Merge branch 'formatting-and-lenting' of https://github.com/OSIPI/pypi …

…into formatting-and-lenting

docs/examples/aif/plot_aif_parker.py

@@ -6,7 +6,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np

docs/examples/aif/plot_dummy.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np

docs/examples/tissue/plot_extended_tofts.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np
@@ -44,13 +43,9 @@
 # %%
 # Comparing different discretization methods for an extracellular
 # volume fraction of 0.2, Ktrans of 0.2 /min and vp of 0.05
-ct = osipi.extended_tofts(
-    t, ca, Ktrans, ve, vp[0]
-)  # Defaults to Convolution
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0])  # Defaults to Convolution
 plt.plot(t, ct, "b-", label="Convolution")
-ct = osipi.extended_tofts(
-    t, ca, Ktrans, ve, vp[0], discretization_method="exp"
-)
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0], discretization_method="exp")
 plt.plot(t, ct, "g-", label="Exponential Convolution")
 plt.title(f"Ktrans = {Ktrans} /min")
 plt.xlabel("Time (sec)")

docs/examples/tissue/plot_tofts.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np
@@ -44,13 +43,9 @@
 # %%
 # Comparing different discretization methods for an extracellular
 # volume fraction of 0.2 and Ktrans of 0.2 /min
-ct = osipi.tofts(
-    t, ca, Ktrans=Ktrans[1], ve=ve
-)  # Defaults to Convolution
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve)  # Defaults to Convolution
 plt.plot(t, ct, "b-", label="Convolution")
-ct = osipi.tofts(
-    t, ca, Ktrans=Ktrans[1], ve=ve, discretization_method="exp"
-)
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve, discretization_method="exp")
 plt.plot(t, ct, "g-", label="Exponential Convolution")
 plt.title(f"Ktrans = {Ktrans[1]} /min")
 plt.xlabel("Time (sec)")

docs/source/conf.py

@@ -4,7 +4,6 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 import os
-
 # -- Project information ---------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 import sys

docs/source/generated/examples/aif/plot_aif_parker.py

@@ -6,7 +6,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np

docs/source/generated/examples/aif/plot_dummy.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np

docs/source/generated/examples/tissue/plot_extended_tofts.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np
@@ -44,13 +43,9 @@
 # %%
 # Comparing different discretization methods for an extracellular
 # volume fraction of 0.2, Ktrans of 0.2 /min and vp of 0.05
-ct = osipi.extended_tofts(
-    t, ca, Ktrans, ve, vp[0]
-)  # Defaults to Convolution
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0])  # Defaults to Convolution
 plt.plot(t, ct, "b-", label="Convolution")
-ct = osipi.extended_tofts(
-    t, ca, Ktrans, ve, vp[0], discretization_method="exp"
-)
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0], discretization_method="exp")
 plt.plot(t, ct, "g-", label="Exponential Convolution")
 plt.title(f"Ktrans = {Ktrans} /min")
 plt.xlabel("Time (sec)")

docs/source/generated/examples/tissue/plot_tofts.py

@@ -7,7 +7,6 @@
 """
 
 import matplotlib.pyplot as plt
-
 # %%
 # Import necessary packages
 import numpy as np
@@ -44,13 +43,9 @@
 # %%
 # Comparing different discretization methods for an extracellular
 # volume fraction of 0.2 and Ktrans of 0.2 /min
-ct = osipi.tofts(
-    t, ca, Ktrans=Ktrans[1], ve=ve
-)  # Defaults to Convolution
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve)  # Defaults to Convolution
 plt.plot(t, ct, "b-", label="Convolution")
-ct = osipi.tofts(
-    t, ca, Ktrans=Ktrans[1], ve=ve, discretization_method="exp"
-)
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve, discretization_method="exp")
 plt.plot(t, ct, "g-", label="Exponential Convolution")
 plt.title(f"Ktrans = {Ktrans[1]} /min")
 plt.xlabel("Time (sec)")

manage.py

@@ -52,9 +52,7 @@ def activate():
     os.makedirs(venv_dir, exist_ok=True)
     venv.create(venv_dir, with_pip=True)
     windows = (
-        (sys.platform == "win32")
-        or (sys.platform == "win64")
-        or (os.name == "nt")
+        (sys.platform == "win32") or (sys.platform == "win64") or (os.name == "nt")
     )
     if windows:
         return os.path.join(venv_dir, "Scripts", "activate")
@@ -66,9 +64,7 @@ def install():
     """Install requirements to a virtual environment."""
 
     logging.info("Installing requirements...")
-    os.system(
-        activate() + " && " + "py -m pip install -r requirements.txt"
-    )
+    os.system(activate() + " && " + "py -m pip install -r requirements.txt")
 
 
 if __name__ == "__main__":

src/osipi/_aif.py

@@ -1,9 +1,7 @@
 import numpy as np
 
 
-def aif_parker(
-    t: np.ndarray, BAT: float = 0.0, Hct: float = 0.0
-) -> np.ndarray:
+def aif_parker(t: np.ndarray, BAT: float = 0.0, Hct: float = 0.0) -> np.ndarray:
     """AIF model as defined by Parker et al (2005)
 
