content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def calcTheta(wavelength, z1):
"""
Calculates the d spacing and the theta angle for a scattering
vector z1
@param wavelength
@param z1 A 3 component numpy array representing the scattering
vector
@param A tuple consisting of the d-value and the theta angle in
radians
... | 9ce846acd4e4b6f211d6b76558d18a54d16f0b75 | 47,950 |
def octahedron():
"""Return the best integral embedding of the octahedral graph."""
v1 = (13 * root(1, 6, 1) - 2) / 3
v2 = -v1.conjugate()
omega = root(1, 3, 1)
v3 = v1 * omega
v4 = v2 * omega
v5 = v1 / omega
v6 = v2 / omega
vertices = (v1, v2, v3, v4, v5, v6)
edges = ((0, 1), (0... | d402fd23e8844bdaad612b17f0930f89b2da248f | 47,951 |
def symbols_for_inclusion(file: CheckFile, match) -> list:
""" Return a list of symbols for a match. """
line_number, column = file.line_number_at(match.end())
line = file.line_at(line_number)
include_suffix = line[column:]
if not is_symbol_list(include_suffix):
return []
listing = ... | 75455942cc7b6abc46ff793522b1297a7157b63e | 47,952 |
def sample_user(email="student@test.com",
password="password123",
name="some name"):
"""Create sample user for tests"""
return get_user_model().objects.create_user(email=email,
password=password,
... | 4d558ca16eb660fe7579ce470e5a2b1c66a369e7 | 47,953 |
def reduce(l):
"""
Args:
Rangelist: generic list of tuples (can be either float or int tuple)
Return:
Reduced int list based on average value of every tuple in input tuple list
"""
result = []
for s in l:
midVal = abs(float(s[0]) - float(s[1])) / 2.0
result.append... | 5db6214bc439dcc149d0d4cef1b66930f40694d7 | 47,954 |
def polygon_iou(pts1, pts2):
"""
Intersection over union between two shapely polygons.
"""
poly1 = Polygon(pts1).convex_hull
poly2 = Polygon(pts2).convex_hull
# union_poly = np.concatenate((pts1, pts2))
if not poly1.intersects(poly2): # this test is fast and can accelerate calculation
... | 0e68901bc422d77f1ba3e14d1285db22f81fd2eb | 47,955 |
import math
def choose_team_levels(num_teams, hackathon_level):
""" Calculates the average experience level per team and distributes any
remaining difference among some of the teams evenly
Returns a list of team_levels """
avg_team_level = math.floor(hackathon_level / num_teams)
team_levels = []
... | aaf372f00969da62b966a2a09aff64a188fbce82 | 47,956 |
def overall_random_accuracy_calc(item):
"""
Calculate overall random accuracy.
:param item: RACC or RACCU
:type item : dict
:return: overall random accuracy as float
"""
try:
return sum(item.values())
except Exception:
return "None" | 0e6cf8ae129235a41db084d2d9d5c92bb5788ed8 | 47,957 |
def infer_time_unit(time_seconds_arr):
"""
Determine the most appropriate time unit for an array of time durations
specified in seconds.
e.g. 5400 seconds => 'minutes', 36000 seconds => 'hours'
"""
if len(time_seconds_arr) == 0:
return 'hours'
max_time_seconds = max(time_seconds_arr... | 11f25a712d8d66e8546fea2f7e36309dcebbcc74 | 47,958 |
def user_is_author_of_snippet(user, snippet_type, snippet_id):
"""
Checks if the current user is
the author of the snippet provided
"""
snippet_models = {
"project": Project,
"skill": Skill,
"profile": Profile
}
snippet = get_object_or_404(snippet_models[snippet_type]... | 8408d18971005f3f00bc961f386193dffc28a9f5 | 47,959 |
def _batch_hard(query_features, positive_features, item_ids):
"""
Adapts the 'Batch Hard' triplet mining strategy of Hermans, Beyer, and Leibe.
This method differs from Batch Hard in that we don't search for the hardest positive
and just use the one positive example provided.
Args:
query_featur... | ac262b089a24dc48aa6c779c1c3b1772ce4a85ff | 47,960 |
def add_paper_grading_sheet(supervisor, modified_df):
"""Adding the Paper Grading sheet with all supervised student Papers and grading metrics"""
src_grading_wb = load_workbook('DataSources/Foik_GradingSheetSeminar.xlsx')
print("The available sheets in the xlsx file")
print(src_grading_wb.sheetnames)
... | 7a9a64eea8718cf56992b7163aadfece17edfd43 | 47,961 |
def addContraints(d, Ca):
""" Ajoute les conditions de Robinson qui ne sont pas encore satisfaites à Ca """
addedConstraints = False
precision=0.0001
nb=0
# print 'Contraintes ajoutees'
for i in range(d.n-2):
for j in range(i+2, d.n):
if (d[i,j] < d[i,j-1] - precision) and ([... | 7967c85ba7207fc2e525451b36432aec973cb95f | 47,963 |
def fprint(prompt: str, question: bool=False, returnstr: bool=False):
"""
Fancy print function
"""
tags = {'[o]': '[\033[01;32m+\033[0m]',
'[ok]': '[\033[01;32m+\033[0m]',
'[+]': '[\033[01;32m+\033[0m]',
'[e]': '[\033[01;31m-\033[0m]',
'[er]': '[\033[01;31... | 27be27f40a112b5f14bbbf728deb319dccb77e7d | 47,965 |
import itertools
import random
def random_tuple_list(lst1, lst2, lb=1):
"""
Generate a random list of tuples (x,y) where x is in lst1 and y is in lst2;
"""
product = list(itertools.product(lst1, lst2))
if len(product) == 0:
k = 0
else:
k = random.randint(lb, len(product))
... | d21846b6d4cfee6479050f78046909329c5596b2 | 47,966 |
def tmp_img(tmpdir):
"""Get temp image fixture used by some test."""
img_path = tmpdir.join('image.jpg')
im = Image.new('RGB', (160, 160))
im.save(img_path.strpath)
return img_path | c6d9335e41b602ab50d54b370b4d671ae7bc433f | 47,967 |
def combine_stage1_stage2(df_1, df_2):
"""Combines the stage 1 and stage 2 submissions.
Replaces the positive class predictions in df_1 with those created in
df_2.
Args:
df_1 (pd.DataFrame): Stage 1 submission
df_2 (pd.DataFrame): Stage 2 submission
Return:
combined_df... | 107c20689ae339a115573a28358d5c29f1b9b269 | 47,968 |
import time
def swap_status_iterator(uuids_list: list, node_proxy: MMProxy) -> dict:
"""Builds swaps statuses dictionary"""
swaps_d = dict.fromkeys(uuids_list, 'unknown')
while True:
work_d = {} # intermediate dictionary to iterate unfinished swaps
values_list = []
for key in swap... | fccb72487afa117963da73457219630620ceddd6 | 47,969 |
from datetime import datetime
def from_utc(utcTime, fmt="%Y-%m-%dT%H:%M:%SZ"):
"""
Convert UTC time string to time.struct_time
"""
return datetime.datetime.strptime(utcTime, fmt) | e4fb9792f8254c6d3a5d20d3da491dbe254c594a | 47,970 |
def intersectGraphs(graph1={}, graph2={}):
"""Given two Graphs, create a new Graph that is their Relational Intersection
` Preconditions: Graph in Xaya Format
Postconditions: returns a graph in XAYA format with intersecting keys and values
Invariants: The input graphs do not change
... | 639d2ad4a0e5da223aefad13d62f11ad9ec74d9d | 47,971 |
def get_r2(reg, X, y):
"""Calculate the goodness of fit of regression model.
Arguments:
reg {model} -- regression model.
X {ndarray} -- 2d array object with int or float.
y {ndarray} -- 1d array object with int.
Returns:
float
"""
if isinstance(y, list):
y =... | 8e478325fd6790218bb89d5556bcf7190b9f72ae | 47,972 |
def h_m_emg_rvs(mu, sigma, *t_args,N_samples=1):
"""Draw random samples from negative skewed hyper-EMG probabaility density
Parameters
----------
mu : float
Nominal position of simulated peak (mean of underlying Gaussian).
sigma : float
Nominal standard deviation (of the underlying ... | 4b23267b7fe8ef4e1596e9ab6e4d8f4fbd32a208 | 47,973 |
def user_ajax_answer(request):
"""Submitting work to task (making Answer of it) is handled here.
Accepts only POST requests.
POST (ajax) params (all required):
wi: work id
ti: task id
ni: network id
a: action, (add or remove)
"""
if request.method !=... | 52138ff6defbce7fa07e6cdad14a991db22c1360 | 47,974 |
def get_diff(a, b):
"""比较图片差异"""
return get_match(a, b)[0] | 9cdeb2d5da40c58d1c295203502cb694a51bc877 | 47,975 |
def get_image_metrics_for_samples(
real_images, generator, prior, data_processor, num_eval_samples):
"""Compute inception score and FID."""
max_classifier_batch = 10
num_batches = num_eval_samples // max_classifier_batch
def sample_fn(arg):
del arg
samples = generator(prior.sample(max_classifier_b... | aa23d8694d0426d90cb3b502c49e13ef4346a87b | 47,976 |
from typing import Tuple
def load_data(
partition: int, num_clients: int
) -> Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]:
"""Load partition of randomly shuffled Fashion-MNIST subset."""
