def testFullLoadCurve_invalid(self):
import numpy as np
import pandas as pd
cases = [
None,
[],
{},
[[1, 2, 3], [4, 5, 6]],
np.array([[1, 2, 3], [4, 5, 6]]),
pd.DataFrame({
"speed": [10, 11, 12],
"foo": [1, 2, 3]
}),
pd.DataFrame({
"velocity": [100, 200, 300],
"alt": [0, 1, 0]
}),
# pd.Series([5,6,'a']),
]
for c in cases:
mdl = datamodel.get_model_base()
mdl = datamodel.merge(datamodel.get_model_base(), mdl)
mdl["wot"] = c
self.checkModel_invalid(mdl)
def testFullLoadCurve_invalid(case):
import numpy as np
import pandas as pd
mdl = datamodel.get_model_base()
mdl = datamodel.merge(datamodel.get_model_base(), mdl)
mdl["wot"] = case
_checkModel_invalid(mdl)
def mdl_from_accdb(props, wot, n2vs: List[float]) -> dict:
"""
:param props:
may have been renamed with :func:`accdb_renames()`
"""
assert isinstance(n2vs, list)
mdl: dict = datamodel.get_model_base()
mdl["f0"] = props.f0
mdl["f1"] = props.f1
mdl["f2"] = props.f2
mdl["unladen_mass"] = props.get("unladen_mass", props.kerb_mass)
mdl["test_mass"] = props.test_mass
mdl["p_rated"] = props.get("p_rated", props.rated_power)
mdl["n_rated"] = props.get("n_rated", props.rated_speed)
mdl["n_idle"] = props.get("n_idle", props.idling_speed)
mdl["v_max"] = props.get("", props.v_max)
# mdl['n_min_drive']= props.n_min_drive
# mdl['n_min_drive_set']= props.n_min_drive_set
mdl["n_min_drive_up"] = props.n_min_drive_up
mdl["n_min_drive_down"] = props.n_min_drive_down
mdl["n_min_drive_up_start"] = props.n_min_drive_start_up # inversed@acdb!
mdl["n_min_drive_down_start"] = props.n_min_drive_start_down # inversed@acdb!
mdl["t_cold_end"] = props.t_end_start_phase
mdl["f_safety_margin"] = props.SM
renames = accdb_renames()
wot = wot.rename(renames, axis=1)
wot["n"] = wot.index
mdl["wot"] = wot
mdl["n2v_ratios"] = n2vs
return mdl
def assemble_model(infiles, model_overrides):
mdl = datamodel.get_model_base()
for filespec in infiles:
try:
mdl = load_model_part(mdl, filespec)
except Exception as ex:
raise Exception("Failed reading %s due to: %s" %
(filespec, ex)) from ex
if model_overrides:
model_overrides = functools.reduce(lambda x, y: x + y,
model_overrides) # join all -m
for (json_path, value) in model_overrides:
try:
if not json_path.startswith("/"):
json_path = _default_model_override_path + json_path
pandel.set_jsonpointer(mdl, json_path, value)
except Exception as ex:
raise Exception(
"Failed setting model-value(%s)@(%s) due to: %s" %
(json_path, value, ex)) from ex
return mdl
def test_default_resistance_coeffs_None(self):
mdl = goodVehicle()
mdl["f0"] = mdl["f1"] = mdl["f1"] = None
mdl = datamodel.merge(datamodel.get_model_base(), mdl)
with pytest.raises(ValidationError,
match="None is not of type 'number'"):
self.checkModel_valid(mdl)
def test_validate_wltc_data(self):
mdl = datamodel.get_model_base()
mdl = datamodel.merge(mdl, goodVehicle())
validator = datamodel.model_validator(validate_wltc_data=True,
validate_schema=True)
validator.validate(mdl)
def test_default_resistance_coeffs_None(self):
mdl = goodVehicle()
mdl["f0"] = mdl["f1"] = mdl["f1"] = None
mdl = datamodel.merge(datamodel.get_model_base(), mdl)
with pytest.raises(ValidationError,
match="'f0' is a required property"):
self.checkModel_valid(mdl)
def test_calc_default_resistance_coeffs_base_model():
tm = 1000 # test_mass
bm = datamodel.get_model_base()
datamodel.upd_resistance_coeffs_regression_curves(bm)
regression_curves = bm["resistance_coeffs_regression_curves"]
res = calc_default_resistance_coeffs(tm, regression_curves)
print(res)
assert len(res) == 3
def _set_model(self, mdl, skip_validation=False, validate_wltc_data=False):
from wltp.datamodel import get_model_base, merge
merged_model = get_model_base()
merge(merged_model, mdl)
if not skip_validation:
datamodel.validate_model(merged_model,
validate_wltc_data=validate_wltc_data)
self._model = merged_model
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 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 _set_model(self, mdl, skip_validation, validate_wltc_data,
additional_properties):
from wltp.datamodel import get_model_base, merge
merged_model = get_model_base()
merge(merged_model, mdl)
if not skip_validation:
errors = list(
datamodel.validate_model(
merged_model,
validate_wltc_data=validate_wltc_data,
additional_properties=additional_properties,
iter_errors=True,
))
if errors:
err_msg = "\n ".join(str(e) for e in errors)
raise ValueError(f"Model validation errors: {err_msg}")
self._model = merged_model
def test_default_resistance_coeffs_missing(self):
mdl = goodVehicle()
mdl = datamodel.merge(datamodel.get_model_base(), mdl)
self.checkModel_valid(mdl)
def testModelBase_plainInvalid(self):
mdl = datamodel.get_model_base()
datamodel.upd_default_load_curve(mdl)
self.checkModel_invalid(mdl)
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s|%(levelname)4.4s|%(module)s:[%(funcName)s]:\n +--> %(message)s",
datefmt="%Y-%m-%d,%H:%M:%S",
)
pd.set_option("display.max_columns", 64)
# %% [markdown]
# ## Run a vehicle with user-specified data
# %%
## For more input data, see https://wltp.readthedocs.io/en/latest/code.html#schema
#
mdl = datamodel.get_model_base()
mdl["f0"] = 395.78
mdl["f1"] = 0.0
mdl["f2"] = 0.15
mdl["unladen_mass"] = 2527.0
mdl["test_mass"] = 2827.0
mdl["p_rated"] = 95.3000030517578
mdl["n_rated"] = 3500
mdl["n_idle"] = 750
mdl["v_max"] = 119.8
# mdl["n_min_drive"] =
# mdl["n_min_drive_up"] = ...
# mdl["n_min_drive_down"] = ...
# mdl["n_min_drive_up_start"] = ...
# mdl["n_min_drive_down_start"] = ...
# mdl["t_cold_end"] = ...
