content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def jump_instruction(args: list, register_copy: dict):
"""
"jnz x y" - Jumps to an instruction y steps away (positive means forward, negative means backward),
but only if x (a constant or a register) is not zero
:param args: Array of arguments passed into this command.
:param register_copy: Dict wit... | 32e855bedc59cd7dbc3bc9de1be6ebfa979abc24 | 3,605,827 |
def pop(the_stack):
""" Returns top of stack if stack not empty. Otherwise, returns None """
if is_empty(the_stack):
return None
else:
item = the_stack[len(the_stack) - 1]
del the_stack[len(the_stack) - 1]
return item | e64b274e7f828e88eb852e96ba8ec29ae8351767 | 3,605,828 |
def build_weighted_graph(G, alpha=0.9, p_null=None):
"""using log-odds ratio probability weighting to model graph edges.
when [p_null] is missing, using a default null model for the weighting:
random degree-preserving rewiring approximation."""
W = G > 0
deg = W.sum(axis=1).reshape((1, -1))
M = ... | b51e5aeb6c8214d609b4ffc119394bb1c763e02d | 3,605,829 |
def get_badge(total, colour=DEFAULT_COLOUR):
"""update total and return shields.io URL
Args:
total:
total coverage (str).
colour:
badge colour (str).
"""
percent = quote("%")
shields_badge = (
f"https://img.shields.io/badge/Coverage-{total}{percent}-{c... | 1f872a12ff3d60847ca9c94248aa8478b12dbefc | 3,605,830 |
import torch
def perm(N, seed: int = None):
"""Generate a tensor with the numbers 0 through N-1 ordered randomly."""
gen = torch.Generator()
if seed is not None: gen.manual_seed(seed)
perm = torch.normal(torch.zeros(N), torch.ones(N), generator=gen)
return torch.argsort(perm) | da12d8783e0aec0bea1d3841c30c554dbaaf3a77 | 3,605,832 |
from typing import Union
from typing import Callable
import torch
def pytorch_load(
torch_object: Union[Callable, TORCH_RECOVERABLE],
path: str,
device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
) -> TORCH_RECOVERABLE:
"""Can instantiate or load on an existing instan... | 4e05323192eb15bbf41b8023a986a5ff41814ee0 | 3,605,833 |
def lowercase(df):
"""
lowercase things
"""
return pd.DataFrame({
"sentence": df.sentence.apply(lambda x: " ".join([i.lower() for i in x.split(" ")])),
"label": df.label
}) | 59dfd02de57f57b1272761bd83d5c6971ded0fc9 | 3,605,834 |
def summarise_tenant(instance_states):
""" Summaries usage of Nova tenant """
core_usages = {}
calculator = InstanceScore()
for state in instance_states:
if state["tenant"] not in core_usages:
core_usages[state["tenant"]] = 0
core_usages[state["tenant"]] += calculator.get_sc... | df9a043b5df6cce81eb73d19b42e63e802f4b508 | 3,605,835 |
import string
def decode(digits, base):
"""Decode given digits in given base to number in base 10.
digits: str -- string representation of number (in given base)
base: int -- base of given number
return: int -- integer representation of number (in base 10)"""
# Handle up to base 36 [0-9a-z]
as... | febdf9973a73de5b3686a20b8c2a5738391a815e | 3,605,836 |
import tempfile
import codecs
def check_vocab(vocab_file, special_vocabs, pad_to_eight):
"""Check if vocab_file doesn't exist, create from corpus_file."""
global UNK, SOS, EOS, UNK_ID
if tf.gfile.Exists(vocab_file):
tf.logging.info("# Vocab file %s exists\n" % vocab_file)
vocab = []
vocab_size = 0
... | f2ba4eb1f16097a4e040b9ec232339262895dd66 | 3,605,837 |
def sub2ind(subs, shape):
"""Convert sub indices (i, j, k) into linear indices.
Parameters
----------
subs : iterable of array_like
List of sub-indices. Its length is the number of dimension.
Each element should have the same number of elements and shape.
shape : iterable
Si... | 3f729716361c4b44cfde362536320c7e1894ebbc | 3,605,839 |
from typing import Literal
def Tokenize(string, separators):
"""Tokenizes the given string based on a list of separator strings.
This is similar to splitting the string based on separators, except
that this function retains the separators. The separators are
wrapped in Separator objects and everything else i... | f1a2c2a6f7174024350c64da9e493bda2e52ca15 | 3,605,840 |
from apysc._file import module_util
from typing import Callable
from typing import Any
def _get_callable_from_package_path_and_callable_name(
*, module_or_class_package_path: str,
callable_name: str) -> Callable:
"""
Get a callable object from a specified package path and
callable name.
