def calculate_electrolyte_mass_concentrations(self):
"""no docstring"""
# ion_mass_concentration = self.data
materials = _properties.get_commen()
materials.index = materials.species_name
material_ions = materials.loc[[
'ammonium', 'sulfate', 'nitrate', 'chloride', 'sodium', 'calcium'
]]
material_ions = material_ions.dropna(axis=1)
material_ions = material_ions.drop(['Species', 'species_name'], axis=1)
# cats = material_ions.loc[material_ions['ion'] == 'cat']
# ans = material_ions.loc[material_ions['ion'] == 'an']
material_elct = materials.loc[[
'ammonium_sulfate', 'ammonium_nitrate', 'ammonium_chloride',
'sodium_chloride', 'sodium_sulfate', 'sodium_nitrate',
'calcium_nitrate', 'calcium_chloride', 'sulfuric_acid',
'ammonium_hydrogen_sulfate'
]]
material_elct = material_elct.dropna(axis=1)
dt = _np.zeros((self.data.shape[0], material_elct.shape[0]))
df = _pd.DataFrame(dt,
columns=material_elct.index,
index=self.data.index)
for i in self.data.index:
cct = self.data.loc[i]
electro = ion2electrolyte_mass_concentration(
cct, material_ions, material_elct)
df.loc[i] = electro.mass_concentration
df['organic_aerosol'] = self.data.organic_aerosol
return AMS_Timeseries_lev02(df)
def calculate_electrolyte_mass_concentrations(self):
"""no docstring"""
# ion_mass_concentration = self.data
materials = _properties.get_commen()
materials.index = materials.species_name
material_ions = materials.loc[['ammonium','sulfate', 'nitrate', 'chloride', 'sodium', 'calcium' ]]
material_ions = material_ions.dropna(axis=1)
material_ions = material_ions.drop(['Species', 'species_name'], axis = 1)
# cats = material_ions.loc[material_ions['ion'] == 'cat']
# ans = material_ions.loc[material_ions['ion'] == 'an']
material_elct = materials.loc[['ammonium_sulfate',
'ammonium_nitrate',
'ammonium_chloride',
'sodium_chloride',
'sodium_sulfate',
'sodium_nitrate',
'calcium_nitrate',
'calcium_chloride',
'sulfuric_acid',
'ammonium_hydrogen_sulfate'
]]
material_elct = material_elct.dropna(axis=1)
dt = _np.zeros((self.data.shape[0],material_elct.shape[0]))
df = _pd.DataFrame(dt,columns=material_elct.index, index = self.data.index)
for i in self.data.index:
cct = self.data.loc[i]
electro = ion2electrolyte_mass_concentration(cct, material_ions, material_elct)
df.loc[i] = electro.mass_concentration
df['organic_aerosol'] = self.data.organic_aerosol
return AMS_Timeseries_lev02(df)
def zdanovskii_stokes_robinson(data, which = 'refractive_Index'):
"""(Stokes and Robinson,1966)
Arguments
---------
data: pandas dataframe
containing chemical composition data
which: str
which property to mix ['refractive_Index', 'density', 'kappa_chem']
"""
materials = _properties.get_commen()
materials.index = materials.species_name
essential_elcts = ['ammonium_sulfate',
'ammonium_nitrate',
'ammonium_chloride',
'sodium_chloride',
'sodium_sulfate',
'sodium_nitrate',
'calcium_nitrate',
'calcium_chloride',
'organic_aerosol'
]
electrolytes = materials.loc[essential_elcts]
electrolytes = electrolytes[['refractive_Index', 'density', 'kappa_chem']]
_pandas_tools.ensure_column_exists(data, 'organic_aerosol', col_alt = ['total_organics'] )
for e in essential_elcts:
_pandas_tools.ensure_column_exists(data, e)
tobemixed = electrolytes[which]
# _pdb.set_trace()
numerator = (data * tobemixed / electrolytes.density).sum(axis=1)
denominator = (data / electrolytes.density).sum(axis=1)
mixed = numerator/denominator
df = _pd.DataFrame(mixed, columns=[which])
ts = _timeseries.TimeSeries(df)
return ts
