id_within_dataset int64 1 55.5k | snippet stringlengths 19 14.2k | tokens listlengths 6 1.63k | nl stringlengths 6 352 | split_within_dataset stringclasses 1 value | is_duplicated bool 2 classes |
|---|---|---|---|---|---|
40,798 | def prepend_timestamp(msg, format):
if (type(format) is str):
timestamp = time.strftime(format, time.localtime())
elif callable(format):
timestamp = str(format())
else:
raise InvalidTimestampFormat
return ('%s DCTB %s' % (timestamp, msg))
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40,800 | def run_callback_threadsafe(loop, callback, *args):
ident = loop.__dict__.get('_thread_ident')
if ((ident is not None) and (ident == threading.get_ident())):
raise RuntimeError('Cannot be called from within the event loop')
future = concurrent.futures.Future()
def run_callback():
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try:
future.set_result(callback(*args))
except Exception as exc:
if future.set_running_or_notify_cancel():
future.set_exception(exc)
else:
_LOGGER.warning('Exception on lost future: ', exc_info=True)
loop.call_soon_threadsafe(run_callback)
return future
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40,801 | def get_repository_categories(app, id):
sa_session = app.model.context.current
return sa_session.query(app.model.RepositoryCategoryAssociation).filter((app.model.RepositoryCategoryAssociation.table.c.repository_id == app.security.decode_id(id)))
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40,804 | def TimestampFromTicks(ticks):
return datetime(*localtime(ticks)[:6])
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] | construct an object holding a timestamp value from the given ticks value . | train | false |
40,805 | def getPublicTypeMembers(type_, onlyValues=False):
for (name, value) in inspect.getmembers(type_):
if (not name.startswith('__')):
if (not onlyValues):
(yield (name, value))
else:
(yield value)
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40,806 | def replaced(fmri):
return _fmadm_action_fmri('replaced', fmri)
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40,807 | def strip_all(string):
return strip_irc(strip(strip_irc(string)))
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] | removes all $() syntax and mirc formatting codes from the input string and returns it . | train | false |
40,808 | @scope.define_info(o_len=2)
def ap_filter_trials(o_idxs, o_vals, l_idxs, l_vals, gamma, gamma_cap=DEFAULT_LF):
(o_idxs, o_vals, l_idxs, l_vals) = list(map(np.asarray, [o_idxs, o_vals, l_idxs, l_vals]))
n_below = min(int(np.ceil((gamma * np.sqrt(len(l_vals))))), gamma_cap)
l_order = np.argsort(l_vals)
keep_idxs = set(l_idxs[l_order[:n_below]])
below = [v for (i, v) in zip(o_idxs, o_vals) if (i in keep_idxs)]
if 0:
print('DEBUG: thresh', l_vals[l_order[:n_below]])
keep_idxs = set(l_idxs[l_order[n_below:]])
above = [v for (i, v) in zip(o_idxs, o_vals) if (i in keep_idxs)]
return (np.asarray(below), np.asarray(above))
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40,809 | def hamming_loss(y_true, y_pred, labels=None, sample_weight=None, classes=None):
if (classes is not None):
warnings.warn("'classes' was renamed to 'labels' in version 0.18 and will be removed in 0.20.", DeprecationWarning)
labels = classes
(y_type, y_true, y_pred) = _check_targets(y_true, y_pred)
if (labels is None):
labels = unique_labels(y_true, y_pred)
else:
labels = np.asarray(labels)
if (sample_weight is None):
weight_average = 1.0
else:
weight_average = np.mean(sample_weight)
if y_type.startswith('multilabel'):
n_differences = count_nonzero((y_true - y_pred), sample_weight=sample_weight)
return (n_differences / ((y_true.shape[0] * len(labels)) * weight_average))
elif (y_type in ['binary', 'multiclass']):
return _weighted_sum((y_true != y_pred), sample_weight, normalize=True)
else:
raise ValueError('{0} is not supported'.format(y_type))
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40,810 | def euler_from_quaternion(quaternion, axes='sxyz'):
return euler_from_matrix(quaternion_matrix(quaternion), axes)
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] | return euler angles from quaternion for specified axis sequence . | train | false |
40,811 | def show_ipsecpolicy(ipsecpolicy, profile=None):
conn = _auth(profile)
return conn.show_ipsecpolicy(ipsecpolicy)
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] | fetches information of a specific ipsecpolicy cli example: . | train | false |
40,812 | @domain_constructor(loss_target=0)
def q1_lognormal():
return {'loss': scope.min((0.1 * ((hp.lognormal('x', 0, 2) - 10) ** 2)), 10), 'status': base.STATUS_OK}
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40,815 | def _has_db_updated_with_new_score(scored_block_usage_key, **kwargs):
if (kwargs['score_db_table'] == ScoreDatabaseTableEnum.courseware_student_module):
score = get_score(kwargs['user_id'], scored_block_usage_key)
found_modified_time = (score.modified if (score is not None) else None)
else:
assert (kwargs['score_db_table'] == ScoreDatabaseTableEnum.submissions)
score = sub_api.get_score({'student_id': kwargs['anonymous_user_id'], 'course_id': unicode(scored_block_usage_key.course_key), 'item_id': unicode(scored_block_usage_key), 'item_type': scored_block_usage_key.block_type})
found_modified_time = (score['created_at'] if (score is not None) else None)
if (score is None):
return kwargs['score_deleted']
return (found_modified_time >= from_timestamp(kwargs['expected_modified_time']))
