def geneva_read(fname, Zstr='014', tar=None):
'''
Reads Geneva model file
Usage:
age, Mstar, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole =
geneva_read(fname, tar=None)
where tar is the read tar(.gz) opened file.
'''
# read tar file
if tar is None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
m = tar.getmember(fname)
fname = tar.extractfile(m)
# (age, M, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole)
cols = (1, 2, 3, 4, 21, 22, 34, 39, 44)
t = _np.loadtxt(fname, usecols=cols, skiprows=2)
age = t[:, 0]
Mstar = t[:, 1]
logL = t[:, 2]
logTeff = t[:, 3]
Hfrac = t[:, 4]
Hefrac = t[:, 5]
oblat = 1. / t[:, 6]
w = t[:, 7]
Rpole = t[:, 8]
return age, Mstar, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole
def geneva_read(fname, Zstr='014', tar=None):
'''
Reads Geneva model file
Usage:
age, Mstar, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole = geneva_read(fname, tar=None)
where tar is the read tar(.gz) opened file.
'''
# read tar file
if tar == None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
m = tar.getmember(fname)
fname = tar.extractfile(m)
# (age, M, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole)
cols = (1, 2, 3, 4, 21, 22, 34, 39, 44)
t = _np.loadtxt(fname, usecols=cols, skiprows=2)
age = t[:, 0]
Mstar = t[:, 1]
logL = t[:, 2]
logTeff = t[:, 3]
Hfrac = t[:, 4]
Hefrac = t[:, 5]
oblat = 1./t[:, 6]
w = t[:, 7]
Rpole = t[:, 8]
return age, Mstar, logL, logTeff, Hfrac, Hefrac, oblat, w, Rpole
def geneva_interp(Mstar, oblat, t, Zstr='014', tar=None, silent=True):
'''
Interpolates Geneva stellar models.
Usage:
Rpole, logL, age = geneva_interp(Mstar, oblat, t, tar=None, silent=True)
where t is given in tMS, and tar is the open tar file. The chosen
metallicity is according to the input tar file. If tar=None, the
code will take Zstr='014' by default.
'''
# oblat to Omega/Omega_c
w = oblat2w(oblat)
# grid
if Mstar <= 20.:
Mlist = _np.array([1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
else:
Mlist = _np.array([20., 25., 32., 40., 60., 85., 120.])
# read tar file
if tar == None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
# interpolation
if (Mstar >= Mlist.min()) * (Mstar <= Mlist.max()):
if (Mstar == Mlist).any():
Rpole, logL, age = geneva_closest(Mstar, oblat, t, tar=tar, Zstr=Zstr, silent=silent)
else:
# nearest value at left
Mleft = Mlist[Mlist < Mstar]
Mleft = Mleft[_np.abs(Mleft - Mstar).argmin()]
iMleft = _np.where(Mlist == Mleft)[0][0]
Rpolel, logLl, agel = geneva_closest(Mlist[iMleft], oblat, t, tar=tar, Zstr=Zstr, silent=silent)
# nearest value at right
Mright = Mlist[Mlist > Mstar]
Mright = Mright[_np.abs(Mright - Mstar).argmin()]
iMright = _np.where(Mlist == Mright)[0][0]
Rpoler, logLr, ager = geneva_closest(Mlist[iMright], oblat, t, tar=tar, Zstr=Zstr, silent=silent)
# interpolate between masses
weight = _np.array([Mright-Mstar, Mstar-Mleft]) / (Mright-Mleft)
Rpole = weight.dot(_np.array([Rpolel, Rpoler]))
logL = weight.dot(_np.array([logLl, logLr]))
age = weight.dot(_np.array([agel, ager]))
else:
if not silent:
print('[geneva_interp] Warning: Mstar out of available range, taking the closest value.')
Rpole, logL, age = geneva_closest(Mstar, oblat, t, tar=tar, Zstr=Zstr, silent=silent)
return Rpole, logL, age
def sigma4b_cranmer(M, w):
'''Computes sigma4b defined in Cranmer 1996 (Eq. 4.22)
Usage:
s4b = sigma4b_cranmer(M, w)
where w=Omega/Omega_c, M=stellar mass in Msun (from 1.7 to 20.)
'''
dir0 = '{0}/refs/tables/'.format(_hdtpath())
tab = _np.load(dir0 + 'sigma4b_cranmer.npz')
s4b = _griddata(tab['parlist'], tab['sigma4b'], _np.array([M, w]), method='linear')[0]
return s4b
def sigma4b_cranmer(M, w):
'''Computes sigma4b defined in Cranmer 1996 (Eq. 4.22)
Usage:
s4b = sigma4b_cranmer(M, w)
where w=Omega/Omega_c, M=stellar mass in Msun (from 1.7 to 20.)
'''
dir0 = '{0}/refs/tables/'.format(_hdtpath())
tab = _np.load(dir0 + 'sigma4b_cranmer.npz')
s4b = _griddata(tab['parlist'],
tab['sigma4b'],
_np.array([M, w]),
method='linear')[0]
return s4b
def geneva_pre_computed(Zstr='014', silent=False):
'''
Create geneva pre-computed grid
'''
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
if _os.path.exists(dir0 + 'geneva_interp_Z{:}.npz'.format(Zstr)):
data = _np.load(dir0 + 'geneva_interp_Z{:}.npz'.format(Zstr))
else:
# par grid
Mstar_arr = _np.array(
[1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
oblat_arr = _np.linspace(1., 1.5, 6)
t_arr = _np.hstack(
[_np.linspace(0., .9, 10),
_np.linspace(1., 1.1, 21)])
Rpole_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
logL_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
age_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
# read tar file
tar = _tarfile.open(dir0 + 'Z{:}.tar.gz'.format(Zstr), 'r:gz')
for iM, Mstar in enumerate(Mstar_arr):
for iob, oblat in enumerate(oblat_arr):
for it, t in enumerate(t_arr):
if not silent:
print(Mstar, oblat, t)
Rp, lL, age = geneva_interp(Mstar,
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
Rpole_grid[iM, iob, it] = Rp
logL_grid[iM, iob, it] = lL
age_grid[iM, iob, it] = age
_np.savez(dir0 + 'geneva_interp_Z{:}'.format(Zstr),
Mstar_arr=Mstar_arr,
oblat_arr=oblat_arr,
t_arr=t_arr,
Rpole_grid=Rpole_grid,
logL_grid=logL_grid,
age_grid=age_grid)
# high M
if _os.path.exists(dir0 + 'geneva_interp_Z{:}_highM.npz'.format(Zstr)):
data = _np.load(dir0 + 'geneva_interp_Z{:}_highM.npz'.format(Zstr))
return
# par grid
Mstar_arr = _np.array([20., 25., 32., 40., 60., 85., 120.])
oblat_arr = _np.linspace(1., 1.05633802817, 2)
t_arr = _np.hstack([_np.linspace(0., .9, 10), _np.linspace(1., 1.1, 21)])
Rpole_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
logL_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
age_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
# read tar file
tar = _tarfile.open(dir0 + 'Z{:}.tar.gz'.format(Zstr), 'r:gz')
for iM, Mstar in enumerate(Mstar_arr):
for iob, oblat in enumerate(oblat_arr):
for it, t in enumerate(t_arr):
if not silent:
print(Mstar, oblat, t)
Rp, lL, age = geneva_interp(Mstar,
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
Rpole_grid[iM, iob, it] = Rp
logL_grid[iM, iob, it] = lL
age_grid[iM, iob, it] = age
_np.savez(dir0 + 'geneva_interp_Z{:}_highM'.format(Zstr),
Mstar_arr=Mstar_arr,
oblat_arr=oblat_arr,
t_arr=t_arr,
Rpole_grid=Rpole_grid,
logL_grid=logL_grid,
age_grid=age_grid)
return
def geneva_interp_fast(Mstar, oblat, t, Zstr='014', silent=True):
'''
Interpolates Geneva stellar models, from grid of
pre-computed interpolations.
Usage:
Rpole, logL, age = geneva_interp_fast(Mstar, oblat, t, Zstr='014')
where t is given in tMS, and tar is the open tar file. For now, only
Zstr='014' is available.
'''
# read grid
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
if Mstar <= 20.:
fname = 'geneva_interp_Z{:}.npz'.format(Zstr)
else:
fname = 'geneva_interp_Z{:}_highM.npz'.format(Zstr)
data = _np.load(dir0 + fname)
Mstar_arr = data['Mstar_arr']
oblat_arr = data['oblat_arr']
t_arr = data['t_arr']
Rpole_grid = data['Rpole_grid']
logL_grid = data['logL_grid']
age_grid = data['age_grid']
# build grid of parameters
par_grid = []
for M in Mstar_arr:
for ob in oblat_arr:
for tt in t_arr:
par_grid.append([M, ob, tt])
par_grid = _np.array(par_grid)
# set input/output parameters
par = _np.array([Mstar, oblat, t])
# set ranges
ranges = _np.array([[par_grid[:, i].min(), par_grid[:, i].max()]
for i in range(len(par))])
# find neighbours
keep, out, inside_ranges, par, par_grid = _phc.find_neighbours(
par, par_grid, ranges)
# interpolation method
if inside_ranges:
interp_method = 'linear'
else:
if not silent:
print('[geneva_interp_fast] Warning: parameters out of available '
'range, taking closest model')
interp_method = 'nearest'
if len(keep[keep]) == 1:
# coincidence
Rpole = Rpole_grid.flatten()[keep][0]
logL = logL_grid.flatten()[keep][0]
age = age_grid.flatten()[keep][0]
else:
# interpolation
Rpole = _griddata(par_grid[keep],
Rpole_grid.flatten()[keep],
par,
method=interp_method,
rescale=True)[0]
logL = _griddata(par_grid[keep],
logL_grid.flatten()[keep],
par,
method=interp_method,
rescale=True)[0]
age = _griddata(par_grid[keep],
age_grid.flatten()[keep],
par,
method=interp_method,
rescale=True)[0]
return Rpole, logL, age
def geneva_interp(Mstar, oblat, t, Zstr='014', tar=None, silent=True):
'''
Interpolates Geneva stellar models.
Usage:
Rpole, logL, age = geneva_interp(Mstar, oblat, t, tar=None, silent=True)
where t is given in tMS, and tar is the open tar file. The chosen
metallicity is according to the input tar file. If tar=None, the
code will take Zstr='014' by default.
'''
# oblat to Omega/Omega_c
# w = oblat2w(oblat)
# grid
if Mstar <= 20.:
Mlist = _np.array([1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
else:
Mlist = _np.array([20., 25., 32., 40., 60., 85., 120.])
# read tar file
if tar is None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
# interpolation
if (Mstar >= Mlist.min()) * (Mstar <= Mlist.max()):
if (Mstar == Mlist).any():
Rpole, logL, age = geneva_closest(Mstar,
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
else:
# nearest value at left
Mleft = Mlist[Mlist < Mstar]
Mleft = Mleft[_np.abs(Mleft - Mstar).argmin()]
iMleft = _np.where(Mlist == Mleft)[0][0]
Rpolel, logLl, agel = geneva_closest(Mlist[iMleft],
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
# nearest value at right
Mright = Mlist[Mlist > Mstar]
Mright = Mright[_np.abs(Mright - Mstar).argmin()]
iMright = _np.where(Mlist == Mright)[0][0]
Rpoler, logLr, ager = geneva_closest(Mlist[iMright],
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
# interpolate between masses
weight = _np.array([Mright - Mstar, Mstar - Mleft
]) / (Mright - Mleft)
Rpole = weight.dot(_np.array([Rpolel, Rpoler]))
logL = weight.dot(_np.array([logLl, logLr]))
age = weight.dot(_np.array([agel, ager]))
else:
if not silent:
print('[geneva_interp] Warning: Mstar out of available range, '
'taking the closest value.')
