XArray Stack - Research Notebook

Data Structures¶

Rasters¶

In general, xarray can handle most of the raster calculations. Shoutout to numpy because it always comes in handy to know how to do some operations by onesself.

xarray

numpy

xarray-dataclasses - create standardized xarray datasets and dataarrays

Vectors¶

If we want to manipulate polygons and convert them into raster format, we need a specialized package to do so.

GeoCube - compatibility between rasters and vectors

Remote Sensing¶

Here, we refer to satellite observations.

SatPy - read some common satellite data into xarray datasets.

GeoWombat - General purpose raster processor

Masks¶

regionmask - working with masks and shape files for xarray

Coordinate Projections¶

rioxarray - rasterio and xarray compatibility

GeoBox - A great little library for handling coordinates and projections. This is an example of GeoCube-Rioxarray Compatibility

Math¶

Linear Algebra¶

xarray-einstats - linear algebra, Einops and statistics

General Array Operations

ds: XRDataset["T X Y"] = ...
# combine dimensions
ds: XRDataset["N"] = rearange(ds, "(T X Y)=N")
ds: XRDataset["N T"] = rearange(ds, "(X Y)=Samples")
# Creating Patches
ds: XRDataset["N"] = rearange(ds, "T X Y -> ")
# aggregate dimensions
# options: mean, min, max, sum, prod
ds: XRDataset["T"] = reduce(ds, "X Y", "mean")
ds: XRDataset["X Y"] = reduce(ds, "T", "mean")
# pooling (max, average)