def plot_red_clump_arches():
logAge = 9.0
AKs = 3.1
distance = 8000
iso = syn.load_isochrone(logAge=logAge, AKs=AKs, distance=distance)
# Magnitude-Temperature
py.clf()
py.plot(iso.T, iso.magKp, 'k-')
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
gca.set_ylim(gca.get_ylim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('Kp Magnitude')
py.title('log(t)=%.2f, AKs=%.1f, d=%4d' % (logAge, AKs, distance))
py.savefig(plotDir + 'rc_arches_mag_temp.png')
# Magnitude-Temperature
py.clf()
py.plot(iso.mag153m - iso.magKp, iso.magKp, 'k-')
gca = py.gca()
gca.set_ylim(gca.get_ylim()[::-1])
py.xlabel('F153M - Kp')
py.ylabel('Kp')
py.title('log(t)=%.2f, AKs=%.1f, d=%4d' % (logAge, AKs, distance))
py.savefig(plotDir + 'rc_arches_cmd_153m_kp.png')
def plot_red_clump_bolometric():
ages = np.array([9.00, 8.95, 8.90, 8.85, 8.78, 8.70])
isos = []
for ii in range(len(ages)):
iso = syn.load_isochrone(logAge=ages[ii], AKs=0, distance=10)
isos.append(iso)
# Make an HR diagram
py.clf()
for ii in range(len(ages)):
iso = isos[ii]
label = '%2d' % (10**(ages[ii] - 9.0))
py.plot(iso.T, iso.logL, '-', label=label)
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('log (L/L_sun)')
py.legend(loc='lower left')
py.savefig(plotDir + 'hr_diagram.png')
# Make an HR diagram
py.clf()
for ii in range(len(ages)):
iso = isos[ii]
label = '%2d' % (10**(ages[ii] - 9.0))
py.plot(iso.T, iso.logL, '.', label=label)
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('log (L/L_sun)')
py.legend(loc='lower left')
py.savefig(plotDir + 'hr_diagram_points.png')
def plot_red_clump_arches():
logAge = 9.0
AKs = 3.1
distance = 8000
iso = syn.load_isochrone(logAge=logAge, AKs=AKs, distance=distance)
# Magnitude-Temperature
py.clf()
py.plot(iso.T, iso.magKp, 'k-')
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
gca.set_ylim(gca.get_ylim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('Kp Magnitude')
py.title('log(t)=%.2f, AKs=%.1f, d=%4d' % (logAge, AKs, distance))
py.savefig(plotDir + 'rc_arches_mag_temp.png')
# Magnitude-Temperature
py.clf()
py.plot(iso.mag153m-iso.magKp, iso.magKp, 'k-')
gca = py.gca()
gca.set_ylim(gca.get_ylim()[::-1])
py.xlabel('F153M - Kp')
py.ylabel('Kp')
py.title('log(t)=%.2f, AKs=%.1f, d=%4d' % (logAge, AKs, distance))
py.savefig(plotDir + 'rc_arches_cmd_153m_kp.png')
def plot_red_clump_bolometric():
ages = np.array([9.00, 8.95, 8.90, 8.85, 8.78, 8.70])
isos = []
for ii in range(len(ages)):
iso = syn.load_isochrone(logAge=ages[ii], AKs=0, distance=10)
isos.append(iso)
# Make an HR diagram
py.clf()
for ii in range(len(ages)):
iso = isos[ii]
label = '%2d' % (10**(ages[ii]-9.0))
py.plot(iso.T, iso.logL, '-', label=label)
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('log (L/L_sun)')
py.legend(loc='lower left')
py.savefig(plotDir + 'hr_diagram.png')
# Make an HR diagram
py.clf()
for ii in range(len(ages)):
iso = isos[ii]
label = '%2d' % (10**(ages[ii]-9.0))
py.plot(iso.T, iso.logL, '.', label=label)
gca = py.gca()
gca.set_xlim(gca.get_xlim()[::-1])
py.xlabel('Teff (K)')
py.ylabel('log (L/L_sun)')
py.legend(loc='lower left')
py.savefig(plotDir + 'hr_diagram_points.png')
def prep_clusters():
arch_iso = arch_syn.load_isochrone(logAge=6.40, AKs=2.4, distance=8000)
#quin_iso = arch_syn.load_isochrone(logAge=6.55, AKs=3.1, distance=8000)
#cent_iso = arch_syn.load_isochrone(logAge=6.80, AKs=2.7, distance=8000)
clust = model_young_cluster(arch_iso, makeMultiples=True)
_out = open(propdir + 'clust_arhces_multi.pickle', 'w')
pickle.dump(clust, _out)
_out.close()
clust = model_young_cluster(arch_iso, makeMultiples=False)
_out = open(propdir + 'clust_arches_single.pickle', 'w')
pickle.dump(clust, _out)
_out.close()
return
def plot_cmd():
iso = syn.load_isochrone(logAge=logAge, AKs=AKs, distance=distance)
mag = iso.mag153m
color = iso.mag127m - iso.mag153m
idx1 = np.abs(iso.M - 1.0).argmin()
idx01 = np.abs(iso.M - 0.1).argmin()
py.clf()
py.plot(color, mag, 'k.')
