davide
5a6cb2d518
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>
184 lines
7.6 KiB
Python
184 lines
7.6 KiB
Python
import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from datetime import datetime
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import numpy as np
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import torch
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import config
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BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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def visualize_inverse(T_pred, x_vals, t_vals, T_fdm, identified_params: dict,
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model=None, device=None, x_s=None, t_s=None, T_meas=None):
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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out_dir = os.path.join(BASE_DIR, 'results', 'inverse', timestamp)
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os.makedirs(out_dir, exist_ok=True)
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x_indices = np.linspace(0, T_fdm.shape[0] - 1, len(x_vals), dtype=int)
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t_indices = np.linspace(0, T_fdm.shape[1] - 1, len(t_vals), dtype=int)
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T_fdm_ds = T_fdm[np.ix_(x_indices, t_indices)]
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param_str = ' | '.join(f'{k}={v:.4f}' for k, v in identified_params.items())
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subtitle = f'Parametri identificati: {param_str}'
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colorscale = 'Hot_r'
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zmin = float(min(np.min(T_pred), np.min(T_fdm_ds)))
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zmax = float(max(np.max(T_pred), np.max(T_fdm_ds)))
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# --- Heatmap ---
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fig_map = make_subplots(
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rows=1, cols=2,
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subplot_titles=["Inverse PINN T(x,t)", "FDM Reference T(x,t)"],
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shared_yaxes=True,
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)
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fig_map.add_trace(go.Heatmap(
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z=T_pred.T, x=x_vals, y=t_vals,
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colorscale=colorscale, zmin=zmin, zmax=zmax,
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colorbar=dict(x=0.46, title='T [°C]'),
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), row=1, col=1)
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fig_map.add_trace(go.Heatmap(
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z=T_fdm_ds.T, x=x_vals, y=t_vals,
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colorscale=colorscale, zmin=zmin, zmax=zmax,
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colorbar=dict(x=1.01, title='T [°C]'),
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), row=1, col=2)
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fig_map.update_xaxes(title_text='x')
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fig_map.update_yaxes(title_text='t', row=1, col=1)
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fig_map.update_layout(title_text=f'Inverse PINN vs FDM<br><sup>{subtitle}</sup>', height=450)
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map_path = os.path.join(out_dir, 'heatmap.html')
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fig_map.write_html(map_path)
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print(f"Heatmap saved → {map_path}")
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# --- Animated profile T(x) ---
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n_frames = len(t_vals)
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frames = [
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go.Frame(
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data=[
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go.Scatter(x=x_vals, y=T_pred[:, i], mode='lines',
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line=dict(color='royalblue', width=2), name='Inverse PINN'),
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go.Scatter(x=x_vals, y=T_fdm_ds[:, i], mode='lines',
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line=dict(color='firebrick', width=2, dash='dash'), name='FDM'),
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],
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name=str(i),
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layout=go.Layout(title_text=f'Inverse PINN vs FDM | t = {t_vals[i]:.3f}'),
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)
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for i in range(n_frames)
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]
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fig_anim = go.Figure(
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data=frames[0].data,
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layout=go.Layout(
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title=f'Inverse PINN vs FDM | t = {t_vals[0]:.3f}',
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xaxis=dict(title='x [m]', range=[-0.02, config.L + 0.02]),
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yaxis=dict(title='T [°C]', range=[zmin - 1, zmax + 1]),
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legend=dict(x=0.75, y=0.95),
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updatemenus=[dict(
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type='buttons', showactive=False, y=1.15, x=0.5, xanchor='center',
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buttons=[
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dict(label='▶ Play', method='animate',
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args=[None, dict(frame=dict(duration=40, redraw=False),
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fromcurrent=True, mode='immediate')]),
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dict(label='⏸ Pause', method='animate',
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args=[[None], dict(frame=dict(duration=0, redraw=False),
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mode='immediate')]),
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],
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)],
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sliders=[dict(
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steps=[dict(method='animate', args=[[str(i)],
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dict(mode='immediate', frame=dict(duration=0, redraw=False))],
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label=f'{t_vals[i]:.2f}') for i in range(n_frames)],
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transition=dict(duration=0), x=0.05, y=0, len=0.9,
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currentvalue=dict(prefix='t = ', font=dict(size=14)),
