irradiance rescale test for value error

rdtools/test/irradiance_rescale_test.py

@@ -7,19 +7,28 @@
 
 @pytest.fixture
 def simple_irradiance():
-    times = pd.date_range('2019-06-01 12:00', freq='15T', periods=5)
+    times = pd.date_range("2019-06-01 12:00", freq="15T", periods=5)
     time_series = pd.Series([1, 2, 3, 4, 5], index=times, dtype=float)
     return time_series
 
 
-@pytest.mark.parametrize("method", ['iterative', 'single_opt'])
+@pytest.mark.parametrize("method", ["iterative", "single_opt", "error"])
 def test_rescale(method, simple_irradiance):
     # test basic functionality
-    modeled = simple_irradiance
-    measured = 1.05 * simple_irradiance
-    rescaled = irradiance_rescale(measured, modeled, method=method)
-    expected = measured
-    assert_series_equal(rescaled, expected, check_exact=False)
+    if method == "error":
+        pytest.raises(
+            ValueError,
+            irradiance_rescale,
+            simple_irradiance,
+            simple_irradiance * 1.05,
+            method=method,
+        )
+    else:
+        modeled = simple_irradiance
+        measured = 1.05 * simple_irradiance
+        rescaled = irradiance_rescale(measured, modeled, method=method)
+        expected = measured
+        assert_series_equal(rescaled, expected, check_exact=False)
 
 
 def test_max_iterations(simple_irradiance):
@@ -31,38 +40,42 @@ def test_max_iterations(simple_irradiance):
     modeled.iloc[4] *= 0.8
 
     with pytest.raises(ConvergenceError):
-        _ = irradiance_rescale(measured, modeled, method='iterative',
-                               max_iterations=2)
+        _ = irradiance_rescale(measured, modeled, method="iterative", max_iterations=2)
 
-    _ = irradiance_rescale(measured, modeled, method='iterative',
-                           max_iterations=10)
+    _ = irradiance_rescale(measured, modeled, method="iterative", max_iterations=10)
 
 
 def test_max_iterations_zero(simple_irradiance):
     # zero is sort of a special case, test it separately
 
     # test series already close enough
     true_factor = 1.0 + 1e-8
-    rescaled = irradiance_rescale(simple_irradiance,
-                                  simple_irradiance * true_factor,
-                                  max_iterations=0,
-                                  method='iterative')
+    rescaled = irradiance_rescale(
+        simple_irradiance,
+        simple_irradiance * true_factor,
+        max_iterations=0,
+        method="iterative",
+    )
     assert_series_equal(rescaled, simple_irradiance, check_exact=False)
 
     # tighten threshold so that it isn't already close enough
     with pytest.raises(ConvergenceError):
-        _ = irradiance_rescale(simple_irradiance,
-                               simple_irradiance * true_factor,
-                               max_iterations=0,
-                               convergence_threshold=1e-9,
-                               method='iterative')
+        _ = irradiance_rescale(
+            simple_irradiance,
+            simple_irradiance * true_factor,
+            max_iterations=0,
+            convergence_threshold=1e-9,
+            method="iterative",
+        )
 
 
 def test_convergence_threshold(simple_irradiance):
     # can't converge if threshold is negative
     with pytest.raises(ConvergenceError):
-        _ = irradiance_rescale(simple_irradiance,
-                               simple_irradiance * 1.05,
-                               max_iterations=5,  # reduced count for speed
-                               convergence_threshold=-1,
-                               method='iterative')
+        _ = irradiance_rescale(
+            simple_irradiance,
+            simple_irradiance * 1.05,
+            max_iterations=5,  # reduced count for speed
+            convergence_threshold=-1,
+            method="iterative",
+        )