refactor(tests): implement pytest fixtures and parameterization

tests/test_tissue.py

@@ -2,20 +2,31 @@
 
 import numpy as np
 import osipi
+import pytest
 
 
-def test_tissue_tofts():
-    """1.
+@pytest.fixture
+def time_array():
+    return np.arange(0, 6 * 60, 1)
 
-    Basic operation of the function - test that the peak tissue concentration is less than the peak
+
+@pytest.fixture
+def aif(time_array):
+    return osipi.aif_parker(time_array)
+
+
+@pytest.fixture
+def base_params():
+    return {"Ktrans": 0.6, "ve": 0.2}
+
+
+def test_tissue_tofts(time_array, aif, base_params):
+    """1.  Basic operation of the function - test that the peak tissue concentration is less than the peak
     AIF
 
     """
-
-    t = np.linspace(0, 6 * 60, 360)
-    ca = osipi.aif_parker(t)
-    ct = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2)
-    assert np.round(np.max(ct)) < np.round(np.max(ca))
+    ct = osipi.tofts(time_array, aif, discretization_method="exp", **base_params)
+    assert np.round(np.max(ct)) < np.round(np.max(aif))
 
     # 2. Basic operation of the function - test with non-uniform spacing of
     # time array
@@ -26,45 +37,42 @@ def test_tissue_tofts():
 
     # 3. The offset option - test that the tissue concentration is shifted
     # from the AIF by the specified offset time
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2, Ta=60.0)
-    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(ca > 0.0)) - 1) * 1 == 60.0
+    ct = osipi.tofts(time_array, aif, Ta=60.0, **base_params)
+    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(aif > 0.0)) - 1) * 1 == 60.0
 
     # 4. Test that the discretization options give almost the same result -
     # time step must be very small
     t = np.arange(0, 6 * 60, 0.01)
     ca = osipi.aif_parker(t)
-    ct_conv = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2)
-    ct_exp = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2, discretization_method="exp")
+    ct_conv = osipi.tofts(t, ca, **base_params)
+    ct_exp = osipi.tofts(t, ca, discretization_method="exp", **base_params)
     assert np.allclose(ct_conv, ct_exp, rtol=1e-4, atol=1e-3)
 
     # 5. Test that the ratio of the area under the ct and ca curves is
     # approximately the extracellular volume
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct_conv = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2)
-    ct_exp = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2, discretization_method="exp")
-    assert math.isclose(np.trapz(ct_conv, t) / np.trapz(ca, t), 0.2, abs_tol=1e-1)
-    assert math.isclose(np.trapz(ct_exp, t) / np.trapz(ca, t), 0.2, abs_tol=1e-1)
+
+    ct_conv = osipi.tofts(time_array, aif, **base_params)
+    ct_exp = osipi.tofts(time_array, aif, discretization_method="exp", **base_params)
+    assert math.isclose(
+        np.trapz(ct_conv, time_array) / np.trapz(aif, time_array), 0.2, abs_tol=1e-1
+    )
+    assert math.isclose(np.trapz(ct_exp, time_array) / np.trapz(aif, time_array), 0.2, abs_tol=1e-1)
 
     # 6. Test specific use cases
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct_conv = osipi.tofts(t, ca, Ktrans=0, ve=0.2)
+    ct_conv = osipi.tofts(time_array, aif, Ktrans=0, ve=0.2)
     assert np.count_nonzero(ct_conv) == 0
 
-    ct_exp = osipi.tofts(t, ca, Ktrans=0, ve=0.2, discretization_method="exp")
+    ct_exp = osipi.tofts(time_array, aif, Ktrans=0, ve=0.2, discretization_method="exp")
     assert np.count_nonzero(ct_exp) == 0
 
-    ct_conv = osipi.tofts(t, ca, Ktrans=0.6, ve=0)
+    ct_conv = osipi.tofts(time_array, aif, Ktrans=0.6, ve=0)
     assert np.count_nonzero(ct_conv) == 0
 
