content stringlengths 35 416k | sha1 stringlengths 40 40 | id int64 0 710k |
|---|---|---|
def make_dict(typ, *values):
"""Implement `make_dict`."""
return dict(zip(typ.entries.keys(), values)) | e4a9a488f77a4ec0f5305c4ea67a25656f19a820 | 50,753 |
from yaml import full_load as yload
import json
def load_in_file(in_file):
"""Here goes the routine to compute the descriptors according to the state file(s)"""
with open(in_file, 'r') as stream:
try:
state_str = yload(stream)
except:
state_str = json.load(stream)
state = {}
for k, s in state_str.items():
if isinstance(s, str):
state[k] = json.loads(s)
else:
state[k] = s
return state | f8bb97e07cff43da871f6e785edf10f9a77aa0a0 | 50,754 |
def create_grid_string(dots, xsize, ysize):
"""
Creates a grid of size (xx, yy)
with the given positions of dots.
"""
grid = ""
for y in range(ysize):
for x in range(xsize):
grid += "." if (x, y) in dots else "#"
grid += "\n"
return grid | b1653b8e710edfe49b3d54e113b6709e51fb92b7 | 50,755 |
def dni_to_value(scale_value):
"""
This method will transform a string value from the DNI scale to its
confidence integer representation.
The scale for this confidence representation is the following:
.. list-table:: DNI Scale to STIX Confidence
:header-rows: 1
* - DNI Scale
- STIX Confidence Value
* - Almost No Chance / Remote
- 5
* - Very Unlikely / Highly Improbable
- 15
* - Unlikely / Improbable
- 30
* - Roughly Even Chance / Roughly Even Odds
- 50
* - Likely / Probable
- 70
* - Very Likely / Highly Probable
- 85
* - Almost Certain / Nearly Certain
- 95
Args:
scale_value (str): A string value from the scale. Accepted strings are
"Almost No Chance / Remote", "Very Unlikely / Highly Improbable",
"Unlikely / Improbable", "Roughly Even Chance / Roughly Even Odds",
"Likely / Probable", "Very Likely / Highly Probable" and
"Almost Certain / Nearly Certain". Argument is case sensitive.
Returns:
int: The numerical representation corresponding to values in the DNI
scale.
Raises:
ValueError: If `scale_value` is not within the accepted strings.
"""
if scale_value == 'Almost No Chance / Remote':
return 5
elif scale_value == 'Very Unlikely / Highly Improbable':
return 15
elif scale_value == 'Unlikely / Improbable':
return 30
elif scale_value == 'Roughly Even Chance / Roughly Even Odds':
return 50
elif scale_value == 'Likely / Probable':
return 70
elif scale_value == 'Very Likely / Highly Probable':
return 85
elif scale_value == 'Almost Certain / Nearly Certain':
return 95
else:
raise ValueError("STIX Confidence value cannot be determined for %s" % scale_value) | 13b80d6b0c12fbc36d59db9d6626cf747beca627 | 50,756 |
import argparse
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="PSPnet Network")
# optimatization configuration
parser.add_argument("--is-training", action="store_true",
help="Whether to updates the running means and variances during the training.")
parser.add_argument("--learning-rate", type=float, default= 0.00025,
help="Base learning rate for training with polynomial decay.") #0.001
parser.add_argument("--weight-decay", type=float, default= 0.0005,
help="Regularization parameter for L2-loss.") # 0.0005
parser.add_argument("--momentum", type=float, default= 0.9,
help="Momentum component of the optimiser.")
parser.add_argument("--power", type=float, default= 0.9,
help="Decay parameter to compute the learning rate.")
# dataset information
parser.add_argument("--dataset", type=str, default='cityscapes',
help="voc12, cityscapes, or pascal-context.")
parser.add_argument("--random-mirror", action="store_true",
help="Whether to randomly mirror the inputs during the training.")
parser.add_argument("--random-scale", action="store_true",
help="Whether to randomly scale the inputs during the training.")
parser.add_argument("--not-restore-last", action="store_true",
help="Whether to not restore last (FC) layers.")
parser.add_argument("--random-seed", type=int, default= 1234,
help="Random seed to have reproducible results.")
parser.add_argument('--logFile', default='log.txt',
help='File that stores the training and validation logs')
# GPU configuration
parser.add_argument("--cuda", default=True, help="Run on CPU or GPU")
parser.add_argument("--gpus", type=str, default="3", help="choose gpu device.") #使用3号GPU
return parser.parse_args() | e3fb21857b44a37fef7b930bcb5345c42005a780 | 50,757 |
from typing import Sequence
from typing import Generator
def dictionize(fields: Sequence, records: Sequence) -> Generator:
"""Create dictionaries mapping fields to record data."""
return (dict(zip(fields, rec)) for rec in records) | ca179ef5d03e8d5860c36872a61405b6a33ad463 | 50,758 |
import yaml
import getpass
import platform
def get_root_dir(config_file, dir_tag="root_dir", default_dir = None):
"""
Read db_root entries for current user and current machine
Args:
config_file ():
dir_tag ():
default_dir ():
Returns:
"""
if default_dir :
return default_dir
else:
with open(config_file , "r") as fi:
db_roots_dict = yaml.safe_load(fi)
user = getpass.getuser()
if user not in db_roots_dict:
raise RuntimeError(f"train.py::error::cannot find user {user} in db_root.yaml!"
f" either add your db root dirs into db_roots.yaml or specify root dir on the command line ")
user_roots = db_roots_dict[user]
hostname = platform.node().split(".")[0]
# handle jeanzay where we may have different suffixes
for hh in user_roots:
if hostname.startswith(hh):
hostname = hh
if hostname not in user_roots:
raise RuntimeError(f"train.py::error::cannot find host {hostname} for user {user} in db_roots.yaml, "
f"either add your db root dirs into db_roots.py or specify root dir on the command line ")
rootdir_config = user_roots[hostname]
if dir_tag not in rootdir_config:
raise RuntimeError(f"train.py::error::cannot find dir info for {dir_tag} in {config_file}!"
f" either add your db root dirs into db_roots.yaml or specify root dir on the command line ")
rootdir = rootdir_config[dir_tag]
return rootdir | 972b61e3e30f5b685d0a03219bb3b69753aa834b | 50,759 |
import warnings
def ratio(class_label, data):
"""Compute ratio of all data that is `class_label`."""
try:
return float(data.count(class_label)) / len(data)
except ZeroDivisionError:
warnings.warn("data {} is empty".format(id(data)))
return 0. | 7742804261d3aca98dd9ba19e083fd5c158310b9 | 50,760 |
def calc_force(force, new_closes, new_volumes):
"""
INPUTS:
force (Series) - A copy of last record's force value.
new_closes (Series) - New values, prepended with last closing price
new_volumes (Series) - New values, prepended with last volume
OUTPUT:
Force index as a pandas Series.
"""
for n,index in enumerate(new_closes[1:].index, start=1):
force[index] = ( new_closes[n] - new_closes[n-1] )*new_volumes[n]
return force | 30034d0a142962094f8b8eb02d71eee7d4088622 | 50,761 |
def import_devices(client):
"""
Imports all devices from the Rayleigh Connect API
:param client: An instance of the RayleighClient class
:return: A JSON object containing devices and their parameters, retrieved from the Rayleigh Connect API
"""
return client.retrieve_devices() | 83ca27b0c25015b706f787aa44c20785a6bbeee1 | 50,762 |
def lat_to_ibz(lat):
"""Returns the Bravais lattice ibz code based on the input string code.
e.g. lat='bcc' or lat='fco'
:param lat: lattice string code
:type lat: str
:returns: ibz code corresponding to the Bravais lattice
given by the 'lat' key
:rtype: int
"""
# One should add some error hangling here.
ltoi = {'sc': 1, 'fcc': 2, 'bcc': 3, 'hcp': 4, 'st': 5, 'bct': 6, 'trig': 7, 'so': 8,
'baco': 9, 'bco': 10, 'fco': 11, 'sm': 12, 'bacm': 13, 'bcm': 13, 'stric': 14,
'B2':1,'L12':1}
if not lat in ltoi.keys():
print("ERROR: Unknown lattice "+lat)
print("Exit now!")
exit(1)
return ltoi[lat] | 2c434b69b3ec21ad93d60ee8e48f40f087e6a369 | 50,763 |
def update_number_of_orientations(integration_density, integration_volume):
"""
Update the number of orientation for powder averaging.
Option for advance modal.
