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def forward(self, inputs: torch.Tensor, word_inputs: torch.Tensor = None) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]: if self._word_embedding is not None and word_inputs is not None: try: mask_without_bos_eos = (word_inputs > 0).long() embedded_inputs = self._word_embedding(word_inputs) type_representation, mask = add_sentence_boundary_token_ids( embedded_inputs, mask_without_bos_eos, self._bos_embedding, self._eos_embedding ) except RuntimeError: token_embedding = self._token_embedder(inputs) mask = token_embedding[] type_representation = token_embedding[] else: token_embedding = self._token_embedder(inputs) mask = token_embedding[] type_representation = token_embedding[] lstm_outputs = self._elmo_lstm(type_representation, mask) output_tensors = [ torch.cat([type_representation, type_representation], dim=-1) * mask.float().unsqueeze(-1) ] for layer_activations in torch.chunk(lstm_outputs, lstm_outputs.size(0), dim=0): output_tensors.append(layer_activations.squeeze(0)) return { : output_tensors, : mask, }
Parameters ---------- inputs: ``torch.Tensor``, required. Shape ``(batch_size, timesteps, 50)`` of character ids representing the current batch. word_inputs : ``torch.Tensor``, required. If you passed a cached vocab, you can in addition pass a tensor of shape ``(batch_size, timesteps)``, which represent word ids which have been pre-cached. Returns ------- Dict with keys: ``'activations'``: ``List[torch.Tensor]`` A list of activations at each layer of the network, each of shape ``(batch_size, timesteps + 2, embedding_dim)`` ``'mask'``: ``torch.Tensor`` Shape ``(batch_size, timesteps + 2)`` long tensor with sequence mask. Note that the output tensors all include additional special begin and end of sequence markers.
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def create_role(name, policy_document=None, path=None, region=None, key=None, keyid=None, profile=None): conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) if role_exists(name, region, key, keyid, profile): return True if not policy_document: policy_document = None try: conn.create_role(name, assume_role_policy_document=policy_document, path=path) log.info(, name) return True except boto.exception.BotoServerError as e: log.error(e) log.error(, name) return False
Create an instance role. CLI Example: .. code-block:: bash salt myminion boto_iam.create_role myrole
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def on_builder_inited(app): app.cache_db_path = ":memory:" if app.config["uqbar_book_use_cache"]: logger.info(bold("[uqbar-book]"), nonl=True) logger.info(" initializing cache db") app.connection = uqbar.book.sphinx.create_cache_db(app.cache_db_path)
Hooks into Sphinx's ``builder-inited`` event.
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def from_urdf_file(cls, urdf_file, base_elements=None, last_link_vector=None, base_element_type="link", active_links_mask=None, name="chain"): if base_elements is None: base_elements = ["base_link"] links = URDF_utils.get_urdf_parameters(urdf_file, base_elements=base_elements, last_link_vector=last_link_vector, base_element_type=base_element_type) return cls([link_lib.OriginLink()] + links, active_links_mask=active_links_mask, name=name)
Creates a chain from an URDF file Parameters ---------- urdf_file: str The path of the URDF file base_elements: list of strings List of the links beginning the chain last_link_vector: numpy.array Optional : The translation vector of the tip. name: str The name of the Chain base_element_type: str active_links_mask: list[bool]
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def start_server(socket, projectname, xmlfilename: str) -> None: state.initialise(projectname, xmlfilename) server = http.server.HTTPServer((, int(socket)), HydPyServer) server.serve_forever()
Start the *HydPy* server using the given socket. The folder with the given `projectname` must be available within the current working directory. The XML configuration file must be placed within the project folder unless `xmlfilename` is an absolute file path. The XML configuration file must be valid concerning the schema file `HydPyConfigMultipleRuns.xsd` (see method |ServerState.initialise| for further information).
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def phistogram(view, a, bins=10, rng=None, normed=False): nengines = len(view.targets) with view.sync_imports(): import numpy rets = view.apply_sync(lambda a, b, rng: numpy.histogram(a,b,rng), Reference(a), bins, rng) hists = [ r[0] for r in rets ] lower_edges = [ r[1] for r in rets ] lower_edges = view.pull(, targets=0) hist_array = numpy.array(hists).reshape(nengines, -1) total_hist = numpy.sum(hist_array, 0) if normed: total_hist = total_hist/numpy.sum(total_hist,dtype=float) return total_hist, lower_edges
Compute the histogram of a remote array a. Parameters ---------- view IPython DirectView instance a : str String name of the remote array bins : int Number of histogram bins rng : (float, float) Tuple of min, max of the range to histogram normed : boolean Should the histogram counts be normalized to 1
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def remove_unicode_dict(input_dict): if isinstance(input_dict, collections.Mapping): return dict(map(remove_unicode_dict, input_dict.iteritems())) elif isinstance(input_dict, collections.Iterable): return type(input_dict)(map(remove_unicode_dict, input_dict)) else: return input_dict
remove unicode keys and values from dict, encoding in utf8
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def parse_encoding(fp): pos = fp.tell() fp.seek(0) try: line1 = fp.readline() has_bom = line1.startswith(codecs.BOM_UTF8) if has_bom: line1 = line1[len(codecs.BOM_UTF8):] m = _PYTHON_MAGIC_COMMENT_re.match(line1.decode(, )) if not m: try: import parser parser.suite(line1.decode(, )) except (ImportError, SyntaxError): pass else: line2 = fp.readline() m = _PYTHON_MAGIC_COMMENT_re.match( line2.decode(, )) if has_bom: if m: raise SyntaxError("python refuses to compile code with both a UTF8" \ " byte-order-mark and a magic encoding comment") return elif m: return m.group(1) else: return None finally: fp.seek(pos)
Deduce the encoding of a Python source file (binary mode) from magic comment. It does this in the same way as the `Python interpreter`__ .. __: http://docs.python.org/ref/encodings.html The ``fp`` argument should be a seekable file object in binary mode.
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def max_entropy_distribution(node_indices, number_of_nodes): distribution = np.ones(repertoire_shape(node_indices, number_of_nodes)) return distribution / distribution.size
Return the maximum entropy distribution over a set of nodes. This is different from the network's uniform distribution because nodes outside ``node_indices`` are fixed and treated as if they have only 1 state. Args: node_indices (tuple[int]): The set of node indices over which to take the distribution. number_of_nodes (int): The total number of nodes in the network. Returns: np.ndarray: The maximum entropy distribution over the set of nodes.
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def partial_dependence(self, term, X=None, width=None, quantiles=None, meshgrid=False): if not self._is_fitted: raise AttributeError() if not isinstance(term, int): raise ValueError(.format(term)) if (term >= len(self.terms)) or (term < -1): raise ValueError(\ .format(term, len(self.terms))) if self.terms[term].isintercept: raise ValueError() if X is None: X = self.generate_X_grid(term=term, meshgrid=meshgrid) if meshgrid: if not isinstance(X, tuple): raise ValueError(\ .format(X)) shape = X[0].shape X = self._flatten_mesh(X, term=term) X = check_X(X, n_feats=self.statistics_[], edge_knots=self.edge_knots_, dtypes=self.dtype, features=self.feature, verbose=self.verbose) modelmat = self._modelmat(X, term=term) pdep = self._linear_predictor(modelmat=modelmat, term=term) out = [pdep] compute_quantiles = (width is not None) or (quantiles is not None) if compute_quantiles: conf_intervals = self._get_quantiles(X, width=width, quantiles=quantiles, modelmat=modelmat, lp=pdep, term=term, xform=False) out += [conf_intervals] if meshgrid: for i, array in enumerate(out): if array.ndim > 1: depth = array.shape[-1] shape += (depth,) out[i] = np.reshape(array, shape) if compute_quantiles: return out return out[0]
Computes the term functions for the GAM and possibly their confidence intervals. if both width=None and quantiles=None, then no confidence intervals are computed Parameters ---------- term : int, optional Term for which to compute the partial dependence functions. X : array-like with input data, optional if `meshgrid=False`, then `X` should be an array-like of shape (n_samples, m_features). if `meshgrid=True`, then `X` should be a tuple containing an array for each feature in the term. if None, an equally spaced grid of points is generated. width : float on (0, 1), optional Width of the confidence interval. quantiles : array-like of floats on (0, 1), optional instead of specifying the prediciton width, one can specify the quantiles. so width=.95 is equivalent to quantiles=[.025, .975]. if None, defaults to width. meshgrid : bool, whether to return and accept meshgrids. Useful for creating outputs that are suitable for 3D plotting. Note, for simple terms with no interactions, the output of this function will be the same for ``meshgrid=True`` and ``meshgrid=False``, but the inputs will need to be different. Returns ------- pdeps : np.array of shape (n_samples,) conf_intervals : list of length len(term) containing np.arrays of shape (n_samples, 2 or len(quantiles)) Raises ------ ValueError : If the term requested is an intercept since it does not make sense to process the intercept term. See Also -------- generate_X_grid : for help creating meshgrids.
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def match_comment(self): match = self.match(r"<%doc>(.*?)</%doc>", re.S) if match: self.append_node(parsetree.Comment, match.group(1)) return True else: return False
matches the multiline version of a comment
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def _find_symbol(self, module, name, fallback=None): if not hasattr(module, name) and fallback: return self._find_symbol(module, fallback, None) return getattr(module, name)
Find the symbol of the specified name inside the module or raise an exception.
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def __parse_dois(self, x): if isinstance(x, str): m = clean_doi(x) if m: self.doi = m elif isinstance(x, list): for entry in x: m = clean_doi(entry) if m: self.doi += m return
Parse the Dataset_DOI field. Could be one DOI string, or a list of DOIs :param any x: Str or List of DOI ids :return none: list is set to self
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def create_request(query): yarr_url = app.config.get(, False) if not yarr_url: raise() api_token = app.config.get(, False) headers = {: api_token} if api_token else {} payload = {: query} url = % yarr_url return requests.get(url, params=payload, headers=headers)
Creates a GET request to Yarr! server :param query: Free-text search query :returns: Requests object
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def insert_lemmatisation_data(germanet_db): germanet_db.lemmatiser.drop() num_lemmas = 0 input_file = gzip.open(os.path.join(os.path.dirname(__file__), LEMMATISATION_FILE)) for line in input_file: line = line.decode().strip().split() assert len(line) == 2 germanet_db.lemmatiser.insert(dict(list(zip((, ), line)))) num_lemmas += 1 input_file.close() germanet_db.lemmatiser.create_index() print(.format(num_lemmas))
Creates the lemmatiser collection in the given MongoDB instance using the data derived from the Projekt deutscher Wortschatz. Arguments: - `germanet_db`: a pymongo.database.Database object
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def vtas2cas(tas, h): p, rho, T = vatmos(h) qdyn = p*((1.+rho*tas*tas/(7.*p))**3.5-1.) cas = np.sqrt(7.*p0/rho0*((qdyn/p0+1.)**(2./7.)-1.)) cas = np.where(tas<0, -1*cas, cas) return cas
tas2cas conversion both m/s
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def paginate(self, url, key, params=None): if params is None: params = {} if self.per_page is not None and "per_page" not in params: params = dict(params, per_page=self.per_page) page = self.request(url, params=params) while True: try: objects = page[key] except (KeyError, TypeError): raise ValueError(\ .format(key)) for obj in objects: yield obj try: url = page["links"]["pages"]["next"] except KeyError: break page = self.request(url)
Fetch a sequence of paginated resources from the API endpoint. The initial request to ``url`` and all subsequent requests must respond with a JSON object; the field specified by ``key`` must be a list, whose elements will be yielded, and the next request will be made to the URL in the ``.links.pages.next`` field until the responses no longer contain that field. :param str url: the URL to make the initial request of. If ``url`` begins with a forward slash, :attr:`endpoint` is prepended to it; otherwise, ``url`` is treated as an absolute URL. :param str key: the field on each page containing a list of values to yield :param dict params: parameters to add to the initial URL's query string. A ``"per_page"`` parameter may be included to override the default :attr:`per_page` setting. :rtype: generator of decoded JSON values :raises ValueError: if a response body is not an object or ``key`` is not one of its keys :raises DOAPIError: if the API endpoint replies with an error
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def get_ip(data): try: ip = data.public_ips[0] except Exception: ip = data.private_ips[0] return ip
Return the IP address of the VM If the VM has public IP as defined by libcloud module then use it Otherwise try to extract the private IP and use that one.
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def rescan_file(self, resource, date=, period=, repeat=, notify_url=, notify_changes_only=, timeout=None): params = {: self.api_key, : resource} try: response = requests.post(self.base + , params=params, proxies=self.proxies, timeout=timeout) except requests.RequestException as e: return dict(error=str(e)) return _return_response_and_status_code(response)
Rescan a previously submitted filed or schedule an scan to be performed in the future. This API allows you to rescan files present in VirusTotal's file store without having to resubmit them, thus saving bandwidth. You only need to know one of the hashes of the file to rescan. :param resource: An md5/sha1/sha256 hash. You can also specify a CSV list made up of a combination of any of the three allowed hashes (up to 25 items), this allows you to perform a batch request with just one single call. Note that the file must already be present in our file store. :param date: (optional) Date in %Y%m%d%H%M%S format (example: 20120725170000) in which the rescan should be performed. If not specified the rescan will be performed immediately. :param period: (optional) Periodicity (in days) with which the file should be rescanned. If this argument is provided the file will be rescanned periodically every period days, if not, the rescan is performed once and not repated again. :param repeat: (optional) Used in conjunction with period to specify the number of times the file should be rescanned. If this argument is provided the file will be rescanned the given amount of times in coherence with the chosen periodicity, if not, the file will be rescanned indefinitely. :param notify_url: (optional) A URL to which a POST notification should be sent when the rescan finishes. :param notify_changes_only: (optional) Used in conjunction with notify_url. Indicates if POST notifications should only be sent if the scan results differ from the previous one. :param timeout: The amount of time in seconds the request should wait before timing out. :return: JSON response that contains scan_id and permalink.
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def _mouseMoveDrag(moveOrDrag, x, y, xOffset, yOffset, duration, tween=linear, button=None): assert moveOrDrag in (, ), "moveOrDrag must be in (, ), not %s" % (moveOrDrag) if sys.platform != : moveOrDrag = xOffset = int(xOffset) if xOffset is not None else 0 yOffset = int(yOffset) if yOffset is not None else 0 if x is None and y is None and xOffset == 0 and yOffset == 0: return startx, starty = position() x = int(x) if x is not None else startx y = int(y) if y is not None else starty x += xOffset y += yOffset width, height = size() x = max(0, min(x, width - 1)) y = max(0, min(y, height - 1)) steps = [(x, y)] if duration > MINIMUM_DURATION: num_steps = max(width, height) sleep_amount = duration / num_steps if sleep_amount < MINIMUM_SLEEP: num_steps = int(duration / MINIMUM_SLEEP) sleep_amount = duration / num_steps steps = [ getPointOnLine(startx, starty, x, y, tween(n / num_steps)) for n in range(num_steps) ] steps.append((x, y)) for tweenX, tweenY in steps: if len(steps) > 1: time.sleep(sleep_amount) _failSafeCheck() tweenX = int(round(tweenX)) tweenY = int(round(tweenY)) if moveOrDrag == : platformModule._moveTo(tweenX, tweenY) elif moveOrDrag == : platformModule._dragTo(tweenX, tweenY, button) else: raise NotImplementedError(.format(moveOrDrag)) _failSafeCheck()
Handles the actual move or drag event, since different platforms implement them differently. On Windows & Linux, a drag is a normal mouse move while a mouse button is held down. On OS X, a distinct "drag" event must be used instead. The code for moving and dragging the mouse is similar, so this function handles both. Users should call the moveTo() or dragTo() functions instead of calling _mouseMoveDrag(). Args: moveOrDrag (str): Either 'move' or 'drag', for the type of action this is. x (int, float, None, optional): How far left (for negative values) or right (for positive values) to move the cursor. 0 by default. y (int, float, None, optional): How far up (for negative values) or down (for positive values) to move the cursor. 0 by default. xOffset (int, float, None, optional): How far left (for negative values) or right (for positive values) to move the cursor. 0 by default. yOffset (int, float, None, optional): How far up (for negative values) or down (for positive values) to move the cursor. 0 by default. duration (float, optional): The amount of time it takes to move the mouse cursor to the new xy coordinates. If 0, then the mouse cursor is moved instantaneously. 0.0 by default. tween (func, optional): The tweening function used if the duration is not 0. A linear tween is used by default. See the tweens.py file for details. button (str, int, optional): The mouse button clicked. Must be one of 'left', 'middle', 'right' (or 1, 2, or 3) respectively. 'left' by default. Returns: None
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def configfile_from_path(path, strict=True): extension = path.split()[-1] conf_type = FILE_TYPES.get(extension) if not conf_type: raise exc.UnrecognizedFileExtension( "Cannot parse file of type {0}. Choices are {1}.".format( extension, FILE_TYPES.keys(), ) ) return conf_type(path=path, strict=strict)
Get a ConfigFile object based on a file path. This method will inspect the file extension and return the appropriate ConfigFile subclass initialized with the given path. Args: path (str): The file path which represents the configuration file. strict (bool): Whether or not to parse the file in strict mode. Returns: confpy.loaders.base.ConfigurationFile: The subclass which is specialized for the given file path. Raises: UnrecognizedFileExtension: If there is no loader for the path.
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def set_option(self, option, value): CONF.set(self.CONF_SECTION, str(option), value)
Set a plugin option in configuration file. Note: Use sig_option_changed to call it from widgets of the same or another plugin.
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def setColumnMapper(self, columnName, callable): columnName = nativestring(columnName) if ( callable is None and columnName in self._columnMappers ): self._columnMappers.pop(columnName) return self._columnMappers[nativestring(columnName)] = callable
Sets the mapper for the given column name to the callable. The inputed callable should accept a single argument for a record from the tree and return the text that should be displayed in the column. :param columnName | <str> callable | <function> || <method> || <lambda>
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async def play_tone(self, pin, tone_command, frequency, duration): if tone_command == Constants.TONE_TONE: if duration: data = [tone_command, pin, frequency & 0x7f, (frequency >> 7) & 0x7f, duration & 0x7f, (duration >> 7) & 0x7f] else: data = [tone_command, pin, frequency & 0x7f, (frequency >> 7) & 0x7f, 0, 0] else: data = [tone_command, pin] await self._send_sysex(PrivateConstants.TONE_DATA, data)
This method will call the Tone library for the selected pin. It requires FirmataPlus to be loaded onto the arduino If the tone command is set to TONE_TONE, then the specified tone will be played. Else, if the tone command is TONE_NO_TONE, then any currently playing tone will be disabled. :param pin: Pin number :param tone_command: Either TONE_TONE, or TONE_NO_TONE :param frequency: Frequency of tone :param duration: Duration of tone in milliseconds :returns: No return value
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def starts(self, layer): starts = [] for data in self[layer]: starts.append(data[START]) return starts
Retrieve start positions of elements if given layer.
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def crypto_core_ed25519_is_valid_point(p): ensure(isinstance(p, bytes) and len(p) == crypto_core_ed25519_BYTES, , raising=exc.TypeError) rc = lib.crypto_core_ed25519_is_valid_point(p) return rc == 1
Check if ``p`` represents a point on the edwards25519 curve, in canonical form, on the main subgroup, and that the point doesn't have a small order. :param p: a :py:data:`.crypto_core_ed25519_BYTES` long bytes sequence representing a point on the edwards25519 curve :type p: bytes :return: point validity :rtype: bool
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def githubtunnel(user1, server1, user2, server2, port, verbose, stanford=False): if stanford: port_shift = 1 else: port_shift = 0 command1 = .format(port-1-port_shift, server2, user1, server1) command2 = .format(port-port_shift, port-port_shift-1, user2) command3 = .format(port, port-1, user2) if verbose: print(command1) if stanford: print(command2) print(command3) try: call(shlex.split(command1)) if stanford: call(shlex.split(command2)) call(shlex.split(command3)) except: print() pass
Opens a nested tunnel, first to *user1*@*server1*, then to *user2*@*server2*, for accessing on *port*. If *verbose* is true, prints various ssh commands. If *stanford* is true, shifts ports up by 1. Attempts to get *user1*, *user2* from environment variable ``USER_NAME`` if called from the command line.
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def perimeter(self): return sum([a.distance(b) for a, b in self.pairs()])
Sum of the length of all sides, float.
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def transfer(self, name, local, remote, **kwargs): try: remote.save(name, local.open(name)) return True except Exception as e: logger.error("Unable to save to remote storage. " "About to retry." % name) logger.exception(e) return False
Transfers the file with the given name from the local to the remote storage backend. :param name: The name of the file to transfer :param local: The local storage backend instance :param remote: The remote storage backend instance :returns: `True` when the transfer succeeded, `False` if not. Retries the task when returning `False` :rtype: bool
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def resize_max(img, max_side): h, w = img.shape[:2] if h > w: nh = max_side nw = w * (nh / h) else: nw = max_side nh = h * (nw / w) return cv.resize(img, (nw, nh))
Resize the image to threshold the maximum dimension within max_side :param img: :param max_side: Length of the maximum height or width :return:
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def filter(self, s, method=, order=30): r s = self.G._check_signal(s) if s.ndim == 1 or s.shape[-1] not in [1, self.Nf]: if s.ndim == 3: raise ValueError( .format(self.Nf, s.shape)) s = np.expand_dims(s, -1) n_features_in = s.shape[-1] if s.ndim < 3: s = np.expand_dims(s, 1) n_signals = s.shape[1] if s.ndim > 3: raise ValueError( ) assert s.ndim == 3 n_features_out = self.Nf if n_features_in == 1 else 1 if method == : axis = 1 if n_features_in == 1 else 2 f = self.evaluate(self.G.e) f = np.expand_dims(f.T, axis) assert f.shape == (self.G.N, n_features_in, n_features_out) s = self.G.gft(s) s = np.matmul(s, f) s = self.G.igft(s) elif method == : c = approximations.compute_cheby_coeff(self, m=order) if n_features_in == 1: s = s.squeeze(axis=2) s = approximations.cheby_op(self.G, c, s) s = s.reshape((self.G.N, n_features_out, n_signals), order=) s = s.swapaxes(1, 2) elif n_features_in == self.Nf: s = s.swapaxes(1, 2) s_in = s.reshape( (self.G.N * n_features_in, n_signals), order=) s = np.zeros((self.G.N, n_signals)) tmpN = np.arange(self.G.N, dtype=int) for i in range(n_features_in): s += approximations.cheby_op(self.G, c[i], s_in[i * self.G.N + tmpN]) s = np.expand_dims(s, 2) else: raise ValueError(.format(method)) return s.squeeze()
r"""Filter signals (analysis or synthesis). A signal is defined as a rank-3 tensor of shape ``(N_NODES, N_SIGNALS, N_FEATURES)``, where ``N_NODES`` is the number of nodes in the graph, ``N_SIGNALS`` is the number of independent signals, and ``N_FEATURES`` is the number of features which compose a graph signal, or the dimensionality of a graph signal. For example if you filter a signal with a filter bank of 8 filters, you're extracting 8 features and decomposing your signal into 8 parts. That is called analysis. Your are thus transforming your signal tensor from ``(G.N, 1, 1)`` to ``(G.N, 1, 8)``. Now you may want to combine back the features to form an unique signal. For this you apply again 8 filters, one filter per feature, and sum the result up. As such you're transforming your ``(G.N, 1, 8)`` tensor signal back to ``(G.N, 1, 1)``. That is known as synthesis. More generally, you may want to map a set of features to another, though that is not implemented yet. The method computes the transform coefficients of a signal :math:`s`, where the atoms of the transform dictionary are generalized translations of each graph spectral filter to each vertex on the graph: .. math:: c = D^* s, where the columns of :math:`D` are :math:`g_{i,m} = T_i g_m` and :math:`T_i` is a generalized translation operator applied to each filter :math:`\hat{g}_m(\cdot)`. Each column of :math:`c` is the response of the signal to one filter. In other words, this function is applying the analysis operator :math:`D^*`, respectively the synthesis operator :math:`D`, associated with the frame defined by the filter bank to the signals. Parameters ---------- s : array_like Graph signals, a tensor of shape ``(N_NODES, N_SIGNALS, N_FEATURES)``, where ``N_NODES`` is the number of nodes in the graph, ``N_SIGNALS`` the number of independent signals you want to filter, and ``N_FEATURES`` is either 1 (analysis) or the number of filters in the filter bank (synthesis). method : {'exact', 'chebyshev'} Whether to use the exact method (via the graph Fourier transform) or the Chebyshev polynomial approximation. A Lanczos approximation is coming. order : int Degree of the Chebyshev polynomials. Returns ------- s : ndarray Graph signals, a tensor of shape ``(N_NODES, N_SIGNALS, N_FEATURES)``, where ``N_NODES`` and ``N_SIGNALS`` are the number of nodes and signals of the signal tensor that pas passed in, and ``N_FEATURES`` is either 1 (synthesis) or the number of filters in the filter bank (analysis). References ---------- See :cite:`hammond2011wavelets` for details on filtering graph signals. Examples -------- Create a bunch of smooth signals by low-pass filtering white noise: >>> import matplotlib.pyplot as plt >>> G = graphs.Ring(N=60) >>> G.estimate_lmax() >>> s = np.random.RandomState(42).uniform(size=(G.N, 10)) >>> taus = [1, 10, 100] >>> s = filters.Heat(G, taus).filter(s) >>> s.shape (60, 10, 3) Plot the 3 smoothed versions of the 10th signal: >>> fig, ax = plt.subplots() >>> G.set_coordinates('line1D') # To visualize multiple signals in 1D. >>> _ = G.plot(s[:, 9, :], ax=ax) >>> legend = [r'$\tau={}$'.format(t) for t in taus] >>> ax.legend(legend) # doctest: +ELLIPSIS <matplotlib.legend.Legend object at ...> Low-pass filter a delta to create a localized smooth signal: >>> G = graphs.Sensor(30, seed=42) >>> G.compute_fourier_basis() # Reproducible computation of lmax. >>> s1 = np.zeros(G.N) >>> s1[13] = 1 >>> s1 = filters.Heat(G, 3).filter(s1) >>> s1.shape (30,) Filter and reconstruct our signal: >>> g = filters.MexicanHat(G, Nf=4) >>> s2 = g.analyze(s1) >>> s2.shape (30, 4) >>> s2 = g.synthesize(s2) >>> s2.shape (30,) Look how well we were able to reconstruct: >>> fig, axes = plt.subplots(1, 2) >>> _ = G.plot(s1, ax=axes[0]) >>> _ = G.plot(s2, ax=axes[1]) >>> print('{:.5f}'.format(np.linalg.norm(s1 - s2))) 0.26808 Perfect reconstruction with Itersine, a tight frame: >>> g = filters.Itersine(G) >>> s2 = g.analyze(s1, method='exact') >>> s2 = g.synthesize(s2, method='exact') >>> np.linalg.norm(s1 - s2) < 1e-10 True
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def lorenz_animation(N_trajectories=20, rseed=1, frames=200, interval=30): from scipy import integrate from mpl_toolkits.mplot3d import Axes3D from matplotlib.colors import cnames def lorentz_deriv(coords, t0, sigma=10., beta=8./3, rho=28.0): x, y, z = coords return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z] np.random.seed(rseed) x0 = -15 + 30 * np.random.random((N_trajectories, 3)) t = np.linspace(0, 2, 500) x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t) for x0i in x0]) fig = plt.figure() ax = fig.add_axes([0, 0, 1, 1], projection=) ax.axis() colors = plt.cm.jet(np.linspace(0, 1, N_trajectories)) lines = sum([ax.plot([], [], [], , c=c) for c in colors], []) pts = sum([ax.plot([], [], [], , c=c, ms=4) for c in colors], []) ax.set_xlim((-25, 25)) ax.set_ylim((-35, 35)) ax.set_zlim((5, 55)) ax.view_init(30, 0) def init(): for line, pt in zip(lines, pts): line.set_data([], []) line.set_3d_properties([]) pt.set_data([], []) pt.set_3d_properties([]) return lines + pts def animate(i): i = (2 * i) % x_t.shape[1] for line, pt, xi in zip(lines, pts, x_t): x, y, z = xi[:i + 1].T line.set_data(x, y) line.set_3d_properties(z) pt.set_data(x[-1:], y[-1:]) pt.set_3d_properties(z[-1:]) ax.view_init(30, 0.3 * i) fig.canvas.draw() return lines + pts return animation.FuncAnimation(fig, animate, init_func=init, frames=frames, interval=interval)