py.plot(color[idx1], mag[idx1], 'r^', ms=20)
py.plot(color[idx01], mag[idx01], 'r^', ms=20)
py.xlabel('F127M - F153M')
py.ylabel('F153M')
py.gca().set_ylim(py.gca().get_ylim()[::-1])
py.savefig('/u/jlu/work/arches/cmd_sim.png')
py.clf()
py.semilogx(iso.T, iso.logL, 'k.')
py.gca().set_xlim(py.gca().get_xlim()[::-1])
py.plot(iso.T[idx1], iso.logL[idx1], 'r^', ms=20)
py.plot(iso.T[idx01], iso.logL[idx01], 'r^', ms=20)
py.xlabel('Teff (K)')
py.ylabel('log(L/Lsun')
py.savefig('/u/jlu/work/arches/hr_sim.png')
wave = np.array([1.27, 1.39, 1.53])
py.clf()
py.plot(wave, [iso.mag127m[idx1], iso.mag139m[idx1], iso.mag153m[idx1]],
'r-')
py.plot(wave, [iso.mag127m[idx01], iso.mag139m[idx01], iso.mag153m[idx01]],
'b-')
py.savefig('/u/jlu/work/arches/sed.png')
c = constants
# Convert luminosity to erg/s
L_all = 10**(iso.logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * iso.T**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# Get the atmosphere model now. Wavelength is in Angstroms
star1 = atm.get_merged_atmosphere(temperature=iso.T[idx1],
gravity=iso.logg[idx1])
# Convert into flux observed at Earth (unreddened)
star1 *= (R_all[idx1] / distance)**2 # in erg s^-1 cm^-2 A^-1
# Get the atmosphere model now. Wavelength is in Angstroms
star01 = atm.get_merged_atmosphere(temperature=iso.T[idx01],
gravity=iso.logg[idx01])
# Convert into flux observed at Earth (unreddened)
star01 *= (R_all[idx01] / distance)**2 # in erg s^-1 cm^-2 A^-1
py.clf()
py.semilogy(star1.wave, star1.flux, 'r-')
py.plot(star01.wave, star01.flux, 'b-')
py.xlim(10000, 20000)
py.ylim(5e-19, 1e-16)
py.savefig('/u/jlu/work/arches/spectra.png')
def plot_cmd():
iso = syn.load_isochrone(logAge=logAge, AKs=AKs, distance=distance)
mag = iso.mag153m
color = iso.mag127m - iso.mag153m
idx1 = np.abs(iso.M - 1.0).argmin()
idx01 = np.abs(iso.M - 0.1).argmin()
py.clf()
py.plot(color, mag, 'k.')
py.plot(color[idx1], mag[idx1], 'r^', ms=20)
py.plot(color[idx01], mag[idx01], 'r^', ms=20)
py.xlabel('F127M - F153M')
py.ylabel('F153M')
py.gca().set_ylim(py.gca().get_ylim()[::-1])
py.savefig('/u/jlu/work/arches/cmd_sim.png')
py.clf()
py.semilogx(iso.T, iso.logL, 'k.')
py.gca().set_xlim(py.gca().get_xlim()[::-1])
py.plot(iso.T[idx1], iso.logL[idx1], 'r^', ms=20)
py.plot(iso.T[idx01], iso.logL[idx01], 'r^', ms=20)
py.xlabel('Teff (K)')
py.ylabel('log(L/Lsun')
py.savefig('/u/jlu/work/arches/hr_sim.png')
wave = np.array([1.27, 1.39, 1.53])
py.clf()
py.plot(wave, [iso.mag127m[idx1], iso.mag139m[idx1], iso.mag153m[idx1]], 'r-')
py.plot(wave, [iso.mag127m[idx01], iso.mag139m[idx01], iso.mag153m[idx01]], 'b-')
py.savefig('/u/jlu/work/arches/sed.png')
c = constants
# Convert luminosity to erg/s
L_all = 10**(iso.logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * iso.T**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# Get the atmosphere model now. Wavelength is in Angstroms
star1 = atm.get_merged_atmosphere(temperature=iso.T[idx1],
gravity=iso.logg[idx1])
# Convert into flux observed at Earth (unreddened)
star1 *= (R_all[idx1] / distance)**2 # in erg s^-1 cm^-2 A^-1
# Get the atmosphere model now. Wavelength is in Angstroms
star01 = atm.get_merged_atmosphere(temperature=iso.T[idx01],
gravity=iso.logg[idx01])
# Convert into flux observed at Earth (unreddened)
star01 *= (R_all[idx01] / distance)**2 # in erg s^-1 cm^-2 A^-1
py.clf()
py.semilogy(star1.wave, star1.flux, 'r-')
py.plot(star01.wave, star01.flux, 'b-')
py.xlim(10000, 20000)
py.ylim(5e-19, 1e-16)
py.savefig('/u/jlu/work/arches/spectra.png')
def plot_arches_ast_err():
catalog = '/u/mwhosek/Desktop/699-2/ks2/submitted_2015_04/ks2_poly/'
catalog += 'catalog.fits'
# Read in data table
t = Table.read(catalog)
mag = t['m_2010_f153m']
vxe = t['fit_vxe'] * 120.
