def export_as_csv(modeladmin, request, queryset):
""" Generic csv export admin action.
Based on http://djangosnippets.org/snippets/2712/
"""
model = modeladmin.model
opts = modeladmin.model._meta
field_names = [field.name for field in opts.fields]
if exclude:
field_names = [f for f in field_names if f not in exclude]
elif fields:
# copy the list, not just assign the list
field_names = list(fields)
labels = prep_label(model, field_names)
if extra:
field_names += extra
labels += prep_extra_label(model, extra)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=%s.csv' % (
unicode(opts).replace('.', '_')
)
writer = UnicodeWriter(response)
if header:
writer.writerow(labels)
for obj in queryset:
writer.writerow([prep_field(obj, field) for field in field_names])
return response
def parse_statistics(self):
time_spent = time.time() - self.start_time
result_string = 'Done!\nSuccessfully parsed {} items.\nFailed to parse {} items.\nSkipped {} items.\nTime spent:{}'.format(
self.parsed_successfully, self.parsed_unsuccessfully, self.skipped,
time_spent)
print result_string
if self.savestats:
output_file_name = 'parse_statistics_' + timezone.now().strftime(
"%Y%m%d-%H%m") + '.txt'
failed_csv_output_file = 'failed_rows_' + timezone.now().strftime(
"%Y%m%d-%H%m") + '.csv'
result_string += '\n Failed rows saved to {}'.format(
failed_csv_output_file)
print 'Saving extended statistics to file: {}'.format(
output_file_name)
f = open(output_file_name, 'w')
f.write(result_string)
f.close()
with open(failed_csv_output_file, 'wb') as csv_output:
writer = UnicodeWriter(csv_output,
quotechar='"',
delimiter=';')
output_file_name.write('Next rows failed to be parsed\n')
for r in self.failed_rows:
try:
for i in xrange(len(
r)): # cleaner way "for x in r" fails to work
r[i] = r[i].decode('UTF-8')
writer.writerow(r)
except BaseException as e:
print e
print r
def __init__(self,
corpus,
parser=sentence_parse,
compute_similarities=True):
"""Return a new ingester for the corpus.
parser may be sentence_parse or ngram_parser(n)
Client must insure that no other ingester is running
concurrently on the same corpus.
"""
self.corpus = corpus
self.parser = parser
self.should_compute_similarities = compute_similarities
max_doc_id = corpus.max_doc_id()
self.next_id = max_doc_id + 1 if max_doc_id is not None else 0
self.document_file = tempfile.TemporaryFile()
self.document_writer = UnicodeWriter(self.document_file)
self.occurrence_file = tempfile.TemporaryFile()
self.sequencer = PhraseSequencer(corpus)
def artists(request):
response = HttpResponse(content_type='text/plain')
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="artists.csv"'
writer = UnicodeWriter(response)
fieldNames = TArtist.field_names()
writer.writerow(fieldNames)
for artist in TArtist.objects.all()[:1000]:
values = []
for fieldName in fieldNames:
values.append(artist.field_value(fieldName))
writer.writerow(values)
return response
def csvwrite(filename, verbose=None, codepage="cp1252"):
""" Write the file on the desired format """
records = 0
table = dbf.Table(filename, codepage=codepage)
table.open()
with open("{f}.csv".format(f=filename), "w") as csvfile:
# Include the first line as a comma-separated line, no-quoting
csvfile.write(",".join(table.field_names)+"\n")
csvwriter = UnicodeWriter(csvfile, quoting=QUOTE_ALL )
for records, row in enumerate(table, start=1):
csvwriter.writerow([field.strip() for field in row])
if verbose:
# print a dot every 1000 records
if records % 1000 == 0:
print('.', end="")
return records
def process_folder(folder):
"""
Writes annotations from all FoLiA .xml-files from the given folder to a .csv-file.
"""
# Create an output .csv-file
with open(os.path.join(folder, 'annotations.csv'), 'wb') as csv_file:
csv_file.write(u'\uFEFF'.encode('utf-8')) # the UTF-8 BOM to hint Excel we are using that...
csv_writer = UnicodeWriter(csv_file, delimiter=';')
csv_writer.writerow(['tekstnummer', 'zin nr',
'geannoteerde passage', 'correctie',
'eenheid', 'probleem', 'woordsoort',
'originele zin', 'gecorrigeerde zin'])
# Loop over all .xml-files in the given folder
for filename in glob.glob(os.path.join(folder, '*.xml')):
print 'Processing ', filename
process_file(csv_writer, filename)
def parse_statistics(self):
time_spent = time.time() - self.start_time
result_string = 'Done!\nSuccessfully parsed {} items.\nFailed to parse {} items.\nSkipped {} items.\nTime spent:{}'.format(
self.parsed_successfully, self.parsed_unsuccessfully, self.skipped, time_spent)
print result_string
if self.savestats:
output_file_name = 'parse_statistics_' + timezone.now().strftime("%Y%m%d-%H%m") + '.txt'
failed_csv_output_file = 'failed_rows_' + timezone.now().strftime("%Y%m%d-%H%m") + '.csv'
result_string += '\n Failed rows saved to {}'.format(failed_csv_output_file)
print 'Saving extended statistics to file: {}'.format(output_file_name)
f = open(output_file_name, 'w')
f.write(result_string)
f.close()
with open(failed_csv_output_file, 'wb') as csv_output:
writer = UnicodeWriter(csv_output, quotechar='"', delimiter=';')
output_file_name.write('Next rows failed to be parsed\n')
for r in self.failed_rows:
try:
for i in xrange(len(r)): # cleaner way "for x in r" fails to work
r[i] = r[i].decode('UTF-8')
writer.writerow(r)
except BaseException as e:
print e
print r
def write_tweets(self, file_name, tweets, columns):
if file_name is None or tweets is None:
return None
unicode_writer = UnicodeWriter(open(file_name, 'w'))
unicode_writer.writerow(columns)
for tweet in tweets:
_tmp = []
for column in columns:
if column in tweet:
_tmp.append(tweet[column])
else:
_tmp.append('')
unicode_writer.writerow(_tmp)
def main():
logging.config.fileConfig('logging.ini', disable_existing_loggers=False)
args = parse_args()
with args.output:
csv_writer = UnicodeWriter(args.output)
csv_writer.writerow(bacparser.models.get_model(args.year)._fields)
for filename in args.filenames:
logging.info('Converting %s' % (filename,))
with open_compressed_file(filename) as f:
unpickler = pickle.Unpickler(f)
try:
while True:
o = unpickler.load()
csv_writer.writerow(o)
except EOFError:
pass
def __init__(self, corpus, parser=sentence_parse, compute_similarities=True):
"""Return a new ingester for the corpus.
parser may be sentence_parse or ngram_parser(n)
Client must insure that no other ingester is running
concurrently on the same corpus.
"""
self.corpus = corpus
self.parser = parser
self.should_compute_similarities = compute_similarities
max_doc_id = corpus.max_doc_id()
self.next_id = max_doc_id + 1 if max_doc_id is not None else 0
self.document_file = tempfile.TemporaryFile()
self.document_writer = UnicodeWriter(self.document_file)
self.occurrence_file = tempfile.TemporaryFile()
self.sequencer = PhraseSequencer(corpus)
def get_company_attr(market, urls, output_file):
col_headers = [ #維基百科上原本就有的欄位
'Industry',
'Products',
'Type',
'Traded as',
'Area served',
'Parent',
'Subsidiaries',
'Key people',
'Genre',
'Services',
'Owner(s)',
'Employees',
'Website',
'Logo',
]
extra_col_headers = [ #經過處理後產生的欄位
'Symbol',
]
csv_writer = UnicodeWriter(open(output_file, 'wb'),
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
encoding='utf-8')
csv_writer.writerow(['Wiki link'] + col_headers + extra_col_headers)
re_obj = re.compile("%s : (?P<symbol>\w*)" % market)
for url in company_wiki_link_generator(urls): #每個公司的wiki頁面
print url
response = get_web_contents(url)
d = pq(response)
p = d(".infobox")
infobox = p.html() #取出公司資料的table
d = pq(infobox)
rows = d.find('tr')
data = [None] * (len(col_headers) + len(extra_col_headers))
symbol = ''
for row in rows: #公司資料table中的每一列
th_txt = pq(row).find('th').text()
td = pq(row).find('td')
if td.attr('class') == 'logo':
td_txt = td.find('img').attr('src')
data[col_headers.index('Logo')] = td_txt
if th_txt in col_headers:
td_txt = td.text()
data[col_headers.index(th_txt)] = td_txt
#從Wiki的Type或Trade as欄位取的股票的Symbol
if th_txt == 'Type':
try:
symbol = re_obj.search(td_txt).group("symbol")
except Exception:
pass
if (not symbol) and th_txt == 'Traded as':
try:
symbol = re_obj.search(td_txt).group("symbol")
except Exception:
pass
data[len(col_headers) + extra_col_headers.index("Symbol")] = symbol
csv_writer.writerow([url] + data)
class DocumentIngester(object):
def __init__(self, corpus, parser=sentence_parse, compute_similarities=True):
"""Return a new ingester for the corpus.
parser may be sentence_parse or ngram_parser(n)
Client must insure that no other ingester is running
concurrently on the same corpus.
"""
self.corpus = corpus
self.parser = parser
self.should_compute_similarities = compute_similarities
max_doc_id = corpus.max_doc_id()
self.next_id = max_doc_id + 1 if max_doc_id is not None else 0
self.document_file = tempfile.TemporaryFile()
self.document_writer = UnicodeWriter(self.document_file)
self.occurrence_file = tempfile.TemporaryFile()
self.sequencer = PhraseSequencer(corpus)
def _record_document(self, text, phrases, metadata):
doc_id = self.next_id
self.next_id += 1
formatted_metadata = ",".join([('"%s"=>"%s"' % (key, value.replace('\\', '\\\\').replace('"', '\\"'))) for (key, value) in metadata.items()])
self.document_writer.writerow([str(self.corpus.id), str(doc_id), text, formatted_metadata])
for (phrase_id, indexes) in phrases:
formatted_indexes = '"{%s}"' % ", ".join(['""(%s, %s)""' % (start, end) for (start, end) in indexes])
self.occurrence_file.write("%s,%s,%s,%s\n" % (self.corpus.id, doc_id, phrase_id, formatted_indexes))
return doc_id
def _upload_new_documents(self):
"""Upload document text and phrase occurrences
Return list of new document_ids
"""
self.document_file.flush()
self.document_file.seek(0)
self.corpus.upload_csv(self.document_file, 'documents')
self.document_file.close()
self.document_file = tempfile.TemporaryFile()
self.document_writer = csv.writer(self.document_file)
self.occurrence_file.flush()
self.occurrence_file.seek(0)
self.corpus.upload_csv(self.occurrence_file, 'phrase_occurrences')
self.occurrence_file.close()
self.occurrence_file = tempfile.TemporaryFile()
def ingest(self, docs):
"""Ingest set of new documents"""
new_doc_ids = list()
print "parsing %s documents..." % len(docs)
for doc in docs:
if isinstance(doc, basestring):
text = doc
metadata = {}
else:
text = doc['text']
metadata = doc['metadata']
phrases = self.parser.__call__(text, self.sequencer)
id = self._record_document(text, phrases, metadata)
new_doc_ids.append(id)
print "uploading documents..."
self.sequencer.upload_new_phrases()
self._upload_new_documents()
if self.should_compute_similarities:
print "computing similarities..."
self.compute_similarities(new_doc_ids)
@staticmethod
def _pairs_for_comparison(all_ids, new_ids):
allowed_ids = set(all_ids)
all_ids = list(all_ids)
all_ids.sort()
new_ids = list(new_ids)
new_ids.sort(reverse=True)
for x in all_ids:
for y in new_ids:
if x >= y:
break
if y in allowed_ids:
yield (x, y)
def compute_similarities(self, new_doc_ids=None, min_similarity=0.5):
docs = self.corpus.all_docs()
# new_doc_ids is used to keep from recomputing already known similarities.
# None is special signal to compute on all doc pairs.
if new_doc_ids is None:
new_doc_ids = docs.keys()
with get_similarity_writer(self.corpus.id) as writer:
i = 0
for (x, y) in self._pairs_for_comparison(docs.keys(), new_doc_ids):
similarity = jaccard(docs[x], docs[y])
if similarity >= min_similarity:
writer.write(x, y, similarity)
i += 1
if i % 10000000 == 0:
writer.flush()
sys.stdout.write('.')
