content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def eperm_crim(por, eperm1, eperm2, eperm3=None, sw=None):
"""Effective electric permittivity after CRIM.
Markov et al., 2012, Journal of Applied Geophysics, Eq. 7.
Parameters
----------
por: float or array
Concentration of constituent 1 (host, wetting phase).
eperm1, eperm2, eperm3 :... | 4daa8528b8e91ff315ee1ecf49f0f88d219cbb87 | 3,605,900 |
def shortest_paths(graph, vertex_key):
"""Uses Dijkstra's algorithm to find the shortest path from
`vertex_key` to all other vertices. If we have no lengths, then each
edge has length 1.
:return: `(lengths, prevs)` where `lengths` is a dictionary from key
to length. A length of -1 means t... | f2ac9abf9292364099748475988d4ee1dbeb4b23 | 3,605,901 |
def process_overall_mode_choice(mode_choice_data):
"""Processing and reorganizing the data in a dataframe ready for plotting
Parameters
----------
mode_choice_data: pandas DataFrame
From the `modeChoice.csv` input file (located in the output directory of the simulation)
Returns
------... | 870685017d223f8a277265f80eea56e50eedec90 | 3,605,902 |
import os
import pathlib
def local_to_cloud(local_path):
"""
takes a path to a local file or directory and converts it to a Dropbox location. This is done by replacing
definitions.HOME_DIR with definitions.CLOUD_HOME_DIR.
:param local_path: path to a local file. Note: file must be somewhere in the cur... | 401f27bdc605ed3d93e7aa8372b8a095abe84ceb | 3,605,903 |
def loadtemplater(ui, spec, defaults=None, resources=None, cache=None):
"""Create a templater from either a literal template or loading from
a map file"""
assert not (spec.tmpl and spec.mapfile)
if spec.mapfile:
frommapfile = templater.templater.frommapfile
return frommapfile(spec.mapfil... | fffc64d1a41486c20c4e61aa4281787730c93cb4 | 3,605,904 |
def vgg16(inputs,
batch_size=100,
num_classes=12,
is_training=True,
dropout=0.5,
weight_decay=0.005,
spatial_squeeze=True,
scope='vgg_16'):
"""Oxford Net VGG 16-Layers version D Example.
Note: All the fully_connected layers have been transfo... | e70abe98dc581c42526a95deb9f03126b7c3a37f | 3,605,905 |
def print_movie_rating_results(genre_to_rating_map):
"""Given a dictionary, prints the average IMBD ratings for each year for
each genre formatted as:
*genre*
*space*
*release years* : *average IMBD rating*
Parameters:
genre_to_rating_map: a dictionary that maps genres to r... | d045cce8a6fe2f73ce27327a6f6aabb97473a5d9 | 3,605,906 |
def flatten_datasets(rel_datasets):
"""Take a dictionary of relations, and returns them in tuple format."""
flattened_datasets = [[], [], []]
for kind in rel_datasets.keys():
for i in range(0, 3):
for rel in rel_datasets[kind][i]:
flattened_datasets[i].append([*rel, kind]... | 70affa370a98c8328effed0bdb015999c5874913 | 3,605,907 |
from typing import Callable
def requires_token(func: Callable):
"""
This annotation protects API calls that require authentication.
It will cause them to raise NoAcccessTokenException if the token is not set in the client.
:return: Function decorated with the protection
"""
def wrapper(self... | 368b223bc824c394072f1f99a7bb33d19015a545 | 3,605,908 |
import torch
def log_sum_exp(x, dim=None):
"""Log-sum-exp trick implementation"""
x_max, _ = torch.max(x, dim=dim, keepdim=True)
x_log = torch.log(torch.sum(torch.exp(x - x_max), dim=dim, keepdim=True))
return x_log+x_max | 45b1f6d198569567d3284bab4116a4703b0589a3 | 3,605,909 |
import hashlib
def feature(self, node="clickhouse1"):
"""Check alter user query syntax.
```sql
ALTER USER [IF EXISTS] name [ON CLUSTER cluster_name]
[RENAME TO new_name]
[IDENTIFIED [WITH {PLAINTEXT_PASSWORD|SHA256_PASSWORD|DOUBLE_SHA1_PASSWORD}] BY {'password'|'hash'}]
[[ADD|DROP] HOST {LOCA... | d9cde1936c78d9d61ca9a446d5018e51c74c221f | 3,605,910 |
def nb_year(p0, percent, aug, p):
"""
Finds the amount of years required for the population to reach a desired amount.
:param p0: integer of starting population.
:param percent: float of percent increase per year.
