How to use the pyxem.signals.reduced_intensity_profile.ReducedIntensityProfile function in pyxem

def test_reduced_intensity_profile_init(RedIntData):
    ri = ReducedIntensityProfile(RedIntData)
    assert isinstance(ri, ReducedIntensityProfile)

def test_damp_exponential(RedIntData):
    ri = ReducedIntensityProfile(RedIntData)
    ri.damp_exponential(b=1)
    compare = np.array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
                          0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
                          0.00000000e+00, 0.00000000e+00],
                         [1.00000000e+00, 3.67879441e-01, 1.83156389e-02, 1.23409804e-04,
                          1.12535175e-07, 1.38879439e-11, 2.31952283e-16, 5.24288566e-22,
                          1.60381089e-28, 6.63967720e-36]],

                        [[2.00000000e+00, 7.35758882e-01, 3.66312778e-02, 2.46819608e-04,
                          2.25070349e-07, 2.77758877e-11, 4.63904566e-16, 1.04857713e-21,
                          3.20762178e-28, 1.32793544e-35],
                         [3.00000000e+00, 1.10363832e+00, 5.49469167e-02, 3.70229412e-04,
                          3.37605524e-07, 4.16638316e-11, 6.95856849e-16, 1.57286570e-21,
                          4.81143267e-28, 1.99190316e-35]]])
    assert np.allclose(ri, compare)
    return

def test_get_reduced_intensity(red_int_generator):

    elements = ['Cu']
    fracs = [1]
    red_int_generator.fit_atomic_scattering(elements=elements, fracs=fracs)
    ri = red_int_generator.get_reduced_intensity()
    assert isinstance(ri, ReducedIntensityProfile)
    ri = red_int_generator.get_reduced_intensity(cutoff=[0,8])
    assert isinstance(ri, ReducedIntensityProfile)
    # better test needed here, an assert allclose or such at least
    return

def test_signal_size():
    spectrum = np.array([5., 4., 3., 2., 2., 1., 1., 1., 0., 0.])
    ri = ReducedIntensityProfile(spectrum)
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([spectrum])
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([[spectrum]])
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([[[spectrum]]])
    pdfgen = PDFGenerator(ri)

def test_get_reduced_intensity(red_int_generator):

    elements = ['Cu']
    fracs = [1]
    red_int_generator.fit_atomic_scattering(elements=elements, fracs=fracs)
    ri = red_int_generator.get_reduced_intensity()
    assert isinstance(ri, ReducedIntensityProfile)
    ri = red_int_generator.get_reduced_intensity(cutoff=[0,8])
    assert isinstance(ri, ReducedIntensityProfile)
    # better test needed here, an assert allclose or such at least
    return

def reduced_intensity_profile():
    data = np.ones((1, 10)) * np.arange(4).reshape(4, 1)
    data = data.reshape(2, 2, 10)
    ri = ReducedIntensityProfile(data)
    return ri

def ri_model(request):
    ri = ReducedIntensityProfile(request.param)
    m = ri.create_model()
    return m

def test_signal_size():
    spectrum = np.array([5., 4., 3., 2., 2., 1., 1., 1., 0., 0.])
    ri = ReducedIntensityProfile(spectrum)
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([spectrum])
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([[spectrum]])
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert isinstance(pdf, PDFProfile)

    ri = ReducedIntensityProfile([[[spectrum]]])
    pdfgen = PDFGenerator(ri)
    pdf = pdfgen.get_pdf(s_cutoff=[0, 10])
    assert pdf is None

def test_damp_low_q_region_erfc(RedIntData):
    ri = ReducedIntensityProfile(RedIntData)
    ri.damp_low_q_region_erfc(scale=20, offset=1.3)
    compare = np.array([[[0., 0., 0., 0., 0.,
                          0., 0., 0., 0., 0.],
                         [0.03299603, 1., 1., 1., 1.,
                          1., 1., 1., 1., 1.]],

                        [[0.06599206, 2., 2., 2., 2.,
                          2., 2., 2., 2., 2.],
                         [0.09898808, 3., 3., 3., 3.,
                          3., 3., 3., 3., 3.]]])
    assert np.allclose(ri, compare)
    return