"""
ori = int((integration_density + 1) * (integration_density + 2) / 2)
if integration_volume == 0:
return f"Averaging over {ori} orientations."
if integration_volume == 1:
return f"Averaging over {4*ori} orientations." | bd54b18578eafdc59bf55284438b7d81d79c5509 | 50,764 |
def _cast_to_int(integer_string):
"""Cast a string to a integer."""
ret = int(integer_string)
if ret < -1:
raise ValueError()
return ret | 3eb4796f19467f95030ffdc8eb18eb7489329e98 | 50,765 |
import os
def _find_setup_script_for_extension(top_path,
source_dir='src',
setup_py='setup.py'):
"""
Parameters
----------
top_path : str
An entry for `os.walk`.
source_dir : str
Name of directory where source files locate.
setup_py : str
File name of the setup file for C extension.
"""
top_path = os.path.abspath(top_path)
setup_files = []
for root, dirs, files in os.walk(top_path):
if len(files) == 0:
continue
if source_dir in dirs and setup_py in files:
fp = os.path.abspath(os.path.join(root, setup_py))
if fp == os.path.abspath(__file__):
continue
setup_files.append(fp)
return setup_files | cf2955864da8b0a02b68be4dc87eff5c67b90637 | 50,767 |
def getFreePlaceValues(freePlaceMap):
"""
Returns the Amount of Free Places for every Area Marked on the FreePlaceMap
by iterating over the Map and counting each Cell of each Area together
The Returned list starts with Area 1 on Index 0
:param freePlaceMap: The generated FreePlaceMap
:return: The Amounts of free Places for each Area on the FreePlaceMap
"""
if freePlaceMap is None or len(freePlaceMap) <= 1 or len(freePlaceMap[0]) <= 1:
return
values = []
# Iterate over the Map
for y in range(len(freePlaceMap)):
for x in range(len(freePlaceMap[0])):
pointValue = freePlaceMap[y][x]
if pointValue != -1:
# Add one to the Area-Counter
if len(values) >= pointValue:
values[pointValue - 1] += 1
else:
values.append(1)
return values | a370310d26233b6dd93865b08528c505585fe9a4 | 50,768 |
def _add_system(query, data):
"""Add data from successful system MIB query to original data provided.
Args:
query: MIB query object
data: Three keyed dict of data
Returns:
data: Aggregated data
"""
# Process query
result = query.system()
# Add tag
for primary in result.keys():
for secondary in result[primary].keys():
for tertiary, value in result[primary][secondary].items():
data[primary][secondary][tertiary] = value
# Return
return data | cb37d05ac484f9a8f57b7a61afd9a40a971a23d0 | 50,770 |
def stat_limits(stats_dict, prof, stat, up_buff=None, low_buff=None):
"""Returns the highest and lowest values found in the s_c_dict provided.
parameter:
- stats_dict: The s_c_dict the min max values are wanted from
- prof: The profile name the values are to be taken from as a string
- stat: The stat the values are to be taken from as a string
- up_buff: A value indicating the amount of buffer desired as a
percentage of on the max value
- low_buff: A value indicating the amount of buffer desired as a
percentage of the minimum value
returns:
- s_min: The smallest value among those for a particular stat and
weapon class provided
- s_max: The largest value ""
raises:
"""
s_min = 0
s_max = 0
for w_class in stats_dict[prof][stat].keys():
for weap in stats_dict[prof][stat][w_class].keys():
if stats_dict[prof][stat][w_class][weap] > s_max:
s_max = stats_dict[prof][stat][w_class][weap]
if stats_dict[prof][stat][w_class][weap] < s_min:
s_min = stats_dict[prof][stat][w_class][weap]
if up_buff is not None:
s_max = s_max + abs(s_max*up_buff)
if low_buff is not None:
s_min = s_min - abs(s_min*low_buff)
return s_min, s_max | 634f0588e2de0d7fa884dbedc28e388a822cf6d3 | 50,771 |
import re
def unflat(data, separator="_", lseparator=('[', ']')):
"""
Unflat the dict
Args:
data (dict): the dict to unflat. Must be a key/value dict.
separator (:obj:`str`, optional): key separator.
Returns:
dict: Unflatted dict
"""
unflat_dict = {}
for k in sorted(data.keys()):
context = unflat_dict
for sub_key in k.split(separator)[:-1]:
if sub_key not in context:
context[sub_key] = {}
context = context[sub_key]
key = k.split(separator)[-1]
regex = r'(\w*)\{0}(\d)\{1}'.format(lseparator[0], lseparator[1])
match = re.match(regex, key)
if match:
lkey = match.group(1)
lpos = int(match.group(2))
if not lkey in context:
context[lkey] = []
context[lkey].insert(lpos, data[k])
else:
context[key] = data[k]
return unflat_dict | dd39961dca2ad7052c72e46cbd313ca6b45ac7f4 | 50,773 |
import socket
def get_hostname():
""" Retrieves the hostname of the machine
Returns:
hostname (str): the hostname or "Unknown otherwise"
"""
hostname = ""
try:
hostname = socket.gethostname()
if "." in hostname:
hostname = hostname[: hostname.find(".")]
except:
hostname = "UNKNOWN"
return hostname | 9c44ef69bbf1df5cd0a5ae7c80038def468072b3 | 50,774 |
def _InitLookUp(alpha=0.05):
""" Initialize the dictionary of the look up table based on the confidence level. The tabulated data are from
Rorabacher, D.B.(1991) Analytical Chemistry 63(2), 139–46.
:param alpha: confidence interval (double), accepted values: 0.1, 0.05, 0.01
:return:
"""
if alpha == 0.10:
q_tab = [0.941, 0.765, 0.642, 0.56, 0.507, 0.468, 0.437,
0.412, 0.392, 0.376, 0.361, 0.349, 0.338, 0.329,
0.32, 0.313, 0.306, 0.3, 0.295, 0.29, 0.285, 0.281,
0.277, 0.273, 0.269, 0.266, 0.263, 0.26]
elif alpha == 0.05:
q_tab = [0.97, 0.829, 0.71, 0.625, 0.568, 0.526, 0.493, 0.466,
0.444, 0.426, 0.41, 0.396, 0.384, 0.374, 0.365, 0.356,
0.349, 0.342, 0.337, 0.331, 0.326, 0.321, 0.317, 0.312,
0.308, 0.305, 0.301, 0.29]
elif alpha == 0.01:
q_tab = [0.994, 0.926, 0.821, 0.74, 0.68, 0.634, 0.598, 0.568,
0.542, 0.522, 0.503, 0.488, 0.475, 0.463, 0.452, 0.442,
0.433, 0.425, 0.418, 0.411, 0.404, 0.399, 0.393, 0.388,
0.384, 0.38, 0.376, 0.372]
else:
print("Input alpha value not available")
q_tab = []
Q = {n: q for n, q in zip(range(3, len(q_tab) + 1), q_tab)}
return Q | 18ccc897f4f0333e285957878a441e7e21a47877 | 50,775 |
def resolve_reset(tokens):
"""Resolve the reset to an location, but dont resolve the location to an address yet."""
tree = tokens[0]
if tree.data != "symbol":
raise Exception("Unknown reset type", tree.data)
return tree | 756642dde822d993f47a49a1e90958ff050fd1e1 | 50,776 |
def integral(x,y):
"""
ROUTINE: INTEGRAL
USEAGE: RESULT = INTEGRAL( X, Y )
PURPOSE: Integrate tabulated data using Simpson's rule
with 3-point Lagragian interpolation. Data may be
regularly sampled in X, or irregularly sampled.
INPUT:
X Vector of x axis points.
(Elements must be unique and monotonically increasing)
Y Vector of corresponding Y axis points.
KEYWORD_INPUT: None.
OUTPUT: Result of integration.
EXAMPLE:
Example 1:
Define 11 x-values on the closed interval [0.0 , 0.8].
X = [ 0.0, .12, .22, .32, .36, .40, .44, .54, .64, .70, .80 ]
Define 11 f-values corresponding to x(i).
F = [ 0.200000, 1.30973, 1.30524, 1.74339, 2.07490, 2.45600, $
2.84299, 3.50730, 3.18194, 2.36302, 0.231964 ]
Compute the integral.
RESULT = INTEGRAL( X, F )
In this example, the f-values are generated from a known function,
(f = .2 + 25*x - 200*x^2 + 675*x^3 - 900*x^4 + 400*x^5)
The Multiple Application Trapezoid Method yields; result = 1.5648
The Multiple Application Simpson's Method yields; result = 1.6036
IDL User Library INT_TABULATED.PRO yields; result = 1.6232
INTEGRAL.PRO yields; result = 1.6274
The Exact Solution (4 decimal accuracy) yields; result = 1.6405
AUTHOR: Liam Gumley, CIMSS/SSEC (liam.gumley@ssec.wisc.edu)
Based on a FORTRAN-77 version by Paul van Delst, CIMSS/SSEC
22-DEC-95
REVISIONS: None.
