semvec/pypi-packages huggingface dataset:
hatch-fancy-pypi-readme 25.1.0
Fancy PyPI READMEs with Hatch
Requires Python: >=3.8
Keywords: documentation, hatch, pypi, readme

# Your ✨Fancy✨ Project Deserves a ✨Fancy✨ PyPI Readme!

*hatch-fancy-pypi-readme* is an MIT-licensed metadata plugin for [Hatch](https://hatch.pypa.io/) by [Hynek Schlawack](https://hynek.me/).

Its purpose is to help you to have fancy PyPI readmes – unlike *this* one you’re looking at right now.

Please check out the [documentation](https://github.com/hynek/hatch-fancy-pypi-readme#readme) to see what *hatch-fancy-pypi-readme* can do for you and your projects!
dxsp 13.0.1
DXSP (DeX SwaP), A defi swap helper package. Swap made easy.
Requires Python: >=3.10
Keywords: dex, swap, crypto, exchange, defi



<table style="border: 1px solid transparent">
  <tr>
    <td>
<a href="http://talky.readthedocs.io"><img src="https://img.shields.io/badge/Wiki-%23000000.svg?style=for-the-badge&logo=wikipedia&logoColor=white"></a>
<a href="https://github.com/mraniki/tt/"><img src="https://img.shields.io/badge/github-%23000000.svg?style=for-the-badge&logo=github&logoColor=white"></a><br>
<a href="https://hub.docker.com/r/mraniki/tt"><img src="https://img.shields.io/docker/pulls/mraniki/tt?style=for-the-badge"></a><br>
  </td>
    <td align="center"><img width="200" alt="Logo" src="https://user-images.githubusercontent.com/8766259/231213427-63ea2752-13d5-4993-aee2-90671b57fc6e.png"></td>
  </tr>
  <tr>
    <td>
      <a href="https://pypi.org/project/dxsp/"><img src="https://img.shields.io/pypi/v/dxsp?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://pypi.org/project/dxsp/"><img src="https://img.shields.io/pypi/dm/dxsp?style=for-the-badge&logo=PyPI&logoColor=white&label=pypi&labelColor=grey"></a><br>
      <a href="https://github.com/mraniki/dxsp/"><img src="https://img.shields.io/github/actions/workflow/status/mraniki/dxsp/%F0%9F%91%B7Flow.yml?style=for-the-badge&logo=GitHub&logoColor=white"></a><br>
   <a href="https://talky.readthedocs.io/projects/dxsp/"><img src="https://readthedocs.org/projects/dxsp/badge/?version=latest&style=for-the-badge"></a><br>
   <a href="https://codebeat.co/projects/github-com-mraniki-dxsp-main"><img src="https://codebeat.co/badges/b1376839-73bc-4b41-bfc1-2fb099f1fc2a"/></a><br>
   <a href="https://codecov.io/gh/mraniki/dxsp"><img src="https://codecov.io/gh/mraniki/dxsp/branch/main/graph/badge.svg?token=39ED0ZA6IH"/> </a><br>
  <a href="https://codeclimate.com/github/mraniki/dxsp/maintainability"><img src="https://api.codeclimate.com/v1/badges/ec80e827d5878e60ba12/maintainability" /></a>
    </td>
    <td align="left"> 
Swap made easy<br>
Trade on any blockchains <br>with uniswap based router, 0x protocol and kwenta sdk. 
    </td> 
  </tr>
</table>

<h5>How to use it</h5>
<pre>
<code>
   from dxsp import DexSwap
    dex = DexSwap()
    #BUY 1% to SWAP with BITCOIN
    #demo_tx = await dex.submit_order({
        "action": "BUY",
        "instrument": "BTC",
        "quantity": 1,
    })
</code>
</pre>

<h5>Documentation</h5>

<a href="https://talky.readthedocs.io/projects/dxsp/"><img src="https://img.shields.io/badge/Documentation-000000?style=for-the-badge&logo=readthedocs&logoColor=white"></a><br>
icspylab 0.1.0
Invariant Coordinate Selection (ICS) for multivariate data analysis.
Requires Python: >=3.9

# ICSpyLab

## Overview

Invariant Coordinate Selection (ICS) is a data transformation method. It transforms the data, via the simultaneous
diagonalization of two scatter matrices, into an invariant coordinate system or independent components,
depending on the underlying assumptions.
It is particularly useful for dimension reduction. Unlike PCA, ICS is not based on variance maximization but on the
maximization/minimization of a generalized kurtosis, and it is invariant not only to orthogonal data transformations but
to any affine transformation.

This package brings the main functionalities of the [ICS R package](https://cran.r-project.org/web/packages/ICS/index.html)
to Python, offering tools for identifying and selecting invariant coordinates in multivariate data.
It includes various covariance estimators, transformation settings,
and plotting utilities. Our extensive testing ensures results consistent with the R package, making it easy for users to
transition from R to Python or start fresh with ICS.

Check out the [documentation](https://icspylab.readthedocs.io/en/latest/) for more details.

## Installation

```bash
pip install numpy pandas scipy scikit-learn seaborn matplotlib 
pip install icspylab
```

### Usage

```python
from icspylab import ICS, cov, covW
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
X = iris.data

# Instantiate ICS object
# ics = ICS() # default parameters
ics = ICS(S1=cov, S2=covW, algorithm='standard', S2_args={'alpha': 1, 'cf': 2})

# Fit and transform the ICS model (equivalent of the function ICS-S3() from the R package ICS)
ics.fit_transform(X)

# Printing a summary
ics.describe()
```