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.array([2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
naper = apertures.shape[0]
# Randomly perform curve of growth on 5 frames
framenum = np.random.randint(1, self.nframes, 5)
apertures = np.linspace(2,20,19)
# Read input image and star position
image = chimera.fitsread(self.sci_file)
pos = np.loadtxt(self.coords, ndmin = 2)
# Iterate through the frames and determine nominal aperture
nom_aper = np.zeros(5, dtype = np.float32)
cnt = 0
for val in framenum:
mags_arr = np.zeros(len(apertures))
objpos = chimera.recenter(image[val,:,:], pos, window_size, method)
for i in range(naper):
flux = self.phot(image[val,:,:], objpos, aper = apertures[i])
try:
mags_arr[i] = -2.5 * np.log10(flux['flux'])
except:
mags_arr[i] = -2.5 * np.log10(flux['flux'][1])
mags_diff = np.diff(mags_arr)
idx = np.where((np.abs(mags_diff) < 0.01) & (np.abs(mags_diff) != 0.0))
if len(idx[0]) != 0:
nom_aper[cnt] = apertures[idx[0][0]]
else:
nom_aper[cnt] = 10.0
cnt += 1
return np.median(nom_aper)
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.array([2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
naper = apertures.shape[0]
# Randomly peform curve of growth on 5 frames
framenum = np.random.randint(1, self.nframes, 5)
apertures = np.linspace(2,20,19)
# Read input image and star position
image = chimera.fitsread(self.sci_file)
pos = np.loadtxt(self.coords, ndmin = 2)
# Iterate through the frames and determine nominal aperture
nom_aper = np.zeros(5, dtype = np.float32)
cnt = 0
for val in framenum:
mags_arr = np.zeros(len(apertures))
objpos = chimera.recenter(image[val,:,:], pos, window_size, method)
for i in range(naper):
flux = self.phot(image[val,:,:], objpos, 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[cnt] = apertures[idx[0][0]]
else:
nom_aper[cnt] = 12.0
cnt += 1
return np.median(nom_aper)
def process(infile, coords, method, inner_radius, outer_radius, cen_method,
window_size, output, zmag, avg):
"""
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
cen_method : string
Centroid method
window_size : int
Centroid finding window size in pixels
output : string
Output file name
zmag : float
Photometric zero point
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)
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)
pos = np.loadtxt(coords, ndmin=2)
nstars = len(pos)
total_phot_data = []
for i in range(ncubes):
sci_file = image_cubes[i]
print(" Processing science image %s" % sci_file)
# Read FITS image and star coordinate
image = chimera.fitsread(sci_file)
if avg != "":
image = time_average(image, avg)
avg = int(avg)
else:
avg = 1
# Instantiate an Aperphot object
ap = chimera.Aperphot(sci_file, coords)
# Set fwhmpsf, sigma, annulus, dannulus and zmag
ap.method = method
ap.inner_radius = inner_radius
ap.outer_radius = outer_radius
if zmag != "":
ap.zmag = float(zmag)
# Determine nominal aperture radius for photometry
if i == 0:
nom_aper = ap.cog(window_size, cen_method)
#nom_aper = 3
print(" Nominal aperture radius : %4.1f pixels" % nom_aper)
# Perform aperture photometry on all the frames
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([nstars, ap.nframes / avg], dtype=dtype)
for j in range(ap.nframes / avg):
print(" Processing frame number : %d" % (j + 1))
objpos = chimera.recenter(image[j, :, :], pos, window_size,
cen_method)
aperphot_data = ap.phot(image[j, :, :], objpos, nom_aper)
pos = np.copy(objpos)
phot_data[:, j]['DATETIME'] = ap.addtime(j * avg *
ap.kintime).isoformat()
phot_data[:, j]['XCEN'] = aperphot_data["xcenter_raw"]
phot_data[:, j]['YCEN'] = aperphot_data["ycenter_raw"]
phot_data[:, j]['MSKY'] = aperphot_data["msky"]
phot_data[:, j]['NSKY'] = aperphot_data["nsky"]
phot_data[:, j]['AREA'] = aperphot_data["area"]
phot_data[:, j]['FLUX_ADU'] = aperphot_data["flux"]
phot_data[:,
j]['FLUX_ELEC'] = phot_data[:, j]['FLUX_ADU'] * ap.epadu
phot_data[:, j]['MAG'] = ap.zmag - 2.5 * np.log10(
phot_data[:, j]['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[:, 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))
total_phot_data.append(phot_data)
# 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")
plotter(phot_data, ap.nframes, ap.kintime, plt_outfile)
'''
# Convert the total_phot_data to array and reshape it
print(' Saving consolidated photometry data...')
total_phot_data_arr = np.concatenate(total_phot_data, axis=1)
# Save the array as npy file
if output != "":
np.save(output + "phot_total.npy", total_phot_data_arr)
else:
np.save("phot_total.npy", total_phot_data_arr)
return
