content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import math
def exponential(max_iteration=1.0, min_transition=1.0, initial_radius=1.0):
"""
Return an exponential radius neighborhood function.
f(iteration) = initial_radius * (min_transition / initial_radius)^(iteration / max_iteration)
Keyword arguments:
max_iteration -- maximum count of itera... | 8f3a8789547ddb93cb884da04c92ecfef9daf5ed | 3,611,730 |
def cal_newpath(dis_mat, path_new, cityNum):
"""
计算所有路径对应的距离
:param dis_mat: 城市距离矩阵 ndarray
:param path_new: 路径矩阵 ndarray
:param cityNum: 城市数量 int
:return: 动态规划最优路径 list
"""
dis_list = []
for each in path_new:
dis = 0
for j in range(cityNum - 1):
dis = dis_mat[each[j]][each[j + 1]] + dis... | 2a4d733e9633a44da3d66d74d54b75efc165d8bf | 3,611,731 |
def load_input(source):
"""load the input"""
if isinstance(source, str): # pragma: no cover
with open(source, 'r', encoding='utf8') as stream:
return stream.read()
else:
data = source.read()
if isinstance(data, bytes):
return data.decode("utf8")
retur... | fa0a9aac1854af59f400bf2344638c8fb4bee96d | 3,611,732 |
def _compute_dci(x_recon, x):
"""Computes score based on both training and testing codes and factors."""
scores = {}
importance_matrix, train_err = compute_importance_gbt(
x_recon, x)
assert importance_matrix.shape[0] == x_recon.shape[0]
assert importance_matrix.shape[1] == x.shape[0]
informativeness_... | 047745df93d73e26578c1d7b7b0bb5e4165a1434 | 3,611,734 |
from typing import Dict
from typing import Any
def sample_sac_params(trial: optuna.Trial,
octree_observations: bool = True,
octree_depth: int = 4,
octree_full_depth: int = 2,
octree_channels_in: int = 7,
octr... | 63d57c7a8de0eb12f01b410ed33ba5b5beb4a725 | 3,611,735 |
import random
def split_vertex_labels(num_vertices, proportion_censored, rng=None):
""" Adapts tensorflow dataset to produce another element in the labels dictionary
corresponding to whether the vertex is in the training or testing set.
Parameters
----------
num_vertices: The number of vertices i... | 63a3419ca171ef006041e12f3cb61b9c368262af | 3,611,736 |
def table():
"""Display base and representative InChI."""
if request.method == "POST" and request.form.get("nmr-inchi-table-data"):
nmr_experiment_type = request.form.get("select-nmr-experiment")
generate_nmr(
nmr_experiment_type=nmr_experiment_type, records=RECORDS
)
... | 29d5552418f98b79036df37446ae940f85873ed6 | 3,611,737 |
def PSO(
directed = False, preprocess = "auto", load_nodes = True, load_node_types = True,
load_edge_weights = True, auto_enable_tradeoffs = True,
sort_tmp_dir = None, verbose = 2, cache = True, cache_path = None,
cache_sys_var = "GRAPH_CACHE_DIR", version = "2020-05-19", **kwargs
) -> Graph:
"""Ret... | 02d9f25998aba2154e8d921ce4667c8803f286df | 3,611,738 |
def se_resnet152(num_classes):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model | 95984b592f695e9423263f8c3cf0edb7fb07e541 | 3,611,739 |
def is_integer(mark_string):
"""Function to check if a supposed pk is an integer."""
try:
mark_id = int(mark_string)
except ValueError:
return False
return mark_id | 1df4842906452cf5672584e5781e0e8a2d3b367d | 3,611,740 |
def getCoordsAndSearchRadius(inputString):
"""getCoordsAndSearchRadius.
Args:
inputString:
"""
coords = {}
ra = None
dec = None
radius = None
sex = COORDS_SEX_REGEX_COMPILED.search(inputString)
decimal = COORDS_DEC_REGEX_COMPILED.search(inputString)
if decimal:
... | 289e6153908ec0553c4560b18d8afd4e57aeebbc | 3,611,741 |
def _extract_command_with_args(cmd):
"""Parse input command with arguments.
Parses the input command in such a way that the user may
provide additional argument to the command. The format used is this:
command=arg1,arg2,arg3,...
all the additional arguments are passed as arguments to the target
... | 3db8aebab04e32f292e2956412bd81e7a07a471e | 3,611,742 |
def filtered_secondary_files(unfiltered_secondary_files: dict) -> list:
"""
Remove unprocessed secondary files.
Interpolated strings and optional inputs in secondary files were added to
CWL in version 1.1.
The CWL libraries we call do successfully resolve the interpolated strings,
but add the ... | 89c0906984ca634b8afc3dd4bdf2e9ccbbad504c | 3,611,743 |
def get_P_rtd_hs(q_rtd_hs, e_rtd):
"""定格消費電力 (4)
Args:
q_rtd_hs(float): 温水暖房用熱源機の定格能力 (W)
e_rtd(float): 当該給湯機の効率
Returns:
float: 定格消費電力
"""
return q_rtd_hs / e_rtd | 61d4a7c2f26b979891936efd6db0195ca3b083b0 | 3,611,744 |
def registration_success(request):
"""
This view is called when the registration succeeded
"""
rcontext = RequestContext(request, {})
return render_to_response('auth/registration_success.haml', rcontext) | 6fe87ed1559b7d0a54a380d543eeab1a6a4fc63b | 3,611,745 |
def match_attr( src1, src2, attrlist=None ):
"""
extension: check that attributes listed are the same for each src
input: string <OR> dict, string <OR> dict, list
output: bool
notes: input sources can be either paths to netcdf files
or dicts {attr_name : attr_val}.
if attrlist is None t... | 4a6eb511dc90c85bc38fa2bd0caef1a85e1c2446 | 3,611,746 |
def slash_validator(
epochs_ctx: EpochsContext,
state: BeaconState,
slashed_index: ValidatorIndex,
config: Eth2Config,
whistleblower_index: ValidatorIndex = None,
) -> BeaconState:
"""
Slash the validator with index ``slashed_index``.
"""
epoch = epochs_ctx.current_shuffling.epoch
... | da36f061397c3909427abc47a30b68e56ac819de | 3,611,747 |
def biot_savart(loop_list, fov_min, fov_max, fov_n, points=None):
"""
Creates coil profiles for arbitrary loops, for use in multichannel shim examples that do not match spherical
harmonics
Args:
centers (list): List of 3D float center points for each loop in mm
normals (list): List of 3D... | 1a116e3299131911bc1a6c19106518c56427efe4 | 3,611,749 |
def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and two lists of labels.
Arguments:
data : A 2D numpy array of shape (N,M)
row_labels : A list or array of length N with the labels
for ... | 5465a92df4edbc94e194a140b0a81b105be68953 | 3,611,750 |
def get_request():
"""Get the current request from anywhere."""
request = request_accessor.send(None)
if request:
return request[0][1]
return None | 92853938db8d7f90551c6ce4febbfedbaccfa6db | 3,611,753 |
def get_aligned_face_and_landmarks(
im, face_cache, aligned_face_size=256, padding=(0, 0)
):
"""
get all aligned faces and landmarks of all images
:param imgs: origin images
:param fa: face_alignment package
:return:
"""
aligned_cur_shapes = []
aligned_cur_im = []
for mat, point... | cdb0989a377c609945058e2903fbec15b94c64ce | 3,611,754 |
def items_per_cart(customers, orders, data):
"""Return customers dataframe with an extra column containing
the average of items_per_cart for each customer."""
mapper = customers_mapper(data)
customers = customers.copy()
orders = orders.copy()
orders['customer_unique_id'] = orders['customer_id'].... | 8fdbdba3bfaaeaeaf89cafcaee0597cf626856d6 | 3,611,755 |
def getParameterDefinitions():
"""Return ParameterDefinitionCollections for each appropriate ArmiObject."""
return {Block: _getBlockParams()} | 502c909754b80c4e0bf40dd74a77e6b2c2df55e1 | 3,611,756 |
def check_fake_image_dataset(method):
"""A wrapper that wraps a parameter checker around the original Dataset(FakeImageDataset)."""
@wraps(method)
def new_method(self, *args, **kwargs):
_, param_dict = parse_user_args(method, *args, **kwargs)
nreq_param_int = ['num_images', 'num_classes', ... | 11b140ca35b5f3f232c1d410671010c9351dd220 | 3,611,757 |
async def validation_exception_handler(request: Request, exc: RequestValidationError):
"""
Handling error in validating requests
"""
return JSONResponse(
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
content=get_error_response(request, exc)
) | a05629d0853b9aadceebda568450373718bd5912 | 3,611,758 |
def cpo(total_cost, total_transactions):
"""Return the CPT (Cost per Order).
Args:
total_cost (float): Total cost of marketing.
total_transactions (int): Total number of transactions.
Returns:
cpt (float) as total cost per order
"""
return total_cost / total_transactions | aaaaf5a96fcbef65e59591954bded1afa13f8c47 | 3,611,760 |
def convert_seq2seq_golds(indices, lengths, rlut, subword_fix=lambda x: x):
"""Convert indices to words and format like a bleu reference corpus.
:param indices: The indices of the gold sentence. Should be in the shape
`[B, T]`. Iterating though axis=1 should yield ints.
:param lengths: The length o... | 152385efd2354486d2e069c9ac80f27032672c70 | 3,611,761 |
def advanced_raster_scan(ny=10, nx=10, fast_axis=1, mirror=[1, 1], theta=0, dy=1, dx=1):
"""
Generates as raster scan.
Parameters
----------
ny, nx : int
Number of steps in *y* (vertical) and *x* (horizontal) direction
*x* is the fast axis
dy, dx : float
Step size (grid... | f3c54a605956c4373654fb466e6b0493048160a0 | 3,611,764 |
def command(func):
"""Decorator to register a fn as an available command."""
if func.__name__ not in COMMANDS:
COMMANDS.append(func.__name__)
return func | 5ed35f6fe82751c95969cd43e5ef2fe3f6fc3dcf | 3,611,765 |
def dendrogram(
df, method='average', filter=None, n=0, p=0, orientation=None, figsize=None, fontsize=16,
label_rotation=45, ax=None
):
"""
Fits a `scipy` hierarchical clustering algorithm to the given DataFrame's variables and visualizes the results as
a `scipy` dendrogram.
The default vertica... | f2f8838547df8c0112e65ff1f6bfb0687f86730b | 3,611,766 |
def intervalrecordlookup(table, start='start', stop='stop', include_stop=False):
"""
As :func:`petl.transform.intervals.intervallookup` but return records
instead of tuples.
"""
tree = recordtree(table, start=start, stop=stop)
return IntervalTreeLookup(tree, include_stop=include_stop) | cc1941e96a0affabcebabb2e39b34b65aa12cb10 | 3,611,767 |
def Convert2targetSeqView(
sampleName,
sampleBamName,
sampleSplitBamName,
vcfFile,
outputDir,
outputFileName):
"""This ``converts`` the Delly Vcf file having tumor normal, to tab-delimited format for input to targetSeqView
:param str sampleName: str for the name... | c2d13107f0e142a17fb10d1933bbed17e17e24ea | 3,611,768 |
def max_valued_dose_dependent_assertion_to_jtms(ev, s, a, ac):
""" convert this continous valued DIKB assertion to a JTMS assertion that includes the dose used in the study/experiment
@param ev:an instance of EvidenceBase containing Assertions instances
@param s:string - representation of the assertion ty... | 6e8312112fa9022d966aca0da6808df7393fa173 | 3,611,769 |
import re
def create_user():
"""注册新用户"""
data = request.get_json()
if not data:
return bad_request('Please post json data.')
message = {}
if 'username' not in data or not data['username']:
message['username'] = 'Please provide a valid username.'
pattern = '^(([^<>()\[\]\\.,;:\... | 97629ef053c298a679dd46f820f76ba49056620f | 3,611,770 |
def rebin_1darray(a, shape, function='sum'):
"""Rebin an array into a new shape by making the sum or mean
"""
sh = shape,a.shape[0]//shape
if function == 'mean':
return a.reshape(sh).mean(-1)
elif function == 'sum':
return a.reshape(sh).sum(-1)
else:
print("WARNING: doing... | 66de60326e081c27aae471281c79c5e86f5180e3 | 3,611,771 |
def affine(src, dst, rcond=1e-6):
"""Estimate the best affine matrix by least-squares in target space.
The implementation is specific to 3-dimensional source and target spaces,
but could be generalized easily.
"""
assert src.shape[1] == dst.shape[1] == 3
flat_dst = dst.flatten(order="C") # ord... | a9d59339813fd0e37e51262a8f13e7af7e4a7918 | 3,611,772 |
def get_ground_truth(obj, image, question):
"""
Get the ground truth value for the image/question combination in reader
study obj.
"""
ground_truths = obj.statistics["ground_truths"]
return ground_truths[image][question] | 522b5550344891e0985bacb9c763c4c52686cb67 | 3,611,773 |
def eval_math(math, compiled_math, workspace):
""" Compile a mathematical expression
Args:
math (:obj:`str`): mathematical expression
compiled_math (:obj:`_ast.Expression`): compiled expression
workspace (:obj:`dict`): values to use for the symbols in the expression
Returns:
... | deeaefa52a048bf218874614062f8e6e43b9e389 | 3,611,776 |
def LF_CD_PARENTHETICAL_DESC(c):
"""
This label function looks for mentions that are in paranthesis.
Some of the gene mentions are abbreviations rather than names of a gene.
"""
if ")" in c[1].get_span() and "(" in list(get_left_tokens(c[1], window=1)):
if LF_CD_DISTANCE_SHORT(c):
... | d4cf44b27cf8ee2139173096a39328c2d841dd96 | 3,611,777 |
def _declare_clang_library_target_files(
ctx,
target,
build_config_path,
clang_custom_info):
"""Declares the outputs for a clang module and returns a struct value describing the clang module.
Args:
ctx: A `ctx` instance.
target: A target `dict` from the package d... | faf39325495eadc5cb775c4ed97f28e871ac8598 | 3,611,778 |
def is_pandas_df(obj):
"""Check if an object is a Pandas dataframe
The benefit here is that Pandas doesn't have to be included
in the dependencies for pydeck
The drawback of course is that the Pandas API might change and break this function
"""
return obj.__class__.__module__ == 'pandas.core.f... | aa226f86d8640903fef4e51121e285ba9c594a3c | 3,611,779 |
def time(days=0, seconds=0, minutes=0, hours=0, **kwargs):
""" Returns a Time that can be added to a Date object.
Other parameters: microseconds, milliseconds, weeks, months, years.
"""
return Time(days=days, seconds=seconds, minutes=minutes, hours=hours, **kwargs) | 3e39559a4f9d9a2da9bc97489a4c1a8582533883 | 3,611,782 |
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
# output: [B, d]
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
# pred: [B, len(topk)] -> [len(topk), B]
# target.reshape(1, -1)... | f8dd618922056eafb7aa1e856137c6a0ea7a3c6e | 3,611,783 |
def password_login(request):
"""
客户端用户使用密码登录
:param request:
:return:
"""
print('api:password_login')
if request.session.get(USER_SESSION_KEY):
print("###api:password_login request.session.session_key已经存在: ", request.session.session_key)
return fail(ResultCode.COMMONERR, "失败：... | f2e794d188d485385170d2e0ae8c5a192a384896 | 3,611,784 |
def gen_node_color_map(table_column,
palette = {'d': palette_color_brewer_q_Set2(),
'c': (palette_color_brewer_s_GnBu(), palette_color_brewer_d_RdYlBu())},
mapping_type='c',
default_color=None,
... | 08fe825e3901cb21c39ea7c32ea6c023b9e50a0f | 3,611,786 |
def unpack_qubit(qubit):
"""
Get a qubit from an object.
:param qubit: An int or Qubit.
:return: A Qubit instance
"""
if isinstance(qubit, integer_types):
return Qubit(qubit)
elif isinstance(qubit, Qubit):
return qubit
elif isinstance(qubit, QubitPlaceholder):
re... | 27f6dd2598bf2cc052849bbf032f4d1cdf545058 | 3,611,787 |
def solve(f, x0):
"""
Solve the equation f(x) = x using a fixed point iteration. x0 is the start value.
"""
x = x0
for n in range(10000): # at most 10000 iterations
oldX = x;
x = f(x);
if abs(x - oldX) < 1.0e-15:
return x; | d5f5409d689c842c1a8c70b95c621360c9ae7c8c | 3,611,788 |
import datasets
def mi_estimate(y_real, gen, enc, masks, k, batch_size, z_dim, s_dim,
z_trans = None):
"""Estimates the MI of the encoder.
Args:
y_real: s_I
gen: generator, (z -> x^)
enc: encoder, (x^ -> dist(z^))
masks: for sampling z_\I from the prior
k: number o... | 68e0d902145ba1eec6d02b195a56413f9feebe4e | 3,611,790 |
from application.plotlydash.dashboard import create_dashboard
from application.assets import compile_assets
import sqlite3
def create_app():
"""Construct core Flask application with embedded Dash app."""
app = Flask(__name__, instance_relative_config=False)
app.config.from_object('config.Config')
wit... | fb1cd994477f7cddd1852edad3d28b685cc69892 | 3,611,791 |
def _transformer_configs(c: Configs):
"""
### Transformer configurations
"""
# We use our
# [configurable transformer implementation](../configs.html#TransformerConfigs)
conf = TransformerConfigs()
# Set the vocabulary sizes for embeddings and generating logits
conf.n_src_vocab = c.n_to... | 6cc7d72cef9a1f607b6f0e712f0a3792247892c2 | 3,611,792 |
def te_diff_template(ruletype):
"""
This is a template for the type_* diff functions.
Parameters:
ruletype The rule type, e.g. "type_transition".
"""
def diff(self):
"""Generate the difference in rules between the policies."""
self.log.info(
"Generating {0} dif... | d2f5b2eca127be8c991ac0c49027bce9c01ee3a2 | 3,611,793 |
def normalize(v1, Rmin=1.0e-8, Rmax=1.0e15):
"""
Do not normalize in place. Not supported in autograd
"""
n = norm(v1)
if n < Rmin or n > Rmax:
print('vector:', v1)
print('norm:', n)
raise Exception("Could not normalize vector. Vector norm beyond tolerance")
else:
... | dddc57693472e5a72ab3caed5e592fd859a4b64e | 3,611,794 |
import re
def find_version() -> str:
"""Only define version in one place."""
version_file = read_file("__init__.py")
version_match = re.search(r'^__version__ = ["\']([^"\']*)["\']', version_file, re.M)
if version_match:
return version_match.group(1)
raise RuntimeError("Unable to find versi... | 8bbd0c331eef2a1918f0fcf0ce63127396e40681 | 3,611,796 |
def p2_allocateByScore(CCA, nameCat, quota, rank, scorelist, final_dic, CCA_dic):
"""
Allocates students to cca where applicants exceed quota
Returns the final dictionaries
"""
cat = ""
for key, value in nameCat.items(): #theoretically it will all be the same anyway
cat = value
quot... | de0ec96eadf396319a7dfbb3942a6917afc6c84c | 3,611,797 |
def _total_solves(color_info):
"""
Return total number of linear solves required based on the given coloring info.
Parameters
----------
color_info : dict
dict['fwd'] = (col_lists, row_maps)
col_lists is a list of column lists, the first being a list of uncolored columns.
... | 32031f2ba834f7d6ed310b0a71ab43884b424459 | 3,611,798 |
def generat_int_pop(n_particles,n_vars,low_bounds,up_bounds):
"""
Generate initial population function.
Here, random values between the lower and the higer bounds are defined for the initial population:
x1^j = rand[xlb^j, xub^j]
Parameters:
n_particles: int
Number of partic... | 27afd97b45b74135633d133ee7ff1163898bf1b8 | 3,611,799 |
def gettext(msg, domain='python-apt'): # real signature unknown; restored from __doc__
"""
gettext(msg: str[, domain: str = 'python-apt']) -> str
Translate the given string. This is much faster than Python's version
and only does translations after setlocale() has been called.
"""
return "" | a6d83a79110ec233f86878fc6aa9f37f2b3e61fa | 3,611,801 |
def as_sparse_or_tensor_variable(x, name=None):
"""
If we can't make a sparse variable, we try to make a tensor variable.
"""
try:
return as_sparse_variable(x,name)
except (ValueError, TypeError):
return theano.tensor.as_tensor_variable(x,name) | 4e9560dc48bd0388b376b9ee29b303850b8ad2e1 | 3,611,802 |
def freeze(slug):
"""
Prevent a builder from updating it's source code to a newer version.
The software will still be re-built if it's dependants are upated.
"""
freeze_file = settings.get('install_path') + 'etc/build-freeze'
return file_filter.AppendUnique(freeze_file, slug).run() | e08b0146fc5c4eafaeba689a3aaf1dff04c06192 | 3,611,803 |
def get_compressor(alg):
"""Return an instance of a compressor
This function returns an object that can be used for data compression.
"""
return _cmp_compressors[alg]() | a40274f069b2fb7bf5afc05efb10b39d5cea5179 | 3,611,804 |
import json
import uuid
import time
def confirm(self, speech, timeout):
"""
Start the view 'confirm'
:param speech: the text that will be use by the Local Manager for tablet and vocal
:type speech: dict
:param timeout: maximum time to wait for a reaction from the local manager
:type timeout: ... | 41a1061879dc14f32a567bb4860195221385609a | 3,611,805 |
def generateAdjMatrix(edgeJsonList, sent_len):
""" Function to generate Adjacency matrix from Depedency Parse Tree """
sparseAdjMatrixPos = [[], [], []]
sparseAdjMatrixValues = []
deptags = []
gov_words = [0] * sent_len
adjMatrix = [0] * sent_len
for i in range(sent_len):
adjMa... | ca8af1991b072c334cab20c317a3cb2e00d8f67f | 3,611,806 |
import operator
def assert_no_duplicate_router_ids(
snapshot=None,
nodes=None,
protocols=None,
soft=False,
session=None,
df_format="table",
):
# type: (Optional[str], Optional[str], Optional[List[str]], bool, Optional[Session], str) -> bool
"""Assert that there are no duplicate router ... | 51d987ece746ea481fa39490aba364765512ce5f | 3,611,807 |
from typing import Generator
def de_bruijn(alphabet: bytes, n: int) -> Generator[str, None, None]:
"""De Bruijn sequence for alphabet and subsequences of length n (for compat. w/ pwnlib)."""
k = len(alphabet)
a = [0] * k * n
def db(t: int, p: int) -> Generator[str, None, None]:
if t > n:
... | bc647a3e140a81c47703eaa2216d49e0aa78f088 | 3,611,809 |
def writexl_alt_getsheetref(path_wbrels, path_wb):
"""
Takes a file path for '/xl/_rels/workbook.xml.rels' and '/xl/workbook.xml' files
and returns a dictionary relationship between sheetID, name and xml worksheet file path
rId: xml's indexing between files (workbook.xml.rels defined rID to xml work... | 84852fc99897b471834ce3a9dc721e18b41047e1 | 3,611,810 |
import re
def createPattern(string):
"""Cria um padrão de comandos de bot."""
return re.compile(f"/{string}|{string}") | b2b013dc71d5f49f041402ecf98db8b923f4d22a | 3,611,811 |
def gnomad_filtered_func(raw_value):
""" We use FILTER in Gnomad3 (GRCh38 only) - need to convert back to bool """
return raw_value not in (None, "PASS") | 6de1d4ef9395e89c11b08cdb43decb045656494b | 3,611,813 |
from bs4 import BeautifulSoup
def parse(html_bytes: bytes, *args, **kwargs) -> BeautifulSoup:
"""Convenience method for ``ZimHtmlParser(html_text).simplify()``."""
return ZimHtmlParser(html_bytes, *args, **kwargs).simplify() | 2b1c711a9ead749e0e8b6279e4861c29b97aa968 | 3,611,814 |
def register(name):
"""Registers a new logs preprocessor function under the given name."""
def add_to_dict(func):
_PREPROCESSORS[name] = func
return func
return add_to_dict | a07008f74ea4a93a6763d1062c78d50a64d9614e | 3,611,815 |
import torch
def _project_z(z, project_method='clip'):
"""To be used for projected gradient descent over z."""
if project_method == 'norm':
z_p = torch.nn.functional.normalize(z, p=2, dim=-1)
#elif project_method == 'clip': #not reimplemented yet
# z_p = tf.clip_by_value(z, -1, 1)
else:
raise Value... | 6cabb462dcbfff7ffdc065601f4a68671822f93f | 3,611,817 |
from datetime import datetime
def __dirnames_matching_format(dirnames, format):
"""
Iterates through dirnames and returns a sorted array of directory names
that match the provided format.
"""
matching_dates = []
for dirname in dirnames:
try:
dt = datetime.strptime(dirname, ... | 52349f0992a0ac0366d75f5328f674ab179246be | 3,611,818 |
import re
def solve(s):
"""doc"""
return "".join([w.capitalize() for w in re.split(r"(\W+)", s)]) | 28f075dd47c3ec57d6bb2a1675ae77815378786e | 3,611,819 |
import requests
def marco_china_hk_trade_diff_ratio() -> pd.DataFrame:
"""
东方财富-经济数据一览-中国香港-香港商品贸易差额年率
https://data.eastmoney.com/cjsj/foreign_8_7.html
:return: 香港商品贸易差额年率
:rtype: pandas.DataFrame
"""
url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
... | 7d60526def9c00944638b80311dbbd6913a11ca2 | 3,611,820 |
from typing import Optional
def make_default_sim_env(
interface: Optional[GymTrainedInterface] = None,
) -> CustomSimEnv:
""" A simulator environment with the following characteristics:
The action and observation spaces are continuous.
An action in this environment is a pilot signal for each EVSE,
... | e023dc37fc81fb8d5e17f87d66b3035e9909a3aa | 3,611,821 |
from iris.cube import Cube
from iris.coords import DimCoord
def make_dummy_1d_cube(job_n=0):
"""
Makes a dummy 1d cube filled with dummy data. It has a scalar job coordinate
to make ensemble stacking convenient
"""
x = np.arange(100)
y = np.sin(x) * np.cos(x + 0.3)
obs = DimCoord(x, var_... | 992251299f13bbfa7581f647602a349b98547635 | 3,611,822 |
def edit_delivery(request, delivery):
"""Edit a delivery as a full network admin: act upon its lifecycle, control which subgroups have been validated,
change the products characteristics, change other users' orders."""
dv = m.Delivery.objects.get(id=delivery)
if dv.network.staff.filter(id=request.user.i... | 7d807af78d2b063555a529a525295352513af919 | 3,611,823 |
def get_fields_by_name(model_cls, *field_names):
"""Return a dict of `models.Field` instances for named fields.
Supports wildcard fetches using `'*'`.
>>> get_fields_by_name(User, 'username', 'password')
{'username': <django.db.models.fields.CharField: username>,
'password': <django.d... | 8ce9c845adbff9bb53da50c7d7e208aa8077e718 | 3,611,824 |
def pyramidnet110_a84_svhn(num_classes=10, **kwargs):
"""
PyramidNet-110 (a=84) model for SVHN from 'Deep Pyramidal Residual Networks,' https://arxiv.org/abs/1610.02915.
Parameters:
----------
num_classes : int, default 10
Number of classification classes.
pretrained : bool, default Fal... | 085571ae5ab102de5e1bdeab100d6244608f899a | 3,611,825 |
def list_of_comments(fname) :
"""Returns list of str objects - comment records from file.
- fname - file name for text file.
"""
#if not os.path.lexists(fname) : raise IOError('File %s is not available' % fname)
f=open(fname,'r')
cmts = []
for rec in f :
if rec.isspace() : conti... | 5aea0668c006b4a4615cab01acd07db8dc1fb2b5 | 3,611,826 |
def publish_branch(branch):
"""Publishes given branch."""
repo_check()
return repo.git.execute([git,
'push', '-u', remote.name, branch]) | 7335704d94efdc5dfba65dab914ab75a70bc24d9 | 3,611,827 |
import base64
def pil_to_base64(pilimage): # pragma: no cover
"""Returns base64 encoded image given a PIL Image"""
buffer = BytesIO()
pilimage.save(buffer, "png")
return base64.b64encode(buffer.getvalue()).decode() | 9c2673b97a86544c5281cbd43ff6dfdad31a4253 | 3,611,828 |
def save_result(x,time,lev,copy_from_source,dflt_units='k'):
"""Saves results from projection in netcdf output format.
Input parameters:
x: projection indices (2dim array with time,mode)
time: coordinates from input netcdf file
lev: level coordinates (PCA modes)
copy_from_s... | 704eb922bf8ccb4f8669df5ee0cf6b134ed4b55c | 3,611,829 |
def linkcode_resolve(domain, info, author, package):
"""Determine the URL corresponding to a Python object."""
try:
module = import_module(info["module"])
except ModuleNotFoundError:
# No URL is returned when the object is not in
# any module, or the module cannot be imported
... | ec849260610890510f0686ab4b1150eb30efe99c | 3,611,831 |
def _max(self, dim=None, keepdim=False):
"""Map of 'max' pytorch method."""
x = self
dim_orig = dim
if dim is None:
dim = _build_fwd_tuple(x.shape)
elif isinstance(dim, int):
dim = (dim,)
dim = _dim_tuple_explicit(x.shape, dim)
ret_max = P.array_max(x, dim)
ret_argmax =... | ecf96de331d24b0b692c12fa24269fb9863121bb | 3,611,832 |
import re
def minify(source):
"""
Removes comments from the source code
"""
multiline = re.compile("(/\*.*?\*/)", re.DOTALL)
singleline = re.compile("//.*?\n")
remove_multiline = lambda f: re.sub(multiline, "", f)
remove_singleline = lambda f: re.sub(singleline, "", f)
return map(remove_multiline, ... | 3743e4071485d1ae085037a4bf4cd492f4f81d29 | 3,611,834 |
def configmap_data_keys():
"""
Build keys for the ``data`` mapping of a ``ConfigMap``.
"""
return builds(
lambda labels, dot: dot + u".".join(labels),
labels=lists(object_name(), average_size=2, min_size=1, max_size=253//2),
dot=sampled_from([u"", u"."]),
).filter(
la... | 4c88839707f37cfb334f6bedce080b4f97a84c58 | 3,611,835 |
from typing import Callable
from typing import Any
def handler_assertions(
handler_name: str,
test_case: str,
error_name: str = None,
) -> Callable:
"""Configurable decorator for testing assertions for a given handler"""
def decorator_function(
decorated_function: Callable... | 7abb8776e029c330c0ad4b060bdf8e0c1ea3af35 | 3,611,836 |
import random
def create_bitmap_dataset(bitmap_data, output_dir, output_name, classnames, output_shards):
"""
Writes the provided bitmap_data as tf.Example in tf.Record
:param bitmap_data: tuple of lists containing (x_train, y_train, x_test, y_test)
:param output_dir: path where to write the outp... | 050fe1ed5a66cf49741d24e21062fadba08a0c7c | 3,611,837 |
import logging
def with_logger(cls):
"""Class decorator to add a logger to a class."""
attr_name = '_logger'
cls_name = cls.__qualname__
module = cls.__module__
if module is not None:
cls_name = module + '.' + cls_name
else:
raise AssertionError
setattr(cls, attr_name, logg... | 77f854ac6d1cbe95ff7804184a1a67a513ac81ae | 3,611,838 |
import itertools
def _generate_mesh_simplices(
*, limits, mesh_size, periodic=False, skip_origin=False
):
"""
Generate the starting simplices for given limits and mesh size.
"""
dim = len(limits)
if len(mesh_size) != dim:
raise ValueError(
"Inconsistent dimensions: ... | 7b2008906aaafb411c22128ffcdd4f2ce1c13471 | 3,611,839 |
def write_tfrecords(data_path,video_paths,action_labels,
n_vids_per_batch,subset,
n_frames_batch = 16,
n_frames_chunk = 512):
"""Function to write tfrecords.
:param data_path: name of tfrecords file to write
:param video_paths: list contain... | 4c6e27a2cc9a2079a8f2828d288ea65542b8e56a | 3,611,840 |
def _set_up_thermo_mets(base_df: pd.DataFrame, mets_list: list, mets_conc_df: pd.DataFrame) -> tuple:
"""
Given the base excel input file, the list of metabolites in the model and a dataframe with metabolite
concentrations averages and respective stdev, fills in the thermoMets sheet.
First fills in conc... | c048a6c220de7055f5cb5ce0a65f969b3d0a9be7 | 3,611,841 |
def setup_constant(prefix, **kwargs):
""" Set up a simple constant function model.
:param prefix: Model prefix
:type prefix: string
:param kwargs: Keyword arguments
:return: LMFIT model and parameters
:rtype: (lmfit.Model, lmfit.Parameters)
"""
amplitude = kwargs.pop('amplitude', 5)
... | 3d0c24887e81c34b499190a79185822fef641bd8 | 3,611,842 |
def subapp_css_static(filename):
"""
set css files.
:param filename: css file name.
:return: static path.
"""
return static_file(filename, root='kokemomo/plugins/subapp/view/resource/css') | bf29bc7a7f9cbab9e2db3da0841eab018c57d96b | 3,611,843 |
def graph1D(n: int, pbc: bool = True) -> Graph:
"""
1D PBC chain with n sites
:param n:
:return:
"""
g = nx.Graph()
for i in range(n):
g.add_node(i)
for i in range(n - 1):
g.add_edge(i, i + 1, weight=1.0)
if pbc is True:
g.add_edge(n - 1, 0, weight=1.0)
... | fdb809ee46c7b9d7ebdd241175e199db747f9227 | 3,611,844 |
def _solve_triangular_right(a, b, left_side, lower, trans_a):
"""An unrolled right-looking triangular solve on (blocked) LapaxMatrices."""
n = a.shape[-1]
def solve(a, b):
return _solve_triangular_left(a, b, left_side, lower, trans_a)
if n == 1:
return solve(a.bview(1), b.bview(1))
out = full_like(... | 1dc6826bfb717a8b162bd2a5d997368276ae0b47 | 3,611,845 |
from numpy import linspace, meshgrid
def _get_latlons(nlat, nlon):
"""Short summary.
Parameters
----------
nlat : type
Description of parameter `nlat`.
nlon : type
Description of parameter `nlon`.
Returns
-------
type
Description of returned object.
"""
... | f43553b4f758ac2a060f609e2781645b79466cb4 | 3,611,846 |
def abs(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
See its docstring for more information.
"""
if x.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in abs")
return Array._new(np.abs(x._array)) | cb37d3486fd82535de77f4df56a30c7e713127b9 | 3,611,847 |
def challenge_hash(peer_challenge, auth_challenge, user_name):
"""
RFC 2759
section 8.2
ChallengeHash
:param peer_challenge:
:param auth_challenge:
:param user_name:
:return: challenge
example
peer_challenge = "\x21\x40\x23\x24\x25\x5E\x26\x2A\x28\x29\x5F\x2B\x3A\x33\x7C\x7E"
... | 153f33509e09ea140433d6ef46aab2225bbb5808 | 3,611,848 |
def var_historic(r, level=5):
"""
Returns the historic Value at Risk at a specified level
i.e. returns the number such that "level" percent of the returns
fall below that number, and the (100-level) percent are above
"""
if isinstance(r, pd.DataFrame):
return r.aggregate(var_historic, le... | dd2b23c15b7aee470886621ba8d64bee571488f9 | 3,611,849 |
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