def test_apertures_exact():
"""Test area as measured by exact aperture modes on array of ones"""
theta = np.random.uniform(-np.pi/2., np.pi/2., naper)
ratio = np.random.uniform(0.2, 1.0, naper)
r = 3.
for dt in SUPPORTED_IMAGE_DTYPES:
data = np.ones(data_shape, dtype=dt)
for r in [0.5, 1., 3.]:
flux, fluxerr, flag = sep.sum_circle(data, x, y, r, subpix=0)
assert_allclose(flux, np.pi*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_circann(data, x, y, r, rout,
subpix=0)
assert_allclose(flux, np.pi*(rout**2 - r**2))
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, 1., ratio,
theta, r=r, subpix=0)
assert_allclose(flux, np.pi*ratio*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_ellipann(data, x, y, 1., ratio,
theta, r, rout, subpix=0)
assert_allclose(flux, np.pi*ratio*(rout**2 - r**2))
def select_sources(file, threshold=30, SNR_limit=5):
objects = []
name = file.split('/')[-1].split('.')[0]
fitsfile = pyfits.open(file, uint=False)
original = fitsfile[1].data
original = np.asarray(original, dtype=np.float)
bias = fits.open('/media/data/Bahtinov/2017_03_15/Bias/Masterbias.fits')
bias = bias[0].data
original = original - bias
data, background = image.subtract_background(original)
sources = sep.extract(data,
threshold,
err=background.globalrms,
gain=1.0,
minarea=100)
sources = np.sort(sources, order='flux')
BG = sep.sum_ellipann(data,
sources['x'],
sources['y'],
sources['a'],
sources['b'],
sources['theta'],
sources['b'] * 1.5,
sources['b'] * 4.5,
err=background.globalrms)[0]
SNR = sources['flux'] / BG
objects.append([
sources['x'][np.where(SNR > SNR_limit)],
sources['x2'][np.where(SNR > SNR_limit)],
sources['y'][np.where(SNR > SNR_limit)],
sources['y2'][np.where(SNR > SNR_limit)],
SNR[np.where(SNR > SNR_limit)]
])
objects = objects[0]
ratio = data.shape[0] * 1.0 / data.shape[1]
fig = figure(figsize=(10, ratio * 10))
axis = fig.add_subplot(111)
axis.imshow(data, cmap='gray', interpolation='nearest', origin='lower')
axis.scatter(sources['x'], sources['y'], color='b', s=1.5)
axis.scatter(objects[0], objects[2], color='r', s=1.5)
fig.savefig(directory_prefix + 'bahtinov_results/Focusrun/' +
today_utc_date + '/' + name + '.png')
plt.close()
return np.asarray(objects)
def mass_weighted_prof(data,
mass_map,
aper,
r_inn,
r_out,
subpix=7,
mask=None,
return_mass=False):
"""Get the stellar mass weighted properties in annulus.
Parameters
----------
data : ndarray
Age or metallicity map, or other quantities that need to be weighted.
mass_map : ndarray
Stellar mass map.
aper : dict
Basic information of the galaxy
r_inn : ndarray
Array of inner radial bins to define the annulus.
r_out : ndarray
Array of outer radial bins to define the annulus.
subpix : int, optional
Subpixel sampling factor. Default: 5.
mask : ndarray, optional
Mask array.
return_mass : bool, optional
Return the stellar mass in each radial bins. Default: False
Returns
-------
prof : dict
A dictionary that contains the mass-weighted and none-weighted profiles.
"""
# Mass weighted data
data_w = copy.deepcopy(data * mass_map)
data_w[np.isnan(data_w)] = 0.0
# Define a mask...just in case
if mask is None:
mask = (mass_map < 1.).astype(np.uint8)
# Sum(data) term
sum_data, _, flag = sep.sum_ellipann(data,
aper['x'],
aper['y'],
1.0,
aper['ba'],
aper['theta'],
r_inn,
r_out,
mask=mask,
subpix=subpix)
# Total number of effective pixels
n_pix_eff, _, flag = sep.sum_ellipann((1.0 - mask),
aper['x'],
aper['y'],
1.0,
aper['ba'],
aper['theta'],
r_inn,
r_out,
mask=mask,
subpix=subpix)
# Sum(mass_map * data) term
sum_data_w, _, _ = sep.sum_ellipann(data_w,
aper['x'],
aper['y'],
1.0,
aper['ba'],
aper['theta'],
r_inn,
r_out,
mask=mask,
subpix=subpix)
# Sum(mass_map) term
sum_mass_map, _, _ = sep.sum_ellipann(mass_map,
aper['x'],
aper['y'],
1.0,
aper['ba'],
aper['theta'],
r_inn,
r_out,
mask=mask,
subpix=subpix)
if return_mass:
return {
'prof_w': sum_data_w / sum_mass_map,
'prof': sum_data / n_pix_eff,
'mass': sum_mass_map,
'flag': flag
}
return {
'prof_w': sum_data_w / sum_mass_map,
'prof': sum_data / n_pix_eff,
'flag': flag
}
def aperture_photometry(img: Union[ndarray, MaskedArray], sources: ndarray,
background: Optional[Union[ndarray,
MaskedArray]] = None,
background_rms: Optional[Union[ndarray,
MaskedArray]] = None,
texp: float = 1,
gain: Union[float, ndarray, MaskedArray] = 1,
sat_level: float = 63000,
a: Optional[float] = None, b: Optional[float] = None,
theta: Optional[float] = 0,
a_in: Optional[float] = None,
a_out: Optional[float] = None,
b_out: Optional[float] = None,
theta_out: Optional[float] = None,
k: float = 0,
k_in: Optional[float] = None,
k_out: Optional[float] = None,
radius: float = 6,
fix_aper: bool = False,
fix_ell: bool = True,
fix_rot: bool = True,
apcorr_tol: float = 0.0001) -> ndarray:
"""
Do automatic or fixed aperture photometry
:param img: input 2D image array
:param sources: record array of sources extracted with
:func:`skylib.extraction.extract_sources`; should contain at least "x"
and "y" columns
:param background: optional sky background map; if omitted, extract
background from the annulus around the aperture, see `a_in` below
:param background_rms: optional sky background RMS map; if omitted,
calculate RMS over the annulus around the aperture
:param texp: exposure time in seconds
:param gain: electrons to data units conversion factor; used to estimate
photometric errors; for variable-gain images (e.g. mosaics), must be
an array of the same shape as the input data
:param sat_level: saturation level in ADUs; used to select only
non-saturated stars for adaptive aperture photometry and aperture
correction
:param a: fixed aperture radius or semi-major axis in pixels; default: use
automatic photometry with a = a_iso*k, where a_iso is the isophotal
semi-major axis
:param b: semi-minor axis in pixels when using a fixed aperture; default:
same as `a`
:param theta: rotation angle of semi-major axis in degrees CCW when using
a fixed aperture and `b` != `a`; default: 0
:param a_in: inner annulus radius or semi-major axis in pixels; used
to estimate the background if `background` or `background_rms` are not
provided, ignored otherwise; default: `a`*`k_in`
:param a_out: outer annulus radius or semi-major axis in pixels; default:
`a`*`k_out`
