def run_fit_helper(true_comp, starcounts, measurement_error,
burnin_step=None,
run_name='default',
trace_orbit_func=None,
):
py_vers = sys.version[0]
data_filename = 'temp_data/{}_compfitter_{}.fits'.format(py_vers, run_name)
log_filename = 'logs/{}_compfitter_{}.log'.format(py_vers, run_name)
plot_dir = 'temp_plots/{}_compfitter_{}'.format(py_vers, run_name)
save_dir = 'temp_data/'
logging.basicConfig(level=logging.INFO,
filename=log_filename,
filemode='w')
synth_data = SynthData(pars=true_comp.get_pars(),
starcounts=starcounts,
measurement_error=measurement_error)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table,
write_table=True,
filename=data_filename)
res = gf.fit_comp(
data=synth_data.table,
plot_it=True,
burnin_steps=burnin_step,
plot_dir=plot_dir,
save_dir=save_dir,
trace_orbit_func=trace_orbit_func,
)
return res
def run_fit_helper(
true_comp,
starcounts,
measurement_error,
burnin_step=None,
run_name='default',
trace_orbit_func=None,
):
py_vers = sys.version[0]
data_filename = 'temp_data/{}_compfitter_{}.fits'.format(py_vers, run_name)
log_filename = 'logs/{}_compfitter_{}.log'.format(py_vers, run_name)
plot_dir = 'temp_plots/{}_compfitter_{}'.format(py_vers, run_name)
save_dir = 'temp_data/'
logging.basicConfig(level=logging.INFO,
filename=log_filename,
filemode='w')
synth_data = SynthData(pars=true_comp.get_pars(),
starcounts=starcounts,
measurement_error=measurement_error)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table,
write_table=True,
filename=data_filename)
res = gf.fit_comp(
data=synth_data.table,
plot_it=True,
burnin_steps=burnin_step,
plot_dir=plot_dir,
save_dir=save_dir,
trace_orbit_func=trace_orbit_func,
)
return res
star_pars_all =\
torb.generateTracebackFile(star_pars, times,
savefile=pdir+xyzuvw_tb_savefile)
logging.info("Generated [{}] traceback file".format(prec))
if INIT_WITH_TRUE_ORIGIN:
init_pars = origin.getInternalSphericalPars()
else:
init_pars = None
# apply traceforward fitting (with lnprob, corner plots as side
# effects)
best_fit, chain, lnprob = gf.fit_comp(
xyzuvw_dict=star_pars_all,
burnin_steps=BURNIN_STEPS,
plot_it=True,
pool=pool,
convergence_tol=C_TOL,
plot_dir=pdir,
sampling_steps=SAMPLING_STEPS,
save_dir=pdir,
init_pars=init_pars,
traceback=True,
)
# store in each directory, for hexplotter
# also used as a flag to confirm this prec already fitted for
np.save(pdir + group_savefile, origin)
if using_mpi:
pool.close()
### COMPUTATIONS #################
##################################
#~ print('SHAPES', memb_probs, all_init_pos, all_init_pars)
np.savetxt('run1message', [2])
log_message('Fitting comp {}'.format(icomp), symbol='.', surround=True)
best_comp, chain, lnprob = compfitter.fit_comp(
data=data_dict,
memb_probs=memb_probs,
init_pos=all_init_pos,
init_pars=all_init_pars,
burnin_steps=global_pars['burnin'],
plot_it=global_pars['plot_it'],
pool=pool,
convergence_tol=global_pars['convergence_tol'],
plot_dir=local_pars['gdir'],
save_dir=local_pars['gdir'],
Component=component,
trace_orbit_func=global_pars['trace_orbit_func'],
store_burnin_chains=global_pars['store_burnin_chains'],
nthreads=global_pars['nthreads'],
)
np.savetxt('run1message_finishedfit_comp', [2])
#~ best_comp, x, lnprob = compfitter.fit_comp_scipy_optimise(data_dict,
#~ memb_probs=memb_probs, init_pos=all_init_pos,
#~ init_pars=all_init_pars, Component=component,
#~ plot_it=global_pars['plot_it'], pool=pool,
#~ convergence_tol=0.25, plot_dir=local_pars['gdir'],
hdul[1].data[0]['pmra_error']
hdul[1].data[0]['parralax_error']
hdul[1].data[0]['parallax_error']
np.sqrt(astr_dict['astr_covs'][0])
import chronostar.retired2.converter as cv
cv.convertMeasurementsToCartesian(astr_dict=astr_dict,
savefile="../data/gaia_dr2_bp_xyzuvw.fits")
import chronostar.compfitter as gf
star_pars = gf.loadXYZUVW("../data/gaia_dr2_bp_xyzuvw.fits")
star_pars['xyzuvw']
import matplotlib.pyplot as plt
plt.plot(star_pars['xyzuvw'][:, 0], star_pars['xyzuvw'][:, 1], '.')
