semvec/pypi-packages huggingface dataset:
dynaplex 0.8.8
DynaPlex toolbox
Requires Python: >=3.12

﻿# DynaPlex 2

DynaPlex 2 is a Python library for solving Markov Decision Problems and similar models (POMDP, HMM). MDP/POMDP Models in DynaPlex 2 are written in (a subset of) Python, and are interpreted using a custom fast interpreter that supports multi-threading and vectorization. DynaPlex 2 focuses on solving problems arising in Operations Management: Supply Chain, Transportation and Logistics, Manufacturing, etc.

## Requirements

- Python 3.12 only
- Windows: AMD64
- Linux: x86_64
- macOS: modern hardware (Apple Silicon) only; no legacy intel hardware supported.
- Algorithms require a compatible PyTorch install; use the PyTorch selector: https://pytorch.org/get-started/locally/

build from source presently not supported.  

## Installation

```bash
pip install dynaplex
```

or 

```bash
pip install dynaplex[complete]
```

## Documentation

Full docs and tutorials are available at https://dynaplex-docs.readthedocs.io/en/latest/  
[ReadTheDocs](https://dynaplex-docs.readthedocs.io/en/latest/)

## Support

Please use the ReadTheDocs issue tracker for support: https://github.com/WillemvJ/DynaPlex-docs/issues
pylookyloo 1.36.3
Python CLI and module for Lookyloo
Requires Python: >=3.10

[![Documentation Status](https://readthedocs.org/projects/pylookyloo/badge/?version=latest)](https://pylookyloo.readthedocs.io/en/latest/?badge=latest)

# PyLookyloo

This is the client API for [Lookyloo](https://www.lookyloo.eu).

## Installation

```bash
pip install pylookyloo
```

## Usage

### Command line

You can use the `lookyloo` command to enqueue a URL.

```bash
usage: lookyloo [-h] [--url URL] --query QUERY

Enqueue a URL on Lookyloo.

optional arguments:
  -h, --help     show this help message and exit
  --url URL      URL of the instance (defaults to https://lookyloo.circl.lu/,
                 the public instance).
  --query QUERY  URL to enqueue.
  --listing      Should the report be publicly listed.
  --redirects    Get redirects for a given capture.

The response is the permanent URL where you can see the result of the capture.
```

### Library

See [API Reference](https://pylookyloo.readthedocs.io/en/latest/api_reference.html)
radiobject 0.1.1
TileDB-backed data structure for radiology data at scale
Requires Python: >=3.11
Keywords: dicom, machine-learning, medical-imaging, nifti, radiology, tiledb

# RadiObject

**What?** A TileDB-backed data structure for radiology data at scale.

**Why?** NIfTI/DICOM must be read from local disk and don't support partial reads.
TileDB enables cloud-native storage (S3), efficient partial reads, and
hierarchical organization of multi-volume datasets.

*[Thoughts](https://souzy.up.railway.app/thoughts/radiology-object)*

## Installation

```bash
pip install radiobject
```

## Quick Start

```python
from radiobject import RadiObject

# Create from NIfTI files using images dict (recommended)
radi = RadiObject.from_niftis(
    uri="./my-dataset",
    images={
        "CT": "./imagesTr/*.nii.gz",      # Glob pattern
        "seg": "./labelsTr",               # Directory path
    },
    validate_alignment=True,               # Ensure matching subjects across collections
    obs_meta=metadata_df,                  # Optional subject-level metadata
)

# Access data (pandas-like)
vol = radi.CT.iloc[0]            # First CT volume
data = vol[100:200, :, :]        # Partial read (only loads needed tiles)

# Filtering (returns views)
subset = radi.filter("age > 40")       # Query expression
subset = radi.head(10)                 # First 10 subjects
subset.materialize("./subset")         # Write to storage
```

Works with local paths or S3 URIs (`s3://bucket/dataset`).

## How It Works

NIfTI requires decompressing entire volumes; TileDB reads only the tiles needed.
This enables **200-660x faster** partial reads. [See benchmarks →](docs/BENCHMARKS.md)

## Sample Data

Download sample datasets for tutorials and testing:

```bash
# Install download dependencies
pip install radiobject[download]

# Download BraTS brain tumor data (for tutorials 00-04)
python scripts/download_dataset.py msd-brain-tumour

# List all available datasets
python scripts/download_dataset.py --list
```

## Documentation

- **[Tutorials](notebooks/README.md)** - Interactive notebooks
- **[Data Access](docs/DATA_ACCESS.md)** - Ingestion, queries, filtering
- **[ML Integration](docs/ML_INTEGRATION.md)** - MONAI/TorchIO setup
- **[Design](docs/DESIGN.md)** - Architecture decisions
- **[Benchmarks](docs/BENCHMARKS.md)** - Performance analysis
- **[Datasets](docs/DATASETS.md)** - Available datasets and download instructions

## License

MIT