def test_angles_creation():
dims = ("axis", "channel", "time")
array = Angles()
np.testing.assert_array_equal(x=array, y=xr.DataArray())
assert array.dims == dims
array = Angles(MARKERS_DATA.values, time=MARKERS_DATA.time)
is_expected_array(array, **EXPECTED_VALUES[57])
size = 10, 10, 100
array = Angles.from_random_data(size=size)
assert array.shape == size
assert array.dims == dims
with pytest.raises(ValueError):
Angles(ANALOGS_DATA)
def test_rotate():
n_frames = 100
n_markers = 10
angles = Angles.from_random_data(size=(3, 1, n_frames))
rt = Rototrans.from_euler_angles(angles, "xyz")
markers = Markers.from_random_data(size=(3, n_markers, n_frames))
rotated_markers = Markers.from_rototrans(markers, rt)
expected_rotated_marker = np.ndarray((4, n_markers, n_frames))
for marker in range(n_markers):
for frame in range(n_frames):
expected_rotated_marker[:, marker, frame] = np.dot(
rt.isel(time=frame),
markers.isel(channel=marker, time=frame),
)
np.testing.assert_array_almost_equal(rotated_markers,
expected_rotated_marker,
decimal=10)
rotated_markers = Markers.from_rototrans(markers.isel(time=0),
rt.isel(time=0))
expected_rotated_marker = np.ndarray(rotated_markers.shape)
for marker in range(n_markers):
expected_rotated_marker[:, marker] = np.dot(
rt.isel(time=0), markers.isel(channel=marker, time=0))
np.testing.assert_array_almost_equal(rotated_markers,
expected_rotated_marker,
decimal=10)
rotated_markers = Markers.from_rototrans(markers, rt.isel(time=0))
expected_rotated_marker = np.ndarray(rotated_markers.shape)
for marker in range(n_markers):
expected_rotated_marker[:,
marker] = np.dot(rt.isel(time=0),
markers.isel(channel=marker))
np.testing.assert_array_almost_equal(rotated_markers,
expected_rotated_marker,
decimal=10)
with pytest.raises(ValueError):
Markers.from_rototrans(markers.isel(time=0), rt)
def test_average_rt():
seq = "xyz"
angles_size = (3, 1, 100)
ref_angles_from_rt_mean = [0.25265133, 0.57436872, 0.79133042]
restart_seed()
angles = Angles.from_random_data(size=angles_size)
# min-max normalization to keep the angles low
angles = angles.pipe(lambda x: (x - x.min()) / (x.max() - x.min()))
rt = Rototrans.from_euler_angles(angles, seq)
rt_mean = Rototrans.from_averaged_rototrans(rt)
angles_from_rt_mean = Angles.from_rototrans(rt_mean, seq)
np.testing.assert_almost_equal(angles_from_rt_mean.data.ravel(),
ref_angles_from_rt_mean,
decimal=4)
def test_rt_transpose():
n_frames = 10
angles = Angles.from_random_data(size=(3, 1, n_frames))
rt = Rototrans.from_euler_angles(angles, angle_sequence="xyz")
rt_t = Rototrans.from_transposed_rototrans(rt)
rt_t_expected = np.zeros((4, 4, n_frames))
rt_t_expected[3, 3, :] = 1
for row in range(rt.row.size):
for col in range(rt.col.size):
for frame in range(rt.time.size):
rt_t_expected[col, row, frame] = rt[row, col, frame]
for frame in range(rt.time.size):
rt_t_expected[:3, 3, frame] = -rt_t_expected[:3, :3, frame].dot(
rt[:3, 3, frame])
np.testing.assert_array_almost_equal(rt_t, rt_t_expected, decimal=10)
def from_random_data(cls,
distribution: str = "normal",
size: tuple = (3, 1, 100),
**kwargs) -> xr.DataArray:
"""
Create random data from a specified distribution (normal by default) using random walk.
Arguments:
distribution: Distribution available in
[numpy.random](https://docs.scipy.org/doc/numpy-1.14.0/reference/routines.random.html#distributions)
size: Shape of the desired array
kwargs: Keyword argument(s) to be passed to numpy.random.`distribution`
Returns:
Random rototrans `xarray.DataArray` sampled from a given distribution
!!! example
To instantiate a `Rototrans` with some random data sampled from a normal distribution:
```python
from pyomeca import Rototrans
n_frames = 100
size = 4, 4, n_frames
rt = Rototrans.from_random_data(size=size)
```
You can choose any distribution available in
[numpy.random](https://docs.scipy.org/doc/numpy-1.14.0/reference/routines.random.html#distributions):
```python
rt = Rototrans.from_random_data(distribution="uniform", size=size, low=1, high=10)
```
"""
return Rototrans.from_euler_angles(
Angles.from_random_data(distribution,
size=(3, 1, size[-1]),
**kwargs),
"xyz",
)