class_docstr: A class to create 3 linked axes for plotting orthogonal
  cuts of 3D maps.
  
  Attributes
  ----------
  
  axes: dictionary of axes
      The 3 axes used to plot each view.
  frame_axes: axes
      The axes framing the whole set of views.
  
  Notes
  -----
  
  The extent of the different axes are adjusted to fit the data
  best in the viewing area.
class_docstr_tok: ["A","class","to","create","3","linked","axes","for","plotting","orthogonal","cuts","of","3D","maps",".","Attributes","--","--","--","--","--","axes",":","dictionary","of","axes","The","3","axes","used","to","plot","each","view",".","frame_axes",":","axes","The","axes","framing","the","whole","set","of","views",".","Notes","--","--","-","The","extent","of","the","different","axes","are","adjusted","to","fit","the","data","best","in","the","viewing","area","."]
code: def find_cut_coords(
      data=None, affine=None, threshold=None, cut_coords=None
  ):
      if cut_coords is None:
          if data is None or data is False:
              cut_coords = (0, 0, 0)
          else:
              x_map, y_map, z_map = find_cut_coords(
                  data, activation_threshold=threshold
              )
              cut_coords = coord_transform(x_map, y_map, z_map, affine)
      return cut_coords
  
imports: import matplotlib
  import matplotlib.pyplot
  import numpy
  from matplotlib import transforms
  from nipy.utils import is_iterable
  from ..datasets import VolumeImg
  from .None import cm
  from .coord_tools import coord_transform, find_cut_coords, get_bounds, get_mask_bounds
  from .edge_detect import _edge_map
label_desc: Use image node_id 1 for calling the OrthoSlicer obj's underlying member method code with example usage: obj.find_cut_coords(data, affine, threshold, cut_coords) and returns: cut_coords