content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def geopad(lon, lat, data, /, nlon=1, nlat=0):
"""
Return array padded circularly along longitude and over the poles for finite
difference methods.
"""
# Pad over longitude seams
if nlon > 0:
pad = ((nlon, nlon),) + (data.ndim - 1) * ((0, 0),)
data = np.pad(data, pad, mode='wrap'... | 8916dde690673b1d278ffab39ee3350f346a4182 | 3,658,607 |
def SL_EAKF(N,loc_rad,taper='GC',ordr='rand',infl=1.0,rot=False,**kwargs):
"""
Serial, covariance-localized EAKF.
Ref: Karspeck, Alicia R., and Jeffrey L. Anderson. (2007):
"Experimental implementation of an ensemble adjustment filter..."
Used without localization, this should be equivalent
(full ensemble... | e7ca69f71cf83a4389086d14791902eb5a661b9e | 3,658,608 |
def CalculateNMaxNCharge(mol):
"""
#################################################################
Most negative charge on N atoms
-->QNmin
Usage:
result=CalculateNMaxNCharge(mol)
Input: mol is a molecule object.
Output: result is a numeric value.
#... | ae63c3f2c6faa8b0d9f7d6ae3b320a9c3b1002d6 | 3,658,609 |
def cnn_5l4(image, **kwargs):
"""
:param in: (TensorFlow Tensor) Image input placeholder
:param kwargs: (dict) Extra keywords parameters for the convolutional layers of the CNN
:return: (TensorFlow Tensor) The CNN output layer
"""
activ = tf.nn.relu
layer_1 = activ(conv(image, 'c1', n_filter... | af059b9a2899c1adcc9f11f4742ffaac8a971dba | 3,658,610 |
def read_dns_data(dns_fn):
"""
Read data in from a DNS file
:param str dns_fn: The filename of the DNS
"""
fed = open(dns_fn, 'r')
begin_data = False
dns_data = {}
for line in fed.readlines():
if begin_data:
if "t = " in line:
... | 2c73289c6284b47901a8f7c91bce6df75849c822 | 3,658,611 |
def arithmetic_mean(iterable):
"""Zero-length-safe arithmetic mean."""
values = np.asarray(iterable)
if not values.size:
return 0
return values.mean() | 3972885d92654d842a163d64c47b585ad6865c98 | 3,658,612 |
def play_process(url):
""" Create and return process to read audio from url and send to analog output"""
return FfmpegProcess(f'ffmpeg -i {url} -f alsa default') | 2246f9385e48dda9398752ecd9fa70914d17c55f | 3,658,613 |
from typing import Iterable
def iterable_to_wikitext(
items: Iterable[object], *, prefix: str = "\n* "
) -> str:
"""
Convert iterable to wikitext.
Pages are converted to links.
All other objects use their string representation.
:param items: Items to iterate
:param prefix: Prefix for eac... | 775bed839d890ab40aeace76a82f881e076cafa2 | 3,658,614 |
def plot_timeSeries(df, col_name, divide=None, xlabel="Days", line=True, title="Time series values", figsize=(9,9)):
"""
Plot a column of the given time series DataFrame.
Parameters
----------
df: pd.DataFrame
DataFrame indexed by days (i.e. the index is a pd.DatetimeIndex).
col_name: s... | 279f74422ae6b186128347cc971a094c13f22c4b | 3,658,615 |
def is_bv(a):
"""Return `True` if `a` is a Z3 bit-vector expression.
>>> b = BitVec('b', 32)
>>> is_bv(b)
True
>>> is_bv(b + 10)
True
>>> is_bv(Int('x'))
False
"""
return isinstance(a, BitVecRef) | 7c1cd1d3d679cdceb12955e61f54861b248ff4a2 | 3,658,617 |
def bgsub_1D(raw_data, energy_axis, edge, **kwargs):
"""
Full background subtraction function for the 1D case-
Optional LBA, log fitting, LCPL, and exponential fitting.
For more information on non-linear fitting function, see information at https://docs.scipy.org/doc/scipy/reference/generated/scipy.opti... | a3f273e55f49811ce9af4ee5c23d4078fe83535a | 3,658,618 |
import random
def about_garble():
"""
about_garble
Returns one of several strings for the about page
"""
garble = ["leverage agile frameworks to provide a robust synopsis for high level overviews.",
"iterate approaches to corporate strategy and foster collaborative thinking t... | c391891f97a7bc6df5287173aa160713cdfff675 | 3,658,619 |
def parse_term_5_elems(expr_list, idx):
"""
Try to parse a terminal node from five elements of {expr_list}, starting
from {idx}.