# Load training and test data (ignoring the test data for now)
(x_train, y_train), (x_test,... | fa4d318d470b83ec0388f9da817f3f92aa435265 | 47,977 |
def flip_y(im):
"""mirrors an image over the x axis."""
source_pix = im.load()
im = im.copy()
dest_pix = im.load()
width, height = im.size
for i in range(width):
for j in range(height):
dest_pix[i,j] = source_pix[i, height-j-1]
return im | 9ad00b2de3e628cc6dd441884103b9d2e3492333 | 47,978 |
def augment_menpo_img_geom_dont_apply(img, p_geom=0):
"""geometric style image augmentation using random face deformations"""
if p_geom > 0.5:
lms_geom_warp = deform_face_geometric_style(img.landmarks['PTS'].points.copy(), p_scale=p_geom, p_shift=p_geom)
return img | 08b7ac4d6e05269ad037c044503110727f2525bd | 47,979 |
import torch
def lower_matrix_to_vector(lower: torch.Tensor) -> torch.Tensor:
"""Convert a lower triangular matrix to a vector.
Parameters
----------
lower : torch.Tensor
lower
Returns
-------
torch.Tensor
"""
shape = lower.shape
assert shape[-1] == shape[-2]
low... | e4fe825caf5926ce3219c4dd7720d1b7f180b998 | 47,980 |
def proxy_line(**kwargs):
"""
Generates a legend proxy for a line region.
Returns
----------
:class:`matplotlib.lines.Line2D`
"""
return matplotlib.lines.Line2D(range(1), range(1), **kwargs) | 62df36751f1de28a6267143844557d3e41adf8f5 | 47,982 |
def test_drawing():
"""Test circuit drawing"""
x = np.array(0.1, requires_grad=True)
y = np.array([0.2, 0.3], requires_grad=True)
z = np.array(0.4, requires_grad=True)
dev = qml.device("default.qubit", wires=2)
@qml.beta.qnode(dev, interface="autograd")
def circuit(p1, p2=y, **kwargs):
... | 1fcb0ab762582b971b70eccc7e09da9ae108377a | 47,984 |
from typing import Union
def encrypt_vault_password(key: bytes, password: Union[str, bytes]) -> bytes:
"""Encrypt and return the given vault password.
:param key: The key to be used during the encryption
:param password: The password to encrypt
"""
if isinstance(password, str):
password ... | 2f366ab3c560a5171e6697bc49fbfd971bc96b31 | 47,985 |
def t_to_col(t, tmin=6, tmax=11):
"""
Given a BPASS time bin, returns a value to input into the time colormap
Parameter
---------
t : float
BPASS log time bin between 6 and 11
Returns
-------
out : int
Input to tcmap to get the right color out
"""
co... | cf589a15d1efafaeef037d478cfae21317c2aac5 | 47,986 |
def blockify(sudoku):
"""
Converts 9x9 sudoku list into a list containing lists of values in given sudoku's blocks
args:
-sudoku - 9x9 sudoku list
returns: List with lists of values of sudoku blocks
"""
i=0
block_row = []
while i<len(sudoku):
j=0
while j<7:
... | 4f24aa3c3f8eb7132ab512bd74c03d8fd1947db0 | 47,988 |
def map_coords_to_scaled_float(coords, orig_size, new_size):
"""
maps coordinates relative to the original 3-D image to coordinates corresponding to the
re-scaled 3-D image, given the coordinates and the shapes of the original and "new" scaled
images. Returns a floating-point coordinate center where t... | f5e1e1523366a9e1e37f9d1a304d9deea8d53e00 | 47,989 |
def getPosition(junctionID):
"""getPosition(string) -> (double, double)
Returns the coordinates of the center of the junction.
"""
return _getUniversal(tc.VAR_POSITION, junctionID) | ecb12bb97a7ab1be52723611edb83ceb7de78614 | 47,990 |
def _structure_summary(structure):
"""
Extract messages from the structure.
Args:
structure: a Pymatgen Structure object
Returns:
dict of the following messages:
nsites (int): number of sites in the structure.
is_ordered (bool): whether the structure is ordered or not.