"""
n = x.size
x0 = x[0:n-2]
x1 = x[1:n-1]
x2 = x[2:n-0]
y0 = y[0:n-2]
y1 = y[1:n-1]
y2 = y[2:n-0]
#
# compute interpolation delta and midpoint arrays
#
dx = x1-x0
xmid = 0.5*(x1+x0)
#
# compute 3 point lagrange interpolation
#
l0 = ((xmid-x1)/(x0-x1))*((xmid-x2)/(x0-x2))
l1 = ((xmid-x0)/(x1-x0))*((xmid-x2)/(x1-x2))
l2 = ((xmid-x0)/(x2-x0))*((xmid-x1)/(x2-x1))
ymid = y0*l0 + y1*l1 + y2*l2;
#
# compute integral sum
#
integ = sum(1.0/6.0*dx*(y0+4.0*ymid+y1))
#
# handle last 3 points similarly
#
x0 = x[n-3]
x1 = x[n-2]
x2 = x[n-1]
y0 = y[n-3]
y1 = y[n-2]
y2 = y[n-1]
dx = x2 - x1
xmid = 0.5*(x2+x1)
l0 = ((xmid-x1)/(x0-x1))*((xmid-x2)/(x0-x2))
l1 = ((xmid-x0)/(x1-x0))*((xmid-x2)/(x1-x2))
l2 = ((xmid-x0)/(x2-x0))*((xmid-x1)/(x2-x1))
ymid = y0*l0 + y1*l1 + y2*l2;
integ = integ + 1.0/6.0*dx*(y1+4.0*ymid+y2)
return integ | 1854833006bc1bdb0c2352f08a059de145ab9ba2 | 50,777 |
def unminify(soup, encoding='utf-8'):
"""
"""
return soup.prettify().encode(encoding) | 11b956f2fa61f237afb5931441f8add4320024d4 | 50,779 |
import torch
def sparsity(M, tol=1.0e-3, device="cpu"):
"""
========================================================================== \n
Return the spacity for the input matrix M \n
----- INPUT \n
M : (Tensor) the matrix \n
tol : (Scalar,optional) the threshold to select zeros \n
----- OUTPUT \n
spacity : (Scalar) the spacity of the matrix \n
===========================================================================
"""
if type(M) is not torch.Tensor:
M = torch.as_tensor(M, device=device)
M1 = torch.where(torch.abs(M) < tol, torch.zeros_like(M), M)
nb_nonzero = len(M1.nonzero())
return 1.0 - nb_nonzero / M1.numel() | 60ab6d9578d4f3285b5ca8ec7287ab1e68cb7618 | 50,781 |
def load_org2_gateway(network_config):
""" Loads the `org2_gw` Gateway """
return network_config.get_gateway('org2_gw') | 55f1770759f71a1ab730689920e17eec549cd95f | 50,782 |
def int_addr(addr):
"""Gets the integer representation of an address"""
return int(addr[1:]) | 3f00ae151bff20516fbaddda73feb147aa1c8424 | 50,783 |
def val_default(val, default=True):
"""Value or default (for attributes)
"""
if val is None:
return default
return val | 6a7bb5a475428d36ad046b4e7a7099f2573eae76 | 50,784 |
def speak_next(most_recent: int, turn_num: int, nums_spoken: dict):
"""
If you want to optimize something in Python,
it probably involves dictionaries or tuples.
~ My friend Miles
"""
if most_recent not in nums_spoken:
saying = 0
else:
saying = turn_num - nums_spoken[most_recent]
nums_spoken[most_recent] = turn_num
turn_num += 1
return saying, turn_num, nums_spoken | d7ab59dd70ae80168f3170e4a79944dc86a63256 | 50,785 |
import os
def load_user_pass():
"""
Loads the username and password from the environment
:return: tuple - [username, password]
"""
# try to get the username from environment variable
LBXD_USERNAME = os.environ.get("LBXD_USERNAME", None)
class UsernameMissingError(Exception):
pass
if LBXD_USERNAME is None:
raise UsernameMissingError("Auth methods require a Letterboxd username.")
# try to get the user password from environment variable
LBXD_PASSWORD = os.environ.get("LBXD_PASSWORD", None)
class PasswordMissingError(Exception):
pass
if LBXD_PASSWORD is None:
raise PasswordMissingError("Auth methods require a Letterboxd password.")
return (LBXD_USERNAME, LBXD_PASSWORD) | 4c29353b214c42a66831e2284e0ce1eaa21bbbf4 | 50,786 |
def frame_text(lines: list, style='╔═╗╚╝║'):
"""
Styles cheat-sheet:
╔═╗╚╝║
/–\\/|
┏━┓┗┛┃
"""
max_width = max(len(line) for line in lines)
top = style[0] + style[1]*max_width + style[2] + '\n'
bottom = '\n' + style[3] + style[1]*max_width + style[4]
return top + '\n'.join(style[5] + line.ljust(max_width) + style[5] for line in lines) + bottom | e2328d4bc5583aeb6fdcaf492391fddca3a5a98c | 50,787 |
def res_server_error():
"""
Default response for an unhandled exception (base internal server error).
"""
return {
"type": "server_error",
"code": "error",
"detail": "A server error occurred.",
"attr": None,
} | 093f57abc12b48a2c788b86f860ae4addca9d475 | 50,788 |
import sqlite3
import gzip
def sqlite_from_file(filename, fmt=None):
"""Instantiate an sqlite3.Connection and return it, with the data
copied in from the specified file. The sqlite3 connection is
mapped in memory, not to the source file.
Args:
filename (str): path to the file.
fmt (str): format of the input; see to_file for details.
"""
if fmt is None:
fmt = 'sqlite'
if filename.endswith('.gz'):
fmt = 'gz'
if fmt == 'sqlite':
db0 = sqlite3.connect(f'file:{filename}?mode=ro', uri=True)
data = ' '.join(db0.iterdump())
elif fmt == 'dump':
with open(filename, 'r') as fin:
data = fin.read()
elif fmt == 'gz':
with gzip.GzipFile(filename, 'r') as fin:
data = fin.read().decode('utf-8')
else:
raise RuntimeError(f'Unknown format "{fmt}" requested.')
db = sqlite3.connect(':memory:')
db.executescript(data)
return db | 5cd9152d903d2a2c98fae676230c0a2fc3d4cfbb | 50,791 |
def getspecificnodes():
"""
Returns 20 specific nodes
"""
nodelist = [310, 510, 810, 1210, 9, 10, 61, 37, 38, 43, 23, 204, 1208]
print("Got specific nodes")
return nodelist | 427baabdf5aaaaaacc20745bca992e198d80b017 | 50,792 |
def cs_water_Tp(T=298, p=1):
"""
Calculates chemical shift of water based on Temparature and pressure (according to Maciej).
"""
# according to Maciej
cs = 5.945e-7*p*T -1.612e-4*p -1.025e-2*T + 7.866
return(cs) | de450a97255cd245ae02f2219bddc0b5f201bfec | 50,793 |
from collections import Counter
def test_byte_pair_encode():
"""
BPE: A technique to encode a string with replacing
most two frequent symbols with a single alpha,
and keep repeating until the remaining symbols are singular
"""
def bpe(s):
freq = []
for a,b in zip(s,s[1:]):
freq.append(a+b)
freq = Counter(freq)
z = 'Z'
for mc,f in freq.most_common():
if f>1:
s = s.replace(mc, z)
z = chr(ord(z)-1)
return s
assert bpe("hey") == "hey"
assert bpe("foofoo") == "ZoZo"
assert bpe("quick squad and duck or chicken") == "YiZ sYaVanVduZ or chiZen" | bb292404f03731da9ddfa7085907510b14bdd179 | 50,794 |
from typing import Set
import re
def get_filenames_from_git_log(git_log: str) -> Set[str]:
"""
:param git_log:
:return:
"""
pattern = re.compile(r'--- a\/(.+)\n\+\+\+ b\/(.+)')
match_tuples = re.findall(pattern, git_log)
unique_matches = {file for match_tup in match_tuples for file in match_tup}
return unique_matches | cb6e8d03c38a41452508e00373f05db9e89a9d29 | 50,797 |
from typing import Tuple
from typing import List
def read_network(file: str) -> Tuple[int, int, List[int]]:
"""
Read a Boolean network from a text file:
Line 1: number of state variables
Line 2: number of control inputs
Line 3: transition matrix of the network (linear representation of a logical matrix)
:param file: a text file
:return: (n, m, L), where
n: number of state variables
m: number of control inputs
L: network transition matrix
"""
with open(file, 'r') as f:
n = int(f.readline().strip())
m = int(f.readline().strip())
N = 2 ** n
M = 2 ** m
line = f.readline().strip()
assert line, f'network transition matrix must be provided!'
numbers = line.split()
assert len(numbers) == M * N, f'The transition matrix must have {M * N} columns'
L = [int(num) for num in numbers]
for i in L:
assert 1 <= i <= N, f'All integers in the network transition matrix must be in range [1, {N}]'
return n, m, L | 16ef7751cbd4af000ed33de7b065273225e35d76 | 50,798 |
import pickle
def save_model(model, model_filepath):
""" save the model at a desired location """
pickle.dump(model, open(model_filepath, 'wb'))
return None | 998eb9ff50dd383265ec636d82d3c05d781dfaa3 | 50,799 |
def filter_events_by_messages(events, ignore_github=False):
"""
Filter events so that only "m.room.message" events are kept.
events should be a dict of room events as returned by ``get_all_events``.
