def read_blondin_toymodel(fname):
header = '''
vel[km/s] rad[cm] dvol[cm^3] dens[g/cm^3] dmass[g] temp[K] c o ne na
mg al si s cl ar k ca sc ti
v cr mn fe co ni ni56 co56 ni57 co57
cr48 v48 cr49 v49 mn51 cr51 co55 fe55 k37 ar37
fe52 mn52 ti44 sc44 ar41 k42 k43 sc43 sc47 co61
ni56_0 co56_0 ni57_0 co57_0 cr48_0 v48_0 cr49_0 v49_0 mn51_0 cr51_0
co55_0 fe55_0 k37_0 ar37_0 fe52_0 mn52_0 ti44_0 sc44_0 ar41_0 k42_0
k43_0 sc43_0 sc47_0 co61_0
'''
header = header.replace('_0', '')
header = header.split()
print(header)
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, delim_whitespace=True, 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_rad'}
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'] = 'Converted {0} to csvy format'.format(fname)
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_rad', unit='K', desc='radiative temperature.'))
for abund in blondin_csv.columns[3:]:
blondin_dict_fields.append(dict(name=abund, desc='Fraction {0} abundance'.format(abund)))
blondin_dict['datatype'] = {'fields':blondin_dict_fields}
return blondin_dict, blondin_csv
def read_cmfgen_density(fname):
"""
Reading a density file of the following structure (example; lines starting with a hash will be ignored):
The first density describes the mean density in the center of the model and is not used.
The file consists of a header row and next row contains unit of the respective attributes
Note that the first column has to contain a running index
Example:
index velocity densities electron_densities temperature
- km/s g/cm^3 /cm^3 K
0 871.66905 4.2537191e-09 2.5953807e+14 7.6395577
1 877.44269 4.2537191e-09 2.5953807e+14 7.6395577
Rest columns contain abundances of elements and isotopes
Parameters
----------
fname : str
filename or path with filename
Returns
-------
time_of_model : astropy.units.Quantity
time at which the model is valid
velocity : np.ndarray
mean_density : np.ndarray
electron_densities : np.ndarray
temperature : np.ndarray
"""
warnings.warn("The current CMFGEN model parser is deprecated",
DeprecationWarning)
df = pd.read_csv(fname, comment="#", delimiter=r"\s+", skiprows=[0, 2])
with open(fname) as fh:
for row_index, line in enumerate(fh):
if row_index == 0:
time_of_model_string = line.strip().replace("t0:", "")
time_of_model = parse_quantity(time_of_model_string)
elif row_index == 2:
quantities = line.split()
velocity = u.Quantity(df["velocity"].values, quantities[1]).to("cm/s")
temperature = u.Quantity(df["temperature"].values, quantities[2])[1:]
mean_density = u.Quantity(df["densities"].values, quantities[3])[1:]
electron_densities = u.Quantity(df["electron_densities"].values,
quantities[4])[1:]
return (
time_of_model,
velocity,
mean_density,
electron_densities,
temperature,
)
def read_cmfgen_density(fname):
"""
Reading a density file of the following structure (example; lines starting with a hash will be ignored):
The first density describes the mean density in the center of the model and is not used.
The file consists of a header row and next row contains unit of the respective attributes
Note that the first column has to contain a running index
Example:
index velocity densities electron_densities temperature
- km/s g/cm^3 /cm^3 K
0 871.66905 4.2537191e-09 2.5953807e+14 7.6395577
1 877.44269 4.2537191e-09 2.5953807e+14 7.6395577
Rest columns contain abundances of elements and isotopes
Parameters
----------
fname: str
filename or path with filename
Returns
-------
time_of_model: ~astropy.units.Quantity
time at which the model is valid
velocity: ~np.ndarray
mean_density: ~np.ndarray
electron_densities: ~np.ndarray
temperature: ~np.ndarray
"""
warnings.warn("The current CMFGEN model parser is deprecated",
DeprecationWarning)
df = pd.read_csv(fname, comment='#', delimiter='\s+', skiprows=[0, 2])
with open(fname) as fh:
for row_index, line in enumerate(fh):
if row_index == 0:
time_of_model_string = line.strip().replace('t0:', '')
time_of_model = parse_quantity(time_of_model_string)
elif row_index == 2:
quantities = line.split()
velocity = u.Quantity(df['velocity'].values, quantities[1]).to('cm/s')
temperature = u.Quantity(df['temperature'].values, quantities[2])[1:]
mean_density = u.Quantity(df['densities'].values, quantities[3])[1:]
electron_densities = u.Quantity(
df['electron_densities'].values, quantities[4])[1:]
return time_of_model, velocity, mean_density, electron_densities, temperature
def test_parse_quantity():
q1 = parse_quantity('5 km/s')
assert q1.value == 5.
