def test_remove_lytaf_events_2():
"""
Test _remove_lytaf_events() with no user artifacts found.
"""
# Run _remove_lytaf_events
with pytest.warns(UserWarning,
match='None of user supplied artifacts were found.'):
time_test, channels_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, channels=CHANNELS, artifacts="Offpoint",
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Generated expected result
time_expected = TIME
channels_expected = CHANNELS
artifacts_status_expected = {
"lytaf": LYTAF_TEST,
"removed": EMPTY_LYTAF,
"not_removed": LYTAF_TEST,
"not_found": ["Offpoint"]
}
# Assert test values are same as expected
assert np.all(time_test == time_expected)
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
# Test correct values are returned when return_artifacts kwarg not
# supplied.
# Case 1: channels kwarg is True
# Run _remove_lytaf_events
with pytest.warns(UserWarning,
match='None of user supplied artifacts were found.'):
time_test, channels_test = lyra._remove_lytaf_events(
TIME,
channels=CHANNELS,
artifacts=["Offpoint"],
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
assert np.all(time_test == time_expected)
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
# Case 2: channels kwarg is False
# Run _remove_lytaf_events
with pytest.warns(UserWarning,
match='None of user supplied artifacts were found.'):
time_test = lyra._remove_lytaf_events(TIME,
artifacts=["Offpoint"],
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
assert np.all(time_test == time_expected)
def test_remove_lytaf_events_1():
"""
Test _remove_lytaf_events() with some artifacts found and others not.
"""
# Run _remove_lytaf_events
time_test, channels_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, channels=CHANNELS, artifacts=["LAR", "Offpoint"],
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Generated expected result
bad_indices = np.logical_and(TIME >= LYTAF_TEST["begin_time"][0],
TIME <= LYTAF_TEST["end_time"][0])
bad_indices = np.arange(len(TIME))[bad_indices]
time_expected = np.delete(TIME, bad_indices)
channels_expected = [
np.delete(CHANNELS[0], bad_indices),
np.delete(CHANNELS[1], bad_indices)
]
artifacts_status_expected = {
"lytaf": LYTAF_TEST,
"removed": LYTAF_TEST[0],
"not_removed": LYTAF_TEST[1],
"not_found": ["Offpoint"]
}
# Assert test values are same as expected
assert time_test.all() == time_expected.all()
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
# Test that correct values are returned when channels kwarg not
# supplied.
# Run _remove_lytaf_events
time_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, artifacts=["LAR", "Offpoint"],
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Assert test values are same as expected
assert time_test.all() == time_expected.all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
def test_remove_lytaf_events_1():
"""Test _remove_lytaf_events() with some artifacts found and others not."""
# Run _remove_lytaf_events
time_test, channels_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, channels=CHANNELS, artifacts=["LAR", "Offpoint"],
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Generated expected result
bad_indices = np.logical_and(TIME >= LYTAF_TEST["begin_time"][0],
TIME <= LYTAF_TEST["end_time"][0])
bad_indices = np.arange(len(TIME))[bad_indices]
time_expected = np.delete(TIME, bad_indices)
channels_expected = [np.delete(CHANNELS[0], bad_indices),
np.delete(CHANNELS[1], bad_indices)]
artifacts_status_expected = {"lytaf": LYTAF_TEST, "removed": LYTAF_TEST[0],
"not_removed": LYTAF_TEST[1],
"not_found": ["Offpoint"]}
# Assert test values are same as expected
assert time_test.all() == time_expected.all()
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
# Test that correct values are returned when channels kwarg not
# supplied.
# Run _remove_lytaf_events
time_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, artifacts=["LAR", "Offpoint"],
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Assert test values are same as expected
assert time_test.all() == time_expected.all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
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_2():
"""Test _remove_lytaf_events() with no user artifacts found."""
# Run _remove_lytaf_events
time_test, channels_test, artifacts_status_test = \
lyra._remove_lytaf_events(
TIME, channels=CHANNELS, artifacts="Offpoint",
return_artifacts=True, lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
# Generated expected result
time_expected = TIME
channels_expected = CHANNELS
artifacts_status_expected = {"lytaf": LYTAF_TEST, "removed": EMPTY_LYTAF,
"not_removed": LYTAF_TEST,
"not_found": ["Offpoint"]}
# Assert test values are same as expected
assert time_test.all() == time_expected.all()
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
assert artifacts_status_test.keys() == artifacts_status_expected.keys()
np.testing.assert_array_equal(artifacts_status_test["lytaf"],
artifacts_status_expected["lytaf"])
np.testing.assert_array_equal(artifacts_status_test["removed"],
artifacts_status_expected["removed"])
np.testing.assert_array_equal(artifacts_status_test["not_removed"],
artifacts_status_expected["not_removed"])
assert artifacts_status_test["not_found"] == \
artifacts_status_expected["not_found"]
