def compute_p_word_given_class(data_paths, vocab_size):
"""
Return a dictionary of word probabilities, P(word | class). All datapaths belong to the same class.
Incorporate Laplacian Smoothing with k=1 here. p_word_given_class should include the probability of UNKNOWN_WORD,
any word that doesn't appear in the training set
"""
p_word_given_class = dict()
# compute number of words in the given class
class_size = 0
for path in data_paths:
message = open_file(path)
words = get_words(message)
class_size += len(words)
# add elements to dictionary
for path in data_paths:
message = open_file(path)
words = get_words(message)
for word in words:
if word in p_word_given_class:
p_word_given_class[word] += 1 / (class_size + vocab_size + 1)
else:
p_word_given_class[word] = 2 / (class_size + vocab_size + 1)
p_word_given_class['UNKNOWN_WORD'] = 1 / (class_size + vocab_size + 1)
return p_word_given_class
def main():
"""Prints a diff between two files."""
parser = optparse.OptionParser(usage='usage: %prog file-a file-b [options]')
parser.add_option('--text-is-case-insensitive', action='store_false',
dest='text_is_case_sensitive', default=True,
help='<pfif:full_name>Jane</pfif:full_name> is the same as '
'<pfif:full_name>JANE</pfif:full_name>')
parser.add_option('--no-grouping', action='store_false',
default=True, dest='group_by_record_id',
help='Rather than grouping all differences pertaining to '
'the same record together, every difference will be '
'displayed individually.')
parser.add_option('--ignore-field', action='append', dest='ignore_fields',
default=[], help='--ignore-field photo_url will mean that '
'there will be no messages for photo_url fields that are '
'added, removed, or changed. To specify multiple fields '
'to ignore, use this flag multiple times.')
parser.add_option('--omit-blank-fields', action='store_true', default=False,
help='Normally, a blank field (ie, <foo></foo>) will count '
'as a different against a file that does not have that '
'field at all. If you pass this flag, a blank field will '
'count as an omitted field.')
(options, args) = parser.parse_args()
assert len(args) >= 2, 'Must provide two files to diff.'
messages = pfif_file_diff(
utils.open_file(args[0]), utils.open_file(args[1]),
text_is_case_sensitive=options.text_is_case_sensitive,
ignore_fields=options.ignore_fields,
omit_blank_fields=options.omit_blank_fields)
print(utils.MessagesOutput.generate_message_summary(messages))
if options.group_by_record_id:
print(utils.MessagesOutput.messages_to_str_by_id(messages))
else:
print(utils.MessagesOutput.messages_to_str(messages))
def btn_click(self, event):
if not os.path.exists(self.icon_path):
return
ext = str(Path(self.icon_path).suffix)
save_path = self.icon_save_path + ext
shutil.copy(self.icon_path, save_path)
utils.open_file(save_path, True)
def dfc2018_loader(folder):
img = open_file(folder + '2018_IEEE_GRSS_DFC_HSI_TR.HDR')[:,:,:-2]
gt = open_file(folder + '2018_IEEE_GRSS_DFC_GT_TR.tif')
gt = gt.astype('uint8')
rgb_bands = (47, 31, 15)
label_values = ["Unclassified",
"Healthy grass",
"Stressed grass",
"Artificial turf",
"Evergreen trees",
"Deciduous trees",
"Bare earth",
"Water",
"Residential buildings",
"Non-residential buildings",
"Roads",
"Sidewalks",
"Crosswalks",
"Major thoroughfares",
"Highways",
"Railways",
"Paved parking lots",
"Unpaved parking lots",
"Cars",
"Trains",
"Stadium seats"]
ignored_labels = [0]
palette = None # 自加，要不报错
return img, gt, rgb_bands, ignored_labels, label_values, palette
def __init__(self, src_path, tgt_path=None):
super(NMTDataset, self).__init__()
self.src = open_file(src_path)
self.tgt = None
if tgt_path is not None:
self.tgt = open_file(tgt_path)
self.size = len(self.src)
def read_features(pos_fname, neg_fname, chrom_lengths, bin_size):
echo('Reading features:', pos_fname, neg_fname)
features = dict(
(chrom, [SKIP] * chrom_lengths[chrom]) for chrom in chrom_lengths)
with open_file(pos_fname) as in_f:
for l in in_f:
chrom, start, end = l.strip().split('\t')[:3]
start_bin = int(start) / bin_size
end_bin = int(end) / bin_size
if chrom not in features:
features[chrom] = []
for bin_i in xrange(start_bin, end_bin + 1):
features[chrom][bin_i] = POS
if neg_fname is not None:
with open_file(neg_fname) as in_f:
for l in in_f:
chrom, start, end = l.strip().split('\t')[:3]
start_bin = int(start) / bin_size
end_bin = int(end) / bin_size
if chrom not in features:
features[chrom] = []
for bin_i in xrange(start_bin, end_bin + 1):
features[chrom][bin_i] = NEG
else:
for chrom in features:
for bin_i in xrange(len(features[chrom])):
if features[chrom][bin_i] != POS:
features[chrom][bin_i] = NEG
return features
def main():
print()
model = int(sys.argv[1])
file_name = sys.argv[2]
n_classes = int(sys.argv[3])
class_list = text_retrieve('class_list.txt')
val_phrase = open_file('data/sign-to-gloss/cleaned/split-files/' +
file_name + '-phrase-' + str(n_classes))
dataset_info = open_file(
'data/sign-to-gloss/cleaned/split-files/dataset-info-' +
str(n_classes))
tar_lines, pred_lines = [], []
for i in range(0, len(val_phrase)):
print(i)
inp, tar = create_batch([val_phrase[i]], dataset_info, n_classes)
pred = translate(inp, model, n_classes)
tar, pred = convert_tar_pred(list(tar[0][1:-1]), pred, class_list)
print('Target phrase: ', tar)
print('Predict phrase: ', pred)
print()
tar_lines.append(tar)
pred_lines.append(pred)
tar_text = lines_to_text(tar_lines, '\n')
pred_text = lines_to_text(pred_lines, '\n')
text_save(
tar_text,
str(n_classes) + '/luong/model_' + str(model) + '/predictions/' +
file_name + '_tar.txt')
text_save(
pred_text,
str(n_classes) + '/luong/model_' + str(model) + '/predictions/' +
file_name + '_pred.txt')
def edit_dataset(src_path, tgt_path):
x = open_file(src_path)
y = open_file(tgt_path)
i = 0
residual = []
with open("./nep_dataset/src.txt", "w", encoding="utf8") as f:
for sent in x:
i += 1
if len(sent.strip()) < 6:
residual.append(i)
continue
if sent.strip()[-1] not in ['ред', '?', '!']:
f.write(sent + 'ред\n')
else:
f.write(sent + '\n')
j = 0
with open("./nep_dataset/tgt.txt", "w", encoding="utf8") as f:
for sent in y:
j += 1
if j in residual:
continue
if sent.strip()[-1] not in ['.', '!', '?']:
f.write(sent + '.\n')
else:
f.write(sent + '\n')
def __get_login_qrcode(self):
"""获取登录二维码并自动打开
https://qr.m.jd.com/show?appid=133&size=147&t=1611304511060
return: boolean
"""
url = "https://qr.m.jd.com/show"
payload = {
"appid": 133,
"size": 147,
"t": utils.get_current_json_timestamp()
}
r = self.session.get(url=url, params=payload, headers=self.headers)
try:
r.raise_for_status()
except requests.HTTPError:
logging.error(f"登录二维码获取({r.status_code})：{r.text}")
return False
qrcode_name = path("qrcode.png").abs_path_str()
utils.dumps_bytes_to_file(r, qrcode_name)
utils.open_file(qrcode_name)
logging.info(f"登录二维码已生成：{qrcode_name}")
logging.info(f"登录二维码已弹出，请打开京东 APP 扫码登录!")
return True
def get_fixed_sets(run, folder_name, dataset_name, mode='both'):
"""
Load fixed training and test set from the file.
Arguments:
run - number of a current run
folder_name - folder with files
dataset_name - name of a loaded dataset
mode - 'both': train and test set,
'train': only training set
'test': only test set
"""
if mode in ('both', 'train'):
train_gt = open_file(folder_name + dataset_name + '/train_gt_' + str(run) + '.npz')
if mode == 'train':
return train_gt
if mode in ('both', 'test'):
test_gt = open_file(folder_name + dataset_name + '/test_gt_' + str(run) + '.npz')
if mode == 'test':
return test_gt
else:
return train_gt, test_gt
else:
print('Wrong name of the mode parameter!')
pass
def main():
print()
n_classes = int(sys.argv[1])
model = int(sys.argv[2])
dataset_info = open_file(
'data/sign-to-gloss/cleaned/split-files/dataset-info-' +
str(n_classes))
print('Dataset Info set size: ', len(dataset_info.keys()))
print()
train_phrase = open_file(
'data/sign-to-gloss/cleaned/split-files/train-phrase-' +
str(n_classes))
val_phrase = open_file(
'data/sign-to-gloss/cleaned/split-files/val-phrase-' + str(n_classes))
test_phrase = open_file(
'data/sign-to-gloss/cleaned/split-files/test-phrase-' + str(n_classes))
print('Training Phrase set size: ', len(train_phrase))
print('Validation Phrase set size: ', len(val_phrase))
print('Testing Phrase set size: ', len(test_phrase))
print()
batch_size = 50
vocab_size = n_classes + 2
parameters = {
'tar_vocab_size': vocab_size,
'emb_size': 512,
'rnn_size': 512,
'batch_size': batch_size,
'epochs': 20,
'train_steps_per_epoch': len(train_phrase) // batch_size,
'rate': 0.3,
'val_steps_per_epoch': len(val_phrase) // batch_size,
'test_steps': len(test_phrase) // batch_size,
'model': model
}
save_file(
parameters, 'results/sign-to-gloss/wlasl-' + str(n_classes) +
'/luong/model_' + str(model) + '/utils/parameters')
print()
print('No. of Training steps per epoch: ',
parameters['train_steps_per_epoch'])
print('No. of Validation steps per epoch: ',
parameters['val_steps_per_epoch'])
print('No. of Testing steps: ', parameters['test_steps'])
print()
train_dataset = tf.data.Dataset.from_tensor_slices(
(train_phrase)).shuffle(len(train_phrase))
train_dataset = train_dataset.batch(batch_size, drop_remainder=True)
val_dataset = tf.data.Dataset.from_tensor_slices(
(val_phrase)).shuffle(len(val_phrase))
val_dataset = val_dataset.batch(batch_size, drop_remainder=True)
test_dataset = tf.data.Dataset.from_tensor_slices(
(test_phrase)).shuffle(len(test_phrase))
test_dataset = test_dataset.batch(batch_size, drop_remainder=True)
print('Model Training started')
print()
#model_training(train_dataset, val_dataset, dataset_info, parameters)
print('Model Testing started')
print()
model_testing(test_dataset, dataset_info, parameters)
def convertData(data_files, modelname, posorneg):
toRet = []
toRet2 = []
if modelname == "naive":
for filename in data_files:
vector = []
raw_data = open_file(filename)
tokenized_data = word_tokenize(raw_data.lower())
length = 1 * len(tokenized_data) - 1
for word in bowvectorsource:
#presence
if word in tokenized_data:
vector.append(1)
else:
vector.append(0)
#normalized frequency
sum = 0
#for tokword in tokenized_data:
# if word == tokword:
# sum += 1 / length
#vector.append(sum)
for i, word in enumerate(bowvectorsource):
if word in tokenized_data:
vector.append(1)
else:
vector.append(0)
#for tokword in tokenized_data:
# if word == tokword:
# sum += 1 / length
toRet.append(vector)
toRet2.append(posorneg)
elif modelname[:6] == "concat":
epoch = modelname[6:]
model1 = Doc2Vec.load("dbow_" + epoch + "e.model")
model2 = Doc2Vec.load("dm_" + epoch + "e.model")
for filename in data_files:
raw_data = open_file(filename)
#print(raw_data)
tokenized_data = word_tokenize(raw_data.lower())
#print(tokenized_data)
vector1 = model1.infer_vector(tokenized_data)
vector2 = model2.infer_vector(tokenized_data)
toRet.append(vector1 + vector2)
toRet2.append(posorneg)
else:
model= Doc2Vec.load(modelname)
for filename in data_files:
raw_data = open_file(filename)
#print(raw_data)
tokenized_data = word_tokenize(raw_data.lower())
#print(tokenized_data)
vector = model.infer_vector(tokenized_data)
toRet.append(vector)
toRet2.append(posorneg)
return toRet, toRet2
def build_file(filename, outfilename, root=u'.', create_dir=True):
filepath = os.path.join(root, filename)
with utils.open_file(filepath) as infile:
try:
output = tags.render(infile.read(), filename=filename, rootdir=root)
except templatelang.ParseBaseException as e:
utils.print_parse_exception(e, filename)
return
with utils.open_file(outfilename, "w", create_dir=create_dir) as outfile:
outfile.write(output)
def gen_anchors(self, output_path, num_clusters):
"""
Generate anchors, which tell YOLO in which scale objects are, which need to be trained and detected.
