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Updated to change optional input from range_label to range_var to be … #1706

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bccacba
Updated to change optional input from range_label to range_var to be …
slonimer Jul 16, 2026
65b1697
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jul 16, 2026
65fe4e1
Updated the tests so that it's not relying on the outdated range_samp…
slonimer Aug 10, 2026
14b637d
Merge branch 'main' of https://github.com/slonimer/echopype
slonimer Aug 10, 2026
eaf1833
Updated to fix the echo_range variable in the test_ds to be consisten…
slonimer Aug 11, 2026
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53 changes: 26 additions & 27 deletions echopype/metrics/summary_statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,22 +13,22 @@
import xarray as xr


def delta_z(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def delta_z(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Helper function to calculate widths between range samples (dz) for discretized integral.

Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
if range_label not in ds:
raise ValueError(f"{range_label} not in the input Dataset!")
dz = ds[range_label].diff(dim="range_sample")
if range_var not in ds:
raise ValueError(f"{range_var} not in the input Dataset!")
dz = ds[range_var].diff(dim=range_var)
return dz.where(dz != 0, other=np.nan)


Expand All @@ -48,70 +48,69 @@ def convert_to_linear(ds: xr.Dataset, Sv_label="Sv") -> xr.DataArray:
return 10 ** (ds[Sv_label] / 10)


def abundance(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def abundance(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Calculates the area-backscattering strength (Sa) [unit: dB re 1 m^2 m^-2].

This quantity is the integral of volumetric backscatter over range (``echo_range``).

Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
dz = delta_z(ds, range_label=range_label)
dz = delta_z(ds, range_var=range_var)
sv = convert_to_linear(ds, "Sv")
return 10 * np.log10((sv * dz).sum(dim="range_sample"))
return 10 * np.log10((sv * dz).sum(dim=range_var))


def center_of_mass(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def center_of_mass(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Calculates the mean backscatter location [unit: m].

This quantity is the weighted average of backscatter along range (``echo_range``).

Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
dz = delta_z(ds, range_label=range_label)
dz = delta_z(ds, range_var=range_var)
sv = convert_to_linear(ds, "Sv")
return (ds[range_label] * sv * dz).sum(dim="range_sample") / (sv * dz).sum(dim="range_sample")
return (ds[range_var] * sv * dz).sum(dim=range_var) / (sv * dz).sum(dim=range_var)


def dispersion(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def dispersion(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Calculates the inertia (I) [unit: m^-2].

This quantity measures dispersion or spread of backscatter from the center of mass.

Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
dz = delta_z(ds, range_label=range_label)
dz = delta_z(ds, range_var=range_var)
sv = convert_to_linear(ds, "Sv")
cm = center_of_mass(ds)
return ((ds[range_label] - cm) ** 2 * sv * dz).sum(dim="range_sample") / (sv * dz).sum(
dim="range_sample"
)
# cm = center_of_mass(ds)
cm = center_of_mass(ds, range_var=range_var)
return ((ds[range_var] - cm) ** 2 * sv * dz).sum(dim=range_var) / (sv * dz).sum(dim=range_var)


def evenness(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def evenness(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Calculates the equivalent area (EA) [unit: m].

This quantity represents the area that would be occupied if all datacells
Expand All @@ -120,19 +119,19 @@ def evenness(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
dz = delta_z(ds, range_label=range_label)
dz = delta_z(ds, range_var=range_var)
sv = convert_to_linear(ds, "Sv")
return ((sv * dz).sum(dim="range_sample")) ** 2 / (sv**2 * dz).sum(dim="range_sample")
return ((sv * dz).sum(dim=range_var)) ** 2 / (sv**2 * dz).sum(dim=range_var)


def aggregation(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
def aggregation(ds: xr.Dataset, range_var="echo_range") -> xr.DataArray:
"""Calculated the index of aggregation (IA) [unit: m^-1].

This quantity is reciprocal of the equivalent area.
Expand All @@ -141,11 +140,11 @@ def aggregation(ds: xr.Dataset, range_label="echo_range") -> xr.DataArray:
Parameters
----------
ds : xr.Dataset
range_label : str
range_var : str
Name of an xarray DataArray in ``ds`` containing ``echo_range`` information.

Returns
-------
xr.DataArray
"""
return 1 / evenness(ds, range_label=range_label)
return 1 / evenness(ds, range_var=range_var)
26 changes: 12 additions & 14 deletions echopype/tests/metrics/test_metrics_summary_statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,16 +19,14 @@
# Utility Function


def create_test_ds(Sv, echo_range):
def create_test_ds(Sv, echo_range, range_var="echo_range"):
freq = [30]
time = pd.date_range("2021-08-28", periods=2)
reference_time = pd.Timestamp("2021-08-27") # noqa: F841
r_b = [0, 1, 2]

testDS = xr.Dataset(
data_vars=dict(
Sv=(["frequency", "ping_time", "range_sample"], Sv),
echo_range=(["frequency", "ping_time", "range_sample"], echo_range),
Sv=(["frequency", "ping_time", range_var], Sv),
),
coords={
'frequency': xr.DataArray(
Expand All @@ -42,10 +40,10 @@ def create_test_ds(Sv, echo_range):
name='ping_time',
dims=['ping_time'],
),
'range_sample': xr.DataArray(
r_b,
name='range_sample',
dims=['range_sample'],
range_var: xr.DataArray(
echo_range,
name=range_var,
dims=[range_var],
),
},
)
Expand All @@ -58,7 +56,7 @@ def create_test_ds(Sv, echo_range):
def test_abundance():
"""Compares summary_statistics.py calculation of abundance with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
echo_range = np.array([1, 2, 3])

ab_ds1 = create_test_ds(Sv, echo_range)
ab_ds1_SOL = np.array([[60.04321374, 30.41392685]])
Expand All @@ -70,9 +68,9 @@ def test_abundance():
def test_center_of_mass():
"""Compares summary_statistics.py calculation of center_of_mass with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
echo_range = np.array([1, 2, 3])
cm_ds1 = create_test_ds(Sv, echo_range)
cm_ds1_SOL = np.array([[2.99009901, 3.90909090]])
cm_ds1_SOL = np.array([[2.99009901, 2.90909090]])
assert np.allclose(
center_of_mass(cm_ds1), cm_ds1_SOL, rtol=1e-09
), 'Calculated output does not match expected output'
Expand All @@ -81,7 +79,7 @@ def test_center_of_mass():
def test_inertia():
"""Compares summary_statistics.py calculation of inertia with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
echo_range = np.array([1, 2, 3])
in_ds1 = create_test_ds(Sv, echo_range)
in_ds1_SOL = np.array([[0.00980296, 0.08264463]])
assert np.allclose(
Expand All @@ -92,7 +90,7 @@ def test_inertia():
def test_evenness():
"""Compares summary_statistics.py calculation of evenness with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
echo_range = np.array([1, 2, 3])
ev_ds1 = create_test_ds(Sv, echo_range)
ev_ds1_SOL = np.array([[1.019998, 1.198019802]])
assert np.allclose(
Expand All @@ -103,7 +101,7 @@ def test_evenness():
def test_aggregation():
"""Compares summary_statistics.py calculation of aggregation with verified outcomes"""
Sv = np.array([[[20, 40, 60], [50, 20, 30]]])
echo_range = np.array([[[1, 2, 3], [2, 3, 4]]])
echo_range = np.array([1, 2, 3])
ag_ds1 = create_test_ds(Sv, echo_range)
ag_ds1_SOL = np.array([[0.9803940792, 0.8347107438]])
assert np.allclose(
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