Plot a 3D visualization of the dynamics of the Lorenz system
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def analyze_internal_angles(self, return_plot=False): angles = self.get_internal_angles().flatten() print(.format(np.min(angles))) print(.format(np.max(angles))) for i in range(10, 100, 10): print(.format( i, np.percentile(angles, i), )) if return_plot: print() fig, ax = plt.subplots(1, 1, figsize=(12 / 2.54, 8 / 2.54)) ax.hist(angles, int(angles.size / 10)) ax.set_xlabel() ax.set_ylabel() fig.tight_layout() return fig, ax
Analyze the internal angles of the grid. Angles shouldn't be too small because this can cause problems/uncertainties in the Finite-Element solution of the forward problem. This function prints the min/max values, as well as quantiles, to the command line, and can also produce a histogram plot of the angles. Parameters ---------- return_plot: bool if true, return (fig, ax) objects of the histogram plot Returns ------- fig: matplotlib.figure figure object ax: matplotlib.axes axes object Examples -------- >>> import crtomo.grid as CRGrid grid = CRGrid.crt_grid() grid.load_elem_file('elem.dat') fig, ax = grid.analyze_internal_angles(Angles) This grid was sorted using CutMcK. The nodes were resorted! Triangular grid found Minimal angle: 22.156368696965796 degrees Maximal angle: 134.99337326279496 degrees Angle percentile 10%: 51.22 degrees Angle percentile 20%: 55.59 degrees Angle percentile 30%: 58.26 degrees Angle percentile 40%: 59.49 degrees Angle percentile 50%: 59.95 degrees Angle percentile 60%: 60.25 degrees Angle percentile 70%: 61.16 degrees Angle percentile 80%: 63.44 degrees Angle percentile 90%: 68.72 degrees generating plot... >>> # save to file with fig.savefig('element_angles.png', dpi=300)
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def write(self, data): if self.closed: raise ConnectionResetError( % self ) else: t = self.transport if self._paused or self._buffer: self._buffer.appendleft(data) self._buffer_size += len(data) self._write_from_buffer() if self._buffer_size > 2 * self._b_limit: if self._waiter and not self._waiter.cancelled(): self.logger.warning( , self._buffer_size, self._b_limit ) else: t.pause_reading() self._waiter = self._loop.create_future() else: t.write(data) self.changed() return self._waiter
Write ``data`` into the wire. Returns an empty tuple or a :class:`~asyncio.Future` if this protocol has paused writing.
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def authenticate(session, username, password): if not username or not password: raise AuthenticationError() user = session.query(PasswordUser).filter( PasswordUser.username == username).first() if not user: raise AuthenticationError() if not user.authenticate(password): raise AuthenticationError() log.info("User %s successfully authenticated", username) return user
Authenticate a PasswordUser with the specified username/password. :param session: An active SQLAlchemy session :param username: The username :param password: The password :raise AuthenticationError: if an error occurred :return: a PasswordUser
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def handle_request(self): try: request, client_address = self.get_request() except socket.error: return if self.verify_request(request, client_address): self.workerpool.run(self.process_request_thread, **{: request, : client_address})
simply collect requests and put them on the queue for the workers.
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def removeAssociation(self, server_url, handle): assoc = self.getAssociation(server_url, handle) if assoc is None: return 0 else: filename = self.getAssociationFilename(server_url, handle) return _removeIfPresent(filename)
Remove an association if it exists. Do nothing if it does not. (str, str) -> bool
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def get(self, model_class, strict=True, returnDict=False, fetchOne=False, **where): self.typeassert(model_class, strict, returnDict, where) table = model_class.__name__.lower() with Session(self.settings) as conn: if not where: query = f else: query = f index= 1 operator = if strict else for key, value in where.items(): if index == 1: query+= " %s %s "%(key, operator, value) else: query+= " AND %s %s "%(key, operator, value) index += 1 try: cursor=conn.cursor() cursor.execute(query) except mysql.Error as e: if e.errno == 1146: print(f"The table {table} does not exist") return [] else: raise e else: if fetchOne: colnames = [d[0] for d in cursor.description] results = cursor.fetchone() if returnDict: return {col: val for col, val in zip(colnames, results)}\ if results else {} return results return self.handleResult(cursor, returnDict)
params: model_class: The queried model class strict: bool -> If True, queries are run with EQUAL(=) operator. If False: Queries are run with RLIKE keyword returnDict: bool -> Return a list if dictionaries(field_names: values) fetchOne: bool -> cursor.fetchone() else: cursor.fetchall() where: **kwargs for quere WHERE condition. if where in {}: Returns all results in the table Usage: print(Session().get(Employee, id=1, returnDict=True))
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def add_text(self, tag, text, global_step=None): self._file_writer.add_summary(text_summary(tag, text), global_step) if tag not in self._text_tags: self._text_tags.append(tag) extension_dir = self.get_logdir() + if not os.path.exists(extension_dir): os.makedirs(extension_dir) with open(extension_dir + , ) as fp: json.dump(self._text_tags, fp)
Add text data to the event file. Parameters ---------- tag : str Name for the `text`. text : str Text to be saved to the event file. global_step : int Global step value to record.
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def extract_secs(self, tx, tx_in_idx): sc = tx.SolutionChecker(tx) tx_context = sc.tx_context_for_idx(tx_in_idx) solution_stack = [] for puzzle_script, solution_stack, flags, sighash_f in sc.puzzle_and_solution_iterator(tx_context): for opcode, data, pc, new_pc in self._script_tools.get_opcodes(puzzle_script): if data and is_sec(data): yield data for data in solution_stack: if is_sec(data): yield data
For a given script solution, iterate yield its sec blobs
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def find_config_section(self, object_type, name=None): possible = [] for name_options in object_type.config_prefixes: for name_prefix in name_options: found = self._find_sections( self.parser.sections(), name_prefix, name) if found: possible.extend(found) break if not possible: raise LookupError( "No section %r (prefixed by %s) found in config %s" % (name, .join(map(repr, _flatten(object_type.config_prefixes))), self.filename)) if len(possible) > 1: raise LookupError( "Ambiguous section names %r for section %r (prefixed by %s) " "found in config %s" % (possible, name, .join(map(repr, _flatten(object_type.config_prefixes))), self.filename)) return possible[0]
Return the section name with the given name prefix (following the same pattern as ``protocol_desc`` in ``config``. It must have the given name, or for ``'main'`` an empty name is allowed. The prefix must be followed by a ``:``. Case is *not* ignored.
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def flow_meter_discharge(D, Do, P1, P2, rho, C, expansibility=1.0): r beta = Do/D beta2 = beta*beta return (0.25*pi*Do*Do)*C*expansibility*( (2.0*rho*(P1 - P2))/(1.0 - beta2*beta2))**0.5
r'''Calculates the flow rate of an orifice plate based on the geometry of the plate, measured pressures of the orifice, and the density of the fluid. .. math:: m = \left(\frac{\pi D_o^2}{4}\right) C \frac{\sqrt{2\Delta P \rho_1}} {\sqrt{1 - \beta^4}}\cdot \epsilon Parameters ---------- D : float Upstream internal pipe diameter, [m] Do : float Diameter of orifice at flow conditions, [m] P1 : float Static pressure of fluid upstream of orifice at the cross-section of the pressure tap, [Pa] P2 : float Static pressure of fluid downstream of orifice at the cross-section of the pressure tap, [Pa] rho : float Density of fluid at `P1`, [kg/m^3] C : float Coefficient of discharge of the orifice, [-] expansibility : float, optional Expansibility factor (1 for incompressible fluids, less than 1 for real fluids), [-] Returns ------- m : float Mass flow rate of fluid, [kg/s] Notes ----- This is formula 1-12 in [1]_ and also [2]_. Examples -------- >>> flow_meter_discharge(D=0.0739, Do=0.0222, P1=1E5, P2=9.9E4, rho=1.1646, ... C=0.5988, expansibility=0.9975) 0.01120390943807026 References ---------- .. [1] American Society of Mechanical Engineers. Mfc-3M-2004 Measurement Of Fluid Flow In Pipes Using Orifice, Nozzle, And Venturi. ASME, 2001. .. [2] ISO 5167-2:2003 - Measurement of Fluid Flow by Means of Pressure Differential Devices Inserted in Circular Cross-Section Conduits Running Full -- Part 2: Orifice Plates.
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def temporal_from_resource(resource): if isinstance(resource.identifier, URIRef): g = Graph().parse(str(resource.identifier)) resource = g.resource(resource.identifier) if resource.value(SCHEMA.startDate): return db.DateRange( start=resource.value(SCHEMA.startDate).toPython(), end=resource.value(SCHEMA.endDate).toPython() ) elif resource.value(SCV.min): return db.DateRange( start=resource.value(SCV.min).toPython(), end=resource.value(SCV.max).toPython() )
Parse a temporal coverage from a RDF class/resource ie. either: - a `dct:PeriodOfTime` with schema.org `startDate` and `endDate` properties - an inline gov.uk Time Interval value - an URI reference to a gov.uk Time Interval ontology http://reference.data.gov.uk/
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def get_parent_vault_ids(self, vault_id): if self._catalog_session is not None: return self._catalog_session.get_parent_catalog_ids(catalog_id=vault_id) return self._hierarchy_session.get_parents(id_=vault_id)
Gets the parent ``Ids`` of the given vault. arg: vault_id (osid.id.Id): a vault ``Id`` return: (osid.id.IdList) - the parent ``Ids`` of the vault raise: NotFound - ``vault_id`` is not found raise: NullArgument - ``vault_id`` is ``null`` raise: OperationFailed - unable to complete request raise: PermissionDenied - authorization failure *compliance: mandatory -- This method must be implemented.*
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def forward(self, input_ids, target=None, mems=None): bsz = input_ids.size(0) tgt_len = input_ids.size(1) last_hidden, new_mems = self.transformer(input_ids, mems) pred_hid = last_hidden[:, -tgt_len:] if self.sample_softmax > 0 and self.training: assert self.config.tie_weight logit = sample_logits(self.transformer.word_emb, self.out_layer.bias, target, pred_hid, self.sampler) softmax_output = -F.log_softmax(logit, -1)[:, :, 0] else: softmax_output = self.crit(pred_hid.view(-1, pred_hid.size(-1)), target) if target is None: softmax_output = softmax_output.view(bsz, tgt_len, -1) else: softmax_output = softmax_output.view(bsz, tgt_len) return (softmax_output, new_mems)
Params: input_ids :: [bsz, len] target :: [bsz, len] Returns: tuple(softmax_output, new_mems) where: new_mems: list (num layers) of hidden states at the entry of each layer shape :: [mem_len, bsz, self.config.d_model] :: Warning: shapes are transposed here w. regards to input_ids softmax_output: output of the (adaptive) softmax: if target is None: Negative log likelihood of shape :: [bsz, len] else: log probabilities of tokens, shape :: [bsz, len, n_tokens]
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def sh_report(self, full=True, latest=False): def pathvar_repr(var): _var = var.replace(, ) return % _var output = [] if not self.remote_url: output.append() output = output + ( [self.label] + self.clone_cmd + [pathvar_repr(self.remote_url)] + [pathvar_repr(self.relpath)] ) yield yield " yield .join(output) if full: checkout_rev = self.current_id relpath = pathvar_repr(self.relpath) if self.relpath else None relpath = relpath if relpath else checkout_branch_cmd = ( [self.label] + self.checkout_branch_cmd + [self.branch] + self.repo_abspath_cmd + [relpath]) checkout_rev_cmd = ( [self.label] + self.checkout_rev_cmd + [checkout_rev] + self.repo_abspath_cmd + [relpath]) if latest: checkout_cmd = checkout_branch_cmd comment = checkout_rev_cmd else: checkout_cmd = checkout_rev_cmd comment = checkout_branch_cmd yield .join(c for c in checkout_cmd if c is not None) yield % .join(c for c in comment if c is not None) for x in self.recreate_remotes_shellcmd(): yield x
Show shell command necessary to clone this repository If there is no primary remote url, prefix-comment the command Keyword Arguments: full (bool): also include commands to recreate branches and remotes latest (bool): checkout repo.branch instead of repo.current_id Yields: str: shell command necessary to clone this repository
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def AddXrefTo(self, ref_kind, classobj, methodobj, offset): self.xrefto[classobj].add((ref_kind, methodobj, offset))
Creates a crossreference to another class. XrefTo means, that the current class calls another class. The current class should also be contained in the another class' XrefFrom list. :param REF_TYPE ref_kind: type of call :param classobj: :class:`ClassAnalysis` object to link :param methodobj: :param offset: Offset in the Methods Bytecode, where the call happens :return:
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def clean_linebreaks(self, tag): stripped = tag.decode(formatter=None) stripped = re.sub(, , stripped) stripped = re.sub(, , stripped) return stripped
get unicode string without any other content transformation. and clean extra spaces
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def columns_used(self): return list(tz.unique(tz.concatv( self.choosers_columns_used(), self.alts_columns_used(), self.interaction_columns_used())))
Columns from any table used in the model. May come from either the choosers or alternatives tables.
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def iterbyscore(self, min=, max=, start=None, num=None, withscores=False, reverse=None): reverse = reverse if reverse is not None else self.reversed zfunc = self._client.zrangebyscore if not reverse \ else self._client.zrevrangebyscore _loads = self._loads for member in zfunc( self.key_prefix, min=min, max=max, start=start, num=num, withscores=withscores, score_cast_func=self.cast): if withscores: yield (_loads(member[0]), self.cast(member[1])) else: yield _loads(member)
Return a range of values from the sorted set name with scores between @min and @max. If @start and @num are specified, then return a slice of the range. @min: #int minimum score, or #str '-inf' @max: #int minimum score, or #str '+inf' @start: #int starting range position @num: #int number of members to fetch @withscores: #bool indicates to return the scores along with the members, as a list of |(member, score)| pairs @reverse: #bool indicating whether to sort the results descendingly -> yields members or |(member, score)| #tuple pairs
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def lookup_by_partial_name(self, partial_name): for k, v in self._name_database.items(): if _uax44lm2transform(partial_name) in k: yield v
Similar to lookup_by_name(name), this method uses loose matching rule UAX44-LM2 to attempt to find the UnicodeCharacter associated with a name. However, it attempts to permit even looser matching by doing a substring search instead of a simple match. This method will return a generator that yields instances of UnicodeCharacter where the partial_name passed in is a substring of the full name. For example: >>> ucd = UnicodeData() >>> for data in ucd.lookup_by_partial_name("SHARP S"): >>> print(data.code + " " + data.name) >>> >>> U+00DF LATIN SMALL LETTER SHARP S >>> U+1E9E LATIN CAPITAL LETTER SHARP S >>> U+266F MUSIC SHARP SIGN :param partial_name: Partial name of the character to look up. :return: Generator that yields instances of UnicodeCharacter.
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def national_significant_number(numobj): national_number = U_EMPTY_STRING if numobj.italian_leading_zero: num_zeros = numobj.number_of_leading_zeros if num_zeros is None: num_zeros = 1 if num_zeros > 0: national_number = U_ZERO * num_zeros national_number += str(numobj.national_number) return national_number
Gets the national significant number of a phone number. Note that a national significant number doesn't contain a national prefix or any formatting. Arguments: numobj -- The PhoneNumber object for which the national significant number is needed. Returns the national significant number of the PhoneNumber object passed in.
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def translate_js_with_compilation_plan(js, HEADER=DEFAULT_HEADER): match_increaser_str, match_increaser_num, compilation_plan = get_compilation_plan( js) cp_hash = hashlib.md5(compilation_plan.encode()).digest() try: python_code = cache[cp_hash][] except: parser = pyjsparser.PyJsParser() parsed = parser.parse(compilation_plan) translating_nodes.clean_stacks() python_code = translating_nodes.trans( parsed) cache[cp_hash] = { : compilation_plan, : python_code, } python_code = match_increaser_str.wrap_up(python_code) python_code = match_increaser_num.wrap_up(python_code) return HEADER + python_code
js has to be a javascript source code. returns equivalent python code. compile plans only work with the following restrictions: - only enabled for oneliner expressions - when there are comments in the js code string substitution is disabled - when there nested escaped quotes string substitution is disabled, so cacheable: Q1 == 1 && name == 'harry' not cacheable: Q1 == 1 && name == 'harry' // some comment not cacheable: Q1 == 1 && name == 'o\'Reilly' not cacheable: Q1 == 1 && name /* some comment */ == 'o\'Reilly'
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def from_las3(cls, string, lexicon=None, source="LAS", dlm=, abbreviations=False): f = re.DOTALL | re.IGNORECASE regex = r pattern = re.compile(regex, flags=f) text = pattern.search(string).group(1) s = re.search(r, string) if s: source = s.group(1).strip() return cls.from_descriptions(text, lexicon, source=source, dlm=dlm, abbreviations=abbreviations)
Turn LAS3 'lithology' section into a Striplog. Args: string (str): A section from an LAS3 file. lexicon (Lexicon): The language for conversion to components. source (str): A source for the data. dlm (str): The delimiter. abbreviations (bool): Whether to expand abbreviations. Returns: Striplog: The ``striplog`` object. Note: Handles multiple 'Data' sections. It would be smarter for it to handle one at a time, and to deal with parsing the multiple sections in the Well object. Does not read an actual LAS file. Use the Well object for that.
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def _insert_stack(stack, sample_count, call_tree): curr_level = call_tree for func in stack: next_level_index = { node[]: node for node in curr_level[]} if func not in next_level_index: new_node = {: func, : [], : 0} curr_level[].append(new_node) curr_level = new_node else: curr_level = next_level_index[func] curr_level[] = sample_count
Inserts stack into the call tree. Args: stack: Call stack. sample_count: Sample count of call stack. call_tree: Call tree.
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def cache_page(**kwargs): cache_timeout = kwargs.pop(, None) key_prefix = kwargs.pop(, None) cache_min_age = kwargs.pop(, None) decorator = decorators.decorator_from_middleware_with_args(CacheMiddleware)( cache_timeout=cache_timeout, key_prefix=key_prefix, cache_min_age=cache_min_age, **kwargs ) return decorator
This decorator is similar to `django.views.decorators.cache.cache_page`
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def on_exception(func): class OnExceptionDecorator(LambdaDecorator): def on_exception(self, exception): return func(exception) return OnExceptionDecorator
Run a function when a handler thows an exception. It's return value is returned to AWS. Usage:: >>> # to create a reusable decorator >>> @on_exception ... def handle_errors(exception): ... print(exception) ... return {'statusCode': 500, 'body': 'uh oh'} >>> @handle_errors ... def handler(event, context): ... raise Exception('it broke!') >>> handler({}, object()) it broke! {'statusCode': 500, 'body': 'uh oh'} >>> # or a one off >>> @on_exception(lambda e: {'statusCode': 500}) ... def handler(body, context): ... raise Exception >>> handler({}, object()) {'statusCode': 500}
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def switch_focus(self, layout, column, widget): for i, l in enumerate(self._layouts): if l is layout: break else: return self._layouts[self._focus].blur() self._focus = i self._layouts[self._focus].focus(force_column=column, force_widget=widget)
Switch focus to the specified widget. :param layout: The layout that owns the widget. :param column: The column the widget is in. :param widget: The index of the widget to take the focus.
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def fetch_github_activity(gen, metadata): if in gen.settings.keys(): gen.context[] = gen.plugin_instance.fetch()
registered handler for the github activity plugin it puts in generator.context the html needed to be displayed on a template
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def load_all_distributions(self): distributions = [] for this in dir(scipy.stats): if "fit" in eval("dir(scipy.stats." + this +")"): distributions.append(this) self.distributions = distributions[:]
Replace the :attr:`distributions` attribute with all scipy distributions
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def format_text_as_docstr(text): r import utool as ut import re min_indent = ut.get_minimum_indentation(text) indent_ = * min_indent formated_text = re.sub( + indent_, + indent_ + , text, flags=re.MULTILINE) formated_text = re.sub(, + indent_ + , formated_text, flags=re.MULTILINE) return formated_text
r""" CommandLine: python ~/local/vim/rc/pyvim_funcs.py --test-format_text_as_docstr Example: >>> # DISABLE_DOCTEST >>> from pyvim_funcs import * # NOQA >>> text = testdata_text() >>> formated_text = format_text_as_docstr(text) >>> result = ('formated_text = \n%s' % (str(formated_text),)) >>> print(result)
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def _get_serialization_name(element_name): known = _KNOWN_SERIALIZATION_XFORMS.get(element_name) if known is not None: return known if element_name.startswith(): return element_name.replace(, ) if element_name.endswith(): element_name = element_name.replace(, ) for name in [, , , ]: if element_name.startswith(name): element_name = element_name.replace(, ) return .join(name.capitalize() for name in element_name.split())
converts a Python name into a serializable name
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def parser(self): if self._parser is None: apkw = { : self.description, : self.epilog, } self._parser = argparse.ArgumentParser(**apkw) if self.version: self._parser.add_argument( , , action=, version="%(prog)s {}".format(self.version), ) return self._parser
Instantiates the argparse parser
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def matches(self, filter_props): if filter_props is None: return False found_one = False for key, value in filter_props.items(): if key in self.properties and value != self.properties[key]: return False elif key in self.properties and value == self.properties[key]: found_one = True return found_one
Check if the filter matches the supplied properties.
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def parse(template, delimiters=None): if type(template) is not unicode: raise Exception("Template is not unicode: %s" % type(template)) parser = _Parser(delimiters) return parser.parse(template)
Parse a unicode template string and return a ParsedTemplate instance. Arguments: template: a unicode template string. delimiters: a 2-tuple of delimiters. Defaults to the package default. Examples: >>> parsed = parse(u"Hey {{#who}}{{name}}!{{/who}}") >>> print str(parsed).replace('u', '') # This is a hack to get the test to pass both in Python 2 and 3. ['Hey ', _SectionNode(key='who', index_begin=12, index_end=21, parsed=[_EscapeNode(key='name'), '!'])]
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def get_template_loader(app, subdir=): dmp = apps.get_app_config() return dmp.engine.get_template_loader(app, subdir, create=True)
Convenience method that calls get_template_loader() on the DMP template engine instance.
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def _make_actor_method_executor(self, method_name, method, actor_imported): def actor_method_executor(dummy_return_id, actor, *args): self._worker.actor_task_counter += 1 try: if is_class_method(method): method_returns = method(*args) else: method_returns = method(actor, *args) except Exception as e: if isinstance(actor, ray.actor.Checkpointable): if self._worker.actor_task_counter == 1: if actor_imported: self._restore_and_log_checkpoint(actor) else: self._save_and_log_checkpoint(actor) return method_returns return actor_method_executor
Make an executor that wraps a user-defined actor method. The wrapped method updates the worker's internal state and performs any necessary checkpointing operations. Args: method_name (str): The name of the actor method. method (instancemethod): The actor method to wrap. This should be a method defined on the actor class and should therefore take an instance of the actor as the first argument. actor_imported (bool): Whether the actor has been imported. Checkpointing operations will not be run if this is set to False. Returns: A function that executes the given actor method on the worker's stored instance of the actor. The function also updates the worker's internal state to record the executed method.
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def spacing(text): if len(text) <= 1 or not ANY_CJK.search(text): return text new_text = text matched = CONVERT_TO_FULLWIDTH_CJK_SYMBOLS_CJK.search(new_text) while matched: start, end = matched.span() new_text = .join((new_text[:start + 1], convert_to_fullwidth(new_text[start + 1:end - 1]), new_text[end - 1:])) matched = CONVERT_TO_FULLWIDTH_CJK_SYMBOLS_CJK.search(new_text) matched = CONVERT_TO_FULLWIDTH_CJK_SYMBOLS.search(new_text) while matched: start, end = matched.span() new_text = .join((new_text[:start + 1].strip(), convert_to_fullwidth(new_text[start + 1:end]), new_text[end:].strip())) matched = CONVERT_TO_FULLWIDTH_CJK_SYMBOLS.search(new_text) new_text = DOTS_CJK.sub(r, new_text) new_text = FIX_CJK_COLON_ANS.sub(r, new_text) new_text = CJK_QUOTE.sub(r, new_text) new_text = QUOTE_CJK.sub(r, new_text) new_text = FIX_QUOTE_ANY_QUOTE.sub(r, new_text) new_text = CJK_SINGLE_QUOTE_BUT_POSSESSIVE.sub(r, new_text) new_text = SINGLE_QUOTE_CJK.sub(r, new_text) new_text = FIX_POSSESSIVE_SINGLE_QUOTE.sub(r"\1\1 \2\3\4 \5\1 \2\1 \3\1 \2 \3\1 \2 \3\1\2\1\2\3\1 \2\1 \2\1\3\5\1 \2\3\4\1\2\3 \4\1 \2\1 \2\1 \2\1 \2\1 \2・', new_text) return new_text.strip()
Perform paranoid text spacing on text.
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def save_new_channel(self): form_info = self.input[] channel = Channel(typ=15, name=form_info[], description=form_info[], owner_id=form_info[]) channel.blocking_save() self.current.task_data[] = channel.key
It saves new channel according to specified channel features.
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def eof_received(self) -> bool: logger.debug("%s - event = eof_received()", self.side) super().eof_received() return False
Close the transport after receiving EOF. Since Python 3.5, `:meth:~StreamReaderProtocol.eof_received` returns ``True`` on non-TLS connections. See http://bugs.python.org/issue24539 for more information. This is inappropriate for websockets for at least three reasons: 1. The use case is to read data until EOF with self.reader.read(-1). Since websockets is a TLV protocol, this never happens. 2. It doesn't work on TLS connections. A falsy value must be returned to have the same behavior on TLS and plain connections. 3. The websockets protocol has its own closing handshake. Endpoints close the TCP connection after sending a close frame. As a consequence we revert to the previous, more useful behavior.
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def saltbridge(poscenter, negcenter, protispos): data = namedtuple( , ) pairings = [] for pc, nc in itertools.product(poscenter, negcenter): if not config.MIN_DIST < euclidean3d(pc.center, nc.center) < config.SALTBRIDGE_DIST_MAX: continue resnr = pc.resnr if protispos else nc.resnr resnr_l = whichresnumber(nc.orig_atoms[0]) if protispos else whichresnumber(pc.orig_atoms[0]) restype = pc.restype if protispos else nc.restype restype_l = whichrestype(nc.orig_atoms[0]) if protispos else whichrestype(pc.orig_atoms[0]) reschain = pc.reschain if protispos else nc.reschain reschain_l = whichchain(nc.orig_atoms[0]) if protispos else whichchain(pc.orig_atoms[0]) contact = data(positive=pc, negative=nc, distance=euclidean3d(pc.center, nc.center), protispos=protispos, resnr=resnr, restype=restype, reschain=reschain, resnr_l=resnr_l, restype_l=restype_l, reschain_l=reschain_l) pairings.append(contact) return filter_contacts(pairings)
Detect all salt bridges (pliprofiler between centers of positive and negative charge)
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def setup_button_connectors(self): self.help_button.clicked.connect(self.show_help) self.run_button.clicked.connect(self.accept) self.about_button.clicked.connect(self.about) self.print_button.clicked.connect(self.show_print_dialog) self.hazard_layer_combo.currentIndexChanged.connect( self.index_changed_hazard_layer_combo) self.exposure_layer_combo.currentIndexChanged.connect( self.index_changed_exposure_layer_combo) self.aggregation_layer_combo.currentIndexChanged.connect( self.index_changed_aggregation_layer_combo)
Setup signal/slot mechanisms for dock buttons.
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def photo_url(self): if self.url is not None: if self.soup is not None: img = self.soup.find(, class_=)[] return img.replace(, ) else: assert (self.card is not None) return PROTOCOL + self.card.img[].replace(, ) else: return
获取用户头像图片地址. :return: 用户头像url :rtype: str
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def Popen(self, cmd, **kwargs): prefixed_cmd = self._prepare_cmd(cmd) return subprocess.Popen(prefixed_cmd, **kwargs)
Remote Popen.
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def build_matlab(static=False): cfg = get_config() if in cfg and cfg[] != : matlab_bin = cfg[].strip() else: matlab_bin = which_matlab() if matlab_bin is None: raise ValueError("specify in cfg file") extcmd = esc(os.path.join(matlab_bin, "mexext")) extension = subprocess.check_output(extcmd, shell=use_shell) extension = extension.decode().rstrip() mex = esc(os.path.join(matlab_bin, "mex")) paths = "-L%(zmq_lib)s -I%(zmq_inc)s" % cfg make_cmd = % (mex, paths) if static: make_cmd += do_build(make_cmd, % extension)
build the messenger mex for MATLAB static : bool Determines if the zmq library has been statically linked. If so, it will append the command line option -DZMQ_STATIC when compiling the mex so it matches libzmq.
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def load(self, path=None): section1option1valueoption2value2 paths = self._paths.copy() if path: if path.is_dir(): path /= .format(self._configuration_name) paths.append(path) paths = [(path_._root / str(x)[1:] if x.is_absolute() else x) for x in paths] if path: path = paths[-1] if not path.exists(): raise ValueError(.format(path)) config_parser = ConfigParser( inline_comment_prefixes=(, ), empty_lines_in_values=False, default_section=, interpolation=ExtendedInterpolation() ) def option_transform(name): return name.replace(, ).replace(, ).lower() config_parser.optionxform = option_transform with suppress(FileNotFoundError): defaults_contents = resource_string(self._package_name, .format(self._configuration_name)) config_parser.read_string(defaults_contents.decode()) config_parser.read([str(x) for x in paths]) config = {k : dict(v) for k,v in config_parser.items()} del config[] return config
Load configuration (from configuration files). Parameters ---------- path : ~pathlib.Path or None Path to configuration file, which must exist; or path to directory containing a configuration file; or None. Returns ------- ~typing.Dict[str, ~typing.Dict[str, str]] The configuration as a dict of sections mapping section name to options. Each options dict maps from option name to option value. The ``default`` section is not included. However, all options from the ``default`` section are included in each returned section. Raises ------ ValueError If ``path`` is a missing file; or if it is a directory which does not contain the configuration file. Examples -------- >>> loader.load() { 'section1': { 'option1': 'value', 'option2': 'value2', } }
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def from_pb(cls, operation_pb, client, **caller_metadata): result = cls(operation_pb.name, client, **caller_metadata) result._update_state(operation_pb) result._from_grpc = True return result
Factory: construct an instance from a protobuf. :type operation_pb: :class:`~google.longrunning.operations_pb2.Operation` :param operation_pb: Protobuf to be parsed. :type client: object: must provide ``_operations_stub`` accessor. :param client: The client used to poll for the status of the operation. :type caller_metadata: dict :param caller_metadata: caller-assigned metadata about the operation :rtype: :class:`Operation` :returns: new instance, with attributes based on the protobuf.
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def get(self): custom_arg = {} if self.key is not None and self.value is not None: custom_arg[self.key] = self.value return custom_arg
Get a JSON-ready representation of this CustomArg. :returns: This CustomArg, ready for use in a request body. :rtype: dict
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def infer_child_relations(graph, node: BaseEntity) -> List[str]: return list(_infer_child_relations_iter(graph, node))
Propagate causal relations to children.
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def get_meta_attributes(self, **kwargs): superuser = kwargs.get(, False) if (self.untl_object.qualifier == or self.untl_object.qualifier == ): if superuser: self.editable = True self.repeatable = True else: self.editable = False self.view_type = elif self.untl_object.qualifier == : self.label = self.view_type = else: self.editable = False self.view_type =
Determine the form attributes for the meta field.
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def parseSOAPMessage(data, ipAddr): "parse raw XML data string, return a (minidom) xml document" try: dom = minidom.parseString(data) except Exception: return None if dom.getElementsByTagNameNS(NS_S, "Fault"): return None soapAction = dom.getElementsByTagNameNS(NS_A, "Action")[0].firstChild.data.strip() if soapAction == ACTION_PROBE: return parseProbeMessage(dom) elif soapAction == ACTION_PROBE_MATCH: return parseProbeMatchMessage(dom) elif soapAction == ACTION_RESOLVE: return parseResolveMessage(dom) elif soapAction == ACTION_RESOLVE_MATCH: return parseResolveMatchMessage(dom) elif soapAction == ACTION_BYE: return parseByeMessage(dom) elif soapAction == ACTION_HELLO: return parseHelloMessage(dom)
parse raw XML data string, return a (minidom) xml document
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def get_acf(x, axis=0, fast=False): x = np.atleast_1d(x) m = [slice(None), ] * len(x.shape) if fast: n = int(2 ** np.floor(np.log2(x.shape[axis]))) m[axis] = slice(0, n) x = x else: n = x.shape[axis] f = np.fft.fft(x - np.mean(x, axis=axis), n=2 * n, axis=axis) m[axis] = slice(0, n) acf = np.fft.ifft(f * np.conjugate(f), axis=axis)[tuple(m)].real m[axis] = 0 return acf / acf[tuple(m)]
Estimate the autocorrelation function of a time series using the FFT. :param x: The time series. If multidimensional, set the time axis using the ``axis`` keyword argument and the function will be computed for every other axis. :param axis: (optional) The time axis of ``x``. Assumed to be the first axis if not specified. :param fast: (optional) If ``True``, only use the largest ``2^n`` entries for efficiency. (default: False)
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def load_credential_file(self, path): c_data = StringIO.StringIO() c_data.write("[Credentials]\n") for line in open(path, "r").readlines(): c_data.write(line.replace("AWSAccessKeyId", "aws_access_key_id").replace("AWSSecretKey", "aws_secret_access_key")) c_data.seek(0) self.readfp(c_data)
Load a credential file as is setup like the Java utilities
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def _get_clause_words( sentence_text, clause_id ): clause = [] isEmbedded = False indices = sentence_text.clause_indices clause_anno = sentence_text.clause_annotations for wid, token in enumerate(sentence_text[WORDS]): if indices[wid] == clause_id: if not clause and clause_anno[wid] == EMBEDDED_CLAUSE_START: isEmbedded = True clause.append((wid, token)) return clause, isEmbedded
Collects clause with index *clause_id* from given *sentence_text*. Returns a pair (clause, isEmbedded), where: *clause* is a list of word tokens in the clause; *isEmbedded* is a bool indicating whether the clause is embedded;
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def make_heading_authors(self, authors): author_element = etree.Element(, {: }) first = True for author in authors: if first: first = False else: append_new_text(author_element, , join_str=) collab = author.find() anon = author.find() if collab is not None: append_all_below(author_element, collab) elif anon is not None: append_new_text(author_element, ) else: author_name, _ = self.get_contrib_names(author) append_new_text(author_element, author_name) first = True for xref in author.xpath("./xref[@ref-type= or @ref-type=]"): _sup = xref.find() sup_text = all_text(_sup) if _sup is not None else auth_sup = etree.SubElement(author_element, ) sup_link = etree.SubElement(auth_sup, , {: self.main_fragment.format(xref.attrib[])}) sup_link.text = sup_text if first: first = False else: append_new_text(auth_sup, , join_str=) return author_element
Constructs the Authors content for the Heading. This should display directly after the Article Title. Metadata element, content derived from FrontMatter
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def add_aacgm_coordinates(inst, glat_label=, glong_label=, alt_label=): import aacgmv2 aalat = []; aalon = []; mlt = [] for lat, lon, alt, time in zip(inst[glat_label], inst[glong_label], inst[alt_label], inst.data.index): tlat, tlon, tmlt = aacgmv2.get_aacgm_coord(lat, lon, alt, time) aalat.append(tlat) aalon.append(tlon) mlt.append(tmlt) inst[] = aalat inst[] = aalon inst[] = mlt inst.meta[] = {:,:} inst.meta[] = {:,:} inst.meta[] = {:,:} return
Uses AACGMV2 package to add AACGM coordinates to instrument object. The Altitude Adjusted Corrected Geomagnetic Coordinates library is used to calculate the latitude, longitude, and local time of the spacecraft with respect to the geomagnetic field. Example ------- # function added velow modifies the inst object upon every inst.load call inst.custom.add(add_quasi_dipole_coordinates, 'modify', glat_label='custom_label') Parameters ---------- inst : pysat.Instrument Designed with pysat_sgp4 in mind glat_label : string label used in inst to identify WGS84 geodetic latitude (degrees N) glong_label : string label used in inst to identify WGS84 geodetic longitude (degrees E) alt_label : string label used in inst to identify WGS84 geodetic altitude (km, height above surface) Returns ------- inst Input pysat.Instrument object modified to include quasi-dipole coordinates, 'aacgm_lat' for magnetic latitude, 'aacgm_long' for longitude, and 'aacgm_mlt' for magnetic local time.
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def target_show(self, id, **kwargs): "https://developer.zendesk.com/rest_api/docs/core/targets api_path = "/api/v2/targets/{id}.json" api_path = api_path.format(id=id) return self.call(api_path, **kwargs)
https://developer.zendesk.com/rest_api/docs/core/targets#show-target
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def avail_locations(call=None): if call == : raise SaltCloudSystemExit( ) ret = {} conn = get_conn() for item in conn.list_locations()[]: reg, loc = item[].split() location = {: item[]} if reg not in ret: ret[reg] = {} ret[reg][loc] = location return ret
Return a dict of all available VM locations on the cloud provider with relevant data
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def _select_position(self, w, h): fitn = ((m.y+h, m.x, w, h, m) for m in self._max_rects if self._rect_fitness(m, w, h) is not None) fitr = ((m.y+w, m.x, h, w, m) for m in self._max_rects if self._rect_fitness(m, h, w) is not None) if not self.rot: fitr = [] fit = itertools.chain(fitn, fitr) try: _, _, w, h, m = min(fit, key=first_item) except ValueError: return None, None return Rectangle(m.x, m.y, w, h), m
Select the position where the y coordinate of the top of the rectangle is lower, if there are severtal pick the one with the smallest x coordinate
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def run(self, lines): if (not self.adjust_path) and (not self.image_ext): return lines ret = [] for line in lines: processed = {} while True: alt = img_name = match = re.search(r, line) if match: if match.group(0) in processed: break if re.match(, match.group(2)): break alt = match.group(1) img_name = match.group(2) else: break if self.image_ext: img_name = re.sub(r, + self.image_ext, img_name) if self.adjust_path and (self.image_path or self.filename): if self.image_path and self.filename: img_name = os.path.join( os.path.abspath(self.image_path), os.path.dirname(self.filename), img_name) if self.filename and (not self.image_path): img_name = os.path.join( os.path.abspath( os.path.dirname(self.filename)), img_name) img_name = img_name.replace(os.path.sep, ) line = re.sub(r, % (alt, img_name), line) processed[match.group(0)] = True ret.append(line) return ret
Filter method
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def asFloat(self, maxval=1.0): x,y,pixels,info = self.asDirect() sourcemaxval = 2**info[]-1 del info[] info[] = float(maxval) factor = float(maxval)/float(sourcemaxval) def iterfloat(): for row in pixels: yield map(factor.__mul__, row) return x,y,iterfloat(),info
Return image pixels as per :meth:`asDirect` method, but scale all pixel values to be floating point values between 0.0 and *maxval*.
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def list_attributes(self): return [attribute for attribute, value in self.iteritems() if issubclass(value.__class__, Attribute)]
Returns the Node attributes names. Usage:: >>> node_a = AbstractNode("MyNodeA", attributeA=Attribute(), attributeB=Attribute()) >>> node_a.list_attributes() ['attributeB', 'attributeA'] :return: Attributes names. :rtype: list
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def _construct_from_permutation(self, significant_pathways): for side, pathway_feature_tuples in significant_pathways.items(): feature_pathway_dict = self._collect_pathways_by_feature( pathway_feature_tuples) self._edges_from_permutation(feature_pathway_dict)
Build the network from a dictionary of (side -> tuple lists), where the side is specified as "pos" and/or "neg" (from the feature gene signature(s)) and mapped to a tuple list of [(pathway, feature)]. Used during the PathCORE-T permutation test by applying the method `permute_pathways_across_features` to an existing CoNetwork.
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def __alterDocstring(self, tail=, writer=None): assert isinstance(tail, str) and isinstance(writer, GeneratorType) lines = [] timeToSend = False inCodeBlock = False inCodeBlockObj = [False] inSection = False prefix = firstLineNum = -1 sectionHeadingIndent = 0 codeChecker = self._checkIfCode(inCodeBlockObj) while True: lineNum, line = (yield) if firstLineNum < 0: firstLineNum = lineNum if line is not None: lines[-1], inCodeBlock = self._endCodeIfNeeded( lines[-1], inCodeBlock) inCodeBlockObj[0] = inCodeBlock writer.send((firstLineNum, lineNum - 1, lines)) lines = [] firstLineNum = lineNum line = line.replace(match.group(1), doxyTag) timeToSend = True if inSection: match = AstWalker.__blanklineRE.match(line) if not match: indent = len(line.expandtabs(self.options.tablength)) - \ len(line.expandtabs(self.options.tablength).lstrip()) if indent <= sectionHeadingIndent: inSection = False else: if lines[-1] == : lines[-1], inCodeBlock = self._endCodeIfNeeded( lines[-1], inCodeBlock) inCodeBlockObj[0] = inCodeBlock line = line.replace(match.group(0), ).rstrip() prefix = else: match = AstWalker.__argsStartRE.match(line) if match: if in prefix: line = .format( prefix, match.groupdict(), linesep) else: line = .format( prefix, match.groupdict()) else: match = AstWalker.__raisesStartRE.match(line) if match: line = line.replace(match.group(0), ).rstrip() if in match.group(1).lower(): prefix = lines[-1], inCodeBlock = self._endCodeIfNeeded( lines[-1], inCodeBlock) inCodeBlockObj[0] = inCodeBlock lines.append( + line) continue else: match = AstWalker.__listRE.match(line) if match and not inCodeBlock: inCodeBlock = True inCodeBlockObj[0] = True line = line.replace(match.group(0), .format(linesep)) else: match = AstWalker.__sectionStartRE.match(line) if match: sectionHeadingIndent = len(line.expandtabs(self.options.tablength)) \ - len(line.expandtabs(self.options.tablength).lstrip()) line = line.replace( match.group(0), .format(match.group(1)) ) if lines[-1] == : lines[-1] = lines[-1], inCodeBlock = self._endCodeIfNeeded( lines[-1], inCodeBlock) inCodeBlockObj[0] = inCodeBlock lines.append( + line) continue elif prefix: match = AstWalker.__singleListItemRE.match(line) if match and not inCodeBlock: line = .format( prefix, match.group(0)) elif self.options.autocode: codeChecker.send( ( line, lines, lineNum - firstLineNum ) ) inCodeBlock = inCodeBlockObj[0] else: if self.options.autocode: codeChecker.send( ( line, lines, lineNum - firstLineNum ) ) inCodeBlock = inCodeBlockObj[0] if tail and lineNum == len(self.docLines) - 1: line = .format(line.rstrip(), linesep, tail) line = .format(line.rstrip()) if lineNum == 0: line = + line lines.append(line.replace( + linesep, linesep)) else: timeToSend = True if timeToSend: lines[-1], inCodeBlock = self._endCodeIfNeeded(lines[-1], inCodeBlock) inCodeBlockObj[0] = inCodeBlock writer.send((firstLineNum, lineNum, lines)) lines = [] firstLineNum = -1 timeToSend = False
Runs eternally, processing docstring lines. Parses docstring lines as they get fed in via send, applies appropriate Doxygen tags, and passes them along in batches for writing.
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def update_long(self, **kwargs): for key, val in six.iteritems(kwargs): self.update_arg(key, long=val)
Update the long optional arguments (those with two leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the long argument names Examples -------- Setting:: >>> parser.update_long(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', long='s') >>> parser.update_arg('something_else', long='se') which in turn is basically comparable to:: >>> parser.add_argument('--s', dest='something', ...) >>> parser.add_argument('--se', dest='something_else', ...) See Also -------- update_short, update_longf
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def e(message, exit_code=None): print_log(message, YELLOW, BOLD) if exit_code is not None: sys.exit(exit_code)
Print an error log message.
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def AAAA(host, nameserver=None): dig = [, , six.text_type(host), ] if nameserver is not None: dig.append(.format(nameserver)) cmd = __salt__[](dig, python_shell=False) if cmd[] != 0: log.warning( %s\, cmd[] ) return [] return [x for x in cmd[].split() if check_ip(x)]
Return the AAAA record for ``host``. Always returns a list. CLI Example: .. code-block:: bash salt ns1 dig.AAAA www.google.com
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def disconnect(receiver, signal=Any, sender=Any, weak=True): if signal is None: raise errors.DispatcherTypeError( %( receiver,sender) ) if weak: receiver = saferef.safeRef(receiver) senderkey = id(sender) try: signals = connections[senderkey] receivers = signals[signal] except KeyError: raise errors.DispatcherKeyError( %( signal, sender ) ) try: _removeOldBackRefs(senderkey, signal, receiver, receivers) except ValueError: raise errors.DispatcherKeyError( %( receiver, signal, sender ) ) _cleanupConnections(senderkey, signal)
Disconnect receiver from sender for signal receiver -- the registered receiver to disconnect signal -- the registered signal to disconnect sender -- the registered sender to disconnect weak -- the weakref state to disconnect disconnect reverses the process of connect, the semantics for the individual elements are logically equivalent to a tuple of (receiver, signal, sender, weak) used as a key to be deleted from the internal routing tables. (The actual process is slightly more complex but the semantics are basically the same). Note: Using disconnect is not required to cleanup routing when an object is deleted, the framework will remove routes for deleted objects automatically. It's only necessary to disconnect if you want to stop routing to a live object. returns None, may raise DispatcherTypeError or DispatcherKeyError
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def filter(self, **kwargs): self._filters.append(ReadsAlignmentsFilter(**kwargs).filter) return self
Add a filter to this C{readsAlignments}. @param kwargs: Keyword arguments, as accepted by C{ReadsAlignmentsFilter}. @return: C{self}
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def sendReset(self, sequenceId=0): for col in xrange(self.numColumns): self.sensorInputs[col].addResetToQueue(sequenceId) self.network.run(1)
Sends a reset signal to the network.