def _find_required_columns(self, columnList):
"""Search and store the index of a list of required columns
The method searches for a list of column names given in the input.
In case that the column does not ecist, an exception is thrown.
The index of existing columns is written into a dictionary with the
column name as key.
@param columnList: list with required columns
@type columnList: []
"""
# go over all columns
for aColumn in columnList:
# find it
colIndex = self.find(aColumn)
# complain if not found
if colIndex < 0:
err_msg = (
"\nThe table: {0:s} does not contain column: {1}".format(
self.filename, aColumn))
raise aXeSIMError(err_msg)
else:
# add the index to the dictionary
self.reqColIndex[aColumn] = colIndex
def find_magnitude_columns(self, mag_wavelength=None):
"""Identify all magnitude columns
The method identifiy all magnitude columns and can select the
one with the wavelength closest to a given input wavelength.
An exception is thrown in case that no magnitude column is found.
@param mag_wavelength: characteristic wavelength
@type mag_wavelength: float
"""
# search for
# magnitude columns in general
self.mag_cols = self._search_mcols()
# if magnitude columns exist,
# search the one closest to the
# desired wavelength
if len(self.mag_cols) > 0:
if (mag_wavelength is not None):
mag_index = self._identify_magcol(self.mag_cols,
mag_wavelength)
else:
mag_index = 0
# set the column number and the wavelength
self.magwave = float(self.mag_cols[mag_index][1])
self.reqColIndex['MAGNITUDE'] = self.mag_cols[mag_index][0]
else:
# enhance the error counter
err_msg = ("\nModel spectrum list: {0:s} does not contain any "
"magnitude column!".format(self.filename))
raise aXeSIMError(err_msg)
def __init__(self, inlist):
"""Initializer for the class
Parameters
----------
inlist: str
name of list with the fits images
"""
# initialize the list of spectra
self._imalist = []
# load the list with input images
ilist = axe_asciidata.open(inlist)
# go over the image names
for item in ilist[0]:
# check whether the file is there
if not os.path.isfile(getSIMDATA(item)):
error_message = ("\nDid not find image: {0:s} !!".format(
str(getSIMDATA(item))))
raise aXeSIMError(error_message)
# apend the fits to the liast
self._imalist.append(ArtImage(getSIMDATA(item.strip())))
def _basic_checks(self):
"""Do some fundamental checks"""
# this check should be removed
# at some time.....
# check that there is at leas one row
if self.nrows < 1:
err_msg = ("\nThe table: {0:s} must contain at least one row!"
.format(self.filename))
raise aXeSIMError(err_msg)
# this check should be removed
# at some time.....
# check that there is at leas one row
if (len(self.header) > 0):
err_msg = ("\nThe table: {0:s} must not contain header comments!"
.format(self.filename))
raise aXeSIMError(err_msg)
def fill_columns(self, WCSimage, WCSext=None):
"""Fill up column information to be ready for aXe
The method completes the model object tables with columns
requested by aXe.
Parameters
----------
WCSimage: str
the reference image with WCS
WCSext: str
the extension to use
ra_dec = iraf.xy2rd(infile=WCSimage, x=self['X_IMAGE'][index],\
y=, hms='NO', Stdout=1)
"""
# check that the image exists
if not os.path.isfile(WCSimage):
err_msg = "The WCS image: {0:s} does not exist!".format(WCSimage)
raise aXeSIMError(err_msg)
# append the image extesnion
if WCSext is not None:
WCSimage = fits.getheader(WCSimage, ext=WCSext)
else:
WCSimage = fits.getheader(WCSimage)
# go over all rows
for index in range(self.nrows):
# just copy some information
# later it would be reasonable
# to give more reasonable values
self['A_WORLD'][index] = self['A_IMAGE'][index]
self['B_WORLD'][index] = self['B_IMAGE'][index]
self['THETA_WORLD'][index] = self['THETA_IMAGE'][index]
# transform x,y to ra and dec
ra, dec = WCSimage.all_world2pix(self['Y_IMAGE'][index],
self['X_IMAGE'][index])
# store ra and dec
self['X_WORLD'][index] = float(ra)
self['Y_WORLD'][index] = float(dec)
# save the changes
self.flush()
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)
