semvec/pypi-packages huggingface dataset:
argparsh 0.0.4
Argparse for shell scripts
Requires Python: >=3.10

# argparsh - python argparse for the shell

Ever wanted to parse arguments in bash but felt frustrated by giant case blocks
and unfriendly syntax? Ever tried `getopts` but ended up curled on the floor
sobbing? Have you ever spent sleepless nights hoping that bash argument parsing
could be as simple as python's `argparse`? Maybe `argparsh` is for you.

`argparsh` aims to provide an easy way to construct an argument parsing program
from any shell.

## Usage

```bash
# Create a parser that accepts a string and an optional int value
parser=$({
    # Initialize the parser with the name of the script and a description
    argparsh new $0 -d "Example parser"

    # Add a positional argument - note that args after -- are options to add_arg
    # and not aliases for the argument
    argparsh add_arg strarg -- --help "My string argument"

    # Add a keyword argument that can either be -i <value> or --intarg <value>
    argparsh add_arg -i --intarg -- \
        --help "My int argument" \
        --type int \
        --default 10
})

# Parse the input arguments with the parser above
eval $(argparsh parse $parser -- "$@")

# Access parsed arguments by name
echo "String argument was" $strarg
echo "Integer argument was" $intarg
```

See `example.sh` for a more complete example, and examples of alternate output
formats (e.g. parsing CLI arguments into an associative array)

## Installation

No dependencies besides python/pip.

```sh
pip install argparsh
```

Or, to build from source:

```sh
git clone https://github.com/aneeshdurg/argparsh.git
cd argparsh
pip install .
```

## Similar Works

argparsh differs from previous attempts at improving shell argument parsing by
being shell agnostic (although only bash is tested, it is relatively easy to add
support for more shells, contributions are welcome), supporting
subcommands/subparsers, and allowing detailed + customizable help text to be
configured.

+ [getopts](https://man7.org/linux/man-pages/man1/getopts.1p.html)
    - the OG argument handling utility. A bit clunky to use.
+ [argparse.sh](https://github.com/yaacov/argparse-sh)
    - similar approach, but not completely shell agnostic, and lacking in more
      advanced features, like subcommands.
+ [fish argparse](https://fishshell.com/docs/current/cmds/argparse.html)
    - Fish specific, but a nice utility. Not easy to use outside of fish and no
      subcommand support.
beforerr 2024.11.12
Personal Python utility library
Requires Python: >=3.10

# beforerr


Python utils for different packages

[![PyPI](https://img.shields.io/pypi/v/beforerr.png)](https://pypi.org/project/beforerr/)
[![Pixi
Badge](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/prefix-dev/pixi/main/assets/badge/v0.json)](https://pixi.sh)
[![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet.png)](https://pdm-project.org)

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

## Install

``` sh
pip install beforerr
```
func_file_cache 0.1.2
Cache decorator for functions
Requires Python: >=3.6

# func-cache

`func-file-cache` is a Python decorator that caches function results. The cache is stored on the local disk as a `.pkl` file.

## Installation

```sh
pip install func_file_cache
```

## Usage

```py
from func_file_cache import cache


@cache(caller_name=None, max_m_bytes=10, verbose=True)
def heavy_computation(x, y):
    # Expensive computation
    return x * y


first_result = heavy_computation(1, 2) # First call, the computation is executed and cached.
second_result = heavy_computation(1, 2) # Second call, the result is retrieved from the cache.
```

Since the cache is stored as a file, subsequent calls to the function will be faster across multiple runs of the program.

## Decorator Argument (option)

- `caller_name`: The name of the caller's filename. If `None`, the name of the caller's file is retrieved using the Python inspect module. If you're using this in a Jupyter Notebook, it is recommended to set this argument to the name of the notebook file.
- `max_m_bytes`: The maximum size of the cache file in MB. If the cache file exceeds this size, the cache is not saved.
- `verbose`: If `True`, prints messages to the console.

## LICENSE

MIT
cluster-pub 0.2.2
CLI to cluster scientific papers
Requires Python: <3.12,>=3.11

# ClusterPub

ClusterPub is a tool developed to help researchers in their processes of bibliographic review, 
helping them to find papers related to their areas of interest,
based on search results returned by papers repositories, like, IEEE Xplore and Pubmed.

## Instalation 🛠

To install and execute ClusterPub it is necessary to have Python 3.11 or above installed.

## Run ClusterPub 🚀

To execute ClusterPub run the following command:

#### Cluster publications present in a bibliographic file
```bash Python installation command
cluster-pub {source_file} {result_file}
```

#### OBS: The result_file name should contain the desired extension.

The allowed extensions for the source file are:

- NBIB
- RIS
- BibTex

The allowed extensions for the result file are:

- EPS
- JPEG
- PDF
- PGF
- PNG
- PS
- Raw (Binary)
- RGBA
- SVG
- SVGZ
- TIF
- TIFF
- Webp


#### To obtain help about the parameters and options available execute the following command:
```bash Python installation command
cluster-pub --help
```

There is a folder in the project directory called sample_files, containing files that could be used to execute tests.

## Extract Clustering Metrics  📈

To calculate clustering metrics, like, Silhouette Score, Davies-Bouldin Score and Calinski-Harabasz Score run the following commands:

OBS: The argument number_of_clusters is not the desired clusters quantity,
but it is the quantity of clusters/categories that might exit in the analysed dataset.

#### Calculate Davies-Bouldin Score
```bash Python installation command
cluster-pub-metrics davies-bouldin-score {source_file} {number_of_clusters}
```

#### Calculate Calinski-Harabasz Score
```bash Python installation command
cluster-pub-metrics calinski-harabasz-score {source_file} {number_of_clusters}
```

#### Calculate Silhouette Score
```bash Python installation command
cluster-pub-metrics silhouette-score {source_file} {number_of_clusters} --distance-metric={distance_metric}
```

#### To obtain help for the score commands listed above run the following command:
```bash Python installation command
cluster-pub-metrics {score_command} -- help
```

## Background Information 🔍

The default hyperparameters and algorithms used in this project are:

- Word Embeddings Technicque: Hash2Vec
- Dimensionality Reduction Technicque: SVD
- Number of singular values used in SVD: 8
- Clustering Algorithm: Hierarchical Clustering
- Distance Metric: Cosine Similarity
- Linkage Method: Weighted
- Supported Languages: English
eidolon-ai-client 0.1.40
Eidolon python client and CLI
Requires Python: <3.13,>=3.8

# Eidolon Client

[![PyPI - Downloads](https://img.shields.io/pypi/dm/eidolon-ai-client)](https://pypistats.org/packages/eidolon-ai-client)
[![Tests - Status](https://img.shields.io/github/actions/workflow/status/eidolon-ai/eidolon/test_python.yml?style=flat&label=test)](https://github.com/eidolon-ai/eidolon/actions/workflows/test_python.yml?query=branch%3Amain)

## Eidolon Client and CLI

This package contains a python client to interact with the Eidolon API. It also contains a CLI which can be called with `eidolon-cli`.
```bash
eidolon-cli --help
```

Visit [eidolonai.com](https://www.eidolonai.com/) for more details.

🚨 To install the dependencies needed for the CLI run the following command: 
```bash
pip install 'eidolon-ai-client[cli]'
```

## Thanks for your support

Eidolon is completely open source and free to use. [Star 🌟 it on GitHub](https://github.com/eidolon-ai/eidolon) to show
your support!