Return the new expression list on success, None on error.
"""
# The only 3 items node is pk_h
if expr_list[idx : idx + 2] != [OP_DUP, OP_HASH160]:
return
... | 8c0c365483c44a767b3e254f957af175125da2d6 | 3,658,620 |
def display_clusters():
"""
Method to display the clusters
"""
offset = int(request.args.get('offset', '0'))
limit = int(request.args.get('limit', '50'))
clusters_id_sorted = sorted(clusters, key=lambda x : -len(clusters[x]))
batches = chunks(range(len(clusters_id_sorted)), size=limit)
r... | e3d578cff54e66ee4b096bcf1e7181a3bac1c845 | 3,658,621 |
def densify_sampled_item_predictions(tf_sample_predictions_serial, tf_n_sampled_items, tf_n_users):
"""
Turns the serial predictions of the sample items in to a dense matrix of shape [ n_users, n_sampled_items ]
:param tf_sample_predictions_serial:
:param tf_n_sampled_items:
:param tf_n_users:
:... | e1dbe0e74c791e1d9b7613fbe52b034a60376497 | 3,658,622 |
def get_market_book(symbols=None, **kwargs):
"""
Top-level function to obtain Book data for a symbol or list of symbols
Parameters
----------
symbols: str or list, default None
A symbol or list of symbols
kwargs:
Additional Request Parameters (see base class)
"""
return ... | 8b1bc8ed07a611cef490f616996aae05ce445ff1 | 3,658,623 |
def ndarange(*args, shape: tuple = None, **kwargs):
"""Generate arange arrays of arbitrary dimensions."""
arr = np.array([np.arange(*args[i], **kwargs) for i in range(len(args))])
return arr.reshape(shape) if shape is not None else arr.T | 42a5070e653386a71a9be7949f5e9341bfbc50c9 | 3,658,624 |
def runningSum(self, nums):
"""
:type nums: List[int]
:rtype: List[int]
5% faster
100% less memory
"""
sum = 0
runningSum = [0] * len(nums)
for i in range(len(nums)):
for j in range(i+1):
runningSum[i] += nums[j]
return runningSum | 393849c4aa1d23b15717748066e21abceaf6d5d9 | 3,658,625 |
def edit_recovery(request, recovery_id):
"""This view is used to edit/update existing tag recoveries."""
clip_codes = sorted(list(CLIP_CODE_CHOICES), key=lambda x: x[0])
tag_types = sorted(list(TAG_TYPE_CHOICES), key=lambda x: x[0])
tag_origin = sorted(list(TAG_ORIGIN_CHOICES), key=lambda x: x[0])
... | f9da1a4377efd436e93cf2be0af2c2e09cc3e31d | 3,658,628 |
def e(string, *args):
"""Function which formats error messages."""
return string.format(*[pformat(arg) for arg in args]) | 8734d01544211fde3f8ee24f0f91dc06763d4a1f | 3,658,629 |
def membership_ending_task(user):
"""
:return: Next task that will end the membership of the user
"""
task = (UserTask.q
.filter_by(user_id=user.id,
status=TaskStatus.OPEN,
type=TaskType.USER_MOVE_OUT)
# Casting jsonb -> bool directl... | 2043c87eaabbf3360f1bec331a03e1c7db8bc783 | 3,658,630 |
import warnings
def hmsstr_to_rad(hmsstr):
"""Convert HH:MM:SS.SS sexigesimal string to radians.
"""
hmsstr = np.atleast_1d(hmsstr)
hours = np.zeros(hmsstr.size)
for i,s in enumerate(hmsstr):
# parse string using regular expressions
match = hms_re.match(s)
if match is None... | e57266c43e3b0f8893f9c71cfbea609cf7c93709 | 3,658,631 |
def find_optimum_transformations(init_trans, s_pts, t_pts, template_spacing,
e_func, temp_tree, errfunc):
"""
Vary the initial transformation by a translation of up to three times the
grid spacing and compute the transformation with the smallest least square
error.
