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_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'))
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 macs0451_multiple_sources():
fig_dir = os.path.join(ROOT_DIR, 'Figures/MACS0451_min_src_pos_var_getlenspy_with_shear/')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
fits_file = '/home/djm241/PycharmProjects/StrongLensing/data/MACS0451/MACS0451_F110W.fits'
if not os.path.isfile(fits_file):
fits_file = '/Users/danmuth/PycharmProjects/StrongLensing/data/MACS0451/MACS0451_F110W.fits'
img_name = '_multiple_sources'
z_lens = 0.43
img_xobs, img_yobs, d = OrderedDict(), OrderedDict(), OrderedDict()
img_xobs['A'] = np.array([2375.942110, 2378.5, 2379.816610, 2381.299088, 2384, 2385.927991, 2389.555816, 2457.694760, 2450.744242, 2442.833333, 2437.857924, 2433.064587, 2427.166666, 2424.099866, 2418.5, 2416.444081, 2462])
img_yobs['A'] = np.array([3038.016677, 3024, 3012.367933, 2999.365293, 2983.5, 2970.435199, 2955.945319, 2737.545077, 2752.305849, 2766.166666, 2782.058508, 2795.293450, 2811.166666, 2823.079067, 2837.5, 2846.943113, 2728])
d['A'] = scale_einstein_radius(z_lens=z_lens, z_src=2.013)
img_xobs['B'] = np.array([3276.693717, 3261.382557, 3427.351819, 3417.043471, 3497.163625, 3486.371962])
img_yobs['B'] = np.array([3482.795501, 3482.854177, 2592.719350, 2590.191799, 3075.107748, 3069.305065])
d['B'] = scale_einstein_radius(z_lens=z_lens, z_src=1.405)
img_xobs['11'] = np.array([3557.178601, 3548.271886, 3541.407488])
img_yobs['11'] = np.array([3363.943860, 3375.285957, 3385.515024])
d['11'] = scale_einstein_radius(z_lens=z_lens, z_src=2.06)
img_xobs['31'] = np.array([2933.063074, 2943.400421, 2890.687234, 2878.906523])
img_yobs['31'] = np.array([3393.715824, 3398.196336, 3044.431729, 3042.460964])
d['31'] = scale_einstein_radius(z_lens=z_lens, z_src=1.904)
img_xobs['41'] = np.array([3222.796159, 3227.700108])
img_yobs['41'] = np.array([3550.903781, 3542.180780])
d['41'] = scale_einstein_radius(z_lens=z_lens, z_src=1.810)
img_xobs['C'] = np.array([3799.999263, 3794.5, 3863.057095, 3861.1])
img_yobs['C'] = np.array([3358.972702, 3367.9, 3059.195359, 3069.9])
d['C'] = scale_einstein_radius(z_lens=z_lens, z_src=2.0)
# Define lens mass model
LX = pymc.Uniform('lx', 2400., 4000., value=3.13876545e+03)
LY = pymc.Uniform('ly', 2000., 3700., value=2.97884105e+03)
LB = pymc.Uniform('lb', 10., 2000., value=1.50779124e+03)
LQ = pymc.Uniform('lq', 0.2, 1., value=4.90424861e-01)
LP = pymc.Uniform('lp', -180., 180., value=1.04010643e+02)
XB = pymc.Uniform('xb', -200., 200., 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]
pars = [LX, LY, LB, LQ, LP]
cov = [400., 400., 400., 0.3, 50.]
lenses += [shear]
pars += [XB, XP]
cov += [40., 30.]
cov = np.array(cov)
nwalkers = 500
nsteps = 30000
burn = 500
best_lens = [ 3.21895080e+03, 3.03726175e+03, 6.67192222e+02, 2.26586238e-01, 5.91357933e+00]
# plot_source_and_pred_lens_positions(best_lens, img_xobs, img_yobs, d, fig_dir, threshold=0.01, plotimage=True, fits_file=fits_file)
run_mcmc(img_xobs, img_yobs, fig_dir, d, lenses, pars, cov, nwalkers=nwalkers, nsteps=nsteps, burn=burn, fits_file=fits_file, img_name=img_name)
