def UL_5sig_err(im, setbl, mtbl, mtbl1, magtype, zp2):
fn = os.path.splitext(im)[0]
from astropy.modeling import models, fitting
import numpy as np
magerrtype = magtype[:3] + 'ERR' + magtype[3:]
if len(mtbl1) < 3:
return 0
x, y = mtbl1[magtype], mtbl1[magerrtype]
#x,y=setbl['MAG_AUTO'],setbl['MAGERR_AUTO']
x, y = x[np.where(y < 1)], y[np.where(y < 1)]
#fit_init=models.Polynomial1D(7)
fit_init = models.Exponential1D()
fit_t = fitting.LevMarLSQFitter()
#fit_t=fitting.LinearLSQFitter()
t = fit_t(fit_init, x, y)
plt.plot(setbl[magtype] + zp2[0], setbl[magerrtype], 'ro')
plt.plot(mtbl[magtype] + zp2[0], mtbl[magerrtype], 'ko')
plt.plot(x + zp2[0], y, 'bo')
plt.xlabel('Mag')
plt.ylabel('Error')
plt.xlim(10, 25)
plt.ylim(-0.1, 0.5)
xp = np.linspace(-20, 0, 20001)
plt.plot(xp + zp2[0], t(xp), '--')
plt.hlines(0.2, 10, 25)
idx_min = np.where(np.abs(t(xp) - 0.198) == np.min(np.abs(t(xp) - 0.198)))
xp[idx_min] + zp2[0] #
plt.vlines(xp[idx_min] + zp2[0], -0.1, 0.5)
# result print, file, header
plt.text(12, 0.4, '5 sigma Detection Limit error=0.198')
plt.text(12, 0.3,
'5sig_UL = ' + '{}'.format(round((xp[idx_min] + zp2[0])[0], 3)))
plt.title(fn + ' ' + magtype + ' ' + '5 sig Detection Limit')
plt.savefig(fn + '_' + magtype + '_' + '5sigUL.png')
plt.close()
return round((xp[idx_min] + zp2[0])[0], 3)
assert_allclose(dvda_h, dvda_w, rtol=1e-9, atol=1e-7 * (1 + 30 / doppler))
@pytest.mark.skipif("not HAS_SCIPY")
def test_KingProjectedAnalytic1D_fit():
km = models.KingProjectedAnalytic1D(amplitude=1, r_core=1, r_tide=2)
xarr = np.linspace(0.1, 2, 10)
yarr = km(xarr)
km_init = models.KingProjectedAnalytic1D(amplitude=1, r_core=1, r_tide=1)
fitter = fitting.LevMarLSQFitter()
km_fit = fitter(km_init, xarr, yarr)
assert_allclose(km_fit.param_sets, km.param_sets)
assert_allclose(km_fit.concentration, 0.30102999566398136)
@pytest.mark.parametrize('model', [models.Exponential1D(), models.Logarithmic1D()])
def test_ExponentialAndLogarithmic1D_fit(model):
xarr = np.linspace(0.1, 10., 200)
assert_allclose(xarr, model.inverse(model(xarr)))
@pytest.mark.parametrize('model', [models.Exponential1D(), models.Logarithmic1D()])
def test_ExponentialAndLogarithmic_set_tau(model):
message = "0 is not an allowed value for tau"
with pytest.raises(ValueError) as err:
model.tau = 0
assert str(err.value) == message
def test_Linear1D_inverse():
astmodels.Mapping((0, 1), n_inputs=3),
astmodels.Shift(2. * u.deg),
astmodels.Scale(3.4 * u.deg),
astmodels.RotateNative2Celestial(5.63 * u.deg, -72.5 * u.deg, 180 * u.deg),
astmodels.RotateCelestial2Native(5.63 * u.deg, -72.5 * u.deg, 180 * u.deg),
astmodels.RotationSequence3D([1.2, 2.3, 3.4, .3], 'xyzx'),
astmodels.SphericalRotationSequence([1.2, 2.3, 3.4, .3], 'xyzy'),
astmodels.AiryDisk2D(amplitude=10., x_0=0.5, y_0=1.5),
astmodels.Box1D(amplitude=10., x_0=0.5, width=5.),
astmodels.Box2D(amplitude=10., x_0=0.5, x_width=5., y_0=1.5, y_width=7.),
astmodels.Const1D(amplitude=5.),
astmodels.Const2D(amplitude=5.),
astmodels.Disk2D(amplitude=10., x_0=0.5, y_0=1.5, R_0=5.),
astmodels.Ellipse2D(amplitude=10., x_0=0.5, y_0=1.5, a=2., b=4.,
theta=0.1),
astmodels.Exponential1D(amplitude=10., tau=3.5),
astmodels.Gaussian1D(amplitude=10., mean=5., stddev=3.),
