semvec/pypi-packages huggingface dataset:
cfmetrics 0.2.1
Python Library for cloudflare analytics web
Requires Python: >=3.7

# Cloudflare Analytics – Because You Need More Graphs 📊


Ever wondered what Cloudflare *thinks* is happening on your website? This incredibly "advanced" tool helps you fetch analytics data using Cloudflare's GraphQL API—because who doesn’t love GraphQL, right? Now you can *almost* understand your website traffic, security threats, and performance (with a pinch of optimism).

## 🚀 Features (Because Everything Needs Bullet Points)

- 📊 Pulls data straight from Cloudflare's GraphQL API (yes, GraphQL, because REST is *so* last decade)
- 🔍 Gives you traffic insights, but *only* if Cloudflare decides to cooperate
- 📈 Shows security metrics—so you can see all those "bad" IPs doing "bad" things
- ⚡ Lightweight, just like your trust in Cloudflare’s analytics

## 📦 Installation (Because Nothing is Easy)

Clone this glorified data-fetcher and install some dependencies:

```sh
# Clone the magical repo
git clone https://github.com/k1m0ch1/cloudflare-analytics.git
cd cloudflare-analytics

# Install the spell components
pip install -r requirements.txt
```

## 🔧 Configuration (Enter Your API Token… If You Dare)

Create a `.env` file and provide your *oh-so-secure* Cloudflare API token:

```sh
CLOUDFLARE_API_TOKEN=your_api_token_here
```

If your token gets leaked, don’t worry—Cloudflare probably already knew about it.

## 🚀 Usage (Let’s See What Cloudflare *Wants* You to See)

Run this beauty and behold the data:

```sh
python analytics.py
```

Example output (or an error message, who knows?):

```
Fetching Cloudflare analytics data...
Total Requests: 120,345 (or whatever Cloudflare decides)
Unique Visitors: 54,678 (probably bots)
Threats Blocked: 1,234 (but not that one shady IP)
...
```

## 🛠 API Reference (A.k.a. "Read the Docs")

This tool "leverages" Cloudflare’s GraphQL API, which you can tweak in `analytics.py`. Or just pretend you understand GraphQL by checking out [Cloudflare’s GraphQL API Docs](https://developers.cloudflare.com/graphql/).

## 📜 License (Because Lawyers Demand It)

This project is licensed under the MIT License—so do whatever you want with it. Just don’t blame us if Cloudflare changes everything overnight.

## 🤝 Contributing (No, Really, Please Help)

Want to make this better? Great! Open an issue, submit a pull request, or just scream into the void.

## 📬 Contact (If Cloudflare Didn’t Block Me Yet)

Got questions, complaints, or just need someone to blame? Reach out to [@k1m0ch1](https://github.com/k1m0ch1) or open an issue. 

---

🚀 **Cloudflare Analytics – Because Staring at Graphs Makes You Feel Productive!**
server-3-free-threads 0.2.13
WSGI server based on threads
Requires Python: >=3.10
Keywords: server, WSGI

<h1>Server_3_free_threads</h1><br>

<p>The WSGI server is based on threads, <br>
which began with experiments on sockets.</p>

><h3>Important!</h3>
>It is supported only on Linux 2.5.44 and later


<ol><h3>the server uses four threads:</h3>
    <li><b>accepting_connections</b>, to accept connections</li>
    <li><b>reading_from_socket</b>, to read data from a client socket</li>
    <li><b>sending_to_socket</b>, to send data to a socket</li>
    <li><b>close_client_sock</b>, to close client sockets</li>
</ol>

><h3>Note</h3>
>It is recommended to use a python 3.13 or later<br>
>build from the source code, with the GIL disabled,<br> 
>to improve performance. [Free-threaded CPython][1]

<h2>Quick start</h2><br>

<h3>log in to your project folder</h3>

![Картинка][2]

![Картинка][3]


<h3>activate the virtual environment</h3>

![Картинка][4]

install the library with the command:<br>
*pip install server-3-free-threads*





[1]: https://docs.python.org/3/whatsnew/3.13.html#whatsnew313-free-threaded-cpython "Free-threaded CPython"
[2]: img/1.1.png
[3]: img/1.2.png
[4]: img/2.1.png
deconomix 1.0.1
Provides methods for cellular composition, hidden background and gene regulation estimation of omics bulk mixtures.

# Deconomix

## Overview

Deconomix is a Python library aimed at the bioinformatics community, offering methods to estimate cell type compositions, hidden background contributions and gene regulation factors of bulk RNA mixtures. Visit the documentation [here](https://medbioinf.pages.gwdg.de/MedicalDataScience/DeconomiX/).

## Features

- **Data Simulation**: Generate artificial bulk mixtures from single-cell data in an efficient way to provide training data for your models.

- **Gene Weighting**: Learn gene weights from artifical bulk mixtures to optimize the cellular composition estimation of real bulk RNA mixtures.

- **Cellular Composition**: Estimate the cellular composition of your bulk RNA profiles or Spatial Transcriptomics spots.

- **Background Estimation**: Refine the composition estimation by estimate a hidden background contribution and profile, which cannot be explained by the cell types featured in the reference.

- **Gene Regulation**: Find out, how cell types in your bulk data is regulated in relation to your reference profiles, for instance in a disease context.

- **Visualization**: Visualize your results with predefined functions.

- **Evaluation**: Perform basic enrichment analysis for the estimated gene regulatory factors.



## Installation

You can install the package using `pip`:
```
pip install deconomix
```

Or directly from the `git` repository:
```
pip install git+https://gitlab.gwdg.de/MedBioinf/MedicalDataScience/DeconomiX.git
```



## Getting Started
For a detailed showcase of the standard workflow please visit our [gitlab page](https://gitlab.gwdg.de/MedBioinf/MedicalDataScience/DeconomiX) and navigate to the example folder. There we provide a jupyter notebook with all neccesary steps to get started. We also encourage the user to take a look at our [documentation](https://medbioinf.pages.gwdg.de/MedicalDataScience/DeconomiX/).


## Publications
- Görtler, F. et al. (2020). Loss-Function Learning for Digital Tissue Deconvolution. Journal of Computational Biology, 27(3), 342–355.

- Görtler, F. et al. (2024). Adaptive digital tissue deconvolution. Bioinformatics, 40(Supplement 1), i100–i109
  
- Mensching-Buhr, M. and Sterr T. et al. (2024) bioRxiv 2024.11.28.625894; doi: https://doi.org/10.1101/2024.11.28.625894 (Preprint)