def run(configObj=None):
# start by interpreting filename and hdrlet inputs
filename = parseinput.parseinput(configObj['filename'])[0]
hdrlet = parseinput.parseinput(configObj['hdrlet'])[0]
if configObj['primary']:
# Call function with properly interpreted input parameters
# Syntax: apply_headerlet_as_primary(filename, hdrlet, attach=True,
# archive=True, force=False, verbose=False)
headerlet.apply_headerlet_as_primary(filename,
hdrlet,
attach=configObj['attach'],
archive=configObj['archive'],
force=configObj['force'],
logging=configObj['logging'])
else:
wcsname = configObj['wcsname']
if wcsname in ['', ' ', 'INDEF']: wcsname = None
wcskey = configObj['wcskey']
if wcskey == '': wcskey = None
# Call function with properly interpreted input parameters
# apply_headerlet_as_alternate(filename, hdrlet, attach=True,
# wcskey=None, wcsname=None, verbose=False)
headerlet.apply_headerlet_as_alternate(filename,
hdrlet,
attach=configObj['attach'],
wcsname=wcsname,
wcskey=wcskey,
logging=configObj['logging'])
def run(configObj=None):
# start by interpreting filename and hdrlet inputs
filename = parseinput.parseinput(configObj['filename'])[0]
hdrlet = parseinput.parseinput(configObj['hdrlet'])[0]
if configObj['primary']:
# Call function with properly interpreted input parameters
# Syntax: apply_headerlet_as_primary(filename, hdrlet, attach=True,
# archive=True, force=False, verbose=False)
headerlet.apply_headerlet_as_primary(filename,
hdrlet, attach=configObj['attach'],
archive=configObj['archive'], force=configObj['force'],
logging=configObj['logging'])
else:
wcsname = configObj['wcsname']
if wcsname in ['', ' ', 'INDEF']: wcsname = None
wcskey = configObj['wcskey']
if wcskey == '': wcskey = None
# Call function with properly interpreted input parameters
# apply_headerlet_as_alternate(filename, hdrlet, attach=True,
# wcskey=None, wcsname=None, verbose=False)
headerlet.apply_headerlet_as_alternate(filename,
hdrlet, attach=configObj['attach'],
wcsname=wcsname, wcskey=wcskey,
logging=configObj['logging'])
def wf3ccd(input, output="", dqicorr="PERFORM", atodcorr="PERFORM",blevcorr="PERFORM",
biascorr="PERFORM", flashcorr="PERFORM", verbose=False, quiet=True ):
"""Run the ``wf3ccd.e`` executable as from the shell."""
call_list = ['wf3ccd.e']
if verbose:
call_list += ['-v','-t']
if (dqicorr == "PERFORM"):
call_list.append('-dqi')
if (atodcorr == "PERFORM"):
call_list.append('-atod')
if (blevcorr == "PERFORM"):
call_list.append('-blev')
if (biascorr == "PERFORM"):
call_list.append('-bias')
if (flashcorr == "PERFORM"):
call_list.append('-flash')
infiles, dummpy_out= parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(str(output))
subprocess.call(call_list)
def wf32d(input, output="", dqicorr="PERFORM", darkcorr="PERFORM",flatcorr="PERFORM",
shadcorr="PERFORM", photcorr="PERFORM", verbose=False, quiet=True ):
""" Call the wf32d.e executable """
if verbose:
call_list += ['-v','-t']
if debug:
call_list.append('-d')
if (darkcorr == "PERFORM"):
call_list.append('-dark')
if (dqicorr == "PERFORM"):
call_list.append('-dqi')
if (flatcorr == "PERFORM"):
call_list.append('-flat')
if (shadcorr == "PERFORM"):
call_list.append('-shad')
if (photcorr == "PERFORM"):
call_list.append('-phot')
infiles, dummpy_out= parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(str(output))
subprocess.call(call_list)
def wf3cte(input, out=None, parallel=True, verbose=False, log_func=print):
"""Run the ``wf3cte.e`` executable as from the shell."""
call_list = ['wf3cte.e']
if verbose:
call_list.append('-v')
if not parallel:
call_list.append('-1')
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if out:
call_list.append(str(out))
print(call_list)
proc = subprocess.Popen(
call_list,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
)
if log_func is not None:
for line in proc.stdout:
log_func(line.decode('utf8'))
return_code = proc.wait()
if return_code != 0:
raise RuntimeError("wf3cte.e exited with code {}".format(return_code))
def acssum(input, output, exec_path='', time_stamps=False, verbose=False,
quiet=False):
"""
Run the acssum.e executable as from the shell.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*flt.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
output : str
Output filename.
If `output` is '' and `input` is '\*_asn.fits',
`output` will be automatically set to '\*_sfl.fits'.
Otherwise, it is an error not to provide a specific `output`.
exec_path : str, optional
The complete path to ACSSUM executable.
If not given, run ACSSUM given by 'acssum.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
"""
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acssum.e']
# Parse input to get list of filenames to process.
# acssum.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(output)
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
subprocess.call(call_list)
def calwf3(input=None, output=None, printtime=False, save_tmp=False,
verbose=False, debug=False, parallel=True, version=False,
log_func=print):
call_list = ['calwf3.e']
return_code = None
if version and input is None:
call_list.append('-r')
else:
if printtime:
call_list.append('-t')
if save_tmp:
call_list.append('-s')
if verbose:
call_list.append('-v')
if debug:
call_list.append('-d')
if not parallel:
call_list.append('-1')
infiles, dummy = parseinput.parseinput(input)
if len(parseinput.irafglob(input)) == 0:
raise IOError("No valid image specified")
if len(parseinput.irafglob(input)) > 1:
raise IOError("calwf3 can only accept 1 file for"
"input at a time: {0}".format(infiles))
for image in infiles:
if not os.path.exists(image):
raise IOError("Input file not found: {0}".format(image))
call_list.append(input)
if output:
call_list.append(str(output))
proc = subprocess.Popen(
call_list,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
)
if log_func is not None:
for line in proc.stdout:
log_func(line.decode('utf8'))
return_code = proc.wait()
ec = error_code(return_code)
if ec:
if ec is None:
print("Unknown return code found!")
ec = return_code
raise RuntimeError("calwf3.e exited with code {}".format(ec))
def run(input,
reference=None,
output='shifts.txt',
findmode='catalog',
**input_dict):
""" Control the entire shift finding operation. """
print('Tweakshifts Version ', __version__, '\n')
# Start by determining what input files we are to work with...
flist, outfile = parseinput.parseinput(input)
verbose = False
if 'verbose' in input_dict and input_dict['verbose']:
print('Computing shifts for :')
print(flist)
verbose = input_dict['verbose']
# Insure that a valid filename gets setup for reference WCS
if reference != None:
refname = reference
if os.path.exists(refname):
err_str = 'Output Reference WCS "' + refname + '" already exists! Please delete.'
raise ValueError(err_str)
else:
refname = 'tweak_wcs.fits'
# split remainder of parameters into algorithm specific sets
# and use them for depending on what algorithm was selected.
if findmode == 'catalog':
match_pars = parseInputPars(input_dict, MATCH_PARS)
match_pars['input'] = input
shift_dict = runCatalog(flist, refname, match_pars)
else:
# Insure that flist does not include the name of the
# reference WCS as well.
ref_img = input_dict['crossref']
if flist.count(ref_img) > 0: flist.remove(ref_img)
# Refname can not be derived from 'refimage' parameter
# as the refimage parameter serves as an output, not an
# input, as needed here... WJH 11 May 2005
crosscor_pars = parseInputPars(input_dict, CROSSCORR_PARS)
shift_dict = runCrosscorr(flist, ref_img, crosscor_pars)
w = wcsutil.WCSObject(ref_img)
w.createReferenceWCS(refname)
if len(shift_dict['order']) > 1:
writeShiftFile(shift_dict, output, refname)
if 'updatehdr' in input_dict and input_dict['updatehdr']:
update_file_headers(shift_dict, refname, verbose=verbose)
else:
print('No suitable data for finding shifts.')
print('No shift file written out.')
if os.path.exists(refname):
os.remove(refname)
def readWCS(input, exts=None, extname=None):
if isinstance(input, str):
if input[0] == '@':
# input is an @ file
filelist = irafglob.irafglob(input)
else:
try:
filelist, output = parseinput.parseinput(input)
except IOError:
raise
elif isinstance(input, list):
if isinstance(input[0], wcsutil.HSTWCS):
# a list of HSTWCS objects
return input
else:
filelist = input[:]
wcso = []
fomited = []
# figure out which FITS extension(s) to use
if exts is None and extname is None:
# Assume it's simple FITS and the data is in the primary HDU
for f in filelist:
try:
wcso = wcsutil.HSTWCS(f)
except AttributeError:
fomited.append(f)
continue
elif exts is not None and validateExt(exts):
exts = [exts]
for f in filelist:
try:
wcso.extend([wcsutil.HSTWCS(f, ext=e) for e in exts])
except KeyError:
fomited.append(f)
continue
elif extname is not None:
for f in filelist:
fobj = fits.open(f)
for i in range(len(fobj)):
try:
ename = fobj[i].header['EXTNAME']
except KeyError:
continue
if ename.lower() == extname.lower():
wcso.append(wcsutil.HSTWCS(f, ext=i))
else:
continue
fobj.close()
if fomited != []:
print("These files were skipped:")
for f in fomited:
print(f)
return wcso
def run(configObj=None):
# Interpret primary parameters from configObj instance
extname = configObj['extname']
input = configObj['input']
# create dictionary of remaining parameters, deleting extraneous ones
# such as those above
cdict = configObj.dict()
# remove any rules defined for the TEAL interface
if "_RULES_" in cdict: del cdict['_RULES_']
del cdict['_task_name_']
del cdict['input']
del cdict['extname']
# parse input
input, altfiles = parseinput.parseinput(configObj['input'])
# Insure that all input files have a correctly archived
# set of OPUS WCS keywords
# Legacy files from OTFR, like all WFPC2 data from OTFR, will only
# have the OPUS WCS keywords archived using a prefix of 'O'
# These keywords need to be converted to the Paper I alternate WCS
# standard using a wcskey (suffix) of 'O'
# If an alternate WCS with wcskey='O' already exists, this will copy
# the values from the old prefix-'O' WCS keywords to insure the correct
# OPUS keyword values get archived for use with updatewcs.
#
for file in input:
# Check to insure that there is a valid reference file to be used
idctab = fits.getval(file, 'idctab')
if not os.path.exists(fileutil.osfn(idctab)):
print('No valid distortion reference file ', idctab, ' found in ',
file, '!')
raise ValueError
# Re-define 'cdict' to only have switches for steps supported by that instrument
# the set of supported steps are defined by the dictionary
# updatewcs.apply_corrections.allowed_corrections
#
for file in input:
# get instrument name from input file
instr = fits.getval(file, 'INSTRUME')
# make copy of input parameters dict for this file
fdict = cdict.copy()
# Remove any parameter that is not part of this instrument's allowed corrections
for step in allowed_corr_dict:
if allowed_corr_dict[
step] not in updatewcs.apply_corrections.allowed_corrections[
instr]:
fdict[step]
# Call 'updatewcs' on correctly archived file
updatewcs.updatewcs(file, **fdict)
def __init__( self, input_file, edit_type=None, hdr_key=None, err_key=None, nref_par=None,
force=None, noclean=False, dry_run=1, verbosity=0):
"""constructor
Parameters
-----------
input_file : string
name of the file or filelist to be processed
edit_type : string type of file to update
hdr_key : string
name of keyword to update in file
err_key : string
name of keyword for error estimate
nref_par : string
name of the directory containing the nonlinearity file
force : string
name of algorithm whose value is to be returned
noclean : {'True', 'False'}
flag to force use of UNCLEANed 0th read.
dry_run : {0,1} [Default: 1]
flag to force not writing to header
verbosity : {0,1,2}
verbosity level (0 for quiet, 1 verbose, 2 very verbose)
"""
if (edit_type == None): edit_type = tfbutil.edit_type
if (hdr_key == None): hdr_key = tfbutil.hdr_key
if (err_key == None): err_key = tfbutil.err_key
if (force == None): force = tfbutil.force
self.input_file = input_file
self.edit_type = edit_type
self.hdr_key = hdr_key
self.err_key = err_key
self.nref_par = nref_par
self.force = force
self.noclean = noclean
self.dry_run = dry_run
self.verbosity = verbosity
self.tfb_version = __version__
self.tfb_run = time.asctime()
outlist = parseinput.parseinput(input_file)
self.num_files = parseinput.countinputs(input_file)[0]
self.outlist0 = outlist[0]
if (( dry_run == 0) & (self.verbosity >0)):
print(' The dry_run option has been selected so keys will not be written.')
if (self.verbosity >1):
print(' Temp_from_bias run on ',self.tfb_run, ', version: ', self.tfb_version)
def countInput(input):
files = parseinput.parseinput(input)
count = len(files[0])
for f in files[0]:
if fileutil.isFits(f)[0]:
try:
ins = fits.getval(f, 'INSTRUME')
except: # allow odd fits files; do not stop the count
ins = None
if ins == 'STIS':
count += (stisObsCount(f)-1)
return count
def countInput(input):
files = parseinput.parseinput(input)
count = len(files[0])
for f in files[0]:
if fileutil.isFits(f)[0]:
try:
ins = fits.getval(f, 'INSTRUME')
except: # allow odd fits files; do not stop the count
ins = None
if ins == 'STIS':
count += (stisObsCount(f) - 1)
return count
def readWCS(input, exts=None, extname=None):
if isinstance(input, str):
if input[0] == '@':
# input is an @ file
filelist = irafglob.irafglob(input)
else:
try:
filelist, output = parseinput.parseinput(input)
except IOError: raise
elif isinstance(input, list):
if isinstance(input[0], wcsutil.HSTWCS):
# a list of HSTWCS objects
return input
else:
filelist = input[:]
wcso = []
fomited = []
# figure out which FITS extension(s) to use
if exts is None and extname is None:
# Assume it's simple FITS and the data is in the primary HDU
for f in filelist:
try:
wcso = wcsutil.HSTWCS(f)
except AttributeError:
fomited.append(f)
continue
elif exts is not None and validateExt(exts):
exts = [exts]
for f in filelist:
try:
wcso.extend([wcsutil.HSTWCS(f, ext=e) for e in exts])
except KeyError:
fomited.append(f)
continue
elif extname is not None:
for f in filelist:
fobj = fits.open(f)
for i in range(len(fobj)):
try:
ename = fobj[i].header['EXTNAME']
except KeyError:
continue
if ename.lower() == extname.lower():
wcso.append(wcsutil.HSTWCS(f, ext=i))
else:
continue
fobj.close()
if fomited != []:
print("These files were skipped:")
for f in fomited:
print(f)
return wcso
def run(configObj=None):
flist, oname = parseinput.parseinput(configObj['filename'])
if len(flist) == 0:
print('=' * 60)
print('ERROR:')
print(' No valid "filename" parameter value provided!')
print(' Please check the working directory and restart this task.')
print('=' * 60)
return
if configObj['hdrname'] in ['', ' ', 'INDEF']:
print('=' * 60)
print('ERROR:')
print(' No valid "hdrname" parameter value provided!')
print(
' Please restart this task and provide a value for this parameter.'
