def probsurf_galstar(mag, err, l=0., b=90.):
infile = tempfile.NamedTemporaryFile()
write_infile(infile.name, mag, err, l, b)
outsurffile = tempfile.NamedTemporaryFile()
outstatsfile = tempfile.NamedTemporaryFile()
logfile = tempfile.NamedTemporaryFile()
os.system('/n/home13/schlafly/galstar/optimized/galstar %s:DM[5,20,120],Ar[0,15,750] --statsfile %s --infile %s 0 --giant &>%s' %
(outsurffile.name, outstatsfile.name, infile.name, logfile.name))
bounds, surfs = galstar_io.load_bins(outsurffile.name)
converged, lnZ, mean, cov = galstar_io.load_stats(outstatsfile.name)
log = logfile.read()
return bounds, surfs, converged, lnZ, mean, cov, log
def main():
parser = argparse.ArgumentParser(
prog='mock-comparison.py',
description='Compares results from galstar for mock data '
'with true stellar parameters.',
add_help=True)
parser.add_argument('bin', type=str, help='Binned pdfs from galstar')
parser.add_argument('stats', type=str, help='Statistics produced by galstar')
parser.add_argument('truth', type=str, help='Test input used by galstar')
parser.add_argument('--stack-out', '-so', type=str, default=None,
help='Output filename for stacked pdf plot.')
parser.add_argument('--pct-out', '-po', type=str, default=None,
help='Output filename for percentile plot.')
if 'python' in sys.argv[0]:
offset = 2
else:
offset = 1
values = parser.parse_args(sys.argv[offset:])
if (values.stack_out == None) and (values.pct_out == None):
print "Either '--stack-out' or '--pct-out' (or both) must be specified."
return 0
test_fname = abspath(values.truth)
dtype = [('DM', 'f8'), ('Ar', 'f8'), ('Mr', 'f8'), ('FeH', 'f8')]
truth = np.loadtxt(test_fname, dtype=dtype, skiprows=7)
stats_fname = abspath(values.stats)
bin_fname = abspath(values.bin)
converged, ln_evidence, mean, cov = load_stats('stats.dat')
bounds, p = load_bins('DM_Ar.dat')
p = smooth_bins(p, [2,2])
norm = np.sum(np.sum(p, axis=1), axis=1)
# Set matplotlib style attributes
mplib.rc('text', usetex=True)
mplib.rc('xtick.major', size=6)
mplib.rc('xtick.minor', size=4)
mplib.rc('ytick.major', size=6)
mplib.rc('ytick.minor', size=4)
mplib.rc('xtick', direction='out')
mplib.rc('ytick', direction='out')
mplib.rc('axes', grid=False)
# Percentile statistics
if values.pct_out != None:
pct_fname = abspath(values.pct_out)
P_indiv = P_star(bounds, p, truth)
print P_indiv
fig = plt.figure(figsize=(4,3), dpi=200)
ax = fig.add_subplot(1,1,1)
ax.hist(P_indiv, alpha=0.6)
lower, upper = binom_confidence(10, p.shape[0], 0.95)
ax.fill_between([0., 1.], [lower, lower], [upper, upper], facecolor='g', alpha=0.2)
lower, upper = binom_confidence(10, p.shape[0], 0.50)
ax.fill_between([0., 1.], [lower, lower], [upper, upper], facecolor='g', alpha=0.2)
ax.set_xlim(0., 1.)
ax.set_xlabel(r'$\% \mathrm{ile}$', fontsize=16)
ax.set_ylabel(r'$\mathrm{\# \ of \ stars}$', fontsize=16)
fig.subplots_adjust(left=0.18, bottom=0.18)
fig.savefig(pct_fname, dpi=300)
# Shifted and stacked pdfs
if values.stack_out != None:
stack_fname = abspath(values.stack_out)
# Simple statistics
Delta_DM = (truth['DM']-mean[:,0]) / np.sqrt(cov[:,0,0])
Delta_Ar = (truth['Ar']-mean[:,1]) / np.sqrt(cov[:,1,1])
Delta_Mr = (truth['Mr']-mean[:,2]) / np.sqrt(cov[:,2,2])
Delta_FeH = (truth['FeH']-mean[:,3]) / np.sqrt(cov[:,3,3])
w_x = bounds[1] - bounds[0]
w_y = bounds[3] - bounds[2]
dx = bounds[0] + 0.5*w_x - truth['DM']
dy = bounds[2] + 0.5*w_y - truth['Ar']
bounds_new = [-0.5*w_x, 0.5*w_x, -0.5*w_y, 0.5*w_y]
stack = stack_shifted(bounds, p, [dx,dy], norm)
DM_range = np.linspace(bounds_new[0], bounds_new[1], stack.shape[0])
p_DM = np.sum(stack, axis=1)
p_DM /= np.sum(p_DM)
Ar_range = np.linspace(bounds_new[2], bounds_new[3], stack.shape[1])
p_Ar = np.sum(stack, axis=0)
p_Ar /= np.sum(p_Ar)
# Determine geometry of density plot and histograms
main_left, main_bottom = 0.18, 0.16
main_width, main_height = 0.63, 0.65
buffer_right, buffer_top = 0., 0.
histx_height, histy_width = 0.12, 0.09
rect_main = [main_left, main_bottom, main_width, main_height]
rect_histx = [main_left, main_bottom+main_height+buffer_top, main_width, histx_height]
rect_histy = [main_left+main_width+buffer_right, main_bottom, histy_width, main_height]
# Set up the figure with a density plot and two histograms
fig = plt.figure(figsize=(4,3), dpi=150)
ax_density = fig.add_axes(rect_main)
ax_histx = fig.add_axes(rect_histx)
ax_histy = fig.add_axes(rect_histy)
xlim = [-2.,2.]
ylim = [-1.,1.]
ax_density.imshow(stack.T, extent=bounds_new, origin='lower', vmin=0.,
aspect='auto', cmap='hot', interpolation='nearest')
ax_density.plot([0., 0.], [ylim[0]-1.,ylim[1]+1.], 'c:', lw=0.5, alpha=0.35)
ax_density.plot([xlim[0]-1., xlim[1]+1.], [0., 0.], 'c:', lw=0.5, alpha=0.35)
ax_density.set_xlim(xlim)
ax_density.set_ylim(ylim)
ax_histx.fill_between(DM_range, y1=p_DM, alpha=0.4, facecolor='b')
ax_histx.plot([0., 0.], [0., 1.1*np.max(p_DM)], 'g-', lw=0.5)
ax_histx.set_ylim(0., 1.1*np.max(p_DM))
ax_histx.set_xlim(xlim)
ax_histx.set_xticklabels([])
ax_histx.set_yticklabels([])
ax_histy.fill_betweenx(Ar_range, x1=p_Ar, alpha=0.4, facecolor='b')
ax_histy.plot([0., 1.1*np.max(p_Ar)], [0., 0.], 'g-', lw=0.5)
ax_histy.set_xlim(0., 1.1*np.max(p_Ar))
ax_histy.set_ylim(ylim)
ax_histy.set_xticklabels([])
ax_histy.set_yticklabels([])
fig.savefig(stack_fname, dpi=300)
plt.show()
return 0
