diff options
Diffstat (limited to 'resources/tools/presentation/generator_CPTA.py')
| -rw-r--r-- | resources/tools/presentation/generator_CPTA.py | 429 |
1 files changed, 429 insertions, 0 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py new file mode 100644 index 0000000000..c1b14f1f55 --- /dev/null +++ b/resources/tools/presentation/generator_CPTA.py @@ -0,0 +1,429 @@ +# Copyright (c) 2018 Cisco and/or its affiliates. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Generation of Continuous Performance Trending and Analysis. +""" + +import datetime +import logging +import plotly.offline as ploff +import plotly.graph_objs as plgo +import numpy as np +import pandas as pd + +from collections import OrderedDict +from utils import find_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 ' \ + '{working_dir} ' \ + '{build_dir}/' + +# .css file for the html format of the report +THEME_OVERRIDES = """/* override table width restrictions */ +.wy-nav-content { + max-width: 1200px !important; +} +""" + +COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink", + "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black", + "Violet", "Blue", "Yellow"] + + +def generate_cpta(spec, data): + """Generate all formats and versions of the Continuous Performance Trending + and Analysis. + + :param spec: Specification read from the specification file. + :param data: Full data set. + :type spec: Specification + :type data: InputData + """ + + logging.info("Generating the Continuous Performance Trending and Analysis " + "...") + + ret_code = _generate_all_charts(spec, data) + + cmd = HTML_BUILDER.format( + date=datetime.date.today().strftime('%d-%b-%Y'), + working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"], + build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"]) + execute_command(cmd) + + with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE]"], "w") as \ + css_file: + css_file.write(THEME_OVERRIDES) + + with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE2]"], "w") as \ + css_file: + css_file.write(THEME_OVERRIDES) + + archive_input_data(spec) + + logging.info("Done.") + + return ret_code + + +def _select_data(in_data, period, fill_missing=False, use_first=False): + """Select the data from the full data set. The selection is done by picking + the samples depending on the period: period = 1: All, period = 2: every + second sample, period = 3: every third sample ... + + :param in_data: Full set of data. + :param period: Sampling period. + :param fill_missing: If the chosen sample is missing in the full set, its + nearest neighbour is used. + :param use_first: Use the first sample even though it is not chosen. + :type in_data: OrderedDict + :type period: int + :type fill_missing: bool + :type use_first: bool + :returns: Reduced data. + :rtype: OrderedDict + """ + + first_idx = min(in_data.keys()) + last_idx = max(in_data.keys()) + + idx = last_idx + data_dict = dict() + if use_first: + data_dict[first_idx] = in_data[first_idx] + while idx >= first_idx: + data = in_data.get(idx, None) + if data is None: + if fill_missing: + threshold = int(round(idx - period / 2)) + 1 - period % 2 + idx_low = first_idx if threshold < first_idx else threshold + threshold = int(round(idx + period / 2)) + idx_high = last_idx if threshold > last_idx else threshold + + flag_l = True + flag_h = True + idx_lst = list() + inc = 1 + while flag_l or flag_h: + if idx + inc > idx_high: + flag_h = False + else: + idx_lst.append(idx + inc) + if idx - inc < idx_low: + flag_l = False + else: + idx_lst.append(idx - inc) + inc += 1 + + for i in idx_lst: + if i in in_data.keys(): + data_dict[i] = in_data[i] + break + else: + data_dict[idx] = data + idx -= period + + return OrderedDict(sorted(data_dict.items(), key=lambda t: t[0])) + + +def _evaluate_results(in_data, 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 + + :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 + :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]): + results.append(0.0) + elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]): + 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: + results.append(1.0) + else: + results = [0.0, ] + try: + median = np.median(in_data) + stdev = np.std(in_data) + if in_data.values[-1] < (median - 3 * stdev): + results.append(0.33) + elif (median - 3 * stdev) <= in_data.values[-1] <= ( + median + 3 * stdev): + results.append(0.66) + else: + results.append(1.0) + except TypeError: + results.append(None) + return results + + +def _generate_trending_traces(in_data, period, moving_win_size=10, + fill_missing=True, use_first=False, + show_moving_median=True, name="", color=""): + """Generate the trending traces: + - samples, + - moving median (trending