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def notification_from_headers(channel, headers): headers = _upper_header_keys(headers) channel_id = headers[X_GOOG_CHANNEL_ID] if (channel.id != channel_id): raise errors.InvalidNotificationError(('Channel id mismatch: %s != %s' % (channel.id, channel_id))) else: message_number = int(headers[X_GOOG_MESSAGE_NUMBER]) state = headers[X_GOOG_RESOURCE_STATE] resource_uri = headers[X_GOOG_RESOURCE_URI] resource_id = headers[X_GOOG_RESOURCE_ID] return Notification(message_number, state, resource_uri, resource_id)
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parse a notification from the webhook request headers .
train
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@task(aliases=['celery']) def celery_worker(ctx, level='debug', hostname=None, beat=False): os.environ['DJANGO_SETTINGS_MODULE'] = 'api.base.settings' cmd = 'celery worker -A framework.celery_tasks -l {0}'.format(level) if hostname: cmd = (cmd + ' --hostname={}'.format(hostname)) if beat: cmd = (cmd + ' --beat') ctx.run(bin_prefix(cmd), pty=True)
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run the celery process .
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def check_vcs_conflict(): try: ok = True for fn in _get_files(only_py=True): with tokenize.open(fn) as f: for line in f: if any((line.startswith((c * 7)) for c in '<>=|')): print 'Found conflict marker in {}'.format(fn) ok = False print () return ok except Exception: traceback.print_exc() return None
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check vcs conflict markers .
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def ip_string_to_num(s): return reduce((lambda a, b: ((a << 8) | b)), map(int, s.split('.')))
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convert dotted ipv4 address to integer .
train
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def publish_course(course): store = modulestore() with store.branch_setting(ModuleStoreEnum.Branch.draft_preferred, course.id): store.publish(course.location, ModuleStoreEnum.UserID.test)
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helper method to publish the course .
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@register.tag def get_obj_perms(parser, token): bits = token.split_contents() format = u'{% get_obj_perms user/group for obj as "context_var" %}' if ((len(bits) != 6) or (bits[2] != u'for') or (bits[4] != u'as')): raise template.TemplateSyntaxError((u'get_obj_perms tag should be in format: %s' % format)) for_whom = bits[1] obj = bits[3] context_var = bits[5] if ((context_var[0] != context_var[(-1)]) or (context_var[0] not in (u'"', u"'"))): raise template.TemplateSyntaxError(u"get_obj_perms tag's context_var argument should be in quotes") context_var = context_var[1:(-1)] return ObjectPermissionsNode(for_whom, obj, context_var)
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returns a list of permissions for a given user/group and obj .
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def test_load_strings_defaults_to_english(): locale.getdefaultlocale = (lambda : ('en_US', 'UTF-8')) strings.load_strings(helpers) assert (strings._('wait_for_hs') == 'Waiting for HS to be ready:')
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load_strings() loads english by default .
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@contextmanager def xml_writer(response, fields, name=None, bom=False): if hasattr(response, u'headers'): response.headers['Content-Type'] = 'text/xml; charset=utf-8' if name: response.headers['Content-disposition'] = 'attachment; filename="{name}.xml"'.format(name=encode_rfc2231(name)) if bom: response.write(UTF8_BOM) response.write('<data>\n') (yield XMLWriter(response, [f['id'] for f in fields])) response.write('</data>\n')
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context manager for writing utf-8 xml data to response .
train
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def remove_users(caller, role, *users): if (not ((len(users) == 1) and (caller == users[0]))): _check_caller_authority(caller, role) role.remove_users(*users)
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the caller requests removing the given users from the role .
train
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def _is_statement_unnamed(statement): return (statement in ('log', 'channel', 'junction', 'options'))
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returns true .
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def add_vlan_binding(vlanid, vlanname, netid): LOG.debug(_('add_vlan_binding() called')) session = db.get_session() try: binding = session.query(network_models_v2.Vlan_Binding).filter_by(vlan_id=vlanid).one() raise c_exc.NetworkVlanBindingAlreadyExists(vlan_id=vlanid, network_id=netid) except exc.NoResultFound: binding = network_models_v2.Vlan_Binding(vlanid, vlanname, netid) session.add(binding) session.flush() return binding
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adds a vlan to network association .
train
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def load_data_str(rel_path): full_path = ((path(__file__).abspath().dirname() / 'data') / rel_path) with open(full_path) as data_file: return data_file.read()
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load a file from the "data" directory as a string .
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@contextlib.contextmanager def redirect_fd(fd): save = os.dup(fd) (r, w) = os.pipe() try: os.dup2(w, fd) (yield io.open(r, 'r')) finally: os.close(w) os.dup2(save, fd) os.close(save)
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temporarily redirect *fd* to a pipes write end and return a file object wrapping the pipes read end .
train
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def check_resource_update(rsrc, template_id, resource_data, engine_id, stack, msg_queue): check_message = functools.partial(_check_for_message, msg_queue) if (rsrc.action == resource.Resource.INIT): rsrc.create_convergence(template_id, resource_data, engine_id, stack.time_remaining(), check_message) else: rsrc.update_convergence(template_id, resource_data, engine_id, stack.time_remaining(), stack, check_message)
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create or update the resource if appropriate .