astmodels.Gaussian2D(amplitude=10.,
x_mean=5.,
y_mean=5.,
x_stddev=3.,
y_stddev=3.),
astmodels.KingProjectedAnalytic1D(amplitude=10., r_core=5., r_tide=2.),
astmodels.Logarithmic1D(amplitude=10., tau=3.5),
astmodels.Lorentz1D(amplitude=10., x_0=0.5, fwhm=2.5),
astmodels.Moffat1D(amplitude=10., x_0=0.5, gamma=1.2, alpha=2.5),
astmodels.Moffat2D(amplitude=10., x_0=0.5, y_0=1.5, gamma=1.2, alpha=2.5),
astmodels.Planar2D(slope_x=0.5, slope_y=1.2, intercept=2.5),
astmodels.RedshiftScaleFactor(z=2.5),
astmodels.RickerWavelet1D(amplitude=10., x_0=0.5, sigma=1.2),
astmodels.RickerWavelet2D(amplitude=10., x_0=0.5, y_0=1.5, sigma=1.2),
def fit_ellipse_for_source(
friendid=None,
detectid=None,
coords=None,
shotid=None,
subcont=True,
convolve_image=False,
pixscale=pixscale,
imsize=imsize,
wave_range=None,
):
if detectid is not None:
global deth5
detectid_obj = detectid
if detectid_obj <= 2190000000:
det_info = deth5.root.Detections.read_where("detectid == detectid_obj")[0]
linewidth = det_info["linewidth"]
wave_obj = det_info["wave"]
redshift = wave_obj / (1216) - 1
else:
det_info = conth5.root.Detections.read_where("detectid == detectid_obj")[0]
redshift = 0
wave_obj = 4500
coords_obj = SkyCoord(det_info["ra"], det_info["dec"], unit="deg")
shotid_obj = det_info["shotid"]
fwhm = surveyh5.root.Survey.read_where("shotid == shotid_obj")["fwhm_virus"][0]
amp = det_info["multiframe"]
if wave_range is not None:
wave_range_obj = wave_range
else:
if detectid_obj <= 2190000000:
wave_range_obj = [wave_obj - 2 * linewidth, wave_obj + 2 * linewidth]
else:
wave_range_obj = [4100, 4200]
if coords is not None:
coords_obj = coords
if shotid is not None:
shotid_obj = shotid
fwhm = surveyh5.root.Survey.read_where("shotid == shotid_obj")[
"fwhm_virus"
][0]
try:
hdu = make_narrowband_image(
coords=coords_obj,
shotid=shotid_obj,
wave_range=wave_range_obj,
imsize=imsize * u.arcsec,
pixscale=pixscale * u.arcsec,
subcont=subcont,
convolve_image=convolve_image,
include_error=True,
)
except:
print("Could not make narrowband image for {}".format(detectid))
return np.nan, np.nan
elif friendid is not None:
global friend_cat
sel = friend_cat["friendid"] == friendid
group = friend_cat[sel]
coords_obj = SkyCoord(ra=group["icx"][0] * u.deg, dec=group["icy"][0] * u.deg)
wave_obj = group["icz"][0]
redshift = wave_obj / (1216) - 1
linewidth = group["linewidth"][0]
shotid_obj = group["shotid"][0]
fwhm = group["fwhm"][0]
amp = group["multiframe"][0]
if wave_range is not None:
wave_range_obj = wave_range
else:
wave_range_obj = [wave_obj - 2 * linewidth, wave_obj + 2 * linewidth]
if shotid is not None:
shotid_obj = shotid
fwhm = surveyh5.root.Survey.read_where("shotid == shotid_obj")[
"fwhm_virus"
][0]
try:
hdu = make_narrowband_image(
coords=coords_obj,
shotid=shotid_obj,
wave_range=wave_range_obj,
imsize=imsize * u.arcsec,
pixscale=pixscale * u.arcsec,
subcont=subcont,
convolve_image=convolve_image,
include_error=True,
)
except:
print("Could not make narrowband image for {}".format(friendid))
return None
elif coords is not None:
coords_obj = coords
if wave_range is not None:
wave_range_obj = wave_range
else:
print(
