issue_comments: 331686038
This data as json
| 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/1115#issuecomment-331686038 | https://api.github.com/repos/pydata/xarray/issues/1115 | 331686038 | MDEyOklzc3VlQ29tbWVudDMzMTY4NjAzOA== | 6334793 | 2017-09-24T04:14:00Z | 2017-09-24T04:14:00Z | NONE | FYI @shoyer @fmaussion , I had to revisit the problem and ended up writing a function to compute vectorized cross-correlation, covariance, regression calculations (along with p-value and standard error) for xr.DataArrays. Essentially, I tried to mimic scipy.stats.linregress() but for multi-dimensional data, and included the ability to compute lagged relationships. Here's the function and its demonstration; please feel free to incorporate it in xarray if deemed useful: https://hrishichandanpurkar.blogspot.com/2017/09/vectorized-functions-for-correlation.html |
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