html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/7295#issuecomment-1558263680,https://api.github.com/repos/pydata/xarray/issues/7295,1558263680,IC_kwDOAMm_X85c4TeA,23738400,2023-05-23T00:26:07Z,2023-05-23T00:26:07Z,CONTRIBUTOR,"Finally had some time to play around with the accessors, I have opened a PR adding them: https://github.com/arviz-devs/xarray-einstats/pull/51","{""total_count"": 2, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 2, ""rocket"": 0, ""eyes"": 0}",,1452291042
https://github.com/pydata/xarray/issues/7295#issuecomment-1439196106,https://api.github.com/repos/pydata/xarray/issues/7295,1439196106,IC_kwDOAMm_X85VyGPK,23738400,2023-02-21T22:52:44Z,2023-02-21T22:52:44Z,CONTRIBUTOR,"I would be happy to integrate einops functionality better with xarray. Also feedback and contributions for xarray-einstats will be very welcome.
That being said, I haven't worked with accessors yet, so I would need some help to try and add this.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1452291042
https://github.com/pydata/xarray/issues/4118#issuecomment-1044853795,https://api.github.com/repos/pydata/xarray/issues/4118,1044853795,IC_kwDOAMm_X84-RzQj,23738400,2022-02-18T17:06:57Z,2022-02-18T17:06:57Z,CONTRIBUTOR,"I am not sure I completely understand option 2, but option 1 seems a better fit to what we are doing at ArviZ (so far we are managing quite well with the InferenceData mentioned above which is a collection of independent xarray datasets). In our case, well defined selection for multiple variables at the same time (i.e. at the dataset level) is very useful.
I was also wondering what changes (if any) would each option imply when using `apply_ufunc`","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,628719058
https://github.com/pydata/xarray/issues/3322#issuecomment-1019753466,https://api.github.com/repos/pydata/xarray/issues/3322,1019753466,IC_kwDOAMm_X848yDP6,23738400,2022-01-24T06:10:27Z,2022-01-24T06:10:44Z,CONTRIBUTOR,"I have created a library for easier linear algebra (plus others) with xarray: https://xarray-einstats.readthedocs.io/en/latest/.
It currently has:
* wrappers for many `numpy.linalg` functions
* wrappers for `scipy.stats` distributions plus a few functions
* wrappers for [einops]() functions
* a `numba.guvectorize`-decorated version of `numpy.histogram` for dataarrays
I have mostly added wrappers for things I personally use, also trying to not overlap with xr-scipy nor xarray-extras, so it might not make much sense as a group, but the modules are completely independent between them and could be reorganized into independent packages or merged into existing ones.
Feedback very welcome! ","{""total_count"": 2, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 1, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,495799492
https://github.com/pydata/xarray/issues/3322#issuecomment-822464839,https://api.github.com/repos/pydata/xarray/issues/3322,822464839,MDEyOklzc3VlQ29tbWVudDgyMjQ2NDgzOQ==,23738400,2021-04-19T13:25:49Z,2021-04-19T13:25:49Z,CONTRIBUTOR,"Great, thanks for the offer :smile: I think I'll start with a minimal repo back at ArviZ and see how much of that can be used more generally. I probably should have named the example above `arviz_dot` instead. I definitely want to be able to do `arviz_dot(a, b)` and have it work automatically because in most ArviZ cases we do have all the information we need for this to be possible. We'll therefore definitely need a higher ArviZ layer, but I think an xarray-linalg would be a great base on which to build and we can keep both in different files/modules so it can be split easily.
I also commented here as I thought it was the most related issue, many of the functions I have in mind are related to linalg, but we'll probably have some other functions too. ","{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,495799492
https://github.com/pydata/xarray/issues/3322#issuecomment-821744766,https://api.github.com/repos/pydata/xarray/issues/3322,821744766,MDEyOklzc3VlQ29tbWVudDgyMTc0NDc2Ng==,23738400,2021-04-17T01:30:34Z,2021-04-17T01:36:41Z,CONTRIBUTOR,"Would there be any interest in an ""xarray linalg library""? Some kind of library of that sort would be really useful to ArviZ and our users, I'll probably write some very basic functions myself on a minimal library or add them to ArviZ directly, but if there are other people interested we can try to find synergies and get something more general.
