semvec/pypi-packages huggingface dataset:
lemma-from-wiki 0.2.0
Generates lemma files for vocabsieve
Requires Python: <3.13,>=3.12

## Overview

Gets a wikipedia dump for a language and creates a lemma table from it for use in [vocabsieve](https://github.com/FreeLanguageTools/vocabsieve/). Uses `spacy` as the lemmatiser but also provides results from `simplemma` for comparison.

Project is AGPL3+ licensed as it re-uses code from [gogadget](https://gogadget.jfox.io).

Needs CUDA toolkit installed and an NVIDIA GPU available: <https://developer.nvidia.com/cuda-toolkit-archive>

On Windows, you will need to install Visual Studio first. I also needed to manually add the following to my PATH: `C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.42.34433\bin\Hostx64\x64`

## Running

Installation instructions assume the use of [uv](https://docs.astral.sh/uv/) to automatically deal with package isolation but will work equally well with a pip venv (if you prefer).

No need to download any extra files. The script will automatically grab the wikipedia articles for your chosen language.

**Install from Pypi**

```sh
uv tool install lemma-from-wiki
```

**Standard analysis**

```sh
lemmafromwiki -l "language code" -n "number of articles to process"
```

**Return only differences from `simplemma`**

```sh
lemmafromwiki -l "language code" -n "number of articles to process" --diff
```

**Getting help**

```sh
lemmafromwiki --help
```

**Getting help (short version)**

```sh
lemmafromwiki
```
LAB-11 0.1.0
Краткое описание mypackage
Requires Python: >=3.6

 # Diabetes dataset
 
 
 ## Установка
 ---------
 Инструкции по установке
 
 Установите пакет с помощью:

 .. code-block:: bash

    pip install mypackage

 ### Использование
 --------------
 Пример использования пакета:

 .. code-block:: python

     import mypackage
     mypackage.do_something()
ld-as-framework 1.1.9
零动AS插件提示包
Requires Python: >=3.8

1. 每个API都配有中文、英文两种使用方法，开发者可根据自己喜好随意选择
2. 每个API都配备了详细的开发文档以及视频形式的讲解，以确保开发者可以完全学会使用
3. 除插件的开发文档外，我们集成了常用工具、公开课、AS-FAQ等多项功能，让开发者从此告别东拼西凑
   文档地址：https://www.yuque.com/xianle-skqwj/ld


# 升级日志：

## 1.1.9
- 统一点击函数名称
- 修复获取_全部元素查找失败返回None问题
- 新增获取_设备版本方法
- 新增循环执行方法函数
- 修复重复使用查询全部元素可能失败问题
- 修复链式操作等待失效的问题
- 增加点击函数持续时间参数
- 修复在循环中链式操作逻辑错误问题

## 1.1.8

- 新增谷歌OCR识别，该函数是调用官网的谷歌OCR，可能程序会崩溃
```python
elements = {
    "任务": {
        "OCR_Mlkit": OcrQuery().mlkitocr_v2().text("系统应用").rect_center()
    }
}
ld.元素_操作("任务1", "OCR_Mlkit").点击_坐标().执行()

# 此外也可以直接识别屏幕某个区域文字
print(ld.文字识别_MlkitV2([85, 1088, 250, 1142]))
```

- `点击`、`滑动`相关函数支持百分比坐标
```python

# 直接使用如下
ld.滑动(["50%", "40%"], ["50%", "60%"])

ld.点击_坐标("50%", "50%")

# 也可以在链式中使用，滑动一样，这里不再过多演示
ld.元素_操作("任务1", "找图_游戏中心").点击_坐标("50%", "50%").随机_延迟(2).执行()
```

## 1.1.7
- 修复`元素_等待`函数失效问题

## 1.1.6
- 新增找图/找色/找字支持屏幕区域，以下以找图为例：
```python
elements = {
    "任务1": {
        "找图_游戏中心": ImageQuery().find().img("yxzx.png").rect_center()
    },
}
# 以上的rect_center()代表找屏幕中心位置，除此之外还支持
# rect_left_top() - 获取右上角范围
# rect_left_top() - 获取左上角范围
# rect_left_bottom() - 获取左下角范围
# rect_right_bottom() - 获取右下角范围
# rect_half_left() - 获取左半屏范围
# rect_half_right() - 获取右半屏范围
# rect_half_top() - 获取上半屏范围
# rect_half_bottom() - 获取下半屏范围
```

