def _transfer_coos(self):
"""Transfer coordinates from the IOL to the GOL"""
# compose the WCS-term for the direct and grism images
dir_term = getDATA("{0:s} [{1:d}]".format(
self.dirname, self.dirname_extinfo['fits_ext']))
gri_term = getDATA("{0:s} [{1:d}]".format(
self.grisim, self.grism_extinfo['fits_ext']))
# generate the WCS objects
dir_wcs = wcsutil.WCSObject(dir_term)
gri_wcs = wcsutil.WCSObject(gri_term)
# go over each row in the catalog
for row in self.gol:
# make a position tuple
try:
xy_direct = (row['X_IMAGE'], row['Y_IMAGE'])
except KeyError:
# self._treat_NULL_table
raise aXeError("No coordinate columns in catalog, empty?")
# convert to RADEC using the direct image
radec_pos = dir_wcs.xy2rd(xy_direct)
# convert to XY on grism image
xy_grism = gri_wcs.rd2xy(radec_pos)
# store projected vals in the GOL
row['X_IMAGE'] = float(xy_grism[0])
row['Y_IMAGE'] = float(xy_grism[1])
def _check_global_backsub(self):
"""Check for global background subtraction"""
# go over all inputs
for one_input in self.axe_inputs:
# load the config file and get the extension information
conf = configfile.ConfigFile(
config_util.getCONF(one_input['config']))
ext_info = config_util.get_ext_info(
config_util.getDATA(one_input['grisim']), conf)
# open the fits image
gri_fits = fits.open(config_util.getDATA(one_input['grisim']),
'readonly')
# go to the correct header
act_header = gri_fits[ext_info['fits_ext']].header
# make sure a sky background value is set
if 'SKY_CPS' in act_header and act_header['SKY_CPS'] >= 0.0:
# close the fits
gri_fits.close()
else:
# close fits, complain and out
gri_fits.close()
err_msg = (
"{0:s}: The grism image: \n{1:s}\nhas no keyword "
"SKY_CPS>=0.0 in the extension {2:d}. This means "
"it had NO global\nsky subtraction, which is "
"required for the CRR version of aXedrizzle!".format(
self.taskname,
config_util.getDATA(one_input['grisim']),
ext_info['fits_ext']))
raise aXeError(err_msg)
def prepare_extraction(self):
"""Prepares the aXe extraction
The module does some preparatory stuff before the extraction
can start. This includes copying the simulated dispersed image
to AXE_IMAGE_PATH and subtracting the background on this copy.
"""
# give brief feedback
_log.info('Dummy extraction on the dispersed image:')
sys.stdout.flush()
# get a random filenames
tmpfile1 = config_util.get_random_filename('t', '.fits')
# copy the grism image to AXE_IMAGE_PATH
shutil.copy(config_util.getOUTSIM(self.simul_grisim),
config_util.getDATA(tmpfile1))
# subtract the background from
# the grism image
# expression = "(a - b)"
# iraf.imexpr(expr=expression, output=tmpfile2,
# a=config_util.getDATA(tmpfile1)+'[SCI]', b=self.bck_flux, Stdout=1)
in_image = fits.open(config_util.getDATA(tmpfile1))
in_image['sci'].data -= self.bck_flux
in_image.close()
# store the name of the background
# subtracted grism image - this was tmpfile
self.dispersed_image = tmpfile1
def _check_files(self):
"""Checks the existence of the input files"""
# check the direct image
if not os.path.isfile(config_util.getDATA(self.direct_image)):
err_msg = ("\nThe direct image is not available: {0:s}"
.format(config_util.getDATA(self.direct_image)))
raise aXeSIMError(err_msg)
# check the configuration file
if not os.path.isfile(config_util.getCONF(self.configfile)):
err_msg = ("\nThe configuration file is not available: {0:s}"
.format(config_util.getCONF(self.configfile)))
raise aXeSIMError(err_msg)
# check the simulated grism image
if not os.path.isfile(config_util.getOUTSIM(self.simul_grisim)):
err_msg = ("\nThe grism image is not available: {0:s}"
.format(config_util.getOUTSIM(self.simul_grisim)))
raise aXeSIMError(err_msg)
# check the IOL
if not os.path.isfile(self.iolname):
err_msg = ("\nThe Input Object List is not available: {0:s}"
.format(self.iolname))
raise aXeSIMError(err_msg)
try:
float(self.bck_flux)
except ValueError:
# check the background image
if not os.path.isfile(config_util.getCONF(self.bck_flux)):
err_msg = ("\nThe background imagage is not available: {0:s}"
.format(config_util.getCONF(self.bck_flux)))
raise aXeSIMError(err_msg)
def _make_objPET(self):
"""Generate the object PET."""
# set the use_direct flag
# NOTE: the flag is only usefull
# for the C-version, the python
# version does not need it!
use_direct = False
if self.dirim is not None:
use_direct = True
# run SEX2GOL
axetasks.sex2gol(grism=self.grisim,
config=config_util.getCONF(self.config),
in_sex=config_util.getDATA(self.objcat),
use_direct=use_direct,
direct=config_util.getDATA(self.dirim),
dir_hdu=None,
spec_hdu=None,
out_sex=None)
# run GOL2AF
axetasks.gol2af(grism=self.grisim,
config=self.config,
mfwhm=self.params['extrfwhm'],
back=False,
orient=self.params['orient'],
slitless_geom=self.params['slitless_geom'],
exclude=self.params['exclude'],
lambda_mark=self.params['lambda_mark'],
dmag=self.dmag,
out_af=None,
in_gol=None)
# run AF2PET
axetasks.af2pet(grism=self.grisim,
config=self.config,
back=False,
out_pet=None)
# run PETCONT
axetasks.petcont(grism=self.grisim,
config=self.config,
cont_model=self.params['cont_model'],
model_scale=self.params['model_scale'],
spec_models=None,
object_models=None,
inter_type=self.params['inter_type'],
lambda_psf=self.params['lambda_psf'],
cont_map=True,
in_af="")
# run PETFF
axetasks.petff(grism=self.grisim,
config=self.config,
back=False,
ffname=None)
def mopup(self):
"""Deleting tmp-files, copying SPC's, STP's"""
# get the root name of the dispersed image
root_name = self.dispersed_image.split('.fits')[0]
# get the root name of the simulated image
result_root = self.simul_grisim.split('.fits')[0]
# move and rename the SPC-file
out_spc = config_util.getOUTPUT(root_name + '_2.SPC.fits')
result_spc = config_util.getOUTSIM(result_root + '_2.SPC.fits')
shutil.move(out_spc, result_spc)
# move and rename the STP-file
out_stp = config_util.getOUTPUT(root_name + '_2.STP.fits')
result_stp = config_util.getOUTSIM(result_root + '_2.STP.fits')
shutil.move(out_stp, result_stp)
# delete the background subtracted
# grism image
os.unlink(config_util.getDATA(self.dispersed_image))
# delete the GOL, the OAF and the PET
result_cat = config_util.getOUTPUT(root_name + '_2.cat')
if os.path.isfile(result_cat):
os.unlink(result_cat)
result_oaf = config_util.getOUTPUT(root_name + '_2.OAF')
if os.path.isfile(result_oaf):
os.unlink(result_oaf)
result_pet = config_util.getOUTPUT(root_name + '_2.PET.fits')
if os.path.isfile(result_pet):
os.unlink(result_pet)
def _get_grism_ext_info(self, grisim='', config='', spec_hdu=None):
"""Determine the extension information on the grism image.
