code stringlengths 4 4.48k | docstring stringlengths 1 6.45k | _id stringlengths 24 24 |
|---|---|---|
def initsysfonts_unix(path="fc-list"): <NEW_LINE> <INDENT> fonts = {} <NEW_LINE> try: <NEW_LINE> <INDENT> flout, flerr = subprocess.Popen('%s : file family style' % path, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True).communicate() <NEW_LINE> <DEDENT> except Exception: <NEW_LINE> <INDENT> r... | use the fc-list from fontconfig to get a list of fonts | 625941d07cff6e4e81117af4 |
def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10, dropout=0, use_batchnorm=False, reg=0.0, weight_scale=1e-2, dtype=np.float32, seed=None): <NEW_LINE> <INDENT> self.use_batchnorm = use_batchnorm <NEW_LINE> self.use_dropout = dropout > 0 <NEW_LINE> self.reg = reg <NEW_LINE> self.num_layers = 1 + len(hid... | Initialize a new FullyConnectedNet.
Inputs:
- hidden_dims: A list of integers giving the size of each hidden layer.
- input_dim: An integer giving the size of the input.
- num_classes: An integer giving the number of classes to classify.
- dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
th... | 625941d0cb5e8a47e48b7c17 |
def join_trigger(registry, xml_parent, data): <NEW_LINE> <INDENT> jointrigger = XML.SubElement(xml_parent, 'join.JoinTrigger') <NEW_LINE> joinProjectsText = ','.join(data.get('projects', [''])) <NEW_LINE> XML.SubElement(jointrigger, 'joinProjects').text = joinProjectsText <NEW_LINE> publishers = XML.SubElement(jointrig... | yaml: join-trigger
Trigger a job after all the immediate downstream jobs have completed
:arg bool even-if-unstable: if true jobs will trigger even if some
downstream jobs are marked as unstable (default false)
:arg list projects: list of projects to trigger
:arg list publishers: list of triggers from publishers mo... | 625941d0d53ae8145f87a3dd |
def describe_trainable_vars(): <NEW_LINE> <INDENT> train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) <NEW_LINE> if len(train_vars) == 0: <NEW_LINE> <INDENT> logger.warn("No trainable variables in the graph!") <NEW_LINE> return <NEW_LINE> <DEDENT> total = 0 <NEW_LINE> total_bytes = 0 <NEW_LINE> data = [] ... | Print a description of the current model parameters.
Skip variables starting with "tower", as they are just duplicates built by data-parallel logic. | 625941d026238365f5f0efdc |
def group_pre(self, group): <NEW_LINE> <INDENT> for package in group: <NEW_LINE> <INDENT> self.package_pre(package) | Called before processing all the packages in a group. It calls
`package_pre` for each package in an arbitrary order. | 625941d0187af65679ca528d |
def mid_rgb( r, g, b ): <NEW_LINE> <INDENT> single_rgb(2, r, g, b, False) <NEW_LINE> single_rgb(3, r, g, b, False) <NEW_LINE> update() | Set the middle backlight to supplied r, g, b colour
Args:
r (int): red value between 0 and 255
g (int): green value between 0 and 255
b (int): blue value between 0 and 255 | 625941d0e1aae11d1e749e25 |
def h(self,node, method='man'): <NEW_LINE> <INDENT> if method == 'man': <NEW_LINE> <INDENT> init_state = node.state <NEW_LINE> goal_state = self.problem.goal_state <NEW_LINE> return sum(abs(b%3 - g%3) + abs(b//3 - g//3) for b, g in ((init_state.index(i), goal_state.index(i)) for i in range(1, 9))) <NEW_LINE> <DEDENT> e... | Returns a lower bound estimate on the cost from node to the goal
using the different heuristics. | 625941d0d6c5a102081441b9 |
def dump_selected(dumpFile, dumpPath, tarPath, dumpAll=False, dumpPersons=False): <NEW_LINE> <INDENT> totalSuccess = True <NEW_LINE> errorMsg = '' <NEW_LINE> if not dumpAll: <NEW_LINE> <INDENT> propagate_selections() <NEW_LINE> <DEDENT> errorMsg += dsh_utils.black_break_msg('dumping KeyWord table...') <NEW_LINE> keyWor... | called by views.dump().
dumps selected items from each table.
if dumpPersons is True,
we're dumping all persons and organizations, nothing else. | 625941d06fb2d068a760f20c |
def compute_gradient(y, tx, w): <NEW_LINE> <INDENT> tmp = np.dot(tx,w) <NEW_LINE> tmpp = y + (tmp<0).astype(np.float) - (tmp>=0).astype(np.float) <NEW_LINE> return -np.dot(tx.T, tmpp)/float(y.shape[0]) | Compute the gradient. | 625941d0d58c6744b4257dce |
def __len__(self): <NEW_LINE> <INDENT> return len(self.__data) | Returns the size of the list of rentals
(Overriding the len() built-in function) | 625941d063f4b57ef0001285 |
def setRelation(self, start, end, length, direction=0): <NEW_LINE> <INDENT> if not self.isIn(start) or not self.isIn(end): <NEW_LINE> <INDENT> raise IndexError('out of index: (%d, %d)' % (start, end)) <NEW_LINE> <DEDENT> if (start == end): <NEW_LINE> <INDENT> raise ValueError('add edge with equal start and end: %d' % s... | direction : 0,1: | start(beginning of node)
2: start(end of node) | | 625941d01f5feb6acb0c4cbe |
def push(self, value: object) -> None: <NEW_LINE> <INDENT> self.sll_val.add_front(value) | TODO: Write this implementation | 625941d0ff9c53063f47c361 |
def test_revcorr_1d(): <NEW_LINE> <INDENT> filt = np.array(((1, 0, 0))) <NEW_LINE> stim = np.zeros((10,)) <NEW_LINE> stim[5] = 1 <NEW_LINE> response = np.convolve(filt, stim, 'full')[:stim.size] <NEW_LINE> recovered, lags = flt.revcorr(stim, response, filt.size) <NEW_LINE> assert np.allclose(recovered, filt[::-1]) <NEW... | Test computation of 1D reverse correlation.
