content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def index(request):
"""Blog home page view"""
blog_settings = BlogSettings.load()
post_list = Post.get_displayable().all()
page = request.GET.get("page")
ret_dict = {
'blog_settings': blog_settings,
'posts': __get_post_page(post_list, page=page,
blo... | 9dac202c7748b8f3aa1740f82fe254352e49c360 | 47,100 |
def find_joins(df, ids, downstream_col="downstream", upstream_col="upstream", expand=0):
"""Find the joins for a given segment id in a joins table.
Parameters
----------
df : DataFrame
data frame containing the joins
ids : list-like
ids to lookup in upstream or downstream columns
... | 39f16985ddd8e79338e520e56ba6ee793558d03f | 47,101 |
def velocity(sigma, xs, ys, X, Y):
"""
Generalizing the one source case:
xs, ys --> (1,)
X, Y --> (nx, ny)
sigma --> (1,)
To the several sources one:
xs, ys --> (ns, 1, 1)
X, Y --> (ns, nx, ny)
sigma --> (ns, 1, 1)
"""
sigma = np.atleast_1d(sigma)... | ab766a16e3bc27f269847d9e130430acd874e925 | 47,102 |
import functools
def rate_limit(limit, period):
"""This decorator implements rate limiting."""
def decorator(f):
@functools.wraps(f)
def wrapped(*args, **kwargs):
if current_app.config['USE_RATE_LIMITS']:
# generate a unique key to represent the decorated function a... | 78a83f85d24083ec7e1155326371a3cb26b56222 | 47,103 |
from typing import Optional
def get_sketch_details(sketch_id: Optional[int] = 0) -> Text:
"""Return back details about an existing sketch.
Args:
sketch_id (int): the sketch ID to check.
Returns:
string containing information about the sketch.
"""
sketch = None
state_obj = state.state()
if not... | 6730aa649b9b74275e3aa92b213b930fdeb2ef9d | 47,104 |
import pkgutil
import sys
import os
def finder_for_path(path):
"""
Return a resource finder for a path, which should represent a container.
:param path: The path.
:return: A :class:`ResourceFinder` instance for the path.
"""
result = None
# calls any path hooks, gets importer into cache
... | d182f41de21f2616d33088a7de30723c77bb4e79 | 47,105 |
import copy
import torch
def dataProcessing(data_x, data_y, batch_size, training_ratio, validation_ratio,
FM_indices, bool_norm=False):
"""
Data preprocessing.
Parameters:
----------
data_x: 2D Array (nDOF x SampleNum).
The deformation data (x SampleNum) of al... | e7dac71df8e11be2839f77f2397660ff12578076 | 47,106 |
def ten_interp(x, a0, a1, a2, a3, a4, a5, a6, a7, a8, a9):
"""``Approximation degree = 10``
"""
return (
a0
+ a1 * x
+ a2 * (x ** 2)
+ a3 * (x ** 3)
+ a4 * (x ** 4)
+ a5 * (x ** 5)
+ a6 * (x ** 6)
+ a7 * (x ** 7)... | 93ce278f784080602990f386164d71fd8290560f | 47,107 |
def mock_interface_settings_match(mock_interface_settings):
"""
Fixture that yields mock USB interface settings that is the correct USB class, subclass, and protocol.
"""
mock_interface_settings.getClass.return_value = libusb.USB_DEVICE_CLASS
mock_interface_settings.getSubClass.return_value = libusb... | ca97bf1af08abbc39407d99ec959caf425f2469e | 47,108 |
def multinomial_naive_bayes_inference(X, W, b):
"""Multinomial naive Bayes classifier inference.
Parameters
----------
X : ndarray, shape (m, n)
input features (one row per feature vector).
W : ndarray, shape (n, k)
weight vectors, each row representing a different class.
b : ... | 4107e7cc5dbf22fdc874fc50d5e0736df74841c4 | 47,109 |
def sort_dist_matrix(mat, row_col_names):
"""
sort the distance matrix by seg_id_nat
:return:
"""
df = pd.DataFrame(mat, columns=row_col_names, index=row_col_names)
df = df.sort_index(axis=0)
df = df.sort_index(axis=1)
sensor_id_to_ind = {}
for i, sensor_id in enumerate(df.columns):
... | 09144b94db1d2f22ca1543e0e52cbab831e52e1c | 47,110 |
def is_complete(step, lst):
"""
Check required field of question for complete state
Required: question is always require user response to be complete
Conditional: Optional question needed depends on reveal_response value of conditional_target.
