def create_aperture_from_ds9_region(region_file):
'''
Loads a ds9 region ('reg') file saved in physical format and returns an astropy aperture instance.
'''
#
f = open(region_file,'rb')
for i in range(3):
f.readline()
region_text = f.read()
pdb.set_trace()
regions = region_text.split('\n')
src_apertures,bkg_apertures = [],[]
for region in regions[:-1]:
# This is a poor example of string handling, but it works.
value_text = region.split('(')[1].split(')')[0]
pos_x,pos_y,r_in,r_out = np.array(value_text.split(','),dtype = float)
src_apertures.append(photutils.CircularAperture((pos_x,pos_y), r=r_in))
bkg_aperture.append(photutils.CircularAnnulus((pos_x,pos_y), r_in=r_in, r_out=r_out))
return src_apertures,bkg_apertures
def _aperture_phot(self, x, y, data, radsize=1,
sky_inner=5, skywidth=5, method="subpixel", subpixels=4):
"""Perform sky subtracted aperture photometry, uses photutils functions, photutil must be installed
Parameters
----------
radsize: int
Size of the radius
sky_inner: int
Inner radius of the sky annulus
skywidth: int
Width of the sky annulus
method: string
Pixel sampling method to use
subpixels: int
How many subpixels to use
Notes
-----
background is taken from sky annulus pixels, check into masking bad pixels
"""
if not photutils_installed:
print("Install photutils to enable")
else:
apertures = photutils.CircularAperture((x, y), radsize)
rawflux_table = photutils.aperture_photometry(
data,
apertures,
subpixels=1,
method="center")
outer = sky_inner + skywidth
annulus_apertures = photutils.CircularAnnulus(
(x, y), r_in=sky_inner, r_out=outer)
bkgflux_table = photutils.aperture_photometry(
data,
annulus_apertures)
# to calculate the mean local background, divide the circular annulus aperture sums
# by the area fo the circular annuls. The bkg sum with the circular aperture is then
# then mean local background tims the circular apreture area.
aperture_area = apertures.area()
annulus_area = annulus_apertures.area()
bkg_sum = (
bkgflux_table['aperture_sum'] *
aperture_area /
annulus_area)[0]
skysub_flux = rawflux_table['aperture_sum'][0] - bkg_sum
return (
float(rawflux_table['aperture_sum'][0]), bkg_sum, skysub_flux)
def get_encircled_energy_and_rad_at_EE(data,
x,
y,
radii,
get_rad_at_EE=0.9,
plot=False,
ax=None):
"""
A function to calculate the encircled energy at a given position, summing up the flux in apertures of size *radii*,
normalizing the EE to the last flux value.
INPUT:
data - a two dimensional np.array
x - x centroid
y - y centroid
radii - an array of radii to calculate the EE
get_rad_at_EE - the EE of which to calculate the radius value
plot - plot a EE vs radii plot
OUTPUT:
df
a dataframe with two columns:
- radii
- EE
rad_at_EE
- the radius when EE is a given input value
NOTE:
Assumes that the data is background subtracted
EXAMPLE:
radii = np.arange(1,450)
df_ee, r_at_EE90 = phothelp.get_encircled_energy(fimg.data,fimg.xcenter,fimg.ycenter,radii,plot=True)
"""
apertures = [photutils.CircularAperture((x, y), r=r) for r in radii]
phot_table = photutils.aperture_photometry(data, apertures)
df = phot_table[phot_table.colnames[3:]].to_pandas()
EE = df.loc[0] / df.loc[0][-1]
df = pd.DataFrame(list(zip(radii, EE)), columns=["radii", "EE"])
#r_at_EE = df[df["EE"] > get_rad_at_EE]["radii"].values[0]
r_at_EE = np.interp(get_rad_at_EE, df.EE.values, df.radii.values)
if plot:
if ax == None:
fig, ax = plt.subplots()
ax.plot(radii, EE.values)
ax.set_xlabel("Radii")
ax.set_ylabel("Encircled Energy")
ax.set_title("EE" + gk.num2str(get_rad_at_EE * 100, 1) + "% at r=" +
str(r_at_EE))
ax.vlines(r_at_EE, 0, get_rad_at_EE, color="green", linestyle="--")
ax.hlines(get_rad_at_EE, 0, r_at_EE, color="green", linestyle="--")
ax.minorticks_on()
return df, r_at_EE
def subtract_background(self,
subtract_min_value=True,
plot_background=False,
ax=None):
"""
A function to subtract the background for the FRD tests
INPUT:
subtract_min_value - subtracts the .min value (no negative numbers)
plot_background - if True, plots the estimated background with the meshes it used.
"""
self.aper = photutils.CircularAperture(positions=(self.x, self.y),
r=self.r)
# get the mask from the aperture
# hack
mask = self.aper.to_mask()[0].to_image(self.data.shape)
# use sigma clipping
sigma_clip = SigmaClip(sigma=3., iters=10)
bkg_estimator = photutils.MedianBackground()
self.bkg = photutils.Background2D(self.data,
self.box_size,
filter_size=(3, 3),
sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator,
mask=mask,
edge_method="crop")
self.data_background = self.data - self.bkg.background
if subtract_min_value:
self.subtracted_value = self.data_background.min()
print("Subtracted min value:", self.subtracted_value)
self.data_background -= self.subtracted_value
if plot_background:
if ax == None:
self.fig, self.ax = plt.subplots()
else:
self.ax = ax
im = self.ax.imshow(self.bkg.background,
origin='lower',
cmap='Greys_r')
self.ax.set_xlim(0, self.bkg.background.shape[1])
self.ax.set_ylim(0, self.bkg.background.shape[0])
self.ax.set_title("Estimated Background", y=1.02)
self.ax.set_xlabel("X pixels")
self.ax.set_ylabel("Y pixels")
if ax == None:
# Only do this if ax is not supplied
# Don't know how to deal with this without passing the figure explicitly
self.fig.colorbar(im)
self.bkg.plot_meshes(outlines=True, color='#1f77b4', ax=self.ax)
return self.data_background
def finish(self, survey, brickname, version_header,
apradec=None, apertures=None):
# apradec = (ra,dec): aperture photometry locations
# apertures: RADII in PIXELS
if apradec is not None:
assert(apertures is not None)
(ra,dec) = apradec
ok,xx,yy = self.wcs.radec2pixelxy(ra, dec)
assert(np.all(ok))
del ok
apxy = np.vstack((xx - 1., yy - 1.)).T
ap_iphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
ap_dphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
ap_rphots = [np.zeros((len(ra), len(apertures)), np.float32)
for band in self.bands]
coimgs = []
comods = []
for iband,band in enumerate(self.bands):
coimg = self.co_images[band]
comod = self.co_models[band]
coiv = self.co_invvars[band]
con = self.co_nobs[band]
with np.errstate(divide='ignore', invalid='ignore'):
coimg /= coiv
comod /= coiv
coimg[coiv == 0] = 0.
comod[coiv == 0] = 0.
coimgs.append(coimg)
comods.append(comod)
hdr = copy_header_with_wcs(version_header, self.wcs)
self.add_to_header(hdr, band)
self.write_coadds(survey, brickname, hdr, band, coimg, comod, coiv, con)
if apradec is not None:
import photutils
mask = (coiv == 0)
with np.errstate(divide='ignore'):
imsigma = 1.0/np.sqrt(coiv)
imsigma[mask] = 0.