plt.show()
plt.plot(star_pars['xyzuvw'][:, 3], star_pars['xyzuvw'][:, 4], '.')
plt.show()
gf.fit_comp(xyzuvw_dict=star_pars, plot_it=True, plot_dir='temp_plots')
get_ipython().magic(u'save gaia_dr2_bp_data_generation.py 1-62')
xyzuvw_dict['xyzuvw'].shape
star_pars
star_pars['xyzuvw'].shae
star_pars['xyzuvw'].shape
plt.plot(star_pars['xyzuvw'][0], star_pars['xyzuvw'][3], '.')
plt.show()
plt.plot(star_pars['xyzuvw'][:, 0], star_pars['xyzuvw'][:, 3], '.')
plt.show()
np.where(star_pars['xyzuvw'][:, 3] > -10)
np.where((star_pars['xyzuvw'][:, 3] > -10) & star_pars['xyzuvw'][:3] < 20)
np.where((star_pars['xyzuvw'][:, 3] > -10) & (star_pars['xyzuvw'][:3] < 20))
np.where((star_pars['xyzuvw'][:, 3] > -10) & (star_pars['xyzuvw'][:, 3] < 20))
bp_membs = np.where((star_pars['xyzuvw'][:, 3] > -10)
& (star_pars['xyzuvw'][:, 3] < 20))
hdul[1].data[0]['error_pmra']
hdul[1].data[0]['pmra_error']
hdul[1].data[0]['parralax_error']
hdul[1].data[0]['parallax_error']
np.sqrt(astr_dict['astr_covs'][0])
import chronostar.retired2.converter as cv
cv.convertMeasurementsToCartesian(astr_dict=astr_dict, savefile="../data/gaia_dr2_bp_xyzuvw.fits")
import chronostar.compfitter as gf
star_pars = gf.loadXYZUVW("../data/gaia_dr2_bp_xyzuvw.fits")
star_pars['xyzuvw']
import matplotlib.pyplot as plt
plt.plot(star_pars['xyzuvw'][:,0], star_pars['xyzuvw'][:,1], '.')
plt.show()
plt.plot(star_pars['xyzuvw'][:,3], star_pars['xyzuvw'][:,4], '.')
plt.show()
gf.fit_comp(xyzuvw_dict=star_pars, plot_it=True, plot_dir='temp_plots')
get_ipython().magic(u'save gaia_dr2_bp_data_generation.py 1-62')
xyzuvw_dict['xyzuvw'].shape
star_pars
star_pars['xyzuvw'].shae
star_pars['xyzuvw'].shape
plt.plot(star_pars['xyzuvw'][0], star_pars['xyzuvw'][3], '.')
plt.show()
plt.plot(star_pars['xyzuvw'][:,0], star_pars['xyzuvw'][:,3], '.')
plt.show()
np.where(star_pars['xyzuvw'][:,3] > -10)
np.where((star_pars['xyzuvw'][:,3] > -10) & star_pars['xyzuvw'][:3] < 20)
np.where((star_pars['xyzuvw'][:,3] > -10) & (star_pars['xyzuvw'][:3] < 20))
np.where((star_pars['xyzuvw'][:,3] > -10) & (star_pars['xyzuvw'][:,3] < 20))
bp_membs = np.where((star_pars['xyzuvw'][:,3] > -10) & (star_pars['xyzuvw'][:,3] < 20))
bp_membs
def run_fit_helper(true_comp,
starcounts,
measurement_error,
burnin_step=None,
run_name='default',
trace_orbit_func=None,
Component=EllipComponent,
init_pars=None):
py_vers = sys.version[0]
save_dir = 'temp_data/%s_compfitter_%s/' % (py_vers, run_name)
data_filename = save_dir + 'synth_data.fits'.format(py_vers, run_name)
plot_dir = save_dir
print("---------", save_dir)
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
log_filename = save_dir + 'log.log'.format(py_vers, run_name)
logging.basicConfig(level=logging.INFO,
filename=log_filename,
filemode='w')
synth_data = SynthData(pars=true_comp.get_pars(),
starcounts=starcounts,
measurement_error=measurement_error,
Components=Component)
synth_data.synthesise_everything()
tabletool.convert_table_astro2cart(synth_data.table,
write_table=True,
filename=data_filename)
print("newPars ------------------------------ \n", init_pars)
if init_pars is None:
internal_pars = None
else:
internal_pars = Component.internalise(init_pars)
res = cf.fit_comp(data=synth_data.table,
plot_it=True,
burnin_steps=burnin_step,
store_burnin_chains=True,
plot_dir=plot_dir,
save_dir=save_dir,
trace_orbit_func=trace_orbit_func,
