X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=0b92db6991d78bf7f5ed2b09dc0a490065cbed53;hp=c2007a1a494bed37f95dc5717affdaf202026f30;hb=06027cf4a33ff75146cbf5e91b029dc801158797;hpb=6e1a15acc7bd3685d71f9d34238181c681dcd4c0 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index c2007a1a49..0b92db6991 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -22,6 +22,7 @@ import pandas as pd from string import replace from math import isnan +from collections import OrderedDict from numpy import nan from xml.etree import ElementTree as ET @@ -360,12 +361,20 @@ def table_performance_comparison(table, input_data): # Prepare the header of the tables try: - header = ["Test case", - "{0} Throughput [Mpps]".format(table["reference"]["title"]), - "{0} stdev [Mpps]".format(table["reference"]["title"]), - "{0} Throughput [Mpps]".format(table["compare"]["title"]), - "{0} stdev [Mpps]".format(table["compare"]["title"]), - "Change [%]"] + header = ["Test case", ] + + history = table.get("history", None) + if history: + for item in history: + header.extend( + ["{0} Throughput [Mpps]".format(item["title"]), + "{0} Stdev [Mpps]".format(item["title"])]) + header.extend( + ["{0} Throughput [Mpps]".format(table["reference"]["title"]), + "{0} Stdev [Mpps]".format(table["reference"]["title"]), + "{0} Throughput [Mpps]".format(table["compare"]["title"]), + "{0} Stdev [Mpps]".format(table["compare"]["title"]), + "Change [%]"]) header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". @@ -400,10 +409,43 @@ def table_performance_comparison(table, input_data): pass except TypeError: tbl_dict.pop(tst_name, None) + if history: + for item in history: + for job, builds in item["data"].items(): + for build in builds: + for tst_name, tst_data in data[job][str(build)].iteritems(): + if tbl_dict.get(tst_name, None) is None: + continue + if tbl_dict[tst_name].get("history", None) is None: + tbl_dict[tst_name]["history"] = OrderedDict() + if tbl_dict[tst_name]["history"].get(item["title"], + None) is None: + tbl_dict[tst_name]["history"][item["title"]] = \ + list() + try: + tbl_dict[tst_name]["history"][item["title"]].\ + append(tst_data["throughput"]["value"]) + except (TypeError, KeyError): + pass tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] + if history: + if tbl_dict[tst_name].get("history", None) is not None: + for hist_data in tbl_dict[tst_name]["history"].values(): + if hist_data: + data_t = remove_outliers( + hist_data, outlier_const=table["outlier-const"]) + if data_t: + item.append(round(mean(data_t) / 1000000, 2)) + item.append(round(stdev(data_t) / 1000000, 2)) + else: + item.extend([None, None]) + else: + item.extend([None, None]) + else: + item.extend([None, None]) if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], outlier_const=table["outlier-const"]) @@ -426,9 +468,9 @@ def table_performance_comparison(table, input_data): item.extend([None, None]) else: item.extend([None, None]) - if item[1] is not None and item[3] is not None: - item.append(int(relative_change(float(item[1]), float(item[3])))) - if len(item) == 6: + if item[-4] is not None and item[-2] is not None and item[-4] != 0: + item.append(int(relative_change(float(item[-4]), float(item[-2])))) + if len(item) == len(header): tbl_lst.append(item) # Sort the table according to the relative change @@ -722,7 +764,8 @@ def table_performance_trending_dashboard(table, input_data): pd_data = pd.Series(tbl_dict[tst_name]["data"]) last_key = pd_data.keys()[-1] win_size = min(pd_data.size, table["window"]) - key_14 = pd_data.keys()[-(pd_data.size - win_size)] + win_first_idx = pd_data.size - win_size + key_14 = pd_data.keys()[win_first_idx] long_win_size = min(pd_data.size, table["long-trend-window"]) data_t, _ = split_outliers(pd_data, outlier_const=1.5, @@ -730,9 +773,9 @@ def table_performance_trending_dashboard(table, input_data): median_t = data_t.rolling(window=win_size, min_periods=2).median() stdev_t = data_t.rolling(window=win_size, min_periods=2).std() - median_idx = pd_data.size - long_win_size + median_first_idx = pd_data.size - long_win_size try: - max_median = max([x for x in median_t.values[median_idx:] + max_median = max([x for x in median_t.values[median_first_idx:] if not isnan(x)]) except ValueError: max_median = nan @@ -764,26 +807,30 @@ def table_performance_trending_dashboard(table, input_data): else: classification_lst.append("normal") - if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0: + if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: rel_change_last = nan else: rel_change_last = round( - (last_median_t - median_t_14) / median_t_14, 2) + ((last_median_t - median_t_14) / median_t_14) * 100, 2) - if isnan(max_median) or isnan(last_median_t) or max_median == 0: + if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: rel_change_long = nan else: rel_change_long = round( - (last_median_t - max_median) / max_median, 2) + ((last_median_t - max_median) / max_median) * 100, 2) + + logging.info("rel_change_last : {}".format(rel_change_last)) + logging.info("rel_change_long : {}".format(rel_change_long)) - tbl_lst.append([name, - '-' if isnan(last_median_t) else - round(last_median_t / 1000000, 2), - '-' if isnan(rel_change_last) else rel_change_last, - '-' if isnan(rel_change_long) else rel_change_long, - classification_lst[win_size:].count("regression"), - classification_lst[win_size:].count("progression"), - classification_lst[win_size:].count("outlier")]) + tbl_lst.append( + [name, + '-' if isnan(last_median_t) else + round(last_median_t / 1000000, 2), + '-' if isnan(rel_change_last) else rel_change_last, + '-' if isnan(rel_change_long) else rel_change_long, + classification_lst[win_first_idx:].count("regression"), + classification_lst[win_first_idx:].count("progression"), + classification_lst[win_first_idx:].count("outlier")]) tbl_lst.sort(key=lambda rel: rel[0]) @@ -793,7 +840,8 @@ def table_performance_trending_dashboard(table, input_data): for nrp in range(table["window"], -1, -1): tbl_pro = [item for item in tbl_reg if item[5] == nrp] for nro in range(table["window"], -1, -1): - tbl_out = [item for item in tbl_pro if item[5] == nro] + tbl_out = [item for item in tbl_pro if item[6] == nro] + tbl_out.sort(key=lambda rel: rel[2]) tbl_sorted.extend(tbl_out) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) @@ -856,8 +904,20 @@ def table_performance_trending_dashboard_html(table, input_data): th.text = item # Rows: + colors = {"regression": ("#ffcccc", "#ff9999"), + "progression": ("#c6ecc6", "#9fdf9f"), + "outlier": ("#e6e6e6", "#cccccc"), + "normal": ("#e9f1fb", "#d4e4f7")} for r_idx, row in enumerate(csv_lst[1:]): - background = "#D4E4F7" if r_idx % 2 else "white" + if int(row[4]): + color = "regression" + elif int(row[5]): + color = "progression" + elif int(row[6]): + color = "outlier" + else: + color = "normal" + background = colors[color][r_idx % 2] tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) # Columns: