test_fishery_dynamics.py creating test file using pytest for fishery_dynamics.py file

.github/workflows/01_test.yml

@@ -32,6 +32,10 @@ jobs:
           python -m pip install --upgrade pip
           pip install .
 
+      - name: Install pytest-mock
+        run: |
+          pip install pytest-mock
+
       - name: Test and generate coverage report
         run: |
           pip install pytest
@@ -49,3 +53,10 @@ jobs:
         if: failure()  # This step will only run if any previous step fails
         run: |
           echo "Build or tests failed. Please check the logs."
+
+      - name: Test and generate coverage report
+        run: |
+          pip install pytest
+          pip install pytest-cov
+          pip install pytest-mock  # Ensure pytest-mock is installed before testing
+          pytest --cov=./ --cov-report=xml  # Run tests and generate a coverage report

msdbook/tests/test_fishery_dynamics.py

@@ -0,0 +1,83 @@
+import pytest
+import numpy as np 
+import matplotlib.pyplot as plt
+from pytest_mock import MockerFixture
+from mpl_toolkits.mplot3d import Axes3D  
+from matplotlib.testing.decorators import check_figures_equal
+from msdbook.fishery_dynamics import plot_objective_performance, plot_factor_performance
+
+@pytest.fixture
+def sample_data():
+    """Fixture to provide sample data for testing."""
+    objective_performance = np.array([
+        [1, 2, 3, 4, 5],
+        [2, 3, 4, 5, 6],
+        [3, 4, 5, 6, 7]
+    ])
+    profit_solution = 1
+    robust_solution = 2
+    param_values = np.random.rand(100, 7)  
+    collapse_days = np.random.rand(100, 2) * 10  
+    b = np.linspace(0, 1, 10)
+    m = np.linspace(0, 1, 10)
+    a = np.linspace(0, 2, 10)
+    return {
+        'objective_performance': objective_performance,
+        'profit_solution': profit_solution,
+        'robust_solution': robust_solution,
+        'param_values': param_values,
+        'collapse_days': collapse_days,
+        'b': b,
+        'm': m,
+        'a': a
+    }
+
+
+def test_plot_objective_performance(sample_data, mocker):
+    """Test the plot_objective_performance function."""
+    fig, ax = plt.subplots()
+    mocker.patch('matplotlib.pyplot.figure', return_value=fig)
+
+    plot_objective_performance(
+        sample_data['objective_performance'],
+        sample_data['profit_solution'],
+        sample_data['robust_solution']
+    )
+
+    # Ensure figure and axes are created
+    assert plt.gcf() == fig
+    assert len(fig.axes) > 0
+
+    # Check for colorbars in the figure
+    colorbars = [c for a in fig.axes for c in a.collections if isinstance(c, plt.cm.ScalarMappable)]
+    assert len(colorbars) > 0
+
+
+def test_plot_factor_performance(sample_data, mocker):
+    """Test the plot_factor_performance function."""
+    fig, axs = plt.subplots(1, 2, subplot_kw={'projection': '3d'})
+    mocker.patch('matplotlib.pyplot.figure', return_value=fig)
+
+    # Reshape b, m, a to 2D arrays for plotting
+    b, m = np.meshgrid(sample_data['b'], sample_data['m'])
+    a = np.tile(sample_data['a'], (len(sample_data['m']), 1))  
+
+    plot_factor_performance(
+        sample_data['param_values'],
+        sample_data['collapse_days'],
+        b,
+        m,
+        a
+    )
+
+    # Ensure figure and axes are created
+    assert plt.gcf() == fig
+    assert len(fig.axes) >= 3  
+
+    for ax in axs:
+        assert isinstance(ax, Axes3D)  
+
+    # Check for colorbars in the figure
+    colorbars = [c for a in fig.axes for c in a.collections if isinstance(c, plt.cm.ScalarMappable)]
+    assert len(colorbars) > 0
+

pyproject.toml

@@ -59,6 +59,7 @@ test = [
     "pytest>=7.0.0",
     "pytest-mock>=3.10",
 ]
+
 [project.urls]
 Homepage = "https://uc-ebook.org/"
 Repository = "https://github.com/IMMM-SFA/msd_uncertainty_ebook"