... | 65fe88a01d53df7ab487ae1d1ab24a4c2c746477 | 47,991 |
def id_type_dict(obj):
"""Creates dictionary with selected field from supplied object."""
if obj is None:
return None
return {
'id': str(obj.id),
'type': obj.type,
} | 7c7d67eee81a3d1553392617304bda4166bce817 | 47,993 |
from typing import Sequence
from typing import Union
from typing import Tuple
from typing import List
from typing import OrderedDict
def _split_selections_property_and_vertex(
selections: Sequence[Union[SelectionNode]],
# OrderedDict is unsubscriptable (pylint E1136)
) -> Tuple["OrderedDict[str, SelectionNode... | 260df2df41c92dfdd8402ca686e03309e96934d8 | 47,994 |
def is_3d(mesh: Mesh) -> bool:
"""Check if a meshio mesh is 3-dimensional"""
for cell_block in mesh.cells:
# first 3D element type is enough.
if meshio_type_to_alpha[cell_block.type] in meshio_3d:
return True
return False | bd6b6bda26c6269121381ff6d9419f5b9129b76b | 47,996 |
import torch
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None):
"""NMS for multi-class bboxes.
Args:
multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
... | f3152e30eda4286ecfedc1b3fa3cf922470e0ada | 47,997 |
def delete_case_study(case_study_id):
"""
Delete a case study
:param case_study_id:
:return:
"""
updater_json = validate_and_return_updater_request()
casestudy = CaseStudy.query.filter(
CaseStudy.id == case_study_id
).first_or_404()
audit = AuditEvent(
audit_type=A... | 4ad9b900f294e99b54f4e88d556a6074e7a0fa94 | 47,998 |
import random
def clustering_kmember(nec_set, k=25):
"""
group record accroding to QID distance. K-member
:param nec_set: natural EC
:param k: k
:return: grouped clusters
"""
clusters = [cluster for cluster in nec_set if len(cluster) >= k]
nec_set = [cluster for cluster in nec_set if l... | 0a18618cf39b688d703694b9700fe1fd0b074de2 | 47,999 |
def get_variables_to_train(trainable_scopes, logger):
"""Returns a list of variables to train.
Returns:
A list of variables to train by the optimizer.
"""
if trainable_scopes is None or trainable_scopes == "":
return tf.trainable_variables()
else:
scopes = [scope.strip() for scop... | ae101730521728859ba81ee888522a705f4749c2 | 48,000 |
def password_change_done(request):
"""
View called when the new user password has been accepted
"""
messages.success(
request, _('Your password has been successfully changed.')
)
return redirect('common:current_user_details') | 3e81e12a270baacf82c3e566353563f78d0ebc4f | 48,001 |
from typing import List
def get_most_representative_notices(notice_ids: List[str], mongodb_client: MongoClient, top_k: int = None) -> List[str]:
"""
This function returns top_k the most representative notices, from the list of notices provided.
:param notice_ids:
:param mongodb_client:
:param ... | e29015e7712f8d6296df70d5dbfbfda2e00569b4 | 48,002 |
def remove_ldra_interface(
device, interface=None, vlan_id=None, policy=None, interface_id=None, **kwargs):
"""
Remove DHCP ldra remote-id
Args:
device ('obj'): device to use
interface ('str'): interface to configure
vlan_id ('str'): vlan_id to attach policy
... | 33a3a2e412dc32552416f074412fae40c9e1f6a8 | 48,003 |
def borders(det):
"""
Calculates the borders for image
Parameters
--------------
det: int array
Detected face
Returns
----------------------
l: int list
Coordinates for left bound of border
r: int list
Coordinates for right bound of border
t: int... | d6358c88ee26e64b7b209d2f5f9725a5b3fad9ba | 48,004 |
from rdkit.Dbase.DbConnection import DbConnect
import pickle
def DBToData(dbName, tableName, user='sysdba', password='masterkey', dupCol=-1, what='*', where='',
join='', pickleCol=-1, pickleClass=None, ensembleIds=None):
""" constructs an _MLData.MLDataSet_ from a database
**Arguments**
... | 39e2531c54501c837d52c4e2d4f148b8e5531f2e | 48,005 |
def get_line_row(lines, row):
"""
- lines: (Array string), array of lines
- row: int, >=0, the row index to grab
RETURN: string, if row greater than or equal to lines length, returns ''
"""
if row < len(lines):
return lines[row]
return '' | f03f230b677fabb3c488c496dad7e35f875023fe | 48,006 |
def VerifyLimitsPotential(potential):
"""Verify if the potential seems to verify the borders conditions (to allow bound states). If it doesn't it ask to the user if he is sure that the potential respects these conditions
Parameters:
-----------
potential (str) : a string that indicates the mathemat... | a1545b0360cb5de43c610442d5135a20c7da5893 | 48,007 |
def _get_head_node_ip(stack_name):
"""
Get the IP Address of the head node.
:param stack_name: The name of the cloudformation stack
:param config: Config object
:return private/public ip address
"""
instances = describe_cluster_instances(stack_name, node_type=NodeType.head_node)
if not ... | c699c21e84f0810b2c78fb0435dac07d8a963e79 | 48,008 |
from typing import Tuple
def build_datasets(config: DataConfig) -> Tuple[DataLoader, DataLoader]:
"""
Build training and validation data loaders for training.
Parameters
----------
config: DataConfig
Data configuration such as batch size and validation set.
Returns
-------
tr... | 5012ce15954e87dcd4a0a4323750d88242a0cf76 | 48,009 |
def fmt_begin_msg(app, fromuser, touser):
"""Format a PM begin message"""
msg = "/me began pestering {touser} {toInit} at {time}".format(touser=touser.display_name,
toInit=getInitials(app, touser,
... | 43753f431835f6b235dd17dc1057d5c218f53a59 | 48,010 |
def filter_dict_null(d):
"""
Filters recursively null values from dictionary
"""
if isinstance(d, dict):
return dict(
(k, filter_dict_null(v))
for k, v in list(d.items())
if filter_dict_null(v) is not None
)
elif isinstance(d, list):
if len... | 13b0288f2e032d0e6ca115d02d6540bb8f8739b5 | 48,011 |
import ast
def merge(predicates):
"""
Invoked with a set of predicates that should
be merged into a single AST. The new AST uses
the PushResults node to return the list of matching
predicates, and Both nodes to combine.
"""
# Merge the AST tree's together first using a tree
all_asts = ... | e5425cb0d87e143a3d6d8ac75e5cad1d77a5916b | 48,012 |
def optimize_threshold(model, X, y, TP=0, FP=0, TN=0, FN=0, minimize=True, pos_label=1):
"""
Find the optimal threshold
Returns a tuple containing:
A pandas dataframe used for plotting
The optimal threshold
The cost associated to that threshold
"""
y_pred = model.predict_prob... | 8f586eb8ec7d72f5a33d6a23becc8e8c6c1ec6f2 | 48,013 |
def get_backend_defaults_url(config, backend_type):
"""Return the URL for a backend's pulse defaults."""
hub = config.get('hub', None)
group = config.get('group', None)
project = config.get('project', None)
if hub and group and project:
return '/Network/{}/Groups/{}/Projects/{}/devices/{}/d... | ac84179e03d132c64e55a87856f420968004f209 | 48,014 |
def ugly_numbers(n: int) -> int:
"""
Uses dynamic programming technique to return the nth ugly number.
>>> ugly_numbers(100)
1536
>>> ugly_numbers(1)
1
>>> ugly_numbers(20)
36
"""
ugly_nums = [0] * (n)
ugly_nums[0] = 1
i2, i3, i5 = 0, 0, 0
next_2 = ugly_nums[i2] * 2
... | 6691ea90eebe1dc659fea244014f73b9638c3a5d | 48,015 |
def get_publish_dates(df):
"""Get time distribution of publish dates for the given dataframe
Returns
-------
list[int]
Time distribution in a format friendly towards HighCharts
"""
return pd.to_datetime(df["publish_date"]).astype(np.int64) / int(1e6) | d80813eefe2bdc10990f5bfd3138fd42c2bb28fe | 48,016 |
def user_delete(request, pk=None):
"""User Delete View
Description:
view to delete users
"""
try:
user = Client.objects.get(id=pk)
except Exception as e:
messages.error(request, e.args[0])
return HttpResponseRedirect('.')
if request.method == 'GET':
_user ... | de0b17445bc203490f57e910544c9109666c3717 | 48,017 |
from pathlib import Path
import requests
def make_posts(
src, src_pattern, dst, layout, category_layout, comment_layout, comment_detail_layout, **params
):
"""Generate posts from posts directory."""
items = []
for posix_path in Path(src).glob(src_pattern):
src_path = str(posix_path)
... | 3cf368d5fe3e78ca02d00137b2894399e78310ec | 48,018 |
def binary_search(array, item):
"""返回 item 在 array 中的下标，没找到返回 None。"""
low = 0
high = len(array) - 1
while low <= high:
mid = (low + high) // 2
guess = array[mid]
if guess == item:
return mid
elif guess > item:
high = mid - 1
else:
... | 921c4594fd83aa891e0ee53d1d4680c8cc6a620c | 48,019 |
def is_update(flags):
"""Is the opcode in flags UPDATE?
*flags*, an ``int``, the DNS message flags.
Returns a ``bool``.
"""
return from_flags(flags) == Opcode.UPDATE | e4f43a77f3128c43ea127a387fc172433d111fec | 48,021 |
def get_1obj_gt_scenario():
"""
Egovehicle stationary (represented by `o`).
Seqeuence of 4-nanosecond timestamps.
|-|
| |
|-|
|-|
| |
|-|
o (x,y,z) = (0,0,0)
|-|
| |
|-|
|-|
| | (x,y,z)=(-3,2,0)
|-|
"""
centers = []
# timestamp 0
cx = -3
cy = 2
cz = 0
centers += [(cx,cy,cz)]
# timestamp 1
... | 5f528802f8b7f131fdba344e7bcf195e9414bd0b | 48,022 |
from typing import Optional
import binascii
def _set_mem(args: [str]) -> Optional[rdcp_command.RDCPCommand]:
"""addr:int hexadecimal_string: str"""
addr = _parse_int_expression(args[0])
value = binascii.unhexlify("".join(args[1:]))
return rdcp_command.SetMem(addr, value, handler=print) | fcba48d5c0c272bd1db99719053f1479128a3760 | 48,024 |
from re import T
def add_board():
"""Lets the user add a board."""
logger.info("My session is: %r" % session)
form = SQLFORM(db.bulletin_boards)
if form.process().accepted:
session.flash = T('Your board was created')
redirect(URL('default', 'bulletin_board', args=[form.vars.id]))
r... | 0d9d6d01a2748c99cba0f9d4ffa485259df86b2f | 48,025 |
def fuel(i: int) -> int:
"""
>>> [fuel(i) for i in [12,14,1969,100756]]
[2, 2, 654, 33583]
"""
return i // 3 - 2 | ea738361f4dc7081c5adeaf628c5424d9919e1bc | 48,026 |
from datetime import datetime
def about(request):
"""Renderea a about"""
assert isinstance(request, HttpRequest)
return render(
request,
'app/about.html',
{
'year':datetime.now().year,
}
) | f03cfb5e50a5150fdf8e0db85fec8f380b854669 | 48,027 |
import psutil
def get_proc_name_from_pid(pid):
"""
using psutil to obtain proc name from pid
:param pid:
:return: proc name
"""
return psutil.Process(pid).name() | 300ce9bcb945fce90a08b8dc2326e899180546fc | 48,028 |
def _create_local(name, shape=None, collections=None):
"""Creates a new local variable.
Args:
name: The name of the new or existing variable.
shape: Shape of the new or existing variable.
collections: A list of collection names to which the Variable will be added.
Returns:
The created variable.
... | 70dffd95cb01212016145120fc8e6e1ef7649f4c | 48,029 |
def ReadStampFile(target_os):
"""Return the contents of the stamp file, or '' if it doesn't exist."""
try:
with open(STAMP_FILE % target_os, 'r') as f:
return f.read().rstrip()
except IOError:
return '' | 6f6fcdafbdbdbb1d46b2464a7569e3cab7a4f789 | 48,030 |
def matmulBackward1(grad: Tensor, t1: 'Tensor', t2: 'Tensor') -> Tensor:
"""Gradient Function that is used when
a tensor is matrix multiplied to a tensor that
requires gradient.
- Math:
let A.shape == m x n
let B.shape == n x p
Y = A @ B
F(Y) = L
... | c0c5b46e8821b6649d6402db8a528513e34accd3 | 48,032 |
from typing import List
def make_rooks() -> List[Rook]:
"""Return list of rooks"""
rooks: List[Rook] = []
for k in range(2):
rooks.extend((
Rook(SQUARE_SIZE * k * 7, SQUARE_SIZE * 7, WHITE,
f'{PIECE_PATH}white_rook.png',),
Rook(SQUARE_SIZE * k * 7, SQUARE_S... | 68f22b46c9275e34dc73e85c19b1327729521799 | 48,034 |
from typing import List
from typing import Iterable
def _domains_in_part(part: dc.DesignPart, exclude_fixed: bool) -> List[Domain]:
"""
:param part:
DesignPart (e.g., :any:`Strand`, :any:`Domani`, Tuple[:any:`Strand`, :any:`Strand`])
:param exclude_fixed:
whether to exclude :any:`Domain`'s... | 1219b666ee93db2fcc06d6fd48f604d03980df14 | 48,035 |
def read_temporal_profiles(tracer, kind, filename):
"""\
Read temporal profiles for given `tracer` for
'weekly' or 'annual' profiles.
"""
data = {}
countries = []
snaps = []
filename = filename.format(tracer=tracer)
with open(filename, "r") as profile_file:
for line in prof... | 36718952419eebb0024321f777cafcaa23dddf56 | 48,036 |
import tests
def test_list():
"""
Retrieve a list of tests as json objects.
See the README for details on the formatting.
"""
test_json_objs = []
for test_id, test_name in enumerate(tests):
exec("name = "+test_name+".name", globals())
exec("desc = "+test_name+".desc", globals... | 89ee001a481490a48120b898b682dbd99ef4ad7d | 48,037 |
def load_embeddings(file_name: str, embeddings_format: str):
"""Load word embeddings from the given file, either in plain-text format ('text') or in gensim format ('gensim')"""
if embeddings_format == 'text':
return _load_w2v_model_file(filename=file_name)
else:
return _load_w2v_model_gensim... | a9131785ed2ffcbff6efd10c6ae736e318717842 | 48,040 |
def favicon():
"""网站图标"""
# send_static_file是flask里面的寻找static静态文件的方法。通过查看源码得知
return current_app.send_static_file('news/favicon.ico') | ba7c36e003118605d1a786305f931cf1f436a9c3 | 48,041 |
def compute_tpm(
counts_df: pd.DataFrame, gene_id2length: pd.DataFrame
) -> pd.DataFrame:
"""Compute TPM values
Args:
counts_df: Dataframe containing counts
gene_id2length: Dictionary of gene ID to gene length
Returns:
pd.DataFrame: containing the TPM values
"""
scaling... | d8fc3bcfcc622b6ffb8f36dc382d23d930d2621e | 48,043 |
import scipy.stats as stats
import scipy.stats as stats
import numpy
def lnprior(pzero_regions, paramSetup):
"""
Function that computes the ln prior probabilities of the model parameters.
"""
priorln = 0.0
mu = 1
# import pdb; pdb.set_trace()
# ensure all parameters are finite
if ... | 22e7b57b8a261a74de22580a8a850f6f3dfbcb26 | 48,045 |
def render_template_to_file(template_path, file_path=None, **values):
"""
Render a 'jinja' template resource to a temporary file.
:param template_path: relative path to the template.
:param file_path: absolute path to the desired output file.
:param values: keyword arguments passed to jinja.
"... | 7af7e2fa2e46bf333ae35985628f81e74cd41f0d | 48,046 |
def fit_generic_continuum(spectrum, median_window=3, model=Chebyshev1D(3),
fitter=LevMarLSQFitter(),
exclude_regions=None, weights=None):
"""
Basic fitting of the continuum of an input spectrum. The input
spectrum is smoothed using a median filter to remov... | b5e279a401a1ff94e6fa7cb0473747dd5cbf0809 | 48,049 |
def sdss_path():
"""Path for absorption/emission lines
"""
return join(_parent_path, 'SDSS') | 894eb310d6d9ad756b365a03ed411bd2569e8a1d | 48,050 |
def read_log_file(
log_file,
log_entries_to_get=[
"x_pixel_um",
"y_pixel_um",
"z_pixel_um",
"image_paths",
"registration_config",
],
separator=": ",
):
"""
Reads an amap log file, and returns a dict of entries corresponding to
"log_entries_to_get"
... | f339d5cffe1ade818eda1c9ed45a4bb115ce159b | 48,052 |
import gzip
import re
def miles_graph():
""" Return the cites example graph in miles_dat.txt
from the Stanford GraphBase.
"""
# open file miles_dat.txt.gz (or miles_dat.txt)
fh = gzip.open('knuth_miles.txt.gz', 'r')
G = nx.Graph()
G.position = {}
G.population = {}
cities = []... | 994005c593573e097f7fb4729b8a4f3d288b002e | 48,054 |
def SetErrorHandler(*args):
"""SetErrorHandler(char const * pszCallbackName=None) -> CPLErr"""
return _gdal.SetErrorHandler(*args) | f76abdc5b689f0ed1f2ea913749b6a6fe116b32f | 48,055 |
def get_shift_bits(n, bit_string, shift):
"""Given a bit-string, return an expression to map to the bit-string.
