diff options
Diffstat (limited to 'resources/tools/presentation/generator_CPTA.py')
| -rw-r--r-- | resources/tools/presentation/generator_CPTA.py | 51 |
1 files changed, 22 insertions, 29 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 9ec196c0d9..72aef537cf 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -164,26 +164,26 @@ def _evaluate_results(in_data, trimmed_data, window=10): if len(in_data) > 2: win_size = in_data.size if in_data.size < window else window - results = [0.0, ] + results = [0.66, ] median = in_data.rolling(window=win_size, min_periods=2).median() stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std() - m_vals = median.values - s_vals = stdev_t.values - d_vals = in_data.values - t_vals = trimmed_data.values - for day in range(1, in_data.size): - if np.isnan(t_vals[day]) \ - or np.isnan(m_vals[day]) \ - or np.isnan(s_vals[day]) \ - or np.isnan(d_vals[day]): + + first = True + for build_nr, value in in_data.iteritems(): + if first: + first = False + continue + if np.isnan(trimmed_data[build_nr]) \ + or np.isnan(median[build_nr]) \ + or np.isnan(stdev_t[build_nr]) \ + or np.isnan(value): results.append(0.0) - elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]): + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): results.append(0.33) - elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \ - (m_vals[day] + 3 * s_vals[day]): - results.append(0.66) - else: + elif value > (median[build_nr] + 3 * stdev_t[build_nr]): results.append(1.0) + else: + results.append(0.66) else: results = [0.0, ] try: @@ -236,30 +236,23 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, in_data = _select_data(in_data, period, fill_missing=fill_missing, use_first=use_first) - # try: - # data_x = ["{0}/{1}".format(key, build_info[str(key)][1].split("~")[-1]) - # for key in in_data.keys()] - # except KeyError: - # data_x = [key for key in in_data.keys()] - hover_text = ["vpp-build: {0}".format(x[1].split("~")[-1]) - for x in build_info.values()] - data_x = [key for key in in_data.keys()] + data_x = [key for key in in_data.keys()] data_y = [val for val in in_data.values()] + + hover_text = list() + for idx in data_x: + hover_text.append("vpp-build: {0}". + format(build_info[str(idx)][1].split("~")[-1])) + data_pd = pd.Series(data_y, index=data_x) t_data, outliers = find_outliers(data_pd, outlier_const=1.5) - results = _evaluate_results(data_pd, t_data, window=moving_win_size) anomalies = pd.Series() anomalies_res = list() for idx, item in enumerate(in_data.items()): - # item_pd = pd.Series([item[1], ], - # index=["{0}/{1}". - # format(item[0], - # build_info[str(item[0])][1].split("~")[-1]), - # ]) item_pd = pd.Series([item[1], ], index=[item[0], ]) if item[0] in outliers.keys(): anomalies = anomalies.append(item_pd) |