)
print('=' * 60)
return
if configObj['output'] in ['', ' ', 'INDEF']:
configObj['output'] = None
str_kw = [
'wcsname', 'destim', 'sipname', 'npolfile', 'd2imfile', 'descrip',
'history', 'author', 'output', 'catalog'
]
# create dictionary of remaining parameters, deleting extraneous ones
# such as those above
cdict = configObj.dict()
# remove any rules defined for the TEAL interface
if "_RULES_" in cdict: del cdict['_RULES_']
del cdict['_task_name_']
del cdict['filename']
del cdict['hdrname']
# Convert blank string input as None
for kw in str_kw:
if cdict[kw] == '': cdict[kw] = None
if cdict['wcskey'].lower() == 'primary': cdict['wcskey'] = ' '
# Call function with properly interpreted input parameters
# Syntax: write_headerlet(filename, hdrname, output, sciext='SCI',
# wcsname=None, wcskey=None, destim=None,
# sipname=None, npolfile=None, d2imfile=None,
# author=None, descrip=None, history=None,
# attach=True, clobber=False)
headerlet.write_headerlet(flist, configObj['hdrname'], **cdict)
def parseMultipleInput(input):
if isinstance(input, str):
if input[0] == '@':
# input is an @ file
filelist = irafglob.irafglob(input)
else:
try:
filelist, output = parseinput.parseinput(input)
except IOError: raise
elif isinstance(input, list):
#if isinstance(input[0], HSTWCS):
## a list of HSTWCS objects
#return input
#else:
filelist = input[:]
return filelist
def parse_input(input, prodonly=False, sort_wildcards=True):
catlist = None
if (isinstance(input, list) == False) and \
('_asn' in input or '_asc' in input) :
# Input is an association table
# Get the input files
oldasndict = asnutil.readASNTable(input, prodonly=prodonly)
filelist = [
fileutil.buildRootname(fname) for fname in oldasndict['order']
]
elif (isinstance(input, list) == False) and \
(input[0] == '@') :
# input is an @ file
f = open(input[1:])
# Read the first line in order to determine whether
# catalog files have been specified in a second column...
line = f.readline()
f.close()
# Parse the @-file with irafglob to extract the input filename
filelist = irafglob.irafglob(input, atfile=atfile_sci)
print(line)
# If there are additional columns for catalog files...
if len(line.split()) > 1:
# ...parse out the names of the catalog files as well
catlist, catdict = parse_atfile_cat(input)
elif (isinstance(input, list)):
# input a python list
filelist = []
for fn in input:
flist, output = parse_input(fn, prodonly=prodonly)
# if wild-cards are given, sort for uniform usage:
if fn.find('*') > -1 and sort_wildcards:
flist.sort()
filelist += flist
else:
# input is either a string or something unrecognizable, so give it a try:
try:
filelist, output = parseinput.parseinput(input)
# if wild-cards are given, sort for uniform usage:
if input.find('*') > -1 and sort_wildcards:
filelist.sort()
except IOError:
raise
return filelist, catlist
def run(configObj=None):
if configObj['hdrname'] == '' and configObj['hdrext'] is None and \
configObj['distname'] == '':
print('=' * 60)
print('ERROR:')
print(' No valid "hdrname", "hdrext" or "distname" parameter value provided!')
print(' Please restart this task and provide a value for one of these parameters.')
print('=' * 60)
return
filename = parseinput.parseinput(configObj['filename'])[0]
# Call function with properly interpreted input parameters
# Syntax: delete_headerlet(filename, hdrname=None, hdrext=None, distname=None)
headerlet.delete_headerlet(filename,
hdrname=configObj['hdrname'],
hdrext=configObj['hdrext'],
distname=configObj['distname'],
logging=configObj['logging'])
def run(configObj=None):
if configObj['hdrname'] == '' and configObj['hdrext'] is None and \
configObj['distname'] == '':
print('=' * 60)
print('ERROR:')
print(' No valid "hdrname", "hdrext" or "distname" parameter value provided!')
print(' Please restart this task and provide a value for one of these parameters.')
print('=' * 60)
return
filename = parseinput.parseinput(configObj['filename'])[0]
# Call function with properly interpreted input parameters
# Syntax: delete_headerlet(filename, hdrname=None, hdrext=None, distname=None)
headerlet.delete_headerlet(filename,
hdrname=configObj['hdrname'],
hdrext=configObj['hdrext'],
distname=configObj['distname'],
logging=configObj['logging'])
def parse_input(input, prodonly=False, sort_wildcards=True):
catlist = None
if (isinstance(input, list) == False) and \
('_asn' in input or '_asc' in input) :
# Input is an association table
# Get the input files
oldasndict = asnutil.readASNTable(input, prodonly=prodonly)
filelist = [fileutil.buildRootname(fname) for fname in oldasndict['order']]
elif (isinstance(input, list) == False) and \
(input[0] == '@') :
# input is an @ file
f = open(input[1:])
# Read the first line in order to determine whether
# catalog files have been specified in a second column...
line = f.readline()
f.close()
# Parse the @-file with irafglob to extract the input filename
filelist = irafglob.irafglob(input, atfile=atfile_sci)
print(line)
# If there are additional columns for catalog files...
if len(line.split()) > 1:
# ...parse out the names of the catalog files as well
catlist,catdict = parse_atfile_cat(input)
elif (isinstance(input, list)):
# input a python list
filelist = []
for fn in input:
flist, output = parse_input(fn, prodonly=prodonly)
# if wild-cards are given, sort for uniform usage:
if fn.find('*') > -1 and sort_wildcards:
flist.sort()
filelist += flist
else:
# input is either a string or something unrecognizable, so give it a try:
try:
filelist, output = parseinput.parseinput(input)
# if wild-cards are given, sort for uniform usage:
if input.find('*') > -1 and sort_wildcards:
filelist.sort()
except IOError: raise
return filelist,catlist
def wf3ir(input, output=None, verbose=False, quiet=True, log_func=print):
"""Call the wf3ir.e executable """
call_list = ['wf3ir.e']
return_code = None
if verbose:
call_list += ['-v', '-t']
infiles, dummy = parseinput.parseinput(input)
if "_asn" in input:
raise IOError("wf3ir does not accept association tables")
if len(parseinput.irafglob(input)) == 0:
raise IOError("No valid image specified")
if len(parseinput.irafglob(input)) > 1:
raise IOError("wf3ir can only accept 1 file for"
"input at a time: {0}".format(infiles))
for image in infiles:
if not os.path.exists(image):
raise IOError("Input file not found: {0}".format(image))
call_list.append(input)
if output:
call_list.append(str(output))
proc = subprocess.Popen(
call_list,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
)
if log_func is not None:
for line in proc.stdout:
log_func(line.decode('utf8'))
return_code = proc.wait()
ec = error_code(return_code)
if return_code:
if ec is None:
print("Unknown return code found!")
ec = return_code
raise RuntimeError("wf3ir.e exited with code {}".format(ec))
def wf3ir(input, output=None, verbose=False, quiet=True, log_func=print):
"""Call the wf3ir.e executable """
call_list = ['wf3ir.e']
return_code = None
if verbose:
call_list += ['-v', '-t']
infiles, dummy = parseinput.parseinput(input)
if "_asn" in input:
raise IOError("wf3ir does not accept association tables")
if len(parseinput.irafglob(input)) == 0:
raise IOError("No valid image specified")
if len(parseinput.irafglob(input)) > 1:
raise IOError("wf3ir can only accept 1 file for"
"input at a time: {0}".format(infiles))
for image in infiles:
if not os.path.exists(image):
raise IOError("Input file not found: {0}".format(image))
call_list.append(input)
if output:
call_list.append(str(output))
proc = subprocess.Popen(
call_list,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
)
if log_func is not None:
for line in proc.stdout:
log_func(line.decode('utf8'))
return_code = proc.wait()
ec = error_code(return_code)
if return_code:
if ec is None:
print("Unknown return code found!")
ec = return_code
raise RuntimeError("wf3ir.e exited with code {}".format(ec))
def _process_input_wcs(infiles, wcskey, updatewcs):
"""
This is a subset of process_input(), for internal use only. This is the
portion of input handling which sets/updates WCS data, and is a performance
hit - a target for parallelization. Returns the expanded list of filenames.
"""
# Run parseinput though it's likely already been done in processFilenames
outfiles = parseinput.parseinput(infiles)[0]
# Disable parallel processing here for now until hardware I/O gets "wider".
# Since this part is IO bound, parallelizing doesn't help more than a little
# in most cases, and may actually slow this down on some desktop nodes.
# cfgval_num_cores = None # get this from paramDict
# pool_size = util.get_pool_size(cfgval_num_cores, len(outfiles))
pool_size = 1
# do the WCS updating
if wcskey in ['', ' ', 'INDEF', None]:
if updatewcs:
log.info('Updating input WCS using "updatewcs"')
else:
log.info('Resetting input WCS to be based on WCS key = %s' % wcskey)
if pool_size > 1:
log.info('Executing %d parallel workers' % pool_size)
subprocs = []
mp_ctx = multiprocessing.get_context('fork')
for fname in outfiles:
p = mp_ctx.Process(
target=_process_input_wcs_single,
name='processInput._process_input_wcs()', # for err msgs
args=(fname, wcskey, updatewcs))
subprocs.append(p)
mputil.launch_and_wait(subprocs, pool_size) # blocks till all done
else:
log.info('Executing serially')
for fname in outfiles:
_process_input_wcs_single(fname, wcskey, updatewcs)
return outfiles
def __init__(self,
filename_list,
keyword_list,
extension='all',
expr="None"):
"""
set initial parameters, call class functions for preforming selection
and formatting to masked astropy table.
"""
self.filename_list = parseinput.parseinput(filename_list)[0]
self.final_key_dict = {}
self.keyword_list = keyword_list.split(",")
self.final_key_set = set()
self.extension = extension
self.expr = expr
self.table = Table()
self.select()
self.__dict_to_table()
def _process_input_wcs(infiles, wcskey, updatewcs):
"""
This is a subset of process_input(), for internal use only. This is the
portion of input handling which sets/updates WCS data, and is a performance
hit - a target for parallelization. Returns the expanded list of filenames.
"""
# Run parseinput though it's likely already been done in processFilenames
outfiles = parseinput.parseinput(infiles)[0]
# Disable parallel processing here for now until hardware I/O gets "wider".
# Since this part is IO bound, parallelizing doesn't help more than a little
# in most cases, and may actually slow this down on some desktop nodes.
# cfgval_num_cores = None # get this from paramDict
# pool_size = util.get_pool_size(cfgval_num_cores, len(outfiles))
pool_size = 1
# do the WCS updating
if wcskey in ['', ' ', 'INDEF', None]:
if updatewcs:
log.info('Updating input WCS using "updatewcs"')
else:
log.info('Resetting input WCS to be based on WCS key = %s' % wcskey)
if pool_size > 1:
log.info('Executing %d parallel workers' % pool_size)
subprocs = []
for fname in outfiles:
p = multiprocessing.Process(target=_process_input_wcs_single,
name='processInput._process_input_wcs()', # for err msgs
args=(fname, wcskey, updatewcs) )
subprocs.append(p)
mputil.launch_and_wait(subprocs, pool_size) # blocks till all done
else:
log.info('Executing serially')
for fname in outfiles:
_process_input_wcs_single(fname, wcskey, updatewcs)
return outfiles
def replace(input, **pars):
""" Replace pixels in `input` that have a value of `pixvalue`
with a value given by `newvalue`.
"""
pixvalue = pars.get('pixvalue', np.nan)
if pixvalue is None: pixvalue = np.nan # insure that None == np.nan
newvalue = pars.get('newvalue', 0.0)
ext = pars.get('ext',None)
if ext in ['',' ','None',None]:
ext = None
files = parseinput.parseinput(input)[0]
for f in files:
fimg = fits.open(f, mode='update')
if ext is None:
# replace pixels in ALL extensions
extn = [i for i in fimg]
else:
if type(ext) == type([]):
extn = [fimg[e] for e in ext]
else:
extn = [fimg[ext]]
for e in extn:
if e.data is not None and e.is_image: # ignore empty Primary HDUs
print("Converting {}[{},{}] value of {} to {}".format(
f,e.name,e.ver,pixvalue,newvalue))
if np.isnan(pixvalue):
e.data[np.isnan(e.data)] = newvalue
else:
e.data[np.where(e.data == pixvalue)] = newvalue
fimg.close()
def replace(input, **pars):
""" Replace pixels in `input` that have a value of `pixvalue`
with a value given by `newvalue`.
"""
pixvalue = pars.get('pixvalue', np.nan)
if pixvalue is None: pixvalue = np.nan # insure that None == np.nan
newvalue = pars.get('newvalue', 0.0)
ext = pars.get('ext', None)
if ext in ['', ' ', 'None', None]:
ext = None
files = parseinput.parseinput(input)[0]
for f in files:
fimg = fits.open(f, mode='update')
if ext is None:
# replace pixels in ALL extensions
extn = [i for i in fimg]
else:
if type(ext) == type([]):
extn = [fimg[e] for e in ext]
else:
extn = [fimg[ext]]
for e in extn:
if e.data is not None and e.is_image: # ignore empty Primary HDUs
print("Converting {}[{},{}] value of {} to {}".format(
f, e.name, e.ver, pixvalue, newvalue))
if np.isnan(pixvalue):
e.data[np.isnan(e.data)] = newvalue
else:
e.data[np.where(e.data == pixvalue)] = newvalue
fimg.close()
def acssum(input,
output,
exec_path='',
time_stamps=False,
verbose=False,
quiet=False,
exe_args=None):
r"""
Run the acssum.e executable as from the shell.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*flt.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
output : str
Output filename.
If `output` is '' and `input` is '\*_asn.fits',
`output` will be automatically set to '\*_sfl.fits'.
Otherwise, it is an error not to provide a specific `output`.
exec_path : str, optional
The complete path to ACSSUM executable.
If not given, run ACSSUM given by 'acssum.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acssum.e']
# Parse input to get list of filenames to process.
# acssum.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(output)
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list)
def acscteforwardmodel(input,
exec_path='',
time_stamps=False,
verbose=False,
quiet=False,
single_core=False,
exe_args=None):
r"""
Run the acscteforwardmodel.e executable as from the shell.
Expect input to be ``*_blc_tmp.fits`` or ``*_flc.fits``.
Output is automatically named ``*_ctefmod.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_blc_tmp.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*blc_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCTE forward model executable.
If not given, run ACSCTE given by 'acscteforwardmodel.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
single_core : bool, optional
CTE correction in the ACSCTE forward model will by default try to use
all available CPUs on your computer. Set this to True to force the use
of just one CPU.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acscteforwardmodel.e']
# Parse input to get list of filenames to process.
# acscte.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if single_core:
call_list.append('-1')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list) # nosec
def create_astrometric_catalog(inputs, **pars):
"""Create an astrometric catalog that covers the inputs' field-of-view.
Parameters
===========
input : str
Filenames of images to be aligned to astrometric catalog
catalog : str, optional
Name of catalog to extract astrometric positions for sources in the
input images' field-of-view. Default: GAIADR2. Options available are
documented on the catalog web page.
output : str, optional
Filename to give to the astrometric catalog read in from the master
catalog web service. If 'None', no file will be written out.
Default: ref_cat.ecsv
gaia_only : bool, optional
Specify whether or not to only use sources from GAIA in output catalog
Default: False
existing_wcs : HST.wcs object
existing WCS object specified by the user
note ::
This function will point to astrometric catalog web service defined
through the use of the ASTROMETRIC_CATALOG_URL environment variable.
Returns
=======
ref_table : object
Astropy Table object of the catalog
"""
# interpret input parameters
catalog = pars.get("catalog", 'GAIADR2')
output = pars.get("output", 'ref_cat.ecsv')
gaia_only = pars.get("gaia_only", False)
table_format = pars.get("table_format", 'ascii.ecsv')
existing_wcs = pars.get("existing_wcs", None)
inputs, _ = parseinput.parseinput(inputs)
# start by creating a composite field-of-view for all inputs
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
if existing_wcs:
outwcs = existing_wcs
else:
outwcs = build_reference_wcs(inputs)
radius = compute_radius(outwcs)
ra, dec = outwcs.wcs.crval
# perform query for this field-of-view
ref_dict = get_catalog(ra, dec, sr=radius, catalog=catalog)
colnames = ('ra', 'dec', 'mag', 'objID', 'GaiaID')
col_types = ('f8', 'f8', 'f4', 'U25', 'U25')
ref_table = Table(names=colnames, dtype=col_types)
# rename coordinate columns to be consistent with tweakwcs
ref_table.rename_column('ra', 'RA')
ref_table.rename_column('dec', 'DEC')
# extract just the columns we want...
num_sources = 0
for source in ref_dict:
if 'GAIAsourceID' in source:
g = source['GAIAsourceID']
if gaia_only and g.strip() is '':
continue
else:
g = -1 # indicator for no source ID extracted
r = float(source['ra'])
d = float(source['dec'])
m = -999.9 # float(source['mag'])
o = source['objID']
num_sources += 1
ref_table.add_row((r, d, m, o, g))
# Write out table to a file, if specified
if output:
ref_table.write(output, format=table_format)
print("Created catalog '{}' with {} sources".format(output, num_sources))
return ref_table
def updatewcs(input, vacorr=True, tddcorr=True, npolcorr=True, d2imcorr=True,
checkfiles=True, verbose=False, use_db=True):
"""
Updates HST science files with the best available calibration information.