:param b_out: outer annulus semi-minor axis in pixels; default: `b`*`k_out`
:param theta_out: annulus orientation in degrees CCW; default: same
as `theta`
:param k: automatic aperture radius in units of isophotal radius; 0 means
find the optimal radius based on SNR; default: 0
:param k_in: inner annulus radius in units of aperture radius (fixed
aperture, i.e. `a` is provided) or isophotal radius (adaptive aperture);
default: 1.5*`k` or 3.75 if `k` is undefined and `a` = None
:param k_out: outer annulus radius in units of aperture radius (fixed
aperture) or isophotal radius (adaptive aperture); default: 2*`k` or
5 if `k` is undefined and `a` = None
:param radius: isophotal analysis radius in pixels used to compute automatic
aperture if ellipse parameters (a,b,theta) are missing
:param fix_aper: use the same aperture radius for all sources when doing
automatic photometry; calculated as flux-weighted median of aperture
sizes based on isophotal parameters
:param fix_ell: use the same major to minor aperture axis ratio for all
sources during automatic photometry; calculated as flux-weighted median
of all ellipticities
:param fix_rot: use the same aperture position angle for all sources during
automatic photometry; calculated as flux-weighted median
of all orientations
:param apcorr_tol: growth curve stopping tolerance for aperture correction;
0 = disable aperture correction
:return: record array containing the input sources, with the following
fields added or updated: "flux", "flux_err", "mag", "mag_err", "aper_a",
"aper_b", "aper_theta", "aper_a_in", "aper_a_out", "aper_b_out",
"aper_theta_out", "aper_area", "background_area", "background",
"background_rms", "phot_flag"
"""
if not len(sources):
return array([])
img = sep_compatible(img)
texp = float(texp)
if isscalar(gain):
gain = float(gain)
k = float(k)
if k <= 0.1:
k = 0 # temporary fix for k = 0 not being allowed in AgA
if k_in:
k_in = float(k_in)
if k_out:
k_out = float(k_out)
x, y = sources['x'] - 1, sources['y'] - 1
area_img = ones(img.shape, dtype=int32)
if isinstance(img, MaskedArray):
mask = img.mask
img = img.data
else:
mask = None
have_background = background is not None and background_rms is not None
if have_background:
background = sep_compatible(background)
if isinstance(background, MaskedArray):
if mask is None:
mask = background.mask
else:
mask |= background.mask
background = background.data
background_rms = sep_compatible(background_rms)
if isinstance(background_rms, MaskedArray):
if mask is None:
mask = background_rms.mask
else:
mask |= background_rms.mask
background_rms = background_rms.data
# Will need this to fill the newly added source table columns
z = zeros(len(sources), float)
fixed_aper = bool(a)
if fixed_aper:
# Use the same fixed aperture and annulus parameters for all sources
a = float(a)
if b:
b = float(b)
else:
b = a
if theta:
theta = float(theta % 180)*pi/180
if theta > pi/2:
theta -= pi
else:
theta = 0
if not have_background:
if theta_out:
theta_out = float(theta_out % 180)*pi/180
if theta_out > pi/2:
theta_out -= pi
elif theta_out != 0:
theta_out = theta
if a_in:
a_in = float(a_in)
else:
a_in = a*k_in if k_in else a*1.5*k if k else a*3.75
if a_out:
a_out = float(a_out)
else:
a_out = a*k_out if k_out else a*2*k if k else a*5