:param aug: integer of new inhabitants.
:param p: integer of desired population.
... | 054496347fc8bedca3424143d48d122712dd1363 | 3,605,911 |
def find_largest_digit(n):
"""
:param n: the number to be processed and compared with
:return: the largest digit in the number
"""
num = abs(n)
initial_largest_digit = num % 10 # set largest digit to the last digit of the number
return helper(num, initial_largest_digit) | 3f9324ab3676bb29b1d81b2eea13c43fc0d338c0 | 3,605,912 |
def compute_overlaps(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
For better performance, pass the largest set first and the smaller second.
"""
# Areas of anchors and GT boxes
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - ... | f224ee968a824984e8463ab43e346e6396cf4325 | 3,605,913 |
def search_master(request):
"""Method to query existing customers."""
try:
results = []
query_id = int(request.GET.get('id'))
query = request.GET.get('q')
school_level = request.GET.get('level')
# Filters for external ids
if query_id == 1:
agents = OVCFacility.objects.filter(facility_name__icontains=qu... | 30ce02b79a05ec5d37c08f36ea25836fc3ba2b5d | 3,605,914 |
def generate_importance_map(map_size):
"""
This function generates a weighted map of "where we want the brain to be".
It is thus 1 at the center and 0 at the edges.
"""
importance_map = np.zeros(map_size)
for i in range(map_size[0]):
for j in range(map_size[1]):
for k in rang... | 4931dd522cbf8e6cec53edd13a6814d47a5ba8f3 | 3,605,915 |
import os
def full_path_to(file):
"""Returns an absolute path to the given file."""
# We need to use full paths to files because `git status --porcelain` shows
# paths relative to the repository's root.
return os.path.join(repository_path(), file) | 49eb10e1513e66dc2a9f85427a024a594f0a7441 | 3,605,916 |
from typing import Optional
def change_email_address(
user_id: UserID,
new_email_address: Optional[str],
verified: bool,
initiator_id: UserID,
*,
reason: Optional[str] = None,
) -> UserEmailAddressChanged:
"""Change the user's e-mail address."""
user = _get_user(user_id)
initiator ... | baae07a39055b302bdabaae0df65f0136da6303f | 3,605,917 |
def write_walks_to_disk(args):
"""
Write random walk into file
:param args: arguments for random walk write
Returns
-------
the name of file containing random walks
"""
num_walks, walk_length, window_size, num_pairs_required, subsample, file_name, iter_function, seed, __current_graph, __vertex2str =... | dcec7907a8c7986fff6f71469ffdd3b629980138 | 3,605,918 |
def tshark_read(
device,
capture_file,
packet_details=False,
filter_str=None,
timeout=60,
rm_file=True,
):
"""Read the packets via tshark
:param device: lan or wan...
:type device: Object
:param capture_file: Filename in which the packets were captured
:type capture_file: St... | 8fc31098e750691a1aa7c27a868abf0d6254adec | 3,605,919 |
def light_similarity(conn, entry_ids_1, entry_ids_2, metric, cpu_cores):
"""
main function
:param conn: db_connection
:param entry_ids_1: list of entries 1
:param entry_ids_2: list of entries 2
:param cpu_cores: number of cores to be used
:param metric: 'lin', 'resnick', 'jc' or 'all'
:r... | f5684a3cc96a456cffd18393f760d740c2d394b6 | 3,605,920 |
def prepare_window(ds_length, window_name, window_kwargs):
"""Window needs special preparation as the parameters can be dependent on dataset length.
Args:
ds_length (int): Length of the dataset.
window_name (str): Name of the window module.
window_kwargs (dit): Key word arguments from t... | 9607cc89227e54b8b477d5fab05ec85d1e3925d1 | 3,605,921 |
def resize_image(img):
"""resize images prior to utilizing in trianing model"""
width, height = img.size
ratio = width/height
new_height = 100
new_width = int(new_height*ratio)
img = img.resize((new_width, new_height))
return img | 1aa0164e1e25ef0f22e55a15a654fda2dfef5b12 | 3,605,922 |
def _filter_calibration(time_field, items, start, stop):
"""filter calibration data based on time stamp range [ns]"""
if len(items) == 0:
return []
def timestamp(x):
return x[time_field]
items = sorted(items, key=timestamp)
calibration_items = [x for x in items if start < timestam... | c7575ec85c7da9f1872150a1da3d7b02718df8a0 | 3,605,923 |
import torch
def phi_inv(D):
""" Inverse of the reallification phi"""
AB,_ = torch.chunk(D,2,dim=0)
A,B = torch.chunk(AB,2,dim=1)
return torch.stack([A,B],dim=len(D.shape)) | b8198764b89f3f1261e96014697cf1346e1c7d43 | 3,605,924 |
import spacy
import subprocess
def load_spacy_nlp(language, disable_components):
"""Load the spaCy nlp object.