:param output_path: path, where YOLO output files should be stored
:param num_clusters: number of clusters need to be found
:return: string of all generated anchors rounded to 2 decimals
"""
np.seterr(divide='ignore', invalid='ignore')
train_txt_path = os.path.join(output_path, self.TRAIN_TXT_FILENAME)
f = utils.open_file(train_txt_path)
lines = [line.rstrip('\n') for line in f.readlines()]
annotation_dims = []
for line in lines:
line = line.replace('JPEGImages', 'labels')
line = line.replace('.jpg', '.txt')
line = line.replace('.png', '.txt')
yolo_img_txt_filename = os.path.join(
output_path,
line.split('/')[-1]) # extract filenames out of 'train.txt'
f2 = utils.open_file(yolo_img_txt_filename)
for line in f2.readlines():
line = line.rstrip('\n')
w, h = line.split(' ')[3:]
annotation_dims.append(tuple(map(float, (w, h))))
annotation_dims = np.array(annotation_dims)
if num_clusters == 0:
for num_clusters_ in range(1, 11): # we make 1 through 10 clusters
anchors_str = None
# kmeans returns None, if no anchors are found, then try it with new random indices
while anchors_str is None:
indices = [
random.randrange(annotation_dims.shape[0])
for i in range(num_clusters_)
]
centroids = annotation_dims[indices]
anchors_str = self.kmeans(annotation_dims, centroids)
else:
anchors_str = None
# kmeans returns None, if no anchors are found, then try it with new random indices
while anchors_str is None:
indices = [
random.randrange(annotation_dims.shape[0])
for i in range(num_clusters)
]
centroids = annotation_dims[indices]
anchors_str = self.kmeans(annotation_dims, centroids)
return anchors_str
def write(self, outfilepath=None, mode="w"):
"""Write objects in file to disk.
Either you can write to a new file (if outfilepath is given),
or you can append what's in storelist to the current filepath.
"""
# If outfilepath is specified, iterate over every object in self
# (which might come from something else) and every object in
# self.storelist and write them out to disk
if outfilepath:
with open_file(outfilepath, mode) as outfile:
logging.info("Writing to disk...")
outfile.write(self.get_header())
for obj in self.source:
line = self.obj_to_str(obj)
outfile.write(line)
for obj in self.storelist:
line = self.obj_to_str(obj)
outfile.write(line)
# Clear storelist now that they've been written to disk
self.clear_storelist()
# Update file attributes (in case of new or converted file)
self.source = self
self.filepath = outfilepath
self.is_new = False
# If outfilepath is not specified, simply iterate over every
# object in self.storelist and append them to the file on disk.
else:
# If the file is new and the path already exist, do not append
if self.is_new and os.path.exists(self.filepath):
raise CancerApiException(
"Output file already exists: {}".format(self.filepath))
with open_file(self.filepath, "a+") as outfile:
logging.info("Writing to disk...")
# If the file is new, start with header
if self.is_new:
outfile.write(self.get_header())
# If file is new and source is not self (i.e., converted file),
# iterate over source
if self.is_new and self.source is not self:
for obj in self.source:
line = self.obj_to_str(obj)
outfile.write(line)
# Proceed with iterating over storelist
for obj in self.storelist:
line = self.obj_to_str(obj)
outfile.write(line)
# Clear storelist now that they've been written to disk
self.clear_storelist()
# Update file attributes (in case of new or converted file)
self.source = self
self.is_new = False
def write(self, outfilepath=None, mode="w"):
"""Write objects in file to disk.
Either you can write to a new file (if outfilepath is given),
or you can append what's in storelist to the current filepath.
"""
# If outfilepath is specified, iterate over every object in self
# (which might come from something else) and every object in
# self.storelist and write them out to disk
if outfilepath:
with open_file(outfilepath, mode) as outfile:
logging.info("Writing to disk...")
outfile.write(self.get_header())
for obj in self.source:
line = self.obj_to_str(obj)
outfile.write(line)
for obj in self.storelist:
line = self.obj_to_str(obj)
outfile.write(line)
# Clear storelist now that they've been written to disk
self.clear_storelist()
# Update file attributes (in case of new or converted file)
self.source = self
self.filepath = outfilepath
self.is_new = False
# If outfilepath is not specified, simply iterate over every
# object in self.storelist and append them to the file on disk.
else:
# If the file is new and the path already exist, do not append
if self.is_new and os.path.exists(self.filepath):
raise CancerApiException("Output file already exists: {}".format(self.filepath))
with open_file(self.filepath, "a+") as outfile:
logging.info("Writing to disk...")
# If the file is new, start with header
if self.is_new:
outfile.write(self.get_header())
# If file is new and source is not self (i.e., converted file),
# iterate over source
if self.is_new and self.source is not self:
for obj in self.source:
line = self.obj_to_str(obj)
outfile.write(line)
# Proceed with iterating over storelist
for obj in self.storelist:
line = self.obj_to_str(obj)
outfile.write(line)
# Clear storelist now that they've been written to disk
self.clear_storelist()
# Update file attributes (in case of new or converted file)
self.source = self
self.is_new = False
def check_mentions(self):
"""Checks mentions for sign up's via email or twitter
via "Sign up / Sign up [email]"""
try:
mentions = self.api.mentions_timeline(count=3)
for mention in mentions:
if "stop" in mention.text.lower():
# Unsubscribe for email
if len(mention.text.split()) == 3:
email = mention.text.split()[2]
email_list = utils.open_file(EMAILS).split()
if email in email_list:
email_list.remove(email)
utils.write_to_file(EMAILS, ' '.join(email_list))
# Unsubscribe for Twitter handle
else:
twitter_name = mention.user.screen_name
twitter_name_list = utils.open_file(
TWITTER_NAMES).split()
if twitter_name in twitter_name_list:
twitter_name_list.remove(twitter_name)
utils.write_to_file(TWITTER_NAMES,
' '.join(twitter_name_list))
elif "sign up" in mention.text.lower():
# Email sign up
if len(mention.text.split()) > 3:
email = mention.text.split()[3]
email_list = utils.open_file(EMAILS).split()
if email not in email_list:
email_list.append(email)
utils.append_to_file(EMAILS, email)
# Twitter handle sign up
else:
twitter_name = mention.user.screen_name
twitter_name_list = utils.open_file(
TWITTER_NAMES).split()
if twitter_name not in twitter_name_list:
twitter_name_list.append(twitter_name)
utils.append_to_file(TWITTER_NAMES, twitter_name)
except tweepy.TweepError as error:
utils.write_to_log(f'Error checking mentions: {error}')
def main():
"""Prints a diff between two files."""
