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rmcdermo merged 1 commit into from
Oct 27, 2025
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Python: fix Heskestad metric comparisons #15491

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147 changes: 108 additions & 39 deletions Utilities/Python/fdsplotlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,11 +371,15 @@ def dataplot(config_filename,**kwargs):
Save_Predicted_Quantity.append(None)

# -------------- PLOTTING + PRINT -----------------
# Read and prepare the experimental (d1) data, same for both cases
E = pd.read_csv(expdir + pp.d1_Filename,
header=int(pp.d1_Col_Name_Row - 1),
sep=',', engine='python', quotechar='"')
header=int(pp.d1_Col_Name_Row - 1),
sep=',', engine='python', quotechar='"',
skip_blank_lines=True).dropna(how='all') # drop fully empty rows

# Drop trailing NaN rows across all columns (MATLAB csvread behavior)
E = E.loc[:E.dropna(how='all').last_valid_index()]
E.columns = E.columns.str.strip()

start_idx = int(pp.d1_Data_Row - pp.d1_Col_Name_Row - 1)
x, _ = get_data(E, pp.d1_Ind_Col_Name, start_idx)
y, _ = get_data(E, pp.d1_Dep_Col_Name, start_idx)
Expand Down Expand Up @@ -449,29 +453,63 @@ def dataplot(config_filename,**kwargs):
# --- Save measured (experimental) metric using MATLAB-equivalent logic ---
if not gtest:
try:
vals_meas, qty_meas, _ = _compute_metrics_block(
x=x,
Y=y,
metric=pp.Metric,
initial_value=float(pp.d1_Initial_Value or 0.0),
comp_start=float(pp.d1_Comp_Start or np.nan),
comp_end=float(pp.d1_Comp_End or np.nan),
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
variant_side="d1",
)
Save_Measured_Metric[-1] = vals_meas
Save_Measured_Quantity[-1] = qty_meas
vals_meas_list = []
qty_meas_list = []

if y.ndim == 2 and x.ndim == 2 and y.shape[1] == x.shape[1]:
for j in range(y.shape[1]):
xj = np.ravel(x[:, j])
yj = np.ravel(y[:, j])
mask = np.isfinite(xj) & np.isfinite(yj)
xj = xj[mask]
yj = yj[mask]
if len(xj) > 0 and len(yj) > 0:
vals_meas, qty_meas, _ = _compute_metrics_block(
x=xj,
Y=yj,
metric=pp.Metric,
initial_value=float(pp.d1_Initial_Value or 0.0),
comp_start=float(pp.d1_Comp_Start or np.nan),
comp_end=float(pp.d1_Comp_End or np.nan),
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
variant_side="d1",
)
vals_meas_list.append(vals_meas)
qty_meas_list.append(qty_meas)
else:
vals_meas, qty_meas, _ = _compute_metrics_block(
x=x,
Y=y,
metric=pp.Metric,
initial_value=float(pp.d1_Initial_Value or 0.0),
comp_start=float(pp.d1_Comp_Start or np.nan),
comp_end=float(pp.d1_Comp_End or np.nan),
dep_comp_start=float(pp.d1_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d1_Dep_Comp_End or np.nan),
variant_side="d1",
)
vals_meas_list = [vals_meas]
qty_meas_list = [qty_meas]

Save_Measured_Metric[-1] = np.array(vals_meas_list, dtype=object)
Save_Measured_Quantity[-1] = np.array(qty_meas_list, dtype=object)

except Exception as e:
print(f"[dataplot] Error computing measured metric for {pp.Dataname}: {e}")
Save_Measured_Metric[-1] = np.array([])
Save_Measured_Quantity[-1] = []

# ------------------- MODEL (d2) -------------------
M = pd.read_csv(cmpdir + pp.d2_Filename,
header=int(pp.d2_Col_Name_Row - 1),
sep=',', engine='python', quotechar='"')
header=int(pp.d2_Col_Name_Row - 1),
sep=',', engine='python', quotechar='"',
skip_blank_lines=True).dropna(how='all') # drop fully empty rows

# Drop trailing NaN rows across all columns (MATLAB csvread behavior)
M = M.loc[:M.dropna(how='all').last_valid_index()]
M.columns = M.columns.str.strip()

start_idx = int(pp.d2_Data_Row - pp.d2_Col_Name_Row - 1)

