content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import logging
import time
from datetime import datetime
import pytz
def get_raw_temperature(request, pymortar_client, weather_mapping):
"""Returns temperatures for a given request or None.
Guarantees that no Nan values in returned data exist."""
logging.info("received request: %s %s %s %s %s", request.bu... | fd7005911058d70481405e5c82b2bee6d18adff7 | 65,900 |
def abs_diff(a, b, base=0):
"""Absolute value of difference between scalars.
abs_diff is symmetric, i.e. `a` and `b` are interchangeable.
Args:
a: First argument. An int.
b: Seconds argument. An int.
base: Dummy argument so that the argument signature matches other scalar
d... | 77d4d4231cd50a6e4cc527979fa806e139759d8f | 65,901 |
def model_name(model):
"""Return model name given model class."""
# Django supports model._meta -> pylint: disable=W0212
return force_text(model._meta) | 62a5e3a2ae757ebad6c8af911d492e44b1f359ec | 65,902 |
def getTitles(db):
"""
Gets all the books titles in the whole database
:param db: database object
:return: sorted books titles
"""
data = []
for book in db.find('books'):
data.append(book['Title'])
return sorted(data) | f63d2138cef4d2ad51767ad62abacce5169bb3a2 | 65,903 |
def mse(x: np.ndarray, y: np.ndarray) -> float:
"""Mean squared error."""
return float(np.mean((x - y) ** 2)) | b72f8717650953c1204319d15952a64c3a9c9b16 | 65,904 |
def plot_title(df, title, save_figure=False):
"""
Plots the skill frequencies of all skill categories for a given title.
Params:
df: (pandas df) the frequency df
title: (str) one of the three job titles:
'data scientist', 'machine learning engineer', 'data engineer'
... | a8e69c6cbb6b7878adddcb843ce80091818392ae | 65,905 |
def overlap(a: Pos, b: Pos, exact: bool = False) -> bool:
"""Calculate if two positions have overlap."""
if a == b:
return True
elif exact:
return False
s0, e0 = a
s1, e1 = b
if in_interval(s1, s0, e0):
return True
if in_interval(e1, s0, e0):
return True
i... | 5860d7be034116a3c0017f4bb0221501f8948386 | 65,906 |
import string
import random
def random_string(string_length=10):
"""Generate a random string of fixed length """
letters = string.ascii_lowercase
return "".join(random.choice(letters) for i in range(string_length)) | b3b13b593f7818e85f836086b1ea6f445a414c38 | 65,907 |
import os
def get_parallel_strategy():
"""Get parallel strategy by train id."""
train_id = get_train_id(request)
profiler_dir = os.path.realpath(os.path.join(settings.SUMMARY_BASE_DIR, train_id))
try:
profiler_dir = validate_and_normalize_path(profiler_dir, 'profiler')
except ValidationErr... | 8ef9140ea65d68cb61210d26e2d78d17d2857019 | 65,908 |
from typing import Dict
def get_dataset_labeling(
dataframe: pd.DataFrame, tag_column: str
) -> Dict[str, int]:
"""Prepares a mapping using unique values from ``tag_column``.
.. code-block:: javascript
{
"class_name_0": 0,
"class_name_1": 1,
...
"c... | 6a968aa16a0cd3b50607d81162a354c8a3f0538e | 65,909 |
def compute_misalignment_matrix_from_bam(bam_fp, bin_size=None, i_know_what_i_am_doing=False):
"""Create a matrix of binned mis-alignments
:param bam_fp: input BAM
:param bin_size: size of bin in mega bases
:param i_know_what_i_am_doing: Set this to override the runtime warning of too many bins
"""
def bi... | f0e92ee274c5a8139ca7183fd8c9b8843725fd34 | 65,910 |
def calc_auc(scored_labels, true_label):
"""Calculates area under the (precision-recall) curve."""
tp = 0
total = 0
for (
_,
l,
) in scored_labels:
if l == true_label:
tp += 1
else:
total += tp
return float(total) / (tp * (len(scored_labels... | f1e907548d3f5897197e97f7340cf3d7f6645e89 | 65,911 |
def give_coordinates(data):
"""
Get ground-truth coordinates (X,Y,Z, room label) where the measurement was
taken, given the JSOn structure as an input.
"""
message = {}
message['true_coordinate_x'] = data['raw_measurement'][1]['receiver_location']['coordinate_x']
message['true_coordinate_y'] = data['raw_m... | d412afbe08d4b1660ab9a12d1e567e4c4e4258fc | 65,912 |
def activate_int8_int8_matrix_multiplications(spec, selector=None):
"""
Utility function that takes in either a full precision (float) spec or
an nbit quantized spec to selectively enable int8 activation + weight quantization
of matrix multiplication operations where the second matrix represents a const... | af94b9e737ea222e85f3c12de698e74fbcd4d420 | 65,913 |
def filter_comment_lines(line):
"""Filter variable comment lines."""
if line.startswith(b"#"):
return True | 6f0441adb79ddf7aebb6466cb6dfdb74c74e1d24 | 65,914 |
def update_shipping_profile(profile_id,
shipping_profile_id,
shipping_form_data):
"""
Update a customer shipping profile and return the CIMResponse.
Arguments:
profile_id -- unique gateway-assigned profile identifier
shipping_profile_id -- uni... | 0d4f180760d0d2628f330128691ad98e3c81d85e | 65,915 |
def orders_by_dow(data_frame):
""" Gives orders_count by day of week.
:param data_frame: DataFrame containing orders data.
:return: DataFrame of order_ids count by day of week.
"""
grouped = data_frame.groupby(['order_dow'], as_index=False)
# count by column: 'order_id'
count = grouped.agg(... | bfca97ced614fb8c309f8edc482fc2136914759b | 65,916 |
def is_user_logged(app):
"""Check for auth_tkt cookies beeing set to see if user is logged in."""
cookies = app.cookies
if 'auth_tkt' in cookies and cookies['auth_tkt']:
return True
return False | 67e670fd70787b35bc9b8d9a724512bfc4e7bac8 | 65,917 |
from pathlib import Path
import numpy as np
import nibabel as nb
def _fix_topup_fieldcoeff(in_coeff, fmap_ref, refpe_reversed=False, out_file=None):
"""Read in a coefficients file generated by TOPUP and fix x-form headers."""
if out_file is None:
out_file = Path("coefficients.nii.gz").absolute()
... | 3a62133034d9e23b4593c5f10f8d048a32a63b8d | 65,918 |
def create_get_service_interface_point_list_by_id(): # noqa: E501
"""Create get-service-interface-point-list by ID
Create operation of resource: get-service-interface-point-list # noqa: E501
:rtype: GetServiceInterfacePointListRPCOutputSchema
"""
return 'do some magic!' | 2f54434f1ecfe77d50034e50b2c4333f24371a08 | 65,919 |
def map_reduce_partitions_nb(a, map_func_nb, map_args, reduce_func_nb, reduce_args):
"""Map using `map_func_nb` and reduce using `reduce_func_nb` each partition of signals in `a`.
`map_func_nb` and `reduce_func_nb` are same as for `map_reduce_between_nb`."""
out = np.full(a.shape[1], np.nan, dtype=np.float... | c424f7fd7b22280fd4ba5df86a2f16a1c55948be | 65,920 |
def lookup(name_in_data, language, metalookup=True, openrefine=False):
"""
Parameters
----------
name_in_data : str
Search string.
Returns
-------
list = [WDdf, how_matched]
WDdf is a dataframe with SPARQL-request
how_matched shows how the string was matched
... | 866180191de041936f3bf9bae4efb7629eaff62a | 65,921 |
import torch
def generate_predictions(model, test_dict):
"""
Function that is used to obtain the test predictions and metrics
"""
test_data = test_dict.map(
lambda batch: batch_tokenize_preprocess(
batch, tokenizer, max_input_length, max_output_length
),
batched=Tr... | 34074065595bf07db3c0d965235a2a8ef1036f25 | 65,922 |
from typing import Dict
from typing import Optional
def issm_likelihood_computations(
precomputed: Dict,
issm_params: Dict,
r_min: int,
r_max: int,
skip_c_d: bool = False,
profiler: Optional[SimpleProfiler] = None,
) -> Dict:
"""
Given `precomputed` from `issm_likelihood_precomputation... | 5f56b547826abbd096f9d4925aab002586f9621f | 65,923 |
def convert_nodata_to_nans(xarr):
"""
Args:
xarr:
Returns:
"""
nodata_attr = [k for k in xarr.attrs.keys() if k.lower().startswith('nodata')][0]
nodata = xarr.attrs[nodata_attr]
if nodata:
if str(xarr.dtype).startswith('int') or str(xarr.dtype).startswith('uint'):
... | f052c4b34141b0823631a61c1f35f163f84d6ddc | 65,924 |
def Main(operation, args):
"""
:param operation:
:param args:
:return:
"""
# 'init' has to be invokded first after deploying the contract to store the necessary and important info in the blockchain
if operation == 'init':
return init()
if operation == 'name':
return name(... | 1d69d5b0f05774d9ea1540b55ceff5a0020a8b66 | 65,925 |
def test_cache_memoize(cache):
"""
Test that cache.memoize() caches the return value of a function using a key based on
function arguments used.
