def test_multiple_scatter_correction(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.fit_thermal_multiple_scattering_correction()
assert ri.data.shape == (2, 2, 10)
def test_s_max_statements(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.damp_lorch()
ri.damp_updated_lorch()
ri.fit_thermal_multiple_scattering_correction(s_max=5, plot=True)
assert isinstance(ri, ReducedIntensity1D)
def test_damp_low_q_region_erfc(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.damp_low_q_region_erfc(scale=20, offset=20)
compare = np.array([
[
[0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
],
[
[0.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
[0.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0],
],
])
assert np.allclose(ri, compare)
def test_damp_exponential(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.damp_exponential(b=1)
compare = np.array(
[
[
[
7.78800783e-01,
1.05399225e-01,
1.93045414e-03,
4.78511739e-06,
1.60522806e-09,
7.28772410e-14,
4.47773244e-19,
3.72336312e-25,
4.19009319e-32,
6.38150345e-40,
],
[
1.55760157e00,
2.10798449e-01,
3.86090827e-03,
9.57023478e-06,
3.21045611e-09,
1.45754482e-13,
8.95546488e-19,
7.44672624e-25,
8.38018639e-32,
1.27630069e-39,
],
],
[
[
2.33640235e00,
3.16197674e-01,
5.79136241e-03,
1.43553522e-05,
4.81568417e-09,
2.18631723e-13,
1.34331973e-18,
1.11700894e-24,
1.25702796e-31,
1.91445103e-39,
],
[
3.11520313e00,
4.21596898e-01,
7.72181654e-03,
1.91404696e-05,
6.42091222e-09,
2.91508964e-13,
1.79109298e-18,
1.48934525e-24,
1.67603728e-31,
2.55260138e-39,
],
],
]
)
assert np.allclose(ri, compare)
def test_reduced_intensity1d_init(RedIntData):
ri = ReducedIntensity1D(RedIntData)
assert isinstance(ri, ReducedIntensity1D)
def test_damp_updated_lorch(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.damp_updated_lorch(s_max=10)
compare = np.array(
[
[
[
0.99753477,
0.97796879,
0.9396585,
0.88420257,
0.81388998,
0.73157686,
0.64053436,
0.54427698,
0.44638168,
0.35030873,
],
[
1.99506954,
1.95593757,
1.879317,
1.76840514,
1.62777996,
1.46315372,
1.28106873,
1.08855397,
0.89276337,
0.70061746,
],
],
[
[
2.99260432,
2.93390636,
2.8189755,
2.65260771,
2.44166995,
2.19473058,
1.92160309,
1.63283095,
1.33914505,
1.05092619,
],
[
3.99013909,
3.91187515,
3.758634,
3.53681029,
3.25555993,
2.92630744,
2.56213745,
2.17710793,
1.78552674,
1.40123492,
],
],
]
)
assert np.allclose(ri, compare)
def test_damp_lorch(RedIntData):
ri = ReducedIntensity1D(RedIntData)
ri.axes_manager.signal_axes[0].scale = 1
ri.axes_manager.signal_axes[0].offset = 0.5
ri.damp_lorch(s_max=10)
compare = np.array(
[
[
[
0.99589274,
0.96339776,
0.90031632,
0.81033196,
0.69864659,
0.57161993,
0.43633259,
0.30010544,
0.17001137,
0.05241541,
],
[
1.99178547,
1.92679552,
1.80063263,
1.62066392,
1.39729317,
1.14323987,
0.87266519,
0.60021088,
0.34002274,
0.10483081,
],
],
[
[
2.98767821,
2.89019329,
2.70094895,
2.43099587,
2.09593976,
1.7148598,
1.30899778,
0.90031632,
0.51003411,
0.15724622,
],
[
3.98357094,
3.85359105,
3.60126526,
3.24132783,
2.79458634,
2.28647973,
1.74533037,
1.20042175,
0.68004548,
0.20966163,
],
],
]
)
assert np.allclose(ri, compare)
def ri_model(request):
ri = ReducedIntensity1D(request.param)
m = ri.create_model()
return m