This allows users to retrieve from the archive self contained science files
which do not require additional reference files.
Basic WCS keywords are updated in the process and new keywords (following WCS
Paper IV and the SIP convention) as well as new extensions are added to the science files.
Examples
--------
>>> from stwcs import updatewcs
>>> updatewcs.updatewcs(filename)
Dependencies
`stsci.tools`
`astropy.io.fits`
`astropy.wcs`
`requests`
`lxml`
Parameters
----------
input : a python list of file names or a string (wild card
characters allowed) input files may be in fits, geis or
waiver fits format
vacorr : boolean
If True, vecocity aberration correction will be applied
tddcorr : boolean
If True, time dependent distortion correction will be applied
npolcorr : boolean
If True, a Lookup table distortion will be applied
d2imcorr : boolean
If True, detector to image correction will be applied
checkfiles : boolean
If True, the format of the input files will be checked,
geis and waiver fits files will be converted to MEF format.
Default value is True for standalone mode.
use_db : boolean
If True, attempt to add astrometric solutions from the
MAST astrometry database.
Default value is True.
"""
if not verbose:
logger.setLevel(100)
write_db_log = False
else:
fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
formatter = logging.Formatter(fmt)
log_filename = 'stwcs.log'
fh = logging.FileHandler(log_filename, mode='w')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.setLevel(verbose)
write_db_log = True
args = "vacorr=%s, tddcorr=%s, npolcorr=%s, d2imcorr=%s, checkfiles=%s, \
" % (str(vacorr), str(tddcorr), str(npolcorr),
str(d2imcorr), str(checkfiles))
logger.info('\n\tStarting UPDATEWCS: %s', time.asctime())
toclose = True
if isinstance(input, fits.HDUList):
input = [input]
if isinstance(input, list) and isinstance(input[0], fits.HDUList):
files = input
file_names = [inp.filename() for inp in files]
toclose = False
else:
file_names = parseinput.parseinput(input)[0]
files = []
for item in file_names:
files.append(fits.open(item, mode='update'))
logger.info("\n\tInput files: {}".format(file_names))
logger.info("\n\tInput arguments: %s" % args)
if checkfiles:
files = checkFiles(files)
file_names = [inp.filename() for inp in files]
if not files:
print('No valid input, quitting ...\n')
return
if use_db:
# Establish any available connection to
# an accessible astrometry web-service
astrometry = astrometry_utils.AstrometryDB(write_log=write_db_log)
for f in files:
acorr = apply_corrections.setCorrections(f, vacorr=vacorr, tddcorr=tddcorr,
npolcorr=npolcorr, d2imcorr=d2imcorr)
if 'MakeWCS' in acorr and newIDCTAB(f):
logger.warning("\n\tNew IDCTAB file detected. All current WCSs will be deleted")
cleanWCS(f)
makecorr(f, acorr)
if use_db:
# Add any new astrometry solutions available from
# an accessible astrometry web-service
astrometry.updateObs(f)
if toclose:
f.close()
return file_names
def clean(input,
suffix,
stat="pmode1",
maxiter=15,
sigrej=2.0,
lower=None,
upper=None,
binwidth=0.3,
mask1=None,
mask2=None,
dqbits=None,
rpt_clean=0,
atol=0.01,
clobber=False,
verbose=True):
r"""Remove horizontal stripes from ACS WFC post-SM4 data.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*flt.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
suffix : str
The string to use to add to each input file name to
indicate an output product. This string will be appended
to the suffix in each input filename to create the
new output filename. For example, setting `suffix='csck'`
will create '\*_csck.fits' images.
stat : { 'pmode1', 'pmode2', 'mean', 'mode', 'median', 'midpt' } (Default = 'pmode1')
Specifies the statistics to be used for computation of the
background in image rows:
* 'pmode1' - SEXTRACTOR-like mode estimate based on a
modified `Pearson's rule <https://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``2.5*median-1.5*mean``;
* 'pmode2' - mode estimate based on
`Pearson's rule <https://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``3*median-2*mean``;
* 'mean' - the mean of the distribution of the "good" pixels (after
clipping, masking, etc.);
* 'mode' - the mode of the distribution of the "good" pixels;
* 'median' - the median of the distribution of the "good" pixels;
* 'midpt' - estimate of the median of the distribution of the "good"
pixels based on an algorithm similar to IRAF's ``imagestats`` task
(``CDF(midpt)=1/2``).
.. note::
The midpoint and mode are computed in two passes through the
image. In the first pass the standard deviation of the pixels
is calculated and used with the *binwidth* parameter to compute
the resolution of the data histogram. The midpoint is estimated
by integrating the histogram and computing by interpolation
the data value at which exactly half the pixels are below that
data value and half are above it. The mode is computed by
locating the maximum of the data histogram and fitting the peak
by parabolic interpolation.
maxiter : int
This parameter controls the maximum number of iterations
to perform when computing the statistics used to compute the
row-by-row corrections.
sigrej : float
This parameters sets the sigma level for the rejection applied
during each iteration of statistics computations for the
row-by-row corrections.
lower : float, None (Default = None)
Lower limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
upper : float, None (Default = None)
Upper limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
binwidth : float (Default = 0.1)
Histogram's bin width, in sigma units, used to sample the
distribution of pixel brightness values in order to compute the
background statistics. This parameter is aplicable *only* to *stat*
parameter values of `'mode'` or `'midpt'`.
clobber : bool
Specify whether or not to 'clobber' (delete then replace)
previously generated products with the same names.
mask1 : str, numpy.ndarray, None, or list of these types
Mask images for ``SCI,1``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
mask2 : str, numpy.ndarray, None, or list of these types
Mask images for ``SCI,2``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
This is not used for subarrays.
dqbits : int, str, None (Default = None)
Integer sum of all the DQ bit values from the input image's DQ array
that should be considered "good" when building masks for de-striping
computations. For example, if pixels in the DQ array can be
combinations of 1, 2, 4, and 8 flags and one wants to consider
DQ "defects" having flags 2 and 4 as being acceptable for de-striping
computations, then `dqbits` should be set to 2+4=6. Then a DQ pixel
having values 2,4, or 6 will be considered a good pixel, while a DQ
pixel with a value, e.g., 1+2=3, 4+8=12, etc. will be flagged
as a "bad" pixel.
Alternatively, one can enter a comma- or '+'-separated list of
integer bit flags that should be added to obtain the final
"good" bits. For example, both ``4,8`` and ``4+8`` are equivalent to
setting `dqbits` to 12.
| Set `dqbits` to 0 to make *all* non-zero pixels in the DQ
mask to be considered "bad" pixels, and the corresponding image
pixels not to be used for de-striping computations.
| Default value (`None`) will turn off the use of image's DQ array
for de-striping computations.
| In order to reverse the meaning of the `dqbits`
parameter from indicating values of the "good" DQ flags
to indicating the "bad" DQ flags, prepend '~' to the string
value. For example, in order not to use pixels with
DQ flags 4 and 8 for sky computations and to consider
as "good" all other pixels (regardless of their DQ flag),
set `dqbits` to ``~4+8``, or ``~4,8``. To obtain the
same effect with an `int` input value (except for 0),
enter -(4+8+1)=-9. Following this convention,
a `dqbits` string value of ``'~0'`` would be equivalent to
setting ``dqbits=None``.
.. note::
DQ masks (if used), *will be* combined with user masks specified
in the `mask1` and `mask2` parameters (if any).
rpt_clean : int
An integer indicating how many *additional* times stripe cleaning
should be performed on the input image. Default = 0.
atol : float, None
The threshold for maximum absolute value of bias stripe correction
below which repeated cleanings can stop. When `atol` is `None`
cleaning will be repeated `rpt_clean` number of times.
Default = 0.01 [e].
verbose : bool
Print informational messages. Default = True.
"""
from stsci.tools import parseinput # Optional package dependency
flist = parseinput.parseinput(input)[0]
if isinstance(mask1, str):
mlist1 = parseinput.parseinput(mask1)[0]
elif isinstance(mask1, np.ndarray):
mlist1 = [mask1.copy()]
elif mask1 is None:
mlist1 = []
elif isinstance(mask1, list):
mlist1 = []
for m in mask1:
if isinstance(m, np.ndarray):
mlist1.append(m.copy())
elif isinstance(m, str):
mlist1 += parseinput.parseinput(m)[0]
else:
raise TypeError("'mask1' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'mask1' must be either a str, or a "
"numpy.ndarray, or a list of the two type of "
"values.")
if isinstance(mask2, str):
mlist2 = parseinput.parseinput(mask2)[0]
elif isinstance(mask2, np.ndarray):
mlist2 = [mask2.copy()]
elif mask2 is None:
mlist2 = []
elif isinstance(mask2, list):
mlist2 = []
for m in mask2:
if isinstance(m, np.ndarray):
mlist2.append(m.copy())
elif isinstance(m, str):
mlist2 += parseinput.parseinput(m)[0]
else:
raise TypeError("'mask2' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'mask2' must be either a str or a "
"numpy.ndarray, or a list of the two type of "
"values.")
n_input = len(flist)
n_mask1 = len(mlist1)
n_mask2 = len(mlist2)
if n_input == 0:
raise ValueError("No input file(s) provided or "
"the file(s) do not exist")
if n_mask1 == 0:
mlist1 = [None] * n_input
elif n_mask1 != n_input:
raise ValueError('Insufficient masks for [SCI,1]')
if n_mask2 == 0:
mlist2 = [None] * n_input
elif n_mask2 != n_input:
raise ValueError('Insufficient masks for [SCI,2]')
for image, maskfile1, maskfile2 in zip(flist, mlist1, mlist2):
# Skip processing pre-SM4 images
if (fits.getval(image, 'EXPSTART') <= SM4_MJD):
LOG.warning(f'{image} is pre-SM4. Skipping...')
continue
# Data must be in ELECTRONS
if (fits.getval(image, 'BUNIT', ext=1) != 'ELECTRONS'):
LOG.warning(f'{image} is not in ELECTRONS. Skipping...')
continue
# Skip processing CTECORR-ed images
if (fits.getval(image, 'PCTECORR') == 'COMPLETE'):
LOG.warning(f'{image} already has PCTECORR applied. Skipping...')
continue
# generate output filename for each input based on specification
# of the output suffix
output = image.replace('.fits', '_' + suffix + '.fits')
LOG.info('Processing ' + image)
# verify masks defined (or not) simultaneously:
if (fits.getval(image, 'CCDAMP') == 'ABCD'
and ((mask1 is not None and mask2 is None) or
(mask1 is None and mask2 is not None))):
raise ValueError("Both 'mask1' and 'mask2' must be specified "
"or not specified together.")
maskdata = _read_mask(maskfile1, maskfile2)
perform_correction(image,
output,
stat=stat,
maxiter=maxiter,
sigrej=sigrej,
lower=lower,
upper=upper,
binwidth=binwidth,
mask=maskdata,
dqbits=dqbits,
rpt_clean=rpt_clean,
atol=atol,
clobber=clobber,
verbose=verbose)
LOG.info(output + ' created')
def acscte(input, exec_path='', time_stamps=False, verbose=False, quiet=False,
single_core=False):
"""
Run the acscte.e executable as from the shell.
Expect input to be ``*_blv_tmp.fits``.
Output is automatically named ``*_blc_tmp.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_blv_tmp.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*blv_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCTE executable.
If not given, run ACSCTE given by 'acscte.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
single_core : bool, optional
CTE correction in ACSCTE will by default try to use all available
CPUs on your computer. Set this to True to force the use of just
one CPU.
"""
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acscte.e']
# Parse input to get list of filenames to process.
# acscte.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if single_core:
call_list.append('-1')
subprocess.call(call_list)
def acsccd(input, exec_path='', time_stamps=False, verbose=False, quiet=False):
"""
Run the acsccd.e executable as from the shell.
Expect input to be ``*_raw.fits``.
Output is automatically named ``*_blv_tmp.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_raw.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*raw.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCCD executable.
If not given, run ACSCCD given by 'acsccd.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
"""
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acsccd.e']
# Parse input to get list of filenames to process.
# acsccd.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
#if dqicorr:
# call_list.append('-dqi')
#if atodcor:
# call_list.append('-atod')
#if blevcorr:
# call_list.append('-blev')
#if biascorr:
# call_list.append('-bias')
subprocess.call(call_list)
def wf3rej(input, output="", crrejtab="", scalense="", initgues="",
skysub="", crsigmas="", crradius=0, crthresh=0,
badinpdq=0, crmask=False, shadcorr=False, verbose=False):
"""call the calwf3.e executable"""
call_list = ["wf3rej.e"]
infiles, dummy_out= parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(str(output))
if verbose:
call_list.append("-v")
call_list.append("-t")
if (shadcorr):
call_list.append("-shadcorr")
if (crmask):
call_list.append("-crmask")
if (crrejtab != ""):
call_list += ["-table",crrejtab]
if (scalense != ""):
call_list += ["-scale",str(scalense)]
if (initgues != ""):
options=["min","med"]
if initgues not in options:
print("Invalid option for intigues")
return ValueError
else:
call_list += ["-init",str(initgues)]
if (skysub != ""):
options=["none","mode","median"]
if skysub not in options:
print(("Invalid skysub option: %s")%(skysub))
print(options)
return ValueError
else:
call_list += ["-sky",str(skysub)]
if (crsigmas != ""):
call_list += ["-sigmas",str(crsigmas)]
if (crradius >= 0.):
call_list += ["-radius",str(crradius)]
else:
print("Invalid crradius specified")
return ValueError
if (crthresh >= 0.):
call_list += ["-thresh",str(crthresh)]
else:
print("Invalid crthresh specified")
return ValueError
if (badinpdq >= 0):
call_list += ["-pdq",str(badinpdq)]
else:
print("Invalid DQ value specified")
return ValueError
subprocess.call(call_list)
def acs2d(input,
exec_path='',
time_stamps=False,
verbose=False,
quiet=False,
exe_args=None):
r"""
Run the acs2d.e executable as from the shell.
Output is automatically named based on input suffix:
+--------------------+----------------+------------------------------+
| INPUT | OUTPUT | EXPECTED DATA |
+====================+================+==============================+
| ``*_raw.fits`` | ``*_flt.fits`` | SBC image. |
+--------------------+----------------+------------------------------+
| ``*_blv_tmp.fits`` | ``*_flt.fits`` | ACSCCD output. |
+--------------------+----------------+------------------------------+
| ``*_blc_tmp.fits`` | ``*_flc.fits`` | ACSCCD output with PCTECORR. |
+--------------------+----------------+------------------------------+
| ``*_crj_tmp.fits`` | ``*_crj.fits`` | ACSREJ output. |
+--------------------+----------------+------------------------------+
| ``*_crc_tmp.fits`` | ``*_crc.fits`` | ACSREJ output with PCTECORR. |
+--------------------+----------------+------------------------------+
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_blv_tmp.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*blv_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACS2D executable.
If not given, run ACS2D given by 'acs2d.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acs2d.e']
# Parse input to get list of filenames to process.
# acs2d.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list)
def acsrej(input,
output,
exec_path='',
time_stamps=False,
verbose=False,
shadcorr=False,
crrejtab='',
crmask=False,
scalense=None,
initgues='',
skysub='',
crsigmas='',
crradius=None,
crthresh=None,
badinpdq=None,
newbias=False,
readnoise_only=False,
exe_args=None):
r"""
Run the acsrej.e executable as from the shell.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*flt.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
output : str
Output filename.
exec_path : str, optional
The complete path to ACSREJ executable.
If not given, run ACSREJ given by 'acsrej.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
shadcorr : bool, optional
Perform shutter shading correction.
If this is False but SHADCORR is set to PERFORM in
the header of the first image, the correction will
be applied anyway.
Only use this with CCD image, not SBC MAMA.
crrejtab : str, optional
CRREJTAB to use. If not given, will use CRREJTAB
given in the primary header of the first input image.
crmask : bool, optional
Flag CR-rejected pixels in input files.
If False, will use CRMASK value in CRREJTAB.
scalense : float, optional
Multiplicative scale factor (in percents) applied to noise.
Acceptable values are 0 to 100, inclusive.
If None, will use SCALENSE from CRREJTAB.
initgues : {'med', 'min'}, optional
Scheme for computing initial-guess image.
If not given, will use INITGUES from CRREJTAB.
skysub : {'none', 'mode'}, optional
Scheme for computing sky levels to be subtracted.