vye = t['fit_vye'] * 120.
ve = np.hypot(vxe, vye)
# Read in isochrone
iso = syn.load_isochrone(logAge=6.4, AKs=2.4, distance=8000)
# Make an array of magnitudes and convert to masses.
mag_arr = np.arange(12, 24, 0.05)
mass_arr = np.zeros(len(mag_arr), dtype=float)
for ii in range(len(mag_arr)):
dm = np.abs(iso.mag153m - mag_arr[ii])
dm_idx = dm.argmin()
mass_arr[ii] = iso.M[dm_idx]
# Establish new limits
lim_mag = 21.7
lim_pme = 1.95
good = np.where((mag < lim_mag) & (ve < lim_pme))[0]
bad = np.where((mag >= lim_mag) | (ve >= lim_pme))[0]
py.clf()
py.subplots_adjust(top=0.88)
py.plot(mag[good], ve[good], 'k.', ms=2)
py.plot(mag[bad], ve[bad], 'k.', ms=2, alpha=0.3)
py.xlim(13, 23)
py.ylim(0.0, 3)
py.xlabel('F153M Magnitude', labelpad=10)
py.ylabel('Proper Motion Error (mas/yr)', labelpad=15)
ax1 = py.gca()
ax2 = ax1.twiny()
top_tick_mag = np.array([14.021, 17, 19.9, 21.65])
top_tick_mass = np.zeros(len(top_tick_mag), dtype=float)
top_tick_label = np.zeros(len(top_tick_mag), dtype='S13')
for nn in range(len(top_tick_mag)):
dm = np.abs(iso.mag153m - top_tick_mag[nn])
dm_idx = dm.argmin()
top_tick_mass[nn] = iso.M[dm_idx]
if top_tick_mass[nn] > 10:
top_tick_label[nn] = '{0:2.0f}'.format(top_tick_mass[nn])
else:
top_tick_label[nn] = '{0:4.1f}'.format(top_tick_mass[nn])
print top_tick_mag
print top_tick_mass
print top_tick_label
# py.arrow(20, 0.65, 0, -0.25, fc='cyan', ec='cyan',
# head_width=0.3, head_length=0.1, linewidth=3)
# py.arrow(20, 0.65, -1, 0.00, fc='cyan', ec='cyan',
# head_width=0.1, head_length=0.3, linewidth=3)
# py.arrow(21.65, 1.95, 0, -0.25, fc='red', ec='red',
# head_width=0.3, head_length=0.1, linewidth=3)
# py.arrow(21.65, 1.95, -1, 0.00, fc='red', ec='red',
# head_width=0.1, head_length=0.3, linewidth=3)
py.plot([13, 20, 20], [0.65, 0.65, 0.0], color='cyan',
linewidth=3)
py.plot([13, 21.7, 21.7], [1.95, 1.95, 0.0], color='red',
linewidth=3)
py.xlim(13, 23)
py.ylim(0.0, 3)
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(top_tick_mag)
ax2.set_xticklabels(top_tick_label)
ax2.set_xlabel(r'Mass (M$_\odot$)', labelpad=10)
py.text(18, 2.02, 'New Limits', color='red', fontweight='bold',
backgroundcolor='white')
py.savefig(prop_dir + 'arches_pm_error.png')
return
def plot_mass_function():
catalog = '/u/mwhosek/Desktop/699-2/ks2/submitted_2015_04/tidal_radius/'
catalog += 'catalog_probs_4.fits'
prop_dir = '/Users/jlu/doc/proposals/hst/cycle23/arches/'
# Read in data table
t = Table.read(catalog)
# Read in isochrone
iso = syn.load_isochrone(logAge=6.4, AKs=2.4, distance=8000)
# Get the completeness (relevant for diff. de-reddened magnitudes).
comp_file = prop_dir + 'arches_completeness_mag.txt'
comp = Table.read(comp_file, format='ascii')
# Pick out the "good" cluster members.
good = np.where((t['Membership'] > 0.3) & (t['m_2010_f127m'] > 0))[0]
clust = t[good]
# Apply a reddening correction (differential only)
magCut = 99
blueCut = -99
clust = profile.apply_redcut(clust, 1, 2.4, blueCut, magCut, 1)
mag = clust['m_2010_f153m']
color = clust['m_2010_f127m'] - clust['m_2010_f153m']
member = clust['Membership']
