class_docstr: Abstract interface for data aggregation functionality for plotly figures.
class_docstr_tok: ["Abstract","interface","for","data","aggregation","functionality","for","plotly","figures","."]
docstr: Copy (i.e. create a new key reference, not a deep copy) of a hf_data dict.
  
  Parameters
  ----------
  hf_data : dict
      The hf_data dict, having the trace 'uid' as key and the
      hf-data, together with its aggregation properties as dict-values
  adjust_default_values: bool
      Whether the default values (of the downsampler, max # shown samples) will
      be adjusted according to the values of this object, by default False
  
  Returns
  -------
  dict
      The copied (& default values adjusted) output dict.
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code: def _copy_hf_data(
      self, hf_data: dict, adjust_default_values: bool = False
  ) -> dict:
      """Copy (i.e. create a new key reference, not a deep copy) of a hf_data dict.
  
      Parameters
      ----------
      hf_data : dict
          The hf_data dict, having the trace 'uid' as key and the
          hf-data, together with its aggregation properties as dict-values
      adjust_default_values: bool
          Whether the default values (of the downsampler, max # shown samples) will
          be adjusted according to the values of this object, by default False
  
      Returns
      -------
      dict
          The copied (& default values adjusted) output dict.
  
      """
      hf_data_cp = {
          uid: {k: hf_dict[k] for k in set(hf_dict.keys())}
          for uid, hf_dict in hf_data.items()
      }
  
      # Adjust the default arguments to the current argument values
      if adjust_default_values:
          for hf_props in hf_data_cp.values():
              if hf_props.get("default_downsampler", False):
                  hf_props["downsampler"] = self._global_downsampler
              if hf_props.get("default_gap_handler", False):
                  hf_props["gap_handler"] = self._global_gap_handler
              if hf_props.get("default_n_samples", False):
                  hf_props[
                      "max_n_samples"
                  ] = self._global_n_shown_samples
  
      return hf_data_cp
  
imports: from __future__ import annotations
  import itertools
  import re
  from abc import ABC
  from collections import namedtuple
  from copy import copy
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  from uuid import uuid4
  import dash
  import numpy
  import pandas
  import plotly.graph_objects
  from pandas.io.json._normalize import nested_to_record
  from plotly.basedatatypes import BaseFigure, BaseTraceType
  from ..aggregation import AbstractAggregator, MedDiffGapHandler, MinMaxLTTB
  from ..aggregation.aggregation_interface import DataPointSelector
  from ..aggregation.gap_handler_interface import AbstractGapHandler
  from ..aggregation.plotly_aggregator_parser import PlotlyAggregatorParser
  from .utils import round_number_str, round_td_str
label_desc: Use image node_id 17 for calling the AbstractFigureAggregator obj's underlying member method code with example usage: obj._copy_hf_data(hf_data, adjust_default_values) and returns: hf_data_cp