semvec/pypi-packages huggingface dataset:
cellsurf 0.1
A self-supervised deep learning method for reference-free deconvolution.

# SURF
A self-supervised deep learning method for reference-free deconvolution. The overall approach is detailed in the official paper out in xxx.

![Fig1](https://github.com/user-attachments/assets/cd371dab-fa9a-474d-9bfa-32b41adb8cbe)

# Data input  
df_expr: (n_spots * n_genes), dataframe, with column names (gene names)  
df_pos: (n_spots * 2), dataframe, with column names [‘x’, ‘y’]  
barcodes: (n_spots,), a numpy array  
  
# Data output     
‘pred.csv’: The predicted cell types proportions in each spot.  
‘beta.csv’: The deconvolved gene expressions of each cell type.  
‘last.pkl’: The saved trained model. 

# Installation
nonebot-plugin-batch-withdrawal 0.0.6
批量撤回
Requires Python: <4.0,>=3.9

# _✨欢迎使用批量撤回插件✨_
本插件主要用于批量撤回消息

## 💿如何安装
<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-batch-withdrawal

</details>

<details open>
<summary>使用 pip 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

> 如果你启用了 **虚拟环境** 且 **nonebot没有加载本插件** 则需要进入.\.venv\Scripts目录下打开命令行,将pip.exe或者pip拖入命令行中再运行以下命令

    pip install nonebot-plugin-batch-withdrawal

</details>

## 🎉指令列表
| 指令 | 权限 | 需要@ | 范围 | 说明 |
|:-----:|:----:|:----:|:----:|:----:|
| delete | 主人/群管理 | 否 | 群聊 | delete 1代表撤回一条消息, delete @被撤回人 1代表撤回一条该成员的消息 |
duomai 10.0.0
Duomai Open Service SDK
Requires Python: >=3.6

## Duomai OpenService Python SDK

### 使用说明 instructions

* 你可以通过此SDK调用内置提供的API接口，SDK将自动提供签名的处理; <br />
  You can use this SDK to call the built-in API interface, and the SDK will automatically provide signature processing.
 
* 只需三步！<br />
  Just three steps!
  
  * 第一步：引入SDK包，初始化Duomai对象，传入你的appkey和appsecret;<br />
    Step 1: Import the SDK package, initialize the Duomai object, and pass in your appkey and appsecret;
  * 第二步：准备好API调用的参数dict，调用对象中的send_client，传入你选择调用的API及API参数;<br />
    Step 2: Prepare the parameters dict for the API call, call send_client in the object, pass in the API and API 
    parameters you choose to call
  * 第三步：通过调用对象的get_result获取返回结果。<br />
    Step 3: Get the returned result by calling the object's get_result.
chi2sim 1.0.2.3
Chi-square test with Monte Carlo simulation
Requires Python: >=3.7
Keywords: statistics, chi-square, monte-carlo, simulation

# chi2sim

Chi-square test with Monte Carlo simulation for contingency tables.
The package and its documentation are still under construction.

## Description

The `chi2sim` package is a Python implementation that contains the translation of R's H tests for contingency tables when `simulate.p.value = TRUE`, originally written in C and based on Patefield's (1981) FORTRAN algorithm. The package provides a fast and reliable method to compute p-values for chi-square tests using Monte Carlo simulation.

## Installation

```bash
pip install chi2sim
```

## Usage

```python
import numpy as np
from chi2sim import chi2_cont_sim

# Example contingency table
table = np.array([
    [10, 5],
    [20, 15]
], dtype=int)

# Perform chi-square test with Monte Carlo simulation
result = chi2_cont_sim(table)
print(result)
```

## Features

- Similar to SciPy's `scipy.stats.chi2_contingency`, but returns 
- Monte Carlo simulation for p-value approximation
- Easy-to-use Python interface

## Requirements

- Python >= 3.9
- NumPy >= 1.15.0

## License

This project is licensed under the MIT License - see the LICENSE file for details.


## Citation

If you use the `chi2sim` package in your study, please don't forget to cite the following literatures:

-  Hope, A. C. A. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society Series B, 30, 582–598. doi:10.1111/j.2517-6161.1968.tb00759.x.

-  Patefield, W. M. (1981). Algorithm AS 159: An efficient method of generating r x c tables with given row and column totals. Applied Statistics, 30, 91–97. doi:10.2307/2346669.
cryptopie 1.4
cryptopie is library crypto for encrypt data pii

# Python-encrypt

This project focuses on encrypting Personally Identifiable Information (PII) using the Python programming language.

## Features

- Encryption and decryption of PII data
- Easy integration with Python applications

### Installation

```sh
pip install cryptopie
```

The below example will print the contents:

```py
from crypto import Crypto
from crypto import AesAlg

crypto = Crypto(aes_key=aes_key, hmac_key=hmac_key)

encrypted_data = crypto.encrypt('Hello, World!', AesAlg.CBC)
print(f'Encrypted Data: {encrypted_data}')

decrypted_data = crypto.decrypt("87eb6382b98f41d7897fcf919e887e08c106c341402fe3ac598de9ab4c35a43959eb4ead70bf355cc2bc2a54d1b506bc", AesAlg.CBC)
print(f'Decrypted Data: {decrypted_data}')
```
htmx-django 0.1.1
Extensions for using HTMX (or Unpoly or Alpine-AJAX) with Django.
Requires Python: >=3.8
Keywords: django, htmx, unpoly, python

# htmx-django

Extensions for using [HTMX](https://htmx.org/)
(or [Unpoly](https://unpoly.com/)
or [Alpine-AJAX](https://alpine-ajax.js.org/))
with [Django](https://www.djangoproject.com/).

By abstracting HTMX (and friends) just a wee bit more than packages like 
[django-htmx](https://django-htmx.readthedocs.io/en/latest/),
htmx-django does not need additional packages like
[django-template-partials](https://github.com/carltongibson/django-template-partials)
to provide a complete HTMX &amp; Django workflow.

htmx-django is simple and lightweight:
Nothing needs to be added to INSTALLED_APPS or to MIDDLEWARE.
In most cases, your Django code does not have even mention HTMX (or Unpoly or Alpine-AJAX).
htmx-django differentiates between full-page and fragment requests for you.

If you have a small brain like me,
you might enjoy the simplicity of htmx-django.
Bigger brained developers might not like it, especially the tiny bits of magic,
and so might prefer the other packages.
That's fine: the others are great packages by great contributors.

### Coming (Very) Soon

### FAQ

* **Why htmx-django?**
  * I was finding that my Django views had lots of boilerplate code
  testing `request.htmx` (see docs for details).
  I tested it to decide which Django template to use for the response.
  So I thought, in the famous words of Raymond Hettinger,
  "[there must be a better way](https://www.google.com/search?q=raymond+hettinger+there+must+be+a+better+way)". 

* **Why not django-htmx?**
  * I tried it. (more answer soon) 

* **Why not django-htmx and django-template-partials?**
  * I tried this too. (more answer soon)

* **Why not just contribute to django-htmx?**
  * I tried. See [issue](https://github.com/adamchainz/django-htmx/issues/445).
    We had a different vision, I guess.