def get_positions(filepath, colour1=None, colour2=None, colour_limits=None, fits_file=None, fig_dir='.', addsavename='', returnflux=False, ignore_points=()):
sex = read_sextractor_output(filepath)
ignore_indexes = []
for xignore, yignore in ignore_points:
ignore_indexes += list(np.where(sex.X_IMAGE == xignore)[0] & np.where(sex.Y_IMAGE == yignore)[0])
sex = sex.drop(ignore_indexes)
x = sex.X_IMAGE
y = sex.Y_IMAGE
flux = sex.FLUX_ISO
plt.figure()
if fits_file is not None:
plot_image(fits_file, vmin=0, vmax=0.35)
plt.scatter(x, y, marker='o', c='b', alpha=0.2)
if colour_limits is not None:
(x1, x2), (y1, y2) = colour_limits
mask = (colour1 > x1) & (colour1 < x2) & (colour2 > y1) & (colour2 < y2)
x = x[mask]
y = y[mask]
flux = flux[mask]
plt.scatter(x, y, marker='+', c='g', alpha=0.9)
plt.xlim(min(x), max(x))
plt.ylim(min(y), max(y))
plt.savefig(os.path.join(fig_dir, 'cluster_members{}'.format(addsavename)))
if returnflux:
return x, y, flux
else:
return x, y
def plot_img_pos(pars, pix_scale=1., threshold=0.8, fits_file=None, img_xobs=None, img_yobs=None, d=None):
xsrcA, ysrcA, sigsrcA, xlens, ylens, blens, qlens, plens = pars
fig_dir = 'Figures/lensed_quasar/'
sa = (0, 100) # search area is 2000 pixels to 5000 pixels
# Define source positions as a Guassian surface brightness profile
X1, Y1, Q1, P1, S1, srcs = {}, {}, {}, {}, {}, {}
X1['A'] = pymc.Uniform('X1A', 0., 100., value=xsrcA)
Y1['A'] = pymc.Uniform('Y1A', 0., 100., value=ysrcA)
Q1['A'] = pymc.Uniform('Q1A', 0.2, 1., value=1.)
P1['A'] = pymc.Uniform('P1A', -180., 180., value=0.)
S1['A'] = pymc.Uniform('N1A', 0., 6., value=sigsrcA)
srcs['A'] = SBObjects.Gauss('', {'x': X1['A'], 'y': Y1['A'], 'q': Q1['A'],'pa': P1['A'], 'sigma': S1['A']})
# Define lens mass model
LX = pymc.Uniform('lx', 0., 100., value=xlens)
LY = pymc.Uniform('ly', 0., 100., value=ylens)
LB = pymc.Uniform('lb', 0., 100., value=blens)
LQ = pymc.Uniform('lq', 0.2, 1., value=qlens)
LP = pymc.Uniform('lp', -180., 180., value=plens)
XB = pymc.Uniform('xb', -0.2, 0.2, value=0.)
XP = pymc.Uniform('xp', -180., 180., value=0.)
lens = MassModels.SIE('', {'x': LX, 'y': LY, 'b': LB, 'q': LQ, 'pa': LP})
shear = MassModels.ExtShear('',{'x':LX,'y':LY,'b':XB,'pa':XP})
lenses = [lens]
x, y = np.meshgrid(np.arange(sa[0], sa[1], pix_scale), np.arange(sa[0], sa[1], pix_scale))
image_plane, image_coords_pred = {}, {}
for name in ['A']:
x_src, y_src = pylens.getDeflections(lenses, [x, y], d=d[name])
image_plane[name] = srcs[name].pixeval(x_src, y_src)
plt.figure()
plt.imshow(image_plane[name], interpolation='nearest', origin='lower')
plt.xlabel('%dx - %d pixels' % (pix_scale, sa[0]))
plt.ylabel('%dy - %d pixels' % (pix_scale, sa[0]))
plt.savefig(os.path.join(ROOT_DIR, fig_dir, 'image_plane%s.png' % name))
image_coords_pred[name] = np.add(np.multiply(np.where(image_plane[name] > threshold), pix_scale)[::-1], sa[0])
lnlike, img_xpred_compare, img_ypred_compare = calc_lnlike(image_coords_pred[name], img_xobs[name], img_yobs[name])
print(img_xpred_compare, img_xobs[name], lnlike)
print(img_ypred_compare, img_yobs[name], lnlike)
colors = (col for col in ['#1f77b4', '#2ca02c', '#9467bd', '#17becf', '#e377c2'])
fig = plt.figure('image_and_position')
plot_image(fits_file, figname='image_and_position', vmax=10.)
plt.xlim(sa[0], sa[1])
plt.ylim(sa[0], sa[1])
for name in ['A']:
plt.scatter(image_coords_pred[name][0], image_coords_pred[name][1], marker='.', alpha=0.3, c=next(colors))
plt.savefig(os.path.join(ROOT_DIR, fig_dir, 'image_with_predicted_image_plane.png'))
def plot_img_pos(pars,
pix_scale=1.,
threshold=0.8,
fits_file=None,
img_xobs=None,
img_yobs=None,
d=None,
fig_dir=None):
names = img_xobs.keys()
sa = (2000, 4500) # search area is 2000 pixels to 5000 pixels
xsrc, ysrc, sigsrc = {}, {}, {}
for i, name in enumerate(names):
xsrc[name], ysrc[name], sigsrc[name] = pars[3 * i:3 * (i + 1)]
xlens, ylens, blens, qlens, plens = pars[-5:]
# Define source positions as a Guassian surface brightness profile
X1, Y1, Q1, P1, S1, srcs = {}, {}, {}, {}, {}, {}
for name in names:
X1[name] = pymc.Uniform('X1%s' % name, 0., 5000., value=xsrc[name])
Y1[name] = pymc.Uniform('Y1%s' % name, 0., 5000., value=ysrc[name])
Q1[name] = pymc.Uniform('Q1%s' % name, 0.2, 1., value=1.)
P1[name] = pymc.Uniform('P1%s' % name, -180., 180., value=0.)
S1[name] = pymc.Uniform('N1%s' % name, 0., 10000., value=sigsrc[name])
srcs[name] = SBObjects.Gauss(
'', {
'x': X1[name],
'y': Y1[name],
'q': Q1[name],
'pa': P1[name],
'sigma': S1[name]
})
# Define lens mass model
LX = pymc.Uniform('lx', 0., 5000., value=xlens)
LY = pymc.Uniform('ly', 0., 5000., value=ylens)
LB = pymc.Uniform('lb', 0., 5000., value=blens)
LQ = pymc.Uniform('lq', 0.2, 1., value=qlens)
LP = pymc.Uniform('lp', -180., 180., value=plens)
XB = pymc.Uniform('xb', -0.2, 0.2, value=0.)
XP = pymc.Uniform('xp', -180., 180., value=0.)
lens = MassModels.SIE('', {'x': LX, 'y': LY, 'b': LB, 'q': LQ, 'pa': LP})
shear = MassModels.ExtShear('', {'x': LX, 'y': LY, 'b': XB, 'pa': XP})
lenses = [lens]
x, y = np.meshgrid(np.arange(sa[0], sa[1], pix_scale),
np.arange(sa[0], sa[1], pix_scale))
image_plane, image_coords_pred = {}, {}
for name in names:
x_src, y_src = pylens.getDeflections(lenses, [x, y], d=d[name])
image_plane[name] = srcs[name].pixeval(x_src, y_src)
plt.figure()
plt.imshow(image_plane[name], interpolation='nearest', origin='lower')
plt.xlabel('%dx - %d pixels' % (pix_scale, sa[0]))
plt.ylabel('%dy - %d pixels' % (pix_scale, sa[0]))
plt.savefig(os.path.join(ROOT_DIR, fig_dir,
'image_plane%s.png' % name))
image_indexes_pred = np.where(image_plane[name] > threshold)
image_coords_pred[name] = np.array(
[x[image_indexes_pred], y[image_indexes_pred]])
print(name, image_coords_pred[name])
colors = (col for col in
['#1f77b4', '#2ca02c', '#9467bd', '#17becf', '#e377c2', 'lime'])
markers = (marker for marker in ['x', 'o', '*', '+', 'v', 'D'])
fig = plt.figure('image_and_position', figsize=(13, 13))
plot_image(fits_file, figname='image_and_position')
plt.xlim(sa[0], sa[1])
plt.ylim(sa[0], sa[1])
for name in names:
plt.scatter(img_xobs[name],
img_yobs[name],
marker=next(markers),
c='white',
label="%s obs" % name,
alpha=0.8)
plt.scatter(image_coords_pred[name][0],
image_coords_pred[name][1],
marker='.',
alpha=0.5,
c=next(colors),
label=name)
plt.legend(loc='upper right')
plt.savefig(
os.path.join(ROOT_DIR, fig_dir,
'image_with_predicted_image_plane.png'))
def plot_source_and_pred_lens_positions(pars, img_xobs, img_yobs, d, fig_dir, threshold=0.01, plotimage=False, fits_file=None, mass_pos=None, flux_dependent_b=False, masses_flux=None, sa=(2000, 4500), pix_scale=10.):
if plotimage:
fig = plt.figure('image_and_position', figsize=(13, 13))
plot_image(fits_file, figname='image_and_position')
names = img_xobs.keys()
xsrc, ysrc, sigsrc = {}, {}, {}
for i, name in enumerate(names):
xsrc[name], ysrc[name], sigsrc[name] = pars[3 * i: 3 * (i + 1)]
xlens, ylens, blens, qlens, plens = pars[-7:-2]
# Define source positions as a Guassian surface brightness profile
X1, Y1, Q1, P1, S1, srcs = {}, {}, {}, {}, {}, {}
for name in names:
X1[name] = pymc.Uniform('X1%s' % name, 0., 5000., value=xsrc[name])
Y1[name] = pymc.Uniform('Y1%s' % name, 0., 5000., value=ysrc[name])
Q1[name] = pymc.Uniform('Q1%s' % name, 0.2, 1., value=1.)
P1[name] = pymc.Uniform('P1%s' % name, -180., 180., value=0.)
S1[name] = pymc.Uniform('N1%s' % name, 0., 10000., value=sigsrc[name])
srcs[name] = SBObjects.Gauss('', {'x': X1[name], 'y': Y1[name], 'q': Q1[name],'pa': P1[name], 'sigma': S1[name]})
# Define lens mass model
LX = pymc.Uniform('lx', 0., 5000., value=xlens)
LY = pymc.Uniform('ly', 0., 5000., value=ylens)
LB = pymc.Uniform('lb', 0., 5000., value=blens)
LQ = pymc.Uniform('lq', 0., 1., value=qlens)
LP = pymc.Uniform('lp', -180., 180., value=plens)
lens = MassModels.SIE('', {'x': LX, 'y': LY, 'b': LB, 'q': LQ, 'pa': LP})
lenses = [lens]
if flux_dependent_b:
for (lx, ly), flux in zip(mass_pos, masses_flux):
slope, intercept = pars[-2:]
LX = pymc.Uniform('lx', 0., 5000., value=lx)
LY = pymc.Uniform('ly', 0., 5000., value=ly)
LB = slope * np.log(flux) + intercept
lens = MassModels.SIS('', {'x': LX, 'y': LY, 'b': LB})
lenses += [lens]
print('lens einstein radius', lens.b)
else:
for b, (lx, ly) in zip(pars[-2:], mass_pos):
LX = pymc.Uniform('lx', 0., 5000., value=lx)
LY = pymc.Uniform('ly', 0., 5000., value=ly)
LB = pymc.Uniform('lb', 0., 5000., value=b)
lens = MassModels.SIS('', {'x': LX, 'y': LY, 'b': LB})
lenses += [lens]
print('lens einstein radius', lens.b)
colors = (col for col in ['#1f77b4', '#2ca02c', '#9467bd', '#17becf', '#e377c2', '#ADFF2F'])
markers = (marker for marker in ['x', 'o', '*', '+', 'v', 'D'])
x_src, y_src = {}, {}
image_plane, image_coords_pred = {}, {}
X1, Y1, Q1, P1, S1, srcs = {}, {}, {}, {}, {}, {}
x, y = np.meshgrid(np.arange(sa[0], sa[1], pix_scale), np.arange(sa[0], sa[1], pix_scale))
plt.xlim(sa[0], sa[1])
plt.ylim(sa[0], sa[1])
for name in names:
plt.figure('image_and_position')
col = next(colors)
plt.scatter(img_xobs[name], img_yobs[name], marker=next(markers), c='white', label="%s obs" % name, alpha=0.8)
plt.scatter(x_src[name], y_src[name], marker='.', alpha=0.5, c=col, label="%s pred src" % name)
# Get Image plane
x_src_all, y_src_all = pylens.getDeflections(lenses, [x, y], d=d[name])
image_plane[name] = srcs[name].pixeval(x_src_all, y_src_all)
image_indexes_pred = np.where(image_plane[name] > threshold)
image_coords_pred[name] = np.array([x[image_indexes_pred], y[image_indexes_pred]])
image_plane_norm = (image_plane[name] - image_plane[name].min())/(image_plane[name].max() - image_plane[name].min())
rgba = np.zeros((len(image_coords_pred[name][0]), 4))
rgba[:, 0:3] = list(int(col[i:i+2], 16)/256. for i in (1, 3, 5))
rgba[:, 3] = 0.8 * np.array(image_plane_norm[image_indexes_pred])
plt.scatter(image_coords_pred[name][0], image_coords_pred[name][1], marker='x', color=rgba, label="%s pred img" % name)
plt.figure(name)
plt.title(name)
plt.imshow(image_plane[name], interpolation='nearest', origin='lower')
print(x_src, y_src)
plt.figure('image_and_position')
plt.legend(loc='upper right')
plt.savefig(os.path.join(fig_dir, 'image_with_contours_and_images.png'))
def plot_source_and_pred_lens_positions(pars, img_xobs, img_yobs, d, fig_dir, threshold=0.01, plotimage=False, fits_file=None):
if plotimage:
fig = plt.figure(figsize=(13, 13))
plot_image(fits_file, fig)
names = img_xobs.keys()
try:
xlens, ylens, blens, qlens, plens = pars
bshear, pshear = 0., 0.
