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def run_recover_tasks(host, guest, instance, on_migration_failure): while on_migration_failure: task = on_migration_failure.popleft() if (task == 'unpause'): try: state = guest.get_power_state(host) if (state == power_state.PAUSED): guest.resume() except Exception as e: LOG.warning(_LW('Failed to resume paused instance before live-migration rollback %s'), e, instance=instance) else: LOG.warning(_LW("Unknown migration task '%(task)s'"), {'task': task}, instance=instance)
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run any pending migration recovery tasks .
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def strip_trailing_semicolon(query): s = _SEMICOLON_WHITESPACE.split(query, 2) if (len(s) > 1): assert (len(s) == 2) assert (s[1] == '') return s[0]
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as a convenience .
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def _get_node(lb, node_id=None, address=None, port=None): for node in getattr(lb, 'nodes', []): match_list = [] if (node_id is not None): match_list.append((getattr(node, 'id', None) == node_id)) if (address is not None): match_list.append((getattr(node, 'address', None) == address)) if (port is not None): match_list.append((getattr(node, 'port', None) == port)) if (match_list and all(match_list)): return node return None
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helper function that returns all information about a named node .
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def fxa_authorize(request): return render(request, 'commonplace/fxa_authorize.html')
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a page to mimic commonplaces fxa-authorize page to handle login .
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def chshell(name, shell): pre_info = info(name) if (not pre_info): raise CommandExecutionError("User '{0}' does not exist".format(name)) if (shell == pre_info['shell']): return True cmd = ['pw', 'usermod', '-s', shell, '-n', name] __salt__['cmd.run'](cmd, python_shell=False) return (info(name).get('shell') == shell)
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change the default shell of the user cli example: .
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def getNextChildIndex(elementNode): for (childNodeIndex, childNode) in enumerate(elementNode.parentNode.childNodes): if (childNode == elementNode): return (childNodeIndex + 1) return len(elementNode.parentNode.childNodes)
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get the next child index .
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def get_url_path(path, use_directory_urls=True): path = get_html_path(path) url = (u'/' + path.replace(os.path.sep, u'/')) if use_directory_urls: return url[:(- len(u'index.html'))] return url
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map a source file path to an output html path .
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def _clean_object_name(name): return (urlquote(name, safe='') if name else None)
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return the url encoded name .
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@pytest.fixture def _history(): history = InMemoryHistory() history.append(u'alpha beta gamma delta') history.append(u'one two three four') return history
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prefilled history .
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def simplify_regex(pattern): pattern = named_group_matcher.sub((lambda m: m.group(1)), pattern) pattern = non_named_group_matcher.sub('<var>', pattern) pattern = pattern.replace('^', '').replace('$', '').replace('?', '').replace('//', '/').replace('\\', '') if (not pattern.startswith('/')): pattern = ('/' + pattern) return pattern
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clean up urlpattern regexes into something more readable by humans .
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def _unpick_search(sort, allowed_fields=None, total=None): sorts = [] split_sort = sort.split(',') for part in split_sort: split_part = part.strip().split() field = split_part[0] if (len(split_part) > 1): order = split_part[1].lower() else: order = 'asc' if allowed_fields: if (field not in allowed_fields): raise ValidationError(('Cannot sort by field `%s`' % field)) if (order not in ['asc', 'desc']): raise ValidationError(('Invalid sort direction `%s`' % order)) sorts.append((field, order)) if (total and (len(sorts) > total)): raise ValidationError(('Too many sort criteria provided only %s allowed' % total)) return sorts
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this is a helper function that takes a sort string eg name asc .
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def _fill_and_one_pad_stride(stride, n): try: return (((1,) + _fill_shape(stride, n)) + (1,)) except TypeError: raise base.IncompatibleShapeError('stride is {} ({}), must be either an integer or an iterable of integers of size {}'.format(stride, type(stride), n))
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expands the provided stride to size n and pads it with 1s .
train
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def make_sample_query_from_filter(sample_filter, require_meter=True): meter = sample_filter.meter if ((not meter) and require_meter): raise RuntimeError('Missing required meter specifier') (start_row, end_row, ts_query) = make_timestamp_query(make_general_rowkey_scan, start=sample_filter.start_timestamp, start_op=sample_filter.start_timestamp_op, end=sample_filter.end_timestamp, end_op=sample_filter.end_timestamp_op, some_id=meter) kwargs = dict(user_id=sample_filter.user, project_id=sample_filter.project, counter_name=meter, resource_id=sample_filter.resource, source=sample_filter.source, message_id=sample_filter.message_id) q = make_query(metaquery=sample_filter.metaquery, **kwargs) if q: res_q = (((q + ' AND ') + ts_query) if ts_query else q) else: res_q = (ts_query if ts_query else None) need_timestamp = ((sample_filter.start_timestamp or sample_filter.end_timestamp) is not None) columns = get_meter_columns(metaquery=sample_filter.metaquery, need_timestamp=need_timestamp, **kwargs) return (res_q, start_row, end_row, columns)
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return a query dictionary based on the settings in the filter .
