semvec/pypi-packages huggingface dataset:
nonebot-plugin-plus-one 0.1.10
全新复读姬，支持文本、图片、表情甚至是转发分享卡片复读，任意群聊触发 +1，姬就 +1。轻巧、专注，不使用任何数据库，不使用任何文件存储
License: MIT
Requires Python: <4.0,>=3.8


<h1 align="center">🐣🐤一个仅有40行代码的复读姬 ✨</h1>
<p align="center">
_✨ 只是一个复读姬：支持群聊白名单、文本复读、图片表情复读、甚至是转发和分享的复读 ✨_
</p>
<p align="center">
  <a href="https://raw.githubusercontent.com/cscs181/QQ-Github-Bot/master/LICENSE">
    <img src="https://img.shields.io/github/license/cscs181/QQ-Github-Bot.svg" alt="license">
  </a>
  <a href="https://pypi.python.org/pypi/nonebot-plugin-analysis-bilibili">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-analysis-bilibili.svg" alt="pypi">
  </a>
  <img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">
</p>


## 快速安装

第一步：右上角 ↗ 点个不要钱的 star 吧，这是不断维护更新的动力。

### nb-cli

```shell
nb plugin install nonebot-plugin-plus-one
```
### pip

```shell
pip install nonebot-plugin-plus-one
```

### git

```shell
cd /your-nonebot-project-home/plugins/
git clone https://github.com/yejue/nonebot-plugin-plus-one.git
```

## 示例
```text
[群1]: 你好
[群1]: 你好
[机器人]: 你好

[群2]: 表情
[群2]: 表情
[机器人]: 表情

[群2]: 分享音乐
[群2]: 分享音乐
[机器人]: 分享音乐
```

## 配置
```text
# 匹配优先级
PLUS_ONE_PRIORITY = 10

# 群聊或私聊白名单，单个或多个示例，下面任选其一，可填入群 QQ 号或个人 QQ 号
PLUS_ONE_WHITE_LIST = ["10000000"]
PLUS_ONE_WHITE_LIST = ["10000000", "1000000"]
```
classification-lib 1.0.0
Module containing various utility functions for classification tasks
License: MIT

# classification_lib
A custom module that grants access to libraries with utility functions for performing classification tasks such as:

* Plotting precision-recall curves for multiple classifiers for easier comparisons.

* Plotting ROC curves for multiple classifiers for easier comparisons.

### Installation
    pip install classification_lib

### Get Started
How to plot different pr-curve(s) for classifier(s) using the __*plot_pr_curve*__ of this library:

```Python
from classification_lib import Plots
import numpy as np

# Instantiating the precisions and recalls for two different classifiers
classifier1_precisions = np.sort(0.791 + 0.168 * np.random.randn(794))
classifier2_precisions = np.sort(0.5 + 0.142 * np.random.randn(794))
classifier1_recalls = sorted(0.70 + 0.299 * np.random.randn(794), reverse=True)
classifier2_recalls = sorted(0.3 + 0.5 * np.random.randn(794), reverse=True)
classifier1_name = "Classifier 1"
classifier2_name = "Classifier 2"
classifiers = [(classifier1_name, classifier1_precisions, classifier1_recalls),
               (classifier2_name, classifier2_precisions, classifier2_recalls)]

# Instantiate a Plot object
plot = Plots(classifiers)

# Call the pr_curve_plot method
result = plot.plot_pr_curve()
```
How to plot different pr-curve(s) for classifier(s) using the __*plot_roc_curve*__ of this library:
```Python
import numpy as np
from classification_lib import Plots

classifier1_false_positive_rate = sorted(0.29 + 0.248 * np.random.randn(219))
classifier2_false_positive_rate = sorted(0.7 + 0.4 * np.random.randn(219))
classifier1_true_positive_rate= sorted(0.788 + 0.2 * np.random.randn(219))
classifier2_true_positive_rate = sorted(0.6 + 0.3 * np.random.randn(219))
classifier1_name = "Classifier 1"
classifier2_name = "Classifier 2"
classifiers = [(classifier1_name, classifier1_false_positive_rate, classifier1_true_positive_rate),
               (classifier2_name, classifier2_false_positive_rate, classifier2_true_positive_rate)]

# Instantiate a Plot object
plot = Plots(classifiers)

# Call the pr_curve_plot method
result = plot.plot_roc_curve()
```
pdm-readiness 0.1.7
A pdm plugin to check if your project dependencies support specific Python version.
License: MIT
Requires Python: >=3.9.0

# pdm-readiness

[![PyPI version](https://badge.fury.io/py/pdm-readiness.svg)](https://pypi.org/project/pdm-readiness/)
![Github Actions](https://github.com/pdm-project/pdm/workflows/Tests/badge.svg)

A `pdm` plugin to check if your project dependencies support specific Python version.

> [!NOTE]  
> Many packages may still work just fine even when they are not listed as supported.
> This plugin only checks the metadata provided by the package authors, so it is not
> a guarantee that the package will work or not.

## Synopsis

The readiness report is divided into 4 sections:

- **Supported dependencies** - currently locked dependencies that support the target Python version.
- **Update required** - currently locked dependencies that do not support the target Python version, but have newer versions that do.
- **Unsupported dependencies** - the most recent version of the dependency does not support the target Python version.
- **Missing metadata** - the package does not provide metadata about the supported Python versions.

Plugin uses [PyPI JSON API](https://warehouse.pypa.io/api-reference/json.html) to get the metadata.
It looks at classifiers like `Programming Language :: Python :: 3.12` to determine which versions are supported.

## Installation

```sh
pdm self add pdm-readiness
```

## Usage

Run `pdm readiness <python_version>` in the root of your pdm project.

Example output:

![image](https://github.com/andriykohut/pdm-readiness/assets/3106616/60b7985f-0cc6-4124-8abe-878690a9d89a)