content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def getContentType(the_type):
"""
Get the content type based on the type name which is in settings
:param the_type:
:return:
"""
if the_type not in settings.XGDS_MAP_SERVER_JS_MAP:
return None
the_model_name = settings.XGDS_MAP_SERVER_JS_MAP[the_type]['model']
splits = the_model... | 25031eb0dce8fe7828f94bdbc99d5c574f0e5ea6 | 3,655,565 |
import scipy
import math
import numpy
def calculateGravityAcceleration(stateVec, epoch, useGeoid):
""" Calculate the acceleration due to gravtiy acting on the satellite at
a given state (3 positions and 3 velocities). Ignore satellite's mass,
i.e. use a restricted two-body problem.
Arguments
... | a2ea0ff1c8feb9f0a678911130e3ca6e96838b7c | 3,655,566 |
import math
def points_on_line(r0, r1, spacing):
"""
Coordinates of points spaced `spacing` apart between points `r0` and `r1`.
The dimensionality is inferred from the length of the tuples `r0` and `r1`,
while the specified `s... | eb2795cba55566823632c75b7a72f34731b5e36e | 3,655,567 |
def index() -> Response:
"""
Return application index.
"""
return APP.send_static_file("index.html") | 37d299ec548fe4f83d8f55f063e3bf9f5fb64c4e | 3,655,568 |
def compare_files(og_maxima,new_maxima, compare_file, until=100, divisor=1000):
"""
given input of the maxima of a graph, compare it to the maxima from data100.txt
maxima will be a series of x,y coordinates corresponding to the x,y values of a maximum from a file.
First see if there is a maxima with t... | 86fe2ffd02785d41284b8edfef44d0dc0e097c90 | 3,655,569 |
import _datetime
def parseDatetimetz(string, local=True):
"""Parse the given string using :func:`parse`.
Return a :class:`datetime.datetime` instance.
"""
y, mo, d, h, m, s, tz = parse(string, local)
s, micro = divmod(s, 1.0)
micro = round(micro * 1000000)
if tz:
offset = _tzoffse... | ce95f42f568b50ffcdc0084dc659a1d5fd0233ff | 3,655,570 |
def median_ratio_flux(spec, smask, ispec, iref, nsig=3., niter=5, **kwargs):
""" Calculate the median ratio between two spectra
Parameters
----------
spec
smask:
True = Good, False = Bad
ispec
iref
nsig
niter
kwargs
Returns
-------
med_scale : float
Medi... | 28872a548ce7569f17f155242aaf4377bf0c1b63 | 3,655,571 |
def get_tags_from_event():
"""List of tags
Arguments:
event {dict} -- Lambda event payload
Returns:
list -- List of AWS tags for use in a CFT
"""
return [
{
"Key": "OwnerContact",
"Value": request_event['OwnerContact']
}
] | e7a0f7da62a4904dbfb716c57b6811053aff3497 | 3,655,572 |
from typing import List
def _verify(symbol_table: SymbolTable, ontology: _hierarchy.Ontology) -> List[Error]:
"""Perform a battery of checks on the consistency of ``symbol_table``."""
errors = _verify_there_are_no_duplicate_symbol_names(symbol_table=symbol_table)
if len(errors) > 0:
return errors... | da9dd12f01107a0c0ea1a8b2df1aa2fb543391ab | 3,655,573 |
def gsl_eigen_symmv_alloc(*args, **kwargs):
"""gsl_eigen_symmv_alloc(size_t n) -> gsl_eigen_symmv_workspace"""
return _gslwrap.gsl_eigen_symmv_alloc(*args, **kwargs) | 54384bfa9787b9a337ad3b9e2d9ea211769238d4 | 3,655,574 |
def add_poll_answers(owner, option):
"""
Add poll answer object. Matching user and option is considered same.
:param owner: User object.
:param option: Chosen poll option.
:return: Poll answer object, Boolean (true, if created).
"""
'''
owner = models.ForeignKey(User, related_name='poll_... | ac667fbfb47aeb7d2450a3d698b0b678c3bdfdbc | 3,655,575 |
def calculate_rrfdi ( red_filename, nir_filename ):
"""
A function to calculate the Normalised Difference Vegetation Index
from red and near infrarred reflectances. The reflectance data ought to
be present on two different files, specified by the varaibles
`red_filename` and `nir_filename`. The file... | 3b8f4d7eadceb38b7f874bfe0a56827f7a8aab09 | 3,655,576 |
import re
def strip_price(header_list):
"""input a list of tag-type values and return list of strings with surrounding html characters removed"""
match_obs = []
regex = '\$(((\d+).\d+)|(\d+))'
string_list = []#['' for item in range(len(header_list))]
for item in range(len(header_list)):
... | 7b3d90416e44f8aa61ababc0e7b68f82ae754413 | 3,655,579 |
import functools
def module(input, output, version):
"""A decorator which turn a function into a module"""
def decorator(f):
class Wrapper(Module):
def __init__(self):
super().__init__(input, output, version)
@property
def name(self):
... | b7d5afcaa8fa52411024f84f979891d19ccf60c0 | 3,655,580 |
def compile_modules_to_ir(
result: BuildResult,
mapper: genops.Mapper,
compiler_options: CompilerOptions,
errors: Errors,
) -> ModuleIRs:
"""Compile a collection of modules into ModuleIRs.
The modules to compile are specified as part of mapper's group_map.
Returns the IR of the modules.
... | e2ea8a87a1ed2450e4c8ed99c7ca8a3142568f45 | 3,655,581 |
def minutes_to_restarttime(minutes) :
"""
converts an int meaning Minutes after midnight into a
restartTime string understood by the bos command
"""
if minutes == -1 :
return "never"
pod = "am"
if minutes > 12*60 :
pod = "pm"
minutes -= 12*60
time = "%d:%02d %s"... | 6d7807cebb7a474553dda8eadfd27e5ce7b2a657 | 3,655,582 |
import tqdm
def ccm_test(x, y,emb_dim = "auto", l_0 = "auto", l_1 = "auto", tau=1, n=10,mean_num = 10,max_dim = 10):
"""
estimate x from y to judge x->y cause
:param x:
:param y:
:param l_0:
:param l_1:
:param emb_dim:
:param tau:
:param n:
:return:
"""
if em... | c03a05e62df36910ea05e361c9683b60befc1b9c | 3,655,583 |
def make_indiv_spacing(subject, ave_subject, template_spacing, subjects_dir):
"""
Identifies the suiting grid space difference of a subject's volume
source space to a template's volume source space, before a planned
morphing takes place.
Parameters:
-----------
subject : str
Sub... | cbe5120093fdf78913c2386820d3388aca0724d1 | 3,655,584 |
def sqlpool_blob_auditing_policy_update(
cmd,
instance,
state=None,
storage_account=None,
storage_endpoint=None,
storage_account_access_key=None,
storage_account_subscription_id=None,
is_storage_secondary_key_in_use=None,
retention_days=None,
... | e99013545172eb03ad5dddeefdb0b36b7bb2edd7 | 3,655,585 |
def format_search_filter(model_fields):
"""
Creates an LDAP search filter for the given set of model
fields.
"""
ldap_fields = convert_model_fields_to_ldap_fields(model_fields);
ldap_fields["objectClass"] = settings.LDAP_AUTH_OBJECT_CLASS
search_filters = import_func(settings.LDAP_AUTH_FORMA... | b6c5c17b566c583a07ef5e9f3ec61cb868f6f8ab | 3,655,587 |
def normalize_img(img):
"""
normalize image (caffe model definition compatible)
input: opencv numpy array image (h, w, c)
output: dnn input array (c, h, w)
"""
scale = 1.0
mean = [104,117,123]
img = img.astype(np.float32)
img = img * scale
img -= mean
img = np.transpose(img, ... | dac9ec8c942d70fb98f0b0989e9643f80dde5448 | 3,655,589 |
from typing import List
from typing import Any
def pages(lst: List[Any], n: int, title: str, *, fmt: str = "```%s```", sep: str = "\n") -> List[discord.Embed]:
# noinspection GrazieInspection
"""
Paginates a list into embeds to use with :class:disputils.BotEmbedPaginator
:param lst: the list t... | f8d9471f2d254b63754128a2e2762520f858edbd | 3,655,590 |
import re
def Substitute_Percent(sentence):
"""
Substitutes percents with special token
"""
sentence = re.sub(r'''(?<![^\s"'[(])[+-]?[.,;]?(\d+[.,;']?)+%(?![^\s.,;!?'")\]])''',
' @percent@ ', sentence)
return sentence | 61bc6970af09703ef018bfcc9378393241ae21ed | 3,655,591 |
def ready_df1(df):
"""
This function prepares the dataframe for EDA.
"""
df = remove_columns(df, columns=[ 'nitrogen_dioxide',
'nitrogen_dioxide_aqi',
'sulfur_dioxide',
'sulfur_dioxide_aqi',... | 3776c571d3eabb39ce27017ac1481e2bd469f68c | 3,655,592 |
def _wrap(func, args, flip=True):
"""Return partial function with flipped args if flip=True
:param function func: Any function
:param args args: Function arguments
:param bool flip: If true reverse order of arguments.
