old_contents: #!/usr/bin/env python
  
  import climate
  import joblib
  import lmj.cubes
  import numpy as np
  
  
  def _check(t):
      t.load()
      t.add_velocities(smooth=0)
      vel = abs(t.df[t.marker_velocity_columns].values).flatten()
      vel = vel[np.isfinite(vel)]
      pct = np.percentile(vel, [1, 2, 5, 10, 20, 50, 80, 90, 95, 98, 99])
      print(t.subject.key, t.block.key, t.key, *pct)
  
  
  def main(root):
      trials = lmj.cubes.Experiment(root).trials_matching('*')
      check = joblib.delayed(_check)
      joblib.Parallel(-1)(check(t) for t in trials)
  
  
  if __name__ == '__main__':
      climate.call(main)
  
new_contents: #!/usr/bin/env python
  
  import climate
  import joblib
  import lmj.cubes
  import numpy as np
  
  
  def _check(t):
      t.load()
      t.add_velocities(smooth=0)
      t.add_accelerations(smooth=0)
      vel = abs(t.df[t.marker_velocity_columns].values).flatten()
      vel = vel[np.isfinite(vel)]
      pct = np.percentile(vel, [1, 2, 5, 10, 20, 50, 80, 90, 95, 98, 99])
      np.set_printoptions(suppress=True, linewidth=1000, precision=2)
      t.log('%s', pct)
  
  
  def main(root):
      trials = lmj.cubes.Experiment(root).trials_matching('*')
      check = joblib.delayed(_check)
      joblib.Parallel(-1)(check(t) for t in trials)
  
  
  if __name__ == '__main__':
      climate.call(main)
  
subject: Use trial logging. Tweak numpy logging output.