Remove unnecessary arg

m2_to_m2.py

@@ -68,7 +68,7 @@ def main(args):
 				# Auto edits
 				if args.auto:
 					# Auto align the parallel sentences and extract the edits.
-					auto_edits = align_text.getAutoAlignedEdits(proc_orig, proc_cor, nlp, args)				
+					auto_edits = align_text.getAutoAlignedEdits(proc_orig, proc_cor, args)				
 					# Loop through the edits.
 					for auto_edit in auto_edits:
 						# Give each edit an automatic error type.

parallel_to_m2.py

@@ -48,7 +48,7 @@ def main(args):
 					# Markup the corrected sentence with spacy (assume tokenized)
 					proc_cor = toolbox.applySpacy(cor_sent.strip().split(), nlp)
 					# Auto align the parallel sentences and extract the edits.
-					auto_edits = align_text.getAutoAlignedEdits(proc_orig, proc_cor, nlp, args)
+					auto_edits = align_text.getAutoAlignedEdits(proc_orig, proc_cor, args)
 					# Loop through the edits.
 					for auto_edit in auto_edits:
 						# Give each edit an automatic error type.

scripts/align_text.py

@@ -6,7 +6,6 @@
 import scripts.rdlextra as DL
 
 # Some global variables
-NLP = None
 CONTENT_POS = {POS.ADJ, POS.ADV, POS.NOUN, POS.VERB}
 
 ### FUNCTIONS ###
@@ -198,14 +197,10 @@ def levSubstitution(a,b,c,d):
 
 # Input 1: A Spacy annotated original sentence.
 # Input 2: A Spacy annotated corrected sentence.
-# Input 3: A preloaded Spacy processing object.
-# Input 4: Command line args.
+# Input 3: Command line args.
 # Output: A list of lists. Each sublist is an edit of the form:
 # edit = [orig_start, orig_end, cat, cor, cor_start, cor_end]
-def getAutoAlignedEdits(orig, cor, spacy, args):
-	# Save the spacy object globally.
-	global NLP
-	NLP = spacy
+def getAutoAlignedEdits(orig, cor, args):
 	# Get a list of strings from the spacy objects.
 	orig_toks = [tok.text for tok in orig]
 	cor_toks = [tok.text for tok in cor]