def run(self):
""" Runs the data reduction algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
self.dataout = DataFits(config=self.config)
self.dataout = self.datain.copy()
self.astrometrymaster()
# Update RA/Dec from astrometry
self.dataout.header.update(self.wcs_out)
try:
w = wcs.WCS(self.dataout.header)
n1 = float( self.dataout.header['NAXIS1']/2 )
n2 = float( self.dataout.header['NAXIS2']/2 )
ra, dec = w.all_pix2world(n1, n2, 1)
# No update because update may affect accuracy of WCS solution
# self.dataout.header['CRPIX1']=n1
# self.dataout.header['CRPIX2']=n2
# self.dataout.header['CRVAL1']=float(ra)
# self.dataout.header['CRVAL2']=float(dec)
self.dataout.header['RA'] = Angle(ra, u.deg).to_string(unit=u.hour, sep=':')
self.dataout.header['Dec'] = Angle(dec, u.deg).to_string(sep=':')
except:
self.log.error('Run: Could not update RA/Dec from Astrometry')
else:
self.log.debug('Run: Updated RA/Dec from Astrometry')
self.log.debug('Run: Done')
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))
def test(self):
""" Test Pipe Step Flat Object: Runs basic tests
"""
# initial log message
self.log.info('Testing pipe step flat')
# get testin and a configuration
if self.config != None and len(
self.config) > 2: # i.e. if real config is loaded
testin = DataFits(config=self.config)
else:
testin = DataFits(config=self.testconf)
# load sample data
datain = DataFits(config=testin.config)
#infile = 'mode_chop/120207_000_00HA012.chop.dmd.fits'
infile = 'mode_chop/120306_000_00HA006.chop.dmd.fits'
#infile = 'mode_chop/120402_000_00HA035.chop.dmd.fits'
#infile = 'sharp/sharc2-048485.dmdsqr.fits'
testfile = os.path.join(datain.config['testing']['testpath'], infile)
#testfile = '/Users/berthoud/testfit.fits'
datain.load(testfile)
if False:
# change data (make complex number array with
# Re=0,1,2,3,4,5,6 . . . in time Im=0)
dataval = numpy.ones(datain.image.shape)
dataval[..., 1] = 0.0
inclist = numpy.arange(dataval.shape[0])
incshape = [1 + i - i for i in dataval.shape[0:-1]]
incshape[0] = dataval.shape[0]
inclist.shape = incshape
dataval[..., 0] = dataval[..., 0] * inclist
#datain.image=dataval
# run first flat
dataout = self(datain)
#print dataout.image[100,...] # print 100th image
#print dataout.image[range(0,dataval.shape[0],1000),0,0] # 1 val per img
dataout.save()
# final log message
self.log.info('Testing pipe step flat - Done')
def __init__(self):
""" Constructor: Initialize data objects and variables
"""
# call superclass constructor (calls setup)
super(StepFlat, self).__init__()
# list of data and flats
self.datalist = [] # used in run() for every new input data file
# flat values
self.flatloaded = 0 # indicates if flat has been loaded
self.flats = [] # list containing arrays with flat values
self.flatdata = DataFits() # Pipedata object containing the flat file
# flat file info and header keywords to fit
self.flatfile = '' # name of selected flat file
self.fitkeys = [] # FITS keywords that have to fit
self.keyvalues = [
] # values of the keywords (from the first data file)
# set configuration
self.log.debug('Init: done')
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
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('Bias calibration frame not found.')
raise RuntimeError('No bias file(s) 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
if (len(filelist) == 1):
self.bias = ccdproc.CCDData.read(filelist[0],
unit='adu',
relax=True)
else:
self.bias = ccdproc.combine(filelist,
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.bias)
# 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',
'MasterBias: %d files used' % len(filelist))
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
''' Select 3 input dataset to use, store in datause '''
#Store number of inputs
num_inputs = len(self.datain)
# Create variable to hold input files
# Copy input to output header and filename
datause = []
self.log.debug('Number of input files = %d' % num_inputs)
# Ensure datause has 3 elements irrespective of number of input files
if num_inputs == 0: # Raise exception for no input
raise ValueError('No input')
elif num_inputs == 1:
datause = [self.datain[0], self.datain[0], self.datain[0]]
elif num_inputs == 2:
datause = [self.datain[0], self.datain[1], self.datain[1]]
else: # If inputs exceed 2 in number
# Here we know there are at least 3 files
ilist = [] # Make empty lists for each filter
rlist = []
glist = []
other = []
for element in self.datain: # Loop through the input files and add to the lists
fname = element.filename.lower()
if 'i-band' in fname or 'iband' in fname or 'iprime' in fname:
ilist.append(element)
elif 'r-band' in fname or 'rband' in fname or 'rprime' in fname:
rlist.append(element)
elif 'g-band' in fname or 'gband' in fname or 'gprime' in fname:
glist.append(element)
else:
other.append(element)
continue
self.log.debug(
'len(ilist) = %d, len(rlist) = %d, len(glist) = %d' %
(len(ilist), len(rlist), len(glist)))
# If there is at least one i-, r-, and g-band filter found in self.datain (best case)
if len(ilist) >= 1 and len(rlist) >= 1 and len(glist) >= 1:
# The first image from each filter list will be reduced in the correct order.
datause = [ilist[0], rlist[0], glist[0]]
elif len(ilist) == 0 and len(rlist) >= 1 and len(glist) >= 1:
# Cases where there is no ilist
if len(rlist) > len(glist):
datause = [rlist[0], rlist[1], glist[0]]
else:
datause = [rlist[0], glist[0], glist[1]]
elif len(glist) == 0 and len(rlist) >= 1 and len(ilist) >= 1:
# Cases where there is no glist
if len(rlist) > len(ilist):
datause = [rlist[0], rlist[1], ilist[0]]
else:
datause = [rlist[0], ilist[0], ilist[1]]
elif len(ilist) == 0 and len(rlist) >= 1 and len(glist) >= 1:
# Cases where there is no rlist
if len(ilist) > len(glist):
datause = [ilist[0], ilist[1], glist[0]]
else:
datause = [ilist[0], glist[0], glist[1]]
elif len(rlist) == 0 and len(glist) == 0:
# Case where there is only ilist
datause = [ilist[0], ilist[1], ilist[2]]
elif len(rlist) == 0 and len(ilist) == 0:
# Case where there is only glist
datause = [glist[0], glist[1], glist[2]]
elif len(ilist) == 0 and len(glist) == 0:
# Case where there is only rlist
datause = [rlist[0], rlist[1], rlist[2]]
self.log.debug(
'Files used: R = %s G = %s B = %s' %
(datause[0].filename, datause[1].filename, datause[2].filename))
self.dataout = DataFits(config=self.config)
self.dataout.header = datause[0].header
self.dataout.filename = datause[0].filename
img = datause[0].image
img1 = datause[1].image
img2 = datause[2].image
''' Finding Min/Max scaling values '''
# Create a Data Cube with floats
datacube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype=float)
# Enter the image data into the cube so an absolute max can be found
datacube[:, :, 0] = img
datacube[:, :, 1] = img1
datacube[:, :, 2] = img2
# Find how many data points are in the data cube
datalength = img.shape[0] * img.shape[1] * 3
# Create a 1-dimensional array with all the data, then sort it
datacube.shape = (datalength, )
datacube.sort()
# Now use arrays for each filter to find separate min values
rarray = img.copy()
garray = img1.copy()
barray = img2.copy()
# Shape and sort the arrays
arrlength = img.shape[0] * img.shape[1]
rarray.shape = (arrlength, )
rarray.sort()
garray.shape = (arrlength, )
garray.sort()
barray.shape = (arrlength, )
barray.sort()
# Find the min/max percentile values in the data for scaling
# Values are determined by parameters in the pipe configuration file
minpercent = int(arrlength * self.getarg('minpercent'))
maxpercent = int(datalength * self.getarg('maxpercent'))
# Find the final data values to use for scaling from the image data
rminsv = rarray[minpercent] #sv stands for "scalevalue"
gminsv = garray[minpercent]
bminsv = barray[minpercent]
maxsv = datacube[maxpercent]
self.log.info(' Scale min r/g/b: %f/%f/%f' % (rminsv, gminsv, bminsv))
self.log.info(' Scale max: %f' % maxsv)
# The same min/max values will be used to scale all filters
''' Finished Finding scaling values '''
''' Combining Function '''
# Make new cube with the proper data type for color images (uint8)
# Use square root (sqrt) scaling for each filter
# log or asinh scaling is also available
#astropy.vidualizations.SqrtStretch()
imgcube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype='uint8')
minsv = [rminsv, gminsv, bminsv]
for i in range(3):
# Make normalization function
norm = simple_norm(datause[i].image,
'sqrt',
min_cut=minsv[i],
max_cut=maxsv)
# Apply it
imgcube[:, :, i] = norm(datause[i].image) * 255.
self.dataout.image = imgcube
# Create variable containing all the scaled image data
imgcolor = Image.fromarray(self.dataout.image, mode='RGB')
# Save colored image as a .tif file (without the labels)
imgcolortif = imgcube.copy()
imgcolortif.astype('uint16')
### tiff.imsave('%s.tif' % self.dataout.filenamebase, imgcolortif)
''' End of combining function '''
''' Add a Label to the Image '''
draw = ImageDraw.Draw(imgcolor)
# Use a variable to make the positions and size of text relative
imgwidth = img.shape[1]
imgheight = img.shape[0]
# Open Sans-Serif Font with a size relative to the picture size
try:
# This should work on Linux
font = ImageFont.truetype(
'/usr/share/fonts/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
except:
try:
# This should work on Mac
font = ImageFont.truetype('/Library/Fonts/Arial Unicode.ttf',
imgheight // 41)
except:
try:
# This should work on Windows
font = ImageFont.truetype('C:\\Windows\\Fonts\\arial.ttf',
imgheight // 41)
except:
# This should work in Colab
font = ImageFont.truetype(
'/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
# If this still doesn't work - then add more code to make it run on YOUR system
# Use the beginning of the FITS filename as the object name
filename = os.path.split(self.dataout.filename)[-1]
try:
objectname = filename.split('_')[0]
objectname = objectname[0].upper() + objectname[1:]
except Exception:
objectname = 'Unknown.'
objectname = 'Object: %s' % objectname
# Read labels at their respective position (kept relative to image size)
# Left corner: object, observer, observatory
# Right corner: Filters used for red, green, and blue colors
draw.text((imgwidth / 100, imgheight / 1.114),
objectname, (255, 255, 255),
font=font)
# Read FITS keywords for the observer, observatory, and filters
if 'OBSERVER' in self.dataout.header:
observer = 'Observer: %s' % self.dataout.getheadval('OBSERVER')
draw.text((imgwidth / 100, imgheight / 1.073),
observer, (255, 255, 255),
font=font)
if 'OBSERVAT' in self.dataout.header:
observatory = 'Observatory: %s' % self.dataout.getheadval(
'OBSERVAT')
draw.text((imgwidth / 100, imgheight / 1.035),
observatory, (255, 255, 255),
font=font)
if 'FILTER' in datause[0].header:
red = 'R: %s' % datause[0].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.114),
red, (255, 255, 255),
font=font)
if 'FILTER' in datause[1].header:
green = 'G: %s' % datause[1].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.073),
green, (255, 255, 255),
font=font)
if 'FILTER' in datause[2].header:
blue = 'B: %s' % datause[2].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.035),
blue, (255, 255, 255),
font=font)
# Make image name
imgname = self.dataout.filenamebegin
if imgname[-1] in '_-,.': imgname = imgname[:-1]
imgname += '.jpg'
# Save the completed image
imgcolor.save(imgname)
self.log.info('Saving file %sjpg' % self.dataout.filenamebegin)
''' End of Label Code '''
# Set complete flag
self.dataout.setheadval('COMPLETE', 1,
'Data Reduction Pipe: Complete Data Flag')
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in jpeg_dataout.
