X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;ds=sidebyside;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=abece8590b772d5a0b08edf0268d54f252eb51e4;hb=a88deeabe7dff7b6e1cd357609c0bc586343aa18;hp=5246952e20457e87bcf917451369ef6576573e00;hpb=6f5de201aadfbb31419c05dfae6495107a745899;p=csit.git diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 5246952e20..abece8590b 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -26,7 +26,8 @@ from numpy import nan, isnan from xml.etree import ElementTree as ET from errors import PresentationError -from utils import mean, stdev, relative_change, remove_outliers, split_outliers +from utils import mean, stdev, relative_change, remove_outliers,\ + split_outliers, classify_anomalies def generate_tables(spec, data): @@ -42,9 +43,9 @@ def generate_tables(spec, data): for table in spec.tables: try: eval(table["algorithm"])(table, data) - except NameError: - logging.error("The algorithm '{0}' is not defined.". - format(table["algorithm"])) + except NameError as err: + logging.error("Probably algorithm '{alg}' is not defined: {err}". + format(alg=table["algorithm"], err=repr(err))) logging.info("Done.") @@ -774,60 +775,52 @@ def table_performance_trending_dashboard(table, input_data): tbl_lst = list() for tst_name in tbl_dict.keys(): - if len(tbl_dict[tst_name]["data"]) > 2: - - pd_data = pd.Series(tbl_dict[tst_name]["data"]) - data_t, _ = split_outliers(pd_data, outlier_const=1.5, - window=table["window"]) - last_key = data_t.keys()[-1] - win_size = min(data_t.size, table["window"]) - win_first_idx = data_t.size - win_size - key_14 = data_t.keys()[win_first_idx] - long_win_size = min(data_t.size, table["long-trend-window"]) - 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_first_idx = median_t.size - long_win_size - try: - max_median = max( - [x for x in median_t.values[median_first_idx:-win_size] - if not isnan(x)]) - except ValueError: - max_median = nan - try: - last_median_t = median_t[last_key] - except KeyError: - last_median_t = nan - try: - median_t_14 = median_t[key_14] - except KeyError: - median_t_14 = nan - - # Classification list: - classification_lst = list() - for build_nr, value in data_t.iteritems(): - if isnan(median_t[build_nr]) \ - or isnan(stdev_t[build_nr]) \ - or isnan(value): - classification_lst.append("outlier") - elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]): - classification_lst.append("regression") - elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]): - classification_lst.append("progression") - else: - classification_lst.append("normal") + if len(tbl_dict[tst_name]["data"]) < 3: + continue + + pd_data = pd.Series(tbl_dict[tst_name]["data"]) + data_t, _ = split_outliers(pd_data, outlier_const=1.5, + window=table["window"]) + last_key = data_t.keys()[-1] + win_size = min(data_t.size, table["window"]) + win_first_idx = data_t.size - win_size + key_14 = data_t.keys()[win_first_idx] + long_win_size = min(data_t.size, table["long-trend-window"]) + median_t = data_t.rolling(window=win_size, min_periods=2).median() + median_first_idx = median_t.size - long_win_size + try: + max_median = max( + [x for x in median_t.values[median_first_idx:-win_size] + if not isnan(x)]) + except ValueError: + max_median = nan + try: + last_median_t = median_t[last_key] + except KeyError: + last_median_t = nan + try: + median_t_14 = median_t[key_14] + except KeyError: + median_t_14 = nan - 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) * 100, 2) + 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) * 100, 2) - 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) * 100, 2) + 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) * 100, 2) + + # Classification list: + classification_lst = classify_anomalies(data_t, window=14) + if classification_lst: + if isnan(rel_change_last) and isnan(rel_change_long): + continue tbl_lst.append( [tbl_dict[tst_name]["name"], '-' if isnan(last_median_t) else @@ -852,7 +845,7 @@ def table_performance_trending_dashboard(table, input_data): file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) - logging.info(" Writing file: '{0}'".format(file_name)) + logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) for test in tbl_sorted: @@ -860,7 +853,7 @@ def table_performance_trending_dashboard(table, input_data): txt_file_name = "{0}.txt".format(table["output-file"]) txt_table = None - logging.info(" Writing file: '{0}'".format(txt_file_name)) + logging.info(" Writing file: '{0}'".format(txt_file_name)) with open(file_name, 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') for row in csv_content: @@ -936,9 +929,18 @@ def table_performance_trending_dashboard_html(table, input_data): anchor = "#" feature = "" if c_idx == 0: - if "memif" in item: + if "lbdpdk" in item or "lbvpp" in item: + file_name = "link_bonding.html" + + elif "testpmd" in item or "l3fwd" in item: + file_name = "dpdk.html" + + elif "memif" in item: file_name = "container_memif.html" + elif "srv6" in item: + file_name = "srv6.html" + elif "vhost" in item: if "l2xcbase" in item or "l2bdbasemaclrn" in item: file_name = "vm_vhost_l2.html" @@ -976,7 +978,7 @@ def table_performance_trending_dashboard_html(table, input_data): if "64b" in item: anchor += "64b-" elif "78b" in item: - anchor += "78b" + anchor += "78b-" elif "imix" in item: anchor += "imix-" elif "9000b" in item: @@ -996,13 +998,12 @@ def table_performance_trending_dashboard_html(table, input_data): ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item - if c_idx > 0: + else: td.text = item try: with open(table["output-file"], 'w') as html_file: - logging.info(" Writing file: '{0}'". - format(table["output-file"])) + logging.info(" Writing file: '{0}'".format(table["output-file"])) html_file.write(".. raw:: html\n\n\t") html_file.write(ET.tostring(dashboard)) html_file.write("\n\t



\n")