dummypackage-dummyname 1.0.15

A dummy package

python3-template

Dummy Python3 project providing structure for development, unit testing, runtime/memory benchmarking, PEP8 check, autodocumentation, and deployment to PyPI and GitHub Releases, automated via Travis CI.

• The actual code is to be developed in the dummypackage library, and used in an application like dummyapp.py, which can be run with python dummyapp.py. To ensure proper function of the tools, all subdirectories must include an __init.py__ file.

• The unit tests are developed in the utest directory. They can also be arbitrarily nested, but also must include an __init.py__ file in each test directory.

• Reports for runtime and memory benchmarks can be generated into timebenchmark and memorybenchmark respectively.

• Autodocs are also generated into docs in PDF as well as HTML (which can be deployed online).

• A setup.py script to create an OS-agnostic package into dist is provided, as well as functionality to deploy it into git releases and PyPI.

• All these tasks can be performed manually as explained later on, and also are automated via Travis CI as it can be seen in the .travis.yml file, and the ci_scripts directory.

This readme has been developed for Ubuntu-like systems, but the Python examples should transfer well to other systems. Also note that this has been tested for Python 3 only, make sure that your python command invokes a Python 3 binary (or that you adapt the exmples presented here).

Dependencies:

Some general dependencies are listed under the addons tag in the .travis.yml file. See requirements.txt for the python-specific dependencies. Also, if not using conda, add this line to your .bashrc to allow executing binaries installed with pip --user:

export PATH=${PATH}:$HOME/.local/bin

Unit Testing:

All unit test files must fulfill the following requirements (see the examples for more details):

• They have to be in the utest directory
• Their name has to end in _test.py (although this is customizable)
• They import functionality from the package like this: from dummypackage.foo_module import Foo
• They import functionality from each other like this: from .foo_test import FooTestCaseCpu
• They import unittest and extend unittest.TestCase classes
• All test method names from the extended unittest classes begin with test (also customizable)
• Nested directories are allowed. However, each test directory has to contain an empty __init__.py file

If these conditions are met, all the tests can be run from the CLI or within Python.

Note: the utest directory includes a special test case, tautology.py which should always pass and can be used to ensure that the testing facilities work correctly. Its name doesn't end in _test.py, so it doesn't get included in the ordinary tests and has to be called explicitly.

Run from CLI:

# run all existing tests
python -m unittest discover -s utest -t . -p "*_test.py" -v
# Run individual test modules (from the repo root dir)
python -m unittest utest/foo_test.py -v
python -m unittest utest/bar_test.py -v
python -m unittest utest/nestedtests/nested_test.py -v
# Run individual classes
python -m unittest utest.foo_test.FooTestCaseCpu -v
python -m unittest utest.nestedtests.nested_test.QuackTestCaseCpu -v
# Run individual methods
python -m unittest utest.bar_test.BarTestCaseCpu.test_inheritance -v

Run within Python:

# run all existing tests and collect results
import utest
results = utest.run_all_tests()
print(results)

# run all tests for a given module and collect results
import utest.foo_test
results = utest.run_module(utest.foo_test);
print(results)

# run for several modules:
import utest
import utest.foo_test as f
import utest.nestedtests.nested_test as n
results = utest.run_modules([f, n]);
print(results)

# run for a single testcase
import utest
from utest.foo_test import FooTestCaseCpu
results = utest.run_testcase(FooTestCaseCpu);
print(results)

# run for several testcases
import utest
from utest.foo_test import FooTestCaseCpu
from utest.bar_test import BarTestCaseCpu
results = utest.run_testcases([FooTestCaseCpu, BarTestCaseCpu]);
print(results)

# run for a single method
import utest
results = utest.run_testmethod("utest.foo_test.FooTestCaseCpu.test_loop");
print(results)

# run for several methods
import utest
results = utest.run_testmethods(["utest.foo_test.FooTestCaseCpu.test_loop",
                                "utest.bar_test.BarTestCaseCpu.test_loop"]);
print(results)

Code Coverage:

Code coverage determines how much of the implemented code do the unit tests go through. Although not particularly sound, it can be used as a way to check that the unit testing relates closely to the developed code. Plus, it is easy to automate.

From CLI:

The usage is very similar to running with python, but prepending coverage run instead, and adding a --source directory, which contains the actual code pool being inspected:

# define the location for the coverage file:
export COVERAGE_FILE=codecoverage/coverage`date "+%Y%m%d_%H%M%S"`

# Run whatever you want to run but prepending 'coverage run'
# the --branch flag activates branch coverage (as opposed to statement coverage)
# the -a flag will accumulate the reports
coverage run --source dummypackage --branch -m unittest discover -s utest -t . -p "*_test.py" -v

# Print report on terminal
coverage report -m

# more elaborated XML report:
coverage xml -o $COVERAGE_FILE.xml

# interactive HTML report
coverage html -d $COVERAGE_FILE\_html
firefox $COVERAGE_FILE\_html/index.html

Within Python:

This sample script performs unit testing AND test coverage wthout creating any files (see the script ci_scripts/utest_with_coverage.py):

import coverage
import utest

# same as with the CLI. A suffix will be automatically added
COVERAGE_FILE = "codecoverage/coverage"

# wrap the testing with the coverage analyzer:
c = coverage.Coverage(data_file=COVERAGE_FILE, data_suffix=True, branch=True,
                      source=["dummypackage"])
c.start()
test_results = utest.run_all_tests()
c.stop()

# at this point c.save() and c.html_report(outfile=etc) would generate files.
# This instead handles the data within Python, as coverage.CoverageData
percentage = c.report()

print("This script did", test_results.testsRun, "tests in total.")
print("No. of test errors:", len(test_results.errors))
print("No. of test failures:", len(test_results.failures))
print("Code coverage of tests (percentage):", percentage)

CPU Runtime Benchmarking:

Run and print on terminal, sorted by total time:

python -m cProfile -s tottime dummyapp.py

It can be sorted by any of these:

calls (call count)
cumulative (cumulative time)
cumtime (cumulative time)
file (file name)
filename (file name)
module (file name)
ncalls (call count)
pcalls (primitive call count)
line (line number)
name (function name)
nfl (name/file/line)
stdname (standard name)
time (internal time)
tottime (internal time)

Sample output:

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
      100    1.496    0.015    1.496    0.015 {method 'index' of 'list' objects}
        1    0.023    0.023    0.023    0.023 bar_module.py:15(__init__)
     93/1    0.016    0.000    1.590    1.590 {built-in method builtins.exec}
        1    0.010    0.010    1.590    1.590 dummyapp.py:6(<module>)
       47    0.005    0.000    0.005    0.000 {built-in method marshal.loads}
  229/227    0.004    0.000    0.011    0.000 {built-in method builtins.__build_class__}

Save to file and explore interactively with web browser using snakeviz:

python -m cProfile -o timebenchmark/dummyapp.`date "+%Y%m%d_%H%M%S"` dummyapp.py
# open results in browser using the snakeviz server
snakeviz timebenchmark/dummyapp.20190112_195256

Within Python:

This small snippet prints the results to the terminal and returns some relevant figures (see the script ci_scripts/runtime_benchmark.py):

import cProfile
import pstats
from io import StringIO

def get_total_time_and_calls(fn, sort_by="time"):
    """
    This function calls the given a parameterless functor while benchmarking
    it via cProfile. A report is printed to the terminal and the tuple
    (n_seconds, n_calls) is returned.
    """
    pr = cProfile.Profile()
    pr.enable()
    fn()
    pr.disable()
    #
    s = StringIO()
    ps = pstats.Stats(pr, stream=s)
    ps.strip_dirs().sort_stats(sort_by)
    ps.print_stats()
    print(s.getvalue())
    #
    return ps.total_tt, ps.total_calls

get_total_time_and_calls(lambda: 2+2)

Memory Benchmarking:

This package reports memory usage of the Python process, line-by-line or as a function of time. As with the runtime profiler, there are several ways to run this functionality.

