def devolatilise(df: pd.DataFrame,
exclude=['H2O', 'H2O_PLUS', 'H2O_MINUS', 'CO2', 'LOI'],
renorm=True):
"""
Recalculates components after exclusion of volatile phases (e.g. H2O, CO2).
"""
keep = [i for i in df.columns if not i in exclude]
if renorm:
return renormalise(df.loc[:, keep])
else:
return df.loc[:, keep]
def devolatilise(df: pd.DataFrame,
exclude=['H2O', 'H2O_PLUS', 'H2O_MINUS', 'CO2', 'LOI'],
renorm=True):
"""
Recalculates components after exclusion of volatile phases (e.g. H2O, CO2).
"""
keep = [i for i in df.columns if not i in exclude]
if renorm:
return renormalise(df.loc[:, keep])
else:
return df.loc[:, keep]
def to_weight(df: pd.DataFrame, renorm=True):
"""
Converts molar quantities to mass quantities of the same order.
E.g.:
mol% --> mass%
mol-ppm --> mass-ppm
"""
MWs = [pt.formula(c).mass for c in df.columns]
if renorm:
return renormalise(df.multiply(MWs))
else:
return df.multiply(MWs)
def to_molecular(df: pd.DataFrame, renorm=True):
"""
Converts mass quantities to molar quantities of the same order.
E.g.:
mass% --> mol%
mass-ppm --> mol-ppm
"""
MWs = [pt.formula(c).mass for c in df.columns]
if renorm:
return renormalise(df.div(MWs))
else:
return df.div(MWs)
def to_weight(df: pd.DataFrame, renorm=True):
"""
Converts molar quantities to mass quantities of the same order.
E.g.:
mol% --> mass%
mol-ppm --> mass-ppm
"""
MWs = [pt.formula(c).mass for c in df.columns]
if renorm:
return renormalise(df.multiply(MWs))
else:
return df.multiply(MWs)
def to_molecular(df: pd.DataFrame, renorm=True):
"""
Converts mass quantities to molar quantities of the same order.
E.g.:
mass% --> mol%
mass-ppm --> mol-ppm
"""
MWs = [pt.formula(c).mass for c in df.columns]
if renorm:
return renormalise(df.div(MWs))
else:
return df.div(MWs)
def recalculate_redox(df: pd.DataFrame,
to_oxidised=False,
renorm=True,
total_suffix='T'):
"""
Recalculates abundances of redox-sensitive components (particularly Fe),
and normalises a dataframe to contain only one oxide species for a given
element.
"""
# Assuming either (a single column) or (FeO + Fe2O3) are reported
# Fe columns - FeO, Fe2O3, FeOT, Fe2O3T
FeO = pt.formula("FeO")
Fe2O3 = pt.formula("Fe2O3")
dfc = df.copy()
ox_species = ['Fe2O3', f"Fe2O3{total_suffix}"]
ox_in_df = [i for i in ox_species if i in dfc.columns]
red_species = ['FeO', f"FeO{total_suffix}"]
red_in_df = [i for i in red_species if i in dfc.columns]
if to_oxidised:
oxFe = swap_simple_oxide(FeO, Fe2O3)
Fe2O3T = dfc.loc[:, ox_in_df].fillna(0).sum(axis=1) + \
oxFe(dfc.loc[:, red_in_df].fillna(0)).sum(axis=1)
dfc.loc[:, 'Fe2O3T'] = Fe2O3T
to_drop = red_in_df + \
[i for i in ox_in_df if not i.endswith(total_suffix)]
else:
reduceFe = swap_simple_oxide(Fe2O3, FeO)
FeOT = dfc.loc[:, red_in_df].fillna(0).sum(axis=1) + \
reduceFe(dfc.loc[:, ox_in_df].fillna(0)).sum(axis=1)
dfc.loc[:, 'FeOT'] = FeOT
to_drop = ox_in_df + \
[i for i in red_in_df if not i.endswith(total_suffix)]
dfc = dfc.drop(columns=to_drop)
if renorm:
return renormalise(dfc)
else:
return dfc
def recalculate_redox(df: pd.DataFrame,
to_oxidised=False,
renorm=True,
total_suffix='T'):
"""
Recalculates abundances of redox-sensitive components (particularly Fe),
and normalises a dataframe to contain only one oxide species for a given
element.