"""
marker = 1
@cache.memoize()
def func(a, b, c, d):
return ((a, b, c, d), marker)
args, markx = func(1, 2, 3, 4)
assert args ==... | 5b93bcab00b334b1c38114f8b700b6ef0d9c3178 | 65,926 |
def get_settings_text(poll):
"""Compile the options text for this poll."""
text = []
locale = poll.user.locale
text.append(
i18n.t(
"settings.poll_type",
locale=locale,
poll_type=translate_poll_type(poll.poll_type, locale),
)
)
text.append(i18... | 34ad92078b0d440d5706ce0f8486a0770900dc10 | 65,927 |
def static():
"""
Returns the live STATIC_ROOT directory.
"""
return python("from django.conf import settings;"
"print(settings.STATIC_ROOT)", show=False).split("\n")[-1] | d6fbb06ed26240881d80189d4f758e3f64902011 | 65,928 |
import json
def parse_osm_streets(fpath):
"""
Parses the API response from *fpath* and converts it to a dataframe.
Args:
fpath (str): File path to read
Returns:
pandas.DataFrame: Contains all streets as well as their geometries
"""
# Helper function
def convert_to_wkt_geo... | 429df1f5015dd2b1c1f0c87a9fe01394b44fa688 | 65,929 |
def setpgid(space, pid, pgrp):
""" setpgid(pid, pgrp)
Call the system call setpgid().
"""
try:
os.setpgid(pid, pgrp)
except OSError, e:
raise wrap_oserror(space, e)
return space.w_None | c4bdfa143b90a57cf941fe0cc5ef442912708a01 | 65,930 |
def obey_reporting_preference(function):
"""
Do not execute this method if report_errors is disabled, even if someone tells you to!
"""
def inner(self, *args, **kwargs):
if not self.reporting_enabled:
return None
else:
return function(self, *args, **kwargs)
... | c0b38babc999b7bf1ace6821d6079f1afb53e540 | 65,931 |
def get_number_of_pages(url):
"""Function that will scrape for the number of pages
with listed apartments available. This number will
then be used to generate urls that will be scraped
for individual apartments.
Args:
url (str): Url to first page of listings.
Returns:
int: The ... | a4ac15e798304d6dd0dd92a30d864426186f6cec | 65,932 |
def paths_edges(random_graph):
"""Fixture for generating path census data frames
for edges and node/global counts based `random_graph` fixture.
"""
_, P = random_graph
E = P.census("edges")
return E, P | 238dfdc767dccd3b048ca7a2781cc692d289554e | 65,933 |
def interpolate_epsilon(teff, dnu, dnu_threshold=30, epsilon_offset=0.601, epsilon_slope=0.632):
"""
Calculate the asymptotic phase term.
This function computes the value of epsilon for either a main-sequence,
subgiant star or a red giant star by exploiting empirical relations linking
epsilon with... | d8df11b9be137a650adf4ce65ba337b4356d346c | 65,934 |
def make_celery(app):
"""
Configure celery for app
:param app: flask application instance
"""
# Create and configure celery
celery = Celery(app.import_name)
celery.config_from_object('config.CeleryConfig')
# Celery task using app context
BaseTask = celery.Task
class ContextTask... | 68e8834d431b734f0d0386ff9cfa7af93f7968a7 | 65,935 |
import warnings
def cycle_matrix(signals, ampl_bins=10, mean_bins=10, rainflow_func=rainflow_windap):
"""Markow load cycle matrix
Calculate the Markow load cycle matrix
Parameters
----------
Signals : array-like or list of tuples
- if array-like, the raw signal\n
- if list of tup... | bef538efa77a5a704effc07ed32e27e90d9f80c7 | 65,936 |
def compare(isamAppliance1, isamAppliance2):
"""
Compare kerberos configuration realms between two appliances
"""
ret_obj1 = get_all(isamAppliance1)
ret_obj2 = get_all(isamAppliance2)
return tools.json_compare(ret_obj1, ret_obj2, deleted_keys=[]) | 09e5322ef408ac1d86857a4f3bcc4d48ca0fcef7 | 65,937 |
def logout_view(request):
"""
注销
:param request:
:return:
"""
logout(request)
return redirect('system:login') | d6174cefbfab31d31e72387f6c2577e0399dd93f | 65,938 |
def logout():
""" Logout Route
The route to log a user out of the portal
Args:
None
Returns:
redirect to the public index page
"""
logout_user()
return redirect(url_for('main.public')) | b75f6cabd8766602d868e09cfff98d1491ea04ce | 65,939 |
def get_nx_graph(sdp_lines, remove_singletons = False):
"""
Return a networkx graph representation of the conll
input.