Each index of the given bit-string corresponds to the index of an n-bit
bit vector. This function returns those bits, using the variable for the bit
if the corresponding index in the bit-string is '1... | 6b0b396b3248eeb4cd971256edd70b8052f462e6 | 48,057 |
def get_bag_count(list_of_bags: list, colour: str) -> int:
"""
Recursive function to loop through list.
Gather rules on line.
While there are still rules to be processed, get colour and recursively call function again.
Append to total.
:return: Total amount of bags inside colour.
:rtype: in... | 593bb1d826cd996ca4725fe7e2c18043c8853021 | 48,058 |
def get_wikidata_complexes():
"""Gets all Wikidata items with a Complex Portal ID property"""
get_macromolecular = """
SELECT ?item ?ComplexPortalID
WHERE
{
?item wdt:P7718 ?ComplexPortalID .
}"""
wikidata_complexes = WDItemEngine.execute_sparql_query(
get_macromolecular, as_da... | a71a3fe63ebd67dec7ce0cb38adaf60daa8a9ad0 | 48,059 |
import socket
def _pick_port():
"""
Returns an unused TCP port.
While we can not guarantee it will stay unused, it is very unlikely
that it will become used within a few seconds.
"""
with socket(AF_INET, SOCK_STREAM) as sock:
sock.bind(("", 0))
return sock.getsockname()[1] | 2f9c0e2aa5619db6212e4dc2c78cf0ff12516447 | 48,060 |
def connection_str():
""" SQLAlchemy connection string to test database """
return "postgresql://nebulo_user:password@localhost:4442/nebulo_db" | b5220a9ce7e44acde4154686af00164d2f065a83 | 48,061 |
def mfi(high, low, close, volume, length=None, drift=None, offset=None, **kwargs):
"""Indicator: Money Flow Index (MFI)"""
# Validate arguments
length = int(length) if length and length > 0 else 14
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, l... | ee73c35c4f704360155a1365cdd8eae78d5d0388 | 48,063 |
def load(filename):
"""
"""
with open(filename, "rb") as file_:
data = read(file_)
return data | efc01f2411e910d169d642ed76117cdcff30d6e7 | 48,064 |
import numpy
def caf_L05burst2(CFR, lMmax, t0, burstamp, ltbegin, ltend):
"""
Generate age distribution (CAF) following L05 & add a starburst
"""
# Assume CIMF is a -2 power-law powerlaw
# Follow the evolve MF notation in Gieles 2009, MNRAS
# Fixed model parameters:
lMlim = 2 # minimum m... | 72b0ad5461d728ed371da7d147af85a193cdfd1f | 48,065 |
def Y_tester():
"""
>>> tmp = Y_tester()
>>> tmp(1)
1
>>> tmp(5)
120
>>> tmp(2)
2
"""
"*** YOUR CODE HERE ***"
return Y(lambda f: lambda x: 1 if x == 1 else x * f(x - 1)) | d374a1226da538b7b105de0a8b2ab1e0e2ab5fbc | 48,066 |
def segment_arm(frame: np.ndarray, abs_depth_dev: int = 14) -> np.ndarray:
"""Segments arm region
This method accepts a single-channel depth image of an arm and
hand region and extracts the segmented arm region.
It is assumed that the hand is placed in the center of the image.
:para... | 2647e66570c12237aa242cf22cf27872d395ef2c | 48,067 |
def tiploc_record(records):
"""return CIF file TIPLOC object from 80-character line string"""
this_array = [[line[0:2],line[2:9],line[9:11],line[11:17],line[17:18],line[18:44],line[44:49],line[49:53],line[53:56],line[56:72],line[72:79]] for line in records]
this_frame = pd.DataFrame(data=this_array, columns... | a2b2737b924ad94ec831fd5206bcf259f3602ee0 | 48,069 |
def sort(args) -> list:
"""Performs a sort based on the given args.
Args is of the format (dist, auc, n0, n1) and is one tuple/list.
Throws an error if the array did not sort correctly.
Returns the results."""
dist, auc, n0, n1 = args
results = list()
data, D0, D1 = continuousScale(n0, n1)
comp = Comparator(dat... | a61156e1de8fd37473c5cfc1a596b8d9511d061c | 48,070 |
def org_new(input_params={}, always_retry=True, **kwargs):
"""
Invokes the /org/new API method.
For more info, see: https://documentation.dnanexus.com/developer/api/organizations#api-method-org-new
"""
input_params_cp = Nonce.update_nonce(input_params)
return DXHTTPRequest('/org/new', input_par... | 200489839e21485c8fb862a66e2a3cb0d42865a0 | 48,073 |
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