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def load_data(connection_string: str): """ Load data from a source. Source could be: - A JSON File - A MongoDB Load data from a file --------------------- If you want to load data from a File, you must to provide this connection string: >>> connection_string = "/path/to/my/file.json"...
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def himmelblau(xy): """ Himmelblau's function, as a set of residuals (cost = sum(residuals**2)) The standard Himmelbau's function is with data as [11, 7], and four minimum at (3.0, 2.0), ~(-2.8, 3.1), ~(-3.8, -3.3), ~(3.6, -1.8). Himmelblau's function is a quadratic model in both x and y. Its data-...
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from typing import Dict def parse_instrument_data(smoothie_response: str) -> Dict[str, bytearray]: """ Parse instrument data. Args: smoothie_response: A string containing a mount prefix (L or R) followed by : and a hex string. Returns: mapping of the mount prefix to the h...
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def _volume_sum_check(props: PropsDict, sum_to=1, atol=1e-3) -> bool: """Check arrays all sum to no more than 1""" check_broadcastable(**props) sum_ar = np.zeros((1,)) for prop in props: sum_ar = sum_ar + props[prop] try: assert sum_ar.max() <= sum_to + atol except AssertionErr...
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def small_view(data, attribute): """ Extract a downsampled view from a dataset, for quick statistical summaries """ shp = data.shape view = tuple([slice(None, None, np.intp(max(s / 50, 1))) for s in shp]) return data[attribute, view]
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def playerStandings(): """Returns a list of the players and their win records, sorted by wins. The first entry in the list should be the player in first place, or a player tied for first place if there is currently a tie. Returns: A list of tuples, each of which contains (id, name, wins, matches...
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def get_all_feature_names(df: pd.DataFrame, target: str = None) -> list: """Get a list of all feature names in a dataframe. Args: df (pd.DataFrame): dataframe of features and target variable target (str): name of target column in df Returns: all_feature_names (list): list of al...
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import random def generate_enhancer_promoter_pair(ep_df): """ """ std_ep_pair = ep_df[['chrom-Enh','chromStart','chromEnd','TSS']] min_ep_gap = abs((std_ep_pair['chromEnd']-std_ep_pair['chromStart']).min()) max_ep_gap = abs((std_ep_pair['chromEnd']-std_ep_pair['chromStart']).max()) fake_samples = [] for enh...
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from typing import Union from typing import Tuple from typing import Dict def constant_lrs( draw, return_kwargs: bool = False ) -> Union[ st.SearchStrategy[lr_scheduler_pb2.ConstantLR], st.SearchStrategy[Tuple[lr_scheduler_pb2.ConstantLR, Dict]], ]: """Returns a SearchStrategy for an ConstantLR plus m...
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def get_read_only_storage_manager(): """Get the current Flask app's read only storage manager, create if necessary""" return current_app.config.setdefault('read_only_storage_manager', ReadOnlyStorageManager())
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def __parse_sql(sql_rows): """ Parse sqlite3 databse output. Modify this function if you have a different database setup. Helper function for sql_get(). Parameters: sql_rows (str): output from SQL SELECT query. Returns: dict """ column_names...
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import scipy import random def _generate_to(qubo, seed, oct_upper_bound, bias=0.5): """ Given a QUBO, an upper bound on oct, and a bias of bipartite vertices, generate an Erdos-Renyi graph such that oct_upper_bound number of vertices form an OCT set and the remaining vertices are partitioned into part...
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def sqlCreate(fields=None, extraFields=None, addCoastGuardFields=True, dbType='postgres'): """Return the sqlhelp object to create the table. @param fields: which fields to put in the create. Defaults to all. @param extraFields: A sequence of tuples containing (name,sql type) for additional fields @par...
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def _section_cohort_management(course, access): """ Provide data for the corresponding cohort management section """ course_key = course.id ccx_enabled = hasattr(course_key, 'ccx') section_data = { 'section_key': 'cohort_management', 'section_display_name': _('Cohorts'), 'access'...
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def rivers_by_station_number(stations, N): """Returns a list of N tuples on the form (river name, number of stations on the river). These tuples are sorted in decreasing order of station numbers. If many stations have the same number of stations as the 'Nth' river, these are also included.""" riversLi...
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import json def load_default_data() -> dict[str, str]: """Finds and opens a .json file with streamer data. Reads from the file and assigns the data to streamer_list. Args: None Returns: A dict mapping keys (Twitch usernames) to their corresponding URLs. Each row is repre...
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def get_value_key(generator, name): """ Return a key for the given generator and name pair. If name None, no key is generated. """ if name is not None: return f"{generator}+{name}" return None
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import math def wav_to_log_spectrogram_clips(wav_file): """convert audio into logrithmic spectorgram, then chop it into 2d-segmentation of 100 frames""" # convert audio into spectorgram sound, sr = librosa.load(wav_file, sr=SR, mono=True) stft = librosa.stft(sound, n_fft=N_FFT, hop_length=HOP_LEN, win...
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def get_title(mods): """ Function takes the objects MODS and extracts and returns the text of the title. """ title = mods.find("{{{0}}}titleInfo/{{{0}}}title".format(MODS_NS)) if title is not None: return title.text
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def sample_ingredient(user, name='Cinnamon'): """ Create and return a sample ingredient :param user: User(custom) object :param name: name of the ingredient :return: Ingredient object """ return Ingredient.objects.create(user=user, name=name)
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def viterbi(O,S,Y, pi, A, B): """Generates a path which is a sequence of most likely states that generates the given observation Y. Args: O (numpy.ndarray): observation space. Size: 1 X N S (numpy.ndarray): state space. Size: 1 X K Y (list): observation sequence. ...
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def int_to_bigint(value): """Convert integers larger than 64 bits to bytearray Smaller integers are left alone """ if value.bit_length() > 63: return value.to_bytes((value.bit_length() + 9) // 8, 'little', signed=True) return value
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def xcorr(S, dtmax=10): """ Cross correlate each pair of columns in S at offsets up to dtmax """ # import pdb; pdb.set_trace() (T,N) = S.shape H = np.zeros((N,N,dtmax)) # Compute cross correlation at each time offset for dt in np.arange(dtmax): # print "Computing cross correlati...
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def register_hooks(): """Exec all the rules files. Gather the hooks from them and load them into the hook dict for later use. """ global HOOKS_LOADED for name, path in load_rules().items(): globals = {} with open(path) as f: exec(compile(f.read(), path, 'exec'), globals)...
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def proxy_rotator(): """Return a cycle object of proxy dict""" return Proxy.get_proxy_rotator()
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def pow(a, b): """ Return an attribute that represents a ^ b. """ return multiplyDivide(a, b, MultiplyDivideOperation.POWER)
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async def send_simple_embed_to_channel(bot: commands.Bot, channel_name: str, message: str, color: str = config["colors"]["default"]) -> discord.Message: """Send a simple embed message to the channel with the given name in the given guild, using the given message and an optional colour. Args: bot (comma...
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def retry_on_server_errors_timeout_or_quota_issues_filter(exception): """Retry on server, timeout and 403 errors. 403 errors can be accessDenied, billingNotEnabled, and also quotaExceeded, rateLimitExceeded.""" if HttpError is not None and isinstance(exception, HttpError): if exception.status_code == 403: ...
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def CSourceForElfSymbolTable(variable_prefix, names, str_offsets): """Generate C source definition for an ELF symbol table. Args: variable_prefix: variable name prefix names: List of symbol names. str_offsets: List of symbol name offsets in string table. Returns: String containing C source fragme...
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from typing import Optional def get_endpoint_access(endpoint_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetEndpointAccessResult: """ Resource schema for a Redshift-managed VPC endpoint. :param str endpoint_name: The name of the endpoint. ...
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def predictor( service: MLFlowDeploymentService, data: np.ndarray, ) -> Output(predictions=np.ndarray): """Run a inference request against a prediction service""" service.start(timeout=10) # should be a NOP if already started prediction = service.predict(data) prediction = prediction.argmax(ax...
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def get_domain(ns, domain): """ Return LMIInstance of given LMI_SSSDDomain. :type domain: string :param domain: Name of the domain to find. :rtype: LMIInstance of LMI_SSSDDomain """ keys = {'Name': domain} try: inst = ns.LMI_SSSDDomain.new_instance_name(keys).to_instance() e...