@param remove_singletons: Indicates whether to remove nodes which
aren't connected to any other node.
"""
graph = nx.MultiDiGraph()
words, pred... | c3ed5c1765eec6b9e829f3eda3bd27f3f3a1bf79 | 65,940 |
def is_in_manifest(dynamodb_client, table_name, run_id):
"""Check if run_id is stored in DynamoDB table.
Return True if run_id is stored or False otherwise.
Arguments:
dynamodb_client - boto3 DynamoDB client (not service)
table_name - string representing existing table name
run_id - string repr... | 5709dd0ffac71bac29d8b585f43f4ea4d4c190c5 | 65,941 |
def audio_slip(signal, delay, overlap=0.5):
""" # Audio Slip
Args:
signal (numpy.ndarray): audio signal.
delay (int): value of delay size.
overlap (float, optional): overlap rate. Defaults to 0.5.
Returns:
tuple: list of energy and list of zero crossing rate list.
"""
... | 35da37bd968052e1cfb33d35e81c23fe25dfc59f | 65,942 |
import json
import logging
import sys
import traceback
async def _create_user():
"""
Method Name : create_user
Parameters : {"userName": Name,"password": password,"firstName":Name,"lastName":Name,"emailId": Mail}
This function will create the details of the user
in access the software by lo... | 9404e5260587b093bb4883e9318a917c0bdf1f6c | 65,943 |
import json
def break_bot():
"""[Slash command for break punch]"""
# break payload
blocks = json.loads("""
[
{
"type": "header",
"text": {
"type": "plain_text",
"text": "Select your status below:",
"emoji": true
}
},
{
"type": "divider"
},
{
"type": "actions",
... | 4664ea206b425a74b2add71b870a9ed904a284dc | 65,944 |
import tqdm
def count_destiantions_from_hex(
feed: gk.Feed,
hex_resolution: int = 8,
include_sum: bool = True
) -> pd.DataFrame:
"""Calculates available directions aggregated to hex.
Args:
feed (gk.Feed): gtfs feed
hex_resolution (int, optional): Defaults to 8.
Returns:
... | b41284f9a7b1c8da1f39c9da24af584a9cabdf16 | 65,945 |
def extract_gloss(phrase, log=None):
"""Extract IGT from example."""
if not phrase.find('words'):
if log:
log.warn("No words in phrase '{}'".format(phrase.attrib.get('guid', '???')))
return {}
morph_glosses = list(map(_parse_morph, _find_morphemes(phrase)))
gloss = {k: [] f... | fde649961bfdddf961ad8e647af1366c02134685 | 65,946 |
def forward_sample(bn, n=1000):
"""
Approximate marginal probabilities from
forward sampling algorithm on a BayesNet object.
This algorithm works by
repeatedly sampling from the BN and taking
the ratio of observations as their marginal probabilities.
One sample is done by first sampling from any prior random
... | cd00006c9149b5db504d567f40a931ae49167dfc | 65,947 |
import re
def mqtt_wildcard(topic, wildcard):
"""Returns True if topic matches the wildcard string.
"""
regex = wildcard.replace('.', r'\.').replace('#', '.*').replace('+', '[^/]*')
if re.fullmatch(regex, topic):
return True
return False | fd7a3a5e8af1e6172decd62f2ba8294dfe07124c | 65,948 |
import _struct
def parse_atp_packet(pkt):
"""Parse a raw ATP packet.
pkt -- raw packet data to be parsed.
Returns: (kind, flags, seqno, tid, udata, pdata)
"""
if len(pkt) < ATP_HEADER_LEN + 1:
raise ValueError("Invalid packet data: Truncated header")
(ddp_tag, kfl, seq, ti... | a14c71006df869ebdc9cc8c5dc475f544385facc | 65,949 |
def make_connection_url(config):
"""
Returns an SqlAlchemy connection URL based upon values in config dict.
