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- Explaining xarray in a single picture · 5 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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1190057727 | https://github.com/pydata/xarray/issues/6771#issuecomment-1190057727 | https://api.github.com/repos/pydata/xarray/issues/6771 | IC_kwDOAMm_X85G7tb_ | alimanfoo 703554 | 2022-07-20T09:40:41Z | 2022-07-20T09:41:07Z | CONTRIBUTOR | Hi @dcherian,
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 :) |
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Explaining xarray in a single picture 1300534066 | |
1190052947 | https://github.com/pydata/xarray/issues/6771#issuecomment-1190052947 | https://api.github.com/repos/pydata/xarray/issues/6771 | IC_kwDOAMm_X85G7sRT | alimanfoo 703554 | 2022-07-20T09:36:10Z | 2022-07-20T09:36:10Z | CONTRIBUTOR | Hi @TomNicholas,
Interesting, I hadn't considered that. Definitely a bit mind-bending though for us non-geoscientists :)
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. |
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Explaining xarray in a single picture 1300534066 | |
1189294740 | https://github.com/pydata/xarray/issues/6771#issuecomment-1189294740 | https://api.github.com/repos/pydata/xarray/issues/6771 | IC_kwDOAMm_X85G4zKU | TomNicholas 35968931 | 2022-07-19T16:21:23Z | 2022-07-19T16:21:23Z | MEMBER | 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 |
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Explaining xarray in a single picture 1300534066 | |
1180550253 | https://github.com/pydata/xarray/issues/6771#issuecomment-1180550253 | https://api.github.com/repos/pydata/xarray/issues/6771 | IC_kwDOAMm_X85GXcRt | dcherian 2448579 | 2022-07-11T15:25:03Z | 2022-07-11T15:25:03Z | MEMBER |
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. |
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Explaining xarray in a single picture 1300534066 | |
1180512949 | https://github.com/pydata/xarray/issues/6771#issuecomment-1180512949 | https://api.github.com/repos/pydata/xarray/issues/6771 | IC_kwDOAMm_X85GXTK1 | TomNicholas 35968931 | 2022-07-11T14:53:43Z | 2022-07-11T14:53:43Z | MEMBER | Hi @alimanfoo, thanks for raising this.
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 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. |
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Explaining xarray in a single picture 1300534066 |
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