semvec/pypi-packages huggingface dataset:
ai-tictactoe 0.1.5
A Tic-Tac-Toe game with AI opponent
Requires Python: >=3.6

# TicTacToe AI
 TicTacToe AI is a Python package that lets you play Tic-Tac-Toe against the computer. It's like the classic game, but with a smart computer opponent that makes the game more interesting. Have fun playing and see if you can beat the AI!
sosq 1.0.5
Building Dataframe from Stack Overflow API with a search query and Saving data to csv file
Requires Python: >=3.8

# stack overflow search queries (sosq)

This project fetches StackOverflow API returns a dataframe

# install

pip install sosq

# for use

1- Get your key and token from stackexchange / follow instructions.

2- sample code below.

```
import sosq
sosq.get_result("your_search_query", "your_key", "your_access_token")
```
g4f-xn 0.0.6
library for easy access to GPT models based on g4f
Keywords: python, g4f, gpt

### g4f_xn

---
#### A library that allows you to use different GPT models!

Also has some other stuff like:
- reading and writing to files
- sound playback(i mean why not)
- other functions for convenience

The project is not ready for public use, but more for a fun and convinient (in my opinion) use of gpt models 

#### code example:

```python
import g4f_xn as xn

# specify model
xn.settings('gpt-3.5-turbo')

prompt = '''
type numbers from 1 to 10
'''

result = xn.gpt(prompt)
print(result)
```

---
also you can provide an array to xn.gpt that will represent history of previous prompts and answers. It will use it as context and append prompt with response.


#### advanced example:

```python
import g4f_xn as xn

# specify model and provider
xn.settings('gpt-4', 'Bing')

p1 = '''
type numbers from 1 to 10
'''
p2 = '''
multiply each number by 2
'''

chat = []

r1 = xn.gpt(p1, chat)
r2 = xn.gpt(p2, chat)
print(r1)  # 1, 2, 3, ...
print(r2)  # 2, 4, 6, ...
```
---

#### gpt -> chat gpt
You can also save chat variable to txt file using xn.write, xn.read <br>
it will be basically chatgpt now

#### example with local chat history:

```python
import g4f_xn as xn

xn.settings('gpt-3.5-turbo')

history = xn.read(r'path/to/file.txt')
chat = [history]
prompt = '''
type numbers from 1 to 10
'''

result = xn.gpt(prompt, chat)
print(result)

xn.write(''.join(chat), r'path/to/file.txt')
```
---
It is highly recommended to use .ipynb files with this library as it is really flexible for that task
REMLA-Test-Lib-version 0.1.1
A version-aware library designed to provide robust version management and utility functions
Requires Python: >=3.6

# LIB-VERSION

LIB-VERSION is a version-aware library designed to provide robust version management and utility functions. This library is ideal for projects requiring precise version tracking, such as in detailed logging or system monitoring environments.

## Features

- **Version Retrieval**: Easily retrieve the current library version using the `VersionUtil` class.
- **Automatic Versioning**: Utilizes Git tags to automatically version the library.
- **CI/CD Integration**: Automatically publishes new versions to a package registry when tagged in Git.

## Getting Started

### Prerequisites

- Git (for versioning)
- Python 3.x (if using Python)
- Access to a package registry pip

### Installation

To install Library Name, use the following command:

```bash
pip install REMLA-Test-Lib-version
```


### Usage
To retrieve the current version of the library:

```python
from REMLA_Test_Lib_version import VersionUtil
print(VersionUtil.VersionUtil.get_version())
```

### How Versioning Works
The library's version is determined by Git tags. When a new tag is pushed to the repository, a CI/CD pipeline updates a VERSION file in the repository with the current tag. This version is then read by the VersionUtil class.

### Setting up Git Tags
To create a new version, tag your commit with the version number:

```bash
git tag -a v1.0.1 -m "Release version 1.0.1"
git push origin v1.0.1
```

### Automated Version Updates
A GitHub Actions workflow is set up to detect when a new tag is pushed and updates the VERSION file automatically. This is then committed back into the repository. See .github/workflows/updateVersion.yml for the workflow configuration.

