
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)


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)

Use trial logging. Tweak numpy logging output.
Use trial logging. Tweak numpy logging output.

  
  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])
-     print(t.subject.key, t.block.key, t.key, *pct)
+     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)
  
Use trial logging. Tweak numpy logging output.
## Code Before:

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)

## Instruction:
Use trial logging. Tweak numpy logging output.
## Code After:

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)
