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TidyPy is a tool that encapsulates a number of other static analysis tools and makes it easy to configure, execute, and review their results.
- It's a consolidated tool for performing static analysis on an entire Python
project -- not just your
*.pysource files. In addition to executing a number of different tools on your code, it can also check your YAML, JSON, PO, POT, and RST files. - Rather than putting yet another specialized configuration file in your
project, TidyPy uses the
pyproject.tomlfile defined by PEP 518. All options for all the tools TidyPy uses are declared in one place, rather than requiring that you configure each tool in a different way. - Honors the pseudo-standard
# noqacomment in your Python source to easily ignore issues reported by any tool. - Includes a number of integrations so you can use it with your favorite toolchain.
- Includes a variety of reporters that allow you to view or use the results of TidyPy's analysis in whatever way works best for you.
- Provides a simple API for you to implement your own tool or reporter and include it in the analysis of your project.
- Supports both Python 2 and 3, as well as PyPy. Even runs on Windows.
When TidyPy is installed (pip install tidypy), the tidypy command
should become available in your environment:
$ tidypy --help
Usage: tidypy [OPTIONS] COMMAND [ARGS]...
A tool that executes several static analysis tools upon a Python project
and aggregates the results.
Options:
--version Show the version and exit.
--help Show this message and exit.
Commands:
check Executes the tools upon the project files.
default-config Outputs a default configuration that can be used to
bootstrap your own configuration file.
extensions Outputs a listing of all available TidyPy extensions.
install-vcs Installs TidyPy as a pre-commit hook into the specified
VCS.
list-codes Outputs a listing of all known issue codes that tools
may report.
purge-config-cache Deletes the cache of configurations retrieved from
outside the primary configuration.
remove-vcs Removes the TidyPy pre-commit hook from the specified
VCS.
To have TidyPy analyze your project, use the check subcommand:
$ tidypy check --help
Usage: tidypy check [OPTIONS] [PATH]
Executes the tools upon the project files.
Accepts one argument, which is the path to the base of the Python project.
If not specified, defaults to the current working directory.
Options:
-x, --exclude REGEX Specifies a regular expression matched
against paths that you want to exclude from
the examination. Can be specified multiple
times. Overrides the expressions specified
in the configuration file.
-t, --tool [bandit|dlint|eradicate|jsonlint|manifest|mccabe|polint|pycodestyle|pydiatra|pydocstyle|pyflakes|pylint|pyroma|rstlint|secrets|vulture|yamllint]
Specifies the name of a tool to use during
the examination. Can be specified multiple
times. Overrides the configuration file.
-r, --report [console,csv,custom,json,null,pycodestyle,pylint,pylint-parseable,toml,yaml][:filename]
Specifies the name of a report to execute
after the examination. Can specify an
optional output file name using the form -r
report:filename. If filename is unset, the
report will be written on stdout. Can be
specified multiple times. Overrides the
configuration file.
-c, --config FILENAME Specifies the path to the TidyPy
configuration file to use instead of the
configuration found in the project's
pyproject.toml.
--workers NUM_WORKERS The number of workers to use to concurrently
execute the tools. Overrides the
configuration file.
--disable-merge Disable the merging of issues from various
tools when TidyPy considers them equivalent.
Overrides the configuration file.
--disable-progress Disable the display of the progress bar.
--disable-noqa Disable the ability to ignore issues using
the "# noqa" comment in Python files.
--disable-config-cache Disable the use of the cache when retrieving
configurations referenced by the "extends"
option.
--help Show this message and exit.
If you need to generate a skeleton configuration file with the default options,
use the default-config subcommand:
$ tidypy default-config --help
Usage: tidypy default-config [OPTIONS]
Outputs a default configuration that can be used to bootstrap your own
configuration file.
Options:
--pyproject Output the config so that it can be used in a pyproject.toml
file.
--help Show this message and exit.
If you'd like to see a list of the possible issue codes that could be returned,
use the list-codes subcommand:
$ tidypy list-codes --help
Usage: tidypy list-codes [OPTIONS]
Outputs a listing of all known issue codes that tools may report.
Options:
-t, --tool [bandit|dlint|eradicate|jsonlint|manifest|mccabe|polint|pycodestyle|pydiatra|pydocstyle|pyflakes|pylint|pyroma|rstlint|secrets|vulture|yamllint]
Specifies the name of a tool whose codes
should be output. If not specified, defaults
to all tools.
