def xr_interp_orientation_in_time(da: xr.DataArray,
time: types_time_like) -> xr.DataArray:
"""Interpolate an xarray DataArray that represents orientation data in time.
Parameters
----------
da :
xarray DataArray containing the orientation as matrix
time :
Time data
Returns
-------
xarray.DataArray
Interpolated data
"""
if "time" not in da.dims:
return da
if len(da.time) == 1: # remove "time dimension" for static case
return da.isel({"time": 0})
time = Time(time).as_pandas_index()
time_da = Time(da).as_pandas_index()
time_ref = da.weldx.time_ref
if not len(time_da) > 1:
raise ValueError("Invalid time format for interpolation.")
# extract intersecting times and add time range boundaries of the data set
times_ds_limits = pd.Index([time_da.min(), time_da.max()])
times_union = time.union(times_ds_limits)
times_intersect = times_union[(times_union >= times_ds_limits[0])
& (times_union <= times_ds_limits[1])]
# interpolate rotations in the intersecting time range
rotations_key = Rot.from_matrix(da.transpose(..., "time", "c", "v").data)
times_key = time_da.view(np.int64)
rotations_interp = Slerp(times_key,
rotations_key)(times_intersect.view(np.int64))
da = xr_3d_matrix(rotations_interp.as_matrix(), times_intersect)
# use interp_like to select original time values and correctly fill time dimension
da = xr_interp_like(da, {"time": time}, fillna=True)
# resync and reset to correct format
if time_ref:
da.weldx.time_ref = time_ref
da = da.weldx.time_ref_restore().transpose(..., "time", "c", "v")
if len(da.time) == 1: # remove "time dimension" for static case
return da.isel({"time": 0})
return da
def xr_interp_orientation_in_time(
dsx: xr.DataArray, times: Union[pd.DatetimeIndex, pd.TimedeltaIndex]
) -> xr.DataArray:
"""Interpolate an xarray DataArray that represents orientation data in time.
Parameters
----------
dsx :
xarray DataArray containing the orientation as matrix
times :
Time data
Returns
-------
xarray.DataArray
Interpolated data
"""
if "time" not in dsx.coords:
return dsx
times = to_pandas_time_index(times)
times_ds = to_pandas_time_index(dsx)
time_ref = dsx.weldx.time_ref
if len(times_ds) > 1:
# extract intersecting times and add time range boundaries of the data set
times_ds_limits = pd.Index([times_ds.min(), times_ds.max()])
times_union = times.union(times_ds_limits)
times_intersect = times_union[
(times_union >= times_ds_limits[0]) & (times_union <= times_ds_limits[1])
]
# interpolate rotations in the intersecting time range
rotations_key = Rot.from_matrix(dsx.transpose(..., "c", "v").data)
times_key = times_ds.astype(np.int64)
rotations_interp = Slerp(times_key, rotations_key)(
times_intersect.astype(np.int64)
)
dsx_out = xr_3d_matrix(rotations_interp.as_matrix(), times_intersect)
else:
# TODO: this case is not really well defined, maybe avoid?
dsx_out = dsx
# use interp_like to select original time values and correctly fill time dimension
dsx_out = xr_interp_like(dsx_out, {"time": times}, fillna=True)
# resync and reset to correct format
if time_ref:
dsx_out.weldx.time_ref = time_ref
dsx_out = dsx_out.weldx.time_ref_restore()
return dsx_out.transpose(..., "c", "v")