If the language's models cannot be found, they are downloaded.
"""
try:
spacy_nlp = spacy.load(language, disable=disable_components)
except OSError:
subprocess.run(["pyt... | 7df4f1714a0a46b7f709509a000df85109af4404 | 3,605,925 |
import os
def is_created():
""" Checks to see if ginger new command has already been run on dir """
return os.path.isfile(os.getcwd()+'/_config.yaml') | 3a91185bd5c17d659e8cd30bf57c781c6a657092 | 3,605,926 |
def activate_wps(wps, endpoint, name):
"""
Activate a WebProcessingService object by calling getcapabilities() on it and handle errors appropriately.
Args:
wps (owslib.wps.WebProcessingService): A owslib.wps.WebProcessingService object.
Returns:
(owslib.wps.WebProcessingService): Returns a... | d146f22db9d13db17e688bfb404366b9e236dc8c | 3,605,927 |
import argparse
def read_param() -> dict:
"""
read parameters from terminal
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--ScreenType", help="type of screen ['enrichment'/'depletion']", type=str, choices=["enrichment", "depletion"]
)
parser.add_argument("--LibFilen... | 5efb8419266b34807b286a411cfd36365c66c628 | 3,605,928 |
def make_low_freq(xx, halfperiod=10, halfamplitude=1.):
""" make low frequncy signals """
omega = np.pi / halfperiod
return halfamplitude * np.sin(omega * xx + np.random.uniform(0, 2*np.pi)) | f3ca7ab174f8e9d3b7f06251009820c753622f15 | 3,605,929 |
from re import T
def tree_weight(pytree: T, weight: float) -> T:
"""Weights tree leaves by weight."""
return jax.tree_map(lambda l: l * weight, pytree) | b6da802e783632fc3986fa5547fad8ca5994e3a7 | 3,605,930 |
def get_sigma_clip(img,sigma=3,iters=100):
"""
Do sigma clipping on the raw images to improve constrast of
target regions.
Reference
=========
[1] sigma clip
http://docs.astropy.org/en/stable/api/astropy.stats.sigma_clip.html
"""
img_clip = sigma_clip(img, sigma=sigma, iters=ite... | a426fea3caafcb382fb0547145eadbeef0f9d7c8 | 3,605,931 |
def Get_foregroundapp(device):
"""Return the foreground app"""
return device.shell("dumpsys activity recents | grep 'Recent #0' | cut -d= -f2 | sed 's| .*||' | cut -d '/' -f1").strip() | 236986e3d08f6a4c7dd4cd8c8441806d25e76654 | 3,605,932 |
import glob
import os
def find_current_eofs(cur_path):
"""Returns a list of SentinelOrbit objects located in `cur_path`"""
return sorted(
[
SentinelOrbit(filename)
for filename in glob.glob(os.path.join(cur_path, "*EOF"))
]
) | a71d75dd5a420cc234a7fe232e686b7c34d92163 | 3,605,933 |
def chunk_size(request):
""" Set the chunk size for the source (or None to use the default).
"""
return request.param | c57269f434790953d475a2791c862d70d204ed86 | 3,605,934 |
import csv
import os
def search_in_database(ip):
"""
search_in_database(ip_number) => (code, country)
returns ('--', 'unknown') if nothing found
"""
global ip_database
if not ip or not ip_database:
return unknown
try:
# do a binary search.
n = sum_ip(ip)
fd ... | d663ff6d2cf011081fd651743abad2059386a92c | 3,605,935 |
def finite_diff_hessian_diag(x, grad, epsilon=FINITE_DIFF_EPSILON):
""" Approximate the diagonal of the Hessian of a function using finite difference in the
partial gradient.
:param np.ndarray x: point at which to evaluate derivative
:param function grad: function that returns the gradient
"""
f... | c1670919376ab554d02f4e954c0e6775328bf469 | 3,605,936 |
import json
def get_new_username():
"""Prompt for a new username."""
username = input("What is your name? ")
filename = 'Excercise_10_13.json'
with open(filename, 'w') as f_obj:
json.dump(username, f_obj)
return username | e25546247a849aca6dae94728c4318ff10a43434 | 3,605,937 |
def densify(*args):
"""
Make the matrix dense and assign nonzeros to a value.
densify(IM x) -> IM
densify(DM x) -> DM
densify(SX x) -> SX
densify(MX x) -> MX
"""
return _casadi.densify(*args) | 122c0f4710ca8b4e6b3274a665caab1ffc568acf | 3,605,938 |
def t0(S, t_r, n):
"""
t0 = t_r - M_r / n
:param S: S angle
:type S: float
:param t_r: reference time
:type t_r: float
:param n: mean movement
:type n: float
:return: t0
:rtype: float
"""
return t_r - 2 / 3 * np.sqrt(2) / n / np.tan(S) | 7534034e64476fcdec3a2585f7716bd8daa26f08 | 3,605,939 |
import re
def parse_ndx(lines):
""" Parses a GROMACS ndx file.
:param lines: Iterable of strings.
:return: Dictionary with group names as key, list of 0-based atom indices as value.
"""
groups = dict()
lastgroup = None
thisvalues = []
for line in lines:
line = line.strip()
# skip comments and empty line... | aeca75ff4ec626e814336b327b11f942a46815b7 | 3,605,940 |
def duration_format(delta: float):
"""
Duration format
:param delta: seconds
:return: string representation: 1 days 1 hour 1 min
"""
if delta < 0:
delta = f'{int(delta * 1000)} ms'
elif delta < 60:
delta = f'{int(delta)} sec'
elif delta < 3600:
delta = f'{int(delt... | 4ea92191076281d2108066a85b2c8662024bdaaf | 3,605,941 |
from datetime import datetime
def isoformat(dt: datetime) -> str:
"""ISO format datetime object with max precision limited to seconds.
Args:
dt: datatime object to be formatted
Returns:
ISO 8601 formatted string
"""
# IMPORTANT should the format be ever changed, be sure to updat... | 679ce7aa71ab30e4c78a0953272c17f487714177 | 3,605,942 |
def BuilderName(build_config, active_waterfall, current_builder):
"""Gets the corresponding builder name of the build.
Args:
build_config: build config (string) of the build.
active_waterfall: active waterfall to run the build.
current_builder: buildbot builder name of the current builder, or None.
... | eaebc50d653b759eff0005e0347f3cd09a4d07e0 | 3,605,943 |
def message_box(message, informativeText, type, question=False):
"""ADD
Parameters
----------
Returns
-------
"""
# TODO: ADD DETAILED TEXT WITH TRACEBACKS AND EXCEPTION CATCHING
msg = QMessageBox()
msg.setText(message)
msg.setInformativeText(informativeText)
if ty... | a15ccbf33e674640e871dbefa7c795c45d7d5b06 | 3,605,944 |
def ndcg_at_k(
rating_true,
rating_pred,
col_user=DEFAULT_USER_COL,
col_item=DEFAULT_ITEM_COL,
col_rating=DEFAULT_RATING_COL,
col_prediction=DEFAULT_PREDICTION_COL,
relevancy_method="top_k",
k=DEFAULT_K,
threshold=DEFAULT_THRESHOLD,
):
"""Normalized Discounted Cumulative Gain (nD... | edcb1da897b218c720c868f597463569d6e06952 | 3,605,945 |
from re import T
def partners():
"""
RESTful CRUD controller for Organisations filtered by Type
"""
# @ToDo: This could need to be a deployment setting
get_vars["organisation_type.name"] = \
"Academic,Bilateral,Government,Intergovernmental,NGO,UN agency"
# Load model
table = ... | 5cb3b9283fd04c854d4e8f4e4b6fd26480ac691f | 3,605,946 |
def _opt_to_mymessage(msg):
"""Transforms dictionary representation of the VkOpt message to the MeMessage obj.
Notes:
Document id of a VkOpt message isn't parsed and may only be -1.
Photos aren't documents (for some reason).
Message is forwarded if only it has attached forwarded message... | e0cd6ce735b175e08375d940820e862aa075ab62 | 3,605,947 |
def _xls_dslx_verilog_impl(ctx):
"""The implementation of the 'xls_dslx_verilog' rule.
Converts a DSLX file to an IR, optimizes the IR, and generates a verilog
file from the optimized IR.
Args:
ctx: The current rule's context object.
Returns:
DslxInfo provider.
