code stringlengths 4 4.48k | docstring stringlengths 1 6.45k | _id stringlengths 24 24 |
|---|---|---|
def _Net_set_mean(self, input_, mean_f, mode='elementwise'): <NEW_LINE> <INDENT> if not hasattr(self, 'mean'): <NEW_LINE> <INDENT> self.mean = {} <NEW_LINE> <DEDENT> if input_ not in self.inputs: <NEW_LINE> <INDENT> raise Exception('Input not in {}'.format(self.inputs)) <NEW_LINE> <DEDENT> in_shape = self.blobs[input_]... | Set the mean to subtract for data centering.
Take
input_: which input to assign this mean.
mean_f: path to mean .npy with ndarray (input dimensional or broadcastable)
mode: elementwise = use the whole mean (and check dimensions)
channel = channel constant (e.g. mean pixel instead of mean image) | 625941c68e7ae83300e4aff4 |
def __Database__export_server(self, dev_info): <NEW_LINE> <INDENT> if not isinstance(dev_info, collections_abc.Sequence) and not isinstance(dev_info, DbDevExportInfo): <NEW_LINE> <INDENT> raise TypeError( 'Value must be a DbDevExportInfos, a seq<DbDevExportInfo> or ' 'a DbDevExportInfo') <NEW_LINE> <DEDENT> ... | export_server(self, dev_info) -> None
Export a group of devices to the database.
Parameters :
- devinfo : (sequence<DbDevExportInfo> | DbDevExportInfos | DbDevExportInfo)
containing the device(s) to export information
Return : None
Throws : ConnectionFailed, Co... | 625941c650485f2cf553cdc1 |
def __call__(self, labels): <NEW_LINE> <INDENT> if not isinstance(labels, torch.Tensor): <NEW_LINE> <INDENT> labels = torch.tensor(labels) <NEW_LINE> <DEDENT> if labels.dim() == 0: <NEW_LINE> <INDENT> n_labels = torch.zeros(self.classes, dtype=self.dtype) <NEW_LINE> n_labels[int(labels)] = 1 <NEW_LINE> return n_labels ... | Args:
labels (torch.tensor): Input to be made categorical.
Returns:
torch.tensor: A categorical representation of the input labels. | 625941c6507cdc57c6306d00 |
def new_service(): <NEW_LINE> <INDENT> sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) <NEW_LINE> try: <NEW_LINE> <INDENT> sock.bind(('localhost', 3368)) <NEW_LINE> sock.listen(1000) <NEW_LINE> print("bind 3368,ready to use") <NEW_LINE> <DEDENT> except: <NEW_LINE> <INDENT> print("Server is already running,quit... | start a service socket and listen
when coms a connection, start a new thread to handle it | 625941c650812a4eaa59c34b |
def get_snmp_request(ibmc): <NEW_LINE> <INDENT> token = ibmc.bmc_token <NEW_LINE> url = ibmc.manager_uri + "/SnmpService" <NEW_LINE> headers = {'content-type': 'application/json', 'X-Auth-Token': token} <NEW_LINE> payload = {} <NEW_LINE> try: <NEW_LINE> <INDENT> request_result = ibmc.request('GET', resource=url, header... | Function:
Get the return result of the redfish interface
Args:
ibmc : Class that contains basic information about iBMC
Returns:
SNMP request info
Raises:
Get SNMP resource info failed!
Date: 2019/10/29 21:47 | 625941c6adb09d7d5db6c7b8 |
def jacobian(self, points): <NEW_LINE> <INDENT> raise NotImplementedError() | Transposed Jacobian of the transform with respect to its parameters
Parameters
----------
x : array-like, shape (n_points, n_dimensions)
Location of the Jacobian to be calculated
Returns
-------
J : array-like, shape (n_points, n_parameters, n_dimensions)
:math:`J = D^T_\theta\phi(x)` | 625941c68a43f66fc4b5408e |
def query_one_row(QUERY, desfile=os.path.join(os.environ['HOME'], '.desservices.ini'), section=None): <NEW_LINE> <INDENT> recs = make_db_query(QUERY, desfile=desfile, section=section) <NEW_LINE> if len(recs) != 1: <NEW_LINE> <INDENT> err = 'Exactly one row expected, got {nr} rows instead!\nQUERY:{Q}' <NEW_LINE> if logg... | Funtion opens a DB connection, gets a cursor, executes the QUERY,
expects exactly one row to be returned, raises Exception otherwise. | 625941c61d351010ab855b44 |
def login(ask_credentials=True): <NEW_LINE> <INDENT> g.CACHE.invalidate() <NEW_LINE> try: <NEW_LINE> <INDENT> if ask_credentials: <NEW_LINE> <INDENT> ui.ask_credentials() <NEW_LINE> <DEDENT> if not common.make_call('login'): <NEW_LINE> <INDENT> return False <NEW_LINE> <DEDENT> return True <NEW_LINE> <DEDENT> except Mis... | Perform a login | 625941c6498bea3a759b9ad7 |
def checkParameters(self): <NEW_LINE> <INDENT> self.DEBUG("EDPluginExecEpydocv1_0.checkParameters") <NEW_LINE> self.checkMandatoryParameters(self.getDataInput(), "Data Input is None") <NEW_LINE> self.checkMandatoryParameters(self.getDataInput().getDocPath(), "No output documentation path provided") <NEW_LINE> self.chec... | Checks the mandatory parameters. | 625941c6a17c0f6771cbe07a |
def printCustomSetup(customDict, uniVals = "Universe"): <NEW_LINE> <INDENT> title = "Custom %s Values" % uniVals <NEW_LINE> print("\n\n\n%s%s%s\n" % ("-" * 20, title, "-" * 20)) <NEW_LINE> for i in iter(customDict): <NEW_LINE> <INDENT> if isinstance(customDict[i], dict): <NEW_LINE> <INDENT> print(i) <NEW_LINE> for x in... | prints out the custom setup. | 625941c6cb5e8a47e48b7ad4 |
def practice0(method = 1): <NEW_LINE> <INDENT> src_num = (1,2,3,4) <NEW_LINE> dst_num = [] <NEW_LINE> if method == 1: <NEW_LINE> <INDENT> for i in src_num: <NEW_LINE> <INDENT> for j in src_num: <NEW_LINE> <INDENT> for k in src_num: <NEW_LINE> <INDENT> if (i != j) and (i != k) and (j != k): <NEW_LINE> <INDENT> str_com =... | 数字组合 | 625941c6379a373c97cfab6c |
