GriddingMachine.jl

Credits

Please cite our paper(s) when you use GriddingMachine:

• Y. Wang, P. Köhler, R. K. Braghiere, M. Longo, R. Doughty, A. A. Bloom, and C. Frankenberg. 2022. GriddingMachine, a database and software for Earth system modeling at global and regional scales. Scientific Data. 9: 258. DOI

About

GriddingMachine.jl includes a collection of global canopy propertie. To best utilize Pkg.Artifacts and FTP storage, GriddingMachine.jl only supports julia 1.7 and above.

Documentation	CI Status	Compatibility	Code Coverage

Installation

julia> using Pkg;
julia> Pkg.add("GriddingMachine");

API

GriddingMachine has the following sub-modules, some of which are in development. The sub-modules are

Sub-module	Functionality	Ready to use
Blender	Regrid the gridded datasets	Testing
Collector	Distribute the gridded datasets	v0.2
Fetcher	Download ungridded datasets	Testing
Indexer	Read the gridded datasets	v0.2
Partitioner	Sort the ungridded datasets	Testing
Requestor	Request gridded datasets	v0.2

See API for more detailed information about how to use GriddingMachine.jl.

To automatically download and query the file path of the dataset, use

julia> using GriddingMachine.Collector;
julia> file_path = query_collection("VCMAX_2X_1Y_V1");

To request a partial dataset from the server without download the entire dataset, use

julia> using GriddingMachine.Requestor;
julia> dat,std = request_LUT("VCMAX_2X_1Y_V1", 35.1, 115.2);
julia> dat,std = request_LUT("VCMAX_2X_1Y_V1", 35.1, 115.2; interpolation=true);

Other language supports

Language	Link to Github repository
Matlab	octave-griddingmachine
Octave	octave-griddingmachine
R	r-griddingmachine
Python	python-griddingmachine

Data contribution

We welcome the contribution of global scale datasets to GriddingMachine.jl. To maximally promote data reuse, we ask data owners to preprocess your datasets before sharing with us, the requirements are:

• The dataset is stored in a NetCDF file
• The dataset is either a 2D or 3D array
• The dataset is cylindrically projected (WGS84 projection)
• The first dimension of the dataset is longitude
• The second dimension of the dataset is latitude
• The third dimension (if available) is the cycle index, e.g., time
• The longitude is oriented from west to east hemisphere (-180° to 180°)
• The latitude is oriented from south to north hemisphere (-90° to 90°)
• The dataset covers the entire globe (missing data allowed)
• Missing data is labeled as NaN (not a number) rather than an unrealistic fill value
• The dataset is not scaled (linearly, exponentially, or logarithmically)
• The dataset has common units, such as μmol m⁻² s⁻¹ for maximum carboxylation rate
• The spatial resolution is uniform longitudinally and latitudinally, e.g., both at 1/2°
• The spatial resolution is an integer division of 1°, such as 1/2°, 1/12°, 1/240°
• Each grid cell represents the average value of everything inside the grid cell area (as opposing to a single point in the middle of the cell)
• The label for the data is "data" (for conveniently loading the data)
• The label for the error is "std" (for conveniently loading the error)
• The dataset must contain one data array and one error array besides the dimensions
• The dataset contains citation information in the attributes
• The dataset contains a log summarizing changes if different from original source

The reprocessed NetCDF file should contain (only) the following fields:

Field	Dimension	Description	Attributes
lon	1D	Longitude in the center of a grid	unit
			description
lat	1D	Latitude in the center of a grid	unit
			description
ind	1D	Cycle index (for 3D data only)	unit
			description
data	2D/3D	Data in the center of a grid	longname
			unit
			about
			authors
			year
			title
			journal
			doi
			changeN
std	2D/3D	Error of data in the center of a grid	same as data

For data contributors who have limited knowledge about Github and Julia, we recommend to contribute your reprocessed data to us by tag an issue via the button New issue in the Issues Tab. See an example table here. See this google doc for an example of this data reprocessing and deployment.

For data contributors who are experienced Github and Julia users, we also welcome that your contribution of code directly. See this pull request for an example of the pull request.