def get_asset_values(self, case='MEAN'):
if case=='WORST':
units = utils.subdf(self.df,
input_key='max_units',
output_key='min_units',
to_numeric=True)
prices = utils.subdf(self.df,
input_key='max_price',
output_key='min_price',
to_numeric=True)
elif case=='BEST':
units = utils.subdf(self.df,
input_key='min_units',
output_key='max_units',
to_numeric=True)
prices = utils.subdf(self.df,
input_key='min_price',
output_key='max_price',
to_numeric=True)
else: # ('MEAN')
units = ( utils.subdf(self.df,
input_key='min_units',
output_key='min_units',
to_numeric=True) +
utils.subdf(self.df,
input_key='max_units',
output_key='max_units',
to_numeric=True) ) / 2.
# Add known means, if 'f_units' is a number:
known_units_means = utils.subdf(self.df, input_key='f_units',
output_key='f_units',to_numeric=True)
# add dataframe with 'f_units', and fillna(.0)
units = utils.replace_known_numeric_values(units, known_units_means)
prices = ( utils.subdf(self.df,
input_key='min_price',
output_key='min_price',
to_numeric=True) +
utils.subdf(self.df,
input_key='max_price',
output_key='max_price',
to_numeric=True) ) / 2.
# Add known means, if 'f_price' is a number:
known_price_means = utils.subdf(self.df, input_key='f_price',
output_key='f_price',to_numeric=True)
# add dataframe with 'f_units', and fillna(.0)
prices = utils.replace_known_numeric_values(prices, known_price_means)
return units * prices * self.get_price_unit_values()
def get_price_unit_values(self, default=pandas.np.nan):
price_units = utils.subdf(self.df, 'price_unit', 'price_unit')
price_unit_values = price_units.applymap(
lambda x: self.rates[x].values[0] if x in self.rates.columns
else pandas.np.nan).apply(pandas.to_numeric,
errors='coerce')
return price_unit_values.fillna(default)