if b_out:
b_out = float(b_out)
else:
b_out = a_out*b/a
else:
# Use automatic apertures derived from ellipse axes; will need image
# with background subtracted
if background is None:
# Estimate background on the fly
tmp_back, tmp_rms = estimate_background(img, size=64)
else:
tmp_back, tmp_rms = background, background_rms
img_back = img - tmp_back
for name in ['a', 'b', 'theta', 'flux']:
if name not in sources.dtype.names:
sources = append_fields(sources, name, z, usemask=False)
a, b, theta = sources['a'], sources['b'], sources['theta']
flux = sources['flux']
bad = (a <= 0) | (b <= 0) | (flux <= 0)
if bad.any():
# Do isophotal analysis to compute ellipse parameters if missing
yy, xx = indices(img.shape)
for i in bad.nonzero()[0]:
ap = (xx - sources[i]['x'])**2 + (yy - sources[i]['y'])**2 <= \
radius**2
if ap.any():
yi, xi = ap.nonzero()
ap_data = img_back[ap].astype(float)
f = ap_data.sum()
if f > 0:
cx = (xi*ap_data).sum()/f
cy = (yi*ap_data).sum()/f
x2 = (xi**2*ap_data).sum()/f - cx**2
y2 = (yi**2*ap_data).sum()/f - cy**2
xy = (xi*yi*ap_data).sum()/f - cx*cy
else:
cx, cy = xi.mean(), yi.mean()
x2 = (xi**2).mean() - cx**2
y2 = (yi**2).mean() - cy**2
xy = (xi*yi).mean() - cx*cy
if x2 == y2:
thetai = 0
else:
thetai = arctan(2*xy/(x2 - y2))/2
if y2 > x2:
thetai += pi/2
m1 = (x2 + y2)/2
m2 = sqrt(max((x2 - y2)**2/4 + xy**2, 0))
ai = max(1/12, sqrt(max(m1 + m2, 0)))
bi = max(1/12, sqrt(max(m1 - m2, 0)))
if ai/bi > 2:
# Prevent too elongated apertures usually occurring for
# faint objects
bi = ai
else:
# Cannot obtain a,b,theta from isophotal analysis, assume
# circular aperture
ai, bi, thetai, f = radius, radius, 0, 0
a[i] = sources[i]['a'] = ai
b[i] = sources[i]['b'] = bi
theta[i] = sources[i]['theta'] = thetai
flux[i] = sources[i]['flux'] = f
bad = (a < b).nonzero()
a[bad], b[bad] = b[bad], a[bad]
theta[bad] += pi/2
theta %= pi
theta[theta > pi/2] -= pi
elongation = a/b
# Obtain the optimal aperture radius from the brightest non-saturated
# source
if not k:
for i in argsort(flux)[::-1]:
if sep.sum_ellipse(
img >= sat_level, [x[i]], [y[i]], a[i], b[i], theta[i],
1, subpix=0)[0][0]:
# Saturated source
continue
try:
# noinspection PyTypeChecker
res = minimize(
calc_flux_err, [a[i]*1.6],
(img_back, x[i], y[i], elongation[i], theta[i], tmp_rms,
mask, gain), bounds=[(1, None)], tol=1e-5)
except ValueError:
continue
if not res.success:
continue
k = res.x[0]/a[i]
break
if not k:
raise ValueError(
'Not enough data for automatic aperture factor; use '
'explicit aperture factor')
# Calculate weighted median of aperture sizes, elongations, and/or
# orientations if requested
r = sqrt(a*b)*k
if r.size > 1 and any([fix_aper, fix_ell, fix_rot]):
flux[flux < 0] = 0
if not flux.any():
raise ValueError(
'Not enough data for weighted median in fixed-aperture '
'automatic photometry; use static fixed-aperture or fully '
'adaptive automatic photometry instead')
if fix_aper:
r = weighted_median(r, flux)
if fix_ell:
elongation = weighted_median(elongation, flux)
if fix_rot:
theta = weighted_median(theta, flux, period=pi)
if theta > pi/2:
theta -= pi
# Calculate the final aperture and annulus sizes
sqrt_el = sqrt(elongation)
a, b = r*sqrt_el, r/sqrt_el
if not have_background:
if not k_in:
k_in = 1.5*k
if not k_out:
k_out = 2*k
a_in = a*(k_in/k)
a_out, b_out = a*(k_out/k), b*(k_out/k)
theta_out = theta
# Calculate mean and RMS of background; to get the pure sigma, set error
# to 1 and don't pass the gain
if have_background:
if fixed_aper and a == b:
bk_area = sep.sum_circle(area_img, x, y, a, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_circle(
background, x, y, a, err=1, mask=mask, subpix=0)[:2]
else:
bk_area = sep.sum_ellipse(
area_img, x, y, a, b, theta, 1, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_ellipse(
background, x, y, a, b, theta, 1, err=1, mask=mask,
subpix=0)[:2]
error = background_rms
elif fixed_aper and a_out == b_out:
bk_area = sep.sum_circann(
area_img, x, y, a_in, a_out, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_circann(
img, x, y, a_in, a_out, err=1, mask=mask, subpix=0)[:2]
error = bk_sigma
else:
bk_area = sep.sum_ellipann(
area_img, x, y, a_out, b_out, theta_out, a_in/a_out, 1, mask=mask,
subpix=0)[0]
bk_mean, bk_sigma = sep.sum_ellipann(
img, x, y, a_out, b_out, theta_out, a_in/a_out, 1, err=1, mask=mask,
subpix=0)[:2]
error = bk_sigma
if have_background:
area = bk_area
elif fixed_aper and a == b:
area = sep.sum_circle(area_img, x, y, a, mask=mask, subpix=0)[0]
else:
area = sep.sum_ellipse(
area_img, x, y, a, b, theta, 1, mask=mask, subpix=0)[0]
if fixed_aper and a == b:
# Fixed circular aperture
if ndim(error) == 1:
# Separate scalar error for each source
flux, flux_err = empty([2, len(sources)], dtype=float)
flags = empty(len(sources), dtype=int)
for i, (_x, _y, _err) in enumerate(zip(x, y, error)):
flux[i], flux_err[i], flags[i] = sep.sum_circle(
img, [_x], [_y], a, err=_err, mask=mask, gain=gain,
subpix=0)
else:
flux, flux_err, flags = sep.sum_circle(
img, x, y, a, err=error, mask=mask, gain=gain, subpix=0)
else:
# Variable or elliptic aperture
if ndim(error) == 1:
# Separate scalar error for each source
flux, flux_err = empty([2, len(sources)], dtype=float)
flags = empty(len(sources), dtype=int)
if isscalar(a):
a = full_like(x, a)
if isscalar(b):
b = full_like(x, b)
if isscalar(theta):
theta = full_like(x, theta)
for i, (_x, _y, _err, _a, _b, _theta) in enumerate(zip(
x, y, error, a, b, theta)):
flux[i], flux_err[i], flags[i] = sep.sum_ellipse(
img, [_x], [_y], _a, _b, _theta, 1, err=_err, mask=mask,
gain=gain, subpix=0)
else:
flux, flux_err, flags = sep.sum_ellipse(
img, x, y, a, b, theta, 1, err=error, mask=mask, gain=gain,
subpix=0)
# Convert background sum to mean and subtract background from fluxes
if have_background:
# Background area equals aperture area
flux -= bk_mean
bk_mean = bk_mean/area
else:
# Background area equals annulus area
bk_mean = bk_mean/bk_area
flux -= bk_mean*area
# Convert ADUs to electrons
flux *= gain
flux_err *= gain
bk_mean *= gain
bk_sigma *= gain
# Calculate aperture correction for all aperture sizes from the brightest
# source
aper_corr = {}
if apcorr_tol > 0:
for i in argsort(flux)[::-1]:
xi, yi = x[i], y[i]
if isscalar(a):
ai = a
else:
ai = a[i]
if isscalar(b):
bi = b
else:
bi = b[i]
if isscalar(theta):
thetai = theta
else:
thetai = theta[i]
if ndim(error) == 1:
err = error[i]
else:
err = error
if ai == bi:
nsat = sep.sum_circle(
img >= sat_level, [xi], [yi], ai, subpix=0)[0][0]
else:
nsat = sep.sum_ellipse(
img >= sat_level, [xi], [yi], ai, bi, thetai, 1,
subpix=0)[0][0]
if nsat:
# Saturated source
continue
# Obtain total flux by increasing aperture size until it grows
# either more than before (i.e. a nearby source in the aperture) or
# less than the threshold (i.e. the growth curve reached saturation)
f0 = f_prev = flux[i]
dap = 0
f_tot = df_prev = None
while True:
dap += 0.1
if ai == bi:
f, f_err, fl = sep.sum_circle(
img, [xi], [yi], ai + dap, err=err, mask=mask,
gain=gain, subpix=0)
area_i = sep.sum_circle(
area_img, [xi], [yi], ai + dap, mask=mask,
subpix=0)[0][0]
else:
f, f_err, fl = sep.sum_ellipse(
img, [xi], [yi], ai + dap, bi*(1 + dap/ai), thetai, 1,
err=err, mask=mask, gain=gain, subpix=0)
area_i = sep.sum_ellipse(
area_img, [xi], [yi], ai + dap, bi + dap, thetai, 1,
mask=mask, subpix=0)[0][0]
f, fl = f[0], fl[0]
if fl:
break
f = (f - bk_mean[i]*area_i)*gain
if f <= 0:
break
df = f/f_prev
if df_prev is not None and df > df_prev:
# Increasing growth, nearby source hit
f_tot = f_prev
break
if df < 1 + apcorr_tol:
# Growth stopped to within the tolerance
f_tot = f
break
f_prev, df_prev = f, df
if f_tot is None:
continue
# Calculate fluxes for the chosen source for all unique aperture
# sizes used for other sources
fluxes_for_ap = {ai: f0}
if not isscalar(a):
for aj in set(a) - {ai}:
bj = aj*bi/ai
if aj == bj:
f, fl = sep.sum_circle(
img, [xi], [yi], aj, err=err, mask=mask, gain=gain,
subpix=0)[::2]
area_j = sep.sum_circle(
area_img, [xi], [yi], aj, mask=mask, subpix=0)[0][0]
else:
f, fl = sep.sum_ellipse(
img, [xi], [yi], aj, bj, thetai, 1, err=err,
mask=mask, gain=gain, subpix=0)[::2]
area_j = sep.sum_ellipse(
area_img, [xi], [yi], aj, bj, thetai, 1,
mask=mask, subpix=0)[0][0]
f, fl = f[0], fl[0]
f = (f - bk_mean[i]*area_j)*gain
if fl or f <= 0:
continue
fluxes_for_ap[aj] = f
# Calculate aperture corrections
for aj, f in fluxes_for_ap.items():
if f < f_tot:
aper_corr[aj] = -2.5*log10(f_tot/f)
break
if 'flux' in sources.dtype.names:
sources['flux'] = flux
else:
sources = append_fields(sources, 'flux', flux, usemask=False)
if 'flux_err' in sources.dtype.names:
sources['flux_err'] = flux_err
else:
sources = append_fields(sources, 'flux_err', flux_err, usemask=False)
if 'flag' in sources.dtype.names:
sources['flag'] |= flags
else:
sources = append_fields(sources, 'flag', flags, usemask=False)
for name in ['mag', 'mag_err', 'aper_a', 'aper_b', 'aper_theta',
'aper_a_in', 'aper_a_out', 'aper_b_out', 'aper_theta_out',
'aper_area', 'background_area', 'background',
'background_rms']:
if name not in sources.dtype.names:
sources = append_fields(sources, name, z, usemask=False)
good = (flux > 0).nonzero()
if len(good[0]):
sources['mag'][good] = -2.5*log10(flux[good]/texp)
sources['mag_err'][good] = 2.5*log10(1 + flux_err[good]/flux[good])
sources['aper_a'] = a
sources['aper_b'] = b
sources['aper_theta'] = theta
if not have_background:
sources['aper_a_in'] = a_in
sources['aper_a_out'] = a_out
sources['aper_b_out'] = b_out
sources['aper_theta_out'] = theta_out
sources['aper_area'] = area
sources['background_area'] = bk_area
sources['background'] = bk_mean
sources['background_rms'] = bk_sigma
# Apply aperture correction
for i in good[0]:
sources['mag'][i] += aper_corr.get(sources['aper_a'][i], 0)
return sources