for irad,rad in enumerate(apertures):
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(coimg, aper, error=imsigma,
mask=mask)
ap_iphots[iband][:,irad] = p.field('aperture_sum')
ap_dphots[iband][:,irad] = p.field('aperture_sum_err')
p = photutils.aperture_photometry(coimg - comod, aper, mask=mask)
ap_rphots[iband][:,irad] = p.field('aperture_sum')
self.write_color_image(survey, brickname, coimgs, comods)
if apradec is not None:
return ap_iphots, ap_dphots, ap_rphots
def psf_norm_2d(array, fwhm, size, threshold, mask_core, full_output,
verbose):
""" 2d case """
if size is not None:
if size < array.shape[0]:
psfs = frame_crop(array, size, force=True, verbose=False)
else:
psfs = array.copy()
else:
psfs = array.copy()
# we check if the psf is centered and fix it if needed
cy, cx = frame_center(psfs, verbose=False)
xcom, ycom = photutils.centroid_com(psfs)
if not (np.allclose(cy, ycom, atol=1e-2) or
np.allclose(cx, xcom, atol=1e-2)):
# first we find the centroid and put it in the center of the array
centry, centrx = fit_2d(psfs)
shiftx, shifty = centrx - cx, centry - cy
psfs = frame_shift(array, -shifty, -shiftx, imlib=imlib,
interpolation=interpolation)
if size is not None:
psfs = frame_crop(psfs, size, force=True, verbose=False)
for _ in range(2):
centry, centrx = fit_2d(psfs)
cy, cx = frame_center(psfs, verbose=False)
shiftx, shifty = centrx - cx, centry - cy
psfs = frame_shift(psfs, -shifty, -shiftx, imlib=imlib,
interpolation=interpolation)
# we check whether the flux is normalized and fix it if needed
fwhm_aper = photutils.CircularAperture((frame_center(psfs)), fwhm/2)
fwhm_aper_phot = photutils.aperture_photometry(psfs, fwhm_aper,
method='exact')
fwhm_flux = np.array(fwhm_aper_phot['aperture_sum'])
if fwhm_flux > 1.1 or fwhm_flux < 0.9:
psf_norm_array = psfs / np.array(fwhm_aper_phot['aperture_sum'])
else:
psf_norm_array = psfs
if threshold is not None:
psf_norm_array[np.where(psf_norm_array < threshold)] = 0
if mask_core is not None:
psf_norm_array = get_circle(psf_norm_array, radius=mask_core)
if verbose:
print("Flux in 1xFWHM aperture: {:.3f}".format(fwhm_flux[0]))
if full_output:
return psf_norm_array, fwhm_flux, fwhm
else:
return psf_norm_array
def _apphot_one((irad, band, rad, img, sigma, isimage, apxy)):
import photutils
result = [irad, band, isimage]
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=sigma)
result.append(p.field('aperture_sum'))
if sigma is not None:
result.append(p.field('aperture_sum_err'))
else:
result.append(None)
return result
def perform_aperture_photometry(xcen, ycen, filename, aperture_radii = np.arange(2, 15), bkg_r_in=16., bkg_r_out=20):
ofile = fits.open(filename)
apertures = [pu.CircularAperture((xcen, ycen), r=r) for r in aperture_radii]
annulus_apertures = pu.CircularAnnulus((xcen, ycen), r_in=bkg_r_in, r_out=bkg_r_out)
apertures.append(annulus_apertures)
phot_table = pu.aperture_photometry(ofile[1].data, apertures )
bkg_colname = phot_table.colnames[-1]
bkg_mean = phot_table[bkg_colname] / annulus_apertures.area()
for aper, icol in zip(apertures, phot_table.colnames[3:]):
bkg_sum = bkg_mean*aper.area()
phot_table.add_column(phot_table[icol]-bkg_sum, name='{}_bkg_sub'.format(icol))
return phot_table, apertures
def aperture_flux(array, yc, xc, fwhm, ap_factor=1, mean=False, verbose=False):
""" Returns the sum of pixel values in a circular aperture centered on the
input coordinates. The radius of the aperture is set as (ap_factor*fwhm)/2.
Parameters
----------
array : array_like
Input frame.
yc, xc : list or 1d arrays
List of y and x coordinates of sources.
fwhm : float
FWHM in pixels.
ap_factor : int, optional
Diameter of aperture in terms of the FWHM.
Returns
-------
flux : list of floats
List of fluxes.
Note
----
From Photutils documentation, the aperture photometry defines the aperture
using one of 3 methods:
'center': A pixel is considered to be entirely in or out of the aperture
depending on whether its center is in or out of the aperture.
'subpixel': A pixel is divided into subpixels and the center of each
subpixel is tested (as above).
'exact': (default) The exact overlap between the aperture and each pixel is
calculated.
"""
n_obj = len(yc)
flux = np.zeros((n_obj))
for i, (y, x) in enumerate(zip(yc, xc)):
if mean:
ind = circle(y, x, (ap_factor * fwhm) / 2)
values = array[ind]
obj_flux = np.mean(values)
else:
aper = photutils.CircularAperture((x, y), (ap_factor * fwhm) / 2)
obj_flux = photutils.aperture_photometry(array,
aper,
method='exact')
obj_flux = np.array(obj_flux['aperture_sum'])
flux[i] = obj_flux
if verbose:
print('Coordinates of object {} : ({},{})'.format(i, y, x))
print('Object Flux = {:.2f}'.format(flux[i]))
return flux
def test_aper_phot(capsys):
"""Check that apertures are as expected from photutils"""
radius = 5
apertures = photutils.CircularAperture((50, 50), radius)
aperture_area = apertures.area()
assert_equal(aperture_area, np.pi * radius**2)
rawflux_table = photutils.aperture_photometry(test_data,
apertures,
subpixels=1,
method="center")
total_flux = float(rawflux_table['aperture_sum'][0])
# Verify the expected circular area sum
assert_equal(total_flux, 207.0)
def doAperturePhotometry(locs, data, fitsfilename, params, bkg_method='2d'):
aplocs = []
for i, pair in enumerate(locs):
aplocs.append(pair[::-1])
apertures = pt.CircularAperture(aplocs, r=params['ap_phot_rad'])
uncert_table = pt.aperture_photometry(data,
apertures,
method='subpixel',
subpixels=10)
bkg = getBackground(data,
fitsfilename,
bkg_method=bkg_method,
locs=locs,
params=params)
if bkg_method == '2d':
phot_table = pt.aperture_photometry(data,
apertures,
method='subpixel',
subpixels=10)
bkg_table = pt.aperture_photometry(bkg.background,
apertures,
method='subpixel',
subpixels=10)
phot_table['residual_aperture_sum'] = phot_table[
'aperture_sum'] - bkg_table['aperture_sum']
elif bkg_method == 'aperture':
phot_table = pt.aperture_photometry(data,
apertures,
method='subpixel',
subpixels=10)
phot_table['residual_aperture_sum'] = phot_table[
'aperture_sum'] - bkg.background * apertures.area()
elif bkg_method == 'median':
phot_table = pt.aperture_photometry(data,
apertures,
method='subpixel',
subpixels=10)
phot_table['residual_aperture_sum'] = phot_table[
'aperture_sum'] - bkg.background * apertures.area()
elif bkg_method == 'None':
phot_table = pt.aperture_photometry(data,
apertures,
method='subpixel',
subpixels=10)
phot_table['residual_aperture_sum'] = phot_table['aperture_sum']
uncert_final = np.sqrt(uncert_table['aperture_sum'])
return phot_table, bkg, uncert_final
def gaussian_kernel(size, size_y=None):
""" Gaussian kernel.
"""
size = int(size)
if not size_y:
size_y = size
else:
size_y = int(size_y)
x, y = np.mgrid[-size:size + 1, -size_y:size_y + 1]
g = np.exp(-(x**2 / float(size) + y**2 / float(size_y)))
fwhm = size
fwhm_aper = photutils.CircularAperture((frame_center(g)), fwhm / 2.)
fwhm_aper_phot = photutils.aperture_photometry(g, fwhm_aper)
g_norm = g / np.array(fwhm_aper_phot['aperture_sum'])
return g_norm / g_norm.max()
def starphot(imdata, position, radius, r_in, r_out):
"""
sources: http://photutils.readthedocs.io/en/stable/
photutils/aperture.html
ARGS:
imdata: Numpy array containing the star.
position: [x,y] coordinates where x corresponds to the second index of
imdata
radius: Radius of the circular aperture used to compute the flux of
the star.
r_in: Inner radius of the annulus used to compute the background
mean.
r_out: Outer radius of the annulus used to compute the background
mean.
Returns [flux, background variance, background mean]
"""
try:
statmask = photutils.make_source_mask(imdata,
snr=5,
npixels=5,
dilate_size=10)
except TypeError:
return None
bkg_annulus = photutils.CircularAnnulus(position, r_in, r_out)
bkg_phot_table = photutils.aperture_photometry(imdata,
bkg_annulus,
method='subpixel',
mask=statmask)
bkg_mean_per_pixel = bkg_phot_table['aperture_sum'] / bkg_annulus.area()
src_aperture = photutils.CircularAperture(position, radius)
src_phot_table = photutils.aperture_photometry(imdata,
src_aperture,
method='subpixel')
signal = src_phot_table['aperture_sum'] - bkg_mean_per_pixel*\
src_aperture.area()
#noise_squared = signal + bkg_mean_per_pixel*src_aperture.area()
mean, median, std = sigma_clipped_stats(imdata,
sigma=3.0,
iters=5,
mask=statmask)
noise_squared = std**2
return float(str(signal.data[0])), noise_squared,\
float(str(bkg_mean_per_pixel.data[0]))
def plot(image, radius, mul):
"""Use photutils DAO starfinder to get the locations of any
objects in the image (above) with the threshold being one of the input
values. Then use astropy's statistics to subtract the median from the image.
Makes an array of two arrays which are our data points locations, then transpose
those points to get the actual locations on our .fits image. Uses photutils CircularAperture
to put an aperture around each potential star in our positions array.
"""
mean, median, std = astropy.stats.sigma_clipped_stats(image.data)
DAO = photutils.detection.DAOStarFinder(threshold=mul * std, fwhm=3.0)
sources = DAO(image.data - median)
source_points = np.array([sources['xcentroid'], sources['ycentroid']])
positions = np.transpose(source_points)
apertures = photutils.CircularAperture(positions, r=radius)
fig, ax = plt.subplots(figsize=(15, 15))
ax.imshow(image.data, cmap='Greys', origin='lower', vmin=100, vmax=5000)
ax.set_title('Interactive Starfinder')
apertures.plot(color='c', lw=1, alpha=1)
def mk_sky_phot(instr, filt):
img = fitsio.getdata(orig_names[instr][filt]["img"])
pixscale = proj_plane_pixel_scales(
WCS(fitsio.getheader(orig_names[instr][filt]["img"])))[0] * 3600.
apersizes = useful.apersizes / pixscale
aperradii = apersizes / 2.
sky_apers = fitsio.getdata(
'/data/highzgal/PUBLICACCESS/SPLASH/PROCESS/errors/sky_apers_%s_%s.fits'
% (instr, filt))
orig_wcs = WCS(
fitsio.getheader(
'/data/highzgal/PUBLICACCESS/SPLASH/PROCESS/data/orig/mosaic_%s_%s.img.fits'
% (instr, filt)))
new_wcs = WCS(fitsio.getheader(orig_names[instr][filt]["img"]))
pos = np.array(zip(sky_apers['IMGX'], sky_apers['IMGY']))
pos = orig_wcs.all_pix2world(pos, 1)
pos = new_wcs.all_world2pix(pos, 1)
# cond = (aperradii[-1] < pos[:,0]) & (pos[:,0] < img.shape[1]-aperradii[-1]) & \
# (aperradii[-1] < pos[:,1]) & (pos[:,1] < img.shape[0]-aperradii[-1])
for i, radius in enumerate(aperradii):
sys.stdout.write(
"\rMeasuring Photometry on sky apertures for %s %s - aperture#%i (%.2f px) ..."
% (instr, filt, i + 1, radius))
sys.stdout.flush()
apertures = photutils.CircularAperture(pos, r=radius)
sky_apers['FLUX_APER'][:, i] = photutils.aperture_photometry(
img, apertures)['aperture_sum']
sys.stdout.write("done!\n")
sys.stdout.flush()
fitsio.writeto('sky_apers_orig_%s_%s.fits' % (instr, filt),
sky_apers,
overwrite=True)
def petrosian_radiu(data, r_step=5., petrosian_ratio=0.2):
""" measure petrosian radiu in units of pixel from data.
Note:
Centroid position is determined from data
Args:
data (2d np array)
r_step=5. (float): aperture radius steps in units of pixel
petrosian_ratio=0.2: the surface brightness at r_petrosian is petrosian_ratio times the surface brightness within r_petrosian.
"""
ny, nx = data.shape
xc, yc = pu.centroid_1dg(data)
rs = np.arange(r_step, ny // 2, r_step)
apts = [pu.CircularAperture(positions=(xc, yc), r=r) for r in rs]
tab = at.Table(pu.aperture_photometry(data, apts))
if len(rs) == 1:
cols = ['aperture_sum']
else:
cols = ['aperture_sum_' + str(i) for i in range(len(rs))]
aperture_sum = np.array(list(tab[cols][0]))
annulus_sum = np.zeros_like(rs)
annulus_sum[0] = aperture_sum[0]
for i in range(1, len(rs)):
annulus_sum[i] = aperture_sum[i] - aperture_sum[i - 1]
aperture_areas = np.pi * rs**2
annulus_areas = np.append(aperture_areas[0], np.diff(aperture_areas))
annulus_avg = annulus_sum / annulus_areas
aperture_avg = aperture_sum / aperture_areas
diff = annulus_avg - aperture_avg * petrosian_ratio
r_petrosian = _find_a_root(x=rs, y=diff)
return r_petrosian
def get_sex_whts(catalog, instr, filt):
radius = 3. / 2. / useful.pix_scale
orig_wcs = WCS(
fitsio.getheader(
'/data/highzgal/PUBLICACCESS/SPLASH/PROCESS/data/orig/mosaic_%s_%s.img.fits'
% (instr, filt)))
new_wcs = WCS(fitsio.getheader(orig_names[instr][filt]["img"]))
pos = zip(catalog['X_IMAGE'], catalog['Y_IMAGE'])
pos = orig_wcs.all_pix2world(pos, 1)
pos = new_wcs.all_world2pix(pos, 1)
wht = fitsio.getdata(
'/data/highzgal/PUBLICACCESS/SPLASH/PROCESS/data/orig/mosaic_%s_%s.wht.fits'
% (instr, filt))
aperture = photutils.CircularAperture(pos, r=radius)
avg_wht = photutils.aperture_photometry(
wht, aperture)['aperture_sum'] / (np.pi * radius**2)
return avg_wht
def BuildEPSF(filter='Ks'):
"""Builds the effective PSF used for the photometry.
Currently uses the SCAO PSF from SimCADO.
"""
src = sim.source.star(mag=19, filter_name=filter, spec_type='M0V')
image = img.MakeImage(src,
exposure=1800,
NDIT=1,
view='wide',
chip='centre',
filter=filter,
ao_mode='scao')
# PSF_AnisoCADO_SCAO_FVPSF_4mas_EsoMedian_20190328.fits
peaks_tbl = phu.find_peaks(image, threshold=135000., box_size=11)
peaks_tbl['peak_value'].info.format = '%.8g' # for consistent table output
# make sure the positions are correct (use the exact ones)
peaks_tbl['x_peak'] = src.x_pix
peaks_tbl['y_peak'] = src.y_pix
positions = (peaks_tbl['x_peak'], peaks_tbl['y_peak'])
apertures = phu.CircularAperture(positions, r=5.)
# extract cutouts of the stars using the extract_stars() function
stars_tbl = apta.Table()
stars_tbl['x'] = peaks_tbl['x_peak']
stars_tbl['y'] = peaks_tbl['y_peak']
mean_val, median_val, std_val = apy.stats.sigma_clipped_stats(img_data,
sigma=2.)
img_data -= median_val # subtract background
nddata = apy.nddata.NDData(data=img_data)
stars = phu.psf.extract_stars(nddata, stars_tbl, size=170)
# build the epsf
epsf_builder = phu.EPSFBuilder(oversampling=4,
maxiters=5,
progress_bar=False)
epsf, fitted_stars = epsf_builder(stars)
# save the epsf
with open(os.path.join('objects', 'epsf-scao-fv.pkl'), 'wb') as output:
pickle.dump(epsf, output, -1)
def _apphot_one(args):
(irad, band, rad, img, sigma, mask, isimage, apxy) = args
import photutils
result = [irad, band, isimage]
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(img, aper, error=sigma, mask=mask)
result.append(p.field('aperture_sum'))
if sigma is not None:
result.append(p.field('aperture_sum_err'))
else:
result.append(None)
# If a mask is passed, also photometer it!
if mask is not None:
p = photutils.aperture_photometry(mask, aper)
maskedpix = p.field('aperture_sum')
# normalize by number of pixels (pi * rad**2)
maskedpix /= (np.pi * rad**2)
result.append(maskedpix)
else:
result.append(None)
return result
def run_forced_phot(cat,
tim,
ceres=True,
derivs=False,
agn=False,
do_forced=True,
do_apphot=True,
get_model=False,
ps=None,
timing=False,
fixed_also=False,
ceres_threads=1):
'''
fixed_also: if derivs=True, also run without derivatives and report
that flux too?
