def plot_lens_bodies(plots):
"""Plots the positions of the lens bodies in the lens plane."""
caustic1 = caus(lens=plots[0], solver='SG12')
caustic2 = caus(lens=plots[1], solver='SG12')
caustic1.plot_lens_bodies()
plt.show()
caustic2.plot_lens_bodies()
plt.show()
def make_plot():
for p in plot:
for plot_type in plot_types:
if plot_type == 'num_images':
p.plot_num_images(errors_only=False,
save=False,
print_errors=True)
caustic = caus(lens=p, solver='SG12')
caustic.plot_caustic(s=1, color='yellow', lw=0)
plt.show()
if plot_type == 'magn':
p.plot_magnification(outliers=False,
log_colorbar=True,
cutoff=cutoff,
save=False)
caustic = caus(lens=p, solver='SG12')
#caustic.plot_caustic(s=1, color='blue')
plt.show()
if plot_type == 'num_iamges_coeff':
p.plot_num_images_coeff(color_magn=True,
log_colorbar=True,
save=False)
if plot_type == 'magn_coeff':
p.plot_magn_coeff(color_num=True,
cutoff=cutoff,
outliers=False,
save=False)
if plot_type == 'coeff':
p.plot_coefficients(cutoff=cutoff,
log_colorbar=False,
outliers=False,
save=False)
if plot_type == 'coeff_tstat':
p.plot_coeff_tstat(cutoff=None,
outliers=False,
sample_res=sample_res,
save=False)
if plot_type == 'position_tstat':
p.plot_position_tstat(cutoff=None,
outliers=False,
sample_res=sample_res,
save=False)
if plot_type == 'fits':
p.write_to_fits()
def plot_num_images(plots, num_images_params):
"""Plots a map of the number of images for a TripleLens object."""
plots[0].plot_num_images(**num_images_params)
caustic = caus(lens=plots[0], solver='SG12')
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.show()
plots[1].plot_num_images(**num_images_params)
caustic = caus(lens=plots[1], solver='SG12')
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.show()
def plot_magnification(plots, magn_params, show_caustic):
"""Plots a magnification map for a TripleLens object."""
plots[0].plot_magnification(**magn_params)
caustic = caus(lens=plots[0], solver='SG12')
if show_caustic:
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.show()
plots[1].plot_magnification(**magn_params)
caustic = caus(lens=plots[1], solver='SG12')
if show_caustic:
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.show()
def plot_trajectory(plot, param_str, v_e, t0, u0, theta1cm, t_start, t_stop):
"""
Plots the source trajectory, determined by the TripleLens object in a
Rhie (2002) configuration, and by varaibles unpacked from source_dict.
"""
z0 = u0 * (math.cos(math.pi / 2. - theta1cm) +
1.j * math.sin(math.pi / 2. - theta1cm))
t_traj = np.linspace(t_start, t_stop, 1000)
z_traj = z0 + (t_traj - t0) * v_e * (math.cos(theta1cm) +
1.j * math.sin(theta1cm))
x_traj = z_traj.real
y_traj = z_traj.imag
caustic = caus(lens=plot, solver='SG12')
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.scatter(x_traj, y_traj, color='blue', s=1, lw=0)
plt.xlim(min(x_traj), max(x_traj))
plt.ylim(min(y_traj), max(y_traj))
plt.title('{} Trajectory with Caustics'.format(param_str))
plt.xlabel('Position - Real')
plt.ylabel('Position - Imaginary')
plt.show()
def plot_images():
num_outer_plots = len(ps_mass_ratios) * len(mp_mass_ratios)
num_inner_plots = len(origins) * len(solvers)
fig = plt.figure(figsize=(10, 8))
outer = gridspec.GridSpec(len(ps_mass_ratios),
len(mp_mass_ratios),
wspace=0.25,
hspace=0.38)
param = [[None] * num_inner_plots for j in range(num_outer_plots)]
plot = [[None] * num_inner_plots for j in range(num_outer_plots)]
for (i, q2), (j, q1) in product(enumerate(mp_mass_ratios),
enumerate(ps_mass_ratios)):
outer_idx = j + i * len(mp_mass_ratios)
inner = gridspec.GridSpecFromSubplotSpec(len(origins),
len(solvers),
subplot_spec=outer[outer_idx],
wspace=0.1,
hspace=0.25)
for (k, origin), (l, solver) in product(enumerate(origins),
enumerate(solvers)):
inner_idx = l + k * len(solvers)
(m, n) = (outer_idx, inner_idx)
