sistemata la visualizzazione con tutti i dati degli scostamenti

debug_brackets.py

@@ -0,0 +1,31 @@
+import json
+
+data = json.load(open('archive_simulations/20251113_170236_scenario_dritto.json'))
+sim = data['simulation_results']['0']['simulated']
+real = data['simulation_results']['0']['real']
+
+print(f"Total simulated states: {len(sim)}")
+print(f"Total real states: {len(real)}")
+print(f"Sim range: {sim[0][0]:.2f} to {sim[-1][0]:.2f}")
+print(f"Real range: {real[0][0]:.2f} to {real[-1][0]:.2f}")
+
+# Check how many real states find brackets
+bracketed = 0
+first_bracketed_time = None
+last_bracketed_time = None
+
+for real_state in real:
+    real_ts = real_state[0]
+    # Find bracketing points
+    for i in range(len(sim) - 1):
+        if sim[i][0] <= real_ts <= sim[i + 1][0]:
+            bracketed += 1
+            if first_bracketed_time is None:
+                first_bracketed_time = real_ts
+            last_bracketed_time = real_ts
+            break
+
+print(f"\nBracketed real states: {bracketed} / {len(real)} ({100*bracketed/len(real):.1f}%)")
+if first_bracketed_time and last_bracketed_time:
+    print(f"Bracketed time range: {first_bracketed_time:.2f} to {last_bracketed_time:.2f}")
+    print(f"Bracketed duration: {last_bracketed_time - first_bracketed_time:.2f}s")

debug_targets.py

@@ -0,0 +1,9 @@
+import json
+
+data = json.load(open('archive_simulations/20251113_170236_scenario_dritto.json'))
+targets = list(data['simulation_results'].keys())
+print(f'Targets in file: {targets}')
+for t in targets:
+    real_count = len(data['simulation_results'][t]['real'])
+    sim_count = len(data['simulation_results'][t]['simulated'])
+    print(f'Target {t}: {real_count} real states, {sim_count} simulated states')

target_simulator/__main__.py

@@ -30,7 +30,10 @@ def main():
     """Initializes and runs the application."""
     import logging
 
+    # Silence verbose loggers from third-party libraries
     logging.getLogger("matplotlib").setLevel(logging.WARNING)
+    logging.getLogger("PIL").setLevel(logging.INFO)
+    logging.getLogger("PIL.PngImagePlugin").setLevel(logging.INFO)
 
     app = MainView()
 

target_simulator/gui/analysis_window.py

@@ -37,6 +37,7 @@ class AnalysisWindow(tk.Toplevel):
         # State variables
         self.selected_target_id = tk.IntVar(value=0)
         self._active = True
+        self._filtered_errors = None  # Cache for spike-filtered errors used in statistics
 
         # Carica i dati e inizializza l'analizzatore
         self._load_data_and_setup(archive_filepath)
@@ -74,8 +75,9 @@ class AnalysisWindow(tk.Toplevel):
             self.latency_timestamps = []
             self.latency_values_ms = []
 
-        # Create a temporary hub and populate it with historical data
-        self._hub = SimulationStateHub()
+        # Create a temporary hub with unlimited history size for analysis
+        # The default history_size of 200 is too small for full simulation playback
+        self._hub = SimulationStateHub(history_size=100000)
         results = archive_data.get("simulation_results", {})
         for target_id_str, data in results.items():
             target_id = int(target_id_str)
@@ -231,7 +233,8 @@ class AnalysisWindow(tk.Toplevel):
         fig = Figure(figsize=(5, 6), dpi=100)
 
         # Use GridSpec for aligned subplots with shared x-axis alignment
-        gs = fig.add_gridspec(2, 1, height_ratios=[2, 1], hspace=0.3)
+        # Increased top margin to avoid title overlap with toolbar
+        gs = fig.add_gridspec(2, 1, height_ratios=[2, 1], hspace=0.35, top=0.95)
 
         # Top subplot: Instantaneous Error
         self.ax = fig.add_subplot(gs[0, 0])
@@ -252,8 +255,8 @@ class AnalysisWindow(tk.Toplevel):
         except Exception:
             pass
 