"You need to supply wave_range=[wave_start, wave_end] for collapsed image"
)
if shotid is not None:
shotid_obj = shotid
fwhm = surveyh5.root.Survey.read_where("shotid == shotid_obj")[
"fwhm_virus"
][0]
else:
print("Enter the shotid to use (eg. 20200123003)")
hdu = make_narrowband_image(
coords=coords_obj,
shotid=shotid_obj,
wave_range=wave_range_obj,
imsize=imsize * u.arcsec,
pixscale=pixscale * u.arcsec,
subcont=subcont,
convolve_image=convolve_image,
include_error=True,
)
else:
print("You must provide a detectid, friendid or coords/wave_range/shotid")
return np.nan, np.nan
w = wcs.WCS(hdu[0].header)
if friendid is not None:
sel_friend_group = friend_cat["friendid"] == friendid
group = friend_cat[sel_friend_group]
eps = 1 - group["a2"][0] / group["b2"][0]
pa = group["pa"][0] * np.pi / 180.0 - 90
sma = group["a"][0] * 3600 / pixscale
coords = SkyCoord(ra=group["icx"][0] * u.deg, dec=group["icy"][0] * u.deg)
wave_obj = group["icz"][0]
redshift = wave_obj / (1216) - 1
linewidth = np.nanmedian(group["linewidth"])
shotid_obj = group["shotid"][0]
fwhm = group["fwhm"][0]
geometry = EllipseGeometry(
x0=w.wcs.crpix[0], y0=w.wcs.crpix[0], sma=sma, eps=eps, pa=pa
)
else:
geometry = EllipseGeometry(
x0=w.wcs.crpix[0], y0=w.wcs.crpix[0], sma=20, eps=0.2, pa=20.0
)
geometry = EllipseGeometry(
x0=w.wcs.crpix[0], y0=w.wcs.crpix[0], sma=20, eps=0.2, pa=20.0
)
# geometry.find_center(hdu.data)
# aper = EllipticalAperture((geometry.x0, geometry.y0), geometry.sma,
# geometry.sma*(1 - geometry.eps), geometry.pa)
# plt.imshow(hdu.data, origin='lower')
# aper.plot(color='white')
ellipse = Ellipse(hdu[0].data)
isolist = ellipse.fit_image()
iso_tab = isolist.to_table()
if len(iso_tab) == 0:
geometry.find_center(hdu[0].data, verbose=False, threshold=0.5)
ellipse = Ellipse(hdu[0].data, geometry)
isolist = ellipse.fit_image()
iso_tab = isolist.to_table()
if len(iso_tab) == 0:
return np.nan, np.nan, np.nan
try:
# compute iso's manually in steps of 3 pixels
ellipse = Ellipse(hdu[0].data) # reset ellipse
iso_list = []
for sma in np.arange(1, 60, 2):
iso = ellipse.fit_isophote(sma)
if np.isnan(iso.intens):
# print('break at {}'.format(sma))
break
else:
iso_list.append(iso)
isolist = IsophoteList(iso_list)
iso_tab = isolist.to_table()
except:
return np.nan, np.nan, np.nan
try:
model_image = build_ellipse_model(hdu[0].data.shape, isolist)
residual = hdu[0].data - model_image
except:
return np.nan, np.nan, np.nan
sma = iso_tab["sma"] * pixscale
const_arcsec_to_kpc = cosmo.kpc_proper_per_arcmin(redshift).value / 60.0
def arcsec_to_kpc(sma):
dist = const_arcsec_to_kpc * sma
return dist
def kpc_to_arcsec(dist):
sma = dist / const_arcsec_to_kpc
return sma
dist_kpc = (
sma * u.arcsec.to(u.arcmin) * u.arcmin * cosmo.kpc_proper_per_arcmin(redshift)
)
dist_arcsec = kpc_to_arcsec(dist_kpc)
# print(shotid_obj, fwhm)
# s_exp1d = models.Exponential1D(amplitude=0.2, tau=-50)
alpha = 3.5
s_moffat = models.Moffat1D(
amplitude=1,
gamma=(0.5 * fwhm) / np.sqrt(2 ** (1.0 / alpha) - 1.0),
x_0=0.0,
alpha=alpha,
fixed={"amplitude": False, "x_0": True, "gamma": True, "alpha": True},
)
s_init = models.Exponential1D(amplitude=0.2, tau=-50)
fit = fitting.LevMarLSQFitter()
s_r = fit(s_init, dist_kpc, iso_tab["intens"])
