class_docstr: Efficient version off LTTB by first reducing really large datasets with
  the [`MinMaxAggregator`][aggregation.aggregators.MinMaxAggregator] and then further aggregating the
  reduced result with [`LTTB`][aggregation.aggregators.LTTB].
  
  Starting from 10M data points, this method performs the MinMax-prefetching of data
  points to enhance computational efficiency.
  
  Inventors: Jonas & Jeroen Van Der Donckt - 2022
  
  Paper: [https://arxiv.org/pdf/2305.00332.pdf](https://arxiv.org/pdf/2305.00332.pdf)
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docstr: Parameters
  ----------
  minmax_ratio: int, optional
      The ratio between the number of data points in the MinMax-prefetching and
      the number of data points that will be outputted by LTTB. By default, 4.
  **downsample_kwargs
      Keyword arguments passed to the `MinMaxLTTBDownsampler`.
      - The `parallel` argument is set to False by default.
      - The `minmax_ratio` argument is set to 4 by default, which was empirically
        proven to be a good default.
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code: def __init__(self, minmax_ratio: int = 4, **downsample_kwargs):
      """
      Parameters
      ----------
      minmax_ratio: int, optional
          The ratio between the number of data points in the MinMax-prefetching and
          the number of data points that will be outputted by LTTB. By default, 4.
      **downsample_kwargs
          Keyword arguments passed to the `MinMaxLTTBDownsampler`.
          - The `parallel` argument is set to False by default.
          - The `minmax_ratio` argument is set to 4 by default, which was empirically
            proven to be a good default.
  
      """
      self.minmaxlttb = MinMaxLTTBDownsampler()
      self.minmax_ratio = minmax_ratio
  
      super().__init__(
          y_dtype_regex_list=[
              rf"{dtype}\d*" for dtype in ("float", "int", "uint")
          ]
          + ["category", "bool"],
          **downsample_kwargs,
      )
  
imports: from __future__ import annotations
  import math
  from typing import Tuple
  import numpy
  from tsdownsample import EveryNthDownsampler, LTTBDownsampler, MinMaxDownsampler, MinMaxLTTBDownsampler
  from ..aggregation.aggregation_interface import DataAggregator, DataPointSelector
label_desc: Use image node_id 1 to create a new MinMaxLTTB object from inherited base classes: DataPointSelector with example: obj = MinMaxLTTB(minmax_ratio)