... | bbc4786827c22158eee33ff9a5e4aaa2939b9705 | 3,658,632 |
def execute_transaction(query):
"""Execute Transaction"""
return Neo4jHelper.run_single_query(query) | 51e8e58bb4cad30b9ae9c7b7d7901ee212c9d26a | 3,658,633 |
from scipy.linalg import null_space
from angle_set import create_theta, get_n_linear, perturbe_points
def generate_linear_constraints(points, verbose=False):
""" Given point coordinates, generate angle constraints. """
N, d = points.shape
num_samples = get_n_linear(N) * 2
if verbose:
print('... | b98354cd6b57d7a33c6e8a43da80b358e358138c | 3,658,634 |
def add_node_to_parent(node, parent_node):
"""
Add given object under the given parent preserving its local transformations
:param node: str
:param parent_node: str
"""
return maya.cmds.parent(node, parent_node, add=True, s=True) | 1f264b7e30c6ebc2285faa987ffc6142ec62d87f | 3,658,635 |
def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
... | 61ccce8b9ffbec3e76aa9e78face469add28437e | 3,658,636 |
def Binary(value):
"""construct an object capable of holding a binary (long) string value."""
return value | 2a33d858b23ac2d72e17ea8ede294c5311cb74be | 3,658,638 |
def _get_domain_session(token, domain_name=None):
"""
Return v3 session for token
"""
domain_name = domain_name or 'default'
auth = v3.Token(auth_url=get_auth_url(),
domain_id=domain_name,
token=token)
return session.Session(auth=auth, user_agent=USER_AGEN... | 1ad7dcd8a9b6ea12e1a73886581c86252602a438 | 3,658,639 |
import six
def fix_troposphere_references(template):
""""Tranverse the troposphere ``template`` looking missing references.
Fix them by adding a new parameter for those references."""
def _fix_references(value):
if isinstance(value, troposphere.Ref):
name = value.data['Ref']
... | 9570e10262d7293a79b76f78508e57289d9b1e2d | 3,658,641 |
import configparser
def parse_config_to_dict(cfg_file, section):
""" Reads config file and returns a dict of parameters.
Args:
cfg_file: <String> path to the configuration ini-file
section: <String> section of the configuration file to read
Returns:
cfg: <dict> configuration param... | 021e3594f3130e502934379c0f5c1ecea228017b | 3,658,642 |
def cnn_net(data,
dict_dim,
emb_dim=128,
hid_dim=128,
hid_dim2=96,
class_dim=2,
win_size=3):
"""
Conv net
"""
# embedding layer
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
# convolution layer
conv... | 47127d5124f48b2be187d15291c2f2bc63f072d7 | 3,658,643 |
def FormatRow(Cn, Row, COLSP):
"""
"""
fRow = ""
for i, c in enumerate(Row):
sc = str(c)
lcn = len(Cn[i])
sc = sc[ 0 : min(len(sc), lcn+COLSP-2) ]
fRow += sc + " "*(COLSP+lcn-len(sc))
return fRow | 53d43fc897d1db5ed3c47d6046d90548939b1298 | 3,658,645 |
def handle_release(pin, evt):
"""
Clears the last tone/light when a button
is released.
"""
if pin > 4:
return False
pin -= 1
explorerhat.light[pin].off()
tone.power_off() | f4833bb289c9dfc45cd572ad754bd270c758ed09 | 3,658,646 |
from typing import List
def makeRoute(start : str, end : str) -> List[str]:
"""Find the shortest route between two systems.
:param str start: string name of the starting system. Must exist in bbData.builtInSystemObjs
:param str end: string name of the target system. Must exist in bbData.builtInSystemObjs... | 6045b07ff5ceceacea4ad43ae2d52a67a0f46ec9 | 3,658,647 |
def norm_error(series):
"""Normalize time series.
"""
# return series
new_series = deepcopy(series)
new_series[:,0] = series[:,0] - np.mean(series[:,0])
return 2*(new_series)/max(abs(new_series[:,0])) | a7af6be8b8ddc800609c3385a96f5a80dfd02853 | 3,658,649 |
def f1d(x):
"""Non-linear function for simulation"""
return(1.7*(1/(1+np.exp(-(x-0.5)*20))+0.75*x)) | 75e3bd8a90fe41dfded9b6063868b6766351a8b0 | 3,658,650 |
def get_field_map(src, flds):
"""
Returns a field map for an arcpy data itme from a list or dictionary.
Useful for operations such as renaming columns merging feature classes.
