import logging
import csv
import prettytable
-import numpy as np
import pandas as pd
from string import replace
from math import isnan
+from xml.etree import ElementTree as ET
from errors import PresentationError
from utils import mean, stdev, relative_change, remove_outliers, find_outliers
table["output-file-ext"])
]
for file_name in tbl_names:
- logging.info(" Writing file: '{}'".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_lst:
for i, txt_name in enumerate(tbl_names_txt):
txt_table = None
- logging.info(" Writing file: '{}'".format(txt_name))
+ 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:
output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[1:]):
output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[-1:0:-1]):
output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[1:]):
output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[-1:0:-1]):
# Prepare the header of the tables
header = ["Test case",
"Thput trend [Mpps]",
- "Change [Mpps]",
+ "Anomaly [Mpps]",
"Change [%]",
- "Anomaly"]
+ "Classification"]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
"data": list()}
try:
tbl_dict[tst_name]["data"]. \
- append(tst_data["throughput"]["value"])
- except TypeError:
+ append(tst_data["result"]["throughput"])
+ except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
# Test name:
name = tbl_dict[tst_name]["name"]
# Throughput trend:
- trend = list(pd_data.rolling(window=win_size).median())[-2]
+ trend = list(pd_data.rolling(window=win_size, min_periods=2).
+ median())[-2]
# Anomaly:
t_data, _ = find_outliers(pd_data)
last = list(t_data)[-1]
t_stdev = list(t_data.rolling(window=win_size, min_periods=2).
std())[-2]
if isnan(last):
- anomaly = "outlier"
+ classification = "outlier"
+ last = list(pd_data)[-1]
elif last < (trend - 3 * t_stdev):
- anomaly = "regression"
+ classification = "regression"
elif last > (trend + 3 * t_stdev):
- anomaly = "progression"
+ classification = "progression"
else:
- anomaly = "normal"
- # Change:
- change = round(float(last - trend) / 1000000, 2)
- # Relative change:
- rel_change = int(relative_change(float(trend), float(last)))
-
- tbl_lst.append([name,
- round(float(last) / 1000000, 2),
- change,
- rel_change,
- anomaly])
+ classification = "normal"
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+ if not isnan(last) and not isnan(trend) and trend != 0:
+ # Relative change:
+ rel_change = int(relative_change(float(trend), float(last)))
- file_name = "{}.{}".format(table["output-file"], table["output-file-ext"])
+ tbl_lst.append([name,
+ round(float(trend) / 1000000, 2),
+ round(float(last) / 1000000, 2),
+ rel_change,
+ classification])
- logging.info(" Writing file: '{}'".format(file_name))
+ # Sort the table according to the classification
+ tbl_sorted = list()
+ for classification in ("regression", "outlier", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_lst if item[4] == classification]
+ tbl_tmp.sort(key=lambda rel: rel[0])
+ tbl_sorted.extend(tbl_tmp)
+
+ 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_lst:
+ for test in tbl_sorted:
file_handler.write(",".join([str(item) for item in test]) + '\n')
- txt_file_name = "{}.txt".format(table["output-file"])
+ txt_file_name = "{0}.txt".format(table["output-file"])
txt_table = None
- logging.info(" Writing file: '{}'".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:
txt_table.align["Test case"] = "l"
with open(txt_file_name, "w") as txt_file:
txt_file.write(str(txt_table))
+
+
+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="#6699ff"))
+ for idx, item in enumerate(csv_lst[0]):
+ alignment = "left" if idx == 0 else "right"
+ th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
+ th.text = item
+
+ # Rows:
+ for r_idx, row in enumerate(csv_lst[1:]):
+ background = "#D4E4F7" if r_idx % 2 else "white"
+ 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))
+ if c_idx == 4:
+ if item == "regression":
+ td.set("bgcolor", "#FF0000")
+ elif item == "outlier":
+ td.set("bgcolor", "#818181")
+ elif item == "progression":
+ td.set("bgcolor", "#008000")
+ 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(dashboard))
+ html_file.write("\n\t<p><br><br></p>\n")
+ except KeyError:
+ logging.warning("The output file is not defined.")
+ return
Code Review / csit.git / blobdiff