def get_Rhie_object(phi, s1, s2, q1, q2):
"""Returns a TL object in the Rhie (2002) parameter space."""
param = ({
's2': s2,
's1': s1,
'phi': phi,
'q2': q2,
'q1': q1,
'res': res,
'origin': origin,
'region': region,
'region_lim': region_lim,
'solver': solver,
'system': system,
'plot_frame': plot_frame,
'refine_region': False
})
plot = TL(**param)
return plot
def get_new_object(plot, phi, s1, s2, q1, q2):
"""
Returns a TL object in some other TripleLens parameter space. The
parameter space is defined according to origin_new and system_new
conversion parameters called before function call.
"""
q1_new = q1
q2_new = q2
s1_new = np.abs(plot.z2 - plot.z1)
s2_new = np.abs(plot.z3 - plot.z1)
s3_temp = np.abs(plot.z2 - plot.z3)
phi_new = ((180. / math.pi) * math.acos(
(s1_new**2 + s2_new**2 - s3_temp**2) / (2. * s1_new * s2_new)))
if plot.phi > 0:
phi_new *= -1. # Fixes accidental reflections over the x-axis since
# law of cosines removes direction
refine_region = True
SFD = True
param_new = ({
's2': s2_new,
's1': s1_new,
'phi': phi_new,
'q2': q2_new,
'q1': q1_new,
'res': res,
'origin': origin_new,
'region': region,
'region_lim': region_lim,
'solver': solver,
'SFD': SFD,
'system': system_new,
'plot_frame': plot_frame,
'refine_region': refine_region
})
plot_new = (TL(**param_new))
return plot_new
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 num_images_demo():
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])
"""
cmap = plt.cm.Blues
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
bounds = np.linspace(-0.5,10.5,12)
norm = colors.BoundaryNorm(bounds, cmap.N)
ticks = np.linspace(0,10,11)
"""
# 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 + 10 * jump,
11) # 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[4]] + blueslist[clist[1]:clist[2]] +
ornglist[clist[4]:clist[5]] + blueslist[clist[3]:clist[4]] +
ornglist[clist[6]:clist[7]] + blueslist[clist[5]:clist[6]] +
ornglist[clist[8]:clist[9]] + blueslist[clist[7]:clist[8]])
# Create new colormap.
cmap_images = LinSegCmap.from_list('Custom cmap', colorlist, 256)
# Discretize the colormap.
bounds = np.linspace(-0.5, 10.5,
12) # This is the discretized boundary.
norm = colors.BoundaryNorm(bounds,
cmap_images.N) # This is the scale.
ticks = np.linspace(0, 10, 11) # These are the tickmark locations.
kwargs = plot[m][n].check_kwargs()
kwargs['cmap'] = cmap_images
kwargs['norm'] = norm
kwargs['s'] = 1
kwargs['lw'] = 0
# Get the data for the plots.
plot[m][n].get_position_arrays()
plot[m][n].get_num_images_array()
(x, y, num_images) = (plot[m][n].x_array, plot[m][n].y_array,
plot[m][n].num_images)
# Create and adjust the plots appropriately.
ax = plt.subplot(inner[n])
sc = ax.scatter(x, y, c=num_images, vmin=0, vmax=10, **kwargs)
# caustic = caus(lens=plot[m][n], solver='SG12')
# caustic.plot_caustic(s=1, color='yellow', points=5000, lw=0)
get_inner_plot_parameters(plot=plot[m][n], ax=ax, k=k, l=l)
fig.add_subplot(ax)
# Add an axis for the color bar.
cbar = fig.add_axes([0.08, 0.89, 0.50, 0.04])
num_color = plt.colorbar(sc,
cax=cbar,
cmap=cmap_images,
ticks=ticks,
orientation='horizontal')
num_color.set_label('Number of Images', fontsize=15, labelpad=-70)
cbar.axes.tick_params(labelsize=12)
get_plot_text(plot, fig)