If not given, will use SKYSUB from CRREJTAB.
crsigmas : str, optional
Cosmic ray rejection thresholds given in the format of 'sig1,sig2,...'.
Number of sigmas given will be the number of rejection
iterations done. At least 1 and at most 20 sigmas accepted.
If not given, will use CRSIGMAS from CRREJTAB.
crradius : float, optional
Radius (in pixels) to propagate the cosmic ray.
If None, will use CRRADIUS from CRREJTAB.
crthresh : float, optional
Cosmic ray rejection propagation threshold.
If None, will use CRTHRESH from CRREJTAB.
badinpdq : int, optional
Data quality flag used for cosmic ray rejection.
If None, will use BADINPDQ from CRREJTAB.
newbias : bool, optional
This option has been deprecated. Use ``readnoise_only``.
readnoise_only : bool, optional
ERR is just read noise, not Poisson noise.
This is used for BIAS images.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acsrej.e']
# Parse input to get list of filenames to process.
# acsrej.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(output)
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if shadcorr:
call_list.append('-shadcorr')
if crrejtab:
call_list += ['-table', crrejtab]
if crmask:
call_list.append('-crmask')
if scalense is not None:
if scalense < 0 or scalense > 100:
raise ValueError('SCALENSE must be 0 to 100')
call_list += ['-scale', str(scalense)]
if initgues:
if initgues not in ('med', 'min'):
raise ValueError('INITGUES must be "med" or "min"')
call_list += ['-init', initgues]
if skysub:
if skysub not in ('none', 'mode'):
raise ValueError('SKYSUB must be "none" or "mode"')
call_list += ['-sky', skysub]
if crsigmas:
call_list += ['-sigmas', crsigmas]
if crradius is not None:
call_list += ['-radius', str(crradius)]
if crthresh is not None:
call_list += ['-thresh ', str(crthresh)]
if badinpdq is not None:
call_list += ['-pdq', str(badinpdq)]
# Backward-compatibility for readnoise_only.
# TODO: Remove this option entirely in a future release.
if newbias:
warnings.warn('newbias is deprecated, use readnoise_only',
ACSREJDeprecationWarning)
readnoise_only = newbias
if readnoise_only:
call_list.append('-readnoise_only')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list)
def updatewcs(input,
vacorr=True,
tddcorr=True,
npolcorr=True,
d2imcorr=True,
checkfiles=True,
verbose=False):
"""
Updates HST science files with the best available calibration information.
This allows users to retrieve from the archive self contained science files
which do not require additional reference files.
Basic WCS keywords are updated in the process and new keywords (following WCS
Paper IV and the SIP convention) as well as new extensions are added to the science files.
Example
-------
>>>from stwcs import updatewcs
>>>updatewcs.updatewcs(filename)
Dependencies
------------
`stsci.tools`
`astropy.io.fits`
`astropy.wcs`
Parameters
----------
input: a python list of file names or a string (wild card characters allowed)
input files may be in fits, geis or waiver fits format
vacorr: boolean
If True, vecocity aberration correction will be applied
tddcorr: boolean
If True, time dependent distortion correction will be applied
npolcorr: boolean
If True, a Lookup table distortion will be applied
d2imcorr: boolean
If True, detector to image correction will be applied
checkfiles: boolean
If True, the format of the input files will be checked,
geis and waiver fits files will be converted to MEF format.
Default value is True for standalone mode.
"""
if verbose == False:
logger.setLevel(100)
else:
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
log_filename = 'stwcs.log'
fh = logging.FileHandler(log_filename, mode='w')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.setLevel(verbose)
args = "vacorr=%s, tddcorr=%s, npolcorr=%s, d2imcorr=%s, checkfiles=%s, \
" % (str(vacorr), str(tddcorr), str(npolcorr), str(d2imcorr),
str(checkfiles))
logger.info('\n\tStarting UPDATEWCS: %s', time.asctime())
files = parseinput.parseinput(input)[0]
logger.info("\n\tInput files: %s, " % [i for i in files])
logger.info("\n\tInput arguments: %s" % args)
if checkfiles:
files = checkFiles(files)
if not files:
print('No valid input, quitting ...\n')
return
for f in files:
acorr = apply_corrections.setCorrections(f, vacorr=vacorr, \
tddcorr=tddcorr,npolcorr=npolcorr, d2imcorr=d2imcorr)
if 'MakeWCS' in acorr and newIDCTAB(f):
logger.warning(
"\n\tNew IDCTAB file detected. All current WCSs will be deleted"
)
cleanWCS(f)
makecorr(f, acorr)
return files
def process_input(input, output=None, ivmlist=None, updatewcs=True, prodonly=False, shiftfile=None):
ivmlist = None
oldasndict = None
if (isinstance(input, list) == False) and \
('_asn' in input or '_asc' in input) :
# Input is an association table
# Get the input files, and run makewcs on them
oldasndict = asnutil.readASNTable(input, prodonly=prodonly)
if not output:
output = oldasndict['output']
filelist = [fileutil.buildRootname(fname) for fname in oldasndict['order']]
elif (isinstance(input, list) == False) and \
(input[0] == '@') :
# input is an @ file
f = open(input[1:])
# Read the first line in order to determine whether
# IVM files have been specified in a second column...
line = f.readline()
f.close()
# Parse the @-file with irafglob to extract the input filename
filelist = irafglob.irafglob(input, atfile=atfile_sci)
# If there is a second column...
if len(line.split()) == 2:
# ...parse out the names of the IVM files as well
ivmlist = irafglob.irafglob(input, atfile=atfile_ivm)
else:
#input is a string or a python list
try:
filelist, output = parseinput.parseinput(input, outputname=output)
#filelist.sort()
except IOError: raise
# sort the list of input files
# this ensures the list of input files has the same order on all platforms
# it can have ifferent order because listdir() uses inode order, not unix type order
filelist.sort()
newfilelist, ivmlist = checkFiles(filelist, ivmlist)
if not newfilelist:
buildEmptyDRZ(input,output)
return None, None, output
#make an asn table at the end
if updatewcs:
pydr_input = runmakewcs(newfilelist)
else:
pydr_input = newfilelist
# AsnTable will handle the case when output==None
if not oldasndict:
oldasndict = asnutil.ASNTable(pydr_input, output=output)
oldasndict.create()
if shiftfile:
oldasndict.update(shiftfile=shiftfile)
asndict = update_member_names(oldasndict, pydr_input)
# Build output filename
drz_extn = '_drz.fits'
for img in newfilelist:
# special case logic to automatically recognize when _flc.fits files
# are provided as input and produce a _drc.fits file instead
if '_flc.fits' in img:
drz_extn = '_drc.fits'
break
if output in [None,'']:
output = fileutil.buildNewRootname(asndict['output'],
extn=drz_extn)
else:
if '.fits' in output.lower():
pass
elif drz_extn[:4] not in output.lower():
output = fileutil.buildNewRootname(output, extn=drz_extn)
print('Setting up output name: ',output)
return asndict, ivmlist, output
def destripe_plus(inputfile, suffix='strp', stat='pmode1', maxiter=15,
sigrej=2.0, lower=None, upper=None, binwidth=0.3,
scimask1=None, scimask2=None,
dqbits=None, rpt_clean=0, atol=0.01,
cte_correct=True, clobber=False, verbose=True):
"""Calibrate post-SM4 ACS/WFC exposure(s) and use
standalone :ref:`acsdestripe`.
This takes a RAW image and generates a FLT file containing
its calibrated and destriped counterpart.
If CTE correction is performed, FLC will also be present.
Parameters
----------
inputfile : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*raw.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
suffix : str
The string to use to add to each input file name to
indicate an output product of ``acs_destripe``.
This only affects the intermediate output file that will
be automatically renamed to ``*blv_tmp.fits`` during the processing.
stat : { 'pmode1', 'pmode2', 'mean', 'mode', 'median', 'midpt' } (Default = 'pmode1')
Specifies the statistics to be used for computation of the
background in image rows:
* 'pmode1' - SEXTRACTOR-like mode estimate based on a
modified `Pearson's rule <http://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``2.5*median-1.5*mean``;
* 'pmode2' - mode estimate based on
`Pearson's rule <http://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``3*median-2*mean``;
* 'mean' - the mean of the distribution of the "good" pixels (after
clipping, masking, etc.);
* 'mode' - the mode of the distribution of the "good" pixels;
* 'median' - the median of the distribution of the "good" pixels;
* 'midpt' - estimate of the median of the distribution of the "good"
pixels based on an algorithm similar to IRAF's `imagestats` task
(``CDF(midpt)=1/2``).
.. note::
The midpoint and mode are computed in two passes through the
image. In the first pass the standard deviation of the pixels
is calculated and used with the *binwidth* parameter to compute
the resolution of the data histogram. The midpoint is estimated
by integrating the histogram and computing by interpolation
the data value at which exactly half the pixels are below that
data value and half are above it. The mode is computed by
locating the maximum of the data histogram and fitting the peak
by parabolic interpolation.
maxiter : int
This parameter controls the maximum number of iterations
to perform when computing the statistics used to compute the
row-by-row corrections.
sigrej : float
This parameters sets the sigma level for the rejection applied
during each iteration of statistics computations for the
row-by-row corrections.
lower : float, None (Default = None)
Lower limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
upper : float, None (Default = None)
Upper limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
binwidth : float (Default = 0.1)
Histogram's bin width, in sigma units, used to sample the
distribution of pixel brightness values in order to compute the
background statistics. This parameter is aplicable *only* to *stat*
parameter values of `'mode'` or `'midpt'`.
clobber : bool
Specify whether or not to 'clobber' (delete then replace)
previously generated products with the same names.
scimask1 : str or list of str
Mask images for *calibrated* ``SCI,1``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
scimask2 : str or list of str
Mask images for *calibrated* ``SCI,2``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
This is not used for subarrays.
dqbits : int, str, None (Default = None)
Integer sum of all the DQ bit values from the input image's DQ array
that should be considered "good" when building masks for de-striping
computations. For example, if pixels in the DQ array can be
combinations of 1, 2, 4, and 8 flags and one wants to consider
DQ "defects" having flags 2 and 4 as being acceptable for de-striping
computations, then `dqbits` should be set to 2+4=6. Then a DQ pixel
having values 2,4, or 6 will be considered a good pixel, while a DQ
pixel with a value, e.g., 1+2=3, 4+8=12, etc. will be flagged
as a "bad" pixel.
Alternatively, one can enter a comma- or '+'-separated list of
integer bit flags that should be added to obtain the final
"good" bits. For example, both ``4,8`` and ``4+8`` are equivalent to
setting `dqbits` to 12.
| Set `dqbits` to 0 to make *all* non-zero pixels in the DQ
mask to be considered "bad" pixels, and the corresponding image
pixels not to be used for de-striping computations.
| Default value (`None`) will turn off the use of image's DQ array
for de-striping computations.
| In order to reverse the meaning of the `dqbits`
parameter from indicating values of the "good" DQ flags
to indicating the "bad" DQ flags, prepend '~' to the string
value. For example, in order not to use pixels with
DQ flags 4 and 8 for sky computations and to consider
as "good" all other pixels (regardless of their DQ flag),
set `dqbits` to ``~4+8``, or ``~4,8``. To obtain the
same effect with an `int` input value (except for 0),
enter -(4+8+1)=-9. Following this convention,
a `dqbits` string value of ``'~0'`` would be equivalent to
setting ``dqbits=None``.
.. note::
DQ masks (if used), *will be* combined with user masks specified
in the `scimask1` and `scimask2` parameters (if any).
rpt_clean : int
An integer indicating how many *additional* times stripe cleaning
should be performed on the input image. Default = 0.
atol : float, None
The threshold for maximum absolute value of bias stripe correction
below which repeated cleanings can stop. When `atol` is `None`
cleaning will be repeated `rpt_clean` number of times.
Default = 0.01 [e].
cte_correct : bool
Perform CTE correction.
verbose : bool
Print informational messages. Default = True.
Raises
------
ImportError
``stsci.tools`` not found.
IOError
Input file does not exist.
ValueError
Invalid header values or CALACS version.
"""
# Optional package dependencies
from stsci.tools import parseinput
try:
from stsci.tools.bitmask import interpret_bit_flags
except ImportError:
from stsci.tools.bitmask import (
interpret_bits_value as interpret_bit_flags
)
# process input file(s) and if we have multiple input files - recursively
# call acs_destripe_plus for each input image:
flist = parseinput.parseinput(inputfile)[0]
if isinstance(scimask1, str):
mlist1 = parseinput.parseinput(scimask1)[0]
elif isinstance(scimask1, np.ndarray):
mlist1 = [ scimask1.copy() ]
elif scimask1 is None:
mlist1 = []
elif isinstance(scimask1, list):
mlist1 = []
for m in scimask1:
if isinstance(m, np.ndarray):
mlist1.append(m.copy())
elif isinstance(m, str):
mlist1 += parseinput.parseinput(m)[0]
else:
raise TypeError("'scimask1' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'scimask1' must be either a str, or a "
"numpy.ndarray, or a list of the two type of "
"values.")
if isinstance(scimask2, str):
mlist2 = parseinput.parseinput(scimask2)[0]
elif isinstance(scimask2, np.ndarray):
mlist2 = [ scimask2.copy() ]
elif scimask2 is None:
mlist2 = []
elif isinstance(scimask2, list):
mlist2 = []
for m in scimask2:
if isinstance(m, np.ndarray):
mlist2.append(m.copy())
elif isinstance(m, str):
mlist2 += parseinput.parseinput(m)[0]
else:
raise TypeError("'scimask2' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'scimask2' must be either a str, or a "
"numpy.ndarray, or a list of the two type of "
"values.")
n_input = len(flist)
n_mask1 = len(mlist1)
n_mask2 = len(mlist2)
if n_input == 0:
raise ValueError(
'No input file(s) provided or the file(s) do not exist')
if n_mask1 == 0:
mlist1 = [None] * n_input
elif n_mask1 != n_input:
raise ValueError('Insufficient masks for [SCI,1]')
if n_mask2 == 0:
mlist2 = [None] * n_input
elif n_mask2 != n_input:
raise ValueError('Insufficient masks for [SCI,2]')
if n_input > 1:
for img, mf1, mf2 in zip(flist, mlist1, mlist2):
destripe_plus(
inputfile=img, suffix=suffix, stat=stat,
lower=lower, upper=upper, binwidth=binwidth,
maxiter=maxiter, sigrej=sigrej,
scimask1=scimask1, scimask2=scimask2, dqbits=dqbits,
cte_correct=cte_correct, clobber=clobber, verbose=verbose
)
return
inputfile = flist[0]
scimask1 = mlist1[0]
scimask2 = mlist2[0]
# verify that the RAW image exists in cwd
cwddir = os.getcwd()
if not os.path.exists(os.path.join(cwddir, inputfile)):
raise IOError("{0} does not exist.".format(inputfile))
# get image's primary header:
header = fits.getheader(inputfile)
# verify masks defined (or not) simultaneously:
if header['CCDAMP'] == 'ABCD' and \
((scimask1 is not None and scimask2 is None) or \
(scimask1 is None and scimask2 is not None)):
raise ValueError("Both 'scimask1' and 'scimask2' must be specified "
"or not specified together.")
calacs_str = subprocess.check_output(['calacs.e', '--version']).split()[0]
calacs_ver = [int(x) for x in calacs_str.decode().split('.')]