# Drop things with too blue colors... this reduces contamination some.
good = np.where(color > 2)[0]
clust = clust[good]
mag = clust['m_2010_f153m']
color = clust['m_2010_f127m'] - clust['m_2010_f153m']
member = clust['Membership']
# Make a finely sampled mass-luminosity relationship by
# interpolating on the isochrone.
iso_mag = iso.mag153m
iso_col = iso.mag127m - iso.mag153m
iso_mass = iso.M.data
iso_WR = iso.isWR
iso_tck, iso_u = interpolate.splprep([iso_mass, iso_mag, iso_col], s=2)
# Find the maximum mass that is NOT a WR star
mass_max = iso_mass[iso_WR == False].max()
u_fine = np.linspace(0, 1, 1e4)
iso_mass_f, iso_mag_f, iso_col_f = interpolate.splev(u_fine, iso_tck)
# Define WR stars.
iso_WR_f = np.zeros(len(iso_mass_f), dtype=bool)
iso_WR_f[iso_mass_f > mass_max] = True
# Make a completeness curve interpolater. First we have to make a
# color-mag grid and figure out the lowest (worst) completeness at
# each point in the grid. Then we can interpolate on that grid.
c_m153_arr = (comp['OuterMag'] + comp['InnerMag']) / 2.0
c_m127_arr = (comp['OuterMag'] + comp['InnerMag']) / 2.0
c_comp_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
c_mag_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
c_col_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
# Loop through an array of F153M mag and F127M-F153M color and
# determine the lowest completness.
for ii in range(len(c_m153_arr)):
for jj in range(len(c_m127_arr)):
c_mag_arr[ii, jj] = c_m153_arr[ii]
c_col_arr[ii, jj] = c_m127_arr[jj] - c_m153_arr[ii]
# if comp['F127m_comp'][jj] < comp['F153m_comp'][ii]:
# c_comp_arr[ii, jj] = comp['F127m_comp'][jj]
# else:
# c_comp_arr[ii, jj] = comp['F153m_comp'][ii]
c_comp_arr[ii, jj] = comp['F153m_comp'][ii] * comp['F127m_comp'][jj]
if c_comp_arr[ii, jj] < 0:
c_comp_arr[ii, jj] = 0
if c_comp_arr[ii, jj] > 1:
c_comp_arr[ii, jj] = 1
comp_int = interpolate.SmoothBivariateSpline(c_mag_arr.flatten(),
c_col_arr.flatten(),
c_comp_arr.flatten(), s=200)
mm_tmp = np.arange(14, 23, 0.1)
cc_tmp = np.arange(0.1, 3.4, 0.1)
comp_tmp = comp_int(mm_tmp, cc_tmp)
py.clf()
py.imshow(comp_tmp, extent=(cc_tmp.min(), cc_tmp.max(),
mm_tmp.min(), mm_tmp.max()))
py.colorbar()
# Loop through data and assign masses and completeness to each star.
mass = np.zeros(len(mag), dtype=float)
isWR = np.zeros(len(mag), dtype=float)
comp = np.zeros(len(mag), dtype=float)
# print mag.min(), mag.max(), color.min(), color.max()
# comp = comp_int(mag, color)
# print comp.shape, mag.shape, color.shape
for ii in range(len(mass)):
dmag = mag[ii] - iso_mag_f
dcol = color[ii] - iso_col_f
delta = np.hypot(dmag, dcol)
# Some funny business - sort and get the closest masses reasonable.
sdx = delta.argsort()
# If the color + mag difference is less than 0.15, then take
# the lowest mass. This helps account for the missing IMF bias.
idx = np.where(delta[sdx] < 0.01)[0]
if len(idx) == 0:
min_idx = delta.argmin()
else:
min_mass_idx = iso_mass_f[sdx[idx]].argmin()
min_idx = sdx[idx][min_mass_idx]
print '{0:4d} {1:4d} {2:4d} {3:5.1f} {4:5.1f}'.format(ii,
min_idx,
delta.argmin(),
iso_mass_f[min_idx],
iso_mass_f[delta.argmin()])
mass[ii] = iso_mass_f[min_idx]
isWR[ii] = iso_WR_f[min_idx]
comp[ii] = comp_int(mag[ii], color[ii])
if comp[ii] > 1:
comp[ii] = 1
if comp[ii] < 0:
comp[ii] = 0
print mag.min(), mag.max(), color.min(), color.max()
print comp.shape, mag.shape, color.shape
# Find the maximum mass where we don't have WR stars anymore
print mass_max, mass.max()
mass_max = mass[isWR == False].max()
print mass_max
# Trim down to just the stars that aren't WR stars.
idx_noWR = np.where(mass <= mass_max)[0]
mass_noWR = mass[idx_noWR]
mag_noWR = mag[idx_noWR]
color_noWR = color[idx_noWR]
isWR_noWR = isWR[idx_noWR]
member_noWR = member[idx_noWR]
comp_noWR = comp[idx_noWR]
