class StepBiasDarkFlat(StepLoadAux, StepParent):
""" Pipeline Step Object to calibrate Bias/Dark/Flat files
"""
stepver = '0.1' # pipe step version
def __init__(self):
""" Constructor: Initialize data objects and variables
"""
# call superclass constructor (calls setup)
super(StepBiasDarkFlat, self).__init__()
# bias values
self.biasloaded = False # indicates if bias has been loaded
self.bias = None # CCD data object containing arrays with bias values
self.biasdata = DataFits() # Pipedata object containing the bias file
# bias file info and header keywords to fit
self.biasname = '' # name of selected bias file
self.biasfitkeys = [] # FITS keywords that are present in bias
self.biaskeyvalues = [] # values of FITS keywords (from data file)
# dark values
self.darkloaded = False # indicates if dark has been loaded
self.dark = None # CCD data object containing arrays with dark values
self.darkdata = DataFits() # Pipedata object containing the dark file
# dark file info and header keywords to fit
self.darkname = '' # name of selected dark file
self.darkfitkeys = [] # FITS keywords that have to fit for dark
self.darkkeyvalues = [] # values of FITS keywords (from data file)
# flat values
self.flatloaded = False # indicates if flat has been loaded
self.flat = None # CCD data object containing arrays with flat values
self.flatdata = DataFits() # Pipedata object containing the flat file
# flat file info and header keywords to fit
self.flatname = '' # name of selected flat file
self.flatfitkeys = [] # FITS keywords that have to fit for flat
self.flatkeyvalues = [] # values of flat keywords (from data file)
# set configuration
self.log.debug('Init: done')
# This function is directly lifted from CCDProc https://github.com/astropy/ccdproc/blob/master/ccdproc/core.py
# Instead of directly calling CCDProc, we have included the function here to increase educational value
# and to decrease reliance on external libraries.
def subtract_bias(self, image, bias):
"""
Subtract master bias from image.
Parameters
----------
image : `~astropy.nddata.CCDData`
Image from which bias will be subtracted.
bias : `~astropy.nddata.CCDData`
Master image to be subtracted from ``ccd``.
{log}
Returns
-------
result : `~astropy.nddata.CCDData`
CCDData object with bias subtracted.
"""
self.log.debug('Subtracting bias...')
result = image.copy()
try:
result.data = image.data - bias.data
# we believe that we should keep this error detection in theory, the bias
# and image both come from seo, so their units should be the same
except ValueError as e:
if 'operand units' in str(e):
raise u.UnitsError(
"Unit '{}' of the uncalibrated image does not "
"match unit '{}' of the calibration "
"image".format(image.unit, bias.unit))
else:
raise e
self.log.debug('Subtracted bias.')
return result
# this code is also lifted from ccdproc https://github.com/astropy/ccdproc/blob/master/ccdproc/core.py
# some of the code is removed from the original ccdproc because it is not relevant to how SEO currently
# processes data. If you are looking at this code in the future, there is more code available to draw from
def subtract_dark(self,
image,
dark,
scale=False,
exposure_time=None,
exposure_unit=None):
"""
Subtract dark current from an image.
Parameters
----------
image : `~astropy.nddata.CCDData`
Image from which dark will be subtracted.
dark : `~astropy.nddata.CCDData`
Dark image.
exposure_time : str or `~ccdproc.Keyword` or None, optional
Name of key in image metadata that contains exposure time.
Default is ``None``.
exposure_unit : `~astropy.units.Unit` or None, optional
Unit of the exposure time if the value in the meta data does not
include a unit.
Default is ``None``.
scale: bool, optional
If True, scale the dark frame by the exposure times.
Default is ``False``.
{log}
Returns
-------
result : `~astropy.nddata.CCDData`
Dark-subtracted image.