parser = optparse.OptionParser(
usage='usage: %prog file-a file-b [options]')
parser.add_option(
'--text-is-case-insensitive',
action='store_false',
dest='text_is_case_sensitive',
default=True,
help='<pfif:full_name>Jane</pfif:full_name> is the same as '
'<pfif:full_name>JANE</pfif:full_name>')
parser.add_option(
'--no-grouping',
action='store_false',
default=True,
dest='group_by_record_id',
help='Rather than grouping all differences pertaining to '
'the same record together, every difference will be '
'displayed individually.')
parser.add_option(
'--ignore-field',
action='append',
dest='ignore_fields',
default=[],
help='--ignore-field photo_url will mean that '
'there will be no messages for photo_url fields that are '
'added, removed, or changed. To specify multiple fields '
'to ignore, use this flag multiple times.')
parser.add_option(
'--omit-blank-fields',
action='store_true',
default=False,
help='Normally, a blank field (ie, <foo></foo>) will count '
'as a different against a file that does not have that '
'field at all. If you pass this flag, a blank field will '
'count as an omitted field.')
(options, args) = parser.parse_args()
assert len(args) >= 2, 'Must provide two files to diff.'
messages = pfif_file_diff(
utils.open_file(args[0]),
utils.open_file(args[1]),
text_is_case_sensitive=options.text_is_case_sensitive,
ignore_fields=options.ignore_fields,
omit_blank_fields=options.omit_blank_fields)
print utils.MessagesOutput.generate_message_summary(messages,
is_html=False)
if options.group_by_record_id:
print utils.MessagesOutput.messages_to_str_by_id(messages)
else:
print utils.MessagesOutput.messages_to_str(messages)
def match(t_file, dm_file):
"""
Open files, convert the text to list and clean it. Check for identical content.
:param t_file: Full path to file 1, as string
:param dm_file: Full path to file 2, as string
:return: Three sets = matching content, not matched from file 1, not matched from file 2.
"""
legacy = utils.open_file(t_file)
ontology = utils.open_file(dm_file)
legacy_occupations = utils.string_to_list(legacy)
ontology_occupations = utils.string_to_list(ontology)
cleaned_legacy = utils.clean_text(legacy_occupations)
cleaned_ontology = utils.clean_text(ontology_occupations)
return match_strings(cleaned_ontology, cleaned_legacy)
def main():
model = 1
file_name = 'train'
n_classes = 100
val_phrase = open_file('data/sign-to-gloss/cleaned/split-files/' +
file_name + '-phrase-' + str(n_classes))
val_info = open_file('data/sign-to-gloss/cleaned/split-files/' +
file_name + '-info-' + str(n_classes))
inp_lines, tar_lines, pred_lines = [], [], []
for i in range(10, 11):
inp, tar = create_batch([val_phrase[i]], val_info, n_classes)
translate(inp, model)
print(tar)
"""print('Input sentence: ', preprocess_inp_tar(inp))
def OwnData(folder):
img = open_file(folder + 'OwnData.mat')['Data']
gt = open_file(folder + 'OwnData_gt.mat')['temp']
# gt = gt.astype('uint8')
rgb_bands = (47, 31, 15) # 没改
label_values = ["Background",
"Class_1",
"Class_2"]
ignored_labels = [0]
palette = None # 自加，要不报错
return img, gt, rgb_bands, ignored_labels, label_values, palette
def _calculate_total_lengths(self):
msg = "Calculating enrichment in regions"
if self.counts_file:
self.sorted_region_path = self.counts_file
if (not self.total_reads_a or not self.total_reads_b or (not self.total_reads_replica and self.use_replica)) and not self.use_MA:
self.logger.info("... counting from counts file...")
self.total_reads_a = 0
self.total_reads_b = 0
if self.total_reads_replica:
self.total_reads_replica = 0
else:
self.total_reads_replica = 1
for line in open(self.counts_file):
try:
enrich = dict(zip(enrichment_keys, line.split()))
self.total_reads_a += float(enrich["signal_a"])
self.total_reads_b += float(enrich["signal_b"])
if self.use_replica:
self.total_reads_replica += float(enrich["signal_prime_2"])
except ValueError:
self.logger.debug("(Counting) skip header...")
else:
self.logger.info("... counting number of lines in files...")
if not self.total_reads_a:
if self.experiment_format == BAM:
self.total_reads_a = bam.size(self.current_experiment_path)
else:
self.total_reads_a = sum(1 for line in utils.open_file(self.current_experiment_path, self.experiment_format, logger=self.logger))
if not self.total_reads_b:
if self.experiment_format == BAM:
self.total_reads_b = bam.size(self.current_control_path)
else:
self.total_reads_b = sum(1 for line in utils.open_file(self.current_control_path, self.control_format, logger=self.logger))
if self.use_replica and not self.total_reads_replica:
if self.experiment_format == BAM:
self.total_reads_replica = bam.size(self.replica_path)
else:
self.total_reads_replica = sum(1 for line in utils.open_file(self.replica_path, self.experiment_format, logger=self.logger))
self.logger.debug("Number lines in experiment A: %s Experiment B: %s"%(self.total_reads_a, self.total_reads_b))
if self.use_replica:
msg = "%s using replicas..."%msg
else:
msg = "%s using swap..."%msg
self.logger.info(msg)
self.average_total_reads = (self.total_reads_a+self.total_reads_b)/2
def delete_question_by_id(question_id):
question_lines = []
questions = utils.open_file(questions_data, QUESTION_HEADER)
for row in questions:
if row['id'] != question_id:
question_lines.append(row)
utils.write_to_file(questions_data, question_lines, QUESTION_HEADER)
answer_lines = []
answers = utils.open_file(answers_data, ANSWER_HEADER)
for row in answers:
if row['question_id'] != question_id:
answer_lines.append(row)
utils.write_to_file(answers_data, answer_lines, ANSWER_HEADER)
def _create_examples(self):
"""Given a file with feature bundle for each term and a list of positive and
negative seeds, create a file with the vectors for the known positive
and negative examples. Also create the model from the vectors."""
