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def topleftlatlon_from_wkt(wkt_string): """Returns the (lat, lon) of the top-left point of a WKT string.""" wkt_type = wkt_string.split('(', 1)[0].rstrip(' ') coord_string = wkt_string.lstrip(wkt_type + ' (') if wkt_type not in ('POINT', 'LINESTRING', 'POLYGON', 'MULTIPOINT', 'MU...
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import time def __date_since_epoch__(value): """Internal function used for Unix datetime format""" day = time.strftime('%d',time.localtime(value)) month = time.strftime('%m',time.localtime(value)) year = time.strftime('%Y',time.localtime(value)) return day,month,year
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def get_unobs_nd_names(gname): """ For a graph named gname, this method returns a list of the names of its unobserved nodes (i.e., either [], or ["U"] or ["U1", "U2"]) Parameters ---------- gname : str Returns ------- list[str] """ if gname in ["G2", "G3", "G5", "G6", "G10...
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def extract_archive(filename, path): """ Extract the contents of the given archive :param filename: The name of the crx file :param path: The path to the crx file :return: Whether the contents of the crx file were extracted successfully or not """ dst_dir = os.path.join(get_script_path(), ...
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import logging def get_commodity_sources(): """ Get a table with prices and emissions manly for 2014. Returns ------- pd.DataFrame Examples -------- >>> get_commodity_sources().loc[2014, 'hard coal']['emission'] * 1000 0.0934 """ logging.info("Get prices and emissions for...
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def named_entity(docs): """Creates a named entity visualization. Taken from https://github.com/wandb/client/blob/master/wandb/plots/named_entity.py """ spacy = util.get_module( "spacy", required="part_of_speech requires the spacy library, install with `pip install spacy`", ) ut...
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def brightness_state(value): """ Returns the brightness and state according to the current data of given value. """ if value.data > 0: return (value.data / 99) * 255, STATE_ON else: return 255, STATE_OFF
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def find_matching_segments(gt_segments, id_key, sensed_segments): """ id_key represents the kind of segment that we are find matches for. e.g. - trip_id for trips - section_id for sections """ matching_segments_map = {} if len(sensed_segments) == 0: print("Found no sensed segmen...
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from typing import List def recall_at_k(y_true: List[int], y_pred: List[List[np.ndarray]], k: int): """ Calculates recall at k ranking metric. Args: y_true: Labels. Not used in the calculation of the metric. y_predicted: Predictions. Each prediction contains ranking score of a...
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def has_datapoint_with_all_dims(fake_ingest, dims): """ Tests if any datapoints has all of the given dimensions """ for datapoint in fake_ingest.datapoints: if has_all_dims(datapoint, dims): return True return False
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def norm_weight(nin, nout=None, scale=0.01, ortho=True, **kwargs): """ Get a (orthogonal) Gaussian weight matrix W, scaled if desired (and not orthogonal). :param nin: :param nout: :param scale: :param ortho: :return W: """ logger.warn('Norm weight with range: +/-{}'.format(scale...
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def key_is_private(key): """Determine if a key has a name indicating it is private """ if PRIVATE_RE.search(key): return True else: return False
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def anonymise_related_objects(obj, anonymised=None): """ See if there are any related models which need to be anonymised. They will be any reverse relations to PrivacyModel subclasses where their OneToOneField and ForeignKey on_delete is ANONYMISE. """ if anonymised is None: anonymised ...
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import inspect def all_links(cls): """ Each object will have the attributes declared directly on the object in the attrs dictionary. In addition there may be attributes declared by a particular object's parent classes. This function walks the class hierarchy to collect the attrs in the object's parent...
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from typing import Dict from typing import Callable def get_losses_dict( names_list: list = [], ) -> Dict[str, Callable]: """Get a dict with all the loss functions for semi supervised training. The training step of a given model will iterate over these, instead of manually computing each. Args: ...
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def prepare(data, args): """ Run trimming and fastq for each fastq file """ workdir = op.abspath(safe_makedir("prepare")) sample = data['name'] data['trimmed'] = _trimming(data['fastq'], workdir, data['name'], args.is_rrbs, args.is_directional) return data
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def individual_margin_diversity(i, ensemble_proba, target, alpha = 0.2): """ Computes the individual diversity of the classifier wrt. to the ensemble and its contribution to the margin. alpha controls the trade-off between both values. The original paper uses :math:`\\alpha = 0.2` in all experiments and reports...