"""
self.log.debug('Subtracting dark...')
result = image.copy()
try:
# if dark current is linear, then this first step scales the provided
# dark to match the exposure time
if scale:
dark_scaled = dark.copy()
data_exposure = image.header[exposure_time]
dark_exposure = dark.header[exposure_time]
# data_exposure and dark_exposure are both quantities,
# so we can just have subtract do the scaling
dark_scaled = dark_scaled.multiply(data_exposure /
dark_exposure)
result.data = image.data - dark_scaled.data
else:
result.data = image.data - dark.data
except (u.UnitsError, u.UnitConversionError, ValueError) as e:
# Make the error message a little more explicit than what is returned
# by default.
raise u.UnitsError("Unit '{}' of the uncalibrated image does not "
"match unit '{}' of the calibration "
"image".format(image.unit, dark.unit))
self.log.debug('Subtracted dark.')
return result
# This code is also from ccdproc. A notable removal is the option to manually choose
# maximum and minimum flat values.
def flat_correct(self, image, flat):
"""Correct the image for flat fielding.
The flat field image is normalized by its mean or a user-supplied value
before flat correcting.
Parameters
----------
ccd : `~astropy.nddata.CCDData`
Data to be transformed.
flat : `~astropy.nddata.CCDData`
Flatfield to apply to the data.
{log}
Returns
-------
ccd : `~astropy.nddata.CCDData`
CCDData object with flat corrected.
"""
self.log.debug('Correcting flat...')
# Use the min_value to replace any values in the flat
flat_corrected = image.copy()
use_flat = flat
flat_mean_val = use_flat.data.mean()
# Normalize the flat.
flat_mean = flat_mean_val * use_flat.unit
flat_normed = use_flat.data / flat_mean
# divide through the flat
flat_corrected.data = image.data / flat_normed
self.log.debug('Corrected flat.')
return flat_corrected
def setup(self):
""" ### Names and Parameters need to be Set Here ###
Sets the internal names for the function and for saved files.
Defines the input parameters for the current pipe step.
The parameters are stored in a list containing the following
information:
- name: The name for the parameter. This name is used when
calling the pipe step from command line or python shell.
It is also used to identify the parameter in the pipeline
configuration file.
- default: A default value for the parameter. If nothing, set
'' for strings, 0 for integers and 0.0 for floats
- help: A short description of the parameter.
"""
### Set Names
# Name of the pipeline reduction step
self.name = 'biasdarkflat'
# Shortcut for pipeline reduction step and identifier for
# saved file names.
self.procname = 'bdf'
# Set Logger for this pipe step
self.log = logging.getLogger('stoneedge.pipe.step.%s' % self.name)
### Set Parameter list
# Clear Parameter list
self.paramlist = []
# Append parameters
self.paramlist.append([
'reload', False,
'Set to True to look for new bias files for every input'
])
# Get parameters for StepLoadAux, replace auxfile with biasfile
self.loadauxsetup('bias')
# Get parameters for StepLoadAux, replace auxfile with darkfile
self.loadauxsetup('dark')
# Get parameters for StepLoadAux, replace auxfile with flatfile
self.loadauxsetup('flat')
# confirm end of setup
self.log.debug('Setup: done')
'''# Looking for similar exptime
def closestExp(self):
input_exptime = self.datain.getheadval('EXPTIME')
dark_exptime = self.loadauxname('dark', multi = True).getheadval('EXPTIME')
nearexp = {abs(dark_ave_exptime - exp): exp for exp in dark_exptime}
return nearexp[min(nearexp.keys())]
'''
def run(self):
""" Runs the calibrating algorithm. The calibrated data is
returned in self.dataout
"""
### Preparation
# Load bias files if necessary
if not self.biasloaded or self.getarg('reload'):
self.loadbias()
# Else: check data for correct instrument configuration - currently not in use(need improvement)
else:
