horizontalalignment='center', verticalalignment='center',
fontsize=24)
ax4.set_xlabel(r'$\tau_{1-1}$',fontsize=24)
ax5.set_xlabel(r'$\tau_{2-2}$',fontsize=24)
ax6.set_xlabel(r'$\tau_{1-1}/\tau_{2-2}$',fontsize=24)
# ax.hlines([3.68],*ax.get_xlim(),linestyles=['--'], colors=['k'], label="H$_2$CO Lower-limit")
# ax.hlines([6.58],*ax.get_xlim(),linestyles=[':'], colors=['k'], label="CO Lower-limit")
#
# ax.legend(loc='best')
#
# ax.set_ylabel(r"$\mathcal{M}_{3D}$")
# ax.set_xlabel('$\\tau_{1-1}/\\tau_{2-2}$',fontsize=24)
savefig(savepath+"sigma_vs_tauratio_sixpanels_abundance%s.png" % abundance)
fig = pl.figure(3+fignum)
pl.clf()
ax2 = pymc_plotting.hist2d(mc_lognormal_freemach_traces, 'tauoneone_mu','sigma', bins=50,
clear=True, fignum=1, varslice=(2.5e5,None,None), colorbar=False,
axis=pl.subplot(222))
ax3 = pymc_plotting.hist2d(mc_lognormal_freemach_traces, 'tautwotwo_mu','sigma', bins=50,
clear=True, fignum=1, varslice=(2.5e5,None,None), colorbar=False,
axis=pl.subplot(223))
ax1 = pymc_plotting.hist2d(mc_lognormal_freemach_traces, 'tau_ratio','sigma', bins=50,
clear=True, fignum=1, varslice=(2.5e5,None,None), colorbar=False,
axis=pl.subplot(221))
pl.subplots_adjust(hspace=0,wspace=0)
ax2.set_yticks([])
ax2.set_xlim(0.101,0.114)
lognormal_statstable = pymc_tools.stats_table(mc_lognormal)
lognormal_statstable.write(
trace_data_path +
'lognormal_statstable_abundance%s_opr%s.fits' % (abundance, opr),
overwrite=True)
lognormal_simple_statstable = pymc_tools.stats_table(mc_simple)
lognormal_simple_statstable.write(
trace_data_path +
'lognormal_simple_statstable_abundance%s_opr%s.fits' % (abundance, opr),
overwrite=True)
lognormal_freemach_statstable = pymc_tools.stats_table(mc_lognormal_freemach)
lognormal_freemach_statstable.write(
trace_data_path +
'lognormal_freemach_statstable_abundance%s_opr%s.fits' % (abundance, opr),
overwrite=True)
pl.figure(33)
pl.clf()
pl.title("Lognormal")
pymc_plotting.plot_mc_hist(
mc_lognormal, 'b', lolim=True, alpha=0.5, bins=25, legloc='lower right')
pl.xlabel('$b$')
savefig(savepath + 'LognormalWithMach_b_1D_restrictions.png')
print "Some statistics used in the paper: "
print 'mc_lognormal_simple sigma: ', mc_simple.stats()['sigma']['quantiles']
print 'mc_lognormal sigma: ', mc_lognormal.stats()['sigma']['quantiles']
print 'mc_lognormal b: ', mc_lognormal.stats(
quantiles=(0.1, 1, 2.5, 5, 50))['b']['quantiles']
stylecycle = itertools.cycle(('-','-.','--',':'))
dashcycle = itertools.cycle(((None,None),(6,2),(10,4),(2,2),(5,5)))
for sigma in np.arange(0.5,4.0,1):
ax.plot(logmeandens,tauratio(meandens,sigma=sigma),color='k',linewidth=2, alpha=0.5, label='$\\sigma_s=%0.1f$' % sigma, dashes=dashcycle.next())
dashcycle = itertools.cycle(((None,None),(6,2),(10,4),(2,2),(5,5)))
for sigma in np.arange(0.5,4.0,1):
ax.plot(logmeandens,tauratio_hopkins(meandens,sigma=sigma),color='orange', label='$\\sigma_s=%0.1f$ Hopkins' % sigma, linewidth=3, alpha=0.8, dashes=dashcycle.next())
ax.legend(loc='best',prop={'size':18})
ax.axis([-1,7,0,15])
ax.set_xlabel('$\\log_{10}\\left(\\langle\\rho\\rangle_V(\\mathrm{H}_2) [\\mathrm{cm}^{-3}]\\right)$',fontsize=24)
ax.set_ylabel('$\\tau_{1-1}/\\tau_{2-2}$',fontsize=24)
savefig(savepath+'lognormalsmooth_density_ratio_massweight_withhopkins_logopr%0.1f_abund%s.png' % (np.log10(opr),str(abundance)),bbox_inches='tight')
dot,caps,bars = ax.errorbar([np.log10(30)],
[ratio],
xerr=np.array([[0.47,0.82]]).T,
yerr=[eratio], # np.array([[0.87,1.11]]).T,
label="G43.17+0.01",
color=(0,0,1,0.5),
alpha=0.5,
marker='o',
linewidth=2)
caps[0].set_marker('$($')
caps[1].set_marker('$)$')
caps[0].set_color((1,0,0,0.6))
