it is desired to make
# the active Grism/Prism WCS be the same for all of the Grism/Prism as well as the direct images
# in the visit. The catch can be if any of the direct images do not have an 'a priori' solution
# which matches the Grism/Prism images. In this case, it will be necessary to "fall back" to
# the pipeline WCS solution.
log.info("\n***** Direct Image Processing *****")
# Loop over all the direct images for this detector in the visit to update the WCS
for edp in tdp.edp_list:
filename = edp.full_filename
hdu = fits.open(filename)
# Make sure the WCS is up-to-date - doing the update for the
# direct images here so there is no need to modify a
# pre-existing class which could cause an issue
drizcorr = hdu[0].header['DRIZCORR']
if drizcorr == "OMIT":
updatewcs.updatewcs(filename, use_db=True)
hdu.close()
# Insure HDRNAME keywords are properly populated in SCI extensions.
_verify_sci_hdrname(filename)
direct_wcs_set, skip_direct_list, d_keyword_wcs_names_dict, direct_dict = collect_wcs_names(tdp.edp_list, 'DIRECT')
log.info("WCS solutions common to all viable direct images: {}".format(direct_wcs_set))
# Are the grism_wcs_set and the direct_wcs_set disjoint? If they are disjoint, there can
# be no common WCS solution.
is_disjoint = grism_wcs_set.isdisjoint(direct_wcs_set)
if not is_disjoint:
# Generate the intersection between the Grism/Prism and direct images
grism_wcs_set &= direct_wcs_set
log.info("Common WCS solutions exist between the Grism/Prism and the direct images.")
log.info(" The common WCS solutions are: {}".format(grism_wcs_set))
# There is a preference for the active WCS for the viable images in the visit
# Check the updated grism_wcs_set for the preferential solutions
match_list = []
final_wcsname = ''
for wcs_item in wcs_preference:
match_list = fnmatch.filter(grism_wcs_set, wcs_item)
if match_list:
final_wcsname = match_list[0]
log.info("Final WCS solution to use for all Grism/Prism and direct images: {}".format(final_wcsname))
break
if final_wcsname:
# Finally, if the image is not in a skip list, reset the primary WCS in all the images
for g_edp in tdp.grism_edp_list:
filename = g_edp.full_filename
if filename not in skip_grism_list:
log.info("")
log.info("Setting the primary WCS for Grism/Prism image {} to {}.".format(filename, final_wcsname))
update_active_wcs(filename, final_wcsname)
# Add the Grism/Prism images to the manifest as all of the files exist.
# Also, add the Grism/Prism trailer filename here. The file will be created by
# by copying the Total Data Product trailer file at the end of processing.
grism_product_list.append(filename)
grism_product_list.append(g_edp.trl_filename)
for edp in tdp.edp_list:
filename = edp.full_filename
if filename not in skip_direct_list:
log.info("")
log.info("Setting the primary WCS for direct image {} to {}.".format(filename, final_wcsname))
update_active_wcs(filename, final_wcsname)
else:
# Do nothing
pass
# Disjoint
else:
log.info("The Grism/Prism and direct images in this visit have no WCS solution common to both sets of data.")
log.info("The active WCS solution for each Grism/Prism and direct image is not changed.")
log.info("Grism product list: {}".format(grism_product_list))
return grism_product_list
# ------------------------------------------------------------------------------
def collect_wcs_names(edp_list, image_type):
"""
Utility to collect all the WCS solution names common to the input image list
Parameters
----------
edp_list: str list
List containing the SVM FLT/FLC filenames
image_type: string
String containing either 'GRISM' or 'DIRECT' to use as a switch for
output information
Returns
-------
image_wcs_set: set of WCS solutions
The set contains the WCS solution names common to all of the
input exposures
skip_image_list: list
This is a list of exposures in the input list which should be
skipped/ignored when updating the active WCS solution
keyword_wcs_names_dict: dictionary {filename: list}
The dictionary is used to associate an individual image/filename with
a list of WCS solution names in the file stored as keywords (not headerlets)
image_dict: dictionary {filename: list}
The dictionary is used to associate an individual image/filename with
a list of *all* WCS solution names in the file
"""
image_wcs_set = set()
skip_image_list = []
exist_image_set = False
image_dict = {}
keyword_wcs_names_dict = {}
# Loop over all the Grism/Prism images for this detector in the visit
for edp in edp_list:
filename = edp.full_filename
# Get all the WCS names which are common to all of the images
# Note that WCS solutions may be represented as FITS keyword values in the
# SCI extension and/or as headerlets in the HDRLET extensions.
# Get the keyword WCS solution names.
keyword_wcs_names = list(wcsutil.altwcs.wcsnames(filename, ext=1).values())
# Get the headerlet WCS solution names
headerlet_wcs_names = wcsutil.headerlet.get_headerlet_kw_names(filename, kw="WCSNAME")
all_wcs_names = keyword_wcs_names + headerlet_wcs_names
keyword_wcs_names_dict[filename] = keyword_wcs_names
image_dict[filename] = all_wcs_names
if all_wcs_names:
log.debug("WCS solutions for file {} are {}.".format(filename, all_wcs_names))
# Initialize a set with wcsnames
if not exist_image_set:
image_wcs_set = set(all_wcs_names)
exist_image_set = True
# Generate the intersection with the set of existing wcsnames and the wcsnames
# from the current image
else:
image_wcs_set &= set(all_wcs_names)
# Oops...no common wcsnames
if not image_wcs_set:
log.error("There are no common WCS solutions with this image {} and previously processed images".format(filename))
log.error(" There is a problem with this image/visit.")
log.error(" Make sure the input data are not *_raw.fits files.")
sys.exit(1)
# If there are no WCS solutions, the image could be bad (e.g., EXPTIME=0 or EXPFLAG="TDF-DOWN...")
else:
log.warning("There are no WCS solutions in the image {} in this visit.".format(filename))
skip_image_list.append(filename)
if image_type == 'GRISM':
log.warning(" Skip and delete this image.")
# Delete the SVM FLT/FlC Grism/Prism image as it has no updated WCS
try:
os.remove(filename)
log.warning("Deleted Grism/Prism image {}.".format(filename))
except OSError:
pass
else:
log.warning(" Skip this image.")
return image_wcs_set, skip_image_list, keyword_wcs_names_dict, image_dict
# ------------------------------------------------------------------------------
def update_active_wcs(filename, wcsname):
"""
Utility to update the active/primary WCS solution
This small utility updates the active/primary WCS solution for the input
file with the WCS solution indicted by the input parameter "wcsname"
Parameters
----------
filename : str
Input/Output SVM FLT/FLC filename - the file is updated in-place
wcsname : str
Name of the desired WCS active/primary solution to be set for the filename
Returns
-------
None
Note: A by-product of this routine is all alternate WCS solutions are saved
as headerlet extensions.
"""
# For exposures with multiple science extensions (multiple chips),
# generate a combined WCS
num_sci_ext, extname = util.count_sci_extensions(filename)
extname_list = [(extname, x + 1) for x in range(num_sci_ext)]
hdu = fits.open(filename)
# Get original value of IDCSCALE keyword
idcscale = hdu['SCI', 1].header['idcscale']
# Get the current key for the desired WCS solution
key = wcsutil.altwcs.getKeyFromName(hdu['SCI', 1].header, wcsname)
# No need to keep this file handle open anymore here
hdu.close()
# Ensure ALL alternate WCS solutions have been saved as headerlets
archive_alternate_wcs(filename)
# Case where the desired active solution is not the current active solution
if key != ' ':
# Get all the latest headerlet HDRNAMES
headerlet_hdrnames = wcsutil.headerlet.get_headerlet_kw_names(filename, kw="HDRNAME")
# Get all the WCS solution names
headerlet_wcsnames = wcsutil.headerlet.get_headerlet_kw_names(filename, kw="WCSNAME")
# Prepare to install a new active WCS, but need to do some checking first
#
# This returns the first matching instance
hdrname = headerlet_hdrnames[headerlet_wcsnames.index(wcsname)]
extensions = []
extensions = wcsutil.headerlet.find_headerlet_HDUs(filename, hdrname=hdrname)
# Install the desired WCS as the active WCS solution
# Since all the alternate WCS solutions have been made into headerlet extensions,
# just restore the desired solution from the headerlet.
try:
wcsutil.headerlet.restore_from_headerlet(filename, hdrname=hdrname, archive=True)
# Update value of nmatches based on headerlet
log.debug("{}: Updating NMATCHES from EXT={}".format(filename, extensions[0]))
fhdu = fits.open(filename, mode='update')
for sciext in range(1, num_sci_ext+1):
nm = fhdu[extensions[0]].header['nmatch'] if 'nmatch' in fhdu[extensions[0]].header else 0
fhdu[(extname, sciext)].header['nmatches'] = nm
if 'idcscale' not in fhdu[(extname, sciext)].header:
fhdu[(extname, sciext)].header['idcscale'] = idcscale # Restore this if it was overwritten/deleted
fhdu.close()
# If the active WCS solution is now 'a priori', need to clean up any 'a posteriori'
# keyword values which may be remaining from a previous WCS solution
reset_keywords_apriori(filename)
except:
_, _, tb = sys.exc_info()
tb_info = traceback.extract_tb(tb)
_, _, _, text = tb_info[-1]
log.warning("Could not restore the common WCS, {}, as the active WCS in this file {}.".format(wcsname, filename))
else:
log.info("No need to update active WCS solution of {} for {} as it is already the active solution.".format(wcsname, filename))
# ------------------------------------------------------------------------------
def archive_alternate_wcs(filename):
"""
Utility to archive (aka create headerlet) for all alternate WCS solutions, as necessary
Parameters
----------
filename : str
Input/Output SVM FLT/FLC filename - the file is updated in-place
Returns
-------
Nothing
Note: There is no strict form for the HDRNAME. For HAP, HDRNAME is of the form
hst_proposid_visit_instrument_detector_filter_ipppssoo_fl[t|c]_wcsname-hlet.fits.
Ex. hst_9029_01_acs_wfc_f775w_j8ca01at_flc_IDC_0461802ej-FIT_SVM_GAIAeDR3-hlet.fits
"""
# Get all the alternate keys and corresponding wcsnames in the science header,
# including the 'active' information
wcs_key_dict = wcsutil.altwcs.wcsnames(filename, ext=1, include_primary=True)
# Loop over the WCSNAMEs looking for the HDRNAMEs. If a corresponding
# HDRNAME does not exist, create one.
header = fits.getheader(filename, ext=0)
# Get all the wcsnames and hdrnames for the existing headerlets
headerlet_wcsnames = wcsutil.headerlet.get_headerlet_kw_names(filename, kw="WCSNAME")
headerlet_hdrnames = wcsutil.headerlet.get_headerlet_kw_names(filename, kw="HDRNAME")
# Create patterns for each WCSNAME to find any 'duplicate' solutions
wcs_patterns = []
for wcsname in headerlet_wcsnames:
wcs_patterns.append([wcsname, re.compile(wcsname+"-[\d]")])
for wkey, wcsname in wcs_key_dict.items():
# Skip the "OPUS" WCS
if wkey.upper().startswith("O"):
continue
# Look for duplicated WCSNAMEs
for (pattern_wcs, pattern) in wcs_patterns:
log.debug(f'Checking {wcsname} against {pattern}')
if pattern.match(wcsname) is not None:
# Reset WCSNAME to original non-duplicated WCSNAME
wcsname = pattern_wcs
log.debug(f'Reset WCSNAME to {wcsname}')
break
try:
keyword = "HDRNAME" + wkey.upper()
hdrname = header[keyword]
# Handle the case where the HDRNAME keyword does not exist - create
# a value from the FITS filename by removing the ".fits" suffix and
# adding information.
except KeyError:
hdrname = header["FILENAME"][:-5] + "_" + wcsname + "-hlet.fits"
# If already a headerlet, then do nothing ...
try:
_ = headerlet_hdrnames[headerlet_wcsnames.index(wcsname)]
# ... else, make a headerlet
except:
wcsutil.headerlet.archive_as_headerlet(filename, hdrname, wcsname=wcsname, wcskey=wkey)
# ------------------------------------------------------------------------------
def delete_ramp_exposures(obj_list, type_of_list):
"""Delete the Ramp filter objects from the Total Product internal lists
The Total Data Product (tdp) object is comprised of a list of Filter Product objects,
as well as a list of Exposure Product objects. The Ramp filter
images need to be processed in the same manner as nominal exposures for at least
some of the processing steps. Because of this, it was deemed best to keep the
Ramp exposures in the tdp list attributes, until their final processing
stage (align_to_gaia), and then delete these objects from the attribute
lists. This function handles the deletion of Ramp images from the input
list.
Parameters
----------
obj_list : list of either FilterProduct ro ExposureProduct objects
The list is updated in-place.
type_of_list : str
String literal indicating if the list contains FILTER or EXPOSURE objects
Returns
-------
ramp_filenames_list : list of str
List of ramp exposure SVM FLT/FLC filenames and their corresponding trailer filenames
"""
reported = False
temp = []
ramp_filenames_list = []
while obj_list:
obj = obj_list.pop()
if obj.filters.lower().find('fr') == -1:
temp.append(obj)
else:
# Add the Ramp images to the manifest as well as the Ramp trailer filename here.
# The file will be created by by copying the Total Data Product trailer file at
# the end of processing.
if type_of_list == 'EXPOSURE':
ramp_filenames_list.append(obj.full_filename)
ramp_filenames_list.append(obj.trl_filename)
if not reported:
log.info('Removing the Ramp images from the Total Data Product exposure list.')
log.info('Theses images are not processed beyond the "align to Gaia" stage.')
reported = True
while temp:
obj_list.append(temp.pop())
return ramp_filenames_list
# ------------------------------------------------------------------------------
def _verify_sci_hdrname(filename):
"""Insures that HDRNAME keyword is populated in SCI extensions.
This function checks to make sure the HDRNAME keyword in the SCI
extension of the science image ``filename`` is populated with a valid
non-empty string.
"""
fhdu, closefits = proc_utils._process_input(filename)
# Find all extensions to be updated
numext = countExtn(fhdu, extname='SCI')
for ext in range(1, numext + 1):
sciext = ('sci', ext)
scihdr = fhdu[sciext].header
if 'hdrname' not in scihdr or scihdr['hdrname'].rstrip() == '':
# We need to create a valid value for the keyword
# Define new HDRNAME value in case it is needed.
# Same value for all SCI extensions, so just precompute it and be ready.
# This code came from 'stwcs.updatewcs.astrometry_utils'
hdrname = "{}_{}".format(filename.replace('.fits', ''), scihdr['wcsname'])
# Create full filename for headerlet:
hfilename = "{}_hlet.fits".format(hdrname)
# Update the header with the new value, inserting after WCSNAME
scihdr.set('hdrname', hfilename, 'Name of headerlet file', after='wcsname')
if closefits:
fhdu.close()
del fhdu
# ------------------------------------------------------------------------------
def reset_keywords_apriori(filename):
"""
Check the SCI extension(s) of the output FLT/FLC and DRZ/DRC files. If the active
WCS solution is 'a priori', delete the following keywords if they are associated
with the active WCS as they are residue from a previous 'a posteriori' solution:
NMATCHES, RMS_RA/RMS_DEC, FITGEOM, and CRDER1/CRDER2. Ensure the WCSTYPE is based
upon the active WCSNAME to clarify any confusion.
Parameters
----------
filename : str
Name of file to evaluate
Returns
-------
None
"""
keys_to_delete = ['NMATCHES', 'RMS_RA', 'RMS_DEC', 'FITGEOM', 'CRDER1', 'CRDER2']
# Determine the number of SCI extensions
num_sci, extname = util.count_sci_extensions(filename)
extname_list = [(extname, x + 1) for x in range(num_sci)]
# Open the file in anticipation of updating 'a priori' solution associated keywords
hdu = fits.open(filename, mode='update')
# Loop over the SCI extensions in the file
for ext in extname_list:
active_wcsname = hdu[ext].header['WCSNAME']
# An 'a priori' solution is of the form <Starting WCS>-<Astrometric Catalog>
# Catalogs: GSC240, HSC30, GAIAD1, GAIADR2, and GAIAeDR3
wcsname_list = active_wcsname.split('-')
# If the active WCS solution is 'a priori', delete the keywords in the keys_to_delete
# list from the header which are remaining from a previous 'a posteriori' solution
is_apriori = len(wcsname_list) == 2 and wcsname_list[1].upper().startswith(('GSC', 'HSC', 'GAIA'))
if is_apriori:
try:
# Make sure the WCSTYPE is consistent with the active 'a priori' WCSNAME
# This keyword is not required for the WCS, but it provides an explanation
# in a succinct way. If WCSTYPE does not exist, create it.
hdu[ext].header['WCSTYPE'] = updatehdr.interpret_wcsname_type(active_wcsname)
# Log a problem, but keep going as this keyword is a convenience.
except KeyError:
log.warning('Could not modify/add the WCSTYPE keyword for the active WCS solution.')
# Loop over the keys_to_delete list
for key in keys_to_delete:
if key in hdu[ext].header:
del hdu[ext].header[key]
hdu.flush()
hdu.close()
del hdu