def output_policies(self, learned_policies, obstacles, policy_file): <NEW_LINE> <INDENT> with open(policy_file, mode='w') as f: <NEW_LINE> <INDENT> for i, (x, y) in enumerate(product(range(self.maze_dimension[0]), range(self.maze_dimension[1]))): <NEW_LINE> <INDENT> if (x, y) not in obstacles: <NEW_LINE> <INDENT> f.wri... | Output learned policies \pi(s) to file. | 625941c67cff6e4e811179ae |
def test_push_configuration_status(device_fixture): <NEW_LINE> <INDENT> device = device_fixture <NEW_LINE> r_value = ('{"message":"[root@LINUX-FW ~]# [root@LINUX-FW ~]#\n ",' '"date":"27-06-2018 09:15:33","status":"ENDED"}') <NEW_LINE> with patch('requests.get') as mock_call_get: <NEW_LINE> <INDENT> mock_call_get.retur... | Test push configuration | 625941c691f36d47f21ac519 |
def strStr(self, haystack, needle): <NEW_LINE> <INDENT> if needle=='': <NEW_LINE> <INDENT> return 0 <NEW_LINE> <DEDENT> if haystack=='' or len(haystack)<len(needle): <NEW_LINE> <INDENT> return -1 <NEW_LINE> <DEDENT> return haystack.find(needle) | :type haystack: str
:type needle: str
:rtype: int | 625941c6be383301e01b54b0 |
def _pretty_logging(data): <NEW_LINE> <INDENT> def _picker(x): <NEW_LINE> <INDENT> if isinstance(x, DataFlowAbstraction): <NEW_LINE> <INDENT> return x.name <NEW_LINE> <DEDENT> elif isinstance(x, DataFrame): <NEW_LINE> <INDENT> return "pd.DataFrame({})".format(str(x.columns)) <NEW_LINE> <DEDENT> elif isinstance(x, tuple... | Returns the dictionary 'data' with a suitable form for logging | 625941c629b78933be1e56d6 |
def message_ports_in(self): <NEW_LINE> <INDENT> return _howto_swig.sc_fdma_interleaver_sptr_message_ports_in(self) | message_ports_in(sc_fdma_interleaver_sptr self) -> swig_int_ptr | 625941c632920d7e50b281f7 |
def get_high_d_function_caller_from_low_d_func(domain_dim, low_d_func, low_d_domain_bounds, low_d_opt_val, low_d_opt_pt, **kwargs): <NEW_LINE> <INDENT> group_dim = len(low_d_domain_bounds) <NEW_LINE> high_d_func, num_groups = get_high_d_function_from_low_d(domain_dim, group_dim, low_d_func) <NEW_LINE> high_d_domain_bou... | Gets a low dimensional function caller from a high dimensional one. | 625941c6d58c6744b4257c88 |
def test_hisHers(self): <NEW_LINE> <INDENT> self.assertEqual(fourForms(hisHers), (u"hers", u"his", u"its", u"theirs")) | L{Noun.hisHers} returns a gender-appropriate substantival possessive
pronoun. | 625941c6bf627c535bc131f7 |
def save_residual_plot(file_path, list_data, height, width, overwrite=True, dpi=100): <NEW_LINE> <INDENT> if "\\" in file_path: <NEW_LINE> <INDENT> raise ValueError( "Please use a file path following the Unix convention") <NEW_LINE> <DEDENT> _create_folder(file_path) <NEW_LINE> if not overwrite: <NEW_LINE> <INDENT> fil... | Save the plot of residual against radius to an image. Useful to check the
accuracy of unwarping results.
Parameters
----------
file_path : str
Output file path.
list_data : array_like
2D array. List of [residual, radius] of each dot.
height : int
Height of the output image.
width : int
Width of the out... | 625941c64e4d5625662d4402 |
def slow(self, callback=False): <NEW_LINE> <INDENT> self.speed += 5 | Down arrow key | 625941c67b25080760e39482 |
def tr(self, message): <NEW_LINE> <INDENT> return QCoreApplication.translate('ocwPlugin', message) | Get the translation for a string using Qt translation API.
We implement this ourselves since we do not inherit QObject.
:param message: String for translation.
:type message: str, QString
:returns: Translated version of message.
:rtype: QString | 625941c67d43ff24873a2cc8 |
def audit_compulsory(self, encrypt_seed, amount, fee): <NEW_LINE> <INDENT> amount = str_num_to_decimal(amount) <NEW_LINE> fee = str_num_to_decimal(fee) <NEW_LINE> if not amount: <NEW_LINE> <INDENT> return CodeMsg.CM(1008, '金额有误') <NEW_LINE> <DEDENT> if not fee: <NEW_LINE> <INDENT> return CodeMsg.CM(1009, '手续费有误') <NEW_... | 审核通用代码 | 625941c6851cf427c661a538 |
@utils.arg('server', metavar='<server>', help=_('Name or ID of server.')) <NEW_LINE> def do_unrescue(cs, args): <NEW_LINE> <INDENT> _find_server(cs, args.server).unrescue() | Unrescue a server. | 625941c6fbf16365ca6f61ea |
def __init__(self, config, basename, centerName): <NEW_LINE> <INDENT> JudgmentVerification.__init__(self, config, basename) <NEW_LINE> self.bi = centerName() <NEW_LINE> pass | :param config: 头文件所在位置
:param basename: 执行用例的文件名
:param centerName: 参数定义的类对象 | 625941c6377c676e912721d1 |
def best_wild_hand(hand): <NEW_LINE> <INDENT> hands = set(best_hand(h) for h in itertools.product(*map(replacements, hand))) <NEW_LINE> return list(max(hands, key=hand_rank)) | Try all values for jokers in all 5-card selections. | 625941c630dc7b7665901990 |
def _timestamp_check(timestamp_client): <NEW_LINE> <INDENT> now_time = int(time.time()) <NEW_LINE> if -300 < now_time-timestamp_client < 300: <NEW_LINE> <INDENT> return True <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> return False | 校队客户端请求的时间戳是否在允许范围之内 | 625941c6a8ecb033257d30f6 |
def box_volume(h, w=None, b=None, base_area=None): <NEW_LINE> <INDENT> if base_area: <NEW_LINE> <INDENT> return h * base_area <NEW_LINE> <DEDENT> if b is None: <NEW_LINE> <INDENT> b = h <NEW_LINE> <DEDENT> if w is None: <NEW_LINE> <INDENT> w = h <NEW_LINE> <DEDENT> base_area = rectangle_area(w, b) <NEW_LINE> return h *... | Calculate the volume of a box (rectangular cuboid) with
side lengths «h», «w» and «b».
