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https://github.com/pydata/xarray/issues/444#issuecomment-115900337 https://api.github.com/repos/pydata/xarray/issues/444 115900337 MDEyOklzc3VlQ29tbWVudDExNTkwMDMzNw== 1177508 2015-06-26T21:50:01Z 2015-06-26T21:53:50Z NONE

Unfortunately I can't use engine='scipy' cause they're not netcdf3 files so it defaults to 'netcdf4'. On the other hand here you can find the back trace from gdb... if that helps in any way...

``` print(arr1.dtype, arr2.dtype) print((arr1 == arr2)) print((arr1 == arr2) | (isnull(arr1) & isnull(arr2)))

gives:

float64 float64 dask.array<x_1, shape=(50, 39, 59), chunks=((50,), (39,), (59,)), dtype=bool> dask.array<x_6, shape=(50, 39, 59), chunks=((50,), (39,), (59,)), dtype=bool> ```

Funny thing is when I'm adding these print statements and so on I get some traceback from Python (some times). Without them I would only get segmetation fault with no additional information. For example, just now, after introducing these prints I got this traceback. This doesn't seem to be an xray bug, I mean it can't since it's just Python code... but any help is appreciated. Thanks!

edit: oh yeah... this is a funny thing. If I do print(((arr1 == arr2) | (isnull(arr1) & isnull(arr2))).all()), I get dask.array<x_13, shape=(), chunks=(), dtype=bool> which I guess it's a problem... so calling that all method kind of screws things up, or at least calls other stuff that screw it up, but I have no idea why calling isnull(arr1 & arr2) before all this... makes it run without segfault.

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