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40,816 | def get_or_set_hash(name, length=8, chars='abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)'):
ret = get(name, None)
if (ret is None):
val = ''.join([random.SystemRandom().choice(chars) for _ in range(length)])
if (DEFAULT_TARGET_DELIM in name):
(root, rest) = name.split(DEFAULT_TARGET_DELIM, 1)
curr = get(root, _infinitedict())
val = _dict_from_path(rest, val)
curr.update(val)
setval(root, curr)
else:
setval(name, val)
return get(name)
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40,818 | def get_native_encoding_type():
if (sys.maxunicode == 65535):
return 'UTF16'
else:
return 'UTF32'
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40,819 | def write_NetCDF_georeference(origin, outfile):
geo_ref = ensure_geo_reference(origin)
geo_ref.write_NetCDF(outfile)
return geo_ref
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40,820 | def _compute_rc(G):
deghist = nx.degree_histogram(G)
total = sum(deghist)
nks = ((total - cs) for cs in accumulate(deghist) if ((total - cs) > 1))
edge_degrees = sorted((sorted(map(G.degree, e)) for e in G.edges()), reverse=True)
ek = G.number_of_edges()
(k1, k2) = edge_degrees.pop()
rc = {}
for (d, nk) in enumerate(nks):
while (k1 <= d):
if (len(edge_degrees) == 0):
ek = 0
break
(k1, k2) = edge_degrees.pop()
ek -= 1
rc[d] = ((2 * ek) / (nk * (nk - 1)))
return rc
| [
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] | returns the rich-club coefficient for each degree in the graph g . | train | false |
40,821 | def countArrayElements(array):
elements = []
counters = []
for element in array:
if (element in elements):
indx = elements.index(element)
counters[indx] += 1
else:
elements.append(element)
counters.append(1)
return (elements, counters)
| [
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] | simple method to count the repetitions of elements in an array . | train | false |
40,822 | @mock_ec2
def test_create_dhcp_options_invalid_options():
conn = boto.connect_vpc(u'the_key', u'the_secret')
servers = [u'f', u'f', u'f', u'f', u'f']
with assert_raises(EC2ResponseError) as cm:
conn.create_dhcp_options(ntp_servers=servers)
cm.exception.code.should.equal(u'InvalidParameterValue')
cm.exception.status.should.equal(400)
cm.exception.request_id.should_not.be.none
with assert_raises(EC2ResponseError) as cm:
conn.create_dhcp_options(netbios_node_type=u'0')
cm.exception.code.should.equal(u'InvalidParameterValue')
cm.exception.status.should.equal(400)
cm.exception.request_id.should_not.be.none
| [
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] | create invalid dhcp options . | train | false |
40,823 | def generate_user(email):
username = slugify(email)
(user, _) = UserProfile.objects.get_or_create(email=email, defaults={'username': username})
return user
| [
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] | generate a userprofile given the email provided . | train | false |
40,825 | def configure_template_filters(app):
app.jinja_env.filters['pretty_date'] = pretty_date
app.jinja_env.filters['format_date'] = format_date
app.jinja_env.filters['nl2br'] = nl2br
| [
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] | configure filters . | train | false |
40,828 | def full_like(array, fill_value, stream=None):
warnings.warn('chainer.cuda.full_like is deprecated. Use cupy.full_like instead.', DeprecationWarning)
check_cuda_available()
assert (stream is None)
if isinstance(array, cupy.ndarray):
return cupy.full_like(array, fill_value)
return cupy.full(array.shape, fill_value, dtype=array.dtype)
| [
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] | compatibility for numpy<1 . | train | false |
40,829 | def tags_eq(tagged_object, tag_names):
eq_(sorted([t.name for t in tagged_object.tags.all()]), sorted(tag_names))
| [
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] | assert that the names of the tags on tagged_object are tag_names . | train | false |
40,830 | def expr_from_dict(rep, *gens):
result = []
for (monom, coeff) in rep.items():
term = [coeff]
for (g, m) in zip(gens, monom):
if m:
term.append(Pow(g, m))
result.append(Mul(*term))
return Add(*result)
| [
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] | convert a multinomial form into an expression . | train | false |
40,831 | def start_filter(app, conn, filter, limit=None, timeout=1.0, ack_messages=False, tasks=None, queues=None, callback=None, forever=False, on_declare_queue=None, consume_from=None, state=None, accept=None, **kwargs):
return Filterer(app, conn, filter, limit=limit, timeout=timeout, ack_messages=ack_messages, tasks=tasks, queues=queues, callback=callback, forever=forever, on_declare_queue=on_declare_queue, consume_from=consume_from, state=state, accept=accept, **kwargs).start()
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] | filter tasks . | train | false |
40,832 | def clean_tables():
for f in TABLES:
if os.path.isfile(f):
os.remove(f)
print((u'Removed ' + f))
| [
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] | remove the lexer/parser modules that are dynamically created . | train | false |
40,833 | def validate_twitter(username):
if username:
username = re.sub('https?://(www\\.)?twitter\\.com/|@', '', username)
if (len(username) > 15):
raise ValidationError(_('Twitter usernames cannot be longer than 15 characters.'))
if (not re.match('^\\w+$', username)):
raise ValidationError(_('Twitter usernames must contain only alphanumeric characters and the underscore.'))
return username
| [
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] | return a twitter username given @ or http(s) strings . | train | false |
40,834 | def _find_method(f, version_float):
qualified_name = _fully_qualified_name(f)
method_list = VERSIONED_METHODS.get(qualified_name, [])
for (min_version, max_version, func) in method_list:
if (min_version <= version_float <= max_version):
return func
raise webob.exc.HTTPNotFound()
| [
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] | look in versioned_methods for method with right name matching version . | train | false |
40,835 | def _updateIndices(db):
db.executescript('\n-- syncing\ncreate index if not exists ix_notes_usn on notes (usn);\ncreate index if not exists ix_cards_usn on cards (usn);\ncreate index if not exists ix_revlog_usn on revlog (usn);\n-- card spacing, etc\ncreate index if not exists ix_cards_nid on cards (nid);\n-- scheduling and deck limiting\ncreate index if not exists ix_cards_sched on cards (did, queue, due);\n-- revlog by card\ncreate index if not exists ix_revlog_cid on revlog (cid);\n-- field uniqueness\ncreate index if not exists ix_notes_csum on notes (csum);\n')
| [
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")"
] | add indices to the db . | train | false |
40,836 | def _items(mappingorseq):
if hasattr(mappingorseq, 'items'):
return iteritems(mappingorseq)
return mappingorseq
| [
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] | wrapper for efficient iteration over mappings represented by dicts or sequences:: . | train | false |
40,837 | def hostgroup_get(name=None, groupids=None, hostids=None, **connection_args):
conn_args = _login(**connection_args)
try:
if conn_args:
method = 'hostgroup.get'
params = {'output': 'extend'}
if ((not groupids) and (not name) and (not hostids)):
return False
if name:
name_dict = {'name': name}
params.setdefault('filter', name_dict)
if groupids:
params.setdefault('groupids', groupids)
if hostids:
params.setdefault('hostids', hostids)
params = _params_extend(params, **connection_args)
ret = _query(method, params, conn_args['url'], conn_args['auth'])
return (ret['result'] if (len(ret['result']) > 0) else False)
else:
raise KeyError
except KeyError:
return False
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] | retrieve host groups according to the given parameters . | train | true |
40,838 | def create_initial_config_file(args, extensions_data):
config_file = args.config_files[(-1)]
if os.path.exists(path.expand_path(config_file)):
return
try:
default = config_lib.format_initial(extensions_data)
path.get_or_create_file(config_file, mkdir=False, content=default)
logger.info(u'Initialized %s with default config', config_file)
except IOError as error:
logger.warning(u'Unable to initialize %s with default config: %s', config_file, encoding.locale_decode(error))
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] | initialize whatever the last config file is with defaults . | train | false |
40,839 | def criteria(course_key):
about_path = reverse('about_course', kwargs={'course_id': unicode(course_key)})
return u'{}{}'.format(site_prefix(), about_path)
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40,840 | def dup_zz_cyclotomic_poly(n, K):
h = [K.one, (- K.one)]
for (p, k) in factorint(n).items():
h = dup_quo(dup_inflate(h, p, K), h, K)
h = dup_inflate(h, (p ** (k - 1)), K)
return h
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40,841 | def timestamp_from_record_tuple(record):
return record[0]['timestamp']
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40,843 | def create_image_bdm(image_ref, boot_index=0):
return BlockDeviceDict({'source_type': 'image', 'image_id': image_ref, 'delete_on_termination': True, 'boot_index': boot_index, 'device_type': 'disk', 'destination_type': 'local'})
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40,844 | @pytest.fixture(autouse=True)
def reset_cache_backend_state(celery_app):
(yield)
backend = celery_app.__dict__.get(u'backend')
if (backend is not None):
if isinstance(backend, CacheBackend):
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backend.client.cache.clear()
backend._cache.clear()
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40,845 | @register(u'operate-and-get-next')
def operate_and_get_next(event):
buff = event.current_buffer
new_index = (buff.working_index + 1)
buff.accept_action.validate_and_handle(event.cli, buff)
def set_working_index():
buff.working_index = new_index
event.cli.pre_run_callables.append(set_working_index)
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40,846 | def _delete_cookie(response):
response.set_cookie(settings.SESSION_COOKIE_NAME, max_age=0, expires='Thu, 01-Jan-1970 00:00:00 GMT', domain=settings.SESSION_COOKIE_DOMAIN, secure=(settings.SESSION_COOKIE_SECURE or None), httponly=(settings.SESSION_COOKIE_HTTPONLY or None))
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40,847 | def mutGaussian(individual, mu, sigma, indpb):
size = len(individual)
if (not isinstance(mu, Sequence)):
mu = repeat(mu, size)
elif (len(mu) < size):
raise IndexError(('mu must be at least the size of individual: %d < %d' % (len(mu), size)))
if (not isinstance(sigma, Sequence)):
sigma = repeat(sigma, size)
elif (len(sigma) < size):
raise IndexError(('sigma must be at least the size of individual: %d < %d' % (len(sigma), size)))
for (i, m, s) in zip(xrange(size), mu, sigma):
if (random.random() < indpb):
individual[i] += random.gauss(m, s)
return (individual,)
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40,849 | def ColorScaleRule(start_type=None, start_value=None, start_color=None, mid_type=None, mid_value=None, mid_color=None, end_type=None, end_value=None, end_color=None):
formats = []
if (start_type is not None):
formats.append(FormatObject(type=start_type, val=start_value))
if (mid_type is not None):
formats.append(FormatObject(type=mid_type, val=mid_value))
if (end_type is not None):
formats.append(FormatObject(type=end_type, val=end_value))
colors = []
for v in (start_color, mid_color, end_color):
if (v is not None):
if (not isinstance(v, Color)):
v = Color(v)
colors.append(v)
cs = ColorScale(cfvo=formats, color=colors)
rule = Rule(type='colorScale', colorScale=cs)
return rule
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40,851 | def dump_privatekey(type, pkey, cipher=None, passphrase=None):
bio = _new_mem_buf()
if (cipher is not None):
if (passphrase is None):
raise TypeError('if a value is given for cipher one must also be given for passphrase')
cipher_obj = _lib.EVP_get_cipherbyname(_byte_string(cipher))
if (cipher_obj == _ffi.NULL):
raise ValueError('Invalid cipher name')
else:
cipher_obj = _ffi.NULL
helper = _PassphraseHelper(type, passphrase)
if (type == FILETYPE_PEM):
result_code = _lib.PEM_write_bio_PrivateKey(bio, pkey._pkey, cipher_obj, _ffi.NULL, 0, helper.callback, helper.callback_args)
helper.raise_if_problem()
elif (type == FILETYPE_ASN1):
result_code = _lib.i2d_PrivateKey_bio(bio, pkey._pkey)
elif (type == FILETYPE_TEXT):
rsa = _lib.EVP_PKEY_get1_RSA(pkey._pkey)
result_code = _lib.RSA_print(bio, rsa, 0)
else:
raise ValueError('type argument must be FILETYPE_PEM, FILETYPE_ASN1, or FILETYPE_TEXT')
if (result_code == 0):
_raise_current_error()
return _bio_to_string(bio)
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40,852 | def pearson_chi_squared_test(observed=[], expected=[], df=None, tail=UPPER):
o = list(observed)
e = (list(expected) or _expected(o))
n = len(o)
m = (len(o[0]) if o else 0)
df = (df or ((n - 1) * (m - 1)))
df = (df or (((m == 1) and (n - 1)) or (m - 1)))
x2 = 0.0
for i in xrange(n):
for j in xrange(m):
if ((o[i][j] != 0) and (e[i][j] != 0)):
x2 += (((o[i][j] - e[i][j]) ** 2.0) / e[i][j])
p = gammai((df * 0.5), (x2 * 0.5), tail)
return (x2, p)
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40,853 | def task_configure_flocker_agent(control_node, dataset_backend, dataset_backend_configuration, logging_config=None):
dataset_backend_configuration = dataset_backend_configuration.copy()
dataset_backend_configuration.update({u'backend': dataset_backend.name})
content = {'version': 1, 'control-service': {'hostname': control_node, 'port': 4524}, 'dataset': dataset_backend_configuration}
if (logging_config is not None):
content['logging'] = logging_config
put_config_file = put(path='/etc/flocker/agent.yml', content=yaml.safe_dump(content), log_content_filter=_remove_dataset_fields)
return sequence([put_config_file])
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] | configure the flocker agents by writing out the configuration file . | train | false |
40,855 | def test_get_words_cplusplus():
expected_words = ['Consider', 'Create', 'Implement', 'Obj', 'ObjContainer', 'Postfix', 'Prefix', 'Return', 'SmartPointer', 'Static', 'Zero', 'a', 'above', 'access', 'actual', 'add', 'an', 'back', 'bool', 'call', 'class', 'const', 'container', 'cout', 'definitions', 'do', 'end', 'endl', 'f', 'false', 'for', 'friend', 'g', 'i', 'if', 'implement', 'include', 'index', 'indicates', 'int', 'iostream', 'iterator', 'j', 'list', 'main', 'member', 'method', 'namespace', 'o', 'obj', 'objc', 'oc', 'of', 'operator', 'overload', 'pointer', 'public', 'push', 'return', 's', 'size', 'smart', 'sp', 'standard', 'static', 'std', 'sz', 'the', 'to', 'true', 'using', 'value', 'vector', 'version', 'void', 'while']
assert (sorted(expected_words) == sorted(get_words_by_filename('example.cpp')))
assert (sorted(expected_words) == sorted(get_words_by_content('example.cpp')))
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] | test for get word from c++ file syntax . | train | false |
40,856 | def generate_dataset(number_items=1000):
data = []
names = get_names()
totalnames = len(names)
random.seed()
for i in range(number_items):
data.append({'name': names[random.randint(0, (totalnames - 1))], 'age': random.randint(1, 100), 'description': li_words(50, False)})
return data
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] | generate a dataset with number_items elements . | train | true |
40,859 | def test_embed_mixture():
em = EmbedMixture(10, 2, 5, dropout_ratio=0.0)
doc_ids = Variable(np.arange(1, dtype='int32'))
doc_vector = em(doc_ids).data
weights = softmax(em.weights.W.data[0, :])
un_weights = softmax(em.unnormalized_weights(doc_ids).data[0, :])
test = np.sum((weights * em.factors.W.data.T), axis=1)
assert np.allclose(doc_vector, test)
assert np.allclose(un_weights, weights)
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] | manually test the projection logic between topic weights and vectors . | train | false |
40,860 | def token_kwargs(bits, parser, support_legacy=False):
if (not bits):
return {}
match = kwarg_re.match(bits[0])
kwarg_format = (match and match.group(1))
if (not kwarg_format):
if (not support_legacy):
return {}
if ((len(bits) < 3) or (bits[1] != 'as')):
return {}
kwargs = {}
while bits:
if kwarg_format:
match = kwarg_re.match(bits[0])
if ((not match) or (not match.group(1))):
return kwargs
(key, value) = match.groups()
del bits[:1]
else:
if ((len(bits) < 3) or (bits[1] != 'as')):
return kwargs
(key, value) = (bits[2], bits[0])
del bits[:3]
kwargs[key] = parser.compile_filter(value)
if (bits and (not kwarg_format)):
if (bits[0] != 'and'):
return kwargs
del bits[:1]
return kwargs
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] | a utility method for parsing token keyword arguments . | train | false |
40,861 | def find_position(string, index, last_index, last_pos):
lines = string.count('\n', last_index, index)
if (lines > 0):
column = (index - string.rfind('\n', last_index, index))
else:
column = (last_pos[1] + (index - last_index))
return ((last_pos[0] + lines), column)
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40,863 | def make_qual(sff_fp, output_fp, use_sfftools=False, no_trim=False):
if use_sfftools:
_fail_on_gzipped_sff(sff_fp)
check_sffinfo()
if no_trim:
_check_call(['sffinfo', '-notrim', '-q', sff_fp], stdout=open(output_fp, 'w'))
else:
_check_call(['sffinfo', '-q', sff_fp], stdout=open(output_fp, 'w'))
else:
try:
format_binary_sff_as_fna(qiime_open(sff_fp, 'rb'), open(output_fp, 'w'), qual=True)
except:
raise IOError(('Could not parse SFF %s' % sff_fp))
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40,864 | def strip_microseconds(delta):
return timedelta(delta.days, delta.seconds)
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] | return the given :py:class:datetime . | train | false |
40,865 | def p_command_for_bad_step(p):
p[0] = 'MALFORMED STEP IN FOR STATEMENT'
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] | command : for id equals expr to expr step error . | train | false |
40,866 | def try_printout(data, out, opts, **kwargs):
try:
printout = get_printout(out, opts)(data, **kwargs)
if (printout is not None):
return printout.rstrip()
except (KeyError, AttributeError, TypeError):
log.debug(traceback.format_exc())
try:
printout = get_printout('nested', opts)(data, **kwargs)
if (printout is not None):
return printout.rstrip()
except (KeyError, AttributeError, TypeError):
log.error('Nested output failed: ', exc_info=True)
printout = get_printout('raw', opts)(data, **kwargs)
if (printout is not None):
return printout.rstrip()
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40,867 | @utils.arg('ip_proto', metavar='<ip-proto>', help=_('IP protocol (icmp, tcp, udp).'))
@utils.arg('from_port', metavar='<from-port>', help=_('Port at start of range.'))
@utils.arg('to_port', metavar='<to-port>', help=_('Port at end of range.'))
@utils.arg('cidr', metavar='<cidr>', help=_('CIDR for address range.'))
@deprecated_network
def do_secgroup_delete_default_rule(cs, args):
for rule in cs.security_group_default_rules.list():
if (rule.ip_protocol and (rule.ip_protocol.upper() == args.ip_proto.upper()) and (rule.from_port == int(args.from_port)) and (rule.to_port == int(args.to_port)) and (rule.ip_range['cidr'] == args.cidr)):
_print_secgroup_rules([rule], show_source_group=False)
return cs.security_group_default_rules.delete(rule.id)
raise exceptions.CommandError(_('Rule not found'))
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40,868 | def instance_type_access_get_by_flavor_id(context, flavor_id):
return IMPL.instance_type_access_get_by_flavor_id(context, flavor_id)
| [
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40,870 | def output():
return s3_rest_controller()
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] | print the output data in json . | train | false |
40,871 | def apply_setup(input_dim, broadcastable, conserve_memory, mean_only, learn_scale, learn_shift):
bn = BatchNormalization(input_dim, broadcastable, conserve_memory, epsilon=0.0001, mean_only=mean_only, learn_scale=learn_scale, learn_shift=learn_shift)
bn.initialize()
b_len = (len(input_dim) if isinstance(input_dim, collections.Sequence) else 1)
x = tensor.TensorType(theano.config.floatX, ([False] * (b_len + 1)))()
return (bn, x)
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40,872 | @manager.command
def createdb():
from june.models import db
db.create_all()
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] | create database for june . | train | false |
40,874 | def send_email_sns(sender, subject, message, topic_ARN, image_png):
from boto3 import resource as boto3_resource
sns = boto3_resource('sns')
topic = sns.Topic(topic_ARN[0])
if (len(subject) > 100):
subject = ((subject[0:48] + '...') + subject[(-49):])
response = topic.publish(Subject=subject, Message=message)
logger.debug('Message sent to SNS.\nMessageId: {},\nRequestId: {},\nHTTPSStatusCode: {}'.format(response['MessageId'], response['ResponseMetadata']['RequestId'], response['ResponseMetadata']['HTTPStatusCode']))
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40,877 | def getGearProfileAnnulus(derivation, pitchRadius, teeth, toothProfile):
gearProfileCylinder = getGearProfileCylinder(teeth, toothProfile)
annulusRadius = ((derivation.dedendum + derivation.rimWidth) - pitchRadius)
return [euclidean.getComplexPolygon(complex(), annulusRadius, (- teeth), (0.5 * math.pi)), gearProfileCylinder]
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40,878 | def addFacesByConvexBottomTopLoop(faces, indexedLoopBottom, indexedLoopTop):
if ((len(indexedLoopBottom) == 0) or (len(indexedLoopTop) == 0)):
return
for indexedPointIndex in xrange(max(len(indexedLoopBottom), len(indexedLoopTop))):
indexedConvex = []
if (len(indexedLoopBottom) > 1):
indexedConvex.append(indexedLoopBottom[indexedPointIndex])
indexedConvex.append(indexedLoopBottom[((indexedPointIndex + 1) % len(indexedLoopBottom))])
else:
indexedConvex.append(indexedLoopBottom[0])
if (len(indexedLoopTop) > 1):
indexedConvex.append(indexedLoopTop[((indexedPointIndex + 1) % len(indexedLoopTop))])
indexedConvex.append(indexedLoopTop[indexedPointIndex])
else:
indexedConvex.append(indexedLoopTop[0])
addFacesByConvex(faces, indexedConvex)
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40,879 | def subtest_fatal(function):
def wrapped(self, *args, **kwds):
self._fatal = True
self.decored()
result = function(self, *args, **kwds)
return result
wrapped.func_name = function.func_name
return wrapped
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] | decorator which mark test critical . | train | false |
40,880 | @register.filter(name='as_crispy_field')
def as_crispy_field(field, template_pack=TEMPLATE_PACK):
if ((not isinstance(field, forms.BoundField)) and settings.DEBUG):
raise CrispyError('|as_crispy_field got passed an invalid or inexistent field')
template = get_template(('%s/field.html' % template_pack))
c = Context({'field': field, 'form_show_errors': True, 'form_show_labels': True}).flatten()
return template.render(c)
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] | renders a form field like a django-crispy-forms field:: {% load crispy_forms_tags %} {{ form . | train | false |
40,881 | def _check_constant_args_pool(ndim, ws, stride, pad, node):
try:
ws = tuple((tensor.get_scalar_constant_value(ws[i]) for i in range(ndim)))
stride = tuple((tensor.get_scalar_constant_value(stride[i]) for i in range(ndim)))
