def load_catalog(filename=False,
header=False,
wcs=False,
ra_key=False,
dec_key=False):
'''
From Anna Marini: get positions from catalog.
'''
if filename and not header:
header = get_fits_header(filename)
if header and not wcs:
wcs = WCS(header)
if ra_key and dec_key:
ra = header[ra_key]
dec = header[dec_key]
ra = wcs.wcs.crval[0]
dec = wcs.wcs.crval[1]
# Diagonal
diag_bound = wcs.pixel_to_world_values([[0, 0], wcs.pixel_shape])
radius = np.mean(diag_bound[1] - diag_bound[0]) / 2
catalog = Catalogs.query_region(
f'{ra} {dec}',
# frame='icrs',
# unit="deg",
radius=radius,
catalog='Gaia',
version=2)
return catalog
def counts(filename,
bias1='1_CCD_Image_34.fits',
bias2='1_CCD_Image_47.fits',
bias3='1_CCD_Image_75.fits',
bias4='2_CCD_Image_191.fits',
bias5='2_CCD_Image_244.fits',
bias6='2_CCD_Image_277.fits',
bias7='3_CCD_Image_52.fits',
bias8='3_CCD_Image_103.fits',
bias9='3_CCD_Image_152.fits'):
header = get_fits_header(filename)
camera = header['INSTRUME']
temp = header['CCD-TEMP']
if camera == 'Atik Cameras':
if temp <= 0.3:
mean_sub = np.mean(
get_fits_data(filename)[1007:3007, 631:2040]) - np.mean(
get_fits_data(bias1))
elif 0.3 < temp <= 1.3:
mean_sub = np.mean(
get_fits_data(filename)[1007:3007, 631:2040]) - np.mean(
get_fits_data(bias2))
else:
mean_sub = np.mean(
get_fits_data(filename)[1007:3007, 631:2040]) - np.mean(
get_fits_data(bias3)) #intorno a 3
elif camera == 'SBIG STL-11000 3 CCD Camera w/ AO':
if -5.5 <= temp <= -3.7:
mean_sub = np.mean(
get_fits_data(filename)[500:3508, 500:2172]) - np.mean(
get_fits_data(bias4))
elif temp < -5.5:
mean_sub = np.mean(
get_fits_data(filename)[500:3508, 500:2172]) - np.mean(
get_fits_data(bias5))
else:
mean_sub = np.mean(
get_fits_data(filename)[500:3508, 500:2172]) - np.mean(
get_fits_data(bias6))
elif camera == 'SBIG STX-16801 3 CCD Camera w/ AO':
if temp <= -19.3:
mean_sub = np.mean(
get_fits_data(filename)[500:3500, 500:3500]) - np.mean(
get_fits_data(bias7))
elif -19.2 < temp <= -14.3:
mean_sub = np.mean(
get_fits_data(filename)[500:3500, 500:3500]) - np.mean(
get_fits_data(bias8))
else:
mean_sub = np.mean(
get_fits_data(filename)[500:3500, 500:3500]) - np.mean(
get_fits_data(bias9))
return (mean_sub)
def ins_temp(filename):
header = get_fits_header(filename)
instru = [header['INSTRUME']]
if 'CCD-TEMP' in header:
temp = [header['CCD-TEMP']]
else:
temp = [' ']
return(instru, temp)
def __init__(self, filenames, fast=FAST):
filenames = sorted(filenames)
self.filenames = filenames
lfil = len(filenames)
log.info(f"dfits {lfil} files. It can take some seconds.")
self.heads = [get_fits_header(f, fast=False) for f in filenames]
for i, p in enumerate(filenames):
self.heads[i]["FULLPATH"] = filenames[i]
self.data = self.heads
def instrument_temperature_time_dateobs(filename):
header = get_fits_header(filename)
instru = [header['INSTRUME']]
if 'CCD-TEMP' in header:
temp = [header['CCD-TEMP']]
else:
temp = [' ']
time = [header['EXPTIME']]
date_obs = header['DATE-OBS']
return (instru, temp, time, date_obs)
def tab(filenames):
filenames = sorted(filenames)
time_table = Table()
counts_unique_table = Table()
for filename in filenames:
header = get_fits_header(filename)
counts_unique = counts(filename)
time = header['EXPTIME']
time_table.add_column([time], rename_duplicate = True)
counts_unique_table.add_column([counts_unique], rename_duplicate = True)
return(time_table, counts_unique_table)
def signal_noise(filenames, my_instr="Mexman"):
for filename in filenames:
catalog = load_catalog(filename)
apers = set_apertures(catalog)
data = get_fits_data(filename)
header = get_fits_header(filename)
wcs = WCS(header)
phot_table = do_photometry(data, apers=apers, wcs=wcs)
pixar = apers[0].to_pixel(wcs)
pixan = apers[1].to_pixel(wcs)
bkg_mean = phot_table['aperture_sum_1'] / pixan.area
R_star = phot_table['residual_aperture_sum']
R_sky = bkg_mean * pixar.area
n_pix_aperture = pixan.area
with open('instruments.json') as jfile:
instrument_dict = json.load(jfile)
instrument = instrument_dict[my_instr]
# if header['INSTRUME'] == 'Mexman':
# key = instrument_dict['Mexman']
# elif header['INSTRUME'] == 'DFOSC_FASU':
# key = instrument_dict['DFOSC_FASU']
# elif header['INSTRUME'] == 'STL-11000 3 CCD Camera':
# key = instrument_dict['STL-11000 3 CCD Camera']
# else:
# log.warning('INSTRUMENT NOT FOUND ')
gain = instrument['gain']
RON = instrument['ron']
if gain is None:
log.warning(f'Gain not found for {instrument}')
if ron is None:
log.warning(f'Ron not found for {instrument}')
signal_noise = (R_star)/[(R_star + R_sky + RON + (gain/2)**2)**1/2]
# for now I'm neglecting Dark Current effects
return(signal_noise)
flats = glob.glob("gj3470/*/flat/*.fit*")
keys = ['ccdxbin', 'filter']
mbias = 'arp.MBIAS.ccdxbin=2.fits'
master_flat(flats, keys, mbias=mbias)
obj_all = glob.glob("gj3470/*/object/*.fit*")
objects = Dfits(obj_all, fast=True).fitsort(['object']).unique_names_for(
('GJ3470', ))
mflat = "arp.MFLAT.ccdxbin=2.filter=vacio+V3.fits"
correct_image(objects, keys, mbias=mbias, mflat=mflat, new_header="Mexman")
solver("*CLEAN*fits")
solved = sorted(glob.glob("solved/*CLEAN*.new"))
datas = [get_fits_header(f, fast=True)["MJD-OBS"] for f in solved]
airmass = [get_fits_header(f, fast=True)["AIRMASS"] for f in solved]
tables = apphot(solved, r=6, r_in=15.5, r_out=25)
tabellone = np.array([tables[0][k] for k in tables[0].keys()])
tabellone = np.insert(tabellone, 0, [datas, airmass], axis=1)
errorone = np.array([tables[1][k] for k in tables[1].keys()])
ascii.write(np.concatenate((tabellone, errorone), axis=1),
"tabellone.txt",
overwrite=True)
#plot f u ($1-58800):(-2.5*log10($5/($4+$14+$16))) w lp pt 7, g u ($2-2458800):($8+2.5*log10(10**(-$10*.4)+10**(-$12*.4)+10**(-$13*0.4) ))-0.000 w lp pt 7 lc rgb "orange"
####################################
# Main
####################################
def apphot(filenames,
reference=0,
display=DISPLAY,
r=False,
r_in=False,
r_out=False):
'''
Perform the aperture photometry
'''
filenames = sorted(filenames)
header0 = get_fits_header(filenames[reference])
wcs0 = WCS(header0)
catalog = load_catalog(wcs=wcs0)
if r and r_in and r_out:
apers = set_apertures(catalog, r=r, r_in=r_in, r_out=r_out)
else:
apers = set_apertures(catalog)
tables = Table()
err_table = Table()
if display:
d = pyds9.DS9("ds9")
for filename in filenames:
header = get_fits_header(filename)
data = get_fits_data(filename)
wcs = WCS(header)
#catalog = load_catalog(wcs=wcs)
#apers = set_apertures(catalog, r=r, r_in=r_in, r_out=r_out)
ron, gain, dark_current = ron_gain_dark()
phot_table = do_photometry(data,
apers,
wcs,
ron,
gain,
dark_current,
obstime=header['MJD-OBS'])
# phot_table = do_photometry(data, apers, wcs, obstime=header['MJD-OBS'],
# flux=False, zero_point_flux=1)
# positions = SkyCoord(catalog['ra'], catalog['dec'],
# frame='icrs',
# unit=(u.deg, u.deg))
if display:
d.set(f"file {filename}")
# for i,pos in enumerate(positions):
# p = pos.to_pixel(wcs)
# circ = f'circle({p[0]}, {p[1]}, {10})'
# d.set("regions", circ)
# d.set("region", f"text {p[0]} {p[1]} "+"{"+str(i)+"}")
for i, aper in enumerate(apers[0].to_pixel(wcs)):
circ = f'circle({aper.positions[0]}, {aper.positions[1]}, {aper.r})'
d.set("regions", circ)
d.set(
"region",
f"text {aper.positions[0]}, {aper.positions[1]} " + "{" +
str(i) + "}")
for aper in apers[1].to_pixel(wcs):
circ = f'circle({aper.positions[0]}, {aper.positions[1]}, {aper.r_in})'
d.set("regions", circ)
circ = f'circle({aper.positions[0]}, {aper.positions[1]}, {aper.r_out})'
d.set("regions", circ)
tables.add_column(phot_table["residual_aperture_sum"],
rename_duplicate=True)
err_table.add_column(phot_table["error"], rename_duplicate=True)
log.info(f"Done {filename}")
return tables, err_table
def filename(self, value):
'''
Setter for filename
'''
self._filename = value
self.head = get_fits_header(value)