# Define our mag and mass bins. We will need both for completeness
# estimation and calculating the mass function.
bins_log_mass = np.arange(0, 1.9, 0.15)
bins_mag = np.zeros(len(bins_log_mass), dtype=float)
for ii in range(len(bins_mag)):
dmass = np.abs((10**bins_log_mass[ii]) - iso_mass_f)
dmass_min_idx = dmass.argmin()
bins_mag[ii] = iso_mag_f[dmass_min_idx]
print 'bins_log_mass = ', bins_log_mass
print '10**bins_log_mass = ', 10**bins_log_mass
print 'bins_mag = ', bins_mag
# compute a preliminary mass function with the propoer weights
weights = member_noWR / comp_noWR
py.figure(1)
py.clf()
py.subplots_adjust(top=0.88)
n_raw, be, p = py.hist(np.log10(mass_noWR), bins=bins_log_mass,
log=True, histtype='step',
label='Unweighted')
py.clf()
n_mem, be, p = py.hist(np.log10(mass_noWR), bins=bins_log_mass,
log=True, histtype='step', color='green',
weights=member_noWR,
label='Observed')
n_fin, be, p = py.hist(np.log10(mass_noWR), bins=bins_log_mass,
log=True, histtype='step', color='black',
weights=weights,
label='Completeness Corr.')
mean_weight = n_fin / n_mem
n_err = (n_fin**0.5) * mean_weight
n_err[0] = 1000.0 # dumb fix for empty bin
bc = bins_log_mass[0:-1] + (np.diff(bins_log_mass) / 2.0)
py.errorbar(bc[1:], n_fin[1:], yerr=n_err[1:], linestyle='none', color='black')
# Fit a powerlaw.
powerlaw = lambda x, amp, index: amp * (x**index)
fitfunc = lambda p, x: p[0] + p[1] * x
errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
log_m = bc[5:]
log_n = np.log10(n_fin)[5:]
log_n_err = n_err[5:] / n_fin[5:]
print 'log_m = ', log_m
print 'log_n = ', log_n
print 'log_n_err = ', log_n_err
pinit = [1.0, -1.0]
out = optimize.leastsq(errfunc, pinit, args=(log_m, log_n, log_n_err), full_output=1)
pfinal = out[0]
covar = out[1]
print 'pfinal = ', pfinal
print 'covar = ', covar
index = pfinal[1]
amp = 10.0**pfinal[0]
indexErr = math.sqrt( covar[0][0] )
ampErr = math.sqrt( covar[1][1] ) * amp
py.plot(log_m, 10**fitfunc(pfinal, log_m), 'k--')
py.text(1.3, 100, r'$\frac{dN}{dm}\propto m^{-2.2}$')
py.axvline(np.log10(mass_max), linestyle='--')
py.ylim(5, 9e2)
py.xlim(0, 1.8)
py.xlabel('log( Mass [Msun])')
py.ylabel('Number of Stars')
py.legend()
ax1 = py.gca()
ax2 = ax1.twiny()
top_tick_mag = np.array([14.0, 17, 20, 21.6])
top_tick_mass = np.zeros(len(top_tick_mag), dtype=float)
top_tick_label = np.zeros(len(top_tick_mag), dtype='S13')
for nn in range(len(top_tick_mag)):
dm = np.abs(iso.mag153m - top_tick_mag[nn])
dm_idx = dm.argmin()
top_tick_mass[nn] = iso.M[dm_idx]
top_tick_label[nn] = '{0:3.1f}'.format(top_tick_mag[nn])
print 'top_tick_mag = ', top_tick_mag
print 'top_tick_msas = ', top_tick_mass
print 'top_tick_label = ', top_tick_label
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(np.log10(top_tick_mass))
ax2.set_xticklabels(top_tick_label)
ax2.set_xlabel(r'F153M (mags)', labelpad=10)
py.savefig(prop_dir + 'arches_imf.png')
py.figure(2)
py.clf()
py.plot(np.log10(iso.M), iso.mag153m)
py.xlim(0, 1.9)
py.xlabel('Log( Mass [Msun] )')
py.ylabel('F153M (mag)')
py.savefig(prop_dir + 'arches_mass_luminosity.png')
py.figure(3)
py.clf()
py.plot(color, mag, 'k.')
py.plot(iso.mag127m - iso.mag153m, iso.mag153m, 'r-')
py.axvline(2)
py.ylim(22, 12)
py.xlim(1, 3.5)
py.xlabel('F127M - F153M (mag)')
py.ylabel('F153M (mag)')
py.savefig(prop_dir + 'arches_cmd_iso_test.png')
return clust
def plot_arches_ast_err():
catalog = '/u/mwhosek/Desktop/699-2/ks2/submitted_2015_04/ks2_poly/'
catalog += 'catalog.fits'
# Read in data table
t = Table.read(catalog)
mag = t['m_2010_f153m']
vxe = t['fit_vxe'] * 120.
vye = t['fit_vye'] * 120.
ve = np.hypot(vxe, vye)
# Read in isochrone
iso = syn.load_isochrone(logAge=6.4, AKs=2.4, distance=8000)
# Make an array of magnitudes and convert to masses.
mag_arr = np.arange(12, 24, 0.05)
mass_arr = np.zeros(len(mag_arr), dtype=float)
for ii in range(len(mag_arr)):
dm = np.abs(iso.mag153m - mag_arr[ii])
dm_idx = dm.argmin()
mass_arr[ii] = iso.M[dm_idx]
# Establish new limits
lim_mag = 21.7
lim_pme = 1.95
good = np.where((mag < lim_mag) & (ve < lim_pme))[0]
bad = np.where((mag >= lim_mag) | (ve >= lim_pme))[0]
py.clf()
py.subplots_adjust(top=0.88)
py.plot(mag[good], ve[good], 'k.', ms=2)
py.plot(mag[bad], ve[bad], 'k.', ms=2, alpha=0.3)
py.xlim(13, 23)
py.ylim(0.0, 3)
py.xlabel('F153M Magnitude', labelpad=10)
py.ylabel('Proper Motion Error (mas/yr)', labelpad=15)
ax1 = py.gca()
ax2 = ax1.twiny()
top_tick_mag = np.array([14.021, 17, 19.9, 21.65])
top_tick_mass = np.zeros(len(top_tick_mag), dtype=float)
top_tick_label = np.zeros(len(top_tick_mag), dtype='S13')
for nn in range(len(top_tick_mag)):
dm = np.abs(iso.mag153m - top_tick_mag[nn])
dm_idx = dm.argmin()
top_tick_mass[nn] = iso.M[dm_idx]
if top_tick_mass[nn] > 10:
top_tick_label[nn] = '{0:2.0f}'.format(top_tick_mass[nn])
else:
top_tick_label[nn] = '{0:4.1f}'.format(top_tick_mass[nn])
print top_tick_mag
print top_tick_mass
print top_tick_label
# py.arrow(20, 0.65, 0, -0.25, fc='cyan', ec='cyan',
# head_width=0.3, head_length=0.1, linewidth=3)
# py.arrow(20, 0.65, -1, 0.00, fc='cyan', ec='cyan',
# head_width=0.1, head_length=0.3, linewidth=3)
# py.arrow(21.65, 1.95, 0, -0.25, fc='red', ec='red',
# head_width=0.3, head_length=0.1, linewidth=3)
# py.arrow(21.65, 1.95, -1, 0.00, fc='red', ec='red',
# head_width=0.1, head_length=0.3, linewidth=3)
py.plot([13, 20, 20], [0.65, 0.65, 0.0], color='cyan', linewidth=3)
py.plot([13, 21.7, 21.7], [1.95, 1.95, 0.0], color='red', linewidth=3)
py.xlim(13, 23)
py.ylim(0.0, 3)
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(top_tick_mag)
ax2.set_xticklabels(top_tick_label)
ax2.set_xlabel(r'Mass (M$_\odot$)', labelpad=10)
py.text(18,
2.02,
'New Limits',
color='red',
fontweight='bold',
backgroundcolor='white')
py.savefig(prop_dir + 'arches_pm_error.png')
return
def plot_mass_function():
catalog = '/u/mwhosek/Desktop/699-2/ks2/submitted_2015_04/tidal_radius/'
catalog += 'catalog_probs_4.fits'
prop_dir = '/Users/jlu/doc/proposals/hst/cycle23/arches/'
# Read in data table
t = Table.read(catalog)
# Read in isochrone
iso = syn.load_isochrone(logAge=6.4, AKs=2.4, distance=8000)
# Get the completeness (relevant for diff. de-reddened magnitudes).
comp_file = prop_dir + 'arches_completeness_mag.txt'
comp = Table.read(comp_file, format='ascii')
# Pick out the "good" cluster members.
good = np.where((t['Membership'] > 0.3) & (t['m_2010_f127m'] > 0))[0]
clust = t[good]
# Apply a reddening correction (differential only)
magCut = 99
blueCut = -99
clust = profile.apply_redcut(clust, 1, 2.4, blueCut, magCut, 1)
mag = clust['m_2010_f153m']
color = clust['m_2010_f127m'] - clust['m_2010_f153m']
member = clust['Membership']
# Drop things with too blue colors... this reduces contamination some.
good = np.where(color > 2)[0]
clust = clust[good]
mag = clust['m_2010_f153m']
color = clust['m_2010_f127m'] - clust['m_2010_f153m']
member = clust['Membership']
