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Python: fix model plotting #15534

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Merged
rmcdermo merged 4 commits into from
Nov 4, 2025
Merged

Python: fix model plotting #15534

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3c0483a
Python: fix model plotting
rmcdermo Nov 4, 2025
e2dcbb2
Python: minor edits
rmcdermo Nov 4, 2025
17a9012
Python: minor edits
rmcdermo Nov 4, 2025
2310089
Python: minor edits
rmcdermo Nov 4, 2025
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142 changes: 103 additions & 39 deletions Utilities/Python/fdsplotlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -268,8 +268,14 @@ def read_csv_cached(path, **kwargs):
}
safe_na_values = default_na

df = pd.read_csv(configdir + config_filename, sep=',', engine='python',
quotechar='"', na_values=safe_na_values, keep_default_na=False)
df = pd.read_csv(
configdir + config_filename,
sep=',',
engine='python',
quotechar='"',
na_values=safe_na_values,
keep_default_na=False
)
df["__orig_index__"] = df.index
C = df.where(pd.notnull(df), None)

Expand Down Expand Up @@ -355,6 +361,7 @@ def read_csv_cached(path, **kwargs):
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)

flip_axis = str(pp.Flip_Axis).strip().lower() in ['yes', 'true', '1']
x_scale = float(pp.Scale_Ind or 1.0)
y_scale = float(pp.Scale_Dep or 1.0)
Expand Down Expand Up @@ -396,15 +403,22 @@ def read_csv_cached(path, **kwargs):
else:
continue

# --- Styles and keys (EXP d1) ---
d1_raw_styles = [c.strip() for c in (pp.d1_Style or '').split('|')] if pp.d1_Style else []
d1_styles = (d1_raw_styles + [None] * len(y_plot_list))[:len(y_plot_list)]
d1_raw_keys = [c.strip() for c in (pp.d1_Key or '').split('|')] if pp.d1_Key else []
d1_key_labels = (d1_raw_keys + [None] * len(y_plot_list))[:len(y_plot_list)]

# --- Plot Exp curves ---
for i, (x_i, y_i) in enumerate(zip(x_plot_list, y_plot_list)):
f = plot_to_fig(
x_data=y_i if flip_axis else x_i,
y_data=x_i if flip_axis else y_i,
figure_handle=None if (first_plot and i == 0) else f,
data_label=key_labels[i],
data_label=d1_key_labels[i],
x_label=pp.Dep_Title if flip_axis else pp.Ind_Title,
y_label=pp.Ind_Title if flip_axis else pp.Dep_Title,
marker_style=styles[i],
marker_style=d1_styles[i],
x_min=pp.Min_Dep if flip_axis else pp.Min_Ind,
x_max=pp.Max_Dep if flip_axis else pp.Max_Ind,
y_min=pp.Min_Ind if flip_axis else pp.Min_Dep,
Expand All @@ -420,7 +434,8 @@ def read_csv_cached(path, **kwargs):
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])
xj = np.ravel(x[:, j])
yj = np.ravel(y[:, j])
mask = np.isfinite(xj) & np.isfinite(yj)
xj, yj = xj[mask], yj[mask]
if len(xj) > 0 and len(yj) > 0:
Expand Down Expand Up @@ -451,7 +466,8 @@ def read_csv_cached(path, **kwargs):
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] = []
Save_Measured_Metric[-1] = np.array([])
Save_Measured_Quantity[-1] = []

# ---------------------- LOAD MODEL ----------------------
M = read_csv_cached(cmpdir + pp.d2_Filename,
Expand All @@ -461,18 +477,24 @@ def read_csv_cached(path, **kwargs):
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)

version_string = revision
if pp.VerStr_Filename:
try:
with open(cmpdir + pp.VerStr_Filename, "r") as fver:
Lines = fver.readlines()
if Lines: version_string = Lines[0].strip()
if Lines:
version_string = Lines[0].strip()
except Exception as e:
print(f"[dataplot] Warning: could not read version string: {e}")

x, _ = get_data(M, pp.d2_Ind_Col_Name, start_idx)
y, _ = get_data(M, pp.d2_Dep_Col_Name, start_idx)
x_scale = float(pp.Scale_Ind or 1.0); y_scale = float(pp.Scale_Dep or 1.0)
x_scaled = np.asarray(x) / x_scale; y_scaled = np.asarray(y) / y_scale

x_scale = float(pp.Scale_Ind or 1.0)
y_scale = float(pp.Scale_Dep or 1.0)
x_scaled = np.asarray(x) / x_scale
y_scaled = np.asarray(y) / y_scale

if x_scaled.ndim == 2 and y_scaled.ndim == 2 and x_scaled.shape[1] == y_scaled.shape[1]:
x_plot_list = [x_scaled[:, i] for i in range(x_scaled.shape[1])]
Expand All @@ -481,19 +503,46 @@ def read_csv_cached(path, **kwargs):
x_plot_list = [x_scaled for _ in range(y_scaled.shape[1])]
y_plot_list = [y_scaled[:, i] for i in range(y_scaled.shape[1])]
else:
x_plot_list = [np.ravel(x_scaled)]; y_plot_list = [np.ravel(y_scaled)]
x_plot_list = [np.ravel(x_scaled)]
y_plot_list = [np.ravel(y_scaled)]

for xi, yi in zip(x_plot_list, y_plot_list):
if len(xi) != len(yi):
print(f"[dataplot] Pair length mismatch in {pp.Dataname}: x={len(xi)}, y={len(yi)}")

# --- Styles and keys (MODEL d2) ---
d2_raw_styles = [c.strip() for c in (pp.d2_Style or '').split('|')] if pp.d2_Style else []
d2_styles = (d2_raw_styles + [None] * len(y_plot_list))[:len(y_plot_list)]
d2_raw_keys = [c.strip() for c in (pp.d2_Key or '').split('|')] if pp.d2_Key else []
d2_key_labels = (d2_raw_keys + [None] * len(y_plot_list))[:len(y_plot_list)]

# --- Plot model curves ---
for i, (x_i, y_i) in enumerate(zip(x_plot_list, y_plot_list)):
f = plot_to_fig(
x_data=y_i if flip_axis else x_i,
y_data=x_i if flip_axis else y_i,
revision_label=version_string,
figure_handle=f, # keep same figure
data_label=d2_key_labels[i],
line_style=d2_styles[i],
x_label=pp.Dep_Title if flip_axis else pp.Ind_Title,
y_label=pp.Ind_Title if flip_axis else pp.Dep_Title,
x_min=pp.Min_Dep if flip_axis else pp.Min_Ind,
x_max=pp.Max_Dep if flip_axis else pp.Max_Ind,
y_min=pp.Min_Ind if flip_axis else pp.Min_Dep,
y_max=pp.Max_Ind if flip_axis else pp.Max_Dep,
legend_location=matlab_legend_to_matplotlib(pp.Key_Position),
plot_type=plot_type,
plot_title=pp.Plot_Title,
)

# --- Interpolated, metric-aware model logic ---
if not gtest:
try:
metric_str = str(pp.Metric or '').strip().lower()
meas_list, pred_list, qty_pred_list = [], [], []

# --- Load experimental data for alignment ---
# Load experimental again for alignment (safe; cached)
E = read_csv_cached(expdir + pp.d1_Filename,
header=int(pp.d1_Col_Name_Row - 1),
sep=',', engine='python', quotechar='"',
Expand All @@ -503,7 +552,7 @@ def read_csv_cached(path, **kwargs):
x_exp, _ = get_data(E, pp.d1_Ind_Col_Name, start_idx_exp)
y_exp, _ = get_data(E, pp.d1_Dep_Col_Name, start_idx_exp)

# Normalize shapes
# Normalize shapes to 2D (col-major semantics)
x_exp = np.atleast_2d(x_exp)
y_exp = np.atleast_2d(y_exp)
x_mod = np.atleast_2d(x)
Expand All @@ -512,6 +561,7 @@ def read_csv_cached(path, **kwargs):
ncols = min(y_exp.shape[1], y_mod.shape[1])

for j in range(ncols):
# Cull NaNs per series keeping pairs aligned
xj_e = np.ravel(x_exp[:, j] if x_exp.shape[1] > 1 else x_exp)
yj_e = np.ravel(y_exp[:, j])
m_e = np.isfinite(xj_e) & np.isfinite(yj_e)
Expand All @@ -523,7 +573,7 @@ def read_csv_cached(path, **kwargs):
xj_m, yj_m = xj_m[m_m], yj_m[m_m]

if metric_str == 'all':
# interpolate model→exp grid
# align by interpolating model to exp x
if xj_m.size < 2 or xj_e.size == 0:
continue
yj_m_i = np.interp(xj_e, xj_m, yj_m, left=np.nan, right=np.nan)
Expand All @@ -533,38 +583,52 @@ def read_csv_cached(path, **kwargs):
x_use = xj_e[mask_pair]
y_exp_use = yj_e[mask_pair]
y_mod_use = yj_m_i[mask_pair]
# compute both on the same x grid
v_meas, _, _ = _compute_metrics_block(
x=x_use, Y=y_exp_use, metric=metric_str,
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",
)
v_pred, qty_pred, _ = _compute_metrics_block(
x=x_use, Y=y_mod_use, metric=metric_str,
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",
)
else:
# no interpolation; operate independently
x_use, y_exp_use, y_mod_use = xj_e, yj_e, yj_m

if y_exp_use.size == 0 or y_mod_use.size == 0:
continue

v_meas, _, _ = _compute_metrics_block(
x=x_use, Y=y_exp_use, metric=metric_str,
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",
)
v_pred, qty_pred, _ = _compute_metrics_block(
x=x_use if metric_str == 'all' else xj_m,
Y=y_mod_use, metric=metric_str,
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",
)
# aggregate metrics: NO interpolation; operate independently
if yj_e.size == 0 or yj_m.size == 0:
continue
v_meas, _, _ = _compute_metrics_block(
x=xj_e, Y=yj_e, metric=metric_str,
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",
)
v_pred, qty_pred, _ = _compute_metrics_block(
x=xj_m, Y=yj_m, metric=metric_str,
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",
)

meas_list.append(np.atleast_1d(v_meas))
pred_list.append(np.atleast_1d(v_pred))
qty_pred_list.append(qty_pred)

# flatten appropriately
flat_meas = np.concatenate(meas_list) if meas_list else np.array([])
flat_pred = np.concatenate(pred_list) if pred_list else np.array([])

Expand Down