caps[1].set_color((1,0,0,0.6))
bars[0].set_color((1,0,0,0.6))
from measure_tau import trace_data_path, abundance, savefig, savepath
import astropy.io.fits as pyfits
from agpy import pymc_plotting
#import pylab as pl
lognormal_freemach_statstable = pyfits.getdata(trace_data_path+'lognormal_freemach_statstable_abundance%s.fits' % abundance)
mc_lognormal_freemach_traces = pyfits.getdata(trace_data_path+"mc_lognormal_freemach_traces.fits")
ax = pymc_plotting.hist2d(mc_lognormal_freemach_traces, 'tau_ratio','mach', bins=50,
clear=True, fignum=1, varslice=(None,None,None), colorbar=True)
ax.hlines([3.68],*ax.get_xlim(),linestyles=['--'], colors=['k'], label="H$_2$CO Lower-limit")
ax.hlines([6.58],*ax.get_xlim(),linestyles=[':'], colors=['k'], label="CO Lower-limit")
ax.legend(loc='best')
ax.set_ylabel(r"$\mathcal{M}_{3D}$")
ax.set_xlabel('$\\tau_{1-1}/\\tau_{2-2}$',fontsize=24)
savefig(savepath+"mach_vs_tauratio_lognormal_mcmc_contours.png")
print "Some statistics used in the paper: "
print "mc_hopkins_simple sigma: ", mc_hopkins_simple.stats()["sigma"]["quantiles"]
print "mc_hopkins sigma: ", mc_hopkins.stats()["sigma"]["quantiles"]
print "mc_hopkins Tval: ", mc_hopkins.stats()["Tval"]["quantiles"]
print "mc_hopkins b: ", mc_hopkins.stats(quantiles=(0.1, 1, 2.5, 5, 50))["b"]["quantiles"]
print "mc_hopkins m: ", mc_hopkins.stats()["mach_mu"]["quantiles"]
print "mc_hopkins_freemach sigma: ", mc_hopkins_freemach.stats()["sigma"]["quantiles"]
print "mc_hopkins_freemach Tval: ", mc_hopkins_freemach.stats()["Tval"]["quantiles"]
print "mc_hopkins_freemach b: ", mc_hopkins_freemach.stats(quantiles=(0.1, 1, 2.5, 5, 50))["b"]["quantiles"]
print "mc_hopkins_freemach m: ", mc_hopkins_freemach.stats()["mach"]["quantiles"]
hopkins_statstable = pymc_tools.stats_table(mc_hopkins)
hopkins_statstable.write(
trace_data_path + "hopkins_statstable_abundance%s_opr%s.fits" % (abundance, opr), overwrite=True
)
hopkins_simple_statstable = pymc_tools.stats_table(mc_hopkins_simple)
hopkins_simple_statstable.write(
trace_data_path + "hopkins_simple_statstable_abundance%s_opr%s.fits" % (abundance, opr), overwrite=True
)
hopkins_freemach_statstable = pymc_tools.stats_table(mc_hopkins_freemach)
hopkins_freemach_statstable.write(
trace_data_path + "hopkins_freemach_statstable_abundance%s_opr%s.fits" % (abundance, opr), overwrite=True
)
pl.figure(32)
pl.clf()
pl.title("Hopkins")
pymc_plotting.plot_mc_hist(mc_hopkins, "b", lolim=True, alpha=0.5, bins=25, legloc="lower right")
savefig(savepath + "HopkinsWithMach_b_1D_restrictions.png")
for withfree in ('with','free'):
logtraces = pyfits.getdata(trace_data_path+"mc_lognormal_%smach_traces%s.fits" % (withfree,abundance))
ax = pymc_plotting.hist2d(logtraces, 'b','mach', bins=50,
clear=True, fignum=1, varslice=(2.5e5,None,None), colorbar=True)
ax.hlines([3.68],*ax.get_xlim(),linestyles=['--'], colors=['k'], label="H$_2$CO")
ax.hlines([6.58],*ax.get_xlim(),linestyles=[':'], colors=['k'], label="CO")
ax.legend(loc='best')
ax.set_ylabel(r"$\mathcal{M}_{3D}$")
ax.set_xlabel('$b$',fontsize=36)
savefig(savepath+"mach_vs_b_lognormal_mcmc_contours_%smach_abundance%s.png" % (withfree,abundance))
hoptraces = pyfits.getdata(trace_data_path+"mc_hopkins_%smach_traces_abundance%s.fits" % (withfree,abundance))
if 'mach' in hoptraces.names:
mach = 'mach'
else:
mach = 'mach_mu'
ax = pymc_plotting.hist2d(hoptraces, 'b',mach, bins=50,
clear=True, fignum=1, varslice=(2.5e5,None,None), colorbar=True)
ax.hlines([3.68],*ax.get_xlim(),linestyles=['--'], colors=['k'], label="H$_2$CO")
ax.hlines([6.58],*ax.get_xlim(),linestyles=[':'], colors=['k'], label="CO")