-        # Bottom subplot: Latency over time
-        self.ax_latency = fig.add_subplot(gs[1, 0], sharex=None)
+        # Bottom subplot: Latency over time - SHARE X-AXIS with top plot for synchronized zoom
+        self.ax_latency = fig.add_subplot(gs[1, 0], sharex=self.ax)
         self.ax_latency.set_title("Latency Evolution")
         self.ax_latency.set_xlabel(
             "Time (s)"
@@ -272,9 +275,24 @@ class AnalysisWindow(tk.Toplevel):
 
         fig.tight_layout()
 
-        self.canvas = FigureCanvasTkAgg(fig, master=parent)
+        # Create frame for toolbar and canvas
+        plot_container = ttk.Frame(parent)
+        plot_container.pack(fill=tk.BOTH, expand=True)
+
+        # Add matplotlib navigation toolbar for zoom/pan functionality at the top
+        from matplotlib.backends.backend_tkagg import NavigationToolbar2Tk
+        
+        toolbar_frame = ttk.Frame(plot_container)
+        toolbar_frame.pack(side=tk.TOP, fill=tk.X)
+        
+        self.canvas = FigureCanvasTkAgg(fig, master=plot_container)
+        
+        toolbar = NavigationToolbar2Tk(self.canvas, toolbar_frame)
+        toolbar.update()
+        
+        # Pack canvas after toolbar so it's below
+        self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
         self.canvas.draw()
-        self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
 
     def _update_target_selector(self):
         """Refresh the target selector Combobox with IDs from the state hub.
@@ -305,24 +323,62 @@ class AnalysisWindow(tk.Toplevel):
     def _update_stats_table(self, results: Dict):
         """Populate the stats Treeview with aggregated error metrics.
         
+        Uses filtered error data (excluding spikes) if available.
+
         Args:
             results (Dict): A mapping containing 'x', 'y', 'z' metrics and
                 numerical aggregates such as mean, std_dev and rmse.
         """
         self.stats_tree.delete(*self.stats_tree.get_children())
 
-        # Add rows for each error axis (X, Y, Z)
-        for axis in ["x", "y", "z"]:
-            self.stats_tree.insert(
-                "",
-                "end",
-                values=(
-                    f"Error {axis.upper()}",
-                    f"{results[axis]['mean']:.3f}",
-                    f"{results[axis]['std_dev']:.3f}",
-                    f"{results[axis]['rmse']:.3f}",
-                ),
-            )
+        # Use filtered errors if available, otherwise fall back to analyzer results
+        if hasattr(self, '_filtered_errors') and self._filtered_errors:
+            import math
+            # Compute statistics from filtered data
+            for axis in ["x", "y", "z"]:
+                errors = self._filtered_errors[axis]
+                if errors:
+                    n = len(errors)
+                    mean = sum(errors) / n
+                    variance = sum((x - mean) ** 2 for x in errors) / n
+                    std_dev = math.sqrt(variance)
+                    rmse = math.sqrt(sum(x**2 for x in errors) / n)
+                    
+                    self.stats_tree.insert(
+                        "",
+                        "end",
+                        values=(
+                            f"Error {axis.upper()}",
+                            f"{mean:.3f}",
+                            f"{std_dev:.3f}",
+                            f"{rmse:.3f}",
+                        ),
+                    )
+                else:
+                    # No data after filtering
+                    self.stats_tree.insert(
+                        "",
+                        "end",
+                        values=(
+                            f"Error {axis.upper()}",
+                            "N/A",
+                            "N/A",
+                            "N/A",
+                        ),
+                    )
+        else:
+            # Fallback to analyzer results (unfiltered)
+            for axis in ["x", "y", "z"]:
+                self.stats_tree.insert(
+                    "",
+                    "end",
+                    values=(
+                        f"Error {axis.upper()}",
+                        f"{results[axis]['mean']:.3f}",
+                        f"{results[axis]['std_dev']:.3f}",
+                        f"{results[axis]['rmse']:.3f}",
+                    ),
+                )
 
         # Add latency row if available
         if self.estimated_latency_ms is not None:
@@ -365,6 +421,8 @@ class AnalysisWindow(tk.Toplevel):
     def _update_plot(self, target_id: int):
         """Update the matplotlib plot for the given target using hub history.
         