# Initialize each triple lens system with the TripleLens class.
param[m][n] = ({
's2': s2,
's1': s1,
'phi': phi,
'q2': q2,
'q1': q1,
'res': res,
'origin': origin,
'region': region,
'region_lim': region_lim,
'solver': solver,
'SFD': SFD,
'system': system,
'plot_frame': plot_frame,
'refine_region': refine_region
})
plot[m][n] = TL(**param[m][n])
roots = plot[m][n].get_roots(x=0., y=0.)
accepted_images = plot[m][n].get_accepted_solutions(x=0., y=0.)
rejected_images = []
for root in roots:
if root in accepted_images:
continue
else:
rejected_images.append(root)
accepted_images = np.array(accepted_images)
rejected_images = np.array(rejected_images)
caustic = caus(lens=plot[m][n], solver='SG12')
z1 = caustic.z1
z2 = caustic.z2
z3 = caustic.z3
ax = plt.subplot(inner[n])
fig.add_subplot(ax)
s = 15
# sc = ax.scatter(z1.real, z1.imag, marker='*', s=10*s, color='blue', lw=0)
# sc = ax.scatter(z2.real, z2.imag, marker='o', s=4*s, color='blue', lw=0)
# sc = ax.scatter(z3.real, z3.imag, marker='o', s=s, color='blue', lw=0)
sc = ax.scatter(accepted_images.real,
accepted_images.imag,
s=s,
color='black',
lw=0)
sc = ax.scatter(rejected_images.real,
rejected_images.imag,
s=s,
color='red',
lw=0)
# caustic.plot_caustic(s=1, color='orange', points=5000, lw=0)
plt.xlim(-5, 5)
plt.ylim(-5, 5)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
# ax.axes.text(4.5, 4.5, 'num accepted: {}'.format(len(accepted_images)), ha='center',
# va='center', fontsize=8)
get_plot_text(plot, fig)
if save_fig:
file_name = '../../Tables/TL/soln_slvr_q_{}_.png'.format(system)
save_png(file_name)
if show_fig:
plt.show()
def get_triple_lens_plot(save_fig=False,
show_fig=True,
arrow_on=False,
caustic_on=False):
param = []
plot = []
param = (({
's1': s1,
's2': s2,
'q1': q1,
'q2': q2,
'system': system,
'origin': origin,
'solver': solver,
'phi': phi,
'plot_frame': plot_frame,
'SFD': SFD
}))
plot = (TL(**param))
causticUL = caus(lens=plot)
z1 = causticUL.z1
z2 = causticUL.z2
z3 = causticUL.z3
plt.scatter(z1.real, z1.imag, s=2000, color='red', marker='*')
plt.scatter(z2.real, z2.imag, s=1000, color='blue')
if system == 'SPM':
plt.scatter(z3.real, z3.imag, s=400, color='purple')
else:
plt.scatter(z3.real, z3.imag, s=(q2 / q1) * 1000, color='blue')
if arrow_on:
ax = plt.axes()
ax.arrow(z2.real - 0.05,
z2.imag,
z1.real - z2.real + 0.1,
z1.imag - z2.imag,
width=1e-6,
length_includes_head=True,
head_width=2e-2,
head_length=5e-2,
fc='k',
ec='k')
ax.arrow(z1.real + 0.05,
z1.imag,
z2.real - z1.real - 0.1,
z1.imag - z2.imag,
width=1e-6,
length_includes_head=True,
head_width=2e-2,
head_length=5e-2,
fc='k',
ec='k')
dx = (z3.real - z2.real)
dy = (z3.imag - z2.imag)
ax.arrow(z2.real + 0.10 * dx,
z2.imag + 0.10 * dy,
