--- a/jython_script.py	Sun Oct 11 13:29:38 2015 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,94 +0,0 @@
-import sys
-import jython_utils
-from ij import ImagePlus, IJ
-from ij.plugin.filter import Analyzer, MaximumFinder
-from ij.process import ImageProcessor
-from jarray import array
-
-# Fiji Jython interpreter implements Python 2.5 which does not
-# provide support for argparse.
-error_log = sys.argv[ -10 ]
-input = sys.argv[ -9 ]
-scale_when_converting = jython_utils.asbool( sys.argv[ -8 ] )
-weighted_rgb_conversions = jython_utils.asbool( sys.argv[ -7 ] )
-noise_tolerance = int( sys.argv[ -6 ] )
-output_type = sys.argv[ -5 ]
-exclude_edge_maxima = jython_utils.asbool( sys.argv[ -4 ] )
-light_background = jython_utils.asbool( sys.argv[ -3 ] )
-tmp_output_path = sys.argv[ -2 ]
-output_datatype = 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()
-bit_depth = image_processor_copy.getBitDepth()
-analyzer = Analyzer( input_image_plus_copy )
-
-try:
-    # Set the conversion options.
-    options = []
-    # The following 2 options are applicable only to RGB images.
-    if bit_depth == 24:
-        if scale_when_converting:
-            option.append( "scale" )
-        if weighted_rgb_conversions:
-            options.append( "weighted" )
-    # Perform conversion - must happen even if no options are set.
-    IJ.run( input_image_plus_copy, "Conversions...", "%s" % " ".join( options ) )
-    if output_type in [ 'List', 'Count' ]:
-        # W're  generating a tabular file for the output.
-        # Set the Find Maxima options.
-        options = [ 'noise=%d' % noise_tolerance ]
-        if output_type.find( '_' ) > 0:
-            output_type_str = 'output=[%s]' % output_type.replace( '_', ' ' )
-        else:
-            output_type_str = 'output=%s' % output_type
-        options.append( output_type_str )
-        if exclude_edge_maxima:
-            options.append( 'exclude' )
-        if light_background:
-            options.append( 'light' )
-        # Run the command.
-        IJ.run( input_image_plus_copy, "Find Maxima...", "%s" % " ".join( options ) )
-        results_table = analyzer.getResultsTable()
-        results_table.saveAs( tmp_output_path )
-    else:
-        # Find the maxima of an image (does not find minima).
-        # LIMITATIONS: With output_type=Segmented_Particles
-        # (watershed segmentation), some segmentation lines
-        # may be improperly placed if local maxima are suppressed
-        # by the tolerance.
-        mf = MaximumFinder()
-        if output_type == 'Single_Points':
-            output_type_param = mf.SINGLE_POINTS
-        elif output_type == 'Maxima_Within_Tolerance':
-            output_type_param = mf.IN_TOLERANCE
-        elif output_type == 'Segmented_Particles':
-            output_type_param = mf.SEGMENTED
-        elif output_type == 'List':
-            output_type_param = mf.LIST
-        elif output_type == 'Count':
-            output_type_param = mf.COUNT
-        # Get a new byteProcessor with a normal (uninverted) LUT where
-        # the marked points are set to 255 (Background 0). Pixels outside
-        # of the roi of the input image_processor_copy are not set. No
-        # output image is created for output types POINT_SELECTION, LIST
-        # and COUNT.  In these cases findMaxima returns null.
-        byte_processor = mf.findMaxima( image_processor_copy,
-                                        noise_tolerance,
-                                        ImageProcessor.NO_THRESHOLD,
-                                        output_type_param,
-                                        exclude_edge_maxima,
-                                        False )
-        # Invert the image or ROI.
-        byte_processor.invert()
-        if output_type == 'Segmented_Particles' and not light_background:
-            # Invert the values in this image's LUT (indexed color model).
-            byte_processor.invertLut()
-        image_plus = ImagePlus( "output", byte_processor )
-        IJ.saveAs( image_plus, output_datatype, tmp_output_path )
-except Exception, e:
-    jython_utils.handle_error( error_log, str( e ) )