plot) + - outliers, regress, progress + + :param in_data: Full data set. + :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. + :param use_first: Use the first sample even though it is not chosen. + :param show_moving_median: Show moving median (trending plot). + :param name: Name of the plot + :param color: Name of the color for the plot. + :type in_data: OrderedDict + :type period: int + :type moving_win_size: int + :type fill_missing: bool + :type use_first: bool + :type show_moving_median: bool + :type name: str + :type color: str + :returns: Generated traces (list) and the evaluated result (float). + :rtype: tuple(traces, result) + """ + + if period > 1: + in_data = _select_data(in_data, period, + fill_missing=fill_missing, + use_first=use_first) + + 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) + + 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=[item[0], ]) + if item[0] in outliers.keys(): + anomalies = anomalies.append(item_pd) + anomalies_res.append(0.0) + elif results[idx] in (0.33, 1.0): + anomalies = anomalies.append(item_pd) + anomalies_res.append(results[idx]) + anomalies_res.extend([0.0, 0.33, 0.66, 1.0]) + + # Create traces + color_scale = [[0.00, "grey"], + [0.25, "grey"], + [0.25, "red"], + [0.50, "red"], + [0.50, "white"], + [0.75, "white"], + [0.75, "green"], + [1.00, "green"]] + + trace_samples = plgo.Scatter( + x=data_x, + y=data_y, + mode='markers', + line={ + "width": 1 + }, + name="{name}-thput".format(name=name), + marker={ + "size": 5, + "color": color, + "symbol": "circle", + }, + ) + traces = [trace_samples, ] + + trace_anomalies = plgo.Scatter( + x=anomalies.keys(), + y=anomalies.values, + mode='markers', + hoverinfo="none", + showlegend=False, + legendgroup=name, + name="{name}: outliers".format(name=name), + marker={ + "size": 15, + "symbol": "circle-open", + "color": anomalies_res, + "colorscale": color_scale, + "showscale": True, + + "colorbar": { + "y": 0.5, + "len": 0.8, + "title": "Results Clasification", + "titleside": 'right', + "titlefont": { + "size": 14 + }, + "tickmode": 'array', + "tickvals": [0.125, 0.375, 0.625, 0.875], + "ticktext": ["Outlier", "Regress", "Normal", "Progress"], + "ticks": 'outside', + "ticklen": 0, + "tickangle": -90, + "thickness": 10 + } + } + ) + traces.append(trace_anomalies) + + if show_moving_median: + data_mean_y = pd.Series(data_y).rolling( + window=moving_win_size).median() + trace_median = plgo.Scatter( + x=data_x, + y=data_mean_y, + mode='lines', + line={ + "shape": "spline", + "width": 1, + "color": color, + }, + name='{name}-trend'.format(name=name, size=moving_win_size) + ) + traces.append(trace_median) + + return traces, results[-1] + + +def _generate_chart(traces, layout, file_name): + """Generates the whole chart using pre-generated traces. + + :param traces: Traces for the chart. + :param layout: Layout of the chart. + :param file_name: File name for the generated chart. + :type traces: list + :type layout: dict + :type file_name: str + """ + + # Create plot + logging.info(" Writing the file '{0}' ...".format(file_name)) + plpl = plgo.Figure(data=traces, layout=layout) + ploff.plot(plpl, show_link=False, auto_open=False, filename=file_name) + + +def _generate_all_charts(spec, input_data): + """Generate all charts specified in the specification file. + + :param spec: Specification. + :param input_data: Full data set. + :type spec: Specification + :type input_data: InputData + """ + + results = list() + for chart in spec.cpta["plots"]: + logging.info(" Generating the chart '{0}' ...". + format(chart.get("title", ""))) + + # Transform the data + data = input_data.filter_data(chart, continue_on_error=True) + if data is None: + logging.error("No data.") + return + + 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() + try: + chart_data[test["name"]][int(idx)] = \ + test["result"]["throughput"] + except (KeyError, TypeError): + chart_data[test["name"]][int(idx)] = None + + for period in chart["periods"]: + # Generate traces: + traces = list() + win_size = 10 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 + trace, result = _generate_trending_traces( + test_data, + period=period, + moving_win_size=win_size, + fill_missing=True, + use_first=False, + name='-'.join(test_name.split('-')[3:-1]), + color=COLORS[idx]) + traces.extend(trace) + results.append(result) + 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) + _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.") + + result = "PASS" + for item in results: + if item is None: + result = "FAIL" + break + if item == 0.66 and result == "PASS": + result = "PASS" + elif item == 0.33 or item == 0.0: + result = "FAIL" + print(results) + print(result) + if result == "FAIL": + return 1 + else: + return 0 |