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def minibatch_discriminator(x): """ Computes minibatch discrimination features from input tensor x""" diffs = K.expand_dims(x, 3) - \ K.expand_dims(K.permute_dimensions(x, [1, 2, 0]), 0) l1_norm = K.sum(K.abs(diffs), axis=2) return K.sum(K.exp(-l1_norm), axis=2)
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def parse_module_descriptors(raw_json: bytes) -> list[ModuleDescriptor]: """Parse the JSON output of `go list -m`.""" if not raw_json: return [] module_descriptors = [] for raw_module_descriptor in ijson.items(raw_json, "", multiple_values=True): if raw_module_descriptor.get("Main", Fal...
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from typing import Any from typing import Tuple from typing import Optional import numbers import torch def _parse_array_or_scalar_like_pair(actual: Any, expected: Any) -> Tuple[Optional[Exception], Optional[_ParsedInputs]]: """Parses an scalar-or-array-like pair. Args: actual: Actual array-or-scalar...
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def atomic(session=default_session): """ with atomic(): # None pass with atomic(session): # Session pass @atomic # Callable def x(): pass @atomic(session): # Session def x(): pass """ if ( not isinstance(session, (Session, scoped_session...
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import mpmath def var(k, theta): """ Variance of the gamma distribution. """ k = mpmath.mpf(k) theta = mpmath.mpf(theta) return k * theta**2
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def count_newlines_fast(path: Pathlike): """ Counts newlines in a file using buffered chunk reads. The fastest possible option in Python according to: https://stackoverflow.com/a/68385697/5285891 (This is a slightly modified variant of that answer.) """ def _make_gen(reader): b = re...
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def crc32(seq): """Returns the crc32 checksum for a sequence (string or Seq object).""" #NOTE - On Python 2 returns a signed int, on Python 3 it is unsigned #Docs suggest should use crc32(x) & 0xffffffff for consistency. #TODO - Should we return crc32(x) & 0xffffffff here? try: #Assume its a...
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def current_date_int(): """ 获取当前时间日期 int值 不使用时间api,直接进行字符串解析,执行效率高 :return: 日期int值 """ date_int = 0 # 首先获取str today = current_str_date() # 手动比系统a时间pi快 today_array = today.split("-") if len(today_array) == 3: date_int = int(today_array[0]) * 10000 + int(today_array[1])...
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import os def load_scalar(layer): """ Load the feature mean / variance scalar for the input layer :type layer: str :param layer: Feature layer :return: scalar """ if not layer in feature_layers: raise NotImplementedError('Feature Layer Type Not Found.') features_path = os.pa...
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import signal def _timeout(seconds): """Decorator for preventing a function from running for too long. Inputs: seconds (int): The number of seconds allowed. Notes: This decorator uses signal.SIGALRM, which is only available on Unix. """ assert isinstance(seconds, int), "@timeout(...
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import asyncio def listen_properties_changed(bus, callback): """Create a future for a PropertiesChanged signal listener. Args: bus: The system bus object to use. callback: The callback function to run when signal is received. Returns: Integer rule id. """ return bus.addM...
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import os async def check_files(hass): """Return bool that indicates if all files are present.""" # Verify that the user downloaded all files. base = f"{hass.config.path()}/custom_components/{DOMAIN}/" missing = [] for file in REQUIRED_FILES: fullpath = "{}{}".format(base, file) if...
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import yaml import subprocess def validate_output(output, fmt, mcf): """compare values in merp2tbl output with merp -d row for row, non-NA must agree Parameters ---------- output : dict as returned by format_output fmt : str 'tsv' or 'yaml' mcf : str path to merp command ...
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import select def get_hadoop_conf_dir(): """ Gets the shared hadoop conf directory using: 1. Start with /etc/hadoop/conf 2. When the stack is greater than HDP-2.2, use /usr/hdp/current/hadoop-client/conf 3. Only when doing a RU and HDP-2.3 or higher, use the value as computed by conf-select. This ...
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from .query import Database from bob.db.base.utils import null import sys def reverse(args): """Returns a list of file database identifiers given the path stems""" db = Database() output = sys.stdout if args.selftest: output = null() try: r = db.reverse(args.path) except KeyError as err: r...
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from typing import List from typing import Tuple def delme_list_to_rangemap(delme_verts: List[int]) -> Tuple[List[int],List[int]]: """ Given an ascending sorted list of ints, build a pair of lists that let me know what indices OTHER things will map to when THESE indices are deleted. list1 is the index each cluster...
