Python issues converting an numpy array from float64 to int

November 25, 2021

I encountered an odd situation in Python (version 3.9.8) and numpy (version 1.20.0)trying to convert float64 numbers to int. It started with calculating the fft coefficients with numpy and then using inverse DFT with

x = np.array([1, -4, 2, 2, 1, -4, 6, 2]) # test vector
X = np.fft.fft(x)                        # calc fft coefficients

The result is fine so far:

print(X)
[ 6. +0.j  0. +4.j -6.+12.j  0. -4.j 14. +0.j  0. +4.j -6.-12.j  0. -4.j]

Now I’ve written my own inverse DFT and get correct results:

N = len(X)       # number of samples N
y = np.zeros(N)  # create empty result vector with N elements

# calc inverse DFT
for n in np.arange(N):
    
    sum = 0
    
    for k in np.arange(N):
        sum = sum + X[k] * np.exp(1j * 2* np.pi * k * n/N)
        
    y[n] = sum.real/N

# print results
print(y)
[ 1. -4.  2.  2.  1. -4.  6.  2.]

The resulting vector is also fine.

But when I convert this vector to int strange things happen. IIn the second half of the vector, values change in ways that are inexplicable to me.

print(y.astype('int'))

[ 1 -4  2  2  0 -3  6  1]

Any idea?

>Solution :

astype(int) will truncate values to integers: it just ditches the part after the decimal period. And the floating point values you see on your screen, are rounded representations: 2. is probably something like 1.99999999 because of floating point imprecisions.

First, round the values, then convert them to integer:

np.round(y)

produces

array([ 1., -4.,  2.,  2.,  1., -4.,  6.,  2.])

which are still floating point values, but now rounded to integer values (note that all (machine-range) integers can be represented exactly by floating point numbers).

Thus do the following: