def timeseries(self, iamc=False, regions=None, variables=None, units=None,
years=None):
"""retrieve timeseries data as a pandas.DataFrame
Parameters
----------
iamc : boolean, default True
returns a pandas.DataFrame either
- 'IAMC-style' format (cols: region, variable unit, <years>)
- in tabular form (cols: region, variable, unit, year)
regions : list of strings
filter by regions
variables : list of strings
filter by variables
units : list of strings
filter by units
years : list of integers
filter by years
"""
# convert filter lists to Java objects
regions = ix.to_jlist(regions)
variables = ix.to_jlist(variables)
units = ix.to_jlist(units)
years = ix.to_jlist(years)
# retrieve data, convert to pandas.DataFrame
data = self._jobj.getTimeseries(regions, variables, units, None, years)
dictionary = {}
# if in tabular format
ts_range = range(data.size())
cols = ['region', 'variable', 'unit']
for i in cols:
dictionary[i] = [str(data.get(j).get(i)) for j in ts_range]
dictionary['year'] = [data.get(j).get('year').intValue()
for j in ts_range]
cols.append("year")
dictionary['value'] = [data.get(j).get('value').floatValue()
for j in ts_range]
cols.append("value")
df = pd.DataFrame
df = df.from_dict(dictionary, orient='columns', dtype=None)
df['model'] = self.model
df['scenario'] = self.scenario
df = df[['model', 'scenario'] + cols]
if iamc:
df = df.pivot_table(index=iamc_idx_cols, columns='year')['value']
df.reset_index(inplace=True)
return df
def add_cat(self, name, cat, keys, is_unique=False):
"""Map elements from *keys* to category *cat* within set *name*.
Parameters
----------
name : str
Name of the set.
cat : str
Name of the category.
keys : str or list of str
Element keys to be added to the category mapping.
is_unique: bool, optional
If `True`, then *cat* must have only one element. An exception is
raised if *cat* already has an element, or if ``len(keys) > 1``.
"""
self._jobj.addCatEle(name, str(cat), ixmp.to_jlist(keys), is_unique)