fix readme formatting

Author: PolarBean

README.md

@@ -94,19 +94,21 @@ folderpath = 'examples/example_brain/GLTa/'
 #try with and without ensemble to find the model which best works for you
 #if you have section numbers included in the filename as _sXXX specify this :)
 Model.predict(folderpath, ensemble=True, section_numbers=True)    
-#This is an optional stage if you have damaged sections, or hemibrains they may negatively effect the propagation for the entire dataset
-#simply set the bad sections here using a string which is unique to those each section you would like to label as bad. DeepSlice will
-#not include it in the propagation and instead it will infer its position based on neighbouring sections.
+#This is an optional stage if you have damaged sections, or hemibrains they may negatively effect the 
+#propagation for the entire dataset simply set the bad sections here using a string which is unique to 
+#those each section you would like to label as bad. DeepSlice will not include it in the propagation 
+#and instead it will infer its position based on neighbouring sections.
 Model.set_bad_sections(bad_sections=["_s094", "s199"])
 #If you would like to normalise the angles (you should)
 Model.propagate_angles()                     
 #To reorder your sections according to the section numbers 
 Model.enforce_index_order()    
-#alternatively if you know the precise spacing (ie; 1, 2, 4, indicates that section 3 has been left out of the series) Then you can use      
+#alternatively if you know the precise spacing (ie; 1, 2, 4, indicates that section 3 has been left out.    
 #Furthermore if you know the exact section thickness in microns this can be included instead of None
 #if your sections are numbered rostral to caudal you will need to specify a negative section_thickness      
 Model.enforce_index_spacing(section_thickness = None)
-#now we save which will produce a json file which can be placed in the same directory as your images and then opened with QuickNII. 
+#now we save which will produce a json file which can be placed in the same directory as your images and
+#then opened with QuickNII. 
 Model.save_predictions(folderpath + 'MyResults')