#xoffset[10:] = [0,-4,0,0,-15,-10,0,0,-20]
axis_range = [-70,60,-40,120]
if (dims[0]==1) & (dims[1]==2):
axis_range = [-40,120,-30,100]
#text_ix = [0,1,4,7]
#xoffset[7]=-15
times = np.linspace(0,max_time, n_times)
if trace_it_back:
tb = traceback.TraceBack(t)
tb.traceback(times,xoffset=xoffset, yoffset=yoffset, axis_range=axis_range, dims=dims,plotit=False,savefile="results/"+pklfile)
if fit_the_group:
star_params = fit_group.read_stars("results/" + pklfile)
beta_pic_group = np.array([ -0.908, 60.998, 27.105, -0.651,-11.470, -0.148, \
8.055, 4.645, 8.221, 0.655, 0.792, 0.911, 0.843, 18.924])
ol_swig = fit_group.lnprob_one_group(beta_pic_group, star_params, use_swig=True, return_overlaps=True)
ol_old = fit_group.lnprob_one_group(beta_pic_group, star_params, use_swig=False, return_overlaps=True)
using_mpi = True
try:
# Initialize the MPI-based pool used for parallelization.
pool = MPIPool()
except:
print("Either MPI doesn't seem to be installed or you aren't running with MPI... ")
using_mpi = False
pool=None
# use_swig=True, return_overlaps=True)
#bpstars = star_params["stars"]["Name1"][np.where(ol_dynamic > 1e-10)]
#allstars = star_params["stars"]["Name1"]
#ol_bp = ol_dynamic[np.where(ol_dynamic > 1e-10)]
#f.write("{} stars with overlaps > 1e-10:\n".format(np.size(bpstars)))
#f.write(str(bpstars)+"\n")
#f.write("\n")
#print_membership(allstars, ol_dynamic)
#print("Just BP stars")
#print_membership(bpstars, ol_bp)
stars, times, xyzuvw, xyzuvw_cov = \
pickle.load(open('results/bp_TGAS2_traceback_save.pkl'))
star_params = fit_group.read_stars('results/bp_TGAS2_traceback_save.pkl')
beta_pic_group = np.array([-6.0, 66.0, 23.0, \
-1.0, -11.0, 0.0, \
10.0, 10.0, 12.0, 5, \
0.9, 0.7, 0.8, \
-35.0, 1.0, -30.0, -4.0, -15.0, -5.0, \
80.0, 60.0, 50.0, \
7, \
-0.2, 0.3, -0.1, \
0.30, \
23.0]) # birth time
#fit from fit_two plus original beta pic fit
big_beta_group = np.array([-22, 34, 26, 0.61, -14, 0.01, \
27, 35, 20,\
#bpstars = star_params["stars"]["Name1"][np.where(ol_dynamic > 1e-10)]
#allstars = star_params["stars"]["Name1"]
#ol_bp = ol_dynamic[np.where(ol_dynamic > 1e-10)]
#f.write("{} stars with overlaps > 1e-10:\n".format(np.size(bpstars)))
#f.write(str(bpstars)+"\n")
#f.write("\n")
#print_membership(allstars, ol_dynamic)
#print("Just BP stars")
#print_membership(bpstars, ol_bp)
stars, times, xyzuvw, xyzuvw_cov = \
pickle.load(open('results/bp_TGAS2_traceback_save.pkl'))
star_params = fit_group.read_stars('results/bp_TGAS2_traceback_save.pkl')
beta_pic_group = np.array([-6.0, 66.0, 23.0, \
-1.0, -11.0, 0.0, \
10.0, 10.0, 12.0, 5, \
0.9, 0.7, 0.8, \
-35.0, 1.0, -30.0, -4.0, -15.0, -5.0, \
80.0, 60.0, 50.0, \
7, \
-0.2, 0.3, -0.1, \
0.30, \
23.0]) # birth time
# The fit being fitted by two gaussians
big_beta_group = np.array([-22, 34, 26, \
0.61, -14, 0.01, \
#xoffset[7]=-15
times = np.linspace(0, max_time, n_times)
if trace_it_back:
tb = traceback.TraceBack(t)
tb.traceback(times,
xoffset=xoffset,
yoffset=yoffset,
axis_range=axis_range,
dims=dims,
plotit=False,
savefile="results/" + pklfile)
if fit_the_group:
star_params = fit_group.read_stars("results/" + pklfile)
beta_pic_group = np.array([ -0.908, 60.998, 27.105, -0.651,-11.470, -0.148, \
8.055, 4.645, 8.221, 0.655, 0.792, 0.911, 0.843, 18.924])
ol_swig = fit_group.lnprob_one_group(beta_pic_group,
star_params,
use_swig=True,
return_overlaps=True)
ol_old = fit_group.lnprob_one_group(beta_pic_group,
star_params,
use_swig=False,
return_overlaps=True)
using_mpi = True
try:
#xoffset[7]=-15
times = np.linspace(0, max_time, n_times)
if trace_it_back:
tb = traceback.TraceBack(t)
tb.traceback(times,
xoffset=xoffset,
yoffset=yoffset,
axis_range=axis_range,
dims=dims,
plotit=True,
savefile="results/bp_TGAS1_traceback_save.pkl")
if fit_the_group:
star_params = fit_group.read_stars("results/bp_TGAS1_traceback_save.pkl")
#Original
beta_pic_group = np.array([-6.574, 66.560, 23.436, -1.327,-11.427, -6.527,\
10.045, 10.319, 12.334, 0.762, 0.932, 0.735, 0.846, 20.589])
#Widened
beta_pic_group = np.array([-6.574, 66.560, 23.436, -1.327,-11.427, 0,\
10.045, 10.319, 12.334, 5, 0.932, 0.735, 0.846, 20.589])
#After one successful fit.
beta_pic_group = np.array([
-0.908, 60.998, 27.105, -0.651, -11.470, -0.148, 8.055, 4.645, 8.221,
0.655, 0.792, 0.911, 0.843, 18.924
])
beta_pic_group = np.array([
-1.96, 60.281, 25.242, 0.359, -11.864, -0.175, 5.516, 4.497, 7.993,
0.848, 0.51, 0.776, 0.765, 18.05