def perform_coverage_delete(self, files):
# perform coverage delete
cleaner = Cleaner('pass # pragma: no cover\n')
for filename in files.keys():
content = self.read(filename)
content = cleaner.clean(content, files[filename])
self.write(filename, content)
def __init__(self):
self.cleaner = Cleaner()
self.maxlen = 900
self.tokenizer = None
self.parent_path = Path(__file__).parent.parent
with open(
self.parent_path /
'data/neural_network_config/tokenizer.pickle', 'rb') as handle:
self.tokenizer = pickle.load(handle)
# load json and create model
json_model_keras = open(
self.parent_path / 'data/neural_network_config/model.json', 'r')
loaded_model_json = json_model_keras.read()
json_model_keras.close()
self.loaded_model = model_from_json(loaded_model_json)
# load weights into new model
self.loaded_model.load_weights(self.parent_path /
"data/neural_network_config/model.h5")
# evaluate loaded model on test data
self.loaded_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae', 'accuracy'])
class Tips(object):
""" Manage Tips Events. """
def __init__(self, enable):
self.enable = enable
self._tips = {}
self._new_tips = set()
self.lock = Lock()
if self.enable:
self.fetcher = Fetcher(self._tips, self.lock, self._new_tips)
self.cleaner = Cleaner(self._tips, self.lock, self._new_tips)
self.fetcher.start()
self.cleaner.start()
def tips(self):
return self._tips.values()
def new_tips(self):
if self._new_tips:
wait_free_acquire(self.lock)
res = [self._tips[x] for x in self._new_tips]
self._new_tips.clear()
self.lock.release()
return res
else:
return []
def stop(self):
if self.enable:
self.fetcher.finnish()
self.cleaner.finnish()
def __init__(self):
self.lda_model = None
self.dictionary = None
self.n_topic = None
self.n_passes = None
self.cleaner = Cleaner()
self.folder_name = "models"
def play(self):
'''
'''
Cleaner.clean_wait_begin(self, self.check_begin)
self.init_slot_descriptions()
#change the picture
self.background_wait_begin = Picture(self.app,
image=PATH_RESSOURCES +
"background_play.jpg",
grid=[0, 0, 40, 40])
response = str(
requests.get(BASE_URL + '/play/round/1/hand/' +
self.token).content)[2:-1]
datas = json.loads(response)
self.cards = []
idx_pos = 1
for data in datas:
self.cards.append(
PushButton(self.app,
image=PATH_RESSOURCES + "cards_min/card_" +
data["image"] + "_min.png",
command=self.show_card,
args=[data],
grid=[idx_pos, 35]))
idx_pos += 1
def main(endpoint_models: list):
""" Loop over endpoint models, search file directory for endpoints
and compare to a standard endpoint config.
"""
audit_list = list()
for model in tqdm(endpoint_models, desc="Looping over endpoint models..."):
# first get the gold standard config file
standard_config_file = get_standard_config(model)
# now open that standard file
with open(standard_config_file, 'r') as standard_config:
standard_config_json = json.load(standard_config)
audit = Parser(standard_config_json)
# gather endpoint filenames
endpoint_config_files = gather_endpoints(model)
for endpoint in tqdm(endpoint_config_files,
desc="Looping over endpoint config files..."):
with open(endpoint, 'r') as endpoint_file:
endpoint_json = json.load(endpoint_file)
config_diff = audit.compare(endpoint_json, endpoint.name)
cleaner = Cleaner(config_diff)
cleaned = cleaner.clean()
audit_list.append({f"{model}": cleaned})
return audit_list
def test_metadata(self):
e = Cleaner('./tests/data/models/meta_2200_model.pkl')
e2 = Cleaner('./tests/data/models/72000_model.pkl')
metadata = e.metadata()
no_metadata = e2.metadata()
self.assertTrue(metadata)
self.assertFalse(no_metadata)
class TopicModeler:
def __init__(self, **kwargs):
'''
kwargs::
