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Working on postprocess tests

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mbsantiago 2025-04-20 15:52:25 +01:00
parent bcf339c40d
commit 1f4454693e
7 changed files with 1280 additions and 49 deletions
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33
batdetect2/postprocess/decoding.py
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@ -29,6 +29,7 @@ The process involves:
from typing import List, Optional
import numpy as np
import xarray as xr
from soundevent import data
from soundevent.geometry import compute_bounds
@ -256,8 +257,17 @@ def convert_raw_prediction_to_sound_event_prediction(
]
),
features=[
data.Feature(term=data.term_from_key(feat_name), value=value)
for feat_name, value in raw_prediction.features
data.Feature(
term=data.Term(
name=f"batdetect2:{feat_name}",
label=feat_name,
definition="Automatically extracted features by BatDetect2",
),
value=value,
)
for feat_name, value in _iterate_over_array(
raw_prediction.features
)
],
)
@ -274,9 +284,7 @@ def convert_raw_prediction_to_sound_event_prediction(
drop=True,
)
for class_name, score in class_scores.sortby(
class_scores, ascending=False
):
for class_name, score in _iterate_sorted(class_scores):
class_tags = sound_event_decoder(class_name)
for tag in class_tags:
@ -295,3 +303,18 @@ def convert_raw_prediction_to_sound_event_prediction(
score=raw_prediction.detection_score,
tags=tags,
)
def _iterate_over_array(array: xr.DataArray):
dim_name = array.dims[0]
coords = array.coords[dim_name]
for value, coord in zip(array.values, coords.values):
yield coord, float(value)
def _iterate_sorted(array: xr.DataArray):
dim_name = array.dims[0]
coords = array.coords[dim_name]
indices = np.argsort(coords.values)
for index in indices:
yield str(coords[index]), coords.values[index]

2
batdetect2/postprocess/detection.py
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@ -103,7 +103,7 @@ def extract_detections_from_array(
top_values = top_values[mask]
top_sorted_indices = top_sorted_indices[mask]
time_indices, freq_indices = np.unravel_index(
freq_indices, time_indices = np.unravel_index(
top_sorted_indices,
detection_array.shape,
)

43
tests/test_postprocessing/test_arrays.py
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@ -1,43 +0,0 @@
from typing import List
import numpy as np
import torch
import xarray as xr
from soundevent import data
from batdetect2.modules import DetectorModel
from batdetect2.postprocess.arrays import to_xarray
from batdetect2.preprocess import preprocess_audio_clip
def test_this(clip: data.Clip, class_names: List[str]):
spec = xr.DataArray(
data=np.random.rand(100, 100),
dims=["time", "frequency"],
coords={
"time": np.linspace(0, 100, 100, endpoint=False),
"frequency": np.linspace(0, 100, 100, endpoint=False),
},
)
model = DetectorModel()
spec = preprocess_audio_clip(
clip,
config=model.config.preprocessing,
)
tensor = torch.from_numpy(spec.data).unsqueeze(0).unsqueeze(0)
outputs = model(tensor)
arrays = to_xarray(
outputs,
start_time=clip.start_time,
end_time=clip.end_time,
class_names=class_names,
)
print(arrays)
assert False

604
tests/test_postprocessing/test_decoding.py Normal file
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@ -0,0 +1,604 @@
from pathlib import Path
from typing import List, Tuple
import numpy as np
import pytest
import xarray as xr
# Removed dataclass import as MockRawPrediction is replaced
from soundevent import data
# Import functions to test
from batdetect2.postprocess.decoding import (
DEFAULT_CLASSIFICATION_THRESHOLD,
convert_raw_prediction_to_sound_event_prediction,
convert_raw_predictions_to_clip_prediction,
convert_xr_dataset_to_raw_prediction,
)
from batdetect2.postprocess.types import RawPrediction
# Dummy GeometryBuilder function fixture
@pytest.fixture
def dummy_geometry_builder():
"""A simple GeometryBuilder that creates a BBox around the point."""
def _builder(
position: Tuple[float, float],
dimensions: xr.DataArray,
) -> data.BoundingBox:
time, freq = position
width = dimensions.sel(dimension="width").item()
height = dimensions.sel(dimension="height").item()
# Assume position is the center
return data.BoundingBox(
coordinates=[
time - width / 2,
freq - height / 2,
time + width / 2,
freq + height / 2,
]
)
return _builder
# Dummy SoundEventDecoder function fixture
@pytest.fixture
def dummy_sound_event_decoder():
"""A simple SoundEventDecoder mapping names to tags."""
tag_map = {
"bat": [
data.Tag(term=data.term_from_key(key="species"), value="Myotis")
],
"noise": [
data.Tag(term=data.term_from_key(key="category"), value="noise")
],
"unknown": [
data.Tag(term=data.term_from_key(key="status"), value="uncertain")
],
}
def _decoder(class_name: str) -> List[data.Tag]:
return tag_map.get(class_name.lower(), [])
return _decoder
@pytest.fixture
def generic_tags() -> List[data.Tag]:
"""Sample generic tags."""
return [
data.Tag(term=data.term_from_key(key="detector"), value="batdetect2")
]
@pytest.fixture
def sample_recording() -> data.Recording:
"""A sample soundevent Recording."""
return data.Recording(
path=Path("/path/to/recording.wav"),
duration=60.0,
channels=1,
samplerate=192000,
)
@pytest.fixture
def sample_clip(sample_recording) -> data.Clip:
"""A sample soundevent Clip."""
return data.Clip(
recording=sample_recording,
start_time=10.0,
end_time=20.0,
)
# Fixture for a detection dataset (adapted from test_extraction)
@pytest.fixture
def sample_detection_dataset() -> xr.Dataset:
"""Creates a sample detection dataset suitable for decoding."""
# Based on test_extraction's corrected expectations
# Detections: (t=20, f=300, s=0.9), (t=10, f=200, s=0.8)
expected_times = np.array([20, 10])
expected_freqs = np.array([300, 200])
detection_coords = {
"time": ("detection", expected_times),
"freq": ("detection", expected_freqs),
}
scores_data = np.array([0.9, 0.8], dtype=np.float64)
scores = xr.DataArray(
scores_data,
coords=detection_coords,
dims=["detection"],
name="scores",
)
dimensions_data = np.array([[7.0, 16.0], [3.0, 12.0]], dtype=np.float32)
dimensions = xr.DataArray(
dimensions_data,
coords={**detection_coords, "dimension": ["width", "height"]},
dims=["detection", "dimension"],
name="dimensions",
)
classes_data = np.array(
[[0.43, 0.85], [0.24, 0.66]],
dtype=np.float32, # Simplified values
)
classes = xr.DataArray(
classes_data,
coords={**detection_coords, "category": ["bat", "noise"]},
dims=["detection", "category"],
name="classes",
)
features_data = np.array(
[[7.0, 16.0, 25.0, 34.0], [3.0, 12.0, 21.0, 30.0]], dtype=np.float32
)
features = xr.DataArray(
features_data,
coords={**detection_coords, "feature": ["f0", "f1", "f2", "f3"]},
dims=["detection", "feature"],
name="features",
)
ds = xr.Dataset(
{
"score": scores,
"dimensions": dimensions,
"classes": classes,
"features": features,
},
coords=detection_coords,
)
return ds
@pytest.fixture
def empty_detection_dataset() -> xr.Dataset:
"""Creates an empty detection dataset with correct structure."""
detection_coords = {
"time": ("detection", np.array([], dtype=np.float64)),
"freq": ("detection", np.array([], dtype=np.float64)),
}
scores = xr.DataArray(
np.array([], dtype=np.float64),
coords=detection_coords,
dims=["detection"],
name="scores",
)
dimensions = xr.DataArray(
np.empty((0, 2), dtype=np.float32),
coords={**detection_coords, "dimension": ["width", "height"]},
dims=["detection", "dimension"],
name="dimensions",
)
classes = xr.DataArray(
np.empty((0, 2), dtype=np.float32),
coords={**detection_coords, "category": ["bat", "noise"]},
dims=["detection", "category"],
name="classes",
)
features = xr.DataArray(
np.empty((0, 4), dtype=np.float32),
coords={**detection_coords, "feature": ["f0", "f1", "f2", "f3"]},
dims=["detection", "feature"],
name="features",
)
return xr.Dataset(
{
"scores": scores,
"dimensions": dimensions,
"classes": classes,
"features": features,
},
coords=detection_coords,
)
# Fixture for sample RawPrediction objects (using the actual type)
@pytest.fixture
def sample_raw_predictions() -> List[RawPrediction]:
"""Manually crafted RawPrediction objects using the actual type."""
# Corresponds roughly to sample_detection_dataset after geometry building
# Det 1: t=20, f=300, s=0.9, w=7, h=16, classes=[0.43, 0.85], feats=[7, 16, 25, 34]
# Det 2: t=10, f=200, s=0.8, w=3, h=12, classes=[0.24, 0.66], feats=[ 3, 12, 21, 30]
pred1_classes = xr.DataArray(
[0.43, 0.85], coords={"category": ["bat", "noise"]}, dims=["category"]
)
pred1_features = xr.DataArray(
[7.0, 16.0, 25.0, 34.0],
coords={"feature": ["f0", "f1", "f2", "f3"]},
dims=["feature"],
)
pred1 = RawPrediction( # Use RawPrediction directly
detection_score=0.9,
start_time=20 - 7 / 2,
end_time=20 + 7 / 2, # 16.5, 23.5
low_freq=300 - 16 / 2,
high_freq=300 + 16 / 2, # 292, 308
class_scores=pred1_classes,
features=pred1_features,
)
pred2_classes = xr.DataArray(
[0.24, 0.66], coords={"category": ["bat", "noise"]}, dims=["category"]
)
pred2_features = xr.DataArray(
[3.0, 12.0, 21.0, 30.0],
coords={"feature": ["f0", "f1", "f2", "f3"]},
dims=["feature"],
)
pred2 = RawPrediction( # Use RawPrediction directly
detection_score=0.8,
start_time=10 - 3 / 2,
end_time=10 + 3 / 2, # 8.5, 11.5
low_freq=200 - 12 / 2,
high_freq=200 + 12 / 2, # 194, 206
class_scores=pred2_classes,
features=pred2_features,
)
pred3_classes = xr.DataArray(
[0.05, 0.02], coords={"category": ["bat", "noise"]}, dims=["category"]
) # Below default threshold
pred3_features = xr.DataArray(
[1.0, 2.0, 3.0, 4.0],
coords={"feature": ["f0", "f1", "f2", "f3"]},
dims=["feature"],
)
pred3 = RawPrediction( # Use RawPrediction directly
detection_score=0.15,
start_time=5.0,
end_time=6.0,
low_freq=50.0,
high_freq=60.0,
class_scores=pred3_classes,
features=pred3_features,
)
return [pred1, pred2, pred3]
# --- Tests for convert_xr_dataset_to_raw_prediction ---
def test_convert_xr_dataset_basic(
sample_detection_dataset, dummy_geometry_builder
):
"""Test basic conversion of a dataset to RawPrediction list."""