## 1.1.5
- 新增统计运行时间日志
```python
# 只需要设置系统日志级别即可
ld.日志_设置级别(LogLevel.INFO)
```
- 新增随机延迟函数
```python
 ld.随机_延迟(second, float_tame)
```
- 新增设备相关函数
```python
ld.获取_设备ID()
ld.获取_设备名称()
ld.获取_设备屏幕信息()
ld.获取_设备品牌()
ld.获取_设备型号()
ld.获取_设备SDK()
ld.获取_设备IP()
ld.获取_当前运行APP信息()
ld.获取_所有安装APP()
ld.获取_当前电量()
ld.获取_设备内存()
```

## 1.1.4
- 新增找图函数可以增加多图片
```python
elements = {
    "测试任务": {
        "多图测试": ImageQuery().find_template().img("小工具-谷歌安装器.png", "小工具-RE文件管理器.png",
                                                     "小工具-主题装扮.png"),
    }
}
```


- 新增字符串函数可以统计字符出现个数

```python
print(文本.字符出现次数("abcabcabc", "ab"))
```

### 升级方法
1.AS当中引用代码为：

```python
plug.load("ld:1.1.4")
```
2.更新提示包插件：

```bash
pip install --upgrade ld-as-framework
```
randomcontentgen 0.0.2
A library for generating random text, numbers, and structured data.
Requires Python: >=3.10

This library provides utilities to generate mock data for testing purposes, including numbers, text, identifiers, and structured data.

## Modules Overview

# **Installation**
!pip install randomcontentgen


1. **Number Generator**
   - Generate random integers, floats, or numbers with custom ranges.
   - Example:
     ```python
     from randomcontent import generate_number
     generate_number(type="int", min=1, max=100)  # Generates an random integer between 1 and 100
     generate_number(type="probability") # Generates an random probability between 0 and 1
     generate_number(type="gauss",mean=0, std_dev=1) # Generates an random number from Gaussian distribution

     ```

2. **Identifier Generator**
   - Generate unique identifiers like UUIDs, credit card numbers, or custom IDs.
   - Example:
     ```python
     from randomcontent import generate_identifier
     generate_identifier(type="uuid")  # Generates and random UUID-like string
     generate_identifier(type="email") # Generates an email address

     ```

3. **Text Generator**
   - Create random names, email addresses, or custom text strings.
   - Examples:
     ```python
     from randomcontent import generate_text
     generate_text(type="sentence", length=12)  # Generates a sentence
     generate_text(type="poem", num_lines=4)   # Generates a poem
     ```

4. **Structured Data Generator**
   - Generate structured mock data based on a schema definition.
   - Example:
     ```python
     from randomcontent import generate_structured_data, format_data
     schema = {"name": "random_name", "email": "random_email","age": "random_int(min=18, max=60)"}
     generate_structured_data(schema, count=5)  # List of 5 data rows
     ```