sys.stdout.flush()
class DocumentIngester(object):
def __init__(self,
corpus,
parser=sentence_parse,
compute_similarities=True):
"""Return a new ingester for the corpus.
parser may be sentence_parse or ngram_parser(n)
Client must insure that no other ingester is running
concurrently on the same corpus.
"""
self.corpus = corpus
self.parser = parser
self.should_compute_similarities = compute_similarities
max_doc_id = corpus.max_doc_id()
self.next_id = max_doc_id + 1 if max_doc_id is not None else 0
self.document_file = tempfile.TemporaryFile()
self.document_writer = UnicodeWriter(self.document_file)
self.occurrence_file = tempfile.TemporaryFile()
self.sequencer = PhraseSequencer(corpus)
def _record_document(self, text, phrases, metadata):
doc_id = self.next_id
self.next_id += 1
formatted_metadata = ",".join([
('"%s"=>"%s"' %
(key, value.replace('\\', '\\\\').replace('"', '\\"')))
for (key, value) in metadata.items()
])
self.document_writer.writerow(
[str(self.corpus.id),
str(doc_id), text, formatted_metadata])
for (phrase_id, indexes) in phrases:
formatted_indexes = '"{%s}"' % ", ".join(
['""(%s, %s)""' % (start, end) for (start, end) in indexes])
self.occurrence_file.write(
"%s,%s,%s,%s\n" %
(self.corpus.id, doc_id, phrase_id, formatted_indexes))
return doc_id
def _upload_new_documents(self):
"""Upload document text and phrase occurrences
Return list of new document_ids
"""
self.document_file.flush()
self.document_file.seek(0)
self.corpus.upload_csv(self.document_file, 'documents')
self.document_file.close()
self.document_file = tempfile.TemporaryFile()
self.document_writer = csv.writer(self.document_file)
self.occurrence_file.flush()
self.occurrence_file.seek(0)
self.corpus.upload_csv(self.occurrence_file, 'phrase_occurrences')
self.occurrence_file.close()
self.occurrence_file = tempfile.TemporaryFile()
def ingest(self, docs):
"""Ingest set of new documents"""
new_doc_ids = list()
print "parsing %s documents..." % len(docs)
for doc in docs:
if isinstance(doc, basestring):
text = doc
metadata = {}
else:
text = doc['text']
metadata = doc['metadata']
phrases = self.parser.__call__(text, self.sequencer)
id = self._record_document(text, phrases, metadata)
new_doc_ids.append(id)
print "uploading documents..."
self.sequencer.upload_new_phrases()
self._upload_new_documents()
if self.should_compute_similarities:
print "computing similarities..."
self.compute_similarities(new_doc_ids)
@staticmethod
def _pairs_for_comparison(all_ids, new_ids):
allowed_ids = set(all_ids)
all_ids = list(all_ids)
all_ids.sort()
new_ids = list(new_ids)
new_ids.sort(reverse=True)
for x in all_ids:
for y in new_ids:
if x >= y:
break
if y in allowed_ids:
yield (x, y)
def compute_similarities(self, new_doc_ids=None, min_similarity=0.5):
docs = self.corpus.all_docs()
# new_doc_ids is used to keep from recomputing already known similarities.
# None is special signal to compute on all doc pairs.
if new_doc_ids is None:
new_doc_ids = docs.keys()
with get_similarity_writer(self.corpus.id) as writer:
i = 0
for (x, y) in self._pairs_for_comparison(docs.keys(), new_doc_ids):
similarity = jaccard(docs[x], docs[y])
if similarity >= min_similarity:
writer.write(x, y, similarity)
i += 1
if i % 10000000 == 0:
writer.flush()
sys.stdout.write('.')
sys.stdout.flush()