"""
''' Select 3 input dataset to use, store in datause '''
#Store number of inputs
num_inputs = len(self.datain)
# Create variable to hold input files
# Copy input to output header and filename
datause = [None, None, None]
self.log.debug('Number of input files = %d' % num_inputs)
if num_inputs == 0: # Raise exception for no input
raise ValueError('No input')
elif num_inputs == 1:
datause = [self.datain[0], self.datain[0], self.datain[0]]
elif num_inputs == 2:
datause = [self.datain[0], self.datain[0], self.datain[1]]
else:
filterorder_list = self.getarg('filterorder').split('|')
filterprefs_list = self.getarg('filterprefs').split('|')
datain_filter_list = [
element.getheadval('filter') for element in self.datain
]
used_filter_flags = [False] * len(self.datain)
if len(filterprefs_list) != 3:
self.log.error(
'Invalid number of preferred filters provided (should be 3): '
+ self.getarg('filterprefs'))
else:
# Locate data matching the filters specified in filterprefs
for i, preferred_filter in enumerate(filterprefs_list):
for j, element in enumerate(self.datain):
if element.getheadval('filter') == preferred_filter:
datause[i] = element
used_filter_flags[j] = True
break
filterorder_walker = 0
for i, channel in enumerate(datause):
if channel == None:
for ordered_filter in filterorder_list[
filterorder_walker:]:
filterorder_walker = filterorder_walker + 1
if ordered_filter in datain_filter_list:
datain_index = datain_filter_list.index(
ordered_filter)
if not used_filter_flags[datain_index]:
datause[i] = self.datain[datain_index]
used_filter_flags[datain_index] = True
break
elif channel.getheadval('filter') in filterorder_list:
filterorder_walker = filterorder_list.index(
channel.getheadval('filter'))
for i, channel in enumerate(datause):
if channel == None:
for j, datain_filter in enumerate(datain_filter_list):
if not used_filter_flags[j]:
datause[i] = self.datain[j]
used_filter_flags[j] = True
break
self.log.debug(
'Files used: R = %s G = %s B = %s' %
(datause[0].filename, datause[1].filename, datause[2].filename))
jpeg_dataout = DataFits(config=self.config)
jpeg_dataout.header = datause[0].header
jpeg_dataout.filename = datause[0].filename
img = datause[0].image
img1 = datause[1].image
img2 = datause[2].image
''' Finding Min/Max scaling values '''
# Create a Data Cube with floats
datacube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype=float)
# Enter the image data into the cube so an absolute max can be found
datacube[:, :, 0] = img
datacube[:, :, 1] = img1
datacube[:, :, 2] = img2
# Find how many data points are in the data cube
datalength = img.shape[0] * img.shape[1] * 3
# Create a 1-dimensional array with all the data, then sort it
datacube.shape = (datalength, )
datacube.sort()
# Now use arrays for each filter to find separate min values
rarray = img.copy()
garray = img1.copy()
barray = img2.copy()
# Shape and sort the arrays
arrlength = img.shape[0] * img.shape[1]
rarray.shape = (arrlength, )
rarray.sort()
garray.shape = (arrlength, )
garray.sort()
barray.shape = (arrlength, )
barray.sort()
# Find the min/max percentile values in the data for scaling
# Values are determined by parameters in the pipe configuration file
minpercent = int(arrlength * self.getarg('minpercent'))
maxpercent = int(datalength * self.getarg('maxpercent'))
# Find the final data values to use for scaling from the image data
rminsv = rarray[minpercent] #sv stands for "scalevalue"
gminsv = garray[minpercent]
bminsv = barray[minpercent]
maxsv = datacube[maxpercent]
self.log.info(' Scale min r/g/b: %f/%f/%f' % (rminsv, gminsv, bminsv))
self.log.info(' Scale max: %f' % maxsv)
# The same min/max values will be used to scale all filters
''' Finished Finding scaling values '''
''' Combining Function '''
# Make new cube with the proper data type for color images (uint8)
# Use square root (sqrt) scaling for each filter
# log or asinh scaling is also available
#astropy.vidualizations.SqrtStretch()
imgcube = numpy.zeros((img.shape[0], img.shape[1], 3), dtype='uint8')
minsv = [rminsv, gminsv, bminsv]
for i in range(3):
# Make normalization function
norm = simple_norm(datause[i].image,
'sqrt',
min_cut=minsv[i],
max_cut=maxsv)
# Apply it
imgcube[:, :, i] = norm(datause[i].image) * 255.
jpeg_dataout.image = imgcube
# Create variable containing all the scaled image data
imgcolor = Image.fromarray(jpeg_dataout.image, mode='RGB')
# Save colored image as a .tif file (without the labels)
imgcolortif = imgcube.copy()
imgcolortif.astype('uint16')
### tiff.imsave('%s.tif' % jpeg_dataout.filenamebase, imgcolortif)
''' End of combining function '''
''' Add a Label to the Image '''
draw = ImageDraw.Draw(imgcolor)
# Use a variable to make the positions and size of text relative
imgwidth = img.shape[1]
imgheight = img.shape[0]
# Open Sans-Serif Font with a size relative to the picture size
try:
# This should work on Linux
font = ImageFont.truetype(
'/usr/share/fonts/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
except:
try:
# This should work on Mac
font = ImageFont.truetype('/Library/Fonts/Arial Unicode.ttf',
imgheight // 41)
except:
try:
# This should work on Windows
font = ImageFont.truetype('C:\\Windows\\Fonts\\arial.ttf',
imgheight // 41)
except:
# This should work in Colab
font = ImageFont.truetype(
'/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf',
imgheight // 41)
# If this still doesn't work - then add more code to make it run on YOUR system
# Use the beginning of the FITS filename as the object name
filename = os.path.split(jpeg_dataout.filename)[-1]
try:
objectname = filename.split('_')[0]
objectname = objectname[0].upper() + objectname[1:]
except Exception:
objectname = 'Unknown.'
