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import copy import collections def get_db_data(relation_data, unprefixed): """Organize database requests into a collections.OrderedDict :param relation_data: shared-db relation data :type relation_data: dict :param unprefixed: Prefix to use for requests without a prefix. This should ...
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def KleinBottle(): """ A minimal triangulation of the Klein bottle, as presented for example in Davide Cervone's thesis [Cer1994]_. EXAMPLES:: sage: simplicial_complexes.KleinBottle() Minimal triangulation of the Klein bottle """ return UniqueSimplicialComplex([[2, 3, 7], [1, 2...
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def speech_to_text(file_name, model_id): """Use Watson Speech to Text to convert audio file to text.""" # create Watson Speech to Text client stt = SpeechToTextV1(iam_apikey=keys.speech_to_text_key) # open the audio file with open(file_name, 'rb') as audio_file: # pass the file to Watson ...
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def auth_connection(connection) -> openeo.Connection: """Connection fixture to a backend of given version with some image collections.""" connection.authenticate_basic(TEST_USER, TEST_PASSWORD) return connection
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def convert(tree,fileName=None): """ Converts input files to be compatible with merge request #412, where we switch from custom XML pathing to standard XPATH nomenclature. @ In, tree, xml.etree.ElementTree.ElementTree object, the contents of a RAVEN input file @ In, fileName, the name for the raven in...
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import logging def merge_object_masks(list_masks, thr_overlap=0.7): """ merge several mask into one multi-class segmentation :param [ndarray] list_masks: :param float thr_overlap: :return ndarray: >>> m1 = np.zeros((5, 6), dtype=int) >>> m1[:4, :4] = 1 >>> m2 = np.zeros((5, 6), dtype=int...
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def s3_lazy_put(filename, assume_immutable=False): """Put file to s3 only if out of sync.""" key = BUCKET.get_key(filename) if key is None: key = BUCKET.new_key(filename) print 'uploading', filename key.set_contents_from_filename(filename) return key elif assume_immutable...
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def moments(Xax, data, vheight=True, estimator=np.mean, negamp=None, veryverbose=False, nsigcut=None, noise_estimate=None, **kwargs): """ Returns the gaussian parameters of a 1D distribution by calculating its moments. Depending on the input parameters, will only output a subset of the abo...
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def preprocess_adj(adj): """Preprocessing of adjacency matrix for simple GCN model""" return normalize_adj(adj + sps.eye(adj.shape[0])).astype(np.float32)
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from apysc._callable import callable_util from apysc._event import handler_circular_calling_util as circ_util from apysc._validation.variable_name_validation import \ def get_handler_name( *, handler: _Handler, instance: Any) -> str: """ Get a handler name. Parameters ---------- handler :...
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def do_lookup(get_locations_func): """ Generic method which gets the locations in [(lat,lng),(lat,lng),...] format by calling get_locations_func and returns an answer ready to go to the client. :return: """ try: locations = get_locations_func() return {'results': [get_elevation(...
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def get_parameter_for_dev_mode(dev_mode_setting): """Return parameter for whether the test should be running on dev_mode. Args: dev_mode_setting: bool. Whether the test is running on dev_mode. Returns: str. A string for the testing mode command line parameter. """ return '--params....
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import re def load_MDU(mdu): """parse MDU and store parameter key/values in dictionary :function: TODO :returns: TODO """ params = {} key_value = re.compile("(.*)=(.*)#.*") with open(mdu, "r") as f: lines = f.readlines() for line in lines: match = key_value...
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def get_username(prompt: str) -> str: """Prompt the user for a username""" username = input(prompt) return username
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import os import pickle def load_tracking_params(base, ext, names = None, init_fname = None): """Loads parameters from file If file doesn't exist, returns empty dictionary Parameters -------------- base : str relative path to file ext : str file extension, must be eit...
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def preprocess(x, y, nb_classes=10, clip_values=None): """Scales `x` to [0, 1] and converts `y` to class categorical confidences. :param x: Data instances. :type x: `np.ndarray` :param y: Labels. :type y: `np.ndarray` :param nb_classes: Number of classes in dataset. :type nb_classes: `int` ...