# Fitting can be done using the uncertainties as weights.
# To get the standard weighting of 1/unc^2 for the case of
# Gaussian errors, the weights to pass to the fitting are 1/unc.
# fitted_line = fit(line_init, x, y, weights=1.0/yunc)
# s_r = fit(s_init, dist_kpc, iso_tab['intens'])#, weights=iso_tab['intens']/iso_tab['intens_err'] )
print(s_r)
try:
r_n = -1.0 * s_r.tau # _0 #* const_arcsec_to_kpc
except:
r_n = np.nan # r_n = -1. * s_r.tau_0
try:
sel_iso = np.where(dist_kpc >= 2 * r_n)[0][0]
except:
sel_iso = -1
aper = EllipticalAperture(
(isolist.x0[sel_iso], isolist.y0[sel_iso]),
isolist.sma[sel_iso],
isolist.sma[sel_iso] * (1 - isolist.eps[sel_iso]),
isolist.pa[sel_iso],
)
phottable = aperture_photometry(hdu[0].data, aper, error=hdu[1].data)
flux = phottable["aperture_sum"][0] * 10 ** -17 * u.erg / (u.cm ** 2 * u.s)
flux_err = phottable["aperture_sum_err"][0] * 10 ** -17 * u.erg / (u.cm ** 2 * u.s)
lum_dist = cosmo.luminosity_distance(redshift).to(u.cm)
lum = flux * 4.0 * np.pi * lum_dist ** 2
lum_err = flux_err * 4.0 * np.pi * lum_dist ** 2
if detectid:
name = detectid
elif friendid:
name = friendid
# Get Image data from Elixer
catlib = catalogs.CatalogLibrary()
try:
cutout = catlib.get_cutouts(
position=coords_obj,
side=imsize,
aperture=None,
dynamic=False,
filter=["r", "g", "f606W"],
first=True,
allow_bad_image=False,
allow_web=True,
)[0]
except:
print("Could not get imaging for " + str(name))
zscale = ZScaleInterval(contrast=0.5, krej=1.5)
vmin, vmax = zscale.get_limits(values=hdu[0].data)
fig = plt.figure(figsize=(20, 12))
fig.suptitle(
"{} ra={:3.2f}, dec={:3.2f}, wave={:5.2f}, z={:3.2f}, mf={}".format(
name, coords_obj.ra.value, coords_obj.dec.value, wave_obj, redshift, amp
),
fontsize=22,
)
ax1 = fig.add_subplot(231, projection=w)
plt.imshow(hdu[0].data, vmin=vmin, vmax=vmax)
plt.xlabel("RA")
plt.ylabel("Dec")
plt.colorbar()
plt.title("Image summed across 4*linewidth")
ax2 = fig.add_subplot(232, projection=w)
plt.imshow(model_image, vmin=vmin, vmax=vmax)
plt.xlabel("RA")
plt.ylabel("Dec")
plt.colorbar()
plt.title("model")
ax3 = fig.add_subplot(233, projection=w)
plt.imshow(residual, vmin=vmin, vmax=vmax)
plt.xlabel("RA")
plt.ylabel("Dec")
plt.colorbar()
plt.title("residuals (image-model)")
# fig = plt.figure(figsize=(10,5))
im_zscale = ZScaleInterval(contrast=0.5, krej=2.5)
im_vmin, im_vmax = im_zscale.get_limits(values=cutout["cutout"].data)
ax4 = fig.add_subplot(234, projection=cutout["cutout"].wcs)
plt.imshow(
cutout["cutout"].data,
vmin=im_vmin,
vmax=im_vmax,
origin="lower",
cmap=plt.get_cmap("gray"),
interpolation="none",
)
plt.text(
0.8,
0.9,
cutout["instrument"] + cutout["filter"],
transform=ax4.transAxes,
fontsize=20,
color="w",
)
plt.contour(hdu[0].data, transform=ax4.get_transform(w))
plt.xlabel("RA")
plt.ylabel("Dec")
aper.plot(
color="white", linestyle="dashed", linewidth=2, transform=ax4.get_transform(w)
)
ax5 = fig.add_subplot(235)
plt.errorbar(
dist_kpc.value,
iso_tab["intens"],
yerr=iso_tab["intens_err"] * iso_tab["intens"],
linestyle="none",
marker="o",
label="Lya SB profile",
)
plt.plot(dist_kpc, s_r(dist_kpc), color="r", label="Lya exp SB model", linewidth=2)
plt.xlabel("Semi-major axis (kpc)")