"""
if not lst:
return False, []
questio... | 0ae26de169e9ee0eacfdb30cdf349345d42b8ca9 | 47,111 |
def mels_spectrogram(spec, sr, n_mels,
fmin=64, fmax=None, top_db=80.0):
""" Extracting mel-filter bands from power spectrum
(i.e. the output from function
`odin.preprocessing.signal.power_spectrogram`)
Parameters
----------
spec : array [nb_samples, n_fft]
power spectrum array
s... | d64f562b50a22704f3f0f94f2c6b3d06539b7ba9 | 47,112 |
import torch
def create_data_loader(data, batch_size=32, shuffle=True, drop_last=False):
""" Create a data loader given numpy array x and y
Args:
data: a tuple (x, y, z, ...) where they have common first shape dim.
Returns: Pytorch data loader
"""
if drop_last:
batch_size = min(... | 573c6d440812e8d9d504d621ace6db572c9acbb5 | 47,113 |
def set_order(order):
"""decorator to set callback's method order
usage:
@set_order(100)
def method(self):
pass
"""
def inner(meth):
def fn(*args, **kwargs):
return meth(*args, **kwargs)
fn._order = order
return fn
return inner | 60a051e87e9ad0b87511892d5d0545555049c1ab | 47,114 |
from typing import Tuple
def calculate_ci(
values: np.ndarray,
ci_level: float,
**kwargs,
) -> Tuple[np.ndarray, np.ndarray]:
"""Calculate confidence/credibility levels using percentiles.
Parameters
----------
values:
The values used to calculate percentiles.
ci_level:
... | cccff0595dcbfb1456a02d16f488997075492610 | 47,115 |
from typing import Optional
from typing import Union
from typing import List
def plot__02__a(
results: pd.DataFrame, ks: Optional[Union[List[int], int]] = None, min_class_support: int = 50,
colormap_name: str = "fixed", sharey: str = "all", cf_level: str = "superclass", n_samples: int = 50,
to... | 1d3e4c501585486ee1943d457f4590a4c2455ba4 | 47,116 |
def display_calibration(probs,
actual,
*,
figure=None,
bins=100,
label=None,
show_ici=True,
alpha=0.05,
n_resamples=None,
... | 7f2545c76f2c37079d15e3874cd5f63421ad6fa1 | 47,117 |
import math
def max_crossing_subarray(given_array, start_index, mid_index, end_index):
"""Function To Calculate The Mid Crossing Sub Array Sum"""
max_left_sum = - math.inf # Used For Sentinel Value
max_right_sum = - math.inf # Used For Sentinel Value
cross_start = None # Just used for variable pr... | 1106b063b652e0d0d475f5b0979a138f4c48113b | 47,118 |
def ignore_warnings(obj=None, category=Warning):
"""Context manager and decorator to ignore warnings.
Note. Using this (in both variants) will clear all warnings
from all python modules loaded. In case you need to test
cross-module-warning-logging this is not your tool of choice.
Parameters
----... | 093fdbbc0728c3f98840c6fa54be2930cf2b9e6c | 47,119 |
import json
def create_sort_spec(model, sort_by, descending):
"""Creates sort_spec."""
sort_spec = []
if sort_by and descending:
for field, direction in zip(sort_by, descending):
direction = "desc" if direction else "asc"
# we have a complex field, we may need to join
... | e2c486cf6b2188646c573ebf02447f6cfcbefdec | 47,120 |
def css_classes():
"""return settings or default"""
return getattr(project_settings, 'DJALOHA_CSS_CLASSES', ()) | 21fd26e50044cd6de06000b9eb38e9b6eca6a487 | 47,121 |
import array
def load_position(dir_path: str, label_file: str) -> array:
"""Loads position of an object from a file.
Parameters
----------
dir_path : str
Folder of the file.
label_file : str
File name.
Returns
-------
array
Position of an object defined by a b... | cc655dc0172aff3cb8571c733e37c30e696c888c | 47,122 |
import re
def camel_2_snake_case(word):
"""
>>> camel_2_snake_case("HTTPResponseCodeXYZ")
'http_response_code_xyz'
From https://stackoverflow.com/a/1176023/548792
"""
return re.sub(r"((?<=[a-z0-9])[A-Z]|(?!^)[A-Z](?=[a-z]))", r"_\1", word).lower() | dc20c832a212f89d51bb05302c9e1677e8f2cb83 | 47,123 |
def copy_to_device(target_device, source_device="/cpu:0"):
"""A transformation that copies dataset elements to the given `target_device`.
Args:
target_device: The name of a device to which elements will be copied.
source_device: The original device on which `input_dataset` will be placed.