:return: Returns function
:rtype: function
"""
@wraps(func)
def ... | 9ac5a814840f821260d46df64b60cd6d71185dbb | 3,655,593 |
def compute_kkt_optimality(g, on_bound):
"""Compute the maximum violation of KKT conditions."""
g_kkt = g * on_bound
free_set = on_bound == 0
g_kkt[free_set] = np.abs(g[free_set])
return np.max(g_kkt) | 216cf110d64d1fd8ec89c0359ebaa9b4e4dcc773 | 3,655,594 |
def replace_cipd_revision(file_path, old_revision, new_revision):
"""Replaces cipd revision strings in file.
Args:
file_path: Path to file.
old_revision: Old cipd revision to be replaced.
new_revision: New cipd revision to use as replacement.
Returns:
Number of replaced occurrences.
Raises:
... | f429e74f0dd7180ab4bf90d662f8042b958b81f8 | 3,655,595 |
def spectral_derivs_plot(spec_der, contrast=0.1, ax=None, freq_range=None,
fft_step=None, fft_size=None):
"""
Plot the spectral derivatives of a song in a grey scale.
spec_der - The spectral derivatives of the song (computed with
`spectral_derivs`) or the song itself... | 5b683d8c49e9bad2fd1fa029af6bc5660bc0e936 | 3,655,596 |
from operator import add
from operator import sub
def scale_center(pnt, fac, center):
"""scale point in relation to a center"""
return add(scale(sub(pnt, center), fac), center) | f69ca54e25d5eb8008b8f08c40500f236005e093 | 3,655,597 |
def gopherize_feed(feed_url, timestamp=False, plug=True):
"""Return a gophermap string for the feed at feed_url."""
return gopherize_feed_object(feedparser.parse(feed_url), timestamp, plug) | aaf4d35044c873e7d0f1a43c4d001ebe5e30714b | 3,655,598 |
def first_sunday_of_month(datetime: pendulum.DateTime) -> pendulum.DateTime:
"""Get the first Sunday of the month based on a given datetime.
:param datetime: the datetime.
:return: the first Sunday of the month.
"""
return datetime.start_of("month").first_of("month", day_of_week=7) | 88c517d1d38785c0d8f9c0f79f3d34199dfceb1e | 3,655,599 |
import pickle
def evaluate_single_model(
model_path, model_index, save_preds_to_db, save_prefix,
metrics, k_values, X, y, labeled_indices):
"""
Evaluate a single model with provided model specifications and data.
Arguments:
- model_path: path to load the model
- model_index: index... | 311589284c46d19e04cd04fd36056e1b53c4bb52 | 3,655,600 |
def sigmoid(x: np.ndarray, derivative: bool = False) -> np.ndarray:
"""
The sigmoid function which is given by
1/(1+exp(-x))
Where x is a number or np vector. if derivative is True it applied the
derivative of the sigmoid function instead.
Examples:
>>> sigmoid(0)
0.5
>>> abs(sigmo... | 7a80b978a9dd8503ba6ec56ce11a5ee9c0564fdb | 3,655,602 |
def create_hierarchy(
num_samples,
bundle_size,
directory_sizes=None,
root=".",
start_sample_id=0,
start_bundle_id=0,
address="",
n_digits=1,
):
"""
SampleIndex Hierarchy Factory method. Wraps
create_hierarchy_from_max_sample, which is a max_sample-based API, not a
numSam... | 6d41b995d664eec2c9d6454abfc485c2c4202220 | 3,655,603 |
def eval_sysu(distmat, q_pids, g_pids, q_camids, g_camids, max_rank = 20):
"""Evaluation with sysu metric
Key: for each query identity, its gallery images from the same camera view are discarded. "Following the original setting in ite dataset"
"""
num_q, num_g = distmat.shape
if num_g < max_rank:
... | ccf61aa9f91e95cebfd63855aea366cb50de8887 | 3,655,604 |
import getpass
def espa_login() -> str:
"""
Get ESPA password using command-line input
:return:
"""
return getpass.getpass("Enter ESPA password: ") | 3ba61567d23ba3771effd6f0aa1a4ac504467378 | 3,655,605 |
def row_up1_array(row, col):
"""This function establishes an array that contains the index for the row above each entry"""
up1_array = np.zeros((row, col), dtype=np.uint8)
for i in range(row):
up1_array[i, :] = np.ones(col, dtype = np.uint8) * ((i - 1) % row)
return up1_array | 19cf1e3ceb9fe174c5cc3c6ba2c336fc58412037 | 3,655,606 |
def lcm(a, b):
"""Return lowest common multiple."""
return a * b // gcd(a, b) | 27a7d5af9001015a0aff459af274a45921d2bc94 | 3,655,607 |
from typing import Callable
def chl_mean_hsl(weights: np.ndarray) -> Callable[[np.ndarray], np.ndarray]:
"""
return a function that can calculate the channel-wise average
of the input picture in HSL color space
"""
return lambda img: np.average(cv2.cvtColor(img, cv2.COLOR_BGR2HLS), axis=(0, 1), w... | b5e337fb3bee18762e31aef3d666906975305b4b | 3,655,608 |
def cosine_mrl_option(labels, predicts):
"""For a minibatch of image and sentences embeddings, computes the pairwise contrastive loss"""
#batch_size, double_n_emd = tensor.shape(predicts)
#res = tensor.split(predicts, [double_n_emd/2, double_n_emd/2], 2, axis=-1)
img = l2norm(labels)
text = l2norm(... | e103b1b0075438270e79913bb59b1117da09b51f | 3,655,609 |
def escape_cdata(cdata):
"""Escape a string for an XML CDATA section"""
return cdata.replace(']]>', ']]>]]><![CDATA[') | c38b934b4c357e8c15fd1f3942f84ca3aaab4ee1 | 3,655,610 |
import inspect
import pprint
def _collect_data_for_docstring(func, annotation):
"""
Collect data to be printed in docstring. The data is collected from
custom annotation (dictionary passed as a parameter for the decorator)
and standard Python annotations for the parameters (if any). Data from
cust... | 32a7ac62506dfc04157c613fa781b3d740a95451 | 3,655,611 |
def _strip_unbalanced_punctuation(text, is_open_char, is_close_char):
"""Remove unbalanced punctuation (e.g parentheses or quotes) from text.
Removes each opening punctuation character for which it can't find
corresponding closing character, and vice versa.
It can only handle one type of punctuation
... | db4b8f201e7b01922e6c06086594a8b73677e2a2 | 3,655,612 |
def read(fin, alphabet=None):
"""Read and parse a fasta file.
Args:
fin -- A stream or file to read
alphabet -- The expected alphabet of the data, if given
Returns:
SeqList -- A list of sequences
Raises:
ValueError -- If the file is unparsable
"""
seqs = [s for s... | 1ff492ac533a318605569f94ef66036c847b21d5 | 3,655,613 |
def get_min_max_value(dfg):
"""
Gets min and max value assigned to edges
in DFG graph
Parameters
-----------
dfg
Directly follows graph
Returns
-----------
min_value
Minimum value in directly follows graph
max_value
Maximum value in directly follows grap... | 17a98350f4e13ec51e72d4357e142ad661e57f54 | 3,655,614 |
def vgg_fcn(num_classes=1000, pretrained=False, batch_norm=False, **kwargs):
"""VGG 16-layer model (configuration "D")
Args:
num_classes(int): the number of classes at dataset
pretrained (bool): If True, returns a model pre-trained on ImageNet
batch_norm: if you want to introduce batch n... | 73c1e80e0ffc6aff670394d1b1ec5e2b7d21cf06 | 3,655,616 |
import time
def fmt_time(timestamp):
"""Return ISO formatted time from seconds from epoch."""
if timestamp:
return time.strftime('%Y-%m-%dT%H:%M:%S', time.localtime(timestamp))
else:
return '-' | c87f1da7b6a3b1b8d8daf7d85a2b0746be58133b | 3,655,618 |
def lislice(iterable, *args):
""" (iterable, stop) or (iterable, start, stop[, step])
>>> lislice('ABCDEFG', 2)
['A', 'B']
>>> lislice('ABCDEFG', 2, 4)
['C', 'D']
>>> lislice('ABCDEFG', 2, None)
['C', 'D', 'E', 'F', 'G']
>>> lislice('ABCDEFG', 0, None, 2)
['A', 'C', 'E', 'G']
"""... | 6c7eb26a9ab5cb913c17f77c2a64929cfc7ebb06 | 3,655,619 |
def calculate_transition_cost(number_objs: int, target_storage_class: str) -> float:
"""
Calculates the cost of transition data from one class to another
Args:
number_objs: the number of objects that are added on a monthly basis
target_storage_class: the storage class the objects will resid... | 01ec7d3e7149dadc020ab6f82033a178366c6ebf | 3,655,620 |
def get_covid():
"""This module sends off a covid notification. You can't get covid from this."""
covid_data = covid_handler()
covid_content = Markup("Date: " + str(covid_data["date"]) + ",<br/>Country: " + str(
covid_data["areaName"]) + ",<br/>New Cases: " + str(
covid_data["newCasesByPubli... | 0c6e4c8e5df7b7e13212eabe46f8a72a7874fde5 | 3,655,622 |
def send_songogram(your_name, artist_first_name, artist_last_name, song_name, number_to_call):
""" Function for sending a Sonogram.
:param your_name: string containing the person sending the sonogram's name.
:param artist_first_name: string containing the musician's first name.
:param artist_last_name: ... | 84e67f7b8b185817596f0fd0173e4cc989616687 | 3,655,623 |
def segm_and_cat(sersic_2d_image):
"""fixture for segmentation and catalog"""
image_mean, image_median, image_stddev = sigma_clipped_stats(sersic_2d_image, sigma=3)
threshold = image_stddev * 3
# Define smoothing kernel
kernel_size = 3
fwhm = 3
# Min Source size (area)
npixels = 4 ** ... | 2a6018f7b4c2a1aea946b6744840bd2216352002 | 3,655,624 |
from typing import Tuple
def break_word_by_trailing_integer(pname_fid: str) -> Tuple[str, str]:
"""
Splits a word that has a value that is an integer
Parameters
----------
pname_fid : str
the DVPRELx term (e.g., A(11), NSM(5))
Returns
-------
word : str
the value not ... | e9b9c85b4225269c94918ce1cc2e746d3c74aa5c | 3,655,625 |
def preprocess_data(image, label, is_training):
"""CIFAR data preprocessing"""
image = tf.image.convert_image_dtype(image, tf.float32)
if is_training:
crop_padding = 4
image = tf.pad(image, [[crop_padding, crop_padding],
[crop_padding, crop_padding], [0, 0]], 'REFLECT')
ima... | 642f384fbf1aa2f884e64de2edf264890317b258 | 3,655,627 |
def load_Counties():
"""
Use load_country() instead of this function
"""
# Get data
# Load data using Pandas
dfd = {
'positive': reread_csv(csv_data_file_Global['confirmed_US']),
'death': reread_csv(csv_data_file_Global['deaths_US']),
}
return dfd | 098d08f3720b6c6148c51000e6e1512d382adeaf | 3,655,628 |
from typing import Iterator
from typing import Any
def issetiterator(object: Iterator[Any]) -> bool:
"""Returns True or False based on whether the given object is a set iterator.
Parameters
----------
object: Any
The object to see if it's a set iterator.
Returns
-------
bool
... | 07ecdcc72c62c4ce3d5fb91181cd1bc785d6cb4d | 3,655,630 |
from mpl_toolkits.mplot3d import Axes3D
def plotModeScatter( pc , xMode=0, yMode=1, zMode=None, pointLabels=None, nTailLabels=3, classes=None):
"""
scatter plot mode projections for up to 3 different modes.
PointLabels is a list of strings corresponding to each shape.
nTailLabels defines number of points that are... | 72bc671d9d4fc0fc8df26965fd4d24d91ab51b72 | 3,655,632 |
import math
def calculatetm(seq):
""" Calculate Tm of a target candidate, nearest neighbor model """
NNlist = chopseq(seq, 2, 1)
NNtable = ['AA', 'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA', 'TC', 'TG', 'TT']
NNendtable = ['A', 'C', 'G', 'T']
NNcount = np.zeros(16)
N... | f53c1aa09cd335d603c721fa9922d85e2de0f612 | 3,655,634 |
def get_data_shape(X_train, X_test, X_val=None):
"""
Creates, updates and returns data_dict containing metadata of the dataset
"""
# Creates data_dict
data_dict = {}
# Updates data_dict with lenght of training, test, validation sets
train_len = len(X_train)
test_len = len(X_test)
d... | 231a334b625d0bfe6aa6e63b79de2b2226b8e684 | 3,655,636 |
def setupAnnotations(context):
"""
set up the annotations if they haven't been set up
already. The rest of the functions in here assume that
this has already been set up
"""
annotations = IAnnotations(context)
if not FAVBY in annotations:
annotations[FAVBY] = PersistentList()
r... | f427c8619452d7143a56d4b881422d01a90ba666 | 3,655,637 |
def _get_media(media_types):
"""Helper method to map the media types."""
get_mapped_media = (lambda x: maps.VIRTUAL_MEDIA_TYPES_MAP[x]
if x in maps.VIRTUAL_MEDIA_TYPES_MAP else None)
return list(map(get_mapped_media, media_types)) | 4dbbcf87c717fca2e1890a5258df023ebbca31c5 | 3,655,638 |
import ctypes
def get_int_property(device_t, property):
""" Search the given device for the specified string property
@param device_t Device to search
@param property String to search for.
@return Python string containing the value, or None if not found.
"""
key = cf.CFStringCreateWithCString... | 75bc08117bb838e8070d3ea4d5134dfbeec9576c | 3,655,639 |
def _get_unique_barcode_ids(pb_index, isoseq_mode=False):
"""
Get a list of sorted, unique fw/rev barcode indices from an index object.
