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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CLIMATE-643) Updating some of examples
Date Thu, 30 Jul 2015 17:06:04 GMT

    [ https://issues.apache.org/jira/browse/CLIMATE-643?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14647952#comment-14647952
] 

ASF GitHub Bot commented on CLIMATE-643:
----------------------------------------

Github user MJJoyce commented on the pull request:

    https://github.com/apache/climate/pull/210#issuecomment-126403057
  
    If the changes build off one another just separate them out into different feature branches
and merge/rebase according during the dev work. Otherwise we end up with overly complicated
pull requests that address multiple tickets and that just makes reviews and integration more
challenging. Small, atomic changes are always easier. If you need help with the branching/merging
let me know. I'm more than happy to show you a few easy ways to take care of this!
    



> Updating some of examples
> -------------------------
>
>                 Key: CLIMATE-643
>                 URL: https://issues.apache.org/jira/browse/CLIMATE-643
>             Project: Apache Open Climate Workbench
>          Issue Type: Improvement
>          Components: general
>    Affects Versions: 1.0.0
>            Reporter: Huikyo Lee
>            Assignee: Huikyo Lee
>             Fix For: 1.0.0
>
>
> Paul L. suggested some ideas to update examples. For example, "knmi_to_cru_full_bias.py"
needs to be updated with better description. The model to model bias could be replaced by
model to observation data bias. The goal is providing 5 examples all based on an actual published
papers. 
> Currently, OCW examples generate wrong results when there is missing data in observational
datasets. It is important to mask those grid points with missing values in model datasets
so that no metrics calculation is done at those grid points. In other words, if any of observation/model
dataset has missing value at a grid point, we need to propagate the missing information to
the other datasets.



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