Returns:
A... | 4b77ea7a13c6cbef5aa31daa3db88b79124cb786 | 47,124 |
def get_run_info():
"""
get run base info
"""
return _global_dict["run_info"] | 3c329e55c970310cbf19967eeee2f77a3eb2ae9e | 47,125 |
def generate_configs(experiment_config):
"""Generate parameter configurations based on an input configuration.
Input is a nested configuration where on each level there can be 'fixed', 'grid', and 'random' parameters.
In essence, we take the cartesian product of all the `grid` parameters and take random s... | 42b380ce9dad365efed0cba0d0bd38abde4ebd37 | 47,126 |
from datetime import datetime
def now_str(time=False):
"""Return string to be used as time-stamp."""
now = datetime.now()
return now.strftime(f"%Y-%m-%d{('_%H:%M:%S' if time else '')}") | 02b73bda5f27e7c25120d50d50244bd103661c90 | 47,127 |
def set_ai_character(controller, character, gamestate, port,
opponent_port):
"""Set the given controller to the STANDARD state playing as the given character.
This is to be called repeatedly each frame while in the character selection menu.
Returns true once it is complete."""
if ga... | 1eaeb2baea4f6977541ae7db3ab696158e8be456 | 47,128 |
def camino_minimo(inicio, fin, rutas, ciudades, grafo):
"""Devuelve una lista con el camino mas corto y de menor coste entre inicio y fin (ambas ciudades de nuestro grafo)
Pre: inicio, fin son las IDs de las ciudades; rutas es un diccionario con objetos Ruta (rutas[id_ciudad1][id_ciudad2]), y ciudades es un dic... | 7076c291a9dec9c2de7c7765ceccfa5a3d7a4f5f | 47,129 |
def get_custom_refinfo(*args):
"""
get_custom_refinfo(crid) -> custom_refinfo_handler_t const *
Get definition of a registered custom refinfo type.
@param crid (C++: int)
"""
return _ida_nalt.get_custom_refinfo(*args) | 031cce0572fed275c29df37f7e928236896b37f9 | 47,130 |
import re
def re_tester(regex, flags=0):
"""Creates a predicate testing passed string with regex."""
if not isinstance(regex, _re_type):
regex = re.compile(regex, flags)
return lambda s: bool(regex.search(s)) | 39a55bc0e9a30b8295a2550f086e4352af6631ab | 47,131 |
def objective_function(n_splits, PopFrenetPath, curv_smoother, tors_smoother, hyperparam, smoothing, alignment, lam, parallel):
"""
Objective function that do the cross validation.
...
"""
print(hyperparam)
# kf = KFold(n_splits=n_splits, shuffle=True, random_state=1)
kf = KFold(n_splits=n_s... | 018a0dd7c2994b9576ba39c310a7f3e36b29033b | 47,132 |
def from_file(path: str) -> set:
"""
Read conditions from a file. Each line contains a separate condition.
:param path: Path to file.
:return: Read conditions.
"""
conditions = set()
with open(path) as f:
for line in f:
conditions.add(line)
return conditions | 3780d540d6f300fe0a97d354ed33fa0aab803d56 | 47,133 |
def from_gca_point(gca_obj, name_header, folder_name, folder_description='',
altitude_mode="ctg", style_to_use=None, pt_hidden=False, folder_collapsed=True):
"""
Save features from GCA object into a kml folder
Parameters
----------
gca_obj : GCA
name_header : str
The ... | 37f1748a8c17e3c380ed5599fabf2ce1f4045abc | 47,134 |
def join_positions(pos1,pos2):
""" Merge two positions and return as a list of strings
pos1: iterable object containing the first positions data
pos2: iterable object containing the second positions data
Example:
>>> join_positions('ABCD','1234')
['A1', 'B2', 'C... | cf5525a9d246501976bcb9ed74b12a13034c7525 | 47,135 |
def get_corrcal_gainsol(data,ant1,ant2,gain_int,noise,sky_cov_vecs,src_vecs,block_edges,gain_fac,maxiter=1000):
"""
This fuction solve for antenna gain per frequency.
Method : Conjugate-gradient Method
Software : python scipy.optimize --- fmin_cg
Parameters
----------
data : array; sh... | 84d47654c0ff0b832260cbf8f42a39ed3acc0703 | 47,136 |
def writePQR(filename, atoms):
"""Write *atoms* in PQR format to a file with name *filename*. Only
current coordinate set is written. Returns *filename* upon success. If
*filename* ends with :file:`.gz`, a compressed file will be written."""
if not isinstance(atoms, Atomic):
raise TypeError(... | d1044594d5231b9fbc36e0091c0eb1aa31f9daf6 | 47,137 |
import os
import re
import syslog
def retrieve_vlan_id():
"""
Retrieves VLAN ids of in-band interfaces.
This function fetchs vlan ids from OLT /broadcom/bal_config.ini file.
:return : vlan id of in-band interface if successfull and None in case of failure.
:rtype : integer(valid vlan_id) in case... | d84ff5215d8c670ff0d2c4e56855a8bd7d1fbe91 | 47,138 |
def read_intersections(fname1, fname2, band1=None, band2=None):
"""Read in the intersection of 2 files as an array"""
bounds = get_intersection_bounds(fname1, fname2)
print(f"bounds: {bounds}")
im1 = copy_vrt(fname1, out_fname="", bbox=bounds)
im2 = copy_vrt(fname2, out_fname="", bbox=bounds)
re... | cb6bee733a078bf47acddf6922ec1334b758af58 | 47,139 |
def transformer(vocab_size, num_layers, units, d_model,
num_heads, dropout, name="transformer"):
"""
transformer的粗粒度的结构实现，在忽略细节的情况下，看作是
encoder和decoder的实现，这里需要注意的是，因为是使用self_attention，
所以在输入的时候，这里需要进行mask，防止暴露句子中带预测的信息，影响
模型的效果
:param vocab_size:token大小
:param num_layers:编码解码... | 3b68ed04cde51f1672414d124b2b74d0aeb0b06d | 47,140 |
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read,
and adds a number of s... | f42afcf25113dd92a0692a411edbc346960d0209 | 47,141 |
import os
def get_device_names():
"""
"""
devices = [comport.device for comport in serial.tools.list_ports.comports()]
devices = [comport.device for comport in serial.tools.list_ports.comports()]
device_names = []
if os.name == 'posix': # macOS
# Strip the device prefix
... | 65161d328cf8242ee31c2b712894c80b40fcfe46 | 47,142 |
def divide(data: tuple, domain='freq'):
"""Divide pyfar audio objects, array likes, and scalars.
Pyfar audio objects are: :py:func:`Signal`, :py:func:`TimeData`, and
:py:func:`FrequencyData`.
Parameters
----------
data : tuple of the form (data_1, data_2, ..., data_N)
Data to be divide... | 1c5c89cba61230b3fcddfc3512be39c04c84f8fd | 47,143 |
import array
def norm_D_sum(D_sum):
"""Normalize a D_sum by the elements for each feature by
smallest absolute size (that element becomes 1 in norm).
For unweighted feature sensitivities, or else it
unweights weighted ones."""
D_n = {}
for feat, pD in D_sum.items():
pD_min_abs = min(ab... | 26eda448ec7db143bc89b5319bbbc18597a88349 | 47,144 |
import torch
def collate_fn(data):
"""Creates mini-batch tensors from the list of tuples (image, caption).
We should build custom collate_fn rather than using default collate_fn,
because merging caption (including padding) is not supported in default.
Args:
data: list of tuple (image, captio... | c60edfb028d9405d76cabded30e5fb74fd1e821e | 47,145 |
def getLeastReplaggedCommons():
"""
Returns the name of the least replagged Commons replica among s1, s2 and s3
"""
return "commonswiki-p.rrdb.toolserver.org"
# broken:
#return urllib.urlopen("http://toolserver.org/~eusebius/leastreplag").readline() | 5726416f7a1cbb09f51de81d967009005a06af1b | 47,146 |
import json
def parse_line(header, line):
"""Parse one line of data from the message file.
Each line is expected to contain chunk key - comma - tile key (CSV style).
Args:
header (dict): Data to join with contents of line to construct a full message.
line (string): Contents of the line.
... | 452dd80f84a35f6e3532330155bade7f424c102a | 47,147 |
from datetime import datetime
from sys import version
async def handle(request):
""" index req """
dt = datetime.datetime.now()
dtstr = str(dt)
myulid = ulid.new()
strmyulid = myulid.str
intmyulid = myulid.int
bmyulid = bazed_ulid(intmyulid)
text = tpl % (
version, dtstr,... | 405459c6270335194a6157ad7f7d4606861a29a6 | 47,148 |
def filequote(text):
"""Transform text to file name."""
trans = str.maketrans(' /()', '____')
return text.translate(trans) | dd6237fe6c66f60c00c8a569636adf17d45d66cc | 47,149 |
def characteristic(text, ontology=None):
"""
Making a ENA Biosamples characteristic
"""
if ontology:
return [{"text": text, "ontologyTerms": [ontology]}]
else:
return [{"text": text}] | e7f175a1ef8137b4c0e19a28a5d74055d9363c66 | 47,150 |
import logging
def get_full_vol_name(vmdk_name, datastore, vm_datastore):
"""
Forms full volume name from vmdk file name an datastore as volume@datastore
For volumes on vm_datastore, just returns volume name
"""
vol_name = vmdk_utils.strip_vmdk_extension(vmdk_name)
logging.debug("get_full_vol_... | 72587f94fbf8f15bbe538a7a6b53bd87a164f709 | 47,151 |
from . import eds
from . import epf
def import_od(source, node_id=None):
"""Parse an EDS, DCF, or EPF file.
:param source:
Path to object dictionary file or a file like object or an EPF XML tree.
:return:
An Object Dictionary instance.
:rtype: canopen.ObjectDictionary
"""
if ... | 5c6ef6056df075c4f0918473e6a3851032bdf27a | 47,152 |
def _strip_trailing_ffs(binary_table):
"""
Strip all FFs down to the last 32 bytes (terminating entry)
"""
while binary_table.endswith("\xFF"*64):
binary_table = binary_table[0:len(binary_table)-32]
return binary_table | 43c14297da709f78316e460180c6c4515650f34d | 47,153 |
def params_schedule_fn_constant_09_01(outside_information):
"""
In this preliminary version, the outside information is ignored
"""
mdp_default_gen_params = {
"inner_shape": (7, 5),
"prop_empty": 0.9,
"prop_feats": 0.1,
"start_all_orders": [
{"ingredients": ["... | 4fa999ee03a8d1fb3178ad6267c5d48a23026855 | 47,154 |
import pathlib
import glob
import os
def getFileListing(files, recurse):
"""Creates recursive or non-recursive file listing.
Args:
file: path to evaluate
recurse: True/False value which dictates whether the listing is recursive
Returns:
fileList: array containing the results of t... | 8c722b372b7ef0fd904cdda7cc153295b8aa4707 | 47,155 |
def is_same_day(t1, t2, refresh_time=None):
"""check two times in same day"""
return get_day_time(t1, refresh_time) == get_day_time(t2, refresh_time) | b8fa216f79f14b419add6b6e96c6923ba791c88e | 47,156 |
from datetime import datetime
def benchmark(synthesizers, datasets=DEFAULT_DATASETS, iterations=3, add_leaderboard=True,
leaderboard_path=LEADERBOARD_PATH, replace_existing=True):
"""Compute the benchmark scores for the synthesizers and return a leaderboard.
The ``synthesizers`` object can eith... | 9168c94921c93765d68d15a32a826622c59bd6ff | 47,157 |
import io
import os
def context_from_format(format_def: str, **kwargs) -> (
InvokeContext, io.BytesIO):
"""
Creates a context from request
:param format_def: function format
:type format_def: str
:param kwargs: request-specific map of parameters
:return: invoke context and data
:rt... | db272011a0f0a62096c6304efb1249b0e167db37 | 47,158 |
def detec_data_space():
"""判断是否有free分区磁盘"""
diskLists = commands.getoutput(""" fdisk -l | grep -iw "^Disk"
| grep "/dev/" | grep -vi "\/mapper\/"
| awk -F ":" '{print $1}' |
awk '{print $2}' | sort """)
diskLists = diskLists.split('\n')
return diskLists | fdcd3016d38c7036cef7a83d2b15c901af8ea9b7 | 47,159 |
def arctand(x):
"""Trigonometric inverse tangent using :func:`np.arctan <numpy.arctan>`, element-wise with an output in degree.
Parameters
----------
x : array_like
Input array.
Returns
-------
y : array_like
The corresponding tangent values. This is a scalar if x is a scal... | 3e10dd7bc3a65b3615e330041891a8aff2f81d5c | 47,160 |
import struct
def byte_to_float(b1, b2, b3, b4):
"""
A function to get a 32 bit float from 4 bytes read in order [b1, b2, b3, b4]
:param b1: first byte
:param b2: second byte
:param b3: third byte
:param b4: fourth byte
:return: the byte array from b1, b2, b3, b4 unpacked as a float using ... | 962480d1b9d2c50e3196b5480e9c62bf696a8f0d | 47,161 |
def interpolate_and_average(xs, ys, interp_points=None):
"""
Average bunch of repetitions (xs, ys)
into one curve. This is done by linearly interpolating
y values to same basis (same xs). Maximum x of returned
curve is smallest x of repetitions.
If interp_points is None, use maximum number of p... | 35e7a0deec29710df4bc868d1c50845e6fc533f9 | 47,162 |
from .PlanheatMappingPlugin import PlanheatMappingPlugin
def classFactory(iface): # pylint: disable=invalid-name
"""Load PlanheatMappingPlugin class from file PlanheatMappingPlugin.
:param iface: A QGIS interface instance.
