Merge 93048ca into 9a09cd1

gpfit/tests/README

@@ -0,0 +1,10 @@
+## Plot unit tests
+
+Plot unit tests are run with [pytest-mpl](https://github.com/matplotlib/pytest-mpl)
+
+To generate the baseline images:
+`pytest --mpl-generate-path=baseline t_plot_fit.py`
+
+To test against the baseline images:
+`pytest --mpl t_plot_fit.py`
+

gpfit/tests/run_tests.py

@@ -18,9 +18,6 @@
 from gpfit.tests import t_print_fit
 TESTS += t_print_fit.TESTS
 
-from gpfit.tests import t_plot_fit
-TESTS += t_plot_fit.TESTS
-
 from gpfit.tests import t_examples
 TESTS += t_examples.TESTS
 

gpfit/tests/t_plot_fit.py

@@ -1,10 +1,12 @@
 "unit tests for plot fit methods"
-import unittest
+import pytest
 import numpy as np
 from gpfit.fit import MaxAffine, SoftmaxAffine, ImplicitSoftmaxAffine
 
+SEED = 33404
 
-class TestPlot(unittest.TestCase):
+
+class TestPlot:
     "Unit tests for plot methods"
 
     N = 51
@@ -14,23 +16,26 @@ class TestPlot(unittest.TestCase):
     y = np.log(w)
     K = 2
 
+    @pytest.mark.mpl_image_compare(filename='ma_1d.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
-        fig.savefig("artifacts/ma_test.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='sma_1d.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
-        fig.savefig("artifacts/sma_test.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='isma_1d.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
-        fig.savefig("artifacts/isma_test.png")
+        return fig
 
 
-class TestPlotSurface(unittest.TestCase):
+class TestPlotSurface:
     "Unit tests for plot_surface methods"
 
     rng = np.random.RandomState(33404)
@@ -42,23 +47,26 @@ class TestPlotSurface(unittest.TestCase):
     y = np.log(P)
     K = 3
 
+    @pytest.mark.mpl_image_compare(filename='ma_2d_surface.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
-        fig.savefig("artifacts/ma_test_surface.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='sma_2d_surface.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
-        fig.savefig("artifacts/sma_test_surface.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='isma_2d_surface.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
-        fig.savefig("artifacts/isma_test_surface.png")
+        return fig
 
 
-class TestPlotSlices(unittest.TestCase):
+class TestPlotSlices:
     "Unit tests for plot_slices method"
 
     Vdd = np.linspace(1, 2, 10)
@@ -71,33 +79,26 @@ class TestPlotSlices(unittest.TestCase):
     y = np.log(P)
     K = 3
 
+    @pytest.mark.mpl_image_compare(filename='ma_2d_slices.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
-        fig.savefig("artifacts/ma_test_slices.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='sma_2d_slices.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
-        fig.savefig("artifacts/sma_test_slices.png")
+        return fig
 
+    @pytest.mark.mpl_image_compare(filename='isma_2d_slices.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
-        fig.savefig("artifacts/isma_test_slices.png")
-
+        return fig
 
 TESTS = [
     TestPlot,
     TestPlotSurface,
     TestPlotSlices,
 ]
-
-if __name__ == '__main__':
-    SUITE = unittest.TestSuite()
-    LOADER = unittest.TestLoader()
-
-    for t in TESTS:
-        SUITE.addTests(LOADER.loadTestsFromTestCase(t))
-
-    unittest.TextTestRunner(verbosity=2).run(SUITE)

setup.py

@@ -30,7 +30,14 @@
     author_email="gpkit@mit.edu",
     url="https://github.com/convexengineering/gpfit",
     python_requires=">=3.4",
-    install_requires=["numpy", "scipy", "gpkit", "matplotlib"],
+    install_requires=[
+        "numpy",
+        "scipy",
+        "gpkit",
+        "matplotlib==3.3.2",
+        "pytest==6.2.5",
+        "pytest-mpl==0.13",
+    ],
     version="0.2.0",
     packages=["gpfit", "gpfit.maths", "gpfit.tests", "gpfit.xfoil"],
     include_package_data=True,