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def test_censure_on_rectangular_images(): rect_image = np.random.rand(300, 200) square_image = np.random.rand(200, 200) CENSURE().detect(square_image) CENSURE().detect(rect_image)
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censure feature detector should work on 2d image of any shape .
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def vm_disk_snapshot_create(name, kwargs=None, call=None): if (call != 'action'): raise SaltCloudSystemExit('The vm_disk_snapshot_create action must be called with -a or --action.') if (kwargs is None): kwargs = {} disk_id = kwargs.get('disk_id', None) description = kwargs.get('description', None) if ((disk_id is None) or (description is None)): raise SaltCloudSystemExit("The vm_disk_snapshot_create function requires a 'disk_id' and a 'description' to be provided.") (server, user, password) = _get_xml_rpc() auth = ':'.join([user, password]) vm_id = int(get_vm_id(kwargs={'name': name})) response = server.one.vm.disksnapshotcreate(auth, vm_id, int(disk_id), description) data = {'action': 'vm.disksnapshotcreate', 'created': response[0], 'snapshot_id': response[1], 'error_code': response[2]} return data
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takes a new snapshot of the disk image .
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@identity_loaded.connect def on_identity_loaded(sender, identity): user = User.query.filter((User.id == identity.id)).first() identity.provides.add(UserNeed(identity.id)) if hasattr(user, u'roles'): for role in user.roles: identity.provides.add(RoleNeed(role.name)) g.user = user
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sets the identity of a given option .
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def enable_trace(case, trace_exclusions=None, f=sys.stdout): if (trace_exclusions is None): trace_exclusions = ['twisted', 'worker_transition.py', 'util/tu', 'log.py', '/mq/', '/db/', 'buildbot/data/', 'fake/reactor.py'] bbbase = os.path.dirname(buildbot.__file__) state = {'indent': 0} def tracefunc(frame, event, arg): if frame.f_code.co_filename.startswith(bbbase): if (not any(((te in frame.f_code.co_filename) for te in trace_exclusions))): if (event == 'call'): state['indent'] += 2 print(('-' * state['indent']), frame.f_code.co_filename.replace(bbbase, ''), frame.f_code.co_name, frame.f_code.co_varnames, file=f) if (event == 'return'): state['indent'] -= 2 return tracefunc sys.settrace(tracefunc) case.addCleanup(sys.settrace, (lambda _a, _b, _c: None))
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enable tracing of app instances .
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def unloadhook(h): def processor(handler): try: result = handler() is_gen = is_iter(result) except: h() raise if is_gen: return wrap(result) else: h() return result def wrap(result): def next_hook(): try: return next(result) except: h() raise result = iter(result) while True: (yield next_hook()) return processor
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converts an unload hook into an application processor .
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def get_fd_or_server(config): lockfile = get_lockfile(config) while True: try: return (os.open(lockfile, ((os.O_CREAT | os.O_EXCL) | os.O_WRONLY)), None) except OSError: pass server = get_server(config) if (server is not None): return (None, server) remove_lockfile(lockfile)
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tries to create the lockfile .
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def clear_script_prefix(): try: del _prefixes.value except AttributeError: pass
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unsets the script prefix for the current thread .
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def inject_ownership_variables(registry, xml_parent, data): ownership = XML.SubElement(xml_parent, 'com.synopsys.arc.jenkins.plugins.ownership.wrappers.OwnershipBuildWrapper') XML.SubElement(ownership, 'injectNodeOwnership').text = str(data.get('node-variables', False)).lower() XML.SubElement(ownership, 'injectJobOwnership').text = str(data.get('job-variables', False)).lower()
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yaml: inject-ownership-variables inject ownership variables to the build as environment variables .
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def show_chain(*chains, **kw): backrefs = kw.pop('backrefs', True) chains = [chain for chain in chains if chain] def in_chains(x, ids=set(map(id, itertools.chain(*chains)))): return (id(x) in ids) max_depth = (max(map(len, chains)) - 1) if backrefs: show_backrefs([chain[(-1)] for chain in chains], max_depth=max_depth, filter=in_chains, **kw) else: show_refs([chain[0] for chain in chains], max_depth=max_depth, filter=in_chains, **kw)
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show a chain of object references .
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def network_create_safe(context, values): return IMPL.network_create_safe(context, values)
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create a network from the values dict .
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def formatter(): output = s3_rest_controller() return output
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restful crud controller .
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def alignment_error_rate(reference, hypothesis, possible=None): if (possible is None): possible = reference else: assert reference.issubset(possible) return (1.0 - ((len((hypothesis & reference)) + len((hypothesis & possible))) / float((len(hypothesis) + len(reference)))))
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return the alignment error rate of an alignment with respect to a "gold standard" reference alignment .
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def _newer(a, b): if (not os.path.exists(a)): return False if (not os.path.exists(b)): return True return (os.path.getmtime(a) >= os.path.getmtime(b))
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inquire whether file a was written since file b .
