Metadata-Version: 2.1
Name: valimp
Version: 0.1
Summary: Validate and parse function inputs
Author: Marcus Read
Author-email: marcusaread.prog@proton.me
License: MIT License
Project-URL: homepage, https://github.com/maread99/valimp
Project-URL: documentation, https://github.com/maread99/valimp
Project-URL: Issue Tracker, https://github.com/maread99/valimp/issues
Project-URL: Source Code, https://github.com/maread99/valimp
Keywords: validation,parsing,validate,parse,coerce,input,function
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: ~=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: flake8-docstrings ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: mypy ; extra == 'dev'
Requires-Dist: pip-tools ; extra == 'dev'
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: pylint ; extra == 'dev'
Provides-Extra: tests
Requires-Dist: black ; extra == 'tests'
Requires-Dist: flake8 ; extra == 'tests'
Requires-Dist: flake8-docstrings ; extra == 'tests'
Requires-Dist: pytest ; extra == 'tests'

<!-- NB any links not defined as absolute will not resolve on PyPI page -->
# valimp

<!-- UPDATE BADGE ADDRESSES! -->
[![PyPI](https://img.shields.io/pypi/v/valimp)](https://pypi.org/project/valimp/) ![Python Support](https://img.shields.io/pypi/pyversions/valimp) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

In Python use type hints to validate, parse and coerce inputs to **public functions**. 

This is the sole use of `valimp`. It's a single short module with no depenencies that does one thing and makes it simple to do.

Works like this:
```python
from valimp import parse, Parser, Coerce
from typing import Annotated, Union, Optional, Any

@parse  # add the `valimp.parse`` decorator to a public function or method
def public_function(
    # validate against built-in or custom types
    a: str,
    # support for type unions
    b: Union[int, float],  # or from Python 3.10 `int | float`
    # validate type of container items
    c: dict[str, Union[int, float]],  # dict[str, int | float]
    # coerce input to a specific type
    d: Annotated[
        Union[int, float, str],  # int | float | str
        Coerce(int)
    ],
    # parse input with reference to earlier inputs...
    e: Annotated[
        str,
        Parser(lambda name, obj, params: obj + f"_{name}_{params['a']}")
    ],
    # coerce and parse input...
    f: Annotated[
        Union[str, int],  # str | int
        Coerce(str),
        Parser(lambda name, obj, _: obj + f"_{name}")
    ],
    *,
    # support for optional types
    g: Optional[str],  # str | None
    # define default values dynamically with reference to earlier inputs
    h: Annotated[
        Optional[float],  # float | None
        Parser(lambda _, obj, params: params["b"] if obj is None else obj)
    ] = None,
) -> dict[str, Any]:
    return {"a":a, "b":b, "c":c, "d":d, "e":e, "f":f, "g":g, "h":h}

public_function(
    # NB parameters 'a' through 'f' could be passed positionally
    a="zero",
    b=1.0,
    c={"two": 2},
    d=3.3, # will be coerced from float to int, i.e. to 3
    e="four",  # will be parsed to "four_e_zero"
    f=5,  # will be coerced to str and then parsed to "5_f"
    g="keyword_arg_g",
    # h, not passed, will be assigned dynamically as parameter b (i.e. 1.0)
)
```
returns:
```
{'a': 'zero',
 'b': 1.0,
 'c': {'two': 2},
 'd': 3,
 'e': 'four_e_zero',
 'f': '5_f',
 'g': 'keyword_arg_g',
 'h': 1.0}
 ```
 And if there are invalid inputs...
```python
public_function(
    a=["not a string"],  # INVALID
    b="not an int or a float",  # INVALID
    c={2: "two"},  # INVALID, key not a str and value not an int or float
    d=3.2, # valid input
    e="valid input",
    f=5.0,  # INVALID, not a str or an int
    g="valid input",
)
```
raises:
```
InputsError: The following inputs to 'public_function' do not conform with the corresponding type annotation:

a
	Takes type <class 'str'> although received '['not a string']' of type <class 'list'>.

b
	Takes input that conforms with <(<class 'int'>, <class 'float'>)> although received 'not an int or a float' of type <class 'str'>.

c
	Takes type <class 'dict'> with keys that conform to the first argument and values that conform to the second argument of <dict[str, typing.Union[int, float]]>, although the received dictionary contains an item with key '2' of type <class 'int'> and value 'two' of type <class 'str'>.

f
	Takes input that conforms with <(<class 'str'>, <class 'int'>)> although received '5.0' of type <class 'float'>.
```
And if the inputs do not match the signature...
```python
public_function(
    "zero",
    "invalid input",  # invalid (not int or float), included in errors
    {"two": 2},
    3.2,
    # no argument passed for required positional arg 'e'
    # no argument passed for required positional arg 'f'
    a="a again",  # passing multiple values for parameter 'a'
    # no argument passed for required keyword arg 'g'
    not_a_kwarg="not a kwarg",  # including an unexpected kwarg
)
```
raises:
```
InputsError: Inputs to 'public_function' do not conform with the function signature:

Got multiple values for argument: 'a'.

Got unexpected keyword argument: 'not_a_kwarg'.

Missing 2 positional arguments: 'e' and 'f'.

Missing 1 keyword-only argument: 'g'.

The following inputs to 'public_function' do not conform with the corresponding type annotation:

b
	Takes input that conforms with <(<class 'int'>, <class 'float'>)> although received 'invalid input' of type <class 'str'>.
```