0.8 * (dx),
0.8 * (dy),
width=1e-6,
length_includes_head=True,
head_width=1.5e-2,
head_length=3e-2,
fc='k',
ec='k')
ax.arrow(z3.real - 0.10 * dx,
z3.imag - 0.10 * dy,
-0.8 * (dx),
-0.8 * (dy),
width=1e-6,
length_includes_head=True,
head_width=1.5e-2,
head_length=3e-2,
fc='k',
ec='k')
if caustic_on:
caustic = caus(lens=plot)
caustic.plot_caustic(s=1, lw=0, color='orange')
title = 'Triple Lens System'
got_plot_adjustments(title=title)
if save_fig:
name = 'TL_model'
_save_fig(name, arrow_on, caustic_on)
if show_fig:
plt.show()
else:
plt.clf()
def get_binary_lens_plot(save_fig=False,
show_fig=True,
arrow_on=True,
caustic_on=False):
param = []
plot = []
param = (({
's': s,
'q': q,
'origin': origin,
'solver': solver,
'plot_frame': plot_frame,
'refine_region': refine_region,
'SFD': SFD
}))
plot = (BL(**param))
causticUL = caus(lens=plot)
z1 = causticUL.z1
z2 = causticUL.z2
plt.scatter(z1.real, z1.imag, s=1000, color='blue')
plt.scatter(z2.real, z2.imag, s=2000, color='red', marker='*')
if arrow_on:
ax = plt.axes()
ax.arrow(z2.real + 0.05,
z2.imag,
z1.real - z2.real - 0.1,
z1.imag - z2.imag,
width=1e-6,
length_includes_head=True,
head_width=2e-2,
head_length=5e-2,
fc='k',
ec='k')
ax.arrow(z1.real - 0.05,
z1.imag,
z2.real - z1.real + 0.1,
z1.imag - z2.imag,
width=1e-6,
length_includes_head=True,
head_width=2e-2,
head_length=5e-2,
fc='k',
ec='k')
if caustic_on:
caustic = caus(lens=plot)
caustic.plot_caustic(s=1, lw=0, color='orange')
title = 'Binary Lens System'
got_plot_adjustments(title=title)
if save_fig:
name = 'BL_model'
_save_fig(name, arrow_on, caustic_on)
if show_fig:
plt.show()
else:
plt.clf()
def num_images_demo():
param = [[None] * len(origins) for j in range(len(mass_ratios))]
plot = [[None] * len(origins) for j in range(len(mass_ratios))]
fig, ax = plt.subplots(len(mass_ratios), len(origins))
for (i, q) in enumerate(mass_ratios):
for (j, origin) in enumerate(origins):
idx = 1 + j + len(origins)*i
# Initialize each binary lens system with the BinaryLens class.
param[i][j] = ({'s': s, 'q': q, 'res': res, 'origin': origin,
'region': region, 'region_lim': region_lim,
'solver': solver, 'SFD': SFD, 'refine_region': refine_region})
plot[i][j] = BL(**param[i][j])
# Use our two base colormaps
orng = plt.cm.Oranges
blues = plt.cm.Blues
# Get the list values from each colormap
ornglist = [orng(i) for i in range(orng.N)] # This list contains 256 colors.
blueslist = [blues(i) for i in range(blues.N)] # This list contains 256 colors.
# Select the regions of the colormaps we want, and slice them together.
start = 0
jump = 24
clist = np.linspace(start, start+8*jump, 6) # Slicing points for merged list.
clist = [int(val) for val in clist] # Convert the list into integers.
