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)