issue_comments

Hi @dcherian,

We are currently reworking https://tutorial.xarray.dev/intro.html and would love to either add your material or link to it if you're creating a consolidated collection of genetics-related material. xref (#3564). We don't have a "domain-specific" section yet but are planning to create one after SciPy.

FWIW we've created a short tutorial on xarray which is meant as a gentle intro to folks coming from the malaria genetics field. We illustrate xarray first using outputs from a geostatistical model of how insecticide-treated bednets are used in Africa. We then give a couple of brief examples of how we use xarray for genomic data. There's video walkthroughs in French and English:

https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html

Please feel free to link to this in the xarray tutorial site if you'd like to :)

Hi @TomNicholas,

I would've thought that latitude and longitude would be 1-dimensional coordinate variables, yet they are drawn as 2-D arrays?

I think that if you assume that the axes of your grid data align with the cardinal directions (East-West / North-South) then you would expect latitude and longitude to be 1D, but if they don't align then the coordinates would need be 2D (i.e. if x and y are merely arbitrary lines along the Earth's surface).

I agree with you though that 2D lat/lon grids are unnecessarily confusing, especially for non-geoscience users.

Interesting, I hadn't considered that. Definitely a bit mind-bending though for us non-geoscientists :)

I like the second diagram you showed more (it's also a neater version of the labelled one I made here). I think it's debatable whether elevation and land_cover constitute coordinates or data variables, but I have no strong opinion on that.

As for improvements, I think it would be clearer to at least use the second image over the first, and perhaps we could improve it further.

SGTM. FWIW on the second diagram I would use "dimensions" instead of "indexes". Getting dimensions first then helps to explain how you can use a coordinate variable to index a dimension.

Whilst trying to use this figure to explain our data model to someone at SciPy I realised that we also need separate versions of this figure for just a DataArray / Variable too, because new users struggle to understand which parts of this diagram are still present in a single DataArray / Variable.

I'm working on a mini-tutorial introducing xarray for some folks in our genetics community

We are currently reworking https://tutorial.xarray.dev/intro.html and would love to either add your material or link to it if you're creating a consolidated collection of genetics-related material. xref (https://github.com/pydata/xarray/issues/3564). We don't have a "domain-specific" section yet but are planning to create one after SciPy.

Hi @alimanfoo, thanks for raising this.

I would've thought that latitude and longitude would be 1-dimensional coordinate variables, yet they are drawn as 2-D arrays?

I think that if you assume that the axes of your grid data align with the cardinal directions (East-West / North-South) then you would expect latitude and longitude to be 1D, but if they don't align then the coordinates would need be 2D (i.e. if x and y are merely arbitrary lines along the Earth's surface).

I agree with you though that 2D lat/lon grids are unnecessarily confusing, especially for non-geoscience users.

I like the second diagram you showed more (it's also a neater version of the labelled one I made here). I think it's debatable whether elevation and land_cover constitute coordinates or data variables, but I have no strong opinion on that.

As for improvements, I think it would be clearer to at least use the second image over the first, and perhaps we could improve it further.