The reverse-correlation should recover the time-reverse of the
linear filter, and the lags should be start at negative values
and be strictly increasing. | 625941d01d351010ab855c8a |
def fusion_liste(liste,n): <NEW_LINE> <INDENT> l=[] <NEW_LINE> i = len(liste) <NEW_LINE> for x in range(i//4): <NEW_LINE> <INDENT> l.append(deepcopy(liste[x][3])) <NEW_LINE> <DEDENT> for x in range(i//4): <NEW_LINE> <INDENT> l.append(fusion1(deepcopy(liste[2*x][3]),deepcopy(liste[2*x+1][3]),n)) <NEW_LINE> <DEDENT> for ... | renvoie la liste :
[0]: score
[1]: nbiteration max
[2]: la table de fin
[3]: la table de debut
[4]: la table de strategie | 625941d076d4e153a657ec9f |
def gen_title(chapelfile): <NEW_LINE> <INDENT> with open(chapelfile, 'r') as handle: <NEW_LINE> <INDENT> line1 = handle.readline() <NEW_LINE> if titlecomment(line1): <NEW_LINE> <INDENT> title = line1.lstrip('//').strip() <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> filename = os.path.split(chapelfile)[1] <NEW_LINE> ti... | Generate file title, based on if title comment exists | 625941d0925a0f43d2549fe6 |
def bootstrap_sample(xs: List[X], n: int = 0) -> List[X]: <NEW_LINE> <INDENT> return [random.choice(xs) for _ in (range(n) if n > 0 else xs)] | Sample a dataset with replacement to get a sub-sample | 625941d0d486a94d0b98e2b4 |
def _create_simulations_skeleton(record_list): <NEW_LINE> <INDENT> from .. import sxs_id <NEW_LINE> return { sxs_id(r.get('title', '')): { 'url': r['links']['conceptdoi'], 'metadata_file_info': max([f for f in r.get('files', []) if '/metadata.json' in f['filename']], default={}, key=lambda f: f['filename']) } for r in ... | Create a dictionary of simulations with information for downloading SXS metadata | 625941d0b7558d58953c5081 |
def explode(self, contactgroups, notificationways): <NEW_LINE> <INDENT> self.apply_partial_inheritance('contactgroups') <NEW_LINE> for prop in Contact.special_properties: <NEW_LINE> <INDENT> if prop == 'contact_name': <NEW_LINE> <INDENT> continue <NEW_LINE> <DEDENT> self.apply_partial_inheritance(prop) <NEW_LINE> <DEDE... | Explode all contact for each contactsgroup
:param contactgroups: contactgroups to explode
:type contactgroups: alignak.objects.contactgroup.Contactgroups
:param notificationways: notificationways to explode
:type notificationways: alignak.objects.notificationway.Notificationways
:return: None | 625941d023849d37ff7b31fd |
def apply_transform( t: Union[List[List[float]], DoubleArray], pos: AxisPosition, ) -> Tuple[float, float, float]: <NEW_LINE> <INDENT> return tuple(dot(t, list(pos))[:3]) | Change of base using a transform matrix. Primarily used to render a point
in space in a way that is more readable for the user.
:param t: A transformation matrix from one 3D space [A] to another [B]
:param pos: XYZ point in space A
:return: corresponding XYZ point in space B | 625941d0d8ef3951e32436ac |
def write_bem_surfaces(fname, surfs): <NEW_LINE> <INDENT> if isinstance(surfs, dict): <NEW_LINE> <INDENT> surfs = [surfs] <NEW_LINE> <DEDENT> with start_file(fname) as fid: <NEW_LINE> <INDENT> start_block(fid, FIFF.FIFFB_BEM) <NEW_LINE> write_int(fid, FIFF.FIFF_BEM_COORD_FRAME, surfs[0]['coord_frame']) <NEW_LINE> _writ... | Write BEM surfaces to a fiff file
Parameters
----------
fname : str
Filename to write.
surfs : dict | list of dict
The surfaces, or a single surface. | 625941d085dfad0860c3afc9 |
def pickf(self, n, **arg): <NEW_LINE> <INDENT> assert is_int(n) <NEW_LINE> defaults = { 'pb': None, 'pattern': r"(['\\])", 'replacement': r'\\\1', 'sep': "', '", 'head': "'", 'tail': "'", 'log_vs_raise': True } <NEW_LINE> for key in defaults: <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> forget = arg[key] <NEW_LINE> <DE... | pickf(self, n,
pb = None,
pattern = "(['])",
replacement = r'\',
sep = "', '",
head = "'",
tail = "'",
log_vs_raise = True
)
Pick `n` randomly selected honey-pots and return a string.
The string is prepended with `head` and appended with `tail`.
The honeypots are escaped with the regular ... | 625941d09c8ee82313fbb8e4 |
def start(self): <NEW_LINE> <INDENT> creds = self.store.get() <NEW_LINE> if not creds or creds.invalid: <NEW_LINE> <INDENT> flow = client.flow_from_clientsecrets(expanduser('~/client_secrets.json'), self.scopes) <NEW_LINE> creds = tools.run_flow(flow, self.store) <NEW_LINE> <DEDENT> http = creds.authorize(Http()) <NEW_... | Initializes G-Sheet authorization using client secret file | 625941d0498bea3a759b9c1d |
def marks(scenario_file_path: Path) -> List: <NEW_LINE> <INDENT> scenario_config = load_resource_file( scenario_file_path.parent, scenario_file_path.stem) <NEW_LINE> markers = [] <NEW_LINE> for mark in scenario_config.get("marks", []): <NEW_LINE> <INDENT> if mark == "canary": <NEW_LINE> <INDENT> markers.append(pytest.m... | Provides pytest markers for the given scenario
Args:
scenario_file_path: test scenario file path
Returns:
pytest markers for the scenario | 625941d063b5f9789fde7254 |
def p_expression_list (self, p): <NEW_LINE> <INDENT> pass | expression_list : expression
| expression_list COMMA expression | 625941d076e4537e8c3517e1 |
def __init__(self, *args, **kwds): <NEW_LINE> <INDENT> if args or kwds: <NEW_LINE> <INDENT> super(PtuGotoGoal, self).__init__(*args, **kwds) <NEW_LINE> if self.pan is None: <NEW_LINE> <INDENT> self.pan = 0. <NEW_LINE> <DEDENT> if self.tilt is None: <NEW_LINE> <INDENT> self.tilt = 0. <NEW_LINE> <DEDENT> if self.pan_vel ... | Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
pan,tilt,pan_vel,tilt_vel
@param ... | 625941d0ac7a0e7691ed423a |
def reset_downloads(self): <NEW_LINE> <INDENT> for download in self.downloads.values(): <NEW_LINE> <INDENT> self.layout.removeWidget(download.progress_bar) <NEW_LINE> download.progress_bar.close() <NEW_LINE> <DEDENT> self.downloads = {} | Reset the downloads back to zero | 625941d05510c4643540f551 |
def where_end_with(self, key, value): <NEW_LINE> <INDENT> self.where(key, 'endswith', value) <NEW_LINE> return self | Make where_ends_with clause.
:@param key
:@param value
:@type key,value: string
:@return self | 625941d0460517430c3942f2 |
def export_image(self, fname, size=sz_plot_img): <NEW_LINE> <INDENT> gc = _chaco.PlotGraphicsContext(self.outer_bounds) <NEW_LINE> gc.render_component(self) <NEW_LINE> gc.save(fname, file_format=None) | Save plot as png image. | 625941d0f548e778e58cd6ec |
def get_objects(self, ids__names): <NEW_LINE> <INDENT> ids, names = parse_ids_names(ids__names) <NEW_LINE> instances = self.storage.filter( self.model_class, any, **{'id.rcontains': ids, 'label.rcontains': names} ) <NEW_LINE> if not instances: <NEW_LINE> <INDENT> raise DoesNotExistException("There aren't any instance."... | Get model list.
Models will match id and label with passed ids__names list. | 625941d097e22403b379d108 |
def getSets(): <NEW_LINE> <INDENT> queries = { 'pathwayComplexes': 'pathwayComplexes/1430728', 'pathwayParticipants': 'pathwayParticipants/1430728', } <NEW_LINE> for key in queries: <NEW_LINE> <INDENT> fn = 'downloads/{}.json'.format(key) <NEW_LINE> if not os.path.isfile(fn): <NEW_LINE> <INDENT> r = requests.get("{}/{}... | Get collections of data from Reactome
pathway participants and reference molecules and proteins | 625941d091f36d47f21ac661 |
def __init__(self, computer, ui): <NEW_LINE> <INDENT> DSKY.dsky_instance = self <NEW_LINE> self.computer = computer <NEW_LINE> output_widgets = ui.get_output_widgets() <NEW_LINE> self.annunciators = output_widgets[0] <NEW_LINE> self._control_registers = output_widgets[1] <NEW_LINE> self._data_registers = output_widgets... | Class constructor.
:type ui: object
:param computer: the instance of the guidance computer
:return: None | 625941d015fb5d323cde0c7f |
def get_deployments(self, refdes, deploy_num="-1", results=pd.DataFrame()): <NEW_LINE> <INDENT> array, node, instrument = refdes.split("-", 2) <NEW_LINE> deploy_url = "/".join((self.urls["deploy"], array, node, instrument, deploy_num)) <NEW_LINE> deployments = self._get_api(deploy_url) <NEW_LINE> while len(deployments)... | Get the deployment information for an instrument. Defaults to all
deployments for a given instrument (reference designator) unless one is
supplied.
Args:
refdes (str): The reference designator for the instrument for which
to request deployment information.
deploy_num (str): Optional to include a specif... | 625941d0b830903b967e9a79 |
def get_default_transcripts(self, **kwargs): <NEW_LINE> <INDENT> return [], '' | Fetch transcripts list from a video platform.
Arguments:
kwargs (dict): Key-value pairs of API-specific identifiers (account_id, video_id, etc.) and tokens,
necessary for API calls.
Returns:
list: List of dicts of transcripts. Example:
[
{
'lang': 'en',
'label': 'En... | 625941d091af0d3eaac9bb88 |
def contrast(self, value): <NEW_LINE> <INDENT> assert(0x00 <= value <= 0xFF) <NEW_LINE> self._brightness = value >> 4 <NEW_LINE> if self._last_image is not None: <NEW_LINE> <INDENT> self.display(self._last_image) | Sets the LED intensity to the desired level, in the range 0-255.
:param level: Desired contrast level in the range of 0-255.
:type level: int | 625941d056b00c62f0f147c8 |
def main(self): <NEW_LINE> <INDENT> loss_history = self.history.history <NEW_LINE> epochs = range(1, len(loss_history['val_loss'])+1) <NEW_LINE> final_data = (pd.DataFrame(loss_history, index=epochs)) <NEW_LINE> final_data.to_csv('results.csv', index=True) | Utility function to save loss history as csv file. | 625941d05fc7496912cc3aec |
def randwords(num, inseed, fn): <NEW_LINE> <INDENT> words = [] <NEW_LINE> numlines = 0 <NEW_LINE> with open(fn, 'r') as tempfile: <NEW_LINE> <INDENT> numlines = sum([1 for x in tempfile]) <NEW_LINE> <DEDENT> with open(fn, 'r') as tempfile: <NEW_LINE> <INDENT> indices = [] <NEW_LINE> if inseed: <NEW_LINE> <INDENT> seed(... | Generate random words from that text file, ensure uniqueness. | 625941d0851cf427c661a67d |
def redo(self): <NEW_LINE> <INDENT> model = self.doc <NEW_LINE> layer = model.layer_stack.deepget(self._layer_path) <NEW_LINE> if self._stroke_seq is None: <NEW_LINE> <INDENT> return <NEW_LINE> <DEDENT> assert self._stroke_seq.finished, "Call stop_recording() first" <NEW_LINE> if self._sshot_after is None: <NEW_LINE> <... | Performs, or re-performs after undo | 625941d016aa5153ce3625e6 |
def search(self, target): <NEW_LINE> <INDENT> tmp = self.head <NEW_LINE> while tmp != None: <NEW_LINE> <INDENT> if tmp.get_data() == target: <NEW_LINE> <INDENT> return tmp <NEW_LINE> <DEDENT> tmp = tmp.next_node <NEW_LINE> <DEDENT> return tmp | Searches the list for the node containing the target data.
Return:
Data within a node or None if the node is not found. | 625941d085dfad0860c3afca |
def cdf(expr, condition=None, evaluate=True, **kwargs): <NEW_LINE> <INDENT> if condition is not None: <NEW_LINE> <INDENT> return cdf(given(expr, condition, **kwargs), **kwargs) <NEW_LINE> <DEDENT> result = pspace(expr).compute_cdf(expr, **kwargs) <NEW_LINE> if evaluate and hasattr(result, 'doit'): <NEW_LINE> <INDENT> r... | Cumulative Distribution Function of a random expression.
optionally given a second condition
This density will take on different forms for different types of
probability spaces.
Discrete variables produce Dicts.
Continuous variables produce Lambdas.
Examples
========
>>> from sympy.stats import density, Die, Normal... | 625941d04f6381625f114ba9 |
def follow_back(self): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> followers_iterator = tweepy.Cursor(self.api.followers).items(self.follower_retrieve_limit) <NEW_LINE> followers = [follower for follower in followers_iterator] <NEW_LINE> for follower in followers: <NEW_LINE> <INDENT> if not self.request_sent(follower.... | Retrieves a follower list of length follower_retrieve_limit and checks with the database to see if a
follow request has been sent to the user in the past. If not, send the user a follow request.
A follow request will ONLY be sent if a request has not been sent already. Users with
protected accounts have one chance to ... | 625941d0a934411ee3751802 |
def main(feature_folder, create_learning_curve=False): <NEW_LINE> <INDENT> with open(os.path.join(feature_folder, "info.yml")) as ymlfile: <NEW_LINE> <INDENT> feature_description = yaml.safe_load(ymlfile) <NEW_LINE> <DEDENT> path_to_data = os.path.join( utils.get_project_root(), feature_description["data-source"] ) <NE... | main function of create_ffiles.py | 625941d00a50d4780f667001 |
def uniformCostSearch(problem): <NEW_LINE> <INDENT> frontier = util.PriorityQueue() <NEW_LINE> cost = {} <NEW_LINE> parent = {} <NEW_LINE> action = {} <NEW_LINE> actions = [] <NEW_LINE> explored = [] <NEW_LINE> currentState = problem.getStartState() <NEW_LINE> cost[currentState] = 0 <NEW_LINE> action[currentState] = No... | Search the node of least total cost first. | 625941d0ad47b63b2c50a0ee |
def __init__(self, model, **kwargs): <NEW_LINE> <INDENT> name = kwargs.pop('out_name', model.__name__.lower() + 's') <NEW_LINE> super(ApiResourceIndex, self).__init__(model, 'get', name, **kwargs) | :type model: subclass(api.api.ModelBase) | 625941d0656771135c3eb9de |
def post(self, meetup_id, rsvp): <NEW_LINE> <INDENT> message = '' <NEW_LINE> status_code = 200 <NEW_LINE> response = {} <NEW_LINE> valid_responses = ('yes', 'no', 'maybe') <NEW_LINE> if not db.exists('id', meetup_id): <NEW_LINE> <INDENT> print('Meetup not found') <NEW_LINE> status_code = 404 <NEW_LINE> message = 'Meetu... | Endpoint to RSVP to meetup | 625941d02eb69b55b151ca1f |
def _walk(top, topdown=True, onerror=None, followlinks=False): <NEW_LINE> <INDENT> dirs = [] <NEW_LINE> nondirs = [] <NEW_LINE> try: <NEW_LINE> <INDENT> scandir_it = scandir(top) <NEW_LINE> <DEDENT> except OSError as error: <NEW_LINE> <INDENT> if onerror is not None: <NEW_LINE> <INDENT> onerror(error) <NEW_LINE> <DEDEN... | Like Python 3.5's implementation of os.walk() -- faster than
the pre-Python 3.5 version as it uses scandir() internally. | 625941d0e8904600ed9f209c |
def calculate_columns(self, item_widths): <NEW_LINE> <INDENT> if not item_widths: <NEW_LINE> <INDENT> return ColumnConfig([], 0) <NEW_LINE> <DEDENT> try: <NEW_LINE> <INDENT> return self.get_column_config(item_widths) <NEW_LINE> <DEDENT> except LineTooSmallError: <NEW_LINE> <INDENT> if self.allow_exceeding and self.num_... | Calculate column widths based on `item_widths`, expecting `item_widths`
to be a sequence of non-negative integers that represent the length of
each corresponding string. The result is returned as a named tuple that
consists of two elements: A sequence of calculated column widths and the
number of lines needed to displa... | 625941d07d43ff24873a2e0f |
def render_ui(self, editor): <NEW_LINE> <INDENT> raise NotImplementedError | 创建ueditor的ui扩展对象的js代码，如button,combo等 | 625941d030bbd722463cbf35 |
def fit(self, train_x, train_y): <NEW_LINE> <INDENT> m = len(train_y) <NEW_LINE> batch = int(math.ceil(m/self._batch_size)) <NEW_LINE> for t in xrange(1, self._max_iter): <NEW_LINE> <INDENT> eta_t = 1.0/(self._lambda_reg*t) <NEW_LINE> dW = [[0 for col in range(self._feature_size)] for row in range(self._label_size)] <N... | :param train_x: list of list
:param train_y: list of list
:return: | 625941d073bcbd0ca4b2c1e5 |
def browser_for(self, user): <NEW_LINE> <INDENT> if user in ['she', 'he', 'user']: <NEW_LINE> <INDENT> return self.browser <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> return self.browsers[user] | Convenience function to look up a given user's
browser, or the current one if a more general term is used. | 625941d0435de62698dfddbc |
def run(self): <NEW_LINE> <INDENT> success = self.open() <NEW_LINE> if not success: <NEW_LINE> <INDENT> print('Failed to initialize!') <NEW_LINE> return <NEW_LINE> <DEDENT> while True: <NEW_LINE> <INDENT> self.step() | Initialize components and start broadcasting.
| 625941d050485f2cf553cf09 |
def crontab_update(content, marker): <NEW_LINE> <INDENT> crontab_remove(marker) <NEW_LINE> crontab_add(content, marker) | Adds or updates a line in crontab. | 625941d0b57a9660fec339f3 |
def test_autogo3(dev): <NEW_LINE> <INDENT> dev[1].global_request("SET p2p_no_group_iface 0") <NEW_LINE> autogo(dev[0], freq=2462) <NEW_LINE> res = connect_cli(dev[0], dev[1], social=True, freq=2462) <NEW_LINE> if "p2p-wlan" not in res['ifname']: <NEW_LINE> <INDENT> raise Exception("Unexpected group interface name on cl... | P2P autonomous GO and client with a separate group interface joining group | 625941d044b2445a33932204 |
def peek(self, **kwargs): <NEW_LINE> <INDENT> self._validate_data_for_ploting() <NEW_LINE> figure = plt.figure() <NEW_LINE> self.plot(**kwargs) <NEW_LINE> figure.show() | Displays the time series in a new figure.
Parameters
----------
**kwargs : `dict`
Any additional plot arguments that should be used when plotting. | 625941d0fb3f5b602dac3803 |
def _evaluate(self,*args,**kwargs): <NEW_LINE> <INDENT> fixed_quad= kwargs.pop('fixed_quad',False) <NEW_LINE> if len(args) == 5: <NEW_LINE> <INDENT> R,vR,vT, z, vz= args <NEW_LINE> <DEDENT> elif len(args) == 6: <NEW_LINE> <INDENT> R,vR,vT, z, vz, phi= args <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> self._parse_eval_... | NAME:
__call__ (_evaluate)
PURPOSE:
evaluate the actions (jr,lz,jz)
INPUT:
Either:
a) R,vR,vT,z,vz[,phi]:
1) floats: phase-space value for single object (phi is optional) (each can be a Quantity)
2) numpy.ndarray: [N] phase-space values for N objects (each can be a Quantity)
b) Or... | 625941d0442bda511e8be587 |
def setUp(self): <NEW_LINE> <INDENT> self.app = create_app("testing") <NEW_LINE> self.client = self.app.test_client <NEW_LINE> self.section = { 'title': 'Test Title', 'contents': 'Some test content' } <NEW_LINE> with self.app.app_context(): <NEW_LINE> <INDENT> db.create_all() | Test SetUp | 625941d023e79379d52ee6d3 |
def get_weakness(self): <NEW_LINE> <INDENT> return self.weakness | Returns a string containing an Enemy's weakness | 625941d0be383301e01b55f4 |
@parametric_function_api("bn", [ ('beta', 'Trainable bias :math:`\\beta`', '<see above>', True), ('gamma', 'Trainable scaling factor :math:`\\gamma`', '<see above>', True), ('mean', 'Moving average of batch mean', '<see above>', False), ('var', 'Moving average of batch variance', '<see above>', False), ]) <NEW_LINE> de... | Batch normalization layer.
.. math::
\begin{array}{lcl}
\mu &=& \frac{1}{M} \sum x_i\\
\sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\
\hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\
y_i &= & \hat{x}_i \gamma + \beta.
\end{array}
where :math:`x_i, y_i` are the inputs.
I... | 625941d0d486a94d0b98e2b5 |
def lineage_for_certname(cli_config, certname): <NEW_LINE> <INDENT> configs_dir = cli_config.renewal_configs_dir <NEW_LINE> util.make_or_verify_dir(configs_dir, mode=0o755, uid=misc.os_geteuid()) <NEW_LINE> try: <NEW_LINE> <INDENT> renewal_file = storage.renewal_file_for_certname(cli_config, certname) <NEW_LINE> <DEDEN... | Find a lineage object with name certname. | 625941d050812a4eaa59c490 |
def forward(self, sentence_outputs, lengths): <NEW_LINE> <INDENT> packed = pack_padded_sequence(sentence_outputs, lengths, batch_first=True) <NEW_LINE> output, _ = self.gru(packed) <NEW_LINE> output, lens = pad_packed_sequence(output, batch_first=True, padding_value=0) <NEW_LINE> return output.float() | :param sentence_outputs: Sentence vecs from the word attention layer
:return: | 625941d099fddb7c1c9de500 |
def test_by_date_and_tag(imgdir): <NEW_LINE> <INDENT> with photo.index.Index(idxfile=imgdir) as idx: <NEW_LINE> <INDENT> date = (datetime.datetime(2016, 2, 28), datetime.datetime(2016, 2, 29)) <NEW_LINE> idxfilter = photo.idxfilter.IdxFilter(tags="Tokyo", date=date) <NEW_LINE> fnames = [ str(i.filename) for i in idxfil... | Select by date and tags.
Multiple selection criteria, such as date and tags may be
combined. | 625941d0283ffb24f3c55a70 |
def main(): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> os.mkdir(TMP_DIR) <NEW_LINE> <DEDENT> except OSError: <NEW_LINE> <INDENT> pass <NEW_LINE> <DEDENT> opts, args = acid.process_args() <NEW_LINE> if args: <NEW_LINE> <INDENT> names = list(args) <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> names = None <NEW_LINE> <D... | Run main. | 625941d03eb6a72ae02ec64f |
def GetMassListFromScanNum(self, scanNumber, scanFilter="", intensityCutoffType=0, intensityCutoffValue=0, maxNumberOfPeaks=0, centroidResult=False, centroidPeakWidth=0.0): <NEW_LINE> <INDENT> peakList = comtypes.automation.VARIANT() <NEW_LINE> peakFlags = comtypes.automation.VARIANT() <NEW_LINE> pnArraySize = c_long()... | This function is only applicable to scanning devices such as MS and PDA.
