semvec/pypi-packages huggingface dataset:
submit-tool 0.2.0
A simple tool to cache the configuration files of the commands
Requires Python: >=3.8

# A simple CLI tool to faciliate config-based jobs

## Background

When submitting a job that requires queueing (for example, in SLURM), the configure files used in the commands are not used at the moment the job was submitted. Changing the configuration files right after the submission may cause unexpected consequences when the job is really launched (read the wrong configuration files).

The simple tool provided by this extension is to copy detected scripts and configurations in a temporary directories and to redirect the arguments to those in the temporary directories (by default is `~/.submit-tool/<random_str>/`).

## Installation

```bash
pip install submit-tool
```

## Usage

```
submit-tool <original command>
```

## Supported extensions

- `sh`
- `json`
- `yaml`
- `yml`
- `toml`
- `ini`
pysare 0.1.2
PySaRe -- Neural Network-Based Survival Analysis and Reliability Engineering
Requires Python: >=3.10

<div align="center">

# ◢ &nbsp; PySaRe &nbsp;  ◣

**Python package for neural network-based survival analysis and reliability engineering**

<p>
<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/oholmer/PySaRe" />
<img alt="Version" src="https://img.shields.io/badge/version-0.1-blue" />
<img alt="Github License" src="https://img.shields.io/badge/License-MIT-green.svg" />
</p>

<br />

</div>

# Introduction
PySaRe is a Python package for neural network-based survival analysis and reliability engineering based on PyTorch.
For an introduction to PySaRe, see [examples/quickstart.ipynb](examples/quickstart.ipynb).


# Installation 
PySaRe is available on pip:
```
pip install pysare
```


# Publications
The package is based on the following publications
- **Energy-Based Survival Models for Predictive Maintenance** https://arxiv.org/abs/2302.00629
hsv-slider 0.1.1
A module for creating HSV color sliders using OpenCV
Requires Python: >=3.6

# HSV Slider

HSV Slider is a Python module that provides an easy way to create HSV color sliders using OpenCV. It allows you to interactively adjust HSV (Hue, Saturation, Value) color ranges, which can be useful for various computer vision tasks such as color-based object detection.

## Installation

You can install HSV Slider using pip:

```
pip install hsv-slider
```

## Usage

Here's a basic example of how to use HSV Slider:

```python
import cv2
from hsv_slider import create_hsv_slider, lower_bound_hsv, upper_bound_hsv

hsv_slider = create_hsv_slider()

while True:
    values = hsv_slider.get_values()
    lower_bound = lower_bound_hsv(values)
    upper_bound = upper_bound_hsv(values)

    # Use lower_bound and upper_bound in your image processing code

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
toshi-client 0.0.1
Python client for the Full-Text Search Engine Toshi
Requires Python: >=3.11

# Toshi Python Client
This repository is a client for the [Full-Text Search Engine Toshi](https://github.com/toshi-search/Toshi). It allows you to use all features implemented in the Toshi engine.

### How to install
The client is available as a package on Pypi and can be installed via:
```shell
pip install toshi_client
```

### Examples
For a full set of examples check out the [integration tests](https://github.com/SmartMonkey-git/py-toshi-client/blob/main/tests/test_integration_sync.py).

Here is a basic example:

1. Create an index and submit it

```python
from toshi_client.client import ToshiClient
from toshi_client.index.index_builder import IndexBuilder
from toshi_client.index.field_options import TextOptionIndexing

builder = IndexBuilder()

builder.add_text_field(name="lyrics", stored=True, indexing=TextOptionIndexing())
builder.add_i64_field(name="year", stored=True, indexed=True)
builder.add_u64_field(name="idx", stored=True, indexed=True)
builder.add_text_field(name="artist", stored=True, indexing=TextOptionIndexing())
builder.add_text_field(name="genre", stored=True, indexing=TextOptionIndexing())
builder.add_text_field(name="song", stored=True, indexing=TextOptionIndexing())
builder.add_facet_field(name="test_facet", stored=True)

index = builder.build("lyrics")
    
client = ToshiClient("http://localhost:8080")
client.create_index(index=index)
```

2. Create a document class according to the just created index

```python
from toshi_client.models.document import Document

class Lyrics(Document):
    @staticmethod
    def index_name() -> str:
        return "lyrics"

    def __init__(
        self,
        lyrics: str,
        year: int,
        idx: int,
        artist: str,
        genre: str,
        song: str,
    ):
        self.lyrics = lyrics
        self.year = year
        self.idx = idx
        self.artist = artist
        self.genre = genre
        self.song = song
```
3. Submit a document to Toshi

```python
doc = Lyrics(
    lyrics="Gold on the ceiling, I ain't blind, just a matter of time",
    year=2011,
    idx=2,
    artist="The Black Keys",
    genre="Rock",
    song="Gold on the Ceiling",
    test_facet="/a/b",
)
client.add_document(document=doc)
```
4. Use a query of your choice to retrieve it

```python
from toshi_client.query.term_query import TermQuery

query = TermQuery(term="ceiling", field_name="lyrics")
documents = client.search(query, Lyrics)
```