# Test correct values are returned when return_artifacts kwarg not
# supplied.
# Case 1: channels kwarg is True
# Run _remove_lytaf_events
time_test, channels_test = lyra._remove_lytaf_events(
TIME, channels=CHANNELS, artifacts=["Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
assert time_test.all() == time_expected.all()
assert (channels_test[0]).all() == (channels_expected[0]).all()
assert (channels_test[1]).all() == (channels_expected[1]).all()
# Case 2: channels kwarg is False
# Run _remove_lytaf_events
time_test = lyra._remove_lytaf_events(
TIME, artifacts=["Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
assert time_test.all() == time_expected.all()
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_lightcurve():
"""Test if artefacts are correctly removed from a LYRAlightCurve."""
# Create sample LYRALightCurve
lyralc = lightcurve.LYRALightCurve.create("2014-01-01")
lyralc.data = pandas.DataFrame(index=TIME,
data={"CHANNEL1": CHANNELS[0],
"CHANNEL2": CHANNELS[1],
"CHANNEL3": CHANNELS[0],
"CHANNEL4": CHANNELS[1]})
# Check correct errors are raised due to bad input
with pytest.raises(TypeError):
lyralc_test = lyra.remove_lytaf_events_from_lightcurve(
[], lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Run remove_artifacts_from_lyralightcurve, returning artifact
# status
lyralc_test, artifact_status_test = \
lyra.remove_lytaf_events_from_lightcurve(
lyralc, 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(
lyralc.data.index, channels=[np.asanyarray(lyralc.data["CHANNEL1"]),
np.asanyarray(lyralc.data["CHANNEL2"]),
np.asanyarray(lyralc.data["CHANNEL3"]),
np.asanyarray(lyralc.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(lyralc_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_lyralightcurve, without returning
# artifact status
lyralc_test = \
lyra.remove_lytaf_events_from_lightcurve(
lyralc, artifacts=["LAR", "Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(lyralc_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)
def test_remove_lytaf_events_3(local_cache):
"""
Test if correct errors are raised by _remove_lytaf_events().
"""
with pytest.raises(TypeError):
lyra._remove_lytaf_events(TIME, channels=6, artifacts=["LAR"],
force_use_local_lytaf=True)
with pytest.raises(ValueError):
lyra._remove_lytaf_events(TIME,
force_use_local_lytaf=True)
with pytest.raises(TypeError):
lyra._remove_lytaf_events(TIME, artifacts=[6],
force_use_local_lytaf=True)
with pytest.raises(ValueError):
lyra._remove_lytaf_events(TIME,
artifacts=["LAR", "incorrect artifact type"],
force_use_local_lytaf=True)
def test_remove_lytaf_events_3():
"""Test if correct errors are raised by _remove_lytaf_events()."""
with pytest.raises(TypeError):
lyra._remove_lytaf_events(TIME, channels=6, artifacts=["LAR"],
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
with pytest.raises(ValueError):
lyra._remove_lytaf_events(TIME,
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
with pytest.raises(TypeError):
lyra._remove_lytaf_events(TIME, artifacts=[6],
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
with pytest.raises(ValueError):
lyra._remove_lytaf_events(TIME,
artifacts=["LAR","incorrect artifact type"],
lytaf_path=TEST_DATA_PATH,
force_use_local_lytaf=True)
def test_remove_lytaf_events_from_lightcurve():
"""Test if artefacts are correctly removed from a LYRAlightCurve."""
# Create sample LYRALightCurve
lyralc = lightcurve.LYRALightCurve.create("2014-01-01")
lyralc.data = pandas.DataFrame(index=TIME,
data={
"CHANNEL1": CHANNELS[0],
"CHANNEL2": CHANNELS[1],
"CHANNEL3": CHANNELS[0],
"CHANNEL4": CHANNELS[1]
})
# Check correct errors are raised due to bad input
with pytest.raises(TypeError):
lyralc_test = lyra.remove_lytaf_events_from_lightcurve(
[], lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Run remove_artifacts_from_lyralightcurve, returning artifact
# status
lyralc_test, artifact_status_test = \
lyra.remove_lytaf_events_from_lightcurve(
lyralc, 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(
lyralc.data.index,
channels=[
np.asanyarray(lyralc.data["CHANNEL1"]),
np.asanyarray(lyralc.data["CHANNEL2"]),
np.asanyarray(lyralc.data["CHANNEL3"]),
np.asanyarray(lyralc.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(lyralc_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_lyralightcurve, without returning
# artifact status
lyralc_test = \
lyra.remove_lytaf_events_from_lightcurve(
lyralc, artifacts=["LAR", "Offpoint"],
lytaf_path=TEST_DATA_PATH, force_use_local_lytaf=True)
# Assert expected result is returned
pandas.util.testing.assert_frame_equal(lyralc_test.data,
dataframe_expected)