content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import re
def remove_unwanted_chars(x: str, *chars: str, to_replace: str = "") -> str:
"""Remove unwanted characters from a string."""
return re.sub(f"[{''.join(chars)}]", to_replace, x) | 4a1e25b1ad12f47f835d4f6cdbac4a6e08413077 | 72,700 |
async def get_by_name(name : str, current_user: User = Depends(get_current_active_user)):
"""
Looks for an item in the database
"""
data = await fetch_item(name)
if data is None:
return HTTPException(404, "Item not found")
return data | 841cfa4854f54076d7e6bcf29ad321ccd73382f1 | 72,701 |
def readMatrix( infile,
format = "full",
separator = "\t",
numeric_type = numpy.float,
take = "all",
headers = True,
missing = None,
):
"""read a matrix from file ane return a numpy matrix.
formats a... | bb63ebf3319ef6a547794fe963fd3323b06a6da8 | 72,702 |
from typing import List
def ranked_followers(username: str, rank_by: str, value: str) -> List[Follower]:
"""
Rank followers based on specified criteria using db queries. Return a list
of ranked followers' user id and their screen names who have not already
been sent a DM.
params:
username... | 232ca94bed58a12b086fccc760fbfd73f5d19208 | 72,703 |
def get_entity_group_version(entity_or_key):
"""Returns the version of the entity group containing `entity_or_key`.
Args:
entity_or_key: a key or entity whose version you want.
Returns: The version of the entity group containing `entity_or_key`. This
version is guaranteed to increase on every change to t... | 11f6a3fa0f0ff93a3201e1208c2b591dd686a7c2 | 72,704 |
def get_first_shape(node):
"""
Returns the first active shape on a given transform
:param node: str, transform node to get shape from
:return: str
"""
shapes = get_shapes(node=node, non_intermediates=True, intermediates=False)
if shapes:
return shapes[0]
return None | c8dfdcbabfce591e9484c8023f6a1ab42b20a9cb | 72,705 |
def UDPOS(*args, **kwargs):
""" Universal Dependencies English Web Treebank
Separately returns the training, validation, and test dataset
Arguments:
root: Directory where the datasets are saved. Default: ".data"
vocabs: A list of voabularies for each columns in the dataset. Must be in an
... | fba09e0dbdeb79fab747ab39b990632563622174 | 72,706 |
def generate_openid_signature(openid):
"""对openid进行加密，返回加密后的openID"""
# 1.创建加密的实例对象
serializer = Serializer(settings.SECRET_KEY, 600)
# 2.调用dumps方法进行加密，返回bytes类型
data = {'openid': openid}
openid_sign = serializer.dumps(data)
# 3.返回
return openid_sign.decode() | 99203760d8e8efaae0731e3c9054898308072697 | 72,707 |
def write_the_species_tree(annotated_species_tree, output_file):
"""
this function writes the species tree to file
args:
annotated_species_tree : a string of annotated species tree in .newick format
output_file : a file name to write to
output:
a file containing the annotated sp... | 7db9d5fc10cd27b1e7a5e51427e7de6991a1157a | 72,708 |
import functools
def compose(*functions):
"""
A functional helper for dealing with function compositions. It ignores any None functions, and if all are None,
it returns None.
Parameters
----------
*functions
function arguments to compose
Returns
-------
function or None
... | 8a18b1e0beef43c0cfac4439fa158675a486b560 | 72,709 |
import os
import shutil
def remove_any_tmp_dir():
"""
Remove the temporary directory if it exists.
Returns its location either way.
"""
tmp_dir = get_tmp_dir()
if os.path.exists(tmp_dir):
shutil.rmtree(tmp_dir)
return tmp_dir | 5156cad84f7455d32a7b9e77f18e9fc94a71a4e1 | 72,710 |
def pretty_print_list(lst, name = 'features', repr_format=True):
""" Pretty print a list to be readable.
"""
if not lst or len(lst) < 8:
if repr_format:
return lst.__repr__()
else:
return ', '.join(map(str, lst))
else:
topk = ', '.join(map(str, lst[:3]))
... | d199261e6b9fd226256a151601d0bd86e7458fe1 | 72,711 |
import json
def load_api_config():
"""Grab our config parameters from our provided config file (JSON format)."""
with open(Config.config_file, 'r', encoding="utf-8") as file_config:
conf = json.loads(file_config.read())
return OAuth2(client_id=conf["falcon_client_id"],
client_se... | 7c73cfd066e4f243c80cfc098c3950b4801f157f | 72,712 |
def factorial(n):
"""
:type n: int
:rtype: int
"""
f = n
for i in range(n - 1, 0, -1):
f = f * i
return f | 17d6e211911417507e5887232f181bf774396c73 | 72,713 |
def train_model(X_train, Y_train):
"""
This function takes in the training data and label arrays
and returns a clf classifier object
"""
n_estimators = 400
max_depth = 5
min_child_weight = 5
learning_rate = 0.1
clf = XGBClassifier(n_estimators=n_estimators,
max_d... | b4e0c26796b14ff1938f736f8dcb5420cccff9c2 | 72,714 |
def pairify(string):
"""Convert a string of the form "key: value" to a tuple ("key",
"value"). May raise a ParseError."""
items = string.split(":", 1)
try:
return items[0].strip(), items[1].strip()
except IndexError:
raise ParseError("'%s': Keys must be terminated with a colon." %
... | 1541e94b4fb6feba91c07f71445e719832afa687 | 72,715 |
def get_provenance_record(attributes, ancestor_files, plot_type):
"""Create a provenance record describing the diagnostic data and plot."""
caption = ("Correlation of {long_name} between {dataset} and "
"{reference_dataset}.".format(**attributes))
record = {
'caption': caption,
... | 3b813a500edd4e93becb469d5cb967a4e32465b4 | 72,716 |
def encryptCharacter(PQ, character, powers):
"""Encrypting a single given character"""
ascii = getAscii(character)
k = 0
powers.setPrevSQR(ascii, k)
modNum = ascii % PQ
powers.setModNum(modNum, k)
for x in range(0, powers.getLength() - 1):
k = k + 1
prevSQR = modNum * modNum
... | dd206c39e5fe80fec0290a226f8341b2fa9a475d | 72,717 |
def matching( obs0, obs1, cost_fn, min_cost, max_cost ):
""" match objects using cost fn where min_cost <= cost <= max_cost, return list of index tuple matches """
m = cost_matrix( obs0, obs1, cost_fn )
row_idx, col_idx = linear_sum_assignment( m )
result = []
for i in range( len(col_idx) ):
... | fc95d8ec487eea2e78db7bb3e0d68f5a8757ba3a | 72,718 |
def _resource_schema_base(available_resources, selected_resources):
"""Resource selection schema."""
known_available_resources = {
sensor_id: sensor[0]
for sensor_id, sensor in SENSOR_TYPES.items()
if sensor_id in available_resources
}
return {
vol.Required(CONF_RESOURC... | 0c41f907fea5792b586ad9bad90085c9ef248906 | 72,719 |
import os
import uuid
import requests
import html
def download_page(url, cookie_jar):
"""
Request page using authenticated cookies (cookiejar).
Download html source and save in browser directory, to
be used by in show_in_browser().
"""
browser_dir = os.path.join(server_path, 'static/browser')
... | c97b39434d5793c7b58229b43eb8365dfc326980 | 72,720 |
import json
import os
import pandas as pd
import numpy as np
def load_fry(frydir, output_format="scRNA", nonzero=False, quiet=False):
"""
load alevin-fry quantification result into an AnnData object
Required Parameters
----------
frydir : `str`
The path to a output directory returned by a... | d77b41f1df3c23c937131a3559691c78cdc3c867 | 72,721 |
from typing import Optional
from operator import or_
def get_resource_holders_permissions(
db_session: Session, resource_id: UUID, holder_id: Optional[UUID] = None
) -> data.ResourceHoldersListResponse:
"""
Get list of permissions for exact holder.
"""
query = (
db_session.query(
... | a64f71eba95a0ab1852c8be097ace96f6361341e | 72,722 |
def delete_user(self):
""""
function to check if the user is deleted
"""
return User.user_list.remove(self) | 6fb17b7d5794174de44954243258d9c83782b8d5 | 72,723 |
def calcMaxEntPolicy(trans_mat, horizon, r_weights, state_features, beta):
"""
For a given reward function and horizon, calculate the MaxEnt policy that gives equal weight to equal reward trajectories
trans_mat: an S x A x S' array of transition probabilites from state s to s' if action a is taken
horizon: t... | 5615fbbc86a0a80cdeae59b5e5790f1b379ac6df | 72,724 |
def bed_count_region_overlaps(site_bed, region_bed, overlap_out,
only_incomplete=False,
intersectBed_f=False):
"""
Overlap sites with regions, and return number of sites overlapping for
each region. Also return the list of site IDs for each region.... | f456b4b0388e5e9c2b57fc1178ee25f9c13b9b64 | 72,725 |
def _generate_fake_input_arg_componentsdk(arg_spec):
"""Generate a fake argument value for inputs of module spec
Args:
arg_spec (dict) : argument specification from yaml module spec
Returns:
object: sample fake value
Raises:
NotImplementedError: if arg type is not implemented
... | 62d92059ff6df74f4f08e6c2d09186f48058b617 | 72,726 |
def get_matrix26():
"""
Return the matrix with ID 26.
"""
return [
[4.0, 4.0, 7.0, 7.0],
[3.0, 3.0, 7.0, 7.0],
[2.0, 2.0, 7.0, 7.0],
[1.0, 1.0, 7.0],
[0.0, 0.0, 7.0, 7.0],
] | 9650bda70c0b7818950d1165edaadc7a6e2df889 | 72,727 |
def get_shortest_path(start, goal):
"""
Returns shortest path. For this problem space
some of the problems have more than one problem
space. This function returns the first occurrence
it finds
:param start: node from
:param goal: node to
:return: list of the path with the shortest walk f... | a41bead86fb21bf99b2035568bc6fc035152de11 | 72,728 |
import os
def format_filename_target(path):
"""
Swaps /source/ to /target/ + extra formatting.
:param path:
:return:
"""
base = os.path.dirname(path.replace("\source\\", "\\target\\"))
# TODO Better this.
if GLOBAL_FLAG is EXTENSIONS.TXT:
return f'{base}\\{os.path.basename(path... | e8b0672526b812a28887a282ad1b76a21d96069d | 72,729 |
from typing import OrderedDict
def finalize_autosummaries() -> None:
"""Create the necessary ops to include autosummaries in TensorBoard report.
Note: This should be done only once per graph.
"""
global _finalized
tfutil.assert_tf_initialized()
if _finalized:
return None
_finaliz... | 50f5520dd9684c957d30bc470ef97f28102e75d9 | 72,730 |
from ..model.types import TypeReference
from typing import Union
def package_local_name(obj: "Union[_Name, TypeReference]") -> str:
"""
Return the name of a DAML object, assuming that the referent exists in the same package as the
target (i.e., _not_ scoped with a package ID).
"""
# TODO: Rewrite... | 2be9a0baafc36760d914ecb5997757d64cb6d046 | 72,731 |
def create_model(model_object, input_data, model_name: str = None) -> Model:
"""
Creates Model instance from arbitrary model objects and sample of input data
:param model_object: model object (function, sklearn model, tensorflow output tensor list etc)
:param input_data: sample of input data (numpy arr... | b54fe92a73bb22ca3da8a8845d364af385003d8b | 72,732 |
def make_disjunctive_chaum_pedersen_zero(
message: ElGamalCiphertext, r: ElementModQ, k: ElementModP, seed: ElementModQ
) -> DisjunctiveChaumPedersenProof:
"""
Produces a "disjunctive" proof that an encryption of zero is either an encrypted zero or one.
:param message: An ElGamal ciphertext
:param ... | 97b0b15b265b795bfcf0e2e8d3ca0737dda8c455 | 72,733 |
def get_conf_dfs(df, log=False):
"""Df is info df, return the collection of dfs in the confirmed_df_dict column with some extra information."""
df_all = pd.DataFrame()
for _, row in df.iterrows():
site = row["site"]
try:
df_frame = pd.DataFrame(row["confirmed_df_dict"])
... | 89e97635edd8062d1ec1cb0878114467e80298ae | 72,734 |
def vib_ent(temp, t_d_perov, t_d_brownm):
"""
Vibrational entropy based on the Debye model
:param temp: temperature
:param delta: non-stoichiometry delta
:return: vibrational entropy
"""
# integral for vibrational entropy using the Debye model
def s_int(temp, t_d... | aa4ceeadc9be5e6f1489c8f9cf6ebb10908d0d05 | 72,735 |
import tqdm
import time
import logging
import torch
from typing import OrderedDict
def train(train_loader,
model,
criterion,
optimizer,
scheduler,
num_classes,
batch_size,
ep_idx,
progress_log,
device,
scale,
... | 73cf675c5446ba407908dca6fdf7b0c6406b1ff4 | 72,736 |
def Object_ClassName():
"""Object_ClassName() -> char const *"""
return _DataModel.Object_ClassName() | 54043519d451286642268c6c971e3cad60a176c2 | 72,737 |
def get_model_from_key(key: str) -> object:
"""
Get model from key
"""
class_name, pk = key.split(":", 1)
return apps.get_model(class_name) | fea798a3626bd516edcda64ad18e278537f3b159 | 72,738 |
def hernquist_profile_3d(cosmo, concentration, halo_mass, odelta, a, r):
"""Calculate the 3D Hernquist halo profile
at a given radius or an array of radii,
for a halo with a given mass, mass definition, and concentration,
at a given scale factor, with a cosmology dependence.
.. note:: Note that thi... | b7257e15408694220aa13d299ea4e346a30fb2c5 | 72,739 |
def masked_softmax(data, mask, dim=-1):
"""Computes the axis wise softmax over chosen elements.
Args:
data: 2-D float `Tensor` of size [n, m].
mask: 2-D boolean `Tensor` of size [n, m].
dim: The dimension over which to compute the softmax.
Returns:
masked_softmax: 2-D float `Tensor` of size [n, ... | befba39ab8a43d5b09618461aa9c46523fce6515 | 72,740 |
def bc(n, k):
""" return the binomial coefficient "n choose k". Can you figure out a method that is less likely to cause an overflow than using the formula(n*(n-1)*...*(n-k+1))/(k*(k-1)*...*2)? """
return special.binom(n, k) | 140b4b84fb370fb8ffd7ae61b3a8c67f4766c50a | 72,741 |
def get_data(lookup_key: str, location: str) -> pd.DataFrame:
"""Retrieves data from an appropriate source.
Parameters
----------
lookup_key
The key that will eventually get put in the artifact with
the requested data.
location
The location to get data for.
Returns
... | 217c9f4dae939f7f073a98885638b9bbc64f342b | 72,742 |
def handle_http_error(response):
"""
Check HTTP response for errors and process them accordingly.
:param response: HTTP response to check
"""
if response.status_code != 200:
# check if there is some info about error in json format
if response.headers.get("content-type") == "applicat... | ca2f9ecc414b1d6a60a29f5e10952acf2cd4ada7 | 72,743 |
import math
import copy
def golden_section_search(var1, func, point=True, e=EPSILON, idx=0, print_stats=False):
"""
Golden section search algorithm
:param var1: Can be list with two elements representing the interval or can be single number from which we find unimodal interval.
:param func: Function t... | 698aa3750dc7632c0e27cb2381da4731af7ba5ae | 72,744 |
def gradient(P, Q, Y):
"""
Calculates the gradient of the Kullback-Leibler divergence between P and Q
:param P: the joint probability matrix
:param Q: the Student-t based joint probability distribution matrix
:param Y: low dimensional matrix representation of high dimensional matrix X
:return: a... | b08116b5bca764b1b9149eaf03f7b3525c99ce3e | 72,745 |
def _exclude_loop_arguments_variables(
param: _pipeline_param.PipelineParam) -> str:
"""Gets the pipeline param name excluding loop argument variables.
Args:
param: The pipeline param object which may or may not be a loop argument.
Returns:
The full name of the pipeline param without loop arguments ... | 5c7d55c75db12afe6862a79d7fc8745443a650b6 | 72,746 |
import logging
def rulextraction(source_side, target_side, alignment, max_nonterminals=2, nodeset_size_limit=5,
initial_rule_nodeset_size_limit=0, subgraph_df_limit=100, parse_graph=False):
"""
Extract the maximum set of rules allowed under constraints from a single example consisting of sour... | c8eb06e737c2f183a8bbeb152b3788894856271b | 72,747 |
import os
import zipfile
def make_lib_c(lib_name, lib_path, deploy_path):
"""
pyc打包单个库
:param lib_name: string jszt_core_py
:param lib_path: string 库路径
:param deploy_path: string 部署文件路径
:return:
"""
lib_path = os.path.join(lib_path, lib_name)
zip_path = "%s.zip" % deploy_path
z... | 68ad788bdc118b7e49386e79321cac6054599b8a | 72,748 |
def get_integration_filter(args, targets): # type: (IntegrationConfig, t.List[IntegrationTarget]) -> t.Set[str]
"""Return a list of test targets to skip based on the host(s) that will be used to run the specified test targets."""
invalid_targets = sorted(target.name for target in targets if target.target_type ... | 46a936aa797b928506ba2328034c1319da3f821a | 72,749 |
def callback(func):
"""Decorator for non blocking functions."""
setattr(func, "_pyhap_callback", True)
return func | 6b1f2297dda8c7b3df5a75142a921e20fd4768f8 | 72,750 |
def docker_compose_project_name():
"""Set a consistent project name to enable optional reuse of containers."""
return "pytest-python-gitlab" | 0c8ed2e63692fec969c042b4e2e8094459045c5b | 72,751 |
def midi_to_seq(
midi_obj, dtype=np.int16, rate=22050, pitch_aug_factor=0, time_aug_factor=1, mode="format", time_shift=0.0125
):
"""method for midi_obj.
Input:
miditoolkit_object, sampling rate
Output:
note_seq: np.array([pitch1,pitch2....]), which length is equal to note.time*rate
... | ab4f6dad327e1b1c5e7715fb765aaefc33146115 | 72,752 |
def units_LexiconFormula(*args):
"""
* Return a unique instance of LexiconFormula.
:rtype: Handle_Units_Lexicon
"""
return _Units.units_LexiconFormula(*args) | 63240baa2e30e3ed0d6fa7ead3768741b57bfb7f | 72,753 |
def stop(backupplan_name, p5_connection=None):
"""
Syntax: BackupPlan <name> stop
Description: Removes the plan <name> from the scheduler
Return Values:
-On Success: the string "1" (the plan was successfully removed)
the string "0" (the plan was not removed or is running)
... | e73dbe04bc0fc8824d144ff12361a03d30ab5c1b | 72,754 |
from twisted.internet import defer
def check_eu_vat_id(vatid):
"""Check an EU VAT ID string (of the form: FI01234567) and return True if
it is currently valid.
"""
def _success(res):
vat_valid = False
if hasattr(res, 'valid'):
if res.valid == 1:
return Tru... | 8bdd8cb25a549bf597057c761f47030c2db0a576 | 72,755 |
def heron(_a, tol, _x0=None, termination_criterion='x'):
"""
Method for finding the non-negative, square root of a real number.
"""
def func(_x):
return _x**2 - _a
def dfunc(_x):
return 2*_x
if _x0 is None:
_x0 = _a
return newton(func, dfunc, _x0, tol,
... | f0580093f1fa5b0d008c290c74b3e5403f36aa30 | 72,756 |
def shopping_graphplan():
"""Solves the shopping problem using GraphPlan"""
return GraphPlan(shopping_problem()).execute() | c0a1cf31ebf796ec2b0b879780661113e6691ed9 | 72,757 |
from operator import gt
def _nodes2dataframe(G: gt.Graph) -> NodeDataFrame:
"""
Takes a Graph-tool graph object and returns nodes data frame.
"""
expect_nodes(G)
if len(G.vp) != 0:
prop_dfs = []
for prop in G.vp:
prop_dfs.append(NodeDataFrame({f"{prop}": list(G.vp[f"{pr... | a517e8ee8aaedde3ebaeb4a916eec35260b49352 | 72,758 |
def get_config_inifile(filename):
"""Read configuration from INI file and return a config dict.
Arguments:
- filename: Name of INI file.
"""
# SR_TODO: default config
parser = SafeConfigParser()
parser.optionxform = str # to preserve case
parser.read(filename)
log.debug("read conf... | 426a7f4e30c9a42aea5427d99482185784816dcc | 72,759 |
import bisect
def clause_b_1_3_3_T_m_i_beam(
I_z_1, I_z_2, I_z_3, I_z_4,
I_f_1, I_f_2, I_f_3, I_f_4,
alpha,
T_z_1, T_z_2,
sigma: float = 5.67e-11,
*_,
**__
):
"""
:param I_z:
:param I_f:
:param alpha:
:param sigma:
:return:
"""
... | 9e0b2f32641c905aedb019ec4bb986297371f198 | 72,760 |
def _partition(entity, sep):
"""Python2.4 doesn't have a partition method so we provide
our own that mimics str.partition from later releases.
Split the string at the first occurrence of sep, and return a
3-tuple containing the part before the separator, the separator
itself, and the part after the... | 0172ce09bd0451eb538122bd9566a3e70d0dcc15 | 72,761 |
def get_config_bool(name, fallback=False):
"""Checks if a config value is set to a valid bool value."""
cli_config = CLIConfig(SF_CLI_CONFIG_DIR, SF_CLI_ENV_VAR_PREFIX)
return cli_config.getboolean('servicefabric', name, fallback) | 3e18b97b12dd7446b973ffb3215d823d75857d26 | 72,762 |
def get_user_access_rights(username: str, is_vendor: bool = False):
"""
Query Redis for information about the user by given username.
Arguments:
:param username (str) authorized user's username
:param is_vendor (bool) whether method should return vendor or SDO accessRigt
"""
... | 90d230129a2a09ea848ef11eb0b7e83cbe8624e8 | 72,763 |
def safe_email(email):
"""Scrub email address before using it."""
if EMAIL_REGEX.match(email) is not None:
return not email.startswith(".") and ".." not in email
logger.warning("Invalid email address: %s.", email)
return False | 68868e5bdfb413caf2f94edb096f319292fe064c | 72,764 |
def read_data(byte_data_directory, x_filename, y_filename=None):
"""
reads in byte date from a list of filenames given in file located
at x_filename. if y_filename is supplied labels will be read in and a map
will be created as well and a label column added to the returned dataframe
"""
X_files... | 3566963b1f9b27957585f2de561b45b0c5d86915 | 72,765 |
def get_region_name():
"""
Return EMR cluster region as string
:return:
"""
return get_extraInstanceData()['region'] | 628a932cb24352b0ca72cc8741172887251ee020 | 72,766 |
def split_into_fixed_length_chunks(dataset, fixed_length, min_length=32, keep_residuals=False):
"""
Splits the sequences into shorter sequences with the same length.
Args:
dataset (dict):
length:
keep_residuals:
Returns:
"""
new_data = {}
# Detect <key, valu... | 4ff93445170060e63ea36cdc4c6aa462b6d0e32a | 72,767 |
def run(args):
"""Admin function used to set the cluster that will be used to
actually perform jobs
"""
try:
authorisation = Authorisation.from_data(args["authorisation"])
except:
authorisation = None
try:
passphrase = str(args["passphrase"])
except:
pass... | 9bbdf9a618c682ef58a1f8f9e4e62c892362b2f3 | 72,768 |
def get_phe_title(db, phename):
"""
Search bar
SciDBnew:
res = str(list(phef[phef['description'] == "asthma_diagnosed_by_doctor"]['title'])[0])
"""
phef = db.get_phenotype_fields(association_set=str(db.list_association_sets()['name'][0]))
if phef.empty:
return None
else:
... | 93ef978e9b79760d0226cc0fd00d827f7b3872ff | 72,769 |
import csv
import sys
def get_next_arrival_given_route(intent, session):
""" Returns the next scheduled bus or train that the user asks for in the utterance.
This function is similar to get_next_arrival().
"""
card_title = intent['name']
if 'attributes' in session:
session_attributes ... | fdc07261f30f284ee09a25dc666bec293c3a3554 | 72,770 |
import random
def make_graceful(ex: Exception, ex_uuid: UUID) -> str:
"""
makes the incoming exception graceful
Args:
ex (Exception): the raised exception to make graceful
ex_uuid(UUID): uuid identifying exception in the logs
Returns:
str: graceful error message
"""
... | af53769ec9ac3880cbab4d7c143549d4f5fc3aa5 | 72,771 |
def run_model(data, labels, return_coef=False):
"""
Runs provided LogisticRegressionCV model with the provided data
and labels.
Parameters:
data (pandas.DataFrame): DataFrame of CMTF components
labels (pandas.Series): Labels for provided data
return_coef (bool, default: False): ... | 99eab8e415bbd5e4f26ef5741292b42510a89e8e | 72,772 |
def hpo_disease_file(request):
"""Get the path to the hpo disease file"""
print('')
return hpo_phenotype_to_terms_reduced_path | 9075f371366428dc8634edf5d35e148ad777652c | 72,773 |
import warnings
def check_already_dropped(labels, dropped_cols, name, dropped_by='numeric_only',
warn=True):
"""Check if columns have already been dropped and return only those that
haven't.
Args:
labels (single label or list-like): Column labels to check.
droppe... | d584691cf2cd073641625b6358000cbe72f736ae | 72,774 |
import uuid
import string
def plot(df,
map_widget=None,
name=None,
renderer_type=None,
symbol_type=None,
symbol_style=None,
col=None,
colors='jet',
alpha=1,
**kwargs):
"""
Plot draws the data on a web map. The user can describe ... | ae9a30a7e5bc0e333ecc96e184739e09610a2085 | 72,775 |
import json
def LoadWorkerOutput(output):
"""Load output from worker processes to our internal format.
