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  • keewis · 2 ✖

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  • consecutive time selection · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
605446570 https://github.com/pydata/xarray/issues/3896#issuecomment-605446570 https://api.github.com/repos/pydata/xarray/issues/3896 MDEyOklzc3VlQ29tbWVudDYwNTQ0NjU3MA== keewis 14808389 2020-03-28T13:19:46Z 2020-03-28T13:19:46Z MEMBER

sounds good. Minor comment / nit: you don't really need the astype(np.float), the result should already be of dtype float since there are missing values after the rolling sum.

I don't know about performance but if it does what you asked for then that's good?

 
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   consecutive time selection 588126763
605084864 https://github.com/pydata/xarray/issues/3896#issuecomment-605084864 https://api.github.com/repos/pydata/xarray/issues/3896 MDEyOklzc3VlQ29tbWVudDYwNTA4NDg2NA== keewis 14808389 2020-03-27T16:08:22Z 2020-03-27T17:58:15Z MEMBER

what about this? python In [86]: def detect(arr): ...: thresh = arr > 0.5 ...: n_found = thresh.rolling(dim={"x": 5}, center=True).sum().fillna(0) ...: detected = ( ...: n_found.rolling(dim={"x": 5}, center=True) ...: .reduce(lambda a, axis: (a == 5).any(axis=axis)) ...: .fillna(False) ...: .astype(bool) ...: ) ...: return detected ...: ...: arr = xr.DataArray( ...: np.array( ...: [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0] ...: ), ...: dims="x", ...: coords={"x": np.arange(24)}, ...: name="sst", ...: ) ...: ( ...: arr.to_dataset() ...: .assign(mask=lambda ds: detect(ds.sst)) ...: .assign(selected=lambda ds: ds.sst.where(ds.mask)) ...: ) Out[86]: <xarray.Dataset> Dimensions: (x: 24) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 14 15 16 17 18 19 20 21 22 23 Data variables: sst (x) int64 0 0 0 0 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 0 0 0 mask (x) bool False False False False False ... True False False False selected (x) float64 nan nan nan nan nan 1.0 ... 1.0 1.0 1.0 nan nan nan

 
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   consecutive time selection 588126763

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