     Args:
@@ -53,19 +51,13 @@ def aif_parker(
     # A1/(SD1*sqrt(2*PI)) * exp(-(t_offset-m1)^2/(2*var1))
     # A1 = 0.833, SD1 = 0.055, m1 = 0.171
     gaussian1 = 5.73258 * np.exp(
-        -1.0
-        * (t_offset - 0.17046)
-        * (t_offset - 0.17046)
-        / (2.0 * 0.0563 * 0.0563)
+        -1.0 * (t_offset - 0.17046) * (t_offset - 0.17046) / (2.0 * 0.0563 * 0.0563)
     )
 
     # A2/(SD2*sqrt(2*PI)) * exp(-(t_offset-m2)^2/(2*var2))
     # A2 = 0.336, SD2 = 0.134, m2 = 0.364
     gaussian2 = 0.997356 * np.exp(
-        -1.0
-        * (t_offset - 0.365)
-        * (t_offset - 0.365)
-        / (2.0 * 0.132 * 0.132)
+        -1.0 * (t_offset - 0.365) * (t_offset - 0.365) / (2.0 * 0.132 * 0.132)
     )
     # alpha*exp(-beta*t_offset) / (1+exp(-s(t_offset-tau)))
     # alpha = 1.064, beta = 0.166, s = 37.772, tau = 0.482

src/osipi/_tissue.py

@@ -76,10 +76,7 @@ def tofts(
     """
     if not np.allclose(np.diff(t), np.diff(t)[0]):
         warnings.warn(
-            (
-                "Non-uniform time spacing detected. Time array may be"
-                " resampled."
-            ),
+            ("Non-uniform time spacing detected. Time array may be" " resampled."),
             stacklevel=2,
         )
 
@@ -90,9 +87,7 @@ def tofts(
         # Convert units
         Ktrans = Ktrans / 60  # from 1/min to 1/sec
 
-        if (
-            discretization_method == "exp"
-        ):  # Use exponential convolution
+        if discretization_method == "exp":  # Use exponential convolution
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
                 f = interp1d(
@@ -134,9 +129,7 @@ def tofts(
             else:
                 # Resample at the smallest spacing
                 dt = np.min(np.diff(t))
-                t_resampled = np.linspace(
-                    t[0], t[-1], int((t[-1] - t[0]) / dt)
-                )
+                t_resampled = np.linspace(t[0], t[-1], int((t[-1] - t[0]) / dt))
                 ca_func = interp1d(
                     t,
                     ca,
@@ -158,9 +151,7 @@ def tofts(
 
                 # Discard unwanted points and make sure time spacing
                 # is correct
-                ct_resampled = (
-                    convolution[0 : len(t_resampled)] * t_resampled[1]
-                )
+                ct_resampled = convolution[0 : len(t_resampled)] * t_resampled[1]
 
                 # Restore time grid spacing
                 ct_func = interp1d(
@@ -251,10 +242,7 @@ def extended_tofts(
 
     if not np.allclose(np.diff(t), np.diff(t)[0]):
         warnings.warn(
-            (
-                "Non-uniform time spacing detected. Time array may be"
-                " resampled."
-            ),
+            ("Non-uniform time spacing detected. Time array may be" " resampled."),
             stacklevel=2,
         )
 
@@ -265,9 +253,7 @@ def extended_tofts(
         # Convert units
         Ktrans = Ktrans / 60  # from 1/min to 1/sec
 
-        if (
-            discretization_method == "exp"
-        ):  # Use exponential convolution
+        if discretization_method == "exp":  # Use exponential convolution
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
                 f = interp1d(
@@ -311,9 +297,7 @@ def extended_tofts(
             else:
                 # Resample at the smallest spacing
                 dt = np.min(np.diff(t))
-                t_resampled = np.linspace(
-                    t[0], t[-1], int((t[-1] - t[0]) / dt)
-                )
+                t_resampled = np.linspace(t[0], t[-1], int((t[-1] - t[0]) / dt))
                 ca_func = interp1d(
                     t,
                     ca,
@@ -335,9 +319,9 @@ def extended_tofts(
 
                 # Discard unwanted points, make sure time spacing is
                 # correct and add vp*ca term for extended model
-                ct_resampled = convolution[
-                    0 : len(t_resampled)
-                ] * t_resampled[1] + (vp * ca_resampled)
+                ct_resampled = convolution[0 : len(t_resampled)] * t_resampled[1] + (
+                    vp * ca_resampled
+                )
 
                 # Restore time grid spacing
                 ct_func = interp1d(

tests/test_tissue.py

@@ -30,52 +30,38 @@ def test_tissue_tofts():
     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
+    assert (np.min(np.where(ct > 0.0)) - np.min(np.where(ca > 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_exp = osipi.tofts(t, ca, Ktrans=0.6, ve=0.2, discretization_method="exp")
     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_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)
 
     # 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)
     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(t, ca, 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)
     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(t, ca, Ktrans=0.6, ve=0, discretization_method="exp")
     assert np.count_nonzero(ct_exp) == 0
 
 
@@ -98,12 +84,8 @@ def test_tissue_extended_tofts():
     # 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(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
 
     # 4. Test that the discretization options give almost the same result -
     # time step must be very small
@@ -129,9 +111,7 @@ def test_tissue_extended_tofts():
         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, t) / np.trapz(ca, t), 0.2 + 0.3, abs_tol=1e-1)
 
     # 6. Test specific use cases
     t = np.arange(0, 6 * 60, 1)