X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_CPTA.py;h=c3784e94ef7dac73f6d9b93699e326b54e7125b9;hp=09d2e444aebc413656b17a9f2dbdf9c2b0de2475;hb=2387340f7050112311cd231f7d30d06731da4836;hpb=a09211590e3de1cb45c1ad1e0aae6fd84bf15c14 diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index 09d2e444ae..c3784e94ef 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -16,6 +16,8 @@ import datetime import logging +import csv +import prettytable import plotly.offline as ploff import plotly.graph_objs as plgo import plotly.exceptions as plerr @@ -162,19 +164,21 @@ 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 + results = [0.0, ] median = in_data.rolling(window=win_size).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]): + for day in range(1, in_data.size): + if np.isnan(m_vals[day]) \ + or np.isnan(s_vals[day]) \ + or np.isnan(d_vals[day]): results.append(0.0) - elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]): + elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]): 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]): + elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \ + (m_vals[day] + 3 * s_vals[day]): results.append(0.66) else: results.append(1.0) @@ -195,7 +199,7 @@ 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: @@ -204,6 +208,7 @@ 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 @@ -213,6 +218,7 @@ 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 @@ -228,18 +234,30 @@ def _generate_trending_traces(in_data, 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_y = [val for val in in_data.values()] data_pd = pd.Series(data_y, index=data_x) - t_data, outliers = find_outliers(data_pd) + 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) @@ -272,6 +290,8 @@ 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, ] @@ -289,19 +309,21 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, "color": anomalies_res, "colorscale": color_scale, "showscale": True, - + "line": { + "width": 2 + }, "colorbar": { "y": 0.5, "len": 0.8, - "title": "Results Clasification", + "title": "Circles Marking Data Classification", "titleside": 'right', "titlefont": { "size": 14 }, "tickmode": 'array', "tickvals": [0.125, 0.375, 0.625, 0.875], - "ticktext": ["Outlier", "Regress", "Normal", "Progress"], - "ticks": 'outside', + "ticktext": ["Outlier", "Regression", "Normal", "Progression"], + "ticks": "", "ticklen": 0, "tickangle": -90, "thickness": 10 @@ -312,7 +334,7 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, if show_moving_median: data_mean_y = pd.Series(data_y).rolling( - window=moving_win_size).median() + window=moving_win_size, min_periods=2).median() trace_median = plgo.Scatter( x=data_x, y=data_mean_y, @@ -322,7 +344,7 @@ def _generate_trending_traces(in_data, period, moving_win_size=10, "width": 1, "color": color, }, - name='{name}-trend'.format(name=name, size=moving_win_size) + name='{name}-trend'.format(name=name) ) traces.append(trace_median) @@ -358,6 +380,39 @@ def _generate_all_charts(spec, input_data): :type input_data: InputData """ + 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: + 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"]: logging.info(" Generating the chart '{0}' ...". @@ -372,27 +427,37 @@ def _generate_all_charts(spec, input_data): chart_data = dict() for job in data: for idx, build in job.items(): - for test in build: - if chart_data.get(test["name"], None) is None: - chart_data[test["name"]] = OrderedDict() + for test_name, test in build.items(): + if chart_data.get(test_name, None) is None: + chart_data[test_name] = OrderedDict() try: - chart_data[test["name"]][int(idx)] = \ + chart_data[test_name][int(idx)] = \ test["result"]["throughput"] except (KeyError, TypeError): - chart_data[test["name"]][int(idx)] = None + pass + + # Add items to the csv table: + for tst_name, tst_data in chart_data.items(): + tst_lst = list() + for build in builds_lst: + item = tst_data.get(int(build), '') + 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: logging.warning("No data for the test '{0}'". format(test_name)) continue + 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, @@ -404,9 +469,8 @@ 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( @@ -417,6 +481,36 @@ def _generate_all_charts(spec, input_data): logging.info(" Done.") + # Write the tables: + file_name = spec.cpta["output-file"] + "-trending" + with open("{0}.csv".format(file_name), 'w') as file_handler: + file_handler.writelines(csv_table) + + 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: + 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)) + + # Evaluate result: result = "PASS" for item in results: if item is None: @@ -426,9 +520,8 @@ def _generate_all_charts(spec, input_data): result = "PASS" elif item == 0.33 or item == 0.0: result = "FAIL" - print(results) - print(result) - if result == "FAIL": - return 1 - else: - return 0 + + logging.info("Partial results: {0}".format(results)) + logging.info("Result: {0}".format(result)) + + return result