If no scanFilter is supplied, the scan corresponding to pnScanNumber is returned. If a
scanFilter is provided, the closest matching scan to pnScanNumber that matches the scanFilter is
returned.
scanFilter must match the Xcalibur scanFilter format... | 625941d05fcc89381b1e182f |
def change_text(self, num): <NEW_LINE> <INDENT> self.bomblabel.config(text='Bombs left {}'.format(num)) | Changes the text on the bomb label | 625941d00a366e3fb873e98a |
def sp_to_vests(self, sp, timestamp=None, use_stored_data=True): <NEW_LINE> <INDENT> return sp * 1e6 / self.get_crea_per_mvest(timestamp, use_stored_data=use_stored_data) | Converts SP to vests
:param float sp: Crea power to convert
:param datetime timestamp: (Optional) Can be used to calculate
the conversion rate from the past | 625941d0cdde0d52a9e531a3 |
def SetForegroundValue(self, *args): <NEW_LINE> <INDENT> return _itkGridForwardWarpImageFilterPython.itkGridForwardWarpImageFilterIVF22IUL2_SetForegroundValue(self, *args) | SetForegroundValue(self, unsigned long _arg) | 625941d0627d3e7fe0d68fc0 |
def get_file_parent_dir_path(level=1): <NEW_LINE> <INDENT> current_dir_path = dirname(abspath(__file__)) <NEW_LINE> path_sep = os.path.sep <NEW_LINE> components = current_dir_path.split(path_sep) <NEW_LINE> return path_sep.join(components[:-level]) | return the path of the parent directory of current file | 625941d0287bf620b61d3bd3 |
def REPLACEB(*args) -> Function: <NEW_LINE> <INDENT> return Function("REPLACEB", args) | Replaces part of a text string, based on a number of bytes, with a different
text string.
Learn more: https//support.google.com/docs/answer/9367752. | 625941d0293b9510aa2c3405 |
def ImageToPdf(outputpath, imagepath): <NEW_LINE> <INDENT> lists = list(imagepath.glob("**/*")) <NEW_LINE> print(f'lists = {lists}') <NEW_LINE> with open(outputpath,"wb") as f: <NEW_LINE> <INDENT> f.write(img2pdf.convert([str(i) for i in lists if i.match("*.jpg") or i.match("*.png")])) <NEW_LINE> <DEDENT> print(outputp... | outputpath: pathlib.Path()
imagepath: pathlib.Path() | 625941d0aad79263cf390bb1 |
def get_sum_frequencies(list_files): <NEW_LINE> <INDENT> df_sum = pd.read_csv(list_files[0], sep="\t", index_col=0) <NEW_LINE> if len(list_files) == 1: <NEW_LINE> <INDENT> return df_sum.sort_values("frequencies", ascending=False) <NEW_LINE> <DEDENT> for i in range(1, len(list_files)): <NEW_LINE> <INDENT> df = pd.read_c... | Get the average frequencies of every hexanucleotide of ``list_files``.
:param list_files: (list of string) list of files
:return: (pandas DataFrame) the average frequencies of every hexanucleotide found in ``list_files`` | 625941d0236d856c2ad4494b |
def __init__(self): <NEW_LINE> <INDENT> self.verts=None <NEW_LINE> self.color=None <NEW_LINE> self.texture=None <NEW_LINE> self.texverts=None | verts. Array de Coord3D
color: Color del poligono is a Color object
texture: Textura del pol´igno que es un TTextures value. Si no tiene debe valer None
texCoord: Array de Coord2D de la textura | 625941d0a934411ee3751803 |
def test_duplicate_normalized_unicode(self): <NEW_LINE> <INDENT> omega_username = 'iamtheΩ' <NEW_LINE> ohm_username = 'iamtheΩ' <NEW_LINE> self.assertNotEqual(omega_username, ohm_username) <NEW_LINE> User.objects.create_user(username=omega_username, password='pwd') <NEW_LINE> data = { 'username': ohm_username, 'passwor... | To prevent almost identical usernames, visually identical but differing
by their unicode code points only, Unicode NFKC normalization should
make appear them equal to Django. | 625941d04a966d76dd55117f |
def clear(self, fill = 0x00): <NEW_LINE> <INDENT> self._buffer = [ fill ] * ( self.width * self._mem_pages ) | !
\~english
Clear buffer data and fill color into buffer
@param fill: a color value, it will fill into buffer.<br>
The SSD1306 only chosen two colors: <br>
0 (0x0): black <br>
1 (0x1): white <br>
\~chinese
清除缓冲区数据并在缓冲区中填充颜色
@param fill: 一个颜色值，它会填充到缓冲区中 <br>
SSD1306只能选择两种颜色： <... | 625941d0be7bc26dc91cd76e |
def cutmix( self, data: torch.Tensor, labels: torch.Tensor, alpha: float = 0.4 ) -> MixupOutput: <NEW_LINE> <INDENT> indices = torch.randperm(data.size(0)) <NEW_LINE> shuffled_data = data[indices] <NEW_LINE> shuffled_labels = labels[indices] <NEW_LINE> lam = np.random.beta(alpha, alpha, size=len(indices)) <NEW_LINE> la... | Transforms input batch into cutmixed batch
Args:
data: input batch data
labels: input batch labels
alpha: Beta distribution argument to generate weights for cutmix
Returns:
MixupOutput with cutmixed data and labels | 625941d0d18da76e23532646 |
def process_update(): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> subprocess.check_output('git pull', stderr=subprocess.STDOUT, shell=True, universal_newlines=True) <NEW_LINE> <DEDENT> except Exception: <NEW_LINE> <INDENT> print(f'\nUnable to update application. Try to manually "git pull" or ' f'"git clone" this repos... | Update (git pull) project.
:return: Void. | 625941d0925a0f43d2549fe7 |
def update(self, fire_after=None, expire_after=None, callback=None, callback_error=None, init=False): <NEW_LINE> <INDENT> now = time.time() <NEW_LINE> if expire_after is not None: <NEW_LINE> <INDENT> self.expire_at = now + expire_after <NEW_LINE> if self.fire_at > self.expire_at: <NEW_LINE> <INDENT> self.fire_at = self... | Update the entry information
Args:
fire_after (float): set callback (periodical) to fire after given time (in second)
expire_after (float): set expiration timer to given time (in second)
callback (obj): callback method that will be called periodically
callback_error (obj): callback method that will be ... | 625941d0435de62698dfddbd |
def redivideClusters(cosmatrix,k,docmatrix): <NEW_LINE> <INDENT> clusters = [[] for _ in range(k)] <NEW_LINE> x,y = np.where(cosmatrix == np.max(cosmatrix,axis=0)) <NEW_LINE> for i,j in zip(x,y): <NEW_LINE> <INDENT> clusters[i].append(j) <NEW_LINE> <DEDENT> nseeds = [] <NEW_LINE> for cluster in clusters: <NEW_LINE> <IN... | cosmatrix：根据中心点计算出的余弦矩阵
将文档划分到对应的簇，并返回新划分的簇和对应簇的中心点 | 625941d0004d5f362079a4a2 |
def nullspace(A, atol=1e-13, rtol=0): <NEW_LINE> <INDENT> A = np.atleast_2d(A) <NEW_LINE> u, s, vh = svd(A) <NEW_LINE> tol = max(atol, rtol * s[0]) <NEW_LINE> nnz = (s >= tol).sum() <NEW_LINE> ns = vh[nnz:].conj().T <NEW_LINE> return scipy.linalg.orth(ns) | Compute an approximate basis for the nullspace of A.
