content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def cropDetectionSegments(ffBinRead, segmentList, cropSize = 64):
""" Crops small images around detections.
ffBinRead: read FF bin structure
segmentList: list of coordinate tuples [(x1, y1), (x2, y2),...]
cropSize: image square size in pixels (e.g. 64x64 pixels)"""
ncols = ffBinRead.ncols - 1... | 749f13efabc60f9b443ea84cabd459d9a8a70241 | 3,632,428 |
def delete_site_request(site_id):
"""Request deletion of a site."""
site = InventorySite.query.filter_by(id=site_id).first()
if site is None:
abort(404)
return render_template('inventory/manage_site.html', site=site) | 3b137e3140fb5fa9cf9f79dce2cc8b283d3a4a70 | 3,632,429 |
def twoD_Gaussian(tup, amplitude, xo, yo, sigma_x, sigma_y, theta, offset):
""" A 2D Gaussian to be used to fit the cross-correlation
Args:
tup (tuple):
A two element tuple containing the (x,y) coordinates where the 2D Gaussian will be evaluated
amplitude (float):
The am... | 35a4d6362f8751294e460dc7ae529bcb7a48022a | 3,632,430 |
from itertools import combinations
def generate_synthetic_example(n_stations=65, lat_lims=(45, 50), lon_lims=(10, 20), u_0=0, A=.1, phi_2=60, B=.01, phi_4=20, amplitude_noise=.05):
"""
Helper function to generate a simple synthetic example.
Constant anisotropy in entire region.
:param n_stations: Num... | 7879af1efcbb7204f454b337f2197f0442673e1f | 3,632,431 |
import yaml
def get_palette(col, col_unique=None, as_dict=True):
"""Get palette for column.
Parameters
----------
col : {'subject_name', 'model', 'scaling', 'cell_type', str}
The column to return the palette for. If we don't have a particular
palette picked out, the palette will conta... | ab134032798d1679366533968c4bc0f277f764bf | 3,632,433 |
import ROOT
def array2hist(array, hist, errors=None):
"""Convert a NumPy array into a ROOT histogram
Parameters
----------
array : numpy array
A 1, 2, or 3-d numpy array that will set the bin contents of the
ROOT histogram.
hist : ROOT TH1, TH2, or TH3
A ROOT histogram.
... | 40522a374321b768fac800a4fce22440991de05f | 3,632,434 |
def adjust_update_rules_for_fixed_nodes(predecessor_node_lists, truth_tables, fixed_nodes):
"""
Adjust "update rules" matrix and its free element vector so that the fixed nodes will end up in their fixed
states on each time step automatically, with no manual interventions required.
:param predecessor_n... | f41609ae25c3622100674372de5a364b095650f8 | 3,632,435 |
def obs_data():
"""
Dictionary with variables as top keys and available observations
directly below. For each observation data set, path and file pattern must
be defined.
"""
meta_dict = {
# ------------------------------------------------------------------------
# 2m temperature
'... | f3db013fa2b99cdaee26b82075674a69a662030c | 3,632,436 |
def create_class_prediction_error_chart(classifier, X_train, X_test, y_train, y_test):
"""Create class prediction error chart.
Tip:
Check Sklearn-Neptune integration
`documentation <https://docs-beta.neptune.ai/essentials/integrations/machine-learning-frameworks/sklearn>`_
for the full ... | c0aadac243614914952d10d484ea4a9a7c89da26 | 3,632,437 |
def footer_embed(message: str, title) -> Embed:
"""
Constructs embed with fixed green color and fixed footer showing website, privacy url and rules url.
:param message: embed description
:param title: title of embed
:return: Embed object
"""
content_footer = (
f"Links: [Website]({co... | cb4637e479d5eabb3afedb965209271553e1f238 | 3,632,438 |
def joins_for_results(basetables, external_info):
"""
Form and return the `results` table
"""
# Get one table per result_type, then stack them,
# kind_problem
# kind_pathproblem
#
# Concatenation with an empty table triggers type conversion to float, so don't
# include empty ta... | 1b2821a11a9a3df65ef9a3dfbf11262175307b9d | 3,632,440 |
def newton_wedge_fringe_sep(alpha, wavelength):
"""Calculate the separation between fringes for an optical flat with angle
alpha."""
d = wavelength/(2*np.sin(alpha))
return d | fc29c6bfcfb6ed19e91588263ef12190bb9ec699 | 3,632,441 |
def train_test_split(shp, savedir, config, client = None):
"""Create the train test split
Args:
shp: a filter pandas dataframe (or geodataframe)
savedir: directly to save train/test and metadata csv files
client: optional dask client
Returns:
None: train.shp and test.shp ar... | 46c8becd416877306e9d28914f3032ff99946321 | 3,632,443 |
def parse_list_from_string(value):
"""
Handle array fields by converting them to a list.
Example:
1,2,3 -> ['1','2','3']
"""
return [x.strip() for x in value.split(",")] | 51e9c654b9d18b8be61c37aab5f5029dfdea2213 | 3,632,444 |
def add_evaluation_args(parser):
"""Evaluation arguments."""
group = parser.add_argument_group('validation', 'validation configurations')
group.add_argument('--eval-batch-size', type=int, default=None,
help='Data Loader batch size for evaluation datasets.'
'De... | 437a77987e9a4a461b98c9cb08b78a016efca9e9 | 3,632,445 |
import itertools
def merge(d1, d2):
"""Merge to dicts into one.
Args:
d1 (dict): dataset 1
d2 (dict): dataset 2
Returns:
dict: merged dict
"""
return dict(itertools.chain(list(d1.items()), list(d2.items()))) | bb1d38f3cb45de6e98855fb04ae1d3d7e73e4a40 | 3,632,446 |
import re
def is_valid(number):
"""
Check if number is roman
:param number: string to check
:type number: str
:return: True or False
:rtype: bool
"""
return re.match(
r"^(M{0,3})(D?C{0,3}|C[DM])(L?X{0,3}|X[LC])(V?I{0,3}|I[VX])$", number
) | 52e1937418d28701ee3d30da139f16ae64cfe480 | 3,632,447 |
def lammps_created_gsd(job):
"""Check if the mdtraj has converted the production to a gsd trajectory for the job."""
return job.isfile("prod.gsd") | 1b05e085970de4d875044e2e6604c1874a0a0e83 | 3,632,448 |
def has_open_quotes(s):
"""Return whether a string has open quotes.
This simply counts whether the number of quote characters of either type in
the string is odd.
Returns
-------
If there is an open quote, the quote character is returned. Else, return
False.
"""
# We check " first... | a9adbcd42518a71458c69c9aa1ff751fa3998573 | 3,632,449 |
def get_task(name, context=None, exception_if_not_exists=True):
"""
Returns item for specified task
:param name: Name of the task
:param context: Lambda context
:param exception_if_not_exists: true if an exception should be raised if the item does not exist
:return: Task item, raises exception i... | 9eb3007c230b75543c5227a44d282ed2ca6b3d9e | 3,632,450 |
def GetResourceReference(project, organization):
"""Get the resource reference of a project or organization.
Args:
project: A project name string.
organization: An organization id string.
Returns:
The resource reference of the given project or organization.
"""
if project:
return resources.R... | fd986df9ced20a6b8edbd910d7268806841d2139 | 3,632,451 |
def pauli_block_y(M, norb):
"""
y compoenent of a matrix, see pauli_block
"""
ret = zeros_like(M)
tmp = (M[:norb, norb:] * 1j + M[norb:, :norb] * (-1j)) / 2
ret[:norb, norb:] = tmp * (-1j)
ret[norb:, :norb] = tmp * 1j
return tmp, ret | 848d70de19723ee22f2adc750c7dd9ec8c47d784 | 3,632,452 |
def permission_required_raise(perm, login_url=None, raise_exception=True):
"""
A permission_required decorator that raises by default.
"""
return permission_required(perm, login_url=login_url, raise_exception=raise_exception) | 7a26f7ac1e858cfcba6961856ecf428d0de03982 | 3,632,453 |
def get_dips_value_around_300(l_cusp):
"""
300°付近の凹みの L* 値および、それを指す Hue の Index を計算する。
"""
dips_300 = np.min(l_cusp[DIPS_300_SAMPLE_ST:DIPS_300_SAMPLE_ED])
dips_300_idx = np.argmin(l_cusp[DIPS_300_SAMPLE_ST:DIPS_300_SAMPLE_ED])
dips_300_idx += DIPS_300_SAMPLE_ST
return dips_300, dips_300_idx | 6db8f0ee9d92c14c50bee096830a9fa0cadc6a94 | 3,632,455 |
def get_filtered_enviro_df(lat_filter, long_filter):
"""
This function takes the latitude and longitude filters and queries the
database to obtain City of Chicago Environmental complaint and enforcement
information that fits within those filters. A pandas dataframe of filtered
database information ... | b09c2cced6f17c8b4814982eb68f2af1589ad97a | 3,632,456 |
def is_valid_time_stamp_normal_response(response):
"""
Returns true if a time_stamp_normal response is valid.
str -> bool
"""
try:
respones_to_datetime(response, constants.DATETIME_FORMATE_NORMAL)
return True
except ValueError:
return False | 37e521ab18f8a21f311b07a96082ea65d2c3a57e | 3,632,457 |
def find_best_capacity_value(desired_capacity, data_file='data/capacities.csv'):
"""Return the closest capacity to the desired one from the possible capacity combinations.