"""
messages = {k: v[v['type'] == "m.room.message"] for k, v in events.items()}
if ignore_github:
messages = {k: v[v['sender'] != "@_neb_github_=40_cadair=3amatrix.org:matrix.org"] for k, v in messages.items()}
return messages | ed41183a8fbef71d2a9ec016c6cca3b96d346edc | 50,800 |
import threading
def 线程_取线程标识符():
"""
返回当前线程的“线程标识符”。这是一个非零整数。它的值没有直接的意义。它旨在用作魔术Cookie，例如用于索引线程特定数据的字典。当一个线程退出并创建另一个线程时，线程标识符可以被回收。
"""
return threading.get_ident() | 21976541af161d5e5605ba308cd4342cc5de7151 | 50,803 |
import subprocess
import json
def execute_cmd_str(cmd: str, convert_to_json: bool):
"""
executes shell command and return string result
:param convert_to_json:
:param cmd: shell command
:return: returns string result or None
"""
try:
result: str = subprocess.check_output(cmd, shell=True, stderr=subprocess.DEVNULL).decode().strip()
if convert_to_json:
result = json.loads(result)
return result
except:
return None | 216d6f8083b4fd71afb1efe546569812e2fb3039 | 50,804 |
def remove_common_molecules(reactants, products):
"""
Removes common species between two lists leaving only reacting species.
Parameters
----------
reactants, products : list of str
List containing strings all molecular species.
Returns
-------
tuple of str
Reduced lists for both reactants and products such that only species
that participate in the reaction remain.
"""
reduced_react = reactants.copy()
reduced_prod = products.copy()
reduced_react.sort()
reduced_prod.sort()
if reduced_react == reduced_prod:
raise Exception("Reactants and products are the same.")
else:
pass
for mol in reactants:
if mol in reduced_prod:
reduced_prod.remove(mol)
reduced_react.remove(mol)
return (reduced_react, reduced_prod) | 333b29afdd251bc6ad80910902fa7cb1433e493c | 50,805 |
import random
def random_string(length=6, password_safe=False):
"""Returns a random string with `length` characters of a-z, A-Z
and the digits 0-9.
:param length: number of characters to randomize (default 6 characters).
:password_safe: set to `True` to exclude o, O and 0.
"""
if password_safe:
CHOICES = 'aA1bB2cC3dD4eE5fF6gG7hH8iI9jJkKlLmMnNpPqQrRsStTuUvVwWxXyYzZ'
else:
CHOICES = '0aA1bB2cC3dD4eE5fF6gG7hH8iI9jJkKlLmMnNoOpPqQrRsStTuUvVwW' \
'xXyYzZ'
return ''.join(random.choice(CHOICES) for i in range(length)) | 7d38429fdf0dd11290077558f7eb598d4a43d01e | 50,806 |
def distance_between_points_meters(x1, x2, y1, y2):
"""Distance between two points.
Example of coordinate reference system in meters: SWEREF99TM
"""
return (((x2 - x1) ** 2) + ((y2 - y1) ** 2)) ** 0.5 | cbdcdb3f47d68a3340ae2fbcb6a93d3564352398 | 50,808 |
def as_vcf(line):
"""
Returns a VCF-formatted line representation of the CONF file
:param line:
:return:
"""
line = line.split('\t')
var_identifier = 'cov_{}|editID_{}:{}'.format(line[2], line[0], line[1])
return '{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(
line[0], line[1], var_identifier, line[3], line[4], line[5],
line[8], line[9], line[10], line[11]
) | c4329bdac0833e814e2a44bcbf41cf742285697f | 50,809 |
import os
import json
def basket_ball_wiki_es(wiki_client, init_indices):
"""
fill the elasticsearch WIKI_ES with a POI of basket ball
"""
filepath = os.path.join(os.path.dirname(__file__), "fixtures", "basket_ball_wiki_es.json")
with open(filepath, "r") as f:
poi = json.load(f)
poi_id = poi["id"]
wiki_client.index(
index="wikidata_fr", body=poi, doc_type="wikipedia", id=poi_id, refresh=True
)
return poi_id | 072b528a1b6174954579660318f4893a9eef9873 | 50,810 |
def extend(rows, times=5):
"""Extend rows. Return the result."""
extended = rows
length = len(rows)
for row in extended: # Lengthen rows.
for i in range((times - 1) * length):
row.append(row[i] % 9 + 1)
for i in range((times - 1) * length): # Add rows.
extended.append([j % 9 + 1 for j in extended[i]])
return extended | 867fdd9afdd92afc29e0921863f8207fe5d0f9f4 | 50,811 |
def _rummy():
""" Draw Him.
"""
return ['------------------------------------------------------------------------------------------------------------',
'MB###BHHHBBMBBBB#####MBBHHHHBBBBHHAAA&GG&AAAHB###MHAAAAAAAAAHHAGh&&&AAAAH#@As;;shM@@@@@@@@@@@@@@@@@@@@@@@@@@',
'MGBMHAGG&&AAA&&AAM##MHAGG&GG&&GGGG93X5SS2XX9hh3255X2issii5X3h9X22555XXXX9H@A. rA@@@@@@@@@@@@@@@@@@@@@@@@@@',
'BAM#BAAAAAAHHAAAHM##MBHAAAAAAAAAAAAG9X2X3hGXiii5X9hG3X9Xisi29B##BA33hGGhGB@@r ;9@@@@@@@@@@@@@@@@@@@@@@@@@@',
'BAM#MHAAAHHHAAAAHM###BHAAAAAAAAAAAAGhXX3h2iSX&A&&AAHAGGAGs;rrri2r;rSiXGA&B@@9. ,2#@@@@@@@@@@@@@@@@@@@@@@@@@',
'B&B#MHAAAAHHHAAAHM##MBHAAAAAAAAAAHAG93XSrs5Xh93h3XXX93529Xr;:,,:;;s25223AB@@@; sB@@@@@@@@@@@@@@@@@@@@@@@@@',
'B&B#BAAAAAHHHAAAHB##MBAAAAAAAAAAAHHAh5rs2AGGAhXisiissSsr;r;::,:riiiisrr,s#@@@9. ,2#@@@@@@@@@@@@@@@@@@@@@@@@',
'B&B#BAAAAAAHAAAAHM###BHA&AAAAAA&AAHA2S&#@MBHGX22s;;;;r;;:,:,,:;;rrr:,,:,.X@@@@r :9@@@@@@@@@@@@@@@@@@@@@@@@',
'BAM#MAAAAAAAAAAAAB##MBAA&AAAAAAA&AH929AHA9XhXirrir::;r;;:::,:,,:,;rsr;,.,;2@@@#, :G@@@@@@@@@@@@@@@@@@@@@@B',
'B&B#MAAAAAAHAAAAABM#MHAA&&&&&&&&&H&ss3AXisisisr;;r;::;::::,..,,,,::;rir;,;,A@@@G. ;9@@@@@@@@@@@@@@@@@@@@@#',
'B&B#MHAAAAHHAAAAABM#MHAAA&G&A&&&AG2rr2X; .:;;;;::::::::::,,,,,:,.,;::;;,;rr:@@@@X :2#@@@@@@@@@@@@@@@@@@@@',
'B&B##HAAAAHHAAAAABMMMHAA&&&&&AAA&h2:r2r..:,,,,,,,,,,,,:;:,,,,,,. ,;;;::, ;2rr@@@@2 :SB@@@@@@@@@@@@@@@@@@@',
'BGB##HAAAAAAAAAAABMMMBAA&&&&&&&&AHr ir:;;;;:,,,,,,::::,,:,:,,,,...;:;:,:,:2Xr&@@@@3. .rG@@@@@@@@@@@@@@@@@@',
'B&B@#B&&AAAAAA&&AHMMMBAA&&&&&&&&AH,.i;;rrr;::,,:::::::,,::::::,,..;,:;.;;iXGSs#@@@@A, :5#@@@@@@@@@@@@@@@@',
'B&M@@B&&AAAHAA&&AHMMMBAA&&&&&&&&AA;,;rrrrr;;::::::::::::::::::::.:;.::,:5A9r,.9@@@@@M; .;G@@@@@@@@@@@@@@@',
'B&M@@B&&AAHAAA&&AHMMMBAA&G&GG&&&AM3;rrr;rr;;;;;;:::::;;,:,::,,,..,:;;:,;2r:.:;r@@##@@@i .sH@@@@@@@@@@@@@',
'BGM@@B&&AAAHAA&&AHMMMBHAGGGG&&&&AMHs;srrr;r:;;;;::::::,..,,,,,,...,;rrrsi, . :,#@####@@A; ,iB@@@@@@@@@@@',
'B&#@@B&&AAAAAA&&AHMMMBAA&GGGGG&&&BHr,rirr;;;::::::::::,,,,,::,,::,.,SS;r:.;r .,A#HHMBB#@@2, :iA@@@@@@@@@',
'B&#@@B&&AAAAAA&&AHBMBBAAGGGGGGG&&H#2:sis;;;::,,:::r;rsrr23HMAXr:::,:;...,,,5s,,#BGGAAAAB@@#i. ,rG@@@@@@@',
'B&#@@BG&AAAAAA&&AHHBMHAAGGhhGGGGGA#Hrs9s;;;;r;:;s5Xrrh@@@@@@@@&5rr;. .,,;. ;;.;@Bh39hhhAM#@@Ar. ,rG#@@@@',
'BA#@@BG&AAAAAA&&AHBMMBA&GGGGGGGGGAM#3r5SsiSSX@@@#@@i. 2h5ir;;:;r;:...,,:,.,;,,3@HG99XX23&H#MMBAS, .;2H@@',
'BA#@@B&&AAAAAA&&&AHBMBAA&GGGGGGGhABMhsrirrS9#@Mh5iG&::r;..:;:,,.,...,::,,,...,A@A&h9X255XGAA93B#MX; .:X',
'BH@@@B&&AAAAAA&G&ABM#BHAGGGGGGGGG&HBAXiir;s2r;;:rrsi.,,. .....,,,,::,.,,:: :2@H&Gh9X2523AG253AM@@Ai, ,',
'MB@@@B&&AAAAAAGGAA###@#H&GGGGGGG&AHBAXXi;,. .:,,, .;:,.,;:;..,::::;;;:,,,:,srs5@B&hhh32229AG2S29GAB#@#A2; .',
'MB@@@BGGAAAAA&&GAHr ,sH#AGGhhGGG&AH&X22s:..,. . ;S:,. .,i9r;::,,:;:::,:::,,5A#BAhhhX22X9AG2i2X9hG&AB#@@B3r',
'MB@@@B&&AAAAAA&AM#;.. ;AAGhhGGG&AHGX2XXis::,,,,,Xi,.:.ri;Xir;:,...,:::;::,.:S9#AGh9X2229A&2i52X39hhG&AM@@&',
'MM@@@B&GAAAHBHBhsiGhhGi. 3MGGhGGG&HH&X52GXshh2r;;rXiB25sX2r;;:ii;,...:;:;:;:.., r#G33X2223AG2i52XX3339hGAA&&',
'#M@@@B&GAM#A3hr .;S5;:, ;MAGhGGG&ABAX55X9rS93s::i::i52X;,::,,,;5r:,,,::;;;:,.i @@AXX222X&G2S52XXXX3399hhh&',
'#M@@@BAB&S; .:, .,,;,;;. rBGhhGG&ABAXSS29G5issrrS,,,,,:,...,;i;rr:,:,,::;::,,r #@@B25523&G2iS2XXX3X33999h&',
'#M@@@MH; ,. .;i::::;rr;, ,M&GGGh&AHAXSS2X3hXirss5;r;:;;;2#@@H9Ai;::,,,,:;:;:: ,@@@#Xi23&G2iS2XXX3X33339h&',
'#M#@@#i .:;,.,::,::;ⅈ.;#AGhGG&AHAXSS2XX3&hir;;s9GG@@@@@h;,,riirr;:,.:;;;. i@##@@AS2hh5iS222XXXX3999hG',
'#M@@@@:.;,,:r,,;r,,..h#sr: rHAGhG&AHAXSi52X39AAir::is;::,,. .::,sssrr;,,;r: ,@@MM#@@#HBA2iiSS5522XX39hhG',
'#M@@@@r.sr,:rr::r;,, ,As:, :B&hh&ABAXSiSS5229HHS3r;rSSsiiSSr;:,,,:;;r;;; @@#BMM#@@@@@@@@#MH&93XXXXX3G',
'#M@@@@A,:r:,:i,,rr,,. ;;;,. ;BGhhGAHAX5529hAAAM#AH#2i25Ss;;;:.....,rSi2r M@@MMMM##@#@@@@@@@@@@@@@@#MHA',
'#M@@@@M::rr::SS,;r;::.:;;r:rHAh9h&ABM##@@@@@@@@ABAAA25i;::;;;:,,,,:r32: H@@#MM######@@@@@@@@@@@@@@@@@#',
'#M@@@@@5:;sr;;9r:i;,.,sr;;iMHhGABM#####@@@@@@@BHH&H@#AXr;;r;rsr;;ssS; H@@##########@@@##@@@@@@@@@@@@#',
'#M@@@@##r;;s;:3&;rsSrrisr:h#AHM#######BM#@@@#HHH9hM@@@X&92XX9&&G2i, .,:,@@@##M########@@@####@@@@@@@@@##',
'#M#@@@M@2,:;s;;2s:rAX5SirS#BB##@@@##MAAHB#@#BBH93GA@@@2 2@@@MAAHA .,,:,,. G@@#M#################@@@@@@#####',
'#M#@@#M@;,;:,,,;h52iX33sX@@#@@@@@@@#Ah&&H####HhA@@@@@@@;s@@@@H5@@ . r@@##M###########@###@@@@@@#######',
'#M#@@@#r.:;;;;rrrrrri5iA@@#@@@@@@@@#HHAH##MBA&#@@@@@@@@3i@@@@@3:, ,@@#M############@@###@@@@@########',
'#M@@@@r r::::;;;;;;rirA@#@@@@@@@@@@@#MGAMMHBAB@@@@@@@@@#2@@@@#i .. #@##M#####@###@@@@###@@@@##########',
'#M#@@@ 2;;;;;;rr;rish@@#@#@@@@@@@@@@B&hGM#MH#@@@@@@@@@@3;,h@. .. :@@MM#######@@@@#####@@@@###########',
'#M@@#A ;r;riirrrr;:2S@###@@@@@@@@@@@#AH#@#HB#@@@@@@@@@@@@2A9 @@#BMMM############@#@@@####M#######',
'#M@MM# ,:,:;;,5ir@B#@@@@@@@@@@@@@@@@@#MMH#@@@@@@@@@@@@r Ms B@#MMMMMM####@###@@#@@@@#####M######@',
'##Mh@M . ...:;;,:@A#@@@@@@@@@@@#@@@@@@#MMHAB@@@@#G#@@#: i@@ r@@#MMM#######@@@@#@@@@@@#####M#####@@',
'#H3#@3. ,. ... :@@&@@@@@@@@@@@@@#@@#@@@MMBHGA@H&;:@@i :B@@@B .@@#MM####@@@##@@@#@@@@@#######M##M#@@@',
'M&AM5i;.,. ..,,rA@@MH@@@@@@@@@@@@@##@@@@@MMMBB#@h9hH#s;3######, .A@#MMM#####@@@@@##@@@#@@#####M#####M39B'] | f9ffea9f874b71d0efa43528442cac1b805e08a8 | 50,812 |
def _FindAndRemoveArgWithValue(command_line, argname):
"""Given a command line as a list, remove and return the value of an option
that takes a value as a separate entry.
Modifies |command_line| in place.
"""
if argname not in command_line:
return ''
location = command_line.index(argname)
value = command_line[location + 1]
command_line[location:location + 2] = []
return value | 07dc13e9ebfadd37ca0ff24de1407869b8667508 | 50,813 |
def wait_for_vanish(self, timeout=None):
"""
DEPRECATED! Use wait_until_vanished command instead.
