def process_shape(self, active_dates_index=None, time_slice_elements=None):
self.num_active_years = len(active_dates_index)/8766.
if active_dates_index is not None:
self.active_dates_index = active_dates_index
if active_dates_index is None:
raise ValueError('processing a shape requires an active date index')
self.time_slice_elements = Shapes.create_time_slice_elements(active_dates_index) if time_slice_elements is None else time_slice_elements
if self.shape_type=='weather date':
self.values = util.reindex_df_level_with_new_elements(self.raw_values, 'weather_datetime', active_dates_index) # this step is slow, consider replacing
if self.values.isnull().values.any():
raise ValueError('Weather data did not give full coverage of the active dates')
elif self.shape_type=='time slice':
self.values = self.create_empty_shape_data()
non_time_elements_in_levels = [list(util.get_elements_from_level(self.values, e)) for e in self._non_time_keys]
time_elements_in_levels = [list(util.get_elements_from_level(self.values, e)) for e in self._active_time_keys]
for ind, value in self.raw_values.iterrows():
non_time_portion = [ind[self._non_time_dict[e]] for e in self._non_time_keys]
time_portion = [ind[self._active_time_dict[e]] for e in self._active_time_keys]
if not np.all([s in l for s, l in zip(non_time_portion+time_portion, non_time_elements_in_levels+time_elements_in_levels)]):
continue
indexer = tuple(non_time_portion + time_portion + [slice(None)])
if self.shape_unit_type=='energy':
len_slice = len(self.values.loc[indexer])
self.values.loc[indexer] = value[0]/float(len_slice)*self.num_active_years
elif self.shape_unit_type=='power':
self.values.loc[indexer] = value[0]
if self.values.isnull().values.any():
raise ValueError('Shape time slice data did not give full coverage of the active dates')
# reindex to remove the helper columns
self.values.index = self.values.index.droplevel(self._active_time_keys)
self.values = self.values.swaplevel('weather_datetime', -1).sort_index()
self.geomap_to_time_zone()
self.localize_shapes()
self.standardize_time_across_timezones()
self.geomap_to_primary_geography()
self.sum_over_time_zone()
self.normalize()
self.add_timeshift_type()
def _add_missing_geographies(self, df, current_geography, current_data_type):
current_number_of_geographies = len(util.get_elements_from_level(df, current_geography))
propper_number_of_geographies = len(cfg.geo.geographies_unfiltered[current_geography])
if current_data_type == 'total' and current_number_of_geographies != propper_number_of_geographies:
# we only want to do it when we have a total, otherwise we can't just fill with zero
df = util.reindex_df_level_with_new_elements(df, current_geography, cfg.geo.geographies_unfiltered[current_geography], fill_value=np.nan)
return df
def produce_flexible_load(shape_df, percent_flexible=None, hr_delay=None, hr_advance=None):
percent_flexible = 0 if percent_flexible is None else percent_flexible
hr_delay = 0 if hr_delay is None else hr_delay
hr_advance = 0 if hr_advance is None else hr_advance
if percent_flexible==0 or (hr_delay==0 and hr_advance==0):
return util.df_slice(shape_df, elements=2, levels='timeshift_type')
timeshift_levels = list(util.get_elements_from_level(shape_df, 'timeshift_type'))
timeshift_levels.sort()
if timeshift_levels==[1, 2, 3]:
delay = util.df_slice(shape_df, elements=1, levels='timeshift_type')
native = util.df_slice(shape_df, elements=2, levels='timeshift_type')
advance = util.df_slice(shape_df, elements=3, levels='timeshift_type')
elif timeshift_levels==[2]:
# TODO this could be a lambda function
def shift(df, hr):
""" positive hours is a shift forward, negative hours a shift back"""
return df.shift(hr).bfill().ffill()
non_weather = [n for n in shape_df.index.names if n!='weather_datetime']
delay = shape_df.groupby(level=non_weather).apply(shift, hr=hr_delay)
native = shape_df
advance = shape_df.groupby(level=non_weather).apply(shift, hr=-hr_advance)
else:
raise ValueError("elements in the level timeshift_type are not recognized")
return pd.concat([delay*percent_flexible + native*(1-percent_flexible),
native,
advance*percent_flexible + native*(1-percent_flexible)], keys=[1,2,3], names=['timeshift_type'])
def process_shape(self):
logging.info(' shape: ' + self.name)
self.num_active_years = num_active_years(self.active_dates_index)
if self.shape_type=='weather date':
self.values = util.reindex_df_level_with_new_elements(self.raw_values, 'weather_datetime', self.active_dates_index)
self.values = self.values.replace(np.nan,0)# this step is slow, consider replacing
if self.values.isnull().values.any():
raise ValueError('Weather data for shape {} did not give full coverage of the active dates'.format(self.name))
elif self.shape_type=='time slice':
self.values = self.create_empty_shape_data()
non_time_elements_in_levels = [list(util.get_elements_from_level(self.values, e)) for e in self._non_time_keys]
time_elements_in_levels = [list(util.get_elements_from_level(self.values, e)) for e in self._active_time_keys]
for ind, value in self.raw_values.iterrows():
non_time_portion = [ind[self._non_time_dict[e]] for e in self._non_time_keys]
time_portion = [ind[self._active_time_dict[e]] for e in self._active_time_keys]
if not np.all([s in l for s, l in zip(non_time_portion+time_portion, non_time_elements_in_levels+time_elements_in_levels)]):
continue
indexer = tuple(non_time_portion + time_portion + [slice(None)])
if self.shape_unit_type=='energy':
len_slice = len(self.values.loc[indexer])
self.values.loc[indexer] = value[0]/float(len_slice)*self.num_active_years
elif self.shape_unit_type=='power':
self.values.loc[indexer] = value[0]
if self.values.isnull().values.any():
raise ValueError('Shape time slice data did not give full coverage of the active dates')
# reindex to remove the helper columns
self.values.index = self.values.index.droplevel(self._active_time_keys)
self.values = cfg.geo.filter_extra_geos_from_df(self.values.swaplevel('weather_datetime', -1).sort())
self.geomap_to_time_zone()
self.localize_shapes()
self.standardize_time_across_timezones()
self.geomap_to_primary_geography()
self.sum_over_time_zone()
self.normalize()
self.add_timeshift_type()
# raw values can be very large, so we delete it in this one case
del self.raw_values
def produce_flexible_load(shape_df,
percent_flexible=None,
hr_delay=None,
hr_advance=None):
percent_flexible = 0 if percent_flexible is None else percent_flexible
hr_delay = 0 if hr_delay is None else hr_delay
hr_advance = 0 if hr_advance is None else hr_advance
if percent_flexible == 0 or (hr_delay == 0 and hr_advance == 0):
return util.df_slice(shape_df, elements=2, levels='timeshift_type')
timeshift_levels = list(
util.get_elements_from_level(shape_df, 'timeshift_type'))
timeshift_levels.sort()
if timeshift_levels == [1, 2, 3]:
delay = util.df_slice(shape_df,
elements=1,
levels='timeshift_type')
native = util.df_slice(shape_df,
elements=2,
levels='timeshift_type')
advance = util.df_slice(shape_df,
elements=3,
levels='timeshift_type')
elif timeshift_levels == [2]:
# TODO this could be a lambda function
def shift(df, hr):
""" positive hours is a shift forward, negative hours a shift back"""
return df.shift(hr).bfill().ffill()
non_weather = [
n for n in shape_df.index.names if n != 'weather_datetime'
]
delay = shape_df.groupby(level=non_weather).apply(shift,
hr=hr_delay)
native = shape_df
advance = shape_df.groupby(level=non_weather).apply(shift,
hr=-hr_advance)
else:
raise ValueError(
"elements in the level timeshift_type are not recognized")
return pd.concat([
delay * percent_flexible + native * (1 - percent_flexible), native,
advance * percent_flexible + native * (1 - percent_flexible)
],
keys=[1, 2, 3],
names=['timeshift_type'])
def produce_flexible_load(shape_df, percent_flexible=None, hr_delay=None, hr_advance=None):
hr_delay = 0 if hr_delay is None else hr_delay
hr_advance = 0 if hr_advance is None else hr_advance
