def match_to_gaia(imcat, refcat, product, output, searchrad=5.0):
"""Create a catalog with sources matched to GAIA sources
Parameters
----------
imcat : str or obj
Filename or astropy.Table of source catalog written out as ECSV file
refcat : str
Filename of GAIA catalog files written out as ECSV file
product : str
Filename of drizzled product used to derive the source catalog
output : str
Rootname for matched catalog file to be written as an ECSV file
"""
if isinstance(imcat, str):
imtab = Table.read(imcat, format='ascii.ecsv')
imtab.rename_column('X-Center', 'x')
imtab.rename_column('Y-Center', 'y')
else:
imtab = imcat
if 'X-Center' in imtab.colnames:
imtab.rename_column('X-Center', 'x')
imtab.rename_column('Y-Center', 'y')
reftab = Table.read(refcat, format='ascii.ecsv')
# define WCS for matching
tpwcs = tweakwcs.FITSWCS(HSTWCS(product, ext=1))
# define matching parameters
tpmatch = tweakwcs.TPMatch(searchrad=searchrad)
# perform match
ref_indx, im_indx = tpmatch(reftab, imtab, tpwcs)
print('Found {} matches'.format(len(ref_indx)))
# Obtain tangent plane positions for both image sources and reference sources
im_x, im_y = tpwcs.det_to_tanp(imtab['x'][im_indx], imtab['y'][im_indx])
ref_x, ref_y = tpwcs.world_to_tanp(reftab['RA'][ref_indx], reftab['DEC'][ref_indx])
if 'RA' not in imtab.colnames:
im_ra, im_dec = tpwcs.det_to_world(imtab['x'][im_indx], imtab['y'][im_indx])
else:
im_ra = imtab['RA'][im_indx]
im_dec = imtab['DEC'][im_indx]
# Compile match table
match_tab = Table(data=[im_x, im_y, im_ra, im_dec,
ref_x, ref_y,
reftab['RA'][ref_indx], reftab['DEC'][ref_indx]],
names=['img_x','img_y', 'img_RA', 'img_DEC',
'ref_x', 'ref_y', 'ref_RA', 'ref_DEC'])
if not output.endswith('.ecsv'):
output = '{}.ecsv'.format(output)
match_tab.write(output, format='ascii.ecsv')
def match_2dhist_fit(imglist, reference_catalog, **fit_pars):
"""Perform cross-matching and final fit using 2dHistogram matching
Parameters
----------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
reference_catalog : Table
Astropy Table of reference sources for this field
Returns
--------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
"""
log.info("{} (match_2dhist_fit) Cross matching and fitting "
"{}".format("-" * 20, "-" * 28))
# Specify matching algorithm to use
match = tweakwcs.TPMatch(**fit_pars)
# Align images and correct WCS
tweakwcs.align_wcs(imglist,
reference_catalog,
match=match,
expand_refcat=False)
# Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
return imglist
def test_generate_catalog(self,input_filenames, truth_file):
""" Verify whether sources from astrometric catalogs can be extracted from images.
Success Criteria
-----------------
* Initially, source catalog matches >80% of 'truth' catalog sources
"""
self.input_loc = 'catalog_tests'
self.curdir = os.getcwd()
truth_path = [self.input_repo, self.tree, self.input_loc, *self.ref_loc]
if not isinstance(input_filenames, list):
input_filenames = [input_filenames]
try:
# Make local copies of input files
local_files = []
for infile in input_filenames:
downloaded_files = self.get_input_file(infile, docopy=True)
local_files.extend(downloaded_files)
test_image = local_files[0]
print("Testing with {}".format(test_image))
imghdu = fits.open(test_image)
instrume = imghdu[0].header['instrume'].lower()
detector = imghdu[0].header['detector'].lower()
instr_pars = detector_specific_params[instrume][detector]
reference_wcs = amutils.build_reference_wcs(local_files)
imcat = amutils.generate_sky_catalog(imghdu, reference_wcs, **instr_pars)
imcat.rename_column('xcentroid', 'x')
imcat.rename_column('ycentroid', 'y')
# create FITS WCS corrector object
wcs_corrector = tweakwcs.FITSWCS(reference_wcs)
# get reference catalog as 'truth' files
reference_catalog = get_bigdata(*truth_path, truth_file, docopy=True)
if os.path.basename(reference_catalog).endswith('ecsv'):
tab_format = 'ascii.ecsv'
else:
tab_format = 'ascii.fast_commented_header'
reference_table = Table.read(reference_catalog, format=tab_format)
num_expected = len(reference_table)
# Perform matching
match = tweakwcs.TPMatch(searchrad=200, separation=0.1, tolerance=5, use2dhist=True)
ridx, iidx = match(reference_table, imcat, wcs_corrector)
nmatches = len(ridx)
except Exception:
exc_type, exc_value, exc_tb = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)
sys.exit()
assert (nmatches > 0.8*num_expected)
def match_relative_fit(imglist, reference_catalog, **fit_pars):
"""Perform cross-matching and final fit using relative matching algorithm
Parameters
----------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
reference_catalog : Table
Astropy Table of reference sources for this field
Returns
--------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
"""
log.info("{} (match_relative_fit) Cross matching and fitting {}".format(
"-" * 20, "-" * 27))
if 'fitgeom' in fit_pars:
fitgeom = fit_pars['fitgeom']
del fit_pars['fitgeom']
else:
fitgeom = 'general'
# 0: Specify matching algorithm to use
match = tweakwcs.TPMatch(**fit_pars)
# match = tweakwcs.TPMatch(searchrad=250, separation=0.1,
# tolerance=100, use2dhist=False)
# Align images and correct WCS
# NOTE: this invocation does not use an astrometric catalog. This call allows all the input images to be aligned in
# a relative way using the first input image as the reference.
# 1: Perform relative alignment
tweakwcs.align_wcs(imglist,
None,
match=match,
expand_refcat=True,
fitgeom=fitgeom)
# Set all the group_id values to be the same so the various images/chips will be aligned to the astrometric
# reference catalog as an ensemble.
# astrometric reference catalog as an ensemble. BEWARE: If additional iterations of solutions are to be
# done, the group_id values need to be restored.
for image in imglist:
image.meta["group_id"] = 1234567
# 2: Perform absolute alignment
tweakwcs.align_wcs(imglist,
reference_catalog,
match=match,
fitgeom=fitgeom)
# 3: Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
return imglist
def match_default_fit(imglist, reference_catalog, **fit_pars):
"""Perform cross-matching and final fit using default tolerance matching
Parameters
----------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
reference_catalog : Table
Astropy Table of reference sources for this field
Returns
--------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
"""
if 'fitgeom' in fit_pars:
fitgeom = fit_pars['fitgeom']
del fit_pars['fitgeom']
else:
fitgeom = 'rscale'
common_pars = fit_pars['pars']
del fit_pars['pars']
log.info("{} (match_default_fit) Cross matching and fitting "
"{}".format("-" * 20, "-" * 27))
# Specify matching algorithm to use
match = tweakwcs.TPMatch(**fit_pars)
# Align images and correct WCS
matched_cat = tweakwcs.align_wcs(imglist,
reference_catalog,
match=match,
minobj=common_pars['MIN_FIT_MATCHES'],
expand_refcat=False,
fitgeom=fitgeom)
# Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
del matched_cat
return imglist
def match_2dhist_fit(imglist, reference_catalog, print_fit_parameters=True):
"""Perform cross-matching and final fit using 2dHistogram matching
Parameters
----------
imglist : list
List of input image NDData objects with metadata and source catalogs
reference_catalog : Table
Astropy Table of reference sources for this field
print_fit_parameters : bool
Specify whether or not to print out FIT results for each chip
Returns
--------
fit_rms : float
Visit level RMS for the FIT
fit_num : int
Number of sources used to generate visit level FIT and `fit_rms`
"""
print(
"-------------------- STEP 5b: (match_2dhist_fit) Cross matching and fitting --------------------"
)
# Specify matching algorithm to use
match = tweakwcs.TPMatch(searchrad=75,
separation=0.1,
tolerance=2.0,
use2dhist=True)
# Align images and correct WCS
tweakwcs.tweak_image_wcs(imglist, reference_catalog, match=match)
# Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
# determine the quality of the fit
fit_rms, fit_num = determine_fit_quality(
imglist, print_fit_parameters=print_fit_parameters)
return fit_rms, fit_num
def test_pipeline(self, input_filenames):
"""Test of new pipeline alignment components (call separately)
This test performs separate fits to each chip separately.
Success Criteria
-----------------
* nmatches > 0 on all chips
* xrms and yrms < 30mas on all chips
"""
self.input_loc = 'catalog_tests'
self.curdir = os.getcwd()
truth_path = [self.input_repo, self.tree, self.input_loc, *self.ref_loc]
if not isinstance(input_filenames, list):
input_filenames = [input_filenames]
# Use this to collect all failures BEFORE throwing AssertionError
failures=False
try:
# Make local copies of input files
local_files = []
for infile in input_filenames:
downloaded_files = self.get_input_file(infile, docopy=True)
local_files.extend(downloaded_files)
# generate reference catalog
refcat = amutils.create_astrometric_catalog(local_files, catalog='GAIADR2')
# Generate source catalogs for each input image
source_catalogs = generate_source_catalogs(local_files)
# Convert input images to tweakwcs-compatible NDData objects and
# attach source catalogs to them.
imglist = []
for group_id,image in enumerate(local_files):
imglist.extend(amutils.build_nddata(image, group_id,
source_catalogs[image]['catalog_table']))
# Specify matching algorithm to use
match = tweakwcs.TPMatch(searchrad=250, separation=0.1,
tolerance=5, use2dhist=True)
# Align images and correct WCS
tweakwcs.tweak_image_wcs(imglist, refcat, match=match)
for chip in imglist:
status = chip.meta['tweakwcs_info']['status']
if status.startswith('FAIL'):
failures = True
print("STATUS for {}: {}".format(chip.meta['filename'], status))
break
except Exception:
failures = True
print("ALIGNMENT EXCEPTION: Failed to align {}".format(infile))
if not failures:
for chip in imglist:
tweak_info = chip.meta.get('tweakwcs_info', None)
chip_id = chip.meta.get('chip_id',1)
# determine 30mas limit in pixels
xylimit = 30
# Perform comparisons
nmatches = tweak_info['nmatches']
xrms,yrms = tweak_info['rms']
xrms *= chip.wcs.pscale*1000
yrms *= chip.wcs.pscale*1000
if any([nmatches==0, xrms>xylimit, yrms>xylimit]):
failures = True
msg1 = "Observation {}[{}] failed alignment with "
msg2 = " RMS=({:.4f},{:.4f})mas [limit:{:.4f}mas] and NMATCHES={}"
print(msg1.format(infile, chip_id))
print(msg2.format(xrms, yrms, xylimit, nmatches))
assert(not failures)
def perform_align(input_list,
archive=False,
clobber=False,
update_hdr_wcs=False):
"""Main calling function.
Parameters
----------
input_list : list
List of one or more IPPSSOOTs (rootnames) to align.
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?
update_hdr_wcs : Boolean
Write newly computed WCS information to image image headers?
Returns
-------
int value 0 if successful, int value 1 if unsuccessful
"""
# Define astrometric catalog list in priority order
catalogList = ['GAIADR2', 'GSC241']
numCatalogs = len(catalogList)
# 0: print git info
print(
"-------------------- STEP 0: Display Git revision info --------------------"
)
full_path = os.path.dirname(__file__) + "/utils"
repo_path = None
if "hlapipeline/hlapipeline" in full_path:
repo_path = full_path.split(
"hlapipeline/hlapipeline")[0] + "hlapipeline"
elif "hlapipeline" in full_path:
repo_path = full_path.split("hlapipeline")[0] + "hlapipeline"
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:
print(
"WARNING: Unable to display Git repository revision information.")