In our case, 99% of the time we want to ""batch"" over the `chain` and `draw` dimensions. So things as simple as:
```
def xarray_dot(a, b, dim=None):
if dim is None:
a_dims = set(a.dims)
b_dims = set(b.dims)
dim_set = a_dims.intersection(b_dims) - {""chain"", ""draw""}
if len(dim_set) == 1:
dim = dim_set.pop()
else:
ValueError
if not isinstance(dim, str):
raise ValueError
return xr.apply_ufunc(
np.einsum,
""...i,...i"",
a,
b,
input_core_dims=[[], [dim], [dim]]
)
```
are already extremely useful.
Being able to invert, cholesky... without using `apply_ufunc` would be convenient, even more so if we define some conventions. i.e. I generally use `dim` and `dim bis` for covariance matrices so they have shape `chain, draw, ..., dim, dim bis`, so I can invert those independently of their number and order of dimensions without even needing to say which are the ""matrix"" dimensions.
In our case, being able to `ds.transpose(""dim1"", ""dim2"")` and have it ignore `chain, draw` would also be convenient, which is probably a very ArviZ specific situation, but maybe there are other libraries/people that commonly have some batch dimensions, even if they use different names for them or if the user chooses those names freely.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,495799492
https://github.com/pydata/xarray/issues/4118#issuecomment-807892921,https://api.github.com/repos/pydata/xarray/issues/4118,807892921,MDEyOklzc3VlQ29tbWVudDgwNzg5MjkyMQ==,23738400,2021-03-26T02:39:24Z,2021-03-26T02:39:24Z,CONTRIBUTOR,"Here are some biomedical papers that are using ArviZ and therefore xarray even if most don't cite xarray and some don't cite ArviZ either. Topics are quite disperse: covid, psychology, biomolecules, oncology...
Some ArviZ recent biomedical citations
* Arroyuelo, A., Vila, J., & Martin, O. A. (2020). Exploring the quality of protein structural models from a Bayesian perspective. bioRxiv.
* Axen, S. D. (2020). Representing Ensembles of Molecules (Doctoral dissertation, UCSF).
* Brauner, J. M., Mindermann, S., Sharma, M., Johnston, D., Salvatier, J., Gavenčiak, T., ... & Kulveit, J. (2021). Inferring the effectiveness of government interventions against COVID-19. Science, 371(6531).
* Busch-Moreno, S., Tuomainen, J., & Vinson, D. (2020). Trait Anxiety Effects on Late Phase Threatening Speech Processing: Evidence from EEG. bioRxiv.
* Busch-Moreno, S., Tuomainen, J., & Vinson, D. (2021). Semantic and prosodic threat processing in trait anxiety: is repetitive thinking influencing responses?. Cognition and Emotion, 35(1), 50-70.
* Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science, 369(6500).
* Heilbron, E., Martìn, O., & Fumagalli, E. (2020). Efectos protectores de los alimentos andinos contra el daño producido por el alcohol a nivel del epitelio intestinal, una aproximación estadística. Ciencia, Docencia y Tecnología, 31(61 nov-mar).
* Legrand, N., Nikolova, N., Correa, C., Brændholt, M., Stuckert, A., Kildahl, N., ... & Allen, M. (2021). The heart rate discrimination task: a psychophysical method to estimate the accuracy and precision of interoceptive beliefs. bioRxiv.
* Wang, Y. (2020, September). Data Analysis of Psychological Measurement of Intelligent Internet-assisted Sports Training based on Bio-Sensors. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 474-477). IEEE.
* WASSERMAN, A., SHRAGER, J., & SHAPIRO, M. A Multilevel Bayesian Model for Precision Oncology.
* Weindel, G., Anders, R., Alario, F. X., & Burle, B. (2020). Assessing model-based inferences in decision making with single-trial response time decomposition. Journal of Experimental Psychology: General.
* Yamagata, Y. (2020). Simultaneous estimation of the effective reproducing number and the detection rate of COVID-19. arXiv e-prints, arXiv-2005.