"""
bc_sel = (pb_index.bcForward != -1) & (pb_index.bcReverse != -1)
bcFw = pb_index.bcForward[bc_sel]
bcRev = pb_index.bcReverse[bc_sel]
bc_ids = sorted(li... | bdfb386d26415a7b3f9f16661d83a38a63958ad0 | 3,655,640 |
def clean_logs(test_yaml, args):
"""Remove the test log files on each test host.
Args:
test_yaml (str): yaml file containing host names
args (argparse.Namespace): command line arguments for this program
"""
# Use the default server yaml and then the test yaml to update the default
#... | 229f34615dc9a6f7ab9c484b9585151814656a77 | 3,655,641 |
def call_posterior_haplotypes(posteriors, threshold=0.01):
"""Call haplotype alleles for VCF output from a population
of genotype posterior distributions.
Parameters
----------
posteriors : list, PosteriorGenotypeDistribution
A list of individual genotype posteriors.
threshold : float
... | 46c26eb38c693d979ea4234af606b3b07ad1e75e | 3,655,642 |
def get_discorded_labels():
"""
Get videos with citizen discorded labels
Partial labels will only be set by citizens
"""
return get_video_labels(discorded_labels) | 6f3cbaf09b43956d14d9abf5cf4e77734c152d2f | 3,655,644 |
def set_common_tags(span: object, result: object):
"""Function used to set a series of common tags
to a span object"""
if not isinstance(result, dict):
return span
for key, val in result.items():
if key.lower() in common_tags:
span.set_tag(key, val)
re... | 365230fb6a69b94684aeac25d14fa4275c1549f8 | 3,655,645 |
import time
def local_timezone():
"""
Returns:
(str): Name of current local timezone
"""
try:
return time.tzname[0]
except (IndexError, TypeError):
return "" | c97c11582b27d8aa0205555535616d6ea11775b9 | 3,655,646 |
import getpass
def ask_credentials():
"""Interactive function asking the user for ASF credentials
:return: tuple of username and password
:rtype: tuple
"""
# SciHub account details (will be asked by execution)
print(
" If you do not have a ASF/NASA Earthdata user account"
" g... | a601a460b3aeddf9939f3acf267e58fdaf9ed7cd | 3,655,647 |
def lab2lch(lab):
"""CIE-LAB to CIE-LCH color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters
----------
lab : array_like
The N-D image in CIE-LAB format. The last (``N+1``-th) dimension must
have at least 3 elements, correspondi... | 711d23d452413d738af162ac5b9e3f34c1a4eab6 | 3,655,648 |
def callattice(twotheta, energy_kev=17.794, hkl=(1, 0, 0)):
"""
Calculate cubic lattice parameter, a from reflection two-theta
:param twotheta: Bragg angle, deg
:param energy_kev: energy in keV
:param hkl: reflection (cubic only
:return: float, lattice contant
"""
qmag = calqmag(twotheta... | 2718e4c44e08f5038ff4119cf477775ed9f3a678 | 3,655,650 |
def reset_password(
*,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_active_user),
background_tasks: BackgroundTasks,
):
"""reset current user password"""
email = current_user.email
# send confirm email
if settings.EMAILS_ENABLED and email:
... | 1f292188b3927c26eb41634acb7fb99e398e94b6 | 3,655,651 |
def rule_valid_histone_target(attr):
""" {
"applies" : ["ChIP-Seq", "experiment_target_histone"],
"description" : "'experiment_target_histone' attributes must be 'NA' only for ChIP-Seq Input"
} """
histone = attr.get('experiment_target_histone', [''])[0]
if attr.get('experiment_type', [""])[0].lo... | 0a10f09c6b9e50cf01583d0c803e5112629e503b | 3,655,652 |
def extend(curve: CustomCurve, deg):
"""returns curve over the deg-th relative extension"""
E = curve.EC
q = curve.q
K = curve.field
if q % 2 != 0:
R = K["x"]
pol = R.irreducible_element(deg)
Fext = GF(q ** deg, name="z", modulus=pol)
return E.base_extend(Fext)
ch... | 8d750b40d91d10d6b51c75765e2083300d7dccf6 | 3,655,653 |
def flatten3D(inputs: tf.Tensor) -> tf.Tensor:
"""
Flatten the given ``inputs`` tensor to 3 dimensions.
:param inputs: >=3d tensor to be flattened
:return: 3d flatten tensor
"""
shape = inputs.get_shape().as_list()
if len(shape) == 3:
return inputs
assert len(shape) > 3
retu... | 11c9c7f7ab955594401468c64323f8f3a52dbe81 | 3,655,654 |
def get_classes(dataset):
"""Get class names of a dataset."""
alias2name = {}
for name, aliases in dataset_aliases.items():
for alias in aliases:
alias2name[alias] = name
if mmcv.is_str(dataset):
if dataset in alias2name:
labels = eval(alias2name[dataset] + '_cla... | d307793a85deef3be239d7dbff746c7c9643dc1b | 3,655,655 |
def split_exclude_string(people):
"""
Function to split a given text of persons' name who wants to exclude
with comma separated for each name e.g. ``Konrad, Titipat``
"""
people = people.replace('Mentor: ', '').replace('Lab-mates: ', '').replace('\r\n', ',').replace(';', ',')
people_list = peop... | 5748a52039548175923f53384474f40ac8fb5e38 | 3,655,656 |
from datetime import datetime
def now(tz=DEFAULT_TZ):
"""
Get the current datetime.
:param tz: The preferred time-zone, defaults to DEFAULT_TZ
:type tz: TzInfo (or similar pytz time-zone)
:return: A time-zone aware datetime set to now
:rtype: datetime
"""
return datetime.now(tz=tz) | 1dcdd78898b726576f69f01cb9f4bfe3aeaef29d | 3,655,657 |
def peek_with_kwargs(init, args=[], permissive=False):
"""
Make datatypes passing keyworded arguments to the constructor.
This is a factory function; returns the actual `peek` routine.
Arguments:
init (callable): type constructor.
args (iterable): arguments NOT to be keyworded; order... | d06df21ab439da1cacb52befa6c619f1efa23d1a | 3,655,658 |
def idc_asset_manage(request,aid=None,action=None):
"""
Manage IDC
"""
if request.user.has_perms(['asset.view_asset', 'asset.edit_asset']):
page_name = ''
if aid:
idc_list = get_object_or_404(IdcAsset, pk=aid)
if action == 'edit':
page_name = '编辑ID... | 7fbf1729c87e9e9921f19cf5cba2810879958848 | 3,655,659 |
def get_detected_column_types(df):
""" Get data type of each columns ('DATETIME', 'NUMERIC' or 'STRING')
Parameters:
df (df): pandas dataframe
Returns
df (df): dataframe that all datatypes are converted (df)
"""
assert isinstance(df, pd.DataFrame), 'Parameter must be DataFrame'
... | 23647127d0e5a125e06fb1932e74ba5f9c885ded | 3,655,661 |
def distance(coords):
"""Calculates the distance of a path between multiple points
Arguments:
coords -- List of coordinates, e.g. [(0,0), (1,1)]
Returns: Total distance as a float
"""
distance = 0
for p1, p2 in zip(coords[:-1], coords[1:]):
distance += ((p2[0] - p1[0]) ** 2... | 9c6088b740f42b839d4aa482c276fe4cc5dc8114 | 3,655,662 |
def roll_dice(dicenum, dicetype, modifier=None, conditional=None, return_tuple=False):
"""
This is a standard dice roller.
Args:
dicenum (int): Number of dice to roll (the result to be added).
dicetype (int): Number of sides of the dice to be rolled.
modifier (tuple): A tuple `(operator, val... | acbc97e4b7720129788c8c5d5d9a1d51936d9dc1 | 3,655,663 |
import math
def build_central_hierarchical_histogram_computation(
lower_bound: float,
upper_bound: float,
num_bins: int,
arity: int = 2,
max_records_per_user: int = 1,
epsilon: float = 1,
delta: float = 1e-5,
secure_sum: bool = False):
"""Create the tff federated computation for cent... | fcd35bc2df5f61174d00079638dda0c04c1490ff | 3,655,664 |
def initialise_halo_params():
"""Initialise the basic parameters needed to simulate a forming Dark matter halo.
Args:
None
Returns:
G: gravitational constant.
epsilon: softening parameter.
limit: width of the simulated universe.
radius: simulated radius of each particle
... | ee3311fd17a40e8658f11d2ddf98d0ff8eb27a6d | 3,655,665 |
def read_data(image_paths, label_list, image_size, batch_size, max_nrof_epochs, num_threads, shuffle, random_flip,
random_brightness, random_contrast):
"""
Creates Tensorflow Queue to batch load images. Applies transformations to images as they are loaded.
:param random_brightness:
:param... | 2bbb7f1be38764634e198f83b82fafb730ec3afa | 3,655,666 |
def reorder_matrix (m, d) :
"""
Reorder similarity matrix : put species in same cluster together.
INPUT:
m - similarity matrix
d - medoid dictionary : {medoid : [list of species index in cluster]}
OUTPUT :
m in new order
new_order - order of species indexes in matrix
"""
new_or... | 5d203ec6f61afe869008fa6749d18946f128ac87 | 3,655,667 |
def reward_penalized_log_p(mol):
"""
Reward that consists of log p penalized by SA and # long cycles,
as described in (Kusner et al. 2017). Scores are normalized based on the
statistics of 250k_rndm_zinc_drugs_clean.smi dataset
:param mol: rdkit mol object
:return: float
"""
# normalizat... | e3e5ebfabf31e4980dc6f3b6c998a08444ce9851 | 3,655,669 |
def loadmat(filename, variable_names=None):
"""
load mat file from h5py files
:param filename: mat filename
:param variable_names: list of variable names that should be loaded
:return: dictionary of loaded data
"""
data = {}
matfile = h5py.File(filename, 'r')
if variable_names is N... | 3b9183968fba56d57c705bce0ec440c630cc0031 | 3,655,670 |
def date_start_search(line):
"""予定開始の日付を検出し,strで返す."""