except ValueError: # includes shear
xlens, ylens, blens, qlens, plens, bshear, pshear = pars
# Define lens mass model
LX = pymc.Uniform('lx', 0., 5000., value=xlens)
LY = pymc.Uniform('ly', 0., 5000., value=ylens)
LB = pymc.Uniform('lb', 0., 5000., value=blens)
LQ = pymc.Uniform('lq', 0.2, 1., value=qlens)
LP = pymc.Uniform('lp', -180., 180., value=plens)
XB = pymc.Uniform('xb', -200., 200., value=bshear)
XP = pymc.Uniform('xp', -180., 180., value=pshear)
lens = MassModels.SIE('', {'x': LX, 'y': LY, 'b': LB, 'q': LQ, 'pa': LP})
lenses = [lens]
if len(pars) == 7:
shear = MassModels.ExtShear('',{'x':LX,'y':LY,'b':XB,'pa':XP})
lenses += [shear]
colors = (col for col in ['#1f77b4', '#2ca02c', '#9467bd', '#17becf', '#e377c2', 'lime'])
markers = (marker for marker in ['x', 'o', '*', '+', 'v', 'D'])
x_src, y_src = {}, {}
image_plane, image_coords_pred = {}, {}
X1, Y1, Q1, P1, S1, srcs = {}, {}, {}, {}, {}, {}
print(float(lens.x), float(lens.y), float(lens.b), float(lens.q), float(lens.pa))
sa = (2000, 4500)
pix_scale = 10.
x, y = np.meshgrid(np.arange(sa[0], sa[1], pix_scale), np.arange(sa[0], sa[1], pix_scale))
plt.xlim(sa[0], sa[1])
plt.ylim(sa[0], sa[1])
for name in names:
x_src[name], y_src[name] = pylens.getDeflections(lenses, [img_xobs[name], img_yobs[name]], d[name])
xs = np.median(x_src[name])
ys = np.median(y_src[name])
lnlike = -0.5 * (x_src[name].var() + y_src[name].var())
print(float(lens.x), float(lens.y), float(lens.b), float(lens.q), float(lens.pa))
print(name, xs, ys, lnlike)
col = next(colors)
plt.scatter(img_xobs[name], img_yobs[name], marker=next(markers), c='white', label="%s obs" % name, alpha=0.8)
plt.scatter(x_src[name], y_src[name], marker='.', alpha=0.5, c=col, label="%s pred src" % name)
# CALC IMG POS
# Assume gaussian surface brightness at (xs, ys)
X1[name] = pymc.Uniform('X1%s' % name, 0., 5000., value=xs)
Y1[name] = pymc.Uniform('Y1%s' % name, 0., 5000., value=ys)
Q1[name] = pymc.Uniform('Q1%s' % name, 0.2, 1., value=1.)
P1[name] = pymc.Uniform('P1%s' % name, -180., 180., value=0.)
S1[name] = pymc.Uniform('N1%s' % name, 0., 10000., value=6.)
srcs[name] = SBObjects.Gauss('', {'x': X1[name], 'y': Y1[name], 'q': Q1[name], 'pa': P1[name], 'sigma': S1[name]})
# Get Image plane
x_src_all, y_src_all = pylens.getDeflections(lenses, [x, y], d=d[name])
image_plane[name] = srcs[name].pixeval(x_src_all, y_src_all)
image_indexes_pred = np.where(image_plane[name] > threshold)
image_coords_pred[name] = np.array([x[image_indexes_pred], y[image_indexes_pred]])
plt.scatter(image_coords_pred[name][0], image_coords_pred[name][1], marker='x', alpha=0.5, c=col, label="%s pred img" % name)
print(x_src, y_src)
plt.legend(loc='upper right')
plt.savefig(os.path.join(fig_dir, 'image_with_contours_and_images.png'))