Parameters:
-----------
src: str, arcpy data item or arcpy.mp layer or table
Source data item containing the de... | 18e6bbae491659b7819aa3584eb40242dea93f11 | 3,658,651 |
def b32qlc_decode(value):
"""
Decodes a value in qlc encoding to bytes using base32 algorithm
with a custom alphabet: '13456789abcdefghijkmnopqrstuwxyz'
:param value: the value to decode
:type: bytes
:return: decoded value
:rtype: bytes
>>> b32qlc_decode(b'fxop4ya=')
b'okay'
"""
... | 8b5bbb0f1900a3b89486c81561fd4c253604287e | 3,658,652 |
def createPreProcessingLayers():
"""
Creates a model with the initial pre-processing layers.
"""
model = Sequential()
model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160, 320, 3)))
model.add(Cropping2D(cropping=((50, 20), (0, 0))))
return model | 1e087ae4bdd1a942845f4f7554e1b27436c6783e | 3,658,653 |
def get_random_atoms(a=2.0, sc_size=2, numbers=[6, 8],
set_seed: int = None):
"""Create a random structure."""
if set_seed:
np.random.seed(set_seed)
cell = np.eye(3) * a
positions = np.array([[0, 0, 0], [a/2, a/2, a/2]])
unit_cell = Atoms(cell=cell, positions=position... | 710592af7db3e24529b68b84e112641b5da63a98 | 3,658,654 |
def vgg16_bn(pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D") with batch normalization
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG(make_layers(cfg['D'], batch_norm=True),... | 34f8e4965555ed4cb046c8ab4e5cde799d887040 | 3,658,656 |
import numpy
def tau(x, cval):
"""Robust estimators of location and scale, with breakdown points of 50%.
Also referred to as: Tau measure of location by Yohai and Zamar
Source: Yohai and Zamar JASA, vol 83 (1988), pp 406-413 and
Maronna and Zamar Technometrics, vol 44 (2002), pp. 307-317"""
... | 6f75ee23f50e94d1ee2754949f5c102d63ac4cab | 3,658,657 |
def shn_gis_location_represent(id, showlink=True):
""" Represent a location given its id """
table = db.gis_location
try:
location = db(table.id == id).select(table.id,
table.name,
table.level,
... | 758dfb8e32178e864f838a790eadf598f65ae6ec | 3,658,658 |
def de_pearson_dataframe(df, genes, pair_by='type', gtex=True, tcga=True):
"""
PearsonR scores of gene differential expression between tumor and normal types.
1. Calculate log2FC of genes for TCGA tumor samples with matching TCGA normal types
2. Compare log2fc to tumor type compared to all other normal... | 29423402b24acc67a278cbdee03916add4228d7d | 3,658,659 |
def load_YUV_as_dic_tensor(path_img):
"""
Construct a dic with 3 entries ('y','u', 'v'), each of them
is a tensor and is loaded from path_img + key + '.png'.
! Return a dictionnary of 3D tensor (i.e. without a dummy batch index)
"""
dic_res = {}
key = ['y', 'u', 'v']
for k i... | b0fe081b36c70ba8a185f151b13c5f046ef26ad6 | 3,658,660 |
def tensor_log10(t1, out_format, dtype=None):
"""
Takes the log base 10 of each input in the tensor.
Note that this is applied to all elements in the tensor not just non-zeros.
Warnings
---------
The log10 of 0 is undefined and is performed on every element in the tensor re... | ff5c1a2f4cee9bc287ac81d3d3e524c1292fa2a7 | 3,658,661 |
def get_file_phenomena_i(index):
"""
Return file phenomena depending on the value of index.
"""
if index <= 99:
return [phen[0]]
elif index >= 100 and index <= 199:
return [phen[1]]
elif index >= 200 and index <= 299:
return [phen[2]]
elif index >= 300 and index <= ... | 18beac08b59aec18b33f6472866a50decd01db30 | 3,658,662 |
def resource_cache_map(resource_id, flush=True):
"""cache resource info"""
if flush:
map_resources(resource_ids=[resource_id, ])
if resource_id not in CDNRESOURCE:
raise InvalidArgument('Resource not exit')
return CDNRESOURCE[resource_id] | 5e67546db9008e805b80c1ed7545d3787444c402 | 3,658,663 |
def _preprocess_html(table_html):
"""Parses HTML with bs4 and fixes some glitches."""
table_html = table_html.replace("<br />", "<br /> ")
table = bs4.BeautifulSoup(table_html, "html5lib")
table = table.find("table")
# Delete hidden style annotations.
for tag in table.find_all(attrs={"style": "display:none"... | 1062c5cdbb058ea36b1c877d7787aebbde87c642 | 3,658,664 |
def parse_campus_hours(data_json, eatery_model):
"""Parses a Cornell Dining json dictionary.
Returns 1) a list of tuples of CampusEateryHour objects for a corresponding CampusEatery object and their unparsed
menu 2) an array of the items an eatery serves.
Args:
data_json (dict): a valid dictio... | 95e7bbc898f4516b9812d3f68749651a32f3535f | 3,658,665 |
from typing import Dict
from typing import Tuple
def _change_relationships(edge: Dict) -> Tuple[bool, bool]:
"""Validate relationship."""
if 'increases' in edge[1]['relation'] or edge[1]['relation'] == 'positive_correlation':
return True, True
elif 'decreases' in edge[1]['relation'] or edge[1]['re... | b826eb1eb7bd1e7eed7fd8577b5c04d827a75e56 | 3,658,666 |
def extract_behaviour_sync(sync, chmap=None, display=False, tmax=np.inf):
"""
Extract wheel positions and times from sync fronts dictionary
:param sync: dictionary 'times', 'polarities' of fronts detected on sync trace for all 16 chans
:param chmap: dictionary containing channel index. Default to const... | b02ec14a5714f1387acb12f1ec2d5bbbc1684f67 | 3,658,667 |
def is_attr_defined(attrs,dic):
"""
Check if the sequence of attributes is defined in dictionary 'dic'.
Valid 'attrs' sequence syntax:
<attr> Return True if single attrbiute is defined.
<attr1>,<attr2>,... Return True if one or more attributes are defined.
<attr1>+<attr2>+... Return True if all ... | 542388846fabc79e126203d80a63db6901a71897 | 3,658,669 |
def c_str_repr(str_):
"""Returns representation of string in C (without quotes)"""
def byte_to_repr(char_):
"""Converts byte to C code string representation"""
char_val = ord(char_)
if char_ in ['"', '\\', '\r', '\n']:
return '\\' + chr(char_val)
elif (ord(' ') <= cha... | e7cce729a00a7d2a35addf95eb097a3caa06bedd | 3,658,670 |
def getActiveTeamAndID():
"""Returns the Team ID and CyTeam for the active player."""
return getActiveTeamID(), getActiveTeam() | edf58aee8d9126ddc25afd94becf641330e13ca2 | 3,658,672 |
from typing import Union
from typing import BinaryIO
from typing import Tuple
from typing import Optional
def is_nitf(
file_name: Union[str, BinaryIO],
return_version=False) -> Union[bool, Tuple[bool, Optional[str]]]:
"""
Test whether the given input is a NITF 2.0 or 2.1 file.
Parameters
... | 6e28baa09d6b8e173db00671e1ed08023630110b | 3,658,673 |
def get_xlsx_filename() -> str:
"""
Get the name of the excel file. Example filename:
kesasetelihakemukset_2021-01-01_23-59-59.xlsx
"""
local_datetime_now_as_str = timezone.localtime(timezone.now()).strftime(
"%Y-%m-%d_%H-%M-%S"
)
filename = f"kesasetelihakemukset_{local_datetime_now... | fb8715f30bd91f39d9836bf59504ad85c205bdf3 | 3,658,675 |
from pathlib import Path
def get_content_directory() -> Path:
"""
Get the path of the markdown `content` directory.
"""
return get_base_directory() / "content" | 2b6f7a9c676e8128fafd43b26cf62aa736aa957c | 3,658,676 |
import math
def mag_inc(x, y, z):
"""
Given *x* (north intensity), *y* (east intensity), and *z*
(vertical intensity) all in [nT], return the magnetic inclincation
angle [deg].
"""
h = math.sqrt(x**2 + y**2)
return math.degrees(math.atan2(z, h)) | f4036358625dd9d032936afc373e53bef7c1e6e1 | 3,658,677 |
import torch
def rgb_to_rgba(image, alpha_val):
"""
Convert an image from RGB to RGBA.
"""
if not isinstance(image, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}")
if len(image.shape) < 3 or image.shape[-3] != 3:
raise ValueError(f"Input ... | 5bab73c37ff81c431ed88ce7d39743cce6c15c56 | 3,658,678 |
def get(identifier):
"""get the activation function"""
if identifier is None:
return linear
if callable(identifier):
return identifier
if isinstance(identifier, str):
activations = {
"relu": relu,
"sigmoid": sigmoid,
"tanh": tanh,
"... | 005789e8cdadff97875f002b9776d8d8bdb22d56 | 3,658,680 |
def df_add_column_codelines(self, key):
"""Generate code lines to add new column to DF"""
func_lines = df_set_column_index_codelines(self) # provide res_index = ...
results = []
for i, col in enumerate(self.columns):
col_loc = self.column_loc[col]
type_id, col_id = col_loc.type_id, col... | 742241d973bb46da2a75b40bf9a76c91ba759d98 | 3,658,681 |
import torch
def resize_bilinear_nd(t, target_shape):
"""Bilinear resizes a tensor t to have shape target_shape.
This function bilinearly resizes a n-dimensional tensor by iteratively
applying tf.image.resize_bilinear (which can only resize 2 dimensions).
For bilinear interpolation, the order in which... | 005266983cca744437826673ff8dd379afb699e2 | 3,658,682 |
def _parse_disambiguate(disambiguatestatsfilename):
"""Parse disambiguation stats from given file.
"""
disambig_stats = [-1, -1, -1]
with open(disambiguatestatsfilename, "r") as in_handle:
header = in_handle.readline().strip().split("\t")
if header == ['sample', 'unique species A pairs',... | bb05ec857181f032ae9c0916b4364b772ff7c412 | 3,658,683 |
def clean_vigenere(text):
"""Convert text to a form compatible with the preconditions imposed by Vigenere cipher."""
return ''.join(ch for ch in text.upper() if ch.isupper()) | d7c3fc656ede6d07d6e9bac84a051581364c63a0 | 3,658,684 |
def select_artist(df_by_artists, df_rate):
"""This method selects artists which perform the same genre as
artists were given
:param df_by_artists:
:param df_rate:
"""
# save the indices of artists, which include any of the genres in the genre profile
list_of_id = []
for index, row in... | 85c09b62553a3257b4f325dd28d26335c9fcb033 | 3,658,685 |
import uuid
def generate_uuid(class_name: str, identifier: str) -> str:
""" Generate a uuid based on an identifier
:param identifier: characters used to generate the uuid
:type identifier: str, required
:param class_name: classname of the object to create a uuid for
:type class_name: str, require... | 10e85effbce04dec62cc55ee709247afa0fb0da7 | 3,658,686 |
def fetch(model, key):
"""Fetch by ID."""
return db.session.query(model).get(key) | 4c3008bec5ed5eac593f2ad8ba2816f121362677 | 3,658,687 |
from typing import Optional
def construct_filename(prefix: str, suffix: Optional[str] = '.csv') -> str:
"""Construct a filename containing the current date.
Examples
--------
.. code:: python
>>> filename = construct_filename('my_file', '.txt')
>>> print(filename)
'my_file_31... | 8269947952d4c8d81cc2855a5776c3677c6a5c57 | 3,658,688 |
def make_friedman_model(point1, point2):
"""
Makes a vtk line source from two set points
:param point1: one end of the line
:param point2: other end of the line
:returns: The line
"""
line = vtkLineSource()
line.SetPoint1(point1)
line.SetPoint2(point2)
return line | f33046307c7c0c2bfeadfbdb4e0815bc5d42d73f | 3,658,689 |
import re
def breadcrumbs_pcoa_plot(pcl_fname, output_plot_fname, **opts):
"""Use breadcrumbs `scriptPcoa.py` script to produce principal
coordinate plots of pcl files.
:param pcl_fname: String; file name of the pcl-formatted taxonomic profile
to visualize via `scriptPcoa.py`.
:... | 06fc9511b21ec3c0111ba91cea8c08852eb2bcaf | 3,658,690 |
def _parse_xml(buff):
"""\
Parses XML and returns the root element.
"""
buff.seek(0)
return etree.parse(buff).getroot() | fa3876f93c0a71b9e4bf6d95dfadbf0714e7c17c | 3,658,691 |
def After(interval):
""" After waits for the duration to elapse and then sends the current time
on the returned channel.
It is equivalent to Timer(interval).c
"""
return Timer(interval).c | 1011151471f839b3e9f7edad369699d76d9f7601 | 3,658,692 |
def f_score(r: float, p: float, b: int = 1):
"""
Calculate f-measure from recall and precision.
