def test_pythonFuncs():
xyzuvw_file = "../data/fed_stars_20_xyzuvw.fits"
xyzuvw_dict = gf.loadXYZUVW(xyzuvw_file)
star_means = xyzuvw_dict['xyzuvw']
star_covs = xyzuvw_dict['xyzuvw_cov']
nstars = star_means.shape[0]
group_mean = np.mean(star_means, axis=0)
group_cov = np.cov(star_means.T)
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_covs, star_means)):
print(i)
co1s.append(co1(group_cov, group_mean, scov, smn))
co2s.append(co2(group_cov, group_mean, scov, smn))
ol.get_lnoverlaps(group_cov, group_mean,
np.array([scov]),
np.array([smn]), 1)
co1s = np.array(co1s)
co2s = np.array(co2s)
co3s = np.exp(sclno(group_cov, group_mean, star_covs, star_means, nstars))
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
# note that most overlaps go to 0, but the log overlaps retains the
# information
co1s = []
co2s = []
for i, (scov, smn) in enumerate(zip(star_covs, star_means)):
co1s.append(co1(star_covs[15], star_means[15], scov, smn))
co2s.append(co2(star_covs[15], star_means[15], scov, smn))
co1s = np.array(co1s)
co2s = np.array(co2s)
lnos = sclno(star_covs[15], star_means[15], star_covs, star_means, 1)
co3s = np.exp(lnos)
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
# coding: utf-8
get_ipython().magic(u'run ipython_primer.py')
import numpy as np
import chronostar.compfitter as gf
xyzuvw_dict = gf.load("../data/gaia_dr2_ok_plx_xyzuvw.fits.gz.fits")
xyzuvw_dict = gf.loadXYZUVW("../data/gaia_dr2_ok_plx_xyzuvw.fits.gz.fits")
xyzuvw_dict.keys()
xyzuvw_dict['xyzuvw'].shape
xyzuvw_dict['xyzuvw_cov'].shape
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'ls ')
get_ipython().magic(u'rm nohup.out')
get_ipython().magic(u'rm log_gaia_converter.log')
get_ipython().magic(u'ls ')
6000 * 5
6000 * 5 / 60
6000 * 5 / 60 / 60
8 * 60
#xyzuvw_file = res_dir + scen + "_xyzuvw_init.npy"
xyzuvw_file = res_dir + scen + "_perf_xyzuvw.npy"
init_xyzuvw = np.load(xyzuvw_file)
astr_table = chronostar.synthdata.measureXYZUVW(init_xyzuvw, prec_val[prec])
cv.convertMeasurementsToCartesian(t=astr_table,
savefile=fits_file)
file_stems = [scen + "_" + prec for scen in scenarios for prec in precs]
#fit_files = [res_dir + "xyzuvw_now_" + stem + ".fits" for stem in file_stems]
assert len(fits_files) == len(file_stems)
for i in range(len(fits_files)):
xyzuvw_dict = gf.loadXYZUVW(fits_files[i])
nstars = xyzuvw_dict['xyzuvw'].shape[0]
origin_file = res_dir + file_stems[i][:-5] + '_origins.npy'
group = np.load(origin_file).item()
true_age = group.age
ntimes = int(2 * true_age + 1)
times = -np.linspace(0, 2 * true_age, ntimes)
tb = torb.trace_many_cartesian_orbit(xyzuvw_dict['xyzuvw'], times=times,
single_age=False,
savefile=res_dir + 'tb_{}.npy'. \
format(int(true_age)))
# for each timestep, get mean of association and distance from mean
dists = np.zeros((nstars, ntimes))
stds = np.zeros(ntimes)
for tix in range(ntimes):
from __future__ import division, print_function """ Generate some BPMG plots for talk """ import sys sys.path.insert(0, "..") import chronostar.retired.hexplotter as hp import chronostar.compfitter as gf xyzuvw_file = "../data/gaia_dr2_bp_xyzuvw.fits" rdir = "../results/em_fit/gaia_dr2_bp/iter11/" star_pars = gf.loadXYZUVW(xyzuvw_file) #final_z = np.load(rdir + "final_groups.npy") #final_med_errs = np.load(rdir + "final_med_errs.npy") #final_groups = np.load(rdir + "final_groups.npy") hp.dataGathererEM(2, 10, rdir, rdir, xyzuvw_file=xyzuvw_file)
if not os.path.exists(rdir_stem): # on server
rdir_stem = "/data/mash/tcrun/em_fit/"
for ncomps in range(1,4):
try:
rdir = rdir_stem + assoc_name + "_{}/".format(ncomps)
