def plot_counter_stat()

in perfrunbook/utilities/measure_and_plot_basic_pmu_counters.py [0:0]

• 22 lines of code
• 4 McCabe index (conditional complexity)

def plot_counter_stat(csv, logfile, plot, stat_name, counter_numerator,
                      counter_denominator, scale):
    """
    Process the returned csv file into a time-series statistic to plot and
    also calculate some useful aggregate stats.
    """
    df = pd.read_csv(csv, sep='|',
                     names=['time', 'count', 'rsrvd1', 'event',
                            'rsrvd2', 'frac', 'rsrvd3', 'rsrvd4'],
                     dtype={'time': np.float64, 'count': np.float64,
                            'rsrvd1': str, 'event': str, 'rsrvd2': str,
                            'frac': np.float64, 'rsrvd3': str, 'rsrvd4': str})
    df_processed = pd.DataFrame()

    df_processed[stat_name] = (df[df['event'] == counter_numerator]['count'].reset_index(drop=True)) / (df[df['event'] == counter_denominator]['count'].reset_index(drop=True)) * scale
    df_processed[counter_numerator] = df[df['event'] == counter_numerator]['count'].reset_index(drop=True)
    df_processed[counter_denominator] = df[df['event'] == counter_denominator]['count'].reset_index(drop=True)
    df_processed.dropna(inplace=True)

    # Calculate some meaningful aggregate stats for comparing time-series plots
    geomean = stats.gmean(df_processed[stat_name])
    p50 = stats.scoreatpercentile(df_processed[stat_name], 50)
    p90 = stats.scoreatpercentile(df_processed[stat_name], 90)
    p99 = stats.scoreatpercentile(df_processed[stat_name], 99)
    xtitle = f"gmean:{geomean:>6.2f} p50:{p50:>6.2f} p90:{p90:>6.2f} p99:{p99:>6.2f}"

    if logfile:
        df_processed.to_csv(logfile)
    if plot:
        plot_terminal(df_processed, stat_name, xtitle)