objectname = 'Object: %s' % objectname
# Read labels at their respective position (kept relative to image size)
# Left corner: object, observer, observatory
# Right corner: Filters used for red, green, and blue colors
draw.text((imgwidth / 100, imgheight / 1.114),
objectname, (255, 255, 255),
font=font)
# Read FITS keywords for the observer, observatory, and filters
if 'OBSERVER' in jpeg_dataout.header:
observer = 'Observer: %s' % jpeg_dataout.getheadval('OBSERVER')
draw.text((imgwidth / 100, imgheight / 1.073),
observer, (255, 255, 255),
font=font)
if 'OBSERVAT' in jpeg_dataout.header:
observatory = 'Observatory: %s' % jpeg_dataout.getheadval(
'OBSERVAT')
draw.text((imgwidth / 100, imgheight / 1.035),
observatory, (255, 255, 255),
font=font)
if 'FILTER' in datause[0].header:
red = 'R: %s' % datause[0].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.114),
red, (255, 255, 255),
font=font)
if 'FILTER' in datause[1].header:
green = 'G: %s' % datause[1].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.073),
green, (255, 255, 255),
font=font)
if 'FILTER' in datause[2].header:
blue = 'B: %s' % datause[2].getheadval('FILTER')
draw.text((imgwidth / 1.15, imgheight / 1.035),
blue, (255, 255, 255),
font=font)
# Make image name
imgname = jpeg_dataout.filenamebegin
if imgname[-1] in '_-,.': imgname = imgname[:-1]
imgname += '.jpg'
# Save the completed image
imgcolor.save(imgname)
self.log.info('Saving file %sjpg' % jpeg_dataout.filenamebegin)
# Optional folder output setup
baseimgname = os.path.basename(imgname)
folderpaths_list = self.getarg('folderpaths').split(':')
for path in folderpaths_list:
path = time.strftime(path, time.localtime())
if not os.path.exists(path):
if self.getarg('createfolders'):
os.makedirs(path)
self.log.info('Creating directory %s' % path)
else:
self.log.info('Invalid folder path %s' % path)
try:
imgcolor.save(os.path.join(path, baseimgname))
except:
self.log.exception('Could not save image to directory %s' %
path)
''' End of Label Code '''
# Set complete flag
jpeg_dataout.setheadval('COMPLETE', 1,
'Data Reduction Pipe: Complete Data Flag')
### Make output data
self.dataout = self.datain.copy()
self.dataout.append(jpeg_dataout)
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])
#convert self.datain to CCD Data object
image = ccdproc.CCDData(self.datain.image, unit='adu')
image.header = self.datain.header
#subtract bias from image
image = ccdproc.subtract_bias(image, self.bias, add_keyword=False)
#subtract dark from image
image = ccdproc.subtract_dark(image,
self.dark,
scale=True,
exposure_time='EXPTIME',
exposure_unit=u.second,
add_keyword=False)
#apply flat correction to image
image = ccdproc.flat_correct(image, self.flat, add_keyword=False)
# copy calibrated image into self.dataout - make sure self.dataout is a pipedata object
self.dataout = DataFits(config=self.datain.config)
self.dataout.image = image.data
self.dataout.header = image.header
self.dataout.filename = self.datain.filename
### 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)
def run(self):
""" Runs the data reduction algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
### Preparation
# construct a temp file name that astrometry will output
fp = tempfile.NamedTemporaryFile(suffix=".fits", dir=os.getcwd())
# split off path name, because a path that is too long causes remap to
# crash sometimes
outname = os.path.split(fp.name)[1]
fp.close()
# Add input file path to ouput file and make new name
outpath = os.path.split(self.datain.filename)[0]
outnewname = os.path.join(outpath, outname.replace('.fits', '.new'))
outwcsname = os.path.join(outpath, outname.replace('.fits', '.wcs'))
# Make sure input data exists as file
if not os.path.exists(self.datain.filename):
self.datain.save()
# Make command string
rawcommand = self.getarg('astrocmd') % (self.datain.filename, outname)
# get estimated RA and DEC center values from the config file or input FITS header
raopt = self.getarg('ra')
if raopt != '':
ra = Angle(raopt, unit=u.hour).degree
else:
try:
ra = Angle(self.datain.getheadval('RA'), unit=u.hour).degree
except:
ra = ''
decopt = self.getarg('dec')
if decopt != '':
dec = Angle(decopt, unit=u.deg).degree
else:
try:
dec = Angle(self.datain.getheadval('DEC'), unit=u.deg).degree
except:
dec = ''
if (ra != '') and (dec != ''):
# update command parameters to use these values
rawcommand = rawcommand + ' --ra %f --dec %f --radius %f' % (
ra, dec, self.getarg('searchradius'))
else:
self.log.debug(
'FITS header missing RA/DEC -> searching entire sky')
### Run Astrometry:
# This loop tries the downsample and param options until the fit is successful
# need either --scale-low 0.5 --scale-high 2.0 --sort-column FLUX
# or --guess-scale
downsamples = self.getarg('downsample')
paramoptions = self.getarg('paramoptions')
for option in range(len(downsamples) * len(paramoptions)):
#for downsample in self.getarg('downsample'):
downsample = downsamples[option % len(downsamples)]
paramoption = paramoptions[option // len(downsamples)]
# Add options to command
command = rawcommand + ' --downsample %d' % downsample + ' ' + paramoption
optionstring = "Downsample=%s Paramopts=%s" % (downsample,
paramoption[:10])
# Run the process - see note at the top of the file if using cron
process = subprocess.Popen(command,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
self.log.debug('running command = %s' % command)
# Wait for the process to be finished or timeout to be reached
timeout = time.time() + self.getarg('timeout')
while time.time() < timeout and process.poll() == None:
time.sleep(1)
poll = process.poll()
if poll == None:
process.kill()
time.sleep(1)
poll = process.poll()
self.log.debug('command returns %d' % poll)
if poll == 0 and os.path.exists(outnewname):
self.log.debug('output file valid -> astrometry successful')
break
else:
self.log.debug('output file missing -> astrometry failed')
# Print the output from astrometry (cut if necessary)
if self.getarg('verbose'):
output = process.stdout.read().decode()
if len(output) > 1000:
outlines = output.split('\n')
output = outlines[:10] + ['...', '...'] + outlines[-7:]
output = '\n'.join(output)
self.log.debug(output)
### Post processing
# Read output file
self.dataout = DataFits(config=self.config)
self.log.debug('Opening astrometry.net output file %s' % outnewname)
try:
self.dataout.load(outnewname)
self.dataout.filename = self.datain.filename
except Exception as error:
self.log.error("Unable to open astrometry. output file = %s" %
outname)
raise error
self.log.debug('Successful parameter options = %s' % optionstring)
# Add history message
histmsg = 'Astrometry.Net: At downsample = %d, search took %d seconds' % (
downsample, time.time() - timeout + 300)
self.dataout.setheadval('HISTORY', histmsg)
# Add RA from astrometry
w = wcs.WCS(self.dataout.header)
n1 = float(self.dataout.header['NAXIS1'] / 2)
n2 = float(self.dataout.header['NAXIS2'] / 2)
ra, dec = w.all_pix2world(n1, n2, 1)
self.dataout.header['CRPIX1'] = n1
self.dataout.header['CRPIX2'] = n2
self.dataout.header['CRVAL1'] = float(ra)
self.dataout.header['CRVAL2'] = float(dec)
self.dataout.header['RA'] = Angle(ra, u.deg).to_string(unit=u.hour,
sep=':')
self.dataout.header['Dec'] = Angle(dec, u.deg).to_string(sep=':')
self.dataout.setheadval('HISTORY',
'Astrometry: Paramopts = ' + optionstring)
# Delete temporary files
if self.getarg('delete_temp'):
os.remove(outnewname)
os.remove(outwcsname)
self.log.debug('Run: Done')
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')))
# print(repr(dfits.header))
### OPTIONAL BUT RECOMMENDED: Check if all necessary files exist
error_flag = False
# Check if configuration file exists
if not os.path.exists(baseconfig):
print(
'ERROR: The config file you specified, %s,\n does NOT exist on your computer, fix "config" above'
% baseconfig)
error_flag = True
# Check if input files exist
for name in infilenames:
if not os.path.exists(name):
print(
'ERROR: The input file you specifed, %s,\n does NOT exist on your computer, fix "inputnames" above'
% name)
error_flag = True
if not error_flag:
print("All Good")
os.chdir('/Users/josh/pipeline/pipeline/Developments/stepwebastrometry')
step = StepWebAstrometry()
indata = []
for f in infilenames:
fits = DataFits(config=baseconfig)
fits.load(f)
indata.append(fits)
outdata = step(indata[0])
print('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.
