semvec/pypi-packages huggingface dataset:
nonebot-plugin-60s 0.2.0
每天60秒读懂世界
Requires Python: >=3.8,<4.0

<div align="center">
  <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>
</div>

<div align="center">

# nonebot-plugin-60s

_✨ 每天60秒读懂世界 ✨_

<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/A-kirami/nonebot-plugin-moyu.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-60s">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-60s.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">

</div>

## 📖 介绍

<details>
  <summary>效果图</summary>

![example](https://raw.githubusercontent.com/techotaku39/nonebot-plugin-60s/master/readme/example.jpg)

</details>

## 💿 安装

<details>
<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-60s

</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-60s
</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-60s
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-60s
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-60s
</details>

打开 nonebot2 项目的 `bot.py` 文件, 在其中写入

    nonebot.load_plugin('nonebot_plugin_60s')

</details>

## 配置说明(可选)
有时候api会失效，就改了下
微信公众号地址：https://mp.weixin.qq.com/
抓取的是公众号的内容，我也就找了一个直接分享图片的公众号：每日60s简报（dailybriefing60s），也是很稳定的每天都会在8点前就会推送当天简报

先登陆你的公众号，因为我们要获取一些你的cookie以及token
登陆后F12查看请求
![](./images/123.png)
红圆框是可以找到的地方（大概），红方框就是cookie以及token(cookies:后面的才是，你也可以右键复制值，token=后面的才是)(这也不懂就使用默认吧)
| 配置项 | 说明 |
|:-----:|:----:|
| calendar_cookie: str = "" | 填写微信公众号的cookie |
| calendar_token: str = "" | 填写微信公众号的token |

## 🎉 使用
### 指令表
| 指令  | 说明 |
|:-----:|:----:|
| 60s/读懂世界 | 查看今天的60s日历 |
| 60s/读懂世界+设置 | 以连续对话的形式设置60s日历的推送时间 |
| 60s/读懂世界+设置 小时:分钟 | 设置60s日历的推送时间 |
| 60s/读懂世界+状态 | 查看本群的60s日历状态 |
| 60s/读懂世界+禁用 | 禁用本群的60s日历推送 |

## 💡 鸣谢

### [A-kirami摸鱼日历](https://github.com/A-kirami/nonebot-plugin-moyu)：本项目就是用大佬的项目改了几行代码，连说明文档也是（）
langchain-chatgpt-browser-api 0.0.1
A Python library that allows you to use Langchain with the ChatGPT instance in your browser.
Requires Python: >=3.7

# Langchain LLM

[![PyPI version](https://badge.fury.io/py/langchain-llm.svg)](https://badge.fury.io/py/langchain-llm)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Description

ChatGPT Browser API is a Python library that allows you to use Langchain with the ChatGPT instance in your browser.

With a Plus subscription, you can take advantage of GPT4 without the need for an API key.

## Installation

You can install ChatGPT Browser API via pip. Run the following command to install it:

```
pip install chatgpt-browser-api
```

Ensure that you have Python 3.6 or a higher version installed on your system.

## Usage

Once installed, you can import ChatGPT Browser API. Here's a basic example to get you started:

```python
from chatgpt_browser_api import BaseChatGPT

# Create an instance of LangchainLLM
llm = BaseChatGPT(chatgpt_web_api_url="http://localhost:3001")

answer = chatgpt("Write your prompt here")

print(answer)
```

To start the web server located at chatgpt_web_api_url, see [ChatGPT-Web-API](https://github.com/lorenzo-polaris/ChatGPT-Web-API)

## Contributing

Contributions are welcome! If you have any ideas, bug reports, or feature requests, please open an issue or submit a pull request on GitHub.

## License

This library is licensed under the MIT License. See the LICENSE file for more information.

```

Please note that you may need to replace certain sections, such as the license badge and the GitHub repository link, with the appropriate information for your project.
```
pytrino 0.0.6
Pytrino: A new way to numerically compute neutrino oscillations
Keywords: probability,neutrino,oscillations

![](logo.png)

We use the recently discovered Eigenvalue-Eigenvector and Adjugate Identities to compute oscillation probabilities with minimal algorithmic steps.

