| Name | fenbux-plum-dispatch JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | |
| Summary | Multiple dispatch in Python |
| upload_time | 2024-01-15 07:51:30 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
multiple
dispatch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
# [Plum: Multiple Dispatch in Python](https://github.com/beartype/plum)
[](https://zenodo.org/badge/latestdoi/110279931)
[](https://github.com/beartype/plum/actions?query=workflow%3ACI)
[](https://coveralls.io/github/beartype/plum?branch=master)
[](https://beartype.github.io/plum)
[](https://github.com/psf/black)
Everybody likes multiple dispatch, just like everybody likes plums.
The design philosophy of Plum is to provide an implementation of multiple dispatch that is Pythonic, yet close to how [Julia](http://julialang.org/) does it.
[See here for a comparison between Plum, `multipledispatch`, and `multimethod`.](https://beartype.github.io/plum/comparison.html)
*Note:*
Plum 2 is now powered by [Beartype](https://github.com/beartype/beartype)!
If you notice any issues with the new release, please open an issue.
# Installation
Plum requires Python 3.8 or higher.
```bash
pip install plum-dispatch
```
# [Documentation](https://beartype.github.io/plum)
See [here](https://beartype.github.io/plum).
# What's This?
Plum brings your type annotations to life:
```python
from numbers import Number
from plum import dispatch
@dispatch
def f(x: str):
return "This is a string!"
@dispatch
def f(x: int):
return "This is an integer!"
@dispatch
def f(x: Number):
return "This is a general number, but I don't know which type."
```
```python
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
>>> f(1.0)
'This is a number, but I don't know which type.'
>>> f(object())
NotFoundLookupError: For function `f`, `(<object object at 0x7fb528458190>,)` could not be resolved.
```
This also works for multiple arguments, enabling some neat design patterns:
```python
from numbers import Number, Real, Rational
from plum import dispatch
@dispatch
def multiply(x: Number, y: Number):
return "Performing fallback implementation of multiplication..."
@dispatch
def multiply(x: Real, y: Real):
return "Performing specialised implementation for reals..."
@dispatch
def multiply(x: Rational, y: Rational):
return "Performing specialised implementation for rationals..."
```
```python
>>> multiply(1, 1)
'Performing specialised implementation for rationals...'
>>> multiply(1.0, 1.0)
'Performing specialised implementation for reals...'
>>> multiply(1j, 1j)
'Performing fallback implementation of multiplication...'
>>> multiply(1, 1.0) # For mixed types, it automatically chooses the right optimisation!
'Performing specialised implementation for reals...'
```
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"description": "# [Plum: Multiple Dispatch in Python](https://github.com/beartype/plum)\n\n[](https://zenodo.org/badge/latestdoi/110279931)\n[](https://github.com/beartype/plum/actions?query=workflow%3ACI)\n[](https://coveralls.io/github/beartype/plum?branch=master)\n[](https://beartype.github.io/plum)\n[](https://github.com/psf/black)\n\nEverybody likes multiple dispatch, just like everybody likes plums.\n\nThe design philosophy of Plum is to provide an implementation of multiple dispatch that is Pythonic, yet close to how [Julia](http://julialang.org/) does it.\n[See here for a comparison between Plum, `multipledispatch`, and `multimethod`.](https://beartype.github.io/plum/comparison.html)\n\n*Note:*\nPlum 2 is now powered by [Beartype](https://github.com/beartype/beartype)!\nIf you notice any issues with the new release, please open an issue.\n\n# Installation\n\nPlum requires Python 3.8 or higher.\n\n```bash\npip install plum-dispatch\n```\n\n# [Documentation](https://beartype.github.io/plum)\n\nSee [here](https://beartype.github.io/plum).\n\n# What's This?\n\nPlum brings your type annotations to life:\n\n```python\nfrom numbers import Number\n\nfrom plum import dispatch\n\n\n@dispatch\ndef f(x: str):\n return \"This is a string!\"\n\n\n@dispatch\ndef f(x: int):\n return \"This is an integer!\"\n\n\n@dispatch\ndef f(x: Number):\n return \"This is a general number, but I don't know which type.\"\n```\n\n```python\n>>> f(\"1\")\n'This is a string!'\n\n>>> f(1)\n'This is an integer!'\n\n>>> f(1.0)\n'This is a number, but I don't know which type.'\n\n>>> f(object())\nNotFoundLookupError: For function `f`, `(<object object at 0x7fb528458190>,)` could not be resolved.\n```\n\nThis also works for multiple arguments, enabling some neat design patterns:\n\n```python\nfrom numbers import Number, Real, Rational\n\nfrom plum import dispatch\n\n\n@dispatch\ndef multiply(x: Number, y: Number):\n return \"Performing fallback implementation of multiplication...\"\n\n\n@dispatch\ndef multiply(x: Real, y: Real):\n return \"Performing specialised implementation for reals...\"\n\n\n@dispatch\ndef multiply(x: Rational, y: Rational):\n return \"Performing specialised implementation for rationals...\"\n```\n\n```python\n>>> multiply(1, 1)\n'Performing specialised implementation for rationals...'\n\n>>> multiply(1.0, 1.0)\n'Performing specialised implementation for reals...'\n\n>>> multiply(1j, 1j)\n'Performing fallback implementation of multiplication...'\n\n>>> multiply(1, 1.0) # For mixed types, it automatically chooses the right optimisation!\n'Performing specialised implementation for reals...'\n```\n",
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