def test_bad_table_spec_save_tables():
ref_data = get_parquet_test_data("faros-internal.parquet", 666)
target_data = get_parquet_test_data("faros-internal.parquet", 667)
reference_signal, target_signal = "Internal", "Faros"
sources = {
reference_signal: {"data": ref_data, "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_data, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=17)
extractor.end_window_length = pd.Timedelta(seconds=10)
extractor.min_length = 3
extractor.threshold = 0.19
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
with tempfile.TemporaryDirectory() as tmp_dir:
with pytest.raises(ValueError):
synchronizer.save_data(
tmp_dir, tables={"N/A": {"Faros": ["N/A"]}}, save_total_table=False
)
with pytest.raises(ValueError):
synchronizer.save_data(
tmp_dir,
tables={"N/A": {"N/A": ["ACCELERATION_Y"]}},
save_total_table=False,
)
def test_happy_path_faros_internal():
ref_data = get_parquet_test_data("faros-internal.parquet", 666)
target_data = get_parquet_test_data("faros-internal.parquet", 667)
reference_signal, target_signal = "Internal", "Faros"
sources = {
reference_signal: {"data": ref_data, "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_data, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=17)
extractor.end_window_length = pd.Timedelta(seconds=10)
extractor.min_length = 3
extractor.threshold = 0.19
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
sync_result = synchronizer.get_sync_params()
assert (
sync_result[reference_signal]["timeshift"] is None
), "Should not have timeshift for reference signal"
assert (
sync_result[reference_signal]["stretch_factor"] == 1
), "Should not stretch reference signal"
assert sync_result[target_signal]["timeshift"] == pd.Timedelta(
"-1 days +23:59:59.070000"
), "Should have timeshift of 0 for equal signal"
assert (
sync_result[target_signal]["stretch_factor"] == 1.0506424792139077
), "Should have stretching factor of 1 for equal signal"
def test_plot_segments(mocked_show):
base_data = get_parquet_test_data("test-data.parquet")
reference_signal, target_signal = "A", "B"
sources = {
reference_signal: {
"data": base_data.copy(),
"ref_column": "ACCELERATION_Z"
},
target_signal: {
"data": base_data,
"ref_column": "ACCELERATION_Z"
},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=5)
extractor.end_window_length = pd.Timedelta(seconds=3)
extractor.min_length = 3
extractor.threshold = 0.5
synchronizer = Synchronizer(sources, reference_signal, extractor)
segments = extractor.get_segments(
get_equidistant_signals(synchronizer.ref_signals,
synchronizer.sampling_freq))
with pytest.raises(ValueError):
plot_segments(synchronizer.ref_signals, segments)
plot_segments(synchronizer.ref_signals, segments, together=True)
plot_segments(synchronizer.ref_signals, segments, separate=True)
def test_happy_path_shifted_data():
base_data = get_parquet_test_data("test-data.parquet")
reference_signal, target_signal = "A", "B"
target_df = base_data.shift(-22, freq="100ms")
sources = {
reference_signal: {"data": base_data.copy(), "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_df, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=5)
extractor.end_window_length = pd.Timedelta(seconds=3)
extractor.min_length = 3
extractor.threshold = 0.5
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
sync_result = synchronizer.get_sync_params()
assert (
sync_result[reference_signal]["timeshift"] is None
), "Should not have timeshift for reference signal"
assert (
sync_result[reference_signal]["stretch_factor"] == 1
), "Should not stretch reference signal"
assert sync_result[target_signal]["timeshift"] == pd.Timedelta(
"0 days 00:00:02.197549725"
), "Should have timeshift of 0 for equal signal"
assert (
sync_result[target_signal]["stretch_factor"] == 1
), "Should have stretching factor of 1 for equal signal"
def test_happy_path_save_tables():
ref_data = get_parquet_test_data("faros-internal.parquet", 666)
target_data = get_parquet_test_data("faros-internal.parquet", 667)
reference_signal, target_signal = "Internal", "Faros"
sources = {
reference_signal: {"data": ref_data, "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_data, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=17)
extractor.end_window_length = pd.Timedelta(seconds=10)
extractor.min_length = 3
extractor.threshold = 0.19
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
acc_columns = ["ACCELERATION_X", "ACCELERATION_Y", "ACCELERATION_Z"]
with tempfile.TemporaryDirectory() as tmp_dir:
tables = {
"ACC": {"Faros": acc_columns, "Internal": acc_columns},
"ECG": {"Faros": ["ECG"]},
}
synchronizer.save_data(tmp_dir, tables=tables, save_total_table=False)
for file in ["ACC", "ECG", "SYNC"]:
file_path = os.path.join(tmp_dir, f"{file}.csv")
assert os.path.isfile(file_path), f"{file_path} should exist"
df = pd.read_csv(file_path)
if file == "ACC":
assert len(df) == 4115, "Should have saved all acc values"