Parameters
----------
grisim : str
The name of the grism images
config : str
The name of the configuration file
spec_hdu : int, None
The extention number of the spectra data
Returns
-------
ext_info : dict
A dictionary that contains the header extension
and the data extension
"""
if spec_hdu is None:
conf = configfile.ConfigFile(getCONF(config))
ext_info = get_ext_info(getDATA(grisim), conf)
else:
# make by hand the extension information
ext_info = {'axe_ext': spec_hdu, 'fits_ext': spec_hdu - 1}
# return the extension info
return ext_info
def _check_dpps(self, back=False):
# go over all inputs
for one_input in self.axe_inputs:
# load the config file and get the extension information
conf = configfile.ConfigFile(
config_util.getCONF(one_input['config']))
ext_info = config_util.get_ext_info(
config_util.getDATA(one_input['grisim']), conf)
# derive the aXe names
axe_names = config_util.get_axe_names(one_input['grisim'],
ext_info)
# check the DPP file
if not os.path.isfile(config_util.getOUTPUT(axe_names['DPP'])):
# error and out
err_msg = ("{0s:}: The DPP file: {1:s} does not exist!".format(
self.taskname, config_util.getOUTPUT(axe_names['DPP'])))
raise aXeError(err_msg)
# check for the background DPP file
if back and not os.path.isfile(
config_util.getOUTPUT(axe_names['BCK_DPP'])):
# error and out
err_msg = ("{0:s}: The background DPP file: {1:s} does not "
"exist!".format(
self.taskname,
config_util.getOUTPUT(axe_names['BCK_DPP'])))
raise aXeError(err_msg)
def _is_prism_data(self):
# define the default
is_prism = False
# make sure there are grism images
for row in self.axe_inputs:
one_grisim = config_util.getDATA(row['grisim'])
# open the fits
one_fits = fits.open(one_grisim, 'readonly')
# read the keyword 'FILTER1'
try:
filter1 = one_fits[0].header['FILTER1']
except KeyError:
filter1 = None
# read the keyword 'FILTER2'
try:
filter2 = one_fits[0].header['FILTER2']
except KeyError:
filter2 = None
# check whether it is prism data
if ((filter1 and filter1.find('PR'))
or (filter2 and filter2.find('PR'))):
# switch to IS_PRISM
is_prism = True
# close the fits
one_fits.close()
# return the index
return is_prism
def _get_dpp_list(self, inima, confterm, back):
"""Determine the name of all DPP files"""
DPP_list = []
# generate the input list for aXe
axe_inputs = axeinputs.aXeInput(inima, confterm)
# go over the list of all inputs
for an_input in axe_inputs:
# load the configuration file
conf = configfile.ConfigFile(config_util.getCONF(an_input['config']))
# get the image extensions
ext_info = config_util.get_ext_info(config_util.getDATA(an_input['grisim']), conf)
# get the name of all axe files
axe_names = config_util.get_axe_names(an_input['grisim'], ext_info)
# if requested,
# append the background DPP file
if back:
DPP_list.append(axe_names['BCK_DPP'])
else:
# append the 'normal' DPP name to the list
DPP_list.append(axe_names['DPP'])
# return the DPP list
return DPP_list
def run(self):
# load the configuration files;
# get the extension info
conf = configfile.ConfigFile(config_util.getCONF(self.config))
ext_info = config_util.get_ext_info(config_util.getDATA(self.grisim),
conf)
del conf
# Does this harm data that was astrodrizzled?
if (('drzfwhm' in self.params) and (self.params['drzfwhm'])
or (('cont_model' in self.params) and
(config_util.is_quant_contam(self.params['cont_model'])))):
# generate the non-linear distortions from the IDCTAB;
# and store them in the fits-file header
_log.info(
"Generating and storing nonlinear distortions in {0}".format(
config_util.getDATA(self.grisim)))
nlins = nlincoeffs.NonLinCoeffs(config_util.getDATA(self.grisim),
ext_info)
nlins.make()
nlins.store_coeffs()
del nlins
# make the object PET's
self._make_objPET()
# make a background PET if necessary
if 'back' in self.params and self.params['back']:
_log.info("\nMaking backpet\n")
self._make_bckPET()
# extract the spectra
if 'spectr' in self.params and self.params['spectr']:
_log.info("\nMaking spectra\n")
self._make_spectra()
# make the proper non-quantitative contamination
if ('drzfwhm' in self.params and self.params['drzfwhm']) and \
('cont_model' in self.params and not config_util.is_quant_contam(self.params['cont_model'])):
_log.info("\nmaking non quant contam\n")
self._make_drzgeocont(ext_info)
def _check_subarray(self):
""" check for and reject subarray images """
# go over all rows in the list
for row in self._inimlist:
# check the existence of the Input Image List
image = config_utils.getDATA(row['grisim'])
subarray = fits.getval(image, "SUBARRAY", ext=0)
if subarray:
err_msg = ("Grism image: {0:s} is a subarray"
" which is not supported".format(image))
raise aXeError(err_msg)
if 'dirim' in self._inimlist.colnames:
image = config_utils.getDATA(row['dirim'])
subarray = fits.getval(image, "SUBARRAY", ext=0)
if subarray:
err_msg = ("Direct image: {0:s} is a subarray"
" which is not supported".format(image))
raise aXeError(err_msg)
def _validate_columns(self):
"""Identify columns according to the Input Image List format"""
columns = self._inimlist.colnames
# check for number of columns
if len(columns) < 2:
err_msg = ("Expected at least 2 columns in {0:s}!".format(
self._inimlist))
raise aXeError(err_msg)
columns.reverse()
name = columns.pop()
if ((np.issubsctype(self._inimlist[name], np.str))
and ('fits' in self._inimlist[name][0])):
self._inimlist.rename_column(name, 'grisim')
else:
err_msg = ("Column 1 should be the names of the grism"
" fits files: {0}".format(self._inimlist))
raise aXeError(err_msg)
# check whether second column has type string
name = columns.pop()
if np.issubsctype(self._inimlist[name], np.str):
self._inimlist.rename_column(name, 'objcat')
else:
err_msg = ("Column 2 should be the names of the object catalogs"
"{0}".format(self._inimlist))
raise aXeError(err_msg)
# do a check on the first object catalog to make sure it's a format
# that we can read
try:
__ = Table.read(config_utils.getDATA(self._inimlist[0]['objcat']),
format='ascii.sextractor')
except IORegistryError:
raise aXeError(
"Catalog format not recognized , checked for: {0:s}".format(
self._inimlist[name][0]))
# go over all remaining rows to find DMAG and DIRIM
while columns:
name = columns.pop()
# assume if it's a number it's DMAG
if np.issubsctype(self._inimlist[name], np.float):
self._inimlist.rename_column(name, 'dmag')
# assume if it's a string its the direct image
elif np.issubsctype(self._inimlist.columns[0], np.str):
if '.fits' in self._inimlist.columns[0][0]:
self._inimlist.rename_column(name, 'dirim')
else:
err_msg = ("Problem identifying last columns in: {0}".format(
self._inimlist))
raise aXeError(err_msg)
def _check_fluxcubes(self):
# go over all inputs
for one_input in self.axe_inputs:
# load the config file and get the extension information
conf = configfile.ConfigFile(
config_util.getCONF(one_input['config']))
ext_info = config_util.get_ext_info(
config_util.getDATA(one_input['grisim']), conf)
# derive the aXe names
axe_names = config_util.get_axe_names(one_input['grisim'],
ext_info)
# check the fluxcube
if not os.path.isfile(config_util.getDATA(axe_names['FLX'])):
# error and out
err_msg = (
"{0:s}: The fluxcube file: {1:s} does not exist!".format(
self.taskname, config_util.getDATA(axe_names['FLX'])))
raise aXeError(err_msg)
def _force_dirim(self):
# go over all inputs
for one_input in self.axe_inputs:
# check whether there is a direct image
if one_input['DIRIM'] is None:
# error and out
err_msg = ("{0:s}: The grism image: {1:s} does NOT have an "
"associated direct image!".format(
self.taskname,
config_util.getDATA(one_input['grisim'])))
raise aXeError(err_msg)
def __init__(self,
grisim="",
objcat="",
dirim="",
config="",
dmag=None,
**params):
if grisim:
self.grisim = config_util.getDATA(grisim)
else:
raise ValueError("No grisim image specified for axeprep")
self.objcat = config_util.getDATA(objcat)
_log.info("\n**Using object catalog: {0}\n".format(self.objcat))
self.dirim = config_util.getDATA(dirim)
self.config = config
self.dmag = dmag
self.params = params
# store the master background
if 'master_bck' in params:
self.master_bck = config_util.getCONF(params['master_bck'])
def _get_dirname_information(self,
dirname=None,
config="",
grisim="",
grism_extinfo=None,
dir_hdu=None):
"""Determine the direct image information.