ConvIRInfo provide... | 4a45b1392e8755218efc593cf3aa3bd9a10c52c9 | 3,605,948 |
import os
def getParentPath():
""" Convenience function. Returns the parent folder of the \\*.sikuli bundle. """
return os.path.dirname(Settings.BundlePath) | dc10d674fe7acbcd46153de462a3e00845be14ac | 3,605,949 |
def generate_X_df_from_descriptor_list(descriptor_list,
default_csv_paths,
col2remove = DEFAULT_INDEX_COLS,
**args,
):
"""
This function generates the combi... | 5c73bbb8d8a3c9621554cbf1efc6dd3746e0b540 | 3,605,950 |
def compile_template(line):
"""
Compile a template expression into a python function (like jsps, but way shorter)
"""
extr = []
def repl(match):
g = match.group
if g('dollar'): return "$"
elif g('backslash'):
return "\\"
elif g('subst'):
extr.a... | 68a275902d20f50c00597194c970237f6c86ae2e | 3,605,951 |
from typing import Optional
def _residual_star(
regular_expression: RegularExpression,
letter: Letter) -> Optional[RegularExpression]:
"""Residual computation, ``STAR`` case
"""
residual_inner = residual(regular_expression.inner, letter)
if residual_inner is not None:
return Re... | aa67c212f2ff063552ef1fd0f1629cf4ee2725b9 | 3,605,952 |
import sqlite3
import os
def get_db_cache(cache_dir: str) -> sqlite3.Connection:
"""
Open cache and return sqlite3 connection
Table is created if it does not exists
"""
cache_file = os.path.join(cache_dir, "cache.sqlite3")
conn = sqlite3.connect(cache_file)
cursor = conn.cursor()
curso... | 7dd6a909ba210a261196ddd1273795d76a27464a | 3,605,953 |
def evaluate_policy(env, policy, args, eval_episodes=1):
""" Runs policy for X episodes and returns average reward """
avg_reward = 0.
avg_episode_steps = 0
save_state = True
if save_state == True:
evaluate_episode_states = []
for _ in range(eval_episodes):
print('eval_episodes', eval_episodes)
obs = env.re... | 47ff74bf41949c78b5e141a34c617909c4347279 | 3,605,954 |
def get_dict(file_name):
"""
This function returns the english to french dictionary given a file where the each column corresponds to a word.
Check out the files this function takes in your workspace.
"""
my_file = pd.read_csv(file_name, delimiter=' ')
etof = {} # the english to french dictiona... | b86d21914c2b978909e7d88b0ca5e9d770a1dd05 | 3,605,955 |
from sklearn.utils import resample
def calc_bootstrap(fcs, obs, func, L, B=1000, bootstrap_range=[2.5, 97.5]):
"""
Calculates moving block bootstrap estimates for an
evaluation metric defined and calculated inside 'func' argument.
INPUT
fcs: forecasted time series
obs: ... | 90b4bcc57e6c337b5638008334e299bdfc70f13d | 3,605,956 |
def histeq(im,nbr_bins = 256):
"""对一幅灰度图像进行直方图均衡化"""
#计算图像的直方图
#在numpy中，也提供了一个计算直方图的函数histogram(),第一个返回的是直方图的统计量，第二个为每个bins的中间值
imhist,bins = histogram(im.flatten(),nbr_bins,normed= True)
cdf = imhist.cumsum() #
cdf = 255.0 * cdf / cdf[-1]
#使用累积分布函数的线性插值，计算新的像素值
im2 = interp(im.flatten... | 11f54f5440eadefa5bd03912d2e5f786eb2a7f29 | 3,605,957 |
def validate(obj, validator, name="object"):
"""Generic function"""
if not isinstance(validator, Validator):
raise TypeError("Not a validator.")
if hasattr(validator, 'validate'):
# Check to ensure that this hasn't looped back to this already
return validator.validate(obj, n... | 527f28f91694593571485ac1d8a828ac7d425906 | 3,605,958 |
def _recursive_namedtuple_convert(data):
"""
Recursively converts the named tuples in the given object to dictionaries
:param data: An object in a named tuple or its children
:return: The converted object
"""
if isinstance(data, list):
# List
return [_recursive_namedtuple_conver... | 292bc249b056c14eb1c700561d366ff4e6e64a10 | 3,605,959 |
def compute_crowd_performance(df_crowd_results, crowd_score_column, experts_score_column):
""" Function to evaluate the answers of the crowd at each posible crowd score threshold"""
rows = []
rows.append(["Thresh", "TP", "TN", "FP", "FN", "Precision", "Recall", "Accuracy", "F1-score"])
precision = 0.0
... | 8316f8af86f9cad7eccf3f2175d0c14e2fbb8e84 | 3,605,960 |
def series_key_from_name(name):
"""Get an ESeries from its name.
Args:
name: The series name as a string, for example 'E24'
Returns:
An ESeries object which can be uses as a series_key.
Raises:
ValueError: If not such series exists.