for keyind in range(len(self.biasfitkeys)):
if self.biaskeyvalues[keyind] != self.datain.getheadval(
self.biasfitkeys[keyind]):
self.log.warn(
'New data has different FITS key value for keyword %s'
% self.biasfitkeys[keyind])
# Load dark files if necessary
if not self.darkloaded or self.getarg('reload'):
self.loaddark()
# Else: check data for correct instrument configuration
else:
for keyind in range(len(self.darkfitkeys)):
if self.darkkeyvalues[keyind] != self.datain.getheadval(
self.darkfitkeys[keyind]):
self.log.warn(
'New data has different FITS key value for keyword %s'
% self.darkfitkeys[keyind])
# Load flat files if necessary
if not self.flatloaded or self.getarg('reload'):
self.loadflat()
# Else: check data for correct instrument configuration
else:
for keyind in range(len(self.flatfitkeys)):
if self.flatkeyvalues[keyind] != self.datain.getheadval(
self.flatfitkeys[keyind]):
self.log.warn(
'New data has different FITS key value for keyword %s'
% self.flatfitkeys[keyind])
# in the config file, set the 'intermediate' variable to either true or false to enable
# saving of intermediate steps
saveIntermediateSteps = self.config['biasdarkflat']['intermediate']
self.dataout = DataFits(config=self.datain.config)
#convert self.datain to CCD Data object
image = CCDData(self.datain.image, unit='adu')
image.header = self.datain.header
# subtract bias from image
image = self.subtract_bias(image, self.bias)
if (saveIntermediateSteps == "true"):
self.dataout.imageset(image.data, imagename="BIAS")
# self.dataout.setheadval('DATATYPE','IMAGE', dataname="BIAS")
self.dataout.setheadval('HISTORY',
'BIAS: %s' % self.biasname,
dataname="BIAS")
# subtract dark from image
image = self.subtract_dark(image,
self.dark,
scale=True,
exposure_time='EXPTIME',
exposure_unit=u.second)
if (saveIntermediateSteps == "true"):
self.dataout.imageset(image.data, imagename="DARK")
# self.dataout.setheadval('DATATYPE','IMAGE', dataname="DARK")
self.dataout.setheadval('HISTORY',
'BIAS: %s' % self.biasname,
dataname="DARK")
self.dataout.setheadval('HISTORY',
'DARK: %s' % self.darkname,
dataname="DARK")
# apply flat correction to image
image = self.flat_correct(image, self.flat)
# if separating bias,dark,flat steps , save the flat-corrected portion into its own hdu
if (saveIntermediateSteps == "true"):
self.dataout.imageset(image.data, imagename="FLAT")
# self.dataout.setheadval('DATATYPE','IMAGE', dataname="FLAT")
self.dataout.setheadval('HISTORY',
'BIAS: %s' % self.biasname,
dataname="FLAT")
self.dataout.setheadval('HISTORY',
'DARK: %s' % self.darkname,
dataname="FLAT")
self.dataout.setheadval('HISTORY',
'FLAT: %s' % self.flatname,
dataname="FLAT")
else:
# copy calibrated image into self.dataout
self.dataout.image = image.data
self.dataout.header = self.datain.header
### Finish - cleanup
# Update DATATYPE
self.dataout.setheadval('DATATYPE', 'IMAGE')
# Add bias, dark files to History
self.dataout.setheadval('HISTORY', 'BIAS: %s' % self.biasname)
self.dataout.setheadval('HISTORY', 'DARK: %s' % self.darkname)
self.dataout.setheadval('HISTORY', 'FLAT: %s' % self.flatname)
self.dataout.filename = self.datain.filename
def loadbias(self):
""" Loads the bias information for the instrument settings
described in the header of self.datain.
If an appropriate file can not be found or the file is invalid
various warnings and errors are returned.
If multiple matching files are found, they are combined into a single
master bias frame by ccdproc.
"""
#master bias frame
#Search for bias and load it into data object
namelist = self.loadauxname('bias', multi=False)
self.log.info('File loaded: %s' % namelist)
if (len(namelist) == 0):
self.log.error('Bias calibration frame not found.')
raise RuntimeError('No bias file loaded')
self.log.debug('Creating master bias frame...')
