from string import replace
from math import isnan
+from numpy import nan
from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, find_outliers
+from utils import mean, stdev, relative_change, remove_outliers, split_outliers
def generate_tables(spec, data):
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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])
if item[1] is not None and item[3] is not None:
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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])
if item[1] is not None and item[3] is not None and item[1] != 0:
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
- header = ["Test case",
- "Throughput Trend [Mpps]",
- "Trend Compliance",
- "Anomaly Value [Mpps]",
- "Change [%]",
- "#Outliers"
+ header = ["Test Case",
+ "Trend [Mpps]",
+ "Short-Term Change [%]",
+ "Long-Term Change [%]",
+ "Regressions [#]",
+ "Progressions [#]",
+ "Outliers [#]"
]
header_str = ",".join(header) + "\n"
if len(tbl_dict[tst_name]["data"]) > 2:
pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
+ last_key = pd_data.keys()[-1]
+ win_size = min(pd_data.size, table["window"])
+ key_14 = pd_data.keys()[-(pd_data.size - win_size)]
+ long_win_size = min(pd_data.size, table["long-trend-window"])
+
+ data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=win_size)
+
+ 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
+ try:
+ max_median = max([x for x in median_t.values[median_idx:]
+ 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
+
# Test name:
name = tbl_dict[tst_name]["name"]
- median = pd_data.rolling(window=win_size, min_periods=2).median()
- trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
- stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
-
- rel_change_lst = [None, ]
- classification_lst = [None, ]
- median_lst = [None, ]
- sample_lst = [None, ]
- first = True
+ # Classification list:
+ classification_lst = list()
for build_nr, value in pd_data.iteritems():
- if first:
- first = False
- continue
- # Relative changes list:
- if not isnan(value) \
- and not isnan(median[build_nr]) \
- and median[build_nr] != 0:
- rel_change_lst.append(
- int(relative_change(float(median[build_nr]),
- float(value))))
- else:
- rel_change_lst.append(None)
- # Classification list:
- if isnan(trimmed_data[build_nr]) \
- or isnan(median[build_nr]) \
+ if isnan(data_t[build_nr]) \
+ or isnan(median_t[build_nr]) \
or isnan(stdev_t[build_nr]) \
or isnan(value):
classification_lst.append("outlier")
- elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
+ elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")
- sample_lst.append(value)
- median_lst.append(median[build_nr])
-
- last_idx = len(classification_lst) - 1
- first_idx = last_idx - int(table["evaluated-window"])
- if first_idx < 0:
- first_idx = 0
-
- if "regression" in classification_lst[first_idx:]:
- classification = "regression"
- elif "outlier" in classification_lst[first_idx:]:
- classification = "outlier"
- elif "progression" in classification_lst[first_idx:]:
- classification = "progression"
- elif "normal" in classification_lst[first_idx:]:
- classification = "normal"
+
+ if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0:
+ rel_change_last = nan
else:
- classification = None
-
- nr_outliers = 0
- consecutive_outliers = 0
- failure = False
- for item in classification_lst[first_idx:]:
- if item == "outlier":
- nr_outliers += 1
- consecutive_outliers += 1
- if consecutive_outliers == 3:
- failure = True
- else:
- consecutive_outliers = 0
-
- idx = len(classification_lst) - 1
- while idx:
- if classification_lst[idx] == classification:
- break
- idx -= 1
-
- if failure:
- classification = "failure"
- elif classification == "outlier":
- classification = "normal"
-
- trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else ''
- sample = round(float(sample_lst[idx]) / 1000000, 2) \
- if not isnan(sample_lst[idx]) else ''
- rel_change = rel_change_lst[idx] \
- if rel_change_lst[idx] is not None else ''
+ rel_change_last = round(
+ (last_median_t - median_t_14) / median_t_14, 2)
+
+ if isnan(max_median) or isnan(last_median_t) or max_median == 0:
+ rel_change_long = nan
+ else:
+ rel_change_long = round(
+ (last_median_t - max_median) / max_median, 2)
+
tbl_lst.append([name,
- trend,
- classification,
- '-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
- nr_outliers])
+ '-' 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.sort(key=lambda rel: rel[0])
- # Sort the table according to the classification
tbl_sorted = list()
- for classification in ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[2] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
+ 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_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_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
# Table header:
- tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
+ 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))
for c_idx, item in enumerate(row):
alignment = "left" if c_idx == 0 else "center"
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
- if c_idx == 2:
- if item == "regression":
- td.set("bgcolor", "#eca1a6")
- elif item == "failure":
- td.set("bgcolor", "#d6cbd3")
- elif item == "progression":
- td.set("bgcolor", "#bdcebe")
- td.text = item
+ # Name:
+ url = "../trending/"
+ file_name = ""
+ anchor = "#"
+ feature = ""
+ if c_idx == 0:
+ if "memif" in item:
+ file_name = "container_memif.html"
+
+ elif "vhost" in item:
+ if "l2xcbase" in item or "l2bdbasemaclrn" in item:
+ file_name = "vm_vhost_l2.html"
+ elif "ip4base" in item:
+ file_name = "vm_vhost_ip4.html"
+
+ elif "ipsec" in item:
+ file_name = "ipsec.html"
+
+ elif "ethip4lispip" in item or "ethip4vxlan" in item:
+ file_name = "ip4_tunnels.html"
+
+ elif "ip4base" in item or "ip4scale" in item:
+ file_name = "ip4.html"
+ if "iacl" in item or "snat" in item or "cop" in item:
+ feature = "-features"
+
+ elif "ip6base" in item or "ip6scale" in item:
+ file_name = "ip6.html"
+
+ elif "l2xcbase" in item or "l2xcscale" in item \
+ or "l2bdbasemaclrn" in item or "l2bdscale" in item \
+ or "l2dbbasemaclrn" in item or "l2dbscale" in item:
+ file_name = "l2.html"
+ if "iacl" in item:
+ feature = "-features"
+
+ if "x520" in item:
+ anchor += "x520-"
+ elif "x710" in item:
+ anchor += "x710-"
+ elif "xl710" in item:
+ anchor += "xl710-"
+
+ if "64b" in item:
+ anchor += "64b-"
+ elif "78b" in item:
+ anchor += "78b"
+ elif "imix" in item:
+ anchor += "imix-"
+ elif "9000b" in item:
+ anchor += "9000b-"
+ elif "1518" in item:
+ anchor += "1518b-"
+
+ if "1t1c" in item:
+ anchor += "1t1c"
+ elif "2t2c" in item:
+ anchor += "2t2c"
+ elif "4t4c" in item:
+ anchor += "4t4c"
+
+ url = url + file_name + anchor + feature
+
+ ref = ET.SubElement(td, "a", attrib=dict(href=url))
+ ref.text = item
+
+ if c_idx > 0:
+ td.text = item
try:
with open(table["output-file"], 'w') as html_file: