content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def state_lookup():
"""Look up state from given zipcode.
Once state is found, redirect to call_senators for forwarding.
"""
zip_digits = request.values.get('Digits', None)
# NB: We don't do any error handling for a missing/erroneous zip code
# in this sample application. You, gentle reader, sho... | 13219025777e50422ab30902f0f3d35f7b73afed | 3,634,829 |
def vmtkmeshtosurface(mesh, cleanoutput=1):
"""Convert a mesh to a surface by throwing out volume elements and (optionally) the relative points
Args:
mesh: Volumetric mesh.
cleanoutput (bool): Remove unused points.
Returns:
vtkPolyData object.
"""
extractor = vmtkscripts.v... | 78b311d8523b495b36be5f64767389bf3c71a13a | 3,634,830 |
def time_delta_calc(contiguous_trajectory,order = 2):
"""Computes the time derivatives of a contiguous trajectory.
INPUT
contiguous_trajectory An array of space-time cordinates
order The order up to which the time derivatives are calculated. If
order=1, the velo... | 17352f37f69e9947c0cea8e9fd7cf54ea3ff6e2e | 3,634,831 |
def tasks_page():
""" Tasks and completions page
"""
return flask.render_template(
'tasks.html',
config=g.project.config,
project=g.project,
version=label_studio.__version__,
**find_editor_files()
) | 56656a8571e33fb40f3790a207faa2fccc7315a0 | 3,634,832 |
def outformathtml(pandasdf):
"""
change a few formating things to prettify and make it match
"""
pandas_table = pandasdf.to_html()
pandas_table = pandas_table.replace(""" border="1" """, " ")
pandas_table = pandas_table.replace("""<tr style="text-align: right;">\n <th></th>\n <th></th>... | 5f8abf88a2aead4f095f52c1f49ab4ad609c04a5 | 3,634,833 |
def list_catalogs(**kwargs):
"""
Return the available Cone Search catalogs as a list of strings.
These can be used for the ``catalog_db`` argument to
:func:`conesearch`.
Parameters
----------
cache : bool
Use caching for VO Service database. Access to actual VO
websites refe... | b539746edb4b6aa256bcbac3153b9a323bb41882 | 3,634,834 |
def rot_decode(data: str, n: int = 13) -> str:
"""Decode a ROT-encoded string that was shifted by `n` places."""
if not 1 <= n < 26:
raise ValueError('n must be in range [1, 26)')
return rot_encode(data, 26 - n) | f001f15684cc77e8ea52cf481626c6bdaf97c071 | 3,634,835 |
from datetime import datetime
def searchlight(x, y, m=None, groups=None, cv=None,
write=False, logger=None, permutations=0, random_state=42, **searchlight_args):
"""
Wrapper to launch searchlight
:param x: Data
:param y: labels
:param m: mask
:param groups: group labels
:pa... | 799f2496c0609050e6914576cfbdaba972320723 | 3,634,836 |
def codegen_reload_data():
"""Parameters to codegen used to generate the fn_ioc_parser_v2 package"""
reload_params = {"package": u"fn_ioc_parser_v2",
"incident_fields": [],
"action_fields": [],
"function_params": [u"ioc_parser_v2_artifact_id", u"ioc_... | 9c279e24b2e05adc5b1573393368216483307071 | 3,634,837 |
def Pattern2(s):
""" Compute the correlator for this pattern:
↓
↓ ↑ ↑
and symmetry-equivalent patterns
"""
res = 0.0
s = np.pad(s, ((0, 0), (2, 2), (2, 2)))
L = s.shape[-1]
for i in range(L-2):
for j in range(L-2):
res += s[1, i, j] * s[0, i+1,... | 390cf6b8262f00d396235fb8ef5d4acc72d1df9e | 3,634,838 |
import math
def gauss(x, x0, sigma):
"""
This function returns a Gaussian distribution.
"""
return (1/(sigma*math.sqrt(2 * math.pi))) * np.exp(-(x - x0)**2 / (2 * sigma**2)) | c4ebd141e68e59567b21355fb3b813c7f4ff914b | 3,634,839 |
import math
def distance():
"""
Calculate the distance between two points.
return:
Distance.
"""
return lambda a, b: math.sqrt((a.x-b.x)*(a.x-b.x)+(a.y-b.y)*+(a.y-b.y)+(a.z-b.z)*(a.z-b.z)) | ab3a14e7033afab66db7c283aefda745158bad65 | 3,634,840 |
import numpy
def rational_sum(numerator, denominator, *argv):
"""Sum of rational numbers."""
if len(argv) < 2:
gcd = numpy.gcd(numerator, denominator)
num_out, den_out = numerator//gcd, denominator//gcd
else:
num_2 = argv[0]
den_2 = argv[1]
num_3 = numerator*den_2 +... | b34d01ea2bcfd072430501828401d5b492b2bae5 | 3,634,841 |
import time
def nonce() -> str:
"""Return a nounce counter (monotonic clock).
References:
* https://support.kraken.com/hc/en-us/articles/360000906023-What-is-a-nonce-
""" # pylint: disable=line-too-long
return str(time.monotonic_ns()) | fb6221fef4c2c8af66200c4c9da8f6253854b186 | 3,634,842 |
def fetch_single_equity(stock_code, start, end):
"""
从本地数据库读取股票期间日线交易数据
注
--
1. 除OHLCV外，还包括涨跌幅、成交额、换手率、流通市值、总市值、流通股本、总股本
2. 添加后复权价格，使用复权价在图中去除间隙断层
3. 使用bcolz格式写入时，由于涨跌幅存在负数，必须剔除该列
Parameters
----------
stock_code : str
要获取数据的股票代码
start_date : datetime-like
自... | 04e63665fe9b05dfcca8387519d2a178d117acb5 | 3,634,843 |
import string
def base62_encode(number):
"""Encode a number in base62 (all digits + a-z + A-Z)."""
base62chars = string.digits + string.ascii_letters
l = []
while number > 0:
remainder = number % 62
number = number // 62
l.insert(0, base62chars[remainder])
return ''.join(l)... | b1f10fe69b6263d54f2e00a32b8260cbb3c42747 | 3,634,845 |
import json
def handle_update(config_path):
"""
handle changes in globalConfig.json
Args:
config_path : path to globalConfig.json
Returns:
dictionary : schema_content (globalConfig.json)
"""
with open(config_path) as config_file:
schema_content = json.load(config_file... | f51f52f9353339ba1cf080b10ad8f28691deefe9 | 3,634,846 |
from typing import Sequence
from typing import Tuple
import urllib
def encode(s: Sequence[Tuple[str, str]], similar_to: str=None) -> str:
"""
Takes a list of (key, value) tuples and returns a urlencoded string.
If similar_to is passed, the output is formatted similar to the provided urlencoded str... | c0153b77ec03708d54574062d15c7f81df59510b | 3,634,847 |
def gis_ellipse_polygon(origin, a, b, orientation=0.0, complexity=128):
"""
Generate a polygon of an ellipse suitable for GIS applications.
:param origin: the center of the ellipse (expected to be UTM, m)
:param a: the a/semi-major axis length (expected to be UTM, m)
:param b: the b/semi-minor axis... | 48df0adbc7fd04508a6d3a8288c6b46fe9f9bdb0 | 3,634,848 |
def activate_lesson():
"""
{
"id": 51,
"active": "A"
}
"""
domain = request.get_json()
return lesson_service.activate_lesson(domain) | a4e48ca2c8e65e9ff0e2fb95b61ef776573a9649 | 3,634,849 |
def split(labels, n_per_class=20, seed=0):
"""
Randomly split the training data.
Parameters
----------
labels: array-like [n_nodes]
The class labels
n_per_class : int
Number of samples per class
seed: int
Seed
Returns
-------
split_train: array-like [n_p... | 69c7510f9be494afe7bb0c1492870c1d7c2d6694 | 3,634,850 |
from setfilter.setfilter import Setfilter
def base():
"""test Setfilter """
# build fixture
# initialization
center = np.array([0, 0, 0, 0.])
body = np.array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
... | b306d6425a6e1e329e0af006585c06be4585972e | 3,634,851 |
def stack(*cons):
"""
Combine constraints into a large constaint
by intersection.
Parameters
----------
cons : [`selection.affine.constraints`_]
A sequence of constraints.
Returns
-------
intersection : `selection.quasi_affine.constraints`_
Notes
-----
Res... | 8ddc52aa41c2ef4ec784067692efa1f3643a130c | 3,634,852 |
def _create_poi_gdf(
tags,
polygon=None,
north=None,
south=None,
east=None,
west=None,
timeout=180,
memory=None,
custom_settings=None,
):
"""
Create GeoDataFrame from POIs json returned by Overpass API.
Parameters
----------
tags : dict
Dict of tags used ... | 4d88c0bb60ee836131bf5197c0caad17c0358f5b | 3,634,853 |
def prepare_data(seqs, labels, maxlen=None, x_dim = 3, mapping=None, max_mapping=None):
"""Create the matrices from the datasets.
This pad each sequence to the same length: the length of the
longest sequence or maxlen.
if maxlen is set, we will cut all sequence to this maximum
length.
This sw... | e2582ce07fa770b78bcd02dae26b345059c051c0 | 3,634,854 |
import types
def _get_prediction_tensor(
predictions_dict):
"""Returns prediction Tensor for a specific Estimators.
Returns the prediction Tensor for some regression Estimators.
Args:
predictions_dict: Predictions dictionary.
Returns:
Predictions tensor, or None if none of the expected keys are... | 0bd509d4021e0f0622b0cf685e5bf8682f47daa0 | 3,634,855 |
def gen_confirm_code():
""" Generate a new email confirmation code and return it
"""
return generate_uuid_readable(9) | 992844a42886892cfa7eebc44e8efb61aa547725 | 3,634,856 |
def update_profile():
"""
Update profile page
"""
form = UpdateProfileForm() # Update profile form
if form.validate_on_submit(): # If form is submitted and validated
if form.picture.data: # If picture is uploaded
picture = save_picture(form.picture.data) # Save picture
... | ad2b427487cc63aa64068b86254c2d5b6b363c2c | 3,634,858 |
def get_version_info(pe):
"""Return version information"""
res = {}
for fileinfo in pe.FileInfo:
if fileinfo.Key == 'StringFileInfo':
for st in fileinfo.StringTable:
for entry in st.entries.items():
res[entry[0]] = entry[1]
if fileinfo.Key == '... | 8a01062de92cd4887f5cc7a292ffb644a701b43c | 3,634,859 |
import torch
def data_prepare(coord, feat, label, split='train', voxel_size=0.04, voxel_max=None, transform=None, shuffle_index=False, origin='min'):
""" coord, feat, label - an entire cloud
"""
if transform:
coord, feat, label = transform(coord, feat, label)
if voxel_size:
# voxelize ... | 988a7068d0fc383f86c853b0593189633a87a53d | 3,634,860 |
def _right_branching(nodes):
"""
Parameters
----------
nodes: list[T], where T denotes NonTerminal or Terminal
Returns
-------
list[T], where T denotes NonTerminal or Terminal
"""
if len(nodes) == 2:
return nodes
lhs = nodes[0] # The left-most child node is head
ind... | 9bfa47daa95be30b7f9a9ada882e1fdceb1295e5 | 3,634,862 |
import random
def random_swap(o_a, o_b):
"""
Randomly swap elements of two observation vectors and return new vectors.
:param o_a: observation vector a
:param o_b: observation vector b
:return: shuffled vectors
"""
X, Y = [], []
tf = [True, False]
for x, y in zip(o_a, o_b):
... | f243e91e5b281c682601fdb8df49bd7e6209274c | 3,634,863 |
def parse_multiplicative(index):
"""Parse multiplicative expression."""
return parse_series(
index, parse_unary,
{token_kinds.star: expr_nodes.Mult,
token_kinds.slash: expr_nodes.Div,
token_kinds.mod: expr_nodes.Mod}) | e314ca44735db31d00fe55268be8a7f69816db45 | 3,634,864 |
def return_factorized_dict(ls):
"""
###### Factorize any list of values in a data frame using this neat function
if your data has any NaN's it automatically marks it as -1 and returns that for NaN's
Returns a dictionary mapping previous values with new values.
"""
factos = pd.unique(pd.factoriz... | 7804f12f953eabcdc7bb398121500c2c8f2e278f | 3,634,865 |
def _strip_asserts(source):
"""
Remove assert method calls from source code.
Using RedBaron, replace some assert calls with print statements that print the actual
value given in the asserts. Depending on the calls, the actual value can be the first or second
argument.
Parameters
----------... | 4c484635328e9610bd07004cfba63acc4ae039d1 | 3,634,866 |
def _index_clusters(feat_mat, init_mat):
"""Creates a hierarchical binary tree till the top-level clusters supplied."""
cluster_feat = init_mat.T.dot(feat_mat)
cluster_feat = cluster_feat.tocsr()
cluster_feat = skprep.normalize(cluster_feat, "l2", axis=1)
init_cluster = HierarchicalKMeans.gen(
... | f48e32507e298c4b089c9358c14727c4b8444ee5 | 3,634,867 |
from functools import reduce
def gcd(numbers):
"""Return greatest common divisor of integer numbers.
Using Euclid's algorithm.
Examples
--------
>>> gcd([4])
4
>>> gcd([3, 6])
3
>>> gcd([6, 7])
1
"""
def _gcd(a, b):
"""Return greatest common divisor of two i... | da7ae2a24649bc05e233533735baf850a37dcc5a | 3,634,868 |
def normal_attention(tensor_base, tensor_to_attend,
mask_for_tensor_base,
mask_for_tensor_to_attend,
similarity_method='inner', hn=100,
use_pooling=False, pooling_method='max',
reverse=False, scope=None):
"""
... | 93df4d084bb76cba4ab227928de6eb72e7f5af76 | 3,634,869 |
from bs4 import BeautifulSoup
def getPageNum(html):
"""解析第一页网页，返回该用户的书评页数
"""
soup=BeautifulSoup(html,'html.parser')
paginator=soup.find('div','paginator')
pas=paginator.findAll('a')
num=int(pas[-2].text)
return num | 626a6f580e5634ba741d0794f6fc4c020aecabd0 | 3,634,870 |
async def get_user(api_management_name=None,resource_group_name=None,user_id=None,opts=None):
"""
Use this data source to access information about an existing API Management User.
> This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/d/api... | c5bdf6aa7d0c69a59b9b8f6a93f63a9cba9a00cd | 3,634,871 |
def makeCC_allpair(spikes, Begin, End, N_thred):
"""
全てのCCをBegin~Endの間で計算する
args:
spikes: list型
Begin, End: int型
N_thred: 並列計算をするときに与えるスレッド数
return:
X: ペアごとに計算したCC. X.shape = (ニューロンのペア数、CCの幅)
index: ペアのニューロンの番号. index.shape = (ニューロンのペア数、2). index[i][0]は結合先ニューロン、in... | e9e5b9bd805251b4244ab89e6a1e11316617101d | 3,634,872 |
def move_position(facility, cur_position, direction):
"""Move position from cur_position in the direction on facility."""
changes = DIRECTIONS[direction]
doors = DIRECTION_TO_DOOR[direction]
for cell in (doors, ROOM):
next_position = []
for coordinate, change in zip(cur_position, changes... | 64601f07378da56eaa03d896987491cd6f93182b | 3,634,873 |
from typing import Callable
import itertools
from typing import OrderedDict
import pprint
def search_for_improvements(
targets : [Exp],
wf_solver : ModelCachingSolver,
context : Context,
examples : [{str:object}],
cost_model : CostModel,
stop_cal... | 374a5a16f6ec4e69e983613aedbb298df1411cca | 3,634,874 |
def down_sample(fft_vec, freq_ratio):
"""
Downsamples the provided data vector
Parameters
----------
fft_vec : 1D complex numpy array
Waveform that is already FFT shifted
freq_ratio : float
new sampling rate / old sampling rate (less than 1)
Returns
-------
... | 1ddfaa075c1d8bc68f9348d4f49807b61eceb60c | 3,634,875 |
import torch
def set_device(cuda: bool) -> int:
"""Set the device for computation.
Args:
cuda (bool): Determine whether to use GPU or not (if available).
Returns:
int: Index of a currently selected device, CPU or GPU.
"""
device = torch.device("cuda" if (torch.cuda.is_available() a... | f65fc5e38f14b8de78d14ac150ac14e5c8788e26 | 3,634,877 |
import torch
def hsic_regular(x, y, sigma=None, use_cuda=True, to_numpy=False):
"""
"""
Kxc = kernelmat(x, sigma)
Kyc = kernelmat(y, sigma)
KtK = torch.mul(Kxc, Kyc.t())
Pxy = torch.mean(KtK)
return Pxy | 551ed76c2f902b662ffe2c693da4474b6eb3958e | 3,634,878 |
def random_correlation(size, n_factors, random_seed=None):
"""
Generates a random correlation matrix with 'size' lines and columns and
'n_factors' factors in the underlying structure of correlation.
:param size: int. Size of the correlation matrix
:param n_factors: int. number of factors in the corr... | 92cd98bdf95e0ffef51c016c1c9ef05391c483dd | 3,634,879 |
def eigendecompose(S):
"""Eigendecompose the input matrix."""
eigvals, V = np.linalg.eig(S)
return eigvals, V | 0658d3b83d54a7435862b89d0a1fa3e82f4082fd | 3,634,881 |
def _CreateSampleDirectoryCoverageData(builder='linux-code-coverage',
modifier_id=0):
"""Returns a sample directory SummaryCoverageData for testing purpose.
Note: only use this method if the exact values don't matter.
"""
return SummaryCoverageData.Create(
server_ho... | 3edebb67f0b4dafa837c14b99fd0b2b241da784d | 3,634,882 |
def sched_time(update_time) -> int:
""" Return interval between the current time and the update time in
seconds
"""
current_time_ss = hhmm_to_seconds(current_time_hhmm())
update = hhmm_to_seconds(update_time)
interval = update-current_time_ss
return interval | 5a57f591550a9f72dc7e9aa87a75c7aacf624cbe | 3,634,883 |
def http_jsonrpc_post(hostname, port, uri, method, params):
"""Perform a plain HTTP JSON RPC post (for task farming)"""
url = "http://%s:%s%s" % (hostname, port, uri)
data = simplejson.dumps({ 'method': method, 'params': params,
'jsonrpc': '2.0', 'id': 1 })
req = urllib2.Request(url, data, {'... | 8ac5e836564c4ddc2a277c4aea8bf0ff6b8836c2 | 3,634,884 |
def get_user_event(current_user, event_id):
"""
Query the user to find and return the event specified by the event Id
:param event_id: Event Id
:param current_user: User
:return:
"""
user_event = User.get_by_id(current_user.id).events.filter_by(event_id=event_id).first()
return user_even... | d65e3a65cb4400a9b4173b8ea4d45f6f94e0993c | 3,634,885 |
def get_distances(scr_data_dict, distance_keys, data_centroid=None):
"""
@param scr_data_dict:
@param distance_keys:
@param data_centroid: Do not provide if calculating distances for establishing s thresholding
@return:
"""
dataset_keys = scr_data_dict.keys()
# Extracting dataset point o... | c0aec2de89bd7c7150d3f5fcbc3161f9f2d441c0 | 3,634,886 |
def compare_numeric_abundances(values_in_taxa_list_1, values_in_taxa_list_2):
"""Retun a Pandas Series with [abundance-in, abundance-out, p-value]."""
mean1 = mean_ignore_nans(values_in_taxa_list_1)
mean2 = mean_ignore_nans(values_in_taxa_list_2)
keyslist1 = list(values_in_taxa_list_1.keys())
keysli... | f2e6e4326b12242a8493b82e37190a47d60b44e6 | 3,634,887 |
def parse_folder(folder, start_time=None, grepcmd=None, tmp_folder=None, force_grep=False):
"""
Args:
grepcmd, tmp_folder, force_grep's default value should be the same with parse_single_log_with_pregrep
Yields:
[[yield], []]: nested yields
"""
def get_first_last_line(filepath):
... | b8d233ba8ad579d38e3353b841ea03d52bb83962 | 3,634,888 |
def search(request):
"""
search function which reads get data from a requests and uses it to find stuff in elasticsearch
"""
context = {"table": []}
if "subject" in request.GET or "predicate" in request.GET or "object" in request.GET:
es = elasticsearch.Elasticsearch(settings.ELASTICSEARCH)
... | 85b1daba7d6a815f3e465b23ef369d439834de9c | 3,634,889 |
from typing import Dict
def parse_measurement_lines(xml_root: Element) -> Dict[int, LineString]:
"""Parses the measurement line from the given xml root
Args:
xml_root (ET.ElementTree): root of the xml file
Returns:
measurement lines to use in the analysis (id, line)
"""
measureme... | 8e7ff2a75189e8f6c1960f6849c1b63bc1ff4821 | 3,634,891 |
from typing import Any
from typing import Optional
def compute_and_apply_approximate_vocabulary(
x: common_types.ConsistentTensorType,
default_value: Any = -1,
top_k: Optional[int] = None,
num_oov_buckets: int = 0,
vocab_filename: Optional[str] = None,
weights: Optional[tf.Tensor] = None,
... | 9662796984852ff255ca923e1048121427debfe6 | 3,634,892 |
import io
def album_cover():
"""Get the current song's album cover."""
cover = app.config["player"].album_cover()
image = io.BytesIO(cover) if cover else "static/no_cover.jpg"
return send_file(image, mimetype="image/jpeg") | 3c2c7b9513bd36e49f27d4b58ca1408932402d72 | 3,634,893 |
def create_new_filename(original_filename: str,
user_response: str):
"""
Creates new file name depending on the users response
:param original_filename: str
:param user_response: str
:return: str | new filename
"""
if user_response == "1":
time = str(today.time())[:8].repla... | 789bf9f73b4647c534859a2ae97b9ec806b02d1c | 3,634,895 |
from typing import OrderedDict
def genertate_info_tree(traces, trace_events, level="module"):
"""
"""
assert level in ["module", "operator", "mixed"]
tree = OrderedDict()
for trace in traces:
path, module = trace
# unwrap all of the events, in case model is called multiple times
... | cc885b41df2d2ecf0484c55a07f9b8ef381263d8 | 3,634,897 |
def interpolate(x, ratio):
"""Interpolate data in time domain. This is used to compensate the
resolution reduction in downsampling of a CNN.
Args:
x: (batch_size, time_steps, classes_num)
ratio: int, ratio to interpolate
Returns:
upsampled: (batch_size, time_steps * ratio, classes_num... | 54ceb6aee15de3c775d3be93bbc48422dc542641 | 3,634,898 |
from astroquery.mast import MastClass
def _resolve_object(target):
"""Ask MAST to resolve an object string to a set of coordinates."""
# Note: `_resolve_object` was renamed `resolve_object` in astroquery 0.3.10 (2019)
return MastClass().resolve_object(target) | bf9c0bb3a09fac1622cc107cf3f9532888b3a4f9 | 3,634,899 |
import torch
def spgr(pd, r1, r2s=None, mt=None, transmit=None, receive=None, gfactor=None,
te=0, tr=25e-3, fa=20, sigma=None, device=None):
"""Simulate data generated by a Spoiled Gradient-Echo (SPGR/FLASH) sequence.
Tissue parameters
-----------------
pd : tensor_like
Proton densit... | 78cfa5f7b264fd85603a5e66ec77974558f05d21 | 3,634,900 |
def create_subject(request):
"""
This is a form
"""
template = loader.get_template('form.html')
form = SubjectForm()
return HttpResponse(template.render({'form':form, 'redirect': '/subject', 'submit':'Create Subject and add to class'}, request)) | 8bea2cb4e6ba26a030a9b3008377c126af551491 | 3,634,902 |
def text2number(text):
"""
Convert arabic text into number, for example convert تسعة و عشرون = >29.
Example:
>>> text2number(u"خمسمئة و ثلاث و عشرون")
523
@param text: input text
@return: number extracted from text
@rtype: integer
"""
number_names = WORDS_NUMBERS.copy()
... | 9a0b28810549efab956d720695534e82aecf2c4f | 3,634,903 |
def parse_prototype(prototype, additional_definitions={}):
"""Given a single annotated C function prototypes (see module docstring),
for syntax), and an optional dict of non-base type definitions,
return the following information that ctypes needs to load and annotate
a function:
function_name: str
... | 93d79407bf0b4175f51dfef0fb39fa288eaa1512 | 3,634,904 |
def create_resource():
"""QonoS resource factory method."""
return wsgi.Resource(SchedulesController()) | caca6c4a6d8b3e67db79eee1937ae0479de1ab05 | 3,634,905 |
def compute_heatmap(model, saved_model, image, pred_index, last_conv_layer):
"""
construct our gradient model by supplying (1) the inputs
to our pre-trained model, (2) the output of the (presumably)
final 4D layer in the network, and (3) the output of the
softmax activations from the model
"""
... | c412a1ddb7300434334b2276df257cda51072f96 | 3,634,906 |
def get_all_orders():
"""Returns a list of ALL the orders for rooms and AGs, with reader and approver specified for each."""
# Get a list of all the orders of access groups of all responsible approvers.
ag_relation = ApprovesAgRequest.query \
.join(AccessGroupRequest, AccessGroupRequest.id == ApprovesAgRequest.ag... | 798c7347e0db7f3f7ba2f66364cbc55be3a641ed | 3,634,907 |
def isAnomaly(lowBand, highBand, value):
"""Condition for anomaly on a certain row"""
if value < lowBand or value > highBand:
return True
return False | 552513115ec9c98c40cd6487b6a33f497c562c87 | 3,634,908 |
def initWeights(positive_count, negative_count, trainingSamples):
"""
Initialise weights.
:param positive_count: Number of positive samples.
:param negative_count: Number of negative samples.
:param trainingSamples: Training samples.
:return: Weights
"""
positiveSampleInitialWeight ... | 640fb32b62fc461b9c638d3a647c53db69424fd9 | 3,634,909 |
def get_distance_calculator(name, *args, **kwargs):
"""returns a pairwise distance calculator
name is converted to lower case"""
name = name.lower()
if "moltype" in kwargs and kwargs.get("moltype") is None:
kwargs.pop("moltype")
if name not in _calculators:
raise ValueError('Unknown... | c8d1d0fca18e7f71742fb8a45829ac4e8f84077b | 3,634,910 |
def compute_escape_peak(spectrum, ratio, params,
escape_e=1.73998):
"""
Calculate the escape peak for given detector.
Parameters
----------
spectrum : array
original, uncorrected spectrum
ratio : float
ratio of shadow to full spectrum
param : dict
... | c6948547a2bb34f32cb423b5ab0cd8d9f9f695e4 | 3,634,912 |
def create_transaction(wallet, purchase):
"""
creates a transaction of crypto from the PSP wallet to a user who has paid in fiat
Note: Ideally this will be done for more than 1 tx at a time.. would have a basket of tx ready to go
and then they will all go out in one TX with all the outputs. Then the s... | d6e31bb698782128e58ccf73e8b62e34f8937377 | 3,634,913 |
import numpy
def _create_globio_lulc_op(
lulc_array, potential_vegetation_array, pasture_array,
ffqi, globio_nodata, pasture_threshold, primary_threshold):
"""Construct GLOBIO lulc given relevant biophysical parameters."""
result = numpy.empty_like(lulc_array, dtype=numpy.int16)
result[:] ... | 1a6ca959e8da30767a264bc529f16ab6f2626851 | 3,634,914 |
import random
def temperature_dist():
"""Random normal variated temperature around RT_MEAN with RT_SIGMA"""
return random.normalvariate(RT_MEAN, RT_SIGMA) | 971b2296878780e9af568784a454d08dfead0e2c | 3,634,915 |
def pair_keys_to_items(items, key):
"""
Convert the list of key:value dicts (nics or disks) into a dict.
The key for the new dict is one value of the current dict identified by the
key parameter. If it does not exist, then the key is the order number in
the list.
"""
new_items = {}
for ... | 92c66bfbb298e767b3fedbfcfd48ad87ac1162ef | 3,634,916 |
def rotateStructure(structure):
"""
Rotate a structure randomly
"""
angle_rad = _np.random.rand(1) * 2 * _np.pi
newstructure = _np.array(
[
(structure[0, :]) * _np.cos(angle_rad)
- (structure[1, :]) * _np.sin(angle_rad),
(structure[0, :]) * _np.sin(angle_r... | 9df411e82caa5827499ae5648a1ec7d24e27cff5 | 3,634,918 |
async def iamalive(name, **params):
"""
Accept services promotions
"""
await state.alive_service(name)
return OK | 82e0e8243dd8eadfc27cab484157c2d1c6db63b1 | 3,634,919 |
def add_wind_rotation_info(res: str, ds: xr.Dataset) -> xr.Dataset:
"""
Add wind rotation information to the dataset
Args:
res: grid resolution, format as f'c{number cells in tile}'
"""
rotation = _load_wind_rotation_matrix(res).drop_vars("tile", errors="ignore")
common_coords = {"x": ... | 242293904998367fe75b112adc1298ead39152a7 | 3,634,920 |
def get_human_size(size):
"""Return a string describing the size in bytes"""
if size < 1024:
return '{} B'.format(size)
if size < 1024 * 1024:
return '{:.2f} KB'.format(float(size) / 1024)
if size < 1024 * 1024 * 1024:
return '{:.2f} MB'.format(float(size) / (1024 * 1024))
if... | 48cee8ca55717d6fb48c5c1dc06becff71c58f0e | 3,634,921 |
def prepare_m2m_data(model, ctx_dict, fields, parent_ids):
"""
Recebe os argumentos e devolve um dicionário com os dados necessários para montar a lista
"""
#print('in_prepare_m2m_data')
name = ctx_dict.get('name')
record = ctx_dict.get('record')
edit_ok = ctx_dict.get('edit_ok')
popup =... | 87ec8e992e63ecf53a3e0c878d1254d70c957d11 | 3,634,923 |
def empty_when_none(_string=None):
"""If _string if None, return an empty string, otherwise return string.
"""
if _string is None:
return ""
else:
return str(_string) | 402186ee7b4ba9c3968f81bee23134067d0f260e | 3,634,924 |
def visualizeFrame(x, y, z, translation, size = 0.1):
""" Input: x - numpy array (x, y, z) or column vector
y - numpy array (x, y, z) or column vector
z - numpy array (x, y, z) or column vector
translation - numpy array (x, y, z) or column vector
... | 2ad8aaf1e16488d4b87d155c5115185a42f68fee | 3,634,925 |
def baseline_dwt(
array,
max_iter,
level=None,
wavelet="sym6",
background_regions=None,
mask=None,
mode="constant",
axis=-1,
):
"""
Iterative method of baseline determination, based on the discrete wavelet transform.
Parameters
----------
array : `~numpy.ndarray`
Data with ba... | 41edf5cc884592ee8c480881702fd02206cd55bd | 3,634,926 |
def get_ego_polygon(ego_state: StateSE2, vehicle: VehicleParameters) -> Polygon:
"""
Return Shapely polygon correspoding to Ego
:param ego_state: x, y (center of rear axle) and heading
:param vehicle: Parameters of the vehicle
:return: Shapely polygon for ego
"""
ego_rectangle = construct_eg... | eb9d3cee0a6c27fe76bbc5525c06c9e094584850 | 3,634,927 |
import hashlib
def file_hashes(f, bufsize=16000000):
"""
computes md5, sha1, sha256 from a file obj. intended for large files.
returns 3-tuple of hexstrings
"""
md5 = hashlib.md5()
sha1 = hashlib.sha1()
sha256 = hashlib.sha256()
while True:
buf = f.read(bufsize)
if len(buf) == 0:
break
... | 4e23a0d99cda07325ba3a14675bfb515c12d2950 | 3,634,928 |
def paginate_update(update):
"""
attempts to get next and previous on updates
"""
time = update.pub_time
event = update.event
try:
next = Update.objects.filter(event=event, pub_time__gt=time).order_by('pub_time').only('title')[0]
except:
next = None
try:
previous... | eda885cfdb538f6e609c097d146c3803b17fb1f8 | 3,634,929 |
def get_timestep(all_params, stuff_for_time_loop):
"""
Gets the full VFP + logging timestep
:param all_params: (dictionary) contains input parameters for simulation
:param stuff_for_time_loop: (dictionary) contains derived parameters for simulation
:return: a function with the above values initializ... | 0ca048c07b9fe230469c7e16e0a847b9b155fc50 | 3,634,930 |
def TDF_ComparisonTool_SourceUnbound(*args):
"""
* Finds from <aRefDataSet> all the keys not bound into <aRelocationTable> and put them into <aDiffDataSet>. Returns True if the difference contains at least one key. (A key is a source object). <anOption> may take the following values: 1 : labels treatment only; 2... | 4f1ad0bbb33ff34f5bbdac37a43bf8ab8c1bd6bd | 3,634,932 |
def sorted_qubits(qbs: Qubits) -> Qubits:
"""Return a sorted list of unique qubits in canonical order.
Qubits can be of different types, so we sort first by type (as a string),
then within types.
"""
return tuple(sorted(list(set(qbs)), key=lambda x: (str(type(x)), x))) | fcff2d13d22875118348566ce6432834c31ba644 | 3,634,933 |
def lu(a: np.ndarray,
permute_l: bool = False,
overwrite_a: bool = False,
check_finite: bool = True,
is_lapack_piv: bool = True):
"""
Compute pivoted LU decomposition of a matrix.
"""
if overwrite_a:
pivot, LU = _lu_internal(a,
permut... | c417a0ec96c92f6662f2dac78b9369ca7feb18f0 | 3,634,934 |
def compute_referendum_result_by_regions(referendum_and_areas):
"""Return a table with the absolute count for each region.
The return DataFrame should be indexed by `code_reg` and have columns:
['name_reg', 'Registered', 'Abstentions', 'Null', 'Choice A', 'Choice B']
"""
tempdf = referendum_and_are... | afd55a1c514c59cdac2a507b9f90eb382f2845ee | 3,634,935 |
from typing import List
import json
def get_edfi_payloads(context, dbt_run_result, table_reference: str) -> List:
"""
Extract BigQUery table and return the
resulting JSON as a dict.
"""
df = context.resources.warehouse.download_table(table_reference)
df_json = df.to_json(orient="records", dat... | c2ad0026ad4e56a256a824a4c1fae0762aaa51b7 | 3,634,937 |
from typing import List
def deinterleaver(pseudo_rand_array: List[int], array: np.ndarray) -> np.ndarray:
"""Random permutations for deinterleaving an array according to a pseudo random sequence"""
dims = array.shape
flat_array = array.flatten()
matrix = np.zeros(array.size, int)
for index, positi... | 8fc1a59921875de3e647f09ba93f6d645d2829c8 | 3,634,938 |
def add_rnn_encoder_arguments(group):
"""Define arguments for RNN encoder."""
group.add_argument(
"--elayers",
default=4,
type=int,
help="Number of encoder layers (for shared recognition part "
"in multi-speaker asr mode)",
)
group.add_argument(
"--eunits"... | 64a65bd496402dedfe98c4bd0d5bbc516c87a398 | 3,634,939 |
def sum_sample(attrfx='merge'):
"""Returns a mapper that computes the sum sample of a dataset.
Parameters
----------
attrfx : 'merge' or callable, optional
Callable that is used to determine the sample attributes of the computed
sum samples. By default this will be a string representation o... | 3a86b1bd169d75a573261313181bb5ca75df3c89 | 3,634,940 |
def match_profile_to_preset(profile_lookup, colour_preset_lookup):
"""Get a list of preset names that match the current profile colour scheme.
"""
match_list = []
for preset_name, preset_colours in colour_preset_lookup.iteritems():
match = True
for colour_name, colour_val in preset_colo... | 7ac13ee2d29970f284269c6ca0e32ed6de27e8f7 | 3,634,941 |
import json
def jsonpify( data, callback ):
"""
Helper to support JSONP
"""
try:
output = json.dumps( data, sort_keys=True, indent=2 )
if callback:
output = '%s(%s)' % ( callback, output )
return output
except Exception as e:
message = 'exception jsonify... | 6b41ca031b45f835aa57bc08e738dbd6c68d59c5 | 3,634,942 |
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