#xarray

New #XArray + #GeoPandas tutorial for fast sampling of geospatial rasters. Learn how XArray can be used efficiently to extract data that is orders of magnitudes faster than other Python packages

Integration of #Xarray and Google #EarthEngine using #Xee library highlighted in this playlist:

Google #EarthEngine Tutorial-207 Published: #Terrain Metrics using #Xarray - Spatial

Launching a new home for all our open-source geospatial python tutorials - covering #GeoPandas, #XArray, #Dask, #XEE, #SamGeo and more. Check them out at

Today I had a raster resampling task to do. Was feeling lazy (#Fridays😬) so I favoured a (possibly) slow but very compact solution using #xarray's coarsen() method. For some reason, resampling a 3GB tile consumed all 64GB of memory. To my dismay, laziness was over!

I quickly jumped to #QGIS to find out why my 3GB tile at 1m resolution could not be resampled at 100m. Luckily, the #grassgis resample module managed this task with no problem and a reduced memory usage. So it was not my computer, but something unknown within #xarray library.

Working with large files like these can be tricky with traditional Python libraries like rasterio. You can easily run out of RAM and processing is slow. The #XArray ecosystem makes it a pleasure to work with the data. We read the bands using #rioxarray. (3/n)

“Processing a 250 TB dataset with Coiled, Dask, and Xarray” #opensource #xarray #dask #coiled #python #graph #sciworkflows #workflow #CompSci #DistributedComputing #hydrology #climate

In a new #Jupyter Notebook developed at NASA's #PODAAC, learn the basics of #xarray and how to use the open-source #Python library, Dask, for parallel computing (data access and data analysis) in the cloud. #CloudComputing ➡️Check it out at:

Want to access EO data in the cloud using Xarray? This beginner-friendly talk by Marco Wolsza (NITheCS Micro School) features DEarthAfrica tools & datasets. Great intro to working with open geospatial data! Watch here: #EO #Xarray #OpenData

When I am working on laptop or desktop, I use xarray+NetCDF but should I know consider using zarr? I see nice things working on cloud storages from brief reading but would like to know more about the advantage (#python, #xarray)

@aazuspan @dmlmont geemap has some functions for exporting Image and ImageCollection to drive. You can grab the source code from the link below if needed. I am hoping to incorporate wxee's #xarray functionality into #geemap

Pipdeptree is a command-line tool that displays installed Python packages in a dependency tree. #pipdeptree + #xarray + #graphwiz = nice DAG visualization of your dependencies 👇👇

Just found a nice #Python package for visualizing package dependency tree with only one line of code pip install pipdeptree graphviz pipdeptree -p xarray --graph-output pdf > dependencies.pdf #xarray #geospatial

Working with GRIB files in Python has never been easier! @ECMWF cfgrib reaches beta - easy & efficient GRIB 1 & 2 driver for #xarray - supports Python 2 & 3 on Linux & MacOS - depends only on ECMWF ecCodes @NumFOCUS @PyData @CopernicusECMWF @bopensolutions

In two new #Jupyter notebooks, learn how to interpret NASA's PACE Ocean Color Instrument data file structures and use #earthaccess to access OCI Level-1B, Level-2, and Level-3 NetCDF files & open them with #xarray. #OBDAAC ➡️Access PACE Notebooks:

A little late for #showyourstripes day, but I was inspired to follow @CedScherer's visualization of warming in Europe with an ocean version using global large marine ecosystems. Temperature data is #NOAA OISSTv2, processing done with #xarray, plotting with #ggplot & #rstats.

✨@EarthmoverHQ is hiring their first #Xarray community developer: Don't miss a chance to be part of something truly transformative! P.S. Excited to see that community building work in open source is being funded. @zarr_dev @xarray_dev

#30DayMapChallenge - day 12 - Map from 2125 - This map will hopefully will NOT be made in the year 2125. This map is based on actual projections (worse case scenario) from IPCC AR6 Sea Level Projections. Python/#xarray

🚀 You like optimization? And you love Python? Then, check out Linopy, a new, open-source Python package 🐍 that makes linear and MILP optimization fast and memory-efficient. The best thing: You can use array-like operations & indexing like you're used to from #pandas & #xarray

#resources: Compared to similar packages, Linopy is nearly as fast as #JuMP, outdoing all others in memory-efficiency 💾, plus it scales very well with the problem size... But wait, this is Python, this cannot be! The secret sauce? Its underlying data model:

Linopy takes Python’s advanced data handling packages like #numpy, #pandas, and #xarray and uses an array-based data model for variables, expressions and constraints. All key data and references are stored efficiently in the background (original idea by @nworbmot).

Slight change in his current design! This is how he will look like from now on #sonicexe #sonicexeoc #xarray