pad = tuple((tensor.get_scalar_constant_value(pad[i]) for i in range(ndim)))
except tensor.NotScalarConstantError:
msg = ('Pool with tensor variable for the window size, stride or padding is only supported in the new GPU backend, so this op will run on CPU. (op %s)' % node)
if (config.assert_no_cpu_op == 'warn'):
_logger.warning(msg)
elif (config.assert_no_cpu_op == 'raise'):
raise AssertionError(msg)
return None
return (ws, stride, pad)
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40,883 | def test_constant_data():
shape = (10, 10)
data = np.ones(shape)
interval = MinMaxInterval()
limits = interval.get_limits(data)
values = interval(data)
np.testing.assert_allclose(limits, (1.0, 1.0))
np.testing.assert_allclose(values, np.zeros(shape))
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] | test intervals with constant data . | train | false |
40,884 | def iso9660(path):
IMPLEMENTATIONS = [('isoinfo', has_isoinfo, Iso9660IsoInfo), ('iso-read', has_isoread, Iso9660IsoRead), ('mount', can_mount, Iso9660Mount)]
for (name, check, klass) in IMPLEMENTATIONS:
if check():
logging.debug('Automatically chosen class for iso9660: %s', name)
return klass(path)
return None
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40,885 | def test_random_sample_different_definitions():
a = db.from_sequence(range(50), npartitions=5)
assert (list(a.random_sample(0.5)) != list(a.random_sample(0.5)))
assert (a.random_sample(0.5).name != a.random_sample(0.5).name)
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40,886 | def ensure_dir_is_templated(dirname):
if ((u'{{' in dirname) and (u'}}' in dirname)):
return True
else:
raise NonTemplatedInputDirException
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40,887 | def merge(file, names, config, coord):
inputs = get_tiles(names, config, coord)
output = {'type': 'Topology', 'transform': inputs[0]['transform'], 'objects': dict(), 'arcs': list()}
for (name, input) in zip(names, inputs):
for (index, object) in enumerate(input['objects'].values()):
if (len(input['objects']) > 1):
output['objects'][('%(name)s-%(index)d' % locals())] = object
else:
output['objects'][name] = object
for geometry in object['geometries']:
update_arc_indexes(geometry, output['arcs'], input['arcs'])
file.write(json.dumps(output, separators=(',', ':')).encode('utf8'))
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] | merges all . | train | false |
40,888 | def remove_property_value(prop, predicate):
removed_vals = []
removed_vals = filter(predicate, prop.propertyValue)
if (len(removed_vals) == len(prop.propertyValue)):
prop.parent.removeProperty(prop.name)
else:
x = prop.propertyValue.cssText
for v in removed_vals:
x = x.replace(v.cssText, u'').strip()
prop.propertyValue.cssText = x
return bool(removed_vals)
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"return",
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")"
] | remove the values that match the predicate from this property . | train | false |
40,889 | @frappe.whitelist()
def get_default_address_template():
return ((((((u'{{ address_line1 }}<br>{% if address_line2 %}{{ address_line2 }}<br>{% endif -%}{{ city }}<br>\n{% if state %}{{ state }}<br>{% endif -%}\n{% if pincode %}{{ pincode }}<br>{% endif -%}\n{{ country }}<br>\n{% if phone %}' + _(u'Phone')) + u': {{ phone }}<br>{% endif -%}\n{% if fax %}') + _(u'Fax')) + u': {{ fax }}<br>{% endif -%}\n{% if email_id %}') + _(u'Email')) + u': {{ email_id }}<br>{% endif -%}')
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] | get default address template . | train | false |
40,890 | def test_dynamically_emptied_directories(tmpdir):
adir = tmpdir.join('adir').ensure(dir=True)
bdir = adir.join('bdir').ensure(dir=True)
some_file = bdir.join('afile')
some_file.write('1234567890')
base_dir = adir.strpath
(spec, parts) = tar_partition.partition(base_dir)
tar_paths = []
for part in parts:
for tar_info in part:
rel_path = os.path.relpath(tar_info.submitted_path, base_dir)
tar_paths.append(rel_path)
assert ('bdir' in tar_paths)
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40,891 | def clone_bench_repo(args):
if os.path.exists(tmp_bench_repo):
return 0
elif args.without_bench_setup:
clone_path = os.path.join(os.path.expanduser('~'), 'bench')
else:
clone_path = tmp_bench_repo
branch = (args.bench_branch or 'master')
repo_url = (args.repo_url or 'https://github.com/frappe/bench')
success = run_os_command({'git': 'git clone {repo_url} {bench_repo} --depth 1 --branch {branch}'.format(repo_url=repo_url, bench_repo=clone_path, branch=branch)})
return success
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] | clones the bench repository in the user folder . | train | false |
40,892 | def legendre(n, monic=False):
if (n < 0):
raise ValueError('n must be nonnegative.')
