def test_emcam_lowest_bin_non_monotonic(reliability_table_slice):
"""Test expected values are returned where the lowest observation
count bin is non-monotonic."""
expected_data = np.array([[1000, 500, 500, 750], [250, 500, 750, 1000],
[2000, 1000, 1000, 1000]])
expected_bin_coord_points = np.array([0.2, 0.5, 0.7, 0.9],
dtype=np.float32)
expected_bin_coord_bounds = np.array(
[[0.0, 0.4], [0.4, 0.6], [0.6, 0.8], [0.8, 1.0]],
dtype=np.float32,
)
reliability_table_slice.data = np.array([
[1000, 0, 250, 500, 750], # Observation count
[0, 250, 500, 750, 1000], # Sum of forecast probability
[1000, 1000, 1000, 1000, 1000], # Forecast count
])
result = Plugin()._enforce_min_count_and_montonicity(
reliability_table_slice.copy())
assert_array_equal(result.data, expected_data)
assert_allclose(
result.coord("probability_bin").points, expected_bin_coord_points)
assert_allclose(
result.coord("probability_bin").bounds, expected_bin_coord_bounds)
def test_emcam_combine_undersampled_bins_non_monotonic(
reliability_table_slice):
"""Test expected values are returned when a bin is below the minimum
forecast count when the observed frequency is non-monotonic."""
expected_data = np.array([[1000, 425, 1000], [1000, 425, 1000],
[2000, 600, 1000]])
expected_bin_coord_points = np.array([0.2, 0.6, 0.9], dtype=np.float32)
expected_bin_coord_bounds = np.array(
[[0.0, 0.4], [0.4, 0.8], [0.8, 1.0]],
dtype=np.float32,
)
reliability_table_slice.data = np.array(
[
[750, 250, 50, 375, 1000], # Observation count
[750, 250, 50, 375, 1000], # Sum of forecast probability
[1000, 1000, 100, 500, 1000], # Forecast count
],
dtype=np.float32,
)
result = Plugin()._enforce_min_count_and_montonicity(
reliability_table_slice.copy())
assert_array_equal(result.data, expected_data)
assert_allclose(
result.coord("probability_bin").points, expected_bin_coord_points)
assert_allclose(
result.coord("probability_bin").bounds, expected_bin_coord_bounds)