https://en.wikipedia.org/wiki/Cuboid | 625941c6442bda511e8be442 |
def t_QUOTE(t): <NEW_LINE> <INDENT> t.lexer.begin('quoted') <NEW_LINE> return t | " | 625941c610dbd63aa1bd2bcc |
def test_appending_records_with_io_stream(): <NEW_LINE> <INDENT> schema = { "type": "record", "name": "test_appending_records_with_io_stream", "fields": [{ "name": "field", "type": "string", }] } <NEW_LINE> stream = MemoryIO() <NEW_LINE> fastavro.writer(stream, schema, [{"field": "foo"}]) <NEW_LINE> fastavro.writer(str... | https://github.com/fastavro/fastavro/issues/276 | 625941c6956e5f7376d70e97 |
def __eq__(self, other): <NEW_LINE> <INDENT> if not isinstance(other, AchievementDefinitionResource): <NEW_LINE> <INDENT> return False <NEW_LINE> <DEDENT> return self.__dict__ == other.__dict__ | Returns true if both objects are equal | 625941c615baa723493c3f9d |
def accept(self, *args): <NEW_LINE> <INDENT> return _pilot.OptimizerLoop_accept(self, *args) | accept(OptimizerLoop self, Visitor visitor) | 625941c6ad47b63b2c509fa8 |
def do_schedule(self, timer=True): <NEW_LINE> <INDENT> state = self.get_state() <NEW_LINE> self._update_torrents() <NEW_LINE> if state == 'Green': <NEW_LINE> <INDENT> self.__apply_set_functions() <NEW_LINE> <DEDENT> elif state == 'Yellow': <NEW_LINE> <INDENT> settings = { 'active_limit': self.config['low_active'], 'act... | This is where we apply schedule rules. | 625941c666673b3332b920b9 |
def __init__(self, id=None, reference_id=None, from_state=None, to_state=None, location_id=None, catalog_object_id=None, catalog_object_type=None, quantity=None, total_price_money=None, occurred_at=None, created_at=None, source=None, employee_id=None, transaction_id=None, refund_id=None, purchase_order_id=None, goods_r... | InventoryAdjustment - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition. | 625941c6c4546d3d9de72a5b |
def Grandezza_vettore(Vettore): <NEW_LINE> <INDENT> Grand = round(np.sqrt(np.dot(Vettore, Vettore)), Vers_dec) <NEW_LINE> return Grand | Calcola la grandezza di un vettore.
=================================================================================
ARGOMENTI
---------------------------------------------------------------------------------
:param Vettore: ==> Tipo: numpy.array
Vettore, deve essere in form... | 625941c68c3a8732951583e2 |
def start_soffice(self): <NEW_LINE> <INDENT> if self.sub: <NEW_LINE> <INDENT> return_code = self.sub.poll() <NEW_LINE> if return_code: <NEW_LINE> <INDENT> self.sub.wait() <NEW_LINE> <DEDENT> <DEDENT> self.sub = subprocess.Popen( args=[ "/usr/bin/soffice", "--impress", "--accept=socket,host=localhost," "port=2002;urp;St... | Start a new soffice process and listen on port 2002. | 625941c657b8e32f524834c3 |
def create_token( self, registry_address, initial_alloc=10 ** 6, name='raidentester', symbol='RDT', decimals=2, timeout=60, auto_register=True, ): <NEW_LINE> <INDENT> contract_path = get_contract_path('HumanStandardToken.sol') <NEW_LINE> with gevent.Timeout(timeout): <NEW_LINE> <INDENT> token_proxy = self._chain.client... | Create a proxy for a new HumanStandardToken (ERC20), that is
initialized with Args(below).
Per default it will be registered with 'raiden'.
Args:
initial_alloc (int): amount of initial tokens.
name (str): human readable token name.
symbol (str): token shorthand symbol.
decimals (int): decimal places.
... | 625941c699fddb7c1c9de3ba |
def FWHM_s(FWHM): <NEW_LINE> <INDENT> return FWHM/(2*np.sqrt(2*np.log(2))) | Given the FWHM of a Gaussian, returns the width (sigma) | 625941c6435de62698dfdc75 |
def __init__(self, name=None, callerid_id=None, whitelabel=None, phone=None, extension=None): <NEW_LINE> <INDENT> self.swagger_types = { 'name': 'str', 'callerid_id': 'str', 'whitelabel': 'bool', 'phone': 'str', 'extension': 'str' } <NEW_LINE> self.attribute_map = { 'name': 'name', 'callerid_id': 'callerid_id', 'whitel... | CreateRecordingByPhoneParameters - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition. | 625941c656b00c62f0f14681 |
def decrypt(self, data): <NEW_LINE> <INDENT> d = b64decode(data) <NEW_LINE> c = AES.new(self._key, self._mode, d[:self._bs]) <NEW_LINE> return self._unpad(c.decrypt(d[self._bs:])).decode('utf-8') | Decrypts data specified
Args:
data (str): Base64-encoded data to be decrypted
Returns:
(str): Decrypted data | 625941c6e76e3b2f99f3a836 |
def _configure_learning_rate(num_samples_per_epoch, global_step): <NEW_LINE> <INDENT> decay_steps = int(num_samples_per_epoch * FLAGS.num_epochs_per_decay / FLAGS.batch_size) <NEW_LINE> if FLAGS.sync_replicas: <NEW_LINE> <INDENT> decay_steps /= FLAGS.replicas_to_aggregate <NEW_LINE> <DEDENT> if FLAGS.learning_rate_deca... | Configures the learning rate.
Args:
num_samples_per_epoch: The number of samples in each epoch of training.
global_step: The global_step tensor.
Returns:
A `Tensor` representing the learning rate.
Raises:
ValueError: if | 625941c60a366e3fb873e842 |
def _collect_pp_hooks(self, descriptor): <NEW_LINE> <INDENT> pp_graph_hooks = set() <NEW_LINE> pp_node_hooks = set() <NEW_LINE> for feat in descriptor: <NEW_LINE> <INDENT> pp_graph_hooks.update(feat.pp_graph_hooks) <NEW_LINE> pp_node_hooks.update(feat.pp_node_hooks) <NEW_LINE> <DEDENT> return pp_graph_hooks, pp_node_ho... | collect all preprocessing hooks | 625941c610dbd63aa1bd2bcd |
def loss(self, X, y=None): <NEW_LINE> <INDENT> scores = None <NEW_LINE> out = self.modules["linear1"].forward(X) <NEW_LINE> out = self.modules["relu1"].forward(out) <NEW_LINE> scores = self.modules["linear2"].forward(out) <NEW_LINE> if y is None: <NEW_LINE> <INDENT> return scores <NEW_LINE> <DEDENT> loss, dout = self.m... | Compute loss and gradient for a minibatch of data.
Args:
- X: Array of input data of shape (N, C), where N is batch size
and C is input_dim.
- y: Array of labels of shape (N,). y[i] gives the label for X[i].
Return:
- loss: Loss for a current minibatch of data. | 625941c656ac1b37e62641fb |
def __eq__(self, other): <NEW_LINE> <INDENT> if not isinstance(other, TransactionsResponseMeta): <NEW_LINE> <INDENT> return False <NEW_LINE> <DEDENT> return self.__dict__ == other.__dict__ | Returns true if both objects are equal | 625941c6c4546d3d9de72a5c |
def to_dict(self): <NEW_LINE> <INDENT> result = {} <NEW_LINE> for attr, _ in six.iteritems(self.swagger_types): <NEW_LINE> <INDENT> value = getattr(self, attr) <NEW_LINE> if isinstance(value, list): <NEW_LINE> <INDENT> result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) <NEW_LINE> <... | Returns the model properties as a dict | 625941c6e1aae11d1e749cdf |
def moveZeroes(self, nums: List[int]) -> None: <NEW_LINE> <INDENT> lastfound = 0 <NEW_LINE> for current in range(len(nums)): <NEW_LINE> <INDENT> if nums[current] != 0 : <NEW_LINE> <INDENT> nums[lastfound],nums[current] = nums[current],nums[lastfound] <NEW_LINE> lastfound+=1 | Do not return anything, modify nums in-place instead. | 625941c6a4f1c619b28b0065 |
def step(self, new_state, reward, terminal, action_list, test=False): <NEW_LINE> <INDENT> assert len(action_list) > 0, "action_list has to have at least one action" <NEW_LINE> new_state_ = self._encode_state(new_state) <NEW_LINE> if not test: <NEW_LINE> <INDENT> self._add_new_state_action_if_unknown(new_state_, action_... | Take a sigle step of the agent
:param new_state: state observed by agent
:param float reward: reward as a result of (state, action, new_state) triple
:param bool terminal: Terminal flag
:param list action_list: action set at new_state A(s) as a list of strings
:param bool test:
:return: | 625941c63cc13d1c6d3c73a4 |
def __init__(self, shape, xlim, ycenter): <NEW_LINE> <INDENT> self.xmin = 0. <NEW_LINE> self.xmax = 0. <NEW_LINE> self.set_shape(shape) <NEW_LINE> self.set_xlim(xlim) <NEW_LINE> self.set_ycenter(ycenter) <NEW_LINE> self.components = [] | .. todo::
WRITEME | 625941c62c8b7c6e89b357ea |
def authorize(name=None, source_group_name=None, source_group_owner_id=None, ip_protocol=None, from_port=None, to_port=None, cidr_ip=None, group_id=None, source_group_group_id=None, region=None, key=None, keyid=None, profile=None, vpc_id=None, vpc_name=None, egress=False): <NEW_LINE> <INDENT> conn = _get_conn(region=re... | Add a new rule to an existing security group.
CLI example::
salt myminion boto_secgroup.authorize mysecgroup ip_protocol=tcp from_port=80 to_port=80 cidr_ip='['10.0.0.0/8', '192.168.0.0/24']' | 625941c6c432627299f04c6e |
def hr_to_triggers(hr, first, last): <NEW_LINE> <INDENT> rr = 1 / hr * 60 * 1000 <NEW_LINE> num_beats = math.ceil((last - first) / rr) + 1 <NEW_LINE> triggers = np.arange(num_beats) * rr + first <NEW_LINE> return triggers | Calculates trigger times based on a constant heart rate.
Args:
hr (float): A constant heart rate (bpm).
first (float): Time stamp of first k-space line (in ms).
last (float): Time stamp of last k-space line (in ms).
Returns:
triggers (array): 1D array containing simulated cardiac trigger times. | 625941c660cbc95b062c656c |
def preprocess_LCG(formula, LCG, num_vars): <NEW_LINE> <INDENT> for cn in range(len(formula)): <NEW_LINE> <INDENT> for var in formula[cn]: <NEW_LINE> <INDENT> if var > 0: <NEW_LINE> <INDENT> LCG.add_edge(var, cn + 2 * num_vars + 1) <NEW_LINE> <DEDENT> elif var < 0: <NEW_LINE> <INDENT> LCG.add_edge(abs(var) + num_vars, ... | Builds LCG | 625941c650812a4eaa59c34c |
def Clone(self): <NEW_LINE> <INDENT> pass | Clone(self: DataGridViewColumn) -> object
Returns: An System.Object that represents the cloned System.Windows.Forms.DataGridViewBand. | 625941c6fb3f5b602dac36bb |
def update_lr_ind(opt, lr): <NEW_LINE> <INDENT> for param_group in opt.param_groups: <NEW_LINE> <INDENT> param_group['lr'] = lr | Decay learning rates of the generator and discriminator. | 625941c623e79379d52ee58e |
def subdMatchTopology(*args, **kwargs): <NEW_LINE> <INDENT> pass | Command matches topology across multiple subdiv surfaces - at all levels.
Flags:
- frontOfChain : foc (bool) [create]
This command is used to specify that the new addTopology node should be placed ahead (upstream) of existing deformer and
skin nodes in the shape's history (but not ah... | 625941c65fcc89381b1e16e7 |
def read(self, timeout=None) -> Message: <NEW_LINE> <INDENT> if self.m2_used: <NEW_LINE> <INDENT> response = "" <NEW_LINE> start_reading = False <NEW_LINE> char = '' <NEW_LINE> self._m2.timeout = timeout <NEW_LINE> while True: <NEW_LINE> <INDENT> if not self._acknowledged_flush: <NEW_LINE> <INDENT> response = "" <NEW_L... | Reads one message from device, creates python-can Message, and returns it
If optional timeout occurs, return None | 625941c66fece00bbac2d766 |
def load(self, istream, **kwargs): <NEW_LINE> <INDENT> encoding = kwargs.pop('encoding', 'utf-8') <NEW_LINE> expression = istream.read().decode(encoding) <NEW_LINE> return self.loads(expression, **kwargs) | Deserializes a haiku expression from an input stream in “Simple
Expression” notation to Python objects. | 625941c6cc0a2c11143dceba |
def _delete(self, thing): <NEW_LINE> <INDENT> self._db.execute( 'DELETE FROM potential_subreddits WHERE subreddit_name = ?', (thing.subreddit.display_name,), ) | Removes the thing's subreddit from the database | 625941c676d4e153a657eb5a |
def test_IntegerPosStep(self): <NEW_LINE> <INDENT> ir = infrange() <NEW_LINE> for i in range(0, 10): <NEW_LINE> <INDENT> self.assertEqual(i, next(ir)) | Range of positive integers from 0-9 | 625941c6de87d2750b85fdbb |
def _ND(omega, delta): <NEW_LINE> <INDENT> return ne.evaluate( "((omega*(omega+2.0))/(2.0*(omega+1.0)**2.0)) * (1.0 + (sqrt(2*omega)*(omega+3.0)*arctan(sqrt(2.0/omega)))/(4.0*(omega+1.0)) + (delta*sqrt(2.0*omega)*(omega-1.0)*(omega+2.0)*log((1.0+delta*sqrt(2.0*omega))/(1.0-delta*sqrt(2.0*omega))) ) / (8.0*(omega+1.0)) ... | See page 210 | 625941c60fa83653e4656fe5 |
def as_training_data(self): <NEW_LINE> <INDENT> raise NotImplementedError | Convert this ``IndexedInstance`` to NumPy arrays suitable for use as
training data to models.
Returns
-------
train_data : (inputs, label)
The ``IndexedInstance`` as NumPy arrays to be used in the model.
Note that ``inputs`` might itself be a complex tuple, depending
on the ``Instance`` type. | 625941c6ac7a0e7691ed40f8 |
def main(): <NEW_LINE> <INDENT> parser = get_parser() <NEW_LINE> args = parser.parse_args() <NEW_LINE> with open(args.inf, 'r') if args.inf else sys.stdin as inf: <NEW_LINE> <INDENT> reader = csv.DictReader(filter(lambda row: row[0] != '#', inf), delimiter='\t') <NEW_LINE> line = 0 <NEW_LINE> for in_row in reader: <NEW... | Validate BEDPE format. | 625941c629b78933be1e56d7 |
def trans(ttable, s): <NEW_LINE> <INDENT> translation = "" <NEW_LINE> for c in s: <NEW_LINE> <INDENT> translation += ttable.get(c, c) <NEW_LINE> <DEDENT> return translation | Return Translation of string s using translation table ttable | 625941c67c178a314d6ef487 |
def inverse_target_map(data, cls=Token): <NEW_LINE> <INDENT> dataitems = data.find(instance_of=cls) <NEW_LINE> if len(dataitems) == 0: <NEW_LINE> <INDENT> return {} <NEW_LINE> <DEDENT> inverse_map = {} <NEW_LINE> for k, v in dataitems[0].target_map.items(): <NEW_LINE> <INDENT> inverse_map[int(as_scalar(v))] = k <NEW_LI... | Return mapping from index to tag string. | 625941c6d10714528d5ffd0b |
def draw_rectangle_rounded(center_x, center_y, width, height, radius, color): <NEW_LINE> <INDENT> shape_list = arcade.ShapeElementList() <NEW_LINE> shape_list.append(arcade.create_rectangle_filled(center_x, center_y, width, height - radius*2, color)) <NEW_LINE> shape_list.append(arcade.create_rectangle_filled(center_x,... | Custom function to draw a rounded rectangle | 625941c685dfad0860c3ae84 |
def cliCmd(self,cmd,log = True): <NEW_LINE> <INDENT> cmd = 'echo "%s" | /opt/tms/bin/cli -m config' % cmd <NEW_LINE> return self.runCmd(cmd,log) | To run cli command | 625941c6e64d504609d74869 |
def _import_batch_file(self, sg_publish_data): <NEW_LINE> <INDENT> app = self.parent <NEW_LINE> app.log_debug("Importing batch file using '%s'" % sg_publish_data) <NEW_LINE> setup_path = self.get_publish_path(sg_publish_data) <NEW_LINE> if setup_path and os.path.exists(setup_path): <NEW_LINE> <INDENT> flame.batch.go_to... | Imports a Batch setup into Flame.
This function import the Batch setup into the current Batch Group.