#if there is just one, use it as biasfile or else combine all to make a master bias
self.bias = CCDData.read(namelist, unit='adu', relax=True)
# Finish up
self.biasloaded = True
self.biasname = namelist
self.log.debug('LoadBias: done')
def loaddark(self):
""" Loads the dark information for the instrument settings
described in the header of self.datain.
If an appropriate file can not be found or the file is invalid
various warnings and errors are returned.
If multiple matching files are found, they are combined into a single
master dark frame by ccdproc.
Also bias corrects dark files if not already done.
"""
#master dark frame
dark_is_bias_corrected = False
dark_bias = None
namelist = self.loadauxname('dark', multi=False)
if (len(namelist) == 0):
self.log.error('Dark calibration frame(s) not found.')
raise RuntimeError('No dark file loaded')
# This has been commented out as it is now in StepMasterDark
# darks = None
# for name in namelist:
# #is (any) dark file bias corrected?
# header = fits.getheader(name)
# if(header.get('BIAS') != None):
# dark_is_bias_corrected = True
# dark_bias = header.get('BIAS')
# elif(header.get('BIASCORR') != None):
# dark_is_bias_corrected = True
# dark_bias = header.get('BIASCORR')
# if(darks):
# darks += ','+name
# else:
# darks = name
self.log.debug('Creating master dark frame...')
#if there is just one, use it as darkfile or else combine all to make a master dark
self.dark = CCDData.read(namelist, unit='adu', relax=True)
#bias correct, if necessary
# if(not dark_is_bias_corrected):
# #Subtracting master bias frame from master dark frame
# self.dark = ccdproc.subtract_bias(self.dark, self.bias, add_keyword=False)
# else:
# self.log.debug('Master dark frame is *already* bias corrected (%s).' % dark_bias)
# Finish up
self.darkloaded = True
self.darkname = namelist
self.log.debug('LoadDark: done')
def loadflat(self):
""" Loads the dark information for the instrument settings
described in the header of self.datain.
If an appropriate file can not be found or the file is invalid
various warnings and errors are returned.
If multiple matching files are found, they are combined into a single
master flat frame by ccdproc.
Also biascorrects and dark corrects flat files if not already done.
"""
#create master flat frame
flat_is_bias_corrected = False
flat_bias = None
flat_is_dark_corrected = False
flat_dark = None
flat_ave_exptime = 0
namelist = self.loadauxname('flat', multi=False)
if (len(namelist) == 0):
self.log.error('Flat calibration frame not found.')
raise RuntimeError('No flat file loaded')
count = 0
datalist = []
flat_corrected = None
# This has been commented out as it is now in StepMasterFlat
#check a few things in these flat component frames
# for name in namelist:
# header = fits.getheader(name)
#is this flat bias corrected?