Rpole, logL, age = geneva_closest(Mstar,
oblat,
t,
tar=tar,
Zstr=Zstr,
silent=silent)
return Rpole, logL, age
def geneva_closest(Mstar, oblat, t, Zstr='014', tar=None, silent=True):
'''
Interpolate models between rotation rates, at closest Mstar.
Usage:
Rpole, logL = geneva_closest(Mstar, oblat, t, Zstr='014', tar=None,
silent=True)
where t is given in tMS, and tar is the open tar file. The chosen
metallicity is according to the input tar file. If tar=None, the
code will take Zstr='014' by default.
'''
# oblat to Omega/Omega_c
w = oblat2w(oblat)
# grid
if Mstar <= 20.:
Mlist = _np.array([1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
Mstr = _np.array([
'1p700', '2p000', '2p500', '3p000', '4p000', '5p000', '7p000',
'9p000', '12p00', '15p00', '20p00'
])
Vlist = _np.array([0., 0.1, 0.3, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95])
Vstr = _np.array([
'00000', '10000', '30000', '50000', '60000', '70000', '80000',
'90000', '95000'
])
else:
Mlist = _np.array([20., 25., 32., 40., 60., 85., 120.])
Mstr = _np.array(
['20p00', '25p00', '32p00', '40p00', '60p00', '85p00', '120p0'])
Vlist = _np.array([0., 0.568])
Vstr = _np.array(['00000', '56800'])
# read tar file
if tar is None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
# find closest Mstar
iM = _np.where(_np.abs(Mstar - Mlist) == _np.min(_np.abs(Mstar -
Mlist)))[0][0]
# find values at selected age
nw = len(Vlist)
wlist = _np.zeros(nw)
Rplist = _np.zeros(nw)
logLlist = _np.zeros(nw)
agelist = _np.zeros(nw)
for iw, vs in enumerate(Vstr):
fname = 'M{:}Z{:}00V{:}.dat'.format(Mstr[iM], Zstr, vs)
age1, _, logL1, _, Hfrac1, _, _, w1, Rpole1 = geneva_read(fname,
tar=tar)
t1 = age1 / age1[_np.where(Hfrac1 == 0.)[0][0] - 1]
if t > t1.max() and not silent:
print('[geneva_closest] Warning: requested age not available, '
'taking t/tMS={:.2f} instead of t/tMS={:.2f}.'.format(
t1.max(), t))
it = _np.where(_np.abs(t - t1) == _np.min(_np.abs(t - t1)))[0][0]
wlist[iw] = w1[it]
Rplist[iw] = Rpole1[it]
logLlist[iw] = logL1[it]
agelist[iw] = age1[it] / 1e6
# interpolate between rotation rates
if w <= wlist.max():
Rpole = _griddata(wlist, Rplist, [w], method='linear')[0]
logL = _griddata(wlist, logLlist, [w], method='linear')[0]
age = _griddata(wlist, agelist, [w], method='linear')[0]
else:
if not silent:
print('[geneva_closest] Warning: no model rotating this fast at '
'this age, taking closest model instead. (omega={:.2f} '
'instead of omega={:.2f})'.format(wlist.max(), w))
iwmax = _np.where(wlist == wlist.max())[0][0]
Rpole = Rplist[iwmax]
logL = logLlist[iwmax]
age = agelist[iwmax]
return Rpole, logL, age
def geneva_pre_computed(Zstr='014', silent=False):
'''
Create geneva pre-computed grid
'''
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
try:
data = _np.load(dir0 + 'geneva_interp_Z{:}.npz'.format(Zstr))
except:
# par grid
Mstar_arr = _np.array([1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
oblat_arr = _np.linspace(1., 1.5, 6)
t_arr = _np.hstack([_np.linspace(0., .9, 10), _np.linspace(1., 1.1, 21)])
Rpole_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
logL_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
age_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
# read tar file
tar = _tarfile.open(dir0 + 'Z{:}.tar.gz'.format(Zstr), 'r:gz')
for iM, Mstar in enumerate(Mstar_arr):
for iob, oblat in enumerate(oblat_arr):
for it, t in enumerate(t_arr):
if not silent:
print(Mstar, oblat, t)
Rp, lL, age = geneva_interp(Mstar, oblat, t, tar=tar, Zstr=Zstr, silent=silent)
Rpole_grid[iM, iob, it] = Rp
logL_grid[iM, iob, it] = lL
age_grid[iM, iob, it] = age
_np.savez(dir0 + 'geneva_interp_Z{:}'.format(Zstr), Mstar_arr=Mstar_arr, oblat_arr=oblat_arr, t_arr=t_arr, \
Rpole_grid=Rpole_grid, logL_grid=logL_grid, age_grid=age_grid)
# high M
try:
data = _np.load(dir0 + 'geneva_interp_Z{:}_highM.npz'.format(Zstr))
except:
# par grid
Mstar_arr = _np.array([20., 25., 32., 40., 60., 85., 120.])
