From a392bb13276d833165c8195d62a19225b8cc8a80 Mon Sep 17 00:00:00 2001
From: Clovis Gladstone <clovisgladstone@gmail.com>
Date: Wed, 3 Apr 2024 16:30:15 -0500
Subject: [PATCH] more clean-up

---
 python/philologic/loadtime/LoadFilters.py | 35 -----------------------
 1 file changed, 35 deletions(-)

diff --git a/python/philologic/loadtime/LoadFilters.py b/python/philologic/loadtime/LoadFilters.py
index fd87e90c..f16b0a6a 100755
--- a/python/philologic/loadtime/LoadFilters.py
+++ b/python/philologic/loadtime/LoadFilters.py
@@ -47,41 +47,6 @@ def generate_words_sorted(loader_obj, text):
     os.system(wordcommand)
 
 
-def old_spacy_tagger(loader_obj, text):
-    """Tag words with Spacy"""
-    with open(text["raw"] + ".tmp", "w", encoding="utf8") as tmp_file:
-        with open(text["raw"], encoding="utf8") as fh:
-            sentence = []
-            current_sent_id = None
-            for line in fh:
-                philo_type, word, philo_id, attrib = line.split("\t")
-                if philo_type in ("word", "sent", "punct"):
-                    sent_id = " ".join(philo_id.split()[:6])
-                    record = Record(philo_type, word, philo_id.split())
-                    record.attrib = loads(attrib)
-                    if current_sent_id is not None and sent_id != current_sent_id:
-                        spacy_sentence = SpacyDoc(loader_obj.nlp.vocab, [r.name for r in sentence])
-                        parsed_sentence = loader_obj.nlp(spacy_sentence)
-                        for saved_record, parsed_word in zip(sentence, parsed_sentence):
-                            saved_record.attrib["pos"] = parsed_word.pos_
-                            saved_record.attrib["tag"] = parsed_word.tag_
-                            saved_record.attrib["ent_type"] = parsed_word.ent_type_
-                            saved_record.attrib["lemma"] = parsed_word.lemma_
-                            print(saved_record, file=tmp_file)
-                        sentence = []
-                    sentence.append(record)
-                    current_sent_id = sent_id
-        if sentence:
-            spacy_sentence = SpacyDoc(loader_obj.nlp.vocab, [r.name for r in sentence])
-            parsed_sentence = loader_obj.nlp(spacy_sentence)
-            for saved_record, parsed_word in zip(sentence, parsed_sentence):
-                saved_record.attrib["pos"] = parsed_word.pos_
-                saved_record.attrib["tag"] = parsed_word.tag_
-                saved_record.attrib["ent_type"] = parsed_word.ent_type_
-                saved_record.attrib["lemma"] = parsed_word.lemma_
-                print(saved_record, file=tmp_file)
-
-
 def spacy_tagger(loader_obj, text):
     """Tag words with Spacy"""