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def mkatas(u_t, v_t, wap, t_t, ttt, g_w, p_l, lat, nlat, ntp, nlev): """Compute the stat.-trans. eddy APE conversions from u, v, wap and t. Arguments: - u_t: a 3D zonal velocity field; - v_t: a 3D meridional velocity field; - wap: a 3D vertical velocity field; - t_t: a 3D temperature field; ...
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def fourier_model(cn, Nfreqs): """ Calculate a 1D (complex) Fourier series model from a set of complex coefficients. Parameters ---------- coeffs : array_like Array of complex Fourier coefficients, ordered from (-n, n), where n is the highest harmonic mode in the model. Nfreqs ...
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def remove_low_information_features(feature_matrix, features=None): """Select features that have at least 2 unique values and that are not all null Args: feature_matrix (:class:`pd.DataFrame`): DataFrame whose columns are feature names and rows are instances features (list[:class:`f...
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def latlt2cart(lat,lt,hemisphere): """ Latitude and local time to cartesian for a top-down dialplot """ r,theta = latlt2polar(lat,lt,hemisphere) return r*cos(theta),r*sin(theta)
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import hashlib def md5_password(password, salt): """获取原始密码+salt的md5值 """ trans_str = password + salt md = hashlib.md5() md.update(trans_str.encode('utf-8')) return md.hexdigest()
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import math def build_belief(belief_type: Belief, num_agents=NUM_AGENTS, **kwargs): """Evenly distributes the agents beliefs into subgroups. """ if belief_type is Belief.MILD: middle = math.ceil(num_agents / 2) return [0.2 + 0.2 * i / middle if i < middle else 0.6 + 0.2 * (i - middle)...
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import warnings def find_time_delay(s1, s2, datarate=1, resolution: bool=False): """ Finds the time shift or delay between two signals If s1 is advanced to s2, then the delay is positive. Parameters ---------- s1: array-like s2: array-like datarate: int, optional Input data sa...
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def create(workout_type, user_id): """Creates a workout object of the specified type.""" workout = None if workout_type == Keys.WORKOUT_TYPE_EVENT: workout = Event.Event(user_id) elif workout_type == Keys.WORKOUT_TYPE_REST: workout = Rest.Rest(user_id) else: workout = Workout...
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def clean_dfg_based_on_noise_thresh(dfg, activities, noise_threshold, parameters=None): """ Clean Directly-Follows graph based on noise threshold Parameters ---------- dfg Directly-Follows graph activities Activities in the DFG graph noise_threshold Noise threshold ...
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def post_form(lti): # pylint: disable=unused-argument, """ Access route with 'initial' request. :param lti: `lti` object :return: string "hi" """ return "hi"
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def cached_accessor(func_or_att): """Decorated function checks in-memory cache and disc cache for att first""" if callable(func_or_att): #allows decorator to be called without arguments att = func_or_att.__name__ return cached_accessor(func_or_att.__name__)(func_or_att) att = func_or_att ...
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import os import glob def function_parts(subdir='./'): """ Parse the gsw m-files in a given subdirectory. Returns a list of parts: function base name, arguments, inputs, and the help lines with a section index dictionary. """ d = os.path.join(mfiledir, subdir) mfilelist = glob.glob(os.pa...
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import os def make_graph(): """Convert the data to a BEL graph.""" if not os.path.exists(DATA_PATH): download_data() df = extract_data() return _make_graph(df)
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from typing import List def find_valid_background_scan_segments( N2_CVs, maximum_scanrate=0.011, scan_length=1950, segment_key="Segment #" ) -> List[int]: """checks if the data possibly contains a N2 background scan""" # finder_warning = None if not isinstance(N2_CVs, pd.DataFrame): # option...
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import collections def api_root(request, format=None): """ GET: Display all available urls. Since some urls have specific permissions, you might not be able to access them all. """ apis = PUBLIC_APIS(request, format) if request.user.is_staff: apis += PROTECTED_APIS(request, fo...
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from typing import Tuple import requests import time def get_gas_from_etherscan(key: str, verbose: bool = False) -> Tuple[int, int, int]: """ Fetch gas from Etherscan API """ r = requests.get('https://api.etherscan.io/api', params={'module': 'gastracker'...
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def bit(value, position, length=1): """ Return bit of number value at position Position starts from 0 (LSB) :param value: :param position: :param length: :return: """ binary = bin(value)[2:] size = len(binary) - 1 if position > size: return 0 else: return ...
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from operator import mod def sidereal_lunar_longitude(tee): """Return sidereal lunar longitude at moment, tee.""" return mod(lunar_longitude(tee) - precession(tee) + SIDEREAL_START, 360)
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def create_view_event_from_tensor_basic_info(tensors_info): """ Create view event reply according to tensor names. Args: tensors_info (list[TensorBasicInfo]): The list of TensorBasicInfo. Each element has keys: `full_name`, `node_type`, `iter`. Returns: EventReply, the even...
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def diff_template(page, label=None): """ Return a Template:Diff2 string for the given Page. """ if label is None: label = page.title() return f"{{{{Diff2|{page.latest_revision_id}|{label}}}}}"
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def is_builtin(func): """Return true if the function is a DGL builtin function.""" return isinstance(func, fn.BuiltinFunction)
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import warnings def compute_sun_angle( position, pose, utc_datetime, sensor_orientation, ): """ compute the sun angle using pysolar functions""" altitude = 0 azimuth = 0 with warnings.catch_warnings(): # Ignore pysolar leap seconds offset warning warnings.simplefilter("ignore")...
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def find_tuple_with_max_ncp(base, T, key, T_max_vals, T_min_vals): """ Scan through the whole table T, and find the i-th tuple that maximizes NCP(base, i). Parameters ---------- :param base: list of int Tuple to compare T's tuples against :param key: T the table :param...
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import re def make_dataset_name(name): """Dataset name contains "letters, numbers, -, _" only, any other content will be replaced with "-" """ def may_replace(c): if re.match("\w", c) is None: if c == '-': return c else: return "_" e...
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def averageOfNumbers(): """ averageOfNumbers Outputs the average of a series of numbers entered by the user. returns: The average of the numbers input by the user. """ numbers = [] amount_of_numbers = input("How many numbers do you wish to input? ") for x in range(1, int(amount...
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import csv import tqdm def process_covid_ctset_data(covid_ctset_meta_csv, covid_ctset_dir, output_dir): """Processes slices for all patients in the given COVID-CTSet CSV file""" filenames = [] classes = [] with open(covid_ctset_meta_csv, 'r') as f: reader = list(csv.DictReader(f, delimiter=','...
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import sys def validate(xmlfile=None, type='request'): """ :type xmlfile: str """ if type not in ['request', 'response', 'cixml']: print('Invalid xml document type') sys.exit() else: schema = xmlschema.XMLSchema('{}{}.xsd'.format(SCHEMAS_FOLDER, type)) return schem...
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import time def pinsage_sample(graph, nodes, samples, top_k=10, proba=0.5, norm_bais=1.0, ignore_edges=set()): """Implement of graphsage sample. Reference paper: . Args: graph: A pgl...
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import sys def nbytes(obj, pprint=False): """ return the number of bytes of the object, which includes size of nested structures Parameters ---------- obj: object object to find the size of pprint: bool, optional (default=False) if set, returns the result after calling pretty...
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import math def calc_entropy(data, base=2): """ Calculate the entropy of data. Using documentation from scipy.stats.entropy as the basis for this code (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html). :param data: Measure the entropy of this object :return: Calcu...
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import functools def singledispatch_method(func): """Singledispatch on second argument, i.e. the one that isn't `self`.""" dispatcher = functools.singledispatch(func) def wrapper(*args, **kw): return dispatcher.dispatch(args[1].__class__)(*args, **kw) wrapper.register = dispatcher.register ...
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def set_slops_from_read_orientation(slop, tpl_reads_orientation, cipos, ciend, buffer=0): # TODO: from Hardcoded to Dynamic Variable for buffer if requested """ set the slop boundaries according to the svtype and the reads orientation fro that svtype :param slop: minimum slop value :param tpl_reads_ori...
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import functools def authenticate_user_registration(some_function): """ Decorator for functions (pages) that require a user to provide identification. Returns 403 (forbidden) or 401 (depending on beiwe-api-version) if the identifying info (username, password, device ID) are invalid. In any function wr...
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def getValidPaths(author_collabs, year): """ selects the neighbours with valid paths returns a list where each result is a tuple of author name, collaborations < year """ valid_neighbours = list() for n in author_collabs: valid_collabs = list() for c in author_collabs[n]: ...
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from typing import Optional from typing import Dict from typing import Any from typing import Tuple def plot_image( image: ArrayLike, imshow_kwargs: Optional[Dict] = None, text_kwargs: Optional[Dict] = None, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plots given image. Parameter...
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def get_same_pinyin(char): """ 取同音字 :param char: :return: """ return same_pinyin.get(char, set())
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def get_chart_params(title, output, show_legend=False): """Parse output type and output options and return related chart parameters. For example: returns filename if output_type is file or server it output_type is server Args: title (str): the title of your plot. output (str): selected ...
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def preprocess(img, net_input_height_size=368, stride=8, pad_value=(0, 0, 0), img_mean=(128, 128, 128), img_scale=1/256): """ Args: img: Returns: """ height, width, _ = img.shape scale = net_input_height_size / height scaled_img = cv2.resize(img, (0, 0), fx=scale,...
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def compute_DVARS(func_img, mean_img=None, mask=None, apply_mask=False): """Computes the DVARS for a given fMRI image.""" if mean_img is None: mean_img = np.mean(func_img, axis=3) if apply_mask: if mask is None: mask = mask_image(mean_img) mean_img[np.logical_not(mask)]...
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def count_unplayed_cards(r, progress): """Returns the number of cards which are not yet played, including cards which are unplayable because all those cards (or cards of a value below it) are already discarded""" n = 0 for suit in r.suits: n += 5 - progress[suit] return n
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def index(request): """View function for home page of site.""" # Render the HTML template index.html with the data in the context variable. return render( request, 'index.html', context = {}, )
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def find_start_time(arr, reps=1): """! A function to find when simulations reaches a steady state with respect to array, arr. @param arr: Array to find steady state in @param reps: repetitions of recursion @return: st Start time, the index of time array when the simulation first reaches a the s...
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def execute_sql(sql, model=Award, fetcher=fetchall_fetcher, read_only=True): """ Executes a sql query against a database. sql - Can be either a sql string statement or a psycopg2 Composable object (Identifier, Literal, SQL, etc). model - A Django model that represents a ...
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import torch def calc_region(bbox, ratio, featmap_size=None): """Calculate a proportional bbox region. The bbox center are fixed and the new h' and w' is h * ratio and w * ratio. Args: bbox (Tensor): Bboxes to calculate regions, shape (n, 4) ratio (float): Ratio of the output region. ...
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def toFENhash(fen): """ Removes the two last parts of the FEN notation """ return ' '.join(fen.split(" ")[:-2])
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def remap_lads(lad_name): """Some LADs of MSOA census data do not match the LAD geographies of the LAD population data """ if lad_name == 'E06000048': lad_mapped = 'E06000057' #Northumberland elif lad_name == 'E08000020': lad_mapped = 'E08000037' elif lad_name == 'E07000097': ...
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def generate_dummy_ip(): """ makes a random ip address as a string """ return str(randint(0,255)) + "." + str(randint(0,255)) + "." + str(randint(0,255)) + "." + str(randint(0,255))
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import os import sys def _upload_and_get_command(boto3_factory, args, job_s3_folder, job_name, config, log): """ Get command by parsing args and config. The function will also perform an s3 upload, if needed. :param boto3_factory: initialized Boto3ClientFactory object :param args: input arguments...
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from typing import Union import os def get_cached(path: str, category: str=None) -> Union[str, dict, list]: """Get contents of a file. The file contents are cached in memory, and all subsequent calls to `get_cached()` with identical `path` & `category` arguments return cached contents rather than rea...
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def get_paying_proxy_contract(w3: Web3, address=None): """ Get Paying Proxy Contract. This should be used just for contract creation/changing master_copy If you want to call Safe methods you should use `get_safe_contract` with the Proxy address, so you can access every method of the Safe :param w3: ...
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import regex def search_motif_positions_debug(sequence, motif): """ Search a sequence for a given motif (as regex), and return position as list. Return False if the motif is absent from the sequence. """ print("Found motifs: %s" % regex.findall(r"%s" % motif, sequence)) positions = [m.start(...
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def validate(detector, val_data, metric): """Test on validation dataset.""" metric.reset() for img, label in val_data: # scores, bboxes = detector.detect(img) scores, bboxes = detector.ms_detect(img) metric.update(bboxes, scores, label) return metric.get()
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def calculate_EcefPoints(lats, longs): """ Construct a numpy array of EcefPoints from 2 arrays containing Latitudes and Longitudes. Note: lats and longs must be of equal lengths. Parameters ---------- lats: float array An array of Latitudes in [degrees]. longs: float array ...
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def compute_flow_map(u, v, gran=8): """ Plot optical flow map """ flow_map = np.zeros(u.shape) for y in range(flow_map.shape[0]): for x in range(flow_map.shape[1]): if y % gran == 0 and x % gran == 0: dx = 2 * int(u[y, x]) dy = 2 * int(v[y, x]) ...
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def brand_usage(number_of_brand_purchasers, total_purchasers): """Returns the percentage of brand usage for a period. Args: number_of_brand_purchasers (int): Total number of unique purchasers of a brand in a period. total_purchasers (int): Total unique purchasers in a period. Returns: ...
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def sim_walks(num_steps, num_trials, d_class): """Assumes num_steps an int >= 0, num_trials an int > 0, d_class a subclass of Drunk Simulates num_trials walks of num_steps steps each. Returns a list of the final distances for each trial""" Homer = d_class() origin = Location(0, 0) ...
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import os def colormaps_path(): """Returns the path to user-defined colormaps""" return os.path.join(user_path(), "colormaps")
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def _construct_request_parameters(args: dict, keys: list, params={}): """A helper function to add the keys arguments to the dict parameters""" parameters = {} if params is not None: for p in params: parameters[p] = params[p] for (arg_field, filter_field) in keys: value = ar...
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from typing import Optional def __run_query_table(context, client, sql: String) -> Optional[DataFrame]: """ Execute an SQL :param context: execution context :param sql: the SQL select query to execute :return: panda DataFrame or None :rtype: panda.DataFrame """ df = None close_dow...
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import six import uuid def add_parts_json(ibs, annot_uuid_list, part_bbox_list, part_theta_list, **kwargs): """ REST: Method: POST URL: /api/part/json/ Ignore: sudo pip install boto Args: annot_uuid_list (list of str) : list of annot UUIDs to be used in IBEIS IA ...
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def single_re_sub_empty_test(): """A function without arguments, needed for timeit. NB: uses global variable text to solve the argument problem""" return single_re_sub_empty(text, repl_chars)
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import sys def A_STAR(global_map, start, end, Type = '8c',e = 1, heuristic = 'eu' ): """ Implements A* Algorithm for the Given Grid Map Arguments: global_map - np.array() start - start location [x,y] end - end location [x,y] Type - Type of Neibhours ('8c' or '4c') e - Heurestic Factor ...
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def create_templates(filename): """Create templates with the service.""" data_to_use = _load_json(filename) service = current_records_marc21.templates_service templates = [] for data in data_to_use: template = service.create(data=data["values"], name=data["name"]) templates.append(t...
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def coherence_limit(nQ=2, T1_list=None, T2_list=None, gatelen=0.1): """ The error per gate (1-average_gate_fidelity) given by the T1,T2 limit. Args: nQ: number of qubits (1 and 2 supported). T1_list: list of T1's (Q1,...,Qn). T2_list: list of T2's (as measured, ...
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from typing import Sequence import warnings def connection_from_list_slice( list_slice: Sequence, args: ConnectionArguments = None, connection_type: ConnectionConstructor = Connection, edge_type: EdgeConstructor = Edge, pageinfo_type: PageInfoConstructor = PageInfo, slice_start=0, list_len...
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def logp_model(residuals, params_nodes, Pk, Variance, distribution_parameters): """returns -2log p of residuals""" Pk_double = np.concatenate((Pk, Pk)) return 2*np.sum(np.square(params_nodes) / Pk_double) + negative_2loglikelihood(residuals, Variance, distribution_parameters)
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def _loadParamFromFile(config, section, paramName): """ read param from file validate it and load to to global conf dict """ # Get paramName from answer file value = config.get(section, paramName) # Validate param value using its validation func param = controller.getParamByName(pa...
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def nonConv(wSpace, pts, val= -100, vis= False, img_name= 'img'): """This function takes in list of convex shapes with there points list for each shape and the union of these shapes will give us the desired nonConvex shape""" convPts = [] ctr=0 for conv in pts: ctr+=1 wSpace1 = convPolygon(wSpace, conv, vis=...
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import os def restart_run(db) -> list: """When restarting a hash run, identify which files need to be skipped from the database Args: db (TinyDB database): A TinyDB database objects containing filepaths and hashes of the search so far """ logger.info("Restarting hashing proces...
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from typing import Dict from typing import List from typing import Tuple def votes_per_month(values: Dict[str, List[str] | Tuple[str]]) -> Dict[str, Dict[str, int]]: """ :param values: a dictionary formatted like: {month: list of votes} :returns: dictionary every person for every month """ all_per...
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import array def CalculationElement(curr_doc, elementlist): """ :param elementlist: list of all elements :return: all_functions: [[functions of elements 1][functions of element 2]...[functions of element n]] --> order of functions: N, u, Q, M, w, phi x: Symbol to plot over ...
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def adjacency2digraph( adj_matrix: np.ndarray, similar_this_dgraph: nx.DiGraph = None) -> nx.DiGraph: """Converts the adjacency matrix to directed graph. If similar_this_graph is given, then the final directed graph has the same node labeling as the given graph has. Using dgraph2adjacen...
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def move_cursor(move): """Return move value for cursor.""" return { 'left': -1, 'right': 1, }[move]
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def accuracy(output, target): """Computes the precision@k for the specified values of k""" topk = (1,4) maxk = max(topk) batch_size = target.size(0) # 上位何件のデータを参照するかターゲットデータのラベル数を参照 label_count = np.count_nonzero(target == 1, axis = 1) # one-hot配列からクラス番号を取得 class_index_array, class_num...
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def unsubscribe_from_basket_action(newsletter): """Unsubscribe from Basket action.""" def unsubscribe_from_basket(modeladmin, request, queryset): """Unsubscribe from Basket.""" ts = [(mozillians.users.tasks.unsubscribe_from_basket_task .subtask(args=[userprofile.user.email, userp...
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from typing import Tuple from typing import Dict from typing import List import os import shutil def pgi_params() -> Tuple[Dict[str, str], List[str]]: """ Nvidia PGI compilers pgc++ is not available on Windows at this time """ compilers = {'FC': 'pgfortran', 'CC': 'pgcc'} i...
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def find_possible_pt2262_device(device_id): """Look for the device which id matches the given device_id parameter.""" for dev_id, device in RFX_DEVICES.items(): if hasattr(device, 'is_lighting4') and len(dev_id) == len(device_id): size = None for i, (char1, char2) in enumerate(zi...
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import math def gcd(*nums): """ Find the greatest common divisor (GCD) of a list of numbers. Args: *nums (tuple[int]): The input numbers. Returns: gcd_val (int): The value of the greatest common divisor (GCD). Examples: >>> gcd(12, 24, 18) 6 >>> gcd(12, 2...
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def create_app(debug=True, base_url="/api/v1/data"): """Create main Flask app if this module is the entrypoint, e.g. python3 -m data.api""" app = Flask(__name__) app.debug = debug configure_app(app, base_url=base_url) return app
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def exposure_compensation(img, illuminant_lux, # ambient illumination strength in lux luminance_target=300, # will compensate for this luminance, in cd/m2 luminance_display=300, # the luminance of the display cd/m2 ...
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from typing import Union import os def get_generate_dicom_seg_cmd( resampled_aseg: Union[str, bytes, os.PathLike], aseg_dicom_seg_metadata: Union[str, bytes, os.PathLike], t1_dicom_file: Union[str, bytes, os.PathLike], aseg_dicom_seg_output: Union[str, bytes, os.PathLike], ) -> str: """Generate th...
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from typing import Callable from pathlib import Path from typing import Tuple from typing import Type def template_and_parser( template_file: Callable[[str, str], Path] ) -> Callable[[str, str], Tuple[Path, Type[AbstractParser]]]: """ Retrieve the parser and test template for a particular supported fi...
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def sum_2(strg): """Sums last 3 digits""" sum = 0 for i in strg[3:]: sum += int(i) if sum == 0: sum = 1 return sum
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def URLs(integration: bool): """ summary: return URLs dict (funName:url) arguments: (integration: bool [test type]) """ URLs = { "get_access_token": "/token", "GetMe": "/users/me/", "ListUsers": "/users", "GetAdmins": "/users/admins", "CreateUser": "/user", ...
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