def add_timeseries(self, df, meta=False):
"""add a timeseries dataframe to the TimeSeries instance
Parameters
----------
df : a Pandas dataframe either
- in tabular form (cols: region, variable, unit, year)
- in 'IAMC-style' format (cols: region, variable, unit, [years])
meta : boolean
indicator whether this timeseries is 'meta-data'
(special treatment during cloning for MESSAGE-scheme scenarios)
"""
meta = 1 if meta else 0
if "time" in df.columns:
raise ("sub-annual time slices not supported by Python interface!")
# if in tabular format
if ("value" in df.columns):
df = df.sort_values(by=['region', 'variable', 'unit', 'year'])\
.reset_index(drop=True)
region = df.region[0]
variable = df.variable[0]
unit = df.unit[0]
time = None
jData = java.LinkedHashMap()
for i in df.index:
if not (region == df.region[i] and variable == df.variable[i]
and unit == df.unit[i]):
# if new 'line', pass to Java interface, start a new
# LinkedHashMap
self._jobj.addTimeseries(region, variable, time, jData,
unit, meta)
region = df.region[i]
variable = df.variable[i]
unit = df.unit[i]
jData = java.LinkedHashMap()
jData.put(java.Integer(int(df.year[i])),
java.Double(float(df.value[i])))
# add the final iteration of the loop
self._jobj.addTimeseries(region, variable, time, jData, unit, meta)
# if in 'IAMC-style' format
else:
for i in df.index:
jData = java.LinkedHashMap()
for j in ix.utils.numcols(df):
jData.put(java.Integer(int(j)),
java.Double(float(df[j][i])))
time = None
self._jobj.addTimeseries(df.region[i], df.variable[i], time,
jData, df.unit[i], meta)