Decorators:

This way requires to import and use the @profile decorator in the desired def declarations of your Python files. Unlike the other methods, it has the drawback that it requires to alter or duplicate the source code. To perform the profiling, run the decorated files (see e.g. the memorybenchmark directory) with the python interpreter. Sample output:

Filename: memorybenchmark/loop_benchmark.py
Line     Mem usage    Increment   Line Contents
================================================
    21     14.6 MiB     14.6 MiB   @profile  # decorator for (https://pypi.org/project/memory-profiler/)
    22                             def do_loop(clss, memsize, loopsize):
    23                                 """
    24                                 Create a Foo instance and loop it.
    25                                 """
    26     14.6 MiB      0.0 MiB       x = clss(memsize)
    27     14.6 MiB      0.0 MiB       x.loop(loopsize)

    21     14.6 MiB     14.6 MiB   @profile  # decorator for (https://pypi.org/project/memory-profiler/)
    22                             def do_loop(clss, memsize, loopsize):
    23                                 """
    24                                 Create a Foo instance and loop it.
    25                                 """
    26     53.3 MiB     38.7 MiB       x = clss(memsize)
    27     53.3 MiB      0.0 MiB       x.loop(loopsize)

Time-based memory usage:

The module provides the mprof binary to perform time-based analysis. Calling mprof run loop_benchmark.py will generate a mprofile_TIMESTAMP.dat file, which can be visualized with mprof plot(if pylab/matplotlib is installed) and looks like this:

FUNC __main__.do_loop 14.4766 1547322141.5961 14.4766 1547322141.5963
FUNC __main__.do_loop 14.4766 1547322141.5964 14.8281 1547322143.1454
CMDLINE /usr/bin/python dummyapp.py
MEM 1.511719 1547322141.5060
MEM 31.281250 1547322141.6064
MEM 53.195312 1547322141.7067
MEM 53.195312 1547322141.8071
MEM 53.195312 1547322141.9074
MEM 53.195312 1547322142.0078
MEM 53.195312 1547322142.1081
MEM 53.195312 1547322142.2085

The available mprof commands are:

mprof run: running an executable, recording memory usage
mprof plot: plotting one the recorded memory usage (by default, the last one)
mprof list: listing all recorded memory usage files in a user-friendly way.
mprof clean: removing all recorded memory usage files.
mprof rm: removing specific recorded memory usage files

Further information can be found in the package homepage.

Within Python:

This small snippet returns memory usage as a function of time (see the script ci_scripts/runtime_benchmark.py). Line-by-line analysis using the LineProfiler class doesn't seem to be supported by this method, see the first and second approaches for alternatives:

from memory_profiler import memory_usage

def fn(times=1000, message="hello!"):
    l = []
    for i in range(times):
        l.append(message)
    for i in range(times**2):
        l.append(message)

usage = memory_usage((fn, (1000, "goodbye")), interval=0.05)
print(usage)

Codestyle Check:

Using it is pretty straightforward: simply call flake8 or python -m flake8 in the repository root directory. If any errors are present, the command will print them and return with error status.

Note that some "errors" are actually proper design decissions. These can be bypassed with the noqa directive, like this: import environment # noqa: F401.

Autodoc:

This project uses sphinx and LaTeX to automatically generate the docs. The ci_scripts/maje_sphinx_docs.sh script provides a most automated and flexible doc generation as HTML and PDF. It only requires the user to provide projectname and author. At the moment, it doesn't assert for input or environment errors and has to be called from the repo root as follows:

./ci_scripts/make_sphinx_docs.sh dummypackage "Dummy Dumson"

It will then generate the files into docs/_build (they are also being uploaded to the repository as they aren't filtered by .gitignore).

Optionally, you can deploy your docs into https://readthedocs.org/ by synchronizing it with your github account. Importing the repository should be straightforward: the page will automatically find your conf.py and generate the docs. The docs homepage of the project should provide a badge like the one at the top of this README and a link to the online docs. Note that the advertisment can be removed in the "Admin" tab. This repo's docs are being deployed to https://python3-template.readthedocs.io

By default, it will provide two versions of the doc: latest and stable. See this link about versioning in readthedocs: here.

Build and Deploy:

Tagging:

Once we reach a given milestone, we can label that specific commit with a version tag (like v1.0.13). The basics are discussed here: https://git-scm.com/book/en/v2/Git-Basics-Tagging. Especially, note that tags have to be explicitly included in the push: git push --follow-tags, otherwise, they won't be sent to the server. If you want this to be the default behaviour (as assumed in this project), call git config --global push.followTags true, after that every regular git push will also push all the valid existing tags.

There are different ways of handling the tag system, the git CLI and gitpython being very popular among them. Since we want here very specific functionality, we use the bump2version third-party library: It allows to automatically increment and set the version tag following semantic versioning (see https://semver.org/), which is highly encouraged. It works as follows:

• The .bumpversion.cfg file holds the current version and the configuration.
• Calling bump2version <ADVANCE> will automatically update the version in the .bumpversion.cfg and setup.py files, and add the corresponding tag to the git repo. The <ADVANCE> parameter can be either major, minor, or patch.

So we just need to start our repo by setting the desired version in the .bumpversion.cfg file (usually 0.1.0), and then commit normally. The process of adding a tag will be something like:

# after finished making all changes and saving all files (to avoid 'Git working directory is not clean' error):
git add . # or whatever you want to add
git commit -m "finished implementing the last feature we needed"
bump2version major

After bump2version, doing a git push will send the commit to the server as usual, and the tag will automatically appear under the GitHub Releases tab, holding a snapshot of the current repo's state as .zip and .tar.gz.

Apparently, the format of .bumpversion.cfg is not standard and the tool doesn't provide a direct means to read the current version. Therefore, the following solutions are provided:

1. From CLI: version=`grep "current_version" .bumpversion.cfg | cut -d'=' -f2 | xargs`
2. Within Python: See ci_scripts/get_version.py

Build and Local Install:

The setup.py script will build the package in source dist (sdist) and wheel forms. The package will be in the dist directory, and some files will be generated into the build directory too. Important: remember to adapt the variables in the setup function call to your repo's needs (except version, which as we seen is handled by bump2version).

# optionally clean cache to prevent side effects
python setup.py clean --all
rm -r *.egg-info
# package the software
python setup.py sdist bdist_wheel
# now something like this will install the package locally:
pip install dist/dummypackage_dummyname-0.0.1-py3-none-any.whl
# this will update it
pip install -U dist/dummypackage_dummyname-0.0.1-py3-none-any.whl
# and this will remove it:
pip uninstall dist/dummypackage_dummyname-0.0.1-py3-none-any.whl

Note that in principle it is not necessary to build the package manually if you have automated the deployment via Travis.

GitHub Release:

We saw that tags get automatically tracked by GitHub. But for each release, we want to add two things to this default behaviour:

1. Specify a changelog
2. Pip-installable package in wheel format.

This can be performed manually via browser in the GitHub webpage, and also via CLI as follows:

TODO

Note that in principle it is not necessary to deploy manually if you have automated the deployment via Travis.

PyPI Release:

The built package can also be released to PyPI, a popular global repository for Python packages. The following command uploads then everything in the dist directory. You will need to provide your PyPI credentials (make a user if you don't have one):

twine upload dist/*

Note that in principle it is not necessary to deploy manually if you have automated the deployment via Travis.

Travis CI:

Intro:

In the context of of CD/CI, a git repository (or any other similar object to automatize tasks on) is regarded as a material, with its specific set of trigger actions (e.g. commit or pull request in a git repo, or certain API requests in a custom REST system). It probably helps to think of materials as classes, and the actions as their methods.

For each of those actions, a CD/CI pipeline may be defined, which will be executed by the CD/CI system upon trigger by some event. Depending on the CI system, the pipelines allow some complexity like branching, but it is best practice to keep them as simple as possible.

Although different materials have different sets of actions, the spirit is in general similar, in what is known as CI. Quoting:

"Continuous Integration is the umbrella for various techniques and practices that every self-respecting software project should employ:

• Build automation - every commit/push to an scm repo automatically triggers a new build (anything in the master branch is deployable)
• Test automation - tests are evaluated as integral part of the build process (that's old news, right?)
• SCM integration - primarily to facilitate build automation, but also to be able to report on issues, versions, milestones, ...
• Project management integration - Basecamp, Campfire, Acunote and other PM tools (people, tasks, schedules, ...)
• Project Dashboard - everything is out in the open (generally without any read access restrictions, making it available to all interested parties)

Travis and this repo:

• https://docs.travis-ci.com
• https://docs.travis-ci.com/user/languages/python/

Travis is a CI/CD tool that can be easily linked to a GitHub account for free, and managed through a travis.yaml file present at the repo's root (More info: https://docs.travis-ci.com/user/languages/python/). The goal for this repository is to trigger Travis after each commit to master, to perform the following tasks:

1. Run in parallel:

   1. Check coding style with flake
   2. Check that the tautology is true
   3. Check that all unit tests are passing
   4. Check that code coverage ratio is above a number
   5. Check that running a given routine never surpasses a certain memory usage
   6. Check that running a given routine never exceedes a certain duration

2. If all checks success, build the docs and dummypackage into our desired architectures.

3. Deploy package in a way that can be installed via pip+ repo link

For that, Travis implements two basic concepts, stages and jobs: A stage is a group of jobs that are allowed to run in parallel. However, each one of the stages runs one after another, and will only proceed if all jobs in the previous stage have passed successfully. If one job fails in one stage, all other jobs on the same stage will still complete, but all jobs in subsequent stages will be canceled, and the build fails.

Each job can be mapped to a "script", which is a call to a program. If the script returns a nonzero status, deployment is considered a failure, and the build will be marked as “errored”.

By default, Travis starts a new virtual machine from scratch for every single job. All the required dependencies have to be installed prior to running the jobs. This can take a while, so it is helpful to design the CI pipeline in a way that minimizes overhead. Some of the dependencies can be shared across jobs using the cache keyword. Additionaly, the file cleanup that happens prior to some tasks can be also skipped.

Steps:

1. sync your GitHub account with Travis, and allow access etc to your repositories. Finally, "activate" the desired repo in Travis. You should see the typical [build | failing] badge. Click on it and copy the markdown link into the main README of the repo, as shown on the top of this file.

2. In your GitHub activated_repo->settings->webhooks, select which events you want to notify to Travis. In our case we will activate commits and pull requests. Any time any of these happens, GitHub will send a request to Travis with all the infos involved.

3. Now both sides know about each other, make sure you understand the .travis.yml file and customize it as desired.

4. For deployment, Travis will need your user credentials. As it wouldn't be safe to distribute your passwords in plain text, Travis offers you the possibility of encrypting them. In this project, we will need credentials for PyPI and GitHub:

GitHub releases config:

Travis can be configured to upload to GitHub releases by running travis setup releases. If you do this yourself, be careful because this will reformat the travis.yml file, deleting all comments and blank lines (although the functionality won't be affected). A temporary backup will help with this.

In the repo root, run travis setup releases and follow the steps:

1. Confirm repo name
2. Provide GitHub credentials
3. Deploy only from ... -> yes
4. Encrypt API key -> yes

Conveniently, this repo's travis.yml already provides structure for releasing into GitHub. You will, in any case, have to replace the credentials with your own ones. You can see how to do that here: https://docs.travis-ci.com/user/deployment/releases#authenticating-with-an-oauth-token.

Also note that the release can be customized with keywords like name, body, draft, prerelease.

https://docs-staging.travis-ci.com/user/deployment/releases/.

PyPI release config:

As with GitHub releases, the .travis.yml file already provides structure for deploying to PyPI. You just have to replace the credentials with your own. To encrypt your password, use the following command (more info here: https://docs.travis-ci.com/user/encryption-keys/). Again, be careful because this will reformat the travis.yml file, deleting all comments and blank lines (although the functionality won't be affected). A temporary backup will help with this:

travis encrypt <PASSWORD> --add deploy.password

For that, make sure you installed the Travis CLI (see https://github.com/travis-ci/travis.rb#installation).

TODO:

• Releases: use a tool like release-it or semantic-release? bumpversion problem: it requires a previous commit, and then commits only 2 small changes. The message provided affects the "tag commit message" only, find a way to edit the proper release title and message. Maybe HISTORY.rst?

• improve create_docs.sh: it should succeed only if everything succeeds, check for environment and input consistency and should be callable from everywhere (requiring or computing repo root path).

• https://github.com/travis-ci/dpl/issues/155

• Update this readme

MAYBE TODO:

• add all OS to travis file: for the moment not supported! https://github.com/travis-ci/travis-ci/issues/9744#issuecomment-419426053

• add online codecov https://codecov.io/