## Formatting Data
Structured data can be saved in JSON, CSV, or Parquet formats:
- Example:
  ```python
  from randomcontent import format_data
  format_data(data, format_type="json", file_name="data")  # Save as data.json
  format_data(data, format_type="csv", file_name="data")   # Save as data.csv
  format_data(data, format_type="parquet", file_name="data")  # Save as data.parquet
2024-assignment2-gruppoBLVG-Matteo 0.0.2
2024_assignment2_gruppoBLVG_
Requires Python: >=3.8

# 2024_assignment2_gruppo_BLVG



******
## Gestore Stocklist
*****
******
******
## Report of the project

####  questa parte del readme è funzionale solo alla consegna del progetto universitario di Processo e Sviluppo Software.
https://gitlab.com/gruppo-blvg/2024_assignment2_gruppo_blvg

progetto in python

stages della pipeline fatti:

  - build (utilizzo pip e requirements files)
  - verify (ruff + bandit + prospector)
  - test (unit test + integration test)
  - package (utilizzo build per creazione dei file tar e whl e salvtaggio negli artifacts in dist/)
  - release (utilizzo twine per upload su pypi, utlizzo variabili per automatizzare la fase di identificazione )

stages della pipeline da fare/completare:
  - docs (creato questo documento utilizzando markdown)

##### membri
- Matteo Varè 856479
- 
- 
- 
******
******
******

## Description

questo programma prende in input un file excel contenente nel primo foglio una stocklist grezza presa dal database e:

1) elimina gli elementi che non sono prodotti finiti
2) ordina per tipologia di prodotto i prodotti/ordini rimanenti
3) elimina gli eventuali duplicati degli ordini

## Installation
questo è un progetto python, quindi è necessario installare python e pip.

se pip non risulta installato seguire le istruzioni al link qui si seguito:
_[Come installare pacchetti](https://packaging.python.org/en/latest/tutorials/installing-packages/)_
per installare il progetto da pypi.
``` 
py -m pip install <<NomeProgetto>>
```
ove <<NomeProgetto>> è il nome di questo progetto.

se vuoi installare una versione particalr di questo progetto fare
``` 
py -m pip install "<<NomeProgetto>>==<<versioneProgetto>>"
```
ove <<versioneProgetto>> è la versione desiderata del progetto 

## Usage
1) ASSICURARSI CHE LA CARTELLA "INPUT" SIA VUOTA, NEL CASO CONTRARIO SVUOTARLA

 2) INSERIRE LA STOCKLIST NELLA CARTELLA "INPUT"

3) DOPPIO CLICK SU "STOCKLIST.BAT" 
se non è presente "stoklist.bat" aprire la console e navigare nella cartella del progetto e digitare:

        python stocklist.py & pause

4) SEGUIRE LE ISTRUZIONI A CONSOLE (DIGITARE Y O N E PREMERE INVIO)
## Support
##### SE CI SONO PROBLEMI IMPREVISTI IL TERMINALE SI CHIUDE DA SOLO:
contattare l'assistenza

##### PROBLEMI COMUNI:
1.	IL FILE IN "OUTPUT" è QUELLO DI UNA VERSIONE PRECEDENTE/CI SONO TROPPI FILE DI OUTPUT:

_SOLUZIONE:_
_SVUOTARE LA CARTELLA "OUTPUT" E RI-ESEGUIRE IL PROGRAMMA_

2.	DA ERRORE DURANTE L'ESECUZIONE/ DICE CHE NON CI SONO FILE VALIDI IN "INPUT":

_SOLUZIONE:_
_VERIFICARE CHE CI SIA SOLO UN FILE IN "INPUT",_
_VERIFICARE CHE L'ESTENSIONE DEL FILE SI .XLS/.XLSX_

## Authors and acknowledgment
creatore del programma : Matteo Varè.

## Project status
under development.
RandomContent 0.0.1
A library for generating random text, numbers, and structured data.
Requires Python: >=3.10

This library provides utilities to generate mock data for testing purposes, including numbers, text, identifiers, and structured data.

## Modules Overview

1. **Number Generator**
   - Generate random integers, floats, or numbers with custom ranges.
   - Example:
     ```python
     from randomcontent import generate_number
     generate_number(type="int", min=1, max=100)  # Generates an random integer between 1 and 100
     generate_number(type="probability") # Generates an random probability between 0 and 1
     generate_number(type="gauss",mean=0, std_dev=1) # Generates an random number from Gaussian distribution

     ```

2. **Identifier Generator**
   - Generate unique identifiers like UUIDs, credit card numbers, or custom IDs.
   - Example:
     ```python
     from randomcontent import generate_identifier
     generate_identifier(type="uuid")  # Generates and random UUID-like string
     generate_identifier(type="email") # Generates an email address

     ```

3. **Text Generator**
   - Create random names, email addresses, or custom text strings.
   - Examples:
     ```python
     from randomcontent import generate_text
     generate_text(type="sentence", length=12)  # Generates a sentence
     generate_text(type="poem", num_lines=4)   # Generates a poem
     ```

4. **Structured Data Generator**
   - Generate structured mock data based on a schema definition.
   - Example:
     ```python
     from randomcontent import generate_structured_data, format_data
     schema = {"name": "random_name", "email": "random_email","age": "random_int(min=18, max=60)"}
     generate_structured_data(schema, count=5)  # List of 5 data rows
     ```

## Formatting Data
Structured data can be saved in JSON, CSV, or Parquet formats:
- Example:
  ```python
  from randomcontent import format_data
  format_data(data, format_type="json", file_name="data")  # Save as data.json
  format_data(data, format_type="csv", file_name="data")   # Save as data.csv
  format_data(data, format_type="parquet", file_name="data")  # Save as data.parquet
search-prompts 1.3.0
A library for efficient text-based prompt search with resource monitoring.
Requires Python: >=3.7

