xarray.Dataset.traj.filter#

abstract Dataset.traj.filter(method='speed', max_speed=10.0, nsigma=5.0, side_half_width=2)#

Filter outlier positions from trajectories.

Parameters:

  • method (str) – Outlier detection method:

    • 'speed': walk through non-NaN positions in order and compare each to the last accepted position. Any position whose speed from the last accepted position exceeds max_speed [m/s] is masked. Crucially, when a position is masked the “last accepted” pointer is not advanced, so entire consecutive runs of invalid positions (e.g. GPS no-fix sentinel values near (0, 0)) are cleared in a single O(N) pass without falsely masking the valid positions that bracket the bad run.

    • 'nsigma_sliding': mask positions whose latitude or longitude deviates more than nsigma standard deviations from the local mean computed over a sliding window of half-width side_half_width. Applied independently to latitude and longitude. Note: this method is designed for isolated single- point spikes; it is less effective for consecutive runs of outliers.

  • max_speed (float) – Maximum allowed speed [m/s]. Used with method='speed'. Default: 10.

  • nsigma (float) – Outlier threshold in number of standard deviations. Used with method='nsigma_sliding'. Default: 5.

  • side_half_width (int) – Half-width of the sliding window (number of neighbours on each side). Used with method='nsigma_sliding'. Default: 2.

Returns:

xarray.Dataset – Dataset with outlier lat/lon positions set to NaN. Time and other variables are left unchanged.

See also

speed

Calculate the speed along trajectories.

Examples

>>> import xarray as xr
>>> import trajan as _
>>> import lzma
>>> b = lzma.open('examples/barents.nc.xz')
>>> ds = xr.open_dataset(b)
>>> filtered = ds.traj.filter(method='speed', max_speed=3.)
>>> filtered = ds.traj.filter(method='nsigma_sliding', nsigma=5., side_half_width=2)