def load_iso():
Zs = numpy.arange(0.0005, 0.0605, 0.0005)
return isodist.PadovaIsochrone(type='sdss-2mass', parsec=True, Z=Zs)
def __init__(self,
loggmin=None,
loggmax=None,
imfmodel='lognormalChabrier2001',
Z=None,
expsfh=False,
band='Ks',
dontgather=False,
basti=False,
parsec=True,
minage=None,
maxage=10.,
jkmin=None,
stage=None,
eta=None):
"""
NAME:
__init__
PURPOSE:
initialize isocmodel
INPUT:
Z= metallicity (if not set, use flat prior in Z over all Z; can be list)
loggmin= if set, cut logg at this minimum
loggmax= if set, cut logg at this maximum, if 'rc', then this is the function of teff and z appropriate for the APOGEE RC sample
imfmodel= (default: 'lognormalChabrier2001') IMF model to use (see isodist.imf code for options)
band= band to use for M_X (JHK)
expsfh= if True, use an exponentially-declining star-formation history (can be set to a decay time-scale in Gyr)
dontgather= if True, don't gather surrounding Zs
basti= if True, use Basti isochrones (if False, use PARSEC)
parsec= if True (=default), use PARSEC isochrones, if False, use Padova
stage= if True, only use this evolutionary stage
minage= (None) minimum log10 of age/yr
maxage= (10.) maximum log10 of age/yr
jkmin= (None) if set, only consider J-Ks greater than this
eta= (None) mass-loss efficiency parameter
OUTPUT:
object
HISTORY:
2012-11-07 - Written - Bovy (IAS)
"""
self._band = band
self._loggmin = loggmin
self._loggmax = loggmax
if isinstance(expsfh, (int, float, numpy.float32, numpy.float64)):
self._expsfh = expsfh
elif expsfh:
self._expsfh = 8.
else:
self._expsfh = False
self._Z = Z
self._imfmodel = imfmodel
self._basti = basti
self._eta = eta
if isinstance(loggmax, str) and loggmax.lower() == 'rc':
from apogee.samples.rc import loggteffcut
#Read isochrones
if basti:
zs = numpy.array([
0.0001, 0.0003, 0.0006, 0.001, 0.002, 0.004, 0.008, 0.01,
0.0198, 0.03, 0.04
])
elif parsec:
zs = numpy.arange(0.0005, 0.06005, 0.0005)
else:
zs = numpy.arange(0.0005, 0.03005, 0.0005)
if Z is None:
Zs = zs
elif isinstance(Z, float):
if basti or dontgather:
Zs = [Z]
elif Z < 0.001 or Z > 0.0295:
Zs = [Z]
elif Z < 0.0015 or Z > 0.029:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
elif Z < 0.01:
Zs = [Z - 0.001, Z - 0.0005, Z, Z + 0.0005,
Z + 0.001] #build up statistics
else:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
if basti:
p = isodist.BastiIsochrone(Z=Zs, eta=eta)
else:
p = isodist.PadovaIsochrone(Z=Zs, parsec=parsec, eta=eta)
if basti:
#Force BaSTI to have equal age sampling
lages = list(numpy.log10(numpy.arange(0.1, 1., 0.1)) + 9.)
lages.extend(list(numpy.log10(numpy.arange(1.0, 10.5, 0.5)) + 9.))
lages = numpy.array(lages)
#Get relevant data
sample = []
weights = []
massweights = []
loggs = []
teffs = []
pmasses = []
plages = []
pjks = []
for logage in p.logages():
if logage > maxage: continue
if not minage is None and logage < minage: continue
if basti and numpy.sum((logage == lages)) == 0: continue
for zz in range(len(Zs)):
thisiso = p(logage, Zs[zz], asrecarray=True, stage=stage)
if len(thisiso.M_ini) == 0: continue
#Calculate int_IMF for this IMF model
if not imfmodel == 'lognormalChabrier2001': #That would be the default
if imfmodel == 'exponentialChabrier2001':
int_IMF = isodist.imf.exponentialChabrier2001(
thisiso.M_ini, int=True)
elif imfmodel == 'kroupa2003':
int_IMF = isodist.imf.kroupa2003(thisiso.M_ini,
int=True)
elif imfmodel == 'chabrier2003':
int_IMF = isodist.imf.chabrier2003(thisiso.M_ini,
int=True)
else:
raise IOError(
"imfmodel option not understood (non-existing model)"
)
elif basti:
int_IMF = isodist.imf.lognormalChabrier2001(thisiso.M_ini,
int=True)
else:
int_IMF = thisiso.int_IMF
dN = (numpy.roll(int_IMF, -1) -
int_IMF) / (int_IMF[-1] - int_IMF[0]) / 10**(logage - 7.)