Args:
config: dict-like object with keys: type, username, password,
host, and database.
Returns:
str URL which SqlAlchemy can use to connect to a database.
"""... | d7dbd16184efe17355230b59e1157c7dfe164351 | 65,950 |
def make_df_from_csv(fname):
"""Make df"""
df = pd.read_csv(fname)
df.index = pd.to_datetime(df['DateTime'])
df.drop(columns=['DateTime'], inplace=True)
return df | 8084bd47938d42abbf7a34b016180f0b35deda02 | 65,951 |
from datetime import datetime
from typing import List
def get_obsnums(h5file: h5py.File,
start_date: datetime.datetime = None,
end_date: datetime.datetime = None,
rcu_modes: List[int] = None,
station_name: str = None,
subband: int = None,... | 124ba13a82baf01f502b3fca46e19b66fa46713d | 65,952 |
def build_table_row(obj,admin_class):
"""生成一条记录的html element"""
ele = ""
for column_name in admin_class.list_display:
column_obj = admin_class.model._meta.get_field(column_name)
if column_obj.choices: #get_xxx_display
column_data = getattr(obj,'get_%s_display'% column_name)()
... | 1aaa614cd3a118b3d5d5a55d311f9cf55e222706 | 65,953 |
import re
import string
def sanitize_name(name):
"""
Sanitizes the given name such that it conforms to unix policy.
Args:
name: the name to sanitize.
Returns:
The sanitized form of name.
"""
if len(name) == 0:
raise Exception("Can not sanitize an empty field.")
... | 5b85146c529e37924dd045cc52014cd2c05b9aaa | 65,954 |
import optparse
import os
import logging
import re
import sys
def ParseOpts():
"""Parses the command line arguments and returns a dictionary of flags."""
parser = optparse.OptionParser()
default_credentials_file = os.path.join(os.environ['HOME'],
'.tracker2gcal-auth.in... | 17056064ba571403cc876f7210728357e1d7916c | 65,955 |
from re import T
import functools
def deco_factory_retry(retry_exceptions=None, max_retries: int = 3,
enable_default=False, default=None,
exception_predicate: T.Callable[[Exception], bool] = None,
exception_queue: T.QueueType = None) -> T.Decorator:... | 5bd03aef00de70438915745204d0d61800953204 | 65,956 |
import pickle
def run_ns(
dd_s: DynamicDress,
base_path: str,
rho_6T_min,
rho_6T_max,
dM_chirp_abs,
gamma_s,
rho_6T,
) -> Results:
"""
Runs nested sampling for a dark dress system given a signal system dd_s.
Saves results and posterior plots.
"""
# Setup
gamma_s_ran... | 7e8aa245a25f5b2fccf44145e00b3342d5b77b34 | 65,957 |
import tempfile
import shutil
def process_nxml(nxml_filename, pmid=None, extra_annotations=None, **kwargs):
"""Process an NXML file using the ISI reader
First converts NXML to plain text and preprocesses it, then runs the ISI
reader, and processes the output to extract INDRA Statements.
Parameters
... | 620c9b972642e095f9d9ad4ae9f4b51092468283 | 65,958 |
def distinct_brightness(dictionary):
"""Given the brightness dictionary returns the dictionary that has
no items with the same brightness."""
distinct, unique_values = {}, set()
for char, brightness in dictionary.items():
if brightness not in unique_values:
distinct[char] = brightnes... | 7f7bb5dba9bab113e15cc4f90ddd4dfda7bb5f01 | 65,959 |
import os
def subset_variant_regions(variant_regions, region, out_file):
"""Return BED file subset by a specified chromosome region.
variant_regions is a BED file, region is a chromosome name or tuple
of (name, start, end) for a genomic region.
"""
if region is None:
return variant_region... | db4ae89b0833bbaf51a6bab0199a3510af9cb15c | 65,960 |
def hash_file(fpath, blocksize=65536, stride=1, hasher=NoParam,
hashlen=NoParam, base=NoParam):
"""
Hashes the data in a file on disk.
The results of this function agree with the standard UNIX commands (e.g.
sha1sum, sha512sum, md5sum, etc...)
