--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/imagej2_analyze_skeleton_jython_script.py	Tue Sep 17 16:55:25 2019 -0400
@@ -0,0 +1,147 @@
+import jython_utils
+import math
+import sys
+from ij import IJ
+from sc.fiji.analyzeSkeleton import AnalyzeSkeleton_
+
+BASIC_NAMES = [ 'Branches', 'Junctions', 'End-point Voxels',
+                'Junction Voxels', 'Slab Voxels', 'Average branch length',
+                'Triple Points', 'Quadruple Points', 'Maximum Branch Length' ]
+DETAIL_NAMES = [ 'Skeleton ID', 'Branch length', 'V1 x', 'V1 y', 'V1 z', 'V2 x',
+                 'V2 y', 'V2 z', 'Euclidean distance' ]
+
+def get_euclidean_distance( vertex1, vertex2 ):
+    x1, y1, z1 = get_points( vertex1 )
+    x2, y2, z2 = get_points( vertex2 )
+    return math.sqrt( math.pow( ( x2 - x1 ), 2 ) +
+                      math.pow( ( y2 - y1 ), 2 ) +
+                      math.pow( ( z2 - z1 ), 2 ) )
+
+def get_graph_length( graph ):
+    length = 0
+    for edge in graph.getEdges():
+        length = length + edge.getLength()
+    return length
+
+def get_points( vertex ):
+    # An array of Point, which has attributes x,y,z.
+    point = vertex.getPoints()[ 0 ]
+    return point.x, point.y, point.z
+    
+def get_sorted_edge_lengths( graph ):
+    # Return graph edges sorted from longest to shortest.
+    edges = graph.getEdges()
+    edges = sorted( edges, key=lambda edge: edge.getLength(), reverse=True )
+    return edges
+
+def get_sorted_graph_lengths( result ):
+    # Get the separate graphs (skeletons).
+    graphs = result.getGraph()
+    # Sort graphs from longest to shortest.
+    graphs = sorted( graphs, key=lambda g: get_graph_length( g ), reverse=True )
+    return graphs
+
+def save( result, output, show_detailed_info, calculate_largest_shortest_path, sep='\t' ):
+    num_trees = int( result.getNumOfTrees() )
+    outf = open( output, 'wb' )
+    outf.write( '# %s\n' % sep.join( BASIC_NAMES ) )
+    for index in range( num_trees ):
+        outf.write( '%d%s' % ( result.getBranches()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getJunctions()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getEndPoints()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getJunctionVoxels()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getSlabs()[ index ], sep ) )
+        outf.write( '%.3f%s' % ( result.getAverageBranchLength()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getTriples()[ index ], sep ) )
+        outf.write( '%d%s' % ( result.getQuadruples()[ index ], sep ) )
+        outf.write( '%.3f' % result.getMaximumBranchLength()[ index ] )
+        if calculate_largest_shortest_path:
+            outf.write( '%s%.3f%s' % ( sep, result.shortestPathList.get( index ), sep ) )
+            outf.write( '%d%s' % ( result.spStartPosition[ index ][ 0 ], sep ) )
+            outf.write( '%d%s' % ( result.spStartPosition[ index ][ 1 ], sep ) )
+            outf.write( '%d\n' % result.spStartPosition[ index ][ 2 ] )
+        else:
+            outf.write( '\n' )
+    if show_detailed_info:
+        outf.write( '# %s\n' % sep.join( DETAIL_NAMES ) )
+        # The following index is a placeholder for the skeleton ID.
+        # The terms "graph" and "skeleton" refer to the same thing.
+        # Also, the SkeletonResult.java code states that the
+        # private Graph[] graph object is an array of graphs (one
+        # per tree).
+        for index, graph in enumerate( get_sorted_graph_lengths( result ) ):
+            for edge in get_sorted_edge_lengths( graph ):
+                vertex1 = edge.getV1()
+                x1, y1, z1 = get_points( vertex1 )
+                vertex2 = edge.getV2()
+                x2, y2, z2 = get_points( vertex2 )
+                outf.write( '%d%s' % ( index+1, sep ) )
+                outf.write( '%.3f%s' % ( edge.getLength(), sep ) )
+                outf.write( '%d%s' % ( x1, sep ) )
+                outf.write( '%d%s' % ( y1, sep ) )
+                outf.write( '%d%s' % ( z1, sep ) )
+                outf.write( '%d%s' % ( x2, sep ) )
+                outf.write( '%d%s' % ( y2, sep ) )
+                outf.write( '%d%s' % ( z2, sep ) )
+                outf.write( '%.3f' % get_euclidean_distance( vertex1, vertex2 ) )
+                if calculate_largest_shortest_path:
+                    # Keep number of separated items the same for each line.
+                    outf.write( '%s %s' % ( sep, sep ) )
+                    outf.write( ' %s' % sep )
+                    outf.write( ' %s' % sep )
+                    outf.write( ' \n' )
+                else:
+                    outf.write( '\n' )
+    outf.close()
+
+# Fiji Jython interpreter implements Python 2.5 which does not
+# provide support for argparse.
+error_log = sys.argv[ -8 ]
+input = sys.argv[ -7 ]
+black_background = jython_utils.asbool( sys.argv[ -6 ] )
+prune_cycle_method = sys.argv[ -5 ]
+prune_ends = jython_utils.asbool( sys.argv[ -4 ] )
+calculate_largest_shortest_path = jython_utils.asbool( sys.argv[ -3 ] )
+if calculate_largest_shortest_path:
+    BASIC_NAMES.extend( [ 'Longest Shortest Path', 'spx', 'spy', 'spz' ] )
+    DETAIL_NAMES.extend( [ ' ', ' ', ' ', ' ' ] )
+show_detailed_info = jython_utils.asbool( sys.argv[ -2 ] )
+output = sys.argv[ -1 ]
+
+# Open the input image file.
+input_image_plus = IJ.openImage( input )
+
+# Create a copy of the image.
+input_image_plus_copy = input_image_plus.duplicate()
+image_processor_copy = input_image_plus_copy.getProcessor()
+
+try:
+    # Set binary options.
+    options = jython_utils.get_binary_options( black_background=black_background )
+    IJ.run( input_image_plus_copy, "Options...", options )
+
+    # Convert image to binary if necessary.
+    if not image_processor_copy.isBinary():
+        IJ.run( input_image_plus_copy, "Make Binary", "" )
+
+    # Run AnalyzeSkeleton
+    analyze_skeleton = AnalyzeSkeleton_()
+    analyze_skeleton.setup( "", input_image_plus_copy )
+    if prune_cycle_method == 'none':
+        prune_index  = analyze_skeleton.NONE
+    elif prune_cycle_method == 'shortest_branch':
+        prune_index  = analyze_skeleton.SHORTEST_BRANCH
+    elif prune_cycle_method == 'lowest_intensity_voxel':
+        prune_index  = analyze_skeleton.LOWEST_INTENSITY_VOXEL
+    elif prune_cycle_method == 'lowest_intensity_branch':
+        prune_index  = analyze_skeleton.LOWEST_INTENSITY_BRANCH
+    result = analyze_skeleton.run( prune_index,
+                                   prune_ends,
+                                   calculate_largest_shortest_path,
+                                   input_image_plus_copy,
+                                   True,
+                                   True )
+    # Save the results.
+    save( result, output, show_detailed_info, calculate_largest_shortest_path )
+except Exception, e:
+    jython_utils.handle_error( error_log, str( e ) )