def cog(self, window_size, method, tolerance=0.01):
"""
Curve of growth to determine nominal aperture for photometry using
astropy photutils.
Parameters
----------
tolerance : float
Magnitude difference tolerance between different apertures
Returns
-------
aperture : float
Nominal aperture radius for photmetry
"""
# Aperture values in pixels
apertures = np.linspace(2, 25, 24)
naper = apertures.shape[0]
# Read input image and star position
image = csusb.fitsread(self.sci_file)
pos = np.loadtxt(self.coords, ndmin=2)
nstars = pos.shape[0]
# Iterate through the frames and determine nominal aperture
mags_arr = np.zeros(len(apertures))
objpos = csusb.recenter(image, pos, window_size, method)
for i in range(naper):
flux = self.phot(image, objpos[0, :], aper=apertures[i])
mags_arr[i] = -2.5 * np.log10(flux["flux"])
mags_diff = np.diff(mags_arr)
idx = np.where(np.abs(mags_diff) < 0.01)
if len(idx[0]) != 0:
nom_aper = apertures[idx[0][0]]
else:
nom_aper = 16.0
return nom_aper
def process(infile, coords, method, inner_radius, outer_radius, cen_method, window_size, threshold, output):
"""
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
method : string
FWHM of the stelar psf in pixels
inner_radius : float
Sky background sigma
outer_radius : int
Inner sky annulus radius in pixels
Returns
-------
None
"""
print "FASTPHOT: CHIMERA Fast Aperture Photometry Routine"
inner_radius = float(inner_radius)
outer_radius = float(outer_radius)
# 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):
print "REGISTER: Not able to locate file %s" %infile
images = []
with open(infile, "r") as fd:
for line in fd.readlines():
if len(line) > 1:
images.append(line.replace("\n", ""))
else:
images = infile.split(",")
# Number of images
nimgs = len(images)
# Read star coordinate
pos = np.loadtxt(coords, ndmin = 2)
nstars = pos.shape[0]
dtype = [("DATETIME", "S25"),("XCEN", "f4"),("YCEN", "f4"),("MSKY", "f8"),("NSKY", "f8"),("AREA", "f8"),("FLUX_ADU", "f8"),("FLUX_ELEC", "f8"),("FERR", "f8"),("MAG", "f8")]
phot_data = np.zeros([nimgs, nstars], dtype = dtype)
for i in range(nimgs):
sci_file = images[i]
print " Processing science image %s" %sci_file
# Read FITS image
image = csusb.fitsread(sci_file)
# Instantiate an Aperphot object
ap = csusb.Aperphot(sci_file, coords)
# Set sigma, annulus and dannulus
ap.method = method
ap.inner_radius = inner_radius
ap.outer_radius = outer_radius
# Determine nominal aperture radius for photometry
if i == 0:
nom_aper = ap.cog(window_size, cen_method)
print " Nominal aperture radius : %4.1f pixels" %nom_aper
# Perform aperture photometry on all the frames
objpos = csusb.recenter(image, pos, window_size, cen_method, threshold)
aperphot_data = ap.phot(image, objpos, nom_aper)
pos = np.copy(objpos)
phot_data[i,:]['DATETIME'] = ap.utcstart
phot_data[i,:]['XCEN'] = aperphot_data["xcenter_raw"]
phot_data[i,:]['YCEN'] = aperphot_data["ycenter_raw"]
phot_data[i,:]['MSKY'] = aperphot_data["msky"]
phot_data[i,:]['NSKY'] = aperphot_data["nsky"]
phot_data[i,:]['AREA'] = aperphot_data["area"]
phot_data[i,:]['FLUX_ADU'] = aperphot_data["flux"]
phot_data[i,:]['FLUX_ELEC'] = phot_data[i,:]['FLUX_ADU'] * ap.epadu
phot_data[i,:]['MAG'] = ap.zmag - 2.5 * np.log10(phot_data[i,:]['FLUX_ELEC']/ap.exptime)
# Calculate error in flux - using the formula
# err = sqrt(flux * gain + npix * (1 + (npix/nsky)) * (flux_sky * gain + R**2))
phot_data[i,:]['FERR'] = np.sqrt(phot_data[i]['FLUX_ELEC'] + phot_data[i]['AREA'] * (1 + phot_data[i]['AREA']/phot_data[i]['NSKY']) * (phot_data[i]['MSKY'] * ap.epadu + ap.readnoise**2))
# Save photometry data in numpy binary format
print " Saving photometry data as numpy binary"
if output != "":
npy_outfile = output + ".npy"
else:
npy_outfile = sci_file.replace(".fits", ".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 = sci_file.replace(".fits", ".lc.png")
ts = np.zeros(nimgs, dtype = np.int32)
for i in range(nimgs):
ts[i] = timedelta(phot_data[0,0]['DATETIME'], phot_data[i,0]['DATETIME'])
plotter(phot_data, ts, plt_outfile)
return