changeset 2:bdee06a1bcfa draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5

--- a/imagej2_adjust_threshold_binary_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_adjust_threshold_binary_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -26,7 +26,11 @@
 # Convert image to binary if necessary.
 if not image_processor_copy.isBinary():
     # Convert the image to binary grayscale.
-    IJ.run(input_image_plus_copy, "Make Binary", "iterations=1 count=1 edm=Overwrite do=Nothing")
+    IJ.run(
+        input_image_plus_copy,
+        "Make Binary",
+        "iterations=1 count=1 edm=Overwrite do=Nothing",
+    )
 # Set the options.
 if black_background:
     method_str = "%s dark" % method

--- a/imagej2_analyze_particles_binary_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_analyze_particles_binary_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -3,8 +3,7 @@
 from ij import IJ
 from ij.plugin.filter import Analyzer
 
-
-OPTIONS = ['edm=Overwrite', 'iterations=1', 'count=1']
+OPTIONS = ["edm=Overwrite", "iterations=1", "count=1"]
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
@@ -43,24 +42,24 @@
     IJ.run(input_image_plus_copy, "Make Binary", "")
 
 # Set the options.
-options = ['size=%s' % size]
-circularity_str = '%.3f-%.3f' % (circularity_min, circularity_max)
-options.append('circularity=%s' % circularity_str)
-if show.find('_') >= 0:
-    show_str = '[%s]' % show.replace('_', ' ')
+options = ["size=%s" % size]
+circularity_str = "%.3f-%.3f" % (circularity_min, circularity_max)
+options.append("circularity=%s" % circularity_str)
+if show.find("_") >= 0:
+    show_str = "[%s]" % show.replace("_", " ")
 else:
     show_str = show
-options.append('show=%s' % show_str)
+options.append("show=%s" % show_str)
 if display_results:
-    options.append('display')
+    options.append("display")
     if not all_results:
-        options.append('summarize')
+        options.append("summarize")
 if exclude_edges:
-    options.append('exclude')
+    options.append("exclude")
 if include_holes:
-    options.append('include')
+    options.append("include")
 # Always run "in_situ".
-options.append('in_situ')
+options.append("in_situ")
 
 # Run the command.
 IJ.run(input_image_plus_copy, "Analyze Particles...", " ".join(options))

--- a/imagej2_analyze_skeleton.xml	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_analyze_skeleton.xml	Sun Nov 05 14:21:17 2023 +0000
@@ -1,8 +1,15 @@
-<tool id="imagej2_analyze_skeleton" name="Analyze skeleton" version="@WRAPPER_VERSION@.0">
-    <description></description>
+<tool id="imagej2_analyze_skeleton" name="Analyze skeleton" version="@WRAPPER_VERSION@.1">
+    <description>with ImageJ2</description>
     <macros>
         <import>imagej2_macros.xml</import>
     </macros>
+    <edam_operations>
+        <edam_operation>operation_3443</edam_operation>
+    </edam_operations>
+    <xrefs>
+        <xref type="bio.tools">imagej</xref>
+        <xref type="biii">imagej2</xref>
+    </xrefs>
     <expand macro="fiji_requirements"/>
     <command detect_errors="exit_code"><![CDATA[
 #import os

--- a/imagej2_analyze_skeleton_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_analyze_skeleton_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -4,18 +4,37 @@
 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']
-OPTIONS = ['edm=Overwrite', 'iterations=1', 'count=1']
+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",
+]
+OPTIONS = ["edm=Overwrite", "iterations=1", "count=1"]
 
 
 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))
+    return math.sqrt(
+        math.pow((x2 - x1), 2) + math.pow((y2 - y1), 2) + math.pow((z2 - z1), 2)
+    )
 
 
 def get_graph_length(graph):
@@ -46,29 +65,29 @@
     return graphs
 
 
-def save(result, output, show_detailed_info, calculate_largest_shortest_path, sep='\t'):
+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))
+    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])
+        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])
+            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')
+            outf.write("\n")
     if show_detailed_info:
-        outf.write('# %s\n' % sep.join(DETAIL_NAMES))
+        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
@@ -80,23 +99,23 @@
                 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))
+                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')
+                    outf.write("%s %s" % (sep, sep))
+                    outf.write(" %s" % sep)
+                    outf.write(" %s" % sep)
+                    outf.write(" \n")
                 else:
-                    outf.write('\n')
+                    outf.write("\n")
     outf.close()
 
 
@@ -109,8 +128,8 @@
 prune_ends = sys.argv[-4] == "yes"
 calculate_largest_shortest_path = sys.argv[-3] == "yes"
 if calculate_largest_shortest_path:
-    BASIC_NAMES.extend(['Longest Shortest Path', 'spx', 'spy', 'spz'])
-    DETAIL_NAMES.extend([' ', ' ', ' ', ' '])
+    BASIC_NAMES.extend(["Longest Shortest Path", "spx", "spy", "spz"])
+    DETAIL_NAMES.extend([" ", " ", " ", " "])
 show_detailed_info = sys.argv[-2] == "yes"
 output = sys.argv[-1]
 
@@ -135,14 +154,21 @@
 # Run AnalyzeSkeleton
 analyze_skeleton = AnalyzeSkeleton_()
 analyze_skeleton.setup("", input_image_plus_copy)
-if prune_cycle_method == 'none':
+if prune_cycle_method == "none":
     prune_index = analyze_skeleton.NONE
-elif prune_cycle_method == 'shortest_branch':
+elif prune_cycle_method == "shortest_branch":
     prune_index = analyze_skeleton.SHORTEST_BRANCH
-elif prune_cycle_method == 'lowest_intensity_voxel':
+elif prune_cycle_method == "lowest_intensity_voxel":
     prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL
-elif prune_cycle_method == 'lowest_intensity_branch':
+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)
+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)

--- a/imagej2_binary_to_edm_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_binary_to_edm_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -21,7 +21,7 @@
 image_processor_copy = input_image_plus_copy.getProcessor()
 
 # Set binary options.
-options_list = ['edm=Overwrite', 'iterations=%d' % iterations, 'count=%d' % count]
+options_list = ["edm=Overwrite", "iterations=%d" % iterations, "count=%d" % count]
 if black_background:
     options_list.append("black")
 if pad_edges_when_eroding:

--- a/imagej2_create_image_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_create_image_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -8,7 +8,7 @@
 width = int(sys.argv[-5])
 height = int(sys.argv[-4])
 depth = int(sys.argv[-3])
-type = sys.argv[-2].replace('_', ' ')
+type = sys.argv[-2].replace("_", " ")
 tmp_image_path = sys.argv[-1]
 
 imp = IJ.newImage(title, type, width, height, depth)

--- a/imagej2_enhance_contrast_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_enhance_contrast_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -24,16 +24,16 @@
 options = []
 # If equalize_histogram, saturated_pixels and normalize are ignored.
 if equalize_histogram:
-    options.append('equalize')
+    options.append("equalize")
 else:
-    if saturated_pixels not in [None, 'None']:
+    if saturated_pixels not in [None, "None"]:
         # Fiji allows only a single decimal place for this value.
-        options.append('saturated=%.3f' % float(saturated_pixels))
+        options.append("saturated=%.3f" % float(saturated_pixels))
     # Normalization of RGB images is not supported.
     if bit_depth != 24 and normalize:
-        options.append('normalize')
+        options.append("normalize")
 # Run the command.
-options = "%s" % ' '.join(options)
+options = "%s" % " ".join(options)
 IJ.run(input_image_plus_copy, "Enhance Contrast...", options)
 # Save the ImagePlus object as a new image.
 IJ.saveAs(input_image_plus_copy, output_datatype, tmp_output_path)

--- a/imagej2_find_maxima_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_find_maxima_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -8,11 +8,11 @@
 # provide support for argparse.
 error_log = sys.argv[-10]
 input_file = sys.argv[-9]
-scale_when_converting = sys.argv[-8] == 'yes'
-weighted_rgb_conversions = sys.argv[-7] == 'yes'
+scale_when_converting = sys.argv[-8] == "yes"
+weighted_rgb_conversions = sys.argv[-7] == "yes"
 noise_tolerance = int(sys.argv[-6])
 output_type = sys.argv[-5]
-exclude_edge_maxima = sys.argv[-4] == 'yes'
+exclude_edge_maxima = sys.argv[-4] == "yes"
 light_background = sys.argv[-3]
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
@@ -36,19 +36,19 @@
         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']:
+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('_', ' ')
+    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
+        output_type_str = "output=%s" % output_type
     options.append(output_type_str)
     if exclude_edge_maxima:
-        options.append('exclude')
+        options.append("exclude")
     if light_background:
-        options.append('light')
+        options.append("light")
     # Run the command.
     IJ.run(input_image_plus_copy, "Find Maxima...", "%s" % " ".join(options))
     results_table = analyzer.getResultsTable()
@@ -60,30 +60,32 @@
     # may be improperly placed if local maxima are suppressed
     # by the tolerance.
     mf = MaximumFinder()
-    if output_type == 'Single_Points':
+    if output_type == "Single_Points":
         output_type_param = mf.SINGLE_POINTS
-    elif output_type == 'Maxima_Within_Tolerance':
+    elif output_type == "Maxima_Within_Tolerance":
         output_type_param = mf.IN_TOLERANCE
-    elif output_type == 'Segmented_Particles':
+    elif output_type == "Segmented_Particles":
         output_type_param = mf.SEGMENTED
-    elif output_type == 'List':
+    elif output_type == "List":
         output_type_param = mf.LIST
-    elif output_type == 'Count':
+    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)
+    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:
+    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)

--- a/imagej2_make_binary_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_make_binary_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -8,8 +8,8 @@
 input = sys.argv[-7]
 iterations = int(sys.argv[-6])
 count = int(sys.argv[-5])
-black_background = sys.argv[-4] == 'yes'
-pad_edges_when_eroding = sys.argv[-3] == 'yes'
+black_background = sys.argv[-4] == "yes"
+pad_edges_when_eroding = sys.argv[-3] == "yes"
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
 
@@ -21,12 +21,12 @@
 image_processor_copy = input_image_plus_copy.getProcessor()
 
 # Set binary options.
-options = ['edm=Overwrite', 'iterations=%d' % iterations, 'count=%d' % count]
+options = ["edm=Overwrite", "iterations=%d" % iterations, "count=%d" % count]
 if pad_edges_when_eroding:
-    options.append('pad')
+    options.append("pad")
 if black_background:
-    options.append('black')
-options = ' '.join(options)
+    options.append("black")
+options = " ".join(options)
 IJ.run(input_image_plus_copy, "Options...", options)
 
 # Run the command.

--- a/imagej2_math_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_math_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -8,11 +8,11 @@
 input_file = sys.argv[-7]
 operation = sys.argv[-6]
 expression = sys.argv[-5]
-if sys.argv[-4] in [None, 'None']:
+if sys.argv[-4] in [None, "None"]:
     bin_constant = None
 else:
     bin_constant = int(sys.argv[-4])
-if sys.argv[-3] in [None, 'None']:
+if sys.argv[-3] in [None, "None"]:
     float_constant = None
 else:
     float_constant = float(sys.argv[-3])
@@ -36,49 +36,49 @@
 image_processor_copy = input_image_plus_copy.getProcessor()
 bit_depth = image_processor_copy.getBitDepth()
 
-if operation.find('_') > 0:
+if operation.find("_") > 0:
     # Square_Root.
-    new_operation = operation.replace('_', ' ')
-elif operation in ['Square', 'Log', 'Exp', 'Abs', 'Reciprocal']:
+    new_operation = operation.replace("_", " ")
+elif operation in ["Square", "Log", "Exp", "Abs", "Reciprocal"]:
     # Unfortunately some ImageJ commands require a "..." ending
     # while others do not.  There seems to be no pattern.
-    new_operation = '%s' % operation
+    new_operation = "%s" % operation
 else:
-    new_operation = '%s...' % operation
+    new_operation = "%s..." % operation
 
-if operation == 'Macro':
+if operation == "Macro":
     # Apply the macro code to the image via a call to it's
     # ImageProcessor since this option does not work using
     # the IJ.run() method.
     new_expression = expression.lstrip('"').rstrip('"')
-    options = 'code=%s' % new_expression
+    options = "code=%s" % new_expression
     image_processor_copy.applyMacro(new_expression)
-elif operation == 'Min':
+elif operation == "Min":
     # Min does not work without using the ImageProcessor.
     image_processor_copy.min(float_constant)
-elif operation == 'Max':
+elif operation == "Max":
     # Max does not work without using the ImageProcessor.
     image_processor_copy.max(float_constant)
-elif operation == 'Abs':
+elif operation == "Abs":
     if bit_depth not in [16, 32]:
         # Convert the image to 32-bit.
         IJ.run(input_image_plus_copy, "32-bit", "")
         IJ.run(input_image_plus_copy, new_operation, "")
-elif operation == 'Reciprocal':
+elif operation == "Reciprocal":
     if bit_depth != 32:
         # Convert the image to 32 bit.
         IJ.run(input_image_plus_copy, "32-bit", "")
         IJ.run(input_image_plus_copy, new_operation, "")
 else:
-    if operation in ['AND', 'OR', 'XOR']:
+    if operation in ["AND", "OR", "XOR"]:
         # Value is a binary number.
-        options = 'value=%d' % bin_constant
-    elif operation in ['Log', 'Exp', 'Square', 'Square_Root']:
+        options = "value=%d" % bin_constant
+    elif operation in ["Log", "Exp", "Square", "Square_Root"]:
         # No constant value.
-        options = ''
+        options = ""
     else:
         # Value is a floating point number.
-        options = 'value=%.3f' % float_constant
+        options = "value=%.3f" % float_constant
     IJ.run(input_image_plus_copy, "%s" % new_operation, "%s" % options)
 # Save the ImagePlus object as a new image.
 IJ.saveAs(input_image_plus_copy, output_datatype, tmp_output_path)

--- a/imagej2_noise_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_noise_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -35,33 +35,51 @@
 image_processor_copy = image_plus_copy.getProcessor()
 
 # Perform the analysis on the ImagePlus object.
-if noise == 'add_noise':
+if noise == "add_noise":
     IJ.run(image_plus_copy, "Add Noise", "")
-elif noise == 'add_specified_noise':
+elif noise == "add_specified_noise":
     IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation")
-elif noise == 'salt_and_pepper':
+elif noise == "salt_and_pepper":
     IJ.run(image_plus_copy, "Salt and Pepper", "")
-elif noise == 'despeckle':
+elif noise == "despeckle":
     IJ.run(image_plus_copy, "Despeckle", "")
-elif noise == 'remove_outliers':
-    IJ.run(image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers")
-elif noise == 'remove_nans':
+elif noise == "remove_outliers":
+    IJ.run(
+        image_plus_copy,
+        "Remove Outliers",
+        "radius=&radius threshold=&threshold which=&which_outliers",
+    )
+elif noise == "remove_nans":
     IJ.run(image_plus_copy, "Remove NaNs", "")
-elif noise == 'rof_denoise':
+elif noise == "rof_denoise":
     IJ.run(image_plus_copy, "ROF Denoise", "")
-elif noise == 'randomj':
-    if randomj == 'randomj_binomial':
-        IJ.run(image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion")
-    elif randomj == 'randomj_exponential':
-        IJ.run(image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion")
-    elif randomj == 'randomj_gamma':
+elif noise == "randomj":
+    if randomj == "randomj_binomial":
+        IJ.run(
+            image_plus_copy,
+            "RandomJ Binomial",
+            "trials=&trials probability=&probability insertion=&insertion",
+        )
+    elif randomj == "randomj_exponential":
+        IJ.run(
+            image_plus_copy,
+            "RandomJ Exponential",
+            "lambda=&lammbda insertion=&insertion",
+        )
+    elif randomj == "randomj_gamma":
         IJ.run(image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion")
-    elif randomj == 'randomj_gaussian':
-        IJ.run(image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion")
-    elif randomj == 'randomj_poisson':
+    elif randomj == "randomj_gaussian":
+        IJ.run(
+            image_plus_copy,
+            "RandomJ Gaussian",
+            "mean=&mean sigma=&sigma insertion=&insertion",
+        )
+    elif randomj == "randomj_poisson":
         IJ.run(image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion")
-    elif randomj == 'randomj_uniform':
-        IJ.run(image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion")
+    elif randomj == "randomj_uniform":
+        IJ.run(
+            image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion"
+        )
 
 # Save the ImagePlus object as a new image.
 IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)

--- a/imagej2_skeletonize3d_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_skeletonize3d_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -6,7 +6,7 @@
 # provide support for argparse.
 error_log = sys.argv[-5]
 input_file = sys.argv[-4]
-black_background = sys.argv[-3] == 'yes'
+black_background = sys.argv[-3] == "yes"
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
 
@@ -18,9 +18,9 @@
 image_processor_copy = input_image_plus_copy.getProcessor()
 
 # Set binary options.
-options = ['edm=Overwrite', 'iterations=1', 'count=1']
-if (black_background):
-    options.append('black')
+options = ["edm=Overwrite", "iterations=1", "count=1"]
+if black_background:
+    options.append("black")
 options = " ".join(options)
 IJ.run(input_image_plus_copy, "Options...", options)
 

--- a/imagej2_watershed_binary_jython_script.py	Mon Sep 28 16:52:29 2020 +0000
+++ b/imagej2_watershed_binary_jython_script.py	Sun Nov 05 14:21:17 2023 +0000
@@ -6,7 +6,7 @@
 # provide support for argparse.
 error_log = sys.argv[-5]
 input = sys.argv[-4]
-black_background = sys.argv[-3] == 'yes'
+black_background = sys.argv[-3] == "yes"
 tmp_output_path = sys.argv[-2]
 output_datatype = sys.argv[-1]
 
@@ -18,9 +18,9 @@
 image_processor_copy = input_image_plus_copy.getProcessor()
 
 # Set binary options.
-options = ['edm=Overwrite', 'iterations=1', 'count=1']
-if (black_background):
-    options.append('black')
+options = ["edm=Overwrite", "iterations=1", "count=1"]
+if black_background:
+    options.append("black")
 options = " ".join(options)
 IJ.run(input_image_plus_copy, "Options...", options)