def do_phot(image,position,radius = 5, r_in=15., r_out=20.):
aperture = CircularAperture(position,r=radius)
bkg_aperture = CircularAnnulus(position,r_in=r_in,r_out=r_out)
# perform the photometry; the default method is 'exact'
phot = aperture_photometry(image, aperture)
bkg = aperture_photometry(image, bkg_aperture)
# calculate the mean background level (per pixel) in the annuli
bkg_mean = bkg['aperture_sum'] / bkg_aperture.area()
bkg_sum = bkg_mean * aperture.area()
#look at ipython notebook; his may need editing; 'phot' in second line below may need brackets with 'flux_sum' inside
#phot['bkg_sum'] = bkg_sum
#phot['flux_sum'] = phot['flux'] - bkg_sum
#these two lines from ipython notebook
flux_bkgsub = phot['aperture_sum'] - bkg_sum
phot['aperture_sum_bkgsub'] = flux_bkgsub
return phot
def GetAP(cut, theta, reso=0.5):
"""
Performs aperture photometry.
Calculates the average value of the cutout within a circle of radius theta minus the average in the outer ring [theta, sqrt(2)*theta]
Params
------
- cut: the cutout (numpy array)
- theta: radius (float, in arcmin)
- reso: pixel resolution (float, in arcmin/pix)
"""
theta_R_pix = theta / reso
positions = [(cut.shape[1] / 2., cut.shape[0] / 2.)]
apertures = CircularAperture(positions, r=theta_R_pix)
annulus_apertures = CircularAnnulus(positions,
r_in=theta_R_pix,
r_out=theta_R_pix * np.sqrt(2))
apers = [apertures, annulus_apertures]
phot_table = aperture_photometry(cut, apers)
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
return phot_table['residual_aperture_sum'][0] / apertures.area()
def snr_astropy(image,sx,sy,r,r_in,r_out):
"""
Returns signal to noise ratio, signal value, noise value using Astropy's Photutils module
Args:
image (2d float array): Image array extracted from fits file
sx,sy (float): x and y pixel locations for the center of the source
r (float): radius for aperture
r_in,r_out (float): inner and outer radii for annulus
Return:
snr (float): signal-to-noise ratio
signal (float): sum of all pixel values within the specified aperture minus sky background
noise (float): noise in the signal calculated as std deviation of pixels in the sky annulus times sqrt of the area of the
signal aperture
poisson_noise (float): snr determined from the poisson noise in the source signal. Poisson noise = sqrt(counts) [because variance
of a poisson distribution is the parameter itself]. Poisson snr = Signal/sqrt(signal) = sqrt(signal)
Written by: Logan Pearce, 2018
"""
import warnings
warnings.filterwarnings('ignore')
from photutils import CircularAperture, CircularAnnulus
positions = (cx-1,cy-1)
ap = CircularAperture(positions,r=r)
skyan = CircularAnnulus(positions,r_in=11,r_out=14)
apsum = ap.do_photometry(image)[0]
skysum = skyan.do_photometry(image)[0]
averagesky = skysum/skyan.area()
signal = (apsum - ap.area()*averagesky)[0]
n = ap.area()
ap_an = aperture_annulus(sx,sy,r,r_in,r_out)
skyan = ap_an[1]
poisson_noise = np.sqrt(signal)
noise = noise = np.std(image[skyan[1],skyan[0]])
noise = noise*np.sqrt(n)
snr = signal/noise
return snr,signal,noise,poisson_noise
def do_phot(fname, datadir, phot_dir_name, apsize, annsize):
hdul = fits.open(fname)
if '.fz' in fname:
data = hdul[1].data
header = hdul[1].header
hdul.close()
else:
data = hdul[0].data
header = hdul[0].header
hdul.close()
gain = header.get('GAIN')
if gain == None:
a = 1
else:
data = data.clip(min=0)
rdnoise = header['RDNOISE']
error = np.sqrt(data * gain) + rdnoise
mean, median, std = sigma_clipped_stats(data, sigma=3.0, iters=5)
daofind = DAOStarFinder(fwhm=5.0, threshold=5. * std)
sources = daofind(data - median)
positions = (sources['xcentroid'], sources['ycentroid'])
apertures_pix = CircularAperture(positions, r=apsize)
annulus_pix = CircularAnnulus(positions,
r_in=annsize[0],
r_out=annsize[1])
apertures_sky = apertures_pix.to_sky(wcs.WCS(header))
annulus_sky = annulus_pix.to_sky(wcs.WCS(header))
apers = [apertures_sky, annulus_sky]
# plt.imshow(data)#, cmap='Greys', origin='lower', norm=norm)
# apertures_pix.plot(color='white', lw=1.5, alpha=0.5)
# annulus_pix.plot(color='white', lw=1.5, alpha=0.5)
# plt.show()
# pdb.set_trace()
phot_table = aperture_photometry(data,
apers,
wcs=wcs.WCS(header),
error=error)
bkg_mean = phot_table['aperture_sum_1'] / annulus_pix.area()
bkg_sum = bkg_mean * apertures_pix.area()
apertures_pix.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
final_error = np.sqrt(phot_table['aperture_sum_err_0']**2 +
phot_table['aperture_sum_err_1']**2)
phot_table['residual_aperture_sum'] = final_sum
phot_table['residual_err'] = final_error
fname = datadir + phot_dir_name + "phot_table_{0:.7f}_{1}s.txt".format(
header['MJD-OBS'], header['EXPTIME'])
phot_table.write(fname, format='ascii')
replace_comma(fname)
def measure_source_fwhm(detection, data, rmax=10):
"""
TO USE CAREFULLY
Function used to estimate the FWHM of a source.
It performs aperture photometry with inscreasing radius from the source position, and then, tries to find where half of the maximum of flux is reached.
Parameters
----------
detection : :class:`~pandas:pandas.Series`
Row of a DataFrame containing detected sources. Should have columns ``xcentroid`` and ``ycentroid``, in classic convention (not astropy)
data : 2D :class:`~numpy:numpy.ndarray`
Flux map used for the photometry
"""
x, y = np.array(detection[['xcentroid', 'ycentroid']])
photo_flux = np.zeros(rmax)
for r in range(rmax):
if r == 0:
aper = CircularAperture((x, y), 1.)
else:
aper = CircularAnnulus((x, y), r, r + 1)
photo_flux[r] = aper.do_photometry(data)[0] / aper.area()
def get_fwhm(flux):
#We assume max is on 0. If not, source is probably contaminated
flux = flux - flux.min()
spline = UnivariateSpline(np.arange(rmax), flux - flux[0] / 2., s=0)
if spline.roots().shape != (1, ):
return np.nan
return spline.roots()[0]
return (get_fwhm(photo_flux))
def ap_phot(sources, data, source_r=6., sky_in=15, sky_out=20, plot=False):
global fig
centroids = (sources['xcentroid'], sources['ycentroid'])
source_aperture = CircularAperture(centroids, r=source_r)
source_area = source_aperture.area()
source_table = aperture_photometry(data, source_aperture)
sky_aperture = CircularAnnulus(centroids, r_in=sky_in, r_out=sky_out)
sky_area = sky_aperture.area()
sky_table = aperture_photometry(data, sky_aperture)
sky_subtracted_source = deepcopy(source_table)
for i in range(np.shape(centroids)[1]):
sky_value = sky_table[i][3]
sky_per_pix = sky_value / sky_area
sky_behind_source = sky_per_pix * source_area
sky_subtracted_source[i][3] -= sky_behind_source
if plot:
fig = plt.figure(figsize=(17, 17))
plt.imshow(data, cmap='gray', origin='lower', vmin=0, vmax=1500)
for i in range(np.shape(centroids)[1]):
plt.annotate(str(source_table[i][0]),
xy=(np.float64(source_table[i][1]) + 15.,
np.float64(source_table[i][2]) + 15.),
color="white")
source_aperture.plot(color='blue', lw=1.5, alpha=0.5)
sky_aperture.plot(color="orange", lw=0.5, alpha=0.5)
plt.tight_layout()
plt.show()
return sky_subtracted_source
def autocorr(self, ell_mask_scale=2, aperture_radius=5, annulus_width=4):
# Compute 2D autocorrelation function
try:
masked_image = self.masked_image.copy()
if ell_mask_scale > 0:
masked_image.mask |= ~self.elliptical_mask(ell_mask_scale)
masked_image = masked_image.filled(0.0)
fft_imgae = np.fft.fft2(masked_image)
acorr_image = np.fft.ifft2(fft_imgae * np.conjugate(fft_imgae)).real
acorr_image = np.fft.ifftshift(acorr_image)
ny, nx = masked_image.shape
yy, xx = np.mgrid[:ny, :nx]
circ = CircularAperture([nx // 2, ny // 2], r=aperture_radius)
ann = CircularAnnulus([nx // 2, ny // 2],
r_in=aperture_radius,
r_out=aperture_radius + annulus_width)
ann_mean = aperture_photometry(
acorr_image, ann)['aperture_sum'][0] / ann.area()
circ_mean = aperture_photometry(
acorr_image, circ)['aperture_sum'][0] / circ.area()
except:
acorr_image = np.nan
circ_mean = np.nan
ann_mean = np.nan
return acorr_image, circ_mean, ann_mean
def get_flux(frame, background, centroids, r, n, x, y):
data = np.nan_to_num(frame)
pixel_fluxes = []
for i in range(y - n / 2, y + n / 2 + 1):
for j in range(x - n / 2, x + n / 2 + 1):
pixel_fluxes.append(data[i][j] - background)
pixel_fluxes_sum = np.sum(pixel_fluxes)
pixel_fluxes = [i / pixel_fluxes_sum for i in pixel_fluxes]
#calculate flux as sum of discrete pixels
if r == 'pld':
flux = pixel_fluxes_sum
return [flux] + pixel_fluxes
#calculate flux using a variable radius
if r == 'var':
#calculate beta, noise pixel parameter from
#http://iopscience.iop.org/article/10.1088/0004-637X/766/2/95/pdf
b = np.sum(frame)**2 / (np.sum(frame**2))
star_aperture = CircularAperture(centroids, r=b**0.5)
else:
star_aperture = CircularAperture(centroids, r=r)
rawflux = aperture_photometry(data, star_aperture)
#how much to remove from star aperture
bkg_sum = background * star_aperture.area()
flux = rawflux['aperture_sum'] - bkg_sum
return [flux[0]] + pixel_fluxes
def aper_phot(data, header, positions, aper, data_out, gain=1, rdnoise=0):
exptime = header['EXPTIME']
apertures = CircularAperture(positions, r=aper[0])
annulus_apertures = CircularAnnulus(positions, r_in=aper[1], r_out=aper[2])
annulus_masks = annulus_apertures.to_mask(method='center')
bkg_median = []
for mask in annulus_masks:
annulus_data = mask.multiply(data)
annulus_data_1d = annulus_data[mask.data > 0]
_, median_sigclip, _ = sigma_clipped_stats(annulus_data_1d)
bkg_median.append(median_sigclip)
bkg_intensity = np.array(bkg_median) * apertures.area() * gain
phot = aperture_photometry(data, apertures)
phot['signal_intensity'] = phot['aperture_sum'] * gain - bkg_intensity
phot['SNR'] = phot['signal_intensity'] / (phot['signal_intensity'] +
bkg_intensity + rdnoise**2)**0.5
phot['magnitude'] = -2.5 * np.log10(
phot['signal_intensity'] / exptime) + 24
phot['delta_magnitude'] = 2.5 * np.log10(1 + 1 / phot['SNR'])
for col in phot.colnames:
phot[col].info.format = '%.4f' # for consistent table output
imageshow(data, positions, aper=aper)
return phot
def autocorr(self, ell_mask_scale=2, aperture_radius=5, annulus_width=4):
# Compute 2D autocorrelation function
try:
masked_image = self.masked_image.copy()
if ell_mask_scale > 0:
masked_image.mask |= ~self.elliptical_mask(ell_mask_scale)
masked_image = masked_image.filled(0.0)
fft_imgae = np.fft.fft2(masked_image)
acorr_image = np.fft.ifft2(fft_imgae *
np.conjugate(fft_imgae)).real
acorr_image = np.fft.ifftshift(acorr_image)
ny, nx = masked_image.shape
yy, xx = np.mgrid[:ny, :nx]
circ = CircularAperture([nx // 2, ny // 2], r=aperture_radius)
ann = CircularAnnulus([nx // 2, ny // 2],
r_in=aperture_radius,
r_out=aperture_radius + annulus_width)
ann_mean = aperture_photometry(
acorr_image, ann)['aperture_sum'][0] / ann.area()
circ_mean = aperture_photometry(
acorr_image, circ)['aperture_sum'][0] / circ.area()
except:
acorr_image = np.nan
circ_mean = np.nan
ann_mean = np.nan
return acorr_image, circ_mean, ann_mean
def compute_snr(x, y):
positions = (x - 1, y - 1)
ap = CircularAperture(positions, r=radius)
skyan = CircularAnnulus(positions, r_in=11, r_out=14)
ap = CircularAperture(positions, r=radius)
skyan = CircularAnnulus(positions, r_in=11, r_out=14)
apsum = ap.do_photometry(image)[0]
skysum = skyan.do_photometry(image)[0]
averagesky = skysum / skyan.area()
signal = (apsum - ap.area() * averagesky)[0]
n = ap.area()
box = image[y + 12:y + 12 + 15, x + 12:x + 12 + 15]
noise = np.std(box)
noise = noise * np.sqrt(n)
snr = signal / noise
return snr
def do_Circ_phot(pos, FWHM, ap_min=3., ap_factor=1.5, rin=None, rout=None, \
sky_nsigma=3., sky_iter=10):
'''
In rigorous manner, we should use
error = sqrt (flux / epadu + area * stdev**2 + area**2 * stdev**2 / nsky)
as in http://stsdas.stsci.edu/cgi-bin/gethelp.cgi?phot .
Here flux == aperture_sum - sky, i.e., flux from image_reduc.
stdev should include sky error AND ronoise.
