added tests for coveralls

tests/test_calibration.py

@@ -1,16 +1,17 @@
 """Test suite for the calibration algorithm in calibration.py."""
 
 # Also tests the dummy model implementation.
-
 import numpy as np
+import pytest
 
 from xhydro.modelling.calibration import perform_calibration
 from xhydro.modelling.hydrological_modelling import dummy_model
-from xhydro.modelling.obj_funcs import get_objective_function
+from xhydro.modelling.obj_funcs import get_objective_function, transform_flows
 
 
 def test_spotpy_calibration():
-    """Make sure the calibration works for a few test cases."""
+    """Make sure the calibration works under possible test cases."""
+
     bounds_low = np.array([0, 0, 0])
     bounds_high = np.array([10, 10, 10])
 
@@ -51,3 +52,139 @@ def test_spotpy_calibration():
     model_config["parameters"] = [5, 5, 5]
     Qsim = dummy_model(model_config)
     assert Qsim[3] == 3500.00
+
+    # Also test to ensure SCEUA and take_minimize is required.
+    best_parameters_sceua, best_simulation, best_objfun = perform_calibration(
+        model_config,
+        "mae",
+        bounds_low=bounds_low,
+        bounds_high=bounds_high,
+        evaluations=10,
+        algorithm="SCEUA",
+    )
+
+    assert len(best_parameters_sceua) == len(bounds_high)
+
+    # Also test to ensure SCEUA and take_minimize is required.
+    best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+        model_config,
+        "nse",
+        bounds_low=bounds_low,
+        bounds_high=bounds_high,
+        evaluations=10,
+        algorithm="SCEUA",
+    )
+    assert len(best_parameters_negative) == len(bounds_high)
+
+    # Test to see if transform works
+    best_parameters_transform, best_simulation, best_objfun = perform_calibration(
+        model_config,
+        "nse",
+        bounds_low=bounds_low,
+        bounds_high=bounds_high,
+        evaluations=10,
+        algorithm="SCEUA",
+        transform="inv",
+        epsilon=0.01,
+    )
+    assert len(best_parameters_transform) == len(bounds_high)
+
+
+def test_calibration_failures():
+    """Test for the calibration algorithm failure modes"""
+    bounds_low = np.array([0, 0, 0])
+    bounds_high = np.array([10, 10, 10])
+    model_config = {
+        "precip": np.array([10, 11, 12, 13, 14, 15]),
+        "temperature": np.array([10, 3, -5, 1, 15, 0]),
+        "Qobs": np.array([120, 130, 140, 150, 160, 170]),
+        "drainage_area": np.array([10]),
+        "model_name": "Dummy",
+    }
+
+    # Test Qobs different length than Qsim
+    with pytest.raises(SystemExit) as pytest_wrapped_e:
+        best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+            model_config.update(Qobs=np.array([100, 100, 100])),
+            "nse",
+            bounds_low=bounds_low,
+            bounds_high=bounds_high,
+            evaluations=1000,
+            algorithm="OTHER",
+        )
+        assert pytest_wrapped_e.type == SystemExit
+
+        # Test mask not 1 or 0
+        mask = np.array([0, 0, 0, 0.5, 1, 1])
+        best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+            model_config,
+            "nse",
+            bounds_low=bounds_low,
+            bounds_high=bounds_high,
+            evaluations=1000,
+            algorithm="DDS",
+            mask=mask,
+        )
+        assert pytest_wrapped_e.type == SystemExit
+
+        # test not same length in mask
+        mask = np.array([0, 0, 0, 1, 1])
+        best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+            model_config,
+            "nse",
+            bounds_low=bounds_low,
+            bounds_high=bounds_high,
+            evaluations=1000,
+            algorithm="DDS",
+            mask=mask,
+        )
+        assert pytest_wrapped_e.type == SystemExit
+
+        # Test objective function fail is caught
+        mask = np.array([0, 0, 0, 0, 1, 1])
+        best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+            model_config,
+            "nse_fake",
+            bounds_low=bounds_low,
+            bounds_high=bounds_high,
+            evaluations=1000,
+            algorithm="DDS",
+            mask=mask,
+        )
+        assert pytest_wrapped_e.type == SystemExit
+
+        # Test objective function that cannot be minimized
+        best_parameters_negative, best_simulation, best_objfun = perform_calibration(
+            model_config,
+            "bias",
+            bounds_low=bounds_low,
+            bounds_high=bounds_high,
+            evaluations=1000,
+            algorithm="DDS",
+            mask=mask,
+        )
+        assert pytest_wrapped_e.type == SystemExit
+
+
+def test_transform():
+    """Test the transformer"""
+
+    Qsim = np.array([10, 10, 10])
+    Qobs = np.array([5, 5, 5])
+
+    Qsim_r, Qobs_r = transform_flows(Qsim, Qobs, transform="inv", epsilon=0.01)
+    np.testing.assert_array_almost_equal(Qsim_r[1], 0.0995024, 6)
+    np.testing.assert_array_almost_equal(Qobs_r[1], 0.1980198, 6)
+
+    Qsim_r, Qobs_r = transform_flows(Qsim, Qobs, transform="sqrt")
+    np.testing.assert_array_almost_equal(Qsim_r[1], 3.1622776, 6)
+    np.testing.assert_array_almost_equal(Qobs_r[1], 2.2360679, 6)
+
+    Qsim_r, Qobs_r = transform_flows(Qsim, Qobs, transform="log", epsilon=0.01)
+    np.testing.assert_array_almost_equal(Qsim_r[1], 2.3075726, 6)
+    np.testing.assert_array_almost_equal(Qobs_r[1], 1.6193882, 6)
+
+    # Test Qobs different length than Qsim
+    with pytest.raises(SystemExit) as pytest_wrapped_e:
+        Qobs_r, Qobs_r = transform_flows(Qsim, Qobs, transform="a", epsilon=0.01)
+        assert pytest_wrapped_e.type == SystemExit

tests/test_hydrological_modelling.py

@@ -0,0 +1,35 @@
+"""Test suite for hydrological modelling in hydrological_modelling.py"""
+import unittest
+
+import numpy as np
+import pytest
+
+from xhydro.modelling.hydrological_modelling import hydrological_model_selector
+
+
+def test_hydrological_modelling():
+    """Test the hydrological models as they become online"""
+
+    # Test the dummy model
+    model_config = {
+        "precip": np.array([10, 11, 12, 13, 14, 15]),
+        "temperature": np.array([10, 3, -5, 1, 15, 0]),
+        "Qobs": np.array([120, 130, 140, 150, 160, 170]),
+        "drainage_area": np.array([10]),
+        "model_name": "Dummy",
+        "parameters": np.array([5, 5, 5]),
+    }
+    Qsim = hydrological_model_selector(model_config)
+    assert Qsim[3] == 3500.00
+
+    # Test the exceptions for new models
+    model_config.update(model_name="ADD_OTHER_HERE")
+    Qsim = hydrological_model_selector(model_config)
+    assert Qsim == 0
+
+
+@pytest.mark.xfail(raises=NotImplementedError)
+def import_unknown_model():
+    model_config = {"model_name": "fake_model"}
+    Qsim = hydrological_model_selector(model_config)
+    assert Qsim == None