def lnprob(mass, luminosity=2e7, luminosity_error=5e6):
if mass < 1:
return -np.inf, 0
if mass > 1e5:
# use a best-fit relation; scatter is minimal
lum = 10**(np.log10(mass) + 3.55)
# n > 20 msun
nostars = 10**(np.log10(mass) - 2.5)
else:
cluster = imf.make_cluster(mass, silent=True)
lum = 10**imf.lum_of_cluster(cluster)
nostars = (cluster > mmin_ostar).sum()
return -(luminosity-lum)**2/(2*luminosity_error**2), nostars
def lnprob(mass, luminosity=2e7, luminosity_error=5e6):
if mass < 1:
return -np.inf, 0
if mass > 1e5:
# use a best-fit relation; scatter is minimal
lum = 10**(np.log10(mass) + 3.55)
# n > 20 msun
nostars = 10**(np.log10(mass) - 2.5)
else:
cluster = imf.make_cluster(mass, silent=True)
lum = 10**imf.lum_of_cluster(cluster)
nostars = (cluster > mmin_ostar).sum()
return -(luminosity - lum)**2 / (2 * luminosity_error**2), nostars
closest_type = np.argmin(np.abs(tbl['lyclum'] - qmax))
print("Closest spectral type = {0}".format(tbl['SpT'][closest_type]))
print(tbl['SpT', 'Msun', 'lyclum', 'logL',
'Teff'][closest_type - 1:closest_type + 1])
# IMF examinations
from imf import imf
cluster_masses = [250, 500, 1000, 2000]
nsamp = 100
clusters = {
mass: [imf.make_cluster(mass, silent=True) for ii in range(nsamp)]
for mass in cluster_masses
}
luminosities = {
mass: [imf.lum_of_cluster(cl) for cl in clusters[mass]]
for mass in cluster_masses
}
lycluminosities = {
mass: [imf.lyc_of_cluster(cl) for cl in clusters[mass]]
for mass in cluster_masses
}
for mass in cluster_masses:
pl.plot(luminosities[mass], lycluminosities[mass], '.', label=mass)
pl.plot(np.log10(2e4), np.log10(4e45), 'x')
pl.legend(loc='best')
import numpy as np
from imf import imf
import pylab as pl
from astropy.utils.console import ProgressBar
clusters, luminosities, masses, mean_luminosities, mean_masses, max_masses = {},{},{},{},{},{}
# uniform random sampling from 100 to 10^5 msun
for clmass in ProgressBar(np.concatenate([10**(np.random.rand(int(1e3))*1 + 4), 10**(np.random.rand(int(1e4))*2.5+1.5)])):
clusters[clmass] = imf.make_cluster(clmass, 'kroupa', mmax=150, silent=True)
# cluster luminosities
luminosities[clmass] = imf.lum_of_cluster(clusters[clmass])
masses[clmass] = clmass
mean_luminosities[clmass] = np.mean(luminosities[clmass])
mean_masses[clmass] = np.mean(clusters[clmass])
max_masses[clmass] = np.max(clusters[clmass])
mass_to_light = np.array([mean_masses[k]/mean_luminosities[k] for k in sorted(clusters.keys())])
pl.figure(2).clf()
pl.semilogx(sorted(clusters.keys()), mass_to_light**-1, '.', alpha=0.1)
pl.xlabel("Cluster Mass")
pl.ylabel("Light to Mass $L_\odot / M_\odot$")
pl.ylim(6,21)
pl.savefig("light_to_mass_vs_mass.pdf")
pl.figure(3).clf()
pl.loglog(max_masses.keys(), max_masses.values(), '.', alpha=0.1)
pl.xlabel("Cluster Mass")
pl.ylabel("Maximum stellar mass")
import numpy as np
from imf import imf
import pylab as pl
from astropy.utils.console import ProgressBar
import paths
pl.matplotlib.rc_file('pubfiguresrc')
nclusters_per_bin = 100
maxmass = 3e4
mmin_bstar = 8
mmin_ostar = 20
cl_masses = np.logspace(2.5,np.log10(maxmass))
clusters = np.array([[imf.make_cluster(mass*(np.random.randn()/20.+1.),silent=True) for ii in range(nclusters_per_bin)] for mass in ProgressBar(cl_masses)])
luminosities = [imf.lum_of_cluster(cl) for section in ProgressBar(clusters) for cl in section]
#cl_masses_flat = np.array([x for mass in cl_masses for x in [mass for ii in range(nclusters_per_bin)]])
cl_masses_flat = np.array([sum(cl) for section in clusters for cl in section])
n_obstars = np.array([(cl>mmin_bstar).sum() for section in clusters for cl in section])
n_ostars = np.array([(cl>mmin_ostar).sum() for section in clusters for cl in section])
# guess, not measurement
nobstars_w51 = n_obstars[(cl_masses_flat > 4e3) & (cl_masses_flat < 9e3)]
nostars_w51 = n_ostars[(cl_masses_flat > 4e3) & (cl_masses_flat < 9e3)]