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def simple_moving_average(iterable, k=10): a = (iterable if isinstance(iterable, list) else list(iterable)) for m in xrange(len(a)): i = (m - k) j = ((m + k) + 1) w = a[max(0, i):j] (yield (float(sum(w)) / (len(w) or 1)))
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returns an iterator over the simple moving average of the given list of values .
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@register.filter @template.defaultfilters.stringfilter def cssid(value): return value.replace(u'.', u'-').replace(u'@', u'-').replace(u'+', u'-').replace(u' ', u'-')
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replaces all .
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def files_with_suffix(base_path, suffix): if os.path.isfile(base_path): if base_path.endswith(suffix): (yield base_path) else: for (root, _, files) in os.walk(base_path): for filename in files: if filename.endswith(suffix): (yield os.path.join(root, filename))
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iterates over files in a given directory .
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def retry(func, retry_classes=None, fail_classes=None, exc_callback=None, backoff_delay=BACKOFF_DELAY): if (fail_classes and retry_classes and set(fail_classes).intersection(retry_classes)): raise ValueError("Can't include exception classes in both fail_on and retry_on") def should_retry_on(exc): if (fail_classes and isinstance(exc, tuple(fail_classes))): return False if (retry_classes and (not isinstance(exc, tuple(retry_classes)))): return False return True @functools.wraps(func) def wrapped(*args, **kwargs): while True: try: return func(*args, **kwargs) except gevent.GreenletExit as e: raise except Exception as e: if (not should_retry_on(e)): raise if (exc_callback is not None): exc_callback(e) gevent.sleep((backoff_delay + int(random.uniform(1, 10)))) return wrapped
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retry calling the decorated function using an exponential backoff .
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def setup_config(test, control_address=u'10.0.0.1', control_port=1234, name=None, log_config=None): if (name is None): name = random_name(test) ca_set = get_credential_sets()[0] scratch_directory = test.make_temporary_directory() contents = {u'control-service': {u'hostname': control_address, u'port': control_port}, u'dataset': {u'backend': u'zfs', u'name': name, u'mount_root': scratch_directory.child('mount_root').path, u'volume_config_path': scratch_directory.child('volume_config.json').path}, u'version': 1} if (log_config is not None): contents[u'logging'] = log_config test.config = scratch_directory.child('dataset-config.yml') test.config.setContent(yaml.safe_dump(contents)) ca_set.copy_to(scratch_directory, node=True) test.ca_set = ca_set
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overwrite or make config .
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def _compile_func(body): body = u'def {0}():\n {1}'.format(FUNC_NAME, body.replace('\n', '\n ')) code = compile(body, 'inline', 'exec') env = {} eval(code, env) return env[FUNC_NAME]
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given python code for a function body .
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def get_user_profile(user): try: listeners = get_memcached(get_key('listeners')) return listeners[user]['profile'] except: return user
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return user profile .
train
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def clear_inputhook(): pyos_inputhook_ptr = ctypes.c_void_p.in_dll(ctypes.pythonapi, 'PyOS_InputHook') pyos_inputhook_ptr.value = ctypes.c_void_p(None).value allow_CTRL_C()
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set pyos_inputhook to null and return the previous one .
train
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def posixpath_normpath(pathname): backslash = '\\' pathname2 = (os.path.normpath(pathname) or '.') if (backslash in pathname2): pathname2 = pathname2.replace(backslash, '/') return pathname2
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convert path into posix path: * normalize path * replace backslash with slash .
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def get_token_expiry(public=True): if public: return (now() + EXPIRE_DELTA_PUBLIC) else: return (now() + EXPIRE_DELTA)
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return a datetime object indicating when an access token should expire .
train
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def _ls_emr_bootstrap_stderr_logs(fs, log_dir_stream, action_num=None, node_id=None): matches = _ls_logs(fs, log_dir_stream, _match_emr_bootstrap_stderr_path, action_num=None, node_id=None) return sorted(matches, key=(lambda m: ((- m['action_num']), m['node_id'])))
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find all stderr from bootstrap actions in the given dir .
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false
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def skipUnlessGISLookup(*gis_lookups): def decorator(test_func): @wraps(test_func) def skip_wrapper(*args, **kwargs): if any(((key not in connection.ops.gis_operators) for key in gis_lookups)): raise unittest.SkipTest(("Database doesn't support all the lookups: %s" % ', '.join(gis_lookups))) return test_func(*args, **kwargs) return skip_wrapper return decorator
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skip a test unless a database supports all of gis_lookups .
train
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def MAMA(ds, count, fastlimit=(-4e+37), slowlimit=(-4e+37)): ret = call_talib_with_ds(ds, count, talib.MAMA, fastlimit, slowlimit) if (ret is None): ret = (None, None) return ret
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mesa adaptive moving average .
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def get_enabled(): return _get_svc_list('YES')
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return what services are set to run on boot .
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def task_enable_updates_testing(distribution): raise DistributionNotSupported(distribution=distribution)
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enable the distributions proposed updates repository .
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def is_listlike(x): return (hasattr(x, '__iter__') and (not isinstance(x, (six.text_type, bytes))))
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is this a sequence that isnt like a string or bytes? .
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def increment_lineno(node, n=1): for node in zip((node,), walk(node)): if ('lineno' in node._attributes): node.lineno = (getattr(node, 'lineno', 0) + n)
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increment the line number of each node in the tree starting at *node* by *n* .
train
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def ensure_home_directory(fs, user): userprofile = get_profile(user) if ((userprofile is not None) and userprofile.home_directory): fs.do_as_user(user.username, fs.create_home_dir, userprofile.home_directory) else: LOG.warn(('Not creating home directory of %s as his profile is empty' % user))
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adds a users home directory if it doesnt already exist .
train
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def test_logarithmic_big_scale(): line = Line(logarithmic=True) line.add('_', [(10 ** (-10)), (10 ** 10), 1]) q = line.render_pyquery() assert (len(q('.y.axis .guides')) == 21)
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test logarithmic option with a large range of value .
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def difftool_run(): files = selection.selected_group() if (not files): return s = selection.selection() model = main.model() difftool_launch_with_head(files, bool(s.staged), model.head)
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start a default difftool session .
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def verify_int(signed, ekey, n): return encrypt_int(signed, ekey, n)
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verifies signed using key ekey .
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def saturate(color, percent): return adjust(color, 1, percent)
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return a fully saturated color with the same hue .
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def idst(x, type=2, n=None, axis=(-1), norm=None, overwrite_x=False): if ((type == 1) and (norm is not None)): raise NotImplementedError('Orthonormalization not yet supported for IDCT-I') _TP = {1: 1, 2: 3, 3: 2} return _dst(x, _TP[type], n, axis, normalize=norm, overwrite_x=overwrite_x)
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return the inverse discrete sine transform of an arbitrary type sequence .
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def import_submodule(mod, subname, fullname): global found_now if ((fullname in found_now) and (fullname in sys.modules)): m = sys.modules[fullname] else: print ('Reloading', fullname) found_now[fullname] = 1 oldm = sys.modules.get(fullname, None) if (mod is None): path = None elif hasattr(mod, '__path__'): path = mod.__path__ else: return None try: with replace_import_hook(original_import): (fp, filename, stuff) = imp.find_module(subname, path) except ImportError: return None try: m = imp.load_module(fullname, fp, filename, stuff) except: if oldm: sys.modules[fullname] = oldm raise finally: if fp: fp.close() add_submodule(mod, m, fullname, subname) return m
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m = import_submodule .
train
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def p_declaration_specifiers_5(t): pass
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declaration_specifiers : type_specifier .
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def release_vlanid(vlan_id): LOG.debug(_('release_vlanid() called')) session = db.get_session() try: vlanid = session.query(l2network_models.VlanID).filter_by(vlan_id=vlan_id).one() vlanid['vlan_used'] = False session.merge(vlanid) session.flush() return vlanid['vlan_used'] except exc.NoResultFound: raise c_exc.VlanIDNotFound(vlan_id=vlan_id) return
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sets the vlanid state to be unused .
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@with_setup(step_runner_environ) def test_failing_behave_as_step_doesnt_pass(): runnable_step = Step.from_string('Given I have a step which calls the "other step fails" step with behave_as') try: runnable_step.run(True) except: pass assert_false(runnable_step.passed)
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when a step definition calls another step definition with behave_as .
train
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def _hashed_key(key): return md5(key.encode(u'utf-8')).hexdigest()
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hash keys when talking directly to the cache api .
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false
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def isIPAddress(addr, family=AF_INET): if isinstance(addr, bytes): try: addr = addr.decode('ascii') except UnicodeDecodeError: return False if (family == AF_INET6): addr = addr.split(u'%', 1)[0] elif (family == AF_INET): if (addr.count(u'.') != 3): return False else: raise ValueError('unknown address family {!r}'.format(family)) try: inet_pton(family, addr) except (ValueError, error): return False return True
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determine whether the given string represents an ipv4 address .
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def load_class(dotted_path): dotted_path_split = dotted_path.split('.') if (len(dotted_path_split) > 1): klass_name = dotted_path_split[(-1)] module_name = '.'.join(dotted_path_split[:(-1)]) module = load_module(module_name) if has_attribute(module, klass_name): klass = getattr(module, klass_name) return klass else: raise AttributeError(('Module %s does not have class attribute %s' % (module_name, klass_name))) else: raise ValueError(('Dotted module path %s must contain a module name and a classname' % dotted_path))
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finds the class registered to the alias .
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def value_for_key(tuple_of_tuples, key): for t in tuple_of_tuples: if (t[0] == key): return t[1] else: return key
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processes a tuple of 2-element tuples and returns the value corresponding to the given key .