... | 263cc2f677f402889f5878cfdb51100b6f021a10 | 3,605,841 |
def interseccion_todas_capas(c, f, lc, l_index, args):
""" Return true if exist intesection with other layers, false otherwise. """
uri2 = QgsDataSourceUri()
uri2.setConnection(args.server, str(args.port), args.dbname, args.user, args.password)
geom = f.geometry()
bbox_geom = geom.boundingBox()
... | 1f06f60fd33748f254c80e9ce709f0032825f80b | 3,605,842 |
def validinstruction(instruction):
"""
Check whether the given instruction is valid
:param instruction: a cardinal point
:return: If the instruction is a valid input
"""
if instruction in CARDINAL_POINTS:
return True
return False | 4c9668615992a479e6c029fc73360cd4a206d0cd | 3,605,843 |
def read_file(fn, guess_product = True):
"""
Reads a file with the ICARTT format.
https://www-air.larc.nasa.gov/missions/intexna/DataManagement_plan.htm
Parameters
----------
fn : TYPE
DESCRIPTION.
guess_product : TYPE, optional
DESCRIPTION. The default is True.
Returns... | 4fde6bd665093bf4e4f698811fa64018a799fdcc | 3,605,844 |
def generate_phone_numbers():
"""Returns example phone numbers."""
return [PhoneNumber(number="0123456789", location="Mobile"),
PhoneNumber(number="+490", location="Work"),
PhoneNumber(number="", location="")] | 733106d534bed56c456c4cc7853472c4fd13bccc | 3,605,846 |
from numpy import linspace
def gbox_boundary(gbox, pts_per_side=16):
"""Return points in pixel space along the perimeter of a GeoBox, or a 2d array.
"""
H, W = gbox.shape[:2]
xx = linspace(0, W, pts_per_side, dtype='float32')
yy = linspace(0, H, pts_per_side, dtype='float32')
return polygon... | e06176435163694da539cc2f354f64deb8c04734 | 3,605,847 |
def get_model_fields(model):
"""
For a given model, returns all fields with their names
"""
_fields = []
for key, val in dict(model.__dict__).iteritems():
if issubclass(val.__class__, ModelAttribute):
_fields.append((key, val.__class__))
return _fields | e9ce1ab931a5e59624a29f4ef6662c832343a9e5 | 3,605,848 |
def map_pixels_to_ascii_chars(image, range_width=25):
"""Maps each pixel to an ascii char based on the range
in which it lies.
0-255 is divided into 11 ranges of 25 pixels each.
"""
pixels_in_image = list(image.getdata())
pixels_to_chars = [ASCII_CHARS[pixel_value//range_width] for pixel_value... | 31d31ecdda9d1479bbe4c4c9091c4c21e60812ee | 3,605,849 |
import re
def is_good_photo(photo):
"""Return True if photo is usable; False if it is a bad photo."""
return all([re.search(pat, photo['img_src']) is None for pat in BAD_PATS]) | f5163769483ee0e266fedd730fd9c487b92a6315 | 3,605,850 |
def descent_set(t):
"""
Return the descent set of a standard tableau ``t``
(encoded as a sorted list).
The *descent set* of a standard tableau `t` is defined as
the set of all entries `i` of `t` such that the number `i+1`
appears in a row below `i` in `t`.
EXAMPLES::
sage: from sa... | 01d91600ab23646ebb471a1b65c137a6e8287183 | 3,605,851 |
def softmax_with_cross_entropy(preds, target_index):
"""
Computes softmax and cross-entropy loss for model predictions,
including the gradient
Arguments:
predictions, np array, shape is either (N) or (batch_size, N) -
classifier output
target_index: np array of int, shape is (1) or ... | 946c2fa76d6bbab53be311b6062b44a1fdef5810 | 3,605,852 |
def evaluate(inputs):
"""
Evaluates a weighted sum function.
$sum = \Sigma_{i=0} (i+1)*x_{i}$
min with replacement = n*(n-1)/2*lb occurs at x_{i} = lb (i.e., lower bound of the discrete variables)
max with replacement = n*(n-1)/2*ub occurs at x_{i} = ub (i.e., upper bound of the discrete variables)
... | c09e4c8973d069e46e981a02d09f8754f893f95d | 3,605,853 |
def _boot_time_amiga() -> "float|None":
"""Returns uptime in seconds or None, on AmigaOS."""
try:
return os.stat("RAM:").st_ctime
except (NameError, OSError):
return None | 45ab403609f18321c039f1e112f433fa6366ba8e | 3,605,854 |
from sklearn.ensemble import IsolationForest
def run_iforest(X):
""" Predict the anomaly score with iForest
"""
clf = IsolationForest()
clf.fit(X)
scores = clf.decision_function(X)
return scores | 56b2ebcd300faca8ef0224f7aacb150e0bf2c133 | 3,605,856 |
def get_A1_hom(s, scalarKP=False):
"""
Builds A1 for the spatial error GM estimation with homoscedasticity as in
Drukker et al. [Drukker2011]_ (p. 9).
.. math::
A_1 = \{1 + [n^{-1} tr(W'W)]^2\}^{-1} \[W'W - n^{-1} tr(W'W) I\]
...
Parameters
----------
s : csr_m... | 7c90fde361ceb2fc7349812b8743264e486c7e01 | 3,605,857 |
import ctypes
def register_node(type_key=None):
"""register node type
Parameters
----------
type_key : str or cls
The type key of the node
"""
node_name = type_key if isinstance(type_key, str) else type_key.__name__
def register(cls):
"""internal register function"""
... | 8105395cc7c19677a04612a3702c6ed1806ca35d | 3,605,858 |
def sort_url_by_query_keys(url):
"""A helper function which sorts the keys of the query string of a url.
For example, an input of '/v2/tasks?sort_key=id&sort_dir=asc&limit=10'
returns '/v2/tasks?limit=10&sort_dir=asc&sort_key=id'. This is to
prevent non-deterministic ordering of the query stri... | 694201c1dc6c5d30df6d589e338e325f538fff47 | 3,605,859 |
import pandas
def add_measured_to_input(time_df, input_df, measured_df):
"""adds info of measurement dataframe to time and layer execution dataframe
Args:
time_df: DataFrame with input layer names as columns, stores runtime
input_df: DataFrame with input layes names as columns, stores executi... | e6871ea8693726ffc33f4e52d0875b6489e672ae | 3,605,860 |
def conditional_lateness_plots(rows=None,
degrees_sep=1,
conds=(0,60,300,600,1200)):
"""
Plots the (weighted) conditional lateness distribution as
F( lateness at stop | lateness at Dth stop previous )
where D = degrees_sep. This is plotted ... | 1c434458cbaf61ff83e2117f17898e63a9401271 | 3,605,861 |
def sample_rois(rois, gt_boxes, num_classes, rois_per_image, fg_rois_per_image, fg_overlap, box_stds, seg, im_info):
"""
generate random sample of ROIs comprising foreground and background examples
:param rois: [n, 5] (batch_index, x1, y1, x2, y2)
:param gt_boxes: [n, 5] (x1, y1, x2, y2, cls)
:param... | 42920de5251879da80346c603a1690f95c2453c8 | 3,605,862 |
import requests
from bs4 import BeautifulSoup
def get_departures(vazi_od, dan_u_nedelji, linija):
"""dan_u_nedelji: R - radni, S - subota, N - nedelja"""
URL = f'http://gspns.co.rs/red-voznje/ispis-polazaka?rv=rvp&vaziod={vazi_od}&dan={dan_u_nedelji}&linija[]={linija}'
r = requests.get(URL)
soup = Bea... | 986d5c175e5d27896cf68c096f2e7201c2dfa778 | 3,605,863 |
def getaccesskeys(show):
"""
This function is used to get the list of all accesskeys
"""
try:
accesskeys=iam.list_access_keys()
except botocore.exceptions.ClientError as e:
coloredtext("There was an error while getting access key data: \n\n\n")
print(... | 52c907385e628638c524ac33f83e38986b081648 | 3,605,864 |
def convert_categorical_column(col : ColumnObject) -> pd.Series:
"""
Convert a categorical column to a Series instance
"""
ordered, is_dict, mapping = col.describe_categorical
if not is_dict:
raise NotImplementedError('Non-dictionary categoricals not supported yet')
# If you want to che... | 30c99adacc32ed8f51b85055494817ae1b58529c | 3,605,865 |
from .RNN.values_arrangement import get_outputs_by_neuron
from typing import Union
from typing import List
def autocorrelation_periodicity(audio_path: Union[str, List[str]], downbeat_model: bool = True) -> float:
"""
Autocorrelation periodicity pulse clarity metric
Args:
audio_path: string or lis... | 80c4d2203d6261599673a4bff0e851225e906e16 | 3,605,866 |
def random_ddm(drift, threshold, ndt, rel_sp=.5, noise_constant=1, dt=0.001, max_rt=10):
"""Simulates behavior (rt and accuracy) according to the diffusion decision model.
In this parametrization, it is assumed that 0 is the lower threshold,
and, when rel_sp=1/2, the diffusion process starts halfway throug... | 8a3df7d78a617d7ed397810b65522eaf0f5a7681 | 3,605,867 |
def flow_camera_motion_loss(gt_masks, camera_motion, depth, flow, camera_intrinsics):
"""Supervise camera_motion with optical flow.
Args:
gt_masks: tensor of shape [batch_size, num_boxes, image_height, image_width]
camera_motion: tensor of shape [batch_size, 7]
depth: tensor of shape [batch_size, image_... | 6a3d2eebd2f96fea2025bd6a02fd4dbe6adcf479 | 3,605,868 |
import random
import string
def user_generate_key(id):
"""Generate new key for user"""
user = User.query.get(id)
key = Key()
key.key = ''.join(random.sample(
string.ascii_letters + string.digits, 32
))
key.user_id = user.id
db.session.add(key)
db.session.commit()
flash('Suc... | d20024c036f678cdafe18d27c2437556fe49c8d3 | 3,605,870 |
def interp2dxy(field3d, xy, meta=True):
"""Return a cross section for a three-dimensional field.
The returned array will hold the vertical cross section data along the
line described by *xy*.
This method differs from :meth:`wrf.vertcross` in that it will return
all vertical levels found ... | 1cda7b44055a61efdcb5a824256e57071d01460a | 3,605,871 |
def calc_prestress(params, calc_eta_func, coords):
"""Calculate the prestress
Calculate -h(eta) * [ C_ijkl(eta) * epsilonT_kl ]
Note that C_1211, C_1222, C_2111 and C_2122 are zero.
Args:
calc_eta_func: function to calculate the phase field
coords: the Sfepy coordinate array
epsilon... | 782ad01fbb1bdba8c399a6543f01481b172516e2 | 3,605,872 |
def pass_args(args):
"""Possible argument to attr_handler()"""
return args | 5681a5f80c1f01bcae099d4baaf7fb07785c5983 | 3,605,873 |
def post_detail(request, id):
"""
Create a view that return a single
Post object based on the post ID and
and render it to the 'postdetail.html'
template. Or return a 404 error if the
post is not found
"""
post = get_object_or_404(Post, pk=id)
post.views += 1 # clock up the number o... | ed806e3061a4fbb9ab28068678341d96b4406b4b | 3,605,875 |
def bin_ip_2_ip(bin_ip):
"""
convert a binary string of IP to decimal IP in a numpy array
:param bin_ip: binary string of an IP
:type bin_ip: string
:return: a decimal IP address in numpy array
:rtype: np.array
"""
length = int(len(bin_ip) / 8)
ip = np.zeros(length, dtype=np.uint8)... | cf9e3543335503ec559426f11ddbd8ce52823201 | 3,605,876 |
def gen_rand_params(include=(), cond_dict=None, seed=None):
"""Returns a dict of DDM parameters with random values.
:Optional:
include : tuple
Which optional parameters include. Can be
any combination of:
* 'z' (bias, default=0.5)
... | 0a044a4b79a452e6d6bb54f3a8765f864a3fe217 | 3,605,877 |
def resnet18_ibn_b(**kwargs):
"""
Constructs a ResNet-18-IBN-b model.
"""
model = ResNet_IBN(block=BasicBlock_IBN,
layers=[2, 2, 2, 2],
ibn_cfg=('b', 'b', None, None),
**kwargs)
return model | 3338e7a48fb27e5baca16e750c46f245e062ae4f | 3,605,878 |
def dict_to_tfexample(mol_dict):
"""Convert dictionary of molecular info to tfExample.
Args:
mol_dict : dictionary containing molecule info.
Returns:
example : tf.example containing mol_dict info.
"""
example = tf.train.Example()
feature_map = example.features.feature
feature_map[fmap_constants.... | e3eb8335b4e5ea12b378b978c941fd960ed334c1 | 3,605,879 |
def normalize_pygcn(a):
""" normalize adjacency matrix with normalization-trick. This variant
is proposed in https://github.com/tkipf/pygcn .
Refer https://github.com/tkipf/pygcn/issues/11 for the author's comment.
Arguments:
a (scipy.sparse.coo_matrix): Unnormalied adjacency matrix
Return... | 48ab37ae1b5acb8fce97a61449277966dc3d3545 | 3,605,880 |
def least_significan_bit(n):
"""Least significant bit of a number
num AND -num = LSB
Args:
n ([type]): [description]
Raises:
TypeError: [description]
Returns:
[type]: [description]
"""
if type(n) != int:
raise TypeError("Number must be Integer.")
if... | 5fcde70104f885eeb753697fb74d8ec2e7156eae | 3,605,881 |
def filter_contigs(assembler):
"""Remove junk from the assembled contigs."""
log.info('Saving assembled contigs: iteration {}'.format(
assembler.state['iteration']))
blast_db = blast.temp_db_name(
assembler.iter_dir(), assembler.state['blast_db'])
hits_file = blast.output_file_name(
... | 7c7394a093502d0a62aa8f3a7f5925e0e3c5376e | 3,605,882 |
def filter_factory(global_conf, **local_conf):
"""Returns a WSGI filter app for use with paste.deploy."""
conf = global_conf.copy()
conf.update(local_conf)
def moon_agent_filter(app):
return MoonAgentKeystoneMiddleware(app, conf)
return moon_agent_filter | 5abf18ddcb13f54a5872e7596edcf25878670a17 | 3,605,883 |
def add(T, w, i=0):
"""
:param T: trie
:param string w: word to be added to T
:returns: new trie consisting of w added into T
:complexity: O(len(w))
"""
if T is None:
T = TrieNode()
if i == len(w): # 叶子节点
T.isWord = True
else:
T.s[w[i]] = add(T.s[w[i]], w, i... | c8977bd383572b1accb671199589fc519e3805d1 | 3,605,884 |
def make_polar_stereo(attrs_dict, globe):
"""Handle polar stereographic projection."""
attr_mapping = [('central_longitude', 'straight_vertical_longitude_from_pole'),
('true_scale_latitude', 'standard_parallel'),
('scale_factor', 'scale_factor_at_projection_origin')]
... | dcbb25c24c705c7c9503bcb605a2b74f097613a8 | 3,605,885 |
def is_k_anonymous(df,partition,sensitive_column,k =3):
"""
params
df: The dataframe on which to check the partition.
partition: The partition of the dataframe to check.
sensitive_column: The name of the sensitive column
k: The desired k
returns
True if the partition... | e58ae8f65524c8a9f7b404d391c2b1d3d0b188c2 | 3,605,886 |
def normalize_variant(variant: str) -> str:
"""
Normalize variant.
Reformat variant replace colons as separators
to underscore.
chromosome:position:reference:alternative
to
chromosome_position_reference_alternative
:param variant: string representation of variant
:return: reformat... | 2dc97b7f7b09add6a8062db94376c1ab030ff07c | 3,605,887 |
from typing import Union
from typing import Any
from typing import Callable
import warnings
def add_deactivated_after_edit_handler(parent : Union[int, str], *, label: str =None, user_data: Any =None, use_internal_label: bool =True, tag: Union[int, str] =0, callback: Callable =None, show: bool =True) -> Union[int, str... | dfc362c55448f7a21df79ab51d944334a0505931 | 3,605,888 |
from typing import Tuple
def compute_limits(
numdata: int, numblocks: int, blocksize: int, blockn: int
) -> Tuple[int, ...]:
"""Generates the limit of indices corresponding to a specific block. It
takes into account the non-exact divisibility of numdata into numblocks
letting the last block to take th... | 748344d60baa8f2ecd31ce822c0e33aca981bc13 | 3,605,889 |
from astropy.utils.data import download_file
from astropy.utils.data import download_file
def initialise_ephemeris(
ephem="DE405",
units="TCB",
earthfile=None,
sunfile=None,
timefile=None,
ssonly=False,
timeonly=False,
filenames=False,
):
"""
Download/read and return solar syst... | 5161f22ad433b901ca0c35cadc90288e5e30c7b1 | 3,605,890 |
def eval_en_performance(representations: np.array,
):
"""
entropy of probe representations at output layer and at origin.
"""
res = drv.entropy_pmf(representations).mean()
return res | d940d83d8c2eae0115c0f3300ea3582f85b13d59 | 3,605,891 |
def binarized_loss(x, l):
"""Cross-entropy loss
Args:
x: B x 1 x H x W floating point ground truth image, [-1, 1] scale
l: B x 2 x H x W output of neural network
Returns:
loss: 0-dimensional NLL loss tensor
"""
assert l.size(1) == 2
x = _binarized_label(x)
# cross_e... | 599e830165b8bb6b70725e664f5d5d0586a5554e | 3,605,892 |
import json
def getToken(response):
"""
Get the tokenised card reference from the API response
:param response: Response object in JSON
:return: String - token
"""
resp_dict = json.loads(response.text)
try:
token = resp_dict["token"]
except KeyError:
print('Retrieval ... | b849f3b021b995b164b99690a82ecabf881bb18b | 3,605,893 |
def _read_transition_statistics_from_files(model, verbose):
"""Parses the transitions statistics from the simulation output files for later analysis
Parameters
-------
model : obj
object containing all anchor and milestone information
Returns
---------
total_steps : int
total number ... | 1a4f326bd628e6ddd9475c9610b92cb2ba564bba | 3,605,894 |
def _extend_to_nparray(item, n):
"""If item is already list/array return np.array, otherwise extend to length n."""
data = item if isinstance(item, (list, np.ndarray)) else [item for _ in range(n)]
return np.asarray(data) | 69a344c9df8e8b8dd079e7b72c7cf911e5c7ed6a | 3,605,895 |
def maskData(data):
"""
- Occasionally, the simulation may crash due to a Riemann solver failure
- This is typically fixed by a restart from a checkpoint with a lower courant number
- However, the data is still there in the history file
- This function masks out incorrect data, by finding areas where "time tr... | 2321555a87a959fca63c44cc18be1362e1ddb491 | 3,605,896 |
import re
def read_urls(filename):
"""Returns a list of the puzzle urls from the given log file,
extracting the hostname from the filename itself.
Screens out duplicate urls and returns the urls sorted into
increasing order."""
# +++your code here+++
f = open(filename, 'r')
paths = re.findall(r'GET (.*... | ff2b9be867c75fd5b5d4e419a58818a319246d21 | 3,605,897 |
from typing import List
import ipaddress
import json
def k8s_resolve(
runner: Runner, remote_info: RemoteInfo, hosts_or_ips: List[str]
) -> List[str]:
"""
Resolve a list of host and/or ip addresses inside the cluster
using the context, namespace, and remote_info supplied. Note that
if any hostname... | 545412ef7807a57cc46a4a5403681c7047f9c8a1 | 3,605,898 |
def createSubsetGafDict(subset, gafDict):
"""
Generates a dictionary that maps the subset's Uniprot ACs to the GO IDs,
based on the provided gene subset and the gaf dictionary.
Parameters
----------
subset : set of str
A subset of Uniprot ACs of interest.
gafDict : dict of str mappi... | 76e69cd79c984a19254df171403c008405276408 | 3,605,899 |
def eperm_crim(por, eperm1, eperm2, eperm3=None, sw=None):
"""Effective electric permittivity after CRIM.
Markov et al., 2012, Journal of Applied Geophysics, Eq. 7.
Parameters
----------
por: float or array
Concentration of constituent 1 (host, wetting phase).
eperm1, eperm2, eperm3 :... | 4daa8528b8e91ff315ee1ecf49f0f88d219cbb87 | 3,605,900 |
def shortest_paths(graph, vertex_key):
"""Uses Dijkstra's algorithm to find the shortest path from
`vertex_key` to all other vertices. If we have no lengths, then each
edge has length 1.
:return: `(lengths, prevs)` where `lengths` is a dictionary from key
to length. A length of -1 means t... | f2ac9abf9292364099748475988d4ee1dbeb4b23 | 3,605,901 |
def process_overall_mode_choice(mode_choice_data):
"""Processing and reorganizing the data in a dataframe ready for plotting
Parameters
----------
mode_choice_data: pandas DataFrame
From the `modeChoice.csv` input file (located in the output directory of the simulation)
Returns
------... | 870685017d223f8a277265f80eea56e50eedec90 | 3,605,902 |
def loadtemplater(ui, spec, defaults=None, resources=None, cache=None):
"""Create a templater from either a literal template or loading from
a map file"""
assert not (spec.tmpl and spec.mapfile)
if spec.mapfile:
frommapfile = templater.templater.frommapfile
return frommapfile(spec.mapfil... | fffc64d1a41486c20c4e61aa4281787730c93cb4 | 3,605,904 |
def vgg16(inputs,
batch_size=100,
num_classes=12,
is_training=True,
dropout=0.5,
weight_decay=0.005,
spatial_squeeze=True,
scope='vgg_16'):
"""Oxford Net VGG 16-Layers version D Example.
Note: All the fully_connected layers have been transfo... | e70abe98dc581c42526a95deb9f03126b7c3a37f | 3,605,905 |
def print_movie_rating_results(genre_to_rating_map):
"""Given a dictionary, prints the average IMBD ratings for each year for
each genre formatted as:
*genre*
*space*
*release years* : *average IMBD rating*
Parameters:
genre_to_rating_map: a dictionary that maps genres to r... | d045cce8a6fe2f73ce27327a6f6aabb97473a5d9 | 3,605,906 |
def flatten_datasets(rel_datasets):
"""Take a dictionary of relations, and returns them in tuple format."""
flattened_datasets = [[], [], []]
for kind in rel_datasets.keys():
for i in range(0, 3):
for rel in rel_datasets[kind][i]:
flattened_datasets[i].append([*rel, kind]... | 70affa370a98c8328effed0bdb015999c5874913 | 3,605,907 |
from typing import Callable
def requires_token(func: Callable):
"""
This annotation protects API calls that require authentication.
It will cause them to raise NoAcccessTokenException if the token is not set in the client.
:return: Function decorated with the protection
"""
def wrapper(self... | 368b223bc824c394072f1f99a7bb33d19015a545 | 3,605,908 |
import torch
def log_sum_exp(x, dim=None):
"""Log-sum-exp trick implementation"""
x_max, _ = torch.max(x, dim=dim, keepdim=True)
x_log = torch.log(torch.sum(torch.exp(x - x_max), dim=dim, keepdim=True))
return x_log+x_max | 45b1f6d198569567d3284bab4116a4703b0589a3 | 3,605,909 |
import hashlib
def feature(self, node="clickhouse1"):
"""Check alter user query syntax.
```sql
ALTER USER [IF EXISTS] name [ON CLUSTER cluster_name]
[RENAME TO new_name]
[IDENTIFIED [WITH {PLAINTEXT_PASSWORD|SHA256_PASSWORD|DOUBLE_SHA1_PASSWORD}] BY {'password'|'hash'}]
[[ADD|DROP] HOST {LOCA... | d9cde1936c78d9d61ca9a446d5018e51c74c221f | 3,605,910 |
def nb_year(p0, percent, aug, p):
"""
Finds the amount of years required for the population to reach a desired amount.
:param p0: integer of starting population.
:param percent: float of percent increase per year.
:param aug: integer of new inhabitants.
:param p: integer of desired population.
... | 054496347fc8bedca3424143d48d122712dd1363 | 3,605,911 |
def find_largest_digit(n):
"""
:param n: the number to be processed and compared with
:return: the largest digit in the number
"""
num = abs(n)
initial_largest_digit = num % 10 # set largest digit to the last digit of the number
return helper(num, initial_largest_digit) | 3f9324ab3676bb29b1d81b2eea13c43fc0d338c0 | 3,605,912 |
def compute_overlaps(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
For better performance, pass the largest set first and the smaller second.
"""
# Areas of anchors and GT boxes
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - ... | f224ee968a824984e8463ab43e346e6396cf4325 | 3,605,913 |
def search_master(request):
"""Method to query existing customers."""
try:
results = []
query_id = int(request.GET.get('id'))
query = request.GET.get('q')
school_level = request.GET.get('level')
# Filters for external ids
if query_id == 1:
agents = OVCFacility.objects.filter(facility_name__icontains=qu... | 30ce02b79a05ec5d37c08f36ea25836fc3ba2b5d | 3,605,914 |
def generate_importance_map(map_size):
"""
This function generates a weighted map of "where we want the brain to be".
It is thus 1 at the center and 0 at the edges.
"""
importance_map = np.zeros(map_size)
for i in range(map_size[0]):
for j in range(map_size[1]):
for k in rang... | 4931dd522cbf8e6cec53edd13a6814d47a5ba8f3 | 3,605,915 |
from typing import Optional
def change_email_address(
user_id: UserID,
new_email_address: Optional[str],
verified: bool,
initiator_id: UserID,
*,
reason: Optional[str] = None,
) -> UserEmailAddressChanged:
"""Change the user's e-mail address."""
user = _get_user(user_id)
initiator ... | baae07a39055b302bdabaae0df65f0136da6303f | 3,605,917 |
def write_walks_to_disk(args):
"""
Write random walk into file
:param args: arguments for random walk write
Returns
-------
the name of file containing random walks
"""
num_walks, walk_length, window_size, num_pairs_required, subsample, file_name, iter_function, seed, __current_graph, __vertex2str =... | dcec7907a8c7986fff6f71469ffdd3b629980138 | 3,605,918 |
def tshark_read(
device,
capture_file,
packet_details=False,
filter_str=None,
timeout=60,
rm_file=True,
):
"""Read the packets via tshark
:param device: lan or wan...
:type device: Object
:param capture_file: Filename in which the packets were captured
:type capture_file: St... | 8fc31098e750691a1aa7c27a868abf0d6254adec | 3,605,919 |
def light_similarity(conn, entry_ids_1, entry_ids_2, metric, cpu_cores):
"""
main function
:param conn: db_connection
:param entry_ids_1: list of entries 1
:param entry_ids_2: list of entries 2
:param cpu_cores: number of cores to be used
:param metric: 'lin', 'resnick', 'jc' or 'all'
:r... | f5684a3cc96a456cffd18393f760d740c2d394b6 | 3,605,920 |
def prepare_window(ds_length, window_name, window_kwargs):
"""Window needs special preparation as the parameters can be dependent on dataset length.