Parameters
----------
desired_capacity: float
The desired capacity value needed for the circuit.
data_file: str
Re... | 19fd7cca9b45b088c2dafa97f75d37c8bae570d8 | 3,632,459 |
def _qt(add_row, secondary_dict_ptr, cols, key):
"""
This sub-function is called by view_utils.qt to add keys to the secondary_dict and
is NOT meant to be called directly.
"""
if cols[key]:
if cols[key] in secondary_dict_ptr:
return add_row, secondary_dict_ptr[cols[key]]
... | ce1cec842822077cbfbd908ff92b1552626cd5f2 | 3,632,460 |
from datetime import datetime
def conv_to_schedule(src: datetime) -> str:
"""Convert given datetime to schedule date string."""
return datetime.strftime(src, FMT_STD) | 571fd18bff08e4e9be9929a75b23c5b023122400 | 3,632,461 |
def create(title):
"""Create a Tk root
title - a title for the application
"""
assert isinstance(title, str)
root = tk.Tk()
root.title = title
rx.concurrency.TkinterScheduler(root)
return root | aaa710b6429c0abafe40e7c7ea51f886f15624c4 | 3,632,462 |
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up Homekit lock."""
hkid = config_entry.data["AccessoryPairingID"]
conn = hass.data[KNOWN_DEVICES][hkid]
@callback
def async_add_service(service):
if service.short_type != ServicesTypes.LOCK_MECHANISM:
r... | 1ebfa72ffc700a873cff642cbd0a92b2a64cdc35 | 3,632,463 |
from typing import List
from typing import Tuple
import torch
def bounding_boxes_to_tensor(bboxes: List[dict], image_size: Tuple[int, int],
cell_size: Tuple[int, int], classes: List[str],
device: torch.device) -> Tuple[torch.Tensor]:
"""
Converts a lis... | 1530748ddc02527edea938542b1e60dcfac5a36b | 3,632,464 |
def compartment_size_uncommon_keys(base):
"""
Provide a model with different amounts metabolites for each compartment.
"""
base.add_metabolites(
[cobra.Metabolite(i, compartment='ml') for i in "ABCD"])
base.add_metabolites(
[cobra.Metabolite(i, compartment='om') for i in "EFG"])
... | d00f1da728b5f8cf9399a8e8810d90cd919c45c6 | 3,632,465 |
def steady_state_step(population: list, reproduction_pipeline: list, insert, probes = (), evaluation_op = ops.evaluate):
"""An operator that performs steady-state evolution when placed in an (otherwise
generational) pipeline.
This is a metaheuristic component that can be parameterized to define many kinds ... | e6e03e4b0d70ba4b3a10124e4170988b0774e9f1 | 3,632,466 |
def get_gym_environs():
""" List all valid OpenAI ``gym`` environment ids. """
return [e.id for e in gym.envs.registry.all()] | 899013b5621e63b44bd0600bd037da389fcdb0ff | 3,632,467 |
import time
def single_classic_cv_evaluation(
dx_train, dy_train, name, model, sample_weight, scoring, outer_cv,
average_scores_across_outer_folds, scores_of_best_model, results,
names, random_state):
"""Non nested cross validation of single model."""
if (isinstance(scoring, list) or i... | 530fd65ed867a4f43a1fcc688970c6a6b1e6ffc7 | 3,632,468 |
from nnabla import logger
def get_extension_context(ext_name, **kw):
"""Get the context of the specified extension.
All extension's module must provide `context(**kw)` function.
Args:
ext_name (str) : Module path relative to `nnabla_ext`.
kw (dict) : Additional keyword arguments for cont... | c5f3bf4c6f4053207009e3412c23133df28d6b61 | 3,632,469 |
def resolve_byprop(prop, value, minimum=1, timeout=FOREVER):
"""Resolve all streams with a specific value for a given property.
If the goal is to resolve a specific stream, this method is preferred over
resolving all streams and then selecting the desired one.
Keyword arguments:
prop -- The St... | a3c81185ad3d972e997399d41480e3f814945c52 | 3,632,470 |
import math
def create_plot(model_filenames, ncols=3, projection=None, nplots_increment=0):
"""Create base figure for multipanel plot.
Creates matplotlib figure and set of axis that corespond to
the number of models that should be plotted.
