+++ /dev/null
-# 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 plots.
-"""
-
-
-import logging
-import pandas as pd
-import plotly.offline as ploff
-import plotly.graph_objs as plgo
-
-from plotly.exceptions import PlotlyError
-
-from utils import mean
-
-
-def generate_plots(spec, data):
- """Generate all plots 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 plots ...")
- for index, plot in enumerate(spec.plots):
- try:
- logging.info(" Plot nr {0}:".format(index + 1))
- eval(plot["algorithm"])(plot, data)
- except NameError as err:
- logging.error("Probably algorithm '{alg}' is not defined: {err}".
- format(alg=plot["algorithm"], err=repr(err)))
- logging.info("Done.")
-
-
-def plot_performance_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_performance_box
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
- # Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
- data = input_data.filter_data(plot)
- if data is None:
- logging.error("No data.")
- return
-
- # Prepare the data for the plot
- y_vals = dict()
- for job in data:
- for build in job:
- for test in build:
- if y_vals.get(test["parent"], None) is None:
- y_vals[test["parent"]] = list()
- try:
- y_vals[test["parent"]].append(test["throughput"]["value"])
- except (KeyError, TypeError):
- y_vals[test["parent"]].append(None)
-
- # Add None to the lists with missing data
- max_len = 0
- for val in y_vals.values():
- if len(val) > max_len:
- max_len = len(val)
- for key, val in y_vals.items():
- if len(val) < max_len:
- val.extend([None for _ in range(max_len - len(val))])
-
- # Add plot traces
- traces = list()
- df = pd.DataFrame(y_vals)
- df.head()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
- traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
- y=df[col],
- name=name,
- **plot["traces"]))
-
- try:
- # Create plot
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
-
- # Export Plot
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
- except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- logging.info(" Done.")
-
-
-def plot_latency_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_latency_box
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
- # Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
- data = input_data.filter_data(plot)
- if data is None:
- logging.error("No data.")
- return
-
- # Prepare the data for the plot
- y_tmp_vals = dict()
- for job in data:
- for build in job:
- for test in build:
- if y_tmp_vals.get(test["parent"], None) is None:
- y_tmp_vals[test["parent"]] = [
- list(), # direction1, min
- list(), # direction1, avg
- list(), # direction1, max
- list(), # direction2, min
- list(), # direction2, avg
- list() # direction2, max
- ]
- try:
- y_tmp_vals[test["parent"]][0].append(
- test["latency"]["direction1"]["50"]["min"])
- y_tmp_vals[test["parent"]][1].append(
- test["latency"]["direction1"]["50"]["avg"])
- y_tmp_vals[test["parent"]][2].append(
- test["latency"]["direction1"]["50"]["max"])
- y_tmp_vals[test["parent"]][3].append(
- test["latency"]["direction2"]["50"]["min"])
- y_tmp_vals[test["parent"]][4].append(
- test["latency"]["direction2"]["50"]["avg"])
- y_tmp_vals[test["parent"]][5].append(
- test["latency"]["direction2"]["50"]["max"])
- except (KeyError, TypeError):
- pass
-
- y_vals = dict()
- for key, values in y_tmp_vals.items():
- y_vals[key] = list()
- for val in values:
- if val:
- average = mean(val)
- else:
- average = None
- y_vals[key].append(average)
- y_vals[key].append(average) # Twice for plot.ly
-
- # Add plot traces
- traces = list()
- try:
- df = pd.DataFrame(y_vals)
- df.head()
- except ValueError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
- traces.append(plgo.Box(x=['TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1'],
- y=df[col],
- name=name,
- **plot["traces"]))
-
- try:
- # Create plot
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
-
- # Export Plot
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
- except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- logging.info(" Done.")
-
-
-def plot_throughput_speedup_analysis(plot, input_data):
- """Generate the plot(s) with algorithm: plot_throughput_speedup_analysis
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
- # Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
- data = input_data.filter_data(plot)
- if data is None:
- logging.error("No data.")
- return
-
- throughput = dict()
- for job in data:
- for build in job:
- for test in build:
- if throughput.get(test["parent"], None) is None:
- throughput[test["parent"]] = {"1": list(),
- "2": list(),
- "4": list()}
- try:
- if "1T1C" in test["tags"]:
- throughput[test["parent"]]["1"].\
- append(test["throughput"]["value"])
- elif "2T2C" in test["tags"]:
- throughput[test["parent"]]["2"]. \
- append(test["throughput"]["value"])
- elif "4T4C" in test["tags"]:
- throughput[test["parent"]]["4"]. \
- append(test["throughput"]["value"])
- except (KeyError, TypeError):
- pass
-
- if not throughput:
- logging.warning("No data for the plot '{}'".
- format(plot.get("title", "")))
- return
-
- for test_name, test_vals in throughput.items():
- for key, test_val in test_vals.items():
- if test_val:
- throughput[test_name][key] = sum(test_val) / len(test_val)
-
- names = ['1 core', '2 cores', '4 cores']
- x_vals = list()
- y_vals_1 = list()
- y_vals_2 = list()
- y_vals_4 = list()
-
- for test_name, test_vals in throughput.items():
- if test_vals["1"]:
- x_vals.append("-".join(test_name.split('-')[1:-1]))
- y_vals_1.append(1)
- if test_vals["2"]:
- y_vals_2.append(
- round(float(test_vals["2"]) / float(test_vals["1"]), 2))
- else:
- y_vals_2.append(None)
- if test_vals["4"]:
- y_vals_4.append(
- round(float(test_vals["4"]) / float(test_vals["1"]), 2))
- else:
- y_vals_4.append(None)
-
- y_vals = [y_vals_1, y_vals_2, y_vals_4]
-
- y_vals_zipped = zip(names, y_vals)
- traces = list()
- for val in y_vals_zipped:
- traces.append(plgo.Bar(x=x_vals,
- y=val[1],
- name=val[0]))
-
- try:
- # Create plot
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
-
- # Export Plot
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
- except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- logging.info(" Done.")
-
-
-def plot_http_server_performance_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_http_server_performance_box
- specified in the specification file.
-
- :param plot: Plot to generate.
- :param input_data: Data to process.
- :type plot: pandas.Series
- :type input_data: InputData
- """
-
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
- # Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
- data = input_data.filter_data(plot)
- if data is None:
- logging.error("No data.")
- return
-
- # Prepare the data for the plot
- y_vals = dict()
- for job in data:
- for build in job:
- for test in build:
- if y_vals.get(test["name"], None) is None:
- y_vals[test["name"]] = list()
- try:
- y_vals[test["name"]].append(test["result"]["value"])
- except (KeyError, TypeError):
- y_vals[test["name"]].append(None)
-
- # Add None to the lists with missing data
- max_len = 0
- for val in y_vals.values():
- if len(val) > max_len:
- max_len = len(val)
- for key, val in y_vals.items():
- if len(val) < max_len:
- val.extend([None for _ in range(max_len - len(val))])
-
- # Add plot traces
- traces = list()
- df = pd.DataFrame(y_vals)
- df.head()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-cps', '').
- replace('-rps', ''))
- traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
- y=df[col],
- name=name,
- **plot["traces"]))
- try:
- # Create plot
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
-
- # Export Plot
- logging.info(" Writing file '{0}{1}'.".
- format(plot["output-file"], plot["output-file-type"]))
- ploff.plot(plpl,
- show_link=False, auto_open=False,
- filename='{0}{1}'.format(plot["output-file"],
- plot["output-file-type"]))
- except PlotlyError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
-
- logging.info(" Done.")
Code Review / csit.git / blobdiff