def init_header_reduced(image):
'''
IQWCS = 1 or 0 / Indicates astrometry has been solved for the field
IQZEROPT = 1 or 0 /indicates if the zero point was calculated for the image
SKYBKG = FLOAT / Average sky background given in counts
SEEPIX = FLOAT / Seeing expressed in pixels
ZPCAT = 'String' / Catalog used to calculate zero point
ZEROPTU = FLOAT / Zero point uncertainty
ZEROPT = 'FLOAT' / Zero point by comparison to catalog
'''
pardic = {"IQWCS" : 0,\
"IQZEROPT" : 0,\
"SKYBKG": 0,\
"SEEPIX": 0,\
"ZPCAT" : "none",\
"ZEROPTU" : 0.,\
"ZEROPT" : 0.,\
"CRREJ" : 0}
fitsutils.update_pars(image, pardic)
def init_header_reduced(image):
'''
IQWCS = 1 or 0 / Indicates astrometry has been solved for the field
IQZEROPT = 1 or 0 /indicates if the zero point was calculated for the image
SKYBKG = FLOAT / Average sky background given in counts
SEEPIX = FLOAT / Seeing expressed in pixels
ZPCAT = 'String' / Catalog used to calculate zero point
ZEROPTU = FLOAT / Zero point uncertainty
ZEROPT = 'FLOAT' / Zero point by comparison to catalog
'''
pardic = {"IQWCS" : 0,\
"IQZEROPT" : 0,\
"SKYBKG": 0,\
"SEEPIX": 0,\
"ZPCAT" : "none",\
"ZEROPTU" : 0.,\
"ZEROPT" : 0.,\
"CRREJ" : 0}
fitsutils.update_pars(image, pardic)
def reduce_image(image, flatdir=None, biasdir=None, cosmic=False, astrometry=True, channel='rc', target_dir='reduced', overwrite=False):
'''
Applies Flat field and bias calibrations to the image.
Steps:
1. - Solve astrometry on the entire image.
2. - Computes cosmic ray rejectionon the entire image.
3. - Compute master bias (if it does not exist) and de-bias the image.
4. - Separate the image into 4 filters.
5. - Compute flat field for each filter (if it does not exist) and apply flat fielding on the image.
6. - Compute the image zeropoint.
'''
logger.info("Reducing image %s"% image)
print "Reducing image ", image
image = os.path.abspath(image)
imname = os.path.basename(image).replace(".fits", "")
try:
objectname = fitsutils.get_par(image, "NAME").replace(" ","")+"_"+fitsutils.get_par(image, "FILTER")
except:
logger.error( "ERROR, image "+ image + " does not have a NAME or a FILTER!!!")
return
print "For object", objectname
logger.info( "For object %s"% objectname)
#Change to image directory
mydir = os.path.dirname(image)
if mydir=="": mydir = "."
mydir = os.path.abspath(mydir)
os.chdir(mydir)
#Create destination directory
if (not os.path.isdir(target_dir)):
os.makedirs(target_dir)
#If we don't want to overwrite the already extracted images, we check wether they exist.
if (not overwrite):
existing = True
for band in ['u', 'g', 'r', 'i']:
destfile = os.path.join(target_dir, imname + "_f_b_a_%s_%s_0.fits"%(objectname, band))
logger.info( "Looking if file %s exists: %s"%( destfile, \
(os.path.isfile(destfile) ) ) )
existing = existing and (os.path.isfile( destfile ) )
if existing:
return []
#Initialize the basic parameters.
init_header_reduced(image)
astro = ""
if (astrometry):
logger.info( "Solving astometry for the whole image...")
img = solve_astrometry(image)
if (os.path.isfile(img)):
astro="a_"
fitsutils.update_par(img, "IQWCS", 1)
else:
logger.error( "ASTROMETRY DID NOT SOLVE ON IMAGE %s"% image)
img = image
#Update noise parameters needed for cosmic reection
if (fitsutils.get_par(img, "ADCSPEED")==2):
fitsutils.update_par(img, "RDNOISE", 20.)
else:
fitsutils.update_par(img, "RDNOISE", 4.)
if (cosmic):
logger.info( "Correcting for cosmic rays...")
