X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_CPTA.py;h=12ebd46922addb2272a4f840e41606fd012192ea;hb=30f205347619919d15691f4b01da5e730840db38;hp=c326aa634fa434b21d118ff7fe758d2c7d5773d2;hpb=4914a78f593124788c754695b79be146c3916c6f;p=csit.git diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py index c326aa634f..12ebd46922 100644 --- a/resources/tools/presentation/generator_CPTA.py +++ b/resources/tools/presentation/generator_CPTA.py @@ -14,7 +14,6 @@ """Generation of Continuous Performance Trending and Analysis. """ -import datetime import logging import csv import prettytable @@ -25,14 +24,16 @@ import numpy as np import pandas as pd from collections import OrderedDict -from utils import find_outliers, archive_input_data, execute_command +from datetime import datetime, timedelta + +from utils import split_outliers, archive_input_data, execute_command # Command to build the html format of the report HTML_BUILDER = 'sphinx-build -v -c conf_cpta -a ' \ '-b html -E ' \ '-t html ' \ - '-D version="Generated on {date}" ' \ + '-D version="{date}" ' \ '{working_dir} ' \ '{build_dir}/' @@ -64,7 +65,7 @@ def generate_cpta(spec, data): ret_code = _generate_all_charts(spec, data) cmd = HTML_BUILDER.format( - date=datetime.date.today().strftime('%d-%b-%Y'), + date=datetime.utcnow().strftime('%m/%d/%Y %H:%M UTC'), working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"], build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"]) execute_command(cmd) @@ -144,51 +145,53 @@ def _select_data(in_data, period, fill_missing=False, use_first=False): return OrderedDict(sorted(data_dict.items(), key=lambda t: t[0])) -def _evaluate_results(in_data, trimmed_data, window=10): +def _evaluate_results(trimmed_data, window=10): """Evaluates if the sample value is regress, normal or progress compared to previous data within the window. We use the intervals defined as: - - regress: less than median - 3 * stdev - - normal: between median - 3 * stdev and median + 3 * stdev - - progress: more than median + 3 * stdev + - regress: less than trimmed moving median - 3 * stdev + - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev + - progress: more than trimmed moving median + 3 * stdev + where stdev is trimmed moving standard deviation. - :param in_data: Full data set. - :param trimmed_data: Full data set without the outliers. - :param window: Window size used to calculate moving median and moving stdev. - :type in_data: pandas.Series + :param trimmed_data: Full data set with the outliers replaced by nan. + :param window: Window size used to calculate moving average and moving stdev. :type trimmed_data: pandas.Series :type window: int :returns: Evaluated results. :rtype: list """ - 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() - 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]): + if len(trimmed_data) > 2: + win_size = trimmed_data.size if trimmed_data.size < window else window + results = [0.66, ] + tmm = trimmed_data.rolling(window=win_size, min_periods=2).median() + tmstd = trimmed_data.rolling(window=win_size, min_periods=2).std() + + first = True + for build_nr, value in trimmed_data.iteritems(): + if first: + first = False + continue + if (np.isnan(value) + or np.isnan(tmm[build_nr]) + or np.isnan(tmstd[build_nr])): results.append(0.0) - elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]): + elif value < (tmm[build_nr] - 3 * tmstd[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 > (tmm[build_nr] + 3 * tmstd[build_nr]): results.append(1.0) + else: + results.append(0.66) else: results = [0.0, ] try: - median = np.median(in_data) - stdev = np.std(in_data) - if in_data.values[-1] < (median - 3 * stdev): + tmm = np.median(trimmed_data) + tmstd = np.std(trimmed_data) + if trimmed_data.values[-1] < (tmm - 3 * tmstd): results.append(0.33) - elif (median - 3 * stdev) <= in_data.values[-1] <= ( - median + 3 * stdev): + elif (tmm - 3 * tmstd) <= trimmed_data.values[-1] <= ( + tmm + 3 * tmstd): results.append(0.66) else: results.append(1.0) @@ -199,10 +202,10 @@ def _evaluate_results(in_data, trimmed_data, window=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=""): + show_trend_line=True, name="", color=""): """Generate the trending traces: - samples, - - moving median (trending plot) + - trimmed moving median (trending line) - outliers, regress, progress :param in_data: Full data set. @@ -210,9 +213,9 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, :param period: Sampling period. :param