# Make a finely sampled mass-luminosity relationship by
# interpolating on the isochrone.
iso_mag = iso.mag153m
iso_col = iso.mag127m - iso.mag153m
iso_mass = iso.M.data
iso_WR = iso.isWR
iso_tck, iso_u = interpolate.splprep([iso_mass, iso_mag, iso_col], s=2)
# Find the maximum mass that is NOT a WR star
mass_max = iso_mass[iso_WR == False].max()
u_fine = np.linspace(0, 1, 1e4)
iso_mass_f, iso_mag_f, iso_col_f = interpolate.splev(u_fine, iso_tck)
# Define WR stars.
iso_WR_f = np.zeros(len(iso_mass_f), dtype=bool)
iso_WR_f[iso_mass_f > mass_max] = True
# Make a completeness curve interpolater. First we have to make a
# color-mag grid and figure out the lowest (worst) completeness at
# each point in the grid. Then we can interpolate on that grid.
c_m153_arr = (comp['OuterMag'] + comp['InnerMag']) / 2.0
c_m127_arr = (comp['OuterMag'] + comp['InnerMag']) / 2.0
c_comp_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
c_mag_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
c_col_arr = np.zeros((len(c_m153_arr), len(c_m127_arr)), dtype=float)
# Loop through an array of F153M mag and F127M-F153M color and
# determine the lowest completness.
for ii in range(len(c_m153_arr)):
for jj in range(len(c_m127_arr)):
c_mag_arr[ii, jj] = c_m153_arr[ii]
c_col_arr[ii, jj] = c_m127_arr[jj] - c_m153_arr[ii]
# if comp['F127m_comp'][jj] < comp['F153m_comp'][ii]:
# c_comp_arr[ii, jj] = comp['F127m_comp'][jj]
# else:
# c_comp_arr[ii, jj] = comp['F153m_comp'][ii]
c_comp_arr[ii,
jj] = comp['F153m_comp'][ii] * comp['F127m_comp'][jj]
if c_comp_arr[ii, jj] < 0:
c_comp_arr[ii, jj] = 0
if c_comp_arr[ii, jj] > 1:
c_comp_arr[ii, jj] = 1
comp_int = interpolate.SmoothBivariateSpline(c_mag_arr.flatten(),
c_col_arr.flatten(),
c_comp_arr.flatten(),
s=200)
mm_tmp = np.arange(14, 23, 0.1)
cc_tmp = np.arange(0.1, 3.4, 0.1)
comp_tmp = comp_int(mm_tmp, cc_tmp)
py.clf()
py.imshow(comp_tmp,
extent=(cc_tmp.min(), cc_tmp.max(), mm_tmp.min(), mm_tmp.max()))
py.colorbar()
# Loop through data and assign masses and completeness to each star.
mass = np.zeros(len(mag), dtype=float)
isWR = np.zeros(len(mag), dtype=float)
comp = np.zeros(len(mag), dtype=float)
# print mag.min(), mag.max(), color.min(), color.max()
# comp = comp_int(mag, color)
# print comp.shape, mag.shape, color.shape
for ii in range(len(mass)):
dmag = mag[ii] - iso_mag_f
dcol = color[ii] - iso_col_f
delta = np.hypot(dmag, dcol)
# Some funny business - sort and get the closest masses reasonable.
sdx = delta.argsort()
# If the color + mag difference is less than 0.15, then take
# the lowest mass. This helps account for the missing IMF bias.
idx = np.where(delta[sdx] < 0.01)[0]
if len(idx) == 0:
min_idx = delta.argmin()
else:
min_mass_idx = iso_mass_f[sdx[idx]].argmin()
min_idx = sdx[idx][min_mass_idx]
print '{0:4d} {1:4d} {2:4d} {3:5.1f} {4:5.1f}'.format(
ii, min_idx, delta.argmin(), iso_mass_f[min_idx],
iso_mass_f[delta.argmin()])
mass[ii] = iso_mass_f[min_idx]
isWR[ii] = iso_WR_f[min_idx]
comp[ii] = comp_int(mag[ii], color[ii])
if comp[ii] > 1:
comp[ii] = 1
if comp[ii] < 0:
comp[ii] = 0
print mag.min(), mag.max(), color.min(), color.max()
print comp.shape, mag.shape, color.shape
# Find the maximum mass where we don't have WR stars anymore
print mass_max, mass.max()
mass_max = mass[isWR == False].max()
print mass_max
# Trim down to just the stars that aren't WR stars.
idx_noWR = np.where(mass <= mass_max)[0]
mass_noWR = mass[idx_noWR]
mag_noWR = mag[idx_noWR]
color_noWR = color[idx_noWR]
isWR_noWR = isWR[idx_noWR]
member_noWR = member[idx_noWR]
comp_noWR = comp[idx_noWR]