class gensim.models.ldamodel.LdaModel(corpus=None, num_topics=100, id2word=None, distributed=False, chunksize=2000, passes=1, update_every=1, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf=None, minimum_phi_value=0.01, per_word_topics=False, callbacks=None, dtype=<class 'numpy.float32'>)¶
'''
self.cleaner = Cleaner()
self.lda_model = None
self.lda_kwargs = kwargs
self.dictionary = None
def update(self, docs):
cleaned = [list(self.cleaner.clean(doc)) for doc in docs]
self.dictionary = corpora.Dictionary(cleaned)
corpus = [self.dictionary.doc2bow(text) for text in cleaned]
if self.lda_model is None:
self.lda_model = models.ldamodel.LdaModel(corpus,
id2word=self.dictionary,
**self.lda_kwargs)
else:
self.lda_model.update(corpus, id2word=self.dictionary)
def classify(self, doc):
bow = self.dictionary.doc2bow(list(self.cleaner.clean(doc)))
topic = max(self.lda_model.get_document_topics(bow),
key=lambda x: x[1])[0]
return self.lda_model.show_topic(topic)
def print_topics(self):
print(self.lda_model.print_topics(num_topics=10, num_words=3))
def format_text(self, text, document, formatting):
par_len = 150
applicator = Applicator()
cleaner = Cleaner()
formatter = Formatter()
new_text = self.get_string(text)
cleaner.create_sentc_list(new_text)
sentc_list = cleaner.get_sentc_list()
formatter.set_sentlist(sentc_list)
if formatting == "list":
formatter.frmt_textlist()
elif formatting == "block":
formatter.frmt_textblock(par_len=par_len)
elif formatting == "string":
formatter.frmt_textstring()
else:
print("format not supported")
raise SystemExit
format_text = formatter.get_text()
applicator.apply_text(format_text, document=document)
def faction(self):
'''
'''
Cleaner.clean_wait_player(self, self.check_players)
self.background_faction = Picture(self.app,
image=PATH_RESSOURCES +
"background_faction.png",
grid=[0, 0, 20, 20])
self.button_faction_1 = PushButton(self.app,
image=PATH_RESSOURCES +
"faction1.png",
command=self.choose_faction,
args=[1],
grid=[7, 12])
self.button_faction_2 = PushButton(self.app,
image=PATH_RESSOURCES +
"faction2.png",
command=self.choose_faction,
args=[2],
grid=[8, 12])
self.button_faction_3 = PushButton(self.app,
image=PATH_RESSOURCES +
"faction3.png",
command=self.choose_faction,
args=[3],
grid=[9, 12])
self.button_faction_4 = PushButton(self.app,
image=PATH_RESSOURCES +
"faction4.png",
command=self.choose_faction,
args=[4],
grid=[10, 12])
def show_card(self, card):
'''
'''
if self.card_img_big is not None:
Cleaner.clean_slot_descriptions(self)
self.card_img_big = Picture(self.app,
image=PATH_RESSOURCES + "cards/card_" +
card["image"] + ".png",
grid=[30, 5, 7, 19])
self.name_card = Text(self.app,
text=card["name"],
font="Impact",
color="white",
size=20,
align="left",
grid=[30, 25, 10, 1])
self.description_card = Text(self.app,
text="\"" +
self.cut_line(card["description"]) + "\"",
font="Impact",
color="white",
size=18,
align="left",
grid=[30, 27, 10, 3])
self.description_power_card = Text(self.app,
text=self.cut_line(
card["description_power"]),
font="Impact",
color="white",
size=18,
align="left",
grid=[30, 31, 10, 3])
def __init__(self):
self.initLogger()
self.examiner = Examiner()
self.sec = Secretary()
self.clr = Cleaner()
self.login()
self.init()
def test_to_submission_format(self):
e = Cleaner('./tests/data/models/ae3_213750_model.pkl')
img = '../data/test/10.png'
img, id = e.clean(img)
csv = e.to_submission_format(img, id)
row = csv[300].split(',')
self.assertEqual(row[0], '%s_%d_%d' % (id, 1, 301))
self.assertTrue(float(row[1]) <= 1.0)
def __init__(self):
self.initLogger()
self.examiner = Examiner()
self.sec = Secretary()
self.clr = Cleaner()
self.questionsDb = QuestionsDb('XFQuestionsLib.db')
self.login()
self.init()
def do(task):
logging.debug("Start doing task: %s" % task)
cleaner = Cleaner()
try:
return cleaner.clean(task)
except:
traceback.print_exc(file=sys.stderr)
logging.critical('Failed while cleaning for task %s' % (task['ID']))
return False
def build(self):
'''build the database'''
reddit = Reddit()
cleaner = Cleaner()
for subreddit in reddit.get_subreddits():
for post in reddit.get_posts(subreddit):
self.database.insert(cleaner.clean(post))
for comment in reddit.get_comments(post):
self.database.insert(cleaner.clean(comment))
def __init__(self):
self.__crawler = Crawler()
self.__cleaner = Cleaner()
self.__file_manager = FileManager()
self.__search_engine = GoogleSearch(config.SEARCH_TOPIC,
config.MAX_ITEM,
config.NUMBER_OF_RESULTS_PER_PAGE,
config.PAUSE_BTW_REQUEST)
self.__csf_manager = CSFManager()
def __init__(self):
self.config_parser = ConfigParser()
self.out_dir = os.path.join(os.path.dirname(__file__), "backups/")
self.create_empty_dir()
self.backup_name = "%s-%s.zip" % (os.getlogin(), time.strftime("%d-%m-%Y"))
self.directorys = self.config_parser.directories_to_backup
self.path = os.path.join(self.out_dir, self.backup_name)
self.zip_creator = ZipCreator(self.path, self.directorys)
self.drive_connector = DriveConnector(self.out_dir, self.config_parser)
self.cleaner = Cleaner(self.out_dir, self.config_parser.get_clean_time())
def __init__(self, **kwargs):
'''
kwargs::
class gensim.models.ldamodel.LdaModel(corpus=None, num_topics=100, id2word=None, distributed=False, chunksize=2000, passes=1, update_every=1, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf=None, minimum_phi_value=0.01, per_word_topics=False, callbacks=None, dtype=<class 'numpy.float32'>)¶
'''
self.cleaner = Cleaner()
self.lda_model = None
self.lda_kwargs = kwargs
self.dictionary = None
def wait_begin(self):
'''
'''
Cleaner.clean_choose_faction(self)
#change the picture
self.background_wait_begin = Picture(self.app,
image=PATH_RESSOURCES +
"background_waiting.png",
grid=[0, 0, 20, 20])
self.background_wait_begin.repeat(1000, self.check_begin)
def test_cleaner_erase_given_data(self):
""" Checks if it is possible to delete a file
Notes:
58 is hard-coded. DO NOT CHANGE
"""
file = open('plushkin1', 'w+')
file.close()
file = open('plushkin2', 'w+')
file.close()
cc = Cleaner(['plushkin1', 'plushkin2'], 0)
self.assertEqual(cc.clean_and_report(), ([], 1, 58, 0))
def collect_all_data(cls, df):
judges = df.copy()
#
for plaintext in judges.plain_text:
top_split, bottom_split = Cleaner.splitter(plaintext)
bad_names = Cleaner._unclean_names(top_split)
good_names = Cleaner._clean_names(bad_names)
author = Cleaner._clean_author(bottom_split)
yield list(good_names), author
def cleanupTask(task):
# cleanup task at hand
# ----------------------------------------------------------------------------------------------
# Get all parameters for the production
# ----------------------------------------------------------------------------------------------
cleaner = Cleaner(task)
cleaner.logCleanup()
print ''
return
def display_db(self):
'''
***not meant to stay***
display everything in database
'''
cleaner = Cleaner()
for post in self.database.posts.find():
loaded = cleaner.load(post)
pprint.pprint(loaded.data)
for comment in self.database.comments.find():
loaded = cleaner.load(comment)
pprint.pprint(loaded.data)
def performance_modelo(dataset, model):
Sentenca = dataset.iloc[:, 1]
Intencoes = dataset.iloc[:, 0]
cleaner = Cleaner()
Sentenca_cleaned = [cleaner.clean_text(x) for x in Sentenca]
Sentenca_counts = CountVectorizer().fit_transform(Sentenca_cleaned)
X_train, X_test, y_train, y_test = train_test_split(Sentenca_counts,
Intencoes,
test_size=0.15,
random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy_score_model = accuracy_score(y_test, y_pred)
return print(f"A acurácia do model é de {accuracy_score_model*100:.1f}%")
def __init__(self):
"""
Description
-----------
Sets our default variable path.
