semvec/pypi-packages huggingface dataset:
nada-ai 0.6.0
Nada-AI is a Python library designed for AI/ML on top of Nada DSL and Nillion Network.
Requires Python: <4.0,>=3.10

# Nada AI

![GitHub License](https://img.shields.io/github/license/NillionNetwork/nada-ai?style=for-the-badge&logo=apache&logoColor=white&color=%23D22128&link=https%3A%2F%2Fgithub.com%2FNillionNetwork%2Fnada-ai%2Fblob%2Fmain%2FLICENSE&link=https%3A%2F%2Fgithub.com%2FNillionNetwork%2Fnada-ai%2Fblob%2Fmain%2FLICENSE)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/NillionNetwork/nada-ai/test.yml?style=for-the-badge&logo=python&logoColor=white&link=https://github.com/NillionNetwork/nada-ai/actions/workflows/test.yml&link=https://github.com/NillionNetwork/nada-ai/actions/workflows/test.yml)
![GitHub Release](https://img.shields.io/github/v/release/NillionNetwork/nada-ai?sort=date&display_name=release&style=for-the-badge&logo=dependabot&label=LATEST%20RELEASE&color=0000FE&link=https%3A%2F%2Fpypi.org%2Fproject%2Fnada-ai&link=https%3A%2F%2Fpypi.org%2Fproject%2Fnada-ai)

Nada AI is a Python library designed for performing ML/AI on top of Nada DSL and the Nillion Network.

It provides an intuitive ML interface and frictionless integration with existing ML frameworks such as PyTorch and Sci-kit learn.

## Features

- **Exporting model state**: Integrates with models from existing ML frameworks and provides an easy way to export them to the Nillion network - to be used in Nada programs.
- **AI Modules**: A PyTorch-esque interface to create arbitrary ML models in Nada by stacking pre-built common ML components - with the possibility of easily creating custom components.
- **Importing model state**: Easily import an exported model state that lives in the Nillion network to be used in a Nada program.

## Installation
### Using pip

```bash
pip install nada-ai
```

### From Sources
You can install the nada-ai library using Poetry:

```bash
git clone https://github.com/NillionNetwork/nada-ai.git
pip install ./nada-ai
```
### Advanced Options

In certain cases, you may want to install Nada AI with different development libraries. The options may be installed as follows:
```
pip install ./nada-ai[examples] # To include the libraries required for the examples
pip install ./nada-ai[linter] # To include automatic linting tools for development
```

In a normal scenario, these libraries won't be installed by Nada AI.
## License

This project is licensed under the Apache2 License. See the LICENSE file for details.
swimos 1.6.0a0
Standalone Python framework for building massively real-time streaming WARP clients.
Keywords: swim client

# Swim System Python Implementation
[![PyPI version](https://badge.fury.io/py/swimos.svg)](https://badge.fury.io/py/swimos)
[![Build Status](https://dev.azure.com/swimai-build/swim-rust/_apis/build/status/swimos.swim-system-python?branchName=main)](https://dev.azure.com/swimai-build/swim-rust/_build/latest?definitionId=6&branchName=main)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Code of Conduct](https://img.shields.io/badge/code-of%20conduct-green.svg)](CODE_OF_CONDUCT.md)
[![chat](https://img.shields.io/badge/chat-Gitter-green.svg)](https://gitter.im/swimos/community)
<a href="https://www.swimos.org"><img src="https://docs.swimos.org/readme/marlin-blue.svg" align="left"></a>

The **Swim System** Python implementation provides a standalone set of
frameworks for building massively real-time streaming WARP clients.

The **Swim Python Client** is a streaming API client for linking to lanes 
of stateful Web Agents using the WARP protocol, enabling massively 
real-time applications that continuously synchronize all shared states 
with ping latency. WARP is like pub-sub without the broker, 
enabling every state of a Web API to be streamed, without 
interference from billions of queues.
<br>
## Installation
`pip install swimos`
## Usage
```python
# Setting the value of a value lane on a remote agent.
import time

from swimos import SwimClient

with SwimClient() as swim_client:
    host_uri = 'ws://localhost:9001'
    node_uri = '/unit/foo'
    lane_uri = 'info'

    value_downlink = swim_client.downlink_value()
    value_downlink.set_host_uri('ws://localhost:9001')
    value_downlink.set_node_uri('/unit/foo')
    value_downlink.set_lane_uri('info')
    value_downlink.open()

    new_value = 'Hello from Python!'
    value_downlink.set(new_value)

    print('Stopping the client in 2 seconds')
    time.sleep(2)
```
## Development

### Dependencies
##### Code dependencies
`pip install -r requirements.txt`
##### Development tools dependencies
`pip install -r requirements-dev.txt`
### Run unit tests
##### Basic:
1) Install async test package: `pip install aiounittest`
2) Run tests: `python -m unittest`

##### With coverage:
1) Install async test package: `pip install aiounittest`
2) Install coverage package: `pip install coverage`
3) Generate report: `coverage run --source=swimos -m unittest`
4) View report: `coverage report -m`

### Run Lint
##### Manual
1) Install lint package: `pip install flake8`
2) Run checks: `flake8`
##### Automatic (before commit)
1) Install commit hook package: `pip install pre-commit`
2) Run hook installation: `pre-commit install`
### Build package
##### Building package distribution
1) Install build package: `pip install build`
2) Run: `python -m build`
##### Releasing a new version
1) Add version tag: `git tag VERSION (e.g. 0.1.0)`
2) Push to remote: `git push origin VERSION`
srlife 2.0.2
Evaluate the structural life of a solar receiver
Requires Python: >=3.6
Keywords: materials structures modeling

# srlife: solar receiver life estimation tool

![Test Status](https://github.com/applied-material-modeling/srlife/workflows/tests/badge.svg) ![Documentation Status](https://readthedocs.org/projects/srlife/badge/?version=latest)

This python package evaluates the structural life of tubular solar receivers against
creep rupture, fatigue, and creep-fatigue damage modes.

Full documentation is available [here](https://srlife.readthedocs.io/).

## Prerequisites

The package itself is pure python, however it relies on several additional
python packages listed in [requirements.txt](requirements.txt).
Note that srlife uses Python3.

## License

The package is provided under an MIT license found in the
[LICENSE](LICENSE) file.
x22223673-ewarranty-management 0.0.4
Electronic Warranty Management System Library
Requires Python: >=3.6

# x22223673-ewarranty-library
The ```warranty_management_system library``` is intended to streamline warranty and claim management with robust validation, flexible data handling, precise calculations, and detailed error handling. This modular approach allows for easy expansion and customization as business needs evolve.

## Modules in warranty_management_system:
### 1.Validators:
  This module contains validation classes and functions to ensure that data entered into the system meets defined criteria. The validators are tailored to check the integrity and consistency of records related to:
  ``` Customer, Warranty, Products, Claims, Extended Warranty ```
### 2.Data Helper:
  This module provides utility functions to retrieve, sort, filter, and limit data records within the application, enabling more efficient data manipulation and display.
  
    Retrieve Data: Fetches specific datasets (e.g., customer products, warranty claims) based on parameters.
    Sort: Offers customizable sorting functionality for displaying lists in ascending or descending order based on fields like purchase date, expiration date, or claim amount.
    Limit: Limits the number of records shown in response to pagination needs.
    Filter: Filters data based on specific criteria such as product categories, warranty status, or claim status.
### 3.Exception Classes:
  Custom exception classes in this module allow for clear, context-specific error handling and reporting within the warranty management system. Examples include:

        WarrantyException, ClaimException, ProductException etc.
### 4. Calculators:
  This module contains calculation functions critical for processing claims and managing extended warranties. Calculations are designed to ensure accuracy in financial and time-based components of the system.

      Claim Calculation: Determines the eligible claim amount based on factors like product value, issue severity, and the warranty coverage.
      Extended Warranty Premium Calculation: Calculates the premium required for extended warranty periods based on product purchase amount, extension period, and monthly premium rates.
      Claim Amount Calculation: Calculates the maximum claim amount for extended warranty products, considering factors such as the product’s original value and the allowable claim percentage.

opensource LICENSE
ocpiupdater 0.1.0
An updater for OpenCPI Projects
Requires Python: >=3.8

# ocpiupdater - An updater for OpenCPI Projects

`ocpiupdater` automatically updated OpenCPI Projects to use the semantics
associated with newer versions of OpenCPI.

For example:

- Using `<worker>-<model>.xml` for the OWD files, instead of `<worker>.xml`.
- Using `<component>.comp/<component>-comp.xml` for OCS files instead of
  `specs/<component>[-_]spec.xml`.
ocpi-project-updater 0.1.0
An updater for OpenCPI Projects
Requires Python: >=3.8

# ocpiupdater - An updater for OpenCPI Projects

`ocpiupdater` automatically updated OpenCPI Projects to use the semantics
associated with newer versions of OpenCPI.

For example:

- Using `<worker>-<model>.xml` for the OWD files, instead of `<worker>.xml`.
- Using `<component>.comp/<component>-comp.xml` for OCS files instead of
  `specs/<component>[-_]spec.xml`.
django-variable 0.1.2
Django Variable
Requires Python: >=3.8

# Django Variable

django_variable is a library to define dynamic global settings
that can be set within a regular django form and edited
within django's admin panel.

## How it works

Set the config values using admin (admin/variable_config/configuration/)
Those values are persisted in the database (one per row)
and stored in an in-memory cache for later access.

## Compatibility

* Python +3.8
* Django >= 5

## Documentation

[Read The Docs](http://django-variable.readthedocs.org)

## License

MIT


## Usage

from django_variable.utils import get_config

get_config('KEY_CONFIG', 'DEFAULT_VALUE')
pyidaungsu-numpy2 0.1.4
Python library for Pyidaungsu Myanmar languages
Requires Python: >=3.6

# Pyidaungsu (Project discontinued)

Python library for Myanmar language. Useful in Natural Language Processing and text preprocessing for Myanmar language.

## Installation

```sh
pip install pyidaungsu
```

## Usage

### ~~Zawgyi-Unicode detection~~ Language detection (Myanmar <Zawgyi, Unicode>, Karen, Mon, Shan)
Starting from the pyidaungsu 0.0.9, it does not only detect Zawgyi and Unicode for Myanmar language but also other languages such as Mon, Karen, Shan as well.

Language detection for Mon and Shan is temporarily disabled starting from 0.1.3 as the accuracy for those languages wasn't as good.
Supported languages ATM: English, Spanish, French, Chinese, Japanese, Korean, Myanmar (unicode), Myanmar (zawgyi), Karen

```sh
import pyidaungsu as pds

# language detection
pds.detect("ထမင်းစားပြီးပြီလား")
>> "mm_uni"
pds.detect("ထမင္းစားၿပီးၿပီလား")
>> "mm_zg"
pds.detect("တၢ်သိၣ်လိတၢ်ဖးလံာ် ကွဲးလံာ်အိၣ်လၢ မ့ရ့ၣ်အစုပူၤလီၤ.")
>> "karen"
pds.detect("ဇၟာပ်မၞိဟ်ဂှ် ကတဵုဒှ်ကၠုင် ပ္ဍဲကဵုဂကောံမွဲ ဖအိုတ်ရ၊၊")
>> "mon"
pds.detect("ၼႂ်းဢိူင်ႇမိူင်းၽူင်း ၸႄႈဝဵင်းတႃႈၶီႈလဵၵ်း ၾႆးမႆႈႁိူၼ်း ၵူၼ်းဝၢၼ်ႈ လင်ၼိုင်ႈ")
>> "shan"
```

### Zawgyi-Unicode conversion

```sh
# convert to zawgyi
pds.cvt2zgi("ထမင်းစားပြီးပြီလား")
>> "ထမင္းစားၿပီးၿပီလား"

# convert to unicode
pds.cvt2uni("ထမင္းစားၿပီးၿပီလား")
>> "ထမင်းစားပြီးပြီလား"
```

### Tokenization

```sh
# syllable level tokenization for Burmese
pds.tokenize("Alan TuringကိုArtificial Intelligenceနဲ့Computerတွေရဲ့ဖခင်ဆိုပြီးလူသိများပါတယ်") # lang parameter for default function is 'mm'
>> ['Alan', 'Turing', 'ကို', 'Artificial', 'Intelligence', 'နဲ့', 'Computer', 'တွေ', 'ရဲ့', 'ဖ', 'ခင်', 'ဆို', 'ပြီး', 'လူ', 'သိ', 'များ', 'ပါ', 'တယ်']

# syllable level tokenization for Karen
pds.tokenize("သရၣ်,သရၣ်မုၣ် ခဲလၢာ်ဟးထီၣ် (၃၅) ဂၤန့ၣ်လီၤ.", lang="karen")
>> ['ကၠိ', 'သ', 'ရၣ်', ',', 'သ', 'ရၣ်', 'မုၣ်', 'ခဲ', 'လၢာ်', 'ဟး', 'ထီၣ်', '(', '၃၅', ')', 'ဂၤ', 'န့ၣ်', 'လီၤ', '.']

# word level tokenization
pds.tokenize("ဖေဖေနဲ့မေမေ၏ကျေးဇူးတရားမှာကြီးမားလှပေသည်", form="word")
>> ['ဖေဖေ', 'နဲ့', 'မေမေ', '၏', 'ကျေးဇူးတရား', 'မှာ', 'ကြီးမား', 'လှ', 'ပေ', 'သည်']

```

Syllable-level tokenization supports for 4 languages (Burmese, Karen, Shan, Mon). Word-level tokenization supports only Burmese currently.</br>
Available values for `lang` parameter in `tokenize` function: "mm", "karen", "mon", "shan"

## Future work

- [x] Add tokenizer for Burmese (Syllabel and word-level tokenization)
- [ ] Add more tokenizer (BPE, WordPiece etc.)
- [ ] Add Part-of-Speech (POS) tagger for Burmese
- [ ] Add Named-entities Recognition (NER) classifier for Burmese
- [ ] Add thorough documentation
yjxj-jupyter 0.3.0
Jupyter工具包【单个单元格时间与内存消耗】
Requires Python: >=3.6

# Jupyter Memory Monitor

一个简单的Jupyter笔记本内存监控工具，可以实时显示每个单元格执行后的内存和时间使用情况。

## 功能特点

- 实时监控内存使用情况
- 自动显示每个单元格执行后的内存占用
- 显示系统总内存使用率
- 轻量级，无需额外配置

## 安装

```bash
pip install jupyter-memory-monitor
```

## 快速开始

在Jupyter笔记本中导入并初始化：

```python
import jupyter_memory_monitor
jupyter_memory_monitor.init()
```


初始化后，每次执行单元格都会在输出区域看到类似下面的信息：

内存使用情况: 当前使用: 256.45 MB 使用率: 12.3% 单元格运行时间: 1.23秒


## 依赖要求

- Python >= 3.6
- IPython >= 7.0.0
- psutil >= 5.0.0

## 许可证

本项目采用 MIT 许可证

## 贡献指南

欢迎提交 Issue 和 Pull Request！

## 更新日志

### 0.2.2
- 增加单元格执行时间显示

### 0.1.0
- 首次发布
- 实现基本的内存监控功能