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def unrepr(s): if (not s): return s obj = _astnode(s) return _Builder().build(obj)
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return a python object compiled from a string .
train
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def chisquare_power(effect_size, nobs, n_bins, alpha=0.05, ddof=0): crit = stats.chi2.isf(alpha, ((n_bins - 1) - ddof)) power = stats.ncx2.sf(crit, ((n_bins - 1) - ddof), ((effect_size ** 2) * nobs)) return power
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power of chisquare goodness of fit test effect size is sqrt of chisquare statistic divided by nobs parameters effect_size : float this is the deviation from the null of the normalized chi_square statistic .
train
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def test_scharr_h_mask(): np.random.seed(0) result = filters.scharr_h(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0)
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horizontal scharr on a masked array should be zero .
train
false
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def memory_usage(): return _GetSystemStats().memory()
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provides the memory usage in bytes for the given process .
train
false
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def test_close_process_when_normal(): with pipeline.get_cat_pipeline(pipeline.PIPE, pipeline.PIPE) as pl: assert (len(pl.commands) == 1) assert (pl.commands[0]._process.poll() is None) pipeline_wait(pl)
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process leaks must not occur in successful cases .
train
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def _has_kit_refs(info, picks): for p in picks: if (info['chs'][p]['coil_type'] == FIFF.FIFFV_COIL_KIT_REF_MAG): return True return False
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determine if kit ref channels are chosen .
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false
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def resolve_reverse_ipv6(packed_ip, flags=0): waiter = Waiter() core.dns_resolve_reverse_ipv6(packed_ip, flags, waiter.switch_args) (result, _type, ttl, addrs) = waiter.get() if (result != core.DNS_ERR_NONE): raise DNSError(result) return (ttl, addrs)
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lookup a ptr record for a given ipv6 address .
train
false
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def server_services(): from evennia.server.sessionhandler import SESSIONS if (hasattr(SESSIONS, 'server') and hasattr(SESSIONS.server, 'services')): server = SESSIONS.server.services.namedServices else: server = {} del SESSIONS return server
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lists all services active on the server .
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false
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def interpret_distro_name(location, basename, metadata, py_version=None, precedence=SOURCE_DIST, platform=None): parts = basename.split('-') if (not py_version): for (i, p) in enumerate(parts[2:]): if ((len(p) == 5) and p.startswith('py2.')): return for p in range(1, (len(parts) + 1)): (yield Distribution(location, metadata, '-'.join(parts[:p]), '-'.join(parts[p:]), py_version=py_version, precedence=precedence, platform=platform))
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generate alternative interpretations of a source distro name note: if location is a filesystem filename .
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true
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def get_default_role(request): global DEFAULT_ROLE default = getattr(settings, 'OPENSTACK_KEYSTONE_DEFAULT_ROLE', None) if (default and (DEFAULT_ROLE is None)): try: roles = keystoneclient(request, admin=True).roles.list() except Exception: roles = [] exceptions.handle(request) for role in roles: if ((role.id == default) or (role.name == default)): DEFAULT_ROLE = role break return DEFAULT_ROLE
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gets the default role object from keystone and saves it as a global .
train
true
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def _MakeSyncSearchServiceCall(call, request, response, deadline): _ValidateDeadline(deadline) logging.warning('_MakeSyncSearchServiceCall is deprecated; please use API.') try: if (deadline is None): apiproxy_stub_map.MakeSyncCall('search', call, request, response) else: rpc = apiproxy_stub_map.UserRPC('search', deadline=deadline) rpc.make_call(call, request, response) rpc.wait() rpc.check_success() except apiproxy_errors.ApplicationError as e: raise _ToSearchError(e)
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deprecated: make a synchronous call to search service .
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def get_displayable_exp_summary_dicts_matching_ids(exploration_ids, editor_user_id=None): exploration_summaries = exp_services.get_exploration_summaries_matching_ids(exploration_ids) filtered_exploration_summaries = [] for exploration_summary in exploration_summaries: if (exploration_summary is None): continue if (exploration_summary.status == rights_manager.ACTIVITY_STATUS_PRIVATE): if (editor_user_id is None): continue if (not rights_manager.Actor(editor_user_id).can_edit(feconf.ACTIVITY_TYPE_EXPLORATION, exploration_summary.id)): continue filtered_exploration_summaries.append(exploration_summary) return get_displayable_exp_summary_dicts(filtered_exploration_summaries)
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given a list of exploration ids .
train
false
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def convert_background_to_jpg(background_url): file_path = get_path_of_temp_url(background_url) im = Image.open(file_path) out_im = file_path.replace('png', 'jpg') bg = Image.new('RGB', im.size, (255, 255, 255)) bg.paste(im, (0, 0), im) bg.save(out_im, quality=55) return out_im
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convert the background image to jpg to reduce the file size .
train
false
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def _lombscargle_cdf(z, N, normalization, dH=1, dK=3): if ((dK - dH) != 2): raise NotImplementedError('Degrees of freedom != 2') Nk = (N - dK) if (normalization == 'psd'): return (1 - np.exp((- z))) elif (normalization == 'standard'): return (1 - ((1 + z) ** ((-0.5) * Nk))) elif (normalization == 'model'): return (1 - ((1 - z) ** (0.5 * Nk))) elif (normalization == 'log'): return (1 - np.exp((((-0.5) * Nk) * z))) else: raise ValueError("normalization='{0}' is not recognized".format(normalization))
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cumulative distribution for the lomb-scargle periodogram compute the expected cumulative distribution of the periodogram for the null hypothesis - i .
train
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def get_unpack_formats(): formats = [(name, info[0], info[3]) for (name, info) in _UNPACK_FORMATS.items()] formats.sort() return formats
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returns a list of supported formats for unpacking .
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def copy_table(dataset_name, table_name, new_table_name, project=None): bigquery_client = bigquery.Client(project=project) dataset = bigquery_client.dataset(dataset_name) table = dataset.table(table_name) destination_table = dataset.table(new_table_name) job_id = str(uuid.uuid4()) job = bigquery_client.copy_table(job_id, destination_table, table) job.create_disposition = google.cloud.bigquery.job.CreateDisposition.CREATE_IF_NEEDED job.begin() print 'Waiting for job to finish...' wait_for_job(job) print 'Table {} copied to {}.'.format(table_name, new_table_name)
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copies a table .
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def get_fields(node): return dict(iter_fields(node))
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retrieve field list for a given table .
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false
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def show_hc(kwargs=None, call=None): if (call != 'function'): raise SaltCloudSystemExit('The show_hc function must be called with -f or --function.') if ((not kwargs) or ('name' not in kwargs)): log.error('Must specify name of health check.') return False conn = get_conn() return _expand_item(conn.ex_get_healthcheck(kwargs['name']))
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show the details of an existing health check .
train
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def test_crop_append(): raw = _test_raw_reader(read_raw_bti, pdf_fname=pdf_fnames[0], config_fname=config_fnames[0], head_shape_fname=hs_fnames[0]) (y, t) = raw[:] (t0, t1) = ((0.25 * t[(-1)]), (0.75 * t[(-1)])) mask = ((t0 <= t) * (t <= t1)) raw_ = raw.copy().crop(t0, t1) (y_, _) = raw_[:] assert_true((y_.shape[1] == mask.sum())) assert_true((y_.shape[0] == y.shape[0]))
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test crop and append raw .
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def timeconvert(timestr): timestamp = None timetuple = email.utils.parsedate_tz(timestr) if (timetuple is not None): timestamp = email.utils.mktime_tz(timetuple) return timestamp
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convert rfc 2822 defined time string into system timestamp .
train
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@verbose def resample(x, up, down, npad=100, axis=(-1), window='boxcar', n_jobs=1, verbose=None): from scipy.signal import get_window if (not isinstance(axis, int)): err = ('The axis parameter needs to be an integer (got %s). The axis parameter was missing from this function for a period of time, you might be intending to specify the subsequent window parameter.' % repr(axis)) raise TypeError(err) x = np.asanyarray(x) ratio = (float(up) / down) if (axis < 0): axis = (x.ndim + axis) orig_last_axis = (x.ndim - 1) if (axis != orig_last_axis): x = x.swapaxes(axis, orig_last_axis) orig_shape = x.shape x_len = orig_shape[(-1)] if (x_len == 0): warn('x has zero length along last axis, returning a copy of x') return x.copy() bad_msg = 'npad must be "auto" or an integer' if isinstance(npad, string_types): if (npad != 'auto'): raise ValueError(bad_msg) min_add = (min((x_len // 8), 100) * 2) npad = ((2 ** int(np.ceil(np.log2((x_len + min_add))))) - x_len) (npad, extra) = divmod(npad, 2) npads = np.array([npad, (npad + extra)], int) else: if (npad != int(npad)): raise ValueError(bad_msg) npads = np.array([npad, npad], int) del npad x_flat = x.reshape(((-1), x_len)) orig_len = (x_len + npads.sum()) new_len = int(round((ratio * orig_len))) final_len = int(round((ratio * x_len))) to_removes = [int(round((ratio * npads[0])))] to_removes.append(((new_len - final_len) - to_removes[0])) to_removes = np.array(to_removes) if (window is not None): if callable(window): W = window(fftfreq(orig_len)) elif (isinstance(window, np.ndarray) and (window.shape == (orig_len,))): W = window else: W = ifftshift(get_window(window, orig_len)) else: W = np.ones(orig_len) W *= (float(new_len) / float(orig_len)) W = W.astype(np.complex128) (n_jobs, cuda_dict, W) = setup_cuda_fft_resample(n_jobs, W, new_len) if (n_jobs == 1): y = np.zeros((len(x_flat), (new_len - to_removes.sum())), dtype=x.dtype) for (xi, x_) in enumerate(x_flat): y[xi] = fft_resample(x_, W, new_len, npads, to_removes, cuda_dict) else: (parallel, p_fun, _) = parallel_func(fft_resample, n_jobs) y = parallel((p_fun(x_, W, new_len, npads, to_removes, cuda_dict) for x_ in x_flat)) y = np.array(y) y.shape = (orig_shape[:(-1)] + (y.shape[1],)) if (axis != orig_last_axis): y = y.swapaxes(axis, orig_last_axis) return y
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create a timegrouper and return our resampler .
