def process(sci_files, sci_bias_file, flat_file, nskip, threshold, flat_bias_file, output):
"""
Entry point function to process science images.
Parameters
----------
sci_files : string
Science image file names
sci_bias_file : string
Bias image file name for science images
flat_file : string
Flat field file name
skip : int
Number of bias frames to skip before averaging the frames. Default is 0.
threshold : float
Threshold for normalized fat field (value between 0 and 1.0).
Default is 0.8.
flat_bias_file : string
Bias image file name for flat field images
Returns
-------
None
"""
print "REDUCE: CHIMERA Image Reduction Rotuine"
nskip = int(nskip)
threshold = float(threshold)
# Check if input is a string of FITS images or a text file with file names
if sci_files[0] == "@":
infile = sci_files[1:]
if not os.path.exists(infile):
print " Not able to locate file %s" % infile
image_cubes = []
with open(infile, "r") as fd:
for line in fd.readlines():
if len(line) > 1:
image_cubes.append(line.replace("\n", ""))
else:
image_cubes = sci_files.split(",")
# Read bias and flat field images
sci_bias_image = chimera.fitsread(sci_bias_file)
flat_image = chimera.fitsread(flat_file)
if flat_bias_file != "":
flat_bias_image = chimera.fitsread(flat_bias_file)
else:
flat_bias_image = sci_bias_image
# Create master bias image
print " Generating master bias image"
master_sci_bias_image = chimera.masterbias(sci_bias_image)
master_flat_bias_image = chimera.masterbias(flat_bias_image)
# Create normalized flat field
print " Generating normalized flat field image"
master_flat_image = chimera.masterflat(flat_image, master_flat_bias_image)
ncubes = len(image_cubes)
for i in range(ncubes):
sci_file = image_cubes[i]
print " Reducing science image : ", sci_file
sci_image, header = chimera.fitsread(sci_file, header=True)
# Reduced the science frames
sci_red_image, sci_avg_image = chimera.imreduce(sci_image, master_sci_bias_image, master_flat_image)
# Write the reduced and average FITS image
if output != "":
red_file = output + "_final.fits"
avg_file = output + "_avg.fits"
else:
red_file = sci_file.replace(".fits", "_final.fits")
avg_file = sci_file.replace(".fits", "_avg.fits")
if os.path.exists(red_file):
os.remove(red_file)
if os.path.exists(avg_file):
os.remove(avg_file)
chimera.fitswrite(sci_red_image, red_file, header=header)
chimera.fitswrite(sci_avg_image, avg_file, header=header)
print " Reduced science image : ", red_file
return
def process(infile, coords, fwhmpsf, sigma, aperture, annulus, dannulus,
output, zmag, debug):
"""
Entry point function to process science image.
Parameters
----------
infile : string
Science image or list of science images
coords : string
Input text file with coordinates of stars
fwhmpsf : float
FWHM of the stelar psf in pixels
sigma : float
Sky background sigma
annulus : int
Inner sky annulus radius in pixels
dannulus : int
Radius of sky annulus in pixels
output : string
Output file name
zmag : string
Photometric zero point
Returns
-------
None
"""
print("PHOTOMETRY: CHIMERA Aperture Photometry Routine")
fwhmpsf = float(fwhmpsf)
sigma = float(sigma)
annulus = int(annulus)
dannulus = int(dannulus)
# Check if input is a string of FITS images or a text file with file names
if infile[0] == "@":
infile = infile[1:]
if not os.path.exists(infile):
raise IOError("PHOTOMETRY: Not able to locate file %s. Stopping." %
infile)
image_cubes = []
with open(infile, "r") as fd:
for line in fd.readlines():
if len(line) > 1:
image_cubes.append(line.replace("\n", ""))
else:
image_cubes = infile.split(",")
# Number of images
ncubes = len(image_cubes)
# Check if the input coordinates file exists. Take a tmp copy of the input
# file and use that. Delete at the end of processing.
if not os.path.exists(coords):
raise IOError("Input coordinate file %s does not exist. Stopping." %
coords)
else:
tmp_coords = coords + ".tmp"
iraf.copy(coords, tmp_coords)
# Fields to extract from phot file
fields = "XCEN,YCEN,CIER,MSKY,STDEV,NSKY,SIER,SUM,AREA,FLUX,MERR,PIER"
total_phot_data = []
img_sec = []
for i in range(ncubes):
sci_file = image_cubes[i]
if not os.path.exists(sci_file):
raise IOError("FITS image %s does not exist. Stopping." % sci_file)
fpath, fname = os.path.split(sci_file)
print("\n Processing science image %s" % fname)
# Instantiate an Aperphot object
ap = chimera.Aperphot(sci_file, coords)
# Set fwhmpsf, sigma, annulus. dannulus and zero point
ap.fwhmpsf = fwhmpsf
ap.sigma = sigma
ap.annulus = annulus
ap.dannulus = dannulus
if zmag != "":
ap.zmag = float(zmag)
# Read the input FITS image
if i == 0:
img, imghdr = chimera.fitsread(ap.sci_file, header=True)
else:
img = chimera.fitsread(ap.sci_file)
# Determine nominal aperture radius for photometry
if i == 0:
if aperture:
nom_aper = float(aperture)
else:
nom_aper = ap.daocog()
print(" Nominal aperture radius : %4.1f pixels" % nom_aper)
# Perform aperture photometry on all the frames
dtype = [
("DATETIME", "S25"),
("XCEN", "f4"),
("YCEN", "f4"),
("CIER", "i4"),
("MSKY", "f4"),
("STDEV", "f4"),
("NSKY", "i4"),
("SIER", "i4"),
("SUM", "f4"),
("AREA", "f4"),
("FLUX_ADU", "f4"),
("FLUX_ELEC", "f4"),
("FERR", "f4"),
("MAG", "f4"),
("MERR", "f4"),
("PIER", "i4"),
]
phot_data = np.zeros([ap.nframes], dtype=dtype)
for j in range(ap.nframes):
print(" Processing frame number : %d" % (j + 1))
outfile = sci_file.replace(".fits", "_" + str(j) + ".phot.1")
ap.daophot(j + 1, tmp_coords, outfile, nom_aper)
objcen = dump(outfile, "XCEN,YCEN")
with open(tmp_coords, "w") as fd:
fd.write(objcen + '\n')
aperphot_data = dump(outfile, fields).split()
phot_data[j]['DATETIME'] = ap.addtime(j * ap.kintime).isoformat()
phot_data[j]['XCEN'] = float(aperphot_data[0])
phot_data[j]['YCEN'] = float(aperphot_data[1])
phot_data[j]['CIER'] = int(aperphot_data[2])
phot_data[j]['MSKY'] = float(aperphot_data[3])
phot_data[j]['STDEV'] = float(aperphot_data[4])
phot_data[j]['NSKY'] = int(aperphot_data[5])
phot_data[j]['SIER'] = int(aperphot_data[6])
phot_data[j]['SUM'] = float(aperphot_data[7])
phot_data[j]['AREA'] = float(aperphot_data[8])
phot_data[j]['FLUX_ADU'] = float(aperphot_data[9])
phot_data[j]['FLUX_ELEC'] = float(aperphot_data[9]) * ap.epadu
phot_data[j]['MAG'] = ap.zmag - 2.5 * np.log10(
phot_data[j]['FLUX_ELEC'] / ap.exptime)
if aperphot_data[10] == 'INDEF':
phot_data[j]['MERR'] = -10
else:
phot_data[j]['MERR'] = float(aperphot_data[10])
phot_data[j]['PIER'] = int(aperphot_data[11])
# Calculate error in flux - using the formula
# err = sqrt(flux * gain + npix * (1 + (npix/nsky)) * (flux_sky * gain + R**2))
phot_data[j]['FERR'] = np.sqrt(
phot_data[j]['FLUX_ELEC'] + phot_data[j]['AREA'] *
(1 + phot_data[j]['AREA'] / phot_data[j]['NSKY']) *
(phot_data[j]['MSKY'] * ap.epadu + ap.readnoise**2))
# Save a 51x51 image section of the object
xmin, xmax = int(phot_data[j]['XCEN']) - 25, int(
phot_data[j]['XCEN']) + 25
ymin, ymax = int(phot_data[j]['YCEN']) - 25, int(
phot_data[j]['YCEN']) + 25
img_sec.append(img[j, ymin:ymax, xmin:xmax])
# Save photometry of all the image cubes in a single file
total_phot_data.append(phot_data)
# If debug mode -
# 1. save DAOPHOT phot files
# 2. save individual phot data as npy file
# 3. Plot light cuve for each data cube separatly
if debug:
# Save photometry data in numpy binary format
print(" Saving photometry data as numpy binary")
if output != "":
npy_outfile = output + ".npy"
else:
npy_outfile = coords + "_phot.npy"
if os.path.exists(npy_outfile):
os.remove(npy_outfile)
np.save(npy_outfile, phot_data)
# Plot first pass light curve
if plot_flag:
print(" Plotting normalized light curve")
if output != "":
plt_outfile = output + ".png"
else:
plt_outfile = coords + "_lc.png"
plotter(phot_data, ap.nframes, ap.kintime, plt_outfile)
else:
# Delete intermediate files is not debug mode
iraf.delete(os.path.join(fpath, '*.phot.1'))
# Convert the total_phot_data to array and reshape it
print(' Saving consolidated photometry data...')
total_phot_data_arr = np.concatenate(total_phot_data)
# Save the array as npy file
np.save(coords + "_total.phot.npy", total_phot_data_arr)
# Save the image section with object as FITS file
print(' Saving image section with object as FITS image...')
img_sec_arr = np.asarray(img_sec)
img_fname = coords + "_obj.fits"
if os.path.exists(img_fname):
os.remove(img_fname)
chimera.fitswrite(img_sec_arr, coords + "_obj.fits", header=imghdr)
# Delete temporary coordinate file
if os.path.exists(tmp_coords):
os.remove(tmp_coords)
return
def process(sci_files, sci_bias_file, flat_file, nskip, threshold,
flat_bias_file, output):
"""
Entry point function to process science images.
Parameters
----------
sci_files : string
Science image file names
sci_bias_file : string
Bias image file name for science images
flat_file : string
Flat field file name
skip : int
Number of bias frames to skip before averaging the frames. Default is 0.
threshold : float
Threshold for normalized fat field (value between 0 and 1.0).
Default is 0.8.
flat_bias_file : string
Bias image file name for flat field images
Returns
-------
None
"""
print "REDUCE: CHIMERA Image Reduction Rotuine"
nskip = int(nskip)
threshold = float(threshold)
# Check if input is a string of FITS images or a text file with file names
if sci_files[0] == "@":
infile = sci_files[1:]
if not os.path.exists(infile):
print " Not able to locate file %s" % infile
image_cubes = []
with open(infile, "r") as fd:
for line in fd.readlines():
if len(line) > 1:
image_cubes.append(line.replace("\n", ""))
else:
image_cubes = sci_files.split(",")
# Read bias and flat field images
sci_bias_image = chimera.fitsread(sci_bias_file)
flat_image = chimera.fitsread(flat_file)
if flat_bias_file != "":
flat_bias_image = chimera.fitsread(flat_bias_file)
else:
flat_bias_image = sci_bias_image
# Create master bias image
print " Generating master bias image"
master_sci_bias_image = chimera.masterbias(sci_bias_image)
master_flat_bias_image = chimera.masterbias(flat_bias_image)
# Create normalized flat field
print " Generating normalized flat field image"
master_flat_image = chimera.masterflat(flat_image, master_flat_bias_image)
ncubes = len(image_cubes)
for i in range(ncubes):
sci_file = image_cubes[i]
print " Reducing science image : ", sci_file
sci_image, header = chimera.fitsread(sci_file, header=True)
# Reduced the science frames
sci_red_image, sci_avg_image = chimera.imreduce(
sci_image, master_sci_bias_image, master_flat_image)
# Write the reduced and average FITS image
if output != "":
red_file = output + "_final.fits"
avg_file = output + "_avg.fits"
else:
red_file = sci_file.replace('.fits', '_final.fits')
avg_file = sci_file.replace('.fits', '_avg.fits')
if os.path.exists(red_file):
os.remove(red_file)
if os.path.exists(avg_file):
os.remove(avg_file)
chimera.fitswrite(sci_red_image, red_file, header=header)
chimera.fitswrite(sci_avg_image, avg_file, header=header)
print " Reduced science image : ", red_file
return
def process(infile, coords, fwhmpsf, sigma, aperture, annulus, dannulus, output, zmag, debug):
"""
Entry point function to process science image.
Parameters
----------
infile : string
Science image or list of science images
coords : string
Input text file with coordinates of stars
fwhmpsf : float
FWHM of the stelar psf in pixels
sigma : float
Sky background sigma
annulus : int
Inner sky annulus radius in pixels
dannulus : int
Radius of sky annulus in pixels
output : string
Output file name
zmag : string
Photometric zero point
Returns
-------
None
"""
print "PHOTOMETRY: CHIMERA Aperture Photometry Routine"
fwhmpsf = float(fwhmpsf)
sigma = float(sigma)
annulus = int(annulus)
dannulus = int(dannulus)
# Check if input is a string of FITS images or a text file with file names
if infile[0] == "@":
infile = infile[1:]
if not os.path.exists(infile):
raise IOError("PHOTOMETRY: Not able to locate file %s. Stopping." %infile)
image_cubes = []
with open(infile, "r") as fd:
for line in fd.readlines():
if len(line) > 1:
image_cubes.append(line.replace("\n", ""))
else:
image_cubes = infile.split(",")
# Number of images
ncubes = len(image_cubes)
# Check if the input coordinates file exists. Take a tmp copy of the input
# file and use that. Delete at the end of processing.
if not os.path.exists(coords):
raise IOError("Input coordinate file %s does not exist. Stopping." %coords)
else:
tmp_coords = coords + ".tmp"
iraf.copy(coords, tmp_coords)
# Fields to extract from phot file
fields = "XCEN,YCEN,CIER,MSKY,STDEV,NSKY,SIER,SUM,AREA,FLUX,MERR,PIER"
total_phot_data = []
img_sec = []
for i in range(ncubes):
sci_file = image_cubes[i]
if not os.path.exists(sci_file):
raise IOError("FITS image %s does not exist. Stopping." %sci_file)
fpath, fname = os.path.split(sci_file)
print "\n Processing science image %s" %fname
# Instantiate an Aperphot object
ap = chimera.Aperphot(sci_file, coords)
# Set fwhmpsf, sigma, annulus. dannulus and zero point
ap.fwhmpsf = fwhmpsf
ap.sigma = sigma
ap.annulus = annulus
ap.dannulus = dannulus
if zmag != "":
ap.zmag = float(zmag)
# Read the input FITS image
if i == 0:
img, imghdr = chimera.fitsread(ap.sci_file, header = True)
else:
img = chimera.fitsread(ap.sci_file)
# Determine nominal aperture radius for photometry
if i == 0:
if aperture:
nom_aper = float(aperture)
else:
nom_aper = ap.daocog()
print " Nominal aperture radius : %4.1f pixels" %nom_aper
# Perform aperture photometry on all the frames
dtype = [("DATETIME", "S25"),("XCEN", "f4"),("YCEN", "f4"),("CIER", "i4"),("MSKY", "f4"),("STDEV", "f4"),("NSKY", "i4"),("SIER", "i4"),("SUM", "f4"),("AREA", "f4"),("FLUX_ADU", "f4"),("FLUX_ELEC", "f4"),("FERR", "f4"),("MAG", "f4"),("MERR", "f4"),("PIER", "i4"),]
phot_data = np.zeros([ap.nframes], dtype = dtype)
for j in range(ap.nframes):
print " Processing frame number : %d" %(j+1)
outfile = sci_file.replace(".fits", "_" + str(j) + ".phot.1")
ap.daophot(j+1, tmp_coords, outfile, nom_aper)
objcen = dump(outfile, "XCEN,YCEN")
with open(tmp_coords, "w") as fd:
fd.write(objcen + '\n')
aperphot_data = dump(outfile, fields).split()
phot_data[j]['DATETIME'] = ap.addtime(j * ap.kintime).isoformat()
phot_data[j]['XCEN'] = float(aperphot_data[0])
phot_data[j]['YCEN'] = float(aperphot_data[1])
phot_data[j]['CIER'] = int(aperphot_data[2])
phot_data[j]['MSKY'] = float(aperphot_data[3])
phot_data[j]['STDEV'] = float(aperphot_data[4])
phot_data[j]['NSKY'] = int(aperphot_data[5])
phot_data[j]['SIER'] = int(aperphot_data[6])
phot_data[j]['SUM'] = float(aperphot_data[7])
phot_data[j]['AREA'] = float(aperphot_data[8])
phot_data[j]['FLUX_ADU'] = float(aperphot_data[9])
phot_data[j]['FLUX_ELEC'] = float(aperphot_data[9]) * ap.epadu
phot_data[j]['MAG'] = ap.zmag - 2.5 * np.log10(phot_data[j]['FLUX_ELEC']/ap.exptime)
if aperphot_data[10] == 'INDEF':
phot_data[j]['MERR'] = -10
else:
phot_data[j]['MERR'] = float(aperphot_data[10])
phot_data[j]['PIER'] = int(aperphot_data[11])
# Calculate error in flux - using the formula
# err = sqrt(flux * gain + npix * (1 + (npix/nsky)) * (flux_sky * gain + R**2))
phot_data[j]['FERR'] = np.sqrt(phot_data[j]['FLUX_ELEC'] + phot_data[j]['AREA'] * (1 + phot_data[j]['AREA']/phot_data[j]['NSKY']) * (phot_data[j]['MSKY'] * ap.epadu + ap.readnoise**2))
# Save a 51x51 image section of the object
xmin, xmax = int(phot_data[j]['XCEN']) - 25, int(phot_data[j]['XCEN']) + 25
ymin, ymax = int(phot_data[j]['YCEN']) - 25, int(phot_data[j]['YCEN']) + 25
img_sec.append(img[j, ymin:ymax, xmin:xmax])
# Save photometry of all the image cubes in a single file
total_phot_data.append(phot_data)
# If debug mode -
# 1. save DAOPHOT phot files
# 2. save individual phot data as npy file
# 3. Plot light cuve for each data cube separatly
if debug:
# Save photometry data in numpy binary format
print " Saving photometry data as numpy binary"
if output != "":
npy_outfile = output + ".npy"
else:
npy_outfile = coords + "_phot.npy"
if os.path.exists(npy_outfile):
os.remove(npy_outfile)
np.save(npy_outfile, phot_data)
# Plot first pass light curve
if plot_flag:
print " Plotting normalized light curve"
if output != "":
plt_outfile = output + ".png"
else:
plt_outfile = coords + "_lc.png"
plotter(phot_data, ap.nframes, ap.kintime, plt_outfile)
else:
# Delete intermediate files is not debug mode
iraf.delete(os.path.join(fpath, '*.phot.1'))
# Convert the total_phot_data to array and reshape it
print ' Saving consolidated photometry data...'
total_phot_data_arr = np.concatenate(total_phot_data)
# Save the array as npy file
np.save(coords + "_total.phot.npy", total_phot_data_arr)
# Save the image section with object as FITS file
print ' Saving image section with object as FITS image...'
img_sec_arr = np.asarray(img_sec)
img_fname = coords + "_obj.fits"
if os.path.exists(img_fname):
os.remove(img_fname)
chimera.fitswrite(img_sec_arr, coords + "_obj.fits", header = imghdr)
# Delete temporary coordinate file
if os.path.exists(tmp_coords):
os.remove(tmp_coords)
return