Args:
fpath (PathLike):
... | 78764a14c2d1604e5662e1ad957f1c1c210b38cf | 65,961 |
def start(start):
"""Start"""
# Create the Session
session = Session(engine)
# Query the data
results = session.query(func.min(Measurement.tobs), func.max(Measurement.tobs), func.avg(Measurement.tobs))\
.filter(Measurement.date >= start)\
.all()
# Convert the tuples into a normal list... | a82b675f72fe8ebf91e1f569e933868405ce53df | 65,962 |
def _cc_library_func(ctx, name, hdrs, srcs, copts, dep_ccinfos):
"""Like cc_library(), but callable from rules.
Args:
ctx: Rule context.
name: Unique name used to generate output files.
hdrs: Public headers that can be #included from other rules.
srcs: C/C++ source files.
dep_ccin... | b4372ce07ae967f9bb1011e6c3fa9922fa68a568 | 65,963 |
def get_interaction_extension_pos(frame, primary):
"""Get the maximum extension position of the interaction/cascade of the
first interaction of the primary particle.
Parameters
----------
frame : current frame
Needed to retrieve I3MCTree
primary : I3Particle
The particle for whi... | f6ae4023bd2b7d48fc83f70a803a555bb217b3ca | 65,964 |
import re
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),\+!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
st... | 7151767d754ebf4591f30762f8708fe464954ba6 | 65,965 |
def import_string(dotted_path):
"""
Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImportError if the import fails.
"""
try:
module_path, class_name = dotted_path.strip(' ').rsplit('.', 1)
except... | 3f8ed0fca9c56fcb11458a4a76fd31593cf2fc0a | 65,966 |
def mean_filter(
array,
support,
half_width=0,
width_z=None,
width_y=None,
width_x=None,
vmin=np.nan,
vmax=np.nan,
title="Object",
debugging=False,
):
"""
Apply a mean filter to an object defined by a support.
Only voxels belonging to the object are taken into accoun... | 6ea0d7349ae4eb6a97451b1ea4466aee81658e0c | 65,967 |
def get(cmd, resource_group_name=None, workspace_name=None):
"""
Get the details of the given (or current) Azure Quantum workspace.
"""
client = cf_workspaces(cmd.cli_ctx)
info = WorkspaceInfo(cmd, resource_group_name, workspace_name, None)
if (not info.resource_group) or (not info.name):
... | 076c382cee4ced54e206513c54663fd00603cef6 | 65,968 |
def get_HNet_D(reactions, compounds):
"""Representing a metabolic pathway as a bipartite graph and returns the
graph resulting from the projection of the bipartite graph on its set of
reactions.
:param reactions: dict of dicts storing reaction information (obtained by
parsing a KGML file)
:... | f2db776a2ff99b6b8da928da98f331b51d8172cb | 65,969 |
def debugger_products_list_select_related():
"""Run debugger queries for products_list_select_related function."""
return debugger_queries(products_list_select_related)() | e6ed289f8e90c50743cfd197132d1b329ca75d15 | 65,970 |
import os
def read_config( vkey ):
"""
Read given configuration file from root directoty and returns options from
it as a dict.
"""
conf_dict = read_configuration(os.path.dirname(os.path.abspath(__file__))+'/'+'setup.cfg' )
return conf_dict.get('metadata')[vkey] | 56e3ce00cf783c3a87f949f7fa9306f2e658ca4e | 65,971 |
def transition(input_tensor, nb_filter, dropout_rate=None, weight_decay=1e-4):
"""Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D.
:param input_tensor: keras model
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weig... | e2264b36a5caf2e15e69262b8d7df8979dff2815 | 65,972 |
import math
def weight_variable(shape, name):
"""Initializes a tensorflow weight variable with random
values centered around 0.
"""
initial = tf.truncated_normal(shape, stddev=1.0 / math.sqrt(float(shape[0])), dtype=tf.float32)
return tf.Variable(initial, name=name) | 9f8cf6e4e06cf35c5d0fb32ca465b6c3a34b0faf | 65,973 |
from typing import List
def get_nouns(spacy_doc: spacy.tokens.Doc) -> List[spacy.tokens.Token]:
"""Return a list of noun tokens within a given document.
Parameters
----------
spacy_doc : spacy.tokens.Doc
spaCy document to parse
Returns
-------
List[spacy.tokens.Token]
A l... | b6cb2a5f3c10f18fc5418db241688132e9400a18 | 65,974 |
def _matmul_flops_compute(input, other, *, out=None):
"""
Count flops for the matmul operation.
"""
macs = _prod(input.shape) * other.shape[-1]
return 2 * macs, macs | 20a21bbd75d863a47ad273fde866c4749358ab9c | 65,975 |
def calc_hyper_param_importance(df, hyper_param, target):
"""Calc hyper param importance relative to given target."""
logger.debug("Calculating importance for hyper_param %s, target is %s.", hyper_param, target)
new_df = df[[hyper_param, target]]
no_missing_value_df = new_df.dropna()
# Can not cal... | 289252f781f49775d037e68357791988950a516d | 65,976 |
def render(content, for_link_cb=None):
""" render the content, which is wiki markup.
:param for_link_cb: takes a link and returns the wiki content for it.
"""
# if content is None or content is '':
# return ''
if content is None or content is '':
return ''
return render_pq(conte... | 9482fdd3a744378dc9cc5f83137212049c53a85a | 65,977 |
import torch
def norm(x):
"""
Normalize a tensor to a tensor with unit norm (treating first dim as batch dim)
:param x:
:return:
"""
b = x.size()[0]
n = torch.norm(x.view(b, -1), p=2, dim=1)
while len(n.size()) < len(x.size()):
n = n.unsqueeze(1)
n.expand_as(x)
retu... | b40ba939c80db85e2ac8377b21ea7a17589b1c0f | 65,978 |
def topleftlatlon_from_wkt(wkt_string):
"""Returns the (lat, lon) of the top-left point of a WKT string."""
wkt_type = wkt_string.split('(', 1)[0].rstrip(' ')
coord_string = wkt_string.lstrip(wkt_type + ' (')
if wkt_type not in ('POINT', 'LINESTRING', 'POLYGON', 'MULTIPOINT',
'MU... | c26b6eb78d8e9dca3ab38e04470cc025a15db4fa | 65,979 |
import time
def __date_since_epoch__(value):
"""Internal function used for Unix datetime format"""
day = time.strftime('%d',time.localtime(value))
month = time.strftime('%m',time.localtime(value))
year = time.strftime('%Y',time.localtime(value))
return day,month,year | 34e8ee15f7fa5d6f662a7850b3252a37a3926371 | 65,980 |
from typing import Optional
def _make_qsub_string(
command: str,
folder: str,
job_name: str = "submitit",
tmem: str = "4G", # physical memory limit
h_vmem: str = "4G", # virtual memory limit
h_rt: str = "00:02:00", # wall time
num_gpus: int = 0,
smp: int = 1,
shell: str = "/bin/bas... | 2a0e2416f51b60a54a7113e75b8b885a723ee7d4 | 65,981 |
def get_unobs_nd_names(gname):
"""
For a graph named gname, this method returns a list of the names of its
unobserved nodes (i.e., either [], or ["U"] or ["U1", "U2"])
Parameters
----------
gname : str
Returns
-------
list[str]
"""
if gname in ["G2", "G3", "G5", "G6", "G10... | 3450293f464b1e7cc7ab343888a606bd96d0f094 | 65,982 |
def extract_archive(filename, path):
"""
Extract the contents of the given archive
:param filename: The name of the crx file
:param path: The path to the crx file
:return: Whether the contents of the crx file were extracted successfully or not
"""
dst_dir = os.path.join(get_script_path(), ... | 6307dafb19ddf5cef64dc6022e38cd0a15bf37eb | 65,983 |
import logging
def get_commodity_sources():
"""
Get a table with prices and emissions manly for 2014.
Returns
-------
pd.DataFrame
Examples
--------
>>> get_commodity_sources().loc[2014, 'hard coal']['emission'] * 1000
0.0934
"""
logging.info("Get prices and emissions for... | b8648a0c2abd8d02f00fca8fd4b945fb4d5ca6bd | 65,984 |
def criterion_rpc(true_labels, pred_scores, p=1, debug=False):
"""
Generalised reversed P-Classification with Squared Hinge Loss:
\ell_+(v) = 1/p * [1 - pv]_+^2 = [1/sqrt(p) - sqrt(p) * v]_+^2
\ell_-(v) = [1 + v]_+^2
"""
if debug:
assert len(true_labels.shape) == 2
assert true_la... | aa272e1cbcf7809f71267aaa5a8e4b75fc0ee93b | 65,985 |
from typing import Iterable
import sys
import subprocess
def launch_minecraft(ports, launch_script=DEFAULT_SCRIPT, keep_alive=False, verbose=False):
"""
Launch Minecraft instances in the background.
Function will block until all instances are ready to receive commands.
ports - List of ports you want t... | d74b375978145e7dd4e04b60d69e0ab5a403aa45 | 65,986 |
def named_entity(docs):
"""Creates a named entity visualization.