# Usage


## Imports
```Python
import numpy as np
np.set_printoptions(precision=3)

from pytrino.oscprobs import OscProbIdentities
```

## Define your oscillation parameters

```Python
a = 0.2
theta12 = np.radians(33.2)
theta13 = np.radians(4.4)
theta23 = np.radians(46.1)
alpha = 0.026
delta = 50
deltacp = np.pi/6
```

## Compute probabilities
```Python
prob = OscProbIdentities(alpha, a, delta, deltacp, theta12, theta13, theta23)

print(prob.mat_angles_phase()) # prints mixing angles and phase in matter
# (29.988882340423384, 84.48628984963665, 3.5930259417910695, 44.188561816129464)

print(prob.PMNS()) # prints values of PMNS matrix elements
"""
[[ 0.849+0.000e+00j -0.297+0.000e+00j -0.062-4.318e-01j]
 [-0.335+8.346e-02j -0.92 -2.921e-02j -0.184+1.342e-18j]
 [ 0.01 +3.989e-01j  0.214-1.396e-01j -0.881-7.870e-19j]]
"""

print(prob.PMNS() @ prob.PMNS().H) # unitarity check
"""
[[1.000e+00-1.696e-22j 0.000e+00+1.561e-17j 6.939e-18+5.551e-17j]
 [0.000e+00-1.561e-17j 1.000e+00-4.276e-19j 5.551e-17-1.327e-18j]
 [1.388e-17-4.857e-17j 5.551e-17+0.000e+00j 1.000e+00-8.097e-19j]]
"""

print(prob.probabilities()) # prints all 9 probabilities
"""
[[0.97  0.015 0.014]
 [0.014 0.236 0.75 ]
 [0.015 0.749 0.236]]
"""
```
serialdatalog 0.1.7
Logs data coming from a serial port
Requires Python: >=3.10,<4.0

# Serial Data Logger

A simple python script that logs the input received from a serial (USB) input to an SQLite database along with logging the time stamp (in the format `YYMMDDHHMMSS`) of the received message.
Serial data logger expects the serial inputs to be comma separated.

## Install

```
pip install serialdatalog
```

## Usage

Here's an example using a Finometer pressure sensor.

```python
import logging
import serialdatalog as sdl

logging.basicConfig(level=logging.INFO)
# table_dict is formed of key : value pairs consisting of:
#   sql_header : sql_data_type
# If the number of headers is different to that read from the serial input then the input is not
# logged.
table_dict = {
    "Elapsed_Time" : "REAL",
    "Finger_Pressure" : "REAL",
    "Height" : "REAL",
    "Arm_Pressure" : "REAL",
    "Finger_Plethysmogram" : "REAL",
}
sdl.logger(
    table_dict,
    source="/dev/ttyACM0",
    dest="datalog.sqlite3",
    encoding='utf-8',
    timeout=10
)
```
image-fragment 0.2.3
Data Section
Requires Python: ~=3.6
Keywords: numpy, Window, Section, Array, Stitch, Split

# Fragment
![GitHub](https://img.shields.io/github/license/fuzailpalnak/fragment)
![Python](https://img.shields.io/badge/python-v3.6+-blue.svg)
![Contributions welcome](https://img.shields.io/badge/contributions-welcome-orange.svg)
![Downloads](https://pepy.tech/badge/image-fragment)

Any extent is a set of smaller fragment extent , which can be as small as *1 x 1* to the *size* of the extent given.
This library will section the given extent in to specified smaller fragment extent


## Installation

    pip install image-fragment


## Image Fragments

![fragments](https://user-images.githubusercontent.com/24665570/91711219-2b7c8980-eba3-11ea-94d2-8239cf6713c4.gif)

An image extent can be sectioned into many fragments, these fragments holds positional information to where it is located 
to the original extent

Now this information can be used to extract selected fragments from the Image, perform operation, and transfer the new 
data to selected position.


```python
import numpy as np
from image_fragment.fragment import ImageFragment

image = np.zeros((1, 1024, 1024, 3))
new_image = np.zeros((1, 1024, 1024, 3))
image_fragment = ImageFragment.image_fragment_4d(fragment_size=(1, 512, 512, 3), org_size=(1, 1024, 1024, 3))
for fragment in image_fragment:
    # GET DATA THAT BELONGS TO THE FRAGMENT
    fragmented_image = fragment.get_fragment_data(image)
    
    # DO SOME OPERATION ON THE FRAGMENTED DATA
    operation_done_on_fragmented_data = np.rot(fragmented_image)
    
    # TRANSFER OPERATED IMAGE ON NEW IMAGE ON THE FRAGMENT POSITION
    new_image = fragment.transfer_fragment(transfer_from=operation_done_on_fragmented_data, transfer_to=new_image)
    
```

If image is 3 Dimensional then switch to `image_fragment = ImageFragment.image_fragment_3d(fragment_size=(512, 512, 3), org_size=(1024, 1024, 3))`, 
this will provide fragments for 3 dimensional image