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We have been using the new groupby logic with Flox and numpy_groupies; however, when we run the following, the dtype is not recognized as a valid argument.
This breaks API compatibility for cases where you may not have the acceleration libraries installed.
Not sure if this has to be upstream in
In addition to base Xarray we have the following extras installed:
Flox
numpy_groupies
Bottleneck
We do this because our data is float32 but we want the accumulator in mean to be float64 for accuracy.
One solution is to cast the variable to float64 before mean, which may cause a copy and spike in memory usage.
When Flox and numpy_groupies are not installed, it works as expected.
We are working with multi-dimensional time-series of weather forecast models.
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We have been using the new groupby logic with Flox and numpy_groupies; however, when we run the following, the dtype is not recognized as a valid argument.
This breaks API compatibility for cases where you may not have the acceleration libraries installed.
Not sure if this has to be upstream in
In addition to base Xarray we have the following extras installed:
Flox
numpy_groupies
Bottleneck
We do this because our data is
float32
but we want the accumulator in mean to befloat64
for accuracy.One solution is to cast the variable to float64 before mean, which may cause a copy and spike in memory usage.
When Flox and numpy_groupies are not installed, it works as expected.
We are working with multi-dimensional time-series of weather forecast models.
Here is the end of the traceback and it appears it is on Flox.
What is the best way to handle this, maybe fix it in Flox?
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