'''
if timing:
tlast = Time()
if ps is not None:
import pylab as plt
opti = None
forced_kwargs = {}
if ceres:
from tractor.ceres_optimizer import CeresOptimizer
B = 8
try:
opti = CeresOptimizer(BW=B, BH=B, threads=ceres_threads)
except:
if ceres_threads > 1:
raise RuntimeError(
'ceres_threads requested but not supported by tractor.ceres version'
)
opti = CeresOptimizer(BW=B, BH=B)
#forced_kwargs.update(verbose=True)
# nsize = 0
for src in cat:
# Limit sizes of huge models
# from tractor.galaxy import ProfileGalaxy
# if isinstance(src, ProfileGalaxy):
# px,py = tim.wcs.positionToPixel(src.getPosition())
# h = src._getUnitFluxPatchSize(tim, px, py, tim.modelMinval)
# MAXHALF = 128
# if h > MAXHALF:
# #print('halfsize', h,'for',src,'-> setting to',MAXHALF)
# nsize += 1
# src.halfsize = MAXHALF
src.freezeAllBut('brightness')
src.getBrightness().freezeAllBut(tim.band)
#print('Limited the size of', nsize, 'large galaxy models')
if derivs:
realsrcs = []
derivsrcs = []
Iderivs = []
for i, src in enumerate(cat):
from tractor import PointSource
realsrcs.append(src)
if not isinstance(src, PointSource):
continue
Iderivs.append(i)
brightness_dra = src.getBrightness().copy()
brightness_ddec = src.getBrightness().copy()
brightness_dra.setParams(np.zeros(brightness_dra.numberOfParams()))
brightness_ddec.setParams(
np.zeros(brightness_ddec.numberOfParams()))
brightness_dra.freezeAllBut(tim.band)
brightness_ddec.freezeAllBut(tim.band)
dsrc = SourceDerivatives(src, [brightness_dra, brightness_ddec],
tim, ps)
derivsrcs.append(dsrc)
Iderivs = np.array(Iderivs)
if fixed_also:
pass
else:
# For convenience, put all the real sources at the front of
# the list, so we can pull the IVs off the front of the list.
cat = realsrcs + derivsrcs
if agn:
from tractor.galaxy import ExpGalaxy, DevGalaxy, FixedCompositeGalaxy
from tractor import PointSource
from legacypipe.survey import SimpleGalaxy, RexGalaxy
realsrcs = []
agnsrcs = []
iagn = []
for i, src in enumerate(cat):
realsrcs.append(src)
## ??
if isinstance(src, (SimpleGalaxy, RexGalaxy)):
#print('Skipping SIMP or REX:', src)
continue
if isinstance(src, (ExpGalaxy, DevGalaxy, FixedCompositeGalaxy)):
iagn.append(i)
bright = src.getBrightness().copy()
bright.setParams(np.zeros(bright.numberOfParams()))
bright.freezeAllBut(tim.band)
agn = PointSource(src.pos, bright)
agn.freezeAllBut('brightness')
#print('Adding "agn"', agn, 'to', src)
#print('agn params:', agn.getParamNames())
agnsrcs.append(src)
iagn = np.array(iagn)
cat = realsrcs + agnsrcs
print('Added AGN to', len(iagn), 'galaxies')
tr = Tractor([tim], cat, optimizer=opti)
tr.freezeParam('images')
disable_galaxy_cache()
F = fits_table()
if do_forced:
if timing and (derivs or agn):
t = Time()
print('Setting up:', t - tlast)
tlast = t
if derivs:
if fixed_also:
print('Forced photom with fixed positions:')
R = tr.optimize_forced_photometry(variance=True,
fitstats=False,
shared_params=False,
priors=False,
**forced_kwargs)
F.flux_fixed = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
N = len(cat)
F.flux_fixed_ivar = R.IV[:N].astype(np.float32)
if timing:
t = Time()
print('Forced photom with fixed positions finished:',
t - tlast)
tlast = t
cat = realsrcs + derivsrcs
tr.setCatalog(Catalog(*cat))
print('Forced photom with position derivatives:')
if ps is None and not get_model:
forced_kwargs.update(wantims=False)
R = tr.optimize_forced_photometry(variance=True,
fitstats=True,
shared_params=False,
priors=False,
**forced_kwargs)
if ps is not None or get_model:
(data, mod, ie, chi, roi) = R.ims1[0]
if ps is not None:
ima = dict(vmin=-2. * tim.sig1,
vmax=5. * tim.sig1,
interpolation='nearest',
origin='lower',
cmap='gray')
imchi = dict(interpolation='nearest',
origin='lower',
vmin=-5,
vmax=5,
cmap='RdBu')
plt.clf()
plt.imshow(data, **ima)
plt.title('Data: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(mod, **ima)
plt.title('Model: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chi, **imchi)
plt.title('Chi: %s' % tim.name)
ps.savefig()
if derivs:
trx = Tractor([tim], realsrcs)
trx.freezeParam('images')
modx = trx.getModelImage(0)
chix = (data - modx) * tim.getInvError()
plt.clf()
plt.imshow(modx, **ima)
plt.title('Model without derivatives: %s' % tim.name)
ps.savefig()
plt.clf()
plt.imshow(chix, **imchi)
plt.title('Chi without derivatives: %s' % tim.name)
ps.savefig()
if derivs or agn:
cat = realsrcs
N = len(cat)
F.flux = np.array([
src.getBrightness().getFlux(tim.band) for src in cat
]).astype(np.float32)
F.flux_ivar = R.IV[:N].astype(np.float32)
F.fracflux = R.fitstats.profracflux[:N].astype(np.float32)
F.rchisq = R.fitstats.prochi2[:N].astype(np.float32)
try:
F.fracmasked = R.fitstats.promasked[:N].astype(np.float32)
except:
print(
'No "fracmasked" available (only in recent Tractor versions)')
if derivs:
F.flux_dra = np.zeros(len(F), np.float32)
F.flux_ddec = np.zeros(len(F), np.float32)
F.flux_dra[Iderivs] = np.array(
[src.getParams()[0] for src in derivsrcs]).astype(np.float32)
F.flux_ddec[Iderivs] = np.array(
[src.getParams()[1] for src in derivsrcs]).astype(np.float32)
F.flux_dra_ivar = np.zeros(len(F), np.float32)
F.flux_ddec_ivar = np.zeros(len(F), np.float32)
F.flux_dra_ivar[Iderivs] = R.IV[N::2].astype(np.float32)
F.flux_ddec_ivar[Iderivs] = R.IV[N + 1::2].astype(np.float32)
if agn:
F.flux_agn = np.zeros(len(F), np.float32)
F.flux_agn_ivar = np.zeros(len(F), np.float32)
F.flux_agn[iagn] = np.array(
[src.getParams()[0] for src in agnsrcs])
F.flux_agn_ivar[iagn] = R.IV[N:].astype(np.float32)
if timing:
t = Time()
print('Forced photom:', t - tlast)
tlast = t
if do_apphot:
import photutils
img = tim.getImage()
ie = tim.getInvError()
with np.errstate(divide='ignore'):
imsigma = 1. / ie
imsigma[ie == 0] = 0.
apimg = []
apimgerr = []
# Aperture photometry locations
xxyy = np.vstack(
[tim.wcs.positionToPixel(src.getPosition()) for src in cat]).T
apxy = xxyy - 1.
apertures = apertures_arcsec / tim.wcs.pixel_scale()
#print('Apertures:', apertures, 'pixels')
#print('apxy shape', apxy.shape) # --> (2,N)
# The aperture photometry routine doesn't like pixel positions outside the image
H, W = img.shape
Iap = np.flatnonzero((apxy[0, :] >= 0) * (apxy[1, :] >= 0) *
(apxy[0, :] <= W - 1) * (apxy[1, :] <= H - 1))
print('Aperture photometry for', len(Iap), 'of', len(apxy[0, :]),
'sources within image bounds')
for rad in apertures:
aper = photutils.CircularAperture(apxy[:, Iap], rad)
p = photutils.aperture_photometry(img, aper, error=imsigma)
apimg.append(p.field('aperture_sum'))
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
F.apflux = np.zeros((len(F), len(apertures)), np.float32)
F.apflux[Iap, :] = ap.astype(np.float32)
apimgerr = np.vstack(apimgerr).T
apiv = np.zeros(apimgerr.shape, np.float32)
apiv[apimgerr != 0] = 1. / apimgerr[apimgerr != 0]**2
F.apflux_ivar = np.zeros((len(F), len(apertures)), np.float32)
F.apflux_ivar[Iap, :] = apiv
if timing:
print('Aperture photom:', Time() - tlast)
if get_model:
return F, mod
return F
def photometry(ax, image, pos, ap_size=10, r_in=20, r_out=30, back_photo=True):
'''
Create apertures around specified list of stars
Parameters:
-----------
ax : axis class
axis of a subplot.
image : ndarray
Image for aperture photometry to be performed on.
pos : 2darray
A list of positions in x,y pixel coordinates for each star.
