def setup_isochrone(self):
isochrone = self.isochrone
for b in self.bands:
isochrone[b + 'flux'] = 10**(isochrone['hsc_' + b] / -2.5)
# Select a minimum mass for the stochastic component
self.minmass_stochastic = min(
1.0, isochrone['initial_mass'][isochrone['phase'] < 0.1][-1])
# Compute the fraction of the total mass in the stochastic component (the rest will be in the smooth component)
self.imf = imf.Kroupa(mmin=0.1,
mmax=120,
p1=0.3,
p2=1.3,
p3=2.3,
break1=0.08,
break2=0.5)
self.imf.normalize()
mmax = 200
self.stochastic_mass_fraction = (
self.imf.m_integrate(self.minmass_stochastic, mmax)[0] /
self.imf.m_integrate(self.imf.mmin, mmax)[0])
m = isochrone['initial_mass']
dn_dm = self.imf(m)
self.smooth_flux = {}
selection = isochrone['initial_mass'] <= self.minmass_stochastic
for b in self.bands:
self.smooth_flux[b] = integrate.simps(
m[selection],
isochrone[b + 'flux'][selection] * dn_dm[selection])
def make_plot_with_mw_and_gal_ocs(savefig=False):
xmin=0.1
xmax=3000
plt.figure(figsize=(12, 5))
ax1 = plt.gca()
ax2 = ax1.twinx()
import imf
x = np.logspace(-3, 2, 100)
kroupa = imf.Kroupa(mmin=1e-3, mmax=1e1)
yk = kroupa(x) * x
plt.loglog(x*0.5e3, yk, c="k", ls=":", alpha=0.3)
plt.ylim(ymax=1)
plt.gca().get_yaxis().set_visible(False)
plt.sca(ax1)
plot_notable_galaxy_distances(end=-1)
plot_with_combined_mwocs_galocs_table()
plt.semilogx()
plt.xlim(0.1, 3000)
plt.ylim(0, 3000)
plt.legend(loc=2)
ax3 = ax1.twiny()
plot_spec_type_dropouts(xmin=xmin*1e3, xmax=xmax*1e3, lm=None)
plt.tight_layout()
if savefig:
plt.savefig("young_clusters_within_2Mpc_incl_MW.pdf", format="pdf")
plt.show()
pl.clf()
cluster,yax,colors = coolplot(1000, massfunc=massfunc, log=False)
pl.scatter(cluster, yax, c=colors, s=np.log10(cluster+3)*85,
linewidths=0.5, edgecolors=(0,0,0,0.25), alpha=0.95)
pl.gca().set_xscale('log')
masses = np.logspace(np.log10(cluster.min()), np.log10(cluster.max()),10000)
pl.plot(masses,(massfunc(masses)),'r--',linewidth=2,alpha=0.5)
pl.xlabel("Stellar Mass")
pl.ylabel("dN(M)/dM")
pl.gca().axis([min(cluster)/1.1,max(cluster)*1.1,min(yax)-0.2,max(yax)+0.5])
pl.savefig("{0}_imf_figure_linear.png".format(name),bbox_inches='tight')
# make one more plot, now showing a top-heavy (shallow-tail) IMF
massfunc = imf.Kroupa(p3=1.75)
name='KroupaTopHeavy'
pl.figure(1, figsize=(10,8))
pl.clf()
cluster,yax,colors = coolplot(1000, massfunc=massfunc)
pl.scatter(cluster, yax, c=colors, s=np.log10(cluster+3)*85,
linewidths=0.5, edgecolors=(0,0,0,0.25), alpha=0.95)
pl.gca().set_xscale('log')
masses = np.logspace(np.log10(cluster.min()), np.log10(cluster.max()),10000)
pl.plot(masses,np.log10(massfunc(masses)),'r--',linewidth=2,alpha=0.5)
pl.xlabel("Stellar Mass")
pl.ylabel("log(dN(M)/dM)")
pl.gca().axis([min(cluster)/1.1,max(cluster)*1.1,min(yax)-0.2,max(yax)+0.5])
pl.savefig("{0}_imf_figure_log.png".format(name),bbox_inches='tight', dpi=150)
25,
linestyle='--',
color='k',
linewidth=2)
ax2.vlines(completeness_3mm * 1e3,
0,
25,
linestyle='--',
color='k',
linewidth=2)
# now compare to a simple IMF...
import dust_emissivity
import imf
masses = np.logspace(-2, 2, 1000) * u.M_sun
probabilities = imf.Kroupa()(masses)
core_efficiency = 1 / 3.
beta = 1.5
model_fluxes_1mm_20K = dust_emissivity.dust.snuofmass(226 * u.GHz,
masses / core_efficiency,
distance=5.4 * u.kpc,
beta=beta,
temperature=20 * u.K)
model_fluxes_3mm_20K = dust_emissivity.dust.snuofmass(93 * u.GHz,
masses / core_efficiency,
distance=5.4 * u.kpc,
beta=beta,
temperature=20 * u.K)
# number not hii:
n_not_hii_gt_1pt5 = ((~hii) & (u.Quantity(core_phot_tbl['peak'], u.Jy/u.beam) > 1.5*u.mJy/u.beam)).sum()
# should be 140
print("Number of not-HII regions at flux >1.5 mJy: {0}".format(n_not_hii_gt_1pt5))
n_not_hii_mid = ((~hii) & (u.Quantity(core_phot_tbl['peak'], u.Jy/u.beam) > 1.5*u.mJy/u.beam) & ((u.Quantity(core_phot_tbl['peak'], u.Jy/u.beam) < 10*u.mJy/u.beam))).sum()
# should be 119
print("Number of not-HII regions at 10 mJy > flux >1.5 mJy: {0}".format(n_not_hii_mid))
nbright = (u.Quantity(core_phot_tbl['peak'], u.Jy/u.beam) > 10*u.mJy/u.beam).sum()
print("nbright = {0}".format(nbright))
nbright_hii = (hii & (u.Quantity(core_phot_tbl['peak'], u.Jy/u.beam) > 10*u.mJy/u.beam)).sum()
print("nbright,hii = {0}".format(nbright_hii))
# in order to produce a >10 mJy flux, a star must produce > 2e47 photons, which
# implies a star B1.5V or earlier, >10 Msun.
kroupa = imf.Kroupa()
mmax = 200
cutoff1 = 8
cutoff2 = 10
x = np.linspace(cutoff2,mmax,50000)
y = kroupa(x)
over10mean = (x*y).sum()/y.sum()
x = np.linspace(cutoff1,cutoff2,50000)
y = kroupa(x)
eighttotenmean = (x*y).sum()/y.sum()
over8fraction = (kroupa.m_integrate(cutoff1, mmax)[0] /
kroupa.m_integrate(kroupa.mmin, mmax)[0])
over10fraction = (kroupa.m_integrate(cutoff2, mmax)[0] /
kroupa.m_integrate(kroupa.mmin, mmax)[0])