if calacs_ver < [8, 3, 1]:
raise ValueError('CALACS {0} is incomptible. '
'Must be 8.3.1 or later.'.format(calacs_str))
# check date for post-SM4 and if 2K subarray or full frame
is_sub2K = False
ctecorr = header['PCTECORR']
aperture = header['APERTURE']
detector = header['DETECTOR']
date_obs = Time(header['DATE-OBS'])
# intermediate filenames
blvtmp_name = inputfile.replace('raw', 'blv_tmp')
blctmp_name = inputfile.replace('raw', 'blc_tmp')
# output filenames
tra_name = inputfile.replace('_raw.fits', '.tra')
flt_name = inputfile.replace('raw', 'flt')
flc_name = inputfile.replace('raw', 'flc')
if detector != 'WFC':
raise ValueError("{0} is not a WFC image, please check the 'DETECTOR'"
" keyword.".format(inputfile))
if date_obs < SM4_DATE:
raise ValueError(
"{0} is a pre-SM4 image.".format(inputfile))
if header['SUBARRAY'] and cte_correct:
if aperture in SUBARRAY_LIST:
is_sub2K = True
else:
LOG.warning('Using non-2K subarray, turning CTE correction off')
cte_correct = False
# delete files from previous CALACS runs
if clobber:
for tmpfilename in [blvtmp_name, blctmp_name, flt_name, flc_name,
tra_name]:
if os.path.exists(tmpfilename):
os.remove(tmpfilename)
# run ACSCCD on RAW subarray
acsccd.acsccd(inputfile)
# modify user mask with DQ masks if requested
dqbits = interpret_bit_flags(dqbits)
if dqbits is not None:
# save 'tra' file in memory to trick the log file
# not to save first acs2d log as this is done only
# for the purpose of obtaining DQ masks.
# WISH: it would have been nice is there was an easy way of obtaining
# just the DQ masks as if data were calibrated but without
# having to recalibrate them with acs2d.
if os.path.isfile(tra_name):
fh = open(tra_name)
tra_lines = fh.readlines()
fh.close()
else:
tra_lines = None
# apply flats, etc.
acs2d.acs2d(blvtmp_name, verbose=False, quiet=True)
# extract DQ arrays from the FLT image:
dq1, dq2 = _read_DQ_arrays(flt_name)
if isinstance(scimask1, str):
if scimask1.strip() is '':
mask1 = None
scimask1 = None
else:
mask1 = fits.getdata(scimask1)
elif isinstance(scimask1, np.ndarray):
mask1 = scimask1.copy()
elif scimask1 is None:
mask1 = None
else:
raise TypeError("'scimask1' must be either a str file name, "
"a numpy.ndarray, or None.")
scimask1 = acs_destripe._mergeUserMaskAndDQ(dq1, mask1, dqbits)
if isinstance(scimask2, str):
if scimask2.strip() is '':
mask2 = None
scimask2 = None
else:
mask2 = fits.getdata(scimask2)
elif isinstance(scimask2, np.ndarray):
mask2 = scimask2.copy()
elif scimask2 is None:
mask2 = None
else:
raise TypeError("'scimask2' must be either a str file name, "
"a numpy.ndarray, or None.")
if dq2 is not None:
scimask2 = acs_destripe._mergeUserMaskAndDQ(dq2, mask2, dqbits)
# reconstruct trailer file:
if tra_lines is not None:
fh = open(tra_name, mode='w')
fh.writelines(tra_lines)
fh.close()
# delete temporary FLT image:
if os.path.isfile(flt_name):
os.remove(flt_name)
# execute destriping of the subarray (post-SM4 data only)
acs_destripe.clean(
blvtmp_name, suffix, stat=stat, maxiter=maxiter, sigrej=sigrej,
lower=lower, upper=upper, binwidth=binwidth,
mask1=scimask1, mask2=scimask2, dqbits=dqbits,
rpt_clean=rpt_clean, atol=atol, clobber=clobber, verbose=verbose)
blvtmpsfx = 'blv_tmp_{0}'.format(suffix)
os.rename(inputfile.replace('raw', blvtmpsfx), blvtmp_name)
# update subarray header
if is_sub2K and cte_correct:
fits.setval(blvtmp_name, 'PCTECORR', value='PERFORM')
ctecorr = 'PERFORM'
# perform CTE correction on destriped image
if cte_correct:
if ctecorr == 'PERFORM':
acscte.acscte(blvtmp_name)
else:
LOG.warning(
"PCTECORR={0}, cannot run CTE correction".format(ctecorr))
cte_correct = False
# run ACS2D to get FLT and FLC images
acs2d.acs2d(blvtmp_name)
if cte_correct:
acs2d.acs2d(blctmp_name)
# delete intermediate files
os.remove(blvtmp_name)
if cte_correct and os.path.isfile(blctmp_name):
os.remove(blctmp_name)
info_str = 'Done.\nFLT: {0}\n'.format(flt_name)
if cte_correct:
info_str += 'FLC: {0}\n'.format(flc_name)
LOG.info(info_str)
def buildEmptyDRZ(input, output):
"""
METHOD : _buildEmptyDRZ
PURPOSE : Create an empty DRZ file in a valid FITS format so that the HST
pipeline can handle the Multidrizzle zero expossure time exception
where all data has been excluded from processing.
INPUT : None
OUTPUT : DRZ file on disk
"""
if output == None:
if len(input) == 1:
oname = fu.buildNewRootname(input[0])
else:
oname = 'final'
_drzextn = '_drz.fits'
if '_flc.fits' in input[0]:
_drzextn = '_drc.fits'
output = fileutil.buildNewRootname(oname,extn=_drzextn)
else:
if 'drz' not in output:
output = fileutil.buildNewRootname(output,extn='_drz.fits')
print('Setting up output name: ',output)
# Open the first image of the excludedFileList to use as a template to build
# the DRZ file.
inputfile = parseinput.parseinput(input)[0]
try :
img = pyfits.open(inputfile[0])
except:
raise IOError('Unable to open file %s \n' %inputfile)
# Create the fitsobject
fitsobj = pyfits.HDUList()
# Copy the primary header
hdu = img[0].copy()
fitsobj.append(hdu)
# Modify the 'NEXTEND' keyword of the primary header to 3 for the
#'sci, wht, and ctx' extensions of the newly created file.
fitsobj[0].header['NEXTEND'] = 3
# Create the 'SCI' extension
hdu = pyfits.ImageHDU(header=img['sci',1].header.copy(),data=None)
hdu.header['EXTNAME'] = 'SCI'
fitsobj.append(hdu)
# Create the 'WHT' extension
hdu = pyfits.ImageHDU(header=img['sci',1].header.copy(),data=None)
hdu.header['EXTNAME'] = 'WHT'
fitsobj.append(hdu)
# Create the 'CTX' extension
hdu = pyfits.ImageHDU(header=img['sci',1].header.copy(),data=None)
hdu.header['EXTNAME'] = 'CTX'
fitsobj.append(hdu)
# Add HISTORY comments explaining the creation of this file.
fitsobj[0].header.add_history("** Multidrizzle has created this empty DRZ **")
fitsobj[0].header.add_history("** product because all input images were **")
fitsobj[0].header.add_history("** excluded from processing because their **")
fitsobj[0].header.add_history("** header EXPTIME values were 0.0. If you **")
fitsobj[0].header.add_history("** still wish to use this data make the **")
fitsobj[0].header.add_history("** EXPTIME values in the header non-zero. **")
# Change the filename in the primary header to reflect the name of the output
# filename.
fitsobj[0].header['FILENAME'] = str(output) #+"_drz.fits"
# Change the ROOTNAME keyword to the ROOTNAME of the output PRODUCT
_drzsuffix = 'drz'
if 'drc' in output:
_drzsuffix = 'drc'
fitsobj[0].header['ROOTNAME'] = str(output.split('_%s.fits'%_drzsuffix)[0])
print('self.output', output)
# Modify the ASN_MTYP keyword to contain "PROD-DTH" so it can be properly
# ingested into the archive catalog.
fitsobj[0].header['ASN_MTYP'] = 'PROD-DTH'
errstr = "#############################################\n"
errstr += "# #\n"
errstr += "# ERROR: #\n"
errstr += "# Multidrizzle has created this empty DRZ #\n"
errstr += "# product because all input images were #\n"
errstr += "# excluded from processing because their #\n"
errstr += "# header EXPTIME values were 0.0. If you #\n"
errstr += "# still wish to use this data make the #\n"
errstr += "# EXPTIME values in the header non-zero. #\n"
errstr += "# #\n"
errstr += "#############################################\n\n"
print(errstr)
# If the file is already on disk delete it and replace it with the
# new file
dirfiles = os.listdir(os.curdir)
if (dirfiles.count(output) > 0):
os.remove(output)
print(" Replacing "+output+"...")
# Write out the empty DRZ file
fitsobj.writeto(output)
return
def processFilenames(input=None,output=None,infilesOnly=False):
"""Process the input string which contains the input file information and
return a filelist,output
"""
ivmlist = None
oldasndict = None
if input is None:
print("No input files provided to processInput")
raise ValueError
if not isinstance(input, list) and ('_asn' in input or '_asc' in input):
# Input is an association table
# Get the input files, and run makewcs on them
oldasndict = asnutil.readASNTable(input, prodonly=infilesOnly)
if not infilesOnly:
if output in ["",None,"None"]:
output = oldasndict['output'].lower() # insure output name is lower case
asnhdr = fits.getheader(input, memmap=False)
# Only perform duplication check if not already completed...
dupcheck = asnhdr.get('DUPCHECK',default="PERFORM") == "PERFORM"
#filelist = [fileutil.buildRootname(fname) for fname in oldasndict['order']]
filelist = buildASNList(oldasndict['order'],input,check_for_duplicates=dupcheck)
elif (not isinstance(input, list)) and \
(input[0] == '@') :
# input is an @ file
f = open(input[1:])
# Read the first line in order to determine whether
# IVM files have been specified in a second column...
line = f.readline()
f.close()
# Parse the @-file with irafglob to extract the input filename
filelist = irafglob.irafglob(input, atfile=util.atfile_sci)
# If there is a second column...
if len(line.split()) == 2:
# ...parse out the names of the IVM files as well
ivmlist = irafglob.irafglob(input, atfile=util.atfile_ivm)
if output in ['',None,"None"]:
if len(filelist) == 1:
output = fileutil.buildNewRootname(filelist[0])
else:
output = 'final'
else:
#input is a string or a python list
try:
filelist, output = parseinput.parseinput(input, outputname=output)
if output in ['',None,"None"]:
if len(filelist) == 1:
output = fileutil.buildNewRootname(filelist[0])
else:
output = 'final'
if not isinstance(input, list):
filelist.sort()
except IOError: raise
# sort the list of input files
# this ensures the list of input files has the same order on all platforms
# it can have ifferent order because listdir() uses inode order, not unix type order
#filelist.sort()
return filelist, output, ivmlist, oldasndict
def acssum(input, output, exec_path='', time_stamps=False, verbose=False,
quiet=False, exe_args=None):
r"""
Run the acssum.e executable as from the shell.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*flt.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
output : str
Output filename.
If `output` is '' and `input` is '\*_asn.fits',
`output` will be automatically set to '\*_sfl.fits'.
Otherwise, it is an error not to provide a specific `output`.
exec_path : str, optional
The complete path to ACSSUM executable.
If not given, run ACSSUM given by 'acssum.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acssum.e']
# Parse input to get list of filenames to process.
# acssum.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
call_list.append(output)
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list)
def buildEmptyDRZ(input, output):
"""
Create an empty DRZ file.
This module creates an empty DRZ file in a valid FITS format so that the HST
pipeline can handle the Multidrizzle zero expossure time exception
where all data has been excluded from processing.
Parameters
----------
input : str
filename of the initial input to process_input
output : str
filename of the default empty _drz.fits file to be generated
"""
# Identify the first input image
inputfile = parseinput.parseinput(input)[0]
if not inputfile:
print('\n******* ERROR *******', file=sys.stderr)
print(
'No input file found! Check specification of parameter '
'"input". ', file=sys.stderr)
print('Quitting...', file=sys.stderr)
print('******* ***** *******\n', file=sys.stderr)
return # raise IOError, "No input file found!"
# Set up output file here...
if output is None:
if len(input) == 1:
oname = fileutil.buildNewRootname(input[0])
else:
oname = 'final'
output = fileutil.buildNewRootname(oname, extn='_drz.fits')
else:
if 'drz' not in output:
output = fileutil.buildNewRootname(output, extn='_drz.fits')
log.info('Setting up output name: %s' % output)
# Open the first image (of the excludedFileList?) to use as a template to build
# the DRZ file.
try :
log.info('Building empty DRZ file from %s' % inputfile[0])
img = fits.open(inputfile[0], memmap=False)
except:
raise IOError('Unable to open file %s \n' % inputfile)
# Create the fitsobject
fitsobj = fits.HDUList()
# Copy the primary header
hdu = img[0].copy()
fitsobj.append(hdu)
# Modify the 'NEXTEND' keyword of the primary header to 3 for the
#'sci, wht, and ctx' extensions of the newly created file.
fitsobj[0].header['NEXTEND'] = 3
# Create the 'SCI' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'SCI'
fitsobj.append(hdu)
# Create the 'WHT' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'WHT'
fitsobj.append(hdu)
# Create the 'CTX' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'CTX'
fitsobj.append(hdu)
# Add HISTORY comments explaining the creation of this file.
fitsobj[0].header.add_history("** AstroDrizzle has created this empty "
"DRZ product because**")
fitsobj[0].header.add_history("** all input images were excluded from "
"processing.**")
# Change the filename in the primary header to reflect the name of the output
# filename.
fitsobj[0].header['FILENAME'] = str(output) # +"_drz.fits"
# Change the ROOTNAME keyword to the ROOTNAME of the output PRODUCT
fitsobj[0].header['ROOTNAME'] = str(output.split('_drz.fits')[0])
# Modify the ASN_MTYP keyword to contain "PROD-DTH" so it can be properly
# ingested into the archive catalog.
# stis has this keyword in the [1] header, so I am directing the code
#t o first look in the primary, then the 1
try:
fitsobj[0].header['ASN_MTYP'] = 'PROD-DTH'
except:
fitsobj[1].header['ASN_MTYP'] = 'PROD-DTH'
# If the file is already on disk delete it and replace it with the
# new file
dirfiles = os.listdir(os.curdir)
if dirfiles.count(output) > 0:
os.remove(output)
log.info(" Replacing %s..." % output)
# Write out the empty DRZ file
fitsobj.writeto(output)
print(textutil.textbox(
'ERROR:\nAstroDrizzle has created an empty DRZ product because all '
'input images were excluded from processing or a user requested the '
'program to stop.') + '\n', file=sys.stderr)
return
def tweakback(drzfile, input=None, origwcs = None,
newname = None, wcsname = None,
extname='SCI', force=False, verbose=False):
"""
Apply WCS solution recorded in drizzled file to distorted input images
(``_flt.fits`` files) used to create the drizzled file. This task relies on
the original WCS and updated WCS to be recorded in the drizzled image's
header as the last 2 alternate WCSs.
Parameters
----------
drzfile : str (Default = '')
filename of undistorted image which contains the new WCS
and WCS prior to being updated
newname : str (Default = None)
Value of ``WCSNAME`` to be used to label the updated solution in the
output (eq., ``_flt.fits``) files. If left blank or None, it will
default to using the current ``WCSNAME`` value from the input drzfile.
input : str (Default = '')
filenames of distorted images to be updated using new WCS
from 'drzfile'. These can be provided either as an ``@-file``,
a comma-separated list of filenames or using wildcards.
.. note:: A blank value will indicate that the task should derive the
filenames from the 'drzfile' itself, if possible. The filenames will be
derived from the ``D*DATA`` keywords written out by
``AstroDrizzle``. If they can not be found, the task will quit.
origwcs : str (Default = None)
Value of ``WCSNAME`` keyword prior to the drzfile image being updated
by ``TweakReg``. If left blank or None, it will default to using the
second to last ``WCSNAME*`` keyword value found in the header.
wcsname : str (Default = None)
Value of WCSNAME for updated solution written out by ``TweakReg`` as
specified by the `wcsname` parameter from ``TweakReg``. If this is
left blank or `None`, it will default to the current ``WCSNAME``
value from the input drzfile.
extname : str (Default = 'SCI')
Name of extension in `input` files to be updated with new WCS
force : bool (Default = False)
This parameters specified whether or not to force an update of the WCS
even though WCS already exists with this solution or `wcsname`?
verbose : bool (Default = False)
This parameter specifies whether or not to print out additional
messages during processing.
Notes
-----
The algorithm used by this function is based on linearization of
the exact compound operator that converts input image coordinates
to the coordinates (in the input image) that would result in
alignment with the new drizzled image WCS.
If no input distorted files are specified as input, this task will attempt
to generate the list of filenames from the drizzled input file's own
header.
EXAMPLES
--------
An image named ``acswfc_mos2_drz.fits`` was created from 4 images using
astrodrizzle. This drizzled image was then aligned to another image using
tweakreg and the header was updated using the ``WCSNAME`` = ``TWEAK_DRZ``.