Needs to be as [[0,0],[1,1]...]
ap_size : int
The radius of a circular aperture in pixel size
r_in : int
The inner radius of background aperture in pixel size
(Ignore if back_photo=False)
r_out : int
The outer radius of background aperture in pixel size
(Ignore if back_photo=False)
back_photo : bool
Set to True if want to return an array of background values, False
to ignore anything to do with background
Returns
-------
photometry of each star : array
average background pixel value around each star : array (If back_photo=True)
plots image with the aperture and centroids located for each star
'''
nstars = np.arange(0, np.shape(pos)[0], 1)
name_stars = []
for i in nstars:
name_stars.append('Star {}'.format(i))
#print name_stars
aperture = photutils.CircularAperture(pos, r=ap_size)
if back_photo == True:
back_aperture = photutils.CircularAnnulus(pos, r_in, r_out)
pos = np.array(pos)
#plt.imshow(image,origin='lower')
for i in range(len(name_stars)):
circle1 = plt.Circle((pos[i, 0], pos[i, 1]),
ap_size,
color='black',
fill=False,
zorder=100)
ax.add_artist(circle1)
plt.axhline(pos[i, 1],
xmin=pos[i, 0] / 100. - .01,
xmax=pos[i, 0] / 100. + .02,
color='black')
plt.axvline(pos[i, 0],
ymin=pos[i, 1] / 100. - .01,
ymax=pos[i, 1] / 100. + .02,
color='black')
plt.text(pos[i, 0] + ap_size + 1, pos[i, 1], name_stars[i], zorder=11)
#print pos[i,1]
#print pos[i,0]
if back_photo == True:
circle2 = plt.Circle((pos[i, 0], pos[i, 1]),
r_in,
color='cyan',
fill=False,
zorder=10)
circle3 = plt.Circle((pos[i, 0], pos[i, 1]),
r_out,
color='cyan',
fill=False,
zorder=10)
ax.add_artist(circle2)
ax.add_artist(circle3)
#aperture.plot(origin=(0,0),indices=None,ax=ax,fill=False)
#plt.ion()
phot_table = photutils.aperture_photometry(image, aperture)
flux_values = phot_table[
'aperture_sum'].data #gets a list of the total flux in specified aperture for each star
if back_photo == True:
back_table = photutils.aperture_photometry(image, back_aperture)
area = np.pi * (r_out**2 - r_in**2)
a = np.ones((np.shape(image)[0], np.shape(image)[1]))
area = photutils.aperture_photometry(a, back_aperture)
back_values = back_table['aperture_sum'].data / area[
'aperture_sum'].data
return flux_values, back_values * np.pi * ap_size**2
else:
return flux_values
ph, pw = psfimg.shape
print('psf img shape', psfimg.shape)
apertures = [1., 3., 3.5, 3.75, 4., 6., 8., 10.]
#apxy = np.array([[pw/2., ph/2.]])
#for ioff,apxy in enumerate([np.array([[pw/2., ph/2.]]),
# np.array([[pw/2, ph/2]])]):
for ioff, apxy in enumerate([np.array([[pw / 2, ph / 2]])]):
print('apxy', apxy)
ap = []
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(psfimg, aper)
ap.append(p.field('aperture_sum'))
ap = np.vstack(ap).T
ap = ap[0, :]
print('Aperture fluxes:', ap)
#plt.plot(apertures, ap, '.-', color='br'[ioff], label='%i,%i + %.1f,%.1f' % (x, y, apxy[0,0], apxy[0,1]))
plt.plot(apertures, ap, '.-', label='%i,%i' % (x, y))
plt.legend(loc='lower right')
plt.xlabel('Aperture')
plt.ylabel('Flux of PSF model')
plt.ylim(0.8, 1.2)
plt.xlim(2, 6)
plt.axhline(1.0, color='k', alpha=0.25)
def aperture_photometry(calibratable,
ra,
dec,
apply_calibration=False,
assume_background_subtracted=False,
use_cutout=False,
direct_load=None):
import photutils
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.table import vstack
from astropy.wcs import WCS
ra = np.atleast_1d(ra)
dec = np.atleast_1d(dec)
coord = SkyCoord(ra, dec, unit='deg')
if not use_cutout:
wcs = calibratable.wcs
apertures = photutils.SkyCircularAperture(coord, r=APERTURE_RADIUS)
# something that is photometerable implements mask, background, and wcs
if not assume_background_subtracted:
pixels_bkgsub = calibratable.background_subtracted_image.data
else:
pixels_bkgsub = calibratable.data
bkgrms = calibratable.rms_image.data
mask = calibratable.mask_image.data
phot_table = photutils.aperture_photometry(pixels_bkgsub,
apertures,
error=bkgrms,
wcs=wcs)
phot_table['zp'] = calibratable.header['MAGZP'] + calibratable.header[
'APCOR4']
phot_table['obsjd'] = calibratable.header['OBSJD']
phot_table['filtercode'] = 'z' + calibratable.header['FILTER'][-1]
pixap = apertures.to_pixel(wcs)
annulus_masks = pixap.to_mask(method='center')
maskpix = [
annulus_mask.cutout(mask.data) for annulus_mask in annulus_masks
]
else:
phot_table = []
maskpix = []
for s in coord:
if direct_load is not None and 'sci' in direct_load:
sci_path = direct_load['sci']
else:
if assume_background_subtracted:
sci_path = calibratable.local_path
else:
sci_path = calibratable.background_subtracted_image.local_path
if direct_load is not None and 'mask' in direct_load:
mask_path = direct_load['mask']
else:
mask_path = calibratable.mask_image.local_path
if direct_load is not None and 'rms' in direct_load:
rms_path = direct_load['rms']
else:
rms_path = calibratable.rms_image.local_path
with fits.open(sci_path, memmap=True) as f:
wcs = WCS(f[0].header)
pixcoord = wcs.all_world2pix([[s.ra.deg, s.dec.deg]], 0)[0]
pixx, pixy = pixcoord
nx = calibratable.header['NAXIS1']
ny = calibratable.header['NAXIS2']
xmin = max(0, pixx - 1.5 * APERTURE_RADIUS.value)
xmax = min(nx, pixx + 1.5 * APERTURE_RADIUS.value)
ymin = max(0, pixy - 1.5 * APERTURE_RADIUS.value)
ymax = min(ny, pixy + 1.5 * APERTURE_RADIUS.value)
ixmin = int(np.floor(xmin))
ixmax = int(np.ceil(xmax))
iymin = int(np.floor(ymin))
iymax = int(np.ceil(ymax))
ap = photutils.CircularAperture([pixx - ixmin, pixy - iymin],
APERTURE_RADIUS.value)
# something that is photometerable implements mask, background, and wcs
with fits.open(sci_path, memmap=True) as f:
pixels_bkgsub = f[0].data[iymin:iymax, ixmin:ixmax]
with fits.open(rms_path, memmap=True) as f:
bkgrms = f[0].data[iymin:iymax, ixmin:ixmax]
with fits.open(mask_path, memmap=True) as f:
mask = f[0].data[iymin:iymax, ixmin:ixmax]
pt = photutils.aperture_photometry(pixels_bkgsub, ap, error=bkgrms)
annulus_mask = ap.to_mask(method='center')
mp = annulus_mask.cutout(mask.data)
maskpix.append(mp)
phot_table.append(pt)
phot_table = vstack(phot_table)
if apply_calibration:
magzp = calibratable.header['MAGZP']
apcor = calibratable.header[APER_KEY]
phot_table['mag'] = -2.5 * np.log10(
phot_table['aperture_sum']) + magzp + apcor
phot_table['magerr'] = 1.0826 * phot_table[
'aperture_sum_err'] / phot_table['aperture_sum']
# check for invalid photometry on masked pixels
phot_table['flags'] = [
int(np.bitwise_or.reduce(m, axis=(0, 1))) for m in maskpix
]
# rename some columns
phot_table.rename_column('aperture_sum', 'flux')
phot_table.rename_column('aperture_sum_err', 'fluxerr')
return phot_table
def psf_norm(array,
fwhm=4,
size=None,
threshold=None,
mask_core=None,
full_output=False,
verbose=False):
""" Scales a PSF, so the 1*FWHM aperture flux equals 1.
Parameters
----------
array: array_like
The psf 2d array.
fwhm: float, optional
The the Full Width Half Maximum in pixels.
size : int or None, optional
If int it will correspond to the size of the squared subimage to be
cropped form the psf array.
threshold : None of float, optional
Sets to zero small values, trying to leave only the core of the PSF.
mask_core : None of float, optional
Sets the radius of a circular aperture for the core of the PSF,
everything else will be set to zero.
Returns
-------
psf_norm: array_like
The normalized psf.
