merged from format and lint branch

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,4 +1,4 @@
-name: ✨ Idea [Feature request] 
+name: ✨ Idea [Feature request]
 description: Tell us about the idea you have !
 title: "[Feature] <title>"
 labels: [idea]

.github/workflows/documentation.yaml

@@ -2,7 +2,7 @@ name: Docs
 run-name: "Updating Docs as ${{ github.actor }} has pushed changes to main"
 on:
   push:
-    branches: 
+    branches:
     - main
 
 jobs:
@@ -43,4 +43,3 @@ jobs:
           force_orphan: true
 
       - run: echo "Status of job = ${{ job.status }}."
-

.github/workflows/lint.yaml

@@ -0,0 +1,23 @@
+name: Lint and Auto-Fix Code
+
+on:
+  push:
+  pull_request:
+    branches:
+      - main
+
+jobs:
+
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v3
+
+      - name: Install pre-commit
+        run: pip install pre-commit
+
+      - name: Run pre-commit
+        run: pre-commit run --all-files

.gitignore

@@ -1,5 +1,6 @@
 .idea/
 /.venv
 dist
+pytest_cache
+src/osipi/__pycache__
 tests/__pycache__
-src/osipi/__pycache__

.pre-commit-config.yaml

@@ -0,0 +1,32 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.8  # Use the latest version
+    hooks:
+      - id: ruff
+        args: [--fix]  # Optional: to enable lint fixes
+      - id: ruff-format
+  - repo: https://github.com/pycqa/flake8
+    rev: 7.0.0
+    hooks:
+      - id: flake8
+        args:
+          - --exclude=venv,.git,__pycache__,.eggs,.mypy_cache,.pytest_cache
+          - --max-line-length=100
+          - --ignore=E203
+          - --per-file-ignores=__init__.py:F401
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+
+  - repo: https://github.com/rstcheck/rstcheck
+    rev: v6.1.2  # Use the latest version
+    hooks:
+      - id: rstcheck
+        entry: rstcheck --ignore-messages="(Hyperlink target .* is not referenced.|No directive entry for
+          .*|Unknown directive type .*|Duplicate explicit target name.*)"
+        args: [
+        "--ignore-roles", "ref",
+        ]

README.md

@@ -33,20 +33,11 @@ We welcome contributions to OSIPI! To contribute, follow these steps:
   - `pip install poetry`
   - `poetry install`
   - `poetry shell`
-  
+
 - Make your changes and commit them with descriptive messages.
 - Push your changes to your fork.
 - Submit a pull request to the main OSIPI repository.
-#### As mentioned before, this project is still in the early stages of development. If you'd like to contribute by adding functionality, we recommend opening an issue first to discuss your proposed functionality and the best ways to implement it. 
+#### As mentioned before, this project is still in the early stages of development. If you'd like to contribute by adding functionality, we recommend opening an issue first to discuss your proposed functionality and the best ways to implement it.
 
 ## Details
 For more detail please see the [documentation](https://osipi.github.io/pypi/).
-
-
-
-
-
-
-
-
-

docs/examples/README.rst

@@ -3,5 +3,3 @@ Examples
 ########
 
 Illustrating common use cases of osipi.
-
-

docs/examples/aif/README.rst

@@ -1,5 +1,3 @@
 ************************
 Arterial Input Functions
 ************************
-
-

docs/examples/aif/plot_aif_parker.py

@@ -1,46 +1,48 @@
-"""
-======================================
-The Parker AIF - a play with variables
-======================================
+"""======================================
+
+The Parker AIF - a play with variables ======================================  Simulating a Parker
+AIF with different settings.
 
-Simulating a Parker AIF with different settings. 
 """
 
+import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
-import matplotlib.pyplot as plt
 import osipi
 
 # %%
 # Generate synthetic AIF with default settings and plot the result.
 
-# Define time points in units of seconds - in this case we use a time resolution of 0.5 sec and a total duration of 6 minutes.
-t = np.arange(0, 6*60, 0.5)
+# Define time points in units of seconds - in this case we use a time
+# resolution of 0.5 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 0.5)
 
 # Create an AIF with default settings
 ca = osipi.aif_parker(t)
 
 # Plot the AIF over the full range
-plt.plot(t, ca, 'r-')
-plt.plot(t, 0*t, 'k-')
-plt.xlabel('Time (sec)')
-plt.ylabel('Plasma concentration (mM)')
+plt.plot(t, ca, "r-")
+plt.plot(t, 0 * t, "k-")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
 plt.show()
 
