{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Atmospheric Science","url":"https://skillfed.io/packages/category/scientific-engineering-atmospheric-science"}],"enrichment":{"capability":"Py-ART provides weather radar data processing, analysis, and visualization algorithms built on the Scientific Python stack for examining precipitation and cloud radar data.","skillfed_tags":["radar-processing","atmospheric-science","geospatial-data"],"use_cases":["Read and quality-control raw radar data from multiple file formats (cfradial, netCDF4, HDF5) for scientific analysis.","Apply published correction algorithms (attenuation, clutter removal, velocity dealiasing) to radar reflectivity and velocity fields.","Generate publication-ready radar plots with geographic overlays, colormaps, and custom field metadata.","Retrieve wind kinematics or precipitation estimates from single or dual-Doppler radar observations.","Integrate radar data into larger atmospheric science workflows using xarray and pandas for time-series or spatial analysis."],"what_it_does":"Py-ART is a Python library for working with weather radar data, developed and used by the ARM User Facility for processing data from precipitation and cloud radars. It sits on top of NumPy, SciPy, matplotlib, and xarray to provide radar-specific algorithms for data correction, retrieval, and visualization. The package handles reading multiple radar file formats, applying quality control and correction algorithms, and producing publication-ready plots with geographic context via cartopy. It's designed for atmospheric scientists and radar engineers but is general enough to work with many radar types beyond ARM's own instruments.\n\nThe library bundles published scientific methods for radar processing\u2014wind retrieval, beam blockage calculation, turbulence detection\u2014and integrates with the broader open-radar ecosystem through xradar and open-radar-data. Installation is straightforward via conda or pip, though the dependency stack (netcdf4, cartopy, xarray, scipy) means setup time and potential compilation on some platforms. The package is actively maintained, supports current Python versions (3.11\u20133.13), and carries no known security vulnerabilities.","worth_installing":"Yes, if you work with weather radar data in research or operational settings. Py-ART is production-stable (Development Status 5), actively maintained, carries no vulnerabilities, and integrates well with the Scientific Python ecosystem. The medium install friction is justified by the comprehensive radar algorithm library and active community. Not necessary for general atmospheric modeling or visualization work that doesn't involve radar-specific corrections or retrievals."},"id":"arm-pyart","links":{"html":"https://skillfed.io/packages/arm-pyart","md":"https://skillfed.io/packages/arm-pyart.md","pypi":"https://pypi.org/project/arm-pyart/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-07","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"arm-pyart","python_support":"supports_current","summary":"Py-ART: Python ARM Radar Toolkit"},"popularity":{"monthly_downloads":156959,"position":10770,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.5"}