ci.eval.compare: format cmp-stats.py with ruff

# Define metrics of interest (can be expanded as needed)

METRIC_PREFIXES = ("nr", "gc")

def flatten_data(json_data: dict) -> dict:

    """

    Extracts and flattens metrics from JSON data.

    return flat_metrics

def load_all_metrics(directory: Path) -> dict:

    """

    Loads all stats JSON files in the specified directory and extracts metrics.

    return metrics

def dataframe_to_markdown(df: pd.DataFrame) -> str:

    df = df.sort_values(by=df.columns[0], ascending=True)

    markdown_lines = []

    # Header (get column names and format them)

    header = '\n| ' + ' | '.join(df.columns) + ' |'

    header = "\n| " + " | ".join(df.columns) + " |"

    markdown_lines.append(header)

    markdown_lines.append("| - " * (len(df.columns)) + "|")  # Separator line

        # Check for no change and NaN in p_value/t_stat

        row_values = []

        for val in row:

            if isinstance(val, float) and np.isnan(val):  # For NaN values in p-value or t-stat

            if isinstance(val, float) and np.isnan(

                val

            ):  # For NaN values in p-value or t-stat

                row_values.append("-")  # Custom symbol for NaN

            elif isinstance(val, float) and val == 0:  # For no change (mean_diff == 0)

                row_values.append("-")  # Custom symbol for no change

            else:

                row_values.append(fmt(f"{val:.4f}" if isinstance(val, float) else str(val)))

                row_values.append(

                    fmt(f"{val:.4f}" if isinstance(val, float) else str(val))

                )

        markdown_lines.append('| ' + ' | '.join(row_values) + ' |')

        markdown_lines.append("| " + " | ".join(row_values) + " |")

    return '\n'.join(markdown_lines)

    return "\n".join(markdown_lines)

def perform_pairwise_tests(before_metrics: dict, after_metrics: dict) -> pd.DataFrame:

    common_files = sorted(set(before_metrics) & set(after_metrics))

    all_keys = sorted({ metric_keys for file_metrics in before_metrics.values() for metric_keys in file_metrics.keys() })

    all_keys = sorted(

        {

            metric_keys

            for file_metrics in before_metrics.values()

            for metric_keys in file_metrics.keys()

        }

    )

    results = []

            pct_change = 100 * diff / before_arr

            t_stat, p_val = ttest_rel(after_arr, before_arr)

            results.append({

            results.append(

                {

                    "metric": key,

                    "mean_before": np.mean(before_arr),

                    "mean_after": np.mean(after_arr),

                    "mean_diff": np.mean(diff),

                    "mean_%_change": np.mean(pct_change),

                    "p_value": p_val,

                "t_stat": t_stat

            })

                    "t_stat": t_stat,

                }

            )

    df = pd.DataFrame(results).sort_values("p_value")

    return df

    # This may happen if the pull request target does not include PR#399720 yet.

    if not before_stats.exists():

        print("⚠️  Skipping comparison: stats directory is missing in the target commit.")

        print(

            "⚠️  Skipping comparison: stats directory is missing in the target commit."

        )

        exit(0)

    # This should never happen, but we're exiting gracefully anyways

    if not after_stats.exists():

        print("⚠️  Skipping comparison: stats directory missing in current PR evaluation.")

        print(

            "⚠️  Skipping comparison: stats directory missing in current PR evaluation."

        )

        exit(0)

    before_metrics = load_all_metrics(before_stats)