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    Ravin Kumar
    @canyon289
    Hey @syclik! Thanks for hosting the whole conference
    When you mean test do you mean unittest or test netcdf files?
    if its test netcdf files theres a couple "precompiled" https://github.com/arviz-devs/arviz/blob/master/arviz/data/datasets.py#L50
    the ones in that url for example
    if its unittests its all in the test folder https://github.com/arviz-devs/arviz/tree/master/arviz/data
    Daniel Lee
    @syclik

    hey @canyon289! Thanks for presenting -- btw, that's for all the people involved. I know these things are team efforts!

    Thanks! That's what I was looking for.

    I'm taking a quick look to see how hard it would be to generate InferenceData NetCDF directly from CmdStan (C++). If it's possible, the next decision would be software design... whether it makes sense to implement directly in CmdStan or whether it makes sense to just write a general C++ library (that conceptually could be at the ArviZ level).
    Daniel Lee
    @syclik
    I'm running into some NetCDF C++ client problems, so... I'm not sure this is going to be straightforward.
    Ravin Kumar
    @canyon289
    For precedent on an external library arviz.jl is an example of an "external" library that provides functionality in another language https://github.com/arviz-devs/ArviZ.jl
    not exactly one of the two options, but from the two you mentioned it sounds like either will work. I'm curious to see which route ends up working better
    Daniel Lee
    @syclik
    me too. Not sure exactly what will work better.
    Sayam Kumar
    @Sayam753
    Hello all. I am new to both gitter and arviz community.
    Sayam Kumar
    @Sayam753
    I am looking for adding log_likelihood in InferenceData from TFP trace. After computing log_likelihood, I am confused whether to add it as a variable in sample_stats or as a separate group. I see it can be possible in both ways. But I am wondering if adding it in a certain way affects underneath computations. Any help in this regard? Thanks
    Ravin Kumar
    @canyon289
    @OriolAbril deferring this one to you
    and @Sayam753 great to see you here :)
    Sayam Kumar
    @Sayam753
    Thanks @canyon289 . Do we have something like discourse as in PyMC for arviz as well?
    I observed this channel for a few days before asking, suspecting if the community has moved to a new place.
    Ravin Kumar
    @canyon289
    No dedicated ArviZ discourse, we actually use the Stan and PyMC discourses for ArviZ pretty regularly :D. You should feel free to post there
    Sayam Kumar
    @Sayam753
    Yeah sure
    Oriol Abril-Pla
    @OriolAbril
    Storing the pointwise log likelihood values in sample_stats has been deprecated and only works for backward compatibility, you should store them in log_likelihood group with the same name as the variable in observed_data and posterior_predictive (if present)
    The cookbook has a good example of this
    And in case it helped, it used to be stored in the sample_stats group with the name log_likelihood, but this approach was not really compatible with multiple likelihood functions nor with custom model comparison tasks which is why this was moved to its own group
    Therefore, everything still works with both options, but the log_likelihood group is preferred and is the only option that supports multiple likelihoods (as seen in this example)
    Sayam Kumar
    @Sayam753
    Thanks @OriolAbril. The resources are awesome. It indeed makes sense to have log_likelihood as a group.
    Sayam Kumar
    @Sayam753
    I have been waiting for a newer release of ArviZ since long. Any updates on this?
    Oriol Abril-Pla
    @OriolAbril
    Now that GSoC has finished a new release should come very soon, not sure exactly when though
    Sayam Kumar
    @Sayam753
    That's awesome.
    Madhu Charan
    @madhucharan
    Hey Guys!My name is Madhu.I am from India and I would like to get started contributing to ArviZ and would like to know about some getting started guide as well as some beginner issues to get a grasp of the codebase.Kindly guide me through some beginner issues as well as some Information/resources to better understand about the codebase
    ken
    @kenkirito
    I am interested in GSoC and have done with basic setup and soon making a pr
    Oriol Abril-Pla
    @OriolAbril
    Great, welcome @kenkirito! We also have a specific gsoc channel if you want to discuss projects or have doubts about the application procedure, general ArviZ questions and introductions are fine here :)
    Sarthak Bhardwaj
    @thesarthakbhardwaj:matrix.org
    [m]
    hey @OriolAbril can you please send the link of specific GSoC channel where we can discuss project ideas?
    Oriol Abril-Pla
    @OriolAbril
    Welcome to the community @thesarthakbhardwaj:matrix.org, this https://gitter.im/arviz-devs/GSoC is the channel to discuss gsoc topics
    Xavier Fernández i Marín
    @xfim
    Hello to everyone. After writing ggmcmc for R (https://cran.r-project.org/web/packages/ggmcmc/index.html) and keeping it alive to use it with JAGS and stan, I am trying to also start with pyMC3.
    However, there is something very basic with ArviZ that is keeping me from the joy of seeing my first figure. I am using GNU/Linux and the figures do not show up. See this: https://discourse.pymc.io/t/arviz-figures-not-showing-up-after-sampling-gnu-linux/7978. I am sure it is something very simple, but would anyone suggest some kewords or ideas on how to solve it and move forward? Thank you very much.
    Xavier Fernández i Marín
    @xfim
    Oh, sorry. It seems someone already pointed my towards the right direction: https://discourse.pymc.io/t/arviz-figures-not-showing-up-after-sampling-gnu-linux/7978. Anyway, very nice to be able to share ideas with you.
    Ari Hartikainen
    @ahartikainen
    hi, try plt.show()
    Xavier Fernández i Marín
    @xfim
    Yes, @ahartikainen , thank you. It worked. Now I start to understand the link between matplotlib and arviz.
    Tobias Bartsch
    @tobiasbartsch
    Hi, I am running into OOM errors with my pymc3 idata objects (during the computation of the pointwise log likelihood values), and it sounds like the arviz/dask integration may be the solution to this issue (see here https://discourse.pymc.io/t/memory-spike-at-the-end-of-the-mcmc-sampling/5669). I saw that the corresponding PR has been merged (https://github.com/arviz-devs/arviz/pull/1229) but could not yet find any example notebooks or documentation that would explain how to switch Dask integration on. Could anyone point me in the right direction? Thanks a lot!
    Ravin Kumar
    @canyon289
    Hey Tobias, Sorry about the trouble
    Youre right it doenst seem weve made that notebook...... https://arviz-devs.github.io/arviz/user_guide/computation.html
    i dont have a good answer off of the top of my head but give me a couple of days and ill think through this one, my apologies for the subpar experience
    Tobias Bartsch
    @tobiasbartsch
    Hey Ravin, great, thank you! If you don't get to it, I might be able to just figure this out myself by looking through the source code. Could you point me to where I should start looking to understand how this is implemented? Thanks a lot!
    Ravin Kumar
    @canyon289
    Thanks for your williingness to work with on this one
    so heres what it looks like. Theres a Dask class in arviz.utils
    I think the way to enable ask is to do az.utils.Dask.enable_dask(**kwargs)
    and give it a shot
    if things dont work it would be great if you could create an issue ticket or post here, we didnt know whether people wanted dask, but postnig and issue shows it being used and what the specific errors are. With both of those we can address problems :)
    Tobias Bartsch
    @tobiasbartsch
    Thanks Ravin! I will give it a try and report back