moving_win_size: Window size. :param fill_missing: If the chosen sample is missing in the full set, its - nearest neighbour is used. + nearest neighbour is used. :param use_first: Use the first sample even though it is not chosen. - :param show_moving_median: Show moving median (trending plot). + :param show_trend_line: Show moving median (trending plot). :param name: Name of the plot :param color: Name of the color for the plot. :type in_data: OrderedDict @@ -221,10 +224,10 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, :type moving_win_size: int :type fill_missing: bool :type use_first: bool - :type show_moving_median: bool + :type show_trend_line: bool :type name: str :type color: str - :returns: Generated traces (list) and the evaluated result (float). + :returns: Generated traces (list) and the evaluated result. :rtype: tuple(traces, result) """ @@ -232,25 +235,30 @@ 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()] - 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) + data_x = list(in_data.keys()) + data_y = list(in_data.values()) + + hover_text = list() + xaxis = list() + for idx in data_x: + hover_text.append("vpp-ref: {0}
csit-ref: mrr-daily-build-{1}". + format(build_info[str(idx)][1].rsplit('~', 1)[0], + idx)) + date = build_info[str(idx)][0] + xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]), + int(date[9:11]), int(date[12:]))) - results = _evaluate_results(data_pd, t_data, window=moving_win_size) + data_pd = pd.Series(data_y, index=xaxis) + + t_data, outliers = split_outliers(data_pd, outlier_const=1.5, + window=moving_win_size) + results = _evaluate_results(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]), ]) + for idx, item in enumerate(data_pd.items()): + item_pd = pd.Series([item[1], ], index=[item[0], ]) if item[0] in outliers.keys(): anomalies = anomalies.append(item_pd) anomalies_res.append(0.0) @@ -270,18 +278,21 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, [1.00, "green"]] trace_samples = plgo.Scatter( - x=data_x, + x=xaxis, y=data_y, mode='markers', line={ "width": 1 }, + legendgroup=name, name="{name}-thput".format(name=name), marker={ "size": 5, "color": color, "symbol": "circle", }, + text=hover_text, + hoverinfo="x+y+text+name" ) traces = [trace_samples, ] @@ -290,9 +301,9 @@ def _generate_trending_traces(in_data, build_info, 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", @@ -322,21 +333,22 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10, ) traces.append(trace_anomalies) - if show_moving_median: - data_mean_y = pd.Series(data_y).rolling( - window=moving_win_size, min_periods=2).median() - trace_median = plgo.Scatter( - x=data_x, - y=data_mean_y, + if show_trend_line: + data_trend = t_data.rolling(window=moving_win_size, + min_periods=2).median() + trace_trend = plgo.Scatter( + x=data_trend.keys(), + y=data_trend.tolist(), mode='lines', line={ "shape": "spline", "width": 1, "color": color, }, + legendgroup=name, name='{name}-trend'.format(name=name) ) - traces.append(trace_median) + traces.append(trace_trend) return traces, results[-1] @@ -379,7 +391,7 @@ def _generate_all_charts(spec, input_data): builds_lst.append(str(build["build"])) # Get "build ID": "date" dict: - build_info = dict() + build_info = OrderedDict() for build in builds_lst: try: build_info[build] = ( @@ -388,6 +400,9 @@ def _generate_all_charts(spec, input_data): ) except KeyError: build_info[build] = ("", "") + logging.info("{}: {}, {}".format(build, + build_info[build][0], + build_info[build][1])) # Create the header: csv_table = list() @@ -428,13 +443,13 @@ 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)) 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 idx = 0 for test_name, test_data in chart_data.items(): if not test_data: @@ -455,16 +470,17 @@ def _generate_all_charts(spec, input_data): results.append(result) idx += 1 - # Generate the chart: - chart["layout"]["xaxis"]["title"] = \ - chart["layout"]["xaxis"]["title"].format(job=job_name) - _generate_chart(traces, - chart["layout"], - file_name="{0}-{1}-{2}{3}".format( - spec.cpta["output-file"], - chart["output-file-name"], - period, - spec.cpta["output-file-type"])) + if traces: + # Generate the chart: + chart["layout"]["xaxis"]["title"] = \ + chart["layout"]["xaxis"]["title"].format(job=job_name) + _generate_chart(traces, + chart["layout"], + file_name="{0}-{1}-{2}{3}".format( + spec.cpta["output-file"], + chart["output-file-name"], + period, + spec.cpta["output-file-type"])) logging.info(" Done.")