import numpy as np
import pandas as pd
from radioactivedecay import Nuclide
from radioactivedecay.utils import Z_DICT, elem_to_Z
[docs]def read_csv_isotope_abundances(
fname, delimiter=r"\s+", skip_columns=0, skip_rows=[1]
):
"""
A generic parser for a TARDIS composition stored as a CSV file
The parser can read in both elemental and isotopic abundances. The first
column is always expected to contain a running index, labelling the grid
cells. The parser also allows for additional information to be stored in
the first skip_columns columns. These will be ignored if skip_columns > 0.
Note that the first column, containing the cell index is not taken into
account here.
Specific header lines can be skipped by the skip_rows keyword argument
It is expected that the first row of the date block (after skipping the
rows specified in skip_rows) specifies the different elements and isotopes.
Each row after contains the composition in the corresponding grid shell.
The first composition row describes the composition of the photosphere and
is essentially ignored (for the default value of skip_rows).
Example:
Index C O Ni56
0 1 1 1
1 0.4 0.3 0.2
Parameters
----------
fname : str
filename or path with filename
Returns
-------
index : np.ndarray
abundances : pandas.DataFrame
isotope_abundance : pandas.MultiIndex
"""
df = pd.read_csv(
fname, comment="#", sep=delimiter, skiprows=skip_rows, index_col=0
)
df = df.transpose()
abundance = pd.DataFrame(
columns=np.arange(df.shape[1]),
index=pd.Index([], name="atomic_number"),
dtype=np.float64,
)
isotope_index = pd.MultiIndex(
[[]] * 2, [[]] * 2, names=["atomic_number", "mass_number"]
)
isotope_abundance = pd.DataFrame(
columns=np.arange(df.shape[1]), index=isotope_index, dtype=np.float64
)
for element_symbol_string in df.index[skip_columns:]:
if element_symbol_string in Z_DICT.values():
z = elem_to_Z(element_symbol_string)
abundance.loc[z, :] = df.loc[element_symbol_string].tolist()
else:
nuc = Nuclide(element_symbol_string)
z = nuc.Z
mass_no = nuc.A
isotope_abundance.loc[(z, mass_no), :] = df.loc[
element_symbol_string
].tolist()
return abundance.index, abundance, isotope_abundance