assert q1.unit == u.Unit('km/s')
with pytest.raises(MalformedQuantityError):
parse_quantity(5)
with pytest.raises(MalformedQuantityError):
parse_quantity('abcd')
with pytest.raises(MalformedQuantityError):
parse_quantity('a abcd')
with pytest.raises(MalformedQuantityError):
parse_quantity('5 abcd')
def test_parse_quantity():
q1 = parse_quantity('5 km/s')
assert q1.value == 5.
assert q1.unit == u.Unit('km/s')
with pytest.raises(MalformedQuantityError):
parse_quantity(5)
with pytest.raises(MalformedQuantityError):
parse_quantity('abcd')
with pytest.raises(MalformedQuantityError):
parse_quantity('a abcd')
with pytest.raises(MalformedQuantityError):
parse_quantity('5 abcd')
def test_parse_quantity():
q1 = parse_quantity("5 km/s")
assert q1.value == 5.0
assert q1.unit == u.Unit("km/s")
with pytest.raises(MalformedQuantityError):
parse_quantity(5)
with pytest.raises(MalformedQuantityError):
parse_quantity("abcd")
with pytest.raises(MalformedQuantityError):
parse_quantity("a abcd")
with pytest.raises(MalformedQuantityError):
parse_quantity("5 abcd")
def read_simple_ascii_density(fname):
"""
Reading a density file of the following structure (example; lines starting with a hash will be ignored):
The first density describes the mean density in the center of the model and is not used.
5 s
#index velocity [km/s] density [g/cm^3]
0 1.1e4 1.6e8
1 1.2e4 1.7e8
Parameters
----------
fname: str
filename or path with filename
Returns
-------
time_of_model: ~astropy.units.Quantity
time at which the model is valid
data: ~pandas.DataFrame
data frame containing index, velocity (in km/s) and density
"""
with open(fname) as fh:
time_of_model_string = fh.readline().strip()
time_of_model = parse_quantity(time_of_model_string)
data = recfromtxt(
fname,
skip_header=1,
names=("index", "velocity", "density"),
dtype=(int, float, float),
)
velocity = (data["velocity"] * u.km / u.s).to("cm/s")
mean_density = (data["density"] * u.Unit("g/cm^3"))[1:]
return time_of_model, velocity, mean_density
def read_simple_ascii_density(fname):
"""
Reading a density file of the following structure (example; lines starting with a hash will be ignored):
The first density describes the mean density in the center of the model and is not used.
5 s
#index velocity [km/s] density [g/cm^3]
0 1.1e4 1.6e8
1 1.2e4 1.7e8
Parameters
----------
fname: str
filename or path with filename
Returns
-------
time_of_model: ~astropy.units.Quantity
time at which the model is valid
data: ~pandas.DataFrame
data frame containing index, velocity (in km/s) and density
"""
with open(fname) as fh:
time_of_model_string = fh.readline().strip()
time_of_model = parse_quantity(time_of_model_string)
data = recfromtxt(fname, skip_header=1,
names=('index', 'velocity', 'density'),
dtype=(int, float, float))
velocity = (data['velocity'] * u.km / u.s).to('cm/s')
mean_density = (data['density'] * u.Unit('g/cm^3'))[1:]
return time_of_model, velocity, mean_density
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,
delim_whitespace=True,
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"] = "Converted {0} to csvy format".format(fname)
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="Fraction {0} abundance".format(abund)))
blondin_dict["datatype"] = {"fields": blondin_dict_fields}
return blondin_dict, blondin_csv
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,
delim_whitespace=True,
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'] = 'Converted {0} to csvy format'.format(fname)
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='Fraction {0} abundance'.format(abund)))
blondin_dict['datatype'] = {'fields': blondin_dict_fields}
return blondin_dict, blondin_csv
