def cost_json(constant_cost, constant_cost_json, timeofday_cost,
timeofday_cost_json, datetime_cost, datetime_cost_json,
banded_cost_params, banded_cost_params_json, request):
if request.param == 'constant':
return constant_cost_json, datamodel.Cost(
name='constantcost', type='constant', parameters=constant_cost)
elif request.param == 'timeofday':
return timeofday_cost_json, datamodel.Cost(
name='timeofdaycost', type='timeofday', parameters=timeofday_cost)
elif request.param == 'datetime':
return datetime_cost_json, datamodel.Cost(
name='datetimecost', type='datetime', parameters=datetime_cost)
elif request.param == 'errorband':
return banded_cost_params_json, banded_cost_params
def test_cost_types(which, constant_cost, datetime_cost, timeofday_cost,
errorband_cost):
dd = {'constant': constant_cost, 'timeofday': timeofday_cost,
'datetime': datetime_cost, 'errorband': errorband_cost,
'other': constant_cost}
out = datamodel.Cost(
name='test',
type=which,
parameters=dd[which]
)
assert isinstance(out, datamodel.Cost)
assert str(out.parameters).split('(')[0].lower() == which + 'cost'
)
)]
)
fx = default_cost_err
obs = pd.Series(0, index=fx.index)
err = deterministic.error_band_cost(
obs, fx, params, deterministic.error)
assert err == 0
@pytest.mark.parametrize('params,exp', [
('banded', 12.06),
(None, np.nan),
(datamodel.Cost(
name='cost',
type='constant',
parameters=datamodel.ConstantCost(
cost=1.9, aggregation='sum', net=True)
), 8.6 * 1.9),
(datamodel.Cost(
name='cost',
type='timeofday',
parameters=datamodel.TimeOfDayCost(
cost=[1.0, 2.0, 3.0],
times=[dt.time(3), dt.time(4), dt.time(8, 1)],
fill='forward',
aggregation='sum',
net=True
)), 16.1),
(datamodel.Cost(
name='cost',
type='datetime',