def test_set_data(self, mock_store, mock_model):
"""should allow write access to output data
"""
expected = np.array([[1.0]])
data_handle = DataHandle(mock_store, 1, 2015, [2015, 2020], mock_model)
data_handle.set_results("test", expected)
assert data_handle["test"] == expected
mock_store.write_results.assert_called_with(1, 'test_model', 'test',
expected, 'test_regions',
'test_intervals', 2015,
None, None)
def save_decision_metrics(self, data_handle: DataHandle):
"""Compute decision metrics for the current system and save to disk
Expects outputs for this wrapper to be defined with the same name as the
list of attributed below
Arguments
---------
data_handle
"""
technologies = self.outputs['rollout_costs'].dim_coords(
'technology').ids
exchanges = self.outputs['rollout_costs'].dim_coords('exchanges').ids
attributes = [
'rollout_costs', 'rollout_bcr', 'total_potential_benefit',
'total_potential_bcr'
]
for attribute in attributes:
data = self.save_exchange_attribute(technologies, exchanges,
attribute)
data_handle.set_results(attribute, data)
def simulate(self, data_handle: DataHandle):
"""Implement smif.SectorModel simulate
"""
# -----
# Start
# -----
now = data_handle.current_timestep
total_cost = 0
interventions = data_handle.get_current_interventions()
dc_assets = []
for name, intervention in interventions.items():
if intervention['build_year'] == now:
technology = intervention['technology']
asset_id = intervention['id']
exchange = [
exchange for exchange in self.system._exchanges
if exchange.id == asset_id
][0]
total_cost += exchange.rollout_costs[technology]
asset = (intervention['id'], intervention['technology'])
dc_assets.append(asset)
self.logger.debug(self.system.capacity('exchange'))
data_handle.set_results('total_cost', total_cost)
self.logger.debug("Upgrading dc assets with %s", dc_assets)
self.system.upgrade(dc_assets)
self.logger.debug(self.system.capacity('exchange'))
adoption = self.update_adoption_desirability(data_handle)
self.save_decision_metrics(data_handle)
# -------------
# Write outputs
# -------------
lad_names = self.outputs['lad_premises_with_fttp'].dim_coords(
'lad_uk_2016').ids
num_lads = len(lad_names)
num_fttp = np.zeros((num_lads))
num_fttdp = np.zeros((num_lads))
num_fttc = np.zeros((num_lads))
num_adsl = np.zeros((num_lads))
coverage = self.system.coverage()
for i, lad in enumerate(lad_names):
if lad not in coverage:
continue
stats = coverage[lad]
num_fttp[i] = stats['num_fttp']
num_fttdp[i] = stats['num_fttdp']
num_fttc[i] = stats['num_fttc']
num_adsl[i] = stats['num_adsl']
data_handle.set_results('lad_premises_with_fttp', num_fttp)
data_handle.set_results('lad_premises_with_fttdp', num_fttdp)
data_handle.set_results('lad_premises_with_fttc', num_fttc)
data_handle.set_results('lad_premises_with_adsl', num_adsl)
aggregate_coverage = self.system.aggregate_coverage('lad')
perc_fttp = np.zeros((num_lads))
perc_fttdp = np.zeros((num_lads))
perc_fttc = np.zeros((num_lads))
perc_docsis3 = np.zeros((num_lads))
perc_adsl = np.zeros((num_lads))
sum_of_premises = np.zeros((num_lads))
for i, lad in enumerate(lad_names):
if lad not in aggregate_coverage:
continue
datum = aggregate_coverage[lad]
perc_fttp[i] = datum['percentage_of_premises_with_fttp']
perc_fttdp[i] = datum['percentage_of_premises_with_fttdp']
perc_fttc[i] = datum['percentage_of_premises_with_fttc']
perc_docsis3[i] = datum['percentage_of_premises_with_docsis3']
perc_adsl[i] = datum['percentage_of_premises_with_adsl']
sum_of_premises[i] = datum['sum_of_premises']
data_handle.set_results('percentage_of_premises_connected_with_fttp',
perc_fttp)
data_handle.set_results('percentage_of_premises_connected_with_fttdp',
perc_fttdp)
data_handle.set_results('percentage_of_premises_connected_with_fttc',
perc_fttc)
data_handle.set_results(
'percentage_of_premises_connected_with_docsis3', perc_docsis3)
data_handle.set_results('percentage_of_premises_connected_with_adsl',
perc_adsl)