class_docstr: Lag correlation (ACF/PACF) plots, commonly known as lollipop, for analysing correlation on
  a given lag. Useful for auto-, partial-, cross- and partial-cross-correlation.
class_docstr_tok: ["Lag","correlation","(","ACF\/PACF",")","plots",",","commonly","known","as","lollipop",",","for","analysing","correlation","on","a","given","lag",".","Useful","for","auto-",",","partial-",",","cross-","and","partial-cross-correlation","."]
docstr: Args:
      data (pd.DataFrame): Dataframe with lag, correlation and optionally upper and lower CI.
      col_corr (str): Name of the correlation column.
      col_lag (str): Name of the lag column.
      col_up_bound (str): Name of the upper boundary column.
      col_lo_bound (str): Name of the lower boundary column.
      label_corr (str): Label for the correlation, defaults to `col_corr` if not provided.
      label_lag (str): Label for the lag, defaults to `col_lag` if not provided.
      label_up_bound (str): Label for the upper boundary, defaults to `col_up_bound`.
      label_lo_bound (str): Label for the upper boundary, defaults to `col_lo_bound`.
      legend_position (str): Where to position the legend, default 'right'.
      label_xaxis (str): X-axis label, default 'Lag'.
      label_yaxis (str): Y-axis label, default 'Correlation'.
      color_area (str): Color of the shaded area between the boundaries.
      color_vlines (str): Color of the vertical lines/spikes.
      color_up_bound (str): Color of the upper boundary line.
      color_lo_bound (str): Color of the lower boundary line.
      color_circle (str): Color of the scatter points.
      scatter_size (float): Size of the scatter points, default 6.
      vlines_width (float): Width of the vertical lines/spikes, default 2.
docstr_tok: ["Args",":","data","(","pd.DataFrame",")",":","Dataframe","with","lag",",","correlation","and","optionally","upper","and","lower","CI",".","col_corr","(","str",")",":","Name","of","the","correlation","column",".","col_lag","(","str",")",":","Name","of","the","lag","column",".","col_up_bound","(","str",")",":","Name","of","the","upper","boundary","column",".","col_lo_bound","(","str",")",":","Name","of","the","lower","boundary","column",".","label_corr","(","str",")",":","Label","for","the","correlation",",","defaults","to","`","col_corr","`","if","not","provided",".","label_lag","(","str",")",":","Label","for","the","lag",",","defaults","to","`","col_lag","`","if","not","provided",".","label_up_bound","(","str",")",":","Label","for","the","upper","boundary",",","defaults","to","`","col_up_bound","`",".","label_lo_bound","(","str",")",":","Label","for","the","upper","boundary",",","defaults","to","`","col_lo_bound","`",".","legend_position","(","str",")",":","Where","to","position","the","legend",",","default","'right","'",".","label_xaxis","(","str",")",":","X-axis","label",",","default","'Lag","'",".","label_yaxis","(","str",")",":","Y-axis","label",",","default","'Correlation","'",".","color_area","(","str",")",":","Color","of","the","shaded","area","between","the","boundaries",".","color_vlines","(","str",")",":","Color","of","the","vertical","lines\/spikes",".","color_up_bound","(","str",")",":","Color","of","the","upper","boundary","line",".","color_lo_bound","(","str",")",":","Color","of","the","lower","boundary","line",".","color_circle","(","str",")",":","Color","of","the","scatter","points",".","scatter_size","(","float",")",":","Size","of","the","scatter","points",",","default","6.","vlines_width","(","float",")",":","Width","of","the","vertical","lines\/spikes",",","default","2","."]
code: def __init__(
      self,
      data: pd.DataFrame,
      col_corr: Optional[str] = "correlation",
      col_lag: Optional[str] = "lag",
      col_up_bound: Optional[str] = "up",
      col_lo_bound: Optional[str] = "low",
      label_corr: Optional[str] = None,
      label_lag: Optional[str] = None,
      label_up_bound: Optional[str] = None,
      label_lo_bound: Optional[str] = None,
      label_xaxis: str = "Lag",
      label_yaxis: str = "Correlation",
      color_area: str = "rgba(160, 158, 187, 0.25)",
      color_vlines: str = "black",
      color_up_bound: str = VizBase.PALETTE[0],
      color_lo_bound: str = VizBase.PALETTE[0],
      color_circle: str = VizBase.PALETTE[0],
      scatter_size: int = 6,
      vlines_width: float = 2,
      **kwargs: Any,
  ) -> None:
      """
      Args:
          data (pd.DataFrame): Dataframe with lag, correlation and optionally upper and lower CI.
          col_corr (str): Name of the correlation column.
          col_lag (str): Name of the lag column.
          col_up_bound (str): Name of the upper boundary column.
          col_lo_bound (str): Name of the lower boundary column.
          label_corr (str): Label for the correlation, defaults to `col_corr` if not provided.
          label_lag (str): Label for the lag, defaults to `col_lag` if not provided.
          label_up_bound (str): Label for the upper boundary, defaults to `col_up_bound`.
          label_lo_bound (str): Label for the upper boundary, defaults to `col_lo_bound`.
          legend_position (str): Where to position the legend, default 'right'.
          label_xaxis (str): X-axis label, default 'Lag'.
          label_yaxis (str): Y-axis label, default 'Correlation'.
          color_area (str): Color of the shaded area between the boundaries.
          color_vlines (str): Color of the vertical lines/spikes.
          color_up_bound (str): Color of the upper boundary line.
          color_lo_bound (str): Color of the lower boundary line.
          color_circle (str): Color of the scatter points.
          scatter_size (float): Size of the scatter points, default 6.
          vlines_width (float): Width of the vertical lines/spikes, default 2.
      """
      super().__init__(**kwargs)
      self._data = data
  
      self._col_corr = col_corr
      self._col_lag = col_lag
      self._col_up_bound = col_up_bound
      self._col_lo_bound = col_lo_bound
  
      self._label_corr = label_corr if label_corr else self._col_corr
      self._label_lag = label_lag if label_lag else self._col_lag
      self._label_up_bound = (
          label_up_bound if label_up_bound else self._col_up_bound
      )
      self._label_lo_bound = (
          label_lo_bound if label_lo_bound else self._col_lo_bound
      )
  
      self._label_xaxis = label_xaxis
      self._label_yaxis = label_yaxis
  
      self._color_area = color_area
      self._color_vlines = color_vlines
      self._color_up_bound = color_up_bound
      self._color_lo_bound = color_lo_bound
      self._color_circle = color_circle
  
      self._scatter_size = scatter_size
      self._vlines_width = vlines_width
  
      self._padding = 0.1
      self._h_line_width = 1
      self._h_line_color = "black"
  
imports: from typing import Union, Optional, Any
  from copy import deepcopy
  import pandas
  import numpy
  from scipy.stats import gaussian_kde
  from tslumen.plot.interactive.base import VizBase, go, make_subplots
  from tslumen.plot.utils import cmapper
label_desc: Use image node_id 1 to create a new LagCorrelation object from inherited base classes: VizBase with example: obj = LagCorrelation(data, col_corr, col_lag, col_up_bound, col_lo_bound, label_corr, label_lag, label_up_bound, label_lo_bound, label_xaxis, label_yaxis, color_area, color_vlines, color_up_bound, color_lo_bound, color_circle, scatter_size, vlines_width)