def test_full_init(self):
self.eval = Evaluation(
self.test_dataset, [self.test_dataset, self.another_test_dataset],
[Bias(), Bias(), TemporalStdDev()])
ref_dataset = self.test_dataset
target_datasets = [self.test_dataset, self.another_test_dataset]
metrics = [Bias(), Bias()]
unary_metrics = [TemporalStdDev()]
self.eval = Evaluation(ref_dataset, target_datasets,
metrics + unary_metrics)
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
# Make sure the two target datasets were added properly
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
# Make sure the three metrics were added properly
# The two Bias metrics are "binary" metrics
self.assertEqual(len(self.eval.metrics), 2)
# TemporalStdDev is a "unary" metric and should be stored as such
self.assertEqual(len(self.eval.unary_metrics), 1)
self.eval.run()
out_str = ("<Evaluation - ref_dataset: {}, "
"target_dataset(s): {}, "
"binary_metric(s): {}, "
"unary_metric(s): {}, "
"subregion(s): {}>").format(str(
self.test_dataset), [str(ds) for ds in target_datasets],
[str(m) for m in metrics],
[str(u)
for u in unary_metrics], None)
self.assertEqual(str(self.eval), out_str)
def test_subregion_unary_result_shape(self):
bound = Bounds(
lat_min=10, lat_max=18,
lon_min=100, lon_max=108,
start=dt.datetime(2000, 1, 1), end=dt.datetime(2000, 3, 1))
new_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset],
[TemporalStdDev(), TemporalStdDev()],
[bound, bound, bound, bound, bound]
)
new_eval.run()
# Expected result shape is
# [
# [ # Subregions cause this extra layer
# [3, temporalstddev.run(reference).shape],
# ]
# ]
# 5 = number of subregions
self.assertTrue(len(new_eval.unary_results) == 5)
# number of metrics
self.assertTrue(len(new_eval.unary_results[0]) == 2)
self.assertTrue(isinstance(new_eval.unary_results, type([])))
# number of datasets (ref + target)
self.assertTrue(new_eval.unary_results[0][0].shape[0] == 3)
def setUp(self):
self.temporal_std_dev = TemporalStdDev()
# Initialize target dataset
self.target_lat = np.array([10, 12, 14, 16, 18])
self.target_lon = np.array([100, 102, 104, 106, 108])
self.target_time = np.array(
[dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'prec'
self.target_dataset = Dataset(self.target_lat, self.target_lon,
self.target_time, self.target_value,
self.target_variable)
def test_add_valid_metric(self):
# Add a "binary" metric
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metric(Bias())
self.assertEqual(len(self.eval.metrics), 1)
# Add a "unary" metric
self.assertEqual(len(self.eval.unary_metrics), 0)
self.eval.add_metric(TemporalStdDev())
self.assertEqual(len(self.eval.unary_metrics), 1)
def setUp(self):
self.temporal_std_dev = TemporalStdDev()
# Initialize target dataset
self.target_lat = np.array([10, 12, 14, 16, 18])
self.target_lon = np.array([100, 102, 104, 106, 108])
self.target_time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'prec'
self.target_dataset = Dataset(self.target_lat, self.target_lon, self.target_time,
self.target_value, self.target_variable)
def test_unary_result_shape(self):
new_eval = Evaluation(self.test_dataset, [
self.another_test_dataset, self.another_test_dataset,
self.another_test_dataset, self.another_test_dataset
], [TemporalStdDev()])
new_eval.run()
# Expected result shape is
# [stddev] where stddev.shape[0] = number of datasets
self.assertTrue(len(new_eval.unary_results) == 1)
self.assertTrue(new_eval.unary_results[0].shape[0] == 5)
def test_full_init(self):
self.eval = Evaluation(
self.test_dataset, [self.test_dataset, self.another_test_dataset],
[Bias(), Bias(), TemporalStdDev()])
self.assertEqual(self.eval.ref_dataset.variable, self.variable)
# Make sure the two target datasets were added properly
self.assertEqual(self.eval.target_datasets[0].variable, self.variable)
self.assertEqual(self.eval.target_datasets[1].variable, self.other_var)
# Make sure the three metrics were added properly
# The two Bias metrics are "binary" metrics
self.assertEqual(len(self.eval.metrics), 2)
# TemporalStdDev is a "unary" metric and should be stored as such
self.assertEqual(len(self.eval.unary_metrics), 1)
class TestTemporalStdDev(unittest.TestCase):
'''Test the metrics.TemporalStdDev metric.'''
def setUp(self):
self.temporal_std_dev = TemporalStdDev()
# Initialize target dataset
self.target_lat = np.array([10, 12, 14, 16, 18])
self.target_lon = np.array([100, 102, 104, 106, 108])
self.target_time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'prec'
self.target_dataset = Dataset(self.target_lat, self.target_lon, self.target_time,
self.target_value, self.target_variable)
def test_function_run(self):
'''Test TemporalStdDev function for target dataset.'''
expected_result = np.zeros((5, 5),)
expected_result.fill(90.13878189)
npt.assert_almost_equal(self.temporal_std_dev.run(self.target_dataset), expected_result)
class TestTemporalStdDev(unittest.TestCase):
'''Test the metrics.TemporalStdDev metric.'''
def setUp(self):
self.temporal_std_dev = TemporalStdDev()
# Initialize target dataset
self.target_lat = np.array([10, 12, 14, 16, 18])
self.target_lon = np.array([100, 102, 104, 106, 108])
self.target_time = np.array(
[dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'prec'
self.target_dataset = Dataset(self.target_lat, self.target_lon,
self.target_time, self.target_value,
self.target_variable)
def test_function_run(self):
'''Test TemporalStdDev function for target dataset.'''
expected_result = np.zeros((5, 5), )
expected_result.fill(90.13878189)
npt.assert_almost_equal(self.temporal_std_dev.run(self.target_dataset),
expected_result)