"""
# Assuming either (a single column) or (FeO + Fe2O3) are reported
# Fe columns - FeO, Fe2O3, FeOT, Fe2O3T
FeO = pt.formula("FeO")
Fe2O3 = pt.formula("Fe2O3")
dfc = df.copy()
ox_species = ['Fe2O3', f"Fe2O3{total_suffix}"]
ox_in_df = [i for i in ox_species if i in dfc.columns]
red_species = ['FeO', f"FeO{total_suffix}"]
red_in_df = [i for i in red_species if i in dfc.columns]
if to_oxidised:
oxFe = swap_simple_oxide(FeO, Fe2O3)
Fe2O3T = dfc.loc[:, ox_in_df].fillna(0).sum(axis=1) + \
oxFe(dfc.loc[:, red_in_df].fillna(0)).sum(axis=1)
dfc.loc[:, 'Fe2O3T'] = Fe2O3T
to_drop = red_in_df + \
[i for i in ox_in_df if not i.endswith(total_suffix)]
else:
reduceFe = swap_simple_oxide(Fe2O3, FeO)
FeOT = dfc.loc[:, red_in_df].fillna(0).sum(axis=1) + \
reduceFe(dfc.loc[:, ox_in_df].fillna(0)).sum(axis=1)
dfc.loc[:, 'FeOT'] = FeOT
to_drop = ox_in_df + \
[i for i in red_in_df if not i.endswith(total_suffix)]
dfc = dfc.drop(columns=to_drop)
if renorm:
return renormalise(dfc)
else:
return dfc
"""
Olivine-Orthopyroxene-Clinopyroxene
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, deterministic=True, **kwargs)
def build_clsf(self):
# Build
clsf = 'thing'
return clsf
if __name__ == '__main__':
#%matplotlib inline
logging.basicConfig(stream=sys.stdout, level=logging.WARNING)
cm = Geochemistry.TAS(rebuild=False)
#cm = Petrology.apahnitic()
fig, ax = plt.subplots(1)
cm.add_to_axes(ax=ax, alpha=0.4, color='k')
plt.show()
from compositions import renormalise
c2 = Geochemistry.peralkalinity()
df = pd.DataFrame(index=np.arange(100))
df['Al2O3'] = np.linspace(20, 50, len(df.index))
df['Na2O'] = np.linspace(20, 10, len(df.index))
df['K2O'] = np.linspace(10, 122, len(df.index))
df['CaO'] = np.linspace(5, 30, len(df.index))
df = renormalise(df)
c2.classify(df)
"""
Olivine-Orthopyroxene-Clinopyroxene
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, deterministic=True, **kwargs)
def build_clsf(self):
# Build
clsf = 'thing'
return clsf
if __name__ == '__main__':
#%matplotlib inline
logging.basicConfig(stream=sys.stdout, level=logging.WARNING)
cm = Geochemistry.TAS(rebuild=False)
#cm = Petrology.apahnitic()
fig, ax = plt.subplots(1)
cm.add_to_axes(ax=ax, alpha=0.4, color='k')
plt.show()
from compositions import renormalise
c2 = Geochemistry.peralkalinity()
df = pd.DataFrame(index=np.arange(100))
df['Al2O3'] = np.linspace(20, 50, len(df.index))
df['Na2O'] = np.linspace(20, 10, len(df.index))
df['K2O'] = np.linspace(10, 122, len(df.index))
df['CaO'] = np.linspace(5, 30, len(df.index))
df = renormalise(df)
c2.classify(df)