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)],
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),
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frames=frames,
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)
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anim_path = os.path.join(out_dir, 'animation.html')
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fig_anim.write_html(anim_path)
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print(f"Animation saved → {anim_path}")
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# --- Time-series a x=0, x=L/2, x=L ---
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nx = len(x_vals)
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points = [(0, 'x=0', 'blue'), (nx // 2, 'x=L/2', 'green'), (nx - 1, 'x=L', 'red')]
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fig_ts = go.Figure()
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for idx, label, color in points:
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fig_ts.add_trace(go.Scatter(x=t_vals, y=T_pred[idx, :], mode='lines',
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line=dict(color=color, width=2), name=f'Inv.PINN {label}'))
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fig_ts.add_trace(go.Scatter(x=t_vals, y=T_fdm_ds[idx, :], mode='lines',
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line=dict(color=color, width=2, dash='dash'), name=f'FDM {label}'))
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fig_ts.add_vline(x=config.T_STEP, line=dict(color='red', dash='dash', width=1.5),
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annotation_text='Heat ON', annotation_position='top right')
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fig_ts.update_layout(
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title=f'Inverse PINN vs FDM — Time Series<br><sup>{subtitle}</sup>',
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xaxis_title='t', yaxis_title='T(x,t)',
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legend=dict(x=0.01, y=0.99), height=500,
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)
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comparison_path = os.path.join(out_dir, 'comparison.html')
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fig_ts.write_html(comparison_path)
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print(f"Comparison saved → {comparison_path}")
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if model is not None and x_s is not None:
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fit_path = plot_data_fit(model, device, x_s, t_s, T_meas, out_dir)
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print(f"Data fit saved → {fit_path}")
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def plot_data_fit(model, device, x_s, t_s, T_meas, out_dir):
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"""Grafico data-fit: curva T_pred per ogni sensore + punti di training sovrapposti."""
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x_s_np = x_s.cpu().numpy() if isinstance(x_s, torch.Tensor) else np.asarray(x_s)
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t_s_np = t_s.cpu().numpy() if isinstance(t_s, torch.Tensor) else np.asarray(t_s)
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T_meas_np = T_meas.cpu().numpy() if isinstance(T_meas, torch.Tensor) else np.asarray(T_meas)
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sensors = sorted(np.unique(np.round(x_s_np, 6)))
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t_dense = np.linspace(0, config.T_END, 200)
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colors = [
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'royalblue', 'firebrick', 'seagreen', 'darkorange',
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'mediumpurple', 'deeppink', 'saddlebrown', 'teal',
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]
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param_str = f'α={model.alpha.item():.5f} k={model.k.item():.5f} h={model.h_conv.item():.4f}'
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fig = go.Figure()
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for i, x_val in enumerate(sensors):
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color = colors[i % len(colors)]
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# Curva predetta densa
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x_in = torch.full((len(t_dense),), float(x_val), dtype=torch.float32, device=device)
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t_in = torch.tensor(t_dense, dtype=torch.float32, device=device)
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with torch.no_grad():
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T_line = model(torch.stack([x_in, t_in], dim=1)).cpu().numpy().ravel()
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label = f'x={x_val:.3f} m'
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fig.add_trace(go.Scatter(
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x=t_dense, y=T_line, mode='lines',
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line=dict(color=color, width=2),
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name=f'PINN {label}',
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legendgroup=label,
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))
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# Punti di training per questo sensore
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mask = np.abs(x_s_np - x_val) < 1e-5
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fig.add_trace(go.Scatter(
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x=t_s_np[mask], y=T_meas_np[mask], mode='markers',
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marker=dict(color=color, size=6, symbol='circle-open', line=dict(width=1.5)),
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name=f'Misure {label}',
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legendgroup=label,
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))
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fig.add_vline(x=config.T_STEP, line=dict(color='gray', dash='dash', width=1),
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annotation_text='Heat ON', annotation_position='top right')
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fig.update_layout(
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title=f'Data fit — T(x_sensor, t)<br><sup>Parametri identificati: {param_str}</sup>',
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xaxis_title='t [s]',
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yaxis_title='T [°C]',
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legend=dict(groupclick='toggleitem'),
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height=520,
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)
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path = os.path.join(out_dir, 'data_fit.html')
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fig.write_html(path)
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return path
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