'''
if rin == None:
rin = 4 * FWHM
if rout == None:
rout = 6 * FWHM
N = len(pos)
if pos.ndim == 1:
N = 1
an = CircAn(pos, r_in=rin, r_out=rout)
ap_size = np.max([ap_min, ap_factor*FWHM])
aperture = CircAp(pos, r=ap_size)
flux = aperture.do_photometry(image_reduc, method='exact')[0]
# do phot and get sum from aperture. [0] is sum and [1] is error.
#For test:
#N=len(pos_star_fit)
#an = CircAn(pos_star_fit, r_in=4*FWHM_moffat, r_out=6*FWHM_moffat)
#ap_size = 1.5*FWHM_moffat
#aperture = CircAp(pos_star_fit, r=ap_size)
#flux = aperture.do_photometry(image_reduc, method='exact')[0]
flux_ss = np.zeros(N)
error = np.zeros(N)
for i in range(0, N):
mask_an = (an.to_mask(method='center'))[i]
# cf: test = mask_an.cutout(image_reduc) <-- will make cutout image.
sky_an = mask_an.apply(image_reduc)
all_sky = sky_an[np.nonzero(sky_an)]
# only annulus region will be saved as np.ndarray
msky, stdev, nsky, nrej = sky_fit(all_sky, method='Mode', mode_option='sex')
area = aperture.area()
flux_ss[i] = flux[i] - msky*area # sky subtracted flux
error[i] = np.sqrt( flux_ss[i]/gain \
+ area * stdev**2 \
+ area**2 * stdev**2 / nsky )
# To know rejected number, uncomment the following.
# plt.imshow(sky_an, cmap='gray_r', vmin=-20)
# plt.colorbar()
# plt.show()
# mask_ap = (aperture.to_mask(method='exact'))[i]
# star_ap = mask_ap.apply(image_reduc)
# plt.imshow(star_ap)
# plt.colorbar()
# plt.show()
# plt.cla()
if pos.ndim > 1:
print('\t[{x:7.2f}, {y:7.2f}], {nsky:3d} {nrej:3d} {msky:7.2f} {stdev:7.2f} {flux:7.1f} {ferr:3.1f}'.format(\
x=pos[i][0], y=pos[i][1], \
nsky=nsky, nrej=nrej, msky=msky, stdev=stdev,\
flux=flux_ss[i], ferr=error[i]))
return flux_ss, error
def GetApertureTau(self,
M,
z,
theta_R,
x_c=0.5,
gamma=-0.2,
reso=0.2,
theta_max=10.,
chi=1.,
fwhm_arcmin=0.,
profile='Battaglia',
lowpass=False):
# if theta_R > theta_max:
# theta_max = theta_R * 2.
tau_profile = self.GetTauProfile(M,
z,
x_c=x_c,
gamma=gamma,
reso=reso,
theta_max=theta_max,
chi=chi,
fwhm_arcmin=fwhm_arcmin,
profile=profile,
lowpass=lowpass)
theta_R_pix = theta_R / reso
apertures = CircularAperture(
[(tau_profile.shape[1] / 2., tau_profile.shape[0] / 2.)],
r=theta_R_pix)
annulus_apertures = CircularAnnulus(
[(tau_profile.shape[1] / 2., tau_profile.shape[0] / 2.)],
r_in=theta_R_pix,
r_out=theta_R_pix * np.sqrt(2))
apers = [apertures, annulus_apertures]
phot_table = aperture_photometry(tau_profile, apers)
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
return phot_table['residual_aperture_sum'][0] / apertures.area()
def phot(self, image, objpos, aper):
"""
Aperture photometry using Astropy's photutils.
Parameters
----------
image : numpy array
2D image array
objpos : list of tuple
Object poistions as list of tuples
aper : float
Aperture radius in pixels
Returns
-------
phot_table : astropy table
Output table with stellar photometry
"""
try:
from astropy.table import hstack
from photutils import aperture_photometry, CircularAnnulus, CircularAperture
except ImportError:
pass
apertures = CircularAperture(objpos, r = aper)
annulus_apertures = CircularAnnulus(objpos, r_in = self.inner_radius, r_out = self.outer_radius)
rawflux_table = aperture_photometry(image, apertures = apertures, method = self.method)
bkgflux_table = aperture_photometry(image, apertures = annulus_apertures, method = self.method)
phot_table = hstack([rawflux_table, bkgflux_table], table_names = ["raw", "bkg"])
bkg = phot_table["aperture_sum_bkg"] / annulus_apertures.area()
phot_table["msky"] = bkg
phot_table["area"] = apertures.area()
phot_table["nsky"] = annulus_apertures.area()
bkg_sum = bkg * apertures.area()
final_sum = phot_table["aperture_sum_raw"] - bkg_sum
phot_table["flux"] = final_sum
return phot_table
def phot(self, image, objpos, aper):
"""
Aperture photometry using Astropy's photutils.
Parameters
----------
image : numpy array
2D image array
objpos : list of tuple
Object poistions as list of tuples
aper : float
Aperture radius in pixels
Returns
-------
phot_table : astropy table
Output table with stellar photometry
"""
try:
from astropy.table import hstack
from photutils import aperture_photometry, CircularAnnulus, CircularAperture
except ImportError:
pass
apertures = CircularAperture(objpos, r = aper)
annulus_apertures = CircularAnnulus(objpos, r_in = self.inner_radius, r_out = self.outer_radius)
rawflux_table = aperture_photometry(image, apertures = apertures, method = self.method)
bkgflux_table = aperture_photometry(image, apertures = annulus_apertures, method = self.method)
phot_table = hstack([rawflux_table, bkgflux_table], table_names = ["raw", "bkg"])
bkg = phot_table["aperture_sum_bkg"] / annulus_apertures.area()
phot_table["msky"] = bkg
phot_table["area"] = apertures.area()
phot_table["nsky"] = annulus_apertures.area()
bkg_sum = bkg * apertures.area()
final_sum = phot_table["aperture_sum_raw"] - bkg_sum
phot_table["flux"] = final_sum
return phot_table
def ap_phot(sources, data, source_r=3.):
global fig
centroids = (sources['xcentroid'], sources['ycentroid'])
source_aperture = CircularAperture(centroids, r=source_r)
source_area = source_aperture.area()
source_table = aperture_photometry(data, source_aperture)
sky_aperture = CircularAperture((290, 130), r=3.)
sky_area = sky_aperture.area()
sky_table = aperture_photometry(data, sky_aperture)
sky_subtracted_source = deepcopy(source_table)
for i in range(np.shape(centroids)[1]):
sky_value = sky_table[0][3]
sky_per_pix = sky_value / sky_area
sky_behind_source = sky_per_pix * source_area
sky_subtracted_source[i][3] -= sky_behind_source
return sky_subtracted_source
def ap_phot(im, cen, r1, r2, r3):
"""
Simple aperture photometry, using a circular source aperture and sky annulus.
"""
apertures = CircularAperture(cen, r1)
annulus_apertures = CircularAnnulus(cen, r2, r3)
apers = [apertures, annulus_apertures]
phot_table = aperture_photometry(im, apers)
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
return float(final_sum)
def aperture_photometry(self, image, positions, r, r_int, r_ext):
# Define apertures and anulluses at all found sources:
object_aperture = CircularAperture(positions, r)
background_annulus = CircularAnnulus(positions, r_int, r_ext)
# Calculate the flux in each aperture (star, and background, and subtract)
backgound_solution = aperture_photometry(image, background_annulus)
object_solution = aperture_photometry(image, object_aperture)
background_per_pixel = backgound_solution[
"aperture_sum"] / background_annulus.area()
object_solution[
"aperture_sum"] -= background_per_pixel * object_aperture.area()
return np.array(object_solution["aperture_sum"]), background_per_pixel
def GetAvg(cut, theta, reso=0.5):
"""
Calculates the average value of the cutout within a circle of radius theta.
Params
------
- cut: the cutout (numpy array)
- theta: radius (float, in arcmin)
- reso: pixel resolution (float, in arcmin/pix)
"""
theta_R_pix = theta / reso
apertures = CircularAperture([(cut.shape[1] / 2., cut.shape[0] / 2.)],
r=theta_R_pix)
phot_table = aperture_photometry(cut, apertures)
bkg_mean = phot_table['aperture_sum']
return bkg_mean / apertures.area()
def photometry(positionX, positionY, radius, radiusInner, radiusOuter,
imageForPhotometry, sessionId):
try:
positionX = int(positionX)
positionY = int(positionY)
radius = int(radius)
radiusInner = int(radiusInner)
radiusOuter = int(radiusOuter)
positions = [(positionX, positionY)]
apertures = CircularAperture(positions, r=radius)
annulus_apertures = CircularAnnulus(positions,
r_in=radiusInner,
r_out=radiusOuter)
#Open data
dataList = []
dataList = openFile(backendInputFits + sessionId + "_Processed_" +
imageForPhotometry)
#object aperture
try:
rawflux_table = aperture_photometry(dataList, apertures)
except (RuntimeError, TypeError, NameError):
print 'I cannot'
#backround apertures
bkgflux_table = aperture_photometry(dataList, annulus_apertures)
try:
phot_table = astropy.table.hstack([rawflux_table, bkgflux_table],
table_names=['raw', 'bkg'])
except (RuntimeError, TypeError, NameError):
print 'error'
bkg_mean = phot_table['aperture_sum_bkg'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_raw'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
#instrumental magnitude
instrumentalMag = 2.5 * math.log10(
(rawflux_table[0][3] - phot_table['residual_aperture_sum'][0]))
return instrumentalMag
except (RuntimeError, TypeError, NameError):
print 'error in photometry function'
def getPhotometry(
self, x, y, data, rphot, rin, rout, plot_on_ds9,
image_name): #function to retrieve photometry of a target
#rphot=5.
#rin=10.
#rout=20.
#rin=rphot+2 #give dead zone outside of the rphot value
#rout=rin+4 #provide plenty of room for background measurment
#pos=[(30.,30.),(40.,40.)] #positions of photometry
pos = [(x, y)]
apertures = CircularAperture(pos, r=rphot)
annulus_apertures = CircularAnnulus(pos, r_in=rin, r_out=rout)
rawflux_table = aperture_photometry(
data, apertures, method='exact'
) #subpixel divides pixels up to determine aperature in or out
bkgflux_table = aperture_photometry(data,
annulus_apertures,
method='exact')
phot_table = hstack([rawflux_table, bkgflux_table],
table_names=['raw', 'bkg'])
bkg_mean = phot_table['aperture_sum_bkg'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_raw'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
#print bkg_mean
#print bkg_sum
#print phot_table
a = phot_table['residual_aperture_sum'][0]
#print a
#stop
if plot_on_ds9 == True:
ds = DS9()
ds.set('file %s' % (image_name))
reg1 = 'regions command "annulus %s %s %s %s #color=blue"' % (
x, y, rin, rout)
reg2 = 'regions command "circle %s %s %s #color=yellow"' % (x, y,
rphot)
ds.set('%s' % (reg1))
ds.set('%s' % (reg2))
return round(a, 3)
def simpleapphot(fileName, pos, r, rI, rO, frame='image'):
'''Performs simple circular aperture photometry with local bg subtraction
'''
# Check Type of pos first
if not isinstance(pos, np.ndarray): pos = np.array(pos)
if pos.ndim == 1:
pos = pos.reshape((-1, 2))
# Create Aperture
frame = frame.lower()
if frame == 'image':
# Create the Aperatures
cirAps = CircularAperture(pos, r)
annAps = CircularAnnulus(pos, rI, rO)
elif frame == 'fk5':
# Create Sky Apertures
pos = SkyCoord(frame=frame,
ra=pos[:, 0] * u.deg,
dec=pos[:, 1] * u.deg)
cirAps = SkyCircularAperture(pos, r * u.arcsec)
annAps = SkyCircularAnnulus(pos, rI * u.arcsec, rO * u.arcsec)
else:
raise ValueError('Unsupported coordinate system.')
# Load in the files and do photometry
hdu = fits.open(fileName)
cirPhotTab = aperture_photometry(hdu, cirAps)
annPhotTab = aperture_photometry(hdu, annAps)
if frame == 'fk5':
cirAps = cirAps.to_pixel(WCS(header=hdu['SCI'].header))
annAps = annAps.to_pixel(WCS(header=hdu['SCI'].header))
hdu.close()
# Get Photometry as ndarray
phot = cirPhotTab['aperture_sum'].data - \
(cirAps.area()/annAps.area())*annPhotTab['aperture_sum'].data
return phot
def aper_phot(img_data,positions, r = 10., r_in = 14., r_out = 18,bkg_sub=False,plot=False): """ :params: r: Aperture Radius :params: r_in: annulus aperture inside radius :params: r_out: annulus aperture outside radius :params: bkg_sub: True if background subtraction is needed :params: plot: True to plot :out: phot_table: Table with the values of the aperture photometry """ #Background subtraction if bkg_sub == True: sigma_clip = SigmaClip(sigma=3., iters=10) bkg_estimator = MedianBackground() bkg = Background2D(img_data, (50, 50), filter_size=(3, 3),sigma_clip=sigma_clip, bkg_estimator=bkg_estimator) data_sub = img_data - bkg.background else: data_sub = img_data #Aperture Photometry using a circular aperture and a circular annulus apertures = CircularAperture(positions, r=r) annulus_apertures = CircularAnnulus(positions,r_in = r_in,r_out = r_out) apers = [apertures,annulus_apertures] phot_table = aperture_photometry(data_sub, apers) bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area() bkg_sum = bkg_mean * apertures.area() final_sum = phot_table['aperture_sum_0'] - bkg_sum phot_table['residual_aperture_sum'] = final_sum positions = np.array(positions) if plot == True: #Ploting norm = ImageNormalize(data_sub, interval=ZScaleInterval(),stretch=LinearStretch()) plt.imshow(data_sub, cmap='Greys', origin='lower',norm=norm) apertures.plot(color='blue', lw=1.5, alpha=0.5) annulus_apertures.plot(color='green',lw=1.5,alpha=0.5) plt.plot(positions[:,0],positions[:,1], ls='none', color = 'red', marker='.', ms=10, lw=1.5) plt.xlim(0, data_sub.shape[1]-1) plt.ylim(0, data_sub.shape[0]-1) plt.show() return phot_table
def ap_phot(image, star_tbl, read_noise, exposure, r=1.5, r_in=1.5, r_out=3.):
'''
Given an image, go do some aperture photometry
'''
from astropy.stats import sigma_clipped_stats
from photutils import aperture_photometry, CircularAperture, CircularAnnulus
from photutils.utils import calc_total_error
# Build apertures from star_tbl
positions = np.transpose([star_tbl['x'], star_tbl['y']])
apertures = CircularAperture(positions, r=r)
annulus_apertures = CircularAnnulus(positions, r_in=r_in, r_out=r_out)
annulus_masks = annulus_apertures.to_mask(method='center')
# Get backgrounds in annuli
bkg_median = []
for mask in annulus_masks:
annulus_data = mask.multiply(image)
annulus_data_1d = annulus_data[mask.data > 0]
_, median_sigclip, _ = sigma_clipped_stats(annulus_data_1d)
bkg_median.append(median_sigclip.value)
bkg_median = np.array(bkg_median) * image.unit
# Set error
error = calc_total_error(image.value, read_noise / exposure.value,
exposure.value)
error *= image.unit
# Perform aperture photometry
result = aperture_photometry(image, apertures, error=error)
result['annulus_median'] = bkg_median
result['aper_bkg'] = bkg_median * apertures.area()
result['aper_sum_bkgsub'] = result['aperture_sum'] - result['aper_bkg']
# To-do: fold an error on background level into the aperture photometry error
for col in result.colnames:
result[col].info.format = '%.8g' # for consistent table output
# print("Aperture photometry complete")
return result, apertures, annulus_apertures
def flux_aperture(image, Xc, Yc, sigx):
from photutils import CircularAperture
from photutils import aperture_photometry
from photutils import CircularAnnulus
#local where we will apply th photometry
apertures = CircularAperture((Xc, Yc), r=sigx)
annulus_apertures = CircularAnnulus((Xc, Yc),
r_in=sigx + 2,
r_out=sigx + 4)
#perform photometry
apers = [apertures, annulus_apertures]
phot_table = aperture_photometry(image, apers)
# phot_table = aperture_photometry(image- bkg, apertures)
#subtracted the sky background
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
return final_sum
def calc_bkg_rms(ap, image, src_ap_area, rpsrc, mask=None, min_ap=6):
if isinstance(ap, CircularAnnulus):
aback = bback = ap.r_in + rpsrc
ap_theta = 0
elif isinstance(ap, EllipticalAnnulus):
aback = ap.a_in + rpsrc
bback = ap.b_in + rpsrc
ap_theta = ap.theta
ecirc = ellip_circumference(aback, bback)