:type iface: QgsInterface
"""
#
return PlanheatMappingPlugin(iface) | c048ea0cc82c4571dbe9459a237fe8733b666da7 | 47,163 |
def plot_marker_3d(x, y, z, max_size=0.75, min_size=0.05, marker_type='scatter', num_lines=8, ax=None, **kwargs):
"""Pseudo-3D scatter plot using marker size to indicate height.
This plots markers at given ``(x, y)`` positions, with marker size determined
by *z* values. This is an alternative to :func:`mat... | 895aa1a78ba0229d33bf65deac35102bf5f75d81 | 47,164 |
def is_record(obj):
"""Check whether ``obj`` is a "record" -- that is, a (text, metadata) 2-tuple."""
if (
isinstance(obj, (tuple, list))
and len(obj) == 2
and isinstance(obj[0], compat.unicode_)
and isinstance(obj[1], dict)
):
return True
else:
return Fal... | e95b7e885afb3594b688c90d1d46fc4ce1e326c8 | 47,165 |
import re
def get_used_by_from_comments(lines: "list[str]") -> "tuple[int, list[str]]":
"""Read the module-used-by block comment from a module file.
Args:
lines (list[str]): The content of the module file as a list of strings.
Returns:
tuple[int, list[str]]: The integer indicates the las... | 6ac30266524373d0de7cf7bb9ad9fd8dcd1933a2 | 47,166 |
def text_to_layer(block, unit, return_sequences=False):
"""Build tensorflow layer, easily."""
layer = None
if block == "CNN":
layer = tf.keras.layers.Conv1D(unit, kernel_size=1, strides=1, padding='same', activation='relu')
elif block == "LCNN":
layer = tf.keras.layers.LocallyConnected1D... | 6ad7fb22ce222c6011e05beb9b6120ba43bd2abf | 47,167 |
from pathlib import Path
def construct_target_path(participant_name, model_name, roi):
"""Construct path to save results to."""
project_root = Path(__file__).parents[1]
return project_root / "results" / participant_name / f"model_{model_name}"\
/ f"roi_{roi}" | 072681647a3362563829c25d4890aa13425cff2c | 47,168 |
import codecs
def txidFromBroadcast (hexStr):
"""Extracts the hex txid from a broadcast in hex."""
# The prevout txid is the first part of the broadcast data
# in serialised form. But we need to reverse the bytes.
hexRev = hexStr[:64]
bytesRev = codecs.decode (hexRev, "hex")
return bytesRev[::-1].hex ... | 96690f4fdef5f0cff857188045696e427914b887 | 47,169 |
def _tm_range_from_secs(start, dur, rate=25.0):
"""
Return a TimeRange for the given timestamp and duration (in
seconds).
"""
return otio.opentime.TimeRange(
_rat_tm_from_secs(start), _rat_tm_from_secs(dur)) | 9c68f34f5456252d4062c5bccfb82b3fc6f17537 | 47,170 |
def is_known_scalar(value):
"""
Return True if value is a type we expect in a dataframe
"""
def _is_datetime_or_timedelta(value):
# Using pandas.Series helps catch python, numpy and pandas
# versions of these types
return pd.Series(value).dtype.kind in ('M', 'm')
return not ... | 231035c6c8282a4b2c632112e74fee6194660a78 | 47,171 |
def get_model_results(ldamodel, corpus, dictionary):
""" Create doc-topic probabilities table and visualization for the LDA model
"""
vis = pyLDAvis.gensim.prepare(ldamodel, corpus, dictionary, sort_topics=False)
transformed = ldamodel.get_document_topics(corpus)
df = pd.DataFrame.from_records([{... | db622cdefa51337985c0b3dba9987d0be3b6f704 | 47,172 |
from django.conf import settings
def i18n(request):
"""
Set client language preference, lasts for one month
"""
next = request.META.get('HTTP_REFERER', settings.SITE_ROOT)
lang = request.GET.get('lang', settings.LANGUAGE_CODE)
if lang not in [e[0] for e in settings.LANGUAGES]:
# lang... | e9f2e1cc69a81e1766abfaae7efc4463e1103273 | 47,173 |
from typing import Optional
def repr_errors(res, estimator=None, method: Optional[str] = None) -> str:
"""Pretty print original docstring and the obtained errors
Parameters
----------
res : dict
result of numpydoc.validate.validate
estimator : {estimator, None}
estimator object or... | fd24899717b209ee1419a7c46f006587a0b8323e | 47,174 |
from hstore_flattenfields.db import fields
def get_modelfield(typo):
"""
>>> get_modelfield('Input')
<class 'hstore_flattenfields.db.fields.HstoreCharField'>
>>> get_modelfield('Integer')
<class 'hstore_flattenfields.db.fields.HstoreIntegerField'>
>>> get_modelfield('Random')
<class 'hstor... | a41e79d6f297f5da4d21e1e0645713afeff9415f | 47,175 |
def thread_get_messages(service, thread):
"""Get the list of messages in a given thread.
