X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=40eda7b8d96302c88bbb26602124d0900a3c4edc;hp=92f0ceed450c8d5a38611a290cf315c603116de1;hb=c298d66734d2d40e343ac4c60703b9838bdd6301;hpb=52cb667958d954d6233d0865a59d90cca82db026
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 92f0ceed45..40eda7b8d9 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2018 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
@@ -17,17 +17,16 @@
import logging
import csv
-import prettytable
import pandas as pd
from string import replace
-from math import isnan
from collections import OrderedDict
-from numpy import nan
+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, classify_anomalies, \
+ convert_csv_to_pretty_txt
def generate_tables(spec, data):
@@ -43,9 +42,9 @@ 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.")
@@ -63,6 +62,8 @@ def table_details(table, input_data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
@@ -129,10 +130,14 @@ def table_merged_details(table, input_data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
data = input_data.merge_data(data)
data.sort_index(inplace=True)
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
suites = input_data.filter_data(table, data_set="suites")
suites = input_data.merge_data(suites)
@@ -226,6 +231,8 @@ def table_performance_improvements(table, input_data):
return None
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
@@ -357,6 +364,8 @@ def table_performance_comparison(table, input_data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
@@ -432,41 +441,29 @@ def table_performance_comparison(table, input_data):
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if history:
- 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))
+ if tbl_dict[tst_name].get("history", None) is not None:
+ for hist_data in tbl_dict[tst_name]["history"].values():
+ if hist_data:
+ item.append(round(mean(hist_data) / 1000000, 2))
+ item.append(round(stdev(hist_data) / 1000000, 2))
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"])
- # 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 = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- 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])
+ 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[-5] is not None and item[-3] is not None and item[-5] != 0:
- item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+ 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)
@@ -509,18 +506,8 @@ def table_performance_comparison(table, input_data):
]
for i, txt_name in enumerate(tbl_names_txt):
- txt_table = None
logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
+ convert_csv_to_pretty_txt(tbl_names[i], txt_name)
# Selected tests in csv:
input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
@@ -592,6 +579,8 @@ def table_performance_comparison_mrr(table, input_data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
@@ -640,26 +629,16 @@ def table_performance_comparison_mrr(table, input_data):
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 = remove_outliers(tbl_dict[tst_name]["ref-data"],
- 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])
+ 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 = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- 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])
+ 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:
@@ -696,22 +675,13 @@ def table_performance_comparison_mrr(table, input_data):
]
for i, txt_name in enumerate(tbl_names_txt):
- txt_table = None
logging.info(" Writing file: '{0}'".format(txt_name))
- with open(tbl_names[i], 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_name, "w") as txt_file:
- txt_file.write(str(txt_table))
+ convert_csv_to_pretty_txt(tbl_names[i], txt_name)
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.
@@ -724,6 +694,8 @@ def table_performance_trending_dashboard(table, input_data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
@@ -732,8 +704,7 @@ def table_performance_trending_dashboard(table, input_data):
"Short-Term Change [%]",
"Long-Term Change [%]",
"Regressions [#]",
- "Progressions [#]",
- "Outliers [#]"
+ "Progressions [#]"
]
header_str = ",".join(header) + "\n"
@@ -742,12 +713,14 @@ def table_performance_trending_dashboard(table, input_data):
for job, builds in table["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tst_name.lower() in table["ignore-list"]:
+ continue
if tbl_dict.get(tst_name, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
"-".join(tst_data["name"].
