Being relatively new to numpy, I'm learning as I go. Your typing suggestion helped me come to the conclusion that I need to type and coerce my input arguments as I'm doing complex math. My test arguments were real, and thus numpy was producing real results. Doing .astype(np.complex128) to my input values that are generally complex resolved my issue.

On Mon, May 25, 2020 at 10:09 AM Brian Racey <raceybe@gmail.com> wrote:
Thanks. That is for performance and memory which of course is valid for most use cases. Would it really be much different than doing type/size checking of all the np.array arguments to a function to ensure the appropriate final np.array sizes are allocated? I'm not trying to question current practice, just trying to understand as performance will eventually be important to me.

Would a "complex default" mode ever make it into numpy, to behave more like Matlab and other packages with respect to complex number handling? Sure it would make it marginally slower if enabled, but it might open the door to better compatibility when porting code to Python.

On Mon, May 25, 2020 at 9:49 AM Eric Wieser <wieser.eric+numpy@gmail.com> wrote:

One explanation for this behavior is that doing otherwise would be slow.

Consider an array like

arr = np.array([1]*10**6 + [-1])
ret = np.log(arr)

Today, what happens is:

  • The output array is allocated as np.double
  • The input array is iterated over, and log evaluated on each element in turn

For what you describe to happen, the behavior would have to be either:

  • The output array is allocated as np.double

  • The input array is iterated over, and log evaluated on each element in turn

  • If any negative element is encountered, allocate a new array as np.cdouble, copy all the data over, then continue. This results in the whole array being promoted.

or:

  • The input array is iterated over, and checked to see if all the values are positive

  • The output array is allocated as np.double or np.cdouble based on this result

  • The input array is iterated over, and log evaluated on each element in turn

In either case, you’ve converted a 1-pass iteration to a 2-pass one.

There are static-typing-based explanations for this behavior too, but I’ll let someone else present one of those.

Eric


On Mon, 25 May 2020 at 14:33, Brian Racey <raceybe@gmail.com> wrote:
Why does numpy produce a runtime warning (invalid value encountered in log) when taking the log of a negative number? I noticed that if you coerce the argument to complex by adding 0j to the negative number, the expected result is produced (i.e. ln(-1) = pi*i).

I was surprised I couldn't find a discussion on this, as I would have expected others to have come across this before. Packages like Matlab handle negative numbers automatically by doing the complex conversion.
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion