Function explode_big

tests/plot.py:35–61

(mean: float, stddev: float)

def explode_big(mean: float, stddev: float) -> List[float]:
    num_samples = 10
    desired_mean = mean
    desired_std_dev = stddev

    samples = np.random.normal(loc=0.0, scale=desired_std_dev, size=num_samples)

    actual_mean = np.mean(samples)
    # actual_std = np.std(samples)
    # print("Initial samples stats   : mean = {:.4f} stdv = {:.4f}".format(actual_mean, actual_std))

    zero_mean_samples = samples - (actual_mean)

    # zero_mean_mean = np.mean(zero_mean_samples)
    zero_mean_std = np.std(zero_mean_samples)
    # print("True zero samples stats : mean = {:.4f} stdv = {:.4f}".format(zero_mean_mean, zero_mean_std))

    scaled_samples = zero_mean_samples * (desired_std_dev / zero_mean_std)
    # scaled_mean = np.mean(scaled_samples)
    # scaled_std = np.std(scaled_samples)
    # print("Scaled samples stats    : mean = {:.4f} stdv = {:.4f}".format(scaled_mean, scaled_std))

    final_samples = scaled_samples + desired_mean
    # final_mean = np.mean(final_samples)
    # final_std = np.std(final_samples)
    # print("Final samples stats     : mean = {:.4f} stdv = {:.4f}".format(final_mean, final_std))
    return list(final_samples)


def systems_order(df: pd.DataFrame) -> List[str]: