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def productory(matrix_list): """ Multiplies from lest to right the matrices in the list matrix_list. Arguments ---------- matrix_list : list of numpy.ndarray List of matrices to be multiplied. Returns ---------- M : numpy.ndarray Product of the matrices in matrix_list. ...
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def writeDictCfg(dct, cfgname): """ Write out a config file from a dictionary. - Entries: 'key=value\n' :param dct: Dictionary to be written as a config file. :param cfgname: Filename or path/to/filename for config file. :return: Config filename or path/to/filename """ cfg_out = open(cfg...
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from textwrap import dedent def trim(text): """Like textwrap.dedent, but also eliminate leading and trailing lines if they are whitespace or empty. This is useful for writing code as triple-quoted multi-line strings. """ lines = text.split('\n') if len(lines) > 0: if len(lines...
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def generate_div(i): """ Generate a div with the style-<i> class. eg. >>> generate_div(0) "<div class="box style-0"></div> """ return '<div class="box style-' + str(i) + '"></div>'
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import re def remove_url(text): """ Supprime les URLs :param text: texte à transformer :return: texte transformé """ return re.sub(r'http\S+', '', text)
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from typing import Any from typing import Optional def _guess_crs_str(crs_spec: Any)->Optional[str]: """ Returns a string representation of the crs spec. Returns `None` if it does not understand the spec. """ if isinstance(crs_spec, str): return crs_spec if hasattr(crs_spec, 'to_epsg')...
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def none_formatter(error): """ Formatter that does nothing, no escaping HTML, nothin' """ return error
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def egg_drop(n: int, k: int) -> int: """ What is the minimum number of trials we need to drop eggs to determine which floors of a building are safe for dropping eggs, given n eggs and k floors? :param n: number of eggs :param k: number of floors :return: the minimum number of trials >>> egg...
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import requests import re def extract_conceptnet(phrase): """Access ConceptNet API and read relational triples as well as their weight and simple example""" url_head = 'http://api.conceptnet.io/c/en/' # access ConceptNet API raw_json = requests.get(url_head + phrase).json() edges = raw_json['edges'] ...
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from typing import Union def get_error_message(traceback: str) -> Union[str, None]: """Extracts the error message from the traceback. If no error message is found, will return None. Here's an example: input: Traceback (most recent call last): File "example_code.py", line 2, in <module> ...
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import re def get_unique_name_from_tag(image_uri): """ Return the unique from the image tag. :param image_uri: ECR image URI :return: unique name """ return re.sub('[^A-Za-z0-9]+', '', image_uri)
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def _enforce_shape(points): """Ensure that datapoints are in a shape of (2, N).""" if points.shape[0] != 2: return points.T return points
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def parseLineForExpression(line): """Return parsed SPDX expression if tag found in line, or None otherwise.""" p = line.partition("SPDX-License-Identifier:") if p[2] == "": return None # strip away trailing comment marks and whitespace, if any expression = p[2].strip() expression = expre...
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import hashlib def hash_bytes(data: bytes): """Compute the same hash on bytes as git would (e.g. via git hash-object). The hash is the sha1 digest, but with a header added to the bytes first: the word "blob", followed by a space, followed by the content length, followed by a zero byte. """ ass...
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def add_suffix_to_parameter_set(parameters, suffix, divider='__'): """ Adds a suffix ('__suffix') to the keys of a dictionary of MyTardis parameters. Returns a copy of the dict with suffixes on keys. (eg to prevent name clashes with identical parameters at the Run Experiment level) """ s...