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def mmd_loss(embedding, auxiliary_labels, weights_pos, weights_neg, params): """ Computes mmd loss, weighted or unweighted """ if weights_pos is None: return mmd_loss_unweighted(embedding, auxiliary_labels, params) return mmd_loss_weighted(embedding, auxiliary_labels, weights_pos, weights_neg, params)
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def perdidas (n_r,n_inv,n_x,**kwargs): """Calcula las perdidas por equipos""" n_t=n_r*n_inv*n_x for kwargs in kwargs: n_t=n_t*kwargs return n_t
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def _symlink_dep_cmd(lib, deps_dir, in_runfiles): """ Helper function to construct a command for symlinking a library into the deps directory. """ lib_path = lib.short_path if in_runfiles else lib.path return ( "ln -sf " + relative_path(deps_dir, lib_path) + " " + deps_dir + "/" ...
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def boundingBoxEdgeLengths(domain): """ Returns the edge lengths of the bounding box of a domain :param domain: a domain :type domain: `escript.Domain` :rtype: ``list`` of ``float`` """ return [ v[1]-v[0] for v in boundingBox(domain) ]
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def get_user(isamAppliance, user): """ Get permitted features for user NOTE: Getting an unexplained error for this function, URL maybe wrong """ return isamAppliance.invoke_get("Get permitted features for user", "/authorization/features/users/{0}/v1".format(user))
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from typing import Sequence from typing import List from typing import Optional def autoupdate( config_file: str, store: Store, tags_only: bool, freeze: bool, repos: Sequence[str] = (), add_unused_hooks: bool = False, ) -> int: """Auto-update the pre-commit config t...
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def plot_energy_resolution_cta_performance(cta_site, ax=None, **kwargs): """ Plot the cta performances (June 2018) for the true_energy resolution Parameters ---------- cta_site: string see `ctaplot.ana.cta_performance` ax: `matplotlib.pyplot.axes` kwargs: args for `matplotlib.pyplot...
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from typing import OrderedDict def _n_nested_blocked_random_indices(sizes, n_iterations): """ Returns indices to randomly resample blocks of an array (with replacement) in a nested manner many times. Here, "nested" resampling means to randomly resample the first dimension, then for each randomly sampl...
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def phase_type_from_parallel_erlang2(theta1, theta2, n1, n2): """Returns initial probabilities :math:`\\alpha` and generator matrix :math:`S` for a phase-type representation of two parallel Erlang channels with parametrisation :math:`(\\theta_1, n_1)` and :math:`(\\theta_2, n_2)` (rate and steps of Erlang ...
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def get_regions(max_time_value): """ Partition R into a finite collection of one-dimensional regions depending on the appearing max time value. """ regions = [] bound = 2 * max_time_value + 1 for i in range(0, bound + 1): if i % 2 == 0: temp = i // 2 r = Const...
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def label_edges(g: nx.DiGraph) -> nx.DiGraph: """Label all the edges automatically. Args: g: the original directed graph. Raises: Exception: when some edge already has attribute "label_". Returns: The original directed graph with all edges labelled. """ g_labelled = nx...
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from datetime import datetime import calendar def report_charts(request, report, casetype='Call'): """Return charts for the last 4 days based on the Call Summary Data""" # The ussual filters. query = request.GET.get('q', '') interval = request.GET.get('interval', 'daily') category = request.GET.ge...
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def _get_optimizer(learning_rate: float, gradient_clip_norm: float): """Gets model optimizer.""" kwargs = {'clipnorm': gradient_clip_norm} if gradient_clip_norm > 0 else {} return tf.keras.optimizers.Adagrad(learning_rate, **kwargs)
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def is_my_message(msg): """ Функция для проверки, какому боту отправлено сообщение. Для того, чтобы не реагировать на команды для других ботов. :param msg: Объект сообщения, для которого проводится проверка. """ text = msg.text.split()[0].split("@") if len(text) > 1: if text[1] != config.bot_name: return Fa...
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from typing import Any from typing import Dict def execute_search_query(client: Client, query: Any, data_range: str) -> Dict[str, Any]: """Execute search job and waiting for the results :type client: ``Client`` :param client: Http client :type query: ``Any`` :param query: Search query :type...
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def static(directory: str) -> WSGIApp: """Return a WSGI app that serves static files under the given directory. Powered by WhiteNoise. """ app = WhiteNoise(empty_wsgi_app()) if exists(directory): app.add_files(directory) return app
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def check_filter(id): """ Helper function to determine if the current crime is in the dictionary """ if id not in important_crime: return 30 else: return important_crime[id] * 30
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def rand_email(domain=None): """Generate a random zone name :return: a random zone name e.g. example.org. :rtype: string """ domain = domain or rand_zone_name() return 'example@%s' % domain.rstrip('.')
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def wordcount_for_reddit(data, search_word): """Return the number of times a word has been used.""" count = 0 for result in data: # do something which each result from scrape for key in result: stringed_list = str(result[key]) text_list = stringed_list.split() fo...
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def calc_precision(gnd_assignments, pred_assignments): """ gnd_clusters should be a torch tensor of longs, containing the assignment to each cluster assumes that cluster assignments are 0-based, and no 'holes' """ precision_sum = 0 assert len(gnd_assignments.size()) == 1 assert len(pred...
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def __get_app_package_path(package_type, app_or_model_class): """ :param package_type: :return: """ models_path = [] found = False if isinstance(app_or_model_class, str): app_path_str = app_or_model_class elif hasattr(app_or_model_class, '__module__'): app_path_str = ap...
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import base64 def dict_from_payload(base64_input: str, fport: int = None): """ Decodes a base64-encoded binary payload into JSON. Parameters ---------- base64_input : str Base64-encoded binary payload fport: int FPort as provided in ...
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def handle(req): """POST""" im = Image.open(BytesIO(req.files[list(req.files.keys())[0]].body)) w, h = im.size im2 = ImageOps.mirror(im.crop((0, 0, w / 2, h))) im.paste(im2, (int(w / 2), 0)) io = BytesIO() im.save(io, format='PNG') return req.Response( body=io.getvalue(), mime_...
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import glob def read_sachs_all(folder_path): """Reads all the sachs data specified in the folder_path. Args: folder_path: str specifying the folder containing the sachs data Returns: An np.array containing all the sachs data """ sachs_data = list() # Divides the Sachs dataset into environments...
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def user_permitted_tree(user): """Generate a dictionary of the representing a folder tree composed of the elements the user is allowed to acccess. """ # Init user_tree = {} # Dynamically collect permission to avoid hardcoding # Note: Any permission to an element is the same as read permiss...
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def extract_failure(d): """ Returns the failure object the given deferred was errback'ed with. If the deferred has result, not a failure a `ValueError` is raised. If the deferred has no result yet a :class:`NotCalledError` is raised. """ if not has_result(d): raise NotCalledError() e...
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def convert_where_clause(clause: dict) -> str: """ Convert a dictionary of clauses to a string for use in a query Parameters ---------- clause : dict Dictionary of clauses Returns ------- str A string representation of the clauses """ out = "{" for key in c...
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from unittest.mock import call def all_metadata_async(): """Retrieves all available metadata for an instance async""" loop = trollius.get_event_loop() res = loop.run_until_complete(call()) return res
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def median_std_from_ma(data: np.ma, axis=0): """On the assumption that there are bit-flips in the *data*, attempt to find a value that might represent the standard deviation of the 'real' data. The *data* object must be a numpy masked array. The value of *axis* determines which way the data are ha...
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def __check_value_range(x: int) -> bool: """ Checks if integer is in valid value range to be a coordinate for Tic-Tac-Toe. """ if x < 1 or x > 3: print(__standard_error_text + "Coordinates have to be between 1 and 3.\n") return False return True
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def find_option(opt): """ This function checks for option defined with optcode; it could be implemented differently - by checking entries in world.cliopts """ # received msg from client must not be changed - make a copy of it tmp = world.climsg[world.clntCounter].copy() # 0 - ether, 1 - ipv...
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from typing import Set def generic_add_model_components( m, d, reserve_zone_param, reserve_zone_set, reserve_generator_set, generator_reserve_provision_variable, total_reserve_provision_expression, ): """ Generic treatment of reserves. This function creates model components rel...
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def return_all_content(content): """Help function to return untruncated stripped content.""" return mark_safe(str(content).replace('><', '> <')) if content else None
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def get_trailing_returns(uid): """ Get trailing return chart """ connection = pymysql.connect(host=DB_SRV, user=DB_USR, password=DB_PWD, db=DB_NAME, charset='utf8mb4', ...
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def bitsNotSet(bitmask, maskbits): """ Given a bitmask, returns True where any of maskbits are set and False otherwise. Parameters ---------- bitmask : ndarray Input bitmask. maskbits : ndarray Bits to check if set in the bitmask """ goodLocs...
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def plot(foo, x, y): """x, y are tuples of 3 values: xmin, xmax, xnum""" np_foo = np.vectorize(foo) x_space = np.linspace(*x) y_space = np.linspace(*y) xx, yy = np.meshgrid(x_space, y_space) xx = xx.flatten() yy = yy.flatten() zz = np_foo(xx, yy) num_x = x[-1] num_y = y[-1] p...
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import struct def _StructPackEncoder(wire_type, format): """Return a constructor for an encoder for a fixed-width field. Args: wire_type: The field's wire type, for encoding tags. format: The format string to pass to struct.pack(). """ value_size = struct.calcsize(format) def SpecificEncode...
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def transition_temperature(wavelength): """ To get temperature of the transition in K Wavelength in micros T = h*f / kB """ w = u.Quantity(wavelength, u.um) l = w.to(u.m) c = _si.c.to(u.m / u.s) h = _si.h.to(u.eV * u.s) kb = _si.k_B.to(u.eV / u.K) f = c/l t = h*f/kb r...
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def truncate_string(string: str, max_length: int) -> str: """ Truncate a string to a specified maximum length. :param string: String to truncate. :param max_length: Maximum length of the output string. :return: Possibly shortened string. """ if len(string) <= max_length: return strin...
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import requests import json def geoinfo_from_ip(ip: str) -> dict: # pylint: disable=invalid-name """Looks up the geolocation of an IP address using ipinfo.io Example ipinfo output: { "ip": "1.1.1.1", "hostname": "one.one.one.one", "anycast": true, "city": "Miami", "region":...
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def mpileup2acgt(pileup, quality, depth, reference, qlimit=53, noend=False, nostart=False): """ This function was written by Francesco Favero, from: sequenza-utils pileup2acgt URL: https://bitbucket.org/sequenza_tools/sequenza-utils original code were protected under GPLv3 license...
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def normalize_missing(xs): """Normalize missing values to avoid string 'None' inputs. """ if isinstance(xs, dict): for k, v in xs.items(): xs[k] = normalize_missing(v) elif isinstance(xs, (list, tuple)): xs = [normalize_missing(x) for x in xs] elif isinstance(xs, basestri...
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def ini_inventory(nhosts=10): """Return a .INI representation of inventory""" output = list() inv_list = generate_inventory(nhosts) for group in inv_list.keys(): if group == '_meta': continue # output host groups output.append('[%s]' % group) for host in inv...
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def get_crp_constrained_partition_counts(Z, Cd): """Compute effective counts at each table given dependence constraints. Z is a dictionary mapping customer to table, and Cd is a list of lists encoding the dependence constraints. """ # Compute the effective partition. counts = defaultdict(int) ...
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import re from re import T def create_doc(): """Test basic layer creation and node creation.""" # Stupid tokenizer tokenizer = re.compile(r"[a-zA-Z]+|[0-9]+|[^\s]") doc = Document() main_text = doc.add_text("main", "This code was written in Lund, Sweden.") # 01...
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def jissue_get_chunked(jira_in, project, issue_max_count, chunks=100): """ This method is used to get the issue list with references, in case the number of issues is more than 1000 """ result = [] # step and rest simple calc step = issue_max_count / chunks rest = issue_max_count % chunks...
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def negative_height_check(height): """Check the height return modified if negative.""" if height > 0x7FFFFFFF: return height - 4294967296 return height
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def linked_gallery_view(request, obj_uuid): """ View For Permalinks """ gallery = get_object_or_404(Gallery, uuid=obj_uuid) images = gallery.images.all().order_by(*gallery.display_sort_string) paginator = Paginator(images, gallery.gallery_pagination_count) page = request.GET.get('page') ...
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def _gm_cluster_assign_id(gm_list, track_id, num_tracks, weight_threshold, z_dim, max_id, max_iteration=1000): """The cluster algorithm that assign a new ID to the track Args: gm_list (:obj:`list`): List of ``GaussianComponent`` representing current multi-target PH...
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def get_bprop_scatter_nd(self): """Generate bprop for ScatterNd""" op = P.GatherNd() def bprop(indices, x, shape, out, dout): return zeros_like(indices), op(dout, indices), zeros_like(shape) return bprop
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import requests def delete_policy_rule(policy_key, key, access_token): """ Deletes a policy rule with the given key. Returns the response JSON. See http://localhost:8080/docs#/Policy/delete_rule_api_v1_policy__policy_key__rule__rule_key__delete """ return requests.delete( f"{FIDESOPS...
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def knn_search_parallel(data, K, qin=None, qout=None, tree=None, t0=None, eps=None, leafsize=None, copy_data=False): """ find the K nearest neighbours for data points in data, using an O(n log n) kd-tree, exploiting all logical processors on the computer. if eps <= 0, it returns the distance to the ...
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from typing import Tuple def compute_vectors_from_coordinates( x: np.ndarray, y: np.ndarray, fps: int = 1 ) -> Tuple[Vector, Vector, Vector, Vector, np.array]: """ Given the X and Y position at each frame - Compute vectors: i. velocity vector ii. unit tangent ...
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import requests def get_now(pair): """ Return last info for crypto currency pair :param pair: ex: btc-ltc :return: """ info = {'marketName': pair, 'tickInterval': 'oneMin'} return requests.get('https://bittrex.com/Api/v2.0/pub/market/GetLatestTick', params=info).json()
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def hsv(h: float, s: float, v: float) -> int: """Convert HSV to RGB. :param h: Hue (0.0 to 1.0) :param s: Saturation (0.0 to 1.0) :param v: Value (0.0 to 1.0) """ return 0xFFFF
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def make_exponential_mask(img, locations, radius, alpha, INbreast=False): """Creating exponential proximity function mask. Args: img (np.array, 2-dim): the image, only it's size is important locations (np.array, 2-dim): array should be (n_locs x 2) in size and each row should corres...
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from typing import Tuple def delete(client, url: str, payload: dict) -> Tuple[dict, bool]: """Make DELETE requests to K8s (see `k8s_request`).""" resp, code = request(client, 'DELETE', url, payload, headers=None) err = (code not in (200, 202)) if err: logit.error(f"{code} - DELETE - {url} - {r...
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def f(x): """The objective is defined as the cost + a per-demographic penalty for each demographic not reached.""" n = len(x) assert n == n_venues reached = np.zeros(n_demographics, dtype=int) cost = 0.0 for xi, ri, ci in zip(x, r, c): if xi: reached = reached | ri # ...
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def clamp(minVal, val, maxVal): """Clamp a `val` to be no lower than `minVal`, and no higher than `maxVal`.""" return max(minVal, min(maxVal, val))
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def get_closest_spot( lat: float, lng: float, area: config.Area ) -> t.Optional[config.Spot]: """Return closest spot if image taken within 100 m""" if not area.spots: return None distances = [ (great_circle((spot.lat, spot.lng), (lat, lng)).meters, spot) for spot in area.spots ...
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def drawBezier( page: Page, p1: point_like, p2: point_like, p3: point_like, p4: point_like, color: OptSeq = None, fill: OptSeq = None, dashes: OptStr = None, width: float = 1, morph: OptStr = None, closePath: bool = False, lineCap: int = 0, lineJoin: int = 0, over...
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from functools import reduce import operator def product(numbers): """Return the product of the numbers. >>> product([1,2,3,4]) 24 """ return reduce(operator.mul, numbers, 1)
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def load_ref_system(): """ Returns l-phenylalanine as found in the IQMol fragment library. All credit to https://github.com/nutjunkie/IQmol """ return psr.make_system(""" N 0.7060 -1.9967 -0.0757 C 1.1211 -0.6335 -0.4814 C 0.6291 0.4897 ...
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from typing import List def range_with_bounds(start: int, stop: int, interval: int) -> List[int]: """Return list""" result = [int(val) for val in range(start, stop, interval)] if not isclose(result[-1], stop): result.append(stop) return result
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import warnings def iou_score(box1, box2): """Returns the Intersection-over-Union score, defined as the area of the intersection divided by the intersection over the union of the two bounding boxes. This measure is symmetric. Args: box1: The coordinates for box 1 as a list of points b...
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def _actually_on_chip(ra, dec, obs_md): """ Take a numpy array of RA in degrees, a numpy array of Decin degrees and an ObservationMetaData and return a boolean array indicating which of the objects are actually on a chip and which are not """ out_arr = np.array([False]*len(ra)) d_ang = 2.11 ...
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from typing import Tuple def outlier_dataset(seed=None) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Generates Outliers dataset, containing 10'000 inliers and 50 outliers Args: seed: random seed for generating points Returns: Tuple containing the inlier features, inlier l...
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def posts(request, payload={}, short_id=None): """ Posts endpoint of the example.com public api Request with an id parameter: /public_api/posts/1qkx8 POST JSON in the following format: POST /public_api/posts/ {"ids":["1qkx8","ma6fz"]} """ Metrics.api_comment.record(re...
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