def __init__(self, filename):
self.filename = filename
self.model = ModelParams(filename)
self.particles = numpy.loadtxt(filename)[:, 0:6]
if vel_error != 0:
print "Assumed error of", vel_error, "km/s in velocity"
if phase_space_info_mode <= 5:
self.particles[:, 2] *= numpy.nan # remove z-coordinate
if phase_space_info_mode <= 3:
self.particles[:, 3:5] *= numpy.nan # remove vx and vy
if phase_space_info_mode != 6 or vel_error != 0:
self.samples, self.weights = sampleMissingData(
numpy.hstack(
(self.particles, numpy.ones(
(self.particles.shape[0], 3)) * vel_error)),
num_subsamples)
# check if we may restart the search from already existing parameters
try:
self.values = numpy.loadtxt(self.filename + ".best")
if self.values.ndim == 1: # only one set of parameters - this occurs after the deterministic search
self.values = self.values[:
-1] # the last column is the likelihood, strip it
else: # a number of MCMC walkers, each with its own set of parameters
self.values = self.values[:, :-1]
print "Loaded from saved file: (nwalkers,nparams)=", self.values.shape
except:
self.values = None
return
def __init__(self, filename):
self.filename = filename
self.model = ModelParams(filename)
try:
self.particles = numpy.loadtxt(filename)[:,0:6]
except Exception as ex:
print(str(ex)+"\nYou need to run this script from a directory containing files "\
"from the Gaia Challenge spherical/triaxial mock data.")
exit()
if vel_error!=0:
print("Assumed error of %f km/s in velocity" % vel_error)
if phase_space_info_mode <= 5:
self.particles[:,2] *= numpy.nan # remove z-coordinate
if phase_space_info_mode <= 3:
self.particles[:,3:5] *= numpy.nan # remove vx and vy
if phase_space_info_mode != 6 or vel_error != 0:
self.samples, self.weights = sampleMissingData(
numpy.hstack((self.particles, numpy.ones((self.particles.shape[0], 3)) * vel_error)),
num_subsamples )
# check if we may restart the search from already existing parameters
try:
self.values = numpy.loadtxt(self.filename+".best")
if self.values.ndim==1: # only one set of parameters - this occurs after the deterministic search
self.values = self.values[:-1] # the last column is the likelihood, strip it
else: # a number of MCMC walkers, each with its own set of parameters
self.values = self.values[:,:-1]
print("Loaded from saved file: (nwalkers,nparams)=" + str(self.values.shape))
except:
self.values = None
return
#axes[1,indx].legend(loc='lower left')
axes[1,indx].set_xlim(rmin, rmax)
axes[1,indx].set_ylim(densmin, densmax)
axes[1,indx].set_xlabel('$r$')
axes[1,indx].set_ylabel(r'$\rho$')
axes[1,indx].text( (rmin*rmax)**0.5, densmin*2, label, ha='center')
################ MAIN PROGRAM ##################
#base = "gs010_bs050_rcrs100_rarcinf_core_0400mpc3_df"
if len(sys.argv)<=1:
print("Provide the data file name as the command-line argument")
exit()
agama.setUnits(mass=1, length=1, velocity=1)
base = sys.argv[1]
model = ModelParams(base)
rmin = 0.01
rmax = 100.
velmin = 0.
velmax = 40.
radii = numpy.logspace(numpy.log10(rmin), numpy.log10(rmax), 25)
midradii = (radii[1:] * radii[:-1])**0.5
xyz = numpy.vstack((radii, numpy.zeros_like(radii), numpy.zeros_like(radii))).T
# plot the inferred density of dark matter and its log-slope as functions of radius
fig,axes = pyplot.subplots(2, 3, figsize=(12,8))
plot_profiles("6"+base+"/"+base+"_1000_0.dat", 0, '6d, no errors')
plot_profiles("5"+base+"/"+base+"_1000_0_err.dat", 1, r'5d, $\delta v$=2 km/s')
plot_profiles("3"+base+"/"+base+"_1000_0_err.dat", 2, r'3d, $\delta v$=2 km/s')
fig.tight_layout()
pyplot.savefig(base+"_darkmatter.png")