 # %%
-# The bolus arrival time (BAT) defaults to 0s. What happens if we change it? Let's try, by changing it in steps of 30s:
+# The bolus arrival time (BAT) defaults to 0s. What happens if we
+# change it? Let's try, by changing it in steps of 30s:
 
 ca = osipi.aif_parker(t, BAT=0)
-plt.plot(t, ca, 'b-', label='BAT = 0s')
+plt.plot(t, ca, "b-", label="BAT = 0s")
 ca = osipi.aif_parker(t, BAT=30)
-plt.plot(t, ca, 'r-', label='BAT = 30s')
+plt.plot(t, ca, "r-", label="BAT = 30s")
 ca = osipi.aif_parker(t, BAT=60)
-plt.plot(t, ca, 'g-', label='BAT = 60s')
+plt.plot(t, ca, "g-", label="BAT = 60s")
 ca = osipi.aif_parker(t, BAT=90)
-plt.plot(t, ca, 'm-', label='BAT = 90s')
-plt.xlabel('Time (sec)')
-plt.ylabel('Plasma concentration (mM)')
+plt.plot(t, ca, "m-", label="BAT = 90s")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
 plt.legend()
 plt.show()
 

docs/examples/aif/plot_dummy.py

@@ -3,44 +3,47 @@
 A dummy script
 ==============
 
-Dummy script to illustrate structure of examples folder 
+Dummy script to illustrate structure of examples folder
 """
 
+import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
-import matplotlib.pyplot as plt
 import osipi
 
 # %%
 # Generate synthetic AIF with default settings and plot the result.
 
-# Define time points in units of seconds - in this case we use a time resolution of 0.5 sec and a total duration of 6 minutes.
-t = np.arange(0, 6*60, 0.5)
+# Define time points in units of seconds - in this case we use a time
+# resolution of 0.5 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 0.5)
 
 # Create an AIF with default settings
 ca = osipi.aif_parker(t)
 
 # Plot the AIF over the full range
-plt.plot(t, ca, 'r-')
-plt.plot(t, 0*t, 'k-')
-plt.xlabel('Time (sec)')
-plt.ylabel('Plasma concentration (mM)')
+plt.plot(t, ca, "r-")
+plt.plot(t, 0 * t, "k-")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
 plt.show()
 
 # %%
-# The bolus arrival time (BAT) defaults to 30s. What happens if we change it? Let's try, by changing it in steps of 30s:
+# The bolus arrival time (BAT) defaults to 30s. What happens if we
+# change it? Let's try, by changing it in steps of 30s:
 
 ca = osipi.aif_parker(t, BAT=0)
-plt.plot(t, ca, 'b-', label='BAT = 0s')
+plt.plot(t, ca, "b-", label="BAT = 0s")
 ca = osipi.aif_parker(t, BAT=30)
-plt.plot(t, ca, 'r-', label='BAT = 30s')
+plt.plot(t, ca, "r-", label="BAT = 30s")
 ca = osipi.aif_parker(t, BAT=60)
-plt.plot(t, ca, 'g-', label='BAT = 60s')
+plt.plot(t, ca, "g-", label="BAT = 60s")
 ca = osipi.aif_parker(t, BAT=90)
-plt.plot(t, ca, 'm-', label='BAT = 90s')
-plt.xlabel('Time (sec)')
-plt.ylabel('Plasma concentration (mM)')
+plt.plot(t, ca, "m-", label="BAT = 90s")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
 plt.legend()
 plt.show()
 

docs/examples/tissue/README.rst

@@ -1,5 +1,3 @@
 *********************
 Tissue concentrations
 *********************
-
-

docs/examples/tissue/plot_extended_tofts.py

@@ -6,46 +6,50 @@
 Simulating tissue concentrations from extended Tofts model with different settings.
 """
 
+import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
-import matplotlib.pyplot as plt
 import osipi
 
 # %%
 # Generate Parker AIF with default settings.
 
-# Define time points in units of seconds - in this case we use a time resolution of 1 sec and a total duration of 6 minutes.
-t = np.arange(0, 6*60, 1)
+# Define time points in units of seconds - in this case we use a time
+# resolution of 1 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 1)
 
 # Create an AIF with default settings
 ca = osipi.aif_parker(t)
 
 # %%
-# Plot the tissue concentrations for an extracellular volume fraction of 0.2 and 3 different plasma volumes of 0.05, 0.2 and 0.6
-Ktrans = 0.2 # in units of 1/min
+# Plot the tissue concentrations for an extracellular volume fraction
+# of 0.2 and 3 different plasma volumes of 0.05, 0.2 and 0.6
+Ktrans = 0.2  # in units of 1/min
 ve = 0.2  # volume fraction between 0 and 1
-vp = [0.05, 0.2, 0.6] # volume fraction between 0 and 1
+vp = [0.05, 0.2, 0.6]  # volume fraction between 0 and 1
 ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0])
-plt.plot(t, ct, 'b-', label=f'vp = {vp[0]}')
+plt.plot(t, ct, "b-", label=f"vp = {vp[0]}")
 ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[1])
-plt.plot(t, ct, 'g-', label=f'vp = {vp[1]}')
+plt.plot(t, ct, "g-", label=f"vp = {vp[1]}")
 ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[2])
-plt.plot(t, ct, 'm-', label=f'vp = {vp[2]}')
-plt.xlabel('Time (sec)')
-plt.ylabel('Tissue concentration (mM)')
+plt.plot(t, ct, "m-", label=f"vp = {vp[2]}")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
 plt.legend()
 plt.show()
 
 # %%
-# 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')
-plt.plot(t, ct, 'g-', label='Exponential Convolution')
-plt.title(f'Ktrans = {Ktrans} /min')
-plt.xlabel('Time (sec)')
-plt.ylabel('Tissue concentration (mM)')
+# 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")
+plt.plot(t, ct, "g-", label="Exponential Convolution")
+plt.title(f"Ktrans = {Ktrans} /min")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
 plt.legend()
 plt.show()
 

docs/examples/tissue/plot_tofts.py

@@ -6,45 +6,50 @@
 Simulating tissue concentrations from Tofts model with different settings.
 """
 
+import matplotlib.pyplot as plt
+
 # %%
 # Import necessary packages
 import numpy as np
-import matplotlib.pyplot as plt
 import osipi
 
 # %%
 # Generate Parker AIF with default settings.
 
-# Define time points in units of seconds - in this case we use a time resolution of 1 sec and a total duration of 6 minutes.
-t = np.arange(0, 6*60, 1)
+# Define time points in units of seconds - in this case we use a time
+# resolution of 1 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 1)
 
 # Create an AIF with default settings
 ca = osipi.aif_parker(t)
 
 # %%
-# Plot the tissue concentrations for an extracellular volume fraction of 0.2 and 3 different transfer rate constants of 0.05, 0.2 and 0.6 /min
+# Plot the tissue concentrations for an extracellular volume fraction
+# of 0.2 and 3 different transfer rate constants of 0.05, 0.2 and 0.6
+# /min
 Ktrans = [0.05, 0.2, 0.6]  # in units of 1/min
 ve = 0.2  # volume fraction between 0 and 1
 ct = osipi.tofts(t, ca, Ktrans=Ktrans[0], ve=ve)
-plt.plot(t, ct, 'b-', label=f'Ktrans = {Ktrans[0]} /min')
+plt.plot(t, ct, "b-", label=f"Ktrans = {Ktrans[0]} /min")
 ct = osipi.tofts(t, ca, Ktrans[1], ve)
-plt.plot(t, ct, 'g-', label=f'Ktrans = {Ktrans[1]} /min')
+plt.plot(t, ct, "g-", label=f"Ktrans = {Ktrans[1]} /min")
 ct = osipi.tofts(t, ca, Ktrans[2], ve)
-plt.plot(t, ct, 'm-', label=f'Ktrans = {Ktrans[2]} /min')
-plt.xlabel('Time (sec)')
-plt.ylabel('Tissue concentration (mM)')
+plt.plot(t, ct, "m-", label=f"Ktrans = {Ktrans[2]} /min")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
 plt.legend()
 plt.show()
 
 # %%
-# 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
-plt.plot(t, ct, 'b-', label='Convolution')
-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)')
-plt.ylabel('Tissue concentration (mM)')
+# 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
+plt.plot(t, ct, "b-", label="Convolution")
+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)")
+plt.ylabel("Tissue concentration (mM)")
 plt.legend()
 plt.show()