[Numpy-discussion] Regression: in-place operations (possibly intentional)

Travis Oliphant travis@continuum...
Mon Sep 17 16:40:32 CDT 2012


On Sep 17, 2012, at 8:42 AM, Benjamin Root wrote:

> Consider the following code:
> 
> import numpy as np
> a = np.array([1, 2, 3, 4, 5], dtype=np.int16)
> a *= float(255) / 15
> 
> In v1.6.x, this yields:
> array([17, 34, 51, 68, 85], dtype=int16)
> 
> But in master, this throws an exception about failing to cast via same_kind.
> 
> Note that numpy was smart about this operation before, consider:
> a = np.array([1, 2, 3, 4, 5], dtype=np.int16)
> a *= float(128) / 256

> yields:
> array([0, 1, 1, 2, 2], dtype=int16)
> 
> Of course, this is different than if one does it in a non-in-place manner:
> np.array([1, 2, 3, 4, 5], dtype=np.int16) * 0.5
> 
> which yields an array with floating point dtype in both versions.  I can appreciate the arguments for preventing this kind of implicit casting between non-same_kind dtypes, but I argue that because the operation is in-place, then I (as the programmer) am explicitly stating that I desire to utilize the current array to store the results of the operation, dtype and all.  Obviously, we can't completely turn off this rule (for example, an in-place addition between integer array and a datetime64 makes no sense), but surely there is some sort of happy medium that would allow these sort of operations to take place?
> 
> Lastly, if it is determined that it is desirable to allow in-place operations to continue working like they have before, I would like to see such a fix in v1.7 because if it isn't in 1.7, then other libraries (such as matplotlib, where this issue was first found) would have to change their code anyway just to be compatible with numpy.

I agree that in-place operations should allow different casting rules.  There are different opinions on this, of course, but generally this is how NumPy has worked in the past.  

We did decide to change the default casting rule to "same_kind" but making an exception for in-place seems reasonable. 

-Travis






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