assert "timestamp" in df.columns, "Should have saved timestamp column"
for col in acc_columns:
for device in tables["ACC"]:
assert (
f"{device}_{col}" in df.columns
), f"Should have saved {device}_{col}"
elif file == "ECG":
assert len(df) == 15100, "Should have saved all ecg values"
assert "timestamp" in df.columns, "Should have saved timestamp column"
elif file == "SYNC":
for source in ["Faros", "Internal"]:
assert (
source in df.columns
), f"Should have saved {source} in SYNC.csv"
assert "Unnamed: 0" in df.columns, "Should have saved index column"
def test_infer_freq():
test_data = get_parquet_test_data("faros-internal.parquet", 667)
assert infer_freq(test_data["ECG"]) == 500, "ECG should be 500 Hz"
assert infer_freq(
test_data["ACCELERATION_X"]) == 100, "Acc. should be 100 Hz"
assert infer_freq(
test_data["ACCELERATION_Y"]) == 100, "Acc. should be 100 Hz"
assert infer_freq(
test_data["ACCELERATION_Z"]) == 100, "Acc. should be 100 Hz"
def test_get_equidistant_signals():
test_data = get_parquet_test_data("faros-internal.parquet", 667)
result = get_equidistant_signals(test_data, frequency=1_000)
for col in result.columns:
assert infer_freq(result[col]) == 1000, f"{col} should have 1000 Hz"
result = get_equidistant_signals(test_data, frequency=500)
for col in result.columns:
assert infer_freq(result[col]) == 500, f"{col} should have 500 Hz"
result = get_equidistant_signals(test_data, frequency=1)
for col in result.columns:
assert infer_freq(result[col]) == 1, f"{col} should have 1 Hz"
def test_plot_reference_columns(mocked_show):
base_data = get_parquet_test_data("test-data.parquet")
reference_signal, target_signal = "A", "B"
sources = {
reference_signal: {
"data": base_data.copy(),
"ref_column": "ACCELERATION_Z"
},
target_signal: {
"data": base_data,
"ref_column": "ACCELERATION_Z"
},
}
plot_reference_columns(sources)
def test_happy_path_save_total_table():
ref_data = get_parquet_test_data("faros-internal.parquet", 666)
target_data = get_parquet_test_data("faros-internal.parquet", 667)
reference_signal, target_signal = "Internal", "Faros"
sources = {
reference_signal: {"data": ref_data, "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_data, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=17)
extractor.end_window_length = pd.Timedelta(seconds=10)
extractor.min_length = 3
extractor.threshold = 0.19
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
with tempfile.TemporaryDirectory() as tmp_dir:
synchronizer.save_data(tmp_dir, tables=None, save_total_table=True)
file_path = os.path.join(tmp_dir, "TOTAL.csv")
assert os.path.isfile(file_path), f"{file_path} should exist"
df = pd.read_csv(file_path)
assert len(df.columns) == 21, "Should save all sensors from internal and faros"
assert len(df) == 18518, "Should create exact number of synced result items"
def test_happy_path_save_pickles():
ref_data = get_parquet_test_data("faros-internal.parquet", 666)
target_data = get_parquet_test_data("faros-internal.parquet", 667)
reference_signal, target_signal = "Internal", "Faros"
sources = {
reference_signal: {"data": ref_data, "ref_column": "ACCELERATION_Z"},
target_signal: {"data": target_data, "ref_column": "ACCELERATION_Z"},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=17)
extractor.end_window_length = pd.Timedelta(seconds=10)
extractor.min_length = 3
extractor.threshold = 0.19
synchronizer = jointly.Synchronizer(sources, reference_signal, extractor)
with tempfile.TemporaryDirectory() as tmp_dir:
synchronizer.save_pickles(tmp_dir)
synced_data = synchronizer.get_synced_data()
for signal, signal_df in synced_data.items():
pickle_path = os.path.join(tmp_dir, f"{signal.upper()}.PICKLE")
assert os.path.isfile(pickle_path)
assert pd.read_pickle(pickle_path).equals(signal_df)
def test_get_max_ref_frequency():
test_data = get_parquet_test_data("faros-internal.parquet", 667)
assert get_max_ref_frequency(test_data) == 500, "max(all) should be 500 Hz"
assert (get_max_ref_frequency(
test_data[["ACCELERATION_X",
"ACCELERATION_Y"]]) == 100), "max(acc) should be 100 Hz"
assert (get_max_ref_frequency(
test_data["ACCELERATION_Y"].to_frame()) == 100
), "max(acc) should be 100 Hz"
with pytest.raises(ValueError):
get_max_ref_frequency(test_data["ACCELERATION_X"])
with pytest.raises(ValueError):
get_max_ref_frequency(pd.DataFrame())
def test_window_length_error():
base_data = get_parquet_test_data("test-data.parquet")
reference_signal, target_signal = "A", "B"
sources = {
reference_signal: {
"data": base_data.copy(),
"ref_column": "ACCELERATION_Z"
},
target_signal: {
"data": base_data,
"ref_column": "ACCELERATION_Z"
},
}
extractor = ShakeExtractor()
extractor.start_window_length = pd.Timedelta(seconds=50)
extractor.end_window_length = pd.Timedelta(seconds=3)
extractor.min_length = 3
extractor.threshold = 0.5
with pytest.raises(BadWindowException):
Synchronizer(sources, reference_signal, extractor).get_sync_params()