Args:
ds_length (int): Length of the dataset.
window_name (str): Name of the window module.
window_kwargs (dit): Key word arguments from t... | 9607cc89227e54b8b477d5fab05ec85d1e3925d1 | 3,605,921 |
def resize_image(img):
"""resize images prior to utilizing in trianing model"""
width, height = img.size
ratio = width/height
new_height = 100
new_width = int(new_height*ratio)
img = img.resize((new_width, new_height))
return img | 1aa0164e1e25ef0f22e55a15a654fda2dfef5b12 | 3,605,922 |
def _filter_calibration(time_field, items, start, stop):
"""filter calibration data based on time stamp range [ns]"""
if len(items) == 0:
return []
def timestamp(x):
return x[time_field]
items = sorted(items, key=timestamp)
calibration_items = [x for x in items if start < timestam... | c7575ec85c7da9f1872150a1da3d7b02718df8a0 | 3,605,923 |
import torch
def phi_inv(D):
""" Inverse of the reallification phi"""
AB,_ = torch.chunk(D,2,dim=0)
A,B = torch.chunk(AB,2,dim=1)
return torch.stack([A,B],dim=len(D.shape)) | b8198764b89f3f1261e96014697cf1346e1c7d43 | 3,605,924 |
def activate_wps(wps, endpoint, name):
"""
Activate a WebProcessingService object by calling getcapabilities() on it and handle errors appropriately.
Args:
wps (owslib.wps.WebProcessingService): A owslib.wps.WebProcessingService object.
Returns:
(owslib.wps.WebProcessingService): Returns a... | d146f22db9d13db17e688bfb404366b9e236dc8c | 3,605,927 |
def make_low_freq(xx, halfperiod=10, halfamplitude=1.):
""" make low frequncy signals """
omega = np.pi / halfperiod
return halfamplitude * np.sin(omega * xx + np.random.uniform(0, 2*np.pi)) | f3ca7ab174f8e9d3b7f06251009820c753622f15 | 3,605,929 |
from re import T
def tree_weight(pytree: T, weight: float) -> T:
"""Weights tree leaves by weight."""
return jax.tree_map(lambda l: l * weight, pytree) | b6da802e783632fc3986fa5547fad8ca5994e3a7 | 3,605,930 |
def get_sigma_clip(img,sigma=3,iters=100):
"""
Do sigma clipping on the raw images to improve constrast of
target regions.
Reference
=========
[1] sigma clip
http://docs.astropy.org/en/stable/api/astropy.stats.sigma_clip.html
"""
img_clip = sigma_clip(img, sigma=sigma, iters=ite... | a426fea3caafcb382fb0547145eadbeef0f9d7c8 | 3,605,931 |
def Get_foregroundapp(device):
"""Return the foreground app"""
return device.shell("dumpsys activity recents | grep 'Recent #0' | cut -d= -f2 | sed 's| .*||' | cut -d '/' -f1").strip() | 236986e3d08f6a4c7dd4cd8c8441806d25e76654 | 3,605,932 |
def chunk_size(request):
""" Set the chunk size for the source (or None to use the default).
"""
return request.param | c57269f434790953d475a2791c862d70d204ed86 | 3,605,934 |
def finite_diff_hessian_diag(x, grad, epsilon=FINITE_DIFF_EPSILON):
""" Approximate the diagonal of the Hessian of a function using finite difference in the
partial gradient.
:param np.ndarray x: point at which to evaluate derivative
:param function grad: function that returns the gradient
"""
f... | c1670919376ab554d02f4e954c0e6775328bf469 | 3,605,936 |
import json
def get_new_username():
"""Prompt for a new username."""
username = input("What is your name? ")
filename = 'Excercise_10_13.json'
with open(filename, 'w') as f_obj:
json.dump(username, f_obj)
return username | e25546247a849aca6dae94728c4318ff10a43434 | 3,605,937 |
def densify(*args):
"""
Make the matrix dense and assign nonzeros to a value.
densify(IM x) -> IM
densify(DM x) -> DM
densify(SX x) -> SX
densify(MX x) -> MX
"""
return _casadi.densify(*args) | 122c0f4710ca8b4e6b3274a665caab1ffc568acf | 3,605,938 |
def t0(S, t_r, n):
"""
t0 = t_r - M_r / n
:param S: S angle
:type S: float
:param t_r: reference time
:type t_r: float
:param n: mean movement
:type n: float
:return: t0
:rtype: float
"""
return t_r - 2 / 3 * np.sqrt(2) / n / np.tan(S) | 7534034e64476fcdec3a2585f7716bd8daa26f08 | 3,605,939 |
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