Package: pyanalyze
pyanalyzePyanalyze is a tool for programmatically detecting common mistakes in Python code, such as references to undefined variables and type errors.
It can be extended to add additional rules and perform checks specific to particular functions.Some use cases for this tool include:Catching bugs before they reach production. The script will catch accidental mistakes like writing "collections.defalutdict" instead of "collections.defaultdict", so that they won't cause errors in production. Other categories of bugs it can find include variables that may be undefined at runtime, duplicate keys in dict literals, and missingawaitkeywords.Making refactoring easier. When you make a change like removing an object attribute or moving a class from one file to another, pyanalyze will often be able to flag code that you forgot to change.Finding dead code. It has an option for finding Python objects (functions and classes) that are not used anywhere in the codebase.Checking type annotations. Type annotations are useful as documentation for readers of code, but only when they are actually correct. Although pyanalyze does not support the full Python type system (seeherefor details), it can often detect incorrect type annotations.UsageYou can install pyanalyze with:$pipinstallpyanalyzeOnce it is installed, you can run pyanalyze on a Python file or package as follows:$python-mpyanalyzefile.py
$python-mpyanalyzepackage/But note that this will try to import all Python files it is passed. If you have scripts that perform operations withoutif __name__ == "__main__":blocks, pyanalyze may end up executing them.In order to run successfully, pyanalyze needs to be able to import the code it checks. To make this work you may have to manually adjust Python's import path using the$PYTHONPATHenvironment variable.ConfigurationPyanalyze has a number of command-line options, which you can see by runningpython -m pyanalyze --help. Important ones include-f, which runs an interactive prompt that lets you examine and fix each error found by pyanalyze, and--enable/--disable, which enable and disable specific error codes.Configuration through apyproject.tomlfile is also supported. Seethe documentationfor
details.Extending pyanalyzeThe main way to extend pyanalyze is by providing a specification for a particular function. This allows you to run arbitrary code that inspects the arguments to the function and raises errors if something is wrong.As an example, suppose your codebase contains a functiondatabase.run_query()that takes as an argument a SQL string, like this:database.run_query("SELECT answer, question FROM content")You want to detect when a call torun_query()contains syntactically invalid SQL or refers to a non-existent table or column. You could set that up with code like this:frompyanalyze.error_codeimportErrorCodefrompyanalyze.signatureimportCallContext,Signature,SigParameterfrompyanalyze.valueimportKnownValue,TypedValue,AnyValue,AnySource,Valuefromdatabaseimportrun_query,parse_sqldefrun_query_impl(ctx:CallContext)->Value:sql=ctx.vars["sql"]ifnotisinstance(sql,KnownValue)ornotisinstance(sql.val,str):ctx.show_error("Argument to run_query() must be a string literal",ErrorCode.incompatible_call,)returnAnyValue(AnySource.error)try:parsed=parse_sql(sql)exceptValueErrorase:ctx.show_error(f"Invalid sql passed to run_query():{e}",ErrorCode.incompatible_call,)returnAnyValue(AnySource.error)# check that the parsed SQL is valid...# pyanalyze will use this as the inferred return type for the functionreturnTypedValue(list)# in pyproject.toml, set:# known_signatures = ["<module>.get_known_argspecs"]defget_known_argspecs(arg_spec_cache):return{# This infers the parameter types and names from the function signaturerun_query:arg_spec_cache.get_argspec(run_query,impl=run_query_impl),# You can also write the signature manuallyrun_query:Signature.make([SigParameter("sql",annotation=TypedValue(str))],callable=run_query,impl=run_query_impl,),}Displaying and checking the type of an expressionYou can usetyping_extensions.reveal_type(expr)to display the type pyanalyze infers for an expression. This can be
useful to understand errors or to debug why pyanalyze does not catch a particular issue. For example:fromtyping_extensionsimportreveal_typereveal_type(1)# Revealed type is 'Literal[1]' (code: reveal_type)This function is also considered a builtin while type checking, so you can usereveal_type()in code that is type checked but not run.For callable objects,reveal_type()will also display the signature inferred by pyanalyze:fromtyping_extensionsimportreveal_typereveal_type(reveal_type)# Revealed type is 'Literal[<function reveal_type at 0x104bf55e0>]', signature is (value, /) -> None (code: reveal_type)A similar function,pyanalyze.dump_value, can be used to get lower-level details of theValueobject pyanalyze infers for an expression.Similarly, you can usepyanalyze.assert_is_valueto assert that pyanalyze infers a particular type for
an expression. This requires importing the appropriateValuesubclass frompyanalyze.value. For example:frompyanalyzeimportassert_is_valuefrompyanalyze.valueimportKnownValueassert_is_value(1,KnownValue(1))# succeedsassert_is_value(int("2"),KnownValue(1))# Bad value inference: expected KnownValue(val=1), got TypedValue(typ=<class 'int'>) (code: inference_failure)This function is mostly useful when writing unit tests for pyanalyze or an extension.Ignoring errorsSometimes pyanalyze gets things wrong and you need to ignore an error it emits. This can be done as follows:Add# static analysis: ignoreon a line by itself before the line that generates the erorr.Add# static analysis: ignoreat the end of the line that generates the error.Add# static analysis: ignoreat the top of the file; this will ignore errors in the entire file.You can add an error code, like# static analysis: ignore[undefined_name], to ignore only a specific error code. This does not work for whole-file ignores. If thebare_ignoreerror code is turned on, pyanalyze will emit an error if you don't specify an error code on an ignore comment.Python version supportPyanalyze supports Python 3.6 through 3.10. Because it imports the code it checks, you have to run it using the same version of Python you use to run your code.ContributingWe welcome your contributions. SeeCONTRIBUTING.mdfor how to get started.DocumentationDocumentation is available atReadTheDocsor onGitHub.