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def xfail(reason=''): __tracebackhide__ = True raise XFailed(reason)
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xfail an executing test or setup functions with the given reason .
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@cache_permission def can_edit_subproject(user, project): return check_permission(user, project, 'trans.change_subproject')
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checks whether user can edit subprojects on given project .
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def has_sub_tasks(task): if istask(task): return True elif isinstance(task, list): return any((has_sub_tasks(i) for i in task)) else: return False
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returns true if the task has sub tasks .
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def create_catalog_api_client(user, catalog_integration): scopes = ['email', 'profile'] expires_in = settings.OAUTH_ID_TOKEN_EXPIRATION jwt = JwtBuilder(user).build_token(scopes, expires_in) return EdxRestApiClient(catalog_integration.internal_api_url, jwt=jwt)
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returns an api client which can be used to make catalog api requests .
train
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def __get_image(conn, vm_): img = config.get_cloud_config_value('image', vm_, __opts__, default='debian-7', search_global=False) return conn.ex_get_image(img)
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the get_image for gce allows partial name matching and returns a libcloud object .
train
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def init_params(options, preemb=None): params = OrderedDict() if (preemb == None): params['Wemb'] = norm_weight(options['n_words'], options['dim_word']) else: params['Wemb'] = preemb params = get_layer('ff')[0](options, params, prefix='ff_state', nin=options['dimctx'], nout=options['dim']) params = get_layer(options['decoder'])[0](options, params, prefix='decoder', nin=options['dim_word'], dim=options['dim']) if options['doutput']: params = get_layer('ff')[0](options, params, prefix='ff_hid', nin=options['dim'], nout=options['dim_word']) params = get_layer('ff')[0](options, params, prefix='ff_logit', nin=options['dim_word'], nout=options['n_words']) else: params = get_layer('ff')[0](options, params, prefix='ff_logit', nin=options['dim'], nout=options['n_words']) return params
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initialize all parameters .
train
false
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@task() @timeit def index_task(cls, id_list, **kw): statsd.incr(('search.tasks.index_task.%s' % cls.get_mapping_type_name())) try: pin_this_thread() qs = cls.get_model().objects.filter(pk__in=id_list).values_list('pk', flat=True) for id_ in qs: try: cls.index(cls.extract_document(id_), id_=id_) except UnindexMeBro: cls.unindex(id_) except Exception as exc: retries = index_task.request.retries if (retries >= MAX_RETRIES): raise IndexingTaskError() statsd.incr('search.tasks.index_task.retry', 1) statsd.incr(('search.tasks.index_task.retry%d' % RETRY_TIMES[retries]), 1) index_task.retry(exc=exc, max_retries=MAX_RETRIES, countdown=RETRY_TIMES[retries]) finally: unpin_this_thread()
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index documents specified by cls and ids .
train
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def only_active_assets(reference_date_value, assets): return [a for a in assets if was_active(reference_date_value, a)]
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filter an iterable of asset objects down to just assets that were alive at the time corresponding to reference_date_value .
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def decov(h): return DeCov()(h)
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computes the decov loss of h args: h : variable holding a matrix where the first dimension corresponds to the batches .
train
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def truncate(content, length=100, suffix='...'): if (len(content) <= length): return content else: return (content[:length].rsplit(' ', 1)[0] + suffix)
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truncate datetime expression examples .
train
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def select_device(device_id): context = devices.get_context(device_id) return context.device
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make the context associated with device *device_id* the current context .
train
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def histogram_bins(timeseries): series = scipy.array([x[1] for x in timeseries]) t = tail_avg(timeseries) h = np.histogram(series, bins=15) bins = h[1] for (index, bin_size) in enumerate(h[0]): if (bin_size <= 20): if (index == 0): if (t <= bins[0]): return True elif ((t >= bins[index]) and (t < bins[(index + 1)])): return True return False
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a timeseries is anomalous if the average of the last three datapoints falls into a histogram bin with less than 20 other datapoints returns: the size of the bin which contains the tail_avg .
train
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def set_cache_dir(cache_dir): if ((cache_dir is not None) and (not op.exists(cache_dir))): raise IOError(('Directory %s does not exist' % cache_dir)) set_config('MNE_CACHE_DIR', cache_dir, set_env=False)
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set the directory to be used for temporary file storage .
train
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def _run_ems(objective_function, data, cond_idx, train, test): d = objective_function(*(data[np.intersect1d(c, train)] for c in cond_idx)) d /= np.sqrt(np.sum((d ** 2), axis=0))[None, :] return (np.sum((data[test[0]] * d), axis=0), d)
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run ems .
