# 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:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Algorithms to generate tables.
"""
import logging
import csv
import prettytable
import pandas as pd
from string import replace
from collections import OrderedDict
from numpy import nan, isnan
from xml.etree import ElementTree as ET
from errors import PresentationError
from utils import mean, stdev, relative_change, classify_anomalies
def generate_tables(spec, data):
"""Generate all tables specified in the specification file.
:param spec: Specification read from the specification file.
:param data: Data to process.
:type spec: Specification
:type data: InputData
"""
logging.info("Generating the tables ...")
for table in spec.tables:
try:
eval(table["algorithm"])(table, data)
except NameError as err:
logging.error("Probably algorithm '{alg}' is not defined: {err}".
format(alg=table["algorithm"], err=repr(err)))
logging.info("Done.")
def table_details(table, input_data):
"""Generate the table(s) with algorithm: table_detailed_test_results
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)
# Prepare the header of the tables
header = list()
for column in table["columns"]:
header.append('"{0}"'.format(str(column["title"]).replace('"', '""')))
# Generate the data for the table according to the model in the table
# specification
job = table["data"].keys()[0]
build = str(table["data"][job][0])
try:
suites = input_data.suites(job, build)
except KeyError:
logging.error(" No data available. The table will not be generated.")
return
for suite_longname, suite in suites.iteritems():
# Generate data
suite_name = suite["name"]
table_lst = list()
for test in data[job][build].keys():
if data[job][build][test]["parent"] in suite_name:
row_lst = list()
for column in table["columns"]:
try:
col_data = str(data[job][build][test][column["data"].
split(" ")[1]]).replace('"', '""')
if column["data"].split(" ")[1] in ("vat-history",
"show-run"):
col_data = replace(col_data, " |br| ", "",
maxreplace=1)
col_data = " |prein| {0} |preout| ".\
format(col_data[:-5])
row_lst.append('"{0}"'.format(col_data))
except KeyError:
row_lst.append("No data")
table_lst.append(row_lst)
# Write the data to file
if table_lst:
file_name = "{0}_{1}{2}".format(table["output-file"], suite_name,
table["output-file-ext"])
logging.info(" Writing file: '{}'".format(file_name))
with open(file_name, "w") as file_handler:
file_handler.write(",".join(header) + "\n")
for item in table_lst:
file_handler.write(",".join(item) + "\n")
logging.info(" Done.")
def table_merged_details(table, input_data):
"""Generate the table(s) with algorithm: table_merged_details
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(