:param dict sg_publish_data: Shotgun data dictionary with all the standard publish fields. | 625941c607f4c71912b114ab |
def _resolve_mapping_column(self, p_column): <NEW_LINE> <INDENT> if p_column in self.m_cache: <NEW_LINE> <INDENT> return self.m_cache[p_column] <NEW_LINE> <DEDENT> samples = self.mapping.get_samples() <NEW_LINE> column_count = {} <NEW_LINE> for sample in samples: <NEW_LINE> <INDENT> if sample[p_column] in column_count:... | Help ``_resolve_column`` | 625941c67047854f462a1435 |
def _start_threads(self): <NEW_LINE> <INDENT> for i in range(self._thread_count): <NEW_LINE> <INDENT> if self._thread_items is None: <NEW_LINE> <INDENT> args = () <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> args = (self._thread_items[i],) <NEW_LINE> <DEDENT> self._threads.append(threading.Thread(target=self.download_... | Distribute items to threads as equally as possible, then start threads. | 625941c61f037a2d8b946228 |
def radial_sort(points, origin, normal): <NEW_LINE> <INDENT> axis0 = [normal[0], normal[2], -normal[1]] <NEW_LINE> axis1 = np.cross(normal, axis0) <NEW_LINE> ptVec = points - origin <NEW_LINE> pr0 = np.dot(ptVec, axis0) <NEW_LINE> pr1 = np.dot(ptVec, axis1) <NEW_LINE> angles = np.arctan2(pr0, pr1) <NEW_LINE> return poi... | Sorts a set of points radially (by angle) around an
an axis specified by origin and normal vector.
Parameters
--------------
points : (n, 3) float
Points in space
origin : (3,) float
Origin to sort around
normal : (3,) float
Vector to sort around
Returns
--------------
ordered : (n, 3) float
Same as input p... | 625941c6eab8aa0e5d26db81 |
def parseError(error): <NEW_LINE> <INDENT> errorCondition = True <NEW_LINE> errorMsg = error <NEW_LINE> if 'ERROR: proxy has expired\n' in error: <NEW_LINE> <INDENT> errorCondition = True <NEW_LINE> errorMsg = 'CRITICAL ERROR: Your proxy has expired!\n' <NEW_LINE> <DEDENT> elif '999100\n' in error: <NEW_LINE> <IN... | Do some basic condor error parsing | 625941c626068e7796caed06 |
def load_files(self, directory: str, file_suffix: str) -> namedtuple: <NEW_LINE> <INDENT> self._directory = directory <NEW_LINE> self._file_suffix = file_suffix <NEW_LINE> self.file_names = self._get_file_names() <NEW_LINE> self._file_tuple = self._create_file_tuple() <NEW_LINE> self.loaded_files = self._file_tuple(*se... | "Reads all files within directory and returns a namedtuple containing files read | 625941c68e05c05ec3eea39d |
def test_executes_interactor(self): <NEW_LINE> <INDENT> self.__target.get_page(self.__params) <NEW_LINE> self.__interactor.execute.assert_called_with(Genre.from_dict(self.__params)) | Test that AddGenreHandler.get_page executes AddGenreInteractor.execute | 625941c65fc7496912cc39a8 |
def build_constructor(): <NEW_LINE> <INDENT> construct = CONSTRUCT_IN.render( build_channel=CONDA_OUT.as_uri(), py_min=PY_MIN, py_max=PY_MAX, node_min=NODE_MIN, node_max=NODE_MAX, rf_version=RF_VERSION, version=VERSION, cd_version=CHROMEDRIVER_VERSION, ipyw_version=IPYWIDGETS_VERSION, ) <NEW_LINE> CONSTRUCT.write_text(... | Use the local build artifacts in constructor
Care should be taken to avoid `noarch: python` packages | 625941c6dd821e528d63b1d4 |
def cross_entropy_loss(logits, labels, label_smoothing=0.0, mode=tf.estimator.ModeKeys.TRAIN): <NEW_LINE> <INDENT> cross_entropy = _softmax_cross_entropy(logits, labels, label_smoothing, mode) <NEW_LINE> loss = tf.reduce_sum(cross_entropy) <NEW_LINE> loss_normalizer = tf.cast(tf.shape(cross_entropy)[0], loss.dtype) <NE... | Computes the cross entropy loss.
Args:
logits: The unscaled probabilities.
labels: The true labels.
label_smoothing: The label smoothing value.
mode: A ``tf.estimator.ModeKeys`` mode.
Returns:
The cumulated loss and the loss normalizer. | 625941c6d7e4931a7ee9df47 |
def sample(self, bqm, chain_strength=1.0, force_embed=False, chain_break_fraction=True, **parameters): <NEW_LINE> <INDENT> embedding_parameters = self._embedding_parameters <NEW_LINE> candidates_parameters = self._candidates_parameters <NEW_LINE> child = self.child <NEW_LINE> __, target_edgelist, target_adjacency = chi... | Sample from the provided binary quadratic model.
Args:
bqm (:obj:`dimod.BinaryQuadraticModel`):
Binary quadratic model to be sampled from.
chain_strength (float, optional, default=1.0):
Magnitude of the quadratic bias (in SPIN-space) applied between variables to create
chains. Note tha... | 625941c6656771135c3eb897 |
def _load_geometry(self): <NEW_LINE> <INDENT> self.template = getattr(neuron.h, self.templatename)(self.templateargs) <NEW_LINE> seccount = 0 <NEW_LINE> for sec in self.template.all: <NEW_LINE> <INDENT> seccount += 1 <NEW_LINE> <DEDENT> if seccount == 0: <NEW_LINE> <INDENT> fileEnding = self.morphology.split('.')[-1] <... | Load the morphology-file in NEURON | 625941c60a50d4780f666ebb |
def get_absolute_url(self, link='detail'): <NEW_LINE> <INDENT> if link == 'detail': <NEW_LINE> <INDENT> url = reverse('user_view', args=[str(self.pk)]) <NEW_LINE> <DEDENT> elif link == 'update': <NEW_LINE> <INDENT> url = reverse('user_edit', args=[str(self.pk)]) <NEW_LINE> <DEDENT> elif link == 'delete': <NEW_LINE> <IN... | Return the URL to the user. | 625941c626238365f5f0ee97 |
def _run_once_naive_(self): <NEW_LINE> <INDENT> box_list = np.zeros(12) <NEW_LINE> for i in range(10): <NEW_LINE> <INDENT> index = random.randint(0, 11) <NEW_LINE> box_list[index] += 1 <NEW_LINE> <DEDENT> count = 0 <NEW_LINE> for box in box_list: <NEW_LINE> <INDENT> if box == 0: <NEW_LINE> <INDENT> count += 1 <NEW_LINE... | Return 0 for False, return 1 for True | 625941c6d99f1b3c44c675ba |
def count_steps(input_str): <NEW_LINE> <INDENT> counter = Counter(input_str) <NEW_LINE> return counter['('] - counter[')'] | Count steps up/down ( '(' / ')' ) | 625941c6c432627299f04c6f |
@autojit_py3doc <NEW_LINE> def test_bitwise_and(a, b): <NEW_LINE> <INDENT> return a & b | >>> test_bitwise_and(0b01, 0b10)
0
>>> test_bitwise_and(0b01, 0b11)
1
>>> test_bitwise_and(0b01, 2.0)
Traceback (most recent call last):
...