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@pytest.fixture def hass_recorder(): hass = get_test_home_assistant() def setup_recorder(config): 'Setup with params.' db_uri = 'sqlite://' conf = {recorder.CONF_DB_URL: db_uri} conf.update(config) assert setup_component(hass, recorder.DOMAIN, {recorder.DOMAIN: conf}) hass.start() hass.block_till_done() recorder._verify_instance() recorder._INSTANCE.block_till_done() return hass (yield setup_recorder) hass.stop()
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hass fixture with in-memory recorder .
train
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def _get_old_unhelpful(): old_formatted = {} cursor = connection.cursor() cursor.execute('SELECT doc_id, yes, no\n FROM\n (SELECT wiki_revision.document_id as doc_id,\n SUM(limitedvotes.helpful) as yes,\n SUM(NOT(limitedvotes.helpful)) as no\n FROM\n (SELECT * FROM wiki_helpfulvote\n WHERE created <= DATE_SUB(CURDATE(), INTERVAL 1 WEEK)\n AND created >= DATE_SUB(DATE_SUB(CURDATE(),\n INTERVAL 1 WEEK), INTERVAL 1 WEEK)\n ) as limitedvotes\n INNER JOIN wiki_revision ON\n limitedvotes.revision_id=wiki_revision.id\n INNER JOIN wiki_document ON\n wiki_document.id=wiki_revision.document_id\n WHERE wiki_document.locale="en-US"\n GROUP BY doc_id\n HAVING no > yes\n ) as calculated') old_data = cursor.fetchall() for data in old_data: doc_id = data[0] yes = float(data[1]) no = float(data[2]) total = (yes + no) if (total == 0): continue old_formatted[doc_id] = {'total': total, 'percentage': (yes / total)} return old_formatted
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gets the data from 2 weeks ago and formats it as output so that we can get a percent change .
train
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def reply_published_cb(sender, user, reply, trivial, **kwargs): siteconfig = SiteConfiguration.objects.get_current() if (siteconfig.get(u'mail_send_review_mail') and (not trivial)): mail_reply(reply, user)
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send e-mail when a review reply is published .
train
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@handle_response_format @treeio_login_required def transaction_edit(request, transaction_id, response_format='html'): transaction = get_object_or_404(Transaction, pk=transaction_id) if request.POST: if ('cancel' not in request.POST): form = TransactionForm(request.user.profile, None, None, request.POST, instance=transaction) if form.is_valid(): transaction = form.save(commit=False) convert(transaction, 'value') return HttpResponseRedirect(reverse('finance_transaction_view', args=[transaction.id])) else: return HttpResponseRedirect(reverse('finance_transaction_view', args=[transaction.id])) else: form = TransactionForm(request.user.profile, None, None, instance=transaction) return render_to_response('finance/transaction_edit', {'form': form, 'transaction': transaction}, context_instance=RequestContext(request), response_format=response_format)
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transaction edit page .
train
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def setup_editor(qtbot): text = 'a = 1\nprint(a)\n\nx = 2' editorStack = EditorStack(None, []) editorStack.set_introspector(Mock()) editorStack.set_find_widget(Mock()) editorStack.set_io_actions(Mock(), Mock(), Mock(), Mock()) finfo = editorStack.new('foo.py', 'utf-8', text) qtbot.addWidget(editorStack) return (editorStack, finfo.editor)
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set up editorstack with codeeditor containing some python code .
train
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def clean_xml_string(s): return u''.join((c for c in s if is_valid_xml_char_ordinal(ord(c))))
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cleans string from invalid xml chars solution was found there:: URL .
train
false
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def send_item_json(): try: item_id = request.args[0] except: raise HTTP(400, current.xml.json_message(False, 400, 'No value provided!')) stable = s3db.org_site istable = s3db.inv_send ittable = s3db.inv_track_item inv_ship_status = s3db.inv_ship_status istable.date.represent = (lambda dt: dt[:10]) query = (((((ittable.req_item_id == item_id) & (istable.id == ittable.send_id)) & (istable.site_id == stable.id)) & ((istable.status == inv_ship_status['SENT']) | (istable.status == inv_ship_status['RECEIVED']))) & (ittable.deleted == False)) records = db(query).select(istable.id, istable.date, stable.name, ittable.quantity) output = ('[%s,%s' % (json.dumps(dict(id=str(T('Sent')), quantity='#')), records.json()[1:])) response.headers['Content-Type'] = 'application/json' return output
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used by s3 .
train
false
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def ATR(barDs, count, timeperiod=(- (2 ** 31))): return call_talib_with_hlc(barDs, count, talib.ATR, timeperiod)
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average true range .
train
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def list_transaction(hostname, username, password, label): bigip_session = _build_session(username, password) trans_id = __salt__['grains.get']('bigip_f5_trans:{label}'.format(label=label)) if trans_id: try: response = bigip_session.get((BIG_IP_URL_BASE.format(host=hostname) + '/transaction/{trans_id}/commands'.format(trans_id=trans_id))) return _load_response(response) except requests.exceptions.ConnectionError as e: return _load_connection_error(hostname, e) else: return 'Error: the label for this transaction was not defined as a grain. Begin a new transaction using the bigip.start_transaction function'
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a function to connect to a bigip device and list an existing transaction .
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def Normal(name, mean, std): return rv(name, NormalDistribution, (mean, std))
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create a continuous random variable with a normal distribution .
train
false
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def get_stack(context=1): frame = sys._getframe(1) framelist = [] while frame: framelist.append(((frame,) + getframeinfo(frame, context))) frame = frame.f_back return framelist
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get a list of records for a frame and all higher frames .
train
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def ensure_utf8(s): if isinstance(s, unicode): return s.encode('utf8') return s
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not all of pyobjc and python understand unicode paths very well yet .
train
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def _ListSeparatorsInXMLFormat(separators, indent=''): result = '' separators = list(separators) separators.sort() for sep_char in separators: result += ('%s<list_separator>%s</list_separator>\n' % (indent, repr(sep_char))) return result
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generates xml encoding of a list of list separators .
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def onInterfaceAppShutDown(): INFO_MSG('onInterfaceAppShutDown()') g_poller.stop()
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kbengine method .
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def postorder_3color(graph, root): color = dict() order = [] def dfs_walk(node): color[node] = 'grey' for succ in graph.successors(node): if (color.get(succ) == 'grey'): print 'CYCLE: {0}-->{1}'.format(node, succ) if (succ not in color): dfs_walk(succ) order.append(node) color[node] = 'black' dfs_walk(root) return order
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return a post-order ordering of nodes in the graph .
train
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def VAR(x, B, const=0): p = B.shape[0] T = x.shape[0] xhat = np.zeros(x.shape) for t in range(p, T): xhat[t, :] = (const + (x[(t - p):t, :, np.newaxis] * B).sum(axis=1).sum(axis=0)) return xhat
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multivariate linear filter parameters x: array columns are variables .
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def upgrade_tools(name, reboot=False, call=None): if (call != 'action'): raise SaltCloudSystemExit('The upgrade_tools action must be called with -a or --action.') vm_ref = salt.utils.vmware.get_mor_by_property(_get_si(), vim.VirtualMachine, name) return _upg_tools_helper(vm_ref, reboot)
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to upgrade vmware tools on a specified virtual machine .
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def _find_flavor(cs, flavor): try: return utils.find_resource(cs.flavors, flavor, is_public=None) except exceptions.NotFound: return cs.flavors.find(ram=flavor)
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get a flavor by name .
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def strip_leading_tabs(docs): lines = docs.splitlines() start = 0 for line in lines: if (line != u''): break start += 1 if start: lines = lines[start:] if (len(lines) > 1): start_line = 1 ref_line = lines[start_line] while (not ref_line): start_line += 1 if (start_line > len(lines)): break ref_line = lines[start_line] strip_left = (len(ref_line) - len(ref_line.lstrip())) if strip_left: docs = u'\n'.join(([lines[0]] + [l[strip_left:] for l in lines[1:]])) return docs
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strip leading tabs from __doc__ text .
train
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def process_po_folder(domain, folder, extra=''): result = True for fname in glob.glob(os.path.join(folder, '*.po')): basename = os.path.split(fname)[1] name = os.path.splitext(basename)[0] mo_path = os.path.normpath(('%s/%s%s' % (MO_DIR, name, MO_LOCALE))) mo_name = ('%s.mo' % domain) if (not os.path.exists(mo_path)): os.makedirs(mo_path) mo_file = os.path.join(mo_path, mo_name) print ('Compile %s' % mo_file) (ret, output) = run(('%s %s -o "%s" "%s"' % (TOOL, extra, mo_file, fname))) if (ret != 0): print ('\nMissing %s. Please install this package first.' % TOOL) exit(1) if ('WARNING:' in output): print output result = False return result
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process each po file in folder .
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def _my_principal_branch(expr, period, full_pb=False): from sympy import principal_branch res = principal_branch(expr, period) if (not full_pb): res = res.replace(principal_branch, (lambda x, y: x)) return res
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bring expr nearer to its principal branch by removing superfluous factors .
train
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def is_loopback_connection(request): host_ip = socket.gethostbyname(socket.gethostname()) remote_ip = get_request_ip(request) return (remote_ip in ['127.0.0.1', 'localhost', host_ip])
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test whether the ip making the request is the same as the ip serving the request .
train
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def maybe_convert_indices(indices, n): if isinstance(indices, list): indices = np.array(indices) if (len(indices) == 0): return np.empty(0, dtype=np.int_) mask = (indices < 0) if mask.any(): indices[mask] += n mask = ((indices >= n) | (indices < 0)) if mask.any(): raise IndexError('indices are out-of-bounds') return indices
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if we have negative indicies .
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def test_make_table(table_types, mixin_cols): t = table_types.Table(mixin_cols) check_mixin_type(t, t['m'], mixin_cols['m']) cols = list(mixin_cols.values()) t = table_types.Table(cols, names=('i', 'a', 'b', 'm')) check_mixin_type(t, t['m'], mixin_cols['m']) t = table_types.Table(cols) check_mixin_type(t, t['col3'], mixin_cols['m'])
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make a table with the columns in mixin_cols .
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@contextfunction def projects_time_slot_list(context, time_slots, no_dates=False): request = context['request'] response_format = 'html' if ('response_format' in context): response_format = context['response_format'] return Markup(render_to_string('projects/tags/time_slot_list', {'time_slots': time_slots, 'no_dates': no_dates}, context_instance=RequestContext(request), response_format=response_format))
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print a list of time slots .
train
false
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def parse_reflog_line(line): (begin, message) = line.split(' DCTB ', 1) (old_sha, new_sha, rest) = begin.split(' ', 2) (committer, timestamp_str, timezone_str) = rest.rsplit(' ', 2) return Entry(old_sha, new_sha, committer, int(timestamp_str), parse_timezone(timezone_str)[0], message)
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parse a reflog line .
train
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def attribute_rule(allowed_attrs): def fn(tag): for (attr, val) in list(tag.attrs.items()): rule = allowed_attrs.get(attr) if rule: if callable(rule): new_val = rule(val) if (new_val is None): del tag[attr] else: tag[attr] = new_val else: pass else: del tag[attr] return fn
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generator for functions that can be used as entries in whitelister .
train
false
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def getNewRepository(): return ExportRepository()
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get new repository .
train
false
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def emits_warning(*messages): @decorator def decorate(fn, *args, **kw): with expect_warnings(assert_=False, *messages): return fn(*args, **kw) return decorate
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mark a test as emitting a warning .
train
false
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def Save(root=None, formats=None, **options): clf = options.pop('clf', True) Config(**options) if (formats is None): formats = ['pdf', 'eps'] try: formats.remove('plotly') Plotly(clf=False) except ValueError: pass if root: for fmt in formats: SaveFormat(root, fmt) if clf: Clf()
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saves the plot in the given formats and clears the figure .
train
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@frappe.whitelist() def archive_restore_column(board_name, column_title, status): doc = frappe.get_doc(u'Kanban Board', board_name) for col in doc.columns: if (column_title == col.column_name): col.status = status doc.save() return doc.columns
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set columns status to status .
train
false
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def is_ha_router(router): try: requested_router_type = router.extra_attributes.ha except AttributeError: requested_router_type = router.get('ha') if validators.is_attr_set(requested_router_type): return requested_router_type return cfg.CONF.l3_ha
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return true if router to be handled is ha .
train
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def install_cache(cache_name='cache', backend='sqlite', expire_after=None, allowable_codes=(200,), allowable_methods=('GET',), session_factory=CachedSession, **backend_options): _patch_session_factory((lambda : session_factory(cache_name=cache_name, backend=backend, expire_after=expire_after, allowable_codes=allowable_codes, allowable_methods=allowable_methods, **backend_options)))
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installs cache for all requests requests by monkey-patching session parameters are the same as in :class:cachedsession .
train
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@docfiller def uniform_filter1d(input, size, axis=(-1), output=None, mode='reflect', cval=0.0, origin=0): input = numpy.asarray(input) if numpy.iscomplexobj(input): raise TypeError('Complex type not supported') axis = _ni_support._check_axis(axis, input.ndim) if (size < 1): raise RuntimeError('incorrect filter size') (output, return_value) = _ni_support._get_output(output, input) if ((((size // 2) + origin) < 0) or (((size // 2) + origin) >= size)): raise ValueError('invalid origin') mode = _ni_support._extend_mode_to_code(mode) _nd_image.uniform_filter1d(input, size, axis, output, mode, cval, origin) return return_value
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calculate a one-dimensional uniform filter along the given axis .
train
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def coarsen(reduction, x, axes, trim_excess=False): for i in range(x.ndim): if (i not in axes): axes[i] = 1 if trim_excess: ind = tuple(((slice(0, (- (d % axes[i]))) if (d % axes[i]) else slice(None, None)) for (i, d) in enumerate(x.shape))) x = x[ind] newshape = tuple(concat([((x.shape[i] / axes[i]), axes[i]) for i in range(x.ndim)])) return reduction(x.reshape(newshape), axis=tuple(range(1, (x.ndim * 2), 2)))
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coarsen array by applying reduction to fixed size neighborhoods parameters reduction: function function like np .
train
false
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def _rows_differ(row, _row): row_copy = copy.deepcopy(row) _row_copy = copy.deepcopy(_row) for panel in row_copy['panels']: if ('id' in panel): del panel['id'] for _panel in _row_copy['panels']: if ('id' in _panel): del _panel['id'] diff = DictDiffer(row_copy, _row_copy) return (diff.changed() or diff.added() or diff.removed())
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check if grafana dashboard row and _row differ .
train
true
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def get_all(context, namespace_name, session, filters=None, marker=None, limit=None, sort_key='created_at', sort_dir='desc'): namespace = namespace_api.get(context, namespace_name, session) query = session.query(models.MetadefTag).filter_by(namespace_id=namespace['id']) marker_tag = None if (marker is not None): marker_tag = _get(context, marker, session) sort_keys = ['created_at', 'id'] (sort_keys.insert(0, sort_key) if (sort_key not in sort_keys) else sort_keys) query = paginate_query(query=query, model=models.MetadefTag, limit=limit, sort_keys=sort_keys, marker=marker_tag, sort_dir=sort_dir) metadef_tag = query.all() metadef_tag_list = [] for tag in metadef_tag: metadef_tag_list.append(tag.to_dict()) return metadef_tag_list
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return a list of all available services .
train
false
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def parse_rx_excludes(options, fatal): excluded_patterns = [] for flag in options: (option, parameter) = flag if (option == '--exclude-rx'): try: excluded_patterns.append(re.compile(parameter)) except re.error as ex: fatal(('invalid --exclude-rx pattern (%s): %s' % (parameter, ex))) elif (option == '--exclude-rx-from'): try: f = open(resolve_parent(parameter)) except IOError as e: raise fatal(("couldn't read %s" % parameter)) for pattern in f.readlines(): spattern = pattern.rstrip('\n') if (not spattern): continue try: excluded_patterns.append(re.compile(spattern)) except re.error as ex: fatal(('invalid --exclude-rx pattern (%s): %s' % (spattern, ex))) return excluded_patterns
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traverse the options and extract all rx excludes .
