semvec/pypi-packages huggingface dataset:
python-ecvrf-4o 0.1.1
An Elliptic Curve Verifiable Random Function (ECVRF) library, initially designed for Omne.
License: MIT
Requires Python: <4,>=3.7
Keywords: ecvrf cryptography vrf elliptic-curve verifiable-random-function

# ECVRF Library

A custom Elliptic Curve Verifiable Random Function (ECVRF) implementation.

## Features

- Key Generation and Management
- Proof Generation and Verification
- Hash-to-Curve Mapping
- Comprehensive Testing Suite
- Security Audits and Static Analysis

## Installation

```bash
pip install -e .
```

## Usage
```python
from ecvrf import KeyManager, VRF, VRFVerifier
```

# Initialize KeyManager
```python
km = KeyManager()
```

# Generate Key Pair
```python
private_pem, public_pem = km.generate_keypair()
```

# Initialize VRF and Verifier
```python
vrf = VRF(km)
verifier = VRFVerifier(km)
```

# Input Data
```python
alpha = b"Sample input for ECVRF."
```

# Generate Proof and Beta
```python
beta, proof = vrf.prove(private_pem, alpha)
```

# Verify Proof
```python
is_valid = verifier.verify(public_pem, alpha, beta, proof)
print(f"Verification successful: {is_valid}")
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

MIT License
eda-helper-tool 0.0.2
Python functions for EDA: univariate, bivariate, and multivariate analysis.
License: MIT
Requires Python: >=3.7

**This Python project provides EDA (Exploratory Data Analysis) functions that can be applied to dataframes and individual columns. It generates analysis, visualizations, and key observations.**


# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle
rust-itertools 0.0.1
Run Rusts itertools and more in Python.
License: MIT
Requires Python: >=3.10
Keywords: itertools

# rust_itertools
Experimental repo to run Rusts itertools in Python.

## Setup
Initially, this will be mostly used as a proof of concept. This is to investigate the comparisons between:
- rust's itertools functionality vs python's itertools functionality
- rust's itertools functionality vs more_itertools functionality
- new rust functions vs more_itertools functionality
- new rust functions vs new python functions around itertools

Practically, this means not all functionality from rust's itertools will be available

## Notes on reasons to use this repository
Often, the major goal of porting Rust functionality over to Python is for speed. So far, my initial experimentation
has shown that that gain is very low (or even non-existant) due to 2 main reasons:
- `itertools` is mostly written in C so already quite fast and roughly on par with Rust
- even for `more_itertools` and new python functions: not having to copy over the results back to Python is a major gain
    vs the rust implementations for the type of functions we deal with here. Example: there are over 36 million
    permutations of the numbers 0 to 9 (incl 9). Generating in Rust and then accessing from Python takes 2.24 seconds
    on my pc, where the CPython itertools takes 0.557 seconds to do the same.

There are some exceptions though, especially for:
- newly written Python code that is not in CPython (both `more_itertools` and additional functions in this repo)
- situations where the Rust compiler can work wonders in reducing logic down to more direct logic
- some tools for which the ratio of compute vs iteration is not as much towards iteration

Next to this, you can use this repository for functionality not available in Python's `itertools` or if you prefer
the way the Rust version is setup.

## Acknowledgements
The Github Actions setup to automatically crossbuild Python packages across operating systems has been based on the
setup in the [rtoml](https://github.com/samuelcolvin/rtoml) project (although first using the old `setuptools` based
setup, because of the link on the `setuptools-rust` page..).

Of course, also credit to the Rust `itertools` [project](https://github.com/rust-itertools/itertools) and inspiration
from the [more-itertools](https://github.com/more-itertools/more-itertools) and of course the core Python itertools
projects.

## Some big ideal TODO's
- Add support for all functionality of any of the `itertools` packages
- Make both a tuple and a list version of each method where applicable
- Make an iterable PyClass (see https://pyo3.rs/v0.22.3/class/protocols#iterable-objects) that can be reused