# Correct for cosmics each filter
cleanimg = clean_cosmic(os.path.join(os.path.abspath(mydir), img))
img = cleanimg
#Get basic statistics for the image
nsrc, fwhm, ellip, bkg = sextractor.get_image_pars(img)
logger.info( "Sextractor statistics: nscr %d, fwhm (pixel) %.2f, ellipticity %.2f"% (nsrc, fwhm, ellip))
print "Sextractor statistics: nscr %d, fwhm (pixel) %.2f, ellipticity %.2f"% (nsrc, fwhm, ellip)
dic = {"SEEPIX": fwhm/0.394, "NSRC":nsrc, "ELLIP":ellip}
#Update the seeing information from sextractor
fitsutils.update_pars(img, dic)
#Compute BIAS
if (biasdir is None or biasdir==""): biasdir = "."
create_masterbias(biasdir)
bias_slow = os.path.join(biasdir, "Bias_%s_%s.fits"%(channel, 'slow'))
bias_fast = os.path.join(biasdir, "Bias_%s_%s.fits"%(channel, 'fast'))
# Running IRAF to DE-BIAS
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
#Compute flat field
if (flatdir is None or flatdir==""): flatdir = "."
create_masterflat(flatdir, biasdir)
#New names for the object.
debiased = os.path.join(os.path.dirname(img), "b_" + os.path.basename(img))
logger.info( "Creating debiased file, %s"%debiased)
if ( (fitsutils.get_par(img, "ADCSPEED")==0.1 and not os.path.isfile(bias_slow)) \
or (fitsutils.get_par(img, "ADCSPEED")==2 and not os.path.isfile(bias_fast)) ):
logger.warn( "Master bias not found! Tryting to copy from reference folder...")
copy_ref_calib(mydir, "Bias")
if ( (fitsutils.get_par(img, "ADCSPEED")==0.1 and not os.path.isfile(bias_slow)) \
or (fitsutils.get_par(img, "ADCSPEED")==2 and not os.path.isfile(bias_fast)) ):
logger.error( "Bias not found in reference folder")
return
#Clean first
if (os.path.isfile(debiased)):
os.remove(debiased)
#Debias
if (fitsutils.get_par(img, "ADCSPEED")==2):
iraf.imarith(img, "-", bias_fast, debiased)
fitsutils.update_par(debiased, "BIASFILE", bias_fast)
fitsutils.update_par(debiased, "RDNOISE", 20.)
else:
iraf.imarith(img, "-", bias_slow, debiased)
fitsutils.update_par(debiased, "BIASFILE", bias_slow)
fitsutils.update_par(debiased, "RDNOISE", 4.)
#Set negative counts to zero
hdu = fits.open(debiased)
header = hdu[0].header
hdu[0].data[hdu[0].data<0] = 0
hdu.writeto(debiased, clobber=True)
#Slicing the image for flats
slice_names = slice_rc(debiased)
print "Creating sliced files, ", slice_names
#Remove un-sliced image
os.remove(debiased)
# DE-flat each filter and store under object name
for i, debiased_f in enumerate(slice_names):
b = fitsutils.get_par(debiased_f, 'filter')
deflatted = os.path.join(os.path.dirname(image), target_dir, imname + "_f_b_" + astro + objectname + "_%s.fits"%b)
#Flat to be used for that filter
flat = os.path.join(flatdir, "Flat_%s_%s_norm.fits"%(channel, b))
if (not os.path.isfile(flat)):
logger.warn( "Master flat not found in %s"% flat)
copy_ref_calib(mydir, "Flat_%s_%s_norm"%(channel, b))
continue
else:
logger.info( "Using flat %s"%flat)
#Cleans the deflatted file if exists
if (os.path.isfile(deflatted)):
os.remove(deflatted)
if (os.path.isfile(debiased_f) and os.path.isfile(flat)):
logger.info( "Storing de-flatted %s as %s"%(debiased_f, deflatted))
time.sleep(1)
iraf.imarith(debiased_f, "/", flat, deflatted)
else:
logger.error( "SOMETHING IS WRONG. Error when dividing %s by the flat field %s!"%(debiased_f, flat))
#Removes the de-biased file
os.remove(debiased_f)
logger.info( "Updating header with original filename and flat field used.")
fitsutils.update_par(deflatted, "ORIGFILE", os.path.basename(image))
fitsutils.update_par(deflatted, "FLATFILE", flat)
slice_names[i] = deflatted
#Moving files to the target directory
for image in slice_names:
bkg = get_median_bkg(image)
fitsutils.update_par(image, "SKYBKG", bkg)
#shutil.move(name, newname)
#Compute the zeropoints
for image in slice_names:
zeropoint.calibrate_zeropoint(image)
return slice_names
def reduce_image(image,
flatdir=None,
biasdir=None,
cosmic=False,
astrometry=True,
channel='rc',
target_dir='reduced',
overwrite=False):
'''
Applies Flat field and bias calibrations to the image.
Steps:
1. - Solve astrometry on the entire image.
2. - Computes cosmic ray rejectionon the entire image.
3. - Compute master bias (if it does not exist) and de-bias the image.
4. - Separate the image into 4 filters.
5. - Compute flat field for each filter (if it does not exist) and apply flat fielding on the image.
6. - Compute the image zeropoint.
'''
logger.info("Reducing image %s" % image)
print "Reducing image ", image
image = os.path.abspath(image)
imname = os.path.basename(image).replace(".fits", "")
try:
objectname = fitsutils.get_par(image, "NAME").replace(
" ", "") + "_" + fitsutils.get_par(image, "FILTER")
except:
logger.error("ERROR, image " + image +
" does not have a NAME or a FILTER!!!")
return
print "For object", objectname
logger.info("For object %s" % objectname)
#Change to image directory
mydir = os.path.dirname(image)
if mydir == "": mydir = "."
mydir = os.path.abspath(mydir)
os.chdir(mydir)
#Create destination directory
if (not os.path.isdir(target_dir)):
os.makedirs(target_dir)
#If we don't want to overwrite the already extracted images, we check wether they exist.
if (not overwrite):
existing = True
for band in ['u', 'g', 'r', 'i']:
destfile = os.path.join(
target_dir,
imname + "_f_b_a_%s_%s_0.fits" % (objectname, band))
logger.info( "Looking if file %s exists: %s"%( destfile, \
(os.path.isfile(destfile) ) ) )
existing = existing and (os.path.isfile(destfile))
if existing:
return []
#Initialize the basic parameters.
init_header_reduced(image)
astro = ""
if (astrometry):
logger.info("Solving astometry for the whole image...")
img = solve_astrometry(image)
if (os.path.isfile(img)):
astro = "a_"
fitsutils.update_par(img, "IQWCS", 1)
else:
logger.error("ASTROMETRY DID NOT SOLVE ON IMAGE %s" % image)
img = image
#Update noise parameters needed for cosmic reection
if (fitsutils.get_par(img, "ADCSPEED") == 2):
fitsutils.update_par(img, "RDNOISE", 20.)
else:
fitsutils.update_par(img, "RDNOISE", 4.)
if (cosmic):
logger.info("Correcting for cosmic rays...")
# Correct for cosmics each filter
cleanimg = clean_cosmic(os.path.join(os.path.abspath(mydir), img))
img = cleanimg
#Compute BIAS
if (biasdir is None or biasdir == ""): biasdir = "."
create_masterbias(biasdir)
bias_slow = os.path.join(biasdir, "Bias_%s_%s.fits" % (channel, 'slow'))
bias_fast = os.path.join(biasdir, "Bias_%s_%s.fits" % (channel, 'fast'))
# Running IRAF to DE-BIAS
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
#Compute flat field
if (flatdir is None or flatdir == ""): flatdir = "."
create_masterflat(flatdir, biasdir)
#New names for the object.
debiased = os.path.join(os.path.dirname(img), "b_" + os.path.basename(img))
logger.info("Creating debiased file, %s" % debiased)
if ( (fitsutils.get_par(img, "ADCSPEED")==0.1 and not os.path.isfile(bias_slow)) \
or (fitsutils.get_par(img, "ADCSPEED")==2 and not os.path.isfile(bias_fast)) ):
logger.warn(
"Master bias not found! Tryting to copy from reference folder...")