"""
### identify dark file to load, search if requested
darkfile = self.getarg('darkfile')
if darkfile == 'search' :
# get list of keywords to fit
fitkeys = self.getarg('fitkeys')
# check format (make first element uppercase)
try:
_ = fitkeys[0].upper()
except AttributeError:
# AttributeError if it's not a string
self.log.error('LoadDark: fitkeys config parameter is ' +
'incorrect format')
raise TypeError('fitkeys config parameter is incorrect format')
# get keywords from data
datakeys=[]
for fitkey in fitkeys:
datakeys.append(self.datain.getheadval(fitkey))
# get dark files from darkdir folder
darkfolder = self.getarg('darkfolder')
filelist=[name for name in os.listdir(darkfolder)
if name[0] != '.' and name.find('.fit') > -1 ]
if len(filelist) < 1:
self.log.error('LoadDark: no dark files found in folder ' +
darkfolder)
raise ValueError('no dark files found in folder ' +
darkfolder)
# match dark files, return best dark file
bestind = 0 # index of file with best match in filelist
bestfitn = 0 # number of keywords that match in best match
fileind = 0 # index for going through the list
while fileind < len(filelist) and bestfitn < len(fitkeys):
# load keys of dark file
filehead = pyfits.getheader(darkfolder+'/'+filelist[fileind])
filekeys=[]
for fitkey in fitkeys:
try:
filekeys.append(filehead[fitkey])
except KeyError:
self.log.warning('LoadDark: missing key [%s] in dark <%s>'
% (fitkey, filelist[fileind] ) )
filekeys.append('')
# determine number of fitting keywords
keyfitn=0
while ( keyfitn < len(fitkeys) and
datakeys[keyfitn] == filekeys[keyfitn] ):
keyfitn = keyfitn + 1
# compare with previous best find
if keyfitn > bestfitn:
bestind = fileind
bestfitn = keyfitn
fileind=fileind+1
darkfile = darkfolder+'/'+filelist[ bestind ]
if bestfitn < len(fitkeys):
self.log.warn('Could not find perfect dark file match')
self.log.warn('Best dark file found is <%s>'
% filelist[bestind] )
else:
self.log.info('Best dark file found is <%s>'
% filelist[bestind] )
self.fitkeys = fitkeys
self.keyvalues = datakeys
### load dark data into a DataFits object
self.darkfile = darkfile
darkdata = DataFits(config = self.config)
darkdata.load(self.darkfile)
### find dark image data arrays and store them
# get sizes of input data
datalist = self.getarg('datalist')
if len(datalist) > 0:
# There are items in datalist -> loop over items
self.darks = []
# Check if necessary number of images in darkdata
if len(darkdata.imgdata) < len(datalist):
msg = 'Number of images in dark file < '
msg += 'number of entries in datalist'
self.log.error('LoadDark: %s' % msg)
raise ValueError(msg)
# Loop through datalist items
for dataind in range(len(datalist)):
dataitem = datalist[dataind]
# Search for dataitem in self.datain images
if dataitem.upper() in self.datain.imgnames:
dataimg = self.datain.imageget(dataitem)
self.log.debug('LoadDark: Found image <%s> to subtract dark'
% dataitem)
# Search dataitem in self.table columns
else:
try:
dataimg = self.datain.table[dataitem]
self.log.debug('LoadDark: Found column <%s> to subtract dark'
% dataitem)
except:
msg = 'No data found for <%s>' % dataitem
self.log.error('LoadDark: %s' % msg)
raise ValueError(msg)
# Get dimensions - append dark to list
datasiz = dataimg.shape
if self.getarg('l0method').upper() != 'NO':
datasiz = datasiz[1:]
darksiz = darkdata.imgdata[dataind].shape
self.darks.append(darkdata.imgdata[dataind])
# Check dimension with dark data
print(datasiz,darksiz)
if len(datasiz) >= len(darksiz):
# Data has >= dimensions than dark -> compare
begind = len(datasiz)-len(darksiz)
if datasiz[begind:] != darksiz:
msg = 'Dark "%s" does not fit data - A' % dataitem
self.log.error('LoadDark: %s' % msg)
raise ValueError(msg)
else:
# More dimensions in dark data -> report error
msg = 'Dark "%s" does not fit data - B' % dataitem
self.log.error('LoadDark: %s' % dataitem)
raise ValueError(msg)
else:
# Empty datalist -> Subtract dark from first image in data with first dark
datasiz = self.datain.image.shape
if self.getarg('l0method').upper() != 'NO':
datasiz = datasiz[1:]
darksiz = darkdata.image.shape
if len(datasiz) >= len(darksiz):
# Data has >= dimensions than dark -> compare
begind = len(datasiz)-len(darksiz)
if datasiz[begind:] != darksiz:
self.log.error('LoadDark: Dark does not fit data - A')
raise ValueError('Dark does not fit data - A')
else:
# More dimensions in dark data -> report error
self.log.error('LoadDark: Dark does not fit data - B')
raise ValueError('Dark does not fit data - B')
self.log.debug('LoadDark: Subtracting Dark from first data image with first dark')
self.darks=[darkdata.image]
### make good pixel map for each detector and add to
#darktemp = numpy.abs(data[0,...])+numpy.abs(data[1,...])
