content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import math
def format_float(number, decimal_places):
"""
Accurately round a floating-point number to the specified decimal
places (useful for formatting results).
"""
divisor = math.pow(10, decimal_places)
value = number * divisor + .5
value = str(int(value) / divisor)
frac = value.sp... | e7aaa92025284489075ce053319c27310bb96a00 | 3,631,961 |
def make_mean_edisp(
observations,
position,
e_true,
e_reco,
low_reco_threshold=Energy(0.002, "TeV"),
high_reco_threshold=Energy(150, "TeV"),
):
"""Compute mean energy dispersion.
Compute the mean edisp of a set of observations j at a given position
The stacking is implemented in :... | 3f8ba4d8f6434dd0e6711f691dc650a061ee2a9e | 3,631,962 |
import time
def formatTime ( sec, nsec, fmt ):
""" Convert given time to a string presentation according to
a given control sequence """
# replace %f (and its variations) with fractional seconds
match = _ffmtre.search ( fmt, 0 )
while match :
# make replacement string
sub... | ad7e2b553545093b7834007453774bdb4cd62507 | 3,631,963 |
def CalculateChi6ch(mol):
"""
#################################################################
Calculation of molecular connectivity chi index for cycles of 6
---->Chi6ch
Usage:
result=CalculateChi6ch(mol)
Input: mol is a molecule object.
... | 240d97be1740b9af691598cd71d47473ce770d53 | 3,631,964 |
def decode_transaction_filter(metadata_bytes):
"""Decodes transaction filter from metadata bytes
Args:
metadata_bytes (str): Encoded list of transaction filters
Returns: decoded transaction_filter list
"""
transaction_filter = []
if not metadata_bytes:
return None
for i in... | c76638f6592fb098e2878471746152aa9df9a694 | 3,631,965 |
def get_signin_box(burl):
""" xxx """
box_content = ''
if user_is_login() == 0:
l_app_header_title = 'Create unlimited optimized trading strategies'
l_app_header_desc = 'Chart patterns, price movements, '+\
'and news analysed using quantitative methods '+\
'with the power of... | 4181779ad6e8ce7924c2854e4e6e9b7d0d47926b | 3,631,966 |
import json
def jsonify(*args, **kwargs):
""" jsonify with support for MongoDB ObjectId
"""
return Response(
json.dumps(
dict(
*args,
**kwargs),
cls=MongoJSONEncoder),
mimetype='application/json') | 8001fe488e412bbf63cad7c9c359431fe9108b2c | 3,631,967 |
def parse_variable(srcline, funcname=None):
"""Return a Variable for the variable declared on the line (or None)."""
line = srcline.strip()
# XXX Handle more than just static variables.
if line.startswith('static '):
if '(' in line and '[' not in line:
# a function
retur... | ff4027f1e3919087016c8169ff1ad8ac7ebf111b | 3,631,968 |
def _assert_df_is_valid_cforest(candidate_model):
"""Assert *df* represents valid causal forest.
A valid causal forest model is given by a pd.DataFrame which fulfills the
following criteria: 1 (MultiIndex). The data frame *df* must have a
MultiIndex with the first layer 'tree_id' and the second layer '... | cf1c52037705ce86940ca4c9e6fdf4b1055dea65 | 3,631,969 |
def is_ip(str_value: str) -> bool:
"""Returns True if string represents and IP address (either IPv4 or IPv6), else False.
:param str str_value: String to evaluate.
"""
return is_ipv4(str_value) or is_ipv6(str_value) | 1438148ab98ce882cd5e27961268726bae4450e0 | 3,631,970 |
def parse_ply(fin):
"""Parse vertex data from a PLY format
Retuns a dictionary of keys to numpy arrays
"""
num_pts, attr_key, attr_type = parse_ply_header(fin)
data = [[] for k in attr_key]
for i in range(num_pts):
line = next(fin)
tokens = line.split()
for j, t in enum... | 13558f96b55d2155cc032771acda272cfa7e5d0b | 3,631,971 |
def energy_change_charge_qa_atom(
df_qc, df_qats, target_label, delta_charge, target_initial_charge=0,
change_signs=False, basis_set='aug-cc-pV5Z', use_ts=True,
ignore_one_row=True, considered_lambdas=None, return_qats_vs_qa=False):
"""Calculate the energy difference to change the charge of a target ato... | 30c8b15a5e25edd0d05352066a1e3533c5182cd6 | 3,631,972 |
def load_key_string_pubkey(string, callback=util.passphrase_callback):
# type: (str, Callable) -> PKey
"""
Load an M2Crypto.EC.PKey from a public key as a string.
:param string: String containing the key in PEM format.
:param callback: A Python callable object that is invoked
... | 9283aff352a84cb99a382f88d6f7cca5ea0ee837 | 3,631,974 |
def merge(left, right, path=None):
"""Merge dicts"""
if path is None:
path = []
for key in right:
if key in left:
if isinstance(left[key], dict) and isinstance(right[key], dict):
merge(left[key], right[key], path + [str(key)])
elif left[key] == right[... | cb313f153225af41626885ae0ee066215dce3b0e | 3,631,976 |
def resize_min_side(pil_img, min_len):
"""
Resize image such that the shortest side length = mins_len pixels
:param pil_img:
:param mins_len:
:return:
"""
# What's the min side?
w, h = pil_img.size
if w < h:
new_w = min_len
new_h = int(np.round(h * (new_w / float(w)))... | 38aeeedf107bedf2c82948248fbdc2483d6d2c10 | 3,631,977 |
def get_numeric_boundaries(df: DataFrame, column_name: str) -> (float, float):
"""
get the min and max values in a numric column. forces a cast to float.
If the column can't be casted as such then this wil throw an error which is currently not trapped
:param df:
:param column_name:
:return: (min... | a02eefd1d6e6f2697350d5e48201e57cbb24c870 | 3,631,978 |
from typing import Optional
def concatenate(data: tvm.te.Tensor, axis: Optional[int] = 0):
"""Join a sequence of arrays along an existing axis. Optimized for CPU exeution.
Parameters
----------
data : tuple of tvm.te.Tensor
The arrays to concatenate
axis : int, optional
The axis ... | d9bb934f9518a565dab341316247294c525ea2c1 | 3,631,979 |
def defgrad_from_strain(E, kappa, flatten=1):
"""Compute the deformation gradient from the strain measure
Parameters
----------
E : ndarray (6,)
Strain measure
kappa : int or float
Seth-Hill strain parameter
flatten : bool, optional
If True (default), return a flattened ... | 3b18515562c3dd30757f9942627ac082eb3947b7 | 3,631,980 |
import hashlib
def get_url_gravatar(email):
"""
Obtenemos una url de gravatar
"""
m = hashlib.md5()
m.update(email.encode('utf-8'))
url = "http://www.gravatar.com/avatar/{0}.jpg?s=300".format(m.hexdigest())
return url | bf48d903445869ee91c685dd1b84e11034dc528c | 3,631,981 |
def volume_type_qos_disassociate_all(context, qos_specs_id):
"""Disassociate all volume types from specific qos specs."""
return IMPL.volume_type_qos_disassociate_all(context,
qos_specs_id) | 16ff0f985dd96d1f2a3aa32022c06c84a1c5f531 | 3,631,982 |
def create_command_at_set(command_set, command):
""" create a command on set """
command_entry = CommandEntry.objects.create(
command_set=command_set,
command=command
)
return command_entry | a4e8367077f5a42b62e27be1e66f2a4c5cf490d0 | 3,631,983 |
def get_index_image():
"""Formats html.
Returns:
Modified index.html content
"""
return """<!DOCTYPE HTML><html lang="en-us">
<head>
</head>
<body style='margin:0'>
<img src='image.[[image_ext]]'>
</body>
</html>""" | 41ea7fbc31e49879216e46083b102294edb5c76f | 3,631,984 |
def merge_schema(original: dict, other: dict) -> dict:
"""Merge two schema dictionaries into single dict
Args:
original (dict): Source schema dictionary
other (dict): Schema dictionary to append to the source
Returns:
dict: Dictionary value of new merged schema
"""
source =... | 6425b64e6ab166ac14afc2e47392745903b8fd12 | 3,631,985 |
def noise_eq_bandwidth(window, axis=-1):
"""
Calculate the noise equivalent bandwidth (NEB) of a windowing function
as
sqrt(window.size * window.max ** 2 / sum(window ** 2))
See https://analog.intgckts.com/equivalent-noise-bandwidth/
Args:
window : float ndarray
axis : int,... | dd13abac6b9d39b68a1b3658fe4fba90be8c82cf | 3,631,986 |
import hashlib
def hash160(s: bytes) -> bytes:
"""
sha256 followed by ripemd160
:param s: data
:return: hashed data
"""
return hashlib.new('ripemd160', hashlib.sha256(s).digest()).digest() | 7b18fcdf51db707a17d5408c7b364818a6c5ee0c | 3,631,987 |
def trim_frame(fr: NDFrame, freq: str) -> NDFrame:
"""Trim index of frame to only keep full periods of certain frequency.
Parameters
----------
fr : NDFrame
The (untrimmed) pandas series or dataframe.
freq : str
Frequency to trim to. E.g. 'MS' to only keep full months.
Returns
... | c8b87ea993510725dc8f4074eeb07ea029445f0a | 3,631,988 |
def settings_alert_rules(request):
"""
To allow users to manage alert
rules for given sites
"""
context_dict = {}
sites = _get_user_sites(request)
user_sites = _get_user_sites(request)
context_dict['permitted'] = get_org_edit_permissions(request.user)
context_dict['sites_stats'] = g... | b8e1326abdb96929f3451c6aae06093f94de0723 | 3,631,989 |
from typing import List
def generate_states_1qubit(c_sys: CompositeSystem, names: List[str]) -> List[State]:
"""returns a list of states on a common 1-qubit system.
Parameters
----------
c_sys: CompositeSystem
1-qubit system
names: List[str]
list of 1-qubit state names
Retur... | 3c4188d50181a9a7c21b50f2b655cb212249c9e2 | 3,631,990 |
def fix_columns(data, text_1_name=None, text_2_name=None, label_name=None):
"""
Rename columns in an input data frame to the ones bisemantic expects. Drop unused columns. If an argument is not
None the corresponding column must already be in the raw data.
:param data: raw data
:type data: pandas.Da... | 7a87e853f5f5e41afcb4ec0a40ebddea234ca289 | 3,631,991 |
def unpack_kgrid(n, vals, log=null_log):
"""
Unpack the 'pyramid' of values u>=v>=w into the (n,n,n) k-grid.
n - the size of the grid
vals - m(m+1)(m+2)/6 values in the pyramid
returns out - (n,n,n) float64 array.
"""
lib = _initlib(log)
v = require(vals, dtype=float64, require... | 62d7a364d43cc2c4cf1c181aa054d94200ea53a1 | 3,631,993 |
import re
def valid_email(email):
"""Check for a valid email address.
Args:
email (str): Email.
Returns:
bool: Return True if in valid email format and False if not.
"""
return bool(re.match('^[a-zA-Z0-9.!#$%&โ*+/=?^_`{|}~-]+@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)*$', email)) | 01c343008229fb2fdf2af3a9a74f3059930696eb | 3,631,994 |
import traceback
def db_remove(key):
""" Endpoint Function to interact with PupDB's remove() method. """
try:
if not key:
return {'error': 'Missing parameter \'key\''}, 400
try:
result = DB.remove(key)
except KeyError as key_err:
return {'error': s... | e354fe2f648cd4ad5c8ff7804789488b01edbe6c | 3,631,995 |
def rgb2gray(img):
""" Given an RGB image return the gray scale image.
Based on http://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale
img = 0.299 R + 0.587 G + 0.114 B
"""
print('Converting RGB image to gray scale.')
return np.uint8(np.dot(img[...,:3], [0.299, 0.587, 0.114])) | 37207a3f66e5008a358f3e4961e809ca658687bc | 3,631,996 |
def search():
"""่ทๅ้ๆบๆจ่"""
html = '<form action="/blog/search/" method="get" name="form" ><div class="my-search" >' \
'<input type="text" name="q" autocomplete="off" placeholder="่ฏท่พๅ
ฅๆ็ดขๅ
ๅฎน" class="search-input">' \
'<i class="layui-icon layui-icon-search search-btn" onclick="javascript:form.... | 86a02c2de79476ae6bea7cd733e502658a5f7555 | 3,631,997 |
def find_number(text, ignore_spaces=False, make_int=True,
ignore_chars=None):
"""
Find the number in the `text`.
:param text: unicode or byte-string text
:param ignore_spaces: if True then groups of digits delimited
by spaces are considered as one number
:raises: :class:`Dat... | d15c5468e965913a6b885f3f5835ed7441481e6f | 3,631,998 |
def PeerDownHasBgpNotification(reason):
"""Determine whether or not a BMP Peer Down message as a BGP notification.
Args:
reason: the Peer Down reason code (from the draft)
Returns:
True if there will be a BGP Notification, False if not
"""
return reason == 1 or reason == 3 | 8ee214798f6766916e8784dd907eeb45ff6620db | 3,631,999 |
def productos_pos():
"""
Muestra las configuraciones para productos pos
"""
productos = db(db.maestro_pos).select()
return dict(productos=productos) | 87e80d5789f65df46c3178a9d83e575321b423a7 | 3,632,000 |
def _build_measurement_vectors(ppci):
"""
Building measurement vector z, pandapower to ppci measurement mapping and covariance matrix R
:param ppci: generated ppci which contains the measurement columns
:param branch_cols: number of columns in original ppci["branch"] without measurements
:param bus_... | b51be7da841ce5c54942834133d64263fcf29ff1 | 3,632,001 |
import zipfile
from io import StringIO
import numpy
import json
import pickle
def load(path):
"""Load data and reconstruct model."""
with zipfile.ZipFile(path,'r') as zf:
buf = StringIO.StringIO(zf.read('weights.npy'))
weights = numpy.load(buf)
config = json.loads(zf.read('config.json... | bee50593f16a534c16f9961e53aaf6a31b5bd3d1 | 3,632,002 |
def env(
a,
import_models=False,
c=None,
f=None,
dir='',
):
"""
Return web2py execution environment for application (a), controller (c),
function (f).
If import_models is True the exec all application models into the
environment.
"""
request = Request()
response ... | 3d78e71866eb1daf06e6e20e9437a0bf39210363 | 3,632,003 |
import copy
import hashlib
import json
def get_payload_hash(payload):
"""Return unique hash of HySDS job JSON payload."""
clean_payload = copy.deepcopy(payload)
for k in ('_disk_usage', '_sciflo_job_num', '_sciflo_wuid'):
if k in clean_payload:
del clean_payload[k]
return hashlib.... | e5122c85c3bfc358dda0404d029d06efbe2fefde | 3,632,004 |
def binStack(array, bins, id0=1):
"""
Bin a hyperstack according to a known list of frames per bin
@param array: input array to be binned, hyperstack or otherwise.
Binning occurs along the first axis
@type array: numpy.ndarray
@param bins: list of frames in each bin. Each index in bins is a... | 3367566a37e6328cd8af039e91a40d176b488617 | 3,632,005 |
import torch
def knn(A, B, k, distFcn):
"""
Returns the indices of the k-nearest neighbors of A in B
Parameters
----------
A : Tensor
a (N,F,) tensor
B : Tensor
a (M,F,) tensor
k : int
the number of neighbors to find
distFcn : callable
the distance func... | f9c3bac58ff3fcffe53bb9b2a25ffbcc57870ec9 | 3,632,006 |
def set_purpose(slack_client, channel, purpose):
"""
Set the purpose of a given channel.
"""
response = slack_client.api_call("channels.setPurpose",
purpose=purpose, channel=channel)
return response | 786a495b55300b955e2f7ec525117be75b251a07 | 3,632,007 |
def masked_minimum(data, mask, dim=1):
"""Computes the axis wise minimum over chosen elements.
Args:
data: 2-D float `Tensor` of size [n, m].
mask: 2-D boolean `Tensor` of size [n, m].
dim: The dimension over which to compute the minimum.
Returns:
masked_minimum: N-D `Tensor`.
The minimize... | 7629c1a2ba9c2089935a68354d748a80e1eff488 | 3,632,009 |
import json
def decode_stderr_json(stderr):
""" return a list of decoded json messages in stderr """
# - check for blank input
if not stderr:
# - nothing to do
return list()
# - split the input (based on newlines) into list of json strings
output = list()
for line in stderr.spl... | d527730d8d9a77a1ec434ee6203c4e08433306c9 | 3,632,011 |
import collections
def hash_params(params):
"""
Construct a data structure of parameters that is hashable.
This requires changing any mutable data structures into immutable ones.
We chose a frozenset because role parameters have to be unique.
.. warning:: this does not handle unhashable scalars... | eb83122e2b1f7097917f1029e84f2053352127f4 | 3,632,012 |
def get_validate_result_form(tel_num, validate_code):
"""
Assemble form for get_validate_result
:param tel_num: Tel number
:param validate_code: Validate code from capcha image
:return: Param in dict
"""
post_data_dict = dict()
post_data_dict['source'] = 'wsyyt'
post_data_dict['telno... | 6340c97522a097c0cf96170e08466fb795e16dc3 | 3,632,013 |
def create_user():
"""Create a new user record."""
request = flask.request.get_json()
try:
# NOTE(jk0): We expect all of these keys to exist to be considered a
# valid user record. Ignore all others.
user = _lookup_user(request["userid"])
if user:
flask.abort(40... | 8d98667aba51172535768d6c57d9fc8f2e2e7821 | 3,632,014 |
def softmax_cross_entropy(logits, labels):
"""
Cross-entropy loss applied to softmax.
"""
one_hot = hk.one_hot(labels, logits.shape[-1])
return -jnp.sum(jax.nn.log_softmax(logits) * one_hot, axis=-1) | c0850fdebbf69629763e94c489ed0aaf67e67184 | 3,632,015 |
def get_category_detail(comp_id, cat_id):
"""Retrives information about the category and the reviews in it"""
json = [Category.query.filter_by(id=cat_id).filter_by(comp_id=comp_id).first_or_404().to_json()]
submissions = Submission.query.filter_by(comp_id=comp_id).all()
cat_submissions = []
for _, s... | d6b780a8d2c985f55a6500faf386c437669d0e9a | 3,632,016 |
def build_input_from_segments(persona, history, reply, vocab,
labels=False, with_eos=True):
"""
Build a sequence of input from 3 segments:
persona, history and last reply.
"""
bos, eos, speaker1, speaker2 = vocab[SPECIAL_TOKENS[:-1]]
sequence = [[bos] + list(chain(*... | 3b47bbc0fb7c666188f36a1e1df520d84327d336 | 3,632,017 |
from scipy.stats import norm
def dual_gaussian(x, amp1=1.0, mean1=0.0, std1=1.0, amp2=1.0, mean2=0.0, std2=1.0):
"""Sum of two Gaussians.
Parameters
----------
x : array
Function argument
amp1: float
Amplitude parameter of the first Gaussian
mean1: float
Mean parameter of th... | 6d46ffcfcfd0327d06ccc8c92157bd9f82813124 | 3,632,018 |
def get_district_info(request, code):
"""
Get district info by 'code'
"""
try:
district = District.objects.get(code=code)
child_districts = District.objects.filter(parent=district.code)
data = district_model2dict(district)
children = [district_model2dict(c) for c in child... | 57136fe7d77375b6d9c258b42bccf24e4c7a3750 | 3,632,020 |
def calculate_price(prices, concentrations):
"""
From a list of prices in $USD / kg and concentrations in mass %,
calculate the price of the formulation in $USD / kg
"""
# Normalise ingredient concentrations
concentrations = np.asarray(concentrations) / np.sum(concentrations)
# Calculate a... | 7131bb0aa5f9502f564e43eba76ee3b878041b22 | 3,632,021 |
def cos_convolve(evidence):
"""Take as input the classifier evidence for single trials in dictionary format
and return alligned evidence and evidence convolved with a cosine.
Input:
dictionary:
accuracy : ndarray
dimensions: time
matrix containing class predi... | cf292eb0af8d4f44a0d64c02b9a09e6b7d149eb9 | 3,632,022 |
from io import StringIO
def get_image_info(real_image_type, body):
""" only in webp, gif, png, jpeg, bmp
"""
image_fp = StringIO(body)
if real_image_type == 'webp':
data = image_fp.read()
width, height = decode.GetInfo(data)
image_pix_count = int(width) * int(height)
else:... | 9570de917deafdcaa2d2a2d208e40710bcb12d04 | 3,632,023 |
def estimate_ranks(layer):
""" Unfold the 2 modes of the Tensor the decomposition will
be performed on, and estimates the ranks of the matrices using VBMF
source: https://github.com/jacobgil/pytorch-tensor-decompositions/blob/master/decompositions.py
"""
weights = layer.weight.data
unfold_0 = ... | 3c4efeb5ad56a32ad3908657c013bfb153aaf01e | 3,632,024 |
def _add_batch_dim(img):
"""Many TF functions require NWHC input. Convert WHC image to NWHC of batch size 1."""
get_hwc(img) # validate dimensions
return tf.expand_dims(img, 0) | 53a995d6a1398f137abb69b38aa91b98257e50c3 | 3,632,025 |
def read_inventory_file(inventory):
"""
Read an inventory file, return the list of dicts
:param str inventory: The inventory file
:return list[dict, ..]: List of hostname and IP definitions
"""
log.info("Reading and validating inventory file")
inventory_hosts = load_json(inventory)
if no... | cff1d3c84b3617b12e4bee7b4726207d4d4747cd | 3,632,026 |
def ground_truth_to_word(ground_truth):
"""
Return the word string based on the input ground_truth
"""
try:
return ''.join([config.CHAR_VECTOR[np.argmax(arr)] for arr in ground_truth if np.argmax(arr) < len(config.CHAR_VECTOR)])
except Exception as ex:
print(ground_truth)
... | ae07266f34f01d605705d60e7c331cefb1fb845a | 3,632,027 |
import itertools
def build_dataframe(dimension_names, dimension_members, data_values,
null_values, sd_values):
"""Build a dataframe from dimensions and data.
Adds the cartesian product of dimension members plus the series of data.
Args:
dimension_names (list of string)
... | 1d5621d753466a69bd0bef4120c2c445e959bbb9 | 3,632,028 |
def fs_url_exists(fs_url):
"""
verifies for a valid fs url
:param fs_url: fs_url string
:return: boolean
"""
try:
fs.open_fs(fs_url)
except fs.errors.CreateFailed:
return False
return True | 98aad242d04b169e1a1e3204bf40e0b4ac9c4018 | 3,632,029 |
def _serialize_noise_model(config):
"""Traverse the dictionary looking for noise_model keys and apply
a transformation so it can be serialized.
Args:
config (dict): The dictionary to traverse
Returns:
dict: The transformed dictionary
"""
for k, v in config.items(... | f3453e174d5ba858b9eec678e7bc1574f74d50eb | 3,632,030 |
def create_app() -> falcon.API:
"""
Typical application factory style setup.
Returns:
falcon.API: The falcon API object.
"""
engine = create_engine("sqlite:///")
app = falcon.API(middleware=[DbSessionMiddleware(engine)])
app.add_route("/", ExampleResource())
return app | ab134f8d25644da01718a16e4887d023f5e0221f | 3,632,031 |
def show_tracker(secure=False):
"""
Output the analytics tracker code.
"""
google = getattr(settings, 'ANALYTICS', {})
if google:
analytics_code = google.get('ANALYTICS_CODE')
if analytics_code:
return {"analytics_code": analytics_code}
return {} | 32d30b031e979cf91165dba4872a93995289bbc4 | 3,632,032 |
def metric_max_over_ground_truths(metric_fn, predictions, ground_truths):
"""Take the average best score against all ground truth answers.
This is a bit different than SQuAD in that there are multiple answers
**and** predictions that we average over. For some situations (e.g., *top k*
beams or multiple human r... | 7c78fc1cca29bc9784a4e4687d794c1f2b6872c9 | 3,632,036 |
def parse_gsod_data(filename):
"""Parse Global Summary of the Day (GSOD) data from a comma separated
.txt file.
Source: https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD&countryabbv=&georegionabbv=
Format Specification: https://www7.ncdc.noaa.gov/CDO/GSOD_DESC.txt
Parameters:
... | 963444222e7627c25354ec6d0d891df4fee4fe8c | 3,632,037 |
def deserialize(xml):
""" Deserializes a Pubmed response into an article object."""
article = {}
root = ET.fromstring(xml)
article_el = root.find('.//PubmedArticle')
if article_el is None:
print('INFO: XML did not contain a Pubmed Article.')
return None
pmid_el = article_el.find('.//MedlineC... | 5cdb8c622f9155eaf36659bd9cab092e3adc4c44 | 3,632,038 |
def attSummaryDict(request, reqs, flist):
""" Return a dictionary summarizing the field values for the chosen most interesting fields """
sumd = {}
for req in reqs:
for f in flist:
if f in req and req[f]:
if not f in sumd: sumd[f] = {}
if not req[f] i... | bafbbe51555cb46c664d33ea31a0e36c56152fa9 | 3,632,039 |
def immutable_kwargs(
kwargs: tp.Dict[str, str]
) -> tp.Tuple[tp.Tuple[str, str], ...]:
"""
Convert str-typed kwargs into a hashable tuple.
"""
return tuple((k, v) for k, v in kwargs.items()) | 900e263e0a7928bfb2c65e3dc7f9c8e405014fb5 | 3,632,040 |
import numpy
def revise_max_intake(
max_intake, total_digestibility, energy_intake, energy_maintenance,
degr_protein_intake, protein_req, animal_type, CRD1, CRD2):
"""Calculate revised maximum intake from protein content of the diet.
When animals are unable to obtain enough protein from the d... | cfe61d2717fcf42104423499e1e1343873c905a0 | 3,632,042 |
from pegasusio.cylib.io import read_fcs
def load_fcs_file(input_fcs: str, genome: str = None) -> MultimodalData:
"""Load Cyto data from a FCS file, support v2.0, v3.0 and v3.1.
Parameters
----------
input_fcs : `str`
The FCS file.
genome : `str`, optional (default None)
The genom... | 232b849679c209863fdc3cc17ad1dc254ed23ab0 | 3,632,043 |
def ext_bottom_up_cut_rod(price, length):
""" bottom up implementation of cut rod memoized algorithm """
incomelst = [float("-Inf") for _ in range(length + 1)]
cutlst = [0 for _ in range(length + 1)]
# set zero income for zero length
incomelst[0] = 0
for j in range(1, length + 1):
income... | 7dd8c43afa9f71793d372b474963ff84d2ce607f | 3,632,044 |
def _build_config_dict(cfg_node):
"""
Updates the config dict provided from the given etcd node, which
should point at a config directory.
"""
config_dict = {}
for child in cfg_node.children:
key = child.key.rsplit("/").pop()
value = str(child.value)
config_dict[key] = va... | 567fca19a6e1890c881170200ba44fc262148948 | 3,632,045 |
import time
def stamp_to_ymd(timestamp):
"""
Caller sends a timestamp in seconds of epoch. Return string for
year month day of that time as YYYYMMDD' as used by url requests, as in
http://<fitsstore_server>/qaforgui/20130616
parameters: <float>, seconds of epochs.
return: <string>, YYYYM... | 2928e93a48f1a5c3abdddcb6285bed7b0cebb369 | 3,632,046 |
def ESMP_GridCreateCubedSphere(tilesize, regDecompPTile=None,
#decompFlagPTile=None, deLabelList=None,
staggerLocList=None, name=None):
"""
Preconditions: ESMP has been initialized.\n
Postconditions: An ESMP_Grid has been created.\n
Arguments... | 999d9b995671af9e410f73e29b2e1af8d79ad5a4 | 3,632,047 |
def version() -> str:
"""็ๆฌๅท"""
return f'Version: {VERSION}' | df0dee3edebdaf24b52a9ad128b5198c89c779a5 | 3,632,050 |
import ctypes
def UnpackMessage(swig_obj_pointer, msg_name):
"""Unpack a SWIG-wrapped memory object into an AIO message.
Args:
swig_obj_pointer: A SWIG-wrapped memory object pointing to the raw AIO
message payload.
msg_name: Name or short name of the message type.
Returns:
An AIO message s... | 2e445f5248ba023190298eec30e0e473804f3df5 | 3,632,051 |
def a2b_hashed_base58(s):
"""
If the passed string is hashed_base58, return the binary data.
Otherwise raises an EncodingError.
"""
data = a2b_base58(s)
data, the_hash = data[:-4], data[-4:]
if double_sha256(data)[:4] == the_hash:
return data
raise EncodingError("hashed base58 ha... | 82276533405e952f8f89cf3caefce6e653ab5694 | 3,632,052 |
def predict(theta, X):
""" computes the predictions for X using a threshold at 0.5
(i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
"""
return np.array([1 if theta.dot(xi) >= 0.5 else 0 for xi in X]) | 3a80add19d08989f94cb3f9e4c058a37bd128f20 | 3,632,053 |
def not_contains(a, b):
"""Evaluates a does not contain b"""
result = False if b in a else True
return result | a0dc087049c8e93c1acdf0e59e3530a6ff8b54e5 | 3,632,054 |
def build_positional_encoding(cfg, default_args=None):
"""Builder for Position Encoding."""
return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args) | 9db2eb7d88b5d4ceea0a9d62adc3239035a34cec | 3,632,055 |
def create_func_result_identifier(func, params_str, key=None, key_separator="__"):
"""
Creates a string of the following format:
If ``key`` is None:
``<FUNC_NAME><PARAMS_STR>``
If ``key`` is not None:
``<FUNC_NAME><PARAMS_STR>__key``
In both cases, ``<FUNC_NAME>`` represents the name of... | 6f3a7a6a8a94629dae7817403d78ef1f970ad5b2 | 3,632,056 |
def quat_to_euler(q):
"""
Converts a unit quaternion:
q = (w, x, y, z) = w + (x i, y j, z k)
into the aircraft Euler angles (roll, pitch, yaw) = (phi, th, psi).
"""
R00 = 1 - 2*q[2]**2 - 2*q[3]**2
R10 = 2*q[1]*q[2] + 2*q[3]*q[0]
if np.sqrt(R00**2 + R10**2) >= .000001:
phi = np... | 507e26d657a868bc136c901612ffba5dff62975d | 3,632,057 |
def idc_get_local_type_name(*args):
"""
idc_get_local_type_name(ordinal) -> char
"""
return _ida_typeinf.idc_get_local_type_name(*args) | c0921c70f0f42d913bbbe4e6d41a02a58660da0e | 3,632,058 |
import csv
def import_town(data_file):
"""
Reads town raster data from a CSV file.
Parameters
----------
data_file : str
Name of CSV raster data file to use for the town.
Returns
-------
town : list
List (cols) of lists (rows) representing raster data of the town.
... | b7749dfd4d698fddfe610c6a51c8ccc43c375cc2 | 3,632,059 |
def init_network():
"""์ ๊ฒฝ๋ง(neural network)์์ ์ฌ์ฉ๋๋ ๊ฐ์ค์น ํ๋ ฌ๊ณผ bias ํ๋ ฌ์ ์์ฑ
๊ต์ฌ p.88
์
๋ ฅ์ธต: (x1, x2) -> 1x2 ํ๋ ฌ
์๋์ธต:
- 1st ์๋์ธต: ๋ด๋ฐ 3๊ฐ (x @ W1 + b1)
- 2nd ์๋์ธต: ๋ด๋ฐ 2๊ฐ
์ถ๋ ฅ์ธต: (y1, y2) -> 1x2 ํ๋ ฌ
W1, W2, W3, b1, b2, b3๋ฅผ ๋์๋ก ์์ฑ
1x2 2x3 3x2
"""
np.random.seed(1... | ddf727e651d46523f83d5d4e870c26e6bbc0b54c | 3,632,060 |
def make_connection():
"""Connection function to establish database connection. During development, the connection information will be
hard coded, however during actual deployment to AWS, the connection information will be retrieved from
other services eg aws secret manager etc.
Returns:
... | fa69c2752444cbee1d5630ee829bd5ebc4c0e0c5 | 3,632,063 |
import random
def a_noun(random=random, *args, **kwargs):
"""
Return a noun, but with an 'a' in front of it. Or an 'an', depending!
>>> mock_random.seed(0)
>>> a_noun(random=mock_random)
'an onion'
>>> a_noun(random=mock_random, capitalize=True)
'A Chimp'
>>> a_noun(random=mock_random... | 1eae1f7b445017d64fc17fe0364275d73ead1b87 | 3,632,064 |
def _tree_flatten_with_names(tree):
"""Populates tree_flatten with leaf names.
This function populates output of tree_flatten with leaf names, using a
custom traversal that produces names is provided. The custom traversal does
NOT have to traverse tree in the same order as jax, as we take care of
automatical... | 2878ece5d63d09d42d4b195706a4594b135ce849 | 3,632,065 |
def inhomogeneous_poisson_process(rate, as_array=False,
refractory_period=None):
"""
Returns a spike train whose spikes are a realization of an inhomogeneous
Poisson process with the given rate profile.
Parameters
----------
rate : neo.AnalogSignal
A `n... | 240b04f5e5ed316d911aad0ee690516982f123cd | 3,632,066 |
import re
def enumerate_quotes(filename, encoding="utf-8", empty_name="Inconnu"):
"""
Enumerates quote from a filename or a stream
@param filename filename or stream
@param encoding applicable only if filename
@param empty_name replces an empty author name
@r... | 51e3a406c05ab81ade918123ee4fba801fb9ef9e | 3,632,067 |
def _params_to_df(params: Parameters) -> DataFrame:
"""Convert lmfit.Parameters to pandas.DataFrame."""
return DataFrame(
[
(p.name, p.vary, p.value, p.stderr, p.min, p.max, p.brute_step, p.expr)
for p in params.values()
],
columns=(
"name",
... | 7485f43a474eef1ebb20ddb73bb411dad52cd918 | 3,632,068 |
def w(P, T, region = 0):
""" Speed of sound [m / s]"""
if region is 0:
region = idRegion(P, T)
if region is 1:
return region1.w(P, T)
elif region is 2:
return region2.w(P, T)
else:
return 0.000 | 7589c57071484d0f46e67a18bd125ad46edbe777 | 3,632,069 |
def WaitForOperation(api_version, response, asynchronous):
"""Handles waiting for the operation and printing information about it.
Args:
api_version: Cloud Domains API version to call.
response: Response from the API call
asynchronous: If true, do not wait for the operation
Returns:
The last inf... | 518e68421082bb3a2a90b38ae62c5be02f20d9fb | 3,632,070 |
from datetime import datetime
def generateVtBar(symbol, d):
"""็ๆK็บฟ"""
bar = VtBarData()
bar.symbol = symbol
bar.vtSymbol = symbol
bar.open = d['open']
bar.high = d['high']
bar.low = d['low']
bar.close = d['close']
bar.volume = d['volume']
bar.openInterest = d['open_oi']
... | d155bda31fba71f07af4d23d17fcaa09c6a277e4 | 3,632,071 |
def search_file(drive_service, num_of_responses, query):
"""
Search for files and store results of query in pd.DataFrame
"""
results = (
drive_service.files()
.list(
pageSize=num_of_responses,
q=query,
fields="nextPageToken, files(id, name, kind, expor... | b7bb340f0c1bb89bc76ecc420b473609a0bbbb2c | 3,632,073 |
from datetime import datetime
import numpy
def L6_summary_daily(ds,series_dict):
"""
Purpose:
Calculate the daily averages or sums of various quantities and write
them to a worksheet in an Excel workbook.
Usage:
L6_summary_daily(ds,series_dict)
where ds is an OzFluxQC data structure
... | d1f57bb364abd7c92b73eab2d9787f5893264a3c | 3,632,074 |
def check_eol(file, eol):
"""Check file EOL.
:param file: Path to file to check
:param eol: Expected End of Line
:return: Resulting error messages
:rtype: str
"""
error = ''
with open(file, 'rb') as open_file:
content = open_file.read()
if eol == '\n':
if b'\r\n' i... | 44bb060531c50ab5d072906414b8c948c9d6ecfa | 3,632,075 |
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