old_contents: """Module to compute and show Voronoi figure
  """
  import os
  import typing as tg
  import json
  
  from .. import NumpyAndPandasEncoder
  
  import matplotlib
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  from scipy.spatial import Voronoi
  
  __location__ = os.path.join(os.getcwd(), os.path.dirname(os.path.realpath(__file__)))
  
  
  def voronoi_plot(cell_info: pd.DataFrame, color_set: tg.Optional[tg.Sequence]=None, target_axes: tg.Optional[matplotlib.axes.Axes]=None) -> None:
      """the function to compute and show voronoi figure
      cell_info is supposed to have column x and column y which contains the position of each point
      """
      cells = np.asarray(cell_info[["x", "y"]])
      color = np.asarray(color_set)
  
      # compute Voronoi tesselation
      vor = Voronoi(cells)
  
      # plot
      if target_axes:
          ax = target_axes
      else:
          ax = plt.gca()
      ax.set_aspect(1.)
  
      # colorize
      for i in range(len(cells)):
          region = vor.regions[vor.point_region[i]]
          if -1 not in region:
              polygon = [vor.vertices[j] for j in region]
              z = list(zip(*polygon))
              ax.fill(z[0], z[1], color=color[i])
  
  
  def voronoi_dump(cell_info: pd.DataFrame, out_file=tg.TextIO, label: tg.Optional[tg.Sequence]=None) -> None:
      cells = np.asarray(cell_info[['x', 'y']])
      if label is None:
          labels = np.zeros((cells.shape[0], 1), dtype=int)
      else:
          labels = np.asarray(label)
  
      vor = Voronoi(cells)
  
      clusters = np.unique(labels)
  
      output = {}
      for x in clusters:
          output[int(x)] = []
  
      for i in range(len(cells)):
          region = vor.regions[vor.point_region[i]]
          if -1 not in region:
              output[int(labels[i])] = [vor.vertices[j] for j in region]
  
      print("data=", file=out_file, end='')
      json.dump(output, out_file, cls=NumpyAndPandasEncoder)
      print(";", file=out_file)
  
new_contents: """Module to compute and show Voronoi figure
  """
  import os
  import typing as tg
  import json
  
  from .. import NumpyAndPandasEncoder
  
  import matplotlib
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  from scipy.spatial import Voronoi
  
  __location__ = os.path.join(os.getcwd(), os.path.dirname(os.path.realpath(__file__)))
  
  
  def voronoi_plot(cell_info: pd.DataFrame, color_set: tg.Optional[tg.Sequence]=None, target_axes: tg.Optional[matplotlib.axes.Axes]=None) -> None:
      """the function to compute and show voronoi figure
      cell_info is supposed to have column x and column y which contains the position of each point
      """
      cells = np.asarray(cell_info[["x", "y"]])
      color = np.asarray(color_set)
  
      # compute Voronoi tesselation
      vor = Voronoi(cells)
  
      # plot
      if target_axes:
          ax = target_axes
      else:
          ax = plt.gca()
      ax.set_aspect(1.)
  
      # colorize
      for i in range(len(cells)):
          region = vor.regions[vor.point_region[i]]
          if -1 not in region:
              polygon = [vor.vertices[j] for j in region]
              z = list(zip(*polygon))
              ax.fill(z[0], z[1], color=color[i])
  
  
  def voronoi_dump(cell_info: pd.DataFrame, out_file=tg.TextIO, label: tg.Optional[tg.Sequence]=None) -> None:
      cells = np.asarray(cell_info[['x', 'y']])
      if label is None:
          labels = np.zeros((cells.shape[0], 1), dtype=int)
      else:
          labels = np.asarray(label)
  
      vor = Voronoi(cells)
  
      clusters = np.unique(labels)
  
      output = {}
      for x in clusters:
          output[int(x)] = []
  
      for i in range(len(cells)):
          region = vor.regions[vor.point_region[i]]
          if -1 not in region:
              output[int(labels[i])].append([vor.vertices[j] for j in region])
  
      print("data=", file=out_file, end='')
      json.dump(output, out_file, cls=NumpyAndPandasEncoder)
      print(";", file=out_file)
  
subject: fix wrong assignment