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def plot_chirpam_fit(cell_mean, param_d, QI=None, fit_f=sinexp_sigm, start=420, stop=960, ax=None): """ Helper function to visualize the fit of a cell response to a chirp_am stimulus. params: - cell_mean: Cell's mean response to the stimulus - param_d: Parameter diction...
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def command_ltc(bot, user, channel, args): """Display current LRC exchange rates from BTC-E""" r = bot.get_url("https://btc-e.com/api/2/ltc_usd/ticker") j = r.json()['ticker'] return bot.say(channel, "BTC-E: avg:$%s last:$%s low:$%s high:$%s vol:%s" % (j['avg'], j['last'], j['low'], j['high'], j['vol']...
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import random def new_match(request, cmd_args): """ Return a slack message with a link to a new match """ # Could potentially add arguments to allow game configuration here serializer = LiveMatchSerializer(data={"config": cmd_args}) if serializer.is_valid(): live_match = serializer.sa...
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def head(feats, anchors, num_classes): """Convert final layer features to bounding box parameters. Parameters ---------- feats : tensor Final convolutional layer features. anchors : array-like Anchor box widths and heights. num_classes : int Number of target classes. ...
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def socfaker_azurevmtopology_get(): """ None """ if validate_request(request): return jsonify(str(socfaker.products.azure.vm.topology))
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import json def read_json_file(filename): """Load json object from a file.""" with open(filename, 'r') as f: content = json.load(f) return content
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import copy def createVectorisedTargValObjFunction(functTypeStr:str, averageMethod="mean",catchOverflow=True, errorRetVal=1e30, normToErrorRetVal=False, greaterThanIsOk=False, lessThanIsOk=False, useAbsVals=False, divideErrorsByNormFactor=None): """ Creates a comparison function that operators on (iterA,iterB) and r...
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def get_nonoverlap_ra_dataset_conf(dataset_conf): """extract segments by shifting segment length""" if dataset_conf["if_rand"]: info("disabled dataset_conf if_rand") dataset_conf["if_rand"] = False if dataset_conf["seg_rand"]: info("disabled dataset_conf seg_rand") dataset_co...
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def _f_model_snaive_wday(a_x, a_date, params, is_mult=False, df_actuals=None): """Naive model - takes last valid weekly sample""" if df_actuals is None: raise ValueError('model_snaive_wday requires a df_actuals argument') # df_actuals_model - table with actuals samples, # adding y_out column w...
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from subprocess import Popen, PIPE import os def read_fts(self,s, orders=None, filename=None, pfits=True, verb=True): """ SYNTAX: read_fts(filename) OUTPUT: namedtuple('spectrum', 'w f berv bjd blaze drift timeid sn55 ') w - wavelength f - flux berv - Barycentric Earth ...
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def union_exprs(La, Lb): """ Union two lists of Exprs. """ b_strs = set([node.unique_str() for node in Lb]) a_extra_nodes = [node for node in La if node.unique_str() not in b_strs] return a_extra_nodes + Lb
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def programs_reload(): """Reload programs from config file Parameters (default): - do_create (True) - do_update (True) - do_pause (False) """ try: result = dar.reload_programs(**request.args) except TypeError as e: log.info("Caught TypeError: %s" % (str(e))) ...
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import requests def generate_urls(): """Gathers clinical trials from clinicaltrials.gov for search term defined in build_url() function and downloads to specified file format. """ api_call = build_url(expr='Cancer', max_rnk=1, fmt='json') r = requests.get(api_call) data = r.json() ...
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def xyz2luv(xyz, illuminant="D65", observer="2"): """XYZ to CIE-Luv color space conversion. Parameters ---------- xyz : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in XYZ format. Final dimension denotes channels. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, option...
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import os def safe_open_w(path): """ Open "path" for writing, creating any parent directories as needed. """ mkdir_p(os.path.dirname(path)) return open(path, 'wb')
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def today(): """ Today Page: Displays all the notifications like the word of the day, news highlights or friends who added you or shared a note with you """ return render_template('main/today.html', username=session['username'])
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import re import itertools def compile_read_regex(read_tags, file_extension): """Generate regular expressions to disern direction in paired-end reads.""" read_regex = [re.compile(r'{}\.{}$'.format(x, y))\ for x, y in itertools.product(read_tags, [file_extension])] return read_regex
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def filter(start=None, stop=None, **kwargs): """ Get commands with ``start`` <= date < ``stop``. Additional ``key=val`` pairs can be supplied to further filter the results. Both ``key`` and ``val`` are case insensitive. In addition to the any of the command parameters such as TLMSID, MSID, SCS, S...