# 1: Interpret input data and optional parameters
print("-------------------- STEP 1: Get data --------------------")
imglist = check_and_get_data(input_list, archive=archive, clobber=clobber)
print("\nSUCCESS")
# 2: Apply filter to input observations to insure that they meet minimum criteria for being able to be aligned
print("-------------------- STEP 2: Filter data --------------------")
filteredTable = filter.analyze_data(imglist)
# Check the table to determine if there is any viable data to be aligned. The
# 'doProcess' column (bool) indicates the image/file should or should not be used
# for alignment purposes.
if filteredTable['doProcess'].sum() == 0:
print("No viable images in filtered table - no processing done.\n")
return (1)
# Get the list of all "good" files to use for the alignment
processList = filteredTable['imageName'][np.where(
filteredTable['doProcess'])]
processList = list(
processList
) #Convert processList from numpy list to regular python list
print("\nSUCCESS")
# 3: Build WCS for full set of input observations
print("-------------------- STEP 3: Build WCS --------------------")
refwcs = amutils.build_reference_wcs(processList)
print("\nSUCCESS")
# 4: Retrieve list of astrometric sources from database
# While loop to accommodate using multiple catalogs
doneFitting = False
catalogIndex = 0
extracted_sources = None
while not doneFitting:
skip_all_other_steps = False
retry_fit = False
print(
"-------------------- STEP 4: Detect astrometric sources --------------------"
)
print("Astrometric Catalog: ", catalogList[catalogIndex])
reference_catalog = generate_astrometric_catalog(
processList, catalog=catalogList[catalogIndex])
# The table must have at least MIN_CATALOG_THRESHOLD entries to be useful
if len(reference_catalog) >= MIN_CATALOG_THRESHOLD:
print("\nSUCCESS")
else:
if catalogIndex < numCatalogs - 1:
print("Not enough sources found in catalog " +
catalogList[catalogIndex])
print("Try again with the next catalog")
catalogIndex += 1
retry_fit = True
skip_all_other_steps = True
else:
print(
"Not enough sources found in any catalog - no processing done."
)
return (1)
if not skip_all_other_steps:
# 5: Extract catalog of observable sources from each input image
print(
"-------------------- STEP 5: Source finding --------------------"
)
if not extracted_sources:
extracted_sources = generate_source_catalogs(processList)
for imgname in extracted_sources.keys():
table = extracted_sources[imgname]["catalog_table"]
# 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])
if total_num_sources < MIN_OBSERVABLE_THRESHOLD:
print(
"Not enough sources ({}) found in image {}".format(
total_num_sources, imgname))
return (1)
# Convert input images to tweakwcs-compatible NDData objects and
# attach source catalogs to them.
imglist = []
for group_id, image in enumerate(processList):
imglist.extend(
amutils.build_nddata(
image, group_id,
extracted_sources[image]['catalog_table']))
print("\nSUCCESS")
# 6: Cross-match source catalog with astrometric reference source catalog, Perform fit between source catalog and reference catalog
print(
"-------------------- STEP 6: Cross matching and fitting --------------------"
)
# Specify matching algorithm to use
match = tweakwcs.TPMatch(searchrad=250,
separation=0.1,
tolerance=100,
use2dhist=False)
# Align images and correct WCS
tweakwcs.tweak_image_wcs(imglist, reference_catalog, match=match)
# Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
tweakwcs_info_keys = OrderedDict(
imglist[0].meta['tweakwcs_info']).keys()
imgctr = 0
for item in imglist:
retry_fit = False
#Handle fitting failures (no matches found)
if item.meta['tweakwcs_info']['status'].startswith(
"FAILED") == True:
if catalogIndex < numCatalogs - 1:
print(
"No cross matches found between astrometric catalog and sources found in images"
)
print("Try again with the next catalog")
catalogIndex += 1
retry_fit = True
break
else:
print(
"No cross matches found in any catalog - no processing done."