## Installation

`$ pip install valimp`

No dependencies!

## Documentation
[tutorial.ipynb](https://github.com/maread99/valimp/blob/master/docs/tutorials/tutorial.ipynb) offers a walk-through of all the functionality.

Further documentation can be found in the module docstring of [valimp.py](https://github.com/maread99/valimp/blob/master/src/valimp/valimp.py).

## Why another validation library!?

### Why even validate input type?
Some may argue that validating the type of public inputs is not pythonic and we can 'duck' out of it and let the errors arise where they may. I'd argue that for the sake of adding a decorator I'd rather raise an intelligible error message than have to respond to an issue asking 'why am I getting this error...'.

> :information_source: `valimp` is only intended for handling inputs to **public functions**. For internal validation, consider using a type checker (for example, [mypy](https://github.com/python/mypy)). 

Also, I like the option of abstracting away all parsing, coercion and validation of public inputs and just receiving the formal parameter as required. For example, public methods in [market-prices](https://github.com/maread99/market_prices) often include a 'date' parameter. I like to offer users the convenience to pass this as either a `str`, a `datetime.date` or a `pandas.Timestamp`, although internally I want it as a `pandas.Timestamp`. I can do this with Valimp by simply including `Coerce(pandas.Timestamp)` to the metadata of the type annotation of each 'date' parameter. I also need to validate that the input is timezone-naive and does indeed represent a date rather than a time. I can do this be defining a single `valimp.Parser` and similarly including it to the annotation metadata of the 'date' parameters. Everything's abstracted away. With a little understanding of type annotations the user can see what's going on by simple inspection of the function's signature (as included within the standard help).

### Why wouldn't I just use Pydantic?
[Pydantic](https://github.com/pydantic/pydantic) is orientated towards the validation of inputs to dataclasses. If that's what you're after then you should definitely be looking there.

The Pydantic V2 `@validate_call` decorator does provide for validating function input against type hints. However, I think it's [fair to say](https://github.com/pydantic/pydantic/issues/6794) that it's less functional than the `@validate_arguments` decorator of Pydantic V1 (of note, in V2 it's not possible to validate later parameters based on values received by earlier parameters). This loss of functionality, together with finding `@validate_arguments` somewhat clunky to do anything beyond simple type validation, led me to write `valimp`.

If you only want to validate the type of function inputs then Pydantic V2 `@validate_call` will do the trick. If you're after additional validation, parsing or coercion then chances are you'll find `valimp` to be a simpler option. If you want to be able to dynamically reference earlier parameters when parsing/validating then definitely have a look at `valimp`.

## Limitations and Development

`valimp` does not currently support:
* variable length arguments (i.e. *args in the function signature).
* variable length keyword arguments (i.e. **kwargs in the function signature).
* precluding positional-only arguments being passed as keyword arguments.

`valimp` currently supports:
* use of the following type annotations:
    * built-in classes, for example `int`, `str`, `list`, `dict` etc
    * custom classes
    * `collections.abc.Sequence`
    * `collections.abc.Mapping`
    * typing.Any
    * typing.Literal
    * typing.Union ( `|` from 3.10 )
    * typing.Optional ( `<cls> | None` from 3.10)
    * collections.abc.Callable, although validation of subscripted types is **not** supported
* validation of container items for the following generic classes:
    * `list`
    * `dict`
    * `tuple`
    * `set`
    * `collections.abc.Sequence`
    * `collections.abc.Mapping`

That's it for now, although the library has been built with development in mind and PRs are very much welcome!

## License

[MIT License][license]


[license]: https://github.com/maread99/valimp/blob/master/LICENSE.txt
