def test_n_max_pipeline():
pipe = pipelines.n_max_pipeline()
props = goodvehicle.goodVehicle()
wot = engine.preproc_wot(props, props["wot"])
sol = pipe.compute(
{
**dtz.keyfilter(
lambda k: k in ("n_rated", "p_rated", "n2v_ratios"), props),
"wot":
wot,
"cycle": {
"V": pd.Series([120])
},
"v_max":
190.3,
"g_vmax":
6,
})
assert (list(sol) == """
p_rated n_rated n2v_ratios wot cycle v_max g_vmax n2v_g_vmax
n_95_low n_95_high n_max_cycle n_max_vehicle n_max
""".split())
steps = [getattr(n, "name", n) for n in sol.plan.steps]
steps_executed = [getattr(n, "name", n) for n in sol.executed]
print(steps, steps_executed)
exp_steps = "calc_n2v_g_vmax calc_n_95 calc_n_max_cycle calc_n_max_vehicle calc_n_max".split(
)
assert steps == steps_executed == exp_steps
def test_calc_n_95(h5_accdb):
results = []
visited_wots = set()
for case in vehdb.all_vehnums(h5_accdb):
(props, wot, _a) = vehdb.load_vehicle_accdb(h5_accdb, case)
vehnum = props["vehicle_no"]
if vehnum in visited_wots:
continue
visited_wots.add(vehnum)
props = props.rename({
"idling_speed": "n_idle",
"rated_speed": "n_rated",
"rated_power": "p_rated",
})
wot = wot.rename({"Pwot": "p", "Pwot_norm": "p_norm"}, axis=1)
wot["n"] = wot.index
wot = engine.preproc_wot(
props.rename({
"idling_speed": "n_idle",
"rated_speed": "n_rated"
}), wot)
n95 = engine.calc_n_95(wot, props["n_rated"], props["p_rated"])
results.append(n95)
df = pd.DataFrame(results,
columns=["n95_low", "n95_high"],
dtype="float64")
# print(df.describe().values)
aggregate_tol = 1e-4 # The digits copied from terminal.
exp = np.array([
[116.0, 116.0],
[3656.11189008, 4784.66622629],
[1337.23040635, 1428.79658641],
[1680.0, 2897.4405215],
[2837.63712814, 3750.68145459],
[3215.01884177, 4142.35055724],
[4512.5, 6000.03571429],
[7843.72440418, 8817.60270757],
])
assert (df.describe().values - exp < aggregate_tol).all(None)
def test_preproc_wot_equals(mdl, xy):
got = engine.preproc_wot(mdl, xy)
assert isinstance(got, pd.DataFrame) and got.shape == (3, 4)
got = got.reset_index(drop=True)
exp = engine.preproc_wot(mdl, pd.DataFrame(_NP, columns=["n", "p"]))
assert (got.to_numpy() == exp.to_numpy()).all(None)
def test_pre_proc_high_level(mdl, wot):
engine.preproc_wot(mdl, wot)