dmass = thisiso.M_ini * (
numpy.roll(int_IMF, -1) - int_IMF) / numpy.sum(
(thisiso.M_ini *
(numpy.roll(int_IMF, -1) - int_IMF))[:-1]) / 10**(
logage - 7.)
for ii in range(1, len(int_IMF) - 1):
if basti:
JK = 0.996 * (thisiso.J[ii] - thisiso.K[ii]) + 0.00923
else:
JK = thisiso.J[ii] - thisiso.Ks[ii]
if not jkmin is None and JK < jkmin: continue
if band.lower() == 'h':
if basti:
raise NotImplementedError(
"'H' not implemented for BaSTI yet")
J = JK + thisiso.K[ii] - 0.044
H = J - (0.980 *
(thisiso.J[ii] - thisiso.H[ii]) - 0.045)
else:
H = thisiso.H[ii]
elif band.lower() == 'j':
if basti:
raise NotImplementedError(
"'J' not implemented for BaSTI yet")
J = JK + thisiso.K[ii] - 0.044
else:
H = thisiso.J[ii]
elif band.lower() == 'k' or band.lower() == 'ks':
if basti:
H = thisiso.K[ii] - 0.046
else:
H = thisiso.Ks[ii]
if JK < 0.3 \
or (isinstance(loggmax,str) and loggmax == 'rc' and (thisiso['logg'][ii] > loggteffcut(10.**thisiso['logTe'][ii],Zs[zz],upper=True))) \
or (not isinstance(loggmax,str) and not loggmax is None and thisiso['logg'][ii] > loggmax) \
or (not loggmin is None and thisiso['logg'][ii] < loggmin):
continue
if dN[ii] > 0.:
sample.append([JK, H])
loggs.append([thisiso.logg[ii]])
teffs.append([10.**thisiso.logTe[ii]])
pmasses.append(thisiso.M_ini[ii])
plages.append(logage)
pjks.append(JK)
if basti: #BaSTI is sampled uniformly in age, not logage, but has a finer sampling below 1 Gyr
if logage < 9.:
if self._expsfh:
weights.append(dN[ii] / 5. * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(
dmass[ii] / 5. * numpy.exp(
(10.**(logage - 7.)) / self._expsfh
/ 100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii] / 5.)
massweights.append(dmass[ii] / 5.)
else:
if self._expsfh:
weights.append(dN[ii] * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(dmass[ii] * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii])
massweights.append(dmass[ii])
else:
if self._expsfh:
weights.append(
dN[ii] * 10**(logage - 7.) * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
massweights.append(
dmass[ii] * 10**(logage - 7.) * numpy.exp(
(10.**(logage - 7.)) / self._expsfh /
100.)) #e.g., Binney (2010)
else:
weights.append(dN[ii] * 10**(logage - 7.))
massweights.append(dmass[ii] *
10**(logage - 7.))
else:
continue #no use in continuing here
#Form array
sample = numpy.array(sample)
loggs = numpy.array(loggs)
teffs = numpy.array(teffs)
pmasses = numpy.array(pmasses)
plages = numpy.array(plages) - 9.
pjks = numpy.array(pjks)
weights = numpy.array(weights)
massweights = numpy.array(massweights)
#Cut out low weights
if False:
indx = (weights > 10.**-5. * numpy.sum(weights))
else:
indx = numpy.ones(len(weights), dtype='bool')
self._sample = sample[indx, :]
self._weights = weights[indx]
self._massweights = massweights[indx]
self._loggs = loggs[indx]
self._teffs = teffs[indx]
self._masses = pmasses[indx]
self._lages = plages[indx]
self._jks = pjks[indx]
#Setup KDE
self._kde = dens_kde.densKDE(
self._sample,
w=self._weights,
h=2. * self._sample.shape[0]**(-1. / 5.), #h='scott',
kernel='biweight',
variable=True,
variablenitt=3,
variableexp=0.5)
return None
def imf_h_jk(plotfile,Z=None,dwarf=False,log=False,h=12.,basti=False,
dartmouth=False,kroupa=False):
#Read isochrones
if basti:
zs= numpy.array([0.0001,0.0003,0.0006,0.001,0.002,0.004,0.008,
0.01,0.0198,0.03,0.04])
elif dartmouth:
zs= isodist.FEH2Z(numpy.array([-2.5,-2.,-1.5,-1.,-0.5,0.,0.2,0.3,0.5]))
else:
zs= numpy.arange(0.0005,0.03005,0.0005)
if Z is None:
Zs= zs
elif not basti and not dartmouth:
if Z < 0.01:
Zs= [Z-0.001,Z-0.0005,Z,Z+0.0005,Z+0.001] #build up statistics
else:
Zs= [Z-0.0005,Z,Z+0.0005] #build up statistics
else:
Zs= [Z]
if basti:
p= isodist.BastiIsochrone(Z=Zs)
elif dartmouth:
p= isodist.DartmouthIsochrone(feh=isodist.Z2FEH(Zs),onlyold=True)
else:
p= isodist.PadovaIsochrone(Z=Zs)
#Get relevant data
sample= []
weights= []
for logage in p.logages():
for z in Zs:
thisiso= p(logage,z)
if basti: mini= thisiso['M_ini']
elif dartmouth: mini= thisiso['M']
else: mini= thisiso['M_ini']
if basti:
int_IMF= isodist.imf.lognormalChabrier2001(thisiso['M_ini'],
int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
elif dartmouth:
int_IMF= isodist.imf.lognormalChabrier2001(thisiso['M'],
int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
else:
if kroupa:
int_IMF= isodist.imf.kroupa2003(thisiso['M_ini'],int=True)
dmpm= numpy.roll(int_IMF,-1)-int_IMF
else:
dmpm= numpy.roll(thisiso['int_IMF'],-1)-thisiso['int_IMF']
for ii in range(1,len(mini)-1):
if basti:
JK= thisiso['J'][ii]-thisiso['K'][ii]
else:
JK= thisiso['J'][ii]-thisiso['Ks'][ii]
H= thisiso['H'][ii]
if JK < 0.: # or thisiso['logg'][ii] > 3.5:
continue
if dmpm[ii] > 0.:
if basti:
sample.append([thisiso['J'][ii]-thisiso['K'][ii],
thisiso['H'][ii]])
else:
sample.append([thisiso['J'][ii]-thisiso['Ks'][ii],
thisiso['H'][ii]])
if dartmouth:
if logage > numpy.log10(5.)+9.:
weights.append(2.*dmpm[ii]) #Dartmouth are linearly spaced, but spacing is bigger at > 5 Gyr
else:
weights.append(dmpm[ii]) #Dartmouth are linearly spaced?
else:
weights.append(dmpm[ii]*10**(logage-7.))
#weights.append(dmpm[ii]*10**(logage-7.)*numpy.exp((10.**(logage-7.))/800.))
else:
continue #no use in continuing here
#Form array
sample= numpy.array(sample)
weights= numpy.array(weights)
#Histogram
if dwarf:
hist, edges= numpy.histogramdd(sample,weights=weights,bins=51,
range=[[0.,1.6],[2.,9.]])