train
false
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def bool_to_int(value): if (value is True): return '1' elif (value is False): return '0' else: return value
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translates python booleans to rpc-safe integers .
train
false
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def getCraftedText(fileName, text='', repository=None): return getCraftedTextFromText(archive.getTextIfEmpty(fileName, text), repository)
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reversal a gcode linear move file or text .
train
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@depends(HAS_ESX_CLI) def esxcli_cmd(cmd_str, host=None, username=None, password=None, protocol=None, port=None, esxi_hosts=None): ret = {} if esxi_hosts: if (not isinstance(esxi_hosts, list)): raise CommandExecutionError("'esxi_hosts' must be a list.") for esxi_host in esxi_hosts: response = salt.utils.vmware.esxcli(host, username, password, cmd_str, protocol=protocol, port=port, esxi_host=esxi_host) if (response['retcode'] != 0): ret.update({esxi_host: {'Error': response.get('stdout')}}) else: ret.update({esxi_host: response}) else: response = salt.utils.vmware.esxcli(host, username, password, cmd_str, protocol=protocol, port=port) if (response['retcode'] != 0): ret.update({host: {'Error': response.get('stdout')}}) else: ret.update({host: response}) return ret
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run an esxcli command directly on the host or list of hosts .
train
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def getComplexByMultiplierPrefix(multiplier, prefix, valueComplex, xmlElement): if (multiplier == 0.0): return valueComplex oldMultipliedValueComplex = (valueComplex * multiplier) complexByPrefix = getComplexByPrefix(prefix, oldMultipliedValueComplex, xmlElement) if (complexByPrefix == oldMultipliedValueComplex): return valueComplex return (complexByPrefix / multiplier)
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get complex from multiplier .
train
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def type_from_value(value): return _type_mapping[type(value)]
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fetch type based on a primitive value .
train
false
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@handle_response_format @treeio_login_required def account_edit(request, response_format='html'): profile = request.user.profile if request.POST: form = AccountForm(request.POST, instance=profile) if form.is_valid(): form.save() return HttpResponseRedirect(reverse('account_view')) else: form = AccountForm(instance=profile) return render_to_response('account/account_edit', {'profile': profile, 'form': Markup(form.as_ul())}, context_instance=RequestContext(request), response_format=response_format)
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account edit page .
train
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@register.inclusion_tag(u'reviews/review_reply_section.html', takes_context=True) def reply_section(context, review, comment, context_type, context_id, reply_to_text=u''): if (comment != u''): if (type(comment) is ScreenshotComment): context_id += u's' elif (type(comment) is FileAttachmentComment): context_id += u'f' elif (type(comment) is GeneralComment): context_id += u'g' context_id += six.text_type(comment.id) return {u'review': review, u'comment': comment, u'context_type': context_type, u'context_id': context_id, u'user': context.get(u'user', None), u'local_site_name': context.get(u'local_site_name'), u'reply_to_is_empty': (reply_to_text == u''), u'request': context[u'request']}
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renders a template for displaying a reply .
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def EncodeUrl(base, params, escape_url, use_html_entities): real_params = [] for (key, value) in params.iteritems(): if escape_url: value = urllib.quote(value) if value: real_params.append(('%s=%s' % (key, value))) if real_params: url = ('%s?%s' % (base, '&'.join(real_params))) else: url = base if use_html_entities: url = cgi.escape(url, quote=True) return url
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escape params .
train
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def AddPost(content): t = time.strftime('%c', time.localtime()) DB.append((t, content))
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add a new post to the database .
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def random_dense_design_matrix_for_regression(rng, num_examples, dim, reg_min, reg_max): X = rng.randn(num_examples, dim) Y = rng.randint(reg_min, reg_max, (num_examples, 1)) return DenseDesignMatrix(X=X, y=Y)
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creates a random dense design matrix for regression .
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def _enrollment_mode_display(enrollment_mode, verification_status, course_id): course_mode_slugs = [mode.slug for mode in CourseMode.modes_for_course(course_id)] if (enrollment_mode == CourseMode.VERIFIED): if (verification_status in [VERIFY_STATUS_NEED_TO_VERIFY, VERIFY_STATUS_SUBMITTED, VERIFY_STATUS_APPROVED]): display_mode = DISPLAY_VERIFIED elif (DISPLAY_HONOR in course_mode_slugs): display_mode = DISPLAY_HONOR else: display_mode = DISPLAY_AUDIT elif (enrollment_mode in [CourseMode.PROFESSIONAL, CourseMode.NO_ID_PROFESSIONAL_MODE]): display_mode = DISPLAY_PROFESSIONAL else: display_mode = enrollment_mode return display_mode
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checking enrollment mode and status and returns the display mode args: enrollment_mode : enrollment mode .
