X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_CPTA.py;h=51787e43c51ad78e9c372e56c15fe5840d5180ce;hp=3b8c9fd3abeeb546fe07068b4dbc9f9c423f59f4;hb=0f662ea0defa9b30fa7a7d9256857fce92d20a6e;hpb=fe3b245f128cb3204cbf57e2e474f006f7ceacd5 diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 3b8c9fd3ab..51787e43c5 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -25,7 +25,7 @@ import numpy as np import pandas as pd from collections import OrderedDict -from utils import find_outliers, archive_input_data, execute_command +from utils import split_outliers, archive_input_data, execute_command # Command to build the html format of the report @@ -164,22 +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, ] * win_size - median = in_data.rolling(window=win_size).median() + results = [0.66, ] + median = trimmed_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 - for day in range(win_size, in_data.size): - if np.isnan(m_vals[day - 1]) or np.isnan(s_vals[day - 1]): + + 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 - 1] - 3 * s_vals[day - 1]): + elif value < (median[build_nr] - 3 * stdev_t[build_nr]): results.append(0.33) - elif (m_vals[day - 1] - 3 * s_vals[day - 1]) <= d_vals[day] <= \ - (m_vals[day - 1] + 3 * s_vals[day - 1]): - 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: @@ -197,7 +201,7 @@ def _evaluate_results(in_data, trimmed_data, window=10): return results -def _generate_trending_traces(in_data, period, moving_win_size=10, +def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, fill_missing=True, use_first=False, show_moving_median=True, name="", color=""): """Generate the trending traces: @@ -206,6 +210,7 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, - outliers, regress, progress :param in_data: Full data set. + :param build_info: Information about the builds. :param period: Sampling period. :param moving_win_size: Window size. :param fill_missing: If the chosen sample is missing in the full set, its @@ -215,6 +220,7 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, :param name: Name of the plot :param color: Name of the color for the plot. :type in_data: OrderedDict + :type build_info: dict :type period: int :type moving_win_size: int :type fill_missing: bool @@ -233,10 +239,16 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, data_x = [key for key in in_data.keys()] data_y = [val for val in in_data.values()] - data_pd = pd.Series(data_y, index=data_x) - t_data, outliers = find_outliers(data_pd) + 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 = split_outliers(data_pd, outlier_const=1.5, + window=moving_win_size) results = _evaluate_results(data_pd, t_data, window=moving_win_size) anomalies = pd.Series() @@ -274,6 +286,8 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, "color": color, "symbol": "circle", }, + text=hover_text, + hoverinfo="x+y+text+name" ) traces = [trace_samples, ] @@ -282,9 +296,9 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, y=anomalies.values, mode='markers', hoverinfo="none", - showlegend=False, + showlegend=True, legendgroup=name, - name="{name}: outliers".format(name=name), + name="{name}-anomalies".format(name=name), marker={ "size": 15, "symbol": "circle-open", @@ -362,12 +376,38 @@ def _generate_all_charts(spec, input_data): :type input_data: InputData """ - csv_table = list() + job_name = spec.cpta["data"].keys()[0] + + builds_lst = list() + for build in spec.input["builds"][job_name]: + status = build["status"] + if status != "failed" and status != "not found": + builds_lst.append(str(build["build"])) + + # Get "build ID": "date" dict: + build_info = OrderedDict() + for build in builds_lst: + try: + build_info[build] = ( + input_data.metadata(job_name, build)["generated"][:14], + input_data.metadata(job_name, build)["version"] + ) + except KeyError: + build_info[build] = ("", "") + logging.info("{}: {}, {}".format(build, + build_info[build][0], + build_info[build][1])) + # Create the header: - builds = spec.cpta["data"].values()[0] - builds_lst = [str(build) for build in range(builds[0], builds[-1] + 1)] + csv_table = list() header = "Build Number:," + ",".join(builds_lst) + '\n' csv_table.append(header) + build_dates = [x[0] for x in build_info.values()] + header = "Build Date:," + ",".join(build_dates) + '\n' + csv_table.append(header) + vpp_versions = [x[1] for x in build_info.values()] + header = "VPP Version:," + ",".join(vpp_versions) + '\n' + csv_table.append(header) results = list() for chart in spec.cpta["plots"]: @@ -397,13 +437,14 @@ def _generate_all_charts(spec, input_data): tst_lst = list() for build in builds_lst: item = tst_data.get(int(build), '') - tst_lst.append(str(item) if item else '') + tst_lst.append(str(item)) + # tst_lst.append(str(item) if item else '') csv_table.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n') for period in chart["periods"]: # Generate traces: traces = list() - win_size = 10 if period == 1 else 5 if period < 20 else 3 + win_size = 14 if period == 1 else 5 if period < 20 else 3 idx = 0 for test_name, test_data in chart_data.items(): if not test_data: @@ -413,6 +454,7 @@ def _generate_all_charts(spec, input_data): test_name = test_name.split('.')[-1] trace, result = _generate_trending_traces( test_data, + build_info=build_info, period=period, moving_win_size=win_size, fill_missing=True, @@ -424,9 +466,8 @@ def _generate_all_charts(spec, input_data): idx += 1 # Generate the chart: - period_name = "Daily" if period == 1 else \ - "Weekly" if period < 20 else "Monthly" - chart["layout"]["title"] = chart["title"].format(period=period_name) + chart["layout"]["xaxis"]["title"] = \ + chart["layout"]["xaxis"]["title"].format(job=job_name) _generate_chart(traces, chart["layout"], file_name="{0}-{1}-{2}{3}".format( @@ -445,11 +486,23 @@ def _generate_all_charts(spec, input_data): txt_table = None with open("{0}.csv".format(file_name), 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') + line_nr = 0 for row in csv_content: if txt_table is None: txt_table = prettytable.PrettyTable(row) else: - txt_table.add_row(row) + if line_nr > 1: + for idx, item in enumerate(row): + try: + row[idx] = str(round(float(item) / 1000000, 2)) + except ValueError: + pass + try: + txt_table.add_row(row) + except Exception as err: + logging.warning("Error occurred while generating TXT table:" + "\n{0}".format(err)) + line_nr += 1 txt_table.align["Build Number:"] = "l" with open("{0}.txt".format(file_name), "w") as txt_file: txt_file.write(str(txt_table))