def test_read_analogs(
usecols,
shape_val,
first_last_val,
mean_val,
median_val,
sum_val,
nans_val,
extension,
):
if extension == "csv":
data = Analogs.from_csv(**analogs_csv_kwargs, usecols=usecols)
decimal = 0
elif extension == "c3d":
data = Analogs.from_c3d(MARKERS_ANALOGS_C3D,
prefix_delimiter=".",
usecols=usecols)
decimal = 4
else:
raise ValueError("wrong extension provided")
if usecols and isinstance(usecols[0], str):
np.testing.assert_array_equal(x=data.channel, y=usecols)
is_expected_array(
data,
shape_val,
first_last_val,
mean_val,
median_val,
sum_val,
nans_val,
decimal=decimal,
)
def test_csv_trailing_columns():
last_column_to_remove = 5
ref = Analogs.from_csv(
**analogs_csv_kwargs).channel[:-last_column_to_remove]
without_last_columns = Analogs.from_csv(
**analogs_csv_kwargs, trailing_columns=last_column_to_remove).channel
np.testing.assert_array_equal(x=ref, y=without_last_columns)
def test_proc_filters():
freq = ANALOGS_DATA.rate
order = 2
is_expected_array(
ANALOGS_DATA.meca.low_pass(order=order, cutoff=5, freq=freq),
**EXPECTED_VALUES[32],
)
is_expected_array(
ANALOGS_DATA.meca.low_pass(order=order, cutoff=5),
**EXPECTED_VALUES[32],
)
is_expected_array(
ANALOGS_DATA.meca.high_pass(order=order, cutoff=100),
**EXPECTED_VALUES[33],
)
is_expected_array(
ANALOGS_DATA.meca.band_pass(order=order, cutoff=[10, 200]),
**EXPECTED_VALUES[34],
)
is_expected_array(
ANALOGS_DATA.meca.band_stop(order=order, cutoff=[40, 60]),
**EXPECTED_VALUES[35],
)
freq = MARKERS_DATA.rate
is_expected_array(
MARKERS_DATA.meca.low_pass(freq=freq, order=order, cutoff=5),
**EXPECTED_VALUES[36],
)
is_expected_array(
MARKERS_DATA.meca.low_pass(order=order, cutoff=5),
**EXPECTED_VALUES[36],
)
is_expected_array(
MARKERS_DATA.meca.high_pass(order=order, cutoff=10),
**EXPECTED_VALUES[37],
)
is_expected_array(
MARKERS_DATA.meca.band_pass(order=order, cutoff=[1, 10]),
**EXPECTED_VALUES[38],
)
is_expected_array(
MARKERS_DATA.meca.band_stop(order=order, cutoff=[5, 6]),
**EXPECTED_VALUES[39],
)
with pytest.raises(ValueError):
Analogs.from_random_data().meca.band_stop(order=order, cutoff=[5, 6])
def test_analogs_creation():
dims = ("channel", "time")
array = Analogs()
np.testing.assert_array_equal(x=array, y=xr.DataArray())
assert array.dims == dims
array = Analogs(ANALOGS_DATA.values)
is_expected_array(array, **EXPECTED_VALUES[56])
size = 10, 100
array = Analogs.from_random_data(size=size)
assert array.shape == size
assert array.dims == dims
with pytest.raises(ValueError):
Analogs(MARKERS_DATA)
def test_csv_without_header():
is_expected_array(
Analogs.from_csv(ANALOGS_CSV, first_row=5, first_column=2),
**EXPECTED_VALUES[59],
)
is_expected_array(
Markers.from_csv(MARKERS_CSV_WITHOUT_HEADER, first_column=2),
**EXPECTED_VALUES[58],
)
def test_read_catch_error(
usecols,
extension,
):
with pytest.raises(IndexError):
Markers.from_csv(MARKERS_CSV)
if extension == "csv":
with pytest.raises(ValueError):
Analogs.from_csv(**analogs_csv_kwargs, usecols=usecols)
with pytest.raises(ValueError):
Markers.from_csv(**markers_csv_kwargs, usecols=usecols)
elif extension == "c3d":
with pytest.raises(ValueError):
Analogs.from_c3d(MARKERS_ANALOGS_C3D, usecols=usecols)
reader = getattr(Markers, f"from_{extension}")
with pytest.raises(ValueError):
reader(MARKERS_ANALOGS_C3D, usecols=usecols)
def test_read_xlsx():
is_expected_array(
Markers.from_excel(**{
**markers_csv_kwargs,
**dict(filename=MARKERS_XLSX)
}),
**EXPECTED_VALUES[60],
)
is_expected_array(
Analogs.from_excel(**{
**analogs_csv_kwargs,
**dict(filename=ANALOGS_XLSX)
}),
**EXPECTED_VALUES[61],
)
def test_csv_edge_cases():
time_with_rate = Analogs.from_csv(**analogs_csv_kwargs,
attrs={
"rate": 2000
}).time
assert time_with_rate[-1] == 5.7995
time_column_with_id = Analogs.from_csv(**analogs_csv_kwargs,
time_column="Frame")
time_column_with_name = Analogs.from_csv(**analogs_csv_kwargs,
time_column=0)
np.testing.assert_array_equal(time_column_with_id.time,
time_column_with_name.time)
np.testing.assert_array_equal(time_column_with_id, time_column_with_name)
with pytest.raises(ValueError):
Analogs.from_csv(**analogs_csv_kwargs, time_column=[20.0])
assert Analogs.from_csv(**analogs_csv_kwargs,
prefix_delimiter="G",
suffix_delimiter="M").shape == (38, 11600)
MARKERS_CSV = DATA_FOLDER / "markers.csv"
MARKERS_ANALOGS_C3D = DATA_FOLDER / "markers_analogs.c3d"
ANALOGS_CSV = DATA_FOLDER / "analogs.csv"
MARKERS_CSV_WITHOUT_HEADER = DATA_FOLDER / "markers_without_header.csv"
MARKERS_XLSX = DATA_FOLDER / "markers.xlsx"
MARKERS_TRC = DATA_FOLDER / "markers.trc"
ANALOGS_XLSX = DATA_FOLDER / "analogs.xlsx"
ANALOGS_STO = DATA_FOLDER / "inverse_dyn.sto"
ANALOGS_MOT = DATA_FOLDER / "inverse_kin.mot"
EXPECTED_VALUES_CSV = DATA_FOLDER / "is_expected_array_val.csv"
MARKERS_DATA = Markers.from_c3d(
MARKERS_ANALOGS_C3D,
usecols=["CLAV_post", "PSISl", "STERr", "CLAV_post"],
prefix_delimiter=":",
)
ANALOGS_DATA = Analogs.from_c3d(
MARKERS_ANALOGS_C3D,
usecols=["EMG1", "EMG10", "EMG11", "EMG12"],
prefix_delimiter=".",
)
EXPECTED_VALUES = pd.read_csv(
EXPECTED_VALUES_CSV,
index_col=[0],
converters={
"shape_val": eval,
"first_last_val": eval
},
).to_dict("index")
# --- EMG --- #
muscles_names = [
"pect.IM EMG7",
"deltpost.EMG3",
"triceps.EMG5",
"deltant.EMG1",
"deltmed.EMG2",
"ssp.EMG8",
"isp.EMG9",
"subs.EMG10",
"biceps.EMG4",
"uptrap.EMG6",
]
a = Analogs.from_c3d(data_path, usecols=muscles_names)
emg_rate = int(a.rate)
emg = (
a.meca.band_pass(order=4,
cutoff=[10, 425]).meca.center().meca.abs().meca.low_pass(
order=4, cutoff=5, freq=a.rate)
# .meca.normalize()
)
# emg.plot(x="time", col="channel", col_wrap=3)
# plt.show()
# emg.meca.to_matlab("EMG_flex3.mat")
# --- Markers --- #
markers_full_names = [
"XIPH", "STER", "STERback", "CLAV_SC", "CLAV_AC", "SCAP_CP", "SCAP_AA",
"SCAP_TS", "SCAP_IA", "DELT", "ARMl", "EPICl", "EPICm", "LARM_ant",
def test_read_mot():
is_expected_array(Analogs.from_mot(ANALOGS_MOT), **EXPECTED_VALUES[63])
def test_read_sto():
is_expected_array(Analogs.from_sto(ANALOGS_STO), **EXPECTED_VALUES[62])
with pytest.raises(IndexError):
Analogs.from_sto(ANALOGS_CSV)