def test_remove_lytaf_events_from_timeseries(lyra_ts):
"""
Test if artifact are correctly removed from a TimeSeries.
"""
# Check correct errors are raised due to bad input
with pytest.raises(AttributeError):
ts_test = lyra.remove_lytaf_events_from_timeseries(
[], force_use_local_lytaf=True)
# Run remove_artifacts_from_timeseries, returning artifact
# status
ts_test, artifact_status_test = \
lyra.remove_lytaf_events_from_timeseries(
lyra_ts, artifacts=["LAR", "Offpoint"], return_artifacts=True,
force_use_local_lytaf=True)
# Generate expected data by calling _remove_lytaf_events and
# constructing expected dataframe manually.
time, channels, artifact_status_expected = lyra._remove_lytaf_events(
lyra_ts.to_dataframe().index,
channels=[
np.asanyarray(lyra_ts.to_dataframe()["CHANNEL1"]),
np.asanyarray(lyra_ts.to_dataframe()["CHANNEL2"]),
np.asanyarray(lyra_ts.to_dataframe()["CHANNEL3"]),
np.asanyarray(lyra_ts.to_dataframe()["CHANNEL4"])
],
artifacts=["LAR", "Offpoint"],
return_artifacts=True,
force_use_local_lytaf=True)
dataframe_expected = pandas.DataFrame(index=time,
data={
"CHANNEL1": channels[0],
"CHANNEL2": channels[1],
"CHANNEL3": channels[2],
"CHANNEL4": channels[3]
})
# Assert expected result is returned
pandas.testing.assert_frame_equal(ts_test.to_dataframe(),
dataframe_expected)
assert artifact_status_test.keys() == artifact_status_expected.keys()
np.testing.assert_array_equal(artifact_status_test["lytaf"],
artifact_status_expected["lytaf"])
np.testing.assert_array_equal(artifact_status_test["removed"],
artifact_status_expected["removed"])
np.testing.assert_array_equal(artifact_status_test["not_removed"],
artifact_status_expected["not_removed"])
assert artifact_status_test["not_found"] == \
artifact_status_expected["not_found"]
# Run remove_artifacts_from_timeseries, without returning
# artifact status
ts_test = \
lyra.remove_lytaf_events_from_timeseries(
lyra_ts, artifacts=["LAR", "Offpoint"],
force_use_local_lytaf=True)
# Assert expected result is returned
pandas.testing.assert_frame_equal(ts_test.to_dataframe(),
dataframe_expected)
def test_remove_lytaf_events_from_timeseries(dtype):
"""Test if artefacts are correctly removed from a TimeSeries.
Also test LYRALightCurve for backwards compatibility."""
# Check correct errors are raised due to bad input
with pytest.raises(AttributeError):
ts_test = lyra.remove_lytaf_events_from_timeseries(
[], lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Run remove_artifacts_from_timeseries, returning artifact
# status
ts = get_lyradata(dtype)
ts_test, artifact_status_test = \
lyra.remove_lytaf_events_from_timeseries(
ts, artifacts=["LAR", "Offpoint"], return_artifacts=True,
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Generate expected data by calling _remove_lytaf_events and
# constructing expected dataframe manually.
time, channels, artifact_status_expected = lyra._remove_lytaf_events(
ts.data.index,
channels=[
np.asanyarray(ts.data["CHANNEL1"]),
np.asanyarray(ts.data["CHANNEL2"]),
np.asanyarray(ts.data["CHANNEL3"]),
np.asanyarray(ts.data["CHANNEL4"])
],
artifacts=["LAR", "Offpoint"],
return_artifacts=True,
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
dataframe_expected = pandas.DataFrame(index=time,
data={
"CHANNEL1": channels[0],
"CHANNEL2": channels[1],
"CHANNEL3": channels[2],
"CHANNEL4": channels[3]
})
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(ts_test.data, dataframe_expected)
assert artifact_status_test.keys() == artifact_status_expected.keys()
np.testing.assert_array_equal(artifact_status_test["lytaf"],
artifact_status_expected["lytaf"])
np.testing.assert_array_equal(artifact_status_test["removed"],
artifact_status_expected["removed"])
np.testing.assert_array_equal(artifact_status_test["not_removed"],
artifact_status_expected["not_removed"])
assert artifact_status_test["not_found"] == \
artifact_status_expected["not_found"]
# Run remove_artifacts_from_timeseries, without returning
# artifact status
ts_test = \
lyra.remove_lytaf_events_from_timeseries(
ts, artifacts=["LAR", "Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(ts_test.data, dataframe_expected)
def test_remove_lytaf_events_from_timeseries(dtype):
"""Test if artefacts are correctly removed from a TimeSeries.
Also test LYRALightCurve for backwards compatibility."""
# Check correct errors are raised due to bad input
with pytest.raises(AttributeError):
ts_test = lyra.remove_lytaf_events_from_timeseries(
[], lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Run remove_artifacts_from_timeseries, returning artifact
# status
ts = get_lyradata(dtype)
ts_test, artifact_status_test = \
lyra.remove_lytaf_events_from_timeseries(
ts, artifacts=["LAR", "Offpoint"], return_artifacts=True,
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Generate expected data by calling _remove_lytaf_events and
# constructing expected dataframe manually.
time, channels, artifact_status_expected = lyra._remove_lytaf_events(
ts.data.index, channels=[np.asanyarray(ts.data["CHANNEL1"]),
np.asanyarray(ts.data["CHANNEL2"]),
np.asanyarray(ts.data["CHANNEL3"]),
np.asanyarray(ts.data["CHANNEL4"])],
artifacts=["LAR", "Offpoint"], return_artifacts=True,
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
dataframe_expected = pandas.DataFrame(index=time,
data={"CHANNEL1": channels[0],
"CHANNEL2": channels[1],
"CHANNEL3": channels[2],
"CHANNEL4": channels[3]})
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(ts_test.data, dataframe_expected)
assert artifact_status_test.keys() == artifact_status_expected.keys()
np.testing.assert_array_equal(artifact_status_test["lytaf"],
artifact_status_expected["lytaf"])
np.testing.assert_array_equal(artifact_status_test["removed"],
artifact_status_expected["removed"])
np.testing.assert_array_equal(artifact_status_test["not_removed"],
artifact_status_expected["not_removed"])
assert artifact_status_test["not_found"] == \
artifact_status_expected["not_found"]
# Run remove_artifacts_from_timeseries, without returning
# artifact status
ts_test = \
lyra.remove_lytaf_events_from_timeseries(
ts, artifacts=["LAR", "Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(ts_test.data, dataframe_expected)