Like Nathaniel said, it would not improve much when compared to the modulo operator. It could handle the edge cases better, but really the biggest benefit would be that it is more convenient. And as the "unwrap" function already exists, people would expect that and look for a function for the inverse operation (at least I did). On Tue, 24 Nov 2020 at 09:22, Daniele Nicolodi wrote:

On 24/11/2020 02:49, Nathaniel Smith wrote:

...

How would this proposed function compare to using the modulo operator, like 'arr % (2*pi)'?

I wrote almost the same word bu word reply, before realizing that taking the modulo looses the sign. The correct operation is slightly more complex (untested):

def wrap(alpha): return (alpha + np.pi) % 2.0 * np.pi - np.pi

However, I don't think there is much value in adding something so trivial as a function to numpy: I cannot think to any commonly used algorithm that requires wrapping the phase, and it is going to be an infinite source of bikesheeding whether the wrapped range should be [-pi, pi) or (-pi, pi] or (0, 2*pi] or [0, 2*pi)

Cheers, Dan

...

On Mon, Nov 23, 2020, 16:13 Thomas mailto:thomasbbrunner@gmail.com> wrote:

Hi,

I have a proposal for a feature and I hope this is the right place to post this.

The idea is to have a function to map any input angle to the range of [ 0, 2*pi ] or [ - pi, pi ].

There already is a function called 'unwrap' that does the opposite, so I'd suggest calling this function 'wrap'.

Example usage: # wrap to range [ 0, 2*pi ] >>> np.wrap([ -2*pi, -pi, 0, 4*pi ]) [0, pi, 0, 2*pi]

There is some ambiguity regarding what the solution should be for the extremes. An example would be an input of 4*pi, as both 0 and 2*pi would be valid mappings.

There has been interest for this topic in the community (see https://stackoverflow.com/questions/15927755/opposite-of-numpy-unwrap < https://stackoverflow.com/questions/15927755/opposite-of-numpy-unwrap>).

Similar functions exist for Matlab (see https://de.mathworks.com/help/map/ref/wrapto2pi.html https://de.mathworks.com/help/map/ref/wrapto2pi.html). They solved the ambiguity by mapping "positive multiples of 2*pi map to 2*pi and negative multiples of 2*pi map to 0." for the 0 to 2*pi case. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org mailto:NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion https://mail.python.org/mailman/listinfo/numpy-discussion

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On Tue, Nov 24, 2020 at 11:37 AM Daniele Nicolodi wrote:

On 24/11/2020 10:25, Thomas wrote:

...

Like Nathaniel said, it would not improve much when compared to the modulo operator.

It could handle the edge cases better, but really the biggest benefit would be that it is more convenient.

Which edge cases? Better how?

...

And as the "unwrap" function already exists,

The unwrap() function exists because it is not as trivial.

I agree, we prefer not to add trivial functions like this. To help those few people that may need this, maybe just add the one-liner Daniele gave to the Notes section of unwrap()? Cheers, Ralf

...

people would expect that and look for a function for the inverse operation (at least I did).

What is your use of a wrap() function? I cannot think of any.

Cheers, Dan _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion

On Tue, Nov 24, 2020 at 6:38 AM Daniele Nicolodi wrote:

On 24/11/2020 10:25, Thomas wrote:

...

Like Nathaniel said, it would not improve much when compared to the modulo operator.

It could handle the edge cases better, but really the biggest benefit would be that it is more convenient.

Which edge cases? Better how?

...

And as the "unwrap" function already exists,

The unwrap() function exists because it is not as trivial.

...

people would expect that and look for a function for the inverse operation (at least I did).

What is your use of a wrap() function? I cannot think of any.

Cheers, Dan

For what it’s worth, this kind of range reduction can be extremely nontrivial depending on the needs of your application. Look at the efforts needed to ensure that the trigonometric functions give good results. This is discussed in this paper: https://www.csee.umbc.edu/~phatak/645/supl/Ng-ArgReduction.pdf. I don’t think that this belongs in Numpy, but it certainly isn’t a one liner. Best, Eric