ENH(mdl): check wot <= n_min_set, REORDER nmins before wot validation

tests/test_engine.py

@@ -109,9 +109,17 @@ def test_validate_wot_errors(mdl, wot, n_idle, n_rated, p_rated, err):
         p_rated = mdl["p_rated"]
     wot = engine.denorm_wot(wot, n_idle, n_rated, p_rated)
     wot = engine.norm_wot(wot, n_idle, n_rated, p_rated)
+    n_min_drive_set = None
     with pytest.raises(type(err), match=str(err)):
-        for err in engine.validate_wot(wot, n_idle, n_rated, p_rated):
-            raise err
+        for verr in engine.validate_wot(wot, n_idle, n_rated, p_rated, n_min_drive_set):
+            raise verr
+
+    n_min_drive_set = n_idle + 0.125 * (n_rated - n_idle)
+    with pytest.raises(type(err), match=str(err)):
+        for verr in engine.validate_wot(wot, n_idle, n_rated, p_rated, n_min_drive_set):
+            raise verr
+
+    ## Needs more tests.
 
 
 @pytest.mark.parametrize(

wltp/datamodel.py

@@ -883,8 +883,8 @@ def validate_model(
     )
     validators = [
         validator.iter_errors(mdl),
-        yield_load_curve_errors(mdl),
         yield_n_min_errors(mdl),
+        yield_load_curve_errors(mdl),
         yield_forced_cycle_errors(mdl, additional_properties),
     ]
     errors = it.chain(*[v for v in validators if not v is None])
@@ -934,7 +934,8 @@ def yield_load_curve_errors(mdl):
         yield ValidationError(f"Failed (de)normalizing wot due to: {ex}")
         return
 
-    for err in engine.validate_wot(wot, n_idle, n_rated, p_rated):
+    n_min_drive_set = mdl.get(d.n_min_drive_set)
+    for err in engine.validate_wot(wot, n_idle, n_rated, p_rated, n_min_drive_set):
         raise err
 
 
@@ -950,6 +951,11 @@ def yield_n_min_errors(mdl):
         # Bail out, jsonschema errors already reported.
         return
 
+    if mdl[d.n_rated] <= mdl[d.n_idle]:
+        yield ValidationError(
+            f"n_idle({mdl[d.n_idle]}) is higher than n_rated({mdl[d.n_rated]}!"
+        )
+
     try:
         nmins = engine.calc_fixed_n_min_drives(mdl, mdl[d.n_idle], mdl[d.n_rated])
         for n in (

wltp/engine.py

@@ -149,7 +149,9 @@ def parse_wot(wot) -> pd.DataFrame:
     return wot
 
 
-def validate_wot(wot: pd.DataFrame, n_idle, n_rated, p_rated) -> pd.DataFrame:
+def validate_wot(
+    wot: pd.DataFrame, n_idle, n_rated, p_rated, n_min_drive_set
+) -> pd.DataFrame:
     """Higher-level validation of the wot-curves with repect to model."""
     w = wio.pstep_factory.get().wot
 
@@ -173,6 +175,10 @@ def validate_wot(wot: pd.DataFrame, n_idle, n_rated, p_rated) -> pd.DataFrame:
             f"`p_wot_max`({wot[w.p].max()}) much lower than p_rated({p_rated})!\n{wot}"
         )
 
+    if n_min_drive_set and wot[w.n].min() > n_min_drive_set:
+        yield ValidationError(
+            f"wot(N) starts above n_min_drive_set({n_min_drive_set})!\n{wot}"
+        )
     if wot[w.n_norm].min() < -0.1:
         yield ValidationError(f"wot(N) starts much lower than n_idle({n_idle})!\n{wot}")
     if wot[w.n].max() < n_rated <= wot[w.n].max():
@@ -205,7 +211,7 @@ def preproc_wot(mdl: Mapping, wot) -> pd.DataFrame:
     wot = denorm_wot(wot, n_idle, n_rated, p_rated)
     wot = norm_wot(wot, n_idle, n_rated, p_rated)
 
-    for err in validate_wot(wot, n_idle, n_rated, p_rated):
+    for err in validate_wot(wot, n_idle, n_rated, p_rated, mdl.get(d.n_min_drive_set)):
         raise err
 
     return wot
@@ -227,7 +233,12 @@ def calc_p_available(P: Column, ASM: Column, f_safety_margin) -> Column:
 
 
 def interpolate_wot_on_v_grid(wot: pd.DataFrame):
-    """Return a new linearly interpolated df on v with v_decimals. """
+    """
+    Interpolated wot on a v-grid from v_min_wot --> v_max_wot, rounded. 
+    
+    :param wot:
+        must contain aat least `v` column
+    """
     w = wio.pstep_factory.get().wot
 
     assert wot.size, "Empty wot!"