import re
import numpy as np
import pandas as pd
import yaml
from astropy import units as u
from tardis.util.base import parse_quantity
PATTERN_REMOVE_BRACKET = re.compile(r"\[.+\]")
T0_PATTERN = re.compile("tend = (.+)\n")
[docs]def read_blondin_toymodel(fname):
"""
Reading the Blondin toy-model format and returns a dictionary and a
dataframe
Parameters
----------
fname : str
path or filename to blondin toymodel
Returns
-------
blondin_dict : dict
dictionary containing most of the meta data of the model
blondin_csv : pandas.DataFrame
DataFrame containing the csv part of the toymodel
"""
with open(fname, "r") as fh:
for line in fh:
if line.startswith("#idx"):
break
else:
raise ValueError(
"File {0} does not conform to Toy Model format as it does "
"not contain #idx"
)
columns = [
PATTERN_REMOVE_BRACKET.sub("", item) for item in line[1:].split()
]
raw_blondin_csv = pd.read_csv(
fname,
# The argument `delim_whitespace` was changed to `sep`
# because the first one is deprecated since version 2.2.0.
# The regular expression means: the separation is one or
# more spaces together (simple space, tabs, new lines).
sep=r"\s+",
comment="#",
header=None,
names=columns,
)
raw_blondin_csv.set_index("idx", inplace=True)
blondin_csv = raw_blondin_csv.loc[
:,
[
"vel",
"dens",
"temp",
"X_56Ni0",
"X_Ti",
"X_Ca",
"X_S",
"X_Si",
"X_O",
"X_C",
],
]
rename_col_dict = {
"vel": "velocity",
"dens": "density",
"temp": "t_electron",
}
rename_col_dict.update({item: item[2:] for item in blondin_csv.columns[3:]})
rename_col_dict["X_56Ni0"] = "Ni56"
blondin_csv.rename(columns=rename_col_dict, inplace=True)
blondin_csv.iloc[:, 3:] = blondin_csv.iloc[:, 3:].divide(
blondin_csv.iloc[:, 3:].sum(axis=1), axis=0
)
# changing velocities to outer boundary
new_velocities = 0.5 * (
blondin_csv.velocity.iloc[:-1].values
+ blondin_csv.velocity.iloc[1:].values
)
new_velocities = np.hstack(
(new_velocities, [2 * new_velocities[-1] - new_velocities[-2]])
)
blondin_csv["velocity"] = new_velocities
with open(fname, "r") as fh:
t0_string = T0_PATTERN.findall(fh.read())[0]
t0 = parse_quantity(t0_string.replace("DAYS", "day"))
blondin_dict = {}
blondin_dict["model_density_time_0"] = str(t0)
blondin_dict["description"] = f"Converted {fname} to csvy format"
blondin_dict["tardis_model_config_version"] = "v1.0"
blondin_dict_fields = [
dict(
name="velocity",
unit="km/s",
desc="velocities of shell outer bounderies.",
)
]
blondin_dict_fields.append(
dict(name="density", unit="g/cm^3", desc="mean density of shell.")
)
blondin_dict_fields.append(
dict(name="t_electron", unit="K", desc="electron temperature.")
)
for abund in blondin_csv.columns[3:]:
blondin_dict_fields.append(
dict(name=abund, desc=f"Fraction {abund} abundance")
)
blondin_dict["datatype"] = {"fields": blondin_dict_fields}
return blondin_dict, blondin_csv
[docs]def convert_blondin_toymodel(
in_fname, out_fname, v_inner, v_outer, conversion_t_electron_rad=None
):
"""
Parameters
----------
in_fname : str
input toymodel file
out_fname : str
output csvy file
conversion_t_electron_rad : float or None
multiplicative conversion factor from t_electron to t_rad.
if `None` t_rad is not calculated
v_inner : float or astropy.unit.Quantity
inner boundary velocity. If float will be interpreted as km/s
v_outer : float or astropy.unit.Quantity
outer boundary velocity. If float will be interpreted as km/s
"""
blondin_dict, blondin_csv = read_blondin_toymodel(in_fname)
blondin_dict["v_inner_boundary"] = str(u.Quantity(v_inner, u.km / u.s))
blondin_dict["v_outer_boundary"] = str(u.Quantity(v_outer, u.km / u.s))
if conversion_t_electron_rad is not None:
blondin_dict["datatype"]["fields"].append(
{
"desc": "converted radiation temperature "
f"using multiplicative factor={conversion_t_electron_rad}",
"name": "t_rad",
"unit": "K",
}
)
blondin_csv["t_rad"] = (
conversion_t_electron_rad * blondin_csv.t_electron
)
csvy_file = f"---\n{yaml.dump(blondin_dict, default_flow_style=False)}\n---\n{blondin_csv.to_csv(index=False)}"
with open(out_fname, "w") as fh:
fh.write(csvy_file)