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def from_array(array, copy=True): """Convert a NumPy array to a tensor. Initializes a taco tensor from a NumPy array and copies the array by default. This always creates a dense tensor. Parameters ------------ array: numpy.array A NumPy array to convert to a taco tensor copy: boo...
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def maybeName(obj): """ Returns an object's __name__ attribute or it's string representation. @param obj any object @return obj name or string representation """ try: return obj.__name__ except (AttributeError, ): return str(obj)
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from typing import List import os import subprocess import glob def ped_datasets() -> List[str]: """Returns paths after downloading pedestrian datasets.""" if not os.path.exists("datasets"): subprocess.call( ["wget", "https://www.dropbox.com/s/8n02xqv3l9q18r1/datasets.zip"] ) ...
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def compare(attr_a, attr_b=0, operation=0): """Create math_Compare-node to get boolean of logical comparison between given attrs. Args: attr_a (NcNode or NcAttrs or string): Maya node attribute. attr_b (NcNode or NcAttrs or float): Maya node attribute. operation (NcNode or NcAttrs or in...
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from sys import flags def train_eval_input_fn(mode, params, restrict_classes=None, shift_classes=0): """Mode-aware input function. restrict_classes: for use with intra fid shift_classes: for use with restrict_classes """ is_train = mode == tf.estimator.ModeKeys.TRAIN split = 'train' if is_train else fl...
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import inspect def gc_nc_60_79(mqc): """ Analogous to gc_nc_0_19. """ k = inspect.currentframe().f_code.co_name try: d = next(iter(mqc["multiqc_picard_gcbias"].values())) v = d["GC_NC_60_79"] v = np.round(v, DECIMALS) except KeyError: v = "NA" return k, v
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import math import random def generate_random_pose(): """ generate a random rod pose in the room with center at (0, 0), width 10 meters and depth 10 meters. The robot has 0.2 meters as radius Returns: random_pose """ def angleRange(x, y, room, L): """ Compute rod an...
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import logging def version_description(project_arn: str, version_name: str = None): """[Describes a Project on Rekognition in AWS] Args: project_arn (str): [Unique Identifier for a Project on Rekognition in AWS] version_name (str, optional): [Display Name]. Defaults to None. Raises: ...
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def feature_importance(residuals, analysis_type="collective", date_from=None, date_till=None, weigh=True): """Feature importance calculation Parameters ---------- residuals : pandas.DataFrame() analysis_type : str, "single"/"collective", "single" by default date_from : str in format 'yyyy-mm-...
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def utest(a, b): """ MannWhitney U statistic scipy.stats.mannwhitneyu tests for a != b Use only when the number of observation in each sample is > 20 and yo have 2 independent samples of ranks. Mann-Whitney U is significant if the u-obtained is LESS THAN or equal to the critical value of U....
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from typing import Callable from typing import Any import asyncio import time async def run_async(func: Callable[..., Any], *args, **kwargs) -> Any: """ Runs a callable on the database thread pool executor using the current thread's I/O loop instance. Usage: def blocking_task(arg1, arg2, ar...
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def constant_schedule_with_warmup(epoch, warmup_epochs=0, lr_start=1e-4, lr_max=1e-3): """ Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between {lr_start} and {lr_max}. """ if epoch < warmup_epochs: lr = (lr_max - ...
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def gen_lc(x,p0,struct,pmax=85.): """ From gen_lc.pro """ #p0 = m0i, m0v, m0b, period, phase shift, tbreak1, tbreak2 p = np.array(p0).copy() if len(p) < 11: # use the poly fits to fill in the pca1-pca4 coeffs c_need = 11 - len(p0) poly_fits = struct["POLY_FITS"][0] new_coeffs = [...
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def create_embed(author, level, ascendency_name, class_name, main_skill: Skill, is_support): """ Create the basic embed we add information to :param author: of the parsed message - str :param level: of the build :param ascendency_name: to display :param class_name: to display if no ascendency ha...
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import os import json def getVersionFolder(baseVersioningFolder, versionNumber=None): """ If versionNumber is None the newest version is returned if available. If the specified version is not available None is returned. """ if not os.path.exists(baseVersioningFolder): raise Exception(f"The gi...