-    ct_exp = osipi.tofts(t, ca, Ktrans=0.6, ve=0, discretization_method="exp")
+    ct_exp = osipi.tofts(time_array, aif, Ktrans=0.6, ve=0, discretization_method="exp")
     assert np.count_nonzero(ct_exp) == 0
 
 
-def test_tissue_extended_tofts():
+def test_tissue_extended_tofts(time_array, aif):
     # 1. Basic operation of the function - test that the peak tissue
     # concentration is less than the peak AIF
     t = np.linspace(0, 6 * 60, 360)
@@ -81,10 +89,8 @@ def test_tissue_extended_tofts():
 
     # 3. The offset option - test that the tissue concentration is shifted
     # from the AIF by the specified offset time
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct = osipi.extended_tofts(t, ca, Ktrans=0.6, ve=0.2, vp=0.3, Ta=60.0)
-    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(ca > 0.0)) - 1) * 1 == 60.0
+    ct = osipi.extended_tofts(time_array, aif, Ktrans=0.6, ve=0.2, vp=0.3, Ta=60.0)
+    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(aif > 0.0)) - 1) * 1 == 60.0
 
     # 4. Test that the discretization options give almost the same result -
     # time step must be very small
@@ -96,67 +102,65 @@ def test_tissue_extended_tofts():
 
     # 5. Test that the ratio of the area under the ct and ca curves is
     # approximately the extracellular volume plus the plasma volume
-
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct_conv = osipi.extended_tofts(t, ca, Ktrans=0.6, ve=0.2, vp=0.3)
-    ct_exp = osipi.extended_tofts(t, ca, Ktrans=0.6, ve=0.2, vp=0.3, discretization_method="exp")
+    ct_conv = osipi.extended_tofts(time_array, aif, Ktrans=0.6, ve=0.2, vp=0.3)
+    ct_exp = osipi.extended_tofts(
+        time_array, aif, Ktrans=0.6, ve=0.2, vp=0.3, discretization_method="exp"
+    )
     assert math.isclose(
-        np.trapz(ct_conv, t) / np.trapz(ca, t),
+        np.trapz(ct_conv, time_array) / np.trapz(aif, time_array),
         0.2 + 0.3,
         abs_tol=1e-1,
     )
-    assert math.isclose(np.trapz(ct_exp, t) / np.trapz(ca, t), 0.2 + 0.3, abs_tol=1e-1)
+    assert math.isclose(
+        np.trapz(ct_exp, time_array) / np.trapz(aif, time_array), 0.2 + 0.3, abs_tol=1e-1
+    )
 
     # 6. Test specific use cases
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct_conv = osipi.extended_tofts(t, ca, Ktrans=0, ve=0.2, vp=0.3)
-    assert np.allclose(ct_conv, ca * 0.3, rtol=1e-4, atol=1e-3)
 
-    ct_exp = osipi.extended_tofts(t, ca, Ktrans=0, ve=0.2, vp=0.3, discretization_method="exp")
-    assert np.allclose(ct_conv, ca * 0.3, rtol=1e-4, atol=1e-3)
+    ct_conv = osipi.extended_tofts(time_array, aif, Ktrans=0, ve=0.2, vp=0.3)
+    assert np.allclose(ct_conv, aif * 0.3, rtol=1e-4, atol=1e-3)
 
-    ct_conv = osipi.extended_tofts(t, ca, Ktrans=0.6, ve=0, vp=0.3)
-    assert np.allclose(ct_conv, ca * 0.3, rtol=1e-4, atol=1e-3)
+    ct_exp = osipi.extended_tofts(
+        time_array, aif, Ktrans=0, ve=0.2, vp=0.3, discretization_method="exp"
+    )
+    assert np.allclose(ct_conv, aif * 0.3, rtol=1e-4, atol=1e-3)
 