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def _get_energy_at_time(masses, pos, vel, time_idx): """ Internal function used to calculate kinetic energy and potential energy at a give time index using a vectorized direct sum approach. This function is necessary to facilitate the parallelization of the energy calculation across multiple CPU cor...
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def get_root_url_for_date(date): """ Returns the root URL of the TMCDB web I/F for the given date. The argument date should be an ISO-8601 date string (YYYY-MM-DD). The returned URL already contains the date. """ year = date[:4] mm = date[5:7] hostname = get_host_name() return "%s/i...
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def dummy_annotation_txt_one_segment(tmpdir_factory): """Create empty TXT annotations.""" content = ("# MNE-Annotations\n" "# onset, duration, description\n" "3.14, 42, AA") fname = tmpdir_factory.mktemp('data').join('one-annotations.txt') fname.write(content) return fn...
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def article_title_meets_posting_requirements(website, article_title): """ Validates that the article title meets all requirements to post the list to Reddit. The validations below check if: (1) The article contains a number (2) The article title doesn't contain certain pre-defined keywords ...
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import json import csv def about(): """ returns about page """ counter = json.load(open(r'filter/OXAs_dict/counter.txt')) ids = [*counter] r = csv.reader(open(r'Training_data/Training_data_IC.csv')) df = pd.DataFrame(data=list(r)) svm_table = df.to_html(index=False, header=False) return re...
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from typing import List def phys_mem_regions_from_elf(elf: ElfFile, alignment: int) -> List[MemoryRegion]: """Determine the physical memory regions for an ELF file with a given alignment. The returned region shall be extended (if necessary) so that the start and end are congruent with the specified a...
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def force_float_to_int_in_any_way(x): """This force a float to be converted to an int. Any float is fine. The result is truncated. Like PHP, if the input float is greater than 2**63, then the result is 0, even if intmask(int(f)) would return some bits.""" # magic values coming from pypy.rlib.rarith...
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from typing import Union def where(condition : pdarray, A : Union[Union[int,float], pdarray], B : Union[Union[int,float], pdarray]) -> pdarray: """ Returns an array with elements chosen from A and B based upon a conditioning array. As is the case with numpy.where, the return arra...
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def pre_training_configs(m): """ Before training the model, configure it """ ordering = range(m.n_visible) np.random.shuffle(ordering) trainer = Optimization.MomentumSGD(m.nade, m.nade.__getattribute__(m.loss_function)) trainer.set_datasets([m.training_dataset, m.masks_dataset]) trainer...
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import os def si_pbesol_nomeshsym(request): """Return Phono3py instance of Si 2x2x2. * without mesh-symmetry * no fc """ yaml_filename = os.path.join(current_dir, "phono3py_si_pbesol.yaml") forces_fc3_filename = os.path.join(current_dir, "FORCES_FC3_si_pbesol") enable_v2 = request.config...
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import torch def loadClusterModule(pathCheckpoint): """ Load CPC Clustering Module from Clustering checkpoint file. """ state_dict = torch.load(pathCheckpoint, map_location=torch.device('cpu')) clusterModule = kMeanCluster(torch.zeros(1, state_dict["n_clusters"], state_dict["dim"])) clusterMod...
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import base64 def bash_inline_create_file(name, contents): """ Turns a file into bash command. Parameters ---------- name : str File name. contents : bytes File contents. Returns ------- result : str The resulting command that creates this file. """ ...
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import html def rtf_encode(unicode_string): """ Converts HTML encoding and Unicode encoding to RTF. Be sure that autoescaping is off in the template. Autoescaping converts <, >, ", ', & The unescape function used here is helpful for catching additional escape sequences used for special characters,...
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def get_P(A): """ Markov matrix. P = D^{-1}*A """ D = get_D(A) return np.linalg.inv(D).dot(A)
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def get_movie_and_zmw_from_name(name): """Given a string of pacbio zmw name or read name, return movie and zmw""" try: fs = name.strip().split(' ')[0].split('/') movie, zmw = fs[0], fs[1] return movie, int(zmw) except ValueError: raise ValueError("Read %r is not a PacBio read...
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def create_sp_history_copy(sp): """ Create a history copy of SP, with end_at value and new pk return: created service provider object """ admins = sp.admins.all() admin_groups = sp.admin_groups.all() nameidformat = sp.nameidformat.all() grant_types = sp.grant_types.all() response_ty...
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def onehot(arr, num_classes=None, safe=True): """ Function to take in a 1D label array and returns the one hot encoded transformation. """ arr = exactly_1d(arr) if num_classes is None: num_classes = np.unique(arr).shape[0] if safe: if num_classes != np.unique(arr).shape[0]: ...
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def scale_relative_sea_level_rise_rate(mmyr: float, If: float = 1) -> float: """Scale a relative sea level rise rate to model time. This function scales any relative sea level rise rate (RSLR) (e.g., sea level rise, subsidence) to a rate appropriate for the model time. This is helpful, because most dis...
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def get_covalent1_radius(Z): """ Converts array of nuclear charges to array of corresponding valence. Args: Z (numpy ndarray): array with nuclear charges Returns: numpy ndarray: array of the same size as Z with the valence of the corresponding atom """ global _elements if _elements is None: _elem...
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from typing import List def get_bank_sizes(num_constraints: int, beam_size: int, candidate_counts: List[int]) -> List[int]: """ Evenly distributes the beam across the banks, where each bank is a portion of the beam devoted to hypotheses having met the same number of c...
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def ssh_connect(openstack_properties): """Create a connection to a server via SSH. Args: openstack_properties (dict): OpenStack facts and variables from Ansible which can be used to manipulate OpenStack objects. Returns: def: A factory function object. """ connections ...
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def prepare(args: dict, overwriting: bool): """Load config and key file,create output directories and setup log files. Args: args (dict): argparser dictionary Returns: Path: output directory path """ output_dir = make_dir(args, "results_tmp", "activity_formatting", overwriting) ...
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import ast def parse_data(data): """Takes a string from a repr(WSGIRequest) and transliterates it This is incredibly gross "parsing" code that takes the WSGIRequest string from an error email and turns it into something that vaguely resembles the original WSGIRequest so that we can send it throug...
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from typing import List def create_slack_context_block(elements: List[SlackBlock]) -> dict: """ Creates a "context block" as described in the slack documentation here: https://api.slack.com/reference/messaging/blocks#context """ return { 'type': 'context', 'elements': [element.get_...
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import os def script_synthesize_filters(config): """ The scripting version of `synthesize_masks`. This function applies the filter to the entire directory (or single file). It combines the filter files from the data directory. Parameters ---------- config : ConfigObj The configur...
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def sample_bar_report(request): """ Demonstrates a basic horizontal bar chart report. """ profile = request.get_user_profile() if not profile.super_admin: raise PermissionDenied('Only super admins can view this report.') # Run your custom report logic to build the following lists: #...
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def _is_ref_path(path_elements): """ Determine whether the given object path, expressed as an element list (see _element_list_to_object_path()), ends with a reference and is therefore eligible for continuation through the reference. The given object path is assumed to be "completed" down to a singl...
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import numpy def apply_along_axis(func1d, axis, arr, *args, **kwargs): """Apply the harmonic analysis to 1-D slices along the given axis.""" arr = dask.array.core.asarray(arr) # Validate and normalize axis. arr.shape[axis] axis = len(arr.shape[:axis]) # Rechunk so that analyze is applied ove...
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import pytz from datetime import datetime def current_time_utc(conf): """ Get time in UTC """ UTC = pytz.utc curr_time = datetime.now(UTC) return curr_time
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def CORe50(root=expanduser("~") + "/.avalanche/data/core50/", scenario="nicv2_391", run=0, train_transform=None, eval_transform=None): """ Creates a CL scenario for CORe50. If the dataset is not present in the computer, this method will automatically download...
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def merge(line): """ Function that merges a single row or column in 2048. """ res = [] for ele in line: res.append(ele) for num in res: if num == 0: res = shift(res,res.index(num)) for inde in range(len(res)-1): if res[inde] == res[inde+1]: res...
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import PIL def image_to_bytes(image: "PIL.Image.Image") -> bytes: """Convert a PIL Image object to bytes using native compression if possible, otherwise use PNG compression.""" buffer = BytesIO() format = image.format if image.format in list_image_compression_formats() else "PNG" image.save(buffer, fo...
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def add_transform(transform_type, transform_tag=None, priority=0, status=TransformStatus.New, locking=TransformLocking.Idle, retries=0, expired_at=None, transform_metadata=None, workprogress_id=None, session=None): """ Add a transform. :param transform_type: Transform type. :param tra...
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import joblib def load_memmap(path): """load_memmap方法用于读取共享数据 Parameters ---------- path : str 文件路径 Returns ---------- """ memmap_data = joblib.load(path, mmap_mode='r+') return memmap_data
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from typing import Tuple import enum from typing import Optional def _classical_routine_on_result( N: int, t: int, x: int, measurement ) -> Tuple[enum.Enum, Optional[Tuple[int, ...]]]: """Try to find factors, given x,N,t and the result of a single quantum measurement. :param N: number to factorise :p...
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def get_rst_cls_file_header(collection_name, class_name): """produce the rst content to begin an attribute-level *.rst file""" # use :doc:`class_name<index>` syntax to create reference back to the index.rst file title = ":doc:`%s<../index>` %s" % (collection_name.capitalize(), class_name) return get_rs...
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def is_leap(year): """ Simply returns true or false depending on if it's leap or not. """ return not year%400 or not (year%4 and year%100)
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def ForwardDynamics(thetalist, dthetalist, taulist, g, Ftip, Mlist, \ Glist, Slist): """Computes forward dynamics in the space frame for an open chain robot :param thetalist: A list of joint variables :param dthetalist: A list of joint rates :param taulist: An n-vector of joint force...
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def union(dataframe1, dataframe2) -> pd.DataFrame: """The set union between dataframe1 (S) and dataframe2 (T), i.e. it returns the elements that are both in dataframe1 and dataframe2. Formally S ∩ T = {s|s ∈ S and s ∈ T}. If duplicates exists in either dataframe they are dropped and a UserWarning is issued...
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def get_axis_periodic(self, Nper, is_antiperiod=False): """Returns the vector 'axis' taking symmetries into account. Parameters ---------- self: Data1D a Data1D object Nper: int number of periods is_antiperiod: bool return values on a semi period (only for antiperiodic si...
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def knapsack_fractional(weights,values,capacity): """ takes weights and values of items and capacity of knapsack as input and returns the maximum profit possible for the given capacity of knapsack using the fractional knapsack algorithm""" #initialisaing the value of max_profit variable max_profit=...
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def invert(values: np.array, inversion: int) -> np.array: """Return the specified musical inversion of the values.""" if np.abs(inversion) > (len(values) - 1): raise ValueError("Inversion out of range") return np.hstack([values[inversion:], values[:inversion]]).astype(int)
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import os from os.path import dirname, abspath, realpath from platform import system def get_libpath(): """ Get the library path of the the distributed SSA library. """ root = dirname(abspath(realpath(__file__))) if system() == 'Linux': library = 'Linux-SSA.so' elif system() == 'D...
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from typing import Any def engine_factory(database_url: str) -> Engine: """Construct database connection pool.""" url = make_url(database_url) engine_kwargs: dict[str, Any] = {} if url.host == "localhost": engine_kwargs = { **engine_kwargs, "connect_args": {"connect_tim...
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import torch def _shear_x(video: torch.Tensor, factor: float, **kwargs): """ Shear the video along the horizontal axis. Args: video (torch.Tensor): Video tensor with shape (T, C, H, W). factor (float): How much to shear along the horizontal axis using the affine matrix. ""...
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from typing import Optional from typing import Sequence def get_smtp_credentials(filters: Optional[Sequence[pulumi.InputType['GetSmtpCredentialsFilterArgs']]] = None, user_id: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetSmtpCreden...
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def coord(row, col): """ returns coordinate values of specific cell within Sudoku Puzzle""" return row*9+col
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def post_authn_parse(request, client_id, endpoint_context, **kwargs): """ :param request: :param client_id: :param endpoint_context: :param kwargs: :return: """ if endpoint_context.args["pkce"]["essential"] is True: if not "code_challenge" in request: raise ValueErro...
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import argparse def get_args(): """get command-line arguments""" parser = argparse.ArgumentParser( description='Argparse Python script', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument( 'positional', metavar='DIR', help='A positional argument', nargs='+...
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import torch def token2hot(seq, max_length, n_features): """ takes in tokenized sequences and returns 1-hot encoded [1 2 2] -> [1 0 0 0], [0 1 0 0 ], [0 1 0 0] """ N = max_length - len(seq) x = np.pad(seq, (0, N), 'constant') x = F.one_hot(torch.tensor(x),num_classes=n_features) retur...
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import re def is_repo_on_known_branch(path): """Check if we're on the most recent commit in a well known branch, 'master' or a version branch.""" remote = find_upstream_remote(None, path) branches = execute_git( None, path, [ "for-each-ref", "--format=...
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from pyadtpulse import PyADTPulse def setup(hass, config): """Initialize the ADTPulse integration""" conf = config[ADTPULSE_DOMAIN] username = conf.get(CONF_USERNAME) password = conf.get(CONF_PASSWORD) try: # share reference to the service with other components/platforms running within H...
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import argparse def get_args(): """Get the command line arguments for ``tcp-h2-describe``. Returns: Tuple[int, int, Optional[str]]: A triple of * The port for the "describe" proxy * The port for the server that is being proxied * The hostname for the server that is being proxied (...
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def make_key(ns_sep, namespace, *names): """Make a redis namespaced key. >>> make_key(":", "YOO:HOO", "a", "b", "c") == "YOO:HOO:a:b:c" True """ return ns_sep.join(chain((namespace,), names))
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import os def gas_strand_2pipes(method="nikuradse"): """ :param method: Which results should be loaded: nikuradse or prandtl-colebrook :type method: str, default "nikuradse" :return: net - STANET network converted to a pandapipes network :rtype: pandapipesNet :Example: >>> pandapipes...
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from typing import Counter def directly_follows(logs_traces, all_activs, noise_threshold=0): """ Gets the allowed directly-follows relations given the traces of the log Parameters ------------- logs_traces Traces of the log all_activs All the activities noise_threshold ...
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from typing import Union from pathlib import Path import subprocess def python_name(env_dirpath: Union[Path, str]) -> str: """Find the name of the Python in a virtual environment. Args: env_dirpath: The path to the root of a virtual environment (Path or str). Returns: A descriptive strin...
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def hfc(x, framesize=1024, hopsize=512, fs=44100): """ Calculate HFC (High Frequency Content) Parameters: inData: ndarray input signal framesize: int framesize hopsize: int hopsize fs: int samplingrate Returns: result: ndarray ...
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from functools import reduce def acronym_buster(message): """ Find the first group of words that is in all caps check if it is in the ACRONYMS dict if it is, return the first occurrence of the acronym else return [acronym] is an acronym. I do not like acronyms. Please remove them from your email. ...
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import logging def get_backup_temp_2(): """ This is the third way to get the temperature """ try: temp = RFM69.temperature logging.warning("Got second backup temperature") return temp except RuntimeError: logging.error("RFM69 not connected") return 2222 ...
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def constructed(function): """A decorator function for calling when a class is constructed.""" def store_constructed(class_reference): """Store the key map.""" setattr(class_reference, "__deserialize_constructed__", function) return class_reference return store_constructed
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def generate_functions( function, parameters, name, name_func, tag_dict, tag_func, docstring_func, summarize, num_passing, num_failing, key_combs_limit, execution_group, timeout, ): """ Generate test cases using the given parameter context, use the name_func ...
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import os import copy def tdt(input_dir_path, experiment_name="Thellier", meas_file_name="measurements.txt", spec_file_name="specimens.txt", samp_file_name="samples.txt", site_file_name="sites.txt", loc_file_name="locations.txt", user="", location="", lab_dec=0, lab_inc=90, moment_units="mA/m"...
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from typing import List def post_multi_tag_datapoints(timeseries_with_datapoints: List[TimeseriesWithDatapoints], **kwargs): """Insert data into multiple timeseries. Args: timeseries_with_datapoints (List[v04.dto.TimeseriesWithDatapoints]): The timeseries with data to insert. Keyword Args: ...
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def get_version(pname: str, url: str) -> str: """Extract the package version from the url returned by `get_url`.""" match = search(r"/(\d+\.\d+\.\d+)/(\w+(?:-\w+)?)-(\d+\.\d+\.\d+)\.tar\.gz$", url) # Assert that the package name matches. assert match[2] == pname # As a sanity check, also assert that...
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import numpy def SmoothMu(trimset, smoothing): """ Smooth mu or P(mu>thresh). """ Stot = trimset[0] newmu = trimset[1] maxflux = Stot.max() xfine = numpy.exp(numpy.linspace(0, numpy.log(maxflux), 300)) #tck = interpolate.splrep(Stot, newmu, s=1, k=5) #yfine = interpolate.splev(xfine, tc...
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def spdiags(data, diags, m, n, format=None): """ Return a sparse matrix from diagonals. Parameters ---------- data : array_like matrix diagonals stored row-wise diags : diagonals to set - k = 0 the main diagonal - k > 0 the k-th upper diagonal - k < 0 the k...
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import os def find_object(sha1_prefix) -> str: """ Find object with given SHA-1 prefix and return path to object in object store, or raise ValueError if there are no objects or multiple objects with this prefix. """ if len(sha1_prefix) < 2: raise ValueError("Hash Prefix must be 2 or ...
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from typing import List import os def list_files(path: str) -> List[str]: """ List files inside a directory Parameters ---------- path directory path Returns ------- list list of file names """ return [f.name for f in os.scandir(path) if os.path.isfile(f)]
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def precision_with_fixed_recall(y_true, y_pred_proba, fixed_recall): """ Compute precision with a fixed recall, for class 1. The chosen threshold for this couple precision is also returned. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (corr...
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def sample_kollege(user, name='Miguel', crm='222'): """Create and return a sample tag""" return Kollege.objects.create(user=user, name=name, crm=crm)
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from course_lib.Base.Evaluation.metrics import average_precision, precision, recall def get_singular_user_metrics(URM_test, recommender_object: BaseRecommender, cutoff=10): """ Return a pandas.DataFrame containing the precision, recall and average precision of all the users :param URM_test: URM to be tes...
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def detect_license(document, cleaned=False): """ Finds a license that is most similar to the provided `document`. / :document: a license, whose name should be identified :type document: string / :cleaned: shows whether a `document` is prepared for vectorization. / Returns the name of...
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from models import Participant def user_profile(request): """ Puts user_profile into context if available. """ profile = None if request.user.is_authenticated(): try: profile = Participant.objects.get(user__id=request.user.id) except ObjectDoesNotExist: pass return {'user_profile': profile}
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import torch def get_reduced_model(model: torch.nn.Module, x_sample: torch.Tensor, bias: bool = True, activation: bool = True) -> torch.nn.Module: """ Get 1-layer model corresponding to the firing path of the model for a specific sample. :param model: pytorch model :param x_samp...
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def cosine_sim(a: np.ndarray, b: np.ndarray) -> float: """ Computes the cosine similarity between two vectors Parameters ---------- a: np.ndarray the first vector b: np.ndarray the second vector Returns ------- float: the cosine similarity of the two vectors """...
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def datetime_to_hours(dt): """Converts datetime.timedelta to hours Parameters: ----------- dt: datetime.timedelta Returns: -------- float """ return dt.days * 24 + dt.seconds / 3600
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def create_data_element(ds: "Dataset") -> "DataElement": """Return a ``gdcm.DataElement`` for the *Pixel Data*. Parameters ---------- ds : dataset.Dataset The :class:`~pydicom.dataset.Dataset` containing the *Pixel Data*. Returns ------- gdcm.DataElement The convert...
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def thermo_paths(spc_dct, spc_locs_dct, spc_mods, run_prefix): """ Set up the path for saving the pf input and output. Placed in a MESSPF, NASA dirs high in run filesys. """ thm_path_dct = {} for spc_name in spc_locs_dct: spc_thm_path_dct = {} spc_info = sinfo.from_dct(spc_dct[s...
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def create_da_model_std(filename, eta_rho=10, xi_rho=10, s_rho=1, reftime=default_epoch, clobber=False, cdl=None, title="My Model STD"): """ Create an time varying model standard deviation file Parameters ---------- filename : string name and ...
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from rmgpy.chemkin import writeThermoEntry def thermoEntry(request, section, subsection, index): """ A view for showing an entry in a thermodynamics database. """ # Load the thermo database if necessary loadDatabase('thermo', section) # Determine the entry we wish to view try: da...
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import os def get_clean_iris(): """ If file exists in data/processed/, recover it If it doesn't, import and clean it again Compress it if possible """ if os.path.exists(f"{path_}/data/processed/iris.csv"): print("Retrieving clean data from backup...") df = pd.read_csv(f"{path_}...
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