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Use geometric mean for HIT energy bin centers #3574

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Merged
daralynnrhode merged 1 commit into from
Oct 19, 2026
Merged

Use geometric mean for HIT energy bin centers #3574

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10 changes: 8 additions & 2 deletions imap_processing/hit/hit_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -356,7 +356,12 @@ def add_energy_variables(
attr_mgr: ImapCdfAttributes | None = None,
) -> xr.Dataset:
"""
Add energy min and max variables to the dataset.
Add energy mean and delta variables to the dataset.

The energy mean for each energy range is the geometric mean of the
minimum and maximum energies, sqrt(energy_min * energy_max), rounded to
three decimal places. The deltas are the distances from that mean to the
minimum (delta minus) and maximum (delta plus) energies.

Parameters
----------
Expand Down Expand Up @@ -385,8 +390,9 @@ def add_energy_variables(
"""
updated_ds = dataset.copy()

# Geometric mean of the minimum and maximum energy of each range
energy_mean: np.ndarray = np.round(
np.mean(np.array([energy_min_values, energy_max_values]), axis=0), 3
np.sqrt(np.array(energy_min_values) * np.array(energy_max_values)), 3
).astype(np.float32)

def _attrs(var_name: str) -> dict:
Expand Down
31 changes: 20 additions & 11 deletions imap_processing/tests/hit/test_hit_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,7 +259,7 @@ def test_add_energy_variables():
particle = "test_particle"
energy_min = np.array([1.8, 4.0, 6.0], dtype=np.float32)
energy_max = np.array([2.2, 6.0, 10.0], dtype=np.float32)
energy_mean = np.mean([energy_min, energy_max], axis=0)
energy_mean = np.array([1.99, 4.899, 7.746], dtype=np.float32)

# Call the function
dataset = add_energy_variables(dataset, particle, energy_min, energy_max)
Expand All @@ -268,15 +268,21 @@ def test_add_energy_variables():
assert f"{particle}_energy_delta_minus" in dataset.data_vars
assert f"{particle}_energy_delta_plus" in dataset.data_vars
assert f"{particle}_energy_mean" in dataset.coords
assert np.all(
dataset[f"{particle}_energy_delta_minus"].values
== np.array(energy_mean - np.array(energy_min), dtype=np.float32)
np.testing.assert_allclose(
dataset[f"{particle}_energy_mean"].values, energy_mean, rtol=0, atol=1e-6
)
assert np.all(
dataset[f"{particle}_energy_delta_plus"].values
== np.array(energy_max - energy_mean, dtype=np.float32)
np.testing.assert_allclose(
dataset[f"{particle}_energy_delta_minus"].values,
energy_mean - energy_min,
rtol=0,
atol=1e-6,
)
np.testing.assert_allclose(
dataset[f"{particle}_energy_delta_plus"].values,
energy_max - energy_mean,
rtol=0,
atol=1e-6,
)
assert np.all(dataset[f"{particle}_energy_mean"].values == energy_mean)


def test_sum_particle_data(sample_dataset):
Expand Down Expand Up @@ -364,9 +370,12 @@ def test_add_summed_particle_data_to_dataset(sample_dataset):
len(energy_ranges),
)

assert np.all(
dataset_to_update[f"{particle}_energy_mean"].values
== np.mean([[1.8, 4.0], [2.2, 6.0]], axis=0)
# Geometric means: sqrt(1.8 * 2.2) = 1.98997, sqrt(4.0 * 6.0) = 4.89898
np.testing.assert_allclose(
dataset_to_update[f"{particle}_energy_mean"].values,
np.array([1.99, 4.899], dtype=np.float32),
rtol=0,
atol=1e-6,
)


Expand Down
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