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def qr_for_geolocation(**kwargs): """Accepts a *'coordinates'* keyword argument or a triplet *'latitude'*, *'longitude'*, and *'altitude'*.""" return _make_geolocation_qr_code(embedded=True, **kwargs)
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import requests def update_host(token, current_ip, domain, subdomain='@'): """ Formulate and Execute an Update request on EntryDNS API for a given access token / IP Args: token: (string) Access Token for an EntryDNS Domain current_ip: (string) IP to point EasyDNS Domain to domain:...
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def nick_that_sent_message(tags, prefix): """Returns a nick that sent the message based on the given data passed to the callback. """ # 'tags' is a comma-separated list of tags that WeeChat passed to the # callback. It should contain a tag of the following form: nick_XYZ, where # XYZ is the nick...
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def Code_encoding(pred): """ Returns 2-D numpy array with codes corresponding to it's type in predict vector 0 column is peninsula cnt 1 column is bay cnt pred: numpy 1-D array with types of puzzles """ code = np.ones((pred.shape[0], 2), dtype=int) for i, typ in enumerate(pred): ...
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from typing import Union import pathlib def pathlibify(item: Union[str, pathlib.Path]) -> pathlib.Path: """Converts string 'path' to pathlib.Path object. Args: item (Union[str, pathlib.Path]): either a string summary of a path or a pathlib.Path object. Returns: pathlib.Path o...
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import torch def get_topk_logits_selector(logits, k=3): """ takes logits[batch, nout, voc_size] and returns a mask with ones at k largest logits """ topk_logit_indices = torch.topk(logits, k=k).indices.cpu() # indices = torch.stack([ # (torch.range(start=0, end=logits.shape[0] * logits.shape[1] * ...
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async def upload_tracks(csv_file: UploadFile = File(...)): """Upload a utf-8 encoded CSV file containing columns (with headings): - `provider` - `license` - `title` - `artist` - `external_url` - `mp3` (as url of audio file in format ffmpeg can read) - `internal_preview_uri` (optional) ...
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def and_(left: ds.Expression, right: ds.Expression) -> ds.Expression: """ Given a left and right expression combined them using the `AND` logical operator Parameters ---------- left : pyarrow._dataset.Expression A Dataset `Expression` to logically `AND` right : pyarrow._dataset.Expressi...
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from re import T def interp(times, values, fill=0, name=None, **kw): """ Generic interpolation function of t given `times` and `values` Imterpolator such that: f(times[i]) = values[i] if t < times[0] or t > times[-1]: f(t) = fill See ``scipy.interpolate.interp1d`` for details of interp...
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def rename_target(blend_shape, target, new_name): """ Rename the given blendshape target :param blend_shape: str, name of blendshape to rename target from :param target: str, blenshape target to rename :param new_name: str, new name for the blendshpae target :return: str """ check_blend...
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from scipy.stats import ks_2samp def p_value_inflation_test(p_values): """ Test for p-value inflation using the Kolmogorov-Smirnov test. However there are no assumptions to fail for this test. :param p_values: array of p-values :return: p-value (significant is bad) """ h_null = np.random....
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from typing import Dict def templated_supplier(supplier_map: Dict[str, ValueSupplierInterface], engine: RecordProcessor) -> ValueSupplierInterface: """ Creates a supplier that populates the template string from the supplier map Args: supplier_map: map of field name -> value...
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def fp_mask_to_list(fp_mask): """Parse a fp_mask, eg 1-4,2,3 or 0x202 Returns fp cpus based on given mask """ if '0x' in fp_mask: fp_cpus = [i for i in range(0, 64) if (1 << i) & int(fp_mask, 16)] else: fp_cpus = [] for rule in fp_mask.split(','): if '-' in rule...
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def get_sub_objects(obj): """Get all the children of an object that were extracted and are BioPaxObjects that need to be registered in the model.""" sub_objs = [] for attr in [a for a in dir(obj) if not a.startswith('__')]: val = getattr(obj, attr) if isinstance(val, BioPaxObject): ...
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def color_check(origin_color: list, target_color: list) -> bool: """ Function that check if it is the color we're looking for in C.I. Set C.I. properly for handling between accuracy and robustness. """ CI = 20 # Confidential Interval if target_color[0]-CI <= origin_color[0] <= target_color[0]+CI\ and target_col...
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def get_obesity_label_bmi(pct, bmi, age, gender): """ Returns the obesity label as underweight, normal, overweight, obese, class I or class II severe obesity """ if 0 <= pct < 0.05: return 'underweight' elif 0.05 <= pct < 0.85: return 'normal' elif 0.85 <= pct < 0.95: ret...
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def make_bin_mtx(num_mtx, aa_table): """ Convert a numeric matrix into a binary matrix. Each column of the alignment occupies a submatrix that has as many columns as there are letters in the amino acid alphabet. If we consider an amino acid alphabet of 21 letters (all standard 20 amino acids pl...
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import gzip import pickle def read_pickle(filename): """ Helper to read pickle file which may be zipped or not. Args: filename (str): A valid string path. Returns: The file object. """ try: with gzip.open(filename, "rb") as f: loaded_object = pickle.load(f...
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def y0(si): """The initial conditions for y :param si: the simulation info object :type si: SimInfo """ i = 0 end_pg = 1 + si.n_pg end_dg = 1 + si.n_pg + si.n_dg f = np.zeros(shape=(si.n_entries(),), dtype=float) f[i] = 1.0 # power is normalized is 1 for j in range(0, si.n_pg):...
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def show_saxs_qmap( img, pargs, width=200, vmin=0.1, vmax=300, logs=True, image_name="", show_colorbar=True, file_name="", show_time=False, save=False, show_pixel=False, aspect=1, save_format="png", cmap="viridis", ): """ Show a SAXS q-map by giving ...
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def load_img(path, gs=False): """Load an image and return it as a numpy ndarray""" image = Image.open(path) if gs: #Convert to single-channel greyscale, if desired image = image.convert('LA') width, height = image.size return np.array(image)[0:height, 0:width, 0] return np.array(...
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def results(hometeam, awayteam, scores_mat): """ Predict head-to-head score function input: hometeam: name of the home team as string awayteam: name of the away team as string scores_mat: stores the scores output: The result of the game """ #hometeam, awaytea...
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def decimate_within_epochs(tsfinname, finname, foutname, dtype, sos, buffer_len=16777216, max_len=None): """Docstring goes here. This is TEMPORARY! Not how Jagular should be used! But we need resultzzz. So, this function takes in a memmapped raw channel (say 15 GB) and an IN...
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import multiprocessing as mp def gen_new_phiw_div_phib_arr(N_PROCESSES, phiw_div_phib_arr_new, cond_GT, fcn_D, fcn_eta, z_div_L_arr, phiw_div_phib_arr, Pi_div_DLP_arr, weight, gp_arr, gm_arr, yt_arr, phi_yt_arr, ID_yt_arr, Ieta_yt_arr): """ Calculation phi_w/phi_b at the given z using Eq. (D3) in [1] The deta...
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from typing import Counter def getRepeatedList(mapping_argmat, score_mat_size): """ Count the numbers in the mapping dictionary and create lists that contain repeated indices to be used for creating the repeated affinity matrix for fusing the affinity values. """ count_dict = dict(Counter(mapp...
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def particle_velocity_fields(model, save, space_order): """ Create the particle velocity fields """ if model.grid.dim == 2: x, z = model.space_dimensions stagg_x = x stagg_z = z # Create symbols for forward wavefield, source and receivers vx = TimeFunction(name='v...
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import time def _random_fixed_norm_serial(shape, dtype, seed, norm): """ Handles `random_fixed_norm` for `serial=False`. """ if seed is None: seed = int(time.time()) key = jax.random.PRNGKey(seed) matrix = jax.random.normal(key, shape=shape, dtype=dtype) if norm is not None: matrix *= norm / jnp.l...
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import torch def model_logprobs(model, collator, input_texts, output_texts, length_penalty=0, batchsize=128): """Computes logprobabilities of input and output texts for a given model Inputs: model: conditional generation model to use collator: collator to process texts input_texts: it...
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def no_repeats(n): """Returns number of arrangments with no repeats.""" return sum((-1)**k * comb(n//2, k) * num_pairs(n - 2*k) for k in range(n//2+1))
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def process(orography, landmask, site_list, metadata_dict=None, all_methods=False, land_constraint=None, minimum_dz=None, search_radius=None, node_limit=None, site_coordinate_system=None, site_x_coordinate=None, site_y_coordinate=None): """Module to create neighbour cubes for ext...
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def _get_text_alignment(point1, point2): """Get the horizontal and vertical text alignment keywords for text placed at the end of a line segment from point1 to point2 args: point1 - x,y pair point2 - x,y pair returns: ha - horizontal alignment string va - vertical alignment s...
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def ldns_rr_list2buffer_str(*args): """LDNS buffer.""" return _ldns.ldns_rr_list2buffer_str(*args)
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def param(key, **kwargs): """Decorator for Agent operation functions to assist with checking params prior to actually trying to execute the code. Example:: class MyAgent: ... @param('voltage', type=float) @param('delay', default=0., type=float) @inlineCallbacks de...
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def weights_none(mol): """ Calculates the QED descriptor using unit weights. """ return qed(mol,w=[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00])
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from typing import Set from typing import Tuple def unrecognized_footprints_check(bom_rows: Rows, footprint_index: int) -> str: """Return an error string if unrecognized footprints are detected.""" # Sweep through looking for acceptable vs unacceptable footprints: unrecognized_footprints: Set[str] = set()...
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def sample_on_ellipsoid(numpts, radius=(1.,1.,1.), rot=(0.,0.,0.), trans=(0.,0.,0.)): """Sample points uniformly randomly on the surface of an ellipsoid. Args: - numpts (int): Number of points to sample - radius (tuple, float): radius of each axis (X, Y, Z) of the ellipsoid - rot (np.array): rotation parameter...
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from typing import Any def avro_mocked_request(data: Any, schema: Any, **kwargs) -> web.Request: """For testng purposes.""" return mocked_request(MinosAvroProtocol.encode(data, schema), content_type="avro/binary", **kwargs)
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def MOLT_query(collection): """ Returns a list of the possible MOLT (objects) that contain the collection. Accepts either a list of midi tones, a list of strings, a list of pitch objecs, or a list of note objects. """ # Convert everything to pitch classes. query_collection = set() if all(isinstance(x, int) or i...
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import json def admin_team_insert(): """ API for inserting a team """ prv_id = None team_id = None prjid = None bgt_id = None teamname = request.form['Name'] div_id = clean_jsgrid_int(request.form['DivisionID']) myids = admindb.AdmIDs(prv_id, team_id, prjid, bgt_id, div_id) retu...
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def wavenumber(f, er=1, ur=1): """Calculate freespace wavenumber of dielectric material (or vacuum). Typically represented by: k Args: f: frequency, in units [Hz] er: relative permittivity ur: relative permeability Returns: np.ndarray: freespace wavenumber, in units [1...
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import re def getPolyPhen2input(data, filesize=150000): """ Reads data and creates files needed for PolyPhen2 input. Saves and returns file names to be used as input. File size is restricted to 150000. Arguments: data = dataframe containing COSMIC data filesize = int restricting lines ...
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def load_cell_expression_5platform(ncols=None, scaling='std', add_prefix=True): """Load 5-platform averaged cell line expression data, sub-select columns of gene expression randomly if specificed, scale the selected data and return a pandas dataframe. Parameters ---------- ncols : int o...
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def normalize_rectangle(rect): """Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis. Input should be of the format (x0, y0, x1, y1). (x0, y0) and (x1, y1) define the lower left and upper right corners of the rectangle, respectively.""" assert len(rect) == 4, '...
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def flip_joints_3d(joints_3d, width, joint_pairs): """Flip 3d joints. Parameters ---------- joints_3d : numpy.ndarray Joints in shape (num_joints, 3, 2) width : int Image width. joint_pairs : list List of joint pairs. Returns ------- numpy.ndarray Flip...
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def forward(internal_func, f_x0, params_value, i, h): """Calculate the forward difference. Args: internal_func (func): A function with a 1d numpy array as it's only argument. f_x0 (numpy array): The function value at params_value. params_value (numpy array): The value column of param_df...
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import inspect import warnings def valid_func_args(func, *args): """ Helper function to test that a function's parameters match the desired signature, if not then issue a deprecation warning. """ keys = inspect.signature(func).parameters.keys() if set(args) == set(keys): return True ...
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import pdb def emlines_from_alis_output(alis_file): """ Generate a list of emission lines from a standard ALIS output file Currently loads Parameters ---------- alis_file : str Returns ------- emlines : list List of EmLine objects """ linelist = LineList('Galaxy') ...
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import re def load_off(filename, verbose=True): """Load 3D model in .off format.""" with open(filename, 'r') as f: obj_file = f.read().splitlines() v = [] # list of vertices f = [] # list of faces e = [] # list of edges num_vertices = None num_faces = None num_edges = None...