"""
self.dir_to_watch = os.getenv("DIR_TO_WATCH")
self.destination_dir = os.getenv("DESTINATION_DIR")
self.logger = Logger()
self.logger.write(f'Automated Maid', figlet=True)
self.cleaner = Cleaner()
def main(dataset, model):
Sentenca = dataset.iloc[:, 1]
Intencoes = dataset.iloc[:, 0]
cleaner = Cleaner()
Sentenca_cleaned = [cleaner.clean_text(x) for x in Sentenca]
vectorizer = CountVectorizer()
Sentenca_counts = vectorizer.fit_transform(Sentenca_cleaned)
model.fit(Sentenca_counts, Intencoes)
print("Digite um comando:")
nova_sentenca = input()
nova_sentenca_clean = cleaner.clean_text(nova_sentenca)
#nova_sentenca = "liga a luz"
counts_da_nova_sentenca = vectorizer.transform(
[cleaner.clean_text(nova_sentenca_clean)])
interpretacao_sentenca(counts_da_nova_sentenca, nova_sentenca)
def create_db(self) -> None:
"""Create database, collect data and insert them."""
print("creating tables...")
Base.metadata.create_all(self.engine)
print("tables created")
print("uploading data from api...")
collector = Collector()
data = collector.collect()
cleaner = Cleaner()
data_cleaned = cleaner.cleaner(data)
print("upload successful")
print("adding data to tables...")
installer = Installer()
installer.install(data_cleaned, self.engine)
print("database install with success")
def infer():
if request.method == "POST":
body = json.loads(request.data.decode("utf-8"))
text = body.get("text", "")
if text == "":
return Jsonify(
result=""
)
cleaner = Cleaner()
text = clean_text(cleaner, text)
tokenizer = joblib.load(os.path.join(MODELS,"tokenizer.pkl"))
sequence = tokenizer.texts_to_sequences([text])
test = pad_sequences(
sequence, maxlen=max_len
)
_, model = create_BiLSTMRNN()
model.load_weights(
os.path.join(MODELS, 'BiLSTM.hdf5'))
return jsonify(
result= sentiment[
np.around(model.predict(test), decimals=0).argmax(axis=1)[0]]
)
else:
return jsonify(
result="POST API call is required"
)
def process_html_page(coredb, driver, page, config, locks):
source = get_clean_source(driver.page_source)
text_content_dirty = driver.find_element_by_tag_name("body").text
text_content = Cleaner.clean_all(text_content_dirty)
text_content_hash = get_source_hash(text_content)
duplicate_page = coredb.get_page_with_hash(text_content_hash)
if duplicate_page is not None:
logging.debug('Duplicate page found with url: ' + duplicate_page['url'])
# TODO: add column to db to list which page it's duplicate off
coredb.update_page(page['id'], PageType.DUPLICATE.value, 200, None, None, duplicate_page['id'])
return
if False: # dont add new links
links = get_links_from_page(driver, source)
for url in links:
handle_new_link(coredb, config, url, page['id'], locks)
#imgs = driver.find_elements_by_xpath('//img[@src]')
#img_srcs = set([img.get_attribute('src') for img in imgs])
#for img_src in img_srcs:
#if ',' in img_src:
#continue # for example svg+xml, ....
#handle_new_image(coredb, page['id'], img_src)
coredb.update_page(page['id'], PageType.HTML.value, 200, text_content, text_content_hash)
class DomesticViolenceClassifier:
def __init__(self):
self.cleaner = Cleaner()
self.maxlen = 900
self.tokenizer = None
self.parent_path = Path(__file__).parent.parent
with open(
self.parent_path /
'data/neural_network_config/tokenizer.pickle', 'rb') as handle:
self.tokenizer = pickle.load(handle)
# load json and create model
json_model_keras = open(
self.parent_path / 'data/neural_network_config/model.json', 'r')
loaded_model_json = json_model_keras.read()
json_model_keras.close()
self.loaded_model = model_from_json(loaded_model_json)
# load weights into new model
self.loaded_model.load_weights(self.parent_path /
"data/neural_network_config/model.h5")
# evaluate loaded model on test data
self.loaded_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae', 'accuracy'])
#returns the sentiment of a text string
def domestic_violence_subject_probability(self, text: str):
x = self.tokenizer.texts_to_sequences([self.cleaner.clean_text(text)])
x = pad_sequences(x, padding='post', maxlen=self.maxlen)
y_new = self.loaded_model.predict(x)
return y_new[0][0]
def main():
#istantiate all the useful classes
linker=EntityLinker()
cleaner=Cleaner()
sentiment=Sentiment()
utility= Utility()
def process(self):
maxlen, chars, x, y, text, chars, char_indices, indices_char, next_chars = Cleaner(
).mainClean()
model = self.createModel(maxlen, chars)
print_callback = LambdaCallback(
on_epoch_end=self.on_epoch_end(model, self.on_epoch_end(
), text, maxlen, chars, char_indices, indices_char))
ModelTrainer().main(print_callback, model, x, y)
def __init__(self, enable):
self.enable = enable
self._tips = {}
self._new_tips = set()
self.lock = Lock()
if self.enable:
self.fetcher = Fetcher(self._tips, self.lock, self._new_tips)
self.cleaner = Cleaner(self._tips, self.lock, self._new_tips)
self.fetcher.start()
self.cleaner.start()
def clean_invalid_glyphs_and_remove_hinting(fontfile, hinting, output): whitespace_and_ignorable_list = get_whitespace_and_ignorable_list() cleaner = Cleaner(fontfile, hinting, whitespace_and_ignorable_list) cleaner.clean() # Flatten cmap format 4 (no idRangeOffset/glyphIdArray) so it is a simple # subset of format 12. change_method(_c_m_a_p.cmap_format_4,_cmap_format_4_compile, 'compile') cleaner.save(output) cleaner.close()
def sqliteCustomer():
sqlite = Sqlite()
sqlite.createTable() #创建表
cl = Cleaner(sqlite)
while 1:
if not gl.QTFLAG: #退出检测
gl.DCFLAG = False
break
try:
data = gl.MYQ.get()
if data[0]==3:
addImg(sqlite,data)
elif data[0]==7:
print 'clear'
cl.cleanOTImg()
except Queue.Empty:
time.sleep(1)
except Exception,e:
logger.error(str(e))
raise
gl.TRIGGER.emit("<font %s>%s</font>"%(gl.style_red,getTime()+str(e)))
def clean_invalid_glyphs_and_remove_hinting(fontfile, hinting, output, verbose):
whitespace_and_ignorable_list = get_whitespace_and_ignorable_list()
cleaner = Cleaner(fontfile, hinting, whitespace_and_ignorable_list)
cleaner.clean(verbose)
