def plot_maphz(plotname):
# Load the two fit
with open('../mapfits/tribrokentwoexp.sav', 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthisz1 = numpy.zeros(len(bf)) + numpy.nan
plotthisz1e = numpy.zeros(len(bf)) + numpy.nan
plotthisz2 = numpy.zeros(len(bf)) + numpy.nan
plotthisz2e = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
hzindx= (True-numpy.isnan(samples[ii,4]))\
*(True-numpy.isnan(samples[ii,5]))\
*(densprofiles.ilogit(samples[ii,4]) > 0.15)
tmed = numpy.median(1. / samples[ii, 1, hzindx])
terr = numpy.std(1. / samples[ii, 1, hzindx])
plotthisz1[ii] = tmed
plotthisz1e[ii] = terr
tmed = numpy.median(1. / samples[ii, 5, hzindx])
terr = numpy.std(1. / samples[ii, 5, hzindx])
plotthisz2[ii] = tmed
plotthisz2e[ii] = terr
plotthisz1[plotthisz1e / plotthisz1 > 0.5] = numpy.nan
bovy_plot.bovy_print()
bovy_plot.bovy_plot(
plotthisz2 * 1000.,
plotthisz1 * 1000.,
'ko',
xrange=[0., 1200.],
yrange=[0., 1200.],
xlabel=r'$2^\mathrm{nd}\ \mathrm{scale\ height\,(pc)}$',
ylabel=r'$1^\mathrm{st}\ \mathrm{scale\ height\,(pc)}$',
zorder=2)
bovy_plot.bovy_plot([0, 1200], [0, 1200], 'k--', overplot=True, lw=2.)
pyplot.errorbar(plotthisz2 * 1000.,
plotthisz1 * 1000.,
yerr=plotthisz1e * 1000.,
marker='o',
color='k',
ls='none',
zorder=1)
bovy_plot.bovy_end_print(plotname)
return None
def _format_results(type, extmap):
if type.lower() == 'tribrokenexpflare':
tsamples = samples_brexp
out = {
'hr1':
numpy.median(tsamples[0]),
'hr1err':
numpy.std(tsamples[0]),
'hr2':
numpy.median(tsamples[2]),
'hr2err':
numpy.std(tsamples[2]),
'rmax':
numpy.median(numpy.exp(tsamples[3])),
'rmaxerr':
1.4826 * numpy.median(
numpy.fabs(
numpy.exp(tsamples[3]) -
numpy.median(numpy.exp(tsamples[3])))),
'hz':
numpy.median(1. / tsamples[1]),
'hzerr':
numpy.std(1. / tsamples[1]),
'rf':
numpy.median(tsamples[4]),
'rferr':
numpy.std(tsamples[4]),
'ml':
0.
}
if out['rmax'] < 4.:
out['rmax'] = sorted(numpy.exp(tsamples[3]))[int(
round(0.95 * tsamples.shape[1]))]
return "${hr1:.2f}\pm{hr1err:.2f}$&${hr2:.2f}\pm{hr2err:.2f}$&$<{rmax:.1f}$&${hz:.2f}\pm{hzerr:.2f}$&${rf:.2f}\pm{rferr:.2f}$&\ldots&{ml:.0f}".format(
**out)
else:
return "${hr1:.2f}\pm{hr1err:.2f}$&${hr2:.2f}\pm{hr2err:.2f}$&${rmax:.1f}\pm{rmaxerr:.1f}$&${hz:.2f}\pm{hzerr:.2f}$&${rf:.2f}\pm{rferr:.2f}$&\ldots&{ml:.0f}".format(
**out)
elif type.lower() == 'expplusconst':
tsamples = samples_exp
out = {
'hr': numpy.median(tsamples[0]),
'hrerr': numpy.std(tsamples[0]),
'hz': numpy.median(1. / tsamples[1]),
'hzerr': numpy.std(1. / tsamples[1]),
'ml': -2. * (ml_exp - ml_brexp)
}
return "\ldots&${hr:.2f}\pm{hrerr:.2f}$&\ldots&${hz:.2f}\pm{hzerr:.2f}$&\ldots&&{ml:.0f}".format(
**out)
if type.lower() == 'tribrokentwoexp':
tsamples = samples_twoexp
hzindx = numpy.fabs(
(1. / tsamples[1] - 1. / tsamples[5]) * tsamples[1]) > 0.5
out = {
'hr1':
numpy.median(tsamples[0]),
'hr1err':
numpy.std(tsamples[0]),
'hr2':
numpy.median(tsamples[2]),
'hr2err':
numpy.std(tsamples[2]),
'rmax':
numpy.median(numpy.exp(tsamples[3])),
'rmaxerr':
numpy.std(numpy.exp(tsamples[3])),
'hz1':
numpy.median(1. / tsamples[1]),
'hz1err':
numpy.std(1. / tsamples[1]),
'amp':
sorted(densprofiles.ilogit(tsamples[4, hzindx]))[int(
round(0.95 * numpy.sum(hzindx)))],
'hz2':
numpy.median(1. / tsamples[5, hzindx]),
'hz2err':
numpy.std(1. / tsamples[5, hzindx]),
'ml':
-2. * (ml_twoexp - ml_brexp)
}
return "${hr1:.2f}\pm{hr1err:.2f}$&${hr2:.2f}\pm{hr2err:.2f}$&${rmax:.1f}\pm{rmaxerr:.1f}$&${hz1:.2f}\pm{hz1err:.2f}$&$<{amp:.2f}$&${hz2:.2f}\pm{hz2err:.2f}$&{ml:.0f}".format(
**out)