"""
try:
return ESeries[name... | c571a8975fadf4de14471a0b83157a06d2999080 | 3,605,961 |
import json
import os
import sys
import re
def initialize_exp(params):
"""
Initialize the experiment:
- dump parameters
- create a logger
"""
# dump parameters
exp_folder = get_dump_path(params)
json.dump(vars(params), open(os.path.join(exp_folder, 'params.pkl'), 'w'), indent=4)
#... | dcfd58f020741051a96ee28528a5ad2b8fe018d2 | 3,605,962 |
def make_transform_sql2(sqlname, typefun, pyfun=None):
""" Makes a sql transformer that accepts two arguments.
sqlname: the name of the sql function.
typefun: a function that accepts a list of datatypes and returns a datatype.
numargs: the number of arguments accepted by this transformer.
pyfun: a python fun... | d2a2b02253670ea4cc7ea6d4ab67ea992c5869cd | 3,605,963 |
def _find_start(score_matrix, align_globally):
"""Return a list of starting points (score, (row, col)).
Indicating every possible place to start the tracebacks.
"""
nrows, ncols = len(score_matrix), len(score_matrix[0])
# In this implementation of the global algorithm, the start will always be
... | 361a1ea87ecf9bbef0950521ed0fdcfd70b7b608 | 3,605,964 |
def get_arguments_by_statement(statement: Statement, issue: Issue) -> dict:
"""
Collects every argument which uses the given statement.
:param statement: Statement which is used for query
:param issue: Extract information for url manager from issue
:rtype: dict
:return: prepared collection with... | faa7d60fa3d4ac073b6ff1cf9104d0f3ccd92299 | 3,605,965 |
def plot_fig(model_name, crypto_list: list, epoch: int, loss: list, acc: list):
"""
draw a figure for loss and acc
"""
cl = concat2str(crypto_list)
pic_name = model_name + '_' + cl
x = np.linspace(1, epoch, epoch)
plt.figure(figsize=(4.5, 5))
plt.subplot(211)
plt.plot(x, loss)
... | 3ebdf445862955c6a1f4f5ac7b3e710578e486bc | 3,605,966 |
def KLT(a):
"""
Returns Karhunen Loeve Transform of the input and the transformation matrix
and eigenvalues.
*** IN DEVELOPMENT ***
Ex:
import numpy as np
a = np.array([[1,2,4],[2,3,10]])
kk,m = KLT(a)
print(kk)
print(m)
# to check, the following should return... | 29ad1baebdb34f474a6a8fdfa89f188b7cc02edc | 3,605,967 |
def get_f1_score(precision, recall):
"""
Calculate and return F1 score
:param precision: precision score
:param recall: recall score
:return: F1 score
"""
return (2 * (precision * recall)) / (precision + recall) | e94dd20acac443be9856b9dbb43adf2ead2e0ba5 | 3,605,968 |
def bh2u(x: bytes) -> str:
"""
str with hex representation of a bytes-like object
>>> x = bytes((1, 2, 10))
>>> bh2u(x)
'01020A'
"""
return x.hex() | 8ab7bf9b536d13a1944e014ea83a4302917c2306 | 3,605,969 |
def chromAndPosSort(x, y):
"""
Comparison function for use in sort routines. Compares strings of the form
chr10:0-100. Sorting is done first by chromosome, in alphabetical order, and then
by start position in numerical order.
"""
xChrom = x.split("_")[-1].split(":")[0]
yChrom = y.split("_")[... | 27db2d05e918f1652ebf154c1004cfad112b5891 | 3,605,970 |
def vis_FasterRCNN_loss(self, scale_weight):
"""
Calculate the roi losses for faster rcnn.
Args:
-- self: FastRCNNOutputs.
-- scale_weight: the weight for loss from different scale.
Returns:
-- losses.
"""
return{
"loss_cls": self.vis_softmax_cross_entropy_loss_(scale_weigh... | 5832d7f28179085db939a7bd624e9ffb08461fe4 | 3,605,971 |
def simple_mask(model, init_fn,
masked_param):
"""Creates a mask given a model and numpy initialization function.
Args:
model: The model to create a mask for.
init_fn: The numpy initialization function, e.g. numpy.ones.
masked_param: The list of parameters to mask.