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from typing import Sequence from typing import Iterable import functools def generate_aggregate_pubkeys(validators: Sequence['ValidatorRecord'], vote_data: 'SlashableVoteData') -> Iterable[BLSPubkey]: """ Compute the aggregate pubkey we expect based on the proof-of-custody i...
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def get_dimensions(unit): """get the set of basis units""" if hasattr(unit, 'dimension'): return unit.dimension if isinstance(unit, Mul): return Mul.fromiter([get_dimensions(arg) for arg in unit.args]) if isinstance(unit, Pow): base, exp = unit.as_base_exp() return Pow(ge...
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from typing import Tuple def mutual_information( indices_a: np.ndarray, indices_b: np.ndarray ) -> Tuple[float, float]: """ Mutual Information Parameters ---------- indices_a : array-like, shape(n_samples) Symbolic time series. indices_b : array-like, shape(n_samples) Symbol...
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import json def validate_token(func): """Decorator for validating the JWT token required for API requests.""" @wraps(func) def wrapper(*args, **kwargs): """Tries to decode the JWT token using the SECRET KEY. Executes the original function if token is valid. Otherwise ret...
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from typing import Sequence from typing import Tuple import numpy def image_stabilisation_proj_( projections: Sequence[Tuple], ndim: int, keep_best: bool = True, debug_output: str = None, **kwargs ) -> SequenceRegistrationModel: """ Same as 'sequence_stabilisation_proj' but takes projections instead, ...
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def get_node_properties(node_ids, **options): """Get properties associated with nodes.""" fields = options.pop('fields', None) functions = { 'type': 'labels(n)', } if fields is not None: prop_string = ', '.join([f'{key}:{functions[key]}' if key in functions else f'{key}:n.{key}' for...
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import os def RunPipe(pipe_list, infile=None, outfile=None, capture=False, capture_stderr=False, oneline=False, raise_on_error=True, cwd=None, **kwargs): """ Perform a command pipeline, with optional input/output filenames. Args: pipe_list: List of command lines to execute...
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def reverse(insts): """ Reverse the order of instances. This function should be passed to :meth:`InstanceSet.select` without any argument. >>> insts = InstanceSet('prediction_result_path').select(reverse) """ return list(reversed(insts))
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def destination(φ1: float, λ1: float, α: float, d: float): """Destination point, provided origin, bearing, and distance. This is the solution to the "first" (or "forward") geodetic problem, assuming a spherical Earth. Parameters ---------- φ1, λ1 : float initial latitude, longitude ...
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def create_volumes(kname, cname): """ Creates volume bindings for Kraken name `kname` and container name `cname`. """ volumes = {} # Mount configuration directory. This is necessary for components which # populate templates and need to mount the populated template into the # container. ...
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def double_pend( y: list, t: float, m_1: float, l_1: float, m_2: float, l_2: float, g: float ) -> list: """ Calculate one step for coupled pendulums. Args: y: (theta_1, theta_d_1, theta_2, theta_d_2) at this timestep. t: Time (unused). m_1: Mass 1. ...
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import functools def ClientOnly(f): """A decorator that asserts _IS_CLIENT.""" @functools.wraps(f) def Wrapper(*args, **kwargs): assert _IS_CLIENT return f(*args, **kwargs) return Wrapper
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def read_var_def(line: str, type_word: str = None, fun_only: bool = False): """Attempt to read variable definition line""" if type_word is None: type_match = NAT_VAR_REGEX.match(line) if type_match is None: return None else: type_word = type_match.group(0).strip()...
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def get_user_by_api_key(api_key): """Looks up a user by an API key. Args: api_key: API key. Returns: A user dictionary. Raises: KeyError: If the specified user is not found. """ if not api_key: raise KeyError('User not found by API key') user = _db.users.fi...
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def ROI(pred_thresh, im_crop, w, h, im_i=0, save_file=False): """ Finalize prediced ROI :param pred_thresh: image region to analyze - :param im_crop: RGB input :param w,h: width and height to be resized to :param im_i: serial ID of the input image :param save_file: switch for saving file Available option...
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def _parse_comparison(sql, example, anonymize_values): """Parse the part relative to a comparison in the SQL query.""" successful_copy = True for item in sql: if item.ttype == sqlparse.tokens.Text.Whitespace: continue if _is_identifier(item): successful_copy = _parse_identifier(item, example, ...
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def tanh(X): """an activation function which outputs the value between (-1,1)""" if isinstance(X,np.ndarray): return (2.0/(1.0+np.exp(-(2*X))))-1 else: X=np.array(X) return tanh(X)
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from typing import Optional from typing import Mapping from typing import Any def char_key_value_linear_source_from_params( params: Parameters, *, input_namespace: str = "input", eval_context: Optional[Mapping[Any, Any]] = None, ) -> KeyValueLinearSource[str, str]: """ Get a key-value source b...
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def get_train(opt): """Get training dataset for KDD 10 percent""" return _get_adapted_dataset(opt, "train")
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def sort_by_time(raw_folded, pdc_channels, return_indices=False): """:param pdc_channels (tuple of lightkurve.FoldedlightCurve)""" sort_idx_raw = np.argsort(raw_folded.time_original) sort_idx_pdc = np.argsort(pdc_channels[0].time_original) apply_indices_to_light_curve(raw_folded, sort_idx_raw) for ...
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def register_deserializer(instance, **options): """ registers a new deserializer or replaces the existing one. if `replace=True` is provided. otherwise, it raises an error on adding a deserializer which is already registered. :param AbstractDeserializerBase instance: deserializer to be registered....
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import numpy def maximum(input, labels=None, index=None): """ Find the maxima over an image at specified subregions. Parameters ---------- input : ndarray N-D image data labels : ndarray, optional Image features noted by integers. If None (default), all values. index : int...
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def getViolationFromAria2(ariaViolation, constraint, violationList): """Descrn: Make a constraint's violation object in the input violation list given an Aria2 violation analysis object. Inputs: Aria2 violation analysis object, NmrConstraint.DistanceConstraint, NmrConstraint.ViolationList Outpu...
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def enforce_connectivity(image, outside): """ Function to filter out any objects not connected to the largest cluster in image. Inputs: image (np.ndarray) : Binary input raster with 1's corresponding to hydrodynamically active areas, which get mapped to high resolution, a...
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def psnd_chlA_vi(imgData, wave, mask=0, bands=[-1,-1]): """ Function that calculates the Pigment Sensitive Normalized Difference for Chlorophyll A. This functions uses bands 675 and 800 nm. The closest bands to these values will be used. Citation: Blackburn, G.A. 1998. Spectral indices for estimating p...
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def test_request_without_setting_receive(): """ If Request is instantiated without the receive channel, then .body() is not available. """ def app(scope): async def asgi(receive, send): request = Request(scope) try: data = await request.json() ...
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def fraction_of_death_40(): """ Real Name: b'fraction of death 40' Original Eqn: b'0.4' Units: b'dmnl' Limits: (None, None) Type: constant b'' """ return 0.4
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def test_video_file(): # type: () -> str """ Fixture for test video file path (ensures file exists). Access in test case by adding a test_video_file argument to obtain the path. """ return get_absolute_path("resources/testvideo.mp4")
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from typing import Optional from typing import Mapping def _description_xml_to_dict(description_xml: str) -> Optional[Mapping[str, str]]: """Convert description (XML) to dict.""" try: tree = DET.fromstring(description_xml) except DET.ParseError as err: _LOGGER.debug("Error parsing %s: %s",...
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import torch import tqdm import os def train_single_scale(D, G, reals, generators, noise_maps, input_from_prev_scale, noise_amplitudes, opt): """ Train one scale. D and G are the current discriminator and generator, reals are the scaled versions of the original level, generators and noise_maps contain informa...
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import logging import sys def get_simultaneous_models(request, fit_problem, setup_request=False): """ Find related models and return a list of dictionary representing them. :param Request request: http request object :param FitProblem fit_problem: FitProblem object :param bool set...
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def write_ometiff(filepath, img, scalex=0.1, scaley=0.1, scalez=1.0, dimorder='TZCYX', pixeltype=np.uint16, swapxyaxes=True, series=1): """ONLY FOR INTERNAL TESTING - DO NOT USE! This f...
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def mnist8m_data(): """ Loads and returns data of MNIST8M dataset for all digits. Returns ------- data : list A list of length = 1, where each element is a dictionary which contains ``train_outputs``, ``train_inputs``, ``test_outputs``, ``test_inputs``, and ``id`` References ...
