semvec/pypi-packages huggingface dataset:
DHMS-aiff-to-csv 1.0
Convert aiff vibration raw data to csv file
Requires Python: >=3.6

# DHMS_aiff_to_csv
Aiff振动加速度音频文件转csv文件Python库

所有者：苏州德姆斯信息技术有限公司

维护者：包岱杰

创建日期：2023年5月19日

说明：此Python库仅限于下载德姆斯设备健康管理系统中非主线环境的设备原始振动加速度aiff数据

# 使用范例：

下载aiff原始振动文件：

from aiff_data_download_tool import download_data_from_DHMS_Server

调用转换aiff音频文件的python函数：

from DHMS_aiff_converter.convert_aiff import convert_aiff_to_df, save_aiff_as_csv, convert_aiff_to_df_include_time, save_aiff_as_csv_include_time

from DHMS_aiff_converter.aiff_data_download_tool import download_data_from_DHMS_Server
python-eol 0.0.3
Simple tool to check if python version is past EOL
Requires Python: >=3.7

# python-eol
A simple tool to check if the current running python version is beyond or close to end of life (eol).

Warns 60 days before eol, and errors when using a python version that is beyond eol.

## Motivation
We tend to often forget which python version we are using for a project and that those version eventually will not get security updates once they are beyond eol.
This tool can be used to check this, for example, in a CI/CD system or as a pre-commit hook.

## Installation
```sh
pip install python-eol
```

## Usage
Simply invoke `eol` from your command line
```sh
eol
```
**Options**:
```sh
usage: eol [-h] [--fail-close-to-eol] [--check-docker-files] [--nep29]

eol check if your python version is beyond end of life

options:
  -h, --help            show this help message and exit
  --fail-close-to-eol   Fail if the python version is close to eol instead of just warn
  --check-docker-files  Search for Dockerfile (**/*Dockerfile*) and check the python versions specified inside them
  --nep29               Use NEP0029 end of life policy
```

## Pre-commit-hook
Add the following to your `.pre-commit-config.yaml`:
```yaml
repos:
  - repo: https://github.com/mimre25/python-eol/
    rev: v0.0.3
    hooks:
      - id: python-eol-check
```
python-apimatic-calculator 1.0.899
Simple calculator API hosted on APIMATIC
Requires Python: >=3.7


# Getting Started with APIMATIC Calculator

## Introduction

Simple calculator API hosted on APIMATIC

## Install the Package

The package is compatible with Python versions `3 >=3.7, <= 3.11`.
Install the package from PyPi using the following pip command:

```python
pip install myUniquePythonPackageName==1.0.899
```

You can also view the package at:
https://pypi.python.org/pypi/myUniquePythonPackageName/1.0.899

## Test the SDK

You can test the generated SDK and the server with test cases. `unittest` is used as the testing framework and `pytest` is used as the test runner. You can run the tests as follows:

Navigate to the root directory of the SDK and run the following commands

```
pip install -r test-requirements.txt
pytest
```

## Initialize the API Client

**_Note:_** Documentation for the client can be found [here.](https://github.com/apimatic/calculator-sdk/blob/0e65eb4/doc/client.md)

The following parameters are configurable for the API Client:

| Parameter | Type | Description |
|  --- | --- | --- |
| `environment` | Environment | The API environment. <br> **Default: `Environment.PRODUCTION`** |
| `http_client_instance` | `HttpClient` | The Http Client passed from the sdk user for making requests |
| `override_http_client_configuration` | `bool` | The value which determines to override properties of the passed Http Client from the sdk user |
| `http_call_back` | `HttpCallBack` | The callback value that is invoked before and after an HTTP call is made to an endpoint |
| `timeout` | `float` | The value to use for connection timeout. <br> **Default: 60** |
| `max_retries` | `int` | The number of times to retry an endpoint call if it fails. <br> **Default: 0** |
| `backoff_factor` | `float` | A backoff factor to apply between attempts after the second try. <br> **Default: 2** |
| `retry_statuses` | `Array of int` | The http statuses on which retry is to be done. <br> **Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]** |
| `retry_methods` | `Array of string` | The http methods on which retry is to be done. <br> **Default: ['GET', 'PUT']** |