)
return (1)
max_rms_val = item.meta['tweakwcs_info']['TOTAL_RMS']
num_xmatches = item.meta['tweakwcs_info']['nmatches']
# print fit params to screen
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FIT PARAMETERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
if item.meta['chip'] == 1:
image_name = processList[imgctr]
imgctr += 1
print("image: {}".format(image_name))
print("chip: {}".format(item.meta['chip']))
print("group_id: {}".format(item.meta['group_id']))
for tweakwcs_info_key in tweakwcs_info_keys:
if not tweakwcs_info_key.startswith("matched"):
print("{} : {}".format(
tweakwcs_info_key,
item.meta['tweakwcs_info'][tweakwcs_info_key]))
# print("Radial shift: {}".format(math.sqrt(item.meta['tweakwcs_info']['shift'][0]**2+item.meta['tweakwcs_info']['shift'][1]**2)))
print(
"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
)
if num_xmatches < MIN_CROSS_MATCHES:
if catalogIndex < numCatalogs - 1:
print(
"Not enough cross matches found between astrometric catalog and sources found in images"
)
print("Try again with the next catalog")
catalogIndex += 1
retry_fit = True
break
else:
print(
"Not enough cross matches found in any catalog - no processing done."
)
return (1)
elif max_rms_val > MAX_FIT_RMS:
if catalogIndex < numCatalogs - 1:
print(
"Fit RMS value = {}mas greater than the maximum threshold value {}."
.format(
item.meta['tweakwcs_info']['FIT_RMS'].value,
MAX_FIT_RMS))
print("Try again with the next catalog")
catalogIndex += 1
retry_fit = True
break
else:
print(
"Fit RMS values too large using any catalog - no processing done."
)
return (1)
else:
print("Fit calculations successful.")
if not retry_fit:
print("\nSUCCESS")
# 7: Write new fit solution to input image headers
print(
"-------------------- STEP 7: Update image headers with new WCS information --------------------"
)
if update_hdr_wcs:
update_image_wcs_info(imglist, processList)
print("\nSUCCESS")
else:
print("\n STEP SKIPPED")
return (0)
def match_relative_fit(imglist, reference_catalog, **fit_pars):
"""Perform cross-matching and final fit using relative matching algorithm
Parameters
----------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
reference_catalog : Table
Astropy Table of reference sources for this field
Returns
--------
imglist : list
List of input image `~tweakwcs.tpwcs.FITSWCS` objects with metadata and source catalogs
"""
log.info("{} (match_relative_fit) Cross matching and fitting {}".format(
"-" * 20, "-" * 27))
if 'fitgeom' in fit_pars:
fitgeom = fit_pars['fitgeom']
del fit_pars['fitgeom']
else:
fitgeom = 'rscale'
common_pars = fit_pars['pars']
del fit_pars['pars']
# 0: Specify matching algorithm to use
match = tweakwcs.TPMatch(**fit_pars)
# match = tweakwcs.TPMatch(searchrad=250, separation=0.1,
# tolerance=100, use2dhist=False)
# Align images and correct WCS
# NOTE: this invocation does not use an astrometric catalog. This call
# allows all the input images to be aligned in
# a relative way using the first input image as the reference.
# 1: Perform relative alignment
match_relcat = tweakwcs.align_wcs(imglist,
None,
match=match,
minobj=common_pars['MIN_FIT_MATCHES'],
expand_refcat=True,
fitgeom=fitgeom)
log.info("Relative alignment found: ")
for i in imglist:
info = i.meta['fit_info']
if 'shift' not in info:
off = (0., 0.)
rot = 0.
scale = -1.
