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- Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data · 18 ✖
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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589154086 | https://github.com/pydata/xarray/issues/1115#issuecomment-589154086 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDU4OTE1NDA4Ng== | max-sixty 5635139 | 2020-02-20T16:00:56Z | 2020-02-20T16:00:56Z | MEMBER | @r-beer I checked back on this and realized I didn't reply to your question: yes re completing #2652, if you're up for giving this a push |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
555564450 | https://github.com/pydata/xarray/issues/1115#issuecomment-555564450 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDU1NTU2NDQ1MA== | max-sixty 5635139 | 2019-11-19T15:39:17Z | 2019-11-19T15:39:17Z | MEMBER | @r-beer would be great to finish this off! I think this would be a popular feature. You could take @hrishikeshac 's code (which is close!) and make the final changes. |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
546176175 | https://github.com/pydata/xarray/issues/1115#issuecomment-546176175 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDU0NjE3NjE3NQ== | max-sixty 5635139 | 2019-10-25T02:38:40Z | 2019-10-25T02:38:40Z | MEMBER | Would be great to get this in, if anyone wants to have a go. A small, focused, PR would be a good start. In the meantime you can use one of the solutions above... |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
451589152 | https://github.com/pydata/xarray/issues/1115#issuecomment-451589152 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQ1MTU4OTE1Mg== | max-sixty 5635139 | 2019-01-04T22:35:15Z | 2019-01-04T22:35:15Z | MEMBER | @hrishikeshac that looks great! Well done for getting an MVP running. Do you want to do a PR from this? Should be v close from here. Others can comment from there. I'd suggest we get something close to this in and iterate from there. How abstract do we want the dimensions to be (i.e. currently we can only pass one dimension in, which is fine, but potentially we could enable multiple). One nit - no need to use |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
445388428 | https://github.com/pydata/xarray/issues/1115#issuecomment-445388428 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQ0NTM4ODQyOA== | max-sixty 5635139 | 2018-12-07T22:42:57Z | 2018-12-07T22:42:57Z | MEMBER | Yes for useful, but not sure whether they should be on the same method. They're also fairly easy for a user to construct (call correlation on a And increments are easy to build on! I'm the worst offender, but don't let completeness get in the way of incremental improvement (OK, I'll go and finish the |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
445386281 | https://github.com/pydata/xarray/issues/1115#issuecomment-445386281 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQ0NTM4NjI4MQ== | dcherian 2448579 | 2018-12-07T22:32:50Z | 2018-12-07T22:32:50Z | MEMBER | I think lagged correlations would be a useful feature. |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
442858136 | https://github.com/pydata/xarray/issues/1115#issuecomment-442858136 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQ0Mjg1ODEzNg== | rabernat 1197350 | 2018-11-29T14:43:13Z | 2018-11-29T14:43:13Z | MEMBER | Hey @hrishikeshac -- any progress on this? Need any help / advice from xarray devs? |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
438370603 | https://github.com/pydata/xarray/issues/1115#issuecomment-438370603 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQzODM3MDYwMw== | max-sixty 5635139 | 2018-11-13T17:51:56Z | 2018-11-13T17:51:56Z | MEMBER | And one that handles ```python untested!def covariance(x, y, dim=None): valid_values = x.notnull() & y.notnull() valid_count = valid_values.sum(dim)
def correlation(x, y, dim=None): # dim should default to the intersection of x.dims and y.dims return covariance(x, y, dim) / (x.std(dim) * y.std(dim)) ``` |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
436784481 | https://github.com/pydata/xarray/issues/1115#issuecomment-436784481 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQzNjc4NDQ4MQ== | max-sixty 5635139 | 2018-11-07T21:31:12Z | 2018-11-07T21:31:18Z | MEMBER | For posterity, I made a small adjustment to @shoyer 's draft: ```python untested!def covariance(x, y, dim=None): # need to ensure the dim lengths are the same - i.e. no auto-aligning # could use count-1 for sample return xr.dot(x - x.mean(dim), y - y.mean(dim), dims=dim) / x.count(dim) def correlation(x, y, dim=None): # dim should default to the intersection of x.dims and y.dims return covariance(x, y, dim) / (x.std(dim) * y.std(dim)) ``` |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