# Define our mag and mass bins. We will need both for completeness
# estimation and calculating the mass function.
bins_log_mass = np.arange(0, 1.9, 0.15)
bins_mag = np.zeros(len(bins_log_mass), dtype=float)
for ii in range(len(bins_mag)):
dmass = np.abs((10**bins_log_mass[ii]) - iso_mass_f)
dmass_min_idx = dmass.argmin()
bins_mag[ii] = iso_mag_f[dmass_min_idx]
print 'bins_log_mass = ', bins_log_mass
print '10**bins_log_mass = ', 10**bins_log_mass
print 'bins_mag = ', bins_mag
# compute a preliminary mass function with the propoer weights
weights = member_noWR / comp_noWR
py.figure(1)
py.clf()
py.subplots_adjust(top=0.88)
n_raw, be, p = py.hist(np.log10(mass_noWR),
bins=bins_log_mass,
log=True,
histtype='step',
label='Unweighted')
py.clf()
n_mem, be, p = py.hist(np.log10(mass_noWR),
bins=bins_log_mass,
log=True,
histtype='step',
color='green',
weights=member_noWR,
label='Observed')
n_fin, be, p = py.hist(np.log10(mass_noWR),
bins=bins_log_mass,
log=True,
histtype='step',
color='black',
weights=weights,
label='Completeness Corr.')
mean_weight = n_fin / n_mem
n_err = (n_fin**0.5) * mean_weight
n_err[0] = 1000.0 # dumb fix for empty bin
bc = bins_log_mass[0:-1] + (np.diff(bins_log_mass) / 2.0)
py.errorbar(bc[1:],
n_fin[1:],
yerr=n_err[1:],
linestyle='none',
color='black')
# Fit a powerlaw.
powerlaw = lambda x, amp, index: amp * (x**index)
fitfunc = lambda p, x: p[0] + p[1] * x
errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
log_m = bc[5:]
log_n = np.log10(n_fin)[5:]
log_n_err = n_err[5:] / n_fin[5:]
print 'log_m = ', log_m
print 'log_n = ', log_n
print 'log_n_err = ', log_n_err
pinit = [1.0, -1.0]
out = optimize.leastsq(errfunc,
pinit,
args=(log_m, log_n, log_n_err),
full_output=1)
pfinal = out[0]
covar = out[1]
print 'pfinal = ', pfinal
print 'covar = ', covar
index = pfinal[1]
amp = 10.0**pfinal[0]
indexErr = math.sqrt(covar[0][0])
ampErr = math.sqrt(covar[1][1]) * amp
py.plot(log_m, 10**fitfunc(pfinal, log_m), 'k--')
py.text(1.3, 100, r'$\frac{dN}{dm}\propto m^{-2.2}$')
py.axvline(np.log10(mass_max), linestyle='--')
py.ylim(5, 9e2)
py.xlim(0, 1.8)
py.xlabel('log( Mass [Msun])')
py.ylabel('Number of Stars')
py.legend()
ax1 = py.gca()
ax2 = ax1.twiny()
top_tick_mag = np.array([14.0, 17, 20, 21.6])
top_tick_mass = np.zeros(len(top_tick_mag), dtype=float)
top_tick_label = np.zeros(len(top_tick_mag), dtype='S13')
for nn in range(len(top_tick_mag)):
dm = np.abs(iso.mag153m - top_tick_mag[nn])
dm_idx = dm.argmin()
top_tick_mass[nn] = iso.M[dm_idx]
top_tick_label[nn] = '{0:3.1f}'.format(top_tick_mag[nn])
print 'top_tick_mag = ', top_tick_mag
print 'top_tick_msas = ', top_tick_mass
print 'top_tick_label = ', top_tick_label
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(np.log10(top_tick_mass))
ax2.set_xticklabels(top_tick_label)
ax2.set_xlabel(r'F153M (mags)', labelpad=10)
py.savefig(prop_dir + 'arches_imf.png')
py.figure(2)
py.clf()
py.plot(np.log10(iso.M), iso.mag153m)
py.xlim(0, 1.9)
py.xlabel('Log( Mass [Msun] )')
py.ylabel('F153M (mag)')
py.savefig(prop_dir + 'arches_mass_luminosity.png')
py.figure(3)
py.clf()
py.plot(color, mag, 'k.')
py.plot(iso.mag127m - iso.mag153m, iso.mag153m, 'r-')
py.axvline(2)
py.ylim(22, 12)
py.xlim(1, 3.5)
py.xlabel('F127M - F153M (mag)')
py.ylabel('F153M (mag)')
py.savefig(prop_dir + 'arches_cmd_iso_test.png')
return clust
def make_models():
# Make one as would be observed at the GC.
syn.load_isochrone(logAge=9.78, AKs=3.1, distance=8000)
# Make some for absolute photometry. 10 pc, no extinction.
syn.load_isochrone(logAge=9.00, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.30, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.48, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.60, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.70, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.78, AKs=0.0, distance=10)
def make_models():
# Make one as would be observed at the GC.
syn.load_isochrone(logAge=9.78, AKs=3.1, distance=8000)
# Make some for absolute photometry. 10 pc, no extinction.
syn.load_isochrone(logAge=9.00, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.30, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.48, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.60, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.70, AKs=0.0, distance=10)
syn.load_isochrone(logAge=9.78, AKs=0.0, distance=10)