diam = 2*rpsrc
# Estimate the number of background apertures that can fit around the source
# aperture.
naps = np.int(np.round(ecirc/diam))
# Use a minimum of 6 apertures
naps = np.max([naps, min_ap])
#naps = 6
theta_back = np.linspace(0, 2*np.pi, naps, endpoint=False)
# Get the x, y positions of the background apertures
x, y = ellip_point(ap.positions[0], aback, bback, ap_theta, theta_back)
# Create the background apertures and calculate flux within each
bkg_aps = CircularAperture(np.vstack([x,y]).T, rpsrc)
flux_bkg = aperture_photometry(image, bkg_aps, mask=mask)
flux_bkg = flux_bkg['aperture_sum']
flux_bkg_adj = flux_bkg/bkg_aps.area() * src_ap_area
# Use sigma-clipping to determine the RMS of the background
# Scale to the area of the source aperture
me, md, sd = sigma_clipped_stats(flux_bkg_adj, sigma=3)
bkg_rms = sd
return bkg_rms, bkg_aps
def circleApp(x, y, r, img_data):
numAps = 20
maxR = 1.5
myPhot = list()
myRs = list()
myCounts = list()
myCountsNoBack = list()
positions = [(x, y)]
# print positions
print r
print(maxR * r)
myBackAp = CircularAnnulus(positions,
r_in=maxR * r,
r_out=(maxR + 0.5) * r) # / numAps)
myBackPhot = aperture_photometry(img_data, myBackAp)
print myBackPhot['aperture_sum'][0]
myBackRate = myBackPhot['aperture_sum'][0] / myBackAp.area(
) # (math.pi * (((maxR + 0.5) * r) ** 2 - (maxR * r) ** 2))
print myBackRate
for j in range(numAps):
curR = maxR * r * (j + 1) / numAps
aperture = CircularAperture(positions, r=curR)
myRs.append(curR) # maxR*(j+1.0)/numAps
myPhot.append(aperture_photometry(img_data, aperture, method="exact"))
tmp = myPhot[j]
myCounts.append(
tmp['aperture_sum'][0] -
(myBackRate * aperture.area())) # math.pi * (curR) ** 2))
myCountsNoBack.append(tmp['aperture_sum'][0])
yerr = np.sqrt(myCountsNoBack) / myCountsNoBack[
numAps - 1] # don't use subtract background
return myRs, myCountsNoBack, yerr
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"point" or "extended"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each pixel.
net : ndarray, 1-D
The count rate (or flux) minus the background at each pixel.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source count rate to get `net`.
npixels : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `net`.
dq : ndarray, 1-D, uint32
The data quality array.
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, npixels, and dq arrays
# no matter what. We may need to divide by npixels, so the default
# is 1 rather than 0.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
npixels = np.ones(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.uint32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
radius = None
inner_bkg = None
outer_bkg = None
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
subtract_background = False
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
(ra, dec) = (0., 0.)
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, npixels, dq) # all bad
x0 = float(shape[2]) / 2.
y0 = float(shape[1]) / 2.
(ra, dec, wavelength) = get_coordinates(input_model, x0, y0)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg, r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
npixels[:] = aperture.area()
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
(wavelength, net, background, npixels, dq) = \
nans_in_wavelength(wavelength, net, background, npixels, dq)
return (ra, dec, wavelength, net, background, npixels, dq)
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"point" or "extended"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each pixel.
net : ndarray, 1-D
The count rate (counts / s) minus the background at each pixel.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source count rate to get `net`.
dq : ndarray, 1-D, int32
The data quality array.
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, and dq arrays no matter what.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.int32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
radius = None
subtract_background = False
inner_bkg = None
outer_bkg = None
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
(ra, dec) = (0., 0.)
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq) # all bad
if hasattr(input_model.meta, 'wcs'):
wcs = input_model.meta.wcs
else:
log.warning("WCS function not found in input.")
wcs = None
if wcs is not None:
x_array = np.empty(shape[0], dtype=np.float64)
x_array.fill(float(shape[2]) / 2.)
y_array = np.empty(shape[0], dtype=np.float64)
y_array.fill(float(shape[1]) / 2.)
z_array = np.arange(shape[0], dtype=np.float64) # for wavelengths
ra, dec, wavelength = wcs(x_array, y_array, z_array)
nelem = len(wavelength)
ra = ra[nelem // 2]
dec = dec[nelem // 2]
else:
(ra, dec) = (0., 0.)
wavelength = np.arange(1, shape[0] + 1, dtype=np.float64)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg, r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
nan_mask = np.isnan(wavelength)
n_nan = nan_mask.sum(dtype=np.intp)
if n_nan > 0:
log.warning("%d NaNs in wavelength array.", n_nan)
dq[nan_mask] = np.bitwise_or(dq[nan_mask], dqflags.pixel['DO_NOT_USE'])
not_nan = np.logical_not(nan_mask)
flag = np.where(not_nan)
if len(flag[0]) > 0:
n_trimmed = flag[0][0] + nelem - (flag[0][-1] + 1)
if n_trimmed > 0:
log.info("Output arrays have been trimmed by %d elements",
n_trimmed)
slc = slice(flag[0][0], flag[0][-1] + 1)
wavelength = wavelength[slc]
net = net[slc]
background = background[slc]
dq = dq[slc]
else:
dq |= dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq)
def do_detection(self):
"""Flag outlier pixels in DQ of input images."""
self.build_suffix(**self.outlierpars)
self._convert_inputs()
pars = self.outlierpars
save_intermediate_results = pars['save_intermediate_results']
# Start by performing initial TSO Photometry on stack of DataModels
# TODO: need information about the actual source position in
# TSO imaging mode (for all subarrays).
# Meanwhile, this is a placeholder representing the geometric
# center of the image.
nints, ny, nx = self.inputs.data.shape
xcenter = (ny - 1) / 2.
ycenter = (ny - 1) / 2.
# all radii are in pixel units
if self.inputs.meta.instrument.pupil == 'WLP8':
radius = 50
radius_inner = 60
radius_outer = 70
else:
radius = 3
radius_inner = 4
radius_outer = 5
apertures = CircularAperture((xcenter, ycenter), r=radius)
aperture_mask = apertures.to_mask(method='center')[0]
# This mask has 1 for mask region, 0 for outside of mask
median_mask = aperture_mask.to_image((ny, nx))
inv_median_mask = np.abs(median_mask - 1)
# Perform photometry
catalog = tso_aperture_photometry(self.inputs, xcenter, ycenter,
radius, radius_inner,
radius_outer)
# Extract net photometry for the source
# This will be the value used for scaling the median image within
# the aperture region
phot_values = catalog['net_aperture_sum']
# Convert CubeModel into ModelContainer of 2-D DataModels
for image in self.input_models:
image.wht = resample_utils.build_driz_weight(
image,
weight_type='exptime',
good_bits=pars['good_bits']
)
# Initialize intermediate products used in the outlier detection
input_shape = self.input_models[0].data.shape
median_model = datamodels.ImageModel(init=input_shape)
median_model.meta = deepcopy(self.input_models[0].meta)
base_filename = self.inputs.meta.filename
median_model.meta.filename = self.make_output_path(
basepath=base_filename, suffix='median'
)
# Perform median combination on set of drizzled mosaics
median_model.data = self.create_median(self.input_models)
aper2 = CircularAnnulus((xcenter, ycenter), r_in=radius_inner,
r_out=radius_outer)
tbl1 = aperture_photometry(median_model.data, apertures,
error=median_model.data * 0.0 + 1.0)
tbl2 = aperture_photometry(median_model.data, aper2,
error=median_model.data * 0.0 + 1.0)
aperture_sum = u.Quantity(tbl1['aperture_sum'][0])
annulus_sum = u.Quantity(tbl2['aperture_sum'][0])
annulus_mean = annulus_sum / aper2.area()
aperture_bkg = annulus_mean * apertures.area()
median_phot_value = aperture_sum - aperture_bkg
if save_intermediate_results:
log.info("Writing out MEDIAN image to: {}".format(
median_model.meta.filename))
median_model.save(median_model.meta.filename)
# Scale the median image by the initial photometry (only in aperture)
# to create equivalent of 'blot' images
# Area outside of aperture in median will remain unchanged
blot_models = datamodels.ModelContainer()
for i in range(nints):
scale_factor = float(phot_values[i] / median_phot_value)
scaled_image = datamodels.ImageModel(init=median_model.data.shape)
scaled_image.meta = deepcopy(median_model.meta)
scaled_data = (median_model.data * (scale_factor * median_mask) + (
median_model.data * inv_median_mask))
scaled_image.data = scaled_data
blot_models.append(scaled_image)
if save_intermediate_results:
log.info("Writing out Scaled Median images...")
def make_output_path(ignored, idx=None):
output_path = self.make_output_path(
basepath=base_filename, suffix='blot', idx=idx,
component_format='_{asn_id}_{idx}'
)
return output_path
blot_models.save(make_output_path)
# Perform outlier detection using statistical comparisons between
# each original input image and its blotted version of the median image
self.detect_outliers(blot_models)
# clean-up (just to be explicit about being finished
# with these results)
del median_model, blot_models
annulus_apertures = CircularAnnulus(
(result['source_x'], result['source_y']),
r_in=apr + 1.0,
r_out=2.0 * apr + 1.0)
# Can make it nice like http://docs.astropy.org/en/stable/visualization/wcsaxes/,
# save the files for future use.
# Do aperture_photometry. Local background subtraction version,
# http://photutils.readthedocs.io/en/stable/aperture.html#local-background-subtraction
# For an estimate of the error, assume all errors are Poisson and that
# the sky/source signal dominates the error.
apers = [apertures, annulus_apertures]
phot_table = aperture_photometry(data, apers, error=np.sqrt(data.copy()))
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
# Based on error propagation calculation by Nicole.
phot_table['residual_aperture_sum_err'] = np.sqrt(
phot_table['aperture_sum_err_0']**2 +
(apertures.area() / annulus_apertures.area() *
phot_table['aperture_sum_err_1'])**2)
# Put this in instrumental magnitude.
phot_table['instrmag'] = 2.5 * np.log10(
phot_table['residual_aperture_sum'] / hdu[0].header['EXPTIME'])
# Based on error propagation calculation by Nicole.
phot_table['instrmag_err'] = 1.085 * phot_table[
'residual_aperture_sum_err'] / phot_table['residual_aperture_sum']
# Add this to the results table. Add errors as well.
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model: IFUCubeModel
The input model.
source_type: string
"point" or "extended"
extract_params: dict
The extraction parameters for aperture photometry.
Returns
-------
(ra, dec, wavelength, net, background, dq)
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, and dq arrays no matter what.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.int32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
radius = None
subtract_background = False
inner_bkg = None
outer_bkg = None
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
(ra, dec) = (0., 0.)
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq) # all bad
if hasattr(input_model.meta, 'wcs'):
wcs = input_model.meta.wcs
else:
log.warning("WCS function not found in input.")
wcs = None
if wcs is not None:
x_array = np.empty(shape[0], dtype=np.float64)
x_array.fill(float(shape[2]) / 2.)
y_array = np.empty(shape[0], dtype=np.float64)
y_array.fill(float(shape[1]) / 2.)
z_array = np.arange(shape[0], dtype=np.float64) # for wavelengths
ra, dec, wavelength = wcs(x_array, y_array, z_array)
nelem = len(wavelength)
ra = ra[nelem // 2]
dec = dec[nelem // 2]
else:
(ra, dec) = (0., 0.)
wavelength = np.arange(1, shape[0] + 1, dtype=np.float64)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg,
r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :],
aperture,
method=method,
subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :],
annulus,
method=method,
subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
nan_mask = np.isnan(wavelength)
n_nan = nan_mask.sum(dtype=np.intp)
if n_nan > 0:
log.warning("%d NaNs in wavelength array.", n_nan)
dq[nan_mask] = np.bitwise_or(dq[nan_mask], dqflags.pixel['DO_NOT_USE'])
not_nan = np.logical_not(nan_mask)
flag = np.where(not_nan)
if len(flag[0]) > 0:
n_trimmed = flag[0][0] + nelem - (flag[0][-1] + 1)
if n_trimmed > 0:
log.info("Output arrays have been trimmed by %d elements",
n_trimmed)
slc = slice(flag[0][0], flag[0][-1] + 1)
wavelength = wavelength[slc]
net = net[slc]
background = background[slc]
dq = dq[slc]
else:
dq |= dqflags.pixel['DO_NOT_USE']
return (ra, dec, wavelength, net, background, dq)
def do_detection(self):
"""Flag outlier pixels in DQ of input images."""
self.build_suffix(**self.outlierpars)
self._convert_inputs()
pars = self.outlierpars
save_intermediate_results = pars['save_intermediate_results']
# Start by performing initial TSO Photometry on stack of DataModels
# TODO: need information about the actual source position in
# TSO imaging mode (for all subarrays).
# Meanwhile, this is a placeholder representing the geometric
# center of the image.
nints, ny, nx = self.inputs.data.shape
xcenter = (ny - 1) / 2.
ycenter = (ny - 1) / 2.
