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def devices_to_string(devices): """ Format device list to string :param devices: list of devices :type devices: int or str or list :return: string of device list :rtype: str """ if isinstance(devices, str): return devices if isinstance(devices, int): devices = [devic...
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def dict_encode(dict, encoding='cp1251'): """Encode dict values to encoding (default: cp1251).""" encoded_dict = {} for key in dict: encoded_dict[key] = dict[key].encode(encoding) return encoded_dict
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def convert_pyr_coeffs_to_pyr(pyr_coeffs): """this function takes a 'new pyramid' and returns the coefficients as a list this is to enable backwards compatibility Parameters ---------- pyr_coeffs : `dict` The `pyr_coeffs` attribute of a `pyramid`. Returns ------- coeffs : `lis...
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def is_ganache(ethereum_network): """true if we're using ganache""" return not ethereum_network
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def format_resp(number, digits=4): """ Formats a number according to the RESP format. """ format_string = "%%-10.%dE" % digits return format_string % (number)
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import torch def relative_time_to_absolute(batch, relative_lens, rate): """Converts SpeechBrain style relative length to the absolute duration. Operates on batch level. Arguments --------- batch : torch.tensor Sequences to determine the duration for. relative_lens : torch.tensor ...
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def _escape(st): """Escape Strings for Dot exporter.""" return st.replace('"', '\\"')
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def get_good_qa_ints_to_keep_l2cloud(): """ https://ecostress.jpl.nasa.gov/downloads/psd/ECOSTRESS_SDS_PSD_L2_ver1-1.pdf for qa flag bits """ cloud_mask_flag = ["0", "1"] # 1 is determined, 0 is not determined cloud_flag = "0" # 0 is no 1 is yes, either bit 2 3 or 4 set thermal_brightness_tes...
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def multiply(x, y): """Multiplies two numbers. Equivalent to a * b but curried""" return x * y
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from typing import List def find_dup(numbers: List) -> int: """ Solution: Iterate through the list and store items in a set. When an item is found that already exists in the set return that item. Complexity: Time: O(n) - Iterate through our list once Space: O(n) - We could potentially store each item found "...
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import argparse import os def get_args(): """Parses command line arguments. Configures and runs argparse.ArgumentParser to extract command line arguments. Returns: An argparse.Namespace containing the arguments parsed from the command line """ parser = argparse.ArgumentParser...
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def conjugate(x: complex) -> complex: """ Returns the conjugate of a complex number """ return x.real - x.imag * 1j
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import math def radians(degrees): """ Converts degrees to radians, which is used in math functions. """ return math.pi / 180.0 * degrees
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def to_lower_camel(s: str) -> str: """ Convert a snake-case string into lower camel case. """ parts = s.split("_") return parts[0] + "".join([p.title() for p in parts[1:]])
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def get_export_options(defaults=None): """code export related options """ if defaults is None: defaults = {} options = { # ID of the model to generate a local model '--model': { 'action': 'store', 'dest': 'model', 'default': defaults.get('mo...
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import logging def _setup_text_logger(name, stream, level): """Setup a text logger.""" res = logging.getLogger(name) handler = logging.StreamHandler(stream) handler.setFormatter(logging.Formatter(fmt="%(message)s")) res.addHandler(handler) res.setLevel(level) return res
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def normalize_ipam_config_key(key): """Normalizes IPAM config keys returned by Docker API to match Ansible keys. :param key: Docker API key :type key: str :return Ansible module key :rtype str """ special_cases = { 'AuxiliaryAddresses': 'aux_addresses' } return special_cases...
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import yaml def fetch_mapping(filepath: str) -> dict: """Returns a dictionary from a YML file""" with open(filepath, "r") as stream: map = yaml.safe_load(stream) return map
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def upcase(val: str) -> str: """Make all characters in a string upper case.""" return val.upper()
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from typing import Union from pathlib import Path import wave def empty_wav(wav_path: Union[Path, str]) -> bool: """Check if a wav contains data""" with wave.open(str(wav_path), 'rb') as wav_f: return wav_f.getnframes() == 0
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def _blockdevice_volume_from_datasetid(volumes, dataset_id): """ A helper to get the volume for a given dataset_id. :param list volumes: The ``BlockDeviceVolume`` instances to inspect for a match. :param UUID dataset_id: The identifier of the dataset the volume of which to find. :r...
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def kebab_case_to_human(word): """Should NOT be used to unslugify as '-' are also used to replace other special characters""" if word: return word.replace("-", " ").title()
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import os import logging def get_l3energy(job, par, bls=0): """ Get the L3, single-point energies. This is not object oriented. """ if bls: key = par['barrierless_saddle_single_point_key'] else: key = par['single_point_key'] if par['single_point_qc'] == 'molpro': ...
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import os def count_files(root_folder): """ Count files in source folder. :param root_folder: :return: """ print('Contagem de arquivos ... Aguarde') file_counter = 0 for root, dirs, files in os.walk(root_folder): for _ in files: file_counter += 1 print('Total de...
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import torch def batch_diag1(A): """ A :(t,d,d) returns : (t,d) """ t = A.shape[0] sollist = [torch.diag(A[i,:,:]) for i in range(t)] result = torch.stack(sollist,dim=0) return result
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def parse_word_expression(expr): """ Parses a word expression such as "thermoelectric - PbTe + LiFePO4" into positive and negative words :param expr: a string expression, with " +" and " -" strings separating the words in the expression. :return: Returns a tuple of lists (positive, negative) """ ...
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import re def __modify_name_replace(file_name, string, replace_string, position): """ Core method to replace a string inside the base name of a file. """ file_newname = "" if position == "any": file_newname = file_name.replace(string, replace_string) elif position == "prefix": ...
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import random import string def rand_string(count=12): """Return random string of length count with letters and numbers, mixed case. Uses Python randomness.""" return ''.join(random.choice(string.ascii_letters + string.digits) for x in range(count))
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from typing import Union from typing import Sequence from typing import Tuple def parse_vspace( ncols: int, vspace: Union[Sequence[int], bool] = False, ) -> Tuple[bool, Sequence[int]]: """ :param ncols: :type ncols: int :param vspace: :return: :rtype: """ if isinstance(vspace, Sequence): add_vspace...
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def setBits( lst ): """ set the bits in lst to 1. also set bits 18, 20, 22, 24, and 28 to one (since they should always be set) all other bits will be 0 """ res = 0 for b in lst + [18,20,22,24,28]: res |= (1 << b) return res
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def predicate(printer, ast): """Prints a predicate.""" return f'{printer.uppaal_c_printer.ast_to_string(ast["expr"])}'
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import string import random def set_xrf(): """ Create XRF key used to prevent cross site request forgery """ characters = string.ascii_letters + string.digits return ''.join(random.sample(characters, 16))
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from typing import List from typing import Callable def create_matcher(queries: List[str], query_type: str) -> Callable[[str], bool]: """ Create a matcher for a list of queries Parameters ---------- queries : list List of queries query_type: str Type of query to run: ["or"|"a...
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def reverseSentence(s): """i am a student. -> student. a am i""" s = s[::-1] sNums = s.split(' ') for i in range(len(sNums)): sNums[i] = sNums[i][::-1] return " ".join(sNums)
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async def format_embed_description(records: dict, format_str: str): """Formats an embed description to use. Args: records (dict): records to convert to description format_str (str, optional): the format string to use; do not pass a F-String! Returns: str: the formatted ...
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def remove_suffix(s: str, suffix: str) -> str: """Remove the suffix from the string. I.e., str.removesuffix in Python 3.9.""" # suffix="" should not call s[:-0] return s[: -len(suffix)] if suffix and s.endswith(suffix) else s
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import re def split_camel_cased(text: str) -> str: """Split camelCased elements with a space. Arguments: text: The text to be processed. Returns: The text with all camelCased elements split into different elements. """ return re.sub("(?!^)([A-Z][a-z]+)", r" \1", text)
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from bs4 import BeautifulSoup import re def default_db_connection(publish_settings): """"Takes PublishSettings looks for Azure default db connection, returns default db connection string for local environment and SQL to add user to local db""" username, password = '', '' soup = BeautifulSoup(publish_settings...
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import torch import pathlib def migrate_180826_models(f_folder: str, f_glob: str) -> int: """ inside <EXPERIMENT>/compressor/ provide f_glob -> '*.torch' Converts the old *.torch files containing 'codebooks' to 'components' """ p_glob = pathlib.Path(f_folder).glob(f_glob) old_key ...
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import math def triangle(v): """ Return a value corresponding to a triangle wave """ return (2 * abs(v - math.floor(v + (1/2)))) - 1
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def get_datatypes(xml_data_model, tag_name='nodeset', data_type='type', max_str=None): """ :param xml_data_model: data model list read from ODK config (read_odk_data_model) :param tag_name: column name containing the tag names of the data model :param data_type: column name containing the data type bel...
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def filter_tagged_vocabulary(tagged_vocabulary, vocabulary, split="|"): """Filters tagged_vocabulary (tokens merged with tags) for tokens occurring in vocabulary. Parameters ---------- tagged_vocabulary : collection vocabulary of tokens (can be merged with tags) vocabulary : collection ...
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def _f(X, y, clf): """Returns the flattened coefficients of a fitted classifier. This function exists at the module level instead of as an anonymous or subordinate function so that it is importable by `multiprocessing`. Parameters ---------- X : array Design matrix. y : array ...
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def load_languages(language, languages_dict): """loads the language from the string-dict loaded from the config Args: language (str): selectbox choice for the language model from the user languages_dict (dict): dict containing to-evaluate strings of the language Returns: tfhub lang...
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def get_data_symptom(driver, options): """ Returns record with selected symptom """ return {"symptom": options[1].text.strip()}
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def inputthis(question='-> ', expected_tuple=('Y', 'N'), error='ERRO! Resposta inesperada!'): """ :param question: Input text :param expected_tuple: Tuple containing all the options from wich the user should choose from :param error: Error message for when the input isn't cointained in the tuple :re...
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def standardize_json_string(json_string): """ Replace " with ' if they occur within square brackets eg {"key":"["Key":"value"]"} => {"key":"['Key':'value']"} """ inside_brackets_flag = False standard_json_string = "" for i in range(0, len(json_string)): if json_string[i] == '[': ...
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from typing import Any import ctypes def di(id_: int) -> Any: """ Hacky inverse for id """ return ctypes.cast(id_, ctypes.py_object).value
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def get_default_model_settings(): """Return some default settings for a DeepSphere model.""" model_settings = {"pretrained_model_name": None, "model_name_prefix": None, "model_name": None, "model_name_suffix": None, # Arc...
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def _find_match(needle: dict, haystack: list, keys: list): """Find a dictionary in a list of dictionary based on a set of keys""" for item in haystack: for key in keys: if item.get(key) != needle[key]: break else: return item return None
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def _extract_params_from_i0(only_variable_parameters, parameters_with_variability, initial_conditions_with_variability): """ Used within the distance/cost function to create the current kinetic parameter and initial condition vectors to be used during that interaction, using current values in i0. This ...
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def handler(mocker, aws_request_id): """ this fixture provides a way to call the function handlers so as to insert a canned context (which is assumed by the logger setup) """ def _handler(func_module, event, context=None): if context is None: context = mocker.Mock(aws_request_id...
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import os def getOutputFileName(originalFileName, outputExtension, index=None): """ Return a filename which is the same as C{originalFileName} except for the extension, which is replaced with C{outputExtension}. For example, if C{originalFileName} is C{'/foo/bar.baz'} and C{outputExtension} is C{...
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def classify(instruction): """Classify instruction. Return name of instruction handler and arguments. """ if instruction in (0x00e0, 0x00ee): return f"op_{instruction:04x}", opcode = instruction >> 12 if 0 <= opcode <= 2: return f"op_{opcode}nnn", instruction & 0x0fff if 3...
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def get_cached_stickers(context, fuzzy=False): """Return cached search results from a previous inline query request.""" query_id = context.inline_query_id cache = context.tg_context.bot_data[query_id] if fuzzy: results = cache["fuzzy"] offset = context.fuzzy_offset else: res...
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from typing import List from typing import Tuple def find_two_smallest(L:List[float]) -> Tuple[int, int]: """ (see above) """ # Find the index of the minimum and remove that item smallest = min(L) min1 = L.index(smallest) L.remove(smallest) # Find the index of the new minimum item in the list...
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def get_attribute(name): """ Gets the value of the element's attribute named ``name``. """ def evaluator(element): return element.attrib.get(name) return evaluator
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import requests def get_attd_as_json(): """ We can fetch the attendance 4PM report via the SODA api using the requests library, as the SODA api is REST-ful. The data updates every day, and includes previous data, so we only need to fetch this on a daily basis after 4 in the afternoon. ...
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from typing import Counter def unique_words_by_tag(tokens, tag_value='N/A'): """Return a counter of the tokens with the given tag value.""" nouns = [] for word, tag in tokens: if tag.startswith(tag_value) or tag_value == 'N/A': nouns.append(word.lower()) return Counter(nouns)
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def total_per_person(single_plan): """Returns total cost per person Parameter single_plan: single plan from database Returns: total cost per person""" costs = single_plan.cost_set.all() if costs: total_cost = 0 for cost in costs: cost_per_person = round(cost.cost /...