raw_predictions = convert_xr_dataset_to_raw_prediction(
sample_detection_dataset, dummy_geometry_builder
)
assert isinstance(raw_predictions, list)
assert len(raw_predictions) == 2
# Check first prediction (score=0.9)
pred1 = raw_predictions[0]
assert isinstance(pred1, RawPrediction) # Check against the actual type
assert pred1.detection_score == pytest.approx(0.9)
# Check bounds derived from dummy_geometry_builder (center pos assumed)
# t=20, f=300, w=7, h=16
assert pred1.start_time == pytest.approx(20 - 7 / 2)
assert pred1.end_time == pytest.approx(20 + 7 / 2)
assert pred1.low_freq == pytest.approx(300 - 16 / 2)
assert pred1.high_freq == pytest.approx(300 + 16 / 2)
xr.testing.assert_allclose(
pred1.class_scores,
sample_detection_dataset["classes"].sel(detection=0),
)
xr.testing.assert_allclose(
pred1.features, sample_detection_dataset["features"].sel(detection=0)
)
# Check second prediction (score=0.8)
pred2 = raw_predictions[1]
assert isinstance(pred2, RawPrediction) # Check against the actual type
assert pred2.detection_score == pytest.approx(0.8)
# t=10, f=200, w=3, h=12
assert pred2.start_time == pytest.approx(10 - 3 / 2)
assert pred2.end_time == pytest.approx(10 + 3 / 2)
assert pred2.low_freq == pytest.approx(200 - 12 / 2)
assert pred2.high_freq == pytest.approx(200 + 12 / 2)
xr.testing.assert_allclose(
pred2.class_scores,
sample_detection_dataset["classes"].sel(detection=1),
)
xr.testing.assert_allclose(
pred2.features, sample_detection_dataset["features"].sel(detection=1)
)
# ...(rest of the tests remain unchanged as they accessed attributes correctly)...
def test_convert_xr_dataset_empty(
empty_detection_dataset, dummy_geometry_builder
):
"""Test conversion of an empty dataset."""
raw_predictions = convert_xr_dataset_to_raw_prediction(
empty_detection_dataset, dummy_geometry_builder
)
assert isinstance(raw_predictions, list)
assert len(raw_predictions) == 0
# --- Tests for convert_raw_prediction_to_sound_event_prediction ---
def test_convert_raw_to_sound_event_basic(
sample_raw_predictions,
sample_recording,
dummy_sound_event_decoder,
generic_tags,
):
"""Test basic conversion, default threshold, multi-label."""
# score=0.9, classes=[0.43(bat), 0.85(noise)]
raw_pred = sample_raw_predictions[0]
se_pred = convert_raw_prediction_to_sound_event_prediction(
raw_prediction=raw_pred,
recording=sample_recording,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
# classification_threshold=DEFAULT_CLASSIFICATION_THRESHOLD (0.1),
# top_class_only=False,
)
assert isinstance(se_pred, data.SoundEventPrediction)
assert se_pred.score == pytest.approx(raw_pred.detection_score)
# Check SoundEvent
se = se_pred.sound_event
assert isinstance(se, data.SoundEvent)
assert se.recording == sample_recording
assert isinstance(se.geometry, data.BoundingBox)
np.testing.assert_allclose(
se.geometry.coordinates,
[
raw_pred.start_time,
raw_pred.low_freq,
raw_pred.end_time,
raw_pred.high_freq,
],
)
assert len(se.features) == len(raw_pred.features)
# Simple check for feature presence and value type
feat_dict = {f.term.name: f.value for f in se.features}
assert "batdetect2:f0" in feat_dict and isinstance(
feat_dict["batdetect2:f0"], float
)
assert feat_dict["batdetect2:f0"] == pytest.approx(7.0)
# Check Tags
# Expected: Generic(0.9), Noise(0.85), Bat(0.43)
# Note: Order might depend on sortby implementation detail, compare as sets
expected_tags = {
# Generic Tag
(generic_tags[0].key, generic_tags[0].value, 0.9),
# Noise Tag (score 0.85 > 0.1)
("category", "noise", 0.85),
# Bat Tag (score 0.43 > 0.1)
("species", "Myotis", 0.43),
}
print("expected", expected_tags)
actual_tags = {(pt.tag.key, pt.tag.value, pt.score) for pt in se_pred.tags}
print("actual", actual_tags)
assert actual_tags == expected_tags
def test_convert_raw_to_sound_event_thresholding(
sample_raw_predictions,
sample_recording,
dummy_sound_event_decoder,
generic_tags,
):
"""Test effect of classification threshold."""
raw_pred = sample_raw_predictions[
0
] # score=0.9, classes=[0.43(bat), 0.85(noise)]
high_threshold = 0.5
se_pred = convert_raw_prediction_to_sound_event_prediction(
raw_prediction=raw_pred,
recording=sample_recording,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=high_threshold, # Only noise should pass
top_class_only=False,
)
# Expected: Generic(0.9), Noise(0.85) - Bat (0.43) is below threshold
expected_tags = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.9)),
("category", "noise", pytest.approx(0.85)),
}
actual_tags = {(pt.tag.key, pt.tag.value, pt.score) for pt in se_pred.tags}
assert actual_tags == expected_tags
def test_convert_raw_to_sound_event_no_threshold(
sample_raw_predictions,
sample_recording,
dummy_sound_event_decoder,
generic_tags,
):
"""Test when classification_threshold is None."""
raw_pred = sample_raw_predictions[
2
] # score=0.15, classes=[0.05(bat), 0.02(noise)]
# Both classes are below default threshold, but should be included if None
se_pred = convert_raw_prediction_to_sound_event_prediction(
raw_prediction=raw_pred,
recording=sample_recording,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=None, # No thresholding
top_class_only=False,
)
# Expected: Generic(0.15), Bat(0.05), Noise(0.02)
expected_tags = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.15)),
("species", "Myotis", pytest.approx(0.05)),
("category", "noise", pytest.approx(0.02)),
}
actual_tags = {(pt.tag.key, pt.tag.value, pt.score) for pt in se_pred.tags}
assert actual_tags == expected_tags
def test_convert_raw_to_sound_event_top_class(
sample_raw_predictions,
sample_recording,
dummy_sound_event_decoder,
generic_tags,
):
"""Test top_class_only=True behavior."""
raw_pred = sample_raw_predictions[
0
] # score=0.9, classes=[0.43(bat), 0.85(noise)]
# Highest score is noise (0.85)
se_pred = convert_raw_prediction_to_sound_event_prediction(
raw_prediction=raw_pred,
recording=sample_recording,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=DEFAULT_CLASSIFICATION_THRESHOLD,
top_class_only=True, # Only include top class (noise)
)
# Expected: Generic(0.9), Noise(0.85)
expected_tags = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.9)),
("category", "noise", pytest.approx(0.85)),
}
actual_tags = {(pt.tag.key, pt.tag.value, pt.score) for pt in se_pred.tags}
assert actual_tags == expected_tags
def test_convert_raw_to_sound_event_all_below_threshold(
sample_raw_predictions,
sample_recording,
dummy_sound_event_decoder,
generic_tags,
):
"""Test when all class scores are below the default threshold."""
raw_pred = sample_raw_predictions[
2
] # score=0.15, classes=[0.05(bat), 0.02(noise)]
se_pred = convert_raw_prediction_to_sound_event_prediction(
raw_prediction=raw_pred,
recording=sample_recording,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=DEFAULT_CLASSIFICATION_THRESHOLD, # 0.1
top_class_only=False,
)
# Expected: Only Generic(0.15) tag, as others are below threshold
expected_tags = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.15)),
}
actual_tags = {(pt.tag.key, pt.tag.value, pt.score) for pt in se_pred.tags}
assert actual_tags == expected_tags
# --- Tests for convert_raw_predictions_to_clip_prediction ---
def test_convert_raw_list_to_clip_basic(
sample_raw_predictions,
sample_clip,
dummy_sound_event_decoder,
generic_tags,
):
"""Test converting a list of RawPredictions to a ClipPrediction."""
clip_pred = convert_raw_predictions_to_clip_prediction(
raw_predictions=sample_raw_predictions,
clip=sample_clip,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=DEFAULT_CLASSIFICATION_THRESHOLD,
top_class_only=False,
)
assert isinstance(clip_pred, data.ClipPrediction)
assert clip_pred.clip == sample_clip
assert len(clip_pred.sound_events) == len(sample_raw_predictions)
# Check if the contained sound events seem correct (basic check)
assert clip_pred.sound_events[0].score == pytest.approx(
sample_raw_predictions[0].detection_score
)
assert clip_pred.sound_events[1].score == pytest.approx(
sample_raw_predictions[1].detection_score
)
assert clip_pred.sound_events[2].score == pytest.approx(
sample_raw_predictions[2].detection_score
)
# Check if tags were generated correctly for one event (e.g., the last one)
# Pred 3 has score 0.15, classes [0.05, 0.02]. Only generic tag expected.
se_pred3_tags = {
(pt.tag.key, pt.tag.value, pt.score)
for pt in clip_pred.sound_events[2].tags
}
expected_tags3 = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.15)),
}
assert se_pred3_tags == expected_tags3
def test_convert_raw_list_to_clip_empty(
sample_clip,
dummy_sound_event_decoder,
generic_tags,
):
"""Test converting an empty list of RawPredictions."""
clip_pred = convert_raw_predictions_to_clip_prediction(
raw_predictions=[],
clip=sample_clip,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
)
assert isinstance(clip_pred, data.ClipPrediction)
assert clip_pred.clip == sample_clip
assert len(clip_pred.sound_events) == 0
def test_convert_raw_list_to_clip_passes_args(
sample_raw_predictions,
sample_clip,
dummy_sound_event_decoder,
generic_tags,
):
"""Test that arguments like top_class_only are passed through."""