# Search Prompts
search-prompts is a library for efficient text search in Hugging Face datasets with resource monitoring (CPU, memory, network).

# Installation
You can install the library via pip:
pip install search-prompts

# Example Usage
from search_prompts import search_prompts_with_monitoring

## Search parameters
query = "You are an AI assistant"
dataset_name = "rombodawg/Everything_Instruct"
config_name = "default"

## Perform search with resource monitoring
matching_prompts, resource_usage = search_prompts_with_monitoring(
    query=query,
    dataset_name=dataset_name,
    config_name=config_name,
    batch_size=1000,
    max_results=5,
    chunk_size=5000,
    monitor_interval=1,
)

## Perform search without resource monitoring
matching_prompts = search_prompts_direct(
    query=query,
    dataset_name=dataset_name,
    config_name=config_name,
    batch_size=1000,
    max_results=5,
    chunk_size=5000,
)

## Display search results
print("\nSearch Results:")
if matching_prompts:
    for instruction in matching_prompts:
        print(f"- {instruction}")
else:
    print("No matches found.")

## Display resource usage
print("\nResource Usage During Execution:")
print(f"Average CPU Usage: {resource_usage['avg_cpu_usage']:.2f}%")
print(f"Peak Memory Usage: {resource_usage['peak_memory_usage']:.2f} MB")
print(f"Network Data Sent: {resource_usage['network_sent_mb']:.2f} MB")
print(f"Network Data Received: {resource_usage['network_recv_mb']:.2f} MB")
print(f"Total Execution Time: {resource_usage['total_execution_time']:.2f} seconds")
ngao 0.3.0
NGAO - Tunnel local ports to public URLs and inspect traffic
Requires Python: >=3.6

ngao - tunnel local ports to public URLs and inspect traffic

USAGE:
  ngao [command] [flags]

AUTHOR:
  ngao - <support@ngao.pro>

COMMANDS: 
  config          update or migrate ngao's configuration file
  http            start an HTTP tunnel
  tcp             start a TCP tunnel
  tunnel          start a tunnel for use with a tunnel-group backend

EXAMPLES: 
  ngao http 80                                                 # secure public URL for port 80 web server
  ngao http --domain baz.ngao.dev 8080                        # port 8080 available at baz.ngao.dev
  ngao tcp 22                                                  # tunnel arbitrary TCP traffic to port 22
  ngao http 80 --oauth=google --oauth-allow-email=foo@foo.com  # secure your app with oauth

Paid Features: 
  ngao http 80 --domain mydomain.com                           # run ngao with your own custom domain
  ngao http 80 --allow-cidr 2600:8c00::a03c:91ee:fe69:9695/32  # run ngao with IP policy restrictions
  Upgrade your account at https://dashboard.ngao.pro/billing/subscription to access paid features

Upgrade your account at https://dashboard.ngao.pro/billing/subscription to access paid features

Flags:
  -h, --help      help for ngao

Use "ngao [command] --help" for more information about a command.