Parameters
----------
dirname : str
Diretory name
config : str
The name of the config file
grisim : str
The name of the grisim image
grism_extinfo : dict
Dictionary of header information
dir_hdu : fits.HDU
FITS header data unit
Returns
-------
A tuple of the direct image name and a dictionary of
extension information
"""
# check whether ANY direct image information exists
if ((dirname is None) and (dir_hdu is None)):
# set the grism image as direct image
dirname = grisim
dirname_extinfo = grism_extinfo
elif ((dirname is not None) and (dir_hdu is None)):
# load the configuration file;
# determine the extension information
conf = configfile.ConfigFile(getCONF(config))
dirname_extinfo = get_ext_info(getDATA(grisim), conf)
del conf
elif ((dirname is not None) and (dir_hdu is not None)):
# make by hand the extension information
dirname_extinfo = {'axe_ext': dir_hdu, 'fits_ext': dir_hdu - 1}
else:
# error and out
err_msg = ("Specifying NO direct image but a direct image HDU: "
"{0:d} makrs NO sense!".format(dir_hdu))
raise aXeError(err_msg)
# return the name and the extension info
return dirname, dirname_extinfo
def check_simdirim_input(self, incat, config, tpass_direct, model_spectra,
model_images, nx, ny, exptime, bck_flux):
"""Does basic checks on the parameters
The method checks whether all input values are reasonable, e.g.
the exposure time and background flux >= 0.0 and similar.
Input files are checked for existence. Also the input type is
checked for the numbers.
Parameters
----------
incat: str
name of model object table
config: str
aXe configuration file name
tpass_direct: str
total passband file
model_spectra: str
name of model spectra
model_images: str
name of model images
nx: int
number of pixels in x
ny: int
number of pixels in y
exptime: float
exposure time
bck_flux: float
flux in background
"""
# do the setup
config_util.axe_setup(axesim=True)
# check the existence of the
# model object table
if not os.path.isfile(config_util.getDATA(incat)):
error_message = (
"The Model Object Table does not exist: {0}".format(
config_util.getDATA(incat)))
raise aXeSIMError(error_message)
# check the existence of the
# axe configuration file
if not os.path.isfile(config_util.getCONF(config)):
error_message = (
"The aXe configuration file does not exist: {0}".format(
config_util.getCONF(config)))
raise aXeSIMError(error_message)
else:
# load the aXe configuration file
conf = configfile.ConfigFile(config_util.getCONF(config))
# make the internal checks
n_sens = conf.check_files(check_glob=False)
# make sure there is
# at least one sens. file
if n_sens < 1:
error_message = ("There must be at least one sensitivity "
"file in: {0}".format(
config_util.getCONF(config)))
raise aXeSIMError(error_message)
# check the existence of the
# total passband file
if not os.path.isfile(config_util.getSIMDATA(tpass_direct)):
error_message = (
"The total passband file does not exist: {0}".format(
config_util.getSIMDATA(tpass_direct)))
raise aXeSIMError(error_message)
if model_spectra is not None:
# check the existence of the
# model spectra file
if not os.path.isfile(config_util.getDATA(model_spectra)):
error_message = (
"The model spectra file does not exist: {0}".format(
config_util.getDATA(config)))
raise aXeSIMError(error_message)
if model_images is not None:
# check the existence of the
# model images file
if not os.path.isfile(config_util.getDATA(model_images)):
error_message = (
"The model images file does not exist: {0}".format(
