+ if len(tbl_dict[tst_name]["data"]) < 2:
+ continue
+
+ data_t = pd.Series(tbl_dict[tst_name]["data"])
+
+ classification_lst, avgs = classify_anomalies(data_t)
+
+ win_size = min(data_t.size, table["window"])
+ long_win_size = min(data_t.size, table["long-trend-window"])
+ try:
+ max_long_avg = max(
+ [x for x in avgs[-long_win_size:-win_size]
+ if not isnan(x)])
+ except ValueError:
+ max_long_avg = nan
+ last_avg = avgs[-1]
+ avg_week_ago = avgs[max(-win_size, -len(avgs))]
+
+ if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
+ rel_change_last = nan
+ else:
+ rel_change_last = round(
+ ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
+
+ if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
+ rel_change_long = nan
+ else:
+ rel_change_long = round(
+ ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
+
+ 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_avg) else
+ round(last_avg / 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")])
+
+ tbl_lst.sort(key=lambda rel: rel[0])
+
+ tbl_sorted = list()
+ for nrr in range(table["window"], -1, -1):
+ tbl_reg = [item for item in tbl_lst if item[4] == nrr]
+ for nrp in range(table["window"], -1, -1):
+ tbl_out = [item for item in tbl_reg if item[5] == nrp]
+ 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"])
+
+ 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:
+ file_handler.write(",".join([str(item) for item in test]) + '\n')
+
+ txt_file_name = "{0}.txt".format(table["output-file"])
+ logging.info(" Writing file: '{0}'".format(txt_file_name))
+ convert_csv_to_pretty_txt(file_name, txt_file_name)
+
+
+def _generate_url(base, test_name):
+ """Generate URL to a trending plot from the name of the test case.
+
+ :param base: The base part of URL common to all test cases.
+ :param test_name: The name of the test case.
+ :type base: str
+ :type test_name: str
+ :returns: The URL to the plot with the trending data for the given test
+ case.
+ :rtype str
+ """
+
+ url = base
+ file_name = ""
+ anchor = "#"
+ feature = ""
+
+ if "lbdpdk" in test_name or "lbvpp" in test_name:
+ file_name = "link_bonding.html"
+
+ elif "testpmd" in test_name or "l3fwd" in test_name:
+ file_name = "dpdk.html"
+
+ elif "memif" in test_name:
+ file_name = "container_memif.html"
+
+ elif "srv6" in test_name:
+ file_name = "srv6.html"
+
+ elif "vhost" in test_name:
+ if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
+ file_name = "vm_vhost_l2.html"
+ elif "ip4base" in test_name:
+ file_name = "vm_vhost_ip4.html"
+
+ elif "ipsec" in test_name:
+ file_name = "ipsec.html"
+
+ elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
+ file_name = "ip4_tunnels.html"
+
+ elif "ip4base" in test_name or "ip4scale" in test_name:
+ file_name = "ip4.html"
+ if "iacl" in test_name or "snat" in test_name or "cop" in test_name:
+ feature = "-features"
+
+ elif "ip6base" in test_name or "ip6scale" in test_name:
+ file_name = "ip6.html"
+
+ elif "l2xcbase" in test_name or "l2xcscale" in test_name \
+ or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
+ or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
+ file_name = "l2.html"
+ if "iacl" in test_name:
+ feature = "-features"
+
+ if "x520" in test_name:
+ anchor += "x520-"
+ elif "x710" in test_name:
+ anchor += "x710-"
+ elif "xl710" in test_name:
+ anchor += "xl710-"
+
+ if "64b" in test_name:
+ anchor += "64b-"
+ elif "78b" in test_name:
+ anchor += "78b-"
+ elif "imix" in test_name:
+ anchor += "imix-"
+ elif "9000b" in test_name:
+ anchor += "9000b-"
+ elif "1518" in test_name:
+ anchor += "1518b-"
+
+ if "1t1c" in test_name:
+ anchor += "1t1c"
+ elif "2t2c" in test_name:
+ anchor += "2t2c"
+ elif "4t4c" in test_name:
+ anchor += "4t4c"
+
+ return url + file_name + anchor + feature
+
+
+def table_performance_trending_dashboard_html(table, input_data):
+ """Generate the table(s) with algorithm:
+ table_performance_trending_dashboard_html specified in the specification
+ file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ try:
+ with open(table["input-file"], 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ csv_lst = [item for item in csv_content]
+ except KeyError:
+ logging.warning("The input file is not defined.")
+ return
+ except csv.Error as err:
+ logging.warning("Not possible to process the file '{0}'.\n{1}".
+ format(table["input-file"], err))
+ return
+
+ # Table:
+ dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
+
+ # Table header:
+ tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
+ for idx, item in enumerate(csv_lst[0]):
+ alignment = "left" if idx == 0 else "center"
+ th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
+ th.text = item
+
+ # Rows:
+ colors = {"regression": ("#ffcccc", "#ff9999"),
+ "progression": ("#c6ecc6", "#9fdf9f"),
+ "normal": ("#e9f1fb", "#d4e4f7")}
+ for r_idx, row in enumerate(csv_lst[1:]):
+ if int(row[4]):
+ color = "regression"
+ elif int(row[5]):
+ color = "progression"
+ else:
+ color = "normal"
+ background = colors[color][r_idx % 2]
+ tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
+
+ # Columns:
+ for c_idx, item in enumerate(row):
+ alignment = "left" if c_idx == 0 else "center"
+ td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
+ # Name:
+ if c_idx == 0:
+ url = _generate_url("../trending/", item)
+ ref = ET.SubElement(td, "a", attrib=dict(href=url))
+ ref.text = item