nmatches = 0
else:
off = info['shift']
rot = info['<rot>']
scale = info['<scale>']
nmatches = info['nmatches']
msg = "Image {} --".format(i.meta['name'])
msg += "\n SHIFT:({:9.4f},{:9.4f}) NMATCHES: {} ".format(
off[0], off[1], nmatches)
msg += "\n ROT:{:9.4f} SCALE:{:9.4f}".format(rot, scale)
msg += "\n Using fitgeom = '{}'".format(fitgeom)
log.info(msg)
# This logic enables performing only relative fitting and skipping fitting to GAIA
if reference_catalog is not None:
# Set all the group_id values to be the same so the various images/chips will be aligned to the astrometric
# reference catalog as an ensemble.
# astrometric reference catalog as an ensemble. BEWARE: If additional iterations of solutions are to be
# done, the group_id values need to be restored.
for image in imglist:
image.meta["group_id"] = 1234567
# 2: Perform absolute alignment
matched_cat = tweakwcs.align_wcs(imglist,
reference_catalog,
match=match,
minobj=common_pars['MIN_FIT_MATCHES'],
fitgeom=fitgeom)
else:
# Insure the expanded reference catalog has all the information needed
# to complete processing.
# TODO: Work out how to get the 'mag' column from input source catalog
# into this extended reference catalog...
reference_catalog = match_relcat
reference_catalog['mag'] = np.array([-999.9] * len(reference_catalog),
np.float32)
reference_catalog.meta['catalog'] = 'relative'
# 3: Interpret RMS values from tweakwcs
interpret_fit_rms(imglist, reference_catalog)
del match_relcat
return imglist
def determine_alignment_residuals(input,
files,
catalogs=None,
max_srcs=1000,
json_timestamp=None,
json_time_since_epoch=None,
log_level=logutil.logging.INFO):
"""Determine the relative alignment between members of an association.
Parameters
-----------
input : string
Original pipeline input filename. This filename will be used to
define the output analysis results filename.
files : list
Set of files on which to actually perform comparison. The original
pipeline can work on both CTE-corrected and non-CTE-corrected files,
but this comparison will only be performed on CTE-corrected
products when available.
catalogs : list, optional
List of dictionaries containing the source catalogs for each input chip.
The list NEEDS to be in the same order as the filenames given in `files`.
Each dictionary for each file will need to have numerical (integer) keys
for each 'sci' extension. If left as `None`, this function will create
it's own set of catalogs using `astrometric_utils.extract_point_sources`.
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
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 'NOTSET'.
Returns
--------
resids_files : list of string
Name of JSON files containing all the extracted results from the comparisons
being performed.
"""
log.setLevel(log_level)
if catalogs is None:
# Open all files as HDUList objects
hdus = [fits.open(f) for f in files]
# Determine sources from each chip
src_cats = []
num_srcs = []
for hdu in hdus:
numsci = countExtn(hdu)
nums = 0
img_cats = {}
if hdu[("SCI", 1)].data.max() == 0.0:
log.info(
"SKIPPING point-source finding for blank image: {}".format(
hdu.filename()))
continue
log.info("Determining point-sources for {}".format(hdu.filename()))
for chip in range(numsci):
chip += 1
img_cats[chip] = amutils.extract_point_sources(
hdu[("SCI", chip)].data, nbright=max_srcs)
nums += len(img_cats[chip])
log.info("Identified {} point-sources from {}".format(
nums, hdu.filename()))
num_srcs.append(nums)
src_cats.append(img_cats)
else:
src_cats = catalogs
num_srcs = []
for img in src_cats:
num_img = 0
for chip in img:
num_img += len(img[chip])
num_srcs.append(num_img)
if len(num_srcs) == 0 or (len(num_srcs) > 0 and max(num_srcs) <= 3):
log.warning(
"Not enough sources identified in input images for comparison")
return []
# Combine WCS from HDULists and source catalogs into tweakwcs-compatible input
imglist = []
for i, (f, cat) in enumerate(zip(files, src_cats)):
imglist += amutils.build_wcscat(f, i, cat)
# Setup matching algorithm using parameters tuned to well-aligned images
match = tweakwcs.TPMatch(searchrad=5,
separation=4.0,
tolerance=1.0,
use2dhist=True)
try:
# perform relative fitting
matchlist = tweakwcs.align_wcs(imglist,
None,
minobj=6,
match=match,
expand_refcat=False)
del matchlist
except Exception:
try:
# Try without 2dHist use to see whether we can get any matches at all
match = tweakwcs.TPMatch(searchrad=5,
separation=4.0,
tolerance=1.0,
use2dhist=False)
matchlist = tweakwcs.align_wcs(imglist,
None,
minobj=6,
match=match,
expand_refcat=False)
del matchlist
except Exception:
log.warning("Problem encountered during matching of sources")
return []
# Check to see whether there were any successful fits...
align_success = False
for img in imglist:
wcsname = fits.getval(img.meta['filename'], 'wcsname', ext=("sci", 1))
img.meta['wcsname'] = wcsname
img.meta['fit_info']['aligned_to'] = imglist[0].meta['filename']
img.meta['reference_catalog'] = None
for img in imglist:
if img.meta['fit_info']['status'] == 'SUCCESS' and '-FIT' in wcsname:
align_success = True
break
resids_files = []
if align_success:
# extract results in the style of 'tweakreg'
resids = extract_residuals(imglist)
if resids is not None:
resids_files = generate_output_files(
resids,
json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch,
exclude_fields=['group_id'])
return resids_files
def determine_alignment_residuals(input, files, max_srcs=2000):
"""Determine the relative alignment between members of an association.
Parameters
-----------
input : string
Original pipeline input filename. This filename will be used to
define the output analysis results filename.
files : list
Set of files on which to actually perform comparison. The original
pipeline can work on both CTE-corrected and non-CTE-corrected files,
but this comparison will only be performed on CTE-corrected
products when available.
Returns
--------
results : string
Name of JSON file containing all the extracted results from the comparisons
being performed.
"""
# Open all files as HDUList objects
hdus = [fits.open(f) for f in files]
# Determine sources from each chip
src_cats = []
num_srcs = []
for hdu in hdus:
numsci = countExtn(hdu)
nums = 0
img_cats = {}
for chip in range(numsci):
chip += 1
img_cats[chip] = amutils.extract_point_sources(hdu[("SCI",
chip)].data,
nbright=max_srcs)
nums += len(img_cats[chip])
num_srcs.append(nums)
src_cats.append(img_cats)
if len(num_srcs) == 0 or (len(num_srcs) > 0 and max(num_srcs) <= 3):
return None
# src_cats = [amutils.generate_source_catalog(hdu) for hdu in hdus]
# Combine WCS from HDULists and source catalogs into tweakwcs-compatible input
imglist = []
for i, (f, cat) in enumerate(zip(files, src_cats)):
imglist += amutils.build_wcscat(f, i, cat)
# Setup matching algorithm using parameters tuned to well-aligned images
match = tweakwcs.TPMatch(searchrad=5,
separation=1.0,
tolerance=4.0,
use2dhist=True)
try:
# perform relative fitting
matchlist = tweakwcs.align_wcs(imglist,
None,
match=match,
expand_refcat=False)
del matchlist
except Exception:
return None
# Check to see whether there were any successful fits...
align_success = False
for img in imglist:
if img.meta['fit_info']['status'] == 'SUCCESS':
align_success = True
break
if align_success:
# extract results in the style of 'tweakreg'
resids = extract_residuals(imglist)
if resids is None:
resids_file = None
else:
# Define name for output JSON file...
resids_file = "{}_astrometry_resids.json".format(input[:9])
# Remove any previously computed results
if os.path.exists(resids_file):
os.remove(resids_file)
# Dump the results to a JSON file now...
with open(resids_file, 'w') as jfile:
json.dump(resids, jfile)
else:
resids_file = None
return resids_file
