# Introduction to Zyra

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**Jump to:**  
[Kid Version](#kid-version) | [High School Version](#high-school-version) | [College Version](#college-version) | [White Paper Version](#white-paper-version)

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## Kid Version
Imagine you have a big box of LEGO bricks mixed together — 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.

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## 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 — you can swap out any step for your own custom tool.

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## 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.

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## 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.