--- a/imagej2_noise_jython_script.py	Tue Sep 17 17:03:42 2019 -0400
+++ b/imagej2_noise_jython_script.py	Mon Sep 28 16:39:18 2020 +0000
@@ -1,35 +1,32 @@
 import sys
+
 from ij import IJ
-from ij import ImagePlus
-import jython_utils
 
 # Fiji Jython interpreter implements Python 2.5 which does not
 # provide support for argparse.
-error_log = sys.argv[ -19 ]
-input = sys.argv[ -18 ]
-image_datatype = sys.argv[ -17 ]
-noise = sys.argv[ -16 ]
-standard_deviation = sys.argv[ -15 ]
-radius = sys.argv[ -14 ]
-threshold = sys.argv[ -13 ]
-which_outliers = sys.argv[ -12 ]
-randomj = sys.argv[ -11 ]
-trials = sys.argv[ -10 ]
-probability = sys.argv[ -9 ]
+error_log = sys.argv[-19]
+input_file = sys.argv[-18]
+image_datatype = sys.argv[-17]
+noise = sys.argv[-16]
+standard_deviation = sys.argv[-15]
+radius = sys.argv[-14]
+threshold = sys.argv[-13]
+which_outliers = sys.argv[-12]
+randomj = sys.argv[-11]
+trials = sys.argv[-10]
+probability = sys.argv[-9]
 # Note the spelling - so things don't get confused due to Python lambda function.
-lammbda = sys.argv[ -8 ]
-order = sys.argv[ -7 ]
-mean = sys.argv[ -6 ]
-sigma = sys.argv[ -5 ]
-min = sys.argv[ -4 ]
-max = sys.argv[ -3 ]
-insertion = sys.argv[ -2 ]
-tmp_output_path = sys.argv[ -1 ]
-
-error = False
+lammbda = sys.argv[-8]
+order = sys.argv[-7]
+mean = sys.argv[-6]
+sigma = sys.argv[-5]
+min = sys.argv[-4]
+max = sys.argv[-3]
+insertion = sys.argv[-2]
+tmp_output_path = sys.argv[-1]
 
 # Open the input image file.
-image_plus = IJ.openImage( input )
+image_plus = IJ.openImage(input_file)
 bit_depth = image_plus.getBitDepth()
 image_type = image_plus.getType()
 # Create an ImagePlus object for the image.
@@ -39,46 +36,32 @@
 
 # Perform the analysis on the ImagePlus object.
 if noise == 'add_noise':
-    IJ.run( image_plus_copy, "Add Noise", "" )
+    IJ.run(image_plus_copy, "Add Noise", "")
 elif noise == 'add_specified_noise':
-    IJ.run( image_plus_copy, "Add Specified Noise", "standard=&standard_deviation" )
+    IJ.run(image_plus_copy, "Add Specified Noise", "standard=&standard_deviation")
 elif noise == 'salt_and_pepper':
-    IJ.run( image_plus_copy, "Salt and Pepper", "" )
+    IJ.run(image_plus_copy, "Salt and Pepper", "")
 elif noise == 'despeckle':
-    IJ.run( image_plus_copy, "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" )
+    IJ.run(image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers")
 elif noise == 'remove_nans':
-    if bit_depth == 32:
-        IJ.run( image_plus_copy, "Remove NaNs", "" )
-    else:
-        # When Galaxy metadata for images is enhanced to include information like this,
-        # we'll be able to write tool validators rather than having to stop the job in
-        # an error state.
-        msg = "Remove NaNs requires a 32-bit image, the selected image is %d-bit" % bit_depth
-        jython_utils.handle_error( error_log, msg )
-        error = True
+    IJ.run(image_plus_copy, "Remove NaNs", "")
 elif noise == 'rof_denoise':
-    if image_type == ImagePlus.GRAY32:
-        IJ.run( image_plus_copy, "ROF Denoise", "" )
-    else:
-        msg = "ROF Denoise requires an image of type 32-bit grayscale, the selected image is %d-bit" % ( bit_depth )
-        jython_utils.handle_error( error_log, msg )
-        error = True
+    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" )
+        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" )
+        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" )
+        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" )
+        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" )
+        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" )
+        IJ.run(image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion")
 
-if not error:
-    # Save the ImagePlus object as a new image.
-    IJ.saveAs( image_plus_copy, image_datatype, tmp_output_path )
+# Save the ImagePlus object as a new image.
+IJ.saveAs(image_plus_copy, image_datatype, tmp_output_path)