The API client can be initialized as follows:

```python
from apimaticcalculator.apimaticcalculator_client import ApimaticcalculatorClient
from apimaticcalculator.configuration import Environment

client = ApimaticcalculatorClient()
```

## List of APIs

* [Simple Calculator](https://github.com/apimatic/calculator-sdk/blob/0e65eb4/doc/controllers/simple-calculator.md)

## Classes Documentation

* [Utility Classes](https://github.com/apimatic/calculator-sdk/blob/0e65eb4/doc/utility-classes.md)
* [HttpResponse](https://github.com/apimatic/calculator-sdk/blob/0e65eb4/doc/http-response.md)
* [HttpRequest](https://github.com/apimatic/calculator-sdk/blob/0e65eb4/doc/http-request.md)
sonicsynth 1.0.0
An audio synthesis library for sound generation and manipulation

# SonicSynth

SonicSynth is an audio synthesis library that provides a high-level interface for generating and manipulating various types of sounds. It allows users to create custom sound waves, apply digital signal processing (DSP) effects, and export the synthesized audio data to various audio file formats.

## Key Features

- **Sound Wave Generation**: Generate different types of sound waves, such as sine, square, triangle, and sawtooth waves, with control over frequency, amplitude, and phase.
- **Envelope Generation**: Create amplitude envelopes, including ADSR (Attack, Decay, Sustain, Release), to shape the volume contour of sounds over time.
- **Digital Signal Processing (DSP) Effects**: Apply filters, delays, reverbs, modulation effects, and more to manipulate the synthesized audio.
- **Multi-track Composition**: Combine multiple sound waves or synthesized audio segments to create multi-track compositions, with control over volume, panning, and timing.
- **Real-time Audio Playback**: Listen to the synthesized audio in real-time, facilitating interactive sound design and experimentation.
- **Audio Export**: Export the synthesized audio data to common audio file formats such as WAV, MP3, and OGG.
- **Comprehensive Documentation and Examples**: Detailed documentation and examples are provided to help users quickly get started and explore SonicSynth's capabilities.

## Installation

You can install SonicSynth using `pip`:

```shell
pip install sonicsynth
```

## Usage
```python
import sonicsynth

# Generate a sine wave
waveform = sonicsynth.generate_sine_wave(frequency=440, amplitude=0.5, duration=2)

# Apply a low-pass filter
filtered_wave = sonicsynth.apply_low_pass_filter(waveform, cutoff_frequency=1000)

# Create a multi-track composition
track1 = sonicsynth.mix_tracks(waveform, filtered_wave, volume=0.8, panning=0.5)

# Play the synthesized audio
player = sonicsynth.Playback(44100)
player.play(track1)

# Export the audio to a WAV file
sonicsynth.export_audio(track1, filename="output.wav", format="wav")
```
gpterminal 0.1.9
A CLI tool to interact with GPT-3/GPT-4, answer Git questions, and execute commands
Requires Python: >=3.6

# GPTerminal

GPTerminal 是一个基于 GPT 模型的命令行工具，旨在帮助用户解决各种编程问题，提供智能问答和代码执行功能。

## 功能

1. 聊天：与 GPT-3 或 GPT-4 进行智能聊天。
2. Git 命令行助手：根据您的问题提供 Git 命令帮助。
3.代码执行：执行命令并捕获错误输出及其上下文，提供智能解决方案。

## 安装

确保安装 Python 3.6 或更高版本。然后运行：

```bash
pip install gpterminal

或者

pip install gpterminal -i https://pypi.org/simple/ 
```

## 使用

在命令行中输入以下命令启动 gpterminal

```bash
gpterminal
```

然后选择您想要使用的功能。

### 示例

1. 聊天

   ```
   gt chat
   ```

2. Git 命令行助手

   ```
   gt git
   ```

3. 代码执行

   ```
   gt run your-command
   ```


当您在终端中输入 `gt`令时，如果提示 `Command not found`，这可能是因为您的系统未将 `gt` 命令添加到 PATH 环境变量中。在这种情况下，您可以按照以下步骤操作：

