semvec/pypi-packages huggingface dataset:
nonebot-plugin-picsetu 0.0.4
Default template for PDM package
License: MIT
Requires Python: >=3.10

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  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
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# nonebot-plugin-picsetu

_✨ 基于NoneBot的涩图插件 ✨_


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## 📖 介绍

一个涩图插件，就是你们想的那样

## 💿 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-picsetu

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot-plugin-picsetu
</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-picsetu
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-picsetu
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-picsetu
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_picsetu"]

</details>

## 🎉 使用
### 指令表
| 指令 | 权限 | 需要@ | 范围 | 说明 |
|:-----:|:----:|:----:|:----:|:----:|
| setu | 所有人 | 否 | 通用 | 别忘了加前缀啊 |
confit2canard 0.1.1
Minimalistic configuration library with encryption and a tasty name
License: MIT
Requires Python: <4.0,>=3.9
Keywords: configuration

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/dolead/confit2canard/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/dolead/confit2canard/tree/main) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/76833f3568f2479d8470a7320429cbfe)](https://app.codacy.com/gh/dolead/confit2canard/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)

# Confit2Canard 🦆

Minimalistic library to load settings from JSON and YAML files that also
provides configuration encryption with AES-GCM.

[The history of the confit de canard](https://en.wikipedia.org/wiki/Duck_confit).

## How to use

Configuration files written to a relative path:
```yaml
# app.yml
title: How to cook?
```

```yaml
# production/tokens.yml
api:
  token: AZERTY1234
```

```yaml
# production/secrets.yml
$Vault;UZXhoxTumLPE6zkT;kRg9bGtmKFn/BeFODX5+;zFgpPMrgxAaVQW6Wv5Q6Ow==
```

The script:

```python
# main.py
from confit2canard import Config

passkey = "ThisIsSupposedToBeAKey0000000000"
configuration_files = ["app.yml",
                       "production/tokens.yml",
                       "production/secrets.yml"]
configuration = Config(configuration_files, passkey=passkey)

print(configurgation.title)
print(configurgation.find("api.token"))
```

The configuration files can be JSON or YAML.

## Command line tool

The `VAULT_PASSKEY` environment variable must be set to write encrypted
configuration with a AES-GCM key:

```shell
VAULT_PASSKEY="ThisIsSupposedToBeAKey0000000000" python \
  -m confit2canard production/secrets.yml
```
JIMG-analyst-tool 0.0.5
JIMG-analyst-tool
License: MIT
Requires Python: >=3.6
Keywords: python, nuclei, intenisty, image, high-resolution, comparison



The JIMG_analyst_tool is a Python library that extends the JIMG image processing tool, specifically tailored for analyzing high-resolution confocal microscope images [Opera-Phoenix](https://www.revvity.com/product/opera-phenix-plus-system-hh14001000?srsltid=AfmBOoohz1LiEemNbG4SJnaEtScwr16MyFL8Ulf9NyDDEAffV2NLJXoe) and other technologies. This library enables detailed examination of nuclei and their chromatin organization, supporting high-resolution analysis of nuclear morphology and chromatin structure.

It also provides algorithms for measuring the intensity of specific protein markers using customizable image masks on high-resolution microscope images. These measurements are normalized using a background mask for consistent data comparison. The collected intensity data can be statistically analyzed to detect differences in marker localization, occurrence, and intensity.

In addition to microscopy, flow cytometry [Amnis-ImageStream](https://cytekbio.com/pages/imagestream) analysis capabilities are integrated into the tool. It can analyze flow cytometry images, applying nuclear and chromatin analysis methods similar to those used for confocal microscopy. Furthermore, the tool enables advanced analysis of cell populations from cytometric data, offering options to select distinguishing cell characteristics, perform clustering of cell sets based on these features, and analyze clusters using statistical methods to identify unique attributes.

With these combined functionalities, the JIMG_analyst_tool is a versatile resource for researchers requiring in-depth quantitative analysis of nuclear and chromatin features in both confocal microscopy and flow cytometry datasets.

Detailed description available at: https://github.com/jkubis96/JIMG-analyst-tool/