# if(header.get('BIAS') != None):
# flat_is_bias_corrected = True
# flat_bias = header.get('BIAS')
# elif(header.get('BIASCORR') != None):
# flat_is_bias_corrected = True
# flat_bias = header.get('BIASCORR')
# #is this flat dark corrected?
# if(header.get('DARK') != None):
# flat_is_dark_corrected = True
# flat_dark = header.get('DARK')
# elif(header.get('DARKCORR') != None):
# flat_is_dark_corrected = True
# flat_dark = header.get('DARKCORR')
# flat_corrected = "%s"%(name.rsplit('.',1)[0])+".corrected"
# shutil.copy(name, flat_corrected)
# self.log.debug('Copying %s to %s' % (name, flat_corrected))
# self.flat = ccdproc.CCDData.read(flat_corrected, unit='adu', relax=True)
# #bias correct, if necessary
# if(not flat_is_bias_corrected):
# self.log.debug('Subtracting master bias frame from flat frame...')
# self.flat = ccdproc.subtract_bias(self.flat, self.bias, add_keyword=False)
# else:
# self.log.debug('Flat frame (%s) is *already* bias corrected.'%flat_bias)
# #dark correct, if necessary
# if(not flat_is_dark_corrected):
# self.log.debug('Subtracting master dark frame from flat frame...')
# self.flat = ccdproc.subtract_dark(self.flat, self.dark, scale=True, exposure_time='EXPTIME', exposure_unit=u.second, add_keyword=False)
# else:
# self.log.debug('Flat frame (%s) is *already* dark corrected.'%flat_dark)
# #create CCD Data object list with corrected flat files
# datalist.append(self.flat)
# #calc average exposure time for potential dark correction
# if(header.get('EXPTIME') != None):
# try:
# exptime = float(header.get('EXPTIME'))
# flat_ave_exptime += exptime
# except ValueError:
# self.log.error('Exposure time (EXPTIME) is not a float (%s).'%(header.get('EXPTIME')))
# count += 1
# #calc average exposure time
# if(count > 0):
# flat_ave_exptime = flat_ave_exptime/count
# self.flat.header['EXPTIME'] = flat_ave_exptime
# self.log.info("Average exposure time for flats is %f"%flat_ave_exptime)
self.log.debug('Creating master flat frame...')
#if there is just one, use it as flatfile or else combine all to make a master flat
self.flat = CCDData.read(namelist, unit='adu', relax=True)
# Finish up
self.flatloaded = True
self.flatname = namelist
self.log.debug('LoadFlat: done')
def reset(self):
""" Resets the step to the same condition as it was when it was
created. Internal variables are reset, any stored data is
erased.
"""
self.biasloaded = False
self.bias = None
self.darkloaded = False
self.dark = None
self.flatloaded = False
self.flat = None
self.log.debug('Reset: done')
class StepMasterDark(StepLoadAux, StepMIParent):
""" Stone Edge Pipeline Step Master Dark Object
The object is callable. It requires a valid configuration input
(file or object) when it runs.
"""
stepver = '0.1' # pipe step version
def setup(self):
""" ### Names and Parameters need to be Set Here ###
Sets the internal names for the function and for saved files.
Defines the input parameters for the current pipe step.
Setup() is called at the end of __init__
The parameters are stored in a list containing the following
information:
- name: The name for the parameter. This name is used when
calling the pipe step from command line or python shell.
It is also used to identify the parameter in the pipeline
configuration file.
- default: A default value for the parameter. If nothing, set
'' for strings, 0 for integers and 0.0 for floats
- help: A short description of the parameter.
"""
### Set Names
# Name of the pipeline reduction step
self.name = 'masterdark'
# Shortcut for pipeline reduction step and identifier for
# saved file names.
self.procname = 'mdark'
# Set Logger for this pipe step
self.log = logging.getLogger('stoneedge.pipe.step.%s' % self.name)
### Set Parameter list
# Clear Parameter list
self.paramlist = []
# Append parameters !!!! WHAT PARAMETERS ARE NEEDED ????? !!!!!
self.paramlist.append([
'combinemethod', 'median',
'Specifies how the files should be combined - options are median, average, sum'
])
self.paramlist.append([
'outputfolder', '',
'Output directory location - default is the folder of the input files'
])
# Get parameters for StepLoadAux, replace auxfile with biasfile
self.loadauxsetup('bias')
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
# Find master bias to subtract from master dark
biaslist = self.loadauxname('bias', multi=False)
if (len(biaslist) == 0):
self.log.error('No bias calibration frames found.')
self.bias = ccdproc.CCDData.read(biaslist, unit='adu', relax=True)
# Create empy list for filenames of loaded frames
filelist = []
for fin in self.datain:
self.log.debug("Input filename = %s" % fin.filename)
filelist.append(fin.filename)
# Make a dummy dataout
self.dataout = DataFits(config=self.config)
if len(self.datain) == 0:
self.log.error('Flat calibration frame not found.')
raise RuntimeError('No flat file(s) loaded')
self.log.debug('Creating master flat frame...')
