class_docstr: Combined Histogram, P-P and Q-Q plots.
class_docstr_tok: ["Combined","Histogram",",","P-P","and","Q-Q","plots","."]
code: def _plot(self) -> plt.figure:
      fig = plt.figure(figsize=self.figsize)
      layout = (2, 2)
  
      ax_hist = plt.subplot2grid(layout, (0, 0), colspan=2)
      ax_qq = plt.subplot2grid(layout, (1, 0))
      ax_pp = plt.subplot2grid(layout, (1, 1))
  
      ax_hist.bar(
          self.series.index,
          self.series.values,
          width=0.75 * np.diff(self.series.index).mean(),
          color=self.PALETTE[0],
      )
      ax_hist.spines["top"].set_visible(False)
      ax_hist.spines["right"].set_visible(False)
  
      self._pplot(
          ax=ax_qq,
          x=self.df_quantiles[self.col_theoretical_quantiles],
          y=self.df_quantiles[self.col_sample_quantiles],
          reference=self.df_quantiles[self.col_qq_ref],
          xlabel="Theoretical Quantiles",
          ylabel="Sample Quantiles",
      )
  
      self._pplot(
          ax=ax_pp,
          x=self.df_percentiles[self.col_theoretical_percentiles],
          y=self.df_percentiles[self.col_sample_percentiles],
          reference=self.df_percentiles[self.col_pp_ref],
          xlabel="Theoretical Percentiles",
          ylabel="Sample Percentiles",
      )
      ax_pp.set_xlim([0.0, 1.0])
      ax_pp.set_ylim([0.0, 1.0])
  
      plt.tight_layout()
      plt.close(fig)
      return fig
  
file_path: tslumen/tslumen/plot/static/distribution.py
imports: from typing import Tuple
  from dataclasses import dataclass
  import numpy
  import pandas
  import matplotlib.pyplot
  from tslumen.plot.static.base import Figure
label_desc: Use image node_id 2 for calling the Distribution obj's underlying member method code with example usage: obj._plot() and returns: fig