semvec/pypi-packages huggingface dataset:
fractaltext 0.1.0
FractalText: recursive plaintext data format
Requires Python: >=3.10
Keywords: fractaltext

###########
FractalText
###########

Python implementation of `FractalText <https://github.com/0y2k/fractaltext-spec>`_.
FractalText is a recursive plaintext data format.

***************
Install and Run
***************

build
  ::

    uv build
test
  ::

    uv run --group test pytest

***********
Development
***********

install pre-commit
  ::

    uv run pre-commit install
lint and format
  ::

    uv run pre-commit run -a
generate doc
  ::

    uv run --group doc make -C doc html
metaidigittts 0.1.3
Speech Recognition Library
Requires Python: >=3.10

# metaidigitstt

## Overview
**metaidigitstt** is a simple and lightweight speech-to-text and text-to-speech recognition module. It leverages **Google Text-to-Speech (gTTS)** for text-to-speech conversion and **pydub** for audio format conversion.

## Features
- Convert text to speech using gTTS
- Save speech as an MP3 file
- Convert MP3 to WAV format
- Play the generated audio file
- Cross-platform support (Windows, macOS, Linux)

## Installation

### Prerequisites
Ensure you have Python **3.10+** ; **google-cloud-texttospeech** & **pydub** libraries installed on your system.

### Install Dependencies
Run the following command to install required dependencies:
```sh
pip install metaidigittts
pip install google-cloud-texttospeech
pip install pydub
```

## Usage

### Import and Use in Your Python Code
```python
from metaidigitstt import text_to_speech_with_gtts

input_text = "Hello, this is a text-to-speech test!"
text_to_speech_with_gtts(input_text, "output.mp3")
```

### Convert MP3 to WAV
```python
from metaidigitstt import convert_mp3_to_wav

convert_mp3_to_wav("output.mp3", "output.wav")
```

### Play an Audio File
```python
from metaidigitstt import play_audio

play_audio("output.wav")
```

## License
This project is licensed under the **MIT License**. See [LICENSE](LICENSE) for more details.

## Author
Developed by **Suhal Samad**  
📧 Email: samadsuhal@gmail.com
AxisLabeling 0.0.2
Implementation of axis-labeling algorithms

What is it?
===========

AxisLabeling is a Python package that implements several axis-labeling algorithms. The package is ideal for generating aesthetically pleasing axis tick locations for data visualizations. It includes implementations of:

  - **Heckbert’s algorithm**
  - **Wilkinson’s algorithm**
  - **Extended Wilkinson’s algorithm**
  - **Nelder’s algorithm**
  - **R’s pretty algorithm**
  - **Matplotlib’s algorithm**
  - **Gnuplot’s algorithm**
  - **Sparks’ algorithm**
  - **Thayer & Storer’s algorithm**

Input parameters
================

  - **dmin**: Minimum value of the data range.
  - **dmax**: Maximum value of the data range.
  - **m**: Desired (or target) number of labels.


How to use is it?
=================

```python

from AxisLabeling import Labeler

# Define data range and desired number of labels
dmin, dmax, m = 7.1, 14.1, 4
labeler = Labeler(dmin, dmax, m)

print("Heckbert labels:", labeler.heckbert())
print("Wilkinson labels:", labeler.wilkinson())
print("Extended labels:", labeler.extended())
print("Nelder labels:", labeler.nelder())
print("R pretty labels:", labeler.rpretty())
print("Matplotlib labels:", labeler.matplotlib_labeling())
print("Gnuplot labels:", labeler.gnuplot_labeling())
print("Sparks labels:", labeler.sparks())
print("Thayer labels:", labeler.thayer())

```

Where to get it?
================

`pip install AxisLabeling`


References
============

1. Heckbert, P. S. (1990) Nice numbers for graph labels, Graphics Gems I.
2. Wilkinson, L. (2005) The Grammar of Graphics.
3. Talbot, J., Lin, S., Hanrahan, P. (2010) An Extension of Wilkinson’s Algorithm for Positioning Tick Labels on Axes, InfoVis 2010.
mib-notebook 0.1.2
A literate programming tool for static notebook as a library
Requires Python: >=3.9

# mib-notebook

Mib is a literate programming tool to publish static html notebooks from python code.

Install it (`pip install` or `uv add`) as `mib-notebook`, import it as `mib`.

You put your code in a file like [docs/example.py](docs/example.py),
you run it like a normal python script (e.g. `uv run python example.py`)
and you get out [example.html](https://pietroppeter.github.io/mib/example.html).

It is [nimib.py] without [nim].

The goal is to experiment with a [nimib]-like python api.

[nimib]: https://github.com/pietroppeter/nimib
[nimib.py]: https://github.com/nimib-land/nimib.py
[nim]: https://nim-lang.org/
model-monitor 0.1.1
A comprehensive library for automated model monitoring and drift detection
Requires Python: >=3.8

# Model Monitor

![PyPI](https://img.shields.io/pypi/v/model-monitor)
![Python](https://img.shields.io/pypi/pyversions/model-monitor)
![License](https://img.shields.io/pypi/l/model-monitor)
![Build Status](https://img.shields.io/github/workflow/status/biswanathroul/model-monitor/Python%20package)

A comprehensive Python library for automated model monitoring and drift detection in machine learning systems.

## Features

- **Data Drift Detection**: Identify shifts in feature distributions using statistical tests and distance metrics
- **Model Performance Monitoring**: Track key metrics over time and detect degradation
- **Prediction Drift Analysis**: Monitor changes in model output distributions
- **Customizable Alerting**: Set thresholds and receive notifications when drift is detected
- **Visualization Tools**: Generate comprehensive reports and visualizations of drift metrics
- **Integration Flexibility**: Works with various ML frameworks and data sources
- **Scalable Architecture**: Efficiently handle large datasets and high-throughput models

## Installation

```bash
pip install model-monitor
```

## Quick Start

```python
from model_monitor import Monitor
import pandas as pd

# Initialize monitor with baseline data
baseline_data = pd.read_csv("baseline_data.csv")
production_data = pd.read_csv("production_data.csv") 

monitor = Monitor()
monitor.set_baseline(baseline_data)

# Run drift detection
results = monitor.detect_drift(production_data)

# Generate report
monitor.generate_report("drift_report.html")

# Set up automated monitoring
monitor.schedule(
    data_source="s3://bucket/path/to/data/",
    frequency="daily",
    alert_threshold=0.05,
    notification_email="alerts@example.com"
)
```


## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

- **Biswanath Roul** - [GitHub](https://github.com/biswanathroul/model-monitor)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.