Args:
output: list of strings. The stdouts of all worker processes.
Returns:
A tuple of start_time, latency, size. Each of these is a list of
numpy arrays, one array per worker process. start_tim... | 554ffd0821764f6ddc312e93575c56713bc45ac2 | 72,776 |
import requests
import json
def req(method, url, headers={}, payload=None, status_code=False, timeout=30):
"""
Request function with error handlers (output: dict)
"""
pass_headers = {}
const_headers = {
'User-Agent': '{}/{}'.format(
__prog__, __version__
)
}... | aca322eaec9df33e870563d3672b002213d13a3f | 72,777 |
def stack(arrays, axis=-1):
"""
Stack of all arrays from a stream. Generalization of numpy.stack
and numpy.concatenate.
Parameters
----------
arrays : iterable
Stream of NumPy arrays. Arrays must have shapes that broadcast together.
axis : int, optional
Stacking direction. I... | 796df83f2900122ba23c746aa9ca1453fc52effa | 72,778 |
def wrn40_8_cifar10(classes=10, **kwargs):
"""
WRN-40-8 model for CIFAR-10 from 'Wide Residual Networks,' https://arxiv.org/abs/1605.07146.
Parameters:
----------
classes : int, default 10
Number of classification classes.
pretrained : bool, default False
Whether to load the pre... | 73214a88ff2b9500326f889278cea7c99ab97fd8 | 72,779 |
from typing import List
def gen_from_dataframe(df: DataFrame,
caption: str = None, header: List[str] = None, line_names: List[str] = None,
index: bool = False, index_start: int = 1, horizontal: bool = False) -> str:
"""
Генерирует таблицу latex из DataFame.
Ну... | 47c8720ee89def6b616641eca510acedbe8210c5 | 72,780 |
def perform_extended_GST_on_data(filename_data_input, filename_target_gateset,
filename_fiducials, filename_germs,
maxlength_germseq):
"""Performs extended/long GST on data
-------------------------------------------------------------------------... | 4ba557ac2e1d9258837660b5949ddcf3c6cb2173 | 72,781 |
def custom_x_bounds_max(current_shooting_point: int, n_elements: int, n_shooting: int) -> np.ndarray:
"""
The custom function for the x bound (this particular one mimics linear interpolation)
Parameters
----------
current_shooting_point: int
The current point to return the value, it is defi... | 7e2faeee8750966674f976be317c0f9a775d51e5 | 72,782 |
def compatibility_g_m(gen_1, gen_2, name_1, name_2):
"""Check compatibility."""
print("Checking compatibility of {} with {}.".format(name_1, name_2))
r_code = 0
for seq in gen_1:
if seq not in gen_2:
print("FAIL\t{} sequence not found in {}.".format(seq, name_2))
r_code =... | 0ce34eb6ebe97bca179425939c95a2a6a19f6e6a | 72,783 |
def review_list(request, resource_type):
"""
:param request:
:param resource_type: The type of resource to generate a review list for.
:return: Returns a list of reviews of that particular resource_type.
"""
resources = Review.objects.filter(resource_type__slug__exact=resource_type.lower())
... | c4d83b403d1b60c80f61f11998b1f1f4b3fe1e7c | 72,784 |
def meanPtVersusPartonicPt(histFile='./combined.sum.hist.root', trigKey='notrig', subProcess=''):
"""note: errors are absolutely fake"""
f = ROOT.TFile(histFile)
m = MiniMcHistos(trigKey, subProcess, f)
c = ROOT.TCanvas()
m['partonicPt'].GetZaxis().SetRangeUser(m['partonicPt'].GetMinimum(1e-10... | 709d9f8624c5115e0a954eb5a51b9a097f4aeb5d | 72,785 |
import sys
import subprocess
def _GetGitRevision(in_directory):
"""Returns the git hash tag for the given directory.
Args:
in_directory: The directory where git is to be run.
Returns:
The git SHA1 hash string.
"""
git_exe = 'git.bat' if sys.platform.startswith('win') else 'git'
p = subprocess.Po... | 4ee7abe93c3d63bec84e478725d7ca086599d74b | 72,786 |
def create_regional_summary (results):
"""Creates the regional summary
Parameters
----------
results : dictionary
results from the model, dictionary with each community or project
as key
Returns
-------
DataFrame
containing regional results
... | c90d3ac11919ce6330c52a2133f2733193a78373 | 72,787 |
import socket
def getaddrinfo(host, port=None, family=0, socktype=0, proto=0, flags=0):
"""return (family, socktype, proto, canonname, sockaddr)
>>> socket.getaddrinfo("www.python.org", 80, 0, 0, socket.SOL_TCP)
[(2, 1, 6, '', ('82.94.164.162', 80)),
(10, 1, 6, '', ('2001:888:2000:d::a2', 80... | 5fde1bc04887e002199e119b24cde08227d14c0b | 72,788 |
def scatter_element_add(tensor, index, value, like=None):
"""In-place addition of a multidimensional value over various
indices of a tensor.
Args:
tensor (tensor_like[float]): Tensor to add the value to
index (tuple or list[tuple]): Indices to which to add the value
value (float or ... | 2bc08d37be6948a820e3b2edd408547b14948516 | 72,789 |
from typing import Optional
from typing import Sequence
def get_volume_backups(compartment_id: Optional[str] = None,
display_name: Optional[str] = None,
filters: Optional[Sequence[pulumi.InputType['GetVolumeBackupsFilterArgs']]] = None,
source_volum... | 4cc5d688b2af608b17ea8ecf3d2844be176fa95f | 72,790 |
from os import environ
def broadcast_addresses():
"""A list if IP adresses to use for name resolution broadcasts"""
addresses = []
if "EPICS_CA_AUTO_ADDR_LIST" in environ and \
environ["EPICS_CA_AUTO_ADDR_LIST"] == "NO": pass
else:
try: addresses += broadcast_addresses_psutil()
... | 1c554cc97b5667c2c83480dc09b5acdf795708a0 | 72,791 |
def getParam(prompt, default, helpMessage="No help implemented yet!"):
"""Get a parameter from the user interactively. Also sets a default and
displays a help message if a user types "h" or "help".
"""
print "\n" + bcolors.OKBLUE + helpMessage + bcolors.ENDC + "\n"
param = raw_input(prompt)
if ... | ed9d2afd663f1c4d1bc36e4470de0f4c15adf2a7 | 72,792 |
def showCategories():
""" Display the main catalog page using any of the listed routes."""
lCategories = session.query(Category).order_by(Category.name).all()
if len(lCategories)==0:
sMsg = ""
if 'username' not in login_session:
sMsg = "There are currently no categories in the system. "
sMsg += "Please ... | f7bd569ba132445e7ea072c136c96d18cec0b0d9 | 72,793 |
def docker_registry_username() -> str:
"""Retrieve the name of the user to use for authentication to the secure registry service."""
return _docker_registry_username(scale_factor=1)[0] | d02a1f870305b6b8c30f69068dbd301b70d7ba55 | 72,794 |
def diaresnet110_cifar100(classes=100, **kwargs):
"""
DIA-ResNet-110 model for CIFAR-100 from 'DIANet: Dense-and-Implicit Attention Network,'
https://arxiv.org/abs/1905.10671.
Parameters:
----------
classes : int, default 100
Number of classification classes.
pretrained : bool, defa... | 82021a5cc88ec5c3c470fafe6790da6dc1eae387 | 72,795 |
import os
import torch
def load_model(model, optimizer, num_epochs, resume=True):
"""Load and resume from existing model.
:return: model path"""
model_name = os.path.join(data_dir,
str(model.__class__.__name__)+ '_' +
str(optimizer.__class__.__na... | e81ab46e1734f4ef4bd16d69a742b70d09b1df73 | 72,796 |
import argparse
def parse_args() -> argparse.Namespace:
"""
Parse the arguments.
Returns:
The argument namespace.
"""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'folder',
type=str,
hel... | 5e09d7573fd107c402607d20a29c2fe33a3526e7 | 72,797 |
def multiple(op1: int, op2: int) -> int:
"""
:param op1:
:param op2:
:return:
"""
return float(op1 * op2) | c9681de0655ba07c051a8bd64515e826df82a18d | 72,798 |
def dense_layer(x, config):
"""Configurable dense layer.
Supports scale layer.
Args:
x: Input tensorflow tensor.
config: network_config.DenseLayer object.
Returns:
An output tensor.
"""
if config.num_outputs < 0:
raise ValueError('Please set num_outputs to a positive number:'
... | 6b77b50598dd19f0632dbb100c3119bf2793ce25 | 72,799 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.