NumbaError: 27:15: Expected an int, or object, or bool
>>> test_bitwise_and(2.0, 0b01)
Traceback (most recent call last):
...
NumbaError: 27:11: Expected an int, or obje... | 625941c632920d7e50b281f9 |
def test_mini_missing_attributes(self, mock_resp): <NEW_LINE> <INDENT> camera = BlinkCameraMini(self.blink.sync) <NEW_LINE> self.blink.sync.network_id = None <NEW_LINE> self.blink.sync.name = None <NEW_LINE> attr = camera.attributes <NEW_LINE> for key in attr: <NEW_LINE> <INDENT> self.assertEqual(attr[key], None) | Test that attributes return None if missing. | 625941c629b78933be1e56d8 |
def search(index, doc_type, q_string): <NEW_LINE> <INDENT> multi_query = { 'query': { 'multi_match': { 'query': q_string, 'type': 'best_fields', 'analyzer': 'english_synonym', 'fields': ['name', 'description', 'tags'], 'fuzziness': 'AUTO' } } } <NEW_LINE> single_query = { 'query': { 'match': { 'description': { 'query':... | search index for matching query docs | 625941c6be383301e01b54b2 |
def reverseString(self, s): <NEW_LINE> <INDENT> j=len(s)-1 <NEW_LINE> for i in range(len(s)//2): <NEW_LINE> <INDENT> s[i],s[j] = s[j],s[i] <NEW_LINE> j-=1 <NEW_LINE> <DEDENT> return s | Do not return anything, modify s in-place instead. | 625941c6a219f33f34628996 |
def set_token(self, token: Optional[str] = None) -> None: <NEW_LINE> <INDENT> self._requests.session.cookies[".ROBLOSECURITY"] = token | Authenticates the client with the passed .ROBLOSECURITY token.
This method does not send any requests and will not throw if the token is invalid.
Arguments:
token: A .ROBLOSECURITY token to authenticate the client with. | 625941c64f6381625f114a65 |
def is_active(self, key, *instances, **kwargs): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> switch = self[key] <NEW_LINE> <DEDENT> except KeyError: <NEW_LINE> <INDENT> return False <NEW_LINE> <DEDENT> if switch.status == GLOBAL: <NEW_LINE> <INDENT> return True <NEW_LINE> <DEDENT> elif switch.status == DISABLED: <NEW_L... | Returns ``True`` if any of ``instances`` match an active switch. Otherwise
returns ``False``.
>>> gargoyle.is_active('my_feature', request) #doctest: +SKIP | 625941c6283ffb24f3c5592c |
def choose_word(wordlist): <NEW_LINE> <INDENT> return random.choice(wordlist) | Function to choose a random word from a wordlist. | 625941c64f6381625f114a66 |
def run(self): <NEW_LINE> <INDENT> self.Edone = False <NEW_LINE> g4mp2_function = [ self.quick_optimize, self.optimize, self.E_zpe, self.prepare_scf_vectors, self.E_hf_g3lxp, self.E_hf1, self.E_hf2, self.reset_symmetry, self.E_mp2, self.E_ccsdt, self.E_mp2_g3lxp, self.E_cbs, self.E_hlc, self.spin_orbit_energy, self.E_g... | Calculate G4MP2 energy for a system that has already been prepared.
| 625941c68e71fb1e9831d7d4 |
def _get_piuparts_content(self, suite): <NEW_LINE> <INDENT> url = 'https://piuparts.debian.org/{suite}/sources.txt' <NEW_LINE> return get_resource_content(url.format(suite=suite)) | :returns: The content of the piuparts report for the given package
or ``None`` if there is no data for the particular suite. | 625941c68da39b475bd64f9c |
def roll_pitch_yaw_rates_thrust_setpoint_encode(self, time_boot_ms, roll_rate, pitch_rate, yaw_rate, thrust): <NEW_LINE> <INDENT> msg = MAVLink_roll_pitch_yaw_rates_thrust_setpoint_message(time_boot_ms, roll_rate, pitch_rate, yaw_rate, thrust) <NEW_LINE> msg.pack(self) <NEW_LINE> return msg | Setpoint in roll, pitch, yaw rates and thrust currently active on the
system.
time_boot_ms : Timestamp in milliseconds since system boot (uint32_t)
roll_rate : Desired roll rate in radians per second (float)
pitch_rate : Desired pitch rate in radians per second (float)
yaw_r... | 625941c6b57a9660fec338ad |
def remove_target(tree, target_id): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> target = find_target(tree, target_id) <NEW_LINE> target.getparent().remove(target) <NEW_LINE> <DEDENT> except AclTargetNotFound: <NEW_LINE> <INDENT> raise LibraryError(reports.id_not_found(target_id, "user")) | Removes acl_target element from tree with specified id.
