How to use the tsfresh.feature_extraction.ReasonableFeatureExtractionSettings function in tsfresh

minmax_fp_ts_csv = '%s/fpid.%s.%s.minmax_fp_ts.tsfresh.input.std.csv' % (
        settings.SKYLINE_TMP_DIR, str(fp_id), base_name)
    if os.path.isfile(minmax_fp_ts_csv):
        os.remove(minmax_fp_ts_csv)
    minmax_fp_fname_out = minmax_fp_ts_csv + '.transposed.csv'
    if os.path.isfile(minmax_fp_fname_out):
        os.remove(minmax_fp_fname_out)
    anomalous_ts_csv = '%s/%s.%s.minmax_anomalous_ts.tsfresh.std.csv' % (
        settings.SKYLINE_TMP_DIR, metric_timestamp, base_name)
    if os.path.isfile(anomalous_ts_csv):
        os.remove(anomalous_ts_csv)
    anomalous_fp_fname_out = anomalous_ts_csv + '.transposed.csv'
    if os.path.isfile(anomalous_fp_fname_out):
        os.remove(anomalous_fp_fname_out)

    tsf_settings = ReasonableFeatureExtractionSettings()
    tsf_settings.disable_progressbar = True
    minmax_fp_features_sum = None
    minmax_anomalous_features_sum = None
    if minmax_anomalous_ts and minmax_fp_ts:
        if not os.path.isfile(minmax_fp_ts_csv):
            datapoints = minmax_fp_ts
            converted = []
            for datapoint in datapoints:
                try:
                    new_datapoint = [float(datapoint[0]), float(datapoint[1])]
                    converted.append(new_datapoint)
                except:  # nosec
                    continue
            for ts, value in converted:
                try:
                    utc_ts_line = '%s,%s,%s\n' % (base_name, str(int(ts)), str(value))

continue

    if os.path.isfile(ts_csv):
        os.remove(ts_csv)

    for ts, value in converted:
        # print('%s,%s' % (str(int(ts)), str(value)))
        utc_ts_line = '%s,%s,%s\n' % (metric, str(int(ts)), str(value))
        with open(ts_csv, 'a') as fh:
            fh.write(utc_ts_line)
    del converted

    df = pd.read_csv(ts_csv, delimiter=',', header=None, names=['metric', 'timestamp', 'value'])
#    print('DataFrame created with %s' % ts_csv)
    df.columns = ['metric', 'timestamp', 'value']
    tsf_settings = ReasonableFeatureExtractionSettings()
    # Disable tqdm progress bar
    tsf_settings.disable_progressbar = True
    df_features = extract_features(
        df, column_id='metric', column_sort='timestamp', column_kind=None,
        column_value=None, feature_extraction_settings=tsf_settings)
    del df
#    print('features extracted from %s data' % ts_csv)
    # write to disk
    fname_out = fname_in + '.features.csv'
    # Transpose
    df_t = df_features.transpose()
#    print('features transposed')
    # Create transposed features csv
    t_fname_out = fname_in + '.features.transposed.csv'
    df_t.to_csv(t_fname_out)
    del df_t

logger.error('error :: could not minmax scale current time series anomalous_timeseries for %s' % (str(fp_id), str(base_name)))
            if len(minmax_anomalous_ts) > 0:
                logger.info('minmax_anomalous_ts is populated with %s data points' % str(len(minmax_anomalous_ts)))
            else:
                logger.error('error :: minmax_anomalous_ts is not populated')
        else:
            logger.info('minmax scaled check will be skipped - anomalous_ts_values_count is %s and minmax_fp_ts is %s' % (str(anomalous_ts_values_count), str(minmax_fp_ts_values_count)))

    minmax_fp_ts_csv = '%s/fpid.%s.%s.minmax_fp_ts.tsfresh.input.std.csv' % (
        settings.SKYLINE_TMP_DIR, str(fp_id), base_name)
    minmax_fp_fname_out = minmax_fp_ts_csv + '.transposed.csv'
    anomalous_ts_csv = '%s/%s.%s.minmax_anomalous_ts.tsfresh.std.csv' % (
        settings.SKYLINE_TMP_DIR, metric_timestamp, base_name)
    anomalous_fp_fname_out = anomalous_ts_csv + '.transposed.csv'

    tsf_settings = ReasonableFeatureExtractionSettings()
    tsf_settings.disable_progressbar = True
    minmax_fp_features_sum = None
    minmax_anomalous_features_sum = None
    if minmax_anomalous_ts and minmax_fp_ts:
        if LOCAL_DEBUG:
            logger.debug('debug :: analyzing minmax_fp_ts and minmax_anomalous_ts')
        if not os.path.isfile(minmax_fp_ts_csv):
            if LOCAL_DEBUG:
                logger.debug('debug :: creating %s from minmax_fp_ts' % minmax_fp_ts_csv)
            datapoints = minmax_fp_ts
            converted = []
            for datapoint in datapoints:
                try:
                    new_datapoint = [float(datapoint[0]), float(datapoint[1])]
                    converted.append(new_datapoint)
                except:  # nosec

# Added log_context to report the context
    current_logger.info('%s :: starting extract_features with %s' % (
        log_context, str(TSFRESH_VERSION)))
    df_features = False
    try:
        # @modified 20161226 - Bug #1822: tsfresh extract_features process stalling
        # Changed to use the new ReasonableFeatureExtractionSettings that was
        # introduced in tsfresh-0.4.0 to exclude the computationally high cost
        # of extracting features from very static timeseries that has little to
        # no variation is the values, which results in features taking up to
        # almost 600 seconds to calculate on a timeseries of length 10075
        # (168h - 1 datapoint per 60s)
        # In terms of inline feature calculatation, always exclude
        # high_comp_cost features.
        # df_features = extract_features(df, column_id='metric', column_sort='timestamp', column_kind=None, column_value=None)
        tsf_settings = ReasonableFeatureExtractionSettings()
        # Disable tqdm progress bar
        tsf_settings.disable_progressbar = True
        df_features = extract_features(
            df, column_id='metric', column_sort='timestamp', column_kind=None,
            column_value=None, feature_extraction_settings=tsf_settings)
        # @modified 20190413 - Bug #2934: Ionosphere - no mirage.redis.24h.json file
        # Added log_context to report the context
        current_logger.info('%s :: features extracted from %s data' % (
            log_context, ts_csv))
    except:
        trace = traceback.print_exc()
        current_logger.debug(trace)
        # @modified 20190413 - Bug #2934: Ionosphere - no mirage.redis.24h.json file
        # Added log_context to report the context
        fail_msg = 'error: %s :: extracting features with tsfresh from - %s' % (log_context, ts_csv)
        current_logger.error('%s' % fail_msg)