semvec/pypi-packages huggingface dataset:
graintools 0.1
Grain segmentation from Laue patterns
Requires Python: >=3.8

# graintools

[![PyPI version](https://img.shields.io/pypi/v/graintools.svg)](https://pypi.org/project/graintools/)
[![Python versions](https://img.shields.io/pypi/pyversions/graintools.svg)](https://pypi.org/project/graintools/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](#license)
[![Tests](https://img.shields.io/badge/tests-passing-brightgreen.svg)](#)

## What is it?
graintools is a set of Python tools is aimed at segmenting X-ray diffraction data coming from polycrystalline materials. The data is acquired using the LAUEMAX setup at BM32 of the European Synchrotron Radiation Facility (ESRF).

---

## Installation
Install the test version
```bash
python3 -m pip install --index-url https://test.pypi.org/simple/ graintools
```

Or add it to your PYTHONPAH
```bash
git clone https://github.com/serbng/graintools.git
```
Inside your script or notebook
```python
>>> sys.path.append("path/to/repo/graintools")
```

### Optional extras

Install dependencies necessary to run the notebooks and the simulation:

```bash
pip install "graintools[full]"
```

---

## Get started
Create a virtual environment and activate it. For example

```bash
python -m venv ~/graintools
source ~/graintools/bin/activate
```

Inside the virtual environment

```bash
pip install "graintools[full]"
```
Download and run the notebook:

```bash
git clone https://github.com/serbng/graintools.git
jupyter lab graintools/examples/simulation
```

---

## Contacts
myemail, TBD which one

---
datarobot-pulumi-utils 0.1.2
A set of Pulumi CustomResources and other utilities built on top of the pulumi-datarobot provider.
Requires Python: >=3.9

<div align="center">
  <h1>DataRobot Pulumi Utils</h1>
</div>
<div align="center">
  <a href="https://pypi.python.org/pypi/datarobot-pulumi-utils"><img src="https://img.shields.io/pypi/v/datarobot-pulumi-utils.svg" alt="PyPI"></a>
  <a href="https://github.com/datarobot-oss/datarobot-pulumi-utils"><img src="https://img.shields.io/pypi/pyversions/datarobot-pulumi-utils.svg" alt="versions"></a>
  <a href="https://github.com/datarobot-oss/datarobot-pulumi-utils/blob/main/LICENSE"><img src="https://img.shields.io/github/license/datarobot-oss/datarobot-pulumi-utils.svg?v" alt="license"></a>
</div>

---

A set of Pulumi CustomResources and other utils on top of the `pulumi-datarobot` provider.
roka 0.1.0
Lightning-fast toolkit for hackathon dominance.
Requires Python: >=3.8

# ROKA: The Hackathon Accelerator

ROKA is a high-speed Python library built to eliminate boilerplate code. It handles APIs, scraping, and ML architecture so you can focus on building your core product during high-pressure development sessions.

---

## Installation

Install via pip:

bash
pip install roka


---

## Core Features

* **Zero-Config Scraping:** Extract clean data and metadata from any URL instantly.
* **Instant ML Wrappers:** One-line deployments for sentiment analysis, OCR, and summarization.
* **Streamlined API Connectors:** Pre-configured interfaces for OpenAI, Firebase, and Twilio.
* **Lightweight Design:** Minimal dependencies to ensure fast deployment and environment stability.

---

## Usage Examples

### 1. Web Scraping
Extract headlines and main content without manual parsing.

python
from roka import Scraper

data = Scraper.quick_get("https://example-news-site.com")
print(data.headlines)
print(data.main_content)


### 2. Sentiment Analysis
Deploy a model for immediate text processing.

python
from roka.ml import QuickAnalyze

result = QuickAnalyze.sentiment("This project is moving fast.")
# Output: {'label': 'POSITIVE', 'score': 0.99}


### 3. OpenAI Integration
Simplified setup for LLM features.

python
from roka.connect import OpenAI

ai = OpenAI(api_key="your_key")
response = ai.ask("Provide a concept for a hackathon project.")


---

## Efficiency Gains

| Task | Traditional Method | With ROKA |
| :--- | :--- | :--- |
| API Setup | 20 Minutes | 2 Minutes |
| Data Scraping | 1 Hour+ | 5 Minutes |
| ML Implementation | 30 Minutes | 1 Line |

---

## Contributing

To contribute a new utility or feature:
1. Fork the repository.
2. Create a feature branch.
3. Submit a Pull Request with a description of the utility added.

---

## License

Distributed under the MIT License.