# --- Define version string ---
Expand Down Expand Up @@ -546,42 +584,73 @@ def dataplot(config_filename,**kwargs):
# --- Save predicted (model) metric using MATLAB-equivalent logic ---
if not gtest:
try:
vals_pred, qty_pred, _ = _compute_metrics_block(
x=x,
Y=y,
metric=pp.Metric,
initial_value=float(pp.d2_Initial_Value or 0.0),
comp_start=float(pp.d2_Comp_Start or np.nan),
comp_end=float(pp.d2_Comp_End or np.nan),
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
variant_side="d2",
)

# --- Early consistency & padding for Metric='all' ---
vals_pred_list = []
qty_pred_list = []

if y.ndim == 2 and x.ndim == 2 and y.shape[1] == x.shape[1]:
# Multiple paired curves (e.g., z/L jet, 62 kW, 31 kW)
for j in range(y.shape[1]):
xj = np.ravel(x[:, j])
yj = np.ravel(y[:, j])
mask = np.isfinite(xj) & np.isfinite(yj)
xj = xj[mask]
yj = yj[mask]
if len(xj) > 0 and len(yj) > 0:
vals_pred, qty_pred, _ = _compute_metrics_block(
x=xj,
Y=yj,
metric=pp.Metric,
initial_value=float(pp.d2_Initial_Value or 0.0),
comp_start=float(pp.d2_Comp_Start or np.nan),
comp_end=float(pp.d2_Comp_End or np.nan),
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
variant_side="d2",
)
vals_pred_list.append(vals_pred)
qty_pred_list.append(qty_pred)
else:
# Single curve (1D)
vals_pred, qty_pred, _ = _compute_metrics_block(
x=x,
Y=y,
metric=pp.Metric,
initial_value=float(pp.d2_Initial_Value or 0.0),
comp_start=float(pp.d2_Comp_Start or np.nan),
comp_end=float(pp.d2_Comp_End or np.nan),
dep_comp_start=float(pp.d2_Dep_Comp_Start or np.nan),
dep_comp_end=float(pp.d2_Dep_Comp_End or np.nan),
variant_side="d2",
)
vals_pred_list = [vals_pred]
qty_pred_list = [qty_pred]

# --- MATLAB-compatible consistency checks (Metric='all' padding, etc.) ---
vals_meas_entry = Save_Measured_Metric[-1]
if isinstance(pp.Metric, str) and pp.Metric.strip().lower() == 'all':
mvec = np.atleast_1d(Save_Measured_Metric[-1])
pvec = np.atleast_1d(vals_pred)
len_m, len_p = mvec.size, pvec.size
mvec = np.atleast_1d(vals_meas_entry)
pvec = np.atleast_1d(vals_pred_list)
len_m, len_p = mvec.size, len(pvec)
if len_m != len_p:
maxlen = max(len_m, len_p)
if len_m < maxlen:
mvec = np.pad(mvec, (0, maxlen - len_m), constant_values=np.nan)
if len_p < maxlen:
pvec = np.pad(pvec, (0, maxlen - len_p), constant_values=np.nan)
Save_Measured_Metric[-1] = mvec
vals_pred = pvec
vals_pred_list = pvec
print(f"[dataplot] Padded {pp.Dataname} ({pp.Quantity}) from ({len_m},{len_p}) to {maxlen}")
else:
len_m = np.size(Save_Measured_Metric[-1])
len_p = np.size(vals_pred)
len_m = np.size(vals_meas_entry)
len_p = np.size(vals_pred_list)
if len_m != len_p:
print(f"[dataplot] Length mismatch at index {csv_rownum}: "
f"{pp.Dataname} | {pp.Quantity} | "
f"Measured={len_m}, Predicted={len_p}")

Save_Predicted_Metric[-1] = vals_pred
Save_Predicted_Quantity[-1] = qty_pred
Save_Predicted_Metric[-1] = np.array(vals_pred_list, dtype=object)
Save_Predicted_Quantity[-1] = np.array(qty_pred_list, dtype=object)

except Exception as e:
print(f"[dataplot] Error computing predicted metric for {pp.Dataname}: {e}")
Save_Predicted_Metric[-1] = np.array([])
Expand Down