train
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def quickstart_generator(war_path, sdk_root=None): if (not sdk_root): sdk_root = _SDK_ROOT quickstart_xml_path = os.path.join(war_path, 'WEB-INF', 'quickstart-web.xml') if os.path.exists(quickstart_xml_path): os.remove(quickstart_xml_path) (java_home, exec_suffix) = java_utils.JavaHomeAndSuffix() java_command = (os.path.join(java_home, 'bin', 'java') + exec_suffix) quickstartgenerator_jar = os.path.join(sdk_root, _QUICKSTART_JAR_PATH) webdefaultxml = os.path.join(sdk_root, _JAVA_VMRUNTIME_PATH, 'etc', 'webdefault.xml') command = [java_command, '-jar', quickstartgenerator_jar, war_path, webdefaultxml] subprocess.check_call(command) with open(quickstart_xml_path) as f: return (f.read(), quickstart_xml_path)
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run the quickstart-web .
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def checkDeprecatedOptions(args): for _ in args: if (_ in DEPRECATED_OPTIONS): errMsg = ("switch/option '%s' is deprecated" % _) if DEPRECATED_OPTIONS[_]: errMsg += (' (hint: %s)' % DEPRECATED_OPTIONS[_]) raise SqlmapSyntaxException(errMsg)
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checks for deprecated options .
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def getMinimumByVector3Path(path): minimum = Vector3(9.876543219876543e+17, 9.876543219876543e+17, 9.876543219876543e+17) for point in path: minimum.minimize(point) return minimum
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get a vector3 with each component the minimum of the respective components of a vector3 path .
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def ge(a, b): return (a >= b)
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same as a >= b .
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def save_project(project, file_path): json.dump(project, codecs.open(file_path, u'w', u'utf8'), indent=4)
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saves given project as a json file keyword arguments: project -- the project to store filename -- the filename to store the project in .
train
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def required_char_field_attrs(self, widget, *args, **kwargs): attrs = super(fields.CharField, self).widget_attrs(widget, *args, **kwargs) original_attrs = (charfield_widget_attrs(self, widget) or {}) attrs.update(original_attrs) return attrs
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this function is for use on the charfield class .
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def name_value_to_dict(content): result = {} for match in re.finditer('^([A-Za-z_][A-Za-z0-9_]*) *= *([^#]*)', content, re.MULTILINE): result[match.group(1)] = match.group(2).strip() return result
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converts a list of name=value pairs to a dictionary .
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def _process_defaults(G, schemas): for schema in schemas: for (name, value) in six.iteritems(schema): absent = (name not in G.node) is_none = (G.node.get(name, {}).get('value') is None) immutable = value.get('immutable', False) if (absent or is_none or immutable): _process(G, name, value.get('default'))
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process dependencies for parameters default values in the order schemas are defined .
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@requires_segment_info def modified_indicator(pl, segment_info, text=u'+'): return (text if int(vim_getbufoption(segment_info, u'modified')) else None)
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return a file modified indicator .
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def connect_to_services(region=None): global cloudservers, cloudfiles, cloud_loadbalancers, cloud_databases global cloud_blockstorage, cloud_dns, cloud_networks, cloud_monitoring global autoscale, images, queues, cloud_cdn cloudservers = connect_to_cloudservers(region=region) cloudfiles = connect_to_cloudfiles(region=region) cloud_cdn = connect_to_cloud_cdn(region=region) cloud_loadbalancers = connect_to_cloud_loadbalancers(region=region) cloud_databases = connect_to_cloud_databases(region=region) cloud_blockstorage = connect_to_cloud_blockstorage(region=region) cloud_dns = connect_to_cloud_dns(region=region) cloud_networks = connect_to_cloud_networks(region=region) cloud_monitoring = connect_to_cloud_monitoring(region=region) autoscale = connect_to_autoscale(region=region) images = connect_to_images(region=region) queues = connect_to_queues(region=region)
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establishes authenticated connections to the various cloud apis .
train
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def disable_api_key(apiKey, region=None, key=None, keyid=None, profile=None): try: conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) response = _api_key_patch_replace(conn, apiKey, '/enabled', 'False') return {'apiKey': _convert_datetime_str(response)} except ClientError as e: return {'error': salt.utils.boto3.get_error(e)}
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disable the given apikey .
train
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def convertToPaths(dictionary): if ((dictionary.__class__ == Vector3) or (dictionary.__class__.__name__ == 'Vector3Index')): return keys = getKeys(dictionary) if (keys == None): return for key in keys: value = dictionary[key] if (value.__class__.__name__ == 'ElementNode'): if (value.xmlObject != None): dictionary[key] = getFloatListListsByPaths(value.xmlObject.getPaths()) else: convertToPaths(dictionary[key])
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recursively convert any xmlelements to paths .
train
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def _walk_js_scopes(scope, lpath=None): if (lpath is None): lpath = [] for subscope in scope: if ((subscope.tag == 'variable') and (not subscope)): continue sublpath = (lpath + [subscope.get('name')]) (yield (subscope, sublpath)) for r in _walk_js_scopes(subscope, sublpath): (yield r)
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walk the subscopes of the given element .
