semvec/pypi-packages huggingface dataset:
testcase-gen-tool 1.0.1
An automated test case generation tool that simplifies the process of creating test cases for software projects.
Requires Python: >=3.12

# Test Case Generation System

A system that automatically generates formal requirements and test cases from PDF documents using AI agents.

## Features

- PDF document processing
- Automatic formal requirement generation
- Test case generation with multiple test design methods
- User-friendly Gradio interface


## Usage

1. Run the application using uvx and visit website http://127.0.0.1:7860/ to use
```bash
uvx testcase-gen-tool
```

2. Open the provided Gradio interface URL in your browser
3. Upload a PDF document containing requirements
4. Click "Process Document" to generate formal requirements and test cases

## Project Structure

```
.
├── app/
│   └── interface.py            # Application Entry
|   └── testcase_gen_app.py     # Testcase generation Logic
├── pyproject.toml              # Project configuration
├── agents/
│   ├── requirement_agent.py    # Formal requirement generation agent
│   └── testcase_agent.py       # Test case generation agent
└── utils/
    └── pdf_processor.py        # PDF processing utility
```

## Development

The project uses:
- Gradio for the web interface
- SmolAgents for AI agent implementation
- PyPDF2 for PDF processing

## License

MIT License
pants-backend-secrets 0.5.0
A secrets plumbing plugin for the Pants build system
Keywords: pantsbuild, pants, backend, secrets

# Secrets backend for Pants

[![PyPI](https://img.shields.io/pypi/v/pants-backend-secrets?label=Latest%20release)](https://pypi.org/project/pants-backend-secrets)

> **Warning**
> This plugin is in development. No stability is guaranteed! Contributions welcome.

This backends implements utilities for handling secrets.

## Planned and missing features

* Setting and creating secrets via Pants

## Targets

### `env_secret`

A secret to be read from the environment.

``` python
env_secret(
    name="bw_session_key",
    key="BW_SESSION",
)
```

| Argument      | Meaning                           | Default value                                         |
|---------------|-----------------------------------|-------------------------------------------------------|
| `name`        | The target name                   | Same as any other target, which is the directory name |
| `key`         | The environment variable to read. | **Required**                                          |
| `decsription` | A description of the target       | ` `                                                   |
| `tags`        | List of tags                      | `[]`                                                  |


## Goals

### `decrypt`

Decrypts and prints a secret.

```console
BW_SESSION="..." pants decrypt //examples/bitwarden:pypi_token
22:29:42.17 [INFO] Completed: Decrypting examples/bitwarden:pypi_token
Secret examples/bitwarden:pypi_token from BitWarden: pypi-...
```
pants-backend-kustomize 0.5.0
A Kustomize plugin for the Pants build system
Keywords: pantsbuild, pants, backend, kustomize, kubernetes

# Kustomize backend for Pants

![PyPI](https://img.shields.io/pypi/v/pants-backend-kustomize?label=Latest%20release)

> **Warning**
> This plugin is in development. No stability is guaranteed! Contributions welcome.

This backends implements targets for kustomize templates.

* [kustomize](https://github.com/kubernetes-sigs/kustomize) for overlaying state ontop of raw kubernetes files

## Planned and missing features

* Key/secret/... generation from built artifacts

## Targets

There's currently one target.


### `kustomize`

A code-generation target for converting a bundle of kubernetes files into a single multi-docuent YAML file with state
injected from other Pants targets.


``` python
kustomize(
    name="kustomize",
    sources=[
        "deployment.yaml",
        "server.py",
        "service.yaml",
        "namespace.yaml",
		"kustomization.yaml",
    ],
    dependencies=[":bin"],
)
```

| Argument | Meaning | Default value |
| --- | --- | --- |
| `name` | The target name | Same as any other target, which is the directory name |
| `sources` | Resources used by this target | **Required** |
| `dependencies` | Targets to package and pass to the build context, as well as bases | `[]` |
| `decsription` | A description of the target | ` ` |
| `tags` | List of tags | `[]` |

For dependencies, the builder will replace labels in the kustomization.yaml with the path of the built package.
spin-stepper 0.0.8
Python interface for stepper motor drivers from the STMicroelectronics STSPIN family (L6470).
Requires Python: >=3.11
Keywords: L6470, L6470HTR, STSPIN, STMicroelectronics, Stepper Motor, Raspberry Pi, SPI

A Python interface for the STSPIN stepper motor drivers from STMicroelectronics (ST).

The STSPIN family of stepper motor drives has a lot of really nice built-in functions
that You normally must implement in Python.
Position counter
Advanced commands such as go_to, go_home


This library has been developed and tested using the L6470HTR circuit connected via SPI to
a Raspberry Pi 4 with Raspbian Bookworm.
This will probably work with most circuits in this family, but scaling of some values might need adjustment.

Documentation on the L6470 circuit can be downloaded here: https://www.st.com/en/motor-drivers/l6470.html
SparkFun has developed a really nice board ned AutoDriver for playing with this circuit,
but this product is now retired. https://github.com/sparkfun/L6470-AutoDriver
ST has an evaluation board called EVAL6470H https://www.st.com/en/evaluation-tools/eval6470h.html

This is a fork from the st-spin package https://github.com/m-laniakea/st_spin by eir.
But it has been re-worked from the ground up to be more pythonic and to handle most of the commands.

Our purpose with this library was to solve our internal needs.
We use this for internally developed lab and production equipment.
We have no affiliation with STMicroelectronics.
tbacktest 0.1.4.dev6
事件驅動式的 Python 金融投資工具回測套件
Requires Python: >=3.6

## 套件簡介

這是一個自製的 Python 套件，實作了 backtesting 功能。
參考OREILLY的Python演算法交易該書本中所描述，以事件觸發導向的交易策略回測。
此回測方法是這麼多種回測方法中，作者認為最為細緻且彈性與變化度最高的方法。

此套件結合finlab package，方便取得台股市場的相關資料數據，方便投資人回測。


## 安裝方式

```bash
pip install tbacktest
```

## 指令範例

動能突破策略，以ETF:00713為例

```bash

from tbacktest import BacktestLongOnly

clo713 = BacktestLongOnly(symbol='00713', start='20171001', newest_day='20250512',amount=1000000,
                          datasource='finlab',
                          finlab_password='finlab_api_token')
#將LongOnly類別實體化出clo713

clo713.run_break_high_strategy(Hday=10, Lday=10,SMA1=5,SMA2=10)
#執行該類別下的方法，及突破進場買進ETF，跌破平倉出場ETF。

clo713.plot_backtest()  
#將策略結果圖示化
clo713.plot_data()
#將原本資料畫出來

clo713.show_backtest_all_information()
#顯示所有回測結果時間序列
```
ifri-mini-ml-lib 0.2.1
A lightweight machine learning library built from scratch by IFRI IA students
Requires Python: >=3.9
Keywords: machine-learning education from-scratch ml-library ifri ai

# ifri_mini_ml_lib

[![PyPI version](https://img.shields.io/pypi/v/ifri-mini-ml-lib.svg)](https://pypi.org/project/ifri-mini-ml-lib/) ![Coverage](https://img.shields.io/badge/coverage-dynamic-lightgrey?style=flat&logo=codecov)

A lightweight, educational machine learning library reimplementing core algorithms from scratch, inspired by scikit-learn. Developed by IFRI AI students for the Concepts & Applications of Machine Learning course.

---

## Features

- Core machine learning algorithms for:
  - Classification (Decision Trees, KNN, Logistic Regression)
  - Regression (Linear, Polynomial, SVR)
  - Clustering (K-means, DBSCAN, Hierarchical)
  - Association Rules (Apriori, Eclat, FP-Growth)
  - Neural Networks (MLP)
- Model selection tools (Cross-validation, Grid Search, etc.)
- Preprocessing utilities (scalers, encoders, missing value handlers, etc.)
- Focus on transparency and understanding of ML model internals

## Installation

Install from PyPI:

```bash
pip install ifri-mini-ml-lib
```

Or install from source:

```bash
git clone https://github.com/IFRI-AI-Classes/ifri_mini_ml_lib.git
cd ifri_mini_ml_lib
pip install -e .
```

## Quick Start

Here's a simple example using the KNN classifier:

```python
from ifri_mini_ml_lib.classification import KNN

# Example data
data = [[0, 0], [1, 1], [0, 1], [1, 0]]
labels = [0, 1, 1, 0]

# Initialize and fit the model
knn = KNN(k=3)
knn.fit(data, labels)

# Predict
prediction = knn.predict([[0.9, 0.8]])
print(prediction)
```

## Documentation

Full documentation is available at: [ifri_mini_ml_lib.github.io](https://ifri-ai-classes.github.io/ifri_mini_ml_lib/)

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).

## Acknowledgments

Thanks to the IFRI AI students and faculty who contributed to this project.
doublylist 0.2.0
Python wrapper for a C doubly linked list implementation
Requires Python: >=3.10

# Implementation Example

Python wrapper for a C doubly linked list implementation

``` python from src.node_list import NodeList

if __name__ == '__main__':
    sl = NodeList()
    sl.add("49643", "Brs", "Arg1", "Arge", "Expert")
    sl.add("51722", "Sl", "Dev", "Arge", "Mid")
    sl.show_reverse()
    sl.search(no="49643")
    sl.count()
    sl.remove(1)
    sl.count()
    sl.destroy() ```
elfi-python-sdk 1.0.9
ELFi Protocol python sdk
Requires Python: >=3.10

# ELFi Protocol Python SDK

## Pip Install

The SDK can be installed via pip:

```
pip install elfi-python-sdk
```

## Requirements

Developed using:
```python
  python=3.10.5
```

## Examples

There are several examples which can be run and can be found in [examples.](https://github.com/0xCedar/elfi_python_sdk/blob/main/examples/)


## Release Notes

V1.0.7
- support base mainnet

V1.0.8
- add create degen order