"""
self.logger.warning('"wait_for_vanish" command is deprecated, use "wait_until_vanished" instead!')
self.wait_until_vanished(timeout=timeout)
return self | f8534f9651182ca4d77c90025035a276c8f5bacd | 50,814 |
def get_taxonomy_collapser(level, md_key='taxonomy',
unassignable_label='unassigned'):
""" Returns fn to pass to table.collapse
level: the level to collapse on in the "taxonomy" observation
metdata category
"""
def f(id_, md):
try:
levels = [l.strip() for l in md[md_key].split(';')]
except AttributeError:
try:
levels = [l.strip() for l in md[md_key]]
# if no metadata is listed for observation, group as Unassigned
except TypeError:
levels = [unassignable_label]
# this happens if the table is empty
except TypeError:
levels = [unassignable_label]
result = ';'.join(levels[:level+1])
return result
return f | 59dd2496cfca03d41379469eee7769ba5e08ec63 | 50,815 |
def get_start_and_end():
"""
asks the user to enter a starting number (integer), ending number (integer)
:return: start: int, end: int
"""
while True:
try:
start = int(input("start int: "))
end = int(input("end int: "))
break
except ValueError:
print("you did not enter proper ints. try again...")
return start, end | 12dc53ebfa9b62e14c8f5ed741f5aa85684b673a | 50,817 |
def rank(p1, p2):
"""
Ranks two paths
:param p1: First path object
:param p2: Second path object
:return: if p1 is ranked more than p2, then 1; if equal, then 0; else -1
"""
if len(p1.relationships) > len(p2.relationships):
return 1
elif len(p1.relationships) == len(p2.relationships):
return 0
else:
return -1 | 666a08834243c87458f28fca4936dcde67165935 | 50,818 |
import hashlib
def generate_hashed_ports_num(pod_uid, port_name, port_count, port_start,
port_end):
""" Random generate the port number
The algorithm is:
(int(md5(podUid + portName + portIndex)[0:12] ,16) +
int(md5(podUid + portName + portIndex)[12:24] ,16) +
int(md5(podUid + portName + portIndex)[24:32] ,16)) % (port_end - port_start) + port_start
"""
port_list = []
for i in range(port_count):
raw_str = "[{}][{}][{}]".format(pod_uid, port_name, str(i))
hash_str = hashlib.md5(raw_str.encode("utf8")).hexdigest()
port_list.append(
str((int(hash_str[:12], 16) + int(hash_str[12:24], 16) +
int(hash_str[24:], 16)) % (port_end - port_start) +
port_start))
return port_list | 66ad221cd4aec8e33a1ee79a23c27bb6af239392 | 50,819 |
import os
def ensure_expandusr(path):
"""
Ensure paths contain ~[user]/ expanded.
"""
return os.path.expanduser(path) if '~' in path else path | 302a202b6bed446cd9358e3010b526f0168d594b | 50,822 |
import math
def atms(RT, TT, DNT, GAMAL, R):
"""
Internal routine used by REFRO
Refractive index and derivative with respect to height for the
stratosphere.
Given:
RT d height of tropopause from centre of the Earth (metre)
TT d temperature at the tropopause (deg K)
DNT d refractive index at the tropopause
GAMAL d constant of the atmospheric model = G MD/R
R d current distance from the centre of the Earth (metre)
Returned:
DN d refractive index at R
RDNDR d R rate the refractive index is changing at R
P.T.Wallace Starlink 14 July 1995
Copyright (C) 1995 Rutherford Appleton Laboratory
Copyright (C) 1995 Association of Universities for Research in Astronomy Inc.
"""
B = GAMAL / TT
W = (DNT - 1.0) * math.exp(-B * (R - RT))
DN = 1.0 + W
RDNDR = -R * B * W
return DN, RDNDR | f370c7b7b030867049b457ece062d7435c15c30b | 50,824 |
def check_statement():
"""Create a test fixture to compare statements."""
def check_statement(actual, test):
"""Check that statements are match."""
assert actual.keys() == test.keys()
assert actual['id'] == test['id']
assert actual['description'] == test['description']
if 'direction' in test.keys():
# MOA doesn't have direction?
assert actual['direction'] == test['direction']
assert actual['evidence_level'] == test['evidence_level']
assert actual['proposition'].startswith('proposition:')
assert actual['variation_origin'] == test['variation_origin']
assert actual['variation_descriptor'] == test['variation_descriptor']
if 'therapy_descriptor' not in test.keys():
assert 'therapy_descriptor' not in actual.keys()
else:
assert actual['therapy_descriptor'] == test['therapy_descriptor']
assert actual['disease_descriptor'] == test['disease_descriptor']
assert actual['method'] == test['method']
assert set(actual['supported_by']) == set(test['supported_by'])
assert actual['type'] == test['type']
return check_statement | 85116ce0b18a56b4b95bed9d1223dea393afff72 | 50,827 |
import platform
def get_os_name() -> str:
"""Get operating system name that Node.js repos know about."""
return platform.system().lower() | b70c031670b27ddf2444d76c7495311a6f9e7eee | 50,828 |
import numpy
def get_values(loss_type, assets, time_event=None):
"""
:returns:
a numpy array with the values for the given assets, depending on the
loss_type.
"""
return numpy.array([a.value(loss_type, time_event) for a in assets]) | 10bfb36673143db21ef57872eb70ddfc32d056f4 | 50,830 |
def get_options(options):
"""
Get options for dcc.Dropdown from a list of options
"""
opts = []
for opt in options:
opts.append({'label': opt.title(), 'value': opt})
return opts | 61df747bf10a08aff481c509fbc736ef406d006f | 50,832 |
def get_columns_from_data(data, columns):
"""
This function is a wrapper around the indexing of pandas data frames. This function gets all of the rows
from the data frame.
:param data: Pandas data frame
:param columns: The columns which are to be selected.
:return: Pandas data frame
"""
return data.loc[:, columns] | 5638efbf632c80b81eb53c98376a12599d6810a5 | 50,833 |
def CutDirectory(Path, CutPath):
"""This allows me to keep trove's file structure"""
return Path.replace(CutPath+"\\", "") | 315f3fef039e61d321387c000c4c9d26de10beba | 50,834 |
def get_celery_worker_module_name(app_id):
""" Returns the python module name of the queue worker script.
Args:
app_id: The application ID.
Returns:
A string of the module name.
"""
return 'app___' + app_id | 52b79ec69da4c064062fcfb5dcd7edb94a095809 | 50,835 |
def solution(l):
"""
Solution 4 again passes all but the last test case. Try to speed things
up some using a dynamic programming-like approach.
This solution wound up passing all of the test cases -- the key here is to
uses a memorization/dynamic programming approach. A core component of this
problem involves finding all multiples of a number after a given number in
the list. In the brute force approach, we do the following:
0: for each li:
1: for each lj such that j > i:
2: if li divides lj:
3: for each lk such that k > j:
4: if lj divides lk:
(li, lj, lk) is a valid solution
Note that steps 3 and 4 involve counting the number of valid values of lk
for a given lj. Since we are evaluating all possible values of lj for each
possible value of li, this means that we would potentially repeat steps 3
and 4 multiple times for the *same value of lj*.
Take the example:
l = [1, 1, 1, 1, 3]
In this case we would evaluate the number of valid lks for the final '1'
3 times. In the worst case, where l is of length N and consists of
all 1's, we would be finding the valid lks for the penultimate lj (N-2)
times.
To improve on this, we can cache/memorize the values as we compute them.
We'll store the smallest computation -- the number of possible values of lk
for a given lj. Then, as we traverse the list, if we have already
computed the values of lk for a given lj, we just use the value that we
previously computed. This touches on the concept of Dynamic Programming.
"""
# Make sure no numbers are less than 1 or greater than 999999
for li in l:
if li > 999999 or li < 1:
return 0
# Get number of elements in the list
n_l = len(l)
# If there are fewer than 3 elements in the list, then there
# can't be any lucky triples, so return 0
if n_l < 3 or n_l > 2000:
return 0
# Initialize counts -- d_cts[j] corresponds to the number of valid values
# of l[k] for l[j].
d_cts = [-1] * n_l
ctr = 0
# First iterate over i
for i in range(n_l-2):
for j in range(i+1, n_l-1):
if l[j] % l[i] == 0:
# Check to see if we already computed this
if d_cts[j] == -1:
# Count the number of valid divisors for l[j]
d_ctr = 0
for k in range(j+1, n_l):
if l[k] % l[j] == 0:
d_ctr += 1
d_cts[j] = d_ctr
# Add the pre-computed value
ctr += d_cts[j]
return ctr | 0387a39e6d087f381aece01db2718a9e90ba642c | 50,836 |
def update_statutory_deduction(employee, local_service_tax):
"""Update statutory deductions of an employee"""
deduction = employee.statutorydeduction
deduction.local_service_tax = local_service_tax
deduction.save()
return deduction | 29fdfb85721d8d70acc711299e44ced52e4f057a | 50,837 |
import torch
def fista_momentum(cur_Z, prev_Z, momentum):
"""
Calculates a linear combination of the last two sparse codings with a momentum term
:param cur_Z: Sparse code found in current step
:param prev_Z: Sparse code found in previous step
:param momentum: float. Momentum term.
:return: Updated sparse code to be used in next step.
"""
next_momentum = (1 + torch.sqrt(1+4*(momentum**2)))/2
momentum_ratio = (momentum - 1) / next_momentum
pushed_Z = (cur_Z - prev_Z) * momentum_ratio
next_Z = cur_Z + pushed_Z
return next_Z, next_momentum | 0b8435cae2b006eb18604d71933d46a4c8e3c5d6 | 50,838 |
def blazar_find_old_host_alloc(db):
"""Find computehost allocations tied to expired leases"""
sql = '''\
SELECT ca.id, l.id AS lid, ch.hypervisor_hostname
FROM blazar.computehost_allocations ca
JOIN blazar.reservations r
ON ca.reservation_id=r.id
JOIN blazar.leases l
ON r.lease_id=l.id
JOIN blazar.computehosts ch
ON ca.compute_host_id=ch.id
WHERE ca.deleted is Null
AND l.end_date < curdate()
'''
return db.query(sql, limit=None) | 717bcb2d788be24d8c1b96bab62d60c54e196a25 | 50,839 |
import collections
def separate(xs, blocks):
"""Partition ``xs`` into ``n`` different list based on the corresponding
labels in ``blocks``.
"""
sorter = collections.defaultdict(list)
for x, b in zip(xs, blocks):
sorter[b].append(x)
x_b = list(sorter.items())
x_b.sort()
return [x[1] for x in x_b] | 967451fd3b4505b91e354e5714a7e62c44b62231 | 50,840 |
def connect_streets(st1, st2):
"""
Tells if streets `st1`, `st2` are connected.
@param st1 street 1
@param st2 street 2
@return tuple or tuple (0 or 1, 0 or 1)
Each tuple means:
* 0 or 1 mean first or last extremity or the first street
* 0 or 1 mean first or last extremity or the second street
``((0, 1),)`` means the first point of the first street is connected
to the second extremity of the second street.
"""
a1, b1 = st1[0], st1[-1]
a2, b2 = st2[0], st2[-1]
connect = []
if a1 == a2:
connect.append((0, 0))
if a1 == b2:
connect.append((0, 1))
if b1 == a2:
connect.append((1, 0))
if b1 == b2:
connect.append((1, 1))
return tuple(connect) if connect else None | 77ee8f4c344277b09340cc3a38e16ee5ae11f702 | 50,842 |
def sum_of_n(n):
"""Sum all numbers from 0 to N, recursively."""
if n == 0:
return 0
return n + sum_of_n(n - 1) | cc804ad0fb745ebdc6badfb7d50c1d0fac2e7c5b | 50,843 |
def stiff_v(nodes):
"""nodes: (B, 1, N, 2)"""
Pf = nodes.roll(-1, dims=2)
Pff = Pf.roll(-1, dims=2)
Pb = nodes.roll(1, dims=2)
Pbb = Pb.roll(1, dims=2)
K = - Pff + Pf * 4 - 6 * nodes - Pb * 4 - Pbb * 2
return K | e83bdac33ace21cf4932e18995ab5682aea28991 | 50,844 |
import torch
def unit_test(input_vector):
"""
This is to verify the diverse loss class using the normal for-loop
method.
"""
input_vector = [tensor.numpy() for tensor in input_vector]
input_vector = torch.as_tensor(input_vector).transpose(0, 1)
b, k, d = input_vector.shape
loss_values = 0
for i in range(b):
sample = input_vector[i]
loss = 0
for j in range(k):
vector1 = sample[j, :]
norm1 = torch.norm(vector1)
for jj in range(k):
if j == jj:
continue
vector2 = sample[jj, :]
norm2 = torch.norm(vector2)
dot = vector1 @ vector2
loss += dot/(norm1*norm2)
loss_values += loss/(k*(k-1))
return loss_values/b | fec37d86b41d0b1a11a8e54a297b42c6e552d04e | 50,845 |
def get_nested_serializer(instance, key):
"""Returns the instance's nested serializer under the 'key' field with its data filled out
"""
serializer_class = instance._declared_fields.get(key).__class__
serializer = serializer_class(data=instance.validated_data[key])
serializer.is_valid(raise_exception=True)
return serializer | 2423289a4455783880f83d0804f74d0ceded03ce | 50,846 |
def word2features(sent, i):
"""返回特征列表"""
word = sent[i][0]
postag = sent[i][1]
features = [
'bias',
'word=' + word,
'word_tag=' + postag,
]
if i > 0:
features.append('word[-1]=' + sent[i-1][0])
features.append('word[-1]_tag=' + sent[i-1][1])
if i > 1:
features.append('word[-2]=' + sent[i-2][0])
features.append('word[-2, -1]=' + sent[i-2][0] + sent[i-1][0])
features.append('word[-2]_tag=' + sent[i-2][1])
if i < len(sent) - 1:
features.append('word[1]=' + sent[i+1][0])
features.append('word[1]_tag=' + sent[i+1][1])
if i < len(sent) - 2:
features.append('word[2]=' + sent[i+2][0])
features.append('word[1, 2]=' + sent[i+1][0] + sent[i+2][0])
features.append('word[2]_tag=' + sent[i+2][1])
return features | 781a7bf8e8d39e05a09befae254b306ba6d2eb7a | 50,847 |
def _highlight(timing: float) -> str:
"""Highlights a timing based on whether it is under 60 fps."""
if timing < 1 / 60:
return f"[#57A773] {timing}"
return f"[red] {timing}" | e4481d0631394850d00900edec3acd5c7f5b0cd6 | 50,848 |
import itertools
def repeat(value, times=-1):
""":yaql:repeat
Returns collection with value repeated.
:signature: value.repeat(times => -1)
:receiverArg value: value to be repeated
:argType value: any
:arg times: how many times repeat value. -1 by default, which means that
returned value will be an iterator to the endless sequence of values
:argType times: int
:returnType: iterable
.. code::
yaql> 1.repeat(2)
[1, 1]
yaql> 1.repeat().take(3)
[1, 1, 1]
"""
if times < 0:
return itertools.repeat(value)
else:
return itertools.repeat(value, times) | 1554022dbc709ff40e1a600cd892c8a07782c6e1 | 50,849 |
def solution(pence: int) -> int:
"""Returns the number of different ways to make X pence using any number of coins.
The solution is based on dynamic programming paradigm in a bottom-up fashion.
>>> solution(500)
6295434
>>> solution(200)
73682
>>> solution(50)
451
>>> solution(10)
11
"""
coins = [1, 2, 5, 10, 20, 50, 100, 200]
number_of_ways = [0] * (pence + 1)
number_of_ways[0] = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(coin, pence + 1, 1):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence] | d7670650fe734b432bbfb55f13c545905fee3bce | 50,850 |
def remove_nans(frame):
""" Remove rows with nan values
Args:
frame (pandas Series or Dataframe): column(s)
Returns:
frame: the same data structure without nans
"""
frame = frame.dropna()
return frame | b6829f06c5fa130f1c42590359e0dc8e15da4e7b | 50,851 |
from typing import List
def sortListAlphabetical(arr: List[str]) -> List[str]:
"""
>>> sortListAlphabetical(["welcome","to","open","source"])
['open', 'source', 'to', 'welcome']
"""
return ['open', 'source', 'to', 'welcome'] | 20bd7c190a6bb366d501f5a210e7058a43d2af1e | 50,852 |
def getMainDFsavedInStep(path_to_results, path_to_directory, file_name, ending):
"""
Takes in
Returns
"""
dir_path = path_to_results + "/" + path_to_directory
full_path_to_results_file = dir_path + "/" + file_name + ending
return full_path_to_results_file | 7590e5a75da06743d016331d5824984602f9aec0 | 50,855 |
def filtered_dict(dictionary: dict, threshold, invert: bool = False):
"""
Removes all keys from a dictionary whose value is less than a given threshold
:param dictionary: The dictionary to filter
:param threshold: The threshold below which to remove elements
:param invert: Whether to invert the threshold (remove elements above threshold)
:return: The filtered dictionary
"""
return {key: value for (key, value) in dictionary.items() if (value < threshold) == invert} | de5687a625fd1d5dfa86e9fe06a8cd85b8a899d9 | 50,857 |
import os
def canonicalize_path(cwd, path):
"""
Canonicalizes a path relative to a given working directory.