# plt.xlabel('Semi-major axis (arcsec)')
plt.ylabel("Flux ({})".format(10 ** -17 * (u.erg / (u.s * u.cm ** 2))))
plt.text(0.4, 0.7, "r_n={:3.2f}".format(r_n), transform=ax5.transAxes, fontsize=16)
plt.text(
0.4, 0.6, "L_lya={:3.2e}".format(lum), transform=ax5.transAxes, fontsize=16
)
secax = ax5.secondary_xaxis("top", functions=(kpc_to_arcsec, kpc_to_arcsec))
secax.set_xlabel("Semi-major axis (arcsec)")
# secax.set_xlabel('Semi-major axis (kpc)')
plt.xlim(0, 100)
# plt.plot(sma, s_r(sma), label='moffat psf')
# plt.plot(dist_kpc.value, s1(kpc_to_arcsec(dist_kpc.value)),
# linestyle='dashed', linewidth=2,
# color='green', label='PSF seeing:{:3.2f}'.format(fwhm))
# These two are the exact same
# s1 = models.Moffat1D()
# s1.amplitude = iso_tab['intens'][0]
# alpha=3.5
# s1.gamma = 0.5*(fwhm*const_arcsec_to_kpc)/ np.sqrt(2 ** (1.0 / alpha) - 1.0)
# s1.alpha = alpha
# plt.plot(r_1d, moffat_1d, color='orange')
# plt.plot(dist_kpc.value, (s1(dist_kpc.value)),
# linestyle='dashed', linewidth=2,
# color='blue', label='PSF seeing:{:3.2f}'.format(fwhm))
E = Extract()
E.load_shot(shotid_obj)
moffat_psf = E.moffat_psf(seeing=fwhm, boxsize=imsize, scale=pixscale)
moffat_shape = np.shape(moffat_psf)
xcen = int(moffat_shape[1] / 2)
ycen = int(moffat_shape[2] / 2)
moffat_1d = (
moffat_psf[0, xcen:-1, ycen] / moffat_psf[0, xcen, ycen] * iso_tab["intens"][0]
)
r_1d = moffat_psf[1, xcen:-1, ycen]
E.close()
plt.plot(
arcsec_to_kpc(pixscale * np.arange(80)),
iso_tab["intens"][0] * (moffat_psf[0, 80:-1, 80] / moffat_psf[0, 80, 80]),
linestyle="dashed",
color="green",
label="PSF seeing:{:3.2f}".format(fwhm),
)
plt.legend()
if friendid is not None:
ax6 = fig.add_subplot(236, projection=cutout["cutout"].wcs)
plot_friends(friendid, friend_cat, cutout, ax=ax6, label=False)
plt.savefig("fit2d_{}.png".format(name))
# filename = 'param_{}.txt'.format(name)
# np.savetxt(filename, (r_n.value, lum.value))
return r_n, lum, lum_err
def test_ExponentialAndLogarithmic1D_fit():
xarr = np.linspace(0.1, 10., 200)
em_model = models.Exponential1D(amplitude=1, tau=1)
log_model = models.Logarithmic1D(amplitude=1, tau=1)
assert_allclose(xarr, em_model.inverse(em_model(xarr)))
assert_allclose(xarr, log_model.inverse(log_model(xarr)))
@pytest.mark.skipif("not HAS_SCIPY")
def test_KingProjectedAnalytic1D_fit():
km = models.KingProjectedAnalytic1D(amplitude=1, r_core=1, r_tide=2)
xarr = np.linspace(0.1, 2, 10)
yarr = km(xarr)
km_init = models.KingProjectedAnalytic1D(amplitude=1, r_core=1, r_tide=1)
fitter = fitting.LevMarLSQFitter()
km_fit = fitter(km_init, xarr, yarr)
assert_allclose(km_fit.param_sets, km.param_sets)
assert_allclose(km_fit.concentration, 0.30102999566398136)
@pytest.mark.parametrize(
'model',
[models.Exponential1D(), models.Logarithmic1D()])
def test_ExponentialAndLogarithmic1D_fit(model):
xarr = np.linspace(0.1, 10., 200)
assert_allclose(xarr, model.inverse(model(xarr)))
@pytest.mark.parametrize(
'model',
[models.Exponential1D(), models.Logarithmic1D()])
def test_ExponentialAndLogarithmic_set_tau(model):
message = "0 is not an allowed value for tau"
with pytest.raises(ValueError) as err:
model.tau = 0
assert str(err.value) == message