Parameters
----------
model_filenames: OrderedDict
... | 11a5e36b641946c5994919845818dcecef98eadf | 3,632,471 |
import time
def get_sample_records(n):
"""get sample records for testing"""
tsk, target = get_sample_task()
inps, ress = [], []
for i in range(n):
inps.append(MeasureInput(target, tsk, tsk.config_space.get(i)))
ress.append(MeasureResult((i + 1,), 0, i, time.time()))
return list(zi... | 136398926d2638aa0542e76e1e6757484aaf82d1 | 3,632,472 |
def build_get_boolean_tfft_request(
**kwargs # type: Any
):
# type: (...) -> HttpRequest
"""Get boolean array value [true, false, false, true].
See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder
into your code flow.
:return: Returns an :class:`~azu... | 0711739e4a97a7356f5f69a12e27f940e03e79ff | 3,632,473 |
def grøn(tekst: str):
"""
Farv en tekst der udskrives via Click grøn.
"""
return farvelæg(tekst, "green") | b85629c384c8918bca38093af99a7840cce0aec2 | 3,632,474 |
def update(dbs, user, role_id=None, org_id=None, create_user=None):
"""
更新用户信息
:param dbs:
:param user:
:param role_id:
:param org_id:
:param create_user:
:return:
"""
try:
with transaction.manager:
if org_id and org_id != '' and org_id != 0:
d... | a345e9edfd0e7174c7ce8e881d3b3699a9222610 | 3,632,476 |
def analogy_making_model(inputs, params, is_training, reuse, output_length=None):
"""Factory function to retrieve analogy-making model."""
latent_encoder = _get_network(Z_ENC_FN)
latent_decoder = _get_network(Z_DEC_FN)
outputs = analogy_seq_encoding_model(inputs, params, is_training, reuse)
with tf.variable... | 3c83c27ebefcc9d3b080441be5a45c52193a40eb | 3,632,477 |
import torch
def test_model(dataloader, model, gpu=False):
"""Tests model performance on a data from dataloader and prints accuracy.
Args:
dataloader (DataLoader)
model (torchvision model)
gpu (bool): Use GPU if True, otherwise CPU
Returns:
test_acc (float): model predict... | f5e1f63b8e0a3e2ee94d579f552807af103e2c1a | 3,632,478 |
import io
import struct
def decrypt_chunk(chunk, password=None):
"""Decrypts the given encrypted chunk with the given password and returns the decrypted chunk.
If password is None then saq.ENCRYPTION_PASSWORD is used instead.
password must be a byte string 32 bytes in length."""
if password is ... | f6578fd445c44a2fd4aecc3009278ddd2a45add0 | 3,632,479 |
import time
from datetime import datetime
def epochFromNice(timeToConvert=None):
""" Get the epoch time from the passed in string of the format:
YYYY-mm-dd_HH-MM-SS.UUUUUU
returns time.time() if timeToConvert is not specified """
if timeToConvert is None:
retTime = time.time()
else:
year... | db6378dd2d47cc17377507474d9726a2f6de22b0 | 3,632,480 |
def prepare_tensor_SVD(tensor, direction, D=None, thresh=1E-32, normalize=False):
"""
prepares and truncates an mps tensor using svd
Parameters:
---------------------
tensor: np.ndarray of shape(D1,D2,d)
an mps tensor
direction: int
if >0 returns left orthogonal decomp... | ad71c7f2b16624bc9f24e060376a9359c8585c07 | 3,632,481 |
from datetime import datetime
def calc_easter(year):
"""
Returns Easter as a date object. Because Easter is a floating mess
year - the year to calc easter for
"""
a = year % 19
b = year // 100
c = year % 100
d = (19 * a + b - b // 4 - ((b - (b + 8) // 25 + 1) // 3) + 15) % 30
... | c02c2a45f55a8f80273759bbf9c06bb2234b0b8a | 3,632,482 |
def MACDFIX(ds, count, signalperiod=-2**31):
"""Moving Average Convergence/Divergence Fix 12/26"""
ret = call_talib_with_ds(ds, count, talib.MACDFIX, signalperiod)
if ret == None:
ret = (None, None, None)
return ret | 8b440e666d0ac1c669e26da9974136fdd18f5e6e | 3,632,483 |
def create_dataset(form_data, params=None, use_doi=False):
"""
Create dataset in Metax.
Arguments:
form_data {object} -- Object with the dataset data that has been validated and converted to comply with the Metax schema.
params {dict} -- Dictionary of key-value pairs of query parameters.
... | b288c021df3cf37467ea304c2105eceb0fc5f2be | 3,632,484 |
def blrPredict(W, data):
"""
blrObjFunction predicts the label of data given the data and parameter W
of Logistic Regression
Input:
W: the matrix of weight of size (D + 1) x 10. Each column is the weight
vector of a Logistic Regression classifier.
X: the data matrix of siz... | 86374a43e6c7cbe69a6789f532ea3be3bf3238f5 | 3,632,485 |
def get_pair(expr, index):
"""Get the field of an expression using python syntax
Arguments:
- `expr`: an expression
- `index`: an integer equal to 0 or 1
"""
if index == 0:
return Fst(expr)
elif index == 1:
return Snd(expr)
else:
raise Exception("Index applie... | 5b25163562fb8399d2e948dd8fc9d47aa6467b07 | 3,632,488 |
import functools
def get_standardized_layers(hparams, dp=None, ps_devices=None):
"""Get the common attention and feed-forward layers.
The returned layer functions will have the following signature:
y, extra_loss = fct(x)
extra_loss is set to 0.0 if the layer doesn't have extra loss.
If dp is provided, ... | 99972e4106928ff4c71e0f52c159a74650310c74 | 3,632,489 |
def row_contains_data(fieldnames, row):
"""Returns True if the value of atleast on of the fields is truthy"""
for field in fieldnames:
if row.get(field):
return True
return False | 7575d1280186c582a652ab37deb4a93e667b51b2 | 3,632,490 |
def create_model(name, batch_size, learning_rate = 0.0001, wd = 0.00001, concat = False, l2_loss = False, penalty = False, coef = 0.4, verbosity = 0):
"""
Create a model from model.py with the given configuration
Args:
name : name of the model (used to create a specific folder to save/load para... | 63b4755e20fc877d9231427ed2cf118efbe37bb0 | 3,632,491 |
def fs(path:str, mode:Mode='rb'):
"""Opens file locally or via s3 depending on path string."""
s3 = s3fs.S3FileSystem()
return partial(
{True: s3.open, False: open}[is_s3_path(path)],
mode=mode
)(path) | 18ffc654e66bdd2c9315d5104fa145a195ee2ce3 | 3,632,492 |
import six
def _check_stop_list(stop):
"""
Check stop words list
ref: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L87-L95
"""
if stop == "thai":
return THAI_STOP_WORDS
elif isinstance(stop, six.string_types):
raise ValueError("not... | 8c47875f42fdfcb1f0b7c7c18b8363783f1ebcbb | 3,632,494 |
from typing import Callable
from typing import Any
def protect_with_lock() -> Callable:
""" This is a decorator for protecting a call of an object with a lock
The objects must adhere to the interface of having:
- A mapping of ids to query_lock objects
Objects adhering to this interface(LockableQu... | 60901e775f1a6a8d3408000f3517f1c3ddedd199 | 3,632,495 |
from datetime import datetime
def _handle_dates():
"""Collect and return data information."""
currentdate = datetime.date.today()
year = currentdate.year
month = currentdate.month
print(f"[INFO] Current year / month: {year:04d} / {month:02d}")
return currentdate | 54735562fe0366f286eeac33f897a98a8d34374a | 3,632,496 |
def add_to_five(number):
"""Add to the 5 by any number.
:param number:
"""
gifs = FrozenGif()
acceptable = (int, float)
type_number = type(number)
if type_number in acceptable:
# get answer
answer = 5 + number
response = int(input("Ente... | 0387af4a7d8587865c3c7e38eeab1af5a00423a2 | 3,632,497 |
def test_url(url, size=17):
"""Test whether the given URL is accessible."""
try:
with gopen(url) as stream:
data = stream.read(size)
if len(data) == size:
return True
return False
except Exception as e:
print(e)
return False | 13023fc5fd346572b3c31e4f4dd491e285e33b6b | 3,632,499 |
import gdata.gauth
def credentials_to_token(credentials):
"""
Transforms an Oauth2 credentials object into an OAuth2Token object
to be used with the legacy gdata API
"""
credentials.refresh(httplib2.Http())
token = gdata.gauth.OAuth2Token(
client_id=credentials.client_id,
clie... | 549b1041c275c542d96e1d048832f71969d727d7 | 3,632,500 |
def solve2x2(lhs, rhs):
"""Solve a square 2 x 2 system via LU factorization.
This is meant to be a stand-in for LAPACK's ``dgesv``, which just wraps
two calls to ``dgetrf`` and ``dgetrs``. We wrap for two reasons:
* We seek to avoid exceptions as part of the control flow (which is
what :func:`nu... | 2f773a0e452ce1401dc5fb5c17256b662614c366 | 3,632,501 |
def fetch_global_notifications(count=0) -> dict:
"""
Always returns notifications in user view.
"""
cfg = get_config()
if count == 0:
count = cfg.default_max_notes
global_feed = get_global_feed()
global_notes = global_feed.get_notifications(count=count, user_view=True)
return glo... | 661232b2477eabd0c5a3b706b8253e4956e1aabd | 3,632,502 |
def getClusterPositionsRedshift(hd_clu, cluster_params, redshift_limit):
"""
Function to get the positions and redshifts of the clusters that pass the required criterion
@hd_clu :: list of cluster headers (each header ahs info on 1000 clusters alone)
@cluster_params :: contains halo_mass_500c and centra... | 4b30bb44a82daa3f2f113c14b7f044ac22d20c6c | 3,632,503 |
def lark_to_field_definition_node(tree: "Tree") -> "FieldDefinitionNode":
"""
Creates and returns a FieldDefinitionNode instance extracted from the
parsing of the tree instance.
:param tree: the Tree to parse in order to extract the proper node
:type tree: Tree
:return: a FieldDefinitionNode ins... | f00d0ed11d3ff61d8017cecbb0226b88accb9854 | 3,632,504 |
def variant_wraps(vfunc, wrapped_attributes=VARIANT_WRAPPED_ATTRIBUTES):
"""Update the variant function wrapper a la ``functools.wraps``."""
f = vfunc.__main_form__
class SentinelObject:
"""A unique sentinel that is not None."""
sentinel = SentinelObject()
for attr in wrapped_attributes:
... | 4143a0e2cb5676c80314096575c9d33fbdcdb788 | 3,632,505 |
from typing import Iterable
def gradient_activity(activity, periods=1, append=True, columns=None):
"""Compute the gradient for all given columns.
Read more in the :ref:`User Guide <gradient>`.
Parameters
----------
activity : DataFrame
The activity to use to compute the gradient.
pe... | fd05c9323d406b9f0c5089f7715b8ec55ceeaae2 | 3,632,506 |
def password_validators_help_texts(password_validators=None):
"""
Return a list of all help texts of all configured validators.
"""
help_texts = []
if password_validators is None:
password_validators = get_default_password_validators()
for validator in password_validators:
help_t... | 3df7b1a669ee7ef01b1e28645d3d7fca5816cf8d | 3,632,507 |
import torch
def spherical_schwarzchild_metric(x,M=1):
""" Computes the schwarzchild metric in cartesian like coordinates"""
bs,d = x.shape
t,r,theta,phi = x.T
rs = 2*M
a = (1-rs/r)
gdiag = torch.stack([-a,1/a,r**2,r**2*theta.sin()**2],dim=-1)
g = torch.diag_embed(gdiag)
print(g.shape)... | 4e65d520a88f4b9212bab43c7ebc4dfc30245bd3 | 3,632,508 |
def _get_pathless_grib_file_names(
init_time_unix_sec, model_name, grid_id=None, lead_time_hours=None):
"""Returns possible pathless file names for the given model/grid.
:param init_time_unix_sec: Model-initialization time.
:param model_name: See doc for `nwp_model_utils.check_grid_name`.
:para... | 6435348fa760b5bd36e8c54df192b024210dcf4f | 3,632,509 |
def get_picard_mrkdup(config):
"""
input: sample config file output from BALSAMIC
output: mrkdup or rmdup strings
"""
picard_str = "mrkdup"
if "picard_rmdup" in config["QC"]:
if config["QC"]["picard_rmdup"] == True:
picard_str = "rmdup"
return picard_str | 87e24c0bf43f9ac854a1588b80731ed445b6dfa5 | 3,632,510 |
import random
def ChoiceColor():
""" 模板中随机选择bootstrap内置颜色 """
color = ["default", "primary", "success", "info", "warning", "danger"]
return random.choice(color) | 15779e8039c6b389301edef3e6d954dbe2283d54 | 3,632,511 |
import platform
def filter_command_line(line):
"""Returns and updates the command line starting with the flag"""
line_flag = line.strip().split(":")[0].strip()
new_line = line.strip()[len(line_flag) + 1:].strip().lstrip(":").strip()
if line_flag == EXEC_FLAG:
return new_line
elif line_flag... | ff5560b1ba23544902e0904bc08030b2d24a40e4 | 3,632,512 |
def load_image(path: str):
"""
Return a loaded image from a given path.
:param path: (str) relative path of the iamge.
:return: PIL image.
"""
image = Image.open(path)
return image | 8c9d96d5cea2fdac67ba937b7d01bc01a860d1d7 | 3,632,513 |
from operator import concat
def mergeSeries(sdata, resetIdx=False):
"""
Merge Series
Inputs:
> sdata: Either a list of dictionary of Series data
> resetIdx (False by default): should we reset the indices?
Output:
> The merged Series
"""
if isinstance(sdata, list):... | dc0382581d3e14dc46abe9c5b40d685f3d80ec20 | 3,632,514 |
def ranking_overview(request):
"""
Show history of rankings for top N teams in current ranking
"""
# Check which rounds are complete
rnd_complete = get_completed_rounds()
# Calculate ranking after each of these rounds
increment_rnds = []
ranking_matrix = []
round_names = []
... | c68505833f72d529d4ea1617d4d8455120c5321d | 3,632,515 |
def release_lock(lock_name, identifier):
"""
:param lock_name: 锁名称
:param identifier: uid
:return: True or False
"""
lock = "string:lock:" + lock_name
pip = redis_client.pipeline(True)
while True:
try:
pip.watch(lock)
lock_value = redis_client.get(lock)
... | 8df9f174d11a36ffad9ed2310c2293630669dc4c | 3,632,516 |
def add_user():
""" Add a user"""
payload = request.json
for required_key in users_schema:
if required_key not in payload.keys():
return jsonify({"message": f"Missing {required_key} parameter"}), 400
user = db.users.find_one({"email": payload["email"]})
if user is not None:
... | 1c674dbb70caa0ae43819391c97694d3dd82a235 | 3,632,517 |
import hashlib
def __get_str_md5(string):
"""
一个字符串的MD5值
返回一个字符串的MD5值
"""
m0 = hashlib.md5()
m0.update(string.encode('utf-8'))
result = m0.hexdigest()
return result | 1d55cd42dc16a4bf674907c9fb352f3b2a100d6c | 3,632,518 |
from typing import List
def create_optimizers() -> List[OptimizationProcedure]:
"""Creates a list of all optimization procedures"""
optimizers: List[OptimizationProcedure] = []
ordering_rules = TaskOrderingRule.__subclasses__()
for rule in ordering_rules:
optimizers.append(StationOriented... | f5cade59c03b017435c18a97423eb059418a6e20 | 3,632,519 |
import urllib
def command_get_file_list(ip_addr, directory):
"""command.cgi?op=100: Get list of files in a directory. Not recursive.
:raise FlashAirBadResponse: When API returns unexpected/malformed data.
:raise FlashAirDirNotFoundError: When the queried directory does not exist on the card.
:raise F... | 40f8c9b84113be84358959f246a868424d382947 | 3,632,520 |
def axml_content(d):
"""
OwcContent dict to Atom XML
:param d:
:return:
"""
# <owc:content type="image/tiff" href=".."
if is_empty(d):
return None
else:
try:
content_elem = etree.Element(ns_elem("owc", "content"), nsmap=ns)
mimetype = extract_p(... | c35c266d4ae7c5026958cbb271598f76369ecc6f | 3,632,521 |
def average_water_consumed(wn):
"""
Compute average water consumed at each node, qbar, computed as follows:
.. math:: qbar=\dfrac{\sum_{k=1}^{K}\sum_{t=1}^{lcm_n}qbase_n m_n(k,t mod (L(k)))}{lcm_n}
where
:math:`K` is the number of demand patterns at node :math:`n`,
:math:`L(k)` is the number o... | bf88a45035b993d00fb31ff7151e48a0a1d59c83 | 3,632,522 |
def _shuffle(arr1, arr2):
"""
Shuffles arr1 and arr2 in the same order
"""
random_idxs = np.arange(len(arr1))
np.random.shuffle(random_idxs)
return arr1[random_idxs], arr2[random_idxs] | 785ecd0b9e92d5695cd2466fec6649d463f55feb | 3,632,523 |
def make_pipeline(tfidf_vectorizer, model):
""" Creates sklearn NLP pipeline
:param vectorizer: Vectorizer object
:param model: Model object
:return: Pipeline object
"""
tfidf_vectorizer = tfidf_vectorizer
model = model
pipeline = Pipeline([("tfidf", tfidf_vectorizer),
... | 423b59d2635bf34169091c7456e71eca2dbdf5d6 | 3,632,525 |
def pnf_peeling_mechanism(item_counts, k, epsilon):
"""Computes epsilon-DP top-k counts by the permute-and-flip peeling mechanism.
The peeling mechanism (https://arxiv.org/pdf/1905.04273.pdf) adaptively uses
the counts as a utility function for the exponential mechanism. Once an item
is selected, the item is ... | 2c2fe9e59addc4905211ca70efd1f88bdeb5a976 | 3,632,526 |
from datetime import datetime
def format_date(timestamp):
"""Reusable timestamp -> date."""
return datetime.date.fromtimestamp(timestamp).isoformat() | 0f735dc18700332238ab4441677c786f9accc069 | 3,632,527 |
import time
def train(model, optimizer, loader, epoch):
"""
Train the models on the dataset.
"""
# running statistics
batch_time = AverageMeter("time", ":.2f")
data_time = AverageMeter("data time", ":.2f")
# training statistics
top1 = AverageMeter("top1", ":.3f")
top5 = AverageMet... | 3d65714a50f1842c32c85fe0cd3c02d7069a88f9 | 3,632,529 |
import functools
def ResidualBlock(name, input_dim, output_dim, filter_size, inputs, resample=None, he_init=True):
"""
resample: None, 'down', or 'up'
"""
if resample == 'down':
conv_shortcut = functools.partial(lib.ops.conv2d.Conv2D, stride=2)
conv_1 = functools.partial(
l... | 1b78b86ea42dd225f5a2bde6bcb6ed47b055ec00 | 3,632,530 |
def run_uGLAD_direct(
Xb,
trueTheta=None,
eval_offset=0.1,
EPOCHS=250,
lr=0.002,
INIT_DIAG=0,
L=15,
VERBOSE=True
):
"""Running the uGLAD algorithm in direct mode
Args:
Xb (np.array 1xMxD): The input sample matrix
trueTheta (np.array 1xDxD): The corresp... | 5b362abfec18a217c768bbe45bce3059ece8be22 | 3,632,531 |
def twitter_api():
"""Returns an authenticated tweepy.API instance.
Returns None on failure.
"""
try:
auth = tweepy.OAuthHandler(TWITTER_API_KEY, TWITTER_API_KEY_SECRET)
auth.set_access_token(TWITTER_ACCESS_TOKEN,
TWITTER_ACCESS_TOKEN_SECRET)
ap... | 5f0e748740025d4065c6317fdf03eead25e7ba95 | 3,632,532 |
def as_general_categories(cats, name="cats"):
"""Return a tuple of Unicode categories in a normalised order.
This function expands one-letter designations of a major class to include
all subclasses:
>>> as_general_categories(['N'])
('Nd', 'Nl', 'No')
See section 4.5 of the Unicode standard fo... | 391185d75dce63df7deb724f8dea035389122b94 | 3,632,533 |
from pathlib import Path
import glob
def upload_directory(
directory: str = './',
upsert=False,
ignore_duplicate_error=False,
recursive=False,
pattern='*'):
"""
Upload files in a directory to the database.
:param directory: [Optional] The root directory to upload. ... | d156c44f511182958172ba30bfb5422bb5da8dcd | 3,632,534 |
def tExtract(rft, T_INDEX):
"""T_INDEX is either of T_WL, T_SPEC, T_COM """
tdat = [dat[T_INDEX] for dat in rft[RFT_T] if (dat[T_WL] >= rft[RFT_R][R_WL] and dat[T_WL] <= rft[RFT_R][R_WH])]
return tdat | c1cdf75b377851316a5da050b8f248f14cfd34e5 | 3,632,535 |
from typing import Union
from pathlib import Path
from typing import Optional
def open_txt(path: Union[str, Path], cf_table: Optional[dict] = cmor) -> xr.Dataset:
"""Extract daily HQ meteorological data and convert to xr.DataArray with CF-Convention attributes."""
meta, data = extract_daily(path)
return t... | 3a77ed5a501c1d455299504e4dd36e55cea580e9 | 3,632,536 |
def dc_coordinates():
"""Return coordinates for a DC-wide map"""
dc_longitude = -77.016243706276569
dc_latitude = 38.894858329321485
dc_zoom_level = 10.3
return dc_longitude, dc_latitude, dc_zoom_level | c07812ad0a486f549c63b81787a9d312d3276c32 | 3,632,537 |
def request_id_to_key(request_id):
"""Converts a request id into a TaskRequest key.
Note that this function does NOT accept a task id. This functions is primarily
meant for limiting queries to a task creation range.
"""
return ndb.Key(TaskRequest, request_id ^ task_pack.TASK_REQUEST_KEY_ID_MASK) | a5c3ef9939390d43264ba397e4c123af44758471 | 3,632,539 |
from typing import Optional
def get_web_app_premier_add_on_slot(name: Optional[str] = None,
premier_add_on_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
slot: Optional[str] = None,
... | 764101a407305b1f6e03fe8b1ccb17cb2ecbd86f | 3,632,540 |
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