Returns:
A mas... | 053ed147fe780296de133e9d438127569609d3ed | 3,605,972 |
def word_dropout(tokens, dropout):
""" Randomly dropout tokens (IDs) and replace them with <UNK> tokens. """
return [constant.UNK_ID if x != constant.UNK_ID and np.random.random() < dropout \
else x for x in tokens] | d4a50bccef6e562bd4edb0320c6c5ecdb83fb4bc | 3,605,973 |
def createSimpleResourceMap(ore_pid, sci_meta_pid, data_pids):
"""Create a simple resource map with one metadata document and n data objects."""
ore = d1_common.resource_map.ResourceMap()
ore.initialize(ore_pid)
ore.addMetadataDocument(sci_meta_pid)
ore.addDataDocuments(data_pids, sci_meta_pid)
... | 8837d120804dc75330f8d50a1086a83dabd25059 | 3,605,974 |
import binascii
def crc32(data):
"""计算输入流的crc32检验码:
Args:
data: 待计算校验码的字符流
Returns:
输入流的crc32校验码。
"""
return binascii.crc32(b(data)) & 0xffffffff | ed8966e87070e4fb26e9468fb5ec1bc7b30ebcd1 | 3,605,975 |
def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
"""
Scale operator.
Putting scale and bias to the input Tensor as following:
``bias_after_scale`` is True:
.. math::
Out=scale*X+bias
``bias_after_scale`` is False:
.. math::
... | b68c737ebc0fb10dc43dc9d15b919705d2171555 | 3,605,976 |
def get_version_data(session, url, authenticated=None):
"""Retrieve raw version data from a url."""
headers = {'Accept': 'application/json'}
resp = session.get(url, headers=headers, authenticated=authenticated)
try:
body_resp = resp.json()
except ValueError:
pass
else:
... | b48550583286a7f3941a1ffd71c8129803ec077e | 3,605,977 |
def _select_relevant_files(api_type):
""" Select the folder related to the api_type
Exclude certain files and directories based on api_type
:param api_type: framework or plugin api
:return: The base file path for the api files to document
The list of files to exclude from api
"""
if api_... | fedd27d728aa7e164e709ca0cf5daa473de58927 | 3,605,978 |
from typing import Union
def datetime_to_string(
date: dt.datetime, date_format: str = "%Y-%m-%d %H:%M:%S"
) -> Union[float, str]:
"""Returns a string representation of a datetime object
Args:
date: dt.datetime the date
date_format: what is the format of the date? see datetime documentati... | 9dd7f8f6f53662cdaf6158b100658465893d286b | 3,605,979 |
import json
def request_game(
request: game_server_pb2.GameRequest
) -> game_structs_pb2.GameRequestResponse:
"""Request a game."""
handler = get_default_tictactoe_cache_handler()
# Set a random number seed based on the fractional second part
# of the timestamp. This makes it more reliable on hig... | 3263b33c557ccaf9448f63e4dc22bddbc878e41f | 3,605,980 |
from typing import List
from functools import reduce
def decode(obs: int, spaces: List[int]) -> List[int]:
"""
Decode an observation from a list of gym.Discrete spaces in a list of integers.
It assumes that obs has been encoded by using the 'utils.encode' function.
:param obs: the encoded observation
... | 6c3c1348776b7b164cf70a5bfa9da3e8b53a280f | 3,605,981 |
def anagram_solution_1(words):
""" Complexity O(n2)
If it is possible to “checkoff” each character, then the two strings must be anagrams
:param words: Tuple
:return: bool
"""
s1, s2 = words
still_ok = True
if len(s1) != len(s2):
still_ok = False
a_list = list(s2)
po... | 942ef7bb631bd803d89e71643e994505cb9b827a | 3,605,982 |
def allocation_shimen_wpp():
"""
Real Name: Allocation ShiMen WPP
Original Eqn: IF THEN ELSE( ShiMen Reservoir Depth>=ShiMenReservoir Operation Rule Lower Limit , Water Right ShiMenLongTan WPP\ *0.387, IF THEN ELSE( ShiMen Reservoir Depth >=ShiMenReservoir Operation Rule Lower Severe Limit , Water Right Shi... | 3f489eec46527d59b305bcaced216c83e90ef6bc | 3,605,983 |
def bleached(source):
"""Render a string through the bleach library, caching the result."""
render_function = partial(bleach.clean,
tags=settings.BLEACH.allowed_tags,
attributes=settings.BLEACH.allowed_attrs)
return cached_render(render_function, s... | d734ae5d7997be878bf002c0e1fcba7516b9591b | 3,605,984 |
import os
def read(path, do_tail = 0):
"""
Read file content.
