full upload so not to lose anything important

tools/plot_score.py

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+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+if __name__ == "__main__":
+    # Path to your .npy file with anomaly scores
+    scores_path = Path(
+        "/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/"
+        "DeepSAD/subter_test/inference/3_smoke_human_walking_2023-01-23.npy"
+    )
+
+    # Frames per second
+    fps = 10
+
+    # Load all scores
+    all_scores = np.load(scores_path)
+
+    # (Optional) If your scores are a 1D array, remove or adjust this reshape
+    # The final shape is expected to be (1, total_frames) or (N,) if you want only one line.
+    all_scores = all_scores.reshape(1, -1)  # shape becomes (1, total_frames)
+
+    # We only want frames [0..299] => 300 frames
+    max_frames = 300
+    scores = all_scores[0, :max_frames]  # Take the first row's first 300 frames
+
+    # Convert frames to time in seconds: time[i] = i / fps
+    time_values = np.arange(len(scores)) / fps  # shape is (max_frames,)
+
+    # Recalculate y-limit based on the selected slice
+    y_limit = 1.1 * np.max(scores[np.isfinite(scores)])
+    # If you want to ensure y_limit >= 10, uncomment below:
+    # y_limit = max(y_limit, 10)
+
+    print("Selected Scores Shape:", scores.shape)
+    print("Time Range:", time_values[0], "->", time_values[-1])
+    print("Y-limit:", y_limit)
+
+    # --------------------------
+    # Improve default font sizes
+    # --------------------------
+    plt.rcParams.update(
+        {
+            "figure.dpi": 150,
+            "font.size": 14,
+            "axes.labelsize": 16,
+            "axes.titlesize": 16,
+            "xtick.labelsize": 14,
+            "ytick.labelsize": 14,
+            "legend.fontsize": 14,
+        }
+    )
+
+    # --------------------------------------------------
+    # Create the figure (half the previous width = 3.84)
+    # --------------------------------------------------
+    fig, ax = plt.subplots(figsize=(3.84, 7))
+
+    # Plot the scores vs. time
+    ax.plot(time_values, scores, label="Anomaly Score", color="blue")
+
+    # Set axis labels
+    ax.set_xlabel("Time [s]", fontsize=16)
+    ax.set_ylabel("Score", fontsize=16)
+
+    # Restrict X-axis from 0 to last time value (or 30 s if exactly 300 frames)
+    ax.set_xlim([0, time_values[-1]])
+    ax.set_ylim([0, y_limit])
+
+    # -------------------------
+    # Customize the ticks
+    # -------------------------
+    # Example: only 4 ticks on the x-axis (0, 10, 20, 30) if 300 frames => 30 s
+    ax.set_xticks([0, 10, 20, 30])
+
+    # Guarantee at least one tick at y=10.
+    # Adjust other ticks as you like; here we use 0 and the y_limit.
+    # If y_limit <= 10, you might want to override it.
+    if y_limit > 10:
+        ax.set_yticks([0, 10, round(y_limit, 1)])  # round for cleaner display
+    else:
+        # If your data doesn't go that high, just keep 0 and y_limit
+        ax.set_yticks([0, round(y_limit, 1)])
+
+    # Optionally add a legend
+    ax.legend(loc="upper right")
+
+    plt.axvline(x=22, color="r", label="shown image")
+
+    # Tight layout for small figure
+    plt.tight_layout()
+
+    # ----------------------
+    # Save to disk, no show
+    # ----------------------
+    output_filename = f"{scores_path.stem}_static_time_plot.png"
+    plt.savefig(output_filename, dpi=150)
+
+    # Clear the figure if you continue using matplotlib
+    plt.clf()