"""
if size is not None:
psfs = frame_crop(array, min(int(size), array.shape[0]), verbose=False)
else:
psfs = array.copy()
# If frame size is even we drop last row and last column
if psfs.shape[0] % 2 == 0:
psfs = psfs[:-1, :]
if psfs.shape[1] % 2 == 0:
psfs = psfs[:, :-1]
# we check if the psf is centered and fix it if needed
cy, cx = frame_center(psfs, verbose=False)
if cy != np.where(psfs == psfs.max())[0] or cx != np.where(
psfs == psfs.max())[1]:
# first we find the centroid and put it in the center of the array
centroidy, centroidx = fit_2dgaussian(psfs, fwhmx=fwhm, fwhmy=fwhm)
shiftx, shifty = centroidx - cx, centroidy - cy
psfs = frame_shift(psfs, -shifty, -shiftx)
for _ in range(2):
centroidy, centroidx = fit_2dgaussian(psfs, fwhmx=fwhm, fwhmy=fwhm)
cy, cx = frame_center(psfs, verbose=False)
shiftx, shifty = centroidx - cx, centroidy - cy
psfs = frame_shift(psfs, -shifty, -shiftx)
# we check whether the flux is normalized and fix it if needed
fwhm_aper = photutils.CircularAperture((frame_center(psfs)), fwhm / 2.)
fwhm_aper_phot = photutils.aperture_photometry(psfs,
fwhm_aper,
method='exact')
fwhm_flux = np.array(fwhm_aper_phot['aperture_sum'])
if verbose:
print "Flux in 1xFWHM aperture: {}".format(fwhm_flux)
if fwhm_flux > 1.1 or fwhm_flux < 0.9:
psf_norm_array = psfs / np.array(fwhm_aper_phot['aperture_sum'])
else:
psf_norm_array = psfs
if threshold is not None:
psf_norm_array[np.where(psf_norm_array < threshold)] = 0
if mask_core is not None:
psf_norm_array = get_circle(psf_norm_array, radius=mask_core)
if full_output:
return psf_norm_array, fwhm_flux
else:
return psf_norm_array
def stage_4(resid=None,
sky=None,
ps=None,
tim=None,
mask=None,
sourcepix=None,
filters=None,
radii_arcsec=None,
orig_catalog=None,
plots=False,
lsbcat='lsb.fits',
expnum=None,
extname=None,
pixscale=None,
**kwa):
#ok,x,y = tim.subwcs.radec2pixelxy(188.7543, 13.3847)
#print 'X,Y', x,y
fstack = np.dstack(filters)
amax = np.argmax(fstack, axis=2)
fmax = np.max(fstack, axis=2)
if plots:
sna = dict(interpolation='nearest',
origin='lower',
vmin=-2.,
vmax=32.,
cmap='gray')
plt.clf()
plt.imshow(fmax, **sna)
plt.title('Filter max')
ps.savefig()
from matplotlib.ticker import FixedFormatter
radformat = FixedFormatter(['%i' % r for r in radii_arcsec])
plt.clf()
plt.imshow(amax, interpolation='nearest', origin='lower', cmap='jet')
plt.title('Filter argmax')
plt.colorbar(ticks=np.arange(amax.max() + 1), format=radformat)
ps.savefig()
peak_amax = np.zeros_like(amax)
hot = (fmax > 10.)
blobs, nblobs = label(hot)
print 'Nblobs', nblobs
#print 'blobs max', blobs.max()
peaks = []
for blob in range(1, nblobs + 1):
I = (blobs == blob)
imax = np.argmax(fmax * I)
py, px = np.unravel_index(imax, fmax.shape)
peaks.append((px, py))
#print 'blob imax', imax
print 'px,py', px, py
print 'blob max', fmax.flat[imax]
ifilt = amax.flat[imax]
print 'blob argmax', ifilt
#print ' =', amax[py,px]
if plots:
peak_amax[I] = ifilt
px = [x for x, y in peaks]
py = [y for x, y in peaks]
if plots:
plt.clf()
plt.imshow(amax * hot,
interpolation='nearest',
origin='lower',
cmap='jet')
ax = plt.axis()
plt.plot(px, py, '+', color='w')
plt.axis(ax)
plt.title('Filter argmax')
plt.colorbar(ticks=np.arange((amax * hot).max() + 1), format=radformat)
ps.savefig()
# Total flux estimate
# fmax is S/N in the amax filter. Back out...
fluxes = []
for x, y in zip(px, py):
print 'peak', x, y
ifilt = amax[y, x]
print 'ifilt', ifilt
sn = fmax[y, x]
print 'S/N', sn
r = radii_arcsec[ifilt]
sigma = r / pixscale
knorm = 1. / (2. * np.sqrt(np.pi) * sigma)
blurred = sn * (knorm * tim.sig1)
fluxest = blurred * 2. * np.pi * (2. * sigma**2)
fluxes.append(fluxest)
fluxes = np.array(fluxes)
mags = 22.5 - 2.5 * np.log10(fluxes)
if plots:
for bg in [1, 2]:
plt.clf()
if bg == 1:
plt.imshow(peak_amax,
interpolation='nearest',
origin='lower',
cmap='jet')
else:
plt.imshow(tim.getImage(), **tim.ima)
ax = plt.axis()
plt.plot(px, py, '+', color='w', ms=10, mew=2)
# Extended sources in the catalog
if len(orig_catalog):
E = orig_catalog[np.maximum(orig_catalog.shapeexp_r,
orig_catalog.shapedev_r) >= 3.]
plt.plot(E.x, E.y, 'x', color='k', ms=10, mew=2)
for x, y, m in zip(px, py, mags):
ra, dec = tim.subwcs.pixelxy2radec(x + 1, y + 1)
plt.text(x,
y,
'%.1f (%.2f,%.2f)' % (m, ra, dec),
color='w',
ha='left',
fontsize=12)
''' evcc.fits:
https://sites.google.com/site/extendedvcc/
wget "https://sites.google.com/site/extendedvcc/table/Table2_2014_8_7.txt?attredirects=0&d=1" -O table2.txt
text2fits -S 35 -H "id_evcc id_vcc ngc ra dec ra_fiber dec_fiber delta cz_sdss cz_ned mem_evcc mem_vcc morph_pri morph_sec morph_index morph_vcc u u_err g g_err r r_err i i_err z z_err r_kron r_50" -f sssddddfffssssssffffffffffff table2.txt evcc.fits -n -
vcc.fits:
text2fits -S 27 -H "id_vcc ngc ra dec cz_sdss cz_ned mem morph_vcc u u_err g g_err r r_err i i_err z z_err r_kron r_50" -f ssddffssffffffffffff table3.txt vcc.fits -n -
'''
mydir = os.path.dirname(__file__)
fn = os.path.join(mydir, 'evcc.fits')
T = fits_table(fn)
ok, x, y = tim.subwcs.radec2pixelxy(T.ra, T.dec)
x = x[ok]
y = y[ok]
plt.plot(x, y, 'o', mec=(0, 1, 0), mfc='none', ms=50, mew=5)
fn = os.path.join(mydir, 'vcc.fits')
T = fits_table(fn)
ok, x, y = tim.subwcs.radec2pixelxy(T.ra, T.dec)
x = x[ok]
y = y[ok]
plt.plot(x, y, 'o', mec=(0, 1, 1), mfc='none', ms=50, mew=5)
plt.axis(ax)
plt.title('Peaks')
#plt.title('Filter peak argmax')
#plt.colorbar(ticks=np.arange((peak_amax*hot).max()+1), format=radformat)
ps.savefig()
LSB = fits_table()
LSB.filter = np.array([tim.band] * len(px))
LSB.expnum = np.array([expnum] * len(px)).astype(np.int32)
LSB.extname = np.array([extname] * len(px))
LSB.x = np.array(px).astype(np.int16)
LSB.y = np.array(py).astype(np.int16)
ra, dec = tim.subwcs.pixelxy2radec(LSB.x, LSB.y)
LSB.ra = ra
LSB.dec = dec
LSB.flux = fluxes.astype(np.float32)
LSB.mag = mags.astype(np.float32)
LSB.radius = np.array(radii_arcsec)[amax[py, px]].astype(np.float32)
LSB.sn = fmax[py, px].astype(np.float32)
# Apertures, radii in ARCSEC.
apertures_arcsec = np.array([0.5, 0.75, 1., 1.5, 2., 3.5, 5., 7.])
apertures = apertures_arcsec / pixscale
apxy = np.vstack((px, py)).T
import photutils
with np.errstate(divide='ignore'):
imsigma = 1.0 / tim.getInvError()
imsigma[tim.getInvError() == 0] = 0
for photimg, err, name, errname in [
(resid, imsigma, 'apflux', 'apflux_ivar'),
(tim.getImage() - sky, None, 'apimgflux', None),
((tim.getImage() - sky) * mask, None, 'apmaskedimgflux', None),
(1. - (mask * 1.), None, 'apgoodpix', None),
(sourcepix, None, 'apsources', None),
]:
apimg = []
apimgerr = []
for rad in apertures:
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(photimg, aper, error=err)
apimg.append(p.field('aperture_sum'))
if err is not None:
apimgerr.append(p.field('aperture_sum_err'))
ap = np.vstack(apimg).T
ap[np.logical_not(np.isfinite(ap))] = 0.
LSB.set(name, ap.astype(np.float32))
if err is not None:
apiv = 1. / (np.vstack(apimgerr).T)**2
apiv[np.logical_not(np.isfinite(apiv))] = 0.
