rusty-utils 0.1.5

Bring some useful tools from Rust to Python

rusty-utils

Bringing useful tools from Rust to Python.

🚪 Portals

• 🚪 Portals
• 📥 Installation
• 📚 Features
  • Result[T, E]
  • Option[T]
• ⚙️ Usage Examples
  • Example: Handling API Responses
  • Example: Safely Unwrapping Values

📥 Installation

rusty-utils is available on PyPI.

Install using pip:

pip install rusty-utils

Or with poetry:

poetry add rusty-utils

📚 Features

rusty-utils brings Rust-inspired constructs like Result and Option to Python, allowing developers to write cleaner and more robust error-handling code.

For more details, check out the full documentation.

Result[T, E]

The Result type is inspired by Rust, enabling you to handle success (Ok) and failure (Err) in a clean, expressive way.

from rusty_utils import Result, Ok, Err

# Success case
success: Result[int, Exception] = Ok(42)

# Failure case
failure: Result[int, Exception] = Err(Exception("An error occurred"))

Custom Result Types

You can alias your own custom Result types to suit your domain-specific needs.

from typing import TypeVar
from rusty_utils import Result, Ok, Err


class MyError(Exception): pass


_T = TypeVar("_T")
MyResult = Result[_T, MyError]

# Custom success and failure cases
success: MyResult[int] = Ok(42)
failure: MyResult[int] = Err(MyError("Something went wrong"))

? Operator

In Rust, the ? operator is used to easily propagate errors up the call stack, allowing you to return early if a function fails, and to handle success (Ok) and failure (Err) values concisely.

fn side_effect() -> Resut<i32, Error> {
    // some code that may fail
    Ok(42)
}

fn process() -> Result<(), Error> {
  let result = side_effect()?; // Propagates the error if it occurs
  ... // Other operation
}

In Python, you can't overload the ? operator (we even didn't treat ? as a valid operator in Python)

result = side_effect()?  # What???

In Python, something has similar ability to throw the Err and remain the Ok value is:
Python built-in try-except.

from rusty_utils import Catch


def side_effect() -> float:
    # Simulate a potential failure (e.g., division by zero)
    return 42 / 0


@Catch(Exception, ZeroDivisionError)
def wrapped_side_effect() -> float:
    return side_effect()


@Catch(Exception)
def process() -> None:
    result1 = wrapped_side_effect().unwrap_or_raise()  # You achieve same functionality with `unwrap_or_raise()`!
    result2 = Catch(Exception, ZeroDivisionError, func=side_effect).unwrap_or_raise() 

In this example:

• We use the @Catch(Exception) decorator to make sure we can capture the raised Err and return to the caller.
  • What the @Catch(E) do is transform the function into a capturer which returns Result[T, E]
• We can use the Catch in this way (result2) to capture the result of a fallible function call into a Result.
• The wrapped_side_effect() function returns a Result that might be an error (Err) or a valid value (Ok).
  • Since the function returns a float and we mark it might raise an Exception, so it actually returns a Result[float, Exception].
• Then use unwrap_or_raise() to handle the result: if it's an error, it raises the exception, effectively mimicking Rust's ? operator.

This approach enables cleaner error propagation and handling in Python, much like in Rust, but using Python’s exception-handling style.

Although the @Catch decorator accpets multiple exception types, it's recommended to use it only for one type of exception at a time, or your linter might can't resolve the type hints correctly. (like it might think the wrapped_side_effect returns a Result[float, Any])

API Overview

• Querying Result Type:

  • is_ok(): Returns True if the Result is Ok.
  • is_err(): Returns True if the Result is Err.

• Unwrapping Values:

  • expect(message): Unwraps the value or raises UnwrapError with a custom message.
  • unwrap(): Unwraps the value or raises UnwrapError.
  • unwrap_or(default): Returns the provided default value if Err.
  • unwrap_or_else(func): Returns the result of the provided function if Err.
  • unwrap_or_raise(): Raises the exception contained in Err.

• Transforming Results:

  • ok(): Transforms Ok to Option[T]
  • err(): Transforms Err to Option[E]
  • map(func): Applies func to the Ok value.
  • map_err(func): Applies func to the Err value.
  • map_or(default, func): Applies func to Ok or returns default if Err.
  • map_or_else(f_err, f_ok): Applies different functions depending on whether the Result is Ok or Err.

• Logical Operations:

  • and_(other): Returns the second Result if the first is Ok; otherwise returns the original Err.
  • or_(other): Returns the first Ok, or the second Result if the first is Err.
  • and_then(func): Chains another operation based on the Ok value.
  • or_else(func): Chains another operation based on the Err value.

Option[T]

The Option type expands Python's Optional, representing a value that may or may not be present (Some or None).

from rusty_utils import Option

some_value: Option[int] = Option(42)
none_value: Option[int] = Option()

API Overview

• Querying Option Type:

  • is_some(): Returns True if the Option contains a value.
  • is_none(): Returns True if the Option contains no value.

• Unwrapping Values:

  • expect(message): Unwraps the value or raises UnwrapError with a custom message.
  • unwrap(): Unwraps the value or raises UnwrapError.
  • unwrap_or(default): Returns the provided default value if None.
  • unwrap_or_else(func): Returns the result of a provided function if None.

• Transforming Options:

  • map(func): Transforms the Some value.
  • map_or(default, func): Transforms the Some value or returns a default if None.
  • map_or_else(default_func, func): Transforms the Some value or returns the result of a default function if None.

• Logical Operations:

  • and_(other): Returns the second Option if the first is Some; otherwise returns None.
  • or_(other): Returns the first Some, or the second Option if the first is None.
  • and_then(func): Chains another operation based on the Some value.
  • or_else(func): Chains another operation based on the None value.

⚙️ Usage Examples

Here are more practical examples of using Result and Option in real-world scenarios.

Example: Handling API Responses

from rusty_utils import Result, Ok, Err


def fetch_data() -> Result[dict, Exception]:
    try:
        # Simulating an API call
        data = {"id": 824, "name": "Kobe Bryant"}
        return Ok(data)
    except Exception as e:
        return Err(e)


result = fetch_data()

if result.is_ok():
    print("Success:", result.unwrap())
else:
    print("Error:", result.unwrap_err())

Example: Safely Unwrapping Values

from rusty_utils import Option


def get_value() -> Option[int]:
    return Option(42)


some_value = get_value()

print(some_value.unwrap_or(0))  # Outputs: 42

For more advanced use cases, consult the full documentation.