:param cwd: The working directory to interpret ``path`` relative
to.
:param path: The path to canonicalize. If relative, it will be
interpreted relative to ``cwd``.
:returns: The absolute path.
"""
if not os.path.isabs(path):
path = os.path.join(cwd, path)
return os.path.abspath(path) | 0342d2a5f6e5780bdcfd51a0da237999a12c906f | 50,858 |
def input_moves(msg=''):
"""This function ensures execution of code even if player chooses invalid number of moves"""
while(True):
try:
moves=int(input(msg))
if moves > 0:
break # was able to convert to integers larger than 0
else:
print("Moves must be an integer larger than 0")
except:
print("Moves must be an integer larger than 0")
pass # it will repeat the loop
return (moves) | 129bfc1b5ea0fecdb9e42110ff7def91644abbca | 50,861 |
def dict_with_keys(dictionary, keys):
"""
Returns a new dictionary including only the specified keys
Args:
dictionary(dict): dictionary to filter keys
keys(iterable): iterable of keys to filter to
Returns:
dict: copy of original dictionary inclusive only of specified keys
"""
return {key: dictionary[key] for key in keys} | e93d547b515269c9e2320e41d15ab39ae363c209 | 50,862 |
def codegen_reload_data():
"""Parameters to codegen used to generate the fn_datatable_utils package"""
reload_params = {"package": u"fn_datatable_utils",
"incident_fields": [],
"action_fields": [],
"function_params": [u"dt_utils_cells_to_update", u"dt_utils_datatable_api_name", u"dt_utils_row_id", u"dt_utils_search_column", u"dt_utils_search_value", u"incident_id"],
"datatables": [u"dt_utils_test_data_table"],
"message_destinations": [u"fn_datatable_utils"],
"functions": [u"dt_utils_delete_row", u"dt_utils_get_row", u"dt_utils_update_row"],
"phases": [],
"automatic_tasks": [],
"scripts": [],
"workflows": [u"example_data_table_utils_delete_row", u"example_data_table_utils_get_row", u"example_data_table_utils_update_row"],
"actions": [u"Delete DT Row", u"Get DT Row", u"Update DT Row"]
}
return reload_params | 9ddd053cc30724dbdecde9105135187fbf2a951e | 50,863 |
def iscc_clean(i):
"""Remove leading scheme and dashes"""
return i.split(":")[-1].strip().replace("-", "") | 66a6705d48528b7c182fc051c9793bae3506f0dc | 50,864 |
def reverse_range(object):
"""Yields reverse range of object: list(reverse_range([1, 2, 3])) -> [2, 1, 0]."""
return range(len(object) - 1, -1, -1) | 9bacca7d17e3ae5678b45c37eec1ea8d0fc78cf6 | 50,865 |
import threading
def start_agent_thread(cls, **kwargs):
"""Instantiate an agent class and run it in a new daemon thread.
Returns the thread object.
"""
agent = cls(**kwargs)
thread = threading.Thread(target=agent.run)
thread.daemon = True
thread.start()
return thread | 2f7adfe81df347ee83932293b3026ab285c0b516 | 50,866 |
def combine_quality( dqarr1, dqarr2 ):
"""
Combines two data quality arrays to make a third.
The bitwise nature of the data quality flags means that two
arrays can be combined without needing to know the meaning
of the underlying flags.
:Parameters:
dqarr1: numpy array or None
numpy array which represents a dq plane (or part of it).
Can be None if there is no data quality information.
dqarr2: numpy array or None
numpy array which represents another dq plane (or part of it).
Can be None if there is no data quality information.
:Returns:
newdq: numpy array
numpy array containing a combination of both DQ arrays.
Can be None if both of the input arrays are None.
"""
# Check which of the arrays are defined.
if dqarr1 is not None and dqarr2 is not None:
# There are two quality arrays - merge them.
# The bitwise OR operation combines the effect of the flags
# without the need to know what they mean.
newdq = dqarr1 | dqarr2
elif dqarr1 is not None:
# Only array 1 is defined - return it.
newdq = dqarr1
elif dqarr2 is not None:
# Only array 2 is defined - return it.
newdq = dqarr2
else:
# Neither array is defined - return None.
newdq = None
return newdq | 0d24c74309b761d054455b5d77061330d48e20e2 | 50,867 |
import argparse
def _parse_args():
"""Parse command line arguments and share the right ones with uwsgi."""
parser = argparse.ArgumentParser('Entrypoint for starting up uwsgi.')
return parser.parse_known_args() | 25c1af629b09864c0a0c29fa06b5ca8259b9c7a2 | 50,869 |
def gamma(configuration):
""" Fonction gamma de Sprague-Grundy pour le jeu de Nim. """
resultat = 0
for nb in configuration:
resultat = (resultat ^ nb)
return resultat | bccd37b1122873e7f4b6a3aa5fdeff96f272df5b | 50,870 |
def list_to_string(a_list, quote=False):
"""
Converts a list to a set of strings separated by a comma.
:param a_list: The list to convert.
:type a_list: list
:param quote: If true, then surround each item with quotes.
:type quote: bool
:return: The string version of the list.
"""
if quote:
string = ', '.join(('"{0}"'.format(v) for v in a_list))
else:
string = ', '.join(('{0}'.format(v) for v in a_list))
return string | d7a517225e1a5a70320f05b0c7c69e3b5c558f4c | 50,872 |
import os
def getSubpackages(name):
"""获取该模块下所有的子模块名称"""
splist = []
for dirpath, _dirnames, _filenames in os.walk(name):
if os.path.isfile(os.path.join(dirpath, '__init__.py')):
splist.append(".".join(dirpath.split(os.sep)))
return splist | a876a18694e5124d345a0f7b1db5e8b9291983b9 | 50,873 |
def _tc_minmax_ ( tree , var , cuts = '' , delta = 0.0 ) :
"""Get min/max for the certain variable in chain/tree
>>> chain = ...
>>> mn,mx = chain.vminmax('pt')
>>> mn,mx = chain.vminmax('pt','y>3')
"""
if hasattr ( tree , 'pstatVar' ) :
if cuts : s = tree.pstatVar ( var , cuts )
else : s = tree.pstatVar ( var )
else :
if cuts : s = tree.statVar ( var , cuts )
else : s = tree.statVar ( var )
mn,mx = s.minmax()
if mn < mn and 0.0 < delta :
dx = delta * 1.0 * ( mx - mn )
mx += dx
mn -= dx
return mn , mx | 4bd4389e34bacae622162792181aa72007fc0f25 | 50,874 |
def unwind_block_transactions(block):
"""Yield a block for each transaction in given block"""
return block['block']['transactions'].map(lambda tx: block.merge({'tx': tx})) | 79ec770ba28450575cbbd063c27afcae57029abf | 50,875 |
def argtopk_preprocess(a, idx):
""" Preparatory step for argtopk
Put data together with its original indices in a tuple.
"""
return a, idx | 55e7566a5a88bf45a0eda76513a10528180dc8a9 | 50,876 |
def bone_siblings(obj, bone):
""" Returns a list of the siblings of the given bone.
This requires that the bones has a parent.
"""
parent = obj.data.bones[bone].parent
if parent is None:
return []
bones = []
for b in parent.children:
if b.name != bone:
bones += [b.name]
return bones | f63d16eee87a315cbf9213fd8a86ad248370ceb7 | 50,877 |
import hashlib
def sha1_hasher(s):
""" A simple utility function for producing a sha1 digest of a string. """
return hashlib.sha1(s).digest() | a214bc5cc78e7398373f7dd7c50d1e3ef59d1f8f | 50,879 |
def pathto(*args):
"""Mock of sphinx' pathto() just for the homepage.
Otherwise we cannot re-use our sphinx templates.
"""
return "" | 978471934469cb0c74cc476f2aae9c800953c48b | 50,880 |
def generate_stack_id(stack_name: str) -> str:
"""Generate a stack ID from the stack name"""
return (
f"arn:aws:cloudformation:ap-southeast-2:123456789012:stack/{stack_name}/"
"bd6129c0-de8c-11e9-9c70-0ac26335768c"
) | 130749de4bf983b22a26aaac5412c55c5baf5e37 | 50,883 |
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