### 在 macOS 或 Linux 上

1. 打开终端并输入以下命令：

   ```
   echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
   ```

   这将向您的 `.bashrc` 文件添加一行，该行将 `~/.local/bin` 目录添加到 PATH 环境变量中。

2. 然后，重新加载您的 `.bashrc` 文件：

   ```bash
   source ~/.bashrc
   ```

   现在，您应该能够在终端中使用 `gt` 命令了。

### 在 Windows 上

1. 打开命令提示符并输入以下命令：

   ```batch
   setx PATH "%USERPROFILE%\AppData\Roaming\\PythonXX\Scripts;%PATH%"
   ```

   请注意，将 `PythonXX` 替换为您的 Python 版本号，例如 `Python36` 或 `Python39`。

2. 然后，关闭并重新打开命令提示符，或者注销并重新登录您的 Windows 帐户。

   现在，您应该能够在命令提示符中使用 `gt` 命令了。


## 问题
1. 需要设置api_key(必填)以及base_url(可选)
2. 需要 api_key 的也可以耐心等待，后续我会修改代码中获取 api_key 的方式，可以让大家直接使用
3. 具体免费 api_key 的获取方式我还在开发中，后续会更新，敬请期待

## 贡献

欢迎对 GPTerminal 进行贡献！请在提交 Pull Request 之前确保您的代码符合项目的质量标准。

## 许可

GPTerminal 采用 MIT 许可证。有关详细信息，请参阅 [LICENSE](LICENSE) 文件。
ai-sketcher 0.0.0.1
Text-to-image generation using Huggingface stable diffusion ControlNet conditioning and AWS Translate's prompt translation function package

[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg?style=flat-square)](https://raw.githubusercontent.com/hyeonsangjeon/youtube-dl-nas/master/LICENSE)
[![Downloads](https://pepy.tech/badge/ai-sketcher)](https://pepy.tech/project/ai-sketcher)
#ai-sketcher





Text-to-image generation using Huggingface stable diffusion ControlNet conditioning and AWS Translate's prompt translation function

```text

```
```python


# prints: wer -> 0.0
```



### References 
- `[1]`.
- `[2]`.
cukcuk 0.5.3
Connect to Cukcuk Shop API
Requires Python: >=3.8

## Cukcuk SDK

### Examples:
1. Get all invoices in a date range
```python
import cukcuk
from datetime import datetime, timedelta

session = cukcuk.LoginSession.from_json("login.json")
last_sync_date = datetime(2023, 5, 1)
before_date = datetime(2023, 5, 5)
invoices = session.get_invoices(last_sync_date=last_sync_date, before_date=before_date)
df = invoices.to_df() # Convert to pandas DataFrame
print(df)
```

2. Using asynchronous session to speed up requests
```python
import asyncio
import cukcuk
from datetime import datetime, timedelta

session = cukcuk.AsyncLoginSession.from_json("login.json")
last_sync_date = datetime(2023, 5, 1)
before_date = datetime(2023, 5, 5)
invoices = asyncio.run(session.get_invoices(last_sync_date=last_sync_date, before_date=before_date))
df = invoices.to_df() # Convert to pandas DataFrame
print(df)
```
AIFloodMaster 1.1.0
贵仁-智能雨洪管理模型

# AI Flood Master

*贵仁 - 智能雨洪管理模型*

随着人工智能技术的发展，“AI+“成为诸多传统业务场景智能化升级的新需求，当然也包括水利水务行业。目前，公司已经基本建立了涵盖水文、一二维水力、水质的较为完整的数字仿真模型体系（简称GRMS），并为公司的一系列项目提供了稳定、有力的技术支撑。

但是随着业务领域的加深和拓展，不断新增的业务需求，如智能预报、智能调度、污染溯源等，都对我们的技术服务能力提出了更高更新的要求。

面对行业的需求升级和技术革新，基于最新的工业级人工智能框架“tensorflow2.x“、结合贵仁多年深耕水利水务行业的技术积累，我们研发了新一代的、针对雨洪场景的智能预报调度模型——贵仁智能雨洪管理模型（GR - AIFloodMaster）。
nonebot-plugin-forjyf 0.1.0
为JYF定制的复读插件
Requires Python: >=3.10,<4.0

# nonebot_plugin_forjyf
JYF复读插件，如果JYF说了富有JYF特征的词汇，例如"酷"，“这样么”,"噢"，“来点XX”,"沃趣"，“xm”，“哦哈哟”等等，包括发fufu的图片、说日语，全都会进行复读，在不断完善中