import numpy as np beta= 0.9 A=[1+1j,2+2j] real=np.zeros((1,2)) for i in range(1): for l in range(2): real[i,j] = real[i,j]-beta*A[i,j] I am not familiar with the computation of different types of arrays in numpy. How can I make the code work?
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1 Answer
The problem with your original code is that the result of
real[i, j] - beta * A[i, j] will be complex, but you created real using np.zeros, which will give you a float64 array unless you explicitly specify a different dtype. Since there is no safe way to cast a complex value to a float, the assignment to real[i, j] will raise a TypeError.
One way to solve the problem would be to initialize real with a complex dtype:
real = np.zeros((1, 2), dtype=np.complex) If you make A a numpy array, you can use broadcasting to do the multiplication in one go without pre-allocating real and without looping:
import numpy as np beta = 0.9 A = np.array([1 + 1j, 2 + 2j]) real = -beta * A print(repr(real)) # array([-0.9-0.9j, -1.8-1.8j]) It looks like you'd probably benefit from reading some of the examples here.
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tom10
@RayHsu: If this answers your question (which it looks to me like it does), please accept it.