# Use top_class_only = True
clip_pred = convert_raw_predictions_to_clip_prediction(
raw_predictions=sample_raw_predictions,
clip=sample_clip,
sound_event_decoder=dummy_sound_event_decoder,
generic_class_tags=generic_tags,
classification_threshold=DEFAULT_CLASSIFICATION_THRESHOLD,
top_class_only=True, # <<-- Argument being tested
)
assert len(clip_pred.sound_events) == 3
# Check tags for the first prediction (score=0.9, classes=[0.43(bat), 0.85(noise)])
# With top_class_only=True, expect Generic(0.9) and Noise(0.85) only
se_pred1_tags = {
(pt.tag.key, pt.tag.value, pt.score)
for pt in clip_pred.sound_events[0].tags
}
expected_tags1 = {
(generic_tags[0].key, generic_tags[0].value, pytest.approx(0.9)),
("category", "noise", pytest.approx(0.85)),
}
assert se_pred1_tags == expected_tags1

214
tests/test_postprocessing/test_detection.py Normal file
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@ -0,0 +1,214 @@
import numpy as np
import pytest
import xarray as xr
from soundevent.arrays import Dimensions
from batdetect2.postprocess.detection import extract_detections_from_array
@pytest.fixture
def sample_data_array():
"""Provides a basic 3x3 DataArray.
Top values: 0.9 (f=300, t=20), 0.8 (f=200, t=10), 0.7 (f=300, t=30)
"""
array = xr.DataArray(
np.zeros([3, 3]),
coords={
Dimensions.frequency.value: [100, 200, 300],
Dimensions.time.value: [10, 20, 30],
},
dims=[
Dimensions.frequency.value,
Dimensions.time.value,
],
)
array.loc[dict(time=10, frequency=100)] = 0.005
array.loc[dict(time=10, frequency=200)] = 0.5
array.loc[dict(time=10, frequency=300)] = 0.03
array.loc[dict(time=20, frequency=100)] = 0.8
array.loc[dict(time=20, frequency=200)] = 0.02
array.loc[dict(time=20, frequency=300)] = 0.6
array.loc[dict(time=30, frequency=100)] = 0.04
array.loc[dict(time=30, frequency=200)] = 0.9
array.loc[dict(time=30, frequency=300)] = 0.7
return array
@pytest.fixture
def data_array_with_nans(sample_data_array: xr.DataArray):
"""Provides a 2D DataArray containing NaN values."""
array = sample_data_array.copy()
array.loc[dict(time=10, frequency=300)] = np.nan
array.loc[dict(time=30, frequency=100)] = np.nan
return array
def test_basic_extraction(sample_data_array: xr.DataArray):
threshold = 0.1
max_detections = 3
actual_result = extract_detections_from_array(
sample_data_array,
threshold=threshold,
max_detections=max_detections,
)
expected_values = np.array([0.9, 0.8, 0.7])
expected_times = np.array([30, 20, 30])
expected_freqs = np.array([200, 100, 300])
expected_coords = {
Dimensions.frequency.value: ("detection", expected_freqs),
Dimensions.time.value: ("detection", expected_times),
}
expected_result = xr.DataArray(
expected_values,
coords=expected_coords,
dims="detection",
name="score",
)
xr.testing.assert_equal(actual_result, expected_result)
def test_threshold_only(sample_data_array):
input_array = sample_data_array
threshold = 0.5
actual_result = extract_detections_from_array(
input_array, threshold=threshold
)
expected_values = np.array([0.9, 0.8, 0.7, 0.6])
expected_times = np.array([30, 20, 30, 20])
expected_freqs = np.array([200, 100, 300, 300])
expected_coords = {
Dimensions.time.value: ("detection", expected_times),
Dimensions.frequency.value: ("detection", expected_freqs),
}
expected_result = xr.DataArray(
expected_values,
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)
def test_max_detections_only(sample_data_array):
input_array = sample_data_array
max_detections = 4
actual_result = extract_detections_from_array(
input_array, max_detections=max_detections
)
expected_values = np.array([0.9, 0.8, 0.7, 0.6])
expected_times = np.array([30, 20, 30, 20])
expected_freqs = np.array([200, 100, 300, 300])
expected_coords = {
Dimensions.time.value: ("detection", expected_times),
Dimensions.frequency.value: ("detection", expected_freqs),
}
expected_result = xr.DataArray(
expected_values,
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)
def test_no_optional_args(sample_data_array):
input_array = sample_data_array
actual_result = extract_detections_from_array(input_array)
expected_values = np.array([0.9, 0.8, 0.7, 0.6, 0.5, 0.04, 0.03, 0.02])
expected_times = np.array([30, 20, 30, 20, 10, 30, 10, 20])
expected_freqs = np.array([200, 100, 300, 300, 200, 100, 300, 200])
expected_coords = {
Dimensions.time.value: ("detection", expected_times),
Dimensions.frequency.value: ("detection", expected_freqs),
}
expected_result = xr.DataArray(
expected_values,
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)
def test_no_values_above_threshold(sample_data_array):
input_array = sample_data_array
threshold = 1.0
actual_result = extract_detections_from_array(
input_array, threshold=threshold
)
expected_coords = {
Dimensions.time.value: ("detection", np.array([], dtype=np.int64)),
Dimensions.frequency.value: (
"detection",
np.array([], dtype=np.int64),
),
}
expected_result = xr.DataArray(
np.array([], dtype=np.float64),
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)
assert actual_result.sizes["detection"] == 0
def test_max_detections_zero(sample_data_array):
input_array = sample_data_array
max_detections = 0
with pytest.raises(ValueError):
extract_detections_from_array(
input_array,
max_detections=max_detections,
)
def test_empty_input_array():
empty_array = xr.DataArray(
np.empty((0, 0)),
coords={Dimensions.time.value: [], Dimensions.frequency.value: []},
dims=[Dimensions.time.value, Dimensions.frequency.value],
)
actual_result = extract_detections_from_array(empty_array)
expected_coords = {
Dimensions.time.value: ("detection", np.array([], dtype=np.int64)),
Dimensions.frequency.value: (
"detection",
np.array([], dtype=np.int64),
),
}
expected_result = xr.DataArray(
np.array([], dtype=np.float64),
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)
assert actual_result.sizes["detection"] == 0
def test_nan_handling(data_array_with_nans):
input_array = data_array_with_nans
threshold = 0.1
max_detections = 3
actual_result = extract_detections_from_array(
input_array, threshold=threshold, max_detections=max_detections
)
expected_values = np.array([0.9, 0.8, 0.7])
expected_times = np.array([30, 20, 30])
expected_freqs = np.array([200, 100, 300])
expected_coords = {
Dimensions.time.value: ("detection", expected_times),
Dimensions.frequency.value: ("detection", expected_freqs),
}
expected_result = xr.DataArray(
expected_values,
coords=expected_coords,
dims="detection",
name="detection_value",
)
xr.testing.assert_equal(actual_result, expected_result)

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import numpy as np
import pytest
import xarray as xr
from soundevent.arrays import Dimensions
from batdetect2.postprocess.detection import extract_detections_from_array
from batdetect2.postprocess.extraction import (
extract_detection_xr_dataset,
extract_values_at_positions,
)
@pytest.fixture
def sample_data_array():
"""Provides a basic 3x3 DataArray.
Top values: 0.9 (f=300, t=20), 0.8 (f=200, t=10), 0.7 (f=300, t=30)
"""
coords = {
Dimensions.frequency.value: [100, 200, 300],
Dimensions.time.value: [10, 20, 30],
}
array = xr.DataArray(
np.zeros([3, 3]),
coords=coords,
dims=[
Dimensions.frequency.value,
Dimensions.time.value,
],
)
array.loc[dict(time=10, frequency=100)] = 0.005
array.loc[dict(time=10, frequency=200)] = 0.5
array.loc[dict(time=10, frequency=300)] = 0.03
array.loc[dict(time=20, frequency=100)] = 0.8
array.loc[dict(time=20, frequency=200)] = 0.02
array.loc[dict(time=20, frequency=300)] = 0.6
array.loc[dict(time=30, frequency=100)] = 0.04
array.loc[dict(time=30, frequency=200)] = 0.9
array.loc[dict(time=30, frequency=300)] = 0.7
return array
@pytest.fixture
def sample_array_for_extraction():
"""Provides a simple array (1-9) for value extraction tests."""
data = np.arange(1, 10).reshape(3, 3)
coords = {
Dimensions.frequency.value: [100, 200, 300],
Dimensions.time.value: [10, 20, 30],
}
return xr.DataArray(
data,
coords=coords,
dims=[
Dimensions.frequency.value,
Dimensions.time.value,
],
name="test_values",
)
@pytest.fixture
def sample_positions_top3(sample_data_array):
"""Get top 3 detection positions from sample_data_array."""
# Expected: (f=300, t=20, s=0.9), (f=200, t=10, s=0.8), (f=300, t=30, s=0.7)
return extract_detections_from_array(
sample_data_array, max_detections=3, threshold=None
)
@pytest.fixture
def sample_positions_top2(sample_data_array):
"""Get top 2 detection positions from sample_data_array."""
# Expected: (f=300, t=20, s=0.9), (f=200, t=10, s=0.8)
return extract_detections_from_array(
sample_data_array, max_detections=2, threshold=None
)
@pytest.fixture
def empty_positions(sample_data_array):
"""Get an empty positions array (high threshold)."""
return extract_detections_from_array(
sample_data_array,
threshold=1.0, # No values > 1.0
)
@pytest.fixture
def sample_sizes_array(sample_data_array):
"""Provides a sample sizes array matching sample_data_array coords."""
coords = sample_data_array.coords
# Data: [[0, 1, 2], [3, 4, 5]] # Dim 0 (width)
# [[9,10,11], [12,13,14]] # Dim 1 (height)
# Reshaped: (2, 3, 3) -> (dim, freq, time)
data = np.array(
[
[
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
], # width (freq increases down, time across)
[[9, 10, 11], [12, 13, 14], [15, 16, 17]], # height
],
dtype=np.float32,
)
return xr.DataArray(
data,
coords={
"dimension": ["width", "height"],
Dimensions.frequency.value: coords[Dimensions.frequency.value],
Dimensions.time.value: coords[Dimensions.time.value],
},
dims=["dimension", Dimensions.frequency.value, Dimensions.time.value],
name="sizes",
)
@pytest.fixture
def sample_classes_array(sample_data_array):
"""Provides a sample classes array matching sample_data_array coords."""
coords = sample_data_array.coords
# Example: (2 cats, 3 freqs, 3 times)
data = np.linspace(0.1, 0.9, 18, dtype=np.float32).reshape(2, 3, 3)
# data[0, 2, 1] -> cat=0, f=300, t=20 -> val for 0.9 detection
# data[0, 1, 0] -> cat=0, f=200, t=10 -> val for 0.8 detection
return xr.DataArray(
data,
coords={
"category": ["bat", "noise"],
Dimensions.frequency.value: coords[Dimensions.frequency.value],
Dimensions.time.value: coords[Dimensions.time.value],
},
dims=["category", Dimensions.frequency.value, Dimensions.time.value],
name="class_scores",
)
@pytest.fixture
def sample_features_array(sample_data_array):
"""Provides a sample features array matching sample_data_array coords."""
coords = sample_data_array.coords
# Example: (4 features, 3 freqs, 3 times)
data = np.arange(0, 36, dtype=np.float32).reshape(4, 3, 3)
# data[:, 2, 1] -> feats, f=300, t=20 -> vals for 0.9 detection
# data[:, 1, 0] -> feats, f=200, t=10 -> vals for 0.8 detection
return xr.DataArray(
data,
coords={
"feature": ["f0", "f1", "f2", "f3"],
Dimensions.frequency.value: coords[Dimensions.frequency.value],
Dimensions.time.value: coords[Dimensions.time.value],
},
dims=["feature", Dimensions.frequency.value, Dimensions.time.value],
name="features",
)
# --- Tests for extract_values_at_positions ---
def test_extract_values_at_positions_correct(
sample_array_for_extraction, sample_positions_top3
):
"""Verify correct values are extracted based on positions coords."""
# Positions: (f=300, t=20), (f=200, t=10), (f=300, t=30)
# Corresponding values in sample_array_for_extraction (1-9):
# f=300, t=20 -> index (2, 1) -> value 8
# f=200, t=10 -> index (1, 0) -> value 4
# f=300, t=30 -> index (2, 2) -> value 9
expected_values = np.array([8, 4, 9])
print(sample_positions_top3)
expected = xr.DataArray(
expected_values,
coords=sample_positions_top3.coords, # Should inherit coords
dims="detection",
name="test_values", # Should inherit name
)
extracted = extract_values_at_positions(
sample_array_for_extraction, sample_positions_top3
)
xr.testing.assert_allclose(extracted, expected)
def test_extract_values_at_positions_extra_dims(
sample_sizes_array, sample_positions_top2
):
"""Test extraction preserves other dimensions in the source array."""
# Positions: (f=300, t=20), (f=200, t=10)
# Extract from sample_sizes_array (dim, freq, time)
# Det 1 (f=300, t=20) -> index (:, 2, 1) -> values [7, 16]
# Det 2 (f=200, t=10) -> index (:, 1, 0) -> values [3, 12]
# Expected shape: (dimension, detection)
expected_values = np.array([[7.0, 3.0], [16.0, 12.0]], dtype=np.float32)
expected = xr.DataArray(
expected_values,
coords={
"dimension": ["width", "height"],
Dimensions.frequency.value: sample_positions_top2.coords[
Dimensions.frequency.value
],
Dimensions.time.value: sample_positions_top2.coords[
Dimensions.time.value
],
},
dims=["dimension", "detection"],
name="sizes", # Inherits name
)
extracted = extract_values_at_positions(
sample_sizes_array, sample_positions_top2
)
xr.testing.assert_allclose(extracted, expected)
def test_extract_values_at_positions_empty(
sample_array_for_extraction, empty_positions
):
"""Test extraction with empty positions returns empty array."""
extracted = extract_values_at_positions(
sample_array_for_extraction, empty_positions
)
assert extracted.sizes["detection"] == 0
# Check coordinates are also empty but defined
assert Dimensions.time.value in extracted.coords
assert Dimensions.frequency.value in extracted.coords
assert extracted.coords[Dimensions.time.value].size == 0
assert extracted.coords[Dimensions.frequency.value].size == 0
assert extracted.name == sample_array_for_extraction.name
def test_extract_values_at_positions_missing_coord_in_array(
sample_array_for_extraction, sample_positions_top2
):
"""Test error if source array misses required coordinates."""
array_no_time = sample_array_for_extraction.copy()
del array_no_time.coords[Dimensions.time.value]
with pytest.raises(IndexError):
extract_values_at_positions(array_no_time, sample_positions_top2)
array_no_freq = sample_array_for_extraction.copy()
del array_no_freq.coords[Dimensions.frequency.value]
with pytest.raises(IndexError):
extract_values_at_positions(array_no_freq, sample_positions_top2)
def test_extract_values_at_positions_missing_coord_in_positions(
sample_array_for_extraction, sample_positions_top2
):
"""Test error if positions array misses required coordinates."""
positions_no_time = sample_positions_top2.copy()
del positions_no_time.coords[Dimensions.time.value]
with pytest.raises(KeyError):
extract_values_at_positions(
sample_array_for_extraction, positions_no_time
)
positions_no_freq = sample_positions_top2.copy()
del positions_no_freq.coords[Dimensions.frequency.value]
with pytest.raises(KeyError):
extract_values_at_positions(
sample_array_for_extraction, positions_no_freq
)
def test_extract_values_at_positions_mismatched_coords(
sample_array_for_extraction, sample_positions_top2
):
"""Test error if positions requests coords not in source array."""
