def magnification_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])
# Get the data for the plots.
cmap = plt.cm.YlOrRd
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
ticks = np.array([1,10,100])
kwargs = plot[i][j].check_kwargs()
kwargs['cmap'] = cmap
kwargs['norm'] = colors.LogNorm()
kwargs['s'] = 1
kwargs['lw'] = 0
plot[i][j].get_position_arrays()
plot[i][j].get_magnification_array()
(x, y, magnification) = (plot[i][j].x_array, plot[i][j].y_array,
plot[i][j].magn_array)
# 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=magnification, vmin=1, vmax=100, **kwargs)
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, 0.89, 0.60, 0.05])
magn_color = plt.colorbar(sc, cax=cbar, cmap=kwargs['cmap'], ticks=ticks,
orientation='horizontal')
magn_color.set_label('Magnification', fontsize=15, labelpad=-66)
cbar.axes.tick_params(labelsize=12)
get_plot_text(plot, fig)
# Save the plot as a .png file.
if save_fig:
file_name = '../../Tables/magn_origin_.png'
save_png(file_name)
if show_fig:
plt.show()
for solver in solvers:
for origin in origins:
for q in mass_ratios:
param.append(({
's': s,
'q': q,
'res': res,
'origin': origin,
'solver': solver,
'plot_frame': plot_frame,
'region': region,
'region_lim': region_lim,
'refine_region': refine_region,
'SFD': SFD
}))
plot.append(BL(**param[-1]))
plot_types = []
plot_types.append('num_images')
#plot_types.append('magn')
#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()
"""
causticMM = mm.Caustics(s=s, q=plot[0].q)
def num_images_demo():
param = [[None] * len(separations) for j in range(len(mass_ratios))]
plot = [[None] * len(separations) for j in range(len(mass_ratios))]
fig, ax = plt.subplots(len(mass_ratios), len(separations))
for (i, q) in enumerate(mass_ratios):
for (j, s) in enumerate(separations):
idx = 1 + j + len(separations)*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(separations), 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=2000, 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([0.12, 0.90, 0.60, 0.035])
num_color = plt.colorbar(sc, cax=cbar, cmap=kwargs['cmap'], ticks=ticks,
orientation='horizontal')
num_color.set_label('Number of Images', fontsize=16, 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_plan_.png'
save_png(file_name)
if show_fig:
plt.show()
s1 = s
s2 = 1.2
q1 = q
q2 = 1e-3
phi = 0
system = 'SPM'
param = ({
's': s,
'q': q,
'origin': originBL,
'region': region,
'solver': solver,
'plot_frame': plot_frame
})
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)
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()
# Zoey Samples
# Created: Jun 7, 2018
# test.py
# Last Updated: Jun 25, 2018
from BinaryLens import BinaryLens as BL
import MulensModel as mm
import matplotlib.pyplot as plt
solver = 'zroots'
origin = 'plan'
x = [0.0, 1.3219, 1.0799, 1.2489]
y = [0.0, -0.0771, 0.0985, 0.0209]
s = [1.0, 1.35, 1.1, 0.9357]
q = [1.0, 0.00578, 0.99, 0.99]
param = []
test = []
for i in range(len(x)):
param.append({'x': x[i], 'y': y[i], 's': s[i], 'q': q[i], 'origin': origin,
'solver': solver})
test.append(BL(**param[-1]))
for t in test:
t.print_image_position(print_input=False)
t.print_magnification(print_input=False)
print('_'*40)