clean HEAD

phantoms/brain/data/ballistic_snr200.bval

@@ -0,0 +1 @@
+0 10 20 30 40 50 60 70 80 90 100 120 140 160 180 200 10 20 30 40 50 60 70 80 90 100 120 140 160 180 200

phantoms/brain/data/ballistic_snr200.cval

@@ -0,0 +1 @@
+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

@@ -0,0 +1 @@
+0 10 20 30 40 80 110 140 170 200 300 400 500 600 700 800 900

tests/IVIMmodels/data/test_GenerateData.py

@@ -0,0 +1,71 @@
+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])