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def share_get(context, share_id): """Get share by id.""" return IMPL.share_get(context, share_id)
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def put_ldap_group_schema_settings( api_client, group_required=None, group_base=None, group_id_attribute=None, group_list_filter=None, group_member_attribute=None, group_member_attr_format=None, group_search_scope=None, **kwargs ): """Put update LDAP group schema settings # noqa...
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def pp(obj, maxwidth=79): """ Pretty-print the given object. """ ctx = PrettyPrinter(maxwidth=maxwidth) ctx.pp(obj) return str(ctx)
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def connect(addr, name=None, group=None, blocking=True, header_len=16, cache_size=-1): """ Creates a `HiSockClient` instance. See HiSockClient for more details :param addr: A two-element tuple containing the IP address and the port number of the server. :type addr: tuple :param name: A stri...
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def bodypart(actions: Actions) -> Features: """Get the body part used to perform each action. Parameters ---------- actions : Actions The actions of a game. Returns ------- Features The 'bodypart_id' of each action. """ return actions[['bodypart_id']]
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import shutil def binary_available() -> bool: """Returns True if the GitHub CLI binary (gh) is availabile in $PATH, otherwise returns False. """ return shutil.which("gh") is not None
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import copy import six def _config_list(conf_tuples=None, only_net=False, **kwargs): """ Return a list of dicts from the salt level configurations conf_tuples _LXCConfig compatible list of entries which can contain - string line - tuple (lxc config param,value) ...
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def rectify_analysis(notice_xml, dry=False): """ Parses through the analysis section and fixes common problems: - analysisSection with only a title: adds an empty analysisParagraph """ new_xml = deepcopy(notice_xml) notice_analysis = new_xml.find('.//{eregs}analysis') # If there is no noti...
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import torch def add_k(c, active_k, max_k, gamma): """ replace the cluster indictors of 0 to active_K+1 :param c: a sparse matrix to indicate the cluster membership. :return: a new sparse matrix to indicate the cluster membership. """ indices = c._indices() values = c._values() values...
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def parse_yahoo_historical_ohlc(fh, adjusted=True, asobject=False): """Parse the historical data in file handle fh from yahoo finance. Parameters ---------- adjusted : bool If True (default) replace open, high, low, close prices with their adjusted values. The adjustment is by a scale fact...
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def clean_colnames( df: pd.DataFrame, keep_names=True, attr_name="column_map", ) -> pd.DataFrame: """Dataframe method, that applies :func:`clean_names` dataframe columns.""" return clean_names(df, keep_names=keep_names, attr_name=attr_name)
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def getAllValidSequences(rule): """ Loops over all possible lists of three cards and returns the list of three cards that are accepted by the given rule. Parameters ---------- rule: Tree The rule that we want to find valid cards for Returns ------- goodList: list Li...
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def reverse_complement(sequence): """ Return reverse complement of DNA sequence. """ return ''.join(COMPLEMENT[b] for b in sequence[::-1])
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async def async_api_turn_on(hass, config, request, context, entity): """Process a turn on request.""" domain = entity.domain if entity.domain == group.DOMAIN: domain = ha.DOMAIN service = SERVICE_TURN_ON if entity.domain == cover.DOMAIN: service = cover.SERVICE_OPEN_COVER await...
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def ring_float_to_class_int(rings:float, step=0.1): """Ring value rounded to classifier value; rounded to nearest step size""" return round(rings/step)
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def capture(cohesin, occupied, args): """ We are describing CTCF capture here. This function is specific to this particular project, and users are encouraged to write functions like this Note the for-loop over left/right sites below, and using cohesin[side] to get left/right leg. ...
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def vh_u(Xcp,Ycp,Zcp,Gamma,R,h,psih=0,nB=3,bWT=True,method='wrench',bSemi=True,polar_out=True): """ Induced velocity by nB azimuthally dstributed helices INPUTS: - psih : reference azimuthal position of the first helix - bSemi: Semi-infinite helices may be obtained using bSemi=True, but then ...
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def action_github(commands, args, repos, data, options=cfg.OPTIONS): """Вывод данных, полученных из функции get_data_for_actions, в файл вашего репозитария на github.com Необходимо заранее сгенерить personal access token (https://github.com/settings/tokens) и прописать его в конфигурационный файл: gith...
