class_docstr: Dynamic Constrained Hamiltonian/Hybrid Monte Carlo technique
  
  Run a billiard ball inside the likelihood constrained.
  The ball reflects off the constraint.
  
  The trajectory is explored in steps of stepsize epsilon.
  A No-U-turn criterion and randomized doubling of forward or backward
  steps is used to avoid repeating circular trajectories.
  Because of this, the number of steps is dynamic.
  
  Parameters
  -----------
  nsteps: int
      number of accepted steps until the sample is considered independent.
  adaptive_nsteps: False, 'proposal-distance', 'move-distance'
      if not false, allow earlier termination than nsteps.
      The 'proposal-distance' strategy stops when the sum of
      all proposed vectors exceeds the mean distance
      between pairs of live points.
      As distance, the Mahalanobis distance is used.
      The 'move-distance' strategy stops when the distance between
      start point and current position exceeds the mean distance
      between pairs of live points.
  delta: float
      step size
  nudge: float
      change in step size, must be >1.
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docstr: React to change of region. 
docstr_tok: ["React","to","change","of","region","."]
code: def region_changed(self, Ls, region):
      """React to change of region."""
      self.adjust_stepsize()
      self.create_problem(Ls, region)
  
      if self.adaptive_nsteps or True:
          self.mean_pair_distance = region.compute_mean_pair_distance()
  
file_path: ultranest/ultranest/dychmc.py
imports: from __future__ import print_function, division
  import numpy
  import matplotlib.pyplot
label_desc: Use image node_id 9 for calling the DynamicCHMCSampler obj's underlying member method code with example usage: obj.region_changed(Ls, region) without return types