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def get_cluster_name(): """ Retrieves the current cluster name from the CLUSTERNAME environment variable This should probably be in conf.py, but it creates dependency issues with ZMQ :return: str cluster name """ return environ.get('CLUSTERNAME')
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def ml_ssl(stft, sv, compression=0, eps=1e-8, norm=False, mask=None): """ Maximum likelihood SSL Arguments: stft: STFT transform result, M x T x F sv: steer vector in each directions, A x M x F norm: normalze STFT or not mask: TF-mask for source, T x F x (N) Return: ...
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def vonMisesStressUtilization(axial_stress, hoop_stress, shear_stress, gamma, sigma_y): """combine stress for von Mises""" # von mises stress a = ((axial_stress + hoop_stress)/2.0)**2 b = ((axial_stress - hoop_stress)/2.0)**2 c = shear_stress**2 von_mises = np.sqrt(a + 3.0*(b+c)) # stress ...
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def fit(X, estimator, beta=0.05, N=None, start=1, step=1, tol=1e-5, max_iter=20, debug=False): """Run the StARS algorithm to select the regularization parameter for the given estimator. Parameters: - X (np.array): Array containing n observations of p variables. Columns are the observations of a...
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def bar_chart(data2): """ A bar chart, like the one above but with small custom alterations title and size """ Chart2 = alt.Chart(data2).mark_bar().encode( alt.X ('movies', title="My Favorite Movies"), alt.Y ('num_oscars', title="# of Academy Awards"), color='movies', )....
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def nSpecies(): """ Returns the total number of species in the model. """ return 62
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def plot_convergence(*args, **kwargs): """Plot one or several convergence traces. Parameters ---------- * `args[i]` [`OptimizeResult`, list of `OptimizeResult`, or tuple]: The result(s) for which to plot the convergence trace. - if `OptimizeResult`, then draw the corresponding single t...
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def server_player_id(player_id): """Serve player ID endpoint. This endpoint is used to indicate a buzzer has been triggered, or register a buzzer. Args: player_id: Player identifier. """ gameshow = flask.current_app gameshow.state_machine.process( state_machine.Events.TRIGG...
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import math def crt(cong): """Use the Chinese Remainder THeorem to solve the given system of congruences, cong = [(mod1, rem1), (mod2, rem2), ...] where val = rem1 mod mod1 val = rem2 mod mod2 ... The val satisfying the congruences is returned. """ result = 0 nprod = math.prod([v[0...
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import base64 def compose_message(body, subject, contacts): """ composes message using message text, subject, contacts and returns it encoded """ message = MIMEMultipart() message['subject'] = subject message['from'] = me message['to'] = contacts plaintext_body = html_to_text(...
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def getFileList(dbid, fnr, printi=0, type='A'): """ Get list of Sdicfile objects from Predict file :param dbid: dbid of Predic file :param fnr: file number of Predic file :param printi: print file list if True :param type: select by file type ('A' is Adabas file) :returns: list of Sdicfile na...
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def insertWarnings(lines, badCommand): """Insert warnings after a command that is probably to be checked. The text is to be given as a list of lines. A command in this sense can be any text that is not to be followed by an asciiletter """ lineNumber = 0 while lineNumber < len(lines): ...
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def rho_p(rank_vector): """Compares each element in the vector to its corresponding value in the null distribution vector, using the probability mass function of the binomial distribution. Assigns a p-value to each element in the vector, creating the betaScore vector. Uses minimum betaScore as rho ...
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import copy def occupy_seats(data, seat_tolerance, only_adjacent): """ Occupies seats according to rules. :param data: seat map (2d array) :param seat_tolerance: number of discovered seats that can be occupied to still make the person occupy the seat :param only_adjacent: boolean, if True only check ...
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def get_orthogonal_grid_edges(pix_x, pix_y, scale_aspect=True): """calculate the bin edges of the slanted, orthogonal pixel grid to resample the pixel signals with np.histogramdd right after. Parameters ---------- pix_x, pix_y : 1D numpy arrays the list of x and y coordinates of the slanted...
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def rom(a, b,f, eps = 1e-8): """Approximate the definite integral of f from a to b by Romberg's method. eps is the desired accuracy.""" R = [[0.5 * (b - a) * (f(a) + f(b))]] # R[0][0] #print_row(R[0]) n = 1 while True: h = float(b-a)/2**n R.append((n+1)*[None]) # Add an empty r...
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def map_pair_name_to_exchange_name(pair_name): """ We're preparing to add the notion that exchanges can have multiple trading pairs into our system. Each exchange is going to have a single ExchangeData db object but have one wrapper for each pair. Order.exchange_name is going to refer to the pair, b...
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from typing import Optional async def handle_list_comparisons( project_id: str, session: Session = Depends(database.session_scope), kubeflow_userid: Optional[str] = Header(database.DB_TENANT), ): """ Handles GET requests to /. Parameters ---------- project_id : str session : sqlal...
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import argparse def get_args(): """ Get command-line arguments """ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description="Howler's Second Program") parser.add_argument("input", type=str, nargs="+", metavar="str", help="Input messages or files") parser.add...
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import pickle def load_model(file_name="model.pkl"): """ Parameters: file_name (string): exact path of the target saved model Returns: built chefboost model """ f = open('outputs/rules/'+file_name, 'rb') model = pickle.load(f) #restore modules from its references modules = [] for model_name in model["...
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import os def locate_template(template): """Locate the template file of a given name.""" *base, ext = template.split('.') if ext != 'yaml': template += '.yaml' return os.path.abspath(os.path.join(TEMPLATE_DIR, template))
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def lower_first(string): """Return a new string with the first letter capitalized.""" if len(string) > 0: return string[0].lower() + string[1:] else: return string
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def get_annotations_dict(members): """Get __annotations__ from a members map. Returns None rather than {} if the dict does not exist so that callers always have a reference to the actual dictionary, and can mutate it if needed. Args: members: A dict of member name to variable Returns: members['__an...
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def exp(pda : pdarray) -> pdarray: """ Return the element-wise exponential of the array. Parameters ---------- pda : pdarray Returns ------- pdarray A pdarray containing exponential values of the input array elements Raises ------ TypeError Rai...
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from typing import Optional def findchallenge(name: str) -> Optional[HTBChallenge]: """Finds a specific challenge by name. Searches HTB for a specific challenge matching the specified name and returns it if one is found. Otherwise returns None. Args: name: An exact challenge name to lookup. ...
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def create_result_dataframe(y_test: DataFrame, y_pred: DataFrame, model_name: str) -> DataFrame: """ :param y_test: DataFrame with target values :param y_pred: DataFrame with predicted values :param model_name: Actual model name :return: DataFrame with models scores """ scores = _prepare_sc...
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def create_custom_tiling_node(tile_mode, tile_level=TileLevel.L1, tensor_name=DEFAULT_STRING, tile_pos=DEFAULT_VALUE, tile_band=DEFAULT_VALUE, tile_axis=DEFAULT_VALUE, ...
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import torch def get_surface_distance(seg_pred, seg_gt, distance_metric="euclidean"): """ (from MONAI) This function is used to compute the surface distances from `seg_pred` to `seg_gt`. Args: seg_pred: the edge of the predictions. seg_gt: the edge of the ground truth. di...
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def makeState(fromacct,toacct,amount): """ make a tranfer state parameter currently due to the compiler problem, must be created as this format :param fromacct: :param toacct: :param amount: :return: """ return state(fromacct, toacct, amount)
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import os def params_used(self): """Check for that params in ``nextflow.config`` are mentioned in ``main.nf``.""" ignore_params_template = [ "params.custom_config_version", "params.custom_config_base", "params.config_profile_name", "params.show_hidden_params", "params....
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def brocher_vp(f): """ V_p derived from V_s via Brocher (2005) eqn 9. """ f *= 0.001 f = 0.9409 + f * (2.0947 - f * (0.8206 - f * (0.2683 - f * 0.0251))) f *= 1000.0 return f
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from typing import Dict def new_swapped_deployment( old_deployment: Dict, container_to_update: str, run_id: str, expose: PortMapping, add_custom_nameserver: bool, ) -> Dict: """ Create a new Deployment that uses telepresence-k8s image. Makes the following changes: 1. Changes to s...
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def final(data): """ Last evolution time point, can be used to obtain parameters at the very end of the run. :param data: numpy ndarray containing the history of the system. :return: selection of the last evolution point. """ return ([data.shape[0]-1],)
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def detach_project_from_that_group(request): """ detach that group from the requested project """ id_project = request.matchdict[u'id'] id_group = request.matchdict[u'group_id'] project = request.dbsession.query(Project).options( joinedload(Project.groups)).get(id_project) group = r...
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def dec_resource(resource_port, expr, stream_port): """Decrease the count of available resource by the specified number, waiting for the processes capturing the resource as needed.""" r = resource_port e = expr s = stream_port expect_resource(r) expect_expr(e) expect_stream(s) expect_sam...
