class_docstr: Compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for
  streaming data
  
  Parameters
  ----------
  mp : numpy.ndarray
      The first column consists of the matrix profile, the second column
      consists of the matrix profile indices, the third column consists of
      the left matrix profile indices, and the fourth column consists of
      the right matrix profile indices.
  
  T : numpy.ndarray
      A 1-D time series data used to generate the matrix profile and matrix profile
      indices found in `mp`. Note that the the right matrix profile index is used and
      the right matrix profile is intelligently recomputed on the fly from `T` instead
      of using the bidirectional matrix profile.
  
  m : int
      The window size for computing sliding window mass. This is identical
      to the window size used in the matrix profile calculation. For managing
      edge effects, see the `L` parameter.
  
  L : int
      The subsequence length that is set roughly to be one period length.
      This is likely to be the same value as the window size, `m`, used
      to compute the matrix profile and matrix profile index but it can
      be different since this is only used to manage edge effects
      and has no bearing on any of the IAC or CAC core calculations.
  
  excl_factor : int, default 5
      The multiplying factor for the regime exclusion zone. Note that this
      is unrelated to the `excl_zone` used in to compute the matrix profile.
  
  n_iter : int, default 1000
      Number of iterations to average over when determining the parameters for
      the IAC beta distribution
  
  n_samples : int, default 1000
      Number of distribution samples to draw during each iteration when
      computing the IAC
  
  custom_iac : numpy.ndarray, default None
      A custom idealized arc curve (IAC) that will used for correcting the
      arc curve
  
  normalize : bool, default True
      When set to `True`, this z-normalizes subsequences prior to computing distances
  
  p : float, default 2.0
      The p-norm to apply for computing the Minkowski distance. Minkowski distance is
      typically used with `p` being 1 or 2, which correspond to the Manhattan distance
      and the Euclidean distance, respectively. This parameter is ignored when
      `normalize == True`.
  
  T_subseq_isconstant_func : function, default None
      A custom, user-defined function that returns a boolean array that indicates
      whether a subsequence in `T` is constant (True). The function must only take
      two arguments, `a`, a 1-D array, and `w`, the window size, while additional
      arguments may be specified by currying the user-defined function using
      `functools.partial`. Any subsequence with at least one np.nan/np.inf will
      automatically have its corresponding value set to False in this boolean array.
  
  Attributes
  ----------
  cac_1d_ : numpy.ndarray
      A 1-dimensional corrected arc curve (CAC) updated as a result of ingressing a
      single new data point and egressing a single old data point.
  
  P_ : numpy.ndarray
      The matrix profile updated as a result of ingressing a single new data
      point and egressing a single old data point.
  
  I_ : numpy.ndarray
      The (right) matrix profile indices updated as a result of ingressing a single
      new data point and egressing a single old data point.
  
  T_ : numpy.ndarray
      The updated time series, `T`
  
  Methods
  -------
  update(t)
      Ingress a new data point, `t`, onto the time series, `T`, followed by egressing
      the oldest single data point from `T`. Then, update the 1-dimensional corrected
      arc curve (CAC_1D) and the matrix profile.
  
  See Also
  --------
  stumpy.fluss : Compute the Fast Low-cost Unipotent Semantic Segmentation (FLUSS)
      for static data (i.e., batch processing)
  
  Notes
  -----
  `DOI: 10.1109/ICDM.2017.21 <https://www.cs.ucr.edu/~eamonn/Segmentation_ICDM.pdf>`__
  
  See Section C
  
  This is the implementation for Fast Low-cost Online Semantic
  Segmentation (FLOSS).
  
  Examples
  --------
  >>> import stumpy
  >>> import numpy as np
  >>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0.]), m=3)
  >>> stream = stumpy.floss(
  ...     mp,
  ...     np.array([584., -11., 23., 79., 1001., 0.]),
  ...     m=3,
  ...     L=3)
  >>> stream.update(19.)
  >>> stream.cac_1d_
  array([1., 1., 1., 1.])
docstr: Get the updated 1-dimensional corrected arc curve (CAC_1D)
  
  Parameters
  ----------
  None
  
  Returns
  -------
  None
docstr_tok: ["Get","the","updated","1-dimensional","corrected","arc","curve","(","CAC_1D",")","Parameters","--","--","--","--","--","None","Returns","--","--","--","-","None"]
code: def cac_1d_(self):
      """
      Get the updated 1-dimensional corrected arc curve (CAC_1D)
  
      Parameters
      ----------
      None
  
      Returns
      -------
      None
      """
      return self._cac.astype(np.float64)
  
file_path: stumpy/stumpy/floss.py
imports: import copy
  import numpy
  import scipy.stats
  from .None import config, core
label_desc: Use image node_id 3 for calling the floss obj's underlying member method code with example usage: obj.cac_1d_() and returns: self