Exemple #1
0
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
Exemple #2
0
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