train
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def test_reshape_configure(backend_default): bsz = backend_default.bsz reshape_0 = Reshape((0, 10, 10)) reshape_0.configure((10, 2, 50)) assert (reshape_0.out_shape == (10, 10, 10)) reshape_1 = Reshape((10, 25, (-1))) reshape_1.configure((10, 2, 50)) assert (reshape_1.out_shape == (10, 25, 4)) reshape_2 = Reshape((5, (-1))) reshape_2.configure((10, 2, 25)) assert (reshape_2.out_shape == (5, 100)) assert (reshape_2.out_shape_t == (5, (100 * bsz))) reshape_3 = Reshape((5, (-1), 5)) reshape_3.configure((10, 25)) assert (reshape_3.out_shape == (5, 10, 5))
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test cases: - reshape with 0 - reshape with -1 - reshape with collapsing dimensions - reshape with expanding dimensions .
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def network_count_reserved_ips(context, network_id): return IMPL.network_count_reserved_ips(context, network_id)
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return the number of reserved ips in the network .
train
false
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def merge_mappings(target, other, function=(lambda x, y: (x + y))): assert (set(target) == set(other)), 'keys must match' return {k: function(v, other[k]) for (k, v) in target.items()}
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merge two mappings into a single mapping .
train
false
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def notify_about_aggregate_update(context, event_suffix, aggregate_payload): aggregate_identifier = aggregate_payload.get('aggregate_id', None) if (not aggregate_identifier): aggregate_identifier = aggregate_payload.get('name', None) if (not aggregate_identifier): LOG.debug('No aggregate id or name specified for this notification and it will be ignored') return notifier = rpc.get_notifier(service='aggregate', host=aggregate_identifier) notifier.info(context, ('aggregate.%s' % event_suffix), aggregate_payload)
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send a notification about aggregate update .
train
false
1,433
def _reg_dir(): return os.path.join(__opts__['cachedir'], 'thorium')
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return the reg_dir for the given job id .
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@register.inclusion_tag('zinnia/tags/dummy.html', takes_context=True) def get_categories_tree(context, template='zinnia/tags/categories_tree.html'): return {'template': template, 'categories': Category.objects.all().annotate(count_entries=Count('entries')), 'context_category': context.get('category')}
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return the categories as a tree .
train
false
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def load_le32(buf, pos): end = (pos + 4) if (end > len(buf)): raise BadRarFile('cannot load le32') return (S_LONG.unpack_from(buf, pos)[0], (pos + 4))
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load little-endian 32-bit integer .
train
true
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def copy_file(src, dst, preserve_mode=1, preserve_times=1, update=0, link=None, verbose=1, dry_run=0): from distutils.dep_util import newer from stat import ST_ATIME, ST_MTIME, ST_MODE, S_IMODE if (not os.path.isfile(src)): raise DistutilsFileError(("can't copy '%s': doesn't exist or not a regular file" % src)) if os.path.isdir(dst): dir = dst dst = os.path.join(dst, os.path.basename(src)) else: dir = os.path.dirname(dst) if (update and (not newer(src, dst))): if (verbose >= 1): log.debug('not copying %s (output up-to-date)', src) return (dst, 0) try: action = _copy_action[link] except KeyError: raise ValueError(("invalid value '%s' for 'link' argument" % link)) if (verbose >= 1): if (os.path.basename(dst) == os.path.basename(src)): log.info('%s %s -> %s', action, src, dir) else: log.info('%s %s -> %s', action, src, dst) if dry_run: return (dst, 1) elif (link == 'hard'): if (not (os.path.exists(dst) and os.path.samefile(src, dst))): try: os.link(src, dst) return (dst, 1) except OSError: pass elif (link == 'sym'): if (not (os.path.exists(dst) and os.path.samefile(src, dst))): os.symlink(src, dst) return (dst, 1) _copy_file_contents(src, dst) if (preserve_mode or preserve_times): st = os.stat(src) if preserve_times: os.utime(dst, (st[ST_ATIME], st[ST_MTIME])) if preserve_mode: os.chmod(dst, S_IMODE(st[ST_MODE])) return (dst, 1)
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copy source_path to output_path .
train
false
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def create_ssh_wrapper(): ssh_wrapper = ssh_file(SSH_WRAPPER) with open(ssh_wrapper, u'w') as handle: handle.write(SSH_WRAPPER_TEMPLATE.format(known_hosts=ssh_file(KNOWN_HOSTS), identity=ssh_file(RSA_KEY))) os.chmod(ssh_wrapper, 493)
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creates wrapper for ssh to pass custom known hosts and key .
train
false
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def test_nvidia_driver3(): var = cuda.fvector() f = theano.function([var], (var + 1), mode=mode_with_gpu, profile=False) topo = f.maker.fgraph.toposort() assert any([isinstance(node.op, cuda.GpuElemwise) for node in topo]) assert (theano.sandbox.cuda.use.device_number is not None)
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test that the gpu device is initialized by theano when we build a function with gpu op .
train
false
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def _get_dpi_from(cmd, pattern, func): try: (out, _) = run_subprocess([cmd]) except (OSError, CalledProcessError): pass else: match = re.search(pattern, out) if match: return func(*map(float, match.groups()))
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match pattern against the output of func .
train
true
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def status_show(context, data_dict): return {'site_title': config.get('ckan.site_title'), 'site_description': config.get('ckan.site_description'), 'site_url': config.get('ckan.site_url'), 'ckan_version': ckan.__version__, 'error_emails_to': config.get('email_to'), 'locale_default': config.get('ckan.locale_default'), 'extensions': config.get('ckan.plugins').split()}
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return a dictionary with information about the sites configuration .
train
false
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def tools_nuget(): nuget_url = CONFIG['nuget']['url'] mobsf_subdir_tools = CONFIG['MobSF']['tools'] nuget_file_path = CONFIG['nuget']['file'] nuget_file_local = open(os.path.join(mobsf_subdir_tools, nuget_file_path), 'wb') print '[*] Downloading nuget..' nuget_file = urlrequest.urlopen(nuget_url) print '[*] Saving to File {}'.format(nuget_file_path) nuget_file_local.write(bytes(nuget_file.read())) nuget_file_local.close()
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download nuget .
train
false
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def get_configuration_dict(name, default=None): default = (default or {}) output = default.copy() output.update((get_configuration_value(name, {}) or {})) return output
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returns a dictionary product after merging the current sites configuration and the default value .
train
false
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def has_required(programs): try: return check_required(programs) except exception.CommandNotFound: return False
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same as check_required but returns false if not all commands exist .
train
false
1,447
def _add_metadata(bt, md_key, lines): taxonomy_md = biom_taxonomy_formatter(bt, md_key) if (taxonomy_md is not None): for i in range((len(lines) - 1)): lines[(i + 1)] = ((lines[(i + 1)] + ' DCTB ') + taxonomy_md[i]) return lines else: nls = ([' DCTB '.join(lines[0].split(' DCTB ')[:(-1)])] + lines[1:]) return nls
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add metadata to formatted correlation output lines .
train
false
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def get_component(app, id): sa_session = app.model.context.current return sa_session.query(app.model.Component).get(app.security.decode_id(id))
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given an object .
train
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def is_aa(residue, standard=False): if (not isinstance(residue, basestring)): residue = residue.get_resname() residue = residue.upper() if standard: return (residue in d3_to_index) else: return (residue in SCOPData.protein_letters_3to1)
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return true if residue object/string is an amino acid .
train
false
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def test_negative_distance(): with pytest.raises(ValueError): Distance([(-2), 3.1], u.kpc) with pytest.raises(ValueError): Distance([(-2), (-3.1)], u.kpc) with pytest.raises(ValueError): Distance((-2), u.kpc) d = Distance((-2), u.kpc, allow_negative=True) assert (d.value == (-2))
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test optional kwarg allow_negative .
train
false
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def pts_to_prestep(x, *args): steps = np.zeros(((1 + len(args)), ((2 * len(x)) - 1))) steps[0, 0::2] = x steps[0, 1::2] = steps[0, 0:(-2):2] steps[1:, 0::2] = args steps[1:, 1::2] = steps[1:, 2::2] return steps
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convert continuous line to pre-steps .
train
false
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def getVoronoiLoopByPoints(inside, loop, outsides): for outside in outsides: loop = getVoronoiLoopByPoint(inside, loop, outside) return loop
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get voronoi loop enclosing the inside .
train
false
1,453
def list_tab(user): data = raw_cron(user) ret = {'pre': [], 'crons': [], 'special': [], 'env': []} flag = False comment = None identifier = None for line in data.splitlines(): if (line == '# Lines below here are managed by Salt, do not edit'): flag = True continue if flag: commented_cron_job = False if line.startswith('#DISABLED#'): line = line[10:] commented_cron_job = True if line.startswith('@'): dat = {} comps = line.split() if (len(comps) < 2): continue dat['spec'] = comps[0] dat['cmd'] = ' '.join(comps[1:]) ret['special'].append(dat) elif line.startswith('#'): comment_line = line.lstrip('# ') if (SALT_CRON_IDENTIFIER in comment_line): parts = comment_line.split(SALT_CRON_IDENTIFIER) comment_line = parts[0].rstrip() if (len(parts[1]) > 1): identifier = parts[1][1:] if (comment is None): comment = comment_line else: comment += ('\n' + comment_line) elif ((line.find('=') > 0) and ((' ' not in line) or (line.index('=') < line.index(' ')))): comps = line.split('=', 1) dat = {} dat['name'] = comps[0] dat['value'] = comps[1] ret['env'].append(dat) elif (len(line.split(' ')) > 5): comps = line.split(' ') dat = {'minute': comps[0], 'hour': comps[1], 'daymonth': comps[2], 'month': comps[3], 'dayweek': comps[4], 'identifier': identifier, 'cmd': ' '.join(comps[5:]), 'comment': comment, 'commented': False} if commented_cron_job: dat['commented'] = True ret['crons'].append(dat) identifier = None comment = None commented_cron_job = False else: ret['pre'].append(line) return ret
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return the contents of the specified users incrontab cli example: .
