open-radar-data
Provides utility functions for accessing data repository for openradar examples/notebooks
Decision gist · record as of 2026-08-14
Yes, if you are working with openradar or need sample weather radar/lidar data for research or examples. The package is actively maintained, has no security vulnerabilities, low install friction, and a permissive license. It solves a real problem—centralized, versioned access to shared datasets—for the openradar community.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; internet connection needed to download datasets on first fetch.
- Low install friction with a single lightweight dependency (pooch).
- Active maintenance with recent commits and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions—suitable for both academic and commercial projects.
last release 2026-05-08 (98 days) · last repo commit 2026-08-03 · 33 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,890 downloads/mo, #14,193 on PyPI
Alternatives
Verify before relying
pip install open-radar-data
from open_radar_data import DATASETS
filepath = DATASETS.fetch('sample_sgp_data.nc')
print(filepath)- Total number and size of available datasets in the registry
- Performance characteristics when fetching large radar volume files
- Whether datasets are updated regularly or represent a static snapshot
What it is and what it does
Open-radar-data is a data distribution package that manages a registry of sample weather radar and lidar datasets for use in openradar examples and notebooks. It wraps the pooch library to handle downloading, caching, and local path retrieval of these datasets, so users can focus on analysis rather than data management.
The package maintains a registry file listing available datasets (single sweep PPI, RHI, and complete volume files in various source formats) and provides a simple fetch interface that downloads files on first use and returns cached local paths on subsequent calls. Users can override the default cache location via an environment variable if needed.
Use it for
- Download and cache sample radar data for reproducible openradar tutorials and example notebooks.
- Retrieve pre-curated weather radar datasets for algorithm development and testing without manual file management.
- Access lidar data alongside radar data for multi-instrument research workflows.
- Integrate standardized sample datasets into automated data processing pipelines.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with openradar or need sample weather radar/lidar data for research or examples.
The package is actively maintained, has no security vulnerabilities, low install friction, and a permissive license. It solves a real problem—centralized, versioned access to shared datasets—for the openradar community.
Install
open-radar-data on PyPI
Before you install
Low install friction with a single lightweight dependency (pooch). Active maintenance with recent commits and no known vulnerabilities.
Requires Python 3.9 or later; internet connection needed to download datasets on first fetch.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions—suitable for both academic and commercial projects.
Quickstart
pip install open-radar-data
from open_radar_data import DATASETS
filepath = DATASETS.fetch('sample_sgp_data.nc')
print(filepath)
Verify before relying
- Total number and size of available datasets in the registry
- Performance characteristics when fetching large radar volume files
- Whether datasets are updated regularly or represent a static snapshot
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepooch |
| Maintenance | Actively maintained 98 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 81,890 / month, #14,193 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: open_radar_data-0.8.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “radar data download cache”
- open-radar-dataProvides a registry and download utility for accessing sample weather…
- arm-pyartPy-ART provides weather radar data processing, analysis, and…
- xradarXradar reads and writes weather radar data in multiple formats…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also xradar · arm-pyart · sodapy · liac-arff · ouster-sdk · env_canada · irm-kmi-api · laspy · pystac-ext-sar · herbie-data