def _specific_angular_momentum_z(field, data):
xv, yv, zv = obtain_relative_velocity_vector(data)
rv = obtain_position_vector(data)
units = rv.units
rv = np.rollaxis(rv, 0, len(rv.shape))
rv = data.ds.arr(rv, units=units)
return rv[..., 0] * yv - rv[..., 1] * xv
def get_periodic_rvec(data):
coords = obtain_position_vector(data).d
if sum(data.ds.periodicity) == 0: return coords
le = data.ds.domain_left_edge.in_units("code_length").d
dw = data.ds.domain_width.in_units("code_length").d
for i in range(coords.shape[0]):
if not data.ds.periodicity[i]: continue
coords[i, ...] -= le[i]
#figure out which measure is less
mins = np.argmin([
np.abs(np.mod(coords[i, ...], dw[i])),
np.abs(np.mod(coords[i, ...], -dw[i]))
],
axis=0)
temp_coords = np.mod(coords[i, ...], dw[i])
#Where second measure is better, updating temporary coords
ii = mins == 1
temp_coords[ii] = np.mod(coords[i, ...], -dw[i])[ii]
# Putting the temporary coords into the actual storage
coords[i, ...] = temp_coords
coords[i, ...] + le[i]
return coords
def _specific_angular_momentum_y(field, data):
xv, yv, zv = obtain_relative_velocity_vector(data)
rv = obtain_position_vector(data)
units = rv.units
rv = np.rollaxis(rv, 0, len(rv.shape))
rv = data.ds.arr(rv, input_units=units)
return -(xv * rv[..., 2] - zv * rv[..., 0])
def _relative_particle_position(field, data):
"""The cartesian particle positions in a rotated reference frame
Relative to the coordinate system defined by *center* field parameter.
Note that the orientation of the x and y axes are arbitrary.
"""
field_names = [(ptype, f"particle_position_{ax}") for ax in "xyz"]
return obtain_position_vector(data, field_names=field_names).T
def test_obtain_position_vector():
ds = fake_random_ds(64,
nprocs=8,
fields=_fields,
negative=[False, True, True, True])
dd = ds.sphere((0.5, 0.5, 0.5), 0.2)
coords = obtain_position_vector(dd)
r = np.sqrt(np.sum(coords * coords, axis=0))
assert_array_less(r.max(), 0.2)
assert_array_less(0.0, r.min())
def _specific_angular_momentum_z(field, data):
xv, yv, zv = obtain_relative_velocity_vector(data)
rv = obtain_position_vector(data)
rv = np.rollaxis(rv, 0, len(rv.shape))
rv = data.ds.arr(rv, input_units=data["index", "x"].units)
return xv * rv[..., 1] - yv * rv[..., 0]
