Do continuous integration and codecoverage as in reduceSPI / ioSPI

[geoffwoollard]

Like this PR for ioSPI, but with simSPI https://github.com/compSPI/ioSPI/pulls?q=is%3Apr+is%3Aclosed

[ninamiolane]

LGTM if tests pass!

[ninamiolane]

replace to simSPI

[codecov]

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[geoffwoollard]

The tests were failing because of low test coverage. For some reasons using a numba.jit decorator, and then testing this function, doesn't count for code coverage. Instead, I defined the function (to be jitted) without a decorator, then make a numba.jitted version of it and called it a different name. The slow plain vanilla function is used in the tests. The fast jitted function is used in other functions

https://github.com/geoffwoollard/simSPI/blob/ad591eff3bc32ab609673dee294977b01222b216/simSPI/transfer.py#L102

[geoffwoollard]

I really don't understand how or why the tests passed in Python 3.7 and 3.8, but not 3.9.

There is a shape mis match, but the shapes should be even

=================================== FAILURES ===================================
________________________________ test_apply_ntf ________________________________

    def test_apply_ntf():
        """Test apply_ntf."""
        N_random = np.random.uniform(low=50, high=100)
        N = int(2 * (N_random // 2))
        ones = np.ones((N, N))
        freq_A_2d = transfer.ctf_freqs(N, dim=2)[0]
        ntf = np.sinc(freq_A_2d)
>       i = multislice.apply_ntf(shot_noise_sample=ones, ntf=ntf)

tests/test_multislice.py:96: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

shot_noise_sample = array([[1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       ...,
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.]])
ntf = array([[0.35818779, 0.36798049, 0.37768329, ..., 0.37768329, 0.36798049,
        0.35818779],
       [0.36798049, 0.37...76,
        0.36798049],
       [0.35818779, 0.36798049, 0.37768329, ..., 0.37768329, 0.36798049,
        0.35818779]])

    def apply_ntf(shot_noise_sample, ntf):
        """Convolution with detector's NTF.
    
        Convolution of (ctf and dqe applied) exit wave
        with the noise transfer function.
    
        Parameters
        ----------
        shot_noise_sample : numpy.ndarray, shape (N,N)
            Image with ctf applied.
        ntf : numpy.ndarray, shape (N,N)
            Noise transfer function (in 2D).
    
        Returns
        -------
        i : numpy.ndarray, shape (N,N)
            Exit wave with ntf applied (in real space).
        """
>       i = fourier.do_ifft(fourier.do_fft(shot_noise_sample, dim=2) * ntf, dim=2)
E       ValueError: operands could not be broadcast together with shapes (98,98) (99,99)

simSPI/multislice.py:93: ValueError

[geoffwoollard]

well it passed the tests for 3.7,3.8,3.9. Maybe some issue with the way the integer is rounded to even??? https://github.com/geoffwoollard/simSPI/blob/ci/tests/test_multislice.py#L92