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def submitData(): """ Controller to manage the AJAX import of vulnerability data """ # Get the action to be performed action = request.post_vars.action if action == "vulnerability": return import_vul_ui() elif action == "vulnerability_part1": return import_vul_csv_part1() elif a...
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async def get_product(product: ProductGinoModel = Depends(get_product)): # noqa: B008 """API for getting a product.""" return product
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import torch def prep_image(img, img_size=(1088, 608)): """ Prepare image for inputting to the neural network. Returns a Variable """ orig_im = cv2.imread(img) dim = orig_im.shape[1], orig_im.shape[0] img = letterbox_image(orig_im, img_size) img_ = img[:, :, ::-1].transpose((2, 0, 1)...
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def ifft_flat(ft, tod, nthread=1, axes=[-1], flags=None): """Same workaround as fft_flat but now for the inverse transform.""" shape_tod = tod.shape naxes = np.atleast_1d(axes).size axes_new = list(range(-1, -1 - naxes, -1)) tod = utils.partial_flatten(tod, axes=axes, pos=0) ft = utils.partial_flatten(ft, axes=ax...
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def weekday_list_to_hexweek(weekday_list): """ Helper to convert list of integers represending weekdays into speaker's hex representation of weekdays. :param hexweek: List of weekday integers e.g. [0, 1, 2, 3, 4] :returns: Hex string .e.g. 0x3E """ # Mon, Tue, Wed, Thu, Fri, Sat, Sun weekda...
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def set_markers(board): """ Function creates the lists of the markers to the side of the game board which indicates the number of ship pieces in each row and column. board: list of board tiles returns the 2 lists of markers with number of ship pieces in each row (xmarkers) and column (y...
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def makeBdc(bus, branch): """Builds the B matrices and phase shift injections for DC power flow. Returns the B matrices and phase shift injection vectors needed for a DC power flow. The bus real power injections are related to bus voltage angles by:: P = Bbus * Va + PBusinj The real power f...
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def Cx2(c1, c2, q1, q2, kind='std'): """ Helper function to create pulse block for a pair of single-qubit Cliffords """ clifford_fun = clifford_map[kind.upper()] seq1 = clifford_fun(q1, c1) seq2 = clifford_fun(q2, c2) #Create the pulse block return seq1 * seq2
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def can_create_teams(user, organization): """ Returns a boolean describing whether a user has the ability to create new teams. """ if user.is_superuser: return True if not is_organization_admin(user, organization): return False result = plugins.first('has_perm', user, 'add_...
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def article_seen(article_title:str) -> list: """ Description: Function to turn a certain article to seen if the user has clicked the 'x' on a news article. Arguments: article_title {str} : string containing the title of an article the user has pressed 'x' on Returns: article...
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import hashlib def get_hash(*args) -> str: """ Convert to String all given args Adding SECRET_KEY and hashing it with sha3_256 @param args: Any argument convertible to String @return: String with HEX digest """ hashing_list = [] for arg in args: hashing_list.append(str(arg)) ...
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from typing import Callable from typing import Iterable from typing import Iterator def map_n(func: Callable[..., _T], /, *iterables: Iterable, max_workers: int | None = None, prefetch: int | None = 2, mp: bool = False, chunksize: int | None = None, ...
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from typing import Set from typing import Type from typing import List def create_approach(name: str, initial_predicates: Set[Predicate], initial_options: Set[ParameterizedOption], types: Set[Type], action_space: Box, train_tasks: List[Task]) -> BaseApproach...
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import torch def validate_adv(val_loader, model, criterion): """ Run evaluation """ losses = AverageMeter() top1 = AverageMeter() # switch to evaluate mode model.eval() for i, (input, target) in enumerate(val_loader): input = input.cuda() target = target.cuda() ...
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import torchvision def interpolate( input, size=None, scale_factor=None, mode="nearest", align_corners=None ): """ Equivalent to nn.functional.interpolate, but with support for empty batch sizes. This will eventually be supported natively by PyTorch, and this class can go away. """ return ...
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from typing import Any from typing import Optional def def_sensitivity( *, default: Any = NOTHING, dtype: Optional[Any] = None, description: Optional[str] = None, converter: Optional[callable] = None, validator: Optional[callable] = None, **kwargs, ): """Define a sensitivity attribute ...
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def is_matched_html(raw): """Return True if all HTML tags are properly match; False otherwise.""" S = ArrayStack() j = raw.find('<') # find first '<' character (if any) while j != -1: k = raw.find('>', j + 1) # find next '>' character if k == -1: ...
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