if (n == 0):
n1 = (n + 1)
else:
n1 = n
(x, w, mu0) = roots_legendre(n1, mu=True)
if (n == 0):
(x, w) = ([], [])
hn = (2.0 / ((2 * n) + 1))
kn = ((_gam(((2 * n) + 1)) / (_gam((n + 1)) ** 2)) / (2.0 ** n))
p = orthopoly1d(x, w, hn, kn, wfunc=(lambda x: 1.0), limits=((-1), 1), monic=monic, eval_func=(lambda x: eval_legendre(n, x)))
return p
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40,894 | def register_context(name, cls, *args, **kwargs):
instance = cls(*args, **kwargs)
proxy = ProxyContext(instance)
_contexts[name] = {'cls': cls, 'args': args, 'kwargs': kwargs, 'proxy': proxy}
_default_context[name] = instance
return proxy
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] | register a new context . | train | false |
40,896 | def _strip_once(value):
s = MLStripper()
try:
s.feed(value)
except HTMLParseError:
return value
try:
s.close()
except HTMLParseError:
return (s.get_data() + s.rawdata)
else:
return s.get_data()
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] | internal tag stripping utility used by strip_tags . | train | false |
40,898 | def print_query(results):
print('Query Parameters:')
query = results.get('query')
for (key, value) in query.iteritems():
print(('%s = %s' % (key, value)))
print()
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] | the query returns the original report query as a dict . | train | false |
40,899 | def filter_css(container, properties, names=()):
properties = normalize_filter_css(properties)
return transform_css(container, transform_sheet=partial(filter_sheet, properties=properties), transform_style=partial(filter_declaration, properties=properties), names=names)
| [
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] | remove the specified css properties from all css rules in the book . | train | false |
40,900 | def test_video():
gif_path = os.path.join(BASE_DIRECTORY, 'artwork', 'example.gif')
assert hasattr(hug.output_format.mp4_video(gif_path, hug.Response()), 'read')
with open(gif_path, 'rb') as image_file:
assert hasattr(hug.output_format.mp4_video(image_file, hug.Response()), 'read')
assert (hug.output_format.mp4_video('Not Existent', hug.Response()) is None)
class FakeVideoWithSave:
def save(self, to, format):
to.write('test')
assert hasattr(hug.output_format.mp4_video(FakeVideoWithSave(), hug.Response()), 'read')
class FakeVideoWithSave:
def render(self):
return 'test'
assert (hug.output_format.avi_video(FakeVideoWithSave(), hug.Response()) == 'test')
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] | ensure that its possible to output videos with hug . | train | false |
40,901 | def get_continuous_computations_info(cc_classes):
cc_models = job_models.ContinuousComputationModel.get_multi([cc_class.__name__ for cc_class in cc_classes])
result = []
for (ind, model) in enumerate(cc_models):
if (model is None):
cc_dict = {'computation_type': cc_classes[ind].__name__, 'status_code': 'never_started', 'last_started_msec': None, 'last_finished_msec': None, 'last_stopped_msec': None, 'active_realtime_layer_index': None, 'is_startable': True, 'is_stoppable': False}
else:
cc_dict = {'computation_type': cc_classes[ind].__name__, 'status_code': model.status_code, 'last_started_msec': model.last_started_msec, 'last_finished_msec': model.last_finished_msec, 'last_stopped_msec': model.last_stopped_msec, 'active_realtime_layer_index': model.active_realtime_layer_index, 'is_startable': (model.status_code == job_models.CONTINUOUS_COMPUTATION_STATUS_CODE_IDLE), 'is_stoppable': (model.status_code == job_models.CONTINUOUS_COMPUTATION_STATUS_CODE_RUNNING)}
result.append(cc_dict)
return result
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40,902 | def _send_decision_email(instance):
context = {'name': instance.user.username, 'api_management_url': urlunsplit((('https' if (settings.HTTPS == 'on') else 'http'), instance.site.domain, reverse('api_admin:api-status'), '', '')), 'authentication_docs_url': settings.AUTH_DOCUMENTATION_URL, 'api_docs_url': settings.API_DOCUMENTATION_URL, 'support_email_address': settings.API_ACCESS_FROM_EMAIL, 'platform_name': configuration_helpers.get_value('PLATFORM_NAME', settings.PLATFORM_NAME)}
message = render_to_string('api_admin/api_access_request_email_{status}.txt'.format(status=instance.status), context)
try:
send_mail(_('API access request'), message, settings.API_ACCESS_FROM_EMAIL, [instance.user.email], fail_silently=False)
instance.contacted = True
except SMTPException:
log.exception('Error sending API user notification email for request [%s].', instance.id)
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] | send an email to requesting user with the decision made about their request . | train | false |
40,904 | def pil_to_nparray(pil_image):
pil_image.load()
return np.asarray(pil_image, dtype='float32')
| [
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"np",
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",",
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"'float32'",
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] | convert a pil . | train | false |
40,905 | def Step(data=None, x=None, y=None, **kws):
kws['x'] = x
kws['y'] = y
return create_and_build(StepBuilder, data, **kws)
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] | create a step chart using :class:stepbuilder <bokeh . | train | false |
40,908 | def mr(n, bases):
from sympy.ntheory.factor_ import trailing
from sympy.polys.domains import ZZ
n = as_int(n)
if (n < 2):
return False
s = trailing((n - 1))
t = (n >> s)
for base in bases:
if (base >= n):
base %= n
if (base >= 2):
base = ZZ(base)
if (not _test(n, base, s, t)):
return False
return True
| [
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")",
":",
"return",
"False",
"return",
"True"
] | perform a miller-rabin strong pseudoprime test on n using a given list of bases/witnesses . | train | false |
40,909 | @task
def haproxy_install():
fprint('Rsync thirdparty/haproxy ~/haproxy')
rsync_project(local_dir='third_party/haproxy/', remote_dir='~/haproxy/', ssh_opts='-o StrictHostKeyChecking=no')
fprint('Building haproxy')
run('haproxy/build.sh ~/')
fprint('Generating viewfinder.pem for haproxy')
vf_passphrase = load_passphrase_from_file()
local(('scripts/generate_haproxy_certificate.sh viewfinder.co %s viewfinder.pem' % vf_passphrase))
run('mkdir -p ~/conf')
run('rm -f ~/conf/viewfinder.pem')
put('viewfinder.pem', '~/conf/viewfinder.pem')
run('chmod 400 ~/conf/viewfinder.pem')
local('rm -f viewfinder.pem')
fprint('Pushing haproxy configs')
assert env.nodetype, 'no nodetype specified'
run('ln -f -s ~/viewfinder/scripts/haproxy.conf ~/conf/haproxy.conf')
run(('ln -f -s ~/viewfinder/scripts/haproxy.redirect.%s.conf ~/conf/haproxy.redirect.conf' % env.nodetype.lower()))
| [
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"%",
"env",
".",
"nodetype",
".",
"lower",
"(",
")",
")",
")"
] | install and configure haproxy . | train | false |
40,912 | def AnalyticsDataFeedFromString(xml_string):
feed = atom.CreateClassFromXMLString(AnalyticsDataFeed, xml_string)
if feed.entry:
for entry in feed.entry:
for met in entry.metric:
entry.__dict__[met.name.replace('ga:', '')] = met
if (entry.dimension is not None):
for dim in entry.dimension:
entry.__dict__[dim.name.replace('ga:', '')] = dim
return feed
| [
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"return",
"feed"
] | converts an xml string into an accountlistfeed object . | train | false |
40,913 | def convert_config_string_to_dict(config_string):
resultant_dict = {}
try:
st = config_string.replace('=', ':')
st = st.replace(' ', ', ')
resultant_dict = ast.literal_eval(st)
except Exception:
LOG.warning(_LW('Error encountered translating config_string: %(config_string)s to dict'), {'config_string': config_string})
return resultant_dict
| [
"def",
"convert_config_string_to_dict",
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"resultant_dict"
] | convert config file replication string to a dict . | train | false |
40,914 | def offline_sync_database_to_version(version=None):
global USE_TRIGGERS
USE_TRIGGERS = False
if version:
_sync_common_repo(version)
else:
expand_schema()
migrate_data()
contract_schema()
| [
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"expand_schema",
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"migrate_data",
"(",
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"contract_schema",
"(",
")"
] | perform and off-line sync of the database . | train | false |
40,915 | def assert_calculated_changes(case, node_state, node_config, nonmanifest_datasets, expected_changes, additional_node_states=frozenset(), leases=Leases(), discovered_datasets=None):
api = UnusableAPI()
deployer = BlockDeviceDeployer(node_uuid=node_state.uuid, hostname=node_state.hostname, block_device_api=api)
cluster_state = compute_cluster_state(node_state, additional_node_states, nonmanifest_datasets)
if (discovered_datasets is None):
local_state = local_state_from_shared_state(node_state=node_state, nonmanifest_datasets=cluster_state.nonmanifest_datasets)
else:
local_state = BlockDeviceDeployerLocalState(node_uuid=node_state.uuid, hostname=node_state.hostname, datasets=dataset_map_from_iterable(discovered_datasets))
case.assertEqual(local_state.shared_state_changes(), (node_state.set('applications', None), NonManifestDatasets(datasets=cluster_state.nonmanifest_datasets)), 'Inconsistent test data.')
return assert_calculated_changes_for_deployer(case, deployer, node_state, node_config, nonmanifest_datasets, additional_node_states, set(), expected_changes, local_state, leases=leases)
| [
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",",
"leases",
"=",
"leases",
")"
] | assert that blockdevicedeployer calculates certain changes in a certain circumstance . | train | false |
40,916 | def builtin_lookup(name):
builtin_astroid = MANAGER.ast_from_module(builtins)
if (name == '__dict__'):
return (builtin_astroid, ())
try:
stmts = builtin_astroid.locals[name]
except KeyError:
stmts = ()
return (builtin_astroid, stmts)
| [
"def",
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"(",
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"(",
"builtin_astroid",
",",
"stmts",
")"
] | lookup a name into the builtin module return the list of matching statements and the astroid for the builtin module . | train | true |
40,917 | def ErrCheckHandle(result, func, args):
if (not result):
raise WinError()
return AutoHANDLE(result)
| [
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"raise",
"WinError",
"(",
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"AutoHANDLE",
"(",
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] | errcheck function for windows functions that return a handle . | train | false |
40,918 | def show_quick_panel(window, list, on_done):
flags = 0
if (int(sublime.version()) >= 3070):
flags = sublime.KEEP_OPEN_ON_FOCUS_LOST
return window.show_quick_panel(list, on_done, flags)
| [
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",",
"flags",
")"
] | wrapper for the window . | train | false |
40,919 | def iter_all(class_name):
for (cls, wdict) in six.iteritems(live_refs):
if (cls.__name__ == class_name):
return six.iterkeys(wdict)
| [
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"iterkeys",
"(",
"wdict",
")"
] | iterate over all objects of the same class by its class name . | train | false |
40,921 | def fig_to_vincent(fig):
renderer = VincentRenderer()
exporter = Exporter(renderer)
exporter.run(fig)
return renderer.chart
| [
"def",
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] | convert a matplotlib figure to a vincent object . | train | false |
40,924 | def make_region(*arg, **kw):
return CacheRegion(*arg, **kw)
| [
"def",
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"CacheRegion",
"(",
"*",
"arg",
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"**",
"kw",
")"
] | instantiate a new :class: . | train | false |
40,925 | def distill_naming(dictionary):
d = {key: value for (key, value) in dictionary.items() if (key in NAMING_DEFAULTS)}
return Naming(d)
| [
"def",
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] | distill only keys and values related to the naming conventions . | train | false |
40,926 | def allocate_lock():
return LockType()
| [
"def",
"allocate_lock",
"(",
")",
":",
"return",
"LockType",
"(",
")"
] | dummy implementation of thread . | train | false |
40,927 | def safe_filter(error_output=u''):
def inner(f):
@wraps(f)
def wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as err:
if sorl_settings.THUMBNAIL_DEBUG:
raise
logger.error((u'Thumbnail filter failed: %s' % err.message), exc_info=sys.exc_info())
return error_output
return wrapper
return inner
| [
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"error_output",
"return",
"wrapper",
"return",
"inner"
] | a safe filter decorator only raising errors when thumbnail_debug is true otherwise returning error_output . | train | true |
40,928 | def getVertexGivenBinary(byteIndex, stlData):
return Vector3(getFloatGivenBinary(byteIndex, stlData), getFloatGivenBinary((byteIndex + 4), stlData), getFloatGivenBinary((byteIndex + 8), stlData))
| [
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"+",
"8",
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"stlData",
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")"
] | get vertex given stl vertex line . | train | false |
40,930 | def equalsIgnoreCase(a, b):
return ((a == b) or (string.lower(a) == string.lower(b)))
| [
"def",
"equalsIgnoreCase",
"(",
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"==",
"string",
".",
"lower",
"(",
"b",
")",
")",
")"
] | return true iff a and b have the same lowercase representation . | train | false |
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