+        Also computes and stores filtered errors (excluding outliers) for statistics.
+
         Args:
             target_id (int): The identifier of the target to plot errors for.
         """
@@ -376,6 +434,8 @@ class AnalysisWindow(tk.Toplevel):
             self.ax.relim()
             self.ax.autoscale_view()
             self.canvas.draw_idle()
+            # Clear filtered data cache
+            self._filtered_errors = None
             return
 
         times, errors_x, errors_y, errors_z = [], [], [], []
@@ -394,9 +454,88 @@ class AnalysisWindow(tk.Toplevel):
                 errors_y.append(real_y - interp_y)
                 errors_z.append(real_z - interp_z)
 
-        self.line_x.set_data(times, errors_x)
-        self.line_y.set_data(times, errors_y)
-        self.line_z.set_data(times, errors_z)
+        if not times:
+            self.line_x.set_data([], [])
+            self.line_y.set_data([], [])
+            self.line_z.set_data([], [])
+            self.ax.relim()
+            self.ax.autoscale_view()
+            self.canvas.draw_idle()
+            self._filtered_errors = None
+            return
+
+        # Filter initial transient/acquisition spikes
+        # Use a threshold based on the median of stable data (after initial period)
+        filtered_times, filtered_x, filtered_y, filtered_z = [], [], [], []
+        outlier_times, outlier_x, outlier_y, outlier_z = [], [], [], []
+        
+        # Take a sample window after initial seconds to compute typical error magnitude
+        min_time = min(times)
+        sample_window_start = min_time + 5.0  # Skip first 5 seconds
+        sample_window_end = min_time + 15.0   # Sample from 5s to 15s
+        
+        sample_errors = []
+        for i, t in enumerate(times):
+            if sample_window_start <= t <= sample_window_end:
+                err_magnitude = (errors_x[i]**2 + errors_y[i]**2 + errors_z[i]**2) ** 0.5
+                sample_errors.append(err_magnitude)
+        
+        # Compute threshold: median of sample + 10x (very permissive for normal errors)
+        if sample_errors:
+            import statistics
+            median_err = statistics.median(sample_errors)
+            # Use 20x median as threshold to catch only extreme outliers
+            threshold = max(median_err * 20, 500.0)  # At least 500 ft threshold
+        else:
+            # Fallback if no sample data available
+            threshold = 1000.0
+        
+        # Classify points as normal or outliers
+        for i, t in enumerate(times):
+            err_magnitude = (errors_x[i]**2 + errors_y[i]**2 + errors_z[i]**2) ** 0.5
+            if err_magnitude > threshold:
+                outlier_times.append(t)
+                outlier_x.append(errors_x[i])
+                outlier_y.append(errors_y[i])
+                outlier_z.append(errors_z[i])
+            else:
+                filtered_times.append(t)
+                filtered_x.append(errors_x[i])
+                filtered_y.append(errors_y[i])
+                filtered_z.append(errors_z[i])
+
+        # Store filtered errors for statistics computation
+        self._filtered_errors = {
+            'x': filtered_x,
+            'y': filtered_y,
+            'z': filtered_z
+        }
+
+        # Plot filtered (normal) data
+        self.line_x.set_data(filtered_times, filtered_x)
+        self.line_y.set_data(filtered_times, filtered_y)
+        self.line_z.set_data(filtered_times, filtered_z)
+
+        # Add annotation if outliers were detected
+        # Clear previous annotations
+        for txt in getattr(self.ax, '_spike_annotations', []):
+            txt.remove()
+        self.ax._spike_annotations = []
+        
+        if outlier_times:
+            # Add text annotation about filtered spikes
+            outlier_count = len(outlier_times)
+            max_outlier_mag = max((outlier_x[i]**2 + outlier_y[i]**2 + outlier_z[i]**2)**0.5 
+                                 for i in range(len(outlier_times)))
+            annotation_text = (f"⚠ {outlier_count} acquisition spike(s) filtered\n"
+                             f"(max error: {max_outlier_mag:.0f} ft at t={outlier_times[0]:.1f}s)\n"
+                             f"Spikes excluded from statistics")
+            txt = self.ax.text(0.02, 0.98, annotation_text,
+                             transform=self.ax.transAxes,
+                             verticalalignment='top',
+                             bbox=dict(boxstyle='round', facecolor='yellow', alpha=0.7),
+                             fontsize=9)
+            self.ax._spike_annotations = [txt]
 
         self.ax.relim()
         self.ax.autoscale_view()