YAXArraysToolbox.jl

High-performance spatio-temporal analysis for Earth System data cubes

YAXArraysToolbox.jl extends YAXArrays.jl with high-level functions for analyzing spatio-temporal data cubes from climate science and Earth observation.

Why YAXArraysToolbox?

Because laziness is not only good when reading big data — it should also apply to common analysis tasks! This package provides ready-to-use functions for plotting, aggregation, and analysis so you can focus on science, not boilerplate code.

✨ Features at a Glance

YAXArraysToolbox extends YAXArrays.jl with high-level functions for climate and Earth observation data analysis:

Feature	Description
📈 Time Series Plotting	Visualize temporal evolution with automatic spatial aggregation
🗺️ Spatial Mapping	Create publication-ready maps with temporal aggregation
⏱️ Temporal Aggregation	Resample data to monthly, yearly, or custom periods
🔄 Space-for-Time Analysis	Estimate land cover change impacts on climate variables
😷 Flexible Masking	Apply spatial, temporal, and altitude-based masks

📦 Installation

using Pkg
Pkg.add(url="https://github.com/dpabon/YAXArraysToolbox.jl")

Or using the package manager:

julia> ]
pkg> add https://github.com/dpabon/YAXArraysToolbox.jl

🚀 Quick Start

using YAXArraysToolbox
using YAXArrays
using CairoMakie
using Dates

# Load Earth System Data Cube
esdc = Cube(open_dataset(
    "https://s3.bgc-jena.mpg.de:9000/esdl-esdc-v2.1.1/esdc-8d-0.25deg-184x90x90-2.1.1.zarr"
))

# Select a region and variable
cube = esdc[
    lon = -10 .. 0,
    lat = 35 .. 45,
    time = Date(2010) .. Date(2012),
    Variable = At("leaf_area_index")
]

# Plot time series (spatial mean)
plot_time(cube; fun="mean")

# Create spatial map (temporal median)
plot_space(cube; fun="median")

# Aggregate to monthly resolution
monthly = aggregate_time(cube; new_resolution="month", fun="mean")

📚 Tutorials

Get hands-on experience with our step-by-step tutorials:

📊 Basic Operations

Learn the core functions: plot_time, plot_space, and aggregate_time.

Start Tutorial →

🌍 Space-for-Time Method

Understand and apply the space4time methodology for land cover change analysis.

Start Tutorial →

Tutorial Overview

Basic Operations — Essential functions for data visualization and aggregation
Space-for-Time Method — Advanced methodology for estimating land cover change impacts

🏗️ Package Architecture

YAXArraysToolbox
├── Basic Operations
│   ├── plot_time      → Time series visualization
│   ├── plot_space     → Spatial mapping
│   └── aggregate_time → Temporal resampling
├── Masking
│   ├── masking_time     → Filter by time period
│   ├── masking_space    → Spatial filtering
│   └── masking_altitude → Elevation-based filtering
└── Spatio-Temporal Analysis
    └── space4time_proc  → Land cover change impact analysis

🔧 Main Functions

Basic Operations

# Time series visualization with spatial aggregation
plot_time(cube; fun="mean", var="temperature")

# Spatial mapping with temporal aggregation
plot_space(cube; fun="median", var="lai")

# Temporal aggregation/resampling
aggregate_time(cube; new_resolution="month", fun="mean")

Masking

# Mask by spatial extent
masking_space(cube, mask_cube; threshold=0.5)

# Mask by time period
masking_time(cube; start_date=Date(2010), end_date=Date(2015))

Spatio-Temporal Analysis

# Space-for-time analysis for land cover change impacts
space4time_proc(climate_cube, landcover_cube, class_list)

📖 Dependencies

YAXArraysToolbox builds on these excellent Julia packages:

Package	Purpose
YAXArrays.jl	Data cube handling and lazy operations
CairoMakie.jl	High-quality plotting
GeoMakie.jl	Geographic projections and mapping

🤝 Contributing

Contributions are welcome! Here's how you can help:

Report bugs — Open an issue describing the problem
Suggest features — Share your ideas in the issues section
Submit PRs — Fork the repo and submit pull requests

Please check existing issues before creating new ones.

Open-Earth-Monitor — Open infrastructure for monitoring the European environment
NFDI4Earth — National Research Data Infrastructure for Earth System Science

This project has received funding from the Open-Earth-Monitor Cyberinfrastructure project that is part of European Union's Horizon Europe research and innovation programme under grant 101059548.