Ujaval Gandhi@spatialthoughts
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
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Post pubblici su X che usano #xarray.
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Daily Python Tip 🐍🐧@python_tipPipdeptree is a command-line tool that displays installed Python packages in a dependency tree. #pipdeptree + #xarray + #graphwiz = nice DAG visualization of your dependencies 👇👇Qiusheng Wu @giswqsJust 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 #geospatial05387.2K
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
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- Ujaval Gandhi@spatialthoughts@spatialthoughtsWorking 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)
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Exciting news for the geospatial community! A new dedicated platform is now available for open-source geospatial Python tutorials, featuring #GeoPandas, #XArray, #Dask, and more. A great resource for anyone working with geospatial data. Thank you, @spatialthoughts, for making these valuable tutorials accessible! #Geospatial #Python #DataScience #OpenSourceUjaval Gandhi @spatialthoughtsLaunching a new home for all our open-source geospatial python tutorials - covering #GeoPandas, #XArray, #Dask, #XEE, #SamGeo and more. Check them out at0171.4K- Ujaval Gandhi@spatialthoughtsLaunching a new home for all our open-source geospatial python tutorials - covering #GeoPandas, #XArray, #Dask, #XEE, #SamGeo and more. Check them out at816965127.1K
- Ujaval Gandhi@spatialthoughtsWant to level up your #Python Dataviz skills? I am launching our Mapping and Data Visualization with Python course on YouTube and designed a free #PythonDatavizChallenge where you spend 30 mins a day for the next 30 days to learn a new skill. Start at
413663131.6K - Ujaval Gandhi@spatialthoughts@spatialthoughtsWe learn how to visualize satellite imagery by querying a STAC Catalog of Sentinel-2 imagery and learning how to work with time-series data with #XArray. We create a median RGB composite and visualize it using #MatPlotlib101432
- Ujaval Gandhi@spatialthoughts@spatialthoughtsWe end the day by learning how #XArray makes temporal aggregation a breeze and create monthly median composites with just 1 line of code and visualize all 12 images using #Matplotlib132544
Richard Signell@rsignellI've been using #hvplot for over a year now, but I'm still blown away by how easy it is to get rich, interactive visualizations of model output with #xarray and #holoviz. @xarray_dev, @holoviz, @pangeo_data.
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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:
04116213.5K- Creating a global wind velocity animation with #ipyleaflet and #xarray Notebook: #geemap #dataviz #geospatial #netcdf432252
Julien Emile-Geay@StellarGeayFor those of you who like their #PMIP4 data analysis-ready + cloud-optimized, the good folk at pangeo-forge have some sweet recipes cooking: Once the data are in the cloud, loading them in #xarray (e.g. in a @ProjectJupyter notebook) is as easy as this:
4631- Irene@igarciamarti@igarciamartiI 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.112274
New #xarray release v0.10.1 is out, with new IO (Iris and Zarr support) and plotting options, among many other option. Thanks to everyone who contributed!02358- Ujaval Gandhi@spatialthoughts@spatialthoughtsI spent most of the week researching the methodology and correct parameters for converting the raw DN values to TOA reflectances. This is a critical step and there was no ready implementation that I could find. #XArray shines at band-math using fast vectorized operations. (4/n)
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Ellen Brock@ellenarun#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
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Fabian Hofmann@fabianmarik🚀 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
2196212.5K- Fabian Hofmann@fabianmarik@fabianmarik#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:115424
- Fabian Hofmann@fabianmarik@fabianmarikLinopy 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).109325
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