optimisation_method='emcee',
Component=Component,
init_pars=internal_pars)
comps_filename = save_dir + 'true_and_best_comp.py'
best_comp = res[0]
EllipComponent.store_raw_components(comps_filename, [true_comp, best_comp])
star_pars = tabletool.build_data_dict_from_table(synth_data.table)
plot_results(true_comp,
best_fit_comp=res[0],
star_pars=star_pars,
plt_dir=save_dir)
return res
pool.wait()
sys.exit(0)
logging.info("Only one thread is master")
logging.info("Attempting to load prior memberships")
try:
z = np.load(init_members_file)
logging.info("Prior memberships successfully loaded")
except IOError:
z = None
logging.info("'init_z.npy' not found")
# can optionally initialise fit around approximate coords
#xyzuvw_dict = gf.loadXYZUVW(xyzuvw_file)
#approx_mean = np.mean(xyzuvw_dict['xyzuvw'], axis=0)
#approx_dx = np.prod(np.std(xyzuvw_dict['xyzuvw'][:,:3], axis=0))**(1./3.)
#approx_dv = np.prod(np.std(xyzuvw_dict['xyzuvw'][:,3:], axis=0))**(1./3.)
#init_pars = np.hstack((approx_mean, np.log(approx_dx), np.log(approx_dv), 1.0))
logging.info("applying fit")
best_fit, chain, lnprob = gf.fit_comp(
xyzuvw_file=xyzuvw_file, burnin_steps=BURNIN_STEPS, plot_it=True,
pool=pool, convergence_tol=C_TOL, save_dir=results_dir, #init_pars=init_pars,
plot_dir=results_dir, sampling_steps=SAMPLING_STEPS, z=z,
)
np.save(best_fit_file, best_fit)
if using_mpi:
pool.close()
mkpath(pdir)
np.save(pdir+group_savefile, origin) # store in each directory, for hexplotter
try:
best_group = dt.loadGroups(pdir + 'final_groups.npy')
logging.info("Precision [{}] already fitted for".format(prec))
except IOError:
# convert XYZUVW data into astrometry
astro_table = chronostar.synthdata.measureXYZUVW(xyzuvw_now_perf, prec_val[prec],
savefile=pdir+astro_savefile)
star_pars =\
cv.convertMeasurementsToCartesian(astro_table,
savefile=pdir+xyzuvw_conv_savefile)
logging.info("Generated [{}] traceback file".format(prec))
# apply traceforward fitting (with lnprob, corner plots as side effects)
best_fit, chain, lnprob = gf.fit_comp(
xyzuvw_dict=star_pars, burnin_steps=BURNIN_STEPS, plot_it=True,
pool=pool, convergence_tol=C_TOL, plot_dir=pdir, save_dir=pdir,
sampling_steps=SAMPLING_STEPS
)
best_group = chronostar.component.Component(best_fit, form=True,
internal=True)
np.save(pdir + 'final_best_groups.npy', best_group)
med_and_span = dt.calcMedAndSpan(chain)
np.save(pdit+'med_and_span.npy', med_and_span)
#hp.dataGatherer(save_dir=prec)
if using_mpi:
pool.close()
# Performing fit for each precision
for prec in precs:
logging.info("Fitting to prec: {}".format(prec))
mkpath(prec)
os.chdir(prec)
np.save(group_savefile, origin) # store in each directory, for hexplotter
try:
res = np.load(result_file)
logging.info("Precision [{}] already fitted for".format(prec))
except IOError:
# convert XYZUVW data into astrometry
astro_table = chronostar.synthdata.measureXYZUVW(xyzuvw_now_perf, prec_val[prec],
savefile=astro_savefile)
star_pars =\
cv.convertMeasurementsToCartesian(astro_table,
savefile=xyzuvw_conv_savefile)
logging.info("Generated [{}] traceback file".format(prec))
# apply traceforward fitting (with lnprob, corner plots as side effects)
best_fit, chain, lnprob = gf.fit_comp(
xyzuvw_dict=star_pars, burnin_steps=BURNIN_STEPS, plot_it=True,
pool=pool, convergence_tol=C_TOL
)
#hp.dataGatherer(save_dir=prec)
finally:
os.chdir('..')
if using_mpi:
pool.close()