train
true
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def _sss(l1, l2, l3): c1 = Circle((0, 0), l3) c2 = Circle((l1, 0), l2) inter = [a for a in c1.intersection(c2) if a.y.is_nonnegative] if (not inter): return None pt = inter[0] return Triangle((0, 0), (l1, 0), pt)
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return triangle having side of length l1 on the x-axis .
train
false
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def standard_normal(size=None, dtype=float): return normal(size=size, dtype=dtype)
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returns an array of samples drawn from the standard normal distribution .
train
false
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@skip('silverlight') @skip('netstandard') def test_xequals_call_for_optimization(): import clr clr.AddReference('System.Configuration') from System.Configuration import ConfigurationManager c = ConfigurationManager.ConnectionStrings for i in xrange(3): if is_posix: AreEqual(2, c.Count) else: AreEqual(1, c.Count) for i in xrange(3): count = c.Count if is_posix: AreEqual(2, count) else: AreEqual(1, count) AreEqual(c.Count, count) for i in xrange(3): c[0].Name c.Count c.Count
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testing specifically for system .
train
false
1,457
def lookup_lastlines(lastlines_dirpath, path): underscored = path.replace('/', '_') try: lastlines_file = open(os.path.join(lastlines_dirpath, underscored)) except (OSError, IOError): return lastlines = lastlines_file.read() lastlines_file.close() os.remove(lastlines_file.name) if (not lastlines): return try: target_file = open(path) except (OSError, IOError): return target_data = target_file.read() target_file.close() loc = target_data.rfind(lastlines) if (loc == (-1)): return reverse_lineno = target_data.count('\n', (loc + len(lastlines))) return reverse_lineno
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retrieve last lines seen for path .
train
false
1,459
def directory_exists(path): return os.path.isdir(os.path.expanduser(path))
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tests to see if path is a valid directory .
train
false
1,460
def _get_data_id(annotation): if isinstance(annotation, HtmlTag): return annotation.attributes[TAGID]
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get id of an annotation .
train
false
1,461
def chunks(l, n): for i in xrange(0, len(l), n): (yield l[i:(i + n)])
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yield successive n-sized chunks from l .
train
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def create_local_srs(): for host_ref in _db_content['host'].keys(): create_sr(name_label='Local storage', type='lvm', other_config={'i18n-original-value-name_label': 'Local storage', 'i18n-key': 'local-storage'}, physical_size=40000, physical_utilisation=20000, virtual_allocation=10000, host_ref=host_ref) create_sr(name_label='Local storage ISO', type='iso', other_config={'i18n-original-value-name_label': 'Local storage ISO', 'i18n-key': 'local-storage-iso'}, physical_size=80000, physical_utilisation=40000, virtual_allocation=80000, host_ref=host_ref)
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create an sr that looks like the one created on the local disk by default by the xenserver installer .
train
false
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def get_profile_from_user(user): for field in user._meta.get_fields(): try: if hasattr(user, field.name): attribute = getattr(user, field.name) if (get_profile_model() == type(attribute)): return attribute except Exception: logger.exception('Error getting profile attribute from user.') logger.info('Could not find profile attribute.') return None
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tries to get the profile according to the class configured on auth_profile_module .
train
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def inverse_sine_transform(F, k, x, **hints): return InverseSineTransform(F, k, x).doit(**hints)
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compute the unitary .
train
false
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def match_paren(parens): stack = Stack() for b in parens: if (b == '('): stack.push(1) elif (not stack.isEmpty()): stack.pop() else: return False return stack.isEmpty()
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returns true or false if parenthesis expression passed is matching .
train
false
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def listen_fds(unset_environment=True): fds = int(os.environ.get('LISTEN_FDS', 0)) listen_pid = int(os.environ.get('LISTEN_PID', 0)) if (listen_pid != os.getpid()): return 0 if unset_environment: os.environ.pop('LISTEN_PID', None) os.environ.pop('LISTEN_FDS', None) return fds
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get the number of sockets inherited from systemd socket activation .
train
false
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def convert_uptime_hours(sys_uptime): return ((int(sys_uptime) / 100.0) / 3600.0)
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sys_uptime is in hundredths of seconds returns a float .
train
false