-    ct_exp = osipi.extended_tofts(t, ca, Ktrans=0.6, ve=0, vp=0.3, discretization_method="exp")
-    assert np.allclose(ct_conv, ca * 0.3, rtol=1e-4, atol=1e-3)
+    ct_conv = osipi.extended_tofts(time_array, aif, Ktrans=0.6, ve=0, vp=0.3)
+    assert np.allclose(ct_conv, aif * 0.3, rtol=1e-4, atol=1e-3)
 
+    ct_exp = osipi.extended_tofts(
+        time_array, aif, Ktrans=0.6, ve=0, vp=0.3, discretization_method="exp"
+    )
+    assert np.allclose(ct_conv, aif * 0.3, rtol=1e-4, atol=1e-3)
 
-def test_tissue_two_compartment_exchange_model():
-    # 1. Basic operation of the function - test that the peak tissue
-    # concentration is less than the peak AIF
-    t = np.linspace(0, 6 * 60, 360)
-    ca = osipi.aif_parker(t)
-    ct = osipi.two_compartment_exchange_model(t, ca, Fp=10, PS=5, ve=0.2, vp=0.3)
-    assert np.round(np.max(ct)) < np.round(np.max(ca))
 
-    # 2. Basic operation of the function - test with non-uniform spacing of
-    # time array
-    t = np.geomspace(1, 6 * 60 + 1, num=360) - 1
+@pytest.mark.parametrize(
+    "time_points",
+    [
+        (np.linspace(0, 6 * 60, 360), "uniform"),
+        (np.geomspace(1, 6 * 60 + 1, num=360) - 1, "non-uniform"),
+    ],
+)
+def test_2cxm_basic_operation(time_points):
+    """Test that the peak tissue concentration is less than the peak AIF for different time arrays"""
+    t, _ = time_points
     ca = osipi.aif_parker(t)
     ct = osipi.two_compartment_exchange_model(t, ca, Fp=10, PS=5, ve=0.2, vp=0.3)
     assert np.round(np.max(ct)) < np.round(np.max(ca))
 
-    # 3. The offset option - test that the tissue concentration is shifted
-    # from the AIF by the specified offset time
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct = osipi.two_compartment_exchange_model(t, ca, Fp=10, PS=5, ve=0.2, vp=0.3, Ta=60.0)
-    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(ca > 0.0)) - 1) * 1 == 60.0
 
-    # 4. Handle case where VP is 0, it should behave like the tofts model
-    t = np.arange(0, 6 * 60, 1)
-    ca = osipi.aif_parker(t)
-    ct = osipi.two_compartment_exchange_model(t, ca, Fp=10, PS=5, ve=0.2, vp=0)
-    ct_tofts = osipi.tofts(t, ca, Ktrans=3.93, ve=0.2)
+def test_2cxm_time_offset(time_array, aif):
+    """Test that the tissue concentration is shifted from the AIF by the specified offset time"""
+    ct = osipi.two_compartment_exchange_model(time_array, aif, Fp=10, PS=5, ve=0.2, vp=0.3, Ta=60.0)
+    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(aif > 0.0)) - 1) * 1 == 60.0
+
+
+def test_2cxm_zero_vp_matches_tofts(time_array, aif):
+    """Test that 2CXM with vp=0 behaves like the Tofts model"""
+    ct = osipi.two_compartment_exchange_model(time_array, aif, Fp=10, PS=5, ve=0.2, vp=0)
+    ct_tofts = osipi.tofts(time_array, aif, Ktrans=3.93, ve=0.2)
     assert np.allclose(ct, ct_tofts, rtol=1e-4, atol=1e-3)
 
 
 if __name__ == "__main__":
-    test_tissue_tofts()
-    test_tissue_extended_tofts()
-    test_tissue_two_compartment_exchange_model()
-
-    print("All tissue concentration model tests passed!!")
+    pytest.main([__file__])