def initiate_active_shapes(self):
logging.info(' reading data for:')
if cfg.cfgfile.get('case', 'parallel_process').lower() == 'true':
shapes = helper_multiprocess.safe_pool(helper_multiprocess.shapes_populate, self.data.values())
self.data = dict(zip(self.data.keys(), shapes))
else:
for id in self.active_shape_ids:
shape = self.data[id]
logging.info(' shape: ' + shape.name)
if hasattr(shape, 'raw_values'):
return
shape.read_timeseries_data()
for id in self.active_shape_ids:
shape = self.data[id]
if shape.shape_type=='weather date':
shape.convert_index_to_datetime('raw_values', 'weather_datetime')
date_position = util.position_in_index(shape.raw_values, 'weather_datetime')
shape.start_date, shape.end_date = min(shape.raw_values.index.levels[date_position]), max(shape.raw_values.index.levels[date_position])
self.start_date = shape.start_date if self.start_date is None else max(shape.start_date, self.start_date)
self.end_date = shape.end_date if self.end_date is None else min(shape.end_date, self.end_date)
self.set_active_dates()
def initiate_active_shapes(self):
print ' reading data for:'
for id in self.active_shape_ids:
shape = self.data[id]
print ' shape: ' + shape.name
if hasattr(shape, 'raw_values'):
return
shape.read_timeseries_data()
if shape.shape_type == 'weather date':
shape.convert_index_to_datetime('raw_values',
'weather_datetime')
date_position = util.position_in_index(shape.raw_values,
'weather_datetime')
shape.start_date, shape.end_date = min(
shape.raw_values.index.levels[date_position]), max(
shape.raw_values.index.levels[date_position])
self.start_date = shape.start_date if self.start_date is None else max(
shape.start_date, self.start_date)
self.end_date = shape.end_date if self.end_date is None else min(
shape.end_date, self.end_date)
self.set_active_dates()
def remap(self, map_from='raw_values', map_to='values', drivers=None, time_index_name='year',
time_index=None, fill_timeseries=True, interpolation_method='missing', extrapolation_method='missing',
converted_geography=None, current_geography=None, current_data_type=None, fill_value=0., lower=0, upper=None):
""" Map data to drivers and geography
Args:
map_from (string): starting variable name (defaults to 'raw_values')
map_to (string): ending variable name (defaults to 'values')
drivers (list of or single dataframe): drivers for the remap
input_type_override (string): either 'total' or 'intensity' (defaults to self.type)
"""
converted_geography = cfg.cfgfile.get('case', 'primary_geography') if converted_geography is None else converted_geography
current_data_type = self.input_type if current_data_type is None else current_data_type
current_geography = self.geography if current_geography is None else current_geography
# TODO fix pluralization
if time_index is None:
time_index = getattr(self, time_index_name + "s") if hasattr(self, time_index_name + "s") else cfg.cfgfile.get('case', 'years')
setattr(self, map_to, getattr(self, map_from).copy())
mapf = getattr(self, map_from)
if current_geography not in (mapf.index.names if mapf.index.nlevels > 1 else [mapf.index.name]):
raise ValueError('current geography does not match the geography of the dataframe in remap')
else:
current_geography_index_levels = mapf.index.levels[util.position_in_index(mapf, current_geography)] if mapf.index.nlevels > 1 else mapf.index.tolist()
if (drivers is None) or (not len(drivers)):
if fill_timeseries:
self.clean_timeseries(attr=map_to, inplace=True, time_index=time_index, time_index_name=time_index_name, interpolation_method=interpolation_method, extrapolation_method=extrapolation_method, lower=lower, upper=upper)
if current_geography != converted_geography:
self.geo_map(converted_geography, attr=map_to, inplace=True, current_geography=current_geography,
current_data_type=current_data_type, fill_value=fill_value)
current_geography = converted_geography
else:
total_driver = DfOper.mult(util.put_in_list(drivers))
if len(current_geography_index_levels) > 1 and current_geography != converted_geography:
# While not on primary geography, geography does have some information we would like to preserve
self.geo_map(converted_geography, attr=map_to, inplace=True, current_geography=current_geography,
current_data_type=current_data_type, fill_value=fill_value)
current_geography = converted_geography
if current_data_type == 'total':