Args:
r: recall score
p: precision score
b: weight of precision in harmonic mean
Returns:
val: value of f-measure
"""
try:
val = (1 + b ** 2) * (p * r) / (b *... | d12af20e30fd80cb31b2cc119d5ea79ce2507c4b | 3,658,693 |
def show_inventory():
"""Show the user what is in stock."""
context = {
'inventory': [ # Could contain any items
{'name': 'apple', 'price': 1.00},
{'name': 'banana', 'price': 1.20},
{'name': 'carrot', 'price': 2.00},
]
}
return render_template('sho... | be2b67abb1ebd60bacfad117dab166a08d6915b1 | 3,658,695 |
import numpy as np
import re
def rebuild_schema(doc, r, df):
"""Rebuild the schema for a resource based on a dataframe"""
# Re-get the resource in the doc, since it may be different.
try:
r = doc.resource(r.name)
except AttributeError:
# Maybe r is actually a resource name
r =... | ed212e5cff26dcfece99e3361df9d61823c2bfde | 3,658,697 |
def compute_similarity(image, reference):
"""Compute a similarity index for an image compared to a reference image.
Similarity index is based on a the general algorithm used in the AmphiIndex algorithm.
- identify slice of image that is a factor of 256 in size
- rebin image slice down to a (25... | 0b49009bfdd0697999e61825390a8f883ae8dd79 | 3,658,698 |
def _create_npu_quantization(
scale,
zero_point,
):
"""This is a helper function to capture a list
of arguments to create Vela NpuQuantization object
"""
# Scale could be an ndarray if per-channel quantization is available
if not isinstance(scale, tvm.tir.expr.Load):
if isinstance(sc... | 71f7e20a760940e6d46301ccd9130265de140b29 | 3,658,699 |
def article_markdown(text):
""" 对传入的text文本进行markdown """
renderer = ArticleRenderer()
markdown = mistune.Markdown(renderer=renderer)
return markdown(text) | 32d1edc0d5155c62b0dc0ff18dc9a44f1ec85d7a | 3,658,700 |
def _gen_key(user_id, key_name):
""" Tuck this into UserManager """
try:
manager = users.UserManager.instance()
private_key, fingerprint = manager.generate_key_pair(user_id, key_name)
except Exception as ex:
return {'exception': ex}
return {'private_key': private_key, 'fingerprin... | f5babf523bded37ba624295a7435e2709488d47a | 3,658,703 |
def svhn_loader(size=None,root="./shvn",set="train",batch_size=32,mean=0.5,std=0.5,transform="default",download=True,target_transform=None,**loader_args):
"""
:param size:
:param root:
:param set:
:param batch_size:
:param mean:
:param std:
:param transform:
:param download:
:pa... | f40cd95338f4e745cbbb849ac8a9999f98245cf0 | 3,658,704 |
import pkg_resources
def get_supervisees():
"""Pull the supervisor specifications out of the entry point."""
eps = list(pkg_resources.iter_entry_points(ENTRY_POINT_GROUP))
return dict((ep.name, ep.load()) for ep in eps) | 6a812bb8422382c6e481bab8b27651786984ea66 | 3,658,705 |
async def index(request):
"""
This is the view handler for the "/" url.
**Note: returning html without a template engine like jinja2 is ugly, no way around that.**
:param request: the request object see http://aiohttp.readthedocs.io/en/stable/web_reference.html#request
:return: aiohttp.web.Respons... | f90ba225055bf77b39942da7fc1b1b2f5b4a7286 | 3,658,706 |
def adtg(s, t, p):
"""
Calculates adiabatic temperature gradient as per UNESCO 1983 routines.
Parameters
----------
s(p) : array_like
salinity [psu (PSS-78)]
t(p) : array_like
temperature [℃ (ITS-90)]
p : array_like
pressure [db]
Returns
-------
ad... | 8d195810ad52215135db4ef8f9825a914b01522c | 3,658,707 |
import re
def calculate_ion_mz(seq: str,
ion: str = 'M',
charge: int = 0
) -> float:
"""
given a peptide sequence and ion type, count the number of atoms, accounting for ion
type and whether cysteines are measured by IAA
- ion type
M:... | e032ad439414314511b008d98dadb23b84012798 | 3,658,708 |
def hhc_to_int(s):
"""Parse a number expressed in sortable hhc as an integer (or long).
>>> hhc_to_int('-')
0
>>> hhc_to_int('.')
1
>>> hhc_to_int('~')
65
>>> hhc_to_int('.-')
66
>>> hhc_to_int('..')
67
>>> hhc_to_int('.XW')
6700
>>> hhc_to_int('----..')
67
... | 25f6e8097f1fbf0f6ceed08fc8ac0195fb88acb4 | 3,658,710 |
def initializeSens(P, B, idxs):
"""
This function initializes the sensitivities using the bicriteria algorithm, to be the distance between each
point to it's closest flat from the set of flats B divided by the sum of distances between self.P.P and B.
