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rmcdermormcdermo
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Merge pull request #15491 from rmcdermo/master
Python: fix Heskestad metric comparisons
2 parents eb2d542 + da5ef19 commit 3e481ac

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‎Utilities/Python/fdsplotlib.py‎

Lines changed: 108 additions & 39 deletions
Original file line numberDiff line numberDiff line change
@@ -371,11 +371,15 @@ def dataplot(config_filename,**kwargs):
371371
Save_Predicted_Quantity.append(None)
372372

373373
# -------------- PLOTTING + PRINT -----------------
374-
# Read and prepare the experimental (d1) data, same for both cases
375374
E = pd.read_csv(expdir + pp.d1_Filename,
376-
header=int(pp.d1_Col_Name_Row - 1),
377-
sep=',', engine='python', quotechar='"')
375+
header=int(pp.d1_Col_Name_Row - 1),
376+
sep=',', engine='python', quotechar='"',
377+
skip_blank_lines=True).dropna(how='all') # drop fully empty rows
378+
379+
# Drop trailing NaN rows across all columns (MATLAB csvread behavior)
380+
E = E.loc[:E.dropna(how='all').last_valid_index()]
378381
E.columns = E.columns.str.strip()
382+
379383
start_idx = int(pp.d1_Data_Row - pp.d1_Col_Name_Row - 1)
380384
x, _ = get_data(E, pp.d1_Ind_Col_Name, start_idx)
381385
y, _ = get_data(E, pp.d1_Dep_Col_Name, start_idx)
@@ -449,29 +453,63 @@ def dataplot(config_filename,**kwargs):
449453
# --- Save measured (experimental) metric using MATLAB-equivalent logic ---
450454
if not gtest:
451455
try:
452-
vals_meas, qty_meas, _ = _compute_metrics_block(
453-
x=x,
454-
Y=y,
455-
metric=pp.Metric,
456-
initial_value=float(pp.d1_Initial_Value or 0.0),
457-
comp_start=float(pp.d1_Comp_Start or np.nan),
458-
comp_end=float(pp.d1_Comp_End or np.nan),
459-
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
460-
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
461-
variant_side="d1",
462-
)
463-
Save_Measured_Metric[-1] = vals_meas
464-
Save_Measured_Quantity[-1] = qty_meas
456+
vals_meas_list = []
457+
qty_meas_list = []
458+
459+
if y.ndim == 2 and x.ndim == 2 and y.shape[1] == x.shape[1]:
460+
for j in range(y.shape[1]):
461+
xj = np.ravel(x[:, j])
462+
yj = np.ravel(y[:, j])
463+
mask = np.isfinite(xj) & np.isfinite(yj)
464+
xj = xj[mask]
465+
yj = yj[mask]
466+
if len(xj) > 0 and len(yj) > 0:
467+
vals_meas, qty_meas, _ = _compute_metrics_block(
468+
x=xj,
469+
Y=yj,
470+
metric=pp.Metric,
471+
initial_value=float(pp.d1_Initial_Value or 0.0),
472+
comp_start=float(pp.d1_Comp_Start or np.nan),
473+
comp_end=float(pp.d1_Comp_End or np.nan),
474+
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
475+
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
476+
variant_side="d1",
477+
)
478+
vals_meas_list.append(vals_meas)
479+
qty_meas_list.append(qty_meas)
480+
else:
481+
vals_meas, qty_meas, _ = _compute_metrics_block(
482+
x=x,
483+
Y=y,
484+
metric=pp.Metric,
485+
initial_value=float(pp.d1_Initial_Value or 0.0),
486+
comp_start=float(pp.d1_Comp_Start or np.nan),
487+
comp_end=float(pp.d1_Comp_End or np.nan),
488+
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
489+
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
490+
variant_side="d1",
491+
)
492+
vals_meas_list = [vals_meas]
493+
qty_meas_list = [qty_meas]
494+
495+
Save_Measured_Metric[-1] = np.array(vals_meas_list, dtype=object)
496+
Save_Measured_Quantity[-1] = np.array(qty_meas_list, dtype=object)
497+
465498
except Exception as e:
466499
print(f"[dataplot] Error computing measured metric for {pp.Dataname}: {e}")
467500
Save_Measured_Metric[-1] = np.array([])
468501
Save_Measured_Quantity[-1] = []
469502

470503
# ------------------- MODEL (d2) -------------------
471504
M = pd.read_csv(cmpdir + pp.d2_Filename,
472-
header=int(pp.d2_Col_Name_Row - 1),
473-
sep=',', engine='python', quotechar='"')
505+
header=int(pp.d2_Col_Name_Row - 1),
506+
sep=',', engine='python', quotechar='"',
507+
skip_blank_lines=True).dropna(how='all') # drop fully empty rows
508+
509+
# Drop trailing NaN rows across all columns (MATLAB csvread behavior)
510+
M = M.loc[:M.dropna(how='all').last_valid_index()]
474511
M.columns = M.columns.str.strip()
512+
475513
start_idx = int(pp.d2_Data_Row - pp.d2_Col_Name_Row - 1)
476514