# Save the plot as a .png file.
if save_fig:
file_name = '../../Tables/TL/num_slvr_q_{}_.png'.format(system)
save_png(file_name)
if show_fig:
plt.show()
def magnification_demo():
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])
# cmap = plt.cm.YlOrRd
cmap = plt.cm.gray
cmaplist = [cmap(i) for i in range(cmap.N)]
cmaplist = cmaplist[20:]
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
ticks = np.array([1, 10])
kwargs = plot[m][n].check_kwargs()
kwargs['cmap'] = cmap
kwargs['norm'] = colors.LogNorm()
kwargs['s'] = 1
kwargs['lw'] = 0
# Get the data for the plots.
plot[m][n].get_position_arrays()
plot[m][n].get_magnification_array()
(x, y, magnification) = (plot[m][n].x_array, plot[m][n].y_array,
plot[m][n].magn_array)
# Create and adjust the plots appropriately.
ax = plt.subplot(inner[n])
sc = ax.scatter(x, y, c=magnification, vmin=1, vmax=10, **kwargs)
# caustic = caus(lens=plot[m][n], solver='SG12')
# caustic.plot_caustic(s=1, color='yellow', points=5000, lw=0)
get_inner_plot_parameters(plot=plot[m][n], ax=ax, k=k, l=l)
fig.add_subplot(ax)
# Add an axis for the color bar.
cbar = fig.add_axes([0.08, 0.89, 0.50, 0.04])
magn_color = plt.colorbar(sc,
cax=cbar,
cmap=kwargs['cmap'],
ticks=ticks,
orientation='horizontal')
magn_color.set_label('Magnification', fontsize=15, labelpad=-70)
cbar.axes.tick_params(labelsize=12)
get_plot_text(plot, fig)
# Save the plot as a .png file.
if save_fig:
file_name = '../../Tables/TL/mag_slvr_q_{}_.png'.format(system)
save_png(file_name)
if show_fig:
plt.show()
testBL = BL(**param)
causticBL = Caus(lens=testBL)
param = ({
's1': s1,
's2': s2,
'q1': q1,
'q2': q2,
'phi': phi,
'origin': originTL,
'region': region,
'solver': solver,
'system': system,
'plot_frame': plot_frame
})
testTL = TL(**param)
causticTL = Caus(lens=testTL, solver=solver)
# Plot the caustic alone
causticBL.plot_caustic(s=1, color='blue')
#causticTL.plot_caustic(s=1, color='red')
plt.show()
#Interesting parameters:
solver = 'SG12'
originBL = 'star'
originTL = 'body3'
plot_frame = 'caustic'
s = 0.8
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()
x = [0.0, 1.3219, 1.0799, 1.2489]
y = [0.0, -0.0771, 0.0985, 0.0209]
s1 = [1.0, 1.35, 1.1, 0.9357]
s2 = [1.5, 1.5, 1.5, 1.5]
q1 = [1.0, 0.00578, 0.99, 0.99]
q2 = [1e-1, 1e-1, 1e-1, 1e-1]
phi = 0
param = []
test = []
for i in range(len(x)):
param.append({
'x': x[i],
'y': y[i],
's1': s1[i],
's2': s2[i],
'phi': phi,
'q1': q1[i],
'q2': q2[i],
'origin': origin,
'solver': solver,
'system': system,
'SFD': SFD
})
test.append(TL(**param[-1]))
for t in test:
# t.print_image_position(print_input=False)
t.print_magnification(print_input=False)
print('_' * 40)
'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.append(TL(**param[-1]))
plot_types = []
plot_types.append('num_images')
plot_types.append('magn')
#plot_types.append('lens_bodies')
#plot_types.append('num_iamges_coeff')
#plot_types.append('magn_coeff')
#plot_types.append('coeff')
#plot_types.append('fits')
#plot_types.append('coeff_tstat')
#plot_types.append('position_tstat')
make_plot()
print('\n')