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def _PutSigningKeyset(io_loop, secret): _GetSecretsManager().PutSecret(secret, json.dumps(secrets.CreateSigningKeyset(secret))) io_loop.stop()
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creates a new keyczar crypt keyset used for signing and signature verification and writes it to secrets subdir .
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def check_phylip_reject_duplicate(): handle = StringIO() sequences = [SeqRecord(Seq('AAAA'), id='longsequencename1'), SeqRecord(Seq('AAAA'), id='longsequencename2'), SeqRecord(Seq('AAAA'), id='other_sequence')] alignment = MultipleSeqAlignment(sequences) try: AlignIO.write(alignment, handle, 'phylip') assert False, 'Duplicate IDs after truncation are not allowed.' except ValueError as e: assert ("Repeated name 'longsequen'" in str(e))
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ensure that attempting to write sequences with duplicate ids after truncation fails for phylip format .
train
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def idz_sfrmi(l, m): return _id.idz_sfrmi(l, m)
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initialize data for :func:idz_sfrm .
train
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def write_bem_solution(fname, bem): _check_bem_size(bem['surfs']) with start_file(fname) as fid: start_block(fid, FIFF.FIFFB_BEM) write_int(fid, FIFF.FIFF_BEM_COORD_FRAME, bem['surfs'][0]['coord_frame']) _write_bem_surfaces_block(fid, bem['surfs']) if ('solution' in bem): if (bem['bem_method'] != FIFF.FWD_BEM_LINEAR_COLL): raise RuntimeError('Only linear collocation supported') write_int(fid, FIFF.FIFF_BEM_APPROX, FIFF.FIFFV_BEM_APPROX_LINEAR) write_float_matrix(fid, FIFF.FIFF_BEM_POT_SOLUTION, bem['solution']) end_block(fid, FIFF.FIFFB_BEM) end_file(fid)
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write a bem model with solution .
train
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def test_tf_mxne(): alpha_space = 10.0 alpha_time = 5.0 (M, G, active_set) = _generate_tf_data() (X_hat_tf, active_set_hat_tf, E) = tf_mixed_norm_solver(M, G, alpha_space, alpha_time, maxit=200, tol=1e-08, verbose=True, n_orient=1, tstep=4, wsize=32) assert_array_equal(np.where(active_set_hat_tf)[0], active_set)
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test convergence of tf-mxne solver .
train
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@contextmanager def inside_dir(dirpath): old_path = os.getcwd() try: os.chdir(dirpath) (yield) finally: os.chdir(old_path)
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execute code from inside the given directory .
train
false
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def find_ndk_project_root(source): ndk_directory = os.path.abspath(source) while (ndk_directory != '/'): if os.path.exists(os.path.join(ndk_directory, 'jni')): break ndk_directory = os.path.dirname(ndk_directory) else: return None return ndk_directory
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given a directory path .
train
false
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def sdm_zero(): return []
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return the zero module element .
train
false
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def _iterate_sagittal_slices(array, limits=None): shape = array.shape[0] for ind in range(shape): if (limits and (ind not in limits)): continue (yield (ind, array[ind, :, :]))
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iterate sagittal slices .
train
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@hsa.jit(device=True) def device_scan(tid, data, temp, inclusive): lane = (tid & (_WARPSIZE - 1)) warpid = (tid >> 6) temp[tid] = data hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) warp_scan_res = warp_scan(tid, temp, inclusive) hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) if (lane == (_WARPSIZE - 1)): temp[warpid] = temp[tid] hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) if (warpid == 0): warp_scan(tid, temp, True) hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) if (warpid > 0): warp_scan_res += temp[(warpid - 1)] hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) if (tid == (temp.size - 1)): if inclusive: temp[0] = warp_scan_res else: temp[0] = (warp_scan_res + data) hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) prefixsum = temp[0] hsa.barrier(hsa.CLK_GLOBAL_MEM_FENCE) return (warp_scan_res, prefixsum)
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args tid: thread id data: scalar input for tid temp: shared memory for temporary work .
train
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def runWithWarningsSuppressed(suppressedWarnings, f, *a, **kw): for (args, kwargs) in suppressedWarnings: warnings.filterwarnings(*args, **kwargs) addedFilters = warnings.filters[:len(suppressedWarnings)] try: result = f(*a, **kw) except: exc_info = sys.exc_info() _resetWarningFilters(None, addedFilters) raise exc_info[0], exc_info[1], exc_info[2] else: if isinstance(result, defer.Deferred): result.addBoth(_resetWarningFilters, addedFilters) else: _resetWarningFilters(None, addedFilters) return result
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run c{f} .
train
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def _val_or_dict(tk, options, *args): options = _format_optdict(options) res = tk.call(*(args + options)) if (len(options) % 2): return res return _dict_from_tcltuple(tk.splitlist(res))
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format options then call func with args and options and return the appropriate result .
train
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def add_or_update_given_trace_db(trace_db, action_executions=None, rules=None, trigger_instances=None): if (trace_db is None): raise ValueError('trace_db should be non-None.') if (not action_executions): action_executions = [] action_executions = [_to_trace_component_db(component=action_execution) for action_execution in action_executions] if (not rules): rules = [] rules = [_to_trace_component_db(component=rule) for rule in rules] if (not trigger_instances): trigger_instances = [] trigger_instances = [_to_trace_component_db(component=trigger_instance) for trigger_instance in trigger_instances] if trace_db.id: return Trace.push_components(trace_db, action_executions=action_executions, rules=rules, trigger_instances=trigger_instances) trace_db.action_executions = action_executions trace_db.rules = rules trace_db.trigger_instances = trigger_instances return Trace.add_or_update(trace_db)
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will update an existing trace .
train
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def wait_for_download_folder(): while (not cfg.download_dir.test_path()): logging.debug('Waiting for "incomplete" folder') time.sleep(2.0)
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wait for download folder to become available .
train
false
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def add_checks(actions): if (FETCH in actions): actions.setdefault(CHECK_FETCH, [True]) if (EXTRACT in actions): actions.setdefault(CHECK_EXTRACT, [True])
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adds appropriate checks to a given dict of actions .
train
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def _ros_sort(df, observations, censorship, warn=False): censored = sort_values(df[df[censorship]], observations, axis=0) uncensored = sort_values(df[(~ df[censorship])], observations, axis=0) if (censored[observations].max() > uncensored[observations].max()): censored = censored[(censored[observations] <= uncensored[observations].max())] if warn: msg = 'Dropping censored observations greater than the max uncensored observation.' warnings.warn(msg) return censored.append(uncensored)[[observations, censorship]].reset_index(drop=True)
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this function prepares a dataframe for ros .
train
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def as_string(value): if six.PY2: buffer_types = (buffer, memoryview) else: buffer_types = memoryview if (value is None): return u'' elif isinstance(value, buffer_types): return bytes(value).decode('utf-8', 'ignore') elif isinstance(value, bytes): return value.decode('utf-8', 'ignore') else: return six.text_type(value)
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convert a value to a unicode object for matching with a query .
train
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def _get_eth_link(vif, ifc_num): link_id = vif.get('devname') if (not link_id): link_id = ('interface%d' % ifc_num) if (vif.get('type') == 'ethernet'): nic_type = 'phy' else: nic_type = vif.get('type') link = {'id': link_id, 'vif_id': vif['id'], 'type': nic_type, 'mtu': vif['network']['meta'].get('mtu'), 'ethernet_mac_address': vif.get('address')} return link
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get a vif or physical nic representation .
train
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def is_valid_hidden_service_address(entry): try: return bool(HS_ADDRESS_PATTERN.match(entry)) except TypeError: return False
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checks if a string is a valid format for being a hidden service address .
train
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@receiver(job_was_approved) def on_job_was_approved(sender, job, approving_user, **kwargs): send_job_review_message(job, approving_user, 'jobs/email/job_was_approved_subject.txt', 'jobs/email/job_was_approved.txt')
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handle approving job offer .
train
false
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def find_gui_and_backend(gui=None, gui_select=None): import matplotlib if (gui and (gui != 'auto')): backend = backends[gui] if (gui == 'agg'): gui = None else: backend = matplotlib.rcParamsOrig['backend'] gui = backend2gui.get(backend, None) if (gui_select and (gui != gui_select)): gui = gui_select backend = backends[gui] return (gui, backend)
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given a gui string return the gui and mpl backend .
train
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@register.filter def all_ancestors_are_published(page, language): page = page.parent while page: if (not page.is_published(language)): return False page = page.parent return True
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returns false if any of the ancestors of page are unpublished .
train
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def generate_adjlist(G, delimiter=' '): directed = G.is_directed() seen = set() for (s, nbrs) in G.adjacency(): line = (make_str(s) + delimiter) for (t, data) in nbrs.items(): if ((not directed) and (t in seen)): continue if G.is_multigraph(): for d in data.values(): line += (make_str(t) + delimiter) else: line += (make_str(t) + delimiter) if (not directed): seen.add(s) (yield line[:(- len(delimiter))])
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generate a single line of the graph g in adjacency list format .
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def grains_refresh(): DETAILS['grains_cache'] = {} return grains()
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refresh the grains from the proxy device .
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def simulate_noise_evoked(evoked, cov, iir_filter=None, random_state=None): noise = evoked.copy() noise.data = _generate_noise(evoked.info, cov, iir_filter, random_state, evoked.data.shape[1])[0] return noise
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create noise as a multivariate gaussian .
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def username(): global _username if (not _username): uid = os.getuid() _username = (pwd_from_uid(uid)[0] or ('user%d' % uid)) return _username
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check that the username leads to an existing player .
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def normalize_slice(s): (start, stop, step) = (s.start, s.stop, s.step) if (start is None): start = 0 if (step is None): step = 1 if ((start < 0) or (step < 0) or ((stop is not None) and (stop < 0))): raise NotImplementedError() return slice(start, stop, step)
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replace nones in slices with integers .
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def get_attrib_file(path, size): attribs = [] path = os.path.join(path, ATTRIB_FILE) try: f = open(path, 'r') except: return [None for unused in xrange(size)] for unused in xrange(size): line = f.readline().strip('\r\n ') if line: if (line.lower() == 'none'): line = None try: line = int(line) except: pass attribs.append(line) else: attribs.append(None) f.close() return attribs
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read jobs attributes from file .
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def regexp2pattern(string): if (type(string) is REGEXP_T): flags = string.flags string = string.pattern if string.startswith('^'): string = string[1:] else: string = ('.*' + string) if string.endswith('$'): string = string[:(-1)] else: string += '.*' return (string, flags) else: return (re.escape(string), 0)
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this function takes a regexp or a string and returns a pattern and some flags .
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def relevant_issues(issues, after): logging.info('finding relevant issues after {}...'.format(after)) seen = set() for issue in issues: if (relevent_issue(issue, after) and (issue['title'] not in seen)): seen.add(issue['title']) (yield issue)
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yields relevant closed issues given a list of issues .
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def write_index(group): return (u'%s_%s' % (settings.ES_INDEX_PREFIX, settings.ES_WRITE_INDEXES[group]))
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write an index file .
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def determine_disk_image_type(image_meta): if (not image_meta.obj_attr_is_set('disk_format')): return None disk_format_map = {'ami': ImageType.DISK, 'aki': ImageType.KERNEL, 'ari': ImageType.RAMDISK, 'raw': ImageType.DISK_RAW, 'vhd': ImageType.DISK_VHD, 'iso': ImageType.DISK_ISO} try: image_type = disk_format_map[image_meta.disk_format] except KeyError: raise exception.InvalidDiskFormat(disk_format=image_meta.disk_format) LOG.debug('Detected %(type)s format for image %(image)s', {'type': ImageType.to_string(image_type), 'image': image_meta}) return image_type
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disk image types are used to determine where the kernel will reside within an image .
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def test_tie_situation(): clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)], voting='hard') assert_equal(clf1.fit(X, y).predict(X)[73], 2) assert_equal(clf2.fit(X, y).predict(X)[73], 1) assert_equal(eclf.fit(X, y).predict(X)[73], 1)
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check voting classifier selects smaller class label in tie situation .
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def merge_pr(num): pr = gh_api.get_pull_request(gh_project, num) repo = pr['head']['repo']['clone_url'] branch = pr['head']['ref'] mergeable = merge_branch(repo=repo, branch=branch) if (not mergeable): cmd = ((('git pull ' + repo) + ' ') + branch) not_merged[str(num)] = cmd print '==============================================================================' print 'Something went wrong merging this branch, you can try it manually by runngin :' print cmd print '=============================================================================='
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try to merge the branch of pr num into current branch .
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def verify_challenge(uri): while True: try: resp = urlopen(uri) challenge_status = json.loads(resp.read().decode('utf8')) except IOError as e: raise ValueError('Error checking challenge: {0} {1}'.format(e.code, json.loads(e.read().decode('utf8')))) if (challenge_status['status'] == 'pending'): time.sleep(2) elif (challenge_status['status'] == 'valid'): LOGGER.info('Domain verified!') break else: raise ValueError('Domain challenge did not pass: {0}'.format(challenge_status))
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loop until our challenge is verified .
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def _find_option_with_arg(argv, short_opts=None, long_opts=None): for (i, arg) in enumerate(argv): if arg.startswith(u'-'): if (long_opts and arg.startswith(u'--')): (name, sep, val) = arg.partition(u'=') if (name in long_opts): return (val if sep else argv[(i + 1)]) if (short_opts and (arg in short_opts)): return argv[(i + 1)] raise KeyError(u'|'.join((short_opts or ([] + long_opts) or [])))
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search argv for options specifying short and longopt alternatives .
train
false
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def split_filename(fname): special_extensions = [u'.nii.gz', u'.tar.gz'] pth = os.path.dirname(fname) fname = os.path.basename(fname) ext = None for special_ext in special_extensions: ext_len = len(special_ext) if ((len(fname) > ext_len) and (fname[(- ext_len):].lower() == special_ext.lower())): ext = fname[(- ext_len):] fname = fname[:(- ext_len)] break if (not ext): (fname, ext) = os.path.splitext(fname) return (pth, fname, ext)
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extract name .
train
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def poll_until(predicate, steps, sleep=None): if (sleep is None): sleep = time.sleep for step in steps: result = predicate() if result: return result sleep(step) result = predicate() if result: return result raise LoopExceeded(predicate, result)
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perform steps until a non-false result is returned .
train
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@commands(u'suggest') def suggest(bot, trigger): if (not trigger.group(2)): return bot.reply(u'No query term.') query = trigger.group(2) uri = u'http://websitedev.de/temp-bin/suggest.pl?q=' answer = web.get((uri + query.replace(u'+', u'%2B'))) if answer: bot.say(answer) else: bot.reply(u'Sorry, no result.')
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processes translation suggestions and stores them in the database .
train
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def rs_swap(a, b): d = {} for rsa in a: d[rsa] = [rsb for rsb in b if (rsa.symbol == rsb.symbol)][0] return d
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build a dictionary to swap randomsymbols based on their underlying symbol .
train
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def _check_for_int(x): try: y = int(x) except (OverflowError, ValueError): pass else: if ((x == x) and (y == x)): return y return x
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this is a compatibility function that takes a c{float} and converts it to an c{int} if the values are equal .
train
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def unzipIter(filename, directory='.', overwrite=0): zf = zipfile.ZipFile(filename, 'r') names = zf.namelist() if (not os.path.exists(directory)): os.makedirs(directory) remaining = len(zf.namelist()) for entry in names: remaining -= 1 isdir = (zf.getinfo(entry).external_attr & DIR_BIT) f = os.path.join(directory, entry) if isdir: if (not os.path.exists(f)): os.makedirs(f) else: fdir = os.path.split(f)[0] if (not os.path.exists(fdir)): os.makedirs(fdir) if (overwrite or (not os.path.exists(f))): outfile = file(f, 'wb') outfile.write(zf.read(entry)) outfile.close() (yield remaining)
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return a generator for the zipfile .
train
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def package_dirname(package): if isinstance(package, str): package = __import__(package, fromlist=['']) filename = package.__file__ dirname = os.path.dirname(filename) return dirname
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return the directory path where package is located .
train
false
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def random_string_from_module(module): return random.choice(string_from_module(module))
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returns a random global string from a module .
train
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def _build_bounding_box_lookup(bounding_box_file): lines = tf.gfile.FastGFile(bounding_box_file, 'r').readlines() images_to_bboxes = {} num_bbox = 0 num_image = 0 for l in lines: if l: parts = l.split(',') assert (len(parts) == 5), ('Failed to parse: %s' % l) filename = parts[0] xmin = float(parts[1]) ymin = float(parts[2]) xmax = float(parts[3]) ymax = float(parts[4]) box = [xmin, ymin, xmax, ymax] if (filename not in images_to_bboxes): images_to_bboxes[filename] = [] num_image += 1 images_to_bboxes[filename].append(box) num_bbox += 1 print(('Successfully read %d bounding boxes across %d images.' % (num_bbox, num_image))) return images_to_bboxes
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build a lookup from image file to bounding boxes .
train
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def convert_PostalAddressProperty(model, prop, kwargs): return get_TextField(kwargs)
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returns a form field for a db .
train
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def _flatten_artist_credit(credit): artist_parts = [] artist_sort_parts = [] artist_credit_parts = [] for el in credit: if isinstance(el, basestring): artist_parts.append(el) artist_credit_parts.append(el) artist_sort_parts.append(el) else: alias = _preferred_alias(el['artist'].get('alias-list', ())) if alias: cur_artist_name = alias['alias'] else: cur_artist_name = el['artist']['name'] artist_parts.append(cur_artist_name) if alias: artist_sort_parts.append(alias['sort-name']) elif ('sort-name' in el['artist']): artist_sort_parts.append(el['artist']['sort-name']) else: artist_sort_parts.append(cur_artist_name) if ('name' in el): artist_credit_parts.append(el['name']) else: artist_credit_parts.append(cur_artist_name) return (''.join(artist_parts), ''.join(artist_sort_parts), ''.join(artist_credit_parts))
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given a list representing an artist-credit block .
train
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def adjust_string(original, length): if (not (length > 0)): raise AssertionError resulting_string = original if (len(resulting_string) > length): unit_db = (resulting_string.endswith('dB') and (resulting_string.find('.') != (-1))) if ((len(resulting_string.strip()) > length) and unit_db): resulting_string = resulting_string[:(-2)] if (len(resulting_string) > length): for char in (' ', '_', 'i', 'o', 'u', 'e', 'a'): offset = (0 if (char == ' ') else 1) while ((len(resulting_string) > length) and (resulting_string.rfind(char, offset) > 0)): char_pos = resulting_string.rfind(char, offset) resulting_string = (resulting_string[:char_pos] + resulting_string[(char_pos + 1):]) resulting_string = resulting_string[:length] resulting_string = ((len(resulting_string) < length) and resulting_string.ljust(length)) return resulting_string
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brings the string to the given length by either removing characters or adding spaces .
train
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def testAgent(path, agent, port=DEFAULT_PORT): agent = adaptAgentObject(BenchmarkingAgent(agent)) experiment = RLCExperiment(path, str(port)) experiment.start() clientAgent = ClientAgent(agent) clientAgent.connect(DEFAULT_HOST, port, CLIENT_TIMEOUT) logging.info('Agent connected') clientAgent.runAgentEventLoop() clientAgent.close() logging.info('Agent finished') experiment.stop() return agent.agent.benchmark
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test an agent once on a rlcompetition experiment .
train
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@np.deprecate(new_name='expm') def expm3(A, q=20): A = _asarray_square(A) n = A.shape[0] t = A.dtype.char if (t not in ['f', 'F', 'd', 'D']): A = A.astype('d') t = 'd' eA = np.identity(n, dtype=t) trm = np.identity(n, dtype=t) castfunc = cast[t] for k in range(1, q): trm[:] = (trm.dot(A) / castfunc(k)) eA += trm return eA
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compute the matrix exponential using taylor series .
train
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@login_required @require_POST def add_member(request, group_slug): prof = get_object_or_404(GroupProfile, slug=group_slug) if (not _user_can_edit(request.user, prof)): raise PermissionDenied form = AddUserForm(request.POST) if form.is_valid(): for user in form.cleaned_data['users']: user.groups.add(prof.group) msg = _('{users} added to the group successfully!').format(users=request.POST.get('users')) messages.add_message(request, messages.SUCCESS, msg) return HttpResponseRedirect(prof.get_absolute_url()) msg = _('There were errors adding members to the group, see below.') messages.add_message(request, messages.ERROR, msg) return profile(request, group_slug, member_form=form)
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add a member to the group .
train
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def isstdin(): if (not _state): raise RuntimeError('no active input()') return _state.isstdin()
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returns true if the last line was read from sys .
train
false
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def files_contains(file1, file2, attributes=None): local_file = open(file1, 'U').readlines() history_data = open(file2, 'U').read() lines_diff = int(attributes.get('lines_diff', 0)) line_diff_count = 0 while local_file: contains = local_file.pop(0).rstrip('\n\r') if (contains not in history_data): line_diff_count += 1 if (line_diff_count > lines_diff): raise AssertionError(("Failed to find '%s' in history data. (lines_diff=%i):\n" % (contains, lines_diff)))
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check the contents of file2 for substrings found in file1 .
train
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def new_post_mails(reply, users_and_watches): post_url = add_utm(reply.get_absolute_url(), 'kbforums-post') c = {'post': reply.content, 'post_html': reply.content_parsed, 'author': reply.creator, 'host': Site.objects.get_current().domain, 'thread': reply.thread.title, 'forum': reply.thread.document.title, 'post_url': post_url} return emails_with_users_and_watches(subject=_lazy(u'Re: {forum} - {thread}'), text_template='kbforums/email/new_post.ltxt', html_template='kbforums/email/new_post.html', context_vars=c, users_and_watches=users_and_watches)
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return an interable of emailmessages to send when a new post is created .
train
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def cds(): rebulk = Rebulk().regex_defaults(flags=re.IGNORECASE, abbreviations=[dash]) rebulk.regex('cd-?(?P<cd>\\d+)(?:-?of-?(?P<cd_count>\\d+))?', validator={'cd': (lambda match: (0 < match.value < 100)), 'cd_count': (lambda match: (0 < match.value < 100))}, formatter={'cd': int, 'cd_count': int}, children=True, private_parent=True, properties={'cd': [None], 'cd_count': [None]}) rebulk.regex('(?P<cd_count>\\d+)-?cds?', validator={'cd': (lambda match: (0 < match.value < 100)), 'cd_count': (lambda match: (0 < match.value < 100))}, formatter={'cd_count': int}, children=True, private_parent=True, properties={'cd': [None], 'cd_count': [None]}) return rebulk
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builder for rebulk object .
train
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def _is_single_paragraph(node): if (len(node) == 0): return False elif (len(node) > 1): for subnode in node[1:]: if (not isinstance(subnode, nodes.system_message)): return False if isinstance(node[0], nodes.paragraph): return True return False
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true if the node only contains one paragraph .
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def dispatch_method(self, basename, arg, **options): method_name = ('%s_%s' % (basename, arg.__class__.__name__)) if hasattr(self, method_name): f = getattr(self, method_name) result = f(arg, **options) if (result is not None): return result raise NotImplementedError(("%s.%s can't handle: %r" % (self.__class__.__name__, basename, arg)))
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dispatch a method to the proper handlers .
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def convert_sysv_runlevel(level): runlevel = str(level) if (runlevel == '0'): target = 'poweroff.target' elif (runlevel in ['1', 's', 'single']): target = 'rescue.target' elif (runlevel in ['2', '3', '4']): target = 'multi-user.target' elif (runlevel == '5'): target = 'graphical.target' elif (runlevel == '6'): target = 'reboot.target' else: raise ValueError(('unknown runlevel %s' % level)) return target
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convert runlevel to systemd target .
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def _path_to_string(path): return '.'.join(path)
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convert a list of path elements into a single path string .
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def argrelmax(data, axis=0, order=1, mode='clip'): return argrelextrema(data, np.greater, axis, order, mode)
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calculate the relative maxima of data .
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def min_weighted_vertex_cover(G, weight=None): cost = dict(G.nodes(data=weight, default=1)) for (u, v) in G.edges(): min_cost = min(cost[u], cost[v]) cost[u] -= min_cost cost[v] -= min_cost return {u for (u, c) in cost.items() if (c == 0)}
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returns an approximate minimum weighted vertex cover .