copy_ref_calib(mydir, "Bias")
if ( (fitsutils.get_par(img, "ADCSPEED")==0.1 and not os.path.isfile(bias_slow)) \
or (fitsutils.get_par(img, "ADCSPEED")==2 and not os.path.isfile(bias_fast)) ):
logger.error("Bias not found in reference folder")
return
#Clean first
if (os.path.isfile(debiased)):
os.remove(debiased)
#Debias
if (fitsutils.get_par(img, "ADCSPEED") == 2):
iraf.imarith(img, "-", bias_fast, debiased)
fitsutils.update_par(debiased, "BIASFILE", bias_fast)
fitsutils.update_par(debiased, "RDNOISE", 20.)
else:
iraf.imarith(img, "-", bias_slow, debiased)
fitsutils.update_par(debiased, "BIASFILE", bias_slow)
fitsutils.update_par(debiased, "RDNOISE", 4.)
#Set negative counts to zero
hdu = fits.open(debiased)
header = hdu[0].header
hdu[0].data[hdu[0].data < 0] = 0
hdu.writeto(debiased, clobber=True)
#Slicing the image for flats
slice_names = slice_rc(debiased)
print "Creating sliced files, ", slice_names
#Remove un-sliced image
os.remove(debiased)
# DE-flat each filter and store under object name
for i, debiased_f in enumerate(slice_names):
b = fitsutils.get_par(debiased_f, 'filter')
deflatted = os.path.join(
os.path.dirname(image), target_dir,
imname + "_f_b_" + astro + objectname + "_%s.fits" % b)
#Flat to be used for that filter
flat = os.path.join(flatdir, "Flat_%s_%s_norm.fits" % (channel, b))
if (not os.path.isfile(flat)):
logger.warn("Master flat not found in %s" % flat)
copy_ref_calib(mydir, "Flat")
continue
else:
logger.info("Using flat %s" % flat)
#Cleans the deflatted file if exists
if (os.path.isfile(deflatted)):
os.remove(deflatted)
if (os.path.isfile(debiased_f) and os.path.isfile(flat)):
logger.info("Storing de-flatted %s as %s" %
(debiased_f, deflatted))
time.sleep(1)
iraf.imarith(debiased_f, "/", flat, deflatted)
else:
logger.error(
"SOMETHING IS WRONG. Error when dividing %s by the flat field %s!"
% (debiased_f, flat))
#Removes the de-biased file
os.remove(debiased_f)
logger.info(
"Updating header with original filename and flat field used.")
fitsutils.update_par(deflatted, "ORIGFILE", os.path.basename(image))
fitsutils.update_par(deflatted, "FLATFILE", flat)
slice_names[i] = deflatted
#Moving files to the target directory
for image in slice_names:
bkg = get_median_bkg(image)
fitsutils.update_par(image, "SKYBKG", bkg)
#Get basic statistics for the image
nsrc, fwhm, ellip, bkg = sextractor.get_image_pars(image)
plot_image(image)
logger.info(
"Sextractor statistics: nscr %d, fwhm (arcsec) %.2f, ellipticity %.2f"
% (nsrc, fwhm, ellip))
print "Sextractor statistics: nscr %d, fwhm (arcsec) %.2f, ellipticity %.2f" % (
nsrc, fwhm, ellip)
dic = {
"FWHM": np.round(fwhm, 3),
"FWHMPIX": np.round(fwhm / 0.394, 3),
"NSRC": nsrc,
"ELLIP": np.round(ellip, 3)
}
#Update the seeing information from sextractor
fitsutils.update_pars(image, dic)
#Compute the zeropoints
for image in slice_names:
zeropoint.calibrate_zeropoint(image)
return slice_names
def interpolate_zp(reduced, logfile):
"""
Uses the zeropoint coefficients derived from SDSS fields to interpolate the
zeropoint for the images that are outside of SDSS field.
"""
a = np.genfromtxt(logfile, dtype=None, names=True, delimiter=",")
a.sort(order=["jd"], axis=0)
a = a[a["inst"] != 0]
a = a[a["insterr"] > 0]
jdmin = np.min(a["jd"])
jdmax = np.max(a["jd"])
zpfiles = glob.glob(os.path.join(reduced, "*fits"))
zpfiles = [
zf
for zf in zpfiles
if fitsutils.has_par(zf, "IQZEROPT")
and (
fitsutils.get_par(zf, "IQZEROPT") == 0
or fitsutils.get_par(zf, "ZEROPT") == 0
or fitsutils.get_par(zf, "ZPCAT") == "SDSSinterpolated"
)
]
# Load the coefficients.
coefs = {}
for fi in ["u", "g", "r", "i"]:
coeffile = os.path.join(reduced, "coefs_%s.txt" % fi)
if os.path.isfile(coeffile):
coefs[fi] = np.genfromtxt(coeffile)
# Load the rms.
rms = {}
for fi in ["u", "g", "r", "i"]:
rmsfile = os.path.join(reduced, "rms_%s.txt" % fi)
if os.path.isfile(rmsfile):
rms[fi] = np.genfromtxt(os.path.join(reduced, "rms_%s.txt" % fi))
for image in zpfiles:
filt = fitsutils.get_par(image, "FILTER")
# To not extrapolate outside of the valid interval.
jd = np.maximum(np.percentile(a["jd"] - jdmin, 10), fitsutils.get_par(image, "JD") - jdmin)
jd = np.minimum(np.percentile(a["jd"] - jdmin, 90), fitsutils.get_par(image, "JD") - jdmin)
airmass = fitsutils.get_par(image, "AIRMASS")
# If there are coefficients for that filter, load them and interpolate.
# Otherwise, skip this file.
if not coefs.has_key(filt):
continue
# est_zp = coef[0] +ab['color']*coef[1] +(ab['airmass']-1.3)*coef[2] + coef[3]*ab['jd'] + coef[4]*ab['jd']**2 + coef[5]*ab['jd']**3 + coef[6]*ab['jd']**4 + coef[7]*ab['jd']**5
values = np.array([1, 0, airmass - 1.3, jd, jd ** 2])
est_zp = np.sum(coefs[filt] * values)
# Update the header with the computed zeropoint.
pardic = {"IQZEROPT": 1, "ZPCAT": "SDSSinterpolated", "ZEROPTU": float(rms[filt]), "ZEROPT": est_zp}
fitsutils.update_pars(image, pardic)
def calibrate_zp_fourshot(logfile, plot=True):
"""
The field of view is quite small, therefore, all rc shots are used to calibrate the zeropoint.
This routine retrieves all the stars taken with the same filter when pointing to the science object.
"""
a = np.genfromtxt(logfile, dtype=None, names=True, delimiter=",")
a.sort(order=["jd"], axis=0)
a = a[a["inst"] != 0]
plotdir = os.path.join(os.path.dirname(os.path.abspath(logfile)), "photometry")
for name in set(a["object"]):
for b in set(a["filter"]):
aib = a[(a["filter"] == b) * (a["object"] == name) * (np.abs(a["color"]) < 1)]
if len(aib) < 3:
print "Less than 3 stars found with %s %s" % (name, b)
continue
# First fit for the linear and detect the deviations
coefs, residuals, rank, singular_values, rcond = np.polyfit(
aib["std"],
aib["inst"],
w=1.0 / np.maximum(0.3, np.sqrt(aib["stderr"] ** 2 + aib["insterr"] ** 2)),
deg=1,
full=True,
)
p = np.poly1d(coefs)
if plot:
plt.figure()
plt.title("%s Filter %s" % (name, b))
plt.errorbar(aib["std"], aib["inst"], yerr=np.sqrt(aib["stderr"] ** 2 + aib["insterr"] ** 2), fmt="o")
plt.plot(aib["std"], p(aib["std"]))
diff = np.abs(aib["inst"] - p(aib["std"]))
mad = stats.funcs.median_absolute_deviation(diff)
aib = aib[diff < mad * 5]
if plot:
plt.errorbar(aib["std"], aib["inst"], yerr=np.sqrt(aib["stderr"] ** 2 + aib["insterr"] ** 2), fmt="o")
plt.plot(aib["std"], p(aib["std"]))
plt.savefig(os.path.join(plotdir, "zp_mag_mag_%s_%s.png" % (name, b)))
plt.close()
# Then fit for the colour
coefs, residuals, rank, singular_values, rcond = np.polyfit(
aib["color"],
aib["std"] - aib["inst"],
w=1.0 / np.maximum(0.15, np.sqrt(aib["stderr"] ** 2 + aib["insterr"] ** 2)),
deg=1,
full=True,
)
p = np.poly1d(coefs)
color, zp = coefs
mad = stats.funcs.median_absolute_deviation(p(aib["color"]) - (aib["std"] - aib["inst"]))
print coefs, residuals, mad
for f in aib["filename"]:
# Add these values to the header.
pardic = {
"IQZEROPT": 1,
"ZPCAT": "SDSS4shot",
"ZEROPTU": float("%.3f" % mad),
"ZEROPT": float("%.3f" % zp),
"ZP": float("%.3f" % zp),
"ZPERR": float("%.3f" % mad),
}
fitsutils.update_pars(f, pardic)
if plot:
plt.figure()
plt.title("%s Filter %s" % (name, b))
plt.errorbar(
aib["color"],
aib["std"] - aib["inst"],
yerr=np.sqrt(aib["stderr"] ** 2 + aib["insterr"] ** 2),
fmt="o",
)
plt.plot(aib["color"], p(aib["color"]))
plt.savefig(os.path.join(plotdir, "zp_col_mag_%s_%s.png" % (name, b)))
plt.close()
return
def calibrate_zeropoint(image, plot=True, plotdir=None, debug=False, refstars=None):
"""
Calibrates the zeropoint using SDSS catalogue.
"""
if plot and plotdir is None:
plotdir = os.path.join(os.path.dirname(image), "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
filt = fitsutils.get_par(image, "filter")
exptime = fitsutils.get_par(image, "exptime")
if fitsutils.has_par(image, "JD"):
date = fitsutils.get_par(image, "JD")
elif fitsutils.has_par(image, "MJD"):
date = fitsutils.get_par(image, "MJD")
elif fitsutils.has_par(image, "MJD-OBS"):
date = fitsutils.get_par(image, "MJD-OBS")
else:
date = 0
if fitsutils.has_par(image, "AIRMASS"):
airmass = fitsutils.get_par(image, "AIRMASS")
else:
airmass = 1.3
objname = fitsutils.get_par(image, "OBJECT")
band = fitsutils.get_par(image, "FILTER")
logger.info("Starting calibration of ZP for image %s for object %s with filter %s." % (image, objname, band))
if exptime < 10:
logger.error("ERROR. Exposure time too short for image (%s) to see anything..." % image)
return
extracted = extract_star_sequence(
os.path.abspath(image), filt, plot=plot, survey="sdss", debug=debug, refstars=refstars, plotdir=plotdir
)
if not extracted:
logger.warn(
"Field not in SDSS or error when retrieving the catalogue... Skipping. Image %s not zeropoint calibrated."
% image
)
# Add these values to the header.
pardic = {"IQZEROPT": 0, "ZPCAT": "None", "ZEROPTU": 0, "ZEROPT": 0, "ZP": 0, "ZPERR": 0}
fitsutils.update_pars(image, pardic)
return
# If extraction worked, we can get the FWHM
fwhm = fitsutils.get_par(image, "fwhm")
fwhm_as = fwhm * 0.394
app_phot.get_app_phot("/tmp/sdss_cat_det_%s.txt" % creationdate, image, wcsin="logic", plotdir=plotdir, box=20)
# Compute the zeropoint for the specific image.
z, c, err = find_zeropoint_noid("/tmp/sdss_cat_det_%s.txt" % creationdate, image, plot=plot, plotdir=plotdir)
# Add these values to the header.
pardic = {"IQZEROPT": 1, "ZPCAT": "SDSS", "ZEROPTU": np.round(err, 3), "ZEROPT": np.round(z, 3)}
fitsutils.update_pars(image, pardic)
# Log the current zeropoint for this image
logname = os.path.join(os.path.dirname(image), "zeropoint.log")
# Add the data to a later stage zeropoint calibrtion with all-sky data.
zplogname = os.path.join(os.path.dirname(image), "allstars_zp.log")
add_to_zp_cal("/tmp/sdss_cat_det_%s.txt" % creationdate, image, zplogname)
if not os.path.isfile(logname):
with open(logname, "a") as f:
f.write("#filename,exptime,filter,date,airmass,fwhm_pix,fwhm_as,zeropoint,color,err\n")
with open(logname, "a") as f:
f.write(
"%s,%.1f,%s,%3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f\n"
% (image, exptime, filt, date, airmass, fwhm, fwhm_as, z, c, err)
)
clean_tmp_files()