# Divide by drivers to turn a total to intensity. multindex_operation will aggregate to common levels.
df_intensity = DfOper.divi((getattr(self, map_to), total_driver), expandable=(False, True), collapsible=(False, True))
setattr(self, map_to, df_intensity)
# Clean the timeseries as an intensity
if fill_timeseries:
# print getattr(self,map_to)
# print time_index
self.clean_timeseries(attr=map_to, inplace=True, time_index=time_index, interpolation_method=interpolation_method, extrapolation_method=extrapolation_method)
if current_data_type == 'total':
setattr(self, map_to, DfOper.mult((getattr(self, map_to), total_driver)))
else:
setattr(self, map_to, DfOper.mult((getattr(self, map_to), total_driver), expandable=(True, False),
collapsible=(False, True)))
self.ensure_correct_geography(map_to, converted_geography, current_geography, current_data_type)
def min_year(self):
"""calculates the minimum or start year of data in the technology specification.
Used to determine start year of subsector for analysis."""
attributes = vars(self)
self.min_year = cfg.cfgfile.get('case', 'current_year')
for att in attributes:
obj = getattr(self, att)
if inspect.isclass(type(obj)) and hasattr(obj, '__dict__') and hasattr(obj, 'raw_values'):
try:
att_min_year = min(obj.raw_values.index.levels[util.position_in_index(obj.raw_values, 'vintage')])
except:
att_min_year = self.min_year
if att_min_year < self.min_year:
self.min_year = att_min_year
else:
pass
def min_year(self):
"""calculates the minimum or start year of data in the technology specification.
Used to determine start year of subsector for analysis."""
attributes = vars(self)
self.min_year = cfg.cfgfile.get('case', 'current_year')
for att in attributes:
obj = getattr(self, att)
if inspect.isclass(type(obj)) and hasattr(obj, '__dict__') and hasattr(obj, 'raw_values'):
try:
att_min_year = min(obj.raw_values.index.levels[util.position_in_index(obj.raw_values, 'vintage')])
except:
att_min_year = self.min_year
if att_min_year < self.min_year:
self.min_year = att_min_year
else:
pass
def initiate_active_shapes(self):
for id in self.active_shape_ids:
shape = self.data[id]
if hasattr(shape, 'raw_values'):
return
shape.read_timeseries_data()
if shape.shape_type=='weather date':
date_position = util.position_in_index(shape.raw_values, 'weather_datetime')
shape.start_date = DT.datetime.strptime(shape.raw_values.index.levels[date_position][0], '%m/%d/%y %H:%M')
shape.end_date = DT.datetime.strptime(shape.raw_values.index.levels[date_position][-1], '%m/%d/%y %H:%M')
self.start_date = shape.start_date if self.start_date is None else max(shape.start_date, self.start_date)
self.end_date = shape.end_date if self.end_date is None else min(shape.end_date, self.end_date)
self.set_active_dates()
def initiate_active_shapes(self):
print ' reading data for:'
for id in self.active_shape_ids:
shape = self.data[id]
print ' shape: ' + shape.name
if hasattr(shape, 'raw_values'):
return
shape.read_timeseries_data()
if shape.shape_type=='weather date':
shape.convert_index_to_datetime('raw_values', 'weather_datetime')
date_position = util.position_in_index(shape.raw_values, 'weather_datetime')
shape.start_date, shape.end_date = min(shape.raw_values.index.levels[date_position]), max(shape.raw_values.index.levels[date_position])
self.start_date = shape.start_date if self.start_date is None else max(shape.start_date, self.start_date)
self.end_date = shape.end_date if self.end_date is None else min(shape.end_date, self.end_date)
self.set_active_dates()