split("-")[1:]))
tbl_dict[tst_name] = {"name": name,
- "data": dict()}
+ "data": OrderedDict()}
try:
tbl_dict[tst_name]["data"][str(build)] = \
tst_data["result"]["throughput"]
@@ -756,86 +729,47 @@ 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"])
- 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"])
-
- 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_first_idx = pd_data.size - long_win_size
- try:
- max_median = max([x for x in median_t.values[median_first_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"]
-
- logging.info("{}".format(name))
- logging.info("pd_data : {}".format(pd_data))
- logging.info("data_t : {}".format(data_t))
- logging.info("median_t : {}".format(median_t))
- logging.info("last_median_t : {}".format(last_median_t))
- logging.info("median_t_14 : {}".format(median_t_14))
- logging.info("max_median : {}".format(max_median))
-
- # Classification list:
- classification_lst = list()
- for build_nr, value in pd_data.iteritems():
-
- 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_t[build_nr] - 2 * stdev_t[build_nr]):
- classification_lst.append("regression")
- elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
- classification_lst.append("progression")
- else:
- classification_lst.append("normal")
+ if len(tbl_dict[tst_name]["data"]) < 2:
+ continue
- 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)
+ data_t = pd.Series(tbl_dict[tst_name]["data"])
- 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_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)
- logging.info("rel_change_last : {}".format(rel_change_last))
- logging.info("rel_change_long : {}".format(rel_change_long))
+ 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(
- [name,
- '-' if isnan(last_median_t) else
- round(last_median_t / 1000000, 2),
+ [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_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])
@@ -843,33 +777,105 @@ def table_performance_trending_dashboard(table, input_data):
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:
file_handler.write(",".join([str(item) for item in test]) + '\n')
txt_file_name = "{0}.txt".format(table["output-file"])
- txt_table = None
- 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:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- txt_table.add_row(row)
- txt_table.align["Test case"] = "l"
- with open(txt_file_name, "w") as txt_file:
- txt_file.write(str(txt_table))
+ 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):
@@ -909,8 +915,17 @@ def table_performance_trending_dashboard_html(table, input_data):
th.text = item
# Rows:
+ colors = {"regression": ("#ffcccc", "#ff9999"),
+ "progression": ("#c6ecc6", "#9fdf9f"),
+ "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"
+ else:
+ color = "normal"
+ background = colors[color][r_idx % 2]
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
# Columns:
@@ -918,81 +933,165 @@ def table_performance_trending_dashboard_html(table, input_data):
alignment = "left" if c_idx == 0 else "center"
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
# 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
-
+ url = _generate_url("../trending/", 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
\n")
except KeyError:
logging.warning("The output file is not defined.")
return
+
+
+def table_failed_tests(table, input_data):
+ """Generate the table(s) with algorithm: table_failed_tests
+ 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", "")))
+
+ # Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
+ data = input_data.filter_data(table, continue_on_error=True)
+
+ # Prepare the header of the tables
+ header = ["Test Case",
+ "Failures [#]",
+ "Last Failure [Time]",
+ "Last Failure [VPP-Build-Id]",
+ "Last Failure [CSIT-Job-Build-Id]"]
+
+ # Generate the data for the table according to the model in the table
+ # specification
+ tbl_dict = dict()
+ for job, builds in table["data"].items():
+ for build in builds:
+ build = str(build)
+ for tst_name, tst_data in data[job][build].iteritems():
+ if tst_name.lower() in table["ignore-list"]:
+ continue
+ if tbl_dict.get(tst_name, None) is None:
+ name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+ "-".join(tst_data["name"].
+ split("-")[1:]))
+ tbl_dict[tst_name] = {"name": name,
+ "data": OrderedDict()}
+ try:
+ tbl_dict[tst_name]["data"][build] = (
+ tst_data["status"],
+ input_data.metadata(job, build).get("generated", ""),
+ input_data.metadata(job, build).get("version", ""),
+ build)
+ except (TypeError, KeyError):
+ pass # No data in output.xml for this test
+
+ tbl_lst = list()
+ for tst_data in tbl_dict.values():
+ win_size = min(len(tst_data["data"]), table["window"])
+ fails_nr = 0
+ for val in tst_data["data"].values()[-win_size:]:
+ if val[0] == "FAIL":
+ fails_nr += 1
+ fails_last_date = val[1]
+ fails_last_vpp = val[2]
+ fails_last_csit = val[3]
+ if fails_nr:
+ tbl_lst.append([tst_data["name"],
+ fails_nr,
+ fails_last_date,
+ fails_last_vpp,
+ "mrr-daily-build-{0}".format(fails_last_csit)])
+
+ tbl_lst.sort(key=lambda rel: rel[2], reverse=True)
+ tbl_sorted = list()
+ for nrf in range(table["window"], -1, -1):
+ tbl_fails = [item for item in tbl_lst if item[1] == nrf]
+ tbl_sorted.extend(tbl_fails)
+ 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(",".join(header) + "\n")
+ 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 table_failed_tests_html(table, input_data):
+ """Generate the table(s) with algorithm: table_failed_tests_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:
+ failed_tests = ET.Element("table", attrib=dict(width="100%", border='0'))
+
+ # Table header:
+ tr = ET.SubElement(failed_tests, "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 = ("#e9f1fb", "#d4e4f7")
+ for r_idx, row in enumerate(csv_lst[1:]):
+ background = colors[r_idx % 2]
+ tr = ET.SubElement(failed_tests, "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
+ else:
+ td.text = item
+ try:
+ with open(table["output-file"], 'w') as html_file:
+ logging.info(" Writing file: '{0}'".format(table["output-file"]))
+ html_file.write(".. raw:: html\n\n\t")
+ html_file.write(ET.tostring(failed_tests))
+ html_file.write("\n\t
\n")
+ except KeyError:
+ logging.warning("The output file is not defined.")
+ return