feat: aggiunge grafico data-fit nella visualizzazione inversa (opzione 4)

Sovrappone la curva T predetta ai punti di training per ogni sensore,
salvando data_fit.html nella stessa cartella degli altri grafici.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

inverse/engine.py

@@ -183,6 +183,7 @@ def evaluate_inverse():
     print(f"Errore massimo assoluto:            {max_err:.4f} °C")
 
 
+
 def generate_visualization_inverse():
     device = _get_device()
 
@@ -208,5 +209,13 @@ def generate_visualization_inverse():
 
     identified_params = {'α': model.alpha.item(), 'k': model.k.item(), 'h': model.h_conv.item()}
 
+    x_s = t_s = T_meas = None
+    from inverse.data import load_measurements
+    try:
+        x_s, t_s, T_meas = load_measurements(device)
+    except FileNotFoundError:
+        print("[skip data-fit] measurements.csv non trovato — esegui prima 'Carica misure'.")
+
     from inverse.visualizer import visualize_inverse
-    visualize_inverse(T_pred, x_vals.cpu().numpy(), t_vals.cpu().numpy(), T_fdm, identified_params)
+    visualize_inverse(T_pred, x_vals.cpu().numpy(), t_vals.cpu().numpy(), T_fdm, identified_params,
+                      model=model, device=device, x_s=x_s, t_s=t_s, T_meas=T_meas)

inverse/visualizer.py

@@ -4,6 +4,7 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from datetime import datetime
 import numpy as np
+import torch
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import config
@@ -11,7 +12,8 @@ import config
 BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
 
-def visualize_inverse(T_pred, x_vals, t_vals, T_fdm, identified_params: dict):
+def visualize_inverse(T_pred, x_vals, t_vals, T_fdm, identified_params: dict,
+                      model=None, device=None, x_s=None, t_s=None, T_meas=None):
     timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
     out_dir = os.path.join(BASE_DIR, 'results', 'inverse', timestamp)
     os.makedirs(out_dir, exist_ok=True)
@@ -116,3 +118,66 @@ def visualize_inverse(T_pred, x_vals, t_vals, T_fdm, identified_params: dict):
     comparison_path = os.path.join(out_dir, 'comparison.html')
     fig_ts.write_html(comparison_path)
     print(f"Comparison saved     → {comparison_path}")
+
+    if model is not None and x_s is not None:
+        fit_path = plot_data_fit(model, device, x_s, t_s, T_meas, out_dir)
+        print(f"Data fit saved       → {fit_path}")
+
+
+def plot_data_fit(model, device, x_s, t_s, T_meas, out_dir):
+    """Grafico data-fit: curva T_pred per ogni sensore + punti di training sovrapposti."""
+    x_s_np = x_s.cpu().numpy() if isinstance(x_s, torch.Tensor) else np.asarray(x_s)
+    t_s_np = t_s.cpu().numpy() if isinstance(t_s, torch.Tensor) else np.asarray(t_s)
+    T_meas_np = T_meas.cpu().numpy() if isinstance(T_meas, torch.Tensor) else np.asarray(T_meas)
+
+    sensors = sorted(np.unique(np.round(x_s_np, 6)))
+    t_dense = np.linspace(0, config.T_END, 200)
+
+    colors = [
+        'royalblue', 'firebrick', 'seagreen', 'darkorange',
+        'mediumpurple', 'deeppink', 'saddlebrown', 'teal',
+    ]
+
+    param_str = f'α={model.alpha.item():.5f}  k={model.k.item():.5f}  h={model.h_conv.item():.4f}'
+    fig = go.Figure()
+
+    for i, x_val in enumerate(sensors):
+        color = colors[i % len(colors)]
+
+        # Curva predetta densa
+        x_in = torch.full((len(t_dense),), float(x_val), dtype=torch.float32, device=device)
+        t_in = torch.tensor(t_dense, dtype=torch.float32, device=device)
+        with torch.no_grad():
+            T_line = model(torch.stack([x_in, t_in], dim=1)).cpu().numpy().ravel()
+
+        label = f'x={x_val:.3f} m'
+        fig.add_trace(go.Scatter(
+            x=t_dense, y=T_line, mode='lines',
+            line=dict(color=color, width=2),
+            name=f'PINN {label}',
+            legendgroup=label,
+        ))
+
+        # Punti di training per questo sensore
+        mask = np.abs(x_s_np - x_val) < 1e-5
+        fig.add_trace(go.Scatter(
+            x=t_s_np[mask], y=T_meas_np[mask], mode='markers',
+            marker=dict(color=color, size=6, symbol='circle-open', line=dict(width=1.5)),
+            name=f'Misure {label}',
+            legendgroup=label,
+        ))
+
+    fig.add_vline(x=config.T_STEP, line=dict(color='gray', dash='dash', width=1),
+                  annotation_text='Heat ON', annotation_position='top right')
+    fig.update_layout(
+        title=f'Data fit — T(x_sensor, t)<br><sup>Parametri identificati: {param_str}</sup>',
+        xaxis_title='t [s]',
+        yaxis_title='T [°C]',
+        legend=dict(groupclick='toggleitem'),
+        height=520,
+    )
+
+    path = os.path.join(out_dir, 'data_fit.html')
+    fig.write_html(path)
+    return path
+