Why I get an assertion error in this example

[Alireza Ranjbar]

I have a signal r which was evaluated(sampled) at time t and the sampling frequency is not constant. The data are:

r = np.array([119.78410766, 119.76753142, 117.62636578, 103.44442714,
       100.20643576, 100.07845224, 100.8635375 , 105.7013656 ,
       115.19253386, 118.8410111 , 119.56967806, 119.49016405,
       115.673267  , 102.86539977, 100.18330076, 100.19053755,
       100.96717742, 104.64258496, 112.76883508, 118.42145725,
       119.46248759])

t = np.array([-408.60214685, -367.98645179, -326.11763398, -283.30427476,
       -240.51508415, -198.71111261, -158.2850487 , -118.96667683,
        -79.99044514,  -40.53604325,    0.        ,   41.86292241,
         84.76209664,  127.57962255,  169.0752062 ,  209.26407576,
        248.94525408,  288.63548604,  328.64737256,  369.17828499,
        410.49942551])

plt.plot(t, r, label='data')
plt.legend()

In the t variable the distance between each sampling instance is between 39 to 42 and not constant and this made the fft inaccurate for me.

When I use nfft however, I get the following assertion error but I am not sure what my mistake is in using the nfft package.

from nfft import nfft
f = nfft(z, r)

---

AssertionError Traceback (most recent call last)
in
1 from nfft import nfft
----> 2 f = nfft(z, r)

~/.local/share/virtualenvs/tf-tRAPLeXL/lib/python3.6/site-packages/nfft/core.py in nfft(x, f_hat, sigma, tol, m, kernel, use_fft, truncated)
123
124 N = len(f_hat)
--> 125 assert N % 2 == 0
126
127 sigma = int(sigma)

AssertionError:

[Alireza Ranjbar]

So I noticed the problem was that the number of samples was not a power of 2.