# Create the new list with segments of the Oranges and Blues colormaps.
colorlist = (ornglist[clist[0]:clist[3]] + blueslist[clist[2]:clist[3]] +
ornglist[clist[4]:clist[5]] + blueslist[clist[4]:clist[5]])
# Create new colormap.
cmap_images = LinSegCmap.from_list('Custom cmap', colorlist, 256)
# Discretize the colormap.
bounds = np.linspace(-0.5, 5.5, 7) # This is the discretized boundary.
norm = colors.BoundaryNorm(bounds, cmap_images.N) # This is the scale.
ticks = np.linspace(0,5,6) # These are the tickmark locations.
kwargs = plot[i][j].check_kwargs()
kwargs['cmap'] = cmap_images
kwargs['norm'] = norm
kwargs['s'] = 1
kwargs['lw'] = 0
plot[i][j].get_position_arrays()
plot[i][j].get_num_images_array()
(x, y, num_images) = (plot[i][j].x_array, plot[i][j].y_array,
plot[i][j].num_images)
# Create and adjust the plots appropriately.
ax[i][j] = plt.subplot(len(mass_ratios), len(origins), idx)
sc = ax[i][j].scatter(x, y, c=num_images, vmin=0, vmax=5, **kwargs)
caustic = caus(lens=plot[i][j], solver='SG12')
caustic.plot_caustic(s=1, color='yellow', points=5000, lw=0)
get_plot_parameters(plot=plot[i][j], ax=ax[i][j], i=i, j=j)
# Add an axis for the color bar.
cbar = fig.add_axes([0.08, 0.895, 0.60, 0.05])
num_color = plt.colorbar(sc, cax=cbar, cmap=kwargs['cmap'], ticks=ticks, orientation='horizontal')
num_color.set_label('Number of Images', fontsize=12+len(origins), labelpad=-62)
cbar.axes.tick_params(labelsize=12)
get_plot_text(plot, fig)
# Save the plot as a .png file.
if save_fig:
file_name = '../../Tables/images_origin_.png'
save_png(file_name)
if show_fig:
plt.show()
def make_plot():
for p in plot:
for plot_type in plot_types:
if plot_type == 'num_images':
p.plot_num_images(errors_only=False,
save=False,
print_errors=False,
s=3)
caustic = caus(lens=p, solver='SG12')
caustic.plot_caustic(points=10000, s=1, color='red', lw=0)
plt.show()
if plot_type == 'magn':
#FIXME: Caustic does not show up properly on magnification plots
p.plot_magnification(outliers=False,
log_colorbar=True,
cutoff=None,
save=False)
caustic = caus(lens=p, solver='SG12')
# caustic.plot_caustic(s=1, color='red', lw=0)
plt.show()
if plot_type == 'lens_bodies':
caustic = caus(lens=plot[0], solver='SG12')
caustic.plot_lens_bodies()
caustic.plot_caustic(s=1, color='red', lw=0)
plt.show()
if plot_type == 'num_images_coeff':
p.plot_num_images_coeff(color_magn=TPrue,
log_colorbar=True,
region='caustic',
region_lim=None,
save=False)
if plot_type == 'magn_coeff':
p.plot_magn_coeff(color_num=True,
cutoff=cutoff,
outliers=False,
region=region,
region_lim=region_lim,
save=False)
if plot_type == 'coeff':
p.plot_coefficients(cutoff=cutoff,
log_colorbar=False,
outliers=False,
region=region,
region_lim=region_lim,
save=False)
if plot_type == 'coeff_tstat':
p.plot_coeff_tstat(cutoff=None,
outliers=False,
region=region,
region_lim=region_lim,
sample_res=sample_res,
save=False)
if plot_type == 'position_tstat':
p.plot_position_tstat(cutoff=None,
outliers=False,
region=region,
region_lim=region_lim,
sample_res=sample_res,
save=False)
if plot_type == 'fits':
p.write_to_fits()