Raises LibraryError if target with id target_id doesn't exist.
tree -- etree node
target_id -- id of target element to remove | 625941c64428ac0f6e5ba81c |
def __str__(self) -> str: <NEW_LINE> <INDENT> res = '' <NEW_LINE> for atom in self.atoms: <NEW_LINE> <INDENT> res = res + str(atom) + ', ' <NEW_LINE> <DEDENT> res = res[:-2] <NEW_LINE> return '({0}, ({1}))'.format(self.name, res) | Return a string representation of this Molecule in this format:
(NAME, (ATOM1, ATOM2, ...)) | 625941c6ac7a0e7691ed40f9 |
def calculate_avg_score(res): <NEW_LINE> <INDENT> c_star_score = 6 <NEW_LINE> avg_score = 0.0 <NEW_LINE> nb_reviews = 0 <NEW_LINE> for comment in res: <NEW_LINE> <INDENT> if comment[c_star_score] > 0: <NEW_LINE> <INDENT> avg_score += comment[c_star_score] <NEW_LINE> nb_reviews += 1 <NEW_LINE> <DEDENT> <DEDENT> if nb_re... | private function
Calculate the avg score of reviews present in res
:param res: tuple of tuple returned from query_retrieve_comments_or_remarks
:return: a float of the average score rounded to the closest 0.5 | 625941c67d43ff24873a2cca |
def run_search_query( self , query_string , search_locs=None , results_fmt="excerpt" , adv_syntax=False , page_no=1 , page_size=10 ) : <NEW_LINE> <INDENT> api_args = { "query": query_string , "fidf": results_fmt , "advsyn": adv_syntax , "page": page_no , "pageSize": page_size , "format": "json" } <NEW_LINE> if search_l... | Run the specified search query. | 625941c65f7d997b87174ac1 |
def start_node(i, dirname, extra_args=None, rpchost=None): <NEW_LINE> <INDENT> datadir = os.path.join(dirname, "node"+str(i)) <NEW_LINE> args = [ os.getenv("BITCOIND", "suprad"), "-datadir="+datadir, "-keypool=1", "-discover=0", "-rest" ] <NEW_LINE> if extra_args is not None: args.extend(extra_args) <NEW_LINE> bitcoind... | Start a suprad and return RPC connection to it | 625941c6fbf16365ca6f61ec |
def choose_action(self, state): <NEW_LINE> <INDENT> self.state = state <NEW_LINE> self.next_waypoint = self.planner.next_waypoint() <NEW_LINE> action = None <NEW_LINE> actions = [None, 'left', 'forward', 'right'] <NEW_LINE> random_action = random.choice(actions) <NEW_LINE> highest_Q = self.get_maxQ(state) <NEW_LINE> ac... | The choose_action function is called when the agent is asked to choose
which action to take, based on the 'state' the smartcab is in. | 625941c6004d5f362079a35e |
def rrsa(myArray,bestval,val): <NEW_LINE> <INDENT> sacount = 0 <NEW_LINE> max_eval = 50 <NEW_LINE> totalval = 0 <NEW_LINE> while sacount < max_eval: <NEW_LINE> <INDENT> remaining_eval = max_eval - sacount <NEW_LINE> sa(myArray) <NEW_LINE> if val > bestval or bestval is None: <NEW_LINE> <INDENT> val = bestval <NEW_LINE>... | Repeatedly calls hillclimb for 50 times
:return: max and avg of values | 625941c63eb6a72ae02ec504 |
def addBinary(self, a, b): <NEW_LINE> <INDENT> n1 = len(a)-1 <NEW_LINE> n2 = len(b)-1 <NEW_LINE> carry = 0 <NEW_LINE> ret = "" <NEW_LINE> while n1>=0 or n2>=0: <NEW_LINE> <INDENT> left = int(a[n1]) if n1>=0 else 0 <NEW_LINE> right = int(b[n2]) if n2>=0 else 0 <NEW_LINE> if left!= right: <NEW_LINE> <INDENT> if carry == ... | :type a: str
:type b: str
:rtype: str | 625941c64d74a7450ccd41ee |
def menu(fenetre): <NEW_LINE> <INDENT> top=Menu(fenetre) <NEW_LINE> fenetre.config(menu=top) <NEW_LINE> J=Menu(top) <NEW_LINE> top.add_cascade(label='Jeu',menu=J,underline=0) <NEW_LINE> J.add_command(label='Nouvelle partie',command=jouer,underline=0) <NEW_LINE> J.add_command(label='Essai',command=nouvelessai,underline=... | Barre de menu | 625941c6287bf620b61d3a8f |
def split_letters(image, num_letters=4, debug=False): <NEW_LINE> <INDENT> binary = prep_img(image) <NEW_LINE> contours = find_contours(binary, 0.5) <NEW_LINE> contours = [[ [int(floor(min(contour[:, 1]))), 0], [int(ceil(max(contour[:, 1]))), 30] ] for contour in contours] <NEW_LINE> contours = sorted(contours, key=lamb... | split full captcha image into `num_letters` lettersself.
return list of letters binary image (0: white, 255: black) | 625941c65fdd1c0f98dc025d |
def test_all_options_links_no_pks(self): <NEW_LINE> <INDENT> class Fake(ResourceBase): <NEW_LINE> <INDENT> @apimethod(no_pks=True) <NEW_LINE> def fake(cls, request): <NEW_LINE> <INDENT> return cls() <NEW_LINE> <DEDENT> <DEDENT> class MyResource(AllOptionsResource): <NEW_LINE> <INDENT> linked_resource_classes = (Fake,) ... | Tests getting the links for a class with
only no_pks apimethods | 625941c630dc7b7665901992 |
def create_windows(x, y, window_size, overlap=True): <NEW_LINE> <INDENT> windows_x = [] <NEW_LINE> windows_y = [] <NEW_LINE> i = 0 <NEW_LINE> while i < len(y)-window_size: <NEW_LINE> <INDENT> window_x = np.expand_dims(np.concatenate(x[i:i+window_size], axis=0), axis=0) <NEW_LINE> window_y = y[i+window_size-1] <NEW_LINE... | Concatenate along dim-1 to meet the desired window_size. We'll skip any
windows that reach beyond the end.
Two options (examples for window_size=5):
Overlap - e.g. window 0 will be a list of examples 0,1,2,3,4 and the
label of example 4; and window 1 will be 1,2,3,4,5 and the label of
example 5
... | 625941c69f2886367277a8b9 |
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