The new WCS can then be used to update each of the 4 images that were
combined to make up this drizzled image using:
>>> from drizzlepac import tweakback
>>> tweakback.tweakback('acswfc_mos2_drz.fits')
If the same WCS should be applied to a specific set of images, those images
can be updated using:
>>> tweakback.tweakback('acswfc_mos2_drz.fits',
... input='img_mos2a_flt.fits,img_mos2e_flt.fits')
See Also
--------
stwcs.wcsutil.altwcs: Alternate WCS implementation
"""
print("TweakBack Version {:s}({:s}) started at: {:s}\n"
.format(__version__,__version_date__,util._ptime()[0]))
# Interpret input list/string into list of filename(s)
fltfiles = parseinput.parseinput(input)[0]
if fltfiles is None or len(fltfiles) == 0:
# try to extract the filenames from the drizzled file's header
fltfiles = extract_input_filenames(drzfile)
if fltfiles is None:
print('*'*60)
print('*')
print('* ERROR:')
print('* No input filenames found! ')
print('* Please specify "fltfiles" or insure that input drizzled')
print('* image contains D*DATA keywords. ')
print('*')
print('*'*60)
raise ValueError
if not isinstance(fltfiles,list):
fltfiles = [fltfiles]
sciext = determine_extnum(drzfile, extname='SCI')
scihdr = fits.getheader(drzfile, ext=sciext, memmap=False)
### Step 1: Read in updated and original WCS solutions
# determine keys for all alternate WCS solutions in drizzled image header
wkeys = wcsutil.altwcs.wcskeys(drzfile, ext=sciext)
wnames = wcsutil.altwcs.wcsnames(drzfile, ext=sciext)
if not util.is_blank(newname):
final_name = newname
else:
final_name = wnames[wkeys[-1]]
# Read in HSTWCS objects for final,updated WCS and previous WCS from
# from drizzled image header
# The final solution also serves as reference WCS when using updatehdr
if not util.is_blank(wcsname):
for k in wnames:
if wnames[k] == wcsname:
wcskey = k
break
else:
wcskey = wkeys[-1]
final_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=wkeys[-1])
if not util.is_blank(origwcs):
for k in wnames:
if wnames[k] == origwcs:
orig_wcskey = k
orig_wcsname = origwcs
break
else:
orig_wcsname,orig_wcskey = determine_orig_wcsname(scihdr,wnames,wkeys)
orig_wcs = wcsutil.HSTWCS(drzfile,ext=sciext,wcskey=orig_wcskey)
# read in RMS values reported for new solution
crderr1kw = 'CRDER1'+wkeys[-1]
crderr2kw = 'CRDER2'+wkeys[-1]
if crderr1kw in scihdr:
crderr1 = fits.getval(drzfile, crderr1kw, ext=sciext, memmap=False)
else:
crderr1 = 0.0
if crderr2kw in scihdr:
crderr2 = fits.getval(drzfile, crderr2kw, ext=sciext, memmap=False)
else:
crderr2 = 0.0
del scihdr
### Step 2: Apply solution to input file headers
for fname in fltfiles:
logstr = "....Updating header for {:s}...".format(fname)
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
# reset header WCS keywords to original (OPUS generated) values
imhdulist = fits.open(fname, mode='update', memmap=False)
extlist = get_ext_list(imhdulist, extname='SCI')
if not extlist:
extlist = [0]
# insure that input PRIMARY WCS has been archived before overwriting
# with new solution
wcsutil.altwcs.archiveWCS(imhdulist, extlist, reusekey=True)
# Process MEF images...
for ext in extlist:
logstr = "Processing {:s}[{:s}]".format(imhdulist.filename(),
ext2str(ext))
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
chip_wcs = wcsutil.HSTWCS(imhdulist, ext=ext)
update_chip_wcs(chip_wcs, orig_wcs, final_wcs,
xrms=crderr1, yrms = crderr2)
# Update FITS file with newly updated WCS for this chip
extnum = imhdulist.index(imhdulist[ext])
updatehdr.update_wcs(imhdulist, extnum, chip_wcs,
wcsname=final_name, reusename=False,
verbose=verbose)
imhdulist.close()
def acsccd(input, exec_path='', time_stamps=False, verbose=False, quiet=False):
"""
Run the acsccd.e executable as from the shell.
Expect input to be ``*_raw.fits``.
Output is automatically named ``*_blv_tmp.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_raw.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*raw.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCCD executable.
If not given, run ACSCCD given by 'acsccd.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acsccd.e']
# Parse input to get list of filenames to process.
# acsccd.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
#if dqicorr:
# call_list.append('-dqi')
#if atodcor:
# call_list.append('-atod')
#if blevcorr:
# call_list.append('-blev')
#if biascorr:
# call_list.append('-bias')
subprocess.call(call_list)
def acscteforwardmodel(input, exec_path='', time_stamps=False, verbose=False,
quiet=False, single_core=False, exe_args=None):
"""
Run the acscteforwardmodel.e executable as from the shell.
Expect input to be ``*_blc_tmp.fits`` or ``*_flc.fits``.
Output is automatically named ``*_ctefmod.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_blc_tmp.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*blc_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCTE forward model executable.
If not given, run ACSCTE given by 'acscteforwardmodel.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
single_core : bool, optional
CTE correction in the ACSCTE forward model will by default try to use
all available CPUs on your computer. Set this to True to force the use
of just one CPU.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acscteforwardmodel.e']
# Parse input to get list of filenames to process.
# acscte.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if single_core:
call_list.append('-1')
if exe_args:
call_list.extend(exe_args)
subprocess.check_call(call_list)
def tweakback(drzfile,
input=None,
origwcs=None,
newname=None,
wcsname=None,
extname='SCI',
force=False,
verbose=False):
"""
Apply WCS solution recorded in drizzled file to distorted input images
(``_flt.fits`` files) used to create the drizzled file. This task relies on
the original WCS and updated WCS to be recorded in the drizzled image's
header as the last 2 alternate WCSs.
Parameters
----------
drzfile : str (Default = '')
filename of undistorted image which contains the new WCS
and WCS prior to being updated
newname : str (Default = None)
Value of ``WCSNAME`` to be used to label the updated solution in the
output (eq., ``_flt.fits``) files. If left blank or None, it will
default to using the current ``WCSNAME`` value from the input drzfile.
input : str (Default = '')
filenames of distorted images to be updated using new WCS
from 'drzfile'. These can be provided either as an ``@-file``,
a comma-separated list of filenames or using wildcards.
.. note:: A blank value will indicate that the task should derive the
filenames from the 'drzfile' itself, if possible. The filenames will be
derived from the ``D*DATA`` keywords written out by
``AstroDrizzle``. If they can not be found, the task will quit.
origwcs : str (Default = None)
Value of ``WCSNAME`` keyword prior to the drzfile image being updated
by ``TweakReg``. If left blank or None, it will default to using the
second to last ``WCSNAME*`` keyword value found in the header.
wcsname : str (Default = None)
Value of WCSNAME for updated solution written out by ``TweakReg`` as
specified by the `wcsname` parameter from ``TweakReg``. If this is
left blank or `None`, it will default to the current ``WCSNAME``
value from the input drzfile.
extname : str (Default = 'SCI')
Name of extension in `input` files to be updated with new WCS
force : bool (Default = False)
This parameters specified whether or not to force an update of the WCS
even though WCS already exists with this solution or `wcsname`?
verbose : bool (Default = False)
This parameter specifies whether or not to print out additional
messages during processing.
Notes
-----
The algorithm used by this function is based on linearization of
the exact compound operator that converts input image coordinates
to the coordinates (in the input image) that would result in
alignment with the new drizzled image WCS.
If no input distorted files are specified as input, this task will attempt
to generate the list of filenames from the drizzled input file's own
header.
EXAMPLES
--------
An image named ``acswfc_mos2_drz.fits`` was created from 4 images using
astrodrizzle. This drizzled image was then aligned to another image using
tweakreg and the header was updated using the ``WCSNAME`` = ``TWEAK_DRZ``.
The new WCS can then be used to update each of the 4 images that were
combined to make up this drizzled image using:
>>> from drizzlepac import tweakback
>>> tweakback.tweakback('acswfc_mos2_drz.fits')
If the same WCS should be applied to a specific set of images, those images
can be updated using:
>>> tweakback.tweakback('acswfc_mos2_drz.fits',
... input='img_mos2a_flt.fits,img_mos2e_flt.fits')
See Also
--------
stwcs.wcsutil.altwcs: Alternate WCS implementation
"""
print("TweakBack Version {:s} started at: {:s}\n".format(
__version__,
util._ptime()[0]))
# Interpret input list/string into list of filename(s)
fltfiles = parseinput.parseinput(input)[0]
if fltfiles is None or len(fltfiles) == 0:
# try to extract the filenames from the drizzled file's header
fltfiles = extract_input_filenames(drzfile)
if fltfiles is None:
print('*' * 60)
print('*')
print('* ERROR:')
print('* No input filenames found! ')
print(
'* Please specify "fltfiles" or insure that input drizzled')
print('* image contains D*DATA keywords. ')
print('*')
print('*' * 60)
raise ValueError
if not isinstance(fltfiles, list):
fltfiles = [fltfiles]
sciext = determine_extnum(drzfile, extname='SCI')
scihdr = fits.getheader(drzfile, ext=sciext, memmap=False)
### Step 1: Read in updated and original WCS solutions
# determine keys for all alternate WCS solutions in drizzled image header
wkeys = wcsutil.altwcs.wcskeys(drzfile, ext=sciext)
if len(wkeys) < 2:
raise ValueError(
f"'{drzfile}' must contain at least two valid WCS: original and updated."
)
wnames = wcsutil.altwcs.wcsnames(drzfile, ext=sciext)
if not util.is_blank(newname):
final_name = newname
else:
final_name = wnames[wkeys[-1]]
# Read in HSTWCS objects for final,updated WCS and previous WCS from
# from drizzled image header
# The final solution also serves as reference WCS when using updatehdr
if not util.is_blank(wcsname):
for wkey, wname in wnames.items():
if wname == wcsname:
wcskey = wkey
break
else:
raise ValueError(
f"WCS with name '{wcsname}' not found in '{drzfile}'")
else:
wcskey = wkeys[-1]
final_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=wcskey)
if not util.is_blank(origwcs):
for wkey, wname in wnames.items():
if wname == origwcs:
orig_wcskey = wkey
break
else:
raise ValueError(
f"WCS with name '{origwcs}' not found in '{drzfile}'")
else:
_, orig_wcskey = determine_orig_wcsname(scihdr, wnames, wkeys)
orig_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=orig_wcskey)
# read in RMS values reported for new solution
crderr1kw = 'CRDER1' + wkeys[-1]
crderr2kw = 'CRDER2' + wkeys[-1]
if crderr1kw in scihdr:
crderr1 = fits.getval(drzfile, crderr1kw, ext=sciext, memmap=False)
else:
crderr1 = 0.0
if crderr2kw in scihdr:
crderr2 = fits.getval(drzfile, crderr2kw, ext=sciext, memmap=False)
else:
crderr2 = 0.0
del scihdr
### Step 2: Apply solution to input file headers
for fname in fltfiles:
logstr = "....Updating header for {:s}...".format(fname)
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
# reset header WCS keywords to original (OPUS generated) values
imhdulist = fits.open(fname, mode='update', memmap=False)
extlist = get_ext_list(imhdulist, extname='SCI')
if not extlist:
extlist = [0]
# Process MEF images...
for ext in extlist:
logstr = "Processing {:s}[{:s}]".format(imhdulist.filename(),
ext2str(ext))
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
chip_wcs = wcsutil.HSTWCS(imhdulist, ext=ext)
update_chip_wcs(chip_wcs,
orig_wcs,
final_wcs,
xrms=crderr1,
yrms=crderr2)
# Update FITS file with newly updated WCS for this chip
extnum = imhdulist.index(imhdulist[ext])
updatehdr.update_wcs(imhdulist,
extnum,
chip_wcs,
wcsname=final_name,
reusename=False,
verbose=verbose)
imhdulist.close()
def destripe_plus(inputfile,
suffix='strp',
stat='pmode1',
maxiter=15,
sigrej=2.0,
lower=None,
upper=None,
binwidth=0.3,
scimask1=None,
scimask2=None,
dqbits=None,
rpt_clean=0,
atol=0.01,
cte_correct=True,
clobber=False,
verbose=True):
r"""Calibrate post-SM4 ACS/WFC exposure(s) and use
standalone :ref:`acsdestripe`.
This takes a RAW image and generates a FLT file containing
its calibrated and destriped counterpart.
If CTE correction is performed, FLC will also be present.
Parameters
----------
inputfile : str or list of str
Input filenames in one of these formats:
* a Python list of filenames
* a partial filename with wildcards ('\*raw.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
suffix : str
The string to use to add to each input file name to
indicate an output product of ``acs_destripe``.
This only affects the intermediate output file that will
be automatically renamed to ``*blv_tmp.fits`` during the processing.
stat : { 'pmode1', 'pmode2', 'mean', 'mode', 'median', 'midpt' } (Default = 'pmode1')
Specifies the statistics to be used for computation of the
background in image rows:
* 'pmode1' - SEXTRACTOR-like mode estimate based on a
modified `Pearson's rule <https://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``2.5*median-1.5*mean``;
* 'pmode2' - mode estimate based on
`Pearson's rule <https://en.wikipedia.org/wiki/Nonparametric_skew#Pearson.27s_rule>`_:
``3*median-2*mean``;
* 'mean' - the mean of the distribution of the "good" pixels (after
clipping, masking, etc.);
* 'mode' - the mode of the distribution of the "good" pixels;
* 'median' - the median of the distribution of the "good" pixels;
* 'midpt' - estimate of the median of the distribution of the "good"
pixels based on an algorithm similar to IRAF's `imagestats` task
(``CDF(midpt)=1/2``).
.. note::
The midpoint and mode are computed in two passes through the
image. In the first pass the standard deviation of the pixels
is calculated and used with the *binwidth* parameter to compute
the resolution of the data histogram. The midpoint is estimated
by integrating the histogram and computing by interpolation
the data value at which exactly half the pixels are below that
data value and half are above it. The mode is computed by
locating the maximum of the data histogram and fitting the peak
by parabolic interpolation.
maxiter : int
This parameter controls the maximum number of iterations
to perform when computing the statistics used to compute the
row-by-row corrections.
sigrej : float
This parameters sets the sigma level for the rejection applied
during each iteration of statistics computations for the
row-by-row corrections.
lower : float, None (Default = None)
Lower limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
upper : float, None (Default = None)
Upper limit of usable pixel values for computing the background.
This value should be specified in the units of the input image(s).
binwidth : float (Default = 0.1)
Histogram's bin width, in sigma units, used to sample the
distribution of pixel brightness values in order to compute the
background statistics. This parameter is aplicable *only* to *stat*
parameter values of `'mode'` or `'midpt'`.
clobber : bool
Specify whether or not to 'clobber' (delete then replace)
previously generated products with the same names.
scimask1 : str or list of str
Mask images for *calibrated* ``SCI,1``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
scimask2 : str or list of str
Mask images for *calibrated* ``SCI,2``, one for each input file.
Pixels with zero values will be masked out, in addition to clipping.
This is not used for subarrays.
dqbits : int, str, None (Default = None)
Integer sum of all the DQ bit values from the input image's DQ array
that should be considered "good" when building masks for de-striping
computations. For example, if pixels in the DQ array can be
combinations of 1, 2, 4, and 8 flags and one wants to consider
DQ "defects" having flags 2 and 4 as being acceptable for de-striping
computations, then `dqbits` should be set to 2+4=6. Then a DQ pixel
having values 2,4, or 6 will be considered a good pixel, while a DQ
pixel with a value, e.g., 1+2=3, 4+8=12, etc. will be flagged
as a "bad" pixel.
Alternatively, one can enter a comma- or '+'-separated list of
integer bit flags that should be added to obtain the final
"good" bits. For example, both ``4,8`` and ``4+8`` are equivalent to
setting `dqbits` to 12.
| Set `dqbits` to 0 to make *all* non-zero pixels in the DQ
mask to be considered "bad" pixels, and the corresponding image
pixels not to be used for de-striping computations.
| Default value (`None`) will turn off the use of image's DQ array
for de-striping computations.
| In order to reverse the meaning of the `dqbits`
parameter from indicating values of the "good" DQ flags
to indicating the "bad" DQ flags, prepend '~' to the string
value. For example, in order not to use pixels with
DQ flags 4 and 8 for sky computations and to consider
as "good" all other pixels (regardless of their DQ flag),
set `dqbits` to ``~4+8``, or ``~4,8``. To obtain the
same effect with an `int` input value (except for 0),
enter -(4+8+1)=-9. Following this convention,
a `dqbits` string value of ``'~0'`` would be equivalent to
setting ``dqbits=None``.
