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 |
|---|---|---|---|---|---|
38,168 | def upload_file_to_s3_by_job_id(file_path, content_type='text/html', extra_message=''):
s3_filename = join(job_id, file_path)
return upload_file_to_s3(file_path, s3_filename, content_type, extra_message)
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] | uploads a file to bokeh-travis s3 bucket under a job_id folder . | train | false |
38,170 | def remote_pm(conn):
import pdb
pdb.post_mortem(conn.modules.sys.last_traceback)
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] | a version of pdb . | train | false |
38,172 | @task(ignore_result=True)
def member_removed_email(group_pk, user_pk):
from mozillians.groups.models import Group
group = Group.objects.get(pk=group_pk)
user = User.objects.get(pk=user_pk)
activate('en-us')
template_name = 'groups/email/member_removed.txt'
subject = (_('Removed from Mozillians group "%s"') % group.name)
template = get_template(template_name)
context = {'group': group, 'user': user}
body = template.render(context)
send_mail(subject, body, settings.FROM_NOREPLY, [user.email], fail_silently=False)
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38,173 | def incident_report():
def prep(r):
if (r.http == 'GET'):
if (r.method in ('create', 'create.popup')):
field = r.table.location_id
lat = get_vars.get('lat', None)
if (lat is not None):
lon = get_vars.get('lon', None)
if (lon is not None):
form_vars = Storage(lat=float(lat), lon=float(lon))
form = Storage(vars=form_vars)
s3db.gis_location_onvalidation(form)
id = s3db.gis_location.insert(**form_vars)
field.default = id
wkt = get_vars.get('wkt', None)
if (wkt is not None):
form_vars = Storage(wkt=wkt)
form = Storage(vars=form_vars)
s3db.gis_location_onvalidation(form)
id = s3db.gis_location.insert(**form_vars)
field.default = id
return True
s3.prep = prep
return s3_rest_controller()
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38,174 | def _cplxpair(z, tol=None):
z = atleast_1d(z)
if ((z.size == 0) or np.isrealobj(z)):
return np.sort(z)
if (z.ndim != 1):
raise ValueError('z must be 1-dimensional')
(zc, zr) = _cplxreal(z, tol)
zc = np.dstack((zc.conj(), zc)).flatten()
z = np.append(zc, zr)
return z
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] | sort into pairs of complex conjugates . | train | false |
38,175 | def dataSources():
dsn = create_buffer(1024)
desc = create_buffer(1024)
dsn_len = c_short()
desc_len = c_short()
dsn_list = {}
try:
lock.acquire()
if (shared_env_h is None):
AllocateEnv()
finally:
lock.release()
while 1:
ret = ODBC_API.SQLDataSources(shared_env_h, SQL_FETCH_NEXT, dsn, len(dsn), ADDR(dsn_len), desc, len(desc), ADDR(desc_len))
if (ret == SQL_NO_DATA_FOUND):
break
elif (not (ret in (SQL_SUCCESS, SQL_SUCCESS_WITH_INFO))):
ctrl_err(SQL_HANDLE_ENV, shared_env_h, ret)
else:
dsn_list[dsn.value] = desc.value
return dsn_list
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] | return a list with [name . | train | false |
38,177 | def _default_key_normalizer(key_class, request_context):
context = {}
for key in key_class._fields:
context[key] = request_context.get(key)
context['scheme'] = context['scheme'].lower()
context['host'] = context['host'].lower()
return key_class(**context)
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] | create a pool key of type key_class for a request . | train | false |
38,179 | def printfile(aFileName):
print ('\nMission file: %s' % aFileName)
with open(aFileName) as f:
for line in f:
print (' %s' % line.strip())
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] | print a mission file to demonstrate "round trip" . | train | true |
38,181 | def construct_relative_path(current_template_name, relative_name):
if (not any((relative_name.startswith(x) for x in ["'./", "'../", '"./', '"../']))):
return relative_name
new_name = posixpath.normpath(posixpath.join(posixpath.dirname(current_template_name.lstrip('/')), relative_name.strip('\'"')))
if new_name.startswith('../'):
raise TemplateSyntaxError(("The relative path '%s' points outside the file hierarchy that template '%s' is in." % (relative_name, current_template_name)))
if (current_template_name.lstrip('/') == new_name):
raise TemplateSyntaxError(("The relative path '%s' was translated to template name '%s', the same template in which the tag appears." % (relative_name, current_template_name)))
return ('"%s"' % new_name)
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38,182 | def list_package_resources(package, include_depends, subdir, rfilter=os.path.isfile):
package_dir = roslib.packages.get_pkg_dir(package)
return list_package_resources_by_dir(package_dir, include_depends, subdir, rfilter)
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38,183 | def getImportPluginFileNames():
return archive.getPluginFileNamesFromDirectoryPath(getPluginsDirectoryPath())
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] | get interpret plugin filenames . | train | false |
38,185 | def test_uninstallpathset_no_paths(caplog):
from pip.req.req_uninstall import UninstallPathSet
from pkg_resources import get_distribution
test_dist = get_distribution('pip')
uninstall_set = UninstallPathSet(test_dist)
uninstall_set.remove()
assert ("Can't uninstall 'pip'. No files were found to uninstall." in caplog.text())
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] | test uninstallpathset logs notification when there are no paths to uninstall . | train | false |
38,186 | @contextmanager
def no_handlers_for_logger(name=None):
log = logging.getLogger(name)
old_handlers = log.handlers
old_propagate = log.propagate
log.handlers = [NullHandler()]
try:
(yield)
finally:
if old_handlers:
log.handlers = old_handlers
else:
log.propagate = old_propagate
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] | temporarily remove handlers all handlers from a logger . | train | false |
38,187 | def _position_is_bracketed(string, position):
position = len(string[:position])
(index, length) = (0, len(string))
while (index < position):
char = string[index]
index += 1
if (char == '['):
closing_index = _end_of_set_index(string, index)
if (closing_index < length):
if (index <= position < closing_index):
return True
index = (closing_index + 1)
else:
return False
return False
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] | tests whether the char at string[position] is inside a valid pair of brackets . | train | false |
38,190 | def _is_mri_subject(subject, subjects_dir=None):
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
return bool((_find_head_bem(subject, subjects_dir) or _find_head_bem(subject, subjects_dir, high_res=True)))
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] | check whether a directory in subjects_dir is an mri subject directory . | train | false |
38,191 | def sign_hmac(secret, payload):
payload = payload.encode('ascii', 'strict')
secret = secret.encode('ascii', 'strict')
sig = hmac.new(base64.urlsafe_b64decode(secret), payload, hashlib.sha1)
out = base64.urlsafe_b64encode(sig.digest())
return out.decode('utf-8')
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")",
")",
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"out",
".",
"decode",
"(",
"'utf-8'",
")"
] | returns a base64-encoded hmac-sha1 signature of a given string . | train | true |
38,192 | def argrelmin(data, axis=0, order=1, mode='clip'):
return argrelextrema(data, np.less, axis, order, mode)
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",",
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] | calculate the relative minima of data . | train | true |
38,193 | def force_html():
c.render_style = 'html'
c.extension = None
c.content_type = 'text/html; charset=UTF-8'
| [
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"=",
"'text/html; charset=UTF-8'"
] | because we can take uris like /s/URL and we can guarantee that the toolbar will never be used with a non-html render style . | train | false |
38,194 | def get_best_cpu_topology(flavor, image_meta, allow_threads=True, numa_topology=None):
return _get_desirable_cpu_topologies(flavor, image_meta, allow_threads, numa_topology)[0]
| [
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] | identify best cpu topology for given constraints . | train | false |
38,195 | def convert_colorspace(arr, fromspace, tospace):
fromdict = {'RGB': (lambda im: im), 'HSV': hsv2rgb, 'RGB CIE': rgbcie2rgb, 'XYZ': xyz2rgb, 'YUV': yuv2rgb, 'YIQ': yiq2rgb, 'YPbPr': ypbpr2rgb, 'YCbCr': ycbcr2rgb}
todict = {'RGB': (lambda im: im), 'HSV': rgb2hsv, 'RGB CIE': rgb2rgbcie, 'XYZ': rgb2xyz, 'YUV': rgb2yuv, 'YIQ': rgb2yiq, 'YPbPr': rgb2ypbpr, 'YCbCr': rgb2ycbcr}
fromspace = fromspace.upper()
tospace = tospace.upper()
if (fromspace not in fromdict.keys()):
raise ValueError(('fromspace needs to be one of %s' % fromdict.keys()))
if (tospace not in todict.keys()):
raise ValueError(('tospace needs to be one of %s' % todict.keys()))
return todict[tospace](fromdict[fromspace](arr))
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"(",
"fromdict",
"[",
"fromspace",
"]",
"(",
"arr",
")",
")"
] | convert an image array to a new color space . | train | false |
38,196 | @require_context
def snapshot_get_all_by_project(context, project_id, filters=None, marker=None, limit=None, sort_keys=None, sort_dirs=None, offset=None):
if (filters and (not is_valid_model_filters(models.Snapshot, filters))):
return []
authorize_project_context(context, project_id)
filters = (filters.copy() if filters else {})
filters['project_id'] = project_id
session = get_session()
with session.begin():
query = _generate_paginate_query(context, session, marker, limit, sort_keys, sort_dirs, filters, offset, models.Snapshot)
if (not query):
return []
query = query.options(joinedload('snapshot_metadata'))
return query.all()
| [
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"(",
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] | get all snapshots belonging to a project . | train | false |
38,197 | def _bits_to_bytes_len(length_in_bits):
return ((length_in_bits + 7) // 8)
| [
"def",
"_bits_to_bytes_len",
"(",
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")",
":",
"return",
"(",
"(",
"length_in_bits",
"+",
"7",
")",
"//",
"8",
")"
] | helper function that returns the numbers of bytes necessary to store the given number of bits . | train | false |
38,199 | @task
def remove_incomplete_accounts(days=INCOMPLETE_ACC_MAX_DAYS):
from mozillians.users.models import UserProfile
now = (datetime.now() - timedelta(days=days))
UserProfile.objects.filter(full_name='').filter(user__date_joined__lt=now).delete()
| [
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"=",
"now",
")",
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"delete",
"(",
")"
] | remove incomplete accounts older than incomplete_acc_max_days old . | train | false |
38,200 | def check_yn(option, opt, value):
if isinstance(value, int):
return bool(value)
if (value in ('y', 'yes')):
return True
if (value in ('n', 'no')):
return False
msg = 'option %s: invalid yn value %r, should be in (y, yes, n, no)'
raise OptionValueError((msg % (opt, value)))
| [
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"value",
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")",
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] | check a yn value return true for yes and false for no . | train | false |
38,202 | def test_stoch_matrix():
print ((__name__ + '.') + test_stoch_matrix.__name__)
matrices = Matrices()
for matrix_dict in matrices.stoch_matrix_dicts:
x = gth_solve(matrix_dict['A'])
(yield (StationaryDistSumOne(), x))
(yield (StationaryDistNonnegative(), x))
(yield (StationaryDistEqualToKnown(), matrix_dict['stationary_dist'], x))
| [
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"[",
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"]",
",",
"x",
")",
")"
] | test with stochastic matrices . | train | false |
38,205 | def get_download_link(cookie, tokens, path):
metas = get_metas(cookie, tokens, path)
if ((not metas) or (metas.get('errno', (-1)) != 0) or ('info' not in metas) or (len(metas['info']) != 1)):
logger.error(('pcs.get_download_link(): %s' % metas))
return None
dlink = metas['info'][0]['dlink']
url = '{0}&cflg={1}'.format(dlink, cookie.get('cflag').value)
req = net.urlopen_without_redirect(url, headers={'Cookie': cookie.sub_output('BAIDUID', 'BDUSS', 'cflag'), 'Accept': const.ACCEPT_HTML})
if (not req):
return url
else:
return req.getheader('Location', url)
| [
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"else",
":",
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"req",
".",
"getheader",
"(",
"'Location'",
",",
"url",
")"
] | path - 一个文件的绝对路径 . | train | true |
38,206 | @step(u'the directory "{directory}" exists')
def step_directory_exists(context, directory):
step_the_directory_should_exist(context, directory)
| [
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")",
":",
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"(",
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",",
"directory",
")"
] | verifies that a directory exists . | train | false |
38,207 | def test_type_error_if_not_dict_context(replay_test_dir, template_name):
with pytest.raises(TypeError):
replay.dump(replay_test_dir, template_name, 'not_a_dict')
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] | test that replay . | train | false |
38,208 | @command('user\\s+(.+)')
def usersearch(q_user, identify='forUsername'):
(user, _, term) = (x.strip() for x in q_user.partition('/'))
if (identify == 'forUsername'):
ret = channelfromname(user)
if (not ret):
return
(user, channel_id) = ret
else:
channel_id = user
usersearch_id(user, channel_id, term)
| [
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"(",
"user",
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",",
"term",
")"
] | fetch uploads by a youtube user . | train | false |
38,209 | def CDLUPSIDEGAP2CROWS(barDs, count):
return call_talib_with_ohlc(barDs, count, talib.CDLUPSIDEGAP2CROWS)
| [
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"(",
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",",
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",",
"talib",
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] | upside gap two crows . | train | false |
38,211 | def attribute_mapped_collection(attr_name):
getter = _SerializableAttrGetter(attr_name)
return (lambda : MappedCollection(getter))
| [
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] | a dictionary-based collection type with attribute-based keying . | train | false |
38,212 | def _error(name, msg):
return {'name': name, 'result': False, 'comment': msg, 'changes': {}}
| [
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"}",
"}"
] | print msg and optionally exit with return code exit_ . | train | false |
38,213 | def get_component_by_name(app, name):
sa_session = app.model.context.current
return sa_session.query(app.model.Component).filter((app.model.Component.table.c.name == name)).first()
| [
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] | get a component from the database via a name . | train | false |
38,214 | def getRCWFromProgID(prog_id):
if is_cli:
return Activator.CreateInstance(getTypeFromProgID(prog_id))
else:
return win32com.client.Dispatch(prog_id)
| [
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] | returns an instance of prog_id . | train | false |
38,217 | def _string_to_rgb(color):
if (not color.startswith('#')):
if (color.lower() not in _color_dict):
raise ValueError(('Color "%s" unknown' % color))
color = _color_dict[color]
assert (color[0] == '#')
color = color[1:]
lc = len(color)
if (lc in (3, 4)):
color = ''.join(((c + c) for c in color))
lc = len(color)
if (lc not in (6, 8)):
raise ValueError('Hex color must have exactly six or eight elements following the # sign')
color = np.array([(int(color[i:(i + 2)], 16) / 255.0) for i in range(0, lc, 2)])
return color
| [
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"(",
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"2",
")",
"]",
")",
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] | convert user string or hex color to color array . | train | true |
38,220 | def lvremove(lvname, vgname):
cmd = ['lvremove', '-f', '{0}/{1}'.format(vgname, lvname)]
out = __salt__['cmd.run'](cmd, python_shell=False)
return out.strip()
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] | remove a given existing logical volume from a named existing volume group cli example: . | train | true |
38,221 | def to_time(wmi_time):
def int_or_none(s, start, end):
try:
return int(s[start:end])
except ValueError:
return None
year = int_or_none(wmi_time, 0, 4)
month = int_or_none(wmi_time, 4, 6)
day = int_or_none(wmi_time, 6, 8)
hours = int_or_none(wmi_time, 8, 10)
minutes = int_or_none(wmi_time, 10, 12)
seconds = int_or_none(wmi_time, 12, 14)
microseconds = int_or_none(wmi_time, 15, 21)
timezone = wmi_time[22:]
if (timezone == '***'):
timezone = None
return (year, month, day, hours, minutes, seconds, microseconds, timezone)
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38,222 | def direct_to_user_template(request, username, template_name, extra_context=None):
user = get_object_or_404(get_user_model(), username__iexact=username)
if (not extra_context):
extra_context = dict()
extra_context['viewed_user'] = user
extra_context['profile'] = get_user_profile(user=user)
return ExtraContextTemplateView.as_view(template_name=template_name, extra_context=extra_context)(request)
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] | simple wrapper for djangos :func:direct_to_template view . | train | true |
38,223 | def getModule(moduleName):
return theSystemPath[moduleName]
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] | retrieve a module from the system path . | train | false |
38,224 | def refactor_with_2to3(source_text, fixer_names, filename=u''):
from lib2to3.refactor import RefactoringTool
fixers = [(u'lib2to3.fixes.fix_' + name) for name in fixer_names]
tool = RefactoringTool(fixer_names=fixers, explicit=fixers)
from lib2to3.pgen2 import tokenize as lib2to3_tokenize
try:
return unicode(tool.refactor_string(source_text, name=filename))
except lib2to3_tokenize.TokenError:
return source_text
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] | use lib2to3 to refactor the source . | train | true |
38,225 | def m_quadratic_sum(A, B, max_it=50):
gamma1 = solve_discrete_lyapunov(A, B, max_it)
return gamma1
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38,228 | def spawn(coro):
if (not isinstance(coro, types.GeneratorType)):
raise ValueError((u'%s is not a coroutine' % coro))
return SpawnEvent(coro)
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] | create a spawned process . | train | false |
38,230 | def random_rainbow(s):
colors_shuffle = [(globals()[i.encode('utf8')] if (not str(i).isdigit()) else term_color(int(i))) for i in c['CYCLE_COLOR']]
colored = [random.choice(colors_shuffle)(i) for i in s]
return ''.join(colored)
| [
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")"
] | print a string with random color with each character . | train | false |
38,231 | def report():
t = Twitter(auth=authen())
screen_name = g['stuff'].split()[0]
if screen_name.startswith('@'):
t.users.report_spam(screen_name=screen_name[1:])
printNicely(green((('You reported ' + screen_name) + '.')))
else:
printNicely(red("Sorry I can't understand."))
| [
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")",
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] | provides report about git status of all repos . | train | false |
38,232 | def entropy(pk, qk=None, base=None):
pk = asarray(pk)
pk = ((1.0 * pk) / np.sum(pk, axis=0))
if (qk is None):
vec = entr(pk)
else:
qk = asarray(qk)
if (len(qk) != len(pk)):
raise ValueError('qk and pk must have same length.')
qk = ((1.0 * qk) / np.sum(qk, axis=0))
vec = rel_entr(pk, qk)
S = np.sum(vec, axis=0)
if (base is not None):
S /= log(base)
return S
| [
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38,233 | def _check_pillar(kwargs):
if kwargs.get('force'):
return True
if ('_errors' in __pillar__):
return False
return True
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] | check the pillar for errors . | train | false |
38,235 | def _parse_snippet_file(content, full_filename):
filename = full_filename[:(- len('.snippet'))]
segments = _splitall(filename)
segments = segments[(segments.index('snippets') + 1):]
assert (len(segments) in (2, 3))
trigger = segments[1]
description = (segments[2] if (2 < len(segments)) else '')
if (content and content.endswith(os.linesep)):
content = content[:(- len(os.linesep))]
(yield ('snippet', (SnipMateSnippetDefinition(trigger, content, description, full_filename),)))
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38,236 | def make_enum(enum_type='enum', base_classes=None, methods=None, **attrs):
def __init__(instance, *args, **kwargs):
raise RuntimeError(('%s types can not be initialized.' % enum_type))
if (base_classes is None):
base_classes = ()
if (methods is None):
methods = {}
base_classes = (base_classes + (object,))
for (k, v) in methods.iteritems():
methods[k] = classmethod(v)
attrs['enums'] = attrs.copy()
methods.update(attrs)
methods['__init__'] = __init__
return type(enum_type, base_classes, methods)
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38,240 | @register.filter(name='rule_member_count')
def rule_member_count(instance, member):
member = getattr(instance, member)
counts = member.all().count()
return str(counts)
| [
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38,244 | def autoload_filenode(must_be=None, default_root=False):
def _autoload_filenode(func):
@handle_odm_errors
@must_have_addon('osfstorage', 'node')
@functools.wraps(func)
def wrapped(*args, **kwargs):
node = kwargs['node']
if (('fid' not in kwargs) and default_root):
file_node = kwargs['node_addon'].get_root()
else:
file_node = models.OsfStorageFileNode.get(kwargs.get('fid'), node)
if (must_be and (file_node.kind != must_be)):
raise HTTPError(httplib.BAD_REQUEST, data={'message_short': 'incorrect type', 'message_long': 'FileNode must be of type {} not {}'.format(must_be, file_node.kind)})
kwargs['file_node'] = file_node
return func(*args, **kwargs)
return wrapped
return _autoload_filenode
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] | implies both must_have_addon osfstorage node and handle_odm_errors attempts to load fid as a osfstoragefilenode with viable constraints . | train | false |
38,245 | def max_call_gas(gas):
return (gas - (gas // opcodes.CALL_CHILD_LIMIT_DENOM))
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")"
] | since eip150 calls will send only all but 1/64th of the available gas . | train | false |
38,247 | def user_can_edit_snippet_type(user, model):
for action in (u'add', u'change', u'delete'):
if user.has_perm(get_permission_name(action, model)):
return True
return False
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38,248 | def _update_first_contribution_msec(user_id, first_contribution_msec):
user_settings = get_user_settings(user_id, strict=True)
user_settings.first_contribution_msec = first_contribution_msec
_save_user_settings(user_settings)
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] | updates first_contribution_msec of user with given user_id . | train | false |
38,250 | def _api_get_files(name, output, kwargs):
value = kwargs.get('value')
if value:
return report(output, keyword='files', data=build_file_list(value))
else:
return report(output, _MSG_NO_VALUE)
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] | api: accepts output . | train | false |
38,252 | def basename_from_filename(filename):
mimetype = mimetypes.guess_type(filename)[0]
if (mimetype is not None):
mimetype = mimetype.lower()
for (filetype, icon_name) in KNOWN_FILE_MIME_TYPES:
if (filetype in mimetype):
return icon_name
extension = os.path.splitext(filename)[1]
return KNOWN_FILE_EXTENSIONS.get(extension.lower(), u'file-text.svg')
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] | returns an icon name based on the filename . | train | false |
38,253 | def jbig2Encode(stream, parameters):
encodedStream = ''
return ((-1), 'Jbig2Encode not supported yet')
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] | method to encode streams using the jbig2 standard . | train | false |
38,254 | def _upstart_is_enabled(name):
return (not _upstart_is_disabled(name))
| [
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] | assume that if an upstart service is not disabled then it must be enabled . | train | false |
38,258 | def sparse2cvxopt(value):
import cvxopt
if isinstance(value, (np.ndarray, np.matrix)):
return cvxopt.sparse(cvxopt.matrix(value.astype('float64')), tc='d')
elif sp.issparse(value):
value = value.tocoo()
return cvxopt.spmatrix(value.data.tolist(), value.row.tolist(), value.col.tolist(), size=value.shape, tc='d')
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] | converts a scipy sparse matrix to a cvxopt sparse matrix . | train | false |
38,259 | def compareAreaDescending(loopArea, otherLoopArea):
if (loopArea.area > otherLoopArea.area):
return (-1)
return int((loopArea.area < otherLoopArea.area))
| [
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] | get comparison in order to sort loop areas in descending order of area . | train | false |
38,260 | def _load_yaml_with_clear_tag(stream):
loader = yaml.SafeLoader(stream)
loader.add_constructor('!clear', _cleared_value_constructor)
try:
return loader.get_single_data()
finally:
if hasattr(loader, 'dispose'):
loader.dispose()
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] | like yaml . | train | false |
38,261 | def has_application(backend=None, has=(), capable=()):
from ..app.backends import BACKEND_NAMES
if (backend is None):
for backend in BACKEND_NAMES:
if has_backend(backend, has=has, capable=capable):
good = True
msg = backend
break
else:
good = False
msg = 'Requires application backend'
else:
(good, why) = has_backend(backend, has=has, capable=capable, out=['why_not'])
if (not good):
msg = ('Requires %s: %s' % (backend, why))
else:
msg = backend
return (good, msg)
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38,262 | def dict_contains(superset, subset):
for (key, value) in subset.iteritems():
ok_((key in superset))
eq_(superset[key], value)
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] | assert that all key/val pairs in dict subset also exist in superset . | train | false |
38,263 | def commutation_matrix(p, q):
K = np.eye((p * q))
indices = np.arange((p * q)).reshape((p, q), order='F')
return K.take(indices.ravel(), axis=0)
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] | create the commutation matrix k_{p . | train | false |
38,264 | def lc_random(lower, upper, stepsize):
nstep = int(((upper - lower) / (1.0 * stepsize)))
choices = [(lower + (x * stepsize)) for x in range(nstep)]
return random.choice(choices)
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] | like random . | train | false |
38,265 | def new_host_state(self, host, node, capabilities=None, service=None):
if (capabilities is None):
capabilities = {}
cap = capabilities.get('compute', {})
if bool(cap.get('baremetal_driver')):
return BaremetalNodeState(host, node, capabilities, service)
else:
return host_manager.HostState(host, node, capabilities, service)
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] | returns an instance of baremetalhoststate or hoststate according to capabilities . | train | false |
38,267 | def print_debug_info(qs, file=None):
opts = qs.model._mptt_meta
writer = csv.writer((sys.stdout if (file is None) else file))
header = (u'pk', opts.level_attr, (u'%s_id' % opts.parent_attr), opts.tree_id_attr, opts.left_attr, opts.right_attr, u'pretty')
writer.writerow(header)
for n in qs.order_by(u'tree_id', u'lft'):
level = getattr(n, opts.level_attr)
row = []
for field in header[:(-1)]:
row.append(getattr(n, field))
row.append((u'%s%s' % ((u'- ' * level), text_type(n).encode(u'utf-8'))))
writer.writerow(row)
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] | given an mptt queryset . | train | false |
38,269 | def custom_layer(incoming, custom_fn, **kwargs):
name = 'CustomLayer'
if ('name' in kwargs):
name = kwargs['name']
with tf.name_scope(name):
inference = custom_fn(incoming, **kwargs)
return inference
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38,270 | def compression_matrix(data, q, n_power_iter=0, seed=None):
n = data.shape[1]
comp_level = compression_level(n, q)
state = RandomState(seed)
omega = state.standard_normal(size=(n, comp_level), chunks=(data.chunks[1], (comp_level,)))
mat_h = data.dot(omega)
for j in range(n_power_iter):
mat_h = data.dot(data.T.dot(mat_h))
(q, _) = tsqr(mat_h)
return q.T
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38,271 | def check_palette(palette):
if (palette is None):
return None
p = list(palette)
if (not (0 < len(p) <= 256)):
raise ValueError('a palette must have between 1 and 256 entries')
seen_triple = False
for (i, t) in enumerate(p):
if (len(t) not in (3, 4)):
raise ValueError(('palette entry %d: entries must be 3- or 4-tuples.' % i))
if (len(t) == 3):
seen_triple = True
if (seen_triple and (len(t) == 4)):
raise ValueError(('palette entry %d: all 4-tuples must precede all 3-tuples' % i))
for x in t:
if ((int(x) != x) or (not (0 <= x <= 255))):
raise ValueError(('palette entry %d: values must be integer: 0 <= x <= 255' % i))
return p
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38,272 | def test_messages(client):
login(client, flaskr.app.config['USERNAME'], flaskr.app.config['PASSWORD'])
rv = client.post('/add', data=dict(title='<Hello>', text='<strong>HTML</strong> allowed here'), follow_redirects=True)
assert ('No entries here so far' not in rv.data)
assert ('<Hello>' in rv.data)
assert ('<strong>HTML</strong> allowed here' in rv.data)
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38,274 | def is_focused_on_element(browser, selector):
return browser.execute_script("return $('{}').is(':focus')".format(selector))
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] | check if the focus is on the element that matches the selector . | train | false |
38,276 | def square_n_sort(L):
L_square = []
L_sorted = []
count = len(L)
if (L[0] >= 0):
for i in L:
L_square.append((i ** 2))
return L_square
while (count > 0):
if (abs(L[0]) >= abs(L[(-1)])):
L_square.append((L[0] ** 2))
L.remove(L[0])
else:
L_square.append((L[(-1)] ** 2))
L.remove(L[(-1)])
count -= 1
L_sorted = L_square[::(-1)]
return L_sorted
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38,277 | def register_builtin_transform(transform, builtin_name):
def _transform_wrapper(node, context=None):
result = transform(node, context=context)
if result:
result.parent = node
result.lineno = node.lineno
result.col_offset = node.col_offset
return iter([result])
MANAGER.register_transform(nodes.CallFunc, inference_tip(_transform_wrapper), (lambda n: (isinstance(n.func, nodes.Name) and (n.func.name == builtin_name))))
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] | register a new transform function for the given *builtin_name* . | train | false |
38,278 | def format_satoshis_plain(x, decimal_point=8):
scale_factor = pow(10, decimal_point)
return '{:.8f}'.format((Decimal(x) / scale_factor)).rstrip('0').rstrip('.')
| [
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38,282 | def vocabulary_create(context, data_dict):
model = context['model']
schema = (context.get('schema') or ckan.logic.schema.default_create_vocabulary_schema())
_check_access('vocabulary_create', context, data_dict)
(data, errors) = _validate(data_dict, schema, context)
if errors:
model.Session.rollback()
raise ValidationError(errors)
vocabulary = model_save.vocabulary_dict_save(data, context)
if (not context.get('defer_commit')):
model.repo.commit()
log.debug(('Created Vocabulary %s' % vocabulary.name))
return model_dictize.vocabulary_dictize(vocabulary, context)
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38,284 | @not_implemented_for('directed')
def biconnected_components(G):
for comp in _biconnected_dfs(G, components=True):
(yield set(chain.from_iterable(comp)))
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38,285 | def normalize_series_name(name):
name = name.lower()
name = name.replace(u'&', u' and ')
name = name.translate(TRANSLATE_MAP)
name = u' '.join(name.split())
return name
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38,286 | def install_as_gi():
import sys
if ('gi.repository' in const.PREFIX):
return
for mod in iterkeys(sys.modules):
if ((mod == 'gi') or mod.startswith('gi.')):
raise AssertionError('pgi has to be imported before gi')
import pgi
import pgi.repository
sys.modules['gi'] = pgi
sys.modules['gi.repository'] = pgi.repository
const.PREFIX.append('gi.repository')
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] | call before the first gi import to redirect gi imports to pgi . | train | true |
38,287 | def _version_from_file(lines):
is_version_line = (lambda line: line.lower().startswith('version:'))
version_lines = filter(is_version_line, lines)
line = next(iter(version_lines), '')
(_, _, value) = line.partition(':')
return (safe_version(value.strip()) or None)
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38,289 | @pytest.mark.skipif(no_fsl(), reason=u'fsl is not installed')
def test_fast_list_outputs(setup_infile):
def _run_and_test(opts, output_base):
outputs = fsl.FAST(**opts)._list_outputs()
for output in outputs.values():
if output:
for filename in filename_to_list(output):
assert os.path.realpath(filename).startswith(os.path.realpath(output_base))
(tmp_infile, indir) = setup_infile
cwd = tempfile.mkdtemp()
os.chdir(cwd)
assert (indir != cwd)
out_basename = u'a_basename'
opts = {u'in_files': tmp_infile}
(input_path, input_filename, input_ext) = split_filename(tmp_infile)
_run_and_test(opts, os.path.join(input_path, input_filename))
opts[u'out_basename'] = out_basename
_run_and_test(opts, os.path.join(cwd, out_basename))
| [
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38,290 | def ishashable(x):
try:
hash(x)
return True
except TypeError:
return False
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] | is x hashable? examples . | train | false |
38,292 | def set_user_lang(user, user_language=None):
from frappe.translate import get_user_lang
local.lang = get_user_lang(user)
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38,293 | def length_of_indexer(indexer, target=None):
if ((target is not None) and isinstance(indexer, slice)):
l = len(target)
start = indexer.start
stop = indexer.stop
step = indexer.step
if (start is None):
start = 0
elif (start < 0):
start += l
if ((stop is None) or (stop > l)):
stop = l
elif (stop < 0):
stop += l
if (step is None):
step = 1
elif (step < 0):
step = (- step)
return ((((stop - start) + step) - 1) // step)
elif isinstance(indexer, (ABCSeries, Index, np.ndarray, list)):
return len(indexer)
elif (not is_list_like_indexer(indexer)):
return 1
raise AssertionError('cannot find the length of the indexer')
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"raise",
"AssertionError",
"(",
"'cannot find the length of the indexer'",
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] | return the length of a single non-tuple indexer which could be a slice . | train | true |
38,297 | def replace(s, old, new, maxsplit=0):
return s.replace(old, new, maxsplit)
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"s",
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"replace",
"(",
"old",
",",
"new",
",",
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] | return x . | train | false |
38,298 | def is_running(proxyname):
return {'result': _is_proxy_running(proxyname)}
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] | return true if an inspected container is in a state we consider "running . | train | false |
38,299 | def output_file(filename, title='Bokeh Plot', mode='cdn', root_dir=None):
_state.output_file(filename, title=title, mode=mode, root_dir=root_dir)
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] | configure the default output state to generate output saved to a file when :func:show is called . | train | false |
38,300 | def tiny2zero(x, eps=1e-15):
mask = (np.abs(x.copy()) < eps)
x[mask] = 0
return x
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] | replace abs values smaller than eps by zero . | train | false |
38,301 | @contextlib.contextmanager
def override_config(name, value):
old_value = getattr(config, name)
setattr(config, name, value)
try:
(yield)
finally:
setattr(config, name, old_value)
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38,304 | @image_comparison(baseline_images=[u'EventCollection_plot__extend_positions'])
def test__EventCollection__extend_positions():
(splt, coll, props) = generate_EventCollection_plot()
new_positions = np.hstack([props[u'positions'], props[u'extra_positions'][1:]])
coll.extend_positions(props[u'extra_positions'][1:])
np.testing.assert_array_equal(new_positions, coll.get_positions())
check_segments(coll, new_positions, props[u'linelength'], props[u'lineoffset'], props[u'orientation'])
splt.set_title(u'EventCollection: extend_positions')
splt.set_xlim((-1), 90)
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"-",
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] | check to make sure extend_positions works properly . | train | false |
38,305 | def test_feature_representation_without_colors():
feature_file = ojoin('..', 'simple_features', '1st_feature_dir', 'some.feature')
feature = Feature.from_file(feature_file)
assert_lines(feature.represented(), 'Feature: Addition # tests/functional/simple_features/1st_feature_dir/some.feature:5\n In order to avoid silly mistakes # tests/functional/simple_features/1st_feature_dir/some.feature:6\n As a math idiot # tests/functional/simple_features/1st_feature_dir/some.feature:7\n I want to be told the sum of two numbers # tests/functional/simple_features/1st_feature_dir/some.feature:8\n')
| [
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")"
] | feature represented without colors . | train | false |
38,306 | def buildSubsamplingNetwork():
n = FeedForwardNetwork()
n.addInputModule(LinearLayer(6, 'in'))
n.addOutputModule(LinearLayer(1, 'out'))
n.addConnection(SubsamplingConnection(n['in'], n['out'], inSliceTo=4))
n.addConnection(SubsamplingConnection(n['in'], n['out'], inSliceFrom=4))
n.sortModules()
return n
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] | builds a network with subsampling connections . | train | false |
38,307 | @gen.coroutine
def _Init(init_db=True, server_logging=True):
httpclient.AsyncHTTPClient.configure('tornado.curl_httpclient.CurlAsyncHTTPClient', max_clients=100)
if options.options.devbox:
metadata = ami_metadata.Metadata()
else:
metadata = (yield gen.Task(ami_metadata.Metadata))
if (metadata is None):
raise Exception('failed to fetch AWS instance metadata; if running on dev box, use the --devbox option')
ami_metadata.SetAMIMetadata(metadata)
logging.info('AMI metadata initialized')
ServerEnvironment.InitServerEnvironment()
logging.info('server environment initialized')
(yield gen.Task(secrets.InitSecrets, can_prompt=sys.stderr.isatty()))
logging.info('secrets initialized')
if init_db:
(yield gen.Task(db_client.InitDB, vf_schema.SCHEMA))
logging.info('DB client initialized')
object_store.InitObjectStore(temporary=False)
logging.info('object store initialized')
if server_logging:
server_log.InitServerLog()
logging.info('main.py initialization complete')
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".",
"InitServerLog",
"(",
")",
"logging",
".",
"info",
"(",
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")"
] | completes viewfinder initialization . | train | false |
38,312 | def rpm_rebuilddb():
module.run_command(['/usr/bin/rpm', '--rebuilddb'])
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"]",
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] | runs rpm --rebuilddb to ensure the db is in good shape . | train | false |
38,313 | def subscribe_to_messages(observer_function):
all_output_plugins = om.manager.get_output_plugin_inst()
for plugin_inst in all_output_plugins:
if isinstance(plugin_inst, GtkOutput):
plugin_inst.subscribe(observer_function)
break
else:
gtk_output = GtkOutput()
om.manager.set_output_plugin_inst(gtk_output)
gtk_output.subscribe(observer_function)
| [
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"subscribe",
"(",
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] | subscribe observer_function to the gtkoutput messages . | train | false |
38,314 | def test_language_portuguese():
lang = Language('pt-br')
assert_equals(lang.code, u'pt-br')
assert_equals(lang.name, u'Portuguese')
assert_equals(lang.native, u'Portugu\xeas')
assert_equals(lang.feature, u'Funcionalidade')
assert_equals(lang.scenario, u'Cen\xe1rio|Cenario')
assert_equals(lang.examples, u'Exemplos|Cen\xe1rios')
assert_equals(lang.scenario_outline, u'Esquema do Cen\xe1rio|Esquema do Cenario')
| [
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"u'Esquema do Cen\\xe1rio|Esquema do Cenario'",
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] | language: pt-br -> language class supports portuguese through code "pt-br" . | train | false |
38,315 | def get_swap_size(vm_):
return config.get_cloud_config_value('swap', vm_, __opts__, default=128)
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] | returns the amoutn of swap space to be used in mb . | train | false |
38,316 | def _centos7_install_commands(version):
installable_version = get_installable_version(flocker_version)
return sequence([run(command='yum clean all'), run(command='yum install -y {}'.format(get_repository_url(distribution='centos-7', flocker_version=installable_version))), run_from_args(((['yum', 'install'] + get_repo_options(installable_version)) + ['-y', ('clusterhq-flocker-node' + version)]))])
| [
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] | construct the command sequence expected for installing flocker on centos 7 . | train | false |
38,317 | def for_int_dtypes(name='dtype', no_bool=False):
if no_bool:
return for_dtypes(_int_dtypes, name=name)
else:
return for_dtypes(_int_bool_dtypes, name=name)
| [
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] | decorator that checks the fixture with integer and optionally bool dtypes . | train | false |
38,319 | def aic(llf, nobs, df_modelwc):
return (((-2.0) * llf) + (2.0 * df_modelwc))
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] | akaike information criterion parameters llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant returns aic : float information criterion references URL . | train | false |
38,320 | def _handle_voted_field(form_value, cc_content, api_content, request, context):
signal = (thread_voted if (cc_content.type == 'thread') else comment_voted)
signal.send(sender=None, user=context['request'].user, post=cc_content)
if form_value:
context['cc_requester'].vote(cc_content, 'up')
api_content['vote_count'] += 1
else:
context['cc_requester'].unvote(cc_content)
api_content['vote_count'] -= 1
track_voted_event(request, context['course'], cc_content, vote_value='up', undo_vote=(False if form_value else True))
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] | vote or undo vote on thread/comment . | train | false |
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