def METHOD_NAME(self):
    widths = [1.0]
    delta_wavelet = lambda s, t: np.array([1])
    len_data = 100
    test_data = np.sin(np.pi * np.arange(0, len_data) / 10.0)
    #Test delta function input gives same data as output
    cwt_dat = wavelets.cwt(test_data, delta_wavelet, widths)
    assert_(cwt_dat.shape == (len(widths), len_data))
    assert_array_almost_equal(test_data, cwt_dat.flatten())
    #Check proper shape on output
    widths = [1, 3, 4, 5, 10]
    cwt_dat = wavelets.cwt(test_data, wavelets.ricker, widths)
    assert_(cwt_dat.shape == (len(widths), len_data))
    widths = [len_data * 10]
    #Note: this wavelet isn't defined quite right, but is fine for this test
    flat_wavelet = lambda l, w: np.full(w, 1 / w)
    cwt_dat = wavelets.cwt(test_data, flat_wavelet, widths)
    assert_array_almost_equal(cwt_dat, np.mean(test_data))