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xml2csv.py
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xml2csv.py
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import argparse
import csv
import os
import sys
from collections import OrderedDict
from pathlib import Path
from typing import List
from supermat.supermat_tei_parser import process_file_to_json
from supermat.utils import get_in_paths_from_directory
def process_file(finput, use_paragraphs=False):
json = process_file_to_json(finput, use_paragraphs=use_paragraphs)
data_list = []
spans_map = OrderedDict()
spans_links_map = OrderedDict()
spans_links_reverse_map = OrderedDict()
filename = Path(finput).name
passages = json['passages']
for passage_id, passage in enumerate(passages):
passage_common_parts = {
'id': passage_id,
'filename': filename,
'passage_id': str(passage['group_id']) + "|" + str(passage['id']) if 'group_id' in passage else str(
passage['id']),
'text': passage['text']
}
passage['passage_id'] = passage_common_parts['passage_id']
spans_passage_map, spans_passage_links_map, spans_passage_links_reverse_map = get_span_maps(passage)
spans_map.update(spans_passage_map)
spans_links_map.update(spans_passage_links_map)
spans_links_reverse_map.update(spans_passage_links_reverse_map)
spans_by_type = OrderedDict()
for t in ['tcValue', 'material', 'pressure', 'me_method']:
spans_by_type[t] = {key: submap for key, submap in spans_passage_map.items() if 'type' in submap and submap['type'] == t}
records_in_passage = []
for span_id, span in spans_by_type['tcValue'].items():
# for span_id, span in spans_by_type[t].items():
outbound = spans_links_map[span_id] if span_id in spans_links_map.keys() else []
inbound = spans_links_reverse_map[span_id] if span_id in spans_links_reverse_map.keys() else []
# if passage['id'] == 17:
# print(inbound)
for out in sorted(list(set(outbound + inbound))):
if out in spans_map:
span_out = spans_map[out]
records_to_update = list(filter(
lambda rip: span['type'] in rip and span_id == rip[span['type']][0] and span_out['type'] not in rip,
records_in_passage))
if len(records_to_update) == 0:
records_in_passage.append({'tcValue': (span_id, spans_map[span_id]['text']), span_out['type']: (span_out['id'], span_out['text'])})
else:
for record_to_update in records_to_update:
record_to_update[span_out['type']] = (span_out['id'], span_out['text'])
for re in records_in_passage:
ids = [re[key][0] for key in re.keys()]
is_same_passage = all(id_value in spans_passage_map.keys() for id_value in ids)
for key, value in passage_common_parts.items():
if not is_same_passage:
if key != "text":
re[key] = value
else:
re[key] = value
data_list.extend(records_in_passage)
spans_by_type = {}
## Recover possible items not tcValue, that were not linked before
for ent_type in ['material', 'pressure', 'me_method']:
spans_by_type[ent_type] = {key: submap for key, submap in spans_map.items() if 'type' in submap and submap['type'] == ent_type}
for span_id, span in spans_by_type[ent_type].items():
outbound = spans_links_map[span_id] if span_id in spans_links_map.keys() else []
inbound = spans_links_reverse_map[span_id] if span_id in spans_links_reverse_map.keys() else []
for out in list(set(outbound + inbound)):
if out in spans_map:
span_out = spans_map[out]
records_to_update = list(filter(lambda rip: span['type'] not in rip and span_out['type'] in rip and span_out['id'] ==
rip[span_out['type']][0], data_list))
if len(records_to_update) > 0:
for record_to_update in records_to_update:
record_to_update[span['type']] = (span['id'], span['text'])
if span['passage_id'] != record_to_update['passage_id']:
record_to_update['text'] = ""
for re in data_list:
for column in re.keys():
if column not in ['id', 'filename', 'passage_id', 'text']:
re[column] = re[column][1]
return data_list
def get_span_maps(passage):
span_links_map = OrderedDict()
span_links_reverse_map = OrderedDict()
span_map = OrderedDict()
for span in passage['spans']:
if 'id' in span:
if span['id'] not in span_map:
span_map[span['id']] = span
span['passage_id'] = passage['passage_id']
if 'links' in span:
for link in span['links']:
target_id = link['targetId']
source_id = span['id']
if target_id not in span_links_map:
span_links_map[target_id] = [source_id]
else:
span_links_map[target_id].append(source_id)
if source_id not in span_links_reverse_map:
span_links_reverse_map[source_id] = [target_id]
else:
span_links_reverse_map[source_id].append(target_id)
else:
print("error")
return span_map, span_links_map, span_links_reverse_map
def write_output(output_path: List, data: List[dict], columns: List):
with open(output_path, encoding='utf-8', mode='w') as fo:
fw = csv.writer(fo, delimiter=",", quotechar='"')
fw.writerow(columns)
for d in data:
fw.writerow([d[c] if c in d else '' for c in columns])
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Converter XML (Supermat) to a tabular values (CSV, TSV)")
parser.add_argument("--input",
help="Input file or directory",
required=True)
parser.add_argument("--output",
help="Output directory",
required=True)
parser.add_argument("--recursive",
action="store_true",
default=False,
help="Process input directory recursively. If input is a file, this parameter is ignored.")
parser.add_argument("--format",
default='csv',
choices=['tsv', 'csv'],
help="Output format.")
parser.add_argument("--use-paragraphs",
default=False,
action="store_true",
help="Uses paragraphs instead of sentences")
args = parser.parse_args()
input = args.input
output = args.output
recursive = args.recursive
format = args.format
use_paragraphs = args.use_paragraphs
if os.path.isdir(input):
path_list = get_in_paths_from_directory(input, ".xml", recursive=recursive)
output_data = []
for path in path_list:
print("Processing: ", path)
file_output_data = process_file(path, use_paragraphs=use_paragraphs)
# data = sorted(file_data, key=lambda k: k['passage_id'])
output_data.extend(file_output_data)
if os.path.isdir(str(output)):
output_path = os.path.join(output, "output") + "." + format
else:
parent_dir = Path(output).parent
output_path = os.path.join(parent_dir, "output." + format)
elif os.path.isfile(input):
input_path = Path(input)
output_data = process_file(input_path, use_paragraphs=use_paragraphs)
# data_sorted = sorted(data, key=lambda k: k['paragraph_id'])
output_filename = input_path.stem
output_path = os.path.join(output, str(output_filename) + "." + format)
else:
print("The input should be either a file or a directory")
sys.exit(-1)
data = [{**record, **{"id": idx}} for idx, record in enumerate(output_data)]
columns = ['id', 'filename', 'passage_id', 'material', 'tcValue', 'pressure', 'me_method', 'text']
write_output(output_path, data, columns)