oblat_arr = _np.linspace(1., 1.05633802817, 2)
t_arr = _np.hstack([_np.linspace(0., .9, 10), _np.linspace(1., 1.1, 21)])
Rpole_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
logL_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
age_grid = _np.zeros([len(Mstar_arr), len(oblat_arr), len(t_arr)])
# read tar file
tar = _tarfile.open(dir0 + 'Z{:}.tar.gz'.format(Zstr), 'r:gz')
for iM, Mstar in enumerate(Mstar_arr):
for iob, oblat in enumerate(oblat_arr):
for it, t in enumerate(t_arr):
if not silent:
print(Mstar, oblat, t)
Rp, lL, age = geneva_interp(Mstar, oblat, t, tar=tar, Zstr=Zstr, silent=silent)
Rpole_grid[iM, iob, it] = Rp
logL_grid[iM, iob, it] = lL
age_grid[iM, iob, it] = age
_np.savez(dir0 + 'geneva_interp_Z{:}_highM'.format(Zstr), Mstar_arr=Mstar_arr, oblat_arr=oblat_arr, t_arr=t_arr, \
Rpole_grid=Rpole_grid, logL_grid=logL_grid, age_grid=age_grid)
return
def geneva_interp_fast(Mstar, oblat, t, Zstr='014', silent=True):
'''
Interpolates Geneva stellar models, from grid of
pre-computed interpolations.
Usage:
Rpole, logL, age = geneva_interp_fast(Mstar, oblat, t, Zstr='014')
where t is given in tMS, and tar is the open tar file. For now, only
Zstr='014' is available.
'''
# read grid
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
if Mstar <= 20.:
fname = 'geneva_interp_Z{:}.npz'.format(Zstr)
else:
fname = 'geneva_interp_Z{:}_highM.npz'.format(Zstr)
data = _np.load(dir0 + fname)
Mstar_arr = data['Mstar_arr']
oblat_arr =data['oblat_arr']
t_arr = data['t_arr']
Rpole_grid = data['Rpole_grid']
logL_grid = data['logL_grid']
age_grid = data['age_grid']
# build grid of parameters
par_grid = []
for M in Mstar_arr:
for ob in oblat_arr:
for tt in t_arr:
par_grid.append([M, ob, tt])
par_grid = _np.array(par_grid)
# set input/output parameters
par = _np.array([Mstar, oblat, t])
# set ranges
ranges = _np.array([[par_grid[:, i].min(), par_grid[:, i].max()] for i in range(len(par))])
# find neighbours
keep, out, inside_ranges, par, par_grid = _phc.find_neighbours(par, par_grid, ranges)
# interpolation method
if inside_ranges:
interp_method = 'linear'
else:
if not silent:
print('[geneva_interp_fast] Warning: parameters out of available range, taking closest model')
interp_method = 'nearest'
if len(keep[keep]) == 1:
# coincidence
Rpole = Rpole_grid.flatten()[keep][0]
logL = logL_grid.flatten()[keep][0]
age = age_grid.flatten()[keep][0]
else:
# interpolation
Rpole = _griddata(par_grid[keep], Rpole_grid.flatten()[keep], par, method=interp_method, rescale=True)[0]
logL = _griddata(par_grid[keep], logL_grid.flatten()[keep], par, method=interp_method, rescale=True)[0]
age = _griddata(par_grid[keep], age_grid.flatten()[keep], par, method=interp_method, rescale=True)[0]
return Rpole, logL, age
def geneva_closest(Mstar, oblat, t, Zstr='014', tar=None, silent=True):
'''
Interpolate models between rotation rates, at closest Mstar.
Usage:
Rpole, logL = geneva_closest(Mstar, oblat, t, Zstr='014', tar=None, silent=True)
where t is given in tMS, and tar is the open tar file. The chosen
metallicity is according to the input tar file. If tar=None, the
code will take Zstr='014' by default.
'''
# oblat to Omega/Omega_c
w = oblat2w(oblat)
# grid
if Mstar <= 20.:
Mlist = _np.array([1.7, 2., 2.5, 3., 4., 5., 7., 9., 12., 15., 20.])
Mstr = _np.array(['1p700', '2p000', '2p500', '3p000', '4p000', '5p000', \
'7p000', '9p000', '12p00', '15p00', '20p00'])
Vlist = _np.array([0., 0.1, 0.3, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95])
Vstr = _np.array(['00000', '10000', '30000', '50000', '60000', '70000', \
'80000', '90000', '95000'])
else:
Mlist = _np.array([20., 25., 32., 40., 60., 85., 120.])
Mstr = _np.array(['20p00', '25p00', '32p00', '40p00', '60p00', '85p00', \
'120p0'])
Vlist = _np.array([0., 0.568])
Vstr = _np.array(['00000', '56800'])
# read tar file
if tar == None:
dir0 = '{0}/refs/geneva_models/'.format(_hdtpath())
fmod = 'Z{:}.tar.gz'.format(Zstr)
tar = _tarfile.open(dir0 + fmod, 'r:gz')
else:
Zstr = tar.getnames()[0][7:10]
# find closest Mstar
iM = _np.where(_np.abs(Mstar-Mlist) == _np.min(_np.abs(Mstar-Mlist)))[0][0]
# find values at selected age
nw = len(Vlist)
wlist = _np.zeros(nw)
Rplist = _np.zeros(nw)
logLlist = _np.zeros(nw)
agelist = _np.zeros(nw)
for iw, vs in enumerate(Vstr):
fname = 'M{:}Z{:}00V{:}.dat'.format(Mstr[iM], Zstr, vs)
age1, _, logL1, _, Hfrac1, _, _, w1, Rpole1 = geneva_read(fname, tar=tar)
t1 = age1 / age1[_np.where(Hfrac1 == 0.)[0][0]-1]
if t > t1.max() and not silent:
print('[geneva_closest] Warning: requested age not available, taking t/tMS={:.2f} instead of t/tMS={:.2f}.'.format(t1.max(),t))
it = _np.where(_np.abs(t-t1) == _np.min(_np.abs(t-t1)))[0][0]
wlist[iw] = w1[it]
Rplist[iw] = Rpole1[it]
logLlist[iw] = logL1[it]
agelist[iw] = age1[it] / 1e6
# interpolate between rotation rates
if w <= wlist.max():
Rpole = _griddata(wlist, Rplist, [w], method='linear')[0]
logL = _griddata(wlist, logLlist, [w], method='linear')[0]
age = _griddata(wlist, agelist, [w], method='linear')[0]
else:
if not silent:
print('[geneva_closest] Warning: no model rotating this fast at this age, taking closest model instead. (omega={:.2f} instead of omega={:.2f})'.format(wlist.max(),w))
iwmax = _np.where(wlist == wlist.max())[0][0]
Rpole = Rplist[iwmax]
logL = logLlist[iwmax]
age = agelist[iwmax]
return Rpole, logL, age