# Create master frame: if there is just one file, turn it into master bias or else combine all to make master bias
if (len(filelist) == 1):
self.dark = ccdproc.CCDData.read(filelist[0],
unit='adu',
relax=True)
self.dark = ccdproc.subtract_bias(self.dark,
self.bias,
add_keyword=False)
else:
darklist = []
for i in filelist:
dark = ccdproc.CCDData.read(i, unit='adu', relax=True)
darksubbias = ccdproc.subtract_bias(dark,
self.bias,
add_keyword=False)
darklist.append(darksubbias)
self.dark = ccdproc.combine(darklist,
method=self.getarg('combinemethod'),
unit='adu',
add_keyword=True)
# set output header, put image into output
self.dataout.header = self.datain[0].header
self.dataout.imageset(self.dark)
# rename output filename
outputfolder = self.getarg('outputfolder')
if outputfolder != '':
outputfolder = os.path.expandvars(outputfolder)
self.dataout.filename = os.path.join(outputfolder,
os.path.split(filelist[0])[1])
else:
self.dataout.filename = filelist[0]
# Add history
self.dataout.setheadval('HISTORY',
'MasterDark: %d files used' % len(filelist))
class StepCoadd(StepMIParent):
""" Stone Edge Pipeline Step Master Bias Object
The object is callable. It requires a valid configuration input
(file or object) when it runs.
"""
stepver = '1.2' # pipe step version
def setup(self):
""" ### Names and Parameters need to be Set Here ###
Sets the internal names for the function and for saved files.
Defines the input parameters for the current pipe step.
Setup() is called at the end of __init__
The parameters are stored in a list containing the following
information:
- name: The name for the parameter. This name is used when
calling the pipe step from command line or python shell.
It is also used to identify the parameter in the pipeline
configuration file.
- default: A default value for the parameter. If nothing, set
'' for strings, 0 for integers and 0.0 for floats
- help: A short description of the parameter.
"""
### Set Names
# Name of the pipeline reduction step
self.name='coadd'
# Shortcut for pipeline reduction step and identifier for
# saved file names.
self.procname = 'coadd'
# Set Logger for this pipe step
self.log = logging.getLogger('pipe.step.%s' % self.name)
### Set Parameter list
# Clear Parameter list
self.paramlist = []
# Append parameters
self.paramlist.append(['kernel','square',
'Specifies the kernel used to determine spreading of input pixels onto output pixels \
- options are square, point, gaussian, smoothing, tophat'])
self.paramlist.append(['pixfrac', 1.,
'The fraction of an output pixel(s) that an input pixel\'s flux is confined to'])
self.paramlist.append(['resolution', 1.,
'Pixel scale divisor for output image (higher gives more resolution, lower gives less)'])
self.paramlist.append(['pad', 0,
'Extra padding outside maximum extent of inputs'])
self.paramlist.append(['fillval', np.nan,
'Value for filling in the area(s) in the output where there is no input data'])
self.paramlist.append(['drizzleweights','exptime',
'How each input image should be weighted when added to the output \
- options are exptime, expsq and uniform'])
self.paramlist.append(['outangle',0.,
'Output angle of drizzled image (currently not functional)'])
def run(self):
""" Runs the mosaicing algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
#calculate platescale of first input image
try:
det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.cd)
pscale = np.sqrt(np.abs(det))*3600.
except:
try:
det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.pc)
pscale = np.sqrt(np.abs(det))*3600.
except:
pscale = self.datain[0].header['PIXSCAL']
#filtering out images which are too far away from the others
#passing images added to a list of (image, WCS) tuples
'''
image_centers = []
for f in self.datain:
image_centers.append((f.header['CRVAL1'], f.header['CRVAL2']))
filtered_datain = []
dist_list = [[[0]*(len(image_centers)-1)]*len(image_centers)]
for i in range(len(image_centers)):
for j in range(len(image_centers)-1):
dist_list[i][j+1] = np.sqrt((image_)**2+()**2)
'''
#calculations necessary for updating wcs information
px = []
py = []
#in order to avoid NaN interactions, creating weight map
weights=[]
for f in self.datain:
weights.append((np.where(np.isnan(f.image) == True, 0, 1)))
for f in self.datain:
px.extend(wcs.WCS(f.header).calc_footprint()[:,0])
py.extend(wcs.WCS(f.header).calc_footprint()[:,1])
x0 = (max(px)+min(px))/2.
