docstr: Saves LMM plot with segment/disk number on x-axis and
  -log10(pvalues) on y-axis in out_dir folder.
  
  Parameters
  ----------
  plot_file : string
      Path to the plot file. This path may
      contain wildcards to process multiple inputs at once.
  title : string
      Title for the plot.
  bundle_name : string
  x : list
      list containing segment/disk number for x-axis.
  y : list
      list containing -log10(pvalues) per segment/disk number for y-axis.
docstr_tok: ["Saves","LMM","plot","with","segment\/disk","number","on","x-axis","and","-log10","(","pvalues",")","on","y-axis","in","out_dir","folder",".","Parameters","--","--","--","--","--","plot_file",":","string","Path","to","the","plot","file",".","This","path","may","contain","wildcards","to","process","multiple","inputs","at","once",".","title",":","string","Title","for","the","plot",".","bundle_name",":","string","x",":","list","list","containing","segment\/disk","number","for","x-axis",".","y",":","list","list","containing","-log10","(","pvalues",")","per","segment\/disk","number","for","y-axis","."]
code: def save_lmm_plot(self, plot_file, title, bundle_name, x, y):
      """Saves LMM plot with segment/disk number on x-axis and
      -log10(pvalues) on y-axis in out_dir folder.
  
      Parameters
      ----------
      plot_file : string
          Path to the plot file. This path may
          contain wildcards to process multiple inputs at once.
      title : string
          Title for the plot.
      bundle_name : string
      x : list
          list containing segment/disk number for x-axis.
      y : list
          list containing -log10(pvalues) per segment/disk number for y-axis.
  
      """
  
      n = len(x)
      dotted = np.ones(n)
      dotted[:] = 2
      c1 = np.random.rand(1, 3)
  
      y_pos = np.arange(n)
  
      (l1,) = plt.plot(
          y_pos,
          dotted,
          color="red",
          marker=".",
          linestyle="solid",
          linewidth=0.6,
          markersize=0.7,
          label="p-value < 0.01",
      )
  
      (l2,) = plt.plot(
          y_pos,
          dotted + 1,
          color="black",
          marker=".",
          linestyle="solid",
          linewidth=0.4,
          markersize=0.4,
          label="p-value < 0.001",
      )
  
      first_legend = plt.legend(handles=[l1, l2], loc="upper right")
  
      axes = plt.gca()
      axes.add_artist(first_legend)
      axes.set_ylim([0, 6])
  
      l3 = plt.bar(y_pos, y, color=c1, alpha=0.5, label=bundle_name)
      plt.legend(handles=[l3], loc="upper left")
      plt.title(title.upper())
      plt.xlabel("Segment Number")
      plt.ylabel("-log10(Pvalues)")
      plt.savefig(plot_file)
      plt.clf()
  
imports: import logging
  import numpy
  import os
  import json
  import warnings
  from time import time
  from scipy.ndimage import binary_dilation
  from dipy.utils.optpkg import optional_package
  from dipy.io import read_bvals_bvecs
  from dipy.io.image import load_nifti, save_nifti
  from dipy.core.gradients import gradient_table
  from dipy.reconst.dti import TensorModel
  from dipy.io.peaks import load_peaks
  from dipy.io.streamline import load_tractogram
  from dipy.segment.mask import segment_from_cfa
  from dipy.segment.mask import bounding_box
  from dipy.tracking.streamline import transform_streamlines
  from glob import glob
  from dipy.workflows.workflow import Workflow
  from dipy.segment.bundles import bundle_shape_similarity
  from dipy.stats.analysis import assignment_map
  from dipy.stats.analysis import anatomical_measures
  from dipy.stats.analysis import peak_values
label_desc: Use image node_id 3 for calling the LinearMixedModelsFlow obj's underlying member method code with example usage: obj.save_lmm_plot(plot_file, title, bundle_name, x, y) without return types