Use a linter in workflow to enforce PEP8 compliance

.circleci/config.yml

@@ -37,7 +37,7 @@ jobs:
           name: Run tests
           # This assumes pytest is installed via the install-package step above
           command: pytest
-
+          
 # Invoke jobs via workflows
 # See: https://circleci.com/docs/2.0/configuration-reference/#workflows
 workflows:

.github/workflows/automated-testing.yml

@@ -1,33 +1,41 @@
-# This workflow will install Python dependencies and run tests with a single version of Python
-# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
-
-name: Python application
-
 on:
   push:
-    branches: [ "main" ]
+    branches:
+      - main
   pull_request:
-    branches: [ "main" ]
-
-permissions:
-  contents: read
-
+    branches:
+      - main
+name: CI
 jobs:
   build:
-
     runs-on: ubuntu-latest
+    steps:
+     - uses: actions/checkout@v3
+     - name: Set up Python 3.10
+       uses: actions/setup-python@v3
+       with:
+         python-version: "3.10"
+     - name: Install dependencies
+       run: |
+         python -m pip install --upgrade pip
+         pip install pytest
+         python -m pip install .
+     - name: Test with pytest
+       run: |
+         python -m pytest 
 
+  flake8-lint:
+    name: flake8
+    runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v3
-      with:
-        python-version: "3.10"
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flake8 pytest
-        python -m pip install .
-    - name: Test with pytest
-      run: |
-        python -m pytest 
+      - uses: actions/checkout@v3
+      - name: Set up Python environment
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      # Install flake8 extensions (this step is not required. Default is "None").
+      - name: Set up flake8
+        run: pip install flake8-quotes
+      - name: flake8 Lint
+        run: |
+         flake8 hogben

hogben/__init__.py

@@ -1,3 +1,3 @@
 """Top-level package for HOGBEN"""
 
-__version__ = "1.2.1"
+__version__ = '1.2.1'

hogben/angles.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import time
 
 import numpy as np
@@ -16,18 +15,14 @@ def _angle_results_visualise(save_path):
         save_path (str): path to directory to save results to.
 
     """
-    from models.samples import similar_sld_sample_1, similar_sld_sample_2
-    from models.samples import thin_layer_sample_1, thin_layer_sample_2
-    from models.samples import simple_sample, many_param_sample
-    from models.bilayers import BilayerDMPC, BilayerDPPC
-    from models.magnetic import SampleYIG
+    from models.samples import simple_sample
 
     # Choose sample here.
     sample = simple_sample()
 
     # Choose contrasts here (only bilayers should use this).
     contrasts = []
-    #contrasts = [-0.56, 6.36]
+    # contrasts = [-0.56, 6.36]
 
     # Number of points and counting time for the initial angle choice.
     points = 100
@@ -37,43 +32,38 @@ def _angle_results_visualise(save_path):
     angle_range = np.linspace(0.2, 4, 500)
     initial_angle = angle_choice(sample, [], angle_range, points, time,
                                  save_path, 'initial', contrasts)
-
     # Plot how the choice of angle changes with initial angle counting time.
     angle_range = np.linspace(0.2, 4, 50)
-    time_range = np.linspace(0, time*8, 50)
+    time_range = np.linspace(0, time * 8, 50)
     angle_choice_with_time(sample, initial_angle, angle_range, time_range,
                            points, time, save_path, contrasts)
 
+
 def _angle_results_optimise(save_path):
     """Optimises the choice measurement angles and counting times for a sample.
 
     Args:
         save_path (str): path to directory to save results to.
 
     """
-    from models.samples import similar_sld_sample_1, similar_sld_sample_2
-    from models.samples import thin_layer_sample_1, thin_layer_sample_2
-    from models.samples import simple_sample, many_param_sample
-    from models.bilayers import BilayerDMPC, BilayerDPPC
-    from models.magnetic import SampleYIG
-
+    from models.samples import simple_sample
     # Choose sample here.
     sample = simple_sample()
 
     # Choose contrasts here (only bilayers should use this).
     contrasts = []
-    #contrasts = [-0.56, 6.36]
+    # contrasts = [-0.56, 6.36]
 
     # Total time budget.
-    total_time = 1000 # A large time improves DE convergence.
+    total_time = 1000  # A large time improves DE convergence.
 
     # Interval containing angles to consider.
     angle_bounds = (0.2, 4.0)
 
     # Create a new .txt file for the results.
     save_path = os.path.join(save_path, sample.name)
     with open(os.path.join(save_path, 'optimised_angles.txt'), 'w') as file:
-        optimiser = Optimiser(sample) # Optimiser for the experiment.
+        optimiser = Optimiser(sample)  # Optimiser for the experiment.
 
         # Optimise the experiment using 1-4 angles.
         for i, num_angles in enumerate([1, 2, 3, 4]):
@@ -89,18 +79,24 @@ def _angle_results_optimise(save_path):
 
             # Convert to percentages.
             angles, splits, val = results
-            splits = np.array(splits)*100
+            splits = np.array(splits) * 100
 
             # Round the optimisation function value to 4 significant figures.
-            val = np.format_float_positional(val, precision=4, unique=False,
-                                             fractional=False, trim='k')
+            val = np.format_float_positional(
+                val, precision=4, unique=False, fractional=False, trim='k'
+            )
 
             # Save the conditions, objective value and computation time.
-            file.write('----------- {} Angles -----------\n'.format(num_angles))
+            file.write(
+                '----------- {} Angles -----------\n'.format(num_angles)
+            )
             file.write('Angles: {}\n'.format(list(np.round(angles, 2))))
             file.write('Splits (%): {}\n'.format(list(np.round(splits, 1))))
             file.write('Objective value: {}\n'.format(val))
-            file.write('Computation time: {}\n\n'.format(round(end-start, 1)))
+            file.write(
+                'Computation time: {}\n\n'.format(round(end - start, 1))
+            )
+
 
 if __name__ == '__main__':
     save_path = './results'

hogben/contrasts.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import time
 
 import matplotlib.pyplot as plt
@@ -17,20 +16,25 @@ def _contrast_results_visualise(save_path):
         save_path (str): path to directory to save results to.
 
     """
-    from models.bilayers import BilayerDMPC, BilayerDPPC
+    from models.bilayers import BilayerDMPC
 
     # Choose sample here.
     bilayer = BilayerDMPC()
 
     # Number of points and counting times for each angle to simulate.
     angle_times = [(0.7, 100, 10), (2.3, 100, 40)]
 
-    # Visualise single contrast choices assuming different initial measurements.
+    # Visualise single contrast choices assuming different initial
+    # measurements.
     contrast_range = np.linspace(-0.56, 6.36, 500)
-    contrast_choice_single(bilayer, contrast_range, [], angle_times, save_path, 'initial')
-    contrast_choice_single(bilayer, contrast_range, [6.36], angle_times, save_path, 'D2O')
-    contrast_choice_single(bilayer, contrast_range, [-0.56], angle_times, save_path, 'H2O')
-    contrast_choice_single(bilayer, contrast_range, [-0.56, 6.36], angle_times, save_path, 'D2O_H2O')
+    contrast_choice_single(bilayer, contrast_range, [], angle_times,
+                           save_path, 'initial')
+    contrast_choice_single(bilayer, contrast_range, [6.36], angle_times,
+                           save_path, 'D2O')
+    contrast_choice_single(bilayer, contrast_range, [-0.56], angle_times,
+                           save_path, 'H2O')
+    contrast_choice_single(bilayer, contrast_range, [-0.56, 6.36], angle_times,
+                           save_path, 'D2O_H2O')
 
     # Investigate contrast pair choices assuming no prior measurement.
     contrast_range = np.linspace(-0.56, 6.36, 75)
@@ -40,20 +44,21 @@ def _contrast_results_visualise(save_path):
     bilayer.nested_sampling([6.36, 6.36], angle_times, save_path, 'D2O_D2O')
     bilayer.nested_sampling([-0.56, 6.36], angle_times, save_path, 'H2O_D2O')
 
+
 def _contrast_results_optimise(save_path):
     """Optimises the choice of contrasts for a bilayer sample.
 
     Args:
         save_path (str): path to directory to save results to.
 
     """
-    from bilayers import BilayerDMPC, BilayerDPPC
+    from bilayers import BilayerDMPC
 
     # Choose sample here.
     bilayer = BilayerDMPC()
 
     # Time budget for experiment.
-    total_time = 1000 # A large time improves DE convergence.
+    total_time = 1000  # A large time improves DE convergence.
 
     # Points and proportion of total counting time for each angle.
     angle_splits = [(0.7, 100, 0.2), (2.3, 100, 0.8)]
@@ -64,7 +69,7 @@ def _contrast_results_optimise(save_path):
     # Create a new .txt file for the results.
     save_path = os.path.join(save_path, bilayer.name)
     with open(os.path.join(save_path, 'optimised_contrasts.txt'), 'w') as file:
-        optimiser = Optimiser(bilayer) # Optimiser for the experiment.
+        optimiser = Optimiser(bilayer)  # Optimiser for the experiment.
 
         # Optimise the experiment using 1-4 contrasts.
         for i, num_contrasts in enumerate([1, 2, 3, 4]):
@@ -79,18 +84,23 @@ def _contrast_results_optimise(save_path):
             end = time.time()
 
             contrasts, splits, val = results
-            splits = np.array(splits)*100 # Convert to percentages.
+            splits = np.array(splits) * 100  # Convert to percentages.
 
             # Round the optimisation function value to 4 significant figures.
             val = np.format_float_positional(val, precision=4, unique=False,
                                              fractional=False, trim='k')
 
             # Write the conditions, objective value and computation time.
-            file.write('----------- {} Contrasts -----------\n'.format(num_contrasts))
+            file.write(
+                '----------- {} Contrasts -----------\n'.format(num_contrasts))
             file.write('Contrasts: {}\n'.format(list(np.round(contrasts, 2))))
             file.write('Splits (%): {}\n'.format(list(np.round(splits, 1))))
             file.write('Objective value: {}\n'.format(val))
-            file.write('Computation time: {}\n\n'.format(round(end-start, 1)))
+            file.write(
+                'Computation time: {}\n\n'.format(
+                    round(
+                        end - start, 1)))
+
 
 def _figure_2(save_path):
     """Creates figure 2 for the paper. The figure shows the minimum eigenvalue
@@ -117,36 +127,36 @@ def _figure_2(save_path):
     h2o_split_2 = 0.245
 
     # Define how much the third contrast will be measured for.
-    nxt_split_1 = 1-d2o_split_1-h2o_split_1
-    nxt_split_2 = 1-d2o_split_2-h2o_split_2
+    nxt_split_1 = 1 - d2o_split_1 - h2o_split_1
+    nxt_split_2 = 1 - d2o_split_2 - h2o_split_2
 
     # Counting times for each angle when measuring D2O.
-    d2o_angle_times_1 = [(angle, points, total_time*split*d2o_split_1)
+    d2o_angle_times_1 = [(angle, points, total_time * split * d2o_split_1)
                          for angle, points, split in angle_splits]
 
-    d2o_angle_times_2 = [(angle, points, total_time*split*d2o_split_2)
+    d2o_angle_times_2 = [(angle, points, total_time * split * d2o_split_2)
                          for angle, points, split in angle_splits]
 
     # Counting times for each angle when measuring H2O.
-    h2o_angle_times_1 = [(angle, points, total_time*split*h2o_split_1)
+    h2o_angle_times_1 = [(angle, points, total_time * split * h2o_split_1)
                          for angle, points, split in angle_splits]
 
-    h2o_angle_times_2 = [(angle, points, total_time*split*h2o_split_2)
+    h2o_angle_times_2 = [(angle, points, total_time * split * h2o_split_2)
                          for angle, points, split in angle_splits]
 
     # Counting times for each angle when measuring the third contrast.
-    nxt_angle_times_1 = [(angle, points, total_time*split*(nxt_split_1))
+    nxt_angle_times_1 = [(angle, points, total_time * split * (nxt_split_1))
                          for angle, points, split in angle_splits]
 
-    nxt_angle_times_2 = [(angle, points, total_time*split*(nxt_split_2))
+    nxt_angle_times_2 = [(angle, points, total_time * split * (nxt_split_2))
                          for angle, points, split in angle_splits]
 
     # Information from the D2O and H2O contrasts for each model.
-    g_init_1 = (sample_1.angle_info(d2o_angle_times_1, [6.36]) +
-                sample_1.angle_info(h2o_angle_times_1, [-0.56]))
+    g_init_1 = (sample_1.angle_info(d2o_angle_times_1, [6.36])
+                + sample_1.angle_info(h2o_angle_times_1, [-0.56]))
 
-    g_init_2 = (sample_2.angle_info(d2o_angle_times_2, [6.36]) +
-                sample_2.angle_info(h2o_angle_times_2, [-0.56]))
+    g_init_2 = (sample_2.angle_info(d2o_angle_times_2, [6.36])
+                + sample_2.angle_info(h2o_angle_times_2, [-0.56]))
 
     # Calculate the minimum eigenvalue for each choice of third contrast.
     min_eigs_1, min_eigs_2 = [], []
@@ -156,8 +166,8 @@ def _figure_2(save_path):
         g_new_1 = sample_1.contrast_info(nxt_angle_times_1, [new_contrast])
         g_new_2 = sample_2.contrast_info(nxt_angle_times_2, [new_contrast])
 
-        min_eigs_1.append(np.linalg.eigvalsh(g_init_1+g_new_1)[0])
-        min_eigs_2.append(np.linalg.eigvalsh(g_init_2+g_new_2)[0])
+        min_eigs_1.append(np.linalg.eigvalsh(g_init_1 + g_new_1)[0])
+        min_eigs_2.append(np.linalg.eigvalsh(g_init_2 + g_new_2)[0])
 
     # Create the plot of third contrast versus minimum eigenvalue.
     fig = plt.figure()
@@ -174,11 +184,12 @@ def _figure_2(save_path):
     ax1.set_xlabel(x_label, fontsize=11, weight='bold')
     ax1.set_ylabel(y_label, fontsize=11, weight='bold', color='b')
     ax2.set_ylabel(y_label, fontsize=11, weight='bold', color='g')
-    ax1.legend(line_1+line_2, ['DMPC', 'DPPC/RaLPS'], loc=0)
+    ax1.legend(line_1 + line_2, ['DMPC', 'DPPC/RaLPS'], loc=0)
 
     # Save the plot.
     save_plot(fig, save_path, 'figure_2')
 
+
 if __name__ == '__main__':
     save_path = './results'
     _contrast_results_visualise(save_path)