419519217 | https://github.com/pydata/xarray/issues/1115#issuecomment-419519217 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQxOTUxOTIxNw== | max-sixty 5635139 | 2018-09-07T17:59:55Z | 2018-09-07T17:59:55Z | MEMBER | Great! Ping me / the issues with any questions at all! |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
418530212 | https://github.com/pydata/xarray/issues/1115#issuecomment-418530212 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQxODUzMDIxMg== | max-sixty 5635139 | 2018-09-04T21:52:22Z | 2018-09-04T21:52:22Z | MEMBER | @hrishikeshac if you'd like to contribute, we can help you along - xarray is a v welcoming project! And from Let us know |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
417816234 | https://github.com/pydata/xarray/issues/1115#issuecomment-417816234 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQxNzgxNjIzNA== | shoyer 1217238 | 2018-08-31T23:55:06Z | 2018-08-31T23:55:06Z | MEMBER | I tend to view the second case as a generalization of the first case. I would also hesitate to implement the I think the basic implementation of this looks quite similar to what I wrote here for calculating the Pearson correlation as a NumPy gufunc: http://xarray.pydata.org/en/stable/dask.html#automatic-parallelization The main difference is that we might naturally want to support summing over multiple dimensions at once via the untested!def covariance(x, y, dim=None): return xarray.dot(x - x.mean(dim), y - y.mean(dim), dim=dim) def corrrelation(x, y, dim=None): # dim should default to the intersection of x.dims and y.dims return covariance(x, y, dim) / (x.std(dim) * y.std(dim)) ``` If you want to achieve the equivalent of |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
417802624 | https://github.com/pydata/xarray/issues/1115#issuecomment-417802624 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDQxNzgwMjYyNA== | max-sixty 5635139 | 2018-08-31T22:14:19Z | 2018-08-31T22:14:19Z | MEMBER | I'm up for adding What do want this to look like? It's a bit different from most xarray functions, which either return the same shape or reduce one dimension.
- The basic case here would take a Should I start with the first case and then we can expand as needed? |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
266525792 | https://github.com/pydata/xarray/issues/1115#issuecomment-266525792 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDI2NjUyNTc5Mg== | fmaussion 10050469 | 2016-12-12T19:23:48Z | 2016-12-12T19:25:29Z | MEMBER | I'll chime in here to ask a usage question: what is the recommended way to compute correlation maps with xarray? I.e. I have a dataarray of dims |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
260387059 | https://github.com/pydata/xarray/issues/1115#issuecomment-260387059 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDI2MDM4NzA1OQ== | rabernat 1197350 | 2016-11-14T16:37:05Z | 2016-11-14T16:37:05Z | MEMBER | To be clear, I am not say that this does not belong in xarray. I'm saying that we lack clear general guidelines for how to determine whether a particular function belongs in xarray. The criterion of a "pretty fundamental operation for working with data" is a good starting point. I would add: - used across a wide range of scientific disciplines - clear, unambiguous / uncontroversial definition - numpy implementation already exists
Many of these things will be pretty trivial once |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
260382091 | https://github.com/pydata/xarray/issues/1115#issuecomment-260382091 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDI2MDM4MjA5MQ== | shoyer 1217238 | 2016-11-14T16:20:14Z | 2016-11-14T16:20:14Z | MEMBER | That said, correlation coefficients are a pretty fundamental operation for working with data. I could see implementing a basic |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
260333267 | https://github.com/pydata/xarray/issues/1115#issuecomment-260333267 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDI2MDMzMzI2Nw== | rabernat 1197350 | 2016-11-14T13:24:10Z | 2016-11-14T13:24:10Z | MEMBER | I agree this would be very useful. But it is also feature creep. There is an extremely wide range of such functions that could hypothetically be put into the xarray package. (all of scipy.signal for example) At some point the community should decide what is the intended scope of xarray itself vs. packages built on top of xarray. |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 | |
260219462 | https://github.com/pydata/xarray/issues/1115#issuecomment-260219462 | https://api.github.com/repos/pydata/xarray/issues/1115 | MDEyOklzc3VlQ29tbWVudDI2MDIxOTQ2Mg== | shoyer 1217238 | 2016-11-13T22:57:02Z | 2016-11-13T22:57:02Z | MEMBER | The first step here is to find a library that implements the desired functionality on pure NumPy arrays, ideally in a vectorized fashion. Then it should be pretty straightforward to wrap in xarray. |
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Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339 |
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