I am really looking forward to a time when scientific data analysis is less of a constant fuckaround and fight with technical bullshit. I'd *really* like
- #netCDF natively supporting complex numbers
- #Python #xarray and #pandas to natively support physical units (#pint is great on its own but the integrations leave a LOT to be desired)
- #Jupyter notebooks to suck less (crashes, glitches, widget plots not saved statically, an effing BUILTIN formatter, etc.)
- proper data pipeline systems
...
Seeking recommendations for a #WebMapping tutorial / course?
Slightly at sea on where to start.
- My current JS skill level is _extreme novice_.
- I don't have access to ArcGIS.
- Comfortable with #QGIS [*] and the #python #geospatial ecosystem (#geopandas #xarray #rasterio and plotting with #matplotlib)
Suggestions welcome. TIA. 
* I have looked at the qgis2web plugin, but having some issues associated with my aged laptop (2012 mbp running Ubuntu) and a 'Wayland session'.
How did I miss this?! This could be a nice way to manage big datasets with multiple files.
xarray.DataTree for hierarchical data structures
GSPy - A New Toolbox And Data Standard For Geophysical Datasets
--
https://doi.org/10.3389/feart.2022.907614 <-- shared paper
--
https://doi.org/10.5066/P9XNQVGQ | https://code.usgs.gov/g3sc/gspy <-- shared code repository
--
[an older paper, but code is in active and ongoing development/evolution]
#GIS #spatial #mapping #geophysics #geophysical #NetCDF #datatypes #code #opensource #library #dataformats #standardisation #standardization #openstandard #portable #metadata #Python #package #GSPy #methods #workflows #xarray #CRS #opendata #architecture #toolbox
I am moving all my computing libraries to #xarray, no regrets. It is a natural way to manipulate datasets of rectangular arrays, with named coordinates and dimensions: https://xarray.dev/
There are several possible backends, including #dask which allows lazy data loading.
I had the pleasure of meeting some of the devs last week, who showed me a preview of the upcoming `DataTree` structure which is going to make this library even more versatile!
Want to work with NetCDF files in Python? My tutorial series covers everything from opening and plotting NetCDF data to creating CF-compliant files for FAIR data publication.
Whether you're new to NetCDF or looking to enhance your skills, I've got you covered! 
Check it out: https://lhmarsden.github.io/NetCDF_in_Python_from_beginner_to_pro
Topics include:
Extracting data 
Plotting 
Creating CF-compliant files 
Granularity 
CF & ACDD
Suggestions? Let me know! #Python #DataScience #NetCDF #xarray #FAIRData #ClimateData
I dreamt that #xarray kerchunk/VirtualiZarr was being used to stream music
My mental picture of image files has always been of pixels covering a surface as tiles each like a tiny rectangular shapefile.
Investigating #Python #xarray has made me see the elegance of handling images as a grid of equally spaced dimensionless sensor readings. Upscaling/downscaling and interpolation become more meaningful and lossless, and image data is functionally identical to (although denser than) other point-based sensor data (e.g. weather stations).
The data science becomes so clean.
Excellent #OpenSource #OpenScience job opportunity: #xarray community developer at Earthmover PBC:
https://github.com/pydata/xarray/discussions/9059
US-only 
, but remote available 
. And the Earthmover folks are awesome!
I released gsw-xarray v0.4.0
https://pypi.org/project/gsw-xarray/0.4.0/
this release brings a new accessor for xarray, which may be a game changer for clarity and ease:
`ds.gsw[["sigma0", "alpha"]]`
v0.3.0 of yt_xarray is out!
This includes the initial release of the embedded transformation framework -- the main perk being how it simplifies the process of using yt's volume rendering methods with non-cartesian data (which is common in geophysical datasets)!
Full release notes at https://github.com/data-exp-lab/yt_xarray/releases/tag/v0.3.0
Figure uses relative humidity from a MERRA-2, see https://chrishavlin.github.io/NASASoftwareWorkshop2024/yt-xr-dev-02-transformations.html for an overview.
I am deepening my knowledge of #xarray by actually using it to store intermediate data for the interferometric processing of Sentinel-1. I found that learning by doing is much more efficient for me than just reading about a topic.
Checking in from New Zealand! I'm developing a library to decode #GeoTIFF files in #rustlang (without using GDAL 
). Currently trying to add an #xarray backend (in Python) at https://github.com/weiji14/cog3pio/pull/14.
Stoked about recent progress on yt_xarray's embedded transformation framework! This new feature takes a non-cartesian #xarray dataset and wraps it in a cartesian #yt dataset. As yt needs data, it will interpolate data on demand. This let's you use yt's volume rendering without saving off interpolated versions of datasets (which is how I used to do things...). Makes it **much** easier to generate volume renderings of geophysical fields with yt.
#python #data_viz #3d
I have ace colleagues, part 243:
My colleague Luke (LukeDataManager) has a Youtube channel where he helps people prepare #OpenData #FAIRdata:
His most recent series is about #Python, #Xarray, #NetCDF, etc...
Give him a follow (on Youtube) and a retoot (here)? https://youtube.com/@LukeDataManager?si=VMU9ehIPE8eyRaCt
Greg Cocks 