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def wpm(typed, elapsed): """Return the words-per-minute (WPM) of the TYPED string.""" assert elapsed > 0, 'Elapsed time must be positive' # BEGIN PROBLEM 4 return len(typed) / 5 * 60 / elapsed # END PROBLEM 4
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from typing import Optional import pkg_resources def get_installed_model_version(name: str) -> Optional[str]: """ Return the version of an installed package """ try: return pkg_resources.get_distribution(name).version except pkg_resources.DistributionNotFound: return None
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def split_into_groups(s, length=3): """splits given string into groups of length""" if length < 0: raise TypeError('split length must be >= 0') s = ''.join(reversed(s)) groups = [s[i:i + length] for i in range(0, len(s), length)] return [''.join(reversed(g)) for g in reversed(groups)]
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def get_sequence(center_idx, half_len, sample_rate, max_num_frames): """ Sample frames among the corresponding clip. Args: center_idx (int): center frame idx for current clip half_len (int): half of the clip length sample_rate (int): sampling rate for sampling frames inside of the c...
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def format_file(path, tool): """Format file with clang-format and return True.""" tool("-i", path) return True
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import re def preprocess_title(text): """Pre-process an episode title string by removing unnecessary quotations, brackets, and whitespaces.""" text = re.sub('[\(\[].*?[\)\]]', '', text) # remove brackets text = re.sub(' +', ' ', text) # remove multiple whitespaces text = text.strip().strip('\"').str...
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def clear_sesam_attributes(sesam_object: dict): """ Return same dict but without properties starting with "_" :param sesam_object: input object from Sesam :return: object cleared from Sesam properties """ return {k: v for k, v in sesam_object.items() if not k.startswith('_')}
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import torch def remove_first_sv(emb, first_sv): """ Projects out the first singular value (first_sv) from the embedding (emb). Inputs: emb: torch Tensor shape (glove_dim) first_sv: torch Tensor shape (glove_dim) Returns: new emb: torch Tensor shape (glove_dim) """ # Calcul...
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def last_slash_check(a_path): """Check if the path provided has an ending slash. If it does, remove it and return a revised path. If not, do nothing. """ if a_path[-1] == '/': new_path = a_path[:-1] else: new_path = a_path return new_path
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def export_sheet(ss, sheet_id, export_format, export_path, sheet_name): """ Exports a sheet, given export filetype and location. Allows export format 'csv', 'pdf', or 'xlsx'. :param ss: initialized smartsheet client instance :param sheet_id: int, required; sheet id :param export_...
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def resolve_cardinality(class_property_name, class_property_attributes, class_definition): """Resolve class property cardinality from yaml definition""" if class_property_name in class_definition.get('required', []): min_count = '1' elif class_property_name in class_definition.get('heritable_require...
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def scan_uv_parsing(inp, uv_alpha_univariate, uv_fold_change, uv_decision_tree, uv_paired_samples, uv_correction, uv_labelsize_vulcano, uv_figsize_vulcano, uv_label_full_vulcano): """ Initialize univariate analysis. Initialization function for univariate analysis. inp : dict Method dictionary....
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from typing import Sequence from typing import Optional def split_round_robin(values: Sequence, num_procs: Optional[int]) -> Sequence[Sequence]: """ Split values into a `num_procs` sequences. ```python split_round_robin(range(10), 3) == [[0, 3, 6, 9], [1, 4, 7], [2, 5, 8]] ``` """ if num_...
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def get_std_dev(total_size: int, comm_size: int) -> float: """ In community generation, larger communities should have a smaller standard deviation (representing tighter-knit communities). This generates a std dev based on the ratio of the number of nodes in this community to the number of nodes in ...
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from typing import Tuple def extgcd(a: int, b: int) -> Tuple[int, int, int]: """a * x + b * y = gcd(a, b) See: https://qiita.com/drken/items/b97ff231e43bce50199a https://www.youtube.com/watch?v=hY2FicqnAcc O(gcd(a, b)) """ if b == 0: return (a, 1, 0) g, x, y = extgcd(b, a %...
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def epsilon(n): """ Compute Jacobi symbol (5/n). """ if n % 5 in [1, 4]: return 1 elif n % 5 in [2, 3]: return -1 else: return 0
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import requests def http_get(url, api_uri, token, **params): """ Helper function to perform an http get, with optional parameters :param url: The url of the endpoint :param api_url: The URI of the specific api :param token: The access token :param params: kwargs for optional parameter to the ...