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@must_have_permission('write') @must_not_be_registration @must_have_addon('forward', 'node') def forward_config_put(auth, node_addon, **kwargs): try: node_addon.url = request.json['url'] node_addon.label = request.json.get('label') except (KeyError, TypeError, ValueError): raise HTTPError(http.BAD_REQUEST) try: dirty_fields = node_addon.get_dirty_fields() node_addon.save() except ValidationValueError: raise HTTPError(http.BAD_REQUEST) if ('url' in dirty_fields): node_addon.owner.add_log(action='forward_url_changed', params=dict(node=node_addon.owner._id, project=node_addon.owner.parent_id, forward_url=node_addon.url), auth=auth, save=True) return {}
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set configuration for forward node settings .
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def stem_text(text): text = utils.to_unicode(text) p = PorterStemmer() return ' '.join((p.stem(word) for word in text.split()))
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return lowercase and stemmed version of string text .
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def profile_func(filename=None): def proffunc(fun): def profiled_func(*args, **kwargs): logging.info('Profiling function {0}'.format(fun.__name__)) try: profiler = cProfile.Profile() retval = profiler.runcall(fun, *args, **kwargs) profiler.dump_stats((filename or '{0}_func.profile'.format(fun.__name__))) except IOError: logging.exception('Could not open profile file {0}'.format(filename)) return retval return profiled_func return proffunc
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decorator for adding profiling to a nested function in salt .
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def getTranslateTetragrid(elementNode, prefix): translation = getCumulativeVector3Remove(Vector3(), elementNode, prefix) if translation.getIsDefault(): return None return getTranslateTetragridByTranslation(translation)
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get translate matrix and delete the translate attributes .
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def unsafe_inline_enabled(response): non_report_only_policies = retrieve_csp_policies(response) report_only_policies = retrieve_csp_policies(response, True) policies_all = merge_policies_dict(non_report_only_policies, report_only_policies) if (len(policies_all) > 0): for directive_name in policies_all: if ((directive_name.lower() != CSP_DIRECTIVE_SCRIPT) and (directive_name.lower() != CSP_DIRECTIVE_STYLE)): continue for directive_value in policies_all[directive_name]: if (directive_value.strip().lower() == CSP_DIRECTIVE_VALUE_UNSAFE_INLINE): return True return False
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method to detect if csp policies are specified for script/style .
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def norm_dlldy(y): return (- y)
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derivative of log pdf of standard normal with respect to y .
train
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def isfloat(x): try: a = float(x) except ValueError: return False else: return True
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check if argument is float .
train
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@transaction.atomic def mass_get_or_create(model_class, base_queryset, id_field, default_dict, global_defaults): current_instances = list(base_queryset) current_ids = set([unicode(getattr(c, id_field)) for c in current_instances]) given_ids = map(unicode, default_dict.keys()) new_ids = [g for g in given_ids if (g not in current_ids)] prepared_models = [] for new_id in new_ids: defaults = default_dict[new_id] defaults[id_field] = new_id defaults.update(global_defaults) model_instance = model_class(**defaults) prepared_models.append(model_instance) if hasattr(model_class.objects, 'bulk_create'): model_class.objects.bulk_create(prepared_models) else: [m.save() for m in prepared_models] inserted_model_instances = prepared_models return (current_instances, inserted_model_instances)
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updates the data by inserting all not found records doesnt delete records if not in the new data example usage .
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def decode_utf8(string): if isinstance(string, str): for encoding in (('utf-8',), ('windows-1252',), ('utf-8', 'ignore')): try: return string.decode(*encoding) except: pass return string return unicode(string)
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returns the given string as a unicode string .
train
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def add_callers(target, source): new_callers = {} for (func, caller) in target.iteritems(): new_callers[func] = caller for (func, caller) in source.iteritems(): if (func in new_callers): new_callers[func] = tuple([(i[0] + i[1]) for i in zip(caller, new_callers[func])]) else: new_callers[func] = caller return new_callers
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combine two caller lists in a single list .
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def _strip_cache_events(data, opts): event_data = copy.deepcopy(data) strip_fields = opts.get('cache_event_strip_fields', []) for field in strip_fields: if (field in event_data): del event_data[field] return event_data
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strip out user-configured sensitive event data .
train
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def _compute_node_to_inventory_dict(compute_node): result = {} if (compute_node.vcpus > 0): result[VCPU] = {'total': compute_node.vcpus, 'reserved': 0, 'min_unit': 1, 'max_unit': compute_node.vcpus, 'step_size': 1, 'allocation_ratio': compute_node.cpu_allocation_ratio} if (compute_node.memory_mb > 0): result[MEMORY_MB] = {'total': compute_node.memory_mb, 'reserved': CONF.reserved_host_memory_mb, 'min_unit': 1, 'max_unit': compute_node.memory_mb, 'step_size': 1, 'allocation_ratio': compute_node.ram_allocation_ratio} if (compute_node.local_gb > 0): result[DISK_GB] = {'total': compute_node.local_gb, 'reserved': (CONF.reserved_host_disk_mb * 1024), 'min_unit': 1, 'max_unit': compute_node.local_gb, 'step_size': 1, 'allocation_ratio': compute_node.disk_allocation_ratio} return result
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given a supplied objects .
train
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def docs_up_to_date(path): if (hasattr(sys, 'frozen') or (not is_git_repo())): return True html_path = os.path.join(qutebrowser.basedir, 'html', 'doc', path) filename = os.path.splitext(path)[0] asciidoc_path = os.path.join(qutebrowser.basedir, os.path.pardir, 'doc', 'help', (filename + '.asciidoc')) try: html_time = os.path.getmtime(html_path) asciidoc_time = os.path.getmtime(asciidoc_path) except FileNotFoundError: return True return (asciidoc_time <= html_time)
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check if the generated html documentation is up to date .