LSB.set(errname, apiv.astype(np.float32))
LSB.cut(np.argsort(-LSB.sn))
LSB.writeto(lsbcat)
return dict(LSB=LSB, filters=None, mod=None, sky=None)
def run(self,
plot_suffix,
plot_folder,
get_rad_at_EE=0.96,
MAXRAD_FACTOR=0.54,
fwzm_z=20,
use_azimuthal_averaging=True,
force_max_radii_to_390=False,
soft_bg_est=True):
"""
Run the analysis
INPUT:
plot_suffix = "f01"
plot_folder = "results/"
get_rad_at_EE = 0.96
FACTOR = 0.54
fwzm_z=20
soft_bg_est = True : If False, will not use software background estimator
OUTPUT:
Saves files to folders
"""
utils.make_dir(plot_folder)
fig, ax = plt.subplots(ncols=3, nrows=2, figsize=(20, 15))
plt.title('FIND ME')
ax_img = ax.flat[0]
ax_img_bkg = ax.flat[1]
ax_img_bkg_sub = ax.flat[2]
ax_cut = ax.flat[3]
ax_ee = ax.flat[4]
ax_img_bkg_sub2 = ax.flat[5]
# -------------
# Plot #1: Normal image
x, y = self.fimg.get_centroid(plot_lines=True, ax=ax_img)
self.fimg.ax.set_title("Hist stretch")
#cbar_ax = fig.add_axes([0.38, 0.59, 0.015, 0.3],label="Counts")
#fig.colorbar(self.fimg.im, cax=cbar_ax)
# -------------
# Plot #2: Background estimation
if soft_bg_est == True:
#x,y = self.fimg.get_centroid()
#print("Centroids",x,y)
CBS = phothelp.CircularBackgroundSubractor(self.fimg.data,
x=x,
y=y,
r=400)
data_back_sub = CBS.subtract_background(plot_background=True,
ax=ax_img_bkg)
self.subtracted_value = CBS.subtracted_value
# -------------
# Plot #3: Background subtracted image
self.fimg.data = data_back_sub
self.fimg.plot(ax=ax_img_bkg_sub,
colorbar=False,
title="Background subtracted (linear stretch)",
stretch="linear")
# add colorbar
#cbar_ax = fig.add_axes([0.38, 0.09, 0.015, 0.3],label="Counts")
#fig.colorbar(self.fimg.im, cax=cbar_ax)
# Get centroid again after taking out the background
x, y = self.fimg.get_centroid()
else:
print('Not subtracting backround')
self.subtracted_value = 0
# -------------
# Plot #4: Cuts
if use_azimuthal_averaging == True:
#self.max_radii_for_EE =
print("Using azimuthal averaging")
_r, _mean, _r_hwzm = self.fimg.get_radial_profile(
rmax=500,
plot=True,
z=fwzm_z,
return_hwzm=True,
ax=ax_cut,
xcen=x,
ycen=y,
subtract_min=True) # 500 ~2/1024
self.max_radii_for_EE = (_r_hwzm * 2. * MAXRAD_FACTOR)[0]
if force_max_radii_to_390 == True:
print("FORCING MAX RADII TO 390")
self.max_radii_for_EE = 390.
else:
print("Not using azimuthal averaging, Using mean(X+Y) cutouts")
cut_x, self.fwhm_x = self.fimg.get_centroid_line_cut(
line="X",
return_FWHM=True,
plot=True,
ax=ax_cut,
use_butter_filter=True,
butter_cutoff_freq=0.03,
fwzm_z=fwzm_z)
cut_y, self.fwhm_y = self.fimg.get_centroid_line_cut(
line="Y",
return_FWHM=True,
plot=True,
ax=ax_cut,
use_butter_filter=True,
butter_cutoff_freq=0.03,
fwzm_z=fwzm_z)
#return cut_x, cut_y
ax_cut.plot(butter.low_pass_butter(cut_x, cutoff_freq=0.03))
ax_cut.plot(butter.low_pass_butter(cut_y, cutoff_freq=0.03))
self.max_radii_for_EE = (
(self.fwhm_x + self.fwhm_y) / 2.) * MAXRAD_FACTOR
ax_cut.set_ylim(0, 64000.)
# -------------
# Plot #4: Encircled energy plot
print('Max radii for EE = {}'.format(self.max_radii_for_EE))
radii = np.arange(1, self.max_radii_for_EE)
df, self.r_ee = phothelp.get_encircled_energy_and_rad_at_EE(
self.fimg.data,
self.fimg.xcenter,
self.fimg.ycenter,
radii,
plot=True,
ax=ax_ee,
get_rad_at_EE=get_rad_at_EE)
ax_ee.set_xlim(0, 500)
ax_ee.set_xlabel("Radii (pixels)")
# Overplot aperture in figure $ with EE
aper = photutils.CircularAperture((x, y), r=self.r_ee)
aper.plot(ax=ax_img_bkg_sub, color="green")
ax_img_bkg_sub.annotate("EE" + str(100 * get_rad_at_EE) + "%",
xy=(x, y + self.r_ee),
color="white",
fontsize=8)
# overplot max aperture
aper = photutils.CircularAperture((x, y), r=self.max_radii_for_EE)
aper.plot(ax=ax_img_bkg_sub, color="red")
ax_img_bkg_sub.annotate("EE100% @ r = " + str(MAXRAD_FACTOR * 2) +
" HWHM",
xy=(x, y + self.max_radii_for_EE),
color="white",
fontsize=8)
# Hist plot
self.fimg.plot(ax=ax_img_bkg_sub2,
colorbar=False,
title="Background subtracted (Hist stretch)",
stretch="hist")
aper = photutils.CircularAperture((x, y), r=self.r_ee)
aper.plot(ax=ax_img_bkg_sub2, color="green")
ax_img_bkg_sub2.annotate("EE" + str(100 * get_rad_at_EE) + "%",
xy=(x, y + self.r_ee),
color="white",
fontsize=8)
# overplot max aperture
aper = photutils.CircularAperture((x, y), r=self.max_radii_for_EE)
aper.plot(ax=ax_img_bkg_sub2, color="red")
ax_img_bkg_sub2.annotate("EE100% @ r = " + str(MAXRAD_FACTOR * 2) +
" HWHM",
xy=(x, y + self.max_radii_for_EE),
color="white",
fontsize=8)
fig.tight_layout()
fig.subplots_adjust(top=0.9)
fig.suptitle("FRD Overview Plot, file: " + self.basename, fontsize=20)
fig.savefig(plot_folder + self.basename + "_" + plot_suffix + ".png")
print("Saved file:",
plot_folder + self.basename + "_" + plot_suffix + ".png")
df.to_csv(plot_folder + self.basename + "_" + plot_suffix + ".csv")
print("Saved file:",
plot_folder + self.basename + "_" + plot_suffix + ".csv")
def noise_per_annulus(array,
separation,
fwhm,
init_rad=None,
wedge=(0, 360),
verbose=False,
debug=False,
mask=None):
""" Measures the noise as the standard deviation of apertures defined in
each annulus with a given separation.
Parameters
----------
array : array_like
Input frame.
separation : float
Separation in pixels of the centers of the annuli measured from the
center of the frame.
fwhm : float
FWHM in pixels.
init_rad : float
Initial radial distance to be used. If None then the init_rad = FWHM.
wedge : tuple of floats, optional
Initial and Final angles for using a wedge. For example (-90,90) only
considers the right side of an image. Be careful when using small
wedges, this leads to computing a standard deviation of very small
samples (<10 values).
verbose : bool, optional
If True prints information.
debug : bool, optional
If True plots the positioning of the apertures.
Returns
-------
noise : array_like
Vector with the noise value per annulus.
vector_radd : array_like
Vector with the radial distances values.