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def writeable(doc): """ Return a writeable tuple for doc. writeable tuple is any 2-tuple of `output_path`, `bytes`. `lettersmith.write` knows how to write these tuples to disk. """ return doc.output_path, doc.content.encode()
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def coco_valid_joints(jointsx, jointsy, jointsv, dims): """ coco_valid_joints: Function to decide which joints are valid. Each dataset has different criteria, so can't have a single function. Boo. jointsx: ndarray joints x cood jointsy: ndarray joints y coord dims: (width, hei...
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def fsm_submit_button(transition): """ Render a submit button that requests an fsm state transition for a single state. """ fsm_field_name, button_value, transition_name = transition return { 'button_value': button_value, 'fsm_field_name': fsm_field_name, 'transition_name...
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def get_attrs(): """get attrs.""" attrs = { "enable_feature_library": True } return attrs
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def pointOnTrianglePlane(P, A, B, C): """ Projects the point P onto the plane containing traingle A, B, C """ N = (B - A).cross(C - A).normalize() return P + (-(P - A).dot(N))*N
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def clean(code): """ Removes characters other than eight language commands. """ return filter(lambda x: x in ['.', ',', '[', ']', '<', '>', '+', '-'], code)
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def get_analysis_type(normal, umi): """ return analysis type """ return "paired" if normal else "single"
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import argparse import os def get_cleaner_ArgumentParser(clean_func): """Get an ArgumentParser instance for clean up functions. Parameters ---------- clean_func : str The name of the cleaner function to get arguments for. Must be one of: "wrapper", "output", "logs". Returns -...
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def list_to_ol(input_list): """ Creates a html list based on input list @param input_list: list of items @return: html for list of items """ return '<ol>{}</ol>'.format('\n'.join(['<li>%s</li>' % cell_type for cell_type in input_list]))
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from typing import Dict from typing import Tuple from typing import List from typing import Any def _traverse_foreign_key_tree( tree: Dict[str, Dict[Tuple[str, ...], dict]], name: str, fields: Tuple[str, ...] ) -> List[Dict[str, Any]]: """Traverse foreign key tree. Args: tree: Foreign key tree (s...
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from typing import List from typing import Dict def generate_currency_endowments( agent_addresses: List[str], currency_ids: List[str], money_endowment: int ) -> Dict[str, Dict[str, int]]: """ Compute the initial money amounts for each agent. :param agent_addresses: addresses of the agents. :param...
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def key_to_hump(key): """ 将变量转化为驼峰命名 :return: 返回驼峰命名变量 """ return key.lower().title().replace("_", "")
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def constrained_domain(**namespace): """ Returns e.g. periodic domain. """ return None
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def sort_list_by_other(to_sort, other, reverse=True): """Sort a list by an other.""" return [el for _, el in sorted(zip(other, to_sort), reverse=reverse)]
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import plistlib def uid_convert(obj): """To JSON uid convertor""" if isinstance(obj, plistlib.UID): return "UID(" + str(obj.data) + ")" # return obj
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import random def _uniq_id(): """ Create a random 64-bit signed integer appropriate for use as trace and span IDs. XXX: By experimentation zipkin has trouble recording traces with ids larger than (2 ** 56) - 1 """ return random.randint(0, (2 ** 56) - 1)
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def update_analyze_button(disabled): """ Updates the color of the analyze button depending on its disabled status :param disabled: if the button is disabled """ if not disabled: style = {"width": "100%", "text-transform": "uppercase", "font-weight": "700", "background":...
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import json def add_environment_endpoint(app, config): """ Create a /_environ endpoint. This creates a /_environ endpoint where expose some variables used in the application to verify if they are ok. :param app: :param config: :return: A json file that expose the configuration variables ...
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from typing import Tuple from pathlib import Path import subprocess def walksat(input_dimacs: str, solver_exe: str = "binary/walksat_linux", ) -> Tuple[bool, list, float]: """ WalkSAT v56 (https://gitlab.com/HenryKautz/Walksat) :param input_dimacs: Correctly formatted DIMACS file ...
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def format_id(ident): """ Convert a message ID to its canonical string form """ return '%016X' % ident
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import sys import imp def main_is_frozen(): """ Returns whether or not it is frozen in an executable #http://www.py2exe.org/index.cgi/HowToDetermineIfRunningFromExe :rtype : bool """ return (hasattr(sys, "frozen") or # new py2exe hasattr(sys, "importers") # old py2exe ...
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def catch_all(path): """ Redirect arbitrary pages to homepage. """ return "we gotta help the pope"
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def read_classes(classes_path): """Reads classes from file. Args: classes_path (str): Path to file containing names of all classes. Returns: list: List containing names of all classes. """ with open(classes_path) as f: class_names = f.readlines() cla...
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import torch def generate_sparse_one_hot(num_ents, dtype=torch.float32): """ Creates a two-dimensional sparse tensor with ones along the diagnoal as one-hot encoding. """ diag_size = num_ents diag_range = list(range(num_ents)) diag_range = torch.tensor(diag_range) return torch.sparse_coo_tensor( ...
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def __is_prefix(pref, word): """ SUMMARY returns whether a string is prefix of another PARAMETERS pref: the string we check whether is prefix word: the string we search the prefix in RETURNS boolean: True if <pref> is prefix of <word> otherwise False """ if word...
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from typing import Optional from typing import List from typing import Dict def generate_info_json( prev_photo: Optional[str], next_photo: Optional[str], exif: List ) -> Dict: """Generate the content of the story metadata file.""" data = { "title": {"en": "", "fi": "", "fr": ""}, "descript...
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def catGram(mp, categories): """ Selects the grammatical categories of the minimal pairs """ pairs = mp[mp.cgram_1.isin(categories) & mp.cgram_2.isin(categories)] return pairs
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def splev_wikipedia(k: int, x: int, t, c, p: int): """Evaluates S(x). Arguments --------- k: Index of knot interval that contains x. x: Position. t: Array of knot positions, needs to be padded as described above. c: Array of control points. We will look at c[k-p .. k] p: Degree o...
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def merge_sort(arr): """ Implementation of a merge_sort algorithm using recursion. """ def _merge(a, b): """ This will be recalled recursevly. Takes in two pre-sorted arrays and returns a single sorted array. """ i = 0 j = 0 k = 0 r = [[] for _ in...
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import os def get_folder_path(folder_name): """ Get path for temp folder with folder name. """ folder_path = os.path.join(str("."), str(folder_name)) if not os.path.exists(folder_path): os.mkdir(folder_path) return folder_path
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def split_string_by_fields(string, fields): """ Helper function to split a string by a set of ordered strings, primarily used for Maccor metadata parsing. >>>split_string_by_fields("first name: Joey last name Montoya", >>> ["first name:", "last name"]) ["Joey", "Montoya"]...
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def footer(id1, id2 = None): """ Build SMT formula footer Args: id1 (str): ID of policy 1 in SMT formula id2 (str, optional): ID of policy 2 in SMT formula. Defaults to None. Returns: str: SMT footer """ smt = '(assert {}.allows)\n'.format(id1) if id2: smt...
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def area_square(length): """ Calculates the area of a square. Parameters ---------- length (float or int) length of one side of a square Returns ------- area (float) - area of the square """ return length ** 2
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def select(func): """ select :: (a -> Bool) -> [a] -> [a] Grab elements based on a filter function Similar to builtins.filter() """ def imap(data): if not isinstance(data, list): return list(filter(func, [data])) return list(filter(func, data)) return imap
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def create_final_conv(image_sizes, h_out = 1, w_out = 1): """ This function generates a convolution with h,w = 1. Used the formula from here :https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d ---------- image_sizes : the h,w of the output of each convolution layer in a l...
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def calculate_time(cents_per_kWh, wattage, dollar_amount): """ Returns the time (in hours) that it would take to reach the monetary price (dollar_amount) given the cost of energy usage (cents_per_kWh) and power (wattage) of a device using that energy. """ return 1 / (cents_per_kWh) * 1e5 * dollar_am...
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def flatten(filenames): """Takes a list which may contain other lists and returns a single, flattened list Args: filenames (list): list of filenames Returns: flattened list of filenames """ flat_filenames = [file for i in filenames for file in i] return flat_filenames
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def vals_vec_from_lmfit(lmfit_params): """Return Python list of parameter values from LMFIT Parameters object.""" vals = [value.value for value in lmfit_params.values()] return vals
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def _get_yaml_path(path, parameter): """Compose the parameter path following the YAML Path standard. Standard: https://github.com/wwkimball/yamlpath/wiki/Segments-of-a-YAML-Path#yaml-path-standard """ yaml_path = [] if path: yaml_path.extend(path) if parameter: yaml_path.append(...
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import os import warnings def filter_valid_images(df, img_directory, x_col, img_format): """Keep only dataframe rows with valid filenames # Arguments df: Pandas dataframe containing filenames in a column x_col: string, column in `df` that contains the filenames or filepaths # Returns ...
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