.. note::
DQ masks (if used), *will be* combined with user masks specified
in the `scimask1` and `scimask2` parameters (if any).
rpt_clean : int
An integer indicating how many *additional* times stripe cleaning
should be performed on the input image. Default = 0.
atol : float, None
The threshold for maximum absolute value of bias stripe correction
below which repeated cleanings can stop. When `atol` is `None`
cleaning will be repeated `rpt_clean` number of times.
Default = 0.01 [e].
cte_correct : bool
Perform CTE correction.
verbose : bool
Print informational messages. Default = True.
Raises
------
ImportError
``stsci.tools`` not found.
IOError
Input file does not exist.
ValueError
Invalid header values or CALACS version.
"""
# Optional package dependencies
from stsci.tools import parseinput
try:
from stsci.tools.bitmask import interpret_bit_flags
except ImportError:
from stsci.tools.bitmask import (interpret_bits_value as
interpret_bit_flags)
# process input file(s) and if we have multiple input files - recursively
# call acs_destripe_plus for each input image:
flist = parseinput.parseinput(inputfile)[0]
if isinstance(scimask1, str):
mlist1 = parseinput.parseinput(scimask1)[0]
elif isinstance(scimask1, np.ndarray):
mlist1 = [scimask1.copy()]
elif scimask1 is None:
mlist1 = []
elif isinstance(scimask1, list):
mlist1 = []
for m in scimask1:
if isinstance(m, np.ndarray):
mlist1.append(m.copy())
elif isinstance(m, str):
mlist1 += parseinput.parseinput(m)[0]
else:
raise TypeError("'scimask1' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'scimask1' must be either a str, or a "
"numpy.ndarray, or a list of the two type of "
"values.")
if isinstance(scimask2, str):
mlist2 = parseinput.parseinput(scimask2)[0]
elif isinstance(scimask2, np.ndarray):
mlist2 = [scimask2.copy()]
elif scimask2 is None:
mlist2 = []
elif isinstance(scimask2, list):
mlist2 = []
for m in scimask2:
if isinstance(m, np.ndarray):
mlist2.append(m.copy())
elif isinstance(m, str):
mlist2 += parseinput.parseinput(m)[0]
else:
raise TypeError("'scimask2' must be a list of str or "
"numpy.ndarray values.")
else:
raise TypeError("'scimask2' must be either a str, or a "
"numpy.ndarray, or a list of the two type of "
"values.")
n_input = len(flist)
n_mask1 = len(mlist1)
n_mask2 = len(mlist2)
if n_input == 0:
raise ValueError(
'No input file(s) provided or the file(s) do not exist')
if n_mask1 == 0:
mlist1 = [None] * n_input
elif n_mask1 != n_input:
raise ValueError('Insufficient masks for [SCI,1]')
if n_mask2 == 0:
mlist2 = [None] * n_input
elif n_mask2 != n_input:
raise ValueError('Insufficient masks for [SCI,2]')
if n_input > 1:
for img, mf1, mf2 in zip(flist, mlist1, mlist2):
destripe_plus(inputfile=img,
suffix=suffix,
stat=stat,
lower=lower,
upper=upper,
binwidth=binwidth,
maxiter=maxiter,
sigrej=sigrej,
scimask1=scimask1,
scimask2=scimask2,
dqbits=dqbits,
cte_correct=cte_correct,
clobber=clobber,
verbose=verbose)
return
inputfile = flist[0]
scimask1 = mlist1[0]
scimask2 = mlist2[0]
# verify that the RAW image exists in cwd
cwddir = os.getcwd()
if not os.path.exists(os.path.join(cwddir, inputfile)):
raise IOError(f"{inputfile} does not exist.")
# get image's primary header:
header = fits.getheader(inputfile)
# verify masks defined (or not) simultaneously:
if (header['CCDAMP'] == 'ABCD'
and ((scimask1 is not None and scimask2 is None) or
(scimask1 is None and scimask2 is not None))):
raise ValueError("Both 'scimask1' and 'scimask2' must be specified "
"or not specified together.")
calacs_str = subprocess.check_output(['calacs.e', '--version'
]).split()[0] # nosec # noqa
calacs_ver = [int(x) for x in calacs_str.decode().split('.')]
if calacs_ver < [8, 3, 1]:
raise ValueError(f'CALACS {calacs_str} is incomptible. '
'Must be 8.3.1 or later.')
# check date for post-SM4 and if supported subarray or full frame
is_subarray = False
ctecorr = header['PCTECORR']
aperture = header['APERTURE']
detector = header['DETECTOR']
date_obs = Time(header['DATE-OBS'])
# intermediate filenames
blvtmp_name = inputfile.replace('raw', 'blv_tmp')
blctmp_name = inputfile.replace('raw', 'blc_tmp')
# output filenames
tra_name = inputfile.replace('_raw.fits', '.tra')
flt_name = inputfile.replace('raw', 'flt')
flc_name = inputfile.replace('raw', 'flc')
if detector != 'WFC':
raise ValueError(f"{inputfile} is not a WFC image, please check the "
"'DETECTOR' keyword.")
if date_obs < SM4_DATE:
raise ValueError(f"{inputfile} is a pre-SM4 image.")
if header['SUBARRAY'] and cte_correct:
if aperture in SUBARRAY_LIST:
is_subarray = True
else:
LOG.warning('Using non-supported subarray, '
'turning CTE correction off')
cte_correct = False
# delete files from previous CALACS runs
if clobber:
for tmpfilename in [
blvtmp_name, blctmp_name, flt_name, flc_name, tra_name
]:
if os.path.exists(tmpfilename):
os.remove(tmpfilename)
# run ACSCCD on RAW
acsccd.acsccd(inputfile)
# modify user mask with DQ masks if requested
dqbits = interpret_bit_flags(dqbits)
if dqbits is not None:
# save 'tra' file in memory to trick the log file
# not to save first acs2d log as this is done only
# for the purpose of obtaining DQ masks.
# WISH: it would have been nice is there was an easy way of obtaining
# just the DQ masks as if data were calibrated but without
# having to recalibrate them with acs2d.
if os.path.isfile(tra_name):
with open(tra_name) as fh:
tra_lines = fh.readlines()
else:
tra_lines = None
# apply flats, etc.
acs2d.acs2d(blvtmp_name, verbose=False, quiet=True)
# extract DQ arrays from the FLT image:
dq1, dq2 = _read_DQ_arrays(flt_name)
mask1 = _get_mask(scimask1, 1)
scimask1 = acs_destripe._mergeUserMaskAndDQ(dq1, mask1, dqbits)
mask2 = _get_mask(scimask2, 2)
if dq2 is not None:
scimask2 = acs_destripe._mergeUserMaskAndDQ(dq2, mask2, dqbits)
elif mask2 is None:
scimask2 = None
# reconstruct trailer file:
if tra_lines is not None:
with open(tra_name, mode='w') as fh:
fh.writelines(tra_lines)
# delete temporary FLT image:
if os.path.isfile(flt_name):
os.remove(flt_name)
# execute destriping (post-SM4 data only)
acs_destripe.clean(blvtmp_name,
suffix,
stat=stat,
maxiter=maxiter,
sigrej=sigrej,
lower=lower,
upper=upper,
binwidth=binwidth,
mask1=scimask1,
mask2=scimask2,
dqbits=dqbits,
rpt_clean=rpt_clean,
atol=atol,
clobber=clobber,
verbose=verbose)
blvtmpsfx = f'blv_tmp_{suffix}'
os.rename(inputfile.replace('raw', blvtmpsfx), blvtmp_name)
# update subarray header
if is_subarray and cte_correct:
fits.setval(blvtmp_name, 'PCTECORR', value='PERFORM')
ctecorr = 'PERFORM'
# perform CTE correction on destriped image
if cte_correct:
if ctecorr == 'PERFORM':
acscte.acscte(blvtmp_name)
else:
LOG.warning(f"PCTECORR={ctecorr}, cannot run CTE correction")
cte_correct = False
# run ACS2D to get FLT and FLC images
acs2d.acs2d(blvtmp_name)
if cte_correct:
acs2d.acs2d(blctmp_name)
# delete intermediate files
os.remove(blvtmp_name)
if cte_correct and os.path.isfile(blctmp_name):
os.remove(blctmp_name)
info_str = f'Done.\nFLT: {flt_name}\n'
if cte_correct:
info_str += f'FLC: {flc_name}\n'
LOG.info(info_str)
def acsccd(input, exec_path='', time_stamps=False, verbose=False, quiet=False,
exe_args=None):
r"""
Run the acsccd.e executable as from the shell.
Expect input to be ``*_raw.fits``.
Output is automatically named ``*_blv_tmp.fits``.
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_raw.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*raw.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACSCCD executable.
If not given, run ACSCCD given by 'acsccd.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
exe_args : list, optional
Arbitrary arguments passed to underlying executable call.
Note: Implementation uses subprocess.call and whitespace is not
permitted. E.g. use exe_args=['--nThreads', '1']
"""
from stsci.tools import parseinput # Optional package dependency
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acsccd.e']
# Parse input to get list of filenames to process.
# acsccd.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
if exe_args:
call_list.extend(exe_args)
#if dqicorr:
# call_list.append('-dqi')
#if atodcor:
# call_list.append('-atod')
#if blevcorr:
# call_list.append('-blev')
#if biascorr:
# call_list.append('-bias')
subprocess.check_call(call_list)
def buildEmptyDRZ(input, output):
"""
Create an empty DRZ file.
This module creates an empty DRZ file in a valid FITS format so that the HST
pipeline can handle the Multidrizzle zero expossure time exception
where all data has been excluded from processing.
Parameters
----------
input : str
filename of the initial input to process_input
output : str
filename of the default empty _drz.fits file to be generated
"""
# Identify the first input image
inputfile = parseinput.parseinput(input)[0]
if not inputfile:
print('\n******* ERROR *******', file=sys.stderr)
print(
'No input file found! Check specification of parameter '
'"input". ',
file=sys.stderr)
print('Quitting...', file=sys.stderr)
print('******* ***** *******\n', file=sys.stderr)
return # raise IOError, "No input file found!"
# Set up output file here...
if output is None:
if len(input) == 1:
oname = fileutil.buildNewRootname(input[0])
else:
oname = 'final'
output = fileutil.buildNewRootname(oname, extn='_drz.fits')
else:
if '_drz' not in output:
output = fileutil.buildNewRootname(output, extn='_drz.fits')
print('Building emtpy DRZ file with output name: %s' % output)
# Open the first image (of the excludedFileList?) to use as a template to build
# the DRZ file.
try:
log.info('Building empty DRZ file from %s' % inputfile[0])
img = fits.open(inputfile[0], memmap=False)
except:
raise IOError('Unable to open file %s \n' % inputfile)
# Create the fitsobject
fitsobj = fits.HDUList()
# Copy the primary header
hdu = img[0].copy()
fitsobj.append(hdu)
# Modify the 'NEXTEND' keyword of the primary header to 3 for the
#'sci, wht, and ctx' extensions of the newly created file.
fitsobj[0].header['NEXTEND'] = 3
# Create the 'SCI' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'SCI'
fitsobj.append(hdu)
# Create the 'WHT' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'WHT'
fitsobj.append(hdu)
# Create the 'CTX' extension
hdu = fits.ImageHDU(header=img['sci', 1].header.copy())
hdu.header['EXTNAME'] = 'CTX'
fitsobj.append(hdu)
# Add HISTORY comments explaining the creation of this file.
fitsobj[0].header.add_history("** AstroDrizzle has created this empty "
"DRZ product because**")
fitsobj[0].header.add_history("** all input images were excluded from "
"processing.**")
# Change the filename in the primary header to reflect the name of the output
# filename.
fitsobj[0].header['FILENAME'] = str(output) # +"_drz.fits"
# Change the ROOTNAME keyword to the ROOTNAME of the output PRODUCT
fitsobj[0].header['ROOTNAME'] = str(output.split('_drz.fits')[0])
# Modify the ASN_MTYP keyword to contain "PROD-DTH" so it can be properly
# ingested into the archive catalog.
# stis has this keyword in the [1] header, so I am directing the code
#t o first look in the primary, then the 1
try:
fitsobj[0].header['ASN_MTYP'] = 'PROD-DTH'
except:
fitsobj[1].header['ASN_MTYP'] = 'PROD-DTH'
# If the file is already on disk delete it and replace it with the
# new file
dirfiles = os.listdir(os.curdir)
if dirfiles.count(output) > 0:
os.remove(output)
log.info(" Replacing %s..." % output)
# Write out the empty DRZ file
fitsobj.writeto(output)
print(textutil.textbox(
'ERROR:\nAstroDrizzle has created an empty DRZ product because all '
'input images were excluded from processing or a user requested the '
'program to stop.') + '\n',
file=sys.stderr)
return
def blendheaders(drzfile,
inputs=None,
output=None,
sciext='SCI',
errext='ERR',
dqext='DQ',
verbose=False):
""" Blend headers that went into creating the original drzfile into a
new header with table that contains keyword values from all input images.
The drzfile will be used to determine the names of the input files, should
no filenames be provided in the 'inputs' parameter.
The drzfile will be updated 'in-place' with the new headers and table if
no output filename has been provided.
Parameters
----------
drzfile : str
Name of drizzled image(s) with headers that need updating. This can
be specified as a single filename, or using wildcards, or '@'-file or
python list of filenames.
When no value for 'inputs' has been provided, this file(or set of files)
will be used to determine the names of the input (flt.fits) files
whose headers need to be blended to create the new drzfile header.
inputs : list, optional
If provided, the filenames with extensions for each chip
provided in this list will be used to get
the headers which will be blended into the final output headers.
For example, ['j9cd01kqq_flt.fits[sci,1]','j9cd01kqq_flt.fits[sci,2]']
would create a blended header based on these chips headers.
output : str, optional
If specified, a new file will be written out that contains the updated
(blended) headers.
sciext: str, optional [Default: 'SCI']
EXTNAME of extensions with science data from the input FITS files. The
header of this extension will be used as the basis for the SCI header
of the drizzled product FITS file.
errext: str, optional [Default: 'ERR']
EXTNAME of extensions with the error array from the input FITS files. The
header of this extension will be used as the basis for the WHT header
of the drizzled product FITS file. If blank or "INDEF", it will use
the 'SCI' header as the basis for the output header for the WHT array.
dqext: str, optional [Default: 'DQ']
EXTNAME of extensions with the data quality array from the input FITS
files. The header of this extension will be used as the basis for the
CTX header of the drizzled product FITS file (when a CTX extension gets
created). If blank or "INDEF", it will use the 'SCI' header as the
basis for the header of any generated CTX array.
verbose : bool, optional [Default: False]
Print out additional messages during processing when specified.
