import numpy as np
import pandas as pd
[docs]class AbundanceSolar(object):
"""Class to read protosolar abundances of the elements with Z <= 32
(Table 1.4 in Draine 2011; based on Asplund et al. 2009) and compute
mean molecular weight, etc.
Properties
----------
df : pandas DataFrame
Table 1.4 in Draine (2011)
Parameters
----------
xHe : float
Elemental abundance of helium. Default value is 0.0955 (Asplund+2009)
Zprime : float
Metallicity scaled to solar abundances.
"""
def __init__(self, xHe=9.55e-2, Zprime=1.0):
self.Zprime = Zprime
data = {
'Z': np.arange(32) + 1,
'X': np.array([ 'H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O',
'F', 'Ne', 'Na', 'Mg', 'Al', 'Si', 'P', 'S',
'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr',
'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Ga', 'Ge']),
'mX_amu': np.array([1.008, 4.0026, 6.941, 9.012, 10.811, 12.011, 14.007, 15.999,
18.998, 20.180, 22.990, 24.305, 26.982, 28.086, 30.974, 32.065,
35.453, 39.948, 39.098, 40.078, 44.956, 47.867, 50.942, 51.996,
54.938, 55.845, 58.933, 58.693, 63.546, 65.380, 69.723, 72.640]),
'NX_NH': np.array([1.0, 9.55e-2, 2.00e-9, 2.19e-11, 6.76e-10, 2.95e-4, 7.41e-5, 5.37e-4,
2.88e-8, 9.33e-5, 2.04e-6, 4.37e-5, 2.95e-6, 3.55e-5, 3.23e-7, 1.45e-5,
1.86e-7, 2.75e-6, 1.32e-7, 2.14e-6, 1.23e-9, 8.91e-8, 1.00e-8, 4.79e-7,
3.31e-7, 3.47e-5, 8.13e-8, 1.74e-6, 1.95e-8, 4.68e-8, 1.32e-9, 4.17e-9])
}
df = pd.DataFrame.from_dict(data)
# Adjust Helium abundance
df.loc[df['X'] == 'He', 'NX_NH'] = xHe
# Scale metal abundance by Zprime
df.loc[df['Z'] > 2, 'NX_NH'] *= self.Zprime
# TODO: Check these again
# Mass [amu] for nH=1
df['MX_per_H'] = df['mX_amu']*df['NX_NH']
# Mass fraction
# divide by MH = 1.008 amu so that MX_MH = 1.0 for H
df['MX_MH'] = df['MX_per_H']/df.loc[0, 'MX_per_H']
self.df = df
[docs] def get_XYZ_muH_mu(self, verbose=False):
"""Compute mass fractions of hydrogen (X), helium (Y), and metals (Z),
mean molecular weight per H (muH), and mean molecular weight per
particle (mu). Dust depletion is ignored.
Parameters
----------
verbose : bool
Print summary of results.
"""
df = self.df
xHe = df[df['X'] == 'He']['NX_NH'].iloc[0]
xMetal = df[df['Z'] > 2]['NX_NH'].sum()
# Total mass (in units of amu) per H
Mtot = df['MX_per_H'].sum()
# Mean molecular weight per H (in units of mH)
muH = Mtot/1.008
# Mass fraction of H, He, and Metals
X = df[df['X'] == 'H']['MX_MH'].iloc[0]/Mtot
Y = df[df['X'] == 'He']['MX_MH'].iloc[0]/Mtot
Z = df[df['Z'] > 2]['MX_MH'].sum()/Mtot
# Mean molecular weight (per particle)
# Fully atomic
mu_atom = df['MX_MH'].sum()/df['NX_NH'].sum()
# Fully ionized
xe_ion = (df['NX_NH']*df['Z']).sum()
mu_ion = df['MX_MH'].sum()/(df['NX_NH']*(1.0 + df['Z'])).sum()
res = dict()
res['xHe'] = xHe
res['xMetal'] = xMetal
res['X'] = X
res['Y'] = Y
res['Z'] = Z
res['muH'] = muH
res['mu_atom'] = mu_atom
res['mu_ion'] = mu_ion
res['xe_ion'] = xe_ion
res['df'] = df
if verbose:
print('Elemental abundance of Helium xHe :', res['xHe'])
print('xMetal :', res['xMetal'])
print('Hydrogen mass fraction X:', res['X'])
print('Helium mass fraction Y:', res['Y'])
print('Metal mass fraction Z:', res['Z'])
print('Mean molecular weight per H:', res['muH'])
print('Mean molecular weight per ptcl (atomic gas):', res['mu_atom'])
print('Mean molecular weight per ptcl (fully ionized):', res['mu_ion'])
print('Electron fraction x_e (fully ionized):', res['xe_ion'])
return res