y0 = (max(py)+min(py))/2.
sx = (max(px)-min(px))*np.cos(y0/180*np.pi) # arcsec
sy = (max(py)-min(py)) # arcsec
size = (sx*3600+self.getarg('pad')*2, sy*3600+self.getarg('pad')*2)
xpix = size[0]//pscale
ypix = size[1]//pscale
cdelt = [pscale/3600.]*2
#create self.dataout and give it a copy of an input's header
self.dataout = DataFits(config = self.config)
self.dataout.header = self.datain[0].header.copy()
#update header wcs information
self.log.info('Creating new WCS header')
self.dataout.header['CRPIX1'] = xpix/2
self.dataout.header['CRPIX2'] = ypix/2
self.dataout.header['CRVAL1'] = x0
self.dataout.header['CRVAL2'] = y0
self.dataout.header['CD1_1'] = -cdelt[0]
self.dataout.header['CD1_2'] = self.dataout.header['CD2_1'] = 0.
self.dataout.header['CD2_2'] = cdelt[1]
self.dataout.header['NAXIS1'] = int(xpix)
self.dataout.header['NAXIS2'] = int(ypix)
self.dataout.header['CTYPE1'] = 'RA---TAN-SIP'
self.dataout.header['CTYPE2'] = 'DEC--TAN-SIP'
self.dataout.header['RADESYS'] = 'ICRS'
self.dataout.header['EQUINOX'] = 2000
self.dataout.header['LATPOLE'] = self.datain[0].header['CRVAL2']
self.dataout.header['LONPOLE'] = 180
self.dataout.header['PIXASEC'] = pscale
theta_rad = np.deg2rad(self.getarg('outangle'))
rot_matrix = np.array([[np.cos(theta_rad), -np.sin(theta_rad)],
[np.sin(theta_rad), np.cos(theta_rad)]])
rot_cd = np.dot(rot_matrix, np.array([[self.dataout.header['CD1_1'], 0.],[0., self.dataout.header['CD2_2']]]))
for i in [0,1]:
for j in [0,1]:
self.dataout.header['CD{0:d}_{1:d}'.format(i+1, j+1)] = rot_cd[i,j]
#check drizzle arguments
if self.getarg('kernel') == 'smoothing':
kernel = 'lanczos3'
elif self.getarg('kernel') in ['square', 'point', 'gaussian', 'tophat']:
kernel = self.getarg('kernel')
else:
self.log.error('Kernel name not recognized, using default')
kernel = 'square'
if self.getarg('drizzleweights') == 'uniform':
driz_wt = ''
elif self.getarg('drizzleweights') in ['exptime', 'expsq']:
driz_wt = self.getarg('drizzleweights')
else:
self.log.error('Drizzle weighting not recognized, using default')
driz_wt = ''
#create drizzle object and add input images
fullwcs = wcs.WCS(self.dataout.header)
self.log.info('Starting drizzle')
driz = drz.Drizzle(outwcs = fullwcs, pixfrac=self.getarg('pixfrac'), \
kernel=kernel, fillval='10000', wt_scl=driz_wt)
for i,f in enumerate(self.datain):
self.log.info('Adding %s to drizzle stack' % f.filename)
driz.add_image(f.imgdata[0], wcs.WCS(f.header), inwht=weights[i])
try:
fillval=float(self.getarg('fillval'))
except:
fillval=np.nan
self.log.error('Fillvalue not recognized or missing, using default')
#creates output fits file from drizzle output
self.dataout.imageset(np.where(driz.outsci == 10000, fillval, driz.outsci))
self.dataout.imageset(driz.outwht,'OutWeight', self.dataout.header)
self.dataout.filename = self.datain[0].filename
#add history
self.dataout.setheadval('HISTORY','Coadd: %d files combined with %s kernel, pixfrac %f at %f times resolution' \
% (len(self.datain), kernel, self.getarg('pixfrac'), self.getarg('resolution')))