print("Implied stellar mass of e1 cluster, assuming 7 stars are OB stars: {0} +/- {1} M_sun".format(cl_masses_flat[n_obstars==7].mean(), cl_masses_flat[n_obstars==7].std()))
print("Implied stellar mass of e1 cluster, assuming 7 stars are O stars: {0} +/- {1} M_sun".format(cl_masses_flat[n_ostars==7].mean(), cl_masses_flat[n_ostars==7].std()))
print("Implied stellar mass of e1 cluster, assuming 6 stars are OB stars: {0} +/- {1} M_sun".format(cl_masses_flat[n_obstars==6].mean(), cl_masses_flat[n_obstars==6].std()))
print("Implied stellar mass of e1 cluster, assuming 6 stars are O stars: {0} +/- {1} M_sun".format(cl_masses_flat[n_ostars==6].mean(), cl_masses_flat[n_ostars==6].std()))
print("Implied stellar mass of e1 cluster, assuming 5 stars are OB stars: {0} +/- {1} M_sun".format(cl_masses_flat[n_obstars==5].mean(), cl_masses_flat[n_obstars==6].std()))
import numpy as np
from imf import imf
import pylab as pl
from astropy.utils.console import ProgressBar
clusters, luminosities, masses, mean_luminosities, mean_masses, max_masses = {},{},{},{},{},{}
# uniform random sampling from 100 to 10^5 msun
for clmass in ProgressBar(np.concatenate([10**(np.random.rand(int(1e3))*1 + 4), 10**(np.random.rand(int(1e4))*2.5+1.5)])):
clusters[clmass] = imf.make_cluster(clmass, 'kroupa', mmax=150, silent=True)
# cluster luminosities
luminosities[clmass] = imf.lum_of_cluster(clusters[clmass])
masses[clmass] = clmass
mean_luminosities[clmass] = np.mean(luminosities[clmass])
mean_masses[clmass] = np.mean(clusters[clmass])
max_masses[clmass] = np.max(clusters[clmass])
mass_to_light = np.array([mean_masses[k]/mean_luminosities[k] for k in sorted(clusters.keys())])
pl.figure(2).clf()
pl.semilogx(sorted(clusters.keys()), mass_to_light**-1, '.', alpha=0.1)
pl.xlabel("Cluster Mass")
pl.ylabel("Light to Mass $L_\odot / M_\odot$")
pl.ylim(6,21)
pl.savefig("light_to_mass_vs_mass.pdf")
pl.figure(3).clf()
pl.loglog(max_masses.keys(), max_masses.values(), '.', alpha=0.1)
pl.xlabel("Cluster Mass")
pl.ylabel("Maximum stellar mass")
print(stop_crit)
if stop_crit not in synth_data:
clusters, luminosities, masses, mean_luminosities, mean_masses, max_masses, number = {},{},{},{},{},{},{}
for clmass in ProgressBar(
np.concatenate([
10**(np.random.rand(int(1e3)) * 1 + 4),
10**(np.random.rand(int(1e4)) * 2.5 + 1.5)
])):
key = str(clmass) # for jsonification
clusters[key] = imf.make_cluster(clmass,
'kroupa',
mmax=150,
silent=True,
stop_criterion=stop_crit)
# cluster luminosities
luminosities[key] = imf.lum_of_cluster(clusters[key])
masses[key] = clmass
number[key] = len(clusters[key])
#mean_luminosities[clmass] = np.mean(luminosities[clmass])
mean_masses[key] = np.mean(clusters[key])
max_masses[key] = np.max(clusters[key])
synth_data[stop_crit] = { #'clusters': clusters,
'number': number,
'luminosities': luminosities,
'masses': masses,
'mean_luminosities': mean_luminosities,
'mean_masses': mean_masses,
'max_masses': max_masses
}
else:
import paths
pl.matplotlib.rc_file('pubfiguresrc')
nclusters_per_bin = 100
maxmass = 3e4
mmin_bstar = 8
mmin_ostar = 20
cl_masses = np.logspace(2.5, np.log10(maxmass))
clusters = np.array([[
imf.make_cluster(mass * (np.random.randn() / 20. + 1.), silent=True)
for ii in range(nclusters_per_bin)
] for mass in ProgressBar(cl_masses)])
luminosities = [
imf.lum_of_cluster(cl) for section in ProgressBar(clusters)
for cl in section
]
#cl_masses_flat = np.array([x for mass in cl_masses for x in [mass for ii in range(nclusters_per_bin)]])
cl_masses_flat = np.array([sum(cl) for section in clusters for cl in section])
n_obstars = np.array([(cl > mmin_bstar).sum() for section in clusters
for cl in section])
n_ostars = np.array([(cl > mmin_ostar).sum() for section in clusters
for cl in section])
# guess, not measurement
nobstars_w51 = n_obstars[(cl_masses_flat > 4e3) & (cl_masses_flat < 9e3)]
nostars_w51 = n_ostars[(cl_masses_flat > 4e3) & (cl_masses_flat < 9e3)]
print(
"Implied stellar mass of e1 cluster, assuming 7 stars are OB stars: {0} +/- {1} M_sun"
