We really ought to have a special page for all of Robert's little gems! DG On Tue, Oct 29, 2013 at 10:00 AM, wrote:

-----------------------------Message: 5 Date: Tue, 29 Oct 2013 17:02:33 +0000 From: Robert Kern Subject: Re: [Numpy-discussion] getting the equivalent complex dtype from a real or int array To: Discussion of Numerical Python Message-ID: < CAF6FJiuYnDbE1Uo9J6OnL1pq+oVZX-ecqKz0Qe9MigyQT69V_g@mail.gmail.com> Content-Type: text/plain; charset="utf-8"

On Tue, Oct 29, 2013 at 4:47 PM, Henry Gomersall wrote:

...

Is there a way to extract the size of array that would be created by doing 1j*array?

The problem I'm having is in creating an empty array to fill with complex values without knowing a priori what the input data type is.

For example, I have a real or int array `a`.

I want to create an array `b` which can hold values from 1j*a in such a way that I don't need to compute those explicitly (because I only need parts of the array say), without upcasting (or indeed downcasting) the result.

So if `a` was dtype 'float32`, `b` would be of dtype `complex64`. If `a` was `int64`, `b` would be of dtype `complex128` etc.

Quick and dirty:

# Get a tiny array from `a` to test the dtype of its output when multiplied # by a complex float. It must be an array rather than a scalar since the # casting rules are different for array*scalar and scalar*scalar. dt = (a.flat[:2] * 1j).dtype b = np.empty(shape, dtype=dt)

-- Robert Kern