Merge pull request #333 from Lnaden/pytest

Convert to PyTest

.github/workflows/CI.yaml

@@ -56,18 +56,9 @@ jobs:
         python -m pip install . --no-deps
         conda list --show-channel-urls
 
-#    - name: Run tests (pytest)
-#        shell: bash
-#        run: |
-#          . devtools/github-actions/initialize_conda.sh
-#          conda activate test
-#          pytest -v --cov=$PACKAGE --cov-report=xml --color=yes $PACKAGE/tests/
-
-    - name: Run tests (nose)
+    - name: Run tests (pytest)
       shell: bash
       run: |
         . devtools/github-actions/initialize_conda.sh
         conda activate test
-        cd devtools
-        nosetests $PACKAGE --nocapture --verbosity=2 --with-doctest --with-timer
-        cd ..
+        pytest -v --cov=$PACKAGE --cov-report=xml --color=yes $PACKAGE/tests/

devtools/conda-envs/test_env.yaml

@@ -12,10 +12,8 @@ dependencies:
   - six
 
     # Testing
-#  - pytest
-#  - pytest-cov
+  - pytest
+  - pytest-cov
   - codecov
-  - nose
-  - nose-timer
   - statsmodels
 

pymbar/__init__.py

@@ -28,7 +28,7 @@
 __maintainer__ = "Levi N. Naden, Michael R. Shirts and John D. Chodera"
 __email__ = "levi.naden@choderalab.org,michael.shirts@colorado.edu,john.chodera@choderalab.org"
 
-from pymbar import timeseries, testsystems, confidenceintervals, version
+from pymbar import timeseries, testsystems, confidenceintervals
 from pymbar.mbar import MBAR
 from pymbar.bar import BAR, BARzero
 from pymbar.exp import EXP, EXPGauss

pymbar/tests/test_covariance.py

@@ -1,71 +1,41 @@
 import numpy as np
+import pytest
 import pymbar
-from pymbar.utils_for_testing import eq, suppress_derivative_warnings_for_tests
-
-def load_oscillators(n_states, n_samples):
-    name = "%dx%d oscillators" % (n_states, n_samples)
-    O_k = np.linspace(1, 5, n_states)
-    k_k = np.linspace(1, 3, n_states)
-    N_k = (np.ones(n_states) * n_samples).astype('int')
-    test = pymbar.testsystems.harmonic_oscillators.HarmonicOscillatorsTestCase(O_k, k_k)
-    x_n, u_kn, N_k_output, s_n = test.sample(N_k, mode='u_kn')
-    return name, u_kn, N_k_output, s_n
-
-
-def load_exponentials(n_states, n_samples):
-    name = "%dx%d exponentials" % (n_states, n_samples)
-    rates = np.linspace(1, 3, n_states)
-    N_k = (np.ones(n_states) * n_samples).astype('int')
-    test = pymbar.testsystems.exponential_distributions.ExponentialTestCase(rates)
-    x_n, u_kn, N_k_output, s_n = test.sample(N_k, mode='u_kn')
-    return name, u_kn, N_k_output, s_n
-
-
-def _test(data_generator):
-    name, U, N_k, s_n = data_generator()
+from pymbar.utils_for_testing import (suppress_derivative_warnings_for_tests, assert_almost_equal,
+                                      oscillators, exponentials)
+
+
+@pytest.mark.parametrize(
+    'statesa, statesb, test_system',
+    [(100, 100, oscillators),
+     (200, 50, oscillators),
+     (200, 50, exponentials)]
+)
+def _test(statesa, statesb, test_system):
+    name, U, N_k, s_n = test_system(statesa, statesb)
     print(name)
     mbar = pymbar.MBAR(U, N_k)
     results1 = mbar.getFreeEnergyDifferences(uncertainty_method="svd")
-    fij1_t, dfij1_t = mbar.getFreeEnergyDifferences(uncertainty_method="svd")
     results2 = mbar.getFreeEnergyDifferences(uncertainty_method="svd-ew")
     fij1 = results1['Delta_f']
     dfij1 = results1['dDelta_f']
     fij2 = results2['Delta_f']
     dfij2 = results2['dDelta_f']
 
-    # Check to make sure the returns from with and w/o dict are the same
-    eq(fij1, fij1_t)
-    eq(dfij1, dfij1_t)
-
-    eq(pymbar.mbar_solvers.mbar_gradient(U, N_k, mbar.f_k), np.zeros(N_k.shape), decimal=8)
-    eq(np.exp(mbar.Log_W_nk).sum(0), np.ones(len(N_k)), decimal=10)
-    eq(np.exp(mbar.Log_W_nk).dot(N_k), np.ones(U.shape[1]), decimal=10)
-    eq(pymbar.mbar_solvers.self_consistent_update(U, N_k, mbar.f_k), mbar.f_k, decimal=10)
+    assert_almost_equal(pymbar.mbar_solvers.mbar_gradient(U, N_k, mbar.f_k), np.zeros(N_k.shape), decimal=8)
+    assert_almost_equal(np.exp(mbar.Log_W_nk).sum(0), np.ones(len(N_k)), decimal=10)
+    assert_almost_equal(np.exp(mbar.Log_W_nk).dot(N_k), np.ones(U.shape[1]), decimal=10)
+    assert_almost_equal(pymbar.mbar_solvers.self_consistent_update(U, N_k, mbar.f_k), mbar.f_k, decimal=10)
 
     # Test against old MBAR code.
     with suppress_derivative_warnings_for_tests():
         mbar0 = pymbar.old_mbar.MBAR(U, N_k)
     fij0, dfij0 = mbar0.getFreeEnergyDifferences(uncertainty_method="svd")
-    eq(mbar.f_k, mbar0.f_k, decimal=8)
-    eq(np.exp(mbar.Log_W_nk), np.exp(mbar0.Log_W_nk), decimal=5)
-
-    eq(fij0, fij1, decimal=8)
-    eq(dfij0, dfij1, decimal=8)
-
-    eq(fij0, fij2, decimal=8)
-    eq(dfij0, dfij2, decimal=8)
-
-
-def test_100x100_oscillators():
-    data_generator = lambda: load_oscillators(100, 100)
-    _test(data_generator)
-
-
-def test_200x50_oscillators():
-    data_generator = lambda: load_oscillators(200, 50)
-    _test(data_generator)
+    assert_almost_equal(mbar.f_k, mbar0.f_k, decimal=8)
+    assert_almost_equal(np.exp(mbar.Log_W_nk), np.exp(mbar0.Log_W_nk), decimal=5)
 
+    assert_almost_equal(fij0, fij1, decimal=8)
+    assert_almost_equal(dfij0, dfij1, decimal=8)
 
-def test_200x50_exponentials():
-    data_generator = lambda: load_exponentials(200, 50)
-    _test(data_generator)
+    assert_almost_equal(fij0, fij2, decimal=8)
+    assert_almost_equal(dfij0, dfij2, decimal=8)