Introduction to Zyra

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Kid Version | High School Version | College Version | White Paper Version

Kid Version

Imagine you have a big box of LEGO bricks mixed together β€” some from space sets, some from castles, some from race cars.
Zyra is like a magical robot helper that:

  1. Finds the bricks you want (getting data from the internet or your computer)

  2. Puts them in order (sorting and cleaning the data)

  3. Builds something amazing (turning the data into pictures, videos, or maps you can show to friends).

It makes science data less messy and more fun to look at.

High School Version

Zyra is a Python tool that:

  • Collects data from many sources like websites, cloud storage, and special science file formats.

  • Processes it so it’s easier to work with (cutting, reshaping, converting formats).

  • Visualizes it in charts, maps, and animations.

Think of it like a 3-step factory:

  1. Input: Raw data from the web, satellites, or experiments.

  2. Processing: Filtering, analyzing, or reformatting.

  3. Output: Graphs, weather maps, or animated videos you can share.

It’s modular β€” you can swap out any step for your own custom tool.

College Version

Zyra is an open-source, modular Python framework for reproducible scientific data workflows.
It organizes work into four layers:

  1. Acquisition Layer – Connects to FTP, HTTP/S, S3, and local sources; supports GRIB, NetCDF, GeoTIFF, and streaming video.

  2. Processing Layer – Extracts subsets, applies transformations, and converts between scientific formats. Includes tools like VideoProcessor and GRIBDataProcessor.

  3. Visualization Layer – Uses Matplotlib and Basemap to produce static plots, animations, and composites with consistent color maps and overlays.

  4. Utilities Layer – Handles credentials, date parsing, file management, and small shared helpers.

The system is designed for flexibility, reproducibility, and interoperability, making it suitable for research, teaching, and operational pipelines.

White Paper Version

Abstract:
Zyra is a composable Python framework for end-to-end scientific data workflows, enabling acquisition, transformation, and visualization across diverse environmental and geospatial datasets. It is designed to address reproducibility, modularity, and interoperability challenges in modern data science.

Architecture:

  • Acquisition Managers implement standardized connect/list/fetch/upload APIs for heterogeneous data sources (e.g., FTPManager, HTTPManager, S3Manager).

  • Processing Managers support domain-specific operations, including video encoding/decoding (FFMPEG), GRIB parsing, NetCDF extraction, and geospatial transformations.

  • Visualization Managers integrate with Matplotlib and Basemap to generate consistent, publication-quality graphics, with support for packaged basemaps and overlays.

  • Utility Managers provide cross-cutting capabilities for credential handling, temporal range calculations, file path operations, and metadata management.

Supported Formats & Protocols: GRIB2, NetCDF, GeoTIFF, MP4, PNG, JPEG; FTP, HTTP/S, AWS S3, local filesystem.

Use Cases: Operational forecasting pipelines, climate research, geospatial analysis, educational demonstrations, and public communication products.