def _getElementList(jItem, filters=None, has_value=False, has_level=False):
# get list of elements, with filter HashMap if provided
if filters is not None:
jFilter = java.HashMap()
for idx_name in filters.keys():
jFilter.put(idx_name, to_jlist(filters[idx_name]))
jList = jItem.getElements(jFilter)
else:
jList = jItem.getElements()
# return a dataframe if this is a mapping or multi-dimensional parameter
dim = jItem.getDim()
if dim > 0:
idx_names = np.array(jItem.getIdxNames().toArray()[:])
idx_sets = np.array(jItem.getIdxSets().toArray()[:])
data = {}
for d in range(dim):
ary = np.array(jItem.getCol(d, jList)[:])
if idx_sets[d] == "year":
# numpy tricks to avoid extra copy
# _ary = ary.view('int')
# _ary[:] = ary
ary = ary.astype('int')
data[idx_names[d]] = ary
if has_value:
data['value'] = np.array(jItem.getValues(jList)[:])
data['unit'] = np.array(jItem.getUnits(jList)[:])
if has_level:
data['lvl'] = np.array(jItem.getLevels(jList)[:])
data['mrg'] = np.array(jItem.getMarginals(jList)[:])
df = pd.DataFrame.from_dict(data, orient='columns', dtype=None)
return df
else:
# for index sets
if not (has_value or has_level):
return pd.Series(jItem.getCol(0, jList)[:])
data = {}
# for parameters as scalars
if has_value:
data['value'] = jItem.getScalarValue().floatValue()
data['unit'] = str(jItem.getScalarUnit())
# for variables as scalars
elif has_level:
data['lvl'] = jItem.getScalarLevel().floatValue()
data['mrg'] = jItem.getScalarMarginal().floatValue()
return data
