semvec/pypi-packages huggingface dataset:
probed 0.0.11
Probed collections
License: MIT
Requires Python: >=3.5
Keywords: library,collections,notification,change-tracking,dict,set,list,containers,probed-dict,probed-set,probed-list,lightweight,pyrustic

# Probed collections

This project is part of the [Pyrustic Open Ecosystem](https://pyrustic.github.io).
> [Installation](#installation) . [Latest](https://github.com/pyrustic/probed/tags) . [Documentation](https://github.com/pyrustic/probed/tree/master/docs/modules#readme)


# Overview
Sometimes you need to know when the content of a data collection has changed.

`Probed` is a library that exposes three classes: `ProbedDict`, `ProbedList` and `ProbedSet`.

These classes are containers like the built-in Python containers (dict, list, set) which they subclass but with not a twist but two twists:

- be notified when the content of your data collection changes (if you wish to be notified);
- being able to put a probe into data collection to do more than just be notified.

Let's write a script to see `Probed` in action:

```python
# script.py
from probed import ProbedList


def on_change(context):
    msg = "\nThe {} operation changed the {} collection\n{}"
    print(msg.format(context.operation, context.container, context.collection))


plist = ProbedList(on_change=on_change)
plist.append("hi")
plist.insert(1, "friend")

```

Let's run the script:

```bash
$ python3 script.py

The append operation changed the list collection
['hi']

The insert operation changed the list collection
['hi', 'friend']
```

Now, let's discover what the `probe` feature does and how to use it:

```python
# script.py
from probed import ProbedSet


def probe(context):
    # this probe will lower strings added to the collection
    # also, the object None isn't allowed in the collection
    if context.operation == "add":
        if context.item is None:
            context = None
        else:
            context.item = context.item.lower()
    return context


pset = ProbedSet(probe=probe)
# add items
pset.add("RED")
pset.add(None)
pset.add("Green")
# print
print(pset)  # {'red', 'green'}

```

In the last script, the `probe` was used to control the items added to the data collection.

All operations that change the contents of the built-in containers are covered by `probed`.

> **Read the [modules documentation](https://github.com/pyrustic/probed/tree/master/docs/modules#readme) !**

# Related project
The **Shared** data exchange and persistence library uses `Probed` to implement the `autosave` feature !

> **Discover [Shared](https://github.com/pyrustic/shared) !**



# Installation
**Probed** is **cross platform** and versions under **1.0.0** will be considered **Beta** at best. It is built on [Ubuntu](https://ubuntu.com/download/desktop) with [Python 3.8](https://www.python.org/downloads/) and should work on **Python 3.5** or **newer**.

## For the first time

```bash
$ pip install probed
```

## Upgrade
```bash
$ pip install probed --upgrade --upgrade-strategy eager

```

<br>
<br>
<br>

[Back to top](#readme)
corporate-finance 0.0.1
A comprehensive library for corporate finance in Python
License: MIT
Keywords: corporate finance,pandas,yahoo finance,numpy,pandas datareader,seaborn,matplotlib

Python Corporate Finance Library

Introduction

This library is designed for professionals and researchers in the field of corporate finance. It provides a comprehensive suite of tools for conducting technical analysis, fundamental analysis, and machine learning, all within the Python programming environment. With this library, users can quickly and easily extract insights from financial data and make informed decisions.

Features

Technical analysis: The library provides a range of tools for conducting technical analysis, including trend analysis, momentum indicators, and charting tools.
Fundamental analysis: The library also includes a comprehensive set of tools for fundamental analysis, including financial statement analysis, valuation methods, and risk assessment.
Machine learning: The library incorporates state-of-the-art machine learning algorithms to provide predictions and insights.
Great insights: The library provides an intuitive and user-friendly interface for visualizing and interpreting results, making it easy for users to extract insights from their financial data.

Installation

To install the library, simply run the following command in your terminal:

pip install python-corporate-finance


Documentation

The library is fully documented and comes with a range of examples and tutorials to help you get started. To access the documentation, simply visit the following link:

Python Corporate Finance Library Documentation

Support

If you need help or have any questions, please feel free to reach out to us at hemantthapa1998@gamil.com 
I am always here to help!
tinyShakespeareLoader 1.0.2
This is a PyTorch DataLoader for the TinyShakespeare dataset.
License: MIT

.. These are examples of badges you might want to add to your README:
   please update the URLs accordingly

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        :alt: Built Status
        :target: https://cirrus-ci.com/github/<USER>/tinyShakespeareLoader
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        :target: https://tinyShakespeareLoader.readthedocs.io/en/stable/
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        :alt: Coveralls
        :target: https://coveralls.io/r/<USER>/tinyShakespeareLoader
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        :alt: PyPI-Server
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        :alt: Twitter
        :target: https://twitter.com/tinyShakespeareLoader

.. image:: https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold
    :alt: Project generated with PyScaffold
    :target: https://pyscaffold.org/

|

=====================
tinyShakespeareLoader
=====================


    A PyTorch DataLoader for the TinyShakespeare Dataset


If you followed Andrej Karparthy's tutorial on GPT, you will notice he used the TinyShakespeare dataset, but not with the PyTorch DataLoader. 
This repository fills that gap.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.4. For details and usage
information on PyScaffold see https://pyscaffold.org/.
nonebot-plugin-mcport 0.1.6
一个可以在QQ群操控JAVA服务器的插件.
License: MIT
Requires Python: >=3.6.0


# 请保护好自己的rcon配置项，任何问题与本插件无关
* 一个可以远程操控我的世界java服务器的bot
* 此插件配置视频教程：[点我查看视频教程](https://www.bilibili.com/video/BV1LY411S7kD/?share_source=copy_web&vd_source=e0ca427d4461aaedc892cf4bc908d052)
* 关于如何使用
# 前提(必要)
* 克隆整个仓库,将本目录下的async_mcrcon.py放入site-packages目录下
* 虚拟环境同理，放入python包路径即可
* pip install pillow
* pip install async-mcrcon
* nb plugin install nonebot-plugin-txt2img
# 我的世界功能部分
1.   请务必保护好自己的rcon信息，此信息为你的敏感信息
2.   在.env中配置服务器的rcon项
# bot配置
* 配置一个go-cq端作为无头QQ端
# 主体部分
* 自行配置nonebot2客户端，具体教程参考b站小狐狸[点此跳转nb2配置教程](https://www.bilibili.com/read/cv21231223?spm_id_from=333.999.0.0)
# 功能介绍
1. 向我的世界JAVA版服务端发送指令并返回图片回执
2. 无脑同意进群(需要在mcrcon.py中124行处配置群号)
3. 响应关键词回复
4. 入群欢迎
5. 定时消息
# 如何开启rcon配置
1. 在服务端根目录server.properties内添加三个配置项
2. rcon.port=
3. rcon.password=
4. enable-rcon=true
# env配置项
| config | example | usage | 
| -------- | -------- | -------- | 
| rconhost| rconhost = "127.0.0.1"| 服务器ip|
| rconpassword| rconpassword = "114514"| 服务器rcon密码 | 
| rconpor| rconport = 114514| 服务器rcon端口| 
|groupset| groupset=[114514]| 开启入群欢迎的群号| 
| zr|zr=[1919810] | 响应执行命令的主人QQ号| 
|tit|tit=仙贝|图片的title|