print('Reading feature vectors and extracting pos and neg examples...')
with open_file(self.features_file) as feats, \
open_file(self.vectors_file, 'w') as vectors:
for line in feats:
try:
term = line.split('\t')[2]
label = self._get_label(term)
if label in ('y', 'n'):
vectors.write("%s\t%s" % (label, line))
except Exception as e:
print('ERROR:', e)
def include(self, in_relpath, context):
#print("including: " + in_relpath);
output_str = "";
context["blog"] = self.blog;
def include2(relpath):
return self.include(relpath, context);
context["include"] = include2;
fd = utils.open_file(self.in_base_path + "/" + in_relpath, "rb");
lines = collections.deque(fd.readlines());
while (len(lines) > 0):
line = lines.popleft();
# XXX: this is not recursive
if (re.search("#{for_each_post}", line)):
template = "";
while (len(lines) > 0):
line = lines.popleft();
if (re.search("#{done}", line)):
for p in self.posts:
context["post"] = p;
output_str += self.replace_commands(template, context);
del context["post"];
break;
template += line;
else:
output_str += self.replace_commands(line, context);
#print("include returned'" + output_str + "'");
return output_str;
def edit_note(note, upload, session, trainee=False):
# Write the current data to a temporary file
(fd, temp_filename) = utils.temp_filename()
editor = os.environ.get("EDITOR","vi")
answer = 'E'
while answer == 'E':
os.system("%s %s" % (editor, temp_filename))
temp_file = utils.open_file(temp_filename)
newnote = temp_file.read().rstrip()
temp_file.close()
print "New Note:"
print utils.prefix_multi_line_string(newnote," ")
prompt = "[D]one, Edit, Abandon, Quit ?"
answer = "XXX"
while prompt.find(answer) == -1:
answer = utils.our_raw_input(prompt)
m = re_default_answer.search(prompt)
if answer == "":
answer = m.group(1)
answer = answer[:1].upper()
os.unlink(temp_filename)
if answer == 'A':
return
elif answer == 'Q':
return 0
comment = NewComment()
comment.policy_queue = upload.policy_queue
comment.package = upload.changes.source
comment.version = upload.changes.version
comment.comment = newnote
comment.author = utils.whoami()
comment.trainee = trainee
session.add(comment)
session.commit()
def get_all_quetions_by_latest(order=False, by='submission_time'):
data = []
questions = utils.open_file(questions_data, QUESTION_HEADER)
for row in questions:
data.append(row)
data = sorted(data, key=lambda i: i[by], reverse=order)
return data
def __init__(self, *args, **kwargs):
self.__dict__ = self.__shared_state
if not getattr(self, 'initialised', False):
self.initialised = True
self.timestamp = time.strftime("%Y%m%d%H%M%S")
cnf = Config()
if cnf.has_key("Dir::UrgencyLog"):
# Create the log directory if it doesn't exist
self.log_dir = cnf["Dir::UrgencyLog"]
if not os.path.exists(self.log_dir) or not os.access(self.log_dir, os.W_OK):
warn("UrgencyLog directory %s does not exist or is not writeable, using /srv/ftp.debian.org/tmp/ instead" % (self.log_dir))
self.log_dir = '/srv/ftp.debian.org/tmp/'
# Open the logfile
self.log_filename = "%s/.install-urgencies-%s.new" % (self.log_dir, self.timestamp)
self.log_file = open_file(self.log_filename, 'w')
else:
self.log_dir = None
self.log_filename = None
self.log_file = None
self.writes = 0
def prod_maintainer(notes, upload):
cnf = Config()
changes = upload.changes
whitelists = [upload.target_suite.mail_whitelist]
# Here we prepare an editor and get them ready to prod...
(fd, temp_filename) = utils.temp_filename()
temp_file = os.fdopen(fd, 'w')
temp_file.write("\n\n=====\n\n".join([note.comment for note in notes]))
temp_file.close()
editor = os.environ.get("EDITOR", "vi")
answer = 'E'
while answer == 'E':
os.system("%s %s" % (editor, temp_filename))
temp_fh = utils.open_file(temp_filename)
prod_message = "".join(temp_fh.readlines())
temp_fh.close()
print "Prod message:"
print utils.prefix_multi_line_string(prod_message,
" ",
include_blank_lines=1)
prompt = "[P]rod, Edit, Abandon, Quit ?"
answer = "XXX"
while prompt.find(answer) == -1:
answer = utils.our_raw_input(prompt)
m = re_default_answer.search(prompt)
if answer == "":
answer = m.group(1)
answer = answer[:1].upper()
os.unlink(temp_filename)
if answer == 'A':
return
elif answer == 'Q':
return 0
# Otherwise, do the proding...
user_email_address = utils.whoami() + " <%s>" % (
cnf["Dinstall::MyAdminAddress"])
changed_by = changes.changedby or changes.maintainer
maintainer = changes.maintainer
maintainer_to = utils.mail_addresses_for_upload(maintainer, changed_by,
changes.fingerprint)
Subst = {
'__SOURCE__': upload.changes.source,
'__CHANGES_FILENAME__': upload.changes.changesname,
'__MAINTAINER_TO__': ", ".join(maintainer_to),
}
Subst["__FROM_ADDRESS__"] = user_email_address
Subst["__PROD_MESSAGE__"] = prod_message
Subst["__CC__"] = "Cc: " + cnf["Dinstall::MyEmailAddress"]
prod_mail_message = utils.TemplateSubst(
Subst, cnf["Dir::Templates"] + "/process-new.prod")
# Send the prod mail
utils.send_mail(prod_mail_message, whitelists=whitelists)
print "Sent prodding message"
def main(): """Runs all validations on the provided PFIF XML file""" assert len(sys.argv) == 2, 'Usage: python pfif_validator.py my-pyif-xml-file' validator = PfifValidator(utils.open_file(sys.argv[1], 'r')) messages = validator.run_validations() print(utils.MessagesOutput.generate_message_summary(messages)) print(validator.validator_messages_to_str(messages))
def addSequence(self, fastafile):
'''Add sequences on Fasta format to genome browser.
Arguments:
fastafile -- a string or iterable representing the input Fasta file(s) to be added in the genome browser.
'''
directory = self.__directory__
try:
os.mkdir(os.path.join(directory, 'sequences'))
except:
pass
if isinstance(fastafile, str):
fastafile = (fastafile, )
for i in fastafile:
con = open_file(i)
seq = ''
for line in con:
try:
line = line.decode('utf-8')
except:
pass
line = line.rstrip()
if line.startswith('>'):
if not isinstance(seq, str):
seq.close()
chrom = re.split(' |\|', re.sub('^>', '', line))[0]
seq = open(
os.path.join(directory, 'sequences', chrom + '.fa'),
'w')
seq.write(">" + chrom + "\n")
else:
seq.write(line)
seq.close()
con.close()
def __init__(self, *args, **kwargs):
self.__dict__ = self.__shared_state
if not getattr(self, 'initialised', False):
self.initialised = True
self.timestamp = time.strftime("%Y%m%d%H%M%S")
cnf = Config()
if "Dir::UrgencyLog" in cnf:
# Create the log directory if it doesn't exist
self.log_dir = cnf["Dir::UrgencyLog"]
if not os.path.exists(self.log_dir) or not os.access(
self.log_dir, os.W_OK):
warn(
"UrgencyLog directory %s does not exist or is not writeable, using /srv/ftp.debian.org/tmp/ instead"
% (self.log_dir))
self.log_dir = '/srv/ftp.debian.org/tmp/'
# Open the logfile
self.log_filename = "%s/.install-urgencies-%s.new" % (
self.log_dir, self.timestamp)
self.log_file = open_file(self.log_filename, 'w')
else:
self.log_dir = None
self.log_filename = None
self.log_file = None
self.writes = 0
def create_train_test_valid_data(src_path, tgt_path, base_path):
x = open_file(src_path)
y = open_file(tgt_path)
src_train, src, tgt_train, tgt = train_test_split(x,
y,
test_size=0.2,
shuffle=True)
src_valid, src_test, tgt_valid, tgt_test = train_test_split(
src, tgt, test_size=0.5, shuffle=True)
NMTDataset.save_file(src_train, base_path + "src_train.txt")
NMTDataset.save_file(tgt_train, base_path + "tgt_train.txt")
NMTDataset.save_file(src_valid, base_path + "src_valid.txt")
NMTDataset.save_file(tgt_valid, base_path + "tgt_valid.txt")
NMTDataset.save_file(src_test, base_path + "src_test.txt")
NMTDataset.save_file(tgt_test, base_path + "tgt_test.txt")
def infer(self, model_path, data_path, output):
test_reader = paddle.batch(paddle.reader.buffered(
reader.create_reader(data_path, self.settings),
size=self.conf.batch_size * 1000),
batch_size=self.conf.batch_size)
# load the trained models
parameters = paddle.parameters.Parameters.from_tar(
utils.open_file(model_path, "r"))
inferer = paddle.inference.Inference(output_layer=self.tags_layer,
parameters=parameters)
def count_evi_ids(test_batch):
num = 0
for sample in test_batch:
num += len(sample[reader.E_IDS])
return num
for test_batch in test_reader():
tags = inferer.infer(input=test_batch,
field=["id"],
feeding=network.feeding)
evi_ids_num = count_evi_ids(test_batch)
assert len(tags) == evi_ids_num
print >> output, ";\n".join(str(tag) for tag in tags) + ";"
def right_click_call(self, _):
"""
右键点击 行动按钮
"""
fp = self.txt['text']
if not fp:
utils.showinfo('你还没有选择文件/目录')
return
if not os.path.exists(fp):
utils.showinfo('文件/目录 不存在 "{}"'.format(fp))
return
if self.is_folder:
utils.open_dir(fp)
else:
utils.open_file(fp, True)
def get_taxonomy():
a_file = open_file("resources/occupations_from_legacy_taxonomy.txt")
strings = string_to_list(a_file)
cleaned_strings = clean_text(strings)
split_strings = map(split_slash, cleaned_strings)
flatten_list = flatten(split_strings)
return flatten_list
def edit_note(note, upload, session, trainee=False):
# Write the current data to a temporary file
(fd, temp_filename) = utils.temp_filename()
editor = os.environ.get("EDITOR", "vi")
answer = 'E'
while answer == 'E':
os.system("%s %s" % (editor, temp_filename))
temp_file = utils.open_file(temp_filename)
newnote = temp_file.read().rstrip()
temp_file.close()
print "New Note:"
print utils.prefix_multi_line_string(newnote, " ")
prompt = "[D]one, Edit, Abandon, Quit ?"
answer = "XXX"
while prompt.find(answer) == -1:
answer = utils.our_raw_input(prompt)
m = re_default_answer.search(prompt)
if answer == "":
answer = m.group(1)
answer = answer[:1].upper()
os.unlink(temp_filename)
if answer == 'A':
return
elif answer == 'Q':
return 0
comment = NewComment()
comment.policy_queue = upload.policy_queue
comment.package = upload.changes.source
comment.version = upload.changes.version
comment.comment = newnote
comment.author = utils.whoami()
comment.trainee = trainee
session.add(comment)
session.commit()
def filter(alias2ent, freq_chat, th_chat, freq_general, th_general, freq_lex, th_lex, filter_set, complex_filter_set, exceptions):
entities = []
for filename in filter_set:
t_entities = load_from_file(filename, full = True)
entities.extend(t_entities)
entities = set(entities)
complex_entities = []
for filename in complex_filter_set:
t_entities = load_from_file(filename, full = True)
complex_entities.extend(t_entities)
complex_entities = set(complex_entities)
keys = alias2ent.keys()
exceptions = set(exceptions)
fout = utils.open_file('filter.txt', 'w')
for alias in keys:
if alias in exceptions: continue
# if len(alias) <=1 or alias in entities:
if len(alias) <= 1 or alias in entities or decouple(complex_entities, alias, True):
alias2ent[alias] = []
continue
if freq_chat[alias] > th_chat or freq_general[alias] > th_general or freq_lex[alias] > th_lex:
alias2ent[alias] = []
fout.write(u"{}\t{}\t{}\t{}\n".format(alias, freq_chat[alias], freq_general[alias], freq_lex[alias]))
fout.close()
def generate(self, in_base_path, out_base_path):
self.in_base_path = in_base_path;
self.out_base_path = out_base_path;
utils.makedirs(out_base_path);
imgutils.init(in_base_path);
utils.init(in_base_path);
self.blog = Struct(json.load(utils.open_file(self.in_base_path + "/blog.json")));
# copy static content
cmd = "cp -rf " + in_base_path + "/static/* " + out_base_path;
print("copy static content: " + cmd)
proc = utils.execute_shell(cmd);
# 'dynamic' content
for c in ["sticky", "posts"]:
setattr(self, c, []);
self.generate_content(c);
# home page
self.generate_home();
# feed
self.generate_feed();
def prod_maintainer(notes, upload):
cnf = Config()
changes = upload.changes
whitelists = [ upload.target_suite.mail_whitelist ]
# Here we prepare an editor and get them ready to prod...
(fd, temp_filename) = utils.temp_filename()
temp_file = os.fdopen(fd, 'w')
temp_file.write("\n\n=====\n\n".join([note.comment for note in notes]))
temp_file.close()
editor = os.environ.get("EDITOR","vi")
answer = 'E'
while answer == 'E':
os.system("%s %s" % (editor, temp_filename))
temp_fh = utils.open_file(temp_filename)
prod_message = "".join(temp_fh.readlines())
temp_fh.close()
print "Prod message:"
print utils.prefix_multi_line_string(prod_message," ",include_blank_lines=1)
prompt = "[P]rod, Edit, Abandon, Quit ?"
answer = "XXX"
while prompt.find(answer) == -1:
answer = utils.our_raw_input(prompt)
m = re_default_answer.search(prompt)
if answer == "":
answer = m.group(1)
answer = answer[:1].upper()
os.unlink(temp_filename)
if answer == 'A':
return
elif answer == 'Q':
return 0
# Otherwise, do the proding...
user_email_address = utils.whoami() + " <%s>" % (
cnf["Dinstall::MyAdminAddress"])
changed_by = changes.changedby or changes.maintainer
maintainer = changes.maintainer
maintainer_to = utils.mail_addresses_for_upload(maintainer, changed_by, changes.fingerprint)
Subst = {
'__SOURCE__': upload.changes.source,
'__CHANGES_FILENAME__': upload.changes.changesname,
'__MAINTAINER_TO__': ", ".join(maintainer_to),
}
Subst["__FROM_ADDRESS__"] = user_email_address
Subst["__PROD_MESSAGE__"] = prod_message
Subst["__CC__"] = "Cc: " + cnf["Dinstall::MyEmailAddress"]
prod_mail_message = utils.TemplateSubst(
Subst,cnf["Dir::Templates"]+"/process-new.prod")
# Send the prod mail
utils.send_mail(prod_mail_message, whitelists=whitelists)
print "Sent prodding message"
def isValid(text): """ A valid input should be in the form of: weather <city> """ text = utils.escape_query(text) content = utils.open_file(os.path.join(os.getcwd(),"glaucobot/assets/salutations.txt")) for line in content: if bool(re.search(r'%s' %(line.lower()), text, re.IGNORECASE)): return True return False
def file_summary(self):
# changes["distribution"] may not exist in corner cases
# (e.g. unreadable changes files)
if not self.changes.has_key("distribution") or not \
isinstance(self.changes["distribution"], dict):
self.changes["distribution"] = {}
byhand = False
new = False
summary = ""
override_summary = ""
for name, entry in sorted(self.files.items()):
if entry.has_key("byhand"):
byhand = True
summary += name + " byhand\n"
elif entry.has_key("new"):
new = True
summary += "(new) %s %s %s\n" % (name, entry["priority"], entry["section"])
if entry.has_key("othercomponents"):
summary += "WARNING: Already present in %s distribution.\n" % (entry["othercomponents"])
if entry["type"] == "deb":
deb_fh = open_file(name)
summary += TagSection(deb_extract_control(deb_fh))["Description"] + '\n'
deb_fh.close()
else:
entry["pool name"] = poolify(self.changes.get("source", ""), entry["component"])
destination = entry["pool name"] + name
summary += name + "\n to " + destination + "\n"
if not entry.has_key("type"):
entry["type"] = "unknown"
if entry["type"] in ["deb", "udeb", "dsc"]:
# (queue/unchecked), there we have override entries already, use them
# (process-new), there we dont have override entries, use the newly generated ones.
override_prio = entry.get("override priority", entry["priority"])
override_sect = entry.get("override section", entry["section"])
override_summary += "%s - %s %s\n" % (name, override_prio, override_sect)
return (byhand, new, summary, override_summary)
def new_site(root=u'.', force=False):
try:
os.stat(os.path.join(root, 'index.html'))
if not force:
msg = "Oops, there's already an index.html file in the source \n"+\
"folder. If you want to overwrite this folder with a new \n"+\
"site, use the --force option."
