semvec/pypi-packages huggingface dataset:
syntera-ai 0.1.3
An AI-powered DevOps toolkit for infrastructure automation and analysis
Requires Python: >=3.8

# SynteraAI DevOps Toolkit

An AI-powered DevOps toolkit for infrastructure automation and analysis. This tool helps DevOps engineers automate infrastructure recommendations, security scanning, code quality analysis, and more using AI.

## Features

- 🤖 AI-powered infrastructure recommendations
- 🔒 Security scanning and analysis
- 📊 Code quality analysis
- 🐳 Docker file generation
- 📦 Dependency checking
- 👥 Contributor statistics
- 📝 Log analysis
- ⚡ Performance optimization suggestions

## Installation

```bash
pip install syntera-ai
```

## Quick Start

1. Set up your OpenAI API key:
```bash
export OPENAI_API_KEY='your-api-key-here'
```

2. Run the dashboard:
```bash
syntera-ai dashboard
```

3. Enter your GitHub repository URL when prompted.

4. Use the interactive dashboard to:
   - Generate infrastructure recommendations
   - Perform security scans
   - Analyze code quality
   - Generate Docker files
   - And more!

## Usage Examples

### Infrastructure Recommendations
```bash
syntera-ai dashboard
# Select option 2 for infrastructure recommendations
```

### Security Scanning
```bash
syntera-ai dashboard
# Select option 3 for security scanning
```

### Code Quality Analysis
```bash
syntera-ai dashboard
# Select option 6 for code quality analysis
```

## Requirements

- Python 3.8 or higher
- OpenAI API key
- Git (for repository analysis)

## Contributing

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

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

Fouad Mahmoud
- GitHub: [@fouadmahmoud281](https://github.com/fouadmahmoud281)
- Email: fouadmahmoud281@gmail.com
fennet-mhc 0.0.1.dev0
Fennet-MHC
Requires Python: <3.12,>=3.10
Keywords: contrastive learning, protein language model, immunopeptidomics, mass spectrometry, HLA, MHC, peptide-MHC binding prediction

# Fennet
Deep learning for multi-dimensional proteomics


# mhc
Foundation model for MHC-peptide binding prediction based on deep contrastive learning
algo-trade-v1 0.1.0
A Python library for algorithmic trading analysis
Requires Python: >=3.7

# Algo Trade V1

A Python library for algorithmic trading analysis that provides various tools for market analysis, including technical analysis, fundamental analysis, and pump detection.

## Features

- Technical Analysis Tools
- Fundamental Analysis
- Pump Detection
- Volume Profile Analysis
- Signal Analysis
- Data Fetching Utilities

## Installation

You can install the package using pip:

```bash
pip install algo-trade-v1
```

## Usage

Here's a basic example of how to use the library:

```python
from algo_trade_v1.libs import technical_analyzer, fundamental_analyzer, pump_ranking_analyzer

# Technical Analysis
analyzer = technical_analyzer.TechnicalAnalyzer()
# Your analysis code here

# Fundamental Analysis
fundamental = fundamental_analyzer.FundamentalAnalyzer()
# Your fundamental analysis code here

# Pump Detection
pump_detector = pump_ranking_analyzer.PumpRankingAnalyzer()
# Your pump detection code here
```

## Documentation

For detailed documentation, please visit our [documentation page](https://github.com/yourusername/Algo_Trade_v1/wiki).

## Contributing

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

## License

This project is licensed under the MIT License - see the LICENSE file for details.
autohex 0.1.1
Generate unique and vibrant hex color codes from text.
Requires Python: >=3.8
Keywords: color, hex, generator, hash, hsl

 # AutoHex

Generate unique and vibrant hex color codes from text using deterministic hashing.

## Features

- **Deterministic**: Same input text always produces the same color
- **Vibrant Colors**: Uses HSL color space to generate visually appealing colors
- **Customizable**: Support for seeding and different algorithms
- **Lightweight**: No external dependencies

## Installation

```bash
pip install autohex
```

## Quick Start

```python
from autohex import AutoHex

# Create an instance
generator = AutoHex()

# Generate a hex color from text
color = generator.gen("hello world")
print(color)  # #664df9

# Use a seed for consistent variations
seeded_generator = AutoHex(seed="my-project")
color = seeded_generator.gen("hello world")
print(color)  # #ec5915
```

## API Reference

### AutoHex(seed=None, algorithm='vibrant_hsl')

Creates a new color generator instance.

**Parameters:**
- `seed` (str, optional): A seed string to influence color generation
- `algorithm` (str): Color generation algorithm (currently only 'vibrant_hsl')

### gen(text: str) -> str

Generates a hex color code from the input text.

**Parameters:**
- `text` (str): Input text to generate color from

**Returns:**
- `str`: Hex color code (e.g., '#7c5ab8')

## Algorithm

The `vibrant_hsl` algorithm:
1. Hashes the input text using SHA-256
2. Converts hash to HSL color space
3. Ensures vibrant colors by constraining saturation (70-100%) and lightness (50-65%)
4. Converts to RGB and formats as hex

## License

MIT License - see [LICENSE.txt](LICENSE.txt) for details.
deepvoxnet3 3.0.1
Framework on top of Tensorflow/Keras to transform datasets and save entire pipelines.
Requires Python: >=3.11
Keywords: deep learning, image segmentation, semantic segmentation, medical image analysis, medical image segmentation, deepvoxnet, deepvoxnet2, deepvoxnet3

# deepvoxnet3
Framework on top of Tensorflow/Keras to transform datasets and save entire pipelines.
alissimplemath 0.0.2
Um pacote simples de operações matemáticas para fins de aprendizado
Requires Python: >=3.6

# Mathsimple

Este Ã© um pacote de exemplo para realizar operaÃ§Ãµes matemÃ¡ticas simples: adiÃ§Ã£o e subtraÃ§Ã£o.

## InstalaÃ§Ã£o

```bash
pip install alissonimplemath
```

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

## Usage

```
from mathsimple import somar, subtrair

print(somar(2, 3))      # SaÃ­da: 5
print(subtrair(5, 3))   # SaÃ­da: 2
```

## Author
[Alisson GuarniÃªr AraÃºjo Faria](https://github.com/alissonguarnier)

## License
[MIT](https://choosealicense.com/licenses/mit/)
nepali-toolkit 0.1.3
A collection of tools for working with Nepali data, digits, dates and more.
Requires Python: >=3.10
Keywords: nepali, districts, municipalities, validators, postal codes, bs, b.s, date, datetime, time, timezone, nepal, bikram, nepali-date, nepali-datetime, nepal-time, npt, nepal-timezone

# Nepali Toolkit
![PyPI version](https://img.shields.io/pypi/v/nepali-toolkit)
![PyPI downloads](https://img.shields.io/pypi/dm/nepali-toolkit)
![License](https://img.shields.io/pypi/l/nepali-toolkit)

---
**Nepali Toolkit** is a Python package that provides a collection of utilities for working with Nepali data, calender, locations, digits, dates, and more.

## Features

- Convert between Nepali and English digits
- Work with Nepali locations, districts, municipalities, wards.

## Installation

```bash
pip install nepali-toolkit
```
fh-init 0.1.4
Quickly scaffold FastHTML apps!
Requires Python: >=3.12

# FastHTML CLI

[![PyPI](https://img.shields.io/pypi/v/fh-init)](https://pypi.org/project/fh-init/)
[![License](https://img.shields.io/github/license/ExploringML/fasthtml-cli)](https://github.com/your-username/your-repo/blob/main/LICENSE)
[![Sponsor](https://img.shields.io/badge/Sponsor-FastHTML%20CLI-pink?logo=github)](https://github.com/sponsors/ExploringML)

Fastest way to scaffold FastHTML apps!

## Usage

To create a new FastHTML application, use the `fh-init` command. Make sure [`uv`](https://docs.astral.sh/uv/getting-started/installation/) is installed before running `uvx`:

```bash
uvx fh-init [OPTIONS] NAME
```

### Arguments

*   `NAME`: The name of your FastHTML application (required).

### Options

*   `--template, -p TEXT`: The name of the FastHTML template to use (default: `base`).
*   `--reload, -r`: Enable live reload.
*   `--pico, -p`: Enable Pico CSS (default: `True`).
*   `--uv / --no-uv`: Use uv to manage project dependencies (default: `uv`).
*   `--tailwind, -t`: Enable Tailwind CSS.
*   `--install-completion`: Install completion for the current shell.
*   `--show-completion`: Show completion for the current shell.
*   `--help`: Show the help message and exit.

### Example

```bash
uvx fh-init my_awesome_app --reload --tailwind
```

This command will create a new FastHTML application named `my_awesome_app` with live reload and Tailwind CSS enabled.

Then to run the FastHTML app:

```bash
cd my_awesome_app
uv run main.py
```
Jeesum680 0.1.0
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# Jeesum680 📊

*Jeesum680* is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install Jeesum680


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/jeevan7674/Jeesum680.git




In your Python project:




from Jeesum680 import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from Jeesum680 import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from Jeesum680 import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from Jeesum680 import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from Jeesum680 import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from Jeesum680 import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from Jeesum680 import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Reddy Jeevan Reddy
Computer Science & Engineering | Web design & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
bnrrcpy 0.1
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# bnrrcpy 📊

bnrrcpy is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install bnrrcpy


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/Nithin789b/bnrrcpy.git




In your Python project:




from bnrrcpy import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from bnrrcpy import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from bnrrcpy import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from bnrrcpy import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from bnrrcpy import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from bnrrcpy import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from bnrrcpy import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Nitheswar Reddy Bommepalli
Artificial Intelligence & Data Science | ML & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package