config_util.getDATA(config)))
raise aXeSIMError(error_message)
# check the nx-value
if ((nx is not None) and (nx <= 0.0)):
error_message = ("Value for 'nx' or 'nx_dir' must be positive: "
"{0:s}".format(str(nx)))
raise aXeSIMError(error_message)
# check the ny-value
if ((ny is not None) and (ny <= 0)):
error_message = ("Value for 'ny' or 'ny_dir' must be positive: "
"{0:s}".format(str(ny)))
raise aXeSIMError(error_message)
# check the exptime-value
if ((exptime is not None) and (exptime < 0)):
error_message = ("Value for 'exptime' or 'exptime_dir' must be "
"positive: {0:s}".format(str(exptime)))
raise aXeSIMError(error_message)
if bck_flux is not None:
# check the bck_flux-value
try:
# convert to float
bck = float(bck_flux)
# check for positive value
if bck < 0:
error_message = ("Value for 'bck_flux' or 'bck_flux_dir'"
" must be positive: {0:s}".format(
str(bck_flux)))
raise aXeSIMError(error_message)
# catch a string
except ValueError:
# check for existence of file
if not os.path.isfile(config_util.getCONF(bck_flux)):
error_message = (
"The background file does not exist: {0}".format(
config_util.getCONF(bck_flux)))
raise aXeSIMError(error_message)
def check_simdispim_input(self, incat, config, lambda_psf, model_spectra,
model_images, nx, ny, exptime, bck_flux,
extraction, extrfwhm, orient, slitless_geom,
adj_sens):
"""Does basic checks on the parameters
The method checks whether all input values are reasonable, e.g.
the exposure time and background flux >= 0.0 and similar.
Input files are checked for existence. Also the input type is
checked for the numbers.
Parameters
----------
incat: str
name of model object table
config: str
aXe configuration file name
lambda_psf: float
wavelength the object shapes were determined at
model_spectra: str
name of model spectra
model_images: str
name of model images
nx: int
number of pixels in x
ny: int
number of pixels in y
exptime: float
exposure time
bck_flux: float
flux in background
extraction: bool
flag for default extraction
extrfwhm: float
multiplier for extraction width
orient: bool
flag for tilted extraction
slitless_geom: bool
flag for slitless optimized extraction
adj_sens: bool
flag for adjusted flux conversion
"""
# do the setup
config_util.axe_setup(axesim=True)
# check the existence of the
# model object table
if not os.path.isfile(config_util.getDATA(incat)):
msg = ("The Model Object Table does not exist: {}".format(
config_util.getDATA(incat)))
raise aXeSIMError(msg)
# check the existence of the
# axe configuration file
if not os.path.isfile(config_util.getCONF(config)):
msg = ("The aXe configuration file does not exist: {}".format(
config_util.getCONF(config)))
raise aXeSIMError(msg)
else:
# load the aXe configuration file
conf = configfile.ConfigFile(config_util.getCONF(config))
# make the internal checks
n_sens = conf.check_files(check_glob=False)
# make sure there is
# at least one sens. file
if n_sens < 1:
msg = ("There must be at least one sensitivity file in: {}".
format(config_util.getCONF(config)))
raise aXeSIMError(msg)
# check whether the configuration files
# allows the requested extraction
if extraction and (slitless_geom or adj_sens):
extr_ready = conf.confirm_extrkeys()
# error and out
if not extr_ready:
msg = ("It is not possible to perform the requested"
"extraction. The likely cause is that the configuration"
"file does NOT contain the keywords 'POBJSIZE' or "
"'SMFACTOR' or their values are NOT reasonable "
"(e.g. <0.0)!")