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def _compute_v2_quantities(v2_arr, bias_arr, n_blocks): """Compute the squared visibilities quantities: - average ('v2') over the cube, - covariance ('v2_cov'), - avar ('avar') and - 'err_avar'.""" n_ps = v2_arr.shape[0] n_baselines = v2_arr.shape[1] v2 = np.zeros(n_baselines) v2_cov = np.zeros...
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def make_default_config(project): """ Return a default configuration for exhale. **Parameters** ``project`` (str) The name of the project that will be searched for in ``testing/projects/{project}``. **Return** ``dict`` The global default testing conf...
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def remove_bad_data(net, init='flat', tolerance=1e-6, maximum_iterations=10, calculate_voltage_angles=True, rn_max_threshold=3.0): """ Wrapper function for bad data removal. INPUT: **net** - The net within this line should be created **init** - (string) Initial voltage ...
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from imjoy_rpc.hypha import RPC import msgpack import json async def execute_model( inputs, server_url=None, model_name=None, config=None, select_outputs=None, request_id="", model_version="", compression_algorithm="gzip", serialization="triton", decode_bytes=False, decode_...
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def find_suspicious_regions(misassemblies, min_cutoff = 2): """ Given a list of miassemblies in gff format """ regions =[] for misassembly in misassemblies: regions.append([misassembly[0], misassembly[3], 'START', misassembly[2]]) regions.append([misassembly[0], misassembly[4], 'EN...
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from typing import List import uuid from datetime import datetime import pytz def make_accounts(n) -> List[AccountSchema]: """Make n test accounts.""" return [ AccountSchema( uuid=uuid.uuid4(), bank_name=f"Starling Personal {i}", account_name=f"Account {i}", ...
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def fabonacci(n): """ Return the n'th number of the fabonacci sequence """ if n == 0: return 0 elif n == 1: return 1 else: return fabonacci(n-1) + fabonacci(n-2)
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def read_file(filepath, *args, **kwargs) -> str: """Try different encoding to open a file in readonly mode.""" for mode in ("utf-8", "gbk", "cp1252", "windows-1252", "latin-1", "ascii"): try: with open(filepath, *args, encoding=mode, **kwargs) as f: content = f.read() ...
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def s3_set_extension(url, extension=None): """ Add a file extension to the path of a url, replacing all other extensions in the path. @param url: the URL (as string) @param extension: the extension, defaults to the extension of current. request """ ...
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def fill_from_root(filename, spectrum_name="", config=None, spectrum=None, bipo=False, **kwargs): """ This function fills in the ndarray (dimensions specified in the config) with weights. It takes the parameter specified in the config from the events in the root file. Args: fil...
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def low_rank_cov_root(covs, rank, implementation='randomized_svd'): """ return X: (n_data, n_dim, rank) matrix so that X[i].dot(X[i].T) ~ covs[i] """ n_data, n_dim = covs.shape[:2] if implementation == 'randomized_svd': X = np.empty((n_data, n_dim, rank)) for i in xrange(n_data): ...
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def _map(term, predicate): """ A 'generic-function' verison of map, which defers to term.map, if one exists, and otherwise calls predicate on term. Actually iterable mapping is handled by term.map """ # validate(predicate, collections.Callable) if _IS_LOGICAL(term): return term.m...
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def has_byobu() -> bool: """Determines whether byobu can run.""" return _has_command('byobu')
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def cylinder_divergence(xi, yi, zi, r, v): """ Calculate the divergence of the velocity field returned by the cylinder_flow() function given the path of a streamtube providing its path components xi, yi, zi. The theoretical formula used to calculate the returned variable 'div' has be...
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def aggregate(df, signal_names, geo_resolution='county'): """Aggregate signals to appropriate resolution and produce standard errors. Parameters ---------- df: pd.DataFrame County block group-level data with prepared signals (output of construct_signals(). signal_names: List[str] ...
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def _equal_mstype(x, y): """ Determine if two mindspore types are equal. Args: x (mstype): first input mindspore type. y (mstype): second input mindspore type. Returns: bool, if x == y return true, x != y return false. """ return const_utils.mstype_eq(x, y)
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def LM(f, *gens, **args): """ Return the leading monomial of ``f``. **Examples** >>> from sympy import LM >>> from sympy.abc import x, y >>> LM(4*x**2 + 2*x*y**2 + x*y + 3*y) x**2 """ options.allowed_flags(args, ['polys']) try: F, opt = poly_from_expr(f, *gens, **arg...
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from typing import Dict def markers( data: AnnData, head: int = None, de_key: str = "de_res", sort_by: str = "auroc,WAD_score", alpha: float = 0.05, ) -> Dict[str, Dict[str, pd.DataFrame]]: """ Parameters ---------- data: ``anndata.AnnData`` Annotated data matrix with rows...
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def vggcif16_bn(): """VGG 16-layer model (configuration "D") with batch normalization""" return VGGcif(make_layers(cfg['D'], batch_norm=True))
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def get_platforms(): """Return the list of all the platforms""" controller = PlatformController return controller.get_list(MySQLFactory.get())
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def metade(x=0, cvsao=False): """ ==> Divide o valor de x pela metade :param x: valor recebido. :param cvsao: (opcional) Se deseja ou não exibir o valor convertido em moeda local :return: valor de x pela metade com ou não conversão para moeda local. """ x /= 2 if cvsao: x = moeda...
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def to_conll_iob(annotated_sentence): """ `annotated_sentence` = list of triplets [(w1, t1, iob1), ...] Transform a pseudo-IOB notation: O, PERSON, PERSON, O, O, LOCATION, O to proper IOB notation: O, B-PERSON, I-PERSON, O, O, B-LOCATION, O """ proper_iob_tokens = [] for idx, annotated_token in enumerate(annotat...
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def get_moves(player): """Based on th tuple of player's position, return the list of acceptable moves Parameters ---------- player : tuple Player move data >>> GAME_DIMENSIONS = (2, 2) >>> get_moves((0, 2)) ['RIGHT', 'UP', 'DOWN'] """ x, y = player moves = ['LEFT',...
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def get_models(model_names): """Retrieve Odoo models :param model_names: a list of Odoo model names :return: a list of dictionaries describing Odoo models """ query = create_model_query(model_names) app.logger.debug(query) return query_odoo("ir.model", "search_read", query)
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def find_auto_threshold(trace_log, variants, decreasingFactor): """ Find automatically variants filtering threshold based on specified decreasing factor Parameters ---------- trace_log Trace log variants Dictionary with variant as the key and the list of traces as the va...
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import re def insert_references(text, last_ref=0): """ Insert references section to the page according to local manual of style. last_ref parameter is used for transfering last reference position: it will be used for additional checks. If last_ref equals -1, references section will not be added. ...
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def KK_RC44(w, Rs, R_values, t_values): """ Kramers-Kronig Function: -RC- Kristian B. Knudsen (kknu@berkeley.edu / kristianbknudsen@gmail.com) """ return ( Rs + (R_values[0] / (1 + w * 1j * t_values[0])) + (R_values[1] / (1 + w * 1j * t_values[1])) + (R_values[2] / (...
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import torch def collate_rank_eval(data): """Collate multiple datapoints for candidate product ranking during evaluation Parameters ---------- data : list of 3-tuples Each tuple is for a single datapoint, consisting of DGLGraphs for reactants and candidate products, scores for candida...
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import gc def garbage(): """ Collect garbage and return an :class:`~refcycle.object_graph.ObjectGraph` based on collected garbage. The collected elements are removed from ``gc.garbage``, but are still kept alive by the references in the graph. Deleting the :class:`~refcycle.object_graph.Obje...
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from typing import IO from re import U def fifo(FALL_THROUGH=0, DATA_WIDTH=32, DEPTH=8): """ args: FALL_THROUGH: fifo is in fall-through mode DATA_WIDTH: default data width if the fifo is of type DEPTH: depth can be arbitrary from 0 to 2**32 """ ADDR_DEPTH = int...
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def from_snbt(snbt : str, pos : int = 0): """Create a TAG from SNBT when type is unknown""" #print(f'Starting tests at {pos}') for i in sorted(Base.subtypes, key = lambda i : i.snbtPriority): try: value, pos = i.from_snbt(snbt, pos) except ValueError: ...
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def parse_data_txt(zip_file_path): """ Extract the tables in fastqc_data.txt from FastQC results contained in a zip file. Returns a dictionary representing the tables in the file, keyed by section headers in the form: {u'Basic Statistics': {'column_labels': (u'Measure', u'Value'), ...