# all radii are in pixel units
if self.inputs.meta.instrument.pupil == 'WLP8':
radius = 50
radius_inner = 60
radius_outer = 70
else:
radius = 3
radius_inner = 4
radius_outer = 5
apertures = CircularAperture((xcenter, ycenter), r=radius)
aperture_mask = apertures.to_mask(method='center')[0]
# This mask has 1 for mask region, 0 for outside of mask
median_mask = aperture_mask.to_image((ny, nx))
inv_median_mask = np.abs(median_mask - 1)
# Perform photometry
catalog = tso_aperture_photometry(self.inputs, xcenter, ycenter,
radius, radius_inner, radius_outer)
# Extract net photometry for the source
# This will be the value used for scaling the median image within
# the aperture region
phot_values = catalog['net_aperture_sum']
# Convert CubeModel into ModelContainer of 2-D DataModels
for image in self.input_models:
image.wht = resample_utils.build_driz_weight(
image, weight_type='exptime', good_bits=pars['good_bits'])
# Initialize intermediate products used in the outlier detection
input_shape = self.input_models[0].data.shape
median_model = datamodels.ImageModel(init=input_shape)
median_model.meta = deepcopy(self.input_models[0].meta)
base_filename = self.inputs.meta.filename
median_model.meta.filename = self.make_output_path(
basepath=base_filename, suffix='median')
# Perform median combination on set of drizzled mosaics
median_model.data = self.create_median(self.input_models)
aper2 = CircularAnnulus((xcenter, ycenter),
r_in=radius_inner,
r_out=radius_outer)
tbl1 = aperture_photometry(median_model.data,
apertures,
error=median_model.data * 0.0 + 1.0)
tbl2 = aperture_photometry(median_model.data,
aper2,
error=median_model.data * 0.0 + 1.0)
aperture_sum = u.Quantity(tbl1['aperture_sum'][0])
annulus_sum = u.Quantity(tbl2['aperture_sum'][0])
annulus_mean = annulus_sum / aper2.area()
aperture_bkg = annulus_mean * apertures.area()
median_phot_value = aperture_sum - aperture_bkg
if save_intermediate_results:
log.info("Writing out MEDIAN image to: {}".format(
median_model.meta.filename))
median_model.save(median_model.meta.filename)
# Scale the median image by the initial photometry (only in aperture)
# to create equivalent of 'blot' images
# Area outside of aperture in median will remain unchanged
blot_models = datamodels.ModelContainer()
for i in range(nints):
scale_factor = float(phot_values[i] / median_phot_value)
scaled_image = datamodels.ImageModel(init=median_model.data.shape)
scaled_image.meta = deepcopy(median_model.meta)
scaled_data = (median_model.data * (scale_factor * median_mask) +
(median_model.data * inv_median_mask))
scaled_image.data = scaled_data
blot_models.append(scaled_image)
if save_intermediate_results:
log.info("Writing out Scaled Median images...")
def make_output_path(ignored, idx=None):
output_path = self.make_output_path(
basepath=base_filename,
suffix='blot',
idx=idx,
component_format='_{asn_id}_{idx}')
return output_path
blot_models.save(make_output_path)
# Perform outlier detection using statistical comparisons between
# each original input image and its blotted version of the median image
self.detect_outliers(blot_models)
# clean-up (just to be explicit about being finished
# with these results)
del median_model, blot_models
phot_table = aperture_photometry(data, [aperture, annulus])
#new background estimation
bkg_mask = np.ma.masked_outside(radial[0].reshape(new_image.shape),
inner_sky_radius, outer_sky_radius)
bkg_map = Background2D(new_image,
tuple(np.array(new_image.shape) / 4),
mask=bkg_mask.mask,
exclude_mesh_method='all')
bkg_map_med = MMMBackground().calc_background(
bkg_map.data) #bkg_map_med=np.median(bkg_map.background)
#print 'Map sky mean '+str(bkg_map_med)
#bkg_mean=phot_table['aperture_sum_1']/annulus.area()
#print 'Aperture sky mean '+str(bkg_mean)
#phot_table['residual_aperture_sum']=phot_table['aperture_sum_0']-bkg_mean*aperture.area()
phot_table['residual_aperture_sum'] = phot_table[
'aperture_sum_0'] - bkg_map_med * aperture.area()
#print 'Map sky result: '+str(phot_table['aperture_sum_0']-bkg_map_med*aperture.area())
#print "Aperture Photometry Result: "+str(phot_table['residual_aperture_sum'])
fig2 = Figure(figsize=(int(6 * scaling), int(6 * scaling)))
fig2.set_facecolor('0.85')
ax2 = fig2.add_axes([0.1, 0.1, .9, .9])
ax2.grid(True, color='white', linestyle='-', linewidth=1)
ax2.set_axisbelow(True)
ax2.scatter(radial[0], radial[1])
ax2.plot(np.linspace(0, new_image_halfwidth, num=50),
gauss1d(np.linspace(0, new_image_halfwidth, num=50), popt[0], 0,
np.mean([popt[3], popt[4]]), popt[6]),
c='k',
lw=2)
#guess=(np.amax(new_image)-new_image[0,0],x,y,3,3,0,new_image[0,0])
def addzb(fitsname, redo=False, fau_dir=None):
telescopes = ['1','2','3','4']
# night = (fitsname.split('/'))[3]
night = (fitsname.split('/'))[-2]
print 'beginning ' + fitsname
# 2D spectrum
h = pyfits.open(fitsname,mode='update')
if 'BARYSRC4' in h[0].header.keys() and not redo:
print fitsname + " already done"
h.flush()
h.close()
return
specstart = datetime.datetime.strptime(h[0].header['DATE-OBS'],"%Y-%m-%dT%H:%M:%S.%f")
specmid = specstart + datetime.timedelta(seconds = h[0].header['EXPTIME']/2.0)
specend = specstart + datetime.timedelta(seconds = h[0].header['EXPTIME'])
t0 = datetime.datetime(2000,1,1)
t0jd = 2451544.5
aperture_radius = 3.398 # fiber radius in pixels
annulus_inner = 2.0*aperture_radius
annulus_outer = 3.0*aperture_radius
for telescope in telescopes:
print 'beginning telescope ' + telescope + ' on ' + fitsname
# get the barycentric redshift for each time
ra = h[0].header['TARGRA' + telescope]
dec = h[0].header['TARGDEC' + telescope]
try: pmra = h[0].header['PMRA' + telescope]
except: pmra = 0.0
try: pmdec = h[0].header['PMDEC' + telescope]
except: pmdec = 0.0
try: parallax = h[0].header['PARLAX' + telescope]
except: parallax = 0.0
try: rv = h[0].header['RV' + telescope]
except: rv = 0.0
objname = h[0].header['OBJECT' + telescope]
if fau_dir is None:
faupath = '/Data/t' + telescope + '/' + night + '/' + night + '.T' + telescope + '.FAU.' + objname + '.????.fits'
else:
faupath = fau_dir + '/t' + telescope + '/' + night + '/' + night + '.T' + telescope + '.FAU.' + objname + '.????.fits'
guideimages = glob.glob(faupath)
# if telescope == '2' and "HD62613" in fitsname: ipdb.set_trace()
times = []
fluxes = np.array([])
for guideimage in guideimages:
try:
fauimage = pyfits.open(guideimage)
except:
print "corrupt file for " + guideimage
continue
# midtime of the guide image (UTC)
midtime = datetime.datetime.strptime(fauimage[0].header['DATE-OBS'],"%Y-%m-%dT%H:%M:%S") +\
datetime.timedelta(seconds=fauimage[0].header['EXPTIME']/2.0)
# convert to Julian date
midjd = t0jd + (midtime-t0).total_seconds()/86400.0
# only look at images during the spectrum
if midtime < specstart or midtime > specend: continue
# find the fiber position
try:
fiber_x = fauimage[0].header['XFIBER' + telescope]
fiber_y = fauimage[0].header['YFIBER' + telescope]
except:
print "keywords missing for " + guideimage
continue
# do aperture photometry
positions = [(fiber_x,fiber_y)]
apertures = CircularAperture(positions,r=aperture_radius)
annulus_apertures = CircularAnnulus(positions, r_in=annulus_inner, r_out=annulus_outer)
# calculate the background-subtracted flux at the fiber position
rawflux_table = aperture_photometry(fauimage[0].data, apertures)
bkgflux_table = aperture_photometry(fauimage[0].data, annulus_apertures)
bkg_mean = bkgflux_table['aperture_sum'].sum() / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
flux = rawflux_table['aperture_sum'].sum() - bkg_sum
# append to the time and flux arrays
times.append(midjd)
fluxes = np.append(fluxes,flux)
if len(times) == 0:
print "No guider images for " + fitsname + " on Telescope " + telescope + "; assuming mid time"
if 'daytimeSky' in fitsname:
h[0].header['BARYCOR' + telescope] = ('UNKNOWN','Barycentric redshift')
h[0].header['BARYSRC' + telescope] = ('UNKNOWN','Source for the barycentric redshift')
h[0].header['FLUXMID' + telescope] = (midjd,'Flux-weighted mid exposure time (JD_UTC)')
continue
# convert specmid to Julian date
midjd = t0jd + (specmid-t0).total_seconds()/86400.0
print midjd
zb = barycorr(midjd, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv)/2.99792458e8
h[0].header['BARYCOR' + telescope] = (zb,'Barycentric redshift')
h[0].header['BARYSRC' + telescope] = ('MIDTIME','Source for the barycentric redshift')
h[0].header['FLUXMID' + telescope] = (midjd,'Flux-weighted mid exposure time (JD_UTC)')
continue
zb = np.asarray(barycorr(times, ra, dec, pmra=pmra, pmdec=pmdec, parallax=parallax, rv=rv))/2.99792458e8
# weight the barycentric correction by the flux
#*******************!*!*!*!*!*!*
#this assumes guider images were taken ~uniformly throughout the spectroscopic exposure!
#****************!*!*!*!*!**!*!*!*!**!*!*!*!*
wzb = np.sum(zb*fluxes)/np.sum(fluxes)
wmidjd = np.sum(times*fluxes)/np.sum(fluxes)
# update the header to include aperture photometry and barycentric redshift
h[0].header['BARYCOR' + telescope] = (wzb,'Barycentric redshift')
h[0].header['BARYSRC' + telescope] = ('FAU Flux Weighted','Source for the barycentric redshift')
h[0].header['FLUXMID' + telescope] = (wmidjd,'Flux-weighted mid exposure time (JD_UTC)')
hdu = pyfits.PrimaryHDU(zip(times,fluxes))
hdu.header['TELESCOP'] = ('T' + telescope,'Telescope')
h.append(hdu)
# write updates to the disk
h.flush()
h.close()
def tso_aperture_photometry(datamodel, xcenter, ycenter, radius, radius_inner,
radius_outer):
"""
Create a photometric catalog for NIRCam TSO imaging observations.
Parameters
----------
datamodel : `CubeModel`
The input `CubeModel` of a NIRCam TSO imaging observation.
xcenter, ycenter : float
The ``x`` and ``y`` center of the aperture.
radius : float
The radius (in pixels) of the circular aperture.
radius_inner, radius_outer : float
The inner and outer radii (in pixels) of the circular-annulus
aperture, used for local background estimation.
Returns
-------
catalog : `~astropy.table.QTable`
An astropy QTable (Quantity Table) containing the source
photometry.
"""
if not isinstance(datamodel, CubeModel):
raise ValueError('The input data model must be a CubeModel.')
aper1 = CircularAperture((xcenter, ycenter), r=radius)
aper2 = CircularAnnulus((xcenter, ycenter), r_in=radius_inner,
r_out=radius_outer)
nimg = datamodel.data.shape[0]
aperture_sum = []
aperture_sum_err = []
annulus_sum = []
annulus_sum_err = []
for i in np.arange(nimg):
tbl1 = aperture_photometry(datamodel.data[i, :, :], aper1,
error=datamodel.err[i, :, :])
tbl2 = aperture_photometry(datamodel.data[i, :, :], aper2,
error=datamodel.err[i, :, :])
aperture_sum.append(tbl1['aperture_sum'][0])
aperture_sum_err.append(tbl1['aperture_sum_err'][0])
annulus_sum.append(tbl2['aperture_sum'][0])
annulus_sum_err.append(tbl2['aperture_sum_err'][0])
# convert array of Quantities to Quantity arrays
aperture_sum = u.Quantity(aperture_sum)
aperture_sum_err = u.Quantity(aperture_sum_err)
annulus_sum = u.Quantity(annulus_sum)
annulus_sum_err = u.Quantity(annulus_sum_err)
# construct metadata for output table
meta = OrderedDict()
meta['instrument'] = datamodel.meta.instrument.name
meta['detector'] = datamodel.meta.instrument.detector
meta['channel'] = datamodel.meta.instrument.channel
meta['subarray'] = datamodel.meta.subarray.name
meta['filter'] = datamodel.meta.instrument.filter
meta['pupil'] = datamodel.meta.instrument.pupil
meta['target_name'] = datamodel.meta.target.catalog_name
meta['xcenter'] = xcenter
meta['ycenter'] = ycenter
ra_icrs, dec_icrs = datamodel.meta.wcs(xcenter, ycenter)
meta['ra_icrs'] = ra_icrs
meta['dec_icrs'] = dec_icrs
info = ('Photometry measured in a circular aperture of r={0} pixels. '
'Background calculated as the mean in a circular annulus with '
'r_inner={1} pixels and r_outer={2} pixels.'
.format(radius, radius_inner, radius_outer))
meta['apertures'] = info
tbl = QTable(meta=meta)
dt = (datamodel.meta.exposure.group_time *
(datamodel.meta.exposure.ngroups + 1))
dt_arr = (np.arange(1, 1 + datamodel.meta.exposure.nints) *
dt - (dt / 2.))
int_dt = TimeDelta(dt_arr, format='sec')
int_times = (Time(datamodel.meta.exposure.start_time, format='mjd') +
int_dt)
tbl['MJD'] = int_times.mjd
tbl['aperture_sum'] = aperture_sum
tbl['aperture_sum_err'] = aperture_sum_err
tbl['annulus_sum'] = annulus_sum
tbl['annulus_sum_err'] = annulus_sum_err
annulus_mean = annulus_sum / aper2.area()
annulus_mean_err = annulus_sum_err / aper2.area()
tbl['annulus_mean'] = annulus_mean
tbl['annulus_mean_err'] = annulus_mean_err
aperture_bkg = annulus_mean * aper1.area()
aperture_bkg_err = annulus_mean_err * aper1.area()
tbl['aperture_bkg'] = aperture_bkg
tbl['aperture_bkg_err'] = aperture_bkg_err
net_aperture_sum = aperture_sum - aperture_bkg
net_aperture_sum_err = np.sqrt(aperture_sum_err ** 2 +
aperture_bkg_err ** 2)
tbl['net_aperture_sum'] = net_aperture_sum
tbl['net_aperture_sum_err'] = net_aperture_sum_err
return tbl
def fors2_pol_phot(folder_path,apermul):
""" Script runs photometry for FORS2 polarimetry images """
# Read in four wave plate angle files and set up arrays for later
file_ang0 = os.path.join(folder_path,'0ang.fits')
file_ang225 = os.path.join(folder_path,'225ang.fits')
file_ang45 = os.path.join(folder_path,'45ang.fits')
file_ang675 = os.path.join(folder_path,'675ang.fits')
files = [file_ang0,file_ang225,file_ang45,file_ang675]
angle = ['0','225','45','675']
ang_dec = ['0','22.5','45','67.5']
label = ['$0^{\circ}$ image','$22.5^{\circ}$ image',
'$45^{\circ}$ image','$67.5^{\circ}$ image']
# Set up array to store FWHM and number of sources per half-wave plate
fwhm = []
numsource = []
# Loop over files for the four wave plate files
for k in range(0,len(angle),1):
# Open fits file, extract pixel flux data and remove saturated pixels
try:
hdulist = fits.open(files[k])
image_data = hdulist[0].data
except FileNotFoundError as e:
print("Cannot find the fits file(s) you are looking for!")
print("Please check the input!")