477515
# --- Define version string ---
@@ -546,42 +584,73 @@ def dataplot(config_filename,**kwargs):
546584
# --- Save predicted (model) metric using MATLAB-equivalent logic ---
547585
if not gtest:
548586
try:
549-
vals_pred, qty_pred, _ = _compute_metrics_block(
550-
x=x,
551-
Y=y,
552-
metric=pp.Metric,
553-
initial_value=float(pp.d2_Initial_Value or 0.0),
554-
comp_start=float(pp.d2_Comp_Start or np.nan),
555-
comp_end=float(pp.d2_Comp_End or np.nan),
556-
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
557-
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
558-
variant_side="d2",
559-
)
560-
561-
# --- Early consistency & padding for Metric='all' ---
587+
vals_pred_list = []
588+
qty_pred_list = []
589+
590+
if y.ndim == 2 and x.ndim == 2 and y.shape[1] == x.shape[1]:
591+
# Multiple paired curves (e.g., z/L jet, 62 kW, 31 kW)
592+
for j in range(y.shape[1]):
593+
xj = np.ravel(x[:, j])
594+
yj = np.ravel(y[:, j])
595+
mask = np.isfinite(xj) & np.isfinite(yj)
596+
xj = xj[mask]
597+
yj = yj[mask]
598+
if len(xj) > 0 and len(yj) > 0:
599+
vals_pred, qty_pred, _ = _compute_metrics_block(
600+
x=xj,
601+
Y=yj,
602+
metric=pp.Metric,
603+
initial_value=float(pp.d2_Initial_Value or 0.0),
604+
comp_start=float(pp.d2_Comp_Start or np.nan),
605+
comp_end=float(pp.d2_Comp_End or np.nan),
606+
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
607+
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
608+
variant_side="d2",
609+
)
610+
vals_pred_list.append(vals_pred)
611+
qty_pred_list.append(qty_pred)
612+
else:
613+
# Single curve (1D)
614+
vals_pred, qty_pred, _ = _compute_metrics_block(
615+
x=x,
616+
Y=y,
617+
metric=pp.Metric,
618+
initial_value=float(pp.d2_Initial_Value or 0.0),
619+
comp_start=float(pp.d2_Comp_Start or np.nan),
620+
comp_end=float(pp.d2_Comp_End or np.nan),
621+
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
622+
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
623+
variant_side="d2",
624+
)
625+
vals_pred_list = [vals_pred]
626+
qty_pred_list = [qty_pred]
627+
628+
# --- MATLAB-compatible consistency checks (Metric='all' padding, etc.) ---
629+
vals_meas_entry = Save_Measured_Metric[-1]
562630
if isinstance(pp.Metric, str) and pp.Metric.strip().lower() == 'all':
563-
mvec = np.atleast_1d(Save_Measured_Metric[-1])
564-
pvec = np.atleast_1d(vals_pred)
565-
len_m, len_p = mvec.size, pvec.size
631+
mvec = np.atleast_1d(vals_meas_entry)
632+
pvec = np.atleast_1d(vals_pred_list)
633+
len_m, len_p = mvec.size, len(pvec)
566634
if len_m != len_p:
567635
maxlen = max(len_m, len_p)
568636
if len_m < maxlen:
569637
mvec = np.pad(mvec, (0, maxlen - len_m), constant_values=np.nan)
570638
if len_p < maxlen:
571639
pvec = np.pad(pvec, (0, maxlen - len_p), constant_values=np.nan)
572640
Save_Measured_Metric[-1] = mvec
573-
vals_pred = pvec
641+
vals_pred_list = pvec
574642
print(f"[dataplot] Padded {pp.Dataname} ({pp.Quantity}) from ({len_m},{len_p}) to {maxlen}")
575643
else:
576-
len_m = np.size(Save_Measured_Metric[-1])
577-
len_p = np.size(vals_pred)
644+
len_m = np.size(vals_meas_entry)
645+
len_p = np.size(vals_pred_list)
578646
if len_m != len_p:
579647
print(f"[dataplot] Length mismatch at index {csv_rownum}: "
580648
f"{pp.Dataname} | {pp.Quantity} | "
581649
f"Measured={len_m}, Predicted={len_p}")
582650

583-
Save_Predicted_Metric[-1] = vals_pred
584-
Save_Predicted_Quantity[-1] = qty_pred
651+
Save_Predicted_Metric[-1] = np.array(vals_pred_list, dtype=object)
652+
Save_Predicted_Quantity[-1] = np.array(qty_pred_list, dtype=object)
653+
585654
except Exception as e:
586655
print(f"[dataplot] Error computing predicted metric for {pp.Dataname}: {e}")
587656
Save_Predicted_Metric[-1] = np.array([])

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