#self.goodpixmap = numpy.ones(data.shape[1:])
#self.goodpixmap [ numpy.where(darktemp == 0.0)] = 0.0
# Finish up
self.darkloaded = 1
self.log.debug('LoadDark: done')
def run(self):
self.dataout = DataFits(config=self.config)
self.dataout.load(self.mask())
def run(self):
""" Runs the combining algorithm. The self.datain is run
through the code, the result is in self.dataout.
"""
# Find master dark to subtract from master dark
biaslist = self.loadauxname('bias', multi=False)
darklist = self.loadauxname('dark', multi=False)
if (len(biaslist) == 0):
self.log.error('No bias calibration frames found.')
if (len(darklist) == 0):
self.log.error('No bias calibration frames found.')
self.bias = ccdproc.CCDData.read(biaslist, unit='adu', relax=True)
self.dark = ccdproc.CCDData.read(darklist, 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.flat = ccdproc.CCDData.read(filelist[0],
unit='adu',
relax=True)
self.flat = ccdproc.subtract_bias(self.flat,
self.bias,
add_keyword=False)
self.flat = ccdproc.subtract_dark(self.flat,
self.dark,
scale=True,
exposure_time='EXPTIME',
exposure_unit=u.second,
add_keyword=False)
else:
#bias and dark correct frames
flatlist = []
for i in filelist:
flat = ccdproc.CCDData.read(i, unit='adu', relax=True)
flatsubbias = ccdproc.subtract_bias(flat,
self.bias,
add_keyword=False)
flatsubbiasdark = ccdproc.subtract_dark(
flatsubbias,
self.dark,
scale=True,
exposure_time='EXPTIME',
exposure_unit=u.second,
add_keyword=False)
flatlist.append(flatsubbiasdark)
#scale the flat component frames to have the same mean value, 10000.0
scaling_func = lambda arr: 10000.0 / numpy.ma.median(arr)
#combine them
self.flat = ccdproc.combine(flatlist,
method=self.getarg('combinemethod'),
scale=scaling_func,
unit='adu',
add_keyword=False)
# set output header, put image into output
self.dataout.header = self.datain[0].header
self.dataout.imageset(self.flat)
# 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',
'MasterFlat: %d files used' % len(filelist))
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
from darepype.drp import DataFits from astropy.io import fits config = '/Users/josh/pipeline/pipeline/Developments/stepwebastrometry/pipeconf_stonedge_auto.txt' fp = '/Users/josh/Desktop/pipeline_test/data/M5_r-band_60s_bin2_200711_053415_itzamna_seo_0_RAW_TABLE.fits' fts = DataFits(config=config) fts.load(fp) # print(repr(fits.HDUList(file=fp))) # fts.header['RA'] = 0 # fts.header['Dec'] = 0 print(repr(fts.header)) print(repr(fts.image)) # print(repr(fts.table))
def run(self):
self.dataout = DataFits(config=self.config)
self.dataout.load(self.source_extract())
self.dataout.header['RA'] = self.datain.header['RA']
self.dataout.header['Dec'] = self.datain.header['Dec']
self.dataout.save()