The algorithm used by this function is based on the singular value
decomposition of `A`.
Parameters
----------
A : ndarray
A should be at most 2-D. A 1-D array with length k will be treated
as a 2-D with shape (1, k)
atol : float
The absolute tolerance... | 625941d031939e2706e4cfd9 |
def suggest_model_using_sensitivity(self): <NEW_LINE> <INDENT> threshold = self._option[self._tenv.regression_sval_threshold] <NEW_LINE> norm_s = self.get_normalized_sensitivity() <NEW_LINE> dv = self.get_response() <NEW_LINE> predictors = self.get_predictors() <NEW_LINE> self.dv_iv_map = dict( [(d, [p for i, p in enum... | After doing linear regression (run()), one may want to call this function to see which terms are significant
Select terms from a full expansion list by observing the Normalized Input Sensitivity (NIS) in [%]
NIS >= threhold in [%] | 625941d0bde94217f3682f60 |
def generate_monthly_time_axis(startyear, nyears, timefmt="ncar"): <NEW_LINE> <INDENT> nyears = nyears + 1 <NEW_LINE> years = np.arange(startyear, startyear + nyears) <NEW_LINE> years = [year for year in years for x in range(12)] <NEW_LINE> months = list(np.arange(1, 13)) * nyears <NEW_LINE> days = 1 if timefmt == "nca... | Construct a monthly noleap time dimension with associated bounds
Parameters
----------
startyear : int
Start year for requested time axis
nyears : int
Number of years in requested time axis
timefmt : str, optional
Time axis format, either "cmip", "gfdl" or "ncar", "ncar" by default
Returns
-------
xarray.... | 625941d0e5267d203edcde0c |
def canDouble(self,hand): <NEW_LINE> <INDENT> if (self._wallet - hand._bet) <= 0: <NEW_LINE> <INDENT> return False <NEW_LINE> <DEDENT> else: <NEW_LINE> <INDENT> return True | Determines whether a player has enough money to double down | 625941d026238365f5f0efde |
def get_moved_pages_redirects(self): <NEW_LINE> <INDENT> if self.offset <= 0: <NEW_LINE> <INDENT> self.offset = 1 <NEW_LINE> <DEDENT> start = (datetime.datetime.utcnow() - datetime.timedelta(0, self.offset * 3600)) <NEW_LINE> offset_time = start.strftime("%Y%m%d%H%M%S") <NEW_LINE> pywikibot.output(u'Retrieving %s moved... | Generate redirects to recently-moved pages. | 625941d0ac7a0e7691ed423b |
def _set_B_and_lmove_(self,M,nmax=None,tol=None): <NEW_LINE> <INDENT> if self.cut==0: raise ValueError('MPS _set_B_and_lmove_ error: the cut is already zero.') <NEW_LINE> L,S,R=M.labels[MPS.L],M.labels[MPS.S],M.labels[MPS.R] <NEW_LINE> u,s,v=svd(M,row=[L],new=Label('__MPS_set_B_and_lmove__',None,None),col=[S,R],nmax=nm... | Set the B matrix at self.cut and move leftward.
Parameters
----------
M : DTensor/STensor
The tensor used to set the B matrix.
nmax : int, optional
The maximum number of singular values to be kept.
tol : float, optional
The truncation tolerance. | 625941d08a349b6b435e82e3 |
def get_save_file_path(name): <NEW_LINE> <INDENT> return constants.SAVE_DATA_PATH + constants.SAVED_DATA_NAME_TAG + '_' + name + '.csv' | Returns the save file path of a file given its name.
str -> str | 625941d07c178a314d6ef5d1 |
def add(self, *objects): <NEW_LINE> <INDENT> for obj in objects: <NEW_LINE> <INDENT> if obj not in self._plotcontext.children: <NEW_LINE> <INDENT> self._plotcontext.children.append(obj) <NEW_LINE> self._plotcontext._dirty = True <NEW_LINE> <DEDENT> self._add(*obj.references()) | Add top level objects to this Document. Also traverses
references and adds those as well. This function should only
be called on top level objects. lower level objects are
added using _add
Args:
*objects (PlotObject) : objects to add to the Document
Returns:
None | 625941d073bcbd0ca4b2c1e6 |
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 | 625941d0167d2b6e31218d06 |
def coverage_report_plain(): <NEW_LINE> <INDENT> test() <NEW_LINE> local('coverage report -m --fail-under=77') | Runs all tests and prints the coverage report. | 625941d0fbf16365ca6f6336 |
def select_keypairs_name_substring(self, search_substring): <NEW_LINE> <INDENT> for keypair in self._cloud.list_keypairs(): <NEW_LINE> <INDENT> if search_substring in keypair['name']: <NEW_LINE> <INDENT> if keypair['name'] in ('rhos-jenkins'): <NEW_LINE> <INDENT> continue <NEW_LINE> <DEDENT> self._add('keypairs', keypa... | Select keypairs based on substring. | 625941d0a8370b7717052a0f |
def prepare_empty_partition_btrfs(self, rootfs, oe_builddir, native_sysroot): <NEW_LINE> <INDENT> size = self.disk_size <NEW_LINE> with open(rootfs, 'w') as sparse: <NEW_LINE> <INDENT> os.ftruncate(sparse.fileno(), size * 1024) <NEW_LINE> <DEDENT> label_str = "" <NEW_LINE> if self.label: <NEW_LINE> <INDENT> label_str =... | Prepare an empty btrfs partition. | 625941d06fb2d068a760f20e |
def train(data_loader, model, optimizer, device): <NEW_LINE> <INDENT> model.train() <NEW_LINE> for data in data_loader: <NEW_LINE> <INDENT> reviews = data["reviews"] <NEW_LINE> targets = data["target"] <NEW_LINE> reviews = reviews.to(device, dtype=torch.long) <NEW_LINE> targets = targets.to(device, dtype=torch.float) <... | This is the main training function that trains model
for one epoch
:param data_loader: this is the torch dataloader
:param model: model (lstm model)
:param optimizer: torch optimizer, e.g. adam, sgd, etc.
