added max line of 70

.pre-commit-config.yaml

@@ -6,22 +6,14 @@ repos:
       - id: end-of-file-fixer
       - id: check-yaml
 
+#  - repo: https://github.com/pycqa/flake8
+#    rev: 6.0.0
+#    hooks:
+#      - id: flake8
+#        args: [ '--max-line-length=80' ]
+
   - repo: https://github.com/psf/black
     rev: 23.3.0
     hooks:
       - id: black
-
-  - repo: https://github.com/pre-commit/mirrors-isort
-    rev: v5.10.1
-    hooks:
-      - id: isort
-
-  - repo: https://github.com/pre-commit/mirrors-autopep8
-    rev: v1.6.0
-    hooks:
-      - id: autopep8
-
-  - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
-    hooks:
-      - id: flake8
+        args: [--line-length=80]

docs/examples/aif/plot_aif_parker.py

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

docs/examples/aif/plot_dummy.py

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

docs/examples/tissue/plot_extended_tofts.py

@@ -7,6 +7,7 @@
 """
 
 import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
@@ -42,8 +43,7 @@
 # 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
 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

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

docs/source/conf.py

@@ -3,10 +3,12 @@
 # For the full list of built-in configuration values, see the documentation:
 # 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
-import os
+
 project = "osipi"
 copyright = "2022, OSIPI"
 author = "OSIPI"

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

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

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

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

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

@@ -7,6 +7,7 @@
 """
 
 import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
@@ -42,8 +43,7 @@
 # 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
 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

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

manage.py

@@ -5,7 +5,8 @@
 
 # logging root configuration
 logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
 )
 
 
@@ -52,8 +53,9 @@ 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")

src/osipi/_aif.py

@@ -47,8 +47,10 @@ def aif_parker(t: np.ndarray, BAT: float = 0.0, Hct: float = 0.0) -> np.ndarray:
     # 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))

src/osipi/_convolution.py

@@ -18,14 +18,14 @@ def exp_conv(T: float, t: np.ndarray, a: np.ndarray) -> np.ndarray:
     n = len(t)
     f = np.zeros((n,))
 
-    x = (t[1: n - 1] - t[0: n - 2]) / T
-    da = (a[1: n - 1] - a[0: n - 2]) / x
+    x = (t[1 : n - 1] - t[0 : n - 2]) / T
+    da = (a[1 : n - 1] - a[0 : n - 2]) / x
 
     E = np.exp(-x)
     E0 = 1 - E
     E1 = x - E0
 
-    add = a[0: n - 2] * E0 + da * E1
+    add = a[0 : n - 2] * E0 + da * E1
 
     for i in range(0, n - 2):
         f[i + 1] = E[i] * f[i] + add[i]

src/osipi/_tissue.py

@@ -80,8 +80,13 @@ def tofts(
         if discretization_method == "exp":  # Use exponential convolution
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
-                f = interp1d(t, ca, kind="linear",
-                             bounds_error=False, fill_value=0)
+                f = interp1d(
+                    t,
+                    ca,
+                    kind="linear",
+                    bounds_error=False,
+                    fill_value=0,
+                )
                 ca = (t > Ta) * f(t - Ta)
 
             Tc = ve / Ktrans
@@ -94,8 +99,13 @@ def tofts(
 
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
-                f = interp1d(t, ca, kind="linear",
-                             bounds_error=False, fill_value=0)
+                f = interp1d(
+                    t,
+                    ca,
+                    kind="linear",
+                    bounds_error=False,
+                    fill_value=0,
+                )
                 ca = (t > Ta) * f(t - Ta)
 
             # Check if time data grid is uniformly spaced
@@ -104,26 +114,34 @@ def tofts(
                 convolution = np.convolve(ca, imp)
 
                 # Discard unwanted points and make sure time spacing is correct
-                ct = convolution[0: len(t)] * t[1]
+                ct = convolution[0 : len(t)] * t[1]
             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, kind="quadratic", bounds_error=False, fill_value=0
+                    t,
+                    ca,
+                    kind="quadratic",
+                    bounds_error=False,
+                    fill_value=0,
                 )
                 imp_func = interp1d(
-                    t, imp, kind="quadratic", bounds_error=False, fill_value=0
+                    t,
+                    imp,
+                    kind="quadratic",
+                    bounds_error=False,
+                    fill_value=0,
                 )
                 ca_resampled = ca_func(t_resampled)
                 imp_resampled = imp_func(t_resampled)
                 # Convolve impulse response with AIF
                 convolution = np.convolve(ca_resampled, imp_resampled)
 
                 # 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(
@@ -215,8 +233,13 @@ def extended_tofts(
         if discretization_method == "exp":  # Use exponential convolution
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
-                f = interp1d(t, ca, kind="linear",
-                             bounds_error=False, fill_value=0)
+                f = interp1d(
+                    t,
+                    ca,
+                    kind="linear",
+                    bounds_error=False,
+                    fill_value=0,
+                )
                 ca = (t > Ta) * f(t - Ta)
 
             Tc = ve / Ktrans
@@ -230,8 +253,13 @@ def extended_tofts(
 
             # Shift the AIF by the arterial delay time (if not zero)
             if Ta != 0:
-                f = interp1d(t, ca, kind="linear",
-                             bounds_error=False, fill_value=0)
+                f = interp1d(
+                    t,
+                    ca,
+                    kind="linear",
+                    bounds_error=False,
+                    fill_value=0,
+                )
                 ca = (t > Ta) * f(t - Ta)
 
             # Check if time data grid is uniformly spaced
@@ -240,27 +268,34 @@ def extended_tofts(
                 convolution = np.convolve(ca, imp)
 
                 # Discard unwanted points, make sure time spacing is correct and add vp*ca term for extended model
-                ct = convolution[0: len(t)] * t[1] + (vp * ca)
+                ct = convolution[0 : len(t)] * t[1] + (vp * ca)
             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, kind="quadratic", bounds_error=False, fill_value=0
+                    t,
+                    ca,
+                    kind="quadratic",
+                    bounds_error=False,
+                    fill_value=0,
                 )
                 imp_func = interp1d(
-                    t, imp, kind="quadratic", bounds_error=False, fill_value=0
+                    t,
+                    imp,
+                    kind="quadratic",
+                    bounds_error=False,
+                    fill_value=0,
                 )
                 ca_resampled = ca_func(t_resampled)
                 imp_resampled = imp_func(t_resampled)
                 # Convolve impulse response with AIF
                 convolution = np.convolve(ca_resampled, imp_resampled)
 
                 # 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_aif.py

@@ -15,8 +15,8 @@ def test_aif_georgiou():
     # Not implemented yet so need to raise an error
     t = np.arange(0, 6 * 60, 1)
     try:
-        ca = osipi.aif_georgiou(t)
-    except:
+        osipi.aif_georgiou(t)
+    except Exception as ex:
         assert True
     else:
         assert False