"""
# interpret input
drzfiles = parseinput.parseinput(drzfile)[0]
# operate on each drzfile specified
for drzfile in drzfiles:
if inputs in [None, '', ' ', 'INDEF', 'None']:
inputs = extract_filenames_from_drz(drzfile)
if verbose:
print('Creating blended headers from: ')
for i in inputs:
print(' ', i)
newhdrs, newtab = get_blended_headers(inputs, verbose=verbose)
# Remove distortion related keywords not included in rules
for hdr in newhdrs:
remove_distortion_keywords(hdr)
# open drizzle product to update headers with new headers
open_mode = 'update'
if output not in [None, '', ' ', 'INDEF', 'None']:
open_mode = 'readonly'
drzimg = fits.open(drzfile, mode=open_mode)
# Determine whether we are working with a simple DRZ FITS file or
# a full multi-extension DRZ FITS file.
if len(drzimg) < 3:
# We are working with a simple FITS image, so concatenate the
# blended PRIMARY and SCI headers
drzimg[0].header = cat_headers(newhdrs[0], newhdrs[1])
drzimg.append(newtab)
else:
# We are working with a full MEF file, so update all extension headers
for i, extn in enumerate(drzimg):
if isinstance(extn, fits.BinTableHDU):
break
# Update new headers with correct array sizes
if isinstance(extn, fits.ImageHDU):
extn_naxis = extn.header['NAXIS']
newhdrs[i]['NAXIS'] = extn_naxis
newhdrs[i]['BITPIX'] = extn.header['BITPIX']
for card in newhdrs[i]['naxis*']:
if len(card.key) > 5: # naxisj keywords
if extn_naxis > 0:
newhdrs[i][card.keyword] = extn.header[
card.keyword]
else:
try:
del newhdrs[i][card.keyword]
except KeyError:
pass
newhdrs[i].set('EXTNAME',
value=extn.header['EXTNAME'],
after='ORIGIN')
newhdrs[i].set('EXTVER',
value=extn.header['EXTVER'],
after='EXTNAME')
for kw in WCS_KEYWORDS:
if kw in extn.header:
newhdrs[i][kw] = extn.header[kw]
if isinstance(extn, fits.PrimaryHDU):
for card in extn.header['exp*']:
newhdrs[i][card.keyword] = card.value
newhdrs[i]['NEXTEND'] = len(drzimg) - 1
newhdrs[i]['ROOTNAME'] = extn.header['rootname']
newhdrs[i]['BITPIX'] = extn.header['bitpix']
# Determine which keywords are included in the table but not
# the new dict(header). These will be removed from the output
# header altogether
tabcols = newtab.data.dtype.names
hdrkws = list(newhdrs[i].keys())
del_kws = list(set(tabcols) - set(hdrkws))
del_kws.append('HISTORY')
for kw in extn.header:
if kw not in newhdrs[i] and kw not in del_kws:
newhdrs[i][kw] = extn.header[kw]
extn.header = newhdrs[i]
# Now append table with remaining header keyword values
drzimg.append(newtab)
if 'nextend' in drzimg[0].header:
drzimg[0].header['nextend'] = len(drzimg) - 1
# Write out the updated product
if open_mode == 'update':
drzimg.close()
print('Updated ', drzfile, ' with blended headers.')
else:
if os.path.exists(output): os.remove(output)
drzimg.writeto(output)
drzimg.close()
print('Created new file ', output, ' with blended headers.')
# Clean up for the next run
del drzimg, newhdrs, newtab
def processFilenames(input=None, output=None, infilesOnly=False):
"""Process the input string which contains the input file information and
return a filelist,output
"""
ivmlist = None
oldasndict = None
if input is None:
print("No input files provided to processInput")
raise ValueError
if not isinstance(input, list) and ('_asn' in input or '_asc' in input):
# Input is an association table
# Get the input files, and run makewcs on them
oldasndict = asnutil.readASNTable(input, prodonly=infilesOnly)
if not infilesOnly:
if output in ["", None, "None"]:
output = oldasndict['output'].lower(
) # insure output name is lower case
asnhdr = fits.getheader(input, memmap=False)
# Only perform duplication check if not already completed...
dupcheck = asnhdr.get('DUPCHECK', default="PERFORM") == "PERFORM"
#filelist = [fileutil.buildRootname(fname) for fname in oldasndict['order']]
filelist = buildASNList(oldasndict['order'],
input,
check_for_duplicates=dupcheck)
elif (not isinstance(input, list)) and \
(input[0] == '@') :
# input is an @ file
f = open(input[1:])
# Read the first line in order to determine whether
# IVM files have been specified in a second column...
line = f.readline()
f.close()
# Parse the @-file with irafglob to extract the input filename
filelist = irafglob.irafglob(input, atfile=util.atfile_sci)
# If there is a second column...
if len(line.split()) == 2:
# ...parse out the names of the IVM files as well
ivmlist = irafglob.irafglob(input, atfile=util.atfile_ivm)
if output in ['', None, "None"]:
if len(filelist) == 1:
output = fileutil.buildNewRootname(filelist[0])
else:
output = 'final'
else:
#input is a string or a python list
try:
filelist, output = parseinput.parseinput(input, outputname=output)
if output in ['', None, "None"]:
if len(filelist) == 1:
output = fileutil.buildNewRootname(filelist[0])
else:
output = 'final'
if not isinstance(input, list):
filelist.sort()
except IOError:
raise
# sort the list of input files
# this ensures the list of input files has the same order on all platforms
# it can have ifferent order because listdir() uses inode order, not unix type order
#filelist.sort()
return filelist, output, ivmlist, oldasndict
def create_astrometric_catalog(inputs, **pars):
"""Create an astrometric catalog that covers the inputs' field-of-view.
Parameters
===========
input : str
Filenames of images to be aligned to astrometric catalog
catalog : str, optional
Name of catalog to extract astrometric positions for sources in the
input images' field-of-view. Default: GAIADR2. Options available are
documented on the catalog web page.
output : str, optional
Filename to give to the astrometric catalog read in from the master
catalog web service. If 'None', no file will be written out.
Default: ref_cat.ecsv
gaia_only : bool, optional
Specify whether or not to only use sources from GAIA in output catalog
Default: False
existing_wcs : HST.wcs object
existing WCS object specified by the user
note ::
This function will point to astrometric catalog web service defined
through the use of the ASTROMETRIC_CATALOG_URL environment variable.
Returns
=======
ref_table : object
Astropy Table object of the catalog
"""
# interpret input parameters
catalog = pars.get("catalog", 'GAIADR2')
output = pars.get("output", 'ref_cat.ecsv')
gaia_only = pars.get("gaia_only", False)
table_format = pars.get("table_format", 'ascii.ecsv')
existing_wcs = pars.get("existing_wcs", None)
inputs, _ = parseinput.parseinput(inputs)
# start by creating a composite field-of-view for all inputs
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
if existing_wcs:
outwcs = existing_wcs
else:
outwcs = build_reference_wcs(inputs)
radius = compute_radius(outwcs)
ra, dec = outwcs.wcs.crval
# perform query for this field-of-view
ref_dict = get_catalog(ra, dec, sr=radius, catalog=catalog)
colnames = ('ra','dec', 'mag', 'objID', 'GaiaID')
col_types = ('f8', 'f8', 'f4', 'U25', 'U25')
ref_table = Table(names = colnames, dtype=col_types)
# Add catalog name as meta data
ref_table.meta['catalog']=catalog
ref_table.meta['gaia_only'] = gaia_only
# rename coordinate columns to be consistent with tweakwcs
ref_table.rename_column('ra', 'RA')
ref_table.rename_column('dec', 'DEC')
# extract just the columns we want...
num_sources = 0
for source in ref_dict:
if 'GAIAsourceID' in source:
g = source['GAIAsourceID']
if gaia_only and g.strip() is '':
continue
else:
g = -1 # indicator for no source ID extracted
r = float(source['ra'])
d = float(source['dec'])
m = -999.9 # float(source['mag'])
o = source['objID']
num_sources += 1
ref_table.add_row((r,d,m,o,g))
# Write out table to a file, if specified
if output:
ref_table.write(output, format=table_format)
log.info("Created catalog '{}' with {} sources".format(output, num_sources))
return ref_table
def reset_dq_bits(input,bits,extver=None,extname='dq'):
""" This function resets bits in the integer array(s) of a FITS file.
Parameters
----------
filename : str
full filename with path
bits : str
sum or list of integers corresponding to all the bits to be reset
extver : int, optional
List of version numbers of the DQ arrays
to be corrected [Default Value: None, will do all]
extname : str, optional
EXTNAME of the DQ arrays in the FITS file
[Default Value: 'dq']
Notes
-----
The default value of None for the 'extver' parameter specifies that all
extensions with EXTNAME matching 'dq' (as specified by the 'extname'
parameter) will have their bits reset.
Examples
--------
1. The following command will reset the 4096 bits in all
the DQ arrays of the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", 4096)
2. To reset the 2,32,64 and 4096 bits in the second DQ array,
specified as 'dq,2', in the file input_file_flt.fits::
reset_dq_bits("input_file_flt.fits", "2,32,64,4096", extver=2)
"""
# Interpret bits value
bits = interpret_bit_flags(bits)
flist, fcol = parseinput.parseinput(input)
for filename in flist:
# open input file in write mode to allow updating the DQ array in-place
p = fits.open(filename, mode='update', memmap=False)
# Identify the DQ array to be updated
# If no extver is specified, build a list of all DQ arrays in the file
if extver is None:
extver = []
for hdu in p:
# find only those extensions which match the input extname
# using case-insensitive name comparisons for 'extname'
if 'extver' in hdu.header and \
hdu.header['extname'].lower() == extname.lower():
extver.append(int(hdu.header['extver']))
else:
# Otherwise, insure that input extver values are a list
if not isinstance(extver, list): extver = [extver]
# for each DQ array identified in the file...
for extn in extver:
dqarr = p[extname,extn].data
dqdtype = dqarr.dtype
# reset the desired bits
p[extname,extn].data = (dqarr & ~bits).astype(dqdtype) # preserve original dtype
log.info('Reset bit values of %s to a value of 0 in %s[%s,%s]' %
(bits, filename, extname, extn))
# close the file with the updated DQ array(s)
p.close()
def update(input,refdir="jref$",local=None,interactive=False,wcsupdate=True):
"""
Updates headers of files given as input to point to the new reference files
NPOLFILE and D2IMFILE required with the new C version of MultiDrizzle.
Parameters
-----------
input : string or list
Name of input file or files acceptable forms:
- single filename with or without directory
- @-file
- association table
- python list of filenames
- wildcard specification of filenames
refdir : string
Path to directory containing new reference files, either
environment variable or full path.
local : boolean
Specifies whether or not to copy new reference files to local
directory for use with the input files.
interactive : boolean
Specifies whether or not to interactively ask the user for the
exact names of the new reference files instead of automatically
searching a directory for them.
updatewcs : boolean
Specifies whether or not to update the WCS information in this
file to use the new reference files.
Examples
--------
1. A set of associated images specified by an ASN file can be updated to use
the NPOLFILEs and D2IMFILE found in the local directory defined using
the `myjref$` environment variable under PyRAF using::
>>>import updatenpol
>>>updatenpol.update('j8bt06010_asn.fits', 'myref$')
2. Another use under Python would be to feed it a specific list of files
to be updated using::
>>> updatenpol.update(['file1_flt.fits','file2_flt.fits'],'myjref$')
3. Files in another directory can also be processed using::
>>> updatenpol.update('data$*flt.fits','../new/ref/')
Notes
-----
.. warning::
This program requires access to the `jref$` directory in order
to evaluate the DGEOFILE specified in the input image header.
This evaluation allows the program to get the information it
needs to identify the correct NPOLFILE.
The use of this program now requires that a directory be set up with
all the new NPOLFILE and D2IMFILE reference files for ACS (a single
directory for all files for all ACS detectors will be fine, much like
jref). Currently, all the files generated by the ACS team has initially
been made available at::
/grp/hst/acs/lucas/new-npl/
The one known limitation to how this program works comes from
confusion if more than 1 file could possibly be used as the new
reference file. This would only happen when NPOLFILE reference files
have been checked into CDBS multiple times, and there are several
versions that apply to the same detector/filter combination. However,
that can be sorted out later if we get into that situation at all.
"""
print('UPDATENPOL Version',__version__+'('+__vdate__+')')
# expand (as needed) the list of input files
files,fcol = parseinput.parseinput(input)
if not interactive:
# expand reference directory name (if necessary) to
# interpret IRAF or environment variable names
rdir = fu.osfn(refdir)
ngeofiles,ngcol = parseinput.parseinput(os.path.join(rdir,'*npl.fits'))
# Find D2IMFILE in refdir for updating input file header as well
d2ifiles,d2col = parseinput.parseinput(os.path.join(rdir,"*d2i.fits"))
# Now, build a matched list of input files and DGEOFILE reference files
# to use for selecting the appropriate new reference file from the
# refdir directory.
for f in files:
print('Updating: ',f)
fdir = os.path.split(f)[0]
# Open each file...
fimg = fits.open(f, mode='update')
phdr = fimg['PRIMARY'].header
fdet = phdr['detector']
# get header of DGEOFILE
dfile = phdr.get('DGEOFILE','')
if dfile in ['N/A','',' ',None]:
npolname = ''
else:
dhdr = fits.getheader(fu.osfn(dfile))
if not interactive:
# search all new NPOLFILEs for one that matches current DGEOFILE config
npol = find_npolfile(ngeofiles,fdet,[phdr['filter1'],phdr['filter2']])
else:
if sys.version_info[0] >= 3:
npol = input("Enter name of NPOLFILE for %s:"%f)
else:
npol = raw_input("Enter name of NPOLFILE for %s:"%f)
if npol == "": npol = None
if npol is None:
errstr = "No valid NPOLFILE found in "+rdir+" for detector="+fdet+"\n"
errstr += " filters = "+phdr['filter1']+","+phdr['filter2']
raise ValueError(errstr)
npolname = os.path.split(npol)[1]
if local:
npolname = os.path.join(fdir,npolname)
# clobber any previous copies of this reference file
if os.path.exists(npolname): os.remove(npolname)
shutil.copy(npol,npolname)
else:
if '$' in refdir:
npolname = refdir+npolname
else:
npolname = os.path.join(refdir,npolname)
phdr.set('NPOLFILE', value=npolname,
comment="Non-polynomial corrections in Paper IV LUT",
after='DGEOFILE')
# Now find correct D2IFILE
if not interactive:
d2i = find_d2ifile(d2ifiles,fdet)
else:
if sys.version_info[0] >= 3:
d2i = input("Enter name of D2IMFILE for %s:"%f)
else:
d2i = raw_input("Enter name of D2IMFILE for %s:"%f)
if d2i == "": d2i = None
if d2i is None:
print('=============\nWARNING:')
print(" No valid D2IMFILE found in "+rdir+" for detector ="+fdet)
print(" D2IMFILE correction will not be applied.")
print('=============\n')
d2iname = ""
else:
d2iname = os.path.split(d2i)[1]
if local:
# Copy D2IMFILE to local data directory alongside input file as well
d2iname = os.path.join(fdir,d2iname)
# clobber any previous copies of this reference file
if os.path.exists(d2iname): os.remove(d2iname)
shutil.copy(d2i,d2iname)
else:
if '$' in refdir:
d2iname = refdir+d2iname
else:
d2iname = os.path.join(refdir,d2iname)
phdr.set('D2IMFILE', value=d2iname,
comment="Column correction table",
after='DGEOFILE')
# Close this input file header and go on to the next
fimg.close()
if wcsupdate:
updatewcs.updatewcs(f)
def acs2d(input, exec_path='', time_stamps=False, verbose=False, quiet=False):
"""
Run the acs2d.e executable as from the shell.
Output is automatically named based on input suffix:
+--------------------+----------------+------------------------------+
| INPUT | OUTPUT | EXPECTED DATA |
+====================+================+==============================+
| ``*_raw.fits`` | ``*_flt.fits`` | SBC image. |
+--------------------+----------------+------------------------------+
| ``*_blv_tmp.fits`` | ``*_flt.fits`` | ACSCCD output. |
+--------------------+----------------+------------------------------+
| ``*_blc_tmp.fits`` | ``*_flc.fits`` | ACSCCD output with PCTECORR. |
+--------------------+----------------+------------------------------+
| ``*_crj_tmp.fits`` | ``*_crj.fits`` | ACSREJ output. |
+--------------------+----------------+------------------------------+
| ``*_crc_tmp.fits`` | ``*_crc.fits`` | ACSREJ output with PCTECORR. |
+--------------------+----------------+------------------------------+
Parameters
----------
input : str or list of str
Input filenames in one of these formats:
* a single filename ('j1234567q_blv_tmp.fits')
* a Python list of filenames
* a partial filename with wildcards ('\*blv_tmp.fits')
* filename of an ASN table ('j12345670_asn.fits')
* an at-file (``@input``)
exec_path : str, optional
The complete path to ACS2D executable.
If not given, run ACS2D given by 'acs2d.e'.
time_stamps : bool, optional
Set to True to turn on the printing of time stamps.
verbose : bool, optional
Set to True for verbose output.
quiet : bool, optional
Set to True for quiet output.
"""
if exec_path:
if not os.path.exists(exec_path):
raise OSError('Executable not found: ' + exec_path)
call_list = [exec_path]
else:
call_list = ['acs2d.e']
# Parse input to get list of filenames to process.
# acs2d.e only takes 'file1,file2,...'
infiles, dummy_out = parseinput.parseinput(input)
call_list.append(','.join(infiles))
if time_stamps:
call_list.append('-t')
if verbose:
call_list.append('-v')
if quiet:
call_list.append('-q')
subprocess.call(call_list)