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def dict_reverse(dictionary): """ Reverse a dictionary. If values are not unique, only one will be used. Which one is not specified Args: dictionary (dict): dict to reverse Returns: reversed (dict): reversed dictionary """ return {v: k for k, v in dictionary.items()}
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def dfs(i, j, mat, visited): """ Applies Depth first search. Args: i (int): The row index of the src element j (int): The column index of the src element mat (List[List[int]]): The input matrix visited (List[List[bool]]): The visited matrx """ if visited[i][j]: ...
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import importlib def import_class_from_string(class_string): """ Import (and return) a class from its name Arguments keywords: class_string -- name of class to import """ module_name, class_name = class_string.rsplit('.', 1) module = importlib.import_module(module_name) return getattr...
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import re def getIndsPropString(wkwdata): """Get the string for the property containing the indices of train, validation and test data""" allAttributes = dir(wkwdata) r = re.compile('^data_.*_inds$') return list(filter(r.match, allAttributes))
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def parse_errata_checksum(data): """ Parse the errata checksum and return the bz2 checksum """ for line in data.splitlines(): if line.endswith('errata.latest.xml.bz2'): return line.split()[0]
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def _strip_whitespace(text_content: str) -> str: """Return text_content with leading and trailing whitespace removed """ output_content: str = str(text_content) if text_content is not None and len(text_content) > 0: output_content = text_content.strip() return output_content
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def identity(x): """Identity activation function. Input equals output. Args: x (ndarray): weighted sum of inputs. """ return x
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def to_lowercase(text: str) -> str: """ Returns the given text with all characters in lowercase. :param text: The text to be converted to lowercase. (String) :return: The given text with all characters in lowercase. (String) """ text = text.lower() return text
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def field_names(model): """Return a set of all field names for a model.""" return {field.name for field in model._meta.get_fields()}
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def read_define(filename, define): """ Read the value of a #define from a file. filename is the name of the file. define is the name of the #define. None is returned if there was no such #define. """ f = open(filename) for l in f: wl = l.split() if len(wl) >= 3 and wl[0] == ...
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def single_high_initializer(low_genome='LOW', high_genome='HIGH'): """Generate a population that contains two types of genomes: all of the population will be 'LOW' except for one, which will be 'HIGH'.""" first = True def create(): nonlocal first if first: first = False ...
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def dist(x, y, S): """ Mahalanobis distance: d(x,y) = \\sqrt( (x-y)^T*S^-1*(x-y) ) where S^-1 = diag(1/s_i^2 for s_i in std) Input must be torch.Tensor((1,N)) for x,y,S """ d = ((x-y).pow(2)/S).sum(1).sqrt() return d
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from typing import Any import logging def search(model: dict[str, Any], search_keys: list[str]) -> list: """ Search an AaC model structure by key(s). Searches a dict for the contents given a set of keys. Search returns a list of the entries in the model that correspond to those keys. This search wil...
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def describe(data_matrix): """Get the shape of a sparse matrix and its average nnz.""" return 'Instances: %3d ; Features: %d with an avg of %d per instance' % \ (data_matrix.shape[0], data_matrix.shape[1], data_matrix.getnnz() / data_matrix.shape[0])
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import random def happens(prob): """ Trigger a random event to happen Arguments: prob {float} -- chances to return true Returns: [bool] -- if the random event happens """ chance = random.uniform(0,1) return chance <= prob
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def rescale(var, dt): """ Rescale variable to fit dt, based on quantities library :param var: Variable to rescale :param dt: Time steps :return: Rescaled integer """ return (var.rescale(dt.units) / dt).magnitude.astype(int).item()
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def filter_instances_by_family(instances, family_name=None): """Yield instances whose 'familyName' custom parameter is equal to 'family_name'. """ return (i for i in instances if i.familyName == family_name)
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def from_datastore(entity): """Translates Datastore results into the format expected by the application. Datastore typically returns: [Entity{key: (kind, id), prop: val, ...}] This returns: [ name, street, city, state, zip, open_hr, close_hr, phone, drink, rating, website ] where n...
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def _strip_unsafe_kubernetes_special_chars(string: str) -> str: """ Kubernetes only supports lowercase alphanumeric characters, "-" and "." in the pod name. However, there are special rules about how "-" and "." can be used so let's only keep alphanumeric chars see here for detail: https://...
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import torch def kld_loss(mu, logvar, mean_reduction=True): """ First it computes the KLD over each datapoint in the batch as a sum over all latent dims. It returns the mean KLD over the batch size. The KLD is computed in comparison to a multivariate Gaussian with zero mean and identity covarianc...
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from datetime import datetime def format_bitmex_api_timestamp(timestamp: datetime) -> str: """Format BitMEX API timestamp.""" return timestamp.replace(tzinfo=None).isoformat()
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def get_position_by_substring(tofind, list): """ Get index where `tofind` is a substring of the entry of `list` :param tofind: string to find in each element of `list` :param list: list to search through :return: index in `list` where `tofind` is found as a substring """ for i, e in enumera...
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def get_combined_params(*models): """ Returns the combine parameter list of all the models given as input. """ params = [] for model in models: params.extend(list(model.parameters())) return params
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def drop_irrelevant_features(df): """ Drop irrelevant columns from features set :param data_df: features_data :return: a dataframe of relevant features """ cols_to_exclude = ['index', 'level_0', 'id'] df = df.drop(cols_to_exclude, axis=1) return df
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def KGtoLB(mkg): """ Convertie une masse en kg vers lb note: 1 kg = 2.20462 lb :param mkg: masse [kg] :return mlb: masse [lb] """ mlb = mkg * 2.20462 return mlb
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def bounding_box(rects): """ Get a new Rect which circumscribes all the rects. :param list rects :return bounding Rect :rtype Rect """ top = float('inf') left = float('inf') bottom = 0 right = 0 for rect in rects: top_left, bottom_right = rect top = min(top,...
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def valid_line(line: str) -> bool: """ 行バリデーション Parameters ---------- line: str 行テキスト Returns ------- valid: bool validであるフラグ """ lstriped = line.lstrip() if len(lstriped) == 0: return False if lstriped[0] == '#': return False if l...
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def check_wide_data_for_blank_choices(choice_col, wide_data): """ Checks `wide_data` for null values in the choice column, and raises a helpful ValueError if null values are found. Parameters ---------- choice_col : str. Denotes the column in `wide_data` that is used to record each ...
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from typing import Optional def flatten_dict( d: dict, s: str = '', exc: Optional[list] = None ) -> dict: """Reformat a multi-layer dictionary into a flat one. :param d: Input dict. :param s: Prefix to be added to keys. :param exc: List of keys to exclude from the resulting di...
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import re def norm_freqs(data, expression, count_name=str, normalize=True, analyze=True): """Get frequencies (normalized = optional) of a regex pattern in a Series with one or more strings. Args: data (DataFrame): a dataframe with texts to extract frequencies from. expression (re.compile): a...
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import importlib def get_module(modulename): """Returns a module object for given Python module name using `importlib.import_module`.""" return importlib.import_module(modulename)
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def _create_default_branch_id(repo_url: str, default_branch_ref_id: str) -> str: """ Return a unique node id for a repo's defaultBranchId using the given repo_url and default_branch_ref_id. This ensures that default branches for each GitHub repo are unique nodes in the graph. """ return f"{repo_url}...
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def get_conv_output_shape_flattened(input_shape, structure): """ Input_shape: (Channels, Height, Width) of input image structure: List containing tuple (out_channels, kernel_size, stride, padding) per conv layer """ def get_layer_output(input, layer_structure): # See shape calculatio...
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def remove_node(G, node): """ Wrapper around networkx.classes.graph.Graph.remove_node to return a dict mapping an incident node to its edge attributes """ rv = {} for edge in G.edges(node, data=True): u = edge[0] v = edge[1] edge_attr = edge[2] t = None if u =...
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import pkgutil def is_installed(package): """Check if a python package (Kipoi plugin) is installed Args: package (str): package/plugin name Returns: True if package/plugin is installed """ return pkgutil.find_loader(package) is not None
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import random import string def generate_verification_code() -> str: """Generate a random 6-digit code Returns: str: a 6-digit code in string format .e.g '654874' """ return "".join(random.choices(string.digits + string.digits, k=6))
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import torch def create_eye_batch(batch_size, eye_size): """Creates a batch of identity matrices of shape Bx3x3 """ return torch.eye(eye_size).view( 1, eye_size, eye_size).expand(batch_size, -1, -1)
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import io def read_file(path): """Returns all the lines of a file at path as a List""" file_lines = [] with io.open(path, mode="rt", encoding="utf-8") as the_file: file_lines = the_file.readlines() return file_lines
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import requests def get_recognitions(mem_skip, api_key): """This function triggers the GET RECOGNITIONS API. https://api.highground.com/#api-Recognition-GetRecognition Keyword Arguments: mem_skip -- tracks member records to skip for looping through all records api_key...
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async def read_exactly(stream, count): """Read the specified number of bytes from stream. Keep trying until we either get the desired amount, or we hit EOF. """ s = b'' while count > 0: n = await stream.receive_some(count) if n == b'': raise EOFError count = coun...
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def maxi(a,b): """ Maximal value >>> maxi(3,4) 4 """ if a > b: return a return b
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def CreateSafeUserEmailForTaskName(user_email): """Tasks have naming rules that exclude '@' and '.'. Task names must match: ^[a-zA-Z0-9_-]{1,500}$ Args: user_email: String user email of the task owner. Returns: String with unacceptable chars swapped. """ return user_email.replace('@', '-AT-').rep...
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import hashlib def calculahash(cadena): """Devuelve el hash SHA256 de una cadena dada como entrada :param cadena: texto de entrada para calcular su hash :return: el hash calculado con SHA256 """ return hashlib.sha256(cadena.encode()).hexdigest()
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def bisection(f, a, b, TOL, N): """To find a solution to f(x) = 0 Finds the solution to f(x) = 0 given the continuous function f on the interval [a, b], where f(a) and f(b) have opposite signs. Args: f: a function to be evaluated (can be a lambda or def). a: the lower bound in the giv...
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def _RGB2sRGB(RGB): """ Convert the 24-bits Adobe RGB color to the standard RGB color defined in Web Content Accessibility Guidelines (WCAG) 2.0 see https://www.w3.org/TR/2008/REC-WCAG20-20081211/#relativeluminancedef for more references :param RGB: The input RGB color or colors shape== ... x 3 (R,...
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def le(h): """ Little-endian, takes a 16b number and returns an array arrange in little endian or [low_byte, high_byte]. """ h &= 0xFFFF # make sure it is 16 bits return [h & 0xFF, h >> 8]
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def get_fontext_synonyms(fontext): """ Return a list of file extensions extensions that are synonyms for the given file extension *fileext*. """ return {'ttf': ('ttf', 'otf'), 'otf': ('ttf', 'otf'), 'afm': ('afm',)}[fontext]
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def verify_splits(splits, keyfunc): """ Verifies that the splits of each transaction sum to 0 Args: splits (dict): return value of group_transactions() keyfunc (func): function that returns the transaction amount Returns: (bool): true on success Examples: >>> from operat...
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def normalize_json_fields(result_list): """ Makes sure that each json contains the same fields. Adds them with None as value if any field is missing. """ found_keys = set() for item in result_list: found_keys.update(item.keys()) for item in result_list: for key in f...
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import collections def attrize(container_name=None, **dct): """Given a dict, return a named tuple where the values are accessible as named attributes. A collection name can be given as an optional parameter. Examples -------- >>> foo = attrize(**{'a': 1, 'b': 2}) >>> foo.a 1 """ ...
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import json def get_filepaths(sets): """ Extracts the file paths from the provided .json file and returns, the content of the .json file as a dictionary as well as a potential error message. """ # Reading in the json file with the settings try: with open(sets, "r") as read_file: ...
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import yaml def load_conf(conf_path): """Load a yaml encoded conf file.""" try: with open(conf_path) as f: y = yaml.load(f, Loader=yaml.CLoader) return y or {} except Exception as e: raise ValueError('Invalid or nonexistent config filename')
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def vector_product(vec1, vec2, do_alignment=True): """ Computes the dot product of two vectors. Parameters ---------- vec1, vec2 : ProbabilityVector Returns ------- product_scalar : float """ if do_alignment: series1, series2 = vec1.series.align(vec2.series) else: ...
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def byte_compare(stream_a, stream_b): """Byte compare two files (early out on first difference). Returns: (bool, int): offset of first mismatch or 0 if equal """ bufsize = 16 * 1024 equal = True ofs = 0 while True: b1 = stream_a.read(bufsize) b2 = stream_b.read(bufsi...
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def linepoint(t, x0, y0, x1, y1): """Returns coordinates for point at t on the line. Calculates the coordinates of x and y for a point at t on a straight line. The t parameter is a number between 0.0 and 1.0, x0 and y0 define the starting point of the line, x1 and y1 the ending point of the l...
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def calc_cov(samples): """ Calculates covariance matrix as described in paper. Args: samples (list of np.matrix) - a list of sample-matrices Returns: np.matrix - the covariance matrix >>> s1 = np.matrix([[1, 0], [0, 1]]) >>> s2 = np.matrix([[2, 0], [0, 2]]) >>> calc_cov([s...
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from pathlib import Path def fixture_sample_file() -> Path: """Return a sample file.""" return Path(__file__).parent.joinpath("sample.txt")
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import hashlib def generate_token(login: str, auth_salt: str) -> str: """Generate token based on login and hash salt.""" encoded_key = (login + auth_salt).encode('utf-8') token = hashlib.sha512(encoded_key).hexdigest() return token
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def get_port(device): """ Get the port, as string, of a device """ return str(device.port)
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def _get_model_features(model_pipe): """Get features from a `civis.ml.ModelPipeline` """ model_features = [] target_cols = model_pipe.train_result_.metadata['data']['target_columns'] for feat in model_pipe.train_result_.metadata['data']['column_names_read']: if feat not in target_cols: ...
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def format_flux_mode_indices(atom_idxs): """ Formates the atom indices into a string that is used to define the fluxional (torsional) modes of a molecular species for Monte Carlo calculations in MESS. :param atom_idxs: idxs of atoms involved in fluxional mode :type atom_idxs: list(i...
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def to_var(log_std): """Returns the variance given the log standard deviation.""" return log_std.exp().pow(2)
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import math def calculate_temp_NTC(raw_val): """ Converts the raw value read from the NTC temperature sensor module into degrees celsius. """ voltage = raw_val * 5 / 1023 resistance = (5 * 10000 / voltage) - 10000 temp = ((3.354016 * (10**-3)) + (2.569850 * (10**-4)) * math.l...
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def get_fuel_needed(mass: int) -> int: """Calculate fuel needed to launch mass.""" return mass // 3 - 2
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def wild2regex(string): """Convert a Unix wildcard glob into a regular expression""" return string.replace('.','\.').replace('*','.*').replace('?','.').replace('!','^')
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import re def sanitized_name(name, wid): """Clean an action name and change it to proper format. It replaces all the unwanted characters with `_`. Args: name (str): The crude action name. Returns: str: The sanitized action name. """ return "popper_{}_{}".format( r...
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import math def get_product_factors(n): """ get product factors of n """ if n <= 0 or not isinstance(n, int): return [] else: product_factors = [] for i in range(1, math.ceil(math.sqrt(n)) + 1): if n % i == 0: product_factors.append(i) ...
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def n_subsystems(request): """Number of qubits or modes.""" return request.param
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