:param str path: path to file
:param int tail: number of lines to read (from end), entire file if 0
:return: file content splitted by newline
:rtype: :py:obj:`list` [ :py:obj:`str` ... ]
"""
try:
if do_tail:
... | d942c4ba037dc20ede8615abfa950cb9ed639389 | 3,605,985 |
def cna(mac):
"""Builds a mock Client Network Adapter for unit tests."""
return mock.Mock(spec=pvm_net.CNA, mac=mac, vswitch_uri='fake_href') | 28ae759612d9b608b8288f627ac5d013914bbbd4 | 3,605,986 |
def product_from_hand(cards):
"""
Expects a list of cards in integer form.
"""
product = 1
card_symbols = []
i = 0
for n in cards:
product *= (n & 0xFF)
card_symbols.append(RANKS[i])
i += 1
return card_symbols, product | 74eb8df29ed20c886745619d712595c975132f2e | 3,605,987 |
def most_popular(request, username=None, search_key=None):
"""
Shows the most popular search results.
The ``username`` kwarg should be the ``username`` field of
``django.contrib.auth.models.User``. The ``search_key`` can be any string.
Template::
``saved_searches/most_popular.html``
Co... | 79e505270a783e188a913113f67bcecc5ae5814f | 3,605,988 |
from typing import List
from typing import Dict
from typing import Any
def replace_foreign_columns_with_local_columns(foreign_columns: List[ForeignColumnPath],
fks_by_name: Dict[str, Any], src_table: str
) -> List[ForeignCol... | 58346be57c746f1db136c50f786a4c2d4e95667d | 3,605,989 |
from typing import Dict
from typing import List
def split_data(data: pd.DataFrame, parameters: Dict) -> List:
"""Splits data into training and test sets.
Args:
data: Source data.
parameters: Parameters defined in parameters.yml.
Returns:
A list containing split... | a91b8bde176280635f4474e9965a54d6b7fb8cd0 | 3,605,990 |
def enum_pair():
"""
枚举所有的对子
"""
return [(cards2str(pair), w) for w, pair in enumerate(CARD_PAIR)] | 1f993b9014250d71185da3d34d89394b398e75ab | 3,605,991 |
def _normalize_type(i_type: str) -> str:
"""Normalize AXI4-Stream names"""
if i_type in _m_type:
i_type = 'INITIATOR'
elif i_type in _s_type:
i_type = 'TARGET'
else:
raise ValueError("Unknown BUSINTERFACE type {}".format(i_type))
return i_type | 35e9ecc7d3f8bab6d0d996e8ec47382dc7113999 | 3,605,992 |
def gt(input, other, out=None):
"""Compute *input* > *other* element-wise.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
... | 978937f5917a9a7917f5d033815d6f17476dea43 | 3,605,993 |
def do_train(config, plugin_factory=None):
# type: (MyNLUConfig, Optional[PluginFactory]) -> Tuple[Trainer, Interpreter, Text]
"""Loads the trainer and the data and runs the training of the specified model."""
# Ensure we are training a model that we can save in the end
# WARN: there is still a race co... | 053262e4678fd40f1adfc84682911cf073fc521e | 3,605,994 |
def remove_invalid_rows(data):
"""
Removes invalid rows from data.
A row is invalid if
* the text is NaN
* session_id is 0
"""
progress = progressbar.ProgressBar(max_value=data.shape[0]).start()
invalid_utterances = []
for i, row in data.iterrows():
if type(r... | 7b409669261289784d0df49702d653318cea4a17 | 3,605,995 |
def load_feature_extractors(rt=None, wp=None, ap=None, slm=None, lm=None) -> 'tuple':
"""
Load feature extractors depending on command line options.
For now we have the following extractors:
* RuleTable
* WordPenalty
* ArityPenalty
* StatelessLM
* KenLM
:return... | 675728cddce5687df44db7d022594dc27aad3675 | 3,605,996 |
def oneYear(token="", version="stable", filter="", format="json", **timeseries_kwargs):
"""Rates data
https://iexcloud.io/docs/api/#treasuries
Args:
token (str): Access token
version (str): API version
filter (str): filters: https://iexcloud.io/docs/api/#filter-results
form... | a11973e8af3b02f5c89bd9a8c527a990f61d7d7d | 3,605,997 |
from typing import Union
from typing import Optional
from typing import List
from typing import Dict
from typing import Any
from typing import Tuple
def sharpen(
image: Union[str, Image.Image],
output_path: Optional[str] = None,
factor: float = 1.0,
metadata: Optional[List[Dict[str, Any]]] = None,
... | 9c9c59638c23761ac3ff9612cb9c256cbde8b24a | 3,605,998 |
def get_unlisted_addons():
"""Load the unlisted addons file as a set."""
return set_from_file('validations/unlisted-addons.txt') | 1519dddfb84ee6f3e600fe044b4431e524fd1c38 | 3,605,999 |
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