Question

i’m using python + numpy + scipy to do some convolution filtering over a complex-number array.

field = np.zeros((field_size, field_size), dtype=complex)
...
field = scipy.signal.convolve(field, kernel, 'same')

So, when i want to use a complex array in numpy all i need to do is pass the dtype=complex parameter.
For my research i need to implement two other types of complex numbers: dual (i*i=0) and double (i*i=1). It’s not a big deal – i just take the python source code for complex numbers and change the multiplication function.
The problem: how do i make a numpy array of those exotic numeric types?

Answer 1

Here’s my solution:

from iComplex import SplitComplex as c_split
...
ctype = c_split
constructor = np.vectorize(ctype, otypes=[np.object])
field = constructor(np.zeros((field_size, field_size)))

That is the easy way to create numpy object array.
What about scipy.signal.convolve – it doesn’t seem to work with my complex numbers and i had to make my own convolution and it works deadly slow. So now i am looking for ways to speed it up.