class_docstr: This class maintains a pandas.dataframe of filenames for all input sections.
  The list of filenames are obtained by searching the specified folders
  or loading from an existing csv file.
  
  Users can query a subset of the dataframe by train/valid/infer partition
  label and input section names.
docstr: Set the data partitioner parameters
  
  :param data_param: corresponding to all config sections
  :param new_partition: bool value indicating whether to generate new
      partition ids and overwrite csv file
      (this class will write partition file iff new_partition)
  :param data_split_file: location of the partition id file
  :param ratios: a tuple/list with two elements:
      ``(fraction of the validation set, fraction of the inference set)``
      initialise to None will disable data partitioning
      and get_file_list always returns all subjects.
docstr_tok: ["Set","the","data","partitioner","parameters",":","param","data_param",":","corresponding","to","all","config","sections",":","param","new_partition",":","bool","value","indicating","whether","to","generate","new","partition","ids","and","overwrite","csv","file","(","this","class","will","write","partition","file","iff","new_partition",")",":","param","data_split_file",":","location","of","the","partition","id","file",":","param","ratios",":","a","tuple\/list","with","two","elements",":","``","(","fraction","of","the","validation","set",",","fraction","of","the","inference","set",")","``","initialise","to","None","will","disable","data","partitioning","and","get_file_list","always","returns","all","subjects","."]
code: def initialise(
      self,
      data_param,
      new_partition=False,
      data_split_file=None,
      ratios=None,
  ):
      """
      Set the data partitioner parameters
  
      :param data_param: corresponding to all config sections
      :param new_partition: bool value indicating whether to generate new
          partition ids and overwrite csv file
          (this class will write partition file iff new_partition)
      :param data_split_file: location of the partition id file
      :param ratios: a tuple/list with two elements:
          ``(fraction of the validation set, fraction of the inference set)``
          initialise to None will disable data partitioning
          and get_file_list always returns all subjects.
      """
      self.data_param = data_param
      if data_split_file is None:
          self.data_split_file = os.path.join(".", "dataset_split.csv")
      else:
          self.data_split_file = data_split_file
      self.ratios = ratios
  
      self._file_list = None
      self._partition_ids = None
  
      self.load_data_sections_by_subject()
      self.new_partition = new_partition
      self.randomly_split_dataset(overwrite=new_partition)
      tf.logging.info(self)
      return self
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  import shutil
  import pandas
  import tensorflow
  from niftynet.engine.signal import TRAIN, VALID, INFER, ALL
  from niftynet.utilities.decorators import singleton
  from niftynet.utilities.filename_matching import KeywordsMatching
  from niftynet.utilities.niftynet_global_config import NiftyNetGlobalConfig
  from niftynet.utilities.util_common import look_up_operations
  from niftynet.utilities.util_csv import match_and_write_filenames_to_csv
  from niftynet.utilities.util_csv import write_csv
label_desc: Use image node_id 1 for calling the ImageSetsPartitioner obj's underlying member method code with example usage: obj.initialise(data_param, new_partition, data_split_file, ratios) and returns: self