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def backends_data(user): available = get_backends().keys() values = {'associated': [], 'not_associated': available, 'backends': available} if (hasattr(user, 'is_authenticated') and user.is_authenticated()): associated = UserSocialAuth.get_social_auth_for_user(user) not_associated = list((set(available) - set((assoc.provider for assoc in associated)))) values['associated'] = associated values['not_associated'] = not_associated return values
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return backends data for given user .
train
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def _GetFD(filename, file_pos, fd_cache): if (filename in fd_cache): (at, fd) = fd_cache[filename] fd_cache[filename] = (time.time(), fd) return fd if (len(fd_cache) == MAX_OPEN_FILES): lru = sorted(fd_cache.items(), key=itemgetter(1))[:int((0.1 * MAX_OPEN_FILES))] for (key, (at, fd)) in lru: fd.close() del fd_cache[key] if (filename.endswith('.gz') or filename.endswith('.gz.tmp')): fd = gzip.open(filename, 'rb') else: fd = open(filename, 'r') fd.seek(file_pos) fd_cache[filename] = (time.time(), fd) return fd
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returns the file descriptor matching "filename" if present in the cache .
train
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def setup_modifiers(node, field=None, context=None, in_tree_view=False): modifiers = {} if (field is not None): transfer_field_to_modifiers(field, modifiers) transfer_node_to_modifiers(node, modifiers, context=context, in_tree_view=in_tree_view) transfer_modifiers_to_node(modifiers, node)
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processes node attributes and field descriptors to generate the modifiers node attribute and set it on the provided node .
train
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def branch2(tree): if isinstance(tree, tuple): (name, subtree) = tree print(name, data2[name]) print('subtree', subtree) if testxb: branchsum = data2[name] else: branchsum = name for b in subtree: branchsum = (branchsum + branch2(b)) else: leavessum = sum((data2[bi] for bi in tree)) print('final branch with', tree, ''.join(tree), leavessum) if testxb: return leavessum else: return ''.join(tree) print('working on branch', tree, branchsum) return branchsum
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walking a tree bottom-up based on dictionary .
train
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def isdescriptor(x): for item in ('__get__', '__set__', '__delete__'): if hasattr(safe_getattr(x, item, None), '__call__'): return True return False
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check if the object is some kind of descriptor .
train
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def create_agent_config_map(config): try: bridge_mappings = q_utils.parse_mappings(config.OVS.bridge_mappings) except ValueError as e: raise ValueError((_('Parsing bridge_mappings failed: %s.') % e)) kwargs = dict(integ_br=config.OVS.integration_bridge, tun_br=config.OVS.tunnel_bridge, local_ip=config.OVS.local_ip, bridge_mappings=bridge_mappings, root_helper=config.AGENT.root_helper, polling_interval=config.AGENT.polling_interval, enable_tunneling=config.OVS.enable_tunneling) if (kwargs['enable_tunneling'] and (not kwargs['local_ip'])): msg = _('Tunnelling cannot be enabled without a valid local_ip.') raise ValueError(msg) return kwargs
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create a map of agent config parameters .
train
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def _existing_file_path_option(option_name, option_value): file_path = FilePath(option_value) if (not file_path.exists()): raise UsageError(u"Problem with --{}. File does not exist: '{}'.".format(option_name, file_path.path)) return file_path
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validate a command line option containing a filepath .
train
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@pytest.mark.skipif(u'not HAS_SCIPY') def test_z_at_value_roundtrip(): z = 0.5 skip = (u'Ok', u'angular_diameter_distance_z1z2', u'clone', u'de_density_scale', u'w') import inspect methods = inspect.getmembers(core.Planck13, predicate=inspect.ismethod) for (name, func) in methods: if (name.startswith(u'_') or (name in skip)): continue print(u'Round-trip testing {0}'.format(name)) fval = func(z) assert allclose(z, funcs.z_at_value(func, fval, zmax=1.5), rtol=2e-08) z2 = 2.0 func_z1z2 = [(lambda z1: core.Planck13._comoving_distance_z1z2(z1, z2)), (lambda z1: core.Planck13._comoving_transverse_distance_z1z2(z1, z2)), (lambda z1: core.Planck13.angular_diameter_distance_z1z2(z1, z2))] for func in func_z1z2: fval = func(z) assert allclose(z, funcs.z_at_value(func, fval, zmax=1.5), rtol=2e-08)
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calculate values from a known redshift .
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def get_container_data_volumes(container, volumes_option): volumes = [] volumes_option = (volumes_option or []) container_mounts = dict(((mount[u'Destination'], mount) for mount in (container.get(u'Mounts') or {}))) image_volumes = [VolumeSpec.parse(volume) for volume in (container.image_config[u'ContainerConfig'].get(u'Volumes') or {})] for volume in set((volumes_option + image_volumes)): if volume.external: continue mount = container_mounts.get(volume.internal) if (not mount): continue if (not mount.get(u'Name')): continue volume = volume._replace(external=mount[u'Name']) volumes.append(volume) return volumes
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find the container data volumes that are in volumes_option .
train
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def getPrime(N, randfunc=None): if (randfunc is None): _import_Random() randfunc = Random.new().read number = (getRandomNBitInteger(N, randfunc) | 1) while (not isPrime(number, randfunc=randfunc)): number = (number + 2) return number
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getprime:long return a random n-bit prime number .
train
false