raise aXeSIMError(msg)
# check the lambda_psf-value
if ((lambda_psf is not None) and (lambda_psf <= 0.0)):
msg = ("Value for 'lambda_psf' must be positive: {0:s}".format(
str(lambda_psf)))
raise aXeSIMError(msg)
if (model_spectra is not None):
# check the existence of the
# model spectra file
if not os.path.isfile(config_util.getDATA(model_spectra)):
msg = ("The model spectra file does not exist: {}".format(
config_util.getDATA(model_spectra)))
raise aXeSIMError(msg)
if model_images is not None:
# check the existence of the
# model images file
if not os.path.isfile(config_util.getDATA(model_images)):
msg = ("The model images file does not exist: ".format(
config_util.getDATA(model_images)))
raise aXeSIMError(msg)
# check the nx-value
if ((nx is not None) and (nx <= 0.0)):
msg = ("Value for 'nx' or 'nx_disp' must be positive: {0:g}".
format(nx))
raise aXeSIMError(msg)
# check the ny-value
if ((ny is not None) and (ny <= 0)):
error_message = ("Value for 'ny' or 'ny_disp' must be "
"positive: {0:g}".format(ny))
raise aXeSIMError(error_message)
# check the exptime-value
if ((exptime is not None) and (exptime < 0)):
error_message = ("Value for 'exptime' or 'exptime_disp' must be "
"positive: {0:g}".format(exptime))
raise aXeSIMError(error_message)
# the extraction width must be set!
if not extrfwhm:
error_message = ("Value for 'extrfwhm' must not be 0.0 to create"
"PETs, but extrfwhm={0:0.1f}!".format(extrfwhm))
raise aXeSIMError(error_message)
# negative extraction width is significant ONLY
# if orient="NO"
if orient and extrfwhm < 0.0:
error_message = (
"Negative width extrfwhm={0:0.1f} together with "
"extraction orient=yes does NOT make sense!".format(extrfwhm))
raise aXeSIMError(error_message)
try:
# convert to float
bck = float(bck_flux)
# check for positive value
if bck < 0:
error_message = ("Value for 'bck_flux' or 'bck_flux_disp'"
" most be positive: {0:g}".format(bck_flux))
raise aXeSIMError(error_message)
# catch a string
except ValueError:
# check for existence of file
if not os.path.isfile(config_util.getCONF(bck_flux)):
error_message = (
"The background file does not exist: {0}".format(
config_util.getCONF(bck_flux)))
raise aXeSIMError(error_message)
def tofits(self, fitsname, indata_copy=0):
"""
Converts and stores the spectra in a fits file
Converts all images stored in the class instance to a
multi-extension fits image with a name gvien as input.
A flagg indicates whether, besides the normal output to
AXE_OUTSIM_PATH, a copy to AXE_IMAGE_PATH is desired.
Parameters
----------
fitsname: str
name for the MEX fits file
indata_copy: int
flag to save also a copy
"""
# create a HDU list
hdulist = fits.HDUList()
# create an empty primary HDU
phdu = fits.PrimaryHDU()
# put the primary to the list
hdulist.append(phdu)
# give a linefeed
print()
# check whther the fitsname
# ends with '.fits'
if ('.fits' not in fitsname[-5:]):
fitsname += '.fits'
# initialize an index
index = 0
# go over all spectra
for ima in self._imalist:
# enhance the counter
index += 1
# append the the image HDU
# to the output fits
hdulist.append(ima.imgHDU)
# print what you do on the screen
print("Adding: {0:s} to {1:s}, ext: {2:s}"
.format(os.path.basename(ima.filename),
fitsname,
str(index)))
# delete older versions
# of the fits name
if os.path.isfile(getOUTSIM(fitsname)):
os.unlink(getOUTSIM(fitsname))
print("\nWriting images to file: {0:s} ..."
.format(getOUTSIM(fitsname)))
# write it to fits
hdulist.writeto(getOUTSIM(fitsname))
# give an end notice
print('Done')
# check whether a copy
# is needed at AXE_IMAGE_PATH directory
if indata_copy:
# delete older versions
# of the fits name
if os.path.isfile(getDATA(fitsname)):
os.unlink(getDATA(fitsname))
print("Writing images to file: {0:s} ..."
.format(getDATA(fitsname)))
# write it to fits
hdulist.writeto(getDATA(fitsname))
# give an end notice
print('Done')
# add an extra linefeed
print('')