Taken from https://github.com/wandb/client/blob/master/wandb/plots/named_entity.py
"""
spacy = util.get_module(
"spacy",
required="part_of_speech requires the spacy library, install with `pip install spacy`",
)
ut... | e217e244ceef03e62decfdcfb1985171733eedea | 65,987 |
def brightness_state(value):
"""
Returns the brightness and state according to the current data of given
value.
"""
if value.data > 0:
return (value.data / 99) * 255, STATE_ON
else:
return 255, STATE_OFF | c8e6e8dfcf27bd62164478dbf1d899727246329e | 65,988 |
def find_matching_segments(gt_segments, id_key, sensed_segments):
"""
id_key represents the kind of segment that we are find matches for. e.g.
- trip_id for trips
- section_id for sections
"""
matching_segments_map = {}
if len(sensed_segments) == 0:
print("Found no sensed segmen... | e94f26b335d1c42af9a8fd1182dbee986543bb80 | 65,989 |
from typing import List
def recall_at_k(y_true: List[int], y_pred: List[List[np.ndarray]], k: int):
"""
Calculates recall at k ranking metric.
Args:
y_true: Labels. Not used in the calculation of the metric.
y_predicted: Predictions.
Each prediction contains ranking score of a... | cecf2ec6fc3e0d071672c096f42063a3962e081e | 65,990 |
def has_datapoint_with_all_dims(fake_ingest, dims):
"""
Tests if any datapoints has all of the given dimensions
"""
for datapoint in fake_ingest.datapoints:
if has_all_dims(datapoint, dims):
return True
return False | 9c3ed9ab42d6ac5eae80c30aaf6b8d123520601d | 65,991 |
def norm_weight(nin, nout=None, scale=0.01, ortho=True, **kwargs):
"""
Get a (orthogonal) Gaussian weight matrix W, scaled
if desired (and not orthogonal).
:param nin:
:param nout:
:param scale:
:param ortho:
:return W:
"""
logger.warn('Norm weight with range: +/-{}'.format(scale... | ce8120ec5e9a65bcfc5862bb95706b619049d194 | 65,992 |
def key_is_private(key):
"""Determine if a key has a name indicating it is private
"""
if PRIVATE_RE.search(key):
return True
else:
return False | 99809e5e0ea1e87b9deb7d01c01d2f9a83b73edc | 65,993 |
def anonymise_related_objects(obj, anonymised=None):
"""
See if there are any related models which need to be anonymised.
They will be any reverse relations to PrivacyModel subclasses where their
OneToOneField and ForeignKey on_delete is ANONYMISE.
"""
if anonymised is None:
anonymised ... | d1f168dbb42df28c44b19e2b7cae86c672d8794e | 65,994 |
from datetime import datetime
import os
def register_global_func(app):
"""register function for jinja
"""
@app.template_global()
def current_year():
return datetime.now().year
@app.template_global()
def is_start():
return Competition.is_start()
@app.template_global()
... | 388848924bc2b2abe18bdd0a0b5f3822737c6e0f | 65,995 |
import matplotlib.pyplot as plt
from matplotlib import ticker
def plot_evoked_joint(evoked, times="peaks", title='', picks=None,
exclude=None, show=True, ts_args=None,
topomap_args=None):
"""Plot evoked data as butterfly plot and add topomaps for time points.
Param... | f810e7092bff0ed8fd6378f45306d2d60bb95761 | 65,996 |
import inspect
def all_links(cls):
"""
Each object will have the attributes declared directly on the object in the attrs dictionary. In addition there
may be attributes declared by a particular object's parent classes. This function walks the class hierarchy to
collect the attrs in the object's parent... | 2e3b1e4e527d339b0972345c81b6967857e54298 | 65,997 |
from typing import Dict
from typing import Callable
def get_losses_dict(
names_list: list = [],
) -> Dict[str, Callable]:
"""Get a dict with all the loss functions for semi supervised training.
The training step of a given model will iterate over these, instead of
manually computing each.
Args:
... | 9d6906b1a654b4e09d5a39753bf2e1955dd535ad | 65,998 |
def prepare(data, args):
"""
Run trimming and fastq for each fastq file
"""
workdir = op.abspath(safe_makedir("prepare"))
sample = data['name']
data['trimmed'] = _trimming(data['fastq'], workdir, data['name'], args.is_rrbs, args.is_directional)
return data | 79eed3f800acbf1d1ceb140913e1a10679053043 | 65,999 |
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