# Create positions requesting a time=40 not present in sample_array
bad_positions = sample_positions_top2.copy()
bad_positions.coords[Dimensions.time.value] = (
"detection",
np.array([40, 10]), # First time is invalid
)
with pytest.raises(
KeyError
): # xarray.sel raises KeyError for missing labels
extract_values_at_positions(sample_array_for_extraction, bad_positions)
# --- Tests for extract_detection_xr_dataset ---
def test_extract_detection_xr_dataset_correct(
sample_positions_top2,
sample_sizes_array,
sample_classes_array,
sample_features_array,
):
"""Tests extracting and bundling info for top 2 detections."""
actual_dataset = extract_detection_xr_dataset(
sample_positions_top2,
sample_sizes_array,
sample_classes_array,
sample_features_array,
)
# Expected positions (top 2):
# 1. Score 0.9, Time 20, Freq 300. Indices (freq=2, time=1)
# 2. Score 0.8, Time 10, Freq 200. Indices (freq=1, time=0)
expected_times = np.array([20, 10])
expected_freqs = np.array([300, 200])
detection_coords = {
Dimensions.time.value: ("detection", expected_times),
Dimensions.frequency.value: ("detection", expected_freqs),
}
# --- Manually Calculate Expected Data ---
# Scores (already correct in sample_positions_top2)
expected_score = sample_positions_top2.rename(
"scores"
) # Rename to match output
# Dimensions Data (width, height) -> Transposed to (detection, dimension)
# sample_sizes_array data: (dim, freq, time)
# Det 1 (f=300, t=20): index (:, 2, 1) -> values [ 7., 16.]
# Det 2 (f=200, t=10): index (:, 1, 0) -> values [ 3., 12.]
expected_dimensions_data = np.array(
[
[7.0, 16.0], # Detection 1 [width, height]
[3.0, 12.0],
], # Detection 2 [width, height]
dtype=np.float32,
)
expected_dimensions = xr.DataArray(
expected_dimensions_data,
coords={**detection_coords, "dimension": ["width", "height"]},
dims=["detection", "dimension"],
name="dimensions",
)
# Classes Data (bat, noise) -> Transposed to (detection, category)
# sample_classes_array data: np.linspace(0.1, 0.9, 18).reshape(2, 3, 3)
# linspace vals: [0.1, 0.147, 0.194, 0.241, 0.288, 0.335, 0.382, 0.429, 0.476, # cat 0
# 0.523, 0.570, 0.617, 0.664, 0.711, 0.758, 0.805, 0.852, 0.9] # cat 1
# Det 1 (cat, f=2, t=1): index (:, 2, 1) -> values [idx 7=0.429, idx 16=0.852]
# Det 2 (cat, f=1, t=0): index (:, 1, 0) -> values [idx 3=0.241, idx 12=0.664]
expected_classes_data = np.array(
[
[0.42941177, 0.85294118], # Detection 1 [bat_prob, noise_prob]
[0.24117647, 0.66470588],
], # Detection 2 [bat_prob, noise_prob]
dtype=np.float32,
)
expected_classes = xr.DataArray(
expected_classes_data,
coords={**detection_coords, "category": ["bat", "noise"]},
dims=["detection", "category"],
name="classes",
)
# Features Data (f0..f3) -> Transposed to (detection, feature)
# sample_features_array data: np.arange(36).reshape(4, 3, 3)
# Det 1 (feat, f=2, t=1): index (:, 2, 1) -> values [ 7, 16, 25, 34]
# Det 2 (feat, f=1, t=0): index (:, 1, 0) -> values [ 3, 12, 21, 30]
expected_features_data = np.array(
[
[7.0, 16.0, 25.0, 34.0], # Detection 1 [f0, f1, f2, f3]
[3.0, 12.0, 21.0, 30.0],
], # Detection 2 [f0, f1, f2, f3]
dtype=np.float32,
)
expected_features = xr.DataArray(
expected_features_data,
coords={**detection_coords, "feature": ["f0", "f1", "f2", "f3"]},
dims=["detection", "feature"],
name="features",
)
# Construct Expected Dataset
expected_dataset = xr.Dataset(
{
"scores": expected_score,
"dimensions": expected_dimensions,
"classes": expected_classes,
"features": expected_features,
}
)
# Add coords explicitly to ensure they match
expected_dataset = expected_dataset.assign_coords(detection_coords)
# --- Assert Equality ---
xr.testing.assert_allclose(actual_dataset, expected_dataset)
def test_extract_detection_xr_dataset_empty(
empty_positions,
sample_sizes_array,
sample_classes_array,
sample_features_array,
):
"""Test extraction with empty positions yields an empty dataset."""
actual_dataset = extract_detection_xr_dataset(
empty_positions,
sample_sizes_array,
sample_classes_array,
sample_features_array,
)
assert isinstance(actual_dataset, xr.Dataset)
assert "detection" in actual_dataset.dims
assert actual_dataset.dims["detection"] == 0
# Check variables exist and have 0 size along detection dim
assert "scores" in actual_dataset
assert actual_dataset["scores"].dims == ("detection",)
assert actual_dataset["scores"].size == 0
assert "dimensions" in actual_dataset
assert actual_dataset["dimensions"].dims == ("detection", "dimension")
assert actual_dataset["dimensions"].shape == (0, 2) # Check both dims
assert "classes" in actual_dataset
assert actual_dataset["classes"].dims == ("detection", "category")
assert actual_dataset["classes"].shape == (0, 2)
assert "features" in actual_dataset
assert actual_dataset["features"].dims == ("detection", "feature")
assert actual_dataset["features"].shape == (0, 4)
# Check coordinates exist and are empty
assert Dimensions.time.value in actual_dataset.coords
assert Dimensions.frequency.value in actual_dataset.coords
assert actual_dataset.coords[Dimensions.time.value].size == 0
assert actual_dataset.coords[Dimensions.frequency.value].size == 0

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tests/test_postprocessing/test_remapping.py Normal file
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