ddir = "../data/"
# final_z_file = rdir + "final/final_membership.npy"
final_z_file = rdir + "memberships.npy"
final_groups_file = rdir + "final_groups.npy"
bg_hists_file = rdir + "bg_hists.npy"
data_file = ddir + assoc_name + "_xyzuvw.fits"
star_pars = gf.loadXYZUVW(data_file)
z = np.load(final_z_file)
# import pdb; pdb.set_trace()
groups = np.load(final_groups_file)
bg_hists = np.load(bg_hists_file)
bg_ln_ols = em.backgroundLogOverlaps(star_pars['xyzuvw'], bg_hists)
overall_lnlike, z = em.get_overall_lnlikelihood(star_pars, groups,
bg_ln_ols, return_memb_probs=True)
print("overall_lnlike with {} comps is: {:.5}".format(ncomps, overall_lnlike))
bic = calcBIC(star_pars, ncomps, overall_lnlike)
print("BIC is: {}".format(bic))
print("With {:.2} stars accounted for by background"\
.format(np.sum(z[:,-1])))
rdir_stem = "../results/em_fit/"
if not os.path.exists(rdir_stem): # on server
rdir_stem = "/data/mash/tcrun/em_fit/"
for ncomps in range(1, 4):
try:
rdir = rdir_stem + assoc_name + "_{}/".format(ncomps)
ddir = "../data/"
# final_z_file = rdir + "final/final_membership.npy"
final_z_file = rdir + "memberships.npy"
final_groups_file = rdir + "final_groups.npy"
bg_hists_file = rdir + "bg_hists.npy"
data_file = ddir + assoc_name + "_xyzuvw.fits"
star_pars = gf.loadXYZUVW(data_file)
z = np.load(final_z_file)
# import pdb; pdb.set_trace()
groups = np.load(final_groups_file)
bg_hists = np.load(bg_hists_file)
bg_ln_ols = em.backgroundLogOverlaps(star_pars['xyzuvw'], bg_hists)
overall_lnlike, z = em.get_overall_lnlikelihood(star_pars,
groups,
bg_ln_ols,
return_memb_probs=True)
print("overall_lnlike with {} comps is: {:.5}".format(
ncomps, overall_lnlike))
bic = calcBIC(star_pars, ncomps, overall_lnlike)
# coding: utf-8
get_ipython().magic(u'run ipython_primer.py')
import numpy as np
import chronostar.compfitter as gf
xyzuvw_dict = gf.load("../data/gaia_dr2_ok_plx_xyzuvw.fits.gz.fits")
xyzuvw_dict = gf.loadXYZUVW ("../data/gaia_dr2_ok_plx_xyzuvw.fits.gz.fits")
xyzuvw_dict.keys()
xyzuvw_dict['xyzuvw'].shape
xyzuvw_dict['xyzuvw_cov'].shape
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'cat log_gaia_converter.log')
get_ipython().magic(u'ls ')
get_ipython().magic(u'rm nohup.out')
get_ipython().magic(u'rm log_gaia_converter.log')
get_ipython().magic(u'ls ')
6000*5
6000*5/60
6000*5/60/60
8*60
weight)
lnalpha_priors.append(lnalpha_prior)
print("nstars: {:6.3f} | age: {:6.3f} | dX: {:6.3f} | dV: {:6.3f} |"
"lnalpha_pr: {:6.3f}"\
.format(weight, group_obj.age, group_obj.dx, group_obj.dv,
lnalpha_prior))
#print(lnalpha_priors)
# for deeper insight, lets investigate the ratio of overlap between
# the flat bg field and the crappy 1.5 Myr component (3_comp[1]) and
# see if the difference will be corrected for by the large prior
rdir = "../results/em_fit/cf-15/"
star_pars_file = "../data/bpmg_cand_w_gaia_dr2_astrometry_comb_binars_xyzuvw.fits"
star_pars = gf.loadXYZUVW(star_pars_file)
bg_hists = np.load(rdir + "bg_hists.npy")
final_z = np.load(rdir + "final/final_membership.npy")
final_groups = np.load(rdir + "final_groups.npy")
for i in range(len(three_group_pars_ex)):
spec_comp_stars_mask = np.where(final_z[:, i] > .5)
spec_comp_star_pars = {
'xyzuvw': star_pars['xyzuvw'][spec_comp_stars_mask],
'xyzuvw_cov': star_pars['xyzuvw_cov'][spec_comp_stars_mask]
}
spec_comp_group = chronostar.component.Component(three_group_pars_ex[1],
internal=False)
spec_ln_bg_ols = em.backgroundLogOverlaps(spec_comp_star_pars['xyzuvw'],
bg_hists,
import logging
import sys
sys.path.insert(0, '..')
import chronostar.retired2.converter as cv
import chronostar.compfitter as gf
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
temp_dir = 'temp_data/'
astro_table_file = temp_dir + 'astro_table.txt'
temp_xyzuvw_save_file = temp_dir + 'xyzuvw_now.fits'
xyzuvw_dict_orig = cv.convertMeasurementsToCartesian(
loadfile=astro_table_file, savefile=temp_xyzuvw_save_file
)
xyzuvw_dict_load = gf.loadXYZUVW(temp_xyzuvw_save_file)
from __future__ import division, print_function """ Generate some BPMG plots for talk """ import sys sys.path.insert(0, "..") import chronostar.retired.hexplotter as hp import chronostar.compfitter as gf xyzuvw_file = "../data/gaia_dr2_bp_xyzuvw.fits" rdir = "../results/em_fit/gaia_dr2_bp/iter11/" star_pars = gf.loadXYZUVW(xyzuvw_file) #final_z = np.load(rdir + "final_groups.npy") #final_med_errs = np.load(rdir + "final_med_errs.npy") #final_groups = np.load(rdir + "final_groups.npy") hp.dataGathererEM(2, 10, rdir, rdir, xyzuvw_file=xyzuvw_file)
pass
except IOError:
#xyzuvw_file = res_dir + scen + "_xyzuvw_init.npy"
xyzuvw_file = res_dir + scen + "_perf_xyzuvw.npy"
init_xyzuvw = np.load(xyzuvw_file)
astr_table = chronostar.synthdata.measureXYZUVW(
init_xyzuvw, prec_val[prec])
cv.convertMeasurementsToCartesian(t=astr_table, savefile=fits_file)
file_stems = [scen + "_" + prec for scen in scenarios for prec in precs]
#fit_files = [res_dir + "xyzuvw_now_" + stem + ".fits" for stem in file_stems]
assert len(fits_files) == len(file_stems)
for i in range(len(fits_files)):
xyzuvw_dict = gf.loadXYZUVW(fits_files[i])
nstars = xyzuvw_dict['xyzuvw'].shape[0]
origin_file = res_dir + file_stems[i][:-5] + '_origins.npy'
group = np.load(origin_file).item()
true_age = group.age
ntimes = int(2 * true_age + 1)
times = -np.linspace(0, 2 * true_age, ntimes)
tb = torb.trace_many_cartesian_orbit(xyzuvw_dict['xyzuvw'], times=times,
single_age=False,
savefile=res_dir + 'tb_{}.npy'. \
format(int(true_age)))
# for each timestep, get mean of association and distance from mean
dists = np.zeros((nstars, ntimes))
stds = np.zeros(ntimes)
for tix in range(ntimes):
import numpy as np
import sys
sys.path.insert(0, '..')
import integration_tests.traceback_plotter as tp
import chronostar.compfitter as gf
rdir = '../results/em_fit/gaia_dr2_bp/'
sdir = rdir
xyzuvw_now_file = '../data/gaia_dr2_bp_xyzuvw.fits'
final_z_file = rdir + 'final/final_membership.npy'
maxtime = 30
ntimes = maxtime + 1
times = np.linspace(0, np.int(-maxtime), ntimes)
final_z = np.load(final_z_file)
xyzuvw_dict = gf.loadXYZUVW(xyzuvw_now_file)
tp.plotSeparation(xyzuvw_dict['xyzuvw'][np.where(final_z[:, 0])],
times,
prec='real-data')
mc_xyzuvws = np.zeros((0, 6))
nsamples = 10
for (mn, cov) in zip(xyzuvw_dict['xyzuvw'][np.where(final_z[:, 0])],
xyzuvw_dict['xyzuvw_cov']):
samples = np.random.multivariate_normal(mn, cov, size=nsamples)
mc_xyzuvws = np.vstack((mc_xyzuvws, samples))
tp.plotSeparation(mc_xyzuvws, times, prec='real-data' + '_emcee')
lnalpha_priors.append(lnalpha_prior)
print("nstars: {:6.3f} | age: {:6.3f} | dX: {:6.3f} | dV: {:6.3f} |"
"lnalpha_pr: {:6.3f}"\
.format(weight, group_obj.age, group_obj.dx, group_obj.dv,
lnalpha_prior))
#print(lnalpha_priors)
# for deeper insight, lets investigate the ratio of overlap between
# the flat bg field and the crappy 1.5 Myr component (3_comp[1]) and
# see if the difference will be corrected for by the large prior
rdir = "../results/em_fit/cf-15/"
star_pars_file = "../data/bpmg_cand_w_gaia_dr2_astrometry_comb_binars_xyzuvw.fits"
star_pars = gf.loadXYZUVW(star_pars_file)
bg_hists = np.load(rdir + "bg_hists.npy")
final_z = np.load(rdir + "final/final_membership.npy")
final_groups = np.load(rdir + "final_groups.npy")
for i in range(len(three_group_pars_ex)):
spec_comp_stars_mask = np.where(final_z[:,i] > .5)
spec_comp_star_pars = {'xyzuvw':star_pars['xyzuvw'][spec_comp_stars_mask],
'xyzuvw_cov':star_pars['xyzuvw_cov'][
spec_comp_stars_mask]}
spec_comp_group = chronostar.component.Component(three_group_pars_ex[1],
internal=False)
spec_ln_bg_ols = em.backgroundLogOverlaps(spec_comp_star_pars['xyzuvw'], bg_hists,
correction_factor=1.)
import logging
import sys
sys.path.insert(0, '..')
import chronostar.retired2.converter as cv
import chronostar.compfitter as gf
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
temp_dir = 'temp_data/'
astro_table_file = temp_dir + 'astro_table.txt'
temp_xyzuvw_save_file = temp_dir + 'xyzuvw_now.fits'
xyzuvw_dict_orig = cv.convertMeasurementsToCartesian(
loadfile=astro_table_file, savefile=temp_xyzuvw_save_file)
xyzuvw_dict_load = gf.loadXYZUVW(temp_xyzuvw_save_file)
co2s = np.array(co2s)
lnos = sclno(star_covs[15], star_means[15], star_covs, star_means, 1)
co3s = np.exp(lnos)
assert np.allclose(co1s, co2s)
assert np.allclose(co2s, co3s)
assert np.allclose(co1s, co3s)
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)
DEBUG_SINGULAR = False
DEBUG_UNDERFLOW = True
co1s = []
co2s = []
xyzuvw_file = "../data/fed_stars_20_xyzuvw.fits"
xyzuvw_dict = gf.loadXYZUVW(xyzuvw_file)
star_means = xyzuvw_dict['xyzuvw']
star_covs = xyzuvw_dict['xyzuvw_cov']
nstars = star_means.shape[0]
gmn = np.mean(star_means, axis=0)
gcov = np.cov(star_means.T)
if DEBUG_SINGULAR:
scov = star_covs[20]
smn = star_means[20]
py_lnol = sclno(gcov, gmn, np.array([scov]), np.array([smn]), 1)
ol.get_lnoverlaps(gcov, gmn,
np.array([scov]),
np.array([smn]), 1)