native_slice = shape_df.xs(2, level='timeshift_type')
native_slice_stacked = pd.concat([native_slice]*3, keys=[1,2,3], names=['timeshift_type'])
pflex_stacked = pd.concat([percent_flexible]*3, keys=[1,2,3], names=['timeshift_type'])
timeshift_levels = sorted(list(util.get_elements_from_level(shape_df, 'timeshift_type')))
if timeshift_levels==[1, 2, 3]:
# here, we have flexible load profiles already specified by the user
names = shape_df.index.names
full_load = shape_df.squeeze().unstack('timeshift_type')
group_by_names = [n for n in full_load.index.names if n != 'weather_datetime']
full_load = full_load.groupby(level=group_by_names).apply(Shape.ensure_feasible_flexible_load)
full_load = full_load.stack('timeshift_type').reorder_levels(names).sort_index().to_frame()
full_load.columns = ['value']
elif timeshift_levels==[2]:
non_weather = [n for n in native_slice.index.names if n!='weather_datetime']
# positive hours is a shift forward, negative hours a shift back
shift = lambda df, hr: df.shift(hr).ffill().fillna(value=0)
delay_load = native_slice.groupby(level=non_weather).apply(shift, hr=hr_delay)
def advance_load_function(df, hr):
df_adv = df.shift(-hr).ffill().fillna(value=0)
df_adv.iloc[0] += df.iloc[:hr].sum().sum()
return df_adv
advance_load = native_slice.groupby(level=non_weather).apply(advance_load_function, hr=hr_advance)
full_load = pd.concat([delay_load, native_slice, advance_load], keys=[1,2,3], names=['timeshift_type'])
else:
raise ValueError("elements in the level timeshift_type are not recognized")
return util.DfOper.add((util.DfOper.mult((full_load, pflex_stacked), collapsible=False),
util.DfOper.mult((native_slice_stacked, 1-pflex_stacked), collapsible=False)))
def process_shape(self, active_dates_index=None, time_slice_elements=None):
self.num_active_years = len(active_dates_index) / 8766.
if active_dates_index is not None:
self.active_dates_index = active_dates_index
if active_dates_index is None:
raise ValueError(
'processing a shape requires an active date index')
self.time_slice_elements = Shapes.create_time_slice_elements(
active_dates_index
) if time_slice_elements is None else time_slice_elements
if self.shape_type == 'weather date':
self.values = util.reindex_df_level_with_new_elements(
self.raw_values, 'weather_datetime',
active_dates_index) # this step is slow, consider replacing
if self.values.isnull().values.any():
raise ValueError(
'Weather data did not give full coverage of the active dates'
)
elif self.shape_type == 'time slice':
self.values = self.create_empty_shape_data()
non_time_elements_in_levels = [
list(util.get_elements_from_level(self.values, e))
for e in self._non_time_keys
]
time_elements_in_levels = [
list(util.get_elements_from_level(self.values, e))
for e in self._active_time_keys
]
for ind, value in self.raw_values.iterrows():
non_time_portion = [
ind[self._non_time_dict[e]] for e in self._non_time_keys
]
time_portion = [
ind[self._active_time_dict[e]]
for e in self._active_time_keys
]
if not np.all([
s in l for s, l in zip(
non_time_portion +
time_portion, non_time_elements_in_levels +
time_elements_in_levels)
]):
continue
indexer = tuple(non_time_portion + time_portion +
[slice(None)])
if self.shape_unit_type == 'energy':
len_slice = len(self.values.loc[indexer])
self.values.loc[indexer] = value[0] / float(
len_slice) * self.num_active_years
elif self.shape_unit_type == 'power':
self.values.loc[indexer] = value[0]
if self.values.isnull().values.any():
raise ValueError(
'Shape time slice data did not give full coverage of the active dates'
)
# reindex to remove the helper columns
self.values.index = self.values.index.droplevel(
self._active_time_keys)
self.values = self.values.swaplevel('weather_datetime',
-1).sort_index()
self.geomap_to_time_zone()
self.localize_shapes()
self.standardize_time_across_timezones()
self.geomap_to_primary_geography()
self.sum_over_time_zone()
self.normalize()
self.add_timeshift_type()
