Function create_histogram

scripts_python/profiling_statistics.py:317–354

(data: np.ndarray, title: str, xlabel: str, output_path: Path)

def create_histogram(data: np.ndarray, title: str, xlabel: str, output_path: Path):
    if len(data) == 0:
        return

    mean_val = np.mean(data)
    std_val = np.std(data)

    fig, ax = plt.subplots(figsize=(10, 6))

    counts, bins, patches = ax.hist(data, bins=24, alpha=0.7, color="blue", edgecolor="black")

    if std_val > 1e-10 and data.max() > data.min():
        x = np.linspace(data.min(), data.max(), 100)
        normal_curve = scipy_stats.norm.pdf(x, mean_val, std_val) * len(data) * (bins[1] - bins[0])
        ax.plot(x, normal_curve, "g-", linewidth=2, label="Normal Distribution")

    ax.axvline(mean_val, color="red", linestyle="--", linewidth=2, label=f"Mean: {mean_val:.2f}")

    if std_val > 1e-10:
        for i in [-3, 3]:
            val = mean_val + i * std_val
            ax.axvline(
                val,
                color="lightblue",
                linestyle="--",
                linewidth=1.5,
                label=f'{"+3" if i > 0 else "-3"}σ: {val:.2f}',
            )

    ax.set_xlabel(xlabel)
    ax.set_ylabel("Frequency")
    ax.set_title(title)
    ax.legend(title=f"μ={mean_val:.2f}, σ={std_val:.2f}")
    ax.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.savefig(output_path, dpi=150, bbox_inches="tight")
    plt.close()


def create_scatterplot(