item.extend([None, None])
else:
item.extend([None, None])
- if tbl_dict[tst_name]["ref-data"]:
- data_t = tbl_dict[tst_name]["ref-data"]
- # 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])
+ data_t = tbl_dict[tst_name]["ref-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = tbl_dict[tst_name]["cmp-data"]
- # 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])
+ data_t = tbl_dict[tst_name]["cmp-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[-4] is not None and item[-2] is not None and item[-4] != 0:
tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = tbl_dict[tst_name]["ref-data"]
- # 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])
+ data_t = tbl_dict[tst_name]["ref-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = tbl_dict[tst_name]["cmp-data"]
- # 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])
+ data_t = tbl_dict[tst_name]["cmp-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None and item[1] != 0:
def table_performance_trending_dashboard(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ """Generate the table(s) with algorithm:
+ table_performance_trending_dashboard
specified in the specification file.
:param table: Table to generate.
"Short-Term Change [%]",
"Long-Term Change [%]",
"Regressions [#]",
- "Progressions [#]",
- "Outliers [#]"
+ "Progressions [#]"
]
header_str = ",".join(header) + "\n"
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) < 3:
+ if len(tbl_dict[tst_name]["data"]) < 2:
continue
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- last_key = pd_data.keys()[-1]
- win_size = min(pd_data.size, table["window"])
- 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"])
- median_t = pd_data.rolling(window=win_size, min_periods=2).median()
- median_first_idx = median_t.size - long_win_size
+ 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_median = max(
- [x for x in median_t.values[median_first_idx:-win_size]
+ max_long_avg = max(
+ [x for x in avgs[-long_win_size:-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
+ max_long_avg = nan
+ last_avg = avgs[-1]
+ avg_week_ago = avgs[max(-win_size, -len(avgs))]
- if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+ 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_median_t - median_t_14) / median_t_14) * 100, 2)
+ ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
- if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+ 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_median_t - max_median) / max_median) * 100, 2)
-
- # Classification list:
- classification_lst, _ = classify_anomalies(pd_data)
+ ((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_median_t) else
- round(last_median_t / 1000000, 2),
+ '-' 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_first_idx:].count("regression"),
- classification_lst[win_first_idx:].count("progression"),
- classification_lst[win_first_idx:].count("outlier")])
+ classification_lst[-win_size:].count("regression"),
+ classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
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[6] == nro]
- tbl_out.sort(key=lambda rel: rel[2])
- tbl_sorted.extend(tbl_out)
+ 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))
+ 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:
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:
# Rows:
colors = {"regression": ("#ffcccc", "#ff9999"),
"progression": ("#c6ecc6", "#9fdf9f"),
- "outlier": ("#e6e6e6", "#cccccc"),
"normal": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
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]
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:
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<p><br><br></p>\n")
Code Review / csit.git / blobdiff