:param B: A set of flats where each flat is represen... | 6726c892311d1590adea62babde6023d4b7d67a3 | 3,658,711 |
def fast_knn(data, k=3, eps=0, p=2, distance_upper_bound=np.inf, leafsize=10, idw=util_idw.shepards):
""" Impute using a variant of the nearest neighbours approach
Basic idea: Impute array with a basic mean impute and then use the resulting complete
array to construct a KDTree. Use this KDTree to compute n... | 976e51c66878643965b099f764595629b379d440 | 3,658,712 |
def role_generator(role):
"""Closure function returning a role function."""
return lambda *args, **kwargs: role.run(*args, **kwargs) | 35dd1a54cb53a6435633c39608413c2d0b9fe841 | 3,658,713 |
def pick_slices(img, num_slices_per_view):
"""
Picks the slices to display in each dimension,
skipping any empty slices (without any segmentation at all).
"""
slices = list()
for view in range(len(img.shape)):
dim_size = img.shape[view]
non_empty_slices = np.array(
... | ed80e4bd53e6a72c6ad7cad899d875ac320b33b7 | 3,658,714 |
import json
def cate2(request):
"""
DB에서 Cate2의 분류 이름을 반환
"""
cate1 = Cate1.objects.get(cate1_name=request.GET.get('cate1'))
cate2 = list(map(lambda cate2 : cate2['cate2_name'],
Cate2.objects.filter(cate1=cate1).values('cate2_name')))
json_data = json.dumps({'cate2': cate2})
retu... | ebeca48bb9d6550fb34d68c453ef1fb47225fb4a | 3,658,715 |
def dayChange():
"""
Day Change
Calculates and stores in a dictionary the total current change in position
value since yesterday, which is (current_price - lastday_price)* qty.
:return: dictionary
"""
daychange = dict()
for position in portfolio:
# Strings are returned from API; ... | f33979f25ffe44a0de8ec0abc1c02284e8fe5427 | 3,658,717 |
def look_for_section(line):
"""Look for one of the sections in a line of text."""
for key in SECTIONS:
if line.startswith(key):
return key
return None | d44ad97312528c4fea856e705be8e6820695fd9a | 3,658,718 |
def SetStrucIdx(sid, index):
"""
Change structure index
@param sid: structure type ID
@param index: new index of the structure
@return: != 0 - ok
@note: See GetFirstStrucIdx() for the explanation of
structure indices and IDs.
"""
s = idaapi.get_struc(sid)
if not s:
... | 8b246d6e2fb155bdc789536f75e20971a54ddfc3 | 3,658,719 |
import json
def extract_user_dict_from_tweet( tweet: Tweet ):
"""Takes the other_data field from a tweet object and
extracts the data for the user from it.
It returns a dictionary rather than a User model object
because we might want to try looking up whether the user
exists before creating a new ... | 533d8795c652e5c7f1299f3dcc04fc30de644222 | 3,658,720 |
def in_scope(repository_data):
"""Return whether the given repository is in scope for the configuration.
Keyword arguments:
repository_data -- data for the repository
"""
if "scope" in repository_data["configuration"] and repository_data["configuration"]["scope"] == "all":
return True
... | 0e521f805f69a1c6f306700680d42fbe76595c3a | 3,658,721 |
from typing import Union
from typing import Tuple
from typing import Optional
from typing import List
from typing import Any
def run_image_container_checks(
image_container: Union[AICSImage, Reader],
set_scene: str,
expected_scenes: Tuple[str, ...],
expected_current_scene: str,
expected_shape: Tup... | a502650fb227aa4425a2501f112603b681b41fbf | 3,658,722 |
from typing import Type
def collect_validation_helper(package_names: str) -> Type[ValidationHelper]:
"""Finds subclasses of the validate.ValidationHelper from a
list of package names.
Args:
package_names: A list of Python package names as strings.
Returns:
A validator class that are ... | bb38b734641a025a9d7fd26d46ebfb1476879c82 | 3,658,723 |
def heartbeat(request):
"""Test that ElasticSearch is operationnal.
:param request: current request object
:type request: :class:`~pyramid:pyramid.request.Request`
:returns: ``True`` is everything is ok, ``False`` otherwise.
:rtype: bool
"""
indexer = request.registry.indexer
try:
... | 6acf0b21b6fcc64f70ca75cb6795df0b5109f273 | 3,658,724 |
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