print(msg)
sys.exit(1)
except OSError:
pass
print("Creating new site in '{0}'.".format(root))
for fname, text in NEW_SITE.items():
fpath = os.path.join(root, fname)
with utils.open_file(fpath, "w", create_dir=True) as afile:
afile.write(text)
def _open_log(self, debug):
# Create the log directory if it doesn't exist
from daklib.config import Config
logdir = Config()["Dir::Log"]
if not os.path.exists(logdir):
umask = os.umask(00000)
os.makedirs(logdir, 0o2775)
os.umask(umask)
# Open the logfile
logfilename = "%s/%s" % (logdir, time.strftime("%Y-%m"))
logfile = None
if debug:
logfile = sys.stderr
else:
umask = os.umask(0o0002)
logfile = utils.open_file(logfilename, 'a')
os.umask(umask)
self.logfile = logfile
def processHeader(self):
fd = utils.open_file(self.page_path, "rb");
header_json="";
m="";
in_header = True;
for line in fd:
if (in_header):
if (line == "\n"):
in_header = False;
header_json += line;
else:
m += line;
try:
self.header = json.loads(header_json);
except ValueError as e:
utils.fatal("malformed content header in " + self.page_path + ":\n" + str(e));
for k in self.header:
setattr(self, k, self.header[k]);
self.html = markdown.markdown(m);
def open_tag_file(self):
tag_file = "{}.tag.txt".format(self.acquisition_path.encode("utf-8"))
open_file(tag_file)
def process_file(self, in_relpath, out_relfpath, context):
print("=== " + out_relfpath + " ===");
out_fpath = self.out_base_path + "/" + out_relfpath;
utils.makedirs(os.path.dirname(out_fpath));
utils.open_file(out_fpath, "wb").write(self.include(in_relpath, context));
def open_rr_file(self):
rr_file = "{}.rr.txt".format(self.acquisition_path.encode("utf-8"))
open_file(rr_file)
def _open(self):
"""Use the open_file function on self.source.filepath in 'r' mode"""
return open_file(self.source.filepath)
def mc_study(self):
"""
perform psedudo-experiments by generating random
data and models (using statistical bin uncertainty)
pseudo data:
- sum all model hists
- then randomise
model:
- randomise each input hist individually
"""
samples = self.samples
ntrials = 10000
if self.plot_toy_fits: ntrials = 10
## save options before mc study
tag = self.tag
quiet = self.quiet
self.quiet = True
toy_arrays = {}
for s in samples:
if not s in toy_arrays: toy_arrays[s] = {}
toy_arrays[s]['mean'] = []
toy_arrays[s]['error'] = []
toy_arrays[s]['pull'] = []
toy_arrays[s]['diff'] = []
toy_arrays[s]['mc'] = []
data_array = []
## initialise toy fitter
## VERY important to prepare hists first
## before initialising, so TFractionFitter
## is not initialise with the real data
self.prepare_toy_hists()
self.init_fitter(toy=True)
for i in xrange(ntrials):
if i%100==0: print 'trial ',i
#print
#print 'trial%d summary: ' % i
self.tag = '%s_trial%d'% (tag,i)
self.randomise_hists()
self.toy_fit()
if not self.fit_status == 0: continue
if self.plot_toy_fits: self.plot()
temp_means = {}
temp_errors = {}
temp_pulls = {}
temp_diffs = {}
temp_mcs = {}
has_zero = False
n_tot_mc = self.ntot_orig()
#print 'n_tot_mc: %.1f, h_orig_tot.int: %.1f'% (n_tot_mc,self.h_orig_total.Integral())
n_tot_fit = self.ntot_fit()
#print 'n_tot_fit: %.1f, n_data: %.1f' % (n_tot_fit,self.ndata_curr())
for s in samples:
n_mc = self.nsamp_orig(s)
n_fit = self.nsamp_fit(s)
en_fit = self.ensamp_fit(s)
pull = (n_fit-n_mc)/en_fit if en_fit else 0.0
diff = n_fit - n_mc
temp_means[s] = n_fit
temp_errors[s] = en_fit
temp_pulls[s] = pull
temp_diffs[s] = diff
temp_mcs[s] = self.nsamp_curr(s)
f_fit = self.fsamp_fit(s)
#if f_fit == 0.: has_zero = True
if f_fit < 0.0000001: has_zero = True
f_mc = n_mc / n_tot_mc if n_tot_mc else 0.0
f_fit = n_fit / n_tot_fit if n_tot_fit else 0.0
#print '%s, mc: %.1f, fit: %.1f, fmc: %.4f, ffit: %.4f' % (s,n_mc,n_fit,f_mc,f_fit)
#print 'data, mc: %.1f, fit: %.1f' % (n_tot_mc,n_tot_fit)
## remove cases where any component is fit to 0
## argument is that we would not take this
## result if we got it in data
## probably should try to do something
## better in future
if not has_zero:
for s in samples:
toy_arrays[s]['mean'].append(temp_means[s])
toy_arrays[s]['error'].append(temp_errors[s])
toy_arrays[s]['pull'].append(temp_pulls[s])
toy_arrays[s]['diff'].append(temp_diffs[s])
toy_arrays[s]['mc'].append(temp_mcs[s])
data_array.append(self.ndata_curr())
else:
print 'ERROR - component fit to zero'
## restore to original state before toys
self.reset_hists()
self.tag = tag
self.quiet = quiet
## set corrections from toy study
filename = 'toy_%s.root'%(self.tag)
for s in samples:
a_mean = toy_arrays[s]['mean']
a_error = toy_arrays[s]['error']
a_pull = toy_arrays[s]['pull']
a_diff = toy_arrays[s]['diff']
a_mc = toy_arrays[s]['mc']
if not s in self.toy_results: self.toy_results[s] = {}
self.toy_results[s]['meanm'] = numpy.mean(a_mean)
self.toy_results[s]['meane'] = numpy.std(a_mean)
self.toy_results[s]['errorm'] = numpy.mean(a_error)
self.toy_results[s]['errore'] = numpy.std(a_error)
self.toy_results[s]['pullm'] = numpy.mean(a_pull)
self.toy_results[s]['pulle'] = numpy.std(a_pull)
## create plots
h_mean = create_mean_hist(s)
h_error = create_error_hist(s)
h_pull = create_pull_hist(s)
h_diff = create_diff_hist(s)
h_mc = create_mc_hist(s)
for v in a_mean: h_mean.Fill(v)
for v in a_error: h_error.Fill(v)
for v in a_pull: h_pull.Fill(v)
for v in a_diff: h_diff.Fill(v)
for v in a_mc : h_mc.Fill(v)
utils.save_object(h_mean,filename)
utils.save_object(h_error,filename)
utils.save_object(h_pull,filename)
utils.save_object(h_diff,filename)
utils.save_object(h_mc,filename)
h_mc_data = create_mc_hist('data')
for v in data_array: h_mc_data.Fill(v)
utils.save_object(h_mc_data,filename)
for isamp in xrange(len(samples)):
s1 = samples[isamp]
for isamp2 in xrange(len(samples)):
if not isamp2 < isamp: continue
s2 = samples[isamp2]
h = create_2d_mean_hist(s1,s2)
for ns1,ns2 in zip(toy_arrays[s1]['mean'],
toy_arrays[s2]['mean']):
h.Fill(ns1,ns2)
utils.save_object(h,filename)
for s in samples:
h = create_2d_mean_hist('%s_mc'%s,'%s_fit'%s)
for ns1,ns2 in zip(toy_arrays[s]['mc'],
toy_arrays[s]['mean']):
h.Fill(ns1,ns2)
utils.save_object(h,filename)
f = utils.open_file(filename)
f.Close()
def view(self):
utils.open_file(self.localfile)
self.watched = True
def handle(text): content = utils.open_file(os.path.join(os.getcwd(),"glaucobot/assets/affirmatives.txt")) return random.choice(content)