sys.exit()
# Remove bad pixels and mask edges
image_data[image_data > 60000] = 0
image_data[image_data < 0] = 0
rows = len(image_data[:,0])
cols = len(image_data[0,:])
hdulist.close()
# Calculate estimate of background using sigma-clipping and detect
# the sources using DAOStarFinder
xord, yord = 843, 164
xexord, yexord = 843, 72
go_bmean, go_bmedian, go_bstd = sigma_clipped_stats(image_data
[yord-40:yord+40,xord-40:xord+40],sigma=3.0,iters=5)
ge_bmean, ge_bmedian, ge_bstd = sigma_clipped_stats(image_data
[yexord-40:yexord+40,xord-40:xord+40],sigma=3.0,iters=5)
daofind_o = DAOStarFinder(fwhm=5,threshold=5*go_bstd,
exclude_border=True)
daofind_e = DAOStarFinder(fwhm=5,threshold=5*ge_bstd,
exclude_border=True)
sources_o = daofind_o(image_data[yord-20:yord+20,xord-20:xord+20])
sources_e = daofind_e(image_data[yexord-20:yexord+20,xexord-20:
xexord+20])
if (len(sources_o) < 1 or len(sources_e) < 1):
print("No source detected in",ang_dec[k],"degree image")
sys.exit()
if len(sources_o) != len(sources_e):
print("Unequal number of sources detected in o and e images!")
sys.exit()
glob_bgm = [go_bmean,ge_bmean]
glob_bgerr = [go_bstd,ge_bstd]
# Convert the source centroids back into detector pixels
sources_o['xcentroid'] = sources_o['xcentroid'] + xord - 20
sources_o['ycentroid'] = sources_o['ycentroid'] + yord - 20
sources_e['xcentroid'] = sources_e['xcentroid'] + xexord - 20
sources_e['ycentroid'] = sources_e['ycentroid'] + yexord - 20
# Estimate the FWHM of the source by simulating a 2D Gaussian
# (only done on 0 angle image ensuring aperture sizes are equal)
if not fwhm:
xpeaks_o = []
xpeaks_e = []
ypeaks_o = []
ypeaks_e = []
for i in range(0,len(sources_o),1):
data_o = image_data[yord-20:yord+20,xord-20:xord+20]
xpeaks_o.append(int(sources_o[i]['xcentroid']) - (xord - 20))
ypeaks_o.append(int(sources_o[i]['ycentroid']) - (yord - 20))
data_e = image_data[yexord-20:yexord+20,xexord-20:xexord+20]
xpeaks_e.append(int(sources_e[i]['xcentroid']) -
(xexord - 20))
ypeaks_e.append(int(sources_e[i]['ycentroid']) -
(yexord - 20))
min_count_o = np.min(data_o)
min_count_e = np.min(data_e)
max_count_o = data_o[ypeaks_o[i],xpeaks_e[i]]
max_count_e = data_e[ypeaks_o[i],xpeaks_e[i]]
half_max_o = (max_count_o + min_count_o)/2
half_max_e = (max_count_e + min_count_e)/2
# Crude calculation for each source
nearest_above_x_o = ((np.abs(data_o[ypeaks_o[i],
xpeaks_o[i]:-1] - half_max_o)).argmin())
nearest_below_x_o = ((np.abs(data_o[ypeaks_o[i],0:
xpeaks_o[i]] - half_max_o)).argmin())
nearest_above_x_e = ((np.abs(data_e[ypeaks_e[i],
xpeaks_e[i]:-1] - half_max_e)).argmin())
nearest_below_x_e = ((np.abs(data_e[ypeaks_e[i],0:
xpeaks_e[i]] - half_max_e)).argmin())
nearest_above_y_o = ((np.abs(data_o[ypeaks_o[i]:-1,
xpeaks_o[i]] - half_max_o)).argmin())
nearest_below_y_o = ((np.abs(data_o[0:ypeaks_o[i],
xpeaks_o[i]] - half_max_o)).argmin())
nearest_above_y_e = ((np.abs(data_e[ypeaks_e[i]:-1,
xpeaks_e[i]] - half_max_e)).argmin())
nearest_below_y_e = ((np.abs(data_e[0:ypeaks_e[i],
xpeaks_e[i]] - half_max_e)).argmin())
fwhm.append((nearest_above_x_o + (xpeaks_o[i] -
nearest_below_x_o)))
fwhm.append((nearest_above_y_o + (ypeaks_o[i] -
nearest_below_y_o)))
fwhm.append((nearest_above_x_e + (xpeaks_e[i] -
nearest_below_x_e)))
fwhm.append((nearest_above_y_e + (ypeaks_e[i] -
nearest_below_y_e)))
fwhm = np.mean(fwhm)
# Stack both ord and exord sources together
tot_sources = vstack([sources_o,sources_e])
# Store the ordinary and extraordinary beam source images and
# create apertures for aperture photometry
positions = np.swapaxes(np.array((tot_sources['xcentroid'],
tot_sources['ycentroid']),dtype='float'),0,1)
aperture = CircularAperture(positions, r=0.5*apermul*fwhm)
phot_table = aperture_photometry(image_data,aperture)
# Set up arrays of ord and exord source parameters
s_id = np.zeros([len(np.array(phot_table['id']))])
xp = np.zeros([len(s_id)])
yp = np.zeros([len(s_id)])
fluxbgs = np.zeros([len(s_id)])
mean_bg = np.zeros([len(s_id)])
bg_err = np.zeros([len(s_id)])
s_area = []
ann_area = []
for i in range(0,len(np.array(phot_table['id'])),1):
s_id[i] = np.array(phot_table['id'][i])
xpos = np.array(phot_table['xcenter'][i])
ypos = np.array(phot_table['ycenter'][i])
xp[i] = xpos
yp[i] = ypos
s_area.append(np.pi*(0.5*apermul*fwhm)**2)
j = i%2
fluxbgs[i] = (phot_table['aperture_sum'][i] -
aperture.area()*glob_bgm[j])
mean_bg[i] = glob_bgm[j]
bg_err[i] = glob_bgerr[j]
ann_area.append(80*80)
# Create and save the image in z scale and overplot the ordinary and
# extraordinary apertures and local background annuli if applicable
fig = plt.figure()
zscale = ZScaleInterval(image_data)
norm = ImageNormalize(stretch=SqrtStretch(),interval=zscale)
image = plt.imshow(image_data,cmap='gray',origin='lower',norm=norm)
bg_annulus_o = RectangularAnnulus((843,159),w_in=0,w_out=80,h_out=80,
theta=0)
bg_annulus_e = RectangularAnnulus((843,69),w_in=0,w_out=80,h_out=80,
theta=0)
bg_annulus_o.plot(color='skyblue',lw=1.5,alpha=0.5)
bg_annulus_e.plot(color='lightgreen',lw=1.5,alpha=0.5)
for i in range(0,len(np.array(phot_table['id'])),1):
aperture = CircularAperture((xp[i],yp[i]),r=0.5*apermul*fwhm)
if i < int(len(np.array(phot_table['id']))/2):
aperture.plot(color='blue',lw=1.5,alpha=0.5)
else:
aperture.plot(color='green',lw=1.5,alpha=0.5)
plt.xlim(760,920)
plt.ylim(20,210)
plt.title(label[k])
image_fn = folder_path + angle[k] + '_image.png'
fig.savefig(image_fn)
# Write ordinary and extraordinary beams to file following the
# convention angleXXX_ord.txt and angleXXX_exord.txt
orig_stdout = sys.stdout
ord_result_file= folder_path + 'angle' + angle[k] + '_ord.txt'
ordresultf = open(ord_result_file, 'w')
sys.stdout = ordresultf
print("# id, xpix, ypix, fluxbgs, sourcearea, meanbg, bgerr, bgarea")
for i in range(0,int(len(np.array(phot_table['id']))/2),1):
print(i+1,xp[i],yp[i],fluxbgs[i],s_area[i],mean_bg[i],bg_err[i],
ann_area[i])
sys.stdout = orig_stdout
ordresultf.close()
orig_stdout = sys.stdout
exord_result_file = folder_path + 'angle' + angle[k] + '_exord.txt'
exordresultf = open(exord_result_file, 'w')
sys.stdout = exordresultf
print("# id, xpix, ypix, fluxbgs, sourcearea, meanbg, bgerr, bgarea")
for i in range(int(len(np.array(phot_table['id']))/2),len(np.array
(phot_table['id'])),1):
print(i+1-int(len(np.array(phot_table['id']))/2),xp[i],yp[i],
fluxbgs[i],s_area[i],mean_bg[i],bg_err[i],ann_area[i])
sys.stdout = orig_stdout
exordresultf.close()
# Save the number of sources in each beam to a list
numsource.append(int(len(np.array(phot_table['id']))/2))
# Print number of sources per half-wave plate image
for i in range(0,len(numsource),1):
print("No of sources detected at",ang_dec[i],"degrees:",numsource[i])
return 0
iraf.ptools.txdump(textfiles='mag_test_g.dat', fields="id,mag,merr,sum,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image", expr='yes', headers='no', Stdout=txdump_out)
txdump_out.close()
g_iraf, ge_iraf, gf_iraf, gsky_iraf = np.loadtxt('phot_test_g.txdump', usecols=(1,2,3,4), unpack=True)
i_iraf, ie_iraf, if_iraf, isky_iraf = np.loadtxt('phot_test_i.txdump', usecols=(1,2,3,4), unpack=True)
# now try python
x, y = np.loadtxt(coords_file, usecols=(0,1), unpack=True)
positions = np.array(zip(x,y))
hdu_g = fits.open(fits_g)
hdu_i = fits.open(fits_i)
apertures = CircularAperture(positions, r=8.)
annulus_apertures = CircularAnnulus(positions, r_in=10., r_out=14.)
print apertures.area()
ap_mask = apertures.to_mask(method='subpixel', subpixels=7)
dummy = np.ones_like(hdu_g[0].data)
ann_mask = annulus_apertures.to_mask(method='center')
ap_g = [m.apply(hdu_g[0].data) for i,m in enumerate(ap_mask)]
ap_i = [m.apply(hdu_i[0].data) for i,m in enumerate(ap_mask)]
area_g = [np.sum(m.apply(dummy)) for i,m in enumerate(ap_mask)]
area_i = [np.sum(m.apply(dummy)) for i,m in enumerate(ap_mask)]
print area_g, area_i
# plt.imshow(ap_g[0], interpolation='nearest')
# plt.show()
ann_g = [m.apply(hdu_g[0].data, fill_value=-999.) for i,m in enumerate(ann_mask)]
ann_i = [m.apply(hdu_i[0].data, fill_value=-999.) for i,m in enumerate(ann_mask)]
flux_g = np.array([np.sum(a) for j,a in enumerate(ap_g)])
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model: IFUCubeModel
The input model.
source_type: string
"point" or "extended"
extract_params: dict
The extraction parameters for aperture photometry.
Returns
-------
(wavelength, net, background, dq)
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, and dq arrays no matter what.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.int32)
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[2]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[1]) / 2.
else:
y_center = float(y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[1], shape[2])) # for defaults
if source_type == 'point':
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
width = None
height = None
theta = None
else:
width = extract_params['width']
if width is None:
width = smaller_axis / 2.
height = extract_params['height']
if height is None:
height = smaller_axis / 2.
theta = extract_params['theta'] * math.pi / 180.
radius = None
inner_bkg = None
outer_bkg = None
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
subtract_background = False
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(extract_params['theta']))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
# Check for out of bounds.
# The problem with having the background aperture extend beyond the
# image is that the normalization would not account for the resulting
# decrease in the area of the annulus, so the background subtraction
# would be systematically low.
outside = False
f_nx = float(shape[2])
f_ny = float(shape[1])
if x_center < 0. or x_center >= f_nx - 1. or \
y_center < 0. or y_center >= f_ny - 1.:
outside = True
log.error("Target location is outside the image.")
if subtract_background and \
(x_center - outer_bkg < -0.5 or x_center + outer_bkg > f_nx - 0.5 or
y_center - outer_bkg < -0.5 or y_center + outer_bkg > f_ny - 0.5):
outside = True
log.error("Background region extends outside the image.")
if outside:
wavelength = np.zeros(shape[0], dtype=np.float64)
dq[:] = dqflags.pixel['DO_NOT_USE']
return (wavelength, net, background, dq) # all bad
wcs = input_model.meta.wcs
x_array = np.empty(shape[0], dtype=np.float64)
x_array.fill(float(shape[2]) / 2.)
y_array = np.empty(shape[0], dtype=np.float64)
y_array.fill(float(shape[1]) / 2.)
z_array = np.arange(shape[0], dtype=np.float64) # for wavelengths
_, _, wavelength = wcs(x_array, y_array, z_array)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
if subtract_background:
annulus = CircularAnnulus(position,
r_in=inner_bkg, r_out=outer_bkg)
normalization = aperture.area() / annulus.area()
else:
aperture = RectangularAperture(position, width, height, theta)
# No background is computed for an extended source.
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
return (wavelength, net, background, dq)
#Lines 35-37 create aperture objects (a circle around each source with a concentric annulus around each circle) and group them in a 2x1 array called apers.
apertures = CircularAperture(positions, r=5.)
annuli = CircularAnnulus(positions, r_in=6., r_out=8.)
apers = [apertures, annuli]
#Lines 40-41 replace all negatives in 'data' with , estimate the uncertainty in the number of counts at each pixel, and define that uncertainty as 'error'
bkg_err = np.zeros(shape=(500, 500))
gain_eff = 7058 #seconds
error = calc_total_error(data, bkg_err, gain_eff)
#Line 44 calculates the total number of counts/s in both the center circles and annuli around each source and propogates the uncertainty from each pixel over the entire source (note: each pixel in a drizzled image like this one has information about counts/s, not counts. You can use the observation time as an "effective gain" and multiply each data point by this gain if you desire total counts)
phot_table = aperture_photometry(data, apers, error=error)
#Lines 47-50 do the math necessary for aperture photometry. Using the annulus, and average background flux rate level is determined (counts/s in annulus divided by area of annulus), which is then multiplied by the area inside the center circle to find the total counts/s due to the background that are inside the circle. That value is subtracted from the measured flux rate inside the circle and you have an accurate estimate of the flux rate of the star itself. Line 48 adds a new column to phot_table with this information.
bkg_mean = phot_table['aperture_sum_1'] / annuli.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
#Lines 53-56 calculate the total error in the same way as the flux rate in counts/s is calculated above
err_mean = phot_table['aperture_sum_err_1'] / annuli.area()
err_sum = err_mean * apertures.area()
final_err = phot_table['aperture_sum_err_0'] - err_sum
phot_table['residual_err_sum'] = final_err
#Lines 59-62 save an untruncated text file called 'phot_table.txt' that has columns "id, xcenter pix, ycenter pix, aperture_sum_0 (counts/s in the inner circle), aperture_sum_1 (counts/s in the annulus), aperture_sum_err_0 (total uncertainty from annulus), aperture_sum_err_1 (total uncertainty in center circle), residual_aperture_sum (counts/s due to sources inside the circle), and residual_sum_err (total uncertainty in source flux rate)"
flux = phot_table['residual_aperture_sum']
flux[flux < 0] = 0
phot_table = np.array(phot_table)
np.set_printoptions(threshold=np.nan)
log = open("/Users/computationalphysics/Desktop/phot_table.txt", "w")
def photom_av(ima, pos, radius, r_in=False, r_out=False, mode='median'):
'''
Aperture photometry in an aperture located at pixel coordinates
pos = ( (x0, y0), (x1, y1), ... ) with a radius=radius.
