code: def warmstart_from_similar_file(
      usample_filename,
      param_names,
      loglike,
      transform,
      vectorized=False,
      min_num_samples=50,
  ):
      """Warmstart from a previous run.
  
      Usage::
  
          aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
              'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
  
          aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
          aux_sampler.run()
          posterior_samples = aux_results['samples'][:,-1]
  
      See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
      for more information.
  
      The remaining parameters have the same meaning as in :py:class:`ReactiveNestedSampler`.
  
      Parameters
      ------------
      usample_filename: str
          'directory/chains/weighted_post_untransformed.txt'
          contains posteriors in u-space (untransformed) of a previous run.
          Columns are weight, logl, param1, param2, ...
      min_num_samples: int
          minimum number of samples in the usample_filename file required.
          Too few samples will give a poor approximation.
  
      Other Parameters
      -----------------
      param_names: list
      loglike: function
      transform: function
      vectorized: bool
  
      Returns
      ---------
      aux_param_names: list
          new parameter list
      aux_loglikelihood: function
          new loglikelihood function
      aux_transform: function
          new prior transform function
      vectorized: bool
          whether the new functions are vectorized
      """
      # load samples
      try:
          with open(usample_filename) as f:
              old_param_names = f.readline().lstrip("#").strip().split()
              auxiliary_usamples = np.loadtxt(f)
      except IOError:
          warnings.warn(
              'not hot-resuming, could not load file "%s"'
              % usample_filename
          )
          return param_names, loglike, transform, vectorized
  
      ulogl = auxiliary_usamples[:, 1]
      uweights_full = auxiliary_usamples[:, 0] * np.exp(
          ulogl - ulogl.max()
      )
      mask = uweights_full > 0
      uweights = uweights_full[mask]
      uweights /= uweights.sum()
      upoints = auxiliary_usamples[mask, 2:]
      del auxiliary_usamples
  
      nsamples = len(upoints)
      if nsamples < min_num_samples:
          raise ValueError(
              'file "%s" has too few samples (%d) to hot-resume'
              % (usample_filename, nsamples)
          )
  
      # check that the parameter meanings have not changed
      if old_param_names != ["weight", "logl"] + param_names:
          raise ValueError(
              'file "%s" has parameters %s, expected %s, cannot hot-resume.'
              % (usample_filename, old_param_names, param_names)
          )
  
      return get_auxiliary_contbox_parameterization(
          param_names,
          loglike=loglike,
          transform=transform,
          vectorized=vectorized,
          upoints=upoints,
          uweights=uweights,
      )
  
imports: from __future__ import print_function, division
  import os
  import sys
  import csv
  import json
  import operator
  import time
  import warnings
  from numpy import log, exp, logaddexp
  import numpy
  from .utils import create_logger, make_run_dir, resample_equal, vol_prefactor, vectorize, listify
  from .utils import is_affine_transform, normalised_kendall_tau_distance, distributed_work_chunk_size
  from ultranest.mlfriends import MLFriends, AffineLayer, ScalingLayer, find_nearby, WrappingEllipsoid, RobustEllipsoidRegion
  from .store import HDF5PointStore, TextPointStore, NullPointStore
  from .viz import get_default_viz_callback
  from .ordertest import UniformOrderAccumulator
  from .netiter import PointPile, SingleCounter, MultiCounter, BreadthFirstIterator, TreeNode, count_tree_between, find_nodes_before, logz_sequence
  from .netiter import dump_tree, combine_results
  from .hotstart import get_auxiliary_contbox_parameterization
label_desc: Use image node_id 6 for calling a global function with example usage: warmstart_from_similar_file(usample_filename, param_names, loglike, transform, vectorized, min_num_samples) and returns: get_auxiliary_contbox_parameterization, param_names,  loglike,  transform,  vectorized