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def _construct_lookup( orders, dists, growth, recurrence_algorithm, rules, tolerance, scaling, n_max, ): """ Create abscissas and weights look-up table so values do not need to be re-calculatated on the fly. """ x_lookup = [] w_look...
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def feedback_system(request): """ get: Get the contents of the system feedback. post: Add a new system feedback from a user. delete: Delete a the user's system feedback. """ if request.method == 'GET': feedback = SystemFeedback.objects.all() serializer = S...
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import requests import os def get_vm_by_id(id): """Returns the VM setting information of a VM""" r = requests.get(api_url + '/vms/' + id, headers=headers).json() # populate name for vm in get_vms(): if vm['id'] == id: r['name'] = os.path.split(vm['path'])[1].split('.')[0] ...
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def parse_mr_job_stderr(stderr, counters=None): """Parse counters and status messages out of MRJob output. :param stderr: a filehandle, a list of lines (bytes), or bytes :param counters: Counters so far, to update; a map from group (string to counter name (string) to count. Return...
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def predict_coefficients(inputs: tf.Tensor, hparams: tf.contrib.training.HParams, reuse: object = tf.AUTO_REUSE) -> tf.Tensor: """Predict finite difference coefficients with a neural networks. Args: inputs: float32 Tensor with dimensions [batch, x]. hparams...
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from typing import Union def ckd(xparentkey: Octets, index: Union[Octets, int]) -> bytes: """Child Key Derivation (CDK) Key derivation is normal if the extended parent key is public or child_index is less than 0x80000000. Key derivation is hardened if the extended parent key is private and child...
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import copy def _treeizeAvailabilityZone(zone): """Build a tree view for availability zones.""" AvailabilityZone = availability_zones.AvailabilityZone az = AvailabilityZone(zone.manager, copy.deepcopy(zone._info), zone._loaded) result = [] # Zone tree view item az.z...
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def reducer(state, action): """Add HTML loaded action to state""" if isinstance(action, dict): try: action = forest.actions.Action.from_dict(action) except TypeError: # TODO: Remove try/except when Actions are supported return state if isinstance(state, di...
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from typing import OrderedDict def parseDisambig(html): """Parse disambiguation page and return list of (article, text) tuples.""" sections = OrderedDict() soup = bs4.BeautifulSoup(html, 'lxml') for i in soup.find_all(True, class_=skipclass): i.decompose() sections[''] = _processDisambigSe...
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import json def get_rookie_prediction(input): """This function receives the data from the requests, parses it as a dictionary and gets the prediction Args: input (json) The request body Returns: The prediction for the input value """ try: data = json.loads(input) ...
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import json import logging import os def task_rebuild_lowering_directory(gearman_worker, gearman_job): """ Verify and create if necessary all the lowering sub-directories """ job_results = {'parts':[]} payload_obj = json.loads(gearman_job.data) logging.debug("Payload: %s", json.dumps(payload...
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import os def make_makeblastdb_cmd(filename): """ Construct a makeblastdb command line to make a BLAST nucleotide database from the passed fragmented input sequence FASTA file. - filename is the location of the fragmented input FASTA sequence file, for constructing the database ...
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def config_register(value): """Register a value or values. Parameters: -A Value """ return ConfigurationSettings().register(value)
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import unicodedata import re def normalize(text): """ Normalizes text before keyword matching. Converts to lowercase, performs KD unicode normalization and replaces multiple whitespace characters with single spaces. """ return unicodedata.normalize('NFKD', re.sub(r'\s+', ' ', text.lower()))
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def gdb_path(): """ Get path to the gdb """ return _get_ya_plugin_instance().gdb_path
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import base64 def pass_to_key(password): """Return a key from the given password for encryption.""" byte_pass = password.encode() kdf = PBKDF2HMAC( algorithm=hashes.SHA256(), length=32, salt=b"\r7\xb6bT\xa0\xd5\xdcG.'+\xb7\xdb\r\xcd", iterations=100000, backend=defa...
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def _ProcessCards(data): """Process the set lines and corresponding data""" # Reset all values # ------------------------------------------------------------------------- setLine = [] sd = {"brN": None, "branches": None, "histN": None, "histories": None, "times": None, "units": None} ...
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def sigmoid_backward(x): """ derivation of sigmoid Paras ----------------------------------- x: output of the linear layer Returns ----------------------------------- max of nums """ s = sigmoid(x) return s * (1 - s)
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