### Publishing to a Registry
When a new tag is pushed, another GitHub Actions workflow triggers to build and publish the package to the designated package registry. See .github/workflows/updateVersion.yml for the details.

### License
This project is licensed under the MIT - see the LICENSE file for details.
sleeplab-format 0.4.1
An interoperable and harmonized format for polysomnography recordings.
Requires Python: >=3.10

[![DOI](https://zenodo.org/badge/558261131.svg)](https://zenodo.org/badge/latestdoi/558261131)
[![PyPI version](https://badge.fury.io/py/sleeplab-format.svg)](https://badge.fury.io/py/sleeplab-format)
# sleeplab-format

Sleeplab format (SLF) is a both machine and human-readable format for storing and processing polysomnography data. SLF provides reader and writer with validation of data types and structures. The goal is to make it easier to apply analytics and machine learning pipelines to multiple datasets from different sources.

## Documentation

See [full documentation](https://uef-smartsleeplab.github.io/sleeplab-format/) and [the paper](https://arxiv.org/abs/2402.06702v1) for details.

## Related tools

[sleeplab-converters](https://github.com/UEF-SmartSleepLab/sleeplab-converters) for converting other formats exported from PSG software to sleeplab format.

[sleeplab-tf-dataset](https://github.com/UEF-SmartSleepLab/sleeplab-tf-dataset) for reading data in sleeplab format as a tensorflow Dataset.

## Installation

```bash
pip install sleeplab-format
```

## Usage

``` py
import sleeplab_format as slf
import pandas as pd
from pathlib import Path

# Read a toy dataset
DS_DIR = Path('tests/datasets/dataset1')
ds = slf.reader.read_dataset(DS_DIR)

# Get the list of subjects from series1
subjects = ds.series['series1'].subjects.values()

# Flatten the nested annotations and cast Pydantic models to dicts
all_events_dict = [dict(a)
    for s in subjects
    for a_model in s.annotations.values()
    for a in a_model.annotations
]

# Create a pandas DataFrame for analyses
event_df = pd.DataFrame(all_events_dict)

# Calculate the mean duration of hypopneas
hypopneas = event_df[event_df['name'] == 'HYPOPNEA']
mean_duration = sum(hypopneas['duration']) / len(hypopneas)

# Modify the dataset
additional_info = {'neck_size': 40.0}
ds.series['series1'].subjects['10001'].metadata.additional_info = additional_info
ds.name = 'dataset2'

# Write the modified dataset
MODIFIED_DS_DIR = Path('/tmp/datasets')
slf.writer.write_dataset(ds, MODIFIED_DS_DIR)
```

See [the automatic sleep staging example](examples/dod_sleep_staging/README.md) for a full end-to-end example.

## Contributing

See [contributing](docs/contributing.md).
tfrecord-slow 0.6.0
TFRecord reader and writer without protobuf.
Requires Python: >=3.9

# tfrecord-slow

[![Github Actions](https://img.shields.io/github/actions/workflow/status/SunDoge/tfrecord-slow/python-package.yml?branch=main&style=for-the-badge)](https://github.com/SunDoge/tfrecord-slow/actions/workflows/python-package.yml)
[![Pypi](https://img.shields.io/pypi/v/tfrecord-slow?style=for-the-badge)](https://pypi.org/project/tfrecord-slow/)

TFRecord reader and writer without protobuf.

## Install

### Reader only (without masked crc32 check support)

```shell
pip install tfrecord-slow
```

### Reader with masked crc32 check support

```shell
pip install tfrecord-slow[crc32c]
```

### Writer (must have crc32c installed)

```shell
pip install tfrecord-slow[crc32c]
```

### Use ndarray msgpack support

```shell
pip install tfrecord-slow[msgpack]
```