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def __determine_list_kwargs(options): list_kwargs = __determine_delete_kwargs(options) list_kwargs.update(__kwargs_option_to_dict(options.list_kwargs)) return list_kwargs
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determine the standard keyword arguments to pass to list() method .
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def _kb_readout(request, readout_slug, readouts, locale=None, mode=None, product=None): if (readout_slug not in readouts): raise Http404 return readouts[readout_slug](request, locale=locale, mode=mode, product=product)
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instantiate and return the readout with the given slug .
train
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def MergeGlobalXcodeSettingsToSpec(global_dict, spec): global_xcode_settings = global_dict.get('xcode_settings', {}) for config in spec['configurations'].values(): if ('xcode_settings' in config): new_settings = global_xcode_settings.copy() new_settings.update(config['xcode_settings']) config['xcode_settings'] = new_settings
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merges the global xcode_settings dictionary into each configuration of the target represented by spec .
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def serialize_field(value): if isinstance(value, basestring): return value return json.dumps(value, cls=EdxJSONEncoder)
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return a string version of the value .
train
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def _rec_eval_in(g, a, v, i, j, K): if (i == j): return dmp_eval(g, a, v, K) (v, i) = ((v - 1), (i + 1)) return dmp_strip([_rec_eval_in(c, a, v, i, j, K) for c in g], v)
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recursive helper for :func:dmp_eval_in .
train
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def getSharedFace(firstEdge, faces, secondEdge): for firstEdgeFaceIndex in firstEdge.faceIndexes: for secondEdgeFaceIndex in secondEdge.faceIndexes: if (firstEdgeFaceIndex == secondEdgeFaceIndex): return faces[firstEdgeFaceIndex] return None
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get the face which is shared by two edges .
train
false
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def deflate_long(n, add_sign_padding=True): s = bytes() n = long(n) while ((n != 0) and (n != (-1))): s = (struct.pack('>I', (n & xffffffff)) + s) n >>= 32 for i in enumerate(s): if ((n == 0) and (i[1] != deflate_zero)): break if ((n == (-1)) and (i[1] != deflate_ff)): break else: i = (0,) if (n == 0): s = zero_byte else: s = max_byte s = s[i[0]:] if add_sign_padding: if ((n == 0) and (byte_ord(s[0]) >= 128)): s = (zero_byte + s) if ((n == (-1)) and (byte_ord(s[0]) < 128)): s = (max_byte + s) return s
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turns a long-int into a normalized byte string .
train
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def promoted_param(registry, xml_parent, data): pdef = base_param(registry, xml_parent, data, False, 'hudson.plugins.promoted__builds.parameters.PromotedBuildParameterDefinition') try: XML.SubElement(pdef, 'projectName').text = data['project-name'] except KeyError: raise MissingAttributeError('project-name') XML.SubElement(pdef, 'promotionProcessName').text = data.get('promotion-name', None)
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yaml: promoted build a promoted build parameter .
train
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def snapshot_in_progress(client, repository=None, snapshot=None): allsnaps = get_snapshot_data(client, repository=repository) inprogress = [snap['snapshot'] for snap in allsnaps if (('state' in snap.keys()) and (snap['state'] == 'IN_PROGRESS'))] if snapshot: return (snapshot if (snapshot in inprogress) else False) elif (len(inprogress) == 0): return False elif (len(inprogress) == 1): return inprogress[0] else: raise CuratorException('More than 1 snapshot in progress: {0}'.format(inprogress))
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determine whether the provided snapshot in repository is in_progress .
train
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def discovery_agent(rpyc_port): data = struct.pack('<H', rpyc_port) s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.bind(('', UDP_DISCOVERY_PORT)) log('discovery_agent: started') while True: (query, addr) = s.recvfrom(MAX_DGRAM_SIZE) if (query == QUERY_MAGIC): log('discovery_agent: now answering', addr) s.sendto(data, addr)
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answers broadcasted queries with the port of the rpyc server on this machine .
train
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def consistencygroup_destroy(context, consistencygroup_id): return IMPL.consistencygroup_destroy(context, consistencygroup_id)
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destroy the consistencygroup or raise if it does not exist .
train
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def obedient_process(*args, **kwargs): stopped = [] def handler(sig, frame): send('SIGINT') stopped.append(1) signal.signal(signal.SIGINT, handler) send('STARTED') while (not stopped): signal.pause() send('STOPPED')
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waits for sigint and exits normally .
train
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def startswith_(a, fragment, msg=None): assert a.startswith(fragment), (msg or ('%r does not start with %r' % (a, fragment)))
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assert a .
train
false