"""
# dprint(fwhm)
def find_coords(rad, sep, init_angle, fin_angle):
angular_range = fin_angle - init_angle
npoints = (np.deg2rad(angular_range) * rad) / sep #(2*np.pi*rad)/sep
ang_step = angular_range / npoints #360/npoints
x = []
y = []
for i in range(int(npoints)):
newx = rad * np.cos(np.deg2rad(ang_step * i + init_angle))
newy = rad * np.sin(np.deg2rad(ang_step * i + init_angle))
x.append(newx)
y.append(newy)
return np.array(y), np.array(x)
###
if array.ndim != 2:
raise TypeError('Input array is not a frame or 2d array')
if not isinstance(wedge, tuple):
raise TypeError('Wedge must be a tuple with the initial and final '
'angles')
init_angle, fin_angle = wedge
centery, centerx = frame_center(array)
n_annuli = int(np.floor((centery) / separation)) - 1
noise = []
vector_radd = []
if verbose:
print('{} annuli'.format(n_annuli))
if init_rad is None:
init_rad = fwhm
if debug:
_, ax = plt.subplots(figsize=(6, 6))
ax.imshow(array,
origin='lower',
interpolation='nearest',
alpha=0.5,
cmap='gray')
for i in range(n_annuli):
y = centery + init_rad + separation * i
rad = dist(centery, centerx, y, centerx)
yy, xx = find_coords(rad, fwhm, init_angle, fin_angle)
yy += centery
xx += centerx
apertures = photutils.CircularAperture((xx, yy), fwhm / 2)
mask = np.bool_(mask)
# quick2D(mask)
if mask:
fluxes = photutils.aperture_photometry(array, apertures, mask=mask)
else:
fluxes = photutils.aperture_photometry(array, apertures)
fluxes = np.array(fluxes['aperture_sum'])
noise_ann = np.std(fluxes)
noise.append(noise_ann)
vector_radd.append(rad)
# if debug:
# if i == 1:
# plt.plot(fluxes)
# plt.figure()
# plt.hist(fluxes, bins=50)
if debug:
for j in range(xx.shape[0]):
# Circle takes coordinates as (X,Y)
aper = plt.Circle((xx[j], yy[j]),
radius=fwhm / 2,
color='r',
fill=False,
alpha=0.8)
ax.add_patch(aper)
cent = plt.Circle((xx[j], yy[j]),
radius=0.8,
color='r',
fill=True,
alpha=0.5)
ax.add_patch(cent)
if verbose:
print('Radius(px) = {}, Noise = {:.3f} '.format(rad, noise_ann))
return np.array(noise), np.array(vector_radd)
def aper_phot(self, x, y):
"""Perform aperture photometry, uses photutils functions, photutils must be available
"""
if not photutils_installed:
print("Install photutil to enable")
else:
sigma = 0. # no centering
amp = 0. # no centering
if self.aperphot_pars["center"][0]:
center = True
delta = 10
popt = self.gauss_center(x, y, delta)
if 5 > popt.count(0) > 1: # an error occurred in centering
warnings.warn(
"Problem fitting center, using original coordinates")
else:
amp, x, y, sigma, offset = popt
radius = int(self.aperphot_pars["radius"][0])
width = int(self.aperphot_pars["width"][0])
inner = int(self.aperphot_pars["skyrad"][0])
subsky = bool(self.aperphot_pars["subsky"][0])
aper_flux = photutils.aperture_photometry(
self._data,
x,
y,
apertures=photutils.CircularAperture(radius),
subpixels=1,
method="center")
aperture_area = np.pi * (radius)**2
if subsky:
outer = inner + width
annulus_sky = photutils.annulus_circular(
self._data, x, y, inner, outer)
annulus_area = np.pi * (outer**2 - inner**2)
total_flux = aper_flux - annulus_sky * (aperture_area /
annulus_area)
else:
total_flux = aper_flux
# compute the magnitude of the sky corrected flux
magzero = float(self.aperphot_pars["zmag"][0])
mag = magzero - 2.5 * (np.log10(total_flux))
pheader = ("x\ty\tradius\tflux\tmag(zpt={0:0.2f})\tsky\t".format(
magzero)).expandtabs(15)
if center:
pheader += ("fwhm")
pstr = "\n{0:.2f}\t{1:0.2f}\t{2:d}\t{3:0.2f}\t{4:0.2f}\t{5:0.2f}\t{6:0.2f}".format(
x + 1, y + 1, radius,
total_flux, mag, annulus_sky / annulus_area,
math_helper.gfwhm(sigma)).expandtabs(15)
else:
pstr = "\n{0:0.2f}\t{1:0.2f}\t{2:d}\t{3:0.2f}\t{4:0.2f}\t{5:0.2f}".format(
x + 1,
y + 1,
radius,
total_flux,
mag,
annulus_sky / annulus_area,
).expandtabs(15)
print(pheader + pstr)
logging.info(pheader + pstr)
def frame_quick_report(array, fwhm, source_xy=None, verbose=True):
""" Gets information from given frame: Integrated flux in aperture, SNR of
central pixel (max or given coordinates), mean SNR in aperture.
Parameters
----------
array : array_like
2d array or input frame.
fwhm : float
Size of the FWHM in pixels.
source_xy : tuple of floats
X and Y coordinates of the source center.
verbose : {True, False}, bool optional
If True prints to stdout the frame info.
Returns
-------
obj_flux : float
Integrated flux in aperture.
snr_pixels : array_like
SNR of pixels in 1FWHM aperture.
fy, fx : float
Fitted coordinates.
"""
if not array.ndim == 2: raise TypeError('Array is not 2d.')
if source_xy is not None:
x, y = source_xy
if verbose:
print
print('Coordinates of chosen px X,Y = {:},{:}'.format(x,y))
else:
y, x = np.where(array == array.max())
y = y[0]
x = x[0]
if verbose:
print
print('Coordinates of Max px X,Y = {:},{:}'.format(x,y))
# we get integrated flux on aperture with diameter=1FWHM
aper = photutils.CircularAperture((x, y), fwhm/2.)
obj_flux = photutils.aperture_photometry(array, aper, method='exact')
obj_flux = obj_flux['aperture_sum'][0]
# we get the mean and stddev of SNRs on aperture
yy, xx = draw.circle(y, x, fwhm/2.)
snr_pixels = [snr_ss(array, (x_,y_), fwhm, plot=False, verbose=False) for \
y_, x_ in zip(yy, xx)]
meansnr = np.mean(snr_pixels)
stdsnr = np.std(snr_pixels)
if verbose:
print('Central pixel SNR: ')
snr_ss(array, (x,y), fwhm, plot=False, verbose=True)
print('-----------------------------------------')
print('In 1*FWHM circular aperture:')
print('Integrated flux = {:.3f}'.format(obj_flux))
print('Mean SNR = {:.3f}'.format(meansnr))
print('Max SNR = {:.3f}, stddev SNRs = {:.3f}'.format(np.max(snr_pixels),
stdsnr))
print('-----------------------------------------')
return obj_flux, snr_pixels
def sky_fibers_for_brick(
survey,
brickname,
bands=['g', 'r', 'z'],
apertures_arcsec=[0.5, 0.75, 1., 1.5, 2., 3.5, 5., 7.]):
'''
Produces a table of possible DESI sky fiber locations for a given
"brickname" (eg, "0001p000") read from the given LegacySurveyData object *survey*.
'''
import fitsio
from astrometry.util.fits import fits_table
from astrometry.util.util import Tan
import photutils
from legacypipe.utils import find_unique_pixels
fn = survey.find_file('blobmap', brick=brickname)
blobs = fitsio.read(fn)
print('Blobs:', blobs.min(), blobs.max())
header = fitsio.read_header(fn)
wcs = Tan(header)
goodpix = (blobs == -1)
for band in bands:
fn = survey.find_file('nexp', brick=brickname, band=band)
if not os.path.exists(fn):
# Skip
continue
nexp = fitsio.read(fn)
goodpix[nexp == 0] = False
# Cut to unique brick area... required since the blob map drops
# blobs that are completely outside the brick's unique area, thus
# those locations are not masked.
brick = survey.get_brick_by_name(brickname)
H, W = wcs.shape
U = find_unique_pixels(wcs, W, H, None, brick.ra1, brick.ra2, brick.dec1,
brick.dec2)
goodpix[U == 0] = False
del U
x, y, blobdist = sky_fiber_locations(goodpix)
skyfibers = fits_table()
skyfibers.brickid = np.zeros(len(x), np.int32) + brick.brickid
skyfibers.brickname = np.array([brickname] * len(x))
skyfibers.x = x.astype(np.int16)
skyfibers.y = y.astype(np.int16)
skyfibers.blobdist = blobdist
skyfibers.ra, skyfibers.dec = wcs.pixelxy2radec(x + 1, y + 1)
pixscale = wcs.pixel_scale()
apertures = np.array(apertures_arcsec) / pixscale
# Now, do aperture photometry at these points in the coadd images
for band in bands:
imfn = survey.find_file('image', brick=brickname, band=band)
ivfn = survey.find_file('invvar', brick=brickname, band=band)
if not (os.path.exists(imfn) and os.path.exists(ivfn)):
continue
coimg = fitsio.read(imfn)
coiv = fitsio.read(ivfn)
apflux = np.zeros((len(skyfibers), len(apertures)), np.float32)
apiv = np.zeros((len(skyfibers), len(apertures)), np.float32)
skyfibers.set('apflux_%s' % band, apflux)
skyfibers.set('apflux_ivar_%s' % band, apiv)
with np.errstate(divide='ignore', invalid='ignore'):
imsigma = 1. / np.sqrt(coiv)
imsigma[coiv == 0] = 0
apxy = np.vstack((skyfibers.x, skyfibers.y)).T
for irad, rad in enumerate(apertures):
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(coimg, aper, error=imsigma)
apflux[:, irad] = p.field('aperture_sum')
err = p.field('aperture_sum_err')
apiv[:, irad] = 1. / err**2
header = fitsio.FITSHDR()
for i, ap in enumerate(apertures_arcsec):
header.add_record(
dict(name='AP%i' % i, value=ap,
comment='Aperture radius (arcsec)'))
skyfibers._header = header
return skyfibers