else:
hist, edges= numpy.histogramdd(sample,weights=weights,bins=49,
range=[[0.3,1.6],[-11.,2]])
#Normalize each J-K
for ii in range(len(hist[:,0])):
hist[ii,:]/= numpy.nanmax(hist[ii,:])/numpy.nanmax(hist)
rev= copy.copy(hist[ii,::-1]) #reverse, but in one go does not always work
hist[ii,:]= rev
#Plot
bovy_plot.bovy_print()
if log:
hist= numpy.log(hist)
bovy_plot.bovy_dens2d(hist.T,origin='lower',cmap='gist_yarg',
xrange=[edges[0][0],edges[0][-1]],
yrange=[edges[1][-1],edges[1][0]],
aspect=(edges[0][-1]-edges[0][0])/float(edges[1][-1]-edges[1][0]),
xlabel=r'$(J-K_s)_0\ [\mathrm{mag}]$',
ylabel=r'$M_H\ [\mathrm{mag}]$',
interpolation='nearest')
#Add extinction arrow
djk= 0.45
dh= 1.55/1.5*djk
from matplotlib.patches import FancyArrowPatch
ax=pyplot.gca()
ax.add_patch(FancyArrowPatch((1.,-2.),(1+djk,-2+dh),
arrowstyle='->',mutation_scale=20,fill=True,
lw=1.25))
bovy_plot.bovy_text(1.05,-2.05,r'$\mathrm{extinction}$',
rotation=-math.atan(1.5/1.55*1.3/13.)/math.pi*180.,
size=14.)
#Add color cut
bovy_plot.bovy_plot([0.5,0.5],[-20.,20.],'--',color='0.6',overplot=True)
ax.add_patch(FancyArrowPatch((0.5,-6.),(0.7,-6.),
arrowstyle='->',mutation_scale=20,fill=True,
lw=1.25,ls='dashed',color='0.6'))
bovy_plot.bovy_text(0.43,-8.,r'$\mathrm{APOGEE\ color\ cut}$',rotation=90.,
size=14.)
#Add twin y axis
ax= pyplot.gca()
def my_formatter(x, pos):
"""distance in kpc for m=h"""
xs= 10.**((h-x)/5.-2.)
return r'$%.0f$' % xs
def my_formatter2(x, pos):
"""distance in kpc for m=h"""
xs= 10.**((h-x)/5.+1.)
return r'$%.0f$' % xs
ax2= pyplot.twinx()
if dwarf:
major_formatter = FuncFormatter(my_formatter2)
else:
major_formatter = FuncFormatter(my_formatter)
ax2.yaxis.set_major_formatter(major_formatter)
ystep= ax.yaxis.get_majorticklocs()
ystep= ystep[1]-ystep[0]
ax2.yaxis.set_minor_locator(MultipleLocator(ystep/5.))
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
ymin, ymax= ax.yaxis.get_view_interval()
ax2.yaxis.set_view_interval(ymin,ymax,ignore=True)
if dwarf:
ax2.set_ylabel('$\mathrm{distance\ for}\ H_0\ =\ %.1f\ [\mathrm{pc}]$' % h)
else:
ax2.set_ylabel('$\mathrm{distance\ for}\ H_0\ =\ %.1f\ [\mathrm{kpc}]$' % h)
xstep= ax.xaxis.get_majorticklocs()
xstep= xstep[1]-xstep[0]
ax2.xaxis.set_minor_locator(MultipleLocator(xstep/5.))
if Z is None:
bovy_plot.bovy_end_print(plotfile)
else:
bovy_plot.bovy_text(r'$Z\ =\ %.3f$' % Z,top_right=True,size=14.)
bovy_plot.bovy_end_print(plotfile)
return None
def generate_isogrid():
"""
generate a recarray with all the entries from PARSEC isochrones in isodist
"""
zs = np.arange(0.0005, 0.0605, 0.0005)
zlist = []
for i in range(len(zs)):
zlist.append(format(zs[i], '.4f'))
iso = isodist.PadovaIsochrone(type='2mass-spitzer-wise', Z=zs, parsec=True)
logages = []
mets = []
js = []
hs = []
ks = []
loggs = []
teffs = []
imf = []
deltam = []
M_ini = []
M_act = []
logL
iso_logages = iso._logages
iso_Zs = iso._ZS
for i in tqdm.tqdm(range(len(iso_logages))):
for j in range(len(iso_Zs)):
thisage = iso_logages[i]
thisZ = iso_Zs[j]
thisiso = iso(thisage, Z=thisZ)
so = np.argsort(thisiso['M_ini'])
loggs.extend(thisiso['logg'][so][1:])
logages.extend(thisiso['logage'][so][1:])
mets.extend(np.ones(len(thisiso['H'][so]) - 1) * thisZ)
js.extend(thisiso['J'][so][1:])
hs.extend(thisiso['H'][so][1:])
ks.extend(thisiso['Ks'][so][1:])
teffs.extend(thisiso['logTe'][so][1:])
imf.extend(thisiso['int_IMF'][so][1:])
deltam.extend(thisiso['int_IMF'][so][1:] -
thisiso['int_IMF'][so][:-1])
M_ini.extend(thisiso['M_ini'][so][1:])
M_act.extend(thisiso['M_act'][so][1:])
logL.extend(thisiso['logL'][so][1:])
