#xarray — Public Fediverse posts

Will we finally get nice html representations of @movingpandas Trajectories and TrajectoryCollections?

Inspired by #pydata #xarray 🤩

WIP 👩‍💻 : https://github.com/movingpandas/movingpandas/issues/293

Any feedback / ideas welcome!

#GISChat #MovementDataAnalysis

RE: https://wisskomm.social/@ioer/115899330915763542

I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.

I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].

I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.

Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR

Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).

[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW

RE: https://wisskomm.social/@ioer/115899330915763542

I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.

I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].

I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.

Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR

Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).

[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW

🚨 New version of xarray-grass 🚨

I'm glad to announce that I've release version 0.4.0 of xarray-grass! It comes with many improvements: 🚀 Lazy loading of GRASS space-time datasets, 📅 Better management of time dimensions, including support of units when writing relative-time series to GRASS
🗘 Automatic transposition of arrays when writing to GRASS.

Try it today !

https://pypi.org/project/xarray-grass/

@grassgis #xarray

I am excited to announce xarray-grass, a new free software Python library designed to bridge two open source data science heavy weights: @grassgis and #xarray (https://xarray.dev/).

Although xarray-grass is in its nascent phase, I encourage you to check out the repository on GitHub (https://github.com/lrntct/xarray-grass) and experiment with it. Your insights and contributions will play a significant role in the project's future.

#geo #RemoteSensing #earthobservation people! Has anyone got an example of using the #Copernicus #DataSpace -> https://documentation.dataspace.copernicus.eu/ to go from searching using #pystac to getting a working #xarray dataset for #Sentinel2 reflectances? #python #geopython

#geo #RemoteSensing #earthobservation people! Has anyone got an example of using the #Copernicus #DataSpace -> https://documentation.dataspace.copernicus.eu/ to go from searching using #pystac to getting a working #xarray dataset for #Sentinel2 reflectances? #python #geopython

#geo #RemoteSensing #earthobservation people! Has anyone got an example of using the #Copernicus #DataSpace -> https://documentation.dataspace.copernicus.eu/ to go from searching using #pystac to getting a working #xarray dataset for #Sentinel2 reflectances? #python #geopython

#geo #RemoteSensing #earthobservation people! Has anyone got an example of using the #Copernicus #DataSpace -> https://documentation.dataspace.copernicus.eu/ to go from searching using #pystac to getting a working #xarray dataset for #Sentinel2 reflectances? #python #geopython

#geo #RemoteSensing #earthobservation people! Has anyone got an example of using the #Copernicus #DataSpace -> https://documentation.dataspace.copernicus.eu/ to go from searching using #pystac to getting a working #xarray dataset for #Sentinel2 reflectances? #python #geopython

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
...

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
...

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
...

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
...

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
...

𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀
SpatialThoughts provides tutorials which cover a broad range of geospatial topics and technologies, e.g., #GeoPandas, #XArray, #dask, and more. Each technology is described in a notebook with step-by-step explanation. Check it out.
https://www.geopythontutorials.com

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

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

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

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

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

Exporting an #xarray array containing an irregular mesh to geotiff is still something that is “not a breeze”

Justus made a great intro on using #DGGS through #xarray #xdggs at the #Pangeo showcase talk. Xdggs is now in a stage where you can use it fairly robustly with #HEALPIX and #H3. Other integrations like for #DGGRID are developed as separate plugins.

https://youtube.com/watch?v=bAMGFKsxsjs&si=hnfpk04VMn-rggEV

Justus made a great intro on using #DGGS through #xarray #xdggs at the #Pangeo showcase talk. Xdggs is now in a stage where you can use it fairly robustly with #HEALPIX and #H3. Other integrations like for #DGGRID are developed as separate plugins.

https://youtube.com/watch?v=bAMGFKsxsjs&si=hnfpk04VMn-rggEV

Justus made a great intro on using #DGGS through #xarray #xdggs at the #Pangeo showcase talk. Xdggs is now in a stage where you can use it fairly robustly with #HEALPIX and #H3. Other integrations like for #DGGRID are developed as separate plugins.

https://youtube.com/watch?v=bAMGFKsxsjs&si=hnfpk04VMn-rggEV

Justus made a great intro on using #DGGS through #xarray #xdggs at the #Pangeo showcase talk. Xdggs is now in a stage where you can use it fairly robustly with #HEALPIX and #H3. Other integrations like for #DGGRID are developed as separate plugins.

https://youtube.com/watch?v=bAMGFKsxsjs&si=hnfpk04VMn-rggEV

Justus made a great intro on using #DGGS through #xarray #xdggs at the #Pangeo showcase talk. Xdggs is now in a stage where you can use it fairly robustly with #HEALPIX and #H3. Other integrations like for #DGGRID are developed as separate plugins.

https://youtube.com/watch?v=bAMGFKsxsjs&si=hnfpk04VMn-rggEV

... while I find ChatGPT increasingly useful for technical things like telling me how to manipulate #xarray datasets in #python, or how to add a docstring to my python routine.

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!

#Python #numpy #ScientificComputing

🌍📊 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

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.

Py3DEP [hydroclimate analysis]
--
https://pypi.org/project/py3dep/ <-- link to app / resources
--
“Py3DEP is a part of [PyGeoUtils] HyRiver software stack that is designed to aid in hydroclimate analysis through web services. This package provides access to the 3DEP database which is a part of the National Map services. The 3DEP service has multi-resolution sources and depending on the user-provided resolution, the data is resampled on the server-side based on all the available data sources. Py3DEP returns the requests as xarray dataset…”
#GIS #spatial #mapping #Py3DEP #PyGeoUtils #HyRiver #softwarestack #elevation #3DEP #USGS #NationalMap #opendata #processing #spatialanalysis #hydroclimate #water #hydrology #webservices #xarray
@HyRiver @USGS

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!

#GetFediHired

@conorosully @lavergnetho Also, consider getting a Microsoft Planetary Computer account. Although Google's equivalent has been around longer, #PlanetaryComputer allows you to do these things easily with standard Python tools and libraries (eg #xarray )

For all #xarray and #PlanetaryComputer folk, here's some nice #Sentinel 1 accessing and visualisation using #Python:
👉 https://discourse.pangeo.io/t/wednesday-november-2nd-2022-jupyter-book-tutorials-demonstrating-xarray-based-workflows-for-cloud-hosted-remote-sensing-data/2834
#RemoteSensing #EarthObservation

It's sprint day of #SciPy2023! Fun with teams #xarray and #astropy so far. Now, post-lunch, we're ready for round two.

As always, stop by if you're in interested in the remaining swag.

@scipy2023

Going back to more #dataAnalysis stuff - who wants to learn to use #XArray and #Pangeo? Apparently this was left over from the #CMIP6ArcticBootcamp (see birdsite for more on that #) but I shared it with student the other day and this really is a nice introduction to the tools..
6/
https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Preparing demos for next week at #jupytercon. Here is a sneak peek (turn on the audio, best with headphones). See you there!

💨💨 🔊💨💨

#sonification #xarray #ipyleaflet #ipytone #jupyter

So it begins!
Climatematch Summer School starts today and we are really excited to see what you will achieve during the course!
This week will start with an intro chapter on the Earth’s Climate Systems and we will learn about Earth’s past, present and future climate. The main focus will be on Xarray Python package 🐍​ 📦​ and how to manipulate large climate dataset. A huge thank to #ProjectPythia, code and data of this tutorial is based on their content.
We hope all our students and staff will learn a lot during this two weeks. Send us pictures, video, audio or artwork from your learning journey!🛫

#python #climatematch #xarray #climate

It's sprint day of #SciPy2023! Fun with teams #xarray and #astropy so far. Now, post-lunch, we're ready for round two.

As always, stop by if you're in interested in the remaining swag.

@scipy2023

It's sprint day of #SciPy2023! Fun with teams #xarray and #astropy so far. Now, post-lunch, we're ready for round two.

As always, stop by if you're in interested in the remaining swag.

@scipy2023

It's sprint day of #SciPy2023! Fun with teams #xarray and #astropy so far. Now, post-lunch, we're ready for round two.

As always, stop by if you're in interested in the remaining swag.

@scipy2023

It's sprint day of #SciPy2023! Fun with teams #xarray and #astropy so far. Now, post-lunch, we're ready for round two.

As always, stop by if you're in interested in the remaining swag.

@scipy2023

Going back to more #dataAnalysis stuff - who wants to learn to use #XArray and #Pangeo? Apparently this was left over from the #CMIP6ArcticBootcamp (see birdsite for more on that #) but I shared it with student the other day and this really is a nice introduction to the tools..
6/
https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Going back to more #dataAnalysis stuff - who wants to learn to use #XArray and #Pangeo? Apparently this was left over from the #CMIP6ArcticBootcamp (see birdsite for more on that #) but I shared it with student the other day and this really is a nice introduction to the tools..
6/
https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Going back to more #dataAnalysis stuff - who wants to learn to use #XArray and #Pangeo? Apparently this was left over from the #CMIP6ArcticBootcamp (see birdsite for more on that #) but I shared it with student the other day and this really is a nice introduction to the tools..
6/
https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Going back to more #dataAnalysis stuff - who wants to learn to use #XArray and #Pangeo? Apparently this was left over from the #CMIP6ArcticBootcamp (see birdsite for more on that #) but I shared it with student the other day and this really is a nice introduction to the tools..
6/
https://medium.com/pangeo/easy-ipcc-part-1-multi-model-datatree-469b87cf9114

Hey #RemoteSensing enthusiasts! I've also released #spyndex v0.3.0 and now you have access to #AwesomeSpectralIndices v0.3.0 in #Python! 🐍🛰️🌿

Check it here: https://github.com/awesome-spectral-indices/spyndex

You can use it for #numpy, (geo)pandas, #xarray, #dask and #EarthEngine! 🚀😉

#GISchat #EOchat

So what's the easiest way to handle time and date data with timezone information in #python (#pandas, #datetime, #numpy, or #xarray). I find myself switching back and forth between datetime64, Timestamp, adding timedelta or tzinfo haphazardly and have never really settled on what's the best way to handle these data. I'm primarily working with pandas dataframes or xarray datasets. #programmingHelp

Py3DEP [hydroclimate analysis]
--
https://pypi.org/project/py3dep/ <-- link to app / resources
--
“Py3DEP is a part of [PyGeoUtils] HyRiver software stack that is designed to aid in hydroclimate analysis through web services. This package provides access to the 3DEP database which is a part of the National Map services. The 3DEP service has multi-resolution sources and depending on the user-provided resolution, the data is resampled on the server-side based on all the available data sources. Py3DEP returns the requests as xarray dataset…”
#GIS #spatial #mapping #Py3DEP #PyGeoUtils #HyRiver #softwarestack #elevation #3DEP #USGS #NationalMap #opendata #processing #spatialanalysis #hydroclimate #water #hydrology #webservices #xarray
@HyRiver @USGS