original_docstring: 
          Fills gaps in input values range, finds major ticks and saves all the
          information to PointsGrid.
  
          Say, the input values are [1, 3, 4], but drawing only these 3 values in
          a row would ruin the scale because by doing so, we unintentionally show
          that the distance between 1 and 3 is the same as the distance between
          3 and 4, which is, of course, false.
  
          To avoid this, we firstly fill the gaps by converting the [1, 3, 4] to
          [1, 2, 3, 4]. Now, the grid contains all the values to correctly
          visualize the scale.
  
          At the same time, if the values are, for example, [1, 4, 46, 98] - the
          list with filled gaps would contain almost 100 elements and labeling
          all of them would be a mess. That's why we find "major ticks", in this
          case: [0, 10, 20, 30, ..., 100]. These are the only values that will be
          labeled along spines, although we still keep track of all 100 to display
          the actual plot.
  
          
docstring: Fills gaps in input values range, finds major ticks and saves all the
  information to PointsGrid.
  
  Say, the input values are [1, 3, 4], but drawing only these 3 values in
  a row would ruin the scale because by doing so, we unintentionally show
  that the distance between 1 and 3 is the same as the distance between
  3 and 4, which is, of course, false.
  
  To avoid this, we firstly fill the gaps by converting the [1, 3, 4] to
  [1, 2, 3, 4]. Now, the grid contains all the values to correctly
  visualize the scale.
  
  At the same time, if the values are, for example, [1, 4, 46, 98] - the
  list with filled gaps would contain almost 100 elements and labeling
  all of them would be a mess.
docstring_tokens: Fills gaps in input values range finds major ticks and saves all the information to PointsGrid . Say the input values are [ 1 3 4 ] but drawing only these 3 values in a row would ruin the scale because by doing so we unintentionally show that the distance between 1 and 3 is the same as the distance between 3 and 4 which is of course false . To avoid this we firstly fill the gaps by converting the [ 1 3 4 ] to [ 1 2 3 4 ] . Now the grid contains all the values to correctly visualize the scale . At the same time if the values are for example [ 1 4 46 98 ] - the list with filled gaps would contain almost 100 elements and labeling all of them would be a mess .
code: def _configure_grid_settings(self) -> None:
          """
          Fills gaps in input values range, finds major ticks and saves all the
          information to PointsGrid.
  
          Say, the input values are [1, 3, 4], but drawing only these 3 values in
          a row would ruin the scale because by doing so, we unintentionally show
          that the distance between 1 and 3 is the same as the distance between
          3 and 4, which is, of course, false.
  
          To avoid this, we firstly fill the gaps by converting the [1, 3, 4] to
          [1, 2, 3, 4]. Now, the grid contains all the values to correctly
          visualize the scale.
  
          At the same time, if the values are, for example, [1, 4, 46, 98] - the
          list with filled gaps would contain almost 100 elements and labeling
          all of them would be a mess. That's why we find "major ticks", in this
          case: [0, 10, 20, 30, ..., 100]. These are the only values that will be
          labeled along spines, although we still keep track of all 100 to display
          the actual plot.
  
          """
  
          xvalues = np.concatenate([axes.xvalues for axes in self.axes])
          x_major_ticks = self.x_locator.tick_values(np.min(xvalues), np.max(xvalues))
          xvmin, xvmax = np.min(x_major_ticks), np.max(x_major_ticks)
  
          self.grid.xvalues = smartrange(xvmin, xvmax, xvalues)
          self.grid.cell_width = self.grid.width / max(len(self.grid.xvalues) - 1, 1)
          self.grid.x_major_ticks = get_indices_of_values_in_list(x_major_ticks,
                                                                  self.grid.xvalues)
  
          #-----------------------------------------------------------------------
  
          yvalues = np.concatenate([axes.yvalues for axes in self.axes])
          y_major_ticks = self.y_locator.tick_values(np.min(yvalues), np.max(yvalues))
          yvmin, yvmax = np.min(y_major_ticks), np.max(y_major_ticks)
  
          self.grid.yvalues = smartrange(yvmin, yvmax, yvalues)
          self.grid.cell_height = self.grid.height / max(len(self.grid.yvalues) - 1, 1)
          self.grid.y_major_ticks = get_indices_of_values_in_list(y_major_ticks,
                                                                  self.grid.yvalues)
code_tokens: def _configure_grid_settings ( self ) -> None : xvalues = np . concatenate ( [ axes . xvalues for axes in self . axes ] ) x_major_ticks = self . x_locator . tick_values ( np . min ( xvalues ) , np . max ( xvalues ) ) xvmin , xvmax = np . min ( x_major_ticks ) , np . max ( x_major_ticks ) self . grid . xvalues = smartrange ( xvmin , xvmax , xvalues ) self . grid . cell_width = self . grid . width / max ( len ( self . grid . xvalues ) - 1 , 1 ) self . grid . x_major_ticks = get_indices_of_values_in_list ( x_major_ticks , self . grid . xvalues ) yvalues = np . concatenate ( [ axes . yvalues for axes in self . axes ] ) y_major_ticks = self . y_locator . tick_values ( np . min ( yvalues ) , np . max ( yvalues ) ) yvmin , yvmax = np . min ( y_major_ticks ) , np . max ( y_major_ticks ) self . grid . yvalues = smartrange ( yvmin , yvmax , yvalues ) self . grid . cell_height = self . grid . height / max ( len ( self . grid . yvalues ) - 1 , 1 ) self . grid . y_major_ticks = get_indices_of_values_in_list ( y_major_ticks , self . grid . yvalues )
short_docstring: Fills gaps in input values range, finds major ticks and saves all the
  information to PointsGrid.
short_docstring_tokens: Fills gaps in input values range finds major ticks and saves all the information to PointsGrid .
comment: """
          Fills gaps in input values range, finds major ticks and saves all the
          information to PointsGrid.
  
          Say, the input values are [1, 3, 4], but drawing only these 3 values in
          a row would ruin the scale because by doing so, we unintentionally show
          that the distance between 1 and 3 is the same as the distance between
          3 and 4, which is, of course, false.
  
          To avoid this, we firstly fill the gaps by converting the [1, 3, 4] to
          [1, 2, 3, 4]. Now, the grid contains all the values to correctly
          visualize the scale.
  
          At the same time, if the values are, for example, [1, 4, 46, 98] - the
          list with filled gaps would contain almost 100 elements and labeling
          all of them would be a mess. That's why we find "major ticks", in this
          case: [0, 10, 20, 30, ..., 100]. These are the only values that will be
          labeled along spines, although we still keep track of all 100 to display
          the actual plot.
  
          """$SEP$#-----------------------------------------------------------------------