:param device: this can be "cuda" or "cpu" | 625941d08a349b6b435e82e4 |
def count_events(self): <NEW_LINE> <INDENT> count = 0 <NEW_LINE> self.fp.seek(0) <NEW_LINE> while True: <NEW_LINE> <INDENT> size = self._read_event_size() <NEW_LINE> if not size: <NEW_LINE> <INDENT> break <NEW_LINE> <DEDENT> self.fp.seek(size, 1) <NEW_LINE> count += 1 <NEW_LINE> <DEDENT> return count | Count events from file. This skips parsing any data so
should be quite fast. Useful for progress bars etc. | 625941d00fa83653e465712b |
def describe_snapshots(DirectoryId=None, SnapshotIds=None, NextToken=None, Limit=None): <NEW_LINE> <INDENT> pass | Obtains information about the directory snapshots that belong to this account.
This operation supports pagination with the use of the NextToken request and response parameters. If more results are available, the DescribeSnapshots.NextToken member contains a token that you pass in the next call to DescribeSnaps... | 625941d01d351010ab855c8c |
def vectordsc(corpus, train_text, test_text): <NEW_LINE> <INDENT> word_vectorizer = TfidfVectorizer( sublinear_tf=True, strip_accents='unicode', analyzer='word', token_pattern=r'\w{1,}', stop_words='english', ngram_range=(1, 2), max_features=10000) <NEW_LINE> word_vectorizer.fit(corpus) <NEW_LINE> train_word_features =... | Convert the description text into ngram vector of features. Sparse matrix format | 625941d0566aa707497f46d8 |
def viewDelta(self, dt, file_parameters): <NEW_LINE> <INDENT> x = np.zeros((dt.num_rows, 1)) <NEW_LINE> y = np.zeros((dt.num_rows, 1)) <NEW_LINE> x[:, 0] = dt.data[:, 0] <NEW_LINE> y[:, 0] = np.arctan2(dt.data[:, 2], dt.data[:, 1]) * 180 / np.pi <NEW_LINE> return x, y, True | Loss or phase angle :math:`\delta(\omega)=\arctan(G''/G')\cdot 180/\pi` (in degrees, in logarithmic scale) vs :math:`\omega` (in logarithmic scale) | 625941d0d8ef3951e32436ae |
def create_product(self, data: Dict) -> Dict: <NEW_LINE> <INDENT> data = dict(product=data) <NEW_LINE> return self._post(self.URL_PRODUCTS, data)['product'] | Create new product in the shop
:param data: Data to be set on the product
:return: Newly created product | 625941d0a05bb46b383ec991 |
def tell_bots(self, lines, silently=False): <NEW_LINE> <INDENT> for bot in self.bots: <NEW_LINE> <INDENT> self.__tell_bot(bot, lines, silently) <NEW_LINE> silently = True | Tell all bots something through STDIN | 625941d0dd821e528d63b319 |
def elementwise_sub(a, b): <NEW_LINE> <INDENT> c = copy.deepcopy(a) <NEW_LINE> for i, row in enumerate(a): <NEW_LINE> <INDENT> for j, num in enumerate(row): <NEW_LINE> <INDENT> c[i][j] -= b[i][j] <NEW_LINE> <DEDENT> <DEDENT> return c | Elementwise substraction. | 625941d056ac1b37e626433e |
def finish_job(self, job): <NEW_LINE> <INDENT> if not job.complete(): <NEW_LINE> <INDENT> raise eva.exceptions.RetryException("NcML aggregation to file '%s' failed.", job.output_filename) <NEW_LINE> <DEDENT> job.logger.info("NcML aggregation to file '%s' successful.", job.output_filename) | Retry on failure, log on completion. | 625941d0e5267d203edcde0d |
def run(action, errormsg="Connection error: %s", graceperiod=0): <NEW_LINE> <INDENT> starttime = time.time() <NEW_LINE> def routine(): <NEW_LINE> <INDENT> timeout = 1.0 <NEW_LINE> while True: <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> action() <NEW_LINE> timeout = 1.0 <NEW_LINE> <DEDENT> except Exception as e: <NEW_L... | Run action() in background forever and retry with exponential backoff in case of errors. | 625941d0d10714528d5ffe54 |
def create_new(arxiv_id_str: str, arxiv_ver: int, payload: Dict[str, Any]) -> Response: <NEW_LINE> <INDENT> arxiv_id: ArXivID = resolve_arxiv_id(arxiv_id_str) <NEW_LINE> try: <NEW_LINE> <INDENT> rel: Relation = create.create(arxiv_id, arxiv_ver, payload['resource_type'], payload['resource_id'], payload.get('description... | Create a new relation for an e-print.
Parameters
----------
arxiv_id_str: str
The arXiv ID of the e-print.
arxiv_ver: int
The version of the e-print.
payload: Dict[str, Any]
Payload info.
Returns
-------
Dict[str, Any]
The newly-created relation.
HTTPStatus
An HTTP status code.
Dict[str, str]
... | 625941d0379a373c97cfacb5 |
def add_fontSize_method(self, text_str, num): <NEW_LINE> <INDENT> try: <NEW_LINE> <INDENT> text_str.font.size = Pt(num) <NEW_LINE> <DEDENT> except Exception: <NEW_LINE> <INDENT> mylog.error('add_fontSize error') <NEW_LINE> self.ret['state'] = 1 <NEW_LINE> self.ret['stateMessage'] = 'add_fontSize error' | 对文本，设置字号 | 625941d0f548e778e58cd6ee |
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