logages = np.array(logages)
mets = np.array(mets)
js = np.array(js)
hs = np.array(hs)
ks = np.array(ks)
loggs = np.array(loggs)
teffs = 10**np.array(teffs)
imf = np.array(imf)
deltam = np.array(deltam)
M_ini = np.array(M_ini)
M_act = np.array(M_act)
logL = np.array(logL)
rec = np.recarray(len(deltam),
dtype=[('logageyr', float), ('Z', float), ('J', float),
('H', float), ('K', float), ('logg', float),
('teff', float), ('int_IMF', float),
('deltaM', float), ('M_ini', float),
('M_act', float), ('logL', float)])
rec['logageyr'] = logages
rec['Z'] = mets
rec['J'] = js
rec['H'] = hs
rec['K'] = ks
rec['logg'] = loggs
rec['teff'] = teffs
rec['int_IMF'] = imf
rec['deltaM'] = deltam
rec['M_ini'] = M_ini
rec['M_act'] = M_act
rec['logL'] = logL
return rec
def __init__(self,
dwarf=False,
imfmodel='lognormalChabrier2001',
Z=None,
interpolate=False,
expsfh=False,
marginalizefeh=False,
glon=None,
dontgather=False,
loggmax=None,
basti=False):
"""
NAME:
__init__
PURPOSE:
initialize isomodel
INPUT:
Z= metallicity (if not set, use flat prior in Z over all Z; can be list)
dwarf= (default: False) if True, use dwarf part
imfmodel= (default: 'lognormalChabrier2001') IMF model to use (see isodist.imf code for options)
interpolate= (default: False) if True, interpolate the binned representation
expsfh= if True, use an exponentially-declining star-formation history
marginalizefeh= if True, marginalize over the FeH distribution along line of sight glon
glon - galactic longitude in rad of los for marginalizefeh
dontgather= if True, don't gather surrounding Zs
loggmax= if set, cut logg at this maximum
basti= if True, use Basti isochrones (if False, use Padova)
OUTPUT:
object
HISTORY:
2012-02-17 - Written - Bovy (IAS)
"""
#Read isochrones
if basti:
zs = numpy.array([
0.0001, 0.0003, 0.0006, 0.001, 0.002, 0.004, 0.008, 0.01,
0.0198, 0.03, 0.04
])
else:
zs = numpy.arange(0.0005, 0.03005, 0.0005)
if marginalizefeh:
Zs = numpy.arange(0.008, 0.031, 0.001)
dFeHdZ = 1. / Zs
pZs = feh_l(glon, isodist.Z2FEH(Zs)) * dFeHdZ
elif Z is None:
Zs = zs
elif isinstance(Z, float):
if basti or dontgather:
Zs = [Z]
elif Z < 0.001 or Z > 0.0295:
Zs = [Z]
elif Z < 0.0015 or Z > 0.029:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
elif Z < 0.01:
Zs = [Z - 0.001, Z - 0.0005, Z, Z + 0.0005,
Z + 0.001] #build up statistics
else:
Zs = [Z - 0.0005, Z, Z + 0.0005] #build up statistics
if basti:
p = isodist.BastiIsochrone(Z=Zs)
else:
p = isodist.PadovaIsochrone(Z=Zs)
#Get relevant data
sample = []
weights = []
for logage in p.logages():
for zz in range(len(Zs)):
thisiso = p(logage, Zs[zz], asrecarray=True)
#Calculate int_IMF for this IMF model
if not imfmodel == 'lognormalChabrier2001': #That would be the default
if imfmodel == 'exponentialChabrier2001':
int_IMF = isodist.imf.exponentialChabrier2001(
thisiso.M_ini, int=True)
elif imfmodel == 'kroupa2003':
int_IMF = isodist.imf.kroupa2003(thisiso.M_ini,
int=True)
elif imfmodel == 'chabrier2003':
int_IMF = isodist.imf.chabrier2003(thisiso.M_ini,
int=True)
else:
raise IOError(
"imfmodel option not understood (non-existing model)"
)
elif basti:
int_IMF = isodist.imf.lognormalChabrier2001(thisiso.M_ini,
int=True)
else:
int_IMF = thisiso.int_IMF
dN = numpy.roll(int_IMF, -1) - int_IMF
for ii in range(1, len(int_IMF) - 1):
if basti:
JK = thisiso.J[ii] - thisiso.K[ii]
else:
JK = thisiso.J[ii] - thisiso.Ks[ii]
H = thisiso.H[ii]
if JK < 0.3 or (not loggmax is None
and thisiso['logg'][ii] > loggmax):
continue
if dN[ii] > 0.:
sample.append([JK, H])
if marginalizefeh:
if H < (11. / -1.3 * (JK - 0.3)):
weights.append(
0.
) #HACK TO GET RID OF UNWANTED BRIGHT POINTS
elif expsfh:
weights.append(pZs[zz] * dN[ii] *
10**(logage - 7.) * numpy.exp(
(10.**(logage - 7.)) /
800.)) #e.g., Binney (2010)
else:
weights.append(pZs[zz] * dN[ii] *
10**(logage - 7.))
else:
if expsfh:
weights.append(dN[ii] * 10**(logage - 7.) *
numpy.exp(
(10.**(logage - 7.)) /
800.)) #e.g., Binney (2010)
else:
weights.append(dN[ii] * 10**(logage - 7.))
else:
continue #no use in continuing here
#Form array
sample = numpy.array(sample)
weights = numpy.array(weights)
self._sample = sample
self._weights = weights
#Histogram
self._jkmin, self._jkmax = 0.3, 1.6
if dwarf:
self._hmin, self._hmax = 2., 9.
self._nbins = 51
else:
self._hmin, self._hmax = -11., 2.
self._nbins = 26 #49
self._djk = (self._jkmax - self._jkmin) / float(self._nbins)
self._dh = (self._hmax - self._hmin) / float(self._nbins)
self._hist, self._edges = numpy.histogramdd(
sample,
weights=weights,
bins=self._nbins,
range=[[self._jkmin, self._jkmax], [self._hmin, self._hmax]])
#Save
self._Zs = Zs
self._interpolate = interpolate
self._dwarf = dwarf
self._loghist = numpy.log(self._hist)
self._loghist[(
self._hist == 0.)] = -numpy.finfo(numpy.dtype(numpy.float64)).max
if interpolate:
#Form histogram grid
jks = numpy.linspace(self._jkmin + self._djk / 2.,
self._jkmax - self._djk / 2., self._nbins)
hs = numpy.linspace(self._hmin + self._dh / 2.,
self._hmax - self._dh / 2., self._nbins)
self._interpolatedhist = scipy.interpolate.RectBivariateSpline(
jks,
hs,
self._hist,
bbox=[self._jkmin, self._jkmax, self._hmin, self._hmax],
s=0.)
#raise NotImplementedError("'interpolate=True' option for isomodel not implemented yet")
return None
import numpy as np
import matplotlib.pyplot as plt
import isodist
import os
import glob
import pickle
from tqdm import tqdm
ISO_PATH = os.getenv('ISODIST_DATA')
#load isochrones
path = ISO_PATH + '/parsec-sdss-2mass/'
Zs = [float(file[79:85]) for file in glob.glob(os.path.join(path, '*.dat.gz'))]
isochrone = isodist.PadovaIsochrone(Z=Zs, parsec=True)
logages = isochrone.logages()
logages = logages[logages >= 8.]
Zs = isochrone.Zs()
#Generate isochrone grid
def isochrone_grid(logages, Zs, imf):
grid = []
for lage in tqdm(range(len(logages))):
for met in Zs:
#print 'generating grid entry for Age='+str(round((10**lage)/1e9,1))+'Gyr and Fe/H='+str(round(isodist.Z2FEH(met),2))+'...'
iso = isochrone(logages[lage], Z=met)
H_iso = iso['H'][1:]
M_iso = iso['M_ini'][1:]
logg_iso = iso['logg'][1:]
J_K_iso = iso['J'][1:] - iso['Ks'][1:]