semvec/pypi-packages huggingface dataset:
zhei 0.0.98
Deep learning tools
Requires Python: >=3.6
Keywords: zhei,deep learning,pytorch

# 安装
```bash
pip install zhei
```


# 使用
```python
import zhei as j

j.hi()
```


# 功能支持

### 帮助信息
- hi()：打印 Banner
- help()：打印帮助信息，待完善
- print_error_info(e: Exception)：打印错误信息


### 通知
- notice(msg, warning=False, access_token="", secret="")：钉钉通知，默认读取 init_env() 设置的环境变量中的凭证
    ```
    Args:
        msg (str): 通知内容
        warning (bool, optional): 是否为警告. Defaults to False.
        access_token (str, optional): 钉钉机器人 access_token. Defaults to "".
        secret (str, optional): 钉钉机器人 secret. Defaults to "".
    ```

### 配置
- init_env(config: Union[DictConfig, dict])：初始化环境
    ```
    包括随机种子、可见GPU、Comet.ml环境变量、进程名

    Args:
        config (DictConfig): 配置文件
            seed (str, optional): 种子. Defaults to '3407'.
            use_deterministic (bool, optional): 是否使用确定性算法，使用了训练会变慢. Defaults to False.
            visibale_cuda (str, optional): 设置可见 GPU，如果不使用设置为 ''. Defaults to 'all'.
            comet_exp (dict, optional): Comet.ml相关环境变量设置. Defaults to {}.
            proctitle (str, optional): 进程名. Defaults to 'python'.
            proctitle_prefix_id (bool, optional): 是否在进程名前边添加进程 ID. Defaults to True.
    ```

### 处理器
- gpu_ready())：检查 GPU 是否可用
- set_processing_units(config: Union[DictConfig, dict] = {})：设置处理器
    ```
    支持自动选择和手动选择、排队和不排队

    Args:
        config (dict, optional): Defaults to {}.
            processing_unit: 处理器类型，可选值为：cpu、mps, 1, ^2 等，其中 ^2 表示自动选择两块 GPU，默认为 cpu
            processing_unit_type: 选择处理器，可选值为：cpu、mps、gpu-a-s、gpu-a-m、gpu-m-s、gpu-m-m
            processing_unit_min_free_memory: 最小空闲内存，单位为 GiB，默认为 10
            processing_unit_min_free_memory_ratio: 最小空闲内存比例，默认为 0.5
            queuing: 是否排队，依赖 Redis，默认为 False
            visible_devices: 可见的 GPU 序号，用逗号分隔，默认为 None

    Returns:
        Union[DictConfig, dict]: config
    ```

### 包装类
- Result：包装类，用于作为模块间通信的媒介，可使用字典进行初始化


### 文件读取和保存
- load_in(path, data_name="")：读取文件
    ```
    根据文件后缀名自动选择读取方法
        目前支持保存类型有：‘pkl’、‘txt’、‘pt’、‘json’, 'jsonl'、'csv'

    Args:
        data_name: str, 打印提示时需要，便于控制台查看保存的文件是什么文件, 默认为空

    Returns:
        data：Object
    ```

- save_as(data, save_path, file_format="pt", data_name="", protocol=4)：保存文件
    ```
    将参数中的文件对象保存为指定格式格式文件
        目前支持保存类型有：‘pkl’、‘txt’、‘pt’、‘json’, 'jsonl'
        默认为‘pt’

    Args:
        data: obj, 要保存的文件对象
        save_path: str, 文件的保存路径，应当包含文件名和后缀名
        data_name: str, 打印提示时需要，便于控制台查看保存的文件是什么文件, 默认为空
        protocol: int, 当文件特别大的时候，需要将此参数调到4以上, 默认为4
        file_format: str, 要保存的文件类型，支持‘pkl’、‘txt’、‘pt’、‘json’、‘jsonl’

    Returns:
        None
    ```
recmetrics-pyspark 0.0.1
A library of recommender systems metrics for big data

# recmetrics-pyspark: recommender systems metrics for big data

**recmetrics-pyspark** obtains the most relevant internal metrics for items recommendations from pySpark DataFrames. It efficiently handles huge amounts of data. Most routines are adapted from the **recmetrics** library which works with pandas DataFrames. 

DISCLAIMER: recmetrics-pyspark is not affiliated nor endorsed by recmetrics or its authors.
Some routines have been adapted from recmetrics to work with pySpark DataFrames
and/or to handle bigger datasets. Therefore, some chunks of code have been copied verbatim,
and functions and parameters names have been kept the same (as much as possible) for better usability.

Furthermore, if you are dealing with small datasets, we recommend to use the recmetrics library (https://github.com/statisticianinstilettos/recmetrics) instead, as it most efficiently handles smaller datasets.

## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/camiloakv/recmetrics-pyspark

Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/recmetrics-pyspark).

```sh
pip install recmetrics-pyspark
```

Available metrics as of version 0.0.1:

 - `long_tail_plot`
 - `coverage`
 - Novelty:
   - `novelty_refac` A small refactoring of recmetrics' implementation.
   - `novelty_pandas` Similar implementation to novelty_refac but using pandas DataFrames as inputs
   - `novelty` pySpark implementation
 - `personalization`
 - `intra_list_similarities`
frics 1.0.0
Textual app to view Frida Codeshare snippets
Requires Python: >=3.10

# frics

[![PyPI - Version](https://img.shields.io/pypi/v/frics.svg)](https://pypi.org/project/frics)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/frics.svg)](https://pypi.org/project/frics)


Frics is a Textual TUI app to search, view, and copy code from [Frida Codeshare](https://codeshare.frida.re/). A local copy of the database is created to speed up search. Searches are performed against the project name, author, description, and even the source code itself, making this a great tool for a quick reference.



-----

## Installation

Installation is recommended using [pipx](https://pypa.github.io/pipx/).

```console
pipx install frics
```

## Screenshot
![Screenshot1](https://github.com/daddycocoaman/frics/blob/main/docs/screenshot1.png?raw=True)

## License

`frics` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
eyes-soatra 0.0.33
Eyes
Keywords: python,crawler,scanner,requests


# How to setup

## First, type
#### Window
```
python install -r requirements.txt
```
#### Mac, Linux
```
python3 install -r requirements.txt
```

## Second, type
#### Window
```
python start.py install --user
```
#### Mac, Linux
```
python3 start.py install --user
```

Now, you can use this package to achieve your purpose.

---
readDataFile 0.0.9
uno test data file read
Requires Python: >=3.6

# Uno Read data File
- this is function for read datafile 

# Example Code

 import readDataFile
 
### readDataFile.ReadDataFile(파일명)
 - 단일 파일 또는 여러 파일명 / 탐색기 없이 파일 불러오기
### readDataFile.ReadDataFile(폴더명)  
 - 해당 폴더에 탐색기 실행
### readDataFile.ReadDataFile()
 - 기본 폴더에 탐색기 실행
pyinvert 0.0.1
A simple invert function implementation using python and cv2
Requires Python: >=3.10

# invert
Image inversion in python (OpenCV & numpy)

## Usage
### example
```
import cv2
from pyinvert import invert

image = cv2.imread('house.tiff')

new = invert(image)
cv2.imwrite('invertedhouse.tiff', new)
```
**Original Image**
![A Blue car in front of a house](https://github.com/garbage1010/invert/blob/main/tests/house.tiff)

**Inverted**
![Inversion of the previous image](https://github.com/garbage1010/invert/blob/main/tests/invertedhouse.tiff)