semvec/pypi-packages huggingface dataset:
feynmodel 0.0.11
Models for constructing Feynman diagrams
Requires Python: <4.0,>=3.8

# FeynModel

FeynModel is a project to develop an XML dialect for describing Feynman Models.
It is in design very close to the UFO format, but not as restrictive (-> less complete).

[![PyPI version][pypi image]][pypi link] [![PyPI version][pypi versions]][pypi link]  ![downloads](https://img.shields.io/pypi/dm/feynml.svg)


[![test][a t image]][a t link]     [![Coverage Status][c t i]][c t l] [![Codacy Badge][cc c i]][cc c l]  [![Codacy Badge][cc q i]][cc q l]  [![Documentation][rtd t i]][rtd t l]

## Installation
```sh
pip install [--user] feynmodel
```

or from cloned source:

```sh
poerty install --with docs --with dev
poetry shell
```

## Documentation

*   <https://pyfeyn2.readthedocs.io/en/stable/feynml/>
*   <https://apn-pucky.github.io/pyfeyn2/feynml/index.html>

## Related:

*   <https://github.com/APN-Pucky/feynml>
*   <https://github.com/APN-Pucky/pyfeyn2>


## Development


### package/python structure:

*   <https://mathspp.com/blog/how-to-create-a-python-package-in-2022>
*   <https://www.brainsorting.com/posts/publish-a-package-on-pypi-using-poetry/>

[doc stable]: https://apn-pucky.github.io/feynmodel/index.html
[doc test]: https://apn-pucky.github.io/feynmodel/test/index.html

[pypi image]: https://badge.fury.io/py/feynmodel.svg
[pypi link]: https://pypi.org/project/feynmodel/
[pypi versions]: https://img.shields.io/pypi/pyversions/feynmodel.svg

[a t link]: https://github.com/APN-Pucky/feynmodel/actions/workflows/test.yml
[a t image]: https://github.com/APN-Pucky/feynmodel/actions/workflows/test.yml/badge.svg

[cc q i]: https://app.codacy.com/project/badge/Grade/6604fe515a7e4ebf927b44f8f5f79dc0
[cc q l]: https://www.codacy.com/gh/APN-Pucky/feynmodel/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=APN-Pucky/feynmodel&amp;utm_campaign=Badge_Grade
[cc c i]: https://app.codacy.com/project/badge/Coverage/6604fe515a7e4ebf927b44f8f5f79dc0
[cc c l]: https://www.codacy.com/gh/APN-Pucky/feynmodel/dashboard?utm_source=github.com&utm_medium=referral&utm_content=APN-Pucky/feynmodel&utm_campaign=Badge_Coverage

[c t l]: https://coveralls.io/github/APN-Pucky/feynmodel?branch=master
[c t i]: https://coveralls.io/repos/github/APN-Pucky/feynmodel/badge.svg?branch=master

[rtd t i]: https://readthedocs.org/projects/pyfeyn2/badge/?version=latest
[rtd t l]: https://pyfeyn2.readthedocs.io/en/latest/?badge=latest
ufo_draw 0.0.6
Draw Feynman diagrams from UFO models
Requires Python: <4.0,>=3.8

# ufo_draw

Allows you to quickly draw feynman diagrams from ufo model files.

## Installation

```bash
pip install ufo_draw # Install it
```
with optional `--user` or `--break-system-packages`. 
However due to the amount of dependencies it might be better to install it using pipx to have dependencies in isolated versions

```bash
pipx install ufo_draw
```

## Example

```
ufo_draw --initial "nu_e nu_e_bar" --final "nu_e nu_e_bar" -o diagram -m ufo_sm
```
creates following diagrams via [pyfeyn2](https://github.com/APN-Pucky/pyfeyn2) from the [ufo_sm](https://github.com/APN-Pucky/ufo_sm) model using [pyqgraf](https://github.com/APN-Pucky/pyqgraf).

![diag0](./img/diagram_0.png)
![diag1](./img/diagram_1.png)
fastk 1.1
Python wrappers for FastK tools
Requires Python: >=3

# FASTK_python

Python interface for [FASTK](https://github.com/thegenemyers/FASTK) (currently Histex and Profex). Python >= 3 is required.

## How to install

```bash
$ git clone https://github.com/yoshihikosuzuki/FASTK_python
$ cd FASTK_python
$ make
$ pip3 install .
```

## How to use

### Quick usage

```python
import fastk

# Histex
hist = fastk.histex("/path/to/source[.hist]")

# Profex
prof = fastk.profex("/path/to/source[.prof]", read_id=1)
```

### `histex()`

```
Signature: fastk.histex(fastk_prefix: str, min_count: int = 1, max_count: int = 100) -> bits.util._counter.RelCounter
Docstring:
Run Histex and return a histogram of k-mer count frequencies.

positional arguments:
  @ fastk_prefix : Prefix of the output files of FastK.

optional arguments:
  @ [min|max]_count : Specify the range of the k-mer count.
```

### `profex()`

```
fastk.profex(
    fastk_prefix: str,
    read_id: int,
    zero_padding: bool = False,
    return_k: bool = False,
) -> Union[List[int], Tuple[List[int], int]]
Docstring:
Run Profex and return the k-mer count profile of a single read.

positional arguments:
  @ fastk_prefix : Prefix of the output files of FastK.
  @ read_id      : Read ID (1, 2, ...)
  @ zero_padding : If True, add (K - 1) zero counts to the prefix.
  @ return_k     : If True, return the value of K as well.
```