When r_in and r_out are given, background is estimated in CircularAnnulus and subtracted.
mode refers to how the background is estimated within the circlar annulus.
Can be 'median' or 'mean'
Photometry is calculating by median averaging the pixels within the aperture and
multiplying by the number of pixels in the aperture (including fractions of pixels).
'''
# Setting up the mask
if hasattr(ima, 'mask'):
if ima.mask.size == 1:
mask = np.zeros(ima.shape, dtype=np.bool) | ima.mask
else:
mask = ima.mask.copy()
else:
mask = np.zeros(ima.shape, dtype=np.bool)
### Performing the actual photometry - identical for each method
# Median averaging of flux in aperture
# Setting up the aperture
apertures = CircularAperture(pos, r = radius)
ap_mask = apertures.to_mask(method='center')
# Setting up arrays to store data
nflx = len(ap_mask)
flx = np.zeros(nflx, dtype=np.float)
flux_max = np.zeros(nflx, dtype=np.float)
flux_min = np.zeros(nflx, dtype=np.float)
# Median averaging of flux
for i, am in enumerate(ap_mask):
fluxmask = ~mask & am.to_image(shape=mask.shape).astype(np.bool)
flx[i] = np.median(ima[fluxmask])
flux_max[i] = np.max(ima[fluxmask])
flux_min[i] = np.min(ima[fluxmask])
# Aperture photometry on mask to see how many masked pixels are in the
# aperture
apm = aperture_photometry(mask.astype(int), apertures)
# Number of unmasked pixels in aperture
ap_area = Column(name = 'area_aper',
data=apertures.area() - apm['aperture_sum'].data)
# Flux in aperture using median av flux and fractional no. pixels in aperture
flux_init = flx*ap_area
### Two different modes for analysing the background
if ( r_in and r_out and mode in ('mean', 'median') ):
### This stuff is the same regardless of method
# Setting up the annulus
anulus_apertures = CircularAnnulus(pos, r_in=r_in, r_out=r_out)
# Performing annulus photometry on the mask
bkgm = aperture_photometry(mask.astype(int), anulus_apertures)
# Number of masked pixels in bkg
mbkg_area = Column(name = 'bpix_bkg',
data=bkgm['aperture_sum'])
# Number of non-masked pixels in aperture and bkg
bkg_area = Column(name = 'area_bkg',
data=anulus_apertures.area() - bkgm['aperture_sum'])
### This stuff is specific to the mean
if mode == 'mean':
# Perform the annulus photometry on the image
bkg = aperture_photometry(ima, anulus_apertures, mask=mask)
# Average bkg where this divides by only number of NONMASKED pixels
# as the aperture photometry ignores the masked pixels
bkga = Column(name='background',
data=bkg['aperture_sum']/bkg_area)
# Bkg subtracted flux
flux = flux_init - bkga*ap_area
# Adding that data
ap.add_column(bkga)
elif mode == 'median':
# Number of pixels in the annulus, a different method
aperture_mask = anulus_apertures.to_mask(method='center')
nbkg = len(aperture_mask)
# Background mask
bkgm = np.zeros(nbkg, dtype=np.float)
# Median averaging
for i, am in enumerate(aperture_mask):
bmask = ~mask & am.to_image(shape=mask.shape).astype(np.bool)
bkgm[i] = np.median(ima[bmask])
flux = flux_init - bkgm*ap_area
bkgm = Column(name = 'background', data = bkgm)
return flux, apm, flx, ap_area, flux_max, flux_min #flux, no.masked pixels in ap, median av flux
def qphot(data,
coord,
rad,
skyradin,
skyradout,
wcs=None,
calfctr=None,
skycoord=None,
unit='Jy',
error=None,
filter=None):
if is_pix_coord(coord):
coord = [np.float(coord[0]), np.float(coord[1])]
aperture = CircularAperture(coord, r=rad)
else:
coord = SkyCoord(' '.join(coord),
unit=(u.hourangle, u.deg),
frame='icrs')
aperture = SkyCircularAperture(coord, r=rad * u.arcsec)
if skycoord:
if is_pix_coord(skycoord):
scoord = [np.float(skycoord[0]), np.float(skycoord[1])]
annulus = CircularAnnulus(scoord, skyradin, skyradout)
else:
scoord = SkyCoord(' '.join(skycoord),
unit=(u.hourangle, u.deg),
frame='icrs')
annulus = SkyCircularAnnulus(scoord, skyradin * u.arcsec,
skyradout * u.arcsec)
else:
if isinstance(coord, SkyCoord):
annulus = SkyCircularAnnulus(coord, skyradin * u.arcsec,
skyradout * u.arcsec)
else:
annulus = CircularAnnulus(coord, skyradin, skyradout)
#mask out nans or negative
mask = np.logical_or(np.isnan(data), data < 0.)
apflux = aperture_photometry(data,
aperture,
wcs=wcs,
error=error,
mask=mask)
skyflux = aperture_photometry(data,
annulus,
wcs=wcs,
error=error,
mask=mask)
phot_table = hstack([apflux, skyflux], table_names=['src', 'sky'])
#calculate mean local background in annulus
if isinstance(annulus, SkyCircularAnnulus):
sky_area = annulus.to_pixel(wcs).area()
else:
sky_area = annulus.area()
if isinstance(aperture, SkyCircularAperture):
src_area = aperture.to_pixel(wcs).area()
else:
src_area = aperture.area()
sky_mean = phot_table['aperture_sum_sky'] / sky_area
sky_sum = sky_mean * src_area
final_sum = phot_table['aperture_sum_src'] - sky_sum
phot_table['residual_aperture_sum'] = final_sum
phot_table['residual_aperture_sum'].unit = unit
phot_table['aperture_area_sky'] = sky_area
phot_table['aperture_area_src'] = src_area
phot_table['aperture_mean_sky'] = sky_mean
phot_table['aperture_mean_sky'].unit = unit
phot_table['aperture_rad_src'] = rad
phot_table['aperture_irad_sky'] = skyradin
phot_table['aperture_orad_sky'] = skyradout
phot_table['aperture_area_sky'].unit = u.pix**2
phot_table['aperture_area_src'].unit = u.pix**2
if error is not None:
src_err = phot_table['aperture_sum_err_src']
sky_err = phot_table['aperture_sum_err_sky']
src_var = src_err / phot_table['residual_aperture_sum']
sky_var = sky_err / sky_sum
color_err = 0. # 10 percent photoerr
color_var = color_err * phot_table['residual_aperture_sum']
phot_table['residual_aperture_err'] = np.sqrt(src_var**2 + sky_var**2 +
color_var**2)
else:
color_err = 0.07 # 7 percent photoerr
color_var = color_err * sky_sum
phot_table.add_column(
Column([color_var for x in phot_table],
name='residual_aperture_err'))
phot_table.remove_columns(
['xcenter_src', 'ycenter_src', 'xcenter_sky', 'ycenter_sky'])
if 'center_input' in phot_table.colnames:
phot_table.remove_columns('center_input')
if 'center_input_src' in phot_table.colnames:
phot_table.remove_columns('center_input_src')
if 'center_input_sky' in phot_table.colnames:
phot_table.remove_columns('center_input_sky')
if isinstance(coord, SkyCoord):
phot_table['xcen'], phot_table['ycen'] = wcs2pix(coord, wcs)
phot_table['ra'], phot_table['dec'] = coord.to_string('hmsdms',
sep=':').split()
else:
phot_table['xcen'] = coord[0]
phot_table['ycen'] = coord[1]
if skycoord:
if isinstance(scoord, SkyCoord):
phot_table['xcensky'], phot_table['ycensky'] = wcs2pix(scoord, wcs)
phot_table['rasky'], phot_table['decsky'] = scoord.to_string(
'hmsdms', sep=':').split()
else:
phot_table['xcensky'] = scoord[0]
phot_table['ycensky'] = scoord[1]
if wcs:
if 'ra' not in phot_table.colnames:
ra, dec = pix2wcs(coord, wcs)
phot_table['ra'] = ra
phot_table['dec'] = dec
if skycoord:
if 'rasky' not in phot_table.colnames:
skyra, skydec = pix2wcs(scoord, wcs)
phot_table['rasky'] = skyra
phot_table['decsky'] = skydec
if calfctr:
flux = phot_table['residual_aperture_sum'] / calfctr
phot_table.add_column(Column(flux, name='ap_flux', unit=unit))
#if error is not None:
fluxerr = phot_table['residual_aperture_err'] / calfctr
phot_table.add_column(Column(fluxerr, name='ap_err', unit=unit))
if filter:
phot_table['filter'] = str(filter)
return phot_table
error in photometry is assumed as Poissonian -- sqrt(N)
but smooth background error not included for now
"""
for eachFile in imFiles:
print('Prcoessing %s ...' %(eachFile))
try:
hdu = pyfits.open(eachFile)
im = hdu[0].data
except: print('File could not be opened: %s' %(eachFile))
# create aperture and annulus objects
apertures = CircularAperture(coords, r=aper_size)
annulus_apertures = CircularAnnulus(coords, r_in=annulus, r_out=dannulus)
npix_src, npix_bkg = apertures.area(), annulus_apertures.area()
# calculate the object and annulus flux
data_error = np.sqrt(im)
rawflux_table = aperture_photometry(im, apertures, error=data_error)
bkgflux_table = aperture_photometry(im, annulus_apertures, error=data_error)
# stack the two tables into one
phot_table = hstack([rawflux_table, bkgflux_table], table_names=['raw', 'bkg'])
# calculate bkg mean & normalize by area
bkg_mean = phot_table['aperture_sum_bkg'] / npix_bkg
# final photometric counts -- in ADUs
Nsrc = phot_table['aperture_sum_raw'] - bkg_mean * npix_src
# change counts to flux
flux = gain * Nsrc / hdu[0].header['EXPTIME']
def onpress(event):
if event.key == 'p':
filename = d.get('file')
#hdu=fits.open(filename)
hdu = d.get_pyfits()
data = hdu[0].data
x = d.get('crosshair image')
x, y = x.split()
x, y = int(float(x)), int(float(y))
new_image_halfwidth = int(entry1.get())
aperture_radius = int(entry2.get())
inner_sky_radius = int(entry3.get())
outer_sky_radius = int(entry4.get())
new_image = data[y - new_image_halfwidth:y + new_image_halfwidth,
x - new_image_halfwidth:x + new_image_halfwidth]
x_grid = np.arange(x - new_image_halfwidth, x + new_image_halfwidth, 1)
y_grid = np.arange(y - new_image_halfwidth, y + new_image_halfwidth, 1)
x_grid, y_grid = np.meshgrid(x_grid, y_grid)
guess = (np.amax(new_image) - new_image[0, 0], x, y, 3, 3, 0,
new_image[0, 0])
popt, pcov = curve_fit(gauss2d, (x_grid, y_grid),
new_image.ravel(),
p0=guess)
#print popt
x = int(popt[1])
y = int(popt[2])
labeltext.set('X: ' + str(x))
label2text.set('Y: ' + str(y))
new_image = data[y - new_image_halfwidth:y + new_image_halfwidth,
x - new_image_halfwidth:x + new_image_halfwidth]
# for artist in ax1.get_children():
# if hasattr(artist,'get_label') and artist.get_label()=='centroid':
# artist.remove()
ax1.clear()
ax1.matshow(np.flip(new_image, axis=0),
cmap='gray',
origin='upper',
clim=zscale(new_image),
zorder=0)
ax1.scatter(
[new_image_halfwidth + 1], [new_image_halfwidth - 1],
marker='+',
s=120,
c='k',
zorder=1
) #ax1.scatter([popt[1]-x+new_image_halfwidth],[popt[2]-y+new_image_halfwidth],marker='+',s=120,c='k',zorder=1)
aperture_circle = plt.Circle((popt[1] - x + new_image_halfwidth,
popt[2] - y + new_image_halfwidth),
radius=aperture_radius,
linewidth=3,
color='hotpink',
fill=False,
lw=3,
zorder=2)
ax1.add_patch(aperture_circle)
inner_sky = plt.Circle((popt[1] - x + new_image_halfwidth,
popt[2] - y + new_image_halfwidth),
radius=inner_sky_radius,
linewidth=3,
color='lime',
fill=False,
zorder=2)
ax1.add_patch(inner_sky)
outer_sky = plt.Circle((popt[1] - x + new_image_halfwidth,
popt[2] - y + new_image_halfwidth),
radius=outer_sky_radius,
linewidth=3,
color='red',
fill=False,
zorder=2)
ax1.add_patch(outer_sky)
canvas.draw()
#update the radial plot
ax2.clear()
radial = radial_profile(new_image,
[new_image_halfwidth, new_image_halfwidth])
#perform the aperture photometry
#currently 2% different from IDL atv
aperture = CircularAperture((x, y), r=aperture_radius)
annulus = CircularAnnulus((x, y),
r_in=inner_sky_radius,
r_out=outer_sky_radius)
phot_table = aperture_photometry(data, [aperture, annulus])
#new background estimation
bkg_mask = np.ma.masked_outside(radial[0].reshape(new_image.shape),
inner_sky_radius, outer_sky_radius)
bkg_map = Background2D(new_image,
tuple(np.array(new_image.shape) / 4),
mask=bkg_mask.mask,
exclude_mesh_method='all')
bkg_map_med = MMMBackground().calc_background(
bkg_map.data) #bkg_map_med=np.median(bkg_map.background)
#print 'Map sky mean '+str(bkg_map_med)
#bkg_mean=phot_table['aperture_sum_1']/annulus.area()
#print 'Aperture sky mean '+str(bkg_mean)
#phot_table['residual_aperture_sum']=phot_table['aperture_sum_0']-bkg_mean*aperture.area()
phot_table['residual_aperture_sum'] = phot_table[
'aperture_sum_0'] - bkg_map_med * aperture.area()
#print 'Map sky result: '+str(phot_table['aperture_sum_0']-bkg_map_med*aperture.area())
#print "Aperture Photometry Result: "+str(phot_table['residual_aperture_sum'])
label8text.set('Sky Value: ' + str(int(bkg_map_med)))
label9text.set('Aperture Counts: ' +
str(int(phot_table['residual_aperture_sum'][0])))
label10text.set(
'Mag: ' +
str(-2.5 * np.log10(int(phot_table['residual_aperture_sum'][0])) +
25.)[:5])
ax2.scatter(radial[0], radial[1])
if var10.get() == 1:
ax2.plot(np.linspace(0, new_image_halfwidth, num=50),
gauss1d(np.linspace(0, new_image_halfwidth, num=50),
popt[0], 0, np.mean([popt[3], popt[4]]), popt[6]),
c='k',
lw=2)
ax2.text(0.5,
0.93,
'Gaussian FWHM: ' +
str(2.35482 * np.mean([popt[3], popt[4]]))[:5],
transform=ax2.transAxes,
fontsize=int(15 * scaling))
if var11.get() == 1:
moffat1d_guess = (np.amax(new_image) - bkg_map_med, 0, 3, 1,
bkg_map_med)
popt2, pcov2 = curve_fit(moffat1d,
radial[0],
radial[1],
p0=moffat1d_guess)
ax2.plot(np.linspace(0, new_image_halfwidth, num=50),
moffat1d(np.linspace(0, new_image_halfwidth,
num=50), popt2[0], popt2[1],
popt2[2], popt2[3], popt2[4]),
c='r',
lw=2)
ax2.text(
0.5,
0.85,
'Moffat FWHM: ' +
str(2.0 * popt2[2] * np.sqrt(2.0**(1. / popt2[3]) - 1.))[:5],
transform=ax2.transAxes,
fontsize=int(15 * scaling))
ax2.grid(True, color='white', linestyle='-', linewidth=1)
ax2.set_axisbelow(True)
ax2.autoscale(False)
ax2.set_xlim([0, new_image_halfwidth])
ax2.set_xlabel('Radius (Pixels)')
ax2.set_ylim([np.amin(radial[1]), np.amax(radial[1])])
ax2.set_axis_bgcolor('0.85')
ax2.axvline(aperture_radius, linewidth=2, color='hotpink')
ax2.axvline(inner_sky_radius, linewidth=2, color='lime')
ax2.axvline(outer_sky_radius, linewidth=2, color='red')
ax2.axhline(bkg_map_med, linewidth=2, color='yellow')
canvas2.draw()
def tso_aperture_photometry(datamodel, xcenter, ycenter, radius, radius_inner,
radius_outer):
"""
Create a photometric catalog for NIRCam TSO imaging observations.
