clean HEAD

Author: DKuppens

phantoms/brain/data/ballistic_snr200.bval

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phantoms/brain/data/ballistic_snr200.cval

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+0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.471 0.666 0.816 0.942 1.054 1.154 1.247 1.333 1.414 1.490 1.632 1.763 1.885 1.999 2.107 

phantoms/brain/data/diffusive_snr200.bval

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tests/IVIMmodels/data/__init__.py

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+import numpy as np
+import numpy.testing as npt
+import pytest
+#import torch
+
+from utilities.data_simulation.GenerateData import GenerateData
+
+#run using python -m pytest from the root folder
+
+test_monoexponential_data = [
+    pytest.param(0, np.linspace(0, 1000, 11), id='0'),
+    pytest.param(0.1, np.linspace(0, 1000, 11), id='0.1'),
+    pytest.param(0.2, np.linspace(0, 1000, 11), id='0.2'),
+    pytest.param(0.3, np.linspace(0, 1000, 11), id='0.3'),
+    pytest.param(0.4, np.linspace(0, 1000, 11), id='0.4'),
+    pytest.param(0.5, np.linspace(0, 1000, 11), id='0.5'),
+    pytest.param(0.8, np.linspace(0, 1000, 11), id='0.8'),
+    pytest.param(1, np.linspace(0, 1000, 11), id='1'),
+]
+@pytest.mark.parametrize("D, bvals", test_monoexponential_data)
+def test_monoexponential(D, bvals):
+    gd = GenerateData()
+    gd_signal = gd.exponential_signal(D, bvals)
+    testing_signal = np.exp(-D * np.asarray(bvals, dtype='float64'))
+    npt.assert_allclose(gd_signal, testing_signal)
+    assert(gd_signal[0] >= testing_signal[0])
+
+test_ivim_data = [
+    pytest.param(0.01, 0.0, 1, 1, np.linspace(0, 1000, 11), None, False),
+    pytest.param(0.01, 0.1, 0.05, 1, np.linspace(0, 1000, 11), None, False),
+    pytest.param(0.05, 0.2, 0.1, 1, np.linspace(0, 800, 11), None, False),
+    pytest.param(0.04, 0.15, 0.25, 1.5, np.linspace(0, 1000, 2), 10, True),
+    pytest.param(0.1, 0.5, 0.5, 0.5, np.linspace(0, 1500, 5), 100, True),
+    pytest.param(0.01, 0.2, 0.1, 1, np.linspace(10, 1500, 11), 100, False),
+    pytest.param(0.1, 0.15, 0.05, 1, np.linspace(10, 1000, 8), 5, False)
+]
+@pytest.mark.parametrize('D, Dp, f, S0, bvals, snr, rician_noise', test_ivim_data)
+def test_ivim(D, Dp, f, S0, bvals, snr, rician_noise):
+    gd = GenerateData()
+    gd_signal = gd.ivim_signal(D, Dp, f, S0, bvals, snr, rician_noise)
+    testing_signal = S0 * ((1 - f) * np.exp(-D * bvals) + f * np.exp(-Dp * bvals))
+    atol = 0.0
+    if snr is not None:
+        atol = 4 / snr
+    npt.assert_allclose(gd_signal, testing_signal, atol=atol)
+
+
+test_linear_data = [
+    pytest.param(0, np.linspace(0, 1000, 11), 0, id='0'),
+    pytest.param(0.1, np.linspace(0, 1000, 11), 10, id='0.1'),
+    pytest.param(0.2, np.linspace(0, 1000, 11), -10, id='0.2'),
+    pytest.param(0.3, np.linspace(0, 1000, 11), 0, id='0.3'),
+    pytest.param(0.4, np.linspace(0, 1000, 11), 0, id='0.4'),
+    pytest.param(0.5, np.linspace(0, 1000, 11), 0, id='0.5'),
+    pytest.param(0.8, np.linspace(0, 1000, 11), 0, id='0.8'),
+    pytest.param(1, np.linspace(0, 1000, 11), 0, id='1'),
+]
+@pytest.mark.parametrize("D, bvals, offset", test_linear_data)
+def test_linear(D, bvals, offset):
+    gd = GenerateData()
+    gd_signal = gd.linear_signal(D, bvals, offset)
+    testing_signal = -D * np.asarray(bvals, dtype='float64')
+    testing_signal += offset
+    npt.assert_allclose(gd_signal, testing_signal)
+    assert(gd_signal[0] >= testing_signal[0])
+        
+    gd_exponential = gd.exponential_signal(D, bvals)
+    gd_log_exponential = np.log(gd_exponential) + offset
+    real_mask = np.isfinite(gd_log_exponential)
+    
+    npt.assert_allclose(gd_log_exponential[real_mask], gd_signal[real_mask])