Args: service: gmail api service object.
thread: thread from which to get the messages.
Returns: list of messages objects (minimal format: only id and label).
"""
_Context.set('Thread {}: retrieving l... | da356d3e4b5dcfe9330002eb54b32b57b62e1990 | 47,176 |
from typing import Tuple
def cc_cyclic(amp: Tuple[int, int] = (10, 15), freq: Tuple[int, int] = (10, 15)) -> DeltaGenerator:
"""
Creates a cyclic shape control chart sequence
Parameters
----------
amp: Tuple. Defaults to (10,15)
Chooses randomly a number between the tuple values as am... | d53062923ab9a7231dfc5fbec64df08104588618 | 47,177 |
import os
def activate():
"""
Return the path to the `activate` shell script within a virtual environment.
"""
path_to_venv = os.environ.get("PATH_TO_VENV", "venv")
if os.name != "nt":
# Posix
return os.path.join(path_to_venv, "bin", "activate")
else:
# Windows
... | baf7846ae2ce9433fcf444457e5d251deed06ec3 | 47,178 |
def predict(X, y, parameters):
"""
This function is used to predict the results of a L-layer neural network.
Arguments:
X -- data set of examples you would like to label
parameters -- parameters of the trained model
Returns:
p -- predictions for the given dataset X
"""
... | e4d374577c2fe0499bc5233a843290e59512dad9 | 47,179 |
def build_log_src_prior(prior_type, xs, ys):
"""
Construct a log-probability distribution from a prior type specifier.
Units are probability per area.
"""
shape = (len(xs), len(ys))
dx = np.mean(np.diff(xs))
dy = np.mean(np.diff(ys))
if prior_type == 'uniform':
log_p_unnormaliz... | 394b700779f1936d92189a2c9d39c0805043c079 | 47,180 |
def query_lat_long_with_fallback(address):
""" Make a query for latlong from address with fallback mechanism.
The method will first attempt to query latlong from Google geocoding service for it's higher precision. If the server cannot
connect to the Google service or a timeout happens, the server tries to ... | 5caa21c13fb3061c36aab7171eb88acbeee4d64e | 47,181 |
def GEV_mean(mu, sigma, xi):
"""Calculate the mean of a GEV distribution.
The arguments mu, sigma, and xi are the location, scale, and shape
parameter of the GEV distribution, respectively.
"""
if xi >= 1:
return np.inf
elif xi == 0:
return mu + sigma * np.euler_gamma
else:
... | 82b7e9137bb3365a1c661059b4de3356fb05f97b | 47,182 |
import tqdm
def create_training_instances(input_files, tokenizer, max_seq_length,
dupe_factor, short_seq_prob, masked_lm_prob,
max_predictions_per_seq, rng, aa_features,
do_hydro, do_charge, do_pks, do_solubility,
... | 3980b1aafb550ee0d4999e9f968fbbbff29c981e | 47,183 |
import re
def delete_dup_greater_than(text):
"""Processes html text deleting duplicated '>' generated after
previous processing steps.
Args:
text (string): html text that is going to be processed.
Returns:
string: text once it's processed.
