| Name | plum-dispatch JSON |
| Version |
2.5.4
JSON |
| download |
| home_page | None |
| Summary | Multiple dispatch in Python |
| upload_time | 2024-11-26 15:04:15 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
multiple
dispatch
|
| VCS |
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| bugtrack_url |
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No requirements were recorded.
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No Travis.
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| 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: `f(<object object at 0x7fd3b01cd330>)` could not be resolved.
Closest candidates are the following:
f(x: str)
<function f at 0x7fd400644ee0> @ /<ipython-input-2-c9f6cdbea9f3>:6
f(x: int)
<function f at 0x7fd3a0235ca0> @ /<ipython-input-2-c9f6cdbea9f3>:11
f(x: numbers.Number)
<function f at 0x7fd3a0235d30> @ /<ipython-input-2-c9f6cdbea9f3>:16
```
> [!IMPORTANT]
> Dispatch, as implemented by Plum, is based on the _positional_ arguments to a function.
> Keyword arguments are not used in the decision making for which method to call.
> In particular, this means that _positional arguments without a default value must
> always be given as positional arguments_!
>
> Example:
> ```python
> from plum import dispatch
>
> @dispatch
> def f(x: int):
> return x
>
> >>> f(1) # OK
> 1
>
> >> try: f(x=1) # Not OK
> ... except Exception as e: print(f"{type(e).__name__}: {e}")
> NotFoundLookupError: `f()` 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...'
```
# Projects Using Plum
The following projects are using Plum to do multiple dispatch!
Would you like to add your project here?
Please feel free to open a PR to add it to the list!
- [Coordinax](https://github.com/GalacticDynamics/coordinax) implements coordinates in JAX.
- [GPAR](https://github.com/wesselb/gpar) is an implementation of the [Gaussian Process Autoregressive Model](https://arxiv.org/abs/1802.07182).
- [GPCM](https://github.com/wesselb/gpcm) is an implementation of various [Gaussian Process Convolution Models](https://arxiv.org/abs/2203.06997).
- [Galax](https://github.com/GalacticDynamics/galax) does galactic and gravitational dynamics.
- [Geometric Kernels](https://github.com/GPflow/GeometricKernels) implements kernels on non-Euclidean spaces, such as Riemannian manifolds, graphs, and meshes.
- [LAB](https://github.com/wesselb/lab) uses Plum to provide backend-agnostic linear algebra (something that works with PyTorch/TF/JAX/etc).
- [MLKernels](https://github.com/wesselb/mlkernels) implements standard kernels.
- [MMEval](https://github.com/open-mmlab/mmeval) is a unified evaluation library for multiple machine learning libraries.
- [Matrix](https://github.com/wesselb/matrix) extends LAB and implements structured matrix types, such as low-rank matrices and Kronecker products.
- [NetKet](https://github.com/netket/netket), a library for machine learning with JAX/Flax targeted at quantum physics, uses Plum extensively to pick the right, efficient implementation for a large combination of objects that interact.
- [NeuralProcesses](https://github.com/wesselb/neuralprocesses) is a framework for composing Neural Processes.
- [OILMM](https://github.com/wesselb/oilmm) is an implementation of the [Orthogonal Linear Mixing Model](https://arxiv.org/abs/1911.06287).
- [PySAGES](https://github.com/SSAGESLabs/PySAGES) is a suite for advanced general ensemble simulations.
- [Quax](https://github.com/patrick-kidger/quax) implements multiple dispatch over abstract array types in JAX.
- [Unxt](https://github.com/GalacticDynamics/unxt) implements unitful quantities in JAX.
- [Varz](https://github.com/wesselb/varz) uses Plum to provide backend-agnostic tools for non-linear optimisation.
[See the docs for a comparison of Plum to other implementations of multiple dispatch.](https://beartype.github.io/plum/comparison.html)
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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: `f(<object object at 0x7fd3b01cd330>)` could not be resolved.\n\nClosest candidates are the following:\n f(x: str)\n <function f at 0x7fd400644ee0> @ /<ipython-input-2-c9f6cdbea9f3>:6\n f(x: int)\n <function f at 0x7fd3a0235ca0> @ /<ipython-input-2-c9f6cdbea9f3>:11\n f(x: numbers.Number)\n <function f at 0x7fd3a0235d30> @ /<ipython-input-2-c9f6cdbea9f3>:16\n```\n\n\n> [!IMPORTANT]\n> Dispatch, as implemented by Plum, is based on the _positional_ arguments to a function.\n> Keyword arguments are not used in the decision making for which method to call.\n> In particular, this means that _positional arguments without a default value must\n> always be given as positional arguments_!\n>\n> Example:\n> ```python\n> from plum import dispatch\n>\n> @dispatch\n> def f(x: int):\n> return x\n>\n> >>> f(1) # OK\n> 1\n>\n> >> try: f(x=1) # Not OK\n> ... except Exception as e: print(f\"{type(e).__name__}: {e}\")\n> NotFoundLookupError: `f()` could not be resolved...\n> ```\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# Projects Using Plum\n\nThe following projects are using Plum to do multiple dispatch!\nWould you like to add your project here?\nPlease feel free to open a PR to add it to the list!\n\n- [Coordinax](https://github.com/GalacticDynamics/coordinax) implements coordinates in JAX.\n- [GPAR](https://github.com/wesselb/gpar) is an implementation of the [Gaussian Process Autoregressive Model](https://arxiv.org/abs/1802.07182).\n- [GPCM](https://github.com/wesselb/gpcm) is an implementation of various [Gaussian Process Convolution Models](https://arxiv.org/abs/2203.06997).\n- [Galax](https://github.com/GalacticDynamics/galax) does galactic and gravitational dynamics.\n- [Geometric Kernels](https://github.com/GPflow/GeometricKernels) implements kernels on non-Euclidean spaces, such as Riemannian manifolds, graphs, and meshes.\n- [LAB](https://github.com/wesselb/lab) uses Plum to provide backend-agnostic linear algebra (something that works with PyTorch/TF/JAX/etc).\n- [MLKernels](https://github.com/wesselb/mlkernels) implements standard kernels.\n- [MMEval](https://github.com/open-mmlab/mmeval) is a unified evaluation library for multiple machine learning libraries.\n- [Matrix](https://github.com/wesselb/matrix) extends LAB and implements structured matrix types, such as low-rank matrices and Kronecker products.\n- [NetKet](https://github.com/netket/netket), a library for machine learning with JAX/Flax targeted at quantum physics, uses Plum extensively to pick the right, efficient implementation for a large combination of objects that interact.\n- [NeuralProcesses](https://github.com/wesselb/neuralprocesses) is a framework for composing Neural Processes.\n- [OILMM](https://github.com/wesselb/oilmm) is an implementation of the [Orthogonal Linear Mixing Model](https://arxiv.org/abs/1911.06287).\n- [PySAGES](https://github.com/SSAGESLabs/PySAGES) is a suite for advanced general ensemble simulations.\n- [Quax](https://github.com/patrick-kidger/quax) implements multiple dispatch over abstract array types in JAX.\n- [Unxt](https://github.com/GalacticDynamics/unxt) implements unitful quantities in JAX.\n- [Varz](https://github.com/wesselb/varz) uses Plum to provide backend-agnostic tools for non-linear optimisation.\n\n[See the docs for a comparison of Plum to other implementations of multiple dispatch.](https://beartype.github.io/plum/comparison.html)\n",
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