# 全角スペース
zen_space = ' '
# 全角0
zen_zero = '０'
nichi = '日'
dollar = '$'
# 全角スペースを0に置き換えることで無理やり対応
line = line.replace(zen_space, zen_zero)
index = line.find(nichi)
# 日と曜日の位置関係から誤表記を訂正
index_first_dollar = line.find(dol... | f89e332a2a0031acdf6fa443ea9752e528674b32 | 3,655,671 |
def train_sub1(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes,
nb_epochs_s, batch_size, learning_rate, data_aug, lmbda,
aug_batch_size, rng, img_rows=48, img_cols=48,
nchannels=3):
"""
This function creates the substitute by alternatively
augmenting the training d... | a5433f78c60f6beec14a6d4fd414d45dc8c65999 | 3,655,672 |
def divideArray(array, factor):
"""Dzielimy tablice na #factor tablic, kazda podtablica ma tyle samo elem oprocz ostatniej"""
factor = min(factor, len(array))
length = floor(len(array) * 1.0 / factor)
res = []
for i in range(factor - 1):
res = res + list([array[i * length:(i + 1) * length]]... | d94441e6036e78f9b541b9d170d03681740c81d3 | 3,655,673 |
def argMax(scores):
"""
Returns the key with the highest value.
"""
if len(scores) == 0: return None
all = scores.items()
values = [x[1] for x in all]
maxIndex = values.index(max(values))
return all[maxIndex][0] | 9310988a0f8aa1279882d060ade7febdc102b0c5 | 3,655,674 |
def rotateright(arr,k)->list:
"""
Rotate the array right side k number of times.
"""
temp=a[0]
poi=0
for i in range(len(arr)):
for j in range(0,k):
poi+=1
if(poi==len(arr)):
poi=0
temp1=arr[poi]
arr[poi]=temp
temp=temp1
return arr | 7d303f5b57cb10a1a28f5c78ffa848d2a9cb593f | 3,655,675 |
def get_ratio(numerator, denominator):
"""Get ratio from numerator and denominator."""
return (
0 if not denominator else round(float(numerator or 0) / float(denominator), 2)
) | e51a860292d54d2e44909ad878d0b1d8e66c37c2 | 3,655,677 |
import io
def create_app():
"""
Create a Flask application for face alignment
Returns:
flask.Flask -> Flask application
"""
app = Flask(__name__)
model = setup_model()
app.config.from_mapping(MODEL=model)
@app.route("/", methods=["GET"])
def howto():
instruction ... | d9a5d59f64dc9227949bbe73065d18bcc8142b9d | 3,655,678 |
def grad_clip(x:Tensor) -> Tensor:
"""
Clips too big and too small gradients.
Example::
grad = grad_clip(grad)
Args:
x(:obj:`Tensor`): Gradient with too large or small values
Returns:
:obj:`Tensor`: Cliped Gradient
"""
x[x>5] = 5
x[x<-5] = -5
... | 5c07c4432fda16d06bda8569aca34cbbaf45b076 | 3,655,679 |
def unfold_kernel(kernel):
"""
In pytorch format, kernel is stored as [out_channel, in_channel, height, width]
Unfold kernel into a 2-dimension weights: [height * width * in_channel, out_channel]
:param kernel: numpy ndarray
:return:
"""
k_shape = kernel.shape
weight = np.zeros([k_shape[... | 7106ead9b4953024731d918fb3c356b056bca156 | 3,655,680 |
def _parse_polyline_locations(locations, max_n_locations):
"""Parse and validate locations in Google polyline format.
The "locations" argument of the query should be a string of ascii characters above 63.
Args:
locations: The location query string.
max_n_locations: The max allowable numbe... | 3ebff7a35c86bad5986ee87c194dd9128936abb0 | 3,655,681 |
def dense(data, weight, bias=None, out_dtype=None):
"""The default implementation of dense in topi.
Parameters
----------
data : tvm.Tensor
2-D with shape [batch, in_dim]
weight : tvm.Tensor
2-D with shape [out_dim, in_dim]
bias : tvm.Tensor, optional
1-D with shape [o... | ac5550f901d1a7c94fee4b8e65fa9957d4b2ff78 | 3,655,682 |
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