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Python: fix length mismatch for LNG Dispersion #15510

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rmcdermo merged 1 commit into from
Oct 29, 2025
Merged

Python: fix length mismatch for LNG Dispersion #15510

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50 changes: 27 additions & 23 deletions Utilities/Python/fdsplotlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,8 +89,8 @@ def _compute_metrics_block(

Returns:
vals_flat : 1D np.array (metrics for each curve, or concatenated for 'all')
titles : list of curve labels
per_curve_series : list of 1D arrays (for Metric='all')
titles : list of metric labels
per_curve_series : list of per-curve metric arrays (for Metric='all')
"""
import numpy as np

Expand Down Expand Up @@ -163,18 +163,19 @@ def _parse_stat_xy(m):
else:
out = np.nan

# MATLAB passes tiny value instead of exact zero (survives nonzeros())
if out == 0.0:
out = 1e-12

return np.array([out]), [f"curve{idx_first}"], []

# --- metric='all': return concatenated series for each curve (minus initial) ---
# --- metric='all': return all finite Y values (one per data point) ---
if metric_str == "all":
for j in range(ncols):
yj = Y_sel[:, j].reshape(-1) - initial_value
yj = Y_sel[:, j].reshape(-1)
mask = np.isfinite(yj)
yj = yj[mask] - initial_value
per_curve_series.append(yj)
titles.extend([f"curve{j+1}"] * yj.size)
titles.extend([f"point{k+1}_curve{j+1}" for k in range(len(yj))])
vals_flat = np.concatenate(per_curve_series) if per_curve_series else np.array([])
return vals_flat, titles, per_curve_series

Expand Down Expand Up @@ -203,10 +204,8 @@ def _parse_stat_xy(m):
elif metric_str == "start":
out = yj[0]
elif metric_str == "ipct":
# Not implemented in this port; keep parity-friendly placeholder
out = 1e-12
out = 1e-12 # placeholder for parity
else:
# Default fallback consistent with MATLAB's behavior path to non-zeros
out = 1e-12

if out == 0.0:
Expand Down Expand Up @@ -643,28 +642,33 @@ def read_csv_cached(path, **kwargs):

# --- 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(vals_meas_entry)
pvec = np.atleast_1d(vals_pred_list)
len_m, len_p = mvec.size, len(pvec)
metric_str = str(pp.Metric or '').strip().lower()

# Always flatten any nested list-of-arrays first
flat_pred = np.concatenate([
np.atleast_1d(v) for v in vals_pred_list if v is not None and np.size(v) > 0
]) if any(np.size(v) > 0 for v in vals_pred_list) else np.array([])

flat_meas = np.atleast_1d(vals_meas_entry).ravel()

if metric_str == 'all':
len_m, len_p = flat_meas.size, flat_pred.size
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)
flat_meas = np.pad(flat_meas, (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_list = pvec
flat_pred = np.pad(flat_pred, (0, maxlen - len_p), constant_values=np.nan)
Save_Measured_Metric[-1] = flat_meas
print(f"[dataplot] Padded {pp.Dataname} ({pp.Quantity}) from ({len_m},{len_p}) to {maxlen}")
else:
len_m = np.size(vals_meas_entry)
len_p = np.size(vals_pred_list)
len_m = flat_meas.size
len_p = flat_pred.size
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}")
print(f"[dataplot] Length mismatch at CSV row {csv_rownum}: "
f"{pp.Dataname} | {pp.Quantity} | Measured={len_m}, Predicted={len_p}")

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

except Exception as e:
Expand Down