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 setUp(self):
self.bias = Bias()
# Initialize reference dataset
self.reference_lat = np.array([10, 12, 14, 16, 18])
self.reference_lon = np.array([100, 102, 104, 106, 108])
self.reference_time = np.array(
[dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.reference_value = flat_array.reshape(12, 5, 5)
self.reference_variable = 'prec'
self.reference_dataset = Dataset(self.reference_lat,
self.reference_lon,
self.reference_time,
self.reference_value,
self.reference_variable)
# Initialize target dataset
self.target_lat = np.array([1, 2, 4, 6, 8])
self.target_lon = np.array([10, 12, 14, 16, 18])
self.target_time = np.array(
[dt.datetime(2001, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300, 600))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'tasmax'
self.target_dataset = Dataset(self.target_lat, self.target_lon,
self.target_time, self.target_value,
self.target_variable)
def test_result_shape(self):
bias_eval = Evaluation(self.test_dataset, [
self.another_test_dataset, self.another_test_dataset,
self.another_test_dataset
], [Bias(), Bias()])
bias_eval.run()
# Expected result shape is
# [bias, bias] where bias.shape[0] = number of datasets
self.assertTrue(len(bias_eval.results) == 2)
self.assertTrue(bias_eval.results[0].shape[0] == 3)
def test_bias_output_shape(self):
bias_eval = Evaluation(self.test_dataset, [
self.another_test_dataset], [Bias()])
bias_eval.run()
input_shape = tuple(self.test_dataset.values.shape)
bias_results_shape = tuple(bias_eval.results[0][0].shape)
self.assertEqual(input_shape, bias_results_shape)
class TestBias(unittest.TestCase):
'''Test the metrics.Bias metric.'''
def setUp(self):
self.bias = Bias()
# Initialize reference dataset
self.reference_lat = np.array([10, 12, 14, 16, 18])
self.reference_lon = np.array([100, 102, 104, 106, 108])
self.reference_time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.reference_value = flat_array.reshape(12, 5, 5)
self.reference_variable = 'prec'
self.reference_dataset = Dataset(self.reference_lat, self.reference_lon,
self.reference_time, self.reference_value, self.reference_variable)
# Initialize target dataset
self.target_lat = np.array([1, 2, 4, 6, 8])
self.target_lon = np.array([10, 12, 14, 16, 18])
self.target_time = np.array([dt.datetime(2001, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300, 600))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'tasmax'
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 bias function between reference dataset and target dataset.'''
expected_result = np.zeros((12, 5, 5), dtype=np.int)
expected_result.fill(-300)
np.testing.assert_array_equal(self.bias.run(self.reference_dataset, self.target_dataset), expected_result)
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)
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 test_subregion_result_shape(self):
bound = Bounds(10, 18, 100, 108, dt.datetime(2000, 1, 1),
dt.datetime(2000, 3, 1))
bias_eval = Evaluation(
self.test_dataset,
[self.another_test_dataset, self.another_test_dataset], [Bias()],
[bound])
bias_eval.run()
# Expected result shape is
# [
# [ # Subregions cause this extra layer
# [number of targets, bias.run(reference, target1).shape]
# ]
# ],
self.assertTrue(len(bias_eval.results) == 1)
self.assertTrue(len(bias_eval.results[0]) == 1)
self.assertTrue(bias_eval.results[0][0].shape[0] == 2)
self.assertTrue(isinstance(bias_eval.results, type([])))
def setUp(self):
self.bias = Bias()
# Initialize reference dataset
self.reference_lat = np.array([10, 12, 14, 16, 18])
self.reference_lon = np.array([100, 102, 104, 106, 108])
self.reference_time = np.array([dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.reference_value = flat_array.reshape(12, 5, 5)
self.reference_variable = 'prec'
self.reference_dataset = Dataset(self.reference_lat, self.reference_lon,
self.reference_time, self.reference_value, self.reference_variable)
# Initialize target dataset
self.target_lat = np.array([1, 2, 4, 6, 8])
self.target_lon = np.array([10, 12, 14, 16, 18])
self.target_time = np.array([dt.datetime(2001, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300, 600))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'tasmax'
self.target_dataset = Dataset(self.target_lat, self.target_lon, self.target_time,
self.target_value, self.target_variable)
class TestBias(unittest.TestCase):
'''Test the metrics.Bias metric.'''
def setUp(self):
self.bias = Bias()
# Initialize reference dataset
self.reference_lat = np.array([10, 12, 14, 16, 18])
self.reference_lon = np.array([100, 102, 104, 106, 108])
self.reference_time = np.array(
[dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.reference_value = flat_array.reshape(12, 5, 5)
self.reference_variable = 'prec'
self.reference_dataset = Dataset(self.reference_lat,
self.reference_lon,
self.reference_time,
self.reference_value,
self.reference_variable)
# Initialize target dataset
self.target_lat = np.array([1, 2, 4, 6, 8])
self.target_lon = np.array([10, 12, 14, 16, 18])
self.target_time = np.array(
[dt.datetime(2001, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300, 600))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'tasmax'
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 bias function between reference dataset and target dataset.'''
expected_result = np.zeros((12, 5, 5), dtype=np.int)
expected_result.fill(-300)
np.testing.assert_array_equal(
self.bias.run(self.reference_dataset, self.target_dataset),
expected_result)
def test_add_metrics(self):
self.assertEqual(len(self.eval.metrics), 0)
self.eval.add_metrics([Bias(), Bias()])
self.assertEqual(len(self.eval.metrics), 2)