Parameters
----------
datamodel : `CubeModel`
The input `CubeModel` of a NIRCam TSO imaging observation.
xcenter, ycenter : float
The ``x`` and ``y`` center of the aperture.
radius : float
The radius (in pixels) of the circular aperture.
radius_inner, radius_outer : float
The inner and outer radii (in pixels) of the circular-annulus
aperture, used for local background estimation.
Returns
-------
catalog : `~astropy.table.QTable`
An astropy QTable (Quantity Table) containing the source
photometry.
"""
if not isinstance(datamodel, CubeModel):
raise ValueError('The input data model must be a CubeModel.')
# For the SUB64P subarray with the WLP8 pupil, the circular aperture
# extends beyond the image and the circular annulus does not have any
# overlap with the image. In that case, we simply sum all values
# in the array and skip the background subtraction.
sub64p_wlp8 = False
if (datamodel.meta.instrument.pupil == 'WLP8' and
datamodel.meta.subarray.name == 'SUB64P'):
sub64p_wlp8 = True
if not sub64p_wlp8:
phot_aper = CircularAperture((xcenter, ycenter), r=radius)
bkg_aper = CircularAnnulus((xcenter, ycenter), r_in=radius_inner,
r_out=radius_outer)
aperture_sum = []
aperture_sum_err = []
annulus_sum = []
annulus_sum_err = []
nimg = datamodel.data.shape[0]
if sub64p_wlp8:
info = ('Photometry measured as the sum of all values in the '
'subarray. No background subtraction was performed.')
for i in np.arange(nimg):
aperture_sum.append(np.sum(datamodel.data[i, :, :]))
aperture_sum_err.append(
np.sqrt(np.sum(datamodel.err[i, :, :]**2)))
else:
info = ('Photometry measured in a circular aperture of r={0} '
'pixels. Background calculated as the mean in a '
'circular annulus with r_inner={1} pixels and '
'r_outer={2} pixels.'.format(radius, radius_inner,
radius_outer))
for i in np.arange(nimg):
aper_sum, aper_sum_err = phot_aper.do_photometry(
datamodel.data[i, :, :], error=datamodel.err[i, :, :])
ann_sum, ann_sum_err = bkg_aper.do_photometry(
datamodel.data[i, :, :], error=datamodel.err[i, :, :])
aperture_sum.append(aper_sum[0])
aperture_sum_err.append(aper_sum_err[0])
annulus_sum.append(ann_sum[0])
annulus_sum_err.append(ann_sum_err[0])
aperture_sum = np.array(aperture_sum)
aperture_sum_err = np.array(aperture_sum_err)
annulus_sum = np.array(annulus_sum)
annulus_sum_err = np.array(annulus_sum_err)
# construct metadata for output table
meta = OrderedDict()
meta['instrument'] = datamodel.meta.instrument.name
meta['detector'] = datamodel.meta.instrument.detector
meta['channel'] = datamodel.meta.instrument.channel
meta['subarray'] = datamodel.meta.subarray.name
meta['filter'] = datamodel.meta.instrument.filter
meta['pupil'] = datamodel.meta.instrument.pupil
meta['target_name'] = datamodel.meta.target.catalog_name
meta['xcenter'] = xcenter
meta['ycenter'] = ycenter
ra_icrs, dec_icrs = datamodel.meta.wcs(xcenter, ycenter)
meta['ra_icrs'] = ra_icrs
meta['dec_icrs'] = dec_icrs
meta['apertures'] = info
# initialize the output table
tbl = QTable(meta=meta)
if hasattr(datamodel, 'int_times') and datamodel.int_times is not None:
nrows = len(datamodel.int_times)
else:
nrows = 0
if nrows == 0:
log.warning("There is no INT_TIMES table in the input file.")
if nrows > 0:
shape = datamodel.data.shape
if len(shape) == 2:
num_integ = 1
else: # len(shape) == 3
num_integ = shape[0]
int_start = datamodel.meta.exposure.integration_start
if int_start is None:
int_start = 1
log.warning("INTSTART not found; assuming a value of %d",
int_start)
# Columns of integration numbers & times of integration from the
# INT_TIMES table.
int_num = datamodel.int_times['integration_number']
mid_utc = datamodel.int_times['int_mid_MJD_UTC']
offset = int_start - int_num[0] # both are one-indexed
if offset < 0:
log.warning("Range of integration numbers in science data extends "
"outside the range in INT_TIMES table.")
log.warning("Can't use INT_TIMES table.")
del int_num, mid_utc
nrows = 0 # flag as bad
else:
log.debug("Times are from the INT_TIMES table.")
time_arr = mid_utc[offset: offset + num_integ]
int_times = Time(time_arr, format='mjd', scale='utc')
else:
log.debug("Times were computed from EXPSTART and TGROUP.")
dt = (datamodel.meta.exposure.group_time *
(datamodel.meta.exposure.ngroups + 1))
dt_arr = (np.arange(1, 1 + datamodel.meta.exposure.nints) *
dt - (dt / 2.))
int_dt = TimeDelta(dt_arr, format='sec')
int_times = (Time(datamodel.meta.exposure.start_time, format='mjd') +
int_dt)
tbl['MJD'] = int_times.mjd
tbl['aperture_sum'] = aperture_sum
tbl['aperture_sum_err'] = aperture_sum_err
if not sub64p_wlp8:
tbl['annulus_sum'] = annulus_sum
tbl['annulus_sum_err'] = annulus_sum_err
annulus_mean = annulus_sum / bkg_aper.area()
annulus_mean_err = annulus_sum_err / bkg_aper.area()
tbl['annulus_mean'] = annulus_mean
tbl['annulus_mean_err'] = annulus_mean_err
aperture_bkg = annulus_mean * phot_aper.area()
aperture_bkg_err = annulus_mean_err * phot_aper.area()
tbl['aperture_bkg'] = aperture_bkg
tbl['aperture_bkg_err'] = aperture_bkg_err
net_aperture_sum = aperture_sum - aperture_bkg
net_aperture_sum_err = np.sqrt(aperture_sum_err ** 2 +
aperture_bkg_err ** 2)
tbl['net_aperture_sum'] = net_aperture_sum
tbl['net_aperture_sum_err'] = net_aperture_sum_err
else:
colnames = ['annulus_sum', 'annulus_sum_err', 'annulus_mean',
'annulus_mean_err', 'aperture_bkg', 'aperture_bkg_err']
for col in colnames:
tbl[col] = np.full(nimg, np.nan)
tbl['net_aperture_sum'] = aperture_sum
tbl['net_aperture_sum_err'] = aperture_sum_err
return tbl
def photom(ima, pos, radius, r_in=None, r_out=None, method='median'):
'''
Aperture photometry in an aperture located at pixel coordinates
pos = ( (x0, y0), (x1, y1), ... ) with a radius=radius.
When r_in and r_out are given, background is estimated in CircularAnnulus and subtracted.
method refers to how the background is estimated within the circlar annulus.
Can be 'median' or 'mean' or 'mode'
'''
ima_local = np.ma.asanyarray(ima.copy())
ima_local.fill_value = np.nan
mask_ = ima_local.mask
ima_ = ima_local.filled()
### Do photometry - identical for each method
apertures = CircularAperture(pos, r = radius)
ap = aperture_photometry(ima_, apertures,
mask=mask_, method='exact')
# Aperture photometry on mask to estimate # of masked pixels in aperture
apm = aperture_photometry(mask_.astype(int), apertures,
method='exact')
ap.add_columns( [apertures.area()-apm['aperture_sum'], apm['aperture_sum']],
names=['aperture_area', 'aperture_badpix'])
ap.add_column(ap['aperture_sum'], index=3, name='Flux')
if ( r_in == None or r_out == None or not method in ('mean', 'median', 'mode') ):
# Quit here if background correction is not requested
return ap
annulus_apertures = CircularAnnulus(pos, r_in=r_in, r_out=r_out)
annulus_masks = annulus_apertures.to_mask(method='center')
bg_values = []
for annulus_mask in annulus_masks:
bg_ima = annulus_mask.cutout(ima_)
bg_mask = annulus_mask.cutout(mask_.astype(np.int))
bg_ima = np.ma.array(bg_ima, mask= bg_mask.astype(np.bool) | ~annulus_mask.data.astype(np.bool))
if method == 'mean': bg_val = bg_ima.mean()
elif method == 'median': bg_val = np.ma.median(bg_ima)
elif method == 'mode':
kernel = gaussian_kde(bg_ima.data[~bg_ima.mask], bw_method='silverman')
mode = bg_ima.mean()
std = bg_ima.std()
mode = minimize_scalar(lambda x: -kernel(x), bounds=(mode-3*std, mode+3*std),
method='bounded')
bg_val=mode.x[0]
if False:
median = np.ma.median(bg_ima)
h, b = np.histogram(bg_ima.data[~bg_ima.mask], bins=15, normed=True)
bc = 0.5*(b[1:]+ b[:-1])
plt.figure(33); plt.clf(); plt.ioff()
fig, (ax0,ax1) = plt.subplots(ncols=2, nrows=1, num=33)
ax0.plot(bc, h, 'x')
x = np.linspace(bc.min(), bc.max(), 100)
ax0.plot(x, kernel(x))
ax0.vlines(mode.x, ax0.get_ylim()[0], ax0.get_ylim()[1])
ax0.vlines(median, ax0.get_ylim()[0], ax0.get_ylim()[1])
ax1.imshow(bg_ima)
plt.show()
bg_values.append(bg_val)
ap.add_column(Column(data=bg_values, name = 'background'))
ap['Flux'] = ap['Flux'] - ap['aperture_area']*ap['background']
return ap, bg_ima
def align_norm(fnlist, tolerance=5, thresh=3.5):
"""Aligns a set of images to each other, as well as normalizing the images
to the same average brightness.
Both the alignment and normalization are accomplished through stellar
photometry using the IRAF routine 'daophot'. The centroids of a handful
of stars are found and used to run the IRAF routine 'imalign'. The
instrumental magnitudes of the stars are used to determine by how much
each image must be scaled for the photometry to match across images.
The images are simply updated with their rescaled, shifted selves. This
overwrites the previous images and adds the header keyword 'fpphot' to
the images.
A handful of temporary files are created during this process, which should
all be deleted by the routine at the end. But if it is interrupted, they
might not be.
If the uncertainty images exist, this routine also shifts them by the same
amounts as the intensity images, as well as updating the uncertainty values
for both the new normalization and the uncertainties in normalizing the
images.
Inputs:
fnlist -> List of strings, each the path to a fits image.
tolerance -> How close two objects can be and still be considered the same
object. Default is 3 pixels.
thresh -> Optional. Level above sky background variation to look for objs.
Default is 3.5 (times SkySigma). Decrease if center positions
aren't being found accurately. Increase for crowded fields to
decrease computation time.
"""
# Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = FPImage(fnlist[0])
toggle = firstimage.fwhm
axcen = firstimage.axcen
aycen = firstimage.aycen
arad = firstimage.arad
firstimage.close()
if axcen is None:
print "Error! Images have not yet been aperture-masked! Do this first!"
crash()
if toggle is None:
print "Warning! FWHMs have not been measured!"
print "Assuming 5 pixel FWHM for all images."
for i in range(len(fnlist)):
fwhm[i] = 5
else:
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
fwhm[i] = image.fwhm
image.close()
# Get sky background levels
skyavg = np.empty(len(fnlist))
skysig = np.empty(len(fnlist))
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
skyavg[i], skysig[i], _skyvar = image.skybackground()
image.close()
# Identify the stars in each image
xlists = []
ylists = []
print "Identifying stars in each image..."
for i in range(len(fnlist)):
xlists.append([])
ylists.append([])
image = FPImage(fnlist[i])
axcen = image.axcen
aycen = image.aycen
arad = image.arad
sources = daofind(image.inty-skyavg[i],
fwhm=fwhm[i],
threshold=thresh*skysig[i]).as_array()
for j in range(len(sources)):
# If the source is not near the center or edge
centermask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 > (0.05*arad)**2)
edgemask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 < (0.95*arad)**2)
if np.logical_and(centermask, edgemask):
xlists[i].append(sources[j][1])
ylists[i].append(sources[j][2])
image.close()
# Match objects between fields
print "Matching objects between images..."
xcoo = []
ycoo = []
for i in range(len(xlists[0])):
# For each object in the first image
accept = True
for j in range(1, len(fnlist)):
# For each other image
dist2 = ((np.array(xlists[j])-xlists[0][i])**2 +
(np.array(ylists[j])-ylists[0][i])**2)
if (min(dist2) > tolerance**2):
accept = False
break
if accept:
# We found an object at that position in every image
xcoo.append(xlists[0][i])
ycoo.append(ylists[0][i])
# Create coordinate arrays for the photometry and shifting
x = np.zeros((len(fnlist), len(xcoo)))
y = np.zeros_like(x)
for i in range(len(xcoo)):
# For every object found in the first image
for j in range(len(fnlist)):
# Find that object in every image
dist2 = ((np.array(xlists[j])-xcoo[i])**2 +
(np.array(ylists[j])-ycoo[i])**2)
index = np.argmin(dist2)
x[j, i] = xlists[j][index]
y[j, i] = ylists[j][index]
# Do aperture photometry on the matched objects
print "Performing photometry on matched stars..."
