Generating Numpy Arrays of Complex Numbers

Question

How can I quickly generate a numpy array of calculated complex numbers? By which I mean, the imaginary component is calculated from an array of values.

I have tried using python's literal j without luck. It seems to only accept float and int cases. cmath refuses to handle arrays. Below is my current attempt.

import numpy as np
import cmath

E0, k, z, w = np.ones(4)

def Ex(t):
    exp = complex(0,k*z-w*t)
    return E0*np.exp(exp)

t = np.arange(0,10,0.01)
E = Ex(t)

this nets the following error:

~\AppData\Local\Temp/ipykernel_8444/3633149291.py in Ex(t)
      7 
      8 def Ex(t):
----> 9     exp = complex(0,k*z-w*t)
     10     return E0*np.exp(exp)
     11 

TypeError: only size-1 arrays can be converted to Python scalars

I am also open to solutions which do not utilize cmath but I had no luck forming arrays of complex numbers without the following workaround:

times = np.arange(0,10,0.01)
E = [Ex(t) for t in times]

complex(1,3) is a python function that takes 2 numbers and returns one complex one. It isn't designed to take numpy arrays (or lists). But 1j can be used in computations, multiplying and adding. — hpaulj
– hpaulj, Commented Mar 31, 2022 at 2:51

yut23 · Accepted Answer · 2022-03-31 02:37:39Z

1

The standard way to do this is exp = 1j*(k*z-w*t).

answered Mar 31, 2022 at 2:37

yut23

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2 Comments

Daniel F Over a year ago

Or, as one line, just E0 * np.exp( 1j * (k * z - w * t) )

Matt Over a year ago

Thanks, this is exactly what I was after. Couldn't find a use like this in the documentation, or in random searching online.

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