# Flatten cmap format 4 (no idRangeOffset/glyphIdArray) so it is a simple
# subset of format 12.
# do we still what this?
change_method(_c_m_a_p.cmap_format_4, _cmap_format_4_compile, "compile")
old_12_or_13_compile = change_method(_c_m_a_p.cmap_format_12_or_13, _cmap_format_12_or_13_compile, "compile")
cleaner.save(output)
cleaner.close()
change_method(_c_m_a_p.cmap_format_12_or_13, old_12_or_13_compile, "compile")
def test_clean(self):
img = config.data_dir_path + 'test/10.png'
PIL.Image.open(img).show()
e = Cleaner('./tests/data/models/13_72000_model.pkl')
e.clean_and_show(img)
e = Cleaner('./tests/data/models/ae3_213750_model.pkl')
e.clean_and_show(img)
pass
def __init__(self, config={}):
Cleaner.__init__(self, config)
self.repo = git.Repo(self.cwd)
self.remote = getattr(self.repo.remotes, self.remote) #or remote/whatev
def main(p):
start = time.time()
# 选择文件名以'json.gz'结尾的记录
file_name_list = filter(lambda x: x.endswith('json.gz'), os.listdir(p))
# TODO 添加文件是否是24个的判断(glob模块)
for file_name in file_name_list:
with open(os.path.join(p, file_name), 'r') as f:
raw_json_file = gzip.GzipFile(fileobj=f)
record_cleaner = Cleaner()
record_grouper = Grouper(db)
record_normalizer = Normalizer(db)
mongo_helper = MongoHelper(db)
counter = ActorCounter()
evaluater = Evaluater()
# 数据清洗
record_cleaner.set_dirty_data(raw_json_file)
record_cleaner.clean()
clean_record = record_cleaner.get_clean_data()
log.log('clean record %s' % len(clean_record))
# 数据处理
# 分组
record_grouper.set_records(clean_record)
record_grouper.group()
record_actor_exist = record_grouper.get_group_1()
record_actor_new= record_grouper.get_group_2()
log.log('record_actor_exist: %s' % len(record_actor_exist))
log.log('record_actor_new: %s' % len(record_actor_new))
# 处理记录的actor已存在的记录
log.log('Begin processing actor-exist records...')
# 只需要删掉记录的actor_attrs即可
for record in record_actor_exist:
del record['actor_attributes']
log.log('Finished.')
# 处理记录的actor不存在的记录
record_normalizer.set_records(record_actor_new)
record_normalizer.normalize()
record_actor_new = record_normalizer.get_record_actor_new()
new_actors = record_normalizer.get_new_actors()
# 把本地的今日新增的Actor更新到数据库
actors = new_actors.values()
mongo_helper.insert_new_actors(actors)
# 对新增的Actor, 改变Redis中相应的计数
counter.count_actor_list(actors)
# 计算每条记录的val
evaluater.set_records(record_actor_exist)
evaluater.evaluate()
val_actor_exist = evaluater.get_val_cache()
evaluater.set_records(record_actor_new)
evaluater.evaluate()
val_actor_new = evaluater.get_val_cache()
# 将记录插入数据库
mongo_helper.insert_new_reocrds(record_actor_new)
mongo_helper.insert_new_reocrds(record_actor_exist)
# 将今日用户新增的val更新到数据库
mongo_helper.update_val(val_actor_new)
mongo_helper.update_val(val_actor_exist)
record_cleaner.free_mem()
del record_cleaner
del record_grouper
del record_normalizer
del mongo_helper
del counter
del evaluater
# 生成CSV文件
util.grcount2csv()
end = time.time()
log.log('total: %s s' % (end - start))
from cleaner import Cleaner cleaner = Cleaner() cleaner.run()
from feeder import FeedDownloader
from webhelper import WebHelper
from cleaner import Cleaner
from threading import Thread
import time
t1 = time.time()
fd = FeedDownloader("http://mybroadband.co.za/news/feed", "My Broadband")
wh = WebHelper()
cleaner = Cleaner()
articles = fd.parse()
print time.time() - t1, "setup complete"
t1 = time.time()
wh.get_html_threaded(articles)
print time.time() - t1, "threaded download complete"
t1 = time.time()
for a in articles:
a.html = wh.attempt_get_html(a.url)
print time.time() - t1, "non threaded download complete"
t1 = time.time()
for a in articles:
if a.html:
a.plaintext = cleaner.clean(a.html)
def clean(self, base, file_base): cleaner = Cleaner(logger=self.logger, options=self.options) cleaner.handle_aux(base, file_base)
def main():
parser = OptionParser(prog="reaper",
version="0.1.0",
usage="%prog [options] <path to folder> "+
"[<path to folder...>]",
description="PyReaper is a small tool that detects " +
"duplicated files by hashing them and then deletes " +
"these duplicated files leaving just one of them",
epilog="CAUTION: handle with EXTREME CARE, " +
"use -n option first if you are not sure of " +
"what are you doing, this thing deletes stuff!!!")
parser.add_option("-n",
"--no-action",
dest="noaction",
action="store_true",
help="does not executes any file action")
parser.add_option("-d",
"--delete",
dest="delete",
action="store_true",
help="delete every duplicated file")
parser.add_option("-m",
"--move-to",
dest="moveto",
metavar="DIR",
help='Moves duplicated files instead of deleting them')
parser.add_option("-p",
"--print-rm-commands",
dest="rmcommands",
action="store_true",
help="skips delete process and prints a set of \"rm\" " +
"commands so you can delete the duplicate files yourself")
parser.add_option("-i",
"--interactive",
dest="interactive",
action="store_true",
help="interactive mode, will ask for each duplicate. " +
"By default it deletes every duplicate found but " +
"the first one")
parser.add_option("-y",
"--dont-ask-confirmation",
dest="noconfirmation",
action="store_true",
help="skips confirmation question. ")
parser.add_option("-s",
"--store-hashes",
dest="storehash",
action="store_true",
help="store and keep calculated hashes in .digest hidden files ")
parser.add_option("-t",
"--delete-empty-trees",
dest="deletedirs",
action="store_true",
help="deletes empty trees when finishes")
parser.add_option("-e",
"--ext",
dest="extension",
action="store",
help="only digests files with the given extension" )
parser.add_option("-v",
"--verbose",
dest="verbose",
action="store_true",
help="outputs much more information during process " +
"(sometimes even too much)")
parser.add_option("",
"--ignore-stored-hashes",
dest="ignorehashes",
action="store_true",
help="ignores stored calculated hashes in .digest " +
"hidden files, this means every hash will be " +
"recalculated")
(options, args) = parser.parse_args()
if not args:
exit_with_error('', parser)
br = Walker(options.extension, \
options.storehash, \
options.verbose, \
options.ignorehashes)
action = None
moveto = None
rmcommands = False
if options.noaction:
action = 'n'
elif options.moveto:
action = 'm'
moveto = options.moveto
if not moveto:
exit_with_error('No "move to" target provided', parser)
elif not os.path.exists(moveto):
exit_with_error('Path %s does not exists' % moveto, parser)
elif not os.path.isdir(moveto):
exit_with_error('Path %s is not a directory' % moveto, parser)
elif options.delete:
action = 'd'
rmcommands = options.rmcommands
if action is None:
exit_with_error('No action selected', parser)
for path in args:
if not os.path.exists(path):
exit_with_error("path {0} does not exists".format(path), parser)
br.digest(path)
duplicates = br.collisions()
clean = False
if duplicates:
print "Duplicates found, cleaning..."
c = Cleaner(
duplicates,
options.interactive,
options.verbose,
action,
rmcommands,
options.noconfirmation,
moveto)
clean = c.clean()
else:
print "No duplicates found"
if not options.storehash:
print "Deleting digest files..."
c = Cleaner(verbose = options.verbose)
c.delete(br.digestFiles(), -1, True)
if options.deletedirs:
c = Cleaner(verbose = options.verbose)
for path in args:
empty_dirs = br.findEmptyDirs(path)
for dir in empty_dirs:
if options.rmcommands or options.noaction:
print "Keeping empty tree {0}".format(dir)
else:
c.deleteDir(dir)
if clean:
sys.exit(0)
else:
sys.exit(1)
from os import environ as environment
import argparse, yaml
import logging
from cleaner import Cleaner
parser = argparse.ArgumentParser()
parser.add_argument("--path", help="path to run cleaner on", type=str)
args = parser.parse_args()
# logging.basicConfig(level=logging.DEBUG)
with open("config.yml") as sets:
config = yaml.load(sets)
path = args.path
if not path:
path = config["cleaner"]["general_pattern"]
cleaner = Cleaner(config["cleaner"])
print "Cleaning path: " + str(path)
cleaner.clean(path, True)
def clean_invalid_glyphs_and_remove_hinting(fontfile, hinting, output): whitespace_list = get_whitespace_list() cleaner = Cleaner(fontfile, hinting, whitespace_list) cleaner.clean() cleaner.save(output) cleaner.close()