def testModelInstance_defaultLoadCurve(self):
mdl = datamodel.get_model_base()
mdl.update(goodVehicle())
datamodel.upd_default_load_curve(mdl)
validator = datamodel.model_validator()
validator.validate(mdl)
datamodel.upd_default_load_curve(mdl, "diesel")
validator = datamodel.model_validator()
validator.validate(mdl)
def testGoodVehicle():
mdl = goodVehicle()
exp = Experiment(mdl)
mdl = exp._model
defwot = datamodel.upd_default_load_curve({})["wot"]
assert pd.DataFrame(mdl["wot"][["n_norm",
"p_norm"]]).equals(pd.DataFrame(defwot))
def testModelInstance_simplInstanceeFullLoadCurve(self):
mdl = datamodel.get_model_base()
mdl.update(goodVehicle())
mdl.update({
"wot": [
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23, 0.23],
]
})
datamodel.model_validator().validate(mdl)
dwot = datamodel.upd_default_load_curve({})["wot"]
self.assertNotEqual(mdl["wot"], dwot)
def goodVehicle():
from wltp import datamodel
goodVehicle = {
"test_mass": 1500,
"p_rated": 100,
"n_rated": 5450,
"n_idle": 950,
# "n_min": None, # Can be overriden by manufacturer.
"gear_ratios": [120.5, 75, 50, 43, 37, 32],
}
goodVehicle = datamodel.upd_default_load_curve(goodVehicle)
return goodVehicle
def goodVehicle():
mdl = {
"test_mass": 1500,
"p_rated": 100,
"n_rated": 5450,
"n_idle": 950,
# "n_min": None, # Can be overridden by manufacturer.
"n2v_ratios": [120.5, 75, 50, 43, 37, 32],
}
mdl = datamodel.upd_default_load_curve(mdl)
mdl = datamodel.upd_resistance_coeffs_regression_curves(mdl)
(f0, f1, f2) = vehicle.calc_default_resistance_coeffs(
mdl["test_mass"], mdl["resistance_coeffs_regression_curves"])
mdl.update(f0=f0, f1=f1, f2=f2)
return mdl
def testModelBase_plainInvalid(self):
mdl = datamodel.get_model_base()
datamodel.upd_default_load_curve(mdl)
self.checkModel_invalid(mdl)