"""
p1 = re.compile(r'(<br>)(>)(</)', re.UNICODE)
processed_text... | dd4383047c17addd9d32dd8d3e6d5f9d35911dd2 | 47,184 |
def svc_model_adjustment_optimization_score(X_train,X_test,y_train,y_test):
"""
支持向量机 Support Vector Machine
"""
svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}
return model_adjustment_optimization(SVC(),'Support Vector Machine',svc_params,X_train,X_test,y_trai... | 188a76d35452f765a158479768f9ba23ea221577 | 47,185 |
def msg_handle_KOR(string)->list:
"""Sort for data, double check with isit_covid"""
isit_covid = False
day = 0 #(0: default, 1: 오늘, 2:어제, 3:그저께)
my_list = split_string(string)
print(string)
#0 기본 언어 : 코로나, 확진자, 몇, 명
if check_item(my_list, '코로나'):
if check_item(my_list, '명') or check_... | f2e5bf3f579203d115e1e571d82a980e7912b1f1 | 47,186 |
import difflib
import re
from typing import Any
import json
def activity_diff(context: Context, data_dict: DataDict) -> dict[str, Any]:
"""Returns a diff of the activity, compared to the previous version of the
object
:param id: the id of the activity
:type id: string
:param object_type: 'package... | 286bcd88b8adbd2ab2ec2d44e3e524f97aa8cd71 | 47,187 |
def property_graph(graph='g1'):
"""
Define the properties of the graph to generate
:graph : type of desired graph. Options : ['g1','g2', 'g3', 'g4', 'g5']
"""
if graph == 'g1':
method = 'partition'
sizes = [75, 75]
probs = [[0.10, 0.005], [0.005, 0.10]]
number_class =... | 57aa301801213f4b88e9b2c77b3bf44c372b09ee | 47,188 |
import re
def readxtalkcoeff(xtalkfile):
"""read crosstalk coefficent file"""
xdict = {}
try:
xfile = open(xtalkfile,'r')
for line in xfile:
if (len(line.strip()) > 0 and line[0] != '#'):
line = line.rstrip('\r\n')
line = line.rstrip()
... | 759e9264b605256580bf4c3dd59859d50524f195 | 47,189 |
import copy
import warnings
from re import U
def radec2altaz(radec, location, obstime=None, epoch_RA=2000.0, time_type=None):
"""
----------------------------------------------------------------------------
Convert RA-Dec to Alt-Az with accurate ephemeris
Inputs:
radec [numpy array] Altitude ... | b4e1dff708e78adf90937d0c886cd7347f7b71ae | 47,190 |
from jiant.utils import gcp
from jiant.utils import emails
import argparse
import io
import os
import random
import torch
import tokenizers
def initial_setup(args: config.Params, cl_args: argparse.Namespace) -> (config.Params, int):
"""Perform setup steps:
1. create project, exp, and run dirs if they don't a... | f7498290ba55f325a06f87bd158f7d3e8afb0c49 | 47,191 |
def filter_properties(person, PERSON_PROPERTIES):
"""
Extract specific properties of the given person into a new dictionary.
Parameters:
person (dict): the dictionary containing properties of a person.
PERSON_PROPERTIES (tupl): a tuple containing the characteristics of a person
Returns... | 2a3ec4ab32c5d99d475ebffaefe0d8c40ce137af | 47,192 |
import argparse
def OriginFromArg(arg):
"""Constructs the origin for the token from a command line argument.
Returns None if this is not possible (neither a valid hostname nor a
valid origin URL was provided.)
"""
# Does it look like a hostname?
hostname = HostnameFromArg(arg)
if hostname:
return "... | f2418f2b0800dce8c682a66e40840413b173086b | 47,193 |
import importlib
def import_module(module, app):
"""Handle the import of a module by mocking them with autodoc config.
Query the value of ``autodoc_mock_imports`` inside the ``conf.py`` module.
Arguments:
module (str): The name of the module to import.
app (Sphinx): The current sphinx ap... | d83633c09cf6c9f56e5a415b1d350587e7d79564 | 47,194 |
def landsat8_spectral_indices(img):
"""
Function that computes several spectral indices for Landsat 8 sensor.
The indices specifically focus in detecting vegetation phenology,
and water and salt content in the soil. The indices are added to the
input image as bands. The function can be used either w... | 2392382e6c6f1bbb222c2a7e42c77d1a654ea9b5 | 47,195 |
def rphiz_to_xyz(values):
"""Converts axis values from cylindrical coordinates into cartesian coordinates
Parameters
----------
values: array
Values of the axis to convert (Nx3)
Returns
-------
ndarray of the axis (Nx3)
"""
r = values[:, 0]
phi = values[:, 1]
if len... | 1b9c7ff031bec97b588844da9a7498f2dd79a85c | 47,196 |
import logging
import pandas
def get_opt_solution(model=None):
"""Returns the solution found for the optimization parameters as data frame
The resulting data frame will have the columns:
* `name`: the name of the parameter
* `lower`: the parameters lower bound
* `upper`: the parameters upper bou... | 3466d6f208cfbe4d9de4854f949f163fcb44b930 | 47,197 |
import os
def load_conv(save_path, name):
"""Load convolution layer parameters"""
fname = os.path.join(save_path, '%s.msg' % name)
with open(fname) as f:
weight = mp.unpack(f)
bias = mp.unpack(f)
weight = np.asarray(weight).astype(np.float32)
bias = np.asarray(bias).as... | 95ca4dbec1cd4c6622842c48c66855b3f9e353b9 | 47,198 |
def invert(image):
"""Invert the given image.
:param image: image
:returns: inverted image
"""
assert np.amin(image) >= -1e-6
assert np.amax(image) <= 1+1e-6
return 1.0 - np.clip(image, 0, 1.0) | 4b9237fb1e2c76eaab08a4b7632165ede5951a7f | 47,199 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.