semvec/pypi-packages huggingface dataset:
nonebot-plugin-xiuxian-GCDI 1.0.3
修仙插件，QQ群与QQ频道均可使用，并且数据互通
Keywords: pip,nonebot2,文游,修仙

# nonebot_plugin_xiuxian_GCDI
修仙模拟器--QQ-群与QQ频道数据互通
## 简介

本插件主要为实现群聊修仙功能,最近经常封号，请自行判断后再使用，已默认转成图片模式，如需关闭，可在config.py处调整img字段为false
并适配了频道与群聊数据互通

## 设定征集中，有好的想法可以推送给我哦~~~

## 安装
1、下载插件文件

- 使用github处拉取源码使用
无镜像：
```
git clone https://github.com/QingMuCat/nonebot_plugin_xiuxian_GCDI
```
镜像：https://ghproxy.com/
```
git clone https://ghproxy.com/https://github.com/QingMuCat/nonebot_plugin_xiuxian_GCDI
```

2、下载数据文件

使用git clone后，进入插件目录，把data文件夹中的全部内容移动到bot的数据文件夹中<br>

3、安装频道补丁：

```
频道补丁
pip install nonebot_plugin_guild_patch -i https://pypi.tuna.tsinghua.edu.cn/simple/
```

4、如果遇到问题，请先百度和查看下方的 【一些问题】

5、如解决不了进交流群：588907147 提问，提问请贴上完整的日志

## 配置文件
1、配置文件一般在data/xiuxian文件夹下，自行按照json格式修改即可，一些字段的含义可以进群交流<br>
2、子插件的配置会在插件运行后在子插件文件中生成config.json文件，该文件字段含义在同级目录的xxxconfig.py有备注。注意：修改配置只需要修改json即可，修改.py文件的话需要删除json文件才会生效，任何修改都需要重启bot

## 更新
使用github处拉取源码使用的
进入插件目录执行命令：
```
git pull
```


## 群聊与频道部分功能展示

![image](https://raw.githubusercontent.com/QingMuCat/qm/master/xiuxian/%E7%BE%A4%E8%81%8A%E7%B4%A0%E6%9D%901.png)
![image](https://raw.githubusercontent.com/QingMuCat/qm/master/xiuxian/%E7%BE%A4%E8%81%8A%E7%B4%A0%E6%9D%902.png)
![image](https://raw.githubusercontent.com/QingMuCat/qm/master/xiuxian/%E9%A2%91%E9%81%93%E7%B4%A0%E6%9D%901.png)
![image](https://raw.githubusercontent.com/QingMuCat/qm/master/xiuxian/%E9%A2%91%E9%81%93%E7%B4%A0%E6%9D%902.png)
## 一些问题

- 当前首次使用，未自动创建json文件及sql文件，请在[github](https://github.com/QingMuCat/nonebot_plugin_xiuxian_GCDI)处，目录data -> xiuxian
处下载的文件，放置于bot目录，data -> xiuxian文件夹处
- 当为放置为plugins目录使用时，请修改根目录下__init__.py文件中的42行：src=''中的内容，填写的是存放插件的目录，一般情况下 src='src.plugins.'  如有不同请按照格式修改
## 特别感谢

- [NoneBot2](https://github.com/nonebot/nonebot2)：本插件实装的开发框架，NB天下第一可爱。
- [go-cqhttp](https://github.com/Mrs4s/go-cqhttp)：稳定完善的 CQHTTP 实现。
- [nonebot_plugin_xiuxian](https://github.com/s52047qwas/nonebot_plugin_xiuxian)：原版修仙

## 插件依赖

- nonebot2
- nonebot-adapter-onebot
- go-cqhttp

## 支持

大家喜欢的话可以给这个项目点个star

有bug、意见和建议都欢迎提交 [Issues](https://github.com/QingMuCat/nonebot_plugin_xiuxian_GCDI/issues) 
或者联系进入QQ交流群：588907147

## 许可证
本项目使用 [MIT](https://choosealicense.com/licenses/mit/) 作为开源许可证
mechcalc 1.3.3
mechanical engineering calculators
Requires Python: >=3.9

# mechcalc

Mechanical Engineering Calculators, Chuck McKyes<br>
This is a gui app based on wxPython.<br>
v1.3.2 October 2021<br>
<br>
Copyright (C) 2021 Chuck McKyes</br>
<br>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.

<br>
*** Verify calculations produced by this software. ***<br><br>

All calculations are approximate estimates. Actual specifications
are available from equipment manufacturers.

Pump power and head assume cold water being pumped.
Vapour pressure of a mixture uses Raoult's law.

wxPython is required. In Windows, Microsoft Visual C++
redistributable is required. I find that everything works
with Python 3.7.3 in Debian and Python 3.8.6 in Windows 10,
but Python 3.9.0 does not appear to work on Windows 10.

Debian requires python3-wxgtk to be installed via your package manager.
For example, on a Raspberry Pi 4 as of this writing, the version is
python3-wxgtk4.0-4.0.4+dfsg-2.

This python script can be run using "python3 -m mechcalc" on the
command line or by creating a shortcut.

In Debian, a desktop shortcut can be created by creating
a file with a .desktop extension (e.g., "MechCalc.desktop").
Make the contents this:

[Desktop Entry]<br>
Version=1.1.2<br>
Type=Application<br>
Name=MechCalc<br>
Comment=<br>
Exec=python3 -m mechcalc<br>
Icon=<br>
Path=<br>
Terminal=false<br>
StartupNotify=false<br>

In Windows, right-click on the desktop or in file explorer
and create a shortcut. Right click again and select Properties.
Enter "<your python install directory>\pythonw.exe -m mechcalc"
in the "Target:" field, and <your python install directory>
in the "Start in:" field. ("pythonw.exe" starts the script without
a command line console.)
tiddlywiki 0.0.6
A full featured desktop client for editing and managing TiddlyWiki html files - simple, powerful, elegant and efficient.
Requires Python: >=3.6

![](https://raw.githubusercontent.com/rzel/twpy/main/twpy_app_icon_1_vecta_compressed.svg)

**twpy** was designed to be the *best* desktop GUI client for editing and managing **TiddlyWiki** files - simple, powerful, elegant, efficient.

Installation is as simple as: 

`pip install tiddlywiki`

Two terminal commands are installed:

**twpym** for starting the **twpy** TiddlyWiki Manager. **--verbose** is an option. You can open TiddlyWiki files in the Editor from the Manager.

**twpye** for starting the **twpy** TiddlyWiki Editor. Requires **--file your-tiddly-wiki-path.html** argument. **--verbose** is also an option. 


**TiddlyWiki Manager**

![](https://raw.githubusercontent.com/rzel/twpy/main/twpy-screenshot-2022-10-17--22-49-32.png)

**TiddlyWiki Editor**

![](https://raw.githubusercontent.com/rzel/twpy/main/twpy-screenshot-2022-10-17--22-50-38.png)
topicmodeltuner 0.3.4
HDBSCAN Tuning for BERTopic Models
Requires Python: >=3

# TopicTuner &#8212; Tune BERTopic HDBSCAN Models

To install from PyPi :
>pip install topicmodeltuner

## The Problem
Out of the box, [BERTopic](https://github.com/MaartenGr/BERTopic) relies upon [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) to cluster topics. Two of the most important HDBSCAN parameters, `min_cluster_size` and `sample_size` will almost always have a dramatic effect on cluster formation. They dictate the number of clusters created including the -`1` or *uncategorized* cluster. While with some datasets a large number of uncategorized documents may be the *right* clustering, in practice BERTopic will essentially discard a large percentage of *"good"* documents and not use them for cluster formation and topic formation. 

HDBSCAN is quite sensitive to the values of these two parameters relative to the text being clustered. This means that when using the BERTopic default value of `min_topic_size=10` (which is assigned to HDBSCAN's `min_cluster_size`) the default parameters will more often than not result in an unmanageable number of topics; as well as a sub-optimal number of uncategorized documents. Additionally, documents assigned to the -1 category will not be used to determine topic vocabularly results. 

## The Solution
TopicTuner provides a TopicModelTuner class&#8201;&#8212;&#8201;a convenience wrapper for BERTopic Models that efficiently manages the process of discovering optimized min_cluster_size and sample_size parameters, providing:

- Random and grid search functionality to quickly discover optimized parameters for a given BERTopic model.
- An internal datastore that records all searches for a given model, making parameter selection fast and easy.
- Visualizations to assist in parameter tuning and selection.
- Two way Import/Export functionality so that you can start from scratch, or with an existing BERTopic model and export a BERTopic model with optimized parameters at the end of your session.
- Save and Load for persistance.

To get you started this release includes both a [demo notebook](https://githubtocolab.com/drob-xx/TopicTuner/blob/main/TopicTunerDemo.ipynb) and [API documentation](https://drob-xx.github.io/TopicTuner)