plt.clf()
for c in all_comps:
# only plot component if it exists
# print('plotting {}'.format(c))
if c.get_age() > time:
# print('{} is greater than {}'.format(c.get_age(), time))
c_copy = SphereComponent(c.get_pars())
# modify age so that 'comp_now' is plotted at time `time`
# time is how long ago, we want copy.age to be time since
# birth, that is c.get_age - time
c_copy.update_attribute({'age':c.get_age()-time})
# print('pars updated to: {}'.format(c_copy.get_pars()))
c_copy.plot(dim1=dim1, dim2=dim2, comp_now=True, comp_then=True,
comp_orbit=True)
# There was a prior issue (
# if c_copy.get_mean_now()[1] < -200:
# import pdb; pdb.set_trace()
plt.xlim()
plt.xlabel('{} [{}]'.format(labels[dim1], units[dim1]))
plt.ylabel('{} [{}]'.format(labels[dim2], units[dim2]))
lims[dim1] = update_1D_lims(lims[dim1], plt.xlim())
lims[dim2] = update_1D_lims(lims[dim2], plt.ylim())
plt.xlim(lims[dim1])
plt.ylim(lims[dim2])
star_data = tabletool.build_data_dict_from_table(star_data_file)
# Set up base subplots, plotting everything that is the same across iterative
# plots. We will then store this via Pickle to save time
base_fig, base_ax = plt.subplots(nrows=2, ncols=2)
base_fig.set_size_inches(8, 8)
base_fig.set_tight_layout(True)
lims = 6 * [None]
for ax, (dim1, dim2) in zip(base_ax.flatten(), dims):
true_comp.plot(ax=ax,
dim1=dim1,
dim2=dim2,
comp_now=False,
comp_then=True,
comp_orbit=True,
linestyle='-.',
color='grey',
alpha=0.15)
for star_mn, star_cov in zip(star_data['means'], star_data['covs']):
star_comp = SphereComponent(attributes={
'mean': star_mn,
'covmatrix': star_cov,
'age': 0.
})
star_comp.plot(dim1=dim1,
dim2=dim2,
ax=ax,
color='blue',
comp_now=False,