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from typing import Dict import pathlib def dir_parser(path_to_dir: str) -> Dict[str, Dict[str, str]]: """ Parses the given directory, and returns the path, stem and suffix for files. """ files = pathlib.Path(path_to_dir).resolve().glob("*.*") files_data = {} for file in files: files_...
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import os import re def get_core_dump_regs(path, pid, log): """ Read all register values from a core dump MacOS: all core dumps are stored as /cores/core.[pid] Linux: the core dump is stored as a 'core' file in the cwd Windows: If the given registry file was used, core dumps are stored in %LOCALAP...
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def logsumexp(x): """ Compute log(sum(exp(x))) avoiding over/underflow """ x = np.asarray(x) m = x.max() return np.log(np.sum(np.exp(x - m))) + m
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def GetConfig(user_config): """Load and return benchmark config. Args: user_config: user supplied configuration (flags and config file) Returns: loaded benchmark configuration """ config = configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME) if FLAGS['ycsb_client_vms'].present: co...
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def is_left(element1, element2): """Is element2 on the left of element1. Args: element1: XML element1 dump from UI. element2: XML element2 dump from UI. Returns: Whether element2 is on the left of element1. """ [e1_x1, e1_y1, e1_x2, e1_y2] = get_element_bounds(element1) [e2_x1, e2_y1, e2_x2, e...
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def return_error_response(): """Return error response sample""" return make_response(jsonify({}), 403)
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import inspect def is_redefined_dataclass_with_slots(old: type, new: type) -> bool: """Return True if `new` is the `old` dataclass redefined with `__slots__.""" # Must both be dataclasses. if "__dataclass_fields__" not in old.__dict__ or "__dataclass_fields__" not in new.__dict__: return False ...
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def get_total_characters(data): """ data - list example: data = [ [16,1027791027,993,GLORIA,MASTON,9,09/22/1938,F,W,*********7,*L**IA,******,**/**/****,F,*,8,2,0], [16,1027791028,2412,GEORGE,MASTON,10,09/22/1938,M,W,*********8,*E**GE,******,**/**/****,M,*,8,2,0] ...
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def pyreq_rm_all_samples(httpreq_handler: HTTPRequestHandler, project_id: int, locale: str): """ Remove all samples in a locale of NLU model in Mix project by querying API endpoint with DELETE method using "requests" package API endpoint :: DELETE f"nlu/api/v1/nlu/<PROJECT_ID>/interactions?...
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def places(client, request, geojson=None, bbox=None, buffer=None, filter_category_ids=None, filter_category_group_ids=None, filters_custom=None, limit=None, sortby=None, dry_ru...
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import numpy def read_tmqm_properties(csvfile,xyzfile): """Reads the csv and xyz files comprising the tmqm dataset. Args: csvfile(str): Filename of the tmqm dataset csv file. xyzfile(str): Filename of the tmqm dataset xyz file. Returns: numpy.ndarray: Numpy array of named propert...
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from .GSL import GSL as gsl def gsl(): """ The GSL package manager """ # grab the protocol # and generate a facility return gsl()
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def reference_selection_dop(target_position,receivers): """ select reference receiver with lowest dop""" dop_references = [] H_references = [] for idx in range(len(receivers)): dop,H = calc_dop(target_position,receivers,receivers.keys()[idx]) dop_references.append(dop) H_refere...
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import pickle def BRN_fourier_integrand_splines(links, unwrap, Nvals=None, rvals=None, Bprop=None): """Return normalized, real space Green's function G(R;Rmax) for kinked WLC using spline integration. Parameters ---------- kvals : (29999,) array-like k values for which to compute G(k; N) ...
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from lmfit import Parameters from typing import OrderedDict def residual_PM_IG_gamma(value_dict, time, f, data=None, weight=None, eps=None): """ derived the residual between model and data given params_local. inputs: value_dict dictionary with parameters, or lmfit.parameters instance. ...
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def merge_all(lst, strategy="smart", renderer="yaml", merge_lists=False): """ .. versionadded:: 2019.2.0 Merge a list of objects into each other in order :type lst: Iterable :param lst: List of objects to be merged. :type strategy: String :param strategy: Merge strategy. See utils.dictupd...
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import argparse def cli_parse() -> tuple: """ Parse the command line arguments using the argparse library. returns -- a named tuple with the arguments resolved and named. """ parser = argparse.ArgumentParser(description="Get headless chrome console log output from urls") parser.add_argument...
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from typing import Optional def __parseGregorian(date_str: str) -> Optional[str]: """Tries to parse the given string as a gregorian date. Return the string 'DD.MM.YYYY' on success, `None`else. Args: date_str (str): The string to parse Returns: Optional[str]: The string 'DD.MM.YYYY' o...
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def ecc_overview(): """ For lack of a better alternative redirect users to the main page """ return redirect(url_for('main.screen'))
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def bprop_scalar_sign(x, out, dout): # pragma: no cover """Backpropagator for `scalar_sign`.""" return (zeros_like(dout),)
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from typing import List def calculateFourCornersProduct(tiles: List[Tile]) -> int: """ Finds 4 corners tiles and multiplies IDs """ fourCornersProduct: int = 1 for i in range(len(tiles)): top, bottom, left, right = False, False, False, False for j in range(len(tiles)): if i == j: ...
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from typing import Union import re import random async def urnotgod(ctx: Context) -> Union[Message, str]: """Responds with a variety of messages that oppose a person who claims to be god.""" if re.search(r"\b(i( )?('| a)?( )?m g( )?o( )?d)\b", ctx.message.content.strip().lower()): a...
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def SiteParameters(): """Create the site parameters for this site. Returns: dict containing SiteParameters for ChromeOS. """ # Chrome OS site parameters. site_params = config_lib.DefaultSiteParameters() # Helpers for constructing Chrome OS site parameters. manifest_project = 'chromiumos/manifest' ...
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import reproject def project_to_header(fitsfile, header, **kwargs): """ Reproject an image to a header. Simple wrapper of reproject.reproject_interp Parameters ---------- fitsfile : string a FITS file name header : pyfits.Header A pyfits Header instance with valid WCS to ...
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import tempfile import shutil import os import json def extract_subdir(path): """Extract the subdir from an artifact.""" with tempfile.TemporaryDirectory() as tmpdir: tmpdir = str(tmpdir) shutil.copy2(path, tmpdir) path = os.path.join(tmpdir, path) pkg_dir, pkg = os.path.spli...
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from sys import path def split_path(p): """ Split paths into segments :param p: an path :type p: str :return: path segments :rtype: List[str] """ segments = [] while True: p, tail = path.split(p) if tail: segments.append(tail) else: ...
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def fetch_tae_df(preprocess=False): """ Fetch the `tae`_ dataset from OpenML and add `fairness_info`. It contains information from teaching assistant (TA) evaluations. at the University of Wisconsin--Madison. The prediction task is a classification on the type of rating a TA receives (1=Low, 2=...
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def filter_data(data): """ Filters data for years after 2010 and data originating from Syria. Inputs: data (pd.DataFrame): Input data with column "Year" and "Origin" Returns: data (pd.DataFrame): Filtered data """ if 'Origin' in data.columns: data = data[(data.Year >...
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def parse(tokens): """ Every time you generate a statement, backtrack to see if you have completed an application. """ revlist = list(reversed(tokens)) stack = [] line = linecount(revlist) col = 1 for tok in revlist: col += 1 try: if type(tok) == int: ...
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def _mask_relv_scores(relv_scores, doc_masks): """ fill padded sentences with score of -np.inf for ranking purpose. :param relv_scores: d_batch * max_ns_doc :param doc_masks: d_batch * max_ns_doc :return: d_batch * max_ns_doc """ relv_mask = np.full_like(relv_scores, -np.inf) ...
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def ridge(func, xlims, ylims, ds, *fargs, **kwargs): """Ridge detection """ x1, x2, A = repulsion_rate(func, xlims, ylims, ds, *fargs, plot=False, newfig=False, output=True, **kwargs) X1, X2 = np.meshgrid(x1, x2) V1, V2 = func([X1, X2], *fargs) Ay, Ax = np.gradient(A, ds) return x1, x2, (V2*Ax-V1*Ay)/(V1*...
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def strrep(strg,x): """ A função retorna uma string com n repetições; str, int -> str """ return strg*x
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from datetime import datetime def get_steps(image): """Get build steps for msan-libs-builder.""" timestamp = datetime.datetime.utcnow().strftime('%Y%m%d%H%M') upload_name = 'msan-libs-' + timestamp + '.zip' steps = build_base_images.get_steps([ 'base-sanitizer-libs-builder', 'msan-libs-builder',...
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import logging def prompt_for_description(): # TODO: Show the user the metadata version of the description and ask them if they'd like to change it. """ Prompt the user for a description. :return: a user-supplied string that will be used as the bookmark's title """ while (desc := input("Provi...
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import collections def get_top_n(predictions, n=10): """Return the top-N recommendation for each user from a set of predictions. Args: predictions(list of Prediction objects): The list of predictions, as returned by the test method of an algorithm. n(int): The number of recommendat...
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def list_data_sources(*args, **kwargs): """ List all available data sources to use the hash_search API Variables: None Arguments: None Data Block: None Result example: [ <list of sources> ] """ return make_api_response(sorted(sources.keys()))
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def connect_to_mc_testing_db(forced_db_name='testing'): """ Get a DB object that is connected to the testing M&C database. Parameters ---------- forced_db_name : str Database name to use. Returns ------- DB object An instance of the `DB` class providing access to the te...
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def action_dispatcher(decoy: Decoy) -> ActionDispatcher: """Return a mock in the shape of an ActionDispatcher.""" return decoy.mock(cls=ActionDispatcher)
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import time def get_token(session): """ Checks to see if there's a valid token for the current session and/or whether an existing token is expired. If expired, it grabs the refresh token. """ token_valid = False token_info = session.get("token_info", {}) # Checking if the session a...
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from re import T def leakyrelu_forward(input, negative_slope=0.1): """ forward pass of LeakyReLU """ out = T.leakyrelu(input, negative_slope) return out
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from typing import cast import warnings def register_type( type_: type, *, widget_type: WidgetRef = None, return_callback: ReturnCallback | None = None, **options, ): """Register a ``widget_type`` to be used for all parameters with type ``type_``. Parameters ---------- type_ : typ...
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from io import StringIO def mixed_types_structured(): """ Fixture providing hetergeneous input data with a structured dtype, along with the associated structured array. """ data = StringIO( ( "1000;2.4;alpha;-34\n" "2000;3.1;beta;29\n" "3500;9.9;gamma;12...
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def redirect_back(default='web.index', **kwargs): """ If next is not none, redirect next, if not, redirect index page """ for target in request.args.get('next'), request.referrer: if not target: continue if is_safe_url(target): return redirect(target) return r...
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def take(n, iterable): """ Yield n items of iterable. Args: n: an integer iterable: an iterator Returns: A generator of the items. Used by: 033, 044, 046 """ return islice(iterable, n)
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def build_driver(executable_path: str = None, options: Options = None, headless=True, timeout=20) -> webdriver: """ Build Chrome driver instance :param executable_path: str :param options: Options :param headless: bool :param timeout: int :return: webdriver """ kwargs = {} if e...
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def leaky_relu_gradient_op(node_A, node_B, alpha, ctx=None): """Computes the gradient of the ReLU function. Parameters: ---- node_A : Node LeakyRelu input. node_B : Node Previous gradient node. alpha : float LeakyRelu alpha Returns: ---- A new Node instanc...
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def getFssMaskLoss(cbsd_grant, constraint): """Gets the FSS mask loss for a FSS blocking protection constraint. Args: cbsd_grant: A CBSD grant of type |data.CbsdGrantInfo|. constraint: The protection constraint of type |data.ProtectionConstraint|. The constraint defines the FSS out of band left part,...
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def get_adversarial_data(dataset = "gtsrb",set_type = "Test_fgsm"): """ Get the numpy arrays of the dataset for test Params: set_type: Test_fgsm -> Test set + adversarial examples from FGSM Test_jsma -> Test set + adversarial examples from JSMA Returns: x_s...
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def task_build(): """Create full distribution.""" return { 'actions': ['python -m build'], 'task_dep': ['gitclean', 'mo', 'docs'], }
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def serve_pil_image(pil_img): """ see: https://groups.google.com/forum/?fromgroups=#!topic/python-tornado/B19D6ll_uZE http://stackoverflow.com/questions/7877282/how-to-send-image-generated-by-pil-to-browser """ img_io = cStringIO.StringIO() pil_img.save(img_io, 'JPEG', quality=70) ...
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def _feature_dtype(feature): """Returns a representation of a Feature's type as a tensorflow dtype.""" if feature.type == schema_pb2.BYTES: return tf.string elif feature.type == schema_pb2.INT: return tf.int64 elif feature.type == schema_pb2.FLOAT: return tf.float32 else: raise ValueError('Fea...
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