-f, --format [toml|json|yaml|csv]
Specifies the format in which the tools
should be output. If not specified, defaults
to TOML.
--help Show this message and exit.
If you want to install or remove TidyPy as a pre-commit hook in your project's
VCS, use the install-vcs/remove-vcs subcommands:
$ tidypy install-vcs --help
Usage: tidypy install-vcs [OPTIONS] VCS [PATH]
Installs TidyPy as a pre-commit hook into the specified VCS.
Accepts two arguments:
VCS: The version control system to install the hook into. Choose from:
git, hg
PATH: The path to the base of the repository to install the hook into.
If not specified, defaults to the current working directory.
Options:
--strict Whether or not the hook should prevent the commit if TidyPy finds
issues.
--help Show this message and exit.
$ tidypy remove-vcs --help
Usage: tidypy remove-vcs [OPTIONS] VCS [PATH]
Removes the TidyPy pre-commit hook from the specified VCS.
Accepts two arguments:
VCS: The version control system to remove the hook from. Choose from:
git, hg
PATH: The path to the base of the repository to remove the hook from. If
not specified, defaults to the current working directory.
Options:
--help Show this message and exit.
If you'd like to enable bash completion for TidyPy, run the following in your shell (or put it in your bash startup scripts):
$ eval "$(_TIDYPY_COMPLETE=source tidypy)"
If you don't want to install TidyPy locally on your system or in your virtualenv, you can use the published Docker image:
$ docker run --rm --tty --volume=`pwd`:/project tidypy/tidypy
The command above will run tidypy check on the contents of the current
directory. If you want to run it on a different directory, then change the
`pwd` to whatever path you need (the goal being to mount your project
directory to the container's /project volume).
Running TidyPy in this manner has a few limitiations, mostly around the fact that since TidyPy is running in its own, isolated Python environment, tools like pylint won't be able to introspect the packages your project installed locally, so it may report false positives around "import-error", "no-name-in-module", "no-member", etc.
If you want to run a command other than check, just pass that along when
you invoke docker:
$ docker run --rm --tty --volume=`pwd`:/project tidypy/tidypy tidypy list-codes
TODO
In addition to ignoring entire files, tools, or specific issue types from tools via your configuration file, you can also use comments in your Python source files to ignore issues on specific lines. Some tools have their own built-in support and notation for doing this:
- pylint will respect comments that look like:
# pylint - bandit will respect
comments that look like:
# nosec - pycodestyle will respect comments that look like:
# noqa - pydocstyle will also respect comments that look like:
# noqa - detect-secrets will respect comments that look like:
# pragma: whitelist secret
TidyPy goes beyond these tool-specific flags to implement # noqa on a
global scale for Python source files. It will ignore issues for lines that have
the # noqa comment, regardless of what tools raise the issues. If you only
want to ignore a particular type of issue on a line, you can use syntax like
the following:
# noqa: CODE1,CODE2
Or, if a particular code is used in multiple tools, you can specify the exact tool in the comment:
# noqa: pycodestyle:CODE1,pylint:CODE2
Or, if you want to ignore any issue a specific tool raises on a line, you can specify the tool:
# noqa: @pycodestyle,@pylint
You can, of course, mix and match all three notations in a single comment if you need to:
# noqa: CODE1,pylint:CODE2,@pycodestyle
You can disable TidyPy's NOQA behavior by specifying the --disable-noqa
option on the command line, or by setting the noqa option to false in
your configuration file. A caveat, though: currently pycodestyle and pydocstyle
do not respect this option and will always honor any # noqa comments they
find.
Out of the box, TidyPy includes support for a number of tools:
- pylint
- Pylint is a Python source code analyzer which looks for programming errors, helps enforcing a coding standard and sniffs for some code smells.
- pycodestyle
- pycodestyle is a tool to check your Python code against some of the style conventions in PEP 8.
- pydocstyle
- pydocstyle is a static analysis tool for checking compliance with Python docstring conventions (e.g., PEP 257).
- pyroma
- Pyroma tests your project's packaging friendliness.
- vulture
- Vulture finds unused code in Python programs.
- bandit
- Bandit is a security linter for Python source code.
- eradicate
- Eradicate finds commented-out code in Python files.
- pyflakes
- Pyflakes is a simple program which checks Python source files for errors.
- mccabe
- Ned Batchelder's script to check the McCabe cyclomatic complexity of Python code.
- jsonlint
- A part of the demjson package, this tool validates your JSON documents for strict conformance to the JSON specification, and to detect potential data portability issues.
- yamllint
- The yamllint tool, as its name implies, is a linter for YAML files.
- rstlint
- The restructuredtext-lint tool, as its name implies, is a linter for reStructuredText files.
- polint
- A part of the dennis package, this tool lints PO and POT files for problems.
- manifest
- Uses the check-manifest script to detect discrepancies or problems with your project's MANIFEST.in file.
- pydiatra
- pydiatra is yet another static checker for Python code.
- secrets
- The detect-secrets tool attempts to find secrets (keys, passwords, etc) within a code base.
- dlint
- Dlint is a tool for encouraging best coding practices and helping ensure we're writing secure Python code.
TidyPy includes a number of different methods to present and/or export the results of the analysis of a project. Out of the box, it provides the following:
- console
- The default reporter. Prints a colored report to the console that groups issues by the file they were found in.
- pylint
- Prints a report to the console that is in the same format as Pylint's default output.
- pylint-parseable
- Prints a report to the console that is in roughly the same format as Pylint's "parseable" output.
- pycodestyle
- Prints a report to the console that is in the same format as pycodestyle's default output.
- json
- Generates a JSON-serialized object that contains the results of the analysis.
- yaml
- Generates a YAML-serialized object that contains the results of the analysis.
- toml
- Generates a TOML-serialized object that contains the results of the analysis.
- csv
- Generates a set of CSV records that contains the results of the analysis.
- custom
- Prints ouput to the console that is in the format defined by a template
string specified in the project configuration. The template string is
expected to be one allowed by the str.format() Python method. It will
receive the following arguments:
filename,line,character,tool,code,message.
TidyPy includes a handful of plugins/integrations that hook it into other tools.
- pytest
- TidyPy can be run during execution of your pytest test suite. To enable
it, you need to specify
--tidypyon the command line when you run pytest, or include it as part of theaddoptsproperty in your pytest config. - nose
- TidyPy can be run during execution of your nose test suite. To enable
it, you can either specify
--with-tidypyon the command line when you run nose, or set thewith-tidypyproperty to1in yoursetup.cfg. - pbbt
- TidyPy can be included in your PBBT scripts using the
tidypytest. To enable it, you can either specify--extend=tidypy.plugin.pbbton the command line when you run PBBT, or set theextendproperty in yoursetup.cfgorpbbt.yamltotidypy.plugin.pbbt.
A simple interface exists for extending TidyPy to include more and different
tools and reporters. To add a tool, create a class that extends tidypy.Tool,
and in your setup.py, declare an entry_point for tidypy.tools that
points to your class:
entry_points={
'tidypy.tools': [
'mycooltool = path.to.model:MyCoolToolClassName',
],
}
To add a reporter, the process is nearly identical, except that you extend
tidypy.Report and declare an entry_point for tidypy.reports.
- Aren't there already tools like this?
- Yup. There's prospector, pylama, flake8, and ciocheck just to name a few. But, as is customary in the world of software development, if the wheel isn't as round as you'd like it to be, you must spend countless hours to reinvent it. I've tried a number of these tools (and even contributed to some), but in the end, I always found something lacking or annoying. Thus, TidyPy was born.
- How do I run TidyPy on a single file?
- The short answer is, you don't (at the moment, anyway). It wasn't designed with that use case in mind. TidyPy was built to analyze a whole project, and show you everything.
- I tried TidyPy out on my project and it reported hundreds/thousands of issues. My ego is now bruised.
Yea, that happens. The philosophy I chose to follow with this tool is that I didn't want it to hide anything from me. I wanted its default behavior to execute every tool in its suite using their most obnoxious setting. Then, when I can see the full scope of damage, I can then decide to disable specific tools or issues via a project-level configuration. I figured if someone took the time to implement a check for a particular issue, they must think it has some value. If my tooling hides that from me by default, then I won't be able to gain any benefits from it.
In general, I don't recommend starting to use linters or other sorts of static analyzers when you think you're "done". You should incorporate them into your workflow right at the beginning of a project -- just as you would (or should) your unit tests. That way you find things early and learn from them (or disable them). It's much less daunting a task to deal with when you address them incrementally.
Contributions are most welcome. Particularly if they're bug fixes! To hack on
this code, simply clone it, and then run make setup. This will create a
virtualenv with all the tools you'll need. The Makefile also has a test
target for running the pytest suite, and a lint target for running TidyPy
on itself.
TidyPy is released under the terms of the MIT License.
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