[docs]class IsotopicMassFraction(pd.DataFrame):
_metadata = ["time_0"]
def __init__(self, *args, **kwargs):
if "time_0" in kwargs:
time_0 = kwargs["time_0"]
kwargs.pop("time_0")
else:
time_0 = 0 * u.d
super(IsotopicMassFraction, self).__init__(*args, **kwargs)
self.time_0 = time_0
@property
def _constructor(self):
return IsotopicMassFraction
def _update_inventory(self):
self.comp_dicts = [dict() for i in range(len(self.columns))]
for (atomic_number, mass_number), mass_fractions in self.iterrows():
nuclear_symbol = f"{Z_to_elem(atomic_number)}{mass_number}"
for i in range(len(self.columns)):
self.comp_dicts[i][nuclear_symbol] = mass_fractions[i]
[docs] @classmethod
def from_inventories(cls, inventories):
multi_index_tuples = set([])
for inventory in inventories:
multi_index_tuples.update(
[cls.id_to_tuple(key) for key in inventory.contents.keys()]
)
index = pd.MultiIndex.from_tuples(
multi_index_tuples, names=["atomic_number", "mass_number"]
)
abundances = pd.DataFrame(
data=0.0, index=index, columns=range(len(inventories))
)
for i, inventory in enumerate(inventories):
for nuclide, abundance in inventory.masses("g").items():
abundances.loc[cls.id_to_tuple(nuclide), i] = abundance
return cls(abundances)
[docs] @staticmethod
def id_to_tuple(atomic_id):
nuclide = Nuclide(atomic_id)
return nuclide.Z, nuclide.A
[docs] def to_inventories(self, shell_masses=None):
"""
Convert DataFrame to a list of inventories interpreting the MultiIndex as
atomic_number and mass_number
Returns
-------
list
list of radioactivedecay Inventories
"""
comp_dicts = [dict() for i in range(len(self.columns))]
for (atomic_number, mass_number), abundances in self.iterrows():
nuclear_symbol = f"{Z_to_elem(atomic_number)}{mass_number}"
for i in range(len(self.columns)):
if shell_masses is None:
comp_dicts[i][nuclear_symbol] = abundances[i]
else:
comp_dicts[i][nuclear_symbol] = (
abundances[i] * shell_masses[i].to(u.g).value
)
return [Inventory(comp_dict, "g") for comp_dict in comp_dicts]
[docs] def decay(self, t):
"""
Decay the Model
Parameters
----------
t : float or astropy.units.Quantity
if float it will be understood as days
Returns
-------
pandas.DataFrame
Decayed abundances
"""
inventories = self.to_inventories()
t_second = (
u.Quantity(t, u.day).to(u.s).value - self.time_0.to(u.s).value
)
logger.info(f"Decaying abundances for {t_second} seconds")
if t_second < 0:
logger.warning(
f"Decay time {t_second} is negative. This could indicate a miss-specified input model."
f" A negative decay time can potentially lead to negative abundances."
)
decayed_inventories = [item.decay(t_second) for item in inventories]
df = IsotopicMassFraction.from_inventories(decayed_inventories)
df.sort_index(inplace=True)
assert (
df.ge(0.0).all().all()
), "Negative abundances detected. Please make sure your input abundances are correct."
return df
[docs] def as_atoms(self):
"""
Merge Isotope dataframe according to atomic number
Returns
-------
pandas.DataFrame
Merged isotope abundances
"""
return self.groupby("atomic_number").sum()
[docs] def merge(self, other, normalize=True):
"""
Merge Isotope dataframe with abundance passed as parameter
Parameters
----------
other : pd.DataFrame
normalize : bool
If true, resultant dataframe will be normalized
Returns
-------
pandas.DataFrame
merged abundances
"""
isotope_abundance = self.as_atoms()
isotope_abundance = isotope_abundance.fillna(0.0)
# Merge abundance and isotope dataframe
modified_df = isotope_abundance.add(other, fill_value=0)
if normalize:
norm_factor = modified_df.sum(axis=0)
modified_df /= norm_factor
return modified_df