counts = np.zeros_like(x)
dcounts = np.zeros_like(x)
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
apertures = CircularAperture((x[i], y[i]), r=2*fwhm[i])
annuli = CircularAnnulus((x[i], y[i]), r_in=3*fwhm[i], r_out=4*fwhm[i])
phot_table = aperture_photometry(image.inty,
apertures, error=np.sqrt(image.vari))
sky_phot_table = aperture_photometry(image.inty, annuli,
error=np.sqrt(image.vari))
counts[i] = phot_table["aperture_sum"] / apertures.area()
counts[i] -= sky_phot_table["aperture_sum"] / annuli.area()
counts[i] *= apertures.area()
dcounts[i] = phot_table["aperture_sum_err"] / apertures.area()
image.close()
# Calculate the shifts and normalizations
norm, dnorm = calc_norm(counts, dcounts)
for i in range(x.shape[1]):
x[:, i] = -(x[:, i] - x[0, i])
y[:, i] = -(y[:, i] - y[0, i])
xshifts = np.average(x, axis=1)
yshifts = np.average(y, axis=1)
# Normalize the images and put shifts in the image headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image.phottog = "True"
image.dnorm = dnorm[i]
image.inty /= norm[i]
image.vari = image.vari/norm[i]**2
image.xshift = xshifts[i]
image.yshift = yshifts[i]
image.close()
return
# overplotted apertures, smaller should be better.
circ_apertures = CircularAperture(positions, r=arb.radius[band])
annulus_apertures = CircularAnnulus(
positions, r_in=arb.annuli_r[band][0], r_out=arb.annuli_r[band][1])
apers = [circ_apertures, annulus_apertures]
phot_table = aperture_photometry(image, apers)
#################################
# Local background subtraction. #
#################################
## Method 1: Follow
## http://photutils.readthedocs.io/en/stable/photutils (...)
## /aperture.html#local-background-subtraction
bkg_mean = phot_table['aperture_sum_1'] / annulus_apertures.area()
bkg_sum = bkg_mean * circ_apertures.area()
final_sum = phot_table['aperture_sum_0'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
## Method 2: Follow
## https://github.com/astropy/photutils/pull/453, sigclipping away stars
## in the annulus.
#ann_masks = annulus_apertures.to_mask(method='center')
#ann_masked_data = [am.apply(image) for am in ann_masks]
## Sigma clip stars in annular aperture.
#pre_sc_median = [np.nanmedian(amd[amd != 0])
# for amd in ann_masked_data]
#pre_sc_std = [
# (np.nanmedian(np.abs(amd[amd != 0] - pre_sc_median[ix])))*1.483
np.logical_or(xStars < 40, xStars > (nx - 40)),
np.logical_or(yStars < 40, yStars > (ny - 40)))
# Now let's do aperture photometry on the remaining sources in the image
# 1. Setup the apertures
sourcePos = [xStars, yStars]
apertures = CircularAperture(sourcePos, r = 6.0)
annulus_apertures = CircularAnnulus(sourcePos, r_in=12., r_out=14.)
# 2. Perform the basic photometry
rawflux_table = aperture_photometry(stokesI.arr, apertures)
bkgflux_table = aperture_photometry(stokesI.arr, annulus_apertures)
phot_table = hstack([rawflux_table, bkgflux_table], table_names=['raw', 'bkg'])
# 3. Compute background contribution and subtract from raw photometry
bkg_mean = phot_table['aperture_sum_bkg'] / annulus_apertures.area()
bkg_sum = bkg_mean * apertures.area()
final_sum = phot_table['aperture_sum_raw'] - bkg_sum
phot_table['residual_aperture_sum'] = final_sum
# Compute the signal-to-noise ratio and find the stars with SNR < 3.0
SNR = final_sum/bkg_sum
bkgDominated = SNR < 1.0
# ###########################################################################
# # PRINT OUT THE PHOTOMETRY TO CHECK FOR CONSISTENCY
# ###########################################################################
# xFmtStr = '{x[0]:>6}.{x[1]:<3}'
# yFmtStr = '{y[0]:>6}.{y[1]:<3}'
# starFmtStr = '{star[0]:>9}.{star[1]:<3}'
# bkgFmtStr = '{bkg[0]:>9}.{bkg[1]:<3}'
# snrFmtStr = '{snr[0]:>9}.{snr[1]:<3}'
def extract_ifu(input_model, source_type, extract_params):
"""This function does the extraction.
Parameters
----------
input_model : IFUCubeModel
The input model.
source_type : string
"point" or "extended"
extract_params : dict
The extraction parameters for aperture photometry.
Returns
-------
ra, dec : float
ra and dec are the right ascension and declination respectively
at the nominal center of the image.
wavelength : ndarray, 1-D
The wavelength in micrometers at each pixel.
net : ndarray, 1-D
The count rate (or flux) minus the background at each pixel.
background : ndarray, 1-D
The background count rate that was subtracted from the total
source count rate to get `net`.
npixels : ndarray, 1-D, float64
For each slice, this is the number of pixels that were added
together to get `net`.
dq : ndarray, 1-D, uint32
The data quality array.
"""
data = input_model.data
shape = data.shape
if len(shape) != 3:
log.error("Expected a 3-D IFU cube; dimension is %d.", len(shape))
raise RuntimeError("The IFU cube should be 3-D.")
# We need to allocate net, background, npixels, and dq arrays
# no matter what. We may need to divide by npixels, so the default
# is 1 rather than 0.
net = np.zeros(shape[0], dtype=np.float64)
background = np.zeros(shape[0], dtype=np.float64)
npixels = np.ones(shape[0], dtype=np.float64)
dq = np.zeros(shape[0], dtype=np.uint32)
# For an extended target, the entire aperture will be extracted, so
# it makes no sense to shift the extraction location.
if source_type.lower() != "extended":
ra_targ = input_model.meta.target.ra
dec_targ = input_model.meta.target.dec
locn = locn_from_wcs(input_model, ra_targ, dec_targ)
if locn is None or np.isnan(locn[0]):
log.warning("Couldn't determine pixel location from WCS, so "
"nod/dither correction will not be applied.")
x_center = extract_params['x_center']
y_center = extract_params['y_center']
if x_center is None:
x_center = float(shape[-1]) / 2.
else:
x_center = float(x_center)
if y_center is None:
y_center = float(shape[-2]) / 2.
else:
y_center = float(y_center)
else:
(x_center, y_center) = locn
log.info("Using x_center = %g, y_center = %g, based on "
"TARG_RA and TARG_DEC.", x_center, y_center)
method = extract_params['method']
# subpixels is only needed if method = 'subpixel'.
subpixels = extract_params['subpixels']
subtract_background = extract_params['subtract_background']
smaller_axis = float(min(shape[-2], shape[-1])) # for defaults
radius = None
inner_bkg = None
outer_bkg = None
if source_type == 'extended':
# Ignore any input parameters, and extract the whole image.
width = float(shape[-1])
height = float(shape[-2])
x_center = width / 2. - 0.5
y_center = height / 2. - 0.5
theta = 0.
subtract_background = False
else:
radius = extract_params['radius']
if radius is None:
radius = smaller_axis / 4.
if subtract_background:
inner_bkg = extract_params['inner_bkg']
if inner_bkg is None:
inner_bkg = radius
outer_bkg = extract_params['outer_bkg']
if outer_bkg is None:
outer_bkg = min(inner_bkg * math.sqrt(2.),
smaller_axis / 2. - 1.)
if inner_bkg <= 0. or outer_bkg <= 0. or inner_bkg >= outer_bkg:
log.debug("Turning background subtraction off, due to "
"the values of inner_bkg and outer_bkg.")
subtract_background = False
width = None
height = None
theta = None
log.debug("IFU 1-D extraction parameters:")
log.debug(" x_center = %s", str(x_center))
log.debug(" y_center = %s", str(y_center))
if source_type == 'point':
log.debug(" radius = %s", str(radius))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" inner_bkg = %s", str(inner_bkg))
log.debug(" outer_bkg = %s", str(outer_bkg))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
else:
log.debug(" width = %s", str(width))
log.debug(" height = %s", str(height))
log.debug(" theta = %s degrees", str(theta))
log.debug(" subtract_background = %s", str(subtract_background))
log.debug(" method = %s", method)
if method == "subpixel":
log.debug(" subpixels = %s", str(subpixels))
x0 = float(shape[2]) / 2.
y0 = float(shape[1]) / 2.
(ra, dec, wavelength) = get_coordinates(input_model, x0, y0)
position = (x_center, y_center)
if source_type == 'point':
aperture = CircularAperture(position, r=radius)
else:
aperture = RectangularAperture(position, width, height, theta)
if subtract_background and inner_bkg is not None and outer_bkg is not None:
annulus = CircularAnnulus(position, r_in=inner_bkg, r_out=outer_bkg)
else:
annulus = None
# Compute the area of the aperture and possibly also of the annulus.
normalization = 1.
temp = np.ones(shape[-2:], dtype=np.float64)
phot_table = aperture_photometry(temp, aperture,
method=method, subpixels=subpixels)
aperture_area = float(phot_table['aperture_sum'][0])
log.debug("aperture.area() = %g; aperture_area = %g",
aperture.area(), aperture_area)
if subtract_background and annulus is not None:
# Compute the area of the annulus.
phot_table = aperture_photometry(temp, annulus,
method=method, subpixels=subpixels)
annulus_area = float(phot_table['aperture_sum'][0])
log.debug("annulus.area() = %g; annulus_area = %g",
annulus.area(), annulus_area)
if annulus_area > 0.:
normalization = aperture_area / annulus_area
else:
log.warning("Background annulus has no area, so background "
"subtraction will be turned off.")
subtract_background = False
del temp
npixels[:] = aperture_area
for k in range(shape[0]):
phot_table = aperture_photometry(data[k, :, :], aperture,
method=method, subpixels=subpixels)
net[k] = float(phot_table['aperture_sum'][0])
if subtract_background:
bkg_table = aperture_photometry(data[k, :, :], annulus,
method=method, subpixels=subpixels)
background[k] = float(bkg_table['aperture_sum'][0])
net[k] = net[k] - background[k] * normalization
# Check for NaNs in the wavelength array, flag them in the dq array,
# and truncate the arrays if NaNs are found at endpoints (unless the
# entire array is NaN).
(wavelength, net, background, npixels, dq) = \
nans_in_wavelength(wavelength, net, background, npixels, dq)
return (ra, dec, wavelength, net, background, npixels, dq)
def photometry(star_positions, aperture_radii, centroid_stamp_half_width,
psf_stddev_init, aperture_annulus_radius, output_path):
"""
Parameters
----------
master_dark_path : str
Path to master dark frame
master_flat_path :str
Path to master flat field
target_centroid : `~numpy.ndarray`
position of centroid, with shape (2, 1)
comparison_flux_threshold : float
Minimum fraction of the target star flux required to accept for a
comparison star to be included
aperture_radii : `~numpy.ndarray`
Range of aperture radii to use
centroid_stamp_half_width : int
Centroiding is done within image stamps centered on the stars. This
parameter sets the half-width of the image stamps.
psf_stddev_init : float
Initial guess for the width of the PSF stddev parameter, used for
fitting 2D Gaussian kernels to the target star's PSF.
aperture_annulus_radius : int
For each aperture in ``aperture_radii``, measure the background in an
annulus ``aperture_annulus_radius`` pixels bigger than the aperture
radius
output_path : str
Path to where outputs will be saved.
"""
master_dark = fits.getdata(master_dark_path)
master_flat = fits.getdata(master_flat_path)
master_flat[master_flat < 0.1] = 1.0 # tmp
#star_positions = init_centroids(image_paths[0:3], master_flat, master_dark,
# target_centroid, plots=True,
# min_flux=comparison_flux_threshold).T
# Initialize some empty arrays to fill with data:
times = np.zeros(len(image_paths))
fluxes = np.zeros((len(image_paths), len(star_positions),
len(aperture_radii)))
errors = np.zeros((len(image_paths), len(star_positions),
len(aperture_radii)))
xcentroids = np.zeros((len(image_paths), len(star_positions)))
ycentroids = np.zeros((len(image_paths), len(star_positions)))
airmass = np.zeros(len(image_paths))
airpress = np.zeros(len(image_paths))
humidity = np.zeros(len(image_paths))
telfocus = np.zeros(len(image_paths))
psf_stddev = np.zeros(len(image_paths))
medians = np.zeros(len(image_paths))
with ProgressBar(len(image_paths)) as bar:
for i in range(len(image_paths)):
bar.update()
# Subtract image by the dark frame, normalize by flat field
imagedata = (fits.getdata(image_paths[i]) - master_dark) / master_flat
# Collect information from the header
imageheader = fits.getheader(image_paths[i])
exposure_duration = imageheader['EXPTIME']
times[i] = Time(imageheader['DATE-OBS'], format='isot', scale=imageheader['TIMESYS'].lower()).jd
medians[i] = np.median(imagedata)
airmass[i] = imageheader['AIRMASS']
airpress[i] = imageheader['AIRPRESS']
humidity[i] = imageheader['HUMIDITY']
telfocus[i] = imageheader['TELFOCUS']
# Initial guess for each stellar centroid informed by previous centroid
for j in range(len(star_positions)):
if i == 0:
init_x = star_positions[j][0]
init_y = star_positions[j][1]
else:
init_x = ycentroids[i-1][j]
init_y = xcentroids[i-1][j]
# Cut out a stamp of the full image centered on the star
image_stamp = imagedata[int(init_y) - centroid_stamp_half_width:
int(init_y) + centroid_stamp_half_width,
int(init_x) - centroid_stamp_half_width:
int(init_x) + centroid_stamp_half_width]
# Measure stellar centroid with 2D gaussian fit
x_stamp_centroid, y_stamp_centroid = centroid_com(image_stamp)
y_centroid = x_stamp_centroid + init_x - centroid_stamp_half_width
x_centroid = y_stamp_centroid + init_y - centroid_stamp_half_width
xcentroids[i, j] = x_centroid
ycentroids[i, j] = y_centroid
# For the target star, measure PSF:
if j == 0:
psf_model_init = models.Gaussian2D(amplitude=np.max(image_stamp),
x_mean=centroid_stamp_half_width,
y_mean=centroid_stamp_half_width,
x_stddev=psf_stddev_init,
y_stddev=psf_stddev_init)
fit_p = fitting.LevMarLSQFitter()
y, x = np.mgrid[:image_stamp.shape[0], :image_stamp.shape[1]]
best_psf_model = fit_p(psf_model_init, x, y, image_stamp -
np.median(image_stamp))
psf_stddev[i] = 0.5*(best_psf_model.x_stddev.value +
best_psf_model.y_stddev.value)
positions = np.vstack([ycentroids[i, :], xcentroids[i, :]])
for k, aperture_radius in enumerate(aperture_radii):
target_apertures = CircularAperture(positions, aperture_radius)
background_annuli = CircularAnnulus(positions,
r_in=aperture_radius +
aperture_annulus_radius,
r_out=aperture_radius +
2 * aperture_annulus_radius)
flux_in_annuli = aperture_photometry(imagedata,
background_annuli)['aperture_sum'].data
background = flux_in_annuli/background_annuli.area()
flux = aperture_photometry(imagedata,
target_apertures)['aperture_sum'].data
background_subtracted_flux = (flux - background *
target_apertures.area())
fluxes[i, :, k] = background_subtracted_flux/exposure_duration
errors[i, :, k] = np.sqrt(flux)
## Save some values
results = PhotometryResults(times, fluxes, errors, xcentroids, ycentroids,
airmass, airpress, humidity, medians,
psf_stddev, aperture_radii)
results.save(output_path)
return results
